Performs analysis of one-way multivariate data, for small samples using Nonparametric techniques. Using approximations for ANOVA Type, Wilks' Lambda, Lawley Hotelling, and Bartlett Nanda Pillai Test statics, the package compares the multivariate distributions for a single explanatory variable.  The comparison is also performed using a permutation test for each of the four test statistics. The package also performs an all-subsets algorithm regarding variables and regarding factor levels.
Nonparametric Multivariate Change Point Model
Implements a nonparametric statistical test for rank or
score data from partially-balanced incomplete block-design
experiments.
A method for obtain nonparametric estimates of regression models
with or without factor-by-curve interactions using local polynomial
kernel smoothers. Additionally, a parametric model (allometric model)
can be estimated.
Small functions to make working with survey data in the context of
a Net Promoter programme easier. Specifically, data transformation methods,
some methods for examining the statistical properties of the NPS, such as
its variance and standard errors, and some simple inferential testing
procedures. Net Promoter and NPS are registered trademarks of Bain &
Company, Satmetrix Systems and Fred Reichheld.
Provides a variety of tools for nonparametric and parametric efficiency measurement.
This package contains a collection of functions to to perform nonparametric deconvolution using simulation extrapolation (SIMEX). We propose an estimator that adopts the SIMEX idea but bypasses the simulation step in the original SIMEX algorithm. There is no bandwidth parameter and the estimate is determined by appropriately selecting "design points". See details in: Wang, X.F., Sun, J. and Fan, Z. (2011). Deconvolution density estimation with heteroscedastic errors using SIMEX.
Functions and datasets used in the book Nonparametric Statistical Methods Using R.
Multidimensional nonparametric spatio-temporal geostatistics.
S3 classes and methods for multidimensional: gridded data, linear binning,
local polynomial kernel regression, density and variogram estimation.
Nonparametric methods for trend and/or variogram inferences.
Package 'npst' generalizes Hewitt's (1971) test for seasonality and
Rogerson's (1996) extension based on Monte-Carlo simulation.
Contains functions for non-parametric survival analysis of
exact and interval-censored observations.
Post-normalization of total copy-number estimates.
This package implements two algorithms for canonical correlation
analysis (CCA) that are based on iterated regression
steps. By choosing the appropriate regression algorithm for each data
modality, it is possible to enforce sparsity, non-negativity or other kinds
of constraints on the projection vectors. Multiple canonical variables are
computed sequentially using a generalized deflation scheme, where the
additional correlation not explained by previous variables is maximized.
'nscancor' is used to analyze paired data from two domains, and has the same
interface as the 'cancor' function from the 'stats' package (plus some extra
parameters). 'mcancor' is appropriate for analyzing data from three or more
domains.
This package simulates and estimates spatial clustered point patterns of the Neyman-Scott model and their extensions.
This package provide functions for box-constrained
multiobjective optimization using the elitist non-dominated
sorting genetic algorithm - NSGA-II. Fast non-dominated
sorting, crowding distance, tournament selection, simulated
binary crossover, and polynomial mutation are called in the
main program, nsga2R, to complete the search.
A Gaussian process regression using a Gaussian kernel for both
one-sample and two-sample cases. Includes non-stationary Gaussian kernel
(exponential decay function) and several likelihood ratio tests for
differential testing along target points.
Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods.  Exact procedures are performed when computationally possible.  Monte Carlo and Asymptotic procedures are performed otherwise.  For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.
This package implements two methods for performing a constrained
principal component analysis (PCA), where non-negativity and/or sparsity
constraints are enforced on the principal axes (PAs). The function
'nsprcomp' computes one principal component (PC) after the other. Each PA
is optimized such that the corresponding PC has maximum additional variance
not explained by the previous components. In contrast, the function
'nscumcomp' jointly computes all PCs such that the cumulative variance is
maximal. Both functions have the same interface as the 'prcomp' function
from the 'stats' package (plus some extra parameters), and both return the
result of the analysis as an object of class 'nsprcomp', which inherits
from 'prcomp'.
A collection of statistical tools for objective (non-supervised) applications
of the Regional Frequency Analysis methods in hydrology.
The package refers to the index-value method and, more precisely, helps the
hydrologist to: (1) regionalize the index-value; (2) form homogeneous regions
with similar growth curves; (3) fit distribution functions to the
empirical regional growth curves.
A Bayesian framework for population group size estimation using the Network Scale Up Method (NSUM). Size estimates are based on a random degree model and include options to adjust for barrier and transmission effects.
Tools for visual inference. Generate null data sets
and null plots using permutation and simulation. Calculate distance metrics
for a lineup, and examine the distributions of metrics.
Provides some number-theoretic functions for R.
Methods for calculating (usually) accurate
numerical first and second order derivatives. Accurate calculations
are done using Richardson's extrapolation or, when applicable, a complex
step derivative is available. A simple difference
method is also provided. Simple difference is (usually) less accurate
but is much quicker than Richardson's extrapolation and provides a
useful cross-check.
Methods are provided for real scalar and vector valued functions.
Package for optimizing regular numeric problems in optically stimulated luminescence dating, such as: equivalent dose calculation, annual dose rate determination, growth curve fitting, decay curve decomposition, fast-component equivalent dose estimation, statistical age models optimization, and statistical plots visualization.
This package contains data sets used as examples in the
book "R in a Nutshell" from O'Reilly Media. For more
information on this book, see
http://shop.oreilly.com/product/0636920022008.do
This package contains the Audio Scrobbler data set used as
an example in the book "R in a Nutshell" from O'Reilly Media.
For more information about this book, see
http://shop.oreilly.com/product/0636920022008.do
This package contains the baseball databank data set used
as an example in the book "R in a Nutshell" from O'Reilly
Media. For more information about this book, see
http://shop.oreilly.com/product/0636920022008.do
Provides coordination and parallel execution facilities,
as well as limited cross-language data exchange, using the
netWorkSpaces server developed by REvolution Computing
Airline on-time data for all flights departing NYC in 2013.
Also includes useful metadata on airlines, airports, weather, and planes.
Provides carefully chosen color palettes as used a.o. at OpenAnalytics <http://www.openanalytics.eu>.
A general purpose client to work with any 'OAI-PMH'
service. The 'OAI-PMH' protocol is described at
http://www.openarchives.org/OAI/openarchivesprotocol.html.
Functions are provided to work with the 'OAI-PMH' verbs: 'GetRecord',
'Identify', 'ListIdentifiers', 'ListMetadataFormats', 'ListRecords', and
'ListSets'.
Harvest metadata using the Open Archives Initiative Protocol for Metadata
Harvesting (OAI-PMH) version 2.0.
Interface to D-optimal design generation code of the Orthogonal Array package. Can generate D-optimal designs with specified number of runs and factors. The optimality of the designs is defined in terms of a user specified optimization function based on the D-efficiency and Ds-efficiency.
Offers a suite of functions for enhancing R plots.
Generalise the starting point of the array index
A set of indexes and tests for the analysis of social segregation.
An implementation of the Blinder-Oaxaca decomposition for linear regression models.
Supports the definition of sets of properties on objects. Observers can listen to changes on individual properties or the set as a whole. The properties are meant to be fully self-describing. In support of this, there is a framework for defining enumerated types, as well as other bounded types, as S4 classes.
A mutable Signal object can report changes to its state,
clients could register functions so that they are called whenever
the signal is emited. The signal could be emited, disconnected,
blocked, unblocked, and buffered.
This package conducts factor rotation techniques which
intentd to identify a simple and well-clustered structure in a
factor loading matrix.
Random forest with oblique decision trees for binary
classification tasks. Discriminative node models in the tree
are based on: ridge regression, partial least squares
regression, logistic regression, linear support vector
machines, or random coefficients.
Grows Oblique Trees to Classification Data.
The package includes two functions for measuring agreement. Raw Agreement Indices (RAI) to categorical data and Information-Based Measure of Disagreement (IBMD) to continuous data. It can be used for multiple raters and multiple readings cases.
Implements the objective Bayesian methodology proposed in Consonni and Deldossi in order to choose the optimal experiment that better discriminate between competing models.
Observational studies are limited in that there could be
an unmeasured variable related to both the response variable
and the primary predictor.  If this unmeasured variable were
included in the analysis it would change the relationship
(possibly changing the conclusions).  Sensitivity analysis is a
way to see how much of a relationship needs to exist with the
unmeasured variable before the conclusions change.  This
package provides tools for doing a sensitivity analysis for
regression (linear, logistic, and cox) style models.
Estimates Optimal Classification scores from roll call
votes supplied though a 'rollcall' object from package 'pscl'.
This package provides a generic function for estimating
positron emission tomography (PET) neuroreceptor occupancies
from the total volumes of distribution of a set of regions of
interest. Fittings methods include the simple 'reference
region' and 'ordinary least squares' (sometimes known as
occupancy plot) methods, as well as the more efficient
'restricted maximum likelihood estimation'.
Supports the analysis of Oceanographic data, including ADCP
measurements, CTD measurements, sectional data, sea-level time series,
coastline files, etc.  Handles dozens of specialized functions for calculating
seawater properties such as potential temperature and density, as well as
derived properties such as buoyancy frequency and dynamic height. Map projections
are calculated using PROJ.4 functions included within the oce.
This package contains a set of functions for manipulating and
visualizing output from numerical ocean models. Many of the routines are
targeted towards Lagrangian particle-tracking models with the data stored
in NetCDF files. This package may be useful to spatial marine ecologists
and biological or physical oceanographers.
Functions for transforming and viewing 2-D and 3-D (oceanographic) data and model output.
Several important and Oceanographic datasets are provided. These are particularly useful to the Oce package, but can be helpful in a general context, as well.
Regression model where the response variable is a rank-indexed compositional vector (non-negative values that sum up to one and are ordered from the largest to the smallest). Parameters are estimated in the Bayesian framework using MCMC methods.
Helper methods for accessing data from web service based on OData Protocol.
It provides several helper methods to access the service metadata, the data from datasets and to download some file resources (it only support CSV for now).
For more information about OData go to http://www.odata.org/documentation/.
This package provides functions to create, connect, update
and query HSQL databases embedded in Open Document Databases
(.odb) files, as OpenOffice and LibreOffice do.
Conversion between the most common odds types for sports betting. Hong Kong odds, US odds, Decimal odds, Indonesian odds, Malaysian odds, and raw Probability are covered in this package.
Wraps the Boost 'odeint' library for integration of differential equations.
Sweave processing of Open Document Format (ODF) files
Provides methods for odfTable() for objects in the survey
package.
Transformation of 'ODM' (see http://www.cdisc.org/odm) files into R format, Microsoft Office format, 'CDA' format and vice versa. 'ODM' format is commonly used in clinical trials. Semantic annotation (such as 'UMLS', 'SNOMED' or 'LOINC' codes) are transformed accordingly.
Fit penalized regression with orthogonalizing EM algorithm
Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased. This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects.
R Client for Ohmage 2 server. Implements basic R
functions to retrieve and process data.
A collection of data sets from several sources that may be
useful for teaching, practice, or other purposes. Functions
have also been included to assist in the retrieval of table
data from websites or in visualizing sample data.
Supplemental functions and data for the OpenIntro guide to the survival package in R.
The package provides functions for the Oja median, Oja
signs and ranks and methods based upon them.
okmesonet retrieves and summarizes Oklahoma (USA) Mesonet
climatological data provided by the Oklahoma Climatological Survey.
Measurements are recorded every five minutes at approximately 120 stations
throughout Oklahoma and are available in near real-time.
'Open Location Codes' (http://openlocationcode.com/) are a Google-created standard for identifying geographic locations.
olctools provides utilities for validating, encoding and decoding entries that follow this standard.
Calculate the theorical specificity of a system of
multiple primers used for PCR, qPCR primers or degenerated
primer design
Provides tools for more easily organizing and
plotting individual ordinary least square (OLS) growth curve
trajectories.
This package including two useful function, which can be
used for filter the molecular descriptors matrix for QSAR.
This package provides functions to generate a correlation matrix
from a genetic dataset and to use this matrix to predict the phenotype of an
individual by using the phenotypes of the remaining individuals through
kriging. Kriging is a geostatistical method for optimal prediction or best
unbiased linear prediction. It consists of predicting the value of a
variable at an unobserved location as a weighted sum of the variable at
observed locations. Intuitively, it works as a reverse linear regression:
instead of computing correlation (univariate regression coefficients are
simply scaled correlation) between a dependent variable Y and independent
variables X, it uses known correlation between X and Y to predict Y.
A collection of functions to analyse '--omics' datasets such as DNA
methylation and gene expression profiles.
Computing probabilistic tree models for oncogenesis based
on genetic data using maximum likelihood.
Contains functions to construct and evaluate directed tree structures that
model the process of occurrence of genetic alterations during carcinogenesis.
Purpose of this package is it to plan, monitor and evaluate oncological phase II studies. In general this kind of studies are single-arm trials with planned interim analysis and binary endpoint. To meet the resulting requirements, the package provides functions to calculate and evaluate 'Simon's two-stage designs' and 'so-called' 'subset designs'. If you are unfamiliar with this package a good starting point is to take a closer look at the functions getSolutions() and getSolutionsSub1().
Analysis of molecular marker data from model (backcrosses,
F2 and recombinant inbred lines) and non-model systems (i. e.
outcrossing species). For the later, it allows statistical
analysis by simultaneously estimating linkage and linkage
phases (genetic map construction). All analysis are based on
multipoint approaches using hidden Markov models.
Provides a series of functions designed to enable users to easily search and interact with occupational data from the O*NET API <www.onetonline.org>. The package produces parsed and listed XML data for custom interactions, or pre-packaged functions for easy extraction of specific data (e.g., Knowledge, Skills, Abilities, Work Styles, etc.).
In this package, we introduce an R function
one_two_sample() which can deal with one and two (normal)
samples. For one normal sample x, the function reports
descriptive statistics, plot, interval estimation and test of
hypothesis of x. For two normal samples x and y, the function
reports descriptive statistics, plot, interval estimation and
test of hypothesis of x and y, respectively. It also reports
interval estimation and test of hypothesis of mu1-mu2 (the
difference of the means of x and y) and sigma1^2 / sigma2^2
(the ratio of the variances of x and y), tests whether x and y
are from the same population, finds the correlation coefficient
of x and y if x and y have the same length.
Two or more samples in one-way independent groups design are compared via different tests.
A collection of routines to manipulate and visualize quaternions and
octonions.
Online PCA for multivariate and functional data using perturbation methods, low-rank incremental methods, and stochastic optimization methods.
Orthogonal Nonlinear Least-Squares Regression using Levenberg-Marquardt Minimization.
Contains tools for open assessment and modelling in Opasnet,
a wiki-based web site and workspace for societal decision making
(see <http://en.opasnet.org/w/Main_Page> for more information).
The core principle of the workspace is maximal openness and modularity.
Variables are defined on public wiki pages using wiki inputs/tables,
databases and R code. This package provides the functionality to download and use these
variables. It also contains health impact assessment tools such as
spatial methods for exposure modelling.
Companion package to the book Option Pricing and
Estimation of Financial Models in R, Wiley, Chichester. ISBN:
978-0-470-74584-7.
Tools to analyse, interpret and understand air
pollution data. Data are typically hourly time series
and both monitoring data and dispersion model output
can be analysed.  Many functions can also be applied to
other data, including meteorological and traffic data.
This package provides an interface to OpenCL, allowing R
to leverage computing power of GPUs and other HPC accelerator
devices.
The OpenCPU system exposes an HTTP API for embedded scientific
computing with R. This provides scalable foundations for integrating R
based analysis and visualization modules into pipelines, web applications
or big data infrastructures. The OpenCPU server can run either as a
single-user development server within the interactive R session, or as a
high performance multi-user cloud server that builds on Linux, Nginx and
rApache. The current R package forms the core of the system. When loaded
in R, it automatically initiates the single-user server and displays the
web address in the console. Visit the OpenCPU website for detailed
information and documentation on the API.
This package is a supplement to OpenIntro Statistics,
which is a free textbook available at openintro.org (at cost
paperbacks are also available for under $10 on Amazon). The
package contains data sets used in the textbook along with
custom plotting functions for reproducing book figures. Note
that many functions and examples include color transparency.
Some plotting elements may not show up properly (or at all) in
some Windows versions.
The OpenMPController package provides a function
'omp_set_num_threads()' to set the number of OpenMP threads to
be used.  This may be useful, for example, when linking against
a vendor optimised BLAS/LAPACK library (e.g. the AMD Core Math
Library), since the defaults used by those libraries may not be
highly performant.
Facilitates treatment of statistical model specifications
as things that can be generated and manipulated programmatically.
Structural equation models may be specified with reticular action model matrices or paths,
linear structural relations matrices or paths, or
directly in matrix algebra.
Fit functions include full information maximum likelihood,
maximum likelihood, and weighted least squares.
Example models include confirmatory factor, multiple group, mixture
distribution, categorical threshold, modern test theory, differential
equations, state space, and many others.
An interface to the Apache OpenNLP tools (version 1.5.3).
The Apache OpenNLP library is a machine learning based toolkit for the
processing of natural language text written in Java.
It supports the most common NLP tasks, such as tokenization, sentence
segmentation, part-of-speech tagging, named entity extraction, chunking,
parsing, and coreference resolution.
See <http://opennlp.apache.org/> for more information.
Apache OpenNLP jars and basic English language models.
Bindings to OpenSSL libssl and libcrypto, plus custom SSH pubkey parsers.
Supports RSA, DSA and NIST curves P-256, P-384 and P-521. Cryptographic signatures
can either be created and verified manually or via x509 certificates. AES block
cipher is used in CBC mode for symmetric encryption; RSA for asymmetric (public key)
encryption. High-level envelope functions combine RSA and AES for encrypting arbitrary
sized data. Other utilities include key generators, hash functions (md5, sha1, sha256,
etc), base64 encoder, a secure random number generator, and 'bignum' math methods for
manually performing crypto calculations on large multibyte integers.
Accesses high resolution raster maps using the OpenStreetMap protocol. Dozens of road, satellite,
and topographic map servers are directly supported, including CloudMade,
Mapnik, Bing, stamen, and MapQuest. Maps can be plotted using either base
graphics, or ggplot2. This package is not affiliated with the OpenStreetMap.org mapping project.
opentraj uses the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) for computing simple air parcel trajectories. The functions in this package allow users to run HYSPLIT for trajectory calculations, as well as get its results, directly from R without using any GUI interface.
Simplifies the creation of Excel .xlsx files by providing a
high level interface to writing, styling and editing worksheets. Through
the use of Rcpp, read/write times are comparable to the xlsx and XLConnect
packages with the added benefit of removing the dependency on Java.
A set of binary operators for common tasks such as regex
manipulation.
These utilities allow for programatically working with
R's operators: translating between an operator and its underlying
function, inverting an operator, determining an operator's type, etc.
Implementation of the Open Perimetry Interface (OPI) for simulating and controlling visual field machines using R. The OPI is a standard for interfacing with visual field testing machines (perimeters). It specifies basic functions that allow many visual field tests to be constructed. As of February 2015 it is fully implemented on the Octopus 600 and Octopus 900 and partially on the Heidelberg Edge Perimeter and the Kowa AP 7000. It also has a cousin: the R package visualFields, which has tools for analysing and manipulating visual field data.
Comparison of data by Pearson product-moment correlation
coefficients is prone to outliers. The problem can be
alleviated by normalizing data with outliers before computing
the Pearson correlation coefficient. The sample provides such
normalization by optimal power space transformation.
Searches for optimal linear combination of multiple
diagnostic tests (markers) that maximizes the area under the
receiver operating characteristic curve (AUC); performs an
approximated cross-validation for estimating the AUC associated
with the estimated coefficients.
Estimates optimal number of biomarkers for two-group
classification based on microarray data
Cluster analysis using statistical and biological
validation measures for both continuous and count data.
Tools to assist in safely applying user-generated objective and
derivative functions to optimization programs. "Optimization" at the
time of writing refers primarily to function minimization methods with at most
bounds and masks on the parameters.
Tools are provided to check the basic computation of objective functions that
the user provides, along with proposed gradient and Hessian functions,
as well as to wrap such functions to avoid failures when inadmissible parameters
are provided. Checks on bounds and masks (fixed parameters) are available, as
are a check on scaling or optimality conditions. An axial search to seek lower
points on the objective function surface is included. Though numDeriv is
used as the primary tool for numerical derivative approximations, forward,
central and backward gradient approximation codes for convenience.
Functions to find near-optimal multi-stage designs for continuous outcomes.
Estimation of value and hedging strategy of call and put options, based on optimal hedging and Monte Carlo method, from Chapter 3 of 'Statistical Methods for Financial Engineering', by Bruno Remillard, CRC Press, (2013).
A single function 'options.ifunset(...)' is contained herewith, which allows the user to set a global option ONLY if it is not already set. By this token, for package maintainers this function can be used in preference to the standard 'options(...)' function, making provision for THEIR end user to place 'options(...)' directives within their '.Rprofile' file, which will not be overridden at the point when a package is loaded.
Parse options from the command-line using a simple, clean syntax.
It requires little or no specification and supports short and long options,
GNU-, Java- or Microsoft- style syntaxes, verb commands and more.
This package enables users to compute one or more optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.
Provides a set of commands to manage an abstract
optimization method. The goal is to provide a building block
for a large class of specialized optimization methods. This
package manages: the number of variables, the minimum and
maximum bounds, the number of non linear inequality
constraints, the cost function, the logging system, various
termination criteria, etc...
Provides a building block for optimization algorithms
based on a simplex. The optimsimplex package may be used in the
following optimization methods: the simplex method of Spendley
et al., the method of Nelder and Mead, Box's algorithm for
constrained optimization, the multi-dimensional search by
Torczon, etc...
Provides a replacement and extension of the optim()
function to unify and streamline optimization capabilities in R
for smooth, possibly box constrained functions of several or
many parameters. This is the CRAN version of the package.
Optimal two and three stage designs monitoring
time-to-event endpoints at a specified timepoint
Efficient Monte Carlo Algorithms for the price and the sensitivities of Asian and European Options under Geometric Brownian Motion.
Greatly simplifies the process to read, analyze and report log data
collated by OptiQuant (optiquant.opti55.com) in R. It automates efficient
import of databases while preparing them for analysis and for semi-automated
reporting.
This fills the gaps credit analysts and loan modellers at
Optimum Credit identify in the existing R code body.
It allows for the production of documentation with less coding,
replicates a number of Microsoft Excel functions useful for
modelling loans (without rounding), and other helpful functions
for producing charts and tables.  It also has some additional scales for
use, including a GBP scale.
Tools for performing an optimal scaling transformation on a data
vector
Code for optimal mixture weights in importance sampling. Workhorse
functions penoptpersp() and penoptpersp.alpha.only() minimize estimated
variances with and without control variates respectively. It can be used in
adaptive mixture importance sampling, for example, function batch.estimation() does
two stages, a pilot estimate of mixing alpha and a following importance
sampling.
Provides routines for distance based bipartite matching to reduce
covariate imbalance between treatment and control groups in observational
studies. Routines are provided to generate distances from GLM models (propensity
score matching) and formulas (Euclidean and Mahalanobis matching), stratified
matching (exact matching), and calipers. Results of the fullmatch routine are
guaranteed to provide minimum average within matched set distance.
A command line parser inspired by Python's 'optparse' library to
be used with Rscript to write "#!" shebang scripts that accept short and
long flag/options.
The package contains a set of algorithms for creating
partitions and coverings of objects largely based on operations
on (dis)similarity relations (or matrices). There are several
iterative re-assignment algorithms optimizing different
goodness-of-clustering criteria.  In addition, there are
covering algorithms 'clique' which derives maximal cliques, and
'maxpact' which creates a covering of maximally compact sets.
Graphical analyses and conversion routines are also included.
The optR function provides an option to solve the linear system
using numeric computation approach. The current packages supports Gauss
Elimination, LU decomposition, Gauss-Sideil, CGM and Cholesky methods to
solve equations having relationships of Ax=b form. The package can be used
to build linear and non-linear models by defining them in linear system.
Finds optimal trees in weighted graphs. In
particular, this package provides solving tools for minimum cost spanning
tree problems, minimum cost arborescence problems, shortest path tree
problems and minimum cut tree problem.
Easy-to-use functions to explore Oracle databases and import data
into R. User interface for the ROracle package.
Implements orbit counting using a fast combinatorial approach.
Counts orbits of nodes and edges from edge matrix or data frame, or a
graph object from the graph package.
Computes various confidence intervals for the odds ratio of two independent binomial proportions.
Functions to perform subspace clustering and
classification.
Provides clustering of genes with similar
dose response (or time course) profiles. It implements the method
described by Lin et al. (2012).
This package has been implemented to solve first order
autocorrelation problems using an iterative method. This
procedure estimates both autocorrelation and beta coefficients
recursively until we reach the convergence (8th decimal). The
residuals are computed after estimating Beta using EGLS
approach and Rho is estimated using the previous residuals.
This package extends the Bradley-Terry-Luce model for fitting pair
comparison models with an ordinal response. It is also possible to
incorporate an order effect, or, equivalently, an effect for the home
advantage.
Computes ordinal, statistics and effect sizes as an
alternative to mean comparison: Cliff's delta or success rate
difference (SRD), Vargha and Delaney's A or the Area Under a
Receiver Operating Characteristic Curve (AUC), the discrete
type of McGraw & Wong's Common Language Effect Size (CLES) or
Grissom & Kim's Probability of Superiority (PS), and the Number
needed to treat (NNT) effect size. Moreover, comparisons to
Cohen's d are offered based on Huberty & Lowman's Percentage of
Group (Non-)Overlap considerations.
This package uses B-spline based nonparametric smooth
estimators to estimate parent distributions given observations
on multiple order statistics.
Functions for visualizing and retrieving data for the
state of an orderbook at a particular period in time.
Ordered lasso and time-lag sparse regression. Ordered Lasso fits a
linear model and imposes an order constraint on the coefficients. It writes
the coefficients as positive and negative parts, and requires positive
parts and negative parts are non-increasing and positive. Time-Lag Lasso
generalizes the ordered Lasso to a general data matrix with multiple
predictors. For more details, see Suo, X.,Tibshirani, R., (2014) 'An
Ordered Lasso and Sparse Time-lagged Regression'.
In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.
Calculates ordinated diet breadth with some plotting functions.
Implementation of cumulative link (mixed) models also known
as ordered regression models, proportional odds models, proportional
hazards models for grouped survival times and ordered logit/probit/...
models. Estimation is via maximum likelihood and mixed models are fitted
with the Laplace approximation and adaptive Gauss-Hermite quadrature.
Multiple random effect terms are allowed and they may be nested, crossed or
partially nested/crossed. Restrictions of symmetry and equidistance can be
imposed on the thresholds (cut-points/intercepts). Standard model
methods are available (summary, anova, drop-methods, step,
confint, predict etc.) in addition to profile methods and slice
methods for visualizing the likelihood function and checking
convergence.
A regression framework for response variables which are continuous self-rating scales such as the Visual Analog Scale (VAS) used in pain assessment, or the Linear Analog Self-Assessment (LASA) scales in quality of life studies. These scales  measure subjects' perception of an intangible quantity, and cannot be handled as ratio variables because of their inherent nonlinearity. We treat them as ordinal variables, measured on a continuous scale. A function (the g function, currently the generalized logistic function) connects the scale with an underlying continuous latent  variable.  The link function is the inverse of the CDF of the assumed underlying distribution of the latent variable. Currently the logit link, which corresponds to a standard logistic distribution, is implemented.
This package provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.
Analysis of a matrix of polytomous items using Ordinal Logistic Biplots (OLB)
The OLB procedure extends the binary logistic biplot to ordinal (polytomous) data.
The  individuals are represented as points on a plane and the variables are represented
as lines rather than vectors as in a classical or binary biplot, specifying the points
for each of the categories of the variable.
The set of prediction regions is established by stripes perpendicular to the line
between the category points, in such a way that the prediction for each individual is given
by its projection into the line of the variable.
Fits ordinal regression models with elastic net penalty.
Supported models include cumulative logit, probit, cauchit, and complementary log-log.
The algorithm uses Fisher Scoring with Coordinate Descent updates.
Method for develops a classification model for ordinal responses based on logic regression.
We consider the problem of estimating two isotonic regression curves g1* and g2* under the constraint that they are ordered, i.e. g1* <= g2*. Given two sets of n data points y_1, ..., y_n and z_1, ..., z_n that are observed at (the same) deterministic design points x_1, ..., x_n, the estimates are obtained by minimizing the Least Squares criterion L(a, b) = sum_{i=1}^n (y_i - a_i)^2 w1(x_i) + sum_{i=1}^n (z_i - b_i)^2 w2(x_i) over the class of pairs of vectors (a, b) such that a and b are isotonic and a_i <= b_i for all i = 1, ..., n. We offer two different approaches to compute the estimates: a projected subgradient algorithm where the projection is calculated using a PAVA as well as Dykstra's cyclical projection algorithm.
Implementation of a procedure for generating samples from a mixed distribution of ordinal and normal random variables with pre-specified correlation matrix and marginal distributions.
Selection and/or smoothing of ordinally scaled independent variables using a group lasso or generalized ridge penalty.
Provides an alternative to R's built-in functionality for handling
regular expressions, based on the Oniguruma library. Offers first-class
compiled regex objects, partial matching and function-based substitutions,
amongst other features.
Organic/biological mass spectrometry data analysis.
Provides functionality to process text files created by Emacs' Org mode, and decompose the content to the smallest components (headlines, body, tag, clock entries etc).  Emacs is an extensible, customizable text editor and Org mode is for keeping notes, maintaining TODO lists, planning projects.  Allows users to analyze org files as data frames in R, e.g., to convieniently group tasks by tag into project and calculate total working hours.  Also provides some help functions like search.parent, gg.pie (visualise working hours in ggplot2) and tree.headlines (visualise headline stricture in tree format) to help user managing their complex org files.
ORIClust is a user-friendly R-based software package for
gene clustering. Clusters are given by genes matched to
prespecified profiles across various ordered treatment groups.
It is particularly useful for analyzing data obtained from
short time-course or dose-response microarray experiments.
Representations, conversions and display of orientation
SO(3) data.  See the orientlib help topic for details.
Used primarily for estimates of allele frequency surfaces from point estimates.
It can also place individuals of unknown origin back onto the geographic map with great accuracy.
Additionally, it can place admixed individuals by estimating contributing fractions at each
location on a map. Lastly, it can rank SNPs by their ability to differentiate populations.
See "Fast Spatial Ancestry via Flexible Allele Frequency Surfaces" (John Michael Ranola, John
Novembre, Kenneth Lange) in Bioinformatics 2014 for more info.
This version of the package deals with the min-sum location
problem, also known as Fermat--Weber problem. The min-sum location problem
search for a point such that the weighted sum of the distances to the
demand points are minimized.
Version espanola del paquete orloca que trata el problema
de localizacion min-sum, tambien conocido como problema de
Fermat--Weber. El problema de localizacion min-sum busca un
punto tal que la suma ponderada de las distancias a los puntos
de demanda sea minima.
Odds ratio based multifactor-dimensionality reduction
method for detecting gene-gene interactions
Data input/output functions for data that conform to the
Digital Imaging and Communications in Medicine (DICOM) standard, part
of the Rigorous Analytics bundle.
Functions for the input/output and visualization of
medical imaging data that follow either the ANALYZE, NIfTI or AFNI
formats.  This package is part of the Rigorous Analytics bundle.
Image analysis techniques for positron emission tomography
(PET) that form part of the Rigorous Analytics bundle.
Assess repeatability, accuracy and corss-platform
agreement of titration microarray data based on order
restricted inference procedures
Convert the Odds Ratio to the Relative Risk in Cohort Studies with Partial Data Information.
Represents the basis functions for B-splines in a simple matrix
formulation that facilitates, taking integrals, derivatives, and
making orthogonal the basis functions.
Implements the orthogonal reparameterization
approach recommended by Lancaster (2002) to estimate dynamic panel
models with fixed effects (and optionally: panel specific
intercepts). The approach uses a likelihood-based estimator and
produces estimates that are asymptotically unbiased as N goes to
infinity, with a T as low as 2.
A collection of functions to construct sets of orthogonal
polynomials and their recurrence relations. Additional
functions are provided to calculate the derivative, integral,
value and roots of lists of polynomial objects.
The osDesign serves for planning an observational study. Currently, functionality is focused on the two-phase and case-control designs. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model.
This package provides infrastructure to access
OpenStreetMap data from different sources, to work with the data
in common R manner, and to convert data into available
infrastructure provided by existing R packages (e.g., into sp and
igraph objects).
An interface between R and the OSRM API. OSRM is a routing
service based on OpenStreetMap data. See <http://project-osrm.org/> for more
information. A public API exists but one can run its own instance. This package
allows to compute distance (travel time and kilometric distance) between points
and travel time matrices.
Functions for creating ensembles of optimal trees for regression, classification and class membership probability estimation are given. A few trees are selected from an initial set of trees grown by random forest for the ensemble on the basis of their individual and collective performance. Trees are assessed on out-of-bag data and on an independent training data set for individual and collective performance respectively. The prediction functions return estimates of the test responses and their class membership probabilities. Unexplained variations, error rates, confusion matrix, Brier scores, etc. are also returned for the test data.
Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree.
Bayesian reconstruction of disease outbreaks using epidemiological and genetic information.
Implements basic tools for storing, handling and visualizing disease outbreak data, as well as simple analysis tools. OutbreakTools defines the new formal class obkData which can be used to store any case-base outbreak data, and provides summaries for these objects, alongside a range of functions for subsetting and data manipulation. It implements a range of graphics for visualising timelines, maps, contact networks and genetic analyses. It also includes a simple case-base outbreak simulation tool.
This package provides three algorithms to detect outlying observations for censored survival data.
Detecting outlying values such as genes, peptides or samples for multi-replicated high-throughput high-dimensional data
A collection of some tests commonly used for identifying
outliers.
Functions to process ''outranking'' ELECTRE methods existing in the literature. See, e.g.,
<http://en.wikipedia.org/wiki/ELECTRE> about the outranking approach and the foundations of ELECTRE methods.
Calculates and compares rate differences of continuous character evolution under Brownian motion and a new set of Ornstein-Uhlenbeck-based Hansen models that allow the strength of selection and stochastic motion to vary across selective regimes.
Provides functions to fit kernel density functions to
data on temporal activity patterns of animals; estimate coefficients
of overlapping of densities for two species; and calculate bootstrap
estimates of confidence intervals.
Provides utilities for processing of Oxy-Bisulfite microarray data
(e.g. via the Illumina Infinium platform) with tandem arrays, one using conventional
bisulfite sequencing, the other using oxy-bisulfite sequencing.
Functions for plotting Australia's coastline and state
boundaries.
Conducts posterior predictive checks of coalescent models using gene and species trees generated by 'BEAST' or '*BEAST'. The functionality of P2C2M can be extended via two third-party R packages that are available from the author websites: 'genealogicalSorting' and 'phybase'. To use these optional packages, the installation of the Python libraries 'NumPy' (>= 1.9.0) and 'DendroPy' (= 3.12.0) is required.
The welfare's synthetic indicator provides an ideal tool
for measuring multi-dimensional concepts such as welfare,
development, living standards, etc. It enables information from
the various indicators to be aggregated into a single synthetic
measure.
Analyzing survival data from illness-death model
A package that provides tools for conducting performance attribution for equity portfolios. The package uses two methods: the Brinson method and a regression-based analysis.
Pabon Lasso is a graphical method for monitoring the efficiency of different wards of a hospital or different hospitals.Pabon Lasso graph is divided into 4 parts which are created after drawing the average of BTR and BOR. The part in the left-down side is Zone I, left-up side is Zone II, Right-up side part is Zone III and the last part is Zone IV.
This package is intended to perform estimation and
prediction in high-dimensional additive models, using a sparse
PAC-Bayesian point of view and a MCMC algorithm. The method is
fully described in Guedj and Alquier (2013), 'PAC-Bayesian
Estimation and Prediction in Sparse Additive Models',
Electronic Journal of Statistics, 7, 264--291.
Functions to easily convert data to binary formats other programs/machines can understand.
Allows to get and cache R package download log files
from RStudio's CRAN mirror for analyzing package usage.
Simple algorithm to arrange non-overlapping circles within a rectangle.
This package comes to illustrate the book "Petit Manuel de
Programmation Orientee Objet sous R"
packdep elucidates the dependencies
between user-contributed R packages and
identifies key packages according to social
network analysis metrics.
Various useful functions for statisticians: describe data, plot Kaplan-Meier curves with numbers of subjects at risk, compare data sets, display spaghetti-plot, build multi-contingency tables...
Manage the R packages your project depends
on in an isolated, portable, and reproducible way.
Illustration of the book "Petit Manuel de Programmation Orientee Objet sous R". The english version "A (Not so) Short Introduction to S4" is on CRAN, 'Contributed documentation'.
Tools to more conveniently perform tasks
associated with add-on packages.  pacman conveniently wraps library and
package related functions and names them in an intuitive and consistent
fashion.  It seeks to combine functionality from lower level functions which
can speed up workflow.
Procrustes analyses to infer co-phylogenetic
matching between pairs of (ultrametric) phylogenetic trees.
Given a vector of Taylor series coefficients of sufficient length as input, the function returns the numerator and denominator coefficients for the Padé approximant of appropriate order.
Calculate unadjusted/adjusted attributable fraction function of a set of covariates for a censored survival outcome from a Cox model using the method proposed by Chen, Lin and Zeng (Biometrika 97, 713-726., 2010).
A statistically sound method for estimating jointly the attachment function and node fitness in a temporal complex network by maximizing a suitable penalized log-likelihood function is implemented in this package. Tutorial can be found in the accompanying vignette. More details about the method can be found at http://dx.doi.org/10.1371/journal.pone.0137796. To cite this package in your projects, please run the command: citation("PAFit") for a list of references you should cite.
Pageview data from the 'Wikimedia' sites, such as 'Wikipedia' (https://www.wikipedia.org/), from entire projects to per-article
levels of granularity, through the new RESTful API and data source (https://wikimedia.org/api/rest_v1/?doc).
The package can identify the dysregulated KEGG pathways
based on global influence from the internal effect of pathways
and crosstalk between pathways. (1) The PAGI package can
prioritize the pathways associated with two biological states
by statistical significance or FDR. (2) The PAGI package can
evaluated the global influence factor (GIF) score in the global
gene-gene network constructed based on the relationships of
genes extracted from each pathway in KEGG database and the
overlapped genes between pathways.
The two Bayesian hierarchical pathway analysis methods Sparse normal/Adaptive lasso and Normal/Bayes factors proposed by Evangelou et al (2013) are implemented. Further, the well-known Fisher's method is implemented
The package contains two functions: paired.Loc and
paired.Scale. A parametric and nonparametric confidence
interval can be computed for the ratio of locations
(paired.Loc) and the ratio of scales (paired.Scale). The
samples must be paired and expected values must be positive.
This package provides many datasets and a set of graphics
(based on ggplot2), statistics, effect sizes and hypothesis
tests for analysing paired data with S4 class.
A tool to compare two heatmaps and discover patterns
within and across groups. In the context of biology, group can
be defined based on gene ontology.
Creates an interactive scatterplot matrix using the D3 JavaScript library. See <http://d3js.org/> for more information on D3.
Eulerian tours and Hamiltonian decompositions of complete graphs are used to ameliorate order effects in statistical graphics.
Performs the explicit calculation
-- not estimation! -- of the Rasch item parameters for dichotomous and
polytomous item responses, using a pairwise comparison approach. Person
parameters (WLE) are calculated according to Warm's weighted likelihood
approach.
Calculation of the parametric, nonparametric confidence intervals
for the difference or ratio of location parameters, nonparametric confidence interval
for the Behrens-Fisher problem and for the difference, ratio and odds-ratio of binomial
proportions for comparison of independent samples. Common wrapper functions to split
data sets and apply confidence intervals or tests to these subsets.
A by-statement allows calculation of CI separately for the levels of further factors.
CI are not adjusted for multiplicity.
Tests if quantitative palaeoenvironmental reconstructions are statistically significant.
19 functions to wrap each endpoint of
the PaleobioDB API, plus 8 functions to visualize and process the fossil
data. The API documentation for the Paleobiology Database can be found in
<http://paleobiodb.org/data1.1/>
Tools to extract and analyse charcoal sedimentary data stored in
the Global Charcoal Database. Main functionalities includes data extraction
and sites selection, transformation and interpolation of the charcoal
records as well as compositing.
Transfer functions and statistical operations for
paleoecology
Provides tools for transforming, time-scaling, and
modifying phylogenies containing extinct (i.e. fossil) lineages. In particular,
most users are interested in the functions timePaleoPhy, bin_timePaleoPhy,
cal3TimePaleoPhy and bin_cal3TimePaleoPhy, which time-scale cladograms of
fossil taxa into dated phylogenies. This package also contains a large number
of likelihood functions for estimating sampling and diversification rates from
different types of data available from the fossil record (e.g. range data,
occurrence data, etc). paleotree users can also simulate diversification and
sampling in the fossil record using the function simFossilRecord, which is a
detailed simulator for branching birth-death-sampling processes composed of
discrete taxonomic units arranged in ancestor-descendant relationships. Users
can use simFossilRecord to simulate diversification in incompletely sampled
fossil records, under various models of morphological differentiation (i.e.
the various patterns by which morphotaxa originate from one another), and
with time-dependent, longevity-dependent and/or diversity-dependent rates of
diversification, extinction and sampling. Additional functions allow users to
translate simulated ancestor-descendant data from simFossilRecord into standard
time-scaled phylogenies or unscaled cladograms that reflect the relationships
among taxon units.
Facilitates analysis of paleontological sequences of trait values from an evolving lineage.  Functions are provided to fit, using maximum likelihood, evolutionary models including unbiased random walks, directional evolution, stasis, Ornstein-Uhlenbeck, punctuated change, and evolutionary models in which traits track some measured covariate.
Use Pokemon(R) inspired palettes with additional 'ggplot2' scales. Palettes are the colours in each Pokemon's sprite, ordered by how common they are in the image. The first 386 Pokemon are currently provided.
R package to compute Incoming Solar Radiation (insolation) for paleoclimate studies. Features three solutions: BER78 (Berger, 1978), BER90 (Berger and Loutre, 1991) and LA04 (Laskar et al. 2004). Computes daily-mean, season-averaged and annual means for all latitudes.
Colour palettes for data, based on some well known public data sets.
Correspondence Analysis of Contingency Tables with Simple
and Double Structures Superimposed Representations, Intra
Blocks Correspondence Analysis (IBCA), Weighted Intra Blocks
Correspondence Analysis (WIBCA).
Simulation functions to assess or explore the power of a
dataset to estimates significant random effects (intercept or
slope) in a mixed model. The functions are based on the "lme4"
package.
Implements the Panel Data Approach Method for program evaluation as developed in Hsiao, Ching and Ki Wan (2012). pampe estimates the effect of an intervention by comparing the evolution of the outcome for a unit affected by an intervention or treatment to the evolution of the unit had it not been affected by the intervention.
Some functions for sample classification in microarrays
Multiple imputation for multivariate panel or clustered data.
Provides the tools needed to benchmark the R2 value corresponding to a certain acceptable noise level while also providing a rescaling function based on that noise level yielding a new value of R2 we refer to as R2k which is independent of both the number of degrees of freedom and the noise distribution function.
PANDA (Preferential Attachment based common Neighbor Distribution derived Associations) is designed to perform the following tasks in PPI networks: (1) identify significantly functionally associated protein pairs, (2) predict GO terms and KEGG pathways for proteins, (3) make a cluster of proteins based on the significant protein pairs, (4) identify subclusters whose members are enriched in KEGG pathways. For other types of biological networks, (1) and (3) can still be performed.
Contains some functions catching all messages, stdout and other
useful information while evaluating R code and other helpers to return user
specified text elements (like: header, paragraph, table, image, lists etc.)
in pandoc's markdown or several type of R objects similarly automatically
transformed to markdown format. Also capable of exporting/converting (the
resulting) complex pandoc documents to e.g. HTML, PDF, docx or odt. This
latter reporting feature is supported in brew syntax or with a custom
reference class with a smarty caching backend.
Aggregate Business Tendency Survey Data (and other qualitative
surveys) to time series at various aggregation levels. Run aggregation of
survey data in a speedy, re-traceable and a easily deployable way.
Aggregation is substantially accelerated by use of data.table.
Panelaggregation aims at providing an interface that is more geared towards
survey researchers.
The package estimates linear models on panel data structures in the presence of AR(1)-type autocorrelation as well as panel heteroskedasticity and/or contemporaneous correlation. First, AR(1)-type autocorrelation is addressed via a two-step Prais-Winsten feasible generalized least squares (FGLS) procedure, where the autocorrelation coefficients may be panel-specific. A number of common estimators for the autocorrelation coefficient are supported. In case of panel heteroskedasticty, one can choose to use a sandwich-type robust standard error estimator with OLS or a panel weighted least squares estimator after the two-step Prais-Winsten estimator. Alternatively, if panels are both heteroskedastic and contemporaneously correlated, the package supports panel-corrected standard errors (PCSEs) as well as the Parks-Kmenta FGLS estimator.
A high performance package implementing random effects and/or sample selection models for panel count data.
Linear models for panel data: the fixed effect model and the
random effect model
Tools to interact with the 'Pangaea' Database (http://
www.pangaea.de/), including functions for searching for data, fetching 'datasets'
by 'dataset' 'ID', and working with the 'Pangaea' 'OAI-PMH' service.
This package contains a methodology that makes use of the factor
structure of large dimensional panels to understand the nature of
nonstationarity inherent in data. This is referred to as PANIC - Panel
Analysis of Nonstationarity in Idiosyncratic and Common Components. PANIC
(2004) includes valid pooling methods that allow panel unit root tests
tests to be constructed. PANIC (2004) can detect whether the
nonstationarity in a series is pervasive, or variable specific, or both.
PANIC (2010) includes the Panel Modified Sargan-Bhargava test, three models
for a Moon and Perron style test, and a bias correction for the
idiosyncratic unit root test of PANIC (2004). The PANIC model approximates
the number of factors based on Bai and Ng (2002).
A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'- or 'markdown'-based
reports and to prettify the output of various estimated models.
R package implementing random forest classification using parallel computing, built with Fortran and OpenMP in the backend.
Passing a given function name or a call to the
parallelize/parallelize_call functions analyses and executes
the code, if possible in parallel. Parallel code execution can
be performed locally or on remote batch queuing systems.
Unified parallelization framework for multiple back-end,
designed for internal package and interactive usage.
The main operation is a parallel "map" over lists.
Supports local, multicore, mpi and BatchJobs mode.
Allows "tagging" of the parallel operation
with a level name that can be later selected by the user to
switch on parallel execution for exactly this operation.
Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.
By sampling your data, running the provided classifier on these samples in parallel on your own machine and letting your models vote on a prediction, we return much faster predictions than the regular machine learning algorithm and possibly even more accurate predictions.
Parallelise constraint based causality discovery and causal inference methods. The parallelised algorithms in the package will generate the same results as that of the pcalg package but will be much more efficient.
By sampling your data, running the Support-Vector-Machine algorithm on these samples in parallel on your own machine and letting your models vote on a prediction, we return much faster predictions than the regular Support-Vector-Machine and possibly even more accurate predictions.
Functions for parameter descriptions and operations in black-box
optimization, tuning and machine learning. Parameters can be described
(type, constraints, defaults, etc.), combined to parameter sets and can in
general be programmed on. A useful OptPath object (archive) to log function
evaluations is also provided.
Parametric linkage analysis and other likelihood-based pedigree analyses based on the Elston-Stewart algorithm, including singlepoint LOD scores, power analysis and computation of marker genotype distributions. Multipoint analysis is provided through a convenient MERLIN wrapper. Includes many utility functions for creating, manipulating and plotting pedigrees, markers and linkage results.
An interface to simplify organizing parameters used in a package,
using external configuration files. This attempts to provide a cleaner
alternative to options().
paran is an implementation of Horn's technique for
numerically and graphically evaluating the components or
factors retained in a principle components analysis (PCA) or
common factor analysis (FA). Horn's method contrasts
eigenvalues produced through a PCA or FA on a number of random
data sets of uncorrelated variables with the same number of
variables and observations as the experimental or observational
data set to produce eigenvalues for components or factors that
are adjusted for the sample error-induced inflation. Components
with adjusted eigenvalues greater than one are retained. paran
may also be used to conduct parallel analysis following
Glorfeld's (1995) suggestions to reduce the likelihood of
over-retention.
Distributed gradient boosting based on the mboost package. The
parboost package is designed to scale up component-wise functional
gradient boosting in a distributed memory environment by splitting the
observations into disjoint subsets, or alternatively using bootstrap
samples (bagging). Each cluster node then fits a boosting model to its
subset of the data. These boosting models are combined in an ensemble,
either with equal weights, or by fitting a (penalized) regression
model on the predictions of the individual models on the complete
data.
The package estimates the matrix of partial correlations
based on different regularized regression methods: lasso,
adaptive lasso, PLS, and Ridge Regression. In addition, the
package provides model selection for lasso, adaptive lasso and
Ridge regression based on cross-validation.
Parallelized version of the "segment" function from Bioconductor package "DNAcopy", utilizing multi-core computation on host CPU
Conduct the Parent-Offspring Test Using Monomorphic SNP Markers. The similarity to the parents is computed for each offspring, and a plot of similarity for all offspring is produced. One can keep the offspring above some threshold for the similarity for further studies.
Statistical functions to describe a Pareto Positive Stable (PPS)
distribution and fit it to real data. Graphical and statistical tools to
validate the fits are included.
Fits Parametric Frailty Models by maximum marginal likelihood.
Possible baseline hazards: Weibull, inverse Weibull, exponential, Gompertz, lognormal and loglogistic.
Possible Frailty distributions: gamma, inverse Gaussian, positive stable and lognormal.
The package provides a number of easily parallelized
functions from the fossil package. This package is designed to
be used with some type of parallel computing backend, such as
multicore, snow or MPI.
Methods for Portfolio Optimization and Risk Management.
The package provides a parallel estimation of the mutual
information based on entropy estimates from k-nearest neighbors
distances and algorithms for the reconstruction of gene
regulatory networks.
Implements basic partial order tools for multidimensional poverty evaluation with ordinal variables. Its main goal is to provide socio-economic scholars with an integrated set of elementary functions for multidimensional poverty evaluation, based on ordinal information. The package is organized in four main parts. The first two comprise functions for data management and basic partial order analysis; the third and the fourth are devoted to evaluation and implement both the poset-based approach and a more classical counting procedure.
Parse dates automatically, without the need of
specifying a format. Currently it includes the git date parser.
It can also recognize and parse all ISO 8601 formats.
A novel tool for generating a piecewise
constant estimation list of increasingly complex predictors
based on an intensive and comprehensive search over the entire
covariate space.
Fits time series models which consist of a sum of a permanent and a transient component.
Computes Odds Ratio adjusted for a vector of possibly
continuous covariates
Low-dimensional embedding, using Random Forests for
multiclass classification
partitionMetric computes a distance between two partitions
of a set.
Additive partitions of integers.  Enumerates the
partitions, unequal partitions, and restricted partitions of an
integer; the three corresponding partition functions are also
given.  Set partitions are now included.
Miscellaneous utilities for the 'parallel' package,
cluster-oriented section; "Snowdoop" alternative to MapReduce;
file splitting and distributed operations such as sort and aggregate.
"Software Alchemy" method for parallelizing most statistical methods,
presented in N. Matloff, Parallel Computation for Data Science,
Chapman and Hall, 2015.  Includes a debugging aid.
This package performs basic functions to fit and predict periodic autoregressive time series models. These models are discussed in the book P.H. Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancement are expected, and the maintainer cannot provide any support.
A computational toolbox for recursive partitioning.
The core of the package is ctree(), an implementation of
conditional inference trees which embed tree-structured
regression models into a well defined theory of conditional
inference procedures. This non-parametric class of regression
trees is applicable to all kinds of regression problems, including
nominal, ordinal, numeric, censored as well as multivariate response
variables and arbitrary measurement scales of the covariates.
Based on conditional inference trees, cforest() provides an
implementation of Breiman's random forests. The function mob()
implements an algorithm for recursive partitioning based on
parametric models (e.g. linear models, GLMs or survival
regression) employing parameter instability tests for split
selection. Extensible functionality for visualizing tree-structured
regression models is available.
A toolkit with infrastructure for representing, summarizing, and
visualizing tree-structured regression and classification models. This
unified infrastructure can be used for reading/coercing tree models from
different sources (rpart, RWeka, PMML) yielding objects that share
functionality for print/plot/predict methods. Furthermore, new and improved
reimplementations of conditional inference trees (ctree) and model-based
recursive partitioning (mob) from the party package are provided based
on the new infrastructure.
Parviol combines parallel coordinates and violin plot
An R package for polygenic trait analysis
Pipeline and additional tools designed to handle aligned data from chromatin NGS experiments (less adapted for typical RNAseq applications). Specifically, starting from aligned reads after sequencing, the pipeline function aims at removing artefacts from datasets, estimate in-silico the fragments size, and generate WIG files with corrected values. The package also provides several tools for WIG files manipulations, scaling, merging and subtract (e.g. input).
To implement two methods, Kappa and PASS, for selecting
tuning parameters in regularized procedures such as LASSO,
SCAD, adaptive LASSO, aiming for variable selection in
regularized regression
Regulation, decomposition and analysis of space-time series. The pastecs library is a PNEC-Art4 and IFREMER (Benoit Beliaeff <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 (http://www.obs-vlfr.fr/~enseigne/anado/passtec/passtec.htm) functionalities to R.
A pre-processor for mrBayes that assimilates sequences, taxonomic
information and tree constraints as per xxx.  The main functions of
interest for most users will be pastis_simple, pastis_main and conch. The
main analysis is conducted with pastis_simple or pastis_main followed by a
manual execution of mrBayes (>3.2). The placement of taxa not contained in
the tree constraint can be investigated using conch.
Data and functions for the book PROBABILITY and STATISTICS
WITH R.
Functions and data sets for the text Probability and Statistics
with R, Second Edition
Functions to patch specials in .dvi files,
or entries in .synctex files.  Works with "concordance=TRUE"
in Sweave or knitr to link sources to previews.
Functions to generate scatterplots with images patches
instead of usual glyphs, with associated utilities.
This utility eases the debugging of literate documents
('noweb' files) by patching the synchronization information
(the '.synctex(.gz)' file) produced by 'pdflatex' with
concordance information produced by 'Sweave' or 'knitr' and
'Sweave' or 'knitr' ; this allows for bilateral communication
between a text editor (visualizing the 'noweb' source) and
a viewer (visualizing the resultant 'PDF'), thus bypassing
the intermediate 'TeX' file.
pathClass is a collection of classification methods that
use information about feature connectivity in a biological
network as an additional source of information. This additional
knowledge is incorporated into the classification a priori.
Several authors have shown that this approach significantly
increases the classification performance.
Implementation of simple functions to draw
basic path diagrams just for visualization purposes.
pathmox, the cousin package of plspm, provides a very
interesting solution for handling segmentation variables
in PLS Path Modeling: segmentation trees in PLS Path Modeling.
Utilities for paths, files and directories.
An R package to quickly and easily visualize longitudinal microbiome profiles using standard output from the QIIME microbiome analysis toolkit.
This library provides tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes.  Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made.  The code permits the analysis of single maps or pairs of maps.
Implementation of a Bayesian active learning strategy to carry out sequential experimental design in the context of biochemical network kinetic parameter estimation. This package gathers functions and pre-computed data sets to reproduce results presented in Pauwels E. et. al published in BMC Systems Biology, 2014. Scripts are given to compute all results from scratch or to draw pictures based on pre-computed data sets.
A cohesive framework for parsing, analyzing and organizing color from spectral data.
Process, format and store accelerometer data
Implementation of the Parallel Adaptive Wang-Landau
algorithm. Also implemented for comparison: parallel adaptive
Metropolis-Hastings,SMC sampler.
A lightweight package that adds
progress bar to vectorized R functions
('*apply'). The implementation can easily be added
to functions, where showing the progress is
useful for the user (e.g. bootstrap).
This package provides data analysis via the pbat program,
and an alternative internal implementation of the power
calculations via simulation only.  For analysis, this package
provides a frontend to the PBAT software, automatically reading
in the output from the pbat program and displaying the
corresponding figure when appropriate (i.e. PBAT-logrank). It
includes support for multiple processes and clusters. For
analysis, users must download PBAT (developed by Christoph
Lange) and accept it's license, available on the PBAT webpage.
Both the data analysis and power calculations have command line
and graphical interfaces using tcltk.
Conducts maximum likelihood analysis and simulation of the protracted speciation model.
pbdBASE gives higher packages, like pbdDMAT, access to
low-level scalable Fortran routines. The package consists of a
set of ScaLAPACK wrappers, as well as a host of new C and
Fortran code.
pbdDEMO is a set of demos of pbdR packages, with a useful,
unifying vignette.
pbdDMAT contains high level S3 and S4 methods for creating,
modifying, and performing computations with dense, distributed
matrices. This includes a new class, 'ddmatrix', for storing all
of the distributed data details.  Computation is handled mostly
by routines from the pbdBASE package.
pbdMPI provides an efficient interface to MPI by utilizing S4
classes and methods with a focus on Single Program/Multiple Data (SPMD)
parallel programming style, which is intended for batch parallel
execution.
This package adds collective parallel read and write capability
to the R package ncdf4 version 1.8. Typical use is as a
parallel NetCDF4 file reader in SPMD style programming. Each R
process reads and writes its own data in a synchronized
collective mode, resulting in faster parallel performance.
Performance improvement is conditional on a parallel file system.
pbdSLAP utilizes scalable linear algebra packages mainly
including BLACS, PBLAS, and ScaLAPACK in double precision via
pbdMPI based on ScaLAPACK version 2.0.2.
Providing high level R wrapper functions to utilize
ZeroMQ C API functions in detail manners. pbdZMQ mainly focuses on
client/servers programming framework in an interaction style. A
minimum ZeroMQ library 4.1.0 rc1 is built and linked within pbdZMQ
if no external ZerMQ library is not provided. A few compatible wrapper
functions to rzmq are also provided.
A set of datasets prepared as a support for my classes or
the book ,,Modele liniowe i mieszane w R, wraz z przykladami w
analizie danych''
Provides a vectorized R function for calculating
probabilities from a standard bivariate normal CDF.
Test in linear mixed effects models.

Attention is on linear mixed effects models as implemented in the
lme4 package.

The package implements a parametric bootstrap test.

The package implements a Kenward-Roger modification of F-tests.
Following the method of Bailey et al., computes for a collection
of candidate models the probability of backtest overfitting, the
performance degradation and probability of loss, and the stochastic
dominance.
Adds different kinds of brackets to a plot, including braces, chevrons, parentheses or square brackets.
R Support for ADMB (Automatic Differentiation Model Builder)
This package solves systems of delay differential equations.
by interfacing numerical routines written by Simon N. Wood , with
contributions by Benjamin J. Cairns. These numerical routines first
appeared in Simon Wood's solv95 program.
This package includes a vignette and a complete user's guide.
'PBSddesolve' originally appeared on CRAN under the name 'ddesolve'.
That version is no longer supported. The current name emphasizes a close
association with other PBS packages, particularly 'PBSmodelling'.
This software has evolved from fisheries research conducted at the
Pacific Biological Station (PBS) in `Nanaimo', British Columbia, Canada. It
extends the R language to include two-dimensional plotting features similar
to those commonly available in a Geographic Information System (GIS).
Embedded C code speeds algorithms from computational geometry, such as
finding polygons that contain specified point events or converting between
longitude-latitude and Universal Transverse Mercator (UTM) coordinates.
Additionally, we include `C++' code developed by Angus Johnson for the `Clipper'
library. Also included are data for a global shoreline and other
data sets in the public domain. The R directory `.../library/PBSmapping/doc'
offers a complete user's guide, which should be consulted to use package
functions effectively.
Provides software to facilitate the design, testing, and operation
of computer models. It focuses particularly on tools that make it easy to
construct and edit a customized graphical user interface (GUI). Although our
simplified GUI language depends heavily on the R interface to the Tcl/Tk
package, a user does not need to know Tcl/Tk. Examples illustrate models
built with other R packages, including PBSmapping, PBSddesolve, and BRugs.
A complete user's guide `PBSmodelling-UG.pdf' shows how to use this package
effectively.
Functions simplifying presentation of PCA models in a 3D interactive representation using 'rgl'.
To seek for a contemporaneous linear transformation for
a multivariate time series such that the transformed series is segmented
into several lower-dimensional subseries, and those subseries are
uncorrelated with each other both contemporaneously and serially.
Draws a 2D principal component plot using the first 2 principal
components from the original and bootstrapped data to give some sense of
variability.
Methods to detect genetic markers involved in biological adaptation. 'pcadapt' provides statistical tools for outlier detection based on Principal Component Analysis.
Three implementations of principal component analysis methods
using the L1 norm.  The package depends on COIN-OR Clp version >=
1.12.0.  The methods implemented are PCA-L1 (Kwak 2008), L1-PCA (Ke and
Kanade 2003, 2005), and L1-PCA* (Brooks, Dula, and Boone 2013).
Functions for causal structure
learning and causal inference using graphical models. The main algorithms
for causal structure learning are PC (for observational data without hidden
variables), FCI and RFCI (for observational data with hidden variables),
and GIES (for a mix of data from observational studies
(i.e. observational data) and data from experiments
involving interventions (i.e. interventional data) without hidden
variables). For causal inference the IDA algorithm, the Generalized
Backdoor Criterion (GBC) and the Generalized Adjustment Criterion (GAC)
are implemented.
Principal Component Analysis, orthogonal rotation and multiple factor analysis for a mixture of quantitative and qualitative variables.
Semiparametric regression analysis of panel count data under the non-homogeneous Poisson
process model with and without Gamma frailty using monotone splines.
The package solves linear system of equations Ax=b by using Preconditioned Conjugate Gradient Algorithm where A is real symmetric positive definite matrix. A suitable preconditioner matrix may be provided by user. This can also be used to minimize quadratic function (x'Ax)/2-bx for unknown x.
Contains logic for computing the statistical association of variable groups, i.e., gene sets, with respect to the principal components of genomic data.
Using piecewise constant hazards models is a very flexible approach
for the analysis of survival data. The time line is divided into sub-intervals;
for each interval, a different hazard is estimated using Poisson regression.
This package implements a work-alike to R's POSIXct class
which implements 360- and 365-day calendars in addition to the
gregorian calendar.
pcIRT estimates the multidimensional polytomous Rasch model
(Rasch, 1961) and the Continous Rating Scale model (Mueller, 1987).
Apply Partial Correlation coefficient with Information
Theory (PCIT) to a correlation matrix.
The PCIT algorithm identifies meaningful correlations to define
edges in a weighted network. The algorithm can be applied to
any correlation-based network including but not limited to gene
co-expression networks.
To reduce compute time by making use of multiple compute cores,
simply run PCIT on a computer with has multiple cores and also
has the Rmpi package installed. PCIT will then auto-detect the
multicore environment and run in parallel mode without the need
to rewrite your scripts. This makes scripts, using PCIT, portable
across single core (or no Rmpi package installed) computers
which will run in serial mode and multicore (with Rmpi package
installed) computers which will run in parallel mode.
Provides functions to perform arm-based network meta-analysis for datasets with binary, continuous, and count outcomes.
Computation of the Pedroni (1999) panel cointegration test statistics.  Reported are the empirical and the standardized values.
Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components. Several rotation options are provided in this package, as well as model selection options.
Set of functions for analysis of Principal Coordinates of Phylogenetic Structure (PCPS).
Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations?  This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.
This package contains a function to estimate
panel-corrected standard errors. Data may contain balanced or
unbalanced panels.
Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.
The package performs cluster analysis via nonparametric density
estimation. Operationally, the kernel method is used throughout to estimate
the density. Diagnostics methods for evaluating the quality of the clustering
are available. The package includes also a routine to estimate the
probability density function obtained by the kernel method, given a set of
data with arbitrary dimensions.
Download economic and financial time series from public sources.
Computes the euclidean distance between rows of a matrix X
and rows of another matrix Y.  Previously, this could be done
by binding the two matrices together and calling 'dist', but
this creates unnecessary computation by computing the distances
between a row of X and another row of X, and likewise for Y.
pdist strictly computes distances across the two matrices, not
within the same matrix, making computations significantly
faster for certain use cases.
This package provides a model of Pavlovian conditioning phenomena, such as response extinction and spontaneous recovery, and partial reinforcement extinction effects. Competing proximal and distal reward predictions, computed using fast and slow learning rates, combine according to their uncertainties and the recency of information. The resulting mean prediction drives the response rate.
A collection of tools for approximating the 'PDQ' functions (respectively, the
cumulative distribution, density, and quantile) of probability distributions via classical
expansions involving moments and cumulants.
Threshold model, panel version of Hylleberg et al. (1990) seasonal unit root tests, and panel unit root test of Chang (2002).
A package to compute and tune some positive definite and
sparse covariance estimators
Calculates the periodogram of a time series, maximum-likelihood fits an Ornstein-Uhlenbeck state space (OUSS) null model and evaluates the statistical significance of periodogram peaks against the OUSS null hypothesis. The OUSS is a parsimonious model for stochastically fluctuating variables with linear stabilizing forces, subject to uncorrelated measurement errors. Contrary to the classical white noise null model for detecting cyclicity, the OUSS model can account for temporal correlations typically occurring in ecological and geological time series.
Biologically inspired methods for
detecting peaks in one-dimensional data, such as time series or genomics data.
The algorithms were originally designed by Weber, Ramachandran, and Henikoff,
see documentation.
Spectrum manipulation: background estimation, Markov
smoothing, deconvolution and peaks search functions. Ported
from ROOT/TSpectrum class.
Package for estimating periodic autoregressive models.
Datasets: monthly ozone and Fraser riverflow. Plots: periodic
versions of boxplot, auto/partial correlations, moving-average
expansion.
pearson7 supports maximum likelihood inference for the Pearson VII
distribution with shape parameter 3/2 and free location and scale
parameters. This distribution is relevant when estimating the velocity of
processive motor proteins with random detachment.
Implementation of the Pearson distribution system, including full
support for the (d,p,q,r)-family of functions for probability distributions
and fitting via method of moments and maximum likelihood method.
The Pearson-ICA algorithm is a mutual information-based
method for blind separation of statistically independent source
signals. It has been shown that the minimization of mutual
information leads to iterative use of score functions, i.e.
derivatives of log densities. The Pearson system allows
adaptive modeling of score functions. The flexibility of the
Pearson system makes it possible to model a wide range of
source distributions including asymmetric distributions. The
algorithm is designed especially for problems with asymmetric
sources but it works for symmetric sources as well.
Validation of risk predictions obtained from survival models and
competing risk models based on censored data using inverse weighting and
cross-validation.
Contains functions for sensitivity and power analysis, for calculating statistics describing pedigrees from wild populations, and for viewing pedigrees
An implementation for association analysis with CNV data in R. It
provides two methods for association study: first, the observed probe
intensity measurement can be directly used to detect the association of CNV
with phenotypes of interest. Second, the most probable copy number is
estimated with the proposed likelihood and the association of the most
probable copy number with phenotype is tested. This method can be applied
to both the independent and correlated population.
Gene-level association tests with disease status for pedigree data: kernel and burden association statistics.
Functions to employ many of the tools and techniques used in the
field of pedometrics.
Functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, and for the analysis of population nucleotide sequences and micro-satellites including coalescence analyses.
Several functions introduced in Aster et al.'s book on inverse theory. The functions are often translations of MATLAB code developed by the authors to illustrate concepts of inverse theory as applied to geophysics. Generalized inversion, tomographic inversion algorithms (conjugate gradients, 'ART' and 'SIRT'), non-linear least squares, first and second order Tikhonov regularization, roughness constraints, and procedures for estimating smoothing parameters are included. Includes a wrapper for the FORTRAN based 'LSQR' (Paige and Saunders) routine.
This package provides functions to perform multivariate Gaussian parameter estimation based on data with abundance-dependent missingness. It implements a penalized Expectation-Maximization (EM) algorithm. The package is tailored for but not limited to proteomics data applications, in which a large proportion of the data are often missing-not-at-random with lower values (or absolute values) more likely to be missing.
A package for fitting possibly high dimensional penalized
regression models. The penalty structure can be any combination
of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a
positivity constraint on the regression coefficients. The
supported regression models are linear, logistic and Poisson
regression and the Cox Proportional Hazards model.
Cross-validation routines allow optimization of the tuning
parameters.
Implements the penalized LDA proposal of "Witten and Tibshirani (2011), Penalized classification using Fisher's linear discriminant, to appear in Journal of the Royal Statistical Society, Series B".
This package provides feature selection SVM using penalty
functions. The smoothly clipped absolute deviation (SCAD),
'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
SCAD' (SCAD and 'L2-norm') penalties are availible. The tuning
parameters can be founf using either a fixed grid or a interval
search.
Flexible copula density estimation with penalized hierarchical B-Splines.
Estimation of univariate (conditional) densities using penalized B-splines with automatic selection of optimal smoothing parameter.
Flexible Pair-Copula Estimation in D-vines using Bivariate Penalized Splines.
Structured fusion Lasso penalized estimation of multi-state models with the penalty applied to absolute effects and absolute effect differences (i.e., effects on transition-type specific hazard rates).
Simulation of continuous, correlated high-dimensional data with time to event or binary response, and parallelized functions for Lasso, Ridge, and Elastic Net penalized regression with repeated starts and two-dimensional tuning of the Elastic Net.
Package peperr is designed for prediction error estimation
through resampling techniques, possibly accelerated by parallel
execution on a compute cluster. Newly developed model fitting
routines can be easily incorporated.
This package provides a variety of methods for dealing
with analysis of peptide library data, including clustering,
motif finding, and QSAR model fitting.
Amino acid exchange based on single nucleotide variant (SNV) information and tryptic digestion on peptide sequence. Searching for homologous by comparison of tryptic digested peptide sequences.
Evaluation of diversity in peptide libraries, including NNN, NNB,
NNK/S, and 20/20 schemes. Custom encoding schemes can also be defined.
Metrics for evaluation include expected coverage, relative efficiency, and
the functional diversity of the library. Peptide-level inclusion
probabilities are computable for both the native and custom encoding
schemes.
Calculate physicochemical properties and indices from amino-acid sequences of peptides and proteins. Include also the option to read and plot output files from the 'GROMACS' molecular dynamics package.
Moderated regression with mean and residual centering and simple slopes analysis.
The package includes procedures for identification, model
fitting and estimation for time series with periodic structure.
Additionally procedures for simulation of periodic processes
and real data sets are included.
To find the certainty of dominance interactions with indirect interactions being considered.
Several person-fit statistics (PFSs) are offered. These statistics allow assessing whether
individual response patterns to tests or questionnaires are (im)plausible given
the other respondents in the sample or given a specified item response theory model. Some PFSs apply to
dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs
(personal biserial correlation, caution index, (normed) number of Guttman errors,
agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include
extensions of lz, U3, and (normed) number of Guttman errors.
Package that implements different performance measures for classification and ranking tasks. AUC, precision at a given recall, F-score for single and multiple classes are available.
Collection of econometric functions for
performance and risk analysis. This package aims to aid
practitioners and researchers in utilizing the latest
research in analysis of non-normal return streams.  In
general, it is most tested on return (rather than
price) data on a regular scale, but most functions will
work with irregular return data as well, and increasing
numbers of functions will work with P&L or price data
where possible.
An  infra-structure for estimating the predictive performance
of predictive models. In this context, it can also be used to compare and/or select among
different alternative ways of solving one or more predictive tasks. The main goal of the
package is to provide a generic infra-structure to estimate the values of different metrics
of predictive performance using different estimation procedures. These estimation tasks can be
applied to any solutions (workflows) to the predictive tasks. The package provides easy to use
standard workflows that allow the usage of any available R modeling algorithm together with
some pre-defined data pre-processing steps and also prediction post-processing methods. It also provides means for addressing issues related with the statistical significance of the observed differences.
Perform Exact or Asymptotic permutation tests
This version of the permutational algorithm generates a
dataset in which event and censoring times are conditional on
an user-specified list of covariates, some or all of which are
time-dependent.
Includes functions to work with the Mallows and Generalized Mallows Models. The considered distances are Kendall's-tau, Cayley, Hamming and Ulam and it includes functions for making inference, sampling and learning such distributions, some of which are novel in the literature. As a by-product, PerMallows also includes operations for permutations, paying special attention to those related with the Kendall's-tau, Cayley, Ulam and Hamming distances. It is also possible to generate random permutations at a given distance, or with a given number of inversions, or cycles, or fixed points or even with a given length on LIS (longest increasing subsequence).
A set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. 'permute' also allows split-plot designs, in which the whole-plots or split-plots or both can be freely-exchangeable or one of the restricted designs. The 'permute' package is modelled after the permutation schemes of 'Canoco 3.1' (and later) by Cajo ter Braak.
Tools that allow developers to write functions for
prediction error estimation with minimal programming effort and
assist users with model selection in regression problems.
Translate words from English to Persian (Over 67,000
words)
Visualizes treatment effects using person icons, similar to Cates (NNT) charts.
Provides a robust framework for analyzing the extent to which differential survival with respect to higher level trait variation is reducible to lower level variation. In addition to its primary test, it also provides functions for simulation-based power analysis, reading in common data set formats, and visualizing results. Temporarily contains an edited version of function hr.mcp() from package 'wild1', written by Glen Sargeant. For tutorial see: http://evolve.zoo.ox.ac.uk/Evolve/Perspectev.html.
"perturb" evaluates collinearity by adding random noise to
selected variables. "colldiag" calculates condition numbers and
variance decomposition proportions to test for collinearity and
uncover its sources.
Single item and composite pesticide residue measurements
of fifteen commodity-pesticide combinations plus analytical
tools.
This package implements different analytic/direct and
iterative reconstruction methods of Peter Toft. It also offer
the possibility to simulate PET data.
Eco-phylogenetic and community phylogenetic analyses.
Keeps community ecological and phylogenetic data matched up and
comparable using 'comparative.comm' objects. Wrappers for common
community phylogenetic indices ('pez.shape', 'pez.evenness',
'pez.dispersion', and 'pez.dissimilarity' metrics). Implementation
of Cavender-Bares (2004) correlation of phylogenetic and
ecological matrices ('fingerprint.regression'). Phylogenetic
Generalised Linear Mixed Models (PGLMMs; 'pglmm') following Ives &
Helmus (2011) and Rafferty & Ives (2013). Simulation of null
assemblages, traits, and phylogenies ('scape', 'sim.meta.comm').
This work is an extension of the state space model for
Poisson count data, Poisson-Gamma model, towards a
semiparametric specification. Just like the generalized
additive models (GAM), cubic splines are used for covariate
smoothing. The semiparametric models are fitted by an iterative
process that combines maximization of likelihood and
backfitting algorithm.
A Group ICA Algorithm that can run in parallel on an SGE platform or multi-core PCs
Miscellaneous functions for data analysis in ecology, with special emphasis on spatial data.
Estimation of panel models for glm-like models: this includes binomial models (logit and probit) count models (poisson and negbin) and ordered models (logit and probit)
Based on the Generalized Least Square model for
comparative Phylogenetics (ref).
Construction of balanced incomplete binary blocks designs (BIB) from a projective geometry defined on a Galois fields order 2, using to extract the RBIBD & the uniforms designs associated.
Carries out model-based clustering or classification using parsimonious Gaussian mixture models.
Evaluation of the pdf and the cdf of the univariate,
noncentral, p-generalized normal distribution. Sampling from
the univariate, noncentral, p-generalized normal distribution
using either the p-generalized polar method, the p-generalized
rejecting polar method, the Monty Python method, the Ziggurat
method or the method of Nardon and Pianca. The package also
includes routines for the simulation of the bivariate,
p-generalized uniform distribution and the simulation of the
corresponding angular distribution.
Provides functions to aid the identification of probable/possible
duplicates in Plant Genetic Resources (PGR) collections using
'passport databases' comprising of information records of each constituent
sample. These include methods for cleaning the data, creation of a
searchable Key Word in Context (KWIC) index of keywords associated with
sample records and the identification of nearly identical records with
similar information by fuzzy, phonetic and semantic matching of keywords.
Computation of mean squared errors of stereological predictors.
The phalen package contains (1) clustering and
partitioning algorithms; (2) penalty functions for numeric
vectors; (3) a ranking function; and (4) color palettes and
functions.
Phylogenetic analysis in R: Estimation of phylogenetic
trees and networks using Maximum Likelihood, Maximum Parsimony,
distance methods and Hadamard conjugation.
Contains a set of functions to perform large-scale analysis of pharmacogenomic data.
This package contains functions performing power calculations for mixed (sparse/dense sampled) pharmacokinetic study designs. The input data for these functions is tailored for NONMEM .phi files.
phaseR is an R package for the qualitative analysis of one and
two dimensional autonomous ODE systems, using phase plane methods. Programs
are available to identify and classify equilibrium points, plot the
direction field, and plot trajectories for multiple initial conditions. In
the one dimensional case, a program is also available to plot the phase
portrait. Whilst in the two dimensional case, additionally a program is
available to plot nullclines. Many example systems are provided for the
user.
Functions to perform Bayesian inference on absorption time
data for Phase-type distributions.  Plans to expand this to
include frequentist inference and simulation tools.
phcfM is an R package for modelling anthropogenic
deforestation. It was initially developed to obtain REDD+
baseline scenarios of deforestation for the "programme
holistique de conservation des forets a Madagascar" (from which
the package is named after). Parameter inference is done in a
hierarchical Bayesian framework. Markov chains Monte Carlo
(MCMC) are coded in C++ using the Scythe statistical library to
maximize computation efficiency.
Implementation of heatmaps that offers more control
over dimensions and appearance.
An alternative to carrying out phenotypic adaptability and stability analyses, taking into account nonparametric statistics. Can be used as a robust approach, less sensitive to departures from common genotypic, environmental, and GxE effects data assumptions (e.g., normal distribution of errors).
Provides some easy-to-use functions for
spatial analyses of (plant-) phenological data
sets and satellite observations of vegetation.
Provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984), bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices.
Provides functions to preprocess phenological data, for modelling and result handling.
Provides some easy-to-use functions for time series
analyses of (plant-) phenological data sets. These functions
mainly deal with the estimation of combined phenological time
series and are usually wrappers for functions that are already
implemented in other R packages adapted to the special
structure of phenological data and the needs of phenologists.
Some date conversion functions to handle Julian dates are also
provided.
High-throughput generation of genetic markers from molecular phenotypes for crosses between inbred strains. These markers can be use to saturate existing genetic map or creating a new one.
Functions used to fit and test the phenology of species based on counts.
This package provides tools for the evaluation of the goodness of fit and the predictive capacity of the proportional hazards model.
Analysis of terms in linear, generalized and mixed linear models,
on the basis of multiple comparisons of factor contrasts.  Specially suited
for the analysis of interaction terms.
Fits proportional hazards model incorporating random effects using
an EM algorithm using Markov Chain Monte Carlo at E-step.
Convert English letters to numbers or numbers to English letters as
on a telephone keypad. When converting letters to numbers, a character
vector is returned with "A," "B," or "C" becoming 2, "D," "E", or "F"
becoming 3, etc. When converting numbers to letters, a character vector is
returned with multiple elements (i.e., "2" becomes a vector of "A," "B," and
"C").
Tools for phoneticians and phonologists, including functions for normalization and plotting of vowels.
Contains tools for the organization, display, and analysis of the sorts of data frequently encountered in phonetics research and experimentation, including the easy creation of IPA vowel plots, and the creation and manipulation of WAVE audio files.
A geochemical modeling program developed by the US Geological
Survey that is designed to perform a wide variety of aqueous geochemical
calculations, including speciation, batch-reaction, one-dimensional
reactive-transport, and inverse geochemical calculations.
The package provides estimation procedures for panel data with large dimensions n, T, and general forms of unobservable heterogeneous effects. Particularly, the estimation procedures are those of Bai (2009) and Kneip, Sickles, and Song (2012), which complement one another very well: both models assume the unobservable heterogeneous effects to have a factor structure. The method of Bai (2009) assumes that the factors are stationary, whereas the method of Kneip et al. (2012) allows the factors to be non-stationary. Additionally, the 'phtt' package provides a wide range of dimensionality criteria in order to estimate the number of the unobserved factors simultaneously with the remaining model parameters.
PhViD contains several pharmacovigilance signal detection
methods extended to the multiple comparison setting.
Calls 'Phoenix NLME' (non-linear mixed effects), a population
modeling and simulation software, for pharmacokinetics and pharmacodynamics
analyses and conducts post-processing of the results. This includes creation of
various diagnostic plots, bootstrap and visual predictive checks. See <http://
www.certara.com/software/pkpd-modeling-and-simulation/phoenix-nlme/> for more
information about 'Phoenix NLME'.
Applies an automated algorithm to mark Actigraph accelerometer data as either bedrest or active.  There are separate functions for waist and wrist data.  Its input is a dataframe with a timestamp column and one of counts.  The output is the same dataframe with an additional column, bedrest.  Bedrest will have "a" active or "br" for each line.  Options also allow for "n" nap which only differ than "s" in being for a shorter (specifiable) time period.  Designed to be used after wearmarking function in Physical Activity package.
Phylogenetic clustering (phyloclustering) is an evolutionary
Continuous Time Markov Chain model-based approach to identify
population structure from molecular data without assuming
linkage equilibrium. The package phyclust (Chen 2011) provides a
convenient implementation of phyloclustering for DNA and SNP data,
capable of clustering individuals into subpopulations and identifying
molecular sequences representative of those subpopulations. It is
designed in C for performance, interfaced with R for visualization,
and incorporates other popular open source programs including
ms (Hudson 2002), seq-gen (Rambaut and Grassly 1997),
Hap-Clustering (Tzeng 2005) and PAML baseml (Yang 1997, 2007), for
simulating data, additional analyses, and searching the best tree.
See the phyclust website for more information, documentations and
examples.
Based on (but not identical to) the no-longer-maintained package 'phyext', provides enhancements to 'phylobase' classes, specifically for use by package 'SigTree'; provides classes and methods which help users manipulate branch-annotated trees (as in 'SigTree'); also provides support for a few other extra features.
The spatial interpolation of genetic distances between
samples is based on a modified kriging method that
accepts a genetic distance matrix and generates a map of
probability of lineage presence. This package also offers
tools to generate a map of  potential contact zones
between groups with user-defined thresholds in the tree
to account for old and recent divergence. Additionally,
it has functions for IDW interpolation using genetic data
and midpoints.
Provides a base S4 class for comparative methods, incorporating
one or more trees and trait data.
This package implements some recently developed methods in phyloclimatic modeling.
Tools for studying the evolution of function-valued traits (as well as other high-dimensional traits) including ancestral state reconstruction, estimating phylogenetic signal, and assessing correlated trait evolution. Visit http://www.phylocurve.org for more information.
Manipulation and analysis of phylogenetically simulated
data sets and phylogenetically based analyses using GLS.
Phyloland package models a space colonization process mapped onto a phylogeny, it aims at estimating limited dispersal and ecological competitive exclusion in a Bayesian MCMC statistical phylogeographic framework (please refer to phyloland-package help for details.)
Provides functions for fitting phylogenetic linear models and phylogenetic generalized linear models. The computation uses an algorithm that is linear in the number of tips in the tree. The package also provides functions for simulating continuous or binary traits along the tree.
Given a phylogenetic tree T and a sample S of species represented as
a subset of tips in T, we want to compute a measure of the diversity
of the species in S with respect to T. The current package offers the
implementations of efficient algorithms for several such measures.
Most importantly, the package includes algorithms for computing exactly and
efficiently the expectation and the variance of these measures, which are
essential for null model comparisons. Efficient computation of
richness-standardized versions of these metrics, such as the net
relatedness index (NRI), nearest taxon index (NTI), phylogenetic
diversity index (PDI), and the corresponding indices of two-sample measures
are also available in this package. The package also introduces a new
single-sample measure, the Core Ancestor Cost (CAC); the package provides
functions for computing the value and the standardised index of the CAC and,
more than that, there is an extra function available that can compute exactly
any statistical moment of the measure.
Provides functions to estimate statistical errors of phylogenetic
metrics particularly to detect binary trait influence on diversification, as
well as a function to simulate trees with fixed number of sampled taxa and trait
prevalence.
A collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method.
Building supermatrix for DNA barcodes using different
genes, calculating the inequality among lineages and
phylogenetic similarity for very large dataset using slicing
methods by invoking Phylocom.
Use topological or taxonomic information to maximize the overlap of phylogenetic and comparative data.
Provides general linear model facilities (single y-variable, multiple x-variables with arbitrary mixture of continuous and categorical and arbitrary interactions) for cross-species data. The theory is in A. Grafen (1989, Proc. R. Soc. B 326, 119-157) and aims to cope with both recognised phylogeny (closely related species tend to be similar) and unrecognised phylogeny (a polytomy usually indicates ignorance about the true  sequence of binary splits).
This package contains functions to classify monitor wear and nonwear time intervals in accelerometer data collected to assess physical activity in free-living condition. The package also contains functions to make plot for accelerometer data, and to obtain the summary of daily monitor wear time and the mean of monitor wear time during valid days. A monitored day is considered valid if the total minutes of classified monitor wear time per day is greater than a user defined cutoff.
A variety of formulae are provided for estimation of height,
weight and fluid compartments of adults and children. Each formula is
referenced to the original publication. Warnings can be given for
estimation based on input data outside of normal ranges. Future functions
will cover more material with a focus on anaesthesia, critical
care and peri-operative medicine.
Fits PE and RLC data to one of a four published PE models.
Simulates incident irradiance as a function of time and space.
Calculates phytoplankton production by transposing modeled PE or RLC data
to a water column with a user-defined theoretical in-situ irradiance field.
Methods for estimating the proportion of true null hypotheses, i.e., the pi0, when a very large number of hypotheses are simultaneously tested, especially for the purpose of (local) false discovery rate control for microarray data. It also contains functions to estimate the distribution of noncentrality parameters from a large number of parametric tests.
Phylocom integration, community analyses, null-models, traits and evolution in R
Implement the pathwise calibrated sparse shooting algorithm (PICASSO) for high dimensional regularized sparse linear regression, sparse logistic regression, and sparse undirected graphical model estimation. As a pathwise coordinate optimization algorithm, PICASSO exploits warm start initialization, active set updating, and strong rule for coordinate preselection to boost computation, and attains a linear convergence to a unique sparse local optimum with optimal statistical properties. Moreover, users can choose different regularizers, including L1, MCP, and SCAD regularizers. The computation is memory-optimized using the sparse matrix output.
A collection of scripts and data files for the statistics text:
"Process Improvement using Data". The package contains code for designed
experiments, data sets and other convenience functions used in the book.
Implements piecewise structural equation models.
Extension of the ARTP package for gene- and pathway-environment
interaction
Check if a remote computer is up. It can either
just call the system ping command, or check a specified
TCP port.
An R interface to the Pinnaclesports.com API.
Implements the Product of Independent beta Probabilities dose Escalation (PIPE) design for dual-agent Phase I trials as described in Mander AP, Sweeting MJ. Statistics in Medicine 2015. doi: 10.1002/sim.6434.
Provides various styles of function chaining methods: Pipe
operator, Pipe object, and pipeline function, each representing a distinct
pipeline model yet sharing almost a common set of features: A value can be
piped to the first unnamed argument of a function and to dot symbol in an
enclosed expression. The syntax is designed to make the pipeline more
readable and friendly to a wide range of operations.
Generate Predicted Interval Plots.  Simulate and plot
confidence intervals of an effect estimate given observed data
and a hypothesis about the distribution of future data.
With pitchRx, one can easily obtain Major League Baseball Advanced
Media's Gameday data (as well as store it in a remote database). The
Gameday website hosts a wealth of data in XML format, but perhaps most
interesting is PITCHf/x. Among other things, PITCHf/x data can be used to
recreate a baseball's flight path from a pitcher's hand to home plate. With
pitchRx, one can easily create animations and interactive 3D scatterplots
of the baseball's flight path. PITCHf/x data is also commonly used to
generate a static plot of baseball locations at the moment they cross home
plate. These plots, sometimes called strike-zone plots, can also refer to a
plot of event probabilities over the same region. pitchRx provides an easy
and robust way to generate strike-zone plots using the ggplot2 package.
R interface of Pivotal Data Fabrics running on PostgreSQL
or Pivotal (Greenplum and HAWQ) database with parallel and
distributed computation ability for big data analytics.
PivotalR is a package that enables users of R to interact with
the Pivotal (Greenplum) Database as well as Pivotal HD/HAWQ for
Big Data analytics. It does so by providing an interface to the
operations on tables/views in the database. These operations
are almost the same as those of data.frame. Thus the users of R
do not need to learn SQL when they operate on the objects in
the database. It also provides a wrapper for MADlib, which is
an open-source library for parallel and scalable in-database
analytics.
The introduction of the broom package has made converting model objects into data
frames as simple as a single function.  While the broom package focuses on providing tidy
data frames that can be used in advanced analysis, it deliberately stops short of providing
functionality for reporting models in publication-ready tables.  pixiedust provides this
functionality with a programming interface intended to be similar to ggplot2's system of
layers with fine tuned control over each cell of the table.  Options for output include
printing to the console and to the common markdown formats (markdown, HTML, and LaTeX).
With a little pixiedust (and happy thoughts) tables can really fly.
Functions for import, export, plotting and other
manipulations of bitmapped images.
Estimation of pharmacokinetic parameters using
non-compartmental theory
Set configuration options on a per-package basis.
Options set by a given package only apply to that package,
other packages are unaffected.
Provides a function kitten() which creates cute little
packages which pass R package checks. This sets it apart from
package.skeleton() which it calls, and which leaves imperfect files
behind. As this is not exactly helpful for beginners, kitten() offers
an alternative.
This package provides some low-level utilities to use for package
development. It currently provides managers for multiple package specific
options and registries, vignette, unit test and bibtex related utilities.
It serves as a base package for packages like NMF, RcppOctave, doRNG, and
as an incubator package for other general purposes utilities, that will
eventually be packaged separately.
It is still under heavy development and changes in the interface(s) are
more than likely to happen.
PKgraph provides a graphical user interface for population
pharmacokinetic model diagnosis. It also provides an integrated
and comprehensive platform for the analysis of pharmacokinetic
data including exploratory data analysis, goodness of model
fit, model validation and model comparison. Results from a
variety of modeling fitting software, including NONMEM,
Monolix, SAS and R, can be used. PKgraph is programmed in R,
and uses the R packages lattice, ggplot2 for static graphics,
and rggobi for interactive graphics.
PKI functions such as verifying certificates, RSA encription and signing which can be used to build PKI infrastructure and perform cryptographic tasks.
Compute standard Non-Compartmental Analysis (NCA)
parameters and summarize them.  In addition to this core work, it
also provides standardized plotting routines, basic assessments
for biocomparison or drug interaction, and model-based estimation
routines for calculating doses to reach specific values of AUC or
Cmax.
Provides functions to evaluate common
pharmacokinetic/pharmacodynamic models and their gradients.
PKreport aims to 1) provide automatic pipeline for users
to visualize data and models. It creates a flexible R framework
with automatically generated R scripts to save time and cost
for later usage; 2) implement an archive-oriented management
tool for users to store, retrieve and modify figures. 3) offer
powerful and convenient service to generate high-quality graphs
based on two R packages: lattice and ggplot2.
Fitting and testing probabilistic knowledge structures,
especially the basic local independence model (BLIM, Doignon & Flamagne,
1999), using the minimum discrepancy maximum likelihood (MDML) method.
Parallel Line Assays: Completely randomized design,
Randomized Block design, and Latin squares design.
Balanced data are fitted as described in the Ph.Eur.
In the presence of missing values complete data analysis can be
performed (with computation of Fieller's confidence intervals for
the estimated potency), or imputation of values can be applied.
The package contains a script such that a pdf-document with a
report of an analysis of an assay can be produced from an input file
with data of the assay. Here no knowledge of R is needed by the user.
Supports the creation of burndown charts and gantt diagrams.
Solves the electromagnetic problem of reflection and transmission
of a plane wave or a gaussian beam, at a planar multilayer interface. Also
computed are the decay rates and emission profile for a dipolar emitter
near a multilayer structure.
Automatic generation of regular factorial designs, including fractional designs, orthogonal block designs, row-column designs and split-plots.
Coupled leaf gas exchange model, A-Ci curve simulation and
fitting, leaf energy balance using Penman-Monteith, Cowan-Farquhar
optimization, unit conversions.
Estimation, prediction, thresholding, and plotting for partially linear additive quantile regression.  Intuitive functions for fitting and plotting partially linear additive quantile regression models.  Uses and works with functions from the 'quantreg' package.
This package implements schemes for estimating player or
team skill based on dynamic updating. Implemented methods
include Elo, Glicko and Stephenson. Contains pdf documentation
of a reproducible analysis using approximately one million
chess matches.
A GTK+ graphical user interface for editing and
interacting with R plots.
The package can be used to estimate probabilistic latent feature models
with a disjunctive or a conjunctive mapping rule for two-way two-mode frequency data
Provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.
Sequential Monte Carlo inference for fully Bayesian
Gaussian process (GP) regression and classification models by
particle learning (PL).  The sequential nature of inference
and the active learning (AL) hooks provided facilitate thrifty
sequential design (by entropy) and optimization
(by improvement) for classification and
regression models, respectively.
This package essentially provides a generic
PL interface, and functions (arguments to the interface) which
implement the GP models and AL heuristics.  Functions for
a special, linked, regression/classification GP model and
an integrated expected conditional improvement (IECI) statistic
is provides for optimization in the presence of unknown constraints.
Separable and isotropic Gaussian, and single-index correlation
functions are supported.
See the examples section of ?plgp and demo(package="plgp")
for an index of demos
PLIS is a multiple testing procedure for testing several
groups of hypotheses. Linear dependency is expected from the
hypotheses within the same group and is modeled by hidden
Markov Models. It is noted that, for PLIS, a smaller p value
does not necessarily imply more significance because of
dependency among the hypotheses. A typical application of PLIS
is to analyze genome wide association studies datasets, where
SNPs from the same chromosome are treated as a group and
exhibit strong linear genomic dependency.
A set of estimators and tests for panel data.
A set of tools for identifying genes whose differential
expression is associated with measurements of other covariates
on a continuous scale.  These methods rely on generalized
additive partially linear models which can be fitted
efficiently using a B-spline basis approximation.  Still under
development: methods for interfacing with objects extending the
eSet class and a function to pass linear models in edgeR and
DEseq format.
This package fits the partially linear mixed effects model
(semiparametric random intercept model) using kernel
regression, without distributional assumptions for the random
terms. Estimation procedure is an iterative generalized least
squares type. A nonparametric heteroskedastic variance function
is allowed for the regression error. Bootstrap resampling is
provided for inference. The package implements bandwidth
selection by an alternative cross validation for correlated
data.
Performs bivariate composite likelihood and full
information maximum likelihood estimation for polytomous
logit-normit (graded logistic) item response theory (IRT)
models.
Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likehood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function  is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.
A function to plot scatter points for two
groups of values.  It simultaneously calculates a P
value of t-test and incorporate the P value into the
plot.  Its advantage is jittering the adjacent
points side by side to avoid or reduce overlapping
in the plot.
Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, ... Includes data sets from oceanography
Provides an interactive plot device for handling the geographic data for web browsers, designed for the automatic creation of web maps as a combination of users' data and Google Maps layers.
Writes sp-class, spacetime-class, raster-class and similar spatial and spatio-temporal objects to KML following some basic cartographic rules.
Easily translate ggplot2 graphs to an interactive web-based version and/or create custom web-based visualizations directly from R. Once uploaded to a plotly account, plotly graphs (and the data behind them) can be viewed and modified in a web browser.
Markov chain Monte Carlo diagnostic plots. The purpose of the
package is to combine existing tools from the 'coda' and 'lattice' packages,
and make it easy to adjust graphical details.
Plot a model's response when varying one or two predictors
while holding the other predictors constant.  A poor man's
partial dependence plot.  Also plot model residuals.
Plot principal component histograms around a bivariate
scatter plot.
Useful set of tools for plotting network diagrams in any kind of project.
Creates an envelope around a set of plotted points. The
envelope is compact with a boundary that is continuous, smooth
and convex. Each point is represented as a circle and the
circles and connecting lines are the solution to the multiple
pulley problem. This method can be used to highlight regions in
a two-dimensional space.
Lots of plots, various labeling, axis and color scaling functions.
Most ROC curve plots obscure the cutoff values and inhibit
interpretation and comparison of multiple curves. This attempts to address
those shortcomings by providing plotting and interactive tools. Functions
are provided to generate an interactive ROC curve plot for web use, and
print versions. A Shiny application implementing the functions is also
included.
Contains a graphical user interface to generate the
diagnostic plots proposed by Bauer (2005) and Pek & Chalmers (2015) to investigate
nonlinear bivariate relationships in latent regression models using
structural equation mixture models (SEMMs).
Fit Log Linear by Linear Association models and Rasch family models by pseudolikelihood estimation
This package provides statistical inference tools applied to Partial Linear Regression (PLR)
models. Specifically, point estimation, confidence intervals estimation, bandwidth selection, goodness-of-fit tests and analysis of
covariance are considered.
Kernel-based methods, combined with ordinary least squares estimation, are used and time series
errors are allowed. In addition, these techniques are also implemented for both parametric (linear)
and nonparametric regression models.
Multivariate regression methods
Partial Least Squares Regression (PLSR), Principal Component
Regression (PCR) and Canonical Powered Partial Least Squares (CPPLS).
Principal Component Analysis (PCA) biplots, Covariance monoplots
and biplots, Partial Least Squares (PLS) biplots, Partial Least Squares for
Generalized Linear Model (PLS-GLM) biplots, Sparse Partial Least Squares
(SPLS) biplots and Sparse Partial Least Squares for Generalized Linear
Model (SPLS-GLM) biplots.
plsdepot contains different methods for PLS analysis of
one or two data tables such as Tucker's Inter-Battery, NIPALS,
SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis.
The main reference for this software is the awesome book (in
French) 'La Regression PLS: Theorie et Pratique' by Michel
Tenenhaus.
The plsdof package provides Degrees of Freedom estimates
for Partial Least Squares (PLS) Regression. Model selection for
PLS is based on various information criteria (aic, bic, gmdl)
or on cross-validation. Estimates for the mean and covariance
of the PLS regression coefficients are available. They allow
the construction of approximate confidence intervals and the
application of test procedures. Further, cross-validation
procedures for Ridge Regression and Principal Components
Regression are available.
Routines for PLS-based genomic analyses,
implementing PLS methods for classification with
microarray data and prediction of transcription factor
activities from combined ChIP-chip analysis. The >=1.2-1
versions include two new classification methods for microarray
data: GSIM and Ridge PLS. The >=1.3 versions includes a
new classification method combining variable selection and
compression in logistic regression context: RIRLS-SPLS; and
an adaptive version of the sparse PLS.
Partial Least Squares Path Modeling (PLS-PM)
analysis for both metric and
non-metric data, as well as REBUS analysis.
Provides Partial least squares Regression for (weighted) beta regression models and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings.
Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Kernel density estimation with global bandwidth selection
via "plug-in".
The base R data.frame, like any vector, is
copied upon modification. This behavior is at odds with
that of GUIs and interactive graphics. To rectify this,
plumbr provides a mutable, dynamic tabular data model.
Models may be chained together to form the complex
plumbing necessary for sophisticated graphical
interfaces. Also included is a
general framework for linking datasets; an typical
use case would be a linked brush.
plusser provides an API interface to Google+ so that posts,
profiles and pages can be automatically retrieved.
A set of tools that solves a common set of problems: you
need to break a big problem down into manageable pieces, operate on each
piece and then put all the pieces back together.  For example, you might
want to fit a model to each spatial location or time point in your study,
summarise data by panels or collapse high-dimensional arrays to simpler
summary statistics. The development of 'plyr' has been generously supported
by 'Becton Dickinson'.
Performs Penalized Multivariate Analysis: a penalized
matrix decomposition, sparse principal components analysis, and
sparse canonical correlation analysis, described in the
following papers: (1) Witten, Tibshirani and Hastie (2009) A
penalized matrix decomposition, with applications to sparse
principal components and canonical correlation analysis.
Biostatistics 10(3):515-534. (2) Witten and Tibshirani (2009)
Extensions of sparse canonical correlation analysis, with
applications to genomic data. Statistical Applications in
Genetics and Molecular Biology 8(1): Article 28.
Monte Carlo based model choice for applied phylogenetics of
continuous traits. Method described in  Carl Boettiger, Graham Coop,
Peter Ralph (2012) Is your phylogeny informative? Measuring
the power of comparative methods, Evolution 66 (7)
2240-51. doi:10.1111/j.1558-5646.2011.01574.x.
Parsimonious Mixtures of Contaminated Gaussian Distributions
The pmclust aims to utilize model-based clustering (unsupervised)
for high dimensional and ultra large data, especially in a distributed
manner. The package employs Rmpi to perform a
expectation-gathering-maximization (EGM) algorithm
for finite mixture Gaussian
models. The unstructured dispersion matrices are assumed in the Gaussian
models. The implementation is default in the single program multiple
data (SPMD) programming model. The code can be executed through Rmpi and
independent to most MPI applications. See the High Performance
Statistical Computing (HPSC) website for more information, documents
and examples.
The Kruskal and Wallis one-way analysis of variance by ranks
can be employed, if the data do not meet the assumptions
for one-way ANOVA. Provided that significant differences
were detected by the Kruskal-Wallis-Test, one may be interested
in applying post-hoc tests for pairwise multiple comparisons
of the ranked data (Nemenyi's test, Dunn's test, Conover's test,
van der Waerden's test). Similarly, one-way ANOVA with repeated
measures that is also referred to as ANOVA with unreplicated
block design can also be conducted via the Friedman-Test.
The consequent post-hoc pairwise multiple comparison tests according
to Nemenyi and Conover are also provided in this package.
Extends the approach proposed by Firth (1993) for bias
reduction of MLEs in exponential family models to the
multinomial logistic regression model with general covariate
types.  Modification of the logistic regression score function
to remove first-order bias is equivalent to penalizing the
likelihood by the Jeffreys prior, and yields penalized maximum
likelihood estimates (PLEs) that always exist.  Hypothesis
testing is conducted via likelihood ratio statistics.  Profile
confidence intervals (CI) are constructed for the PLEs.
The Predictive Model Markup Language (PMML) is an XML-based language
which provides a way for applications to define statistical and data
mining models and to share models between PMML compliant applications.
More information about PMML and the Data Mining Group can be found at
http://www.dmg.org.
The generated PMML can be imported into any PMML consuming application,
such as the Zementis ADAPA and UPPI scoring engines which allow for
predictive models built in R to be deployed and executed on site, in
the cloud (Amazon, IBM, and FICO), in-database (IBM Netezza, Pivotal,
Sybase IQ, Teradata and Teradata Aster) or Hadoop (Datameer and Hive).
Allows for data to be transformed before using
it to construct models. Builds structures to allow functions in
the PMML package to output transformation details in
addition to the model in the resulting PMML file.
Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.
Includes functions and examples for Parametric Network Enrichment Analysis.
An R package for prime number generation, primality testing and integer factorization, currently, using simple methods.
This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.
An R package for Probit-Normal Marginalized Transition
Random Effects Models
The program pnn implements the algorithm proposed by
Specht (1990).  It is written in the R statistical language. It
solves a common problem in automatic learning. Knowing a set of
observations described by a vector of quantitative variables,
we classify them in a given number of groups. Then, the
algorithm is trained with this datasets and should guess
afterwards the group of any new observation. This neural
network has the main advantage to begin generalization
instantaneously even with a small set of known observations. It
is delivered with four functions (learn, smooth, perf and
guess) and a dataset. The functions are documented with
examples and provided with unit tests.
Provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user.
Bayesian variable selection for regression models of under-reported
count data as well as for Poisson and binomial logit regression models using
spike and slab priors.
The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment.
This package implements both the exact and approximation
methods for computing the cdf of the Poisson binomial
distribution. It also provides the pmf, quantile function, and
random number generation for the Poisson binomial distribution.
Implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518.
Functions for obtaining the density, random deviates
and maximum likelihood estimates of the Poisson lognormal
distribution and the bivariate Poisson lognormal distribution.
For every spatial point in a list, calculates the density
and temporal tendency (average age) of points within a user defined
neighborhood; also supports point density and temporal tendency
visualization using 'ggmap'.
Functions to calculate and plot event and pointer years as well as components of resilience. Designed for dendroecological applications, but also suitable to analyze patterns in other ecological time series.
Generation of multiple count, binary and continuous variables simultaneously
given the marginal characteristics and association structure. Throughout the package,
the word 'Poisson' is used to imply count data under the assumption of Poisson distribution.
Generation of multiple count, binary and ordinal variables simultaneously
given the marginal characteristics and association structure. Throughout the package,
the word 'Poisson' is used to imply count data under the assumption of Poisson distribution.
Generation of a chosen number of count, binary, ordinal, and continuous (via Fleishman polynomials) random variables, with specified correlations and marginal properties.
Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure.
Functions to create confidence intervals for ratios of Poisson
rates under misclassification using double sampling.
This package generates count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously.
A package for simulating multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture.
Contains functions and classes for simulating, plotting and analysing homogenous and non-homogenous Poisson processes.
High dimensional mixtures of Poisson Generalized Linear models with three different parameterizations of Poisson means are considered. Moreover, partitioning the response variables into a set of blocks is possible. The package estimates parameters via EM algorithm. For an efficient initialization, a random splitting small-EM is introduced.
This package implements a method for normalization,
testing, and false discovery rate estimation for RNA-sequencing
data. The description of the method is in Li J, Witten DM,
Johnstone I, Tibshirani R (2012). Normalization, testing, and
false discovery rate estimation for RNA-sequencing data.
Biostatistics 13(3): 523-38. We estimate the sequencing depths
of experiments using a new method based on Poisson
goodness-of-fit statistic, calculate a score statistic on the
basis of a Poisson log-linear model, and then estimate the
false discovery rate using a modified version of permutation
plug-in method. A more detailed instruction as well as sample
data is available at
http://www.stanford.edu/~junli07/research.html.  In this
version, we changed the way of calculating log foldchange for
two-class data. The FDR estimation part remains unchanged.
Latent class analysis and latent class regression models
for polytomous outcome variables.  Also known as latent structure analysis.
This package provides easy access to various political data APIs
directly from R. For example, you can access Google Civic Information API
(https://developers.google.com/civic-information/) or Sunlight Congress API
(https://sunlightlabs.github.io/congress/) for US Congress data, and POPONG
API (http://data.popong.com/) for South Korea National Assembly data.
Client for the HuffPost Pollster API, which provides
access to U.S. opinion polls since 2004.
Routines for the polynomial spline fitting routines
hazard regression, hazard estimation with flexible tails, logspline,
lspec, polyclass, and polymars, by C. Kooperberg and co-authors.
Functions for evaluating the mass density, cumulative distribution function, quantile function and random variate generation for the Polya-Aeppli distribution, also known as the geometric compound Poisson distribution.
Simulate via Markov chain Monte Carlo (hit-and-run algorithm)
a Dirichlet distribution conditioned to satisfy a finite set of linear
equality and inequality constraints (hence to lie in a convex polytope
that is a subset of the unit simplex).
R port of the Clipper library. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Also computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines.
Computes polychoric and polyserial correlations by quick
"two-step" methods or ML, optionally with standard errors;
tetrachoric and biserial correlations are special cases.
The following methods for cubature (numerical integration)
over polygonal domains are currently implemented:
the two-dimensional midpoint rule as a simple wrapper around
as.im.function() from package spatstat (Baddeley and Turner, 2005),
the product Gauss cubature by Sommariva and Vianello (2007),
an adaptive cubature for isotropic functions via line integrate()
along the boundary (Meyer and Held, 2014),
and quasi-exact methods specific to the integration of the
bivariate Gaussian density over polygonal and circular domains
(based on formulae from the Abramowitz and Stegun (1972) handbook).
For cubature over simple hypercubes, the packages "cubature" and
"R2Cuba" are more appropriate.
Implements a Gibbs sampling algorithm to perform Bayesian inference on biallelic SNP frequencies, genotypes and heterozygosity (observed and expected) in a population of autopolyploids.
A collection of functions to implement a class for univariate
polynomial manipulations.
Implements univariate polynomial operations in R
Functions to perform paternity exclusion via allele
matching, in autopolyploid species having ploidy 4, 6, or 8. The
marker data data used can be genotypic (copy numbers known) or
'phenotypic' (copy numbers not known).
A collection of tools to handle microsatellite data of
any ploidy (and samples of mixed ploidy) where allele copy number is not
known in partially heterozygous genotypes.  It can import and export data in
ABI 'GeneMapper', 'Structure', 'ATetra', 'Tetrasat'/'Tetra', 'GenoDive', 'SPAGeDi',
'POPDIST', 'STRand', and binary presence/absence formats.  It can calculate
pairwise distances between individuals using a stepwise mutation model or
infinite alleles model, with or without taking ploidies and allele frequencies
into account.  These distances can be used for the calculation of clonal
diversity statistics or used for further analysis in R.  Allelic diversity
statistics are also available.  polysat can assist the user in estimating the
ploidy of samples, and it can estimate allele frequencies in
populations, calculate pairwise Fst values based on those frequencies, and
export allele frequencies to 'SPAGeDi' and 'adegenet'.  Functions are also
included for assigning alleles to isoloci in cases where one pair of
microsatellite primers amplifies alleles from two or more independently
segregating isoloci.
Perform classic chi-squared tests and Ripol et al(1999)
binomial confidence interval approach for autopolyploid
dominant markers. Also, dominant markers may be generated
for families of offspring where either one or both of the
parents possess the marker. Missing values and
misclassified markers may be generated at random.
Fits Bayesian mixture models to estimate marker dosage for dominant markers on autopolyploids using JAGS (1.0 or greater) as outlined in Baker et al (2010). May be used in conjunction with polySegratio for simulation studies and comparison with standard methods.
Routines for flexible functional form estimation via basis
regression, with model selection via the adaptive LASSO or SCAD to prevent
overfitting.
This package fits a patch occupancy model
This package propose calculations of an information criterion to check the quality of simulations results of ABM/IBM or other non-linear rule-based models. The POMDEV measure is based on the KL divergence and likelihood theory. It basically indicates the deviance of simulation results from field observations. Once POMDEV scores and metropolis-hasting sampling on different model versions are effectuated, POMIC scores can be calculated. This method is still under development and further work are needed for the incorporation of multiple patterns assessment.
Tools for working with partially observed Markov processes (POMPs, AKA stochastic dynamical systems, state-space models).  'pomp' provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods.  It is also a platform for the implementation of new inference methods.
Finds equivalence classes corresponding to a symmetric relation
or undirected graph.  Finds total order consistent with partial order
or directed graph (so-called topological sort).
Construct and analyze projection matrix models from a demography study of marked individuals classified by age or stage. The package covers methods described in Matrix Population Models by Caswell (2001) and Quantitative Conservation Biology by Morris and Doak (2002).
popdemo provides a set of tools designed for modelling population demography
using population projection matrix (PPM) models.  The focus is mainly on indices
of transient dynamics, and the use of control theory (specifically transfer
functions).  However, popdemo includes a number of tools focussing on other
areas of demographic modelling.  Some tools may prove useful to anyone working
with projection matrix models of any type, or indeed matrices in general.
The package is designed to build on those tools already available in popbio.
Optimal experimental designs for both population and individual
studies based on nonlinear mixed-effect models.  Often this is based on a
computation of the Fisher Information Matrix (FIM). This package was developed
for pharmacometric problems, and examples and predefined models are available
for these types of systems.
Enables computation of various epidemiological statistics where the use of population data such as population counts and hazards is required. Relatively high computation speed achieved by using data.table.
A package that implements a variety of statistical and
population genetic methodology.
There are two main purposes to this package. The first is
to allow batch conversion of Genepop (Rousset 2008) input files
for use with Arlequin (Excoffier and Lischer 2010), which has a
simple GUI to analyze batch files. Two commonly used simulation
software, BottleSim (Kuo & Janzen 2003) and Easypop (Balloux
2001) produce Genepop output files that can be analyzed this
way. There are also functions to convert to and from BottleSim
format, to quickly produce allele frequency tables or to
convert a file directly for use in ordination analyses (e.g.
principal component analysis). This package also includes
functions to calculate allele rarefaction curves, confidence
intervals on heterozygosity and allelic richness with
resampling strategies (bootstrap and jackknife).
Provides efficient tools for population genomics data analysis,
able to process individual loci, large sets of loci, or whole genomes. PopGenome not only
implements a wide range of population genetics statistics, but also facilitates the easy
implementation of new algorithms by other researchers. PopGenome is optimized for speed via
the seamless integration of C code.
Provides beginner friendly framework to analyse population genetic data. It uses 'knitr' to create comprehensive reports on spatial genetic data. For detailed information how to use the package refer to the comprehensive tutorials or visit www.popgenreport.org.
This is a generic package that produces "Population Graphs"
a graph-theoretic topology based upon conditional genetic covariance.  This
functionality used to be within the gstudio package, but has been taken out
to focus on spatial integration of graph topologies with existing packages
like sp, raster, and ggplot2.
Provides a suite of tools for various methods of estimating
confidence intervals for the mean of selected populations.
Provides objects and accompanying methods which facilitates populating and querying SQLite databases.
Population genetic analyses for hierarchical analysis of partially
clonal populations built upon the architecture of the 'adegenet' package.
GenAlEx is a popular Excel macro for genetic analysis. The 'poppr'
R package allows import of GenAlEx formatted CSV data. This package serves as a
small 'poppr' add on for those who have trouble or simply do not want to export
their data into CSV format.
Implements the Bayesian hierarchical model described by Wheldon, Raftery, Clark and Gerland (see: http://www.csss.washington.edu/Papers/wp108.pdf) for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows, at the national level.
A set of routines which are useful in constructing
and evaluating self-organizing maps (SOMs).
The main attribute of 'PopVar' is the prediction of genetic variance in bi-parental populations, from which the package derives its name. 'PopVar' contains a set of functions that use phenotypic and genotypic data from a set of candidate parents to 1) predict the mean, genetic variance, and superior progeny value of all, or a defined set of pairwise bi-parental crosses, and 2) perform cross-validation to estimate genome-wide prediction accuracy of multiple statistical models. More details are available in Mohammadi, Tiede, and Smith (2015). Crop Sci. doi:10.2135/cropsci2015.01.0030. A dataset 'think_barley.rda' is included for reference and examples.
This package contains a set of portmanteau diagnostic
checks for univariate and multivariate time series.
Classes for analysing and implementing equity portfolios.
Portfolio optimization and analysis routines and graphics.
R interface to PortfolioEffect Quant service for estimating
high frequency price variance, quarticity, microstructure noise variance,
and other metrics in both aggregate and rolling window flavors.
Constructed estimators could use client-side market data or access
HF intraday price history for all major US Equities.
See https://www.portfolioeffect.com/ for more information on the
PortfolioEffect high frequency portfolio analytics platform.
R interface to PortfolioEffect Quant service for backtesting
high frequency trading (HFT) strategies, intraday portfolio analysis
and optimization. Includes auto-calibrating model pipeline for market
microstructure noise, risk factors, price jumps/outliers, tail risk
(high-order moments) and price fractality (long memory). Constructed
portfolios could use client-side market data or access HF intraday price
history for all major US Equities. See https://www.portfolioeffect.com/
for more information on the PortfolioEffect high frequency portfolio
analytics platform.
Classes that serve as a framework for designing equity
portfolio simulations.
Risk Attribution of a portfolio with Volatility Risk Analysis.
Does Markov chain Monte Carlo (MCMC) simulation of Potts models
and composite likelihood inference for Potts models.
A package including several functions related to the Potts models.
Optimizes a function using Powell's UObyQA algorithm.
Functions for the computation of power and level tables for hypothesis tests, in LaTeX format, functions to build explanatory graphs for studying power of test statistics.
Functions to obtain the operational characteristics of
bioequivalence studies with 2-stage designs via simulations
- Contains a function to calculate power and sample-size distribution
of 2-stage bioequivalence (BE) studies with a 2x2 crossover design
according to Potvin et al. / Montague et al. modified to include a
futility Nmax and modified to do the sample size estimation step
with PE and mse of stage 1.
- Contains further a function with the modifications according to
Karalis & Macheras which use both point estimate (PE) and mse from stage 1
also for the power monitoring steps.
- The third function power.2stage.fC() calculates power and sample-size
distribution of 2-stage BE studies with a futility criterion
for the PE or CI of T/R from stage 1.
- The fourth function power.2stage.GS() calculates power of non-adaptive
group sequential (2-stage) BE studies.
- The fifth function power.2stage.p() calculates power and sample size
distribution of 2-stage BE studies with 2 parallel groups.
This function has a sibling power.2stage.pAF(), which performs exactly as
described in A.Fuglsang 2014
- Another function power.2stage.ssr() allows the power calculation
for 2-stage studies with (blinded) interim sample size re-estimation.
Basic functions for power analysis and effect size calculation.
Analytical power calculations for GxE and GxG interactions for case-control studies of candidate genes and genome-wide association studies (GWAS). This includes power calculation for four two-step screening and testing procedures.  It can also calculate power for GxE and GxG without any screening.
An implementation of maximum likelihood estimators for a variety
of heavy tailed distributions, including both the discrete and continuous
power law distributions. Additionally, a goodness-of-fit based approach is
used to estimate the lower cut-off for the scaling region.
Functions to
calculate power and sample size for testing
(1) mediation effects;
(2) the slope in a simple linear regression;
(3) odds ratio in a simple logistic regression;
(4) mean change for longitudinal study with 2 time points;
(5) interaction effect in 2-way ANOVA; and
(6) the slope in a simple Poisson regression.
(1) To estimate the power of testing for linkage using an
affected sib pair design, as a function of the recurrence risk
ratios. We will use analytical power formulae as implemented in
R. These are based on a Mathematica notebook created by Martin
Farrall. (2) To examine how the power of the transmission
disequilibrium test (TDT) depends on the disease allele
frequency, the marker allele frequency, the strength of the
linkage disequilibrium, and the magnitude of the genetic
effect. We will use an R program that implements the power
formulae of Abel and Muller-Myhsok (1998). These formulae allow
one to quickly compute power of the TDT approach under a
variety of different conditions. This R program was modeled on
Martin Farrall's Mathematica notebook.
Functions to calculate power and
sample size for testing main effect or interaction effect in
the survival analysis of epidemiological studies
(non-randomized studies), taking into account the
correlation between the covariate of the
interest and other covariates. Some calculations also take
into account the competing risks and stratified analysis.
This package also includes
a set of functions to calculate power and sample size
for testing main effect in the survival analysis of
randomized clinical trials.
The PP package includes estimation of (MLE, WLE, MAP, EAP, ROBUST)
person parameters for the 1,2,3,4-PL model and the GPCM (generalized
partial credit model). The parameters are estimated under the assumption
that the item parameters are known and fixed. The package is useful e.g. in
the case that items from an item pool / item bank with known item parameters
are administered to a new population of test-takers and an ability
estimation for every test-taker is needed.
Calculates partial and semi-partial
(part) correlations along with p-value.
Computing similarities between proteins based on their GO annotation, KEGG annotation and PPI network topology. It integrates seven features (TCSS, IntelliGO, Wang, KEGG, Jaccard, RA and AA) to predict PPIs using an SVM classifier. Some internal functions to calculate GO semantic similarities are re-used from R package GOSemSim authored by Guangchuang Yu.
This package contains linear and nonlinear regression
methods based on Partial Least Squares and Penalization
Techniques. Model parameters are selected via cross-validation,
and confidence intervals ans tests for the regression
coefficients can be conducted via jackknifing.
Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"). Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.
The pps package contains functions to select samples using
PPS (probability proportional to size) sampling. It also
includes a function for stratified simple random sampling, a
function to compute joint inclusion probabilities for
Sampford's method of PPS sampling, and a few utility functions.
The user's guide pps-ug.pdf is included.
Projection pursuit classification tree using LDA, Lr or PDA projection pursuit index
Tools for exploring projection pursuit classification tree using
various projection pursuit indices.
This package allows users to calculate the number of
under-five child deaths caused by consumption of poor quality
antimalarials across 39 sub-Saharan nations. The package supports one
function, that starts an interactive web tool created using
the shiny R package. The web tool runs locally on the user's machine.
The web tool allows users to set input parameters (prevalence of poor
quality antimalarials, case fatality rate of children who take poor
quality antimalarials, and sample size) which are then used to perform
an uncertainty analysis following the Latin hypercube
sampling scheme. Users can download the output figures as PDFs, and the
output data as CSVs. Users can also download their input parameters
for reference. This package was designed to accompany the analysis
presented in:
J. Patrick Renschler, Kelsey Walters, Paul Newton, Ramanan Laxminarayan
"Estimated under-five deaths associated with poor-quality
antimalarials in sub-Saharan Africa", 2014. Paper submitted.
Distance-based parametric bootstrap tests for clustering with
spatial neighborhood information. Some distance measures,
Clustering of presence-absence, abundance and multilocus genetical data
for species delimitation, nearest neighbor
based noise detection. Try package?prabclus for on overview.
Functions from numerical analysis and linear algebra, numerical
optimization, differential equations, plus some special functions.
Uses Matlab function names where appropriate to simplify porting.
Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs. Other functions compute variance components for multistage designs and sample sizes in two-phase designs. A number of example datasets are included.
pragma allows for the use of pragma (also sometimes called
directives or keywords. These allow assigning arbitrary
functionality to a word without requiring the standard function
call syntax i.e. with parens.
The Prais-Winsten estimation procedure takes into account serial correlation of type AR(1) in a linear model. The procedure is an iterative method that recursively estimates the beta coefficients and the error autocorrelation of the specified model until convergence of rho, i.e. the AR(1) coefficient, is attained. All estimates are obtained by OLS.
Build friendly R packages that
praise their users if they have done something
good, or they just need it to feel better.
Kasulikud funktsioonid kvantitatiivsete mudelite kursuse
(SHPH.00.004) jaoks
Estimation, prediction and testing for analyzing serial dilution assay data using paired response curve.
Clustering is unsupervised and exploratory in nature. Yet, it can be performed through penalized regression with grouping pursuit. In this package, we provide two algorithms for fitting the penalized regression-based clustering (PRclust). One algorithm is based on quadratic penalty and difference convex method. Another algorithm is based on difference convex and ADMM, called DC-ADD, which is more efficient. Generalized cross validation was provided to select the tuning parameters. Rand index, adjusted Rand index and Jaccard index were provided to estimate the agreement between estimated cluster memberships and the truth.
It contains functions to analyze the precipitation
intensity, concentration and anomaly.
Accurate calculations and visualization of Precision-Recall and ROC
curves.
PredictABEL includes functions to assess the performance of
risk models. The package contains functions for the various measures that are
used in empirical studies, including univariate and multivariate odds ratios
(OR) of the predictors, the c-statistic (or area under the receiver operating
characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test,
reclassification table, net reclassification improvement (NRI) and
integrated discrimination improvement (IDI). Also included are functions
to create plots, such as risk distributions, ROC curves, calibration plot,
discrimination box plot and predictiveness curves. In addition to functions
to assess the performance of risk models, the package includes functions to
obtain weighted and unweighted risk scores as well as predicted risks using
logistic regression analysis. These logistic regression functions are
specifically written for models that include genetic variables, but they
can also be applied to models that are based on non-genetic risk factors only.
Finally, the package includes function to construct a simulated dataset with
genotypes, genetic risks, and disease status for a hypothetical population, which
is used for the evaluation of genetic risk models.
Three prediction algorithms described in the paper
"On-line predictive linear regression" Annals of Statistics 37,
1566 - 1590 (2009)
This package provides functions to diagnose
and make inferences from various linear models, such as those obtained from 'aov',
'lm', 'glm', 'gls', 'lme', and 'lmer'. Inferences include predicted
means and standard errors, contrasts, multiple comparisons, permutation tests and graphs.
"train_predict_mix" predicts the binary response with
binary features
Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach. Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature. Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.
Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection.
Prepares data collected in an experimental design for statistical
analysis (e.g., analysis of variance ;ANOVA) by taking the  individual data
files and preparing one table that contains several possibilities for
dependent variables. Most suitable when measuring reaction-times and/or
accuracy, or any other variable in an interval or ratio scale. Functions
included: file_merge(), read_data() and prep(). The file_merge() function
vertically merges individual data files (in a long format) in which each line
is a single trial within the experiment to one single dataset. The
read_data() function reads a file in a txt or csv format that contains a
single dataset in a long format table and creates a data frame from it. The
prep() function aggregates the single dataset according to any combination of
between and within grouping variables (i.e., between-subjects and
within-subjects independent variables, respectively), and returns a data
frame with a number of dependent measures for further analysis for each
experimental cell according to the combination of provided grouping
variables. Dependent measures for each experimental cell include among
others means before and after rejecting all values according to a flexible
standard deviation criterion/s, number of rejected values according to the
flexible standard deviation criterion/s, proportions of rejected values
according to the flexible standard deviation criterion/s, number of values
before rejection,  means after rejecting values according to procedures
described in Van Selst & Jolicoeur (1994) (suitable when measuring
reaction-times), standard deviations, medians, means according to any
percentile (e.g., 0.05, 0.25, 0.75, 0.95) and harmonic means. The data frame
prep() returns can also be exported as a txt file to be used for statistical
analysis in other statistical programs.
Preprocessing is often the most time-consuming phase in knowledge
discovery and preprocessing transformations interdependent in unexpected
ways. This package helps to make preprocessing faster and more effective. It
provides an S4 framework for creating and testing preprocessing combinations
for classification, clustering and outlier detection. The framework supports
user-defined and domain-specific preprocessors and preprocessing phases.
Default preprocessors can be used for low variance removal, missing value
imputation, scaling, outlier removal, noise smoothing, feature selection and
class imbalance correction.
This package provides a set of functions useful when
evaluating the results of presence-absence models. Package
includes functions for calculating threshold dependent measures
such as confusion matrices, pcc, sensitivity, specificity, and
Kappa, and produces plots of each measure as the threshold is
varied. It will calculate optimal threshold choice according to
a choice of optimization criteria. It also includes functions
to plot the threshold independent ROC curves along with the
associated AUC (area under the curve).
Makes output files from select PreSens Fiber Optic Oxygen Transmitters easier to work with in R. See <http://www.presens.de> for more information about PreSens (Precision Sensing GmbH).
Estimating species accumulation curves by rational function approximations to the non-parametric empirical Bayes estimator and by the zero-truncated Negative Binomial distribution.
Computes probability-scale residuals and residual correlations
for continuous, ordinal, binary, count, and time-to-event data.
prettyGraphs contains simple, crisp graphics. Graphics produced by prettyGraphs are publication-quality.
Automates the process of creating a scale bar and north arrow in
any package that uses base graphics to plot in R. Bounding box tools help find and manipulate
extents. Finally, there is a function to automate the process of setting margins, plotting
the map, scalebar, and north arrow, and resetting graphic parameters upon completion.
Functions for conventionally formatting descriptive stats,
reshaping data frames and formatting R output as HTML.
Pretty, human readable formatting of quantities.
Time intervals: 1337000 -> 15d 11h 23m 20s.
Vague time intervals: 2674000 -> about a month ago.
Bytes: 1337 -> 1.34 kB.
The prevalence package provides Frequentist and Bayesian methods for prevalence assessment studies. IMPORTANT: the truePrev functions in the prevalence package call on JAGS (Just Another Gibbs Sampler), which therefore has to be available on the user's system. JAGS can be downloaded from http://mcmc-jags.sourceforge.net/.
It provides functions for both likelihood-based
and Bayesian analysis of spatially referenced prevalence data, and is
also an extension of the 'geoR' package.
Spatial estimation of a prevalence surface
or a relative risks surface, using data from a Demographic and Health
Survey (DHS) or an analog survey.
Estimate False Discovery Rates (FDRs) for importance metrics from random forest runs.
Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.
Functions are primarily functions for systems of ordinary
differential equations, difference equations, and eigenanalysis
and projection of demographic matrices; data are for examples.
Visually Assessing the Specificity and Informativeness of Primer
Pairs.
Functions to test whether a number is prime and generate the prime numbers within a specified range. Based around
an implementation of Wilson's theorem for testing for an integer's primality.
Performs a unified treatment of Bump Hunting by Patient Rule Induction Method (PRIM) in Survival, Regression and Classification settings (SRC). The current version is a development release that only implements the case of a survival response. New features will be added soon as they are available.
fits a principal curve to a data matrix in arbitrary
dimensions
Provides capabilities beyond principal components
analysis to focus on finding structure in low variability
subspaces. Constructs and plots simple basis vectors for
pre-defined and user-defined measures of simplicity.
Allows users to access the Oregon State Prism climate data. Using the web service API data can easily downloaded in bulk and loaded into R for spatial analysis. Some user friendly visualizations are also provided.
The PRISMA package is capable of loading and processing huge text
corpora processed with the sally toolbox (http://www.mlsec.org/sally/).
sally acts as a very fast preprocessor which splits the text files into
tokens or n-grams. These output files can then be read with the PRISMA
package which applies testing-based token selection and has some
replicate-aware, highly tuned non-negative matrix factorization and
principal component analysis implementation which allows the processing of
very big data sets even on desktop machines.
PrivateLR implements two differentially private algorithms for
estimating L2-regularized logistic regression coefficients. A randomized
algorithm F is epsilon-differentially private (C. Dwork, Differential
Privacy, ICALP 2006), if
|log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon
for any pair D, D' of datasets that differ in exactly one element, any
set S, and the randomness is taken over the choices F makes.
Estimation of prevalence ratios using logistic models and confidence intervals with delta and bootstrap methods.
Optogenetics is a new tool to study neuronal circuits that have been genetically modified to allow stimulation by flashes of light.  This package implements the methodological framework, Point-process Response model for Optogenetics (PRO), for analyzing data from these experiments.  This method provides explicit nonlinear transformations to link the flash point-process with the spiking point-process.  Such response functions can be used to provide important and interpretable scientific insights into the properties of the biophysical process that governs neural spiking in response to optogenetic stimulation.
This package provides a framework for performing elementary probability
calculations on finite sample spaces, which may be represented by data frames
or lists.  Functionality includes setting up sample spaces, counting tools,
defining probability spaces, performing set algebra, calculating probability
and conditional probability, tools for simulation and checking the law of
large numbers, adding random variables, and finding marginal distributions.
Characteristic functions for all base R distributions are included.
Contains functions that are useful for probing moderation effects (or interactions) including techniques such as pick-a-point (also known as spotlight analysis) and Johnson-Neyman (also known as floodlight analysis). Plot function is also provided to facilitate visualization of results from each of these techniques.
Probabilistic Fisher discriminant analysis (pFDA) is a probabilistic version of the popular and powerful Fisher linear discriminant analysis for dimensionality reduction and classification.
The ProbForecastGOP package contains a main function,
called ProbForecastGOP and other functions, to produce
probabilistic weather forecasts of weather fields using the
Geostatistical Output Perturbation (GOP) method of Gel,
Raftery, and Gneiting (JASA, 2004).
This package provides multiclass conditional probability
estimation for the SVM, which is distributional assumption
free.
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
A direct and flexible method for estimating an ICA model.
This approach estimates the densities for each component
directly via a tilted gaussian. The tilt functions are
estimated via a GAM poisson model. Details can be found in
"Elements of Statistical Learning (2nd Edition)" Section 14.7.4
Fast and user friendly implementation of nonparametric estimators
for censored event history (survival) analysis. Kaplan-Meier and
Aalen-Johansen method.
The package can prioritize the candidate disease metabolites based on the assumption
that functionally related metabolites tend to associate with the same or similar
diseases in the context of metabolic pathway. The PROFANCY package (1) prioritizes the
disease metabolites from global functional similarity and local modularity of the
metabolic network; (2) allows users to select default metabolites or input their
interested metabolites as seed nodes or candidate nodes (3) can prioritize the candidate
metabolites in KEGG or EHMN metabolic network.
This package facilitates profile inference (inference at
the posterior mode) for a class of product partition models
(PPM). The Dirichlet process mixture is currently the only
available member of this class. These methods search for the
maximum posterior (MAP) estimate for the data partition in a
PPM.
Programs to determine student grades and create
examinations from Question banks.  Programs will create numerous
multiple choice exams, randomly shuffled, for different versions of same question list.
This package provides profile likelihoods for a parameter of interest in commonly used statistical models. The models include linear models, generalized linear models, proportional odds models, linear mixed-effects models, and linear models for longitudinal responses fitted by generalized least squares. The package also provides plots for normalized profile likelihoods as well as the maximum profile likelihood estimates and the kth likelihood support intervals.
profileModel provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of *arbitrary* inference functions for *arbitrary* 'glm'-like fitted models with linear predictors.
Fit profile analytic models. This includes the multivariate methods and data visualization tools
to implement profile analysis and cross-validation techniques described
in Davison & Davenport (2002) and Bulut (2013).
Allows users to quickly and reliably profile data in R
using convenience functions. The profiled data returns as a data.frame
and provides a wealth of common and uncommon summary statistics.
profr provides an alternative data structure
and visual rendering for the profiling information
generated by Rprof.
Tools for examining Rprof profile output.
Implementing the Progeny Clustering algorithm, the progenyClust package assesses the clustering stability and identifies the optimal clustering number for a given data matrix. It uses kmeans clustering as default, but can be customized to work with other clustering algorithms and different parameter settings. The package includes one main function progenyClust(), plot and summary methods for progenyClust object, and two example datasets (test and cell) for testing.
sample code, appendices and functions for the text
Programming GUIs in R
Prognostic ROC curve is an alternative graphical approach to represent the discriminative capacity of the marker: a receiver operating characteristic (ROC) curve by plotting 1 minus the survival in the high-risk group against 1 minus the survival in the low-risk group. This package contains functions to assess prognostic ROC curve. The user can enter the survival according to a model previously estimated or the user can also enter individual survival data for estimating the prognostic ROC curve by using Kaplan-Meier estimator. The area under the curve (AUC) corresponds to the probability that a patient in the low-risk group has a longer lifetime than a patient in the high-risk group. The prognostic ROC curve provides complementary information compared to survival curves. The AUC is assessed by using the trapezoidal rules. When survival curves do not reach 0, the prognostic ROC curve is incomplete and the extrapolations of the AUC are performed by assuming pessimist, optimist and non-informative situations.
Terminal progress bars. They are
configurable, may include percentage, elapsed time, and/or
the estimated completion time. They work in the command line,
in Emacs, R Studio, Windows Rgui and Mac OSX R.app. The package
also provides a C++ API, that works with or without Rcpp.
A simple interface to lat/long projection and datum
transformation of the PROJ.4 cartographic projections library.
It allows transformation of geographic coordinates from one
projection and/or datum to another.
ProjectTemplate provides functions to
automatically build a directory structure for a new R
project. Using this structure, ProjectTemplate
automates data loading, preprocessing, library
importing and unit testing.
High-throughput experiments are now widely used in biological researches, which improves both the quality and quantity of omics data. Network-based presentation of these data has become a popular way in data analyses. This package mainly provides functions for biological network construction, visualization and analyses. Networks can be constructed either from experimental data or from a set of proteins and integrated PPI database. Based on them, users can perform traditional visualization, along with the subcellular localization based ones for Homo sapiens and Arabidopsis thaliana. Furthermore, analyses including topological statistics, functional module clustering and go profiling can also be achieved.
Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation.
Computes two-sample confidence intervals for single, paired and independent proportions
This package implements propensity clustering and
decomposition. Propensity decomposition can be viewed on the
one hand as a generalization of the eigenvector-based
approximation of correlation networks, and on the other hand as
a generalization of random multigraph models and
conformity-based decompositions.
The prop.comb.RR package analyzes combination of proportions and relative risk.
The properties package allows to parse Java properties files
in the context of R Service Bus applications.
A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.
This package contains functions to run propensity-biased allocation to balance covariate distributions in sequential trials and propensity-constrained randomization to balance covariate distributions in trials with known baseline covariates at time of randomization.  Currently this package only supports trials comparing two groups.
The package provides functions for pretreatment and sample
selection of visible and near infrared diffuse reflectance spectra
This package provides functions to identify the optimal
solution that maximizes numbers of detectable differentiated
proteins from a multi-stage clinical proteomic study.
Provides methods for making inference in isobaric labelled
LC-MS/MS experiments, i.e. iTRAQ experiments. It provides a function that
reasonably parses a CSV-export from Proteome Discoverer(TM) into a data
frame that can be easily handled in R. Functions and methods are provided
for quality control, filtering, norming, and the calculation of response
variables for further analysis. The merging of multiple iTRAQ experiments
with respect to a reference is also covered.
Method for protein quantification based on identified and
quantified peptides. protiq can be used for absolute and relative protein
quantification. Input peptide abundance scores can come from various
sources, including SRM transition areas and intensities or spectral counts
derived from shotgun experiments. The package is still being extended to
also include the model for protein identification, MIPGEM, presented in
Gerster, S., Qeli, E., Ahrens, C.H. and Buehlmann, P. (2010). Protein and
gene model inference based on statistical modeling in k-partite graphs.
Proceedings of the National Academy of Sciences 107(27):12101-12106.
An object oriented system using object-based, also called
prototype-based, rather than class-based object oriented ideas.
Greedy algorithm described in Bien and Tibshirani (2011)
Prototype Selection for Interpretable Classification. Annals of
Applied Statistics. 5(4). 2403-2424
Performs minimax linkage hierarchical clustering.  Every cluster
has an associated prototype element that represents that cluster as
described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering
with Prototypes via Minimax Linkage," accepted for publication in The
Journal of the American Statistical Association, DOI:
10.1198/jasa.2011.tm10183.
PROTOLIDAR package contains functions for analyze the
LIDAR scan of plants (grapevine) and make 3D maps in GRASS GIS.
'The Proton Game' is a console-based data-crunching game for younger and older data scientists.
Act as a data-hacker and find Slawomir Pietraszko's credentials to the Proton server.
You have to solve four data-based puzzles to find the login and password.
There are many ways to solve these puzzles. You may use loops, data filtering, ordering, aggregation or other tools.
Only basics knowledge of R is required to play the game, yet the more functions you know, the more approaches you can try.
The knowledge of dplyr is not required but may be very helpful.
This game is linked with the ,,Pietraszko's Cave'' story available at http://biecek.pl/BetaBit/Warsaw.
It's a part of Beta and Bit series.
You will find more about the Beta and Bit series at http://biecek.pl/BetaBit.
Comprehensive toolkit for generating various numerical
representation schemes of protein sequence. The descriptors and similarity
scores included are extensively used in bioinformatics and chemogenomics.
For full functionality, the software 'ncbi-blast+' is needed, see
<http://blast.ncbi.nlm.nih.gov/Blast.cgi?CMD=Web&PAGE_TYPE=BlastDocs&DOC_TYPE=Download>
for more information.
'ProTracker' is a popular music tracker to sequence
music on a Commodore Amiga machine. This package offers the
opportunity to import, export, manipulate an play 'ProTracker'
module files. Even though the file format could be considered
archaic, it still remains popular to this date. This package
intends to contribute to this popularity and therewith
keeping the legacy of 'ProTracker' and the Commodore Amiga
alive.
This R package helps with quality checks, visualizations
and analysis of mass spectrometry data, coming from proteomics
experiments. The package is developed, tested and used at the Functional
Genomics Center Zurich. We use this package mainly for prototyping,
teaching, and having fun with proteomics data. But it can also be
used to do data analysis for small scale data sets.
Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov dissimilarity and Sircombe-Hazelton L2-norm. Categorical provenance proxies, such as mineralogical, petrographic or chemical compositions are compared with the Aitchison and Bray-Curtis distances. Also included are tools to plot compositional data on ternary diagrams, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions.
Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.
Determine minimal protein set explaining peptide spectrum matches.
Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method.
Test of periodicity in short time series using response surface regression
Useful tools to pry back the covers of R and understand the
language at a deeper level.
Bootstrapping for propensity score analysis and matching.
A collection of functions that primarily produce graphics
to aid in a Propensity Score Analysis (PSA).  Functions
include: cat.psa and box.psa to test balance within strata of
categorical and quantitative covariates, circ.psa for a
representation of the estimated effect size by stratum,
loess.psa that provides a graphic and loess based effect size
estimate, and various balance functions that provide measures
of the balance achieved via a PSA in a categorical covariate.
Algorithms for fitting penalized semiparametric Bayesian Cox models with shrinkage and grouping priors.
Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome.  Both tumor-normal paired and tumor-only analyses are supported.
Bayesian analysis of item-response theory (IRT) models,
roll call analysis; computing highest density regions; maximum
likelihood estimation of zero-inflated and hurdle models for count
data; goodness-of-fit measures for GLMs; data sets used
in writing	and teaching at the Political Science
Computational Laboratory; seats-votes curves.
Provides a number of functions to
simplify and automate the scoring, comparison, and evaluation of
different ways of creating composites of data.  It is particularly
aimed at facilitating the creation of physiological composites of
metabolic syndrome symptom score (MSSS) and allostatic load (AL).
Produces power spectral density estimates through iterative
refinement of the optimal number of sine-tapers at each frequency. This
optimization procedure is based on the method of Riedel and Sidorenko
(1995), which minimizes the Mean Square Error (sum of variance and bias)
at each frequency, but modified for computational stability.
This R package includes functions for gathering commonly used and
regularly maintained data set in political science. It also includes
functions for combining components from these data sets into variables that
have been suggested in the literature, but are not regularly maintained.
Functions for creating Latin Hypercubes with
prescribed correlations and performing parameter space exploration.
Also implements the PLUE method.
Based on the package sensitivity, by Gilles Pujol,
Bertrand Iooss & Alexandre Janon.
Various functions for computing pseudo-observations for
censored data regression
Implements projected sparse Gaussian process kriging for the intamap package
This package contains functions to calculate the Specificity Index statistic, which can be used for comparative quantitative analysis to identify genes enriched in specific cell populations across a large number of profiles, as well as perform numerous post-processing operations. NOTE:Supplementary data (human & mouse expression sets, calculated pSI datasets, etc.) can be found in pSI.data package located at the following URL: http://genetics.wustl.edu/jdlab/psi_package/
Makes it easy to build panel data in wide format from PSID
delivered raw data. Deals with data downloaded and pre-processed by Stata or SAS,
or can optionally download directly from the PSID server using the SAScii package.
`psidR` takes care of merging data from each wave onto a cross-period index file,
so that individuals can be followed over time. The user must specify which years
they are interested in, and the PSID variable names (e.g. ER21003) for each year
(they differ in each year). There are different panel data designs and
sample subsetting criteria implemented ("SRC", "SEO", "immigrant" and "latino"
samples).
Suite of R functions for the estimation of local false discovery rate (LFDR) using several methods.
This package provides functions for estimation of linear
and non-linear mixed-effects models using stochastic
differential equations. Moreover it provides functions for
finding smoothed estimates of model states and for simulation.
The package allows for any multivariate non-linear time-variant
model to be specified, and it also handles multidimensional
input, co-variates, missing observations and specification of
dosage regimen.
The package provides an implementation of PSO consistent
with the standard PSO 2007/2011 by Maurice Clerc et al.
Additionally a number of ancillary routines are provided for
easy testing and graphics.
Spearman's rank correlation test with precomputed exact
null distribution for n <= 22.
Smoothing splines with penalties on order m derivatives.
Estimates parameters of a piecewise exponential model for time to progression and time from progression to death with interval censoring of the time to progression and covariates for each distribution using proportional hazards.
Provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The  library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. This software results from research work executed within the framework of the Swiss National Centre of Competence in Research LIVES, which is financed by the Swiss National Science Foundation. The authors are grateful to the Swiss National Science Foundation for its financial support.
Functions to simulate Poisson or Normally distributed responses relative to
a baseline and compute achieved significance level and powers for tests on the
simulated responses.
Kappa, ICC, Cronbach alpha, screeplot, mtmm
A general purpose toolbox for personality, psychometrics and experimental psychology.   Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using  factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multi-levels include within and between group statistics, including correlations and factor analysis.   Functions for simulating particular item and test structures are included. Several functions serve as a useful front end for structural equation modeling.  Graphical displays of path diagrams, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometrics as well as publications in personality research. For more information, see the personality-project.org/r webpage.
Contains functions useful for correlation theory,
meta-analysis (validity-generalization), reliability, item
analysis, inter-rater reliability, and classical utility
Psychometric mixture models based on 'flexmix' infrastructure. At the moment Rasch mixture models
with different parameterizations of the score distribution (saturated vs. mean/variance specification)
and Bradley-Terry mixture models are implemented. Both mixture models can be estimated with or without
concomitant variables. See vignette('raschmix', package = 'psychomix') for details on the Rasch mixture
models.
Infrastructure for psychometric modeling such as data classes
(for item response data and paired comparisons), basic model fitting
functions (for Bradley-Terry, Rasch, partial credit, rating scale,
multinomial processing tree models), extractor functions for different types
of parameters (item, person, threshold, discrimination), unified inference
and visualizations, and various datasets for illustration. Intended as a
common lightweight and efficient toolbox for psychometric modeling and a
common building block for fitting psychometric mixture models in package
"psychomix" and psychometric tree models in package "psychotree".
Recursive partitioning based on psychometric models,
employing the general MOB algorithm (from package partykit) to obtain
Bradley-Terry trees, Rasch trees, rating scale and partial credit trees, and
MPT trees.
An assortment of functions that could be useful in analyzing data from psychophysical experiments. It includes functions for calculating d' from several different experimental designs, links for m-alternative forced-choice (mafc) data to be used with the binomial family in glm (and possibly other contexts) and self-Start functions for estimating gamma values for CRT screen calibrations.
Psytabs produces well-formatted tables in .rtf or .html,
which largely conform to "psychological style".
A multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions is also available. The package includes also some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with these extensions.
A tool to draw inference in personalized medicine regimes
Function 'pip3d' tests whether a point in 3D space is
within, exactly on, or outside an enclosed surface defined by a triangular mesh.
Function 'pip2d' tests whether a point in 2D space is within, exactly on, or outside a polygon.
Fits and analyses time dependent marked point process models with an emphasis on earthquake modelling. For a more detailed introduction to the package, see the topic "PtProcess". A list of recent changes can be found in the topic "Change Log".
Parametric Time Warping aligns patterns, i.e. it aims to
put corresponding features at the same locations. The algorithm
searches for an optimal polynomial describing the warping. It
is possible to align one sample to a reference, several samples
to the same reference, or several samples to several
references. One can choose between calculating individual
warpings, or one global warping for a set of samples and one
reference. Two optimization criteria are implemented: RMS (Root
Mean Square error) and WCC (Weighted Cross Correlation). Both
warping of peak profiles and of peak lists are supported.
Bayesian variable selection for linear regression models using hierarchical
priors. There is a prior that combines information across responses and one
that combines information across covariates, as well as a standard spike and
slab prior for comparison. An MCMC samples from the marginal posterior
distribution for the 0-1 variables indicating if each covariate belongs to the
model for each response.
Text mining of PubMed Abstracts (text and XML) from http://www.ncbi.nlm.nih.gov/pubmed.
create a word cloud using the abstract of publications from PubMed
R interface to pullword service for Natural Language Processing
in Chinese. It enables users to extract valuable words from text by deep learning models.
For more details please visit the official site (in Chinese) http://pullword.com/.
R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/).
PurBayes is an MCMC-based algorithm that uses
next-generation sequencing data to estimate tumor purity and
clonality for paired tumor-normal data.
Enables the removal of training data from fitted R models while
retaining predict functionality. The purged models are more portable as their
memory footprints do not scale with the training sample size.
Make your pure functions purr with the 'purrr' package. This
package completes R's functional programming tools with missing features
present in other programming languages.
A package for sending push notifications to mobile devices (iOS and
Android) and the desktop using Pushover
Likelihood based population viability analysis in the
presence of observation error and missing data.
The package can be used to fit, compare, predict,
and forecast various growth model types using data cloning.
The calculation of p-variation of the finite sample data
Computes nonparametric p-values for the potential class
memberships of new observations as well as cross-validated
p-values for the training data. The p-values are based on
permutation tests applied to an estimated Bayesian likelihood
ratio, using a plug-in statistic for the Gaussian model, 'k
nearest neighbors', 'weighted nearest neighbors' or
'penalized logistic regression'.
Additionally, it provides graphical displays and quantitative
analyses of the p-values.
An implementation of multiscale bootstrap resampling for
assessing the uncertainty in hierarchical cluster analysis.
It provides AU (approximately unbiased) p-value as well as
BP (bootstrap probability) value for each cluster in a dendrogram.
Computes rank correlations and their p-values with various options for tied ranks.
The pvsR package facilitates data retrieval from Project
Vote Smart's rich online data base on US politics via the Project Vote
Smart application programming interface (PVS API). The functions in this
package cover most PVS API classes and methods and return the
requested data in a data frame.
This R package contains open source implementations
of several p-value weighting methods, including Spjotvoll, exponential
and Bayes weights. These are methods for improving power in multiple testing
via the use of prior information.
Power analysis functions along the lines of Cohen (1988).
Tools the evaluation of interim analysis plans for sequentially
monitored trials on a survival endpoint; tools to construct efficacy and
futility boundaries, for deriving power of a sequential design at a specified
alternative, template for evaluating the performance of candidate plans at a
set of time varying alternatives.
Statistical power simulation for testing the Rasch Model based on a three-way analysis of variance design with mixed classification.
The Penn World Table provides purchasing power parity and
national income accounts converted to international prices for
189 countries for some or all of the years 1950-2010.
The Penn World Table 8.x provides information on relative levels of
income, output, inputs, and productivity for 167 countries
between 1950 and 2011.
The pxR package provides a set of functions for reading and writing PC-Axis files, used by different statistical organizations around the globe for data dissemination.
Generic interface for the PX-Web/PC-Axis API. The PX-Web/PC-Axis
API is used by organizations such as Statistics Sweden and Statistics
Finland to disseminate data. The R package can interact with all
PX-Web/PC-Axis APIs to fetch information about the data hierarchy, extract
metadata and extract and parse statistics to R data.frame format. PX-Web is
a solution to disseminate PC-Axis data files in dynamic tables on the web.
Since 2013 PX-Web contains an API to disseminate PC-Axis files.
Given a SpatialPolygonsDataFrame and a set of populations for each polygon,
compute a population density estimate based on Tobler's pycnophylactic interpolation
algorithm. The result is a SpatialGridDataFrame.
Drawing population pyramid using (1) data.frame or (2) vectors.
The former is named as pyramid() and the latter pyramids(), as wrapper
function of pyramid().  pyramidf() is the function to draw population
pyramid within the specified frame.
String operations the Python way - a package for those of us who miss Python's string methods while we're working in R.
Implements heuristics for the Quadratic Assignment Problem (QAP). Currently only a simulated annealing heuristic is available.
This package delivers some functions to provide a scientific quality assurance of meteorological data.
An extensive set of functions to perform Qualitative Comparative Analysis:
crisp sets (csQCA), temporal (tQCA), multi-value (mvQCA) and fuzzy sets (fsQCA).
QCA is a methodology that bridges the qualitative and quantitative divide
in social science research. It uses a boolean algorithm that results in a minimal
causal combination which explains a given phenomenon.
A set of functions for Qualitative Comparative Analysis (QCA). It can be used for various types of QCA (csQCA, mvQCA fsQCA and crip set TQCA  and time-serious QCA). It has methods for simplifying assumption, contradictory simplifying assumption.
Implements tests for Type I error in Qualitative Comparative Analysis (QCA) that take into account the multiple hypothesis tests inherent in the procedure. Tests can be carried out on three variants of QCA: crisp-set QCA (csQCA), multi-value QCA (mvQCA) and fuzzy-set QCA (fsQCA). For fsQCA, the fsQCApermTest() command implements a permutation test that provides 95% confidence intervals for the number of counterexamples and degree of consistency, respectively. The distributions of permuted values can be plotted against the observed values. For csQCA and mvQCA, simple binomial tests are implemented in csQCAbinTest() and mvQCAbinTest(), respectively.
An extensive set of functions to perform Qualitative Comparative Analysis:
crisp sets ('csQCA'), temporal ('tQCA'), multi-value ('mvQCA') and fuzzy sets ('fsQCA'),
using a GUI - graphical user interface.
QCA is a methodology that bridges the qualitative and quantitative divide
in social science research. It uses a boolean algorithm that results in a minimal
causal combination which explains a given phenomenon.
Helper functions for Qualitative Comparative Analysis: evaluate and plot Boolean formulae on fuzzy set score data, apply Boolean operations, compute consistency and coverage measures
Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.
Tools for (automated and manual) quality control of
the results of Genome Wide Association Studies
Robust estimation of Gaussian mixture models fitted by modified EM algorithm, robust clustering and classification.
This package allows to generate Shewhart-type charts and to obtain
numerical results of interest to the quality control of a process
(involving  continuous, attribute or count data).
This package provides basic functionality for univariable and multivariable
quality control analysis, including: xbar, xbar-one, S, R, ewna, cusum,
mewna, mcusum  and T2 charts.
Quantile correlation-sure independence screening (QC-SIS) and composite quantile correlation-sure independence screening (CQC-SIS) for ultrahigh-dimensional data.
Automates many of the tasks associated with quantitative
discourse analysis of transcripts containing discourse
including frequency counts of sentence types, words, sentences,
turns of talk, syllables and other assorted analysis tasks. The
package provides parsing tools for preparing transcript data.
Many functions enable the user to aggregate data by any number
of grouping variables, providing analysis and seamless
integration with other R packages that undertake higher level
analysis and visualization of text.  This affords the user a
more efficient and targeted analysis.  'qdap' is designed for
transcript analysis, however, many functions are applicable to
other areas of Text Mining/Natural Language Processing.
A collection of dictionaries and word lists for use with
the 'qdap' package.
A collection of regular expression tools associated with
the 'qdap' package that may be useful outside of the context of
discourse analysis. Tools include
removal/extraction/replacement of abbreviations, dates, dollar
amounts, email addresses, hash tags, numbers, percentages,
citations, person tags, phone numbers, times, and zip codes.
A collection of tools associated with the 'qdap' package
that may be useful outside of the context of text analysis.
This package provides different specifications of a Quadrilateral
Dissimilarity Model which can be used to fit same-different judgments
in order to get a predicted matrix that satisfies regular minimality
[Colonius & Dzhafarov, 2006, Measurement and representations of
sensations, Erlbaum]. From such a matrix, Fechnerian distances can be
computed.
Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc.
This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015.
The qgraph package can be used to visualize data as networks as well as provides an interface for visualizing weighted graphical models.
Two linear mixed model approaches: REML(restricted maximum likelihood) and MINQUE (minimum norm quadratic unbiased estimation) approaches and several resampling techniques are integrated for various quantitative genetics analyses. With these two types of approaches, various unbalanced data structures, missing data, and any irregular genetic  mating designs can be analyzed and statistically tested. This package also offers fast computations for many large data sets. Other functions will be added to this R tool in the future.
Functions for making run charts and  basic Shewhart control
charts for measure and count data.
The main function, qic(), creates run and control charts and has a
simple interface with a rich set of options to control data analysis
and plotting, including options for automatic data aggregation by
subgroups, easy analysis of before-and-after data, exclusion of one
or more data points from analysis, and splitting charts into
sequential time periods.
Missing values and empty subgroups are handled gracefully.
Open QIIME output files in R, compute statistics, and
create plots from the data.
This is a collection of functions to read, recode, and transcode data.
Extension of the functionality of the Matrix package for using sparse matrices. Some of the functions are very general, while other are highly specific for special data format as used for quantitative language comparison (QLC).
Collection of visualizations as used in quantitative language comparison.
Functions to implement Q-learning for estimating optimal
dynamic treatment regimes from two stage sequentially
randomized trials, and to perform inference via m-out-of-n
bootstrap for parameters indexing the optimal regime.
Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.
Analysis of Q methodology, used to identify distinct perspectives existing within a group. This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see http://qmethod.org).
A single function runs the full analysis. Each step can be run separately using the corresponding functions: for automatic flagging of Q-sorts (manual flagging is optional), for statement scores, for distinguishing and consensus statements, and for general characteristics of the factors.
Additional functions are available to import and export data, to print and plot, to import raw data from individual *.CSV files, and to make printable cards.
The package uses principal components and it allows manual or automatic flagging, a number of mathematical methods for rotation, and a number of correlation coefficients for the initial correlation matrix.
See further details in the package documentation, and in the web pages below, which include a cookbook, guidelines for more advanced analysis (how to perform manual flagging or change the sign of factors), data management, and a beta graphical user interface for online and offline use.
Basic functions for building parsers, with an application to PC-AXIS format files.
To generate the scores of the EORTC QLQ-C30 questionnaire and supplementary modules and to determine the time to health-related quality of life score deterioration in longitudinal analysis.
Model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR).
use 2^ddCT methods calculate the relative gene expression,
data file can be export from bio-rad qpcr machine, the results
can be plot with errorbar. ver 1.1 add GroupPlot function, can
plot all the groups once.
Tools to 1) simulate and visualize stochastic differential equations and 2) determine stability of equilibria using the ordered-upwind method to compute the quasi-potential.
Provides the function qqtest which incorporates uncertainty in its
qqplot display(s) so that the user might have a better sense of the
evidence against the specified distributional hypothesis.  qqtest draws a
quantile quantile plot for visually assessing whether the data come from a
test distribution that has been defined in one of many ways.  The vertical
axis plots the data quantiles, the horizontal those of a test distribution.
The default behaviour generates 1000 samples from the test distribution and
overlays the plot with shaded pointwise interval estimates for the ordered
quantiles from the test distribution.  A small number of independently
generated exemplar quantile plots can also be overlaid.  Both the interval
estimates and the exemplars provide different comparative information to
assess the evidence provided by the qqplot for or against the hypothesis
that the data come from the test distribution (default is normal or
gaussian).  Finally, a visual test of significance (a lineup plot) can also
be displayed to test the null hypothesis that the data come from the test
distribution.
Tools that create D3 JavaScript force directed graph from R. D3 JavaScript was created by Michael Bostock. See http://d3js.org/ and, more specifically for Force Directed Graph https://github.com/mbostock/d3/wiki/Force-Layout.
The qrfactor package simultaneously runs both Q and R  mode factor analyses. The package contains only one function called qrfactor() that can perform PCA, R-mode Factor Analysis, Q-mode Factor Analysis, Simultaneous R- and Q-mode Factor Analysis, Principal Coordinate Analysis, as wells as Multidimensional Scaling  (MDS). Loadings and scores can easily be computed from the  simulation.The plot.qrfactor() function offers several annotated  biplots for all possible combinations of eigenvectors, loadings, and scores. Input data includes shapefiles, tables and dataframe
Joint estimation of quantile specific intercept and slope parameters in a linear regression setting.
Quantile regression (QR) for Linear
Mixed-Effects Models via the asymmetric Laplace distribution (ALD).
It uses the Stochastic Approximation of the EM (SAEM) algorithm for
deriving exact maximum likelihood estimates and full inference results
for the fixed-effects and variance components.
It also provides graphical summaries for assessing the algorithm
convergence and fitting results.
This package is designed to accompany the book
Quantitative Risk Management: Concepts, Techniques and Tools by
Alexander J. McNeil, Rudiger Frey, and Paul Embrechts.
Functions and data sets for reproducing selected results from
the book "Quantitative Risk Management: Concepts, Techniques and Tools".
Furthermore, new developments and auxiliary functions for Quantitative
Risk Management practice.
Functionality for generating (randomized) quasi-random numbers in
high dimensions.
Quantile regression (QR) for Nonlinear
Mixed-Effects Models via the asymmetric Laplace distribution (ALD).
It uses the Stochastic Approximation of the EM (SAEM) algorithm for
deriving exact maximum likelihood estimates and full inference results
for the fixed-effects and variance components.
It also provides graphical summaries for assessing the algorithm
convergence and fitting results.
Fit a quantile regression neural network with optional
left censoring using a variant of the finite smoothing
algorithm.
Molecular descriptors and outcomes for several public domain data sets
Dynamic bindings to the Qt library for calling Qt
methods and extending Qt classes from R. Other packages build upon 'qtbase'
to provide special-purpose high-level interfaces to specific parts of Qt.
Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTET).  The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables), (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package).
Analysis of experimental crosses to identify genes
(called quantitative trait loci, QTLs) contributing to variation in
quantitative traits.
Datasets for the book, A Guide to QTL Mapping with R/qtl.
Web-based interactive charts (using D3.js) for the analysis of
experimental crosses to identify genetic loci (quantitative trait
loci, QTL) contributing to variation in quantitative traits.
Functions to infer co-mapping trait hotspots and causal models
Provides tools for joint analysis of multiple traits in a backcross (BC) or recombinant inbred lines (RIL) population. It can be used to select an optimal subset of traits for multiple-trait mapping, analyze multiple traits via the SURE model, which can associate different QTL with different traits, and perform multiple-trait composite multiple-interval mapping.
Functions to Simultaneously Infer Causal Graphs and Genetic Architecture
This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances.
Low-level interface to functionality in Qt for efficiently drawing
dynamic graphics and handling basic user input.
Quantitative Analysis of Complex Networks. This package offers a set of topological network measures to analyze complex Networks structurally.
This package gives you the exact first four permutation moments for the most commonly used quadratic form statistics, which need not be positive definite. The extension of this work to quadratic forms greatly expands the utility of density approximations for these problems, including for high-dimensional applications, where the statistics must be extreme in order to exceed stringent testing thresholds. Approximate p-values are obtained by matching the exact moments to the Pearson family of distributions using the PearsonDS package.
This package contains routines and documentation for
solving quadratic programming problems.
This package fits classical sparse regression models with
efficient active set algorithms by solving quadratic problems. Also
provides a few methods for model selection purpose (cross-validation,
stability selection).
Exact one-sided p-values and confidence intervals for an outcome variable defined on an interval measurement scale with only qualitative and ordinal information available.
Used for testing for qualitative interactions between
treatment effects and patient subgroups. Here the term treatment effect
means a comparison result of two treatments within each patient subgroup.
Models included in this package are Gaussian, binomial and Cox models.
Methods included here are interval based graphical approach and Gail Simon
LRT.
Qualitative methods for the validation of dynamic models.
It contains (i) an orthogonal set of deviance measures for absolute,
relative and ordinal scale and (ii) approaches accounting for time
shifts. The first approach transforms time to take time delays and speed
differences into account. The second divides the time series into
interval units according to their main features and finds the longest
common subsequence (LCS) using a dynamic programming algorithm.
Implements indices of qualitative variation proposed by Wilcox (1973).
Functions for interacting directly with the Quandl API to offer data in a number of formats usable in R, downloading a zip with all data from a Quandl database, and the ability to search. This R package uses the Quandl API. For more information go to https://www.quandl.com/docs/api. For more help on the package itself go to https://www.quandl.com/help/r.
Methods which streamline the descriptive analysis of quantitative matrices.
Statistical evaluation of calibration curves by different
regression techniques: ordinary, weighted, robust (up to 4th
order polynomial).  Log-log and Box-Cox transform, estimation
of optimal power and weighting scheme. Tests for
heteroscedascity and normality of residuals. Different kinds of
plots commonly used in illustrating calibrations. Easy "inverse
prediction" of concentration by given responses and statistical
evaluation of results (comparison of precision and accuracy by
common tests).
A fast, flexible toolset for for the management, processing, and
quantitative analysis of textual data in R.
This package provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach and the balance approach.
Estimation of conditional quantiles using optimal quantization.
Construction of an optimal grid of N quantizers, estimation of conditional
quantiles and data driven selection of the size N of the grid. Graphical
illustrations for the selection of N and of resulting estimated curves or
surfaces when the dimension of the covariate is one or two.
Code for centroid, median and quantile classifiers.
Specify, build, trade, and analyse quantitative financial trading strategies.
Contains functions useful for data screening, testing
moderation, mediation and estimating power.
Estimation and inference methods for models of conditional quantiles:
Linear and nonlinear parametric and non-parametric (total variation penalized) models
for conditional quantiles of a univariate response and several methods for handling
censored survival data.  Portfolio selection methods based on expected shortfall
risk are also included.
Quantile Regression Forests is a tree-based ensemble
method for estimation of conditional quantiles. It is
particularly well suited for high-dimensional data. Predictor
variables of mixed classes can be handled. The package is
dependent on the package randomForests, written by Andy Liaw.
Fits non-crossing regression quantiles as a function of linear covariates and a smooth terms via B-splines with difference penalties.
Methods to determine, smooth and plot quantile periodograms for
univariate and multivariate time series.
Using HTS data, clusters mutations in order to recreate putative clones from the data provided. It requires genotype at the location of the variant as well as the depth of coverage and number of reads supporting the mutation. Additional information may be provided, such as the contamination in the tumor sample. This package also provides a function QuantumCat() which simulates data obtained from tumor sequencing. Latest version can be found at github/DeveauP/QuantumClone .
Identify differentially expressed genes in RNA-seq count data using quasi-Poisson or quasi-negative binomial models with 'QL', 'QLShrink' and 'QLSpline' methods (Lund, Nettleton, McCarthy, and Smyth, 2012).
Set of functions to make the processing and analysis of
surveys easier : interactive shiny apps for data recoding, contingency
tables, dataset metadata handling, and several convenience functions.
It provides versatile tools for analysis of birth and death based Markovian Queueing Models
and Single and Multiclass Product-Form Queueing Networks.
It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K,
Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks,
Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks
and Single Channel Multiple Class Mixed Networks.
Also it provides a B-Erlang, C-Erlang and Engset calculators.
This work is dedicated to the memory of D. Sixto Rios Insua.
Use Newton's method and coordinate descent to solve the
regularized inverse covariance matrix estimation problem.
Please refer to: Sparse Inverse Covariance Matrix Estimation
Using Quadratic Approximation, Cho-Jui Hsieh, Matyas A. Sustik,
Inderjit S. Dhillon, Pradeep Ravikumar, Advances in Neural
Information Processing Systems 24, 2011, p. 2330--2338.
While analyzing geospatial data, easy visualization is often needed
that allows for quick plotting, and simple, but easy interactivity.
Additionally, visualizing geospatial data in projected coordinates is also
desirable.  The 'quickmapr' package provides a simple method to visualize 'sp'
and 'raster' objects, allows for basic zooming, panning, identifying, and
labeling of spatial objects, and does not require that the data be in
geographic coordinates.
Quickly fits and plots psychometric functions (normal, logistic,
Weibull or any or any function defined by the user) for multiple groups.
Grows a qualitative interaction tree. Quint is a tool for subgroup analysis, suitable for data from a two-arm randomized controlled trial.
Gene banks increasingly use molecular markers for routine
characterization of plant collections and farmer managed diversity. The
gene bank of the International Potato Center presently uses a
micro-satellite marker kit to produce molecular profiles for potato
accessions. We have been searching for a compact graphical representation
that shows both molecular diversity and accession characteristics - thus
permitting biologists and collection curators to have a simple way to
interpret molecular data. Inspired by the ancient Andean data recording
system we devised a graph that allows for standardized representation while
leaving room for updates of the marker kit and the collection of
accessions. The graph has been used in several catalogs of potatoes.
Functions to compute confidence statistics
for ordering population quantiles.
Selection of a threshold parameter for GLM-lasso to obtain a sparse model
with a good compromise between high true positive rate and low false discovery rate.
For a given data matrix A and cluster centers/prototypes collected in the matrix P, the functions described here select a subset of statistic variables Q that mostly explains/justifies P as prototypes. The functions are useful to reduce the data dimension for classification and to discard masking variables for clustering.
Functions to compute quasi variances and associated measures of approximation error
A collection of (wrapper) functions the creator found useful for
quickly placing data summaries and formatted regression results into .Rnw or
.Rmd files.  Functions for generating commonly used graphics, such as receiver
operating curves or Bland-Altman plots, are also provided by 'qwraps2'.
'qwraps2' is a updated version of a package 'qwraps'.  The original version
'qwraps' was never submitted to CRAN but can be found at
https://github.com/dewittpe/qwraps.  The implementation and limited scope of the
functions within 'qwraps2' (https://github.com/dewittpe/qwraps2) is fundamentally
different from 'qwraps'.
QZ package provides generalized eigenvalues and QZ
decomposition (generalized Schur form) for an N-by-N
non-symmetric matrix A or paired matrices (A,B) with
eigenvalues reordering mechanism. The package is mainly based
complex*16 and double precision of LAPACK library (version
3.4.2.)
Estimation of reproduction numbers for disease outbreak, based on
incidence data. The R0 package implements several documented methods. It is
therefore possible to compare estimations according to the methods used.
Depending on the methods requested by user, basic reproduction number
(commonly denoted as R0) or real-time reproduction number (referred to as
R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs
will give different graphs depending on the methods requested : basic
reproductive number estimations will only show the epidemic curve
(collected data) and an adjusted model, whereas real-time methods will also
show the R(t) variations throughout the outbreak time period. Sensitivity
analysis tools are also provided, and allow for investigating effects of
varying Generation Time distribution or time window on estimates.
Utilities for sparse signal recovery suitable for compressed sensing. L1, L2 and TV penalties, DFT basis matrix, simple sparse signal generator, mutual cumulative coherence between two matrices and examples, Lp complex norm, scaling back regression coefficients.
A series of functions to call AD Model Builder (i.e.,
compile and run models) from within R, read the results back
into R as 'admb' objects, and provide standard accessors (i.e.
coef(), vcov(), etc.)
An R interface to estimate structured additive regression (STAR) models with BayesX.
It is a wrapper around the Cuba-1.6 library by Thomas Hahn available from the URL http://www.feynarts.de/cuba/. Implement four general-purpose multidimensional integration algorithms: Vegas, Suave, Divonne and Cuhre.
This package provides generic functions to analyze the distribution
of two continuous variables: 'conf2d' to calculate a smooth empirical
confidence region, and 'freq2d' to calculate a frequency distribution.
2dRUE is a methodology to make a diagnostic of land
condition in a large territory during a given time period. The
following projects have funded this package: DeSurvey IP (EC
FP6 Integrated Project contract No. 003950), DesertWatch (ESA
DUE contract No. 18487/04/I-LG) and MesoTopos (Junta de
Andalucia PE ref. RNM-4023).
Converting R CRAN outputs into Google Earth.
Wrapper functions for GUESS, a GPU-enabled sparse Bayesian variable
selection method for linear regression based analysis of possibly
multivariate/correlated outcomes.
Includes HTML function and methods to write in an HTML
file. Thus, making HTML reports is easy. Includes a function
that allows redirection on the fly, which appears to be very
useful for teaching purpose, as the student can keep a copy of
the produced output to keep all that he did during the course.
Package comes with a vignette describing how to write HTML
reports for statistical analysis. Finally, a driver for Sweave
allows to parse HTML flat files containing R code and to
automatically write the corresponding outputs (tables and
graphs).
Providing wrapper functions to implement Bayesian analysis in JAGS.  Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.
generate univariate and bivariate analyses in LaTeX or
HTML formats
An R command interface to the 'MLwiN' multilevel
modelling software package.
Using this package,
it is possible to call a BUGS model, summarize inferences and
convergence in a table and graph, and save the simulations in arrays for easy access
in R.
R2PPT provides a simple set of wrappers to easily use rcom
or RDCOMClient for generating Microsoft PowerPoint
presentations.
R2STATS is a gWidgetsRGtk2 GUI for fitting and comparing GLM or GLMM (based on Douglas Bates' lme4 package) in R. It is designed to make comparisons between numerous models easy, both numerically and graphically, which may be useful for teaching. Relevant plots are automatically produced for each model family. R2STATS is *not* a generic graphical interface for R, but a GUI for statistical modelling in a model comparison approach.
r2stl, R package for visualizing data using a 3D printer
Package r2stl converts R data to STL (stereolithography) files
that can be used to feed a 3-dimensional printer.  The
3-dimensional output from an R function can be materialized
into a solid surface in a plastic material, therefore allowing
more detailed examination. There are many possible uses for
this new R tool, such as to examine mathematical expressions
with very irregular shapes, to aid teaching people with
impaired vision, to create raised relief maps from digital
elevation maps (DEMs), to bridge the gap between mathematical
tools and rapid prototyping, and many more.  Ian Walker created
the function "r2stl" and Jose' Gama assembled the package.
Using the Ming library
(http://www.libming.org/) to create Flash animations.
Users can either use the SWF device swf() to generate SWF file
directly through plotting functions like plot() and lines(),
or convert images of other formats (SVG, PNG, JPEG) into SWF.
This package uses either the statconnDCOM server (via the
rcom package) or the RDCOMClient to communicate with MS-Word
via the COM interface.
Invoke a 'BUGS' model in 'OpenBUGS' or 'WinBUGS', a class "bugs" for 'BUGS'
results and functions to work with that class.
Function write.model() allows a 'BUGS' model file to be written.
The class and auxiliary functions could be used with other MCMC programs, including 'JAGS'.
This is a collection of useful functions and data for
Stats 330
CDISC Dataset-XML and Define-XML are used as file format for clinical trial data and metadata respectively. This functions supports reading both Dataset-XML and Define-XML. The Clinical Data Interchange Standards Consortium (CDISC, http://www.cdisc.org/) is a standards developing organization (SDO) to develop global standards and innovations regarding medical research and healthcare.
Provides a collection of functions for basic
database and document management operations such as add, get, list or
delete. Every cdbFunction() gets and returns a list() containing the
connection setup.
Such a list (in the documentation mostly called cdb) can be generated by
cdb <- cdbIni(). Then, the list cdb provides some functions respectively
functionality e.g cdb$baseUrl() or cdb$getDocRev().
This package provides an R interface with 2-part 4dfp MRI images
(.4dfp.ifh and .4dfp.img files.)
A collection of R functions for use with Stock Synthesis, a
fisheries stock assessment modeling platform written in ADMB by Dr. Richard
D.  Methot at the NMFS Northwest Fisheries Science Center. The functions
include tools for summarizing and plotting results, manipulating files,
visualizing model parameterizations, and various other tasks.
The R6 package allows the creation of classes with reference
semantics, similar to R's built-in reference classes. Compared to reference
classes, R6 classes are simpler and lighter-weight, and they are not built
on S4 classes so they do not require the methods package. These classes
allow public and private members, and they support inheritance, even when
the classes are defined in different packages.
Implementation of some racing methods for the empirical
selection of the best. If the R package `rpvm' is installed
(and if PVM is available, properly configured, and
initialized), the evaluation of the candidates are performed in
parallel on different hosts.
Fit a variety of models to Rank Abundance Data
This package implements import, export, manipulation, visualization, and downstream
(post-clustering) analysis of RADseq data, integrating with the pyRAD package by Deren Eaton.
Fundamental formulas for Radar, for attenuation, range, velocity,
effectiveness, power, scatter, doppler, geometry, radar equations, etc.
Based on Nick Guy's Python package PyRadarMet
Functions to extract first and second order statistics from
images.
RadioSonde is a collection of programs for reading and
plotting SKEW-T,log p diagrams and wind profiles for data
collected by radiosondes (the typical weather balloon-borne
instrument), which we will call "flights", "sondes", or
"profiles" throughout the associated documentation.  The raw
data files are in a common format that has a header followed by
specific variables.  Use "help(ExampleSonde)" for the full
explanation of the data files.
Radioactive doses estimation using individual chromosomal aberrations information. See Higueras M, Puig P, Ainsbury E, Rothkamm K. (2015). A new inverse regression model applied to radiation biodosimetry. Proceedings of the Royal Society A, 471.
Designed for the import, analysis, and visualization of dosimetric and volumetric data in Radiation Oncology, the tools herein enable import of dose-volume histogram information from Varian's 'Eclipse' platform and 3D structural representations and dosimetric information from 'DICOM-RT' files.  These tools also enable subsequent visualization and statistical analysis of these data.
Contains 4 different functions for radon and soil gas transport in a porous medium.
Aims at loading Google Adwords data into R. Adwords is an online
advertising service that enables advertisers to display advertising copy to web users.
Therefore the package implements three main features. First, the package provides an
authentication process for R with the Google Adwords API via OAUTH2. Second, the
package offers an interface to apply the Adwords query language in R and
query the Adwords API with ad-hoc reports. Third, the received data are
transformed into suitable data formats for further data processing and data
analysis.
A series of shortcuts for routine tasks originally developed by Rafael A. Irizarry to facilitate data exploration.
Proper L2-penalized ML estimators for the
precision matrix as well as supporting functions to employ these estimators
in a graphical modeling setting.
Data fusion filters for Attitude Heading Reference System (AHRS) based on
Vlad Maximov's GyroLib AHRS library (quaternion based linearized/extended/unscented Kalman filter,
Euler based LKF, gyro-free with vector matching, SVD calibration and EKF calibration),
Sebastian O.H. Madgwick AHRS algorithms and Sebastian O.H. Madgwick implementation of Mayhony et al AHRS algorithm.
Functions and data sets for functional data display and outlier detection.
The Climate Prediction Center's (CPC) rainfall data for the
world (1979 to present, 50 km resolution) and the USA (1948 to
present, 25 km resolution), is one of the few high quality, long
term, observation based, daily rainfall products available for free.
Although raw data is available at CPC's ftp site, obtaining,
processing and visualizing the data is not straightforward. There are
more than 12,000 files for the world and about 24,000 files for the USA.
Moreover, file formats and file extensions have not been consistent.
This package provides functionality to download, process and visualize
over 35 years of global rainfall data and over 65 years of USA rainfall
data from CPC.
Estimates of rainfall at desired frequency (e.g., 1% annual
chance or 100-year return period) and desired duration (e.g.,
24-hour duration) are often required in the design of dams and other
hydraulic structures, catastrophe risk modeling, environmental
planning and management. One major source of such estimates for the
USA is the NOAA National Weather Service's (NWS) division of
Hydrometeorological Design Studies Center (HDSC). Raw data from
NWS-HDSC is available at 1-km resolution and comes as a huge number
of GIS files. This package provides functionality to easily access
and analyze the 1-km GIS files provided by NWS' PF Data Server for
the entire USA. This package also comes with datasets on record point
rainfall measurements provided by NWS-HDSC.
Programmatic interface to the Altmetric.com API.
Characterizing environmental microbiota diversity using amplicon-based next generation sequencing (NGS) data. Functions are developed to manipulate operational taxonomic unit (OTU) table, perform descriptive and inferential statistics, and generate publication-quality plots.
Estimate the parameters, the number of classes and cluster vertices of a random network into groups with homogeneous connection profiles. The clustering is performed for directed graphs with typed edges (edges are assumed to be drawn from multinomial distributions) for which a partition of the vertices is available.
API for using 'AmCharts' Library. Based on the 'htmlwidgets'
package, it provides a global architecture to generate 'JavaScript' source
code for charts. Most of classes in the library have their equivalent in R
with S4 classes; for those classes, not all properties have been referenced
but can easily be added in the constructors. Complex properties (e.g.
'JavaScript' object) can be passed as named list. See examples at <http://
dataknowledge.github.io/rAmCharts/>. and <http://www.amcharts.com/> for
more information about the library. The package includes the free version of
'AmCharts' Library. Its only limitation is that a small link to the web site
will be displayed in your charts. If you enjoy this library, do not hesitate
to refer to this page <http://www.amcharts.com/online-store/> to purchase a
licence, and thus support its creators and get a period of Priority Support.
See also <http://www.amcharts.com/about/> for more information about
'AmCharts' company.
A "define" function is provided that allows
for inclusion of external scripts without polluting
the global namespace and makes it easier to
write modular R code.
Additional matrix functionality for R including: (1) wrappers
for the base matrix function that allows matrices to be created from character
strings and lists (the former is especially useful for creating block
matrices), (ii) better printing of large matrices via a new generic function
for "pretty" printing, and (iii) a number of convenience functions for users
more familiar with other scientific languages like 'Julia', 'Matlab'/'Octave',
or 'Python'+'NumPy'.
This package provides an efficient procedure for fitting the entire solution path for high-dimensional regularized generalized linear models with interactions effects under the strong heredity constraint.
RAMpath is a rewrite of RAMpath software developed by John McArdle and Steven Boker. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features.  First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variances and covariances into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system.
Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples.  Package performance is tuned for large spatial datasets.
The deterministic part of the Fortuna cryptographic
pseudorandom number generator, described by Schneier & Ferguson
"Practical Cryptography"
Generates random names with additional information including fake SSNs, gender, location, zip, age, address, and nationality.
The true random number service provided by the RANDOM.ORG
website created by Mads Haahr samples atmospheric noise via radio tuned to
an unused broadcasting frequency together with a skew correction algorithm
due to John von Neumann.  More background is available in the included
vignette based on an essay by Mads Haahr.  In its current form, the package
offers functions to retrieve random integers, randomized sequences and
random strings.
Methods for the inference on and the simulation of Gaussian fields are provided, as well as methods for the simulation of extreme value random fields.
Various utilities are provided that might be used in spatial statistics and elsewhere. It delivers a method for solving linear equations that checks the sparsity of the matrix before any algorithm is used. Furthermore, it includes the Struve functions.
Classification and regression based on a forest of trees
using random inputs.
Distributed training and prediction of random forest models based upon 'randomForest' package using 'ddR' (Distributed Data Structures) API in the 'ddR' package.
A unified treatment of Breiman's random forests for survival, regression and classification problems based on Ishwaran and Kogalur's random survival forests (RSF) package.  The package runs in both serial and parallel (OpenMP) modes.
The package implements a bagging predictor based on
general linear models
Allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.
Contains a function to randomize subjects, patients in groups of
sequences (treatment sequences).
If a blocksize is given, the randomization will be done within blocks.
The randomization may be controlled by a Wald-Wolfowitz runs test.
Functions to obtain the p-value of that test are included.
The package is mainly intended for randomization of bioequivalence studies
but may be used also for other clinical crossover studies.
Contains two helper functions sequences() and williams() to get the sequences
of commonly used designs in BE studies.
This tool enables the user to choose a randomization procedure
based on sound scientific criteria. It comprises the generation of randomization
sequences as well the assessment of randomization procedures based on carefully
selected criteria. Furthermore, randomizeR provides a function for the
comparison of randomization procedures.
Generates random assignments for common experimental
designs: simple, complete, blocked, and clustered.
Fits standard and random effects latent class models. The single level random effects model is described in Qu et al (doi: 10.2307/2533043) and the two level random effects model in Beath and Heller (doi: 10.1177/1471082X0800900302). Examples are given for their use in diagnostic testing.
Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.
The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis.
Ensemble model, for classification, regression
and unsupervised learning, based on a forest of unpruned
and randomized binary decision trees. Each tree is grown
by sampling, with replacement, a set of variables at each node.
Each cut-point is generated randomly, according to the continuous
Uniform distribution. For each tree, data are either bootstrapped
or subsampled. The unsupervised mode introduces clustering, dimension reduction
and variable importance, using a three-layer engine. Random Uniform Forests are mainly
aimed to lower correlation between trees (or trees residuals), to provide a deep analysis
of variable importance and to allow native distributed and incremental learning.
Several non parametric randomness tests for numeric sequences
Provides (1) pseudo random generators - general linear congruential generators, multiple recursive generators and generalized feedback shift register (SF-Mersenne Twister algorithm and WELL generators); (2) quasi random generators - the Torus algorithm, the Sobol sequence, the Halton sequence (including the Van der Corput sequence) and (3) some RNG tests - the gap test, the serial test, the poker test. The package depends on rngWELL package but it can be provided without this dependency on demand to the maintainer. For true random number generation, use the 'random' package, for Latin Hypercube Sampling (a hybrid QMC method), use the 'lhs' package. A number of RNGs and tests for RNGs are also provided by 'RDieHarder', all available on CRAN. There is also a small stand-alone package 'rngwell19937' for the WELL19937a RNG.
Implementation of random variables by means of S4 classes and methods
Tools for easy generation of (life-history) traits maps based on species range (extent-of-occurrence) maps.
Generates expected values of species richness, with continuous or
scattered ranges, for data across one or two dimensions.
A fast implementation of Random Forests, particularly suited for high dimensional data.
Ensembles of classification, regression, survival and probability prediction trees are
supported. Data from genome-wide association studies can be analyzed efficiently. In addition to
data frames, datasets of class 'gwaa.data' (R package GenABEL) can be directly analyzed.
This package performs aggregation of ordered lists based
on the ranks using several different algorithms: Borda count,
Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a
brute force algorithm (for small problems)
This package proposes a model-based clustering algorithm for
ranking data. Multivariate rankings as well as partial rankings are taken
into account. This algorithm is based on an extension of the Insertion
Sorting Rank (ISR) model for ranking data, which is a meaningful and
effective model parametrized by a position parameter (the modal ranking,
quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
of the rank population is modelled by a mixture of ISR, whereas conditional
independence assumption is considered for multivariate rankings.
Implements distance based probability models for ranking data.
The supported distance metrics include Kendall distance, Spearman distance, Footrule distance, Hamming distance,
, Weighted-tau distance and Weighted Kendall distance.
Phi-component model and mixture models are also supported.
Rank-hazard plots (Karvanen and Harrell, Statistics in Medicine 2009) visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate.
Methods for ranking responses of a single response question or a multiple response question
Finds the k nearest neighbours for every point in a given dataset
in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
support for approximate as well as exact searches, fixed radius searches
and bd as well as kd trees.
Finds the k nearest neighbours for every point in a given dataset
in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
support for approximate as well as exact searches, fixed radius searches
and 'bd' as well as 'kd' trees. The distance is computed using the L1
(Manhattan, taxicab) metric. Please see package 'RANN' for the same
functionality using the L2 (Euclidean) metric.
To find the reversal association between variables.
Facilitates efficient polygon search using kd trees.
Coordinate level spatial data can be aggregated to higher geographical
identities like census blocks, ZIP codes or police district boundaries.
This process requires mapping each point in the given data set to a
particular identity of the desired geographical hierarchy. Unless efficient
data structures are used, this can be a daunting task. The operation
point.in.polygon() from the package sp is computationally expensive.
Here, we exploit kd-trees as efficient nearest neighbor search algorithm
to dramatically reduce the effective number of polygons being searched.
Package to perform non-invasive fetal testing for aneuploidies
using sequencing count data from cell-free DNA
This package provides other packages with access to the internal
R serialization code. Access to this code is provided at the C function
level by using the registration of native function mechanism. Client
packages simply include a single header file RApiSerializeAPI.h provided by
this package.

This packages builds on the Rhpc package by Junji Nakano and Ei-ji Nakama
which also includes a (partial) copy of the file src/main/serialize.c from R
itself.

The R Core group is the original author of the serialization code made
available by this package.
This package implements R functions that interface to various methods in the Linux kernel related to enforcing security restrictions on a certain task (process). The package can apply mandatory access control (MAC) policies by loading an apparmor profile, and set hardware limits (rlimit), uid, gid, affinity and priority of the current process, directly from within R. The high level function eval.secure builds on these foundations to support dynamic sandboxing: it evaluates a single expression in a temporary fork with hard security restrictions and hardware limits without any side effects on the main R session.
An easy way to determine which directories on the users computer
you should use to save data, caches and logs. A port of Python's Appdirs
(\url{https://github.com/ActiveState/appdirs}) to R.
Facilitating the creation of reproducible statistical
report templates. Once created, rapport templates can be exported to
various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the
converter backend.
Helper functions that act as wrappers to more advanced statistical
methods with the advantage of having sane defaults for quick reporting.
This package uses the functions written by Daniel
Morissette <danmo@videotron.ca> to read geographical information in Arc/Info
V 7.x format and E00 files to import the coverages into R variables.
Tests gene-environment interaction for rare genetic variants using Sequence Kernel Association Test (SKAT) type gene-based tests. Includes two tests for the interaction term only, and one joint test for genetic main effects and gene-environment interaction.
Randomization tests for the statistical comparison of \emph{i} = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the \emph{i} samples were all drawn randomly from a single assemblage, with (necessarily) a single underlying species abundance distribution. The biogeographic null hypothesis is that the \emph{i} samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions
Allows calculation of rarity weights for species and indices of rarity for assemblages of species according to different methods (Leroy et al. 2012, 2013).
An R wrapper of the 'ARPACK' library
<http://www.caam.rice.edu/software/ARPACK/> to solve large scale
eigenvalue/vector problems. It is typically used to compute a few
eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues,
which is usually more efficient than eigen() if k << n. This package
provides the 'eigs()' function which does the similar job as in 'Matlab',
'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function
to calculate the largest k singular values and corresponding
singular vectors of a real matrix. Matrices can be given in either dense
or sparse form.
MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.
This package contains functions to perform supervised and
pixel based raster image classification. It has been designed
to facilitate land-cover analysis. Five classification
algorithms can be used: Maximum Likelihood Classification,
Multinomial Logistic Regression, Neural Networks, Random
Forests and Support Vector Machines. The output includes the
classified raster and standard classification accuracy
assessment such as the accuracy matrix, the overall accuracy
and the kappa coefficient. An option for in-sample verification
is available.
The rase package primarily implements the Range Ancestral State Estimation for phylogeography described in Quintero, I., Keil, P., Jetz, W., & Crawford, F. W. (2015). Historical Biogeography Using Species Geographical Ranges. Systematic biology, 64(6), 1059-1073. It also includes Bayesian inference of ancestral states under a Brownian Motion model of character evolution and Maximum Likelihood estimation of rase for n-dimensional data. Visualizing functions in 3D are implemented using the rgl package.
Reading, writing, manipulating, analyzing and modeling of gridded spatial data. The package implements basic and high-level functions. Processing of very large files is supported.
Methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. See the website for examples.
An implementation of routines for solving rate-distortion problems.
Rate-distortion theory is a field within information theory that
examines optimal lossy compression. That is, given that some
information must be lost, how can a communication channel be designed
that minimizes the cost of communication error? Rate-distortion
theory is concerned with the optimal  (minimal cost) solution to such
tradeoffs. An important tool for solving rate-distortion problems is
the Blahut algorithm, developed by Richard Blahut and described in:

Blahut, R. E. (1972). Computation of channel capacity and
rate-distortion functions. IEEE Transactions on Information Theory,
IT-18(4), 460-473.

This package implements the basic Blahut algorithm, and additionally contains a number of `helper' functions, including a routine for searching for an information channel that minimizes cost subject to a constraint on information rate.
A function which performs exact rate ratio tests and returns an object of class htest.
The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data.
This package provides several functions to
manipulate rational functions, including basic
arithmetic operators, derivatives and integrals with
EXPLICIT forms.
This package provide an easy way to produce atmospheric profiles of Pressure,
Temperature and Density according to the standard atmosphere 1976.
It provides also profiles of molecular volume backscatter coefficients for standard atmosphere,
and approximated estimations of sunset, sunrise and solar zenith angle.
The R Analytic Tool To Learn Easily (Rattle) provides a
Gnome (RGtk2) based interface to R functionality for data mining.
The aim is to provide a simple and intuitive interface
that allows a user to quickly load data from a CSV file
(or via ODBC), transform and explore the data,
build and evaluate models, and export models as PMML (predictive
modelling markup language) or as scores. All of this with knowing little
about R. All R commands are logged and commented through the log tab. Thus they
are available to the user as a script file or as an aide for the user to
learn R or to copy-and-paste directly into R itself.
Rattle also exports a number of utility
functions and the graphical user interface, invoked as rattle(),  does
not need to be run to deploy these.
Parameter estimation for averaging models of Anderson's Information Integration Theory
Interface to <http://proyectoavis.com> database.
It provides means to download data filtered by species, order,
family, and several other criteria. Provides also basic functionality to
plot exploratory maps of the datasets.
Provides an interface to functions of the 'SAMtools' C-Library by Heng Li.
Basic R package to access data structures offered by any
BEFdata portal instance.
rbenchmark is inspired by the Perl module Benchmark, and
is intended to facilitate benchmarking of arbitrary R code. The
library consists of just one function, benchmark, which is a
simple wrapper around system.time.  Given a specification of
the benchmarking process (counts of replications, evaluation
environment) and an arbitrary number of expressions, benchmark
evaluates each of the expressions in the specified environment,
replicating the evaluation as many times as specified, and
returning the results conveniently wrapped into a data frame.
Oracle Berkeley DB -
<http://www.oracle.com/technetwork/database/database-technologies/berkeleydb/overview/index.html> -
is a family of embedded key-value database
libraries providing scalable high-performance data management services
to applications. The Berkeley DB products use simple function-call APIs
for data access and management. This package provides an R interface to
the Berkeley DB API.
The package contains functions to generate random numbers
from the beta distribution and random vectors from the
Dirichlet distribution.
Interface to 'Biodiversity' 'Heritage' Library ('BHL') 'API'
methods. 'BHL' is a repository of 'digitized' literature on 'biodiversity'
studies, including 'floras', research papers, and more.
To facilitate retrieving and processing biochemical reaction data such as Rhea, MetaCyc, KEGG and Unipathway, the package provides the functions to download and parse data, instantiate generic reaction and check mass-balance. The package aims to construct an integrated metabolic network and genome-scale metabolic model.
Functions for connecting to BioUML server, querying BioUML repository and launching BioUML analyses.
Interface to the 'USGS' 'BISON' 'API', a 'database' for
species occurrence data on the web. Data comes from species in the
United States from participating data providers. You can get
data via 'taxonomic' and location based queries. A simple function
is provided to help visualize data.
Utilities related to Bitcoin. Unified markets API interface
(bitstamp, kraken, btce, bitmarket). Both public and private API calls.
Integration of data structures for all markets. Support SSL. Read Rbitcoin
documentation (command: ?btc) for more information.
An R package for the BitCoinCharts.com API.
An R Interface to Bloomberg is provided via the Blp API.
Creation, manipulation, simulation of linear Gaussian Bayesian
networks from text files and more...
Scalable decision tree training and prediction.
Takes matched and unmatched data and calculates Rosenbaum bounds for the treatment effect.  Calculates bounds for binary outcome data, Hodges-Lehmann point estimates, Wilcoxon signed-rank test for matched data and matched IV estimators, Wilcoxon sum rank test, and for data with multiple matched controls. Package is also designed to work with the Matching package and operate on Match() objects.
The RBP curve is a visual tool to assess the
performance of prediction models.
Functions to prepare files needed for running BUGS in
batch-mode, and running BUGS from R. Support for Linux and
Windows systems with OpenBugs is emphasized.
Rbundler manages a project-specific library for dependency
package installation. By specifying dependencies in a DESCRIPTION file
in a project's root directory, one may install and use dependencies
in a repeatable fashion without requiring manual maintenance.
rbundler creates a project-specific R library in
`PROJECT_ROOT/.Rbundle` (by default) and a project-specific
`R_LIBS_USER` value, set in `PROJECT_ROOT/.Renviron`. It supports
dependency management for R standard "Depends", "Imports",
"Suggests", and "LinkingTo" package dependencies. rbundler also
attempts to validate and install versioned dependencies, such
as ">=", "==", "<=". Note that, due to the way R manages package
installation, differing nested versioned dependencies are not
allowed. For example, if your project depends on packages A (== 1),
and B (== 2), but package A depends on B (== 1), then a nested
dependency violation will cause rbundler to error out.
Memoization can be used to speed up repetitive and computational expensive function calls.  The first time a function that implements memoization is called the results are stored in a cache memory.  The next time the function is called with the same set of parameters, the results are momentarily retrieved from the cache avoiding repeating the calculations.  With this package, any R object can be cached in a key-value storage where the key can be an arbitrary set of R objects.  The cache memory is persistent (on the file system).
Calculate the flow of particles between polygons by two integration methods: integration by a cubature method and integration on a grid of points.
Provides an interface to the National Topographic System (NTS), which is
the way in which a number of freely available Canadian datasets
are organized. CanVec and CanVec+ datasets, which include all data used
to create Canadian topographic maps, are two such datasets that are useful
in creating vector-based maps for locations across Canada. This packages searches
CanVec data by location, plots it using pretty defaults, and exports it to
human-readable shapefiles for use in another GIS.
Estimation of abundance and other of demographic parameters for closed
populations, open populations and the robust design in capture-recapture experiments
using loglinear models.
This package makes some maps using shapefiles and
dataframes. Five kinds of maps are available : proportionnal
circles, proportionnal circles colored by a discretized
quantitative variable, proportionnal circles colored by the
modalities of a qualitative variable, choropleth and typology.
This packages provides a direct interface (without the
use of Java) to the most basic functionality of Apache
Cassanda such as login, updates and queries.
Tools for large, sparse optimal matching of treated units
and control units in observational studies.  Provisions are
made for refined covariate balance constraints, which include
fine and near-fine balance as special cases.  Matches are
optimal in the sense that they are computed as solutions to
network optimization problems rather than greedy algorithms.
R interface to (some of) cddlib
(http://www.ifor.math.ethz.ch/~fukuda/cdd_home/cdd.html).
Converts back and forth between two representations of a convex polytope:
as solution of a set of linear equalities and inequalities and as
convex hull of set of points and rays.
Also does linear programming and redundant generator elimination
(for example, convex hull in n dimensions).  All functions can use exact
infinite-precision rational arithmetic.
This package allows the user to access functionality in the
CDK, a Java framework for cheminformatics. This allows the user to load
molecules, evaluate fingerprints, calculate molecular descriptors and so on.
In addition the CDK API allows the user to view structures in 2D.
This package provides the CDK libraries for use
in the R environment. Given the size of the library itself,
this package is not expected to change very frequently. To make
use of the CDK within R, it is suggested that you use the rcdk
package. Note that it is possible to directly interact with the CDK
using rJava. However rcdk exposes functionality in a more idiomatic way.
The CDK library itself is released as LGPL and the sources can be
obtained from https://github.com/cdk/cdk
RCEIM is a package implementing a stochastic heuristic method for performing multidimensional function optimization. The method is inspired in the Cross-Entropy Method. It does not relies on derivatives, neither imposes particularly strong requirements into the function to be optimized. Additionally, it takes profit from multicore processing to enable optimization of time-consuming functions.
Analyze microscopy based cytometry datasets created with 'Cell-ID'. It includes functions for manipulating and plotting the data. It can also create automatic image montages of cells in a user defined layout.
Example dataset for 'Rcell' package. Contains images and cell data object.
To facilitate using 'cereal' with Rcpp.
'cereal' is a header-only C++11 serialization library.
'cereal' takes arbitrary data types and reversibly turns them into
different representations, such as compact binary encodings, XML,
or JSON. 'cereal' was designed to be fast, light-weight, and easy
to extend - it has no external dependencies and can be easily
bundled with other code or used standalone.
Conjugate gradient minimization of nonlinear functions
with box constraints incorporating the Dai/Yuan update. This
implementation should be used in place of the "CG" algorithm
of the optim() function.
A simple data science challenge system using R Markdown and Dropbox <https://www.dropbox.com/>.
It requires no network configuration, does not depend on external platforms
like e.g. Kaggle <https://www.kaggle.com/> and can be easily installed on a personal computer.
R package for chess validations, pieces movements and check
detection. Also integrates functions to plot chess boards given a
Forsyth Edwards and Portable Game notations.
An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data.
Collection of portable choice dialog widgets
Functions to generate stimuli and analyze data of reverse correlation image classification experiments (psychophysical tasks aimed at visualizing cognitive mental representations of faces).
RCircos package provides a simple and flexible way to generate Circos 2D track plot images for genomic data visualization. The types of plots include: heatmap, histogram, lines, scatterplot, tiles and plot items for further decorations include connector, link (lines and ribbons), and text (gene) label. All functions require only R graphics package that comes with R base installation.
This package contains wrapper functions and methods which allow to
use "climdex.pcic" and "RMAWGEN" packages.  With this simple approach it is
possible to calculate climate change indices, suggested by the WMO-CCL,
CLIVAR, ETCCDMI(http://www.climdex.org),on stochastic generations of
temperature and precipitation time series, obtained by the application of
RMAWGEN.  Each index can be applied to both observed data and to synthetic
time series produced by the Weather Generator, over a reference period
(e.g. 1981-2010, as in the example). It contains also functions and methods
to evaluate the generated time series of climate change indices consistency
by statistical tests.Bugs/comments/questions/collaboration of any kind are
warmly welcomed.
R tools for integrating with the www.clinicalcodes.org web
repository
ClinicalTrials.gov is a registry and results database of publicly
and privately supported clinical studies of human participants conducted
around the world. Users can search for information about and results from
those trials. This provides a set of functions to interact with the search
and download features. Results are downloaded to temporary directories and
returned as R objects.
R version of 'GenClone' (a computer program to analyse genotypic data, test for clonality and describe spatial clonal organization, Arnaud-Haond & Belkhir 2007, <http://wwz.ifremer.fr/clonix/content/download/68205/903914/file/GenClone2.0.setup.zip>), this package allows clone handling as 'GenClone' does, plus the possibility to work with several populations, MultiLocus Lineages (MLL) custom definition and use, and p-value calculation for psex statistic (probability of originating from distinct sexual events) and psex_Fis statistic (taking account of Hardy-Weinberg equilibrium departure) as 'MLGsim'/'MLGsim2' (a program for detecting clones using a simulation approach, Stenberg et al. 2003).
Provide flexible native clustering routines that can be
linked against in downstream packages.
Tool for providing access to the Java version 'CMAEvolutionStrategy' of
Nikolaus Hansen. 'CMA-ES' is the Covariance Matrix Adaptation Evolution Strategy,
see https://www.lri.fr/~hansen/cmaes_inmatlab.html#java.
A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.
Various statistical, graphics, and data-management functions used by the Rcmdr package in the R Commander GUI for R.
An Rcmdr "plug-in" to accompany the book Customer and
Business Analytics: Applied Data Mining for Business Decision
Making Using R by Daniel S. Putler and Robert E. Krider.
This package provides a Rcmdr "plug-in" based on coin (Conditional Inference Procedures in a Permutation Test Framework).
This package provides an Rcmdr plug-in based on the
depthTools package, which implements different robust
statistical tools for the description and analysis of gene
expression data based on the Modified Band Depth, namely, the
scale curves for visualizing the dispersion of one or various
groups of samples (e.g. types of tumors), a rank test to decide
whether two groups of samples come from a single distribution
and two methods of supervised classification techniques, the DS
and TAD methods.
The package provides a platform-independent GUI for design of experiments.
It is implemented as a plugin to the R-Commander, which is a more general
graphical user interface for statistics in R based on tcl/tk.
DoE functionality can be accessed through the menu Design that is added to the
R-Commander menus.
This package provides an Rcmdr "plug-in" based on the
Design of experiments class Stat 4309
Este paquete proporciona una interfaz grafica de usuario (GUI) para algunos de los procedimientos estadisticos detallados en un curso de 'Estadistica aplicada a las Ciencias Sociales mediante el programa informatico R' (EACSPIR). LA GUI se ha desarrollado como un Plugin del programa R-Commander.
Rcmdr plug-in GUI extension for Evidence Based Medicine medical indicators calculations (Sensitivity, specificity, absolute risk reduction, relative risk, ...).
This package provides an Rcmdr "plug-in" for the EcoVirtual package and was designed primarily for teaching ecological models using simulations.
This package provides an Rcmdr "plug-in" based on the time
series functions.  Contributors: G. Jay Kerns, John Fox, and
Richard Heiberger.
Export Rcmdr output to LaTeX or HTML code. The
plug-in was originally intended to facilitate exporting Rcmdr
output to formats other than ASCII text and to provide R
novices with an easy-to-use, easy-to-access reference on
exporting R objects to formats suited for printed output. The
package documentation contains several pointers on creating
reports, either by using conventional word processors or
LaTeX/LyX.
EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10901-8 or Nankodo, ISBN: 978-4-524-26158-1), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below.
Rcmdr menu support for many of the functions in the HH package.
The focus is on menu items for functions we use in our introductory
courses.
This package is an R Commander plugin that accompanies IPSUR, an Introduction to Probability and Statistics Using R.
This package is an R Commander plug-in for Kaplan-Meier
plots and other plots by using the ggplot2 package.
This package provides an Rcmdr "plug-in" based on the
lfstat package for low flow analysis.
Easy to use interface for conducting meta-analysis in R. This
package is an Rcmdr-plugin, which allows the user to conduct analyses in a
menu-driven, graphical user interface environment (e.g., CMA, SPSS). It
uses recommended procedures as described in The Handbook of Research
Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
Rcmdr menu items to display mosaic and assoc plots Allows
to visually restructure the underlying structables Developed
after extended discussions with Rich Heiberger
This package provides an R Commander plugin that offers a
unified menu of new and pre-existing statistical functions
related to public management and policy analysis statistics.
Functions and menus have been renamed according to the usage
in PMGT 630 in the Master of Public Administration program at
Brigham Young University.
An R Commander "plug-in" extending functionality of linear models and providing an interface to Partial Least Squares Regression and Linear and Quadratic Discriminant analysis. Several statistical summaries are extended, predictions are offered for additional types of analyses, and extra plots, tests and mixed models are available.
This package provides a GUI for the orloca package, it is
developed as an Rcmdr plug-in.
Rcmdr menu support for some of the graphics by group in
the lattice package
This package provides a Rcmdr "plug-in" to analyze questionnaire data.
This package provides an Rcmdr "plug-in" based on the
Quality control class Stat 4300
Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an Rcmdr "plug-in" to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades).
Rcmdr GUI extension plug-in for Receiver Operator Characteristic tools from pROC and ROCR packages. Also it ads a Rcmdr GUI extension for Hosmer and Lemeshow GOF test from the package ResourceSelection.
This package includes tools for calculating sample sizes and
selecting samples using various sampling designs. This package is an extension
of RcmdrPlugin.EHESsampling which was developed as part of the EHES pilot project.
The EHES Pilot project has received funding from the European Commission and
DG Sanco. The views expressed here are those of the authors and they do not represent
Commission's official position.
Provides a GUI for the SCVA, SCRT and SCMA packages. The package is written as an Rcmdr plugin.
Supports the text book Acevedo, M.F (2013) "Data Analysis
and Statistics for Geography, Environmental Science, and
Engineering" CRC Press
This package provides a GUI for the SLC package, it is
written as an Rcmdr plug-in.
This package provides an Rcmdr "plug-in" for studying
sport management data.
Rcmdr interface to the 'sos' package. The plug-in renders
the 'sos' searching functionality easily accessible via the Rcmdr
menus. It also simplifies the task of performing multiple searches and
subsequently obtaining the union or the intersection of the results.
This package provides a GUI for the steepness package, it
is written as an Rcmdr plug-in.
This package provides an R Commander plug-in for the survival
package, with dialogs for Cox models, parametric survival regression models,
estimation of survival curves, and testing for differences in survival
curves, along with data-management facilities and a variety of tests,
diagnostics and graphs.
This package provides an Rcmdr "plug-in" based on the
TeachingDemos package, and is primarily for illustrative
purposes.
An R Commander plug-in providing an integrated solution to perform
a series of text mining tasks such as importing and cleaning a corpus, and
analyses like terms and documents counts, vocabulary tables, terms
co-occurrences and documents similarity measures, time series analysis,
correspondence analysis and hierarchical clustering. Corpora can be imported
from spreadsheet-like files, directories of raw text files, Twitter queries,
as well as from Dow Jones Factiva, LexisNexis, Europresse and Alceste files.
Some extension to Rcmdr (R Commander) made by R-UCA project and used in teaching statistics at University of Cadiz (UCA).
Working with CMIP5 data can be tricky, forcing scientists to write
custom scripts and programs. The `RCMIP5` package aims to ease this
process, providing a standard, robust, and high-performance set of scripts
to (i) explore what data have been downloaded, (ii) identify missing data,
(iii) average (or apply other mathematical operations) across experimental
ensembles, (iv) produce both temporal and spatial statistical summaries,
and (v) produce easy-to-work-with graphical and data summaries.
Provides programmatic access to Colombos, a web based
interface for exploring and analyzing comprehensive organism-specific
cross-platform expression compendia of bacterial organisms.
Provides color schemes for maps (and other graphics)
designed by Cynthia Brewer as described at http://colorbrewer2.org
Solve some conic related problems (intersection of conics with lines and conics, arc length of an ellipse, polar lines, etc.).
A collection of small text corpora of interesting data.
It contains all data sets from https://github.com/dariusk/corpora.
Some examples:
names of animals: birds, dinosaurs, dogs; foods: beer categories,
pizza toppings; geography: English towns, rivers, oceans;
humans: authors, US presidents, occupations; science: elements,
planets; words: adjectives, verbs, proverbs, US president quotes.
R interface to CPLEX solvers for linear, quadratic, and (linear and quadratic) mixed integer programs. Support for quadratically constrained programming is available. A working installation of CPLEX is required for usage of the Rcplex package. See the file "INSTALL" for details on how to install the Rcplex package in Linux/Unix-like systems and Windows systems. Support for sparse matrices is provided by an S3-style class "simple_triplet_matrix" from package slam and by objects from the Matrix package class hierarchy.
Identifies regions of common (species) profiles (RCPs).  Within a region the probability of sampling all species remains approximately constant.  This is performed using mixtures-of-experts models.  The package also contains associated methods, such as diagnostics.  Currently, the model and methods are defined for species presence-absence data for a number of sites.
The 'Rcpp' package provides R functions as well as C++ classes which
offer a seamless integration of R and C++. Many R data types and objects can be
mapped back and forth to C++ equivalents which facilitates both writing of new
code as well as easier integration of third-party libraries. Documentation
about 'Rcpp' is provided by several vignettes included in this package, via the
'Rcpp Gallery' site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and
Francois (2011, JSS), and the book by Eddelbuettel (2013, Springer); see
'citation("Rcpp")' for details on these last two.
Rcpp11 includes a header only C++11 library that facilitates
integration between R and modern C++.
'Annoy' is a small C++ library for Approximate Nearest Neighbors
written for efficient memory usage as well an ability to load from / save to
disk. This package provides an R interface by relying on the 'Rcpp' package,
exposing the same interface as the original Python wrapper to 'Annoy'. See
<https://github.com/spotify/annoy> for more on 'Annoy'. 'Annoy' is released
under Version 2.0 of the Apache License. Also included is a small Windows
port of 'mmap' which is released under the MIT license.
The APT Package Management System provides Debian and
Debian-derived Linux systems with a powerful system to resolve package
dependencies. This package offers access directly from R.
'Armadillo' is a templated C++ linear algebra library (by Conrad
Sanderson) that aims towards a good balance between speed and ease of use. Integer,
floating point and complex numbers are supported, as well as a subset of
trigonometric and statistics functions. Various matrix decompositions are
provided through optional integration with LAPACK and ATLAS libraries.
The 'RcppArmadillo' package includes the header files from the templated
'Armadillo' library. Thus users do not need to install 'Armadillo' itself in
order to use 'RcppArmadillo'. 'Armadillo' is licensed under the MPL 2.0, while
'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the
GNU GPL version 2 or later, as is the rest of 'Rcpp'.
This package provides R with access to Boost Date_Time
functionality by using Rcpp modules.

Functionality from Boost Date_Time for dates, durations (both for days
and datetimes), timezones, and posix time ("ptime") is provided. The posix
time implementation can support high-resolution of up to nano-second
precision by using 96 bits (instead of R's 64) to present a ptime object.
'Rcpp' Access to the 'CCTZ' timezone library is provided.
'CCTZ' is a C++ library for translating between absolute and civil times
using the rules of a time zone.

The 'CCTZ' source code, released under the Apache 2.0 License, is included in
this package.  See <https://github.com/google/cctz> for more details.
The RcppClassic package provides a deprecated C++ library which
facilitates the integration of R and C++.

New projects should use the new Rcpp API in the Rcpp package.
The Rcpp package contains a C++ library that facilitates
the integration of R and C++ in various ways via a rich API.
This API was preceded by an earlier version which has been
deprecated since 2010 (but is still supported to provide
backwards compatability in the package RcppClassic).  This
package RcppClassicExamples provides usage examples for the
older, deprecated API. There is also a corresponding package
RcppExamples package with examples for the newer, current API
which we strongly recommend as the basis for all new
development.
The cnpy library written by Carl Rogers provides read and write
facilities for files created with (or for) the NumPy extension for Python.
Vectors and matrices of numeric types can be read or written to and from
files as well as compressed files. Support for integer files is available if
the package has been built with -std=c++11 which is the default starting
with release 0.2.3 following the release of R 3.1.0.
An efficient C++ based implementation of the 'DEoptim'
function which performs global optimization by differential evolution.
Its creation was motivated by trying to see if the old approximation "easier,
shorter, faster: pick any two" could in fact be extended to achieving all
three goals while moving the code from plain old C to modern C++.  The
initial version did in fact do so, but a good part of the gain was due to
an implicit code review which eliminated a few inefficiencies which have
since been eliminated in 'DEoptim'.
This package is based on the C++ code from Yusuke Sugomori,
which implements basic machine learning methods with
many layers (deep learning), including dA (Denoising Autoencoder),
SdA (Stacked Denoising Autoencoder), RBM (Restricted Boltzmann machine) and
DBN (Deep Belief Nets).
R and 'Eigen' integration using 'Rcpp'.
'Eigen' is a C++ template library for linear algebra: matrices,
vectors, numerical solvers and related algorithms.  It supports dense
and sparse matrices on integer, floating point and complex numbers,
decompositions of such matrices, and solutions of linear systems. Its
performance on many algorithms is comparable with some of the best
implementations based on 'Lapack' and level-3 'BLAS'.

The 'RcppEigen' package includes the header files from the 'Eigen' C++
template library (currently version 3.2.5). Thus users do not need to
install 'Eigen' itself in order to use 'RcppEigen'.

Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License
(version 2); earlier version were licensed under the GNU LGPL version 3 or
later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under
the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
Examples for Seamless R and C++ integration The Rcpp
package contains a C++ library that facilitates the integration
of R and C++ in various ways. This package provides some usage
examples.

Note that the documentation in this package currently does not cover
all the features in the package. It is not even close. On the
other hand, the site http://gallery.rcpp.org is regrouping a
number of examples for Rcpp.
Access to a family of Gauss error functions for arbitrary complex arguments is provided via the 'Faddeeva' package by Steven G. Johnson (see <http://ab-initio.mit.edu/wiki/index.php/Faddeeva_Package> for more information).
'Rcpp' integration for 'GNU GSL' vectors and matrices
The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for
scientific computing. It is particularly useful for C and C++ programs as it
provides a standard C interface to a wide range of mathematical routines
such as special functions, permutations, combinations, fast fourier
transforms, eigensystems, random numbers, quadrature, random distributions,
quasi-random sequences, Monte Carlo integration, N-tuples, differential
equations, simulated annealing, numerical differentiation, interpolation,
series acceleration, Chebyshev approximations, root-finding, discrete
Hankel transforms physical constants, basis splines and wavelets.  There
are over 1000 functions in total with an extensive test suite.

The 'RcppGSL' package provides an easy-to-use interface between 'GSL' data
structures and R using concepts from 'Rcpp' which is itself a package that
eases the interfaces between R and C++.

This package also serves as a prime example of how to build a package
that uses 'Rcpp' to connect to another third-party library. The 'autoconf'
script, 'inline' plugin and example package can all be used as a stanza to
write a similar package against another library.
MLPACK is an intuitive, fast, scalable C++ machine learning
library, meant to be a machine learning analog to LAPACK. It
aims to implement a wide array of machine learning methods
and function as a Swiss army knife for machine learning
researchers: MLPACK is from http://www.mlpack.org/: sources are included in the package.
Direct interface to Octave. The primary goal is to facilitate the
port of Matlab/Octave scripts to R. The package enables to call any Octave
functions from R and as well as browsing their documentation, passing
variables between R and Octave, using R core RNGs in Octave, which ensures
that stochastic computations are also reproducible.
High level functions for doing parallel programming with 'Rcpp'.
For example, the parallelFor() function can be used to convert the work of
a standard serial "for" loop into a parallel one and the parallelReduce()
function can be used for accumulating aggregate or other values.
This package allows to display a progress bar in the R
console for long running computations taking place in c++ code,
and support for interrupting those computations even in multithreaded
code, typically using OpenMP.
Connection to the 'Redis' key/value store using the
C-language client library 'hiredis'.
Provides fast and efficient routines for
common rolling / windowed operations. Routines for the
efficient computation of windowed mean, median,
sum, product, minimum, maximum, standard deviation
and variance are provided.
An R interface to the C++/Boost Shark machine learning library.
This package provides R with access to the Sequential
Monte Carlo Template Classes by Johansen (Journal of Statistical
Software, 2009, v30, i6).

At present, two additional examples have been added, and the first
example from the JSS paper has been extended. Further integration
and extensions are planned.
The Streamulus (template, header-only) library by
Irit Katriel (at https://github.com/iritkatriel/streamulus)
provides a very powerful yet convenient framework for stream
processing.

This package connects Streamulus to R by providing both the header
files and all examples.
The configuration format defined by 'TOML' (which expands to
"Tom's Obvious Markup Language") specifies an excellent format (described at
<https://github.com/toml-lang/toml>) suitable for both human editing as well
as the common uses of a machine-readable format. This package uses 'Rcpp' to
connect the 'cpptoml' parser written by Chase Geigle (in modern C++11) to R.
This package provides access to some of the C level
functions of the xts package.

In its current state, the package is mostly a proof-of-concept to
support adding useful functions, and does not yet add any of
its own.
The Ziggurat generator for normally distributed random numbers,
originally proposed by Marsaglia and Tsang (JSS, 2000), has been improved
upon a few times starting with Leong et al (JSS, 2005). This package provides
an aggregation in order to compare different implementations. The goal is to
provide an 'faster but good enough' alternative for use with R and C++ code.

The package is still in an early state. Unless you know what you are doing,
sticking with the generators provided by R may be a good idea as these have
been extremely diligently tested.
Aims at loading Criteo online advertising campaign data into R. Criteo <http://www.criteo.com/> is an online
advertising service that enables advertisers to display advertising copy to web users. The
package provides an authentication process for R with the Criteo API <http://kb.criteo.com/advertising/content/5/27/en/api.html>.
Moreover, the package features an interface to query campaign data from the
Criteo API. The data can be downloaded and will be transformed into a R
data frame.
Client for various 'CrossRef' 'APIs', including 'metadata' search
with their old and newer search 'APIs', get 'citations' in various formats
(including 'bibtex', 'citeproc-json', 'rdf-xml', etc.), convert 'DOIs'
to 'PMIDs', and 'vice versa', get citations for 'DOIs', and get links to
full text of articles when available.
Provides easy symmetric file encryption using GPG with
cryptographically strong defaults. Only symmetric encryption is
supported. GPG is pre-installed with most Linux distributions.
Windows users will need to install 'Gpg4win' (http://www.gpg4win.org/).
OS X users will need to install 'GPGTools' (https://gpgtools.org/).
Basic access to market infomation from the public API for the Crypto-Currency exchange Cryptsy.
R interface to the CSDP semidefinite programming library. Installs version 6.1.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. See the INSTALL file for further details.
Functions and objects for CUR matrix decomposition.
A wrapper for 'libcurl' <http://curl.haxx.se/libcurl/>
Provides functions to allow one to compose general HTTP requests
and provides convenient functions to fetch URIs, get & post
forms, etc. and process the results returned by the Web server.
This provides a great deal of control over the HTTP/FTP/...
connection and the form of the request while providing a
higher-level interface than is available just using R socket
connections.  Additionally, the underlying implementation is
robust and extensive, supporting FTP/FTPS/TFTP (uploads and
downloads), SSL/HTTPS, telnet, dict, ldap, and also supports
cookies, redirects, authentication, etc.
Functions to convert Rd to roxygen documentation. It can parse an
Rd file to a list, create the roxygen documentation and update the original
R script (e.g. the one containing the definition of the function)
accordingly. This package also provides utilities which can help developers
build packages using roxygen more easily. The formatR package can be used
to reformat the R code in the examples sections so that the code will be
more readable.
Shrunken Centroids Regularized Discriminant Analysis for
the classification purpose in high dimensional data.
Provides basic functionalities for writing a module
for http://datacanvas.io. The http://datacanvas.io is a big data
analytics platform that helps data scientists to build, manage
and share data pipelines.
Fetches data from DataMarket.com, either as
timeseries in zoo form (dmseries) or as long-form data
frames (dmlist). Metadata including dimension structure
is fetched with dminfo, or just the dimensions with
dmdims.
This package provides the tools to undertake estimation in
Regression Discontinuity Designs. Both sharp and fuzzy designs are
supported. Estimation is accomplished using local linear regression.
A provided function will utilize Imbens-Kalyanaraman optimal
bandwidth calculation. A function is also included to test the
assumption of no-sorting effects.
Set of functions for Regression Discontinuity Design ('RDD'), for
data visualisation, estimation and testing.
Data Envelopment Analysis for R, estimating robust DEA scores without and with environmental variables and doing returns-to-scale tests.
The package provides functions for estimating the relevant
dimension of a data set in feature spaces, applications to
model selection, graphical illustrations and prediction.
Functions for creating plots and image files in a unified way regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.).  Default device options as well as scales and aspect ratios are controlled in a uniform way across all device types.  Switching output format requires minimal changes in code.  This package is ideal for large-scale batch processing, because it will never leave open graphics devices or incomplete image files behind, even on errors or user interrupts.
The package has many function that helps to perform
various quality check on the data.It basically provides many
function that helps in performing Extrapolative data analysis.
The RDieHarder packages provides an R interface to
the dieharder suite of random number generators and tests that
was developed by Robert G. Brown and David Bauer, extending
earlier work by George Marsaglia and others.
Analysis of distance sampling data collected on line transect surveys.  Estimates distance-based detection functions and abundances.
Imports real-time thermo cycler (qPCR) data from Real-time PCR
Data Markup Language (RDML) format files and transforms to the appropriate
formats of the 'qpcR' and 'chipPCR' packages. Contains a dendrogram
visualization for the structure of RDML object.
A package that controls the Java software Discourse Network
Analyzer (DNA) from within R. Network matrices, statement frequency time
series and attributes of actors can be transferred directly into R.
Data analysis functions for Dota2. Dota2 is a popular online game (see, e.g. <http://en.wikipedia.org/wiki/Dota_2> for more information) and there are more and more websites doing data analysis for paid players.The data of the latest hero, Winter Wyvern and the latest game version, 6.83c, are included. The package now contains functions for calculating the best choice given specific lineup, showing unconditional win rates and the global online population.
Functions for manipulation of Rd objects, including function
reprompt() for updating existing Rd documentation for
functions, methods and classes, and function rebib() for
import of references from bibtex files.
Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD	designs: rdrobust to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the 	cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect to perform bandwidth selection for the different procedures implemented, and rdplot to conduct exploratory data analysis (RD plots).
Provides full programmatic access to the Dropbox file hosting platform (dropbox.com), including support for all standard file operations.
Provides functionality for carrying out estimation
with data collected using Respondent-Driven Sampling. This includes
Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential
Sampling estimator. The package is part of the "RDS Analyst" suite of
packages for the analysis of respondent-driven sampling data.
R interface to DSDP semidefinite programming library. The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method.
Provides a threads-type programming environment for R.
The package gives the R programmer the clearer, more concise
shared memory world view, and in some cases gives superior
performance as well.  In addition, it enables parallel processing on
very large, out-of-core matrices.
This package provides an R interface to Pete Warden's Data
Science Toolkit.  See www.datasciencetoolkit.org for more
information.  The source code for this package can be found at
github.com/rtelmore/RDSTK Happy hacking!
The rDVR package allows you to start stop and save a video server
from within R. It does this by way of a REST interface to a JAVA service.
The jar binary relies on the screen recorder included in the Monte Media
Library (CC BY 3.0 licence) developed by Werner Randelshofer
(http://http://www.randelshofer.ch/monte/). The REST interface was modified
from https://github.com/tuenti/VideoRecorderService which has an Apache
licence.
Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions.
Includes transport routines in porous media, in estuaries, and in bodies with variable shape.
Identifies and reads Windows BMP, JPEG and PNG format bitmap
images. Identification defaults to the use of the magic number embedded in
the file rather than the file extension. Reading of JPEG and PNG image
depends on libjpg and libpng libraries. See file INSTALL for details if
necessary.
Reads data files acquired by MALDI-TOF MS on Bruker Daltonics
machines of the *flex series.
A set of functions to simplify reading data from files. The main function, reader(), should read most common R datafile types without needing any parameters except the filename. Other functions provide simple ways of handling file paths and extensions, and automatically detecting file format and structure.
A collection of R functions to read, write, manipulate, and
convert genotype data in GenAlEx format.  GenAlEx is a widely-used Excel
plugin for manipulating and analysing genotype data.  This package reads
GenAlEx data from text or Excel files and creates an annotated data frame
of class 'genalex'. Several functions are provided for accessing,
manipulating, printing, writing and converting this data. GenAlEx and its
documentation are available at http://biology-assets.anu.edu.au/GenAlEx.
Functions for reading data sets in different formats
for testing machine learning tools are provided. This allows to run
a loop over several data sets in their original form, for example
if they are downloaded from UCI Machine Learning Repository.
The data are not part of the package and have to be downloaded
separately.
Functions for reading mass spectrometry data in mzXML format.
Read ODS (OpenDocument Spreadsheet) files and put them into data
frames.
Read flat/tabular text files from disk (or a connection).
Function to read and write the Stata file format.
Import excel files into R. Supports '.xls' via the embedded 'libxls'
C library (http://sourceforge.net/projects/libxls/) and '.xlsx' via the
embedded 'RapidXML' C++ library (http://rapidxml.sourceforge.net). Works
on Windows, Mac and Linux without external dependencies.
The RealVAMs package fits a multivariate value-added model (VAM) (see Broatch and Lohr 2012) with normally distributed test scores and a binary outcome indicator. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.
Resampling methods for adaptive linear model selection.
These can be thought of as extensions of the Akaike information
criterion that account for searching among candidate models.
Kiefer-Wolfowitz maximum likelihood estimation for mixture models
and some other density estimation and regression methods based on convex
optimization.
A programmatic client for the eBird database, including functions
for searching for bird observations by geographic location (latitude,
longitude), eBird hotspots, location identifiers, by notable sightings, by
region, and by taxonomic name.
R functions for random univariate and multivariate finite mixture generation, number of components, component weights and component parameters estimation, printing and plotting of finite mixtures, bootstrapping and class membership prediction. Variables can be either continuous or discrete, may follow normal, lognormal, Weibull, gamma, binomial, Poisson or Dirac parametric families and should be independent within components.
Build regular expressions piece by piece using human readable code.
Relevant Component Analysis (RCA) tries to find a linear
transformation of the feature space such that the effect of irrelevant
variability is reduced in the transformed space.
recalls provides access to U.S. Federal Government recall data on drugs and food from the Food and Drug Administration, car safety from the National Highway Traffic Safety Administration, and product safety recalls from the Consumer Product Safety Commission.
Pathways in a database could have many redundancies among
them. This package allows the user to set a maximum value for
the proportion of these redundancies.
Beta-diversity indices provide dissimilarity matrices with particular distribution of data requiring specific treatment. For example the high frequency of ties and zero values in turnover indices produces hierarchical cluster dendrograms whose topology and bootstrap supports are affected by the order of rows in the original matrix. Moreover, biogeographical regionalization can be facilitated by a combination of hierarchical clustering and multi-dimensional scaling. The recluster package provides robust techniques to analyze pattern of similarity in species composition.
Simple, easy to use, and flexible functionality for recoding variables. It allows for simple piecewise definition of transformations.
Provides a research infrastructure to test and develop
recommender algorithms.
Provides the Book-Crossing Dataset for the package recommenderlab.
Provides the Jester Dataset for package recommenderlab.
Functions to aid in reconstructing sessions
and efficiently calculating an array of metrics from the resulting data,
including bounce rate, time-on-page, and session length. Although primarily
designed for web data and analytics, its approach is plausibly applicable
to other domains.
Provides functions for linking and de-duplicating data sets.
Methods based on a stochastic approach are implemented as well as
classification algorithms from the machine learning domain.
Functions for generating k-record values and k-record
times
R wrapper of the 'libmf' library
(http://www.csie.ntu.edu.tw/~cjlin/libmf/) for recommender
system using matrix factorization. It is typically used to
approximate an incomplete matrix using the product of two
matrices in a latent space. Other common names for this task
include "collaborative filtering", "matrix completion",
"matrix recovery", etc.
Functions that fit gamma frailty model with spline or piecewise
constant baseline rate function for recurrent event data, compute and
plot parametric mean cumulative function (MCF) from a fitted model
as well as nonparametric sample MCF (Nelson-Aalen estimator) are provided.
Most functions are S4 methods that produce S4 class objects.
Provides functions used in the 'R: Einführung durch angewandte Statistik' (second edition).
Access data stored in REDCap databases using the Application
Programming Interface (API).  REDCap (Research Electronic Data CAPture) is
a web application for building and managing online surveys and databases
developed at Vanderbilt University.  The API allows users to access data
and project meta data (such as the data dictionary) from the web
programmatically.  The redcapAPI package facilitates the process of
accessing data with options to prepare an analysis-ready data set
consistent with the definitions in a database's data dictionary.
Encapsulates functions to streamline calls from R to the REDCap
API.  REDCap (Research Electronic Data CAPture) is a web application for
building and managing online surveys and databases developed at Vanderbilt
University.  The Application Programming Interface (API) offers an avenue
to access and modify data programmatically, improving the capacity for
literate and reproducible programming.
Reddit is an online bulletin board and a social networking website
where registered users can submit and discuss content. This package uses
Reddit API to extract Reddit data using Reddit API. Note that due to the API
limitations, the number of comments that can extracted is limited to 500 per
thread. The package consists of 4 functions, one for extracting relevant URLs,
one for extracting features out of given URLs, one that does both together and
one that constructs graphs based on the structure of a thread.
Enables researchers to sample redistricting plans from a pre-specified target distribution using a Markov Chain Monte Carlo algorithm. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements.  The algorithm also can be used in combination with efficient simulation methods such as simulated and parallel tempering algorithms. Tools for analysis such as inverse probability reweighting and plotting functionality are included. The package implements methods described in Fifield, Higgins, Imai and Tarr (2015) ``A New Automated Redistricting Simulator Using Markov Chain Monte Carlo,'' working paper available at <http://http://imai.princeton.edu/research/files/redist.pdf>.
This package estimates regression trees with random
effects as a way to use data mining techniques to describe
longitudinal or panel data.
small package with functions for creating references,
reading from and writing to references and a memory efficient
refdata type that transparently encapsulates matrices and
data.frames
This is a collection of tools to allow the medical professional to
calculate appropriate reference ranges (intervals) with confidence intervals around
the limits for diagnostic purposes.
Reference-free method for conducting EWAS while deconvoluting DNA methylation arising as mixtures of cell types.  This method is similar to surrogate variable analysis (SVA and ISVA), except that it makes additional use of a biological mixture assumption.
Contains functionality for import and managing of downloaded genome annotation Data from Ensembl genome browser (European Bioinformatics Institute) and from UCSC genome browser (University of California, Santa Cruz) and annotation routines for genomic positions and splice site positions.
Provides tools for importing and working with
bibliographic references.  It greatly enhances the 'bibentry' class by
providing a class 'BibEntry' which stores 'BibTeX' and 'BibLaTeX' references,
supports 'UTF-8' encoding, and can be easily searched by any field, by date
ranges, and by various formats for name lists (author by last names,
translator by full names, etc.). Entries can be updated, combined, sorted,
printed in a number of styles, and exported. 'BibTeX' and 'BibLaTeX' '.bib' files
can be read into 'R' and converted to 'BibEntry' objects.  Interfaces to 'NCBI Entrez',
'CrossRef', and 'Zotero' are provided for importing references and
references can be created from locally stored 'PDF' files using 'Poppler'.  Includes
functions for citing and generating a bibliography with hyperlinks for
documents prepared with 'RMarkdown' or 'RHTML'.
Provides subsets with reference semantics, i.e. subsets
which automatically reflect changes in the original object, and which
optionally update the original object when they are changed.
Methods for regression for functional
data, including function-on-scalar, scalar-on-function, and
function-on-function regression. Some of the functions are applicable to
image data.
Interactive plotting for functional data analyses.
Methods for regressing scalar responses on functional or image predictors, via transformation to the wavelet domain and back.
This package clusters regression coefficients using the methods of clustering through linear regression models (CLM) (Qin and Self 2006). Maximum likelihood approach is used to infer the parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the number of clusters.
Detecting mislabeled samples in genetic data.
Produces population distribution of disease risk and statistical risk categories, and predicts risks for individuals with genotype information.
An R framework for constructing and managing human
readable regular expressions.  It aims to provide tools that
enable the user to write regular expressions in a way that is
similar to the ways R code is written. The tools allow the user
to (1) write in smaller, modular, named, sub-expressions, (2)
write top to bottom, rather than a single string (3) comment
individual chunks, (4) indent expressions to clearly present
regular expression groups, (5) add vertical line spaces and R
comments (i.e., #), and (6) test the validity of the
concatenated expression and the modular sub-expressions.
Provides a generic infrastructure for creating and using registries.
Regularized (polychotomous) logistic regression
by Gibbs sampling. The package implements subtly different
MCMC schemes with varying efficiency depending on the data type
(binary v. binomial, say) and the desired estimator (regularized maximum
likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a
unified interface.
The package provides tools for
(1) nonparametric regression (kernel, local linear),
(2) semiparametric regression (single index, additive models), and
(3) quantile regression (linear, kernel)
Functions to fit Gaussian linear model by maximising the
residual log likelihood where the covariance structure can be
written as a linear combination of known matrices.  Can be used
for multivariate models and random effects models.  Easy
straight forward manner to specify random effects models,
including random interactions. Code now optimised to use
Sherman Morrison Woodbury identities for matrix inversion in
random effects models. We've added the ability to fit models
using any kernel as well as a function to return the mean and
covariance of random effects conditional on the data (BLUPs).
The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors' paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.
Performs Random Subspace Method (RSM) for high-dimensional linear regression to obtain variable importance measures. The final model is chosen based on validation set or Generalized Information Criterion.
For a sequence of event occurence times, we are interested in
finding subsequences in it that are too "regular". We define regular as being
significantly different from a homogeneous Poisson process. The departure from
the Poisson process is measured using a L1 distance. See Di and Perlman 2007
for more details.
Functions for unary and binary regression tests
Functions for the detection of footprints of selection on
dense SNP data using Extended Homozygosity Haplotype (EHH)
based tests. The package includes computation of EHH, iHS
(within population) and Rsb (across pairs of populations)
statistics. Various plotting functions are also included to
facilitate visualization and interpretation of the results.
Item analysis with alpha standard error and principal axis
factoring for continuous variable scales (with plots).
relaimpo provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.
Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation.
Data structures and algorithms for k-ary relations with
arbitrary domains, featuring relational algebra, predicate functions,
and fitters for consensus relations.
package relax contains some functions for report
writing, presentation, and programming: relax(), tangleR(),
weaveR(), (g)slider(). "relax" is written in R and Tcl/Tk.
relax creates a new window (top level Tcl/Tk widget) that consists
of two text fields and some buttons and menus.
Text (chunks) and code (chunks) are inserted in the upper text field (report field).
Code chunks are evaluated by clicking on EvalRCode.
Results are shown in the lower text field (output field) and
will be transferred to the report field by pressing on Insert.
In this way you get correct reports. These reports can be
loaded again in cause of presentation, modification and result checking.
tangleR() and weaveR() implement a plain kind of tangling
and weaving. gslider() and slider() are designed to define sliders for interactive
experiments in a simple way.
The syntax rules of code chunks and text chunks are defined by
the noweb system proposed by Norman Ramsey
(http://www.eecs.harvard.edu/~nr/noweb/intro.html).
Extends the glmnet package with "relaxation", done by running glmnet once on the entire predictor matrix, then again on each different subset of variables from along the regularization path. Relaxation may lead to improved prediction accuracy for truly sparse data generating models, as well as fewer false positives (i.e. fewer noncontributing predictors in the final model). Penalty may be lasso (alpha = 1) or elastic net (0 < alpha < 1). For this version, family may be "gaussian" or "binomial" only. Takes advantage of fast FORTRAN code from the glmnet package.
Relaxed Lasso is a generalisation of the Lasso shrinkage
technique for linear regression. Both variable selection and
parameter estimation is achieved by regular Lasso, yet both
steps do not necessarily use the same penalty parameter. The
results include all standard Lasso solutions but allow often
for sparser models while having similar or even slightly better
predictive performance if many predictor variables are present.
The package depends on the LARS package.
R functions for the comparison of distributions.
This includes nonparametric estimation of the relative distribution
PDF and CDF and numerical summaries as described in
"Relative Distribution Methods in the Social Sciences"
by Mark S. Handcock and Martina Morris,
Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789
This function computes the relative entropy (H) as an index for qualitative variation of a factor.
Functions for estimating parameters in software reliability models.
Only infinite failure models are implemented so far.
A variety of tools useful for performing structural
reliability analysis, such as with structure function and
system signatures.  Plans to expand more widely.
A collection of utilities for some reliability
models/probability distributions.
Functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets.
A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations.
Various functions for relative survival analysis.
Classes and functions for analyzing the performance of portfolios relative to a benchmark.
remix provides remix, a quick and easy function for
describing datasets. It can be view as a mix of cast (in
package reshape) and summary.formula (in package Hmisc).
Implements TRACDS (Temporal Relationships
between Clusters for Data Streams), a generalization of
Extensible Markov Model (EMM). TRACDS adds a temporal or order model
to data stream clustering by superimposing a dynamically adapting
Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN
data stream clustering). Development of this
package was supported in part by NSF IIS-0948893 and R21HG005912 from
the National Human Genome Research Institute.
remMap is developed for fitting multivariate response regression models under the high-dimension-low-sample-size setting
Empirical orthogonal teleconnections in R.
'remote' is short for "R(-based) EMpirical Orthogonal TEleconnections".
It implements a collection of functions to facilitate empirical
orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections
(EOTs) denote a regression based approach to decompose spatio-temporal
fields into a set of independent orthogonal patterns. They are quite
similar to Empirical Orthogonal Functions (EOFs) with EOTs producing
less abstract results. In contrast to EOFs, which are orthogonal in both
space and time, EOT analysis produces patterns that are orthogonal in
either space or time.
Peaks Over Threshold (POT) or 'methode du renouvellement'. The distribution for the exceedances can be chosen, and heterogeneous data (including historical data or block data) can be used in a Maximum-Likelihood framework.
Provides an R interface to the NCBI's EUtils API
allowing users to search databases like GenBank and PubMed, process the
results of those searches and pull data into their R sessions.
An R interface to the Encyclopedia of Life API. Includes functions for downloading and extracting information off the EOL pages.
Tools for performing the leaf reordering for the dendrogram that preserves the hierarchical clustering result and at the same time tries to group instances from the same class together.
Performs genome-wide association studies on individuals that are both related and have repeated measurements.
Global tests for expression data of high-dimensional sets of
molecular features.
Estimation of Bayes and local Bayes false discovery rates for
replicability analysis.
Supplies classes and routines to convert data to and from EpiJSON files. This package provides conversion functions for data.frame, sp and obkData.
Test for effects of  both individual factors and their interaction on  replicated spatial patterns in a two factorial design.
A common problem faced by journal reviewers and authors is the
question of whether the results of a replication study are consistent with
the original published study. One solution to this problem is to examine
the effect size from the published study and generate the range of effect
sizes that could reasonably be obtained (due to random sampling) in a
replication attempt (i.e., calculate a replication interval). If a
replication effect size falls outside the replication interval then that
value could not have occurred to due the effects of sampling error alone.
Alternatively, if a replication effect size falls within the replication
interval then the replication results could have reasonably occurred due to
the effects of sampling error alone. This package has functions that
calculate the replication interval for two different types of effect sizes,
namely correlation (i.e., r) and the standardized mean difference
(i.e., d-value).
Tools to load 'R' packages
and automatically generate BibTeX files citing them as well as load
plain-text and 'Excel' formatted data stored on 'Dropbox', 'GitHub', and
from other sources.
This is an R data manager meant to avoid manual storage/retrieval of R data to/from the file system. It builds one (or more) centralized repository where R objects are stored with annotations, tags, dependency notes, provenance traces. It also provides navigation tools to easily locate, load and edit previously stored resources.
Fits regression models to repeated ordinal response
variables, using generalized estimating equations implemented
in package gee.
Create Microsoft Word document (>=2007),
Microsoft Powerpoint document (>=2007) and HTML documents from R. There are
several features to let you format and present R outputs ; e.g. Editable
Vector Graphics, functions for complex tables reporting, reuse of corporate
template document (*.docx and *.pptx). You can use the package as a tool
for fast reporting and as a tool for reporting automation. The package does
not require any installation of Microsoft product to be able to write
Microsoft files (docx and pptx).
External jars required for package ReporteRs. ReporteRs is an
R package for creating Microsoft Word document (>=2007), Microsoft
Powerpoint document (>=2007) and HTML documents from R.
Provides a system for reporting messages, which provides certain useful features over the standard R system, such as the incorporation of output consolidation, message filtering, expression substitution, automatic generation of stack traces for debugging, and conditional reporting based on the current "output level".
reportRx is a set of tools that integrates with LaTeX and knitr to
automatically generate generate reproducible clinical reports. Fuctions to
automatically produce demoraphic tables, outcome summaries, univariate and
multivariate analysis results and more are included.
Assists in writing reports and
presentations by providing a frame work that brings together
existing R, LaTeX/.docx and Pandoc tools. The package is
designed to be used with RStudio, MiKTex/Tex Live/LibreOffice,
knitr, knitcitations, Pandoc and pander. The user will want to
download these free programs/packages to maximize the
effectiveness of the reports package.  Functions with two
letter names are general text formatting functions for copying
text from articles for inclusion as a citation.
These functions are especially helpful when writing reports of data analysis using Sweave.
An R Interface to 'EPP-lab' v1.0. 'EPP-lab' is a Java program for
projection pursuit using genetic algorithms written by Alain Berro and S. Larabi
Marie-Sainte and is included in the package. The 'EPP-lab' sources are available
under https://github.com/fischuu/EPP-lab.git.
Contains workhorse function jrparams(), as well as two
helper functions Mboxtest() and JRsMahaldist(), and four
example data sets.
The reproducer R package includes data analysis functions and data sets (e.g., related to software defect prediction) to streamline reproducible research in software engineering.
This package contains the function run.eqs() which calls
an EQS script file, executes the EQS estimation, and, finally,
imports the results as R objects. These two steps can be
performed separately: call.eqs() calls and executes EQS,
whereas read.eqs() imports existing EQS outputs as objects into
R. It requires EQS 6.2 (build 98 or higher).
General purpose R client for 'ERDDAP' servers. Includes
functions to search for 'datasets', get summary information on
'datasets', and fetch 'datasets', in either 'csv' or 'netCDF' format.
'ERDDAP' information: http://upwell.pfeg.noaa.gov/erddap/information.html.
A collection of regression models for recurrent event process and failure time.
Bootstrap, permutation tests, and other resampling functions,
featuring easy-to-use syntax.
This package implements functions for spectral
similarity/dissimilarity analysis and memory-based learning (MBL) for
non-linear modelling in complex spectral datasets. MBL is
also known as local modelling in the chemometrics literature.
Measure single-storage water supply system performance using resilience, reliability,
and vulnerability metrics; assess storage-yield-reliability relationships;
determine no-fail storage with sequent peak analysis; optimize release
decisions using deterministic and stochastic dynamic programming; evaluate
inflow persistence using the Hurst coefficient.
Reshape lets you flexibly restructure and aggregate data using just two functions: melt and cast.
Flexibly restructure and aggregate data using just two
functions: melt and dcast (or acast).
A tool for learning how to use the functions, melt,
acast/dcast, and ddply.
electrical properties of resistor networks.
Resource Selection (Probability) Functions for
use-availability wildlife data as described in
Lele and Keim (2006, Ecology 87, 3021--3028),
and Lele (2009, J. Wildlife Management 73, 122--127).
Contains three functions that query AuriQ Systems' Essentia Database and return the results in R. 'essQuery' takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. 'read.essentia' takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. 'capture.essentia' takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html.
Contains easy scripts which can be used to quickly create GUI windows for 'Rcmdr' Plugins. No knowledge about Tcl/Tk is required to make use of these scripts (These scripts are a generalisation of the template scripts in the 'RcmdrPlugin.BiclustGUI' package).
Provides the user with functions to develop their trading strategy,
uncover actionable trading ideas, and monitor consensus shifts with
crowdsourced earnings and economic estimate data directly from
<www.estimize.com>. Further information regarding the web services this
package invokes can be found at <www.estimize.com/api>.
The restlos package provides algorithms for robust estimation of location (mean and mode) and scatter based on minimum spanning trees (pMST), self-organizing maps (Flood Algorithm), Delaunay triangulations (RDELA), and nested minimum volume convex sets (MVCH). The functions are also suitable for outlier detection.
Debugging with restore points instead of break points. A restore
point stores all local variables when called inside a function. The stored
values can later be retrieved and evaluated in a modified R console that
replicates the function's environment. To debug step by step, one can
simply copy & paste the function body from the R script. Particularly
convenient in combination with RStudio. See the github page for a tutorial.
Using a database, LaTeX and R easily build attractive resumes.
Reaction time analysis by maximum likelihood
Reconstructs retinae by morphing a flat surface with
cuts (a dissected flat-mount retina) onto a curvilinear surface (the a
standard retinal shape). It can estimate the position of a point on the
intact adult retina to within 8 degrees of arc (3.6% of nasotemporal axis).
The coordinates in reconstructed retinae can be transformed to visuotopic
coordinates.
A collection of tools to import and structure the (currently) single-season
event, game-log, roster, and schedule data available from http://www.retrosheet.org.
In particular, the event (a.k.a. play-by-play) files can be especially difficult to parse.
This package does the parsing on those files, returning the requested data in the most
practical R structure to use for sabermetric or other analyses.
An interface to NCBI databases such as PubMed, GenBank, or GEO
powered by the Entrez Programming Utilities (EUtils). The nine EUtils
provide programmatic access to the NCBI Entrez query and database
system for searching and retrieving biological data.
Simplified scenario testing and sensitivity analysis with R via a
generalized function for one-factor-at-a-time (OFAT) sensitivity analysis,
evaluation of parameter sets and (sampled) parameter permutations. Options
for formatting output and parallel processing are also provided.
Computation of (direct and indirect) revealed preferences, fast non-parametric tests of rationality axioms (WARP, SARP, GARP), simulation of axiom-consistent data, and detection of axiom-consistent subpopulations.
An implementation of the reverse ecology framework. Reverse ecology
refers to the use of genomics to study ecology with no a priori assumptions
about the organism(s) under consideration, linking organisms to their
environment. It allows researchers to reconstruct the metabolic networks and
study the ecology of poorly characterized microbial species from their
genomic information, and has substantial potentials for microbial community
ecological analysis.
Adjusts the weights of survey respondents so that the
marginal distributions of certain variables fit more closely to
those from a more precise source (e.g. Census Bureau's data).
A friendly interface for the construction of regular expressions.
This package wraps some of the matrix exponentiation utilities from EXPOKIT (http://www.maths.uq.edu.au/expokit/), a FORTRAN library that is widely recommended for matrix exponentiation (Sidje RB, 1998. "Expokit: A Software Package for Computing Matrix Exponentials." ACM Trans. Math. Softw. 24(1): 130-156).  EXPOKIT includes functions for exponentiating both small, dense matrices, and large, sparse matrices (in sparse matrices, most of the cells have value 0).  Rapid matrix exponentiation is useful in phylogenetics when we have a large number of states (as we do when we are inferring the history of transitions between the possible geographic ranges of a species), but is probably useful in other ways as well.
Provides an interface to the Facebook API.
This package facilitates searching for and downloading seismic time series in miniSEED format (a minimalist version of the Standard for the Exchange of Earthquake Data) from International Federation of Digital Seismograph Networks repositories. This package can also be used to gather information about seismic networks (stations, channels, locations, etc) and find historical earthquake data (origins, magnitudes, etc).
An R implementation of the random ferns classifier by Ozuysal et al., modified for generic and multi-label classification and featuring OOB error approximation and importance measure.
RFGLS  uses a generalized least-squares method to perform single-marker association analysis,
in datasets of nuclear families containing parents, twins, and/or adoptees
Variable selection tools for groups of variables and functional data based on a new grouped variable importance with random forests.
An interface to 'figshare' (http://figshare.com), a scientific repository to archive and assign 'DOIs' to data, software, figures, and more.
A file set refers to a set of files located in one or more directories on the file system.  This package provides classes and methods to locate, setup, subset, navigate and iterate such sets.  The API is designed such that these classes can be extended via inheritance to provide a richer API for special file formats.  Moreover, a specific name format is defined such that filenames and directories can be considered to have full names which consists of a name followed by comma-separated tags.  This adds additional flexibility to identify file sets and individual files.  NOTE: This package's API should be considered to be in an beta stage.  Its main purpose is currently to support the aroma.* packages, where it is one of the main core components; if you decide to build on top of this package, please contact the author first.
Japanese stock market from Yahoo!-finance-Japan
A programmatic interface to http://www.fishbase.org, re-written
based on an accompanying 'RESTful' API. Access tables describing over 30,000
species of fish, their biology, ecology, morphology, and more. This package also
supports experimental access to http://www.sealifebase.org data, which contains
nearly 200,000 species records for all types of aquatic life not covered by
'FishBase.'
A programmatic interface to openfisheries.org. This package is
part of the rOpenSci suite (http://ropensci.org)
R estimation and inference for linear models.  Estimation
is for general scores and a library of commonly used score
functions is included.
RFLPtools provides functions to analyse DNA fragment samples
(i.e. derived from RFLP-analysis) and standalone BLAST report files
(i.e. DNA sequence analysis).
A collection of tools for multivariate analysis of metabolomics
data, which includes several preprocessing methods (normalization, scaling)
and various exploration and data visualization techniques (Principal
Components Analysis and Multi Dimensional Scaling). The core of the package
is the Random Forest algorithm used for the construction, optimization and
validation of classification models with the aim of identifying potentially
relevant biomarkers.
R access to the FOAAS (F... Off As A Service) web service is provided.
Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps.  Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.
Insert, update,
retrieve, delete and bulk operate datasets with a SaaS based CRM
Salesforce.com and a PaaS based application platform Force.com from R.
Contains all the code examples in the book "R for
Dummies" (1st edition). You can view the table of contents as well as the sample code for each chapter.
A modified version (with great help from Ian J. Wilson) of Ian J. Wilson's program BATWING for calculating forensic trace-suspect match probabilities.
The R Formatter formats R source code. It is very much based on
formatR, but tries to improve it by heuristics. For example, spaces can be
forced around the division operator "/".
Estimate significance of importance metrics
for a Random Forest model by permuting the response
variable. Produces null distribution of importance
metrics for each predictor variable and p-value of
observed.
An R interface to a modified version of the Free Evolutionary Algorithm Kit FrEAK. FrEAK is a toolkit written in Java to design and analyze evolutionary algorithms. Both the R interface and an extended version of FrEAK are contained in the RFreak package. For more information on FrEAK see http://sourceforge.net/projects/freak427/.
Utilities for Random Forest model selection, class balance
correction, significance test, cross validation and partial dependency
plots.
Provides functions for accessing and retrieving data from the
Google Analytics APIs (https://developers.google.com/analytics/). Supports
OAuth 2.0 authorization. Package provides access to the Management, Core
Reporting, Multi-Channel Funnels Reporting, Real Time Reporting and
Metadata APIs. Access to all the Google Analytics accounts which the user
has access to. Auto-pagination to return more than 10,000 rows of the
results by combining multiple data requests. Also package provides
shiny app to explore the core reporting API dimensions and metrics.
This package was created to analyze multi-level one-way
experimental designs. It is designed to handle vectorized
observation and factor data where there are unequal sample
sizes and population variance homogeneity can not be assumed.
To conduct the Gabriel test, create two vectors: one for your
observations and one for the factor level of each observation.
The function, rgabriel, conduct the test and save the output as
a vector to input into the gabriel.plot function, which produces
a confidence interval plot for Multiple Comparison.
This package implements a Gamma convolution model for
background correction.
A programmatic interface to the Web Service methods
provided by the Global Biodiversity Information Facility ('GBIF').
'GBIF' is a database of species occurrence records from sources all
over the globe. 'rgbif' includes functions for searching for
taxonomic names, retrieving information on data providers,
getting species occurrence records, and getting counts of
occurrence records.
We utilize  approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data.  The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce  posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.
Multi-block data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: (i) to study the relationships between blocks and (ii) to identify subsets of variables of each block which are active in their relationships with the other blocks.
Thin plate spline fitting and prediction
Provides bindings to Frank Warmerdam's Geospatial Data Abstraction Library (GDAL) (>= 1.6.3) and access to projection/transformation operations from the PROJ.4 library. The GDAL and PROJ.4 libraries are external to the package, and, when installing the package from source, must be correctly installed first. Both GDAL raster and OGR vector map data can be imported into R, and GDAL raster data and OGR vector data exported. Use is made of classes defined in the sp package. Windows and Mac Intel OS X binaries (including GDAL, PROJ.4 and Expat) are provided on CRAN.
A method 'generate' is implemented in this package for the random
generation of vector time series according to models obtained by 'RMAWGEN',
'vars' or other packages.  This package was created to generalize the
algorithms of the RMAWGEN package for the analysis and generation of any
environmental vector time series.
The method 'generate' is extended for spatial multi-site
stochastic generation of daily precipitation. It generates precipitation
occurrence in several sites using logit regression (Generalized Linear
Models) and D.S. Wilks' approach (Journal of Hydrology, 1998).
A genetic algorithm plus derivative optimizer.
Connectors to online and offline sources for taking IP addresses and geolocating them to country, city, timezone and other geographic ranges. For individual connectors, see the package index.
Interface to Geometry Engine - Open Source (GEOS) using the C API for topology operations on geometries. The GEOS library is external to the package, and, when installing the package from source, must be correctly installed first. Windows and Mac Intel OS X binaries are provided on CRAN.
Create, read and write GEXF (Graph Exchange XML Format) graph files (used in Gephi and others). Using the XML package, it allows the user to easily build/read graph files including attributes, GEXF viz attributes (such as color, size, and position), network dynamics (for both edges and nodes) and edge weighting. Users can build/handle graphs element-by-element or massively through data-frames, visualize the graph on a web browser through "sigmajs" (a javascript library) and interact with the igraph package.
The rggobi package provides a command-line interface to GGobi, an
interactive and dynamic graphics package. Rggobi complements GGobi's
graphical user interface, providing a way to fluidly transition between
analysis and exploration, as well as automating common tasks.
This package provides some functions to create quizzes
in the GIFT format. This format is used by several Virtual Learning
Environments such as Moodle.
Provides medium to high level functions for 3D interactive graphics, including
functions modelled on base graphics (plot3d(), etc.) as well as functions for
constructing representations of geometric objects (cube3d(), etc.).  Output
may be on screen using OpenGL, or to various standard 3D file formats including
WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.
A programmatic interface to the web service methods
provided by Global Biotic Interactions (GloBI). GloBI provides
access to spatial-temporal species interaction records from
sources all over the world. rglobi provides methods to search
species interactions by location, interaction type, and
taxonomic name. In addition, it supports Cypher, a graph query
language, to allow for executing custom queries on the GloBI
aggregate species interaction dataset.
R interface to the GNU Linear Programming Kit.
GLPK is open source software for solving large-scale linear programming (LP),
mixed integer linear programming (MILP) and other related problems.
Provides an 'htmlwidgets' framework for the 'rgl' package.
Interface for gnuplot
Based on gnuplot_i version 1.11, the GPL code from Nicolas Devillard.
Provides functions for accessing and retrieving data from the
Google Analytics API
It fires a query to the API to get the unsampled data in R for Google Analytics Premium Accounts. It retrieves data from the Google drive document and stores it into the local drive. The path to the excel file is returned by this package. The user can read data from the excel file into R using read.csv() function.
This package serves two purposes: (i) Provide a
comfortable R interface to query the Google server for static
maps, and (ii) Use the map as a background image to overlay
plots within R. This requires proper coordinate scaling.
RGP is a simple modular Genetic Programming (GP) system build in
pure R. In addition to general GP tasks, the system supports Symbolic
Regression by GP through the familiar R model formula interface. GP
individuals are represented as R expressions, an (optional) type system
enables domain-specific function sets containing functions of diverse
domain- and range types. A basic set of genetic operators for variation
(mutation and crossover) and selection is provided.
RGP UI provides as modern web-based user interface to the modular
Genetic Programming (GP) system RGP.
Geological Survey of Canada (GSC) functions for exploratory data analysis with applied geochemical data, with special application to the estimation of background ranges and identification of outliers, 'anomalies', to support mineral exploration and environmental studies.  Additional functions are provided to support analytical data QA/QC, ANOVA for investigations of field sampling and analytical variability, and utility tasks.  NOTE: function caplot() for concentration-area plots employs package 'akima', however, 'akima' is only licensed for not-for-profit use.  Therefore, not-for-profit users of 'rgr' will have to independently make package 'akima' available through library(....); and use of function caplot() by for-profit users will fail.
Data and Functions from the book R Graphics, Second Edition. There is a function to produce each figure in the book, plus several functions, classes, and methods defined in Chapter 8.
Interpreted interface between GRASS 7 geographical
information system and R, based on starting R from within the GRASS GIS
environment, or running free-standing R in a temporary GRASS location;
the package provides facilities for using all GRASS commands from the
R command line. This package may not be used for GRASS 6, for which
spgrass6 should be used.
Integrates the Groovy scripting language with the R Project for Statistical Computing.
Facilities in the R language for programming
graphical interfaces using Gtk, the Gimp Tool Kit.
DBI/RJDBC interface to h2 database. h2 version 1.3.175 is included.
An R interface to the 'Handsontable' JavaScript library, which is a
minimalist Excel-like data grid editor.
An R interface for the HealthData.gov data API.  For each data resource, you can filter results (server-side) to select subsets of data.
Evaluating risk (that a patient arises a side effect) during hospitalization is the main purpose of this package. Several methods (Parametric, non parametric and De Vielder estimation) to estimate the risk constant (R) are implemented in this package. There are also functions to simulate the different models of this issue in order to quantify the previous estimators. It is necessary to read at least the first six pages of the report to understand the topic.
Function of apply style using 'MPI' provides better 'HPC' environment on R.
and this package supports long vector, can deal with slightly big data.
Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'ACML' and 'MKL').
and possible to control the number of threads in 'OpenMP'.
get a number of logical cores and physical cores if feasible.
rHpcc is an R package providing an Interface between R and
HPCC.Familiarity with ECL (Enterprise Control Language) is a
must to use this package.HPCC is a massive parallel-processing
computing platform that solves Big Data problems.ECL is the
Enterprise Control Language designed specifically for huge data
projects using the HPCC platform.Its extreme scalability comes
from a design that allows you to leverage every query you
create for re-use in subsequent queries as needed. To do this,
ECL takes a dictionary approach to building queries wherein
each ECL definition defines an Attribute. Each previously
defined Attribute can then be used in succeeding ECL Attribute
definitions as the language extends itself as you use it.
Methods and tools for performing Heart Rate Variability analysis
of ECG records. RHRV allows to import data files containing heartbeat positions
in the most broadly used formats; eliminating outliers or spurious points
present in the time series with unacceptable physiological values; plotting HRV
data and performing time domain, frequency domain and nonlinear HRV analysis.
This package offers functions to perform regularized
Hotelling's T-square test for pathway or gene set analysis. The
package is tailored for but not limited to proteomics data, in
which sample sizes are often small, a large proportion of the
data are missing and/or correlations may be present.
DEPRECATED. Do not start building new projects based on this package. Cross-platform alternatives are the following packages: bigmemory (CRAN), ff (CRAN), BufferedMatrix (Bioconductor).  The main usage of it was inside the aroma.affymetrix package. (The package currently provides a class representing a matrix where the actual data is stored in a binary format on the local file system.  This way the size limit of the data is set by the file system and not the memory.)
This package provides a set of tools for conducting exact
or approximate randomization-based inference for experiments of
arbitrary design. The primary functionality of the package is
in the generation, manipulation and use of permutation matrices
implied by given experimental designs. Among other features,
the package facilitates estimation of average treatment
effects, constant effects variance estimation, randomization
inference for significance testing against sharp null
hypotheses and visualization of data and results.
Computes attributable effects based confidence set, permutation test confidence set, and asymptotic confidence interval for the average treatment effect on a binary outcome.
The Diceware method can be used to generate strong passphrases.
In short, you roll a 6-faced dice 5 times in a row, the number obtained is
matched against a dictionary of easily remembered words. By combining together 7
words thus generated, you obtain a password that is relatively easy to remember,
but would take several millions years (on average) for a powerful computer to guess.
Computes rarefaction curves, cumulated and mean species
richness. Compares these estimates by means of randomization
tests.
Linear and logistic ridge regression for small data sets
and genome-wide SNP data
This package implements ridge fusion methodology for inverse covariance matrix estimation for use in quadratic discriminant analysis.  The package also contains function for model based clustering using ridge fusion for inverse matrix estimation, as well as tuning parameter selection functions.  We have also implemented QDA using joint inverse covariance estimation.
An interface to iDigBio's search API that allows downloading
specimen records. Searches are returned as a data.frame. Other functions
such as the metadata end points return lists of information. iDigBio is a US
project focused on digitizing and serving museum specimen collections on the
web. See <https://www.idigbio.org> for information on iDigBio.
An extension of the Kruskal-Wallis Test that allow
selection of arbitrary reference group. Also provide Mean Ridit
for each group. Mean Ridit of a group is an estimate of
probability a random observation from that group will be
greater than or equal to a random observation from reference
group.
RIFS package provides functionality for generating &
plotting prefractals in R^n with various protofractal sets and
partition coefficient for iterative segments
Interactive data visualization supporting linked graphs can help users easily
explore data and gain valuable insights and enables such visualization and interactive
re-analysis using HTML5 canvas and JavaScript. Its R API is designed to create a seamless
analysis, visualization, and re-analysis workflow for interactive visualization and analysis.
Since HTML5 canvas and JavaScript is supported in virtually all modern web browsers, the
visualizations can be delivered to almost any device/platform for presentation purposes.
Provides functions to read 'ImageJ' (http://imagej.nih.gov/ij/)
Region of Interest (ROI) files, to plot the ROIs and to convert them to
'spatstat' (http://spatstat.org/) spatial patterns.
Cloudera 'Impala', which is a massively parallel processing (MPP) SQL query engine runs natively in Apache Hadoop. 'RImpala' facilitates the connection and execution of distributed queries through 'R'. 'Impala' supports JDBC integration which 'RImpala' utilizes to establish the connection between 'R' and 'Impala'. Thanks to Mu Sigma for their continued support throughout the development of the package.
A programmatic interface to the
API provided by the iNaturalist website.
Index structures allow quickly accessing elements from
large collections. While btree are optimized for disk databases
and ttree for ram databases we use hybrid static indexing which
is quite optimal for R.
C++ classes to embed R in C++ applications
The 'RInside' packages makes it easier to have "R inside" your C++ application
by providing a C++ wrapper class providing the R interpreter.

As R itself is embedded into your application, a shared library build of R
is required. This works on Linux, OS X and even on Windows provided you use
the same tools used to build R itself.

Numerous examples are provided in the eight subdirectories of the examples/
directory of the installed package: standard, mpi (for parallel computing)
qt (showing how to embed 'RInside' inside a Qt GUI application), wt (showing
how to build a "web-application" using the Wt toolkit), armadillo (for
'RInside' use with 'RcppArmadillo') and eigen (for 'RInside' use with 'RcppEigen').
The example use GNUmakefile(s) with GNU extensions, so a GNU make is required
(and will use the GNUmakefile automatically).

Doxygen-generated documentation of the C++ classes is available at the
'RInside' website as well.
Functions to calculate several ecological indices of individual and population niche width
(Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis' W, and Roughgarden's
WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be
quantified by counts of categories, measures of mass/lenght or proportions. Monte Carlo resampling
procedures are available for hypothesis testing against multinomial null models.
Streamlined data import and export by making assumptions that the user is probably willing to make: 'import()' and 'export()' determine the data structure from the file extension, reasonable defaults are used for data import and export (e.g., 'stringsAsFactors=FALSE'), web-based import is natively supported (including from SSL/HTTPS), compressed files can be read directly without explicit decompression, and fast import packages are used where appropriate.
Functions for the analysis of Quaternary science data, including
constrained clustering, WA, WAPLS, IKFA, MLRC and MAT transfer
functions, and stratigraphic diagrams.
Utility functions and S3 classes for IPv4 and IPv6 addresses, including
conversion to and from binary representation.
A package including various functions for image processing and analysis. With this package is possible to process and analyse RGB, LAN (multispectral) and AVIRIS (hyperspectral) images. This packages also provides functions for reading JPEG files, extracted from the archived 'rimage' package.
Computes risk measures from data, as well as performs risk management procedures such as practical risk measurement, capital requirement, capital allocation and decision-making.
Risk regression models for survival analysis with and without
competing risks.
Compute time-dependent Incident/dynamic accuracy measures
(ROC curve, AUC, integrated AUC )from censored survival data
under proportional or non-proportional hazard assumption of
Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).
Implements efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolio. The log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals.
A set of tools to extract bibliographic content from the National Center for Biotechnology Information (NCBI) databases, including PubMed. The name RISmed is a portmanteau of RIS (for Research Information Systems, a common tag format for bibliographic data) and PubMed.
This package implements the simulation and regression of
integrated Isothermal Titration Calorimetry (ITC) data using
the most commonly used one-to-one binding reaction model.
A simple yet powerful script editor built natively in R with tcltk.
Finds a robust instrumental variables estimator using a
high breakdown point S-estimator of multivariate location
and scatter matrix.
Functions for reading, analysing and plotting river networks.
For this package, river networks consist of sections and nodes with associated attributes,
e.g. to characterise their morphological, chemical and biological state.
The package provides functions to read this data from text files, to analyse the network
structure and network paths and regions consisting of sections and nodes that fulfill
prescribed criteria, and to plot the river network and associated properties.
Sankey plots are a type of diagram that is convenient to
illustrate how flow of information, resources etc. separates and joins,
much like observing how rivers split and merge. For example, they can be
used to compare different clusterings.
This R package is a flexible and efficient tool to
visualise both quantitative and qualitative data from river surveys.
It can be used to produce diagrams with the topological structure of
the river network.
This package is devoted to the IVIVC linear level A with
numerical deconvolution method. The latter is working for
inequal and incompatible timepoints between impulse and
response curves. A numerical convolution method is also
available. Application domains include pharamaceutical industry
QA/QC and R&D together with academic research.
A tool for undergraduate and graduate courses in open-channel hydraulics.
Provides functions for computing normal and critical depths, steady-state
water surface profiles (e.g. backwater curves) and unsteady flow
computations (e.g. flood wave routing).
Bayesian analysis of CGH microarrays fitting Hidden Markov
Chain models. The selection of the number of states is made via
their posterior probability computed by Reversible Jump Markov
Chain Monte Carlo Methods. Also returns probabilistic common
regions for gains/losses.
Jade is a high performance template engine heavily influenced by
Haml and implemented with JavaScript for node and browsers.
Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.
RJDBC is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.
A series of useful functions, some available in different
forms in other packages, but which have been extended, sped up, or
otherwise modified in some way considered useful to the author.
R-interface to gene drop programs from Alun Thomas' Java Programs for Statistical Genetics and Computational Statistics (JPSGCS)
R interface to statistical database organized by Japanese government (http://statdb.nstac.go.jp/)
Provides functions to retrieve data and metadata from providers
that disseminate data by means of SDMX web services.
SDMX (Statistical Data and Metadata eXchange) is a standard that
has been developed with the aim of simplifying the exchange of
statistical information.
More about the SDMX standard and the SDMX Web Services
can be found at: http://sdmx.org .
Converts R object into JSON objects and vice-versa
This is a package that allows conversion to and from
data in Javascript object notation (JSON) format.
This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code
and allows R programmers to read and convert JSON content to R objects.
This is an alternative to rjson package. Originally, that was too slow for converting large R objects to JSON
and was not extensible.  rjson's performance is now similar to this package, and perhaps slightly faster in some cases.
This package uses methods and is readily extensible by defining methods for different classes,
vectorized operations, and C code and callbacks to R functions for deserializing JSON objects to R.
The two packages intentionally share the same basic interface. This package (RJSONIO) has many additional
options to allow customizing the generation and processing of JSON content.
This package uses libjson rather than implementing yet another JSON parser. The aim is to support
other general projects by building on their work, providing feedback and benefit from their ongoing development.
Read and write the 'JSON-stat' format (http://json-stat.org) to and
from (lists of) R data frames. Not all features are supported, especially
the extensive metadata features of 'JSON-stat'.
R interface to Python via Jython allowing R to call python
code.
Apache 'Kafka' is an open-source message broker project developed by the Apache Software Foundation which can be thought of as a distributed, partitioned, replicated commit log service.At a high level, producers send messages over the network to the 'Kafka' cluster which in turn serves them up to consumers.See <http://kafka.apache.org/> for more information.Functions included in this package enable:1.Creating 'Kafka' producer 2.Writing messages to a topic 3.Closing 'Kafka' producer 4.Creating 'Kafka' consumer 5.Reading messages from a topic 6.Closing 'Kafka' consumer. The jars required for this package are included in a separate package 'rkafkajars'.Thanks to Mu Sigma for their continued support throughout the development of the package.
The 'rkafkajars' package collects all the external jars required for the 'rkafka' package.
An R interface to KEA (Version 5.0).
KEA (for Keyphrase Extraction Algorithm) allows for extracting
keyphrases from text documents. It can be either used for free
indexing or for indexing with a controlled vocabulary. For more
information see <http://www.nzdl.org/Kea/>.
External jars required for package RKEA.
An interface of R to Klout API v2. It fetches Klout Score for a Twitter Username/handle in real time. Klout is a website and mobile app that uses social media analytics to rank its users according to online social influence via the "Klout Score", which is a numerical value between 1 and 100.  In determining the user score, Klout measures the size of a user's social media network and correlates the content created to measure how other users interact with that content.
Random knn classification and regression are implemented. Random knn based feature selection methods are also included. The approaches are mainly developed for high-dimensional data with small sample size.
Contains function rkt which computes the Mann-Kendall test (MK) and the Seasonal and the Regional Kendall Tests for trend (SKT and RKT) and  Theil-Sen's slope estimator.
This package provides functionality to read files
containing observations which consist of arbitrary key/value
pairs.
Functions and Datasets Required for ST370 class
Allow a user to discover, query and modify data from a LabKey Server.
Standardized methods for calculating common important derived
physical features of lakes including water density based based on
temperature, thermal layers, thermocline depth, lake number, Wedderburn
number, Schmidt stability and others.
Display spatial data with interactive maps powered by the
open-source JavaScript library 'Leaflet' (see <http://leafletjs.com/>). Maps can be rendered in a web browser or
displayed in the HTML viewer pane of 'RStudio'. This package is designed to be easy to use and
can create complex maps with vector and raster data, web served map tiles and interface elements.
Provides an interface to the C implementation of the
random number generator with multiple independent streams
developed by L'Ecuyer et al (2002). The main purpose of this
package is to enable the use of this random number generator in
parallel R applications.
An R interface for C library libeemd for performing the ensemble
empirical mode decomposition (EEMD), its complete variant (CEEMDAN) or the
regular empirical mode decomposition (EMD).
Set of tools for reading, processing and visualizing small set
of LiDAR (Light Detection and Ranging) data for forest inventory applications.
An interface to LINDO API. Supports Linear, Integer, Quadratic, Conic, General Nonlinear, Global, and Stochastic Programming models. To download the trial version LINDO API, please visit www.lindo.com/rlindo.
A series of functions that allow users
to access the LinkedIn API to get information about connections,
search for people and jobs, share updates with their network,
and create group discussions.  For more information about using
the API please visit https://developer.linkedin.com/.
Provides a set of functions for data manipulation with
list objects, including mapping, filtering, grouping, sorting,
updating, searching, and other useful functions. Most functions
are designed to be pipeline friendly so that data processing with
lists can be chained.
Robust fitting of linear model which can take response in matrix form.
R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.
Rapid, simulation-based exact (restricted) likelihood ratio tests
for testing the presence of variance components/nonparametric terms for
models fit with nlme::lme(),lme4::lmer(), gamm4::gamm4(), mgcv::gamm() and
SemiPar::spm().
R interface to LTP-Cloud service for Natural Language Processing
in Chinese. For more details please visit http://www.ltp-cloud.com/. Visit
https://github.com/hetong007/rLTP for up-to-date version.
A collection of 'shiny' applications for the R package 'Luminescence'.
These mainly, but not exclusively, include applications for plotting chronometric
data from e.g. luminescence or radiocarbon dating. It further provides access to
bootstraps tooltip and popover functionality and contains the 'jscolor.js' library
with a custom 'shiny' output binding.
RM2 is a simple package that implements functions used in
revenue management and pricing environments.
Uses refined moving average filter based on the optimal and data-driven moving average lag q or smoothing spline to estimate trend and seasonal components, as well as irregularity (residuals) for univariate time series or data.
An EM algorithm to fit Mallows' Models to full or partial
rankings, with or without ties.
This package implements an algorithm family for continuous
optimization called memetic algorithms with local search chains
(MA-LS-Chains). Memetic algorithms are hybridizations of genetic
algorithms with local search methods. They are especially suited
for continuous optimization.
An interface to the software package MARK that constructs input
files for MARK and extracts the output. MARK was developed by Gary White
and is freely available at (http://www.phidot.org/software/mark/downloads/)
but is not open source.
Convert R Markdown documents into a variety of formats.
rmatio is a package for reading and writing Matlab MAT
files from R. The rmatio package supports reading MAT version 4,
MAT version 5 and MAT compressed version 5. The rmatio package can
write version 5 MAT files and version 5 files with variable
compression.
Methods readMat() and writeMat() for reading and writing MAT files.  For user with MATLAB v6 or newer installed (either locally or on a remote host), the package also provides methods for controlling MATLAB (trademark) via R and sending and retrieving data between R and MATLAB.
S3 and S4 functions are implemented for spatial multi-site
stochastic generation of daily time series of temperature and
precipitation. These tools make use of Vector AutoRegressive models (VARs).
The weather generator model is then saved as an object and is calibrated by
daily instrumental "Gaussianized" time series through the 'vars' package
tools. Once obtained this model, it can it can be used for weather
generations and be adapted to work with several climatic monthly time
series.
Functions for fitting, diagnosing and predicting from a
class of Markov models.
Functions for simple fixed and random effects
meta-analysis for two-sample comparisons and cumulative
meta-analyses. Draws standard summary plots, funnel plots, and
computes summaries and tests for association and heterogeneity
An interface between R and the metasim simulation engine.
Facilitates the use of the metasim engine to build and
run individual based population genetics simulations. The
simulation environment is documented in: Allan
Strand. Metasim 1.0: an individual-based environment for
simulating population genetics of complex population
dynamics. Mol. Ecol. Notes, 2:373-376, 2002. (Please
contact Allan Strand with comments, bug reports, etc).
This version represents a significant alteration of
function names that hopefully increases consistency and
reduces the chances of collisions with other packages
naming conventions.  For a spatially-explicit, but slower, package
with a similar interface, see kernelPop
Methods that simplify the setup of S3 generic functions and S3 methods.  Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers.  For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible.  The method setMethodS3() is a good start for those who in the future may want to migrate to S4.  This is a cross-platform package implemented in pure R that generates standard S3 methods.
Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.
Facilitates the use of data mining algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models/algorithms, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics (improved mmetric function); 1.2 - new input importance methods (improved Importance function); 1.0 - first version.
The Rmisc library contains many functions useful for data analysis
and utility operations.
A collection of functions designed to run supervised and
unsupervised classification with MIXture MODelling.
The Rmixmod package provides model-based clustering by fitting a mixture model (e.g. Gaussian components for quantitative continuous data) to the data and identifying each cluster with one of its components. The number of components can be determined from the data, typically using the BIC criterion. In practice, however, individual clusters can be poorly fitted by Gaussian distributions, and in that case model-based clustering tends to represent one non-Gaussian cluster by a mixture of two or more Gaussian components. If the number of mixture components is interpreted as the number of clusters, this can lead to overestimation of the number of clusters. This is because BIC selects the number of mixture components needed to provide a good approximation to the density. This package, RmixmodCombi, according to \emph{Combining Mixture Components for Clustering} by J.P. Baudry, A.E. Raftery, G. Celeux, K. Lo, R. Gottardo, combines the components of the EM/BIC solution (provided by Rmixmod) hierarchically according to an entropy criterion. This yields a clustering for each number of clusters less than or equal to K. These clusterings can be compared on substantive grounds, and we also provide a way of selecting the number of clusters via a piecewise linear regression fit to the (possibly rescaled) entropy plot.
Provides an interface to many endpoints of Mixpanel's Data Export API. The R functions allow for event and profile data export as well as for segmentation, retention and addiction analysis. Results are always parsed into convenient R objects.
Sundry discrete probability distributions and helper functions.
A collection of miscellaneous functions for medical data analysis. Visualization
of multidimensional data, diagnostic test calibration, pairwise tests for qualitative variables and outcome simulation.
Connect R with MOA (Massive Online Analysis -
http://moa.cms.waikato.ac.nz) to build classification models and
regression models on streaming data or out-of-RAM data
External jars required for package RMOA. RMOA is a framework to
build data stream models on top of MOA (Massive Online Analysis -
http://moa.cms.waikato.ac.nz)
MongoDB Database interface for R. The interface is provided via Java calls to the mongo-java-driver.
This R package provides an interface to the NoSQL MongoDB database
using the MongoDB C-driver version 0.8.
Programmatic access to the Survey Monkey API <https://developer.surveymonkey.com/>, which currently provides extensive functionality for monitoring surveys and retrieving survey results and some functionality for creating new surveys and data collectors.
An interface to the MOSEK optimization library designed to
solve large-scale mathematical optimization problems. Supports
linear, quadratic and second order cone optimization
with/without integer variables, in addition to the more general
separable convex problems.  Trial and free academic licenses
available at http://www.mosek.com.
Performs probability mass function estimation with
Dirichlet process mixtures of rounded kernels
Arithmetic (via S4 classes and methods) for
arbitrary precision floating point numbers, including transcendental
("special") functions.  To this end, Rmpfr interfaces to
the LGPL'ed MPFR (Multiple Precision Floating-Point Reliable) Library
which itself is based on the GMP (GNU Multiple Precision) Library.
Rmpi provides an interface (wrapper) to MPI APIs. It also
provides interactive R slave environment.
This package contains functions and S3 methods for spatial
multi-site stochastic generation of daily precipitation. It generates
precipitation occurrence in several sites using Wilks' Approach (1998).
Bugs/comments/questions/collaboration of any kind are warmly welcomed.
Regression modeling, testing, estimation, validation,
graphics, prediction, and typesetting by storing enhanced model design
attributes in the fit.  'rms' is a collection of functions that
assist with and streamline modeling.  It also contains functions for
binary and ordinal logistic regression models, ordinal models for
continuous Y with a variety of distribution families, and the Buckley-James
multiple regression model for right-censored responses, and implements
penalized maximum likelihood estimation for logistic and ordinary
linear models.  'rms' works with almost any regression model, but it
was especially written to work with binary or ordinal regression
models, Cox regression, accelerated failure time models,
ordinary linear models,	the Buckley-James model, generalized least
squares for serially or spatially correlated observations, generalized
linear models, and quantile regression.
This package can be used to test any simple null
hypothesis using the root-mean-square goodness of fit test.
Monte Carlo estimation is used to calculate the associated
P-value.
Functions for working with the Tracy-Widom laws and other distributions
related to the eigenvalues of large Wishart matrices.
The tables for computing the Tracy-Widom densities and distribution
functions were computed by Momar Dieng's MATLAB package "RMLab"
(formerly available on his homepage at
http://math.arizona.edu/~momar/research.htm ).
This package is part of a collaboration between Iain Johnstone,
Zongming Ma, Patrick Perry, and Morteza Shahram.  It will soon be
replaced by a package with more accuracy and built-in support for
relevant statistical tests.
Implements 'DBI' Interface to 'MySQL' and 'MariaDB' Databases.
This package is designed to streamline several of the common steps for RNA-seq
analysis, including differential expression and variant discovery.
For the development build, or to contribute changes to this package,
please see our repository at https://bitbucket.org/petersmp/rnaseqwrapper/
GUI to explore high dimensional data (including image data) using graphs as navigational infrastructure.
Image data used as examples in the RnavGraph R package.
See the demos in the RnavGraph package.
Provides a set of functions to access a data visualization web service. For more information and a tutorial on how to use it, see https://navicell.curie.fr/pages/nav_web_service.html and https://github.com/sysbio-curie/RNaviCell.
Access to data held by the National Biodiversity Network (NBN, www.nbn.org.uk). The NBN acts as a data warehouse for biological records data in the UK and is the UK node of GBIF (Global Biodiversity Information Facility). In this package NBN data is accessed via its web-services.
Provides the libraries of the EUtils operations for the
RNCBI package.
This package contains functions to retrieve, organize, and visualize weather data from the NCEP/NCAR Reanalysis (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html) and NCEP/DOE Reanalysis II (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html) datasets.  Data are queried via the Internet and may be obtained for a specified spatial and temporal extent or interpolated to a point in space and time.  We also provide functions to visualize these weather data on a map.  There are also functions to simulate flight trajectories according to specified behavior using either NCEP wind data or data specified by the user.
An interface to the Nexus Class Library which allows parsing
of NEXUS, Newick and other phylogenetic tree file formats. It provides
elements of the file that can be used to build phylogenetic objects
such as ape's 'phylo' or phylobase's 'phylo4(d)'.
This package is a collection of various functions to extract the implied risk neutral density from option.
Neo4j, a graph database, allows users to store their data as a property graph. A graph consists of nodes that are connected by relationships; both nodes and relationships can have properties, or key-value pairs. RNeo4j is Neo4j's R driver. It allows users to read and write data from and to Neo4j directly from their R environment by exposing an interface for interacting with nodes, relationships, paths, and more. Most notably, it allows users to retrieve Cypher query results as R data frames, where Cypher is Neo4j's graph query language. Visit <http://www.neo4j.com> to learn more about Neo4j.
Within this package the XML-RPC API to NEOS is implemented. This enables the user to pass optimization problems to NEOS and retrieve results within R.
It provides functions to compute the modularity and modularity-related roles in networks. It is a wrapper around the rgraph library (Guimera & Amaral, 2005, doi:10.1038/nature03288).
An interface to the NetCDF file format designed by Unidata
for efficient storage of array-oriented scientific data and descriptions.
The R interface is closely based on the C API of the NetCDF library,
and it includes calendar conversions from the Unidata UDUNITS library.
The current implementation supports all operations on NetCDF datasets
in classic and 64-bit offset file formats, and NetCDF4-classic format
is supported for reading and modification of existing files.
Interface to use and access Wilensky's NetLogo (Wilensky 1999) from R using either headless (no GUI) or interactive GUI mode. Provides functions to load models, execute commands, and get values from reporters. Mostly analogous to the NetLogo Mathematica Link http://ccl.northwestern.edu/netlogo/docs/mathematica.html
Provides access to phyloinformatic data in NeXML format.  The
package should add new functionality to R such as the possibility to
manipulate NeXML objects in more various and refined way and compatibility
with 'ape' objects.
A function setVectorSeed() is provided. Its argument
is a numeric vector of an arbitrary nonzero length, whose
components have integer values from [0, 2^32-1]. The input
vector is transformed using AES (Advanced Encryption Standard)
algorithm into an initial state of Mersenne-Twister random
number generator. The function provides a better alternative
to the R base function set.seed(), if the input vector is
a single integer. Initializing a stream of random numbers
with a vector is a convenient way to obtain several streams,
each of which is identified by several integer indices.
This package contains a set of functions for working with
Random Number Generators (RNGs). In particular, it defines a generic
S4 framework for getting/setting the current RNG, or RNG data
that are embedded into objects for reproducibility.
Notably, convenient default methods greatly facilitate the way current
RNG settings can be changed.
It is a dedicated package to WELL pseudo random generators, which were introduced in Panneton et al. (2006), ``Improved Long-Period Generators Based on Linear Recurrences Modulo 2'', ACM Transactions on Mathematical Software. But this package is not intended to be used directly, you are strongly __encouraged__ to use the 'randtoolbox' package, which depends on this package.
Long period linear random number generator WELL19937a by
F. Panneton, P. L'Ecuyer and M. Matsumoto. The initialization
algorithm allows to seed the generator with a
numeric vector of an arbitrary length and uses MRG32k5a by
P. L'Ecuyer to achieve good quality of the initialization. The
output function may be set to provide numbers from the interval
(0,1) with 53 (the default) or 32 random bits. WELL19937a is of
similar type as Mersenne Twister and has the same period.
WELL19937a is slightly slower than Mersenne Twister, but has
better equidistribution and "bit-mixing" properties and faster
recovery from states with prevailing zeros than Mersenne
Twister. All WELL generators with orders 512, 1024, 19937 and
44497 can be found in randtoolbox package.
Provides an R interface to the NiftyReg image registration tools <http://sourceforge.net/projects/niftyreg/>. Linear and nonlinear registration are supported, in two and three dimensions.
An implementation of robust nonnegative matrix factorization (rNMF). The rNMF algorithm decomposes a nonnegative high dimension data matrix into the product of two low rank nonnegative matrices, while detecting and trimming outliers. The main function is rnmf(). The package also includes a visualization tool, see(), that arranges and prints vectorized images.
Implementation of a Recurrent Neural Network in R.
Client for many 'NOAA' data sources including the 'NCDC' climate
'API' at http://www.ncdc.noaa.gov/cdo-web/webservices/v2, with functions for
each of the 'API' 'endpoints': data, data categories, data sets, data types,
locations, location categories, and stations. In addition, we have an interface
for 'NOAA' sea ice data, the 'NOAA' severe weather inventory, 'NOAA' Historical
Observing 'Metadata' Repository ('HOMR') data, 'NOAA' storm data via 'IBTrACS',
tornado data via the 'NOAA' storm prediction center, and more.
An interface to the National Oceanic and Atmospheric Administration's Operational Model Archive and Distribution System (NOMADS) that allows R users to quickly and efficiently download global and regional weather model data for processing.  rNOMADS currently supports a variety of models ranging from global weather data to an altitude of 40 km, to high resolution regional weather models, to wave and sea ice models. It can also retrieve archived NOMADS models.  rNOMADS can retrieve binary data in grib format as well as import ascii data directly into R by interfacing with the GrADS-DODS system.
Utility functions to retrieve data from the UK National River Flow
Archive. The package contains R wrappers to the UK NRFA data temporary-API.
There are functions to retrieve stations falling in a bounding box,
to generate a map and extracting time series and general information.
Provides an interface to the OAuth 1.0 specification
allowing users to authenticate via OAuth to the
server of their choice.
Base S4-classes and functions for robust asymptotic statistics.
Methods for analysis of compositional data
including robust methods, imputation, methods to replace rounded zeros, (robust)
outlier detection for compositional data, (robust) principal component analysis for
compositional data, (robust) factor analysis for compositional data,
(robust) discriminant analysis for compositional data (Fisher rule), robust
regression with compositional predictors and (robust) Anderson-Darling
normality tests for compositional data as well as popular log-ratio
transformations (addLR, cenLR, isomLR, and their inverse transformations).
In addition, visualisation and diagnostic tools are implemented as well as
high and low-level plot functions for the ternary diagram.
Robust pairwise correlations based on estimates of scale,
particularly on "FastQn" one-step M-estimate.
Locations problems, M-estimates of coefficients and scale
in linear regression, Weights for bounded influence regression,
Covariance matrix of the coefficient estimates, Asymptotic
relative efficiency of regression M-estimates, Robust testing
in linear models, High breakdown point regression, M-estimates
of covariance matrices, M-estimates for discrete generalized
linear models.
A set of functions to filter time series based on concepts
from robust statistics.
Functions for the determination of optimally robust influence curves and
estimators in case of normal location and/or scale
Functions for the determination of optimally robust influence curves and
estimators for preprocessing omics data, in particular gene expression data.
Calculates periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). Three main functions are included: RobPer calculates the periodogram. Outlying periodogram bars (indicating a period) can be detected with betaCvMfit. Artificial light curves can be generated using the function tsgen. For more details see Thieler, Fried and Rathjens (2013).
Functions for the determination of optimally robust influence curves in case of
linear regression with unknown scale and standard normal distributed errors where the
regressor is random.
This package provides the function to calculate SVD, regularized SVD, robust SVD and robust regularized SVD method. The robust SVD methods use alternating iteratively reweighted least squares methods. The regularized SVD uses generalized cross validation to choose the optimal smoothing parameters.
Data management in hydrology and other fields is facilitated with functions to enter and modify data in a database according to the Observations Data Model (ODM) standard by CUASHI (Consortium of Universities for the Advancement of Hydrologic Science). While this data model has been developed in hydrology, it is also useful for other fields. RObsDat helps in the setup of the database within one of the free database systems MariaDB, PostgreSQL or SQLite. It imports the controlled water vocabulary from the CUASHI web service and provides a smart interface between the analyst and the database: Already existing data entries are detected and duplicates avoided. The data import function converts different data table designs to make import simple. Cleaning and modifications of data are handled with a simple version control system. Variable and location names are treated in a user friendly way, accepting and processing multiple versions. When querying data from the database, it is stored in a spacetime objects within R for subsequent processing.
Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown. Also included are functions for conducting sensitivity analyses under correlated effects weighting and producing RVE-based forest plots.
R functions for the computation of the truncated maximum
likelihood and the robust accelerated failure time regression
for gaussian and log-Weibull case.
"Essential" Robust Statistics.
Tools allowing to analyze data with robust methods.  This includes
regression methodology including model selections and multivariate
statistics where we strive to cover the book "Robust Statistics,
Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.
Robust mixture discriminant analysis (RMDA, Bouveyron & Girard, 2009) allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.
The Spatial-EM is a new robust EM algorithm for the finite
mixture learning procedures. The algorithm utilizes median-
based location and rank-based scatter estimators to replace sample
mean and sample covariance matrix in each M step, hence enhancing
stability and robustness of the algorithm. To understand more about
this algorithm, read the article
''Yu, K., Dang, X., Bart Jr, H. and Chen, Y. (2015). Robust Model-
based Learning via Spatial-EM Algorithm. IEEE Transactions on
Knowledge and Data Engineering, 27(6), 1670-1682.
doi:10.1109/TKDE.2014.2373355''.
An object oriented solution for robust factor analysis. In the solution, new S4 classes "Fa", "FaClassic", "FaRobust", "FaCov", "SummaryFa" are created.
This package provides robust estimation for generalized
additive models. It implements a fast and stable algorithm in
Wong, Yao and Lee (2013). The implementation also contains
three automatic selection methods for smoothing parameter. They
are designed to be robust to outliers. For more details, see
Wong, Yao and Lee (2013).
Robust methods for high-dimensional data, in particular linear
model selection techniques based on least angle regression and sparse
regression.
A method to fit linear mixed effects models robustly.
Robustness is achieved by modification of the scoring equations
combined with the Design Adaptive Scale approach.
Robust estimation of the generalized log gamma model is provided using Quantile Tau estimator and Weighted Likelihood estimator.
Methods for aggregating ranked lists, especially lists of
genes. It implements the Robust Rank Aggregation (Kolde et. al
in preparation) and some other simple algorithms for the task.
RRA method uses a probabilistic model for aggregation that is
robust to noise and also facilitates the calculation of
significance probabilities for all the elements in the final
ranking.
Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm.
Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.
eXperimental, eXtraneous, or eXtraordinary Functionality
for Robust Statistics.  In other words, methods which are not
yet well established, often related to methods in package
robustbase.
This package computes traditional ROC curves and time-dependent ROC curves using the cross-validation, the 0.632 and the 0.632+ estimators. The 0.632+ estimator of time-dependent ROC curve is useful to estimate the predictive accuracy of prognostic signature based on high-dimensional data. For instance, it outperforms the other approaches, especially the cross-validation solution which is often used. The 0.632+ estimators correct the area under the curve in order to adequately estimate the prognostic capacities regardless of the overfitting level. This package also allows for the construction of diagnostic or prognostic scoring systems (penalized regressions). The methodology is adapted to complete data (penalized logistic regression associated with ROC curve) or incomplete time-to-event data (penalized Cox model associated with time-dependent ROC curve).
Functions for a classification method based on receiver
operating characteristics (ROC). Briefly, features are selected
according to their ranked AUC value in the training set. The
selected features are merged by the mean value to form a
metagene. The samples are ranked by their metagene value and
the metagene threshold that has the highest accuracy in
splitting the training samples is determined. A new sample is
classified by its metagene value relative to the threshold. In
the first place, the package is aimed at two class problems in
gene expression data, but might also apply to other problems.
A collection of functions for ease of
presentation and interpretation of regression analysis.
These functions are used to produce the statistics lectures
in http://pj.freefaculty.org/guides. There
are functions to explore the effect (or lack thereof) from
"mean centering," "residual centering", and standardization.
The vignette "rockchalk" offers a fairly comprehensive overview.
The vignette "Rstyle" has advice about coding in R.
See the outreg function for a convenient way to generate
regression models for reports. For nice, easy to use pictures,
see plotSlopes, plotCurves, plotPlane.
The package title "rockchalk" refers to our school motto,
"Rock Chalk Jayhawk, Go K.U.".
Provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.
The rococo package provides a robust gamma rank correlation
coefficient along with a permutation-based rank correlation test.
The rank correlation coefficient and the test are explicitly
designed for dealing with noisy numerical data.
ROC graphs, sensitivity/specificity curves, lift charts,
and precision/recall plots are popular examples of trade-off
visualizations for specific pairs of performance measures. ROCR is a
flexible tool for creating cutoff-parameterized 2D performance curves
by freely combining two from over 25 performance measures (new
performance measures can be added using a standard interface).
Curves from different cross-validation or bootstrapping runs can be
averaged by different methods, and standard deviations, standard
errors or box plots can be used to visualize the variability across
the runs. The parameterization can be visualized by printing cutoff
values at the corresponding curve positions, or by coloring the
curve according to cutoff. All components of a performance plot can
be quickly adjusted using a flexible parameter dispatching
mechanism. Despite its flexibility, ROCR is easy to use, with only
three commands and reasonable default values for all optional
parameters.
Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces.
This package contains functions in order to estimate time-dependent ROC with right-censored data. More precisely, two functions concern the estimation of ROC curves defined by Heagerty, Lumley and Pepe (Biometrics, 2000). The two other functions concern their adaptation for studying the capacity of a marker to predict the excess of mortality of a specific population compared to the general population (same age, gender and calendar year). This last part is based on additive relative survival models and the work of Pohar-Perme (Biometrics, 2011).
Function to estimate the ROC Curve of a continuous-scaled
diagnostic test with the help of a second imperfect diagnostic
test with binary responses.
An implementation of R's DBI interface using ODBC package as a
back-end. This allows R to connect to any DBMS that has a ODBC driver.
An extension for RODBC package adding support for parameterized
queries.
A collection of functions for numerical construction of optimal discriminating designs. At the current moment T-optimal designs (which maximize the lower bound for the power of F-test for regression model discrimination) and their robust analogues can be calculated with the package.
This package implements an interface to Oracle Data Mining
(ODM). It provides an ideal environment for rapid development
of demos and proof of concept data mining studies. It
facilitates the prototyping of vertical applications and makes
ODM and the RDBMS environment easily accessible to
statisticians and data analysts familiar with R but not fluent
in SQL or familiar with the database environment. It also
facilitates the benchmarking and testing of ODM functionality
including the production of summary statistics, performance
metrics and graphics. It enables the scripting and control of
production data mining methodologies from a high-level
environment. Oracle Data Mining (ODM) is an option of Oracle
Relational Database Management System (RDBMS) Enterprise
Edition (EE). It contains several data mining and data analysis
algorithms for classification, prediction, regression,
clustering, associations, feature selection, anomaly detection,
feature extraction, and specialized analytics. It provides
means for the creation, management and operational deployment
of data mining models inside the database environment. For more
information consult the entry for "Oracle Data Mining" in
Wikipedia (en.wikipedia.org).
The R Optimization Infrastructure (ROI) package provides a
sophisticated framework for handling optimization problems in R.
This package enhances the R Optimization Infrastructure (ROI) package
with the GLPK solver for solving mixed integer linear programming (MILP)
problems as well as all variants/combinations of LP, IP.
This package enhances the R Optimization Infrastructure (ROI) package
with the quadprog solver for solving quadratic programming (QP)
problems.
This package enhances the R Optimization Infrastructure (ROI) package
with the symphony solver from the COIN-OR suite for solving mixed integer
linear programming (MILP) problems as well as all variants/combinations of LP, IP.
Apply a function in a moving window, then
combine the results in a data frame.
Provides the possibility to plot Lexis surface maps (heat maps) of rates of mortality improvement. Raw data to be plotted can be read from the Human Mortality Database using code originally written by Tim Riffe. The European Research Council has provided financial support under the European Community's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no. 263744.
Methods and classes for object-oriented programming in R with or without references.  Large effort has been made on making definition of methods as simple as possible with a minimum of maintenance for package developers.  The package has been developed since 2001 and is now considered very stable.  This is a cross-platform package implemented in pure R that defines standard S3 classes without any tricks.
This package contains the Rook specification and
convenience software for building and running Rook applications. To
get started, be sure and read the 'Rook' help file first.
Implementation of the Taylor Regression Estimator method described in Christopoulos (2014,<https://www.researchgate.net/publication/261562841>)
for finding the root, extreme or inflection point of a curve, when we only have a set of probably noisy xy points for it. The method uses a suitable
polynomial regression in order to find the coefficients of the relevant Taylor polynomial for the function that has generated our data.
Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE).
Includes routines that: (1) generate gradient and Jacobian matrices (full and banded),
(2) find roots of non-linear equations by the Newton-Raphson method,
(3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the Newton-Raphson method, or by dynamically running,
(4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ODEs
by numerical differencing (using the method-of-lines approach).
Includes fortran code.
An R package for the OpenSecrets.org web services API.
Optimally robust estimation in general smoothly parameterized models using S4 classes and methods.
Optimally robust estimation using S4 classes and methods. Old version still needed
for current versions of ROptRegTS and RobRex.
R package extracts optimizely test results and test information using Optimizely REST API. Only read functionality is supported for analysis and reporting.
Optimally robust estimation for regression-type models using S4 classes and
methods
An R package for computing both exact- and stochastic
robust ordinal regression, and maximal vectors.
Oracle database interface (DBI) driver for R.
This is a DBI-compliant Oracle driver based on the OCI.
Implementation of Robust Ordinal Regression for value-based sorting with some extensions and additional tools. It is a novel Multiple-Criteria Decision Aiding (MCDA) framework.
The package provides functions to deal with binary classification
problems in the presence of imbalanced classes. Synthetic balanced samples are
generated according to ROSE (Menardi and Torelli, 2013).
Functions that implement more traditional remedies to the class imbalance
are also provided, as well as different metrics to evaluate a learner accuracy.
These are estimated by holdout, bootstrap or cross-validation methods.
Download and plot Open Street Map <http://www.openstreetmap.org/>,
Mapquest <http://www.mapquest.com/>, Bing Maps <http://www.bing.com/maps>
and other tiled map sources in a way that works seamlessly with plotting
from the 'sp' package. Use to create high-resolution basemaps and add hillshade
to vector based maps.
Fit and deploy rotation forest models ("Rodriguez, J.J., Kuncheva, L.I., 2006. Rotation forest: A new classifier ensemble method. IEEE Trans. Pattern Anal. Mach. Intell. 28, 1619-1630") for binary classification. Rotation forest is an ensemble method where each base classifier (tree) is fit on the principal components of the variables of random partitions of the feature set.
Tools for working with rotational data, including
simulation from the most commonly used distributions on SO(3),
methods for different Bayes, mean and median type estimators for
the central orientation of a sample, confidence/credible
regions for the central orientation based on those estimators and
a novel visualization technique for rotation data.  Most recently,
functions to identify potentially discordant (outlying) values
have been added.
R build of Rothermel's (1972) model for surface fire rate of spread with some additional utilities (uncertainty propagation, standard fuel model selection, fuel model optimization by genetic algorithm) and sample datasets.
An interface to the 'Open Tree of Life' API to retrieve phylogenetic
trees, information about studies used to assemble the synthetic tree, and
utilities to match taxonomic names to 'Open Tree identifiers'. The 'Open Tree of
Life' aims at assembling a comprehensive phylogenetic tree for all named
species.
A set of functions to support Xiong K, 'Roughened Random Forests for Binary Classification' (2014). The functions include RRFA, RRFB, RRFC1-RRFC7, RRFD and RRFE. RRFB and RRFC6 are usually recommended. RRFB is much faster than RRFC6.
Rough Sets were introduced by Zdzislaw Pawlak on his book "Rough Sets: Theoretical Aspects of Reasoning About Data". Rough Sets provide a formal method to approximate crisp sets when the set-element belonging relationship is either known or undetermined. This enables the use of Rough Sets for reasoning about incomplete or contradictory knowledge. A decision table is a prescription of the decisions to make given some conditions. Such decision tables can be reduced without losing prescription ability. This package provides the classes and methods for knowledge reduction from decision tables as presented in the chapter 7 of the aforementioned book. This package provides functions for calculating the both the discernibility matrix and the essential parts of decision tables.
Implementations of algorithms for data analysis
based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We
not only provide implementations for the basic concepts of RST and FRST but also
popular algorithms that derive from those theories. The methods included in the
package can be divided into several categories based on their functionality:
discretization, feature selection, instance selection, rule induction and classification
based on nearest neighbors. RST was introduced by Zdzisław Pawlak in 1982
as a sophisticated mathematical tool to
model and process imprecise or incomplete information. By using
the indiscernibility relation for objects/instances, RST does not require
additional parameters to analyze the data. FRST is an extension of RST. The
FRST combines concepts of vagueness and indiscernibility that are expressed
with fuzzy sets (as proposed by Zadeh, in 1965) and RST.
Allows the manipulation of R objects as if they were
organized rows in a way that is familiar to people used to working with
databases.  It allows more consistent and predictable output to common
functions, and generalizes a number of utility functions to to be failsafe
with any number and type of input objects.
A 'Doxygen'-like in-source documentation system
for Rd, collation, and 'NAMESPACE' files.
Performs a multivariate normality test based on Royston's H test
Implements macroevolutionary analyses on phylogenetic trees. See Morlon et al. PLoSB (2010), Morlon et al. PNAS (2011), Condamine et al. Eco Lett (2013), Morlon et al. Eco Lett (2014), Manceau et al. (2015) and Lewitus & Morlon (submitted).
rpanel provides a set of functions to build simple
GUI controls for R functions.  These are built on the tcltk package.
Uses could include changing a parameter on a graph by animating it
with a slider or a "doublebutton", up to more sophisticated control
panels.
Some functions for specific graphical tasks, referred to as 'cartoons',
are provided.
Recursive partitioning for classification,
regression and survival trees.  An implementation of most of the
functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.
Provides algorithims for randomly sampling a feasible set defined
by a given total and number of elements using integer partitioning.
Plot 'rpart' models. Extends plot.rpart() and text.rpart()
in the 'rpart' package.
This package contains functions that allow to build
classification trees for ordinal responses within the CART
framework. The trees are grown using the Generalized Gini
impurity function, where the misclassification costs are given
by the absolute or squared differences in scores assigned to
the categories of the response. Pruning is based on the total
misclassification rate or on the total misclassification cost.
This package contains additional tools for working with rpart
objects.  Most importantly, it includes methods for converting rpart rules
into a series of structured tables sufficient for executing the decision
tree completely in SQL.
Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.
This package implements the R-PCLR (Random-Penalized
Conditional Logistic Regression) algorithm for obtaining
variable importance. The algorithm is applicable for the
analysis of high dimensional data from matched case-control
studies.
Provides tools to read, write, visualize PDB files and perform some structural manipulations.
Monthly Pacific Decadal Oscillation (PDO) index
values from January 1900 to present.
Implements the methodology of "Cannings, T. I. and Samworth, R. J. (2015) Random projection ensemble classification. http://arxiv.org/abs/1504.04595". The random projection ensemble classifier is a very general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment.
The purpose of this package is to factor out logic and math common
to Item Factor Analysis fitting, diagnostics, and analysis. It is
envisioned as core support code suitable for more specialized IRT packages
to build upon. Complete access to optimized C functions are made available
with R_RegisterCCallable.
Allows ad hoc queries and reading and
writing data frames to and from a database.
RPHAST is an R interface to the PHAST software
(Phylogenetic Analysis with Space/Time Models).  It can be used for
many types of analysis in comparative and evolutionary genomics,
such as estimating models of evolution from sequence data, scoring
alignments for conservation or acceleration, and predicting
elements based on conservation or custom phylogenetic hidden Markov
models.  It can also perform many basic operations on multiple
sequence alignments and phylogenetic trees.
Rphylip provides an R interface for the PHYLIP package. All users
of Rphylip will thus first have to install the PHYLIP phylogeny methods
program package (Felsenstein 2013). See http://www.phylip.com for more
information about installing PHYLIP.
Tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data. Available functions allow ancestral state reconstruction, missing data imputation, phylogenetic regression (phylogenetic generalized least squares), and phylogenetic principal components analysis (pPCA).
Build powerful pivot tables (aka Pivot Grid, Pivot Chart, Cross-Tab) and dynamically slice & dice / drag 'n' drop your data.
'rpivotTable' is a wrapper of 'pivottable', a powerful open-source Pivot Table library
implemented in 'JavaScript' by Nicolas Kruchten.  Aligned to 'pivottable' v1.6.3.
Provides an interface to the the many computational resources iPlant offers through their RESTful application programming interface, see <http://www.iplantcollaborative.org/> for more information about iPlant, and <http://www.iplantcollaborative.org/ci/iplant-science-apis> for its APIs.  Currently, 'rPlant' functions interact with the iPlant Agave API, the Taxonomic Name Resolution Service API, and the Phylotastic Taxosaurus API. Before using 'rPlant', users will have to register with the iPlant Collaborative.  See <http://agaveapi.co/> for more information on the Agave API and <http://user.iplantcollaborative.org/> to register with iPlant.
Efficiently read and analyze 'PLEXOS' solutions by converting them
into 'SQLite' databases that can be easily queried. It supports collation of
solutions that may have been divided into different time partitions,
as well as the comparison across different scenarios. 'PLEXOS' is a power systems
production cost model, created and distributed by Energy Exemplar
(see URL below for more information).
A programmatic interface to the 'SOLR' based
search 'API' (http://api.plos.org/) provided by the Public
Library of Science journals to search their articles.
Functions are included for searching for articles, retrieving
articles, making plots, doing 'faceted' searches,
'highlight' searches, and viewing results of 'highlighted'
searches in a browser.
This package is intended for using R as a plotting engine either by custom
applications, or from a small script launched from the system console, or even within
the R console. Two ASCII text files are necessary:
(1) The graph parameters file, which name is passed to the function 'rplotengine()'.
The user can specify the titles, choose the type of the graph, graph output formats
(e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend,
whether to show or not a grid at the background, etc.
(2) The data to be plotted, which name is specified as a parameter ('data_filename')
in the previous file. This data file has a tabulated format, with a single character
(e.g. tab) between each column, and a headers line located in the first row.
Optionally, the file could include data columns for showing confidence intervals.
Really Poor Man's Graphical User Interface, used to create interactive R analysis sessions with simple R commands.
Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures.
This is a model-based clustering algorithm that returns a hierarchy
of classes, similar to hierarchical clustering, but also similar to
finite mixture models.
A set of functions to analyze and print the development of a commodity using the Point and Figure (P&F) approach. A P&F processor can be used to calculate daily statistics for the time series. These statistics can be used for deeper investigations as well as to create plots. Plots can be generated as well known X/O Plots in plain TXT format, and additionally in a more graphical format.
PDF tools based on the Poppler PDF rendering library.
See <http://poppler.freedesktop.org/> for more information on Poppler.
A collection of utility functions to generate
various types of random portfolios.  The weights of these
portfolios are random variables derived from uniformly
distributed random variables
Database interface and PostgreSQL driver for R This
package provides a Database Interface (DBI) compliant driver
for R to access PostgreSQL database systems.

In order to build and install this package from source, PostgreSQL
itself must be present your system to provide PostgreSQL
functionality via its libraries and header files. These files
are provided as postgresql-devel package under some Linux
distributions.

On Microsoft Windows system the attached libpq library source will be
used.

A wiki and issue tracking system for the package are available at
Google Code at https://code.google.com/p/rpostgresql/ .
The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) <doi:10.1080/10543406.2013.860156>.
Using some association schemes to obtain a new series of resolvable partially pairwise balanced designs (RPPBD) and space-filling designs.
Reads in sample description and slide description files and
annotates the expression values taken from gpr files. After
normalization data can be visualized as boxplot, heatmap or
dotplot.
Routines to select and visualize the maxima for a given strict
partial order. This especially includes the computation of the Pareto
frontier, also known as (Top-k) Skyline operator, and some
generalizations (database preferences).
'Eprime' is a set of programs for administering psychological
experiments by computer. This package provides functions for loading,
parsing, filtering and exporting data in the text files produced by
'Eprime' experiments.
Provides a set of functions to facilitate building formatted strings
under various replacement rules: C-style formatting, variable-based formatting,
and number-based formatting. C-style formatting is basically identical to built-in
function 'sprintf'. Variable-based formatting allows users to put variable names
in a formatted string which will be replaced by variable values. Number-based
formatting allows users to use index numbers to represent the corresponding
argument value to appear in the string.
Protocol Buffers are a way of encoding structured data in an
efficient yet extensible format. Google uses Protocol Buffers for almost all
of its internal RPC protocols and file formats.  Additional
documentation is available in the arXiv.org preprint "RProtoBuf:
Efficient Cross-Language Data Serialization in R" by Eddelbuettel,
Stokely, and Ooms (2014) at <http://arxiv.org/abs/1401.7372>.
The rpsychi offers a number of functions for psychiatry, psychiatric nursing, clinical psychology. Functions are primarily for statistical significance testing using published work. For example, you can conduct a factorial analysis of variance (ANOVA), which requires only the mean, standard deviation, and sample size for each cell, rather than the individual data. This package covers fundamental statistical tests such as t-test, chi-square test, analysis of variance, and multiple regression analysis. With some exceptions, you can obtain effect size and its confidence interval. These functions help you to obtain effect size from published work, and then to conduct a priori power analysis or meta-analysis, even if a researcher do not report effect size in a published work.
Access PubChem data (compounds, substance, assays).
Structural information is provided in the form of SMILES
strings. This package only provides access to a subset of the
precalculated data stored by PubChem. Bio-assay data can be
accessed to obtain descriptions as well as the actual data. It
is also possible to search for assay ID's by keyword. Currently
the main limitation is that only 1000 molecules can be
downloaded at a time from the PubChem servers
Client for accessing ProPublica's data journalism APIs
An R interface to the Pushbullet messaging service which
provides fast and efficient notifications (and file transfer) between
computers, phones and tablets.  An account has to be registered at the site
http://www.pushbullet.com site to obtain a (free) API key.
Provide access to (virtually any) ArcGIS Geoprocessing
tool from within R by running Python geoprocessing scripts
without writing Python code or touching ArcGIS. Requires ArcGIS
>=9.2, a suitable version of Python (for ArcGIS 9.2: Python
2.4; for ArcGIS 10.0: 2.6), and Windows.
Run Python code, make function calls,  assign and retrieve variables, etc. from R.
R package for Qualitative Data Analysis. Current version only supports plain text, but it can import PDF highlights if the Enhance package of rjpod, which is available on R-Forge, is installed.
Performs penalized quantile regression for LASSO, SCAD and MCP functions including group penalties. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC.
Hypocenter estimation and analysis of seismic data collected continuously, or in trigger mode. The functions organize other functions from RSEIS and GEOmap to help researchers pick, locate, and store hypocenters for detailed seismic investigation.
The 'RQuantLib' package makes parts of 'QuantLib' accessible from R
The 'QuantLib' project aims to provide a comprehensive software framework
for quantitative finance. The goal is to provide a standard open source library
for quantitative analysis, modeling, trading, and risk management of financial
assets.
Enables researchers to conduct multivariate statistical analyses of survey data with randomized response technique items from several designs, including mirrored question, forced question, and unrelated question. This includes regression with the randomized response as the outcome and logistic regression with the randomized response item as a predictor. In addition, tools for conducting power analysis for designing randomized response items are included. The package implements methods described in Blair, Imai, and Zhou (2015) ''Design and Analysis of the Randomized Response Technique,'' Working paper available at <http://graemeblair.com/papers/randresp.pdf>.
Analyzes and predicts from matrix population models in the way of the Ramas (c) software
Software for genomic prediction with the RR-BLUP mixed model. One application is to estimate marker effects by ridge regression; alternatively, BLUPs can be calculated based on an additive relationship matrix or a Gaussian kernel.
rrBlupMethod6 -- Re-parametrization of mixed model
formulation to allow for a fixed residual variance when using
RR-BLUP for genomwide estimation of marker effects and linear
transformation of the adjusted means proposed by Piepho et
al.(2011)
Robust Location and Scatter Estimation and Robust
Multivariate Analysis with High Breakdown Point.
Robust multivariate methods for high dimensional data:
outlier detection, PCA, PLS, classification
Robust Location and Scatter Estimation and Robust
Multivariate Analysis with High Breakdown Point for Incomplete
Data
Make use of the hardware random number accessed by the 'RdRand'
instruction in recent Intel CPUs (Ivy Bridge and later).
'DRNG' is "Digital Random Number Generator".
R client interface to the "Redis" key-value database.
An R and Repast integration tool for running individual-based (IbM)
simulation models developed using 'Repast Simphony' Agent-Based framework
directly from R code. This package integrates 'Repast Simphony' models within
R environment, making easier the tasks of running and analyzing model output
data for automated parameter calibration and for carrying out uncertainty and
sensitivity analysis using the power of R environment.
Feature Selection with Regularized Random Forest. This
package is based on the 'randomForest' package by Andy Liaw.
The key difference is the RRF function that builds a
regularized random forest.
Collection of functions for fitting distributions to given data or
by known quantiles. Two main functions fit.perc() and fit.cont() provide
users a GUI that allows to choose a most appropriate distribution without
any knowledge of the R syntax. Note, this package is a part of the 'rrisk'
project.
This package offers methods to perform robust regularized
linear discriminant analysis.
Functions for creating and manipulating RNA secondary structure plots.
Univariate and multivariate methods to analyze
randomized response (RR) survey designs (e.g., Warner, S. L. (1965).
Randomized response: A survey technique for eliminating evasive answer
bias. Journal of the American Statistical Association, 60, 63–69).
Besides univariate estimates of true proportions, RR variables can be used
for correlations, as dependent variable in a logistic regression (with or
without random effects), as predictors in a linear regression, or as
dependent variable in a beta-binomial ANOVA. For simulation and bootstrap
purposes, RR data can be generated according to several models.
The RSP markup language makes any text-based document come alive.  RSP provides a powerful markup for controlling the content and output of LaTeX, HTML, Markdown, AsciiDoc, Sweave and knitr documents (and more), e.g. 'Today's date is <%=Sys.Date()%>'.  Contrary to many other literate programming languages, with RSP it is straightforward to loop over mixtures of code and text sections, e.g. in month-by-month summaries.  RSP has also several preprocessing directives for incorporating static and dynamic contents of external files (local or online) among other things.  Functions rstring() and rcat() make it easy to process RSP strings, rsource() sources an RSP file as it was an R script, while rfile() compiles it (even online) into its final output format, e.g. rfile('report.tex.rsp') generates 'report.pdf' and rfile('report.md.rsp') generates 'report.html'.  RSP is ideal for self-contained scientific reports and R package vignettes.  It's easy to use - if you know how to write an R script, you'll be up and running within minutes.
Point and interval estimation of linear parameters with data
obtained from complex surveys (including stratified and clustered samples)
when randomization techniques are used. The randomized response technique
was developed to obtain estimates that are more valid when studying
sensitive topics. Estimators and variances for 14 randomized response
methods for qualitative variables and 7 randomized response methods for
quantitative variables are also implemented. In addition, some data sets
from surveys with these randomization methods are included in the package.
Advanced response surface analysis. The main function RSA computes
and compares several nested polynomial regression models (full polynomial,
shifted and rotated squared differences, rising ridge surfaces, basic
squared differences). The package provides plotting functions for 3d
wireframe surfaces, interactive 3d plots, and contour plots. Calculates
many surface parameters (a1 to a4, principal axes, stationary point,
eigenvalues) and provides standard, robust, or bootstrapped standard errors
and confidence intervals for them.
The package contains all the data sets related to the book
written by the maintainer of the package.
Robust Small Area Estimation. Robust Basic Unit- and Area-Level Models
Provides access to geocomputing and terrain analysis
functions of the geographical information system (GIS) 'SAGA' (System for
Automated Geoscientific Analyses) from within R by running the command
line version of SAGA. This package furthermore provides several R functions
for handling ASCII grids, including a flexible framework for applying local
functions (including predict methods of fitted models) and focal functions to
multiple grids. SAGA GIS is available under GPLv2 / LGPLv2 licence from
http://sourceforge.net/projects/saga-gis/.
Contains functions that are useful for pre- and post-processing the tree output from the MCMC-based C++ program sampletrees. The program sampletrees is used to sample gene genealogies conditional on either phased or unphased SNP genotype data. More information about sampletrees can be found at http://stat.sfu.ca/statgen/research/sampletrees.html . Pre-processing tasks involve creating the settings file, the haplotype estimation files (if input data is genotype), and error checking the settings. Post-processing involves reading in the MCMC output from a run of sampletrees and computing summary statistics.
SaTScan(TM) (http://www.satscan.org) is software for finding regions in
Time, Space, or Time-Space that have excess risk, based on scan statistics, and
uses Monte Carlo hypothesis testing to generate P-values for these regions.  The
rsatscan package provides functions for writing R data frames in
SaTScan-readable formats, for setting SaTScan parameters, for running SaTScan in
the OS, and for reading the files that SaTScan creates.
This package implements a statistical tool for modeling multivariate relationships using a stepwise cluster analysis (SCA) method.
The Scala interpreter is embedded in R and callbacks to R from the embedded interpreter are supported.  Conversely, the R interpreter is embedded in Scala.  Scala versions 2.10 and 2.11 are supported.
Client for Rserve, allowing to connect to Rserve instances and issue commands.
Uses Elsevier Scopus API to download information about authors and their citations.
rscproxy library provides an interface to R used by third
party applications, most notable, but not limited to,
statconnDCOM, ROOo and other systems.
Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for  a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.
Ray Shooting Depth functions are provided for bivariate analysis.
Set of classes and methods to read data and metadata documents
exchanged through the Statistical Data and Metadata Exchange (SDMX) framework,
currently focusing on the SDMX XML standard format (SDMX-ML).
an implementation of the analysis about seed components
from borenstein 2008
Estimate the percentage of seeds in a seedlot that contain stacks
of genetically modified traits.  Estimates are calculated using a
multinomial group testing model with maximum likelihood estimation of the
parameters.
Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms.  Includes general time-series tools, plotting, filtering, interactive display.
The RSelenium package provides a set of R bindings for the
Selenium 2.0 WebDriver using the JsonWireProtocol. Selenium automates web
browsers (commonly referred to as browsers). Using RSelenium you can
automate browsers locally or remotely.
A robust procedure is implemented to estimate means and covariance matrix of multiple variables with missing data using Huber weight and then to estimate a structural equation model.
Rserve acts as a socket server (TCP/IP or local sockets)
which allows binary requests to be sent to R. Every
connection has a separate workspace and working
directory. Client-side implementations are available
for popular languages such as C/C++ and Java, allowing
any application to use facilities of R without the need of
linking to R code. Rserve supports remote connection,
user authentication and file transfer. A simple R client
is included in this package as well.
Slow Feature Analysis in R, ported to R based on the
matlab versions SFA toolkit 1.0 by Pietro Berkes and SFA toolkit
2.8 by Wolfgang Konen for matlab.
This package provides functions for calculating
associations between two genes with five correlation
methods(e.g., the Gini correlation coefficient [GCC], the
Pearson's product moment correlation coefficient [PCC], the
Kendall tau rank correlation coefficient [KCC], the Spearman's
rank correlation coefficient [SCC] and the Tukey's biweight
correlation coefficient [BiWt], and three non-correlation
methods (e.g., mutual information [MI] and the maximal
information-based nonparametric exploration [MINE], and the
euclidean distance [ED]). It can also been implemented to
perform the correlation and clustering analysis of
transcriptomic data profiled by microarray and RNA-Seq
technologies. Additionally, this package can be further applied
to construct gene co-expression networks (GCNs).
Robust estimation of sparse inverse covariance matrix via the gamma-divergence.
Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html See Train's chapter on HB in Discrete Choice with Simulation here: http://elsa.berkeley.edu/books/choice2.html; and his paper on using HB with non-normal distributions here: http://eml.berkeley.edu//~train/trainsonnier.pdf.
Fits models to longitudinal network data
Robust and efficient feature selection algorithm to
identify important features for predicting survival risk.
The method is based on subsampling and averaging linear models
obtained from the (preconditioned) Lasso algorithm, with an extra
shrinking procedure to reduce the size of signatures. An
evaluation procedure using subsampling is also provided.
This package provides a way to transform an image into a mosaic composed from a set of smaller images (tiles). It also contains a simple function for creating the tiles from a folder of images directly through R, without the need of any external code. At this moment only the JPEG format is supported, either as input (image and tiles) or output (mosaic transformed image).
Functions for interacting with the Adobe Analytics API V1.4
(https://api.omniture.com/admin/1.4/rest/).
This package contains a function RSKC which runs the robust sparse K-means clustering algorithm.
Provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis.
Read and analyse Root System Markup Language (RSML) files, used to
store plant root system architecture data. More information can be found
here: http://rootsystemml.github.io/
The Stuttgart Neural Network Simulator (SNNS) is a library
containing many standard implementations of neural networks. This
package wraps the SNNS functionality to make it available from
within R. Using the RSNNS low-level interface, all of the
algorithmic functionality and flexibility of SNNS can be accessed.
Furthermore, the package contains a convenient high-level
interface, so that the most common neural network topologies and
learning algorithms integrate seamlessly into R.
A programmatic interface to various SNP datasets
on the web: openSNP, NBCI's dbSNP database, and Broad Institute SNP
Annotation and Proxy Search. Functions are included for searching for
SNPs for the Broad Institute and NCBI. For OpenSNP, functions are
included for getting SNPs, and data for genotypes, phenotypes,
annotations, and bulk downloads of data by user.
An implementation of the use of efficient score statistics
in genome-wide SNP set analysis with complex traits. Three standard score statistics
(Cox, binomial, and Gaussian) are provided, but the package is easily extensible to
include others. Code implementing the inferential procedure is primarily written in C++ and
utilizes parallelization of the analysis to reduce runtime. A supporting function offers
simple computation of observed, permutation, and FWER and FDR adjusted p-values.
Provides easier interaction with
Socrata open data portals http://dev.socrata.com.
Users can provide a 'Socrata' data set resource URL,
or a 'Socrata' Open Data API (SoDA) web query,
or a 'Socrata' "human-friendly" URL,
returns an R data frame.
Converts dates to 'POSIX' format.
Manages throttling by 'Socrata'.
sofia-ml is a suite of fast incremental algorithms for machine learning that can be used for training models for classification or ranking
General Non-linear Optimization Using Augmented Lagrange Multiplier Method
Somoclu is a massively parallel implementation of self-organizing maps.  It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal .
Based on (optionally sparse) quadratic optimization with the main algorithms implemented in C. Includes features for easy processing of many (smaller) records. The algorithm has been tested on fairly large optimization problems with up to a few million variables and several hundred thousand restrictions.
Conducts a spatially-explicit, simulation-based power analysis for detecting trends in population abundance through occupancy-based modeling. Applicable for evaluating monitoring designs in conservation and ecological settings.
Conversion between attitude representations: DCM, Euler angles, Quaternions, and Euler vectors.
Plus conversion between 2 Euler angle set types (xyx, yzy, zxz, xzx, yxy, zyz, xyz, yzx, zxy, xzy, yxz, zyx).
Fully vectorized code, with warnings/errors for Euler angles (singularity, out of range, invalid angle order),
DCM (orthogonality, not proper, exceeded tolerance to unity determinant) and Euler vectors(not unity).
Also quaternion and other useful functions.
Based on SpinCalc by John Fuller and SpinConv by Paolo de Leva.
Provides functions for estimating power or sample size for RNA-Seq studies. Empirical approach is used and the data is assumed to be count in nature. The underlying distribution of data is assumed to be Poisson or negative binomial. The package contains 6 function; 4 functions provide estimates of sample size or power for Poisson and Negative Binomial distribution; 2 functions provide plots of power for given sample size or sample size for given power.
This package embeds the SQLite database engine in R and
provides an interface compliant with the DBI package.
The source for the SQLite engine (version 3.8.6) is included.
RSQLServer wraps the jTDS Project's JDBC 3.0 SQL Server
driver and extends the RJDBC classes and DBI methods. It defines a
SQLServerDriver, SQLServerConnection & SQLServerRsult S4 classes as
extensions of the RJDBC equivalent classes. Most of the DBI methods will
simply be calls to methods defined by RJDBC classes. However, the dbConnect
and some of the dbGetInfo methods are specific to SQL Server. The jTDS
drivers do extend to Sybase SQL Server, but currently, only Microsoft SQL
Server is supported by this package.
Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series.
Provides fast, persistent (side-effect-free) stack, queue and
deque (double-ended-queue) data structures. While deques include a superset
of functionality provided by queues, in these implementations queues are
more efficient in some specialized situations. See the documentation for
rstack, rdeque, and rpqueue for details.
User-facing R functions are provided by this package to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo and (optionally penalized) maximum likelihood estimation via optimization. In both cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.
Access to the Digital Universe Data set API pulls data on
exoplants, stars, and galaxies.
R interface for china national data http://data.stats.gov.cn/, some convenient functions for accessing the national data are provided.
This package simulates random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate  Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data."
Toolbox for remote sensing image processing and analysis such as
calculating spectral indices, principal component transformation, unsupervised
and supervised classification or fractional cover analyses.
While streaming processing provides opportunities to deal with extremely large and ever growing data sets in (near) real time, the development of streaming algorithms for complex models is often cumbersome: the software packages that facilitate streaming processing in production environments do not provide statisticians with the simulation, estimation, and plotting tools they are used to. Developers of streaming algorithms would thus benefit from the flexibility of [R] to create, plot and compute data while developing streaming algorithms. Package RStorm implements a streaming architecture modeled on Storm for easy development and testing of streaming algorithms in [R]. RStorm is not intended as a production package, but rather a development tool for streaming algorithms.
R implementation of Stata's stpm2 function (flexible link-based survival models), with extensions to different smoothers and penalised models.
Unified object oriented interface for multiple independent streams of random numbers from different sources.
Access the RStudio API (if available) and provide informative error messages
when it's not.
A collection of efficient and effective tools and
algorithms for subgroup discovery and analytics. The package
integrates an R interface to the org.vikamine.kernel library
of the VIKAMINE system (http://www.vikamine.org) implementing
subgroup discovery, pattern mining and analytics in Java.
This package provides an interface for the package of
nonlinear differential algebraic equation solvers that comprise
SUNDIALS. ODEs are expressed as R functions or as compiled
code.
Interface to four 'Sunlight' Foundation 'APIs'
(http://sunlightfoundation.com/api/) for government data, including the
Congress 'API' 'v3', the Capitol Words 'API', the Influence Explorer 'API', and the
Open States 'API'. 'Sunlight' Foundation is a 'nonpartison' 'nonprofit' that
collects and provides data on government activities, and those that
influence government. Functions are provided to interact with each of the
four 'APIs'.
Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.
This package provides a range of functions for the design and
analysis of disease surveillance activities. These functions were
originally developed for animal health surveillance activities but can be
equally applied to aquatic animal, wildlife, plant and human health
surveillance activities. Utilities are included for sample size calculation
and analysis of representative surveys for disease freedom, risk-based
studies for disease freedom and for prevalence estimation.
A processing program for spatially distributed data.
It features graphing tools, query building, and polygon clipping.
A graphical user interface is provided.
Randomized singular value decomposition (rsvd) is a very fast probabilistic
algorithm to compute an approximated low-rank singular value decomposition of large
data sets with high accuracy. SVD plays a central role in data analysis and scientific computing.
SVD is also widely used for computing principal component analysis (PCA), a linear dimensionality reduction technique.
Randomized PCA (rpca) is using the approximated singular value decomposition to compute the most significant principal components. In addition several plot functions are provided.
A graphics device for R that uses the w3.org xml standard
for Scalable Vector Graphics.
A graphics device for R that uses the w3.org xml standard
for Scalable Vector Graphics.  This version supports
tooltips with 1 to 3 lines, hyperlinks, and line styles.
An R interface to the SYMPHONY solver for mixed-integer linear programs.
Access SymPy computer algebra system from R via Jython
Provides tabular reporting functionalities to work with 'ReporteRs'
package: 'as.FlexTable' methods are available for 'ftable' and 'xtable' objects,
function 'FlexPivot' is producing a pivot table and 'freqtable' a percentage table,
a 'knitr' print method and a 'shiny' render function are provided for 'FlexTable' objects.
Contains functions for random generation of R x C and 2 x 2 x K contingency tables. In addition to the generation of contingency tables over predetermined intraclass-correlated clusters, it is possible to generate contingency tables without intraclass correlations under product multinomial, multinomial, and Poisson sampling plans. It also consists of a function for generation of random data from a given discrete probability distribution function.
Storing huge data in RData format causes problems because
of the necessity to load the whole file to the memory in order
to access and manipulate objects inside such file; rtape is a
simple solution to this problem. The package contains several
wrappers of R built-in serialize/unserialize mechanism allowing
user to quickly append objects to a tape-like file and later
iterate over them requiring only one copy of each stored object
to reside in memory a time.
Tools for analyzing sales report files of iTunes Connect.
Robust tail dependence estimation for bivariate models. This package is based on two papers by the authors:'Robust and bias-corrected estimation of the coefficient of tail dependence' and 'Robust and bias-corrected estimation of probabilities of extreme failure sets'. This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).
Provides response time distributions (density/PDF, distribution
function/CDF, and random generation): (a) Ratcliff diffusion model based on
C code by Andreas and Jochen Voss and (b) linear ballistic accumulator (LBA)
with different distribution underlying the drift rate.
Exploit controlled vocabularies organized on tematres servers.
rTensor is a set of tools for creation, manipulation, and modeling
of tensors with arbitrary number of modes. A tensor in the context of data
analysis is a multidimensional array. rTensor does this by providing a S4
class 'Tensor' that wraps around the base 'array' class. rTensor also
provides common tensor operations as methods, including matrix unfolding,
summing/averaging across modes, calculating the Frobenius norm, and taking
the inner product between two tensors. Familiar array operations are
overloaded, such as index subsetting via '[' and element-wise operations.
rTensor also implements various tensor decomposition, including CP, GLRAM,
MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements
transpose, product, and SVD, as defined in Kilmer et al. (2013). Some
auxiliary functions include the Khatri-Rao product, Kronecker product, and
the Hamadard product for a list of matrices. Development of rTensor has
been generously supported by Cornell's Department of Statistical Science.
RTextTools is a machine learning package for automatic
text classification that makes it simple for novice users to
get started with machine learning, while allowing experienced
users to easily experiment with different settings and
algorithm combinations. The package includes nine algorithms
for ensemble classification (svm, slda, boosting, bagging,
random forests, glmnet, decision trees, neural networks,
maximum entropy), comprehensive analytics, and thorough
documentation.
This package contains several functions for calculation of texture metrics for Grey Level Co-occurrence matrices
A set of R functions to output Rich Text Format (RTF) files with high resolution tables and graphics that may be edited with a standard word processor such as Microsoft Word.
RTFBS identifies and scores possible Transcription Factor
Binding Sites and allows for FDR analysis and pruning.  It supports
splitting of sequences based on size or a specified GFF, grouping
by G+C content, and specification of Markov model order.  The heavy
lifting is done in C while all results are made available via R.
Reads and writes TIFF format images and
returns them as a pixmap object. Because the resulting object
can be very large for even modestly sized TIFF images, images
can be reduced as they are read for improved performance.  This
package is a wrapper around libtiff (www.libtiff.org), on which
it depends (i.e. the libtiff shared library must be on your
PATH for the binary to work, and tiffio.h must be on your
system to build the package from source). By using libtiff's
highlevel TIFFReadRGBAImage function, this package inherently
supports a wide range of image formats and compression schemes.
This package also provides an implementation of the Ridler
Autothresholding algorithm for easy generation of binary masks.
Interface to Congress, Campaign Finance, Article Search, and
Geographic 'APIs' from the New York Times. Documentation for New York
Times 'APIs' (http://developer.nytimes.com/docs/). This client covers
a subset of the New York Times 'APIs'.
STK++ (http://www.stkpp.org) is a collection of
C++ classes for statistics, clustering, linear algebra, arrays (with an
Eigen-like API), regression, dimension reduction, etc. The integration of
the library to R is using Rcpp.

The rtkore package includes the header files from the STK++ core library.
All files contain only templated classes or inlined functions.

STK++ is licensed under the GNU LGPL version 2 or later. rtkore
(the stkpp integration into R) is licensed under the
GNU GPL version 2 or later. See file LICENSE.note for details.
STK++ (http://www.stkpp.org) is a collection of
C++ classes for statistics, clustering, linear algebra, arrays (with an
Eigen-like API), regression, dimension reduction, etc. The integration of
the library to R is using Rcpp.

The rtkpp package includes the header files from the STK++
library (currently version 0.8.99). Thus users do not need to
install STK++ itself in order to use it.

STK++ is licensed under the GNU LGPL version 2 or later. rtkpp
(the stkpp integration into R) is licensed under the
GNU GPL version 2 or later.
Aimed at seismic tomography, the package
plots tomographic images, and allows one to interact and query
three-dimensional tomographic models.
Vertical cross-sectional cuts can be extracted by mouse click.
Geographic information can be added easily.
Geostatistical interpolation of data with irregular spatial support such as runoff related data or data from administrative units.
This is a port of Jonathan Shewchuk's Triangle library to
R. From his description: "Triangle generates exact Delaunay
triangulations, constrained Delaunay triangulations, conforming
Delaunay triangulations, Voronoi diagrams, and high-quality
triangular meshes. The latter can be generated with no small or
large angles, and are thus suitable for finite element analysis."
This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).
An R wrapper around the fast T-distributed Stochastic
Neighbor Embedding implementation by Van der Maaten.
TSON, short for Typed JSON, is a binary-encoded serialization of
JSON like document that support JavaScript typed data (https://github.com/amaurel/TSON).
This package contains the program ttf2pt1, for use with the
extrafont package. This product includes software developed by the TTF2PT1
Project and its contributors.
Convert text into speech (voice file in 'wav' format) with API offered by ITRI TTS (Text-To-Speech service, Industrial Technology Research Institute, Taiwan. http://tts.itri.org.tw/). One main function is given, tts_ITRI(). English and Chinese (both traditional and simplified) are supported, and user can specify the speaker accent, speed and volume. Using this package requires internet connection.
The 't-walk' is a general-purpose MCMC sampler for
arbitrary continuous distributions that requires no tuning.
A strong type system for R which supports
symbol declaration and assignment with type checking
and condition checking.
R port of 'universalchardet', that is the encoding detector
library of Mozilla.
Unobserved Components Models (introduced in Harvey, A. (1989),
Forecasting, structural time series models and the Kalman filter, Cambridge
New York: Cambridge University Press) decomposes a time series into
components such as trend, seasonal, cycle, and the regression effects due
to predictor series which captures the salient features of the series to
predict its behavior.
ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.
Contains data and visualization functions for USA unemployment
data. Data comes from the US Bureau of Labor Statistics (BLS). State data
is in ?df_state_unemployment and covers 2000-2013. County data is in
?df_county_unemployment and covers 1990-2013. Choropleth maps of the data
can be generated with ?state_unemployment_choropleth and
?county_unemployment_choropleth respectively. See the vignettes for
additional information.
R functions implementing a standard Unit Testing
framework, with additional code inspection and report
generation tools.
Automatically convert a file or package worth of 'RUnit' test
functions into 'testthat' tests.
This package contains some functions for converting R
objects to Java style variables and XML. Generated Java code is
interpretable by dynamic Java libraries such as Beanshell.
Calling R externally and handling the Java or XML output is an
other way to call R from other languages without native
interfaces. For a Java implementation of this approach visit
http://www.mhsatman.com/rcaller.php and
http://stdioe.blogspot.com/search/label/rcaller
User-friendly interface utilities for MCMC models via
Just Another Gibbs Sampler (JAGS), facilitating the use of parallel
(or distributed) processors for multiple chains, automated control
of convergence and sample length diagnostics, and evaluation of the
performance of a model using drop-k validation or against simulated
data. Template model specifications can be generated using a standard
lme4-style formula interface to assist users less familiar with the
BUGS syntax.  A JAGS extension module provides additional distributions
including the Pareto family of distributions, the DuMouchel prior and
the half-Cauchy prior.
Interface to the UNU.RAN library for Universal Non-Uniform RANdom variate generators.
Thus it allows to build non-uniform random number generators from quite arbitrary
distributions. In particular, it provides an algorithm for fast numerical inversion
for distribution with given density function.
In addition, the package contains densities, distribution functions and quantiles
from a couple of distributions.
This package provides a GUI (Graphical User Interface) for
the UNU.RAN random variate generators. Thus it allows to build
non-uniform random number generators interactively for quite
arbitrary distributions. In addition, R code for the required
calls from package Runuran can be displayed and stored for
later use. Some basic analysis like goodness-of-fit tests can
be performed.
An interface to the web service methods provided by the United States Department of Agriculture (USDA). The Agricultural Research Service (ARS) provides a large set of databases. The current version of the package holds interfaces to the Systematic Mycology and Microbiology Laboratory (SMML), which consists of four databases: Fungus-Host Distributions, Specimens, Literature and the Nomenclature database. It provides functions for querying these databases. The main function is \code{associations}, which allows searching for fungus-host combinations.
Utility functions useful when programming and developing R packages.
Implements the 'RUV' (Remove Unwanted Variation) algorithms.  These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data.  The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well.  These algorithms were proposed by Gagnon-Bartsch and Speed (2012), and by Gagnon-Bartsch, Jacob and Speed (2013).  The algorithms require the user to specify a set of negative control variables, as described in the references.  The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', and 'RUV-rinv', along with various supporting algorithms.
Contains diverse more or less complicated functions, written to simplify user's life: simplifications of existing functions, basic but not implemented tests, easy-to-use tools, bridges between functions of different packages... All functions are presented in the French book 'Aide-memoire de statistique appliquee a la biologie', written by the same author and available on CRAN.
A collection of functions for computing "r-values" from various kinds of user input such as MCMC output or a list of effect size estimates and associated standard errors. Given a large collection of measurement units, the r-value, r, of a particular unit is a reported percentile that may be interpreted as the smallest percentile at which the unit should be placed in the top r-fraction of units.
Operations on triangular meshes based on 'VCGLIB'. This package
integrates nicely with the R-package 'rgl' to render the meshes processed
by 'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is
an open source portable C++ templated library for manipulation, processing
and displaying with OpenGL of triangle and tetrahedral meshes. The library,
composed by more than 100k lines of code, is released under the GPL
license, and it is the base of most of the software tools of the Visual
Computing Lab of the Italian National Research Council Institute ISTI
(http://vcg.isti.cnr.it), like 'metro' and 'MeshLab'.
The 'VCGLIB' source is pulled from
trunk (svn://svn.code.sf.net/p/vcg/code/trunk/vcglib) and patched to work
with options determined by the configure script as well as
to work with the header files included by 'RcppEigen'.
Query the main 'R' 'SVN' repository to find the
versions 'r-release' and 'r-oldrel' refer to, and also all
previous 'R' versions and their release dates.
Retrieve, map and summarize data from the 'VertNet.org' archives.
Functions allow searching by many parameters, including 'taxonomic' names,
places, and dates. In addition, there is an interface for conducting spatially
delimited searches, and another for requesting large 'datasets' via email.
Wrappers around the 'xml2' and 'httr' packages to make it easy to
download, then manipulate, HTML and XML.
The RVFam package provides functions to perform single SNP association analyses and gene-based tests for continuous, binary and survival traits against sequencing data (e.g. exome chip) using family data.
Test suite for non-uniform pseudo-random number generators.
To detecting rare variants for binary traits using general pedigrees, the pedigree disequilibrium tests are proposed by collapsing rare haplotypes/variants with/without weights. To run the test, MERLIN is needed in Linux for haplotyping.
Play Video Poker with R, complete with a graphical user
interface. So far, only "Jacks or Better" is implemented.
ViennaCL is a free open-source linear algebra library
for computations on many-core architectures (GPUs, MIC) and
multi-core CPUs. The library is written in C++ and supports CUDA,
OpenCL, and OpenMP (including switches at runtime).
I have placed these libraries in this package as a more efficient
distribution system for CRAN. The idea is that you can write a package
that depends on the ViennaCL library and yet you do not need to
distribute a copy of this code with your package.
Variable metric nonlinear function minimization with bounds constraints.
R interface to Vowpal Wabbit fast out-of-core learning system
The Vowpal Wabbit (VW) project is a fast out-of-core learning system
sponsored by Yahoo! Research and written by John Langford along with a
number of contributors.

There are two ways to have a fast learning algorithm: (a) start with a slow
algorithm and speed it up, or (b) build an intrinsically fast learning
algorithm. This project is about approach (b), and it has reached a state
where it may be useful to others as a platform for research and
experimentation.

There are several optimization algorithms available with the baseline being
sparse gradient descent (GD) on a loss function (several are available). The
code should be easily usable. Its only external dependence is on the Boost
library, which is often installed by default.

This R package does not include the distributed computing implementation of
the cluster/ directory of the upstream sources.  Use of the software as a
network servie is also not directly supported as the aim is a simpler direct
call from R for validation and comparison.
Computes estimates of the probability of related individuals sharing a rare variant.
Used to compute population controls weighted rare variants transmission distortion test
Use multiple regression methods to test rare variants
association with disease traits.
A set of R functions which provide an
environment for the Time-Frequency analysis of 1-D signals (and
especially for the wavelet and Gabor transforms of noisy
signals). It was originally written for Splus by Rene Carmona,
Bruno Torresani, and Wen L. Hwang, first at the University of
California at Irvine and then at Princeton University.  Credit
should also be given to Andrea Wang whose functions on the
dyadic wavelet transform are included. Rwave is based on the
book: "Practical Time-Frequency Analysis: Gabor and Wavelet
Transforms with an Implementation in S", by Rene Carmona, Wen
L. Hwang and Bruno Torresani, Academic Press, 1998.
This package will download model predictions from 15 different global circulation models in 20 year intervals from the world bank.  Users can also access historical data, and create maps at 2 different spatial scales.
a Bipartite graph and is constructed based on the spatial and/or non-spatial attributes of the spatial objects in the dataset. Secondly, RW techniques are utilized on the graphs to compute the outlierness for each point (the differences between spatial objects and their spatial neighbours). The top k objects with higher outlierness are recognized as outliers.
This package provides programmatic access to Yahoo!
Weather and NOAA APIs
RWebLogo is a wrapper for the WebLogo python package
that allows generating of customised sequence logos. Sequence logos are
graphical representations of the sequence conservation of nucleotides (in a
strand of DNA/RNA) or amino acids (in protein sequences). Each logo
consists of stacks of symbols, one stack for each position in the sequence.
The overall height of the stack indicates the sequence conservation at that
position, while the height of symbols within the stack indicates the
relative frequency of each amino or nucleic acid at that position. In
general, a sequence logo provides a richer and more precise description of,
for example, a binding site, than would a consensus sequence.
An R interface to Weka (Version 3.7.12).
Weka is a collection of machine learning algorithms for data mining
tasks written in Java, containing tools for data pre-processing,
classification, regression, clustering, association rules, and
visualization.  Package RWeka contains the interface code, the Weka
jar is in a separate package RWekajars.  For more information on
Weka see <http://www.cs.waikato.ac.nz/ml/weka/>.
External jars required for package RWeka.
This package provides Wiener process distribution functions,
namely the Wiener first passage time density, CDF, quantile and random
functions.
A plug in for using WinEdt as an editor for R.
The Rwinsteps package facilitates communication between R
and the Rasch modeling software Winsteps. The package currently
includes functions for reading and writing command files,
sending them to Winsteps, reading and writing data according to
command file specifications, reading output into R, and
plotting various results.
A communications simulation package supporting common wireless modulation
formats including BPSK, QPSK, 8-PSK, 16-PSK, 16-QAM and 64-QAM. The package includes AWGN
noise generation, and raised cosine and square root raised cosine pulse shaping. It also
includes convenience functions for plotting constellation diagrams, density plots, stem
plots and eye diagrams. Together these functions enable the evaluation of bit error and
symbol error rates, evaluation of pulse shaping and inter-symbol interference and support
visualization of the respective signals and noise in a variety of formats.
Enables mapping of country level and gridded user datasets.
High resolution vector country boundaries derived from
Natural Earth data, can be plotted in rworldmap.
Provides a set of functions for performing digital signal
processing.
Tools for getting historical weather information and forecasts from wunderground.com.  Historical weather and forecast data includes, but is not limited to, temperature, humidity, windchill, wind speed, dew point, heat index.  Additionally, the weather underground weather API also includes information on sunrise/sunset, tidal conditions, satellite/webcam imagery, weather alerts, hurricane alerts and historical record high/low temperatures.
Fits the R x C inference model described in Greiner and
Quinn (2009).  Allows incorporation of survey results.
This package allows the visualization of your favorite
XKCD comic strip directly from R. XKCD web comic content is
provided under the Creative Commons Attribution-NonCommercial
2.5 License.
The RXMCDA package for the R statistical software allows you to read many XMCDA tags and transform them into R variables which are then usable in your MCDA algorithms written in R. The library also allows to write certain R variables into XML files according to the XMCDA standard.
Methods to compute chemical similarity between two or more reactions and molecules. Allows masking of chemical substructures for weighted similarity computations. Uses packages 'rCDK' and 'fingerprint' for cheminformatics functionality.
Facilities for running simulations from ordinary
differential equation (ODE) models, such as pharmacometrics and other
compartmental models.  A compilation manager translates the ODE model
into C, compiles it, and dynamically loads the object code into R for
improved computational efficiency.  An event table object facilitates
the specification of complex dosing regimens (optional) and sampling
schedules.  NB: The use of this package requires both C and
Fortran compilers, for details on their use with R please see
Section 6.3, Appendix A, and Appendix D in the "R Administration and
Installation" manual.
Identify and display TRACEs for a specified shrinkage path and determine
the extent of shrinkage most likely, under normal distribution theory, to produce an
optimal reduction in MSE Risk in estimates of regression (beta) coefficients.
An interface to the yacas computer algebra system.
You can use this package to translate thousands of words. The Youdao translation open API is applied in this package. But, it just translates less than 1000 English words into Chinese.
Package contains some data and functions that
are used in my forthcoming "R you ready?" book.
A programmatic interface to web-services of Youtheria. Youtheria is an online database of mammalian trait data.
This package is a port to R of the SysGran program, written in Delphi by Camargo (2006). It contains functions for the analysis of grain size samples (in logarithmic (phi) and geometric (micrometers) scale) based on various methods, like Folk & Ward (1957) and Methods of Moments (Tanner, 1995), among others; textural classifications and distribution of unconsolidated sediments are shown in histograms, bivariated plots and ternary diagrams of Shepard (1954) and Pejrup (1988). English and Portuguese languages are supported in outputs
R is very powerful but it lacks some of the functionalities found in
Stata or SPSS to manage survey data. The 'memisc' package provides these
(variable labels, value labels, definable missing values and so on), but to
efficiently work these functions need a graphical interface to allow the user
to get an overview of the data. This package provides such a graphical interface,
similar in fashion to SPSS's Variable View and data managing system. It uses the
'memisc' package as its backend. Additionally, 'Rz' has a powerful plot assistant
interface based on 'ggplot2'.
Interface to the ZeroMQ lightweight messaging kernel (see <http://www.zeromq.org/> for more information).
A set of functions used in teaching STATS 201/208 Data Analysis at the University of Auckland. The functions are designed to make parts of R more accessible to a large undergraduate population who are mostly not statistics majors.
Set of tools to assess the performance of a model through the computation of typical prediction scores against one or more observational datasets or reanalyses (a reanalysis being a physical extrapolation of observations that relies on the equations from a model, not a pure observational dataset).
The main function s4vd() performs a biclustering via sparse
singular value decomposition with a nested stability selection.
The results is an biclust object and thus all methods of the
biclust package can be applied.
A collection of baseball analytics functions for sabermetrics purposes. Among these functions include popular metrics such as OBP, wOBA, runs created functions as well as field-independent pitching metrics.
Semiparametric empirical likelihood ratio
based test of changepoint with one-change or epidemic alternatives
with data-based model diagnostic
Functions for detecting eye fixations in raw eye-tracking
data.  The detection is done using a velocity-based algorithm for
saccade detection proposed by Ralf Engbert and Reinhold Kliegl in
2003.  The algorithm labels segments as saccades when the velocity of
the eye movement exceeds a certain threshold.  Anything between two
saccades is considered a fixation.  Thus the algorithm is not
appropriate for data containing episodes of smooth pursuit eye
movements.
Computes the Exposure-At-Default based on standardized approach of
the Basel III Regulatory framework (SA-CCR). For the generation of the trades an
object-oriented solution has been created.
Performs constrained optimization for expensive black-box problems.
Provides the density, distribution, quantile and generation functions of some obscure probability
distributions, including the doubly non-central t, F, Beta, and Eta distributions;
Lecoutre's lambda-prime and K-prime; the upsilon distribution; the (weighted) sum of
non-central chi-squares to a power; the (weighted) sum of log non-central chi-squares;
the product of non-central chi-squares to powers; the product of doubly non-central
F variates.
Maximum likelihood tools to fit and compare models of species
abundance distributions and of species rank-abundance distributions.
Time series models for small area estimation based on area-level models.
The SAEMIX package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm: - computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, - provides standard errors for the maximum likelihood estimator - estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm. Several applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group (http://group.monolix.org/).
An implementation of a stacked sparse autoencoder for dimension reduction of features and pre-training of feed-forward neural networks
with the 'neuralnet' package is contained within this package. The package also includes a predict function for the stacked autoencoder object to generate the compressed
representation of new data if required. For the purposes of this package, 'stacked' is defined in line with http://ufldl.stanford.edu/wiki/index.php/Stacked_Autoencoders .
The underlying sparse autoencoder is defined in the documentation of 'autoencoder'.
A complete set of functions to calculate several EBLUP (Empirical Best Linear Unbiased Predictor) estimators and their mean squared errors. All estimators are based on an area-level linear mixed model introduced by Rao and Yu in 1994 (see documentation). The REML method is used for fitting this model.
Tools for the simulation of data in the context of small area
estimation. Combine all steps of your simulation - from data generation
over drawing samples to model fitting - in one object. This enables easy
modification and combination of different scenarios. You can store your
results in a folder or start the simulation in parallel.
The aim of the package is to provide some basic functions
for doing statistics with one dimensional Fuzzy Data (in the
form of polygonal fuzzy numbers). In particular, the package
contains functions for the basic operations on the class of
fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference)
as well as for calculating (Bertoluzza) distance,
sample variance, sample covariance, and the
Dempster-Shafer (levelwise) histogram. Moreover a function to
simulate fuzzy random variables, bootstrap tests for the
equality of means, and a function to do linear regression given
trapezoidal fuzzy data is included. Version 1.0 fixes some bugs
of previous versions.
Overloads the glm function in the stats package so that
a test for the existence of the maximum likelihood estimate is included
in the fitting procedure for binary regression models.
Design and sensitivity analysis for computer experiments with scalar-valued output and functional input, e.g. over time or space. The aim is to explore the behavior of the sensitivity over the functional domain.
Implements an information theory approach to the analysis of line
cross data providing model averaged results of parameter estimates and
unconditional standard errors.  Also includes functions to provide a
visualization of models space, custom plots of multi-model inference results, and traditional
line cross analysis plots.
The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC)
sampling of random variables on a simplex. A Self-Adjusting Logit Transform
(SALT) proposal is used so that sampling is still efficient even in difficult
cases, such as those in high dimensions or with parameters that differ by orders
of magnitude. Special care is also taken to maintain accuracy even when some
coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are
included in the package, enabling easy assessment of the convergence and mixing
of the chain within the constrained space.
The package SAM targets at high dimensional predictive
modeling (regression and classification) for complex data
analysis. SAM is short for sparse additive modeling, and adopts
the computationally efficient basis spline technique. We solve
the optimization problems by various computational algorithms
including the block coordinate descent algorithm, fast
iterative soft-thresholding algorithm, and newton method. The
computation is further accelerated by warm-start and active-set
tricks.
A framework for running sets of MCMC samplers on sets of
distributions with a variety of tuning parameters, along with plotting
functions to visualize the results of those simulations.  See sc-intro.pdf
for an introduction.
Two-step estimation
and maximum likelihood estimation
of Heckman-type sample selection models:
standard sample selection models (Tobit-2)
and endogenous switching regression models (Tobit-5).
Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test for categorical data. The t-test function allows paired and unpaired (balanced / unbalanced) designs as well as homogeneous and heterogeneous variances. The Wilcoxon function allows for ties.
Computes the required sample size using the optimal designs with multiple constraints proposed in Mayo et al.(2010). This optimal method is designed for two-arm, randomized phase II clinical trials, and the required sample size can be optimized either using fixed or flexible randomization allocation ratios.
Computes the required sample size for estimation of totals, means
and proportions under complex sampling designs.
A set of R functions for calculating sample size
requirements using three different Bayesian criteria in the
context of designing an experiment to estimate a normal mean or
the difference between two normal means.  Functions for
calculation of required sample sizes for the Average Length
Criterion, the Average Coverage Criterion and the Worst Outcome
Criterion in the context of normal means are provided.
Functions for both the fully Bayesian and the mixed
Bayesian/likelihood approaches are provided.
A set of R functions for calculating sample size
requirements using three different Bayesian criteria in the
context of designing an experiment to estimate the difference
between two binomial proportions. Functions for calculation of
required sample sizes for the Average Length Criterion, the
Average Coverage Criterion and the Worst Outcome Criterion in
the context of binomial observations are provided. In all
cases, estimation of the difference between two binomial
proportions is considered. Functions for both the fully
Bayesian and the mixed Bayesian/likelihood approaches are
provided.
Functions for drawing and calibrating samples.
Sampling procedures from the book 'Stichproben. Methoden
und praktische Umsetzung mit R' by Goeran Kauermann and Helmut
Kuechenhoff (2010)
Functions to estimate from survey data. This package is a user-friendly wrapper of the samplingVarEst package. It considers that the user is more familiar with practical survey data rather than with research on survey sampling (variance estimation). More functionalities are on the way.
In the field of stratified sampling design, this package
offers an approach for the determination of the best
stratification of a sampling frame, the one that ensures the
minimum sample cost under the condition to satisfy precision
constraints in a multivariate and multidomain case. This
approach is based on the use of the genetic algorithm: each
solution (i.e. a particular partition in strata of the sampling
frame) is considered as an individual in a population; the
fitness of all individuals is evaluated applying the
Bethel-Chromy algorithm to calculate the sampling size
satisfying precision constraints on the target estimates.
Functions in the package allows to: (a) analyse the obtained
results of the optimisation step; (b) assign the new strata
labels to the sampling frame; (c) select a sample from the new
frame accordingly to the best allocation.
Functions for the execution of the genetic algorithm are a modified
version of the functions in the 'genalg' package.
Functions to calculate some point estimators and estimating their variance under unequal probability sampling. Single and two stage sampling designs are considered. Some approximations for the second order inclusion probabilities are also available (sample and population based).
Sampling surface simulation is useful in the comparison of different areal sampling methods
in forestry, ecology and natural resources. The sampSurf package allows the simulation
of numerous sampling methods for standing trees and downed woody debris in a spatial context.
It also provides an S4 class and method structure that facilitates the addition of new sampling
methods.
Augmenting a matched data set by generating multiple stochastic, matched samples from the data using a
multi-dimensional histogram constructed from dropping the input matched data into a multi-dimensional grid built on
the full data set. The resulting stochastic, matched sets will likely provide a collectively higher coverage of the full
data set compared to the single matched set. Each stochastic match is without duplication, thus allowing downstream
validation techniques such as cross-validation to be applied to each set without concern for overfitting.
This package contains R functions to gauge the impact of unpublished studies upon the meta-analytic summary effect of a set of published studies. (Credits: The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 282574.)
Data sets for the book 'Statistical Analysis of
Network Data with R'.
Model-robust standard error estimators for cross-sectional, time series, and longitudinal data.
Recent gcc and clang compiler versions provide functionality to
memory violations and other undefined behaviour; this is often referred to as
"Address Sanitizer" (or SAN) and "Undefined Behaviour Sanitizer" (UBSAN).
The Writing R Extension manual describes this in some detail in Section 4.9.

This feature has to be enabled in the corresponding binary, eg in R, which
is somewhat involved as it also required a current compiler toolchain which
is not yet widely available, or in the case of Windows, not available at all
(via the common Rtools mechanism).

As an alternative, the pre-built Docker containers available via the Docker Hub
at https://registry.hub.docker.com/u/eddelbuettel/docker-debian-r/ can be used
on Linux, and via boot2docker on Windows and OS X.

This package then provides a means of testing the compiler setup as the
known code failures provides in the sample code here should be detected
correctly, whereas a default build of R will let the package pass.

The code samples are based on the examples from the Address Sanitizer
Wiki at https://code.google.com/p/address-sanitizer/wiki/AddressSanitizer.
There are several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.
Software for the book Spectral Analysis for Physical
Applications, Donald B. Percival and Andrew T. Walden,
Cambridge University Press, 1993.
Functions for statistical analysis of point processes.
Read SAS files in the sas7bdat data format.
Using any importation code designed for SAS users to read
ASCII files into sas7bdat files, the SAScii package parses
through the INPUT block of a (.sas) syntax file to design the
parameters needed for a read.fwf function call.  This allows
the user to specify the location of the ASCII (often a .dat)
file and the location of the .sas syntax file, and then load
the data frame directly into R in just one step.
Data sets and sample lmer analyses corresponding
to the examples in Littell, Milliken, Stroup and Wolfinger
(1996), "SAS System for Mixed Models", SAS Institute.
A statistical method for significant analysis of
comparative proteomics based on LC-MS/MS Experiments
This smorgasbord provides a variety of functions which are useful
for handling, manipulating and visualizing remote sensing data.
Bayesian inference of graphical model structures using spanning trees.
Implements Bayesian statistical methodology for the
analysis of complex computer models.
It allows for the emulation, calibration, and validation of computer models,
following methodology described in Bayarri et al 2007, Technometrics.
The purpose of this package is to be able to save and load only
the needed variables/columns of a dataframe in special binary files (tar
archives) - which seems to be a lot faster method than loading the whole
binary object (RData files) via load() function, or than loading columns
from SQLite/MySQL databases via SQL commands (see vignettes). Performance
gain on SSD drives is a lot more sensible compared to basic load()
function. The performance improvement gained by loading only the chosen
variables in binary format can be useful in some special cases (e.g. where
merging data tables is not an option and very different datasets are needed
for reporting), but be sure if using this package that you really need
this, as non-standard file formats are used!
Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.
Smooth Backfitting for additive models using
Nadaraya-Watson estimator
This package estimates parameters of a Gaussian copula,
treating the univariate marginal distributions as nuisance
parameters as described in Hoff(2007). It also provides a
semiparametric imputation procedure for missing multivariate
data.
sbioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction
networks with spatial effects.
Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions are defined.
For deterministic solutions, sbioPN creates the associated system of differential equations "on the fly", and
solves it with a Runge Kutta Dormand Prince 45 explicit algorithm.
For stochastic solutions, sbioPN offers two variants of Gillespie algorithm, or SSA.
For hybrid deterministic/stochastic,
it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions.
sbioPN algorithms are developed in C to achieve adequate performance.
An ADMM implementation of SDP-1, a semidefinite programming relaxation of the maximum likelihood estimator for fitting a block model. SDP-1 has a tendency to produce equal-sized blocks and is ideal for producing a form of network histogram approximating a nonparametric graphon model. Alternatively, it can be used for community detection. (This is experimental code, proceed with caution.)
The package uses fitting axes-aligned rectangles to a time series in order to find structural breaks. The algorithm enclose the time series in a number of axes-aligned rectangles and tries to minimize their area and number. As these are conflicting aims, the user has to specify a parameter alpha in [0.0,1.0]. Values close to 0 result in more breakpoints, values close to 1 in fewer. The left edges of the rectangles are the breakpoints. The package supplies two  methods, computeBreakPoints(series,alpha) which returns the indices of the break points and computeRectangles(series,alpha) which returns the rectangles. The algorithm is randomised; it uses a genetic algorithm. Therefore, the break point sequence found can be different in different executions of the method on the same data, especially when used on  longer series of some thousand observations. The algorithm uses a range-tree as background data structure which makes i very fast and suited to analyse series with millions of observations. A detailed description can be found in Paul Fischer, Astrid Hilbert, Fast detection  of structural breaks, Proceedings of Compstat 2014.
Simple Component Analysis (SCA) often provides much more
interpretable components than Principal Components (PCA) while still
representing much of the variability in the data.
Calculates graph theoretic scagnostics. Scagnostics
describe various measures of interest for pairs of variables,
based on their appearance on a scatterplot.  They are useful
tool for discovering interesting or unusual scatterplots from a
scatterplot matrix, without having to look at every individual
plot.
Provides the Scale class and corresponding functions, in order to facilitate data input for scale construction. Reverse items and alternative orders of administration are dealt with by the program. Computes reliability statistics, confirmatory, single factor loadings. It suggests item deletions and produces basic text output in English, for incorporation in reports. Returns list objects of all relevant functions from other packages (see Depends).
Graphical scales map data to aesthetics, and provide
methods for automatically determining breaks and labels
for axes and legends.
Algorithms for fitting scaled sparse linear regression and estimating precision matrices
Routines for generalized additive modelling under shape
constraints on the component functions of the linear predictor.
Models can contain multiple shape constrained (univariate
and/or bivariate) and unconstrained terms. The routines of gam()
in package 'mgcv' are used for setting up the model matrix,
printing and plotting the results.  Penalized likelihood
maximization based on Newton-Raphson method is used to fit a
model with multiple smoothing parameter selection by GCV or
UBRE/AIC.
Import, plot, and diagnose results from statistical
catch-at-age models, used in fisheries stock assessment.
This package computes the maximum likelihood estimator of the generalised additive and index regression with shape constraints. Each additive component function is assumed to obey one of the nine possible shape restrictions: linear, increasing, decreasing, convex, convex increasing, convex decreasing, concave, concave increasing, or concave decreasing.
scaRabee is a port of the Scarabee toolkit originally
written as a Matlab-based application. It provides a framework
for simulation and optimization of pharmacokinetic-pharmacodynamic
models at the individual and population level. It is built on top of the
neldermead package, which provides the direct search algorithm proposed
by Nelder and Mead for model optimization.
Creates 'D3' 'JavaScript' scatterplots from 'R' with interactive
features : panning, zooming, tooltips, etc.
Plots a three dimensional (3D) point cloud.
This package implements statistical methods for estimating and inferring the mean of functional data. The methods are based on simultaneous confidence bands and local polynomial fitting. They feature: bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions.
SCEPtER pipeline for estimating the stellar age, mass, and radius
given observational
effective temperature, [Fe/H], and astroseismic
parameters. The results are obtained adopting a maximum likelihood
technique over a grid of pre-computed stellar models.
SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models.
The Fisher Scoring Algorithm is extended so as to combine Partial Least Squares
regression with Generalized Linear Model estimation in the multivariate context.
Provides schema-on-read tools including a single function call (e.g., schemaOnRead("filename")) that reads text ('TXT'), comma separated value ('CSV'), raster image ('BMP', 'PNG', 'GIF', 'TIFF', and 'JPG'), R data ('RDS'), HDF5, NetCDF, spreadsheet ('XLS', 'XLSX', 'ODS', and 'DIF'), Weka Attribute-Relation File Format ('ARFF'), Epi Info ('REC'), SPSS ('SAV'), Systat ('SYS'), and Stata ('DTA') files. It also recursively reads folders (e.g., schemaOnRead("folder")), returning a nested list of the contained elements.
Provides functions to extract citation data from Google
Scholar.  Convenience functions are also provided for comparing
multiple scholars and predicting future h-index values.
This package contains functions and datasets for math
taught in school. A main focus is set to prime-calculation
Offers a helping hand to psychologists and other behavioral scientists who routinely deal with experimental data from factorial experiments. It includes several functions to format output from other R functions according to the style guidelines of the APA (American Psychological Association). This formatted output can be copied directly into manuscripts to facilitate data reporting. These features are backed up by a toolkit of several small helper functions, e.g., offering out-of-the-box outlier removal. The package lends its name to Georg "Schorsch" Schuessler, ingenious technician at the Department of Psychology III, University of Wuerzburg.
This is a shape preserving spline which is guaranteed
to be monotonic and concave or convex if the data is monotonic
and concave or convex. It does not use any optimisation and is
therefore quick and smoothly converges to a fixed point in
economic dynamics problems including value function iteration.
It also automatically gives the first two derivatives of the
spline and options for determining behaviour when evaluated
outside the interpolation domain.
This package provides detailed functionality for working with the Schwartz 1997 two-factor commodity model. Essentially, it contains pricing formulas for futures and European options and the standard d/p/q/r functions for the distribution of the state variables and futures prices. In addition, a parameter estimation procedure is contained together with many utilities as filtering and plotting functionality. This package is accompanied by futures data of ten commodities.
Functions for generating Standardized Climate Indices (SCI).
SCI is a transformation of (smoothed) climate (or environmental)
time series that  removes seasonality and forces the data to
take values of the standard normal distribution. SCI was
originally developed for precipitation. In this case it is
known as the Standardized Precipitation Index (SPI).
An R interface to the SciDB array database (http://scidb.org).
Provides miscellaneous functions for carrying out political behaviour analysis, including measures of political fragmentation, seats allocation, and graphical demonstrations.
Sparse Column-wise Inverse Operator for estimating the inverse covariance matrix.  Note that this is a preliminary version accompanying the arXiv paper (arXiv:1203.3896) in 2012.   This version contains only the minimal set of functions for estimation and cross validation.
A collection of functions that creates graphs with error
bars for data collected from one-way or higher factorial
designs.
Functions to install SciViews additions to R, and more (various) tools
An R package for single-case meta-analysis.
Given a matrix with results of different algorithms for different problems, the package uses statistical tests and corrections to assess the differences between algorithms.
Provides routines for scoring behavioral questionnaires. Includes scoring procedures for the 'International Physical Activity Questionnaire (IPAQ)' <http://www.ipaq.ki.se>. Compares physical functional performance to the age- and gender-specific normal ranges.
Estimate GGUM Person Parameters Using Pre-Calibrated Item Parameters and Binary or Graded Disagree-Agree Responses
A set of tools to quickly score models commonly used to data
analysis and data science using uncommon scoring metrics. For example,
you might want to use a weighted absolute percent error instead of
a root mean square deviation to score that regression model.
This package contains the functions necessary to run the SCORER 2.0 algorithm. SCORER 2.0 can be used to differentiate between parallel dimeric and trimeric coiled-coil sequence, which are the two most more frequent coiled-coil structures observed naturally. As such, SCORER 2.0 is particularly useful for researchers looking to characterize novel coiled-coil sequences. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences. Also included in this package are functions that allows the user to retrain the SCORER 2.0 algorithm using user-defined training data.
Evaluating probabilistic forecasts via proper scoring rules.  scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families.  Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score.  For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.
Division of an ANOVA experiment treatment means into
homogeneous distinct groups using the clustering method of
Scott & Knott
Implements the Scout method for regression, described in "Covariance-regularized regression and classification for high-dimensional problems", by Witten and Tibshirani (2008),  Journal of the Royal Statistical Society, Series B 71(3): 615-636.
The package implements different inventory models, the
bullwhip effect and other supply chain performance variables.
Given a word will produce that word's scrabble score. Unlike many naive implementations this package takes into consideration the distribution of letter in scrabble. So a word like 'zzz' will be scored '10' rather than '30'.
Tools for Scraping Data from Web-Based Documents
Routines for a collection of screen-and-clean type
variable selection procedures, including UPS and GS.
Tools for the analysis of high-dimensional data developed/implemented
at the group "Statistical Complexity Reduction In Molecular Epidemiology" (SCRIME).
Main focus is on SNP data. But most of the functions can also be applied to other
types of categorical data.
Support for using .Rt (transcript) tests
in the tests directory of a package.  Provides more
convenience and features than the standard .R/.Rout.save
tests.  Tests can be run under R CMD check and also
interactively.  Provides source.pkg() for quickly loading
code, DLLs, and data from a package for use in an
edit/compile/test development cycle.
A coalescent simulator that allows the rapid simulation of
biological sequences under neutral models of evolution. Different to
other coalescent based simulations, it has an optional approximation
parameter that allows for high accuracy while maintaining a linear run time
cost for long sequences. It is optimized for simulating massive data sets as
produced by Next-Generation Sequencing technologies for up to several thousand
sequences.
A package for designing single-case phase, alternation and multiple-baseline experiments, as well as for conducting randomization tests on data gathered by means of such designs.
scrypt is an R package for working with scrypt. Scrypt is a
password-based key derivation function created by Colin Percival. The
algorithm was specifically designed to make it costly to perform
large-scale custom hardware attacks by requiring large amounts of memory.
Code for describing and manipulating scuba diving profiles
(depth-time curves) and decompression models,
for calculating the predictions of decompression models,
for calculating maximum no-decompression time and decompression tables,
and for performing mixed gas calculations.
A package to make graphical representations of single case data and to transform graphical displays back to raw data. The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend.
Provides an efficient framework for
high-dimensional linear and diagonal discriminant analysis with
variable selection.  The classifier is trained using James-Stein-type
shrinkage estimators and predictor variables are ranked using
correlation-adjusted t-scores (CAT scores).  Variable selection error
is controlled using false non-discovery rates or higher criticism.
Functions and Datasets from Lohr, S. (1999), Sampling:
Design and Analysis, Duxbury.
Data from statistical agencies and other institutions are mostly
confidential. This package can be used for the generation of anonymized
(micro)data, i.e. for the creation of public- and scientific-use files.
In addition, various risk estimation methods are included.
Note that the package 'sdcMicroGUI' includes a graphical user interface for various methods
in this package.
A point and click graphical user interface based on top of the
'sdcMicro' package to create anonymized data set. The graphical user
interface provides full reproducibility of any result via the script menu
in the GUI.
Methods for statistical disclosure control in
tabular data such as primary and secondary cell suppression are covered in
this package.
Classes and methods to calculate and evaluate target matrices for
statistical disclosure control.
Allows for  computing (and by default plotting) different types of serial dependence diagrams.
Compares the evolution of an original network X to an augmented network Y by counting the number of Shortcuts, Detours, Dead Ends (SDDE), equal paths and disconnected nodes.
The semidiscrete decomposition (SDD) approximates a matrix
as a weighted sum of outer products formed by vectors with
entries constrained to be in the set {-1, 0, 1}.
Companion package to the book Simulation and Inference for
Stochastic Differential Equations With R Examples, ISBN
978-0-387-75838-1, Springer, NY.
Performs two tests to evaluate if the
experiments are associated and returns a list of interesting
features common to all the experiments.
This packages provides a set of tools for post processing the
outcomes of species distribution modeling exercises. It includes novel
methods for comparing models and tracking changes in distributions through
time. It further includes methods for visualizing outcomes, selecting
thresholds, calculating measures of accuracy and landscape fragmentation
statistics, etc.. This package was made possible in part by financial
support from the Australian Research Council & ARC Research Network for
Earth System Science.
This package includes several methods to create virtual species
distribution map. Those maps can be used for species distribution modelling
(SDM) study. SDM use environmental data for sites of occurrence of a
species to predict all the sites where the environmental conditions are
suitable for the species to persist, and may be expected to occur.
Fitting discrete Bayesian networks using marginal mixture distributions
Utility functions for scale-dependent and alternative hyperpriors.
The main purpose of this package is to provide saddlepoint
approximations to some measures of risk, based on the compound Poisson risk
process that is perturbed by a Brownian motion.  Various approximation
methods for the probability of ruin are also included.  Furthermore, exact
values of both the risk measures as well as the probability of ruin are
available if the individual claims follow a hypo-exponential distribution
(i. e., if it can be represented as a sum of independent exponentially
distributed random variables with different rate parameters).
Implementation of some algorithms for the data mining task called "subgroup discovery" without package dependencies. It also provide a Shiny App for make the analysis easier.
The algorithms works with data sets provided in KEEL format.
If you want more information about this format, please refer to < http://www.keel.es > .
Implements algorithms to help with scenario discovery - currently only modified version of the the Patient Rule Induction Method.
Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties.
Calculates parameters of the seawater carbonate system and assists the design of ocean acidification perturbation experiments.
Standard error adjusted adaptive lasso (SEA-lasso) is a version of the adaptive lasso, which incorporates OLS standard error to the L1 penalty weight. This method is intended for variable selection under linear regression settings (n > p). This new weight assignment strategy is especially useful when the collinearity of the design matrix is a concern.
Provides functionality for searching / subsetting and slicing named
objects using 'stringr/i'-style modifiers by case (in)sensitivity,
regular expressions or fixed expressions; searches uses the standard '['
operator and allows specification of default search behavior to either the
search target (named object) and/or the search pattern.
Provides an interface with the Google Search Console API v3,
formally called Google Webmaster Tools.
This package provides an implementation of the QuadTree
data structure. It uses this to implement fast k-Nearest
Neighbor and Rectangular range lookups in 2 dimenions. The
primary target is high performance interactive graphics.
Capable of deriving seasonal statistics, such as "normals", and
analysis of seasonal data, such as departures. This package also has
graphics capabilities for representing seasonal data, including boxplots for
seasonal parameters, and bars for summed normals. There are many specific
functions related to climatology, including precipitation normals,
temperature normals, cumulative precipitation departures and precipitation
interarrivals. However, this package is designed to represent any
time-varying parameter with a discernible seasonal signal, such as found
in hydrology and ecology.
This package conducts Score Equity Assessment (SEA; Dorans, 2004) by calculating and plotting multiple SEA indices as introduced by a variety of authors and summarized by Huggins and Penfield (2012).
Routines for the seasonal analysis of health data,
including regression models, time-stratified case-crossover,
plotting functions and residual checks. Thanks to Yuming Guo
for checking the case-crossover code.
Easy-to-use interface to X-13-ARIMA-SEATS, the
seasonal adjustment software by the US Census Bureau. It offers full
access to almost all options and outputs of X-13, including X-11 and SEATS,
automatic ARIMA model search, outlier detection and support for user
defined holiday variables, such as Chinese New Year or Indian Diwali. It
also includes a Shiny-based graphical user interface. Seasonal does not
contain the binaries of X-13, which must be installed separately. See the
README file or the vignette for installation notes.
A model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables
Displays state-event charts, for graphical presentation of longitudinal data.
SECP package provides functionality for estimating
parameters of site clusters on 2D & 3D square lattice with
various lattice sizes, relative fractions of accessible sites
(occupation probability), iso- & anisotropy, von Neumann &
Moore (1,d)-neighborhoods
Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
Tools are provided for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package 'secr'.
Tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package 'secr'
Provides a wrapper to access data from the SeeClickFix
web API for R. SeeClickFix is a central platform employed by many cities
that allows citizens to request their city's services. This package
creates several functions to work with all the built-in calls to the
SeeClickFix API. Allows users to download service request data from
numerous locations in easy-to-use dataframe format manipulable in
standard R functions.
Suite of functions for the simulation, visualisation and analysis of bacterial evolution within- and between-host.
Supports the text book "Data Analysis and Statistics for
Geography, Environmental Science, and Engineering"
Supports Simulation of Ecological and Environmental Models
Creates SEER (Surveillance, Epidemiology and End Results) and A-bomb data binaries
from ASCII sources and provides tools for estimating SEER second cancer risks.
Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound).  This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses.
A package that provides functions for measuring spatial
segregation. The methods implemented in this package include
White's P index (1983), Morrill's D(adj) (1991), Wong's D(w)
and D(s) (1993), and Reardon and O'Sullivan's set of spatial
segregation measures (2004).
Performs correlation matrix segmentation and applies a test
procedure to detect highly correlated regions in gene expression.
This package contains functions that help to determine event
boundaries in event segmentation experiments by bootstrapping a critical
segmentation magnitude under the null hypothesis that all key presses were
randomly distributed across the experiment. Segmentation magnitude is
defined as the sum of Gaussians centered at the times of the segmentation
key presses performed by the participants. Within a participant, the maximum
of the overlaid Gaussians is used to prevent an excessive influence of a
single participant on the overall outcome (e.g. if a participant is pressing
the key multiple times in succession). Further functions are included, such
as plotting the results.
Given a regression model, segmented `updates' the model by adding one or more segmented (i.e., piecewise-linear) relationships. Several variables with multiple breakpoints are allowed.
This package performs a fast exact segmentation on data and allows for use of various cost functions
A model coupling polygon and point processes for assessing
risk due to contaminant sources and their impact on exposed individuals.
An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See http://snap.stanford.edu/seismic/ for more information and datasets.
Fast versions of seismic analysis functions that 'roll' over a
vector of values. See the RcppRoll package for alternative
versions of basic statistical functions such as rolling mean,
median, etc.
Set of functions that access information about deputies and votings in Polish diet from webpage http://www.sejm.gov.pl/.
The package was developed as a result of an internship in MI2 Group - http://mi2.mini.pw.edu.pl/, Faculty of Mathematics and Information Science, Warsaw University of Technology.
Sejong(http://www.sejong.or.kr/) corpus and
Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum)
dictionaries for KoNLP
This package implements a novel method for fitting a
bounded probability distribution to quantiles (for example
stated by an expert), see Bornkamp and Ickstadt (2009) for
details.  For this purpose B-splines are used, and the density
is obtained by penalized least squares based on a Brier entropy
penalty.  The package provides methods for fitting the
distribution as well as methods for evaluating the underlying
density and cdf. In addition methods for plotting the
distribution, drawing random numbers and calculating quantiles
of the obtained distribution are provided.
Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.
New tools for inference after selection, for use
with forward stepwise regression, least angle regression, the
lasso, and the many means problem.
Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).
Translates a CSS3 selector into an equivalent XPath
expression. This allows us to use CSS selectors when working with
the XML package as it can only evaluate XPath expressions. Also
provided are convenience functions useful for using CSS selectors on
XML nodes. This package is a port of the Python package "cssselect"
(http://packages.python.org/cssselect/).
Fit and selects point pattern models based on minimum contrast, AIC and and goodness of fit.
Detection of outliers and influential errors using a latent variable model.
Functions using Cohen's effect sizes (Cohen, Jacob. Statistical power analysis for the behavioral sciences. Academic press, 2013) are provided for reliable feature selection in biology data analysis.  In addition to Cohen's effect sizes, p-values are calculated and adjusted from quasi-Poisson GLM, negative binomial GLM and Normal distribution ANOVA.  Significant features (genes, RNAs or proteins) are selected by adjusted p-value and minimum Cohen's effect sizes, calculated to keep certain level of statistical power of biology data analysis given p-value threshold and sample size.
A probability tree allows to compute probabilities of
complex events, such as genotype probabilities in intermediate generations of inbreeding
through recurrent self-fertilization (selfing). This package implements functionality to compute
probability trees for two- and three-marker genotypes in the F2 to F7 selfing
generations. The conditional probabilities are derived automatically
and in symbolic form. The package also provides functionality to
extract and evaluate the relevant probabilities.
Performs a regularization approach to variable selection in the
model-based clustering and classification frameworks.
First, the variables are arranged in order with a lasso-like procedure.
Second, the method of Maugis, Celeux, and Martin-Magniette (2009, 2011)
is adapted to define the role of variables in the two frameworks.
Functions for fitting general linear structural
equation models (with observed and latent variables) using the RAM approach,
and for fitting structural equations in observed-variable models by two-stage least squares.
Outlier and leverage diagnostics for SEM.
This is an add-on package which provides fourteen
goodness-of-fit indexes for structural equation models using
'sem' package.
Package semiArtificial contains methods to generate and evaluate semi-artificial data sets.
Based on a given data set different methods learn data properties using machine learning algorithms and
generate new data with the same properties.
The package currently includes the following data generator:
-a RBF network based generator using rbfDDA from RSNNS package,
-a Random Forest based generator for both classification and regression problems
-a density forest based generator for unsupervised data
Data evaluation support tools include:
-single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance
-evaluation based on clustering using Adjusted Rand Index (ARI) and FM
-evaluation based on classification performance with various learning models, eg, random forests.
Parametric and semi-parametric analyses of semi-competing risks/univariate survival data.
This package provides routines to check identifiability or non-identifiability of linear structural equation models as described in Drton, Foygel & Sullivant (Ann. Statist., 2011) and Foygel, Draisma & Drton (Ann. Statist., 2012).  The routines are based on the graphical representation of structural equation models by a path diagram/mixed graph.
Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model.
Functions for semiparametric regression analysis, to
complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J.
(2003). Semiparametric Regression. Cambridge University Press.
Routine for fitting bivariate models for binary and continuous responses with linear and nonlinear effects in the presence of associated error equations, endogeneity, non-random sample selection or partial observability.
Routine for fitting continuous or discrete response copula sample selection models with semiparametric predictors, including linear and nonlinear effects.
Functions to compute Gaussian and p-Gaussian kernels,
include supervision in these kernels, and perform kernel PCA
projections.
Conduct tests of difference in fit for mean and covariance
structure models as in structural equation modeling (SEM)
Path diagrams and visual analysis of various SEM packages' output
Fits structural equation models using partial least
squares (PLS). The PLS approach is referred to as
'soft-modeling' technique requiring no distributional
assumptions on the observed data.
Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments.
Provides useful tools for structural equation modeling packages.
Package contains a simple SMTP client which provides a
portable solution for sending email, including attachment, from
within R.
A collection of functions for factor screening, global sensitivity analysis and reliability sensitivity analysis of model output.
This package performs sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition.  The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468).  The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al.  The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al.  The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test.  The other functions in the package are internal functions.
Sensitivity analysis in observational studies, including evidence factors and amplification, using the permutation distribution of Huber-Maritz M-statistics, including the permutational t-test.
Sensitivity analysis analysis in matched observational studies with multiple controls using weighted M-statistics to increase design sensitivity.
This package provides functions to perform principal stratification sensitivity analyses on datasets.
Functions that facilitate analysis of
Sensory as well as Consumer data within a mixed effects model
framework are provided. The so-called mixed assessor models,
that correct for the scaling effect are implemented.
The generation of the d-tilde plots forms part of the package.
The shiny application for the  functionalities
forms part of the package.
Contains the function CUUimpute() which performs model-based clustering and imputation simultaneously.
Provides methods for sensory discrimination methods;
duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different,
2-AC and degree-of-difference.
This enables the calculation of d-primes, standard errors of
d-primes, sample size and power computations, and
comparisons of different d-primes. Methods for profile likelihood
confidence intervals and plotting are included.
Collection of datasets from Sen & Srivastava: "Regression
Analysis, Theory, Methods and Applications", Springer.  Sources
for individual data files are more fully documented in the
book.
Provides access and analytic functions for Sensus data.
Functions to generate separation plots for evaluation of
model fit.
This is a method for DNA Copy Number Profiling using
Next-Generation Sequencing. It has new model and test
statistics based on non-homogeneous Poisson Processes with
change point models. It uses an adaptation of Circular Binary
Segmentation. Also included are methods for point-wise Bayesian
Confidence Interval and model selection method for the
change-point model. A case and a control sample reads (normal
and tumor) are required.
A modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.
Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses.
The SeqGrapheR package provide interactive GUI for visualization of DNA sequence clusters. Details and principles of usage are described in user manual and (2010 BMC Bioinformatics 11:378). For full functionality installed NCBI blast is required.
Exploratory data analysis and data visualization
for biological sequence (DNA and protein) data.  Includes also
utilities for sequence data management under the ACNUC system.
Computes necessary information to meta analyze region-based
tests for rare genetic variants (e.g. SKAT, T1) in individual studies, and
performs meta analysis.
Integrate sequencing data (VCF/BCF) or meta-analysis results in R.
A program that computes the probability of crossing
sequential boundaries in a clinical trial. It implements the
Armitage-McPherson and Rowe Algorithm using the method
described in Schoenfeld D. (2001)" A simple Algorithm for
Designing Group Sequential Clinical Trials" Biometrics 27: pp,
972-974
User inputs a range of values r1 and r2, as well as a
number of columns v, in the function sq.pe(r1,r2,v). The
returned statement is a permutation matrix.
This package includes functions for handling DNA
sequences, especially simulated RFLP and TRFLP pattern based on
selected restriction enzyme and DNA sequences.
Educational package used in R courses to illustrate
object-oriented programming and package
development. Using biological sequences (DNA and RNA) as
a working example.
Functions to calculate exact critical values, statistical power, expected time to				signal and required  sample sizes for  performing exact sequential  analysis. All these				calculations can be done for either Poisson or binomial data, for continuous  or group				sequential analyses, and for different types of rejection boundaries.  In case  of group				sequential  analyses, the group  sizes do not  have to be specified in advance and,  for				binomial data, the alpha spending can be arbitrarily settled.
Tools to analyze genomic sequencing data from
paired normal-tumor samples, including cellularity and ploidy estimation; mutation
and copy number (allele-specific and total copy number) detection, quantification
and visualization.
Infrastructure for seriation with an implementation of several
seriation/sequencing techniques to reorder matrices, dissimilarity
matrices, and dendrograms. Also provides (optimally) reordered heatmaps,
color images and clustering visualizations like dissimilarity plots, and
visual assessment of cluster tendency plots (VAT and iVAT).
Translates antibody levels measured in a (cross-sectional)
population sample into an estimate of the frequency with which
seroconversions (infections) occur in the sampled population.
Start an HTTP server in R to serve static files, or dynamic
documents that can be converted to HTML files (e.g., R Markdown) under a
given directory.
Structural equation modeling (SEM) is a powerful statistical
approach for the testing of networks of direct and indirect theoretical
causal relationships in complex datasets with intercorrelated dependent and
independent variables. Here we implement a simple method for spatially
explicit SEM (SE-SEM) based on the analysis of variance covariance matrices
calculated across a range of lag distances. This method provides readily
interpretable plots of the change in path coefficients across scale.
Utility functions for interacting with R processes from
external programs. This package includes functions to save and
restore session information (including loaded packages, and
attached data objects), as well as functions to evaluate
strings containing R commands and return the printed results or
an execution transcript.
SetMethods is a package companion to the book by C. Q.
Schneider and C. Wagemann "Set-Theoretic Methods for the Social
Sciences", Cambridge University Press. It contains some
additional functions not present in other packages and data to
replicate the examples in the book and in the online appendix.
Tests gene expression data from a biological pathway for biologically meaningful differences in the eigenstructure between two classes.  Specifically, it tests the null hypothesis that the two classes' leading eigenvalues and sums of eigenvalues are equal.  A pathway's leading eigenvalue arguably represents the total variability due to variability in pathway activity, while the sum of all its eigenvalues represents the variability due to pathway activity and to other, unregulated causes. Implementation of the method described in Danaher (2015), "Covariance-based analyses of biological pathways".
SetRNG provides utilities to help set and record the setting of
the seed and the uniform and normal generators used when a random
experiment is run. The utilities can be used in other functions
that do random experiments to simplify recording and/or setting all the
necessary information for reproducibility.
See the vignette and reference manual for examples.
Data structures and basic operations for ordinary sets,
generalizations such as fuzzy sets, multisets, and
fuzzy multisets, customizable sets, and intervals.
Provides option settings management that goes
beyond R's default 'options' function. With this package, users can define
their own option settings manager holding option names, default values and
(if so desired) ranges or sets of allowed option values that will be
automatically checked. Settings can then be retrieved, altered and reset
to defaults with ease. For R programmers and package developers it offers
cloning and merging functionality which allows for conveniently defining
global and local options, possibly in a multilevel options hierarchy. See
the package vignette for some examples concerning functions, S4 classes,
and reference classes. There are convenience functions to reset par()
and options() to their 'factory defaults'.
Automatically sets the value of options("width") when the
terminal emulator is resized. The functions of this package only work if R
is compiled for Linux systems and it is running interactively in a terminal
emulator.
This package contains functions for calculating severity
and generating severity curves. Specifically, the simple case
of the one-parameter Normal distribution (i.e., with known
variance) is considered.
This package combines several sighting based estimators of historical extinction, allowing them to be run simultaneously or individually. Code for this package was contributed by Ben Collen, Gene Hunt and Tracy Rout. Additional code was taken from McPherson & Myers (2009).
Stochastic Frontier Analysis
introduced by Aigner, Lovell and Schmidt (1976)
and Battese and Coelli (1992, 1995).
Useful utilities ['goodies'] from Seminar fuer Statistik ETH
Zurich, quite a few related to graphics; many ported from S-plus times.
This package contains a series of tools for analyzing Systems Factorial Technology data.  This includes functions for plotting and statistically testing capacity coefficient functions and survivor interaction contrast functions.
Graph based clustering summaries for spatial point patterns. Includes Connectivity function,
Cumulative connectivity function and clustering function, plus the triangle/triplet intensity function T.
A fast and flexible set of tools for large scale estimation. It
features many stochastic gradient methods, built-in models, visualization
tools, automated hyperparameter tuning, model checking, interval estimation,
and convergence diagnostics.
An Object-oriented Framework for Geostatistical Modeling in S+
containing functions for variogram estimation, variogram fitting and kriging
as well as some plot functions. Written entirely in S, therefore works only
for small data sets in acceptable computing time.
Fit a regularized generalized linear model via penalized
maximum likelihood.  The model is fit for a path of values of
the penalty parameter. Fits linear, logistic and Cox models.
RCON(V, E) models (Højsgaard, et al., 2008) are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. 'sglasso' package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm.
Fast generic solver for sparse group lasso optimization problems. The loss (objective) function must be defined in a C++ module. Use of multiple processors for cross validation and subsampling is supported through OpenMP. The optimization problem is solved using a coordinate gradient descent algorithm. The algorithm is applicable to a broad class of convex loss functions.Convergence of the algorithm is established (see reference). Development version is on github.
Functions for computing power and boundaries for pre-licensure vaccine trials using the Generalized Likelihood Ratio tests proposed by Shih, Lai, Heyse and Chen
seven different methods for multiple testing problems. The SGoF-type methods and the BH and BY false discovery rate controlling procedures.
Functions to calculate student growth percentiles and percentile growth projections/trajectories for students using large scale,
longitudinal assessment data.  Functions use quantile regression to estimate the conditional density associated
with each student's achievement history.  Percentile growth projections/trajectories are calculated using the coefficient matrices derived from
the quantile regression analyses and specify what percentile growth is required for students to reach future achievement targets.
Functions for computing sparse generalized principal components, including functions for modeling structured correlation
Data sets utilized by the SGP Package as exemplars for users to conduct their own SGP analyses.
The Sparse Group Partial Least Square package (sgPLS) provides sparse, group, and sparse group versions of partial least square regression models.
The package for Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.
Provides functions to implement sgRSEA (single-guide RNA Set Enrichment Analysis), which is a robust test for identification of essential genes from genetic screening data using CRISPR (clustered regularly interspaced short palindromic repeats) and Cas9 (CRISPR-associated nuclease 9) system.
Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation.
Functions for plotting graphical shapes
such as ellipses, circles, cylinders, arrows, ...
In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrapping confidence interval for the change-point is incorporated in the main routine.
Functions to read and write ESRI shapefiles
Studies otolith shape variation among fish populations.
Otoliths are calcified structures found in the inner ear of teleost fish and their shape has
been known to vary among several fish populations and stocks, making them very useful in taxonomy,
species identification and to study geographic variations. The package extends previously described
software used for otolith shape analysis by allowing the user to automatically extract closed
contour outlines from a large number of images, perform smoothing to eliminate pixel noise,
choose from conducting either a Fourier or wavelet transform to the outlines and visualize
the mean shape. The output of the package are independent Fourier or wavelet coefficients
which can be directly imported into a wide range of statistical packages in R. The package
might prove useful in studies of any two dimensional objects.
Routines for the statistical analysis of landmark
shapes, including Procrustes analysis, graphical displays, principal
components analysis, permutation and bootstrap tests, thin-plate
spline transformation grids and comparing covariance matrices.
Landsat satellites collect important data about global forest conditions. Documentation about Landsat's role in forest disturbance estimation is available at the site <http://landsat.gsfc.nasa.gov/?p=9513>. By constrained quadratic B-splines, this package delivers an optimal shape-restricted trajectory to a time series of Landsat imagery for the purpose of modeling annual forest disturbance dynamics to behave in an ecologically sensible manner assuming one of seven possible "shapes", namely, flat, decreasing, one-jump (decreasing, jump up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then increasing), linear increasing, and double-jump (decreasing, jump up, decreasing, jump up, decreasing). The main routine selects the best shape according to the minimum Bayes information criterion (BIC) or the cone information criterion (CIC), which is defined as the log of the estimated predictive squared error. The package also provides parameters summarizing the temporal pattern including year(s) of inflection, magnitude of change, pre- and post-inflection rates of growth or recovery. In addition, it contains routines for converting a flat map of disturbance agents to time-series disturbance maps and a graphical routine displaying the fitted trajectory of Landsat imagery.
A collection of tools for analyzing significance of trading
strategies, based on the Sharpe ratio and overfit of the same.
Miscellaneous soil data management, summary, visualization, and conversion utilities to support soil survey.
Data sets and SAR, SARX, HSAR and HSARX
models as described in Solymos and Lele
(2012, GEB 21, 109-120).
Implements various methods for eliciting a probability distribution for a single parameter from an expert or a group of experts. The expert provides a small number of probability or quantile judgements, corresponding to points on his or her cumulative distribution function. A range of parametric distributions can then be fitted and displayed, with feedback provided in the form of additional quantiles. A graphical interface for the roulette elicitation method is also provided. For multiple experts, a weighted linear pool can be calculated.
Makes it incredibly easy to build interactive web
applications with R. Automatic "reactive" binding between inputs and
outputs and extensive pre-built widgets make it possible to build
beautiful, responsive, and powerful applications with minimal effort.
Ace editor bindings to enable a rich text editing environment
within Shiny.
Provides Bootstrap 2 web components for use with the Shiny
package. With versions of Shiny prior to 0.11, these Bootstrap 2 components
were included as part of the package. Later versions of Shiny include
Bootstrap 3, so the Bootstrap 2 components have been moved into this
package for those uses who rely on features specific to Bootstrap 2.
Adds additional Twitter Bootstrap components to Shiny.
Create dashboards with 'Shiny'. This package provides
a theme on top of 'Shiny', making it easy to create attractive dashboards.
Provides functionality for client-side navigation of
the server side file system in shiny apps. In case the app is running
locally this gives the user direct access to the file system without the
need to "download" files to a temporary location. Both file and folder
selection as well as file saving is available.
Perform common JavaScript operations in Shiny applications without
having to know any JavaScript. Examples include: hiding an element, disabling
an input, resetting an input back to its original value, delaying code execution
by a few seconds, and many more useful functions. 'shinyjs' can also be used to
easily run your own custom JavaScript functions from R.
Shiny wrappers for the RGL package. This package exposes RGL's
ability to export WebGL visualization in a shiny-friendly format.
We provide a graphical user interface for interactive Markov chain
Monte Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a
posterior sample. The interface is powered by RStudio's Shiny web application
framework and works with the output of MCMC programs written in any
programming language (and has extended functionality for Stan models fit
using the rstan package and the No-U-Turn sampler).
Themes for use with Shiny. Includes several Bootstrap themes
from http://bootswatch.com/, which are packaged for use with Shiny
applications.
Exposes bindings to jsTree -- a JavaScript library
that supports interactive trees -- to enable a rich, editable trees in
Shiny.
The SHIP-package allows the estimation of various types of
shrinkage covariance matrices. These types differ in terms of
the so-called covariance target (to be chosen by the user), the
highly structured matrix which the standard unbiased sample
covariance matrix is shrunken towards and which optionally
incorporates prior biological knowledge extracted from the
database KEGG. The shrinkage intensity is obtained via an
analytical procedure.
An interface to the API of the E-commerce service Shopify
(http://docs.shopify.com/api)
Analyzes shooting data with respect to group shape,
precision, and accuracy. This includes graphical methods,
descriptive statistics, and inference tests using standard,
but also non-parametric and robust statistical methods.
Implements distributions for radial error in bivariate normal
variables. Works with files exported by OnTarget PC/TDS or
Taran, as well as with custom data files in text format.
Making it easy to use various types of fonts ('TrueType',
'OpenType', Type 1, web fonts, etc.) in R graphs, and supporting most output
formats of R graphics including PNG, PDF and SVG. Text glyphs will be converted
into polygons or raster images, hence after the plot has been created, it no
longer relies on the font files. No external software such as Ghostscript is
needed to use this package.
Providing font files that are needed by the 'showtext' package.
Functions for converting network data from a
SpatialLinesDataFrame object to a "igraph-class" object.
Post-estimation shrinkage of regression coefficients in statistical modeling can be
used to correct for the overestimation of regression coefficients caused by variable selection.
While global shrinkage modifies all regression coefficients by the same factor, parameterwise
shrinkage factors differ between regression coefficients. With variables which are either
highly correlated or associated with regard to contents, such as several columns of a design
matrix describing a nonlinear effect, parameterwise shrinkage factors are not interpretable
and a compromise between global and parameterwise shrinkage, termed 'joint shrinkage', is a
useful extension.
A computational shortcut to resampling-based shrinkage factor estimation based on DFBETA
residuals can be applied.
Global, parameterwise and joint shrinkage for models fitted by lm, glm, coxph, or (for
R <= 3.1.0) mfp is available.
Several shrinkage estimators of the effect-size of a parameter of interest.
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.
Implementation of the shuffle estimator, a non-parametric estimator for signal and noise variance under mild noise correlations.
This package takes data on organism isotopes and fits a
Bayesian model to their dietary habits based upon a Gaussian
likelihood with a mixture dirichlet-distributed prior on the
mean. It also includes SiBER metrics. See siardemo() for an
example. Version 4.1.2 contains bug fixes to allow more than
isotope numbers other than 2. Version 4.2 fixes a bug that
stopped siar working on 64-bit systems
Fits bivariate ellipses to stable isotope data using Bayesian
inference with the aim being to describe and compare their isotopic
niche.
The code computes the structural intervention distance (SID) between a true directed acyclic graph (DAG) and an estimated DAG. Definition and details about the implementation can be found in J. Peters and P. Bühlmann: "Structural intervention distance (SID) for evaluating causal graphs", Neural Computation 27, pages 771-799, 2015.
This package has many purposes: first, it gives to the
community an R implementation of each known side channel attack
and countermeasures as well as data to test it, second it
allows to implement a side channel attack quickly and easily.
Evolutionary reconstruction based on substitutions and insertion-deletion (indels) analyses in a distance-based framework.
Functions for making sieve test reports. Sieve test is widely used to obtain particle-size distribution of powders or granular materials.
Print function signatures and find overly complicated code.
SigClust is a statistical method for testing the
significance of clustering results. SigClust can be applied to
assess the statistical significance of splitting a data set
into two clusters. For more than two clusters, SigClust can be
used iteratively.
Uses logistic regression to model the probability of detection as a function of covariates.
This model is then used with observational survey data to estimate population size, while
accounting for uncertain detection.  See Steinhorst and Samuel (1989).
A collection of tools for estimating the location of a transmitter signal from radio telemetry studies using the maximum likelihood estimation (MLE) approach described in Lenth (1981).
A set of signal processing functions originally written for 'Matlab' and 'Octave'.
Includes filter generation utilities, filtering functions,
resampling routines, and visualization of filter models. It also
includes interpolation functions.
Predicts presence of signal peptides in eukaryotic protein using
hidden semi-Markov models. The implemented algorithm can be accessed both
from command line and GUI.
Perform sign test on one-sample data, which is one of the oldest non-parametric statistical methods. Assume that X comes from a continuous distribution with median = v ( unknown ). Test the null hypothesis H0: median of X v = mu ( mu is the location parameter and is given in the test ) v.s. the alternative hypothesis H1: v > mu ( or v < mu or v != mu ) and calculate the p-value. When the sample size is large, perform the asymptotic sign test. In both ways, calculate the R-estimate of location of X and the distribution free confidence interval for mu.
Takes a phylogenetic tree (of class phylo) and a data frame (or matrix) of corresponding tip labels and p-values, and determines the significance of the branches (families of p-values) and plots the tree with colored branches (corresponding to families) according to their levels of significance.  Also produces a CSV file with the p-values of all of the branches as well as which tips belong to which branches.  Finally, it exports a file that can be opened in FigTree that produces a colored plot (colored branches according to their significance of corresponding groups of p-values) with p-value annotations.
This package calculates ANSI S3.5-1997 Speech Intelligibility Index
(SII), a standard method for computing the intelligibility of
speech from acoustical measurements of speech, noise, and hearing
thresholds. This package includes data frames corresponding to
Tables 1 - 4 in the ANSI standard as well as a function utilizing
these tables and user-provided hearing threshold and noise level
measurements to compute the SII score.  The methods implemented
here extend the standard computations to allow calculation of SII
when the measured frequencies do not match those required by the
standard by applying interpolation to obtain values for the
required frequencies
--
Development of this package was funded by the Center for Bioscience
Education and Technology (CBET) of the Rochester Institute of
Technology (RIT).
Besides functions for the calculation of similarity and
multiple plot similarity measures with binary data (for
instance presence/absence species data) the package contains
some simple wrapper functions for reshaping species lists into
matrices and vice versa and some other functions for further
processing of similarity data (Mantel-like permutation
procedures) as well as some other useful stuff for vegetation
analysis.
Package simboot provides estimation of simultaneous bootstrap and asymptotic confidence intervals for diversity indices, namely the Shannon and the Simpson index. Several pre--specified multiple comparison types are available to choose. Further user--defined contrast matrices are applicable. In addition, simboot estimates adjusted as well as unadjusted p--values for two of the three proposed bootstrap methods. Further simboot allows for comparing biological diversities of two or more groups while simultaneously testing a user-defined selection of Hill numbers of orders q, which are considered as appropriate and useful indices for measuring diversity.
A flexible tool for simulating complex longitudinal data using
structural equations, with emphasis on problems in causal inference.
Specify interventions and simulate from intervened data generating
distributions. Define and evaluate treatment-specific means, the average
treatment effects and coefficients from working marginal structural models.
User interface designed to facilitate the conduct of transparent and
reproducible simulation studies, and allows concise expression of complex
functional dependencies for a large number of time-varying nodes. See the
package vignette for more information, documentation and examples.
Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011) or ratios (Hasler and Hothorn, 2012) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008) or covariance matrices (Hasler, 2014).
Simulates correlated multinomial responses by utilizing threshold approaches under a cumulative link model, a continuation ratio model or a baseline-category logit marginal model specification.
Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures.
Provides tools to help organize Monte Carlo simulations in R. The
tools provided control the structure and back-end of the Monte Carlo simulations
by utilizing a generate-analyse-summarise strategy. The functions control common
simulation issues such as re-simulating non-convergent results, support parallel
back-end computations, save and restore temporary files, aggregate results
across independent nodes, and provide native support for debugging.
Provides the functions for simulation and modeling of stochastic differential equations (SDE's) the type Ito and Stratonovich. This package contains many objects, the numerical methods to find the solutions to SDE's (1, 2 and 3-dim), with a possibility for simulates a flows trajectories,with good accuracy. Many theoretical problems on the SDE's have become the object of practical research, as statistical analysis and simulation of solution of SDE's, enabled many searchers in different domains to use these equations to modeling and to analyse practical problems, in financial and actuarial modeling and other areas of application, for example modelling and simulate of dispersion in shallow water using the attractive center (Boukhetala K, 1996).  We hope that the package presented here and the updated survey on the subject might be of help for practitioners, postgraduate and PhD students, and researchers in the field who might want to implement new methods.
An object oriented framework to simulate
ecological (and other) dynamic systems. It can be used for
differential equations, individual-based (or agent-based) and other
models as well. The package helps to organize scenarios (to avoid copy
and paste) and aims to improve readability and usability of code.
Estimation of function and index vector in single index model with and without shape constraints including different smoothness conditions.
Implementation of the SIMEX-Algorithm by Cook & Stefanski
and MCSIMEX by Küchenhoff, Mwalili & Lesaffre
Implement of the Simulation-Extrapolation (SIMEX) algorithm for the accelerated failure time (AFT) with covariates subject to measurement error.
A general framework for statistical simulation.
Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.
Functions to run and assist four
different similarity measures. The similarity
measures included are: longest common
subsequence (LCSS), Frechet distance, edit distance
and dynamic time warping (DTW). Each of these
similarity measures can be calculated from two
n-dimensional trajectories, both in matrix form.
Allows a Simile model saved as a compiled binary to be
loaded, parameterized, executed and interrogated. This version works
with Simile v5.97 on.
Discrete-Event Simulation (DES) package for R.
Fits a stable isotope mixing model via JAGS in R. The package allows for any number of isotopes or sources, as well as concentration dependencies.
Algorithms to create prognostic biomarkers using biological networks.
Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.
The R package simone implements the inference of
co-expression networks based on partial correlation
coefficients from either steady-state or time-course
transcriptomic data. Note that with both type of data this
package can deal with samples collected in different
experimental conditions and therefore not identically
distributed. In this particular case, multiple but related
networks are inferred on one simone run.
Simulates and plots quantities of interest (relative
hazards, first differences, and hazard ratios) for linear coefficients,
multiplicative interactions, polynomials, penalised splines, and
non-proportional hazards, as well as stratified survival curves from Cox
Proportional Hazard models. It also simulates and plots marginal effects
for multiplicative interactions.
Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.
SimpleTable provides a series of methods to conduct
Bayesian inference and sensitivity analysis for causal effects
from 2 x 2 and 2 x 2 x K tables when unmeasured confounding is
present or suspected.
Simplex density, distribution, quantile functions as well as random variable
generation of the simplex distribution are given. Regression analysis of proportional data
using various kinds of simplex models is available. In addition, GEE method can be applied
to longitudinal data to model the correlation. Residual analysis is also involved. Some
subroutines are written in C with GNU Scientific Library (GSL) so as to facilitate the
computation.
This package provides methods to integrate functions over m-dimensional simplices
in n-dimensional Euclidean space.  There are exact methods for polynomials and
adaptive methods for integrating an arbitrary function.
Basic tools for symbolic expression simplification, e.g. simplify(x*1) => x, or simplify(sin(x)^2+cos(x)^2) => 1. Based on the "Expression v3" (Ev3) 1.0 system by Leo Liberti.
Tools and methods to simulate populations for surveys based on auxiliary data. The tools include model-based methods, calibration and combinatorial optimization algorithms. The package was developed with support of the International Household Survey Network, DFID Trust Fund TF011722.
This package detects instances of Simpson's Paradox in
datasets. It examines subpopulations in the data, either
user-defined or by means of cluster analysis, to test whether a
regression at the level of the group is in the opposite
direction at the level of subpopulations.
Calculate power for generalised linear mixed models, using
simulation. Designed to work with models fit using the 'lme4' package.
This package provides a number of functions to simulate restriction enzyme digestion, library construction and fragments size selection to predict the number of loci expected from most of the Restriction site Associated DNA (RAD) and Genotyping By Sequencing (GBS) approaches. SimRAD aims is to provide an estimation of the number of loci expected from a particular genome depending on the protocol type and parameters allowing to assess feasibility, multiplexing capacity and the amount of sequencing required.
Functions for performing Bayesian `similarity regression', adapted to model the association between an HPO-encoded patient phenotype and binary genotype. A `no association' model is compared with one in which the log odds of having a rare genotype is linked to the semantic similarity between patient phenotype and a latent characteristic phenotype. The method estimates the probability of an association together with an HPO-coded phenotype characteristic of the disease.
Facilitates data simulation from a random regression model where the data properties can be controlled by a few input parameters. The data simulation is based on the concept of relevant latent components and relevant predictors, and was developed for the purpose of testing methods for variable selection for prediction. Included are also functions for designing computer experiments in order to investigate the effects of the data properties on the performance of the tested methods. The design is constructed using the Multi-level Binary Replacement (MBR) design approach which makes it possible to set up fractional designs for multi-factor problems with potentially many levels for each factor.
Tools for setting up, conducting, and evaluating larger-scale
simulation studies, including parallel computations.
Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.
RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. Users control the proportion of genes simulated to be differentially expressed (DE) and can provide a vector of weights to control the distribution of effect sizes. The algorithm requires a matrix of RNA-seq read counts with large sample sizes in at least two treatment groups. Many datasets are available that fit this standard.
simSummary is a small utility package which eases the
process of summarizing simulation results. Simulations often
produce intermediate results - some focal statistics that need
to be summarized over several scenarios and many replications.
This step is in principle easy, but tedious. The package
simSummary fills this niche by providing a generic way of
summarizing the focal statistics of simulations. The useR must
provide properly structured input, holding focal statistics,
and then the summary step can be performed with one line of
code, calling the simSummary function.
The simTool package is designed for statistical simulations that
have two components. One component generates the data and the other one
analyzes the data. The main aims of the simTool package are the reduction
of the administrative source code (mainly loops and management code for the
results) and a simple applicability of the package that allows the user to
quickly learn how to work with the simTool package. Parallel computing is
also supported. Finally, convenient functions are provided to summarize the
simulation results.
This package provides simulation process of 4 selection methods in predicting potent compounds
This package performs classical correspondence analysis (CA) and
simultaneous analysis (SA). Simultaneous analysis is a factorial
methodology developed for the joint treatment of a set of several
contingency tables. In SA tables having the same rows, are concatenated
row-wise.
In this version of the package a multiple option have been included for
the simultaneous analysis
of tables having the same columns, concatenated column-wise.
This way, a MSA allows to perform the analysis of an indicator matrix
where the rows represent individuals.
In this package, functions for computation, summaries
and graphical visualization in two dimensions are provided, including
options to display partial and supplementary points.
This package provides routines to perform SIN model selection
as described in Drton & Perlman (2004, 2008).  The selected models are
represented in the format of the 'ggm' package, which allows in
particular parameter estimation in the selected model.
The sinaplot is a data visualization chart suitable for plotting
any single variable in a multiclass dataset. It is an enhanced jitter strip
chart, where the width of the jitter is controlled by the density
distribution of the data within each class.
A platform for experimenting with spatially explicit individual-based vegetation models.
Calculating daily global solar radiation at horizontal surface using several well-known models (i.e. Angstrom-Prescott, Supit-Van Kappel, Hargreaves, Bristow and Campbell, and Mahmood-Hubbard), and model calibration based on ground-truth data, and (3) model auto-calibration. The FAO Penmann-Monteith equation to calculate evapotranspiration is also included.
This package fits single-index (quadratic) response surface models.
Supplementary item response theory models to complement existing
functions in R, including multidimensional compensatory and
noncompensatory IRT models, MCMC for hierarchical IRT models and
testlet models, NOHARM, faceted and hierarchical rater models,
ordinal IRT model (ISOP), latent regression models,
DETECT statistic, local structural equation modeling (LSEM).
Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Through this publicly available package, we provide a unified environment to carry out variable selection using iterative sure independence screening (SIS) and all of its variants in generalized linear models and the Cox proportional hazards model.
Implements the SISAL algorithm by Tikka and Hollmén. It is
a sequential backward selection algorithm which uses a linear
model in a cross-validation setting. Starting from the full
model, one variable at a time is removed based on the
regression coefficients. From this set of models, a
parsimonious (sparse) model is found by choosing the model with
the smallest number of variables among those models where the
validation error is smaller than a threshold. Also implements
extensions which explore larger parts of the search space
and/or use ridge regression instead of ordinary least squares.
SISUS for source partitioning using stable isotopes.
SISUS reads in a specific Excel-like workbook and performs an IsoSource-type analysis by
returning a sample of feasible solutions to p in the non-over constrained linear systems, b=Ap.
Edit \file{sisus_*_template.xls} and input data values and run parameters.
Run \code{\link{sisus.run}(filename)}. See output in current working directory.
Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.
Functions for fitting and plotting SITAR (Super Imposition by Translation And Rotation) growth curve models. SITAR is a shape-invariant model with a regression B-spline mean curve and subject-specific random effects on both the measurement and age scales.
Format a number (or a list of numbers) to a string (or a
list of strings) with SI prefix. Use SI prefixes as constants
like (4 * milli)^2
Sensitivity indices with dependent correlated inputs, using a
method based on PLS regression.
Functions and utilities to perform
Statistical Analyses in the Six Sigma way.
Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control),
you can manage several Quality Management studies:
Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis,
etc. Data frames used in the books "Six Sigma with R" (Springer, 2012)
and "Quality Control with R" (Springer, 2015)
are also included in the package.
Calculates and plots the SiZer map for scatterplot data.
A SiZer map is a way of examining when the p-th derivative of a
scatterplot-smoother is significantly negative, possibly zero
or significantly positive across a range of smoothing
bandwidths.
Provides a database-independent JDBC interface.
Collection of miscellaneous utility functions (especially intended
for people coming from other statistical software packages like 'SPSS', and/
or who are new to R), supporting following common tasks: 1) Reading and writing
data between R and other statistical software packages like 'SPSS', 'SAS' or
'Stata' and working with labelled data; this includes easy ways to get and set
label attributes, to convert labelled vectors into factors (and vice versa),
or to deal with multiple declared missing values etc. 2) Data transformation
tasks like recoding, dichotomizing or grouping variables, setting and replacing
missing values. 3) Convenient functions to perform frequently used statistical
tests, or to calculate various commonly used statistical coefficients.
Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, PCA and correlation matrices, cluster analyses, scatter plots, Likert scales, effects plots of regression models (including interaction terms) and much more.
Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.
Computes necessary information to meta analyze SKAT
statistics in each individual cohort, and then performs the
meta analysis.
Sparse (Multicategory) Kernel Discriminant Analysis does variable selection for nonparametric classification
Functions for the Skellam distribution, including: density
(pmf), cdf, quantiles and random variates.
Functions are provided for the density function,
distribution function, quantiles and random number generation
for the skew hyperbolic t-distribution. There are also
functions that fit the distribution to data. There are
functions for the mean, variance, skewness, kurtosis and mode
of a given distribution and to calculate moments of any order
about any centre. To assess goodness of fit, there are
functions to generate a Q-Q plot, a P-P plot and a tail plot.
Density, distribution function, quantile function and
random generation for the skewed t distribution of Fernandez
and Steel.
A generalization of the statistic used in Friedman's ANOVA method and in Durbin's rank test. This nonparametric statistical test is useful for the data obtained from block designs with missing observations occurring randomly. A resulting p-value is based on the chi-squared distribution and Monte Carlo method.
Algorithms to compute spherical k-means partitions.
Features several methods, including a genetic and a fixed-point
algorithm and an interface to the CLUTO vcluster program.
Provides directly interpretable estimated coefficients
for four models in connection with the two-group straight line
ANCOVA problem: (A) the full model, which requires the fitting of
two intercepts and two slopes; (B) a reduced model, which requires
the fitting of a single intercept and single slope; (C) a reduced
model, which requires the fitting of two separate intercepts and a
single, common slope; and (D) a reduced model, which requires the
fitting of a single, common intercept and two separate slopes.  The
summary function provides tests of fit for the (null) hypotheses of:
(1) equivalent data sets, (2) equivalent slopes, and
(3) equivalent intercepts.
Slackr contains functions that make it possible to
interact with Slack.com messaging platform. When you need to share
information/data from R, rather than resort to copy/paste in e-mails
or other services like Skype, you can use this package to send
well-formatted output from multiple R objects and expressions
to all teammates at the same time with little effort. You can also
send images from the current graphics device, R objects (as RData),
and upload files.
Data structures and algorithms for sparse arrays and matrices,
based on index arrays and simple triplet representations, respectively.
Estimates the slope and level change present in data after
removing phase A trend. Represents graphically the original and
the detrended data.
The skew logistic distribution is a quantile-defined generalisation
of the logistic distribution (van Staden and King 2015).  Provides random
numbers, quantiles, probabilities, densities and density quantiles for the distribution.
It provides Quantile-Quantile plots and method of L-Moments estimation
(including asymptotic standard errors) for the distribution.
Compute Time series Resistant Smooth 4253H, twice smoothing method.
Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The
Statistical Sleuth: A Course in Methods of Data Analysis (2nd
ed)", Duxbury.
Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The
Statistical Sleuth: A Course in Methods of Data Analysis (3rd
ed)", Cengage Learning.
Set of tools to find coherent patterns in microarray data
using a Bayesian sparse latent factor model (Duarte and Mayrink 2015 -
http://link.springer.com/chapter/10.1007%2F978-3-319-12454-4_15).
Considerable effort has been put into making slfm fast and memory efficient,
turning it an interesting alternative to simpler methods in terms
of execution time. It implements versions of the SLFM using both type
of mixtures: using a degenerate distribution or a very concentrated
normal distribution for the spike part of the mixture. It also implements
additional functions to help pre-process the data and fit the model
for a large number of arrays.
Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".
Efficient procedures for Sorted L1 Penalized Estimation (SLOPE).
The sorted L1 norm is useful for statistical estimation and testing,
particularly for variable selection in the linear model.
Interface for creation of 'slp' class smoother objects for
use in Generalized Additive Models (as implemented by packages
'gam' and 'mgcv').
This is software linked to the book
'Applied Smoothing Techniques for Data Analysis -
The Kernel Approach with S-Plus Illustrations' Oxford University Press.
Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods.
This package provides a solution path for L1-penalized angle-based classification. Three loss functions are implemented in smac, including the deviance loss in logistic regression, the exponential loss in boosting, and the proximal support vector machine loss.
Provides the following approaches for multidimensional
scaling (MDS) based on stress minimization by means of majorization
(smacof): basic MDS on symmetric dissimilarity matrices,
MDS with external constraints on the configuration, individual difference scaling (idioscal, indscal, and friends),
MDS with spherical restrictions, and unfolding. The MDS type can be ratio, interval, ordinal, and monotone splines.
Various tools and extensiosn like jackknife MDS, permutation tests, gravity models, inverse MDS, unidimensional scaling,
classical scaling, and Procrustes.
Various statistical methods for analyzing case-control point data.
The methods available closely follow those in chapter 6 of Applied Spatial
Statistics for Public Health Data by Waller and Gotway (2004).
Inference techniques for Fay Herriot Model.
Animal movement models including moving-resting process
with embedded Brownian motion, Brownian motion with measurement error.
The package "smart" provides a general framework for
analyzing (including estimation, feature selection and
prediction) and visualize big data. It integrates several
novel, efficient and robust data analysis tools, including
Transelliptical Component Analysis (TCA), Transelliptical
Correlation Estimation (TCE) and Group Nearest Shrunken
Centroids (gnsc). We target on high dimensional data
analysis(usually d >> n), and exploit computationally
efficiently approaches. Results are organized to be visualized
properly for users.
Tools for accessing and processing datasets presented on the blog SmarterPoland.pl. Among all: functions for downloading data from eurostat database, coordinates from Google Maps and data from GUS (Central Statistical Office of Poland).
This package provides methods of fitting bivariate lines in allometry using the major axis (MA) or standardised major axis (SMA), and for making inferences about such lines. The available methods of inference include confidence intervals and one-sample tests for slope and elevation, testing for a common slope or elevation amongst several allometric lines, constructing a confidence interval for a common slope or elevation, and testing for no shift along a common axis, amongst several samples.
It categorizes a numeric variable into bins mapped to a binary target variable for its ulterior usage in scoring modeling. Its purpose is to automate the time consuming process of selecting the right cut points, quickly calculate metrics such as Weight of Evidence and Information Value; and also document SQL codes, tables, and plots used throughout the development stage. The package also allows users to establish their own cut points for numeric characteristics and run the analysis for categorical variables.
particle filtering, auxiliary particle filtering and sequential Monte Carlo algorithms
Implements multiple imputation of missing covariates by
Substantive Model Compatible Fully Conditional Specification.
This is a modification of the popular FCS/chained equations
multiple imputation approach, and allows imputation of missing
covariate values from models which are compatible with the user
specified substantive model.
This package is for optimizing non-linear complex
functions based on Monte Carlo random sampling.
A package containing functions for genome-wide association
studies.
Data Sets for Kumar and Petersen (2012).
Statistical Methods in Customer Relationship Management,
Wiley: New York.
An R-package for Estimating Semiparametric PH and AFT
Mixture Cure Models
Provides resampling functions (stratified, residual,
multinomial, systematic, and branching), measures of weight
uniformity (coefficient of variation, effective sample size,
and entropy), and a weight renormalizing function.
Contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.
This package provides similarity among documents.
Symbolic multidimensional scaling for interval-valued dissimilarities. The hypersphere model and the hyperbox model are available.
A package for fitting smoothing-splines mixed-effects models to replicated functional
data sets.
Provides statistical methods for the analysis of data areal data, with a focus on cluster detection.
R functions and data from Chapter 5 of 'Statistical
Methods for Financial Engineering', by Bruno Remillard, CRC
Press, (2013).
This package contains code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second edition of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press, November 2011.
Smooth multivariate interpolation for gridded and scattered data
with possibly large datasets. The code for scattered data is taken from the
'SHEPPACK' Fortran package.
This package accompanies Aitkin et al, Statistical
Modelling in R, OUP, 2009. The package contains some functions
and datasets used in the text.
This tiny package contains one function smirnov() which
calculates two scaled taxonomic coefficients, Txy (coefficient
of similarity) and Txx (coefficient of originality). These two
characteristics may be used for the analysis of similarities
between any number of taxonomic groups, and also for assessing
uniqueness of giving taxon. It is possible to use smirnov()
output as a distance measure: convert it to distance by
"as.dist(1 - smirnov(x))".
Constructs a yield curve by the Smith-Wilson method from a
table of LIBOR and SWAP rates
The SML package is a collection of statistical machine learning methods.
Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions
Fits flexible additive models to data on stream networks, taking account of flow-connectivity of the network.  Models are fitted using penalised least squares.
Provides generators for a high number of both single- and multi-
objective test functions which are frequently used for the benchmarking of
(numerical) optimization algorithms. Moreover, it offers a set of convenient
functions to generate, plot and work with objective functions.
A collection of methods for smoothing numerical data, commencing with a port of the Matlab gaussian window smoothing function. In addition, several functions typically used in smoothing of financial data are included.
SmoothHazard is a package for fitting illness-death (and survival)
model with possibly interval-censored data for transition toward transient
state. Left-truncated and right-censored data are also allowed.
Proportional intensities regression models allow for covariates on each of
the 3 transitions. We use either a parametric approach with Weibull
baseline intensities or a semi-parametric approach with M-splines
approximation of baseline intensities in order to obtain smooth estimates.
Estimation of parameters is conducted by likelihood maximization in the
parametric approach or penalized likelihood maximization in the
semi-parametric approach.
Provides flexible hazard ratio curves allowing non-linear
relationships between continuous predictors and survival. To
better understand the effects that each continuous covariate
has on the outcome, results are ex pressed in terms of hazard
ratio curves, taking a specific covariate value as reference.
Confidence bands for these curves are also derived.
Functions to smooth two-dimensional fields using FFT and the convolution theorem
Some M-estimators for 1-dimensional location (Bisquare, ML
for the Cauchy distribution, and the estimators from
application of the smoothing principle introduced in Hampel,
Hennig and Ronchetti (2011) to the above, the Huber
M-estimator, and the median, main function is smoothm), and
Pitman estimator.
Contains, as a main contribution, a function to fit
a regression model with possibly right, left or interval
censored observations and with the error distribution
expressed as a mixture of G-splines. Core part
of the computation is done in compiled C++ written
using the Scythe Statistical Library Version 0.3.
Given independent and identically distributed observations X(1), ..., X(n) from a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers several estimates to compute estimates of gamma. The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0].
This package contains the datasets and a few functions for
use with the practicals outlined in Appendix A of the book
Statistical Models (Davison, 2003, Cambridge University Press).
The practicals themselves can be found at
http://statwww.epfl.ch/davison/SM/
Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers
Produce small area population estimates by fitting census data to
survey data.
Datasets used in "Statistical Methods for the Social Sciences"
(SMSS) by Alan Agresti and Barbara Finlay.
Implements the structural model for variances in order to
detect differentially expressed genes from gene expression data
Build and manipulate probability distributions of the skew-normal
family and some related ones, notably the skew-t family, and provide related
statistical methods for data fitting and diagnostics, in the univariate and
the multivariate case.
A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.
Functions for reading and writing Gadget N-body snapshots. The Gadget code is popular in astronomy for running N-body / hydrodynamical cosmological and merger simulations. To find out more about Gadget see the main distribution page at www.mpa-garching.mpg.de/gadget/
Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.
Robust and non-robust SNHT tests for changepoint detection.
Implement K-nearest neighbor classifier, weighted nearest neighbor classifier, bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier and stabilized nearest neighbor classifier, and perform model selection via 5 fold cross-validation for them. This package also provides functions for computing the classification error and classification instability of a classification procedure.
Support for simple parallel computing in R.
An R interface to the C libstemmer library that implements
Porter's word stemming algorithm for collapsing words to a common
root to aid comparison of vocabulary. Currently supported languages are
Danish, Dutch, English, Finnish, French, German, Hungarian, Italian,
Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish
and Turkish.
Usability wrapper around snow for easier development of
parallel R programs. This package offers e.g. extended error
checks, and additional functions. All functions work in
sequential mode, too, if no cluster is present or wished.
Package is also designed as connector to the cluster management
tool sfCluster, but can also used without it.
Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed.
contains several supplementary non-parametric statistics methods including quantile test, Cox-Stuart trend test, runs test, normal score test, kernel PDF and CDF estimation, kernel regression estimation and kernel Kolmogorov-Smirnov test.
This package carries out most common analysis when performing whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated.
Implements classes and methods for large scale SNP enrichment analysis (e.g. SNPs associated with genes expression in a GWAS signal).
A set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example for use with the RSNPset package, or to export to a PLINK set.
This package implements asymptotic methods related to
maximally selected statistics, with applications to SNP data.
Creates plots of p-values using single SNP and/or haplotype data.
Main features of the package include options to display a linkage
disequilibrium (LD) plot and the ability to plot multiple datasets
simultaneously. Plots can be created using global and/or individual
haplotype p-values along with single SNP p-values. Images are created as
either PDF/EPS files.
A modification of Breiman and Cutler's classification random forests modified for SNP (Single Nucleotide Polymorphism) data (based on randomForest v4.6-7) to prevent X-chromosome SNP variable importance bias compared to autosomal SNPs by simulating the process of X chromosome inactivation. Classification is based on a forest of trees using random subsets of SNPs and other variables as inputs.
Write snpStats objects to disk in formats suitable for reading by
snphap, phase, mach, IMPUTE, beagle, and (almost) anything else that
expects a rectangular format.
This package queries the SNPs data sets and makes plots of
genes and SNPs.  This package allows users to access these data
sets once they have been compiled into one file and indexed
with Tabix (http://samtools.sourceforge.net/tabix.shtml). There
are functions to retrieve SNPs, subset them for specific
strains, obtain only SNPs that are polymorphic for a subset of
strains, plot SNPs and look for certain allele patterns in
SNPs. We have also added functions to access indels and
structural variants.
Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided.
Contains functions to perform various models and
methods for test equating. It currently implements the traditional
mean, linear and equipercentile equating methods, as well as the
mean-mean, mean-sigma, Haebara and Stocking-Lord IRT linking methods.
It also supports newest methods such that local equating, kernel
equating (using Gaussian, logistic and uniform kernels) with presmoothing,
and IRT parameter linking methods based on asymmetric item characteristic
functions. Functions to obtain both standard error of equating (SEE)
and standard error of equating difference between two equating
functions (SEED) are also implemented for the kernel method of
equating.
Allows objects to be stored on disc and automatically
recalled into memory, as required, by delayed assignment.
Specific and class specific multiple correspondence analysis on survey-like data. Soc.ca is optimized to the needs of the social
scientist and presents easily interpretable results in near publication ready quality.
A suite of tools for collecting and constructing networks from social media data. Provides easy-to-use functions for collecting data across popular platforms (Instagram, Facebook, Twitter, and YouTube) and generating different types of networks for analysis.
SocialMediaMineR is a social media search and analytic tool that takes one or multiple URL(s) and returns the information about the popularity and reach of the URL(s) on social media. The function get_socialmedia retrieves the number of shares, likes, tweets, pins, and hits on Facebook, Twitter, Pinterest, StumbleUpon, LinkedIn, and Reddit. The package also includes dedicated functions for each social networking site and a function to decode shortened URLs.
Generates social networks using either of two
approaches: using either pairwise distances or territorial area intersections.
Provides to sociologists (and related scientists) a toolbox to facilitate the construction of social position indicators from survey data. Social position indicators refer to what is commonly known as social class and social status. There exists in the sociological literature many theoretical conceptualisation and empirical operationalization of social class and social status. This first version of the package offers tools to construct the International Socio-Economic Index of Occupational Status (ISEI) and the Oesch social class schema. It also provides tools to convert several occupational classifications (PCS82, PCS03, and ISCO08) into a common one (ISCO88) to facilitate data harmonisation work, and tools to collapse (i.e. group) modalities of social position indicators.
SOD (Self-Organising-Deltoids) provides multidimensional scaling by gradually reducing the dimensionality of an initial space and using the resulting stress in the configuration to re-arrange nodes. Stress is calculated from the errors in the inter-node distances, and the sum of the stresses at each node is combined to create N-dimensional force vectors that direct the movement of nodes as the dimensionality is iteratively reduced.
Functions, examples and other software related to the book
"Software for Data Analysis: Programming with R". See
package?SoDA for an overview.
Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under quadratic discriminant analysis and logistic regression model.
To implement two clustering methods, ODC and SODC, for
clustering datasets using optimal scoring, can also be used as
an dimension reduction tool.
Bindings to libsodium: a modern, easy-to-use software library for
encryption, decryption, signatures, password hashing and more. Sodium uses
curve25519, a state-of-the-art Diffie-Hellman function by Daniel Bernstein,
which has become very popular after it was discovered that the NSA had
backdoored Dual EC DRBG.
Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos.
An extension of sensitivity, specificity, positive and negative
predictive value to continuous predicted and reference memberships in
[0, 1].
It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.
Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an "EM" flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components)
A collection of functions for reading data from USDA-NCSS soil databases.
Basic and model-based soil physical analyses.
This package provides functions to graphically represent
soil properties. Morphological data gathered in the field
such as horizon boundaries, root abundance and dimensions,
skeletal shape, abundance and dimension as well as
meaningful soil color may be represented via the plot
function. A lattice-based plot.element function has been
designed to represent depth function of a given variable.
This package contains functions for modeling Soil Organic
Matter decomposition in terrestrial ecosystems.
Methods and classes for processing and analyzing soil and plant infrared (MIR, alpha-MIR and VISNIR) spectroscopy readings based on the Africa Soil Information Services (AfSIS) project data.
"The Soil Texture Wizard" is a set of R functions designed to produce texture triangles (also called texture plots, texture diagrams, texture ternary plots), classify and transform soil textures data. These functions virtually allows to plot any soil texture triangle (classification) into any triangle geometry (isosceles, right-angled triangles, etc.). This set of function is expected to be useful to people using soil textures data from different soil texture classification or different particle size systems. Many (> 15) texture triangles from all around the world are predefined in the package. A simple text based graphical user interface is provided: soiltexture_gui().
It is a set of R implementations of parametric formulas of soil water
retention or conductivity curve. At the moment, only Van Genuchten (for
soil water retention curve) and Mualem (for hydraulic conductivity) were
implemented. See reference
\url{http://en.wikipedia.org/wiki/Water_retention_curve}.
Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.
SOLAR is the standard software program to perform linkage and association mappings
of the quantitative trait loci (QTLs) in pedigrees of arbitrary size and complexity.
This package allows the user to exploit the variance component methods implemented in SOLAR.
It automates such routine operations as formatting pedigree and phenotype data.
It also parses the model output and contains summary and plotting functions for exploration of the results.
In addition, solarius enables parallel computing of the linkage and association analyses,
that makes the calculation of genome-wide scans more efficient.
See <http://solar.txbiomedgenetics.org/> for more information about SOLAR.
Predicted solid earth tide displacements in the meridional,
zonal and vertical directions. Based on "Solid" from Dennis Milbert,
modified from "dehanttideinelMJD" by V. Dehant, S. Mathews, J. Gipson,
and C. Bruyninx.
Provides a set of functions for querying and parsing
data from Solr endpoints (local and remote), including search, faceting,
highlighting, stats, and 'more like this'.
Self-Organizing Map (with application in gene clustering)
This package provides an R implementation of the Self-Organising Migrating Algorithm, a general-purpose, stochastic optimisation algorithm. The approach is similar to that of genetic algorithms, although it is based on the idea of a series of ``migrations'' by a fixed set of individuals, rather than the development of successive generations. It can be applied to any cost-minimisation problem with a bounded parameter space, and is robust to local minima.
The stochastic (also called on-line) version of the Self-Organising
Map (SOM) algorithm is provided. Different versions of the
algorithm are implemented, for numeric and relational data and for
contingency tables. The package also contains many plotting
features (to help the user interpret the results) and a graphical
user interface based on shiny (which is also available on-line at
http://shiny.nathalievilla.org/sombrero).
some Brownian motions simulation functions
This package collects some procedures controlling the
Generalized Familywise Error Rate.
It's a collection of functions for Multiplicity Correction and Multiple Testing.
The package provides the plot function som.plot() to
create high quality visualisations of hexagonal Kohonen maps
(self-organising maps).
Estimate the abundance of cell clones from the
distribution of lengths of DNA fragments (as created by
sonication, whence `sonicLength').  The algorithm in
"Estimating abundances of retroviral insertion sites from
DNA fragment length data" by Berry CC, Gillet NA, Melamed
A, Gormley N, Bangham CR, Bushman FD. Bioinformatics;
2012 Mar 15;28(6):755-62 is implemented.  The
experimental setting and estimation details are described
in detail there. Briefly, integration of new DNA in a
host genome (due to retroviral infection or gene therapy)
can be tracked using DNA sequencing, potentially allowing
characterization of the abundance of individual cell
clones bearing distinct integration sites. The locations
of integration sites can be determined by fragmenting the
host DNA (via sonication or fragmentase), breaking the
newly integrated DNA at a known sequence, amplifying the
fragments containing both host and integrated DNA,
sequencing those amplicons, then mapping the host
sequences to positions on the reference genome. The
relative number of fragments containing a given position
in the host genome estimates the relative abundance of
cells hosting the corresponding integration site, but
that number is not available and the count of amplicons
per fragment varies widely.  However, the expected number
of distinct fragment lengths is a function of the
abundance of cells hosting an integration site at a given
position and a certain nuisance parameter. The algorithm
implicitly estimates that function to estimate the
relative abundance.
Collection of different single objective test functions
useful for benchmarks and algorithm development.
Provides functions to non-parametrically estimate the off-pulse interval of a source
function originating from a pulsar. The technique is based on a sequential application of P-values
obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov,
Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.
Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique.  Includes support for binary, count, and continuous data.
SortableHTMLTables writes a data frame to an HTML file
that contains a sortable table. The sorting is done using the
jQuery plugin Tablesorter. The appearance is controlled through
a CSS file and several GIF's.
sortinghat is a classification framework to streamline the
evaluation of classifiers (classification models and algorithms) and seeks
to determine the best classifiers on a variety of simulated and benchmark
data sets. Several error-rate estimators are included to evaluate the
performance of a classifier. This package is intended to complement the
well-known 'caret' package.
Algorithms for Finnish open government data.
Search contributed R packages, sort by package
sos4R is a client for Sensor Observation Services (SOS) as
specified by the Open Geospatial Consortium (OGC).  It allows
users to retrieve metadata from SOS web services and to
interactively create requests for near real-time observation
data based on the available sensors, phenomena, observations et
cetera using thematic, temporal and spatial filtering.
Tools to download data from the Sotkanet open data portal.
Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings.
The SoundexBR package provides an algorithm for decoding names
into phonetic codes, as pronounced in Portuguese. The goal is for
homophones to be encoded to the same representation so that they can be
matched despite minor differences in spelling. The algorithm mainly encodes
consonants; a vowel will not be encoded unless it is the first letter. The
soundex code resultant consists of a four digits long string composed by
one letter followed by three numerical digits: the letter is the first
letter of the name, and the digits encode the remaining consonants.
Construct a ranking of a set of treatments/groups by
gathering together information coming from several response variables.
It can be used with both balanced and unbalanced experiments
(with almost all test statistics) as well as in presence of either
continuous covariates or a stratifying (categorical) variable.
Analogous to source(), but works when given a Gist URL or
ID.
Genomic and multi-environmental soybean data. Soybean Nested Association Mapping (SoyNAM) project dataset funded by the United Soybean Board (USB), pre-formatted for general analysis and genome-wide association analysis using the NAM package.
Classes and methods for spatial
data; the classes document where the spatial location information
resides, for 2D or 3D data. Utility functions are provided, e.g. for
plotting data as maps, spatial selection, as well as methods for
retrieving coordinates, for subsetting, print, summary, etc.
Provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.
Implements the Sequential Predictions Algorithm
The package implements the model-based kernel machine
method for detecting gene-centric gene-gene interactions of Li
and Cui (2012).
Miscellaneous functions for analysing species association
and niche overlap.
Partial correlation estimation with joint sparse
regression model
SPACECAP is a user-friendly software package for
estimating animal densities using closed model
capture-recapture sampling based on photographic captures using
Bayesian spatially-explicit capture-recapture models. This
approach offers advantage such as: substantially dealing with
problems posed by individual heterogeneity in capture
probabilities in conventional capture-recapture analyses. It
also offers non-asymptotic inferences which are more
appropriate for small samples of capture data typical of
photo-capture studies.
undirected graph inference with missing data
This package provides an extension of conditional
independence (CIG) and directed acyclic graph (DAG) estimation
to the case where conditional relationships are (non-linear)
additive models.
Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal combining or aggregating, retrieving coordinates, print, summary, etc.
SPACoDi is primarily designed to characterise the
structure and phylogenetic diversity of communities using
abundance or presence-absence data of species among community
plots.
The package provides tools to construct and exploit spatially weighted context data. Spatial weights are derived by a Kernel function from a user-defined matrix of distances between contextual units. Spatial weights can then be applied either to precise contextual measures or to aggregate estimates based on micro-level survey data, to compute spatially weighted context data. Available aggregation functions include indicators of central tendency, dispersion, or inter-group variability, and take into account survey design weights. The package further allows combining the resulting spatially weighted context data with individual-level predictor and outcome variables, for the purposes of multilevel modelling. An ad hoc stratified bootstrap resampling procedure generates robust point estimates for multilevel regression coefficients and model fit indicators, and computes confidence intervals adjusted for measurement dependency and measurement error of aggregate estimates. As an additional feature, residual and explained spatial dependency can be estimated for the tested models.
Easily implement a variety of simulation models, with a focus on
spatially explicit agent based models. These include raster-based,
event-based, and agent-based models. The core simulation components are
built upon a discrete event simulation framework that facilitates
modularity, and easily enables the user to include additional functionality
by running user-built simulation modules. Included are numerous tools to
visualize raster and other maps. The suggested package `fastshp` can be
installed with `install.packages("fastshp", repos="http://rforge.net",
type="source")`.
Set of functions for sparse matrix algebra.
Differences with SparseM/Matrix are:
(1) we only support (essentially) one sparse matrix format,
(2) based on transparent and simple structure(s),
(3) tailored for MCMC calculations within GMRF.
(4) S3 and S4 like-"compatible"  ...     and it is fast.
Inference in mixed models, including GLMMs with spatial correlations and models with non-Gaussian random effects (e.g., Beta Binomial, or negative-binomial mixed models). Heteroskedasticity can further be fitted by a linear model. The algorithms are currently various Laplace approximations methods for ML or REML, in particular h-likelihood and penalized-likelihood methods.
Fit the spatial panel data models: the fixed effects, random
effects and between models.
Carries out a search for an optimal partition in terms of a
regular Boolean expression
This package performs robust nonparametric tests for rare variants
association analysis using summation of partition approaches that incorporate
gene-gene and gene-environmental interactions
We provide an efficient solver for estimating semiparametric generalized linear models.
Implements the sparse clustering methods of Witten and
Tibshirani (2010): "A framework for feature selection in
clustering"; published in Journal of the American Statistical
Association 105(490): 713-726.
Decide which server to connect to,
based on previous response times, and configuration.
A sparkline is a line chart, without axes and labels.
Its goal is to show the general shape of changes over time, or
another quantity. This package is an 'R' implementation
of the original shell project: <http://zachholman.com/spark/>.
Create sparklines and graphical tables for documents and websites.
Generate syntax for use with the sparklines package for
LaTeX.
Use SPARQL to pose SELECT or UPDATE queries to an end-point.
Provides functions to estimate kernel-smoothed relative risk functions and perform subsequent inference.
Implements the sparse biclustering proposal of Tan and Witten (2014), Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics  23(4):985-1008.
A collection of sparse and regularized discriminant analysis
methods intended for small-sample, high-dimensional data sets. The package
features the High-Dimensional Regularized Discriminant Analysis classifier.
Multidimensional scaling provides a means of uncovering a latent structure underlying observed data, while estimating the number of latent dimensions.  This package presents a means for scaling binary and count data, for example the votes and word counts for legislators.  Future work will include an EM implementation and extend this work to ordinal and continuous data.
SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de
Computes Hessian of a scalar-valued function, and returns it in
sparse Matrix format, using ACM TOMS Algorithm 636. The user must supply the objective function, the
gradient, and the row and column indices of the non-zero elements of the
lower triangle of the Hessian (i.e., the sparsity structure must be known
in advance). The algorithm exploits this sparsity, so Hessians can be
computed quickly even when the number of arguments to the objective
functions is large. This package is intended to be useful for numeric
optimization (e.g., with the trustOptim package) or in simulation (e.g.,
the sparseMVN package). The underlying algorithm is ACM TOMS Algorithm 636,
written by Coleman, Garbow and More (ACM Transactions on Mathematical
Software, 11:4, Dec. 1985).
Performs sparse linear discriminant analysis for gaussians and mixture of gaussians models.
Coefficient estimation and model prediction based on the LASSO sparse
learning algorithm and its improved versions such as Bolasso, bootstrap ranking
LASSO, two-stage hybrid LASSO and others. These LASSO estimation procedures are
applied in the fields of variable selection, graphical modeling and ensemble
learning. The bagging LASSO model uses a Monte Carlo cross-entropy algorithm to
determine the best base-level models and improve predictive performance.
Use RcppEigen to fit least trimmed squares
regression models with an L1 penalty in order to obtain
sparse models.
Some basic linear algebra functionality for sparse matrices is
provided:  including Cholesky decomposition and backsolving as well as
standard R subsetting and Kronecker products.
Computes multivariate normal (MVN) densities, and
samples from MVN distributions, when the covariance or
precision matrix is sparse.
Sparsenet uses the MC+ penalty of Zhang. It computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects.  This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots.  Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to limited dependent variables, models with truncated outcomes, and propensity score and instrumental variable analysis.
Sparse-aware maximum likelihood for structural equation models in inferring gene regulatory networks
Computes sparse vector autoregressive coefficients and precision
matrices for time series chain graphical models.
Computer simulations are becoming a popular technique to use in attempts to further our understanding of complex systems. SPARTAN, described in our 2013 publication in PLoS Computational Biology, provides code for four techniques described in available literature which aid the analysis of simulation results, at both single and multiple timepoints in the simulation run. The first technique addresses aleatory uncertainty in the system caused through inherent stochasticity, and determines the number of replicate runs necessary to generate a representative result. The second examines how robust a simulation is to parameter perturbation, through the use of a one-at-a-time parameter analysis technique. Thirdly, a latin hypercube based sensitivity analysis technique is included which can elucidate non-linear effects between parameters and indicate implications of epistemic uncertainty with reference to the system being modelled. Finally, a further sensitivity analysis technique, the extended Fourier Amplitude Sampling Test (eFAST) has been included to partition the variance in simulation results between input parameters, to determine the parameters which have a significant effect on simulation behaviour. Version 1.3 adds support for Netlogo simulations, aiding simulation developers who use Netlogo to build their simulations perform the same analyses. We have also added user support through the group spartan-group[AT]york[DOT]ac[DOT]uk. Version 2.0 added the ability to read all simulations in from a single CSV file in addition to the prescribed folder structure in previous versions.
Fit spatial CAR count regression models using MCMC
This package provides functionality for spatio-temporal modeling of large data sets. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.
Graphs (or networks) and graph component
calculations for spatial locations in *D.
Functions for kriging and point pattern analysis.
Functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units.  This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration.
Utilities to support spatial data manipulation, query, sampling
and modelling. Functions include models for species population density, download
utilities for climate and global deforestation spatial products, spatial
smoothing, multivariate separability, point process model for creating pseudo-
absences and sub-sampling, polygon and point-distance landscape metrics,
auto-logistic model, sampling models, cluster optimization and statistical
exploratory tools.
Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models.
Filter matrices or (three dimensional) array data using different convolution kernels.
This package fits a hierarchical spatial model for the generalized extreme value distribution with the option of model averaging over the space of covariates.
Edge-corrected kernel density estimation and binary kernel
regression estimation for multivariate spatial point process
data
Network based diffusion analysis (NBDA) allows inference on
the asocial and social transmission of information.  This may involve
the social transmission of a particular behaviour such as tool use, for example.
For the NBDA, the key parameters estimated are the social effect and baseline rate
parameters.  The baseline rate parameter gives the rate at which the behaviour
is first performed (or acquired) asocially amongst the individuals in a given population.
The social effect parameter quantifies the effect of the social associations amongst
the individuals on the rate at which each individual first performs or displays
the behaviour.  Spatial NBDA involves incorporating spatial information in the analysis.
This is done by incorporating social networks derived from
spatial point patterns (of the home bases of the individuals under study).  In addition,
a spatial covariate such as vegetation cover, or slope may be included in the modelling
process.
This package contains test and estimates of location,
tests of independence, tests of sphericity and several
estimates of shape all based on spatial signs, symmetrized
signs, ranks and signed ranks.
This package provides tools to assess the association between two spatial processes.
Currently, three methodologies are implemented: An adapted t-test to perform hypothesis
testing about the independence between the processes, a suitable nonparametric correlation
coefficient, and the codispersion coefficient. SpatialPack gives methods to complement
methodologies that are available in geoR for one spatial process.
Computes spatial position models: Stewart potentials, Reilly
catchment areas, Huff catchment areas.
Bayesian Estimation of Spatial Probit and Tobit Models.
Summaries for measuring segregation/mingling in multitype spatial point patterns with graph based neighbourhood description.
Included indices: Mingling, Shannon, Simpson (also the non-spatial)
Included functionals: Mingling, Shannon, Simpson, ISAR, MCI.
Included neighbourhoods: Geometric, k-nearest neighbours, Gabriel, Delaunay.
Provides functions implementing the pairwise M-estimator for
parametric models for stable tail dependence functions described in "An
M-estimator of spatial tail dependence"
(Einmahl, J.H.J., Kiriliouk, A., Krajina, A. and Segers, J., 2014).
See http://arxiv.org/abs/1403.1975.
Spatial functions meant to enhance the core functionality of the
package "raster", including a parallel processing engine for use with
rasters.
Tools for spatial data analysis.  Emphasis on kriging.  Provides functions for prediction and simulation.  Intended to be relatively straightforward, fast, and flexible.
Spatial forecast verification arose from verifying high-resolution forecasts, where coarser-resolution models generally are favored even when a human forecaster finds the higher-resolution model to be considerably better.  Most newly proposed methods, which largely come from image analysis, computer vision, and similar, are available, with more on the way.
Utilities that estimate, predict and cross-validate the
spatio-temporal model developed for MESA Air.
Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigenfunctions by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data.
Bayesian inference for parametric proportional hazards spatial survival models; flexible spatial survival models.
Fits univariate and multivariate spatio-temporal
models with Markov chain Monte Carlo (MCMC).
Provides several Bayesian survival models for spatial/non-spatial survival data: marginal Bayesian Nonparametric models, marginal Bayesian proportional hazards models, generalized accelerated failure time frailty models, and standard semiparametric frailty models within the context of proportional hazards, proportional odds and accelerated failure time.
Evaluation of control charts by means of
the zero-state, steady-state ARL (Average Run Length) and RL quantiles.
Setting up control charts for given in-control ARL. The control charts
under consideration are one- and two-sided EWMA, CUSUM, and
Shiryaev-Roberts schemes for monitoring the mean of normally
distributed independent data. ARL calculation
of the same set of schemes under drift are added.
Other charts and parameters are in preparation.
Further SPC areas will be covered as well
(sampling plans, capability indices ...).
Calibration of thresholds of control charts such as
CUSUM charts based on past data, taking estimation error into account.
A new reduced-rank LDA method which works for high dimensional multi-class data.
Spatial coverage sampling and random sampling from compact
geographical strata created by k-means.
Provides a covariance estimator for multivariate normal
data that is sparse and positive definite.  Implements the
majorize-minimize algorithm described in Bien, J., and
Tibshirani, R. (2011), "Sparse Estimation of a Covariance
Matrix," Biometrika. 98(4). 807--820.
This package computes the sparse principal component regression. The regularization parameters are optimized by cross-validation.
The Semi Parametric Piecewise Distribution blends the Generalized Pareto Distribution for the tails with a kernel based interior.
A collection of functions to create spatial weights matrix
objects from polygon contiguities, from point patterns by distance and
tessellations, for summarizing these objects, and for permitting their
use in spatial data analysis, including regional aggregation by minimum
spanning tree; a collection of tests for spatial autocorrelation,
including global Moran's I, APLE, Geary's C, Hubert/Mantel general cross
product statistic, Empirical Bayes estimates and Assunção/Reis Index,
Getis/Ord G and multicoloured join count statistics, local Moran's I
and Getis/Ord G, saddlepoint approximations  and exact tests for global
and local Moran's I; and functions for estimating spatial simultaneous
autoregressive (SAR) lag and error models, impact measures for lag
models, weighted and unweighted SAR and CAR spatial regression models,
semi-parametric and Moran eigenvector spatial filtering, GM SAR error
models, and generalized spatial two stage least squares models.
Functions for estimating split-duration regression models and various associated generic function methods.
A spatio-dynamic modelling package that focuses on three
characteristic wetland plant communities in a semiarid Mediterranean
wetland in response to hydrological pressures from the catchment. The
package includes the data on watershed hydrological pressure and the
initial raster maps of plant communities but also allows for random initial
distribution of plant communities. Ongoing developments of the package
focus on offering easy to use tools for creating other spatio-dynamic
models.
Implements stochastic proximity embedding as described by
Agrafiotis et al. in PNAS, 2002, 99, pg 15869 and J. Comput. Chem., 2003,24, pg 1215
We introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data. Related publication is available at http://www.biomedcentral.com/1471-2105/12/405/.
Alternative to the kernlab::specc function. Includes a spectral clustering implementation, a locally adapted kernel function akin to what is already proposed in kernlab, and an optional procedure that automatically estimates the optimal number of clusters. Several sample data sets are also included.
Utility functions for spectroscopy.  1. Functions to simulate spectra for use in teaching or testing.  2.  Functions to process files created by 'LoggerPro' and 'SpectraSuite' software.
SPECIES is an R package for estimation of species richness
or diversity.
Preparation of species occurrences and distribution data for the use in phylogenetic analyses. SpeciesgeocodeR is built for data cleaning, data exploration and data analysis and especially suited for biogeographical and ecological questions on large datasets. The package includes the easy creation of summary-tables and -graphs and geographical maps, the automatic cleaning of geographic occurrence data, the calculating of coexistence matrices and species ranges (EOO) as well as mapping diversity in geographic areas.
Mixtures using BFGS and analytical derivatives
The package helps to test the specificity of personality trait-outcome (or trait-trait) associations by comparing the observed associations to those obtained using randomly created personality scales.
Provides a set of methods for metabolomics
data analysis, including data loading in different formats,
pre-processing, metabolite identification, univariate and multivariate
data analysis, machine learning and feature selection. Case studies
can be found on the website: http://darwin.di.uminho.pt/metabolomics .
A collection of new forecast verification routines for the SPECS FP7 project. The emphasis is on comparative verification of ensemble forecasts.
Routines for creating, manipulating, and performing
Bayesian inference about Gaussian processes in
one and two dimensions using the Fourier basis approximation:
simulation and plotting of processes, calculation of
coefficient variances, calculation of process density,
coefficient proposals (for use in MCMC).  It uses R environments to
store GP objects as references/pointers.
Contains some implementations of Singular Spectrum Analysis (SSA) for the gapfilling and spectral decomposition of time series. It contains the code used by Buttlar et. al. (2014), Nonlinear Processes in Geophysics. In addition, the iterative SSA gapfilling method of Kondrashov and Ghil (2006) is implemented. All SSA calculations are done via the truncated and fast SSA algorithm of Korobeynikov (2010) (package 'Rssa').
Spectra visualization, organizer and data preparation
from within R or stand-alone. Binary (application) part is
installed separately by running spnInstallApp()
immediately after installing the package.
Fitting linear models and generalized linear models to large data sets by updating algorithms.
The package performs estimation and testing of the
treatment effect in a 2-group randomized clinical trial with a
quantitative, dichotomous, or right-censored time-to-event
endpoint. The method improves efficiency by leveraging baseline
predictors of the endpoint. The inverse probability weighting
technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used
to provide unbiased estimation when the endpoint is missing at
random.
A set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI).
Provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.
This package implements spatial error estimation and
permutation-based variable importance measures for predictive
models using spatial cross-validation and spatial block
bootstrap.
A set of tools for estimation of various spatial specifications of stochastic frontier models
Interpreted interface between GRASS 6+ geographical
information system and R, based on starting R from within the GRASS
environment, or running free-standing R in a temporary GRASS location;
the package provides facilities for using all GRASS commands from the
R command line. This package may not be used for GRASS 7, for which
spgrass7 should be used.
A collection of statistical hypothesis tests and other
techniques for identifying certain spatial relationships/phenomena in
DNA sequences. In particular, it provides tests and graphical methods for determining
whether or not DNA sequences comply with Chargaff's second parity rule
or exhibit purine-pyrimidine parity. In addition, there are functions for
efficiently simulating discrete state space Markov chains and testing
arbitrary symbolic sequences of symbols for the presence of first-order
Markovianness.
Also, it has functions for counting words/k-mers (and cylinder patterns) in
arbitrary symbolic sequences. Functions which take a DNA sequence as input
can handle sequences stored as SeqFastadna objects from the 'seqinr' package.
Functions for computing geographically weighted
regressions are provided, based on work by Chris
Brunsdon, Martin Charlton and Stewart Fotheringham.
Various functions for creating spherical coordinate system plots via extensions to rgl.
This package defines several methods to integrate
functions over the unit sphere and ball in n-dimensional
Euclidean space.  Routines for converting to/from multivariate
polar/spherical coordinates are also provided.
Spherical K-function for point-pattern analysis on the sphere.
This package carries out spherical wavelet transform
developed by Li (1999) and Oh (1999), and implements wavelet
thresholding approaches proposed by Oh and Li (2004).
Generalized Method of Moment estimation of Cliff-Ord-type
spatial autoregressive models with and without heteroskedastic
innovations
The SNP Panel Identification Assay (SPIA) is a package
that enables an accurate determination of cell line identity
from the genotype of single nucleotide polymorphisms (SNPs).
The SPIA test allows to discern when two cell lines are close
enough to be called similar and when they are not.
A package for the analysis of species limits and DNA barcoding data
Fits and simulates data from our predator preferences model
Spike and slab for prediction and variable selection in
linear regression models. Uses a generalized elastic net for
variable selection.
Bayesian variable selection, model choice, and regularized
estimation for (spatial) generalized additive mixed regression models
via stochastic search variable selection with spike-and-slab priors.
An optimal weighting strategy to compute
simulation-efficient shortest probability intervals (spins).
Implements a generative model that uses a
spike-and-slab like prior distribution obtained by multiplying a
deterministic binary vector. Such a model allows an EM algorithm,
optimizing a type-II log-likelihood.
The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".
Online data collection tools like Google Forms often export
multiple-response questions with data concatenated in cells. The
concat.split (cSplit) family of functions splits such data into
separate cells. The package also includes functions to stack groups
of columns and to reshape wide data, even when the data are
"unbalanced"---something which reshape (from base R) does not handle,
and which melt and dcast from reshape2 do not easily handle.
ML and GM estimation and diagnostic testing of econometric models for spatial panel data.
This package provides functions for fitting a Sparse
Partial Least Squares Regression and Classification
Currently there are many functions in S-PLUS that are
missing in R. To facilitate the conversion of S-PLUS packages
to R packages, this package provides some missing S-PLUS
functionality in R.
Classes and methods for times and dates (ported from S-PLUS)
Classes and methods for time series (ported from S-PLUS).
Installs SPM12 to the R library directory and has associated functions for fMRI and general imaging utilities, called through MATLAB.
A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields.
Implements the method of general semiparametric maximum likelihood estimation for logistic models in case-mother control-mother designs.
The package provides methods for dealing with spatial social
networks. It allows to plot networks for which actors have a specific
location on a map (cities, participants in a political debate, etc.).
SpatialPolygons objects from the sp package are supported.
A programmatic interface to many species occurrence data sources,
including 'GBIF', 'USGS's' 'BISON', 'iNaturalist', Berkeley 'Ecoinformatics'
Engine 'eBird', 'AntWeb', and `iDigBio`. Includes functionality for
retrieving species occurrence data, and combining that data.
Utilities for use with 'spocc'. Includes functions to
clean occurrence data, find and remove duplicates, combine data,
and make maps with base 'R' plots, 'ggplot2', and various interactive
maps.
SPODT is a spatial partitioning method based on oblique decision trees, in order to classify study area into zones of different risks, determining their boundaries
R implementation of the methods described in "A rank-based
empirical likelihood approach to two-sample proportional odds
model and its goodness-of-fit" by Zhong Guan and Cheng Peng,
Journal of Nonparametric Statistics, to appear.
The aim of this package is to provide infrastructure for
sports analysis. Anyway, currently it is a selection of data
sets, functions to fetch sports data, examples, and demos --
with the ambition to develop bit by bit a set of classes to
represent general concepts of sports analysis.
The Sequential Parameter Optimization Toolbox provides a set of tools for Parameter-tuning, based on modelling techniques, DoE and statistical methods.
The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.
Calculate species richness functions for rarefaction and
extrapolation.
SPRINT (Simple Parallel R INTerface) is a parallel
framework for R. It provides a High Performance Computing (HPC)
harness which allows R scripts to run on HPC clusters. SPRINT
contains a library of selected R functions that have been
parallelized. Functions are named after the original R function
with the added prefix 'p', i.e. the parallel version of cor()
in SPRINT is called pcor(). Call to the parallel R functions
are included directly in standard R scripts.
SPRINT contains functions for correlation (pcor), partitioning around medoids (ppam),
apply (papply), permutation testing (pmaxT), bootstrapping (pboot), random forest (prandomForest),
rank product (pRP) and hamming distance (pstringdistmatrix).
The main function of this package builds prognostic models that consider interactions by combining available statistical components. Furthermore, it provides a function for evaluating the relevance of the selected interactions by resampling techniques.
Robust dimension reduction methods for regression and discriminant analysis are implemented that yield estimates with a partial least squares alike interpretability. Partial robust M regression (PRM) is robust to both vertical outliers and leverage points. Sparse partial robust M regression (SPRM) is a related robust method with sparse coefficient estimate, and therefore with intrinsic variable selection. For bianry classification related discriminant methods are PRM-DA and SPRM-DA.
R functions to mine sparse models from data.
Perform Wald's Sequential Probability Ratio Test on variables with a Normal, Bernoulli, Exponential and Poisson distribution. Plot acceptance and continuation regions, or create your own with the help of closures.
Methods to optimize sample configurations using spatial simulated
annealing. Multiple objective functions are implemented for various
purposes, such as variogram estimation, trend estimation, and spatial
interpolation. A general purpose spatial simulated annealing function
enables the user to define his/her own objective function.
Program uses method of polynomial of variable degrees to interpolate gridded data preserving monotonicity and/or convexity or none. Method is implemented for univariate and bivariate cases. If values of derivatives are provided, spline will fix them,if not program will estimate them numerically. Package written purely in R.
SPSL package provides functionality for labeling of
percolation clusters on 2D & 3D square lattice with various
lattice size, relative fraction of accessible sites (occupation
probability), iso- & anisotropy, von Neumann & Moore
(1,d)-neighborhood
Produces Slepian smoother basis objects for use in Generalized Additive Models, as estimated by package:mgcv.
This group of functions implements algorithms for design and
analysis of probability surveys.  The functions are tailored for Generalized
Random Tessellation Stratified survey designs.
This package collects algorithms related to Sierpinski pedal triangle (SPT).
Fits, spatially predicts, and temporally forecasts space-time data using Gaussian Process (GP): (1) spatially varying coefficient process models and (2) spatio-temporal dynamic linear models.
Implements nonparametric hypothesis tests to check isotropy and
symmetry properties for two-dimensional spatial data.
spThin is a set of functions that can be used to spatially thin
species occurrence data. The resulting thinned data can be used in ecological
modeling, such as ecological niche modeling.
Fits, spatially predicts and temporally forecasts large amounts of space-time data using  [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems.
Semiparametric transformation model, calibration weights
This package provides functions and datasets from the book "Introduction to Scientific Programming and Simulation Using R".
Sparse Quadratic Discriminant Analysis (SQDA) can be performed. In SQDA, the covariance matrix are assumed to be block-diagonal.And, for each block, sparsity assumption is imposed on the covariance matrix. It is useful in high-dimensional setting.
Description: Manipulate R data frames using SQL.
sqliter helps users, mainly data munging practioneers, to organize
their sql calls in a clean structure. It simplifies the process of
extracting and transforming data into useful formats.
This package provides utilities for working with a library of SQL
files.
Normalization based a subset of negative control probes as
described in 'Subset quantile normalization using negative
control features'. Wu Z, Aryee MJ, J Comput Biol. 2010
Oct;17(10):1385-95 [PMID 20976876].
Algorithms for accelerating the convergence of slow,
monotone sequences from smooth, contraction mapping such as the
EM algorithm. It can be used to accelerate any smooth, linearly
convergent acceleration scheme.  A tutorial style introduction
to this package is available in a vignette on the CRAN download
page or, when the package is loaded in an R session, with
vignette("SQUAREM").
Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps.  Lower-level functions  map numeric values to colors, display a matrix as an array of colors, and draw color keys.  Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.
This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models.
Implementation of the SRCS method for a color-based visualization of the
results of multiple pairwise tests on a large number of problem configurations, proposed in:
I.G. del Amo, D.A. Pelta. SRCS: a technique for comparing multiple algorithms under several
factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry
(Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in
Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012.
Draws scaled rectangle diagrams to represent a 2^k contingency
table, for k<=6
This package contains a collection of functions to perform
nonparametric estimation of receiver operating characteristic
(ROC) curves for continuous data.
This package implements the SRRS method introduced in Phoa (2013) into a graphical user interface (GUI) R program.
The ss3sim package develops a framework for fisheries stock
assessment simulation testing with Stock Synthesis 3 (SS3).
A set of functions to calculate sample size for two-sample difference in means tests. Does adjustments for either nonadherence or variability that comes from using data to estimate parameters.
Provides the spatial sign correlation and the two-stage spatial sign correlation as well as a one-sample test for the correlation coefficient.
ssd calculates the sample size needed to detect the differences between two sets of unordered categorical data.
Functions to visually and statistically analyze single system data.
Performs groupwise OLS (gOLS) and groupwise SIR (gSIR).
Spatial Stochastic Frontier Analysis (SSFA) is an original method for controlling the spatial heterogeneity in Stochastic Frontier Analysis (SFA) models, for cross-sectional data, by splitting the inefficiency term into three terms: the first one related to spatial peculiarities of the territory in which each single unit operates, the second one related to the specific production features and the third one representing the error term.
Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods.
This package provides utility functions for system command
execution, both locally and remotely using ssh/scp. The command
output is captured and provided to the caller. This functionality is
intended to streamline calling shell commands from R, retrieving and
using their output, while instrumenting the calls with appropriate
error handling. NOTE: this first version is limited to unix with local
and remote systems running bash as the default shell.
This package contains a set of functions that calculates
appropriate sample sizes for one-sample t-tests, two-sample t-tests,
and F-tests for microarray experiments based on desired power while
controlling for false discovery rates. For all tests, the standard
deviations (variances) among genes can be assumed fixed or random.
This is also true for effect sizes among genes in one-sample and two
sample experiments. Functions also output a chart of power versus sample
size, a table of power at different sample sizes, and a table of critical
test values at different sample sizes.
We propose a procedure for sample size calculation while controlling false discovery rate for RNA-seq experimental design. Our procedure depends on the Voom method proposed for RNA-seq data analysis by Law et al. (2014) and the sample size calculation method proposed for microarray experiments by Liu and Hwang (2007). We develop a set of functions that calculates appropriate sample sizes for two-sample t-test for RNA-seq experiments with fixed or varied set of parameters. The outputs also contain a plot of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes. To install this package, please use "source('http://bioconductor.org/biocLite.R'); biocLite('ssizeRNA', type = 'source')".
Package provides a set of tools for robust estimation and inference for models with sample selectivity.
Geostatistical modeling for data on stream networks, including models based on in-stream distance.  Models are created using moving average constructions. Spatial linear models, including covariates, can be fit with ML or REML.  Mapping and other graphical functions are included.
R package for computing the spherical smoothing splines
An integrated set of tools to estimate the size of a networked population based on respondent-driven sampling data. The package is part of the "RDS Analyst" suite of packages for the analysis of respondent-driven sampling data.
SSRAT is a computer program for two-dimensional sociometric status
determination with rating scales. For each person assessed, SSRAT computes
probability distributions of the total scores for `sympathy' (S),
`antipathy' (A), `social preference' (P) and `social impact' (I), and
applies the criteria for sociometric status categorization.
Calculates the power and sample size based on the difference in Restricted Mean Survival Times.
sss is a set of tools to import survey files
in the .sss (triple-s) format. It provides the function
read.sss that reads the .asc and .sss files of a
triple-s survey data file.  The package is experimental
- feedback, issues and bug reports are welcome.
Fast iterative thresholding sparse SVD, together with an initialization algorithm
Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions.
Implements the "shrinkage t" statistic
introduced in Opgen-Rhein and Strimmer (2007) and a shrinkage estimate
of the "correlation-adjusted t-score" (CAT score) described in
Zuber and Strimmer (2009).  It also offers a convenient interface
to a number of other regularized t-statistics commonly
employed in high-dimensional case-control studies.
Density, Probability and Quantile functions, and random number
generation for (skew) stable distributions, using the parametrizations of
Nolan.
Compute the maximum-likelihood(ML), generalised method of moment with finite and continuum number of points, the iterative Koutrouvelis regression and the Kogon-McCulloch methods to estimate the 4 parameters of stable law. The asymptotic properties of the estimator (covariance matrix, confidence intervals) are also provided.
Resampling procedures to assess the stability of selected variables
with additional finite sample error control for high-dimensional variable
selection procedures such as Lasso or boosting
Stacks rectangular datasets on top of each other, possibly
performing several type coercions along the way. For large datasets,
depends on the ff package. Provides an aggressive version of
ffbase::compact for data that may appear be real-typed but is in fact
int/short/byte. For many purposes plyr::rbind.fill may be more appropriate,
but for some kinds of survey data, the rules here work better.
Consists of helper functions collected from StackOverflow.com, a
question and answer site for professional and enthusiast programmers.
Provides facilities to implement and run population models of
stage-structured species...
stam is an evolving package that target on the various
methods to conduct Spatio-Temporal Analysis and
Modelling,including Exploratory Spatio-Temporal Analysis and
Inferred Spatio-Temporal Modelling.
Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation
Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single
ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values
around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to
utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects
allowing integration with other packages such adegenet.
Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. doi:10.1111/1755-0998.12129 for the appropriate citation and user manual. Thank you in advance.
Provides functions for the analysis of
occupational and environmental data with non-detects. Maximum
likelihood (ML) methods for censored log-normal data and
non-parametric methods based on the product limit estimate (PLE)
for left censored data are used to calculate all of the
statistics recommended by the American Industrial Hygiene
Association (AIHA) for the complete data case. Functions for
the analysis of complete samples using exact methods are also
provided for the lognormal model. Revised from 2007-11-05
'survfit~1'.
Standardizes text according to a template; particularly
useful for country names.
The C++ header files of the Stan project are provided by this package, but it contains no R code, shared objects, vignettes, or function documentation. It is only useful for developers who want to utilize the LinkingTo directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo and (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, templated statistical and linear algebra functions that can handle the automatically differentiable scalar types (and doubles, ints, etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.
Functions to analyze neuronal spike trains from a single
neuron or from several neurons recorded simultaneously.
Produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold
regression analysis results from several models side-by-side, as well as summary
statistics.
Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.
A Set of Tools for Administering SHared Repositories
Datasets for Stat2 textbook (by Cannon, et. al., published
by WH Freeman)
A set of tools to explore data.frames (summarize,
tabulate, xtile, pctile, binscatter) and work with panel data (elapsed
quarters/month, lead/lag, fill in rows of values, rolling functions).
Extract statistics from articles and recompute p values.
This package offers different possibilities to make statistical analysis for Environmental Data.
Support for reading and writing files in StatDataML---an XML-based data exchange format.
statebins is an alternative to choropleth maps for USA States and
is based on work by the Washington Post graphics department in their report
on "The states most threatened by trade". statebins preserves as much of
the geographic placement of the states as possible but has the look and
feel of a traditional heatmap. Functions are provided that allow for use
of both a binned, discrete scale and continuous scale depending on what
is needed for the underlying data.
Statistics Finland (Tilastokeskus) Open Data R Tools
Selectively acquire hourly meteorological data from stations located all over the world.
Integration of two data sources referred to the same target population which share a number of common variables (aka data fusion). Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing  with data from complex sample surveys are available too.
Offers useful functions to perform day-to-day data manipulation
operations, data quality checks and post modelling statistical checks.
One can effortlessly change class of a number of variables to factor,
remove duplicate observations from the data, create deciles of a
variable, perform data quality checks for continuous (integer or numeric),
categorical (factor) and date variables, and compute goodness of fit
measures such as auc for statistical models. The functions are consistent
for objects of class 'data.frame' and 'data.table', which is an enhanced
'data.frame' implemented in the package 'data.table'.
Functions and datasets to support Mayer Alvo and Philip L.H. Yu (2014). "Statistical Methods for Ranking Data", Springer. In this packages, smaller ranks stand for more preferred items.
A collection of algorithms and functions to aid statistical modeling. Includes growth curve comparisons, limiting dilution analysis (aka ELDA), mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models.  Includes advanced generalized linear model functions that implement secure convergence, dispersion modeling and Tweedie power-law families.
An integrated set of tools for the representation, visualization, analysis, and simulation of network data. For an introduction, type help(package='statnet').
Non-statistical utilities used by the software developed by the Statnet Project. They may also be of use to others.
A graphical user interface for network modeling with the 'statnet'
software.
Statomica is a suite of R functions that are needed for
certain packages such as LFDR.MLE, PsiHat, HistogramLFDR, combine and LFDRenrich.
A collection of statistical tools for social network analysis, with strong emphasis on the analysis of discrete powerlaw distributions and statistical hypothesis tests.
A set of methods to implement Generalized Method of Moments and Maximal
Likelihood methods for Random Utility Models. These methods are meant to
provide inference on rank comparison data. These methods accept full,
partial, and pairwise rankings, and provides methods to break down full or
partial rankings into their pairwise components. Please see Generalized
Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of
some of our methods.
Provides a number of functions for carrying out
inference with set-theoretic comparative methods, including
facilities for examining causal paths, assessing the sensitivity
of results to measurement and model specification error,
and performing Random Forest Comparative Analysis.
Functions related to multivariate measures of independence and ICA:
-estimate independent components by minimizing distance covariance;
-conduct a test of mutual independence based on distance covariance;
-estimate independent components via infomax (a popular method but generally performs poorer than mdcovica, ProDenICA, and/or fastICA, but is useful for comparisons);
-order indepedent components by skewness;
-match independent components from multiple estimates;
-other functions useful in ICA.
steepness is a package that computes steepness as a
property of dominance hierarchies. Steepness is defined as the
absolute slope of the straight line fitted to the normalized
David's scores. The normalized David's scores can be obtained
on the basis of dyadic dominance indices corrected for chance
or by means of proportions of wins. Given an observed
sociomatrix, it computes hierarchy's steepness and estimates
statistical significance by means of a randomization test.
A package to manage and display stellar tracks and
isochrones from Pisa low-mass database. Includes tools for
isochrones construction and tracks interpolation.
Estimation of the parameters of a spatio-temporal model
using the EM algorithm, estimation of the parameter standard
errors using a spatio-temporal parametric bootstrap, spatial
mapping.
STEPwise Community Assembly Model collection, applying ABC-SMC in order to infer different community assembly processes. Also includes several plotting functions in order to visualize found estimates.
A method to explore the treatment-covariate interactions in survival or generalized
linear model (GLM) for continuous, binomial and count data arising from two treatment
arms of a clinical trial. A permutation distribution approach to inference is implemented,
based on permuting the covariate values within each treatment group.
L2 penalized logistic regression for both continuous and
discrete predictors, with forward stagewise/forward stepwise
variable selection procedure.
Allows to fit step-functions to univariate serial data where neither the number of jumps nor their positions is known.
A stepwise approach to identifying recombination
breakpoints in a sequence alignment.
Functions for the collection of 3D points and curves using a stereo camera setup.
Functions implementing the procedure of entropy comparison between two data samples after the renormalization of respective probability distributions with the algorithm designed by Klimontovich (Zeitschrift fur Physik B Condensed Matter. 1987, Volume 66, Issue 1, pp 125-127) and extended by Anishchenko (Proc. SPIE 2098, Computer Simulation in Nonlinear Optics. 1994, pp.130-136).  The package also includes data preparation tools which can also be used separately for various applications.
A set of functions for computing the Standardized Temperature Index (STI).
Functions to build and use an interpolator ("emulator") for time series or 1D regularly spaced data in multidimensional space. The standard usage is for interpolating time-resolved computer model output between model parameter settings. It can also be used for interpolating multivariate data (e.g., oceanographic time-series data, etc.) in space.  There are functions to test the emulator using cross-validation, and to produce contour plots over 2D slices in model input parameter (or physical) space.
This package enables you to estimate a regression trunk model, which is an integration of a regression tree and a multiple regression model.
A consistently well behaved method of interpolation based
on piecewise rational functions using Stineman's algorithm
The Structural Topic Model (STM) allows researchers
to estimate topic models with document-level covariates.
The package also includes tools for model selection, visualization,
and estimation of topic-covariate regressions.
This visualization allows users to interactively explore the relationships between topics and the covariates estimated from the stm package in R.
Generates an interactive visualization of topic correlations/hierarchy in a Structural Topic Model (STM) of Roberts, Stewart, and Tingley.  The package performs a hierarchical clustering of topics which are then exported to a JSON object and visualized using D3.
Analysis by spatio-temporal data using the decomposition in n-dimensional arrays and using median polish technique.
Implementation of the family of generalised age-period-cohort
stochastic mortality models.  This family of models encompasses many models
proposed in the actuarial and demographic literature including the Lee-Carter model
and the Cairns-Blake-Dowd model. It includes functions for fitting mortality models,
analysing their goodness-of-fit and performing mortality projections and
simulations.
Plots a QQ-Norm Plot with several Gaussian simulations.
Fit a spatial-temporal occupancy models using
a probit formulation instead of a traditional logit
model.
This is an R package accompanying the paper "Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles" by Sameer S Bajikar, Christiane Fuchs, Andreas Roller, Fabian J Theis and Kevin A Janes (PNAS 2014, 111(5), E626-635). In this paper, we measure expression profiles from small heterogeneous populations of cells, where each cell is assumed to be from a mixture of lognormal distributions. We perform maximum likelihood estimation in order to infer the mixture ratio and the parameters of these lognormal distributions from the cumulated expression measurements.
Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models via Markov chain Monte Carlo (MCMC) methods.
With this package, users can obtain the real-time price and volume information of stocks in China market, as well as the information of the stock index. This package adopted the API from Sina Finance (http://finance.sina.com.cn/).
Download stock data, build single index, constant
correlation, and multigroup models, and estimate optimal stock
portfolios. Plotting functions for the portfolio possibilities
curve and portfolio cloud are included. A function to test a
portfolio on a data set is also provided.
Provides functions for analyzing historical performance of stocks or other investments. Functions are written in C++ to quickly calculate maximum draw-down, Sharpe ratio, Sterling ratio, and other commonly used metrics of stock performance.
Given a list of substance compositions, a list of
substances involved in a process, and a list of constraints in
addition to mass conservation of elementary constituents, the
package contains functions to build the substance composition
matrix, to analyze the uniqueness of process stoichiometry, and
to calculate stoichiometric coefficients if process
stoichiometry is unique.  (See Reichert, P. and Schuwirth, N.,
A generic framework for deriving process stoichiometry in
enviromental models, Environmental Modelling and Software 25,
1241-1251, 2010 for more details.)
Storm is a distributed real-time computation system. Similar to how
Hadoop provides a set of general primitives for doing batch processing, Storm
provides a set of general primitives for doing real-time computation.

Storm includes a "Multi-Language" (or "Multilang") Protocol to allow
implementation of Bolts and Spouts in languages other than Java.  This R
extension provides implementations of utility functions to allow an application
developer to focus on application-specific functionality rather than Storm/R
communications plumbing.
A toolkit for Reliability Availability and Maintainability (RAM) modeling of industrial process systems.
Can be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set.
Functionality and data access tools for transport planning, including origin-destination analysis, route allocation and modelling travel patterns.
A package for analysing, simulating and displaying space-time point patterns
Implement transformation-based and pixel-based residual
analysis of spatial-temporal point process models.
Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics, using demographic and genetic data sampled in the curse of the epidemics
Functions for the stratigraphic analysis of phylogenetic trees.
A toolkit for analyzing stratified population genetic data.
Univariate stratification of survey populations with a generalization of the
Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account
a discrepancy between the stratification variable and the survey variable. The determination
of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all
stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure
that some specific units are in the sample. The well known cumulative root frequency rule of
Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented.
A collection of tools for plotting and analyzing paleontological and geological data distributed through through time in stratigraphic cores or sections. Includes some miscellaneous functions for handling other kinds of palaeontological and paleoecological data.
This package provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of the class "StratSel" as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets.
The main function is icweib, which fits a stratified Weibull proportional hazards model for left censored, right censored, interval censored, and non-censored survival data. We parameterize the Weibull regression model so that it allows a stratum-specific baseline hazard function, but where the effects of other covariates are assumed to be constant across strata.
A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893 and NIH R21HG005912.
I provide functions to calculate Gross Primary Productivity, Net Ecocsystem Production, and Ecosystem Respiration from single station diurnal Oxygen curves.
Interface for data stream clustering algorithms implemented in the MOA (Massive Online Analysis) framework.
This package provides a series of functions that allow R users
to access Twitter's filter, sample, and user streams, and to
parse the output into data frames.
Functions to assist the process of learning structural
equation modeling
Forms queries to submit to the Cleveland Federal Reserve Bank web
site's financial stress index data site.  Provides query functions for both
the composite stress index and the components data. By default the download
includes daily time series data starting September 25, 1991.  The functions
return a class of either type easing or cfsi which contain a list of items
related to the query and its graphical presentation.  The list includes the
time series data as an xts object.  The package provides four lattice time
series plots to render the time series data in a manner similar to the
bank's own presentation.
The package provide functions for computing the reliability of stress-strength models and for building two-sided or one-sided confidence intervals according to different approximate procedures
Implements an approximate string matching version of R's native
'match' function. Can calculate various string distances based on edits
(damerau-levenshtein, hamming, levenshtein, optimal sting alignment), qgrams
(q-gram, cosine, jaccard distance) or heuristic metrics (jaro, jaro-winkler).
An implementation of soundex is provided as well. Distances can be computed
between character vectors while taking proper care of encoding or between
integer vectors representing generic sequences.
A toolbox for a construction of protein-protein interaction networks through the 'STRING' application programming interface, and an inference of Gaussian networks through 'SIMoNe' and 'WGCNA' approach, from DE genes analysis results and expression data. Additional functions are provided to import automatically networks into an active 'Cytoscape' session.
Allows for fast, correct, consistent, portable,
as well as convenient character string/text processing in every locale
and any native encoding. Owing to the use of the ICU library,
the package provides R users with platform-independent functions
known to Java, Perl, Python, PHP, and Ruby programmers. Among available
features there are: pattern searching (e.g., with ICU Java-like regular
expressions or the Unicode Collation Algorithm), random string generation,
case mapping, string transliteration, concatenation,
Unicode normalization, date-time formatting and parsing, etc.
A consistent, simple and easy to use set of wrappers around the
fantastic 'stringi' package. All function and argument names (and positions)
are consistent, all functions deal with "NA"'s and zero length vectors
in the same way, and the output from one function is easy to feed into
the input of another.
Testing, monitoring and dating structural changes in (linear)
regression models. strucchange features tests/methods from
the generalized fluctuation test framework as well as from
the F test (Chow test) framework. This includes methods to
fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM,
recursive/moving estimates) and F statistics, respectively.
It is possible to monitor incoming data online using
fluctuation processes.
Finally, the breakpoints in regression models with structural
changes can be estimated together with confidence intervals.
Emphasis is always given to methods for visualizing the data.
Performs multiple testing corrections that take specific structure of hypotheses into account.
Implements a broad class of latent variable and structural equation models for general pedigree data.
An open source platform for validation and process control.
Tools to analyse data from internal validation of forensic short tandem
repeat (STR) kits are provided. The tools are developed to provide
the necessary data to conform with guidelines for internal validation
issued by the European Network of Forensic Science Institutes (ENFSI)
DNA Working Group, and the Scientific Working Group on DNA Analysis Methods
(SWGDAM). A front-end graphical user interface is provided.
More information about each function can be found in the
respective help documentation.
Fit the basic structural time series model by maximum likelihood.
This package defines an S4 class for structural time series models
and provides some basic methods to work with it.
A number of functions, supplemented by GUI, to perform various analyses in the field of computational stylistics, authorship attribution, etc.
The three functions in the package compute the maximum likelihood estimates of the informants' competence scores, tests for two answer keys with known groups, and finds "best" split of the informants into sub-culture groups.
Produces various measures of expected treatment effect heterogeneity under an assumption of homogeneity across subgroups. Graphical presentations are created to compare these expected differences with the observed differences.
The package implements a convenient procedure for microarray study, which is to do gene selection and classification simultaneously for binary outcomes. Users needn't to tune the parameters and can fix any genes that they desire to keep in the model. The K-folds cross validation results are returned.
Routines for identifying metabolic subpathways mediated by microRNAs (miRNAs) through topologically locating miRNAs and genes within reconstructed Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway graphs embedded by miRNAs. (1) This package can obtain the reconstructed KEGG metabolic pathway graphs with genes and miRNAs as nodes, through converting KEGG metabolic pathways to graphs with genes as nodes and compounds as edges, and then integrating miRNA-target interactions verified by low-throughput experiments from four databases (TarBase, miRecords, mirTarBase and miR2Disease) into converted pathway graphs. (2) This package can locate metabolic subpathways mediated by miRNAs by topologically analyzing the "lenient distance" of miRNAs and genes within reconstructed KEGG metabolic pathway graphs.(3) This package can identify significantly enriched miRNA-mediated metabolic subpathways based on located subpathways by hypergenomic test. (4) This package can support six species for metabolic subpathway identification, such as caenorhabditis elegans, drosophila melanogaster, danio rerio, homo sapiens, mus musculus and rattus norvegicus, and user only need to update interested organism-specific environment variables.
The subplex algorithm for unconstrained optimization, developed by Tom Rowan.
Essentially a discussion tool about estimation of copula using ranks and subsampling.
Functions that compute sub-scores for a test using different methods in classical test theory (CTT).
A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria.
Subsemble is a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a unique form of V-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble.
An interface to 'OpenSubspace', an open source framework for
evaluation and exploration of subspace clustering algorithms in WEKA
(see <http://dme.rwth-aachen.de/de/opensubspace> for more
information).  Also performs visualization.
subtype performs a biclustering procedure on a input
dataset and assess whether resulting clusters are promising
subtypes. Note that the R-package rsmooth should be installed
before implementing subtype. rsmooth can be downloaded from
http://www.meb.ki.se/~yudpaw.
Generates, plays, and solves Sudoku puzzles.  The GUI
playSudoku() needs package "tkrplot" if you are not on Windows.
Tools for making, retrieving, displaying and solving sudoku games.
This package is an alternative to the earlier sudoku-solver package,
'sudoku'.  The present package uses a slightly different algorithm, has a
simpler coding and presents a few more sugar tools, such as plot and print
methods.  Solved sudoku games are of some interest in Experimental Design
as examples of Latin Square designs with additional balance constraints.
This is a package for the subsampling method of robust
estimation of linear regression models
Generates formatted summary tables for vectors and dataframes.
Three summarizing functions: 1) 'freq' generates frequency tables
reporting counts and proportions (including cumulative); 2) 'descr'
gives common central tendency statistics and measures of dispersion
for numerical data; 3) 'dfSummary' gives as much information
as possible about a dataframe and its components in a clear,
legible table. All three functions can either display plain text tables
in the console or write markdown, text or html files to disk. An
additional misc function, 'what.is', extends base 'is' function,
checking the object against all 'is' functions and gives a variety
of other information about the object.
Functions for drawing samples according to Sunter's
sampling design, and for computing first and second order
inclusion probabilities
Methodology for supervised grouping aka "clustering" of
potentially many predictor variables, such as genes etc.
Clusters features under the assumption that each cluster has a
random effect and there is an outcome variable that is related to the random
effects by a linear regression. In this way the cluster analysis is
``supervised'' by the outcome variable. An alternate specification is that
features in each cluster have the same compound symmetric normal distribution,
and the conditional distribution of the outcome given the features
has the same coefficient for each feature in a cluster.
Biclusters are submatrices in the data matrix which
satisfy certain conditions of homogeneity. Package contains
functions for generating robust biclusters with respect to the
initialization parameters for a given bicluster solution
contained in a bicluster set in data, the procedure is also
known as ensemble biclustering. The set of biclusters is
evaluated based on the similarity of its elements (the
overlap), and afterwards the hierarchical tree is constructed
to obtain cut-off points for the classes of robust biclusters.
The result is a number of robust (or super) biclusters with
none or low overlap.
A Comprehensive Test Suite for Markov Chain
Nonconvergence.
Efficient statistical testing and scalable visualization of intersections among multiple sets.
This package implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.
Witten and Tibshirani (2011) Supervised multidimensional scaling for visualization, classification, and bipartite ranking. Computational Statistics and Data Analysis 55(1): 789-801.
Supervised principal components for regression and
survival analsysis. Especially useful for high-dimnesional
data, including microarray data.
Ten distributions supplementing those built into R.
Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi
squared, Spearman's rho, maximum F ratio, the Pearson product
moment correlation coefficiant, Johnson distributions, normal
scores and generalized hypergeometric distributions. In
addition two random number generators of George Marsaglia are
included.
Provides three basic functions that support an implementation of object case (Case 1) best-worst scaling: one for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; one for creating a data set suitable for analysis; and one for calculating count-based scores.
Provides seven basic functions that support an implementation of choice experiments.
SURFACE is a data-driven phylogenetic comparative method for fitting stabilizing selection models to continuous trait data, building on the ouch package. The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak.
In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a biomarker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
suRtex was designed for easy descriptive statistic reporting of categorical survey data (e.g., Likert scales) in LaTeX. suRtex takes a matrix or data frame and produces the LaTeX code necessary for a sideways table creation. Mean, median, standard deviation, and sample size are optional.
Performs inference of several model-free group contrast measures, which include difference/ratio of cumulative incidence rates at given time points, quantiles, and restricted mean survival times (RMST). Two kinds of covariate adjustment procedures (i.e., regression and augmentation) for inference of the metrics based on RMST are also included.
This package provides a function to estimate the AUC, TPR(c),
FPR(c), PPV(c), and NPV(c) for for a specific timepoint and marker cutoff
value c using non-parametric and semi-parametric estimators. Standard errors
and confidence intervals are also computed. Either analytic or bootstrap
standard errors can be computed.
The package provides a variety of functions to estimate
time-dependent true/false positive rates and AUC curves from a
set of censored survival data.
Performs inference for C of risk prediction models with
censored survival data, using the method proposed by Uno et al.
(2011). Inference for the difference in C between two competing
prediction models is also implemented.
Estimates correlation coefficients with associated
confidence limits
for bivariate, partially censored survival times. Uses
the iterative multiple imputation approach proposed
by Schemper, Kaider, Wakounig and Heinze, Statistics
in Medicine 2013. Provides a scatterplot function to visualize the bivariate
distribution, either on the original time scale or as copula.
Implementation of statistical methods for the modeling and
change-point detection in time series of counts, proportions and
categorical data, as well as for the modeling of continuous-time
epidemic phenomena, e.g., discrete-space setups such as the spatially
enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models,
or continuous-space point process data such as the
occurrence of infectious diseases. Main focus is on outbreak
detection in count data time series originating from public health
surveillance of communicable diseases, but applications could just as well
originate from environmetrics, reliability engineering, econometrics or
social sciences.
Currently, the package contains implementations of many typical
outbreak detection procedures such as Farrington et al (1996),
Noufaily et al (2012) or the negative binomial LR-CUSUM method
described in Höhle and Paul (2008). A novel CUSUM approach combining
logistic and multinomial logistic modelling is also included.
Furthermore, inference methods for the retrospective infectious
disease models in Held et al (2005), Held et al (2006),
Paul et al (2008), Paul and Held (2011), Held and Paul (2012),
and Meyer and Held (2014) are provided.
Continuous self-exciting spatio-temporal point processes are
modeled through additive-multiplicative conditional
intensities as described in Höhle (2009) ('twinSIR', discrete
space) and Meyer et al (2012) ('twinstim', continuous space).
The package contains several real-world data sets, the ability
to simulate outbreak data, visualize the results of the
monitoring in temporal, spatial or spatio-temporal fashion.
Note: Using the 'boda' algorithm requires the 'INLA'
package, which should be installed automatically through the
specified Additional_repositories, if uninstalled dependencies
are also requested. As this might not work under OS X it
might be necessary to manually install the 'INLA' package as
specified at <http://www.r-inla.org/download>.
Relative survival, AER and SMR based on French death rates
Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Principal components, factor analysis.
Data obtained from surveys contains information not only about the
survey responses, but also the survey metadata, e.g. the original survey
questions and the answer options. The surveydata package makes it easy to
keep track of this metadata, and to easily extract columns with
specific questions.
Help generate slides for surveys or experiments.
The resulted slides allow the subject to respond with the use of the mouse (usual keyboard input is
replaced with clicking on a virtual keyboard on the slide). Subjects' responses are saved to the user-
specified location in the form of R-readable text file. To allow flexibility, each function in
this package generates a particular type of slides thus general R function writing skills are
required to compile these edited slides.
Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation.
The Gini concentration test for survival data is a nonparametric test based on the Gini index for testing the equality of two survival distributions from the point of view of concentration. The package compares different nonparametric tests (asymptotic Gini test, permutation Gini test, log-rank test, Gray-Tsiatis test and Wilcoxon test) and computes their p-values.
Performs inference for a class of measures to compare
competing risk prediction models with censored survival data.
The class includes the integrated discrimination improvement
index (IDI) and category-less net reclassification index (NRI).
Contains the core survival analysis routines, including
definition of Surv objects,
Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
and parametric accelerated failure time models.
Estimate the regression coefficients and the baseline hazard
of proportional hazard Cox models using maximum penalised likelihood.
A 'non-parametric' smooth estimate of the baseline hazard function
is provided.
Compute time-dependent ROC curve from censored survival
data using Kaplan-Meier (KM) or Nearest Neighbor Estimation
(NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No
2, 2000, PP 337-344)
Microarray gene expression data can be analyzed individually or jointly using merging methods or meta-analysis to predict patients' survival and risk assessment.
Three breast cancer gene expression data sets that can be used for package 'survJamda'. This package contains the gene expression and phenotype data of GSE1992, GSE3143 and GSE4335.
Kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates.
A collection of functions for analysis of survival data. These
extend the methods available in the package survival.
Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data
Estimation of the prediction accuracy in a unified survival AUC
approach. Model selection and prediction estimation based on a survival AUC.
Stepwise model selection, based on several ranking approaches.
Estimation of survival function for recurrent event data
using Peña-Strawderman-Hollander, Whang-Chang estimators and
MLE estimation under a Gamma Frailty model.
The main function of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions.
Performs two-sample comparisons using the restricted mean survival time (RMST) as a summary measure of the survival time distribution. Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed. It performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures.
Simulation of simple and complex survival data including recurrent and multiple events and competing risks.
This package can be used to conduct asymptotic and empirical power and sample size calculations for SNP association studies with right censored time to event outcomes
The sValues package implements the s-values proposed by Ed. Leamer.
Accurate identification of genes that are truly differentially expressed over two sample varieties, after adjusting for hidden subject-specific effects of residual heterogeneity.
2d and 3d space-varying coefficient models are fitted to
regular grid data using either a full B-spline tensor product
approach or a sequential approximation. The latter one is
computationally more efficient. Resolution increment is
enabled.
This package provides various R bindings to various SVD and eigensolvers (PROPACK, nuTRLan)
Rapidly construct dialog boxes for your GUI, including an automatic
function assistant
Reimplementation of the svDialogs dialog boxes in Tcl/Tk
Visualize singular value decompositions (SVD), principal component analysis (PCA), factor analysis (FA) and related methods.
Some visualization tools based on Singular Value Decomposition
This 'htmlwidget' provides pan and zoom
interactivity to R graphics, including 'base', 'lattice', and 'ggplot2'.  The
interactivity is provided through the 'svg-pan-zoom.js' library.  Various
options to the widget can tailor the pan and zoom experience to nearly any
user desire.
Creates 3D animated, interactive visualizations in Scalable Vector Graphics (SVG) format that can be viewed in a web browser.
Implements a simple HTTP server allowing to connect GUI clients to R
Function for the GUI API to interact with external IDE/code editors
Functions to manage the GUI client, like Komodo with the
SciViews-K extension
Supporting functions for the GUI API (various utility functions)
Implements the SVM-Maj algorithm to train data with
Support Vector Machine, this algorithm uses two efficient
updates, one for linear kernel and one for the nonlinear
kernel.
Causal effect estimation in observational data often requires identifying a set of untreated observations that are comparable to some treated group of interest.  This package provides a suite of functions for identifying such a set of observations and for implementing standard and new diagnostics tools.  The primary function, svmmatch(), uses support vector machines to identify a region of common support between treatment and control groups.  A sensitivity analysis, balance checking, and assessment of the region of overlap between treated and control groups is included.  The Bayesian implementation allows for recovery of uncertainty estimates for the treatment effect and all other parameters.
Computes the entire regularization path for the two-class
svm classifier with essentially the same cost as a single SVM
fit.
Various tools for semantic vector spaces, such as
correspondence analysis (simple, multiple and discriminant), latent
semantic analysis, probabilistic latent semantic analysis, non-negative
matrix factorization and EM clustering. Furthermore, there are specialized
distance measures, plotting functions and some helper functions.
Implements a simple socket server allowing to connect GUI clients to R
Supporting functions for the GUI API (Sweave functions)
Set of tools aimed at wrapping some of the functionalities
of the packages tools, utils and codetools into a nicer format so
that an IDE can use them
A complete unit test system and functions to implement its GUI part
High level management of widgets, windows and other graphical resources.
Builds, evaluates and validates a nomogram with survey data and right-censored outcomes.
This package deals with data which stem from survey designs including plausible values. This package has been created to handle data from Large Scale Assessments like PISA, PIAAC etc. which use  complex survey designs to draw the sample and plausible values to report person related estimates. Various functions/statistics (mean, quantile, GLM etc.) are provided to handle this kind of data.
The package contains functions to connect the structure of
the data with the information on the samples. Three types of
associations are covered: 1. linear model of principal
components. 2. hierarchical clustering analysis. 3.
distribution of features-sample annotation associations.
Additionally, the inter-relation between sample annotations can
be analyzed. Simple methods are provided for the correction of
batch effects and removal of principal components.
Three ensemble learning algorithms based on support vector machines.
They all train support vector machines on subset of data and combine the result.
The Soil and Water Assessment Tool is a river basin or
watershed scale model developed by Dr. Jeff Arnold for the
USDA-ARS.
provides utilities for defining R / Rd as Tex-package-listings "language" and
including R / Rd source file (sniplets) copied from R-forge in its most recent version
(or another url) thereby avoiding inconsistencies between vignette and documented
source code
Structural handling of identity numbers used in the Swedish
administration such as personal identity numbers ('personnummer') and
organizational identity numbers ('organisationsnummer').
Collection of conversion, analytical, geodesic, mapping, and
plotting functions. Used to support packages and code written by
researchers at the Southwest Fisheries Science Center of the National
Oceanic and Atmospheric Administration.
swirl turns the R console into an interactive learning
environment. Users receive immediate feedback as they are guided through
self-paced lessons in data science and R programming.
A set of tools for writing and sharing interactive courses
to be used with swirl.
Ozone, NOx (= Sum of Nitrogenmonoxide and
Nitrogendioxide), Nitrogenmonoxide, ambient temperature, dew
point, wind speed and wind direction at 3 sites around lake of
Lucerne in Central Switzerland in 30 min time resolution for
year 2004.
Functions for estimating the parameters from the latent
state process and the functions corresponding to the J states as
proposed by De Souza and Heckman (2013).
Provides an abstraction for managing, installing,
and switching between sets of installed R packages. This allows users to
maintain multiple package libraries simultaneously, e.g. to maintain
strict, package-version-specific reproducibility of many analyses, or
work within a devel/release paradigm. Introduces a generalized package
installation process which supports multiple repository and
non-repository sources and tracks package provenance.
Provides a simple plugin to the switchr
framework which allows users to publish manifests of packages - or of specific versions thereof - as  single-file GitHub repositories (Gists). These manifest files can then be used as remote seeds (see switchr documentation) when creating new package libraries.
Tools for retrieving, organizing, and analyzing environmental
data from the System Wide Monitoring Program of the National Estuarine
Research Reserve System.  These tools address common challenges
associated with continuous time series data for environmental decision
making.
This Systems Biology Library for R implements algorithms for constraint based analyses of metabolic networks (e.g. flux-balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on/off minimization (ROOM), robustness analysis and flux variability analysis). Most of the current LP/MILP solvers are supported via additional packages.
An implementation of a cost constrained flux balance analysis technique (i.e. MetabOlic Modeling with ENzyme kineTics (MOMENT)).
MOMENT uses enzyme kinetic data and enzyme molecular weights to constrain flux balance analysis(FBA) and it is described in
Adadi, R., Volkmer, B., Milo, R., Heinemann, M., & Shlomi, T. (2012). Prediction of Microbial Growth Rate versus Biomass
Yield by a Metabolic Network with Kinetic Parameters, 8(7). doi:10.1371/journal.pcbi.1002575.
This package also implements an improvement of MOMENT that considers multi-functional enzymes.
FBA is a mathematical technique to find fluxes in metabolic models at steady state.
It is described in Orth, J.D., Thiele, I. and Palsson, B.O.  What is flux balance analysis? Nat. Biotech. 28, 245-248(2010).
implement cycle-Free flux balance analysis, flux variability, and Random Sampling of solution space. Flux balance analysis is a technique to find fluxes in metabolic models at steady state. It is described in Orth, J.D., Thiele, I. and Palsson, B.O. What is flux balance analysis? Nat. Biotech. 28, 245-248 (2010).
In this package the dynamic FBA technique proposed by
Varma was implemented.
Three different approaches to use gene expression data (or protein measurements) for improving FBA predictions.
SBML (Systems Biology Markup Language) integration in sybil. Many constraint based metabolic models are published in SBML format (*.xml). Herewith is the ability to read and check SBML files in sybil provided.
Symbolic data analysis methods: importing/ exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic, replication and profiling, artificial symbolic data generation.
Package that implements various symbol plots (bars,
profiles, stars, Chernoff faces, color icons, stick figures).
Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.
A collection of functions required for genomic prediction which were developed within the Synbreed project for synergistic plant and animal breeding (www.synbreed.tum.de). This covers data processing, data visualization, and analysis. All functions are embedded within the framework of a single, unified data object. The implementation is flexible with respect to a wide range of data formats in plant and animal breeding. This research was funded by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr Synbreed - Synergistic plant and animal breeding (FKZ 0315528A).
Data sets for the 'synbreed' package with three data sets from cattle, maize and mice to illustrate the functions in the 'synbreed' R package. All data sets are stored in the gpData format introduced in the 'synbreed'
package. This research was funded by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr Synbreed - Synergistic plant and animal breeding (FKZ 0315528A).
Methods for computing spatial, temporal, and spatiotemporal
statistics including: empirical univariate, bivariate and multivariate
variograms; fitting variogram models; phase locking and synchrony analysis;
generating autocorrelated and cross-correlated matrices.
This package carries out synchrosqueezed wavelet transform. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.
The function 'syncSubsample' subsamples temporal data of different entities so that the result only contains synchronal events. The function 'mci' calculates the Movement Coordination Index (MCI, see reference on help page for function 'mci') of a data set created with the function 'syncSubsample'.
Analysis of metacommunities based on functional traits and phylogeny of the community components. The functions that are offered here implement for the R environment methods that have been available in the SYNCSA application written in C++ (by Valerio Pillar, available at http://ecoqua.ecologia.ufrgs.br/ecoqua/SYNCSA.html).
This package provides programmatic access to
The Synergizer service for translating between sets of
biological identifiers.
Infers sympatry matrices from distributional data and analyzes them in order to identify groups of species cohesively connected.
Framework to perform synthetic likelihood inference
for models where the likelihood function is unavailable or
intractable.
It implements various estimators of mutual information, such as
the maximum likelihood and the Millow-Madow estimator, various Bayesian
estimators, the shrinkage estimator, and the Chao-Shen estimator. It also
offers wrappers to the kNN and kernel density estimators. Furthermore, it
provides various index of performance evaluation such as precision, recall,
FPR, F-Score, ROC-PR Curves and so on. Lastly, it provides a brand new way
of generating synthetic RNA-Seq Network with known dependence structure.
Implements the synthetic control group method for comparative case studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2011, 2014). The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone.
A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data.
Using FreeType to load system fonts
and Google Fonts (https://www.google.com/fonts) into R.
It is supposed to support other packages such as 'R2SWF'
and 'showtext'.
Fitting simultaneous
systems of linear and nonlinear equations using Ordinary Least
Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated
Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted
Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS).
A toolbox for systemic risk based on liabilities matrices. Contains a Gibbs sampler for liabilities matrices where only row and column sums of the liabilities matrix are observed.
Extracts sentiment and sentiment-derived plot arcs
from text using three sentiment dictionaries conveniently
packaged for consumption by R users.  Implemented dictionaries include
"afinn" developed by Finn {\AA}rup Nielsen, "bing" developed by Minqing Hu
and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D.
Applicable references are available in README.md and in the documentation
for the "get_sentiment" function.  The package also provides a method for
implementing Stanford's coreNLP sentiment parser. The package provides
several methods for plot arc normalization.
Contains functions for generating tables for statistical reports written in Microsoft Word or LaTeX. There are functions for I-by-J frequency tables, comparison of means or medians across levels of a categorical variable, and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards regression. Functions are available to handle data simple random samples or survey data. The package is intended to make it easier for researchers to translate results from statistical analyses in R to their reports or manuscripts.
Sometimes you need to split your data and work on the two chunks independently before bringing them back together. 'Taber' allows you to do that with its two functions.
Some functions are included in this package for writing tables in Excel format suitable for distribution.
Table 1 is the classical way to describe the patients in a
clinical study. The amount of splits in the data in such a table is
limited. Table1Heatmap draws a heatmap of all crosstables that can be
generated with the data. Users can choose between showing the actual
crosstables or direction of effect of associations, and highlight
associations by number of patients or p-values.
A collection of convenience wrappers for producing and exporting various commonly-used numerical table types to .xls/.xlsx multi-tab spreadsheets while controlling spreadsheet position and layout, via XLConnect functionality.
Provides a user friendly interface to generation of booktab style
tables using xtable.
Creates "Table 1", i.e., description of baseline patient
characteristics, which is essential in every medical research.
Supports both continuous and categorical variables, as well as
p-values and standardized mean differences. Weighted data are
supported via the survey package. See github for a screencast.
tableone was inspired by descriptive statistics functions in
Deducer , a Java-based GUI package by Ian Fellows. This package
does not require GUI or Java, and intended for command-line users.
Computes and displays complex tables of summary statistics
TableToLongForm automatically converts hierarchical Tables intended for a human reader into a simple LongForm Dataframe that is machine readable.
A tableplot is a visualisation of a (large)
dataset with a dozen of variables, both numeric and
categorical. Each column represents a variable and each
row bin is an aggregate of a certain number of records.
Numeric variables are visualized as bar charts, and
categorical variables as stacked bar charts. Missing
values are taken into account. Also supports large ffdf
datasets from the ff package.
A tableplot is a visualisation of a (large)
dataset with a dozen of variables, both numeric and
categorical. This package contains an interactive version of
tableplot working in your browser.
R based tabu search algorithm for binary configurations
Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.
Includes marginal and joint maximum likelihood
estimation of uni- and multidimensional item response models
(Rasch, 2PL, 3PL, Generalized Partial Credit, Multi Facets,
Nominal Item Response, Structured Latent Class Analysis,
Mixture Distribution IRT Models, Located Latent Class Models).
Latent regression models and plausible value imputation are
also supported.
Functions for performing analysis of variance on time
course microarray data
Implementation of functions for fitting taper curves (a semiparametric linear mixed effects taper model) to diameter measurements along stems. Further functions are provided to estimate the uncertainty around the predicted curves, to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. For cases where tree
heights are not measured, methods for estimating
additional variance in volume predictions resulting from uncertainties in
tree height models (tariffs) are provided.  The example data include the taper curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees and a subset of section-wise diameter measurements of these trees. The functions implemented here are detailed in the following publication: Kublin, E., Breidenbach, J., Kaendler, G. (2013) A flexible stem taper and volume prediction method based on mixed-effects B-spline regression, Eur J For Res, 132:983-997.
Manager of tick-by-tick transaction data that performs 'cleaning', 'aggregation' and 'import' in an efficient and fast way. The package engine, written in C++, exploits the 'zlib' and 'gzstream' libraries to handle gzipped data without need to uncompress them. 'Cleaning' and 'aggregation' are performed according to Brownlees and Gallo (2006). Currently, TAQMNGR processes raw data from WRDS (Wharton Research Data Service).
A collection of various utility and convenience functions.
A collection of miscellaneous spatial object manipulation functions
Evaluates traveltimes and ray paths using predefined Earth
(or other planet) models.  Includes phase plotting routines.
The IASP91 and AK135 Earth models are included, and most
important arrival phases can be evaluated.
Computes the t* statistic corresponding to the tau star population
coefficient introduced by Bergsma and Dassios (Bernoulli 20(2), 2014,
1006-1028) and does so in  O(n^2*log(n)) time. Can provide both the
V-statistic and U-statistic related to the tau star measure depending on
user preference.
Portfolio optimization typically requires an estimate of a covariance matrix of asset returns. There are many approaches for constructing such a covariance matrix, some using the sample covariance matrix as a starting point. This package provides implementations for two such methods: random matrix theory and shrinkage estimation. Each method attempts to clean or remove noise related to the sampling process from the sample covariance matrix.
Base library of types for tawny and related packages
Interacts with a suite of web 'APIs' for taxonomic tasks,
such as verifying species names, and getting taxonomic 'hierarchies'.
Automated standardization of taxonomic names and removal of orthographic errors in plant species names using 'The Plant List' website (www.theplantlist.org).
Solves Teitz and Bart's p-median problem - given a set of
points attempts to find subset of size p such that summed distances of any
point in the set to the nearest point in p is minimised.  Although
generally effective, this algorithm does not guarantee that a globally
optimal subset is found.
This package provides functions to assist researchers
working in the field of tuberculosis diagnostics.  Functions
for the interpretation of two popular interferon-gamma release
assays are provided, and additional functionality is planned.
Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.
Functions to perform the reliability/survival
analysis using a parametric Transform-both-sides (TBS) model.
Automatically downloads and processes TCGA genomics and clinical data into a format convenient for statistical analyses in the R environment.
This package implements the TcGSA, an algorithm to analyze longitudinal gene-expression data at the gene set level.
A series of additional Tcl commands and Tk widgets with style
and various functions (under Windows: DDE exchange, access to the
registry and icon manipulation) to supplement the tcltk package
Platform for the advanced analysis of T cell receptor and Immunoglobulin repertoires data and visualisation of the analysis results.
Tools for the statistical analysis of persistent homology and for density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries GUDHI, Dionysus, and PHAT.
Topological Data Analysis using Mapper (discrete Morse theory).
Generate a 1-dimensional simplicial complex from a filter
function defined on the data:  1. Define a filter function (lens) on the
data.  2. Perform clustering within within each level set and generate
one node (vertex) for each cluster.  3. For each pair of clusters in
adjacent level sets with a nonempty intersection, generate one edge
between vertices.  The function mapper1D uses a filter function with
codomain R, while the the function mapper2D uses a filter function with
codomain R^2.
A boosted Tweedie compound Poisson model using the gradient boosting. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing interactions among predictors.
The Time-Delay Correlation algorithm (TDCor) reconstructs the topology of a gene regulatory network (GRN) from time-series transcriptomic data.  The algorithm is described in details in Lavenus et al., Plant Cell, 2015.  It was initially developed to infer the topology of the GRN controlling lateral root formation in Arabidopsis thaliana.  The time-series transcriptomic dataset which was used in this study is included in the package to illustrate how to use it.
Deconvolution of instrument responses from seismic traces and seismogram lists from RSEIS.  Includes pre-calculated instrument responses for several common instruments.
Tuned Data Mining in R (TDMR) performs a complete tuning of a data
mining task.
Implementation of target diagrams using 'lattice' and 'ggplot2' graphics. Target diagrams provide a graphical overview of the respective contributions of the unbiased RMSE and MBE to the total RMSE (Jolliff, J. et al., 2009. "Summary Diagrams for Coupled Hydrodynamic-Ecosystem Model Skill Assessment." Journal of Marine Systems 76: 64–82.)
Transmission/disequilibrium tests for extended marker haplotypes
This package is a set of demonstration functions that can
be used in a classroom to demonstrate statistical concepts, or
on your own to better understand the concepts or the
programming.
Allows the user to draw probabilistic samples and make inferences from a finite population based on several sampling designs.
Can fit neural networks with up to two hidden layer and two different error functions. Also able to handle a weight decay. But just able to compute one output neuron and very slow.
TED performs Turbulence time series Event Detection and classification.
For fitting multiple linear regressions, the ordinary least squares approach is sensitive to outliers and/or violations of model assumptions. The trimmed elemental estimators are more robust to such situations. This package contains functions for computing the trimmed elemental estimates, as well as for creating the bias-corrected and accelerated bootstrap confidence intervals in the context of trimmed elemental regressions.
Fits mixtures of multivariate t-distributions (with eigen-decomposed covariance structure) via the expectation conditional-maximization algorithm under a clustering or classification paradigm.
Temporal disaggregation methods are used to disaggregate and
interpolate a low frequency time series to a higher frequency series, where
either the sum, the average, the first or the last value of the resulting
high frequency series is consistent with the low frequency series. Temporal
disaggregation can be performed with or without one or more high frequency
indicator series. Contains the methods of Chow-Lin, Fernandez, Litterman,
Denton and Denton-Cholette.
The tensor product of two arrays is notionally an outer
product of the arrays collapsed in specific extents by summing
along the appropriate diagonals.
The package provides convenience functions for advance
linear algebra with tensors and computation with datasets of
tensors on a higher level abstraction. It includes Einstein and
Riemann summing conventions, dragging, co- and contravariate
indices, parallel computations on sequences of tensors.
The target equivalence range (TEQR) design, previously named the toxicity equivalence range design is an up-down design patterned after the modified toxicity probability interval design.The TEQR design extends the well known 3+3 design to allow for: an explicit target range for the dose limiting toxicity (DLT) rate, more subjects at the maximum tolerated dose (MTD), and specification of a too-toxic rate, which closes a dose level, In addition, the TEQR design can be implemented to target a dose based either on toxicity rate or activity rate constrained for toxicity.
This package is for the comparison of various types of A+B
escalation rules for dose finding trials.
An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis.
The package offers a wide range of functions for term
structure estimation based on static and dynamic coupon bond
and yield data sets. The implementation focuses on the cubic
splines approach of McCulloch (1971, 1975) and the Nelson and
Siegel (1987) method with extensions by Svensson (1994),
Diebold and Li (2006) and De Pooter (2007). We propose a
weighted constrained optimization procedure with analytical
gradients and a globally optimal start parameter search
algorithm. Extensive summary statistics and plots are provided
to compare the results of the different estimation methods.
Several demos are available using data from European government
bonds and yields.
A suite of functions for visualising ternary probabilistic
forecasts.
Simulation of reconstructed phylogenetic trees under tree-wide time-heterogeneous birth-death processes and estimation of diversification parameters under the same model. Speciation and extinction rates can be any function of time and mass-extinction events at specific times can be provided. Trees can be simulated either conditioned on the number of species, the time of the process, or both. Additionally, the likelihood equations are implemented for convenience and can be used for Maximum Likelihood (ML) estimation and Bayesian inference.
tester allows you to test characteristics of common R objects.
Provides a bootstrap test which decides whether two dose response curves can be assumed as equal concerning their maximum absolute deviation. A plenty of choices for the model types are available, which can be found in the 'DoseFinding' package, which is used for the fitting of the models.
Provides two convenience functions assert() and test_pkg() to
facilitate testing R packages.
GUI for entering test items and obtaining raw
and transformed scores. The results are shown on the
console and can be saved to a tabular text file for further
statistical analysis. The user can define his own tests and
scoring procedures through a GUI.
These are weighted tests type logrank for recurrent events.
A unit testing system designed to be fun, flexible and easy to
set up.
This package intends to go above and beyond of the features
provided by the testthat package. Namely, stubbing of functions and methods
is provided as well as the ability to architect mock structured hierarchies
of files (sometimes called fixtures).
Intended for developers, testthatsomemore aims to
raise the caliber of R testing frameworks to that of famous libraries
like JUnit and RSpec.
Statistical extreme value modelling of threshold excesses,
maxima and multivariate extremes.  Univariate models for threshold
excesses and maxima are the Generalised Pareto, and Generalised
Extreme Value model respectively.  These models may be fitted by
using maximum (optionally penalised-)likelihood, or Bayesian
estimation, and both classes of models may be fitted with covariates
in any/all model parameters.  Model diagnostics support the fitting
process. Graphical output for visualising fitted models and return
level estimates is provided.  For serially dependent sequences, the
intervals declustering algorithm of Ferro and Segers is provided,
with diagnostic support to aid selection of threshold and declustering
horizon.  Multivariate modelling is performed via the conditional
approach of Heffernan and Tawn, with graphical tools for threshold
selection and to diagnose estimation convergence.
Analysis and visualization of community dynamics in microbial
ecology experiments (that use sequence count data) using the
truncated Poisson lognormal distribution.
TExPosition is an extension of ExPosition for two table analyses, specifically, discriminant analyses.
Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented.
Multinomial [inverse] regression inference for text documents and associated attributes. Provides fast sparse multinomial logistic regression for phrase counts.  A minimalist partial least squares routine is also included.  Note that the topic modeling capability of textir is now a separate package, maptpx.
Statistical exploration of textual corpora using several methods
from French 'Textometrie' (new name of 'Lexicometrie') and French 'Data Analysis' schools.
It includes methods for exploring irregularity of distribution of lexicon features across
text sets or parts of texts (Specificity analysis); multi-dimensional exploration (Factorial analysis), etc.
Those methods are used in the TXM software.
Function for sparse regression on raw text, regressing a labeling
vector onto a feature space consisting of all possible phrases.
Tools for measuring similarity among documents and detecting
passages which have been reused. Implements shingled n-gram, skip n-gram,
and other tokenizers; similarity/dissimilarity functions; pairwise
comparisons; minhash and locality sensitive hashing algorithms; and a
version of the Smith-Waterman local alignment algorithm suitable for
natural language.
The TFDEA algorithm for technology forecasts when future products
will be introduced based upon their features.
It also includes DEA (Data Envelopment Analysis) functions including extensions dealing with
with infeasibility.
In addition it includes some standard technology forecasting data sets.
Statistical interpretation of forensic glass transfer
(Simulation of the probability distribution of recovered glass
fragments).
In putative Transcription Factor Binding Sites (TFBSs)
identification from sequence/alignments,
we are interested in the significance of certain match score.
TFMPvalue provides the accurate calculation of P-value with
score threshold for Position Weight Matrices,
or the score with given P-value.
This package is an interface to code originally made available by
Helene Touzet and Jean-Stephane Varre, 2007,
Algorithms Mol Biol:2, 15.
Utilities for simple manipulation and quick
plotting of time series data. These utilities use the tframe package
which provides a programming kernel for time series. Extensions to
tframe provided in tframePlus can also be used. See the Guide vignette
for examples.
A kernel of functions for programming
time series methods in a way that is relatively independently of the
representation of time. Also provides plotting, time windowing,
and some
other utility functions which are specifically intended for time series.
See the Guide distributed as a vignette, or ?tframe.Intro for more
details. (User utilities are in package tfplot.)
Extensions and additional tframe utilities.
Connects R to TrueFX(tm) for free streaming real-time and
historical tick-by-tick market data for dealable interbank
foreign exchange rates with millisecond detail.
Deconvolving first-order and general-order thermoluminescence glow
curves using a modified Levenberg-Marquardt algorithm. It provides the
possibility of setting constraints or fixing any of parameters. It offers
an interactive way to initialize parameters by clicking with a mouse on a
plot at positions where peak maxima should be located. The optimal estimate
is obtained by "Try-and-error". It also provides routines for simulating
first-, second-, and general-order glow peaks (or glow curves).
Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).
Use Gibbs sampler with Polya-Gamma data augmentation to fit logistic and probit regression under independent Student-t priors (including Cauchy priors and normal priors as special cases).
Bayesian nonstationary, semiparametric nonlinear regression
and design by treed Gaussian processes (GPs) with jumps to the limiting
linear model (LLM).  Special cases also implemented include Bayesian
linear models, CART, treed linear models, stationary separable and
isotropic GPs, and GP single-index models.  Provides 1-d and 2-d plotting functions
(with projection and slice capabilities) and tree drawing, designed for
visualization of tgp-class output.  Sensitivity analysis and
multi-resolution models are supported. Sequential experimental
design and adaptive sampling functions are also provided, including ALM,
ALC, and expected improvement.  The latter supports derivative-free
optimization of noisy black-box functions.
Functions to compute and plot tracheidograms
Contains data sets used in other packages Torsten Hothorn
maintains.
A collection of functions and routines for inputting thermal image video files, plotting and converting binary raw data into estimates of temperature.
Temperature measurement data, equations and methods for thermocouples,
wire RTD, thermistors, IC thermometers, bimetallic strips and the ITS-90.
This package interacts directly with the Thinknum API to offer data
in a number of formats usable in R
Design and analyze three-armed non-inferiority or superiority trials which follow a gold-standard design, i.e. trials with an experimental treatment, an active and a placebo control. The following distribution of endpoints is covered: negative binomial distribution.
This package implements a thresholded version of the EEBoost
algorithm described in [Wolfson (2011, JASA)]. EEBoost is a general-purpose
method for variable selection which can be applied whenever inference would
be based on an estimating equation. The package currently implements
variable selection based on the Generalized Estimating Equations, but can
also accommodate user-provided estimating functions. Thresholded EEBoost is
a generalization which allows multiple variables to enter the model at each
boosting step.
Implements the Maximum Likelihood estimator for baseline, placebo, and treatment groups (three-group) experiments with non-compliance proposed by Gerber, Green, Kaplan, and Kern (2010).
Create interactive 3D scatter plots and globes using the 'three.js' visualization library ("http://threejs.org").
Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.
A connector to the 'What3Words' (http://what3words.com/) service, which represents each 3m by 3m square on earth
with a unique trio of English-language words.
Fit a threshold regression model based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Point and interval estimations of optimum thresholds for continuous diagnostic tests (two- and three- state settings).
Fit the Simultaneous Systems of Linear Equations using Three-stage Least Squares.
Functions to execute orders in backtesting using tick data. A testing platform was established by the four major execution functions, namely 'LimitBuy', 'LimitSell', 'MarketBuy' and 'MarketSell', which enclosed all tedious aspects (such as queueing for order executions and calculate actual executed volumes) for order execution using tick data. Such that one can focus on the logic of strategies, rather than its execution.
This package provides the timing functions 'tic' and 'toc' that
can be nested. One can record all timings while a complex script is
running, and examine the values later. It is also possible to instrument
the timing calls with custom callbacks. In addition, this package provides
class 'Stack', implemented as a vector, and class 'List', implemented as a
list, both of which support operations 'push', 'pop', 'first', 'last' and
'clear'.
Utilities to generate wiki reports in TiddlyWiki format.
Calculate Characteristics of Qasi-Periodic Time Series, e.g. Estuarine Water Levels.
Tide analysis and prediction of predominantly semi-diurnal tides
with two high waters and two low waters during one lunar day (~24.842 hours,
~1.035 days). The analysis should preferably cover an observation period of at
least 19 years. For shorter periods, for example, the nodal cycle can not be
taken into account, which particularly affects the height calculation. The main
objective of this package is to produce tide tables.
An easy and consistent way to turn 'JSON' into tidy data frames
that are natural to work with in 'dplyr', 'ggplot2' and other tools.
An evolution of 'reshape2'. It's designed specifically for data
tidying (not general reshaping or aggregating) and works well with
'dplyr' data pipelines.
This package provides an easy and simple way to read, write and display bitmap images stored in the TIFF format. It can read and write both files and in-memory raw vectors.
Temporally resolved groups of typical differences (errors) between two time series are determined and visualized
A collection of data sets and functions that are useful in the
teaching of statistics at an elementary level to students who may have
little or no previous experience with the command line.  The functions for
elementary inferential procedures follow a uniform interface for user
input.  Some of the functions are instructional applets that can only be
run on the R Studio integrated development environment with package
'manipulate' installed.  Other instructional applets are Shiny apps
that may be run locally. In teaching the package is used alongside of
package 'mosaic', 'mosaicData' and 'abd', which are therefore listed as
dependencies.
This package contains functions for tight clustering
Algorithm.
Download TIGER/Line shapefiles from the United States Census Bureau
and load into R as Spatial DataFrames.
Provides a graphics output device for R
that records plots in a LaTeX-friendly format. The device transforms
plotting commands issued by R functions into LaTeX code blocks. When
included in a LaTeX document, these blocks are interpreted with the
help of 'TikZ'---a graphics package for TeX and friends written by Till
Tantau. Using the 'tikzDevice', the text of R plots can contain LaTeX
commands such as mathematical formula. The device also allows arbitrary
LaTeX code to be inserted into the output stream.
Methods and classes to build HMMs
that are suitable for the analysis of ChIP-chip data. The
provided parameter estimation methods include the Baum-Welch
algorithm and Viterbi training as well as a combination of
both.
This package is intended for processing the output from
functional gene tiling DNA microarray experiments. It produces
hybridization pattern plots for each gene on the array, and
statistics for each gene including mean probe intensity, median
probe intensity, bright probe fraction, bright segment length
dependent score, bright probe mean intensity, and bright probe
median intensity. Output is generated in order of bright
segment length dependent score in both a latex/eps format and
tab-delimited text file. The package works in two modes: single
array, and comparison of two arrays. Array comparison includes
array comparison statistics: median of logarithm of one array
probe divided by its counterpart on the other array, median
absolute deviation of that value, and the binomial test to see
whether the genes are equally abundant in both arrays.
Environment for teaching
"Financial Engineering and Computational Finance".
Managing chronological and calendar objects.
We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for  irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement.
Provides an easy-to-use wrapper to Rprof() and
summaryRprof() in the profiling of both function calls and
whole blocks of code.
Implements the Time Machine, a simulation approach for stochastic
trees.
Methods for incorporating time into network analysis. Construction of time-ordered networks (temporal graphs). Shortest-time and shortest-path-length analyses. Resource spread calculations. Data resampling and rarefaction for null model construction. Reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks. Vector clock latencies. Plotting functionalities.
Extract useful time components of a date object, such as
day of week, weekend, holiday, day of month, etc, and put it in
a data frame.  This can be used to create many predictor
variables out of a single time variable, which can then be used
in a regression or decision tree.  Also includes function
plotCalendarHeatmap which draws a calendar and overlays a
heatmap based on values.
Programs for Martinussen and Scheike (2006), `Dynamic Regression
Models for Survival Data', Springer Verlag.  Plus more recent developments.
Additive survival model, semiparametric proportional odds model, fast cumulative
residuals, excess risk models and more. Flexible competing risks regression
including GOF-tests. Two-stage frailty modelling.
PLS for the additive risk model. Lasso in the 'ahaz' package.
Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed.
Computes bootstrap CI for the sample ACF and periodogram
Uses a negative binomial mixed-effects (NBME) model to detect nonparallel differential expression(NPDE) genes and parallel differential expression(PDE) genes in the time course RNA-seq data.
Environment for teaching
"Financial Engineering and Computational Finance".
Managing financial time series objects.
Store and organize a large amount of low frequency time series data.
The package was designed to manage a large catalog of official statistics which are
typically published on monthly, quarterly or yearly basis. Thus timeseriesdb is
optimized to handle a large number of lower frequency time series as opposed to a
smaller amount of high frequency time series such as real time data from measuring devices.
Hence timeseriesdb provides the opportunity to store extensive multi-lingual
meta information. The package also provides a web GUI to explore the underlying
PostgreSQL database interactively.
Objects to manipulate sequential and seasonal time series. Sequential time series based on time instants and time durations are handled. Both can be regularly or unevenly spaced (overlapping durations are allowed). Only POSIX* format are used for dates and times. The following classes are provided : 'POSIXcti', 'POSIXctp', 'TimeIntervalDataFrame', 'TimeInstantDataFrame', 'SubtimeDataFrame' ; methods to switch from a class to another and to modify the time support of series (hourly time series to daily time series for instance) are also defined. Tools provided can be used for instance to handle environmental monitoring data (not always produced on a regular time base).
A interface to the TimeTree of Life Webpage (www.timetree.org). TimeTree is a public database for information on the evolutionary timescale of life. This package includes functions for searching divergence time for  taxa or all nodes of a phylogeny.
Date sequence, relative date calculations, and date manipulation with business days
and holidays.  Works with Date and POSIXt classes.
Prediction and ranking of drug combinations based on their drug-target interaction profiles and single-drug sensitivities in a given cancer cell line or patient-derived sample.
A problem-solving environment (PSE) for fitting
separable nonlinear models to measurements arising in physics
and chemistry experiments; has been extensively applied to
time-resolved spectroscopy and FLIM-FRET data.
Functions for statistical analysis, prediction and control of time series.
A universal non-uniform random number generator
for quite arbitrary distributions with piecewise twice
differentiable densities.
Non-parametric resampling-based inference tests for TExPosition.
Assist performing tip-dating of phylogenetic trees with BEAST.
TIPOM is based on a methodology that was developed in the
1960s by Bernardino Bagolini. The basic idea is to use
the three simple dimensions of length, width and thickness
of each lithic artefact to classify them in discrete
groups and infer their function.
Functions and S3 classes for time indexes and time indexed
series, which are compatible with FAME frequencies.
GUI to analyze mass spectrometric data on the relative
abundance of two substances from a titration series.
This data set provides information on the fate of passengers on
the fatal maiden voyage of the ocean liner "Titanic", summarized according
to economic status (class), sex, age and survival. Whereas the base R
Titanic data found by calling data("Titanic") is an array resulting from
cross-tabulating 2201 observations, these data sets are the individual
non-aggregated observations and formatted in a machine learning context
with a training sample, a testing sample, and two additional data sets
that can be used for deeper machine learning analysis. These data sets
are also the data sets downloaded from the Kaggle competition and thus
lowers the barrier to entry for users new to R or machine learing.
Pairwise evolutionary distance estimation between protein sequences with the TKF91 and TKF92 model, which consider all the possible paths of transforming from one sequence to another.
simple mechanism for placing R graphics in a Tk widget
Contains functions for training and applying two-level random forest and hidden Markov models for human behavior classification from raw tri-axial accelerometer and/or GPS data. Includes functions for training a two-level model, applying the model to data, and computing performance.
TLE implements a general framework for robust fitting of
finite mixture models. Parameter estimation is performed using
the EM algorithm.
Computation of effects under linear, logistic and Poisson regression models with transformed variables. Logarithm and power transformations are allowed. Effects can be displayed both numerically and graphically in both the original and the transformed space of the variables.
Fit a linear mixed effects model for censored data with
Student-t or normal distributions. The errors are assumed
independent and identically distributed.
Functions for two level normal models as described in
Everson and Morris (2000). J. R. Statist. Soc. B, 62 prt 2,
pp.399--412.
A framework for text mining applications within R.
Thematic maps are geographical maps in which statistical data are visualized. This package offers a flexible, layer-based, way to create thematic maps, such as choropleths and bubble maps.
With this tool, a user should be able to quickly implement complex
random effect models through simple C++ templates. The package combines
CppAD (C++ automatic differentiation), Eigen (templated matrix-vector
library) and CHOLMOD (sparse matrix routines available from R) to obtain an
efficient implementation of the applied Laplace approximation with exact
derivatives. Key features are: Automatic sparseness detection, parallelism
through BLAS and parallel user templates.
Provides an R-interface to the TMDb API (see TMDb API on <http://docs.themoviedb.apiary.io/#>). The Movie Database (TMDb) is a popular user editable database for movies and TV shows (see <https://www.themoviedb.org>).
Random number generation of truncated multivariate Gaussian distributions using Hamiltonian Monte Carlo. The truncation is defined using linear and/or quadratic polynomials.
Miscellaneous data and utility functions
for manipulating data and your R environment.
tmle implements targeted maximum likelihood estimation, first described in van der Laan and Rubin, 2006 (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006.  This version adds the tmleMSM function to the package, for estimating the parameters of a marginal structural model (MSM) for a binary point treatment effect.  The tmle function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes.  Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. Effect estimation stratified by a binary mediating variable is also available. The population mean is calculated when there is missingness, and no variation in the treatment assignment.  An ID argument can be used to identify repeated measures. Default settings call SuperLearner to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
Estimation of average causal effects for single time point
interventions in network-dependent data (e.g., in the presence of spillover
and/or interference). Supports arbitrary interventions (static or
stochastic). Implemented estimation algorithms are the targeted maximum
likelihood estimation (TMLE), the inverse-probability-of-treatment (IPTW)
estimator and the parametric G-computation formula estimator. Asymptotically
correct influence-curve-based confidence intervals are constructed for the
TMLE and IPTW. The data are assumed to consist of rows of unit-specific
observations, each row i represented by variables (F.i,W.i,A.i,Y.i), where
F.i is a vector of friend IDs of unit i (i's network), W.i is a vector of
i's baseline covariates, A.i is i's exposure (can be binary, categorical or
continuous) and Y.i is i's binary outcome. Exposure A.i depends on
(multivariate) user-specified baseline summary measure(s) sW.i, where sW.i
is any function of i's baseline covariates W.i and the baseline covariates
of i's friends in F.i. Outcome Y.i depends on sW.i and (multivariate)
user-specified summary measure(s) sA.i, where sA.i is any function of i's
baseline covariates and exposure (W.i,A.i) and the baseline covariates and
exposures of i's friends. The summary measures are defined with functions
def.sW and def.sA. See ?'tmlenet-package' for a general overview.
Targeted minimum loss estimation (TMLE) of a non-parametric variable importance measure of a continuous exposure 'X' on an outcome 'Y', taking baseline covariates 'W' into account.
The tmod package contains definitions of human blood
transcriptional modules and tools to analyse them.
This package provides a tm Source to create corpora from
a corpus prepared in the format used by the Alceste application (i.e.
a single text file with inline meta-data). It is able to import both
text contents and meta-data (starred) variables.
A plug-in for the text mining framework tm to support text mining
in a distributed way. The package provides a convenient interface for
handling distributed corpus objects based on distributed list objects.
Provides a 'tm' Source to create corpora from
articles exported from the 'Europresse' content provider as
HTML files. It is able to read both text content and meta-data
information (including source, date, title, author and pages).
This package provides a tm Source to create corpora from
articles exported from the Dow Jones Factiva content provider as
XML or HTML files. It is able to read both text content and meta-data
information (including source, date, title, author, subject,
geographical coverage, company, industry, and various
provider-specific fields).
Provides a tm Source to create corpora from
articles exported from the LexisNexis content provider as
HTML files. It is able to read both text content and meta-data
information (including source, date, title, author and pages).
A plug-in for the tm text mining framework providing mail handling
functionality.
Facilitate text retrieval from feed
formats like XML (RSS, ATOM) and JSON. Also direct retrieval from
HTML is supported. As most (news) feeds only incorporate small
fractions of the original text tm.plugin.webmining even retrieves
and extracts the text of the original text source.
Importance sampling from the truncated multivariate normal using the GHK (Geweke-Hajivassiliou-Keane) simulator.
Unlike Gibbs sampling which can get stuck in one truncation sub-region depending on initial values, this package allows
truncation based on disjoint regions that are created by truncation of absolute values. The GHK algorithm uses simple Cholesky
transformation followed by recursive simulation of univariate truncated normals hence there are also no convergence issues.
Importance sample is returned along with sampling weights, based on which, one can calculate integrals over truncated regions
for multivariate normals.
Random number generation for the truncated multivariate normal and Student t distribution.
Computes probabilities, quantiles and densities,
including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.
Temporal and cross-sectional network autocorrelation models (TNAM).
R package for analyzing weighted, two-mode, and longitudinal networks.
A consistent set of tools to perform in-database analytics
on Teradata Aster Big Data Discovery Platform. toaster (a.k.a 'to Aster')
embraces simple 2-step approach: compute in Aster - visualize and analyze
in R. Its `compute` functions use combination of SQL and
SQL/MR functions running in Aster database - highly scalable parallel
and distributed analytical platform. Then `create` functions visualize
results with boxplots, scatterplots, histograms, heatmaps, word clouds,
maps, or slope graphs. Advanced options such as faceting, coloring,
labeling, and others are supported with most functions.
Construction of the Total Operating Characteristic (TOC) Curve and the Receiver (aka Relative) Operating Characteristic (ROC) Curve for spatial and non-spatial data. The TOC method is a modification of the ROC method which measures the ability of an index variable to diagnose either presence or absence of a characteristic. The diagnosis depends on whether the value of an index variable is above a threshold. Each threshold generates a two-by-two contingency table, which contains four entries: hits (H), misses (M), false alarms (FA), and correct rejections (CR). While ROC shows for each threshold only two ratios, H/(H + M) and FA/(FA + CR), TOC reveals the size of every entry in the contingency table for each threshold (Pontius Jr., R.G., Si, K. 2014. The total operating characteristic to measure diagnostic ability for multiple thresholds. Int. J. Geogr. Inf. Sci. 28 (3), 570-583).
Imports the fundamental definitions and utilities of the Time Oriented Language (TOL), focused on time series analysis and stochastic processes, and provides the basis for the integration of TOL in R. See <https://www.tol-project.org> for more information about the TOL project.
Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence.  This package provides functions for estimating tolerance limits for various distributions.  Plotting is also available for tolerance limits of continuous random variables.
Function for converting cardinal to ordinal numbers by adding a language specific ordinal indicator (http://en.wikipedia.org/wiki/Ordinal_indicator) to the number.
Provides an interface to the C code for Latent Dirichlet
Allocation (LDA) models and Correlated Topics Models
(CTM) by David M. Blei and co-authors and the C++ code
for fitting LDA models using Gibbs sampling by Xuan-Hieu
Phan and co-authors.
For multiple ranked input lists (full or partial) representing the same set of N objects, the package TopKLists offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results.
Set of hydrological functions including an R
implementation of the hydrological model TOPMODEL, which is
based on the 1995 FORTRAN version by Keith Beven. From version
0.7.0, the package is put into maintenance mode. New functions
for hydrological analysis are now developed as part of the
RHydro package. RHydro can be found on R-forge and is built on
a set of dedicated S4 classes.
Using Gaussian graphical models we propose a novel approach to
perform pathway analysis using gene expression. Given the
structure of a graph (a pathway) we introduce two statistical
tests to compare the mean and the concentration matrices between
two groups. Specifically, these tests can be performed on the
graph and on its connected components (cliques).
Evaluation of alternatives based on multiple criteria using TOPSIS method.
Fit an Instrumental Variables Two Stage Least Squares model
Implements geodesic interpolation and basis
generation functions that allow you to create new tour
methods from R.
The GUI allows user to control the tour by checkboxes for
the variable selection, slider for the speed, and toggle boxes
for pause.
Calculates sample size and dose allocation for binary toxicity
tests, using the Fish Embryo Toxicity Test as example.
An optimal test design is obtained by running
(i) spoD (calculate the number of individuals to test under control
conditions), (ii) setD (estimate the minimal sample size per treatment
given the users precision requirements) and (iii) doseD (construct
an individual dose scheme).
This package implements the greedy approximation for tree
preserving embedding.
Estimation of transition probabilities for the illness-death model. Both the Aalen-Johansen estimator for a Markov model and a novel non-Markovian estimator (de Una-Alvarez and Meira-Machado, 2015) are included.
Estimation of transition probabilities for the
illness-death model and or the three-state progressive model.
Regression models for temporal process responses with
time-varying coefficient.
Plant ecologists often need to collect "traits" data
about plant species which are often scattered among various
databases: TR8 contains a set of tools which take care of
automatically retrieving some of those functional traits data
for plant species from publicly available databases (Biolflor,
The Ecological Flora of the British Isles, LEDA traitbase, Ellenberg
values for Italian Flora, Mycorrhizal intensity databases, Catminat).
The TR8 name, inspired by "car plates" jokes, was chosen since
it both reminds of the main object of the package and is
extremely short to type.
Contains functions to standardize tracheid profiles
using the traditional method (Vaganov) and a new method to standardize
tracheidograms based on the relative position of tracheids within tree rings.
Automatically stores objects in files on disk
so that files are rewritten when objects are changed, and
so that objects are accessible but do not occupy memory
until they are accessed. Keeps track of times when objects
are created and modified, and caches some basic
characteristics of objects to allow for fast summaries of
objects.  Also provides a command history mechanism that
saves the last command to a history file after each
command completes.
Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data.  Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals.
The tractor.base package consists of functions for working with magnetic resonance images. It can read and write image files stored in Analyze, NIfTI, MGH and DICOM formats (DICOM support is read only), generate images for use as regions of interest, and manipulate and visualise images.
Tree Ring Analysis of Disturbance Events in R (TRADER) package provides only one way for disturbance reconstruction from tree-ring data.
An interface for creating GUIs modeled in part after the
traits UI module for python. The basic design takes advantage of
the model-view-controller design pattern. The interface makes basic
dialogs quite easy to produce.
Species trait data from many different sources, including
sequence data from 'NCBI', plant trait data from 'BETYdb', invasive species
data from the Global Invasive Species Database and 'EOL', 'Traitbank' data
from 'EOL', Coral traits data from http://coraltraits.org, 'nativity' status
('Flora Europaea' or 'ITIS'), and 'Birdlife' International.
Implements a predictive model of community assembly called 'Traitspace' (Laughlin et al. 2012, Ecology Letters). Traitspace is a hierarchical Bayesian model that translates the theory of trait-based environmental filtering into a statistical model that incorporates intraspecific trait variation to predict the relative abundances and the distributions of species.
The package includes functions to plot the predicted and the observed values. It also includes functions to compare the predicted values against the observed values using a variety of different distance measures and to implement permutation tests to test their statistical significance.
Implements the three-step procedure proposed by Leffondree et al. (2004) to identify clusters of individual longitudinal trajectories. The procedure involves (1) calculating 24 measures describing the features of the trajectories; (2) using factor analysis to select a subset of the 24 measures and (3) using cluster analysis to identify clusters of trajectories, and classify each individual trajectory in one of the clusters.
Classes and methods for trajectory data, with nested classes for individual trips, and collections for different entities. Methods include selection, generalization, aggregation, intersection, and plotting.
Toolbox for the manipulation, description and rendering of sequences, and more generally the mining of sequence data in the field of social sciences. Although the toolbox is primarily intended for analyzing state or event sequences that describe life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and simple functions for extracting the most frequent subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Collection of ancillary functions and utilities to be used in conjunction with the TraMineR package for sequence data exploration. Most of the functions are in test phase, lack systematic consistency check of the arguments and are subject to changes. Once fully checked, some of the functions of this collection could be included in a next release of TraMineR.
TRAMPR is an R package for matching terminal restriction
fragment length polymorphism (TRFLP) profiles between unknown
samples and a database of knowns.  TRAMPR facilitates analysis
of many unknown profiles at once, and provides tools for
working directly with electrophoresis output through to
generating summaries suitable for community analyses with R's
rich set of statistical functions.  TRAMPR also resolves the
issues of multiple TRFLP profiles within a species, and shared
TRFLP profiles across species.
Transcribes audio to text with the HP IDOL API. Includes functions to upload files,
retrieve transcriptions, and monitor jobs.
Bindings for the Google Translate API v2
translateR provides easy access to the Google and Microsoft APIs. The package is easy to use with the related R package "stm" for the estimation of multilingual topic models.
Package with translated commands of SPSS. The usage is oriented
on the handling of SPSS-Syntax. Mainly the package has two purposes:
It facilitates SPSS-Users to change over to R and aids migration projects from SPSS to R.
R  version 2.1.0 and later support Korean translations of program messages.  The continuous efforts have been made by <http://developer.r-project.org/TranslationTeams.html>   The R Documentation files are licensed under the General Public License, version 2 or 3.   This means that the pilot project to translate them into Korean has permission to reproduce them and translate them.  This work is done with GNU 'gettext' utilities.  The portable object template is updated a weekly basis or whenever changes are necessary.  Comments and corrections via email to the maintainer is of course most welcome.   In order to voluntarily participate in or offer your help with this translation, please contact the maintainer.   To check the change and progress of Korean translation, please visit <http://www.openstatistics.net>.
A unified estimation procedure for the analysis of censored data using linear transformation models.
Solve optimal transport problems in statistics. Compute Wasserstein metrics (a.k.a. Kantorovitch, Fortet--Mourier, Mallows, Earth Mover's, or minimal L_p metrics), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, point patterns, and mass vectors.
The trapezoid package provides dtrapezoid, ptrapezoid,
qtrapezoid, and rtrapezoid functions for the trapezoidal
distribution.
Transmission Ratio Distortion (TRD) is a genetic phenomenon where the two alleles from either parent are not transmitted to the offspring at the expected 1:1 ratio under Mendelian inheritance, leading to spurious signals in genetic association studies. Functions in this package are developed to account for this phenomenon using loglinear model and Transmission Disequilibrium Test (TDT). Some population information can also be calculated.
Evaluate the performance of markers used to guide treatment selection decisions. Package includes functions to plot risk curves, evaluate marker performance, assess model calibration, and compare markers.
Utilities to investigate sensitivity to unmeasured confounding in parametric models with either binary or continuous treatment.
Interface to the API for 'TreeBASE' (http://treebase.org)
from 'R.' 'TreeBASE' is a repository of user-submitted phylogenetic
trees (of species, population, or genes) and the data used to create
them.
Bootstrapped response and correlation functions,
seasonal correlations and evaluation of reconstruction
skills for use in dendroclimatology and dendroecology.
Create a measure of inter-point dissimilarity useful
for clustering mixed data, and, optionally, perform the clustering.
The centre of mass is a crucial data for arborists in order to
consolidate a tree using steel or dynamic cables. Given field-recorded data
on branchiness of a tree, the package: (i) computes and plots the centre of
mass of the tree itself, (ii) computes branches slenderness coefficient in
order to aid the arborist identify potentially dangerous branches, and
(iii) computes the force acting on a ground plinth and its best position
relating to the tree centre of mass, should the tree need to be stabilized
by a steel cable
Treelets provides a novel construction of multi-scale bases that extends
wavelets to non-smooth signals. It returns a multi-scale orthonormal
basis, where the final computed basis functions are supported
on nested clusters in a hierarchical tree.  Both the tree and
the basis, which are constructed simultaneously, reflect the
internal structure of the data.
A treemap is a space-filling visualization of hierarchical
structures. This package offers great flexibility to draw treemaps.
(i) For a given species phylogeny on present day data which is calibrated to calendar-time, a method for estimating maximum likelihood speciation and extinction processes is provided. The method allows for non-constant rates. Rates may change (1) as a function of time, i.e. rate shifts at specified times or mass extinction events (likelihood implemented as LikShifts, optimization as bd.shifts.optim and visualized as bd.shifts.plot) or (2) as a function of the number of species, i.e. density-dependence (likelihood implemented as LikDD and optimization as bd.densdep.optim) or (3) extinction rate may be a function of species age (likelihood implemented as LikAge and optimization as bd.age.optim.matlab). Note that the methods take into account the whole phylogeny, in particular it accounts for the "pull of the present" effect. (1-3) can take into account incomplete species sampling, as long as each species has the same probability of being sampled. For a given phylogeny on higher taxa (i.e. all but one species per taxa are missing), where the number of species is known within each higher taxa, speciation and extinction rates can be estimated under model (1) (implemented within LikShifts and bd.shifts.optim with groups !=0). (ii) For a given phylogeny with sequentially sampled tips, e.g. a virus phylogeny, rates can be estimated under a model where rates vary across time using bdsky.stt.optim based on likelihood LikShiftsSTT (extending LikShifts and bd.shifts.optim). Furthermore, rates may vary as a function of host types using LikTypesSTT (multitype branching process extending functions in R package diversitree). This function can furthermore calculate the likelihood under an epidemiological model where infected individuals are first exposed and then infectious.
An implementation of permutation tests in R, supporting both exact and asymptotic K sample test of data locations. The p value of exact tests is found using tree algorithms. Tree algorithms treat permutations of input data as tree nodes and perform constraint depth-first searches for permutations that fall into the critical region of a test systematically. Pruning of tree search and optimisations at C level enable exact tests for certain large data sets.
Tools for the exploration of distributions of phylogenetic trees.
This package includes a shiny interface which can be started from R using
'treescapeServer()'.
Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees.
The package is a flexible simulation tool for phylogenetic trees under a general model. It is possible to assume any probability distribution for the waiting time until speciation and extinction independently. Thus, TreeSimGM allows to simulate stochastic phylogenetic trees using any probability distribution and parameters for speciation and  extinction. The speciation modes have all binary splits and are: (i) symmetric, where for every speciation event new waiting times until speciation and extinction are drawn for both daughter lineages; and (ii) asymmetric, where a speciation event results in one species with new waiting times, and another that carries the extinction time and age of its ancestor. Those two modes were inspired by allopatric and peripatric speciation respectively.Both models (symmetric and asymmetric) were created and implemented so that different processes (distributions) for speciation and extinction could be independently and explicitly specified. It is also possible to have an implicit extinction process by setting the extinction rate to zero.
This package implements TreeThresh, a locally adaptive
version of EbayesThresh.
The analysis of environmental data often requires
the detection of trends and change-points.
This package provides the Mann-Kendall Trend Test,
seasonal Mann-Kendall Test,
correlated seasonal Mann-Kendall Test,
partial Mann-Kendall Trend test,
(Seasonal) Sen's slope, partial correlation trend test and
change-point test after Pettitt.
functions and examples in Sample Size Calculation in
Clinical Research.
Provides the "r, q, p, and d" distribution functions for
the triangle distribution
Tribology test devices such as pin-on-disk, ball-on-disk machines and profilometers give important data for tribological analysis. trib is built to analyze and visualize that data into human understandable form. It has functions for finding coefficient of friction, Friction Force vs Distance graphs and calculating Wear Track Volume and Wear Track plotting.
The package contains routines to 1) project unity-summed
triples to unit-square doubles and vice versa, 2) make a grid
of unity-summed triples paired to doubles, 3) evaluate a
function over the grid and 4) make simple plots including
ternary contour plots over a grid of values.
Propensity score matching for non-binary treatments.
Provides various commonly-used response time trimming
methods, including the recursive / moving-criterion methods reported by
Van Selst and Jolicoeur (1994). By passing trimming functions raw data files,
the package will return trimmed data ready for inferential testing.
Creates point and probability forecasts from the trees in a random forest using a trimmed opinion pool.
The package contains functions that 1) estimates
gene-environment interaction between a SNP and a continuous
non-genetic attribute by fitting a generalized additive model
to case-parent trio data, 2) produces graphical displays of
estimated interaction, 3) performs permutation test of
gene-environment interaction; 4) simulates informative
case-parent trios.
Functions for accessing and manipulating spatial data for animal
tracking, with straightforward coercion from and to other formats. Filter
for speed and create time spent maps from animal track data.
A constrained two-dimensional Delaunay triangulation package
providing both triangulation and generation of voronoi mosaics of
irregular spaced data.
Data handling and estimation functions for animal movement
estimation from archival or satellite tags. Helper functions are included
for making image summaries binned by time interval from MCMC simulations of
point data.
Social Relation Model (SRM) analyses for single or multiple
round-robin groups are performed. These analyses are either based on one
manifest variable, one latent construct measured by two manifest variables,
two manifest variables and their bivariate relations, or two latent
constructs each measured by two manifest variables. Within-group t-tests
for variance components and covariances are provided for single groups,
between-groups t-tests for multiple groups. Handling for missing values is
provided.
A new bioinformatic tool for comparing transcriptomes of two biological samples from the same or different species. The mapping is conducted based on the overlap of the associated genes of different samples. More examples and detailed explanations are available in the vignette.
Class definitions and constructors for pseudo-vectors containing
all permutations, combinations and subsets of objects taken from a vector.
Simplifies working with structures commonly encountered in combinatorics.
Functions and datasets for the reader of the book "The R Software: Fundamentals of Programming and Statistical Analysis".
An implementation of the TrueSkill algorithm (Herbrich,
R., Minka, T. and Grapel, T) in R; a Bayesian skill rating
system with inference by approximate message passing on a
factor graph. Used by Xbox to rank gamers and identify
appropriate matches.
http://research.microsoft.com/en-us/projects/trueskill/default.aspx
Current version allows for one player per team. Will update as
time permits.  Requires R version 3.0 as it is written with
Reference Classes.  URL:
https://github.com/bhoung/trueskill-in-r Acknowledgements to
Doug Zongker and Heungsub Lee for their python implementations
of the algorithm and for the liberal reuse of Doug's code
comments (@dougz and @sublee on github).
A collection of functions to deal with the truncated univariate and multivariate normal distributions.
A collection of functions to evalue probability density
functions, cumulative distribution functions, quantile
functions and random numbers for truncated random variables.
Functions are provided to also compute the expected value and
variance. Nadarajah and Kotz (2006) developed most of the
functions. QQ plots can be produced.  All the probability
functions in the stats, stats4 and evd packages are
automatically available for truncation..
Goodness-of-fit tests and some adjusted exploratory tools
allowing for left truncated data
r/d/p/q functions for the truncated normal distribution
Estimation of models for truncated Gaussian variables by maximum likelihood.
Semi-parametric estimation of truncated linear regression models
Does local optimization using two derivatives and trust regions.
Guaranteed to converge to local minimum of objective function.
Trust region algorithm for nonlinear optimization.  Efficient when the Hessian of the objective function is sparse (i.e., relatively few nonzero cross-partial derivatives).
Contains R functions and datasets detailed in the book
"Time Series Analysis with Applications in R (second edition)"
by Jonathan Cryer and Kung-Sik Chan
Tsallis distribution also known as the q-exponential family distribution. Provide distribution d, p, q, r functions, fitting and testing functions. Project initiated by Paul Higbie and based on Cosma Shalizi's code.
The tsbridge package contains a collection of R functions that can be used to estimate normalising constants using the bridge sampler of Meng and Wong (1996). The functions can be applied to calculate posterior model probabilities for a variety of time series Bayesian models, where parameters are estimated using BUGS, and models themselves are created using the tsbugs package.
Different estimates are provided to solve the blind source separation problem for time series with stochastic volatility.
The tsbugs package contains a collection of R functions
that can be used to create time series BUGS models of various
order. Included are function to create BUGS with non-constant
variance such stochastic volatility models and random variance
shift models.
Performs the two-sample comparisons using the following exact test procedures: the exact likelihood-ratio test (LRT) for equality of two normal populations proposed in Zhang et al. (2012); the combined test based on the LRT and Shapiro-Wilk test for normality via the Bonferroni correction technique; the newly proposed density-based empirical likelihood (DBEL) ratio test. To calculate p-values of the DBEL procedures, three procedures are used: (a) the traditional Monte Carlo (MC) method implemented in C++, (b) a new interpolation method based on regression techniques to operate with tabulated critical values of the test statistic; (c) a Bayesian type method that uses the tabulated critical values as the prior information and  MC generated DBEL-test-statistic's values as data.
This package contains a set of measures of dissimilarity between time series to perform time series clustering. Metrics based on raw data, on generating models and on the forecast behavior are implemented. Some additional utilities related to time series clustering are also provided, such as clustering algorithms and cluster evaluation metrics.
Utilities for comparing the equality of series on two databases.
Comprehensive examples of all the 'TS*' packages is provided in the
vignette Guide.pdf with the 'TSdata' package.
Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.
Illustrates the various 'TSdbi' packages with a vignette using time series
data from several sources. The vignette also illustrates some simple time series
manipulation and plotting using packages 'tframe' and 'tfplot'.
Provides a common interface to time series databases. The
objective is to define a standard interface so users can retrieve time
series data from various sources with a simple, common, set of
commands, and so programs can be written to be portable with respect
to the data source. The SQL implementations also provide a database
table design, so users needing to set up a time series database
have a reasonably complete way to do this easily. The interface
provides for a variety of options with respect to the representation
of time series in R. The interface, and the SQL implementations, also
handle vintages of time series data (sometime called editions or
realtime data). There is also a (not yet well tested) mechanism to
handle multilingual data documentation.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
A set of commonly used distance measures and some additional functions which, although initially not designed for this purpose, can be used to measure the dissimilarity between time series. These measures can be used to perform clustering, classification or other data mining tasks which require the definition of a distance measure between time series.
Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).
This function assign and integrate target peaks in
two-dimentional chromatogram.
Time series analysis and computational finance.
Routines for the analysis of nonlinear time series. This
work is largely inspired by the TISEAN project, by Rainer
Hegger, Holger Kantz and Thomas Schreiber:
http://www.mpipks-dresden.mpg.de/~tisean/
Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes.
Extraction of Factors from Multivariate Time Series. See ?00tsfa-Intro for more details.
A 'fame' interface for 'TSdbi'.
Comprehensive examples of all the 'TS*' packages is provided in the
vignette Guide.pdf with the 'TSdata' package.
two-stage procedure for comparing hazard rate functions
which may or may not cross each other
Functions for analysing and forecasting intermittent demand/slow moving items time series.
Implementations of a number of functions used to mine numeric time-series data. It covers the implementation of SAX transformation, univariate motif discovery (based on the random projection method), multivariate motif discovery (based on graph clustering), and several functions used for the ease of visualizing the motifs discovered. The details of SAX transformation can be found in J. Lin. E. Keogh, L. Wei, S. Lonardi, Experiencing SAX: A novel symbolic representation of time series, Data Mining and Knowledge Discovery 15 (2) (2007) 107-144. Details on univariate motif discovery method implemented can be found in B. Chiu, E. Keogh, S. Lonardi, Probabilistic discovery of time series motifs, ACM SIGKDD, Washington, DC, USA, 2003, pp. 493-498. Details on the multivariate motif discovery method implemented can be found in A. Vahdatpour, N. Amini, M. Sarrafzadeh, Towards unsupervised activity discovery using multi-dimensional motif detection in time series, IJCAI 2009 21st International Joint Conference on Artificial Intelligence.
Methods to
retrieve data from several different sources. This include historical
quote data from Yahoo and Oanda, economic data from FRED, and xls and
csv data from different sources.
Comprehensive examples of all the 'TS*' packages is provided in the
vignette Guide.pdf with the 'TSdata' package.
Tools for specifying time series regression models
A 'MySQL' interface for 'TSdbi'.
Comprehensive examples of all the 'TS*' packages is provided in the
vignette Guide.pdf with the 'TSdata' package.
Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the 'networkDynamic' format. This work was supported by grant R01HD68395 from the National Institute of Health.
A "pure R" implementation of the t-SNE algorithm.
An ODBC interface for 'TSdbi'.
Comprehensive examples of all the 'TS*' packages is provided in the
vignette Guide.pdf with the 'TSdata' package.
Detection of outliers in time series following the
Chen and Liu (1993) procedure. Innovative outliers, additive outliers,
level shifts, temporary changes and seasonal level shifts are considered.
Basic infrastructure and some algorithms for the traveling
salesperson problem (also traveling salesman problem; TSP).
The package provides some simple algorithms and
an interface to the Concorde TSP solver and its implementation of the
Chained-Lin-Kernighan heuristic. Concorde
itself is not included in the package and has to be obtained separately
from http://www.math.uwaterloo.ca/tsp/concorde.html.
Adjusts for correlations among the rows and columns via
the Transposable Sphering Algorithm when conducting large-scale
inference on the rows of a data matrix.
Prediction intervals for ARIMA and structural time series models using importance sampling approach with uninformative priors for model parameters, leading to more accurate coverage probabilities in frequentist sense. Instead of sampling the future observations and hidden states of the state space representation of the model, only model parameters are sampled, and the method is based solving the equations corresponding to the conditional coverage probability of the prediction intervals. This makes method relatively fast compared to for example MCMC methods, and standard errors of prediction limits can also be computed straightforwardly.
Instance feature calculation and evolutionary instance generation
for the traveling salesman problem. Also contains code to "morph" two TSP
instances into each other. And the possibility to conveniently run a couple
of solvers on TSP instances.
A 'PostgreSQL' interface for 'TSdbi'.
Comprehensive examples of all the 'TS*' packages is provided in the
vignette Guide.pdf with the 'TSdata' package.
Functions for time series prediction and accuracy assessment using automatic ARIMA modelling. The generated ARIMA models and its yielded prediction errors are intended to be used as baseline for evaluating the practical value of other time series prediction methods and creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Methods to
retrieve data in the Statistical Data and Metadata Exchange ('SDMX') format
from several database. (For example,
EuroStat, the European Central Bank, the Organisation for Economic
Co-operation and Development, the Unesco Institute for Statistics,
and the International Labor Organization.)
This is a wrapper for package 'RJSDMX'.
Comprehensive examples of all the 'TS*' packages is provided in the
vignette Guide.pdf with the 'TSdata' package.
Standard SQL query functions used by
SQL plugins packages for the 'TSdbi' interface to time series databases.
It will mainly be used by other packages rather than directly by end
users. The one exception is the function 'TSquery' which can be used to
construct a time series from a database containing observations over
time (e.g. balance statements for multiple years), but where the database
is not specifically designed to store time series (as with other
'TSdbi' SQL plugin packages).
Comprehensive examples of all the 'TS*' packages is provided in the
vignette Guide.pdf with the 'TSdata' package.
An 'SQLite' interface for 'TSdbi'.
Comprehensive examples of all the 'TS*' packages is provided in the
vignette Guide.pdf with the 'TSdata' package.
A ternary search tree is a type of prefix tree with up to three children and the ability for incremental string search. The package uses this ability for word auto-completion and spell checking. Includes a dataset with the 10001 most frequent English words.
Interactive laboratory of Time Series based in Box-Jenkins methodology.
This interface was created to develop a standard procedure
of temporal trend analysis in the framework of the OSPAR convention.
It is based on four successive steps : simply manipulate your database,
select the parameters you want to analyse, build your regulated
time series, perform analysis and read the results all in one easy to use
interface. Statistical analysis call other package function such as
Kendall tests (wq package) or cusum function (Pastec package).
C source code and R wrappers for the tth/ttm TeX to
HTML/MathML translators.
Computing the first two moments of the truncated multivariate t (TMVT) distribution under the double truncation. Appling the slice sampling algorithm to generate random variates from the TMVT distribution.
Functions and data to construct technical trading rules with R.
Time-Temperature Superposition analysis is often applied to frequency modulated data obtained by Dynamic
Mechanic Analysis (DMA) and Rheometry in the analytical chemistry and physics
areas. These techniques provide estimates of material mechanical properties
(such as moduli) at different temperatures in a wider range of time. This
package provides the Time-Temperature superposition Master Curve at a referred
temperature by the three methods: the two wider used methods, Arrhenius based
methods and WLF, and the newer methodology based on derivatives procedure.
The Master Curve is smoothed by B-splines basis. The package output is composed
of plots of experimental data, horizontal and vertical shifts, TTS data, and TTS
data fitted using B-splines with bootstrap confidence intervals.
This package utilizes training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects.
This package makes Travel To Work Area from a commuting flow data frame.
Get comments posted on YouTube videos, information on how many
times a video has been liked, search for videos with particular content, and
much more. You can also scrape captions from a few videos. To learn more about
the YouTube API, see https://developers.google.com/youtube/v3/.
Custom template and output formats for use with rmarkdown. Produce
Edward Tufte-style handouts in html formats with full support for rmarkdown
features
Perform the conventional Tukey test from aov and aovlist
objects
Provides an R-interface to the Tumblr web API (see Tumblr v2 API on https://www.tumblr.com/docs/en/api/v2). Tumblr is a microblogging platform and social networking website (https://www.tumblr.com).
A tool to obtain tumor growth rates from clinical trial patient data.  Output includes individual and summary data for tumor growth rate estimates as well as optional plots of the observed and predicted tumor quantity over time.
Generic methods for parameter tuning of classification algorithms using multiple scoring functions
Collection of tools to analyze music, extract features
like MFCCs, handling wave files, read mp3, transcription, ...
Also contains functions ported from the rastamat Matlab
package.
Functions to find all matches or non-matches, orphans, and
duplicate or other replicated elements.
Algorithms for accelerating the convergence of slow,
monotone sequences from smooth, contraction mapping such as the
EM and MM algorithms. It can be used to accelerate any smooth,
linearly convergent acceleration scheme.  A tutorial style
introduction to this package is available in a vignette on the
CRAN download page or, when the package is loaded in an R
session, with vignette("turboEM").
Package for analyzing TURF (Total Unduplicated Reach and Frequency) data in R.
No looping in TURF algorithm results in fast processing times. Allows for individual-level
weights, depth specification, and user-truncated combination set(s). Allows user to substitute
Monte Carlo simulated combination set(s) after set(s) exceed a user-specified limit.
Package designed for working with vectors and lists of vectors,
mainly for turning them into other indexed data structures.
An implementation of turtle graphics
(http://en.wikipedia.org/wiki/Turtle_graphics) in R.
Turtle graphics comes from Papert's language Logo and has
been used to teach concepts of computer programming.
The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model.
The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine.
See Parajka, J., R. Merz, G. Bloeschl (2007) Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446.
Total Variation Denoising is a regularized denoising method which
effectively removes noise from piecewise constant signals whilst preserving
edges.  This package contains a C++ implementation of Condat's very fast 1D
squared error loss TVD algorithm.  Additional methods and loss functions may
be added in future versions.
Functions for managing cashflows and interest rate curves.
This package offers functions for propensity score
estimating and weighting, nonresponse weighting, and diagnosis
of the weights.  This package was originally developed by Drs.
Ridgeway, McCaffrey, and Morral. Burgette, Griffin and
McCaffrey updated the package during 2011-2015.
Maximum likelihood computations for Tweedie families.
This is an improved implementation of the package 'StreamR' to
capture tweets and store it into a 'postGIS' data base. The package simplifies
the configuration task to capture tweets and facilitates its store into a
'PostGIS' database.
Twiddler is an interactive tool that automatically creates
a Tcl/Tk GUI for manipulating variables in any R expression.
See the documentation of the function twiddle to get started.
Provides an interface to the Twitter web API.
This package implements the nonparametric test of equality
between two copulas proposed by Remillard and Scaillet in their
2009 JMVA paper.
This program computes thresholds and power for a two-stage
genome-wide association study. It follows the methods described
in Skol AD, Scott, LJ, Abecasis GR, Boehnke M (2006) Nature
Genetics doi:10.1038/ng1706 and in the "CaTS" computer program
(http://www.sph.umich.edu/csg/abecasis/CaTS/)
Implements a variety of non-parametric methods for computing one-stage and two-stage confidence intervals, as well as point estimates of threshold values.
Conditional logistic regression with longitudinal follow up and
individual-level random coefficients: A stable and efficient
two-step estimation method
Provides functions to produce rudimentary ascii graphics
directly in the terminal window. Provides a basic plotting
function (and equivalents of curve, density, acf and barplot)
as well as a boxplot function.
Two Phase I designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method. Simulations tools are also available to estimate the operating characteristics of the methods with several user-dependent options.
This package contains latex, knitr and R Markdown templates that
adhere to the UC Berkeley Graduate Division's thesis guidelines. The
templates are located in the inst/ directory.
An algorithm for general-purpose unconstrained non-linear optimization.
The algorithm is of quasi-Newton type with BFGS updating of the inverse
Hessian and soft line search with a trust region type monitoring of the
input to the line search algorithm. The interface of 'ucminf' is
designed for easy interchange with 'optim'.
Provides simple bindings to version 2 of Unidata's udunits library.
Allows users to access live UK gas market information via National Grid's API.
Does uniformly most powerful (UMP) and uniformly most
powerful unbiased (UMPU) tests.  At present only distribution implemented
is binomial distribution.  Also does fuzzy tests and confidence intervals
(following Geyer and Meeden, Statistical Science, 2005) for the binomial
distribution (one-tailed procedures based on UMP test and two-tailed
procedures based on UMPU test).
Helper functions for making, running, and reporting SEM
and twin models in OpenMx.
If you are just starting, try typing ?umx.
A dataset is said to be unbalanced when the class of interest (minority class) is much rarer than normal behaviour (majority class). The cost of missing a minority class is typically much higher that missing a majority class. Most learning systems are not prepared to cope with unbalanced data and several techniques have been proposed. This package implements some of most well-known techniques and propose a racing algorithm to select adaptively the most appropriate strategy for a given unbalanced task.
The package implements top-down and bottom-up algorithms
for nonparametric function estimation in Gaussian noise using
Unbalanced Haar wavelets.
Provides a basic (random) data, grids, 6 models of uncertainty, 3 automatic interpolations (idw, spline, kriging), variogram and basic data visualization.
Computes a universal numeric fingerprint (UNF) for an R data object. UNF is a cryptographic hash or signature that can be used to uniquely identify (a version of) a rectangular dataset, or a subset thereof. UNF can be used, in tandem with a DOI, to form a persistent citation to a versioned dataset.
Stereological unfolding as implemented in this package consists in
the estimation of the joint size-shape-orientation distribution of spheroidal shaped
particles based on the same measured quantities of corresponding planar section
profiles. A single trivariate discretized version of the (stereological) integral
equation in the case of prolate and oblate spheroids is solved numerically by the
EM algorithm. The estimation of diameter distribution of spheres from planar sections
(Wicksell's corpuscle problem) is also implemented. Further, the package provides routines
for the simulation of a Poisson germ-grain process with either spheroids or spheres as grains
together with functions for planar sections.
Data from Unicode 7.0.0 and related utilities.
Univarate shrinkage prediction for survival analysis using
in the Cox model.. Especially useful for high-dimensional data,
including microarray data.
Goodness-of-fit tests for the uniform distribution.
For each string in a set of strings, determine a unique tag that is a substring of fixed size k unique to that string, if it has one. If no such unique substring exists, the least frequent substring is used. If multiple unique substrings exist, the lexicographically smallest substring is used. This lexicographically smallest substring of size k is called the "UniqTag" of that string.
An alternative implementation and extension (grpDuplicated) of base::duplicated.matrix, base:anyDuplicated.matrix and base::unique.matrix for matrices of atomic mode, avoiding the time consuming collapse of the matrix into a character vector.
Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or
self-defined penalties on the B-spline coefficients.
United is a software tool which can be downloaded at the following
website <http://www.schroepl.net/pbm/software/united/>. In general, it is
a virtual manager game for football teams. This package contains helpful
functions for determining an optimal formation for a virtual match in
United. E.g. knowing that the opponent has a strong defensive it is
advisable to beat him in the midfield. Furthermore, this package contains
functions for computing the optimal usage of hardness in a game.
Concise TAP (http://testanything.org/) compliant unit testing package. Authored tests can be run using CMD check with minimal implementation overhead.
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates.
A collection of utilities for biodiversity data.
Includes the simulation of ecological drift under Hubbell's Unified
Neutral Theory of Biodiversity, and the calculation of various
diagnostics such as Preston curves.  Now includes functionality
provided by Francois Munoz and Andrea Manica.
This package contains a collection of functions which
implement data classification. It uses unlabeled data to obtain
parameter estimates of models. The functions can be implemented
over a number of models with the best model selected and
displayed.
An integrated package for building and testing uplift models
An implementation of the UPMASK method for performing membership
assignment in stellar clusters in R. It is prepared to use photometry and
spatial positions, but it can take into account other types of data. The
method is able to take into account arbitrary error models, and it is
unsupervised, data-driven, physical-model-free and relies on as few
assumptions as possible. The approach followed for membership assessment is
based on an iterative process, principal component analysis, a clustering
algorithm and a kernel density estimation.
Creates visualizations of intersecting sets using a novel matrix design,
along with visualizations of several common set, element and attribute
related tasks.
Provide a set of wrappers to call all the endpoints of UptimeRobot API
which includes various kind of ping, keep-alive and speed tests.
See <https://uptimerobot.com/> for more information.
Unit root and cointegration tests encountered in applied
econometric analysis are implemented.
Allows using different URL shortening services, which also provide
expanding and analytic functions. Specifically developed for Bit.ly, Goo.gl
(both OAUTH2) and is.gd (no API Key). Other can be added by request.
A toolkit for handling URLs that so far includes functions for URL
encoding and decoding, parsing, and parameter extraction. All functions are
designed to be both fast and entirely vectorised. It is intended to be
useful for people dealing with web-related datasets, such as server-side
logs, although may be useful for other situations involving large sets of
URLs.
This package provides spatial objects with the boundaries of
states or counties in the United States of America from 1629 to 2000.
It provides data from the Atlas of Historical County Boundaries.
This package contains functions to read cancer data from SEER (http://seer.cancer.gov/) and IARC (http://www.iarc.fr) to create datasets at the county level based on US census information.
US Census 2000 Designated Places shapefiles and additional
demographic data from the SF1 100 percent files. This data set
contains polygon files in lat/lon coordinates and the
corresponding demographic data for a number of different
variables.
US 2000 Census Tract shapefiles and additional demographic
data from the SF1 100 percent files. This data set contains
polygon files in lat/lon coordinates and the corresponding
demographic data for a number of different variables.
US Census 2010 shape files and additional demographic data
from the SF1 100 percent files. This package contains a number
of helper functions for the UScensus2010blk,
UScensus2010blkgrp, UScensus2010tract, UScensus2010cdp
packages.
This is a framework that aims to provide methods and tools for assessing the impact of different sources of uncertainties (e.g.positional uncertainty) on performance of species distribution models (SDMs).)
A set of little functions that have been found useful to do little odds and ends such as plotting the results of K-means clustering, substituting special text characters, viewing parts of a data.frame, constructing formulas from text and building design and response matrices.
Contains a number of functions that serve
two goals: first, make R more accessible to people migrating from
SPSS by adding a number of functions that behave roughly like their
SPSS equivalents; and second, make a number of slightly more
advanced functions more user friendly to relatively novice users.
A collection of data sets to accompany the
textbook "Using R for Introductory Statistics," second
edition.
Implements mixtures of unrestricted skew-t factor analyzer models via the EM algorithm
The Universal Scalability Law is a model to predict hardware and
software scalability. It uses system capacity as a function of load to
forecast the scalability for the system.
Forms a query to submit for US Treasury yield curve data, posting
this query to the US Treasury web site's data feed service.  By default the
download includes data yield data for 12 products from January 1, 1990,
some of which are NA during this span.  The caller can pass parameters to
limit the query to a certain year or year and month, but the full download
is not especially large.  The download data from the service is in XML
format.  The package's main function transforms that XML data into a numeric
data frame with treasury product items (constant maturity yields for 12 kinds
of bills, notes, and bonds) as columns and dates as row names. The function
returns a list which includes an item for this data frame as well as query-related
values for reference and the update date from the service.
Construct and plot objective hierarchies and associated value and utility functions.
Evaluate the values and utilities and visualize the results as colored objective hierarchies or tables.
Visualize uncertainty by plotting median and quantile intervals within the nodes of objective hierarchies.
Get numerical results of the evaluations in standard R data types for further processing.
Tools for generating and handling of UUIDs (Universally Unique Identifiers).
A method for estimating log-normalizing constants (or free
energies) and expectations from multiple distributions (such as
multiple generalized ensembles).
An R interface to Google's open source JavaScript engine.
V8 is written in C++ and implements ECMAScript as specified in ECMA-262,
5th edition. In addition, this package implements typed arrays as
specified in ECMA 6 used for high-performance computing and libraries
compiled with 'emscripten'.
A likelihood framework for estimating the effective
coverage of vaccination programs using cross-sectional surveys
combined with administrative data.
Declare data validation rules and data quality indicators; confront
data with them and analyze or visualize the results. The package supports
rules that are per-field, in-record, cross-record or cross-dataset. Rules
can be automatically analyzed for rule type and connectivity.
Historical results for the state of Virginia lottery draw games. Data were downloaded from https://www.valottery.com/.
Implements the variational inference procedure for
Bayesian variable selection, as described in the "Scalable
variational inference for Bayesian variable selection in
regression, and its accuracy in genetic association studies"
(Bayesian Analysis 7, March 2012, pages 73-108). This software
has been used to implement Bayesian variable selection for
large problems with over a million variables and thousands of
samples.
Variance component models: REML estimation, testing fixed effect contrasts through Satterthwaite or Kenward-Roger methods, testing the nullity of variance components through (linear or quadratic) score tests or likelihood ratio tests.
Generation of domain variables, linearisation of several nonlinear population statistics (the ratio of two totals, weighted income percentile,  relative median income ratio,  at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, 1953), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y.G.). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.
Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions,  including all commonly known distributions.  Also computed are the corresponding probability density function and cumulative distribution function.
Estimation, Hypothesis Testing, Prediction for Stationary Vector Autoregressive Models
Variables are the fundamental parts of each programming language but handling them might be frustrating for programmers from time to time. This package contains some functions to help user (especially data explorers) to make more sense of their variables and take the most out of variables as well as their hardware. These functions are written, collected and crafted over some years of experience in statistical data analysis and for each of them there was a need. Functions in this package are suppose to be efficient and easy to use, hence they will be frequently updated to make them more convenient.
Presence of interaction between a SNP and another SNP (or another factor) can result
in heterogeneity of variance between the genotypes of an interacting SNP.
Detecting such heterogeneity gives prior knowledge for constructing a genetic model
underlying complex trait.
Uses a Bayesian model to
estimate the variability in a repeated
measure outcome and use that as an outcome or a predictor
in a second stage model.
This package provides functions for the variance gamma
distributions. Density, distribution and quantile functions.
Functions for random number generation and fitting of the
variance gamma to data. Also, functions for computing moments
of the variance gamma distribution of any order about any
location. In addition, there are functions for checking the
validity of parameters and to interchange different sets of
parameterizatons for the variance gamma distribution.
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
Uses a finite mixture model for performing the cluster analysis with variable selection of continuous data by assuming independence between classes. The package deals dataset with missing values by assuming that values are missing at random. The one-dimensional marginals of the components follow Gaussian distributions for facilitating both model interpretation and model selection. The variable selection is led by the Maximum Integrated Complete-Data Likelihood criterion. The maximum likelihood inference is done by an EM algorithm for the selected model. This package also performs the imputation of  missing values.
Variable selection from random forests using both
backwards variable elimination (for the selection of small sets
of non-redundant variables) and selection based on the
importance spectrum (somewhat similar to scree plots; for the
selection of large, potentially highly-correlated variables).
Main applications in high-dimensional data (e.g., microarray
data, and other genomics and proteomics applications).
Computes a portfolio of European options that replicates
the cost of capturing the realised variance of an equity index.
Generates R scripts useful to perform repetitive
tasks on variables of different classes from a simple database. First it
helps to create a tabular file describing the variables. Then the package
could process this tabulate file to create a script in a .R or .Rmd format.
This script will include code blocks for each variable according to the
variable description. It could help to import, adapt to R classes and
perform descriptive analysis on each variable according to its type. This R
script could be used as it is or it could be modified if necessary to
perform additional analysis. The user could write his own R script template
to produce a customized script.
Efficient algorithm for solving discrete mixture
regression model for rare variant association analysis.
Uses variational Bayes algorithm to efficiently search over
model space.  Outputs an approximate likelihood ratio test
as well as variant level posterior probabilities of
association.
A package to fit and simulate latent position and cluster
models for statistical networks using a Variational Bayes approximation.
Variational algorithms and methods for fitting mixture
models. Main functions are varbayes, vbcomp, vbconstr, mppca,
mmppca and gmmkmsock.
Efficient algorithm for solving ultra-sparse
regularized regression models using a variational
Bayes algorithm with a spike (l0) prior.  Algorithm
is solved on a path, with coordinate updates, and is
capable of generating very sparse models.  There are
very general model diagnostics for controling type-1
error included in this package.
ANOVA-type estimation (prediction) of random effects in linear mixed models is implemented, following
Searle et al. 1991 (ANOVA for unbalanced data). For better performance the SWEEP-Operator is now implemented
for generating the ANOVA Type-1 error sum of squares. The primary objective of this package is to perform
Variance Component Analyses (VCA). This is a special type of analysis frequently used in verifying the
precision performance of diagnostics.
The Satterthwaite approximation of the total degrees of freedom and for individual variance components is
implemented. These are used in the Chi-Squared tests against a claimed value, and in the respective confidence intervals.
Satterthwaite's approximation of denominator degrees of freedom in t-/F-tests of fixed effects are also
available.
There are several functions for extracting, random effects, fixed effects, variance-covariance
matrices of random and fixed effects. Residuals (marginal, conditional) can be extracted as raw, standardized
and studentized residuals.
Additionally, plotting methods for residuals and random effects and a variability chart are available. The latter
is useful for visualizing the variability in sub-classes emerging from the experimental design.
Visualization techniques, data sets, summary and inference
procedures aimed particularly at categorical data. Special
emphasis is given to highly extensible grid graphics. The
package was package was originally inspired by the book
"Visualizing Categorical Data" by Michael Friendly and is
now the main support package for a new book,
"Discrete Data Analysis with R" by Michael Friendly and
David Meyer (2015).
Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data
and the 'gnm' package for Generalized Nonlinear Models.
In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and
adds a 3D version in 'mosaic3d'.  Additionally, methods are provided for comparing and visualizing lists of
'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by
Michael Friendly and David Meyer.
Recursive partitioning for varying coefficient generalized linear models and ordinal linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal regression.
Multicategory Vertex Discriminant Analysis: A novel supervised multicategory classification method
Facilities for constructing variance dispersion graphs, fraction-of-design-space plots
and similar graphics for exploring the properties of experimental designs. The design region is
explored via random sampling, which allows for more flexibility than traditional variance
dispersion graphs. A formula interface is leveraged to provide access to complex model
formulae. Graphics can be constructed simultaneously for multiple experimental designs and/or
multiple model formulae. Instead of using pointwise optimization to find the minimum and
maximum scaled prediction variance curves, which can be inaccurate and time consuming, this
package uses quantile regression as an alternative.
Uses a modification of the published FORTRAN code in "A Computer Program for Generating Variance Dispersion Graphs" by G. Vining, Journal of Quality Technology, Vol. 25 No. 1 January 1993, to produce variance dispersion graphs. Also produces fraction of design space plots, and contains data frames for several minimal run response surface designs.
Functions for creating variance dispersion graphs, fraction of design space plots, and contour plots of scaled prediction variances for second-order response surface designs in spherical and cuboidal regions. Also, some standard response surface designs can be generated.
This provides web-based visual data-mining tools by adding
interactive functions to 'ggplot2' graphics. Brushing and linking between the
multiple plots is one of the main feature of this package. Currently
scatter plots, histograms, parallel coordinate plots and choropleth maps are
supported.
A package for applying affine and similarity transformations on vector spatial data (sp objects). Transformations can be defined from control points or directly from parameters. If redundant control points are provided Least Squares is applied allowing to obtain residuals and RMSE.
The base 'sets' tools follow the algebraic definition
that each element of a set must be unique.
Since it's often helpful to compare all elements of two vectors,
this toolset treats every element as unique for counting purposes.
For ease of use, all functions in vecsets have an argument
'multiple' which, when set to FALSE,
reverts them to the base::set tools functionality.
This package performs a 2D statistical analysis, both numerical and graphical, of a set of vectors. Since a vector has two components (module and azimuth) vector analysis is performed in three stages: modules are analyzed by means of linear statistics, azimuths are analyzed by circular statistics, and the joint analysis of modules and azimuths is done using density maps that allow detecting another distribution properties (i.e. anisotropy) and outliers. Tests and circular statistic parameters have associated a full range of graphing: histograms, maps of distributions, point maps, vector maps, density maps, distribution modules and azimuths.
This package performs a 3D statistical analysis, both numerical and graphical, of a set of vectors. Since a vector has three components (a module and two angles) vectorial analysis is performed in two stages: modules are analyzed by means of linear statistics and orientations are analyzed by spherical statistics. Tests and spherical statistic parameters are accompanied by a graphs as: density maps, distribution modules and angles. The tests, spherical statistic parameters and graphs allow us detecting another distribution properties (i.e. anisotropy) and outliers.
Ordination methods, diversity analysis and other
functions for community and vegetation ecologists.
Static and dynamic 3D plots to be used with ordination
results and in diversity analysis, especially with the vegan package.
This package contains functions used to perform fuzzy clustering of vegetation data under different models.
Handling of vegetation data from Turboveg (<http://www.synbiosys.alterra.nl/turboveg/>) and other sources (<http://www.vegetweb.de>). Taxonomic harmonization given appropriate taxonomic lists (e.g. the German taxonomical standard list "GermanSL", <http://www.botanik.uni-greifswald.de/GermanSL.html>).
This package computes diversity for community data sets
using the methods outlined by Jost (2006, 2007). While there
are differing opinions on the ideal way to calculate diversity
(e.g. Magurran 2004), this method offers the advantage of
providing diversity numbers equivalents, independent alpha and
beta diversities, and the ability to incorporate 'order' (q) as
a continuous measure of the importance of rare species in the
metrics. The functions provided in this package largely
correspond with the equations offered by Jost in the cited
papers. The package computes alpha diversities, beta
diversities, gamma diversities, and similarity indices.
Confidence intervals for diversity measures are calculated
using a bootstrap method described by Chao et al. (2008).  For
datasets with many samples (sites, plots), sim.table creates
tables of all pairwise comparisons possible, and for grouped
samples sim.groups calculates pairwise combinations of within-
and between-group comparisons.
A set of functions to generate high-resolution Venn and Euler plots. Includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.
Calculates and displays Venn and Euler Diagrams
Utilities for verifying discrete, continuous and probabilistic forecasts, and forecasts expressed as parametric distributions are included.
Installs specified versions of R packages hosted on CRAN and provides functions to list available versions and the versions of currently installed packages. These tools can be used to help make R projects and packages more reproducible.
'versions' fits in the narrow gap between the 'devtools' install_version() function and the 'checkpoint' package. devtools::install_version() installs a stated package version from source files stored on the CRAN archives. However CRAN does not store binary versions of packages so Windows users need to have RTools installed and Windows and OSX users get longer installation times.
'checkpoint' uses the Revolution Analytics MRAN server to install packages (from source or binary) as they were available on a given date. It also provides a helpful interface to detect the packages in use in a directory and install all of those packages for a given date.
'checkpoint' doesn't provide install.packages-like functionality however, and that's what 'versions' aims to do, by querying MRAN.
As MRAN only goes back to 2014-09-17, 'versions' can't install packages from before this date.
When given a description of a polyhedral set by a system of linear inequalities Ax <= b, produce the list of the vertices of the set.
Integrated data management library that offers a variety of tools concerning the loading and manipulation of environmental data available from different Venezuelan governmental sources. Facilities are provided to plot temporal and spatial data as well as understand the health of a collection of meteorological data.
An implementation of about 6 major classes of
statistical regression models. At the heart of it are the
vector generalized linear and additive model (VGLM/VGAM)
classes, and the book "Vector Generalized Linear and
Additive Models: With an Implementation in R" (Yee, 2015)
gives details of the statistical framework and VGAM package.
Currently only fixed-effects models are implemented,
i.e., no random-effects models. Many (150+) models and
distributions are estimated by maximum likelihood estimation
(MLE) or penalized MLE, using Fisher scoring. VGLMs can be
loosely thought of as multivariate GLMs. VGAMs are data-driven
VGLMs (i.e., with smoothing). The other classes are RR-VGLMs
(reduced-rank VGLMs), quadratic RR-VGLMs, reduced-rank VGAMs,
RCIMs (row-column interaction models)---these classes perform
constrained and unconstrained quadratic ordination (CQO/UQO)
models in ecology, as well as constrained additive ordination
(CAO). Note that these functions are subject to change;
see the NEWS and ChangeLog files for latest changes.
Data sets to accompany the VGAM package and
the book "Vector Generalized Linear and
Additive Models: With an Implementation in R" (Yee, 2015).
These are used to illustrate vector generalized
linear and additive models (VGLMs/VGAMs), and associated models
(Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column
Interaction Models, and constrained and unconstrained ordination
models in ecology).
This package provides an implementation of Linear discriminant analysis and quadratic discriminant analysis that works fine in very high dimension (when there are many more variables than observations).
It will take the vectors of motion for two videos
(coming from a variant of shotdetect code allowing to store
detailed motion vectors in JSON format, for instance) and it
will look for comparing taking out the common chunk.
Then, provided you have some image's hashes it will compare
their signature in order to make up the decision about
chunk similarity of two video files.
ShotDetect is a free software which detects shots and scenes
from a video (http://johmathe.name/shotdetect.html).
This package implements a fast regression algorithm for
building linear model for large data as defined in the paper
"VIF-Regression: A Fast Regression Algorithm for Large Data
(2011), Journal of the American Statistical Association, Vol.
106, No. 493: 232-247" by Dongyu Lin, Dean P. Foster, and Lyle
H. Ungar.
Contains a function to support the paper 'A sequential multiple change-point detection
procedure via VIF regression' (Accepted by Computational Statistics).
Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).
New tools for the visualization of missing
and/or imputed values are introduced, which can be used for exploring the data and the
structure of the missing and/or imputed values. Depending on this structure
of the missing values, the corresponding methods may help to identify the
mechanism generating the missing values and allows to explore the data
including missing values. In addition, the quality of imputation can be
visually explored using various univariate, bivariate, multiple and
multivariate plot methods. A graphical user interface available in the
separate package VIMGUI allows an easy handling of the implemented plot
methods.
This package introduces new tools for the visualization of missing
and/or imputed values, which can be used for exploring the data and the
structure of the missing and/or imputed values. Depending on this structure
of the missing values, the corresponding methods may help to identify the
mechanism generating the missings and allows to explore the data including
missing values. In addition, the quality of imputation can be visually
explored using various univariate, bivariate, multiple and multivariate
plot methods. A graphical user interface allows an easy handling of the
implemented plot methods.
Tools for bivariate exploratory data analysis, bivariate copula selection and (vine) tree construction are provided. Vine copula models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and plotting methods are included. Data is assumed to lie in the unit hypercube (so-called copula data). For C- and D-vines links to the package 'CDVine' are provided.
Implementation of the vine graphical model for building
high-dimensional probability distributions as a factorization of
bivariate copulas and marginal density functions. This package
provides S4 classes for vines (C-vines and D-vines) and methods
for inference, goodness-of-fit tests, density/distribution
function evaluation, and simulation.
A lattice violin-plot is overlayed with the arithmetic
mean and standard deviation.
A violin plot is a combination of a box plot and a kernel density plot.
viopoints draws one dimensional scatter plots with jitter
using kernel density estimates in a similar way to violin
plots.
Generate a violin point plot, a combination of a violin/histogram plot and a scatter plot by offsetting points within a category based on their density using quasirandom noise.
Port of the new 'matplotlib' color maps ('viridis' - the default -,
'magma', 'plasma' and 'inferno') to 'R'. 'matplotlib' <http://matplotlib.org/>
is a popular plotting library for 'python'. These color maps are designed in
such a way that they will analytically be perfectly perceptually-uniform,
both in regular form and also when converted to black-and-white. They are
also designed to be perceived by readers with the most common form of color
blindness.
Port of the new 'matplotlib' color maps ('viridis' - the default
-, 'magma', 'plasma' and 'inferno') to 'R'. 'matplotlib' <http://matplotlib.org/
> is a popular plotting library for 'python'. These color maps are designed
in such a way that they will analytically be perfectly perceptually-uniform,
both in regular form and also when converted to black-and-white. They are
also designed to be perceived by readers with the most common form of color
blindness. This is the 'lite' version of the more complete 'viridis' package
that can be found at <https://cran.r-project.org/package=viridis>.
Provides a framework for generating virtual species distributions,
a procedure increasingly used in ecology to improve species distribution models.
This package integrates the existing methodological approaches with the
objective of generating virtual species distributions with increased
ecological realism.
Provides an R interface to the 'vis.js' JavaScript charting library. It allows an interactive visualization of networks.
Provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R (lm, glm, coxph, rlm, gam, locfit, randomForest, etc.)
A collection of tools for analyzing the field of vision. It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research.
Graphs the pdf or pmf and highlights what area or probability is present in user defined locations. Visualize is able to provide lower tail, bounded, upper tail, and two tail calculations. Supports strict and equal to inequalities.  Also provided on the graph is the mean and variance of the distribution.
Provides fitting routines for four versions of the
Vitality family of mortality models.
Provides individual- and aggregate-level graphical depictions of
patterns of treatment and response in patient registries, and a graphical
tool for examining potential for confounding in analyses of observational
data.
Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences.  The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs.  The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another.  Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures.
Functions, Classes & Methods for estimation, prediction, and
simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.
The tools you need to process, analyze, combine, integrate and
plot your fishery data: the georeferenced dataset from the Vessel Monitoring
System (VMS), from the Automatic Information System (AIS) or other tracking
devices, as well as the catches or landings dataset from the Logbook or Vessel
Register. Package 'vmsbase' is equipped by Viewer Tools to visually inspect data
at different steps of the analyses and to produce effective outputs for reports
and scientific publications. Viewers are conceived to show the VMS pings, to
visualize single or multiple tracks for fishing vessels, or to represent the VMS
data on Google Viewer, so that the user can produce easy to interpret and more
realistic visualization of both fishing effort and effort behaviour. Package
'vmsbase' represents the implementation of several R routines which have been
developed by the "Tor Vergata" University of Rome Team involved in the Italian
National Program for the Data Collection Framework for Fisheries Data between
2009-2012.
Using V-algorithm and Newton-Raphson method to obtain multiple-objective optimal design for estimating the shape of dose-response, the ED50 (the dose producing an effect midway between the expected responses at the extreme doses) and the MED (the minimum effective dose level) for the 2,3,4-parameter logistic models and for evaluating its efficiencies for the three objectives.
Voss package provides functionality for generating
realizations of a fractal Brownian function on uniform 1D & 2D
grid with classic and generic versions of the Voss algorithm
(random sequential additions)
Procedures for the manipulation, normalization, and plotting of phonetic and sociophonetic vowel formant data.  vowels is the backend for the NORM website.
Parametric and semiparametric inference for massively parallel
models, i.e., a large number of models with common design matrix, as often
occurs with brain imaging data.
Tools for tree crown structure description based on T-LiDAR data voxelisation
Variable Penalty Dynamic Time Warping for aligning GC-MS chromatograms to a master signal and more. With the appropriate penalty this method performs good alignment without altering the shape of peaks in GC-MS data.
vrmlgen creates 3D scatter and bar plots, visualizations of 3D meshes, parametric functions and height maps in web-formats like the Virtual Reality Markup Language (VRML, filetype .wrl) and the LiveGraphics3D format.
A collection of statistical tests for martingale difference hypothesis
Performs variable selection/feature reduction under a clustering or classification framework. In particular, it can be used in an automated fashion using mixture model-based methods (tEIGEN and MCLUST are currently supported).
Three steps variable selection procedure based on random forests.
Initially developed to handle high dimensional data (for which number of
variables largely exceeds number of observations), the package is very
versatile and can treat most dimensions of data, for regression and
supervised classification problems. First step is dedicated to eliminate
irrelevant variables from the dataset. Second step aims to select all
variables related to the response for interpretation purpose. Third step
refines the selection by eliminating redundancy in the set of variables
selected by the second step, for prediction purpose.
Designed to facilitate the assimilation, analysis and synthesis of animal location and movement data collected by the VEMCO suite of acoustic transmitters and receivers. As well as database and geographic information capabilities the principal feature of VTrack is the qualification and identification of ecologically relevant events from the acoustic detection and sensor data. This procedure condenses the acoustic detection database by orders of magnitude, greatly enhancing the synthesis of acoustic detection data.
Prepares variables so that data has fewer exceptional cases, making it easier to safely use models in production. Common problems 'vtreat' defends against: Inf, NA, too many categorical values, rare categorical values, new categorical values (levels seen during application, but not during training).   'vtreat::prepare' should be used as you would use 'model.matrix'.
Contains 2 new functions for visualization of univariate data: ccdplot and qddplot, along with input data.
Functions and data for use in visual word recognition research:
Computation of neighbors (Hamming and Levenshtein
distances), average distances to neighbors (e.g., OLD20),
and Coltheart's N. Also includes the LD1NN algorithm to
detect bias in the composition of a lexical decision task. Most of
the functions support parallel execution. Supplies wordlists for
several languages. Uses the string distance functions from the stringdist package by Mark van der Loo.
Set of functions that improves the graphical presentations of the functions 'wave.correlation' and 'spin.correlation' (waveslim package, Whitcher 2012) and the 'wave.multiple.correlation' and 'wave.multiple.cross.correlation' (wavemulcor package, Fernandez-Macho 2012b). The plot outputs (heatmaps) can be displayed in the screen or can be saved as PNG or JPG images or as PDF or EPS formats. The W2CWM2C package also helps to handle the (input data) multivariate time series easily as a list of N elements (times series) and provides a multivariate data set (dataexample) to exemplify its use. A description of the package was published in Computing in Science & Engineering (Volume:16, Issue: 6) on Sep. 09, 2014, doi:10.1109/MCSE.2014.96.
R interface to a W3C Markup Validation service.
See <http://validator.w3.org/> for more information.
waffect (pronounced 'double-u affect' for 'weighted
affectation') is a package to simulate phenotypic (case or
control) datasets under a disease model H1 such that the total
number of cases is constant across all the simulations (the
constrain in the title). The package also makes it possible to
generate phenotypes in the case of more than two classes, so
that the number of phenotypes belonging to each class is
constant across all the simulations. waffect is used to assess
empirically the statistical power of Genome Wide Association
studies.
Square pie charts (a.k.a. waffle charts) can be used
to communicate parts of a whole for categorical quantities. To emulate the
percentage view of a pie chart, a 10x10 grid should be used with each square
representing 1% of the total. Modern uses of waffle charts do not
necessarily adhere to this rule and can be created with a grid of any
rectangular shape. Best practices suggest keeping the number of categories
small, just as should be done when creating pie charts.
Fit the fixed effect panel data model with heteroskedasticity and
autocorrelation correction.
Generates random data sets including: data.frames, lists,
and vectors.
Consider the intersection of two spaces: the complete solution space
to Ax = b and the N-simplex. The intersection of these two spaces is
a non-negative convex polytope. The package walkr samples from this
intersection using two Monte-Carlo Markov Chain (MCMC) methods:
hit-and-run and Dikin walk. walkr also provide tools to examine sample
quality.
A collection of functions to perform the Application
Programming Interface (API) calls associated with the Walk
Score website (www.walkscore.com) within the R environment.
These functions can be used to query the Walk Score and Transit
Score database for a wide variety of information using R
scripts. This package includes the simple Walk Score and
Transit Score API calls, which return the scores associated
with an input location, as well as calls which return some data
used to calculate the scores. These functions are especially
useful for mass data collection and gathering Walk Score and
Transit Score values for large lists of locations.
Offers three overarching categories of functions to obtain bird vocalization recordings from the web, manage sound files, and facilitate (bio)acoustic analysis in R.
This estimates precise weaning ages
for a given skeletal population
by analyzing the stable nitrogen isotope ratios of them.
Bone collagen turnover rates estimated anew and
the approximate Bayesian computation (ABC)
were adopted in this package.
Helpful tools for data processing and visualisation of results of the hydrological model WASIM-ETH.
Tools and functions to calculate actual evapotranspiration
using surface energy balance models.
This package imports U.S. Geological Survey (USGS) daily hydrologic data from USGS web services, plots the data, addresses some common data problems, and calculates and plots anomalies.
Lets you connect to any CUAHSI WaterOneFlow web service from any hydroserver.
To see list of available hydroservers, see http://hiscentral.cuahsi.org.
The package has five data download functions: GetServices(): show all public
HydroServers from the HIS Central Catalog.
HISCentral_GetSites() and HISCentral_GetSeriesCatalog(): search for sites or time series
from the HIS Central catalog based on geographic bounding box, server, or keyword.
GetVariables(): Show a data.frame with all variables on the server.
GetSites(): Show a data.frame with all sites on the server.
GetSiteInfo(): Show what variables, methods and quality control levels are
available at the specific site. GetValues(): Given a site code, variable code, start time
and endtime, fetch a data.frame of all the observation time series data values.
The data.frame has two columns: time and DataValue. This function has an optional "daily"
parameter, you can set daily="max", daily="min" or daily="mean" to get aggregated daily
values. The GetValues function can also retrieve WaterML 1 or WaterML 2.0 data
from a custom URL or from a local file. The package also has five data upload functions:
AddSites(), AddVariables(), AddMethods(), AddSources(), and AddValues().
These functions can be used for uploading data to a HydroServer Lite ODM database
via the JSON data upload web service interface.
Wrap-around Time Series (WATS) plots for interrupted time series
designs with seasonal patterns.
Computes Bayesian wavelet shrinkage credible intervals
Makes available code necessary to reproduce figures and
tables in papers on the WaveD method for wavelet deconvolution
of noisy signals as presented in The WaveD Transform in R,
Journal of Statistical Software Volume 21, No. 3, 2007.
Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms.
This package contains functions for computing and plotting
discrete wavelet transforms (DWT) and maximal overlap discrete
wavelet transforms (MODWT), as well as their inverses.
Additionally, it contains functionality for computing and
plotting wavelet transform filters that are used in the above
decompositions as well as multiresolution analyses.
Wavelet routines that calculate single sets of wavelet
multiple correlations and cross-correlations out of n variables
(either 1D time series, 2D images or 3D arrays). They can later
be plotted in single graphs, as an alternative to trying to
make sense out of several sets of wavelet correlations or
wavelet cross-correlations. The code is based on the
calculation, at each wavelet scale, of the square root of the
coefficient of determination in a linear combination of
variables for which such coefficient of determination is a
maximum. The code provided here is based on the
wave.correlation routine in Brandon Whitcher's waveslim R
package Version: 1.6.4, which in turn is based on wavelet
methodology developed in Percival and Walden (2000); Gencay,
Selcuk and Whitcher (2001) and others.
Basic wavelet routines for time series (1D), image (2D)
and array (3D) analysis.  The code provided here is based on
wavelet methodology developed in Percival and Walden (2000);
Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet
transform (DTCWT) from Kingsbury (1999, 2001) as implemented by
Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002).  All
figures in chapters 4-7 of GSW (2001) are reproducible using this
package and R code available at the book website(s) below.
Performs 1, 2 and 3D real and complex-valued wavelet transforms,
nondecimated transforms, wavelet packet transforms, nondecimated
wavelet packet transforms, multiple wavelet transforms,
complex-valued wavelet transforms, wavelet shrinkage for
various kinds of data, locally stationary wavelet time series,
nonstationary multiscale transfer function modeling, density
estimation.
Supplies bootstrap alternatives to traditional hypothesis-test
and confidence-interval procedures such as one-sample and two-sample
inferences for means, standard deviations, and proportions; simple linear
regression; and more. Suitable for general audiences, including individual
and group users, introductory statistics courses, and more advanced
statistics courses that desire an introduction to bootstrap methods.
Provides efficient implementation of the Wild Binary Segmentation and Binary
Segmentation algorithms for estimation of the number and locations of
multiple change-points in the piecewise constant function plus Gaussian
noise model.
Implements detection for the number and locations of
the change-points in a time series using the Wild Binary Segmentation and
the Locally Stationary Wavelet model.
SOMs can be useful tools to group patterns containing several peaks. If peaks do not always occur at exactly the same position, classical distance measures cannot be used. This package provides SOM technology using the weighted crosscorrelation (WCC) distance.
WCE implements a flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.
The package contains the WCQ method for detection of QTL
effects in a small mapping population. It also contains
implementation of the Chen-Qin two-sample and one-sample test
of means.
Search, extract and format data from the World Bank's
World Development Indicators
Functions that help in fetching weather data from
websites. Given a location and a date range, these functions help fetch
weather data (temperature, pressure etc.) for any weather related analysis.
Handle instant weather forecasts and geographical information. It combines multiple sources of information to obtain instant weather forecasts.
Chemical information from around the web. This package interacts with a suite of web APIs for chemical information.
R is used by a vast array of people for a vast array of purposes - including web
analytics. This package contains functions for consuming and munging various common forms of
request log, including the Common and Combined Web Log formats and AWS logs.
A Stata-style `webuse()` function for importing named datasets from Stata's online collection.
Utility functions for developing web applications. Includes parsers
for application/x-www-form-urlencoded as well as multipart/form-data
and examples of using the parser with either httpuv or rhttpd.
Uses Protovis to provide web graphics for R (exposes most
low-level functions).  Package is still under active
development and shouldn't be considered stable until version
0.1.  Currently uses a web browser to process JavaScript,
although future version will process JavaScript directly and
return the SVG output.  Also does not properly support discrete
labels (e.g. with histograms) or statistical functions.  See
website for more details.
Provides functions to create factor variables with contrasts based on weighted effect coding. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. This is especially useful when a researcher has no directional hypotheses and uses a sample from a population in which the number of observation per group is different The package also provides functionality for interactions between two factor variables based on weighted effect coding. Please note that this is a beta version: while functional, it does not follow all R conventions.
The WeightedCluster library provides functions to cluster states sequences and weighted data. These functionalities include aggregating replicated cases, an optimized weighted PAM algorithm, function computing cluster quality measures for a range of clustering solutions and miscellaneous functions to plot clustering solutions of state sequences.
This packages contains the Weighted Portmanteau Tests as
described in "New Weighted Portmanteau Statistics for Time
Series Goodness-of-Fit Testing' accepted for publication by the
Journal of the American Statistical Association.
Has functions to implement the weighted scores method and CL1 information criteria as an intermediate step for variable/correlation selection for longitudinal categorical and count data in Nikoloulopoulos, Joe and Chaganty (2011, Biostatistics, 12: 653-665) and Nikoloulopoulos (2015a,2015b).
This package currently provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squareds, histograms, and t-tests.  Also now includes some software for quickly recoding survey data.  Future versions of the package will be more closely integrated with anesrake and additional weighting tools and will provide the option to find weighting benchmarks and weight data using a variety of methodologies.  NOTE: Weighted partial correlation calculations temporarily pulled to address a bug.
The weightTAPSPACK subsets The American Panel Survey (TAPS) data by outcome and covariates, models the attrition rates, imputes data for attrited individuals, and finds weights for analysis.
Provides computational support for flow over weirs, such as sharp-crested, broad-crested, and embankments. Initially, the package supports broad- and sharp-crested weirs.
Convert 'WKT' to 'GeoJSON' and 'GeoJSON' to 'WKT'. Functions
included for converting between 'GeoJSON' to 'WKT', creating both
'GeoJSON' features, and non-features, creating WKT from R objects
(e.g., lists, data.frames, vectors), and linting 'WKT'.
Palettes generated mostly from Wes Anderson movies
This R package provides a computationally efficient way
of fitting weighted linear fixed effects estimators for
causal inference with various weighting schemes.  Imai
and Kim (2012) show that weighted linear fixed effects
estimators can be used to estimate the average treatment
effects under different identification strategies.  This
includes stratified randomized experiments, matching and
stratification for observational studies, first
differencing, and difference-in-differences.  The package
also provides various robust standard errors and a
specification test for standard linear fixed effects
estimators.
Integrates sophisticated mixed modelling methods with a whole genome approach to detecting significant QTL in linkage maps.
Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.
Non parametric alternative to Kolmogorov-Smirnov based
standard GSEA testing.
Inferences about counterfactuals are essential for prediction,
answering what if questions, and estimating causal effects.
However, when the counterfactuals posed are too far from the data at
hand, conclusions drawn from well-specified statistical analyses
become based largely on speculation hidden in convenient modeling
assumptions that few would be willing to defend.  Unfortunately,
standard statistical approaches assume the veracity of the model
rather than revealing the degree of model-dependence, which makes this
problem hard to detect.  WhatIf offers easy-to-apply methods to
evaluate counterfactuals that do not require sensitivity testing over
specified classes of models.  If an analysis fails the tests offered
here, then we know that substantive inferences will be sensitive to at
least some modeling choices that are not based on empirical evidence,
no matter what method of inference one chooses to use.  WhatIf
implements the methods for evaluating counterfactuals discussed in
Gary King and Langche Zeng, 2006, "The Dangers of Extreme
Counterfactuals," Political Analysis 14 (2); and Gary
King and Langche Zeng, 2007, "When Can History Be Our Guide?  The
Pitfalls of Counterfactual Inference," International Studies
Quarterly 51 (March).
logicless templating, reuse templates in many programming
languages including R
Shinohara et. al introduced Whitestripe, an
intensity-based normalization of T1 and T2 images, where normal
appearing white matter performs well, but requires segmentation.
This method performs white matter mean and standard deviation
estimates on data that has been rigidly-registered to the MNI
template and uses histogram-based methods.
Look up the username and full name of the current user,
the current user's email address and 'GitHub' username,
using various sources of system and configuration information.
Retrieve data from the 'Whoapi' (https://whoapi.com) store of domain information, including a domain's geographic
location, registration status and search prominence.
Provides very fast access to whole genome, population scale variation data
from VCF files and sequence data from FASTA-formatted files.
It also reads in alignments from FASTA, Phylip, MAF and other file formats.
Provides easy-to-use interfaces to genome annotation from UCSC and Bioconductor and gene ontology data
from AmiGO and is capable to read, modify and write PLINK .PED-format pedigree files.
Fit, forecast, predict massive spacio-temporal data
Extends the glmnet and relaxnet packages with polynomial basis expansions. Basis expansion is applied to the predictors and a subset of the basis functions is chosen using relaxnet. Predictors may be screened using correlation or t-tests. Screening is done separately within cross-validation folds. Cross-validation may be used to select the order of basis expansion and alpha, the elastic net tuning parameter.
The german Wikibook "GNU R" introduces R to new users.
This package is a collection of functions and datas used in the
german WikiBook "GNU R"
An API client for the 'Wikidata' store of semantic data.
Provides an interface to the Wikipedia web application programming interface (API), using internet connection. Three functions provide details for a specific Wikipedia page: all links that are present, all pages that link to, all the contributions (revisions for main pages, and discussions for talk pages). Two functions provide details for a specific user: all contributions, and general information (as name, gender, rights or groups). It provides additional information compared to others packages, as 'WikipediR'. It does not need login. The multiplex network that can be constructed from the results of the functions of 'WikipediaR' can be modeled as Stochastic Block Model as in  Barbillon P., Donnet, S., Lazega E., and Bar-Hen A.: "Stochastic Block Models for Multiplex networks: an application to networks of researchers", ArXiv 1501.06444, http://arxiv.org/abs/1501.06444.
Public attention is an interesting field of study. The
internet not only allows to access information in no time on
virtually any subject but via page access statistics gathered
by website authors the subject of attention as well can be
studied. For the omnipresent Wikipedia those access statistics
are made available via 'http://stats.grok.se' a server
providing the information as file dumps as well as as web API.
This package provides an easy to use, consistent and traffic
minimizing approach to make those data accessible within R.
A wrapper for the MediaWiki API, aimed particularly at the
Wikimedia 'production' wikis, such as Wikipedia. It can be used to retrieve
page text, information about users or the history of pages, and elements of
the category tree.
This package provides functions to perform fast variable
selection based on the Wilcoxon rank sum test in the
cross-validation or Monte-Carlo cross-validation settings, for
use in microarray-based binary classification.
Dynamic interaction refers to spatial-temporal associations in the
movements of two (or more) animals. This package provides tools for
calculating a suite of indices used for quantifying dynamic interaction
with wildlife telemetry data. For more information on each of the methods
employed see the references within. The package draws heavily on the
classes and methods developed in the 'adehabitat' packages.
Provides insight into how the best hand for a poker game changes based on the game dealt, players who stay in until the showdown and wildcards added to the base game.  At this time the package does not support player tactics, so draw poker variants are not included.
Analysing convergent evolution using the Wheatsheaf index.
Helps for interfacing with the operating system
particularly for Windows.
Data sets from the World Input Output Database, for the years 1995-2011.
Perform the Wild Scale-Enhanced (WiSE) bootstrap.  Specifically, the user may supply a single or multiple equally-spaced time series and use the WiSE bootstrap to select a wavelet-smoothed model.  Conversely, a pre-selected smooth level may also be specified for the time series.  Quantities such as the bootstrap sample of wavelet coefficients, smoothed bootstrap samples, and specific hypothesis testing and confidence region results of the wavelet coefficients may be obtained.  Additional functions are available to the user which help format the time series before analysis.  This methodology is recommended to aid in model selection and signal extraction.
Note:  This package specifically uses wavelet bases in the WiSE bootstrap methodology, but the theoretical construct is much more versatile.
A set of functions to run code 'with' safely and temporarily
modified global state. Many of these functions were originally a part of the
devtools package, this provides a simple package with limited dependencies
to provide access to these functions.
WITNESS is a computational model intended to understand the
psychological processes underlying eyewitness identification. This package
contains the WITNESS computational model as well as two methods of fitting
the model to data.
Utility functions to convert between the 'Spatial' classes
specified by the package 'sp', and the well-known binary '(WKB)'
representation  for geometry specified by the Open Geospatial Consortium.
Supports 'Spatial'  objects of class 'SpatialPoints',
'SpatialPointsDataFrame', 'SpatialLines',  'SpatialLinesDataFrame',
'SpatialPolygons', and 'SpatialPolygonsDataFrame'.  Supports 'WKB' geometry
types 'Point', 'LineString', 'Polygon', 'MultiPoint',  'MultiLineString', and
'MultiPolygon'. Includes extensions to enable creation  of maps with
'TIBCO Spotfire'.
Approach to the robustness via Weighted Likelihood.
A GUI R implementation of hierarchical Bayesian models of working memory, used for analyzing change detection data.
Distance discriminant analysis method is one of
classification methods according to multiindex performance
parameters.However,the traditional Mahalanobis distance
discriminant method treats with the importance of all
parameters equally,and exaggerates the role of parameters which
changes a little.The weighted Mahalanobis distance is used in
discriminant analysis method to distinguish the importance of
each parameter.In the concrete application,firstly based on the
principal component analysis scheme,a new group of parameters
and their corresponding percent contributions of the parameters
are calculated ,and the weighted matrix is regarded as the
diagonal matrix of the contributions rates.Setting data to
standardization,then the weighted Mahalanobis distance can be
calculated.Besides the methods metioned above,bayes method is
also given.
Analyzing the texture of an image from a multifractal wavelet leader analysis.
Software to book Wavelet Methods for Time Series Analysis,
Donald B. Percival and Andrew T. Walden, Cambridge University
Press, 2000.
Estimates Poole and Rosenthal W-NOMINATE scores from roll
call votes supplied though a 'rollcall' object from package
'pscl'.
Shows the relationship between an independent and dependent variable through Weight of Evidence and Information Value.
Tools for connecting to Wordbank, an open repository for developmental vocabulary data.
Matches words in one file with words in another file and shows index(row number) for the matches
An interface to WordNet using the Jawbone Java API to WordNet.
WordNet (<http://wordnet.princeton.edu/>) is a large lexical database of
English.  Nouns, verbs, adjectives and adverbs are grouped into sets of
cognitive synonyms (synsets), each expressing a distinct concept.  Synsets
are interlinked by means of conceptual-semantic and lexical relations.
Please note that WordNet(R) is a registered tradename.  Princeton
University makes WordNet available to research and commercial users
free of charge provided the terms of their license
(<http://wordnet.princeton.edu/wordnet/license/>) are followed, and
proper reference is made to the project using an appropriate
citation (<http://wordnet.princeton.edu/wordnet/citing-wordnet/>).
This package collects several classical word pools used
most often to provide lists of words in psychological studies
of learning and memory.
Supplies permutation-test alternatives to traditional hypothesis-test
procedures such as two-sample tests for means, medians, and standard deviations;
correlation tests; tests for homogeneity and independence; and more. Suitable for
general audiences, including individual and group users, introductory statistics
courses, and more advanced statistics courses that desire an introduction to
permutation tests.
Data from the United Nation's World Population Prospects 2008
Data from the United Nation's World Population Prospects
2010
Data from the United Nation's World Population Prospects 2012
Provides data from the United Nation's World Population Prospects 2015.
Explore data in the 'wpp2015' (or 2012, 2010) package using a 'shiny' interface.
Functions to assist in the processing and exploration of data from
environmental monitoring programs. The package name stands for "water quality"
and reflects the original focus on time series data for physical and
chemical properties of water, as well as the plankton. Intended for programs
that sample approximately monthly at discrete stations, a feature of many
legacy data sets. Most of the functions should be useful for analysis of
similar-frequency time series regardless of the subject matter.
Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights.
wrassp is a wrapper around Michel Scheffers's libassp (Advanced
Speech Signal Processor). The libassp library aims at providing
functionality for handling speech signal files in most common audio formats
and for performing analyses common in phonetic science/speech science. This
includes the calculation of formants, fundamental frequency, root mean
square, auto correlation, a variety of spectral analyses, zero crossing
rate, filtering etc. This wrapper provides R with a large subset of
libassp's signal processing functions and provides them to the user in a
(hopefully) user-friendly manner.
A powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The WrightMap package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. eRm or IRToys in R, or Stata, Mplus, etc.),  WrightMap features special integration with ConQuest to facilitate reading and plotting its output directly.The wrightMap function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The CQmodel function reads output files created using ConQuest software and creates a set of data frames for easy data manipulation, bundled in a CQmodel object. The wrightMap function can take a CQmodel object as input or it can be used to create Wright Maps directly from data frames of person and item parameters.
Function for writing a SNNS pattern file from a data.frame
or matrix.
Cross-platform Perl based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX)
files from one or more data frames. Each data frame will be
written to a separate named worksheet in the Excel spreadsheet.
The worksheet name will be the name of the data frame it contains
or can be specified by the user.
A collection of robust statistical methods based on Wilcox' WRS functions. It implements t-tests (independent and dependent samples), ANOVA (including between-within subject designs) and nonparametric ANCOVA models based on robust location measures.
Contains functions for working with the Worldwide Reference System
(WRS) 1 and 2 systems used by NASA. WRS-1 applies to Landsat 1-3, WRS-2
applies to Landsat 4-8. The package has functions for retrieving a given
path and row as a polygon, and for retrieving the path(s) and row(s)
containing a given raster or vector.
Contains the Worldwide Reference System (WRS) 1 and 2 polygon
data from NASA, for use by the wrspathrow package. WRS-1 and WRS-2
Shape files courtesy of the U.S. Geological Survey, from
http://landsat.usgs.gov/tools_wrs-2_shapefile.php
Entropy weighted k-means (ewkm) is a weighted subspace
clustering algorithm that is well suited to very high
dimensional data. Weights are calculated as the importance of
a variable with regard to cluster membership.  The two-level
variable weighting clustering algorithm tw-k-means (twkm)
introduces two types of weights, the weights on individual
variables and the weights on variable groups, and they are
calculated during the clustering process.  The feature group
weighted k-means (fgkm) extends this concept by grouping
features and weighting the group in addition to weighting
individual features.
A parallel implementation of Weighted Subspace Random
Forest.  The Weighted Subspace Random Forest algorithm was
proposed in the International Journal of Data Warehousing and
Mining, 8(2):44-63, 2012, proposed by Baoxun Xu, Joshua Zhexue
Huang, Graham Williams, Qiang Wang, and Yunming Ye.  The algorithm
can classify very high-dimensional data with random forests built
using small subspaces.  A novel variable weighting method is used
for variable subspace selection in place of the traditional random
variable sampling.This new approach is particularly useful in
building models from high-dimensional data.
We propose weighted SVM methods with penalization form. By
adding weights to loss term, we can build up weighted SVM
easily and examine classification algorithm properties under
weighted SVM. Through comparing each of test error rates, we
conclude that our Weighted SVM with boosting has predominant
properties than the standard SVM have, as a whole.
Estimation and regression modeling of subdistribution functions for competing risks data with adjustment for covariate-dependent censoring, as described in He et al. (2015).
Allows form managers to download entries from their respondents using Wufoo JSON API. Additionally, the Wufoo reports - when public - can be also acquired programmatically. Note that building new forms within this package is not supported.
Methods to calculate and interpret climate change signals and time series from climate multi-model ensembles. Climate model output in binary 'NetCDF' format is read in and aggregated over a specified region to a data.frame for statistical analysis. Global Circulation Models, as the 'CMIP5' simulations, can be read in the same way as Regional Climate Models, as e.g. the 'CORDEX' or 'ENSEMBLES' simulations. The package has been developed at the 'Wegener Center for Climate and Global Change' at the University of Graz, Austria.
Book is "Linear Mixed Models: A Practical Guide Using
Statistical Software" published in 2006 by Chapman Hall / CRC
Press
A wrapper function and GUI for the X12 binaries
A graphical user interface for the x12 package
Functions to extract business financial information from
an Extensible Business Reporting Language ('XBRL') instance file and the
associated collection of files that defines its 'Discoverable' Taxonomy
Set ('DTS').
Provides a tool for extracting information (entities and relations between them) in text datasets. It also emphasizes the results exploration with graphical displays. It is a rule-based system and works with hand-made dictionaries and local grammars defined by users. X.ent uses parsing with Perl functions and JavaScript to define user preferences through a browser and R to display and support analysis of the results extracted. Local grammars are defined and compiled with the tool Unitex, a tool developed by University Paris Est that supports multiple languages. See ?xconfig for an introduction.
Extensions of Exponential Random Graph Models (ERGM): Temporal Exponential Random Graph Models (TERGM), Generalized Exponential Random Graph Models (GERGM), Temporal Network Autocorrelation Models (TNAM), and Relational Event Models (REM).
Datasets and definitions of generic functions used in dependencies of the xergm package.
Extreme Gradient Boosting, which is an
efficient implementation of gradient boosting framework.
This package is its R interface. The package includes efficient
linear model solver and tree learning algorithms. The package can automatically
do parallel computation on a single machine which could be more than 10 times faster
than existing gradient boosting packages. It supports various
objective functions, including regression, classification and ranking. The
package is made to be extensible, so that users are also allowed to define
their own objectives easily.
Interface to the XGobi and XGvis programs for graphical
data analysis.
R scripts for plotting and assessing XHMM whole-exome-sequencing-based CNV calls. XHMM (eXome Hidden Markov Model) is a C++ software package (http://atgu.mgh.harvard.edu/xhmm) written to call copy number variation (CNV) from next-generation sequencing projects, where exome capture was used (or targeted sequencing, more generally). This R package enables the user to visualize both the PCA normalization performed by XHMM and the CNVs it has called.
Conduct the likelihood ratio tests for Hardy-Weinberg equilibrium at marker loci on the X chromosome.
Provides a simple XML tree parser/generator. It includes functions
to read XML files into R objects, get information out of and into nodes, and
write R objects back to XML code. It's not as powerful as the 'XML' package
and doesn't aim to be, but for simple XML handling it could be useful. It was
originally developed for the R GUI and IDE RKWard (https://rkward.kde.org), to
make plugin development easier.
This package allows the representation of ggplot2 graphs using the
XKCD style.
Provides comprehensive functionality to read, write and format Excel data.
Provides external JAR dependencies for the XLConnect package
Provide R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.
The xlsxjars package collects all the external jars
required for the xlxs package. This release corresponds to POI
3.10.1.
A toolbox for meta-analysis. This package includes a collection of functions for (1) implementing robust multivariate meta-analysis of continuous or binary outcomes; and (2) testing and correcting of publication bias in multivariate meta-analysis settings.
This is Xiaobei's miscellaneous classes and functions useful when
developing R packages, particularly for OOP using R Reference Class.
Many approaches for both reading and
creating XML (and HTML) documents (including DTDs), both local
and accessible via HTTP or FTP.  Also offers access to an
'XPath' "interpreter".
Work with XML files using a simple, consistent interface. Built on
top of the 'libxml2' C library.
XML2R is a framework that reduces the effort required to transform
XML content into number of tables while preserving parent to child
relationships.
Fit Markov Networks to a wide range of high-throughput genomics data.
Tests whether a set of counts fit a given expected ratio. For
example, a genetic cross might be expected to produce four types in the
relative frequencies of 9:3:3:1. To see whether a set of observed counts
fits this expectation, one can examine all possible outcomes (multi) or a
random sample of them (xmonte) and find the probability of an observation
deviating from the expectation by at least as much as the observed. As a
measure of deviation from the expected, one can use the log-likelihood
ratio, the multinomial probability, or the classic chi-square statistic. A
histogram of the test statistic can also be plotted and compared with the
asymptotic curve.
Analysis of crossover interference in experimental crosses,
particularly regarding the gamma model.
Xpose is collection of functions to be used as a model
building aid for non-linear mixed effects (population) analysis
using NONMEM. It facilitates data set checkout, exploration and
visualization, model diagnostics, candidate covariate identification
and model comparison.
A hierarchical Bayesian approach to assess functional impact of mutations on gene expression in cancer. Given a patient-gene matrix encoding the presence/absence of a mutation, a patient-gene expression matrix encoding continuous value expression data, and a graph structure encoding whether two genes are known to be functionally related, xseq outputs: a) the probability that a recurrently mutated gene g influences gene expression across the population of patients;
and b) the probability that an individual mutation in gene g in an individual patient m influences expression within that patient.
Tool set for crystallographer to design and analyze crystallization experiments of ribosome from Mycobacterium tuberculosis
Can be used for coloring output in terminals.
It was developed for the standard Ubuntu terminal but should be compatible
with any terminal using xterm or ANSI escape sequences. If run in windows,
RStudio, or any other platform not supporting such escape sequences it
gracefully passes on any output without modifying it.
Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.
This package provides an alternative canonical
correlation/redundancy analysis function, with associated
print, plot, and summary methods.  A method for generating
helio plots is also included.
Performs nearest neighbor-based imputation using one or more alternative
approaches to processing multivariate data. These include methods based on canonical
correlation analysis, canonical correspondence analysis, and a multivariate adaptation
of the random forest classification and regression techniques of Leo Breiman and Adele
Cutler. Additional methods are also offered. The package includes functions for
comparing the results from running alternative techniques, detecting imputation targets
that are notably distant from reference observations, detecting and correcting
for bias, bootstrapping and building ensemble imputations, and mapping results.
This is a simple wrapper for the yakmo K-Means library (developed by Naoki Yoshinaga, see http://www.tkl.iis.u-tokyo.ac.jp/~ynaga/yakmo/). It performs fast and robust (orthogonal) K-Means.
This collection of data exploration tools was developed at
Yale University for the graphical exploration of complex
multivariate data; barcode and gpairs now have their own
packages.  The new big.read.table() provided here may be
useful for large files when only a subset is needed.
This package implements the libyaml YAML 1.1 parser and emitter (http://pyyaml.org/wiki/LibYAML) for R.
Yield curve or zero-coupon prices interpolation and extrapolation using the Nelson-Siegel, Svensson, Smith-Wilson models, and Hermite cubic splines.
The purpose of this package is to provide methods to interpret multiple
linear regression and canonical correlation results including beta weights,structure coefficients,
validity coefficients, product measures, relative weights, all-possible-subsets regression,
dominance analysis, commonality analysis, and adjusted effect sizes.
Deploy, maintain, and invoke models via the Yhat
REST API.
Modelling the yield curve with some parametric models.
The models implemented are: Nelson-Siegel, Diebold-Li and
Svensson.  The package also includes the data of the term
structure of interest rate of Federal Reserve Bank and European
Central Bank.
The clustering by k-means of using the target variable.
To determine the number of clusters with the variance of
the target variable in the cluster.
An R implementation of Yplant, combined with the QuasiMC
raytracer. Calculate radiation absorption, transmission and scattering,
photosynthesis and transpiration of virtual 3D plants.
Inference procedures accommodate a flexible range of hazard ratio patterns with a two-sample semi-parametric model. This model contains the proportional hazards model and the proportional odds model as sub-models, and accommodates non-proportional hazards situations to the extreme of having crossing hazards and crossing survivor functions. Overall, this package has four major functions: 1) the parameter estimation, namely short-term and long-term hazard ratio parameters; 2) 95 percent and 90 percent point-wise confidence intervals and simultaneous confidence bands for the hazard ratio function; 3) p-value of the adaptive weighted log-rank test; 4) p-values of two lack-of-fit tests for the model. See the included "read_me_first.pdf" for brief instructions. In this version (1.1), there is no need to sort the data before applying this package.
Simple method for comparison of gene
expression generated across different experiments, and on
different platforms; that does not require global
renormalization, and is not restricted to comparison of
identical probes. YuGene works on a range of microarray dataset
distributions, such as between manufacturers. The resulting
output allows direct comparisons of gene expression between
experiments and experimental platforms.
Simulation and Inference for Stochastic Differential Equations.
Yummly.com is one of the world's largest and most powerful recipe search sites and this package aims to provide R bindings for publicly available Yummly.com Recipe API (https://developer.yummly.com/).
Principled methods to impute multivariate left-censored data and zeros in compositional data sets
R package accompanying the book Working with dynamic
models for agriculture and environment, by Daniel Wallach
(INRA), David Makowski (INRA), James W. Jones (U.of Florida),
Francois Brun (ACTA), in preparation for June 2013.
A framework that brings together an abundance of common
statistical models found across packages into a unified interface, and
provides a common architecture for estimation and interpretation, as well
as bridging functions to absorb increasingly more models into the
collective library.  Zelig allows each individual package, for each
statistical model, to be accessed by a common uniformly structured call and
set of arguments.  Moreover, Zelig automates all the surrounding building
blocks of a statistical workflow --procedures and algorithms that may be
essential to one user's application but which the original package
developer did not use in their own research and might not themselves
support. These include bootstrapping, jackknifing, and reweighting of data.
In particular, Zelig automatically generates predicted and simulated
quantities of interest (such as relative risk ratios, average treatment
effects, first differences and predicted and expected values) to interpret
and visualize complex models.
Add-on package for Zelig 5. Enables the use of a variety of logit
and probit regressions.
Add-on package for Zelig. Enables the use of multilevel (mixed-effect) models.
This package provides an R wrapper for the Zendesk API
Functions to compute compositional turnover using zeta-diversity, the number of species shared by multiple assemblages. The package includes a Zeta.order() function to compute zeta-diversity for a specific number of assemblages, and a Zeta.decline() function to compute zeta-diversity for a range of numbers of assemblages. It also includes additional functions to explain how zeta-diversity varies with distance and with differences of environmental variables between assemblages, using linear regressions and generalized additive models.
Provides MCMC algorithms for the analysis of
zero-inflated count models. The case of stochastic search
variable selection (SVS) is also considered.  All MCMC samplers
are coded in C++ for improved efficiency. A data set
considering the demand for health care is provided.
Fits observation-driven and parameter-driven models for zero-inflated time series
This package contains a database of city, state, latitude,
and longitude information for U.S. ZIP codes from the
CivicSpace Database (August 2004) augmented by Daniel Coven's
federalgovernmentzipcodes.us web site (updated January 22,
2012). Previous versions of this package (before 1.0) were
based solely on the CivicSpace data, so an original version of
the CivicSpace database is also included.
Statistical models and utilities for the analysis of word
frequency distributions.  The utilities include functions for
loading, manipulating and visualizing word frequency data and
vocabulary growth curves.  The package also implements several
statistical models for the distribution of word frequencies in
a population.  (The name of this library derives from the most
famous word frequency distribution, Zipf's law.)
Calculate and plot scattering matrix coefficients for plane waves at interface.
Fits beta regression and zero-or-one inflated beta regression and obtains Bayesian Inference of the model via the Markov Chain Monte Carlo approach implemented in JAGS.
An S3 class with methods for totally ordered indexed
observations. It is particularly aimed at irregular time series
of numeric vectors/matrices and factors. zoo's key design goals
are independence of a particular index/date/time class and
consistency with ts and base R by providing methods to extend
standard generics.
The analysis and inference of faunal remains recovered from archaeological sites concerns the field of zooarchaeology. The zooaRch package provides analytical tools to make inferences on zooarchaeological data. Functions in this package allow users to read, manipulate, visualize, and analyze zooarchaeological data.
ZooImage is a free (open source) solution for analyzing
digital images of zooplankton. In combination with ImageJ, a
free image analysis system, it processes digital images,
measures individuals, trains for automatic classification of
taxa, and finally, measures zooplankton samples (abundances,
total and partial size spectra or biomasses, etc.)
zm(), called with any active plot allow to enter an
interactive session to zoom/navigate any plot. The development
version, as well as binary releases can be found at
https://github.com/cbarbu/R-package-zoom
Combines a forecast of a time series, using the function forecast(), with the dynamic plots from dygraphs.
Makes zebra-striped tables (tables with alternating row colors)
in LaTeX and HTML formats easily from a data.frame, matrix, lm, aov, anova,
glm, coxph, nls, fitdistr, mytable and cbind.mytable objects.
The zyp package contains an efficient implementation of Sen's slope method (Sen, 1968) plus implementation of Xuebin Zhang's (Zhang, 1999) and Yue-Pilon's (Yue, 2002) prewhitening approaches to determining trends in climate data.
Please send questions and comments regarding BH to
http://lists.r-forge.r-project.org/cgi-bin/mailman/listinfo/boostheaders-devel
Please send questions and comments to etiennead@gmail.com.
Please send questions and comments regarding NScluster to
ismrp@jasp.ism.ac.jp
Please send questions and comments regarding pbdR to
RBigData@gmail.com
Please send questions and comments regarding Rcpp to
rcpp-devel@lists.r-forge.r-project.org
Please send questions and comments regarding RInside to
rcpp-devel@lists.r-forge.r-project.org
Please send questions and comments regarding SAPP to
ismrp@jasp.ism.ac.jp
Please send questions and comments regarding timsac to
ismrp@jasp.ism.ac.jp
Dane R. Van Domelen <vandomed@gmail.com>
Pedro Rafael Diniz Marinho <pedro.rafael.marinho@gmail.com>
Carlos J. Gil Bellosta <cgb@datanalytics.com>
Sy Han (Steven) Chiou  <schiou@d.umn.edu>
P Lafaye de Micheaux <lafaye@dms.umontreal.ca>
Robin K. S. Hankin <hankin.robin@gmail.com>
Lafaye de Micheaux Pierre <lafaye@dms.umontreal.ca>
Joao Vitor Dias Monteiro <monte092@umn.edu>
Ricardo de Matos Simoes <r.dematossimoes@qub.ac.uk>
Karel Van den Meersche <karel.van_den_meersche@cirad.fr>
Jose Antonio Garcia Bueno <joseagbueno@gmail.com>
Anderson Rodrigo da Silva <anderson.agro@hotmail.com>
Ana Belen Nieto Librero <ananieto@usal.es>
David Alejandro Martell Juarez <alex91599@gmail.com>
A. Jonathan R. Godfrey <a.j.godfrey@massey.ac.nz>
L. A. van der Ark <L.A.vanderArk@uva.nl>
Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it>
K. Gerald van den Boogaart <support@boogaart.de>
P. Lafaye de Micheaux <lafaye@dms.umontreal.ca>
Dennis J. J. Walvoort <dennis.Walvoort@wur.nl>
Rafael S. de Souza <rafael.2706@gmail.com>
Eduardo L. T. Conceicao <mail@eduardoconceicao.org>
Marcelino de la Cruz Rot <marcelino.delacruz@upm.es>
Alexandre Adalardo de Oliveira <ecovirtualpackage@gmail.com>
Tomas J. Aragon Developer <aragon@berkeley.edu>
Manuel Oviedo de la Fuente <manuel.oviedo@usc.es>
Eduardo San Miguel Martin
<eduardo.san.miguel.martin@gmail.com>
Shaiful Anuar Abu Bakar <saab@um.edu.my>
Vincent T van Hees <vincentvanhees@gmail.com>
Gabriel Coelho Goncalves de Abreu <abreu_ga@yahoo.com.br>
Geraldine Van der Auwera <vdauwera@broadinstitute.org>
Rafael de Andrade Moral <rafael_moral@yahoo.com.br>
"Markus Loecher, Berlin School of Economics and Law (BSEL)" <markus.loecher@gmail.com>
Alejandro Quintela del Rio <aquintela@udc.es>
Jorge Luis Ojeda Cabrera <jojeda@unizar.es>
Eufrasio de A. Lima Neto <eufrasio@de.ufpb.br>
Ricardo Jorge de Almeida Queiroz Filho <rjaqfcin@gmail.com>
Jan van der Laan <djvanderlaan@unrealizedtime.nl>
"Antoine Tremblay, Dalhousie University" <trea26@gmail.com>
"Robin K. S. Hankin" <hankin.robin@gmail.com>
Elizabeth Holmes - NOAA Federal <eli.holmes@noaa.gov>
Fernando Ferraz do Nascimento <fernandofn@ufpi.edu.br>
Antonio Hermes M. da Silva-Junior <hermes@ccet.ufrn.br>
Alysha M De Livera <alyshad@unimelb.edu.au>
Stephen L R Ellison <s.ellison@lgc.co.uk>
Cherise R. Chin Fatt <cherise.chinfatt@utdallas.edu>
Marcelino de la Cruz <marcelino.delacruz@upm.es>
Julio Cesar Hernandez Sanchez <juliocesar_avila@usal.es>
Rune Haubo Bojesen Christensen <rune.haubo@gmail.com>
Euloge Clovis Kenne Pagui <kenne@stat.unipd.it>
Joaquin Bienvenido Ordieres Mere <j.ordieres@upm.es>
Marieke van der Maaten-Theunissen <marieketheunissen@gmail.com>
Peter J. van der Most <p.j.van.der.most@umcg.nl>
"Shingo Yamamoto (gloops, Inc.)" <si.yamamoto@gloops.com>
Vito M. R. Muggeo <vito.muggeo@unipa.it>
Sy Han (Steven) Chiou <schiou@hsph.harvard.edu>
Raymond K. W. Wong <raymondkww.dev@gmail.com>
Pierre Lafaye de Micheaux <lafaye@dms.umontreal.ca>
Titus von der Malsburg <malsburg@posteo.de>
Rutger M. van den Bor <r.m.vandenbor@umcutrecht.nl>
Marcelino de la Cruz <marcelino.delacruz@urjc.es>
Jakson Alves de Aquino <jalvesaq@gmail.com>
Prof. Juan David Velasquez H. <jdvelasq@unal.edu.co>
Willem Robert van Hage <willem.van.hage@synerscope.com>
Frederick Kin Hing Phoa <fredphoa@stat.sinica.edu.tw>
Stephen A Sefick Jr. <ssefick@gmail.com>
Daniel A. Dos Santos <pseudalopex_79@yahoo.com>
Jesus Maria Rodriguez Rodriguez <jesusmro@ono.com>
K. Gerald van den Boogaart <boogaart@math.tu-freiberg.de>
Antonio Fabio Di Narzo <antonio.fabio@gmail.com>
Joerg Schlarmann (aka Produnis) <schlarmann@produnis.de>
Maintainers are not available to give advice on using a package
they did not author.
Several parts are still preliminary and may be changed in the
future. This typically includes function and argument names, as
well as defaults for arguments and return values.
Several parts are still preliminary and may be changed in the
future. this typically includes function and argument names, as
well as defaults for arguments and return values.
SEVERAL PARTS ARE STILL PRELIMINARY AND MAY BE CHANGED IN THE
FUTURE. THIS TYPICALLY INCLUDES FUNCTION AND ARGUMENT NAMES, AS
WELL AS DEFAULTS FOR ARGUMENTS AND RETURN VALUES.
The header file blaswrap.h, f2c.h and fgsg.h are from the
VisualStudio library created by Julie Langou.
This software comes with NO warranty! It is NOT intended to be
used in clinical applications! For evaluation only!
Package stable is available at http://www.robustanalysis.com
In order to use this package you need your personal Project Vote
Smart API key. To get an API-key you need to sign up for access
to PVS' API (see http://votesmart.org/share/api for details).
Whenever you are using pvsR make sure that your personal PVS
API key is saved as a character string in the variable pvs.key
(pvs.key <- "yourkey"). If there is no valid PVS API key saved
in the variable pvs.key the functions in this package won't
work. This program is free software; you can redistribute it
and/or modify it under the terms of the GNU General Public
License as published by the Free Software Foundation; either
version 2 of the License, or (at your option) any later
version. This program is distributed in the hope that it will
be useful, but WITHOUT ANY WARRANTY; without even the implied
warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
PURPOSE. See the GNU General Public License for more details.
More information about the system requirements can be found in
the README file.
For details and derivation, see Braumoeller, Bear F. "Guarding
Against False Positives in Qualitative Comparative Analysis."
Forthcoming, Political Analysis.
This will be used generally to communicate with HTTP servers and
specifically in the SOAP package, HTML forms and for accessing
data on Web sites that require more state information than
simply URL access (e.g. cookies).
NOTICE regarding the transfer of data from Retrosheet: The
information used here was obtained free of charge from and is
copyrighted by Retrosheet. Interested parties may contact
Retrosheet at "www.retrosheet.org".
See http://www.json.org/JSON_checker/test.zip for tests. We now
use code from the libjson project
(http://libjson.sourceforge.net).
The files configure.in, cleanup, Makevars.in and Makevars.win are
based on the same files found in RPostgreSQL. I thank Dirk
Eddelbuettel for giving the go ahead to reuse their build
setup. Copyright of the unmodified protions remains with the
original authors. The files in src/libpq/ are part of
PostgreSQL. Please refer to http://www.postgresql.org/ for
their copyright and license.
The name "Scrabble" is a trademark of Hasbro, Inc. in the United
States and Canada; elsewhere, it is a trademark of Mattel.
In version 2.4.0 of this package, a new approach to garbage
collection has been implemented and it is experimental. You can
disable it via the configuration option --enable-nodegc=no.
However, you are encouraged to use this and report problems as
the results are beneficial and being able to produce any errors
should they occur will be very helpful.
2015-01-03 19:56:29 UTC; Steven Harris Ranney
2015-03-17 02:31:38 UTC; Jose Antonio Garcia
2012-06-11 14:36:21 UTC; Sophie Baillargeon
2010-01-31 13:29:31 UTC; Jens Henrik Badsberg
2015-02-26 17:45:29 UTC; Sophie Baillargeon
2015-08-23 02:08:23 UTC; Fernando de Villar
2015-05-19 12:00:31 UTC; LARMARANGE Joseph
2014-08-28 18:37:46 UTC; Sophie Baillargeon
2014-10-17 18:39:03 UTC; Christiane Dargatz
2014-12-04 17:04:02 UTC; Sophie Baillargeon
2014-10-24 12:34:10 UTC; Sophie Baillargeon
Image Magick (for reading non PPM format), dcraw
(for reading RAW images).
Optionally Latte-integrale, Bertini, and Macaulay2.
Cygwin is required for each of the above for Windows users. See
INSTALL file for details.
ImageMagick (http://imagemagick.org) or
GraphicsMagick (http://www.graphicsmagick.org) or LyX
(http://www.lyx.org) for saveGIF(); (PDF)LaTeX for saveLatex();
SWF Tools (http://swftools.org) for saveSWF(); FFmpeg
(http://ffmpeg.org) or avconv (https://libav.org/avconv.html)
for saveVideo()
JAGS (>= 3.4.0) (see
http://mcmc-jags.sourceforge.net)
jags (>= 3.3.0, see
http://mcmc-jags.sourceforge.net)
OpenBUGS (>= 3.2.2), hence Windows or Linux
MAGeCK (=0.51, from
http://sourceforge.net/p/mageck/wiki/Home/), bowtie2
(http://bowtie-bio.sourceforge.net/bowtie2/index.shtml)
OpenCL library; double-precision AMD or Nvidia GPU;
GNU make
Java (>= 7.0); Stanford CoreNLP
<http://nlp.stanford.edu/software/corenlp.shtml> (>= 3.5.2)
couchDB instance to connect to and work on.
nvcc (release >= 3.1) (NVIDIA Cuda Compiler
driver); Linux operating system; GNU make.
libcurl: libcurl-devel (rpm) or
libcurl4-openssl-dev (deb).
none, one or more of JAGS (>= 3.0.0), WinBUGS (>=
1.4), OpenBUGS (>= 3.2.2)
Xft (only needed for platforms other than OSX and
Windows)
Debian or Ubuntu system with access to Goto Blas,
Intel MKL, Atlas development build as well as a Nvidia GPU with
CUDA support
zlib headers and library. OpenSSL headers and
library. LibSSH2 (optional on non-Windows) to enable the SSH
transport.
Nvidia's CUDA toolkit (>= release 5.0) and Nvidia
GPU with compute capibility >=2.0
C++11 (supporting at least std=c++0x), OpenCL
shared library (provided by an SDK such as AMD/NVIDIA) and
OpenCL headers including the C++ header file (provided by
Khronos if not by SDK)
libhdf5 (>= 1.8.x) with C++ interface
(--enable-cxx=yes) and v18 API enabled
BLAS/LAPACK libraries, PLASMA library , NVIDIA CUDA
toolkit, MAGMA library
Apache Hadoop >= 2.6.0
(https://hadoop.apache.org/releases.html#Download); Obsolete:
Hadoop core >= 0.19.1 and <= 1.0.3 or CDH3
(http://www.cloudera.com); standard unix tools (e.g., chmod)
ImageMagick (http://imagemagick.org) or
GraphicsMagick (http://www.graphicsmagick.org)
A C library with the fesetround/fegetround
functions.
Boost.Asio and Boost.Regex from
http://www.boost.org/. See README.
ImageJ (>=1.48) (see http://imagej.nih.gov/ij/),
Java
JAGS (>= 3.0.0) (see
http://mcmc-jags.sourceforge.net)
At least trial version of LocalSolver to be
downloaded from http://www.localsolver.com/download.html
ADMB version 11 (http://admb-project.org/) for
use.admb=TRUE; see readme.txt
OpenSSL: libssl-dev (deb) or openssl-devel (rpm) or
libssl_dev (csw). SASL: libsasl2-dev (deb) or cyrus-sasl-devel
(rpm) or sasl_dev (csw).
jags (>= 3.0.0) (see
http://mcmc-jags.sourceforge.net)
pandoc, ipython (>= 3.0) or jupyter,
ipython/jupyter notebook optional dependencies, pywin32 (on
windows)
pandoc (http://johnmacfarlane.net/pandoc) for
exporting markdown files to other formats.
For the MERLIN wrapper, MERLIN
(www.sph.umich.edu/csg/abecasis/merlin) must be installed and
pointed to in the PATH environment variable.
The 'Japanese.Rnw' vignette requires uplatex and
dvipdfmx.
OpenMPI (>= 1.5.4) on Solaris, Linux and Mac,
MPICH2 (>= 1.4.1p1) on Windows
OpenMPI (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. MS-MPI (Microsoft HPC Pack 2012) or MPICH2 (>=
1.4.1p1) on Windows.
OpenMPI (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. MS-MPI (Microsoft HPC Pack 2012 R2 MS-MPI
Redistributable Package) on Windows.
OpenMPI (>= 1.5.4) on Solaris, Linux and Mac.
(Parallel) HDF5 and (Parallel) NetCDF4 (4.1 or later)
libraries. No MPI library required on Windows.
OpenMPI (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. No MPI library required on Windows yet.
Linux, Mac OSX, and Windows, or ZeroMQ library >=
4.0.4.
Phoenix NLME with Phoenix Modeling Language (PML)
license
For Windows users, Rtools (see
http://cran.r-project.org/bin/windows/Rtools/).
Python 2.7.x or Python 3.2.x-3.4.x, NumPy (Python
scientific computing package)
JAGS (>= 3.2.0) (see
http://mcmc-jags.sourceforge.net)
proj 4.4.6 or higher (http://proj.maptools.org/)
Python (>= 2.7.0) with header files and shared
library (UNIX) / static library (Windows)
Qt4 libraries and headers (http://qt.nokia.com),
cmake (http://www.cmake.org)
OpenBugs for functions bugs() and openbugs() or
WinBUGS 1.4 for function bugs()
A valid Bloomberg installation. API headers and
dynamic library are downloaded from
<https://github.com/Rblp/blp> during the build step. See
<http://www.bloomberglabs.com/api/> for API documentation.
GMP (GNU MP bignum library from http://gmplib.org/)
pandoc (>= 1.12.3) -
http://johnmacfarlane.net/pandoc
A Debian-based system with the libapt-pkg-dev
package
A 64-bit POSIX OS such as Linux or OS X with IANA
time zone data in /usr/share/zoneinfo as well as a C++11
compiler.
Octave (>= 3.2.4) and its development files
GNU make, Windows: cmd.exe and cscript.exe,
Solaris: g++ is required
The hiredis library (eg via package libhiredis-dev
on Debian/Ubuntu, hiredis-devel on Fedora/RedHat, or directly
from source from https://github.com/redis/hiredis)
A C++11 compiler. Version 4.6.* of g++ (as
currently in Rtools) is insufficient; versions 4.8.*, 4.9.* or
later will be fine.
DieHarder library (>= 3.31.1) from
http://www.phy.duke.edu/~rgb/General/dieharder.php, GNU GSL for
the GSL random-number generators
MOSEK (http://www.mosek.com) and MOSEK License for
use of Rmosek, optional use of the pogs optimizer may require
CUDA/GPU accoutrements.
for building from source: GDAL >= 1.6.3, library
from http://trac.osgeo.org/gdal/wiki/DownloadSource and PROJ.4
(proj >= 4.4.9) from http://trac.osgeo.org/proj/; GDAL OSX
frameworks built by William Kyngesburye at
http://www.kyngchaos.com/ may be used for source installs on
OSX.
GEOS (>= 3.2.0); for building from source: GEOS
from http://trac.osgeo.org/geos/; GEOS OSX frameworks built by
William Kyngesburye at http://www.kyngchaos.com/ may be used
for source installs on OSX.
OpenGL, GLU Library, zlib (optional), libpng
(>=1.2.9, optional), FreeType (optional)
GLPK library package (e.g., libglpk-dev on
Debian/Ubuntu)
R built as a shared or static library, 'MPI'
library.
Java JDK 1.2 or higher (for JRI/REngine JDK 1.4 or
higher), GNU make
Java (>= 1.5), zlib headers and library.
Oracle Java 7,Apache Kafka 2.8.0-0.8.1.1
notepad.exe, mark.exe (>= 8.0) (or mark32.exe and
mark64.exe) and rel_32.exe (see README.txt)
libmariadb-client-dev | libmariadb-client-lgpl-dev
| libmysqlclient-dev (deb), mariadb-devel (rpm), mariadb |
mysql-connector-c (brew), mysql56_dev (csw)
Oracle Instant Client or Oracle Database Client
Poppler Glib interface headers and libraries
(<http://poppler.freedesktop.org/>) [Debian/Ubuntu:
libpoppler-glib-dev, Fedora: poppler-glib-devel]
GNU make, Windows: cmd.exe and cscript.exe
ProtoBuf libraries and compiler version 2.2.0 or
later. On Debian/Ubuntu these are libprotoc-dev and
libprotobuf-dev, while on Fedora protobuf-devel and
protobuf-compiler are needed.
The 'curl' binary has to be in the path (and this
is checked at package startup), a user API key (which can be
obtained from http://www.pushbullet.com), and one or more
devices to push messages to which may be any one of an (Android
or iOS) phone, the (Chrome or Firefox, or Opera or Safari)
browser or (Windows or Mac) desktop application provided the
corresponding Pushbullet 'app' has been installed on any one of
these.
Windows, Python (>= 2.4.0), ArcGIS (>= 9.2)
Python (>= 2.7) and Python headers and libraries
(See the INSTALL file)
QuantLib library (>= 1.4.0) from
http://quantlib.org, Boost library from http://www.boost.org
need the RDRAND instruction on Intel CPU. and C
compiler must be able to compile a GNU-style in-line assembler.
NW RFC SDK downloaded from http://service.sap.com
Tcl/Tk (>= 8.5), Tktable (>= 2.9, optional)
The Boost 'program_options' library
(http://boost.org) is required.
Python (>=2.6) in path and NumPy module installed,
ghostscript (>=9.0)
ZeroMQ >= 3.0.0 libraries and headers (see
<http://www.zeromq.org/>; Debian packages libzmq3, libzmq3-dev,
Fedora packages zeromq3, zeromq3-devel)
GLPK library, including -dev or -devel part
Binary executables of X-13-ARIMA-SEATS
(installation notes: see README or vignette)
zlib headers and libraries, optionally also bzip2
and POSIX-compliant regex functions.
(PDF)LaTeX (http://www.latex-project.org/) with
'pdfpages' package for studentGrowthPlot option in visualizeSGP
to bind together student growth plots into school catalogs
Komodo Edit (http://www.openkomodo.com), SciViews-K
(http://www.sciviews.org/SciViews-K)
Antiword (<http://www.winfield.demon.nl/>) for
reading MS Word files, pdfinfo and pdftotext from Poppler
(<http://poppler.freedesktop.org/>) for reading PDF
Teradata Aster Database 5.10 (AD5.10) or higher,
Teradata Aster Analytical Foundation 5.10 (AA5.10) or higher
(5.11 or higher is recommended)
PARI/GP >= 2.3.0 [strongly recommended for
logkda()]
V8: libv8-dev (deb), v8-devel (rpm), v8-3.14 (arch)
Java (>= 5.0); WordNet database files (direct
download:
<http://wordnetcode.princeton.edu/3.0/WNdb-3.0.tar.gz>; Debian
and Fedora package: wordnet)
Perl (>= 5.0), Unitex (>= 3.0
http://www-igm.univ-mlv.fr/~unitex/)
The standalone program xgobi must be installed
additionally, see file README, or INSTALL.windows under Windows
Accurate, Adaptable, and Accessible Error Metrics for Predictive
Models
Access to Abbyy Optical Character Recognition (OCR) API
Tools for Approximate Bayesian Computation (ABC)
Data Only: Tools for Approximate Bayesian Computation (ABC)
ABCDE_FBA: A-Biologist-Can-Do-Everything of Flux Balance
Analysis with this package
Implementation of Artificial Bee Colony (ABC) Optimization
Approximate Bayesian Computation via Random Forests
Data Modelling with Additive Bayesian Networks
Abundant regression and high-dimensional principal fitted
components
Functions for Processing Minute-to-Minute Accelerometer Data
Creation and Evaluation of Acceptance Sampling Plans
Data Quality Visualization Tools for Partially Accruing Data
Tools for Autoregressive Conditional Duration Models
ace() and avas() for selecting regression transformations
Analysing Conditional Distributions of Income
Align-and-Count Method comparisons of RFLP data
Implements ACME Estimator of Bird and Bat Mortality by Wind
Turbines
Affymetrix SNP Probe-Summarization using Non-Negative Matrix
Factorization
Modelling dependence with multivariate Archimax (or any
user-defined continuous) copulas
Convenience functions for analytical Customer Relationship
Management
Download, manipulate, and present data from the US Census
American Community Survey
Gene Enrichment Analysis from ACSN Maps or Gmt Files
Algorithmic Complexity for Short Strings
Data Only: Algorithmic Complexity of Short Strings (Computed via
Coding Theorem Method)
A Companion Package for the Book "A Course in Statistics with R"
Asymptotic Classification Theory for Cognitive Diagnosis
Actuarial Functions and Heavy Tailed Distributions
ada: an R package for stochastic boosting
Applies Multiclass AdaBoost.M1, SAMME and Bagging
Discrete and Global Optimization Routines
A Class of Adaptive Elastic Net Methods for Censored Data
Adaptive Semiparametric Regression with Simultaneous Confidence
Bands
Implementation of a generic adaptive Monte Carlo Markov Chain
sampler
Fit Additive Hazards Models for Survival Analysis
Additivity Tests in the Two Way Anova with Single Sub-class
Numbers
A Package for Analysis of Accelerated Destructive Degradation
Test Data
Analysis of Ecological Data : Exploratory and Euclidean Methods
in Environmental Sciences
Exploratory Analysis of Genetic and Genomic Data
An S4 Lattice-Based Package for the Representation of
Multivariate Data
Analysis of Habitat Selection by Animals
adephylo: exploratory analyses for the phylogenetic comparative
method
Adequacy of probabilistic models and generation of pseudo-random
numbers
calculate ad hoc distance thresholds for DNA barcoding
identification
An Interpretation of the ADM method - automated detection
algorithm
Adaptive Mixture of Student-t distributions
Fast Clustering Using Adaptive Density Peak Detection
Wavelet Analysis of Genomic Data from Admixed Populations
Download and process AEMO price and demand data
Model-Based Estimation of Confounder-Adjusted Attributable
Fractions
Hybrid Simulation and Genome Scan for Dominant Markers
Accelerated Failure Time Model with Generalized Estimating
Equations
Aggregate Numeric, Date and Categorical Variables by an ID
Aggregation Operators and Preordered Sets
Statistical Tools for Measuring Agreement
Statistical Procedures for Agricultural Research
Calculate Agreement or Consensus in Ordered Rating Scales
Estimation in Adaptive Group Sequential Trials
Miscellaneous plotting and utility functions
Regularization for semiparametric additive hazards regression
Estimation and Testing of Average Hazard Ratios
Model Selection and Multimodel Inference Based on (Q)AIC(c)
Estimation of Box-Cox Power Transformation Parameter
Tools for reading AIDA (http://aida.freehep.org/) files into R
Interpolation of Irregularly and Regularly Spaced Data
Adaptive Kmeans algorithm based on threshold
Quantile Regression Using Asymmetric Laplace Distribution
An R-package for estimating absolute protein quantities from
label-free LC-MS/MS proteomics data
Haplotype Two Snips Out of a Paired Group of Patients
Alignment of Phonetic Sequences Using the 'ALINE' Algorithm
Generate Age-Length Keys for fish populations
Allele Imputation and Haplotype Reconstruction from Pedigree
Databases
Identifying unique multilocus genotypes where genotyping error
and missing data may be present
Allele Retention, Inbreeding, and Demography
A fast, unbiased and exact allelic exact test
Computes all of a word's possible spellings
R Client for the Lagotto Altmetrics Platform
Generalization of the Convex Hull of a Sample of Points in the
Plane
Obtain Alpha-Outlier Regions for Well-Known Probability
Distributions
Implementation of the 3D alpha-shape for the reconstruction of
3D sets from a point cloud
Data to accompany Applied Linear Regression 3rd edition
Data to accompany Applied Linear Regression 4rd edition
Multivariate Curve Resolution Alternating Least Squares
(MCR-ALS)
Accelerated line search algorithm for simultaneous orthogonal
transformation of several positive definite symmetric matrices
to nearly diagonal form
Optimal Experimental Designs for Accelerated Life Testing
Compare Gene Expressions from 2-Treatment RNA-Seq Experiments
European Commission Annual Macro-Economic (AMECO) Database
Adaptive Management of Epidemiological Interventions
Additive and Multiplicative Effects Models for Networks and
Relational Data
This package includes pricing function for selected American
call options with underlying assets that generate payouts
A Multidirectional Optimum Ecotope-Based Algorithm
Statistical Analysis Of Amplicon Data Of The Same Sample To
Identify Artefacts
Simple and Canonical Correspondence Analysis
Analogue and Weighted Averaging Methods for Palaeoecology
Additional functions for use with the analogue package
Model Layer for Automatic Data Analysis via CSV File
Interpretation
Functions for analysis of fMRI datasets stored in the ANALYZE or
NIFTI format
Assigning Ancestry Based on Population References
Statistical analysis of surveys with anchoring vignettes
An extended Bayesian Learning Technique developed by Dr. Geoff
Webb
Adaptive Neuro Fuzzy Inference System in R
Simulate a Bus Route Creel Survey of Anglers
Animal track reconstruction for high frequency 2-dimensional
(2D) or 3-dimensional (3D) movement data
A Gallery of Animations in Statistics and Utilities to Create
Animations
AnnotLists: A tool to annotate multiple lists from a specific
annotation file
Anonymize Data Containing Personally Identifiable Information
Statistical Methods for Anthropometric Data
Analysis of Overdispersed Data using S3 methods
aoristic analysis with spatial output (kml)
Process the Apache Web Server Log Combined Files
Create American Psychological Association (APA) Style Tables
Phylogenetic Methods for Multiple Gene Data
Datasets from Hosmer, Lemeshow and Sturdivant, "Applied Logistic
Regression" (3rd ed.)
Another Plot PACKage: stem.leaf, bagplot, faces, spin3R,
plotsummary, plothulls, and some slider functions
Pre- and Postprocessing for AP-MS data analysis based on
spectral counts
Compute Appell's F1 hypergeometric function
Approximate Path for Penalized Likelihood Estimators
Functions and Data Sets for 'Applied Predictive Modeling'
Amyloid Propensity Prediction Neural Network
Bayesian prediction of complex computer codes
Datasets from Draper and Smith "Applied Regression Analysis"
(3rd Ed., 1998)
Tools for the a Priori Comparison of Regression Modelling
Strategies
Amdahl's Profiler, Directed Optimization Made Easy
General Utility Functions for the 'Agricultural Production
Systems Simulator'
Edit, Run and Evaluate APSIM Simulations Easily Using R
apsrtable model-output formatter for social science
Functions to help display air quality model output and
monitoring data
Interface methods to use with an ActiveQuant Master Server
AquaEnv - an integrated development toolbox for aquatic chemical
model generation
Segmentation of an autoregressive Gaussian process of order 1
Rigorous Data Reduction and Error Propagation of Ar40 / Ar39
Data
Example Datasets from Archaeological Research
Plant Root System Architecture Analysis Using DART and RSML
Files
Tools for Storing, Restoring and Searching for R Objects
Activated Region Fitting, fMRI data analysis (3D)
Arfima-MLM Estimation For Repeated Cross-Sectional Data
Command line optional and positional argument parser
Improved Communication to Users with Respect to Problems in
Function Arguments
Data Analysis Using Regression and Multilevel/Hierarchical
Models
Analysis of Large Affymetrix Microarray Data Sets
A Probe-Level Data File Format Used by 'aroma.affymetrix'
[deprecated]
Copy-Number Analysis of Large Microarray Data Sets
Core Methods and Classes Used by 'aroma.*' Packages Part of the
Aroma Framework
Tools for Simulating Direct Behavioral Observation Recording
Procedures Based on Alternating Renewal Processes
Application in R for Queueing Analysis and Simulation
Aligned Rank Transform for Nonparametric Factorial Analysis
Time-Varying DBN Inference with the ARTIVA (Auto Regressive TIme
VArying) Model
Gene and Pathway p-values computed using the Adaptive Rank
Truncated Product
Mining Association Rules and Frequent Itemsets
Mining NB-Frequent Itemsets and NB-Precise Rules
Visualizing Association Rules and Frequent Itemsets
A Collection of Statistical Tools for Biologists
Export R objects to several markup languages
Simulations for adaptive seamless designs
Linkage Map Construction using the MSTmap Algorithm
A collection of functions for estimating centrographic
statistics and computational geometries for spatial point
patterns
Bayesian Inference on Causal Genetic Variants using Affected
Sib-Pairs Data
A General Framework for Multivariate Analysis with Optimal
Scaling
Augments the Use of 'Asreml' in Fitting Mixed Models
Readable Check Functions to Ensure Code Integrity
A Lightweight Core of the 'assertive' Package
Assertions to Check Properties of Strings
Assertions to Check Properties of Dates and Times
Assertions to Check Properties of Matrices
Assertions to Check Properties of Models
Assertions to Check Properties of Numbers
Assertions to Check Properties of Variables
Assertive Programming for R Analysis Pipelines
Optimal pricing of assets with fixed expiry date
A Suite of R Functions Implementing Spline Smoothing Techniques
Statistical Tests for Genetic Association Studies
A Computational Tool for Astrochronology
Astronomy: time and position functions, misc. utilities
Applied Statistical Time Series Analysis
Average Shifted Visual Predictive Checks
Regression Analysis of Sparse Asynchronous Longitudinal Data
Calculate Power Utilizing Asymptotic Likelihood Ratio Methods
Inference for Average Treatment Effects using Covariate
Balancing
A GTK GUI for teaching basic concepts in statistical inference,
and doing elementary bayesian tests
Automatically Tuned Markov Chain Monte Carlo
Acoustic Traveltime Calculations for 1-D Atmospheric Models
Support Querying Axibase Time-Series Database
Threshold independent performance measures for probabilistic
classifiers
Variable Selection with Random Forest and the Area Under the
Curve
Sparse Autoencoder for Automatic Learning of Representative
Features from Unlabeled Data
Automated Hierarchical Multiple Regression with Assumptions
Checking
Partial Least Squares Regression with Backward Selection of
Predictors
Minimum Distance Estimation in Autoregressive Model
Automated Vector Autoregression Models and Networks
Calculation of Average Population Attributable Fractions and
Confidence Intervals
Class and Methods definitions for packages aws, adimpro, fmri,
dwi
Discover, Publish and Consume Web Services on Microsoft Azure
Machine Learning
Adaptive differential evolution, b6e6rl variant
Bayesian Bacterial Growth Curve Analysis in R
Bayesian Bootstrap Predictive Mean Matching - Multiple and
Single Imputation for Discrete Data
Bubble Chart to Compare Biological Annotations by using DAVID
Bayesian Analysis of Computer Code Output (BACCO)
Learning Causal Cyclic Graphs from Unknown Shift Interventions
Exploring Portfolio-Based Conjectures About Financial
Instruments
Choice of the Hyperparameter Omega in the Bayesian Adjustment
for Confounding (BAC) Algorithm
Bayesian Average Error approach to Sample Size Determination
Ensemble bagging and boosting classifiers
Balanced and spatially balanced sampling
Functions and datasets for books by Jeff Gill
Bayesian Meta-Analysis of Diagnostic Test Data
Analysis and Visualization of Macroevolutionary Dynamics on
Phylogenetic Trees
Functions for simple A/B split test and multi-armed bandit
analysis
Batch Effect Removal (in Phenotype Prediction using Gene Data)
Bayesian Model Averaging using Bayesian Adaptive Sampling
Recovering a Basic Space from Issue Scales
Age-Specific Survival Analysis from Incomplete
Capture-Recapture/Recovery Data
Batching Routines in Parallel and Passing Command-Line Arguments
to R
Statistical Experiments on Batch Computing Clusters
Consistent Batch Means Estimation of Monte Carlo Standard Errors
Convert Categorical Representations of Logicals to Actual
Logicals
An R Package for Data Reduction by Battery Reduction
Bayesian Feature Allocation Model for Tumor Heterogeneity
Power Calculations and Bayesian Analysis of Count Distributions
and FECRT Data using MCMC
Bayesian Analysis of Censored Regression Models Under Scale
Mixture of Skew Normal Distributions
Functions and Datasets for the book "Bayesian Data Analysis"
The Bayesian Dynamic Conditional Correlation GARCH Model
Graphical User Interface for bayesTFR, bayesLife and bayesPop
Computation of Bayes Factors for Common Designs
Bayesian Estimation of the GARCH(1,1) Model with Student-t
Innovations
Scalable Rejection Sampling for Bayesian Hierarchical Models
Bayesian Analysis of Generalized Elliptical Semi-Parametric
Models and Flexible Measurement Error Models
Bayesian Melding of GPS and DR Path for Animal Tracking
Bayesian Beta regression: joint mean and precision modeling
Designing Bayesian Multi-Arm Multi-Stage Studies
Mixtures-of-Experts Markov Chain Clustering and Dirichlet
Multinomial Clustering
Default Bayesian Hypothesis Tests for Correlation, Partial
Correlation, and Mediation
Bayesian Mixture Survival Models using Additive
Mixture-of-Weibull Hazards, with Lasso Shrinkage and
Stratification
BayesNI: Bayesian Testing Procedure for Noninferiority with
Binary Endpoints
Hierarchical Bayesian analysis of ecological count data
Bayesian Analysis of Small Area Estimation
Computation of Bayes factors for interrupted time-series designs
MCMC Sampling of Bayesian Linear Models via Summary Statistics
Bayesian Survival Regression with Flexible Error and Random
Effects Distributions
Bayesian thresholds mixed-effects models for categorical data
Bayes Factors, Model Choice And Variable Selection In Linear
Models
R Utilities Accompanying the Software Package BayesX
R Package Distribution of the BayesX C++ Sources
Bayesian analysis of haplotype association using Markov Chain
Monte Carlo
R Functions for Bayesian Hazard Rate Estimation
R Package for Baylor University Educational Psychology
Quantitative Courses
Bayesian Fitting of Ornstein-Uhlenbeck Models to Phylogenies
Bayesian Analysis of Significantly Mutated Genes in Cancer
On Bayesian analysis of Threshold autoregressive model (BAYSTAR)
Solving and Optimizing Large-Scale Nonlinear Systems
Bayesian bandwidth estimation and semi-metric selection for the
functional kernel regression with unknown error density
Bayesian bandwidth estimation for multivariate kernel regression
with Gaussian error
Bayesian Estimation of Incoherent Neutron Scattering Backgrounds
Miscellaneous Helper Functions for B. Bischl
Tools for general maximum likelihood estimation
Bias-Corrected Bayesian Classification with Selected Features
Business Cycle Dating and Plotting Tools
Bayesian composition estimator: estimating sample (taxonomic)
composition from biomarker data
The Bayesian Causal Effect Estimation Algorithm
Bayesian Models for Cost-Effectiveness Analysis in the Presence
of Structural Zero Costs
Radiocarbon Dating, Age-Depth Modelling, Relative Sea Level Rate
Estimation, and Non-Parametric Phase Modelling
Bayesian Palaeoclimate Reconstruction from Pollen
Bayesian Hierarchical Clustering Using Spike and Slab Models
Bayesian Analysis of Change Point Problems
Behavioral change point analysis of animal movement
Bayesian Multiple Changepoint Detection Using Metadata
Advanced Analysis of B Cell Receptor Repertoire Data
Bayesian Continual Reassessment Method for Phase I
Dose-Escalation Trials
Cross-Validation for the SVD (Bi-Cross-Validation)
Bayesian Graph Selection Based on Birth-Death MCMC Approach
Inference and design for predictive values in binary diagnostic
tests
Remove Weekends and Holidays From ggplot2 Axes
Routines for Block Diagonal Symmetric matrices
Analysis of Illumina BeadArray SNP data including MSV markers
Visualization via Beanplots (like Boxplot/Stripchart/Violin
Plot)
Bayesian Estimate of Age-specific Nest Survival Probabilities
Quantifies effects of geo/eco distance on genetic
differentiation
Matrices Backed by Binary PED Files (PLINK)
Genomic Region Processing using Tools Such as Bedtools, Bedops
and Tabix
Easily Play Notification Sounds on any Platform
The Bee Swarm Plot, an Alternative to Stripchart
28 benchmark densities from Berlinet/Devroye (1994)
Benchmark and Frontier Analysis Using DEA and SFA
Benford Analysis for Data Validation and Forensic Analytics
Statistical Tests for Evaluating Conformity to Benford's Law
Bent-Cable Regression for Independent Data or Autoregressive
Time Series
Bayesian analysis for exponential random graph models
Function Collection Related to Plotting and Hydrology
Bessel -- Bessel Functions Computations and Approximations
Bayesian Estimation Supersedes the t-Test
Detecting rare variants for quantitative traits using nuclear
families
Beta-Diversity Within a Metaweb of Species Interactions
Partitioning beta diversity into turnover and nestedness
components
Functions to incorporate taxonomic uncertainty on multivariate
analyses of ecological data
Simulation, estimation and forecasting of Beta-Skew-t-EGARCH
models
Breaks For Additive Season and Trend (BFAST)
Binary Generalized Extreme Value Additive Models
Bayesian Estimation in Generalized Linear Models
Gaussian Mixture Modeling Algorithms And The Belief-based
Mixture Modeling
Markov Beta and Gamma Processes for Modeling Hazard Rates
Block Gibbs Sampler with Incomplete Multinomial Distribution
Useful Functions for Box, Hunter and Hunter II
Bias correction of the parameter estimates of the beta
regression model
Calculates the bidimensional regression between two 2D
configurations
Inferences for bi- and trifactorial trial designs
Tools for Survey Statistics in Educational Assessment
BLAS routines for native R matrices and big.matrix objects
Utilities for 'big.matrix' Objects from Package 'bigmemory'
Case-Control Analysis of Multi-Allelic Loci
Scalable Least-Angle Regression and Lasso
bounded memory linear and generalized linear models
Manage Massive Matrices with Shared Memory and Memory-Mapped
Files
A shared resource interface for Bigmemory Project packages
PCA, Transpose and Multicore Functionality for 'big.matrix'
Objects
Generalized Ridge Regression (with special advantage for p >> n
cases)
Table, Apply, and Split Functionality for Matrix and
'big.matrix' Objects
Top Scoring Pair based methods for classification
Power for SNP analyses using silver standard cases
Estimates Mixture Models for Case-Control Data
Mean and Variance Modeling of Binary Data
Multi-Class Discriminant Analysis using Binary Predictors
Methods for Analyzing Binned Income Data
Evaluation and experimental design for binomial group testing
Asymptotic simultaneous confidence intervals for many-to-one
comparisons of proportions
Data Generation with Binary and Continuous Non-Normal Components
Simultaneous generation of multivariate binary and normal
variates
Binomial Confidence Intervals For Several Parameterizations
Generates binomial random numbers via the coupling from the past
algorithm
Efficient MCMC for Binomial Logit Models
Confidence intervals and sample size determination for a
binomial proportion under simple random sampling and pooled
sampling
Performing diagnostics on binomial regression models
Concurrent Generation of Binary, Ordinal and Continuous Data
Cut Numeric Values into Evenly Distributed Groups
Exact Binary Sequential Designs and Analysis
Feature Selection and Classification with the Embedded
Validation Procedures for Biomedical Data Analysis
Package for Community Ecology and Suitability Analysis
Process Biogas Data and Predict Biogas Production
BioGeography with Bayesian (and Likelihood) Evolutionary
Analysis in R Scripts
Predict environmental conditions from biological observations
An interface package (beta) for the BIOM file format
Find Biomarkers in Two-Class Discrimination Problems
Functional Annotation and Biological Data Retrieval with R
Ensemble platform for species distribution modeling
Utilities for the BIOM (Biological Observation Matrix) Format
Simulation of deterministic and stochastic biochemical reaction
networks using Petri Nets
Search and Download Papers from the bioRxiv Preprint Server
From BIOlogical Sequences to MultiDimensional Scaling
Tools for Electromyogram Signals (EMG) Analysis
Tools for Biometry and Applied Statistics in Agricultural
Science
Visualising bipartite networks and calculating some (ecological)
indices
Bootstrap on Classical Biplots and Clustering Disjoint Biplot
BIPOD (Bayesian Inference for Partially Observed diffusions)
Methods to Analyse Ring Re-Encounter Data
Tools to find bad commits with git bisect
Toolkit to Identify Candidate Synthetic Lethality
Bayesian Isotonic Regression with Bernstein Polynomials
A S3 Class for Vectors of 64bit Integers
Binarization and Trinarization of One-Dimensional Data
Estimating the Parameters of Some Bivariate Distributions
Sample size calculations for bivariate longitudinal data
Conduct Univariate and Bivariate Wavelet Analyses
Functions to compute the biweight mean vector and covariance &
correlation matrices
Business Days Calculations and Utilities
Plots (slightly extended) Bland-Altman plots
Calculate and Analyze Blau statuses for measuring social
distance
Black-Litterman and Copula Opinion Pooling Frameworks
Analyze biotic homogenization of landscapes
Fitting block ERGM given the block structure on social networks
Binomial linear and linear-expit regression
Data Files and Functions Accompanying the Book "Bayesian Data
Analysis in Ecology using R, BUGS and Stan"
Coclustering Package for Binary, Categorical, Contingency and
Continuous Data-Sets
blockmatrix: Tools to solve algebraic systems with partitioned
matrices
Creates strings that show a text message in 8 by 8 block letters
An R package for Generalized and classical blockmodeling of
valued networks
Latent and Stochastic Block Model Estimation by a 'V-EM'
Algorithm
Randomization for block random clinical trials
Nested Block Designs for Unstructured Treatments
Block, Assign, and Diagnose Potential Interference in Randomized
Experiments
Statistical Comparisons with Distance-Function Based Permutation
Tests
Constrained Optimization Via Stochastic Gradient Descent
Request Data from the U.S. Bureau of Labor Statistics API
Benchmark dose analysis for dose-response data
Mediation analysis with missing data using bootstrap
A Package for Bayesian Meta-Analysis and Meta-Regression
Bayesian Sampling for Stick-Breaking Mixtures
The pseudo-likelihood method for pairwise binary markov networks
Bundle Methods for Regularized Risk Minimization Package
Learning Discrete Bayesian Network Classifiers from Data
Data Generator based on Bayesian Network Model
Bayesian Network Structure Learning, Parameter Learning and
Inference
Bayesian Estimation for Normal Heteroscedastic Nonlinear
Regression Models
Ferguson-Klass Type Algorithm for Posterior Normalized Random
Measures
A Bayesian Nonparametric Algorithm for Time Series Clustering
Bayesian Non- and Semi-Parametric Model Fitting
Bayesian Network Structure Learning from Data with Missing
Values
Bayesian Output Analysis Program (BOA) for MCMC
Bacterium and Virus Analysis of Orthologous Groups (BOG) is a
Package for Identifying Differentially Regulated Genes in the
Light of Gene Functions
Interface to the Boilerpipe Java Library
Bayesian Optimal INterval (BOIN) Design for Single-Agent and
Drug-Combination Phase I Clinical Trials
Construction, Simulation and Analysis of Boolean Networks
A modular framework to bag or boost any estimation procedure
Optimized GWAS cohort subset selection for resequencing studies
Bootstrap Functions (Originally by Angelo Canty for S)
Bootstrapped Confidence Intervals for (Negative) Likelihood
Ratio Tests
Bootstrap Methods for Various Network Estimation Routines
Bootstrap Prediction Intervals and Bias-Corrected Forecasting
Bootstrapped Response and Correlation Functions
A bootstrap test for the probability of ruin in the classical
risk process
Bootstrap Subsamplings of Sparse Partial Least Squares -
Discriminant Analysis for Classification and Signature
Identification
Functions for the Book "An Introduction to the Bootstrap"
Fast, Exact Bootstrap Principal Component Analysis for High
Dimensional Data
Bootstrap Tolerance Levels for Credit Scoring Validation
Statistics
Bayesian Ordination and Regression AnaLysis
Wrapper Algorithm for All-Relevant Feature Selection
Boosted One-Step Statistics: Fast and accurate approximations
for GLM, GEE and Mixed models for use in GWAS
a Bunch of Structure and Sequence Analysis
Analytic Solutions for (ground-water) Boussinesq Equation
Double Box Plot for Two-Axes Correlation
Biplot of Multivariate Data Based on Principal Components
Analysis
Beta Product Confidence Procedure for Right Censored Data
bPeaks: an intuitive peak-calling strategy to detect
transcription factor binding sites from ChIP-seq data in small
eukaryotic genomes
Helper functions to misc3d and rgl packages for brain imaging
Basic wavelet analysis of multivariate time series with a
visualisation and parametrisation using graph theory
Species Richness Estimation and Modeling
Multiple Break-Point Detection via the Cross-Entropy Method
Templating Framework for Report Generation
Extracting Usable Data from the Grad Cafe Results Search
Bias reduction in binomial-response generalized linear models
Brier score decomposition of probabilistic forecasts for binary
events
brnn (Bayesian regularization for feed-forward neural networks)
Convert Statistical Analysis Objects into Tidy Data Frames
A Compression Format Optimized for the Web
Bayesian Inference on the Ratio of Two Poisson Rates
Interface to the 'OpenBUGS' MCMC Software
Statistical methods for safety assessment in agricultural field
trials
Bayesian Survival Model with Lasso Shrinkage Using Generalized
Weibull Regression
Nonparametric Smoothing of the Hazard Function
Bayes Screening and Model Discrimination
bspmma: Bayesian Semiparametric Models for Meta-Analysis
Asymptotic Covariance Matrices of Some BSS Mixing and Unmixing
Matrix Estimates
Birnbaum-Saunders Model Based on Skew-Normal Distribution
Temporal Exponential Random Graph Models by Bootstrapped
Pseudolikelihood
Estimates univariate function via Bayesian trend filtering
Modelling Heterogeneity in Paired Comparison Data
Buckley-James Regression for Survival Data with High-Dimensional
Covariates
Markov model for bursty behavior in streams
Bayesian Analysis of a Vector Autoregressive Model with
Stochastic Volatility and Time-Varying Parameters
A Simple alternative to proportional Venn diagrams
The Stark-Parker algorithm for bounded-variable least squares
Solvers for Boundary Value Problems of Differential Equations
Bayesian Variant Selection: Bayesian Model Uncertainty
Techniques for Genetic Association Studies
Extended Inference for Lasso and Elastic-Net Regularized Cox and
Generalized Linear Models
Infering large-scale gene networks with C3NET
C5.0 Decision Trees and Rule-Based Models
Simple, Multiple and Joint Correspondence Analysis
Bootstrap Confidence Regions for Correspondence Analysis
Classification Accuracy and Consistency under Item Response
Theory
Conditional Density Estimation Network Construction and
Evaluation
This package performs the CADF unit root test proposed in Hansen
(1995)
Conditional Akaike information criterion for lme4
R graphics device using cairo graphics library for creating
high-quality bitmap (PNG, JPEG, TIFF), vector (PDF, SVG,
PostScript) and display (X11 and Win32) output
Multiple imputation using MICE and Random Forest
Calibration of Scatterplot and Biplot Axes
Bayesian calibration of complex computer codes
Improved Allele-Specific Copy Number of SNP Microarrays for
Downstream Segmentation
Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN
Camera Trap Data Management and Preparation of Occupancy and
Spatial Capture-Recapture Analyses
Estimation of Temporal Ordering of Cancer Abnormalities
Visualizing Generalized Canonical Discriminant and Canonical
Correlation Analysis
Accessing Intra-Tumor Heterogeneity and Tracking Longitudinal
and Spatial Clonal Evolutionary History by Next-Generation
Sequencing
Directly Extracts Complete CANSIM Data Tables
Combined analysis of pleiotropy and epistasis
Comparative Analyses of Phylogenetics and Evolution in R
Numbers Figures and Creates Simple Captions
CAlibrating Penalities Using Slope HEuristics
Estimates population size from non-invasive sampling
Spatial Generalised Linear Mixed Models for Areal Unit Data
Data Sets Used in the Vignette Accompanying the CARBayes Package
Spatio-Temporal Generalised Linear Mixed Models for Areal Unit
Data
Estimation of the Number of Fatalities from Carcass Searches
Identification of Cardinal Dates in Ecological Time Series
Cardiovascular Safety Exposure-Response Modeling in Early-Phase
Clinical Studies
High-Dimensional Regression and CAR Score Variable Selection
Statistical package for population size estimation in
capture-recapture models
Estimation of caribou abundance based on large scale
aggregations monitored by radio telemetry
Functions For Statistics Classes At Carleton College
Ecological Quality Ratios Calculation and Plot
A Collection of Database, Data Structure, Visualization, and
Utility Functions for R
CRISPR AnalyzeR for Pooled CRISPR Screens
Reparameterized and marginalized posterior sampling for
conditional autoregressive models, OpenCL implementation
Séries temporelles avec R - Méthodes et cas
Identify similar cases for qualitative case studies
Analysis of categorical-variable datasets with missing values
Fishery Stock Assessment by Generalized Depletion Models
High Dimensional Factor Analysis and Confounder Adjusted Testing
and Estimation
Community Assembly by Traits: Individuals and Beyond
An R Package for Simulating IRT-Based Computerized Adaptive
Tests
Tools: moving window statistics, GIF, Base64, ROC AUC, etc
Procedures to Generate Patterns under Computerized Adaptive
Testing
Special models for categorical variables
Tools for Estimating Causal Dose Response Functions
Deriving Expressions of Joint Interventional Distributions and
Transport Formulas in Causal Models
Methods for Estimating Causal Effects from Observational Data
Estimation of Causal Effects with Generalized Additive Models
Selection Bias Approach to Sensitivity Analysis for Causal
Effects
Analysis utilities for binary classification and Causata users
Bayesian Canonical Correlation Analysis and Group Factor
Analysis
(Robust) Canonical Correlation Analysis via Projection Pursuit
Concordance Correlation Coefficient for Repeated (and
Non-Repeated) Measures
Combined Cluster and Discriminant Analysis
Cox Model for Case-Cohort Data with Stratified
Subcohort-Selection
Convex Clustering Methods and Clustering Indexes
Significance Tests for Canonical Correlation Analysis (CCA)
One and two locus GWAS of binary phenotype with
case-control-population design
Cultural Consensus Theory applications to data
Coupled dipole approximation of light scattering by clusters of
nanoparticles
Retrieve U.S. Flu Season Data from the CDC FluView Portal
Conditional Distance Correlation and Its Related Feature
Screening Method
Coordinate Descent Algorithms for Lasso Penalized L1, L2, and
Logistic Regression
Components of Canadian Monetary and Credit Aggregates
Constrained Dual Scaling for Detecting Response Styles
Statistical Inference of C- And D-Vine Copulas
Benchmark for the CEC 2005 Special Session on Real-Parameter
Optimization
Benchmark functions for the Special Session and Competition on
Real-Parameter Single Objective Optimization at CEC-2013
Collection of Common Astronomical Conversion Routines and
Functions
Translate Spreadsheet Cell Ranges to Rows and Columns
Functions to fit cell volume distributions and thereby estimate
cell growth rates and division times
Censored Linear Mixture Regression Models
Fits Normal and Student-t Censored Regression Model
Cross-Entropy R Package for Optimization
Context Driven Exploratory Projection Pursuit
Outlier detection using the iterated RMCD method of Cerioli
(2010)
Covariance Estimation Regularized by Nuclear Norm Penalties
Cause-Specific Framework for Competing-Risk Analysis
Estimate quantiles using any order Cornish-Fisher expansion
Calculate AUC-type measure when gold standard is continuous and
the corresponding optimal linear combination of variables with
respect to it
R-Based API for Accessing the MSKCC Cancer Genomics Data Server
(CGDS)
Microarray CGH analysis using the Smith-Waterman algorithm
Detecting hot spot on CGH array data with fused lasso
regression
Segmentation methods for array CGH analysis
Statistical Methods and Models for Claims Reserving in General
Insurance
Markov Model for the Online Multi-Channel Attribution Problem
Charge Transfer Rates and Charge Carrier Mobilities
Install Packages from Snapshots on the Checkpoint Server for
Reproducibility
Analysis and Visualisation of Ecological Communities
Calibration Functions for Analytical Chemistry
Multivariate Statistical Analysis in Chemometrics
Chemometrics with R - Multivariate Data Analysis in the Natural
Sciences and Life Sciences
A tool for the design of synthetic experiments in machine
olfaction
Multiple Testing Methods for Exploratory Research
Calculation of standard deviation scores adduced from different
growth standards
Statistical Methods for Phenology Analysis in Temperate Fruit
Trees
Toolkit of Helper Functions to Pre-Process Amplification Data
Chemical Thermodynamics and Activity Diagrams
Simplify the Creation of Choropleth Maps in R
Contains an Administrative-Level-1 Map of the World
Contains maps used by the choroplethr package
Interface to Chromosome Counts Database API
Analysis of chromosomal interactions data (correction,
segmentation and comparison)
Chronological Objects which can Handle Dates and Times
Chunkwise Text-File Processing for 'dplyr'
Generative Models for Complex Networks with Conditionally
Independent Dyadic Structure
Weighted summary of cumulative incidence functions
Co-Information based N-Order Epistasis Detector and Visualizer
CircNNTSR: An R package for the statistical analysis of circular
data using nonnegative trigonometric sums (NNTS) models
Detecting of Outliers in Circular Regression
Circular Statistics, from "Topics in circular Statistics" (2001)
CIT Breast Cancer Molecular SubTypes Prediction
CIT Colon Cancer Molecular SubTypes Prediction
Visualization of structure and contents of a database
Client for the Comprehensive Knowledge Archive Network ('CKAN')
'API'
Optimal k-Means Clustering for One-Dimensional Data
C++ implementations of phylogenetic cladogenesis calculations
Time Series Modeling for Climate Change Proxies
Construct Graphs of S4 Class Hierarchies
Explore classification models in high dimensions
Classification Accuracy and Consistency under IRT models
Robust multivariate classification using highly optimised SVM
ensembles
Cleaning Geometries from Spatial Objects
Simultaneous Variables Clustering and Regression
Model-Based Clustering of Categorical Sequences
Analyzes Clickstreams Based on Markov Chains
Easily Download and Visualise Climate Data from CliFlo
Climate Classification According to Several Indices
Constrained L1-minimization for Inverse (covariance) Matrix
Estimation
Statistical Methods for Climate Sciences
Clinical Trial Design and Data Analysis Functions
Read and Write from the System Clipboard
Constrained Inference for Linear Mixed Effects Models
Fitting exact conditional logistic regression with lasso and
elastic net penalties
Central Limit Theorem Experiments (Theoretical and Simulation)
Clustering Method Based on Local Shrinking
"Finding Groups in Data": Cluster Analysis Extended Rousseeuw et
al.
Explore clustering interactively using R and GGobi
Random Cluster Generation (with Specified Degree of Separation)
Identifying clusters in genomics data by recursive partitioning
Optimal Distance-Based Clustering for Multidimensional Data with
Sequential Constraint
Power calculations for cluster-randomized and cluster-randomized
crossover trials
Reproducibility of gene expression clusters
Calculate Cluster-Robust p-Values and Confidence Intervals
Searching for Optimal Clustering Procedure for a Data Set
Assessment of Stability of Individual Objects or Clusters in
Partitioning Solutions
Clustering of Observations with Geographical Constraints
Variable Selection in Clustering by Mixture Models for Discrete
Data
Methods for joint dimension reduction and clustering
Clustering of Variables Around Latent Variables
Variable Selection for Model-Based Clustering
Covariance Matrix Adapting Evolutionary Strategy
Control Charts for Conway-Maxwell-Poisson Distribution
Subdistribution Analysis of Competing Risks
Misc Functions of the Center for the Mathematical Research
The Complex Multivariate Gaussian Distribution
A Package for Coincidence Analysis (CNA)
Canonical Non-Symmetrical Correspondence Analysis in R
Maximum Likelihood Estimation of a Log-Concave Density Function
Pre-process DNA Copy Number (CN) Data for Detection of CN Events
Association Analysis of CNV Data and Imputed SNPs
Example data sets for association analysis of CNV data
A collection of functions to help with analysis of coarsely
observed data
COnstrained B-Splines (Sparse Matrix Based)
Statistical comparisons of two or more alpha coefficients
Output Analysis and Diagnostics for MCMC
Markov chain Monte Carlo burn-in based on "bridge" statistics
Automated C Code Generation for Use with the 'deSolve' and
'bvpSolve' Packages
Robust Coefficient Alpha and Omega with Missing and Non-Normal
Data
Species coexistence modeling and analysis
Conditional Inference Procedures in a Permutation Test Framework
Simultaneous Confidence Interval for Multinomial Proportion
Collocation Inference for Dynamic Systems
Colocalisation tests of two genetic traits
Implementation of Colored Independent Component Analysis and
Spatial Colored Independent Component Analysis
Plot and add custom coloring to Venn diagrams for 2-dimensional,
3-dimensional and 4-dimensional data
Tools for colors in a Hue-Saturation-Value (HSV) color model
Synergy/Antagonism Analyses of Drug Combinations
Construction Methods of some Series of PBIB Designs
Exact Test from the Combinations of Mutually Exclusive
Alterations (CoMEt) Algorithm
Print Nicely Formatted Comments for Use in Script Files
Bindings to the 'CommonMark' Reference Implementation
Extract and plot common trends from a cointegration system.
Calculate P-value for Johansen Statistics
Comparative Phylogeographic Analysis using the Community Tree
Framework
Creates empty plots with compact axis notation
Compare Two Correlated C Indices with Right-censored Survival
Outcome
Interface to Diverse Estimation Methods of Causal Networks
comparison of medical forms in CDISC ODM format
Correct for Verification Bias in Diagnostic Accuracy & Agreement
Multivariate likelihood ratio calculation and evaluation
Event-specific incidence rates for competing risks data
Tools for Retrieval and Storage of Functional Genomics Data
Conway-Maxwell-Poisson GLM and distribution functions
Numerically Evaluate Integrals and Derivatives (also Higher
Order) of Vector- And Complex-Valued Functions
Robust Linear Regression with Compositional Data as Covariates
Regression Estimation Based on the Compound Covariate Method
Under the Cox Proportional Hazard Model
Distribution function of quadratic forms in normal variables
Composite-Likelihood Based Analysis of Random Fields
Optimisation with Continuous Convex Piecewise (Linear and
Quadratic) Functions
Approximate conditional inference for logistic and loglinear
models
Parameter estimation in conditional GEE for recurrent event gap
times
Conditional Density Estimation with Neural Network Conditional
Mixtures
Condition Number Regularized Covariance Estimation
Primal or Dual Cone Projections with Routines for Constrained
Regression
Confidence Estimation of Environmental State Classifications
Conformal Prediction for Regression and Classification
Configural Frequencies Analysis Using Log-Linear Modeling
Algorithms for Fitting Circles, Ellipses and Conics Based on the
Work by Prof. Nikolai Chernov
A package to check the cancellation axioms of conjoint
measurement
A Tool for Reproducible Research by Converting 'LaTeX' Files
Generated by R Sweave to Rich Text Format Files
Tools for working with connectivity matrices
Conover-Iman Test of Multiple Comparisons Using Rank Sums
Partial Linear Least-Squares Regression using Constrained
Splines
Compute the Median Ranking(s) According to the Kemeny's
Axiomatic Approach
Constrained, Covariance-Matching Constrained and Universal Point
or Block Kriging
Contouring of Non-Regular Three-Dimensional Data
Median Comparison for Two-Sample Right-Censored Survival Data
Quantifies and assesses the significance of convergent evolution
Probabilistic Species Co-occurrence Analysis in R
Cooperative aspects of optimal trees in weighted graphs
Tools for processing COPAS large-particle flow cytometer data
General Bivariate Copula Theory and Many Utility Functions
Fitting the copCAR Regression Model for Discrete Areal Data
Coverage Probability Excursion (CoPE) Sets
Copula Based Bivariate Beta-Binomial Model for Diagnostic Test
Accuracy Studies
Estimation of Distribution Algorithms Based on Copulas
Estimation and Statistical Process Control Under Copula-Based
Time Series Models
Bivariate Copula Based Regression Models
Copula Mixed Effect Models for Bivariate and Trivariate
Meta-Analysis of Diagnostic Test Accuracy Studies
Computing Statistical Indices to Detect Answer Copying on
Multiple-Choice Tests
Baseline Correction for Copy Number Data from Cancer Samples
Generate correlated count random variables
Community Estimation in G-Models via CORD
Classification, Regression and Feature Evaluation
TDT for compound heterozygous and recessive models
The Clustering of Regression Models Method
Efficient Estimation of Covariance and (Partial) Correlation
Statistics and data sets for corpus frequency data
Nonparametrics with Clustered Binary and Multinomial Data
Linear Regression Based on Linear Structure Between Covariates
Functions and Methods for Correspondence Regression
A collection of functions for graphing correlation matrices
Estimate Correlations Between Repeatedly Measured Endpoints
(E.g., Reliability) Based on Linear Mixed-Effects Models
Tools for processing data after a Genome Wide Association Study
Tools for estimating and predicting the cosinor model
Functions for cosmological distances, times, luminosities, etc
Photometric redshift estimation using generalized linear models
Fit Regularized Nonparametric Regression Models Using COSSO
Penalty
Mean and variance modeling of count data
Brownian Motion Processes for 'nlme'-Models
Latent Class Models with Covariate Effects on Underlying and
Measured Variables
A simultaneous regression model for the mean and covariance
Robust Covariance Estimation via Nearest Neighbor Cleaning
Computes covariance test for adaptive linear modelling
Streamlined Plot Theme and Plot Annotations for 'ggplot2'
Messages, Warnings, Strings with Ascii Animals
Cox models by likelihood based boosting for a single survival
endpoint or competing risks
Cox-Type Models for Interval-Censored Data
Cox Regression (Proportional Hazards Model) with Multiple Causes
and Mixed Effects
Confirmatory Path Analysis through the d-sep tests
Methods to perform Common Principal Component Analysis (CPCA)
Concordance Probability Estimates in Survival Analysis
Association Between Methylation and a Phenotype of Interest
Parallelized Genomic Prediction and GWAS
CpG Filtering Method Based on Intra-class Correlation
Coefficients
Find the maximum likelihood estimator of the shape constrained
hazard baseline and the effect parameters in the Cox
proportional hazards model
Sequential and Batch Change Detection Using Parametric and
Nonparametric Methods
Correction of the pvalue after multiple coding
Conditional probability function of a competing event
Quantile, Composite Quantile Regression and Regularized Versions
Conditional Maximum Likelihood for Quadratic Exponential Models
for Binary Panel Data
Power Calculation for Weighted Log-Rank Tests in Cure Rate
Models
Probabilistic damage tolerance analysis for fatigue cracking of
metallic aerospace structures
Multivariate nonparametric Cramer-Test for the
two-sample-problem
Download Logs from the RStudio CRAN Mirror
Fit Continuous-Time Correlated Random Walk Models to Animal
Movement Data
Categorical Randomized Block Data Analysis
Censored Regression with Conditional Heteroscedasticity
Functions for calculating the CreditMetrics risk model
Group-Based Modelling of Longitudinal Data
Statistical Methods for Crime Series Linkage
Continual Reassessment Method (CRM) for Phase I Clinical Trials
CCMN and other noRMalizatioN methods for metabolomics data
Object-Oriented Implementation of CRM Designs
Downloads and Builds datasets for Climate Reference Network
Analysis and Search of Crossover Designs
Confidence intervals for crossover points of two simple
regression lines
Cross-Recurrence Quantification Analysis for Categorical and
Continuous Time-Series
Penalized Variable Selection in Competing Risks Regression
Competing risks regression for Stratified and Clustered data
Stepwise Covariate Selection for the Fine & Gray Competing Risks
Regression Model
Sample Size Estimation Functions for Cluster Randomized Trials
Interface to Climatic Research Unit Time-Series Version 3.21
Data
Identifying cryptic relatedness in genetic association studies
Statistical Tests for Cryptographic Randomness
Functions for Conditional Simulation in Regression-Scale Models
Covariate-adjusted Skewed Functional Model (cSFM)
Methods to Test Hypotheses on the Distribution of Spatial Point
Processes
csSAM - cell-specific Significance Analysis of Microarrays
Substantive significance testing for regression estimates and
marginal effects
Modeling Animal Movement with Continuous-Time Discrete-Space
Markov Chains
Continuous Time Structural Equation Modelling
A Class of Mixture Models for Ordinal Data
Rule- and Instance-Based Regression Modeling
CUDA Parallel Implementation of a Bayesian Multilevel Model for
fMRI Data Analysis
Data sets for the examples used in the package "cudaBayesreg"
Analyze Multivariate Phenotypes by Combining Univariate results
Extends ddply to allow calculation of cumulative quantities
Change point detection in genomic sequences
The package will formally test two curves represented by
discrete data sets to be statistically equal or not when the
errors of the two curves were assumed either equal or not using
the tube formula to calculate the tail probabilities
curvHDR filtering of flow cytometry samples
Cusp-Catastrophe Model Fitting Using Maximum Likelihood
Compute Decision Interval and Average Run Length for CUSUM
Charts
CUTOFF: A Spatio-temporal Imputation Method
An R package to perform LPUE standardization and stock
assessment of the English Channel cuttlefish stock using a
two-stage biomass model
Cross-Validated Area Under the ROC Curve Confidence Intervals
Estimation of the Calibration Equation with Error-in
Observations
Penalized Logistic Regression Model using Majorization
Minimization by Coordinate Descent (MMCD) Algorithm
Calculate the predictive squared correlation coefficient
Fast Cross-Validation via Sequential Testing
Cross-validation tools for regression models
Regularized Estimators of Covariance Matrices with CV Tuning
Miscellaneous Functions for Math, Plotting, Printing,
Statistics, Strings, and Tools
extend cxxfunction by saving the dynamic shared objects
Query Team Cymru IP Address, Autonomous System Number (ASN),
Border Gateway Protocol (BGP), Bogon and Malware Hash Data
Services
Cycle and Phase Identification for mastication data
Predicting Causal Direction from Dependency Features
Interactive Heat Maps Using 'htmlwidgets' and 'D3.js'
Tools for creating D3 JavaScript network, tree, dendrogram, and
Sankey graphs from R
Data Analysis and Graphics Data and Functions
Data Sets and Functions, for demonstrations with expression
arrays and gene sequences
Data Sets and Functions, supplementary to DAAG
Three-Way Data Analysis Through Densities
Functions Useful in the Design and ANOVA of Experiments
Design and Analysis of Experiments with R
Learning directed acyclic graphs (DAGs) through bootstrap
aggregating
Diagnostic accuracy of classification models
Dynamical Assembly of Islands by Speciation, Immigration and
Extinction
DALY Calculator - A GUI for stochastic DALY calculation in R
Dynamic Assembly Model of Colonization, Local Extinction and
Speciation
Dams in the United States from the National Inventory of Dams
(NID)
Dandelion Plot for R-mode Exploratory Factor Analysis
Package for deep architectures and Restricted-Bolzmann-Machines
Statistical Tools to Analyze Your Darts Game
Interactive Data Visualization with D3.js
File-Based Access to Large Matrices Stored on HDD in Binary
Format
Tools for Easily Combining and Cleaning Data Sets
dataframes2xls writes data frames to xls files
Estimating Finite State Machine Models from Data
A system for mapping foreign objects to R variables and
environments
Easily Source Publicly Available Data on Derivatives
Performs variable level data quality checks and generates
summary statistics
Retrieval Functions for USGS and EPA Hydrologic and Water
Quality Data
An Implementation of a SAS-Style Data Step
General Purpose Hierarchical Data Structure
Support functions for packages VBmix, semisupKernelPCA, and
patchPlot
Data and Workspace Browser for Terminals
Deconvolution Analysis Tool for Dynamic Contrast Enhanced MRI
Functions for "Data Analysis in Vegetation Ecology"
Discriminant Analysis with Additional Information
Discrete Bayesian Additive Regression Trees Sampler
Provides a graphical user interface to connect with databases
that use MySQL
Goodness-of-fit and two sample comparison tests using sample
entropy
methods of distance-based gene set functional enrichment
analysis
Discrete Beta Kernel Graduation of Mortality Data
Compute the NPMLE of distribution from doubly censored data
Distance-Based Measures of Spatial Structures
Density Based Clustering of Applications with Noise (DBSCAN) and
Related Algorithms
Inferring Disease Networks via Differential Network Inference
Analyzing Dichotomous Choice Contingent Valuation Data
A Package for Image Analysis of DCE-MRI (S4 Implementation)
Differential Co-expression Analysis and Differential Regulation
Analysis of Gene Expression Microarray Data
Analysis of Ontologies and Protein Domain Annotations
Claims Reserving under the Double Chain Ladder Model
Data Cloning and MCMC Tools for Maximum Likelihood Methods
Functions for the Detection of Spatial Clusters of Diseases
Hierarchical Models Made Easy with Data Cloning
Attribute profile estimation using Diagnostic Classification
Models and MCMC
Conventional Cross-validation statistics for climate-growth
model
Depth-Based Classification and Calculation of Data Depth
Variance Stabilization by Data-Driven Haar-Fisz (for
Microarrays)
Learning Bayesian Networks with Mixed Variables
Deconvolution density estimation with adaptive methods for a
variable prone to measurement error
Get Energy Data from the UK Dept of Energy and Climate Change
DEComposition of Indirect and Direct Effects
Quantitative Support of Decision Making under Uncertainty
Differential Co-Expression and Differential Expression Analysis
Deconvolution Estimation in Measurement Error Models
Additional dialogs and functions for Deducer
Deductive Correction, Deductive Imputation, and Deterministic
Correction
Dirichlet-multinomial Evolutionary Event Profile Randomization
(DEEPR) test
Integration of One-Dimensional Functions with Double Exponential
Formulas
Detection of differentially expressed genes
Models for Skewed Count Distributions Relevant to Networks
Statistical Functions for the Delaporte Distribution
Delaunay Triangulation and Dirichlet (Voronoi) Tessellation
Estimation of Multivariate Densities Using Adaptive Partitions
Identification of dichotomous differential item functioning
(DIF) using Angoff's Delta Plot method
Functions to calculate relatedness on diploid genetic data
Evaluating the genetic differentiation between populations based
on Gst and D values
Differential Expression from Multiple Indicators
Deming, Thiel-Sen and Passing-Bablock Regression
Forecasting mortality, fertility, migration and population data
Kernel Density Estimation for Demonstration Purposes
Faster Dendrogram Manipulation using 'Rcpp'
Extra functions to cut, label and colour dendrogram clusters
Dendrogram seriation: ordering for visualisation
Modular Leaf Ordering Methods for Dendrogram Nodes
Statistical Analyses of De Novo Genetic Variants
Visualization of Multivariate Functions, Sets, and Data
Clustering by fast search and find of density peaks
Density.T.HoldOut: Non-combinatorial T-estimation Hold-Out for
density estimation
Density strips and other methods for compactly illustrating
distributions
Global Optimization by Differential Evolution
Differential Evolution Optimization in Pure R
Statistical Inference for Parametric and Semiparametric Models
Based on Dependently Truncated Data
Dependent Mixture Models - Hidden Markov Models of GLMs and
Other Distributions in S4
Depth functions tools for multivariate analysis
Seasonal Adjustment by Frequency Analysis
R interface to DescribeDisplay (GGobi plugin)
Describe Data in R Using Common Descriptive Statistics
Optimal deseasonalization for geophysical time series using AR
fitting
Computational tool for designing genetical genomics experiments
Desirability Functions for Ranking, Selecting, and Integrating
Data
Desirability Function Optimization and Ranking
Bagging with Distance-based Regression for Differential Gene
Expression Analyses
Solvers for Initial Value Problems of Differential Equations
(ODE, DAE, DDE)
Estimation of Diagonal Elements of Sparse Precision-Matrices
Analyzing Wildlife Data with Detection Error
Detect Data Containing Personally Identifiable Information
DetMCD Algorithm (Robust and Deterministic Estimation of
Location and Scatter)
Start the detrendeR Graphical User Interface (GUI)
A computer program to detect markers responding to selection
Data sets from Devore's "Prob and Stat for Eng (7th ed)"
Tools to Make Developing R Packages Easier
Phase I/II Adaptive Dose-Finding Design for Combination Studies
Dose-finding by the continual reassessment method
Explore data.frames by plotting NA and classes of each variable
Differential Functioning of Items and Tests
Phase I/II Adaptive Dose-Finding Design for MTA
Capture-Recapture Estimation using Bayesian Model Averaging
Differential Geometric LARS (dgLARS) method
Simulating Data for PLS Mode B Structural Models
Double Hierarchical Generalized Linear Models
Block Diagonal Extraction or Replacement
Functions for visualising simple graphs (networks), plotting
flow diagrams
Create Graph Diagrams and Flowcharts Using R
Diagnostic test summary measures for three ordinal groups
Calculate probabilities of various dice-rolling events
Construction and Evaluation of Metamodels
Kriging Methods for Computer Experiments
Kriging-Based Optimization for Computer Experiments
Plot methods for computer experiments design and surrogate
Dictionaries for reading microdata surveys from IBGE
Defines some physical constants and dielectric functions
commonly used in optics, plasmonics
R Interface to the ZEIT ONLINE Content API
Detection of Differential Item Functioning (DIF) in Rasch Models
by Boosting Techniques
Analyzing and Visualizing Differential Correlation Networks in
Biological Data
Calculates Confidence Intervals for two Dependent Proportions
Functions from the book Solving Differential Equations in R
Metrics of Difference for Comparing Pairs of Maps
Diffusion IRT Models for Response and Response Time Data
Display Differences Between Two Files using Codediff Library
Baseline identification and peak decomposition for x-ray
diffractograms
Inference and Analysis for Generalized Quadratic Diffusions
A penalty approach to Differential Item Functioning in Rasch
Models
Collection of Methods to Detect Dichotomous Differential Item
Functioning (DIF)
Item Focused Trees for the Identification of Items in
Differential Item Functioning
Create Cryptographic Hash Digests of R Objects
An application programming interface for generating null models
of social contacts based on individuals' space use
Data-Informed Link Strength. Combine multiple-relationship
networks into a single weighted network. Impute (fill-in)
missing network links
DIME (Differential Identification using Mixture Ensemble)
DiNAMIC A Method To Analyze Recurrent DNA Copy Number
Aberrations in Tumors
Hartigan's Dip Test Statistic for Unimodality - Corrected
Bayesian Clustering of Multivariate Data Under the
Dirichlet-Process Prior
Direct labels for multicolor plots in lattice or ggplot2
Direction Analysis for Pathways and Kinases
Discrete Laplace Mixture Inference using the EM Algorithm
DiscML: An R package for estimating evolutionary rates of
discrete characters using maximum likelihood
Multiple testing procedures for discrete test statistics
Create and Manipulate Discrete Random Variables
Discrete Weibull Distributions (Type 1 and 3)
Data preprocessing, discretization for classification
Discrimination of Alternative Reproductive Tactics (ARTs)
Tools of the Trade for Discriminant Analysis
Modelling Spatial Variation in Disease Risk for Areal Data
2D Hausdorff and Simplex Dispersion Orderings
Disparity Filter Algorithm of Weighted Network
Create Disposable R Packages for Testing
Utilities for making pairwise comparisons of multivariate data
Distance Sampling Detection Function and Abundance Estimation
DiSTATIS Three Way Metric Multidimensional Scaling
Computations over Distributed Data without Aggregation
Method Functions for Confidence Intervals and to Distill
Information from an Object
Object oriented implementation of distributions
Documentation for Packages distr, distrEx, distrSim, distrTEst,
distrTeach, distrMod, and distrEllipse
S4 classes for elliptically contoured distributions
Object Oriented Implementation of Probability Models
Package distr classes for distributions from Rmetrics
Simulation classes based on package distr
Extensions of package distr for teaching Stochastics/Statistics
in secondary school
Estimation and Testing classes based on package distr
Provides tools for quality checks of georeferenced plant species
accessions
diversitree: comparative phylogenetic analyses of
diversification
A Comprehensive, General Purpose Population Genetics Analysis
Package
Functions for re-sampling a community matrix to compute
diversity indices at different sampling levels
Tools for Analysis of Diversity and Similarity in Biological
Systems
Nearest Neighbour Contingency Table Analysis
Distance Measure Based Judgment and Learning
Bayesian and Likelihood Analysis of Dynamic Linear Models
Dynamic Modeling and Parameter Estimation in ODE Models
Delete or Merge Regressors for linear model selection
Functions and data for "Data Mining with R"
Tools for analysing forensic genetic DNA data
Integrative Analysis of Omics Data in Terms of Network,
Evolution and Ontology
Analysis of Discretely Observed linear
Birth-And-Death(-and-immigration) Markov Chains
Groupwise statistics, LSmeans, linear contrasts, utilities
Extract Data Tables from Microsoft Word Documents
Functions for Acceptance Sampling Ideas originated by H.F. Dodge
Full Factorials, Orthogonal Arrays and Base Utilities for DoE
Packages
Wrapper package for design of experiments functionality
ADI (average dominance index), social network graphs with dual
directions, and music notation graph
R Console Bindings for the 'Domino Command-Line Client'
Foreach parallel adaptor for the Rmpi package
Foreach Parallel Adaptor for the 'parallel' Package
Foreach parallel adapter for the rredis package
Generic Reproducible Parallel Backend for foreach Loops
Planning and Analyzing Dose Finding Experiments
Foreach Parallel Adaptor for the 'snow' Package
Dot-and-Whisker Plots of Regression Results
Test against parametric regression function
Differential Exon Usage Test for RNA-Seq Data via Empirical
Bayes Shrinkage of the Dispersion Parameter
Local Linear Hazard Estimation with Do-Validated and
Cross-Validated Bandwidths
Dirichlet Process Mixture model simulation for clustering and
image segmentation
Data Pre-Processing and Visualization Functions for
Classification
Methods for Dimension Reduction for Regression
Doubly Robust Generalized Estimating Equations
Discontinuous Regression and Image Processing
Regression and association models for repeated categorical data
Dual Empirical Likelihood Inference under Density Ratio Models
in the Presence of Multiple Samples
Analyze Drop Out of an Experiment or Survey
Dose-Response Modeling with Smoothing Splines
Discretization-Based Direct Random Sample Generation
Bayesian subgroup analysis in clinical trials
Dynamic Systems Estimation (time series package)
Density Surface Modelling of Distance Sampling Data
Spatial Modelling for Distance Sampling Data
Recursive Partitioning for Discrete-Time Survival Trees
Exploring Disease Similarity in Terms of Dysfunctional
Regulatory Mechanisms
A Wrapper of the JavaScript Library 'DataTables'
Comparison of Binary Diagnostic Tests in a Paired Study Design
Analysis of diffusion weighted imaging (DWI) data
Dunnett-Tukey-Kramer Pairwise Multiple Comparison Test Adjusted
for Unequal Variances and Unequal Sample Sizes
Suite of functions related to discrete-time discrete-state
Markov Chains
Estimation and Comparison of Dynamic Treatment Regimes
Learning Algorithms for Dynamic Treatment Regimes
Time Series Clustering with Dynamic Time Warping
Time-Weighted Dynamic Time Warping for Remote Sensing Time
Series Analysis
Dual Scaling Analysis of Multiple Choice Data
Create dummy/indicator variables flexibly and efficiently
Automatic Creation of Dummies with Support for Predictive
Modeling
Software implementation of step-down and step-up Dunnett test
procedures
Dunn's Test of Multiple Comparisons Using Rank Sums
Bayesian inference from count data using discrete uniform priors
Discrete variations of a fractional Brownian motion
Analyze Dose-Volume Histograms and Check Constraints
Interface to Dygraphs Interactive Time Series Charting Library
Dynamically visualized probability distributions and their
moments
Methods for detection of clusters in hierarchical clustering
dendrograms
Implementation of the Dynamic TOPMODEL hydrological model
Full Interactive GUI for Dynamic Biplot in R
Denoising and clustering for dynamical image sequence (2D or
3D)+T
Dynamic Nomograms for Linear, Generalized Linear and
Proportional Hazard Models
Companion Package to "Dynamic Prediction in Clinical Survival
Analysis"
Dynamic Simulations of Autoregressive Relationships
Methods for Estimating Dynamic Treatment Regimes
Misc Functions of the Department of Statistics, Probability
Theory Group (Formerly: E1071), TU Wien
Plots of the Empirical Attainment Function
Early Warning Signals Toolbox for Detecting Critical Transitions
in Timeseries
Efficient Approximate Bayesian Computation Sampling Schemes
Analysis of variance and other important complementary analyzes
Utility functions for working with mark-recapture data
Sample Size Estimation for Experimental Designs
Evaluation of stratified genome-wide association meta-analysis
results
Ensemble Forecast Verification for Large Datasets
Entropy reweighting to create balanced samples
Empirical Bayes Thresholding and Related Methods
Empirical Bayes Estimation of Dynamic Bayesian Networks
Genotyping using Next Generation Sequencing Data
Empirical Bayesian Lasso and Elastic Net Methods for Generalized
Linear Models
Genotyping and SNP calling using single-sample next generation
sequencing data
Functions for Spatial Point Pattern Analysis
Efficient calculation of fine structure isotope patterns via
Fourier transforms of simplex-based elemental models
Dissimilarity-based functions for ecological analysis
Programmatic Interface to the API Serving UC Berkeley's Natural
History Data
Spatial Analysis of Phenotypic, Genotypic and Environmental Data
A community modeling foundation for Eco-Hydrology
"A practical guide to ecological modelling - using R as a
simulation platform"
Ecological Regression using Aggregate and Individual Data
Simulating Community Assembly and Ecological Diversification
Using Ecospace Frameworks
Procedures for Ecological Assessment of Surface Waters
Non-Parametric Multiple Change-Point Analysis of Multivariate
Data
More Powerful Unconditional Testing of Negative Binomial Means
for Digital Gene Expression Data
Network Reconstruction and Changepoint Detection
Simultaneous Edit-Imputation for Continuous Microdata
Parsing, Applying, and Manipulating Data Cleaning Rules
Empirical Differentially Methylated Regions Calculation
Estimation of the effective dimension reduction (EDR) space
Provides tools for dimension reduction methods
Tools for analysis and classification of electroencephalography
(EEG) data
Read and Preprocess Fluorescence Excitation-Emission Matrix
(EEM) Data
Tools for Pre-Processing Emission-Excitation-Matrix (EEM)
Convenience Functions for Education Data
Wavelet-Based Enhanced FDR for Signal Detection in Noisy Images
Effect Displays for Linear, Generalized Linear, and Other Models
Test changes of a grouped response relative to baseline
Visualization of Categorical Response Models
The Causal Effects for a Causal Loglinear Model
Hierarchical Modelling of Faecal Egg Counts
Tool for ego-centric measures in Social Network Analysis
Exploration and Graphics for RivEr Trends (EGRET)
Exploration and Graphics for RivEr Trends (EGRET) Confidence
Intervals
R Wrapper for the Energy Information Administration (EIA) API
Generates (dense) matrices that have a given set of eigenvalues
Semiparametric factor and regression models for symmetric
relational data
Computes confidence intervals for principal components
eiPack: Ecological Inference and Higher-Dimension Data
Management
An algorithm for reducing errors-in-variable bias in simple
linear regression
Ecological Inference with individual and aggregate data
Enhanced Least Absolute Shrinkage and Selection Operator
Regression Model
General Purpose Interface to 'Elasticsearch'
Elastic-Net for Sparse Estimation and Sparse PCA
Collection of functions for statistical election audits
Functions for election audits using stratified random samples
Data Sets, Functions and Examples from the Book: "The Elements
of Statistical Learning, Data Mining, Inference, and
Prediction" by Trevor Hastie, Robert Tibshirani and Jerome
Friedman
Functions for drawing ellipses and ellipse-like confidence
regions
Implementation of ELM (Extreme Learning Machine ) algorithm for
SLFN ( Single Hidden Layer Feedforward Neural Networks )
Preference Rating for Visual Stimuli Based on Elo Ratings
Animal Dominance Hierarchies by Elo Rating
Empirical Likelihood Analysis for the Cox Model and
Yang-Prentice (2005) Model
Tools to Analyze the Thermal Reaction Norm of Embryo Growth
Evolutionary Monte Carlo (EMC) algorithm
Evolutionary Monte Carlo (EMC) methods for clustering
EM Algorithm for Model-Based Clustering of Finite Mixture
Gaussian Distribution
Empirical Mode Decomposition and Hilbert Spectral Analysis
Support Functions and Data for "Ecological Models and Data"
Exponentially Modified Gaussian (EMG) Distribution
Evaluation of Modeling without Information Leakage
EM Algorithms for Estimating Item Response Theory Models
End-member modelling algorithm and supporting functions for
grain-size analysis
Contrasts in mixed effects for EMMIX model with random effects
The EM Algorithm and Skew Mixture Distribution
Fitting Unrestricted Multivariate Skew t Mixture Models
Fitting Mixed Models with Known Covariance Structures
Eye Movement Analysis Package for Fixation and Saccade Detection
Expected Maximum Profit Classification Performance Measure
Simulation-Based False Discovery Rate in RNA-Seq
Empirical Likelihood Ratio for Censored/Truncated Data
Empirical Likelihood Ratio Test for Two Samples with Censored
Data
Exact Multinomial Test: Goodness-of-Fit Test for Discrete
Multivariate data
Entropy Minimization over Variable Clusters (EMVC)
R Package for Fitting Multinomial Probit Models with Endogenous
Selection
R Package for Analyzing Endorsement Experiments
Data sets from "Introductory Statistics for Engineering
Experimentation"
Automated Runs and Evaluations of Ecological Niche Models
Ensemble Partial Least Squares Regression
An R Package for the Analysis of Multiple ChIP-Seq Data
Enrichment assessment of virtual screening approaches
Extensible Package for Parallel, Batch Training of Base Learners
for Ensemble Modeling
Probabilistic Forecasting using Ensembles and Bayesian Model
Averaging
Extensible Package for Cross-Validation-Based Integration of
Base Learners
Extensible Package for Principal-Component-Regression-based
Integration of Base Learners
Extensible Classes and Methods for Penalized-Regression-based
Integration of Base Learners
Entropy Partitioning to Measure Diversity
Estimation of Entropy, Mutual Information and Related Quantities
Estimation of Entropy and Related Quantities
Tools for Exploring Differential Shannon Entropy, Differential
Coefficient of Variation and Differential Expression
Isotope Pattern, Profile and Centroid Calculation for Mass
Spectrometry
Peak picking for high resolution mass spectrometry data
Statistical Analysis of Environmental Space-Time Processes
Package for Environmental Statistics, Including US EPA Guidance
Statistical Functions for the Censored and Uncensored
Epanechnikov Distribution
Gaussian Approximation of Bayesian Binary Regression Models
A Package for Statistical Analysis in Epidemiology
Elementary Epidemiological Functions for Epidemiology and
Biostatistics
Implements Hierarchical Bayesian Models for Epidemiological
Applications
Epidemiological Tool for Contact Tracing
EpiEstim: a package to estimate time varying reproduction
numbers from epidemic curves
Flexible Modelling Functions for Epidemiological Data Analysis
Mathematical Modeling of Infectious Disease
Tools for the Analysis of Epidemiological Data
Basic Sensitivity Analysis of Epidemiological Results
Density Estimation with Soft Information by Exponential
Epi-splines
EPoC (Endogenous Perturbation analysis of Cancer)
EQS Output Conversion to lavaan Functions
Tools for analyzing eQTL experiments: A complementary to Karl
Broman's 'qtl' package for genome-wide analysis
Direct, Chain and Average Equating Coefficients with Standard
Errors Using IRT Methods
Provides Tests and Graphics for Assessing Tests of Equivalence
Nonparametric Multiple Expectile Regression via ER-Boost
Fit, Simulate and Diagnose Exponential-Family Models for
Networks
Fit, Simulate and Diagnose Exponential-Family Models for
Networks with Count Edges
ERG Modeling Based on Graphlet Properties
Local Health Department network data set
User-specified terms for the statnet suite of packages
Significance analysis of Event-Related Potentials data
Event-Related Potential (ERP) Data Exploration Made Easy
Event-related potentials (ERP) analysis, graphics and utility
functions
Estimate Number of Latent Factors and Factor Matrix for Factor
Analysis
ESEA: Discovering the Dysregulated Pathways based on Edge Set
Enrichment Analysis
Toolkit for the simulation of financial assets and interest
rates models
Ensemble of Subset of K-Nearest Neighbours Classifiers for
Classification and Class Membership Probability Estimation
Calibrating Parameters for the Samejima's Continuous IRT Model
Estimation of Heritability in High Dimensional Sparse Linear
Mixed Models using Variable Selection
Tools for Assessing Estimability of Linear Predictions
Mixed FLP and ML Estimation of ETAS Space-Time Point Processes
Utility Functions to Execute Standard Extract/Transform/Load
Operations (using Package 'ff') on Large Data
eulerian: A package to find eulerian paths from graphs
Bayesian Single Arm Sample Size Calculation Software
Parsing and Evaluation Tools that Provide More Details than the
Default
Modelling Actual, Potential and Reference Crop
Evapotranspiration
Functions for extreme value distributions
Bayesian Analysis in Extreme Value Theory
A Powerful Tool to Quickly Compare Huge Lists and Draw Venn
Diagrams
Extreme Value Mixture Modelling, Threshold Estimation and
Boundary Corrected Kernel Density Estimation
Comparative and Population Genetic Analyses
Tools for Evolutionary Quantitative Genetics
Mean of order p, peaks over random threshold Hill and high
quantile estimates
Evolutionary Learning of Globally Optimal Trees
Goodness-of-Fit Tests for the Exponential and Two-Parameter
Weibull Distributions
Exact Conditional Tests and Confidence Intervals for 2x2 tables
Exact P-Values and Matching Confidence Intervals for Simple
Discrete Parametric Cases
Inductive Confidence Intervals for the difference between two
proportions
Monte Carlo Exact Tests for Log-linear models
Exact solution paths for regularized LASSO regressions with L_1
penalty
Exact Distributions for Rank and Permutation Tests
Confidence regions for exceedance sets and contour lines
Convenient Data Exchange with Microsoft Excel
Excursion Sets and Contour Credibility Regions for Random Fields
Calls Copy Number Variants from Targeted Sequence Data
Expanding Ploidy and Allele-Frequency on Nested Subpopulations
experiment: R package for designing and analyzing randomized
experiments
Modeling without data using expert opinion
Explanation of Predictions for Classification and Regression
Models
Exploratory analysis with the singular value decomposition
Calculate density dependent likelihood of a phylogenetic tree
Spatial analysis of extra-pair paternity
Data Sets from "Forecasting with Exponential Smoothing"
Fast, Reliable and Elegant Reproducible Research
Convenience Functions for Botanists to Create Specimens Indices
Extending the Range of Functions for Probability Distributions
Maximum penalized likelihood estimation with extended lasso
penalty
Extra-binomial approach for pooled sequencing data
Categorical Data Analysis and Visualization
Extremely Randomized Trees (ExtraTrees) Method for
Classification and Regression
Estimation of Extreme Value Dependence for Time Series Data
Estimate Lower Extreme Quantile with the Censored Weibull MLE
and Censored Weibull Mixture
Easy Analysis and Visualization of Factorial Experiments
Easy Interface to Effective Concentration Calculations
selects significant non-additive interaction between two
variables using fast GLM implementation
provide an easy to use framework to conduct simulation
Summarise Data in the Quick and Easy Way
Bayesian Sparse Factor Analysis model for the inference of
pathways responsive to drug treatment
Combination of Factorial Methods and Cluster Analysis
Multivariate Exploratory Data Analysis and Data Mining
Bayesian quantile regression factor models
Perform Factorial Analysis from FactoMineR with a Shiny
Application
Scores the FACT and FACIT Family of Patient-Reported Outcome
Measures
thin wrapper for the Factual.com server API
Variable selection for supervised classification in high
dimension
Distributions that are Sometimes Used in Hydrology
Data from the Book "Multivariate Statistical Modelling Based on
Generalized Linear Models", First Edition, by Ludwig Fahrmeir
and Gerhard Tutz
Faisal Conjoint Model: A New Approach to Conjoint Analysis
Finding Allele-specific Copy Number in Next-Generation
Sequencing Data
Probabilities for Pedigrees Given DNA Data
A Convex Formulation for Modeling Interactions with Strong
Heredity
Maximum Likelihood and Bayesian Estimation of Univariate
Probability Distributions
Factor Analysis for Multiple Testing (FAMT) : simultaneous tests
under dependence in high-dimensional data
Penalized Likelihood Factor Analysis via Nonconvex Penalty
Building Kriging Models from FANOVA Graphs
Visualisation of Sequential Probability Distributions Using Fan
Charts
Download Data from the FAOSTAT Database of the Food and
Agricultural Organization (FAO) of the United Nations
Influential Case Detection Methods for Factor Analysis and
Structural Equation Models
Modelization for Functional AutoRegressive Processes
Functions and datasets for books by Julian Faraway
Rmetrics - Analysing and Modelling Financial Assets
Implementation of the Fourier Amplitude Sensitivity Test (FAST)
Fast Estimation of a Covariance Matrix by Banding the Cholesky
Factor
A Fast Solver for Parameterized Linear Programming Problems and
Constrained L1 Minimization Approach to Sparse Precision Matrix
Estimation
Fast Hierarchical Clustering Routines for R and Python
Lasso and elastic-net penalized Cox's regression in high
dimensions models using the cocktail algorithm
Fast, Low Memory-Footprint Digests of R Objects
Fast Rcpp implementation of Gauss-Hermite quadrature
Efficiently Using Gaussian Processes with Rcpp and RcppEigen
Robust Algorithm for Principal Component Analysis
Hierarchical Independent Component Analysis: a Multi-Scale
Sparse Non-Orthogonal Data-Driven Basis
FastICA Algorithms to perform ICA and Projection Pursuit
Learn from training data then quickly fill in missing data
A Fast Multiple-Kernel Method Based on a Low-Rank Approximation
Fast Computation of Multivariate M-estimators
FastPCS Robust Fit of Multivariate Location and Scatter
Foundations and Applications of Statistics Using R
Fits the FastRCS Robust Multivariable Linear Regression Model
Fast Interactive Framework for Web Scripting Using R
Fast Utility Function for Time Parsing and Conversion
Two-Locus Family-Based Association Test with Polytomic Outcome
Kiener Distributions and Fat Tails in Finance
Functions and Datasets for "Forest Analytics with R"
Gene by Environment Interaction and Conditional Gene Tests for
Nuclear Families
Algorithm for searching the space of Gaussian directed acyclic
graphical models through moment fractional Bayes factors
FISH Based Normalization and Copy Number inference of SNP
microarray data
Association Football (Soccer) Ranking via Poisson Regression
Fast Algorithms to Bootstrap Receiver Operating Characteristics
Curves
Basics of Certificates and Structured Products Valuation
Rmetrics - Bivariate Dependence Structures with Copulae
A Method to Search for Differentially Expressed Genes and to
Detect Recurrent Chromosomal Copy Number Aberrations
Measuring functional diversity (FD) from multiple traits, and
other tools for functional ecology
Functional Data Analysis: K-Mean Alignment
Functional data analysis in a mixed model framework
Interval Testing Procedure for Functional Data
Functional Data Analysis and Utilities for Statistical Computing
Multivariate Dependence with FDG Copulas
Permutation-based FDR Point and Confidence Interval Estimation
False Discovery Rate Procedure Under Discrete Null Distributions
Estimation of (Local) False Discovery Rates and Higher Criticism
Frequency Distribution Tables, Histograms and Polygons
Local Inferential Feature Significance for Multivariate Kernel
Density Estimation
Creates a Model Matrix via Feature Hashing with a Formula
Interface
Feature Extraction for Discretely-Sampled Functional Data
Fechnerian Scaling of Discrete Object Sets
Functions to Automate Downloading Geospatial Data Available from
Several Federated Data Sources
Client package for the U.S. Federal Register API
Fixed Effects Nonlinear Maximum Likelihood Models
fExpressCertificates - Structured Products Valuation for
ExpressCertificates/Autocallables
Rmetrics - Extreme Financial Market Data
memory-efficient storage of large data on disk and fast access
functions
Basic Statistical Functions for Package 'ff'
Force field simulation for a set of points
Wrapper for FFTW3: Includes 1-D, Univariate and Multivariate,
and 2-D Transform
Rmetrics - Autoregressive Conditional Heteroskedastic Modelling
Fractional Gaussian Noise and power law decay time series model
fitting
Feature Grouping and Selection Over an Undirected Graph
Tests for Right and Interval-Censored Survival Data Based on the
Fleming-Harrington Class
Provide functions for forest inventory calculations
Classical, Reloaded and Adaptive FastICA Algorithms
File-Backed Matrix Class with Convenient Read and Write Access
Rmetrics - Economic and Financial Data Import
Financial Instrument Model Infrastructure for R
Classifies implicit trading activity from market quotes and
computes the probability of informed trading
Time Value of Money, time series analysis and Computational
Finance
Covariance Matrix Estimation and Regularization for Finance
Find all root(s) of the equation and Find root(s) of the
equation by dichotomy
Python tools to find an acceptable python binary
Functions to operate on binary fingerprint data
Computation of the probability of ruin within a finite time
horizon
Companion to Tsay (2005) Analysis of Financial Time Series
Fisheye and Hyperbolic-space-alike Interactive Visualization
Tools in R
Iterative FISH-based Calibration of Hi-C Data
Fits Poisson-sum-of-Gammas GLMs, Tweedie GLMs, and delta
log-normal mdoels
FitARMA: Fit ARMA or ARIMA using fast MLE algorithm
Help to Fit of a Parametric Distribution to Non-Censored or
Censored Data
FITS (Flexible Image Transport System) utilities
fitTetra is an R package for assigning tetraploid genotype
scores
Feature-Based Landscape Analysis of Continuous and Constraint
Optimization Problems
Fits Piecewise Constant Models with Data-Adaptive Knots
Implementation of optimal hierarchical clustering
Flexible Parametric Models for Censored and Truncated Data
Flexible Parametric Survival and Multi-State Models
Tools for Interacting with the Brazilian Flora Checklist
Cytometric Diversity Indices from 'FlowJo' Workspaces
Tools for characterizing flowering traits
Forecasts future values of a univariate time series
Streamlining Design and Deployment of Complex Workflows
Path algorithm for the general Fused Lasso Signal Approximator
Flux rate calculation from dynamic closed chamber measurements
Data sets from "Forecasting: methods and applications" by
Makridakis, Wheelwright & Hyndman (1998)
A Flexible Modelling Environment for Inverse Modelling,
Sensitivity, Identifiability, Monte Carlo Analysis
Functions for Medical Statistics Book with some Demographic Data
Variance estimation of FMT method (Fully Moderated t-statistic)
Rmetrics - Analysing and Modeling Multivariate Financial Return
Distributions
Fast Nearest Neighbor Search Algorithms and Applications
Nonlinear and Chaotic Time Series Modelling
visualisation and analysis of food web networks
Rmetrics - Pricing and Evaluating Basic Options
Foraminifera and Community Ecology Analyses
Provides Foreach Looping Construct for R
Forecasting Functions for Time Series and Linear Models
Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat, Weka,
dBase, ...
Statistical Methods in Forensic Genetics
Statistical tools for the interpretation of forensic DNA
mixtures
Visualizes Random Forests with Feature Contributions
Advanced Forest Plot Using 'grid' Graphics
Imputation of Missing Values Through a Forward Imputation
Algorithm
Functions from the 2nd Italian Forest Inventory (INFC)
Elementary arithemtic in formal series rings
Utilities for Formulas, Expressions, Calls and Other Objects
Palaeoecological and Palaeogeographical Analysis Tools
Bayesian 4 Parameter Item Response Model
Restricted MLE for Functional Principal Components Analysis
Reliable Comparison of Floating Point Numbers
Rmetrics - Portfolio Selection and Optimization
Computing the noncentrality parameter of the noncentral F
distribution
Data for "Forecasting: principles and practice"
Approximation of First-Passage-Time Densities for Diffusion
Processes
Fractionally differenced ARIMA aka ARFIMA(p,d,q) models
Fraction Proliferation via a Quiescent Growth Model
Fractal Time Series Modeling and Analysis
Estimation of Parameters p and q for Randomized Sierpinski
Carpet for [p-p-p-q]-Model
Generate fractal time series with non-normal returns
distribution
General Frailty Models: Shared, Joint and Nested Frailty Models
with Prediction
General Semiparametric Shared Frailty Model
Financial Risk Modelling and Portfolio Optimisation with R
Fuzzy Rule-Based Systems for Classification and Regression Tasks
Fast Regularized Canonical Correlation Analysis
Statistical algorithms used in common data mining course
Rmetrics - Regression Based Decision and Prediction
FREQ: Estimate population size from capture frequencies
Frequency Domain Analysis for Multivariate Time Series
Frequency Moving Average Plots (MAP) of Multinomial Data by a
Continuous Covariate
FeatuRE Selection Algorithms for Computer Aided Diagnosis
Fractional Factorial designs with 2-level factors
Catalogues of resolution IV 128 run 2-level fractional
factorials up to 33 factors that do have 5-letter words
Regression Analysis of Fractional Responses
Regression Analysis of Fractional Responses Under Unobserved
Heterogeneity
Regression Analysis of Panel Fractional Responses
The Generalized Hyperbolic Distribution, Related Distributions
and Their Applications in Finance
Functions for Simple Fisheries Stock Assessment Methods
Data to Support Fish Stock Assessment (FSA) Package
Automated Feature Selection from 'caret'
Import and Analysis of OMR Data from FormScanner
Wrapper Functions for FSL (FMRIB Software Library) from
Functional MRI of the Brain (FMRIB)
Social Relations Analyses With Roles ("Family SRM")
Programs for Analyzing Fourier Transform-Ion Cyclotron Resonance
Mass Spectrometry Data
Features and Strings for Nonparametric Regression
R interface to tslib (a time series library in c++)
Spectral Density Estimation and Comparison for Functional Time
Series
FUzzy GEnetic, a machine learning algorithm to construct
prediction model based on fuzzy logic
Full Text of 'Scholarly' Articles Across Many Data Sources
Chi-Square and Exact Tests for Non-Parametric Functional
Dependencies
Functional Profiling of Microarray Expression Data
A package for functional data clustering
An algorithm for gene and gene set network inference
Clustering in the Discriminative Functional Subspace
Fungible Coefficients and Monte Carlo Functions
Model-based clustering in group-specific functional subspaces
Simple Utility Providing Terminal Access to all R Functions
funreg (Functional Regression for Irregularly Timed Data)
Community Detection via Fused Principal Component Analysis
A Tiny Utility Providing Polymorphic Operations
Random matrix generation and manipulation
A framework for working in a functional programming paradigm in
R
Exact calculation of fuzzy decision rules for multiple testing
Fuzzy Rank Tests and Confidence Intervals
Fuzzy Stationary Probabilities from a Sequence of Observations
of an Unknown Markov Chain
Forward search for Mokken scale analysis
Selecting Variables in Regression Models
Fire Weather Index System and Fire Behaviour Prediction System
Calculations
A package performing Dynamic Bayesian Network inference
Grain-Size Statistics and Description of Sediment
A genetic algorithm approach to determine stratum boundaries and
sample sizes of each stratum in stratified sampling
Abbreviating Questionnaires (or Other Measures) Using Genetic
Algorithms
Framework for Generalized Subspace Pattern Mining
GAD: Analysis of variance from general principles
First Passage Time Simulation for Gaussian Diffusion Processes
Graphical Extension with Accuracy in Parameter Estimation
(GAIPE)
Genetic algorithms and C-steps based LTS (Least Trimmed Squares)
estimation
Fit the GamBin Model to Species Abundance Distributions
Generalized linear and additive models by likelihood based
boosting
Estimating multistate models using gamboost()
Functions and Data for a Course on Modern Regression and
Classification
Statistical Estimation of Game-Theoretic Models
Generalised Additive Models for Location Scale and Shape
Fitting an Interval Response Variable Using gamlss.family
Distributions
Distributions to be Used for GAMLSS Modelling
Fitting Mixture Distributions with GAMLSS
Fitting non linear parametric GAMLSS models
Generating and fitting truncated (gamlss.family) distributions
Generalized additive mixed models using mgcv and lme4
classic gamma regression: joint modeling of mean and shape
parameters
Generalized additive mixed model analysis via slice sampling
Fit Regularization Path for Generalized Additive Models
Gene Association Network-based Pathway Analysis
Genetic Algorithm optimization for real-based and
permutation-based problems
Functions for Drawing Gapped Cluster Heatmap with ggplot2
Authorize and Request Google Analytics Data
Genetic Algorithm (GA) for Variable Selection from
High-Dimensional Data
Genetic Data Manipulation (Quality Control, GRM and LD
Computations, PCA), Linear Mixed Models (AIREML Algorithm),
Association Testing
Difference measures for multivariate Gaussian probability
density functions
Collection of functions for Gaussian quadrature
Gazepath Transforms Eye-Tracking Data into Fixations and
Saccades
Generalize Lambda Distribution and Generalized Bootstrapping
Generalized Beta Distribution of the Second Kind: Properties,
Likelihood, Estimation
Utilities for processing Rd objects and files
Guided Correction Approach for Inherited bias (GCAI.bias)
Graph-based two-sample tests for categorical data
GPU/CPU Benchmarking in Debian-based systems
LASSO and (adaptive) Elastic-Net penalized least squares,
logistic regression, HHSVM and squared hinge loss SVM using a
fast GCD algorithm
Concord method for Graphical Model Selection
Datasets for the Book Graphical Data Analysis with R
Wrappers for the Geospatial Data Abstraction Library (GDAL)
Utilities
Various R Programming Tools for Data Manipulation
A toolbox for the analysis of categorical data in social
sciences, and especially Geometric Data Analysis
Download, slice, and normalize GDELT data
Generalized Diffusion Magnetic Resonance Imaging
Distances and Routes on Geographical Grids
Functions for Generalized Dissimilarity Modeling
Modified Variance Estimators for Generalized Estimating
Equations
Calculate Generalized Eigenvalues, the Generalized Schur
Decomposition and the Generalized Singular Value Decomposition
of a Matrix Pair with Lapack
Network Meta-Analysis Using Bayesian Methods
Package contains data which is used by GenABEL example and test
functions
Global ENvironmental ASsessment Information System (GENASIS)
computational tools
Clopper-Pearson Confidence Interval and Generalized Binomial
Distribution
Genetic Class Association Testing (GenCAT)
Predict Gender from Names Using Historical Data
Identification of Periodically Expressed Genes
Bayesian calculation of gene-specific FST from genomic SNP data
Detection of structure from multilocus genetic data
Profiling a gene list into GOslim or KEGG function pie
Genetic Epidemiology Design and Inference
Blinder-Oaxaca Decomposition for Generalized Linear Model
Generate Data Containing Fake Personally Identifiable
Information
Construct time delay gene regulatory network
Identify Favorable Subsets of Germplasm Collections
Collection of Genetic Data Analysis Tools
The Forward Imputation: A Sequential Distance-Based Approach for
Imputing Missing Data
Functions for generating and manipulating binned kernel density
estimates
Path algorithm for generalized lasso problems
Functions for the Bayesian Analysis of GWAS Data
Application of MOSS algorithm to genome-wide association study
(GWAS)
Plot Publication-Grade Gene and Genome Maps
Simulation of Discrete Random Variables with Given Correlation
Matrix and Marginal Distributions
Generalized PATHMOX Algorithm for PLS-PM, LS and LAD Regression
Generalized Ridge Trace Plots for Ridge Regression
Functions for Generalized Simulated Annealing
Spline Based Window Boundaries for Genomic Analyses
Draw and Annotate Maps, Especially Charts of the North Atlantic
Analysis of Geostatistical Data using Bayes and Empirical Bayes
Methods
Analysis and Modeling for Geostatistical Count Data
A geometrical Approach to Differential expression and gene-set
enrichment
Spatial Prediction for Function Value Data
Quantification of the effect of geographic versus environmental
isolation on genetic differentiation
Convert Data from and to 'geoJSON' or 'topoJSON'
Web-Processing of Large Gridded Datasets
Data for topographic and Geologic Mapping
Geometric Morphometric Analyses of 2D/3D Landmark Data
Interface to www.geonames.org web service
Geophysics, Continuum Mechanics, Mogi Models, Gravity
A Package for Generalised Linear Spatial Models
Robust Geostatistical Analysis of Spatial Data
Facilitate Generating of Distance Matrices Used in Package
'spacom' and Plotting Data on Maps
Geostatistical Analysis and Design of Optimal Spatial Sampling
Networks
Spatio-Temporal; Inverse Distance Weighting and Radial Basis
Functions with Distance-Based Regression
Geostatistical Modelling with Likelihood and Bayes
An R Plug-in for the Distributed Hydrological Model GEOtop
Interactive exploratory spatial data analysis
Estimation and Fit Diagnostics for Generalized Exponential
Random Graph Models
Get Meteorological Data for Hydrological Modeling
Parsing Command-Line Arguments and Variable Interpolation
GetR: Calculate Guttman error trees in R
General-to-Specific (GETS) Modelling and Indicator Saturation
Methods
Learning Optimization and Machine Learning for Statistics
GEV Conditional Density Estimation Network
ARMA-GARCH/APARCH Models with GEV and Stable Distributions
A visual, intuitive, easy to use software giving access to a new
type of information buried into your microarray data
Tools for Working with Hansen et al. Global Forest Change
Dataset
Create Dendrograms and Tree Diagrams Using 'ggplot2'
GGEBiplotGUI: Interactive GGE Biplots in R
Visualization Tools for Genealogical Data
Add Marginal Histograms to 'ggplot2', and More 'ggplot2'
Enhancements
Data Visualization Tools for Statistical Analysis Results
Group Lasso Penalized Learning Using A Unified BMD Algorithm
Tools for Analyzing MCMC Simulations from Bayesian Inference
Variations of Parallel Coordinate Plots for Categorical Data
An Implementation of the Grammar of Graphics
North Symbols and Scale Bars for Maps Created with 'ggplot2' or
'ggmap'
Explore complex data by embedding subplots within plots
Offers Various Swiss Maps as ggplot2 Objects
An Extension to 'ggplot2', for the Creation of Ternary Diagrams
Extra Themes, Scales and Geoms for `ggplot2`
A package on the generalized hyperbolic distribution and its
special cases
Gene Set Uncertainty in Enrichment Analysis
Gibbs Sampler for One-Way Mixed-Effects ANOVA and ANCOVA Models
Naive Gibbs Sampling with Metropolis Steps
Random Variate Generator for the GIG Distribution
Gillespie's Stochastic Simulation Algorithm (SSA)
Group Iterative Multiple Model Estimation
Generalized Iterative Proportional Fitting for Relational Models
The giRaph package for graph representation in R
Quantification of Pinned Microbial Cultures
Constructing panels of population pyramid plots based on lattice
Lasso Penalization in Large Scale Generalized Linear Array
Models
Generalized Linear Autoregressive Moving Average Models
Graphical lasso- estimation of Gaussian graphical models
General Linear Ballistic Accumulator Models
Calculate Textures from Grey-Level Co-Occurrence Matrices
(GLCMs)
Estimation and Use of the Generalised (Tukey) Lambda
Distribution
Fitting Single and Mixture of Generalised Lambda Distributions
(RS and FMKL) using Various Methods
An Asymmetry-Steepness Parameterization of the Generalized
Lambda Distribution
Fit GLD Regression Model and GLD Quantile Regression Model to
Empirical Data
Learning Interactions via Hierarchical Group-Lasso
Regularization
Fitting Generalized Linear Models Subject to Constraints
Distributed 'glm' for Big Data using 'ddR' API
Graph-Constrained Regularization for Sparse Generalized Linear
Models
Fit GLM with LEP-based penalized maximum likelihood
Generalized Linear Mixed Models via Monte Carlo Likelihood
Approximation
Generalised Linear Mixed Models and Spatial Models with WinBUGS,
BRugs, or OpenBUGS
Gauss-Seidel Generalized Linear Mixed Model solver
Variable Selection for Generalized Linear Mixed Models by
L1-Penalized Estimation
Generalized linear models with clustering
Lasso and Elastic-Net Regularized Generalized Linear Models
Fit a penalized constrained continuation ratio model for
predicting an ordinal response
L1 Regularization Path for Generalized Linear Models and Cox
Proportional Hazards Model
Fit a penalized continuation ratio model for predicting an
ordinal response
Model selection and multimodel inference made easy
Variable Selection Deviation Measures and Instability Tests for
High-Dimensional Generalized Linear Models
Testing the additional predictive value of high-dimensional data
Bi-Level Optimization of Metabolic Network Models
Global Gene-Set Analysis for Association Studies
Generate Functions to Get or Set Global Options
Objective functions for benchmarking the performance of global
optimization algorithms
Identify Global Objects in R Expressions
Fitting and Testing Generalized Logistic Distributions
Generalized Logrank Tests for Interval-censored Failure Time
Data
Generalized Least Squares with Measurement Error
Distance and Travel Time Between Two Points from Google Maps
Fast Estimation of Gaussian Mixture Copula Models
Graph Based Multiple Comparison Procedures
Generalized Minimum Distance of distributions
Predicting and Forecasting Time Series via GMDH-Type Neural
Network Algorithms
Descriptive Statistics, Transition Plots, and More
Generalized Method of Moments and Generalized Empirical
Likelihood
Multinomial Logit Models with Random Parameters
Various R Programming Tools for Model Fitting
Interface between GMT Map-Making Software and R
Computation of Generalized Nash Equilibria
Generalized Non-negative Matrix Factorization Based on Renyi
Divergence
Read Data from Files Readable by Gnumeric
Weighted and lexicographical goal programming and optimization
Model-diagnostics based on cumulative residuals
Testing Goodness-of-Fit with the Kernel Density Estimator
Tests of Fit for some Probability Distributions
Classical Goodness-of-Fit Tests for Univariate Distributions
Generalized Orthogonal GARCH (GO-GARCH) models
Easy Authentication with Google OAuth2 APIs
Working with Google Public Data Explorer DSPL Metadata Files
Visualization of Functional Analysis Data
functions to get public survey data in Japan
Gaussian Processes for Pareto Front Estimation and Optimization
Batch Effect Detection via Guided Principal Components Analysis
GPCSIV, Generalized Principal Component of Symbolic Interval
variables
Bootstrap goodness-of-fit test for the generalized Pareto
distribution
Apply Gaussian Process in Functional data analysis
Generalized partial linear models (GPLM)
Various R Programming Tools for Plotting Data
Generalized Partially Linear Tree-Based Regression Model
Analysing and plotting genotype-phenotype maps
A Minimalistic package to apply Gaussian Process in R
gpseq: Using the generalized Poisson distribution to model
sequence read counts from high throughput sequencing
experiments
Maximum Likelihood Estimation of Multiple Membership Mixed
Models Used in Value-Added Modeling
Graph-Constrained Estimation and Hypothesis Testing
Species distribution modelling using latent Gaussian random
fields
Graphical Representation and Modeling of Metagenomic Reads
Graphical Analysis of Variance Using ggplot2
Efficient selection of undirected graphical models for
high-dimensional datasets
A Multi-Platform GUI for Drawing Customizable Graphs in R
Graphical VAR for Experience Sampling Data
GraphPCA, Graphical tools of histogram PCA
Cluster Detection with Hypothesis Free Scan Statistic
Inference in Graphical Gaussian Models with Edge and Vertex
Symmetries
Fit a Grey-Zone Model with Survival Data
Miscellaneous Functions for "Grid" Graphics
Redraw Base Graphics Using grid Graphics
Uses Grieg-Smith method on 2 dimentional spatial data
Generalized Regression on Orthogonal Components
The package was developed to fit fit many growth curves obtained
under different conditions
Gromov-Hausdorff Type Distances for Labeled Metric Spaces
Regression Analysis of Grouped and Coarse Data
Regularized Multivariate Regression for Identifying Master
Predictors Using the GroupRemMap Penalty
A GUI-Based Program to Compute Probabilities Regarding Group
Sequential Designs
Multiple Testing Procedure for Grouped Hypotheses
Bayesian Semi and Nonparametric Growth Curve Models that
Additionally Include Multiple Membership Random Effects
Bayesian Non-Parametric Dependent Models for Time-Indexed
Functional Data
Simple Metrics to Summarize Growth Curves
Bayesian reconstruction of growth velocity
Fitting user specified models with Group Lasso penalty
Concave 1-norm and 2-norm group penalty in linear and logistic
regression
Regularization Paths for Regression Models with Grouped
Covariates
Penalized Regression Models with Overlapping Grouped Covariates
Tools for the Analysis of Gutenberg-Richter Distributions of
Earthquake Magnitudes
Gene Set Analysis using the Gamma Method
Two functions for Generalized SARIMA time series simulation
Robust Estimation in the Presence of Cellwise and Casewise
Contamination and Missing Data
Group Sequential Design in Equivalence Studies
Download Google Sheets Using Just the URL
Copula Density and 2-D Hazard Estimation using Smoothing Splines
Spatial and Spatio-Temporal Geostatistical Modelling, Prediction
and Simulation
Utilities for strings and function arguments
Estimation of the Generalized Symmetry Point, an Optimal
Cutpoint in Continuous Diagnostic Tests
Calculate efficiencies of group testing algorithms with
correlated responses
Game-Theoretically OPtimal (GTOP) Reconciliation Method
R Functions to Perform and Display Google Trends Queries
Maps, data and methods related to Guerry (1833) "Moral
Statistics of France"
Implementation of Sampling Formulas for the Unified Neutral
Model of Biodiversity and Biogeography, with or without Guild
Structure
Fast Calculation of the Likelihood of a Stochastic Survival
Model
Regularized Categorical Effects/Categorical Effect
Modifiers/Continuous/Smooth Effects in GLMs
Global Validation of Linear Models Assumptions
Genome-Wide Association/Interaction Analysis and Rare Variant
Analysis with Family Data
Exact Hardy-Weinburg testing for Genome Wide Association Studies
Controlling the genome-wide type I error rate in association
mapping experiments
Calculation of probabilities for inadequate and excessive
gestational weight gain
gWidgets API for building toolkit-independent, interactive GUIs
Rewrite of gWidgets API for Simplified GUI Construction
Implementation of gWidgets2 for the RGtk2 Package
Toolkit Implementation of gWidgets2 for tcltk
Toolkit implementation of gWidgets for RGtk2
Toolkit implementation of gWidgets for tcltk package
Geographically Weighted Logistic Elastic Net Regression
Generalized Waring Regression Model for Count Data
Fits geographically weighted regression models with diagnostic
tools
Genome-wide significance for whole genome sequencing studies
Maximum Likelihood Estimation for Gene-by-Measured Environment
Interaction Models
Software to perform Haar Fisz transforms
Estimation, Simulation and Visualization of Hierarchical
Archimedean Copulae (HAC)
Utilities for using R scripts in Hadoop streaming
Hierarchical Optimal Matching and Machine Learning Toolbox
Univariate non-parametric two-sample test based on empirical
Hankel transforms
Inference of Trait Associations with SNP Haplotypes and Other
Attributes using the EM Algorithm
Analyzing Case-Parent Triad and/or Case-Control Data with SNP
Haplotypes
Estimate Haplotype Relative Risks in Case-Control Data
Statistical Analysis of Haplotypes with Traits and Covariates
when Linkage Phase is Ambiguous
Haplotype Inference and Statistical Analysis of Genetic
Variation
Graphical Tests for Hardy-Weinberg Equilibrium
Harmonic Regression to One or more Time Series
Full feature implementation of hash/associated
arrays/dictionaries
A collection of non-cryptographic hash functions
Generate Short Unique YouTube-Like IDs (Hashes) from Integers
Hawkes process simulation and calibration toolkit
Damage functions from FEMA's HAZUS software for use in modeling
financial losses from natural disasters
Hierarchical Bayesian Regression for GLMs
Hill/Bliss Independence Model for Combination Vaccines
Hierarchical Bayesian Analysis of Recognition Memory
Hierarchical Bayesian Small Area Estimation
Hierarchical Bayesian Space-Time models for Gaussian space-time
data
Interval estimation for the parameters of linear models with
heteroskedasticity (Wild Bootstrap)
Change Point Estimation for Regression with Heteroscedastic Data
High Dimensional Supervised Classification and Clustering
Use known groups in high-dimensional data to derive scores for
plots
Hierarchical DECOmposition of Entropy for Categorical Map
Comparisons
Tests for High Dimensional Generalized Linear Models
Statistical Analysis Tools for High Dimension Molecular Data
(HDMD)
Nomograms for High-Dimensional Cox Models
Highest density regions and conditional density estimation
The Lasso for the Tweedie's Compound Poisson Model Using an
IRLS-BMD Algorithm
Health Effects of Air Pollution and Temperature (HEAT)
Heat exchange calculations during physical activity
Fit statistic for binary dependent variable models
Package for robust estimation using heavy-tailed distributions
Visualizing Hypothesis Tests in Multivariate Linear Models
Hierarchical Exponential-Family Random Graph Models
Marker-Based Estimation of Heritability Using Individual Plant
or Plot Data
Holonomic Gradient Method and Gradient Descent
Handy Functions for Working With HGNC Gene Symbols and
Affymetrix Probeset Identifiers
Statistical Analysis and Data Display: Heiberger and Holland
Heller-Heller-Gorfine Tests of Independence and Equality of
Distributions
The Hilbert-Huang Transform: Tools and Methods
Simulation from distributions supported by nested hyperplanes
Multiple Logistic Regression for 3D Chromatin Domain Border
Analysis
The All-Configurations, Maximum-Interaction F-Test for Hidden
Additivity
Inference on High Dimensional Max-Stable Distributions
Functions For Performing Hierarchical Analysis of Distance
Sampling Data
Hierarchical Multiplicative Partitioning of Complex Phenotypes
Estimation and Tests of Hierarchical F-Statistics
A graphical user interface for calculating power and sample size
for hierarchical data
Convex Hierarchical Testing of Interactions
Two-Sample Tests for Equality of Means in High Dimension
Outlier Detection Algorithms for High-Dimensional Data
Two-Sample Tests for High-Dimensional Mean Vectors
Determining and Evaluating High-Risk Zones
Simultaneous Critical Values for t-Tests in Very High Dimensions
Estimation of Heritability in Linear Mixed Models
H-Index Calculator using Data from a Web of Science (WoS)
Citation Report
Tools for hypothesis testing based on Hypergeometric
Intersection distributions
Hierarchical Semiparametric Regression of Test Statistics
Data Sets from the History of Statistics and Data Visualization
Construction of regular and irregular histograms with different
options for automatic choice of bins
"Hit and Run" and "Shake and Bake" for Sampling Uniformly from
Convex Shapes
HIV calibrated model life tables for countries with generalized
HIV epidemics
Conversion Tools for HK80 Geographical Coordinate System
Diagnostic Tools for Hierarchical (Multilevel) Linear Models
Hierarchical Latent Space Network Model (HLSM)
The H-measure and other scalar classification performance
metrics
Hidden Markov Model for Continuous Observations Processes
Hidden Markov models with discrete non-parametric observation
distributions
Analysing accelerometer data using hidden Markov models
Hypothesis Testing and Power Calculations for Comparing
Metagenomic Samples from HMP
Statistical Object Oriented Data Analysis of RDP-based Taxonomic
trees from Human Microbiome Data: Modeling, Visualization, and
Two-Group Comparison
Flux Estimation with Static Chamber Data
Half-Normal Plots with Simulation Envelopes
Information Retrieval for Genetic Datasets
Holiday and Half-Day Data, for Use with the 'TimeWarp' Package
Theoretical Model to Simulate Radical Polymerization
Homogeneity tests for Regional Frequency Analysis
Horn's (1965) Test to Determine the Number of Components/Factors
Hot Deck Imputation Methods for Missing Data
A lower bound for the number of correct rejections
Heligman Pollard mortality model parameter estimation using
Bayesian Melding with Incremental Mixture Importance Sampling
High Performance Cluster Models Based on Kiefer-Wolfowitz
Recursion
Miscellaneous convenience functions and dataset
Regularization Paths for Huber Loss Regression and Quantile
Regression Penalized by Lasso or Elastic-Net
A Handbook of Statistical Analyses Using R (1st Edition)
A Handbook of Statistical Analyses Using R (2nd Edition)
A Handbook of Statistical Analyses Using R (3rd Edition)
Manage, Analyse and Simulate Hyperspectral Data
hierarchical Bayesian species distribution models
Canonical Correlation Analysis based on Kernel Independence
Measures
Phasing, Pedigree Reconstruction, Sire Imputation and
Recombination Events Identification of Half-sib Families Using
SNP Data
Meta-Analysis of Diagnostic Test Accuracy when Reference Test is
Imperfect
Facilitates Automated HTML Report Creation
Clustering high throughput sequencing (HTS) data
compute HUM value and visualize ROC curves
Models and Tests for Departure from Hardy-Weinberg Equilibrium
and Independence Between Loci
Bayesian investigation of Hardy-Weinberg Equilibrium via
estimation and testing
Objective Bayesian Testing for the Hardy-Weinberg Equilibrium
Problem
Testing Hardy-Weinberg Equilibrium for Multiallelic Genes
HTML Writer - Outputs R objects in HTML format
hwriterPlus: Extending the hwriter Package
Build, Deploy and Evaluate Hybrid Ensembles
Implementation of the Hybrid Monte Carlo and Multipoint Hybrid
Monte Carlo sampling techniques
Hybrid Bayesian Networks Using R and JAGS
Tools and models for hydrological applications
Groundwater data presentation and interpretation
Goodness-of-fit functions for comparison of simulated and
observed hydrological time series
R codes for estimating water retention and infiltration model
parameters using experimental data
Particle Swarm Optimisation, with focus on Environmental Models
Hydrologic Indices for Daily Time Series Data
Time series management, analysis and interpolation for
hydrological modelling
A generalization of the Dirichlet distribution
Work with Hyperspectral Data, i.e. Spectra + Meta Information
(Spatial, Time, Concentration, ...)
High-Dimensional Kernel Density Estimation and Geometry
Operations
Macroevolutionary phylogentic analysis of species trees and gene
trees
Confidence Intervals and Tests of Statistical Hypotheses
Tools for Modeling Rate-Dependent Hysteretic Processes and
Ellipses
Synthesizing Information Across Collaborating Research
The Irvine Robustness Assessment for Qualitative Comparative
Analysis
Model Selection for Index of Asymmetry Distribution
Functions to use with data from the Implicit Association Test
Implicit Association Test Scores Using Robust Statistics
Integrative Bayesian Analysis of Transcriptomic and CGH Data
Functions for the Analysis of IBD Haplo Output
Convert Between Different IBD-State Labelling Schemes
Regression Methods for IBD Linkage With Covariates
Simulation of Chromosomal Regions Shared by Family Members
Irregular-lattice based ensemble empirical mode decomposition
Iboot: iterated bootstrap tests and confidence sets
R API to Interactive Brokers Trader Workstation
An Interface to WinBUGS/OpenBUGS/JAGS by gWidgets
A Copy Number and Expression-Based Classifier for Breast Tumours
Inequality and Concentration Indices and Curves
Standardized high-throughput evaluation of cell-based compound
screens
Independent Components Analysis (ICA) based artifact correction
Bayesian Semiparametric Models for Interval-Censored Data
Facilitating Estimation of the Intraclass Correlation
Coefficient
Multilevel model intraclass correlation for slope heterogeneity
Calculation of Sample Size and Power for ICC
Tools for Working with ICD-9 Codes, and Finding Comorbidities
Individual Conditional Expectation Plot Toolbox
Incremental Cost-Effectiveness (ICE) Statistical Inference from
Two Unbiased Samples
Regression Models for Interval Censored Data
Study Design and Data Analysis in the Presence of Error-Prone
Diagnostic Tests and Self-Reported Outcomes
Estimation of number of clusters and identification of atypical
units
Inequality constrained inference in linear normal situations
Integrative clustering of multiple genomic data types
Tools for Exploring Multivariate Data via ICS/ICA
A package for semiparametric regression analysis of
interval-censored data
Jacquard Condensed Coefficients of Identity
Utilities of Institute of Data Analyses and Process Design
(www.idp.zhaw.ch)
Imprecise Dirichlet Process for Survival Analysis
Identify Turtles by their Plastral Biometries
R Analysis package for iDynoMiCS Simulation Results
Functions to set and get the IEEE rounding mode
An integrative factor analysis model for drug-pathway
association inference
Toolkit for Item Factor Analysis with OpenMx
Italian Fiscal Code ('Codice Fiscale') Utilities
Read Binary Files Saved by 'Igor Pro' (Including 'Neuromatic'
Data)
A Collection of Network Data Sets for the 'igraph' Package
Convert iGraph graps to SoNIA .son files
Instagram 2014 Vancouver Top Colour Dataset
Consistency, Homogeneity and Summary Statistics of
Climatological Data
Improved Laplace Approximation for Integrals of Unimodal
Functions
Lee-Carter Mortality Models using Iterative Fitting Algorithms
Item Response Models for Multiple Ratings
Identifying Causal Effect for Multi-Component Intervention Using
Instrumental Variable Method
Imprecise Inferential Framework for Poisson Sampling Model
Minimum distance estimation in an imprecise probability model
A collection of methods for left-censored missing data
imputation
The Multifactor Dimensionality Reduction (MDR) Analysis for
Incomplete Data
Impute Missing Values in a Predictive Context
Imputing the Last Largest Censored Observation(s) Under Weighted
Least Squares
Mixture models for longitudinal count data
Analysing Inbreeding Based on Genetic Markers
Nonparametric tests of independence between random vectors
Relationship Between Species and Groups of Sites
Integrate INEGI’s (Mexican Stats Office) API with R
Measuring Inequality, Concentration, and Poverty
Interpolation and Extrapolation for Species Diversity
Infine-dimensional model (IDM) to analyse phenotypic variation
in growth trajectories
Functions to extract inferential values of a fitted model object
Inference about the standardized mortality ratio when evaluating
the effect of a screening program on survival
Methods for Causal Inference with Interference
Tools for Detecting Influential Data in Mixed Effects Models
Software Tools to Quantify Structural Importance of Nodes in a
Network
Case Influence in Structural Equation Models
Information Decomposition of Two-phase Experiments
Data Exploration with Information Theory (Weight-of-Evidence and
Information Value)
Performance Analysis and Companion Functions for Accuracy
Improvement for Binary Classification Problems
Sequence Statistics for Relational Event Models
Functions to Inline C, C++, Fortran Function Calls from R
Convert inline comments to documentation
insideRODE includes buildin functions with deSolve solver and
C/FORTRAN interfaces to nlme, together with compiled codes
Probabilistic Verbal Autopsy Coding with InSilicoVA
Check, Install and Load CRAN & USGS GRAN Packages
Using R to Install Stuff (Such As: R, Rtools, RStudio, Git, and
More!)
A Collection of Insurance Datasets Useful in Risk Classification
in Non-life Insurance
procedures for automated interpolation - methods only to be used
interactively, not included in intamap package
Iterated Convex Minorant Algorithm for interval censored event
data
Joint Modelling of the Gene-Expression and Bioassay Data, Taking
Care of the Effect Due to a Fingerprint Feature
Tests for marginal interactions in a 2 class response model
Calculates Critical Test Statistics to Control False Discovery
and Familywise Error Rates in Marginal Effects Plots
interactive network analysis and visualization
Compute Chi-Square Measures with Corrections
Exact Confidence Intervals in the Presence of Interference
Coercion Routines for Network Data Objects
Plot the Effects of Variables in Interaction Terms
Hourly interpolation of multiple temperature daily series
Binary Classifier and Regression Model Interpretation Functions
Simulation of Inter-Related Genomic Datasets
Weighted Logrank Tests and NPMLE for interval censored data
Tools for Working with Points and Intervals
Interventional Inference for Dynamic Bayesian Networks
Numerical Integration for Integrated Likelihood
linear programming solver by the interior point method and
graphically (two dimensions)
methods for analyzing introgression between divergent lineages
Threshold regression that fits the (randomized drift) inverse
Gaussian distribution to survival data
A Unified Framework for Input-Output Operations in R
Calling Copy Number Alterations in Amplicon Sequencing Data
Calculate the Number of Required Flows for Semiconductor
Sequencing
Functions for smoothing with infinite order flat-top kernels
Tools for subgroup analyses with multiple trial data using
aggregate statistics
Spatial Interpolation by Inverse Path Distance Weighting
Fast implementation of the iterative proportional fitting
procedure in C
Builds and analyses Integral Projection Models (IPMs)
Interfaces to Phylogenetic Software in R
Introduction to Probability and Statistics Using R
Manipulate, Validate and Resolve IP Addresses
Regression methods for interval-valued variables
Classes and Methods for Seismic Data Analysis
Fast Truncated SVD, PCA and Symmetric Eigendecomposition for
Large Dense and Sparse Matrices
Various Coefficients of Interrater Reliability and Agreement
Simple interface to the estimation and plotting of IRT models
Utilities and Probability Distributions Related to
Multidimensional Person Item Response Models
Estimation of Tree-Based Item Response Models
Iterative Selection of Blocks of Features - ISBF
Provides datasets for Introduction to Statistical Data Analysis
for the Life Sciences
Fitting Ising models using the eLasso method
Sampling Methods and Distribution Functions for the Ising Model
Data for An Introduction to Statistical Learning with
Applications in R
An Introduction to Statistical Modeling of Extreme Values
Functions to Perform Isotonic Regression
Confidence intervals for current status data based on
transformations and bootstrap
Order-Restricted Inference for Microarray Experiments
Calculation of isotopic pattern for a given molecular formula
Active Set and Generalized PAVA for Isotone Optimization
Penalized Isotonic Regression in one and two dimensions
Week of the year and weekday according to ISO 8601
Approximate probability densities by iterated Laplace
Approximations
Efficient Iterator for Permutations and Combinations
itertools2: Functions creating iterators for efficient looping
Tools for classification and regression trees, with an emphasis
on interpretability
Interpreting Time Series and Autocorrelated Data Using GAMMs
Time series analysis package for students
Incorporating Uncertainties in Propensity Scores
Bayesian Instrumental Variable Estimation and Model
Determination via Conditional Bayes Factors
Instrumental fixed effect panel data model
Uses heteroscedasticity to estimate mismeasured and endogenous
regressor models
Statistical Inference and Sensitivity Analysis for Instrumental
Variables Model
Improved methods for causal inference and missing data problems
Simulation-Based Maximum Likelihood Parameter Estimation
Jackknife Estimates of Kaplan-Meier Estimators or Integrals
Non-parametric Jackstraw for Principal Component Analysis
Blind Source Separation Methods Based on Joint Diagonalization
and Some BSS Performance Criteria
A Wrapper Around 'rjags' to Streamline 'JAGS' Analyses
Joint Analysis of Genotype and Group-Specific Variability Using
a Novel Score Test Approach to Map Expression Quantitative
Trait Loci (eQTL)
Analysis Tools for the 'JAMES' Framework
R modules for JASPAR databases: a collection of transcription
factor DNA-binding preferences, modeled as matrices
Misc Small Tools and Helper Functions for Other Code of J.
Buttlar
One-to-One Gene-Probeset Mapping for Affymetrix Human
Microarrays
Joint Generalized Estimating Equation Solver
Performs the Joint Graphical Lasso for sparse inverse covariance
estimation on multiple classes
Chinese Text Segmentation Data for jiebaR Package
Joint Modeling of Longitudinal and Survival Data
Joint Modeling of Longitudinal and Time-to-Event Data under a
Bayesian Approach
Joint Modeling of Longitudinal and Survival Data - Power
Calculation
Tools for Time Series Analysis Based on Symbolic Aggregate
Dicretization
Joint modelling of repeated measurements and time-to-event data
Penalized Likelihood Estimation and Prediction under the Joint
Cox Models Between Tumour Progression and Death for
Meta-Analysis
Joint Approximate Diagonalization of a set of square matrices
Risk estimation using the joint probability method
Joint Modelling of Mixed Correlated Binary and Continuous
Responses : A Latent Variable Approach
Multilevel Joint Modelling Multiple Imputation
Functions for some Unit-Level Small Area Estimators and their
Variances
Covariance and Inverse Covariance Matrix Estimation Using Joint
Penalty
Methods for population-based cancer survival analysis
Joint Random Forest (JRF) for the Simultaneous Estimation of
Multiple Related Networks
Basic Finance; NPV/IRR/Annuities/Bond-Pricing; Black Scholes
A Robust, High Performance JSON Parser and Generator for R
Create a Simple Web API for your R Functions
Package to identify and sort genes overexpressed
Non-Additive Measure and Integral Manipulation Functions
Sample Size Estimation Functions for Studies of Interobserver
Agreement
Calculates "vectorial Kappa", an index of congruence between
patchy mosaics
K-Adaptive Partitioning for Survival data
Kinetic analysis and visualization of DCE-MRI data
Kernel Smoothing for Bivariate Copula Densities
Nonparametric method for identifying discordant phylogenetic
trees
Kernel Estimator and Bandwidth Selection for Density and Its
Derivatives
Calculate Solutions to the Kelvin Differential Equation using
Bessel Functions
Kendall rank correlation and Mann-Kendall trend test
Nonparametric kernel estimation of the distribution function.
Bandwidth selection and estimation of related functions
Expectile Regression in Reproducing Kernel Hilbert Space
Distance Weighted Discrimination (DWD) and Kernel Methods
Kernel Factory: An Ensemble of Kernel Machines
Kernel Density Estimation for Heaped and Rounded Data
Functions for Kernel Smoothing Supporting Wand & Jones (1995)
Kalman Filter and Smoother for Exponential Family State Space
Models
Integrated Code Chunk Anchoring and Referencing for R Markdown
Documents
Kalman Filter, Smoother and Disturbance Smoother
Framework for the Analysis of Kinetic Visual Field Data
Routines for Fitting Kinetic Models to Chemical Degradation Data
a Keep It Simple Species Migration Model
Spectral response of water wells to harmonic strain and pressure
Linear equations with Kronecker structure
Kaplan-Meier Estimator with Constraints for Right Censored Data
-- a Recursive Computational Algorithm
Confidence intervals for the Kaplan-Meier estimator
Kaplan-Meier Simultaneous Confidence Band for the Survivor
Function
Distributed k-Means for Big Data using 'ddR' API
Kaplan-Meier multiple imputation for the analysis of cumulative
incidence functions in the competing risks setting
Clustering longitudinal data using the likelihood as a metric of
distance
K-Means with Simultaneous Outlier Detection
Data sets from Klein and Moeschberger (1997), Survival Analysis
A General-Purpose Package for Dynamic Report Generation in R
Nearest-neighbor Classification with Categorical Variables
Knockoff Filter for Controlling the False Discovery Rate
Tools for the provision of scientific fisheries management
advice
Knowledge discovery by accuracy maximization
A Genetic Algorithm for Fixed-Size Subset Selection
Functional Summary and Meta-Analysis of Gene Expression Data
Supervised and Unsupervised Self-Organising Maps
An Improved Evaluation of Kolmogorov's Distribution
Koul's Minimum Distance Estimation in Linear Regression and
Autoregression Model
Method for Clustering Partially Observed Data
Kriging-based Inversion for Deterministic and Noisy Computer
Experiments
Kernel-based Regularized Least squares (KRLS)
K-Sample Rank Tests and their Combinations
Identify Kinase Substrate Relationships Using Dynamic Data
Configurable function for solving families of nonlinear
equations
k-Top Scoring Pairs for Microarray Classification
Datasets and functions from the (now non-existing) Faculty of
Life Sciences, University of Copenhagen
Calculation of Groundwater Mounding Beneath an Infiltration
Basin
Utility Functions by Youyi, Krisz and Others
Kolmogorov-Zurbenko Fourier Transform and Applications
Kolmogorov-Zurbenko Spatial Smoothing and Applications
l2boost - Friedman's boosting algorithm for regularized linear
regression
Ordination and Multivariate Analysis for Ecology
Relabelling MCMC Outputs of Mixture Models
Convert labels or tables to a dendrogram
Libreria Del Laboratorio Di Statistica Con R
Estimation of indicators on social exclusion and poverty
Duncan test, Tukey test and Scott-Knott test
Local Approximate Gaussian Process Regression
Tools for the Analysis of Ecosystem Metabolism
Lake Temperatures Collected by Situ and Satellite Methods from
1985-2009
Collection of Internal Consistency Reliability Coefficients
Modeling Data with Functional Programming
Tools for Modeling Data with Functional Programming
Analyze and Gaussianize Heavy-Tailed, Skewed Data
Liquid Association for Network Dynamics Detection
Landmark Estimation of Survival and Treatment Effect
Landmark Prediction of a Survival Outcome
Radiometric and topographic correction of satellite imagery
Landsat 8 Imagery Rescaled to Reflectance, Radiance and/or
Temperature
Langevin Analysis in One and Two Dimensions
Data sets and functions with "Analyzing Linguistic Data: A
practical introduction to statistics"
Laplace Deconvolution with Noisy Discrete Non-Equally Spaced
Observations on a Finite Time Interval
Local Average Response Functions for Instrumental Variable
Estimation of Treatment Effects
Least Angle Regression, Lasso and Forward Stagewise
Likelihood Analysis of Speciation/Extinction Rates from
Phylogenies
High-Dimensional Inference with the Penalized Score Test
L1 regularized regression (Lasso) solver using the Cyclic
Coordinate Descent algorithm aka Lasso Shooting
Draws diagrams useful for checking latent scales
Latent Position and Cluster Models for Statistical Networks
Functions to Calculate Common Laterality Statistics in
Primatology
Density estimation and nonparametric regression on irregular
regions
Extra Graphical Utilities Based on Lattice
Multiresolution Kriging Based on Markov Random Fields
Complex Survey Structural Equation Modeling (SEM)
Client for 'Turfjs' for 'Geospatial' Analysis
Tools for Biostatistics, Public Policy, and Law
Latent Budget Analysis for Compositional Data
Limited Memory BFGS Minimizer with Bounds on Parameters
Length-biased correction to survival curve estimation
Latent Class Analysis (LCA) with familial dependence in extended
pedigrees
Data sets for package ``LMERConvenienceFunctions''
Extended Mixed Models Using Latent Classes and Latent Processes
Local Correlation, Spatial Inequalities and Other Tools
Collapsed Gibbs Sampling Methods for Topic Models
Multivariate Condition Matching by Backwards Elimination Using
Linear Discriminant Analysis
Interactive Visualization of Topic Models
Lan-DeMets Method for Group Sequential Boundaries
Linkage disequilibrium corrected by the structure and the
relatedness
Graphical display of pairwise linkage disequilibria between SNPs
LD LASSO Regression for SNP Association Study
Finding Locally D-optimal optimal designs for some nonlinear and
generalized linear models
Methods for likelihood-based dimension reduction in regression
Exact tests for Linkage Disequilibrium and Hardy-Weinberg
Equilibrium
Analysis and Visualization of Plant Leaf Angle Distributions
Rapid Digital Image Analysis of Leaf Area
Create Interactive Web Maps with the JavaScript 'Leaflet'
Library
Interactive Web-Maps Based on the Leaflet JavaScript Library
Likelihood Estimation of Admixture in Parents From Offspring
Genotypes
latent effect adjustment after primary projection
Functions for Learning Bayesian Inference
Data and functions to accompany the book "Learning R"
An Interactive Environment for Learning Statistics
Phylogenetic and Functional Analyses for Ecology
Functions and datasets to accompany the book "Le logiciel R:
Maitriser le langage, Effectuer des analyses statistiques"
(french)
Tools for Data Handling and Analysis in Macroecology
Estimation of the Local False Discovery Rates by Type II Maximum
Likelihood Estimation
Calculation of Low Flow Statistics for Daily Stream Flow Data
Tools for linear grouping analysis (LGA)
Simulation and Estimation of Log-GARCH Models
Tests for Genetic Association/Gene-Environment Interaction in
Longitudinal Gene-Environment-Wide Interaction Studies
Set-Based Tests for Genetic Association in Longitudinal Studies
A set of methods for longitudinal data objects
Computational Routines for Proton and Ion Radiotherapy
Linear Predictive Models Based on the LIBLINEAR C/C++ Library
Linearized Bregman Algorithms for Generalized Linear Models
Light Cone Reconstruction of States - Predictive State
Estimation From Spatio-Temporal Data
Sensitivity Analysis for Case Weight in Normal Linear Regression
Financial and Actuarial Mathematics for Life Contingencies
Two-Parameter HMD Model Life Table System
Compute the Top Decile Lift and Plot the Lift Curve
Tools for Communication with Lightning-Viz Server
Methods for Maximum Likelihood Estimation
Functions to Analyze and Visualize Likert Type Items
Linear Inverse Model examples and solution methods
Jitter/CI Plot with Ordered Points Below the Limit of Detection
Binning and Plotting of Linearly Referenced Data
Static Univariate Frequentist and Bayesian Linear Calibration
Functions to Explore L-Systems (Lindenmayer Systems)
Minimum Distance Estimation in Linear Regression Model
Tools for Generating, Visualizing, and Analysing Link
Communities in Networks
Linkage information based genotype imputation method
Interactive Interpretation of Linear Regression Models
Fitting lasso penalised additive isotone models
Statistical Methods for the Item Count Technique and List
Experiment
Tests of independence based on the Longest Increasing
Subsequence
Leveraging Learning to Automatically Manage Algorithms
Local Log-linear Models for Capture-Recapture
Add Standardized Regression Coefficients to lm-Objects
Linear Mixed-Effects Models using 'Eigen' and S4
Linear Mixed-Effects Models with Censored Responses
Compute the Personalized Activity Index Based on a Negative
Binomial Model
Compute the Personalized Activity Index Based on a Flexible
Bayesian Negative Binomial Model
Linear Mixed Effects Models with Non-Stationary Stochastic
Processes
Model Selection and Post-hoc Analysis for (G)LMER Models
Add smoothing spline modelling capability to nlme
Latent Markov Models with and without Covariates
Testing for a subset of variance components in linear mixed
models
Functions for estimation and inference of selection in
age-structured populations
Linear Mixed Effect Model Splines for Modelling and Analysis of
Time Course Data
L-moments, Censored L-moments, Trimmed L-moments, L-comoments,
and Many Distributions
Regional frequency analysis using L-moments
Predicting Modulator and Functional/Survival Analysis
Functions for Performing Local FDR Estimation when Null and
Alternative are Poisson
Local Regression, Likelihood and Density Estimation
Test of stationarity and localized autocovariance
Datasets for "Statistics: UnLocking the Power of Data"
Genome-Wide Association Analysis of a Biomarker Accounting for
Limit of Detection
A simple logging system for R, based on log4j
Relative Risk Regression Using the Log-Binomial Model
Log-concave Density Estimation in Arbitrary Dimensions
Maximum likelihood estimation of a log-concave density based on
censored data
Estimate a Log-Concave Probability Density from iid Observations
Compute MLE of Log-Concave Density on R with Fixed Mode, and
Perform Inference for the Mode
Estimate a Log-Concave Probability Mass Function from Discrete
i.i.d. Observations
CoxPH Model with Log Concave Baseline Distribution
LOGICOIL: multi-state prediction of coiled-coil oligomeric
state
Firth's bias reduced logistic regression
Diagnostic Tests for Models with a Binomial Response
Functions, Data and Code for Binary and Binomial Data
Fitting l2-regularized logit choice models via generalized
gradient descent
Infer network based on binary arrays using regularized logistic
regression
Functions for the logitnormal distribution
Double log normal distribution functions
Log-Multiplicative Models, Including Association Models
Logrank Test for Aggregated Survival Data
Kernel Regression Smoothing with Local or Global Plug-in
Bandwidth
Package for Plotting Categorical Longitudinal and Time-Series
Data
Model-Based Clustering and Classification for Longitudinal Data
Statistics for Long-Memory Processes (Jan Beran) -- Data and
Functions
Sample size calculations for longitudinal data
Expand Short URLs Using the 'LongURL' API
Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian
Models
loop decomposition of weighted directed graphs for life cycle
analysis, providing flexbile network plotting methods, and
analyzing food chain properties in ecology
A Collection of Tools to Conduct Levins' Loop Analysis
Uses an Archive to Amend Previous Stages of a Pipe using Current
Output
Logistic Ordinal Regression Differential Item Functioning using
IRT
LOw Rand and sparsE Covariance matrix estimation
Missing morphometric data simulation and estimation
Runs Lotka's Law which is One of the Special Applications of
Zipf's Law
Lassoed principal components for testing significance of
features
Local polynomial estimators of intensity function or its
derivatives
Linear Parametric Models Applied to Hydrological Series
Local Polynomial Estimator in Measurement Error Models
Merging linkage maps by linear programming
Modeler for linear programs (LP) and mixed integer linear
programs (MILP)
Linear Predictor Score, for Binary Inference from Multiple
Continuous Variables
Interface to 'Lp_solve' v. 5.5 to Solve Linear/Integer Programs
R Interface for lp_solve version 5.5.2.0
Learned Pattern Similarity and Representation for Time Series
LP Nonparametric Approach to Non-Gaussian Non-Linear Time Series
Modelling
Dose Response Signal Detection under Model Uncertainty
Different types of estimators to deal with multicollinearity
Locally stationary two-dimensional wavelet process estimation
scheme
A Multiscale Test of Spatial Stationarity for LS2W processes
Applied Latent Semantic Analysis (LSA) Functions
Least-Squares Bilinear Clustering for Three-Way Data
Local Statistical Complexity - Automatic Pattern Discovery in
Spatio-Temporal Data
Solving Least Squares Problems under Equality/Inequality
Constraints
American options pricing with Least Squares Monte Carlo method
Companion to "Learning Statistics with R"
the accelerated failure time model to right censored data based
on least-squares principle
Simulation-Based Bayesian Inference for Latent Traits of Item
Response Models
Longitudinal Targeted Maximum Likelihood Estimation
LTPD and AOQL Plans for Acceptance Sampling Inspection by
Variables
Ridge and Liu Estimates based on LTS (Least Trimmed Squares)
Method
Likelihood inference from case-control data Under Covariate
Assumptions (LUCA)
Printing Floating Point Numbers in a Human-Friendly Format
Comprehensive Luminescence Dating Data Analysis
Lunar Phase & Distance, Seasons and Other Environmental Factors
A Latent Variable Model for Mixed Continuous and Ordinal
Responses
An Automated Approach to Evaluating Dose-Effect Experiments
Following Litchfield and Wilcoxon (1949)
Accurate Estimation of Clonal Coincidences and Abundances from
Biological Replicates
Binary and Multiclass Boosting Algorithms
Class prediction based on microarray data and clinical
parameters
A Flexible Microarray Data Simulation Model
Data Class and Tools for Handling Spatial-Temporal Data
Pretty Scientific Plotting with Minor-Tick and log Minor-Tick
Support
A Graphical User Interface for Microarray Data Analysis
Creates an empty package framework for the LCFD format
Quantitative Analysis of Mass Spectrometry Data
A wrapper around the Java machine learning tool MALLET
Calculation of Minimum Averaged Mean Squared Error (MAMSE)
weights
Matrix Analysis and Normalization by Concordant Information
Enhancement
Access and Process Data and Documents of the Manifesto Project
Manly Mixture Modeling and Model-Based Clustering
Multiple testing procedures of Cox (2011) and Wang and Cox
(2007)
Mapping Markers to the Nearest Genomic Feature
Mapping Smoothed Effect Estimates from Individual-Level Data
Geographic Display of Survey Data Statistics
Tools for Reading and Handling Spatial Objects
Mapping, pruning, and graphing tree models
Multivariate Autoregressive Modeling for Analysis of Community
Time-Series Data
Multiplicative AR(1) with Seasonal Processes
Estimation of Meiotic Recombination Rates Using Marey Maps
Approximate marginal inference for regression-scale models
Mark-Recapture Analysis for Survival and Abundance Estimation
Enhanced Analytics for Marketers Navigating the Ocean of Web
Data
Markov Chain Models for Phylogenetic Trees
Easy Handling Discrete Time Markov Chains
Statistical Significance of the Markowitz Portfolio
Multivariate Analysis Based on Relative Likelihoods
Import, Plot and Analyze Bathymetric and Topographic Data
An algorithm for least-squares curve fitting
Mandallaz' Model-Assisted Small Area Estimators
Support Functions and Datasets for Venables and Ripley's MASS
Body Mass Estimation Equations for Vertebrates
ML and MCMC Methods for Pedigree Reconstruction and Analysis
Model-Averaged Tail Area Wald (MATA-Wald) Confidence Interval
Multivariate and Propensity Score Matching with Balance
Optimization
Computation of the Balance - Sample Size Frontier in Matching
Methods for Causal Inference
MatchIt: Nonparametric Preprocessing for Parametric Casual
Inference
Combining Matching and Linear Regression for Causal Inference
Measuring Association and Testing Independence Efficiently
An Interface for MATLAB using System Calls
Matrix Functions for Teaching and Learning Linear Algebra and
Multivariate Statistics
Sparse and Dense Matrix Classes and Methods
Collection of functions for matrix calculations
Matrix eQTL: Ultra fast eQTL analysis via large matrix
operations
Modelling with Sparse And Dense Matrices
Matrix-Based Partial Least Squares Estimation
Methods that Apply to Rows and Columns of Matrices (and to
Vectors)
Modern Calibration Functions for the Modern Analog Technique
(MAT)
Three methods to identify differentially expressed gene sets,
ordinary least square test, Multivariate Analysis Of Variance
test with n contrasts and Random forest
Exact max-type Cochran-Armitage trend test(CATT)
Low-memory Multinomial Logistic Regression with Support for Text
Classification
Selecting the Best Set of Relevant Environmental Variables along
with the Optimal Regularization Multiplier for Maxent Niche
Modeling
Maximum Likelihood Estimation and Related Tools
Model species distributions by estimating the probability of
occurrence using presence-only data
Mazama Science Spatial Data Download and Utility Functions
Maxwell Boltzmann Bose Einstein Fermi Dirac Distribution and
Destruction Rate Modelling
Moment-Based Estimation for Hierarchical Models
Nucleic Acid Melting Curve Analysis on Microbead Surfaces with R
Model Based Multifactor Dimensionality Reduction
Access Data from the Massachusetts Bay Transit Authority (MBTA)
Web API
Multivariate Bayesian Variable Selection Method Exploiting
Dependence among Outcomes
Tools for Two-Dimensional Monte-Carlo Simulations
Translates information from netcdf files with MC2 output into
inter-PVT transitions
Matrix Computation Based Identification Of Prime Implicants
Functions to Support the Multicriteria Decision Aiding Process
Maximum Likelihood Estimation for Generalized Linear Mixed
Models
Machine coded genetic algorithms for real-valued optimization
problems
Warnes and Raftery's MCGibbsit MCMC diagnostic
Global optimization using Monte Carlo and Quasi Monte Carlo
simulation
Monte Carlo hypothesis tests with Sequential Stopping
IRT models for multiple choice items (mcIRT)
Normal Mixture Modelling for Model-Based Clustering,
Classification, and Density Estimation
Posterior Distribution of Extreme Value Models in R
Bayesian analysis of multivariate counts data
Multiple Criteria Optimization Algorithms and Related Functions
Conjoint Analysis through Averaging of Multiple Analyses
Multiple comparisons using normal approximation
A Monte Carlo permutation method for multiple test correlation
Design and Analysis of Dose-Finding Studies (see also
DoseFinding package)
Functions for Monte Carlo Methods with R
Mixture and Flexible Discriminant Analysis
Multivariate Data Analysis for Chemometrics
Measuring the Difference Between Two Empirical Distributions
Multi-Dimensional MA Normalization for Plate Effect
Detect gene-gene interactions using multifactor dimensionality
reduction
Improved Score Tests for Generalized Linear Models
Functions for Analysis of Data with General Recognition Theory
Detection and Control of Tree-Ring Widths on Scanned Image
Sections
Maximum Entropy Bootstrap for Time Series
Bayesian Methods for Identifying the Most Harmful Medication
Errors
Flexible Mediation Analysis Using Natural Effect Models
Medical Risk and Comorbidity Tools for ICD-9-CM Data
A max-margin supervised Sparse Topical Coding Model
Multivariate Data Handling in Ecology and Biogeography
Multivariate Data Handling with S4 Classes and Sparse Matrices