Selection of k in k-means clustering based on Pham et al. paper
``Selection of k in k-means clustering''.
Using this package you can combine known kinase substrate relationships with experimental data and determine active kinases and their substrates.
Knowledge Space Theory is a set-theoretical framework, which
proposes mathematical formalisms to operationalize knowledge structures in a
particular domain. The kst-package provides basic functionalities to
generate, handle, and manipulate knowledge structures and knowledge spaces.
This function is designed for use with an arbitrary set of equations with
an arbitrary set of unknowns.
The user selects "fixed" values for enough unknowns to leave as many variables as
there are equations, which in most cases means the system is properly
defined and a unique solution exists. The function, the fixed values
and initial values for the remaining unknowns are fed to a nonlinear backsolver.
The original version of "TK!Solver" was the inspiration for this function.
These functions compute the k best pairs of genes used to classify samples based on the relative rank of the genes expression within each profile. A score based on the sensitivity and the specificity is calculated for every possible pair. The k pairs with the highest score will be selected with the restriction that a gene can appear in at most one pair. The value of k is either set as a parameter chosen by the user or computed through crossvalidation. Other functions related to the k-TSP are also available, for example the functions prediction, summary, plot, etc. can be found in the package.
Provides various functions and data sets from experiments at the Faculty of Life Sciences, University of Copenhagen. This package will be discontinued and archived, and the functions and datasets will be maintained and updated in the MESS package
Calculation groundwater mounding beneath an infiltration basin based on the Hantush (1967) equation (http://doi.org/10.1029/WR003i001p00227). The correct implementation is shown with a verification example based on a USGS report (page 25, http://pubs.usgs.gov/sir/2010/5102/support/sir2010-5102.pdf).
A miscellaneous set of functions for printing, plotting, kernels, etc. Additional contributors are acknowledged on individual function help pages.
Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.
A colletion of functions to implement Kolmogorov-Zurbenko
Fourier transform based periodograms and smoothing methods
A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise.
L1 estimation for linear regression and random number generation for
the multivariate Laplace distribution.
Efficient implementation of Friedman's boosting algorithm
with l2-loss function and coordinate direction (design matrix
columns) basis functions.
A variety of ordination and community analyses
useful in analysis of data sets in community ecology.
Includes many of the common ordination methods, with
graphical routines to facilitate their interpretation,
as well as several novel analyses.
Support labeled loop and escape from nested loop
Provides a range of axis labeling algorithms
Work with labelled data imported from
'SPSS' or 'Stata' with 'haven' or 'foreign'.
An implementation of distance-based ranking algorithms to predict rankings of labels.  Two common algorithms are included: the naive Bayes and the nearest neighbor algorithms.
The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.
The package offers a dendrogram representation of series
of labels, this is specially needed in Markov chain Monte Carlo
clustering. If you have a dendrogram in your mind you can
easily put series of meaningful labels in a matrix and heights
in a vector, then convert them to a dendrogram abject.
Insieme di funzioni di supporto al volume "Laboratorio di
Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006.
This package contains sets of functions defined in "Laboratorio
di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia,
2006. Function names and docs are in italian as well.
Contains data sets to accompany the book "Experimental Design for
Laboratory Biologists: Maximising Information and Improving Reproducibility"
(available mid 2016 from Cambridge University Press).
Estimation of indicators on social exclusion and poverty, as well
as Pareto tail modeling for empirical income distributions.
The package contains functions to compare and group means.
Methods for fast access to large ASCII files.  Currently the
following file formats are supported: comma separated format (csv) and fixed
width format. It is assumed that the files are too large to fit into memory,
although the package can also be used to efficiently access files that do
fit into memory. Methods are provided to access and process files blockwise.
Furthermore, an opened file can be accessed as one would an ordinary
data.frame. The LaF vignette gives an overview of the functionality
provided.
Performs approximate GP regression for large computer experiments and spatial datasets.  The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine.  OpenMP and GPU features may require special compilation.  An interface to lower-level (full) GP inference and prediction is also provided, as are associated wrapper routines for blackbox optimization under constraints via an augmented Lagrangian scheme, and large scale computer model calibration.
Provides the tables from Sean Lahman's Baseball Database as a set of R data.frames.
It uses the data on pitching, hitting and fielding performance and other tables
from 1871 through 2014, as recorded in the 2015 version of the database.
A collection of tools for the calculation of freewater metabolism.
Lake morphometry metrics are used by limnologists to understand,
among other things, the ecological processes in a lake. Traditionally, these
metrics are calculated by hand, with planimeters, and increasingly with
commercial GIS products. All of these methods work; however, they are either
outdated, difficult to reproduce, or require expensive licenses to use. The
\code{lakemorpho} package provides the tools to calculate a typical suite
of these metrics from an input elevation model and lake polygon. The metrics
currently supported are: fetch, maximum length, maximum width, mean width,
maximum depth, mean depth, shoreline development, shoreline length, surface
area, and volume.
Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. Climatic drivers and geomorphometric characteristics were also compiled and are included for each lake. Data are part of the associated publication from the Global Lake Temperature Collaboration project (http://www.laketemperature.org). See citation('laketemps') for dataset attribution.
Currently the package includes 14 methods for calculating internal
consistency reliability but is still growing.  The package allows users
access to whichever reliability estimator is deemed most appropriate for
their situation.
A language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety.
Provides tools that manipulate and transform data using methods
and techniques consistent with functional programming. The idea is that
through the use of these tools, a program can be reasoned about insomuch
that the implementation can be proven to be equivalent to the mathematical
model.
Lambert W x F distributions are a generalized framework to analyze
skewed, heavy-tailed data. It is based on an input/output system, where the
output random variable (RV) Y is a non-linearly transformed version of an input
RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed).
The transformed RV Y has a Lambert W x F distribution. This package contains
functions to model and analyze skewed, heavy-tailed data the Lambert Way:
simulate random samples, estimate parameters, compute quantiles, and plot/
print results nicely. Probably the most important function is 'Gaussianize',
which works similarly to 'scale', but actually makes the data Gaussian.
A do-it-yourself toolkit allows users to define their own Lambert W x
'MyFavoriteDistribution' and use it in their analysis right away.
Implements both real-valued branches of the Lambert-W function, also known as the product logarithm, without the need for installing the entire GSL.
Using Liquid Association for Network Dynamics Detection.
Provides functions to estimate survival and a treatment effect using a landmark estimation approach.
This package includes functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307.
Processing of Landsat or other multispectral satellite
imagery. Includes relative normalization, image-based
radiometric correction, and topographic correction options.
Functions for converted Landsat 8 multispectral satellite imagery
rescaled to the top of atmosphere (TOA) reflectance, radiance
and/or at satellite brightness temperature using radiometric
rescaling coefficients provided in the metadata file (MTL file).
This set of functions allows the simulation of categorical maps from scratch in a geographically referenced landscape or the manipulation of existing ones. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion), therefore expands more or less circularly picking cells at the edges randomly.
Estimate drift and diffusion functions from time series and
generate synthetic time series from given drift and diffusion coefficients.
Data sets exemplifying statistical methods, and some
facilitatory utility functions used in "Analyzing Linguistic
Data: A practical introduction to statistics using R",
Cambridge University Press, 2008.
Solves the problem of Laplace deconvolution with noisy discrete
non-equally spaced observations on a finite time interval based on expansions
of the convolution kernel, the unknown function and the observed signal over
Laguerre functions basis. It implements the methodology proposed in the paper
"Laplace deconvolution on the basis of time domain data and its application to
Dynamic Contrast Enhanced imaging" by F. Comte, C-A. Cuenod, M. Pensky and Y.
Rozenholc in ArXiv (http://arxiv.org/abs/1405.7107).
This package is intended for researchers studying historical labour relations (see http://www.historyoflabourrelations.org). The package allows for easy access of excel files in the standard defined by the Global Collaboratory on the History of Labour Relations. The package also allows for visualisation of labour relations according to the Collaboratory's format.
Provides instrumental variable estimation of treatment effects when both the endogenous treatment and its instrument are binary. Applicable to both binary and continuous outcomes.
Efficient procedures for fitting an entire lasso sequence
with the cost of a single least squares fit. Least angle
regression and infinitesimal forward stagewise regression are
related to the lasso, as described in the paper below.
laser implements maximum likelihood methods based on the
birth-death process to test whether diversification rates have
changed over time and whether rates vary among lineages.
Routines and documentation for solving regression problems
while imposing an L1 constraint on the estimates, based on
the algorithm of Osborne et al. (1998)
Implementation of the algorithm introduced in "Shah, R. D.
(2016) Modelling interactions in high-dimensional data with Backtracking, JMLR,
to appear". Data with thousands of predictors can be handled. The algorithm
performs sequential Lasso (Tibshirani, 1996) fits on design matrices containing
increasing sets of candidate interactions. Previous fits are used to greatly
speed up subsequent fits so the algorithm is very efficient.
Use the lasso regression method to perform approximate inference
in high dimensions, by penalizing the effects of nuisance parameters.
L1 regularized regression (Lasso) solver using the Cyclic
Coordinate Descent algorithm aka Lasso Shooting is fast. This
implementation can choose which coefficients to penalize. It
support coefficient-specific penalities and it can take X'X and
X'y instead of X and y.
This is a simple wrapper for the LASVM Solver (see http://leon.bottou.org/projects/lasvm). LASVM is basically an online variant of the SMO solver.
A graph
proposed by Rosenbaum is useful
for checking some properties of various
sorts of latent scale, this program generates commands
to obtain the graph using 'dot' from 'graphviz'.
Fit and simulate latent position and cluster models for statistical networks.
Calculates and plots Handedness index (HI), absolute HI, mean HI and z-score which are commonly used indexes for the study of hand preference (laterality) in non-human primates.
Parses and converts LaTeX math formulas to R's plotmath
expressions, used to enter mathematical formulas and symbols to be rendered as
text, axis labels, etc. throughout R's plotting system.
A powerful and elegant high-level data visualization
system inspired by Trellis graphics, with an emphasis on
multivariate data. Lattice is sufficient for typical graphics needs,
and is also flexible enough to handle most nonstandard requirements.
See ?Lattice for an introduction.
This package contains functions that compute the
lattice-based density estimator of Barry and McIntyre, which
accounts for point processes in two-dimensional regions with
irregular boundaries and holes.  The package also implements
two-dimensional non-parametric regression for similar regions.
Building on the infrastructure provided by the lattice
package, this package provides several new high-level
functions and methods, as well as additional utilities
such as panel and axis annotation functions.
Methods for the interpolation of large spatial
datasets. This package follows a "fixed rank Kriging" approach using
a large number of basis functions and provides spatial estimates
that are comparable to standard families of covariance functions.
Using a large number of basis functions allows for estimates that
can come close to interpolating the observations (a spatial model
with a small nugget variance.)  Moreover, the covariance model for
this method can approximate the Matern covariance family but also
allows for a multi-resolution model and supports efficient
computation of the profile likelihood for estimating covariance
parameters. This is accomplished through compactly supported basis
functions and a Markov random field model for the basis
coefficients. These features lead to sparse matrices for the
computations and this package makes of the R spam package for this.
An extension of this version over previous ones ( < 5.4 ) is the
support for different geometries besides a rectangular domain. The
Markov random field approach combined with a basis function
representation makes the implementation of different geometries
simple where only a few specific functions need to be added with
most of the computation and evaluation done by generic routines that
have been tuned to be efficient.  One benefit of the LatticeKrig
model/approach is the facility to do unconditional and conditional
simulation of the field for large numbers of arbitrary points. There
is also the flexibility for estimating non-stationary covariances
and also the case when the observations are a linear combination
(e.g. an integral) of the spatial process.  Included are generic
methods for prediction, standard errors for prediction, plotting of
the estimated surface and conditional and unconditional simulation.
Estimation and simulation of latent variable models.
Fit a variety of latent variable models, including confirmatory
factor analysis, structural equation modeling and latent growth curve models.
Interactive shiny application for working with different kinds of
latent variable analysis, with the 'lavaan' package. Graphical output for models
are provided and different estimators are supported.
Fit structural equation models (SEM) including factor analysis,
multivariate regression models with latent variables and many other latent
variable models while correcting estimates, standard errors, and
chi-square-derived fit measures for a complex sampling design.
Incorporate clustering, stratification, sampling weights, and
finite population corrections into a SEM analysis.
Wrapper around packages lavaan and survey.
lava plugin allowing combinations of left and right
censored and binary outcomes
Client for 'Turfjs' (http://turfjs.org/) for
'geospatial' analysis. The package revolves around using 'GeoJSON'
data. Functions are included for creating 'GeoJSON' data objects,
measuring aspects of 'GeoJSON', and combining, transforming,
and creating random 'GeoJSON' data objects.
Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness,
measures of relative variability etc, the package contains new robust tests of symmetry,
omnibus and directional tests of normality, and their graphical counterparts such as
Robust QQ plot; a robust trend tests for variances etc. All implemented tests and methods
are illustrated by simulations and real-life examples from legal statistics, economics,
and biostatistics.
By combining constant, linear, and quadratic local models,
lazy estimates the value of an unknown multivariate function on
the basis of a set of possibly noisy samples of the function
itself.  This implementation of lazy learning automatically
adjusts the bandwidth on a query-by-query basis through a
leave-one-out cross-validation.
Supplies a LazyData facility for packages which have data
sets but do not provide LazyData.  A single function is
is included, requireData, which is a drop-in replacement for
base::require, but carrying the additional
functionality. By default, it suppresses package
startup messages as well.  See argument 'reallyQuitely'.
A disciplined approach to non-standard evaluation.
Helper functions to build SQL statements
for dbGetQuery or dbSendQuery under program control.
They are intended to increase speed of coding and
to reduce coding errors. Arguments are carefully checked,
in particular SQL identifiers such as names of tables or columns.
More patterns will be added as required.
Provides the functionality to write LaTeX code from within R
without having to learn LaTeX.  Functionality also exists to create HTML
and Markdown code.  While the functionality still exists to write
complete documents with lazyWeave, it is generally easier to do so with
with markdown and knitr.  lazyWeave's main strength now is the ability
to design custom and complex tables for reporting results.
Latent budget analysis is a method for the analysis of a two-way contingency table with an exploratory variable and a response variable. It is specially designed for compositional data.
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.
Interfacing to Nocedal et al. L-BFGS-B.3.0 (2011) limited
memory BFGS minimizer with bounds on parameters.
The package offers various length-bias corrections to
survival curve estimation.
Performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset.
This package performs a Latent Class Analysis of
phenotypic measurements in pedigrees and a model selection
based on one of two methods: likelihood-based cross-validation
and Bayesian Information Criterion. It computes also individual
and triplet child-parents weights in a pedigree using an
upward-downward algorithm. It takes into account the familial
dependence defined by the pedigree structure by considering
that a class of a child depends on his parents classes via
triplet-transition probabilities of the classes. The package
handles the case where measurements are available on all
subjects and the case where measurements are available only on
symptomatic (i.e. affected) subjects. Distributions for
discrete (or ordinal) and continuous data are currently
implemented. The package can deal with missing data.
Local Discrimination via Latent Class Models
This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model.
Bayesian population size estimation using non parametric latent-class models.
Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models and mixed models for curvilinear univariate or multivariate longitudinal outcomes using a maximum likelihood estimation method.
Collections of functions allowing random number generations and estimation of Liouville copulas.
The main purpose of lctools is to provide researchers and educators with easy-to-learn
user friendly tools for calculating key spatial statistics and to apply simple as well as
advanced methods of spatial analysis in real data. These include: Local Pearson and
Geographically Weighted Pearson Correlation Coefficients, Spatial Inequality Measures
(Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation (Global and Local Moran's I),
several Geographically Weighted Regression techniques and other Spatial Analysis tools
(other geographically weighted statistics). This package also contains functions for
measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.
Implements latent Dirichlet allocation (LDA)
and related models.  This includes (but is not limited
to) sLDA, corrLDA, and the mixed-membership stochastic
blockmodel.  Inference for all of these models is
implemented via a fast collapsed Gibbs sampler written
in C.  Utility functions for reading/writing data
typically used in topic models, as well as tools for
examining posterior distributions are also included.
Performs group matching by backward elimination using linear discriminant analysis.
For this first version only metrics to estimate the best fitting
number of topics are implemented.
Tools to create an interactive web-based visualization of a
topic model that has been fit to a corpus of text data using
Latent Dirichlet Allocation (LDA). Given the estimated parameters of
the topic model, it computes various summary statistics as input to
an interactive visualization built with D3.js that is accessed via
a browser. The goal is to help users interpret the topics in their
LDA topic model.
Computations related to group sequential boundaries.
Includes calculation of bounds using the Lan-DeMets
alpha spending function approach.
The package provides a set of functions which aim is to
propose four measures of linkage disequilibrium: the usual r^2
measure, the r^2_S measure (r^2 corrected by the structure
sample), the r^2_V (r^2 corrected by the relatedness of
genotyped individuals), the r^2_VS measure (r^2 corrected by
both the relatedness of genotyped individuals and the structure
of the sample).
Produces a graphical display, as a heat map, of measures
of pairwise linkage disequilibria between SNPs. Users may
optionally include the physical locations or genetic map
distances of each SNP on the plot.
ldlasso requires data be of class gwaa.data from the the
package GenABEL
this package provides functions for Finding Locally
D-optimal designs for Logistic, Negative Binomial, Poisson,
Michaelis-Menten, Exponential, Log-Linear, Emax, Richards,
Weibull and Inverse Quadratic regression models and also
functions for auto-constructing Fisher information matrix and
Frechet derivative based on some input variables and without
user-interfere.
Implementation of most popular approaches to PD (probability of default) calibration: Quasi Moment Matching algorithm (D. Tasche), algorithm proposed by M. van der Burgt, K. Pluto and D. Tasche's most prudent estimation methodology.
Functions, methods, and data sets for fitting likelihood-based dimension reduction in regression, using principal fitted components (pfc), likelihood acquired directions (lad), covariance reducing models (core).
Linear dimension reduction subspaces can be uniquely defined using orthogonal projection matrices. This package provides tools to compute distances between such subspaces and to compute the average subspace.
Exact tests for Linkage Disequilibrium (LD) and Hardy-Weinberg Equilibrium (HWE). - 2-sided LD tests based on different measures of LD (Kulinskaya and Lewin 2008) - 1-sided Fisher's exact test for LD - 2-sided Haldane test for HWE (Wiggington 2005) - 1-sided test for inbreeding - conditional p-values proposed in Kulinskaya (2008) to overcome the problems of asymetric distributions (for both LD and HWE)
The leader clustering algorithm provides
a means for clustering a set of data points. Unlike many other clustering
algorithms it does not require the user to specify the number of clusters,
but instead requires the approximate radius of a cluster as its primary
tuning parameter. The package provides a fast implementation of this
algorithm in n-dimensions using Lp-distances (with special cases for p=1,2,
and infinity) as well as for spatial data using the Haversine
formula, which takes latitude/longitude pairs as inputs and clusters
based on great circle distances.
A leaf angle distribution is a special distribution that is defined between 0 and 90 degrees, and a number of distributions are used to characterize the leaf angle distribution in real plant canopies. This package includes methods to fit distributions to data, visualize the fit, and compare fits of nine different distributions.
An interface for the image processing program ImageJ, which allows a rapid digital image analysis for particle sizes. This package includes function to write an ImageJ macro which is optimized for a leaf area analysis by default.
Create and customize interactive maps using the 'Leaflet' JavaScript
library and the 'htmlwidgets' package. These maps can be used directly from
the R console, from 'RStudio', in Shiny apps and R Markdown documents.
Display your spatial data on interactive web-maps using the open-source JavaScript library Leaflet. 'leafletR' provides basic web-mapping functionality to combine vector data and online map tiles from different sources. See <http://leafletjs.com> for more information on Leaflet.
Advances in sequencing technology now allow researchers to capture the expression profiles of individual cells. Several algorithms have been developed to attempt to account for these effects by determining a cell's so-called `pseudotime', or relative biological state of transition. By applying these algorithms to single-cell sequencing data, we can sort cells into their pseudotemporal ordering based on gene expression. LEAP (Lag-based Expression Association for Pseudotime-series) then applies a time-series inspired lag-based correlation analysis to reveal linearly dependent genetic associations.
Contains LEAPFrOG Gradient Optimisation and Expectation Maximisation functions for inferring parental admixture proportions from an offspring with SNP genotypes.
These functions take a gene expression value matrix, a
primary covariate vector, an additional known covariates
matrix.  A two stage analysis is applied to counter the effects
of latent variables on the rankings of hypotheses.  The
estimation and adjustment of latent effects are proposed by
Sun, Zhang and Owen (2011).  "leapp" is developed in the
context of microarray experiments, but may be used as a general
tool for high throughput data sets where dependence may be
involved.
Regression subset selection including exhaustive search
LearnBayes contains a collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
Crabs in the English channel, deer skulls, English
monarchs, half-caste Manga characters, Jamaican cities,
Shakespeare's The Tempest, drugged up cyclists and sexually
transmitted diseases.
Implementations of several basic neural network concepts in R, as based on posts on \url{http://qua.st/}.
Allows students to use R as an interactive educational environment
for statistical concepts, ranging from p-values to confidence intervals
to stability in time series.
Utilizing phylogenetic and functional information for the analyses of ecological datasets. The analyses include methods for quantifying the phylogenetic and functional diversity of assemblages.
Estimate the slope and intercept of a bivariate
linear relationship by calculating a posterior density
that is invariant to interchange and scaling of the
coordinates.
This package provides functions and datasets for the
reader of the book "Le logiciel R: Maitriser le langage,
Effectuer des analyse statistiques". The documentation and help
pages are written in french.
Each function accomplishes the work of several or more standard R functions. For example, two function calls, Read() and CountAll(), read the data and generate summary statistics for all variables in the data frame, plus histograms and bar charts as appropriate.  Other functions provide for descriptive statistics, a comprehensive regression analysis, analysis of variance and t-test, plotting, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function call, variable labels, color themes, and a built-in help system. A confirmatory factor analysis of multiple indicator measurement models is available, as are pedagogical routines for data simulation such as for the Central Limit Theorem. Compatible with 'RStudio' and 'knitr' including generation of R markdown instructions for interpretative output.
This package contains some simple objects and functions to do
statistics using linear models and a Bayesian framework.
R functions for handling, processing, and analyzing geographic
data on species' distributions and environmental variables as well as species'
description and conservation information.
Utilities for formatting strings and character
vectors to for capitalization, word break and white space. Supported formats
are: snake_case, spine-case, camelCase, PascalCase, Title Case, UPPERCASE,
lowercase, Sentence case or combinations thereof. 'lettercase' strives to
provide a simple, consistent, intuitive and high performing interface.
Functions to plot Lexis Diagrams for Demographic purposes.
Provides an extension to factors called 'lfactor' that are similar
to factors but allow users to refer to 'lfactor' levels by either the level or
the label.
Functions for performing and visualizing Local Fisher Discriminant Analysis(LFDA), Kernel Fisher Discriminant Analysis(KLFDA), and Semi-supervised Local Fisher Discriminant Analysis(SELF).
Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE).
Transforms away factors with many levels prior to doing an OLS.
Useful for estimating linear models with multiple group fixed effects, and for
estimating linear models which uses factors with many levels as pure control
variables. Includes support for instrumental variables, conditional F statistics
for weak instruments, robust and multi-way clustered standard errors, as well as
limited mobility bias correction.
Various algorithms related to linguistic fuzzy logic: mining for linguistic fuzzy association
rules, composition of fuzzy relations, performing perception-based logical deduction (PbLD),
and forecasting time-series using fuzzy rule-based ensemble (FRBE).
The "Manual on Low-flow Estimation and Prediction", published by the World Meteorological Organisation (WMO), gives a comprehensive summary on how to analyse stream flow data focusing on low-flows. This packages provides functions to compute the described statistics and produces plots similar to the ones in the manual.
Tools for linear grouping analysis. Three user-level
functions: gap, rlga and lga.
Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.
Spatial and spatio-temporal modelling of point patterns using the
log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal,
multivariate and aggregated point processes using Markov chain Monte Carlo.
Functions for testing the genetic association/gene-environment interaction in longitudinal gene-environment-wide interaction studies. Generalized score type tests are used for set based analyses. Then GEE based score tests are applied to all single variants within the defined set.
Functions for the longitudinal genetic random field method (He et al., 2015, <doi:10.1111/biom.12310>) to test the association between a longitudinally measured quantitative outcome and a set of genetic variants in a gene/region.
A very simple implementation of a class for
longitudinal data.
Provides a number of methods for creating and augmenting Latin Hypercube Samples.
R interface to the open-source, ANSI C library 'libamtrack' (http://libamtrack.dkfz.org). 'libamtrack' provides computational routines for the prediction of detector response and radiobiological efficiency in heavy charged particle beams. It is designed for research in proton and ion dosimetry and radiotherapy. 'libamtrack' also includes many auxiliary physics routines for proton and ion beams. Original package and C-to-R conversion routines developed by Felix A. Klein.
A wrapper around the LIBLINEAR C/C++ library for
machine learning (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear).
LIBLINEAR is a simple library for solving large-scale regularized linear
classification and regression. It currently supports L2-regularized
classification (such as logistic regression, L2-loss linear SVM and L1-loss
linear SVM) as well as L1-regularized classification (such as L2-loss linear
SVM and logistic regression) and L2-regularized support vector regression
(with L1- or L2-loss). The main features of LiblineaR include multi-class
classification (one-vs-the rest, and Crammer & Singer method), cross
validation for model selection, probability estimates (logistic regression
only) or weights for unbalanced data. The estimation of the models is
particularly fast as compared to other libraries.
Solving the linear SVM problem with coordinate descent
is very efficient and is implemented in one of the most often used packages,
'LIBLINEAR' (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear).
It has been shown that the uniform selection of coordinates can be
accelerated by using an online adaptation of coordinate frequencies (ACF).
This package implements ACF and is based on 'LIBLINEAR' as well as
the 'LiblineaR' package (<https://cran.r-project.org/package=LiblineaR>).
It currently supports L2-regularized L1-loss as well as L2-loss linear SVM.
Similar to 'LIBLINEAR' multi-class classification (one-vs-the rest, and
Crammer & Singer method) and cross validation for model selection is
supported. The training of the models based on ACF is much faster than
standard 'LIBLINEAR' on many problems.
Efficient procedures for fitting the regularization path
for linear, binomial, multinomial, Ising and Potts models with lasso,
group lasso or column lasso(only for multinomial) penalty.
The package uses Linearized Bregman Algorithm to solve the
regularization path through iterations. Bregman Inverse Scale Space Differential
Inclusion solver is also provided for linear model with lasso penalty.
Estimates predictive states from spatio-temporal data and
consequently can provide provably optimal forecasts.
Currently this implementation
supports an N-dimensional spatial grid observed over equally spaced time
intervals. E.g. a video is a 2D spatial systems observed over time. This
package implements mixed LICORS, has plotting tools (for (1+1)D and (2+1)D
systems), and methods for optimal forecasting.  Due to memory limitations
it is recommend to only analyze (1+1)D systems.
This package presents a general method for assessing the
local influence of minor perturbations of case weight for the
linear regression models.
Classes and methods that allow the user to manage life table,
actuarial tables (also multiple decrement tables). Moreover, functions to easily
perform demographic, financial and actuarial mathematics on life contingencies
insurances calculations are contained therein.
Provides in built datasets and three functions.
Likelihood-based estimation of individual growth and sexual maturity models for organisms, usually fish and invertebrates. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions.
Functions supplied in this package will implement
discriminant analysis to select an appropriate life table
family, select an appropriate alpha level based on a desired
life expectancy at birth, produce a model mortality pattern
based on family and level as well as plot the results.
Compute the top decile lift and plot the lift curve. Cumulative lift curves are also supported.
Dockerize R Markdown documents with support for Rabix.
The purpose of this package is to enable usage of lightningviz server to be accessible from R. The
server itself can be found at http://lightning-viz.org/ and is required to work with this package. Package
by itself cannot and will not create any visualizations.
Lights Out is a puzzle game consisting of a grid of lights
that are either on or off. Pressing any light will toggle it and its
adjacent lights. The goal of the game is to switch all the lights off. This
package provides an interface to play the game on different board sizes,
both through the command line or with a visual application. Puzzles can
also be solved using the automatic solver included. View a demo
online at http://daattali.com/shiny/lightsout/.
A Poisson Subordinated Distribution to capture major
leptokurtic features in log-return time series of financial
data.
Tools for maximum likelihood estimation of parameters
of scientific models.
Functions for computing the r and r* statistics for inference on an arbitrary scalar function of model parameters, plus some code for the (modified) profile likelihood.
Tools to determine DNA profile Weight of Evidence.
For further information see the likeLTD guide provided,
or Balding, D.J. (2013) <DOI:10.1073/pnas.1219739110>.
Functions to analyze and visualize likert type items.
Functions that read and solve linear inverse problems (food web problems, linear programming problems).
These problems find solutions to linear or quadratic functions:
min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(ai*xi)
subject to equality constraints Ex=f and inequality constraints Gx>=h. Uses package limSolve.
Values below a specified limit of detection are stacked in
rows in order to reduce overplotting and create a clear
graphical representation of your data.
Functions that (1.) Find the minimum/maximum of a linear or quadratic function:
min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(ai*xi)
subject to equality constraints Ex=f and/or inequality constraints Gx>=h.
(2.) Sample an underdetermined- or overdetermined system Ex=f subject to Gx>=h, and if applicable Ax~=b.
(3.) Solve a linear system Ax=B for the unknown x. Includes banded and tridiagonal linear systems.
The package calls Fortran functions from LINPACK
Short for 'linear binning', the linbin package provides functions
for manipulating, binning, and plotting linearly referenced data. Although
developed for data collected on river networks, it can be used with any interval
or point data referenced to a 1-dimensional coordinate system. Flexible bin
generation and batch processing makes it easy to compute and visualize variables
at multiple scales, useful for identifying patterns within and between variables
and investigating the influence of scale of observation on data interpretation.
Estimate and confidence/credible intervals for an unknown
regressor x0 given an observed y0.
L-systems or Lindenmayer systems are parallel rewriting systems which can
be used to simulate biological forms and certain kinds of fractals.
Briefly, in an L-system a series of symbols in a string are replaced
iteratively according to rules to give a more complex string. Eventually,
the symbols are translated into turtle graphics for plotting. Wikipedia has
a very good introduction: en.wikipedia.org/wiki/L-system This package
provides basic functions for exploring L-systems.
Implements the probability density function, quantile function,
cumulative distribution function, random number generation and the hazard rate function for
the continuous one-parameter Lindley distribution as well as for 15 of its modifications. Also
is it possible to draw censored random samples, with a desired censoring rate, when the event
times are any continuous lifetime distribution supported by R.
Train and predict using fast prototype-based Linearized
Support-Vector Regression methods.
Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model.
Provides tools to manipulate formulas, such as getting x, y or contrasts from the model/formula, and functions to evaluate and check the marginal effects of a linear model.
Fits a linear excess relative risk model by maximum likelihood, possibly including several variables and allowing for lagged exposures.
Tools for detecting and correcting sample mix-ups between two sets
of measurements, such as between gene expression data on two tissues.
Link communities reveal the nested and overlapping structure in networks, and uncover the key nodes that form connections to multiple communities. linkcomm provides a set of tools for generating, visualizing, and analysing link communities in networks of arbitrary size and type. The linkcomm package also includes tools for generating, visualizing, and analysing Overlapping Cluster Generator (OCG) communities.
Matrices implemented as collections of matrix-like nodes, linked by
columns or rows.
A linkage information based method for imputing missing diploid genotypes
Creates kinematic and static force models of 3D levers and linkage mechanisms, with particular application to the fields of engineering and biomechanics.
This package implements the methodology of
Likelihood-based Imprecise Regression (LIR) for the case of
linear regression with interval data.
This package can be used to solve Linear Programming /
Linear Optimization problems by using the simplex algorithm.
Interactive visualization of effects, response functions
and marginal effects for different kinds of regression models. In this version
linear regression models, generalized linear models, generalized additive
models and linear mixed-effects models are supported.
Major features are the interactive approach and the handling of the effects of categorical covariates:
if two or more factors are used as covariates every combination of the levels of each
factor is treated separately. The automatic calculation of
marginal effects and a number of possibilities to customize the graphical output
are useful features as well.
Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators. In particular it solves the problem of variable selection by choosing the best predictor among  predictors emanating from different methods as lasso, elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.
Tools for checking R code for style compliance.  Tools for
creating style compliance tests.  A suite of pre-built tests is
provided.
Variable elimination (Gaussian elimination, Fourier-Motzkin elimination),
Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution,
projecting a vector on the convex polytope described by a system of (in)equations,
simplify systems by removing spurious columns and rows and collapse implied equalities,
test if a matrix is totally unimodilar, compute variable ranges implied by linear
(in)equalities.
Checks adherence to a given style, syntax errors and possible
semantic issues.  Supports on the fly checking of R code edited with Emacs,
Vim and Sublime Text.
Performs Bayesian linear regression in astronomy. The method accounts for heteroscedastic errors in both the independent and the dependent variables, intrinsic scatters (in both variables), time evolution of slopes, normalization and scatters, Malmquist and Eddington bias, and break of linearity. The posterior distribution of the regression parameters  is sampled with a Gibbs method exploiting the JAGS library.
Fits lasso (total variation) penalised additive isotone models
Though SRFI-1 scopes both list-processing and higher-order
programming, we'll save some list-orthogonal functions for the
`functional' package; this is freely a mixture of
implementation and API.
This is an unofficial package aimed at automating the
import of LISREL output in R.  This package or its maintainer
is not in any way affiliated with the creators of LISREL and
SSI, Inc.
Allows researchers to conduct multivariate statistical analyses of survey data with list experiments. This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011), Blair and Imai (2012), Blair, Imai, and Lyall (2013), Imai, Park, and Greene (2014), and Aronow, Coppock, Crawford, and Green (2015). This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, and a method for combining list experiments with direct questions. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions.
List environments are environments that have list-like properties.  For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting, e.g. 'x <- listenv(a=1, b=2); for (i in seq_along(x)) x[[i]] <- x[[i]]^2; y <- as.list(x)'.
Tests for independence between X and Y computed from a paired sample (x1,y1),...(xn,yn) of (X,Y), using one of the following statistics (a) the Longest Increasing Subsequence (Ln), (b) JLn, a Jackknife version of Ln or (c) JLMn, a Jackknife version of the longest monotonic subsequence. This family of tests can be applied under the assumption of continuity of X and Y.
Provides a function that, as an alternative to base::list, allows default values to be inherited from another list.
A scripting and command-line front-end
is provided by 'r' (aka 'littler') as a lightweight binary wrapper around
the GNU R language and environment for statistical computing and graphics.
While R can be used in batch mode, the r binary adds full support for
both 'shebang'-style scripting (i.e. using a  hash-mark-exclamation-path
expression as the first line in scripts) as well as command-line use in
standard Unix pipelines. In other words, r provides the R language without
the environment.
Provides a wrapper around LiveChat's API. The R functions allow for
one to extract chat sessions, raw text of chats between agents and customers and
events.
Fits and tests logistic joinpoint models.
Provides functionality to train and evaluate algorithm selection models for portfolios.
LLE is a non-linear algorithm for mapping high-dimensional
data into a lower dimensional (intrinsic) space. This package
provides the main functions to performs the LLE alogrithm
including some enhancements like subset selection, calculation
of the intrinsic dimension etc.
Applies local log-linear capture-recapture models (LLLMs) for
closed populations, as described in the doctoral thesis of Zachary Kurtz.
The method is relevant when there are 3-5 capture occasions, with auxiliary
covariates available for all capture occasions.  As part of estimating the
number of missing population units, the method estimates the "rate of
missingness" as it varies over the covariate space.  In addition,
user-friendly functions are provided to recreate (approximately) the method
of Zwane and van der Heijden (2004), which applied the VGAM package
in a way that is closely related to LLLMs.
Analyses experimental data from liquid-liquid phase diagrams and
provide a simple way to obtain its parameters and a simplified report. Designed
initially to analyse Aqueous Two-Phases Systems, the package will include (every
other update) new functions in order to comprise useful tools in liquid-liquid
analysis.
Adds standardized regression coefficients to objects created by lm. Also extends the S3 methods print, summary and coef with additional boolean argument standardized.
Exact significance tests for a changepoint in linear or multiple linear regression.
Confidence regions with exact coverage probabilities for the changepoint.
Fit linear and generalized linear mixed-effects models.
The models and their components are represented using S4 classes and
methods.  The core computational algorithms are implemented using the
'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
This package includes a function to fit a linear
mixed-effects model in the formulation described in Laird and
Ware (1982) but allowing for censored normal responses. In this
version, the with-in group errors are assumed independent and
identically distributed.
Performs Genome-Wide Association analysis for diverse populations and
for multi-environment and multi-trait analysis using linear mixed models.
Performs QTL mapping analysis for balanced and for
multi-environment and multi-trait analysis using mixed models.
Balanced population, single trait, single environment QTL mapping is
performed through marker-regression (Haley and Knott (1992) <DOI:10.1038/hdy.1992.131>,
Martinez and Curnow (1992) <DOI:10.1007/BF00222330>,
while multi-environment and multi-trait QTL
mapping is performed through linear mixed models.
These functions could use any of the following populations: double haploid,
F2, recombinant inbred lines, back-cross, and 4-way crosses.
Performs a Single Marker Analysis, a Single Interval Mapping,
or a Composite Interval Mapping analysis, and then constructs a final model
with all of the relevant QTL.
The functions in this package implement the safety monitoring procedures proposed in the paper titled "Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Zhao, Y., Li, D.K.B., Petkau, A.J., Riddehough, A., Traboulsee, A., published in Journal of the American Statistical Association in 2013. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which can be modelled with a gamma or log-normal distributions. One can also choose the semi-parametric option which does not assume any distribution for the random effect. These mixed-effect models could be useful beyond the application of the safety monitoring. The maximum likelihood methods are used to estimate the unknown fixed effect parameters of the model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNBBayes, which contains the functions to compute the Personalized Activity Index in Bayesian framework.
The functions in this package implement the safety monitoring procedures proposed in the paper titled "A flexible mixed effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Kondo, Y., Zhao, Y. and Petkau, A.J. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which is modelled with the infinite mixture of Beta distributions, very flexible distribution that theoretically allows any form. The package also has the option of a single beta distribution for random effects. These mixed-effect models could be useful beyond the application of the safety monitoring. The inference is based on MCMC samples and this package contains a Gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNB, which contains the functions to compute the Personalized Activity Index in the frequentist framework.
Contains functions to estimate model parameters and filter, smooth and forecast random effects coefficients for mixed models with stationary and non-stationary stochastic processes under multivariate normal and t response distributions, diagnostic checks, bootstrap standard error calculation, etc.
The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data.
Different kinds of tests for linear mixed effects models as implemented
in 'lme4' package are provided. The tests comprise types I - III F tests
for fixed effects, LR tests for random effects.
The package also provides the calculation of population means for fixed factors
with confidence intervals  and corresponding plots. Finally the backward
elimination of non-significant effects is implemented.
Add smoothing spline modelling capability to nlme. Fit
smoothing spline terms in Gaussian linear and nonlinear
mixed-effects models
Fit certain versions of the Latent Markov model for longitudinal categorical data.
Test zero variance components in linear mixed models and
test additivity in nonparametric regression
using the restricted likelihood ratio test and the generalized F-test.
This R package provide methods for estimation and statistical
inference on directional and fluctuating selection in age-structured
populations.
Functions related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, and recovery of tree diameter distributions. Datasets on tree height-diameter relationship and light response of moss photosynthesis are included.
Some improved procedures for linear mixed models.
This package fits (gaussian) linear mixed-effects models
for high-dimensional data (n<<p) using a Lasso-type approach
for the fixed-effects parameter.
Fit right censored Multiple Ordinal Tobit (MOT) model.
Linear Mixed effect Model Splines ('lmms') implements linear mixed
effect model splines for modelling and differential expression for highly
dimensional data sets: investNoise() for quality control and filterNoise() for
removing non-informative trajectories; lmmSpline() to model time course expression
profiles and lmmsDE() performs differential expression analysis to identify
differential expression between groups, time and/or group x time interaction.
Computes model II simple linear regression using ordinary
least squares (OLS), major axis (MA), standard major axis (SMA), and
ranged major axis (RMA).
Functions related to L-moments: computation of L-moments
and trimmed L-moments of distributions and data samples; parameter
estimation; L-moment ratio diagram; plot vs. quantiles of an
extreme-value distribution.
Extensive functions for the theory of L-moments (LMs) in addition to probability-weighted moments (PWMs), and parameter (p) estimation for numerous familiar and not-so-familiar distributions. Estimation of LMs for the same distributions is provided as are L-moment ratio diagrams. Estimation of LMs for right-tail and left-tail censoring by known or unknown threshold and also by indicator variable is available. Asymmetric trimmed LMs (TL-moments, TLMs) are supported as are numerical integrations to dynamically compute trajectories of select TLM ratios for TL-moment ratio diagram construction.  LMs of residual (resid.) and reversed (rev.) resid. life are implemented along with 13 operators on quantile functions that are useful in reliability, lifetime, and survival analysis.  Exact analytical bootstrap estimates of order statistics, LMs, and variances-covariances of LMs are provided. The Harri-Coble Tau34-squared Test for Normality is available. Distribution support with "L" (LMs), "TL" (TLMs) and added (+) support for right-tail censoring (RC) encompasses: Asymmetric (Asy.) Exponential (Exp.) Power [L], Asy. Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen.) Exp. Poisson [L], Gen. Extreme Value [L], Gen. Lambda [L,TL], Gen. Logistic [L), Gen. Normal [L], Gen. Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L], 3-p log-Normal [L], Pearson Type III [L], Rayleigh [L], Rev. Gumbel [L+RC], Rice/Rician (L), Slash [TL], 3-p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L]. LMs have multivariate analogs; the sample L-comoments (LCMs) are implemented and might have considerable application with copulas because LCMs measure asymmetric association and higher comoments or comovements of variables.
Contains functions to estimate
L-moments and trimmed L-moments from the data. Also
contains functions to estimate the parameters of the normal
polynomial quantile mixture and the Cauchy polynomial quantile
mixture from L-moments and trimmed L-moments.
Functions for regional frequency analysis using the methods
of J. R. M. Hosking and J. R. Wallis (1997), "Regional frequency analysis:
an approach based on L-moments".
Accompanies Markus Brauer, Bas Rokers, and John Curtin's two-course
graduate series in General, Generalized, and Multi-level Linear Models (PSY
610/710).
A collection of tests, data sets, and examples
for diagnostic checking in linear regression models. Furthermore,
some generic tools for inference in parametric models are provided.
Predict modulators regulating the ability of effectors to regulate their targets and produce
modulator-effector-target triplets followed by goterm functional enrichment and survival analysis.This
is mainly applied to long non-coding RNAs (lncRNAs) as candidate modulators regulating the ability of
transcription factors (TFs) to regulate their corresponding targets.
Identifies pathways synergisticly regulated by the interested lncRNA(long non-coding RNA) sets based on a lncRNA-mRNA(messenger RNA) interaction network. 1) The lncRNA-mRNA interaction network was built from the protein-protein interactions and the lncRNA-mRNA co-expression relationships in 28 RNA-Seq data sets. 2) The interested lncRNAs can be mapped into networks as seed nodes and a random walk strategy will be performed to evaluate the rate of each coding genes influenced by the seed lncRNAs. 3) Pathways regulated by the lncRNA set will be evaluated by a weighted Kolmogorov-Smirnov statistic as an ES Score. 4) The p value and false discovery rate value will also be calculated through a permutation analysis. 5) The running score of each pathway can be plotted and the heat map of each pathway can also be plotted if an expression profile is provided. 6) The rank and scores of the gene list of each pathway can be printed.
Various plots and functions that make use of the lattice/trellis plotting framework.
The plots (which include 'loaPlot', 'GoogleMap' and 'trianglePlot') use panelPal(), a function that
extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel
and panel-to-key synchronization/management.
Simplicial, Mahalanobis and Ellipsoid Local and Global Depth
Computational routines for estimating and visualizing local Gaussian parameters. Local Gaussian parameters are useful for characterizing and testing for non-linear dependence within bivariate data. See e.g. Tjostheim and Hufthammer, Local Gaussian correlation: A new measure of dependence, Journal of Econometrics, 2013, Volume 172 (1), pages 33-48.
The package converts R data onto input and data for LocalSolver,
executes optimization and exposes optimization results as R data.
LocalSolver (http://www.localsolver.com/) is an optimization engine
developed by Innovation24 (http://www.innovation24.fr/). It is designed to
solve large-scale mixed-variable non-convex optimization problems.  The
localsolver package is developed and maintained by WLOG Solutions
(http://www.wlogsolutions.com/en/) in collaboration with Decision Support
and Analysis Division at Warsaw School of Economics
(http://www.sgh.waw.pl/en/).
Computation of local false discovery rates.
The main idea of the Local FDR algorithm is to estimate both proportion of null observations and the ratio of null and alternative densities. In the case that there are many null observations, this can be done reliably, through maximum likelihood or generalized linear models. This package implements this in the case that the null and alternative densities are Poisson.
Local regression, likelihood and density estimation.
Provides test of second-order stationarity for time
series (for dyadic and arbitrary-n length data). Provides
localized autocovariance, with confidence intervals,
for locally stationary (nonstationary) time series.
Datasets for "Statistics:Unlocking the Power of Data" by
Lock, Lock, Lock, Lock and Lock
Data sets and other utilities for
'Statistics: Unlocking the Power of Data'
by Lock, Lock, Lock, Lock and Lock
(ISBN : 978-0-470-60187-7, http://lock5stat.com/).
Genome-wide association (GWAS) analyses
of a biomarker that account for the limit of detection.
Local Ordinal embedding (LOE) is one of graph embedding methods for unweighted graphs.
logr4 provides an object-oriented logging system that uses an API
roughly equivalent to log4j and its related variants.
Methods for fitting log-link GLMs and GAMs to binomial data,
using EM-type algorithms with more stable convergence properties than standard methods.
Computes a log-concave (maximum likelihood) estimator for
i.i.d. data in any number of dimensions.
Based on right or interval censored data, compute the maximum likelihood estimator of a (sub)probability density under the assumption that it is log-concave. For further information see Duembgen, Rufibach, and Schuhmacher (2011, preprint).
Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.
Computes maximum likelihood estimate of a log-concave density  with fixed and known location of the mode. Performs inference about the mode via a likelihood ratio test. Extension of the logcondens package.
Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k.
Computes a cox PH model with a log concave baseline distribution. If no covariates are provided, estimates the log concave NPMLE. Built specifically for interval censored data, where data is a n by 2 matrix with [i, 1] as the left side of the interval for subject i and [i,2] as right side. Uncensored data can be entered by setting [i,1] = [i,2]. Alternatively, this can also handle uncensored data. If all the data is uncensored, you may enter data as a length(n) vector.
logging is a pure R package that implements the ubiquitous
log4j package.
Two classification ensemble methods based on logic regression models.  LogForest uses a bagging approach to construct an ensemble of logic regression models.  LBoost uses a combination of boosting and cross-validation to construct an ensemble of logic regression models.  Both methods are used for classification of binary responses based on binary predictors and for identification of important variables and variable interactions predictive of a binary outcome.
This package contains the functions necessary to run the LOGICOIL algorithm. LOGICOIL can be used to differentiate between antiparallel dimers, parallel dimers, trimers and higher-order coiled-coil sequence. By covering >90 percent of the known coiled-coil structures, LOGICOIL is a net improvement compared with other existing methods, which achieve a predictive coverage of around 31 percent of this population. As such, LOGICOIL is particularly useful for researchers looking to characterize novel coiled-coil sequences or studying coiled-coil containing protein assemblies. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences.
Access to powerful logic minimization algorithms and data structures that operate on a sum-of-products truth table.  The core algorithms are built on Espresso Version 2.3 developed at UC Berkeley for digital logic synthesis purposes.  Enhancements have been made to integrate within the R framework and support additional logic optimization use cases such as those needed by Qualitative Comparative Analysis (QCA) and Genetic Programming.  There are no expressed or implied warranties.
Routines for fitting Logic Regression models.
Firth's bias reduced logistic regression approach with
penalized profile likelihood based confidence intervals for
parameter estimates.
Diagnostic tests and plots for GLMs (generalized linear models)
with binomial/ binary outcomes, particularly logistic regression.
Dimensionality reduction techniques for binary data including
logistic PCA.
Functions, data and code for Hilbe, J.M. 2015. Practical Guide to Logistic Regression, by Chapman and Hall/CRC.
Fits linear discrete logit choice models with l2 regularization. To handle reasonably sized datasets, we employ an accelerated version of generalized gradient descent.
LogitNet is developed for inferring network of binary
variables under the high-dimension-low-sample-size setting
Density, distribution, quantile and random generation
function for the logitnormal distribution. Estimation of the
mode and the first two moments. Estimation of distribution
parameters.
r,d,p,q functions for the double log normal distribution
Functions to fit log-multiplicative models using gnm, with
support for convenient printing, plots, and jackknife/bootstrap
standard errors. For complex survey data, models can be fitted from
design objects from the 'survey' package. Currently supported models
include UNIDIFF (Erikson & Goldthorpe), a.k.a. log-multiplicative
layer effect model (Xie), and several association models: Goodman's
row-column association models of the RC(M) and RC(M)-L families
with one or several dimensions; two skew-symmetric association
models proposed by Yamaguchi and by van der Heijden & Mooijaart.
LogrankA provides a logrank test across unlimited groups with the
possibility to input aggregated survival data.
Routines for the logspline density estimation. oldlogspline()
uses the same algorithm as the logspline 1.0.x package - the Kooperberg
and Stone (1992) <DOI: 10.2307/1390786> algorithm (with an improved interface).
The recommended routine logspline() uses an algorithm from Stone et al (1997).
<DOI: 10.1214/aos/1031594728>
Kernel regression smoothing with adaptive local or global plug-in
bandwidth selection.
Computes the Lomb-Scargle Periodogram for unevenly sampled time series. Includes a randomization procedure to obtain reliable p-values.
Package for Plotting Categorical Longitudinal and Time-Series Data
Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure.
Contains general data structures and
functions for longitudinal data with multiple variables,
repeated measurements, and irregularly spaced time points.
Also implements a shrinkage estimator of dynamical correlation
and dynamical covariance.
Tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d).
Datasets and Functionality from the textbook Jan Beran
(1994). Statistics for Long-Memory Processes; Chapman & Hall.
The longpower package contains functions for computing
power and sample size for linear models of longitudinal data
based on the formula due to Liu and Liang (1997) and Diggle et
al (2002). Either formula is expressed in terms of marginal
model or Generalized Estimating Equations (GEE) parameters.
This package contains functions which translate pilot mixed
effect model parameters (e.g. random intercept and/or slope)
into marginal model parameters so that the formulas of Diggle
et al or Liu and Liang formula can be applied to produce sample
size calculations for two sample longitudinal designs assuming
known variance.
Interface to the 'LongURL' API to identify known URL shortener services
and expand vectors of short URLs with optional error checking and
URL validation. See <http://longurl.org/> for more information about
'LongURL'.
Efficient approximate leave-one-out cross-validation (LOO)
using Pareto smoothed importance sampling (PSIS), a new procedure for
regularizing importance weights. As a byproduct of the calculations, we also
obtain approximate standard errors for estimated predictive errors and for
the comparison of predictive errors between models. We also compute the
widely applicable information criterion (WAIC).
Fits look-up tables by filling entries with the mean or median values of observations
fall in partitions of the feature space. Partitions can be determined by user of the
package using input argument feature.boundaries, and dimensions of the feature space
can be any combination of continuous and categorical features provided by the data set.
A Predict function directly fetches corresponding entry value, and a default value is
defined as the mean or median of all available observations.
The table and other components are represented using the S4 class lookupTable.
The program can perform loop analysis and plot network
structure (especially for food webs),including minimum spanning
tree, loop decomposition of weighted directed graphs, and other
network properties which may be related to food chain
properties in ecology.
Loop analysis makes qualitative predictions of variable change in a system of causally interdependent variables, where "qualitative" means sign only (i.e. increases, decreases, non change, and ambiguous). This implementation includes output support for graphs in .dot file format for use with visualization software such as graphviz (graphviz.org). 'LoopAnalyst' provides tools for the construction and output of community matrices, computation and output of community effect matrices, tables of correlations, adjoint, absolute feedback, weighted feedback and weighted prediction matrices, change in life expectancy matrices, and feedback, path and loop enumeration tools.
Remedies a common problem in piping: not having access to
intermediate outputs of the pipe. Within a "loop", a piping intermediate
is  stored in a stack archive, data is processed, and then both the
stored intermediate and the current output are reintegrated using an
"ending" function. Two special ending functions are provided: amend and
insert. However, any ending function can be specified, including merge
functions, join functions, setNames(), etc. This framework allows the
following work-flow: focus on a particular aspect or section of a
data set, conduct specific operations, and then reintegrate changes into
the whole.
Analysis of Differential Item Functioning (DIF) for
dichotomous and polytomous items using an iterative hybrid of
ordinal logistic regression and item response theory (IRT).
Estimate covariance matrices that contain low rank and sparse components
LOST includes functions for simulating missing morphometric
data randomly, with taxonomic bias and with anatomical bias. LOST also
includes functions for estimating linear and geometric morphometric data.
Running Lotka's Law following Pao (1985)(DOI: 10.1016/0306-4573(85)90055-X). The Law is based around the proof that the number of authors making n contributions is about 1/n^{a} of those making one contribution.
This package contains routines and documentation for
solving quadratic programming problems where the hessian is
represented as the product of two matrices.
Optimization of bipartite modularity using LP-BRIM (Label propagation
followed by Bipartite Recursively Induced Modularity).
Implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.
Fitting multivariate data patterns with local principal curves; including simple tools for data compression (projection), bandwidth selection, and measuring goodness-of-fit.
Estimates the intensity function, or its derivative of a given order, of a multiplicative counting process using the local polynomial method
Apply Univariate Long Memory Models,
Multivariate Short Memory Models To Hydrological Dataset.
Two local polynomial estimators for solving the errors-in-variables problem: one is in Delaigle, Fan, and Carroll (2009), and one is in Huang and Zhou (2014+). The SIMEX bandwidth selection method is also provided for both estimators.
LPmerge creates a consensus genetic map by merging linkage maps from different populations.  The software uses linear programming (LP) to efficiently minimize the mean absolute error between the consensus map and the linkage maps.  This minimization is performed subject to linear inequality constraints that ensure the ordering of the markers in the linkage maps is preserved.  When marker order is inconsistent between linkage maps, a minimum set of ordinal constraints is deleted to resolve the conflicts.
lpmodeler is a set of user-friendly functions to simplify the modelling of linear programs (LP) and mixed integer programs (MIP). It provides a unified interface compatible with optimization packages: Rsymphony.
Contains a function called "LPR" to estimate coefficients using Lasso and Partial Ridge method and to calculate confidence intervals through bootstrap.
Local Polynomial Regression with Ridging.
An implementation of the Linear Predictor Score approach, as initiated by Radmacher et al. (J Comput Biol 2001) and enhanced by Wright et al. (PNAS 2003) for gene expression signatures. Several tools for unsupervised clustering of gene expression data are also provided.
Lp_solve is freely available (under LGPL 2) software for
solving linear, integer and mixed integer programs. In this
implementation we supply a "wrapper" function in C and some R
functions that solve general linear/integer problems,
assignment problems, and transportation problems. This version
calls lp_solve version 5.5.
The lpSolveAPI package provides an R interface to 'lp_solve',
a Mixed Integer Linear Programming (MILP) solver with support for pure
linear, (mixed) integer/binary, semi-continuous and special ordered sets
(SOS) models.
Learned Pattern Similarity (LPS) for time series.
Implements a novel approach to model the dependency structure
in time series that generalizes the concept of autoregression to local
auto-patterns. Generates a pattern-based representation of time series
along with a similarity measure called Learned Pattern Similarity (LPS).
Introduces a generalized autoregressive kernel.This package is based on the
'randomForest' package by Andy Liaw.
Specially designed rank transform based Legendre Polynomial-like (LP) orthonormal transformations are implemented for non-linear signal processing.
This package provides some basic infrastructure and tools
to fit Generalized Linear Models (GLMs) via penalized
likelihood inference. Estimating procedures already implemented
are the LQA algorithm (that is where its name come from),
P-IRLS, RidgeBoost, GBlockBoost and ForwardBoost.
This is a collection of functions to fit quantile regression models for independent and hierarchical data.
It fits a robust linear quantile regression model using a new
family of zero-quantile distributions for the error term. This family of
distribution includes skewed versions of the Normal, Student's t, Laplace, Slash
and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses
as shown in Bottai et.al.(2009) <doi:10.1002/sim.3781>. It provides estimates and full inference.
It also provides envelopes plots for assessing the fit and confidences bands
when several quantiles are provided simultaneously.
Provides functions for calculating test statistics, simulating quantiles
and simulating p-values of likelihood ratio contrast tests in regression models
with a lack of identifiability.
In the fashion of 'node.js' <https://nodejs.org/>, requires a file,
sourcing into the current environment only the variables explicitly specified
in the module.exports or exports list variable. If the file was already sourced,
the result of the earlier sourcing is returned to the caller.
Implements a Gibbs sampler to do linear regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic scatter, and a covariate prior distribution which is given by either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base distribution.
When multicollinearity exists among predictor variables of the linear model, least square estimators does not provide a better solution for estimating parameters. To deal with multicollinearity several estimators are proposed in the literature. Some of these estimators are Ordinary Least Square Estimator (OLSE), Ordinary Generalized Ordinary Least Square Estimator (OGOLSE), Ordinary Ridge Regression Estimator (ORRE), Ordinary Generalized Ridge Regression Estimator (OGRRE), Restricted Least Square Estimator (RLSE), Ordinary Generalized Restricted Least Square Estimator (OGRLSE), Ordinary Mixed Regression Estimator (OMRE), Ordinary Generalized Mixed Regression Estimator (OGMRE), Liu Estimator (LE), Ordinary Generalized Liu Estimator (OGLE), Restricted Liu Estimator (RLE), Ordinary Generalized Restricted Liu Estimator (OGRLE), Stochastic Restricted Liu Estimator (SRLE), Ordinary Generalized Stochastic Restricted Liu Estimator (OGSRLE), Type (1),(2),(3) Liu Estimator (Type-1,2,3 LTE), Ordinary Generalized Type (1),(2),(3) Liu Estimator (Type-1,2,3 OGLTE), Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 ALTE), Ordinary Generalized Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 OGALTE), Almost Unbiased Ridge Estimator (AURE), Ordinary Generalized Almost Unbiased Ridge Estimator (OGAURE), Almost Unbiased Liu Estimator (AULE), Ordinary Generalized Almost Unbiased Liu Estimator (OGAULE), Stochastic Restricted Ridge Estimator (SRRE), Ordinary Generalized Stochastic Restricted Ridge Estimator (OGSRRE), Restricted Ridge Regression Estimator (RRRE) and Ordinary Generalized Restricted Ridge Regression Estimator (OGRRRE). To select the best estimator in a practical situation the Mean Square Error (MSE) is used. Using this package scalar MSE value of all the above estimators and Prediction Sum of Square (PRESS) values of some of the estimators can be obtained, and the variation of the MSE and PRESS values for the relevant estimators can be shown graphically.
R code of a likelihood ratio test for genome-wide association under genetic
heterogeneity.
Estimates two-dimensional local wavelet spectra
Wavelet-based methods for testing stationarity and quadtree segmenting of images
The basic idea of latent semantic analysis (LSA) is,
that text do have a higher order (=latent semantic) structure which,
however, is obscured by word usage (e.g. through the use of synonyms
or polysemy). By using conceptual indices that are derived statistically
via a truncated singular value decomposition (a two-mode factor analysis)
over a given document-term matrix, this variability problem can be overcome.
Provides functions that allow for convenient working
with Latent Semantic Analysis. For actually building an LSA space, use the
package 'lsa' or other specialized software.
Functions for performing least-squares bilinear clustering of
three-way data. The method uses the bilinear decomposition (or biadditive
model) to model two-way matrix slices while clustering over the third way.
Up to four different types of clusters are included, one for each term of the
bilinear decomposition. In this way, matrices are clustered simultaneously on
(a subset of) their overall means, row margins, column margins and row-column
interactions. The orthogonality of the bilinear model results in separability of
the joint clustering problem into four separate ones. Three of these subproblems
are specific k-means problems, while a special algorithm is implemented for the
interactions. Plotting methods are provided, including biplots for the low-rank
approximations of the interactions.
Estimators and visualization for local statistical complexity of
(N+1)D fields. In particular for 0, 1 and 2 dimensional space this package
provides useful visualization.
Create lots of colorful plots in a plethora of variations (try the LSD demotour() )
Interfaces R with LSD. Reads object-oriented data in results files (.res) produced by LSD and creates appropriate multi-dimensional arrays in R. Also provides functions to extract basic information and statistics from those files. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente (documentation and downloads available at <http://labsimdev.org>).
Fit a least square dummy variable regression
It contains functions that solve least squares linear
regression problems under linear equality/inequality
constraints. It is developed based on the 'Fortran' program of
Lawson and Hanson (1974, 1995), which is public domain and
available at http://www.netlib.org/lawson-hanson.
Linear multiple output using sparse group lasso. The algorithm finds the sparse group lasso penalized maximum likelihood estimator. This result in feature and parameter selection, and parameter estimation. Use of multiple processors for cross validation and subsampling is supported through OpenMP. Development version is on github.
Calculates the (localised) length of the shorth and
supplies corresponding diagnostic plots.
Conduct structural equation modeling via penalized likelihood.
Obtain least-squares means for many linear, generalized linear,
and mixed models. Compute contrasts or linear functions of least-squares
means, and comparisons of slopes. Plots and compact letter displays.
The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution.
Creates plots of peptides from shotgun proteomics analysis of secretome and lysate samples. These plots contain associated protein features and scores for potential secretion and truncation.
Implements the LS-PLS (least squares - partial least
squares) method described in for instance Jørgensen, K.,
Segtnan, V. H., Thyholt, K., Næs, T. (2004) A Comparison of
Methods for Analysing Regression Models with Both Spectral and
Designed Variables.  Journal of Chemometrics, 18(10), 451--464.
A collection of tools intended to make introductory statistics easier to teach, including wrappers for common hypothesis tests and basic data manipulation. It accompanies Navarro, D. J. (2015). Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners, Version 0.5. [Lecture notes] School of Psychology, University of Adelaide, Adelaide, Australia. ISBN: 978-1-326-18972-3. URL: http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/.
Due to lack of proper inference procedure and software,
the ordinary linear regression model is seldom used in practice
for the analysis of right censored data. This paper presents an
S-Plus/R program that implements a recently developed inference
procedure (Jin, Lin and Ying, 2006)\cite{Jin} for the
accelerated failure time model based on the least-squares
principle.
Has a set of functions that allows stationary analysis and locally stationary time series analysis.
Functions for simulating realizations from the posterior distribution of
a latent trait of an item response model. Distributions are conditional on one or
a subset of response patterns (e.g., sum scores). Functions for computing likelihoods,
Fisher and observed information, posterior modes, and profile likelihood confidence
intervals are also included. These functions are designed to be easily amenable to
user-specified models.
Analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.
Targeted Maximum Likelihood Estimation (TMLE) of
treatment/censoring specific mean outcome or marginal structural model for
point-treatment and longitudinal data.
Calculation of rectifying LTPD and AOQL plans for sampling inspection by variables which minimize mean inspection cost per lot of process average quality.
A set of functions to execute the linear-transformation of
replicate (LTR) algorithm for preprocessing of microarray data
Methods of developing linear time series modelling.
Methods are given for loglikelihood computation, forecasting
and simulation.
This is a new tool to estimate Ridge and Liu estimators based on LTS method in multiple linear regression analysis.
Implements local spatial and local spatiotemporal Kriging based on local spatial and local spatiotemporal variograms, respectively.
Functions to work with date-times and timespans: fast and user
friendly parsing of date-time data, extraction and updating of components of
a date-time (years, months, days, hours, minutes, and seconds), algebraic
manipulation on date-time and timespan objects. The 'lubridate' package has
a consistent and memorable syntax that makes working with dates easy and
fun.
Likelihood inference in case-control studies of a rare
disease under independence or simple dependence of genetic and
non-genetic covariates
Print vectors (and data frames) of floating point numbers
using a non-scientific format optimized for human readers.  Vectors
of numbers are rounded using significant digits, aligned at the
decimal point, and all zeros trailing the decimal point are dropped.
Reformat currency-based data as numeric values (or numeric values as currency-based data) and convert between currencies.
Classes and methods for spatially explicit land use change
modelling in R.
A simple client for the Lumen Database <https://lumendatabase.org/> (formerly Chilling Effects) API.
A collection of various R functions for the purpose of Luminescence
dating data analysis. This includes, amongst others, data import, export,
application of age models, curve deconvolution, sequence analysis and
plotting of equivalent dose distributions.
Provides functions to calculate lunar and other environmental
covariates.
Provides flexible but lightweight logging facilities for R scripts.
Supports priority levels for logs and messages, flagging messages, capturing
script output, switching logs, and logging to files or connections.
Latent variable models for network data using fast inferential
procedures.
A model for mixed ordinal and continuous responses is
presented where the heteroscedasticity of the variance of the
continuous response is also modeled. In this model ordinal
response can be dependent on the continuous response. The aim
is to use an approach similar to that of Heckman (1978) for the
joint modelling of the ordinal and continuous responses. With
this model, the dependence between responses can be taken into
account by the correlation between errors in the models for
continuous and ordinal responses
Implements the letter value 'boxplot' which extends the standard
'boxplot' to deal with both larger and smaller number of data points
by dynamically selecting the appropriate number of letter values to display.
The manual approach of Litchfield and Wilcoxon (1949)
<http://jpet.aspetjournals.org/content/96/2/99.abstract>
for evaluating dose-effect experiments
is automated so that the computer can do the work.
Functions to quickly read LXB parameter data.
We provide a clonality score estimator that takes full advantage of the multi-biological-replicate structure of modern sequencing experiments; it specifically takes into account the reality that, typically, the clonal coverage is well below 0.1%.
This package contains functions to read in and manipulate
air quality model output from Models3-formatted files.  This
format is used by the Community Multiscale Air Quaility (CMAQ)
model.
The 10.000 time series from the M4 time series forecasting
competition, including print and plot functions.
Provides binomial tree models for European, American and Asian
Options as well as Interest Rates. Monte Carlo Simulation and Methods for
Solving Differential Equations are also included.
Performs binary and multiclass boosting in maximum-margin, sparse, smooth and normal settings
as described in "A Boosting Framework on Grounds of Online Learning" by T. Naghibi and B. Pfister, (2014).
For further information regarding the algorithms, please refer to http://arxiv.org/abs/1409.7202
This is an integrated meta-analysis package for conducting
a correlational research synthesis. One of the unique features
of this package is in its integration of user-friendly
functions to facilitate statistical analyses at each stage in a
meta-analysis with correlations. It uses recommended procedures
as described in The Handbook of Research Synthesis and
Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
Connects to <https://machi.na/>. and allows the creation
of time series, and running backtests on selected portfolio if requested.
'Maclinical' implements class prediction using both
microarray data and clinical parameters. It addresses the
question of the additional predictive value of microarray data.
Class prediction is performed using a two-step method combining
(pre-validated) PLS dimension reduction and random forests.
A collection of functions for conducting a meta-analysis with mean differences data.  It uses recommended procedures as	described in The 	Handbook of Research Synthesis and Meta-Analysis	(Cooper, Hedges, & Valentine, 2009).
Provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter&Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.
Contains the Maddison Project database, which provides estimates of
GDP per capita for all countries in the world between AD 1 and 2010. See
http://www.ggdc.net/maddison for more information.
An object that supports automatic differentiation
of matrix- and multidimensional-valued functions with
respect to multidimensional independent variables.
Automatic differentiation is via 'forward accumulation'.
Performs distance sampling analyses on a number of species
accounting for unidentified sightings, model uncertainty and covariate
uncertainty.
This function allows to generate two biological conditions
synthetic microarray dataset which has similar behavior to
those currently observed with common platforms. User provides a
subset of parameters.  Available default parameters settings
can be modified.
A bundle of functions for modifying MAESTRA/MAESPA input files,reading output files, and visualizing the stand in 3D. Handy for running sensitivity analyses, scenario analyses, etc.
Data class for increased interoperability working with spatial-temporal data together with corresponding functions and methods (conversions, basic calculations and basic data manipulation). The class distinguishes between spatial, temporal and other dimensions to facilitate the development and interoperability of tools build for it. Additional features are name-based addressing of data and internal consistency checks (e.g. checking for the right data order in calculations).
a collection of efficient, vectorized algorithms for the
creation and investigation of magic squares and hypercubes, including
a variety of functions for the manipulation and analysis of
arbitrarily dimensioned arrays.  The package includes methods for
creating normal magic squares of any order greater than 2.  The
ultimate intention is for the package to be a computerized embodiment
all magic square knowledge, including direct numerical verification
of properties of magic squares (such as recent results on the
determinant of odd-ordered semimagic squares).  Some antimagic
functionality is included.  The package also
serves as a rebuttal to the often-heard comment "I thought R
was just for statistics".
Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.
Provides a mechanism for chaining commands with a
new forward-pipe operator, %>%. This operator will forward a
value, or the result of an expression, into the next function
call/expression. There is flexible support for the type
of right-hand side expressions. For more information, see
package vignette.
To quote Rene Magritte, "Ceci n'est pas un pipe."
Provides a Graphical User Interface for Microarray Data Analysis. Protein protein interactions can also be predicted from two normalized microarray data.
Easy to use package for sending email notifications with status information from R
Interface to Apache Commons Email to send emails
from R.
Implements methodologies for modelling interval data, considering appropriate parameterizations of the variance-covariance matrix that take into account the intrinsic nature of interval data, and lead to five different possible configuration structures.
A user-friendly interface for the construction of
'Makefiles'.
This package creates an empty framework of files and
directories for the "Load, Clean, Func, Do" structure described
by Josh Reich.
A complete analysis pipeline for matrix-assisted laser
desorption/ionization-time-of-flight (MALDI-TOF) and other
two-dimensional mass spectrometry data. In addition to commonly
used plotting and processing methods it includes distinctive
features, namely baseline subtraction methods such as
morphological filters (TopHat) or the statistics-sensitive
non-linear iterative peak-clipping algorithm (SNIP), peak
alignment using warping functions, handling of replicated
measurements as well as allowing spectra with different
resolutions.
Functions for reading (tab, csv, Bruker fid, Ciphergen
XML, mzXML, mzML, imzML, Analyze 7.5, CDF, mMass MSD) and
writing (tab, csv, mMass MSD, mzML, imzML) different file
formats of mass spectrometry data into/from MALDIquant objects.
This package allows you to train topic models in mallet and load results directly into R.
Implementation of methods for microarray meta-analysis.
Designing multi-arm multi-stage studies with normal endpoints and known variance.
This package calculates the nonparametric adaptive MAMSE
weights for univariate, right-censored or multivariate data.
The MAMSE weights can be used in a weighted likelihood or to
define a mixture of empirical distribution functions.
This will allow easier management of a CRAN-style repository on
local networks (i.e. not on CRAN). This might be necessary where hosted
packages contain intellectual property owned by a corporation.
High-dimensional data integration is a critical but difficult problem in genomics research because of potential biases from high-throughput experiments. We present MANCIE, a computational method for integrating two genomic data sets with homogenous dimensions from different sources based on a PCA procedure as an approximation to a Bayesian approach.
Datasets designed to be used in conjunction with Mango Solutions
training materials and the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).
Provides access to coded election programmes from the Manifesto
Corpus and to the Manifesto Project's Main Dataset and routines to analyse this
data. The Manifesto Project (https://manifesto-project.wzb.eu) collects and
analyses election programmes across time and space to measure the political
preferences of parties. The Manifesto Corpus contains the collected and
annotated election programmes in the Corpus format of the package 'tm' to enable
easy use of text processing and text mining functionality. Specific functions
for scaling of coded political texts are included.
Interactive plotting functions for use within RStudio.
The manipulate function accepts a plotting expression and a set of
controls (e.g. slider, picker, checkbox, or button) which are used
to dynamically change values within the expression. When a value is
changed using its corresponding control the expression is
automatically re-executed and the plot is redrawn.
The utility of this package includes finite mixture modeling and model-based clustering through Manly mixture models. It also provides capabilities for forward and backward model selection procedures.
Performs the multiple testing procedures of Cox (2011) and Wang and Cox (2007).
Allows the user to generate a list of features (gene, pseudo, RNA,
CDS, and/or UTR) directly from NCBI database for any species with a current
build available. Option to save downloaded and formatted files is available,
and the user can prioritize the feature list based on type and assembly builds
present in the current build used. The user can then use the list of features
generated or provide a list to map a set of markers (designed for SNP markers
with a single base pair position available) to the closest feature based on
the map build. This function does require map positions of the markers to be
provided and the positions should be based on the build being queried through
NCBI.
Functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure.
Supplement to maps package, providing the larger and/or
higher-resolution databases.
Estimation methods for phase-type
distribution (PH) and Markovian arrival process (MAP) from
empirical data (point and grouped data) and density function.
Contains functions for mapping odds ratios or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting for confounding variables, using methods described by Kelsall and Diggle (1998) and Webster at al. (2006).  Includes convenient functions for mapping, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).
MAPLES is a general method for the estimation of age
profiles that uses standard micro-level demographic survey
data. The aim is to estimate smoothed age profiles and relative
risks for time-fixed and time-varying covariates.
A minimal, light-weight set of tools for producing nice looking maps in R, with support for map projections.
Create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.
Converts latitude/longitude into projected coordinates.
Utilities for 'vizualizing' species occurrence data. Includes
functions to 'vizualize' occurrence data from 'spocc', 'rgbif',
and other packages. Mapping options included for base R plots, 'ggplot2',
and various interactive maps.
Display of maps.  Projection code and larger maps are in
separate packages ('mapproj' and 'mapdata').
Automated calculation and visualization of survey data statistics on a color-coded map.
Set of tools for manipulating and reading geographic data, in particular ESRI shapefiles; C code used from shapelib. It includes binary access to GSHHG shoreline files. The package also provides interface wrappers for exchanging spatial objects with packages such as PBSmapping, spatstat, maps, RArcInfo, Stata tmap, WinBUGS, Mondrian, and others.
Posterior maximization for topic models (LDA) in text analysis,
as described in Taddy (2012) `on estimation and selection for topic models'.  Previous versions of this code were included as part of the textir package.  If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.
Functions with example data for graphing, pruning, and
mapping models from hierarchical clustering, and classification
and regression trees.
Methods to view spatial objects interactively.
R functions for multivariate autoregressive analysis
The MAR1 package provides basic tools for preparing
ecological community time-series data for MAR modeling,
building MAR-1 models via model selection and bootstrapping,
and visualizing and exporting model results. It is intended to
make MAR analysis (sensu Ives et al. (2003) Analysis of
community stability and ecological interactions from
time-series data) a more accessible tool for anyone studying
community dynamics.  The user need not necessarily be familiar
with time-series modeling or command-based statistics programs
such as R.
Multiplicative AR(1) with Seasonal is a stochastic
process model built on top of AR(1). The package provides the
following procedures for MAR(1)S processes: fit, compose, decompose,
advanced simulate and predict.
Computation of various Markovian models for categorical data
including homogeneous Markov chains of any order, MTD models, Hidden Markov
models, and Double Chain Markov Models.
Datasets, constants, conversion factors, utilities for MArine, Riverine,
Estuarine, LAcustrine and Coastal science.
The package contains among others: (1) chemical and
physical constants and datasets, e.g. atomic weights, gas
constants, the earths bathymetry; (2) conversion factors
(e.g. gram to mol to liter, barometric units,
temperature, salinity); (3) physical functions, e.g. to
estimate concentrations of conservative substances, gas
transfer and diffusion coefficients, the Coriolis force
and gravity; (4) thermophysical properties of the
seawater, as from the UNESCO polynomial or from the more
recent derivation based on a Gibbs function.
Local recombination rates are graphically estimated across a genome using marey maps.
Likelihood inference based on higher order approximations
for linear nonnormal regression models
Multivariate arima and arima-x estimation using Spliid's algorithm.
Provides R bindings to the 'Sundown' 'Markdown' rendering library
(https://github.com/vmg/sundown). 'Markdown' is a plain-text formatting
syntax that can be converted to 'XHTML' or other formats. See
http://en.wikipedia.org/wiki/Markdown for more information about 'Markdown'.
Functions for fitting various models to capture-recapture data
including fixed and mixed-effects Cormack-Jolly-Seber(CJS) for survival
estimation and POPAN structured Jolly-Seber models for abundance
estimation. Includes a CJS models that concurrently estimates and corrects
for tag loss. Hidden Markov model (HMM) implementations of CJS and
multistate models with and without state uncertainty.
maRketSim is a market simulator for R.  It was initially designed
around the bond market, with plans to expand to stocks.  maRketSim is
built around the idea of portfolios of fundamental objects.  Therefore
it is slow in its current incarnation, but allows you the flexibility of
seeing exactly what is in your final results, since the objects are retained.
Automatic marking of R assignments for students and teachers based
on 'testthat' test suites.
Allows for fitting of maximum likelihood models using Markov chains
on phylogenetic trees for analysis of discrete character data. Examples of such
discrete character data include restriction sites, gene family presence/absence,
intron presence/absence, and gene family size data. Hypothesis-driven user-
specified substitution rate matrices can be estimated. Allows for biologically
realistic models combining constrained substitution rate matrices, site rate
variation, site partitioning, branch-specific rates, allowing for non-stationary
prior root probabilities, correcting for sampling bias, etc.
Functions and S4 methods to create and manage discrete time Markov chains (DTMC) more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of DTMC proprieties) analysis are provided.
a collection of tools for analyzing significance of
Markowitz portfolios.
Functions provided allow data simulation; construction of weighted relative likelihood functions; clustering and principal component analysis based on weighted relative likelihood functions.
Import xyz data from the NOAA, GEBCO and other sources, plot xyz data to prepare publication-ready figures, analyze xyz data to extract transects, get depth / altitude based on geographical coordinates, or calculate z-constrained least-cost paths.
This algorithm provides a numerical solution to the
problem of minimizing a function. This is more efficient than
the Gauss-Newton-like algorithm when starting from points very
far from the final minimum. A new convergence test is
implemented (RDM) in addition to the usual stopping criterion :
stopping rule is when the gradients are small enough in the
parameters metric (GH-1G).
The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models fit to multivariate time-series data.  Fitting is primarily via an Expectation-Maximization (EM) algorithm, although fitting via the BFGS algorithm (using the optim function) is also provided.  MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model.  Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, bootstrap model selection criteria (AICb), confidences intervals via the Hessian approximation and via bootstrapping and calculation of auxiliary residuals for detecting outliers and shocks.  The user guide shows examples of using MARSS for parameter estimation for a variety of applications, model selection, dynamic factor analysis, outlier and shock detection, and addition of covariates.  Type RShowDoc("UserGuide", package="MARSS") at the R command line to open the MARSS user guide.  Online workshops (lectures and computer labs) at http://faculty.washington.edu/eeholmes/workshops.shtml  See the NEWS file for update information.
An S4 implementation of the unbiased extension of the model-
assisted synthetic-regression estimator proposed by
Mandallaz (2013) <DOI:10.1139/cjfr-2012-0381>,
Mandallaz et al. (2013) <DOI:10.1139/cjfr-2013-0181> and
Mandallaz (2014) <DOI:10.1139/cjfr-2013-0449>.
It yields smaller variances than the standard bias correction,
the generalised regression estimator.
Functions and datasets to support Venables and Ripley,
"Modern Applied Statistics with S" (4th edition, 2002).
Estimation equations are from a variety of sources but are, in general, based on regressions between skeletal measurements (e.g., femoral circumference) and body mass in living taxa.
The primary aim of MasterBayes is to use MCMC techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data.  Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled C++ for efficiency.
Calculates Model-Averaged Tail Area Wald (MATA-Wald) confidence
intervals, which are constructed using single-model estimators and model
weights.
Provides functions for multivariate and propensity score matching
and for finding optimal balance based on a genetic search algorithm.
A variety of univariate and multivariate metrics to
determine if balance has been obtained are also provided.
Returns the subset of the data with the minimum imbalance for
every possible subset size (N - 1, N - 2, ...), down to the data set with the
minimum possible imbalance. Also includes tools for the estimation
of causal effects for each subset size, functions for visualization
and data export, and functions for calculating
model dependence as proposed by Athey and Imbens.
Implements structural estimators to correct for
the sample selection bias from observed outcomes in matching
markets. This includes one-sided matching of agents into
groups as well as two-sided matching of students to schools.
The package also contains R code for three matching algorithms:
the deferred-acceptance (or Gale-Shapley) algorithm for
stable marriage and college admissions problems, the
top-trading-cycles algorithm for house allocation and a
partitioning linear programme for the roommates problem.
Computes matching algorithms quickly using Rcpp.
Implements the Gale-Shapley Algorithm to compute the stable
matching for two-sided markets, such as the stable marriage
problem and the college-admissions problem. Implements Irving's
Algorithm for the stable roommate problem. Implements the top
trading cycle algorithm for the indivisible goods trading problem.
MatchIt selects matched samples of the the original
treated and control groups with similar covariate distributions
-- can be used to match exactly on covariates, to match on
propensity scores, or perform a variety of other matching
procedures.
Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies.
Performs multilevel matches for data with cluster-level treatments and individual-level outcomes using a network optimization algorithm.  Functions for checking balance at the cluster and individual levels are also provided, as are methods for permutation-inference-based outcome analysis.
Transferring over a code base from Matlab to R is often a repetitive
and inefficient use of time. This package provides a translator for Matlab /
Octave code into R code. It does some syntax changes, but most of the heavy
lifting is in the function changes since the languages are so similar.
Options for different data structures and the functions that can be changed
are given. The Matlab code should be mostly in adherence to the standard
style guide but some effort has been made to accommodate different number of
spaces and other small syntax issues. This will not make the code more R
friendly and may not even run afterwards. However, the rudimentary syntax,
base function and data structure conversion is done quickly so that the
maintainer can focus on changes to the design structure.
Tools to simulate, manage, visualize, and analyze
spatially and temporally explicit datasets of mating potential.
Implements methods to calculate synchrony, proximity, and compatibility.
Simple tools for constructing and manipulating objects of
class mathgraph from the book "S Poetry", available at
http://www.burns-stat.com/pages/spoetry.html
Uses a ratio of weighted distributions to estimate association between variables in a data set.
Provides users to call MATLAB from using the "system" command.
Allows users to submit lines of code or MATLAB m files.
This is in comparison to 'R.matlab', which creates a MATLAB server.
A collection of matrix functions for teaching and learning matrix
linear algebra as used in multivariate statistical methods. These functions are
mainly for tutorial purposes in learning matrix algebra ideas using R. In some
cases, functions are provided for concepts available elsewhere in R, but where
the function call or name is not obvious. In other cases, functions are provided
to show or demonstrate an algorithm. In addition, a collection of functions are
provided for drawing vector diagrams in 2D and 3D.
A general framework for computing powers of matrices.  A
key feature is the capability for users to write callback functions,
called after each iteration, thus enabling customization for specific
applications.  Diverse types of matrix classes/matrix multiplication
are accommodated.  If the multiplication type computes in parallel,
then the package computation is also parallel.
An analysis platform for metagenomics combining
specialized tools and workflows, easy handling of the BIOM
format, and transparent access to MG-RAST resources.  matR integrates
easily with other R packages and non-R software.
Classes and methods for dense and sparse matrices and
operations on them using 'LAPACK' and 'SuiteSparse'.
A collection of functions to support matrix calculations
for probability, econometric and numerical analysis. There are
additional functions that are comparable to APL functions which
are useful for actuarial models such as pension mathematics.
This package is used for teaching and research purposes at the
Department of Finance and Risk Engineering, New York
University, Polytechnic Institute, Brooklyn, NY 11201.
Computation and visualization of matrix correlation coefficients.
The main method is the Similarity of Matrices Index, while various related
measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2 and adjusted RV are included
for comparison.
Matrix eQTL is designed for fast eQTL analysis on large datasets.
Matrix eQTL can test for association between genotype and gene expression using linear regression
with either additive or ANOVA genotype effects.
The models can include covariates to account for factors
as population stratification, gender, and clinical variables.
It also supports models with heteroscedastic and/or correlated errors,
false discovery rate estimation and separate treatment of local (cis) and distant (trans) eQTLs.
Modelling with sparse and dense 'Matrix' matrices, using
modular prediction and response module classes.
Partial Least Squares Path Modeling
algorithm and related algorithms. The algorithm implementations aim for
computational efficiency using matrix algebra and covariance data. The
package is designed toward Monte Carlo simulations and includes functions
to perform simple Monte Carlo simulations.
High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds().  Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized.  There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedians().
Implements data manipulation methods such as cast, aggregate, and merge/join for Matrix and matrix-like objects.
This package in includes functions for receiver operating
characteristic (ROC) analyses as well as Monte Carlo
simulation.  It includes specific graphical functions for
interpreting the output of these techniques.
Interactive shiny application for running a meta-analysis,
provides support for both random effects and fixed effects models with the 'metafor' package.
Additional support is included for calculating effect sizes plus
support for single case designs, graphical output, and detecting publication bias.
This package is a gene set analysis function for one-sided test (OLS), two-sided test (multivariate analysis of variance).
If the experimental conditions are equal to 2, the p-value for Hotelling's t^2 test is calculated.
If the experimental conditions are great than 2, the p-value for Wilks' Lambda is determined and post-hoc test is reported too.
Three multiple comparison procedures, Dunnett, Tukey, and sequential pairwise comparison, are implemented.
The program computes the p-values and FDR (false discovery rate) q-values for all gene sets.
The p-values for individual genes in a significant gene set are also listed.
MAVTgsa generates two visualization output: a p-value plot of gene sets (GSA plot) and a GST-plot of the empirical distribution function of the ranked test statistics of a given gene set.
A Random Forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes.
Perform exact MAX3 or MAX2 test for one-locus genetic
association analysis and trend test for dominant, recessive and
additive models. It can also calculate approximated p-value
with the normal approximation method.
maxent is an R package with tools for low-memory
multinomial logistic regression, also known as maximum entropy.
The focus of this maximum entropy classifier is to minimize
memory consumption on very large datasets, particularly sparse
document-term matrices represented by the tm package. The
classifier is based on an efficient C++ implementation written
by Dr. Yoshimasa Tsuruoka.
Complex niche models show low performance in identifying
the most important range-limiting environmental variables and in
transferring habitat suitability to novel environmental conditions
(Warren and Seifert, 2011; Warren et al., 2014). This package helps to
identify the most important set of uncorrelated variables and to
fine-tune Maxent's regularization multiplier. In combination, this
allows to constrain complexity and increase performance of Maxent niche
models (assessed by information criteria, such as AICc (Akaike, 1974) ,
and by the area under the receiver operating characteristic (AUC)
(Fielding and Bell, 1997). Users of this package should be familiar with
Maxent niche modelling.
Functions for Maximum Likelihood (ML) estimation and non-linear
optimization, and related tools.  It includes a unified way to call
different optimizers, and classes and methods to handle the results from
the ML viewpoint.  It also includes a number of convenience tools for testing
and developing your own models.
Provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program MAXENT, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species' distribution.
Generate a maximum projection (MaxPro) design, a MaxPro Latin hypercube design or improve an initial design based on the MaxPro criterion. Details of the MaxPro criterion can be found in: Joseph, V. R., Gul, E., and Ba, S. (2015) "Maximum Projection Designs for Computer Experiments", Biometrika.
Maximally selected rank statistics with
several p-value approximations.
A suite of conversion scripts to create internally standardized
spatial polygons dataframes. Utility scripts use these datasets to return
values such as country, state, timezone, watershed, etc. associated with a
set of longitude/latitude pairs. (They also make cool maps.)
Scattered data interpolation with Multilevel B-Splines
Distributions that are typically used for exposure rating in
general insurance, in particular to price reinsurance contracts.
The vignettes show code snippets to fit the distribution to
empirical data.
Cluster genes based on Poisson or Negative-Binomial model
for RNA-Seq or other digital gene expression (DGE) data
Implements methods that useful in designing research and analyzing data, with
particular emphasis on methods that are developed for or used within the behavioral,
educational, and social sciences (broadly defined). That being said, many of the methods
implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a
suite of functions for a variety of related topics, such as effect sizes, confidence intervals
for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size
planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and
minimum-risk point estimation perspectives), mediation analysis, various properties of
distributions, and a variety of utility functions. MBESS (pronounced ``em-bes'') was originally
an acronym for "Methods for the Behavioral, Educational, and Social Sciences," but at this
point MBESS contains methods applicable and used in a wide variety of fields and is an
orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has
greatly benefited from others, see <http://nd.edu/~kkelley/site/MBESS.html> for a detailed
list of those that have contributed and other details.
Implements methods from the paper
"Fast Moment-Based Estimation for Hierarchical Models," by Perry (2016).
Over 20 multiple-site diversity indices can be calculated.
Later versions will include phylogenetic diversity
This package provides linear models based on Theil-Sen
single median and Siegel repeated medians. They are very robust
(29 or 50 percent breakdown point, respectively), and if no
outliers are present, the estimators are very similar to OLS.
The MBmca package provides data sets and lightweight utilities for
nucleic acid melting curve analysis and presentation on microbead surfaces
but also for reactions in solution (e.g., qPCR).
Model Based Multifactor Dimension Reduction proposed by
Calle et al. (2008) as a dimension reduction method for
exploring gene-gene interactions.
Functional gradient descent algorithm
(boosting) for optimizing general risk functions utilizing
component-wise (penalised) least squares estimates or regression
trees as base-learners for fitting generalized linear, additive
and interaction models to potentially high-dimensional data.
Access to the MBTA API for R. Creates an easy-to-use bundle of
functions to work with all the built-in calls to the MBTA API. Allows users
to download realtime tracking data in dataframe format that is manipulable
in standard R analytics functions.
Bayesian variable selection methods for data with continuous multivariate responses and multiple covariates.
A complete framework to build and study Two-Dimensional Monte-Carlo simulations, aka Second-Order Monte-Carlo simulations. Also includes various distributions (pert, triangular, Bernoulli, empirical discrete and continuous).
Post processes MC2 output, especially for use by Path or ST-Sim.  MC2 (short for "MC1 version 2") is a dynamic global vegetation model (en.wikipedia.org/wiki/DGVM).  Path (essa.com/tools/path) and ST-Sim (www.apexrms.com) are state-and-transition model (STM) engines. MC2 has a user website at sites.google.com/site/mc1dgvmusers.  Since 2001, MC1 has been used to simulate changes in natural vegetation due to climate change at scales from regional to global.  In 2012, MC1 was reimplemented in C++ to make it faster and to reduce storage requirements.  This newer version is referred to as MC2, an abbreviation of "MC1 version 2".  Beginning in 2011, output from MC1 and MC2 has been used to inform regional state-and-transition model simulations by the U.S. Forest Service and the Washington State Department of Natural Resources.  Projects to date have involved study areas in central Oregon, the Olympic Peninsula, the Blue Mountains ecoregion, southwestern Oregon, and southeastern Oregon.  In the first of this series of projects, the netCDF output files from MC2 were manually post-processed, mostly in Excel, to produce input .csv files for the STM engines. Beginning with the second project, R scripts were used to automate the post-processing work.  These R scripts have been collected into the MC2toPath R-package.
Weather and air pollution data, risk estimates, and other
information from the Medicare Air Pollution Study (MCAPS) of
204 U.S. counties, 1999--2002
Computes the prime implicants or a minimal disjunctive normal form for a
logic expression presented by a truth table or a logic tree. Has been particularly
developed for logic expressions resulting from a logic regression analysis, i.e.
logic expressions typically consisting of up to 16 literals, where the prime implicants
are typically composed of a maximum of 4 or 5 literals.
A number of biomedical problems involve performing many hypothesis tests, with an attendant need to apply stringent thresholds. Often the data take the form of a series of predictor vectors, each of which must be compared with a single response vector, perhaps with nuisance covariates. Parametric tests of association are often used, but can result in inaccurate type I error at the extreme thresholds, even for large sample sizes. Furthermore, standard two-sided testing can reduce power compared to the doubled p-value, due to asymmetry in the null distribution. Exact (permutation) testing approaches are attractive, but can be computationally intensive and cumbersome. MCC is an approximation to exact association testing of two vectors that is accurate and fast enough for standard use in high-throughput settings, and can easily provide standard two-sided or doubled p-values.
Implements methods for processing a sample of (hard)
clusterings, e.g. the MCMC output of a Bayesian clustering
model. Among them are methods that find a single best
clustering to represent the sample, which are based on the
posterior similarity matrix or a relabelling algorithm.
Functions which can be useful to support the analyst in the Multicriteria Decision Aiding (MCDA) process involving multiple, conflicting criteria.
An R implementation of Four Multi-Criteria Decision Making (MCDM) Methods: Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), "VIseKriterijumska Optimizacija I Kompromisno Resenje" (VIKOR), both Multi-Objective Optimization by Ratio Analysis and Full Multiplicative Form (Multi-MOORA) and Weighted Aggregated Sum Product ASsessment (WASPAS). In addition, this package provides a MetaRanking function which combines the output of the previous methods.
Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM.
Machine coded genetic algorithm (MCGA) is a fast tool for
real-valued optimization problems. It uses the byte
representation of variables rather than real-values. It
performs the classical crossover operations (uniform) on these
byte representations. Mutation operator is also similar to
classical mutation operator, which is to say, it changes a
randomly selected byte value of a chromosome by +1 or -1 with
probability 1/2. In MCGAs there is no need for
encoding-decoding process and the classical operators are
directly applicable on real-values. It is fast and can handle a
wide range of a search space with high precision. Using a
256-unary alphabet is the main disadvantage of this algorithm
but a moderate size population is convenient for many problems.
Package also includes multi_mcga function for multi objective
optimization problems. This function sorts the chromosomes
using their ranks calculated from the non-dominated sorting
algorithm.
'mcgibbsit' provides an implementation of Warnes & Raftery's
MCGibbsit run-length diagnostic for a set of (not-necessarily
independent) MCMC samplers.  It combines the estimate error-bounding
approach of the Raftery and Lewis MCMC run length diagnostic with
the between verses within chain approach of the Gelman and
Rubin MCMC convergence diagnostic.
The package performs global optimization combining Monte Carlo and Quasi Monte Carlo simulation with a local search. \n       The local searches can be easily speeded-up by using a network of local workstations.
mcheatmaps serves to visualize multiple different symmetric matrices and matrix clusters in a single figure using a dendogram, two half matrices and various color labels.
The package performs Monte Carlo hypothesis tests. It
allows a couple of different sequential stopping boundaries (a
truncated sequential probability ratio test boundary and a
boundary proposed by Besag and Clifford, 1991). Gives valid
p-values and confidence intervals on p-values.
The Multiplicative Competitive Interaction (MCI) Model by Nakanishi & Cooper (1974) <DOI:10.2307/3151146> is an econometric model for analyzing market shares and/or market areas. The functions in this package include fitting the MCI model, the log-centering transformation of MCI datasets and tools for data preparation.
This package provides functions to estimate two popular IRT-models: The Nominal Response Model (Bock 1972) and the quite recently developed Nested Logit Model (Suh & Bolt 2010). These are two models to examine multiple-choice items and other multicategorial response formats.
Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.
Maximum likelihood estimation using a Monte Carlo local likelihood (MCLL) method
This packages provides a function to estimate parameters for
the conditional logit model (also with multinomial counts), and for the
mixed conditional logit model, or conditional logit with random effects
(random intercepts only, no random slopes yet).
The current implementation of random effects is limited to
the PQL technique, which requires large cluster sizes.
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
Simulates continuous distributions of random vectors using
Markov chain Monte Carlo (MCMC).  Users specify the distribution by an
R function that evaluates the log unnormalized density.  Algorithms
are random walk Metropolis algorithm (function metrop), simulated
tempering (function temper), and morphometric random walk Metropolis
(Johnson and Geyer, Annals of Statistics, 2012, function morph.metrop),
which achieves geometric ergodicity by change of variable.
Provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions.It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots.
Poisson-lognormal generalized linear mixed model analysis of multivariate counts data using MCMC, aiming to infer the changes in relative proportions of individual variables. The package was originally designed for sequence-based analysis of microbial communities ("metabarcoding", variables = operational taxonomic units, OTUs), but can be used for other types of multivariate counts, such as in ecological applications (variables = species). The results are summarized and plotted using 'ggplot2' functions. Includes functions to remove sample and variable outliers and reformat counts into normalized log-transformed values for correlation and principal component/coordinate analysis. Walkthrough and examples: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/walkthroughExample_mcmcOTU_R.txt.
Contains functions to perform Bayesian
inference using posterior simulation for a number of
statistical models. Most simulation is done in compiled C++
written in the Scythe Statistical Library Version 1.0.3. All
models return coda mcmc objects that can then be summarized
using the coda package. Some useful
utility functions such as density functions,
pseudo-random number generators for statistical
distributions, a general purpose Metropolis sampling algorithm,
and tools for visualization are provided.
Functions for convenient plotting and viewing of MCMC output.
Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: http://bit.ly/1Nwo4CB.
Provides tools for computing Monte Carlo standard
errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. MCSE
computation for expectation and quantile estimators is
supported as well as multivariate estimations. The package also provides
functions for computing effective sample size and for plotting
Monte Carlo estimates versus sample size.
Functions for multiple criteria optimization using genetic
algorithms and related test problems
The 1001 time series from the M-competition (Makridakis et
al. 1982) and the 3003 time series from the IJF-M3 competition
(Makridakis and Hibon, 2000).
The package aids in creating a Conjoint Analysis design
with extra cards.  Unlike traditional "holdout" cards these
cards are used to create a set of "good" (balanced and low
correlation) designs. Each of these designs is analyzed and the
average calculated.
A Markov chain Monte Carlo method is provided to estimate the preferential attachment function from a single network snapshot. Conventional methods require the complete information about the appearance order of all nodes and edges in the network. This package incorporates the appearance order into the state space and estimates it together with the preferential attachment function. Auxiliary variables are introduced to facilitate fast Gibbs sampling.
Multiple contrast tests and simultaneous confidence
intervals based on normal approximation. With implementations for
binomial proportions in a 2xk setting (risk difference and odds ratio),
poly-3-adjusted tumour rates, biodiversity indices (multinomial data)
and expected values under lognormal assumption. Approximative power
calculation for multiple contrast tests of binomial and Gaussian data.
Provides a function that wraps
mcparallel() and mccollect() from 'parallel' with temporary variables and a
task handler.  Wrapped in this way the results of an mcparallel() call
can be returned to the R session when the fork is complete
without explicitly issuing a specific mccollect() to retrieve the value.
Outside of top-level tasks, multiple mcparallel() jobs can be retrieved with
a single call to mcparallelDoCheck().
A Monte Carlo permutation method for multiple test
correlation.
This package implements a methodology for the design and analysis of
dose-response studies that combines aspects of multiple comparison procedures
and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748).
The package provides tools for the analysis of dose finding trials as well as a variety
of tools necessary to plan a trial to be conducted with the MCPMod methodology.
***NOTE*** The MCPMod package will not be further developed, all future development of
the MCP-Mod methodology will be done in the DoseFinding R-package. ***NOTE***
Calculation of signed root deviance profiles for linear combinations of parameters in a generalized linear model. Multiple tests and simultaneous confidence intervals are provided.
This package provides regression methods to quantify the relation between two measurement methods. In particular it addresses regression problems with errors in both variables and without repeated measurements. The package provides implementations of Deming regression, weighted Deming regression, and Passing-Bablok regression following the CLSI EP09-A3 recommendations for analytical method comparison and bias estimation using patient samples.
Perform the model confidence set procedure of Hansen et al (2011).
mcsm contains a collection of functions that allows the
reenactment of the R programs used in the book EnteR Monte
Carlo Methods without further programming. Programs being
available as well, they can be modified by the user to conduct
one's own simulations.
Locally weighted regression, semiparametric and
conditionally parametric regression, fourier and cubic spline
functions, GMM and linearized spatial logit and probit,
k-density functions and counterfactuals, nonparametric quantile
regression and conditional density functions, Machado-Mata
decomposition for quantile regressions, spatial AR model,
repeat sales models, conditionally parametric logit and probit
Transition matrices (probabilities or counts) estimation for discrete Markov Chains of order n (1 <= n <= 5).
Selects bandwidth for the kernel density estimator with minimum distance method as proposed by Devroye and Lugosi (1996). The minimum distance method directly selects the optimal kernel density estimator from countably infinite kernel density estimators and indirectly selects the optimal bandwidth. This package selects the optimal bandwidth from finite kernel density estimators.
Mixture and flexible discriminant analysis, multivariate
adaptive regression splines (MARS), BRUTO, ...
Package implements projection based methods for preprocessing,
exploring and analysis of multivariate data used in chemometrics.
Provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.
Allows different models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance (overdispersion) can be further modeled as random-effect models.
Normalize data to minimize the difference between sample plates
(batch effects). For given data in a matrix and grouping variable (or
plate), the function 'normn_MA' normalizes the data on MA coordinates.
More details are in the citation. The primary method is 'Multi-MA'. Other
fitting functions on MA coordinates can also be employed e.g. loess.
The multinomial diversity model is a toolbox for relating diversity to complex predictors. It is based on (1) Shannon diversity; (2) the multinomial logit model, and (3) the link between Shannon diversity and the log-likelihood of the MLM.
Allows a user to conduct multivariate distance matrix regression using analytic p-values and compute measures of effect size.
Provides automatization for plot generation succeeding common molecular dynamics analyses.
This includes straightforward plots, such as RMSD (Root-Mean-Square-Deviation) and
RMSF (Root-Mean-Square-Fluctuation) but also more sophisticated ones such as
dihedral angle maps, hydrogen bonds, cluster bar plots and
DSSP (Definition of Secondary Structure of Proteins) analysis.
The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.
Performs multifactor dimensionality reduction (MDR) to
detect potential gene-gene interactions in case-control
studies.
A set of functions to obtain modified score test for generalized linear models.
Tools associated with General
Recognition Theory (Townsend & Ashby, 1986), including Gaussian model fitting of 2x2 and more
general designs, associated plotting and model comparison tools,
and tests of marginal response invariance and report independence.
A graphical user interface (GUI) for performing Multidimensional Scaling applications and interactively analysing the results all within the GUI environment. The MDS-GUI provides means of performing Classical Scaling, Least Squares Scaling, Metric SMACOF, Non-Metric SMACOF, Kruskal's Analysis and Sammon Mapping with animated optimisation.
Clustering of vector data and functional data using the mean shift algorithm (multi-core processing is supported) or its blurring version.
Identification of ring borders on scanned image sections from dendrochronological samples. Processesing of image reflectances to produce gray matrices and a time series of smoothed gray values. Luminance data is plotted on segmented images for users to perform both: visual identification of ring borders, or control of automatic detection. Routines to visually include/exclude ring borders on the R graphical device, or automatically detect ring borders using a linear detection algorithm. This algorithm detects ring borders according to positive/negative extreme values in the smoothed time-series of gray values.
This package performs maximum entropy density based
dependent data bootstrap. An algorithm is provided to create a
population of time series (ensemble) without assuming
stationarity. The reference paper (Vinod, H.D., 2004) explains
how the algorithm satisfies the ergodic theorem and the central
limit theorem.
Two distinct but related statistical approaches to the problem of identifying the combinations of medication error characteristics that are more likely to result in harm are implemented in this package: 1) a Bayesian hierarchical model with optimal Bayesian ranking on the log odds of harm, and 2) an empirical Bayes model that estimates the ratio of the observed count of harm to the count that would be expected if error characteristics and harm were independent. In addition, for the Bayesian hierarchical model, the package provides functions to assess the sensitivity of results to different specifications of the random effects distributions.
Run flexible mediation analyses using natural
effect models as described in Lange, Vansteelandt and Bekaert (2012),
Vansteelandt, Bekaert and Lange (2012) and Loeys, Moerkerke, De Smet,
Buysse, Steen and Vansteelandt (2013).
Calculates MeDiA_K (means Mean Distance Association by K-nearest neighbor) in order to detect nonlinear associations.
Provides a general framework for clinical trial simulations based
on the Clinical Scenario Evaluation (CSE) approach. The package supports a
broad class of data models (including clinical trials with continuous, binary,
survival-type and count-type endpoints as well as multivariate outcomes that are
based on combinations of different endpoints), analysis strategies and commonly
used evaluation criteria.
We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010), Imai, Keele and Tingley (2010), Imai, Tingley and Yamamoto (2013), Imai and Yamamoto (2013) and Yamamoto (2013). In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.
Generates risk estimates and comorbidity flags from ICD-9-CM
codes available in administrative medical datasets. The package supports
the Charlson Comorbidity Index, the Elixhauser Comorbidity
classification, the Revised Cardiac Risk Index, and the Risk Stratification
Index.  Methods are table-based, fast, and use the 'plyr' package, so
parallelization is possible for large jobs. Also includes a sample of
real ICD-9 data for 100 patients from a publicly available dataset.
This package contains the functions used to perform some
confidence statistics based in population median.
This is a C++ implementation of Sparse Topical Coding
(STC), a model of discrete data which is fully described in Zhu
et al. (2011) (http://www.cs.cmu.edu/~junzhu/stc/stc.pdf). It
can be used for multi-class classification and describing
documents with underlying sparse topics.
MEET (Motif Elements Estimation Toolkit) is a R-package
that integrates a set of computational algorithms for the
detection of Transcription Factor Binding Sites (TFBS).
A framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa' is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa' objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples.
An S4 update of the 'mefa' package
using sparse matrices for enhanced efficiency.
Sparse array-like objects are supported via
lists of sparse matrices.
Given a set of taxa and marker, the goal is build a complete and correct sequence alignment for phylogenetic analysis.
Exploratory model analysis.  Fit and graphical
explore ensembles of linear models.
Modelization of influenza epidemics in order to monitor future activity.
Memgene can detect relatively weak spatial genetic patterns by using Moran's Eigenvector Maps (MEM) to extract only the spatial component of genetic variation.  Memgene has applications in landscape genetics where the movement and dispersal of organisms are studied using neutral genetic variation.
One of the aims of this package is to make life easier for
useRs who deal with survey data sets. It provides an
infrastructure for the management of survey data including
value labels, definable missing values, recoding of variables,
production of code books, and import of (subsets of) SPSS and
Stata files. Further, it provides functionality to produce
tables and data frames of arbitrary descriptive statistics and
(almost) publication-ready tables of regression model
estimates, which can be exported to LaTeX and HTML.
Cache the results of a function so that when you call it
again with the same arguments it returns the pre-computed value.
Data sets and sample analyses from Pinheiro and Bates,
"Mixed-effects Models in S and S-PLUS" (Springer, 2000).
How much ram do you need to store a 100,000 by 100,000 matrix?
How much ram is your current R session using? How much ram do you even have?
Learn the scintillating answer to these and many more such questions with
the 'memuse' package.
Set of configurable menus built with GTK+ to graphically interface new functions.
A GUI for merging datasets in R using gWidgets.
N>=3 methods are used to measure each of n items.
The data are used to estimate simultaneously systematic error (bias)
and random error (imprecision). Observed measurements for each method
or device are assumed to be linear functions of the unknown true values
and the errors are assumed normally distributed. Maximum likelihood
estimation is used for the imprecision standard deviation estimates.
Pairwise calibration curves and plots can be easily generated.
Provides methods for extracting results from mixed-effect model
objects fit with the 'lme4' package. Allows construction of prediction intervals
efficiently from large scale linear and generalized linear mixed-effects models.
A mixed collection of useful and semi-useful functions, some of
which may even be referenced in The R Primer book.
User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker, "Meta-Analysis with R" (2015):
- fixed effect and random effects meta-analysis;
- several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble);
- statistical tests and trim-and-fill method to evaluate bias in meta-analysis;
- import data from RevMan 5;
- prediction interval, Hartung-Knapp and Paule-Mandel method for random effects model;
- cumulative meta-analysis and leave-one-out meta-analysis;
- meta-regression (if R package metafor is installed);
- generalised linear mixed models (if R packages metafor, lme4, numDeriv, and BiasedUrn are installed).
Bayesian inference analysis for bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximation with INLA. A purpose built graphic user interface is available. The installation of R package INLA is compulsory for successful usage. The INLA package can be obtained from <http://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: install.packages("INLA", repos="http://www.math.ntnu.no/inla/R/testing").
A package for meta-analysis of genome-wide association
scans between quantitative or binary traits and SNPs
Fits probabilistic principal components analysis,
probabilistic principal components and covariates analysis and
mixtures of probabilistic principal components models to
metabolomic spectral data.
A collection of functions to aid in the statistical analysis of metabolomic data
Functions to analyze coherence, boundary clumping, and turnover
following the pattern-based metacommunity analysis of Leibold and Mikkelson
2002. The package also includes functions to visualize ecological networks, and
to calculate modularity as a replacement to boundary clumping.
Implement the DerSimonian-Laird (DSL) and Olkin-Pratt (OP)
meta-analytical approaches with correlation coefficients as
effect sizes.
Provides two functions-meta2d and meta3d for detecting
rhythmic signals from time-series datasets. For analyzing
time-series datasets without individual information, 'meta2d' is
suggested, which could incorporates multiple methods from ARSER,
JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For
analyzing time-series datasets with individual information, 'meta3d' is
suggested, which takes use of any one of these three methods to analyze
time-series data individual by individual and gives out integrated values
based on analysis result of each individual.
MetaDE package implements 12 major meta-analysis methods
for differential expression analysis.
The metafolio R package is a tool to simulate salmon
metapopulations and apply financial portfolio optimization concepts. The
package accompanies the paper 'Portfolio conservation of metapopulations
under climate change'. See citation("metafolio").
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted.
For each covariate, cluster its coefficient effects across different data sets during
data integration. Supports Gaussian, binomial and Poisson regression models.
Functionalities for facilitating systematic reviews, data extractions, and meta-analyses.  It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter-reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; automated and manual data extraction from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges' d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software.  Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031.
Provides methods for making inference in the random effects meta
regression model such as point estimates and confidence intervals for the
heterogeneity parameter and the regression coefficients vector.  Inference
methods are based on different approaches to statistical inference.
Methods from three different schools are included: methods based on the
method of moments approach, methods based on likelihood, and methods based
on generalised inference.  The package also includes tools to run extensive
simulation studies in parallel on high performance clusters in a modular
way.  This allows extensive testing of custom inferential methods with all
implemented state-of-the-art methods in a standardised way.  Tools for
evaluating the performance of both point and interval estimates are
provided.  Also, a large collection of different pre-defined plotting
functions is implemented in a ready-to-use fashion.
Automation of preprocessing often requires computationally costly
preprocessing combinations. This package helps to find near-best combinations
faster. Sub heuristics supported are random and grid restarts, taboo list,
decreasing probability for accepting inferior solutions and location of
previously best solution candidate is compared to. The package is intended
to be used with package 'preprocomb' and takes its 'GridClass' object as input.
Tools to generate random landscape graphs, evaluate species
occurrence in dynamic landscapes, simulate future landscape occupation and
evaluate range expansion when new empty patches are available (e.g. as a
result of climate change).
First- and higher-order likelihood inference in
meta-analysis and meta-regression models.
Combines either p-values or modified effect sizes from different
studies to find differentially expressed genes
This package provides functions for diagnostic and
prognostic meta-analyses. It estimates univariate, bivariate
and multivariate models, and allows the aggregation of
previously published prediction models with new data.
Resolves complex metagenomic mixtures by analysing
deep sequencing data, using a mixture model based approach.
The use of parallel Monte Carlo Markov chains for the exploration
of the species space enables the identification of the set
of species more likely to contribute to the mixture.
A novel meta-analytic method that allows an unbiased inclusion of studies with Non Statistically-Significant Unreported Effects (NSUEs). Briefly, the method first calculates the interval where the unreported effects (e.g. t-values) should be according to the threshold of statistical significance used in each study. Afterwards, maximizing likelihood techniques are used to impute the expected effect size of each study with NSUEs, accounting for between-study heterogeneity and potential covariates. Multiple imputations of the NSUEs are then randomly created based on the expected value, variance and statistical significance bounds. Finally, a restricted-maximum likelihood random-effects meta-analysis is separately conducted for each set of imputations, and estimations from these meta-analyses are pooled. Please read the reference in 'meta.nsue' for details of the procedure.
The canonical way to perform meta-analysis
involves using effect sizes. When they are not available
this package provides a number of methods for
meta-analysis of significance values, a
number of data-sets to replicate published results, and a
routine for graphical display.
Perform the Meta-analysis for Pathway Enrichment (MAPE)
methods introduced by Shen and Tseng (2010). It includes functions to
automatically perform MAPE_G (integrating multiple studies at
gene level), MAPE_P (integrating multiple studies at pathway
level) and MAPE_I (a hybrid method integrating MAEP_G and
MAPE_P methods). In the simulation and real data analyses in
the paper, MAPE_G and MAPE_P have complementary advantages and
detection power depending on the data structure. In general,
the integrative form of MAPE_I is recommended to use. In the
case that MAPE_G (or MAPE_P) detects almost none pathway, the
integrative MAPE_I does not improve performance and MAPE_P (or
MAPE_G) should be used. Reference: Shen, Kui, and George C
Tseng. Meta-analysis for pathway enrichment analysis when
combining multiple microarray studies.Bioinformatics (Oxford,
England) 26, no. 10 (April 2010): 1316-1323.
doi:10.1093/bioinformatics/btq148.
http://www.ncbi.nlm.nih.gov/pubmed/20410053.
MetaPCA implements simultaneous dimension reduction using
PCA when multiple studies are combined. We propose two basic
ideas to find a common PC subspace by eigenvalue maximization
approach and angle minimization approach, and we extend the
concept to incorporate Robust PCA and Sparse PCA in the
meta-analysis realm.
Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).
MetaQC implements our proposed quantitative quality
control measures: (1) internal homogeneity of co-expression
structure among studies (internal quality control; IQC); (2)
external consistency of co-expression structure correlating
with pathway database (external quality control; EQC); (3)
accuracy of differentially expressed gene detection (accuracy
quality control; AQCg) or pathway identification (AQCp); (4)
consistency of differential expression ranking in genes
(consistency quality control; CQCg) or pathways (CQCp). (See
the reference for detailed explanation.) For each quality
control index, the p-values from statistical hypothesis testing
are minus log transformed and PCA biplots were applied to
assist visualization and decision. Results generate systematic
suggestions to exclude problematic studies in microarray
meta-analysis and potentially can be extended to GWAS or other
types of genomic meta-analysis. The identified problematic
studies can be scrutinized to identify technical and biological
causes (e.g. sample size, platform, tissue collection,
preprocessing etc) of their bad quality or irreproducibility
for final inclusion/exclusion decision.
Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.
A collection of functions for conducting meta-analysis using a
structural equation modeling (SEM) approach via the 'OpenMx' package.
It also implements the two-stage SEM approach to conduct meta-analytic
structural equation modeling on correlation and covariance matrices.
The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis:
- Copas selection model;
- limit meta-analysis;
- upper bound for outcome reporting bias.
Functions for Meta-analysis Burden test, SKAT and SKAT-O. These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants.
This package fits meta regression models and generates a
number of statistics: next to t- and z-tests, the likelihood
ratio, bartlett corrected likelihood ratio and permutation
tests are performed on the model coefficients.
This package allows doing meta-analysis for primary studies with classification outcomes in order to evaluate systematically the accuracies of classifiers, namely, the diagnostic tests. It provides functions to fit the bivariate model of Reitsma et al.(2005). Moreover, if the reference employed in the classification process isn't a gold standard, its deficit can be detected and its influence to the underestimation of the diagnostic test's accuracy can be corrected, as described in Botella et al.(2013).
Spatio-temporal geostatistical mapping of meteorological data. Global spatio-temporal models calculated using publicly available data are stored in package.
Access to several Numerical Weather Prediction services both in raster format and as a time series for a location. Currently it works with GFS, MeteoGalicia, NAM, and RAP.
meteogram is a collection of programs for plotting
meteograms for meteorological data such as atmospheric cross
section, temperatures plots.
Fit and plot macroecological patterns predicted by the Maximum
Entropy Theory of Ecology (METE).
Functions for selection of visual meteor data, calculations of Zenithal Hourly Rate (ZHR) and population index, graphics of population index, ZHR and magnitude distribution.
Meth27QC is a tool for analyzing Illumina Infinium
HumanMethylation27 BeadChip Data and generating QC reports.
This package allows users quickly assess data quality of the
Assay. Users can evaluate the data quality in the way that
Illumina GenomeStudio/BeadStudio recommended based on the
control probes. The package reads files exported from
GenomeStudio/BeadStudio software, generating intensity and
standard deviation plots grouped by the types of the control
probes. Meth27 carries 40 control probes for staining,
hybridization, target removal, extension, bisulfite conversion,
specificity, negative and non-polymorphic controls. Details of
those control probes can be found in the Infinium Assay for
Methylation Protocol Guide from Illumina.We also used the other
non-control probes to plot intensity of detected genes, signal
average for green and red. Outliers can be identified.
Methods (standard and advanced) for analysis of agreement
between measurement methods.
It plots the output from Methpup (https://github.com/XinYang6699/Methpup)
Provides a univariate and several high dimensional multivariate test statistics for detecting differentially methylated regions based on MethylCap-seq data.
Metabolomics data are inevitably subject to a component of unwanted variation, due to factors such as batch effects, matrix effects, and confounding biological variation. This package contains a collection of R functions which can be used to remove unwanted variation and obtain normalized metabolomics data.
Split an untargeted metabolomics data set into a set of likely true
metabolites and a set of likely measurement artifacts. This process involves
comparing missing rates of pooled plasma samples and biological samples. The
functions assume a fixed injection order of samples where biological samples are
randomized and processed between intermittent pooled plasma samples. By comparing
patterns of missing data across injection order, metabolites that appear in blocks
and are likely artifacts can be separated from metabolites that seem to have
random dispersion of missing data. The two main metrics used are: 1. the number of
consecutive blocks of samples with present data and 2. the correlation of missing rates
between biological samples and flanking pooled plasma samples.
Metrics is a set of evaluation metrics that is commonly
used in supervised machine learning.
Provides an 'htmlwidgets' interface to the
'MetricsGraphics.js' ('D3'-based) charting library which is geared towards
displaying time-series data. Chart types include line charts, scatterplots,
histograms and rudimentary bar charts. Support for laying out multiple charts
into a grid layout is also provided. All charts are interactive and many
have an option for line, label and region annotations.
Explore the behavior and performance of phylogenetic metrics and
null models.
metRology provides classes and calculation and plotting functions
for metrology applications, including measurement uncertainty estimation
and inter-laboratory metrology comparison studies.
Implementation of various statistical models for multivariate
event history data. Including multivariate cumulative incidence models, and
bivariate random effects probit models (Liability models). Also contains
two-stage binomial modelling that can do pairwise odds-ratio dependence
modelling based marginal logistic regression models. This is an alternative
to the alternating logistic regression approach (ALR).
An easy to use Graphical User Interface for estimating sample sizes required for metabolomic experiments even when experimental pilot data is not available.
A diverse collection of metabolomics related statistical tools.
Exact simulation from max-stable processes and multivariate extreme value distributions for various parametric models. Threshold selection methods.
Computes the average of a sequence of random vectors
in a moving expanding window using a fixed amount of storage
Fit flexible (excess) hazard regression models with a random effect defined at the cluster level.
MExPosition is for descriptive (i.e., fixed-effects)
multi-table multivariate analysis the singular value
decomposition.
Calculate MF (mitigated fraction) with clustering and bootstrap options. See http://goo.gl/pcXYVr for definition of MF.
Performs Multiple Factor Analysis method for quantitative, categorical, frequency and mixed data, in addition to generating a lot of graphics, also has other useful functions.
Multivariate functional halfspace depth and median for two-dimensional functional data.
The package implements several time series filters useful
for smoothing and extracting trend and cyclical components of a
time series. The routines are commonly used in economics and
finance, however they should also be interest to other areas.
Currently, Christiano-Fitzgerald, Baxter-King,
Hodrick-Prescott, Butterworth, and trigonometric regression
filters are included in the package.
Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994.
Convenience functions for multivariate MCMC using univariate samplers including:
slice sampler with stepout and shrinkage (Neal, 2003),
adaptive rejection sampler (Gilks and Wild, 1992),
adaptive rejection Metropolis (Gilks et al 1995), and
univariate Metropolis with Gaussian proposal.
Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output.
Procedures to simulate, estimate and diagnose MGARCH
processes of BEKK and multivariate GJR (bivariate asymmetric GARCH
model) specification.
GAMs, GAMMs and other generalized ridge regression with
multiple smoothing parameter estimation by GCV, REML or UBRE/AIC.
Includes a gam() function, a wide variety of smoothers, JAGS
support and distributions beyond the exponential family.
Implement algorithms to recover multiple networks by pursuit of both sparseness and cluster.
Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data.  Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.
Tools for univariate and multivariate generalized linear models with model averaging and null model technique.
Estimation of time-varying Mixed Graphical model using L1-regularized neighborhood regression.
Extends distribution and density functions to parametric
multivariate generalized Pareto distributions (MGPD of Type
II), and provides fitting functions which calculate maximum
likelihood estimates for bivariate and trivariate models. (Help
is under progress)
Each function in the package performs three main functions
i) it reads spatial data and produces basic graphs including
pie chart, bar chart, box plots, histogram, scatter plots, and
lines ii)it reads non-spatial data such as "csv", "txt", "dat"
data and produces basic graphs and iii) it plots map(s) of the
input attribute(s) of spatial data by setting "type" parameter
to "map"
Convenience Functions for R Language Access to the v.1 API of the MG-RAST Metagenome Annotation Server, part of the US Department of Energy (DOE) Systems Biology Knowledge Base (KBase).
Implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2015), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA, to appear.
Performs gene set analysis based on GSZ scoring function and asymptotic p-value. It is different from GSZ in that it implements asymptotic p-values instead of empirical p-values. Asymptotic p-values are calculated by fitting suitable distribution model to the null distribution. Unlike empirical p-values, resolution of asymptotic p-values are independent of the number of permutations and hence requires considerably fewer permutations. In addition, this package allows gene set analysis with seven other popular gene set analysis methods.
Performs general Metropolis-Hastings Markov Chain Monte
Carlo sampling of a user defined function which returns the
un-normalized value (likelihood times prior) of a Bayesian
model.  The proposal variance-covariance structure is updated
adaptively for efficient mixing when the structure of the
target distribution is unknown.  The package also provides some
functions for Bayesian inference including Bayesian Credible
Intervals (BCI) and Deviance Information Criterion (DIC)
calculation.
Implementation of a goodness-of-fit test for normality using the Minimum Hellinger Distance.
Runs a minimum-hypergeometric (mHG) test as described in: Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa.
Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences.  Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.
Multiple Hypothesis Testing For Variable Selection in high dimensional linear models. This package performs variable selection with multiple hypothesis testing, either for ordered variable selection or non-ordered variable selection. In both cases, a sequential procedure is performed. It starts to test the null hypothesis "no variable is relevant"; if this hypothesis is rejected, it then tests "only the first variable is relevant", and so on until the null hypothesis is accepted.
Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.
Estimation of models with zero left-censored variables.
Null values may be caused by a selection process, insufficient
resources or infrequency of purchase.
The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm. Each variable has its own imputation
model. Built-in imputation models are provided for continuous data
(predictive mean matching, normal), binary data (logistic regression),
unordered categorical data (polytomous logistic regression) and ordered
categorical data (proportional odds). MICE can also impute continuous
two-level data (normal model, pan, second-level variables). Passive
imputation can be used to maintain consistency between variables. Various
diagnostic plots are available to inspect the quality of the imputations.
Contains some auxiliary functions for multiple
imputation which complements existing functionality
in R.
In addition to some utility functions, main features
include plausible value imputation, multilevel
imputation functions, imputation using partial least
squares (PLS) for high dimensional predictors, nested
multiple imputation, and two-way imputation.
Tools for microeconomic analysis and microeconomic modelling
Demand analysis with the Almost Ideal Demand System (AIDS)
suggested by Deaton and Muellbauer (1980)
Tools for economic analysis and economic modelling
with a Constant Elasticity of Substitution (CES) function
Production analysis with the Symmetric Normalized Quadratic (SNQ) profit function
A procedure for comparing multivariate samples associated with
different groups. It uses principal component analysis to convert
multivariate observations into a set of linearly uncorrelated statistical
measures, which are then compared using a number of statistical methods. The
procedure is independent of the distributional properties of samples and
automatically selects features that best explain their differences, avoiding
manual selection of specific points or summary statistics. It is appropriate
for comparing samples of time series, images, spectrometric measures or
similar multivariate observations.
Fits Semiparametric Promotion Time Cure Models, taking into account (using a
corrected score approach or the SIMEX algorithm) or not the measurement error
in the covariates, using a backfitting approach to maximize the likelihood.
A nature-inspired metaheuristic algorithm based on the echolocation behavior of microbats that uses frequency tuning to optimize problems in both continuous and discrete dimensions. This R package makes it easy to implement the standard bat algorithm on any user-supplied function. The algorithm was first developed by Xin-She Yang in 2010 (<DOI:10.1007/978-3-642-12538-6_6>, <DOI:10.1109/CINTI.2014.7028669>).
Provides infrastructure to accurately measure and compare
the execution time of R expressions.
The MicroDatosEs package provides utilities for reading and processing microdata from Spanish official statistics with R.
This group of functions simplifies the creation of linked micromap
plots.
This package provides the user with the ability to quickly create Linked
Micromap plots of the 50 U.S. states and District of Columbia (51 areas).
Linked Micromap plots are visualizations of geo-referenced data that link statistical
graphics to an organized series of small maps.
The Help description contains examples of how to use the micromapST function.
This package contains the US and State boundary files and U. S. FIPS code to State conversion tables
used by the micromapST function and several data sets used in the micromapST examples.
Copyrighted 2013, 2014 and 2015 by Carr, Pearson and Pickle.
A collection of functions for computations and visualizations of microbial pan-genomes.
Deploys your R Analytic to MicroStrategy
This entry-level toolkit allows performing continuous-time microsimulation for a wide range of demographic applications. Individual life-courses are specified by a continuous-time multi-state model.
Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.
Contains the function mice.impute.midastouch(). Technically this function is to be run from within the 'mice' package (van Buuren et al. 2011), type ??mice. It substitutes the method 'pmm' within mice by 'midastouch'. The authors have shown that 'midastouch' is superior to default 'pmm'. Many ideas are based on Siddique / Belin 2008's  MIDAS.
Apply tests of multiple comparisons based on studentized midrange
and range distributions. The tests are: Tukey Midrange test, Student-Newman-
Keuls Midrange test, Skott-Knott Midrange test and Skott-Knott Range test.
Functions for implementing species dispersal into projections
of species distribution models (e.g. under climate change scenarios).
Indirect methods for estimating bilateral migration flows in the presence of partial or missing data. Methods might be relevant to other categorical data situations on non-migration data, where for example, marginal totals are known and only auxiliary bilateral data is available.
This package provides various indices, like Crude Migration Rate,
different Gini indices or the Coefficient of Variation among others, to
show the (un)equality of migration.
This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.
Implements multiple imputation for proportional hazards regression
with interval censored data or proportional sub-distribution hazards
regression for interval censored competing risks data. The main functions
allow to estimate survival function, cumulative incidence function, Cox
and Fine & Gray regression coefficients and associated variance-covariance
matrix. 'MIICD' functions call 'Surv', 'survfit' and 'coxph' from the
'survival' package, 'crprep' from the 'mstate' package, and 'mvrnorm' from
the 'MASS' package.
Functions for estimating structural equation models using model-
implied instrumental variables.
The MILC package is designed to predict individual trajectories using the continuous time microsimulation model MILC, that describes the natural history of lung cancer.
Multiple imputation for multivariate binary outcome by using Gibbs' Sampler on all potential profiles.
Guesses the MIME type from a filename extension using the data
derived from /etc/mime.types in UNIX-type systems.
Tools to combine results for multiply-imputed data using
mixture approximations
66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv().
R wrapper for 'cmine' implementation of Maximal
Information-based Nonparametric Exploration statistics (MIC and
MINE family). Detailed information of the ANSI C implementation 'cmine'
can be found at 'http://mpba.fbk.eu/cmine'.
This package implements the core idea of games known as
'Minesweeper' on Microsoft Windows or 'KMines' for KDE on
Unix-like operating systems.
Makes it possible to create an internally consistent
repository consisting of selected packages from CRAN-like repositories.
The user specifies a set of desired packages, and miniCRAN recursively
reads the dependency tree for these packages, then downloads only this
subset. The user can then install packages from this repository directly,
rather than from CRAN.  This is useful in production settings, e.g. server
behind a firewall, or remote locations with slow broadband access.
quick and simple tktcl miniGUI to call functions.
Create tile grid maps, which are like choropleth maps except each
region is represented with equal visual space.
The minimax family of distributions is a two-parameter
family like the beta family, but computationally a lot more
tractible.
The 'minimaxdesign' package provides two main functions: mMcPSO() and
miniMaxPro(), which generates minimax designs and minimax projection designs using
clustering and particle swarm optimization (PSO) techniques. These designs can be used
in a variety of settings, e.g., as space-filling designs for computer experiments or
sensor allocation designs. A detailed description of the two designs and the employed
algorithms can be found in Mak and Joseph (2016).
A binding to the minimist JavaScript library. This module implements
the guts of optimist's argument parser without all the fanciful decoration.
Provides UI widget and layout functions for writing Shiny apps
that work well on small screens.
The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds.  The implementation can be used via nls-like calls using the nlsLM function.
Package minPtest is designed for estimating a gene region-level summary for SNP data from case-control studies using a permutation-based resampling method, called min P test, allowing execution on a compute cluster or multicore computer.
Derivative-free optimization by quadratic approximation
based on an interface to Fortran implementations by M. J. D.
Powell.
This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures.
Learns direct microbe-microbe interaction networks using a Poisson
multivariate-normal hierarchical model with an L1 penalized precision
matrix. Optimization is carried out using an iterative conditional modes
algorithm.
For a given set of stoichiometric reactions, this package evaluates the mass balance and extracts all reactants, products, orphan metabolites, metabolite names and compartments. Also are included some options to check the compound names associated to Chemical Entities of Biological Interest (ChEBI) database <https://www.ebi.ac.uk/chebi>.
This package implements entropy optimization distribution
under specified constraints. It also offers an R interface to
the MinxEnt and MaxEnt distributions.
An implementation of the iterative proportional fitting (IPFP),
maximum likelihood, minimum chi-square and weighted least squares procedures
for updating a N-dimensional array with respect to given target marginal
distributions (which, in turn can be multi-dimensional). The package also
provides an application of the IPFP to simulate multivariate Bernoulli
distributions.
This package contains functions to carry out processing of
high throughput data analysis and detection of putative
hits/mutants.  Contents include a function for post-hoc quality
control for removal of outlier sample sets, a median-based
normalization method for use in datasets where there are no
explicit controls and where most of the responses are of the
wildtype/no response class (see accompanying paper).  The
package also includes a way to prioritize individuals of
interest using am empirical cumulative distribution function.
Methods for generating synthetic data as well as data from the
Chloroplast 2010 project are included.
This package collects algorithms/functions developed for
microRNA profiling data analyses.  Analytical platforms include
traditional hybridization microarray, CGH, beads-based
microarray, and qRT-PCR array.
The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
Analysis of dichotomous and polytomous response data using
unidimensional and multidimensional latent trait models under the Item
Response Theory paradigm. Exploratory and confirmatory models can be
estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory
bi-factor and two-tier analyses are available for modeling item testlets.
Multiple group analysis and mixed effects designs also are available for
detecting differential item and test functioning as well as modelling
item and person covariates.
Provides tools to generate an HTML interface for creating adaptive
and non-adaptive educational and psychological tests using the shiny
package. Suitable for applying unidimensional and multidimensional
computerized adaptive tests using item response theory methodology and for
creating simple questionnaires forms to collect response data directly in R.
Package also contains tools useful for performing Monte Carlo simulations
for studying the behaviour of computerized adaptive test banks.
A collection of miscellaneous 3d plots, including
isosurfaces.
Various functions for random number generation, density
estimation, classification, curve fitting, and spatial data
analysis.
LaTeX tables, Kalman filter, web scraping, development tools,
relative risk, odds ratio
A collection of miscellaneous methods to simplify various tasks,
including plotting, data.frame and matrix transformations, environment
functions, regular expression methods, and string and logical operations, as
well as numerical and statistical tools. Most of the methods are simple but
useful wrappers of common base R functions, which extend S3 generics or
provide default values for important parameters.
There is an increasing interest in investigating how the compositions of microbial communities are associated with human health and disease. In this package, we present a novel global testing method called aMiSPU, that is highly adaptive and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. Our simulations and real data analysis demonstrated that aMiSPU test was often more powerful than several competing methods while correctly controlling type I error rates.
Implements two methods: a nonparametric risk adjustment and a data imputation method that use general population mortality tables to allow a correct analysis of time to disease recurrence.
The function 'missForest' in this package is used to
impute missing values particularly in the case of mixed-type
data. It uses a random forest trained on the observed values of
a data matrix to predict the missing values. It can be used to
impute continuous and/or categorical data including complex
interactions and non-linear relations. It yields an out-of-bag
(OOB) imputation error estimate without the need of a test set
or elaborate cross-validation. It can be run in parallel to
save computation time.
Provides numeric and graphical
summaries for the missing values from both categorical
and quantitative variables. A variety of imputation
methods are applied, including the univariate imputations
like fixed or random values, multivariate imputations
like the nearest neighbors and multiple imputations,
and imputations conditioned on a categorical variable.
Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA.
To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR).
For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)," Journal of Statistical Software,  56(6), 1-31. URL http://www.jstatsoft.org/v56/i06/.
Test for association between a set of SNPS/genes and
continuous or binary outcomes by including variant
characteristic information and using (weighted) score
statistics.
This R package is providing all the data sets and statistical analysis applications used in "Modern Industrial Statistics: with applications in R, MINITAB and JMP" by R.S. Kenett and S. Zacks with contributions by D. Amberti, John Wiley and Sons, 2013, which is a second revised and expanded revision of "Modern Industrial Statistics: Design and Control of Quality and Reliability", R. Kenett and S. Zacks, Duxbury/Wadsworth Publishing, 1998.
Various reliability analysis methods for rare event inference:
1) computing failure probability (probability that the output of a numerical model exceeds a threshold),
2) computing quantiles of low or high-order,
3) Wilks formula to compute quantile(s) from a sample or the size of the required i.i.d. sample.
Flexible multivariate function approximation using adapted
Mixture of Student t Distributions. Mixture of t distribution
is obtained using Importance Sampling weighted Expectation
Maximization algorithm.
Provides tools for multiple imputation of missing data in multilevel
modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo',
and several functions for visualization, data management and the analysis
of multiply imputed data sets.
Tools to perform analyses and combine results from
multiple-imputation datasets.
Estimation/multiple imputation programs for mixed categorical
and continuous data.
Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.
Algorithms and methods for estimating parametric mixture models with missing data.
Mixed effects cumulative and baseline logit link models
for the analysis of ordinal or nominal responses, with
non-parametric distribution for the random effects
This package contains functions for fitting finite mixture
distribution models to grouped data and conditional data by the
method of maximum likelihood using a combination of a
Newton-type algorithm and the EM algorithm.
Missing data imputation for continuous and categorical data, using nonparametric Bayesian joint models (specifically the hierarchically coupled mixture model with local dependence described in Murray and Reiter (2015); see 'citation("MixedDataImpute")' or http://arxiv.org/abs/1410.0438). See '?hcmm_impute' for example usage.
Fits mixed membership models with discrete multivariate data (with
or without repeated measures) following the general framework of Erosheva et al
(2004). This package uses a Variational EM approach by approximating the
posterior distribution of latent memberships and selecting hyperparameters
through a pseudo-MLE procedure. Currently supported data types are
Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al     (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models.
The estimation of the parameters in Mixed Poisson models.
Inference on stochastic differential models Ornstein-Uhlenbeck or
Cox-Ingersoll-Ross, with one or two random effects in the drift function.
We provide detailed functions for univariate Mixed Tempered Stable distribution.
Routines for the analysis (unsupervised clustering) of
networks using MIXtures of Erdos-Renyi random graphs
Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.
Evaluation and optimization of the Fisher Information Matrix in NonLinear Mixed Effect Models using Markov Chains Monte Carlo for continuous and discrete data.
Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the  generalized hyperbolic distribution.The first model 'MGHD' is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD', mixture of  multiple scaled generalized hyperbolic distributions. The 'cMSGHD' is a 'MSGHD' with convex contour plots. The 'MCGHD', mixture of  coalesced generalized hyperbolic distributions is a new more flexible model.
The main functions perform mixed models analysis by least squares or REML by adding the function r() to formulas of lm and glm. A collection of text-book statistics for higher education is also included, e.g. modifications of the functions lm, glm and associated summaries from the package stats.
A collection of functions to implement the MixMAP algorithm, which performs gene
level tests of association using data from a previous GWAS or data from a
meta-analysis of several GWAS.  Conceptually, genes are detected as
significant if the collection of p-values within a gene are determined to
be collectively smaller than would be observed by chance.
We provide statistical integrative techniques and
variants to analyse highly dimensional data sets: regularized
Canonical Correlation Analysis ('rCCA') and sparse Partial Least Squares variants ('sPLS')
to unravel relationships between two heterogeneous data sets of size (n times p) and (n times q) where the p and
q variables are measured on the same samples or individuals n. These data may come from high throughput
technologies, such as 'omics' data (e.g. transcriptomics, metabolomics or proteomics
data) that require an integrative or joint analysis. However, 'mixOmics' can also be applied to any other
large data sets where p + q >> n. 'rCCA' is a regularized version of Canonical Correlation Analysis to deal with
the large number of variables. 'sPLS' allows variable selection in a one step procedure and two frameworks
are proposed: regression and canonical analysis. Numerous graphical outputs are provided to help interpreting
the results. Recent methodological developments include: sparse PLS-Discriminant Analysis ('sPLS-DA'), Independent
Principal Component Analysis ('IPCA'), multilevel analysis using variance decomposition of the data and integration
of multiple data sets with regularized Generalised Canonical Correlation Analysis ('rGCCA') and variants (sparse 'GCCA'). More details  can be found
on our website.
Provides the function 'mixord' for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.
A collection of tools implemented to facilitate the analysis of the components of a finite mixture distributions. The package has some functions to generate random samples coming from a finite mixture. The package provides a C++ implementation for the construction of a hierarchy over the components of a given finite mixture.
Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.
Estimates Rasch models and mixture Rasch models, including the dichotomous Rasch model, the rating scale model, and the partial credit model.
Fits mixtures of (possibly multivariate) regressions
(which has been described as doing ANCOVA when you don't
know the levels).
It offers random-forest-based functions to impute clustered
incomplete data. The package is tailored for but not limited to imputing
multitissue expression data, in which a gene's expression is measured on the
collected tissues of an individual but missing on the uncollected tissues.
Separates DNA mixtures using a statistical model within a
greedy algorithm with a useful tcl/tk GUI.
The utility of this package is in simulating mixtures of Gaussian
distributions with different levels of overlap between mixture
components.  Pairwise overlap, defined as a sum of two
misclassification probabilities, measures the degree of
interaction between components and can be readily employed to
control the clustering complexity of datasets simulated from
mixtures. These datasets can then be used for systematic
performance investigation of clustering and finite mixture
modeling algorithms. Among other capabilities of 'MixSim', there
are computing the exact overlap for Gaussian mixtures,
simulating Gaussian and non-Gaussian data, simulating outliers
and noise variables, calculating various measures of agreement
between two partitionings, and constructing parallel
distribution plots for the graphical display of finite mixture
models.
Functions to fit finite mixture of scale mixture of
skew-normal (FM-SMSN) distributions.
Differential expression analysis of RNA-Seq data when replicates under two conditions are available is performed. First, mixtures of Negative Binomial distributions are fitted on the data in order to estimate the dispersions, then the Wald test is computed.
A collection of R functions for analyzing finite mixture models.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.
Curve Fitting for monotonic(sigmoidal) & non-monotonic(J-shaped)
concentration-response data. Prediction of mixture toxicity based on reference
models such as 'concentration addition', 'independent action', and 'generalized
concentration addition'.
An implementation of all 14 Gaussian parsimonious
clustering models (GPCMs) for model-based clustering and
model-based classification.
Functions for computing the penalized maximum likelihood estimate (PMLE) or maximum likelihood estimate (MLE), testing the order of a finite mixture model using EM-test, drawing histogram of observations and the fitted density or probability mass function of the mixture model.
A set of classes and methods to set up and run multispecies, trait
based and community size spectrum ecological models, focussed on the marine
environment.
Provides functions to compute and visualize movement-based kernel density estimates (MKDEs) for animal utilization distributions in 2 or 3 spatial dimensions.
Calculation routines based on the FOCUS Kinetics Report (2006).
Includes a function for conveniently defining differential equation models,
model solution based on eigenvalues if possible or using numerical solvers
and a choice of the optimisation methods made available by the 'FME' package.
If a C compiler (on windows: 'Rtools') is installed, differential
equation models are solved using compiled C functions.
Please note that no warranty is implied for correctness of results or fitness
for a particular purpose.
Package for fast computation of the maximum kernel
likelihood estimator (mkle)
Contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals, and generation of similarity matrices.
mkssd is a package that generates efficient balanced
non-aliased multi-level k-circulant supersaturated designs by
interchanging the elements of the generator vector. The package
tries to generate a supersaturated design that has chisquare
efficiency more than user specified efficiency level (mef). The
package also displays the progress of generation of an
efficient multi-level k-circulant design through a progress
bar. The progress of 100% means that one full round of
interchange is completed. More than one full round (typically
4-5 rounds) of interchange may be required for larger designs.
A collection of artificial and real-world machine learning
benchmark problems, including, e.g., several data sets from the
UCI repository.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version.
Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them.  This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact.
Functions necessary to perform the machine learning-based
differential network analysis of transcriptome data.
Exploratory data analysis and manipulation functions for multi-
label data sets along with an interactive Shiny application to ease their use.
Large collection of multilabel datasets along with the functions
needed to export them to several formats, to make partitions, and to obtain
bibliographic information.
Difference scaling is a method for scaling perceived
supra-threshold differences.  The package contains functions that
allow the user to design and run a difference scaling experiment,
to fit the resulting data by maximum likelihood and test the
internal validity of the estimated scale.
This package provides a unified interface to various
machine learning algorithms. Confusion matrices are provided
too.
This package contains functions to compute the
nonparametric maximum likelihood estimator (MLE) for the
bivariate distribution of (X,Y), when realizations of (X,Y)
cannot be observed directly.  To be more precise, we consider
the situation where we observe a set of rectangles that are
known to contain the unobservable realizations of (X,Y). We
compute the MLE based on such a set of rectangles.  The methods
can also be used for univariate censored data (see data set
'cosmesis'), and for censored data with competing risks (see
data set 'menopause').  We also provide functions to visualize
the observed data and the MLE.
Maximum likelihood Gaussian process modeling for
univariate and multi-dimensional outputs with diagnostic plots.
Contact the maintainer for a package version that implements
sensitivity analysis functionality.
Provides functions for unit root testing using MLE method
Processing and analysis of high throughput (Roche 454)
sequences generated from multiple loci and multiple biological
samples. Sequences are assigned to their locus and sample of
origin, aligned and trimmed. Where possible, genotypes are
called and variants mapped to known alleles.
An R code implementation of the maximum likelihood (fixed
point) algorithm of Hyvaerinen, Karhuna, and Oja for
independent component analysis.
Used for mediation analysis with generalized multilevel models.
An implementation of Multi-level Monte Carlo for R.  This package
builds on the original 'Matlab' and C++ implementations by Mike Giles to provide
a full MLMC driver and example level samplers.  Multi-core parallel sampling
of levels is provided built-in.
A collection of evaluation metrics, including loss, score and
utility functions, that measure regression, classification and ranking performance.
Computational strategies for multivariate linear
mixed-effects models with missing values, Schafer and Yucel
(2002), Journal of Computational and Graphical Statistics, 11,
421-442.
Data and examples from a multilevel modelling software review
as well as other well-known data sets from the multilevel modelling
literature.
Estimation of the multinomial logit model
Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL.
Phase haplotypes from genotypes based on a list of known
haplotypes. Suited to highly diverse loci such as HLA.
Interface to a large number of classification and regression
techniques, including machine-readable parameter descriptions. There is
also an experimental extension for survival analysis, clustering and
general, example-specific cost-sensitive learning. Generic resampling,
including cross-validation, bootstrapping and subsampling. Hyperparameter
tuning with modern optimization techniques, for single- and multi-objective
problems. Filter and wrapper methods for feature selection. Extension of
basic learners with additional operations common in machine learning, also
allowing for easy nested resampling. Most operations can be parallelized.
This package provides Multilinear Regression Model Population
Analysis to build a pool of models between quantitative activity and chemical
descriptors. Also some useful model validation functions. Contains all
molecular descriptors of 101 organic compounds and acticity dataset.
Generate a stream of pseudo-random numbers generated using the MLS
Junk Generator algorithm. Functions exist to generate single pseudo-random
numbers as well as a vector, data frame, or matrix of pseudo-random numbers.
Likelihood-based estimation of conditional transformation
models via the most likely transformation approach.
Additional documentation, a package vignette and
regression tests for package mlt.
Compute estimates of the multivariate vector autoregression model as
used to (but not limited to) analyze experience sampling method data in
clinical psychology. The model can be extended through treatment effects,
covariates and pre- and post-assessment effects.
Simulation and visualization of complex
models for longitudinal data. The models are encoded using the model coding
language 'Mlxtran', automatically converted into C++ codes, compiled on the
fly and linked to R using the 'Rcpp' package. That allows one to implement
very easily complex ODE-based models and complex statistical models,
including mixed effects models, for continuous, count, categorical, and
time-to-event data.
Description: Various utilities for the Multiplicative
Multinomial distribution
Description: A single-sample classifier that generates Medulloblastoma (MB) subtype predictions for single-samples of human MB patients and model systems, including cell lines and mouse-models. The MM2S algorithm uses a systems-based methodology that facilitates application of the algorithm on samples irrespective of their platform or source of origin. MM2S demonstrates > 96% accuracy for patients of well-characterized normal cerebellum, Wingless (WNT), or Sonic hedgehog (SHH) subtypes, and the less-characterized Group4 (86%) and Group3 (78.2%). MM2S also enables classification of MB cell lines and mouse models into their human counterparts.This package contains function for implementing the classifier onto human data and mouse data, as well as graphical rendering of the results as PCA plots and heatmaps.
Gene Expression datasets for the 'MM2S' package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594').
Used for general multiple mediation analysis.
The analysis method is described in Yu et al. 2014, "General Multiple Mediation Analysis With an Application to Explore Racial Disparity in Breast Cancer Survival", published on Journal of Biometrics & Biostatistics, 5(2):189.
Provides tools for performing mathematical morphology operations,
such as erosion and dilation, on data of arbitrary dimensionality. Can also
be used for finding connected components, resampling, filtering, smoothing
and other image processing-style operations.
R interface to POSIX mmap and Window's MapViewOfFile
Provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.
An implementation of modified maximum contrast methods
and the maximum contrast method: Functions 'mmcm.mvt' and
'mcm.mvt' give P-value by using randomized quasi-Monte Carlo
method with 'pmvt' function of package 'mvtnorm', and
'mmcm.resamp' gives P-value by using a permutation method.
This library implements mixture model distance sampling
methods. See Miller and Thomas (in prep.).
mme fit Gaussian Multinomial mixed-effects models for small area estimation: Model 1, with one
random effect in each category of the response variable; Model 2, introducing
independent time effect; Model 3, introducing correlated time effect.
mme calculates analytical and parametric bootstrap MSE estimators.
Fit multivariate mixture of normal distribution using
covariance structure.
This package implements a novel multivariate meta-analysis.
fits multivariate marginal models for multivariate longitudinal data for both continuous and discrete responses
Fits multivariate marginal models with shared regression parameters for discrete and continuous responses
The package implements a multi-marker molecular signature (MMMS) approach
for treatment-specific subgroup identification.
Provides functions for measuring
population divergence from genotypic data.
Compute similarities and distances between marked point processes.
Time-series of count data occur in many different contexts. A
Markov-modulated Poisson process provides a framework for detecting
anomalous events using an unsupervised learning approach.
Fixed effects Selection in Linear Mixed Models
Fits a family of mixtures of multivariate t-distributions under a continuous t-distributed latent variable structure for the purpose of clustering or classification. The alternating expectation-conditional maximization algorithm is used for parameter estimation.
The first day of any MMWR week is Sunday.
MMWR week numbering is sequential beginning with 1
and incrementing with each week to a maximum of 52
or 53. MMWR week #1 of an MMWR year is the first week
of the year that has at least four days in the calendar
year. This package provides functionality to convert
Dates to MMWR day, week, and year and the reverse.
Time and memory efficient estimation of multinomial logit models using maximum likelihood method. Numerical optimization performed by Newton-Raphson method using an optimized, parallel C++ library to achieve fast computation of Hessian matrices. Motivated by large scale multiclass classification problems in econometrics and machine learning.
The package provides multivariate tests, estimates and methods based on the identity score, spatial sign score and spatial rank score. The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components.
Computes integral of f(x)*x_i^k on a product of intervals,
where f is the density of a gaussian law.
This a is small alteration of the mnormt code from A. Genz and A. Azzalini.
Functions are provided for computing the density and the
distribution function of multivariate normal and "t" random variables,
and for generating random vectors sampled from these distributions.
Probabilities are computed via non-Monte Carlo methods; different routines
are used in the case d=1, d=2, d>2, if d denotes the number of dimensions.
MNP is a publicly available R package that fits the
Bayesian multinomial probit model via Markov chain Monte Carlo.
The multinomial probit model is often used to analyze the
discrete choices made by individuals recorded in survey data.
Examples where the multinomial probit model may be useful
include the analysis of product choice by consumers in market
research and the analysis of candidate or party choice by
voters in electoral studies.  The MNP software can also fit the
model with different choice sets for each individual, and
complete or partial individual choice orderings of the
available alternatives from the choice set. The estimation is
based on the efficient marginal data augmentation algorithm
that is developed by Imai and van Dyk (2005). ``A Bayesian
Analysis of the Multinomial Probit Model Using the Data
Augmentation,'' Journal of Econometrics, Vol. 124, No. 2
(February), pp. 311-334. Detailed examples are given in Imai
and van Dyk (2005). ``MNP: R Package for Fitting the
Multinomial Probit Model.'' Journal of Statistical Software,
Vol. 14, No. 3 (May), pp. 1-32.
An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.
Some canned plots and functions designed for the mobilize project.
Designed to be called remotely.
This package provides methods to analyze cluster
alternatives based on multi-objective optimization of cluster
validation indices.
Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors.  These model-based non-parametric clustering techniques can extract  clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.
This package provides estimators of the mode of univariate
unimodal data or univariate unimodal distributions
Given independent and identically distributed observations X(1), ..., X(n) from a density f,
provides five methods to perform a multiscale analysis about f as well as the necessary critical
values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements
for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by
any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of
I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive
correction term Gamma in either method offers another two approaches which are more powerful on small scales and
less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a
compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically
equivalent to the first and was introduced in Rufibach and Walther (2010).
Local linear estimation of psychometric functions.
Provides functions for nonparametric estimation of a
psychometric function and for estimation of a derived threshold
and slope, and their standard deviations and confidence
intervals
Bootstrap cross-validation for ROC, AUC and Brier score to assess
and compare predictions of binary status responses.
Useful functions for performing common
data analysis tasks that I could not find in other packages. For
example, flexible functions for discretizing ("binning") continuous
variables are included, since this is a common technique used in
industry that is not well appreciated in academia. See the vignette
for a more detailed high-level explanation, or the documentation
for full details.
Creates sophisticated models of training data and validates the models with an independent test set, cross validation, or in the case of Random Forest Models, with Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data.
A utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions.  In this way, a user can easily go from linear to non-linear models within the same package.
A collection of tools to deal with statistical models.
The functionality is experimental and the user interface is likely to
change in the future. The documentation is rather terse, but packages `coin'
and `party' have some working examples. However, if you find the
implemented ideas interesting we would be very interested in a discussion
of this proposal. Contributions are more than welcome!
Designed with a dual purpose of
accurately estimating the mode (or modes) as well as characterizing
the modality of data. The specific application area includes complex
or mixture distributions particularly in a big data environment.
The heterogeneous nature of (big) data may require deep introspective
statistical and machine learning techniques, but these statistical tools
often fail when applied without first understanding the data. In small
datasets, this often isn't a big issue, but when dealing with large scale
data analysis or big data thoroughly inspecting each dimension
typically yields an O(n^n-1) problem. As such, dealing with big data
require an alternative toolkit. This package not only identifies the
mode or modes for various data types, it also provides a programmatic
way of understanding the modality (i.e. unimodal, bimodal, etc.) of
a dataset (whether it's big data or not). See
<http://www.sdeevi.com/modes_package> for examples and discussion.
Package for processing downloaded MODIS Cloud Product HDF
files and derived files. Specifically, MOD35_L2 cloud product
files, and the associated MOD03 geolocation files (for
MODIS-TERRA); and MYD35_L2 cloud product files, and the
associated MYD03 geolocation files (for MODIS-AQUA).  The
package will be most effective if the user installs MRTSwath
(MODIS Reprojection Tool for swath products;
https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath),
and adds the directory with the MRTSwath executable to the
default R PATH by editing ~/.Rprofile.
Provides several functions for downloading, storing and processing
subsets of MODIS Land Processes data as a batch process.
The algorithms implemented here are used to detect the community structure of a network.
These algorithms follow different approaches, but are all based on the concept of modularity maximization.
Contains basic tools for performing
multiple-output quantile regression and computing
regression quantile contours by means of directional
regression quantiles. In the location case, one can thus
obtain halfspace depth contours in two to six dimensions.
Fits a Constrained Segmented Distributed Lag regression model
to epidemiological time series of mortality, temperature, and other confounders.
Methods for color labeling, calculation of eigengenes, merging of closely related modules.
Provides modules as an organizational unit for source code. Modules
enforce to be more rigorous when defining dependencies and have
a local search path. They can be used as a sub unit within packages
or in scripts.
Functions for exploring the best subsets in regression with a genetic algorithm. The package is much faster than methods relying on complete enumeration, and is suitable for datasets with large number of variables.
A package for analysis between rare variants and
quantitative traits by CMC (the combined multivariate and
collapsing method).
mokken contains functions for performing Mokken
scale analysis on test and questionnaire data. It includes an automated
item selection algorithm, and various checks of model assumptions.
Surface topography calculations of Dirichlet's normal energy,
relief index, and orientation patch count for teeth using scans of enamel caps.
Importantly, for the relief index and orientation patch count calculations to
work, the scanned tooth files must be oriented with the occlusal plane parallel
to the x and y axes, and perpendicular to the z axis. The files should also be
simplified, and smoothed in some other software prior to uploading into R.
Model selection and parameter estimation based on non-local priors. Routines are provided to compute Bayes factors, marginal densities and to perform variable selection in regression setups. Routines to evaluate prior densities, distribution functions, quantiles and modes are included.
A collection of moment-matching methods for computing the cumulative distribution function of a positively-weighted sum of chi-squared random variables. Methods include the Satterthwaite-Welch method, Hall-Buckley-Eagleson method, Wood's F method, and the Lindsay-Pilla-Basak method.
Functions to calculate: moments, Pearson's kurtosis,
Geary's kurtosis and skewness; tests related to them
(Anscombe-Glynn, D'Agostino, Bonett-Seier).
A complete toolkit for morphometrics, from data extraction to multivariate analyses.
Most common 2D morphometrics approaches are included:
outlines, open outlines, configurations of landmarks, traditional morphometrics,
and facilities for data preparation, manipulation and visualization
with a consistent grammar throughout.
Momocs allows reproducible, complex morphometric analyses,
paves the way for a pure open-source workflow in R,
and other morphometrics approaches should be easy to plug in,
or develop from, on top of this canvas.
'MetaOMineR' suite is a set of R packages that offers many functions and modules needed for the analyses
of quantitative metagenomics data. 'momr' is the core package and contains routines for biomarker identification and exploration.
Developed since the beginning of field, 'momr' has evolved and is structured around the different modules
such as preprocessing, analysis, vizualisation, etc. See package help for more information.
Keep track of dates in terms of months.
Model dates as at close of business.
Perform date arithmetic in units of "months" and "years" (multiples of months).
Allow "infinite" dates to model "ultimate" time spans.
The unique function of this package allows representing in a single graph the relative occurrence and co-occurrence of events measured in a sample.
As examples, the package was applied to describe the occurrence and co-occurrence of different species of bacterial or viral symbionts infecting arthropods at the individual level. The graphics allows determining the prevalence of each symbiont and the patterns of multiple infections (i.e. how different symbionts share or not the same individual hosts).
We named the package after the famous painter as the graphical output recalls Mondrian’s paintings.
An in-process version of MonetDB, a relational database focused on analytical tasks. Similar to SQLite, the database runs entirely inside the R shell, with the main difference  that queries complete much faster thanks to MonetDB's columnar architecture.
Allows to pull data from MonetDB into R. Includes a DBI implementation and a dplyr backend.
High-level, high-performance MongoDB client based on libmongoc and
jsonlite. Includes support for aggregation, indexing, map-reduce, streaming,
SSL encryption and SASL authentication. The vignette gives a brief overview
of the available methods in the package.
Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.
Train and make predictions from a multi-layer perceptron neural
network with partial monotonicity constraints.
Geometric morphometric and evolutionary biology analyses of anchor shape from four-anchored monogeneans.
Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology charts and distributional and diversity maps are also available.
Various imputation methods are utilized in this package, where one can flag and impute non-monotonic data that is outside of a prespecified range.
Estimation of multivariate normal and student-t data of
arbitrary dimension where the pattern of missing data is monotone.
Through the use of parsimonious/shrinkage regressions
(plsr, pcr, lasso, ridge,  etc.), where standard regressions fail,
the package can handle a nearly arbitrary amount of missing data.
The current version supports maximum likelihood inference and
a full Bayesian approach employing scale-mixtures for Gibbs sampling.
Monotone data augmentation extends this
Bayesian approach to arbitrary missingness patterns.
A fully functional standalone interface to the Bayesian lasso
(from Park & Casella), Normal-Gamma (from Griffin & Brown),
Horseshoe (from Carvalho, Polson, & Scott), and ridge regression
with model selection via Reversible Jump, and student-t errors
(from Geweke) is also provided.
Requires rooted resolved phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways.
Functions for fitting monotone polynomials to data.
Estimates monotone regression and variance functions in a
nonparametric model.
Several analysis-related functions for the book entitled "R
statistics and graph for medical articles" (written in Korean), version 1,
by Keon-Woong Moon with Korean demographic data with several plot
functions.
A collection of basic astronomical routines for R based on
"Practical astronomy with your calculator" by Peter
Duffet-Smith.
A multi-objective optimization solver based on particle
swarm optimization with crowding distance.
MOrder provide functions to check time homogeneity and order
of markov chain by using chi-squared test, AIC value and BIC value.
This package is a port of the MATLAB colourmap functions
accompanying the paper M. Geissbuehler and T. Lasser, "How to display data
by color schemes compatible with red-green color perception deficiencies,"
Opt. Express 21, 9862-9874 (2013) to R.
A toolset for Geometric Morphometrics and mesh processing. This
includes (among other stuff) mesh deformations based on reference points,
permutation tests, detection of outliers, processing of sliding
semi-landmarks and semi-automated surface landmark placement.
Tools for ecotoxicologists and regulators dedicated to the
mathematical and statistical modelling of bioassay data. They use advanced and
innovative methods for a valuable quantitative environmental risk assessment.
MorseGen is a program for generating raw data based on
user-specified summary (descriptive) statistics. Samples based
on the supplied statistics are drawn from a normal distribution
(or, in some cases, an exponential distribution) and scaled to
match the desired descriptive statistics. Intended uses include
creating raw data that fits desired characteristics or to
replicate the results in a published study.
Smoothing one- and two-dimensional Poisson counts with
P-splines specifically tailored to mortality data.
Extra-Poisson variation can be accounted as well as forecasting.
Collection of mortality data and a specific function for
selecting those data by country, sex, age and years.
Data sets and utilities from Project MOSAIC (mosaic-web.org) used
to teach mathematics, statistics, computation and modeling.  Funded by the
NSF, Project MOSAIC is a community of educators working to tie together
aspects of quantitative work that students in science, technology,
engineering and mathematics will need in their professional lives, but
which are usually taught in isolation, if at all.
Data sets from Project MOSAIC (mosaic-web.org) used
to teach mathematics, statistics, computation and modeling.  Funded by the
NSF, Project MOSAIC is a community of educators working to tie together
aspects of quantitative work that students in science, technology,
engineering and mathematics will need in their professional lives, but
which are usually taught in isolation, if at all.
Learning, manipulation and  evaluation of mixtures of  truncated basis  functions
(MoTBFs),  which include mixtures of  polynomials (MOPs) and  mixtures of truncated
exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian
networks. The  package provides  functionality for learning  univariate, multivariate and
conditional  densities, with the  possibility of incorporating prior  knowledge. Structural
learning of hybrid Bayesian  networks is also provided. A set of useful tools is provided,
including  plotting, printing  and likelihood  evaluation. This  package  makes use of  S3
objects, with two new classes called 'motbf' and 'jointmotbf'.
Statistics to quantify tracks of moving things (x-y-z-t data),
such as cells, bacteria or animals. Available measures include mean square
displacement, confinement ratio, autocorrelation, straightness, turning angle,
and fractal dimension.
Functions to estimate start and duration of moult from moult
data, based on models developed in Underhill
and Zucchini (1988, 1990).
Lattice functions for drawing folded empirical cumulative
distribution plots, or mountain plots. A mountain plot is similar
to an empirical CDF plot, except that the curve increases from
0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at
the right side.  See: Monti (1995), Folded empirical distribution
function curves-mountain plots.  The American Statistician, 49, 342-345.
Extract from two-dimensional x-y coordinates of an arm-reaching trajectory, several dependent measures such as area under the curve, latency to start the movement, x-flips, etc.; which characterize the action-dynamics of the response. Mainly developed to analyze data coming from mouse-tracking experiments.
Mouse-tracking, the analysis of mouse movements in computerized
experiments, is a method that is becoming increasingly popular in the
cognitive sciences. The mousetrap package offers functions for importing,
preprocessing, analyzing, aggregating, and visualizing mouse-tracking data.
Contains functions to access movement data stored in movebank.org as well as tools to visualize and statistically analyze animal movement data, among others functions to calculate dynamic Brownian Bridge Movement Models. Move helps addressing movement ecology questions.
Provides tools for animal movement modelling using hidden Markov models.
These include processing of tracking data, fitting HMMs to movement data, visualization of
data and fitted model, decoding of the state process...
Fit and simulate mixtures of von Mises-Fisher distributions.
Multidimensional projection techniques are used to create two dimensional representations of multidimensional data sets.
Preprocess and analysis of genomic data. MPAgenomics
provides wrappers from commonly used packages to streamline their repeated
manipulation, offering an easy-to-use pipeline. The segmentation of
successive multiple profiles is performed with an automatic choice of
parameters involved in the wrapped packages. Considering multiple profiles
in the same time, MPAgenomics wraps efficient penalized regression methods
to select relevant markers associated with a given outcome.
Algorithms for fitting model-based penalized coefficient paths. Currently the models include penalized Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models. The penalties include least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP), and each possibly combining with L_2 penalty.
Fits Multinomial Probit Bayesian Additive Regression Trees.
It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.
Multivariate process capability analysis using the multivariate process capability vector. Allows to analyze a multivariate process with both normally and non-normally distributed and also with dependent and independent quality characteristics.
Data sets and scripts for Modeling Psychophysical Data in R (Springer).
A fast approach to compute approximate persistent homology using a multiscale approach
(1) Our system provides a network-based strategies for metabolite pathway identification.(2) The MPINet can support the identification of pathways using Hypergeometric test based on metabolite set. (3)MPINet can support pathways from multiple databases.
Computing modified profile likelihood estimates for Weibull Shape and Regression Parameters. Modified likelihood estimates are provided.
Model stability and variable inclusion plots [Mueller and Welsh
(2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller
(2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al.
(2008, <doi:10.1214/07-AOS517>); Jiang et al.
(2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.
The MplusAutomation package leverages the flexibility of the R
language to automate latent variable model estimation and interpretation
using Mplus, a powerful latent variable modeling program developed by
Muthen and Muthen (www.statmodel.com). Specifically, MplusAutomation
provides routines for creating related groups of models, running batches of
models, and extracting and tabulating model parameters and fit statistics.
Exploratory graphical analysis of multivariate data,
specifically gene expression data with different projection
methods: principal component analysis, correspondence analysis,
spectral map analysis.
Tools for constructing linkage maps, reconstructing
haplotypes, estimating linkage disequilibrium and QTL mapping
in multi-parent RIL designs (e.g. MAGIC)
Functions to compute and plot multivariate (partial)
Mantel correlograms.
Uses a kernel smoothing approach to calculate Mutual Information
for comparisons between all types of variables including continuous vs
continuous, continuous vs discrete and discrete vs discrete. Uses a
nonparametric bias correction giving Bias Corrected Mutual Information
(BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to
large genomic datasets.
Symbolic computing with multivariate polynomials in R.
Mposterior package provides a general framework for estimating a
median of subset posterior measures (M-posterior). Each subset posterior measure
is represented as a matrix of posterior samples; rows represent the atoms and
columns index the dimensions. All subset measures are represented as list of
matrices; length of the list equals the number of subsets (or machines). The
distance between subset posterior measures and M-posterior is measured using the RBF kernel.
M-posterior is represented as a weighted combination of empirical measures based on
subsets measures. These weights are estimated using Weiszfeld algorithm implemented
in this package.
A procedure to test for dependence between point processes on the real line, e.g. causal dependence, correlation, inhibition or anti-correlation. The package also provides a number of utilities for plotting simultaneous point processes, and combining p-values.
Computational tools to represent phylogenetic signals using adapted eigenvector maps.
Fitting and testing multinomial processing tree (MPT) models, a
class of statistical models for categorical data.  The parameters are the
link probabilities of a tree-like graph and represent the latent cognitive
processing steps executed to arrive at observable response categories
(Batchelder & Riefer, 1999; Erdfelder et al., 2009; Riefer &
Batchelder, 1988).
Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g.,  Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.
'RAMAS Metapop' <https://www.ramas.com/ramas.htm#metapop> is a
popular software package for performing spatially-explicit population
viability analysis. It is primarily GUI-driven, but can benefit from
integration into an R workflow, wherein model results can be subjected to
further analysis. 'RAMAS Metapop' stores metapopulation model parameter
settings and population dynamics simulation results in plain text files
(.mp files). This package facilitates access, summary and visualisation of
'RAMAS Metapop 5' outputs in order to better integrate 'RAMAS' analyses into
an R workflow.
Most of this package consists of data sets from the
textbook Introduction
to Linear Regression Analysis (3rd ed), by Montgomery et al.
Some additional data sets and functions useful in an
undergraduate regression course are included.
mQTL provides a complete QTL analysis pipeline for metabolomic data.
Distinctive features include normalisation using PQN approach, peak alignment
using RSPA approach, dimensionality reduction using SRV approach and finally
QTL mapping using R/qtl package.
Analysis of mark-recapture data using covariates. Models: CJS open population; Huggin's closed population. Link functions: logit, sine, hazard.  Model selection and model averaging routines. Plot methods. Simulation routine. CJS methods produce estimates of population size using the Horvitz-Thompson estimator.
With this package, it is possible to perform
(simultaneous) inferences for ratios of linear combinations of
coefficients in the general linear model. In particular, tests
and confidence interval estimations for ratios of treatment
means in the normal one-way layout and confidence interval
estimations like in (multiple) slope ratio and parallel line
assays can be carried out. Moreover, it is possible to
calculate the sample sizes required in comparisons with a
control based on relative margins. For the simple two-sample
problem, functions for a t-test for ratio-formatted hypotheses
and the corresponding Fieller-type confidence interval are
provided assuming homogeneous or heterogeneous group variances.
Multivariate regression with covariance estimation
The MRCV package provides functions for analyzing the association between
one single response categorical variable (SRCV) and one multiple response
categorical variable (MRCV), or between two or three MRCVs.  A modified Pearson
chi-square statistic can be used to test for marginal independence for the one or
two MRCV case, or a more general loglinear modeling approach can be used to examine
various other structures of association for the two or three MRCV case.  Bootstrap-
and asymptotic-based standardized residuals and model-predicted odds ratios are
available, in addition to other descriptive information.
Animal abundance estimation via conventional, multiple covariate
and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function
fitting is performed via maximum likelihood. Also included are diagnostics
and plotting for fitted detection functions. Abundance estimation is via a
Horvitz-Thompson-like estimator.
Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics
Used on survival data to jointly estimate the hazard rate and the effects of covariates on failure times. Can accommodate covariates under the proportional and non-proportional hazards setting, and is ideal for analysis of survival data with long-term follow-up.
Provides three Modified Rand Indices and three Modified Adjusted Rand Indices for comparing two partitions, which are usually obtained on two different sets of units, where one is a subset of another set of units. Splitting and merging of clusters have a different affects on the value of the indices.
Provide a compact storage for medical imaging data with access and display possibilities. Basic tools for processing brain imaging data are proposed like extraction of brain voxels, morphological image segmentation, and filtering / normalization of contrast parameters. Specific tools are also provided for blood perfusion imaging to calculate hypoperfusion and reperfusion volumes.
Various methods for MRI tissue classification.
Conditional maximum likelihood estimation via the EM
algorithm and information-criterion-based model selection in
binary mixed Rasch models.
Conduct multi-locus random-SNP-effect mixed linear model analysis for genome-wide association study. First, each marker is scanned using random-SNP-effect mixed linear model method. Then, all the markers selected from the first step are included in the multi-locus model and their effects are estimated by an EM empirical Bayes method. No multiple test correction is needed.
This package contains a set of function to compute mutual information matrices from continuous, categorical and survival variables. It also contains function to perform feature selection with mRMR and a new ensemble mRMR technique.
We can calculate directly used this package the Minimal Clinically Important Difference by applying the Anchor-based method and the Response shift effect by applying the Then-Test method.
An implementation of the MRS algorithm for comparison across distributions.
The model is based on a nonparametric process taking the form of a Markov model
that transitions between a "null" and an "alternative" state
on a multi-resolution partition tree of the sample space.
MRS effectively detects and characterizes a variety of underlying differences.
These differences can be visualized using several plotting functions.
Fits regularized multinomial response models using penalized loglikelihood methods with structured penalty terms.
This package contains functions, data and examples to compute a multiplicative-regression model for relative survival.
The Bayesian procedure starts with one warplet in the model and uses the posterior distributions as priors for a more extended model with one more warplet. The model is built with adding one warplet at a time and allows for amplitude variations.
Statistical Analyses of Methylation-sensitive Amplification Polymorphism (MSAP) assays.
The output of an affinity-purification mass spectrometry
experiment is typically a list of proteins that were observed
in the experiment, identified by UniProt identifiers
(http://www.uniprot.org/).  This package takes as input a list of
UniProt identifiers, and the associated Mascot scores from the
experiment (which indicate the likelihood that the protein has
been correctly identified), clusters them by gene ontology
category (http://geneontology.org/), then draws diagrams
showing the results in hierarchical clusters by category, with lines
for individual proteins representing the associated Mascot score.
The results are in general not publication-ready, but will rather
require editing via a graphics editor that can interpret SVG (scalable
vector graphics) format.  As an alternative representation, the
package will also generate tag clouds based on the Mascot scores.
Performs Bayesian nonparametric multiscale density estimation and multiscale testing of group differences with multiscale Bernstein polynomials (msBP) mixtures as in Canale and Dunson (2016).
Provides methods for estimating frequentist and
Bayesian Vector Autoregression (VAR) models and Markov-switching
Bayesian VAR (MSBVAR).  Functions for reduced
form and structural VAR models are also available. Includes
methods for the generating posterior inferences for these models,
forecasts, impulse responses (using likelihood-based error bands),
and forecast error decompositions.  Also includes utility functions
for plotting forecasts and impulse responses, and generating draws
from Wishart and singular multivariate normal densities.  Current
version includes functionality to build and evaluate models with
Markov switching.
Find common entities detected in both positive and negative
ionization mode, delete this entity in the less sensible mode and combine both
matrices.
R Client for the Microsoft Cognitive Services Web Language Model
REST API, including Break Into Words, Calculate Conditional
Probability, Calculate Joint Probability, Generate Next Words, and List
Available Models. A valid account MUST be registered at the Microsoft
Cognitive Services website <https://www.microsoft.com/cognitive-services/>
in order to obtain a (free) API key. Without an API key, this package will
not work properly.
Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously.
This package provides functions for metabolite set
enrichment analysis (MSEA) and principal component analysis
(PCA), and converting metabolite set list from your own csv
files or KEGG's tar.gz files to XML documents.  This package is
suitable for computation of MSEA for factor loading in PCA.
Package for the detection of molecules in complex mixtures
of compounds. It creates an initial_DATA matrix from several
GC-MS analyses by collecting and assembling the information
from chromatograms and mass spectra (MS.DataCreation), It can
read several format (ASCII, CDF, mzML, mzXML or mzData).It
tests for the best unsupervised clustering method to group
similar mass spectra into molecules (MS.test.clust).It runs the
optimal unsupervised clustering method on the initial_DATA
matrix, identifies the optimal number of clusters, produces
different files for facilitating the quality control and
identification of putative molecules, and returns
fingerprinting or profiling matrices (MS.clust).It converts
output files from MS.clust for NIST mass spectral library
search and ARISTO webtool search
A Tcl/Tk GUI for some basic functions in the MSeasy
package
A companion to the Chinese book ``Modern Statistical Graphics''.
Multinomial logistic regression with sparse group lasso penalty. Suitable for high dimensional multiclass classification with many classes. The algorithm finds the sparse group lasso penalized maximum likelihood estimator. This result in feature and parameter selection, and parameter estimation. Use of multiple processors for cross validation and subsampling is supported through OpenMP. Development version is on github.
This is the library that can serialize or unserialize MessagePack format data.
This package computes the degrees of freedom of the lasso,
elastic net, generalized elastic net and adaptive lasso based
on the generalized path seeking algorithm.  The optimal model
can be selected by model selection criteria including Mallows'
Cp, bias-corrected AIC (AICc), generalized cross validation
(GCV) and BIC.
An R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.
A decision tree classifier for detecting microsatellite instability (MSI) in somatic mutation data from whole exome sequencing. MSI is detected based on different mutation rates in all sites as well as in simple sequence repeats. This mechanism can also be applied to sequence data of targeted gene panels with shorter sequence length.
Analysis of annual average ocean water level time series
from long (minimum length 80 years) individual records, providing improved
estimates of trend (mean sea level) and associated real-time velocities and
accelerations. Improved trend estimates are based on Singular Spectrum Analysis
methods. Various gap-filling options are included to accommodate incomplete time
series records. The package also contains a forecasting module to consider the
implication of user defined quantum of sea level rise between the end of the
available historical record and the year 2100. A wide range of screen and pdf
plotting options are available in the package.
Functions for fitting continuous-time Markov and hidden
Markov multi-state models to longitudinal data.  Designed for
processes observed at arbitrary times in continuous time (panel data)
but some other observation schemes are supported. Both Markov
transition rates and the hidden Markov output process can be modelled
in terms of covariates, which may be constant or piecewise-constant
in time.
There are several functions to implement the method for analysis in a multiblock multivariable data. If the input is only a matrix, then the principal components analysis (PCA) is implemented. If the input is a list of matrices, then the multiblock PCA is implemented. If the input is two matrices for exploratory and objective variables, then the partial least squares (PLS) analysis is implemented. If the input is two list of matrices for exploratory and objective variables, then the multiblock PLS analysis is implemented. Moreover, if the extra outcome variable is specified, then the supervised version for the methods above is implemented. For each methods, the sparse modeling is also incorporated. Functions to select the number of components and the regularized parameters are also provided.
This package provides functions and datasets from the book "Methods of Statistical Model Estimation".
A fast and general method for restructuring classical longitudinal data into
augmented ones. The reason for this is to facilitate the modeling of longitudinal data under
a multi-state framework using the 'msm' package.
Contains necessary Multivariate Analysis methods and Datasets used in STAT 571 at the University of Illinois at Urbana-Champaign
This package is a toolkit for multivariate process monitoring. It contains the main alternatives of multivariate control chart such as: Hotelling, Chi squared, MEWMA, MCUSUM and Generelized Variance control chart. Also, it includes some tools for assessing multivariate normality like: Mardia, Royston and Henze Zirkler test and the univariate D'Agostino test. Moreover, it possess ten didactic datasets.
Discrete Morse-Smale complex approximation based on kNN graph. The Morse-Smale complex provides a decomposition of the domain. This package provides methods to compute a hierarchical sequence of Morse-Smale complicies and tools that exploit this domain decomposition for regression and visualization of scalar functions.
Calculates ARMSS (age related multiple sclerosis severity), and both local and global MSSS (multiple sclerosis severity score).
Nonparametric estimation for right censored, left truncated time to event data in multistate models.
Constructs trees for multivariate survival data using marginal and frailty models.
The mstate packages contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models.
Univariate Autoregressive Markov Switching Models for Linear and Generalized Models
Read data in the 'MTConnect'<http://www.mtconnect.org/> standard.
You can use the package to read data from historical 'MTConnect logs' along
with the 'devices.xml' describing
the device. The data is organised into a 'MTConnectDevice' S4 data structure
and some convenience methods are also provided for basic read/view operations.
MTK (Mexico ToolKit) is a generic platform for the sensitivity and uncertainty analysis of complex models. It provides functions and facilities for experimental design, model simulation, sensitivity and uncertainty analysis, methods integration and data reporting, etc.
Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis.  (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models.  (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics.  (c) Finally, the MTS package also performs forecasting using diffusion index, transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.
This is an EM algorithm based method for imputation of
missing values in multivariate normal time series. The
imputation algorithm accounts for both spatial and temporal
correlation structures. Temporal patterns can be modelled using
an ARIMA(p,d,q), optionally with seasonal components, a
non-parametric cubic spline or generalised additive models with
exogenous covariates. This algorithm is specially tailored for
climate data with missing measurements from several monitors
along a given region.
Provides programmatic access to the Amazon Mechanical Turk (MTurk) Requester API.
A graphical user interface (GUI) for the MTurkR package.
Functions for downloading flight data from
http://www.munich-airport.de and for analyzing flight patterns.
This package builds multi-fidelity cokriging models from
responses with different levels of fidelity.  Important
functions : MuFicokm, predict.MuFicokm, summary.MuFicokm.
A package for producing a smooth estimate of the hazard
function for censored data.
A simple tool allowing users to easily and dynamically explore or document a data set using a tree structure.
Integer Programming Formulations Applied to Univariate and Multivariate Allocation Problems.
Simultaneous tests and confidence intervals
for general linear hypotheses in parametric models, including
linear, generalized linear, linear mixed effects, and survival models.
The package includes demos reproducing analyzes presented
in the book "Multiple Comparisons Using R" (Bretz, Hothorn,
Westfall, 2010, CRC Press).
Convert a logical vector or a vector of
p-values or a correlation, difference, or distance
matrix into a display identifying the pairs for
which the differences were not significantly
different.  Designed for use in conjunction with
the output of functions like TukeyHSD, dist{stats},
simint, simtest, csimint, csimtest{multcomp},
friedmanmc, kruskalmc{pgirmess}.
Equivalence tests and related confidence intervals for the
comparison of two treatments, simultaneously for one or many
normally distributed, primary response variables (endpoints).
The step-up procedure of Quan et al. (2001) is both applied for
differences and extended to ratios of means. A related
single-step procedure is also available.
GEE solver for correlated nominal or ordinal multinomial responses using a local odds ratios parameterization.
Efficient finite difference method for valuing European and American multi-asset options.
Algorithms for performing joint parameter estimation in
astronomical survey data acquired in multiple bands.
A GUI with which users can construct and interact
with Multibiplot Analysis and provides inferential results by using Bootstrap Methods.
Calculate the polygenic and major gene models for quantitative trait
linkage analysis using variance components approach. The 0.2.2
release includes bug fixes that allow 'multic' to run properly
on 64-bit systems. The 0.3.0 release includes a fully implemented
sw2mloci() function. As of 0.3.8, Splus version is no longer
supported. Additional documentation and examples can be found
in the inst directory.
This package provides a tool for analysis of multiple CNV.
Includes functions designed to examine relationships among multivariate constructs (e.g., personality). This includes functions for profile (within-person) analysis, dealing with large numbers of analyses, lens model analyses, and structural summary methods for data with circumplex structure. The package also includes functions for graphically comparing and displaying group means.
A set of tools to permute multisets without loops or hash tables and to generate integer partitions. The permutation functions are based on C code from Aaron Williams. Cool-lex order is similar to colexicographical order. The algorithm is described in Williams, A. Loopless Generation of Multiset Permutations by Prefix Shifts. SODA 2009, Symposium on Discrete Algorithms, New York, United States. The permutation code is distributed without restrictions. The code for stable and efficient computation of multinomial coefficients comes from Dave Barber. The code can be download from http://home.comcast.net/~tamivox/dave/multinomial/index.html and is distributed without conditions. The package also generates the integer partitions of a positive, non-zero integer n. The C++ code for this is based on Python code from Jerome Kelleher which can be found here http://jeromekelleher.net/partitions.php. The C++ code and Python code are distributed without conditions.
Read a table of fixed width formatted data of different types into
a data.frame for each type.
Several functions are presented in order to study data in a group structure,
where the same set of variables are measured on different groups of
individuals.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).
The functions in this package are designed to be used in
the analysis of multilevel data by applied psychologists.  The
package includes functions for estimating common within-group
agreement and reliability indices. The package also contains
basic data manipulation functions that facilitate the analysis
of multilevel and longitudinal data.
Functions to estimate and visualize propensity
score analysis for multilevel, or clustered, data.
Capture-mark-recapture analysis with multiple non-invasive marks. The models implemented in 'multimark' combine encounter history data arising from two different non-invasive ``marks'', such as images of left-sided and right-sided pelage patterns of bilaterally asymmetrical species, to estimate abundance and related demographic parameters while accounting for imperfect detection. Bayesian models are specified using simple formulae and fitted using Markov chain Monte Carlo. Addressing deficiencies in currently available software, 'multimark' also provides a user-friendly interface for performing Bayesian multimodel inference using capture-recapture data consisting of a single conventional mark or multiple non-invasive marks.
Allows running a meta-analysis of multivariate Genome Wide
Association Studies (GWAS) and easily visualizing results through custom
plotting functions. The multivariate setting implies that results for each
single nucleotide polymorphism (SNP) include several effect sizes (also
known as "beta coefficients", one for each trait), as well as related
variance values, but also covariance between the betas. The main goal of
the package is to provide combined beta coefficients across different
cohorts, together with the combined variance/covariance matrix. The method
is inverse-variance based, thus each beta is weighted by the inverse of its
variance-covariance matrix, before taking the average across all betas. The
default options of the main function \code{multi_meta} will work with files
obtained from GEMMA multivariate option for GWAS (Zhou & Stephens, 2014).
It will work with any other output, as soon as columns are formatted to
have the according names. The package also provides several plotting
functions for QQ-plots, Manhattan Plots and custom summary plots.
This is a likelihood approach for the regression analysis of overdispersed correlated count data with cluster varying covariates. The approach fits a multivariate negative binomial model by maximum likelihood and provides robust estimates of the regression coefficients.
An implementation of a method for building simultaneous
confidence intervals for the probabilities of a multinomial
distribution given a set of observations, proposed by Sison and
Glaz in their paper Sison, C.P and J. Glaz. Simultaneous
confidence intervals and sample size determination for
multinomial proportions. Journal of the American Statistical
Association, 90:366-369 (1995).  The method is an R translation
of the SAS code implemented by May and Johnson in their paper:
May, W.L. and W.D. Johnson. Constructing two-sided simultaneous
confidence intervals for multinomial proportions for small
counts in a large number of cells. Journal of Statistical
Software 5(6) (2000). Paper and code available at
http://www.jstatsoft.org/v05/i06
MNL and overdispersed multinomial regression using robust
(LQD and tanh) estimation
A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006).
Performs genetic association tests between SNPs
(one-at-a-time) and multiple phenotypes (separately or in joint
model).
Performs variable importance analysis using a causal inference approach. This is done by fitting Population Intervention Models. The default is to use a Targeted Maximum Likelihood Estimator (TMLE). The other available estimators are Inverse Probability of Censoring Weighted (IPCW), Double-Robust IPCW (DR-IPCW), and Graphical Computation (G-COMP) estimators. Inference can be obtained from the influence curve (plug-in) or by bootstrapping.
Fit Cox proportional hazard models with a weighted
partial likelihood. It handles one or multiple endpoints, additional matching
and makes it possible to reuse controls for other endpoints.
Algebraic procedures for the analysis of multiple social networks are delivered with
this package. Among other things, it is possible to create and manipulate multivariate network
data with different formats, and there are effective ways available to treat multiple networks
with routines that combine algebraic systems like the partially ordered semigroup or the
semiring structure together with the relational bundles occurring in different types of
multivariate network data sets. As well an algebraic approach for two-mode networks is made
through Galois derivations between families of the pair of subsets.
Provides a new form of data frame backed by shared memory matrices and a way to manipulate them. Upon creation these data frames are shared across multiple local nodes to allow for simple parallel processing.
Various utilities to manipulate multivariate polynomials
Functions to calculate Unique Trait Combinations (UTC) and scaled
Unique Trait Combinations (sUTC) as measures of multivariate richness. The
package can also calculate beta-diversity for trait richness and can
partition this into nestedness-related and turnover components. The code
will also calculate several measures of overlap.
Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.
Functions to perform sensitivity analysis on a model with multivariate output.
Implements two version of Multi-SOM algorithm namely stochastic Multi-SOM and batch Multi-SOM. The package determines also the best number of clusters and offers to the user the best clustering scheme from different results.
The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.
MultiSV is an R package for identification of structural
variations in multiple populations based on whole genome resequencing. It
fits linear mixed model and identifies structural variations in multiple
populations using whole genome sequencing data. It could also be
manipulated to use on RNA-seq data for differential gene expression
(implementation in future releases). Main steps for analysis include
generating read depth in bins using ComputeBinCounts. conversion of bins to
MultiSV format using Bin2MultiSV. Finally, identification of structural
variations using CallMultiSV.
Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates.
In Random Forest, prediction has been done for single
output feature, while linear relation between the
output features has not been considered in other
packages. In this package, using linear relation of
the output features, a multivariate random forest
prediction has been done, which can predict all the
output features with one Random forest. If there is
high correlation among the features, this MRF
prediction gives better result than the individual
RFs.
A multivariate generalization of the emulator package
Computation of an estimation of the long-memory parameters and
the long-run covariance matrix using a multivariate model
(Lobato, 1999; Shimotsu 2007). Two semi-parametric methods are
implemented: a Fourier based approach (Shimotsu 2007) and a wavelet based
approach (Achard and Gannaz 2014).
Fits multi-way component models via alternating least squares algorithms with optional constraints (orthogonality, non-negativity, and structural). Fit models include Individual Differences Scaling, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
Exports two functions implementing
multi-way clustering using the method suggested by Cameron, Gelbach, &
Miller (2011) and cluster (or block)
bootstrapping for estimating variance-covariance matrices. Normal one and
two-way clustering matches the results of other common statistical
packages.  Missing values are handled transparently and rudimentary
parallelization support is provided.
A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods.   Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and some algorithms for calculating exact probabilities associated with one- and two- stage testing involving Mann-Whitney-Wilcoxon statistics.
Implementation of commonly used p-value-based and
parametric multiple testing procedures (computation of adjusted
p-values and simultaneous confidence intervals) and parallel
gatekeeping procedures based on the methodology presented in
the book "Multiple Testing Problems in Pharmaceutical
Statistics" (edited by Alex Dmitrienko, Ajit C. Tamhane and
Frank Bretz) published by Chapman and Hall/CRC Press 2009.
Preprocessing of high-throughput data (normalization and
scalings); Principal Component Analysis with help tool for
choosing best-separating principal components and automatic
testing for outliers; automatic univariate analysis for
parametric and non-parametric data, with generation of specific
reports (volcano and box plots); partial least square
discriminant analysis (PLS-DA); orthogonal partial least square
discriminant analysis (OPLS-DA); Statistical Total Correlation
Spectroscopy (STOCSY); Ratio Analysis Nuclear Magnetic
Resonance (NMR) Spectroscopy (RANSY).
Model selection and model averaging based on information criteria
(AICc and alike).
Multidimensional unfolding using Schoenemann's algorithm for metric
and Procrustes rotation of unfolding results.
Provides easy access to, and manipulation of, the Munsell
colours. Provides a mapping between Munsell's
original notation (e.g. "5R 5/10") and hexadecimal strings suitable
for use directly in R graphics. Also provides utilities
to explore slices through the Munsell colour tree, to transform
Munsell colours and display colour palettes.
Methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, sRGB, CIE Lab or CIE Luv. Chromas can be odd and hue steps can be real numbers. Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".
Provides mailmerge methods for reading spreadsheets of addresses and other relevant information to create standardized but customizable letters.  Provides a method for mapping US ZIP codes, including those of letter recipients.  Provides a method for parsing and processing html code from online job postings of the American Political Science Association.
Data and code for the paper by Ehm, Gneiting, Jordan and Krueger ('Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings', 2015).
This package is a collection of function for converting and modifying mono dimensional nuclear magnetic resonance spectra.
Performs Wilcox rank sum test, Kruskal rank sum test,
Friedman rank sum test and McNemar test.
The Mutoss package and accompanying mutossGUI package are
designed to ease the application and comparison of multiple
hypothesis testing procedures.
The mutossGUI package provides a graphical user interface for the MuToss Project.
An ensemble classifier for multiclass classification. This is a novel classifier that natively works as an ensemble. It projects data on a large number of matrices, and uses very simple classifiers on each of these projections. The results are then combined, ideally via Dempster-Shafer Calculus.
Functions, data sets, analyses and examples from the book
`An Introduction to Applied Multivariate Analysis with R'
(Brian S. Everitt and Torsten Hothorn, Springer, 2011).
A set of tools for displaying, modeling and analysing
multivariate abundance data in community ecology. See
'mvabund-package.Rd' for details of overall package organization.
The package is implemented with the Gnu Scientific Library
(http://www.gnu.org/software/gsl/) and Rcpp
(http://dirk.eddelbuettel.com/code/rcpp.html) R / C++ classes.
Pacote para analise multivariada, que possui funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), analise de multiplos fatores (MFA), para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos. Tambem possui outras funcoes uteis para a analise multivariada.
Fit log-linear model for multivariate Bernoulli
distribution with mixed effect models and LASSO
Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.
Hierarchical workspace tree, code editing and backup, easy
package prep, editing of packages while loaded, per-object
lazy-loading, easy documentation, macro functions, and
miscellaneous utilities. Needed by debug package.
An implementation of Multi-View Clustering (Bickel and Scheffer, 2004). Documents are generated by drawing word values from a categorical distribution for each word, given the cluster. This means words are not counted (multinomial, as in the paper), but words take on different values from a finite set of values (categorical). Thus, it implements Mixture of Categoricals EM (as opposed to Mixture of Multinomials developed in the paper), and Spherical k-Means. The latter represents documents as vectors in the categorical space.
Implementation of multi-view bi-clustering algorithms. When a sample is characterized by two or more sets of input features, it creates multiple data matrices for the same set of examples, each corresponding to a view. For instance, individuals who are diagnosed with a disorder can be described by their clinical symptoms (one view) and their genomic markers (another view). Rows of a data matrix correspond to examples and columns correspond to features. A multi-view bi-clustering algorithm groups examples (rows) consistently across the views and simultaneously identifies the subset of features (columns) in each view that are associated with the row groups. This mvcluster package includes three such methods. (1) MVSVDL1: multi-view bi-clustering based on singular value decomposition where the left singular vectors are used to identify row clusters and the right singular vectors are used to identify features (columns) for each row cluster. Each singular vector is regularized by the L1 vector norm.  (2) MVLRRL0: multi-view bi-clustering based on sparse low rank representation (i.e., matrix approximation) where the decomposed components are regularized by the so-called L0 vector norm (which is not really a vector norm). (3) MVLRRL1: multi-view bi-clustering based on sparse low rank representation (i.e., matrix approximation) where the decomposed components are regularized by the L1 vector norm.
Permutation tests for variance components for 2-level, 3-level and 4-level data with univariate or multivariate responses.
This package computes the continuous wavelet transform of
irregularly sampled time series.
Implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods and classical multivariate statistical tools.
Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation.
This package computes regression deletion diagnostics for
multivariate linear models and provides some associated
diagnostic plots.
Define, manipulate and plot meshes on simplices, spheres, balls, rectangles and tubes.
Directional and other multivariate histograms are provided.
Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees.
Performs multivariate normality tests and graphical approaches and implements multivariate outlier detection and univariate normality of marginal distributions through plots and tests.
This package computes the Nelson-Aalen estimator of the cumulative transition hazard for multistate models.
Provides computationally efficient tools related to
the multivariate normal distribution. The main functionalities are:
simulating multivariate normal random vectors, evaluating multivariate
normal densities and Mahalanobis distances. These tools are very efficient
thanks to the use of C++ code and of the OpenMP API.
Package for  moving grid adjustment
in plant breeding field trials.
Finds the maximum likelihood estimate of the mean vector
and variance-covariance matrix for multivariate normal data
with missing values.
Generalization of shapiro-wilk test for multivariate
variables.
Given a vector of multivariate normal data, a matrix of
covariates and the data covariance matrix, generate new multivariate normal
samples that have the same covariance matrix based on permutations of
the transformed data residuals.
Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test,  Royston test and Henze-Zirkler test.
various methods for multivariate outlier detection.
Tools for estimating multivariate probit models,
calculating conditional and unconditional expectations,
and calculating marginal effects on conditional and unconditional
expectations.
Computes orthant probabilities multivariate normal distribution.
Provides methods to construct multivariate grids, which can be used
for multivariate quadrature. This grids can be based on different quadrature
rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss
quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the
multidimensional grid the product-rule or the combination- technique can be
applied.
This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include:
(i) Normalization and/or variance stabilization of the data,
(ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
(iii) Generation of diverse diagnostic plots,
(iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.
Mean, variance, and random variates for left/right truncated normal distributions.
Generalization of the Shapiro-Francia test for
multivariate variables.
This package implements the generalization of the Shapiro-Wilk test
for multivariate normality proposed by Villasenor-Alva and
Gonzalez-Estrada (2009).
Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history.
Routines to perform estimation and inference under the multivariate t-distribution.
Currently, the following methodologies are implemented: multivariate mean and covariance
estimation, hypothesis testing about the mean, equicorrelation and homogeneity of variances,
the Wilson-Hilferty transformation, QQ-plots with envelopes and random variate
generation. Some auxiliary functions are also provided.
Fits a multivariate model of decision trees for multiple, continuous outcome variables. A model for each outcome variable is fit separately, selecting predictors that explain covariance in the outcomes.  Built on top of 'gbm', which fits an ensemble of decision trees to univariate outcomes.
This package contains functions to run fixed effects or
random effects multivariate meta-analysis.
Computes multivariate normal and t probabilities, quantiles,
random deviates and densities.
A function for plotting multivariate time series data
Matched Wake Analysis (mwa) grants insights into causal relationships in spatiotemporal event data.
Computes the Wavelet deconvolution estimate of a common signal
present in multiple channels that have possible different levels of blur
and long memory additive error.
mxkssd is a package that generates efficient balanced
mixed-level k-circulant supersaturated designs by interchanging
the elements of the generator vector. The package tries to
generate a supersaturated design that has EfNOD efficiency more
than user specified efficiency level (mef). The package also
displays the progress of generation of an efficient mixed-level
k-circulant design through a progress bar. The progress of 100
per cent means that one full round of interchange is completed.
More than one full round (typically 4-5 rounds) of interchange
may be required for larger designs.
Feature selection methods for identifying minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin.
The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.
Perform correlation and linear regression test
among the numeric columns in a data frame automatically
and make plots using pairs or lattice::parallelplot.
Useful functions for accessing MyEpisodes feeds and
episode information as well as other tv episode related actions
through www.myepisodes.com
Recommendation engine based on Myrrix. Myrrix is a complete,
real-time, scalable clustering and recommender system, evolved from Apache
Mahout. It uses Alternating Least Squares to build a recommendation engine.
External jars required for package Myrrix. Myrrix is a
recommendation engine
Investigate the evolution of biological processes by capturing evolutionary signatures in transcriptomes. The aim of this tool is to provide a transcriptome analysis environment for answering questions regarding the evolution of biological processes.
Linear and logistic regression models for quantitative genetic analysis of data from monozygotic twins.
An R wrapper for 'libnabo', an exact or approximate k nearest
neighbour library which is optimised for low dimensional spaces (e.g. 3D).
'libnabo' has speed and space advantages over the 'ANN' library wrapped by
package 'RANN'. 'nabor' includes a knn function that is designed as a
drop-in replacement for 'RANN' function nn2. In addition, objects which
include the k-d tree search structure can be returned to speed up repeated
queries of the same set of target points.
Contains methods described by Dennis R. Helsel in
his book "Nondetects And Data Analysis: Statistics
for Censored Environmental Data"
Constructs (non)additive genetic relationship matrices, and their inverses, from a pedigree to be used in linear mixed effect models (A.K.A. the 'animal model'). Also includes other functions to facilitate the use of animal models. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (http://www.vsni.co.uk/software/asreml).
Contains a sample of the 2005 Grade 8 Mathematics data from the National Assessment of Educational Progress (NAEP). This data set is called the NAEP Primer.
Designed for association studies in nested association mapping (NAM) panels, also handling biparental and random panels. It includes functions for genome-wide associations mapping of multiple populations, marker quality control, solving mixed models and finding variance components through REML and Gibbs sampling.
This package provides user-level functions to manage
namespaces not (yet) available in base R: 'registerNamespace',
'unregisterNamespace', 'makeNamespace', and
'getRegisteredNamespace'

('makeNamespaces' is extracted from the R 'base' package source code:
src/library/base/R/namespace.R)
This software package implements functions to simulate spherical, ellipsoid and cubic polyatomic nanoparticles with arbitrary crystal structures and to calculate the associated pair-distribution function and X-ray/neutron total-scattering signals. It also provides a target function that can be used for simultaneous fitting of small- and wide-angle total scattering data in real and reciprocal spaces. The target function can be generated either as a sum of weighted residuals for individual datasets or as a vector of residuals suitable for optimization using multi-criteria algorithms (e.g. Pareto methods).
A set of tools for normalizing, diagnostics and visualization of NanoString nCounter data.
Enables the processing and normalisation of the standard mRNA data output from the Nanostring nCounter software.
Provides functions to access NASA's Earth Imagery and Assets API
and the Earth Observatory Natural Event Tracker (EONET) webservice.
This package contains tidied data from the ASA 2006 data expo,
as well as a number of useful other related data sets.
NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D
biological image data, especially traced neurons. Reads and writes 3D images
in NRRD and 'Amira' AmiraMesh formats and reads surfaces in 'Amira' hxsurf
format. Traced neurons can be imported from and written to SWC and 'Amira'
LineSet and SkeletonGraph formats. These data can then be visualised in 3D
via 'rgl', manipulated including applying calculated registrations, e.g.
using the 'CMTK' registration suite, and analysed. There is also a simple
representation for neurons that have been subjected to 3D skeletonisation
but not formally traced; this allows morphological comparison between
neurons including searches and clustering (via the 'nat.nblast' extension
package).
Extends package 'nat' (NeuroAnatomy Toolbox) by providing a
collection of NBLAST-related functions.
Extends package 'nat' (NeuroAnatomy Toolbox) by providing objects
and functions for handling template brains.
The package provides functions related to human natural ordering.
It handles adjacent digits in a character sequence as a number
so that natural sort function arranges a character vector by their numbers, not digit characters.
It is typically seen when operating systems lists file names.
For example, a sequence a-1.png, a-2.png, a-10.png looks naturally ordered
because 1 < 2 < 10 and natural sort algorithm arranges so whereas general sort algorithms arrange
it into a-1.png, a-10.png, a-2.png owing to their third and fourth characters.
Utility functions that may be of general interest but are
specifically required by the 'NeuroAnatomy Toolbox' ('nat'). Includes
functions to provide a basic make style system to update files based on
timestamp information, file locking and 'touch' utility. Convenience
functions for working with file paths include 'abs2rel', 'split_path'
and 'common_path'. Finally there are utility functions for working with
'zip' and 'gzip' files including integrity tests.
Estimate the effective population size of a closed population using genetic data collected from two or more data points.
It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.
Calls the 'Jupyter'/'IPython' script 'nbconvert' to create vignettes from notebooks.
Those notebooks ('.ipynb' files) are files containing rich text, code, and its output.
Code cells can be edited and evaluated interactively.
See <http://ipython.org/notebook.html> for more information.
The Dirichlet (aka NBD-Dirichlet) model describes the
purchase incidence and brand choice of consumer products.  We
estimate the model and summarize various theoretical quantities
of interest to marketing researchers. Also provides functions
for making tables that compare observed and theoretical
statistics.
Perform non-bipartite matching and matched randomization. A
"bipartite" matching utilizes two separate groups, e.g. smokers being
matched to nonsmokers or cases being matched to controls. A "non-bipartite"
matching creates mates from one big group, e.g. 100 hospitals being
randomized for a two-arm cluster randomized trial or 5000 children who
have been exposed to various levels of secondhand smoke and are being
paired to form a greater exposure vs. lesser exposure comparison. At the
core of a non-bipartite matching is a N x N distance matrix for N potential
mates. The distance between two units expresses a measure of similarity or
quality as mates (the lower the better). The 'gendistance()' and
'distancematrix()' functions assist in creating this. The 'nonbimatch()'
function creates the matching that minimizes the total sum of distances
between mates; hence, it is referred to as an "optimal" matching. The
'assign.grp()' function aids in performing a matched randomization. Note
bipartite matching can be performed using the prevent option in
'gendistance()'.
Negative Binomial (NB) models for two-group comparisons and
regression inferences from RNA-Sequencing Data.
Performs a Necessary Condition Analysis (NCA). (Dul, J. 2016. Necessary Condition Analysis (NCA). ''Logic and Methodology of 'Necessary but not Sufficient' causality." Organizational Research Methods 19(1), 10-52)
NCA identifies necessary (but not sufficient) conditions in datasets. Instead of drawing a regression line ''through the middle of the data'' in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations.
(Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is ''horizontal'' and the y-axis is ''vertical'' and that values increase ''upwards'' and ''to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g. an effort or input) is necessary but not sufficient for a (desired) level of y (e.g. good performance or output).
Performs nonlinear calibration and curve fitting for data from Luminex, RT-PCR, ELISA, RPPA etc. Its precursor is Ruminex.
A flexible tool is presented here that can perform
(i) traditional non-compartmental analysis (NCA) and
(ii) simulation-based posterior predictive checks for a population
pharmacokinetic (PK) model using NCA metrics.
making NCBI taxonomic data locally available and
searchable as an R object
Provides a high-level R interface to
data files written using Unidata's
netCDF library (version 4 or earlier), which are binary data files that are portable
across platforms and include metadata information in addition to the data sets.
Using this package, netCDF files (either version 4
or "classic" version 3) can be opened and data sets read in easily.
It is also easy to create new netCDF dimensions, variables, and files,
in either version 3 or 4 format, and manipulate existing netCDF files.
This package replaces the former ncdf package, which only worked with
netcdf version 3 files.  For various reasons the names of the functions
have had to be changed from the names in the ncdf package.  The old ncdf package is still
available at the URL given below, if you need to have backward compatibility.
It should be possible to have both the ncdf and ncdf4 packages installed
simultaneously without a problem.
However, the ncdf package does not provide an interface for netcdf version 4 files.
This package contains a collection of helper functions for dealing
with NetCDF files opened using ncdf4.
Set of tools to simplify the handling of 'NetCDF' files with the 'RNetCDF' package.
Most functions are wrappers of basic functions from the 'RNetCDF' package to easily run combinations of these
functions for frequently encountered tasks.
Computes the noncentral Dunnett's test distribution (pdf, cdf and quantile) and generates random numbers.
R functions for analyzing spatial (cross-)covariance: the
nonparametric (cross-)covariance, the spline correlogram, the
nonparametric phase coherence function, and related.
Computes the noncentral gamma function: pdf, cdf, quantile
function and inverse for the noncentrality parameter.
A set of handy functions. Includes a versatile one line progress bar, one
line function timer with detailed output, time delay function, text histogram, object
preview, CRAN package search, simpler package installer, Linux command install check,
a flexible Mode function, top function, simulation of correlated data, and more.
Efficient algorithms for fitting regularization paths for linear or logistic regression models penalized by MCP or SCAD, with optional additional L2 penalty.
Naive discriminative learning implements learning and
classification models based on the Rescorla-Wagner equations and their
equilibrium equations.
Renders dynamic network data from 'networkDynamic' objects as movies, interactive animations, or other representations of changing relational structures and attributes.
Performs network enrichment analysis against functional gene sets.
Benchmarks networks. Renders raw gene profile matrices of dimensionality "Ngenes
x Nsamples" into the space of gene set (typically pathway) enrichment scores of
dimensionality "Npathways x Nsamples".
This package implements a path algorithm for Near-Isotonic
Regression. For more details see the help files
Includes functions and examples to compute NEAT, a network-based test for gene enrichment analysis (Signorelli et al., 2016).
NeatMap is a package to create heatmap like plots in 2 and
3 dimensions, without the need for cluster analysis. Like the
heatmap, the plots created by NeatMap display both a
dimensionally reduced representation of the data as well as the
data itself. They are intended to be used in conjunction with
dimensional reduction techniques such as PCA.
A simple function for easier package loading and auto-installation.
needy is a small utility library designed to make testing function
inputs less difficult. R is a dynamically typed language, but larger
projects need input checking for scalabity. needy offers a single
function, require_a( ), which lets you specify the traits an input object
should have, such as class, size, numerical properties or number of
parameters, while reducing boilerplate code and aiding debugging.
Effective population sizes (often abbreviated as "Neff") are essential in biodiversity monitoring and conservation. For the first time, calculating effective sizes with data obtained within less than a generation but considering demographic parameters is possible. This individual based model uses demographic parameters of a population to calculate annual effective sizes and effective population sizes (per generation). A defined number of alleles and loci will be used to simulate the genotypes of the individuals. Stepwise mutation rates can be included. Variations in life history parameters (sex ratio, sex-specific survival, recruitment rate, reproductive skew) are possible. These results will help managers to define existing populations as viable or not.
Estimating the number of essential genes in a genome on the basis of data from a random transposon mutagenesis experiment, through the use of a Gibbs sampler.
Provides several direct search optimization algorithms based on the
simplex method. The provided algorithms are direct search algorithms, i.e.
algorithms which do not use the derivative of the cost function. They are
based on the update of a simplex. The following algorithms are available: the
fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained
optimization with a fixed shape simplex), the variable shape simplex method of
Nelder and Mead (unconstrained optimization with a variable shape simplex
made), and Box's complex method (constrained optimization with a variable
shape simplex).
Access paleoecological datasets from the Neotoma Paleoecological
Database using the published API (http://api.neotomadb.org/).  The functions
in this package access various pre-built API functions and attempt to return
the results from Neotoma in a usable format for researchers and the public.
Set of functions to estimate the Glomerular Filtration Rate (GFR) using different biomarkers and other phenotypes of interest for nephrology.
Package for a Network assisted algorithm for Epigenetic studies using mean and variance Combined signals: NEpiC. NEpiC combines both signals in mean and variance differences in methylation level between case and control groups searching for differentially methylated sub-networks (modules) using the protein-protein interaction network.
Estimate hazard ratios, survival curves and attributable
risks for cohorts with missing covariates, using Cox models or
Kaplan-Meier estimated for strata.  This handles studies nested
within cohorts, such as case-cohort studies with stratified
sampling.  See
http://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf
Calculate a Mann-Whitney-Wilcoxon test for a difference between treatment levels using nested ranks.  This test can be used when observations are structured into several groups and each group has received both treatment levels. The p-value is determined via bootstrapping.  The nested ranks test is intended to be one possible mixed-model extension of the Mann-Whitney-Wilcoxon test, for which treatment is a fixed effect and group membership is a random effect.
Infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.
netClass is an R package for network-based feature (gene)
selection for biomarkers discovery via integrating biological
information.  This package adapts the following 5 algorithms
for classifying and predicting gene expression data using prior
knowledge: 1) average gene expression of pathway (aep); 2)
pathway activities classification (PAC); 3) Hub network
Classification (hubc); 4) filter via top ranked genes (FrSVM);
5) network smoothed t-statistic (stSVM).
Facilitates network clustering and evaluation of cluster
configurations.
Model fitting procedures for regression with network cohesion effects, when a network connecting sample individuals is available in a regression problem. In the future, other commonly used statistical models will be added, such as gaussian graphical model.
This package contains functions to carry out high
throughput data analysis and to conduct data set comparisons.
Similarity matrices from high throughput phenotypic data
containing uninformative (e.g. wild type) or missing data can
be calculated to report similarity of response.  A suite of
graph comparisons using an adjacency or correlation matrix
format are included to facilitate quick network analysis.
This package contains all data needed for Dan McFarland's
SNA R labs.
Empirical statistical analysis, visualization and simulation
of network models of the diffusion of innovations. The package implements
algorithms for calculating network diffusion statistics such as transmission
rate, hazard rates, exposure models, network threshold levels, infectiousness
(contagion), and susceptibility. The package is inspired by work published in
Valente, et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995)
<ISBN:9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others
(2011) <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among
others.
Methods for the generation of a wide range of network geographies,
e.g., grid networks or clustered networks. Useful for the generation of
benchmarking instances for the investigation of, e.g., Vehicle-Routing-Problems
or Travelling Salesperson Problems.
The netgsa-package contains functions for carrying out Network-based Gene Set Analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data.
Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs.
Network meta-analysis following methods by Rücker (2012) and Krahn et al. (2013). Functions and datasets to support Schwarzer, Carpenter, and Rücker, "Meta-Analysis with R" (2015), Chapter 8 "Network Meta-Analysis".
Package for the pre-processing and normalization of graphs.
NetSim allows to combine and simulate a variety of micro-models to research their impact on the macro-features of social networks.
A set of functions for studying network robustness, resilience and vulnerability.
A collection of network inference methods for co-expression networks, quantitative network distances and a novel framework for network stability analysis.
Tools to create and modify network objects.  The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.
Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey
graphs from 'R'.
Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.
A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects.
Functions useful
for producing estimates from data that were collected using network
reporting techniques like network scale-up, indirect sampling,
network reporting, and sibling history.
Tools to simulate bipartite networks/graphs with the
degrees of the nodes fixed and specified. 'networksis' is part
of the 'statnet' suite of packages for network analysis.
networkTomography implements the methods developed and evaluated in
Blocker and Airoldi (2011) and Airoldi and Blocker (2012). These include the
authors' own dynamic multilevel model with calibration based upon a Gaussian
state-space model in addition to implementations of the methods of Tebaldi &
West (1998; Poisson-Gamma model with MCMC sampling), Zhang et al. (2002;
tomogravity), Cao et al. (2000; Gaussian model with mean-variance relation),
and Vardi (1996; method of moments). Data from the 1router network of Cao et
al. (2000), the Abilene network of Fang et al. (2007), and the CMU network
of Blocker and Airoldi (2011) are included for testing and reproducibility.
RBF and MLP neural networks with graphical user interface
Training of neural networks using backpropagation,
resilient backpropagation with (Riedmiller, 1994) or without
weight backtracking (Riedmiller and Braun, 1993) or the
modified globally convergent version by Anastasiadis et al.
(2005). The package allows flexible settings through
custom-choice of error and activation function. Furthermore,
the calculation of generalized weights (Intrator O & Intrator
N, 1993) is implemented.
Visualization and analysis tools to aid in the interpretation of
neural network models.  Functions are available for plotting,
quantifying variable importance, conducting a sensitivity analysis, and
obtaining a simple list of model weights.
Annotated neuroblastoma copy number profiles,
a benchmark data set for change-point detection algorithms.
A collection of data structures that represent
volumetric brain imaging data. The focus is on basic data handling for 3D
and 4D neuroimaging data. In addition, there are function to read and write
NIFTI files and limited support for reading AFNI files.
The package allows users to generate fMRI time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments.
Computes the probability density function, cumulative distribution function, quantile function, random numbers and measures of inference for the following general families of  distributions (each family defined in terms of an arbitrary cdf G): Marshall Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, and exponentiated exponential Poisson G distributions.
Indices, heuristics and strategies to help determine the number of factors/components to retain:
1. Acceleration factor (af with or without Parallel Analysis);
2. Optimal Coordinates (noc with or without Parallel Analysis);
3. Parallel analysis (components, factors and bootstrap);
4. lambda > mean(lambda) (Kaiser, CFA and related);
5. Cattell-Nelson-Gorsuch (CNG);
6. Zoski and Jurs multiple regression (b, t and p);
7. Zoski and Jurs standard error of the regression coeffcient (sescree);
8. Nelson R2;
9. Bartlett khi-2;
10. Anderson khi-2;
11. Lawley khi-2 and
12. Bentler-Yuan khi-2.
Numerical Formal Concept Analysis (nFCA) is a modern unsupervised learning tool for analyzing general numerical data. Given input data, this R package nFCA outputs two nFCA graphs: a H-graph and an I-graph that reveal systematic, hierarchical clustering and inherent structure of the data.
An n-gram is a sequence of n "words" taken from a
body of text in order.  This package offers utilities for creating,
displaying, summarizing, and "babbling" n-grams.  The
'tokenization' and "babbling" are handled by very efficient C
code, which can even be build as its own standalone library.
The babbler is a simple Markov chain.  The package also offers
a vignette with complete example 'workflows' and information about
the utilities offered in the package.
A simple n-gram (contiguous sequences of n items from a
given sequence of text) tokenizer to be used with the 'tm' package with no
'rJava'/'RWeka' dependency.
ngspatial provides tools for analyzing spatial data, especially
non-Gaussian areal data. The current version supports the sparse spatial
generalized linear mixed model of Hughes and Haran (2013) and the centered
autologistic model of Caragea and Kaiser (2009).
Body Shape and related measurements from the US National Health
and Nutrition Examination Survey (NHANES, 1999-2004).  See
http://www.cdc.gov/nchs/nhanes.htm for details.
Utility to retrieve data from the National Health and Nutrition
Examination Survey (NHANES).
NHEMOtree performs cost-sensitive classification by
solving the two-objective optimization problem of minimizing
misclassification rate and minimizing total costs for
classification. The three methods comprised in NHEMOtree are
based on EMOAs with either tree representation or bitstring
representation with an enclosed classification tree algorithm.
Compiling the NHL Real Time Scoring System Database for easy use in R
Bayesian NHMM modeling for multiple time series. The emission distribution can be mixtures of Gammas, Poissons, Normals and zero inflation is possible.
Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations.  Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the package handle missing values.
Tools for modelling, ML estimation, validation analysis and simulation of non homogeneous Poisson processes in time.
get or set UNIX priority (niceness) of running R process
This package uses a probabilistic method to calculate niche
regions and pairwise niche overlap using multidimensional niche indicator
data (e.g., stable isotopes, environmental variables, etc.). The niche
region is defined as the joint probability density function of the
multidimensional niche indicators at a user-defined probability alpha
(e.g., 95%).  Uncertainty is accounted for in a Bayesian framework, and the
method can be extended to three or more indicator dimensions.  It provides
directional estimates of niche overlap, accounts for species-specific
distributions in multivariate niche space, and produces unique and
consistent bivariate projections of the multivariate niche region.  A
forthcoming article by Swanson et al. (Ecology, 2014) provides a detailed
description of the methodology.  See the package vignette for a worked
example using fish stable isotope data.
Computes and plots the boundary between night and day.
Miscellaneous R functions developed over the course of statistical
research. These include utilities that supplement the existing
idiosyncrasies of R; extend plotting functionality and aesthetics;
provide alternative presentations of matrix decompositions; extend
types of random variables supported for simulation; extend access
to command line tools and system information, making work on remote
systems easier.
Japan-specific data is sometimes too unhandy for R users to manage. The utility functions and data in this package disencumber us from such an unnecessary burden.
Fast and Accurate Trisomy Prediction in Non-Invasive Prenatal
Testing.
Datasets for testing nonlinear regression routines.
Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for
SI (International System of Units) and non-SI, plus unit conversions
Based on the data from NIST (National Institute of Standards and Technology)
Noninferiority tests for difference in failure rates at a prespecified control rate or prespecified time.
Optimization for nonlinear objective and constraint functions.  Linear or nonlinear equality and inequality constraints are allowed.  It accepts the input parameters as a constrained matrix.
Solve a system of nonlinear equations using a Broyden or a Newton method
with a choice of global strategies such as line search and trust region.
There are options for using a numerical or user supplied Jacobian,
for specifying a banded numerical Jacobian and for allowing
a singular or ill-conditioned Jacobian.
Fit and compare Gaussian linear and nonlinear mixed-effects models.
This package combines the odesolve and nlme packages for
mixed-effects modelling using differential equations.
nlmeU: Datasets and utility  functions enhancing functionality of nlme package. Datasets, functions and scripts are described in book titled 'Linear Mixed-Effects Models:
A Step-by-Step Approach' by Galecki and Burzykowski (2013). Package is under development.
Replacement for nls() tools for working with nonlinear least squares problems.
The calling structure is similar to, but much simpler than, that of the nls()
function. Moreover, where nls() specifically does NOT deal with small or zero
residual problems, nlmrt is quite happy to solve them. It also attempts to be
more robust in finding solutions, thereby avoiding 'singular gradient' messages
that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach
in nlmrt generally works more reliably to get a solution, though this may be
one of a set of possibilities, and may also be statistically unsatisfactory.
Added print and summary as of August 28, 2012.
It includes three methods: K-profiles clustering, non-linear network reconstruction, and non-linear hierarchical clustering.
nloptr is an R interface to NLopt. NLopt is a free/open-source library for
nonlinear optimization, providing a common interface for a number of
different free optimization routines available online as well as original
implementations of various other algorithms.
See http://ab-initio.mit.edu/wiki/index.php/NLopt_Introduction for more
information on the available algorithms. During installation on Unix the
NLopt code is downloaded and compiled from the NLopt website.
Basic classes and methods for Natural Language Processing.
Utilities for Natural Language Processing.
Likelihood inference based on higher order approximations
for nonlinear models with possibly non constant variance
This is a package which can help you search for the root
of a equation.
Estimate the non-linear odds ratio and plot it against a continuous exposure.
Adds brute force and multiple starting values to nls.
Estimation of structural equation models with nonlinear effects
and underlying nonnormal distributions.
Non-linear shrinkage estimation of population eigenvalues and covariance
matrices, based on publications by Ledoit and Wolf (2004, 2015, 2016).
Data sets and nonlinear regression models dedicated to predictive microbiology
Fit univariate non-linear scale mixture of
skew-normal(NL-SMSN) regression.
Several tools for assessing the quality of fit of a
gaussian nonlinear model are provided.
Utilities and kinship information for behavior genetics and
developmental research using the National Longitudinal Survey of Youth
(NLSY; <http://www.bls.gov/nls/>).
Uses a modified lifting algorithm on which it builds the
nondecimated lifting transform. It has applications in wavelet
shrinkage.
R functions for (non)linear time series analysis. A core
topic is order estimation through cross-validation.
Provides functions to calculate the normalised Lineage-Through-Time (nLTT) statistic, given two phylogenetic trees. The nLTT statistic measures the difference between two Lineage-Through-Time curves, where each curve is normalised both in time and in number of lineages.
Wald Test for nonlinear restrictions on model parameters and confidence
intervals for nonlinear functions  of parameters using delta-method.  Applicable
after ANY model, provided parameters estimates and their covariance matrix are
available.
Without the need to know the true number of change-points in advance,
this package provides a function for detecting multiple change-points
using  a non-parametric maximum likelihood approach.
Provides a framework to perform Non-negative Matrix
Factorization (NMF). The package implements a set of already published algorithms
and seeding methods, and provides a framework to test, develop and plug
new/custom algorithms. Most of the built-in algorithms have been optimized
in C++, and the main interface function provides an easy way of performing
parallel computations on multicore machines.
Wrapper package for the nmfgpu library, which implements several
Non-negative Matrix Factorization (NMF) algorithms for CUDA platforms.
By using the acceleration of GPGPU computing, the NMF can be used for
real-world problems inside the R environment. All CUDA devices starting with
Kepler architecture are supported by the library.
Functions,  examples and  data  from the  book
'Numerical  Methods  and  Optimization  in Finance'  by  M.
Gilli, D.  Maringer and E.  Schumann.  The package provides
implementations of several optimisation heuristics, such as
Differential  Evolution, Genetic  Algorithms  and Threshold
Accepting.  There  are also functions for  the valuation of
financial  instruments,  such  as  bonds and  options,  and
functions that help with stochastic simulations.
Software for feed-forward neural networks with a single
hidden layer, and for multinomial log-linear models.
Computing prediction intervals of neural network models (e.g.backpropagation) at certain confidence level. It can take the output from models trained by other packages like 'nnet', 'neuralnet', 'RSNNS', etc.
Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.
This is a package for Non-Negative Linear Models (NNLM). It implements
fast sequential coordinate descent algorithms for non-negative linear regression
and non-negative matrix factorization (NMF). It supports mean square error and Kullback-Leibler divergence loss.
Many other features are also implemented, including missing value imputation, domain knowledge integration,
designable W and H matrices and multiple forms of regularizations.
An R interface to the Lawson-Hanson implementation of an
algorithm for non-negative least squares (NNLS).  Also allows
the combination of non-negative and non-positive constraints.
Nonlinear nonparametric statistics using partial moments.
Helps a clinical trial team  discuss
the clinical goals  of a well-defined biomarker with a diagnostic,
staging, prognostic, or predictive purpose.  From this discussion will
come a statistical plan for a (non-randomized) validation trial.
Both prospective and retrospective trials are supported. In a specific
focused discussion, investigators should determine the range of
"discomfort" for the NNT, number needed to treat.  The meaning of
the discomfort range, [NNTlower, NNTupper], is that within this range
most physicians would feel discomfort either in treating or withholding
treatment. A pair of NNT values bracketing that range, NNTpos and NNTneg,
become the targets of the study's design.  If the trial can demonstrate
that a positive biomarker test yields an NNT less than NNTlower,
and that a negative biomarker test yields an NNT less than NNTlower,
then the biomarker may be useful for patients. A highlight of the package
is visualization of a "contra-Bayes" theorem, which produces criteria for
retrospective case-controls studies.
Node harvest is a simple interpretable tree-like estimator for high-dimensional regression and classification. A few nodes are selected from an initially large ensemble of nodes, each associated with a positive weight. New observations can fall into one or several nodes and predictions are the weighted average response across all these groups. The package offers visualization of the estimator. Predictions can return the nodes a new observation fell into, along with the mean response of training observations in each node, offering a simple explanation of the prediction.
An implementation of the nodiv algorithm, see Borregaard, M.K., Rahbek, C., Fjeldsaa, J., Parra, J.L., Whittaker, R.J. & Graham, C.H. 2014. Node-based analysis of species distributions. Methods in Ecology and Evolution 5(11): 1225-1235. <DOI:10.1111/2041-210X.12283>. Package for phylogenetic analysis of species distributions. The main function goes through each node in the phylogeny, compares the distributions of the two descendant nodes, and compares the result to a null model. This highlights nodes where major distributional divergence have occurred. The distributional divergence for these nodes is mapped using the SOS statistic.
The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of genetic effects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations (provided the phenotype and the genotype of every individuals), estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is presented in Le Rouzic & Alvarez-Castro (2008).
Package for hierarchical clustering of objects characterized by nominal variables.
Analysis of a matrix of polytomous items using Nominal Logistic Biplots (NLB)
according to Hernandez-Sanchez and Vicente-Villardon (2013).
The NLB procedure extends the binary logistic biplot to nominal (polytomous) data.
The  individuals are represented as points on a plane and the  variables are represented
as convex prediction regions rather than vectors as in a classical or binary biplot.
Using the methods from Computational Geometry, the set of prediction regions is converted to a set of points
in such a way that the prediction for each individual is established by its closest
"category point". Then interpretation is based on distances rather than on projections.
In this package we implement the geometry of such a representation and construct computational algorithms
for the estimation of parameters and the calculation of prediction regions.
A collection of various regional information determined by the
U.S. Census Bureau along with demographic data.
A finite-population significance test of the 'sharp' causal null hypothesis that
treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers.
A generalized likelihood ratio test statistic is used, and the resulting p-value is exact.
Currently, it is assumed that there are only Compliers and Never Takers in the population.
Functions for nonlinear time series analysis. This package permits
the computation of the  most-used nonlinear statistics/algorithms
including generalized correlation dimension, information dimension,
largest Lyapunov exponent, sample entropy and Recurrence
Quantification Analysis (RQA), among others. Basic routines
for surrogate data testing are also included. Part of this work
was based on the  book "Nonlinear time series analysis" by
Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
Loading NONMEM (NONlinear Mixed-Effect Modeling, <http://www.iconplc.com/innovation/solutions/nonmem/> ) output files and simulate with parameter uncertainty.
Testing non-nested models via theory supplied by Vuong (1989).
Includes tests of model distinguishability and of model fit that can be applied
to both nested and non-nested models. Also includes functionality to obtain
confidence intervals associated with AIC and BIC. This material is based on work
supported by the National Science Foundation under Grant Number SES-1061334.
This package contains functions for measuring efficiency
and productivity of decision making units (DMUs) under the
framework of Data Envelopment Analysis (DEA) and its
variations.
Provides tests of significance for covariates (or groups of covariates) in a fully nonparametric regression model and a variable (or group) selection procedure based on False Discovery Rate. In addition, it provides a function for local polynomial regression for any number of dimensions, using a bandwidth specified by the user or automatically chosen by cross validation or an adaptive procedure.
This package offers a comprehensive data analysis if
stratification and matching by the propensity score is done.
Several functions are implemented, starting from the selection
of the propensity score model up to estimating propensity score
based treatment or exposure effects. All functions can be
applied separately as well as combined.
Screening a HRMS data set for peaks related by (1) isotope patterns, (2) different adducts of the same molecule and/or (3) homologue series. The resulting isotopic pattern and adduct groups can then be combined to so-called components, with homologue series information attached. Also allows plotting and filtering HRMS data for mass defects, frequent m/z distances and components vs. non-components.
Data sets for isotope pattern grouping of LC-HRMS peaks with package nontarget. Based on a vast set of unique PubChem molecular formulas, quantized (a) m/z, (b) m/z differences, (c) intensity ratios and (d) marker centroids of simulated centroid pairs are listed for different instrument resolutions.
Estimation of party/candidate ideological positions
that correspond to a Nash equilibrium along a
one-dimensional space.
Onedimensional Normal Mixture Models Classes, for, e.g.,
density estimation or clustering algorithms research and teaching;
providing the widely used Marron-Wand densities.  Now fitting to data
by ML (Maximum Likelihood) or EM estimation.
The Nordklim dataset 1.0 is a unique and useful achievement for climate
analysis. It includes observations of twelve different climate elements from
more than 100 stations in the Nordic region, in time span over 100 years.
The project contractors were NORDKLIM/NORDMET on behalf of the National
meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI),
Norway (DNMI) and Sweden (SMHI).
Analysis of multivariate normal datasets with missing
values
Functions for parameter estimation, Bayesian posterior simulation
and multiple imputation from incomplete multivariate data under a
normal model.
The functions proposed in this package compute the density of the sum of a Gaussian and a gamma random variables, estimate the parameters and correct the noise effect in a gamma-signal and Gaussian-noise model. This package has been used to implement the background correction method for Illumina microarray data presented in Plancade S., Rozenholc Y. and Lund E. "Generalization of the normal-exponential model : exploration of a more accurate parameterization for the signal distribution on Illumina BeadArrays", BMC Bioinfo 2012, 13(329).
This package provides functions for the normal Laplace
distribution. It is currently under development and provides
only limited functionality. Density, distribution and quantile
functions, random number generation, and moments are provided.
Collection of utilities referred to Exponential Power distribution,
also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4)
Functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided.
Evaluates the probability density function of the sum of
the Gaussian and Student's t density on 3 degrees of freedom.
Evaluates the p.d.f. of the sphered Student's t density
function.  Also evaluates the erf, and erfc functions on
complex-valued arguments. Thanks to Krishna Myneni the function
is calculates the Faddeeva function also!
Tests for the composite hypothesis of normality
Includes Omnibus Univariate and Multivariate Normality
Tests (See Doornik and Hansen (1994)). One variation allows for
the possibility of weak dependence rather than independence in
the variable(s). Also included is an univariate white noise
test where the null hypothesis is "white noise" rather than
strict "white noise".
CIPW Norm (acronym from the surnames of the authors: Cross, Iddings, Pirrson and Washington)
is the most commonly used calculation algorithm to estimate the standard mineral assemblages
for igneous rocks from its geochemical composition. NORRRM (acronym from noRm, R language and
Renee) is the highly consistent program to calculate the CIPW Norm.
An implementation of a specific method for generating
n-dimensional random vectors with given marginal distributions and
correlation matrix. The method uses the NORTA (NORmal To Anything)
approach which generates a standard normal random vector and then
transforms it into a random vector with specified marginal distributions
and the RA (Retrospective Approximation) algorithm which is a generic
stochastic root-finding algorithm. The marginals can be continuous or
discrete. See the vignette of package for more details.
Five omnibus tests for testing the composite hypothesis of
normality.
Tests the goodness-of-fit to the Normal distribution for the errors of an ARMA model.
The nose package consists of a collection of three
functions for classifying sparseness in typical 2 x 2 data sets
with at least one cell should have zero count. These functions
are based on the three widely applied summary measures for 2 x
2 categorical data viz, Risk Difference (RD), Relative Risk
(RR), Odds Ratio (OR). This package helps to identify suitable
continuity correction for zero cells when a multi centre
analysis or a meta analysis is carried out. Further, it can be
considered as a tool for sensitivity analysis for adding a
continuity correction and to identify the presence of Simpson's
paradox.
Provides functions to produce advanced ascii graphics, directly to the terminal window. This package utilizes the txtplot() function from the 'txtplot' package, to produce text-based histograms, empirical cumulative distribution function plots, scatterplots with fitted and regression lines, quantile plots, density plots, image plots, and contour plots.
This Package provides a connection to the pushover.net API
to send push notification to your smartphone directly from R.
(ACCOUNT REQUIRED!)
Estimate Large correlation and covariance matrices and their inverses using
integration of the sample and thresholded correlation and covariance estimators.
The noweb system for source code, implemented in R.
The Nozzle package provides an API to generate HTML
reports with dynamic user interface elements based on
JavaScript and CSS (Cascading Style Sheets). Nozzle was
designed to facilitate summarization and rapid browsing of
complex results in data analysis pipelines where multiple
analyses are performed frequently on big data sets. The package
can be applied to any project where user-friendly reports need
to be created.
This package provides a variety of nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC:www.nserc.ca), the Social Sciences and Humanities Research Council of Canada (SSHRC:www.sshrc.ca), and the Shared Hierarchical Academic Research Computing Network (SHARCNET:www.sharcnet.ca).
Computes interdaily stability (IS), intradaily variability (IV) & the relative amplitude (RA) from actigraphy data as described in van Someren et al. (1999). Additionally, it also computes L5 (i.e. the 5 hours with lowest average actigraphy amplitude) and M10 (the 10 hours with highest average amplitude) as well as the respective start times. The flex versions will also compute the L-value for a user-defined number of minutes. IS describes the strength of coupling of a rhythm to supposedly stable zeitgebers. It varies between 0 (Gaussian Noise) and 1 for perfect IS. IV describes the fragmentation of a rhythm, i.e. the frequency and extent of transitions between rest and activity. It is near 0 for a perfect sine wave, about 2 for Gaussian noise and may be even higher when a definite ultradian period of about 2 hrs is present. RA is the relative amplitude of a rhythm. Note that to obtain reliable results, actigraphy data should cover a reasonable number of days.
With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers.
The package "nparLD" is designed to perform nonparametric
analysis of longitudinal data in factorial experiments.
Longitudinal data are those which are collected from the same
subjects over time, and they frequently arise in biological
sciences. Nonparametric methods do not require distributional
assumptions, and are applicable to a variety of data types
(continuous, discrete, purely ordinal, and dichotomous).  Such
methods are also robust with respect to outliers and for small
sample sizes.
These routines create multiple imputations of missing at random categorical data, with or without structural zeros. Imputations are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling.
A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers  as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods.  Its use is illustrated via a number of empirical applications and simulated examples.
An implementation of nonparametric combination of hypothesis tests.
This package performs nonparametric combination (Pesarin and Salmaso 2010),
a permutation-based procedure for jointly testing multiple hypotheses. The
tests are conducted under the global "sharp" null hypothesis of no effects,
and the component tests are combined on the metric of their p-values. A
key feature of nonparametric combination is that it accounts for the
dependence among tests under the null hypothesis. In addition to the
"NPC" function, which performs nonparametric combination itself, the
package also contains a number of helper functions, many of which calculate
a test statistic given an input of data.
An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model.
This package implements nonparametric smoothing methods for circular data.
Provides nonparametric tests for assessing whether possibly serially dependent univariate or multivariate observations have the same c.d.f. or not. In addition to tests focusing directly on the c.d.f., the package contains tests designed to be particularly sensitive to changes in the underlying copula, Spearman's rho or certain quantities that can be estimated using one-sample U-statistics of order two such as the variance, Gini's mean difference or Kendall's tau. The latest addition is a nonparametric test for detecting changes in the distribution of independent block maxima.
Routines to compute normalised prediction distribution
errors, a metric designed to evaluate non-linear mixed effect
models such as those used in pharmacokinetics and
pharmacodynamics
Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions
modeling oriented towards flow-cytometry data pre-processing applications.
An R-package for calculating sample size of a survival trial with or without cure fractions
Returns nonparametric aligned rank tests for the interaction in two-way factorial designs, on R data sets with repeated measures in 'wide' format. Five ANOVAs tables are reported. A PARAMETRIC one on the original data, one for a CHECK upon the interaction alignments, and three aligned rank tests: one on the aligned REGULAR, one on the FRIEDMAN, and one on the KOCH ranks. In these rank tests, only the resulting values for the interaction are relevant.
This package provides routines for plotting
linkage and association results along a chromosome,
with marker names displayed along the top border.
There are also routines for generating BED and BedGraph
custom tracks for viewing in the UCSC genome browser.
The data reformatting program Mega2 uses this
package to plot output from a variety of
programs.
Performing drug response analyses and IC50 estimations using n-Parameter logistic regression. nplr can also be applied to proliferation analyses.
Given a failure type, the function computes covariate-specific probability of failure over time and covariate-specific conditional hazard rate based on possibly right-censored competing risk data. Specifically, it computes the non-parametric maximum-likelihood estimates of these quantities and their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described in Chang et al. (2007a).
Nonparametric maximum likelihood estimation or Gaussian
quadrature for overdispersed generalized linear models and
variance component models
The main method npmpm calculates bacterial concentrations
during food manufacture after a contamination. Variability and
uncertainty are included by use of probability distributions
and Monte Carlo Simulation. The model aims at predicting
possible bacterial concentrations at one certain point in time
s, e.g. at the end of a process chain. The process steps of
this process chain are run through in linear order.
Experimental data that match current process step conditions
are gathered, and one deterministic primary model is fitted to
every series of measured values. From every fitted curve one
concentration of bacteria at time s is computed, yielding a set
of concentrations. This sample of possible contamination sizes
is assumed to follow a certain probability distribution. After
calculation of distribution parameters, one value is randomly
drawn from this probability distribution. This value may be
modified, and then serves as contamination for the next process
step.
Fit multinomial logistic regression with a penalty on the nuclear
norm of the estimated regression coefficient matrix, using proximal
gradient descent.
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 or splines. Additionally, a parametric model (allometric model) can be
estimated.
Given a sample of class 0 and class 1 and a classification method, the package generates the corresponding Neyman-Pearson classifier with a pre-specified type-I error control and Neyman-Pearson Receiver Operator Curve.
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.
Modelling the vegetation, carbon, nitrogen and water dynamics of undisturbed open bog ecosystems in a temperate to sub-boreal climate. The test data can be downloaded from http://jeroenpullens.github.io/NUCOMBog_data/. And the executable of the model can downloaded from https://github.com/jeroenpullens/source_modelMEE.
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 model optimization, and statistical plot 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'
(Open Archives Initiative Protocol for 'Metadata' Harvesting) 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
Trains and makes predictions from the OASIS method, described in
detail in the paper "OASIS is Automated Statistical Inference for Segmentation,
with applications to multiple sclerosis lesion segmentation in MRI"
<doi:10.1016/j.nicl.2013.03.002>. OASIS is a method
for multiple sclerosis (MS) lesion segmentation on structural magnetic resonance
image (MRI) studies. OASIS creates probability maps of lesion presence using the
FLAIR, T2, T1, and PD structural MRI volumes. This packages allows for training
of the OASIS model and prediction of OASIS probability maps from a trained model
with user supplied studies that have a gold standard lesion segmentation masks.
The package will also create OASIS probability maps for MRI studies using the
OASIS model from the OASIS paper if no gold standard lesion segmentation masks
are available.
A set of indexes and tests for the analysis of social segregation.
An implementation of the Blinder-Oaxaca decomposition for linear regression models.
Data processing, visualisation and analysis of Limit Order Book
event data.
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, measurements made with 'argo' floats, 'CTD' measurements, sectional
data, sea-level time series, coastline and topographic data, etc. Provides
specialized functions for calculating seawater properties such as potential
temperature in either the 'UNESCO' or 'TEOS-10' equation of state. Produces
graphical displays that conform to the conventions of the Oceanographic literature.
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. Wrapper functions to estimate the standard limited dependent variable models are also included.
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.
Functions to calculate 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.
It 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().The web-based tool (https://imbi.shinyapps.io/phaseII-app/)
extends the functionality of our R package by means of a proper dealing with over- and underrunning.
The R function binom.test of the 'stats' R package and the package 'binom' might be
helpful to assess the performance of the corresponding one-stage design as a reference.
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.
Functions for reading ontologies into R as lists and handling sets of ontological terms.
Functions for visualising sets of ontological terms using the graphviz layout system.
Functions for calculating semantic similarities between ontological terms or sets of ontological terms based on term information content and assessing statistical significance of similarity in the context of a collection of sets of ontological terms.
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.
Tool for accessing the OpenCage API, which provides forward
geocoding (from placename to longitude and latitude) and reverse geocoding (from
longitude and latitude to placename).
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. The server can run either as a single-user development
server within the interactive R session, or as a multi-user linux stack
based on rApache and NGINX. The current R package implements the core of
the system. When loaded in R, it automatically initiates the single-user
server and displays the web address in the console. The OpenCPU website
has more detailed API documentation.
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 EC curves P-256, P-384 and P-521. Cryptographic signatures
can either be created and verified manually or via x509 certificates. AES can be
used in cbc, ctr or gcm mode for symmetric encryption; RSA for asymmetric (public
key) encryption or EC for Diffie Hellman. 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.
Implements multiple existing open-source algorithms for coding cause of death from verbal autopsies. It also provides tools for data manipulation tasks commonly used in Verbal Autopsy analysis and implements easy graphical visualization of individual and population level statistics.
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.
Software which helps practitioners optimally design experiments
that measure the slope divided by the intercept (originally invented for HSGC
measurements) and provides confidence intervals. Other useful tools herein.
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.
Computes confidence regions on the location of
response surface optima.
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.
Distance based bipartite matching using the RELAX-IV minimum cost flow solver,
oriented to matching of treatment and control groups in observational
studies. Routines are provided to generate distances from generalised linear models (propensity
score matching), formulas giving variables on which to limit matched distances, stratified or
exact matching directives, or calipers, alone or in combination.
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.
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.
A connector to ORES (<http://ores.wmflabs.org/>), an AI project to provide edit scoring for content
on Wikipedia and other Wikimedia projects. This lets a researcher identify
if edits are likely to be reverted, damaging, or made in good faith.
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.
Get species origin data (whether species is native/invasive) from the
following sources on the web: Encyclopedia of Life (<http://eol.org>), Flora
'Europaea' (<http://rbg-web2.rbge.org.uk/FE/fe.html>), Global Invasive Species
Database (<http://www.iucngisd.org/gisd>), the Native Species Resolver
(<http://bien.nceas.ucsb.edu/bien/tools/nsr/nsr-ws/>), and Integrated Taxonomic
Information Service (<http://www.itis.gov/>).
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
A data set package with the "Orsi" fronts as a
'SpatialLinesDataFrame' object. The Orsi et al. (1995) fronts are published at
the Southern Ocean Atlas Database Page, please see package CITATION for details.
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.
Allows distance based spatial clustering of georeferenced data by implementing the City Clustering Algorithm - CCA. Multiple versions allow clustering for matrix, raster and single coordinates on a plain (euclidean distance) or on a sphere (great-circle or orthodromic distance).
Compound deconvolution for chromatographic data, including gas chromatography - mass spectrometry (GC-MS) and comprehensive gas chromatography - mass spectrometry (GCxGC-MS). The package includes functions to perform independent component analysis - orthogonal signal deconvolution (ICA-OSD), independent component regression (ICR), multivariate curve resolution (MCR-ALS) and orthogonal signal deconvolution (OSD) alone.
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).
Produces customisable images of OpenStreetMap (OSM) data and
enables data visualisation using OSM 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 distances (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.
A penalized regression framework that can simultaneously estimate the optimal treatment strategy and identify important variables. Appropriate for either censored or uncensored continuous response.
Analyses of OTU tables produced by 16S sequencing, as well as example data. It contains the data and scripts used in the paper Linz, et al. (2016) "Seasonal mixing as a barrier to the development of stable bacterial communities in bog lakes" (Manuscript in prep).
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.
Functions for estimating the overlapping area of two or more empirical distributions.
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.
Summarize physical activity (Mets, Proportion, Total, etc.) in the given time period from accelerometer data. This package has been tested by data exported from 'activPAL'. 'activPAL' is a wearable device for medical and healthcare research application developed by PAL Technologies, Glasgow, Scotland. A simulated accelerometer sample dataset is provided in this package. See https://github.com/YukunZhang/PAactivPAL for more information.
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.
Implements Partition-Assisted Clustering, which utilizes a collection of partition based nonparametric density estimation techniques to improve the robustness and accuracy of downstream clustering.
The package also provides functions for effectively visualizing the clustering results.
It is particularly useful for finding and visualizing subpopulations in single-cell data analysis.
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"
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.
A prediction-based approach to the analysis of data from
randomized clinical trials is implemented. Based on response and
covariate data from a randomized clinical trial comparing a new
experimental treatment E versus a control C, the objective is to
develop and internally validate a model that can identify subjects
likely to benefit from E rather than C. Currently,
survival and binary response types are permitted.
Variational Bayesian approximations of Gibbs measures with hinge losses for classification and ranking.
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.
A simple way to add page numbers to base/ggplot/lattice graphics.
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.
Bayesian hierarchical methods for pathway analysis of genomewide association data: Normal/Bayes factors and Sparse Normal/Adaptive lasso. The Frequentist Fisher's product method is included as well.
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, a posteriori 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 a posteriori 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 palaeoclimate studies. Features three solutions: Berger (1978), Berger and Loutre (1991) and 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.
The document converter 'pandoc' <http://pandoc.org/> is widely used
in the R community. One feature of 'pandoc' is that it can produce and consume
JSON-formatted abstract syntax trees (AST). This allows to transform a given
source document into JSON-formatted AST, alter it by so called filters and pass
the altered JSON-formatted AST back to 'pandoc'. This package provides functions
which allow to write such filters in native R code.
Although this package is inspired by the Python package 'pandocfilters'
<https://github.com/jgm/pandocfilters/>, it provides additional convenience functions which make it simple to use the 'pandocfilters' package as a
report generator. Since 'pandocfilters' inherits most of it's functionality
from 'pandoc' it can create documents in many formats
(for more information see <http://pandoc.org/>) but is also bound to the same
limitations as 'pandoc'.
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 (<https://
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).
Users pass images and objects of class 'nifti' from the 'oro.nifti'
package to a Papaya, an interactive lightweight JavaScript viewer.
Although many packages can view individual slices or projections of
image and matrix data, this package allows for quick and easy
interactive browsing of images.  The viewer is based off of the
Mango software, which is a lightweight medical image viewer.
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.
Provides two functions: Group_function() and Parallel_Tree(). Group_function() applies a given function (e.g.,mean()) to input variable(s) by group across levels. Has additional data management options.
Parallel_Tree() uses 'ggplot2' to create a parallel coordinate plots (technically a facsimile of parallel coordinate plots in a Cartesian coordinate system).
Used in combination these functions can create parallel tree plots, a variant of parallel coordinate plots, which are useful for visualizing multilevel data.
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
Create random passwords of letters, numbers and punctuation.
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.
Primarily for use with datasets containing only categorical variables, although continuous variables may be included as independent variables in paths. Using M Plus, backward variable selection is performed on all Total, Total Indirect, and then Direct effects until none of these effects have p-values greater than the specified target p-value. If there are missing values in the data, imputations are performed using the Mice package. Then selection is performed with the imputed data sets, and results are averaged.
Quickly and easily visualize longitudinal microbiome profiles using standard output from the QIIME microbiome analysis toolkit (see <http://qiime.org> for more information).
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 (e.g. bootstrap). The type and style of the
progress bar (with percentages or remaining time)
can be set through options.
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.
An interface to and extensions for the 'PBLAS' and
'ScaLAPACK' numerical libraries.  This enables R to utilize
distributed linear algebra for codes written in the 'SPMD' fashion.
This interface is deliberately low-level and mimics the style of
the native libraries it wraps.  For a much higher level way of
managing distributed matrices, see the 'pbdDMAT' package.
A set of demos of 'pbdR' packages, together with a useful,
unifying vignette.
A set of classes for managing distributed matrices, and
a collection of methods for computing linear algebra and
statistics.  Computation is handled mostly by routines from the
'pbdBASE' package, which itself relies on the 'ScaLAPACK' and
'PBLAS' numerical libraries for distributed computing.
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.
Utilizing scalable linear algebra packages mainly
including BLACS, PBLAS, and ScaLAPACK in double precision via
pbdMPI based on ScaLAPACK version 2.0.2.
'ZeroMQ' is a well-known library for high-performance
asynchronous messaging in scalable, distributed applications.  This
package provides high level R wrapper functions to easily utilize
'ZeroMQ'. We mainly focus on interactive client/server programming
frameworks. For convenience, a minimal 'ZeroMQ' library (4.1.0 rc1)
is shipped with 'pbdZMQ', which can be used if no system installation
of 'ZeroMQ' is available.  A few wrapper functions compatible with
'rzmq' are also provided.
A set of datasets and functions used in the book
'Modele liniowe i mieszane w R, wraz z przykladami w analizie danych'.
Datasets either come from real studies or are created to be as similar
as possible to real studies.
Provides a vectorized R function for calculating
probabilities from a standard bivariate normal CDF.
Test in mixed effects models.

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

This package implements a parametric bootstrap test and a Kenward Roger
modification of F-tests for linear mixed effects models and a parametric bootstrap
test for generalized linear mixed models.
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.
Principal component of explained variance (PCEV) is a statistical tool for the analysis of a multivariate
response vector. It is a dimension-reduction technique, similar to Principal
component analysis (PCA), which seeks the maximize the proportion of
variance (in the response vector) being explained by a set of covariates.
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.
Estimates the multidimensional polytomous Rasch model
(Rasch, 1961) and the Continuous 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).
Simulate the forensic DNA process: generate random or fixed DNA
profiles, create forensic samples including mixtures of diploid and haploid
cells, simulate DNA extraction, normalization, degradation, amplification
including stutters and inter-locus balance, and capillary electrophoresis.
DNA profiles are visualized as electropherograms and saved in tables.
The command pcrsim() opens up a graphical user interface which allow the user
to create projects, to enter, load, and save parameters required for the simulation.
The simulation is transparent and the parameters used in each step of the simulation
can be viewed in the result tables.
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.
Allows the user to convert PDF tables to formats more amenable to
analysis ('.csv', '.xml', or '.xlsx') by wrapping the PDFTables API.
In order to use the package, the user needs to sign up for an API account
on the PDFTables website (<https://pdftables.com/pdf-to-excel-api>).
The package works by taking a PDF file as input, uploading it to PDFTables,
and returning a file with the extracted data.
Utilities based on libpoppler for extracting text,
fonts, attachments and metadata from a pdf file. Also implements
rendering of PDF to bitmaps on supported platforms.
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.
Estimates panel data cointegrating relationships following the estimator of Mark and Sul (2003), <DOI:10.1111/j.1468-0084.2003.00066.x>.
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.
Utility functions for the handling, analysis and visualisation
of data from portable emissions measurement systems ('PEMS') and other
similar mobile activity monitoring devices. The package includes a dedicated
'pems' data class that manages many of the quality control, unit handling
and data archiving issues that can hinder efforts to standardise 'PEMS'
research.
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.
A regularization approach for Cox Frailty Models by penalization methods is provided.
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.
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.
Provides tools for linkage mapping in polyploids.
It implements the method PERGOLA, which is a fast, deterministic method to
calculate the order of markers in a linkage group.
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).
Can be used to carry out
permutation resampling inference in the context of RNA
microarray studies.
Can be used to carry out permutation based gene expression pathway analysis. This work was supported by a National Institute of Allergy and Infectious Disease/National Institutes of Health contract (No. HHSN272200900059C).
All the functions compiled in this package were created to perform permutation-based non-parametric analysis on microbiome data for biomarker discovery aims. This test executes thousands of comparisons in pairwise manner, after random shuffling of data into the different groups of study.
Manipulates invertible functions from a finite set to itself.  Can transform from word form to cycle form and back.
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.
In analysis of high-dimensional DNA methylation data, a penalized exponential tilt model can identify differentially methylated loci between cases and controls, using network based regularization. It is able to detect any differences in means only, in variances only or in both means and variances.
Reproducible, programmatic retrieval of survey datasets from the
Pew Research Center.
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.
Fits penalized generalized estimating equations to longitudinal data with high-dimensional covariates.
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.
An implementation of Bayesian single-arm phase II
design methods for binary outcome based on posterior
probability and predictive probability.
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.
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").
Provides a collection of phonetic algorithms including
Soundex, Metaphone, NYSIIS, Caverphone, and others.
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.
Definitions of classes, methods, operators and functions for use in
'photobiology' and radiation meteorology and climatology. Calculation of
effective (weighted) and not-weighted 'irradiances'/doses, 'fluence' rates,
'transmittance', 'reflectance', 'absorptance', 'absorbance' and diverse
ratios and other derived quantities from spectral data. Local 'maxima' and
'minima'. Conversion between energy- and photon-based units. Wavelength
interpolation. Astronomical calculations related solar angles and day
length. Colours and vision.
Functions for reading and writing foreign files containing
spectral data from spectrometers and their associated software, and output
from simulation models in common use.
Constructors of waveband objects for commonly used biological
spectral weighting functions (BSWFs) and for different wavebands describing
named ranges of wavelengths in the ultraviolet (UV), visible (VIS)
and infrared (IR) regions of the electromagnetic spectrum.
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 high-dimensional traits (morphometric, function-valued, etc.) 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. Other tools include functions to test the adequacy of a population 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.
Tools for calculating and viewing topological properties of phylogenetic trees.
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).
Contains a function to categorize accelerometer readings collected in free-living (e.g., for 24 hours/day for 7 days), preprocessed and compressed as counts (unit-less value) in a specified time period termed epoch (e.g., 1 minute) as either bedrest (sleep) or active.  The input is a matrix with a timestamp column and a column with number of counts per epoch. The output is the same dataframe with an additional column termed bedrest. In the bedrest column each line (epoch) contains a function-generated classification 'br' or 'a' denoting bedrest/sleep and activity, respectively.  The package is designed to be used after wear/nonwear marking function in the 'PhysicalActivity' package.
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.
Screens and sorts phylogenetic trees in both traditional and
extended Newick format. Allows for the fast and flexible screening (within
a tree) of Exclusive clades that comprise only the target taxa and/or Non-
Exclusive clades that includes a defined portion of non-target taxa.
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
Implements 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
Fits models of gene expression evolution to expression data from
coregulated groups of genes, assuming inverse gamma distributed rate
variation.
Global univariate minimization of Lipschitz functions is performed by using Pijavski method, which was published in Pijavski (1972) <DOI:10.1016/0041-5553(72)90115-2>.
Genomic data for the plant pathogen "Phytophthora infestans." It includes a variant file ("vcf") sequence file ("fasta") and an annotation file ("gff"). This package is intended to be used as example data for packages that work with genomic data.
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 (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 'pitchfx'. Among other things, 'pitchfx' 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. 'pitchfx' 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.
A semi-parametric estimation method for the Cox model
with left-truncated data using augmented information
from the marginal of truncation times.
Supports the creation of burndown charts and gantt diagrams.
Solves the electromagnetic problem of reflection and transmission at a planar multilayer interface. Also computed are the decay rates and emission profile for a dipolar emitter.
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.
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 two million chess
matches.
A GTK+ graphical user interface for editing and
interacting with R plots.
Functions for estimating probabilistic latent feature models with a disjunctive or a conjunctive mapping rule on (aggregated) binary three-way 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.
Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, etc. Includes data sets from oceanography.
The 'rgl' implementation of plot3D functions.
When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.
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 marginal effects for interactions estimated
from linear models.
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.
The main objective is to make easy the PLS Path Modeling with R using the package 'plspm'. It compute automatically the inner matrix and the outer list the 'plspm' function need simply by specify the model using formulas.
Computation of sensitivity indexes by using a method based on a truncated Polynomial Chaos Expansion of the response and regression PLS, for computer models with correlated continuous inputs, whatever the input distribution. The truncated Polynomial Chaos Expansion is built from the multivariate Legendre orthogonal polynomials.
The number of runs (rows) can be smaller than the number of monomials. It is possible to select only the most significant monomials.
Of course, this package can also be used if the inputs are independent. Note that, when they are  independent and uniformly distributed, the package 'polychaosbasics' is more appropriate.
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.
Interfaces and methods for variable selection in Partial Least
Squares. The methods include filter methods, wrapper methods and embedded
methods.
Kernel density estimation with global bandwidth selection
via "plug-in".
Gives the ability to automatically generate and serve an HTTP API
from R functions using the annotations in the R documentation around your
functions.
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
Aims to utilize model-based clustering (unsupervised)
for high dimensional and ultra large data, especially in a distributed
manner. The code employs pbdMPI to perform a
expectation-gathering-maximization 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 programming model. The code can be executed
through pbdMPI and independent to most MPI applications.
See the High Performance
Statistical Computing website for more information, documents
and examples.
The Kruskal and Wallis one-way analysis of variance by ranks
or van der Waerden's normal score test can be employed,
if the data do not meet the assumptions
for one-way ANOVA. Provided that significant differences
were detected by the omnibus test, one may be interested
in applying post-hoc tests for pairwise multiple comparisons
(such as 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
or the Quade-test. The consequent post-hoc pairwise
multiple comparison tests according to Nemenyi, Conover and Quade
are also provided in this package. Finally Durbin's test for
a two-way balanced incomplete block design (BIBD) is also given
in this package.
Routines for state estimate in a linear
Gaussian state space model and a simple stochastic volatility model using
particle filtering. Parameter inference is also carried out in these models
using the particle Metropolis-Hastings algorithm that includes the particle
filter to provided an unbiased estimator of the likelihood. This package is
a collection of minimal working examples of these algorithms and is only
meant for educational use and as a start for learning to them on your own.
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 to compute 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 (overdispersed) Poisson, negative binomal 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.
Generation of 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).
Computation of sensitivity indexes by using a method based on a truncated Polynomial Chaos Expansions of the response.
The necessary condition of the method is: the inputs must be uniformly and independently sampled. Since the inputs are uniformly distributed, the truncated Polynomial Chaos Expansion is built from the multivariate Legendre orthogonal polynomials.
R port of Angus Johnson's open source library Clipper. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines. Computes Minkowski Sum of general polygons. There is a function for removing self-intersections from polygon data.
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 used can be genotype data (copy numbers known) or
'allelic phenotype data' (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.
This algorithm can classify the trends as linear, quadratic, cubic, concealed or no-trend. The "concealed trend" are those trends that possess quadratic or cubic forms, but the net change between the start and end of the period hasn't been significant. While, the "no-trend" category includes simple linear trends that are below the significant slope coefficient.
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
Calculations of an information criterion are proposed 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).
Tools for modelling populations and demography using matrix projection
models (MPMs). Designed to build on similar tools already available in 'popbio'.
Specific foci are on indices of transient dynamics and use of control theory
approaches, but 'popdemo' may also be useful for other implementations of MPMs,
or matrix models in a more general sense.
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.
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).
Run population simulations using an Individual-Based Model (IBM) compiled in C.
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.
Simulate the dynamic of wolf populations using a specific Individual-Based Model (IBM) compiled in C.
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 cloud 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 cloud 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.
Functions to convert geometry and 'hstore' data types from
'PostgreSQL' into standard R objects, as well as to simplify
the import of R data frames (including spatial data frames) into 'PostgreSQL'.
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.
Functions for bootstrapping the power of ANOVA designs
based on estimated means and standard deviations of the conditions.
Please refer to the documentation of the boot.power.anova() function
for further details.
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.
Computation of matrix and scalar exponentiation.
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.
A testing workbench for evaluating Precision-Recall curves under various conditions.
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.
Contains functions to analyze the precipitation
intensity, concentration and anomaly.
Accurate calculations and visualization of Precision-Recall and Receiver Operator Characteristics
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.
Provides a Testbench for comparison of prediction models. This
package is inspired from 'imputeTestbench' package
<https://cran.r-project.org/package=imputeTestbench>. It compares prediction
models with reference to RMSE, MAE or MAPE parameters. It allows to add new
proposed methods to test bench and to compare with other methods. The function
'prediction_append()' allows to add multiple numbers of methods to the existing
methods available in test bench.
"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 for statistical analysis (e.g., analysis of variance
;ANOVA) by enabling the user to easily and quickly merge (using the
file_merge() function) raw data files into one merged table and then
aggregate the merged table (using the prep() function) into a finalized
table while keeping track and summarizing every step of the preparation.
The finalized table contain several possibilities for dependent measures of
the dependent variable. Most suitable when measuring variables in an
interval or ratio scale (e.g., reaction-times) and/or discrete values such
as accuracy. Main functions included are file_merge() and prep(). The
file_merge() function vertically merges individual data files (in a long
format) in which each line is a single observation to one single dataset.
The prep() function aggregates the single dataset according to any
combination of 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 cell
according to the combination of provided grouping variables. Dependent
measures for each cell include among others means before and after rejecting
all values according to a flexible standard deviation criteria, number of
rejected values according to the flexible standard deviation criteria,
proportions of rejected values according to the flexible standard deviation
criteria, 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.
Data quality simulation can be used to check the robustness of data
analysis findings and learn about the impact of data quality contaminations on
classification. This package helps to add contaminations (noise, missing values,
outliers, low variance, irrelevant features, class swap (inconsistency), class
imbalance and decrease in data volume) to data and then evaluate the simulated
data sets for classification accuracy. As a lightweight solution simulation runs
can be set up with no or minimal up-front effort.
Data quality issues such as missing values and outliers are often
interdependent, which makes preprocessing both time-consuming and leads to
suboptimal performance in knowledge discovery tasks. This package supports
preprocessing decision making by visualizing interdependent data quality issues
through means of feature construction. The user can define his own application
domain specific constructed features that express the quality of a data point
such as number of missing values in the point or use nine default features.
The outcome can be explored with plot methods and the feature constructed data
acquired with get methods.
The R Markdown template in this package is based on the Center
for Open Science Preregistration Challenge and is, thus, particularly suited to
draft preregistration documents for studies that enter the challenge.
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 the number of species represented r or more times in a random sample.
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, scale bar, 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.
Identifies potential target sequences for a given set of primers
and generates taxonomically annotated phylogenetic trees with the predicted
amplification products.
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).
Binomial Spatial Probit models for big data.
This package provides multiclass conditional probability
estimation for the SVM, which is distributional assumption
free.
Confidence intervals and point estimation for R under various parametric model assumptions; likelihood inference based on classical first-order approximations and higher-order asymptotic procedures.
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.
Generate time series chart for individual values with mean and +/-
3 standard deviation lines and the corresponding mR chart with the upper control
limit. Also execute the 8 Shewhart stability run tests and display the violations.
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
Create a project directory structure, along with typical files
for that project.  This allows projects to be quickly and easily created,
as well as for them to be standardized. Designed specifically with scientists
in mind (mainly bio-medical researchers, but likely applies to other fields).
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.
A suite of multivariate methods and data visualization
tools to implement profile analysis and cross-validation techniques described in
Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and other published and unpublished resources.
The package includes routines to perform criterion-related profile analysis, profile analysis
via multidimensional scaling, moderated profile analysis, profile analysis by group, and a
within-person factor model to derive score profiles.
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.
An alternative data structure for the profiling information
generated by Rprof().
Interactive visualizations for profiling R code.
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 k-means clustering as a default, provides a tailored hierarchical clustering function, and can be customized to work with other clustering algorithms and different parameter settings. The package includes a main function progenyClust(), plot and summary methods for 'progenyClust' object, a function hclust.progenyClust() for hierarchical clustering, 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.
Abundant statistical literature has revealed the importance of constructing and evaluating various methods for constructing confidence intervals (CI) for single binomial proportion (p). We comprehensively provide procedures in frequentist (approximate with or without adding pseudo counts or continuity correction or exact) and in Bayesian cultures. Evaluation procedures for CI warrant active computational attention and required summaries pertaining to  four criterion (coverage probability, expected length, p-confidence, p-bias, and error) are implemented.
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
An implementation of protein descriptors in R. These descriptors
combine the advantages of being fixed length and including partial sequential
effect: Various length of protein sequences are described with fixed length
vectors that are suitable for machine learning algorithms, and still includes
partial sequential effect.
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.
An optimized, permissively licensed C++ implementation for serializing
R objects to protocol-buffers. This lightweight package is complementary to the
much larger 'RProtoBuf' package which provides a full featured toolkit for
working with protocol-buffers in R.
'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.
Procedures for testing for group-wide signal in clusters of variables. Tests can be performed for single groups in isolation (univariate) or multiple groups together (multivariate). Specific tests include the exact and approximate (un)selective likelihood ratio tests described in Reid et al (2015), the selective F test and marginal screening prototype test of Reid and Tibshirani (2015). User may pre-specify columns to be included in prototype formation, or allow the function to select them itself. A mixture of these two is also possible. Any variable selection is accounted for using the selective inference framework. Options for non-sampling and hit-and-run null reference distributions.
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. Includes an intuitive
query-based user interface for users who are not proficient in R.
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.
Tests 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 parametric and semiparametric Bayesian survival 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
Contains the core methods for the evaluation of principal
surrogates in a single clinical trial. Provides a flexible interface for
defining models for the risk given treatment and the surrogate, the models
for integration over the missing counterfactual surrogate responses, and the
estimation methods. Estimated maximum likelihood and pseudo-score can be used
for estimation, and the bootstrap for inference. A variety of post-estimation
summary methods are provided, including print, summary, plot, and testing.
Various functions in Pattern Sequence Based Forecasting (PSF) takes
time series data as input and assist to forecast the future value with
the help of Algorithm PSF. This algorithm forecasts the behavior of time series
based on similarity of pattern sequences. Initially, clustering is done with the
labeling of samples from database. The labels associated with samples are then
used for forecasting the future behaviour of time series data. The further
technical details and references regarding PSF are discussed in Vignette.
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.
Particle swarm optimization - a basic variant.
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.
Produces tables conforming to "psychological style" (i.e. APA style) based on standard R output. The resulting tables can be exported to '.rtf', '.html' or '.doc' format.
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
Takes the output of statistical tests and transforms it in a
publish-friendly pattern. Currently only APA (American Psychological
Association) style is supported with output to HTML, LaTeX, Markdown and
plain text. It is easily customizable, extendable and can be used well
with 'knitr'.  Additionally 'pubprint' offers a memory system that allows
to save and retrieve results of computations.
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.
Contains functions which allow the user to perform time series regression quickly using the Power Weighted Densities (PWD) approach. alphahat_LR_one_Rcpp() is the main workhorse function within this package.
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.
Core functions to perform Qualitative Comparative Analysis.
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 Epigenome-Wide Association Studies.
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.
Accurate estimates of the diets of predators are required
in many areas of ecology, but for many species current methods are
imprecise, limited to the last meal, and often biased. The diversity
of fatty acids and their patterns in organisms, coupled with the
narrow limitations on their biosynthesis, properties of digestion in
monogastric animals, and the prevalence of large storage reservoirs of
lipid in many predators, led us to propose the use of quantitative
fatty acid signature analysis (QFASA) to study predator diets.
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.
Extremely fast algorithm "QICD", Iterative Coordinate
Descent Algorithm for High-dimensional Nonconvex Penalized Quantile
Regression. This algorithm combines the coordinate descent algorithm
in the inner iteration with the majorization minimization step
in the outside step. For each inner univariate minimization problem,
we only need to compute a one-dimensional weighted median,
which ensures fast computation. Tuning parameter selection is based
on two different method: the cross validation and BIC for
quantile regression model. Details are described in the Peng,B and Wang,L. (2015)
linked to via the URL below with <DOI:10.1080/10618600.2014.913516>.
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 also offers functions to print Q cards and to generate Q distributions for study administration.
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.
Parametric modeling of quantile regression coefficient functions.
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.
Accompanying package to the book
Quantitative Risk Management: Concepts, Techniques and Tools by
Alexander J. McNeil, Rüdiger Frey, and Paul Embrechts.
Various data sets (stocks, stock indices, constituent data, FX,
zero-coupon bond yield curves, volatility, commodities) for Quantitative
Risk Management practice.
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.
Densitometric evaluation of the photo-archived quantitative thin-layer chromatography (TLC) plates.
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.
This is a collection of functions for unconditional and conditional quantiles. These include methods for transformation-based quantile regression, quantile-based measures of location, scale and shape, methods for quantiles of discrete variables, quantile-based multiple imputation, and restricted quantile regression.
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.
Contains methods associated with the Define, Measure, Analyze, Improve and Control (i.e. DMAIC) cycle of the Six Sigma Quality Management methodology.It covers distribution fitting, normal and non-normal process capability indices, techniques for Measurement Systems Analysis especially gage capability indices and Gage Repeatability (i.e Gage RR) and Reproducibility studies, factorial and fractional factorial designs as well as response surface methods including the use of desirability functions. Improvement via Six Sigma is project based strategy that covers 5 phases: Define - Pareto Chart; Measure - Probability and Quantile-Quantile Plots, Process Capability Indices for various distributions and Gage RR Analyze i.e. Pareto Chart, Multi-Vari Chart, Dot Plot; Improve - Full and fractional factorial, response surface and mixture designs as well as the desirability approach for simultaneous optimization of more than one response variable. Normal, Pareto and Lenth Plot of effects as well as Interaction Plots; Control - Quality Control Charts can be found in the 'qcc' package. The focus is on teaching the statistical methodology used in the Quality Sciences.
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 'randomForest', 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.
Implements the nonparametric quantile regression method developed by Belloni, Chernozhukov, and Fernandez-Val (2011) to partially linear quantile models. Provides point estimates of the conditional quantile function and its derivatives based on series approximations to the nonparametric part of the model. Provides pointwise and uniform confidence intervals using analytic and resampling methods.
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 and addins 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.
This calculates the Quandle, Rack and Degenerate Homology groups of
Racks and Biracks (as well as Quandles and Biquandles). In addition, a test is
provided to ascertain if a given set with one or two given functions is indeed a
biquandle or not.
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.
A set of functions to extract results from regression models and
plot the effect size using 'ggplot2' seamlessly. While 'broom' is useful to
convert statistical analysis objects into tidy data frames, 'coefplot' is adept at showing
multivariate regression results. With specific outcome, this package could build regression models
automatically, extract results into a data frame and provide a quicker way to summarize
models' statistical findings using 'ggplot2'.
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
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 NOAA Northwest Fisheries Science Center. The functions
include tools for summarizing and plotting results, manipulating files,
visualizing model parameterizations, and various other common stock
assessment 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.
It has tools for normalization of rank abundance
distributions (RAD) to a desired number of ranks using MaxRank
Normalization method.
RADs are commonly used in biology/ecology and mathematically equivalent
to complementary cumulative distributions (CCDFs) which are used in
physics, linguistics and sociology and more generally in data science.
Fundamental formulas for Radar, for attenuation, range, velocity,
effectiveness, power, scatter, doppler, geometry, radar equations, etc.
Based on Nick Guy's Python package PyRadarMet
Create interactive radar charts using the 'Chart.js' JavaScript library
and the 'htmlwidgets' package. Chart.js (http://www.chartjs.org/) is a
lightweight library that supports several types of simple chart using the HTML5
canvas element. This package provides an R interface specifically to the
radar chart, sometimes called a spider chart, for visualising multivariate data.
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 multiple treatment planning system platforms 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.
Ridge maximum likelihood estimation of vector auto-regressive processes and supporting functions for their exploitation.
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.
Parser generator for R using combinatory parsers. It
is inspired by combinatory parsers developed in Haskell.
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:// datastorm-
open.github.io/introduction_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.
Provides a solution path for Reinforced Angle-based Multicategory Support Vector Machines, with linear learning, polynomial learning, and Gaussian kernel learning.
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.
Simple methods to generate attractive random colors. The random
colors are from a wrapper of 'randomColor.js'
<https://github.com/davidmerfield/randomColor>. In addition, it also generates
optimally distinct colors based on k-means (inspired by 'IWantHue'
<https://github.com/medialab/iwanthue>).
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. Now extended to include multivariate and unsupervised forests.
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.
Generate random strings of a dictated size of symbol set and
distribution of the lengths of strings.
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.
Implements random variables by means of S4 classes and methods.
Provides tools for filtering occurrence records, generating alpha-hull-derived range polygons and mapping species richness.
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)
Implementation of 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.
The rankFD() function calculates the Wald-type statistic (WTS) and the ANOVA-type
statistic (ATS) for nonparametric factorial designs, e.g., for count, ordinal or score data
in a crossed design with an arbitrary number of factors.
Rank-hazard plots Karvanen and Harrell (2009) <DOI:10.1002/sim.3591> 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
Implementation of Kernelized score functions and other semi-supervised learning algorithms for node label ranking in biomolecular networks. RANKS can be easily applied to a large set of different relevant problems in computational biology, ranging from automatic protein function prediction, to gene disease prioritization and drug repositioning, and more in general to any bioinformatics problem that can be formalized as a node label ranking problem in a graph. The modular nature of the implementation allows to experiment with different score functions and kernels and to easily compare the results with baseline network-based methods such as label propagation and random walk algorithms, as well as to enlarge the algorithmic scheme by adding novel user-defined score functions and kernels.
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.
Bindings to various methods in the kernel for enforcing security
restrictions. AppArmor can apply mandatory access control (MAC) policies on
a given task (process) via security profiles with detailed ACL definitions.
In addition the package has kernel bindings for setting the process hardware
resource limits (rlimit), uid, gid, affinity and priority. The high level R
function 'eval.secure' builds on these methods to do dynamic sandboxing:
it evaluates a single R expression within a temporary fork which acts as a
sandbox by enforcing fine grained restrictions without affecting the main R
process. Recent versions on this package can also be installed on systems
without libapparmor, in which case some features are automatically disabled.
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).
Previously an R wrapper of the 'ARPACK' library
<http://www.caam.rice.edu/software/ARPACK/>, and now a shell of the
R package 'RSpectra', an R interface to the 'Spectra' library
<http://yixuan.cos.name/spectra/> for solving large scale
eigenvalue/vector problems. The current version of 'rARPACK'
simply imports and exports the functions provided by 'RSpectra'.
New users of 'rARPACK' are advised to switch to the 'RSpectra' package.
MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.
Software 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.
An Pure R implementation of Bayesian Global Optimization with Gaussian Processes.
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...
R interface for creating plots in Bokeh. Bokeh by Continuum
Analytics, \url{http://bokeh.pydata.org/en/latest/}.
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.
Implements the Ranking-Based Variable Selection
algorithm for variable selection in high-dimensional data.
Relational Class Analysis (RCA) is a method for detecting
heterogeneity in attitudinal data (as described in Goldberg
A., 2011, Am. J. Soc, 116(5)).
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.
Provides implementations of rule pruning algorithms based on the "Classification Based on Associations" (CBA). It can be used for building classification models from association rules. Rules are pruned in the order of precedence given by the sort criteria and a default rule is added. CBA was originally proposed by Liu, B. Hsu, W. and Ma, Y (1998). Integrating Classification and Association Rule Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86.
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.
Tools for optimal subset matching of treated units
and control units in observational studies, with support
for refined covariate balance constraints, (including
fine and near-fine balance as special cases). A close
relative is the 'rcbalance' package.
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.
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.
An R interface to the Chemistry Development Kit, a Java library
for chemoinformatics. 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.
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
Provides a list of functions for the statistical analysis and the post-processing of the Markov Chains simulated by ChronoModel (see <http://www.chronomodel.fr> for more information).  ChronoModel is a friendly software to construct a chronological model in a Bayesian framework.  Its output is  a sampled Markov chain from the posterior distribution of dates component  the chronology.
Functions to generate stimuli and analyze data of reverse correlation image classification experiments (psychophysical tasks aimed at visualizing cognitive mental representations of faces).
A simple and flexible way to generate Circos 2D track plot images for genomic data visualization is implemented in this package. 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.
Run 'R CMD check' from 'R' programmatically, and capture the
results of the individual checks.
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 Plugin for the 'FactoMineR' package.
Provides an Rcmdr plug-in based on the 'GWRM' package.
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.
A GUI front-end for ggplot2 allows Kaplan-Meier plot, histogram,
Q-Q plot, box plot, errorbar plot, scatter plot, line chart, pie chart,
bar chart, contour plot, and distribution plot.
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
Extends R Commander with 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' plugin 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.
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 3.2.8). 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 large 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.
A collection of open source libraries for numerical computing
(numerical integration, optimization, etc.) and their integration with
'Rcpp'.
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 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'. 'MsgPack' encoding is optional
if the 'RcppMsgPack' package is detected. You can install via from
the 'ghrr' drat repository listed below.
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.
The numerical treatment of optimal switching problems in a finite time setting when the state evolves as a controlled Markov chain consisting of a uncontrolled continuous component following linear dynamics and a controlled Markov chain taking values in a finite set. The reward functions are assumed to be convex and Lipschitz continuous in the continuous state. The action set is finite.
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.
Client for the web service methods provided
by 'DataCite' (<https://www.datacite.org/>), including functions to interface with
their 'OAI-PMH' 'metadata' service, and a 'RESTful' search 'API'. The 'API'
is backed by 'SOLR', allowing expressive queries, including faceting,
statistics on variables, and 'more-like-this' queries.
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.
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.
A client library for 'The Guardian' (https://www.guardian.com/)
and their API, this package allows users to search for Guardian articles and
retrieve both the content and metadata.
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.
A wrapper for the Deutsche Nationalbibliothek (German National Library) API, available at <http://www.dnb.de>. The German National Library is the German central archival library, collecting, archiving, bibliographically classifying all German and German-language publications from 1913, foreign publications about Germany, translations of German works, and the works of German-speaking emigrants published abroad between 1933 and 1945. A personal access token is required for usage.
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.
Interface to the Dryad Solr API, their OAI-PMH service, and
fetch datasets.
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.
Combine multiple data files from a common directory.
The data files will be read into R and bound together, creating a
single large data.frame. A general function is provided along with
a specific function for data that was collected using the open-source
experiment builder 'OpenSesame' <http://osdoc.cogsci.nl/>.
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.
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) and puts 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.
The rearrangement operator (Hardy,
Littlewood, and Polya 1952) for univariate, bivariate, and
trivariate point estimates of monotonic functions. The package
additionally provides a function that creates simultaneous
confidence intervals for univariate functions and applies the
rearrangement operator to these confidence intervals.
Collection of analysis methods used in regional and urban economics and (quantitative) economic geography, e.g. measures of inequality, regional disparities and regional specialization.
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 model generation, estimation, clustering and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, binomial, Poisson or Dirac parametric families.
Build regular expressions piece by piece using human readable code.
This package is designed for interactive use.  For package development, use
the rebus.* dependencies.
Build regular expressions piece by piece using human readable code.
This package contains core functionality, and is primarily intended to be
used by package developers.
Build regular expressions piece by piece using human readable code.
This package contains date and time functionality, and is primarily intended
to be used by package developers.
Build regular expressions piece by piece using human readable code.
This package contains number-related functionality, and is primarily intended
to be used by package developers.
Build regular expressions piece by piece using human readable code.
This package contains Unicode functionality, and is primarily intended to be
used by package developers.
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.
The 'Echo nest' <http://the.echonest.com> is the industry's leading
music intelligence company, providing developer with deepest understanding of
music content and music fans. This package can be used to access artist's data
including songs, blogs, news, reviews etc. Song's data including audio summary,
style, danceability, tempo etc can also be accessed.
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.
Provides an interface and a C++ implementation
of the RecMap MP2 construction heuristic (see 'citation("recmap")'
for details). This algorithm draws maps according to a given statistical
value (e. g. election results, population or epidemiological data).
The basic idea of the RecMap algorithm is that each map region
(e. g. different countries) is
represented by a rectangle. The area of each rectangle represents the
statistical value given as
input . Documentation about 'RecMap' is provided by a
vignette included in this package and a 'RecMap gallery'
site at <http://cartodraw.science/recmap/gallery>.
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 including UBCF, IBCF, FunkSVD and association
rule-based 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.
Computes quality control to daily precipitation datasets, reconstructs the original series by estimating precipitation in missing values, creates new series in a specified pair of coordinates and creates grids.
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>.
Provides methods to parse, query and serialize information
stored in the Resource Description Framework (RDF). RDF is described at <http://www.w3.org/TR/rdf-primer>.
This package supports RDF by implementing an R interface to the Redland RDF C library,
described at <http://librdf.org/docs/api/index.html>. In brief, RDF provides a structured graph
consisting of Statements composed of Subject, Predicate, and Object Nodes.
Contains C++ compiled objects that use time delay embedding to perform state-space reconstruction and nonlinear forecasting and an R interface to those objects using 'Rcpp'. It supports both the simplex projection method from Sugihara & May (1990) <DOI:10.1038/344734a0> and the S-map algorithm in Sugihara (1994) <DOI:10.1098/rsta.1994.0106>. In addition, this package implements convergent cross mapping as described by Sugihara et al. (2012) <DOI:10.1126/science.1227079>.
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.
The older method (Houseman et al., 2014) is similar to surrogate variable analysis (SVA and ISVA), except that it makes additional use of a biological mixture assumption.
The newer method (Houseman et al., 2016) is similar to non-negative matrix factorization, with additional constraints and additional utilities.
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'.
A tool for refining data frame with formulas.
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.
Tools are provided 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.
Functions for fitting linear and generalized linear models with variable selection. The functions can automatically do Stepwise Regression, Lasso or Elastic Net as variable selection methods. Lasso and Elastic net are improved and handle factors better (they can either include or exclude all factor levels).
Uses both ridge and lasso penalties (and extensions) to penalize
specific parameters in structural equation models. The package offers additional cost functions, cross validation, and other extensions beyond traditional SEM.
Computes linear Bayesian spectral estimates from multirate
data for second-order stationary time series. Provides credible
intervals and methods for plotting various spectral estimates.
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.
Makes relationship inference involving mixtures with unknown profiles and unknown number of contributors.
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.
Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.
A small wrapper on 'regexpr' to extract the matches and
captured groups from the match of a regular expression to a character
vector.
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.
A set of utilities for controlling a remote R session
from a local one.  Simply set up a server (see package vignette
for more details) and connect to it from your local R session,
including 'RStudio'.  Network communication is handled
by the 'ZeroMQ' library by way of the 'pbdZMQ' package.  The
client/server framework is a custom 'REPL'.
Fits linear models with endogenous regressors using Internal
Instrumental Variables (IIV) methods. These are statistical techniques to
correct for endogeneity when no strong, valid external instrumental variables
are available. The first version of the package offers two methods, the Latent
Instrumental Variables (Ebbes et al., 2005) and Lewbel's higher moments approach
(Lewbel, 1997). In a second version of the package, two other methods will be
added, joint estimation using copulas (Park and Gupta, 2012) and multilevel GMM
(Kim and Frees, 2007).
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 original 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 effect likely did not occur
due to the effects of sampling error alone. Alternatively, if a replication
effect size falls within the replication interval, then the replication
effect could have reasonably occurred due to the effects of sampling error
alone. This package has functions that calculate the replication interval for
the correlation (i.e., r), standardized mean difference (i.e., d-value), and mean.
The calculations used in version 2.0.0 and onward differ from past calculations
due to feedback during the journal review process. The new calculations allow
for a more precise interpretation of the replication interval.
Tools to load 'R' packages
and automatically generate BibTeX files citing them as well as load and
cache plain-text and 'Excel' formatted data stored on 'GitHub', and
from other sources.
A data manager meant to avoid manual storage/retrieval of
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 linear models to repeated ordinal scores using GEE methodology.
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. 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.
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.
String and binary representations of objects for several formats /
mime types.
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).
High level and easy 'HTTP' client for 'R'. Provides functions for
building 'HTTP' queries, including query parameters, body requests, headers,
authentication, and more.
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.
Executes a post-rotation algorithm that REorders and/or REflects FACTors (REREFACT) for each replication of a simulation study with exploratory factor analysis.
A collection of regression models for recurrent event process and failure time.
Bootstrap, permutation tests, and other resampling functions,
featuring easy-to-use syntax.
Implementation of functions for spectral similarity/dissimilarity
analysis and memory-based learning (MBL) for non-linear modeling
in complex spectral datasets. In chemometrics MBL is also known as local modeling.
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 for water supply, hydropower, and multi-objective reservoirs
using deterministic and stochastic dynamic programming; generate inflow
replicates using parametric and non-parametric models; 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.
R Markdown format for 'reveal.js' presentations, a framework
for easily creating beautiful presentations using HTML.
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.
Provides convenience functions to communicate with
an Experigen server. It is especially helpful with registered experiments, as
authentication with the server has to happen.
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.
Returns environmental data such as air temperature,
precipitation rate and wind speed from the FetchClimate Web service (<http://fc.itis.cs.msu.ru/>)
based on user specified arguments such as geographical regions or coordinates and time bounds.
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.
Basic R interface for the FLANN C++ library written by Marius Muja and David Lowe.
This package was written primarily for another package, 'rcss'. This packages utilises
a few features from the FLANN C++ library. When I have time (and if there is sufficient
demand), I will add more functions.
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.
Functionality required to efficiently use R with MarkLogic NoSQL Database Server, <http://www.marklogic.com/what-is-marklogic/>. Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of MarkLogic server. In order to use the package you need a MarkLogic Server version 8 or higher.
Various tools for handling fuzzy measures, calculating Shapley value and Interaction index,  Choquet and Sugeno integrals, as well as fitting fuzzy measures to empirical data are provided. Construction of fuzzy measures from empirical data is done by solving a linear programming problem by using 'lpsolve' package, whose source in C adapted to the R environment
is included. The description of the basic theory of fuzzy measures is in the manual in the Doc folder in this package.
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.
Access WHO Global Health Observatory
data from R via the Athena web service,
an application program interface providing
a simple query interface to the World
Health Organization's data and statistics content.
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 data set.
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.
Provides interface to Google Fit REST API v1 (see <https://developers.google.com/fit/rest/v1/reference/>).
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.
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.
Allows to get weather data from Automated Surface Observing System (ASOS) stations (airports) in the
whole world thanks to the Iowa Environment Mesonet website.
RIFS package provides functionality for generating &
plotting prefractals in R^n with various protofractal sets and
partition coefficient for iterative segments
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.
A pure R implementation of the median cut algorithm.
Extracts the dominant colors from an image, and turns them into
a scale for use in plots or for fun!
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.
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.
Performs the JIVE decompositions on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data. It provides two methods of rank selection when the rank is unknown, a permutation test and a BIC selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
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.
KEEL is a popular Java software for a large number of different knowledge data discovery tasks.
This package takes the advantages of KEEL and R, allowing to use KEEL algorithms in simple R code.
The implemented R code layer between R and KEEL makes easy both using KEEL algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way.
It includes more than 100 algorithms for classification, regression and preprocess, which allows a more complete experimentation process.
For more information about KEEL, see <http://www.keel.es/>.
KEEL is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, RKEEL is a package with a R code layer between R and KEEL, for using KEEL in R code. This package includes the datasets from KEEL in .dat format for its use in RKEEL package. For more information about KEEL, see <http://www.keel.es/>.
KEEL is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, 'RKEEL' is a package with a R code layer between R and KEEL, for using KEEL in R code. This package downloads and install the .jar files necessary for 'RKEEL' algorithms execution. For more information about KEEL, see <http://www.keel.es/>.
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 functions for the topological analysis of polymers, with a focus on protein structures.
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.
Processing logical operations such as AND/OR/NOT operations
dynamically. It also handles nesting in the operations.
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 function to simulate luminescence signals in the
mineral quartz based on published models.
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.
MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a
Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data.
HTML formats and templates for 'rmarkdown' documents, with some extra
features such as automatic table of contents, lightboxed figures, dynamic
crosstab helper.
A series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.
'R' 'Markdown' format for 'shower' presentations, see
<https://github.com/shower/shower>.
We provide functions to compute confidence
intervals (CIs) for a well-defined nonlinear function of the model
parameters (e.g., product of k coefficients) in single--level and
multilevel structural equation 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.
The simulation environment is documented in: "Strand, A.(2002) <DOI:10.1046/j.1471-8286.2002.00208.x> Metasim 1.0: an individual-based environment for simulating population genetics of
complex population dynamics. Mol. Ecol. Notes.
Please see the vignettes CreatingLandscapes and Simulating to get some ideas on how to use the packages.
See the rmetasim vignette to get an overview and to see important changes to the
code in the most recent version.
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 and Engage 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. Furthermore it is possible to load and update profiles.
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 nonparametric 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.
Display a randomly selected quote about Richard M. Stallman
based on the collection in the 'GNU Octave' function 'fact()' which was
aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site
<http://www.stallmanfacts.com>.
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.
Exported class can be used for storing sparse matrix
and solving corresponding linear system with one or many right
hand sides. There is a possibility to do separately symbolic analysis,
LU factorization and system solving.
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.
Using as input a 'LaTeX' file with a multiple choice exam, this package will produce several versions with randomized contents of the same exam. Functions for grading and testing for cheating are also available.
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 https://github.com/NetLogo/Mathematica-Link.
A collection of tools for measuring the similarity of news content and tracing the flow of (news) messages over
time and across media.
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.
Programmatic interface to the
Web Service methods provided by the National 'Phenology' Network
(<https://usanpn.org/>), which includes data on various life history
events that occur at specific times.
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.
A collection of methods for the robust analysis of univariate and
multivariate functional data, possibly in high-dimensional cases, and hence
with attention to computational efficiency and simplicity of use.
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.
Includes 'sysdata.rda' file for packages of the 'RobASt' - family of packages; is
currently used by package 'RobExtremes' only.
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.
Provides functions to download and parse 'robots.txt' files.
Ultimately the package makes it easy to check if bots
(spiders, scrapers, ...) are allowed to access specific
resources on a domain.
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 the corresponding article: Thieler, Fried and Rathjens (2016), Journal of Statistical Software 69(9), 1-36, <doi:10.18637/jss.v069.i09>.
Three-step regression and inference for cellwise and casewise contamination.
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''.
Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison.
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, Weighted Likelihood estimator and Truncated Maximum Likelihood estimator. Functions for regression and censored data are also available.
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.
A collection of functions to compute the Rao-Stirling diversity index
(Porter and Rafols, 2009) <DOI:10.1007/s11192-008-2197-2> and its extension to
acknowledge missing data (i.e.,	uncategorized references) by calculating its
interval of uncertainty using	mathematical optimization as proposed in Calatrava
et al. (2016) <DOI:10.1007/s11192-016-1842-4>.
The Rao-Stirling diversity index is a well-established bibliometric indicator
to measure the interdisciplinarity of scientific publications. Apart from the
obligatory dataset of publications with their respective references and	a
taxonomy of disciplines that categorizes references as well as a measure of
similarity between the disciplines, the Rao-Stirling diversity index requires
a complete categorization of all references of a publication into disciplines.
Thus, it fails for a incomplete categorization; in this case, the robust
extension has to be used, which encodes the uncertainty caused by missing
bibliographic data as an uncertainty interval.
Classification / ACM - 2012: Information systems ~ Similarity measures,
Theory of computation ~ Quadratic	programming, Applied computing ~ Digital
libraries and archives.
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 interpretation and presentation
of regression analysis.  These functions are used
to produce the statistics lectures in
http://pj.freefaculty.org/guides. Includes regression
diagnostics, regression tables, and plots of interactions and
"moderator" variables. The emphasis is on "mean-centered" and
"residual-centered" predictors. The vignette 'rockchalk' offers a
fairly comprehensive overview.  The vignette 'Rstyle' has advice
about coding in R.  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.
Contains functions in order to estimate diagnostic and prognostic capacities of continuous markers. More precisely, one function concerns the estimation of time-dependent ROC (ROCt) curve, as proposed by Heagerty et al. (2000) <DOI: 10.1111/j.0006-341X.2000.00337.x>. One function concerns the adaptation of the ROCt theory for studying the capacity of a marker to predict the excess of mortality of a specific population compared to the general population. This last part is based on additive relative survival models and the work of Pohar-Perme et al. (2012) <DOI: 10.1111/j.1541-0420.2011.01640.x>. We also propose two functions for cut-off estimation in medical decision making by maximizing time-dependent expected utility function. Finally, we propose confounder-adjusted estimators of ROC and ROCt curves by using the Inverse Probability Weighting (IPW) approach.  For the confounder-adjusted ROC curve (without censoring), we also proposed the implementation of the estimator based on placement values proposed by Pepe and Cai (2004) <DOI: 10.1111/j.0006-341X.2004.00200.x>.
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), KL-optimal designs (for lognormal errors) and their robust analogues can be calculated with the package.
Provides a reference class and several utility methods to
facilitate the implementation of models based on ordinary
differential equations. The heart of the package is a code generator
that creates compiled 'Fortran' (or 'R') code which can be passed to
a numerical solver. There is direct support for solvers contained
in packages 'deSolve' and 'rootSolve'.
Fetch and process Hillary Rodham Clinton's "personal" emails.
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.
Parallel functions for computing rolling statistics of time-series
data.
Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.
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.
OpenWeatherMap (OWM) <http://openweathermap.org/api> is a service providing weather related data.
This package can be used to access current weather data for one location or several locations.
It can also be used to forecast weather for 5 days with data for every 3 hours.
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.
Client for the 'Orcid.org' 'API' (http://orcid.org/).
Functions included for searching for people, searching by 'DOI',
and searching by 'Orcid' 'ID'.
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.
Rosette is an API for multilingual text analysis and information extraction. More information can be found at https://developer.rosette.com.
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. (2010) <DOI:10.1371/journal.pbio.1000493>, Morlon et al. (2011) <DOI:10.1073/pnas.1102543108>, Condamine et al. (2013) <DOI:10.1111/ele.12062>, Morlon et al. (2014) <DOI:10.1111/ele.12251>, Manceau et al. (2015) <DOI:10.1111/ele.12415>, Lewitus & Morlon (2016) <DOI:10.1093/sysbio/syv116>, and Drury et al. (2016) <DOI:10.1093/sysbio/syw020>.
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.
Allows users to identify relevant clinical codes and
automate the construction of clinical code lists for primary care database
studies. This package is analogous to the Stata command pcdsearch.
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.
Provides monthly Pacific Decadal Oscillation (PDO) index
values from January 1900 to March 2016.
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. Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny.
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 GenePix results files
(text file format used by many microarray scanner and software providers).
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).
Implements a 'DBI' compliant interface to Presto. Presto is
an open source distributed SQL query engine for running interactive
analytic queries against data sources of all sizes ranging from
gigabytes to petabytes: <https://prestodb.io/>.
'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.
Robust, reliable and flexible paths to files below a
project root. The 'root' of a project is defined as a directory
that matches a certain criterion, e.g., it contains a certain
regular file.
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 data journalism APIs from ProPublica <http://www.propublica.org/>.
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 including
outlier detection, PCA, PLS and 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".
Provides implementations of several popular recommendation systems. They can process standard recommendation datasets (user/item matrix) as input and generate rating predictions and recommendation lists.  Standard algorithm implementations included in this package are: Global/Item/User-Average baselines, Item-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology for recommender systems using measures such as MAE, RMSE, Precision, Recall, AUC, NDCG, RankScore and coverage measures. The package is intended for rapid prototyping of recommendation algorithms and education purposes.
R client interface to the "Redis" key-value database.
'IUCN' Red List (<http://apiv3.iucnredlist.org/api/v3/docs>) client.
The 'IUCN' Red List is a global list of threatened and endangered species.
Functions cover all of the Red List 'API' routes. An 'API' key is required.
'OpenRefine' (formerly 'Google Refine') is a popular, open source data cleaning software. This package enables users to programmatically trigger data transfer between R and 'OpenRefine'. Available functionality includes project import, export and deletion.
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/.
Performs parameter estimation for geostatistical data using a resampling-based stochastic approximation (RSA) method.
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.
Tools to create, evaluate, critique,
revise, track progress, and communicate a detailed wilderness or urban
search plan to management. This package uses and creates csv files in the R
working directory to document inputs and results. It also creates a series
of PDF and PNG files to accomplish communication of the plan. The program
creates and revises search plans using Bayesian models. The package
includes functions bestsearch(), searchstatus() and searchme() to model the
number of searchers and hours required to search an area, calculate the
probability of detection, probability of success, and project the best plan
given limited resources.
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.
The SciMark 2.0 benchmark was originally developed in Java as a benchmark for numerical and scientific computational performance. It measures the performance of several computational kernels which are frequently occurring in scientific applications. This package is a simple wrapper around the ANSI C implementation of the benchmark.
Client for Rserve, allowing to connect to Rserve instances and issue commands.
Programmatic deployment interface for 'RPubs', 'shinyapps.io', and
'RStudio Connect'. Supported content types include R Markdown documents,
Shiny applications, plots, and static web content.
Uses Elsevier Scopus API to download information about authors and their citations.
A method to download Department of Education College Scorecard data
using the public API. It is based on the dplyr model of
piped commands to select and filter data in a single chained function call.
An API key from the U.S. Department of Education is required.
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.
Analyses sentiment of a sentence in English and assigns score to it. It can classify sentences to the following categories of sentiments:- Positive, Negative, very Positive, very negative,
Neutral or Sarcasm. For a vector of sentences, it counts the number of sentences in each
category of sentiment.In calculating the score, negation and various degrees
of adjectives are taken into consideration. It deals only with English sentences.
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.
Functions that simplify the R interface the 'SLURM' cluster workload
manager, and automate the process of dividing a parallel calculation across
cluster nodes.
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.
It manages snow and weather local time series as provided by MeteoIO (<http://models.slf.ch/docserver/meteoio/html/index.html>, <http://www.slf.ch/ueber/organisation/schnee_permafrost/projekte/MeteoIO/index_EN>, <https://www.openhub.net/p/MeteoIO>). MeteoIO is a C/C++ Open Source library which "has been designed to accomodate both the needs of carefully crafted simulations for a specific purpose/study and for the needs of operational simulations that run automatically and unattended". It is integrated in physical spatially-distributed models and tackles several issues with weather input/output data. Here a SMET S4 class object is defined and can be imported from/ exported to a SMET ini files of MeteoIO , allowing interoperability from R to MeteoIO and other SMET-compliant software.
Read and analyse Root System Markup Language (RSML) files, used to
store plant root system architecture data. More information can be found
at the address <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 and manages throttling by 'Socrata'.
Users can upload data to Socrata portals directly
from R.
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.
R interface to the 'Spectra' library
<http://yixuan.cos.name/spectra/> for large scale eigenvalue and SVD
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.
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.
Calculates a newly defined coefficient of determination, aka R^2, and coefficient of partial determination, aka partial R^2 by Zhang (2016) for generalized linear models (including quasi models with well defined variance functions). It also avoids overstatement of variation proportion explained by the model or a group of covariates.
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.
Utilises The jTDS Project's JDBC 3.0 SQL Server
driver to extend the RJDBC classes and DBI methods. It defines a
SQLServerDriver, SQLServerConnection & SQLServerRsult S4 classes as
extensions of the RJDBC equivalent classes with most DBI
methods being thin extensions of the methods defined by RJDBC classes.
However, the dbConnect interface is more convenient, and the data reading
and writing capabilities found in RJDBC's fetch and dbWriteTable methods
have been refined. The package also implements a SQL backend to the dplyr
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, rough Bayesian inference via variational approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.
Estimates pre-compiled regression models using the 'rstan' package, which provides
the R interface to the Stan C++ library for Bayesian estimation. Users specify models via the
customary R syntax with a formula and data.frame plus some additional arguments for priors.
Access to the Digital Universe Data set API pulls data on
exoplants, stars, and galaxies.
A simple R -> Stata interface allowing the user to
execute Stata commands (both inline and from a .do file)
from R.
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 generalized survival models, where g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth. For fully parametric models, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects.
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 three 'Sunlight' Foundation 'APIs' (http://
sunlightfoundation.com/api/) for government data, including the
Congress 'API' 'v3', the Capitol Words '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 three '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.
Renders vector-based 'svg' images into high-quality custom-size bitmap
arrays using 'librsvg'. The resulting bitmap can be written to e.g. 'png', 'jpeg'
or 'webp' format. In addition, the package can convert images directly to various
formats such as pdf or postscript.
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, quantile function, and random generation): (a) Ratcliff diffusion
model based on C code by Andreas and Jochen Voss and (b) linear ballistic
accumulator (LBA) with different distributions underlying the drift rate.
Exploit controlled vocabularies organized on tematres servers.
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
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, t-product, and t-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.
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.
A suite of custom R Markdown formats and templates for
authoring journal articles and conference submissions.
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).
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.
Contains functions for launching R Tutorial & Plotting Apps. The R Tutorial app
provides a set of most commonly performed data manipulation tasks in R. The
app structures the contents into different topics and provides an interactive & dynamic interface
for navigation.
The plotting app provides an automated interface for generating plots using the 'ggplot2' package.
Current version of this app supports 10 different plot types along with options to manipulate specific
aesthetics and controls related to each plot type.
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.
An R interface to the 'typeform' (https://typeform.com) application program interface.
Also provides functions for downloading your results.
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.
Vector Graphics devices for 'SVG', 'DrawingML' for
Microsoft Word, PowerPoint and Excel.
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.
This is a collection of the five region-based
rare-variant genetic association tests. The following tests are
currently implemented: ASKAT, ASKAT-Normalized, VC-C1, VC-C2 and
VC-C3.
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.
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 3.9.0).
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'.
Communications simulation package supporting forward error correction.
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.
Functions which allow to read many XMCDA tags and transform them into R variables which are then usable in MCDA algorithms written in R. It also allows to write certain R variables into XML files respecting 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.
'Yandex Translate' (https://translate.yandex.com/) is a statistical machine translation system.
The system translates separate words, complete texts, and webpages.
This package can be used to detect language from text and to translate it to supported target language.
For more info: https://tech.yandex.com/translate/doc/dg/concepts/About-docpage/ .
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 <http://www.utheria.org/>.
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'.
R interface to the 'Zabbix API' data <https://www.zabbix.com/documentation/3.0/manual/api/reference>. Enables easy and direct communication with 'Zabbix API' from 'R'.
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 verify forecasts 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). Intended for seasonal to decadal climate forecasts although can be useful to verify other kinds of forecasts. The package can be helpful in climate sciences for other purposes than forecasting.
Fit a spatial instrumental-variable regression by two-stage least squares.
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.
Perform joint segmentation on two signal dimensions derived from
total read depth (intensity) and allele specific read depth (intensity) for
whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data.
A collection of baseball analytics functions for sabermetrics
purposes. Among these functions include popular metrics such as FIP, wOBA, and
runs created and other advanced 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). Currently, trade types of all
the five major asset classes have been created and, given the inheritance-
based structure of the application, the addition of further trade types is
straightforward. The application automatically separates the trades on the
corresponding hedging and netting sets including the basis and volatility
transactions. All the examples appearing on the regulatory paper (including the
margined and the un-margined workflow) have been implemented.
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'.
Methods to fit robust alternatives to commonly used models used in
Small Area Estimation. The methods here used are based on best linear
unbiased predictions and linear mixed models. At this time available models
include area level models incorporating spatial and temporal correlation in
the random effects.
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.
Functions for Generalized and Safe-Bayesian Ridge and Lasso Regression models with both fixed and varying variance.
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.
Tools for the statistical analysis and visualization of (relative and absolute) quantitative (LFQ,TMT,HRM) Proteomics data.
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.
A coordinate descent algorithm for computing the solution path of the sparse and coupled sparse asymmetric least squares, including the elastic net and (adaptive) Lasso penalized SALES and COSALES regressions.
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.
Programs for fitting Gaussian linear mixed models (LMM).
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 without replacement. 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.
Sankey plots illustrate the flow of information or material.
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
Functions for reading, listing
the contents of, and writing 'SAS' 'xport' format files.
The functions support reading and writing of either
individual data frames or sets of data frames.  Further,
a mechanism has been provided for customizing how
variables of different data types are stored.
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.
Uses the neural gas algorithm to construct
a scenario tree for use in multi-stage stochastic programming.
The primary input is a set of initial scenarios or realizations
of a disturbance. The scenario tree nodal structure must be
predefined using a scenario tree nodal partition matrix.
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 ('H5'), NetCDF ('CS'), 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 functions for analyzing political behavior data, including measures of political fragmentation, seat apportionment, 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
Provides functions to measure growth patterns and align
sampling spots in chronologically deposited materials. The package is
intended for the fields of sclerochronology, dendrochronology and geology.
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 for quickly scoring models in data science and
machine learning. This toolset is written in C++ for blazing fast performance.
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.
Clean biological occurrence records. Includes functionality
for cleaning based on various aspects of spatial coordinates,
unlikely values due to political 'centroids', coordinates based on
where collections of specimens are held, and more.
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.
Solves convex cone programs via operator splitting. Can solve:
linear programs (LPs), second-order cone programs (SOCPs), semidefinite programs
(SDPs), exponential cone programs (ECPs), and power cone programs (PCPs), or
problems with any combination of those cones.    SCS uses AMD (a set of routines for permuting sparse matrices prior to factorization) and LDL (a sparse LDL' factorization and solve package) from 'SuiteSparse' (<http://www.suitesparse.com>).
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.
Test the global null in linear models using marginal approach.
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.
An extensible R framework for developing species distribution
models using individual and community-based approaches, generate ensembles of
models, evaluate the models, and predict species potential distributions in
space and time.
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.
A software package help user to create virtual species for species distribution modelling. It includes
several methods to help user 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 soft-discretized data.  Soft-discretization is based on mixture of normal distributions.  Also implemented is a supervised Bayesian network learning employing Kullback-Leibler divergence.
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 or ARFF format files or data.frame objects. If you want more information about KEEL 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,
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 uses the
X-13 binaries from the 'x13binary' package.
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.
A collection of functions for correcting biased estimates under
selection (range restriction).
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 post-selection inference, 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.
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.
Routines for fitting copula 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. Most of the functions have to be applied on model with scalar output, but several functions support multi-dimensional outputs.
Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.
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.
Designed for fitting hidden (latent)
Markov models and mixture hidden Markov models for social
sequence data and other categorical time series. Also some more restricted
versions of these type of models are available: Markov models, mixture
Markov models, and latent class models. The package supports models for one or
multiple subjects with one or multiple parallel sequences (channels). External
covariates can be added to explain cluster membership in mixture models. The
package provides functions for evaluating and comparing models, as well as
functions for easy plotting of multichannel sequence data and hidden Markov
models. Models are estimated using maximum likelihood via the EM algorithm and/or
direct numerical maximization with analytical gradients.
All main algorithms are written in C++ with support for parallel computation.
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 (Variant call format, e.g. VCF or 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.
Sequential triangular test for the arithmetic mean in one- and two-
samples, proportions in one- and two-samples, and the Pearson's correlation
coefficient.
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 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.
Provides functionality for the use of the internal hardware for
RS232/RS422/RS485 and any other virtual serial interfaces of the
computer.
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".
Implements an algorithm to conduct advanced
gene set enrichment analysis on the results of genomics experiments.
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.
Mutators to set attributes of variables, that work well in a pipe
(much like stats::setNames()).
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 easily manipulating colours and creating colour scales.
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 bootstrap confidence interval for the change-point is incorporated in the main routine.
Functions to read and write ESRI shapefiles
An evolving and growing collection of tools for the quantification, assessment, and comparison of planar shape and pattern. The current flagship functionality is in the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking. The spectra of results are returned in graphic and tabular formats. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented.
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.
Hierarchical models for the analysis of species-area
relationships (SARs) by combining several data sets and covariates;
with a global data set combining individual SAR studies;
as described in Solymos and Lele
(2012, Global Ecology and Biogeography 21, 109-120).
Provides a computational framework for Bayesian estimation of
antigen-driven selection in immunoglobulin (Ig) sequences, providing an
intuitive means of analyzing selection by quantifying the degree of
selective pressure. Also provides tools to profile mutations in Ig
sequences, build models of somatic hypermutation (SHM) in Ig sequences,
and make model-dependent distance comparisons of Ig repertoires.
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. Also includes functions for eliciting beliefs about population distributions.
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' also includes
a colour picker widget, a colour picker RStudio addin, and 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.
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 and rstanarm packages).
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.
A statistical method designed to take advantage of population genetics and microevolutionary theory, specifically by testing the association between haplotype sharing length and trait of interest.
Block-diagonal covariance selection for high dimensional Gaussian
graphical models. The selection procedure is based on the slope heuristics.
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.
Supports inference from range statistics like extreme spread.
Includes a set of web-based graphical user interfaces.
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.
The predictive value of a statistical model can often be improved
by applying shrinkage methods. This can be achieved, e.g., by regularized
regression or empirical Bayes approaches. Various types of shrinkage factors can
also be estimated after a maximum likelihood. 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 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 bi-variate 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).
Several different sigmoid functions are implemented, including a wrapper function, SoftMax preprocessing and inverse functions.
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 the presence of signal peptides in eukaryotic protein
using hidden semi-Markov models. The implemented algorithm can be accessed from
both the 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.
Pathway Analysis is the process of statistically linking observations on the molecular level to biological processes or pathways on the systems (organism, organ, tissue, cell) level.
Traditionally, pathway analysis methods regard pathways as collections of single genes and treat all genes in a pathway as equally informative.  This can lead to identification of spurious (misleading) pathways as statistically significant, since components are often shared amongst pathways.
SIGORA seeks to avoid this pitfall by focusing on genes or gene-pairs that are (as a combination) specific to a single pathway.  In relying on such pathway gene-pair signatures (Pathway-GPS), SIGORA inherently uses the status of other genes in the experimental context to identify the most relevant pathways.
The current version allows for pathway analysis of human and mouse data sets and contains pre-computed Pathway-GPS data for pathways in the KEGG and Reactome pathway repositories as well as mechanisms for extracting GPS for user supplied repositories.
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 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 safely and efficiently organize Monte Carlo simulations in R.
The package controls the structure and back-end of Monte Carlo simulations
by utilizing a general generate-analyse-summarise strategy. The functions provided control
common simulation issues such as re-simulating non-convergent results, support parallel
back-end and MPI distributed 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.
Livestock movements are important for the spread of many
infectious diseases between herds. The package provides an
efficient and flexible framework for stochastic disease spread
modelling that integrates within-herd disease dynamics as
continuous-time Markov chains and livestock movements between
herds as scheduled events. The core simulation solver is
implemented in C and uses 'OpenMP' (if available) to divide work
over multiple processors. The package contains template models and
can be extended with user defined models.
A process-oriented and trajectory-based Discrete-Event Simulation
(DES) package for R. Designed to be a generic framework like SimPy or
SimJulia, it leverages the use of Rcpp to boost the performance and turning
DES in R feasible. As a noteworthy characteristic, simmer exploits the
concept of trajectory: a common path in the simulation model for entities
of the same type.
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.
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.
Simple classic graph algorithms for simple graph classes.
Graphs may possess vertex and edge attributes. 'simplegraph' has
so dependencies and it is written entirely in R, so it is easy to
install.
Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.
Simple result caching in R based on Henrik
Bengtsson's R.cache. The global environment is not considered when caching
results simplifying moving files between multiple instances of R. Relies on
more base functions than R.cache (e.g. cached results are saved using saveRDS()
and readRDS()).
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.
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.  Dirichlet probabilities
are calculated in certain cases.
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 and funds from the World bank.
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.
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 estimates 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',
specialised for modelling the association between a HPO-encoded 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 ("design"), conducting, and evaluating
large-scale simulation studies with graphics and tables, 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), 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.
Provided within is a high quality and fast PPRNG that is able to be used in an 'OpenMP' parallel
environment compiled under either C++98 or C++11. The objective of this package release is to consolidate
the distribution of the 'sitmo' library on CRAN by enabling others to link to the 'sitmo' header file instead
of including a copy of 'sitmo' within their individual package. Lastly, the package contains example
implementations using 'sitmo' and two accompanying vignette that provide additional information.
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. This package supports labelled data.
Collection of convenient functions for common statistical computations,
which are not directly provided by R's base or stats packages.
This package aims at providing, first, shortcuts for statistical
measures, which otherwise could only be calculated with additional
effort (like standard errors or root mean squared errors). Second,
these shortcut functions are generic (if appropriate), and can be
applied not only to vectors, but also to other objects as well
(e.g., the Coefficient of Variation can be computed for vectors,
linear models, or linear mixed models; the r2()-function returns
the r-squared value for 'lm', 'glm', 'merMod' or 'lme' objects).
The focus of most functions lies on summary statistics or fit
measures for regression models, including generalized linear
models and mixed effects models. However, some of the functions
also deal with other statistical measures, like Cronbach's Alpha,
Cramer's V, Phi etc.
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 an alternative to manually process hemispherical pictures. The algorithm processes each picture one by one to determine the proportion of sky pixel. The algorithm uses the Ridler and Calvard method (Ridler and Calvard 1978).
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.
'Slack' <http://slack.com/> provides a service for teams to
collaborate by sharing messages, images, links, files and more. Functions are provided
that make it possible to interact with the 'Slack' platform 'API'. When
you need to share information or data from R, rather than resort to copy/
paste in e-mails or other services like 'Skype' <http://www.skype.com/>, 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 extensions 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 prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.
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.
Implementation of robust and non-robust Standard Normal Homogeneity
Test (SNHT) for changepoint detection. This test statistic is equal sided,
as proposed in "Homogenization of Radiosonde Temperature Time Series Using
Innovation Statistics" by Haimberger, L., 2007. However, the statistic contains
an estimate of sigma^2 in the denominator instead of sigma, which seems to
be a more appropriate value (based on the paper "Homogenization of Swedish
temperature data. Part I: Homogeneity test for linear trends." by Alexandersson,
H., and A. Moberg, 1997).
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.
Functions for analysis of network objects, which are imported or
simulated by the package. The non-parametric methods of analysis center
around snowball and bootstrap sampling.
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.
Bivariate Gaussian genotype clustering and calling for Illumina microarrays, building on the package 'mclust'. Pronounced snip-em-clust.
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.
Scan statistics applied in social network data can be used to test the cluster characteristics among a social network.
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.
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, pins, and hits on Facebook (www.facebook.com), Pinterest (www.pinterest.com), StumbleUpon (www.stumbleupon.com), LinkedIn (www.linkedin.com), and Reddit (www.reddit.com). The package also includes dedicated functions for each social network platform and a function to resolve 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.
Calculation of solar zenith and azimuth angles.
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'.
Provides a set of functions for querying and parsing data
from 'Solr' (<http://lucene.apache.org/solr>) 'endpoints' (local and
remote), including search, 'faceting', 'highlighting', 'stats', and
'more like this'. In addition, some functionality is included for
creating, deleting, and updating documents in a 'Solr' 'database'.
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.
Mixed model equation solver allowing the specification of variance covariance structures of random effects. ML/REML estimates are obtained using the Average Information, Expectation-Maximization, Newton-Raphson, or Efficient Mixed Model Association algorithms. Designed for genomic prediction and genome wide association studies (GWAS) to include additive, dominance and epistatic relationship structures or other covariance structures in R, but also functional as a regular mixed model program.
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.
Used to construct the URLs and parameters of 'Socrata Open Data API' <https://dev.socrata.com> calls, using the API's 'SoQL' parameter format. Has method-chained and sensical syntax. Plays well with pipes.
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.
Basic functions for dealing with wav files and sound samples.
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.
Implements a non-parametric source attribution model
to attribute cases of disease to sources in Bayesian framework with source and
type effects. Type effects are clustered using a Dirichlet Process. Multiple
times and locations are supported.
Tools for the reading and tokenization of R code.
The 'sourcetools' package provides both an R and C++ interface
for the tokenization of R code, and helpers for interacting
with the tokenized representation of R code.
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.
Test the results of any given investment/expense combinations for a series of sliding-window periods of the S&P500 from 1950 to the present.
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 matching or aggregation, 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.
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")`.
Implements methods for anticipating the emergence and eradication
of infectious diseases from surveillance time series. Also provides support
for computational experiments testing the performance of such methods.
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). Heteroscedasticity 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 set of tools for representing and estimating sparse Bayesian networks from continuous and discrete data.
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
Estimates Hessian of a scalar-valued function, and returns it
in a sparse Matrix format. The sparsity pattern must be known in advance. The
algorithm is especially efficient for hierarchical models with a large number of
heterogeneous units.
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.  The method can implement sparse linear regression and sparse probit regression.  Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to  models with truncated outcomes, propensity score, and instrumental variable analysis.
Sparse-aware maximum likelihood for structural equation models in inferring gene regulatory networks
Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix.
Currently, only sparse real matrices in Matrix package format are supported.
Fast algorithm for fitting solution paths of sparse linear SVM with lasso or elastic-net regularization generate sparse solutions.
Computes sparse vector autoregressive coefficients and precision
matrices for time series chain graphical models.
A wrapper for sparse VAR/VECM time series models estimation
using penalties like ENET, SCAD and MCP.
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.
Methods and data for cluster detection and disease mapping.
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 maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data.
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.