Easy-to-use and versatile functions to output R objects in
HTML format
This package extends the package hwriter providing
facilities such as the inclusion of output from R, the
results of an R session, the display of mathematical
expressions using LaTeX notation and the incorporation of
SVG graphical objects.  It uses MathJax to display
mathematical expressions, in contrast to R2HTML which
uses ASCIIMathML.
Tests whether a set of genotype counts fits the HW expectations.
Exact tests performed by an efficient algorithm. Included test statistics
are likelihood ratio, probability, U-score and Pearsons X2.
This package contains functions to build and deploy an ensemble
consisting of six different sub-ensembles: Bagged Logistic Regressions,
Random Forest, Stochastic AdaBoost, Kernel Factory, Bagged Neural Networks,
Bagged Support Vector Machines. Functions to cross-validate the Hybrid
Ensemble and plot and summarize the results are also provided. There is
also a function to assess the importance of the predictors.
hybrid hierarchical clustering via mutual clusters
This package is an R implementation of the Hybrid Monte
Carlo and Multipoint Hybrid Monte Carlo sampling techniques
described in Liu (2001): "Monte Carlo Strategies in Computing".
Package providing tools for hydrological applications and models developed for regional analysis in Northwestern Italy.
Contains one function for drawing Piper diagrams (also
called Piper-Hill diagrams) of water analyses for major ions.
S3 functions implementing both statistical and graphical goodness-of-fit measures between observed and simulated values, mainly oriented to be used during the calibration, validation, and application of hydrological models. Missing values in observed and/or simulated values can be removed before computations. Comments / questions / collaboration of any kind are very welcomed.
This package is version 2 of HydroMe v.1 package. It
estimates the parameters in infiltration and water retention
models by curve-fitting method. The models considered are those
that are commonly used in soil science. It has new models for
water retention characteristic curve and debugging of errors in
HydroMe v.1
This package implements a state-of-the-art version of the Particle Swarm Optimisation (PSO) algorithm (SPSO-2011 and SPSO-2007 capable). hydroPSO can be used as a replacement of the 'optim' R function for (global) optimization of non-smooth and non-linear functions. However, the main focus of hydroPSO is the calibration of environmental and other real-world models that need to be executed from the system console. hydroPSO is model-independent, allowing the user to easily interface any computer simulation model with the calibration engine (PSO). hydroPSO  communicates with the model through the model's own input and output files, without requiring access to the model's source code. Several PSO variants and controlling options are included to fine-tune the performance of the calibration engine to different calibration problems. An advanced sensitivity analysis function together with user-friendly plotting summaries facilitate the interpretation and assessment of the calibration results. hydroPSO is parallel-capable, to alleviate the computational burden of complex models with "long" execution time. Bugs reports/comments/questions are very welcomed (in English, Spanish or Italian).
Calculates a suite of hydrologic indices for daily time series data that are widely used in hydrology and stream ecology.
S3 functions for management, analysis, interpolation and plotting of time series used in hydrology and related environmental sciences. In particular, this package is highly oriented to hydrological modelling tasks. The focus of this package has been put in providing a collection of tools useful for the daily work of hydrologists (although an effort was made to optimise each function as much as possible, functionality has had priority over speed). Bugs / comments / questions / collaboration of any kind are very welcomed, and in particular, datasets that can be included in this package for academic purposes.
This package provides functions for the hyperbolic and
related distributions. Density, distribution and quantile
functions and random number generation are provided for the
hyperbolic distribution, the generalized hyperbolic
distribution, the generalized inverse Gaussian distribution and
the skew-Laplace distribution. Additional functionality is
provided for the hyperbolic distribution, including fitting of
the hyperbolic to data.
A suite of routines for the hyperdirichlet distribution
Performs (exact) hypergeometric tests on IxJ and 2x2x2 contingency tables using parallelised C code
The hypergeometric function, hypergeo(), for complex numbers
This package is an interface to handle hyperspectral data sets in
R.  I.e. spatially or time-resolved spectra, or spectra with any other kind
of information associated with each of the spectra. The spectra can be data
as obtained in XRF, UV/VIS, Fluorescence, AES, NIR, IR, Raman, NMR, MS,
etc. More generally, any data that is recorded over a discretized variable,
e.g. absorbance = f (wavelength), stored as a vector of absorbance values
for discrete wavelengths is suitable.
Estimates the shape and volume of high-dimensional objects and performs set operations: intersection / overlap, union, unique components, inclusion test, and non-convex feature detection. Can measure the n-dimensional ecological hypervolume and perform species distribution modeling.
A Bay Area high level phylogenetic analysis package mostly
using the birth-death process. Analysis of species tree
branching times and simulation of species trees under a number
of different time variable birth-death processes.  Analysis of
gene tree species tree reconciliations and simulations of gene
trees in species trees.
Compute the confidence interval of the population mean
with one sample or of the difference of population means of two
samples from normal distributions or t-distributions.Compute
the confidence interval of population variance with one sample
or of the difference of population variances of two samples by
chi-square tests.Test for population mean or the differences of
two normal samples under normality with the given null
hypothesis H0,which depends on the user,so that he can know if
he can reject H0 or not at the significance level alpha.Do the
chi-square tests with one or two samples which have multinomial
distributions by using an approximate chi-square distribution
when n is large enough.
The package is intended for simulating high density
genomic data. The meiosis is
simulated under a count-location model, the genetic
structure can include additive and dominance effects. The
low level nature of the package provides great
flexibility in creating all kinds of populations.
Fit, summarize and plot sinusoidal hysteretic processes using:
two-step simple harmonic least squares, ellipse-specific non-linear least
squares, the direct method, geometric least squares or linear least squares.
A collection of tools for modeling the shape of 1 dimensional clines.
Calculate AIC's and AICc's of unimodal model (one normal distribution) and bimodal model(a mixture of two normal distributions) which fit the distribution of indices of asymmetry (IAS), and plot their density, to help determine IAS distribution is unimodel or bimodal.
Contains a function to implement the standard D-Scoring algorithm
(Greenwald, Banaji, & Nosek, 2003) for Implicit Association Test (IAT)
data, as well as various other functions to produce plots of IAT data.
Bayesian integrative models of gene expression and comparative genomic hybridization data. The package provides inference on copy number variations and their association with gene expression
This package contains several utility functions related to incomplete block designs. The package contains function to generate efficient incomplete block designs with given numbers of treatments, blocks and block size. The package also contains function to generate an incomplete block design with specified concurrence matrix. There are functions to generate balanced treatment incomplete block designs and incomplete block designs for test versus control treatments comparisons with specified concurrence matrix. Package also allows performing analysis of variance of data and computing least square means of factors from experiments using a connected incomplete block design. Tests of hypothesis of treatment contrasts in incomplete block design set up is supported.
IBDhaploRtools consists of several functions to analyze, plot, and store the output of running IBD_Haplo software package. More information regarding IBD_Haplo can be found at www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
A method to test genetic linkage with covariates by
regression methods with response IBD sharing for relative
pairs.  Account for correlations of IBD statistics and
covariates for relative pairs within the same pedigree.
R package for simulation of IBD sharing among family members. Using sex specific recombination rates from the Decode map (2010) of the human genome, phased chromosomes are simulated for all pedigree members, either by unconditional 'gene dropping' or conditional on a specified IBD pattern. Regions compatible with the query IBD pattern (possibly different from the conditional pattern) are subsequently detected and summarized.
A data-driven and adaptive hierarchical-scale decomposition method for irregular-lattice field (represented by polygons).
Implementation of an incremental model construction method called IBHM which
stands for Incrementally Built Heterogeneous Model. The method is designed for solving
real number approximation problems in a highly automated fashion.
The package implements a general algorithm to obtain
iterated bootstrap tests and confidence sets for a
p-dimensional parameter based on the unstudentized version of
the Rao statistic.
Provides native R access to Interactive Brokers Trader Workstation API.
This package has provided an interface to WinBUGS/OpenBUGS/JAGS
via R2WinBUGS and R2jags. Some options will be intelligently guessed, e.g.
the path to WinBUGS/OpenBUGS/JAGS and the data/parameter list. Users can
specify all the options in a GUI.
iC10 implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group.
Training datasets for iC10; which implements the classifier described in the paper 'Genome-driven integrated classification of breast cancer validated in over 7,500 samples' (Ali HR et al., Genome Biology 2014). It uses copy number and/or expression form breast cancer data, trains a pamr classifier (Tibshirani et al.) with the features available and predicts the iC10 group. Genomic annotation for the training dataset has been obtained from Mark Dunning's lluminaHumanv3.db package.
Lorenz and concentration curves; Atkinson, Generalized
entropy and SGini indices (with decomposition)
Calculation of IC50 values, automatic drawing of
dose-response curves and validation of compound screens on 96-
and 384-well plates.
Independent Component Analysis (ICA) using various algorithms: FastICA, Information-Maximization (Infomax), and Joint Approximate Diagonalization of Eigenmatrices (JADE).
The icamix package provides R functions as well as a C++ library which facilitate the estimation of ICA mixture models. We have developed and implemented the NSMM-ICA algorithm in the function EMFASTICAALG that currently integrates npEM and Fast-ICA for non-parametric estimation of ICA mixture models.
Removes eye-movement and other types of known (i.e., recorded) or unknown (i.e., not recorded) artifacts using the fastICA package. The correction method proposed in this package is largely based on the method described in on Flexer, Bauer, Pripfl, and Dorffner (2005). The process of correcting electro- and magneto-encephalographic data (EEG/MEG) begins by running function ``icac'', which first performs independent components analysis (ICA) to decompose the data frame into independent components (ICs) using function ``fastICA'' from the package of the same name. It then calculates for each trial the correlation between each IC and each one of the noise signals -- there can be one or more, e.g., vertical and horizontal electro-oculograms (VEOG and HEOG), electro-myograms (EMG), electro-cardiograms (ECG), galvanic skin responses (GSR), and other noise signals. Subsequently, portions of an IC corresponding to trials at which the correlation between it and a noise signal was at or above threshold (set to 0.4 by default; Flexer et al., 2005, p. 1001) are zeroed-out in the source matrix, ``S''. The user can then identify which ICs correlate with the noise signals the most by looking at the summary of the ``icac'' object (using function ``summary.icac''), the scalp topography of the ICs (using function ``topo_ic''), the time courses of the ICs (using functions ``plot_tric'' and ``plot_nic''), and other diagnostic plots. Once these ICs have been identified, they can be completely zeroed-out using function ``update.icac'' and the resulting correction checked using functions ``plot_avgba'' and ``plot_trba''. Some worked-out examples with R code are provided in the package vignette.
Implements mixed ICA/PCA model for blind source separation, potentially with inclusion of Gaussian sources
The package includes a number of functions to assist in the estimation of the Intraclass Correlation Coefficient (ICC).  Functions included estimate the ICC from variance components of a one-way ANOVA and also estimate the number of individuals or groups necessary to obtain an ICC estimate with a desired confidence interval width.
This package allows accurate manipulation, conversion, validation
and comparison of ICD-9-CM (clinical modification) codes. Although ICD-9
codes appear numeric, this is not the case, since trailing zeroes, and a
mixture of decimal and non-decimal "short" format codes exist. Tools to map
ICD-9 codes to co-morbidities are also provided, with mappings from Quan
(Deyo and Elixhauser versions), Elixhauser and AHRQ included.
Kernel Estimators for Interval-Censored Data
This package implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.
Given two unbiased samples of patient level data on cost and effectiveness
for a pair of treatments, make head-to-head treatment comparisons by (i) generating the
bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of
the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with
specified confidence fraction within [0.50, 0.99] that is equivariant with respect to
changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge
with economic preferences from an ICE map with specified values of lambda, beta and gamma
parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation
in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves
from an ICE map with specified values of lambda, beta and gamma or eta parameters.
We consider studies in which information from error-prone
diagnostic tests or self-reports are gathered sequentially to determine the
occurrence of a silent event. Using a likelihood-based approach
incorporating the proportional hazards assumption, we provide functions to
estimate the survival distribution and covariate effects. We also provide
functions for power and sample size calculations for this setting.
ICGE is a package that helps to estimate the number of real clusters in data as well as to identify atypical units. The underlying methods are based on distances rather than on unit x variables.
This package implements parameter estimation in normal (linear) models under linear equality and inequality constraints and implements normal likelihood ratio tests involving inequality-constrained hypotheses. For inequality-constrained linear models, averaging over R-squared for different orderings of regressors is also included.
Integrative clustering of multiple genomic data types
using a joint latent variable model.
Calculates the ICOMP criterion and its variations
The package implements Tyler et al.'s and Oja et al.'s method of two different scatter
matrices to obtain an invariant coordinate system or independent
components, depending on the underlying assumptions.
Tools for multivariate nonparametrics, as location tests
based on marginal ranks, spatial median and spatial signs
computation, Hotelling's T-test, estimates of shape
Currently using the proportional hazards (PH) model. More methods under other semiparametric regression models will be included in later versions.
Calculate identity coefficients, based on Mark Abney's C
code.
The IDPmisc package contains different high-level graphics
functions for displaying large datasets, displaying circular
data in a very flexible way, finding local maxima, brewing
color ramps, drawing nice arrows, zooming 2D-plots, creating
figures with differently colored margin and plot region.  In
addition, the package contains auxiliary functions for data
manipulation like omitting observations with irregular values
or selecting data by logical vectors, which include NAs. Other
functions are especially useful in spectroscopy and analyses of
environmental data: robust baseline fitting, finding peaks in
spectra.
This package contains functions to perform robust
nonparametric survival analysis with right censored
data using a prior near-ignorant Dirichlet Process.
This is a package for estimating the copula mixture model and plotting correspondence curves in "Measuring reproducibility of high-throughput experiments" (2011), Annals of Applied Statistics, Vol. 5, No. 3, 1752-1779, by Li, Brown, Huang, and Bickel
iDynoMiCS is a computer program, developed by an international team of researchers, whose purpose is to model and simulate microbial communities in an individual-based way. It is described in detail in the paper "iDynoMiCS: next-generation individual-based modelling of biofilms" by Lardon et al, published in Environmental Microbiology in 2011. The simulation produces results in XML file format, describing the state of each species in each timestep (agent_State), a summary of the species statistics for a timepoint (agent_Sum), the state of each solute grid in each timestep (env_State) and a summary of the solutes for a timestep (env_Sum). This R package provides a means of reading this XML data into R such that the simulation response can be statistically analysed. iDynoMiCS is available from the website iDynoMiCS.org, where a full tutorial on using both the simulation and this R package is provided.
A pair of functions for getting and setting the IEEE
rounding mode for floating point computations.
The package performes Independent Factor Analysis
This package implements a Bayesian sparse factor model for the joint analysis of paired datasets, one is the gene expression dataset and the other is the drug sensitivity profiles, measured across the same panel of samples, e.g., cell lines. Prior knowledge about gene-pathway associations can be easily incorporated in the model to aid the inference of drug-pathway associations.
A collection of statistical tests and utilities for genetic association studies
This package provides function to read data from the Igor
Pro data analysis program by Wavemetrics. The data formats
supported are Igor packed experiment format (pxp) and Igor binary
wave (ibw). See: http://www.wavemetrics.com/ for details. It also
includes some functions to load special pxp files produced by the
Neuromatic/Nclamp packages for recording and analysing neuronal
data. See http://www.neuromatic.thinkrandom.com/ for details.
Routines for simple graphs and network analysis. igraph can
handle large graphs very well and provides functions for generating random
and regular graphs, graph visualization, centrality indices and much more.
A small collection of various network data sets,
to use with the igraph package. They also work with the igraph0
package.
This program facilitates exporting igraph graphs to the
SoNIA file format
The package offers an S4 infrastructure to store climatological
station data of various temporal aggregation scales. In-built quality
control and homogeneity tests follow the methodology from the European
Climate Assessment & Dataset project. Wrappers for climate indices
defined by the Expert Team on Climate Change Detection and Indices
(ETCCDI), a quick summary of important climate statistics and climate
diagram plots provide a fast overview of climatological
characteristics of the station.
Compute moments of images and perform reconstruction from
moments.
Zoom in and out of maps or any supplied lines or points,
with control for color, poly fill, and aspect.
A package to share plots using the image hosting service Imgur.com
IMIS algorithm draws samples from the posterior
distribution. The user has to define the following R functions
in advance: prior(x) calculates prior density of x,
likelihood(x) calculates the likelihood of x, and
sample.prior(n) draws n samples from the prior distribution.
In this package, you can find two functions proposed in
Ding, Geng and Zhou (2011) to estimate direct and indirect
causal effects with randomization and multiple-component
intervention using instrumental variable method.
A minimum distance estimator is calculated for an
imprecise probability model. The imprecise probability model
consists of upper coherent previsions whose credal sets are
given by a finite number of constraints on the expectations.
The parameter set is finite. The estimator chooses that
parameter such that the empirical measure lies next to the
corresponding credal set with respect to the total variation
norm.
The package contains a collection of functions for left-censored missing data imputation. Left-censoring is a special case of missing not at random (MNAR)  mechanism that generates non-responses in proteomics experiments. The package also contains functions to artificially generate peptide/protein expression data (log-transformed) as random draws from a multivariate Gaussian distribution as well as a function to generate missing data (both randomly and non-randomly). For comparison reasons, the package also contains several wrapper functions for the imputation of non-responses that are missing at random.
This package provides various approaches to handling
missing values for the MDR analysis to identify gene-gene
interactions using biallelic marker data in genetic association
studies
This package provides a general imputation framework based on
variable selection methods including regularisation methods,
tree-based models and dimension reduction methods.
If Kaplan-Meier weights are involved in modeling,
especially solving AFT model using weighted least squares
method, the package gives alternative choices to the Efron's
tail correction approach that brings estimates with less bias
and more efficiency
This package provides a Graphical User Interface (GUI) to some of the functions in the package extRemes version >= 2.0.
Fits mixtures models for longitudinal data. Appropriate when the data are counts and when the correlation structure is assumed to be AR(1).
The incReg package calculate the incremental multivariate Regression based on a given dataset or a .csv file, based on the multiple linear regression
Functions for testing mutual independence between many
numerical random vectors or serial independence of a
multivariate stationary sequence. The proposed test works when
some or all of the marginal distributions are singular with
respect to Lebesgue measure.
This package provides a set of functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites. It is also possible to check the statistical significance of such associations.
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
This package contains functions to perform calculations of
the infine-dimensional model (IDM) and to produce 95%
confidence intervals around the model elements through
bootstrapping.
Collection of functions to extract inferential values
(point estimates, confidence intervals, p-values, etc) of a
fitted model object into a matrix-like object that can be used
for table/report generation; transform point estimates via the
delta method.
The InferenceSMR package provides functions to make
inference about the standardized mortality ratio (SMR) when
evaluating the effect of a screening program. The package is
based on methods described in Sasieni (2003) and Talbot et al.
(2011).
Implementation of methods Extremum Surface Estimator and
Extremum Distance Estimator for identifying the inflection
point of a convex/concave curve.
influence.ME provides a collection of tools for
detecting influential cases in generalized mixed effects
models. It analyses models that were estimated using lme4. The
basic rationale behind identifying influential data is that
when iteratively single units are omitted from the data, models
based on these data should not produce substantially different
estimates. To standardize the assessment of how influential a
(single group of) observation(s) is, several measures of
influence are common practice, such as DFBETAS and Cook's Distance.
In addition, we provide a measure of percentage change of the fixed point
estimates and a simple procedure to detect changing levels of significance.
influence.SEM provides a set of tools for evaluating several measures of case influence for Structural Equation Models following the approach of Pek and MacCallum (2011).
InfoDecompuTE is capable of generating the structure of
the analysis of variance (ANOVA) table of the two-phase
experiments. By inputting the design and the relationships of
the random and fixed factors using the Wilkinson-Rogers'
syntax, infoDecompuTE can generate the structure of the ANOVA
table with the coefficients of the variance components for the
expected mean squares. This package can also study the balanced
incomplete block design and provides the efficiency factors of
the fixed effects.
Tools for creating sequence statistics, especially for
Butts's egocentric relational event model fitting software in
the relevent R package
This package implements various measures of information theory based on several entropy estimators.
Calculation of information utility (i.e., Lindley
information) quantities for item response models.
Spatial Econometrics models using Bayesian Model Averaging
on models fitted with INLA. The INLA package can be obtained from
http://www.r-inla.org . We recommend the testing version, which can be
downloaded by running:
source("http://www.math.ntnu.no/inla/givemeINLA-testing.R")
Functionality to dynamically define R functions and S4 methods
with in-lined C, C++ or Fortran code supporting .C and .Call calling conventions.
Generates Rd files from R source code with comments.
The main features of the default syntax are that
(1) docs are defined in comments near the relevant code,
(2) function argument names are not repeated in comments, and
(3) examples are defined in R code, not comments.
It is also easy to define a new syntax.
Non-parametric resampling-based inference tests for ExPosition.
insideRODE package includes buildin functions from
deSolve, compiled functions from compiler, and C/FORTRAN code
interfaces to nlme. It includes nlmLSODA, nlmODE,
nlmVODE,nlmLSODE for general purpose; cfLSODA,cfLSODE, cfODE,
cfVODE call C/FORTRAN compiled dll functions.ver2.0 add
sink()function into example it helps to directly combine
c/fortran source code in R files. Finally, with new compiler
package, we generated compiled functions: nlmODEcp, nlmVODEcp,
nlmLSODEcp,nlmLSODAcp and cpODE, cpLSODA, cpLSODE, cpVODE. They
will help to increase speed.
Functions to compute insolation on complex terrain.
R is great for installing software.  Through the 'installr'
package you can automate the updating of R (on Windows, using updateR())
and install new software. Software installation is initiated through a
gui (just run installr()), or through functions such as: install.Rtools(),
install.pandoc(), install.git(), and many more. The updateR() command
performs the following: finding the latest R version, downloading it,
running the installer, deleting the installation file, copy and updating
old packages to the new R installation.
Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.
A package that provides classes and methods for automated
spatial interpolation.
A package that provides  additional functionality for spatial interpolation in the intamap package.
Implementation of ICM-Algorithm by Wei Pan, J. Comp. & Gr.
Stat. 78: 109-120, 1999 Algorithm for the Cox proportional
hazard model for interval censored data
the integrOmics package is superseded by the mixOmics
package
This package searches for marginal interactions in a
binary response model. Interact uses permutation methods to
estimate false discovery rates for these marginal interactions
and has some, limited visualization capabilities
An extension of the package 'igraph'. This package create
possibly to work with 'igraph' objects interactively.
A shiny application for designing adaptive clinical trials. For
more details, see: http://arxiv.org/abs/1404.0734
R world provides several packages for working with network
data.  This package supplements that functionality by providing
methods for converting network data objects between classes
defined in other packages. Currently supported classes:
network, igraph.
A package for numerical encoding as well as for linear and
non-linear interpolation of amino acid sequences.
Hourly interpolation of daily minimum and maximum temperature
series. Carries out interpolation on multiple series ad once. Requires some
hourly series for calibration (alternatively can use default calibration
table).
The package provides an R version of the InterVA4 software for
coding cause of death from verbal autopsies. It also provides simple
graphical representation of individual and population level statistics.
Functions to fit nonparametric survival curves, plot them, and perform logrank or Wilcoxon type tests.
Tools for working with and comparing sets of points and intervals.
This package allows a dynamic Bayesian network to be inferred from microarray timecourse data with interventions (inhibitors).
This package calculates the integrated likelihood numerically. Given the Likelihood function and the prior function, this package integrates out the nuisance parameters by Metropolis-Hastings (MCMC) Algorithm.
Solves linear programming problems by the interior point
method, and plots the graphical solution of a linear
programming problem of two dimensions.
For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code.
Estimating interval regression models.  Both common and
observation-specific boundaries may be specified.
Performs Goodness of Fit for regression models using
introgress provides functions for analyzing introgression
of genotypes between divergent, hybridizing lineages, including
estimating genomic clines from multi-locus genotype data and
testing for deviations from neutral expectations. Functions are
also provided for maximum likelihood estimation of molecular
hybrid index and graphical analysis.
Provides tools for importing, merging, and analysing data from international assessment studies (TIMSS, PIRLS, PISA and PIAAC)
This package describes the associated cost games to inventory situations.
Functions to facilitate inverse estimation/calibration in linear,
nonlinear, and (linear) mixed models. A generic function is also provided
for plotting fitted regression models with or without confidence/prediction
bands that may be of use to the general user.
invGauss fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.
This package provides qread as a unified method for reading from
files and qwrite for writing to files. Support for additional file or data
types can be extended readily by defining new S3 methods. Further, plotting
to screen, file, or both is streamlined by qdraw.
Density, spectral density, and regression estimation using infinite order kernels.
This package provides functions to estimate an IPD linear
mixed effects model for a continuous outcome and any
categorical covariate from study summary statistics. There are
also functions for estimating the power of a
treatment-covariate interaction test in an individual patient
data meta-analysis from aggregate data.
Interpolation of geo-referenced point data by Inverse
Path Distance Weighting
A fast (C) implementation of the iterative proportional fitting
procedure. Based on corresponding code from the networkTomography package.
IPMpack takes demographic vital rates and (optionally) environmental data to build integral projection models.  A number of functional forms for growth and survival can be incorporated, as well as a range of reproductive strategies.  The package also includes a suite of diagnostic routines, provides classic matrix model output (e.g., lambda, elasticities, sensitivities), and produces post-hoc metrics (e.g., passage time and life expectancy).
Improved predictive models by indirect classification and
bagging for classification, regression and survival problems
as well as resampling based estimators of prediction error.
This package contains the Sweave source code used to
generate IPSUR, an introductory probability and statistics
textbook, alongside other supplementary materials such as the
parsed R code for the book and data for the examples and
exercises.  The book is released under the GNU Free
Documentation License.
Builds statistical control charts with exact limits for
univariate and multivariate cases.
Estimate an optimal dynamic treatment regime using Interactive Q-learning
Iterated racing for automatic algorithm configuration
"iRefR" allows the user to load any version of the consolidated protein interaction database "iRefIndex" and perform tasks such as: selecting databases, pmids, experimental methods, searching for specific proteins, separate binary interactions from complexes and polymers, generate complexes according to an algorithm that looks after possible binary-represented complexes, make general database statistics and create network graphs, among others.
This package contains some important regression methods
for interval-valued variables. For each method, it is available
the fitted values, residuals and some goodness-of-fit measures.
This package calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements. ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects' means and residuals) is provided.
A fast and memory-efficient method for computing a few approximate singular values and singular vectors of large matrices.
Coefficients of Interrater Reliability and Agreement for
quantitative, ordinal and nominal data: ICC, Finn-Coefficient,
Robinson'A, Kendall's W, Cohen's Kappa, ...
Provides a simple common interface to the estimation of item
parameters in IRT models for binary responses with three different programs
(ICL, BILOG-MG, and ltm, and a variety of functions useful with IRT models.
Multidimensional Person Item Response Theory probability distributions
Helper functions and example data sets accompanying De
Boeck, P. and Partchev, I. (2012) IRTrees: Tree-Based Item
Response Models of the GLMM Family, Journal of Statistical
Software - Code Snippets, 48(1), 1-28.
The ISA is a biclustering algorithm that finds modules
in an input matrix. A module or bicluster is a block of the
reordered input matrix.
Selection of features for sparse regression estimation (like the LASSO). Selection of blocks of features when the regression parameter is sparse and constant by blocks (like the Fused-LASSO). Application to cgh arrays. TEST VERSION, USE AT YOUR OWN RISK! Difference with 0.1 version: compatible with R 3.0.
image-based high-throughput RNAi screening tools for data analysis
and visualization
Provides datasets for Introduction to Statistical Data Analysis for the Life Sciences
describes a set of operations for symbolic data type based
on interval-valued. The operations are processing of punctuals
variables to interval variables, construction of a 3D graphic
interval, linear regression interval and interval descriptive
statistics such as mean, median, variance, standard deviation
and mode.
This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.
Classical Ising Model is a land mark system in statistical physics. Model explains physics of spin glasses and magnetic materials, but used in many different context for example phase transitions and neural systems. This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions.
This package can be used to sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractibility of the Ising model the distribution can only be computed up to ~10 nodes.
The collection of datasets used in the book "An
Introduction to Statistical Learning with Applications in R"
Functions to support the computations carried out in `An
Introduction to Statistical Modeling of Extreme Values' by
Stuart Coles. The functions may be divided into the following
groups; maxima/minima, order statistics, peaks over thresholds
and point processes.
Linear order and unimodal order (univariate) isotonic
regression; bivariate isotonic regression with linear order on
both variables.
Some functions for confidence intervals for current status data based on transformations and bootstrap.
This is a package to draw isotonic inference for circular data.
ISO language, territory, currency, script and character codes.
Provides ISO 639 language codes, ISO 3166 territory codes, ISO 4217
currency codes, ISO 15924 script codes, and the ISO 8859 character codes
as well as the UN M.49 area codes.
Several testing procedures including the global likelihood ratio test
(Bartholomew, 1961), Williams (1971, 1972), Marcus (1976), M (Hu et al. 2005) and
the modified M (Lin et al. 2007) are used to test for the monotonic trend in gene
expression with respect to doses. BH (Benjamini and Hochberg 1995) and BY
(Benjamini and Yekutieli 2004) FDR controlling procedures are applied to adjust the
raw p-values obtained from the permutations.
Isopam clustering algorithm and utilities.  Isopam
optimizes clusters and optionally cluster numbers in a brute
force style and aims at an optimum separation by all or some
descriptors (typically species).
The function calculates the isotopic pattern (fine
structures) for a given chemical formula.
Contains two main functions: gpava() for
solving general isotone regression problem using the
pool-adjacent-violators algorithm (PAVA). activeSet() provides
a framework of active set methods for isotone optimization
problems with arbitrary order restrictions. Various types of
loss functions are pre-specified.
Given a response y and a one- or two-dimensional predictor, the isotonic regression estimator is calculated with the usual orderings.
Estimates diet contributions from isotopic sources using JAGS.
Includes estimation of concentration dependence and measurement error.
This is an substitute for the %V and %u formats which are
not implemented on Windows. In addition, the package offers
functions to convert from standard calender format yyyy-mm-dd
to and from ISO 8601 week format yyyy-Www-d.
Independent Surrogate Variable Analysis is an algorithm
for feature selection in the presence of potential confounding
factors.
Data sets and scripts for text examples and exercises in
P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed.,
Springer Verlag, ISBN 978-0387790534.
Iterates through a phylogenetic tree to identify regions
of rate variation using the parametric rate comparison test.
Support for iterators, which allow a programmer to traverse
through all the elements of a vector, list, or other collection
of data.
The iterLap (iterated Laplace approximation) algorithm
approximates a general (possibly non-normalized) probability
density on R^p, by repeated Laplace approximations to the
difference between current approximation and true density (on
log scale). The final approximation is a mixture of
multivariate normal distributions and might be used for example
as a proposal distribution for importance sampling (eg in
Bayesian applications).  The algorithm can be seen as a
computational generalization of the Laplace approximation
suitable for skew or multimodal densities.
This package helps in 1) generating permutations and combinations with or without replacement; 2) with distinct items or non-distinct items (multiset); 3) calculating number of combinations or permutations in various situations. All generated sequences are in lexicographical order (AKA, dictionary order). The algorithms to generate permutations and combinations are memory efficient, only previous sequence is required to generate next sequence. These iteratively algorithms enable users to process all sequences without putting all results in the memory at the same time.  The algorithms are written in C/C++ and hence very computational efficient.
Various tools for creating iterators, many patterned after
functions in the Python itertools module, and others patterned
after functions in the 'snow' package.
A port of Python's excellent itertools module to R for efficient
looping.
This package is based on the code of the rpart package.
It extends rpart by adding additional splitting methods
emphasizing interpretable/parsimonious trees.  Unless indicated
otherwise, it is safe to assume that all functions herein are
extensions of or copied directly from similar or nearly
identical rpart methods.  As such, the authors of rpart are
authors of this package as well.  However, please direct any
error reports or other questions about itree to the maintainer
of this package; they are welcome and appreciated.
The its package contains an S4 class for handling
irregular time series
This package provides a subset of the functionality found in the Windows-based program ITSM. The intended audience is students using the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis.
This package includes functions to incorporate
uncertainties in estimated propensity scores and provide
adjusted standard errors for making valid causal inference.
This package allows one incorporate instrument and covariate uncertainty into instrumental variable regression.
Fit an Instrumental least square dummy variable model
a menu-driven package for IVIVC model building, data analysis and
model validation (only internal validation) based on Wagner-Nelson
Method (only for the one-compartment PK model) for Level A correlation
GMM estimation of triangular systems using heteroscedasticity based instrumental variables as in Lewbel (2012)
This package contains functions for carrying out instrumental variable estimation of causal effects and power analyses for instrumental variable studies.
ivprobit fit an Instrumental variables probit model using the
generalized least squares estimator
Improved methods based on inverse probability weighting
and outcome regression for causal inference and missing data
problems
Jaatha is a composite likelihood method for inferring evolutionary
parameters using genetic data. Given a model of the evolutionary history of
two biological populations as well as SNP data from multiple individuals
from each population, it estimates model parameters like the time of
separation of both species.
The package is for computing both the original and
modified jackknife estimates of Kaplan-Meier estimators.
Significance test for association between variables and their
estimated latent variables
The package ports JF Cardoso's JADE algorithm as well as his function for joint diagonalization. The package also includes several other blind source separation (BSS) methods, like AMUSE and SOBI, as well as some criteria for performance evaluation of BSS algorithms.
Implements a 2 degree-of-freedom score test that measures 1) the overall shift in the gene expression due to genotype, and 2) group-specific changes in gene expression due to genotype (interaction term) in a mixed-effects model framework.
R modules for JASPAR data processing and visualization
Graphics device routing all graphics commands to a Java
program. The actual functionality of the JavaGD depends on the
Java-side implementation. Simple AWT and Swing implementations
are included.
Collection of several tools and helper functions used across the other packages of J. Buttlar (ncdf.tools and spectral.methods).
The Joint Graphical Lasso is a generalized method for
estimating Gaussian graphical models/ sparse inverse covariance
matrices/ biological networks on multiple classes of data.  We
solve JGL under two penalty functions: The Fused Graphical
Lasso (FGL), which employs a fused penalty to encourage inverse
covariance matrices to be similar across classes, and the Group
Graphical Lasso (GGL), which encourages similar network
structure between classes.  FGL is recommended over GGL for
most applications.
Java GUI for R - cross-platform, universal and unified Graphical User Interface for R. For full functionality on Windows and Mac OS X JGR requires a start application which depends on your OS. This can be downloaded from JGR website: http://rforge.net/JGR/
Shared parameter models for the joint modeling of longitudinal and time-to-event data.
Shared parameter models for the joint modeling of longitudinal and time-to-event data using MCMC.
RE.Johnson performs the Johnson Transformation to increase the normality.
Johnson curve distributions.  Implementation of AS100 and
AS99.
Analysis of repeated measurements and time-to-event data
via random effects joint models. Some plotting functions and
the variogram are also included.
Different algorithms to perform approximate joint
diagonalization of a finite set of square matrices. Depending
on the algorithm, orthogonal or non-orthogonal diagonalizer is
found. These algorithms are particularly useful in the context
of blind source separation.
A bivariate integration method to estimate risk caused by two extreme and dependent forcing variables.
A joint regression model for mixed correlated binary and
continuous responses is presented. In this model binary
response can be dependent on the continuous response. With this
model, the dependence between responses can be taken into
account by the correlation between errors in the models for
binary and continuous responses.
JOP is a tool for simultaneous optimization of multiple
responses and visualization of the results. The visualization
is done by the joint optimization plot introduced by Kuhnt and
Erdbruegge (2004).
This package currently implements the unit level EBLUP and GREG estimators as well as the estimate of their variances to further document the publication of Breidenbach and Astrup (2011). It also contains a vignette that explains the use of the implemented functions.
This package provides an easy and simple way to read, write and display bitmap images stored in the JPEG format. It can read and write both files and in-memory raw vectors.
Functions, methods, and datasets for cancer survival
analysis, including the proportional hazard relative survival
model, the join point relative survival model.
This package enables users to access MongoDB by running queries
and returning their results in R data frames. Usually, data in MongoDB is
only available in the form of a JSON document. jSonarR uses data
processing and conversion capabilities in the jSonar Analytics Platform
and the JSON Studio Gateway (http://www.jsonstudio.com), to convert it to
a tabular format which is easy to use with existing R packages.
A fast JSON parser and generator optimized for statistical data and
the web. Started out as a fork of RJSONIO, but has been completely rewritten
in recent versions. The package offers flexible, robust, high performance
tools for working with JSON in R and is particularly powerful for building
pipelines and interacting with web APIs. The parser implementation is based
on the mapping described in the vignette of the package (Ooms, 2014). In
addition to drop-in replacements for toJSON and fromJSON, jsonlite contains
functions to stream, validate, and prettify JSON data, and many unit
tests to verify that all edge cases are encoded and decoded consistently
for use with dynamic data in systems and applications.
Transforming non-normal data to normality using Johnson families
of distributions.
Examines two-party electoral systems and calculates
quantities of interest based on the behaviour of their
districts.
Generates image data for fractals (Julia and Mandelbrot
sets) on the complex plane in the given region and resolution.
Cross-platform, self-contained, and bi-directional interface between R and Scala, Java, and other JVM-based languages.
Identify and sort genes overexpressed and associated to transmembrane protein in Affymetrix expression set or any other results of microarray experiment.
Offers a complete and interactive GUI to work out Cohen's
and Fleiss' Kappa.
Kappalab, which stands for "laboratory for capacities", is
an S4 tool box for capacity (or non-additive measure, fuzzy
measure) and integral manipulation on a finite setting. It
contains routines for handling various types of set functions
such as games or capacities. It can be used to compute several
non-additive integrals: the Choquet integral, the Sugeno
integral, and the symmetric and asymmetric Choquet integrals.
An analysis of capacities in terms of decision behavior can be
performed through the computation of various indices such as
the Shapley value, the interaction index, the orness degree,
etc. The well-known Möbius transform, as well as other
equivalent representations of set functions can also be
computed. Kappalab further contains seven capacity
identification routines: three least squares based approaches,
a method based on linear programming, a maximum entropy like
method based on variance minimization, a minimum distance
approach and an unsupervised approach grounded on parametric
entropies. The functions contained in Kappalab can for instance
be used in the framework of multicriteria decision making or
cooperative game theory.
This package contains basic tools for the purpose of
sample size estimation in studies of interobserver/interrater
agreement (reliability).  This package contains sample size
estimation functions for both the power-based and confidence
interval-based methods, with binary or multinomial outcomes and
two through six raters.
this package allows to quantify the congruence between two patchy
mosaics or landscapes. This "vectorial Kappa" approach extends the
principle of Cohen's Kappa index by calculating areas of intersected
patches between two mosaics rather than agreement between pixels. It
provides an exact alternative for patchy mosaics when a Kappa index is
needed.
This package provides some routines to conduct the K-adaptive partitioning (kaps) algorithm for survival data. A function kaps is an implementation version of our algorithm.
Package for kinetic analysis of longitudinal voxel-wise Dynamic Contrast Enhanced MRI data. Includes tools for visualization and exploration of voxel-wise parametric maps.
Create, Simulate, Fit, Solve k-Cube Thurstonian IRT Models
A non-parametric method for identifying potential
outlying observations in a collection of phylogenetic trees based
on the methods of Owen and Provan (2011). Such discordant trees
may indicate problems with sequence annotation or tree
reconstruction, or they may represent interesting biological
phenomena, such as horizontal gene transfers.
Smoothing techniques and computing bandwidth selectors of the nth derivative of a probability density for one-dimensional data.
Uses Bessel functions to calculate the analytic solutions to the
Kelvin differential equation, namely the fundamental (Be) and equivalent
(Ke) complex functions.
Computes the Kendall rank correlation and Mann-Kendall
trend test. See documentation for use of block bootstrap when
there is autocorrelation.
Implements the kernel method of test equating using the CB, EG, SG, NEAT CE/PSE and NEC designs, supporting gaussian, logistic and uniform kernels and unsmoothed and pre-smoothed input data.
Nonparametric kernel distribution function estimation is
performed. Three automatic bandwidth selection methods for
nonparametric kernel distribution function estimation are
implemented: the plug-in of Altman and Leger, the plug-in of
Polansky and Baker, and the modified cross-validation of
Bowman, Hall and Prvan. The exceedance function, the mean
return period and the return level are also computed.
Classification based on an ensemble of kernel machines
Kernel-based machine learning methods for classification,
regression, clustering, novelty detection, quantile regression
and dimensionality reduction.  Among other methods kernlab
includes Support Vector Machines, Spectral Clustering, Kernel
PCA, Gaussian Processes and a QP solver.
functions for kernel smoothing (and density estimation)
corresponding to the book:
Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
This package fits nonparametric item and option characteristic curves using kernel smoothing. It allows for optimal selection of the smoothing bandwidth using cross-validation and a variety of exploratory plotting tools.
Package KFAS provides functions for Kalman filtering, smoothing,
forecasting and simulation of multivariate exponential family state space
models with exact diffuse initialization when distributions of some or all
elements of initial state vector are unknown.
Naive implementation of the Kalman filter, smoother and disturbance
smoother for state space models. An interface to other implementations of the
filter that are available in other packages is also provided.
A collection of useful functions not found anywhere else, mainly
for programming: Generalized lagged differences, checking containment in an
interval, creating a factor where the levels maintain the order of
appearance, sampling rows from a data frame, converting seconds from
midnight to and from H:M:S format, choosing the first non-NA value,
transposing lists of lists, returning the name of the file currently
sourced, smart named lists and vectors, and an alternative interface to
assign().
Analysis of kin-cohort studies. kin.cohort provides estimates of age-specific
cumulative risk of a disease for carriers and noncarriers of a mutation. The cohorts are
retrospectively built from relatives of probands for whom the genotype is known. Currently
the method of moments and marginal maximum likelihood are implemented. Confidence intervals
are calculated from bootstrap samples.
Most of the code is a translation from previous matlab code by Chatterjee.
Simple calculation routines based on the FOCUS Kinetics
Report (2006). Only kinetic models for parent only datasets are
included (no metabolite formation/decline or multicompartment kinetics).
Please note that no warranty is implied for correctness of results or
fitness for a particular purpose.
Routines to handle family data with a pedigree object. The
initial purpose was to create correlation structures that describe
family relationships such as kinship and identity-by-descent, which
can be used to model family data in mixed effects models, such as in the
coxme function.  Also includes a tool for pedigree drawing which is
focused on producing compact layouts without intervention.  Recent additions
include utilities to trim the pedigree object with various criteria, and
kinship for the X chromosome
This package allows one to compose kintone REST API requests and convenient functions to convert kintone-JSON/R-Objects in R scriptings.
Provides tools to calculate the theoretical hydrodynamic response
of an aquifer undergoing harmonic straining or pressurization. There are
two classes of models here: (1) for sealed wells, based on the model of
Kitagawa et al (2011), and (2) for open wells, based on the models of
Cooper et al (1965), Hsieh et al (1987), Rojstaczer (1988), and Liu et al
(1989). These models treat strain (or aquifer head) as an input to the
physical system, and fluid-pressure (or water height) as the output. The
applicable frequency band of these models is characteristic of seismic
waves, atmospheric pressure fluctuations, and solid earth tides.
Weighted k-Nearest Neighbors Classification, Regression and Clustering
Miscellaneous functions for classification and visualization
developed at the Fakultaet Statistik, Technische Universitaet Dortmund
A set of functions designed to quickly generate results of
a multiple choice test. Generates detailed global results,
lists for anonymous feedback and personalised result feedback
(in LaTeX and/or PDF format), as well as item statistics like
Cronbach's alpha or disciminatory power.
The package implements efficient ways to evaluate and
solve equations of the form Ax=b, where A is a kronecker
product of matrices.  Functions to solve least squares problems
of this type are also included.
Calculate NPMLE with constraints for right censored data. This package give a recursive computational algorithm for the "constrained" Kaplan- Meier estimator. The constraint is assumed given in linear estimating equations or mean functions. We also illustrate how this leads to the empirical likelihood ratio test with right censored data. EM algorithm (emplik) is used to get the initial value.
Computes various confidence intervals for the Kaplan-Meier
estimator, namely: Petos CI, Rothman CI, CI's based on
Greenwoods variance, Thomas and Grunkemeier CI and the
simultaneous confidence bands by Nair and Hall and Wellner.
Computes and plots an exact nonparametric band for any user-specified level of confidence from a single-sample survfit object
The package performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions.
This package contains a collection of functions for common data
extraction and reshaping operations, string manipulation, and
functions for table and plot generation for R Markdown documents.
KmL is an implementation of k-means specifically design
to cluster longitudinal data. It provides facilities to deal with missing
value, compute several quality criterion (Calinski and Harabatz,
Ray and Turie, Davies and Bouldin, BIC, ...) and propose a graphical
interface for choosing the 'best' number of clusters.
KmL3D is an implementation of k-means specifically design
to cluster joint trajectories (longitudinal data on
several variable-trajectories).
Like KmL, it provides facilities to deal with missing
value, compute several quality criterion (Calinski and Harabatz,
Ray and Turie, Davies and Bouldin, BIC,...) and propose a graphical
interface for choosing the 'best' number of clusters. In addition, the 3D graph
representing the mean joint-trajectories of each cluster can be exported through
LaTeX in a 3D dynamic rotating PDF graph.
'kmlcov' Cluster longitudinal data using the likelihood as a
metric of distance. The generalised linear model allow the user to
introduce covariates with different level effects (2 levels).
Data sets and functions for Klein and Moeschberger (1997),
"Survival Analysis, Techniques for Censored and Truncated
Data", Springer.
knitcitations provides the ability to create dynamic citations
in which the bibliographic information is pulled from the web rather
than having to be entered into a local database such as bibtex ahead of
time. Knitcitations is primarily aimed at authoring in the R markdown
format, and can provide outputs for web-based authoring such as linked
text and tooltips over inline citations.  Cite using a DOI, URL, or
bibtex file key.  See the package URL for details.
This package provides a general-purpose tool for dynamic report
generation in R, which can be used to deal with any type of (plain text)
files, including Sweave, HTML, Markdown, reStructuredText, AsciiDoc, and
Textile. R code is evaluated as if it were copied and pasted in an R
terminal thanks to the evaluate package (e.g., we do not need to explicitly
print() plots from ggplot2 or lattice). R code can be reformatted by the
formatR package so that long lines are automatically wrapped, with indent
and spaces added, and comments preserved. A simple caching mechanism is
provided to cache results from computations for the first time and the
computations will be skipped the next time. Almost all common graphics
devices, including those in base R and add-on packages like Cairo,
cairoDevice and tikzDevice, are built-in with this package and it is
straightforward to switch between devices without writing any special
functions. The width and height as well as alignment of plots in the output
document can be specified in chunk options (the size of plots for graphics
devices is also supported). Multiple plots can be recorded in a single code
chunk, and it is also allowed to rearrange plots to the end of a chunk or
just keep the last plot. Warnings, messages and errors are written in the
output document by default (can be turned off). The large collection of
hooks in this package makes it possible for the user to control almost
everything in the R code input and output. Hooks can be used either to
format the output or to run R code fragments before or after a code chunk.
The language in code chunks is not restricted to R (there is simple support
to Python and shell scripts, etc). Many features are borrowed from or
inspired by Sweave, cacheSweave, pgfSweave, brew and decumar.
A framework to create Bootstrap 3 HTML reports from knitr
Rmarkdown and RHTML.
Multi-distance based k-Nearest Neighbors Classification
with K Threshold Value Check and Same K_i Problem Dealing,
Missing Observations Filling
This package provides the implementation of an exact formula of the
ith nearest neighbour distance distribution and implementations of tests of
independence based on that formula. Furthermore the package provides a
general framework to benchmark tests of independence.
The tuna Regional Fisheries Management Organisations (tRFMOs) use a common framework for providing scientific advice, i.e. the Kobe II Framework. This is based on maintaining fishing mortality below FMSY and stock biomass above  BMSY. This package provides methods for summarising results from stock assessments and Management Strategy Evaluations in the Kobe format.
Function kofnGA uses a genetic algorithm to choose a subset of a
fixed size k from the integers 1:n, such that a user-supplied objective function
is minimized at that subset.  The selection step is done by tournament selection
based on ranks, and elitism may be used to retain a portion of the best solutions
from one generation to the next.
Supervised and unsupervised self-organising maps
This package provides an alternative, more efficient
evaluation of extreme probabilities of Kolmogorov's goodness-of-fit measure,
Dn, when compared to the original implementation of Wang, Marsaglia, and
Tsang. These probabilities are used in Kolmogorov-Smirnov tests when
comparing two samples.
Korean language processing package. Morphological
analyzer, POS tagger, Keystroke converter, Hangul automata,
Concordance, Mutual Information..
A set of tools to analyze texts. Includes, amongst others,
functions for automatic language detection, hyphenation, several indices of
lexical diversity (e.g., type token ratio, HD-D/vocd-D, MTLD) and
readability (e.g., Flesch, SMOG, LIX, Dale-Chall). Basic import functions
for language corpora are also provided, to enable frequency analyses
(supports Celex and Leipzig Corpora Collection file formats) and measures
like tf-idf.  #' Note: For full functionality a local installation of
TreeTagger is recommended. koRpus also includes a plugin for the R GUI and
IDE RKWard, providing dialogs for its basic features. To use them, install
RKWard from http://rkward.sf.net (plugins are detected automatically). Due
to some restrictions on CRAN, the full package sources are only available
from the project homepage.
Simple and highly optimized ordinary kriging algorithm to plot geographical data
Criteria and algorithms for sequentially estimating level
sets of a multivariate numerical function, possibly observed in
noise.
Package implements Kernel-based Regularized Least Squares (KRLS), a machine learning method to fit multidimensional functions y=f(x) for regression and classification problems without relying on linearity or additivity assumptions. KRLS finds the best fitting function by minimizing the squared loss of a Tikhonov regularization problem, using Gaussian kernels as radial basis functions. For further details see Hainmueller and Hazlett (2014).
Testing methods for kernel-based regression models.
Kernel smoothers for univariate and multivariate data
Compares k samples using the Anderson-Darling test,
Kruskal-Wallis type tests with different rank score criteria,
and Steel's multiple comparison test.  It computes asymptotic,
simulated or (limited) exact P-values, all valid under
randomization, with or without ties, or conditionally under
random sampling from populations, given the observed tie
pattern.  Except for Steel's test it also combines these tests
across several blocks of samples.  Also analyzed are 2 x t
contingency tables and their blocked combinations using the
Kruskal-Wallis criterion.  Steel's test is inverted to provide
simultaneous confidence bounds for shift parameters.  A
plotting function shows that asymptotic approximations for
small sample sizes provide only very limited utility when
comparing tail probabilities with those obtained via simulation
or exact calculations.
Selection of k in k-means clustering based on Pham et al. paper
``Selection of k in k-means clustering''
Check the power of a statistical model moving sample size
k using half-Cross Validation.
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
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.
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
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.
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
This package 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 2013, as recorded in the 2014 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.  This
package provides the tools to calculate a typical suite of these
metrics from an input elevation model and lake polygon.
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.
The Lambert W framework is a new generalized way to analyze skewed, heavy-tailed data. Lambert W random variables (RV) are based on an input/output framework where the input is a RV X with distribution F(x), and the output Y = func(X) has similar properties as X (but slightly skewed or heavy-tailed). Then this transformed RV Y has a Lambert W x F distribution - for details see References. This package contains functions to perform a Lambert W analysis of skewed and heavy-tailed data: data can be simulated, parameters can be estimated from real world data, quantiles can be computed, and results plotted/printed in a 'nice' way. Probably the most important function is 'Gaussianize', which works the same way as the R function 'scale' but actually makes your data Gaussian. An optional modular toolkit implementation allows users to define their own Lambert W x 'my favorite distribution' and use it for their analysis.
Processing of Landsat or other multispectral satellite
imagery. Includes relative normalization, image-based
radiometric correction, and topographic correction options.
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.
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.
Uses gradient descent to minimize the sum of squared
residuals for the regression problem.  Can include an L2
penalty on the coefficient matrix. This function is very useful
when there is an initial guess of what B should be in Y = XB.
In general, this function performs faster than R's lm function.
GPU acceleration can be used to make this function extremely
fast.  This package suggests cudaMatrixOps, which is not on
CRAN but can be downloaded at stanford.edu/~jeffwong.
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)
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.
Writes a file of commands for the dot program to draw a
graph proposed by Rosenbaum and useful for checking some
properties of various sorts of latent scale
A package to fit and simulate latent position and cluster models for statistical networks.
Laterality package calculates Handedness index (HI) and z-score. It gives also absolute HI and mean HI at the individual and group level. The corresponding graphs can also be plotted. Some functions based on the ade4 package perform correspondence analysis (package ade4 is needed).
Lattice is a powerful and elegant high-level data
visualization system, with an emphasis on multivariate data, that 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.
Extra graphical utilities based on lattice
Functions 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.)  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. One benefit of this
approach is the facilty to do unconditional and conditional
simulation of the field for large numbers of arbitrary points. There
is also the flexibility for estimating nonstationary covariances. 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.
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
An R software package on 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.
Provides the functionality to write LaTeX code from within R without having to learn LaTeX.
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.
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.
Functions for learning chain graphs (and as a special
case, Bayesian networks) via the decomposition approach.
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.
Functions for the 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.
Local Correlation Coefficients (Local Pearson and Geographically Weighted Pearson), Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation (Moran's I) and other Spatial Analysis tools (Geographically Weighted Statistics, Inferential Statistics)
This package 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 writtten in C.  Utility functions for
reading/writing data typically used in topic models, as well as
tools for examining posterior distributions are also included.
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).
LDExplorer is an R package for the memory and time
efficient whole-genome LD based haplotype block recognition.
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.
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 and the corresponding 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)
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.
Display your spatial data on interactive web-maps using the open-source JavaScript library Leaflet. The package provides basic web-mapping functionality to combine vector data and online map tiles from different sources.
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.
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 descriptive 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 and color themes. The function Help provides a help system that suggests specific analyses and functions. Variable labels are available. A confirmatory factor analysis of multiple indicator measurement models is also available as is a pedagogical routines for data simulation such as for the Central Limit Theorem.
This package contains some simple objects and functions to do
statistics using linear models and a Bayesian framework.
R functions for handling and analyzing geographic data, mainly
species' geographic distributions (in ESRI shapefile format) and
environmental variables (in raster format), as well as species' information
related to their description (e.g. taxonomy and year of description) and
conservation status (e.g. category of threat, population trend) as provided
by the IUCN's RedList database. The package includes functions to create
presence-absence matrices based on species distributions and user-defined
grid systems, from which several other functions could be applied to
generate, for example, species richness rasters and maps, and geographical
midpoints of species. In addition, it provides a function to create spatial
correlograms of variables, based on the Moran's I index, under the
equiprobable or equidistant criterion to define distance classes.
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 as pure control variables.
Includes support for instrumented variables, robust and multi-way clustered standard errors.
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 produce 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. A collection of methods can be applied to objects of both the 'lgarch' and 'mlgarch' types: coef, fitted, logLik, print, residuals, summary and vcov.
Spatial and spatio-temporal modelling of point patterns
using the log-Gaussian Cox process
The LGS package simulates the biological steps of a
linkage group selection experiment in order to gain insights
into the underlying biology. See Martinelli et al. (Proc Natl
Acad Sci U S A, 2005) for the LGS experimental procedure and
Hunt et al. (BMC Genomics, 2010) for an application of the
simulations implemented in this package.
A very simple implementation of a class for
longitudinal data.
This package provides a number of methods for creating and
augmenting Latin Hypercube Samples
This package is the 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 provides many auxiliary physics routines for proton and ion beams. Original package and C-to-R conversion routines developed by Felix A. Klein.
LiblineaR is a wrapper around the liblinear C/C++ library for machine learning. liblinear is a linear classifier for data with millions of instances and features. It supports L2-regularized classifiers (such as L2-loss linear SVM, L1-loss linear SVM, and logistic regression) as well as L1-regularized classifiers (such as L2-loss linear SVM and logistic regression). 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. For more information on the C/C++ liblinear library itself, refer to R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. liblinear: A Library for Large Linear Classification, Journal of Machine Learning Research 9(2008), 1871-1874, available at http://www.csie.ntu.edu.tw/~cjlin/liblinear . The two first blocks of the package version indicates which version of liblinear is currently supported by LiblineaR. For example: 1.32-14 means that the package supports the version 1.32 of liblinear.
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.
Financial and actuarial functions to evaluate life contingencies.
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.
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.
Tools to determine DNA profile Weight of Evidence.
For further information see the likeLTD guide at the URL provided,
or the paper under citation.
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
Lindenmayer or L-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.
Train and predict using fast prototype-based Linearized
Support-Vector Regression methods.
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.
A linkage information based method for imputing missing diploid genotypes
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 and marginal effects for
linear regression models and of link functions, response functions and
marginal effects for generalized linear regression models with binary
dependent variables. A major feature is 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
interactive approach, the automatic calculation of marginal effects and
far-reaching possibilities to customize the graphical output are
characteristic features as well.
LINselect allows to 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.
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.
list is a publicly available R package that allows
researchers to conduct multivariate statistical analyses of
survey data with list experiments. In addition, the package
implements the statistical test that is designed to detect
certain failures of 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), and Imai, Park, and Greene (2014).
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, and a joint model of the list experiment that enables
the analysis of the list experiment as a predictor in
outcome regression models.
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 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) a for
closed populations, as described in the ongoing doctoral thesis work of
Zachary Kurtz in the department of statistics at Carnegie Mellon
University.  The method is relevant when there are 3-4 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.
This is the package for implementing the method in "Bias
Correction in RNA-Seq Short-read Counts using Penalized
Regression." It first using a penalized regression to determine
an appropriate surrounding sequence, then refits the model
using the dinucleotide expansion.
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 multivariate 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.
Fit negative binomial mixed-effect regression model. For the distribution of subject-specific random intercept, a gamma or log-normal distributions or semi-parametric procedure are allowed. For the safety monitoring, subject-specific conditional probability index can be computed. For details, see  Zhao, Y., Li, D.K.B., Petkau, J.A., Riddehough, A. & Traboulsee, A.  Detection of unusual increases in MRI lesion counts in multiple sclerosis patients.
This package contains a Gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. For the distribution of subject-specific random intercept, user can choose a beta distribution or an infinite mixture of beta distributions. For the safety monitoring, subject-specific conditional probability indices can be computed.  For details, see Kondo,Y., Zhao, Y. and Petkau, J.A. A Flexible Mixed Effect Negative Binomial Regression for Detecting Abrupt Increase in MRI Lesion Counts in Multiple Sclerosis Patients.
Functions to back-fit fixed effects (on F or t values as well as log-likelihood ratio testing (llrt), AIC, BIC, relLik.AIC or relLik.BIC) and to forward-fit random effects (using log-likelihood ratio testing). NOTE that the back- and forward-fitting of generalized linear mixed-effects regression (glmer) models is now supported by functions ``bfFixefLMER_t.fnc'' and ``ffRanefLMER.fnc''. The package also includes a function to compute ANOVAs with upper- and lower-bound p-values (anti-conservative and conservative, respectively), a function to graph model criticism plots, functions to trim data on model residuals or on a response variable (per subject), a function to perform posthoc analyses (with or without MCMC p-values), a function to generate summaries of mcposthoc objects, a function to generate (dynamic) 3d plots of (i) predicted values of an LMER model for interactions between two numeric variables,(ii) the raw data as a function of two numeric variable, and (iii) kernel density estimates (densities) of two numeric variables, and finally a function to calculate the relative log-likelihood between two models. Also, as of version 2.4, the package gains function ``plotLMER.fnc'' (revived from archived package ``languageR'').
The package provides different kinds of tests on lmer
objects (of lme4 package). The tests comprise type 3 and type 1 F tests for
fixed effects, LRT tests for random effects, calculation of
population means for fixed factors with confidence intervals
and corresponding plots. Package also provides backward
elimination of non-significant effects
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.
Some improved procedures for linear mixed models
Package lmmfit contains three functions evaluating some
goodness-of-fit-measures for linear mixed models with
one-level-grouping fitted using lme() from package nlme.
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.
The package 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 time x group
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.
The package is a comparatively comprehensive implementation of 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 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 Exponential (Exp.) Power [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.
The Lmoments package contains functions to estimate
L-moments and trimmed L-moments from the data. The package 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.
Simplicial, Mahalanobis and Ellipsoid Local and Global Depth
This package provides computational routines for estimating and visualizing local Gaussian parameters.
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/).
Local regression, likelihood and density estimation.
Provides test of second-order stationarity for time
series. 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
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.
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), this package allows to 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), this package 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.
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.
Firth's bias reduced logistic regression approach with
penalized profile likelihood based confidence intervals for
parameter estimates.
Diagnostic tests and plots for logistic regression models.
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) algorithm (with an
improved interface). The recommended routine logspline uses an
algorithm from Stone et al (1997).
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
This package contains general data structures and
functions for longitudinal data with multiple variables,
repeated measurements, and irregularly spaced time points.
It 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.
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). Loop Analyst 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.
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.
This package contains routines and documentation for
solving quadratic programming problems where the hessian is
represented as the product of two matrices.
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
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.
Local Polynomial Regression with Ridging.
This package provides functions implementing the Linear Predictor Score approach, as initiated by Radmacher et al. (J Comput Biol 2001) and enhanced by Wright et al. (PNAS 2003). Given a set of continuous variables (typically gene expression values), it builds a simple score able to retrieve previously defined classes.
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 for 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. This package is based on the
'randomForest' package by Andy Liaw. LPS aims at finding time series patterns to
compute the similarity.
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.
Fit quantile regression models for independent and hierarchical data
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 Ridge Regression Estimator (ORRE), Restricted Least Square Estimator (RLSE), Ordinary Mixed Regression Estimator (OMRE), Liu Estimator (LE), Restricted Liu Estimator (RLE), Stochastic Restricted Liu Estimator (SRLE), Type (1) Liu Estimator (Type-1 LTE), Type (2) Liu Estimator (Type-2 LTE), Type (3) Liu Estimator (Type-3 LTE), Type (1) Adjusted Liu Estimator (Type-1 ALTE), Type (2) Adjusted Liu Estimator (Type-2 ALTE), Type (3) Adjusted Liu Estimator (Type-3 ALTE), Almost Unbiased Ridge Estimator (AURE), Almost Unbiased Liu Estimator (AULE), Stochastic Restricted Ridge Estimator (SRRE) and Restricted Ridge Regression Estimator (RRRE). 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.
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.
This package provides functions that allow for convenient working
with Latent Semantic Analysis. For actually building an LSA space, use the
package 'lsa'.
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.demo.tour() )
Fit a least square dummy variable regression
Linear multiple output using sparse group lasso
Calculates the (localised) length of the shorth and
supplies corresponding diagnostic plots.
This package provides methods for obtaining least-squares means for various linear models. It can also compute contrasts or linear combinations of these least-squares means, 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.
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.
Functions that I have found useful for teaching purposes.
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.
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 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.
Implement local time space variogram estimation and kriging
Lubridate makes it easier to work with dates
and times by providing functions to identify and parse
date-time data, extract and modify components of a
date-time (years, months, days, hours, minutes, and
seconds), perform accurate math on date-times, handle
time zones and Daylight Savings Time. Lubridate has a
consistent, memorable syntax, that makes working with
dates fun instead of frustrating.
Likelihood inference in case-control studies of a rare
disease under independence or simple dependence of genetic and
non-genetic covariates
Package provides a collection of various R functions for Luminescence Dating data analysis.
Provides functions to calculate lunar and other environmental
covariates.
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
This package provides several LVQ algorithms along with
different usage methods and output visualization.
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.
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.
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).
'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.
This is an integrated meta-analysis package for conducting	a research synthesis with mean differences data. 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 mean differences. It uses recommended procedures as	described in The 	Handbook of Research Synthesis and Meta-Analysis	(Cooper, Hedges, & Valentine, 2009).
This package 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.
In this package we provide some basic utilities for
performing meta-analysis. Note that from version 1.2 a complete
redesign of MADAM started to take place, so that code from
versions <1.2 will not be compatible with current versions.
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.
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.
Magma matrix classes and methods for parallel processing
of matrix algebra operations.  Operations are performed with
algorithms developed by the MAGMA research project.  MAGMA aims
to achieve the fastest possible linear algebra libraries on
hybrid multicore CPU and GPU architectures by exploiting their
massive parallelism and minimizing communication latencies.
Provides a mechanism for chaining commands with a
new forward-pipe operator. Ceci n'est pas un pipe.
Easy to use package for sending email notifications with status information from R
Interface to Apache Commons Email to send emails
from within R.
MAINT.Data implements methodologies for modelling Interval Data, considering five different possible configurations structures for the variance-covariance matrix.
This package creates an empty framework of files and
directories for the "Load, Clean, Func, Do" structure described
by Josh Reich.
Using EM algorithm to estimate malaria haplotype
probabilities and the number of infections from multiply
infected human blood samples. Estimated haplotype probabilities
and their standard error are reported.
MALDIquant provides a complete analysis pipeline for
MALDI-TOF and other mass spectrometry data. Distinctive
features include baseline subtraction methods such as TopHat or
SNIP, peak alignment using warping functions, handling of
replicated measurements as well as allowing spectra with
different resolutions.
This package reads (tab, csv, Bruker fid, Ciphergen XML,
mzXML, mzML, imzML, Analyze 7.5, CDF) and writes (tab, csv,
msd, mzML) 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.
Performs the multiple testing procedures of Cox (2011) and Wang and Cox (2007).
Map2NCBI is an R package that 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.
This package contains functions and wrappers for using the Multiple Aggregation Prediction Algorithm, proposed by Kourentzes et al. (2014).
Supplement to maps package, providing the larger and/or
higher-resolution databases.
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.
This package contains various utilities for producing nice looking maps in R.
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.
Display of maps.  Projection code and larger maps are in
separate packages (mapproj and mapdata).
A package for calculating and visualizing 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.
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.
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.
Likelihood inference based on higher order approximations
for linear nonnormal regression models
Calculates the Log Marginal Likelihood for a Normal
Mixture model for given data.
Marginal mean model plots for linear (lm) and generalized
linear models (glm).  Including tools for bandwidth
exploration.  Built on the work from the alr3 and locfit
packages.
Markdown is a plain-text formatting syntax that can be
converted to XHTML or other formats. This package provides R bindings
to the Sundown markdown rendering library.
This package provides a framework for handling
data and analysis for mark-recapture.
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.
A package for easily handling discrete Markov chains
a collection of tools for analyzing significance of
Markowitz portfolios.
Import, plot and analyze bathymetric and topographic data
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), Mandallaz et al. (2013) and Mandallaz (2014).
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.
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.
MatchingFrontier 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. The package also includes tools for the estimation
of causal effects for each subset size, as well as functions for visualization
and data export.
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.
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.
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.
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.
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.
matrixpls is implements 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.
This packages provides methods operating on rows and columns of matrices, e.g. col/rowMedians() and col/rowRanks().  There are also some vector-based methods, e.g. anyMissing(), weightedMedians(), and binMeans().  The objective is to have all methods being optimized for speed and memory usage.
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.
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.
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.
Maximally selected rank statistics with
several p-value approximations.
Scattered data interpolation with Multilevel B-Splines
This package contains probability functions to work with MBBEFD and its exposure curve
Cluster genes based on Poisson or Negative-Binomial model
for RNA-Seq or other digital gene expression (DGE) data
MBESS implements methods that are especially useful to
researchers working within the behavioral, educational, and
social sciences (both substantive researchers and
methodologists), Many of the methods contained within MBESS are
applicable to quantitative research in general,
Implements methods from the paper "Fast Moment-Based Estimation
for Hierarchical Models," by Perry (2014).
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.
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.
Various distributions and utilities to ease the use of R to build and study Two-Dimensional Monte-Carlo simulations
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
Classical multiple correspondence analysis and multiple correspondence analysis via orthogonal polynomials
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.
The MCDA package for the R statistical software allows you to use MCDA methods and functions in R.
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.
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.
Maximum likelihood estimation using a Monte Carlo local likelihood (MCLL) method
This packages provides a function to estimate parameters for the
mixed conditional logit model, or conditional logit with random effects.
The current implementation of random effects is limited to
the PQL technique, which requires large cluster sizes.
Normal Mixture Modeling fitted via EM algorithm for Model-Based Clustering, Classification, and Density Estimation, including Bayesian regularization.
functions for Markov chain Monte Carlo (MCMC).
Hybrid Markov chain Monte Carlo (MCMC) to simulate from a
multimodal target distribution.  A Gaussian process
approximation makes this possible when derivatives are unknown.
The Package serves to minimize the number of function
evaluations in Bayesian calibration of computer models using
parallel tempering.  It allows replacement of the true target
distribution in high temperature chains, or complete
replacement of the target.  Methods used are described in,
"Efficient MCMC schemes for Bayesian calibration of computer
models", Fielding, Mark, Nott, David J. and Liong Shie-Yui,
Technometrics (2010). The authors gratefully acknowledge the
support & contributions of the Singapore-Delft Water Alliance
(SDWA).  The research presented in this work was carried out as
part of the SDWA's Multi-Objective Multi-Reservoir Management
research programme (R-264-001-272).
This package implements 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
This package 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.  MCMCpack also contains some useful
utility functions, including some additional density functions
and pseudo-random number generators for statistical
distributions, a general purpose Metropolis sampling algorithm,
and tools for visualization.
This package implements generalized linear mixed model analysis of qRT-PCR data based on lognormal-Poisson model 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). Also implemented are the 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. Tutorial: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/mcmc.qpcr.tutorial.pdf
mcmcse 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. 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.
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.
This package provides functions for a user to run a
regenerative Markov chain in parallel, split it up into tours
and form Confidence Intervals on parameters of interest.
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.
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
Mixture and flexible discriminant analysis, multivariate
adaptive regression splines (MARS), BRUTO, ...
The package implements projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics.
The package provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.
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.
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.
This package contains a series of tools associated with General
Recognition Theory (Townsend & Ashby, 1986), including Gaussian model fitting of 4x4 and more
general confusion matrices, 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, Kruskals
Analysis and Sammon Mapping with animated optimisation.
This package assists in the identification of ring borders on scanned image sections from dendrochronological samples. It processes the image section and computes luminance data from images producing a matrix of gray values 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. The package provides functions 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 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.
This package implements 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: 1) Bayesian hierarchical models 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.
mediation allows both parametric and nonparametric causal mediation analysis.  It implements 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.
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.
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. Also some convenience tools for graphics,
programming, and simulation are provided.
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?
Learn the scintillating answer to this and many more such questions with
the memuse package.  This package gives the user the size (memory usage)
of an unallocated, in-core, dense, numeric matrix.  The output is an S4
class object that can be manipulated to be presented in different ways
(different units, short/long versions of those units, etc.).  The package
offers similar functionality to that of R's object.size() function, with
the key difference being that memuse does not require an object to already
be allocated.  So one can determine the size of enormous matrices even
from a laptop.
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.
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:
- 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).
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.
Functions to analyze coherence, boundary clumping, and turnover following the pattern-based metacommunity analysis of Leibold and Mikkelson 2002
Implement the DerSimonian-Laird (DSL) and Olkin-Pratt (OP)
meta-analytical approaches with correlation coefficients as
effect sizes.
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").
The metafor package provides 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 (conditional) 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.
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.
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.
Meta-analysis of significance values. The package
provides routines for a number of methods for doing this, a
number of datasets to replicate published results, and a
routine for graphical display
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 using standard and robust methods with confidence intervals from profile likelihood.
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.
This package implements two p-value combination techniques (inverse normal and Fisher methods). It also provides a vignette explaining how to combine data from multiple RNA-seq experiments.
Advanced statistical methods to model and adjust for bias in meta-analysis. More standard methods, i.e. tests for bias in meta-analysis and the trim-and-fill method are provided in R package meta. Main purpose of these methods is to evaluate how sensitive the result of a meta-analysis is to potential bias in meta-analysis. The following methods are available:
- Copas selection model;
- limit meta-analysis;
- upper bound for outcome reporting bias.
Meta analysis for SNP-set (Sequence) Kernel Association Test.
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).
Retrieve meteorological forecast from Meteogalicia and OpenMeteo services
meteogram is a collection of programs for plotting
meteograms for meteorological data such as atmospheric cross
section, temperatures plots.
Functions for selection of visual meteor data, calculations of Zenithal Hourly Rate (ZHR) and population index, graphics of 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)
Metrics is a set of evaluation metrics that is commonly
used in supervised machine learning.
metRology provides classes and calculation and plotting functions
for metrology applications, including measurement uncertainty estimation
and inter-laboratory metrology comparison studies.
Pharmacometric tools for common data preparation tasks, stratified
bootstrap resampling of data sets, NONMEM control stream creation/editing,
NONMEM model execution, creation of standard and user-defined diagnostic plots,
execution and summary of bootstrap and predictive check results, implementation
of simulations from posterior parameter distributions, reporting of output
tables and creation of detailed analysis logs.
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).
A diverse collection of metabolomics related statistical tools.
Computes the average of a sequence of random vectors
in a moving expanding window using a fixed amount of storage
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.
This is the package for doing model based functional
clustering.
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.
Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output.
Routines for GAMs and other generalized ridge regression
with multiple smoothing parameter selection by GCV, REML
or UBRE/AIC. Also GAMMs. Includes a gam() function.
The package 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.
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 (2014), Simultaneous sparse estimation of canonical vectors in the p>>N setting'
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.
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.
Multiple Hypotheses Testing For Variable Selection
Estimation of models with zero left-censored variables.
Null values may be caused by a selection process, insufficient
resources or infrequency of purchase.
Missing-data imputation and model checking
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.
The package miceadds 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 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
Cross-linking immunoprecipitation coupled with high-throughput sequencing (CLIP-Seq) has made it possible to identify targeting sites of RNA-binding proteins in various cell culture systems and tissue types on a genome-wide scale. Here we present MiClip,a novel model-based approach to identify high-confidence protein-RNA binding sites in CLIP-Seq datasets. This approach assigns confidence value to each binding site on a probabilistic basis. The MiClip package can be flexibly applied to analyze both HITS-CLIP data and PAR-CLIP data.
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 users the ability to quickly create Linked
Micromap plots of the 50 U.S. states and District of Columbia (51 areas).
Linked Micromaps are visualizations of georeferenced 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 FIPS/State conversion tables
used by the micromapST function and several data sets used in the micromapST examples.
Copyrighted 2013 and 2014 by Carr, Pearson and Pickle.
A collection of functions for computations and visualizations of microbial pan-genomes.
Deploys your R Analytic to MicroStrategy
The package performs continuous-time microsimulation for population projections.
Econometric methods for mixed frequency time series data analysis
Functions for implementing species dispersal into projections
of species distribution models (e.g. under climate change scenarios).
The migest package contains a collection of R functions that are useful to estimate migration when there is missing data. Included is the ffs routine to estimate migration flows from stock data, as well as various other functions to indirectly estimate flows where marginal totals are known. Functions are illustrated with dummy data.
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.
Graphical User Interface of the mi Package
Applies the Pan's (2000) multiple imputation approach to Cox or Fine and Gray regression for interval censored data.
The MILC package is designed to predict individual trajectories using the continuous time microsimulation model MILC, that describes the natural history of lung cancer.
This package 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
46 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)
This package implements the core idea of games known as
'Minesweeper' on Microsoft Windows or 'KMines' for KDE on
Unix-like operating systems.
Tools to create an internally consistent, mini version of CRAN with
selected packages only.
quick and simple tktcl miniGUI to call functions.
The minimax family of distributions is a two-parameter
family like the beta family, but computationally a lot more
tractible.
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.
This package implements entropy optimization distribution
under specified constraints. It also offers an R interface to
the MinxEnt and MaxEnt distributions.
Plots to visualize missing data and datasets that were
produced by Multiple Imputation. The idea behind this package
is to visualize MI objects produced by the R packages mi, mice
or Amelia II to provide a better understanding of the imputed
data and to evaluate the MI process.
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.
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 functioning and 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.
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 set of miscellaneous R tools to simplify the work with several
data types and formats.
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
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 using 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: 1) computing
failure probability (probability that the output of a numerical
model exceeds a threshold); 2) computing the size of a sample
to estimate a quantile with a confidence level.
Flexible multivariate function approximation using adapted
Mixture of Student t Distributions. Mixture of t distribution
is obtained using Importance Sampling weighted Expectation
Maximization algorithm.
Tools to perform analyses and combine results from
multiple-imputation datasets.
Estimation/multiple imputation programs for mixed
categorical and continuous data
This package 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.
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.
Compute Estimator of variance with quadratic forms, and
computes test on a linar contrasts in fixed effects
This package provides detailed functions for univariate Mixed Tempered Stable distribution distribution with Gamma density. This distribution encompasses, Variance Gamma and Symmetric Geo-Stable as special cases. The package contains routine for mle estimation, for the computation of density, probability, quantile and random numbers
Routines for the analysis (unsupervised clustering) of
networks using MIXtures of Erdos-Renyi random graphs
This package contains functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.
Carries out model-based clustering using three 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 MCGHD, mixture of  coalesced generalized hyperbolic distributions is a new more flexible model.
The main functions mask call to mixed models by least squares or REML by adding r() function to formulas of lm and glm. It also has a collection of text book statistics for higher education.
mixlow software for assessing drug synergism/antagonism
This function implements 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.
The package provide statistical integrative techniques and
variants to analyse highly dimensional data sets: regularized
CCA and sparse PLS to unravel relationships between two
heterogeneous data sets of size (nxp) and (nxq) 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 CCA 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, Independent Principal Component Analysis and
multilevel analysis using variance decomposition of the data.
This package 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).
Separates DNA mixtures using a statistical model within a
greedy algorithm with a useful tcl/tk GUI.
MixSim allows 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.
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.
An implementation of all 14 Gaussian parsimonious
clustering models (GPCMs) for model-based clustering and
model-based classification.
Likelihood-based methods for inference for finite mixture
models
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
(default is a Levenberg-Marquardt variant).  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)
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.
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.
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.
Programs for exploring the maximum likelihood estimates of
penetrance parameters for linkage analysis
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.
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.
Description: Various utilities for the Multiplicative
Multinomial distribution
This package provides tools for performing mathematical morphology operations, such as erosion and dilation, on data of arbitrary dimensionality. It can also be used for resampling, filtering, smoothing and other image processing-style operations.
R interface to POSIX mmap and Window's MapViewOfFile
This package provides an implementation of modified
maximum contrast methods and the maximum contrast method. This
version supports functions mmcm.mvt, mcm.mvt that gives P-value
by using randomized quasi-Monte Carlo method from pmvt function
of package mvtnorm, and mmcm.resamp that gives P-value by using
the 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.
Implement different types of model mixing and model
selection methods for linear or logistic models.
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.
mmod provides functions for measuring
population divergence from genotypic data
Fixed effects Selection in Linear Mixed Models
The datasets from Modern Multivariate Statistical
Techniques by Alan Julian Izenman are contained in this
package.  The documentation descriptions show the page numbers
of references to the data set within the text.  See the text
for detailed descriptions of the datasets.  Also included in
this package is a function for exporting these datasets en
masse.
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.
This package provides functions for computing the density and the
distribution function of multivariate normal and multivariate "t" variates,
and for generating random vectors sampled from these distributions.
Probabilities are computed via a non-Monte Carlo method; different routines
are used for 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.
This package implements random forest method for model
based recursive partitioning. The mob() function, developed by
Zeileis et al (2008), within party package, is modified to
construct model-based decision trees based on random forests
methodology. The main input function mobForestAnalysis() takes
all input parameters to construct trees, compute out-of-bag
errors, predictions, and overall accuracy of forest. The
algorithm performs parallel computation using clusterApply()
function within 'parallel' package.
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,
this package 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.
This package will create sophisticated models of training data and validate 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. It will create graphs and tables of the model validation results. It will apply these models to GIS .img files of predictors to create detailed prediction surfaces. It will handle 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 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!
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.
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.
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.
This package performs model selection based on non-local priors, including MOM, eMOM and iMOM 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.
Functions to calculate: moments, Pearson's kurtosis,
Geary's kurtosis and skewness; tests related to them
(Anscombe-Glynn, D'Agostino, Bonett-Seier).
Momocs is intended to ease and popularize shape analysis
of outlines (especially using elliptical Fourier analysis). It
mostly hinges on the functions developed in Morphometrics with
R (Claude, 2008). From outline extraction of images and
elliptical Fourier calculation to multivariate analysis and the
visualization of transformations within the morphological
space, Momocs provides a complete and convenient toolkit to
specialists within every field that are, or may be, interested
in morphological comparisons of outlines.
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.
Acoustic template detection and monitoring database interface.
Multi-layer perceptron neural network with partial
monotonicity constraints
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
Package contains functions to fit monotone polynomials to
data
Estimates monotone regression and variance functions in a
nonparametric model.
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.
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.
A package for ecotoxicologists and regulators dedicated to the mathematical and statistical modelling of bioassay data. The package uses 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.  A few features take
advantage of the manipulate package when working in RStudio.
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.
Functions to estimate start and duration of moult from moult
data (of types 1 to 5), based on models developed in Underhill
and Zucchini (1988, 1990). Allows modelling parameters as
functions of covariates.
move is a package that contains functions to access movement data stored in movebank.org as well as tools to visualize and statistically analyse animal movement data. Move addresses movement ecological questions.
Fit and simulate mixtures of von Mises-Fisher distributions.
Implementation of multidimensional projection techniques
MPAgenomics provides functions to preprocess and analyze genomic
data. It 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.
MPCI package 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). Moreover, it includes two datasets.
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).  The lme4.0 package, used only in conjunction with the polmer function, is available from R-Forge at http://https://r-forge.r-project.org/projects/lme4/
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.
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.
mpoly allows for the symbolic manipulation of multivariate
polynomials in a general and consistent way designed especially for
sparse and super sparse polynomials.  It also allows for basic
differential calculus with multivariate polynomials and Grobner basis
calculations from computational algebraic geometry.
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 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).
MPTinR 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 contextree language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, MPTinR can fit a wide variety of other cognitive models such as SDT models (see fit.model). MPTinR supports multicore fitting and FIA calculation using the snowfall package. MPTinR can generate data from a model for e.g., simulation or parametric bootstrap and plot predicted versus observed data.
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.
Model-robust, semi-parametric modelling of continuous and
quantal concentration/dose-response data.
This package implements mark-recapture distance sampling
methods as described in D.L. Borchers, W. Zucchini and Fewster,
R.M. (1988), "Mark-recapture models for line transect surveys",
Biometrics 54: 1207-1220. and Laake, J.L. (1999) "Distance sampling
with independent observers: Reducing bias from heterogeneity by
weakening the conditional independence assumption." in Amstrup,
G.W., Garner, S.C., Laake, J.L., Manly, B.F.J., McDonald, L.L. and
Robertson, D.G. (eds) "Marine mammal survey and assessment
methods", Balkema, Rotterdam: 137-148 and Borchers, D.L., Laake,
J.L., Southwell, C. and Paxton, C.L.G. "Accommodating unmodelled
heterogeneity in double-observer distance sampling surveys". 2006.
Biometrics 62:372-378.)
Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics
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.
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.
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.
Clusters Uniprot terms by GO category, then draws diagrams
showing the results in hierarchical clusters, with lines for individual
proteins, showing their score with the length of the line.  The results
are in general not publication-ready, but will rather require editing
via Illustrator or some similar tool.
The package contains functions to: 1) load ah files from Strand software and build a R data
base; 2) plot different graphical displays of the fragment sizes obtained for each locus and their bins; 3)
interact with graphs to sort the data base for a given locus and range, allowing to easily  trace back a
particular group of samples to its original Strand file; bin fragment size data into population genetics files.
4) From version 1.04 on, the user can set manually bin limits and write files combining the manual and
automatic methods to bin alleles.
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.
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
This package implements all the methods in the paper
"Modeling non-uniformity in short-read rates in RNA-Seq data".
Especially, it implements both the iterative glm procedure for
the Poisson linear model and the training procedure of the MART
model. The cross-validation for both of the methods is also
implemented. Version 1.1 and later also implements the Poisson
linear model with dinucleotide composition. Version 1.2
corrected a bug in expData.R. This bug leads to mis-labeled
lines in plotCoef.R, but will not change other parts like
training, testing, or cross-validation.
A companion to the Chinese book ``Modern Statistical
Graphics'' by Yihui Xie.
Sparse group lasso multiclass classification, suitable for
high dimensional problems with many classes. Fast algorithm for
solving the multinomial sparse group lasso convex optimization
problem. This package apply template metaprogramming
techniques, therefore -- when compiling the package from source
-- a high level of optimization is needed to gain full speed
(e.g. for the GCC compiler use -O3). Use of multiple processors
for cross validation and subsampling is supported through
OpenMP. The Armadillo C++ library is used as the primary linear
algebra engine.
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.
Functions for fitting general continuous-time Markov and
hidden Markov multi-state models to longitudinal data.  A variety of
observation schemes are supported, including processes observed at
arbitrary times (panel data), continuously-observed processes, and
censored states. 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.
This package provides functions and datasets from the book "Methods of Statistical Model Estimation".
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.
Package for 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.
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
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
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 R access to the Amazon Mechanical Turk (MTurk) Requester API.
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.
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.
Provides a GUI with which users can construct and interact
with Multibiplot Analysis.
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.
This package provides a tool for analysis of multiple CNV.
This packages includes functions designed to examine relationships among multivariate constructs.
A set of tools to permute multisets without loops or hash tables. 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.
multiDimBio is a package designed to support a systems biology research program from inception through publication.  It focuses on dimension reduction approaches to detect patterns in complex, multivariate experimental data and places an emphasis on informative visualizations.  The goal for this project is to create a package that will evolve over time, thereby remaining relevant and reflective of current methods and techniques.  As a result, we encourage suggested additions to the package, both methodological and graphical.
This package includes several methods to study multigroup data,
where the same set of variables are measured on different groups of
individuals.
This package provides a flexible 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.
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.
This package 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.
multiplex - Analysis of Multiple Social Networks with Algebra is a
package for the study of social systems made of different types of
relationships. 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.
Various utilities to manipulate multivariate polynomials
An R package to perform sensitivity analysis on a
model with multivariate output
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.
Data frames are integral to R.  They provide a standard format for passing data to model-fitting and plotting functions, and this standard makes it easier for experienced users to learn new functions that accept data as a single data frame.  Still, many data sets do not easily fit into a single data frame; data sets in ecology with a so-called fourth-corner problem provide important examples. Manipulating such inherently multiple-table data using several data frames can result in long and difficult-to-read work-flows.  This package provides new data storage objects called data.list objects, which extend the data.frame concept to explicitly multiple-table settings.  Like data frames, data lists are lists of variables stored as vectors; what is new is that these vectors have dimension attributes that make accessing and manipulating them easier.  As data.list objects can be coerced to data.frame objects, they can be used with all R functions that accept an object that can be coerced to a data.frame.
Generates Discrete Prolate Spheroidal Sequences, provides functions and utilities for multitaper spectral analysis and related problems.
A multivariate generalization of the emulator package
This package exports two functions cluster.vcov, which implements
multi-way clustering using the method suggested by Cameron, Gelbach, &
Miller (2011) and cluster.boot, which implements 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 somealgorithms 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).
This package provides Schoeneman's algorithm for metric
multidimensional unfolding and Procrustes rotation of unfolding
results.
Functions for exploring and using the Munsell
colour system
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.
MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences.
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.
A graphical user interface for the MuToss Project
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.
Fit log-linear model for multivariate Bernoulli
distribution with mixed effect models and LASSO
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.
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.
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.
The package mvmeta consists of a collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression in R.
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees
Assessing the assumption of multivariate normality is required by many parametric multivariate statistical methods, such as discriminant analysis, principal component analysis, MANOVA, etc. Here, we present an R package to asses multivariate normality. The MVN package contains three most widely used multivariate normality tests, including Mardia's, Henze-Zirkler's and Royston's multivariate normality tests.
This package computes the Nelson-Aalen estimator of the cumulative transition hazard for multistate models.
The package 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.
various methods for multivariate outlier detection.
The main function of this package, MRT, provides to the following information : the proper cluster membership allocation in the tree, the discriminant species for each cluster, the tree nodes at the proper level of R2 (1-relative error), the total species variance partitioned by species, by the tree, and by the splits of the tree, plus a different graphic representation of the final model in the form of a tree. Moreover, the CascadeMRT function allows a cascade MRT analysis and the CasMRTR2 function allows to illustrate the (unadjusted) variation explained by each wave/drops. Last but not least, the R2aGDF function permits the calculation of an adjusted R^2 for an MRT model.
Calculate power for a given effect size and sample size
using multivariate classification methods: Random forest (RF),
Prediction analysis of microarrays (PAM), K-nearest neighbor
(KNN), or Support vector machine (SVM)
MVR 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 which 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.
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
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. 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
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::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.
An R package for working with nested Archimedean copulas.
Specifically, providing procedures for computing function
values and cube volumes, characteristics such as Kendall's tau
and tail dependence coefficients, efficient sampling
algorithms, various estimators, and goodness-of-fit tests.  The
package also contains related univariate distributions and
special functions such as the Sibuya distribution, the
polylogarithm, Stirling and Eulerian numbers.
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 for ASReml software, which can be obtained upon purchase from VSN international (<www.vsni.co.uk/software/asreml>).
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
This package contains tidied data from the ASA 2006 data expo,
as well as a number of useful other related data sets.
This package is a reboot of the AnalysisSuite
(https://github.com/jefferis/AnalysisSuite) bundle of R code that I have
been using for a number of years to analyse and visualise 3D biological
image data, especially traced neurons. AnalysisSuite is most easily
installed using the nat.as package presently available at
https://github.com/jefferis/nat.as. The nat package is considerably cleaner
but currently provides only a subset of the full functionality of nat.as /
AnalysisSuite. nat can read and write 3D images in NRRD and Amira formats
and read surfaces in Amira's 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. The package also has a simple representation for neurons that
have been subjected to a 3D skeletonisation but not formally traced; this
allows morphological comparison between neurons including searches and
clustering.
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 to support the NeuroAnatomy Toolbox (nat).
Includes functions to provide a basic make style system to update files
based on timestamp information, file locking and other convenience functions
for working with the filesystem.
NbClust package provides 30 indices for determining the optimal number of clusters in a dataset.
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.
Negative Binomial (NB) models for two-group comparisons and
regression inferences from RNA-Sequencing Data.
nCal is a package that performs nonlinear calibration and curve fitting for data from Luminex, RT-PCR, ELISA, RPPA etc. Its precursor is Ruminex.
NCBI2R is an R package that annotates lists of SNPs and/or
genes, with current information from NCBI, including LD
information. Functions are provided that with one command will
provide annotation of the results from genome wide association
studies to provide a broader context of their meaning. Other
functions enable comparisons between a user's GWA results, and
candidate snp/gene lists that are created from keywords, such
as specific diseases, phenotypes or gene ontology terms.
Commands are simple to follow and designed to work with R
objects to integrate into existing workflows. The output
produces text fields and weblinks to more information for items
such as: gene descriptions, nucleotide positions, OMIM,
pathways, phenotypes, and lists of interacting and neighboring
genes. Annotation can then be used in R for further analysis,
or the objects can be customized for use in spreadsheet
programs or web browsers. The NCBI2R package was designed to
allow those performing genome analysis to produce output that
could easily be understood by a person not familiar with R.
Please see the website at http://NCBI2R.wordpress.com for more
information. The internet is required for almost all of these
functions. Use the function PrintNCBI2RInfo() for information.
making NCBI taxonomic data locally available and
searchable as an R object
This package provides a high-level R interface to
Unidata's netCDF data files, which are portable across
platforms and include metadata information in addition to the
data sets. Using this package netCDF files can be opened and
data sets read in easily. It is also easy to create new netCDF
dimensions, variables, and files, or manipulate existing netCDF
files. This interface provides considerably more functionality
than the old "netCDF" package for R, and is not compatible with
the old "netCDF" package for R. Release 1.2 (2005-01-24) adds
better support for character variables, and miscellaneous bug
fixes.  Release 1.3 (2005-03-27) is for miscellaneous bug
fixes, and improves the documentation.  Release 1.4
(2005-06-27) improves the efficiency, and adds small bug fixes.
Release 1.5 (2006-02-27) adds support for byte variables, plus
small bug fixes. Release 1.6 (2006-06-19) adds various bug
fixes, plus support for making dimensions WITHOUT dimvars
(coordinate variables), although I think this is a bad idea in
general.  ALSO, the default behavior for put.var.ncdf with
unlimited variables and NO specified start and count parameters
has changed!  Before, the default was to append to the end of
the existing variable.  Now, the default is to assume a start
of 1 along each dimension, and a count of the current length of
each dimension.  This really can be ambiguous when using an
unlimited dimension. I always specify both start and count when
writing to a variable with an unlimited dimension, and suggest
you do as well.  I may require this in a future release, as it
seems to cause people problems.
This package 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 ncdf files with the RNetCDF package.
Most functions are wrappers for basic functions from RNetCDF 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: 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 ("Mnet").
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 animated movies or other representations of relational structure and node attributes that change over time.
This package implements a path algorithm for Near-Isotonic
Regression. For more details see the help files
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.
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.
NEff is an individual based model. This package uses demographic parameter of a population to calculate annual effective sizes and effective population sizes (per generation). It assumes that no migration and mutation is occurring. A defined number of alleles and loci will be used to simulate the genotypes of the individuals. 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).
Set of functions to estimate the Glomerular Filtration
Rate (GFR) using different biomarkers and other phenotypes of
interest for nephrology.
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
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.
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.
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)
The NETS package provides routines for the estimation of
sparse long run partial correlation networks for time series
data
NetSim allows to combine and simulate a variety of micro-models to research their impact on the macro-features of social networks.
A collection of network inference methods for co-expression networks, quantitative network distances and a novel framework for network stability analysis.
This package summarizes quantified peptide data, fits linear models on
and performs network analysis for proteomics mass spectrometry data.
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.
Simple interface routines to facilitate the handling of network objects with complex intertemporal data. "networkDynamic" is a part of the "statnet" suite of packages for network analysis.
A collection of dynamic network datasets from various sources and multiple authors represented networkDynamic format
networkreporting has a collection of 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.
Annotated neuroblastoma copy number profiles,
a benchmark data set for change-point detection algorithms.
neuroim is 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
ANALYZE7.5 and 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 for the following general families of  distributions (each family defined in terms of an arbitrary cdf G): Marshall Olkin G distributions due to Marshall and Olkin  (1997), exponentiated G distributions due to Gupta et al. (1998), beta G distributions due to Eugene et al. (2002), gamma G distributions due to Zografos and Balakrishnan (2009), Kumaraswamy G distributions due to Cordeiro and Castro (2011), generalized beta G distributions due to Alexander et al. (2012), beta extended G distributions due to Cordeiro et al. (2012), gamma G distributions due to Ristic and Balakrishnan (2012), gamma uniform G distributions due to Torabi and Montazeri (2012), beta exponential G distributions due to Alzaatreh et al. (2013), Weibull G distributions also due to Alzaatreh et al. (2013), log gamma G I distributions due to Amini et al. (2013), log gamma G II distributions also due to Amini et al. (2013), exponentiated generalized G distributions due to Cordeiro et al. (2013), exponentiated Kumaraswamy G distributions due to Lemonte et al. (2013), geometric exponential Poisson G distributions due to Nadarajah et al. (2013a), truncated-exponential skew-symmetric G distributions due to Nadarajah et al. (2013b), modified beta G distributions due to Nadarajah et al. (2013c), and exponentiated exponential Poisson G distributions due to Ristic and Nadarajah (2013).
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.
This package offers utilities for creating, displaying,
and "babbling" n-grams.  The babbler is a simple Markov
process.
Retrieve and plot word frequencies through time from the Google
Ngram Viewer
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.
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.
The NHMMfdr package implements the non-homogeneous Hidden Markov Model based FDR control as described in Kuan et al., 2011 for multiple comparison adjustment under dependence. It allows for prior information to be incorporated in the model to improve detection of significant tests.
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.
This package provides some Japanese utility functions and data, which may be useful for Japanese R users.
Datasets for testing nonlinear regression routines.
Regression in the Normal Linear ADG Model
Solve a system of non linear 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 and
for specifying that a numerical Jacobian is banded.
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.
nlmrt provides tools for working with nonlinear least squares problems
using a calling structure 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.
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.
Datasets and functions for nonlinear regression. Support software for the book "Nonlinear regression with R".
Adds brute force and multiple starting values to nls.
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.
Nonlinear restrictions Wald Test for regression parameters using delta-method
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.
This package provides a framework to perform Non-negative Matrix
Factorization (NMF). It 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.
Functions, examples and data from the book 'Numerical Methods
and Optimization in Finance' by M. Gilli, D. Maringer and E. Schumann.
The package contains, in particular, implementations of several
optimisation heuristics (such as Differential Evolution, Genetic Algorithms
and Threshold Accepting).
Software for feed-forward neural networks with a single
hidden layer, and for multinomial log-linear models.
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.
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.
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).
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.
R package for nonlinear time series analysis
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 package contains functions for measuring efficiency
and productivity of decision making units (DMUs) under the
framework of Data Envelopment Analysis (DEA) and its
variations.
This package 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.
Nordklim data set 1.0 The Nordklim dataset 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. It covers a time span
for more than 100 years.  Project contractors: 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
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.
A collection of utilities referred to exponential power
distributions, also known as General Error Distribution
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".
Five omnibus tests for the composite hypothesis of
normality
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.
This Package provides a connection to the pushover.net API
to send push notification to your smartphone directly from R.
(ACCOUNT REQUIRED!)
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).
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.
This package provides functions for the best known approaches to nonparametric boundary estimation.  The selected methods are concerned with empirical, smooth, unconstrained as well as restricted estimates under  both separate and multiple shape constraints.  The package also allows for Monte Carlo comparisons among these different estimation procedures, illustrating the simulation experiments by Daouia, Noh and Park (2013),  Data envelope fitting with constrained polynomial splines, TSE Working Paper, http://www.tse-fr.eu/images/doc/wp/etrie/wp_tse_449.pdf
This package performs 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 (multivariate) continuous observations have the same c.d.f. or not. In addition to tests focusing directly on the (multivariate) c.d.f., the package contains tests designed to be particularly sensitive to changes in the underlying copula.
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
An R-package for calculating sample size of a survival trial with or without cure fractions
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.
The package computes non-parametric Maximum-Likelihood Estimate and its asymptotic variance in a semiparametric mixture model for competing-risks data, as described in Chang et al. (2007b).
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.
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.
This package provides a variety of nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. This package is a parallel implementation of the np package based on the MPI specification that incorporates the Rmpi package (Hao Yu <hyu@stats.uwo.ca>) with minor modifications and we are extremely grateful to Hao Yu for his contributions to the R community. 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).
This package provides a few small functions to make working with
survey data in an NPS context 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.
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.
Datasets from Bates and Watts (1988) "Nonlinear Regression
Analysis and Its Applications" with sample code.
Calculating the net reclassification improvement (NRI) for risk prediction models with time to event and binary data.
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.
This package was 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.
Generate null datasets and null plots with ease.
Provides some number-theoretic functions for R.
This package provide 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; growth curve fitting; decay curve decomposition and fast-component equivalent dose estimation; statistical age models analysis and radial plot drawing.
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.
Harvest metadata using the Open Archives Initiative Protocol for Metadata
Harvesting (OAI-PMH) version 2.0.
Generalise the starting point of the array index
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.
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 ADP measurements, CTD measurements, sectional
data, sea-level time series, coastline files, etc.
Provides functions for calculating seawater properties
such as potential temperature and density, as well as
derived properties such as buoyancy frequency and
dynamic height.
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.
This package provides functions to create, connect, update
and query HSQL databases embedded in Open Document Databases
(.odb) files, as OpenOffice and LibreOffice do.
Sweave processing of Open Document Format (ODF) files
Provides methods for odfTable() for objects in the survey
package.
Transformation of ODM files into R format, office format, CDA format and vice versa. CDISC 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
R Client for Ohmage 2 server. Implements basic R
functions to retrieve and process data.
A collection of data sets from several sources that may be
useful for teaching, practice, or other purposes. Functions
have also been included to assist in the retrieval of table
data from websites or in visualizing sample data.
Supplemental functions and data for the OpenIntro guide to the survival package in R.
The package provides functions for the Oja median, Oja
signs and ranks and methods based upon them.
okmesonet retrieves and summarizes Oklahoma (USA) Mesonet
climatological data provided by the Oklahoma Climatological
Survey.  Measurements are recorded every five minutes at
approximately 120 stations throughout Oklahoma and are
available in near real-time.
Calculate the theorical specificity of a system of
multiple primers used for PCR, qPCR primers or degenerated
primer design
Provides tools for more easily organizing and
plotting individual ordinary least square (OLS) growth curve
trajectories.
This package including two useful function, which can be
used for filter the molecular descriptors matrix for QSAR.
This package provides functions to generate a correlation matrix
from a genetic dataset and to use this matrix to predict the phenotype of an
individual by using the phenotypes of the remaining individuals through
kriging. Kriging is a geostatistical method for optimal prediction or best
unbiased linear prediction. It consists of predicting the value of a
variable at an unobserved location as a weighted sum of the variable at
observed locations. Intuitively, it works as a reverse linear regression:
instead of computing correlation (univariate regression coefficients are
simply scaled correlation) between a dependent variable Y and independent
variables X, it uses known correlation between X and Y to predict Y.
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.
Predictions of condition-specific TF interactions using
gene activity data, such as mRNA expression measurements and a
TF-target graph.
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.
This package 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.
A collection of routines to manipulate and visualize quaternions and
octonions.
This package is primarily developed for executing open assessments and modeling in Opasnet environment (http://www.opasnet.org).
The operating principle of this package is maximum openness and modularity. Variables are defined publicly on wiki pages using wiki inputs/tables,
our database and R code. Using any predefined variable is very easy: fetch the variable from our servers (or your own) and evaluate it. Actual
evaluation of variables is done lazily by default: when the evaluation of a variable is explicitly called, all variables it is dependent
on are evaluated recursively. There are also a couple of impact assessment tools such as GIS functions in the package.
To learn more go to \url{http://en.opasnet.org/w/Modelling_in_Opasnet}.
Fit an outer-product emlator to the multivariate
evaluations of a computer model.
Companion package to the book Option Pricing and
Estimation of Financial Models in R, Wiley, Chichester. ISBN:
978-0-470-74584-7
Tools to analyse, interpret and understand air
pollution data. Data are typically hourly time series
and both monitoring data and dispersion model output
can be analysed.  Many functions can also be applied to
other data, including meteorological and traffic data.
This package provides an interface to OpenCL, allowing R
to leverage computing power of GPUs and other HPC accelerator
devices.
The OpenCPU system exposes an HTTP API for embedded scientific
computing with R. This provides reliable and scalable foundations for
integrating R based analysis and visualization modules into pipelines,
web applications or big data infrastructures. The OpenCPU server can
run either as a single-user development server within the interactive R
session, or as a high performance multi-user cloud server that builds
on Linux, Nginx and rApache. The current R package forms the core of
the system. When loaded in R, it automatically initiates the single-user
server and displays the web address in the console. For detailed
information and documentation on the API, visit the OpenCPU website.
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.
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.
Accesses high resolution raster maps. 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.
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.
This package simplifies the creation of .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.
Utilities for working with R's operators
This package contains the current version 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 September 2013 it is fully implemented on the Octopus 900 and partially on the Heidelberg Edge Perimeter. Note to use these two machines you will need either the Rhep or OPIOctopus900 packages which require permission from the respective companies to obtain: see the OPI www site. It also has a cousin: the R package visualFields, which has tools for analysing and manipulating visual field data.
Tools for analysing OmniLog(R) and MicroStation(TM)
phenotype microarray (PM) data as produced by the devices
distributed by BIOLOG Inc. as well as similar kinds of data
such as growth curves. Major facilities are plotting data,
accurately estimating curve parameters, comparing and
discretising data, creating phylogenetic formats and reports
for taxonomic journals, drawing the PM analysis results in
biochemical pathway graphs optionally including genome
annotations, running multiple comparisons of means, easy
interaction with powerful feature-selection approaches,
integrating metadata, using the YAML format for the storage of
data and metadata, batch conversion of large numbers of files,
and database I/O. (The suggested mboostDevel is from R-Forge
and not necessary.)
The package opm provides tools for analysing OmniLog(R)
Phenotype Microarray (PM) data as produced by the device
distributed by BIOLOG Inc. and similar kinds of data such as
growth curves. The package opmdata contains the entire example
data set from Vaas et al. (2012), which has been removed from
opm due to its size, as well as the data sets from Wittmann et
al. (2013) and Montero-Calasanz et al. (2013).
This package translates microarray expression data into metagenes and provides various visualizations and second level analyses.
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
Tools to assist in safely applying user generated objective and
derivative function to optimization programs. These are primarily function
minimization methods with at most bounds and masks on the parameters.
Provides a way 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. Check bounds and masks. Check scaling or optimality conditions.
Perform an axial search to seek lower points on the objective function surface.
Includes forward, central and backward gradient approximation codes.
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).
This package enables users to compute one or more optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.
Provides a set of commands to manage an abstract
optimization method. The goal is to provide a building block
for a large class of specialized optimization methods. This
package manages: the number of variables, the minimum and
maximum bounds, the number of non linear inequality
constraints, the cost function, the logging system, various
termination criteria, etc...
Provides a building block for optimization algorithms
based on a simplex. The optimsimplex package may be used in the
following optimization methods: the simplex method of Spendley
et al., the method of Nelder and Mead, Box's algorithm for
constrained optimization, the multi-dimensional search by
Torczon, etc...
Provides a replacement and extension of the optim()
function to unify and streamline optimization capabilities in R
for smooth, possibly box constrained functions of several or
many parameters. This is the CRAN version of the package.
Optimal two and three stage designs monitoring
time-to-event endpoints at a specified timepoint
This packages 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
Functions for optimal matching, including full matching
A command line parser inspired by Python's 'optparse' library to
be used with Rscript to write "#!" shebang scripts that accept short and
long flag/options.
The package contains a set of algorithms for creating
partitions and coverings of objects largely based on operations
on (dis)similarity relations (or matrices). There are several
iterative re-assignment algorithms optimizing different
goodness-of-clustering criteria.  In addition, there are
covering algorithms 'clique' which derives maximal cliques, and
'maxpact' which creates a covering of maximally compact sets.
Graphical analyses and conversion routines are also included.
The optR function provides an option to solve the linear system
using numeric computation approach. The current packages supports Gauss
Elimination, LU decomposition, Gauss-Sideil, CGM and Cholesky methods to
solve equations having relationships of Ax=b form. The package can be used
to build linear and non-linear models by defining them in linear system.
Finds optimal trees in weighted graphs. In
particular, this package provides solving tools for minimum cost spanning
tree problems, minimum cost arborescence problems, shortest path tree
problems and minimum cut tree problem.
Easy-to-use functions to explore Oracle databases and import data
into R. User interface for the ROracle package.
Implements orbit counting using a fast combinatorial approach
Computes various confidence intervals for the odds ratio of two independent binomial proportions.
Functions to perform subspace clustering and
classification.
This package 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.
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.
This package implements 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). 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.
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.
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.
The package implements 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
Organic/biological mass spectrometry data analysis.
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.
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
Converting the Odds Ratio to the Relative Risk in Cohort Studies with Partial Data Information
Represents the basis functions for Bsplines in a simple
matrix formulation that facilitates, taking integrals,
derivatives, and orthogonalizing the basis functions.
A collection of functions to construct sets of orthogonal
polynomials and their recurrence relations. Additional
functions are provided to calculate the derivative, integral,
value and roots of lists of polynomial objects.
The osDesign serves for planning an observational study. Currently, functionality is focused on the two-phase and case-control designs. Functions in this packages provides Monte Carlo based evaluation of operating characteristics such as powers for estimators of the components of a logistic regression model.
This package provides infrastructure to access
OpenStreetMap data from different sources, to work with the data
in common R manner, and to convert data into available
infrastructure provided by existing R packages (e.g., into sp and
igraph objects).
Fit and compare Ornstein-Uhlenbeck models for evolution along a phylogenetic tree.
Bayesian reconstruction of disease outbreaks using epidemiological and genetic information.
basic tools for the analysis of disease outbreaks.
This package provides three algorithms to detect outlying observations for censored survival data.
Detecting outlying observations for replicated high-throughput high-dimensional data based on a MA plot with quantile regression approach.
A collection of some tests commonly used for identifying
outliers.
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 plotting Australia's coastline and state
boundaries.
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.
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.
This package comes to illustrate the book "Petit Manuel de
Programmation Orientee Objet sous R"
packdep elucidates the dependencies
between user-contributed R packages and
identifies key packages according to social
network analysis metrics.
Various useful functions for statisticians: describe data, plot Kaplan-Meier curves with numbers of subjects at risk, compare data sets, display spaghetti-plot, build multi-contingency tables...
Packrat is a tool for managing the R packages your project depends
on in an isolated, portable, and reproducible way.
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).
The package can identify the dysregulated KEGG pathways
based on global influence from the internal effect of pathways
and crosstalk between pathways. (1) The PAGI package can
prioritize the pathways associated with two biological states
by statistical significance or FDR. (2) The PAGI package can
evaluated the global influence factor (GIF) score in the global
gene-gene network constructed based on the relationships of
genes extracted from each pathway in KEGG database and the
overlapped genes between pathways.
The two Bayesian hierarchical pathway analysis methods Sparse normal/Adaptive lasso and Normal/Bayes factors proposed by Evangelou et al (2013) are implemented. Further, the well-known Fisher's method is implemented
The package contains two functions: paired.Loc and
paired.Scale. A parametric and nonparametric confidence
interval can be computed for the ratio of locations
(paired.Loc) and the ratio of scales (paired.Scale). The
samples must be paired and expected values must be positive.
This package provides many datasets and a set of graphics
(based on ggplot2), statistics, effect sizes and hypothesis
tests for analysing paired data with S4 class.
A tool to compare two heatmaps and discover patterns
within and across groups. In the context of biology, group can
be defined based on gene ontology.
Eulerian tours and Hamiltonian decompositions of complete graphs are used to ameliorate order effects in statistical graphics.
The package pairwise offers functions for 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. The package provides 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.
paleobioDB version 0.2 has 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/>.
The paleofire package provides 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
Analyzes, time-scales and simulates phylogenies of extinct/fossil
lineages, along with calculation of diversity curves. Also fits likelihood
models to estimate sampling rates from stratigraphic ranges.
This package 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.
R package to compute Incoming Solar Radiation (insolation) for paleoclimate studies. Features three solutions: BER78 (Berger, 1978), BER90 (Berger and Loutre, 1991) and LA04 (Laskar et al. 2004). Computes daily-mean, season-averaged and annual means for all latitudes.
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.
Some functions for sample classification in microarrays
Multiple imputation for multivariate panel or clustered data
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 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.
Linear models for panel data: the fixed effect model and the
random effect model
A toolbox for writing Sweave or other LaTeX-based reports
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.
Map over lists in parallel. 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.
A small package containing useful functions for parameter
descriptions and operations in black-box optimization, tuning
and machine learning.
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.
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 distribution and fit it to real data. Graphical and statistical tools to validate the fits are included.
Parametric Frailty Models, fitted by maximum marginal likelihood method.
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.
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.
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.
partDSA is 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.
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.
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
To implement two methods, Kappa and PASS, for selecting
tuning parameters in regularized procedures such as LASSO,
SCAD, adaptive LASSO, aiming for variable selection in
regularized regression
Regulation, decomposition and analysis of space-time series. The pastecs library is a PNEC-Art4 and IFREMER (Benoit Beliaeff <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 (http://www.obs-vlfr.fr/~enseigne/anado/passtec/passtec.htm) functionalities to R.
A pre-processor for mrBayes that assimilates sequences, taxonomic
information and tree constraints as per xxx.  The main functions of
interest for most users will be pastis_simple, pastis_main and conch. The
main analysis is conducted with pastis_simple or pastis_main followed by a
manual execution of mrBayes (>3.2). The placement of taxa not contained in
the tree constraint can be investigated using conch.
Data and functions for the book PROBABILITY and STATISTICS
WITH R.
Functions to patch specials in .dvi files;
works with "concordance=TRUE" in Sweave 2.5.0+
Functions to generate scatterplots with images patches
instead of usual glyphs, with associated utilities.
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.
This library provides tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes.  Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made.  The code permits the analysis of single maps or pairs of maps.
Implementation of a Bayesian active learning strategy to carry out sequential experimental design in the context of biochemical network kinetic parameter estimation. This package gathers functions and pre-computed data sets to reproduce results presented in Pauwels E. et. al published in BMC Systems Biology, 2014. Scripts are given to compute all results from scratch or to draw pictures based on pre-computed data sets.
A cohesive framework for parsing, analyzing and organizing color
from spectral data
Process, format and store accelerometer data
Implementation of the Parallel Adaptive Wang-Landau
algorithm. Also implemented for comparison: parallel adaptive
Metropolis-Hastings,SMC sampler.
A lightweight package that adds
progress bar to vectorized R functions
('*apply'). The implementation can easily be added
to functions, where showing the progress is
useful for the user (e.g. bootstrap).
This package provides data analysis via the pbat program,
and an alternative internal implementation of the power
calculations via simulation only.  For analysis, this package
provides a frontend to the PBAT software, automatically reading
in the output from the pbat program and displaying the
corresponding figure when appropriate (i.e. PBAT-logrank). It
includes support for multiple processes and clusters. For
analysis, users must download PBAT (developed by Christoph
Lange) and accept it's license, available on the PBAT webpage.
Both the data analysis and power calculations have command line
and graphical interfaces using tcltk.
This package provides tools for modeling copulas with the PBC model, a class of multivariate copulas based on products of bivariate copulas (G. Mazo G, S. Girard and F. Forbes, 2013). The likelihood is computed thanks to a message-passing algorithm on graphs (J. C. Huang and N. Jojic, 2010).
Maximum likelihood analysis of the protracted speciation model
pbdBASE gives higher packages, like pbdDMAT, access to
low-level scalable Fortran routines. The package consists of a
set of ScaLAPACK wrappers, as well as a host of new C and
Fortran code.
pbdDEMO is a set of demos of pbdR packages, with a useful,
unifying vignette.
pbdDMAT contains high level S3 and S4 methods for creating,
modifying, and performing computations with dense, distributed
matrices. This includes a new class, 'ddmatrix', for storing all
of the distributed data details.  Computation is handled mostly
by routines from the pbdBASE package.
pbdMPI provides an efficient interface to MPI by utilizing S4
classes and methods with a focus on Single Program/Multiple Data (SPMD)
parallel programming style, which is intended for batch parallel
execution.
This package adds collective parallel read and write capability
to the R package ncdf4 version 1.8. Typical use is as a
parallel NetCDF4 file reader in SPMD style programming. Each R
process reads and writes its own data in a synchronized
collective mode, resulting in faster parallel performance.
Performance improvement is conditional on a parallel file system.
pbdSLAP utilizes scalable linear algebra packages mainly
including BLACS, PBLAS, and ScaLAPACK in double precision via
pbdMPI based on ScaLAPACK version 2.0.2.
A set of datasets prepared as a support for my classes or
the book ,,Modele liniowe i mieszane w R, wraz z przykladami w
analizie danych''
Provides a vectorized R function for calculating
probabilities from a standard bivariate normal CDF.
Test in linear mixed effects models.

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

The package implements a parametric bootstrap test

The package implements a Kenward-Roger modification of F-tests
Following the method of Bailey et al., computes for a collection
of candidate models the probability of backtest overfitting, the
performance degradation and probability of loss, and the stochastic
dominance.
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.  PBSmapping also includes 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 PBSmapping-UG.pdf, which should be consulted to use all functions in
the package effectively.
PBS Modelling 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 it depends heavily on the R interface
to the Tcl/Tk package, a user does not need to know Tcl/Tk. The
package contains examples that illustrate models built with other R
packages, including PBSmapping, deSolve, PBSddesolve, and BRugs.
It also serves as a convenient prototype for building new R packages,
along with instructions and batch files to facilitate that process.
The R directory '.../library/PBSmodelling/doc' offers a complete user
guide PBSmodelling-UG.pdf. To use this package effectively,
please consult the guide.
This package provides a function simplifying presentation
of PCA models in a 3D interactive representation using rgl
This package contains 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 2012).
This package contains several 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 observational and interventional data without hidden
variables). For causal inference the IDA algorithm and the generalized
backdoor criterion is implemented.
Principal Component Analysis, orthogonal rotation and multiple factor
analysis for a mixture of quantitative and qualitative variables.
A set of functions to perform parallel analysis for
principal components analysis intended mainly for large data
sets. It performs a parallel analysis of continuous, ordered
(including dichotomous/binary as a special case) or mixed type
of data associated with a principal components analysis.
Polychoric correlations among ordered variables, Pearson
correlations among continuous variables and polyserial
correlation between mixed type variables (one ordered and one
continuous) are used. Whenever the use of polyserial or
polychoric correlations yields a non positive definite
correlation matrix, the resulting matrix is transformed into
the nearest positive definite matrix.
Semiparametric regression analysis of panel count data under the non-homogeneous Poisson
process model with and without Gamma frailty using monotone splines.
The package solves linear system of equations Ax=b by using Preconditioned Conjugate Gradient Algorithm where A is real symmetric positive definite matrix. A suitable preconditioner matrix may be provided by user. This can also be used to minimize quadratic function (x'Ax)/2-bx for unknown x.
Contains logic for computing the statistical association of variable groups, i.e., gene sets, with respect to the principal components of genomic data.
This package implements a work-alike to R's POSIXct class
which implements 360- and 365-day calendars in addition to the
gregorian calendar.
pcIRT estimates the multidimensional polytomous Rasch model (Rasch, 1961) and the Continous Rating Scale model (Mueller, 1987)
This package provides the necessary functions for
performing the Partial Correlation coefficient with Information
Theory (PCIT) algorithm developed by Reverter and Chan (2008).
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.
Provides functions to conduct network meta-analysis using arm-based method, which was proposed by Zhang et al (2014); contains functions to generate summary table for effect sizes (e.g., odds ratio, population-averaged event rate), plot network comparisons, and plot credible interval for population-averaged event rate.
Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components. Several rotation options are provided in this package, as well as model selection options.
Set of functions for analysis of Principal Coordinates of Phylogenetic Structure (PCPS).
Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations?  This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.
This package contains a function to estimate
panel-corrected standard errors. Data may contain balanced or
unbalanced panels.
Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.
The package performs cluster analysis via nonparametric density
estimation. Operationally, the kernel method is used throughout to estimate
the density. Diagnostics methods for evaluating the quality of the clustering
are available. The package includes also a routine to estimate the
probability density function obtained by the kernel method, given a set of
data with arbitrary dimensions.
A package for downloading economic and financial time series from
public sources.
Computes the euclidean distance between rows of a matrix X
and rows of another matrix Y.  Previously, this could be done
by binding the two matrices together and calling 'dist', but
this creates unnecessary computation by computing the distances
between a row of X and another row of X, and likewise for Y.
pdist strictly computes distances across the two matrices, not
within the same matrix, making computations significantly
faster for certain use cases.
This package provides a model of Pavlovian conditioning phenomena, such as response extinction and spontaneous recovery, and partial reinforcement extinction effects. Competing proximal and distal reward predictions, computed using fast and slow learning rates, combine according to their uncertainties and the recency of information. The resulting mean prediction drives the response rate.
A 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.
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 predicted survival probabilities using inverse
weighting and resampling.
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.
This package contains functions to employ many tools and techniques used in the field of pedometrics (see http://en.wikipedia.org/wiki/Pedometrics for definition of pedometrics). These functions were developed to fulfill the demands created by the PhD research project (2012-2016) entitled Contribution to the Construction of Models for Predicting Soil Properties, developed by Alessandro Samuel-Rosa under the supervision of Dr Lucia HC Anjos (Federal Rural University of Rio de Janeiro, Brazil), Dr Gustavo M Vasques (Embrapa Soils, Brazil), and Dr Gerard BM Heuvelink (ISRIC - World Soil Information, the Netherlands). The project is supported by the CAPES Foundation, Ministry of Education of Brazil, Brasilia - DF 70040-020, Brazil, process ID BEX 11677/13-9. Several functions are simple wrapper functions that extend the functionalities of other functions commonly used for the analysis of pedometric data. It should be noted that changes are likely to occur quite often and using this package as a dependency for other packages is strongly discouraged.
pegas provides 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 for Aster Book on Inverse Theory. These functions are translations of MATLAB code developed by the authors to illustrate the concepts of Inverse theory as applied to geophysics. There is one function, rlsqr, written by Keehoon Kim, that is a wrapper for the FORTRAN based Paige and Saunders LSQR routine.
This package provides functions to perform multivariate Gaussian parameter estimation based on data with abundance-dependent missingness. It implements a penalized Expectation-Maximization (EM) algorithm. The package is tailored for but not limited to proteomics data applications, in which a large proportion of the data are often missing-not-at-random with lower values (or absolute values) more likely to be missing.
A package for fitting possibly high dimensional penalized
regression models. The penalty structure can be any combination
of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge)
and a positivity constraint on the regression coefficients. The
supported regression models are linear, logistic and poisson
regression and the Cox Proportional Hazards model.
Cross-validation routines allow optimization of the tuning
parameters.
Implements the penalized LDA proposal of "Witten and
Tibshirani (2011), Penalized classification using Fisher's
linear discriminant, to appear in Journal of the Royal
Statistical Society, Series B".
This package provides feature selection SVM using penalty
functions. The smoothly clipped absolute deviation (SCAD),
'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
SCAD' (SCAD and 'L2-norm') penalties are availible. The tuning
parameters can be founf using either a fixed grid or a interval
search.
Flexible copula density estimation with penalized hierarchical B-Splines.
Flexible Pair-Copula Estimation in D-vines using Bivariate
Penalized Splines.
The package is developed for the cross-sectional study of
diet and stool microbiome data. The model facility variable
selection. The group l1 penalized logistic normal (LN) approach
is also implemented in the package.
This package provides features for L1 penalized estimation of multistate 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.
Functions for evaluating diversity in peptide libraries, including
NNN, NNB, NNK/S, and trimer schemes.  This includes expected coverage,
relative efficiency, and functional diversity of the library.
Calculate physicochemical properties and indices from aminoacid sequences of peptides and proteins. Include also utilities for read and plot GROMACS output files .XVG.
moderated regression with mean and residual centering and
simple slopes analysis
The package includes procedures for identification, model
fitting and estimation for time series with periodic structure.
Additionally procedures for simulation of periodic processes
and real data sets are included.
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.
This package provides an  infra-structure for performance estimation and experimental comparison and selection of predictive models
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.
This package includes functions to work with the Mallows, Generalized Mallows and Weighted 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 in those related with the Kendall's-tau, Cayley, Ulam and Hamming distances and the random generation of permutations.
This R extension package can be used to carry out
permutation resampling inference in the context of RNA
microarray studies
The permtest uses permutations in order to compare the
variability within and distance between two groups within a set
of microarray data.
The 'permute' package implements a set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples). '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.
Functions and Datasets designed to support teaching of regression with applications to Marketing and Finance
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)
"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.
Functions related to PF (prevented fraction). Calculate incidence density ratio, confidence interval, and Rao-Scott weights of PF by multiple methods. See http://goo.gl/eJ6Rxi for definition of PF.
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.
Description: Miscellaneous functions for analysis and display of ecological and 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.
Computation of mean squared errors of stereological predictors.
The phalen package contains (1) clustering and
partitioning algorithms; (2) penalty functions for numeric
vectors; (3) a ranking function; and (4) color palettes and
functions.
Phylogenetic analysis in R (Estimation of phylogenetic
trees and networks using Maximum Likelihood, Maximum Parsimony,
Distance methods & Hadamard conjugation)
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.
A package for drawing pretty heatmaps in R. The ordinary heatmap
function in R has several drawbacks when it comes to producing publication
quality heatmaps. It is hard to produce pictures with consistent text, cell
and overall sizes and shapes. The function pheatmap tries to alleviate the
problems by offering more fine grained control over heatmap dimensions and
appearance.
This package is an alternative to carrying out phenotypic adaptability and stability analyses, taking into account nonparametric statistics, and can be used as a robust approach, and 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 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.
Pheno2Geno is an R package for the computational generation genetic markers and maps from molecular phenotypes
Functions to fit and test the phenology of species based on counts. The package includes also several useful more general functions.
This package provides tools for the evaluation of the goodness of fit and the predictive capacity of the proportional hazards model.
Analysis of terms in linear, generalized and mixed linear models,
on the basis of multiple comparisons of factor contrasts.  Specially suited
for the analysis of interaction terms.
Fits proportional hazards model incorporating random effects using
an EM algorithm using Markov Chain Monte Carlo at E-step.
This package computes persistent homology of filtered simplicial complexes, and provides facilities for constructing complexes from geometric data.
phonR is an R package for phoneticians and phonologists,
including functions for normalization and plotting of vowels.
The functions contained in this package are intended to facilitate 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. Some functions also allow for the creation and manipulation of sounds saved as WAV files.
R interface to PHREEQC (Version 3). PHREEQC is 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.
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.
This package enhancements to phylobase classes.  It
provides classes and methods which help uses manipulate
branch-annotated trees and provides supports for a few other
extra features.
This package provides functions for the spatial interpolation
of genetic distances between samples. The interpolation 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.
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.)
the phylolm package provides functions for fitting phylogenetic linear regression models and phylogenetic logistic regression. 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.
PhyloSim is an extensible object-oriented framework for the Monte Carlo simulation of sequence evolution written in 100 percent R. It is built on the top of the R.oo and ape packages and uses Gillespie's direct method to simulate substitutions, insertions and deletions.
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.
Calculates some topological properties of phylogenetic trees
Provides general linear model facilities (single y-variable, multiple x-variables with arbitrary mixture of continuous and categorical and arbitrary interactions) for cross-species data. The theory is in A. Grafen (1989, Proc. R. Soc. B 326, 119-157) and aims to cope with both recognised phylogeny (closely related species tend to be similar) and unrecognised phylogeny (a polytomy usually indicates ignorance about the true  sequence of binary splits).
This package contains functions to classify monitor wear and nonwear time intervals in accelerometer data collected to assess physical activity in free-living condition. The package also contains functions to make plot for accelerometer data, and to obtain the summary of daily monitor wear time and the mean of monitor wear time during valid days. A monitored day is considered valid if the total minutes of classified monitor wear time per day is greater than a user defined cutoff.
phytools provides various functions for phylogenetic analysis,
mostly relevant to comparative biology.
This package implements method(s) to (approximately
unbiasedly) estimate the proportion of true null hypotheses,
i.e., the pi0, when a very large number of hypotheses are
simultaneously tested, especially for the purpose of (local)
false discovery rate control for microarray data. It also
contains functions to estimate the distribution of
noncentrality parameters from a large number of parametric
tests.
Phylocom integration, community analyses, null-models, traits and evolution in R
Implement a new family of efficient algorithms, called PathwIse CalibrAted Sparse Shooting AlgOrithm, for a variety of sparse learning problems, including Sparse Linear Regression, Sparse Logistic Regression, Sparse Column Inverse Operator and Sparse Multivariate Regression. Different types of active set identification schemes are implemented, such as cyclic search, greedy search, stochastic search and proximal gradient search. Besides, the package provides the choices between convex (L1 norm) and non-convex (MCP and SCAD) regularizations. Moreover, group regularization for Sparse Linear Regression and Sparse Logistic Regression are also implemented.
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
Using information for trade initiation, the package
computes the probability of informed trading of Easley and
O'Hara (1987).
Check if a remote computer is up. It can either
just call the system ping command, or check a specified
TCP port.
Provides Pipe operator and function based on syntax which support
to pipe value to first-argument of a function, to dot in expression,
by formula as lambda expression, for side-effect, and with assignment.
The set of syntax is designed to make the pipeline highly readable.
Generate Predicted Interval Plots.  Simulate and plot
confidence intervals of an effect estimate given observed data
and a hypothesis about the distribution of future data.
With pitchRx, one can easily obtain Major League Baseball Advanced
Media's Gameday data (as well as store it in a remote database). The
Gameday website hosts a wealth of data in XML format, but perhaps most
interesting is PITCHf/x. Among other things, PITCHf/x data can be used to
recreate a baseball's flight path from a pitcher's hand to home plate. With
pitchRx, one can easily create animations and interactive 3D scatterplots
of the baseball's flight path. PITCHf/x data is also commonly used to
generate a static plot of baseball locations at the moment they cross home
plate. These plots, sometimes called strike-zone plots, can also refer to a
plot of event probabilities over the same region. pitchRx provides an easy
and robust way to generate strike-zone plots using the ggplot2 package.
R interface of Pivotal Data Fabrics running on PostgreSQL
or Pivotal (Greenplum) 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.
Functions for import, export, plotting and other
manipulations of bitmapped images.
Estimation of pharmacokinetic parameters using
non-compartmental theory
PKfit is a nonlinear regression program
which is designed to perform model/curve fitting
and model simulations for pharmacokinetics.
Provides a function kitten() which creates cute little
packages which pass R CMD check. 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.
Tools for creating R packages, particularly for checking
the documentation produced by roxygen2 and for adding support
for the documentation of S4 methods. Also included are some
functions of use when coding a package and some of use in
interactive R sessions. Finally, R syntax-highlighting patterns
for some text editors are contained.
This package provides PKI functions such as verifyig
certificates, RSA encription and signing which can be used to
build PKI infrastructure and perform cryptographic tasks.
PKmodelFinder is the software for the exploratory analysis
for pharmacometrics.
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.
computations for WinBUGS, NONMEM V, NLME
Supports the creation of burndown charts and gantt diagrams.
Solves the electromagnetic problem of reflection and transmission
of a plane wave or a gaussian beam, at a planar multilayer interface. Also
computed are the decay rates and emission profile for a dipolar emitter
near a multilayer structure.
planor is dedicated to the automatic generation of regular factorial designs, including fractional designs, orthogonal block designs, row-column designs and split-plots.
This package implements schemes for estimating player or
team skill based on dynamic updating. Implemented methods
include Elo, Glicko and Stephenson. Contains pdf documentation
of a reproducible analysis using approximately one million
chess matches.
A GTK+ graphical user interface for editing and
interacting with R plots.
The package can be used to estimate probabilistic latent feature models
with a disjunctive or a conjunctive mapping rule for two-way two-mode frequency data
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.
A function to plot scatter points for two
groups of values.  It simultaneously calculates a P
value of t-test and incorporate the P value into the
plot.  Its advantage is jittering the adjacent
points side by side to avoid or reduce overlapping
in the plot.
Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, ... Includes data sets from oceanography
This package provides a interactive plot device for handling the geographic data for web browsers. It is 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.
Markov chain Monte Carlo diagnostic plots. The purpose of the
package is to combine existing tools from the 'coda' and 'lattice' packages,
and make it easy to adjust graphical details.
Plot a model's response when varying one or two predictors
while holding the other predictors constant.  A poor man's
partial dependence plot.
Plot principal component histograms around a bivariate
scatter plot.
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.
Contains functions plotSEMM_setup, plotSEMM_contour,
and plotSEMM_probability, which generate the
diagnostic plots proposed by Bauer (2005) to investigate
nonlinear latent variable interactions in SEMM using LISREL
output.
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)
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.
This package provides routines for PLS-based genomic
analyses. It implements 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.
plspm contains a set of functions for performing Partial Least
Squares Path Modeling (PLS-PM) analysis for both metric and non-metric
data, as well as REBUS analysis.
This package 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.
This packages provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings.
This package provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Kernel density estimation with global bandwidth selection
via "plug-in".
The base R data.frame, like any vector, is
copied upon modification. This behavior is at odds with
that of GUIs and interactive graphics. To rectify this,
plumbr provides a mutable, dynamic tabular data model.
Models may be chained together to form the complex
plumbing necessary for sophisticated graphical
interfaces. Also included is a
general framework for linking datasets; an typical
use case would be a linked brush.
plusser provides an API interface to Google+ so that posts,
profiles and pages can be automatically retrieved.
plyr is 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 pieces 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 BD (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.
Parsimonious Mixtures of Contaminated Gaussian Distributions
The pmclust aims to utilize model-based clustering (unsupervised)
for high dimensional and ultra large data, especially in a distributed
manner. The package employs Rmpi to perform a
expectation-gathering-maximization (EGM) algorithm
for finite mixture Gaussian
models. The unstructured dispersion matrices are assumed in the Gaussian
models. The implementation is default in the single program multiple
data (SPMD) programming model. The code can be executed through Rmpi and
independent to most MPI applications. See the High Performance
Statistical Computing (HPSC) website for more information, documents
and examples.
The Kruskal and Wallis one-way analysis of variance by ranks
can be employed, if the data do not meet the assumptions
for one-way ANOVA. Provided that significant differences
were detected by the Kruskal-Wallis-Test, one may be interested
in applying post-hoc tests for pairwise multiple comparisons
of the ranked data. Similarly, one-way ANOVA with repeated
measures that is also referred to as ANOVA with unreplicated
block design can also be conducted via the Friedman-Test.
The consequent post-hoc pairwise multiple comparison test according
to Nemenyi is also provided in this package.
Extends the approach proposed by Firth (1993) for bias
reduction of MLEs in exponential family models to the
multinomial logistic regression model with general covariate
types.  Modification of the logistic regression score function
to remove first-order bias is equivalent to penalizing the
likelihood by the Jeffreys prior, and yields penalized maximum
likelihood estimates (PLEs) that always exist.  Hypothesis
testing is conducted via likelihood ratio statistics.  Profile
confidence intervals (CI) are constructed for the PLEs.
The Predictive Model Markup Language (PMML) is an XML-based language
which provides a way for applications to define statistical and data
mining models and to share models between PMML compliant applications.
More information about PMML and the Data Mining Group can be found at
http://www.dmg.org.
The generated PMML can be imported into any PMML consuming application,
such as the Zementis ADAPA and UPPI scoring engines which allow for
predictive models built in R to be deployed and executed on site, in
the cloud (Amazon, IBM, and FICO), in-database (IBM Netezza, Pivotal,
Sybase IQ, Teradata and Teradata Aster) or Hadoop (Datameer and Hive).
Allows for data to be transformed before using
it to construct models. Builds structures to allow functions in
the PMML package to output transformation details in
addition to the model in the resulting PMML file.
Descriptive statistics (mean rank, pairwise frequencies, and marginal matrix), Analytic Hierarchy Process models (with Saaty's and Koczkodaj's inconsistencies), probability models (Luce models, distance-based models, and rank-ordered logit models) and visualization with multidimensional preference analysis for ranking data are provided. Current, only complete rankings are supported by this package.
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.
This package provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) for use in Phase I trials of combinations of agents. It also 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.
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.
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.
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.
An R interface to the Huffington Post Pollster API, which provides
programmatic interface to political polls.
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.
Generate dependent samples from a non-full dimensional
polytope via a Markov Chain sampler
R port of the Clipper library. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Also computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines.
Computes polychoric and polyserial correlations by quick
"two-step" methods or ML, optionally with standard errors;
tetrachoric and biserial correlations are special cases.
A package providing methods for cubature (numerical integration)
over polygonal domains.
Currently, four cubature methods are 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.
A collection of functions to implement a class for univariate
polynomial manipulations.
Implements univariate polynomial operations in R
Functions to perform paternity exclusion via allele
matching, in autopolyploid species having ploidy 4, 6, or 8. The
marker data data used can be genotypic (copy numbers known) or
'phenotypic' (copy numbers not known).
polysat is 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 lastly it can estimate allele frequencies in
populations, calculate pairwise Fst values based on those frequencies, and
export allele frequencies to SPAGeDi and adegenet.
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.
Logistic regression modeling for polytomous settings (more than two categorical outcomes) with both fixed and mixed effect predictors, and univariate and bivariate analysis of categorical, unordered data.
Routines for flexible functional form estimation via basis
regression, with model selection via the adaptive LASSO or SCAD to prevent
overfitting.
This package fits a patch occupancy model
This package propose calculations of an information criterion to check the quality of simulations results of ABM/IBM or other non-linear rule-based models. The POMDEV measure is based on the KL divergence and likelihood theory. It basically indicates the deviance of simulation results from field observations. Once POMDEV scores and metropolis-hasting sampling on different model versions are effectuated, POMIC scores can be calculated. This method is still under development and further work are needed for the incorporation of multiple patterns assessment.
PoMoS-package aims to determine from a set of N time
series the optimal polynomial structure of a model built on
first-order ordinary differential equations. The core of the
package is based on the poMoS function: an evolutionary
algorithm combined with a least square fitting. Optimality is
estimated with AIC (Akaike, 1974) or AIC-like criterions.
Although efficient in its selection, the identification of the
optimal structure cannot be guaranteed. Therefore, both
selected and rejected models are reconsidered after optimal
solutions are obtained from the evolutionary algorithm for
another analysis. This analysis is based on a statistical
evaluation of the regressors' quality.
Inference methods for partially-observed Markov processes
functions for computing closures of relations
Construct and analyze projection matrix models from a
demography study of marked individuals classified by age or
stage. The package covers methods described in Matrix
Population Models by Caswell (2001) and Quantitative
Conservation Biology by Morris and Doak (2002).
popdemo provides a set of tools designed for modelling population demography
using population projection matrix (PPM) models.  The focus is mainly on indices
of transient dynamics, and the use of control theory (specifically transfer
functions).  However, popdemo includes a number of tools focussing on other
areas of demographic modelling.  Some tools may prove useful to anyone working
with projection matrix models of any type, or indeed matrices in general.
The package is designed to build on those tools already available in popbio.
PopED computes optimal experimental designs for both
population studies and individual studies based on nonlinear mixed-effect models.
Often this is based on a computation of the Fisher Information Matrix (FIM).
A package that implements a variety of statistical and
population genetic methodology.
There are two main purposes to this package. The first is
to allow batch conversion of Genepop (Rousset 2008) input files
for use with Arlequin (Excoffier and Lischer 2010), which has a
simple GUI to analyze batch files. Two commonly used simulation
software, BottleSim (Kuo & Janzen 2003) and Easypop (Balloux
2001) produce Genepop output files that can be analyzed this
way. There are also functions to convert to and from BottleSim
format, to quickly produce allele frequency tables or to
convert a file directly for use in ordination analyses (e.g.
principal component analysis). This package also includes
functions to calculate allele rarefaction curves, confidence
intervals on heterozygosity and allelic richness with
resampling strategies (bootstrap and jackknife).
PopGenome is an R-package for Population Genetic & Genomic Analysis
This package provides an easy way to analyse population genetic data
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.
Population genetic analyses for hierarchical analysis of partially
clonal populations built upon the architecture of the adegenet package.
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.
This package contains a set of routines which are useful in constructing
and evaluating self-organizing maps (SOMs).
This package contains a set of portmanteau diagnostic
checks for univariate and multivariate time series.
Classes for analysing and implementing equity portfolios.
Classes that serve as a framework for designing equity
portfolio simulations.
Risk Attribution of a portfolio with Volatility Risk Analysis.
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.
Contains functions to calculate power and sample size distribution
of 2-stage 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 as well as the power calculation steps according to
Karalis & Macheras.
The third function calculates power and sample size distribution
of 2-stage BE studies with a futility criterion for the point
estimator of T/R from stage 1.
The fourth function calculates power of non-adaptive group
sequential (2-stage) BE studies.
The fifth function calculates power and sample size distribution
of 2-stage BE studies in 2 parallel groups.
Basic functions for power analysis and effect size calculation.
Analytical power calculations for GxE and GxG interactions for case-control studies of candidate genes and genome-wide association studies (GWAS). This includes power calculation for four two-step screening and testing procedures.  It can also calculate power for GxE and GxG without any screening.
This package implements both the discrete and continuous maximum
likelihood estimators for fitting the power-law distribution to data.
Additionally, a goodness-of-fit based approach is used to estimate the
lower cut-off for the scaling region.
The package contains functions (1) for calculating power,
sample size, and minimal detectable mediation effect for
testing mediation effect in linear, logistic, Poisson, or cox
regression; (2) for calculating power, sample size, and minimal detectable slope
for testing the slope in a simple linear regression (only one
predictor); (3) for calculating power, and sample size for testing odds ratio
in a simple logistic regression with continuous predictor or binary predictor;
(4) for calculating power, and sample size for testing mean change for longitudinal study with 2 time points.
(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.
'powerr' is used for electric power system analysis and control,
including basic power flow analysis and CASCADE analysis.
Other features will be updated soon.
This package includes a set of 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.
Contains functions to calculate power and sample size for
various study designs used for bioequivalence studies.
See function known.designs() for study designs covered.
Moreover the package contains functions for power and sample size
based on 'expected' power in case of uncertain (estimated) variability.
-----
Added are functions for the power and sample size for the ratio of
two means with normally distributed data on the original scale
(based on Fieller's confidence ('fiducial') interval).
-----
Contains further functions for power and sample size calculations based on
non-inferiority t-test. This is not a TOST procedure but eventually useful
if the question of 'non-superiority' must be evaluated.
The power and sample size calculations based on non-inferiority test may
also performed via 'expected' power in case of uncertain (estimated)
variability.
-----
Contains functions power.scABEL() and sampleN.scABEL() to calculate power
and sample size for the BE decision via scaled (widened) BE acceptance
limits based on simulations.
Contains further functions power.RSABE() and sampleN.RSABE() to calculate
power and sample size for the BE decision via reference scaled ABE criterion
according to the FDA procedure based on simulations.
Contains further functions power.NTIDFDA() and sampleN.NTIDFDA() to calculate
power and sample size for the BE decision via the FDA procedure for NTID's
based on simulations.
-----
Contains functions for power analysis of a sample size plan for ABE
(pa.ABE()), scaled ABE (pa.scABE()) and scaled ABE for NTID's (pa.NTIDFDA())
analysing power if deviating from assumptions of the plan.
-----
Contains further functions for power calculations / samplesize estimation
for dose proportionality studies using the Power model.
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.
The R package ppcor can calculate parital and semi-partial
(part) correlations along with p-value.
This package implements several functions useful for computing similarities between proteins based on their GO annotation, KEGG annotation and PPI network topology. It integrates these similarities to predict PPIs using an SVM classifier. Twenty species are supported, including Anopheles, Arabidopsis, Bovine, Canine, Chicken, Chimp, E coli strain K12 and strain Sakai, Fly, Human, Malaria, Mouse, Pig, Rhesus, Rat, Worm, Xenopus, Yeast, Streptomyces coelicolor and Zebrafish.
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.
This package contains tools to fit 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
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.
PracTools 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.
Kasulikud funktsioonid kvantitatiivsete mudelite kursuse
(SHPH.00.004) jaoks
This package computes Bayesian predictive inference on totals and averages of finite populations segmented in planned and unplanned domains. Inference is based on Bayesian nonparametric methods using species-sampling models (specifically the Dirichlet process).
PredictABEL includes functions to assess the performance
of risk models. The package contains functions for the various
measures that are used in empirical studies, including
univariate and multivariate odds ratios (OR) of the predictors,
the c-statistic (or area under the receiver operating
characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of
fit test, reclassification table, net reclassification
improvement (NRI) and integrated discrimination improvement
(IDI). Also included are functions to create plots, such as
risk distributions, ROC curves, calibration plot,
discrimination box plot and predictiveness curves. In addition
to functions to assess the performance of risk models, the
package includes functions to obtain weighted and unweighted
risk scores as well as predicted risks using logistic
regression analysis. These logistic regression functions are
specifically written for models that include genetic variables,
but they can also be applied to models that are based on
non-genetic risk factors only.  Finally, the package includes
function to construct a simulated dataset with genotypes,
genetic risks, and disease status for a hypothetical
population, which is used for the evaluation of genetic risk
models.
Three prediction algorithms described in the paper
"On-line predictive linear regression" Annals of Statistics 37,
1566 - 1590 (2009)
This package provides functions to diagnose
and make inferences from various linear models, such as those obtained from 'aov',
'lm', 'glm', 'gls', 'lme', and 'lmer'. Inferences include predicted
means and standard errors, contrasts, multiple comparisons, permutation tests and graphs.
"train_predict_mix" predicts the binary response with
binary features
Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach.  Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature.  Computes pattern models for paired comparisons, rankings, and ratings.  Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.
This is a package for 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.
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).
Estimating species accumulation curves by rational function approximations to the non-parametric empirical Bayes estimator and by the zero-truncated Negative Binomial distribution.
Computes probability scale residuals and residual correlations
for continuous, ordinal, binary, count, and time-to-event data (currently
only implements ordinal data).
prettyGraphs contains simple, crisp graphics. Graphics produced by prettyGraphs are publication-quality.
Functions for conventionally formatting descriptive stats,
reshaping data frames and formatting R output as HTML.
Tools 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/
This package performs spatial estimation of a prevalence surface
or a relative risks surface, using data from a Demographic and Health
Survey (DHS) or an analog survey.
PRIM for bump hunting in high-dimensional data
Functions are primarily functions for systems of ordinary
differential equations, difference equations, and eigenanalysis
and projection of demographic matrices; data are for examples.
Visually Assessing the Specificity and Informativeness of Primer
Pairs.
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.
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.
Differentially private regularized logistic regression
Estimation of prevalence ratios using logistic models and confidence intervals with delta and bootstrap methods.
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.
The ProbForecastGOP package contains a main function,
called ProbForecastGOP and other functions, to produce
probabilistic weather forecasts of weather fields using the
Geostatistical Output Perturbation (GOP) method of Gel,
Raftery, and Gneiting (JASA, 2004).
This package provides multiclass conditional probability
estimation for the SVM, which is distributional assumption
free.
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
A direct and flexible method for estimating an ICA model.
This approach estimates the densities for each component
directly via a tilted gaussian. The tilt functions are
estimated via a GAM poisson model. Details can be found in
"Elements of Statistical Learning (2nd Edition)" Section 14.7.4
Fast and user friendly implementation of nonparametric estimators
for censored event history (survival) analysis.
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.
The package profileR provides a set of multivariate methods and data visualization tools
to implement profile analysis and cross-validation techniques described
in Davison & Davenport (2002) and Bulut (2013).
profr provides an alternative data structure
and visual rendering for the profiling information
generated by Rprof.
Tools for examining Rprof profile output.
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.
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.
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.
A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.
This package contains functions to run propensity-biased allocation to balance covariate distributions in sequential trials and propensity-constrained randomization to balance covariate distributions in trials with known baseline covariates at time of randomization.  Currently this package only supports trials comparing two groups.
The package provides functions for pretreatment and sample
selection of visible and near infrared diffuse reflectance spectra
This package provides functions to identify the optimal
solution that maximizes numbers of detectable differentiated
proteins from a multi-stage clinical proteomic study.
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
PROTOLIDAR package contains functions for analyze the
LIDAR scan of plants (grapevine) and make 3D maps in GRASS GIS.
The protr package focus on offering a unique and comprehensive
toolkit for protein sequence descriptor calculation and similarity
computation. The descriptors included in the protr package are extensively
utilized in Bioinformatics and Chemogenomics research. The qualitative
descriptors listed in protr include Amino Acid Composition (Amino Acid
Composition/Dipeptide Composition/Tripeptide Composition) descriptor,
Autocorrelation (Normalized Moreau-Broto Autocorrelation/Moran
Autocorrelation/Geary Autocorrelation) descriptor, CTD
(Composition/Transition/Distribution) descriptor, Conjoint Traid
descriptor, Quasi-sequence Order (Sequence Order Coupling
Number/Quasi-sequence Order Descriptors) descriptor and Pseudo Amino Acid
Composition (Pseudo Amino Acid Composition/Amphiphilic Pseudo Amino Acid
Composition) descriptor. The quantitative descriptors, for
Proteochemometric (PCM) Modeling, includes the Generalized Scales-Based
Descriptors derived by Principal Components Analysis, Generalized
Scales-Based Descriptors derived by AA-Properties (AAindex), Generalized
Scales-Based Descriptors derived by 20+ classes of 2D and 3D Molecular
Descriptors (Topological, WHIM, VHSE, etc.), Generalized Scales-Based
Descriptors derived by Factor Analysis, Generalized Scales-Based
Descriptors derived by Multidimensional Scaling, and Generalized BLOSUM/PAM
Matrix-Derived Descriptors. The protr package also integrates the
functionality of parallellized similarity computation derived by protein
sequence alignment and Gene Ontology (GO) semantic similarity measures
between a list of protein sequences / GO terms / Entrez Gene IDs. ProtrWeb,
the web service built on protr, is located at:
http://cbdd.csu.edu.cn:8080/protrweb/ . The protr package is developed by
Computational Biology and Drug Design (CBDD) Group, Central South
University.
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.
Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.
Test of periodicity in short time series using response surface regression
Useful tools to pry back the covers of R and understand the
language at a deeper level.
Bootstrapping functions 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 the PSBC 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.
Power spectral density estimates
are produced through iterative refinement of the
optimal number of sine-tapers at each frequency.
The optimization procedure is based on the
method of Riedel and Sidorenko (1995), which applies
smoothing that varies with frequency to minimize the sum
of variance and bias at each point.
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.
Based on the package sensitivity, by Gilles Pujol,
Bertrand Iooss & Alexandre Janon.
Various functions for computing pseudo-observations for
censored data regression
Implements projected sparse Gaussian process kriging for the intamap package
This package contains functions to calculate the Specificity Index statistic, which can be used for comparative quantitative analysis to identify genes enriched in specific cell populations across a large number of profiles, as well as perform numerous post-processing operations. NOTE:Supplementary data (human & mouse expression sets, calculated pSI datasets, etc.) can be found in pSI.data package located at the following URL: http://genetics.wustl.edu/jdlab/psi_package/
Makes it easy to build panel data in wide format from PSID
delivered raw data. Deals with data downloaded and pre-processed by Stata or SAS,
or can optionally download directly from the PSID server using the SAScii package.
psidR takes care of merging data from each wave onto a cross-period index file,
so that individuals can be followed over time. The user must specify which years
they are interested in, and the PSID variable names (e.g. ER21003) for each year
(they differ in each year). There are different panel data designs and two
popular subsetting criteria (heads only and core sample only) implemented.
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.
population structure inference using likelihood-based approach
The package provides an implementation of PSO consistent
with the standard PSO 2007/2011 by Maurice Clerc et al.
Additionally a number of ancillary routines are provided for
easy testing and graphics.
Spearman's rank correlation test with precomputed exact
null distribution for n <= 22.
Smoothing splines with penalties on order m derivatives.
This package 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 number of routines for personality, psychometrics and experimental psychology.   Functions are primarily for scale construction using factor 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 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 parametrizations 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, and rating
scale 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.
An assortment of functions that could be useful in analyzing data from psychophysical experiments. It includes functions for calculating d' from several different experimental designs, links for m-alternative forced-choice (mafc) data to be used with the binomial family in glm (and possibly other contexts) and self-Start functions for estimating gamma values for CRT screen calibrations.
Psytabs produces well-formatted tables in .rtf or .html,
which largely conform to "psychological style".
Implements (cumulative) prospect theory and other
theories of risky decision making. A practically unlimited number of
choices can be specified. The package allows for different probability
weighting functions and utility functions to be specified, as well as their
parameters. Other features include the ability to plot individual
probability weighting curves and families of these curves, as well as
individual utility function curves. Single-stage decision trees can be
drawn. The probability simplex can also be drawn. Certainty equivalents and
risk premiums can be plotted. Theoretical predictions for different risky
decision making theories can be compared against each other and empirical
data.
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.
This package 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).
An R package for text mining of PubMed Abstracts (http://www.ncbi.nlm.nih.gov/pubmed). The algorithms are designed for two formats (text and XML) from PubMed
create a word cloud using the abstract of publications from PubMed
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.
A package for sending push notifications to iOS and Android
devices 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.
pvclust is a package for assessing the uncertainty in
hierarchical cluster analysis. It provides AU (approximately
unbiased) p-values as well as BP (boostrap probability) values
computed via multiscale bootstrap resampling.
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.
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.
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 8.0 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.
PX-Web/PC-Axis API information can be found at:
http://www.scb.se/Grupp/OmSCB/API/API-description.pdf
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.
This package delivers some functions to provide a scientific quality assurance of meteorological data.
This package provides functions for performing Qualitative Comparative Analysis (csQCA, tQCA, mvQCA and fsQCA).
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.
QCAGUI is a graphical user interface (GUI) for the QCA
package, derived from R Commander.  Because QCA has little to
do with statistics, the menus from Rcmdr were stripped down to
the very basics.  In crisp sets QCA, data is binary therefore
it is fairly decent to treat it as categorical (1 - presence; 0
- absence).  In order to ease the primary analysis (e.g. tables
of frequencies) and the creation of basic graphs, this package
activates some menus that are not available in Rcmdr but for
factors. Users should be aware, however, that QCAGUI is not a
package for statistics; Rcmdr is better suited for this
purpose.
Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.
Tools for (automated and manual) quality control of
the results of Genome Wide Association Studies
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.
This package 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.
This package is 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, person tags,
phone numbers, times, and zip codes.
This package is a collection of tools associated with the qdap
package that may be useful outside of the context of text analysis.
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.
Open QIIME output files in R, compute statistics, and
create plots from the data.
This package contains various functions that extend 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).
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.
QLS is a two-stage computational approach for estimation
of the correlation parameters within the framework of GEE. It
helps solving parameters in mean, scale, and correlation
structures for longitudinal data.
Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.
Analyses Q methodology data to identify the 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. The function qmethod() runs the full analysis and each step can be run separately using the corresponding functions: qflag() for automatic flagging of Q-sorts (manual flagging is optional), qzscores() for scores for statements, qda() for distinguishing and consensus statements, and qfcharact() for general characteristics of the factors. It includes a function to import data from PQMethod software, to export plain text data for interpretation in two flavours, and to plot Q method results. The package uses principal components and it offers the following choices: manual or automatic flagging, a number of mathematical methods for rotation, and a number of correlation coefficients for the initial correlation matrix.
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 quality of life score deterioration in longitudinal analysis.
This package is deprecated and it is now only a stub for
the newer version called qpgraph available through the
Bioconductor project. The q-order partial correlation graph
search algorithm, q-partial, or qp, algorithm for short, is a
robust procedure for structure learning of undirected Gaussian
graphical Markov models from "small n, large p" data, that is,
multivariate normal data coming from a number of random
variables p larger than the number of multidimensional data
points n as in the case of, e.g., microarray data.
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.
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
This package is designed to accompany the book
Quantitative Risk Management: Concepts, Techniques and Tools by
Alexander J. McNeil, Rudiger Frey, and Paul Embrechts.
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 clases from R. Other packages build upon
qtbase to provide special-purpose high-level interfaces to
specific parts of Qt.
Analysis of experimental crosses to identify genes
(called quantitative trait loci, QTLs) contributing to variation in
quantitative traits.
Functions for model selection for genetic architecture.
Datasets for the book, A Guide to QTL Mapping with R/qtl
Functions to infer co-mapping trait hotspots and causal models
This software provides tools for joint analysis of
multiple traits in a backcross (BC) or recombinant inbred lines
(RIL) population. It can be used to select an optimal subset of
traits for multiple-trait mapping, analyze multiple traits via
the SURE model, which can associate different QTL with
different traits, and perform multiple-trait composite
multiple-interval mapping.
Functions to Simultaneously Infer Causal Graphs and Genetic Architecture
This software provides tools for quantitative trait mapping in populations such as advanced intercross lines where relatedness among individuals should not be ignored. It can estimate background genetic variance components, impute missing genotypes, simulate genotypes, perform a genome scan for putative quantitative trait loci (QTL), and plot mapping results. It also has functions to calculate identity coefficients from pedigrees, especially suitable for pedigrees that consist of a large number of generations, or estimate identity coefficients from genotypic data in certain circumstances.
Low-level interface to functionality in Qt for efficiently
drawing dynamic graphics and handling basic user input.
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.
This Package 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 models.
This package interacts directly with the Quandl API to offer data
in a number of formats usable in R, as well as the ability to upload and
search.
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).
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.
Specify, build, trade, and analyse quantitative financial
trading strategies
Contains functions useful for data screening, testing
moderation, mediation and estimating power.
Quantile regression and related methods.
Quantile Regression Forests is a tree-based ensemble
method for estimation of conditional quantiles. It is
particularly well suited for high-dimensional data. Predictor
variables of mixed classes can be handled. The package is
dependent on the package randomForests, written by Andy Liaw.
Fits non-crossing regression quantiles as a function of linear covariates and a smooth terms via B-splines with quadratic penalties.
Methods to determine, smooth and plot quantile (i. e., Laplace or
copula) periodograms for univariate time series.
This package is used to apply the QL, QLShrink and QLSpline methods (Lund, Nettleton, McCarthy, and Smyth, 2012) to quasi-Poisson or quasi-negative binomial models for identifying differentially expressed genes in RNA-seq data.
This package provides some convenience functions to make the
processing and analysis of surveys easier.
It provides a versatile tool 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.
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.
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.
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
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.)
This package is designed to estimate reproduction numbers for disease outbreak, based on incidence data. It 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.
Provides miminization routines and related utilities for
compressive sampling. l-1, l-2 and TV (1-D) minimization, DFT
basis matrix, sparse signal generator, mutual cumulative
coherence and examples.
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.
R2Cuba implements 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).
generate univariate and bivariate analyses in LaTeX or
HTML formats
R2MLwiN is an R command interface to the MLwiN multilevel
modelling software package, allowing users to fit multilevel models using
MLwiN from within the R environment.
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.
This package uses 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 familiar plotting functions like plot(),
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.
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 / S-PLUS.
In S-PLUS, the openbugs functionality and the windows emulation
functionality is not yet available.
This is a collection of useful functions and data for
Stats 330
R4CDISC package includes functions for reading Dataset-XML and Define-XML files.
The R4CouchDB package provides a collection of functions for basic
database and document management operations such as add and delete. Every
cdbFunction() gets and returns a list() containing the connection setup.
Such a list (in the documentation mostly called cdb) can be generated by
cdb <- cdbIni(). Then, the list cdb provides some functions respectively
functionality e.g cdb$baseUrl() or cdb$getDocRev().
This package provides an R interface with 2-part 4dfp MRI images
(.4dfp.ifh and .4dfp.img files.)
A collection of R functions for use with Stock Synthesis, a
fisheries stock assessment modeling platform written in ADMB by Dr. Richard
D.  Methot at the NMFS Northwest Fisheries Science Center. The functions
include tools for summarizing and plotting results, manipulating files,
visualizing model parameterizations, and various other tasks.
The R6 package allows the creation of classes with reference
semantics, similar to R's built-in reference classes. Compared to reference
classes, R6 classes are simpler and lighter-weight, and they are not built
on S4 classes so they do not require the methods package. These classes
allow public and private members, and they support inheritance, even when
the classes are defined in different packages.
Implementation of some racing methods for the empirical
selection of the best. If the R package `rpvm' is installed
(and if PVM is available, properly configured, and
initialized), the evaluation of the candidates are performed in
parallel on different hosts.
Fit a variety of models to Rank Abundance Data
This package implements import, export, manipulation, visualization, and downstream
(post-clustering) analysis of RADseq data, integrating with the pyRAD package by Deren Eaton.
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.
This package is designed for the import, analysis, and visualization of dosimetric and volumetric data in Radiation Oncology.  The tools herein enable import of DVH information from Varian's Aria/Eclipse platform and 3D structural representations and dosimetric information from DICOM-RT files.  These tools also enable subsequent visualization and statistical analysis of these data.
Package contains proper L2-penalized ML estimators for the precision matrix as well as supporting functions to employ these estimators in a graphical modeling setting.
R implementation of 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 datasets 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.
This package provides a series of functions to make amplicon-based metagenomic analysis more accessible, and publication-quality plots simple. Amplicon-based (or targeted) metagenomics amplifies and sequences selected DNA regions of environmental samples, but not the entire pool of genetic material, which is referred to as shotgun metagenomics. The amplicon-metagenomics mainly aims at characterizing broad microbiota biodiversity in different environments.
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.
RAMpath is a rewrite of RAMpath software developed by John McArdle and Steven Boker. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features.  First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variances and covariances into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system.
Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples.  Package performance is tuned for large spatial datasets.
The deterministic part of the Fortuna cryptographic
pseudorandom number generator, described by Schneier & Ferguson
"Practical Cryptography"
This package provides an interface to the true random number
service provided by the random.org website created by Mads Haahr.
The random.org web service 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.
Simulation of Gaussian and extreme value random fields;
conditional simulation; kriging; maximum likelihood estimation
Classification and regression based on a forest of trees
using random inputs.
A unified treatment of Breiman's random forests for survival, regression and classification problems based on Ishwaran and Kogalur's random survival forests (RSF) package.  The package runs in both serial and parallel (OpenMP) modes.
The package implements a bagging predictor based on
general linear models
The 'randomizationInference' package 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.
Fits random effects latent class models, as well as standard latent class models.
Function to generate 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.
Random survival forests for right-censored and competing
risks survival data.
Ensemble model, for classification and regression, based on a forest of unpruned and randomized binary 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 Uniform distribution on the support of each candidate variable. Optimal random node is, then, selected by maximizing information gain (classification) or minimizing 'L2' (or 'L1') distance (regression). Data are either bootstrapped or sub-sampled for each tree. Random Uniform Forests are aimed to lower correlation between trees, to offer more details about variable importance and selection and to allow native incremental learning.
Several non parametric randomness tests for numeric sequences
The package provides (1) pseudo random generators - general linear congruential generators (Park Miller) and multiple recursive generators (Knuth TAOCP), generalized feedback shift register (SF-Mersenne Twister algorithm and WELL generators); (2) quasi random generators - the Torus algorithm, the Sobol sequence, the Halton sequence (thus include Van der Corput sequence) and (3) some additional tests such as 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 provided by 'RDieHarder', all available on CRAN. There is also a small stand-alone package 'rngwell19937' for the WELL19937a RNG.
Implementation of random variables by means of S4 classes and methods
A package to manipulate species range (extent-of-occurrence) maps, mainly tools
for easy generation of species richness and (life-history) traits maps.
This package performs aggregation of ordered lists based
on the ranks using several different algorithms: Borda count,
Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a
brute force algorithm (for small problems)
This package proposes a model-based clustering algorithm for
ranking data. Multivariate rankings as well as partial rankings are taken
into account. This algorithm is based on an extension of the Insertion
Sorting Rank (ISR) model for ranking data, which is a meaningful and
effective model parametrized by a position parameter (the modal ranking,
quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
of the rank population is modelled by a mixture of ISR, whereas conditional
independence assumption is considered for multivariate rankings.
Rank-hazard plots (Karvanen and Harrell, Statistics in Medicine 2009) visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate.
Methods for ranking responses of a single response question or a multiple response question
Finds the k nearest neighbours for every point in a given dataset
in O(N log N) time using Arya and Mount's ANN library (v1.1.3). There is
support for approximate as well as exact searches, fixed radius searches
and bd as well as kd trees.
To find the reversal association between variables.
Facilitates efficient polygon search using kd trees.
Coordinate level spatial data can be aggregated to higher geographical
identities like census blocks, ZIP codes or police district boundaries.
This process requires mapping each point in the given data set to a
particular identity of the desired geographical hierarchy. Unless efficient
data structures are used, this can be a daunting task. The operation
point.in.polygon() from the package sp is computationally expensive.
Here, we exploit kd-trees as efficient nearest neighbor search algorithm
to dramatically reduce the effective number of polygons being searched.
Package to perform non-invasive fetal testing for aneuploidies
using sequencing count data from cell-free DNA
This package provides other packages with access to the internal
R serialization code. Access to this code is provided at the C function
level by using the registration of native function mechanism. Client
packages simply include a single header file RApiSerializeAPI.h provided by
this package.

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

The R Core group is the original author of the serialization code made
available by this package.
This package implements R functions that interface to various methods in the Linux kernel related to enforcing security restrictions on a certain task (process). The package can apply mandatory access control (MAC) policies by loading an apparmor profile, and set hardware limits (rlimit), uid, gid, affinity and priority of the current process, directly from within R. The high level function eval.secure builds on these foundations to support dynamic sandboxing: it evaluates a single expression in a temporary fork with hard security restrictions and hardware limits without any side effects on the main R session.
An easy way to determine which directories on the users computer
you should use to save data, caches and logs. A port of Python's Appdirs
(\url{https://github.com/ActiveState/appdirs}) to R.
A package that facilitates 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
This package allows calculation of rarity weights for species and indices of rarity for assemblages of species according to different methods (Leroy et al. 2012, In Press).
rARPACK is an R wrapper of the ARPACK library
(http://www.caam.rice.edu/software/ARPACK/) to solve large scale
eigenvalue/vector problems. It is typically used to compute a few
eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues,
which is usually more efficient than eigen() if k << n. This package
provides the eigs() function which does the similar job as in Matlab,
Octave, Python SciPy and Julia. It also provides the svds() function
to calculate the largest k singular values and corresponding
singular vectors of a real matrix. Matrices can be given in either dense
or sparse form.
Sampling binary matrices with fixed margins
This package contains functions to perform supervised and
pixel based raster image classification. It has been designed
to facilitate land-cover analysis. Five classification
algorithms can be used: Maximum Likelihood Classification,
Multinomial Logistic Regression, Neural Networks, Random
Forests and Support Vector Machines. The output includes the
classified raster and standard classification accuracy
assessment such as the accuracy matrix, the overall accuracy
and the kappa coefficient. An option for in-sample verification
is available.
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.
The raster package defines classes and methods for spatial raster data access and manipulation. The rasterVis package complements raster providing a set of methods for enhanced visualization and interaction.
A function which performs exact rate ratio tests and returns an object of class htest.
An index of inter rater agreement not affected by Kappa paradoxes
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.
Rattle (the R Analytic Tool To Learn Easily) 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 the Averaging model of Anderson's
Information Integration Theory
Interface to the proyectoavis.com bird observations records
database. It provides means to download datasets filtered by species, order,
family, and several other criteria. Provides also basic functionality to
plot exploratory maps of the datasets.
This package provides functions to import FASTA sequence
files into R memory with various bit counts per letter.
This package provides an interface to functions of the
'SAMtools' C-Library by Heng Li.
Basic R package to access data structures offered by any
BEFdata portal instance.
rbenchmark is inspired by the Perl module Benchmark, and
is intended to facilitate benchmarking of arbitrary R code. The
library consists of just one function, benchmark, which is a
simple wrapper around system.time.  Given a specification of
the benchmarking process (counts of replications, evaluation
environment) and an arbitrary number of expressions, benchmark
evaluates each of the expressions in the specified environment,
replicating the evaluation as many times as specified, and
returning the results conveniently wrapped into a data frame.
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.
RbioRXN is an R package that facilitates 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.
provides functions to interact with BioUML server.
This package is an R 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. This package focuses on
getting the data from BISON - but does provide a function to help visualize
data quickly.
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.
Creation, manipulation, simulation of linear Gaussian Bayesian
networks from text files and more...
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.
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.
Relational Class Analysis (RCA) is a method for
detecting heterogeneity in attitudinal data. (as
described in Goldberg A., 2011, Am. J. Soc, 116(5)) .
src/ also contains the source files of igraph-C
which was written by Gabor. LAPACK routines are
included in the igraph source code.
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.
Estimation of abundance and other of demographic parameters for closed
populations, open populations and the robust design in capture-recapture experiments
using loglinear models.
This package makes some maps using shapefiles and
dataframes. Five kinds of maps are available : proportionnal
circles, proportionnal circles colored by a discretized
quantitative variable, proportionnal circles colored by the
modalities of a qualitative variable, choropleth and typology.
This packages provides a direct interface (without the
use of Java) to the most basic functionality of Apache
Cassanda such as login, updates and queries.
Tools for large, sparse optimal matching of treated units
and control units in observational studies.  Provisions are
made for refined covariate balance constraints, which include
fine and near-fine balance as special cases.  Matches are
optimal in the sense that they are computed as solutions to
network optimization problems rather than greedy algorithms.
R interface to (some of) cddlib
(http://www.ifor.math.ethz.ch/~fukuda/cdd_home/cdd.html).
Converts back and forth between two representations of a convex polytope:
as solution of a set of linear equalities and inequalities and as
convex hull of set of points and rays.
Also does linear programming and redundant generator elimination
(for example, convex hull in n dimensions).  All functions can use exact
infinite-precision rational arithmetic.
This package allows the user to access functionality in the
CDK, a Java framework for cheminformatics. This allows the user to load
molecules, evaluate fingerprints, calculate molecular descriptors and so on.
In addition the CDK API allows the user to view structures in 2D.
This package provides the CDK libraries for use
in the R environment. Given the size of the library itself,
this package is not expected to change very frequently. To make
use of the CDK within R, it is suggested that you use the rcdk
package. Note that it is possible to directly interact with the CDK
using rJava. However rcdk exposes functionality in a more idiomatic way.
The CDK library itself is released as LGPL and the sources can be
obtained from https://github.com/cdk/cdk
RCEIM is a package implementing a stochastic heuristic method for performing multidimensional function optimization. The method is inspired in the Cross-Entropy Method. It does not relies on derivatives, neither imposes particularly strong requirements into the function to be optimized. Additionally, it takes profit from multicore processing to enable optimization of time-consuming functions.
Package to analyze microscopy based cytometry datasets created with Cell-ID, Cell-X or CellProfiler datasets. It includes functions for manipulating and plotting the data. It can also create automatic image montages of cells in a user defined layout.
Conjugate gradient minimization of nonlinear functions
with box constraints incorporating Dai/Yuan update
Estimate binary, ordered and count models with random parameters.
Collection of portable choice dialog widgets
Functions to generate stimuli and analyze data of reverse correlation image classification experiments.
RCircos package provides a simple and flexible way to generate Circos 2D track plot images for genomic data visualization. The types of plots include: heatmap, histogram, lines, scatterplot, tiles and plot items for further decorations include connector, link (lines and ribbons), and text (gene) label. All functions require only R graphics package that comes with R base installation.
This package contains wrapper functions and methods which allow to
use "climdex.pcic" and "RMAWGEN" packages.  With this simple approach it is
possible to calculate climate change indices, suggested by the WMO-CCL,
CLIVAR, ETCCDMI(http://www.climdex.org),on stochastic generations of
temperature and precipitation time series, obtained by the application of
RMAWGEN.  Each index can be applied to both observed data and to synthetic
time series produced by the Weather Generator, over a reference period
(e.g. 1981-2010, as in the example). It contains also functions and methods
to evaluate the generated time series of climate change indices consistency
by statistical tests.Bugs/comments/questions/collaboration of any kind are
warmly welcomed.
R tools for integrating with the www.clinicalcodes.org web
repository
Provide flexible native clustering routines that can be
linked against in downstream packages.
rCMA allows access to the Java version
(CMAEvolutionStrategy) of Niko Hansen.
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.
This package provides an 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.
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-10: 4498109007), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below.
Rcmdr menu support for many of the functions in the HH package.
The focus is on menu items for functions we use in our introductory
courses.
This package is an R Commander plugin that accompanies IPSUR, an Introduction to Probability and Statistics Using R.
This package is an R Commander plug-in for Kaplan-Meier
plots and other plots by using the ggplot2 package.
This package provides an Rcmdr "plug-in" based on the
lfstat package for low flow analysis.
Easy to use interface for conducting meta-analysis in R. This
package is an Rcmdr-plugin, which allows the user to conduct analyses in a
menu-driven, graphical user interface environment (e.g., CMA, SPSS). It
uses recommended procedures as described in The Handbook of Research
Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
Rcmdr menu items to display mosaic and assoc plots Allows
to visually restructure the underlying structables Developed
after extended discussions with Rich Heiberger
This package provides an R Commander plugin that offers a
unified menu of new and pre-existing statistical functions
related to public management and policy analysis statistics.
Functions and menus have been renamed according to the usage
in PMGT 630 in the Master of Public Administration program at
Brigham Young University.
This package provides an Rcmdr "plug-in" extending some functions of linear models and providing an interface to PLS, LDA and QDA.
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
This package provides a 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, reclassification table, categorical and continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI) from the PredictABEL package.
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.
This package provides a GUI for the SCVA, SCRT and SCMA packages. It 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 some statistical models with a
different graphic's window that provides Rcmdr. It is developed
as an Rcmdr plug-in.
This package provides an R Commander plug-in for the
survival package, with dialogs for Cox models, parametric
survival regression models, estimation of survival curves, and
testing for differences in survival curves, along with
data-management facilities and a variety of tests, diagnostics
and graphs.
This package provides an Rcmdr "plug-in" based on the
TeachingDemos package, and is primarily for illustrative
purposes.
An R Commander plug-in providing an integrated solution to perform
a series of text mining tasks such as importing and cleaning a corpus, and
analyses like terms and documents counts, vocabulary tables, terms
co-occurrences and documents similarity measures, time series analysis,
correspondence analysis and hierarchical clustering. Corpora can be imported
from spreadsheet-like files, directories of raw text files, Twitter queries,
as well as from Dow Jones Factiva, LexisNexis, Europresse and Alceste files.
This package provides customized function for R-Commander made by R-UCA project.
This package provides programmatic access to Colombos, a web based
interface for exploring and analyzing comprehensive organism-specific
cross-platform expression compendia of bacterial organisms.
The packages provides palettes for drawing nice maps
shaded according to a variable.
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++.
R and Armadillo integration using Rcpp
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.

A delayed evaluation approach is employed (during compile time) to combine
several operations into one, and to reduce (or eliminate) the need for
temporaries. This is accomplished through recursive templates and template
meta-programming.

This library is useful if C++ has been decided as the language of choice
(due to speed and/or integration capabilities), rather than another language.

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.

This Armadillo integration provides a nice illustration of the
capabilities of the Rcpp package for seamless R and C++ integration.

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.
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.
This package use the cnpy library written by Carl Rogers to
provide 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.
This package provides 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.
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 3.2.2). Thus users do not need to
install Eigen itself in order to use RcppEigen.

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

Note that the documentation in this package currently does not cover
all the features in the package. It is not even close. On the
other hand, the site http://gallery.rcpp.org is regrouping a
number of examples for Rcpp.
Rcpp integration for GNU GSL vectors and matrices
The GNU Scientific Library (GSL) is a collection of numerical routines for
scientific computing. It is particularly useful for C and C++ programs as it
provides a standard C interface to a wide range of mathematical routines
such as special functions, permutations, combinations, fast fourier
transforms, eigensystems, random numbers, quadrature, random distributions,
quasi-random sequences, Monte Carlo integration, N-tuples, differential
equations, simulated annealing, numerical differentiation, interpolation,
series acceleration, Chebyshev approximations, root-finding, discrete
Hankel transforms physical constants, basis splines and wavelets.  There
are over 1000 functions in total with an extensive test suite.

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

This package also serves as a prime example of how to build a package
that uses Rcpp to connect to another third-party library. The autoconf
script, inline plugin and example package can all be used as a stanza to
write a similar package against another library.
MLPACK is an intuitive, fast, scalable C++ machine learning
library, meant to be a machine learning analog to LAPACK. It
aims to implement a wide array of machine learning methods
and function as a Swiss army knife for machine learning
researchers: MLPACK is from http://www.mlpack.org/: sources are included in the package.
Direct interface to Octave. The primary goal is to facilitate the
port of Matlab/Octave scripts to R. The package enables to call any Octave
functions from R and as well as browsing their documentation, passing
variables between R and Octave, using R core RNGs in Octave, which ensures
that stochastic computations are also reproducible.
High level functions for doing parallel programming with Rcpp.
For example, the parallelFor function can be used to convert the work of
a standard serial "for" loop into a parallel one and the parallelReduce
function can be used for accumulating aggregate or other values.
This package allows to display a progress bar in the R
console for long running computations taking place in c++ code,
and support for interrupting those computations even in a
multithreaded code, typically using OpenMP.
RcppRedis connects R to the Redis key/value store using the
C client library hiredis
RcppRoll supplies fast functions for 'roll'ing over
vectors and matrices, e.g. rolling means, medians and
variances. It also provides the utility functions 'rollit' and
'rollit_raw' as an interface for generating your own C++ backed
rolling functions.
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.
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.
R interface to the CSDP semidefinite programming library. Installs version 6.1.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. See the INSTALL file for further details.
Functions and objects for CUR matrix decomposition.
The package allows 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.
Fetches data from DataMarket.com, either as timeseries in
zoo form (dmseries) or as long-form data frames (dmlist).
Metadata including dimension structure is fetched with dminfo,
or just the dimensions with dmdims.
This package provides the tools to undertake estimation in
Regression Discontinuity Designs. Both sharp and fuzzy designs are
supported. Estimation is accomplished using local linear regression.
A provided function will utilize Imbens-Kalyanaraman optimal
bandwidth calculation. A function is also included to test the
assumption of no-sorting effects.
The package provides functions for estimating the relevant
dimension of a data set in feature spaces, applications to
model selection, graphical illustrations and prediction.
This package provides functions for creating plots and image files in a unified way regardless of output format (EPS, PDF, PNG, SVG, TIFF, WMF, etc.).  Default device options as well as scales and aspect ratios are controlled in a uniform way across all device types.  Switching output format requires minimal changes in code.  This package is ideal for large-scale batch processing, because it will never leave open graphics devices or incomplete image files behind, even on errors or user interrupts.
The package has many function that helps to perform
various quality check on the data.It basically provides many
function that helps in performing Extrapolative data analysis.
The RDieHarder packages provides an R interface to
the dieharder suite of random number generators and tests that
was developed by Robert G. Brown and David Bauer, extending
earlier work by George Marsaglia and others.
Analysis of line transect surveys.  Estimates
distance-based sightability functions and abundances.
Imports real-time thermo cycler (qPCR) data from RDML format files and transforms to the appropriate formats of the 'qpcR' and 'chipPCR' packages.
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.
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
rdbinselect to conduct exploratory data analysis (RD plots).
This package 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
Sampler estimator.
Provides a threads-type programming environment for R.
The package gives the R programmer the clearer, more concise
shared memory world view, and in some cases gives superior
performance as well.  In addition, it enables parallel processing on
very large, out-of-core matrices.
This package provides an R interface to Pete Warden's Data
Science Toolkit.  See www.datasciencetoolkit.org for more
information.  The source code for this package can be found at
github.com/rtelmore/RDSTK Happy hacking!
The rDVR package allows you to start stop and save a video server
from within R. It does this by way of a REST interface to a JAVA service.
The jar binary relies on the screen recorder included in the Monte Media
Library (CC BY 3.0 licence) developed by Werner Randelshofer
(http://http://www.randelshofer.ch/monte/). The REST interface was modified
from https://github.com/tuenti/VideoRecorderService which has an Apache
licence.
Routines for developing models that describe reaction and
advective-diffusive transport in one, two or three dimensions.
Includes transport routines in porous media, in estuaries, and
in bodies with variable shape.
Identifies and reads Windows BMP, JPEG and PNG format bitmap
images. Identification defaults to the use of the magic number embedded in
the file rather than the file extension. Reading of JPEG and PNG image
depends on libjpg and libpng libraries. See file INSTALL for details if
necessary.
Reads data files acquired by MALDI-TOF MS on Bruker Daltonics
machines of the *flex series.
A set of functions to simplify reading data from files. The main function, reader(), should read most common R datafile types without needing any parameters except the filename. Other functions provide simple ways of handling file paths and extensions, and automatically detecting file format and structure.
The data are not part of the package. The package requires
to receive a path to a local copy of the data and their
description, which consists of an XML file "contents.xml" and R
scripts. Each data set is read into R as a data frame using its
own R script.  An example of the description directory for some
of the data sets from UCI Machine Learning Repository is
available at "http://www.cs.cas.cz/~savicky/readMLData".
Functions for reading mass spectrometry data in mzXML format.
Read ODS (OpenDocument Spreadsheet) files and put them into data
frames.
Resampling methods for adaptive linear model selection.
These can be thought of as extensions of the Akaike information
criterion that account for searching among candidate models.
Kiefer-Wolfowitz maximum likelihood estimation for mixtures models
and some other density estimation and regression methods based on convex optimization.
A programmatic interface to the eBird database
R functions for random univariate and multivariate finite mixture generation, number of components, component weights and component parameters estimation, printing and plotting of finite mixtures, bootstrapping and class membership prediction. Variables can be either continuous or discrete, may follow normal, lognormal, Weibull, gamma, binomial, Poisson or Dirac parametric families and should be independent within components.
Relevant Component Analysis (RCA) tries to find a linear
transformation of the feature space such that the effect of irrelevant
variability is reduced in the transformed space.
recalls provides access to U.S. Federal Government recall data on drugs and food from the Food and Drug Administration, car safety from the National Highway Traffic Safety Administration, and product safety recalls from the Consumer Product Safety Commission.
All this package needs is some idea of a risk model and a
population description and it will generate a random
case-control-study from that risk model and population for you.
You may repeat this to simulate powers of testing models and
such.
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 a research infrastructure to test and develop
recommender algorithms.
Provides the Book-Crossing Dataset for recommenderlab
Provides the Jester Dataset for recommenderlab
Functions for generating k-record values and k-record
times
This package is an 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.
Performs robust and efficient analysis of the effect of exposure on a secondary outcome in a case-control study.
This package provides functions used in the second edition of “R: Einführung durch angewandte Statistik”.
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
This package estimates regression trees with random
effects as a way to use data mining techniques to describe
longitudinal or panel data.
small package with functions for creating references,
reading from and writing to references and a memory efficient
refdata type that transparently encapsulates matrices and
data.frames
This is a collection of tools to allow the medical professional to
calculate appropriate reference ranges (intervals) with confidence intervals around
the limits for diagnostic purposes.
Reference-free method for conducting EWAS while deconvoluting DNA methylation arising as mixtures of cell types.  This method is similar to surrogate variable analysis (SVA and ISVA), except that it makes additional use of a biological mixture assumption.
The packge 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.
RefManageR 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's
Entrez, CrossRef, and Zotero are provided for importing references and
references can be created from locally stored PDFs using Poppler.  Includes
functions for citing and generating a bibliography with hyperlinks for
documents prepared with RMarkdown or RHTML.
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.
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.
Provides a generic infrastructure for creating and using
registries.
Regularized (polychotomous) logistic regression
by Gibbs sampling. The package implements subtly different
MCMC schemes with varying efficiency depending on the data type
(binary v. binomial, say) and the desired estimator (regularized maximum
likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a
unified interface.
The package provides tools for
(1) nonparametric regression (kernel, local linear),
(2) semiparametric regression (single index, additive models), and
(3) quantile regression (linear, kernel)
Functions to fit Gaussian linear model by maximising the
residual log likelihood where the covariance structure can be
written as a linear combination of known matrices.  Can be used
for multivariate models and random effects models.  Easy
straight forward manner to specify random effects models,
including random interactions. Code now optimised to use
Sherman Morrison Woodbury identities for matrix inversion in
random effects models. We've added the ability to fit models
using any kernel as well as a function to return the mean and
covariance of random effects conditional on the data (BLUPs).
Performs Random Subspace Method (RSM) for high-dimensional linear regression to obtain variable importance measures. The final model is chosen based on validation set or Generalized Information Criterion.
For a sequence of event occurence times, we are interested in
finding subsequences in it that are too "regular". We define regular as being
significantly different from a homogeneous Poisson process. The departure from
the Poisson process is measured using a L1 distance. See Di and Perlman 2007
for more details.
Functions for unary and binary regression tests
Functions for the detection of footprints of selection on
dense SNP data using Extended Homozygosity Haplotype (EHH)
based tests. The package includes computation of EHH, iHS
(within population) and Rsb (across pairs of populations)
statistics. Various plotting functions are also included to
facilitate visualization and interpretation of the results.
Item analysis with alpha standard error and principal axis
factoring for continuous variable scales (with plots).
relaimpo provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.
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
Functions for estimating parameters in software reliability models.
Only infinite failure models are implemented so far.
A variety of tools useful for performing structural
reliability analysis, such as with structure function and
system signatures.  Plans to expand more widely.
A collection of utilities for some reliability
models/probability distributions.
Functions to facilitate inference on the relative
importance of predictors in a linear or generalized linear
model, and a couple of useful Tcl/Tk widgets.
A set of tools to explore the behaviour statistics used
for forensic DNA interpretation when close relatives are
involved
Various functions for relative survival analysis.
Classes and functions for analyzing the performance of portfolios relative to a benchmark.
remix provides remix, a quick and easy function for
describing datasets. It can be view as a mix of cast (in
package reshape) and summary.formula (in package Hmisc).
This package implements TRACDS (Temporal Relationships
between Clusters for Data Streams) which is 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. This package implements 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
'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.
R package dedicated to some Extreme values problems and allowing the use of the so-called "methode du renouvellement" which is popular among french-speaking hydrologists.
Rentrez provides an R interact to the NCBI's EUtils API allowing
users to search databases like genbank and pubmed, process the resulting
files 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.
Global tests for expression data of high-dimensional sets of
molecular features.
Estimation of Bayes and local Bayes false discovery rates for
replicability analysis.
A common problem faced by journal reviewers and authors is the
question of whether the results of a replication study are consistent with
the original published study. One solution to this problem is to examine
the effect size from the published study and generate the range of effect
sizes that could reasonably be obtained (due to random sampling) in a
replication attempt (i.e., calculate a replication interval). If a
replication effect size falls outside the replication interval then that
value could not have occurred to due the effects of sampling error alone.
Alternatively, if a replication effect size falls within the replication
interval then the replication results could have reasonably occurred due to
the effects of sampling error alone. This package has functions that
calculate the replication interval for two different types of effect sizes
(i.e., correlation, Cohen's d).
The package includes tools to load R packages
and automatically generate BibTeX files citing them as well as load
plain-text and Excel formatted data stored on Dropbox, GitHub, and from
other sources.
Fits regression models to repeated ordinal response
variables, using generalized estimating equations implemented
in package gee.
An R package for creating Microsoft Word document (>=2007),
Microsoft Powerpoint document (>=2007) and HTML documents from R. There are
several features to let you format and present R outputs ; e.g. Editable
Vector Graphics, functions for complex tables reporting, reuse of corporate
template document (*.docx and *.pptx). You can use the package as a tool
for fast reporting and as a tool for reporting automation. The package does
not require any installation of Microsoft product to be able to write
Microsoft files (docx and pptx).
External jars required for package ReporteRs. ReporteRs is an
R package for creating Microsoft Word document (>=2007), Microsoft
Powerpoint document (>=2007) and HTML documents from R.
The reportr package 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.
reports is a package that 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.
The functions in this package 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.
This package includes methods to calculate resource adequacy
metrics in power systems. These methods are based in the notion of
loss-of-load probability (LOLP) and include the treatment of conventional
and variable renewable generators.
Contains workhorse function jrparams(), as well as two
helper functions Mboxtest() and JRsMahaldist(), and four
example data sets.
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).
Bootstrap, permutation tests, and other resampling functions,
featuring easy-to-use syntax.
This package implements functions for spectral
similarity/dissimilarity analysis and memory-based learning (MBL) for
non-linear modelling in complex spectral datasets. MBL is
also known as local modelling in the chemometrics literature.
Reshape lets you flexibly restructure and aggregate data using just two functions: melt and cast.
Reshape lets you flexibly restructure and
aggregate data using just two functions: melt and cast.
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).
The package implements some recently introduced
methodology for robust estimation of location and scatter as
well as outlier identification.
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.
Reaction time analysis by maximum likelihood
This is an R package for interfacing with NCBI databases such
as PubMed, Genbank, or GEO via the Entrez Programming Utilities (EUtils).
The nine EUtils provide a programmatical interface to the NCBI Entrez query
and database system for searching and retrieving biological data.
Computation of (direct and indirect) revealed preferences, fast non-parametric tests of rationality axioms (WARP, SARP, GARP) and detection of axiom-consistent subpopulations.
Functions for managing logs of reviews of subversioned
files (http://subversion.apache.org/).
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).
This package wraps some of the matrix exponentiation utilities from EXPOKIT (http://www.maths.uq.edu.au/expokit/), a FORTRAN library that is widely recommended for matrix exponentiation (Sidje RB, 1998. "Expokit: A Software Package for Computing Matrix Exponentials." ACM Trans. Math. Softw. 24(1): 130-156).  EXPOKIT includes functions for exponentiating both small, dense matrices, and large, sparse matrices (in sparse matrices, most of the cells have value 0).  Rapid matrix exponentiation is useful in phylogenetics when we have a large number of states (as we do when we are inferring the history of transitions between the possible geographic ranges of a species), but is probably useful in other ways as well.
Provides an interface to the Facebook API
This package facilitates searching for and downloading seismic time series in miniSEED format (a minimalist version of the Standard for the Exchange of Earthquake Data) from International Federation of Digital Seismograph Networks repositories. This package can also be used to gather information about seismic networks (stations, channels, locations, etc) and find historical earthquake data (origins, magnitudes, etc).
An R implementation of the random ferns classifier by Ozuysal et al., modified for generic classification problems 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
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 fishbase.org.
A programmatic interface to openfisheries.org. This package is
part of the rOpenSci suite (http://ropensci.org)
R estimation and inference for linear models.  Estimation
is for general scores and a library of commonly used score
functions is included.
RFLPtools provides functions to analyse DNA fragment samples
(i.e. derived from RFLP-analysis) and standalone BLAST report files
(i.e. DNA sequence analysis).
A varying coefficient on the stimulus effect is introduced
into the voxelwise fMRI regression model. By this the effects
of the canonical hemodynamic response function are allowed to
vary with another variable, like for example time or a
vigilance parameter.
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.
RForcecom provides the connection to Force.com and Salesforce.com from R.
This package contains all the code examples in the book "R for
Dummies" (1st edition)
A modified version (with great help from Ian J. Wilson) of Ian J. Wilson's program BATWING for calculating forensic trace-suspect match probabilities.
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/.
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 the RGA 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.
This package provides functions to interact with the
Gaug.es API. Gaug.es is a web analytics service, like Google
analytics. You have to have a Gaug.es account to use this package.
A programmatic interface to the
Web Service methods provided by the Global
Biodiversity Information Facility.
Rgbp is an R package that utilizes approximate Bayesian machinery to provide a method of estimating two-level hierarchical models for Gaussian, Poisson, and Binomial data in a fast and computationally efficient manner. The main products of this package are point and interval estimates for the true parameters, whose good frequency properties can be validated via its repeated sampling procedure called frequency method checking. It is found that such Bayesian-frequentist reconciliation allows Rgbp to have attributes desirable both sides of the aisle, working well in small samples and yielding good coverage probabilities for its interval estimates.
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, < 2) 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 `RMAWGEN`
algorithms for the analysis and generation of any environmental vector time
series.Bugs/comments/questions/collaboration of any kind are warmly
welcomed.
A genetic algorithm plus derivative optimizer
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.
The first R package to work with GEXF graph files (used in Gephi and others). Using XML library, it allows the user to easily build/read graph files including attributes, GEXF viz attributes (such as colour, size, and position), network dynamics (for both edges and nodes, including spells) and edges weighting. Users can build/handle graphs element-by-element or massively through data-frames, visualize the graph on a web browser through sigmajs javascript library and interact with the igraph package.
The rggobi package provides a command-line interface to GGobi, an
interactive and dynamic graphics package. Rggobi complements GGobi's
graphical user interface, providing a way to fluidly transition between
analysis and exploration, as well as automating common tasks.
This package provides some functions to create quizzes
in the GIFT format. This format is used by several Virtual Learning
Environments such as Moodle.
Provides medium to high level functions for 3D interactive graphics, including
functions modelled on base graphics (plot3d(), etc.) as well as functions for
constructing representations of geometric objects (cube3d(), etc.).  Output
may be on screen using OpenGL, or to various standard 3D file formats including
WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.
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.
Rgnuplot, R 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
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) R 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 both
environmental studies and mineral exploration.  Additionally,
there are functions 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(akima); 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.
Deprecated package, please use the 'questionr' package instead.
This package provides functions for beginners and social sciences students
or researchers. Currently it includes functions for cross-tabulation,
weighting, results export, and maps plotting. The documentation and help
pages are written in french.
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 for the HealthData.gov data API.  For each data resource, you can filter results (server-side) to select subsets of data.
This package includes functions to estimate
heterozygosity-heterozygosity correlation and to calculate
three estimates of individual multilocus heterozygosity:
homozygosity by loci, internal relatedness, and standardized
heterozygosity.
RHive is an R extension facilitating
distributed computing via HIVE query.
It provides an easy to use HQL like SQL
and R objects and functions in HQL.
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.
Rhpc_lapply, Rhpc_lapplyLB and Rhpc_worker_call using MPI provides better HPC environment on R(works fast on HPC).
and this package supports long vector, can deal with slightly big data.
Control the number of threads on BLAS(Aka GotoBLAS, ACML and MKL).
--
GotoBLAS(generic) binary for Windows i386 http://prs.ism.ac.jp/~nakama/SurviveGotoBLAS2/binary/windows/x86/KATMAI/Rblas.dll
GotoBLAS(generic) binary for Windows x64 http://prs.ism.ac.jp/~nakama/SurviveGotoBLAS2/binary/windows/x64/PRESCOTT/Rblas.dll
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.
THIS PACKAGE HAS BEEN DEPRECATED. Do not start building new projects based on it. 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.
Computes rarefaction curves, cumulated and mean species
richness. Compares these estimates by means of randomization
tests.
Linear and logistic ridge regression for small data sets
and genome-wide SNP data
This package implements ridge fusion methodology for inverse covariance matrix estimation for use in quadratic discriminant analysis.  The package also contains function for model based clustering using ridge fusion for inverse matrix estimation, as well as tuning parameter selection functions.  We have also implemented QDA using joint inverse covariance estimation.
An extension of the Kruskal-Wallis Test that allow
selection of arbitrary reference group. Also provide Mean Ridit
for each group. Mean Ridit of a group is an estimate of
probability a random observation from that group will be
greater than or equal to a random observation from reference
group.
RIFS package provides functionality for generating &
plotting prefractals in R^n with various protofractal sets and
partition coefficient for iterative segments
RIGHT enables interactive data visualization and analysis using
HTML5 canvas and JavaScript technology to help users easily explore data
and gain valuable insight. Its R API is designed to create a seamless
analysis-visualization workflow for interactive graphics. Since HTML5
canvas and JavaScript is supported in virtually all modern web browsers,
the visualizations can be delivered to almost any device/platform for
presentation purposes.
Provides functions to read ImageJ (http://imagej.nih.gov/ij/)
Region of Interest (ROI) files, to plot the ROIs and to convert them as
spatstat (http://spatstat.org/) spatial patterns.
RImpala facilitates the connection and execution of distributed queries using Cloudera Impala, which is a massively parallel processing (MPP) SQL query engine that runs natively in Apache Hadoop. Impala supports jdbc integration which RImpala utilizes to establish the connection between R and Impala.
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.
A circular buffer, cyclic buffer or ring buffer is a data
structure that uses a single, fixed-size buffer as if it were
connected end-to-end. This structure lends itself easily to
buffering data streams.  See
http://en.wikipedia.org/wiki/Ring_buffer for more information.
Implementation of the "Ringscale" method as proposed in
the student research project "DETECTION OF FAINT COMPANIONS
AROUND YOUNG STARS IN SPECKLE PATTERNS OF VLT/NACO CUBE MODE
IMAGES BY MEANS OF POST-PROCESSING" at the
Friedrich-Schiller-University of Jena.
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 six 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).

Doxygen-generated documentation of the C++ classes is available at the
RInside website as well.
The package implements several ecological niche metrics to measure individual specialization. Monte Carlo resampling tools are available for hypothesis testing against multinomial models.
Rio is a set of tools aims to simplify the process of
importing/exporting data. The import() and export() functions will
determine data file format base on the file extension, without the need to
select appropriate read/write function for the specific data file format.
For example, exporting the iris data into a csv file can be achieved with
the following command: export(iris, "iris.csv")
Functions for the analysis of Quaternary science data, including
constrained clustering, WA, WAPLS, IKFA, MLRC and MAT transfer
functions, and stratigraphic diagrams.
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.
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).
Download content from NCBI databases. This package is intended to facilitate analyses of NCBI database content. This package is not intended for reference management.
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.
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.
Visualisation for quantitative and qualitative riverine data
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.
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.
Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.
RJDBC is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.
A series of useful functions, some available in different
forms in other packages, but which have been extended, sped up, or
otherwise modified in some way considered useful to the author.
R-interface to gene drop programs from Alun Thomas' Java
Programs for Statistical Genetics and Computational Statistics
(JPSGCS)
R interface to statistical database organized by Japanese government (http://statdb.nstac.go.jp/)
This package provides functions that can be used to retrieve
data and metadata from SDMX compliant data providers.
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.
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/>.
Random KNN Classification and Regression
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
This package allows an R user to discover, query and modify data in 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.
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 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 set of tools for working with lists as dataset,
including filtering, mapping, grouping, sorting, updating,
searching, and many other functions. It recommends functional
programming and collection pipeline in list operations.
Estimates robust rank-based fixed effects and predicts robust random effects in two- and three- level random effects nested models.
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().
RM2 is a simple package that implements functions used in
revenue management and pricing environments.
Use refined moving average filter based on the optimal and data-driven moving average lag q to decompose trend, seasonality and irregularity for an univariate time series or univariate 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.
This package provides an interface to the
software package MARK developed by Gary White. MARK is
freely available at
(http://www.phidot.org/software/mark/download/) but is
not open source.
Convert R Markdown documents into a variety of formats
including HTML, MS Word, PDF, and Beamer.
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.
This package provides methods readMat() and writeMat() to read and write MAT files.  It also makes it possible to communicate (evaluate code, send and retrieve objects etc.) with MATLAB v6 or higher running locally or on a remote host.
This package contains S3 and S4 functions for spatial multi-site
stochastic generation of daily time series of temperature and
precipitation. The implemented tools make use of Vector AutoRegressive
models (VARs). The VAR is calibrated by daily instrumental "Gaussianized"
time series and then works with monthly time series as input. Example
script files about its usage are available on "RMAWGENCodeCorner" GitHub
project (see link below). Bugs/comments/questions/collaboration of any kind
are warmly welcome.
Functions for fitting, diagnosing and predicting from a
class of Markov models.
RMediation package provides 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.
Facilitates the use of the metasim engine to build and
run individual based population genetics simulations. The
simulation environment is documented in: Allan
Strand. Metasim 1.0: an individual-based environment for
simulating population genetics of complex population
dynamics. Mol. Ecol. Notes, 2:373-376, 2002. (Please
contact Allan Strand with comments, bug reports, etc).
This version represents a significant alteration of
function names that hopefully increases consistency and
reduces the chances of collisions with other packages
naming conventions.  For a spatially-explicit, but slower, package
with a similar interface, see kernelPop
Methods that simplify the setup of S3 generic functions and S3 methods.  Major effort has been made in making definition of methods as simple as possible with a minimum of maintenance for package developers.  For example, generic functions are created automatically, if missing, and naming conflict are automatically solved, if possible.  The method setMethodS3() is a good start for those who in the future may want to migrate to S4.  This is a cross-platform package implemented in pure R that generates standard S3 methods.
Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.
This package facilitates the use of data mining algorithms in classification and regression tasks by presenting a short and coherent set of functions. While several DM algorithms can be used, it is particularly suited for Neural Networks (NN) and Support Vector Machines (SVM). Versions: 1.3.1 minor corrections; 1.3 - new classification and regression metrics (improved mmetric function); 1.2 - new input importance methods (improved Importance function); 1.1 - minor error corrections; 1.0 - first version.
The Rmisc library contains many functions useful for data analysis
and utility operations.
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.
A collection of miscellaneous functions. Exploratory data analysis, multidimensional visualization, medico-economic simulation, multiple testing...
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.
An interface to the MOSEK optimization library designed to
solve large-scale mathematical optimization problems. Supports
linear, quadratic and second order cone optimization
with/without integer variables, in addition to the more general
separable convex problems.  Trial and free academic licenses
available at http://www.mosek.com.
Performs probability mass function estimation with
Dirichlet process mixtures of rounded kernels
Rmpfr provides (S4 classes and methods for)
arithmetic including transcendental ("special") functions for
arbitrary precision floating point numbers. To this end, it interfaces to
the LGPL'ed MPFR (Multiple Precision Floating-Point Reliable) Library
which itself is based on the GMP (GNU Multiple Precision) Library.
Rmpi provides an interface (wrapper) to MPI APIs. It also
provides interactive R slave environment.
This package contains functions and S3 methods for spatial
multi-site stochastic generation of daily precipitation. It generates
precipitation occurrence in several sites using Wilks' Approach (1998).
Bugs/comments/questions/collaboration of any kind are warmly welcomed.
Regression modeling, testing, estimation, validation,
graphics, prediction, and typesetting by storing enhanced model design
attributes in the fit.  rms is a collection of functions that
assist with and streamline modeling.  It also contains functions for
binary and ordinal logistic regression models, ordinal models for
continuous Y with a variety of distribution families, and the Buckley-James
multiple regression model for right-censored responses, and implements
penalized maximum likelihood estimation for logistic and ordinary
linear models.  rms works with almost any regression model, but it
was especially written to work with binary or ordinal regression
models, Cox regression, accelerated failure time models,
ordinary linear models,	the Buckley-James model, generalized least
squares for serially or spatially correlated observations, generalized
linear models, and quantile regression.
This package can be used to test any simple null
hypothesis using the root-mean-square goodness of fit test.
Monte Carlo estimation is used to calculate the associated
P-value.
Functions for working with the Tracy-Widom laws and other
distributions related to the eigenvalues of large Wishart
matrices.

The tables for computing the Tracy-Widom densities and distribution
functions were computed by Momar Dieng's MATLAB package
"RMLab", which is 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.
Database interface and MySQL driver for R.  This version
complies with the database interface definition as implemented
in the package DBI 0.2-2.
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.
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 and
NCEP/DOE Reanalysis II 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.
This package is a collection of various functions to extract the implied risk neutral density from option.
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.
This package provides an interface to Unidata's NetCDF
library functions (version 3) and furthermore access to
Unidata's UDUNITS calendar conversions. The routines and the
documentation follow the NetCDF and UDUNITS C interface, so the
corresponding manuals can be consulted for more detailed
information.
R package that allows 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. Note that the Sxslt package suggested can
be installed from the Omegahat repository, though it can also be obtained
from https://github.com/cboettig/Sxslt
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.
A stand-alone package for the random number generator
WELL19937a by F. Panneton, P. L'Ecuyer and M. Matsumoto. The
initialization algorithm allows to seed the generator with a
vector of integers 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.
R interface to nifticlib (nifticlib-2.0.0) (read/write
ANALYZE(TM)7.5/NIfTI-1 volume images)
This package provides an R interface to the NiftyReg image registration tools <http://sourceforge.net/projects/niftyreg/>.
This is an R wrapper to many NOAA APIs including the NCDC climate
API at http://www.ncdc.noaa.gov/cdo-web/webservices/v2. There are 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 that is not part of the climate data
API, the NOAA severe weather inventory, and ERDDAP data. NOAA buoy data is
only on the buoy branch in the Github repository for this package (see url
below).
An interface to the National Oceanic and Atmospheric Administration's Operational Model Archive and Distribution System (NOMADS) that allows R users to quickly and efficiently download global and regional weather model data for processing.  rNOMADS currently supports a variety of models ranging from global weather data to an altitude of 40 km, to high resolution regional weather models, to wave and sea ice models. It can also retrieve archived NOMADS models.  rNOMADS can retrieve binary data in grib format as well as import ascii data directly into R by interfacing with the GrADS-DODS system.
Utility functions to retrieve data from the UK National River Flow
Archive. The package contains R wrappers to the UK NRFA data temporary-API
(http://www.ceh.ac.uk/data/nrfa/). There are functions to retrieve stations
falling in a bounding box, to generate a map and extracting time series and
general information. It also contain the dataset \code{StationSummary}.
This package 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.
The package includes methods for imputation of compositional data
including robust methods, methods to impute 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.
Package to calculate periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). Three main functions are included: RobPer calculates the periodogram. Outlying periodogram bars (indicating a period) can be detected with betaCvMfit. Artificial light curves can be generated using the function tsgen. For more details see Thieler, Fried and Rathjens (2013).
Functions for the determination of optimally robust influence curves in case of
linear regression with unknown scale and standard normal distributed errors where the
regressor is random.
This package provides the function to calculate SVD, regularized SVD, robust SVD and robust regularized SVD method. The robust SVD methods use alternating iteratively reweighted least squares methods. The regularized SVD uses generalized cross validation to choose the optimal smoothing parameters.
This package provides classes and functions for handling observational (time series) data
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 in the robumeta package 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.  The goal is to provide
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.
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.
Robust mixed effects modeling by robustification
of scoring equations using the Design Adaptive Scale approach.
Robust estimation of the generalized log gamma model is provided using Quantile Tau estimator and Weighted Likelihood estimator.
Methods for aggregating ranked lists, especially lists of
genes. It implements the Robust Rank Aggregation (Kolde et. al
in preparation) and some other simple algorithms for the task.
RRA method uses a probabilistic model for aggregation that is
robust to noise and also facilitates the calculation of
significance probabilities for all the elements in the final
ranking.
Linear regression functions using Huber and Bisquare psi
functions
Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.
eXperimental, eXtraneous, or eXtraordinary Functionality
for Robust Statistics.  In other words, methods which are not
yet well established, often related to methods in package
robustbase.
This package computes traditional ROC curves and time-dependent ROC curves using the cross-validation, the 0.632 and the 0.632+ estimators. The 0.632+ estimator of time-dependent ROC curve is useful to estimate the predictive accuracy of prognostic signature based on high-dimensional data. For instance, it outperforms the other approaches, especially the cross-validation solution which is often used. The 0.632+ estimators correct the area under the curve in order to adequately estimate the prognostic capacities regardless of the overfitting level. This package also allows for the construction of diagnostic or prognostic scoring systems (penalized regressions). The methodology is adapted to complete data (penalized logistic regression associated with ROC curve) or incomplete time-to-event data (penalized Cox model associated with time-dependent ROC curve).
Functions for a classification method based on receiver
operating characteristics (ROC). Briefly, features are selected
according to their ranked AUC value in the training set. The
selected features are merged by the mean value to form a
metagene. The samples are ranked by their metagene value and
the metagene threshold that has the highest accuracy in
splitting the training samples is determined. A new sample is
classified by its metagene value relative to the threshold. In
the first place, the package is aimed at two class problems in
gene expression data, but might also apply to other problems.
A collection of functions for ease of
presentation and interpretation of regression analysis.
These functions are used to produce the statistics lectures
in http://pj.freefaculty.org/guides. There
are functions to explore the effect (or lack thereof) from
"mean centering," "residual centering", and standardization.
The vignette "rockchalk" offers a fairly comprehensive overview.
The vignette "Rstyle" has advice about coding in R.
See the outreg function for a convenient way to generate
regression models for reports. For nice, easy to use pictures,
see plotSlopes, plotCurves, plotPlane.
The package title "rockchalk" refers to our school motto,
"Rock Chalk Jayhawk, Go K.U.".
Provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.
The rococo package provides a robust gamma rank correlation
coefficient along with a permutation-based rank correlation test.
The rank correlation coefficient and the test are explicitly
designed for dealing with noisy numerical data.
ROC graphs, sensitivity/specificity curves, lift charts,
and precision/recall plots are popular examples of trade-off
visualizations for specific pairs of performance measures. ROCR
is a flexible tool for creating cutoff-parameterized 2D
performance curves by freely combining two from over 25
performance measures (new performance measures can be added
using a standard interface).  Curves from different
cross-validation or bootstrapping runs can be averaged by
different methods, and standard deviations, standard errors or
box plots can be used to visualize the variability across the
runs. The parameterization can be visualized by printing cutoff
values at the corresponding curve positions, or by coloring the
curve according to cutoff. All components of a performance plot
can be quickly adjusted using a flexible parameter dispatching
mechanism. Despite its flexibility, ROCR is easy to use, with
only three commands and reasonable default values for all
optional parameters.
This package contains functions in order to estimate time-dependent ROC with right-censored data. More precisely, two functions concern the estimation of ROC curves defined by Heagerty, Lumley and Pepe (Biometrics, 2000). The two other functions concern their adaptation for studying the capacity of a marker to predict the excess of mortality of a specific population compared to the general population (same age, gender and calendar year). This last part is based on additive relative survival models and the work of Pohar-Perme (Biometrics, 2011).
Function to estimate the ROC Curve of a continuous-scaled
diagnostic test with the help of a second imperfect diagnostic
test with binary responses.
An extension for RODBC package adding support for parameterized
queries
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.
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.
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.
Optimally robust estimation for regression-type models using S4 classes and
methods
An R package for computing both exact- and stochastic
robust ordinal regression, and maximal vectors.
Oracle database interface (DBI) driver for R.
This is a DBI-compliant Oracle driver based on the OCI.
Implementation of Robust Ordinal Regression for value-based sorting with some extensions and additional tools. It is a novel Multiple-Criteria Decision Aiding (MCDA) framework.
The package provides functions to deal with binary classification
problems in the presence of imbalanced classes. Synthetic balanced samples are
generated according to ROSE (Menardi and Torelli, 2013).
Functions that implement more traditional remedies to the class imbalance
are also provided, as well as different metrics to evaluate a learner accuracy.
These are estimated by holdout, bootstrap or cross-validation methods.
This package implements tools for working with rotational data: it
allows simulation from the most commonly used distributions on SO(3), it
includes methods for different Bayes, mean and median type estimators for the
central orientation of a sample, it provides confidence and credible regions
for the central orientation based on those estimators and it includes a novel
visualization technique for rotation data.
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.
This package provides comprehensive implementations of algorithms
based on rough set theory (RST) and fuzzy rough set theory (FRST), and
integrates these two theories into a single package. It provides
implementations, not only for the basic concepts of RST and FRST, but also
most common methods based on them for handling some: discretization,
feature selection, instance selection, rule induction, and classification
based on nearest neighbors. RST was introduced by Zdzislaw Pawlak in 1982
as a sophisticated mathematical tool based on indiscernibility relations to
model and process imprecise or incomplete information. It works on
symbolic-valued datasets for tackling the data analysis problems. 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. In addition, we
provide a new feature in this version which is missing value completion.
Finally, our package should be considered as an alternative software
library for analyzing data based on RST and FRST. Furthermore, in this
version we provide some algorithms for dealing with missing values.
Rowr 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.
Performs a multivariate normality test based on Royston's
H test
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.
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.
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 package
(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.
rPlant is an R interface to the the many computational
resources iPlant offers through their RESTful application
programming interface. Currently, rPlant functions interact
with the iPlant foundational API, the Taxonomic Name Resolution
Service API, and the Phylotastic Taxosaurus API. Before using
rPlant, users will have to register with the iPlant
Collaborative.
http://www.iplantcollaborative.org/discover/discovery-environment
Read and analyze PLEXOS solutions from R
A programmatic interface to the
Web Service methods provided by the Public Library
of Science journals for search.
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.
Poor Man's Gui: create interactive R analysis sessions
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 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/ .
Reads in sample description and slide description files and
annotates the expression values taken from gpr files. After
normalization data can be visualized as boxplot, heatmap or
dotplot.
Implementing Preferences in R to retrieve Maxima for a given
strict partial order. This also includes the Skyline Operator, which allows
to retrieve the pareto-optimal values.
A set of tools for building formatted string under various
replacement rules: C-style formatting, variable-based formatting, and
number-based formatting
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) http://arxiv.org/abs/1401.7372
The rpsychi offers a number of functions for psychiatry, psychiatric nursing, clinical psychology. Functions are primarily for statistical significance testing using published work. For example, you can conduct a factorial analysis of variance (ANOVA), which requires only the mean, standard deviation, and sample size for each cell, rather than the individual data. This package covers fundamental statistical tests such as t-test, chi-square test, analysis of variance, and multiple regression analysis. With some exceptions, you can obtain effect size and its confidence interval. These functions help you to obtain effect size from published work, and then to conduct a priori power analysis or meta-analysis, even if a researcher do not report effect size in a published work.
Access PubChem data (compounds, substance, assays).
Structural information is provided in the form of SMILES
strings. This package only provides access to a subset of the
precalculated data stored by PubChem. Bio-assay data can be
accessed to obtain descriptions as well as the actual data. It
is also possible to search for assay ID's by keyword. Currently
the main limitation is that only 1000 molecules can be
downloaded at a time from the PubChem servers
Client for accessing ProPublica's data journalism APIs
This package provides R functions for performing
computation on GPU. The implementation is based on the NVIDIA
CUDA Toolkit. Besides the core functionality, it also aims for
robustness, performance, and scalability.
This package provides an easy-to-use interface for the
Pushbullet service which provides fast and efficient notifications
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.

The package currently uses the curl binary directly as using the RCurl
package worked only partially: messages can be sent, but return codes
from the service are being lost.
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.
This package permits calls to Python 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.
Rquake is a package for 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 visible to the R
user. Currently a number option pricing functions are included, both vanilla
and exotic, as well as a broad range of fixed-income functions. Also included
are general calendaring and holiday utilities. Further software contributions
are welcome.

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.

The Windows binary version is self-contained and does not require a QuantLib
(or Boost) installation.

RQuantLib uses the Rcpp R/C++ interface class library. See the Rcpp package
on CRAN (or R-Forge) for more information on Rcpp.

Note that while RQuantLib's code is licensed under the GPL (v2 or later),
QuantLib itself is released under a somewhat less restrictive Open Source
license (see QuantLib-License.txt).
Analyzes and predicts from matrix population models in the way of the Ramas (c) software
Software for genomic prediction with the RR-BLUP mixed model. One application is to estimate marker effects by ridge regression; alternatively, BLUPs can be calculated based on an additive relationship matrix or a Gaussian kernel.
rrBlupMethod6 -- Re-parametrization of mixed model
formulation to allow for a fixed residual variance when using
RR-BLUP for genomwide estimation of marker effects and linear
transformation of the adjusted means proposed by Piepho et
al.(2011)
Robust Location and Scatter Estimation and Robust
Multivariate Analysis with High Breakdown Point.
Robust multivariate methods for high dimensional data:
outlier detection, PCA, PLS, classification
Robust Location and Scatter Estimation and Robust
Multivariate Analysis with High Breakdown Point for Incomplete
Data
Make use of the physical random number accessed by the RdRand
instruction in recent Intel CPUs (Ivy Bridge and later).
An R client for the Redis persistent key-value database available from http://redis.io.
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.
RRreg provides 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 and as predictors in a linear regression. 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.
Apply a set of transformation rules to any R function,
call or expression.
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
RSAGA provides access to geocomputing and terrain analysis
functions of SAGA from within R by running the command line version of
SAGA. RSAGA 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/.
This package implements a statistical tool for modeling multivariate relationships using a stepwise cluster analysis (SCA) method.
Client for Rserve, allowing to connect to Rserve instances and issue commands.
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.
rsdmx provides a set of classes and methods to read data and metadata documents
exchanged through the Statistical Data and Metadata Exchange (SDMX) framework. The
package currently focuses 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.
This package estimates means and covariance matrix of multiple variables with missing data using Huber weight and then estimates a SEM model using lavaan or EQS.
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).
This package can be used to estimate models using a hierarchical Bayesian 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://elsa.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 accessing the Adobe Analytics Reporting API
This package contains a function RSKC which runs the robust sparse K-means clustering algorithm.
Provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis.
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.
This package is a programmatic interface to various SNP datasets
on the web: openSNP, NBCI's dbSNP database, and Broad Institute SNP
Annotation and Proxy Search. This package started as a library to
interact with openSNP alone, so most functions deal with openSNP.
RSNPset is a software implementation of the use of efficient score statistics
in genome-wide SNP set analysis with complex traits. The package provides three standard
score statistics (Cox, Binomial, and Gaussian) but 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.
Provided with a Socrata dataset resource URL,
or a Socrata SoDA web API query,
or a Socrata "human-friendly" URL,
returns an R data frame.
Converts dates to POSIX format.
Manages throttling by Socrata.
sofia-ml is a suite of fast incremental algorithms for machine learning that can be used for training models for classification or ranking
General Non-linear Optimization Using Augmented Lagrange
Multiplier Method
Somoclu is a cluster-oriented implementation of self-organizing maps. It 			     relies on MPI for distributing the workload, and it can be accelerated by 			     CUDA on a GPU cluster. A sparse kernel is also included, which is useful for 		     training maps on vector spaces generated in text mining processes.
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.
Calculate SPEARpesticide in R
(http://www.systemecology.eu/SPEAR/index.php)
A package for converting 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 interface to SPRNG 2.0 APIs, and examples and
documentation for its use.
Database Interface R driver for SQLite.  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 3.7.17) is included.
This package uses SQLite's loadable extension feature to
provide a number of additional SQL functions and aggregates.
The package is a wrapper of extension functions written by Liam
Healy and made available through the SQLite website
(http://www.sqlite.org/contrib).  Math: acos, asin, atan, atn2,
atan2, acosh, asinh, atanh, difference, degrees, radians, cos,
sin, tan, cot, cosh, sinh, tanh, coth, exp, log, log10, power,
sign, sqrt, square, ceil, floor, pi. String: replicate,
charindex, leftstr, rightstr, ltrim, rtrim, trim, replace,
reverse, proper, padl, padr, padc, strfilter. Aggregate: stdev,
variance, mode, median, lower_quartile, upper_quartile.
This package provides various Singular Spectrum Analysis routines.
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."
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.
Unified object oriented interface for multiple independent
streams of random numbers from different sources.
This package provides functions to make it easy to access
the RStudio API when available, and provide informative error messages
when not.
A collection of efficient and effective tools and
algorithms for subgroup discovery and analytics. The package
integrates an R interface to the org.vikamine.kernel library
of the VIKAMINE system (http://www.vikamine.org) implementing
subgroup discovery, pattern mining and analytics in Java.
This package provides an interface for the package of
nonlinear differential algebraic equation solvers that comprise
SUNDIALS. ODEs are expressed as R functions or as compiled
code.
Interface to four Sunlight Foundation APIs
(http://sunlightfoundation.com/api/) for government data, including the
Congress API v3, the Capitol Words API, the Influence Explorer API, and the
Open States API. Sunlight Foundation is a nonpartison nonprofit that
collects and provides data on government activities, and those that
influence government. Functions are provided to interact with each of the
four APIs. rCharts - https://github.com/ramnathv/rCharts
This package is a processing program for spatially
distributed data. It features graphing tools, query building, and
polygon clipping. A graphical user interface is provided.
A graphics device for R that uses the w3.org xml standard
for Scalable Vector Graphics.
A graphics device for R that uses the w3.org xml standard
for Scalable Vector Graphics.  This version supports
tooltips with 1 to 3 lines, hyperlinks, and line styles.
An R interface to the SYMPHONY MILP solver. The package code links to the local SYMPHONY installation.
If no installation is found by the configure script, version 5.4.7 will be installed from the included SYMPHONY sources.
Access SymPy computer algebra system from R via Jython
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 extreme failure sets'. This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).
Exploit controlled vocabularies organized on tematres servers.
rTensor is a set of tools for creation, manipulation, and modeling
of tensors with arbitrary number of modes. A tensor in the context of data
analysis is a multidimensional array. rTensor does this by providing a S4
class 'Tensor' that wraps around the base 'array' class. rTensor also
provides common tensor operations as methods, including matrix unfolding,
summing/averaging across modes, calculating the Frobenius norm, and taking
the inner product between two tensors. Familiar array operations are
overloaded, such as index subsetting via '[' and element-wise operations.
rTensor also implements various tensor decomposition, including CP, GLRAM,
MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements
transpose, product, and SVD, as defined in Kilmer et al. (2013). Some
auxiliary functions include the Khatri-Rao product, Kronecker product, and
the Hamadard product for a list of matrices. Development of rTensor has
been generously supported by Cornell's Department of Statistical Science.
RTextTools is a machine learning package for automatic
text classification that makes it simple for novice users to
get started with machine learning, while allowing experienced
users to easily experiment with different settings and
algorithm combinations. The package includes nine algorithms
for ensemble classification (svm, slda, boosting, bagging,
random forests, glmnet, decision trees, neural networks,
maximum entropy), comprehensive analytics, and thorough
documentation.
This package contains several functions for calculation of texture metrics for Grey Level Co-occurrence matrices
A set of R functions to output Rich Text Format (RTF) files with high resolution tables and graphics that may be edited with a standard word processor such as Microsoft Word.
RTFBS identifies and scores possible Transcription Factor
Binding Sites and allows for FDR analysis and pruning.  It supports
splitting of sequences based on size or a specified GFF, grouping
by G+C content, and specification of Markov model order.  The heavy
lifting is done in C while all results are made available via R.
This package will read and write TIFF format images and
return 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
support 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.
Created mainly for use with seismic tomography, this program
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.
Package for 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 package 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
This package contains the program ttf2pt1, for use with the
extrafont package. This product includes software developed by the TTF2PT1
Project and its contributors.
A strong type system for R which supports
symbol declaration and assignment with type checking
and condition checking.
R port of 'universalchardet', that is the encoding detector
library of Mozilla.
Unobserved Components Models (introduced in Harvey, A. (1989),
Forecasting, structural time series models and the Kalman filter, Cambridge
New York: Cambridge University Press) decomposes a time series into
components such as trend, seasonal, cycle, and the regression effects due
to predictor series which captures the salient features of the series to
predict its behavior.
ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.
R functions implementing a standard Unit Testing
framework, with additional code inspection and report
generation tools
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
This package provides high-level interface utilities for Just Another Gibbs Sampler (JAGS).  The primary functions facilitate running parallel JAGS models for multiple chains, automated control of convergence and sample length diagnostics, evaluation of the performance of a model against simulated data, and compatibility with the WinBUGS syntax of model files with data and initial values lists.  Model fitting can be done locally (via the rjags package or separate JAGS instances) or via distributed computing clusters such as those provided by snow (a Simple Network Of Workstations), Xgrid, or other user specified functions.  A JAGS extension module implementing additional distributions for JAGS models (including the Pareto family of distributions) is also provided.
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.
This package provides utility methods useful when programming and developing R packages.
This package 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.
This package provides several procedures for computing "r-values". 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.
A wrapper to the VertNet collections database API.
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.
Variable metric nonlinear function minimization with bounds constraints
R interface to Vowpal Wabbit fast out-of-core learning system
The Vowpal Wabbit (VW) project is a fast out-of-core learning system
sponsored by Yahoo! Research and written by John Langford along with a
number of contributors.

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

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

This R package does not include the distributed computing implementation of
the cluster/ directory of the upstream sources.  Use of the software as a
network servie is also not directly supported as the aim is a simpler direct
call from R for validation and comparison.
Computes estimates of the probability of related individuals sharing a rare variant.
Used to compute population controls weighted rare variants transmission distortion test
Use multiple regression methods to test rare variants
association with disease traits.
Rwave is a library of R functions which provide an
environment for the Time-Frequency analysis of 1-D signals (and
especially for the wavelet and Gabor transforms of noisy
signals). It was originally written for Splus by Rene Carmona,
Bruno Torresani, and Wen L. Hwang, first at the University of
California at Irvine and then at Princeton University.  Credit
should also be given to Andrea Wang whose functions on the
dyadic wavelet transform are included. Rwave is based on the
book: "Practical Time-Frequency Analysis: Gabor and Wavelet
Transforms with an Implementation in S", by Rene Carmona, Wen
L. Hwang and Bruno Torresani, Academic Press, 1998.
This package will download model predictions from 15 different global circulation models in 20 year intervals from the world bank.  Users can also access historical data, and create maps at 2 different spatial scales.
a Bipartite graph and is constructed based on the spatial and/or non-spatial attributes of the spatial objects in the dataset. Secondly, RW techniques are utilized on the graphs to compute the outlierness for each point (the differences between spatial objects and their spatial neighbours). The top k objects with higher outlierness are recognized as outliers.
This package provides programmatic access to Yahoo!
Weather and NOAA APIs
RWebLogo is a wrapper for the WebLogo python package
that allows generating of customised sequence logos. Sequence logos are
graphical representations of the sequence conservation of nucleotides (in a
strand of DNA/RNA) or amino acids (in protein sequences). Each logo
consists of stacks of symbols, one stack for each position in the sequence.
The overall height of the stack indicates the sequence conservation at that
position, while the height of symbols within the stack indicates the
relative frequency of each amino or nucleic acid at that position. In
general, a sequence logo provides a richer and more precise description of,
for example, a binding site, than would a consensus sequence.
An R interface to Weka (Version 3.7.11).
Weka is a collection of machine learning algorithms for data mining
tasks written in Java, containing tools for data pre-processing,
classification, regression, clustering, association rules, and
visualization.  Package RWeka contains the interface code, the Weka
jar is in a separate package RWekajars.  For more information on
Weka see <http://www.cs.waikato.ac.nz/~ml/weka/>.
External jars required for package RWeka.
This package provides Wiener process distribution functions,
namely the Wiener first passage time density, CDF, quantile and random
functions.
A plug in for using WinEdt as an editor for R
The Rwinsteps package facilitates communication between R
and the Rasch modeling software Winsteps. The package currently
includes functions for reading and writing command files,
sending them to Winsteps, reading and writing data according to
command file specifications, reading output into R, and
plotting various results.
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.
Fits the R x C inference model described in Greiner and
Quinn (2009).  Allows incorporation of survey results.
This package allows the visualization of your favorite
XKCD comic strip directly from R. XKCD web comic content is
provided under the Creative Commons Attribution-NonCommercial
2.5 License.
The RXMCDA package for the R statistical software allows you to read many XMCDA tags and transform them into R variables which are then usable in your MCDA algorithms written in R. The library also allows to write certain R variables into XML files according to the XMCDA standard.
Identify and display TRACEs for a specified shrinkage path and determine
the extent of shrinkage most likely, under normal distribution theory, to produce an
optimal reduction in MSE Risk in estimates of regression (beta) coefficients.
An interface to the yacas computer algebra system.
You can use this package to translate thousands of words. The Youdao translation open API is applied in this package. But, it just translates less than 1000 English words into Chinese.
Data, functions etc. from the 'R you ready?' book
A programmatic interface to web-services of Youtheria. Youtheria is an online database of mammalian trait data.
This package is a port to R of the SysGran program, written in Delphi by Camargo (2006). It contains functions for the analysis of grain size samples (in logarithmic (phi) and geometric (micrometers) scale) based on various methods, like Folk & Ward (1957) and Methods of Moments (Tanner, 1995), among others; textural classifications and distribution of unconsolidated sediments are shown in histograms, bivariated plots and ternary diagrams of Shepard (1954) and Pejrup (1988). English and Portuguese languages are supported in outputs
R is very powerful but it lacks some of the functionalities found in
Stata or SPSS to manage survey data. The 'memisc' package provides these
(variable labels, value labels, definable missing values and so on), but to
efficiently work these functions need a graphical interface to allow the user
to get an overview of the data. This package provides such a graphical interface,
similar in fashion to SPSS's Variable View and data managing system. It uses the
'memisc' package as its backend. Additionally, 'Rz' has a powerful plot assistant
interface based on 'ggplot2'.
Set of tools to assess the performance of a model through the computation of typical prediction scores against one or more observational datasets or reanalyses (a reanalysis being a physical extrapolation of observations that relies on the equations from a model, not a pure observational dataset).
The main function s4vd performs a biclustering via sparse
singular value decomposition with a nested stability selection.
The results is an biclust object and thus all methods of the
biclust package can be applied.
Semiparametric empirical likelihood ratio
based test of changepoint with one-change or epidemic alternatives
with data-based model diagnostic
Maximum likelihood tools to fit and compare models of species
abundance distributions and species rank-abundance distributions.
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/).
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.
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, Hukuhara difference,
quantiles) as well as for calculating (Bertoluzza) distance,
sample variance, sample covariance, sample correlation, 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 0.4 fixes some bugs
of version 0.3 and includes an additional function to calculate
quantiles of samples of polygonal fuzzy numbers.
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.
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.
This package consists of two components: a framework
for running sets of MCMC samplers on sets of distributions with
a variety of tuning parameters and plotting functions to visualize
the results of those simulations.  See sc-intro.pdf for an
introduction.
Computes sample size for Student's t-test and for the
Wilcoxon-Mann-Whitney test for categorical data. The t-test
function allows paired and unpaired (balanced / unbalanced)
designs as well as homogeneous and heterogeneous variances. The
Wilcoxon function allows for ties.
Computes the required sample size using the optimal designs with multiple constraints proposed in Mayo et al.(2010). This optimal method is designed for two-arm, randomized phase II clinical trials, and the required sample size can be optimized either using fixed or flexible randomization allocation ratios.
Computes the required sample size for estimation of totals, means
and proportions under complex sampling designs.
A set of R functions for calculating sample size
requirements using three different Bayesian criteria in the
context of designing an experiment to estimate a normal mean or
the difference between two normal means.  Functions for
calculation of required sample sizes for the Average Length
Criterion, the Average Coverage Criterion and the Worst Outcome
Criterion in the context of normal means are provided.
Functions for both the fully Bayesian and the mixed
Bayesian/likelihood approaches are provided.
A set of R functions for calculating sample size
requirements using three different Bayesian criteria in the
context of designing an experiment to estimate the difference
between two binomial proportions. Functions for calculation of
required sample sizes for the Average Length Criterion, the
Average Coverage Criterion and the Worst Outcome Criterion in
the context of binomial observations are provided. In all
cases, estimation of the difference between two binomial
proportions is considered. Functions for both the fully
Bayesian and the mixed Bayesian/likelihood approaches are
provided.
Functions for drawing and calibrating samples.
Sampling procedures from the book 'Stichproben. Methoden
und praktische Umsetzung mit R' by Goeran Kauermann and Helmut
Kuechenhoff (2010)
Functions to estimate from survey data. This package is a user-friendly wrapper of the samplingVarEst package. It considers that the user is more familiar with practical survey data rather than with research on survey sampling (variance estimation). More functionalities are on the way.
In the field of stratified sampling design, this package
offers an approach for the determination of the best
stratification of a sampling frame, the one that ensures the
minimum sample cost under the condition to satisfy precision
constraints in a multivariate and multidomain case. This
approach is based on the use of the genetic algorithm: each
solution (i.e. a particular partition in strata of the sampling
frame) is considered as an individual in a population; the
fitness of all individuals is evaluated applying the
Bethel-Chromy algorithm to calculate the sampling size
satisfying precision constraints on the target estimates.
Functions in the package allows to: (a) analyse the obtained
results of the optimisation step; (b) assign the new strata
labels to the sampling frame; (c) select a sample from the new
frame accordingly to the best allocation.
Functions for the execution of the genetic algorithm are a modified
version of the functions in the 'genalg' package.
Functions to calculate some point estimators and estimating their variance under unequal probability sampling. Single and two stage sampling designs are considered. Some approximations for the second order inclusion probabilities are also available (sample and population based).
This package provides the base classes and routines for sampling surface simulation
useful in the comparison of different areal sampling methods in forestry, ecology
and natural resources.
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.
This package has several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.
Software for the book Spectral Analysis for Physical
Applications, Donald B. Percival and Andrew T. Walden,
Cambridge University Press, 1993.
Functions for statistical analysis of point processes.
Read SAS files in the sas7bdat data format.
Using any importation code designed for SAS users to read
ASCII files into sas7bdat files, the SAScii package parses
through the INPUT block of a (.sas) syntax file to design the
parameters needed for a read.fwf function call.  This allows
the user to specify the location of the ASCII (often a .dat)
file and the location of the .sas syntax file, and then load
the data frame directly into R in just one step.
Data sets and sample lmer analyses corresponding
to the examples in Littell, Milliken, Stroup and Wolfinger
(1996), "SAS System for Mixed Models", SAS Institute.
A statistical method for significant analysis of
comparative proteomics based on LC-MS/MS Experiments
This package provides 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.
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.
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.
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
mgcv(gam) package 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.
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.
A package to estimate stellar mass and radius given
observational data of effective temperature, [Fe/H], and
astroseismic parameters. The results are obtained adopting a
maximum likelihood technique over a grid of computed stellar
models.
Grid of stellar models computed with various initial
helium abundances and different mixing-length values.
SCGLR extends the Fisher Scoring Algorithm so as to combine PLS
regression with GLM estimation in the multivariate context.
scholar 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
The package 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 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).
SciDB is an open-source array database (http://scidb.org). The scidb package provides an R interface to SciDB.
SciencesPo is a facility package for the political science crowd. It provides well-founded functions for analyzing random and nonrandom data. Future updates will focus on innovative techniques published in relevant journals in the field of political science.
Sparse Column-wise Inverse Operator for estimating the inverse covariance matrix.  Note that this is a preliminary version accompanying the arXiv paper (arXiv:1203.3896) in 2012.   This version contains only the minimal set of functions for estimation and cross validation.
A collection of functions that creates graphs with error
bars for data collected from one-way or higher factorial
designs.
Functions to install SciViews additions to R, and more (various) tools
An R package for single-case meta-analysis.
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 package for designing single-case phase, alternation and multiple-baseline experiments, as well as for conducting randomization tests on data gathered by means of such designs.
scrypt is an R package for working with scrypt. Scrypt is a
password-based key derivation function created by Colin Percival. The
algorithm was specifically designed to make it costly to perform
large-scale custom hardware attacks by requiring large amounts of memory.
dive profiles, decompression models and gas calculations
for scuba diving
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.
This package 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.
Calculating the third-order unbiased p-values via speedy
double bootstrap method.
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.
Implementation of methods for statistical disclosure control in
tabular data
This package allows for  computing (and by default plotting) different types of serial dependence diagrams.
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.
The package performs two tests to evaluate if the
experiments are associated and returns a list of interesting
features common to all the experiments.
This packages provides a set of tools for post processing the
outcomes of species distribution modeling exercises. It includes novel
methods for comparing models and tracking changes in distributions through
time. It further includes methods for visualizing outcomes, selecting
thresholds, calculating measures of accuracy and landscape fragmentation
statistics, etc.. This package was made possible in part by financial
support from the Australian Research Council & ARC Research Network for
Earth System Science.
This package includes several methods to create virtual species
distribution map. Those maps can be used for species distribution modelling
(SDM) study. SDM use environmental data for sites of occurrence of a
species to predict all the sites where the environmental conditions are
suitable for the species to persist, and may be expected to occur.
Fitting discrete Bayesian networks using marginal mixture distributions
This package provides saddlepoint approximations to some measures of risk based on the compound Poisson risk process that is perturbed by a Brownian motion.  It also includes various approximation methods for the probability of ruin.  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).
Implements algorithms to help with scenario discovery - currently only modified version of the the Patient Rule Induction Method.
Calculates parameters of the seawater carbonate system and assists the design of ocean acidification perturbation experiments.
Code and/or corrections were contributed by Andreas Andersson,
Bernard Gentili, Andreas Hofmann, Jim Orr,
Aurelien Proye and Karline Soetaert and James Rae
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.
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.
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.
seasonal is an easy-to-use and full-featured R-interface
to X-13ARIMA-SEATS, the newest seasonal adjustment software from
the United States Census Bureau. See the README file or the vignette for
installation notes.
A model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables
Displays state-event charts, for graphical presentation of longitudinal data.
SECP package provides functionality for estimating
parameters of site clusters on 2D & 3D square lattice with
various lattice sizes, relative fractions of accessible sites
(occupation probability), iso- & anisotropy, von Neumann &
Moore (1,d)-neighborhoods
Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
Tools for designing spatially explicit capture--recapture studies of animal populations (primarily a simulation manager for package secr)
Suite of functions for the simulation and visualisation 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
This package contains codes that setup SEER
(Surveillance, Epidemiology and End Results) data use with R.
It includes SEER and Japanese A-Bomb survivor data analysis examples.
By using it, the portability of SEER R scripts should improve.
This package works only with the latest SEER release of April 15, 2014.
seewave provides 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).
Given a regression model, segmented `updates' the model by adding one or more segmented 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
Predicts standard errors of estimated genetic parameters
for IGE
Sejong(http://www.sejong.or.kr/) corpus and
Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum)
dictionaries for KoNLP
This package implements a novel method for fitting a
bounded probability distribution to quantiles (for example
stated by an expert), see Bornkamp and Ickstadt (2009) for
details.  For this purpose B-splines are used, and the density
is obtained by penalized least squares based on a Brier entropy
penalty.  The package provides methods for fitting the
distribution as well as methods for evaluating the underlying
density and cdf. In addition methods for plotting the
distribution, drawing random numbers and calculating quantiles
of the obtained distribution are provided.
Multi-stage selection is practiced in numerous fields of life and social sciences and particularly in breeding. A special characteristic of multi-stage selection is that candidates are evaluated in successive stages with increasing intensity and effort, and only a fraction of the superior candidates is selected and promoted to the next stage. For the optimum design of such selection programs, the selection gain plays a crucial role. It can be calculated by integration of a truncated multivariate normal (MVN) distribution. While mathematical formulas for calculating the selection gain and the variance among selected candidates were developed long time ago, solutions for numerical calculation were not available. This package can also be used for optimizing multi-stage selection programs for a given total budget and different costs of evaluating the candidates in each stage.
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/).
Detection of outliers and influential errors using a latent variable model.
Regularization for variable selection in model-based clustering and discriminant analysis
This package contains functions for fitting general linear structural
equation models (with observed and unobserved 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.
More generators are planned in near future.
Data evaluation support tools include:
-single attribute based statistical evaluation: mean, median,standard deviation, skewness, kurtosis, KS test, Hellinger distance
-evaluation based on clustering using Adjusted Rand Index (ARI)
-evaluation based on classification performance with various learning models, eg, random forests.
In this package, we provide parametric and semi-parametric regression models to analyze semi-competing risks data.
This package provides routines to check identifiability or non-identifiability of linear structural equation models as described in Drton, Foygel & Sullivant (Ann. Statist., 2011) and Foygel, Draisma & Drton (Ann. Statist., 2012).  The routines are based on the graphical representation of structural equation models by a path diagram/mixed graph.
Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model.
Functions for semiparametric regression analysis, to
complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J.
(2003). Semiparametric Regression. Cambridge University Press.
Routine for fitting bivariate probit models with semiparametric predictors (including linear and nonlinear effects) in the presence of correlated error equations, endogeneity or sample selection. Bivariate copula models are also supported.
Routine for fitting continuous 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.
This package provide useful tools for structural equation
modeling analysis.
Package contains a simple SMTP client which provides a
portable solution for sending email, including attachment, from
within R.
A collection of functions for factor screening, global sensitivity analysis and reliability sensitivity analysis of model output.
This package performs sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition.  The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468).  The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al.  The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al.  The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test.  The other functions in the package are internal functions.
Sensitivity analysis in observational studies, including evidence factors and amplification, using the permutation distribution of Huber-Maritz M-statistics, including the permutational t-test.
Sensitivity analysis analysis in matched observational studies with multiple controls using weighted M-statistics to increase design sensitivity.
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 and
2-AC. 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.
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.
This package implements the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases), and applies generally to individual-randomized clinical trials with multiple active treatment groups and a shared placebo group, with 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 arm 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 outcomes (e.g., stopping early for efficacy futility); unconditional power for comparing treatment efficacy; 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 outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.
Package includes functions to analyse sequence data in FASTA Format with known HLA types or other features and calculate in the case of HLA as feature possible epitopes from them. Further it calculates possible co-mutations from these sequences and generates graphical output in PDF.
SeqGrapheR package provide interactive GUI for visualization of DNA sequence clusters. Details and principles of usage are described in user manual and (2010 BMC Bioinformatics 11:378) ,For full functionality installed NCBI blast is required
Exploratory data analysis and data visualization for
biological sequence (DNA and protein) data. Include also
utilities for sequence data management under the ACNUC system.
Computes necessary information to meta analyze region-based tests for rare genetic variants (e.g. SKAT, T1) in individual studies, and performs meta analysis.
Integrate sequencing data (VCF) or meta-analysis results in R
A program that computes the probability of crossing
sequential boundaries in a clinical trial. It implements the
Armitage-McPherson and Rowe Algorithm using the method
described in Schoenfeld D. (2001)" A simple Algorithm for
Designing Group Sequential Clinical Trials" Biometrics 27: pp,
972-974
User inputs a range of values r1 and r2, as well as a
number of columns v, in the function sq.pe(r1,r2,v). The
returned statement is a permutation matrix.
This package includes functions for handling DNA
sequences, especially simulated RFLP and TRFLP pattern based on
selected restriction enzyme and DNA sequences.
Educational package used in R courses to illustrate OO programming
and package development. Using biological sequences (DNA and RNA)
as a working example.
Calculates exact critical values to perform continuous or group sequential analysis for Poisson type data using the Maximized Sequential Probability Ratio Test (MaxSPRT) with a Wald type rejection boundary. Also calculates properties such as statistical power, expected time to signal when the null is rejected and maximum sample size needed to obtain a certain power.
This package provides tools to analyze NGS DNA-sequencing from paired normal-tumor samples, including cellularity and ploidy estimation; mutation and copy number (allele-specific and total copy number) detection, quantification and visualization.
Infrastructure for seriation with an implementation of several
seriation/sequencing techniques to reorder matrices, dissimilarity
matrices, and dendrograms. Also contains some visualizations techniques
based on seriation.
Start a simple HTTP server in R to serve static files 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.
This package allows computing and visualizing
socioeconomic indices and categories distributions from
datasets of socioeconomic variables (These tools were developed
as part of the EquitArea Project, a public health program).
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.
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.
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 triplet intensity function T (and some other).
An Object-oriented Framework for Geostatistical Modeling
in S+
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) algorithm 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. This package
apply template metaprogramming techniques, therefore -- when compiling the
package from source -- a high level of optimization is needed to gain full
speed (e.g. for the GCC compiler use -O3). Use of multiple processors for
cross validation and subsampling is supported through OpenMP. The Armadillo
C++ library is used as the primary linear algebra engine. (The sglOptim
package version a.b.c.d is interpreted as follows: a - primary
version, b - major updates and fixes, c - source revision as corresponding
to R-Forge, d - minor fixes made only to the CRAN
branch of the source)
Functions for computing power and boundaries for pre-licensure vaccine trials using the Generalized Likelihood Ratio tests proposed by Shih, Lai, Heyse and Chen
This package implements seven different methods for multi-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
The package SGPdata contains three data sets utilized by the SGP Package as exemplars for users to set up their own data for SGP analyses.
The package for Sample Generation by Replacement simulations. 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.
Functions for plotting graphical shapes
such as ellipses, circles, cylinders, arrows, ...
Functions to read and write ESRI shapefiles
Routines for the statistical analysis
of shapes. In particular, the package provides routines for
Procrustes analysis, displaying shapes and principal components,
testing for mean shape difference, thin-plate spline transformation
grids and comparing covariance matrices.
a collection of tools for analyzing significance of trading
strategies, based on the Sharpe ratio and overfit of the same.
A collection of functions to support soil survey
Data sets and SAR, SARX, HSAR and HSARX
models as described in Solymos and Lele
(2012, GEB 21, 109-120).
Shiny 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.
Adds more Twitter Bootstrap components to the shiny interface
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.
Shiny wrappers for the RGL package. This package exposes RGL's
ability to export WebGL visualization in a shiny-friendly format.
The SHIP-package allows the estimation of various types of
shrinkage covariance matrices. These types differ in terms of
the so-called covariance target (to be chosen by the user), the
highly structured matrix which the standard unbiased sample
covariance matrix is shrunken towards and which optionally
incorporates prior biological knowledge extracted from the
database KEGG. The shrinkage intensity is obtained via an
analytical procedure.
An interface to the API of the E-commerce service Shopify
(http://docs.shopify.com/api)
Analyzes shooting data with respect to group shape,
precision, and accuracy. This includes graphical methods,
descriptive statistics, and inference tests using standard,
but also non-parametric and robust statistical methods. Works
with files exported by OnTarget PC and OnTarget TDS or with
custom data files in text format with a similar structure.
This package can override text rendering functions of many
R graphics devices (such as svg(), pdf(), etc.), so that text in R
graphs can be displayed or printed using almost any system fonts.
This is done by analyzing the glyphs contained in the font file and
then rendering the text into lines and polygons, which are all device
independent. This package will be helpful if you want to use non-standard
fonts in your graphics device (for example, drawing Chinese characters in
pdf device).
Functions for converting network data from a
SpatialLinesDataFrame object to a "igraph-class" object.
Post-estimation shrinkage of regression coefficients in statistical modeling can be
used to correct for the overestimation of regression coefficients caused by variable selection.
While global shrinkage modifies all regression coefficients by the same factor, parameterwise
shrinkage factors differ between regression coefficients. With variables which are either
highly correlated or associated with regard to contents, such as several columns of a design
matrix describing a nonlinear effect, parameterwise shrinkage factors are not interpretable
and a compromise between global and parameterwise shrinkage, termed 'joint shrinkage', is a
useful extension.
A computational shortcut to resampling-based shrinkage factor estimation based on DFBETA
residuals can be applied.
Global, parameterwise and joint shrinkage for models fitted by lm, glm, coxph, or (for
R <= 3.1.0) mfp is available.
This package 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
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.
SIDIER provides functions for reading and writing fasta sequences, finding unique haplotypes, estimating genetic distances based on gap positions and lengths, combining distance matrices and estimating and plotting percolation networks.
Functions for making sieve test reports. Sieve test is widely used to obtain particle-size distribution of powders or granular materials.
A really simple package for printing function signatures.
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.
A set of signal processing R functions originally written for Matlab/Octave.
Includes filter generation utilities, filtering functions,
resampling routines, and visualization of filter models. It also
includes interpolation functions.
Predicts presence of signal peptides in eukaryotic protein using
hidden semi-Markov models. The implemented algorithm can be accessed both
from command line and GUI.
SIGORA is a Pathway Analysis package. Pathway Analysis is
the process of statistically linking observations on the
molecular level to biological processes or pathways on the
systems (cell, tissue, organ, organism) level. Traditional
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 many components are 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-GPSs), 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, PID_BIOCARTA, PID_NCI, INOH and REACTOME pathway
repositories. For more information, please see our manuscript
submitted to Bioinformatics, 2012.
SigTree 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.
Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011) or ratios (Hasler and Hothorn, 2012) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008) or covariance matrices (Hasler, 2014).
Simulates correlated multinomial responses by utilizing threshold approaches under a cumulative link model, a continuation ratio model or a baseline category logit marginal model specification.
Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures.
The package Sim.DiffProc is an object created in R environment 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.
simecol is 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 (avoids copy
and paste) and improves readability and usability of code.
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.
Allows a Simile model saved as a compiled binary to be
loaded, parameterized, executed and interrogated. This version
works with Simile v5.97 on.
Algorithms to create prognostic biomarkers using
biological networks
This package provides R functions for simulation of event histories with nonlinear baseline hazard rate functions and nonlinear covariate effects using inversion sampling on all-cause hazard rate functions.
The R package simone implements the inference of
co-expression networks based on partial correlation
coefficients from either steady-state or time-course
transcriptomic data. Note that with both type of data this
package can deal with samples collected in different
experimental conditions and therefore not identically
distributed. In this particular case, multiple but related
networks are inferred on one simone run.
The package 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.
SimpleTable provides a series of methods to conduct
Bayesian inference and sensitivity analysis for causal effects
from 2 x 2 and 2 x 2 x K tables when unmeasured confounding is
present or suspected.
Simplex density, distribution, quantile functions as well as random variable
generation of the simplex distribution are given. Regression analysis of proportional data
using various kinds of simplex models is available. In addition, GEE method can be applied
to longitudinal data to model the correlation. Residual analysis is also involved. Some
subroutines are written in C with GNU Scientific Library (GSL) so as to facilitate the
computation.
This package provides methods to integrate functions over m-dimensional simplices
in n-dimensional Euclidean space.  There are exact methods for polynomials and
adaptive methods for integrating an arbitrary function.
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.
This package provides a number of functions to simulate restriction enzyme digestion, library construction and fragments size selection to predict the number of loci expected from most of the Restriction site Associated DNA (RAD) and Genotyping By Sequencing (GBS) approaches. SimRAD aims is to provide an estimation of the number of loci expected from a particular genome depending on the protocol type and parameters allowing to assess feasibility, multiplexing capacity and the amount of sequencing required.
Tools for setting up, conducting, and evaluating larger-scale
simulation studies, including parallel computations.
This package can be used to generate data using the
structural equation modeling framework. This package is
tailored to use those simulated data for various purposes, such
as model fit evaluation, power analysis, or missing data
handling and planning.
RNA-Seq 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.  Rather than generating data from a questionable parametric model, the SimSeq package provides tools for simulating RNA-Seq data by subsampling from existing datasets.  The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of actual RNA-Seq data.  Users can control the proportion of genes simulated to be differentially expressed (DE) and can adjust the magnitude of differences for DE genes. SimSeq requires a matrix of RNA-Seq read counts with large sample sizes in at least two treatment groups. Although a full version of such a dataset is not distributed with the package, numerous candidate datasets are publicly available.
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 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.
This packages provides and implementation of the Smooth Incremental Graphical Lasso Estimation (SINGLE) which can be used to estimate dynamic networks from non-stationary time series data.
A platform for experimenting with spatially explicit individual-based vegetation models.
Package provides functions to calculate daily solar
radiation at horizontal surface using several well-known
models. It also includes functions for model calibration based
on ground-truth data as well as a function for
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.
(Iterative) Sure Independence Screening for Generalized Linear Models and Cox's Proportional Hazards Models
Functions for fitting and plotting SITAR (SuperImposition by
Translation And Rotation) growth curve models
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
This package contains 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 book "Six Sigma with R" (Springer, 2012)
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 several plotting / table output functions for
visualizing data and utility functions.
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 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.
The Skillings-Mack test statistic is 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. The results from the SkiMack function are the Skillings-Mack statistic, p-values based on the chi-squared distribution and Monte Carlo method. The Monte Carlo method is recommended for approximating p-values when there are many ties and/or small designs with missing values are conducted.
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.
Slackr contains functions that make it possible to
interact with Slack.com messaging platform. When you need to share
information/data from R, rather than resort to copy/paste in e-mails
or other services like Skype, you can use this package to send
well-formatted output from multiple R objects and expressions
to all teammates at the same time with little effort. You can also
send images from the current graphics device, R objects (as RData),
and upload files.
Data structures and algorithms for sparse arrays and matrices,
based on index arrays and simple triplet representations, respectively.
Estimates the slope and level change present in data after
removing phase A trend. Represents graphically the original and
the detrended data.
The skew logistic distribution is a quantile-defined generalisation of the logistic distribution (van Staden 2013).  Provides random numbers, quantiles, probabilities, densities and density quantiles for the distribution.  It provides Quantile-Quantile plots and method of L-Moments estimation for the distribution.
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.
slfm is a set of tools to find coherent patterns in microarray data using a Bayesian sparse latent factor model. 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.
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 the quantitative and the qualitative factors. Details
of the algorithm can be found in Ba, S. (2013), "Optimal Sliced
Latin Hypercube Designs", revised for Technometrics. Important
function in this package is "maximinSLHD".
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 the following approaches of multidimensional
scaling (MDS) based on stress minimization by means of majorization
(smacof): Simple smacof (basic MDS) on symmetric dissimilarity matrices,
smacof for rectangular matrices (unfolding models), smacof with constraints
on the configuration, three-way smacof for individual differences
(including constraints for idioscal, indscal, and identity), and spherical
smacof (primal and dual algorithm). Each of these approaches is implemented
in a metric and nonmetric manner including primary, secondary, and tertiary
approaches for tie handling. Jackknife and permutation tests are included
as well.
Animal movement models including moving-resting process
with embedded Brownian motion, Brownian motion with measurement error.
The package "smart" provides a general framework for
analyzing (including estimation, feature selection and
prediction) and visualize big data. It integrates several
novel, efficient and robust data analysis tools, including
Transelliptical Component Analysis (TCA), Transelliptical
Correlation Estimation (TCE) and Group Nearest Shrunken
Centroids (gnsc). We target on high dimensional data
analysis(usually d >> n), and exploit computationally
efficiently approaches. Results are organized to be visualized
properly for users.
Tools for accessing and processing datasets presented on
the blog SmarterPoland.pl.
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.
particle filtering, auxiliary particle filtering and sequential Monte Carlo algorithms
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.
A package for fitting smoothing-splines mixed-effects models to replicated functional
data sets.
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.
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.
A port of the matlab gaussian window smoothing function
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.
This package contains primarily 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], this package 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 quantil) 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
Define 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.
Support for simple parallel computing in R.
An R interface to the C libstemmer library that implements
Porter's word stemming algorithm for collapsing words to a common
root to aid comparison of vocabulary. Currently supported languages are
Danish, Dutch, English, Finnish, French, German, Hungarian, Italian,
Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish
and Turkish.
Usability wrapper around snow for easier development of
parallel R programs. This package offers e.g. extended error
checks, and additional functions. All functions work in
sequential mode, too, if no cluster is present or wished.
Package is also designed as connector to the cluster management
tool sfCluster, but can also used without it.
Extension of the snow package supporting fault tolerant
and reproducible applications, as well as supporting
easy-to-use parallel programming - only one function is needed.
contains several supplementary non-parametric statistics methods including quantile test, Cox-Stuart trend test, runs test, normal score test, kernel PDF and CDF estimation, kernel regression estimation and kernel Kolmogorov-Smirnov test.
This package carries out most common analysis when performing whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated.
Implements classes and methods for large scale SNP enrichment analysis (e.g. SNPs associated with genes expression in a GWAS signal)
A set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example for use with the RSNPset package, or to export to a PLINK set.
This package implements asymptotic methods related to
maximally selected statistics, with applications to SNP data.
A bivariate Gaussian genotype clustering and calling
algorithm 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.
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.
Multivariate, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability densities. SNS is a Metropolis-Hastings variant, where the proposal function is a local Gaussian approximation to the density obtained via a second order Taylor expansion around the current point. The mean of the Gaussian proposal is taken as the full Newton-Raphson step from the current point. During burn-in, Newton-Raphson optimization is performed to get close to the mode of the pdf which is unique due to convexity.
The package 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), 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.
soc.ca is a package optimized to the needs of scientists within
the social sciences producing specific and class specific multiple
correspondence analysis on survey-like data. Soc.ca is optimized to only
give the most essential statistical output values sorted so as to help in
analysis. A diverse set of plotting functions create plots of near
publication ready quality.
SocialMediaMineR is a social media search and analytic tool that takes one or multiple URL(s) and returns the information about the popularity and reach of the URL(s) on social media. The function get_socialmedia retrieves the number of shares, likes, tweets, pins, and hits on Facebook, Twitter, Pinterest, StumbleUpon, LinkedIn, and Reddit. The package also includes dedicated functions for each social networking site and a function to decode shortened URLs.
Generates social networks using either of two
approaches: using either pairwise distances or territorial area intersections.
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.
To implement two clustering methods, ODC and SODC, for
clustering datasets using optimal scoring, can also be used as
an dimension reduction tool.
Extension of sensitivity, specificity, positive and
negative predictive value to continuous predicted and reference
memberships in [0, 1]
This package comprises some soft clustering algorithms. Presently,
it contains classic k-means and the following rough k-means variants:
Lingras & West, Peters, and PI rough k-means.
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.
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.
A collection of tools customized for preprocessing and analyzing soil and plant infrared spectroscopy data with useful tools included for working with other ancillary 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. Several texture triangles are predefined: USDA; FAO (which is also the
triangle for the soil map of Europe); Aisne (France); GEPPA (France); German triangles
"Bodenkundliche Kartieranleitung 1994", "SEA 1974" and "TGL 1985"; Soil
Survey of England and Wales; Australian triangle; Belgian triangle; Canadian triangle;
ISSS triangle; Romanian triangle; Polish triangle.
It contains implementations of parametric formulas for
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}. Any
contributions or suggestions are warmly welcomed.
Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.
This package provides a set of functions for querying and parsing
data from Solr endpoints (local and remote), including search, faceting,
highlighting, stats, and 'more like this'.
Self-Organizing Map (with application in gene clustering)
This package provides an R implementation of the
Self-Organising Migrating Algorithm, a general-purpose,
stochastic optimisation algorithm. The approach is similar to
that of genetic algorithms, although it is based on the idea of
a series of ``migrations'' by a fixed set of individuals,
rather than the development of successive generations. It can
be applied to any cost-minimisation problem with a bounded
parameter space, and is robust to local minima.
some Brownian motions simulation functions
This library collects some procedures controlling the
Generalized Familywise Error Rate.
It's a collection of functions for Multiplicity Correction and Multiple Testing.
The package provides the plot function som.plot() to
create high quality visualisations of hexagonal Kohonen maps
(self-organising maps).
Estimate the abundance of cell clones from the
distribution of lengths of DNA fragments (as created by
sonication, whence `sonicLength').  The algorithm in
"Estimating abundances of retroviral insertion sites from
DNA fragment length data" by Berry CC, Gillet NA, Melamed
A, Gormley N, Bangham CR, Bushman FD. Bioinformatics;
2012 Mar 15;28(6):755-62 is implemented.  The
experimental setting and estimation details are described
in detail there. Briefly, integration of new DNA in a
host genome (due to retroviral infection or gene therapy)
can be tracked using DNA sequencing, potentially allowing
characterization of the abundance of individual cell
clones bearing distinct integration sites. The locations
of integration sites can be determined by fragmenting the
host DNA (via sonication or fragmentase), breaking the
newly integrated DNA at a known sequence, amplifying the
fragments containing both host and integrated DNA,
sequencing those amplicons, then mapping the host
sequences to positions on the reference genome. The
relative number of fragments containing a given position
in the host genome estimates the relative abundance of
cells hosting the corresponding integration site, but
that number is not available and the count of amplicons
per fragment varies widely.  However, the expected number
of distinct fragment lengths is a function of the
abundance of cells hosting an integration site at a given
position and a certain nuisance parameter. The algorithm
implicitly estimates that function to estimate the
relative abundance.
Collection of different single objective test functions
useful for benchmarks and algorithm development.
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.
Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings.
Analogous to source(), but works when given a Gist URL or
ID.
A package that provides classes and methods for spatial
data. The classes document where the spatial location information
resides, for 2D or 3D data. Utility functions are provided, e.g. for
plotting data as maps, spatial selection, as well as methods for
retrieving coordinates, for subsetting, print, summary, etc.
Provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.
Implements the Sequential Predictions Algorithm
The package implements the model-based kernel machine
method for detecting gene-centric gene-gene interactions of Li
and Cui (2012).
Miscellaneous functions for analysing species association
and niche overlap.
Partial correlation estimation with joint sparse
regression model
SPACECAP is a user-friendly software package for
estimating animal densities using closed model
capture-recapture sampling based on photographic captures using
Bayesian spatially-explicit capture-recapture models. This
approach offers advantage such as: substantially dealing with
problems posed by individual heterogeneity in capture
probabilities in conventional capture-recapture analyses. It
also offers non-asymptotic inferences which are more
appropriate for small samples of capture data typical of
photo-capture studies.
undirected graph inference with missing data
This package provides an extension of conditional
independence (CIG) and directed acyclic graph (DAG) estimation
to the case where conditional relationships are (non-linear)
additive models.
Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal combining or aggregating, retrieving coordinates, print, summary, etc.
SPACoDi is primarily designed to characterise the
structure and phylogenetic diversity of communities using
abundance or presence-absence data of species among community
plots.
The package provides tools to construct and exploit spatially weighted context data. Spatial weights are derived by a Kernel function from a user-defined matrix of distances between contextual units. Spatial weights can then be applied either to precise contextual measures or to aggregate estimates based on micro-level survey data, to compute spatially weighted context data. Available aggregation functions include indicators of central tendency, dispersion, or inter-group variability, and take into account survey design weights. The package further allows combining the resulting spatially weighted context data with individual-level predictor and outcome variables, for the purposes of multilevel modelling. An ad hoc stratified bootstrap resampling procedure generates robust point estimates for multilevel regression coefficients and model fit indicators, and computes confidence intervals adjusted for measurement dependency and measurement error of aggregate estimates. As an additional feature, residual and explained spatial dependency can be estimated for the tested models.
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.
Implements a collection of functions for inference in mixed models. It was developed in particular for GLMMs with spatial correlations, but also fits models with non-Gaussian random effects (e.g., Beta Binomial, or negative-binomial mixed models). Heteroskedasticity can further be fitted by a linear model. The algorithms are currently various Laplace approximations methods for ML or REML, in particular h-likelihood and penalized-likelihood methods.
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.
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 (2013), Sparse biclustering of transposable data, to appear in Journal of Computational and Graphical Statistics".
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.
SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de
Computes Hessian of a scalar-valued function, and returns it in sparse Matrix format.
The user must supply the objective function, the
gradient, and the row and column indices of the non-zero elements of the lower
triangle of the Hessian (i.e., the sparsity structure must be known in advance).
The algorithm exploits this sparsity, so Hessians can be computed quickly even
when the number of arguments to the objective functions is large. This package is
intended to be useful for numeric optimization (e.g., with the trustOptim package)
or in simulation (e.g., the sparseMVN package). The underlying algorithm is ACM TOMS
Algorithm 636, written by Coleman, Garbow and More (ACM Transactions on Mathematical
Software, 11:4, Dec. 1985).
Performs sparse linear discriminant analysis for gaussians
and mixture of gaussians models.
Use RcppEigen to fit least trimmed squares
regression models with an L1 penalty in order to obtain
sparse models.
Basic linear algebra for sparse matrices
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-aware maximum likelihood for structural equation models in inferring gene regulatory networks
Computes sparse vector autoregressive coefficients and precision
matrices for time series chain graphical models.
Computer simulations are becoming a popular technique to use in attempts to further our understanding of complex systems. SPARTAN, described in our 2013 publication in PLoS Computational Biology, provides code for four techniques described in available literature which aid the analysis of simulation results, at both single and multiple timepoints in the simulation run. The first technique addresses aleatory uncertainty in the system caused through inherent stochasticity, and determines the number of replicate runs necessary to generate a representative result. The second examines how robust a simulation is to parameter perturbation, through the use of a one-at-a-time parameter analysis technique. Thirdly, a latin hypercube based sensitivity analysis technique is included which can elucidate non-linear effects between parameters and indicate implications of epistemic uncertainty with reference to the system being modelled. Finally, a further sensitivity analysis technique, the extended Fourier Amplitude Sampling Test (eFAST) has been included to partition the variance in simulation results between input parameters, to determine the parameters which have a significant effect on simulation behaviour. Version 1.3 adds support for Netlogo simulations, aiding simulation developers who use Netlogo to build their simulations perform the same analyses. We have also added user support through the group spartan-group[AT]york[DOT]ac[DOT]uk. Version 2.0 added the ability to read all simulations in from a single CSV file in addition to the prescribed folder structure in previous versions.
Fit spatial CAR count regression models using MCMC
This is an R package for spatio-temporal modeling of large data sets. It provides tools for modeling of Gaussian processes in space and time 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, graph visualization and graph component
calculations, ment to be used as a tool in spatial point
pattern analysis. See package 'spatstat' for more info about
spatial point patterns.
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.
This package proposes several approaches for spatial
extremes modelling.
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.
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.
Functions to perform spatial forecast verification
Utilities that estimate, predict and cross-validate the
spatio-temporal model developed for MESA Air.
an R Package for Bayesian Inference with Spatial Survival Models
spBayes fits univariate and multivariate spatio-temporal
models with Markov chain Monte Carlo (MCMC).
This package provides Bayesian model fitting for several survival models including
spatial copula linear dependent Dirichlet process mixture model, anova Dirichlet process mixture model,
proportional hazards model and marginal spatial proportional hazards model.
Evaluation of control charts by means of
the zero-state, steady-state ARL (Average Run Length) and RL quantiles.
Setting up control charts for given in-control ARL. The control charts
under consideration are one- and two-sided EWMA, CUSUM, and
Shiryaev-Roberts schemes for monitoring the mean of normally
distributed independent data. ARL calculation
of the same set of schemes under drift are added.
Other charts and parameters are in preparation.
Further SPC areas will be covered as well
(sampling plans, capability indices ...).
This package calibrates thresholds of control charts such as
CUSUM charts based on past data, taking estimation error into account.
Spatial coverage sampling and random sampling from compact
geographical strata created by k-means.
Provides a covariance estimator for multivariate normal
data that is sparse and positive definite.  Implements the
majorize-minimize algorithm described in Bien, J., and
Tibshirani, R. (2011), "Sparse Estimation of a Covariance
Matrix," Biometrika. 98(4). 807--820.
This package computes the sparse principal component regression. The regularization parameters are optimized by cross-validation.
A collection of functions to create spatial weights matrix
objects from polygon contiguities, from point patterns by distance and
tessellations, for summarizing these objects, and for permitting their
use in spatial data analysis, including regional aggregation by minimum
spanning tree; a collection of tests for spatial autocorrelation,
including global Moran's I, APLE, Geary's C, Hubert/Mantel general cross
product statistic, Empirical Bayes estimates and Assunção/Reis Index,
Getis/Ord G and multicoloured join count statistics, local Moran's I
and Getis/Ord G, saddlepoint approximations  and exact tests for global
and local Moran's I; and functions for estimating spatial simultaneous
autoregressive (SAR) lag and error models, impact measures for lag
models, weighted and unweighted SAR and CAR spatial regression models,
semi-parametric and Moran eigenvector spatial filtering, GM SAR error
models, and generalized spatial two stage least squares models.
A spatio-dynamic modelling package that focuses on three
characteristic wetland plant communities in a semiarid Mediterranean
wetland in response to hydrological pressures from the catchment. The
package includes the data on watershed hydrological pressure and the
initial raster maps of plant communities but also allows for random initial
distribution of plant communities. Ongoing developments of the package
focus on offering easy to use tools for creating other spatio-dynamic
models.
Implements stochastic proximity embedding as described by
Agrafiotis et al. in PNAS, 2002, 99, pg 15869 and J. Comput. Chem., 2003,24, pg 1215
Alternative to the kernlab::specc function. Includes a spectral clustering implementation, a locally adapted kernel function akin to what is already proposed in kernlab, and an optional procedure that automatically estimates the optimal number of clusters. Several sample data sets are also included.
SPECIES is an R package for estimation of species richness
or diversity.
Mixtures using BFGS and analytical derivatives
The package helps to test the specificity of personality trait-outcome (or trait-trait) associations by comparing the observed associations to those obtained using randomly created personality scales.
A collection of new forecast verification routines for the SPECS FP7 project. The emphasis is on comparative verification of ensemble forecasts.
Routines for creating, manipulating, and performing
Bayesian inference about Gaussian processes in one and two
dimensions using the Fourier basis approximation: simulation
and plotting of processes, calculation of coefficient
variances, calculation of process density, coefficient
proposals (for use in MCMC).  It uses R environments to store
GP objects as references/pointers.
This package contains some implementations of Singular Spectrum Analysis (SSA) for the gapfilling (gapfillSSA() ) and spectral decomposition (filterTseriesSSA() ) of time series. It contains the code used by Buttlar et. al. (2014), Nonlinear Processes in Geophysics (gapfillNcdf). In addition, the iterative SSA gapfilling of Kondrashov and Ghil (2006) is implemented. All SSA calculations are done via the truncated and fast SSA algorithm of Korobeneykov (2010) (package Rssa).
Fitting LMs and GLMs to large data sets by updating algorithms.
The package performs estimation and testing of the
treatment effect in a 2-group randomized clinical trial with a
quantitative, dichotomous, or right-censored time-to-event
endpoint. The method improves efficiency by leveraging baseline
predictors of the endpoint. The inverse probability weighting
technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used
to provide unbiased estimation when the endpoint is missing at
random.
A set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI).
Provides some easy-to-use functions to interpolate species
range based on species occurrences and to estimate centers of
biodiversity
This package implements spatial error estimation and
permutation-based variable importance measures for predictive
models using spatial cross-validation and spatial block
bootstrap.
A set of tools for estimation of various spatial specifications of stochastic frontier models
Interpreted interface between GRASS 6+ geographical
information system and R, based on starting R from within the GRASS
environment, or running free-standing R in a temporary GRASS location;
the package provides facilities for using all GRASS commands from the
R command line.
Functions for computing geographically weighted
regressions based on work by Chris
Brunsdon, Martin Charlton and Stewart Fortheringham,
http://gwr.nuim.ie/
Various functions for creating spherical coordinate system plots via extensions to rgl.
This package defines several methods to integrate
functions over the unit sphere and ball in n-dimensional
Euclidean space.  Routines for converting to/from multivariate
polar/spherical coordinates are also provided.
This package carries out spherical wavelet transform
developed by Li (1999) and Oh (1999), and implements wavelet
thresholding approaches proposed by Oh and Li (2004).
Generalized Method of Moment estimation of Cliff-Ord-type
spatial autoregressive models with and without heteroskedastic
innovations
The SNP Panel Identification Assay (SPIA) is a package
that enables an accurate determination of cell line identity
from the genotype of single nucleotide polymorphisms (SNPs).
The SPIA test allows to discern when two cell lines are close
enough to be called similar and when they are not.
A package for the analysis of species limits and DNA barcoding data
Spike and slab for prediction and variable selection in
linear regression models. Uses a generalized elastic net for
variable selection.
Bayesian variable selection, model choice, and
regularized estimation for (spatial) generalized
additive mixed regression models via SSVS with
spike-and-slab priors.
An optimal weighting strategy to compute
simulation-efficient shortest probability intervals (spins).
Spatial and Space-Time Point Pattern Analysis Functions
Online data collection tools like Google Forms often export
multiple-response questions with data concatenated in cells. The
concat.split family of functions conveniently splits such data into
separate cells. The package also includes functions to conveniently stack
groups of columns and to reshape wide data, even when the data are
"unbalanced"---something which reshape (from base R) does not handle, and
which melt and dcast from reshape2 do not easily handle.
ML and GM estimation and diagnostic testing of econometric
models for spatial panel data.
This package provides functions for fitting a Sparse
Partial Least Squares Regression and Classification
Currently there are many functions in S-PLUS that are
missing in R. To facilitate the conversion of S-PLUS packages
to R packages, this package provides some missing S-PLUS
functionality in R.
Classes and methods for times and dates (ported from S-PLUS)
Classes and methods for time series (ported from S-PLUS).
The main goal of this package is to provide a set of functions for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields.
Implements the method of general semiparametric maximum likelihood estimation for logistic models in case-mother control-mother designs.
Use new methods to mine sub-pathway,but user need to prepare for xml format data of KEGG PATHWAY and store them in "xml" folder under "paths"(paths library is the default directory which is set by user) library,prepare KEGG pathway map data and store them in "png" folder under the "paths"(paths library is the default directory which is set by user) library in advance.
The spnet package offers methods for dealing with spacial social
networks. It allows to plot networks for which actors have a specific
location on a map (participants in a political debate, cities, etc.).
SpatialPolygons objects from the sp package are supported.
A programmatic interface to many species occurrence data sources,
including GBIF, USGS's BISON, iNaturalist, Berkeley Ecoinformatics Engine
eBird, AntWeb, and more as they sources become easily available.
SPODT is a spatial partitioning method based on oblique decision trees, in order to classify study area into zones of different risks, determining their boundaries
R implementation of the methods described in "A rank-based
empirical likelihood approach to two-sample proportional odds
model and its goodness-of-fit" by Zhong Guan and Cheng Peng,
Journal of Nonparametric Statistics, to appear.
The aim of this package is to provide infrastructure for
sports analysis. Anyway, currently it is a selection of data
sets, functions to fetch sports data, examples, and demos --
with the ambition to develop bit by bit a set of classes to
represent general concepts of sports analysis.
R-Package for Sequential Parameter Optimization Toolbox
The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.
SPRINT (Simple Parallel R INTerface) is a parallel
framework for R. It provides a High Performance Computing (HPC)
harness which allows R scripts to run on HPC clusters. SPRINT
contains a library of selected R functions that have been
parallelized. Functions are named after the original R function
with the added prefix 'p', i.e. the parallel version of cor()
in SPRINT is called pcor(). Call to the parallel R functions
are included directly in standard R scripts.
SPRINT contains functions for correlation (pcor), partitioning around medoids (ppam),
apply (papply), permutation testing (pmaxT), bootstrapping (pboot), random forest (prandomForest),
rank product (pRP) and hamming distance (pstringdistmatrix).
R functions to mine sparse models from data.
SPSL package provides functionality for labeling of
percolation clusters on 2D & 3D square lattice with various
lattice size, relative fraction of accessible sites (occupation
probability), iso- & anisotropy, von Neumann & Moore
(1,d)-neighborhood
Produces Slepian smoother basis objects for use in Generalized Additive Models, as estimated by package:mgcv.
This group of functions implements algorithms for design and
analysis of probability surveys.  The functions are tailored for Generalized
Random Tessellation Stratified (GRTS) survey designs.
This package collects algorithms related to Sierpinski pedal triangle (SPT).
The package is able to fit, spatially predict and temporally forecast large amounts of space-time data using  [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for big-n problem.
This package provides functions and datasets from the book "Introduction to Scientific Programming and Simulation Using R".
Sparse Quadratic Discriminant Analysis (SQDA) can be performed. In SQDA, the covariance matrix are assumed to be block-diagonal.And, for each block, sparsity assumption is imposed on the covariance matrix. It is useful in high-dimensional setting.
Description: Manipulate R data frames using SQL.
sqliter helps users, mainly data munging practioneers, to organize
their sql calls in a clean structure. It simplifies the process of
extracting and transforming data into useful formats.
Provides API for executing and retrieving the results of SQL statements on the online SQLShare service provided by the eScience institute at the University of Washington. More information on using SQLShare may be found at the package URL.
This package provides utilities for working with a library of SQL
files.
Normalization based a subset of negative control probes as
described in 'Subset quantile normalization using negative
control features'. Wu Z, Aryee MJ, J Comput Biol. 2010
Oct;17(10):1385-95 [PMID 20976876].
Algorithms for accelerating the convergence of slow,
monotone sequences from smooth, contraction mapping such as the
EM algorithm. It can be used to accelerate any smooth, linearly
convergent acceleration scheme.  A tutorial style introduction
to this package is available in a vignette on the CRAN download
page or, when the package is loaded in an R session, with
vignette("SQUAREM").
This package provides functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps.  Lower-level functions are provided to map numeric values to colors, display a matrix as an array of colors, and draw color keys.  Higher-level plotting functions are provided to generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.
This package (sra) provides a set of tools to analyse
artificial-selection response datasets. The data typically
feature for several generations the average value of a trait in
a population, the variance of the trait, the population size
and the average value of the parents that were chosen to breed.
Sra implements two families of models aiming at describing the
dynamics of the genetic architecture of the trait during the
selection response. The first family relies on purely
descriptive (phenomenological) models, based on an
autoregressive framework. The second family provides different
mechanistic models, accounting e.g. for inbreeding, mutations,
genetic and environmental canalization, or epistasis. The
parameters underlying the dynamics of the time series are
estimated by maximum likelihood. The sra package thus provides
(i) a wrapper for the R functions mle() and optim() aiming at
fitting in a convenient way a predetermined set of models, and
(ii) some functions to plot and analyze the output of the
models.
This package contains a collection of functions to perform
nonparametric estimation of receiver operating characteristic
(ROC) curves for continuous data.
This package implements the SRRS method introduced in Phoa (2013) into a graphical user interface (GUI) R program.
The ss3sim package develops a framework for fisheries stock
assessment simulation testing with Stock Synthesis 3 (SS3).
A set of functions to calculate sample size for two-sample
difference in means tests. Does adjustments for either
nonadherence or variability that comes from using data to
estimate parameters.
Package to visually and statistically analyze single system data
The sse package takes user-defined power functions, evaluates them for a parameter range, and draws a sensitivity plot. It also provides a resampling procedure for semi-parametric sample size estimation and methods for adding informations to a Sweave report.
Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods.
This package provides utility functions for system command
execution, both locally and remotely using ssh/scp. The command
output is captured and provided to the caller. This functionality is
intended to streamline calling shell commands from R, retrieving and
using their output, while instrumenting the calls with appropriate
error handling. NOTE: this first version is limited to unix with local
and remote systems running bash as the default shell.
This package contains a set of functions that calculates
appropriate sample sizes for one-sample t-tests, two-sample
t-tests, and F-tests for microarray experiments based on
desired power while controlling for false discovery rates. For
all tests, the standard deviations (variances) among genes can
be assumed fixed or random.  This is also true for effect sizes
among genes in one-sample and two sample experiments. Functions
also output a chart of power versus sample size, a table of
power at different sample sizes, and a table of critical test
values at different sample sizes.
Package provides a set of tools for robust estimation and inference for models with sample selectivity.
Geostatistical modeling for data on stream networks, including models based on in-stream distance.  Models are created using moving average constructions. Spatial linear models, including covariates, can be fit with ML or REML.  Mapping and other graphical functions are included.
R package for computing the spherical smoothing splines
sss is a set of tools to import survey files
in the .sss (triple-s) format. It provides the function
read.sss that reads the .asc and .sss files of a
triple-s survey data file.  The package is experimental
- feedback, issues and bug reports are welcome.
Fast iterative thresholding sparse SVD, together with an initialization algorithm
This package allows to fit  a regression model suitable for data analysis in which the conditional distribution of the response is symmetric and continuous and where its location and dispersion parameters are modelled through semiparametric functions. Supported distributions for the model error include normal, Student-t, power exponential, symmetric hyperbolic, contaminated normal, slash, sinh-normal and sinh-t distributions.
This package implements the "shrinkage t" statistic
introduced in Opgen-Rhein and Strimmer (2007) and a shrinkage estimate
of the "correlation-adjusted t-score" (CAT score) described in
Zuber and Strimmer (2009).  It also offers a convenient interface
to a number of other regularized t-statistics commonly
employed in high-dimensional case-control studies.
data analysis of drug stability (shelf life estimation)
based on ICH Q1E guideline with single-factor analysis for single-batch
or multiple-batch.
Density, Probability and Quantile functions, and random
number generation for (skew) stable distributions, using the
parametrizations of Nolan.
Compute the maximum-likelihood(ML), generalised method of moment with finite and continuum number of points, the iterative Koutrouvelis regression and the Kogon-McCulloch methods to estimate the 4 parameters of stable law. The asymptotic properties of the estimator (covariance matrix, confidence intervals) are also provided.
Resampling procedures to assess the stability of selected variables
with additional finite sample error control for high-dimensional variable
selection procedures such as Lasso or boosting
Stacks rectangular datasets on top of each other, possibly
performing several type coercions along the way. For large datasets,
depends on the ff package. Provides an aggressive version of
ffbase::compact for data that may appear be real-typed but is in fact
int/short/byte. For many purposes plyr::rbind.fill may be more appropriate,
but for some kinds of survey data, the rules here work better.
stam is an evolving package that target on the various
methods to conduct Spatio-Temporal Analysis and
Modelling,including Exploratory Spatio-Temporal Analysis and
Inferred Spatio-Temporal Modelling.
StAMPP allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation
Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single
ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values
around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to
utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects
allowing integration with other packages such adegenet.
Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. doi:10.1111/1755-0998.12129 for the appropriate citation and user manual. Thank you in advance.
Standardizes text according to a template; particularly
useful for country names.
Functions to analyze neuronal spike trains from a single
neuron or from several neurons recorded simultaneously.
Produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold
regression analysis results from several models side-by-side, as well as summary
statistics.
STARSEQ is an R-package for detecting associations with
rare variants using selected samples in sequence-based
association studies. It corrects for the bias in the secondary
trait distribution induced by selective sampling on the primary
trait. The corrected secondary trait can be analyzed by
standard rare variant tests. In the STARSEQ package, several
popular rare variant tests were implemented, which include 1.)
combined multivariate and collapsing 2.) weighted sum
statistics, 3.) kernel based adaptive cluster, 4.) variable
threshold test 5.) sequence kernel association test.
A Set of Tools for Administering SHared Repositories
Datasets for Stat2 textbook (by Cannon, et. al., published
by WH Freeman)
This package offers different possibilities to make statistical analysis for Environmental Data.
statebins is an alternative to choropleth maps for USA States and
is based on work by the Washington Post graphics department in their report
on "The states most threatened by trade". statebins preserves as much of
the geographic placement of the states as possible but has the look and
feel of a traditional heatmap. Functions are provided that allow for use
of both a binned, discrete scale and continuous scale depending on what
is needed for the underlying data.
Statistics Finland (Tilastokeskus) Open Data R Tools
The package StatMatch provides some R functions to perform statistical matching (aka data fusion), i.e. the integration of two data sources referred to the same target population which share a number of common variables. Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing  with data from complex sample surveys are available too.
Functions and datasets to support Statistical Methods for Ranking Data by Mayer Alvo and Philip L.H. Yu(2014)
Various statistical modeling functions including growth curve comparisons, limiting dilution analysis, mixed linear models, heteroscedastic regression, Tweedie family generalized linear models, the inverse-Gaussian distribution and Gauss quadrature.
An integrated set of tools for the representation, visualization, analysis, and simulation of network data. For an introduction, type help(package='statnet')
This package contains non-statistical utilities used by the software developed by the Statnet Project. They may also be of use to others.
This package implements Generalized Method of Moments and Maximal
Likelihood methods for Random Utility Models. These methods are meant to
provide inference on rank comparison data. These methods accept full,
partial, and pairwise rankings, and provides methods to break down full or
partial rankings into their pairwise components. Please see Generalized
Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of
some of our methods.
steepness is a package that computes steepness as a
property of dominance hierarchies. Steepness is defined as the
absolute slope of the straight line fitted to the normalized
David's scores. The normalized David's scores can be obtained
on the basis of dyadic dominance indices corrected for chance
or by means of proportions of wins. Given an observed
sociomatrix, it computes hierarchy's steepness and estimates
statistical significance by means of a randomization test.
A package to manage and display stellar tracks and
isochrones from Pisa low-mass database. Includes tools for
isochrones construction and tracks interpolation.
Estimation of the parameters of a spatio-temporal model
using the EM algorithm, estimation of the parameter standard
errors using a spatio-temporal parametric bootstrap, spatial
mapping.
This package is an implementation of the process of stepwise community assembly through the inverse process of stepwise species deletion with Stepwise Community Assembly Models (STEPCAM)
A method to explore the treatment-covariate interactions
in survival data arising from two treatment arms of a clinical
trial.  A permutation distribution approach to inference is
implemented, based on permuting the covariate values within
each treatment group.
L2 penalized logistic regression for both continuous and
discrete predictors, with forward stagewise/forward stepwise
variable selection procedure.
A stepwise approach to identifying recombination
breakpoints in a sequence alignment.
StereoMorph provides functions for the collection of 3D points and curves using a stereo camera setup.
Functions to build and use an interpolator ("emulator") for time series or 1D regularly spaced data in multidimensional space. The standard usage is for interpolating time-resolved computer model output between model parameter settings. It can also be used for interpolating multivariate data (e.g., oceanographic time-series data, etc.) in space.  There are functions to test the emulator using cross-validation, and to produce contour plots over 2D slices in model input parameter (or physical) space.
This package enables you to estimate a regression trunk model, which is an integration of a regression tree and a multiple regression model.
A consistently well behaved method of interpolation based
on piecewise rational functions using Stineman's algorithm
The Structural Topic Model (STM) allows researchers
to estimate topic models with document-level covariates.
The package also includes tools for model selection, visualization,
and estimation of topic-covariate regressions.
With this package you can simulate several lines into the
QQ-Norm-Plot under the assumption of Gaussian distribution
Fit a spatial-temporal occupancy models using
a probit formulation instead of a traditional logit
model.
This is an R package accompanying the paper "Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles" by Sameer S Bajikar, Christiane Fuchs, Andreas Roller, Fabian J Theis and Kevin A Janes (PNAS 2014, 111(5), E626-635). In this paper, we measure expression profiles from small heterogeneous populations of cells, where each cell is assumed to be from a mixture of lognormal distributions. We perform maximum likelihood estimation in order to infer the mixture ratio and the parameters of these lognormal distributions from the cumulated expression measurements.
This package provides efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models via Markov chain Monte Carlo (MCMC) methods.
Download stock data, build single index, constant
correlation, and multigroup models, and estimate optimal stock
portfolios. Plotting functions for the portfolio possibilities
curve and portfolio cloud are included. A function to test a
portfolio on a data set is also provided.
Given a list of substance compositions, a list of
substances involved in a process, and a list of constraints in
addition to mass conservation of elementary constituents, the
package contains functions to build the substance composition
matrix, to analyze the uniqueness of process stoichiometry, and
to calculate stoichiometric coefficients if process
stoichiometry is unique.  (See Reichert, P. and Schuwirth, N.,
A generic framework for deriving process stoichiometry in
enviromental models, Environmental Modelling and Software 25,
1241-1251, 2010 for more details.)
Storm is a distributed real-time computation system. Similar to how
Hadoop provides a set of general primitives for doing batch processing, Storm
provides a set of general primitives for doing realtime computation.

Storm includes a "Multi-Language" (or "Multilang") Protocol to allow
implementation of Bolts and Spouts in languages other than Java.  This R
extension provides implementations of utility functions to allow an application
developer to focus on application-specific functionality rather than Storm/R
communications plumbing.
A toolkit for Reliability Availability and Maintainability (RAM) modeling of industrial process systems.
Can be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set.
A package for analysing, simulating and displaying space-time point patterns
Implement transformation-based and pixel-based residual
analysis of spatial-temporal point process models.
Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics, using demographic and genetic data sampled in the curse of the epidemics
Functions for the stratigraphic analysis of phylogenetic trees.
This package performs univariate stratification of survey
populations. The main function implements a generalization of
the Lavallee-Hidiroglou method of stratum construction. The
generalized method takes into account a discrepancy between the
stratification variable and the survey variable. The
determination of the optimal boundaries also incorporate, if
desired, an anticipated non-response, a take-all stratum for
large units, a take-none stratum for small units, and a
certainty stratum to ensure that some specific units are in the
sample. The well known cumulative root frequency rule of
Dalenius and Hodges and the geometric rule of Gunning and
Horgan are also implemented.
A collection of tools for plotting and analyzing
paleontological and geological data distributed through through
time in stratigraphic cores or sections. Includes some
miscellaneous functions for handling other kinds of
palaeontological and paleoecological data.
This package provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of the class "StratSel" as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets.
The main function is icweib, which fits a stratified Weibull proportional hazards model for left censored, right censored, interval censored, and non-censored survival data. We parameterize the Weibull regression model so that it allows a stratum-specific baseline hazard function, but where the effects of other covariates are assumed to be constant across strata.
A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893 and NIH R21HG005912.
This package contains functions for calculating GPP, NDM, and R from single station diurnal Oxygen curves
This package provides an interface to algorithms implemented in the MOA (Massive Online Analysis) framework for data stream mining.
This package provides a series of functions that allow R users
to access Twitter's filter, sample, and user streams, and to
parse the output into data frames.
Functions to assist the process of learning structural
equation modeling
Forms queries to submit to the Cleveland Federal Reserve Bank web
site's financial stress index data site.  Provides query functions for both
the composite stress index and the components data. By default the download
includes daily time series data starting September 25, 1991.  The functions
return a class of either type easing or cfsi which contain a list of items
related to the query and its graphical presentation.  The list includes the
time series data as an xts object.  The package provides four lattice time
series plots to render the time series data in a manner similar to the
bank's own presentation.
The package provide functions for computing the
reliability of stress-strength models and for building
two-sided or one-sided confidence intervals according to
different approximate procedures
Implements an approximate string matching version of R's native
'match' function. Can calculate various string distances based on edits
(damerau-levenshtein, hamming, levenshtein, optimal sting alignment),
qgrams (q-gram, cosine, jaccard distance) or heuristic metrics (jaro,
jaro-winkler). An implementation of soundex is provided as well.
stringi allows for fast, correct, consistent,
and convenient character string/text processing in each locale
and any native encoding. Thanks to the use of the ICU library,
the package provides R users with platform-independent functions
known to Java, Perl, Python, PHP and Ruby programmers.
stringr is a set of simple wrappers that make R's string
functions more consistent, simpler and easier to use.  It does
this by ensuring that: function and argument names (and
positions) are consistent, all functions deal with NA's and
zero length character appropriately, and the output data
structures from each function matches the input data structures
of other functions.
Testing, monitoring and dating structural changes in (linear)
regression models. strucchange features tests/methods from
the generalized fluctuation test framework as well as from
the F test (Chow test) framework. This includes methods to
fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM,
recursive/moving estimates) and F statistics, respectively.
It is possible to monitor incoming data online using
fluctuation processes.
Finally, the breakpoints in regression models with structural
changes can be estimated together with confidence intervals.
Emphasis is always given to methods for visualizing the data.
Performs multiple testing corrections that take specific structure of hypotheses into account.
This package implements a broad class of latent variable and structural equation models for general pedigree data.
strvalidator is an open source platform for validation and process control.
It provide tools that make it very easy to perform an internal
validation of a forensic short tandem repeat (STR) kit for human
identification. The tools are developed to provide all necessary data
to conform with the ENFSI guidelines for internal validation.
More information about each function can be found in the respective help documentation.
This package provides algorithms to fit
structural time series models by maximum likelihood.
This package defines an S4 class for structural time series models
and provides some basic methods to work with it.
A number of functions, supplemented by GUI, to perform various
analyses in the field of computational stylistics, authorship
attribution, etc.
The three functions in the package compute the maximum likelihood estimates of the informants' competence scores, tests for two answer keys with known groups, and finds "best" split of the informants into sub-culture groups.
Produces various measures of expected treatment effect heterogeneity under an assumption of homogeneity across subgroups. Graphical presentations are created to compare these expected differences with the observed differences.
The package implements a convenient procedure for microarray study, which is to do gene selection and classification simultaneously for binary outcomes. Users needn't to tune the parameters and can fix any genes that they desire to keep in the model. The K-folds cross validation results are returned.
The subplex algorithm for unconstrained optimization, developed by Tom Rowan.
essentially a discussion tool about estimation of copula using ranks and subsampling.
A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria.
Subsemble is a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a unique form of V-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble.
subtype performs a biclustering procedure on a input
dataset and assess whether resulting clusters are promising
subtypes. Note that the R-package rsmooth should be installed
before implementing subtype. rsmooth can be downloaded from
http://www.meb.ki.se/~yudpaw.
Generates, plays, and solves Sudoku puzzles.  The GUI
playSudoku() needs package "tkrplot" if you are not on Windows.
This is a package for the subsampling method of robust
estimation of linear regression models
Includes 4 summarizing functions; two are to be used with uni-dimensional data, and the two others with dataframes. For uni-dimensional data, 'unistats' will generate common central tendency statistics and measures of dispersion for numerical data and 'frequencies' will generate a table of frequencies with counts and percentages (including cumulative). Both functions report number and proportion of valid values vs NA values. For dataframes, 'dfSummary' will generate a table containing as many rows as there are columns in the dataframe, each row giving variable information (class and type), labels if any, common statistics for numerical data and frequency tables for any type of data, along with number and proportion of valid (non-missing) values. Finally, 'properties' will conveniently report attributes of a dataframe object itself and also of all its individual components (columns).
Functions for drawing samples according to Sunter's
sampling design, and for computing first and second order
inclusion probabilities
Methodology for supervised grouping aka "clustering" of
potentially many predictor variables, such as genes etc.
Biclusters are submatrices in the data matrix which
satisfy certain conditions of homogeneity. Package contains
functions for generating robust biclusters with respect to the
initialization parameters for a given bicluster solution
contained in a bicluster set in data, the procedure is also
known as ensemble biclustering. The set of biclusters is
evaluated based on the similarity of its elements (the
overlap), and afterwards the hierarchical tree is constructed
to obtain cut-off points for the classes of robust biclusters.
The result is a number of robust (or super) biclusters with
none or low overlap.
A Comprehensive Test Suite for Markov Chain
Nonconvergence.
This package implements the super learner prediction method and contains a library of prediction algorithms to be used in the super learner.
Witten and Tibshirani (2011) Supervised multidimensional scaling for visualization, classification, and bipartite ranking. Computational Statistics and Data Analysis 55(1): 789-801.
Supervised principal components for regression and
survival analsysis. Especially useful for high-dimnesional
data, including microarray data.
Ten distributions supplementing those built into R.
Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi
squared, Spearman's rho, maximum F ratio, the Pearson product
moment correlation coefficiant, Johnson distributions, normal
scores and generalized hypergeometric distributions. In
addition two random number generators of George Marsaglia are
included.
This package provides three basic functions that support an implementation of object case (Case 1) best-worst scaling: one for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; one for creating a data set suitable for analysis; and one for calculating count-based scores.
The support.CEs package provides seven basic functions that support an implementation of choice experiments.
SURFACE is a data-driven phylogenetic comparative method for fitting stabilizing selection models to continuous trait data, building on the ouch package. The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak.
In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a biomarker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
suRtex was designed for easy descriptive statistic reporting of categorical survey data (e.g., Likert scales) in LaTeX. suRtex takes a matrix or data frame and produces the LaTeX code necessary for a sideways table creation. Mean, median, standard deviation, and sample size are optional.
Performs inference of several model-free group contrast measures, which include difference/ratio of cumulative incidence rates at given time points, quantiles, and restricted mean survival times (RMST). Two kinds of covariate adjustment procedures (i.e., regression and augmentation) for inference of the metrics based on RMST are also included.
This package provides a function to estimate the AUC, TPR(c),
FPR(c), PPV(c), and NPV(c) for for a specific timepoint and marker cutoff
value c using non-parametric and semi-parametric estimators. Standard errors
and confidence intervals are also computed. Either analytic or bootstrap
standard errors can be computed.
The package provides a variety of functions to estimate
time-dependent true/false positive rates and AUC curves from a
set of censored survival data.
Performs inference for C of risk prediction models with
censored survival data, using the method proposed by Uno et al.
(2011). Inference for the difference in C between two competing
prediction models is also implemented.
A package implementing statistical methods for the modeling and
change-point detection in time series of counts, proportions and
categorical data, as well as for the modeling of continuous-time
epidemic phenomena, e.g. discrete-space setups such as the spatially
enriched Susceptible-Exposed-Infectious-Recovered (SEIR) models for
surveillance data, or continuous-space point process data such as the
occurrence of disease or earthquakes. Main focus is on outbreak
detection in count data time series originating from public health
surveillance of infectious diseases, but applications could just as well
originate from environmetrics, reliability engineering, econometrics or
social sciences.
Currently the package contains implementations of typical
outbreak detection procedures such as Farrington et al (1996),
Noufaily et al (2012) or the negative binomial LR-CUSUM method
described in Hoehle and Paul (2008). Furthermore, inference
methods for the retrospective infectious disease model in Held
et al (2005), Held et al (2006), Paul et al (2008) and Paul
and Held (2011) are provided. A novel CUSUM approach combining
logistic and multinomial logistic modelling is also included.
Continuous self-exciting spatio-temporal point processes are
modeled through additive-multiplicative conditional
intensities as described in Höhle (2009) ("twinSIR", discrete
space) and Meyer et al (2012) ("twinstim", continuous space).
The package contains several real-world data sets, the ability
to simulate outbreak data, visualize the results of the
monitoring in temporal, spatial or spatio-temporal fashion.
Note: The suggested package INLA is unfortunately not available from
any mainstream repository - in case one wants to use the 'boda'
algorithm one needs to manually install the INLA package
as specified at http://www.r-inla.org/download.
Relative survival, AER and SMR based on French death rates
Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Principal components, factor analysis.
Data obtained from surveys contains information not only about the
survey responses, but also the survey metadata, e.g. the original survey
questions and the answer options. The surveydata package makes it easy to
keep track of this metadata, and to easily extract columns with
specific questions.
The Gini concentration test for survival data is a nonparametric test based on the Gini index for testing the equality of two survival distributions from the point of view of concentration. The package compares different nonparametric tests (asymptotic Gini test, permutation Gini test, log-rank test, Gray-Tsiatis test and Wilcoxon test) and computes their p-values.
Performs inference for a class of measures to compare
competing risk prediction models with censored survival data.
The class includes the integrated discrimination improvement
index (IDI) and category-less net reclassification index (NRI).
survival analysis: descriptive statistics, two-sample
tests, parametric accelerated failure models, Cox model. Delayed entry
(truncation) allowed for all models; interval censoring for parametric
models. Case-cohort designs.
Estimate the regression coefficients and the baseline hazard
of proportional hazard Cox models using maximum penalised likelihood.
A 'non-parametric' smooth estimate of the baseline hazard function
is provided.
Compute time-dependent ROC curve from censored survival
data using Kaplan-Meier (KM) or Nearest Neighbor Estimation
(NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No
2, 2000, PP 337-344)
A collection of functions for analysis of survival data. These
extend the methods available in package survival.
Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data
Estimation of survival function for recurrent event data
using Peña-Strawderman-Hollander, Whang-Chang estimators and
MLE estimation under a Gamma Frailty model.
The main function of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions.
Simulation of simple and complex survival data
This package can be used to conduct asymptotic and empirical power and sample size calculations for SNP association studies with right censored time to event outcomes
Accurate identification of genes that are truly differentially expressed over two sample varieties, after adjusting for hidden subject-specific effects of residual heterogeneity.
2d and 3d space-varying coefficient models are fitted to
regular grid data using either a full B-spline tensor product
approach or a sequential approximation. The latter one is
computationally more efficient. Resolution increment is
enabled.
This package provides various R bindings to various SVD and eigensolvers (PROPACK, nuTRLan)
Rapidly construct dialog boxes for your GUI, including an automatic
function assistant
Reimplementation of the svDialogs dialog boxes in Tcl/Tk
Some visualization tools based on Singular Value Decomposition
Implements a simple HTTP server allowing to connect GUI clients to R
Function for the GUI API to interact with external IDE/code editors
Functions to manage the GUI client, like Komodo with the
SciViews-K extension
Supporting functions for the GUI API (various utility functions)
Implements the SVM-Maj algorithm to train data with
Support Vector Machine, this algorithm uses two efficient
updates, one for linear kernel and one for the nonlinear
kernel.
Computes the entire regularization path for the two-class
svm classifier with essentially the same cost as a single SVM
fit.
Implements a simple socket server allowing to connect GUI clients to R
Supporting functions for the GUI API (Sweave functions)
Set of tools aimed at wrapping some of the functionalities
of the packages tools, utils and codetools into a nicer format so
that an IDE can use them
A complete unit test system and functions to implement its GUI part
High level management of widgets, windows and other graphical resources.
Construction, internal validation and calibration of
nomograms with right-censored outcomes emanating from complex
survery designs
This package deals with data which stem from survey designs including plausible values. This package has been created to handle data from Large Scale Assessments like PISA, PIAAC etc. which use  complex survey designs to draw the sample and plausible values to report person related estimates. Various functions/statistics (mean, quantile, GLM etc.) are provided to handle this kind of data.
The package contains functions to connect the structure of
the data with the information on the samples. Three types of
associations are covered: 1. linear model of principal
components. 2. hierarchical clustering analysis. 3.
distribution of features-sample annotation associations.
Additionally, the inter-relation between sample annotations can
be analyzed. Simple methods are provided for the correction of
batch effects and removal of principal components.
The Soil and Water Assessment Tool is a river basin or
watershed scale model developed by Dr. Jeff Arnold for the
USDA-ARS.
provides utilities for defining R / Rd as
Tex-package-listings "language" and including R / Rd source
file (sniplets) copied from R-forge in its most recent version
(or another url) thereby avoiding inconsistencies between
vignette and documented source code
Structural handling of identity numbers used in the Swedish
administration such as personal identity numbers ('personnummer') and
organizational identity numbers ('organisationsnummer').
Interface for the REST API of Statistics Sweden. The
package offers methods to fetch information about the data hierarchy stored
behind the API; extract metadata; fetch actual data; and clean up the
results.
Collection of conversion, analytical, geodesic, mapping, and
plotting functions. Used to support packages and code written by
researchers at the Southwest Fisheries Science Center of the National
Oceanic and Atmospheric Administration.
swirl turns the R console into an interactive learning
environment. Users receive immediate feedback as they are guided through
self-paced lessons in data science and R programming.
Ozone, NOx (= Sum of Nitrogenmonoxide and
Nitrogendioxide), Nitrogenmonoxide, ambient temperature, dew
point, wind speed and wind direction at 3 sites around lake of
Lucerne in Central Switzerland in 30 min time resolution for
year 2004.
Functions for estimating the parameters from the latent
state process and the functions corresponding to the J states as
proposed by De Souza and Heckman (2013).
The package sybil is a Systems Biology Library for R, implementing algorithms for constraint based analyses of metabolic networks (e.g. flux-balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on/off minimization (ROOM), robustness analysis and flux variability analysis).
implement cost constrained flux balance analysis as described in Adadi, R., Volkmer, B., Milo, R., Heinemann, M., & Shlomi, T. (2012). Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network with Kinetic Parameters, 8(7). doi:10.1371/journal.pcbi.1002575. Flux balance analysis is a technique to find fluxes in metabolic models at steady state. It is described in Orth, J.D., Thiele, I. and Palsson, B.O. What is flux balance analysis? Nat. Biotech. 28, 245-248 (2010)
implement cycle-Free flux balance analysis, flux variability, and Random Sampling of solution space. Flux balance analysis is a technique to find fluxes in metabolic models at steady state. It is described in Orth, J.D., Thiele, I. and Palsson, B.O. What is flux balance analysis? Nat. Biotech. 28, 245-248 (2010).
In this package the dynamic FBA technique proposed by
Varma was implemented.
three different approaches to use gene expression data (or protein measurements) for improving FBA predictions.
SBML (Systems Biology Markup Language) integration in sybil
Symbolic Data Analysis Methods: importing/ exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic, replication and profiling, artificial symbolic data generation.
Package that implements various symbol plots (bars,
profiles, stars, Chernoff faces, color icons, stick figures).
Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.
The package was developed within the Synbreed project for synergistic plant and animal breeding (www.synbreed.tum.de). It contains a collection of functions required for genomic prediction in both plant and animal breeding. This covers data processing, data visualization and analysis. All functions are embedded within the framework of a single, unified data object. The implementation is flexible with respect to a wide range of data formats. This research was funded by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr Synbreed - Synergistic plant and animal breeding (FKZ 0315528A).
This package contains three data sets from cattle, maize and mice to illustrate the functions in the synbreed R package. All data sets are stored in the gpData format introduced in the synbreed package. This research was funded by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr Synbreed - Synergistic plant and animal breeding (FKZ 0315528A).
This package provides Boost mutex functionality in R.
Methods for computing spatial, temporal, and spatiotemporal
statistics including: empirical univariate, bivariate and multivariate
variograms; fitting variogram models; phase locking and synchrony analysis;
generating autocorrelated and cross-correlated matrices.
This package carries out synchrosqueezed wavelet transform. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.
Analysis of metacommunities based on functional traits and phylogeny of the community components. The functions that are offered here implement for the R environment methods that have been available in the SYNCSA application written in C++ (by Valerio Pillar, available at http://ecoqua.ecologia.ufrgs.br/ecoqua/SYNCSA.html).
This package provides programmatic access to
The Synergizer service for translating between sets of
biological identifiers.
Infers sympatry matrices from distributional data and analyzes them in order to identify groups of species cohesively connected.
Framework to perform synthetic likelihood inference
for models where the likelihood function is unavailable or
intractable.
Implements the synthetic control group method for comparative case studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2011, 2014). The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone.
A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data.
This package uses FreeType to load system fonts
and Google Fonts(https://www.google.com/fonts) into R,
mainly used to support other packages like R2SWF
and showtext.
This package contains functions for fitting simultaneous
systems of linear and nonlinear equations using Ordinary Least
Squares (OLS), Weighted Least Squares (WLS), Seemingly
Unrelated Regressions (SUR), Two-Stage Least Squares (2SLS),
Weighted Two-Stage Least Squares (W2SLS), and Three-Stage Least
Squares (3SLS).
Contains functions for generating tables for statistical reports written in Microsoft Word or LaTeX. There are functions for I-by-J frequency tables, comparison of means or medians across levels of a categorical variable, and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards regression. Functions are available to handle data simple random samples or survey data. The package is intended to make it easier for researchers to translate results from statistical analyses in R to their reports or manuscripts.
Table 1 is the classical way to describe the patients in a
clinical study. The amount of splits in the data in such a table is
limited. Table1Heatmap draws a heatmap of all crosstables that can be
generated with the data. Users can choose between showing the actual
crosstables or direction of effect of associations, and highlight
associations by number of patients or p-values.
A collection of convenience wrappers for producing and exporting various commonly-used numerical table types to .xls/.xlsx multi-tab spreadsheets while controlling spreadsheet position and layout, via XLConnect functionality.
Provides a user friendly interface to generation of booktab style
tables using xtable.
This package creates "Table 1", i.e., description of baseline
patient characteristics, which is essential in every medical research. This
package provides functions to create such summaries for continuous and
categorical variables, optionally with subgroup comparisons. The package
was inspired by and based on descriptive statistics functions in Deducer,
a Java-based GUI package by Ian Fellows. This package does not require GUI
or Java, and intended for command-line users.
Computes and displays complex tables of summary statistics
TableToLongForm automatically converts hierarchical Tables intended for a human reader into a simple LongForm Dataframe that is machine readable.
A tableplot is a visualisation of a (large)
dataset with a dozen of variables, both numeric and
categorical. Each column represents a variable and each
row bin is an aggregate of a certain number of records.
Numeric variables are visualized as bar charts, and
categorical variables as stacked bar charts. Missing
values are taken into account. Also supports large ffdf
datasets from the ff package.
A tableplot is a visualisation of a (large)
dataset with a dozen of variables, both numeric and
categorical. This package contains an interactive version of
tableplot working in your browser.
R based tabu search algorithm for binary configurations
The package includes marginal and joint maximum likelihood
estimation of uni- and multidimensional item response models
(Rasch,  2PL, 3PL, Generalized Partial Credit, Multi Facets,
Structured Latent Class Analysis),
fit statistics, standard error estimation, as
well as plausible value imputation and weighted likelihood
estimation of ability.
Facilitates analyzing microchemical profiles (both mono- and multielemental composition) from fish otoliths (stones from the inner ear) using a recursive partitioning approach that can accommodate some form of user-specified constraints. It also allows extracting environmental histories from otolith based on typical elemental sequences generated using methods such as LA-ICPMS. Microchemical analysis is widely used in fisheries management and fisheries biology to identify stocks and characterize fish movements but the provided functions may be considered from the more general perspective of the chronological clustering of multivariate time series using piecewise constant regressions.
Functions for performing analysis of variance on time
course microarray data
This package implements functions for fitting taper curves (a semiparametric linear mixed effects taper model) to diameter measurements along stems. Further functions are provided to estimate the uncertainty around the predicted curves, to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. For cases where tree
heights are not measured, methods for estimating
additional variance in volume predictions resulting from uncertainties in
tree height models (tariffs) are provided.  The example data include the taper curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees and a subset of section-wise diameter measurements of these trees. The functions implemented here are detailed in the following publication: Kublin, E., Breidenbach, J., Kaendler, G. (2013) A flexible stem taper and volume prediction method based on mixed-effects B-spline regression, Eur J For Res, 132:983-997.
TAQMNGR manages tick-by-tick transaction data, performing 'cleaning', 'aggregation' and 'import' in an efficient and fast way (the package engine is developed in C++).
Cleaning and Aggregation are performed according to Brownlees and Gallo (2006). Currently, TAQMNGR processes raw data from WRDS (Wharton Research Data Service).
A collection of various utility and convenience functions.
A collection of miscellaneous spatial object manipulation functions
Evaluates traveltimes and ray paths using predefined Earth
(or other planet) models.  Includes phase plotting routines.
The IASP91 and AK135 Earth models are included, and most
important arrival phases can be evaluated.
Portfolio optimization typically requires an estimate of a covariance matrix of asset returns. There are many approaches for constructing such a covariance matrix, some using the sample covariance matrix as a starting point. This package provides implementations for two such methods: random matrix theory and shrinkage estimation. Each method attempts to clean or remove noise related to the sampling process from the sample covariance matrix.
Base library of types for tawny and related packages
Taxonomic information from around the web. This package
interacts with a suite of web APIs for taxonomic tasks, such
as verifying species names, getting taxonomic hierarchies,
and verifying name spelling.
Automated standardization of taxonomic names and removal of orthographic errors in plant species names using the The Plant List (TPL) website (www.theplantlist.org)
Solves Teitz and Bart's p-median problem - given a set of
points attempts to find subset of size p such that summed distances of any
point in the set to the nearest point in p is minimised.  Although
generally effective, this algorithm does not guarantee that a globally
optimal subset is found.
This package provides functions to assist researchers
working in the field of tuberculosis diagnostics.  Functions
for the interpretation of two popular interferon-gamma release
assays are provided, and additional functionality is planned.
Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.
This package contains functions used to perform the
reliability/survival analysis using a parametric
Transform-both-sides (TBS) model.
This package implements the TcGSA, an algorithm to analyze longitudinal gene-expression data at the gene set level.
A series of additional Tcl commands and Tk widgets with style
and various functions (under Windows: DDE exchange, access to the
registry and icon manipulation) to supplement the tcltk package
This package provides tools for the statistical analysis of persistent homology and for density clustering.
Deconvolution of instrument responses from seismic traces and seismogram lists from RSEIS.  Includes pre-calculated instrument responses for several common instruments.
TDM is designed to estimate individual pharmacokinetic
parameters with one or more drug serum/plasma concentrations
obtained from a single subject or multiple subjects using
JAGS (Just Another Gibbs Sampling) interfaced through rjags
and JAGS. Besides, it also can calculate a suggested dose
with the target drug concentration (C ->D) or calculate a
predicted drug concentration with a given dose (D -> C).
Tuned Data Mining in R (TDMR) performs a complete tuning of a data
mining task.
Transmission/disequilibrium tests for extended marker haplotypes
This package is a set of demonstration functions that can
be used in a classroom to demonstrate statistical concepts, or
on your own to better understand the concepts or the
programming.
Foundations of inference in survey sampling
Can fit neural networks with up to two hidden layer and two different error functions. Also able to handle a weight decay. But just able to compute one output neuron and very slow.
TED performs Turbulence time series Event Detection and classification.
Fits mixtures of multivariate t-distributions (with eigen-decomposed covariance structure) via the multi-cycle ECM algorithm under a clustering or classification paradigm.
Temporal disaggregation methods are used to disaggregate and
interpolate a low frequency time series to a higher frequency series, where
either the sum, the average, the first or the last value of the resulting
high frequency series is consistent with the low frequency series. Temporal
disaggregation can be performed with or without one or more high frequency
indicator series. The R package tempdisagg is a collection of several
methods for temporal disaggregation.
The tensor product of two arrays is notionally an outer
product of the arrays collapsed in specific extents by summing
along the appropriate diagonals.
The package provides convenience functions for advance
linear algebra with tensors and computation with datasets of
tensors on a higher level abstraction. It includes Einstein and
Riemann summing conventions, dragging, co- and contravariate
indices, parallel computations on sequences of tensors.
The target equivalence range (TEQR) design, previously named the toxicity equivalence range design is an up-down design patterned after the modified toxicity probability interval design.The TEQR design extends the well known 3+3 design to allow for: an explicit target range for the dose limiting toxicity (DLT) rate, more subjects at the maximum tolerated dose (MTD), and specification of a too-toxic rate, which closes a dose level, In addition, the TEQR design can be implemented to target a dose based either on toxicity rate or activity rate constrained for toxicity.
This package is for the comparison of various types of A+B
escalation rules for dose finding trials.
An integrated set of extensions to the `ergm` package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). "tergm" is a part of the "statnet" suite of packages for network analysis.
The package offers a wide range of functions for term
structure estimation based on static and dynamic coupon bond
and yield data sets. The implementation focuses on the cubic
splines approach of McCulloch (1971, 1975) and the Nelson and
Siegel (1987) method with extensions by Svensson (1994),
Diebold and Li (2006) and De Pooter (2007). We propose a
weighted constrained optimization procedure with analytical
gradients and a globally optimal start parameter search
algorithm. Extensive summary statistics and plots are provided
to compare the results of the different estimation methods.
Several demos are available using data from European government
bonds and yields.
A suite of functions for visualising ternary probabilistic
forecasts.
TESS is a package for simulation of reconstructed
phylogenetic trees under global, time-dependent birth-death
processes. Speciation and extinction rates can be any function
of time and mass-extinction events at specific times can be
provided. Trees can be simulated either conditioned on the
number of species, the time of the process, or both.
Additionally, the likelihood equations are implemented for
convenience and can be used for Maximum Likelihood (ML)
estimation and Bayesian inference.
tester allows you to test characteristics of common R objects.
This package provides two convenience functions assert() and
test_pkg() to facilitate testing R packages.
GUI for entering test items and obtaining raw
and transformed scores. The results are shown on the
console and can be saved to a tabular text file for further
statistical analysis. The user can define his own tests and
scoring procedures through a GUI.
These are weighted tests type logrank for recurrent events.
A testing package specifically tailored for R
that's fun, flexible and easy to set up.
This package intends to go above and beyond of the features
provided by the testthat package. Namely, stubbing of functions and methods
is provided as well as the ability to architect mock structured hierarchies
of files (sometimes called fixtures).
Intended for developers, testthatsomemore aims to
raise the caliber of R testing frameworks to that of famous libraries
like JUnit and RSpec.
Statistical extreme value modelling of threshold excesses,
maxima and multivariate extremes.  Univariate models for threshold
excesses and maxima are the Generalised Pareto, and Generalised
Extreme Value model respectively.  These models may be fitted by
using maximum (optionally penalised-)likelihood, or Bayesian
estimation, and both classes of models may be fitted with covariates
in any/all model parameters.  Model diagnostics support the fitting
process. Graphical output for visualising fitted models and return
level estimates is provided.  For serially dependent sequences, the
intervals declustering algorithm of Ferro and Segers is provided,
with diagnostic support to aid selection of threshold and declustering
horizon.  Multivariate modelling is performed via the conditional
approach of Heffernan and Tawn, with graphical tools for threshold
selection and to diagnose estimation convergence.
TExPosition is an extension of ExPosition for two table analyses, specifically, discriminant analyses.
texreg converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented.
Multinomial [inverse] regression inference for text documents and associated attributes. Provides fast sparse multinomial logistic regression for phrase counts.  A minimalist partial least squares routine is also included.  Note that the topic modeling capability of textir is now a separate package, maptpx.
Statistical exploration of textual corpora using several methods
from French 'Textometrie' (new name of 'Lexicometrie') and French 'Data Analysis' schools.
It includes methods for exploring irregularity of distribution of lexicon features across
text sets or parts of texts (Specificity analysis); multi-dimensional exploration (Factorial analysis), etc.
Those methods are used in the TXM software.
The package implements the TFDEA algorithm for technology forecasting.
Using DEA it forecasts the capabilities of future technologies. The package also
includes basic DEA (Data Envelopment Analysis) and SDEA (Super-efficiency Data
Envelopment Analysis). In additional the package includes some standard
technology forecasting data sets.
Statistical interpretation of forensic glass transfer
(Simulation of the probability distribution of recovered glass
fragments).
TFMPvalue provides the accurate calculation of P-value with score threshold for Position Weight Matrices, or the score with given P-value. This package is an interface to code originally made available by Helene Touzet and Jean-Stephane Varre, 2007, Algorithms Mol Biol:2, 15.
Utilities in this package are for simple manipulation and quick
plotting of time series data. These utilities use the tframe package
which provides a programming kernel for time series. Extensions to
tframe provided in tframePlus can also be used. See the Guide vignette
for examples.
The tframe package provides a kernel of functions for
programming time series methods in a way that is relatively
independently of the representation of time. It also provides
plotting, time windowing, and some other utility functions
which are specifically intended for time series.  See the Guide
distributed as a vignette, or ?tframe.Intro for more details.
(User utilities other than the programming kernel have been
moved into package tfplot.)
Extensions and additional tframe utilities.
Connects R to TrueFX(tm) for free streaming real-time and
historical tick-by-tick market data for dealable interbank
foreign exchange rates with millisecond detail.
Bayesian nonstationary, semiparametric nonlinear
regression and design by treed Gaussian processes (GPs) with
jumps to the limiting linear model (LLM).  Special cases also
implemented include Bayesian linear models, CART, treed linear
models, stationary separable and isotropic GPs, and GP
single-index models.  Provides 1-d and 2-d plotting functions
(with projection and slice capabilities) and tree drawing,
designed for visualization of tgp-class output.  Sensitivity
analysis and multi-resolution models are supported. Sequential
experimental design and adaptive sampling functions are also
provided, including ALM, ALC, and expected improvement.  The
latter supports derivative-free optimization of noisy black-box
functions.
Functions to compute and plot tracheidograms
Contains data sets used in other packages I maintain.
This package interacts directly with the Thinknum API to offer data
in a number of formats usable in R
This package implements a thresholded version of the EEBoost
algorithm described in [Wolfson (2011, JASA)]. EEBoost is a general-purpose
method for variable selection which can be applied whenever inference would
be based on an estimating equation. The package currently implements
variable selection based on the Generalized Estimating Equations, but can
also accommodate user-provided estimating functions. Thresholded EEBoost is
a generalization which allows multiple variables to enter the model at each
boosting step.
Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models
Fit a threshold regression model based on the
first-hitting-time of a boundary by the sample path of a Wiener
diffusion process. The threshold regression methodology is well
suited to applications involving survival and time-to-event
data.
The ThresholdROC package provides point and interval estimations of the optimum threshold for continuous diagnostic tests (two and three states setting). The point estimation is based on the definition of a cost function which we opt to minimise. An analytical estimator exists for the binormal and trinormal model and the empirical one is used for all settings. The interval estimation is based on the Delta method variance estimator in a binormal parametric setting and on methods on non-linear equations for the trinormal setting. Bootstrap methods are also provided for the confidence intervals.
This package provides the timing functions 'tic' and 'toc' that
can be nested. One can record all timings while a complex script is
running, and examine the values later. It is also possible to instrument
the timing calls with custom callbacks. In addition, this package provides
class 'Stack', implemented as a vector, and class 'List', implemented as a
list, both of which support operations 'push', 'pop', 'first', 'last' and
'clear'.
Utilities to generate wiki reports in TiddlyWiki format.
Functions to calculate characteristics of quasi periodic
time series, e.g. observed estuarine water levels
tidyr is an evolution of reshape2. It's design specifically for
data tidying (not general reshaping or aggregating) and works well with
dplyr data pipelines.
This package provides an easy and simple way to read, write and display bitmap images stored in the TIFF format. It can read and write both files and in-memory raw vectors.
Temporally resolved groups of typical differences (errors) between two time series are determined and visualized
This package consists of datasets and functions that are useful
in the teaching of statistics at an elementary level to students who may
have little or no previous experience with the command line.  The functions
for elementary inferential procedures follow a uniform interface for user
input. The package is typically used with package mosaic but runs
independently of it.  Some of the functions are instructional applets that
can only be run on the RStudio IDE with RStudio's package manipulate
loaded. The RStudio IDE is freely available (www.rstudio.org), and includes
package manipulate.
This package contains functions for tight clustering
Algorithm.
The tikzDevice package provides a graphics output device for R
that records plots in a LaTeX-friendly format. The device transforms
plotting commands issued by R functions into LaTeX code blocks. When
included in a paper typeset by LaTeX, these blocks are interpreted with the
help of TikZ---a graphics package for TeX and friends written by Till
Tantau. Using the tikzDevice, the text of R plots can contain LaTeX
commands such as mathematical formula. The device also allows arbitrary
LaTeX code to be inserted into the output stream.
This package provides methods and classes to build HMMs
that are suitable for the analysis of ChIP-on-chip data. The
provided parameter estimation methods include the Baum-Welch
algorithm and Viterbi training as well as a combination of
both.
This package is intended for processing the output from
functional gene tiling DNA microarray experiments. It produces
hybridization pattern plots for each gene on the array, and
statistics for each gene including mean probe intensity, median
probe intensity, bright probe fraction, bright segment length
dependent score, bright probe mean intensity, and bright probe
median intensity. Output is generated in order of bright
segment length dependent score in both a latex/eps format and
tab-delimited text file. The package works in two modes: single
array, and comparison of two arrays. Array comparison includes
array comparison statistics: median of logarithm of one array
probe divided by its counterpart on the other array, median
absolute deviation of that value, and the binomial test to see
whether the genes are equally abundant in both arrays.
Provides an easy-to-use wrapper to Rprof() and
summaryRprof() in the profiling of both function calls and
whole blocks of code.
Implements the Time Machine, a simulation approach for stochastic
trees.
Methods for incorporating time into network analysis. Construction of time-ordered networks (temporal graphs). Shortest-time and shortest-path-length analyses. Resource spread calculations. Data resampling and rarefaction for null model construction. Reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks. Vector clock latencies. Plotting functionalities.
Extract useful time components of a date object, such as
day of week, weekend, holiday, day of month, etc, and put it in
a data frame.  This can be used to create many predictor
variables out of a single time variable, which can then be used
in a regression or decision tree.  Also includes function
plotCalendarHeatmap which draws a calendar and overlays a
heatmap based on values.
Programs for Martinussen and Scheike (2006), `Dynamic Regression
Models for Survival Data', Springer Verlag.  Plus more recent developments.
Additive survival model, semiparametric proportional odds model, fast cumulative
residuals, excess risk models and more. Flexible competing risks regression
including GOF-tests. Two-stage frailty modelling.
PLS for the additive risk model. Lasso in ahaz package.
Estimation of time-dependent ROC curve and area under time
dependent ROC curve (AUC) in the presence of censored data,
with or without competing risks. Confidence intervals of AUCs
and tests for comparing AUCs of two rival markers measured on
the same subjects can be computed, using the iid-representation
of the AUC estimator. Plot functions for time-dependent ROC
curves and AUC curves are provided. Time-dependent Positive
Predictive Values (PPV) and Negative Predictive Values (NPV)
can also be computed.
Computes bootstrap CI for the sample ACF and periodogram
This package is intended to manipulate irregular time series such as subtime series, series based on time intervals, etc. This package uses only POSIX* format. Main things defined in this package are the classes POSIXcti, POSIXctp, TimeIntervalDataFrame, TimeInstantDataFrame, SubtimeDataFrame, methods to switch from one class to another and the changeSupport method for TimeIntervalDataFrame.
Date sequence, relative date calculations, and date
manipulation with business days and holidays.  Works with Date
and POSIXt classes.
TIMP is a problem solving environment for fitting
separable nonlinear models to measurements arising in physics
and chemistry experiments, and has been extensively applied to
time-resolved spectroscopy and FLIM-FRET data.
Functions for statistical analysis, prediction and control of time series.
A universal non-uniform random number generator for quite
arbitrary distributions with piecewise twice differentiable
densities.
Non-parametric resampling-based inference tests for TExPosition.
TIPOM is based on a methodology that was developed in the
1960s by Bernardino Bagolini. The basic idea is to use
the three simple dimensions of length, width and thickness
of each lithic artefact to classify them in discrete
groups and infer their function.
Functions and S3 classes for time indexes and time indexed
series, which are compatible with FAME frequencies.
GUI to analyze mass spectrometric data on the relative
abundance of two substances from a titration series.
simple mechanism for placing R graphics in a Tk widget
TLE implements a general framework for robust fitting of
finite mixture models. Parameter estimation is performed using
the EM algorithm.
Computation of effects under linear, logistic and Poisson regression models with transformed variables. Logarithm and power transformations are allowed. Effects can be displayed both numerically and graphically in both the original and the transformed space of the variables.
Fit a linear mixed effects model for censored data with
Student-t or normal distributions. The errors are assumed
independent and identically distributed.
Functions for two level normal models as described in
Everson and Morris (2000). J. R. Statist. Soc. B, 62 prt 2,
pp.399--412.
A framework for text mining applications within R.
Thematic maps are geographical maps in which spatial data
distributions are visualized. This package offers a flexible, layer-based,
way to create all kinds of thematic maps, such as choropleths and bubble
maps.
tmle implements targeted maximum likelihood estimation, first described in van der Laan and Rubin, 2006 (Targeted Maximum Likelihood Learning, The International Journal of biostatistics, 2(1), 2006.  This version adds the tmleMSM function to the package, for estimating the parameters of a marginal structural model (MSM) for a binary point treatment effect.  The tmle function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes.  Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. Effect estimation stratified by a binary mediating variable is also available. The population mean is calculated when there is missingness, and no variation in the treatment assignment.  An ID argument can be used to identify repeated measures. Default settings call SuperLearner to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
Targeted minimum loss estimation (TMLE) of a non-parametric variable importance measure of a continuous exposure 'X' on an outcome 'Y', taking baseline covariates 'W' into account.
This package provides a tm Source to create corpora from
a corpus prepared in the format used by the Alceste application (i.e.
a single text file with inline meta-data). It is able to import both
text contents and meta-data (starred) variables.
A plug-in for the text mining framework tm to support text mining
in a distributed way. The package provides a convenient interface for
handling distributed corpus objects based on distributed list objects.
This package provides a tm Source to create corpora from
articles exported from the Europresse content provider as
HTML files. It is able to read both text content and meta-data
information (including source, date, title, author and pages).
This package provides a tm Source to create corpora from
articles exported from the Dow Jones Factiva content provider as
XML or HTML files. It is able to read both text content and meta-data
information (including source, date, title, author, subject,
geographical coverage, company, industry, and various
provider-specific fields).
This package provides a tm Source to create corpora from
articles exported from the LexisNexis content provider as
HTML files. It is able to read both text content and meta-data
information (including source, date, title, author and pages).
A plug-in for the tm text mining framework providing mail handling
functionality.
tm.plugin.webmining facilitates text retrieval from feed
formats like XML (RSS, ATOM) and JSON. Also direct retrieval from
HTML is supported. As most (news) feeds only incorporate small
fractions of the original text tm.plugin.webmining even retrieves
and extracts the text of the original text source.
Random number generation for the truncated multivariate normal and Student t distribution.
Computes probabilities, quantiles and densities,
including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.
R package for analyzing weighted, two-mode, and
longitudinal networks.
toaster (a.k.a 'to Aster') is a set of tools to perform
in-database analytics with Teradata Aster Discovery Platform.
toaster embraces simple approach by dividing tasks into 2 steps:
compute in Aster - visualize and analyze in R. toaster `compute` functions
use distributed, highly scalable, parallel SQL and map-reduce
for processing of large data sets in Aster database. Then
`create` functions visualize results with boxplots, scatterplots,
histograms, heatmaps, word clouds, maps, or slope graphs. Advanced options
such as faceting, coloring, labeling, and others are supported
with most plots.
Tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence.  This package provides functions for estimating tolerance limits for various distributions.  Plotting is also available for tolerance limits of continuous random variables.
Provides an interface to the C code for Latent Dirichlet
Allocation (LDA) models and Correlated Topics Models
(CTM) by David M. Blei and co-authors and the C++ code
for fitting LDA models using Gibbs sampling by Xuan-Hieu
Phan and co-authors.
For multiple ranked input lists (full or partial) representing the same set of N objects, the package TopKLists offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results.
Set of hydrological functions including an R
implementation of the hydrological model TOPMODEL, which is
based on the 1995 FORTRAN version by Keith Beven. From version
0.7.0, the package is put into maintenance mode. New functions
for hydrological analysis are now developed as part of the
RHydro package. RHydro can be found on R-forge and is built on
a set of dedicated S4 classes.
Using Gaussian graphical models we propose a novel approach to
perform pathway analysis using gene expression. Given the
structure of a graph (a pathway) we introduce two statistical
tests to compare the mean and the concentration matrices between
two groups. Specifically, these tests can be performed on the
graph and on its connected components (cliques).
Evaluation of alternatives based on multiple criteria using TOPSIS method.
Fit an Instrumental Variables Two Stage Least Squares model
Implements geodesic interpolation and basis
generation functions that allow you to create new tour
methods from R.
The GUI allows user to control the tour by checkboxes for
the variable selection, slider for the speed, and toggle boxes
for pause.
This package implements the greedy approximation for tree
preserving embedding.
Estimation of transition probabilities for the
illness-death model and or the three-state progressive model
Regression models for temporal process responses with
time-varying coefficient.
Plant ecologists often need to collect "traits" data
about plant species which are often scattered among various
databases: TR8 contains a set of tools which take care of
automatically retrieving some of those functional traits data
for plant species from publicly available databases (Biolflor,
The Ecological Flora of the British Isles, LEDA traitbase, Ellenberg
values for Italian Flora, Mycorrhizal intensity database).
The TR8 name, inspired by "car plates" jokes, was chosen since
it both reminds of the main object of the package and is
extremely short to type.
Automatically stores objects in files on disk so that
files are rewritten when objects are changed, and so that
objects are accessible but do not occupy memory until they are
accessed. Keeps track of times when objects are created and
modified, and caches some basic characteristics of objects to
allow for fast summaries of objects.  Also provides a command
history mechanism that saves the last command to a history file
after each command completes.
Final release of 'trackObjs' - package name changed to
'track' - if using 'trackObjs' please switch to 'track'.
Tracking database are the same with 'track' and 'trackObjs'.
Automatically stores objects in files on disk so that files are
rewritten when objects are changed, and so that objects are
accessible but do not occupy memory until they are accessed.
Keeps track of times when objects are created and modified, and
caches some basic characteristics of objects to allow for fast
summaries of objects.  Also provides a command history
mechanism that saves the last command to a history file after
each command completes.
Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data.  Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals.
The tractor.base package consists of functions for working with magnetic resonance images. It can read and write image files stored in Analyze, NIfTI, MGH and DICOM formats (DICOM support is read only), generate images for use as regions of interest, and manipulate and visualise images.
An interface for creating GUIs modeled in part after the
traits UI module for python. The basic design takes advantage of
the model-view-controller design pattern. The interface makes basic
dialogs quite easy to produce.
Classes and methods for trajectory data, with nested classes for individual trips, and collections for different entities. Methods include selection, generalization, aggregation, intersection, and plotting.
This package is a toolbox for sequence manipulation, description, rendering and more generally the mining of sequence data in the field of social sciences. Though it is primarily intended for analyzing state or event sequences that describe life courses such as family formation histories or professional careers its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for translating sequences from one format to another. It offers several statistical functions for describing and rendering sequences, for computing distances between sequences with different metrics among which optimal matching, the longest common prefix and the longest common subsequence, and simple functions for extracting the most frequent subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Collection of ancillary functions and utilities to be used in conjunction with the TraMineR package for sequence data exploration. Most of the functions are in test phase, lack systematic consistency check of the arguments and are subject to changes. Once fully checked, some of the functions of this collection could be included in a next release of TraMineR.
TRAMPR is an R package for matching terminal restriction
fragment length polymorphism (TRFLP) profiles between unknown
samples and a database of knowns.  TRAMPR facilitates analysis
of many unknown profiles at once, and provides tools for
working directly with electrophoresis output through to
generating summaries suitable for community analyses with R's
rich set of statistical functions.  TRAMPR also resolves the
issues of multiple TRFLP profiles within a species, and shared
TRFLP profiles across species.
Bindings for the Google Translate API v2
translateR provides easy access to the Google and Microsoft APIs. The package is easy to use with the related R package "stm" for the estimation of multilingual topic models.
Solve optimal transport problems in statistics. Compute Wasserstein metrics (a.k.a. Kantorovitch, Fortet--Mourier, Mallows, Earth Mover's, or minimal L_p metrics), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, point patterns, and mass vectors.
The trapezoid package provides dtrapezoid, ptrapezoid,
qtrapezoid, and rtrapezoid functions for the trapezoidal
distribution.
Evaluate the performance of markers used to guide treatment selection decisions. Package includes functions to plot risk curves, evaluate marker performance, assess model calibration, and compare markers.
Utilities to investigate sensitivity to unmeasured confounding in parametric models with either binary or continuous treatment.
Interface to the API for TreeBASE. TreeBASE is a repository of
user-submitted phylogenetic trees (of species, population, or genes) and the
data used to create them.
Bootstrapped response and correlation functions,
seasonal correlations and evaluation of reconstruction
skills for use in dendroclimatology and dendroecology
The centre of mass is a crucial data for arborists in order to
consolidate a tree using steel or dynamic cables. Given field-recorded data
on branchiness of a tree, the package: (i) computes and plots the centre of
mass of the tree itself, (ii) computes branches slenderness coefficient in
order to aid the arborist identify potentially dangerous branches, and
(iii) computes the force acting on a ground plinth and its best position
relating to the tree centre of mass, should the tree need to be stabilized
by a steel cable
Treelets provides a novel construction of multi-scale
bases that extends wavelets to non-smooth signals. It returns a
multi-scale orthonormal basis, where the final computed basis
functions are supported on nested clusters in a hierarchical
tree.  Both the tree and the basis, which are constructed
simultaneously, reflect the internal structure of the data.
A treemap is a space-filling visualization of hierarchical
structures. This package offers great flexibility to draw treemaps.
(i) For a given species phylogeny on present day data which is calibrated to calendar-time, a method for estimating maximum likelihood speciation and extinction processes is provided. The method allows for non-constant rates. Rates may change (1) as a function of time, i.e. rate shifts at specified times or mass extinction events (likelihood implemented as LikShifts, optimization as bd.shifts.optim and visualized as bd.shifts.plot) or (2) as a function of the number of species, i.e. density-dependence (likelihood implemented as LikDD and optimization as bd.densdep.optim). Note that the methods take into account the whole phylogeny, in particular it accounts for the "pull of the present" effect. Both (1) and (2) can take into account incomplete species sampling, as long as each species has the same probability of being sampled. For a given phylogeny on higher taxa (i.e. all but one species per taxa are missing), where the number of species is known within each higher taxa, speciation and extinction rates can be estimated under model (1) (implemented within LikShifts and bd.shifts.optim with groups !=0). (ii) For a given phylogeny with sequentially sampled tips, e.g. a virus phylogeny, rates can be estimated under a model where rates vary across time using bdsky.stt.optim based on likelihood LikShiftsSTT (extending LikShifts and bd.shifts.optim). Furthermore, rates may vary as a function of host types using LikTypesSTT (multitype branching process extending functions in R package diversitree). This function can furthermore calculate the likelihood under an epidemiological model where infected individuals are first exposed and then infectious.
The package simulates phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa), or a fixed age (sim.bd.age), or a fixed age and number of tips (sim.bd.taxa.age). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa). The function sim.bd.age (and sim.rateshift.taxa without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot, LTT.plot.gen and LTT.average.root. TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim). (ii) sim.rateshifts.taxa is generalized to sim.bdsky.stt for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa. This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree simulates coalescent gene trees within birth-death species trees, and sim.genetree simulates coalescent gene trees.
The package is a flexible simulation tool for phylogenetic trees under a general model. It is possible to assume any probability distribution for the waiting time until speciation and extinction independently. Thus, TreeSimGM allows to simulate stochastic phylogenetic trees using any probability distribution and parameters for speciation and  extinction. The speciation modes have all binary splits and are: (i) symmetric, where for every speciation event new waiting times until speciation and extinction are drawn for both daughter lineages; and (ii) asymmetric, where a speciation event results in one species with new waiting times, and another that carries the extinction time and age of its ancestor. Those two modes were inspired by allopatric and peripatric speciation respectively.Both models (symmetric and asymmetric) were created and implemented so that different processes (distributions) for speciation and extinction could be independently and explicitly specified. It is also possible to have an implicit extinction process by setting the extinction rate to zero.
functions and examples in Sample Size Calculation in
Clinical Research.
This is a package for symmetric discrete triangular
distributions
This is a package for general discrete triangular
distributions which is an extension of the symmetric discrete
triangular distributions
Provides the "r, q, p, and d" distribution functions for
the triangle distribution
The package contains routines to 1) project unity-summed
triples to unit-square doubles and vice versa, 2) make a grid
of unity-summed triples paired to doubles, 3) evaluate a
function over the grid and 4) make simple plots including
ternary contour plots over a grid of values.
Propensity score matching for non-binary treatments.
Creates point and probability forecasts from the trees in a random forest using a trimmed opinion pool.
The package contains functions that 1) estimates
gene-environment interaction between a SNP and a continuous
non-genetic attribute by fitting a generalized additive model
to case-parent trio data, 2) produces graphical displays of
estimated interaction, 3) performs permutation test of
gene-environment interaction; 4) simulates informative
case-parent trios.
Functions for accessing and manipulating spatial data for
animal tracking.  Filter for speed and create time spent plots
from animal track data.
A constrained two-dimensional Delaunay triangulation package
Data handling and estimation functions for animal movement estimation from archival or satellite tags. Helper functions are included for making image summaries binned by time interval from MCMC simulations of point data.
Social Relation Model (SRM) analyses for single or multiple
round-robin groups are performed. These analyses are either based on one
manifest variable, one latent construct measured by two manifest variables,
two manifest variables and their bivariate relations, or two latent
constructs each measured by two manifest variables. Within-group t-tests
for variance components and covariances are provided for single groups,
between-groups t-tests for multiple groups. Handling for missing values is
provided.
Class definitions and constructors for pseudo-vectors containing
all permutations, combinations and subsets of objects taken from a vector.
Simplifies working with structures commonly encountered in combinatorics.
An implementation of the TrueSkill algorithm (Herbrich,
R., Minka, T. and Grapel, T) in R; a Bayesian skill rating
system with inference by approximate message passing on a
factor graph. Used by Xbox to rank gamers and identify
appropriate matches.
http://research.microsoft.com/en-us/projects/trueskill/default.aspx
Current version allows for one player per team. Will update as
time permits.  Requires R version 3.0 as it is written with
Reference Classes.  URL:
https://github.com/bhoung/trueskill-in-r Acknowledgements to
Doug Zongker and Heungsub Lee for their python implementations
of the algorithm and for the liberal reuse of Doug's code
comments (@dougz and @sublee on github).
A collection of functions to evalue probability density
functions, cumulative distribution functions, quantile
functions and random numbers for truncated random variables.
Functions are provided to also compute the expected value and
variance. Nadarajah and Kotz (2006) developed most of the
functions. QQ plots can be produced.  All the probability
functions in the stats, stats4 and evd packages are
automatically available for truncation..
Goodness-of-fit tests and some adjusted exploratory tools
allowing for left truncated data
r/d/p/q functions for the truncated normal distribution
Estimation of models for truncated Gaussian variables by maximum likelihood.
Semi-parametric estimation of truncated linear regression models
local optimization using two derivatives and trust regions
Trust region algorithm for nonlinear optimization. In addition to
being more stable and robust than optim, this package includes
methods that are scalable and efficient (in terms
of both speed and memory usage) when the Hessian is sparse.
Contains R functions and datasets detailed in the book
"Time Series Analysis with Applications in R (second edition)"
by Jonathan Cryer and Kung-Sik Chan
Time series aggregation for incomplete time series data.
The tsbridge package contains a collection of R functions that can be used to estimate normalising constants using the bridge sampler of Meng and Wong (1996). The functions can be applied to calculate posterior model probabilities for a variety of time series Bayesian models, where parameters are estimated using BUGS, and models themselves are created using the tsbugs package.
The tsbugs package contains a collection of R functions
that can be used to create time series BUGS models of various
order. Included are function to create BUGS with non-constant
variance such stochastic volatility models and random variance
shift models.
This package contains a set of measures of dissimilarity between time series to perform time series clustering. Metrics based on raw data, on generating models and on the forecast behavior are implemented. Some additional utilities related to time series clustering are also provided, such as clustering algorithms and cluster evaluation metrics.
Utilities for comparing the equality of series on two databases.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
TSdata illustrates the various TSdbi packages using time series
data from several sources. It also illustrates some simple time series
manipulation and plotting using packages tframe and tfplot.
TSdbi provides a common interface to time series databases. The
objective is to define a standard interface so users can retrieve time
series data from various sources with a simple, common, set of
commands, and so programs can be written to be portable with respect
to the data source. The SQL implementations also provide a database
table design, so users needing to set up a time series database
have a reasonably complete way to do this easily. The interface
provides for a variety of options with respect to the representation
of time series in R. The interface, and the SQL implementations, also
handle vintages of time series data (sometime called editions or
realtime data). There is also a (not yet well tested) mechanism to
handle multilingual data documentation.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
This package provides a set of commonly used distance measures for time series data. The application of these distance measures is useful to perform clustering, classification or other data mining tasks.
Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).
This function assign and integrate target peaks in
two-dimentional chromatogram.
Package for time series analysis and computational finance
Routines for the analysis of nonlinear time series. This
work is largely inspired by the TISEAN project, by Rainer
Hegger, Holger Kantz and Thomas Schreiber:
http://www.mpipks-dresden.mpg.de/~tisean/
Extraction of Factors from Multivariate Time Series. See ?00tsfa-Intro for more details.
TSfame provides a fame interface for TSdbi.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
Provides methods for generics in the TSdbi package to
retrieve data with getSymbols, in particular from the Federal Reserve
FRED database.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
Provides methods for generics in the TSdbi package to
retrieve data from historical quote URLs.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
two-stage procedure for comparing hazard rate functions
which may or may not cross each other
This package contains functions for analysing and forecasting intermittent demand/slow moving items time series.
Provides methods for generics in the TSdbi package to
retrieve data from web data sources, importing them using Javascript
object notation. Direct connections or a proxy connection are
possible. (Only Statistics Canada connections are currently supported.)
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
This package will help you to carry out a time series LARS
analysis.
Tools for specifying time series regression models
TSMySQL provides a MySQL interface for TSdbi.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
A "pure R" implementation of the t-SNE algorithm.
TSodbc provides an ODBC interface for TSdbi.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
This package implements a procedure based on the approach described
in Chen and Liu (1993) for automatic detection of outliers in time series.
Innovational outliers, additive outliers, level shifts, temporary changes
and seasonal level shifts are considered.
Basic infrastructure and some algorithms for the traveling
salesperson problem (also traveling salesman problem; TSP).
The package provides some simple algorithms and
an interface to Concorde, the currently fastest TSP solver. Concorde
itself is not included in the package and has to be obtained separately.
Adjusts for correlations among the rows and columns via
the Transposable Sphering Algorithm when conducting large-scale
inference on the rows of a data matrix.
Instance feature calculation and evolutionary instance generation
for the traveling salesman problem. Also contains code to "morph" two TSP
instances into each other. And the possibility to conveniently run a couple
of solvers on TSP instances.
TSPostgreSQL provides a PostgreSQL interface for TSdbi.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
This package provides standard SQL query functions used by
SQL plugins packages for the TSdbi interface to time series databases.
It will mainly be used by other packages rather than directly by end
users. The one exception is the function TSquery which can be used to
construct a time series from a database containing observations over
time (e.g. balance statements for muliple years), but where the database
is not specifically designed to store time series (as with other
TSdbi SQL plugin packages).
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
TSSQLite provides a SQLite interface for TSdbi.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
Interactive laboratory of Time Series based in Box-Jenkins methodology.
Provides TSdbi package methods to retrieve time series data from
spreadsheets, pretending the spreadsheet is a database.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
Provides TSdbi package methods to retrieve time series data from
zipped, as if the files form a database.
Comprehensive examples of all the TS* packages is provided in the
vignette Guide.pdf with the TSdata package.
This interface was created to develop a standard procedure
of temporal trend analysis in the framework of the OSPAR convention.
It is based on four successive steps : simply manipulate your database,
select the parameters you want to analyse, build your regulated
time series, perform analysis and read the results all in one easy to use
interface. Statistical analysis call other package function such as
Kendall tests (wq package) or cusum function (Pastec package).
C source code and R wrappers for the tth/ttm TeX to
HTML/MathML translators.
Functions and data to construct technical trading rules
with R.
This package utilizes training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects.
This package makes Travel To Work Area from a commuting flow data frame.
Perform the conventional Tukey test from aov and aovlist
objects
Generic methods for parameter tuning of classification algorithms using multiple scoring functions
Collection of tools to analyze music, extract features
like MFCCs, handling wave files, read mp3, transcription, ...
Also contains functions ported from the rastamat Matlab
package.
Functions to find all matches, or the orphan, duplicate or
triplicate or other replicated values in a vector.
Algorithms for accelerating the convergence of slow,
monotone sequences from smooth, contraction mapping such as the
EM and MM algorithms. It can be used to accelerate any smooth,
linearly convergent acceleration scheme.  A tutorial style
introduction to this package is available in a vignette on the
CRAN download page or, when the package is loaded in an R
session, with vignette("turboEM").
Package for analyzing TURF (Total Unduplicated Reach and Frequency) data in R.
No looping in TURF algorithm results in fast processing times. Allows for individual-level
weights, depth specification, and user-truncated combination set(s). Allows user to substitute
Monte Carlo simulated combination set(s) after set(s) exceed a user-specified limit.
Package designed for working with vectors and lists of vectors,
mainly for turning them into other indexed data structures.
An implementation of turtle graphics
(http://en.wikipedia.org/wiki/Turtle_graphics) in R.
Turtle graphics comes from Papert's language Logo and has
been used to teach concepts of computer programming.
The model is a lumped conceptual rainfall-runoff model,
following the structure of the HBV model.  The model runs on a
daily time step and consists of a snow routine, a soil moisture
routine and a flow routing routine.  See Parajka, J., R. Merz,
G. Bloeschl (2007) Uncertainty and multiple objective
calibration in regional water balance modelling: case study in
320 Austrian catchments, Hydrological Processes, 21, 435-446.
Total Variation Denoising is a regularized denoising method which
effectively removes noise from piecewise constant signals whilst preserving
edges.  This package contains a C++ implementation of Condat's very fast 1D
squared error loss TVD algorithm.  Additional methods and loss functions may
be added in future versions.
Functions for managing cashflows and interest rate curves
This package offers functions for propensity score
estimating and weighting, nonresponse weighting, and diagnosis
of the weights.  This package was originally developed by Drs.
Ridgeway, McCaffrey, and Morral. Burgette, Griffin and
McCaffrey updated the package in 2011-13.
Maximum likelihood computations for Tweedie families.
Twiddler is an interactive tool that automatically creates
a Tcl/Tk GUI for manipulating variables in any R expression.
See the documentation of the function twiddle to get started.
Provides an interface to the Twitter web API
This package implements the nonparametric test of equality
between two copulas proposed by Remillard and Scaillet in their
2009 JMVA paper.
This program computes thresholds and power for a two-stage
genome-wide association study. It follows the methods described
in Skol AD, Scott, LJ, Abecasis GR, Boehnke M (2006) Nature
Genetics doi:10.1038/ng1706 and in the "CaTS" computer program
(http://www.sph.umich.edu/csg/abecasis/CaTS/)
Implements a variety of nonparametric methods for computing one-stage and two-stage confidence intervals, as well as point estimates of threshold values.
Conditional logistic regression with longitudinal follow
up and individual-level random coefficients: A stable and
efficient two-step estimation method
Provides functions to produce rudimentary ascii graphics
directly in the terminal window. Provides a basic plotting
function (and equivalents of curve, density, acf and barplot)
as well as a boxplot function.
This package contains latex, knitr and R Markdown templates that
adhere to the UC Berkeley Graduate Division's thesis guidelines. The
templates are located in the inst/ directory.
An algorithm for general-purpose unconstrained non-linear optimization.
The algorithm is of quasi-Newton type with BFGS updating of the inverse
Hessian and soft line search with a trust region type monitoring of the
input to the line search algorithm. The interface of 'ucminf' is
designed for easy interchange with 'optim'.
This package provides simple bindings to version 2 of
Unidata's udunits library
A dataset is said to be unbalanced when the class of interest (minority class) is much rarer than normal behaviour (majority class). The cost of missing a minority class is typically much higher that missing a majority class. Most learning systems are not prepared to cope with unbalanced data. Proposed strategies essentially belong to the following categories: sampling and distance-based. Sampling techniques up-sample or down-sample a class of instances. SMOTE generates synthetic minority examples. Distance based techniques use distances between input points to under-sample or to remove noisy and borderline examples.
The package implements top-down and bottom-up algorithms
for nonparametric function estimation in Gaussian noise using
Unbalanced Haar wavelets.
Data from Unicode 6.3.0 and related utilities.
Univarate shrinkage prediction for survival analysis using
in the Cox model.. Especially useful for high-dimensional data,
including microarray data.
Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or
self-defined penalties on the B-spline coefficients.
Concise TAP (http://testanything.org/) compliant unit testing package. Authored tests that can be run using CMD check with minimal implementation overhead.
This package fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates.
A collection of utilities for biodiversity data.
Includes the simulation of ecological drift under Hubbell's Unified
Neutral Theory of Biodiversity, and the calculation of various
diagnostics such as Preston curves.  Now includes functionality
provided by Francois Munoz and Andrea Manica.
This package contains a collection of functions which
implement data classification. It uses unlabeled data to obtain
parameter estimates of models. The functions can be implemented
over a number of models with the best model selected and
displayed.
An integrated package for building and testing uplift models
An implementation of the UPMASK method for performing membership
assignment in stellar clusters in R. It is prepared to use photometry and
spatial positions, but it can take into account other types of data. The
method is able to take into account arbitrary error models, and it is
unsupervised, data-driven, physical-model-free and relies on as few
assumptions as possible. The approach followed for membership assessment is
based on an iterative process, principal component analysis, a clustering
algorithm and a kernel density estimation.
Unit root and cointegration tests encountered in applied
econometric analysis are implemented.
This package contains functions to read cancer data from SEER (http://seer.cancer.gov/) and IARC (http://www.iarc.fr) to create datasets at the county level based on US census information.
US Census 2000 Block Group shapefiles and additional
demographic data from the SF1 100 percent files. This data set
contains polygon files in lat/lon coordinates and the
corresponding demographic data for a number of different
variables.
US Census 2000 Designated Places shapefiles and additional
demographic data from the SF1 100 percent files. This data set
contains polygon files in lat/lon coordinates and the
corresponding demographic data for a number of different
variables.
US 2000 Census Tract shapefiles and additional demographic
data from the SF1 100 percent files. This data set contains
polygon files in lat/lon coordinates and the corresponding
demographic data for a number of different variables.
US Census 2010 shape files and additional demographic data
from the SF1 100 percent files. This package contains a number
of helper functions for the UScensus2010blk,
UScensus2010blkgrp, UScensus2010tract, UScensus2010cdp
packages.
This package aims to provide methods and tools for assessing the impact of different sources of uncertainties (e.g.positional uncertainty) on performance of species distribution models (SDMs).)
A set of little functions that have been found
useful to do little odds and ends
This package contains a number of functions that serve
two goals: first, make R more accessible to people migrating from
SPSS by adding a number of functions that behave roughly like their
SPSS equivalents; and second, make a number of slightly more
advanced functions more userfriendly to relatively novice users.
A collection of data sets to accompany the
textbook "Using R for Introductory Statistics," second
edition.
Implements mixtures of unrestricted skew-t factor analyzer models via the EM algorithm
The Universal Scalability Law is a model to
predict hardware and software scalability. It uses
system capacity as a function of load to forecast the
scalability for the system.
Forms a query to submit for US Treasury yield curve data, posting
this query to the US Treasury web site's data feed service.  By default the
download includes data yield data for 12 products from January 1, 1990,
some of which are NA during this span.  The caller can pass parameters to
limit the query to a certain year or year and month, but the full download
is not especially large.  The download data from the service is in XML
format.  The package's main function transforms that XML data into a numeric
data frame with treasury product items (constant maturity yields for 12 kinds
of bills, notes, and bonds) as columns and dates as row names. The function
returns a list which includes an item for this data frame as well as query-related
values for reference and the update date from the service.
Construct and plot objective hierarchies and associated value and utility functions.
Evaluate the values and utilities and visualize the results as colored objective hierarchies or tables.
Visualize uncertainty by plotting median and quantile intervals within the nodes of objective hierarchies.
Get numerical results of the evaluations in standard R data types for further processing.
Tools for generating and handling of UUIDs
A method for estimating log-normalizing constants (or free
energies) and expectations from multiple distributions (such as
multiple generalized ensembles).
A likelihood framework for estimating the effective
coverage of vaccination programs using cross-sectional surveys
combined with administrative data.
Implements the variational inference procedure for
Bayesian variable selection, as described in the "Scalable
variational inference for Bayesian variable selection in
regression, and its accuracy in genetic association studies"
(Bayesian Analysis 7, March 2012, pages 73-108). This software
has been used to implement Bayesian variable selection for
large problems with over a million variables and thousands of
samples.
Variance component models: REML estimation, testing fixed effect contrasts through Satterthwaite or Kenward-Roger methods, testing the nullity of variance components through (linear or quadratic) score tests or likelihood ratio tests.
This package allows interactive variogram diagnostics.
Generation of domain variables, linearisation of several nonlinear population statistics (the ratio of two totals, weighted income percentile, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, 1953), variance estimation for longitudinal, crossectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y.G.). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.
Functions for estimate the changes of effective population
size with coalescent approach.
Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions,  including all commonly known distributions.  Also computed are the corresponding probability density function and cumulative distribution function.
Estimation, Hypothesis Testing, Prediction for Stationary Vector Autoregressive Models
Presence of interaction between a SNP and another SNP (or another factor) can result
in heterogeneity of variance between the genotypes of an interacting SNP.
Detecting such heterogeneity gives prior knowledge for constructing a genetic model
underlying complex trait.
This package provides functions for the variance gamma
distributions. Density, distribution and quantile functions.
Functions for random number generation and fitting of the
variance gamma to data. Also, functions for computing moments
of the variance gamma distribution of any order about any
location. In addition, there are functions for checking the
validity of parameters and to interchange different sets of
parameterizatons for the variance gamma distribution.
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
Objective Bayes Variable Selection in Linear Regression
and Probit Models.
Variable selection from random forests using both
backwards variable elimination (for the selection of small sets
of non-redundant variables) and selection based on the
importance spectrum (somewhat similar to scree plots; for the
selection of large, potentially highly-correlated variables).
Main applications in high-dimensional data (e.g., microarray
data, and other genomics and proteomics applications). You can
use rpvm instead of Rmpi if you want but I've only tested with
Rmpi.
Computes a portfolio of European options that replicates
the cost of capturing the realised variance of an equity index.
Generates R scripts useful to perform repetitive
tasks on variables of different classes from a simple database. First it
helps to create a tabular file describing the variables. Then the package
could process this tabulate file to create a script in a .R or .Rmd format.
This script will include code blocks for each variable according to the
variable description. It could help to import, adapt to R classes and
perform descriptive analysis on each variable according to its type. This R
script could be used as it is or it could be modified if necessary to
perform additional analysis. The user could write his own R script template
to produce a customized script.
Efficient algorithm for solving discrete mixture
regression model for rare variant association analysis.
Uses variational Bayes algorithm to efficiently search over
model space.  Outputs an approximate likelihood ratio test
as well as variant level posterior probabilities of
association.
A package to fit and simulate latent position and cluster
models for statistical networks using a Variational Bayes approximation.
Variational algorithms and methods for fitting mixture
models. Main functions are varbayes, vbcomp, vbconstr, mppca,
mmppca and gmmkmsock.
Efficient algorithm for solving ultra-sparse
regularized regression models using a variational
Bayes algorithm with a spike (l0) prior.  Algorithm
is solved on a path, with coordinate updates, and is
capable of generating very sparse models.  There are
very general model diagnostics for controling type-1
error included in this package.
Visualization techniques, data sets, summary and inference
procedures aimed particularly at categorical data. Special
emphasis is given to highly extensible grid graphics. The
package was inspired by the book "Visualizing Categorical Data"
by Michael Friendly.
Provides additional data sets, methods and documentation to complement the vcd package for Visualizing Categorical Data
and the gnm package for Generalized Nonlinear Models.
In particular, vcdExtra extends mosaic, assoc and sieve plots from vcd to handle glm() and gnm() models and
adds a 3D version in mosaic3d.  Additionally, methods are provided for comparing and visualizing lists of
glm and loglm objects.
Recursive partitioning algorithm for varying coefficient generalized linear models and ordinal 2-stage linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal ordinal regression.
Multicategory Vertex Discriminant Analysis: A novel supervised multicategory classification method
Construct Variance Dispersion Graphs (VDG),
Fraction-of-design-space (FDS) and similar plots for experimental designs
This package calls a modification of the published FORTRAN code for producing variance dispersion graphs. For more details on variance dispersion graphs see "A Computer Program for Generating Variance Dispersion Graphs" by G. Vining, Journal of Quality Technology, Vol. 25 No. 1 January 1993.
This package creates variance dispersion graphs (VDGs) and fraction of design space (FDS) plots for response surface designs in spherical or cuboidal regions.
This provides web based visual data mining tools by adding
interactive functions to ggplot2 graphics. Brushing and linking between the
multiple plots is one of the main feature of this package. Currently
scatter plot, histogram, parallel coordinate plot and choropleth map are
supported.
The base 'sets' tools follow the algebraic definition
that each element of a set must be unique.
Since it's often helpful to compare all elements of two vectors,
this toolset treats every element as unique for counting purposes.
For ease of use, all functions in vecsets have an argument
'multiple' which, when set to FALSE,
reverts them to the base::set tools functionality.
This package performs a 2D statistical analysis, both numerical and graphical, of a set of vectors. Since a vector has two components (module and azimuth) vector analysis is performed in three stages: modules are analyzed by means of linear statistics, azimuths are analyzed by circular statistics, and the joint analysis of modules and azimuths is done using density maps that allow detecting another distribution properties (i.e. anisotropy) and outliers. Tests and circular statistic parameters have associated a full range of graphing: histograms, maps of distributions, point maps, vector maps, density maps, distribution modules and azimuths.
This package performs a 3D statistical analysis, both
numerical and graphic, of a set of vectors. Since a vector has
three components (a module and two angles) vector analysis is
performed in two stages: modules are analyzed by means of
linear statistics and orientations are analyzed by spherical
statistics. Tests and spherical statistic parameters are
accompanied by a full range of graphing: vector maps, density
maps, distribution modules and angles. The tests, spherical
statistic parameters and graphs allow us detecting another
distribution properties (I.e. anisotropy) and outliers.
Some vectorized optimization methods and speedup using foreach
Ordination methods, diversity analysis and other
functions for community and vegetation ecologists.
This package contains functions used to perform fuzzy clustering of vegetation data under different models.
Handling of vegetation data sets from Turboveg, VegetWeb and ESVeg Exports. Taxonomic harmonization of datasets with appropriate taxonomic lists (e.g. the German taxonomical standard list "GermanSL").
This package computes diversity for community data sets
using the methods outlined by Jost (2006, 2007). While there
are differing opinions on the ideal way to calculate diversity
(e.g. Magurran 2004), this method offers the advantage of
providing diversity numbers equivalents, independent alpha and
beta diversities, and the ability to incorporate 'order' (q) as
a continuous measure of the importance of rare species in the
metrics. The functions provided in this package largely
correspond with the equations offered by Jost in the cited
papers. The package computes alpha diversities, beta
diversities, gamma diversities, and similarity indices.
Confidence intervals for diversity measures are calculated
using a bootstrap method described by Chao et al. (2008).  For
datasets with many samples (sites, plots), sim.table creates
tables of all pairwise comparisons possible, and for grouped
samples sim.groups calculates pairwise combinations of within-
and between-group comparisons.
A set of functions to generate high-resolution Venn and Euler plots. Includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.
Calculates and displays Venn and Euler Diagrams
This package contains utilities for verification of
discrete,continuous, probabilistic forecasts and forecast
expressed as parametric distributions.
This package fits many (150+) models and
distributions by maximum likelihood estimation (MLE)
or penalized MLE, using Fisher scoring. This is done
via the vector generalized linear and additive model
(VGLM/VGAM) classes and associated models (reduced-rank
VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, row-column
interaction models). These are abbreviated RR-VGLMs,
QRR-VGLMs, RR-VGAMs and RCIMs. These include constrained
and unconstrained quadratic ordination (CQO/UQO) models
in ecology as well as constrained additive ordination
(CAO).
Data sets to accompany the VGAM package.
These are used to illustrate vector generalized
linear and additive models (VGLMs/VGAMs), and associated models
(Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column
Interaction Models, and constrained and unconstrained ordination
models in ecology.
This package provides an implementation of Linear discriminant analysis and quadratic discriminant analysis that works fine in very high dimension (when there are many more variables than observations).
It will take the vectors of motion for two videos
(coming from a variant of shotdetect code allowing to store
detailed motion vectors in json format, for instance) and it
will look for comparing taking out the common chunk.
Then, provided you have some image's hashes it will compare
their signature in order to make up the decision about
chunk similarity of two video files.
This package implements a fast regression algorithm for
building linear model for large data as defined in the paper
"VIF-Regression: A Fast Regression Algorithm for Large Data
(2011), Journal of the American Statistical Association, Vol.
106, No. 493: 232-247" by Dongyu Lin, Dean P. Foster, and Lyle
H. Ungar.
This package introduces new tools for the visualization of missing
and/or imputed values, which can be used for exploring the data and the
structure of the missing and/or imputed values. Depending on this structure
of the missing values, the corresponding methods may help to identify the
mechanism generating the missing values and allows to explore the data
including missing values. In addition, the quality of imputation can be
visually explored using various univariate, bivariate, multiple and
multivariate plot methods. A graphical user interface available in the
separate package VIMGUI allows an easy handling of the implemented plot
methods.
This package introduces new tools for the visualization of missing
and/or imputed values, which can be used for exploring the data and the
structure of the missing and/or imputed values. Depending on this structure
of the missing values, the corresponding methods may help to identify the
mechanism generating the missings and allows to explore the data including
missing values. In addition, the quality of imputation can be visually
explored using various univariate, bivariate, multiple and multivariate
plot methods. A graphical user interface allows an easy handling of the
implemented plot methods.
This package provides functions for statistical inference of vine copulas. It contains tools for bivariate exploratory data analysis, bivariate copula selection and (vine) tree construction. Models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and plotting methods are also included. Data is assumed to lie in the unit hypercube (so-called copula data). For C- and D-vines links to the package CDVine are provided.
Vines are graphical models for pair-copula constructions that represent high-dimensional distributions as a factorization of bivariate copulas and marginal density functions. This package contains S4 classes for vines (C-vines and D-vines) and methods for inference, goodness-of-fit tests, density/distribution function evaluation, and simulation.
A lattice violin-plot is overlayed with the arithmetic
mean and standard deviation.
A violin plot is a combination of a box plot and a kernel density plot.
viopoints draws one dimensional scatter plots with jitter
using kernel density estimates in a similar way to violin
plots.
Provides a framework for generating virtual species distributions,
a procedure increasingly used in ecology to improve species distribution models.
This package integrates the existing methodological approaches with the
objective of generating virtual species distributions with increased
ecological realism.
VISOVA(VISualization Of VAriance) is a novel method for exploratory data analysis. It is basically an extension of the trellis graphics and developing their grid concept with parallel coordinates, permitting visualization of many dimensions at once. This package includes functions allowing users to perform VISOVA analysis and compare different column/variable ordering methods for making the high-dimensional structures easier to perceive even when the data is complicated.
Provides convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R (lm, glm, coxph, rlm, gam, locfit, etc.)
A collection of tools for analyzing the field of vision. It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research.
Package contains function to visualize a t-test, the power
of a t-test, control charts and the influence of regulating
them, Oc-curves, the Law of large Numbers and confidence
intervals.
Graphs the pdf or pmf and highlights what area or probability is present in user defined locations. Visualize is able to provide lower tail, bounded, upper tail, and two tail calculations. Supports strict and equal to inequalities.  Also provided on the graph is the mean and variance of the distribution.
The VisuClust package provides functions to display
multivariate data, based on Sammon's nonlinear mapping.
Provides fitting routines for several models in the
Vitality family of mortality models.
Provides individual- and aggregate-level graphical depictions of
patterns of treatment and response in patient registries, and a graphical
tool for examining potential for confounding in analyses of observational
data.
Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences.  The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs.  The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another.  Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures.
Functions, Classes & Methods for estimation, prediction, and
simulation (bootstrap) of VLMC -- Variable Length Markov Chain -- Models
This package implements algorithms that use V-algorithm and Newton Raphson algorithm to find Multiple-objectives optimal design for estimating three objectives: estimating model parameters; estimating ED50; estimating the minimum effective dose (MED).
Voss package provides functionality for generating
realizations of a fractal Brownian function on uniform 1D & 2D
grid with classic and generic versions of the Voss algorithm
(random sequential additions)
vowels is an R package for the manipulation,
normalization, and plotting of phonetic and sociophonetic vowel
formant data.  vowels is the backend for the NORM website.
Parametric and semiparametric inference for massively parallel
models, i.e., a large number of models with common design matrix, as often
occurs with brain imaging data.
Tools for tree crown structure description based on T-LiDAR data voxelisation
Variable Penalty Dynamic Time Warping for aligning GC-MS
chromatograms to a master signal and more. With the appropriate
penalty this method performs good alignment without altering
the shape of peaks in GC-MS data.
vrmlgen creates 3D scatter and bar plots, visualizations of 3D meshes, parametric functions and height maps in web-formats like the Virtual Reality Markup Language (VRML, filetype .wrl) and the LiveGraphics3D format.
A collection of statistical tests for martingale difference hypothesis
Performs variable selection/feature reduction under a clustering or classification framework. In particular, it can be used in an automated fashion using mixture model-based methods (tEIGEN and MCLUST are currently supported).
Three steps variable selection procedure based on random forests.
Initially developed to handle high dimensional data (for which number of
variables largely exceeds number of observations), the package is very
versatile and can treat most dimensions of data, for regression and
supervised classification problems. First step is dedicated to eliminate
irrelevant variables from the dataset. Second step aims to select all
variables related to the response for interpretation purpose. Third step
refines the selection by eliminating redundancy in the set of variables
selected by the second step, for prediction purpose.
Functions and data for use in visual word recognition research:
Computation of neighbors (Hamming and Levenshtein
distances), average distances to neighbors (e.g., OLD20),
and Coltheart's N. Also includes the LD1NN algorithm to
detect bias in the composition of a lexical decision task. Most of
the functions support parallel execution. Supplies wordlists for
several languages. Uses the string distance functions from the stringdist package by Mark van der Loo.
The W2CWM2C package is a set of routines that improves the
graphical presentations of the functions 'wave.correlation' and
'spin.correlation' (wavelet cross correlation) (Waveslim
package, Whitcher 2012) and the 'wave.multiple.correlation' and
'wave.multiple.cross.correlation' (Wavemulcor package,
Fernandez-Macho 2012)
R interface to a W3C Markup Validation service.
See <http://validator.w3.org/> for more information.
waffect (pronounced 'double-u affect' for 'weighted
affectation') is a package to simulate phenotypic (case or
control) datasets under a disease model H1 such that the total
number of cases is constant across all the simulations (the
constrain in the title). The package also makes it possible to
generate phenotypes in the case of more than two classes, so
that the number of phenotypes belonging to each class is
constant across all the simulations. waffect is used to assess
empirically the statistical power of Genome Wide Association
studies.
The Wald-Wolfowitz test is a non-parametric statistical test that checks a randomness hypothesis for a two-valued data sequence
A collection of functions to perform the Application
Programming Interface (API) calls associated with the Walk
Score website (www.walkscore.com) within the R environment.
These functions can be used to query the Walk Score and Transit
Score database for a wide variety of information using R
scripts. This package includes the simple Walk Score and
Transit Score API calls, which return the scores associated
with an input location, as well as calls which return some data
used to calculate the scores. These functions are especially
useful for mass data collection and gathering Walk Score and
Transit Score values for large lists of locations.
This package estimates precise weaning ages for a given
skeletal population by analyzing the stable nitrogen isotope
ratios of them.  Bone collagen turnover rates estimated anew
and the approximate Bayesian computation (ABC) were adopted in
this package.
Helpful tools for data processing and visualisation of results of the hydrological model WASIM-ETH.
This package imports U.S. Geological Survey (USGS) daily hydrologic data from USGS web services, plots the data, addresses some common data problems, and calculates and plots anomalies.
This package provides both traditional and lattice
graphics implementations of waterfall charts.
Wrap-around Time Series (WATS) Plots for Interrupted Time Series
Designs with Seasonal Patterns
Computes Bayesian wavelet shrinkage credible intervals
Makes available code necessary to reproduce figures and
tables in papers on the WaveD method for wavelet deconvolution
of noisy signals as presented in The WaveD Transform in R,
Journal of Statistical Software Volume 21, No. 3, 2007.
This package contains functions for computing and plotting
discrete wavelet transforms (DWT) and maximal overlap discrete
wavelet transforms (MODWT), as well as their inverses.
Additionally, it contains functionality for computing and
plotting wavelet transform filters that are used in the above
decompositions as well as multiresolution analyses.
Wavelet routines that calculate single sets of wavelet
multiple correlations and cross-correlations out of n variables
(either 1D time series, 2D images or 3D arrays). They can later
be plotted in single graphs, as an alternative to trying to
make sense out of several sets of wavelet correlations or
wavelet cross-correlations. The code is based on the
calculation, at each wavelet scale, of the square root of the
coefficient of determination in a linear combination of
variables for which such coefficient of determination is a
maximum. The code provided here is based on the
wave.correlation routine in Brandon Whitcher's waveslim R
package Version: 1.6.4, which in turn is based on wavelet
methodology developed in Percival and Walden (2000); Gencay,
Selcuk and Whitcher (2001) and others.
Basic wavelet routines for time series (1D), image (2D)
and array (3D) analysis.  The code provided here is based on
wavelet methodology developed in Percival and Walden (2000);
Gencay, Selcuk and Whitcher (2001); the dual-tree complex wavelet
transform (DTCWT) from Kingsbury (1999, 2001) as implemented by
Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002).  All
figures in chapters 4-7 of GSW (2001) are reproducible using this
package and R code available at the book website(s) below.
Performs 1, 2 and 3D real and complex-valued wavelet transforms,
nondecimated transforms, wavelet packet transforms, nondecimated
wavelet packet transforms, multiple wavelet transforms,
complex-valued wavelet transforms, wavelet shrinkage for
various kinds of data, locally stationary wavelet time series,
nonstationary multiscale transfer function modeling, density
estimation.
The package implements Wild Binary Segmentation and Binary
Segmentation algorithms for estimation of the number and locations of
multiple change-points in the piecewise constant function plus Gaussian
noise model.
SOM networks for comparing patterns with peak shifts.
The package contains the WCQ method for detection of QTL
effects in a small mapping population. It also contains
implementation of the Chen-Qin two-sample and one-sample test
of means.
Search, extract and format data from the World Bank's
World Development Indicators
Functions that help in fetching weather data from
websites. Given a location and a date range, these functions help fetch
weather data (temperature, pressure etc.) for any weather related analysis.
Two versions of the package are available.  One provides
only the basic script required to facilitate communication
between a site user's web browser and an R script.  The other
consists of a basic web application with a form for the site
user to choose options for the analysis and a results page
showing the results of the analysis.  The package can be viewed
as an extension of CGIwithR, fixing a few technical problems
associated with large data files and long computations,
addressing some security issues, allowing the web programmer to
choose the data interchange format to be used in communications
between the server and the browser, and providing a template
website to reduce the programming burden on the user.
Uses Protovis to provide web graphics for R (exposes most
low-level functions).  Package is still under active
development and shouldn't be considered stable until version
0.1.  Currently uses a web browser to process JavaScript,
although future version will process JavaScript directly and
return the SVG output.  Also does not properly support discrete
labels (e.g. with histograms) or statistical functions.  See
website for more details.
The WeightedCluster library provides functions to cluster states sequences and weighted data. These functionalities include aggregating replicated cases, an optimized weighted PAM algorithm, function computing cluster quality measures for a range of clustering solutions and miscellaneous functions to plot clustering solutions of state sequences.
Entropy weighted kmeans (ewkm) is a weighted subspace
clustering algorithm that is well suited to very high
dimensional data. Weights are calculated as the importance of a
variable with regard to cluster membership. The feature group
weighted kmenas (fgkm) extends this concept by grouping
features and weighting the group in addition to weihgting
individual features.
This packages contains the Weighted Portmanteau Tests as
described in "New Weighted Portmanteau Statistics for Time
Series Goodness-of-Fit Testing' accepted for publication by the
Journal of the American Statistical Association.
This package has functions that handle the steps for the
weighted scores method in Nikoloulopoulos, Joe and Chaganty
(2011, Biostatistics, 12: 653-665) for binary (logistic and
probit), Poisson and negative binomial regression, with
dependent data. Two versions of negative binomial regression
from Cameron and Trivedi (1998) are used. Let NB(tau,xi) be a
parametrization with probability mass function f(y;tau,xi)=
Gamma(tau+y) xi^y / [ Gamma(tau) y! (1+xi)^(tau+y)], for
y=0,1,2,..., tau>0, xi>0, with mean mu=tau*xi = exp(beta^T x)
and variance tau*xi*(1+xi), where x is a vector of covariates.
For NB1, the parameter gamma is defined so that tau=mu/gamma,
xi=gamma; for NB2, the parameter gamma is defined so that
tau=1/gamma, xi=mu*gamma.  In NB1, the convolution parameter
tau is a function of the covariate x and xi is constant; in
NB2, the convolution parameter tau is constant and xi is a
function of the covariate x.
This package currently provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squareds, histograms, and t-tests.  Also now includes some software for quickly recoding survey data.  Future versions of the package will be more closely integrated with anesrake and additional weighting tools and will provide the option to find weighting benchmarks and weight data using a variety of methodologies.  NOTE: Weighted partial correlation calculations temporarily pulled to address a bug.
This hydraulics package provides computational support for flow over weirs, such as sharp-crested, broad-crested, and embankments. Initially, the package supports broad- and sharp-crested weirs.
Palettes generated mostly from
\href{http://wesandersonpalettes.tumblr.com/}{Wes Anderson Palettes}.
This R package provides a computationally efficient way
of fitting weighted linear fixed effects estimators for
causal inference with various weighting schemes.  Imai
and Kim (2012) show that weighted linear fixed effects
estimators can be used to estimate the average treatment
effects under different identification strategies.  This
includes stratified randomized experiments, matching and
stratification for observational studies, first
differencing, and difference-in-differences.  The package
also provides various robust standard errors and a
specification test for standard linear fixed effects
estimators.
This package integrates sophisticated mixed modelling methods with a whole genome approach to detecting significant QTL in linkage maps.
Functions necessary to perform Weighted Correlation Network Analysis.
Non parametric alternative to Kolmogorov-Smirnov based
standard GSEA testing.
Inferences about counterfactuals are essential for prediction,
answering what if questions, and estimating causal effects.
However, when the counterfactuals posed are too far from the data at
hand, conclusions drawn from well-specified statistical analyses
become based largely on speculation hidden in convenient modeling
assumptions that few would be willing to defend.  Unfortunately,
standard statistical approaches assume the veracity of the model
rather than revealing the degree of model-dependence, which makes this
problem hard to detect.  WhatIf offers easy-to-apply methods to
evaluate counterfactuals that do not require sensitivity testing over
specified classes of models.  If an analysis fails the tests offered
here, then we know that substantive inferences will be sensitive to at
least some modeling choices that are not based on empirical evidence,
no matter what method of inference one chooses to use.  WhatIf
implements the methods for evaluating counterfactuals discussed in
Gary King and Langche Zeng, 2006, "The Dangers of Extreme
Counterfactuals," Political Analysis 14 (2); and Gary
King and Langche Zeng, 2007, "When Can History Be Our Guide?  The
Pitfalls of Counterfactual Inference," International Studies
Quarterly 51 (March).
logicless templating, reuse templates in many programming
languages including R
WhopGenome's provides very fast access to whole genome, population scale variation data
from VCF files and sequence data from FASTA-formatted files.
It also reads in alignments from FASTA, Phylip, MAF and other file formats.
Provides easy-to-use interfaces to genome annotation from UCSC and Bioconductor and gene ontology data
from AmiGO and is capable to read, modify and write PLINK .PED-format pedigree files.
Fit, forecast, predict massive spacio-temporal data
WideLM fits large numbers of linear models to a fixed
design matrix and response.  An Nvidia GPU is used to
accelerate model fitting, with the CUDA API enabling parallel
execution of multiple individual fits.  The individual models
must have modest size.  The package implements a chunking
interface to the GPU kernel, allowing the caller to stream
model specifications.  Hence the number of fits to be performed
is essentially unlimited.
Extends the glmnet and relaxnet packages with polynomial basis expansions. Basis expansion is applied to the predictors and a subset of the basis functions is chosen using relaxnet. Predictors may be screened using correlation or t-tests. Screening is done separately within cross-validation folds. Cross-validation may be used to select the order of basis expansion and alpha, the elastic net tuning parameter.
The german Wikibook "GNU R" introduces R to new users.
This package is a collection of functions and datas used in the
german WikiBook "GNU R"
WikipediR is a wrapper for the MediaWiki API, aimed particularly
at the Wikimedia 'production' wikis, such as Wikipedia
This package provides functions to perform fast variable
selection based on the Wilcoxon rank sum test in the
cross-validation or Monte-Carlo cross-validation settings, for
use in microarray-based binary classification.
Dynamic interaction refers to spatial-temporal associations in the
movements of two (or more) animals. This package provides tools for
calculating a suite of indices used for quantifying dynamic interaction
with wildlife telemetry data. For more information on each of the methods
employed see the references within. The package draws heavily on the
classes and methods developed in the 'adehabitat' packages.
WITNESS is a computational model intended to understand the
psychological processes underlying eyewitness identification. This package
contains the WITNESS computational model as well as two methods of fitting
the model to data.
Approach to the robustness via Weighted Likelihood.
A GUI R implementation of hierarchical Bayesian models of working memory, used for analyzing change detection data.
Distance discriminant analysis method is one of
classification methods according to multiindex performance
parameters.However,the traditional Mahalanobis distance
discriminant method treats with the importance of all
parameters equally,and exaggerates the role of parameters which
changes a little.The weighted Mahalanobis distance is used in
discriminant analysis method to distinguish the importance of
each parameter.In the concrete application,firstly based on the
principal component analysis scheme,a new group of parameters
and their corresponding percent contributions of the parameters
are calculated ,and the weighted matrix is regarded as the
diagonal matrix of the contributions rates.Setting data to
standardization,then the weighted Mahalanobis distance can be
calculated.Besides the methods metioned above,bayes method is
also given.
Software to book Wavelet Methods for Time Series Analysis,
Donald B. Percival and Andrew T. Walden, Cambridge University
Press, 2000.
Estimates Poole and Rosenthal W-NOMINATE scores from roll
call votes supplied though a 'rollcall' object from package
'pscl'.
Analyses individually geo-referenced multilocus genotypes
for the inferences of genetic boundaries between populations.
It is based on the wombling method that estimates the systemic
function by looking for the local variation of the allele
frequencies. The systemic function estimation is based on the
local polynomial regression, and a binomial test assess the
significance of boundaries. The method applies to codominant or
dominant markers and allows for missing data.
Matches words in one file with words in another file and shows index(row number) for the matches
An interface to WordNet using the Jawbone Java API to WordNet.
WordNet (<http://wordnet.princeton.edu/>) is a large lexical database of
English.  Nouns, verbs, adjectives and adverbs are grouped into sets of
cognitive synonyms (synsets), each expressing a distinct concept.  Synsets
are interlinked by means of conceptual-semantic and lexical relations.
Please note that WordNet(R) is a registered tradename.  Princeton
University makes WordNet available to research and commercial users
free of charge provided the terms of their license
(<http://wordnet.princeton.edu/wordnet/license/>) are followed, and
proper reference is made to the project using an appropriate
citation (<http://wordnet.princeton.edu/wordnet/citing-wordnet/>).
This package collects several classical word pools used
most often to provide lists of words in psychological studies
of learning and memory.
Data from the United Nation's World Population Prospects 2008
Data from the United Nation's World Population Prospects
2010
Data from the United Nation's World Population Prospects 2012
A shiny interface for exploring data in wpp2008-wpp2012 packages.
Functions to assist in the processing and exploration of data from
environmental monitoring programs. The name "wq" stands for "water quality"
and reflects the original focus on time series data for physical and
chemical properties of water, as well as the plankton. The package is
intended for programs that sample approximately monthly at discrete
stations, a feature of many legacy data sets. Most of the functions should
be useful for analysis of similar-frequency time series regardless of the
subject matter.
A powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The WrightMap package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. eRm or IRToys in R, or Stata, Mplus, etc.),  WrightMap features special integration with ConQuest to facilitate reading and plotting its output directly.The wrightMap function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The CQmodel function reads output files created using ConQuest software and creates a set of data frames for easy data manipulation, bundled in a CQmodel object. The wrightMap function can take a CQmodel object as input or it can be used to create Wright Maps directly from data frames of person and item parameters.
Function for writing a SNNS pattern file from a data.frame
or matrix.
Cross-platform Perl based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX)
files from one or more data frames. Each data frame will be
written to a separate named worksheet in the Excel spreadsheet.
The worksheet name will be the name of the data frame it contains
or can be specified by the user.
Contains functions for working with the Worldwide Reference System
(WRS) 1 and 2 systems used by NASA. WRS-1 applies to Landsat 1-3, WRS-2
applies to Landsat 4-8. The package has functions for retrieving a given
path and row as a polygon, and for retrieving the path(s) and row(s)
containing a given raster or vector.
Contains the Worldwide Reference System (WRS) 1 and 2 polygon
data from NASA, for use by the wrspathrow package. WRS-1 and WRS-2
Shape files courtesy of the U.S. Geological Survey, from
http://landsat.usgs.gov/tools_wrs-2_shapefile.php
Entropy weighted k-means (ewkm) is a weighted subspace
clustering algorithm that is well suited to very high
dimensional data. Weights are calculated as the importance of a
variable with regard to cluster membership.
The two-level variable weighting clustering algorithm TW-k-means (twkm)
introduces two types of weights, the weights on individual variables and
the weights on variable groups, and they are calculated during the clustering process.
The feature group weighted k-means (fgkm) extends this concept by grouping
features and weighting the group in addition to weighting
individual features.
The wsrf package is a parallel implementation of the
Weighted Subspace Random Forest algorithm proposed (wsrf).  A
novel variable weighting method is used for variable subspace
selection in place of the traditional approach of random
variable sampling.  This new approach is particularly useful
in building models for high dimensional data---often
consisting of thousands of variables.  Parallel computation is
used to take advantage of multi-core machines and clusters of
machines to build random forest models from high dimensional
data with reduced elapsed times.
We propose weighted SVM methods with penalization form. By
adding weights to loss term, we can build up weighted SVM
easily and examine classification algorithm properties under
weighted SVM. Through comparing each of test error rates, we
conclude that our Weighted SVM with boosting has predominant
properties than the standard SVM have, as a whole.
Estimation and regression modeling of subdistribution functions for competing risks data with adjustment for covariate-dependent censoring, as described in He et al. (2013).
Book is "Linear Mixed Models: A Practical Guide Using
Statistical Software" published in 2006 by Chapman Hall / CRC
Press
A wrapper function and GUI for the X12 binaries
A graphical user interface for the x12 package
XBRL is a package suited to extract business financial information from
an Extensible Business Reporting Language (XBRL) instance file and the
associated collection of files that defines its Discoverable Taxonomy
Set (DTS). These files are usually disseminated across different remote
locations.
XBRL documents employ many technologies defined by XML standards, such
as XML Schema, Namespaces, XLink, and XPath, which make the extraction
of information involved. The functions provided by this package
address this complexity, returning data frames containing the enclosed
information readily available to perform analyses.
XBRL has been successfully tested in analyzing 10-K and 10-Q
submissions from USA filing companies that are publicly available at
the EDGAR system of the Securities and Exchange Commission (SEC).
As published taxonomy files (such as
"http://xbrl.fasb.org/us-gaap/2013/elts/us-gaap-2013-01-31.xsd")
are immutable and are used by most filers, XBRL offers the option of
downloading them only the first time they are referred, keeping a
local cache copy that can be used from then on. This speeds-up the
process, specially on slow connections.
XBRL is not and does not aim to be (at least yet) a validating XBRL
processor.
XBRL is still a work in progress. As such, functions and produced data
frames may change structure in future versions until stabilization.
x.ent is  a system for extracting information (entities and relations between them) in text datasets. It also emphasizes results exploration with graphical displays. It is a rule-based system and works with hand-made dictionaries and local grammars defined by users. x.ent has been written in perl and use javascript to define user preferences through a browser. Local grammars are defined and compiled with the tool Unitex, developed by University Paris Est, and supporting multiple languages. See ?xconfig  for an introduction.
The xergm package implements temporal exponential random graph models (TERGM) by bootstrapped pseudolikelihood ("btergm") and (temporal) network autocorrelation models ("tnam").
This package is a R wrapper of xgboost, which is short for eXtreme
Gradient Boosting. It is an efficient and scalable implementation of
gradient boosting framework. The package includes efficient linear model
solver and tree learning algorithms. The package can automatically do
parallel computation with OpenMP, and it can be more than 10 times faster
than existing gradient boosting packages such as gbm. It supports various
objective functions, including regression, classification and ranking. The
package is made to be extensible, so that users are also allowed to define
their own objectives easily.
Interface to the XGobi and XGvis programs for graphical
data analysis.
R scripts for plotting and assessing XHMM whole-exome-sequencing-based CNV calls. XHMM (eXome Hidden Markov Model) is a C++ software package (http://atgu.mgh.harvard.edu/xhmm) written to call copy number variation (CNV) from next-generation sequencing projects, where exome capture was used (or targeted sequencing, more generally). This R package enables the user to visualize both the PCA normalization performed by XHMM and the CNVs it has called.
This package provides a simple XML tree parser/generator. It
includes functions to read XML files into R objects, get information out of
and into nodes, and write R objects back to XML code.  It's not as powerful
as the XML package and doesn't aim to be, but for simple XML handling it
could be useful. It was originally programmed for RKWard.
This package allows the representation of ggplot2 graphs using the XKCD style.
Provides external JAR dependencies for the XLConnect package
Provide R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.
The xlsxjars package collects all the external jars
required for the xlxs package. This release corresponds to POI
3.10.1.
This is Xiaobei's miscellaneous classes and functions useful when
developing R packages, particularly for OOP using R Reference Class.
This package provides many approaches for both reading and
creating XML (and HTML) documents (including DTDs), both local
and accessible via HTTP or FTP.  It also offers access to an
XPath "interpreter".
XML2R is a framework that reduces the effort required to transform
XML content into number of tables while preserving parent to child
relationships.
Tests whether a set of counts fit a given expected ratio. For
example, a genetic cross might be expected to produce four types in the
relative frequencies of 9:3:3:1. To see whether a set of observed counts
fits this expectation, one can examine all possible outcomes (multi) or a
random sample of them (xmonte) and find the probability of an observation
deviating from the expectation by at least as much as the observed. As a
measure of deviation from the expected, one can use the log-likelihood
ratio, the multinomial probability, or the classic chi-square statistic. A
histogram of the test statistic can also be plotted and compared with the
asymptotic curve.
Analysis of crossover interference in experimental
crosses, particularly regarding the gamma model.
Xpose is collection of functions to be used as a model
building aid for non-linear mixed effects (population) analysis
using NONMEM. It facilitates data set checkout, exploration and
visualization, model diagnostics, candidate covariate identification
and model comparison.
Text formatting in xterm and ansi terminals using escape
sequences.  Supports colors and various font styles. It began
as a standalone version of the 'xterm256' package by Romain
Francois since that package is interwoven with the syntax
highlighting package 'highlight' but has been developed in
another direction since then.  For some more sophisticated
examples of this package functionality check out the 'dataview'
package as that was the very reason 'xtermStyle' came into
existence. However as 'xtermStyle' can be used independently
they were released separately.
Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.
This package provides an alternative canonical
correlation/redundancy analysis function, with associated
print, plot, and summary methods.  A method for generating
helio plots is also included.
Performs nearest neighbor-based imputation using one or more alternative
approaches to processing multivariate data. These include methods based on canonical
correlation analysis, canonical correspondence analysis, and a multivariate adaptation
of the random forest classification and regression techniques of Leo Breiman and Adele
Cutler. Additional methods are also offered. The package includes functions for
comparing the results from running alternative techniques, detecting imputation targets
that are notably distant from reference observations, detecting and correcting
for bias, bootstrapping and building ensemble imputations, and mapping results.
This collection of data exploration tools was developed at
Yale University for the graphical exploration of complex
multivariate data; barcode and gpairs now have their own
packages.
This package implements the libyaml YAML 1.1 parser and emitter (http://pyyaml.org/wiki/LibYAML) for R.
Yield curve or zero-coupon prices interpolation and extrapolation using the Nelson-Siegel, Svensson, Smith-Wilson models, and Hermite cubic splines.
The purpose of this package is to provide methods to interpret multiple
linear regression and canonical correlation results including beta weights,structure coefficients,
validity coefficients, product measures, relative weights, all-possible-subsets regression,
dominance analysis, commonality analysis, and adjusted effect sizes.
yhatr lets you deploy, maintain, and invoke models via the Yhat
REST API.
Modelling the yield curve with some parametric models.
The models implemented are: Nelson-Siegel, Diebold-Li and
Svensson.  The package also includes the data of the term
structure of interest rate of Federal Reserve Bank and European
Central Bank.
The clustering by k-means of using the target variable.
To determine the number of clusters with the variance of
the target variable in the cluster.
Software accompanying the book by Girosi and King: Demographic Forecasting.
Four different forecast models (i.e, OLS, LC, POISSON, and MAP).
The driver of the simulation is function mortality.driver(),
with your choices of input parameters, such as country, disease, and gender, and
many others; please read the software manual.
An R implementation of Yplant, combined with the QuasiMC
raytracer. Calculate radiation absorption, transmission and
scattering, photosynthesis and transpiration of virtual 3D
plants.
This package contains various inference procedures associated with a two-sample semi-parametric model that can accommodate a flexible range of hazard ratio patterns. This model contains the proportional hazards model and the proportional odds model as sub-models, and accommodates non-proportional hazards situations to the extreme of having crossing hazards and crossing survivor functions. Overall, this package has four major functions: 1) the parameter estimation, namely short-term and long-term hazard ratio parameters; 2) 95 percent and 90 percent point-wise confidence intervals and simultaneous confidence bands for the hazard ratio function; 3) p-value of the adaptive weighted log-rank test; 4) p-values of two lack-of-fit tests for the model. See the included "read_me_first.pdf" for brief instructions. In this version (1.1), there is no need to sort the data before applying this package.
YuGene is a simple method for comparison of gene
expression generated across different experiments, and on
different platforms; that does not require global
renormalization, and is not restricted to comparison of
identical probes. YuGene works on a range of microarray dataset
distributions, such as between manufacturers. The resulting
output allows direct comparisons of gene expression between
experiments and experimental platforms.
Simulation and Inference for Stochastic Differential Equations
Principled methods to impute multivariate left-censored data and zeros in compositional data sets
R package accompanying the book Working with dynamic
models for agriculture and environment, by Daniel Wallach
(INRA), David Makowski (INRA), James W. Jones (U.of Florida),
Francois Brun (ACTA), in preparation for June 2013.
Zelig is an easy-to-use program that can estimate, and
help interpret the results of, an enormous range of statistical
models. It literally is ``everyone's statistical software'' because
Zelig's simple unified framework incorporates everyone else's (R)
code. We also hope it will become ``everyone's statistical
software'' for applications and teaching, and so have designed
Zelig so that anyone can easily use it or add their programs to it.
Zelig also comes with infrastructure that facilitates the use of
any existing method, such as by allowing multiply imputed data for
any model, and mimicking the program Clarify (for Stata) that takes
the raw output of existing statistical procedures and translates
them into quantities of direct interest.
Add-on package for Zelig. Enables the use of a variety of logit
and probit regressions.
General Additive Models extend GLM's to support the application of
smoothing functions to the predictor terms.
Add-on package for Zelig. Enables the use of multilevel (mixed-effect) models.
This package provides an R wrapper for the Zendesk API
This package provides MCMC algorithms for the analysis of
zero-inflated count models. The case of stochastic search
variable selection (SVS) is also considered.  All MCMC samplers
are coded in C++ for improved efficiency. A data set
considering the demand for health care is provided.
Fits observation-driven and parameter-driven models for zero-inflated time series
This package contains a database of city, state, latitude,
and longitude information for U.S. ZIP codes from the
CivicSpace Database (August 2004) augmented by Daniel Coven's
federalgovernmentzipcodes.us web site (updated January 22,
2012). Previous versions of this package (before 1.0) were
based solely on the CivicSpace data, so an original version of
the CivicSpace database is also included.
Statistical models and utilities for the analysis of word
frequency distributions.  The utilities include functions for
loading, manipulating and visualizing word frequency data and
vocabulary growth curves.  The package also implements several
statistical models for the distribution of word frequencies in
a population.  (The name of this library derives from the most
famous word frequency distribution, Zipf's law.)
Calculate and plot scattering matrix coefficients for
plane waves at interface.
zoib fits the zero/one inflated beta regression model and obtains Bayesian Inference of the model via the Markov Chain Monte Carlo approach implemented in JAGS
An S3 class with methods for totally ordered indexed
observations. It is particularly aimed at irregular time series
of numeric vectors/matrices and factors. zoo's key design goals
are independence of a particular index/date/time class and
consistency with ts and base R by providing methods to extend
standard generics.
ZooImage is a free (open source) solution for analyzing
digital images of zooplankton. In combination with ImageJ, a
free image analysis system, it processes digital images,
measures individuals, trains for automatic classification of
taxa, and finally, measures zooplankton samples (abundances,
total and partial size spectra or biomasses, etc.)
zm(), called with any active plot allow to enter an
interactive session to zoom/navigate any plot. The development
version, as well as binary releases can be found at
https://github.com/cbarbu/R-package-zoom
The zyp package contains an efficient implementation of Sen's slope method (Sen, 1968) plus implementation of Xuebin Zhang's (Zhang, 1999) and Yue-Pilon's (Yue, 2002) prewhitening approaches to determining trends in climate data.
Dane R. Van Domelen <vandomed@gmail.com>
Pedro Rafael Diniz Marinho <pedro.rafael.marinho@gmail.com>
Carlos J. Gil Bellosta <cgb@datanalytics.com>
Sy Han (Steven) Chiou <schiou@d.umn.edu>
P Lafaye de Micheaux <lafaye@dms.umontreal.ca>
Robin K. S. Hankin <hankin.robin@gmail.com>
Lafaye de Micheaux Pierre <lafaye@dms.umontreal.ca>
Joao Vitor Dias Monteiro <monte092@umn.edu>
Dr. Pablo Emilio Verde
<pabloemilio.verde@uni-duesseldorf.de>
Ricardo de Matos Simoes <r.dematossimoes@qub.ac.uk>
Karel Van den Meersche <karel.van_den_meersche@cirad.fr>
Anderson, Michael P. (HSC) <Michael-Anderson@ouhsc.edu>
Jose Antonio Garcia Bueno <joseagbueno@gmail.com>
Anderson Rodrigo da Silva <anderson.agro@hotmail.com>
Ana Belen Nieto Librero <ananieto@usal.es>
A. Jonathan R. Godfrey <a.j.godfrey@massey.ac.nz>
Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it>
K. Gerald van den Boogaart <support@boogaart.de>
P. Lafaye de Micheaux <lafaye@dms.umontreal.ca>
Dennis J. J. Walvoort <dennis.Walvoort@wur.nl>
Rafael S. de Souza <rafael.2706@gmail.com>
Eduardo L. T. Conceicao <econceicao@kanguru.pt>
Marcelino de la Cruz Rot <marcelino.delacruz@upm.es>
Alexandre Adalardo de Oliveira <ecovirtualpackage@gmail.com>
Tomas J. Aragon Developer <aragon@berkeley.edu>
Manuel Oviedo de la Fuente <manuel.oviedo@usc.es>
Eduardo San Miguel Martin
<eduardo.san.miguel.martin@gmail.com>
Thiago do Rego Sousa <thiagoestatistico@gmail.com>
Vincent T van Hees <vincent.van-hees@newcastle.ac.uk>
Paul Gilbert and Coen Bernaards <pgilbert.ttv9z@ncf.ca>
Gabriel Coelho Goncalves de Abreu <abreu_ga@yahoo.com.br>
Geraldine Van der Auwera <vdauwera@broadinstitute.org>
Alejandro Quintela del Rio <aquintela@udc.es>
Jorge Luis Ojeda Cabrera <jojeda@unizar.es>
Eufrasio de A. Lima Neto <eufrasio@de.ufpb.br>
Ricardo Jorge de Almeida Queiroz Filho <rjaqfcin@gmail.com>
Jan van der Laan <djvanderlaan@unrealizedtime.nl>
"Antoine Tremblay, Dalhousie University" <trea26@gmail.com>
"Robin K. S. Hankin" <hankin.robin@gmail.com>
Elizabeth Holmes - NOAA Federal <eli.holmes@noaa.gov>
Stephen L R Ellison <s.ellison@lgc.co.uk>
Cherise R. Chin Fatt <cherise.chinfatt@utdallas.edu>
Marcelino de la Cruz <marcelino.delacruz@upm.es>
Julio Cesar Hernandez Sanchez <juliocesar_avila@usal.es>
Rune Haubo Bojesen Christensen <rhbc@dtu.dk>
Peter J. van der Most <p.j.van.der.most@umcg.nl>
Vito M. R. Muggeo <vito.muggeo@unipa.it>
Raymond K. W. Wong <raymondkww.dev@gmail.com>
Rutger M. van den Bor <r.m.vandenbor@umcutrecht.nl>
Jakson Alves de Aquino <jalvesaq@gmail.com>
Prof. Juan David Velasquez H. <jdvelasq@unal.edu.co>
Stephen W. Erickson, PhD <SErickson@uams.edu>
Willem Robert van Hage <willem.van.hage@synerscope.com>
Frederick Kin Hing Phoa <fredphoa@stat.sinica.edu.tw>
Stephen A Sefick Jr. <ssefick@gmail.com>
Daniel A. Dos Santos <pseudalopex_79@yahoo.com>
Hugo Andres Gutierrez Rojas
<hugogutierrez@usantotomas.edu.co>
K. Gerald van den Boogaart <boogaart@math.tu-freiberg.de>
Antonio Fabio Di Narzo <antonio.fabio@gmail.com>
Joerg Schlarmann (aka Produnis) <schlarmann@produnis.de>
A3: Accurate, Adaptable, and Accessible Error Metrics for
Predictive Models
Tools for Approximate Bayesian Computation (ABC)
ABCDE_FBA: A-Biologist-Can-Do-Everything of Flux Balance
Analysis with this package
Implementation of Artificial Bee Colony (ABC) Optimization
Approximate Bayesian Computational model for estimating P2
Load Axon ABF2 files (currently only in gap-free recording mode)
Data Modelling with Additive Bayesian Networks
Abundant regression and high-dimensional principal fitted
components
Functions for processing minute-to-minute accelerometer data
Creation and evaluation of Acceptance Sampling Plans
Visualization tools for partially accruing data
ace() and avas() for selecting regression transformations
Align-and-Count Method comparisons of RFLP data
Affymetrix SNP probe-summarization using non-negative matrix
factorization
Modelling dependence with multivariate Archimax (or any
user-defined continuous) copulas
Convenience functions for analytical Customer Relationship
Management
Download, manipulate, and present data from the US Census
American Community Survey
Algorithmic Complexity for Short Strings
Data Only: Algorithmic Complexity of Short Strings (Computed via
Coding Theorem Method)
Asymptotic Classification Theory for Cognitive Diagnosis
ada: an R package for stochastic boosting
Applies multiclass AdaBoost.M1, AdaBoost-SAMME and Bagging
Discrete and Global Optimization Routines
A class of adaptive elastic net methods for censored data
Adaptive Semiparametric Regression with Simultaneous Confidence
Bands
Implementation of a generic adaptive Monte Carlo Markov Chain
sampler
Additivity tests in the two way ANOVA with single sub-class
numbers
A Package for Analysis of Accelerated Destructive Degradation
Test Data
Analysis of Ecological Data : Exploratory and Euclidean methods
in Environmental sciences
adegenet: an R package for the exploratory analysis of genetic
and genomic data
Analysis of habitat selection by animals
adephylo: exploratory analyses for the phylogenetic comparative
method
Adequacy of probabilistic models and generation of pseudo-random
numbers
calculate ad hoc distance thresholds for DNA barcoding
identification
An Interpretation of the ADM method - automated detection
algorithm
Adaptive Mixture of Student-t distributions
Download and process AEMO price and demand data
Hybrid simulation and genome scan for dominant markers
Accelerated Failure Time Model with Generalized Estimating
Equations
Aggregation Operators and Preordered Sets
Statistical Tools for Measuring Agreement
Statistical Procedures for Agricultural Research
Miscellaneous plotting and utility functions
Regularization for semiparametric additive hazards regression
Model selection and multimodel inference based on (Q)AIC(c)
An R Package to Estimate Box-Cox Power Transformation Parameter
Tools for reading AIDA (http://aida.freehep.org/) files into R
Interpolation of irregularly spaced data
Adaptive Kmeans algorithm based on threshold
Quantile Regression Using Asymmetric Laplace Distribution
An R-package for estimating absolute protein quantities from
label-free LC-MS/MS proteomics data
Generate Age-Length Keys for fish populations
Allele simulation and Haplotype reconstruction from pedigree
databases
Identifying unique multilocus genotypes where genotyping error
and missing data may be present
Allele Retention, Inbreeding, and Demography
A fast, unbiased and exact allelic exact test
Computes all of a word's possible spellings
R wrapper to the almetrics API platform developed by PLoS
Generalization of the convex hull of a sample of points in the
plane
Implementation of the 3D alpha-shape for the reconstruction of
3D sets from a point cloud
Data to accompany Applied Linear Regression 3rd edition
Data to accompany Applied Linear Regression 4rd edition
multivariate curve resolution alternating least squares
(MCR-ALS)
Accelerated line search algorithm for simultaneous orthogonal
transformation of several positive definite symmetric matrices
to nearly diagonal form
Compare Gene Expressions from 2-Treatment RNA-Seq Experiments
Adaptive Management of Epidemiological Interventions
Additive and multiplicative effects modeling of networks and
relational data
This package includes pricing function for selected American
call options with underlying assets that generate payouts
A Multidirectional Optimum Ecotope-Based Algorithm
Statistical Analysis Of Amplicon Data Of The Same Sample To
Identify Artefacts
Simple and Canonical Correspondence Analysis
Analogue and weighted averaging methods for palaeoecology
Functions for analysis of fMRI datasets stored in the ANALYZE or
NIFTI format
Statistical analysis of surveys with anchoring vignettes
An extended Bayesian Learning Technique developed by Dr. Geoff
Webb
Animal track reconstruction for high frequency 2-dimensional
(2D) or 3-dimensional (3D) movement data
A gallery of animations in statistics and utilities to create
animations
AnnotLists: A tool to annotate multiple lists from a specific
annotation file
Statistical methods for anthropometric data oriented towards the
ergonomic design of products
Analysis of Overdispersed Data using S3 methods
aoristic analysis with spatial output (kml)
Another Plot PACKage: stem.leaf, bagplot, faces, spin3R,
plotsummary, plothulls, and some slider functions
Pre- and Postprocessing for AP-MS data analysis based on
spectral counts
Compute Appell's F1 hypergeometric function
Approximate Path for Penalized Likelihood Estimators
Functions and Data Sets for 'Applied Predictive Modeling'
Bayesian prediction of complex computer codes
Amdahl's profiler, directed optimization made easy
Edit, run and evaluate APSIM simulations from R easily
apsrtable model-output formatter for social science
Functions to help display air quality model output and
monitoring data
Interface methods to use with an ActiveQuant Master Server
AquaEnv - an integrated development toolbox for aquatic chemical
model generation
Segmentation of an autoregressive Gaussian process of order 1
Example Datasets from Archaeological Research
Tools for storing, restoring and searching for R objects
Activated Region Fitting, fMRI data analysis (3D)
Command line optional and positional argument parser
Data Analysis Using Regression and Multilevel/Hierarchical
Models
Analysis of large Affymetrix microarray data sets
A probe-level data file format used by aroma.affymetrix
[DEPRECATED]
Copy-number analysis of large microarray data sets
Core methods and classes used by aroma.* packages part of The
Aroma Framework
Tools for simulating different methods of observing behavior
based on alternating renewal processes
Gene and Pathway p-values computed using the Adaptive Rank
Truncated Product
Mining Association Rules and Frequent Itemsets
Mining NB-Frequent Itemsets and NB-Precise Rules
Visualizing Association Rules and Frequent Itemsets
A collection of statistical tools for biologists
Export R objects to several markup languages
Simulations for adaptive seamless designs
Linkage map construction using the MSTmap algorithm
A collection of functions for estimating centrographic
statistics and computational geometries for spatial point
patterns
Readable check functions to ensure code integrity
Optimal pricing of assets with fixed expiry date
Statistical Tests for Genetic Association Studies
Astronomy: time and position functions, misc. utilities
Applied Statistical Time Series Analysis
Calculate Power Utilizing Asymptotic Likelihood Ratio Methods
A GTK GUI for teaching basic concepts in statistical inference,
and doing elementary bayesian tests
Automatically Tuned Markov Chain Monte Carlo
Acoustic Traveltime Calculations for 1-D Atmospheric Models
Threshold independent performance measures for probabilistic
classifiers
Variable Selection with Random Forest and the Area Under the
Curve
Automatic Tracing and Display of Changes to Row Count
An implementation of sparse autoencoder for automatic learning
of representative features from unlabeled data
pls regression with backward selection of predictors
Class and Methods definitions for packages aws, adimpro, fmri,
dwi
Adaptive differential evolution, b6e6rl variant
Bayesian Bootstrap Predictive Mean Matching - Multiple and
single imputation for discrete data
Bayesian Analysis of Computer Code Output (BACCO)
Exploring portfolio-based conjectures about financial
instruments
Sensitivity of the Bayesian Adjustment for Confounding (BAC)
algorithm to the choice of hyperparameter omega
Bayesian Average Error approach to Sample Size Determination
Ensemble bagging and boosting classifiers
Balanced and spatially balanced sampling
Functions and datasets for books by Jeff Gill
Protein Secondary Structure Prediction Using the Bamboo Method
Bayesian Meta-Analysis of diagnostic test data
Analysis and visualization of macroevolutionary dynamics on
phylogenetic trees
Functions for simple A/B split test and multi-armed bandit
analysis
Bayesian Model Averaging using Bayesian Adaptive Sampling
A package for estimating age-specific survival from incomplete
capture-recapture/recovery data
Batching Routines in Parallel and Passing Command-Line Arguments
to R
Statistical experiments on batch computing clusters
Consistent Batch Means Estimation of Monte Carlo Standard Errors
Tests/Searches for significant clusters in genetic data
Power calculations and Bayesian analysis of count distributions
and FECRT data using MCMC
Bayesian analysis of censored linear regression models with
scale mixtures of normal (SMN) distributions
Functions and Datasets for the book "Bayesian Data Analysis"
Graphical User Interface for bayesTFR, bayesLife and bayesPop
Computation of Bayes factors for common designs
Bayesian Estimation of the GARCH(1,1) Model with Student-t
Innovations
Functions to implement Generalized Direct Sampling
Bayesian Analysis of Generalized Elliptical Semiparametric
Models
Bayesian Beta regression: joint mean and precision modeling
Mixtures-of-Experts Markov Chain Clustering and Dirichlet
Multinomial Clustering
Default Bayesian hypothesis tests for correlation, partial
correlation, and mediation
BayesNI: Bayesian Testing Procedure for Noninferiority with
Binary Endpoints
Hierarchical Bayesian analysis of ecological count data
Bayesian Analysis of Small Area Estimation
Computation of Bayes factors for interrupted time-series designs
Bayesian Survival Regression with Flexible Error and Random
Effects Distributions
Bayesian thresholds mixed-effects models for categorical data
Bayesian Variable selection in Linear Models
R Utilities Accompanying the Software Package BayesX
R Package Distribution of the BayesX C++ Sources
Bayesian analysis of haplotype association using Markov Chain
Monte Carlo
R Functions for Bayesian Hazard Rate Estimation
R Package for Baylor University Educational Psychology
Quantitative Courses
Bayesian fitting of Ornstein-Uhlenbeck models to phylogenies
Bayesian Analysis of Significantly Mutated Genes in Cancer
On Bayesian analysis of Threshold autoregressive model (BAYSTAR)
Solving and Optimizing Large-Scale Nonlinear Systems
Bayesian bandwidth estimation and semi-metric selection for the
functional kernel regression with unknown error density
Bayesian bandwidth estimation for multivariate kernel regression
with Gaussian error
Bayesian Estimation of Incoherent Neutron scattering Backgrounds
Miscellaneous helper functions for B. Bischl
Tools for general maximum likelihood estimation
Bias-corrected Bayesian Classification with Selected Features
Business Cycle Dating and Plotting Tools - BCDating
Bayesian composition estimator: estimating sample (taxonomic)
composition from biomarker data
Bayesian models for cost-effectiveness analysis in the presence
of structural zero costs
Radiocarbon dating, age-depth modelling, relative sea level rate
estimation, and non-parametric phase modelling
Bayesian Palaeoclimate Reconstruction from Pollen
Bayesian clustering using spike-and-slab hierarchical model,
suitable for clustering high-dimensional data
Bringing to light significant Columns correlated with Organism
Labels
A Package for Performing a Bayesian Analysis of Change Point
Problems
Behavioral change point analysis of animal movement
Bayesian Multiple Changepoint Detection Using Metadata
Bayesian continual reassessment method (CRM) designs for Phase I
dose-finding trials
Cross-Validation for the SVD (Bi-Cross-Validation)
Density Estimation for Binned/Weighted Data
Graph estimation based on birth-death MCMC approach
Bayesian Discrete Ordered Classification of DNA Barcodes
Inference and design for predictive values in binary diagnostic
tests
Routines for Block Diagonal Symmetric matrices
Analysis of Illumina BeadArray SNP data including MSV markers
Visualization via Beanplots (like Boxplot/Stripchart/Violin
Plot)
Bayesian Estimate of Age-specific Nest Survival Probabilities
Average bioequivalence and bioavailability data analysis tool
Disentangles the effects of geographic and ecological isolation
on genetic differentiation
Easily Play Notification Sounds on any Platform
The bee swarm plot, an alternative to stripchart
28 benchmark densities from Berlinet/Devroye (1994)
Benchmark and frontier analysis using DEA and SFA
Benford Analysis for data validation and forensic analytics
Statistical Tests for Evaluating Conformity to Benford's Law
Bent-Cable Regression for Independent Data or Autoregressive
Time Series
Bayesian analysis for exponential random graph models
function collection related to hydrology, zooming and shapefiles
Bessel -- Bessel Functions Computations and Approximations
Bayesian Estimation Supersedes the t-Test
Detecting rare variants for quantitative traits using nuclear
families
Partitioning beta diversity into turnover and nestedness
components
Functions to incorporate taxonomic uncertainty on multivariate
analyses of ecological data
Simulation, estimation and forecasting of Beta-Skew-t-EGARCH
models
Breaks For Additive Season and Trend (BFAST)
Binary Generalized Extreme Value Additive Models
Gaussian Mixture Modeling algorithms. Including the belief-based
mixture modeling
Markov beta and gamma processes for modeling hazard rates
Block Gibbs Sampler with Incomplete Multinomial Distribution
Useful Functions for Box, Hunter and Hunter II
Bayesian hierarchical multi-subject multiscale analysis of
functional MRI data
Bias correction of the parameter estimates of the beta
regression model
Calculates the bidimensional regression between two 2D
configurations
Inferences for bi- and trifactorial trial designs
Some Tools for Survey Statistics in Educational Assessment,
Developed by BIFIE
BLAS routines for native R matrices and big.matrix objects
A library of utilities for big.matrix objects of package
bigmemory
Scalable Least-Angle Regression and Lasso
bounded memory linear and generalized linear models
Manage massive matrices with shared memory and memory-mapped
files
A shared resource interface for Bigmemory Project packages
PCA, transpose and multicore functionality for big.matrix
objects
Big Random Forests: Classification and Regression Forests for
Large Data Sets
Generalized Ridge Regression (with special advantage for p >> n
cases)
table-, tapply-, and split-like functionality for matrix and
big.matrix objects
Top Scoring Pair based methods for classification
bild: a package for BInary Longitudinal Data
Power for SNP analyses using silver standard cases
Multi-Class Discriminant Analysis using Binary Predictors
Methods for Analyzing Binned Income Data
Evaluation and experimental design for binomial group testing
Asymptotic simultaneous confidence intervals for many-to-one
comparisons of proportions
Simultaneous generation of multivariate binary and normal
variates
Binomial Confidence Intervals For Several Parameterizations
Generates binomial random numbers via the coupling from the past
algorithm
Efficient MCMC for Binomial Logit Models
Confidence intervals and sample size determination for a
binomial proportion under simple random sampling and pooled
sampling
Performing diagnostics on binomial regression models
Cut Numeric Values Into Evenly Distributed Groups
Exact Binary Sequential Designs and Analysis
GUI for biodiversity, suitability and community ecology analysis
BioGeography with Bayesian (and Likelihood) Evolutionary
Analysis in R Scripts
Predict environmental conditions from biological observations
An interface package (beta) for the BIOM file format
Find biomarkers in two-class discrimination problems
Ensemble platform for species distribution modeling
Utilities for the BIOM (Biological Observation Matrix) Format
Simulation of deterministic and stochastic biochemical reaction
networks using Petri Nets
From BIOlogical Sequences to MultiDimensional Scaling
Tools for Biometry and Applied Statistics in Agricultural
Science
Visualising bipartite networks and calculating some (ecological)
indices
BIPOD (Bayesian Inference for Partially Observed diffusions)
Methods to analyse ring re-encounter data
Tools to find bad commits with git bisect
Toolkit to identify synthetic lethality and cell line resistance
relationships based on bimodality in gene expression data
Bayesian Isotonic Regression with Bernstein Polynomials
A S3 class for vectors of 64bit integers
Sample size calculations for bivariate longitudinal data
Conduct univariate and bivariate wavelet analyses
Functions to compute the biweight mean vector and covariance &
correlation matrices
Functions to handle business days calculations
Plots (slightly extended) Bland-Altman plots
Calculate and Analyze Blau statuses for measuring social
distance
Analyze biotic homogenization of landscapes
Fitting block ERGM given the block structure on social networks
Binomial linear and linear-expit regression
Co-Clustering package for binary, contingency, continuous and
categorical data-sets
blockmatrix: Tools to solve algebraic systems with partitioned
matrices
Creates strings that show a text message in 8 by 8 block letters
An R package for Generalized and classical blockmodeling of
valued networks
Randomization for block random clinical trials
Nested block designs for unstructured treatments
Block, assign, and diagnose potential interference in randomized
experiments
Constrained optimization via stochastic gradient descent
Benchmark dose analysis for dose-response data
Mediation analysis with missing data using bootstrap
Bayesian Sampling for Stick-breaking Mixtures
The pseudo-likelihood method for pairwise binary markov networks
Bundle Methods for Regularized Risk Minimization Package
Bayesian network structure learning, parameter learning and
inference
Ferguson-Klass type algorithm for posterior normalized random
measures
Bayesian Output Analysis Program (BOA) for MCMC
Interface to the boilerpipe Java library by Christian
Kohlschutter (http://code.google.com/p/boilerpipe/)
Bayesian Optimal Interval Design for Phase I Clinical Trials
Generation, reconstruction, simulation and analysis of
synchronous, asynchronous, and probabilistic Boolean networks
A modular framework to bag or boost any estimation procedure
Optimized GWAS cohort subset selection for resequencing studies
Bootstrap Functions (originally by Angelo Canty for S)
Use multiple feature selection algorithms to derive robust
feature sets for two or multiclass classification problems
Bootstrapped confidence intervals for (negative) likelihood
ratio tests
Bootstrap Prediction Intervals and Bias-Corrected Forecasting
Bootstrapped Response and Correlation Functions
A bootstrap test for the probability of ruin in the classical
risk process
Functions for the Book "An Introduction to the Bootstrap"
Fast, Exact Bootstrap Principal Component Analysis for High
Dimensional Data
Bayesian Ordination and Regression AnaLysis
A wrapper algorithm for all-relevant feature selection
Boosted One-Step Statistics: Fast and accurate approximations
for GLM, GEE and Mixed models for use in GWAS
a Bunch of Structure and Sequence Analysis
Analytic Solutions for (ground-water) Boussinesq Equation
Double Box Plot for Two-Axes Correlation
Biplot of Multivariate Data Based on Principal Components
Analysis
Beta Product Confidence Procedure for Right Censored Data
bPeaks: an intuitive peak-calling strategy to detect
transcription factor binding sites from ChIP-seq data in small
eukaryotic genomes
Helper functions to misc3d and rgl packages for brain imaging
Basic wavelet analysis of multivariate time series with a
visualisation and parametrisation using graph theory
Species richness estimation and modelling
An R Package for Multiple Break-Point Detection via the
Cross-Entropy Method
Templating Framework for Report Generation
Bias reduction in binomial-response generalized linear models
Brier score decomposition of probabilistic forecasts for binary
events
brnn (Bayesian regularization for feed-forward neural networks)
Bayesian analysis of quantile regression models
R interface to the OpenBUGS MCMC software
Statistical methods for safety assessment in agricultural field
trials
Nonparametric Smoothing of the Hazard Function
Bayes Screening and Model Discrimination
bspmma: Bayesian Semiparametric Models for Meta-Analysis
Asymptotic covariance matrices of some BSS mixing and unmixing
matrix estimates
Estimates univariate function via Bayesian trend filtering
Buckley-James Regression for Survival Data with High-Dimensional
Covariates
Markov model for bursty behavior in streams
Bayesian Analysis of a Vector Autoregressive Model with
Stochastic Volatility and Time-Varying Parameters
A Simple alternative to proportional Venn diagrams
The Stark-Parker algorithm for bounded-variable least squares
Solvers for boundary value problems of ordinary differential
equations
Bayesian Variant Selection: Bayesian Model Uncertainty
Techniques for Genetic Association Studies
Extended Inference for Lasso and Elastic-Net Regularized Cox and
Generalized Linear Models
Infering large-scale gene networks with C3NET
C5.0 Decision Trees and Rule-Based Models
Simple, Multiple and Joint Correspondence Analysis
Bootstrap Confidence Regions for Correspondence Analysis
Classification accuracy and consistency under Item Response
Theory
Conditional Density Estimation Network Construction and
Evaluation
This package performs the CADF unit root test proposed in Hansen
(1995)
Conditional Akaike information criterion for lme4
R graphics device using cairo graphics library for creating
high-quality bitmap (PNG, JPEG, TIFF), vector (PDF, SVG,
PostScript) and display (X11 and Win32) output
Cairo-based cross-platform antialiased graphics device driver
Multiple imputation using MICE and Random Forest
Calibration of Scatterplot and Biplot Axes
Bayesian calibration of complex computer codes
R interface to Fortran 77 implementation of CALINE3
CalMaTe - Improved allele-specific copy number of SNP arrays for
downstream segmentation
Finite Mixture Models and meta-analysis tools - based on C.A.MAN
Estimation of temporal ordering of cancer abnormalities
Visualizing Generalized Canonical Discriminant and Canonical
Correlation Analysis
Combined analysis of pleiotropy and epistasis
Comparative Analyses of Phylogenetics and Evolution in R
Covariate Adjusted Precision Matrix Estimation
Capushe, data-driven slope estimation and dimension jump
Estimates population size from non-invasive sampling
Poisson Log-linear Models with Spatio-temporal Random Effects
Estimation of the number of fatalities from carcass searches
Identification of Cardinal Dates in Ecological Time Series
High-Dimensional Regression and CAR Score Variable Selection
Statistical package for population size estimation in
capture-recapture models
Estimation of caribou abundance based on large scale
aggregations monitored by radio telemetry
Functions For Statistics Classes At Carleton College
A Collection of Database, Data Structure, Visualization, and
Utility Functions for R
Reparameterized and marginalized posterior sampling for
conditional autoregressive models
Reparameterized and marginalized posterior sampling for
conditional autoregressive models, OpenCL implementation
Séries temporelles avec R - Méthodes et cas
Identify similar cases for qualitative case studies
Analysis of categorical-variable datasets with missing values
Fishery Stock Assessment by Catch Dynamic Models
Community Assembly by Traits: Individuals and beyond
An R Package for Simulating IRT-Based Computerized Adaptive
Tests
Tools: moving window statistics, GIF, Base64, ROC AUC, etc
Procedures to generate IRT adaptive tests (CAT)
Special models for categorical variables
Deriving Expressions of Joint Interventional Distributions in
Causal Models
Estimation of Causal Effects with Generalized Additive Models
Selection Bias Approach to Sensitivity Analysis for Causal
Effects
Analysis utilities for binary classification and Causata users
R Package for Covariate Balancing Propensity Score
Bayesian canonical correlation analysis and group factor
analysis
(Robust) canonical correlation analysis via projection pursuit
Combined Cluster and Discriminant Analysis
Convex Clustering Methods and Clustering Indexes
Simulate Congruence Class Model for Networks
Significance Tests for Canonical Correlation Analysis (CCA)
One and two locus GWAS of binary phenotype with
case-control-population design
Cultural Consensus Theory applications to data
Coupled dipole approximation of light scattering by clusters of
nanoparticles
Coordinate Descent Algorithms for Lasso Penalized L1, L2, and
Logistic Regression
Components of Canadian Monetary and Credit Aggregates
Statistical inference of C- and D-vine copulas
Benchmark for the CEC 2005 Special Session on Real-Parameter
Optimization
Benchmark functions for the Special Session and Competition on
Real-Parameter Single Objective Optimization at CEC-2013
Collection of common astronomical conversion routines
Functions to fit cell volume distributions and thereby estimate
cell growth rates and division times
Fitting Normal and Student-t censored regression model
Context Driven Exploratory Projection Pursuit
Outlier detection using the iterated RMCD method of Cerioli
(2010)
Analysis of configuration frequencies (CFA)
Estimate quantiles using any order Cornish-Fisher expansion
Calculate AUC-type measure when gold standard is continuous and
the corresponding optimal linear combination of variables with
respect to it
R-Based API for accessing the MSKCC Cancer Genomics Data Server
(CGDS)
Microarray CGH analysis using the Smith-Waterman algorithm
Detecting hot spot on CGH array data with fused lasso
regression
Segmentation methods for array CGH analysis
Statistical methods and models for the calculation of
outstanding claims reserves in general insurance
Charge Transfer Rates and Charge Carrier Mobilities
Install Packages from Snapshots on the Checkpoint Server for
Reproducibility
Analysis and visualisation of ecological communities
Calibration functions for analytical chemistry
Multivariate Statistical Analysis in Chemometrics
Chemometrics with R - Multivariate Data Analysis in the Natural
Sciences and Life Sciences
A tool for the design of synthetic experiments in machine
olfaction
Multiple testing methods for exploratory research
Calculation of standard deviation scores adduced from different
growth standards
Statistical methods for phenology analysis in temperate fruit
trees
Toolkit of helper functions to pre-process amplification data
Chemical Thermodynamics and Activity Diagrams
Functions to simplify the creation of choropleths (thematic
maps) in R
Analysis of chromosomal interactions data (correction,
segmentation and comparison)
Generative models for complex networks with conditionally
independent dyadic structure
Weighted summary of cumulative incidence functions
Co-Information based N-Order Epistasis Detector and Visualizer
CircNNTSR: An R package for the statistical analysis of circular
data using nonnegative trigonometric sums (NNTS) models
Circular Statistics, from "Topics in circular Statistics" (2001)
CIT Breast Cancer Molecular SubTypes Prediction
CIT Colon Cancer Molecular SubTypes Prediction
Visualization of structure and contents of a database
Optimal k-means clustering for one-dimensional data
C++ implementations of phylogenetic cladogenesis calculations
An unsupervised non hierarchical clustering algorithm handling
biological data
Construct Graphs of S4 Class Hierarchies
Explore classification models in high dimensions
Classification Accuracy and Consistency under IRT models
Robust multivariate classification using highly optimised SVM
ensembles
CLERE methodology for simultaneous variables clustering and
regression
Model-Based Clustering of Categorical Sequences
Analyzes clickstreams based on Markov chains
Constrained L1-minimization for Inverse (covariance) Matrix
Estimation
Clinical Trial Design and Data Analysis Functions
Fitting exact conditional logistic regression with lasso and
elastic net penalties
Clustering Method Based on Local Shrinking
Cluster Analysis Extended Rousseeuw et al
Explore clustering interactively using R and GGobi
A hierarchical clustering method based on genetic algorithms
random cluster generation (with specified degree of separation)
Identifying clusters in genomics data by recursive partitioning
Power calculations for cluster-randomized and cluster-randomized
crossover trials
Reproducibility of gene expression clusters
Searching for optimal clustering procedure for a data set
Methods for joint dimension reduction and clustering
Clustering of variables around Latent Variables
Variable Selection for Model-Based Clustering
Covariance Matrix Adapting Evolutionary Strategy
Control Charts for Conway-Maxwell-Poisson Distribution
Subdistribution Analysis of Competing Risks
Misc Functions of the Center for the Mathematical Research
A Package for Coincidence Analysis (CNA)
Canonical Non-symmetrical Correspondence Analysis in R
pre-process DNA copy number (CN) data for detection of CN events
Example data sets for association analysis of CNV data
A collection of functions to help with analysis of coarsely
observed data
COBS -- Constrained B-splines (Sparse matrix based)
Constrained B-splines -- outdated 1999 version
Statistical comparisons of two or more alpha coefficients
Output analysis and diagnostics for MCMC
Markov chain Monte Carlo burn-in based on "bridge" statistics
Robust Cronbach's alpha with missing and non-normal data
Species coexistence modeling and analysis
Conditional Inference Procedures in a Permutation Test Framework
Simultaneous Confidence Interval for Multinomial Proportion
cold: a package for Count Longitudinal Data
Collocation Inference for Dynamic Systems
Colocalisation tests of two genetic traits
Plot and add custom coloring to Venn diagrams for 2-dimensional,
3-dimensional and 4-dimensional data
Tools for colors in a Hue-Saturation-Value (HSV) color model
Constructions method of rectangular PBIB and rectangular right
angular PBIB(m) (m= 4,5 and 7) designs
Extract and plot common trends from a cointegration system.
Calculate P-value for Johansen Statistics
Categorizes ICD-9-CM codes based on published comorbidity
indices
Creates empty plots with compact axis notation
comparison of medical forms in CDISC ODM format
Estimate diagnostic accuracy (sensitivity, specificity, etc) and
agreement statistics when one test is conducted on only a
subsample of specimens
Multivariate likelihood ratio calculation and evaluation
Event-specific incidence rates for competing risks data
Tools for Storage and Retrieval of Gene Expession Data
Conway-Maxwell-Poisson GLM and distribution functions
Robust linear regression with compositional data as covariates
Regression estimation based on the compound covariate method
under the Cox proportional hazard model
Distribution function of quadratic forms in normal variables
Composite-likelihood based Analysis of Random Fields
Optimisation with Continuous Convex Piecewise (Linear and
Quadratic) Functions
Approximate conditional inference for logistic and loglinear
models
Parameter estimation in conditional GEE for recurrent event gap
times
Conditional Density Estimation with Neural Network Conditional
Mixtures
Condition Number Regularized Covariance Estimation
Primal or Dual Cone Projections with Routines for Constrained
Regression
Confidence Estimation of Environmental State Classifications
Configural Frequencies Analysis Using Loglinear Modeling
Algorithms for fitting circles, ellipses and conics based on the
work by Prof. Nikolai Chernov
A package to check the cancellation axioms of conjoint
measurement
Tools for working with connectivity matrices
Constrained, covariance-matching constrained and universal point
or block kriging
Probabilistic Species Co-occurrence Analysis in R
Cooperative aspects of optimal trees in weighted graphs
Variables selection for index models via correlation pursuit
Tools for processing COPAS large-particle flow cytometer data
Basic Theoretical Copula, Empirical Copula, and Various Utility
Functions
Fitting the copCAR regression model for discrete areal data
Estimation of Distribution Algorithms Based on Copulas
Bivariate Copula Based Regression Models
Computing Statistical Indices to Detect Answer Copying on
Multiple-Choice Tests
Generate correlated count random variables
Classification, regression, feature evaluation and ordinal
evaluation
TDT for compound heterozygous and recessive models
The Clustering of Regression Models Method
Efficient Estimation of Covariance and (Partial) Correlation
Statistics and data sets for corpus frequency data
Nonparametrics with clustered binary and multinomial data
Linear regression based on linear structure between covariates
Multivariate Data Generation by Permutation
A collection of functions for graphing correlation matrices
Tools for processing data after a Genome Wide Association Study
Tools for estimating and predicting the cosinor model
Functions for cosmological distances, times, luminosities, etc
Photometric redshift estimation using generalized linear models
Fit Regularized Nonparametric Regression Models Using COSSO
Penalty
Mean and variance modeling of count data
Latent Class Models with Covariate Effects on Underlying and
Measured Variables
A simultaneous regression model for the mean and covariance
Robust Covariance Estimation via Nearest Neighbor Cleaning
Computes covariance test for adaptive linear modelling
Cox models by likelihood based boosting for a single survival
endpoint or competing risks
Cox-type models for interval-censored data
Confirmatory Path Analysis through the d-sep tests
Methods to perform Common Principal Component Analysis (CPCA)
Concordance Probability Estimates in Survival Analysis
Association between Methylation and a phenotype of interest
Find the maximum likelihood estimator of the shape constrained
hazard baseline and the effect parameters in the Cox
proportional hazards model
Correction of the pvalue after multiple coding
Conditional probability function of a competing event
Power Calculation for Weighted Log-Rank Tests in Cure Rate
Models
Probabilistic damage tolerance analysis for fatigue cracking of
metallic aerospace structures
Multivariate nonparametric Cramer-Test for the
two-sample-problem
Fit continuous-time correlated random walk models to animal
movement data
Categorical Randomized Block Data Analysis
Censored Regression with Conditional Heteroscedasticity
Functions for calculating the CreditMetrics risk model
Group-Based Modelling of Longitudinal Data
Continual Reassessment Method (CRM) for Phase I Clinical Trials
CCMN and other noRMalizatioN methods for metabolomics data
Downloads and Builds datasets for Climate Reference Network
Confidence intervals for crossover points of two simple
regression lines
Cross-Recurrence Quantification Analysis for Categorical and
Continuous Time-Series
Competing risks regression for Stratified and Clustered data
Stepwise covariate selection for the Fine & Gray competing risks
regression model
Sample Size Estimation Functions for Cluster Randomized Trials
Identifying cryptic relatedness in genetic association studies
Functions for Conditional Simulation in Regression-Scale Models
Covariate-adjusted Skewed Functional Model (cSFM)
Extract information from an html document with CSS selectors
csSAM - cell-specific Significance Analysis of Microarrays
Substantive significance testing for regression estimates and
marginal effects
Rule- and Instance-Based Regression Modeling
CUDA Parallel Implementation of a Bayesian Multilevel Model for
fMRI Data Analysis
Data sets for the examples used in the package "cudaBayesreg"
Analyze Multivariate Phenotypes by Combining Univariate results
Extends ddply to allow calculation of cumulative quantities
Change point detection in genomic sequences
The package will formally test two curves represented by
discrete data sets to be statistically equal or not when the
errors of the two curves were assumed either equal or not using
the tube formula to calculate the tail probabilities
curvHDR filtering of flow cytometry samples
CUTOFF: A Spatio-temporal Imputation Method
An R package to perform LPUE standardization and stock
assessment of the English Channel cuttlefish stock using a
two-stage biomass model
Cross-Validated Area Under the ROC Curve Confidence Intervals
Estimation of the Calibration Equation with Error-in
Observations
Penalized Logistic Regression Model using Majorization
Minimization by Coordinate Descent (MMCD) Algorithm
Calculate the predictive squared correlation coefficient
Fast Cross-Validation via Sequential Testing
Cross-validation tools for regression models
Regularized Estimators of Covariance Matrices with CV Tuning
extend cxxfunction by saving the dynamic shared objects
Cycle and Phase Identification for mastication data
Tools for creating D3 JavaScript network, tree, dendrogram, and
Sankey graphs from R
Data Analysis And Graphics data and functions
Data Sets and Functions, for demonstrations with expression
arrays and gene sequences
Data Sets and Functions, supplementary to DAAG
Functions useful in the design and ANOVA of experiments
Design and Analysis of Experiments with R
Learning directed acyclic graphs (DAGs) through bootstrap
aggregating
Diagnostic accuracy of classification models
DALY Calculator - A GUI for stochastic DALY calculation in R
Dams in the United States from the National Inventory of Dams
(NID)
Dandelion Plot for R-mode Exploratory Factor Analysis
Package for deep architectures and Restricted-Bolzmann-Machines
Statistical Tools to Analyze Your Darts Game
file-based access to large matrices stored on HDD in binary
format
R tools for making it easier to combine and clean data sets
dataframes2xls writes data frames to xls files
An R package to convert data sets for import into JAGS, WinBUGS
and OpenBUGS and to generate data.frames for predicting the
effects of particular variables
A system for mapping foreign objects to R variables and
environments
Merging of overlapping and inconsistent data
Performs variable level data quality checks and generates
summary statistics
Support functions for packages VBmix, semisupKernelPCA, and
patchPlot
Deconvolution Analysis Tool for Dynamic Contrast Enhanced MRI
Functions for "Data Analysis in Vegetation Ecology"
Discriminant analysis with additional information
Discrete Bayesian Additive Regression Trees Sampler
Provides a graphical user interface to connect with databases
that use MySQL
Goodness-of-fit and two sample comparison tests using sample
entropy
DBF test of no difference between groups
methods of distance-based gene set functional enrichment
analysis
Discrete Beta Kernel Graduation of Mortality Data
Compute the NPMLE of distribution from doubly censored data
Distance-based measures of spatial structures
A Package for Medical Image Analysis (S4 implementation)
Differential Co-expression Analysis and Differential Regulation
Analysis of Gene Expression Microarray Data
Analysis of ontologies and protein domain annotations
Claims Reserving under the Double Chain Ladder Model
Data Cloning and MCMC Tools for Maximum Likelihood Methods
Functions for the detection of spatial clusters of diseases
Hierarchical Models Made Easy with Data Cloning
Attribute profile estimation using Diagnostic Classification
Models and MCMC
Conventional Cross-validation statistics for climate-growth
model
DDalpha-classifier, depth-calculating methods and associated
functions
Dynamic Deterministic Effects Propagation Networks: Infer
signalling networks for timecourse RPPA data
Variance Stabilization by Data-Driven Haar-Fisz (for
Microarrays)
Learning Bayesian Networks with Mixed Variables
Deconvolution density estimation with adaptive methods for a
variable prone to measurement error
Get energy data from the UK Dept of Energy and Climate Change
DEComposition of Indirect and Direct Effects
Export Decomposition (Wang-Wei-Zhu and source)
Deconvolution Estimation in Measurement Error Models
Additional dialogs and functions for Deducer
deducorrect: Deductive correction, deductive imputation, and
deterministic correction
Detection of differentially expressed genes
Models for Skewed Count Distributions Relevant to Networks
Statistical functions for the Delaporte distribution
Delaunay Triangulation and Dirichlet (Voronoi) Tessellation
Identification of dichotomous differential item functioning
(DIF) using Angoff's Delta Plot method
Functions to calculate relatedness on diploid genetic data
Evaluating the genetic differentiation between populations based
on Gst and D values
Differential Expression from Multiple Indicators
Deming, Thiel-Sen and Passing-Bablock Regression
Forecasting mortality, fertility, migration and population data
Kernel Density Estimation for Demonstration Purposes
Faster dendrogram manipulation using Rcpp
Extra functions to cut, label and colour dendrogram clusters
Dendrogram seriation: ordering for visualisation
Modular Leaf Ordering Methods for Dendrogram Nodes
Visualization of multivariate, functions, sets, and data
Clustering by fast search and find of density peaks
Density.T.HoldOut: Non-combinatorial T-estimation Hold-Out for
density estimation
Density strips and other methods for compactly illustrating
distributions
Global optimization by Differential Evolution
Differential Evolution Optimization in pure R
Statistical inference for parametric and semiparametric models
based on dependently truncated data
Dependent Mixture Models - Hidden Markov Models of GLMs and
Other Distributions in S4
Depth functions tools for multivariate analysis
Seasonal adjustment by frequency analysis
R interface to DescribeDisplay (GGobi plugin)
Optimal deseasonalization for geophysical time series using AR
fitting
Computational tool for designing genetical genomics experiments
Desirabiliy Function Optimization and Ranking
Bagging with Distance-based Regression for Differential Gene
Expression Analyses
General Solvers for Initial Value Problems of Ordinary
Differential Equations (ODE), Partial Differential Equations
(PDE), Differential Algebraic Equations (DAE), and Delay
Differential Equations (DDE)
Analyzing Wildlife Data with Detection Error
Start the detrendeR Graphical User Interface (GUI)
A computer program to detect markers responding to selection
Data sets from Devore's "Prob and Stat for Eng (7th ed)"
Dose-finding by the continual reassessment method
Explore data.frames by plotting NA and classes of each variable
Differential Functioning of Items and Tests (DFIT) framework
analyses
Differential Geometric LARS (dgLARS) method
dgmb Simulating data for PLS structural models
Double Hierarchical Generalized Linear Models
Functions for visualising simple graphs (networks), plotting
flow diagrams
Diagnostic test summary measures for three ordinal groups
Calculate probabilities of various dice-rolling events
Construction and evaluation of metamodels
Kriging methods for computer experiments
Kriging-based optimization for computer experiments
Plot methods for computer experiments design and surrogate
Dictionaries for reading microdata surveys from IBGE
Defines some physical constants and dielectric functions
commonly used in optics, plasmonics
R companion to "Curve and Surface Fitting with Splines"
Calculates Confidence Intervals for two Dependent Proportions
Functions from the book Solving Differential Equations in R
Diffusion IRT models for Response and Response Time Data
Baseline identification and peak decomposition for x-ray
diffractograms
A penalty approach to Differential Item Functioning in Rasch
Models
Collection of methods to detect dichotomous differential item
functioning (DIF) in psychometrics
Create cryptographic hash digests of R objects
An application programming interface for generating null models
of social contacts based on individuals' space use
Data-Informed Link Strength. Combine multiple-relationship
networks into a single weighted network. Impute (fill-in)
missing network links
DIME (Differential Identification using Mixture Ensemble)
DiNAMIC A Method To Analyze Recurrent DNA Copy Number
Aberrations in Tumors
Hartigan's dip test statistic for unimodality - corrected code
Bayesian Clustering of Multivariate Data Under the
Dirichlet-Process Prior
Direct labels for multicolor plots in lattice or ggplot2
Discrete Laplace mixture inference using the EM algorithm
DiscML: An R package for estimating evolutionary rates of
discrete characters using maximum likelihood
Multiple testing procedures for discrete test statistics
Functions to create and manipulate discrete random variables
Data preprocessing, discretization for classification
Discrimination of Alternative Reproductive Tactics (ARTs)
Tools of the Trade for Discriminant Analysis
Calculate observed and expected disease incidence counts from a
case file and population data
2D Hausdorff and Simplex Dispersion Orderings
Utilities for making pairwise comparisons of multivariate data
A simple way to fit detection functions to distance sampling
data and calculate abundance/density for biological
populations
DiSTATIS Three Way Metric Multidimensional Scaling
Method Functions for Confidence Intervals and to Distill
Information from an Object
Object oriented implementation of distributions
Documentation for packages distr, distrEx, distrSim, distrTEst,
distrTeach, distrMod, and distrEllipse
S4 classes for elliptically contoured distributions
Object oriented implementation of probability models
Package distr classes for distributions from Rmetrics
Simulation classes based on package distr
Extensions of package distr for teaching Stochastics/Statistics
in secondary school
Estimation and Testing classes based on package distr
Provides tools for quality checks of georeferenced plant species
accessions
diversitree: comparative phylogenetic analyses of
diversification
diveRsity: Genetic diversity partition statistics and
Informative locus selection using Fst, Gst, Dest(Jost Chao)
G'st and In
Functions for re-sampling a community matrix to compute
diversity indices at different sampling levels
Nearest Neighbour Contingency Table Analysis
Bayesian and Likelihood Analysis of Dynamic Linear Models
Delete or Merge Regressors for linear model selection
Functions and data for "Data Mining with R"
Tools for analysing forensic genetic DNA data
Integrative analysis of omics data in terms of network,
evolution and ontology
Analysis of Discretely Observed linear
Birth-And-Death(-and-immigration) Markov Chains
doBy - Groupwise summary statistics, LSmeans, general linear
contrasts, various utilities
docopt, command-line interface specification language
Functions for Acceptance Sampling Ideas originated by H.F. Dodge
Full factorials, orthogonal arrays and base utilities for DoE
packages
Wrapper package for design of experiments functionality
Foreach parallel adaptor for the multicore package
ADI (average dominance index), social network graphs with dual
directions, and music notation graph
Foreach parallel adaptor for the Rmpi package
Foreach parallel adaptor for the parallel package
Foreach parallel adapter for the rredis package
Generic Reproducible Parallel Backend for foreach Loops
Planning and Analyzing Dose Finding experiments
Foreach parallel adaptor for the snow package
Test against parametric regression function
Differential Exon Usage Test for RNA-Seq data via Empirical
Bayes Shrinkage of the Dispersion Parameter
A package for downloading files over http and https
Dirichlet Process Mixture model simulation for clustering and
image segmentation
Semiparametric Bayesian procedure for selecting a subset
containing the weakest species with an acceptably high
probability
Methods for dimension reduction for regression
Doubly Robust Generalized Estimating Equations
Regression and association models for repeated categorical data
Dual empirical likelihood inference under density ratio models
in the presence of multiple samples
Dose-Response Modeling with Smoothing Splines
Discretization-based Direct Random Sample Generation
Bayesian subgroup analysis in clinical trials
Dynamic Systems Estimation (time series package)
Density surface modelling of distance sampling data
Spatial modelling for distance sampling data
Recursive Partitioning for Discrete-Time Survival Trees
Comparison of Binary Diagnostic Tests in a Paired Study Design
Analysis of diffusion weighted imaging (DWI) data
Dunnett-Tukey-Kramer Pairwise Multiple Comparison Test Adjusted
for Unequal Variances and Unequal Sample Sizes
Suite of functions related to discrete-time discrete-state
Markov Chains
Estimation and comparison of dynamic treatment regimes
Dual Scaling Analysis of Multiple Choice Data
Create dummy/indicator variables flexibly and efficiently
Software implementation of step-down and step-up Dunnett test
procedures
Dunn's test of multiple comparisons using rank sums
Bayesian inference from count data using discrete uniform priors
Discrete variations of a fractional Brownian motion
Dynamically visualized probability distributions and their
moments
Methods for detection of clusters in hierarchical clustering
dendrograms
Implementation of the Dynamic TOPMODEL hydrological model
dynBiplotGUI: Full Interactive GUI for Dynamic Biplot in R
Denoising and clustering for dynamical image sequence (2D or
3D)+T
Misc Functions of the Department of Statistics (e1071), TU Wien
Plots of the Empirical Attainment Function
Early Warning Signals Toolbox for Detecting Critical Transitions
in Timeseries
Multivariate Adaptive Regression Spline Models
Fetch and plot credit easing policy tool data
EasyABC: performing efficient approximate Bayesian computation
sampling schemes
Analysis of variance and other important complementary analyzes
Utility functions for working with mark-recapture data
Evaluation of stratified genome-wide association meta-analysis
results
Entropy reweighting to create balanced samples
Empirical Bayes Thresholding and Related Methods
Empirical Bayes Estimation of Dynamic Bayesian Networks
Genotyping and SNP calling using single-sample next generation
sequencing data
Functions for spatial point pattern analysis
Efficient calculation of fine structure isotope patterns via
Fourier transforms of simplex-based elemental models
Dissimilarity-based functions for ecological analysis
Programmatic interface to the API serving UC Berkeley's Natural
History Data
A community modeling foundation for Eco-Hydrology
"A practical guide to ecological modelling - using R as a
simulation platform"
Ecological regression using aggregate and individual data
Nonparametric Multiple Change Point Analysis of Multivariate
Data
More Powerful Unconditional Testing of Negative Binomial Means
for Digital Gene Expression Data
Network Reconstruction and Changepoint Detection
R package for parsing, applying, and manipulating data cleaning
rules
Estimation of the effective dimension reduction (EDR) space
Provides tools for dimension reduction methods
Tools for analysis and classification of electroencephalography
(EEG) data
Convenience functions for education data
Effect Displays for Linear, Generalized Linear,
Multinomial-Logit, Proportional-Odds Logit Models and
Mixed-Effects Models
Test changes of a grouped response relative to baseline
Visualization of Categorical Response Models
Hierarchical modelling of faecal egg counts
Tool for ego-centric measures in Social Network Analysis
Extended and enhanced Hierarchical Logistic Regression models
(so called Huisman-Olff-Fresco models)
R Wrapper for the Energy Information Administration (EIA) API
Generates (dense) matrices that have a given set of eigenvalues
Semiparametric factor and regression models for symmetric
relational data
Computes confidence intervals for principal components
eiPack: Ecological Inference and Higher-Dimension Data
Management
An algorithm for reducing errors-in-variable bias in simple
linear regression
Ecological Inference with individual and aggregate data
Elastic-Net for Sparse Estimation and Sparse PCA
Collection of functions for statistical election audits
Functions for election audits using stratified random samples
Data sets, functions and examples from the book: "The Elements
of Statistical Learning, Data Mining, Inference, and
Prediction" by Trevor Hastie, Robert Tibshirani and Jerome
Friedman
Functions for drawing ellipses and ellipse-like confidence
regions
Implementation of ELM (Extreme Learning Machine ) algorithm for
SLFN ( Single Hidden Layer Feedforward Neural Networks )
Animal Dominance Hierarchies by Elo Rating
ELT - A package to build Experience Life Tables
Evolutionary Monte Carlo (EMC) algorithm
Evolutionary Monte Carlo (EMC) methods for clustering
EM Algorithm for Model-Based Clustering of Finite Mixture
Gaussian Distribution
Empirical Mode Decomposition and Hilbert Spectral Analysis
Global Disaster Losses from the EMDAT database Using R
Ecological models and data (book support)
Evaluation of Modeling without Information Leakage
EM-Algorithm for Jump Diffusion processes
End-member modelling algorithm and supporting functions for
grain-size analysis
Contrasts in mixed effects for EMMIX model with random effects
The EM Algorithm and Skew Mixture Distribution
Fitting Unrestricted Multivariate Skew t Mixture Models
Fitting mixed models with known covariance structures
Expected Maximum Profit for Credit Scoring
Empirical likelihood ratio for censored/truncated data
Empirical-likelihood test (two-sample, censored data)
Exact Multinomial Test: Goodness-of-Fit Test for Discrete
Multivariate data
Entropy Minimization over Variable Clusters (EMVC)
Tools for ecological network analysis (ena) in R
R Package for Fitting Multinomial Probit Models with Endogenous
Selection
R Package for Analyzing Endorsement Experiments
Data sets from "Introductory Statistics for Engineering
Experimentation"
Automated runs and evaluations of ecological niche models
An R package for the analysis of multiple ChIP-seq data
Enrichment assessment of virtual screening approaches
Probabilistic Forecasting using Ensembles and Bayesian Model
Averaging
Entropy partitioning to measure diversity
Estimation of Entropy, Mutual Information and Related Quantities
Estimation of Entropy and Related Quantities
Isotope pattern, profile and centroid calculation for mass
spectrometry
Peak picking for high resolution mass spectrometry data
Statistical analysis of environmental space-time processes
Package for Environmental Statistics, including US EPA Guidance
A package for statistical analysis in epidemiology
Epistatic and penetrance models for two-locus genetic
interactions
Elementary Epidemiological Functions for Epidemiology and
Biostatistics
Epidemiological tool for contact tracing
EpiEstim: a package to estimate time varying reproduction
numbers from epidemic curves
Mathematical Modeling of Infectious Disease
A collection of epidemic/network-related tools
An R package for the analysis of epidemiological data
EPoC (Endogenous Perturbation analysis of Cancer)
EQS Output Conversion to lavaan Functions
Tools for analyzing eQTL experiments: A complementary to Karl
Broman's 'qtl' package for genome-wide analysis
Direct, chain and average equating coefficients with standard
errors using IRT methods
Provides tests and graphics for assessing tests of equivalence
Nonparametric Multiple Expectile Regression via ER-Boost
Fit, Simulate and Diagnose Exponential-Family Models for
Networks
Fit, Simulate and Diagnose Exponential-Family Models for
Networks with Count Edges
ergm.graphlets: A Package for ERG Modeling Based on Graphlet
Properties
Local Health Department network data set
User-specified terms for the statnet suite of packages
Significance analysis of Event-Related Potentials data
Event-related potentials (ERP) analysis, graphics and utility
functions
Toolkit for the simulation of financial assets and interest
rates models
Calibrating Parameters for the Samejima's Continuous IRT Model
Modeling earthquake data using Epidemic Type Aftershock Sequence
model
Estimation of an ETAS model. Mixed FLP (Forward Likelihood
Predictive) and ML estimation of non-parametric and parametric
components of the ETAS model for earthquake description
Utility functions to execute standard ETL operations (using
package ff) on large data
eulerian: A package to find eulerian paths from graphs
Parsing and evaluation tools that provide more details than the
default
Functions for extreme value distributions
Bayesian Analysis in Extreme Value Theory
eVenn: A powerful tool to compare lists and draw Venn diagrams
Estimation of Variance by Efficient Replication
Extreme Value Mixture Modelling, Threshold Estimation and
Boundary Corrected Kernel Density Estimation
Epigenetic Variable Outliers for Risk prediction Analysis
Mean of order p, peaks over random threshold Hill and high
quantile estimates
Evolutionary Learning of Globally Optimal Trees
Goodness-of-fit tests for the Exponential and two-parameter
Weibull distributions
Exact Conditional Tests and Confidence Intervals for 2x2 tables
Exact P-values and Matching Confidence Intervals for simple
Discrete Parametric Cases
Inductive Confidence Intervals for the difference between two
proportions
Monte Carlo Exact Tests for Log-linear models
Exact solution paths for regularized LASSO regressions with L_1
penalty
Exact Distributions for Rank and Permutation Tests
Confidence regions for exceedance sets and contour lines
Convenient way to work with data in Microsoft Excel
Functions that compute probabilistic excursion sets and contour
credibility regions for latent Gaussian random processes and
fields
experiment: R package for designing and analyzing randomized
experiments
Modeling without data using expert opinion
Exploratory analysis with the singular value decomposition
Calculate density dependent likelihood of a phylogenetic tree
Spatial analysis of extra-pair paternity
Data sets from "Forecasting with exponential smoothing"
Convenience Functions for Botanists to Create Specimens Indices
Maximum penalized likelihood estimation with extended lasso
penalty
Extra-binomial approach for pooled sequencing data
Extremely Randomized Trees (ExtraTrees) method for
classification and regression
Estimate the lower extreme quantile with the censored Weibull
MLE and censored Weibull Mixture
Easy analysis and visualization of factorial experiments
selects significant non-additive interaction between two
variables using fast GLM implementation
provide an easy to use framework to conduct simulation
Bayesian Sparse Factor Analysis model for the inference of
pathways responsive to drug treatment
Combination of Factorial Methods and Cluster Analysis
Multivariate Exploratory Data Analysis and Data Mining with R
Bayesian quantile regression factor models
thin wrapper for the Factual.com server API
Variable selection for supervised classification in high
dimension
Distributions that are sometimes used in hydrology
Data from the book "Multivariate Statistical Modelling Based on
Generalized Linear Models", first edition, by Ludwig Fahrmeir
and Gerhard Tutz
File Abstraction Interface Layer (FAIL) mimicking a key-value
store
Finding Allele-specific Copy Number in Next-Generation
Sequencing Data
Probabilities for Pedigrees given DNA data
Factor Analysis for Multiple Testing (FAMT) : simultaneous tests
under dependence in high-dimensional data
Penalized likelihood factor analysis via nonconvex penalty
Building Kriging models from FANOVA graphs
Visualisation of sequential probability distributions using fan
charts
A complementary package to the FAOSTAT database and the
Statistical Yearbook of the Food and Agricultural Organization
of the United Nations
Influential case detection methods for factor analysis and SEM
Modelization for Functional AutoRegressive processes
Functions and datasets for books by Julian Faraway
Rmetrics - Analysing and Modelling Financial Assets
Implementation of the Fourier Amplitude Sensitivity Test (FAST)
A fast solver for parameterized lp problems and constrained l1
minimization approach to sparse precision matrix estimation
Fast hierarchical clustering routines for R and Python
Lasso and elastic-net penalized Cox's regression in high
dimensions models using the cocktail algorithm
Fast Rcpp implementation of Gauss-Hermite quadrature
R package to compute the FastHCS outlyingness index
Hierarchical Independent Component Analysis: a multi-scale
sparse non-orthogonal data-driven basis
FastICA Algorithms to perform ICA and Projection Pursuit
Learn from training data then quickly fill in missing data
Fast Computation of Multivariate M-estimators
R package to compute the FastPCS outlyingness index
Data sets and utilities for Foundations and Applications of
Statistics by R Pruim
R package to compute the FastRCS outlyingness index
Fast Interactive Framework for Web Scripting Using R
Two-locus Family-based Association Test with Polytomic Outcome
Power Hyperbolic Functions and Kiener Distributions
Functions and Datasets for "Forest Analytics with R"
Gene by Environment Interaction and Conditional Gene Tests for
Nuclear Families
Algorithm for searching the space of Gaussian directed acyclic
graphical models through moment fractional Bayes factors
FISH Based Normalization and Copy Number inference of SNP
microarray data
Association Football (Soccer) Ranking via Poisson Regression
fCertificates - Basics of Certificates and Structured Products
Valuation
Rmetrics - Bivariate Dependence Structures with Copulae
FCROS for detecting differentially expressed genes
Measuring functional diversity (FD) from multiple traits, and
other tools for functional ecology
Functional Data Analysis: K-Mean Alignment
Functional data analysis in a mixed model framework
Interval Testing Procedure for functional data
Functional Data Analysis and Utilities for Statistical Computing
(fda.usc)
Permutation-based FDR Point and Confidence Interval Estimation
Estimation of (Local) False Discovery Rates and Higher Criticism
Frequency Distribution Tables, Histograms and Poligons
Feature Extraction for Discretely-Sampled Functional Data
Fechnerian Scaling of Discrete Object Sets
Client package for the U.S. Federal Register API
fExpressCertificates - Structured Products Valuation for
ExpressCertificates/Autocallables
Rmetrics - Extreme Financial Market Data
memory-efficient storage of large data on disk and fast access
functions
Basic statistical functions for package ff
Force field simulation for a set of points
Wrapper for fftw3: includes 1-D, univariate and multivariate,
and 2-D transform
Rmetrics - Autoregressive Conditional Heteroskedastic Modelling
Fractional Gaussian Noise and power law decay time series model
fitting
Feature grouping and selection over an undirected graph
Tests for right and interval-censored survival data based on the
Fleming-Harrington class
Provide functions for forest inventory calculations
Classic, reloaded and adaptive FastICA algorithms
Rmetrics - Economic and Financial Data Import
Financial Instrument Model Infrastructure for R
Classifies implicit trading activity from market quotes and
computes the probability of informed trading
Time Value of Money, time series analysis and Computational
Finance
Find all root(s) of the equation and Find root(s) of the
equation by dichotomy
R Package for Finding Heterogeneous Treatment Effects
Python tools to find an acceptable python binary
Functions to operate on binary fingerprint data
Computation of the probability of ruin within a finite time
horizon
Companion to Tsay (2005) Analysis of Financial Time Series
Fisheye and Hyperbolic-space-alike Interactive Visualization
Tools in R
Iterative FISH-based Calibration of Hi-C Data
Fits Poisson-sum-of-Gammas GLMs, Tweedie GLMs, and delta
log-normal mdoels
FitARMA: Fit ARMA or ARIMA using fast MLE algorithm
Help to fit of a parametric distribution to non-censored or
censored data
FITS (Flexible Image Transport System) utilities
fitTetra is an R package for assigning tetraploid genotype
scores
Implementation of optimal hierarchical clustering
Flexible parametric survival and multi-state models
tools for interacting with the Brazilian Flora Checklist
Forecasts future values of a univariate time series
Path algorithm for the general Fused Lasso Signal Approximator
Estimation of background-subtracted fluorescence data
Flux rate calculation from dynamic closed chamber measurements
Data sets from "Forecasting: methods and applications" by
Makridakis, Wheelwright & Hyndman (1998)
A Flexible Modelling Environment for Inverse Modelling,
Sensitivity, Identifiability, Monte Carlo Analysis
Functions for medical statistics book with some demographic data
Variance estimation of FMT method (Fully Moderated t-statistic)
Rmetrics - Analysing and Modeling Multivariate Financial Return
Distributions
Fast Nearest Neighbor Search Algorithms and Applications
Nonlinear and Chaotic Time Series Modelling
visualisation and analysis of food web networks
Foraminifera and Community Ecology Analyses
ForeCA - Forecastable Component Analysis
Forecasting functions for time series and linear models
Read Data Stored by Minitab, S, SAS, SPSS, Stata, Systat, Weka,
dBase, ..
Statistical Methods in Forensic Genetics
Statistical tools for the interpretation of forensic DNA
mixtures
Imputation of missing values through a forward imputation
algorithm
Functions from the 2nd Italian Forest Inventory (INFC)
R functions for handling multiple processes
Elementary arithemtic in formal series rings
Utilities for working with formulas, expressions, calls and
other R objects
Palaeoecological and Palaeogeographical Analysis Tools
Restricted MLE for Functional Principal Components Analysis
Computing the noncentrality parameter of the noncentral F
distribution
Data for "Forecasting: principles and practice"
Approximation of first-passage-time densities for diffusion
processes
Fractionally differenced ARIMA aka ARFIMA(p,d,q) models
Fractal Time Series Modeling and Analysis
Generate fractal time series with non-normal returns
distribution
General Frailty models: shared, joint and nested frailty models
with prediction
Financial Risk Modelling and Portfolio Optimisation with R
Fuzzy Rule-based Systems for Classification and Regression Tasks
Fast Regularized Canonical Correlation Analysis
Statistical algorithms used in common data mining course
Rmetrics - Regression Based Decision and Prediction
FREQ: Estimate population size from capture frequencies
Frequency Moving Average Plots (MAP) of Multinomial Data by a
Continuous Covariate
FeatuRE Selection Algorithms for Computer Aided Diagnosis
Fractional Factorial designs with 2-level factors
Catalogues of resolution IV 128 run 2-level fractional
factorials up to 33 factors that do have 5-letter words
Regression analysis of fractional responses
The Generalized Hyperbolic Distribution, Related Distributions
and Their Applications in Finance
Wrapper functions for FSL (FMRIB Software Library) from
Functional MRI. of the Brain (FMRIB)
Social Relations Analyses with roles ("Family SRM")
Programs for Analyzing Fourier Transform-Ion Cyclotron Resonance
Mass Spectrometry Data
Features and Strings for Nonparametric Regression
R interface to tslib (a time series library in c++)
FUzzy GEnetic, a machine learning algorithm to construct
prediction model based on fuzzy logic
Functional Profiling of Microarray Expression Data
A package for functional data clustering
An algorithm for gene and gene set network inference
Clustering in the Discriminative Functional Subspace
Model-based clustering in group-specific functional subspaces
funreg (Functional Regression for Irregularly Timed Data)
Community Detection via Fused Principal Component Analysis
Futile library to provide some polymorphic operations
Random matrix generation and manipulation
A framework for working in a functional programming paradigm in
R
Exact calculation of fuzzy decision rules for multiple testing
Fuzzy Rank Tests and Confidence Intervals
Fuzzy stationary probabilities from a sequence of observations
of an unknown Markov chain
Forward search for Mokken scale analysis
Selecting variables in regression models
Fire Weather Index System and Fire Behaviour Prediction System
Calculations
A package performing Dynamic Bayesian Network inference
Grain-size Statistics and Description of Sediment
A genetic algorithm approach to determine stratum boundaries and
sample sizes of each stratum in stratified sampling
Framework for Generalized Subspace Pattern Mining
GAD: Analysis of variance from general principles
Graphical Extension with Accuracy in Parameter Estimation
(GAIPE)
Genetic algorithms and C-steps based LTS (Least Trimmed Squares)
estimation
Fit the GamBin model to species abundance distributions
Generalized linear and additive models by likelihood based
boosting
Estimating multistate models using gamboost()
Functions and data for a course on modern regression and
classification
Statistical Estimation of Game-Theoretic Models
Generalised Additive Models for Location Scale and Shape
Fitting an interval response variable using gamlss.family
distributions
Distributions to be used for GAMLSS modelling
A GAMLSS add on package for fitting mixture distributions
Fitting non linear parametric GAMLSS models
Generating and fitting truncated (gamlss.family) distributions
Generalized additive mixed models using mgcv and lme4
classic gamma regression: joint modeling of mean and shape
parameters
Gene Association Network-based Pathway Analysis
Genetic Algorithm optimization for real-based and
permutation-based problems
Functions for Drawing Gapped Cluster Heatmap with ggplot2
Difference measures for multivariate Gaussian probability
density functions
Collection of functions for Gaussian quadrature
Generalize Lambda Distribution and Generalized Bootstrapping
Generalized Beta Distribution of the Second Kind: properties,
likelihood, estimation
Utilities for processing Rd objects and files
Package for Generalized Birnbaum-Saunders Distributions
Guided Correction Approach for Inherited bias (GCAI.bias)
Graph-based two-sample tests for categorical data
GPU/CPU Benchmarking in Debian-based systems
LASSO and (adaptive) Elastic-Net penalized least squares,
logistic regression, HHSVM and squared hinge loss SVM using a
fast GCD algorithm
Concord method for Graphical Model Selection
A toolbox for the analysis of categorical data in social
sciences, and especially Geometric Data Analysis
Download, slice, and normalize GDELT data
Generalized Diffusion Magnetic Resonance Imaging
distances and routes on geographical grids
R Interface to CoreArray Genomic Data Structure (GDS) files
Calculate generalized eigenvalues of a matrix pair
Package contains data which is used by GenABEL example and test
functions
Global ENvironmental ASsessment Information System (GENASIS)
computational tools
Clopper-Pearson Confidence Interval and Generalized Binomial
Distribution
Gender: predict gender from names using historical data
Identification of Periodically Expressed Genes
Bayesian calculation of gene-specific FST from genomic SNP data
Detection of structure from multilocus genetic data
Profiling a gene list into GOslim or KEGG function pie
Genetic Epidemiology Design and Inference
Construct time delay gene regulatory network
Collection of Genetic Data Analysis Tools
Functions for generating and manipulating binned kernel density
estimates
Path algorithm for generalized lasso problems
Functions for the Bayesian analysis of GWAS data
Application of MOSS algorithm to genome-wide association study
(GWAS)
Circular Genomic Permutation using GWAS p-values of association
Plot publication-grade gene and genome maps
Simulation of ordinal and discrete variables with given
correlation matrix and marginal distributions
Generalized ridge trace plots for ridge regression
R functions for Generalized Simulated Annealing
Spline Based Window Boundaries for Genomic Analyses
Analysis of geostatistical data using Bayes and empirical Bayes
methods
Analysis and Modeling for Geostatistical Count Data
A geometrical Approach to Differential expression and gene-set
enrichment
Quantification of the effect of geographic versus environmental
isolation on genetic differentiation
Data for topographic and Geologic Mapping
Geometric morphometric analyses of 2d/3d landmark data
Interface to www.geonames.org web service
Geophysics, Continuum Mechanics, Mogi Model
geoRglm - a package for generalised linear spatial models
Robust Geostatistical Analysis of Spatial Data
Helper package to facilitate the generation of distance matrices
used in the package spacom
Spatial geostatistics; some geostatistical and radial basis
functions, prediction and cross validation; design of optimal
spatial sampling networks based on geostatistical modelling
Geostatistics using SpatialPoints and rasters
Interactive exploratory spatial data analysis
Parse command-line arguments and variable interpolation
GetR: Calculate Guttman error trees in R
Getting to the Bottom, A Package for Learning Optimization
Methods
GEV conditional density estimation network
ARMA-GARCH/APARCH models with GEV and stable distributions
A visual, intuitive, easy to use software giving access to a new
type of information buried into your microarray data
Tools for working with Hansen et al. 2013 Global Forest Change
dataset
Tools for extracting dendrogram and tree diagram plot data for
use with ggplot2
GGEBiplotGUI: Interactive GGE Biplots in R
Group Lasso Penalized Learning Using A Unified BMD Algorithm
A package for spatial visualization with Google Maps and
OpenStreetMap
Graphical tools for analyzing Markov Chain Monte Carlo
simulations from Bayesian inference
Variations of Parallel Coordinate Plots for Categorical Data
An implementation of the Grammar of Graphics
Explore complex data by embedding subplots within plots
An extension to ggplot2, for the creation of ternary diagrams
Extra themes, scales and geoms for ggplot
A package on the generalized hyperbolic distribution and its
special cases
Gibbs Sampler for One-Way Mixed-Effects ANOVA and ANCOVA Models
Naive Gibbs Sampling with Metropolis Steps
Gillespie's Stochastic Simulation Algorithm (SSA)
Generalized Iterative Proportional Fitting for Relational Models
The giRaph package for graph representation in R
an R package for quantification of pinned microbial culture
Constructing panels of population pyramid plots based on lattice
Generalized Linear Autoregressive Moving Average Models
Graphical lasso- estimation of Gaussian graphical models
High dimensional Mixture Graph Models selection
Calculate textures from grey-level co-occurrence matrices
(GLCMs) in R
Estimation and use of the generalised (Tukey) lambda
distribution
Fitting Single and Mixture of Generalised Lambda Distributions
(RS and FMKL) using Various Methods
An Asymmetry-Steepness Parameterization of the Generalized
Lambda Distribution
Learning interactions via hierarchical group-lasso
regularization
Fitting Generalized Linear Models Subject to Constraints
Fit GLM with LEP-based penalized maximum likelihood
Generalised Linear Mixed Models and Spatial Models with WinBUGS,
BRugs, or OpenBUGS
Gauss-Seidel Generalized Linear Mixed Model solver
Variable selection for generalized linear mixed models by
L1-penalized estimation
Generalized linear models with clustering
Lasso and elastic-net regularized generalized linear models
Fit a penalized constrained continuation ratio model for
predicting an ordinal response
L1 Regularization Path for Generalized Linear Models and Cox
Proportional Hazards Model
Fit a penalized continuation ratio model for predicting an
ordinal response
Model selection and multimodel inference made easy
Testing the additional predictive value of high-dimensional data
Global Gene-Set Analysis for Association Studies
Objective functions for benchmarking the performance of global
optimization algorithms
Fitting and Testing Generalized Logistic Distributions
Generalized Least Squares with Measurement Error
Fast estimation of Gaussian Mixture Copula Models
Graph Based Multiple Comparison Procedures
Generalized Minimum Distance of distributions
Collection of functions for plotting relations, generating
tables, and more
Generalized Method of Moments and Generalized Empirical
Likelihood
Interface between GMT Map-Making Software and R
Computation of generalized Nash equilibria
Generalized Non-negative Matrix Factorization Based on Renyi
Divergence
Read data from files readable by gnumeric
Weighted and lexicographical goal programming and optimization
Model-diagnostics based on cumulative residuals
GoFKernel: Testing Goodness-of-fit with the Kernel Density
Estimator
Tests of fit for some probability distributions
Classical Goodness-of-Fit Tests for Univariate Distributions
Generalized Orthogonal GARCH (GO-GARCH) models
An R library to build Google's Public Data Explorer DSPL
Metadata files
Word cloud summaries of GO enrichment analysis
functions to get public survey data in Japan
Batch Effect Detection via Guided Principal Components Analysis
GPCSIV, Generalized Principal Component of Symbolic Interval
variables
Bootstrap goodness-of-fit test for the generalized Pareto
distribution
Apply Gaussian Process in Functional data analysis
Generalized partial linear models (GPLM)
Generalized Partially Linear Tree-based Regression Model
Analysing and plotting genotype-phenotype maps
A Minimalistic package to apply Gaussian Process in R
gpseq: Using the generalized Poisson distribution to model
sequence read counts from high throughput sequencing
experiments
Maximum Likelihood Estimation of Multiple Membership Mixed
Models Used in Value-Added Modeling
Species distribution modelling using latent Gaussian random
fields
Graphical Representation and Modeling of Metagenomic Reads
Graphical Analysis of Variance Using ggplot2
Efficient selection of undirected graphical models for
high-dimensional datasets
A multi-platform GUI for drawing customizable graphs in R
GraphPCA, Graphical tools of histogram PCA
Inference in Graphical Gaussian Models with Edge and Vertex
Symmetries
Uses Grieg-Smith method on 2 dimentional spatial data
Generalized Regression on Orthogonal Components
The package was developed to fit fit many growth curves obtained
under different conditions
Regression Analysis of Grouped and Coarse Data
A GUI-based program to compute probabilities regarding group
sequential designs
Bayesian semi and nonparametric growth curve models that
additionally include multiple membership random effects
Bayesian non-parametric dependent models for time-indexed
functional data
Bayesian reconstruction of growth velocity
Fitting user specified models with Group Lasso penalty
Concave 1-norm and 2-norm group penalty in linear and logistic
regression
Regularization paths for regression models with grouped
covariates
Tools for the analysis of Gutenberg-Richter distributions of
earthquake magnitudes
Gene Set Analysis using the Gamma Method
Two functions for Generalized SARIMA time series simulation
Robust Estimation of Multivariate Location and Scatter in the
Presence of Missing Data
Group Sequential Design in Equivalence Studies
Copula Density and 2-D Hazard Estimation using Smoothing Splines
spatial and spatio-temporal geostatistical modelling, prediction
and simulation
Analyses and functions related to the spatial analysis of
genetic marker data
Utilities for strings and function arguments
Calculate efficiencies of group testing algorithms with
correlated responses
Maps, data and methods related to Guerry (1833) "Moral
Statistics of France"
Implementation of sampling formulas for the unified neutral
model of biodiversity and biogeography, with or without guild
structure
GUTS: Fast Calculation of the Likelihood of a Stochastic
Survival Model
Regularized Categorical Effects/Categorical Effect
Modifiers/Continuous/Smooth effects in GLMs
Global Validation of Linear Models Assumptions
Genome-wide association/interaction analysis and rare variant
analysis with family data
Exact Hardy-Weinburg testing for Genome Wide Association Studies
Controlling the genome-wide type I error rate in association
mapping experiments
Calculation of probabilities for inadequate and excessive
gestational weight gain
gWidgets API for building toolkit-independent, interactive GUIs
Rewrite of gWidgets API for simplified GUI construction
Implementation of gWidgets2 for RGtk2 package
Toolkit implementation of gWidgets2 for tcltk package
Toolkit implementation of gWidgets for RGtk2
Toolkit implementation of gWidgets for tcltk package
Fits geographically weighted regression models with diagnostic
tools
Genome-wide significance for whole genome sequencing studies
Maximum Likelihood Estimation for Gene-by-Measured Environment
Interaction Models
Software to perform Haar Fisz transforms
Estimation, simulation and visualization of Hierarchical
Archimedean Copulae (HAC)
Utilities for using R scripts in Hadoop streaming
Hierarchical Optimal Matching and Machine Learning Toolbox
Univariate non-parametric two-sample test based on empirical
Hankel transforms
Inference of trait associations with SNP haplotypes and other
attributes using the EM Algorithm
Analyzing case-parent triad and/or case-control data with SNP
haplotypes
Estimate Haplotype Relative Risks in Case-Control Data
Statistical Analysis of Haplotypes with Traits and Covariates
when Linkage Phase is Ambiguous
Graphical tests for Hardy-Weinberg equilibrium
Full feature implementation of hash/associated
arrays/dictionaries
A collection of non-cryptographic hash functions
Hawkes process simulation and calibration toolkit
Damage functions from FEMA's HAZUS software for use in modeling
financial losses from natural disasters
Hill/Bliss Independence Model for Combination Vaccines
Hierarchical Bayesian Analysis of Recognition Memory
Hierarchical Bayesian Small Area Estimation
Hierarchical Bayesian Space-Time models for Gaussian space-time
data
Interval estimation for the parameters of linear models with
heteroskedasticity (Wild Bootstrap)
High Dimensional Supervised Classification and Clustering
Use known groups in high-dimensional data to derive scores for
plots
Hierarchical DECOmposition of Entropy for Categorical Map
Comparisons
Statistical Analysis Tools for High Dimension Molecular Data
(HDMD)
Highest density regions and conditional density estimation
The Lasso for the Tweedie's Compound Poisson Model Using an
IRLS-BMD Algorithm
Health Effects of Air Pollution and Temperature (HEAT)
Heat exchange calculations during physical activity
Fit statistic for binary dependent variable models
Package for robust estimation using heavy-tailed distributions
Visualizing Hypothesis Tests in Multivariate Linear Models
Hierarchical Exponential-Family Random Graph Models with Local
Dependence
Handy functions for working with HGNC gene symbols and
Affymetrix probeset identifiers
Statistical Analysis and Data Display: Heiberger and Holland
The Hilbert-Huang Transform: Tools and Methods
Simulation from distributions supported by nested hyperplanes
HIBAG -- HLA Genotype Imputation with Attribute Bagging
Hierarchical Climate Regionalization: An Improved Hierarchical
Clustering in R for Climate Regionalization
Conduct the F test for hidden additivity in two-factor
experiments
Functions for performing hierarchical analysis of distance
sampling data
Estimation and tests of hierarchical F-statistics
Determining and evaluating high-risk zones
Estimation of heritability in high dimensional Linear Mixed
Models
Tools for hypothesis testing based on Hypergeometric
Intersection distributions
Hierarchical Semiparametric Regression of Test Statistics
Data sets from the history of statistics and data visualization
Construction of regular and irregular histograms with different
options for automatic choice of bins
"Hit and Run" method for sampling uniformly from convex shapes
HIV calibrated model life tables for countries with generalized
HIV epidemics
Conversion Tools for HK80 Geographical Coordinate System
Diagnostic tools for hierarchical (multilevel) linear models
Hierarchical latent space network model (HLSM)
The H-measure and other scalar classification performance
metrics
Hidden Markov Model for Continuous Observations Processes
Hidden Markov models with discrete non-parametric observation
distributions
Analysing accelerometer data using hidden Markov models
Hypothesis Testing and Power Calculations for Comparing
Metagenomic Samples from HMP
Statistical Object Oriented Data Analysis of RDP-based Taxonomic
trees from Human Microbiome Data: Modeling, Visualization, and
Two-Group Comparison
Flux estimation with static chamber data
Holiday and halfday data, for use with the TimeWarp package
Homogeneity tests for Regional Frequency Analysis
Hot Deck Imputation Methods for Missing Data
A lower bound for the number of correct rejections
Heligman Pollard mortality model parameter estimation using
Bayesian Melding with Incremental Mixture Importance Sampling
High Performance Cluster Models Based on Kiefer-Wolfowitz
Recursion
A set of annotation maps describing the Human Phenotype Ontology
Functions to plot graphviz style graphs of sets of HPO terms
Analysis semantic similarity between HPO terms and HPO-based
phenotypic similarity between genes and between diseases
Miscellaneous convenience functions and dataset
A Handbook of Statistical Analyses Using R
A Handbook of Statistical Analyses Using R (2nd Edition)
A Handbook of Statistical Analyses Using R (3rd Edition)
hierarchical Bayesian species distribution models
Canonical Correlation Analysis based on Kernel Independence
Measures
Phasing, sire imputation, recombination events identification
and pedigree reconstruction of half-sib families using SNP data
Joint meta-analysis of diagnostic test sensitivity and
specificity with or without a gold standard reference test
Facilitates automated HTML report creation
Clustering high throughput sequencing (HTS) data
Differential analysis of RNA-seq data with Poisson mixture
models
compute HUM value and visualize ROC curves
Models and tests for departure from Hardy-Weinberg equilibrium
and independence between loci
Bayesian investigation of Hardy-Weinberg Equilibrium via
estimation and testing
Objective Bayesian Testing for the Hardy-Weinberg Equilibrium
Problem
Testing Hardy-Weinberg Equilibrium for Multiallelic Genes
HTML Writer - Outputs R objects in HTML format
hwriterPlus: Extending the hwriter Package
Exact Tests for Hardy-Weinberg Proportions
Build, deploy and evaluate a Hybrid Ensemble
Implementation of the Hybrid Monte Carlo and Multipoint Hybrid
Monte Carlo sampling techniques
Tools and models for hydrological applications
Groundwater data presentation and interpretation
Goodness-of-fit functions for comparison of simulated and
observed hydrological time series
R codes for estimating water retention and infiltration model
parameters using experimental data
Particle Swarm Optimisation, with focus on Environmental Models
Hydrologic indices for daily time series data
Time series management, analysis and interpolation for
hydrological modelling
A generalization of the Dirichlet distribution
Interface for hyperspectral data, i.e. spectra + meta info
(spatial, time, concentration, ...)
High-dimensional kernel density estimation and geometry
operations
Macroevolutionary phylogentic analysis of species trees and gene
trees
Confidence Intervals and Tests of Statistical Hypotheses
Simulation of genomic data in applied genetics
Tools for Modeling Rate-Dependent Hysteretic Processes and
Ellipses
Model selection for index of asymmetry distribution
Functions to use with data from the Implicit Association Test
Integrative Bayesian Analysis of Transcriptomic and CGH data
Functions for the Analysis of IBD Haplo output
Regression Methods for IBD Linkage With Covariates
Simulation of chromosomal regions shared by family members
Irregular-lattice based ensemble empirical mode decomposition
Iboot: iterated bootstrap tests and confidence sets
R API to Interactive Brokers Trader Workstation
An Interface to WinBUGS/OpenBUGS/JAGS by gWidgets
A copy number and expression-based classifier for breast
tumours
Inequality and Concentration Indices and Curves
Standardized high-throughput evaluation of cell-based compound
screens
Independent Components Analysis (ICA) based artifact correction
Functions facilitating the estimation of the Intraclass
Correlation Coefficient
Tools for Working with ICD-9 Codes, and Finding Comorbidities
Individual Conditional Expectation Plot Toolbox
Incremental Cost-Effectiveness (ICE) Statistical Inference from
Two Unbiased Samples
Study Design and Data Analysis in the presence of error-prone
diagnostic tests and self-reported outcomes
Estimation of number of clusters and identification of atypical
units
Inequality constrained inference in linear normal situations
Integrative clustering of multiple genomic data types
Tools for Exploring Multivariate Data via ICS/ICA
A package for semiparametric regression analysis of
interval-censored data
Jacquard Condensed Coefficients of Identity
Utilities of Institute of Data Analyses and Process Design
(www.idp.zhaw.ch)
Imprecise Dirichlet Process for Survival Analysis
R Analysis package for iDynoMiCS Simulation Results
Functions to set and get the IEEE rounding mode
An integrative factor analysis model for drug-pathway
association inference
Read binary files saved by Igor Pro (including Neuromatic data)
A collection of network data sets for the igraph package
Convert iGraph graps to SoNIA .son files
Consistency, Homogeneity and Summary Statistics of
Climatological Data
Identifying Causal Effect for Multi-Component Intervention Using
Instrumental Variable Method
Minimum distance estimation in an imprecise probability model
A collection of methods for left-censored missing data
imputation
The Multifactor Dimensionality Reduction (MDR) Analysis for
Incomplete Data
Imputing the last largest censored observation/observations
under weighted least squares
Mixture models for longitudinal count data
Nonparametric tests of independence between random vectors
Studying the statistical relationship between species and groups
of sites
Measuring Inequality, Concentration, and Poverty
Infine-dimensional model (IDM) to analyse phenotypic variation
in growth trajectories
Functions to extract inferential values of a fitted model object
Inference about the standardized mortality ratio when evaluating
the effect of a screening program on survival
Tools for detecting influential data in mixed effects models
Case Influence in Structural Equation Models
Information Decomposition of Two-phase Experiments
R Tools for Creating Sequence Statistics
Inline C, C++, Fortran function calls from R
Convert inline comments to documentation
insideRODE includes buildin functions with deSolve solver and
C/FORTRAN interfaces to nlme, together with compiled codes
Using R to install stuff (such as: R, Rtools, RStudio, git, and
more!)
A Collection of Insurance Datasets Useful in Risk Classification
in Non-life Insurance
procedures for automated interpolation - methods only to be used
interactively, not included in intamap package
Iterated Convex Minorant Algorithm for interval censored event
data
Tests for marginal interactions in a 2 class response model
interactive network analysis and visualization
Coercion routines for network data objects in R
Hourly interpolation of multiple temperature daily series
Weighted Logrank Tests and NPMLE for interval censored data
Tools for working with points and intervals
Interventional Inference for Dynamic Bayesian Networks
Numerical Integration for Integrated Likelihood
linear programming solver by the interior point method and
graphically (two dimensions)
methods for analyzing introgression between divergent lineages
Threshold regression that fits the (randomized drift) inverse
Gaussian distribution to survival data
A unified framework for input-output operations in R
Functions for smoothing with infinite order flat-top kernels
Tools for subgroup analyses with multiple trial data using
aggregate statistics
ipdw: Interpolation by Inverse Path Distance Weighting
Fast implementation of the iterative proportional fitting
procedure in C
Builds and analyses Integral Projection Models (IPMs)
Introduction to Probability and Statistics Using R
Regression methods for interval-valued variables
Fast partial SVD by implicitly-restarted Lanczos
bidiagonalization
Various Coefficients of Interrater Reliability and Agreement
Simple interface to the estimation and plotting of IRT models
Utilities and Probability Distributions Related to
Multidimensional Person Item Response Models
Estimation of Tree-Based Item Response Models
Iterative Selection of Blocks of Features - ISBF
image-based High-Throughput RNAi Screening Analysis Tool
Provides datasets for Introduction to Statistical Data Analysis
for the Life Sciences
Fitting Ising models using the eLasso method
Sampling methods and distribution functions for the Ising model
Data for An Introduction to Statistical Learning with
Applications in R
An Introduction to Statistical Modeling of Extreme Values
Functions to perform isotonic regression
Confidence intervals for current status data based on
transformations and bootstrap
Testing for monotonic relationship between gene expression and
doses in a microarray experiment
Calculation of isotopic pattern for a given molecular formula
Active set and generalized PAVA for isotone optimization
Penalized Isotonic Regression in one and two dimensions
Week of the year and weekday according to ISO 8601
Approximate probability densities by iterated Laplace
Approximations
Efficient iterator for permutations and combinations
itertools2: Functions creating iterators for efficient looping
Tools for classification and regression trees, with an emphasis
on interpretability
Time series analysis package for students
Incorporating Uncertainties in Propensity Scores
Bayesian Instrumental Variable Estimation and Model
Determination via Conditional Bayes Factors