Computes the probability density function, cumulative distribution function, quantile function, random numbers of any composite model based on the lognormal distribution.
Provides routines for density and moments of the
Conway-Maxwell-Poisson distribution as well as functions for
fitting the COM-Poisson model for over/under-dispersed count
data.
Fit a CMP regression model to count data. The code provides functions for model estimation, dispersion testing, and diagnostics.
The package provides functions for the consistent analysis of
compositional data (e.g. portions of substances) and positive numbers
(e.g. concentrations) in the way proposed by Aitchison and Pawlowsky-Glahn.
Calculate regression coefficients and their standard deviations under the Cox proportional hazard model with the large number of covariates. A shrinkage type estimation based on the compound covariate method (Emura, Chen & Chen, 2012 PLoS ONE) and a univariate gene selection method under dependent censoring (Emura & Chen, 2014 Statistical Methods in Medical Research) are implemented in this package.
Computing Continuous Distributions Obtained by Compounding
a Continuous and a Discrete Distribution
Computes the distribution function of quadratic forms in
normal variables using Imhof's method, Davies's algorithm,
Farebrother's algorithm or Liu et al.'s algorithm.
Different tools for describing and analysing paired comparison data are presented. Main methods are estimation of products scores according Bradley Terry Luce model. A segmentation of the individual could be conducted on the basis of a mixture distribution approach. The number of classes can be tested by the use of Monte Carlo simulations. This package deals also with multi-criteria paired comparison data.
A set of procedures for the analysis of Random Fields using likelihood and non-standard likelihood methods is provided. Spatial analysis often involves dealing with large dataset. Therefore even simple studies may be too computationally demanding. Composite likelihood inference is emerging as a useful tool for mitigating such computational problems. This methodology shows satisfactory results when compared with other techniques such as the tapering method. Moreover, composite likelihood (and related quantities) have some useful properties similar to those of the standard likelihood.
This package contains several functions for calculating the most
widely used effect sizes (ES), along with their variances, confidence
intervals and p-values.  The output includes ES's of d (mean difference), g
(unbiased estimate of d), r (correlation coefficient), z' (Fisher's z), and
OR (odds ratio and log odds ratio). In addition, NNT (number needed to
treat), U3, CLES (Common Language Effect Size) and Cliff's Delta are
computed. This package uses recommended formulas as described in The
Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, &
Valentine, 2009).
Continuous smoothing of probability density function on a compact or semi-infinite support is performed using four continuous associated kernels: extended beta, gamma, lognormal and reciprocal inverse Gaussian. The cross-validation technique is also implemented for bandwidth selection.
Continuous convex piecewise linear (ccpl) resp. quadratic (ccpq) functions can be implemented with sorted breakpoints and slopes. This includes functions that are ccpl (resp. ccpq) on a convex set (i.e. an interval or a point) and infinite out of the domain. These functions can be very useful for a large class of optimisation problems. Efficient manipulation (such as log(N) insertion) of such data structure is obtained with map standard template library of C++ (that hides balanced trees). This package is a wrapper on such a class based on Rcpp modules.
The four functions svdcp (cp for column partitioned),
svdbip or svdbip2 (bip for bi-partitioned), and svdbips (s for
a simultaneous optimization of one set of r solutions),
correspond to a "SVD by blocks" notion, by supposing each block
depending on relative subspaces, rather than on two whole
spaces as usual SVD does. The other functions, based on this
notion, are relative to two column partitioned data matrices x
and y defining two sets of subsets xi and yj of variables and
amount to estimate a link between xi and yj for the pair (xi,
yj) relatively to the links associated to all the other pairs.
Higher order likelihood-based inference for logistic and
loglinear models
Solves for the mean parameters, the variance parameter, and their asymptotic variance in a conditional GEE for recurrent event gap times, as described by Clement and Strawderman (2009) in the journal Biostatistics. Makes a parametric assumption for the length of the censored gap time.
Conditional density estimation with mixtures for
heavy-tailed distributions
Computes conditional multivariate normal probabilities, random deviates and densities.
Routines doing cone projection and quadratic programming, as well as doing estimation and inference for constrained parametric regression and shape-restricted regression problems.
This small library contains a series of simple tools for
constructing and manipulating confounded and fractional
factorial designs.
Functions for estimating and reporting multiyear averages and
corresponding confidence intervals and distributions. A potential use case
is reporting the chemical and ecological status of surface waters according
to the European Water Framework Directive.
Implementation of conformal prediction using caret models for classification and regression
Offers several functions for Configural Frequencies
Analysis (CFA), which is a useful statistical tool for the analysis of
multiway contingency tables. CFA was introduced by G. A. Lienert as
'Konfigurations Frequenz Analyse - KFA'.
plot conics (ellipses, hyperbolas, parabolas)
Conjoint is a simple package that implements a conjoint
analysis method to measure the preferences.
Implementation of a procedure (Domingue, 2012; see also
Karabatsos, 2001 and Kyngdon, 2011) to test the single and
double cancellation axioms of conjoint measure in data that is
dichotomously coded and measured with error.
ConnMatTools collects several different methods for analyzing and
working with connectivity matrices in R.  Though primarily oriented towards
marine larval dispersal, many of the methods are general and useful for
terrestrial systems as well.
Given response y, continuous predictor x, and covariate matrix, the relationship between E(y) and x is estimated with a shape-constrained regression spline.  Function outputs fits and various types of inference.
Compute the median ranking according the Kemeny's axiomatic approach. Rankings can or cannot contain ties, rankings can be both complete or incomplete.
Provides functions for
efficient computations of nonlinear spatial predictions with
local change of support. This package supplies functions for
tow-dimensional spatial interpolation by constrained,
covariance-matching constrained and universal (external drift)
kriging for points or block of any shape for data with a
nonstationary mean function and an isotropic weakly stationary
variogram. The linear spatial interpolation methods,
constrained and covariance-matching constrained kriging,
provide approximately unbiased prediction for nonlinear target
values under change of support. This package
extends the range of geostatistical tools available in R and
provides a veritable alternative to conditional simulation for
nonlinear spatial prediction problems with local change of
support.
Various utilities for evaluating continued fractions
Bayesian analysis of complete and incomplete contingency tables.
Contrast methods, in the style of the Design package,
for fit objects produced by the lm, glm, gls, and geese functions.
Nonparametric two-sample procedure for comparing the median survival time.
Converts between the Date class and d/m/y for several
calendars, including Persian, Islamic, and Hebrew
This package provides a way to investigate various modes of convergence of random variables.
Quantifies and assesses the significance of convergent evolution.
Fits convolution-based nonstationary
Gaussian process models to point-referenced spatial data. The nonstationary
covariance function allows the user to specify the underlying correlation
structure and which spatial dependence parameters should be allowed to
vary over space: the anisotropy, nugget variance, and process variance.
The parameters are estimated via maximum likelihood, using a local
likelihood approach. Also provided are functions to fit stationary spatial
models for comparison, calculate the kriging predictor and standard errors,
and create various plots to visualize nonstationarity.
This R package applies the probabilistic model of species co-occurrence (Veech 2013) to a set of species distributed among a set of survey or sampling sites. The algorithm calculates the observed and expected frequencies of co-occurrence between each pair of species. The expected frequency is based on the distribution of each species being random and independent of the other species. The analysis returns the probabilities that a more extreme (either low or high) value of co-occurrence could have been obtained by chance. The package also includes functions for visualizing species co-occurrence results and preparing data for downstream analyses.
Computes several cooperative games and allocation rules associated with minimum cost spanning tree problems and minimum cost arborescence problems.
A logical workflow for the reading, processing, and visualization
of data obtained from the Union Biometrica Complex Object Parametric
Analyzer and Sorter (COPAS) platform large-particle flow cytometers and a
powerful suite of functions for the rapid processing and analysis of large
high-throughput screening data sets. It combines the speed of dplyr with
the elegance of ggplot2 to make analysis of COPAS data fast and painless.
Extensive functions for copula computations and related mathematical operations are given that are oriented around the oft cited and fundamental copula theory described by Nelsen (2006), Joe (2015), and other selected works. The lower, upper, product, and select other copulas are implemented. Individual copula functional support is intentionally limited to keep the package focused on an API to general copula theory. Expressions available for an arbitrary copula include the diagonal of a copula, the survival copula, the dual of a copula, co-copula, and numerical copula density. Level curves (sets) are available. Horizontal and vertical copula sections and derivatives of such sections also are supported. The numerical derivatives of a copula are provided as well as inverses; functions for copula simulation thus are supported. Composition of copulas is provided: (1) composition of a single copula using two composition parameters, (2) composition of two copulas using two composition parameters, (3) composition of two copulas using four composition parameters, and (4) random variates of composited copulas.  Characteristics of copulas include Kendall Tau, Spearman Rho, Gini Gamma, Blomqvist Beta, and Schweizer-Wolff Sigma, tail dependency, and tail order. Logical evaluators of positively/negatively quadrant dependency, left-tail decreasing, and right-tail increasing also are available. The Kullback-Leibler divergence and Vuong's Procedure to support inference are available. Quantile and median regression for V with respect to U and U with respect to V is available. Empirical copulas (EC) are supported. ECs are dependent on gridded data structures for which generation capability is provided. The derivatives of the EC grid are computable. Also, the inverses of the derivatives, which are the conditional QDFs of copula sections also are computable. Median and quantile regression of an EC also is supported along with support for EC simulation of V conditional on U.
Provides tools for fitting the copCAR regression model for discrete areal data. Three types of estimation are supported: continuous extension, composite marginal likelihood, and distributional transform.
Provides functions to  compute and plot Coverage
Probability Excursion (CoPE) sets
for real valued functions on a 2-dimensional domain. CoPE sets are obtained
from repeated noisy observations of the function on the entire domain.
They are designed to bound the excursion
set of the target function at a given level from above and below with
a predefined probability. The target
function can be a parameter in spatially-indexed linear regression.
Classes (S4) of commonly used elliptical, Archimedean, extreme
value and some more copula families.  Methods for density, distribution,
random number generation, bivariate dependence measures, perspective and
contour plots.  Fitting copula models including variance estimates.
Independence and serial (univariate and multivariate) independence tests,
and other copula related tests. Empirical copula and multivariate CDF.
Goodness-of-fit tests for copulas based on multipliers, the parametric
bootstrap with several transformation options.
Merged former package 'nacopula' for nested Archimedean copulas: Efficient
sampling algorithms, various estimators, goodness-of-fit tests and related
tools and special functions.
This package provides a platform where Estimation of Distribution Algorithms (EDAs) based on copulas can be implemented and studied. It contains complete implementations of various EDAs based on copulas and vines, and it also offers the possibility of implementing new algorithms. EDAs are implemented using S4 classes with generic functions for its main parts: seeding, selection, learning, sampling, replacement, local optimization, termination, and reporting. The package also provides a group of well-known benchmark problems and utility functions to study the behavior of EDAs.
Estimation and statistical process control are performed under copula-based time-series models.
This R-packages presents a bivariate, copula-based model
for the joint distribution of a pair of continuous and discrete
random variables. The two marginal random variables are modeled
via generalized linear models, and their joint distribution
(represented by a parametric copula family) is estimated using
maximum-likelihood techniques.
It has functions to implement the copula mixed models for bivariate and trivariate meta-analysis of diagnostic test accuracy studies in Nikoloulopoulos, A.K. (2015a,b).
CopyDetect contains several IRT and non-IRT based statistical indices proposed in the literature for detecting answer copying on multiple-choice examinations. CopyDetect includes the indices that have been shown as effective and reliable based on the simulation studies. CopyDetect provides results for the Omega index (Wollack, 1996), Generalized Binomial Test (van der Linden & Sotaridona, 2006), K index (Holland, 1996), K1 and K2 indices (Sotaridona & Meijer, 2002), and S1 and S2 indices (Sotaridona & Meijer, 2003).
The 450k arrays are frequently used in the epigenetic studies, the copy number calling from the 450k data is possible but it faces some difficulties in cancer samples regarding the determination of the copy number status due to the false sample centering and baseline shifting. Without solving this issue the CN calling will be inaccurate. CopyNumber450kCancer-package was designed to correct the baseline in cancer samples using the Maximum Density Peak Estimation (MDPE) method.
The main advantages for CopyNumber450kCancer-package are: Fast (few seconds per sample), high accuracy rate, in-sample correction, no input parameters needed, low computer sources required, and adaptable for 450k-similar technologies.
Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.
Generate high-dimensional correlated count random
variables with a prespecified Pearson correlation.
given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs.
This is a suite of machine learning algorithms written in C++ with R interface.
It contains several machine learning model learning techniques in classification and regression,
for example classification and regression trees with optional constructive induction and models in the leaves,
random forests, kNN, naive Bayes, and locally weighted regression.
It is especially strong in feature evaluation where it contains several variants of Relief
algorithm and many impurity based attribute evaluation functions, e.g., Gini, information gain, MDL, DKM.
These methods can be used for example to discretize numeric attributes.
Its additional strength is OrdEval algorithm and its visualization used for evaluation of data sets with
ordinal features and class enabling analysis according to the Kano model.
Several algorithms support parallel multithreaded execution via OpenMP.
The top-level documentation is reachable through ?CORElearn.
Provides a minimal interface for applying
annotators from the 'Stanford CoreNLP' java library. Methods
are provided for tasks such as tokenisation, part of speech
tagging, lemmatisation, named entity recognition, coreference
detection and sentiment analysis.
Use to analysis case-parent trio sequencing studies. Test the compound heterozygous and recessive disease models
Fits a hidden rates model that allows different transition rate classes on different portions of a phylogeny by treating rate classes as hidden states in a Markov process and various other functions for evaluating models of binary character evolution.
We proposed a new model-based clustering method, called the clustering of
regression models method(CORM), which groups genes that share a similar
relationship to the covariate(s). This method provides a unified approach
for a family of clustering procedures and can be applied to data collected
with various experimental designs. This package includes the implementation
for two such clustering procedures: (1) the Clustering of Linear Models
(CLM) method, and (2) the Clustering of Linear Mixed Models (CLMM) method.
Implements a James-Stein-type shrinkage estimator for
the covariance matrix, with separate shrinkage for variances and correlations.
The details of the method are explained in Sch\"afer and Strimmer (2005) and
Opgen-Rhein and Strimmer (2007).  The approach is both computationally as well
as statistically very efficient, it is applicable to "small n, large p" data,
and always returns a positive definite and well-conditioned covariance matrix.
In addition to inferring the covariance matrix the package also provides
shrinkage estimators for partial correlations and partial variances.
The inverse of the covariance and correlation matrix
can be efficiently computed, as well as any arbitrary power of the
shrinkage correlation matrix.  Furthermore, functions are available for fast
singular value decomposition, for computing the pseudoinverse, and for
checking the rank and positive definiteness of a matrix.
Utility functions and data sets for the statistical
analysis of corpus frequency data, used in the SIGIL statistics
course.
This package implements non-parametric analyses for clustered
binary and multinomial data. The elements of the cluster are assumed
exchangeable, and identical joint distribution (also known as marginal
compatibility, or reproducibility) is assumed for clusters of different
sizes. A trend test based on stochastic ordering is implemented.
Linear regression based on a recursive structural equation model
(explicit correlations) found by a MCMC algorithm. It permits to face
highly correlated covariates. Variable selection is included (by lasso,
elastic net, etc.). It also provides some graphical tools for basic
statistics and regression trees.
Through permutations, obtain a multivariate distribution between univariate variables by specifying a correlation matrix.
Correlplot contains diverse routines for the construction of different plots for representing correlation matrices.
Calculates correlation of variables and displays the results graphically.
Included panel functions can display points, shading, ellipses, and correlation
values with confidence intervals.
In clinical practice and research settings in medicine and the behavioral sciences, it is often of interest to quantify the correlation of a continuous endpoint that was repeatedly measured (e.g., test-retest correlations, ICC, etc.). This package allows for estimating these correlations based on mixed-effects models. 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.
The corrplot package is a graphical display of a
correlation matrix, confidence interval. It also contains some
algorithms to do matrix reordering.
Statistical summary of Structure output.
Designed for analysis of the results of a Genome Wide Association Study. Includes tools to pull lists of Chromosome number and SNP position below a certain significance threshold, refine gene networks (including data I/O for Cytoscape), and check SNP base pair changes.
To identify the globally most discriminative subnetwork from gene
expression profiles using an optimization model and genetic algorithm
cosinor is a set of simple functions that transforms longitudinal
data to estimate the cosinor linear model as described in Tong (1976).
Methods are given to summarize the mean, amplitude and acrophase, to
predict the mean annual outcome value, and to test the coefficients.
Package encapsulates standard expressions for distances,
times, luminosities, and other quantities useful in
observational cosmology, including molecular line observations.
Currently coded for a flat universe only.
User-friendly interfaces to perform fast and reliable photometric
redshift estimation. The code makes use of generalized linear models and
can adopt gamma or inverse gaussian families, either from a frequentist or
a Bayesian perspective. The code additionally provides a Shiny application
providing a simple user interface.
This package aims at the easy production of descriptive and
bivariate statistics tables in HTML. It can load files in csv, xls/xlsx
formats, and read labels and formats from SAS or plain text files. It is
intended to be usable by absolute R beginners. Ce paquet permet la
production facile de tableaux HTML descriptifs et comparatifs, meme pour
les debutants sous R.
COSSO is a new regularization method that automatically
estimates and selects important function components by a
soft-thresholding penalty in the context of smoothing spline
ANOVA models. Implemented models include mean regression,
quantile regression, logistic regression and the Cox regression
models.
Contains functions that can determine whether a time series
is second-order stationary or not (and hence evidence for
locally stationarity). Given two non-stationary series (i.e.
locally stationary series) this package can then discover
time-varying linear combinations that are second-order stationary.
Implements cointegration/cotrending rank selection
algorithm in Guo and Shintani(2011). Paper: "Consistant
Cotrending rank selection when both stochastic and nonlinear
deterministic trends are present", Preprint, Feb 2011.
Functions, data and code for Hilbe, J.M. 2011. Negative Binomial Regression, 2-nd Edition (Cambridge University Press) and Hilbe, J.M. 2014. Modeling Count Data (Cambridge University Press).
Standardize country names, convert them into one of
eleven coding schemes, convert between coding schemes, and
assign region descriptors.
Modeling under- and over-dispersed count data using extended Poisson process models (EPPM)
Estimation of latent class models with covariate effects
on underlying and measured variables. The measured variables
are dichotomous or polytomous, all with the same number of
categories.
Track and report code coverage for your package and (optionally)
upload the results to a coverage service like Codecov (http://codecov.io)
or Coveralls (http://coveralls.io).  Code coverage is a measure of the
amount of code being exercised by the tests.  It is an indirect measure of
test quality. This package is compatible with any testing methodology or
framework and tracks coverage of both R code and compiled C/C++/Fortran code.
This package fits a simultaneous regression model for the mean vectors and covariance matrices of multivariate response variables, as described in Hoff and Niu (2012). The explanatory variables can be continuous or discrete. The current version of the package provides the Bayesian estimates.
The cov.nnve() function for robust covariance estimation
by the nearest neighbor variance estimation (NNVE) method of
Wang and Raftery (2002,JASA)
Model-free selection of covariates under unconfoundedness for situations where the parameter of interest is an average causal effect. This package is based on  model-free backward elimination algorithms proposed in de Luna, Waernbaum and Richardson (2011). Marginal co-ordinate hypothesis testing is used in situations where all covariates are continuous while kernel-based smoothing appropriate for mixed data is used otherwise.
This package computes covariance test for the lasso.
Some helpful extensions and modifications to the 'ggplot2'
library. In particular, this package makes it easy to combine multiple
'ggplot2' plots into one and label them with letters, e.g. A, B, C, etc.,
as is often required for scientific publications. The package also provides
a streamlined and clean theme that is used in the Wilke lab, hence the
package name, which stands for Claus O. Wilke's plot library.
This package provides routines for fitting Cox models by
likelihood based boosting for a single endpoint or in presence
of competing risks
Fits Cox-type models based on interval-censored data from a survival
or illness-death process.
Cox proportional hazards models containing Gaussian random
effects, also known as frailty models.
Cox model regularized with net (L1 and Laplacian), elastic-net (L1 and L2) or lasso (L1) penalty. In addition, it can truncate the estimate by a hard threshold, which is selected simultaneously with other tuning parameters. Moreover, it can handle the adaptive version of these regularization forms, such as adaptive lasso and net adjusting for signs of linked coefficients. The package uses one-step coordinate descent algorithm and runs extremely fast by taking into account the sparsity structure of coefficients.
Cox regression with Firth's penalized likelihood
This package implements weighted estimation in Cox regression as proposed by
Schemper, Wakounig and Heinze (Statistics in Medicine, 2009). Weighted Cox regression
provides unbiased average hazard ratio estimates also in case of non-proportional hazards.
Approximated generalized concordance probability an effect size measure for clear-cut
decisions can be obtained.
Additionally estimation of nonlinear effects using fractional polynomials similar to the
MFP algorithm (Royston, Sauerbrei, 2008) is provided. This feature can also be used to
estimate the interaction of a covariate with a nonlinear function of time.
A package for fitting Cox models with penalized ridge-type partial likelihood. The package includes functions for fitting simple Cox models with all covariates controlled by a ridge penalty. The weight of the penalty is optimised by using a REML type-algorithm. Models with time varying effects of the covariates can also be fitted. Some of the covariates may be allowed to be fixed and thus not controlled by the penalty. There are three different penalty functions, ridge, dynamic and weighted dynamic. Time varying effects can be fitted without the need of an expanded dataset.
Fit robustly proportional hazards regression model
It fits a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.
Functions for calculating the conditional power
for different models in survival time analysis
within randomized clinical trials
with two different treatments to be compared
and survival as an endpoint.
Functions to check whether a vector of p-values respects the assumptions of FDR (false discovery rate) control procedures and to compute adjusted p-values.
The package includes functions to test and compare causal models.
This package contains methods to perform Common Principal
Component Analysis (CPCA). The stepwise method by Trendafilov is published
in the current version. Please see Trendafilov (2010). Stepwise estimation
of common principal components. Computational Statistics & Data Analysis,
54(12), 3446-3457. doi:10.1016/j.csda.2010.03.010
Functions to calculate concordance probability estimates
in survival analysis
Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest,
adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no
need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept.
Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots,
and scatterplots for individual CpG sites.
Filter CpGs based on Intra-class Correlation Coefficients (ICCs) when replicates are available. ICCs are calculated by fitting linear mixed effects models to all samples including the un-replicated samples. Including the large number of un-replicated samples improves ICC estimates dramatically. The method accommodates any replicate design.
This package computes the maximum likelihood estimator (MLE) of the shape-constrained hazard baseline and the effect parameters in the Cox proportional hazards model under IID sampling.  We assume that the data are continuous and allow for right censoring.  The function 'find.shapeMLE' allows for four different shape constraints: increasing, decreasing, unimodal, and u-shaped.
The package cpk provides simplified clinical pharmacokinetic functions for dose regimen design and modification at the point-of-care. Currently, the following functions are available: (1) ttc.fn for target therapeutic concentration, (2) dr.fn for dose rate, (3) di.fn for dosing interval, (4) dm.fn for maintenance dose, (5) bc.ttc.fn for back calculation, (6) ar.fn for accumulation ratio, (7) dpo.fn for orally administered dose, (8) cmax.fn for peak concentration, (9) css.fn for steady-state concentration, (10) cmin.fn for trough,(11) ct.fn for concentration-time predictions, (12) dlcmax.fn for calculating loading dose based on drug's maximum concentration, (13) dlar.fn for calculating loading dose based on drug's accumulation ratio, and (14) R0.fn for calculating drug infusion rate. Reference: Linares O, Linares A. Computational opioid prescribing: A novel application of clinical pharmacokinetics. J Pain Palliat Care Pharmacother 2011;25:125-135.
R Interface to C API of IBM ILOG CPLEX, depends on IBM ILOG CPLEX (>= 12.1)
Likelihood-based and Bayesian methods for various compound Poisson linear models.
Sequential and batch change detection for univariate data streams, using the change point model framework. Functions are provided to allow nonparametric distribution-free change detection in the mean, variance, or general distribution of a given sequence of observations. Parametric change detection methods are also provided for Gaussian, Bernoulli and Exponential sequences. Both the batch (Phase I) and sequential (Phase II) settings are supported, and the sequences may contain either a single or multiple change points.
We propose to determine the correction of the significance level after multiple coding of an explanatory variable in Generalized Linear Model. The different methods of correction of the p-value are the Single step Bonferroni procedure, and resampling based methods developped by P.H.Westfall in 1993. Resampling methods are based on the permutation and the parametric bootstrap procedure. If some continuous, and dichotomous transformations are performed this package offers an exact correction of the p-value developped by B.Liquet & D.Commenges in 2005. The naive method with no correction is also available.
Estimates the conditional probability function of a competing event, and fits, using the temporal process regression or the pseudo-value approach, a proportional-odds model to the conditional probability function
Estimate quantile regression(QR) and composite quantile regression (cqr) and with adaptive lasso penalty using interior point (IP), majorize and minimize(MM), coordinate descent (CD), and alternating direction method of multipliers algorithms(ADMM).
Estimation, based on conditional maximum likelihood, of the quadratic exponential model proposed by Bartolucci, F. & Nigro, V. (2010, Econometrica) and of a simplified and a modified version of this model. The quadratic exponential model is suitable for the analysis of binary longitudinal data when state dependence (further to the effect of the covariates and a time-fixed individual intercept) has to be taken into account. Therefore, this is an alternative to the dynamic logit model having the advantage of easily allowing conditional inference in order to eliminate the individual intercepts and then getting consistent estimates of the parameters of main interest (for the covariates and the lagged response). The simplified version of this model does not distinguish, as the original model does, between the last time occasion and the previous occasions. The modified version formulates in a different way the interaction terms and it may be used to test in a easy way state dependence as shown in Bartolucci, F., Nigro, V. & Pigini, C. (2013, Econometric Reviews). The package also includes estimation of the dynamic logit model by a pseudo conditional estimator based on the quadratic exponential model, as proposed by Bartolucci, F. & Nigro, V. (2012, Journal of Econometrics).
This package contains R-functions to perform power
calculation in a group sequential clinical trial with censored
survival data and possibly unequal patient allocation between
treatment and control groups. The fuctions can also be used to
determine the study duration in a clinical trial with censored
survival data as the sum of the accrual duration, which
determines the sample size in a traditional sense, and the
follow-up duration, which more or less controls the number of
events to be observed. This package also contains R functions
and methods to display the computed results.
R functions for cosmological research.
The main functions are similar to the python library, cosmolopy.
Using a sampling-based approach (either sequential importance sampling or explicit Monte Carlo), this package can be used to perform a probabilistic damage tolerance for aircraft structures. It can model a single crack, or two simultaneously growing fatigue cracks (the so-called continuing damage problem). With a single crack, multiple types of future repairs are possible.
Provides R routine for the so called two-sample
Cramer-Test.  This nonparametric two-sample-test on equality
of the underlying distributions can be applied to
multivariate data as well as univariate data. It offers two
possibilities to approximate the critical value both of which
are included in this package.
Functions for completing and recalculating rankings.
API to the database of CRAN package downloads from the RStudio
CRAN mirror. The database itself is at http://cranlogs.r-pkg.org, see
https://github.com/metacran/cranlogs.app for the raw API.
Various R tools for http://crantastic.org/
The (C)orrelated (RA)ndom (W)alk (L)ibrary of
R functions was designed for fitting continuous-time
correlated random walk (CTCRW) models with time indexed
covariates. The model is fit using the Kalman-Filter on
a state space version of the continuous-time stochastic
movement process.
Colored terminal output on terminals that support 'ANSI'
color and highlight codes. It also works in 'Emacs' 'ESS'. 'ANSI'
color support is automatically detected. Colors and highlighting can
be combined and nested. New styles can also be created easily.
This package was inspired by the 'chalk' 'JavaScript' project.
Implements a statistical test for comparing bar plots or
histograms of categorical data derived from a randomized block
repeated measures layout.
Different approaches to censored or truncated regression with
conditional heteroscedasticity are provided. First, continuous
distributions can be used for the (right and/or left censored or truncated)
response with separate linear predictors for the mean and variance.
Second, cumulative link models for ordinal data
(obtained by interval-censoring continuous data) can be employed for
heteroscedastic extended logistic regression (HXLR). In the latter type of
models, the intercepts depend on the thresholds that define the intervals.
A set of functions for computing the CreditMetrics risk model
One function to bootstrap a credit curve from market quotes (Credit Default Swap spreads) and another function to price Credit Default Swaps from a credit curve.
This package implements modeling and computational tools for
conditional random fields model (CRF) as well as other probabilistic
undirected graphical models of discrete data with pairwise and unary
potentials.
This package fits discrete mixtures of Zero-Inflated
Poisson (ZIP) models for analyzing criminal trajectories.
Statistical Methods for Crime Series Linkage. This package provides
code for criminal case linkage, crime series identification, crime series
clustering, and suspect identification.
CRM simulator for Phase I Clinical Trials
Implements the Cross-contribution Compensating Multiple
standard Normalization (CCMN) method and other normalization
algorithms.
The crn package provides the core functions required to
download and format data from the Climate Reference Network.
Both daily and hourly data are downloaded from the ftp, a
consolidated file of all stations is created, station metadata
is extracted. In addition functions for selecting individual
variables and creating R friendly datasets for them is
provided.
Contains functions for the construction of carryover
balanced crossover designs. In addition contains functions to
check given designs for balance.
This package performs a test for comparing two
multivariate distributions by using the distance between
observations.  The input is a distance matrix and the labels of
the two groups to be compared, the output is the number of
cross-matches and a p-value.
Package Crossover provides different crossover designs from combinatorial or search algorithms as well as from literature and a GUI to access them.
This package provides functions to calculate confidence intervals for crossover points of two simple linear regression lines using
the non-linear regression, the delta method, the Fieller method, and the bootstrap methods.
Contains generic functions for performing
cross validation and for computing diagnostic errors.
Modelling credit risks based on the concept of "CreditRisk+", First Boston Financial Products, 1997 and "CreditRisk+ in the Banking Industry", Gundlach & Lehrbass, Springer, 2003.
Cross-recurrence quantification analysis
of two time-series, of either categorical or
continuous values. It provides different methods
for profiling cross-recurrence, i.e., only looking
at the diagonal recurrent points, as well as more
in-depth measures of the whole cross-recurrence plot,
e.g., recurrence rate.
In competing risks regression, the proportional subdistribution hazards(PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for penalized variable selection for the PSH model. Penalties include LASSO, SCAD, MCP, and their group versions.
Extension of cmprsk to Stratified and Clustered data.
Goodness of fit test for Fine-Gray model.
Performs forward and backwards stepwise regression for the Proportional subdistribution hazards model in competing risks (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection criteria. BICcr has a penalty of k = log(n*), where n* is the number of primary events.
Regression splines that handle a mix of continuous and categorical (discrete) data often encountered in applied settings. I 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).
Sample size estimation in cluster (group) randomized trials.  Contains traditional power-based methods, empirical smoothing (Rotondi and Donner, 2009), and updated meta-analysis techniques (Rotondi and Donner, 2012).
The Crunch.io service (http://crunch.io/) provides a cloud-based
data store and analytic engine, as well as an intuitive web interface.
Using this package, analysts can interact with and manipulate Crunch
datasets from within R. Importantly, this allows technical researchers to
collaborate naturally with team members, managers, and clients who prefer a
point-and-click interface.
Functions for reading in and manipulating CRU TS3.21: Climatic Research Unit (CRU) Time-Series (TS) Version 3.21 data.
Exploratory tools to identify closely related subjects using autosomal genetic marker data.
Performs cryptographic randomness tests on a sequence of random integers or bits. Most of the included tests are not covered by standard test suites. In addition to the chi-square goodness-of-fit test, results of Anderson-Darling, Kolmogorov-Smirnov, and Jarque-Bera tests are also generated by some of the cryptographic randomness tests. Additionally, it includes two functions for the calculation of greatest common divisor and the Stirling numbers of the second kind.
Monte Carlo conditional inference for the parameters of a
linear nonnormal regression model
cSFM is a method to model skewed functional data when considering covariates via a copula-based approach.
Package for CShapes, a GIS dataset of country boundaries
(1946-2012). Includes functions for data extraction and the
computation of distance matrices and -lists.
This package contains functions for likelihood and
posterior analysis of conditionally specified logistic
regression models.  All calculus and simulation is done in
compiled FORTRAN.
Provides functions for computing the density
and the log-likelihood function of closed-skew normal variates,
and for generating random vectors sampled from this distribution.
See Gonzalez-Farias, G., Dominguez-Molina, J., and Gupta, A. (2004).
The closed skew normal distribution,
Skew-elliptical distributions and their applications: a journey beyond normality,
Chapman and Hall/CRC, Boca Raton, FL, pp. 25-42.
Includes two functions to evaluate the hypothesis of complete spatial randomness (csr) in point processes.  The function 'mwin' calculates quadrat counts to estimate the intensity of a spatial point process through the moving window approach proposed by Bailey and Gatrell (1995).  Event counts are computed within a window of a set size over a fine lattice of points within the region of observation.  The function 'pielou' uses the nearest neighbor test statistic and asymptotic distribution proposed by Pielou (1959) to compare the observed point process to one generated under csr.  The value can be compared to that given by the more widely used test proposed by Clark and Evans (1954).
Cell-type specific differential expression of a microarray
experiment of heterogeneous tissue samples, using SAM.
Functions that allow a researcher to examine the robustness of the substantive significance of their findings. Implements ideas set out in Esarey and Danneman (2014).
Functions for loading large (10M+ lines) CSV
and other delimited files, similar to read.csv, but typically faster and
using less memory than the standard R loader. While not entirely general,
it covers many common use cases when the types of columns in the CSV file
are known in advance. In addition, the package provides a class 'int64',
which represents 64-bit integers exactly when reading from a file. The
latter is useful when working with 64-bit integer identifiers exported from
databases. The CSV file loader supports common column types including
'integer', 'double', 'string', and 'int64', leaving further type
transformations  to the user.
Continuous Time Autoregressive Models and the Kalman Filter
Server-side and client-side tools for task views
to CRAN-style repositories
Adaptive multivariate integration over hypercubes
Estimating mutation and selection coefficients on synonymous
codon bias usage based on models of ribosome overhead cost (ROC).
Multinomial logistic regression and Markov Chain Monte Carlo are used to
estimate and predict protein production rates with/without the presence
of expressions and measurement errors. Work flows with examples for
simulation, estimation and prediction processes are also provided
with parallelization speedup. The whole framework is tested with
yeast genome and gene expression data of Yassour (2009).
Regression modeling using rules with added instance-based corrections
Compute Unified Device Architecture (CUDA) is a software
platform for massively parallel high-performance computing on
NVIDIA GPUs. This package provides a CUDA implementation of a
Bayesian multilevel model for the analysis of brain fMRI data.
A fMRI data set consists of time series of volume data in 4D
space. Typically, volumes are collected as slices of 64 x 64
voxels. Analysis of fMRI data often relies on fitting linear
regression models at each voxel of the brain. The volume of the
data to be processed, and the type of statistical analysis to
perform in fMRI analysis, call for high-performance computing
strategies. In this package, the CUDA programming model uses a
separate thread for fitting a linear regression model at each
voxel in parallel. The global statistical model implements a
Gibbs Sampler for hierarchical linear models with a normal
prior. This model has been proposed by Rossi, Allenby and
McCulloch in `Bayesian Statistics and Marketing', Chapter 3,
and is referred to as `rhierLinearModel' in the R-package
bayesm. A notebook equipped with a NVIDIA `GeForce 8400M GS'
card having Compute Capability 1.1 has been used in the tests.
The data sets used in the package's examples are available in
the separate package cudaBayesregData.
FMRI data sets used in the examples of "cudaBayesreg".
Data sets have been separated from the main package
"cudaBayesreg" for convenience.
Reconstruct individual-level values from aggregate-level
summaries.
Combining Univariate Association Test Results of Multiple
Phenotypes for Detecting Pleiotropy
Extends ddply to allow calculation of cumulative
quantities.
Estimation of number and location of change points in
mean-shift (piecewise constant) models. Particularly useful to
model genomic sequences of continuous measurements.
The curl() and curl_download() functions provide highly configurable
drop-in replacements for base url() and download.file() with better performance,
support for encryption (https://, ftps://), 'gzip' compression, authentication,
and other 'libcurl' goodies. The core of the package implements a framework for
performing fully customized requests where data can be processed either in memory,
on disk, or streaming via the callback or connection interfaces. Some knowledge of
'libcurl' is recommended; for a more-user-friendly web client see the 'httr' package
which builds on this package with HTTP specific tools and logic.
The currentSurvival package contains functions for the
estimation of the current cumulative incidence (CCI) and the
current leukaemia-free survival (CLFS). The CCI is the
probability that a patient is alive and in any disease
remission (e.g. complete cytogenetic remission in chronic
myeloid leukaemia) after initiating his or her therapy (e.g.
tyrosine kinase therapy for chronic myeloid leukaemia). The
CLFS is the probability that a patient is alive and in any
disease remission after achieving the first disease remission.
Test Equality of Curves with Homoscedastic or
Heteroscedastic Errors.
Filtering, also known as gating, of
flow cytometry samples using the curvHDR method.
Cobb's maximum likelihood method for cusp-catastrophe modeling
(Grasman, van der Maas, & Wagenmakers, 2009, JSS, 32:8;
Cobb, L, 1981, Behavioral Science, 26:1, 75--78).
Includes a cusp() function for model fitting, and several
utility functions for plotting, and for comparing the
model to linear regression and logistic curve models.
This package provides a set of tools for spatio-temporal imputation
in R. It includes the implementation for then CUTOFF imputation method,
a useful cross-validation function that can be used not only by the
CUOTFF method but also by some other imputation  functions to help
choosing an optimal value for relevant parameters, such as the number
of k-nearest neighbors for the KNN imputation method, or the number of
components for the SVD imputation method. It also contains tools for
simulating data with missing values with respect to some specific
missing pattern, for example, block missing. Some useful visualisation
functions for imputation purposes are also provided in the package.
This package can be used to standardize abundance indices using the delta-GLM method and to model the English Channel cuttlefish stock using a two-stage biomass model
This package contains various tools for working with and evaluating cross-validated area under the ROC curve (AUC) estimators.  The primary functions of the package are ci.cvAUC and ci.pooled.cvAUC, which report cross-validated AUC and compute confidence intervals for cross-validated AUC estimates based on influence curves for i.i.d. and pooled repeated measures data, respectively.  One benefit to using influence curve based confidence intervals is that they require much less computation time than bootstrapping methods.  The utility functions, AUC and cvAUC, are simple wrappers for functions from the ROCR package.
Statistical inferences for estimating the calibration equation with error-in observations
The package uses majorization minimization by coordinate
descent (MMCD) algorithm to compute the solution surface for
concave penalized logistic regression model.  The SCAD and MCP
(default) are two concave penalties considered in this
implementation.  For the MCP penalty, the package also provides
the local linear approximation by coordinate descant (LLA-CD)
and adaptive rescaling algorithms for computing the solutions.
The package also provides a Lasso-concave hybrid penalty for
fast variable selection. The hybrid penalty applies the concave
penalty only to the variables selected by the Lasso.  For all
the implemented methods, the solution surface is computed along
kappa, which is a more smooth fit for the logistic model.
Tuning parameter selection method by k-fold cross-validated
area under ROC curve (CV-AUC) is implemented as well.
The external prediction capability of quantitative structure-activity relationship (QSAR) models is often quantified using the predictive squared correlation coefficient. This value can be calculated with an external data set or by cross validation.
This package implements the fast cross-validation via sequential testing (CVST) procedure. CVST is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.
This package carries out level-dependent cross-validation
method for the selection of thresholding value in wavelet
shrinkage. This procedure is implemented by coupling a
conventional cross validation with an imputation method due to
a limitation of data length, a power of 2. It can be easily
applied to classical leave-one-out and k-fold cross validation.
Since the procedure is computationally fast, a level-dependent
cross validation can be performed for wavelet shrinkage of
various data such as a data with correlated errors.
Tools that allow developers to write functions for
cross-validation with minimal programming effort and assist
users with model selection.
This is a package for selecting tuning parameters based on cross-validation (CV) in regularized estimators of large covariance matrices. Four regularized methods are implemented: banding, tapering, hard-thresholding and soft-thresholding. Two types of matrix norms are applied: Frobenius norm and operator norm. Two types of CV are considered: K-fold CV and random CV. Usually K-fold CV use K-1 folds to train a model and the rest one fold to validate the model. The reverse version trains a model with 1 fold and validates with the rest with K-1 folds. Random CV randomly splits the data set to two parts, a training set and a validation set with user-specified sizes.
An iterative algorithm for solving a convex
formulation of the biclustering problem.
Alternating Minimization Algorithm (AMA) and Alternating Direction
Method of Multipliers (ADMM) splitting methods for convex clustering.
Miscellaneous useful or interesting functions. Some parameters of functions may have changed, so beware!
This package estimates gaussian cluster weighted linear regressions by EM algorithm.
extend cxxfunction by saving the dynamic shared objects
for reusing across R sessions
Tools for calculating coordinate representations of
hypocycloids, epicyloids, hypotrochoids, and epitrochoids
(altogether called 'cycloids' here) with different scaling
and positioning options. The cycloids can be visualised with
any appropriate graphics function in R.
A toolkit for querying Team Cymru <http://team-cymru.org> IP address,
Autonomous System Number (ASN), Border Gateway Protocol (BGP), Bogon
and Malware Hash Data Services.
This library contains a primary function that divides
chewing sequences in cycles and cycles into phases.  See
get.all.breaks for an example.
Calculates ecological diversity indices for a microbial
community
The relationship between statistical dependency and causality lies
at the heart of all statistical approaches to causal inference. The D2C
package implements a supervised machine learning approach to infer the
existence of a directed causal link between two variables in multivariate
settings with n>2 variables. The approach relies on the asymmetry of some
conditional (in)dependence relations between the members of the Markov
blankets of two variables causally connected. The D2C algorithm predicts
the existence of a direct causal link between two variables in a
multivariate setting by (i) creating a set of of features of the
relationship based on asymmetric descriptors of the multivariate dependency
and (ii) using a classifier to learn a mapping between the features and the
presence of a causal link
Create interactive heat maps that are usable from the R console, in the
'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps. Hover the
mouse pointer over a cell to show details, drag a rectangle to zoom, and click
row/column labels to highlight.
Two sample test based on Wasserstein metric. This is motivated from detection of differential DNA-methylation sites based on underlying distributions.
This packages is intended to make it easy to create D3 JavaScript
network, tree, dendrogram, and Sankey graphs from R using data frames.
!!! NOTE: Active development has moved to the networkD3 package. !!!
various data sets used in examples and exercises in the
book Maindonald, J.H. and Braun, W.J. (2003, 2007, 2010) "Data
Analysis and Graphics Using R".
Data sets and functions, for the display of biological
expression array (microarray) data, and for demonstrations with
such data
various data sets used in additional exercises for
the book Maindonald, J.H. and Braun, W.J. (3rd edn 2010)
"Data Analysis and Graphics Using R", and for a
'Data Mining' course.  Note that a number of datasets
that were in earlier versions of this package have been
transferred to the DAAG package.
The content falls into the following groupings: (i) Data, (ii) Factor manipulation
functions, (iii) Design functions, (iv) ANOVA functions, (v) Matrix functions,
(vi) Projector and canonical efficiency functions, and (vii) Miscellaneous functions.
A document 'daeDesignRandomization.pdf', available in the doc subdirectory of the
installation directory for 'dae', describes the use of the package for generating randomized
layouts for experiments. The ANOVA functions facilitate the extraction of information when
the 'Error' function has been used in the call to 'aov'.
Contains Data frames and functions used in the book "Design and Analysis of Experiments with R".
Diff, patch and merge for data frames. Document changes in data sets and use them to apply patches.
Changes to data can be made visible by using render_diff. The V8 package is used to
wrap the 'daff.js' JavaScript library which is included in the package.
Data and miscellanea to support the book "Introduction to
Data analysis with R for Forensic Scientists", Curran, J.M.
2010 CRC Press ISBN: 978-1-4200-8826-7
dagbag is a set of methods that learn DAGs via bootstrap aggregating.
Integrates the information from multiple linkage maps to
create a consensus directed graph, which is then linearized to
produce a consensus map.
Functions to draw, manipulate, evaluate directed
acyclic graphs and simulate corresponding data.
Several functions for evaluating the accuracy of
classification models. The package provides the following
performance measures: repeated k-fold cross-validation,
0.632 and 0.632+ bootstrap estimation of the misclassification rate,
sensitivity, specificity and AUC. If an application is
computationally intensive, parallel execution can be used
to reduce the computational effort.
Simulates and computes the (maximum) likelihood of a dynamical model of island biota assembly through speciation, immigration and extinction. See Valente et al. 2015. Ecology Letters. In press.
Functions and an example dataset for the psychometric
theory of knowledge spaces.  This package implements data
analysis methods and procedures for simulating data and quasi
orders and transforming different formulations in knowledge
space theory.
The DALY Calculator is a free, open-source Graphical User
Interface (GUI) for stochastic disability-adjusted life year
(DALY) calculation.
This package contains a miscellaneous set of functions I use in my teaching either at UWM or the ICPSR Summer Program.  Broadly, the functions help with presentation and interpretation of GLMs.
Simulates and computes (maximum) likelihood of a dynamical model of community assembly that takes into account phylogenetic history.
The single largest source of dams in the United States is the
National Inventory of Dams (NID, http://nid.usace.army.mil) from the US
Army Corps of Engineers. Entire data from the NID cannot be obtained all at
once and NID's website limits extraction of more than a couple of thousand
records at a time. Moreover, selected data from the NID's user interface
cannot not be saved to a file. In order to make the analysis of this data
easier, all the data from NID was extracted manually. Subsequently, the raw
data was checked for potential errors and cleaned. This package provides
sample cleaned data from the NID and provides functionality to access the
entire cleaned NID data.
This package contains the function used to create the Dandelion Plot. Dandelion Plot is a visualization method for R-mode Exploratory Factor Analysis
The darch package is build on the basis of the code from G. E.
Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief
nets : last visit: 01.08.2013). This package is for generating neural
networks with many layers (deep architectures) and train them with the
method introduced by the publications "A fast learning algorithm for deep
belief nets" (G. E. Hinton, S. Osindero, Y. W. Teh) and "Reducing the
dimensionality of data with neural networks" (G. E. Hinton, R. R.
Salakhutdinov). This method includes a pre training with the contrastive
divergence method publishing by G.E Hinton (2002) and a fine tuning with
common known training algorithms like backpropagation or conjugate
gradient.
The recovery of visual sensitivity in a dark environment is known as dark
adaptation.  In a clinical or research setting the recovery is typically
measured after a dazzling flash of light and can be described by the Mahroo,
Lamb and Pugh (MLP) model of dark adaptation. The functions in this package
take dark adaptation data and use nonlinear regression to find the parameters
of the model that 'best' describe the data. They do this by firstly,
generating rapid initial objective estimates of data adaptation parameters,
then a multi-start algorithm is used to reduce the possibility of a local
minimum. There is also a bootstrap method to calculate parameter confidence
intervals. The functions rely upon a 'dark' list or object. This object is
created as the first step in the workflow and parts of the object are
updated as it is processed.
Are you aiming at the right spot in darts? Maybe not! Use
this package to find your optimal aiming location. For a better
explanation, go to
http://www-stat.stanford.edu/~ryantibs/darts/ or see the paper
"A Statistician Plays Darts".
The dashboard package allows users to create web pages which display
interactive data visualizations working in a standard modern browser. It displays them locally
using the Rook server. Nor knowledge about web technologies nor Internet connection are
required. D3.js is a JavaScript library for manipulating documents based on data.
D3 helps the dashboard package bring data to life using HTML, SVG and CSS.
Provides an interface to the C++ FILEVECTOR library
facilitating analysis using large (giga- to tera-bytes) matrices.
Matrix storage is organized in a way that either columns or rows
are quickly accessible. DatABEL is primarily aimed to support
genome-wide association analyses e.g. using GenABEL, MixABEL and
ProbABEL.
Functions to check variables against a
set of data quality rules. A rule file can be accompanied by look-up tables. In
addition, there are some convenience functions that may
serve as an example for defining clearer 'data rules'. An
HTML based user interface facilitates initial exploration of the
functionality.
Tools for combining and cleaning data sets, particularly
with grouped and time series data.
dataframes2xls writes data frames to xls files. It
supports multiple sheets and basic formatting.
datalist is an R package to 1) convert data sets into numeric data
lists for import into JAGS, WinBUGS and OpenBUGS and 2) generate data.frames
that can be passed to models for the purposes of predicting the effects of
particular variables with the other variables held constant.
datamap utilizes variable bindings and objects of class
"UserDefinedDatabase" to provide a simple mapping system to
foreign objects. Maps can be used as environments or attached
to the search path, and changes to either are persistent.
Mapped foreign objects are fetched in real-time and are never
cached by the mapping system.
Provides an S4 infrastructure for unified handling
of internal datasets and web based data sources. The package is
currently in beta; things may break, change or go away without
warning.
The package performs variable level data quality checks including
missing values, unique values, frequency tables, and generates summary
statistics
Collection of functions to help retrieve U.S. Geological Survey
(USGS) and U.S. Environmental Protection Agency (EPA) water quality and
hydrology data from web services. USGS web services are discovered from the
National Water Information System (NWIS) tools. Both EPA and USGS water
quality data are obtained from the Water Quality Portal.
Based on a SAS data step. This allows for row-wise dynamic building of data, iteratively importing slices of existing dataframes, conducting analyses, and exporting to a results frame. This is particularly useful for differential or time-series analyses, which are often not well suited to vector-based operations.
Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns and a fast file reader (fread). Offers a natural and flexible syntax, for faster development.
Create tree structures from hierarchical data, and use the utility
methods to traverse the tree in various orders. Aggregate, print, convert to and from data.frame,
and apply functions to your tree data. Useful for decision trees, machine learning, finance, and
many other applications.
Support functions for packages VBmix, semisupKernelPCA,
and patchPlot.
Tools for deciphering the contents of
unknown objects or environments from within the terminal,
a problem often encountered when working with unfamiliar packages or debugging complex functions.
If working in xterm256 or ANSI terminals the output is coloured by default
to improve readability (e.g. the standard Ubuntu terminal).
This package performs voxel-wise deconvolution analysis of
DCE-MRI contrast agent concentration versus time data and
generates the Impulse Response Function, which can be used to
approximate commonly utilized kinetic parameters such as Ktrans
and ve.  An interactive advanced voxel diagnosis tool (AVDT) is
also provided to facilitate easy navigation of voxel-wise data.
A collection of functions accompanying the book "Data Analysis in Vegetation Ecology", Second edition. 2013, Wiley-Blackwell, Chichester
Various utilities for the Davies distribution and the
generalized lambda distribution
This package performs linear and quadratic discriminant analysis with additional information expressed as inequality restrictions among the populations means and computes several estimations of the true error rate
Fits Bayesian additive regression trees (BART) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/MH sampler. Also serves as a drop-in replacement for package BayesTree.
Using widgets provided by gWidgets this creates a nice
usable interface to help facilitate the learning of SQL.  It
also just gives a nice interface for more advanced users.  Not
all features are provided in the GUI but it is possible for
more advanced users to achieve these goals inside the GUI.
Goodness-of-fit and two sample comparison tests using sample entropy
This package provides methods and examples to support a
method of Gene Set Analysis (GSA).  DBGSA is a novel
distance-based gene set enrichment analysis method. We consider
that, the distance between 2 groups with different phenotype by
focusing on the gene expression should be larger, if a certain
gene functional set was significantly associated with a
particular phenotype.
A database interface (DBI) definition for communication
between R and relational database management systems.  All
classes in this package are virtual and need to be extended by
the various R/DBMS implementations.
This package allows for nonparametric graduation of mortality rates using fixed or adaptive discrete beta kernel estimator.
Use EM algorithm to compute the NPMLE of CDF and also the
two censoring distributions. For doubly censored data (as
described in Chang and Yang (1987) Ann. Stat. 1536-47). You can
also specify a constraint, it will return the constrained NPMLE
and the -2 log empirical likelihood ratio. This can be used to
test the hypothesis about the constraint and find confidence
intervals for probability or quantile via empirical likelihood
ratio theorem. Influence function of hat F may also be
calculated (but may be slow).
Simple computation of spatial statistic functions of distance to characterize the spatial structures of mapped objects, including classical ones (Ripley's K and others) and more recent ones used by spatial economists (Duranton and Overman's Kd, Marcon and Puech's M). Relies on spatstat for some core calculation.
db.r uses OOP style objects (with an API akin to C++, Python, Ruby,
Javascript, etc.) to help make visualizing the tables, columns, and
available operations on your database easier. db.r dynamically loads
database schemas, execute queries, and manage credentials.
A fast reimplementation of the density-based DBSCAN clustering algorithm for spatial data introduced by Ester et al. 'A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,' 1996. This implementation uses the kd-tree data structure (from library ANN) for faster k-nearest neighbor search. The implementation is many times faster than the R-based implementation in package fpc.
Prediction methods where explanatory information is coded as a matrix of distances between individuals. Distances can either be directly input as a distances matrix, a squared distances matrix, an inner-products matrix or computed from observed predictors.
A collection of routines and documentation that allows one to
perform voxel-wise quantitative analysis of dynamic contrast-enhanced MRI
(DEC-MRI) and diffusion-weighted imaging (DWI) data, with emphasis on
oncology applications.
Functions for 1) gene filtration; 2) link filtration; 3) differential co-expression analysis: DCG (Differential Coexpressed
Gene) identification and DCL (Differentially Coexpressed Link)
identification; and 4) differential regulation analysis: DRG (Differential Regulated
Gene) identification, DRL (Differential Regulated Link)
identification, DRL visualization and regulator ranking.
There lacks a package for analysing domain-centric ontologies and annotations, particularly those in the dcGO database. The dcGO (http://supfam.org/SUPERFAMILY/dcGO) is a comprehensive domain-centric database for annotating protein domains using a panel of ontologies including Gene Ontology. With the package, users are expected to analyse and visualise domain-centric ontologies and annotations. Supported analyses include but are not limited to: easy access to a wide range of ontologies and their domain-centric annotations; able to build customised ontologies and annotations; domain-based enrichment analysis and visualisation; construction of a domain (semantic similarity) network according to ontology annotations; significance analysis for estimating a contact (statistical significance) network via Random Walk with Restart; and high-performance parallel computing. The new functionalities are: 1) to create domain-centric ontologies; 2) to predict ontology terms for input protein sequences (precisely domain content in the form of architectures) plus to assess the predictions; 3) to reconstruct ancestral discrete characters using maximum likelihood/parsimony.
Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).
Low level functions for implementing
maximum likelihood estimating procedures for
complex models using data cloning and Bayesian
Markov chain Monte Carlo methods.
Sequential and parallel MCMC support
for JAGS, WinBUGS and OpenBUGS.
A set of functions for the detection of spatial clusters
of disease using count data. Bootstrap is used to estimate
sampling distributions of statistics.
S4 classes around infrastructure
provided by the dclone package to make package
development with data cloning for hierarchical
models easy as a breeze.
Analysis of dichotomous response data to obtain attribute profile
estimates for respondents using Diagnostic Classification Model (DCM) and
Markov Chain Monte Carlo (MCMC) method. The estimation procedure uses a
loglinear cognitive diagnostic modeling (LDCM) framework that allows for
the estimation of a host of DCMs such as NIDO, NIDA, NC-RUM etc.
This package performs several conventional
Cross-validation statistical methods for climate-growth model
in the climate reconstruction from tree rings, including Sign
Test statistic, Reduction of Error statistic, Product Mean
Test, Durbin-Watson statistic etc. This package is at its
primary stage, the functions have not been tested exhaustively
and more functions would be added in the comming days.
Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.
Implements maximum likelihood methods based on the diversity-dependent birth-death process to test whether speciation or extinction are diversity-dependent.
See Etienne et al. 2012, Proc. Roy. Soc. B 279: 1300-1309, doi: 10.1098/rspb.2011.1439.
Also implements maximum likelihood methods to detect various types of key innovations in the light of diversity-dependence.
See Etienne & Haegeman 2012, Am. Nat. 180: E75-E89, doi: 10.1086/667574.
Finally, DDD contains a function to simulate the diversity-dependent process.
A wrapper for the Duke Analytics model deployment API. See <http://www.dukeanalytics.com/products/dukedeployR.php> for more details.
The package contains the normalizing and variance stabilizing
Data-Driven Haar-Fisz algorithm. Also contains related algorithms
for simulating from certain microarray gene intensity models and
evaluation of certain transformations.
This package provides useful functions for various DDI (Data Documentation Initiative) related outputs.
Bayesian networks with continuous and/or discrete
variables can be learned and compared from data.
deamer provides deconvolution algorithms for the
non-parametric estimation of the density f of an error-prone
variable x with additive noise e. The model is y = x + e where
the noisy variable y is observed, while x is unobserved.
Estimation may be performed for i) a known density of the error
ii) with an auxiliary sample of pure noise and iii) with an
auxiliary sample of replicate (repeated) measurements.
Estimation is performed using adaptive model selection and
penalized contrasts.
Debugger for R functions, with code display, graceful
error recovery, line-numbered conditional breakpoints, access
to exit code, flow control, and full keyboard input.
Provides functions for retrieving energy statistics from
the United Kingdom Department of Energy and Climate Change and
related data sources.  The current version focuses on total final
energy consumption statistics at the local authority, MSOA, and
LSOA geographies.  Methods for calculating the generation mix of
grid electricity and its associated carbon intensity are also
provided.
Calculates various estimates for measures of educational
differentials, the relative importance of primary and secondary
effects in the creation of such differentials and compares the
estimates obtained from two datasets.
Supporting the quantitative analysis of binary welfare based
decision making processes using Monte Carlo simulations. Decision support
is given on two levels: (i) The actual decision level is to choose between
two alternatives under probabilistic uncertainty. This package calculates
the optimal decision based on maximizing expected welfare. (ii) The meta
decision level is to allocate resources to reduce the uncertainty in the
underlying decision problem, i.e to increase the current information to
improve the actual decision making process. This problem is dealt with
using the Value of Information Analysis. The Expected Value of
Information for arbitrary prospective estimates can be calculated as
well as Individual and Clustered Expected Value of Perfect Information.
The probabilistic calculations are done via Monte Carlo
simulations. This Monte Carlo functionality can be used on its own.
Integrated differential expression (DE) and differential co-expression (DC) analysis on gene expression data based on DECODE (DifferEntial CO-expression and Differential Expression) algorithm.
Two global-value-chain decompositions are implemented.
Firstly, the Wang-Wei-Zhu (Wang, Wei, and Zhu, 2013) algorithm splits bilateral gross
exports into 16 value-added components. Secondly, the Leontief decomposition
(default) derives the value added origin of exports by country and
industry, which is also based on Wang, Wei, and Zhu
(Wang, Z., S.-J. Wei, and K. Zhu. 2013. "Quantifying International Production Sharing at the Bilateral and Sector Levels.").
This package contains a collection of functions to deal
with nonparametric measurement error problems using
deconvolution kernel methods. We focus two measurement error
models in the package: (1) an additive measurement error model,
where the goal is to estimate the density or distribution
function from contaminated data; (2) nonparametric regression
model with errors-in-variables. The R functions allow the
measurement errors to be either homoscedastic or
heteroscedastic. To make the deconvolution estimators
computationally more efficient in R, we adapt the "Fast Fourier
Transform" (FFT) algorithm for density estimation with
error-free data to the deconvolution kernel estimation. Several
methods for the selection of the data-driven smoothing
parameter are also provided in the package. See details in:
Wang, X.F. and Wang, B. (2011). Deconvolution estimation in
measurement error models: The R package decon. Journal of
Statistical Software, 39(10), 1-24.
An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.
Added functionality for Deducer. This package includes
additional dialogs for calculating distribution function
values, cluster analysis and more.
A example GUI plug-in package to serve as a template.
A Deducer plug-in for factor analysis, reliability
analysis and discriminant analysis, using psych, GPArotation
and mvnormtest packages.
A Deducer plug-in for spatial data analysis. Includes The
ability to plot and explore open street map and Bing satellite
images.
Adds Kaplan-Meier Survival Analysis to the Deducer GUI
A collection of methods for automated data cleaning where all actions are logged.
Implement some deep learning architectures and neural network
algorithms, including BP,RBM,DBN,Deep autoencoder and so on.
Tests for, and describe differences in event count profiles in groups of reconstructed cophylogenies
Detection of differentially expressed genes between two
distinct groups of samples.
Likelihood-based inference for skewed count distributions used in network modeling. "degreenet" is a part of the "statnet" suite of packages for network analysis.
Provides probability mass, distribution, quantile, random-variate generation, and method-of-moments parameter-estimation functions for the Delaporte distribution. The Delaporte is a discrete probability distribution which can be considered the convolution of a negative binomial distribution with a Poisson distribution. Alternatively, it can be considered a counting distribution with both Poisson and negative binomial components. It has been studied in actuarial science as a frequency distribution which has more variability than the Poisson, but less than the negative binomial.
Calculates the Delaunay triangulation and the Dirichlet
or Voronoi tessellation (with respect to the entire plane) of
a planar point set. Plots triangulations and tessellations in
various ways.  Clips tessellations to sub-windows. Calculates
perimeters of tessellations.
We implement methods for estimating multivariate densities.
We include a discretized kernel estimator,
an adaptive histogram (a greedy histogram and a CART-histogram),
stagewise minimization, and bootstrap aggregation.
The deltaPlotR package proposes an implementation of Angoff's Delta Plot method to detect dichotomous DIF. Several detection thresholds are included, either from multivariate normality assumption or by prior determination. Item purification is supported.
Functions to calculate pairwise relatedness on diploid genetic datasets. Different estimators for relatedness can be combined with information on geographical distances. Information on heterozygosity, allele- and genotype diversity as well as genetic F-statistics are provided for each population.
This package allows to calculate the fixation index Gst
(Nei, 1973) and the differentiation index D (Jost, 2008) pairwise
between or averaged over several populations.  P-values, stating the
significance of differentiation, and 95 percent confidence intervals
can be estimated using bootstrap resamplings. In the case that more
than two populations are compared pairwise, the p-values are
adjusted by bonferroni correction and in several other ways due to
the multiple comparison from one data set.
Implementation of the DEMI method for the analysis of high-density microarray data.
Generalized Deming regression, Theil-Sen regression and Passing-Bablock regression functions.
Functions for demographic analysis including lifetable
calculations; Lee-Carter modelling; functional data analysis of
mortality rates, fertility rates, net migration numbers; and
stochastic population forecasting.
Demonstration code showing how (univariate) kernel
density estimates are computed, at least conceptually,
and allowing users to experiment with different kernels,
should they so wish.  NOTE: the density function in the
stats package should be used for computational efficiency.
Offers a set of functions for extending
dendrogram objects in R, letting you visualize and compare trees of
hierarchical clusterings. You can (1) Adjust a trees graphical parameters
- the color, size, type, etc of its branches, nodes and labels. (2)
Visually and statistically compare different dendrograms to one another.
Offers faster manipulation of dendrogram objects in
R. A dendrogram object in R is a list structure with attributes in its
nodes and leaves. Working with dendrogram objects often require a function
to recursively go through all (or most) element in the list object.
Naturally, such function are rather slow in R, but can become much faster
thanks to 'Rcpp'.
This package provides extra functions to manipulate dendrograms
that build on the base functions provided by the stats package. The main
functionality it is designed to add is the ability to colour all the edges
in an object of class dendrogram according to cluster membership i.e. each
subtree is coloured, not just the terminal leaves. In addition it provides
some utility functions to cut dendrogram and hclust objects and to set/get
labels.
Re-arranges a dendrogram to optimize visualisation-based cost functions
This package includes functions to optimize ordering of nodes in a dendrogram,
without affecting the meaning of the dendrogram. A dendrogram can be sorted based
on the average distance of subtrees, or based on the smallest distance value.
These sorting methods improve readability and interpretability of tree structure,
especially for tasks such as comparison of different distance measures or linkage
types and identification of tight clusters and outliers. As a result, it also
introduces more meaningful reordering for a coupled heatmap visualization.
An integrated toolset for the analysis of de novo (sporadic) genetic sequence variants.  denovolyzeR implements a mutational model that estimates the probability of a de novo genetic variant arising in each human gene, from which one can infer the expected number of de novo variants in a given population size.  Observed variant frequencies can then be compared against expectation in a Poisson framework.  denovolyzeR provides a suite of functions to implement these analyses for the interpretation of de novo variation in human disease.
We provide tools to
(1) visualize multivariate density functions and density estimates
with level set trees,
(2) visualize level sets with shape trees,
(3) visualize multivariate data with tail trees,
(4) visualize scales of multivariate density estimates with
mode graphs and branching maps, and
(5) visualize anisotropic spread with 2D volume functions and
2D probability content functions.
Level set trees visualize mode structure,
shape trees visualize shapes of level sets of unimodal densities,
and tail trees visualize connected data sets.
The kernel estimator is implemented
but the package may  also be applied for visualizing other density estimates.
An implementation of the clustering algorithm described by Alex
Rodriguez and Alessandro Laio (Science, 2014 vol. 344), along with tools to
inspect and visualize the results.
Implementation in the density framework of the non-combinatorial algorithm and its greedy version, introduced by Magalhães and Rozenholc (2014), for T-estimation Hold-Out proposed in Birgé (2006, Section 9). The package provide an implementation which uses several families of estimators (regular and irregular histograms, kernel estimators) which may be used alone or combined. As a complement, provides also a comparison with other Held-Out derived from least-squares and maximum-likelihood. This package implements also the T-estimation Hold-Out derived from the test introduced in Baraud (2011).
Graphical methods for compactly illustrating probability distributions, including density strips, density regions, sectioned density plots and varying width strips.
Implements the differential evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector.
An implementation of the jDE variant of the Differential Evolution
stochastic algorithm for global optimization of nonlinear programming problems.
Suppose that one can observe bivariate random variables (X, Y) only when X<=Y holds. Data (Xj, Yj), subject to Xj<=Yj, for all j=1,...,n, are called truncated data. Parametric approach (Emura & Konno 2012 Stat Papers), semiparametric approach (Chaieb et al. 2006 Biometrika; Emura et al. 2011 Sinica; Emura & Murotani 2015 TEST), nonparametric maximum likelihood approach (Emura & Wang 2012 JMVA), and regression approach (Emura & Wang 2015 AISM) are implemented for statistical inference on (X, Y), when X and Y are dependent. Also included is truncated data on the number of deaths at each year (1963-1980) for Japanese male centenarians (Emura & Murotani 2015 TEST).
Fit (multigroup) mixtures of latent Markov models on mixed categorical and continuous (timeseries) data. The Rdonlp2 package can optionally be used for optimization of the log-likelihood and is available from R-forge: https://r-forge.r-project.org/R/?group_id=156
Fit latent (hidden) Markov models on mixed categorical and continuous (time series)
data, otherwise known as dependent mixture models
Depth functions methodology applied to multivariate
analysis. Besides allowing calculation of depth values and
depth-based location estimators, the package includes functions
for drawing contour plots and perspective plots of depth
functions. Euclidian and spherical depths are supported.
Data depth concept offers a variety of powerful and user friendly tools for robust exploration and inference for multivariate data. The offered techniques may be successfully used in cases of lack of our knowledge on parametric models generating data due to their nonparametric nature. The package consist of among others implementations of several data depth techniques involving multivariate quantile-quantile plots, multivariate scatter estimators, multivariate Wilcoxon tests and robust regressions.
depthTools is a package that implements different
statistical tools for the description and analysis of gene
expression data based on the concept of data 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. All these techniques are based on the Modified
Band Depth, which is a recent notion of depth with a low
computational cost, what renders it very appropriate for high
dimensional data such as gene expression data.
Offers a special data structure called a 'deque'
(pronounced like 'deck'), which is a list-like structure.  However,
unlike R's list structure, data put into a 'deque' is not necessarily
stored contiguously, making insertions and deletions at the front/end
of the structure much faster.  The implementation here is new and
uses a doubly linked list, and whence does not rely on R's
environments.  To avoid unnecessary data copying, most 'deque'
operations are performed via side-effects.
R-based solution for symbolic differentiation. It admits
user-defined function as well as function substitution
in arguments of functions to be differentiated. Some symbolic
simplification is part of the work.
Decompose a time series into seasonal, trend and irregular components using transformations to amplitude-frequency domain.
Weighted frequency and contingency tables of categorical
variables and of the comparison of the mean value of a numerical
variable by the levels of a factor, and methods to produce xtable
objects of the tables and to plot them. There are also functions to
facilitate the character encoding conversion of objects, to quickly
convert fixed width files into csv ones, and to export a data.frame to
a text file with the necessary R and SPSS codes to reread the data.
Produce publication quality graphics from output of GGobi's
describe display plugin
A collection of basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'camel style' was consequently applied to functions borrowed from contributed R packages as well.
Deseasonalize daily or monthly time series.
The package provides R scripts for designing genetical
genomics experiments.
Functions for (1) ranking, selection, and prioritisation of genes,
proteins, and metabolites from high dimensional biology experiments, (2)
multivariate hit calling in high content screens, and (3) combining data
from diverse sources.
S3 classes for multivariate optimization using the desirability function by Derringer and Suich (1980).
Harrington and Derringer-Suich type desirability functions
This package implements a statistical data mining method to
compare whole genome gene expression profiles, with respect to the presence
of a recurrent genetic disturbance event, to identify the affected target
genes.
Functions that solve initial value problems of a system
of first-order ordinary differential equations (ODE), of
partial differential equations (PDE), of differential
algebraic equations (DAE), and of delay differential
equations.  The functions provide an interface to the FORTRAN
functions lsoda, lsodar, lsode, lsodes of the ODEPACK
collection, to the FORTRAN functions dvode and daspk and a
C-implementation of solvers of the Runge-Kutta family with
fixed or variable time steps.  The package contains routines
designed for solving ODEs resulting from 1-D, 2-D and 3-D
partial differential equations (PDE) that have been converted
to ODEs by numerical differencing.
Several estimators of the diagonal elements of a sparse precision
(inverse covariance) matrix from a sample of Gaussian vectors for a
given matrix of estimated marginal regression coefficients.
To install package 'gurobi', instructions at
http://user.gurobi.com/download/gurobi-optimizer and
http://www.gurobi.com/documentation/6.0/refman/r_api_overview.html.
The package implements models to analyze
site occupancy and count data models with detection error.
The package development was supported by the
Alberta Biodiversity Monitoring Institute (ABMI)
and the Boreal Avian Modelling (BAM) Project.
Solvers and testset for stiff and nonstiff differential equations, and
differential algebraic equations
DetMCD is a new algorithm for robust and deterministic estimation of location and scatter. The benefits of robust and deterministic estimation are explained in Hubert, M., Rousseeuw, P.J. and Verdonck, T. (2012),"A deterministic algorithm for robust location and scatter", Journal of   Computational and Graphical Statistics, Volume 21, Number 3, Pages 618--637.
Simple GUI to perform some standard tree-ring analyses.
In the new era of population genomics, surveys of genetic
polymorphism ("genome scans") offer the opportunity to
distinguish locus-specific from genome wide effects at many
loci. Identifying presumably neutral regions of the genome that
are assumed to be influenced by genome-wide effects only, and
excluding presumably selected regions, is therefore critical to
infer population demography and phylogenetic history reliably.
Conversely, detecting locus-specific effects may help identify
those genes that have been, or still are, targeted by natural
selection. The software package DetSel has been developed to
identify markers that show deviation from neutral expectation
in pairwise comparisons of diverging populations.
Output graphics to EMF (enhanced metafile).
Data sets and sample analyses from Jay L. Devore (2008),
"Probability and Statistics for Engineering and the Sciences
(7th ed)", Thomson.
Collection of package development tools.
It handles numerics, characters, factors, and logicals.
Phase I/II adaptive dose-finding design for combination
studies. Several methods are proposed depending on the type of
combinations: (1) the combination of two cytotoxic agents, and (2)
combination of a molecularly targeted agent with a cytotoxic agent.
This package provides functions to run the CRM and
TITE-CRM in phase I trials and calibration tools for trial
planning purposes.
Quickly and graphically show missing values and classes of each variable and each observation of a data.frame. The aim is to show pattern of missing values and if there is wrong class attribution.
A set of functions to perform Raju, van der Linden and Fleer's
(1995) Differential Functioning of Items and Tests (DFIT) analyses. It includes
functions to use the Monte Carlo Item Parameter Replication  approach
(Oshima, Raju, & Nanda, 2006) for obtaining the associated statistical
significance tests cut-off points. They may also be used for a priori and
post-hoc power calculations.
Phase I/II adaptive dose-finding design for single-agent
Molecularly Targeted Agent (MTA), according to the paper "Phase
I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau
Determination using Adaptive Randomization" (in review).
Derivative-Free optimization algorithms. These algorithms
do not require gradient information. More importantly, they can
be used to solve non-smooth optimization problems.
Performs Bayesian model averaging for capture-recapture. This includes code to stratify records, check the strata for suitable overlap to be used for capture-recapture, and some functions to plot the estimated population size.
dglars implements the differential geometric extension
of the least angle regression method for logistic and Poisson
regression models. To compute the implicitly defined solution
curve, two different algorithms are implemented, i.e. the
predictor-corrector method and the cyclic coordinate descent
method.
Fitting double  generalized linear models
Random data generation for PLS structural models.
This package contains a proposed revision to the
stats::ks.test() function and the associated ks.test.Rd help
page.  With one minor exception, it does not change the
existing behavior of ks.test(), and it adds features necessary
for doing one-sample tests with hypothesized discrete
distributions.  The package also contains cvm.test(), for doing
one-sample Cramer-von Mises goodness-of-fit tests.
The dhglm package fits double hierarchical generalized
linear models in which the mean, dispersion parameters for
variance of random effects, and residual variance
(overdispersion) can be further modeled as random-effect
models.
Several tools for handling block-matrix diagonals and similar constructs are implemented. Block-diagonal matrices can be extracted or removed using two small functions implemented here. In addition, non-square matrices are supported. Block diagonal matrices occur when two dimensions of a data set are combined along one edge of a matrix. For example, trade-flow data in the 'decompr' and 'gvc' packages have each country-industry combination occur along both edges of the matrix.
Visualises simple graphs (networks) based on a transition matrix, utilities to plot flow diagrams,
visualising webs, electrical networks, ...
Support for the books "A practical guide to ecological modelling -
using R as a simulation platform"
by Karline Soetaert and Peter M.J. Herman (2009). Springer.
and the book "Solving Differential Equations in R"
by Karline Soetaert, Jeff Cash and Francesca Mazzia. Springer.
Includes demo(flowchart), demo(plotmat), demo(plotweb)
Create graph diagrams and flowcharts using R.
The R package provides utilities to estimate and make inference on two summary measures for diagnostic tests when there are three ordinal groups ----volume under ROC surface (VUS) and the extended Youden index, assuming that diagnostic markers increase monotonically and stochastically with disease severity. Point estimates, variance, confidence interval and optimal cut-points both under the normal assumption and also the non-parametric method(s) will be provided for the summary measures. Statistical tests are implemented to compare multiple diagnostic tests and two diagnostic test.  Sample size is calculated to estimate the summary measures within a user-specified margin of error for future study planning.
Visualize one-factor data frame.
Beads plot consists of diamonds of each factor of each data series.
A diamond indicates average and range.
Look over a data frame with many numeric columns and a factor column.
This package provides utilities to calculate the probabilities of various dice-rolling events, such as the probability of rolling a four-sided die six times and getting a 4, a 3, and either a 1 or 2 among the six rolls (in any order); the probability of rolling two six-sided dice three times and getting a 10 on the first roll, followed by a 4 on the second roll, followed by anything but a 7 on the third roll; or the probabilities of each possible sum of rolling five six-sided dice, dropping the lowest two rolls, and summing the remaining dice.
Space-Filling Designs and Uniformity Criteria.
Estimation, validation and prediction of models of different types : linear models, additive models, MARS,PolyMARS and Kriging.
Estimation, validation and prediction of kriging models.
Important functions : km, print.km, plot.km, predict.km.
Expected Improvement. EGO algorithm. Multipoint EI and parallelized versions of EGO. Criteria and algorithms for Noisy Kriging-based Optimization , including AEI, AKG, EQI, and EI with plugin.
View 2D/3D sections or contours of computer experiments designs, surrogates or test functions.
Collapse red-green or green-blue distinctions to simulate
the effects of different types of color-blindness.
This package contains the dictionaries for reading microdata
from IBGE (Brazilian Institute of Geography and Statistics)
surveys PNAD, PME and POF.
Physical constants. Gold, silver and glass permittivities,
together with spline interpolation functions.
A method for identifying pattern changes between 2 experimental
conditions in correlation networks (e.g., gene co-expression networks),
which builds on a commonly used association measure, such as Pearson's
correlation coefficient. This package includes functions to calculate
correlation matrices for high-dimensional dataset and to test
differential correlation, which means the changes in the correlation
relationship among variables (e.g., genes and metabolites) between 2
experimental conditions.
The package includes functions to calculate confidence
intervals for the difference of dependent proportions. There
are two functions implemented to edit the data (dichotomising
with the help of cutpoints, counting accordance and discordance
of two tests or situations). For the calculation of the
confidence intervals entries of the fourfold table are needed.
Functions and examples from the book Solving Differential
Equations in R by Karline Soetaert, Jeff R Cash and Francesca Mazzia.
Springer, 2012.
Metrics of difference for comparing pairs of maps representing real or categorical variables at original and multiple resolutions.
Package to fit diffusion-based IRT models to response and
response time data. Models are fitted using marginal maximum
likelihood. Parameter restrictions (fixed value and equality
constraints) are possible. In addition, factor scores (person drift
rate and person boundary separation) can be estimated. Model fit
assessment tools are also available. The traditional diffusion model
can be estimated as well.
Residual-based baseline identification and peak decomposition for x-ray diffractograms as introduced in Davies/Gather/Mergel/Meise/Mildenberger (2008).
Implements diffusion map method of data
parametrization, including creation and visualization of
diffusion map, clustering with diffusion K-means and
regression using adaptive regression model.
A package to perform DIFlasso, a method to detect DIF (Differential Item Functioning) in Rasch Models. It can handle settings with many covariates and also metric covariates.
Collection of traditional methods to detect differential item functioning among dichotomously scored items.
Item focused recursive partitioning for simultaneous selection of
items and variables that induce Differential Item Functioning (DIF) in Item Response models.
Implementation of a function 'digest()' for the creation
of hash digests of arbitrary R objects (using the md5, sha-1, sha-256,
crc32, xxhash and murmurhash algorithms) permitting easy comparison of R
language objects, as well as a function 'hmac()' to create hash-based
message authentication code.

The md5 algorithm by Ron Rivest is specified in RFC 1321, the sha-1
and sha-256 algorithms are specified in FIPS-180-1 and FIPS-180-2,
and the crc32 algorithm is described in
ftp://ftp.rocksoft.com/cliens/rocksoft/papers/crc_v3.txt.

For md5, sha-1, sha-256 and aes, this package uses small standalone
implementations that were provided by Christophe Devine. For crc32, code
from the zlib library is used. For sha-512, an implementation by Aaron
D. Gifford is used. For xxHash, the implementation by Yann Collet is used.
For murmurhash, an implementation by Shane Day is used.

Please note that this package is not meant to be deployed for
cryptographic purposes for which more comprehensive (and widely
tested) libraries such as OpenSSL should be used.
Digiroo2 is an R package developed by researchers at the
University of Queensland to investigate association patterns
and social structure in wild animal populations. Proximity
between individuals is generally considered to be an
appropriate proxy for associations and pairwise association
indices are the most widely used technique for analysing animal
social structure. However, little attention is given to
identifying how patterns of spatial overlap affect these
association patterns. For example, do individuals associate
randomly with others with whom they share home ranges, or do
some individuals go out of their way to associate with or avoid
particular individuals? This program builds a null model of
random associations based on an individual's space use
determined using home range methodologies. Random points may be
generated within a specified home range contour or according to
the Utilization Distribution (UD). Expected associations of
individuals are extracted based on probability of occurrence
and the proximity between home range weighted random points.
Association matrices can be generated from multiple
permutations for analysis using SOCPROG 2.4 (Whitehead 2009) to
create 'expected' pairwise half-weight association indices
(HWIs). These may be compared with the 'observed' HWIs from
field observations to reveal whether pairs of animals associate
more (= attraction) or less (= avoidance) than expected by
chance.
The assay sensitivity is the minimum number of copies that the digital PCR assay can detect. Users provide serial dilution results in the format of counts of positive and total reaction wells. The output is the estimated assay sensitivity and the copy number per well in the initial dilute.
Combine multiple-relationship networks into a single weighted
network.  The approach is similar to factor analysis in the that
contribution from each constituent network varies so as to maximize the
information gleaned from the multiple-relationship networks.
This implementation uses Principal Component Analysis calculated using
'prcomp' with bootstrap subsampling.  Missing links are imputed using
the method of Chen et al.
(2012).
A robust differential identification method that considers an ensemble of finite mixture models combined with a local false discovery rate (fdr) to analyze ChIP-seq (high-throughput genomic)data comparing two samples allowing for flexible modeling of data.
This function implements the DiNAMIC procedure for
assessing the statistical significance of recurrent DNA copy
number aberrations (Bioinformatics (2011) 27(5) 678 - 685).
Compute Hartigan's dip test statistic for unimodality /
multimodality and provide a test with simulation based p-values,  where
the original public code has been corrected.
Implements the clustering method in Fu, Russell, Bray and
Tavare: Bayesian clustering with the Dirichlet-process prior.
Estimates the number of clusters from the data.  Data are
replicated time series or replicated measurements from multiple
experimental conditions.
An extensible framework
for automatically placing direct labels onto multicolor lattice or
ggplot2 plots.
Label positions are described using Positioning Methods
which can be re-used across several different plots.
There are heuristics for examining "trellis" and "ggplot" objects
and inferring an appropriate Positioning Method.
Direction analysis is a set of tools designed to integrate multiple experimental treatments for analyzing pathways and kinases from microarray, RNA-seq, proteomics, or phosphoproteomics data.
Implements Dirichlet regression models in R.
Estimate parameters in Dirichlet-Multinomial and compute
profile log-likelihoods.
Discrete smoothing of probability mass function is performed using three discrete associated kernels: Dirac Discrete Uniform (DiracDU), Binomial and Discrete Triangular. Two automatic bandwidth selection procedures are implemented: the cross-validation method for the three kernels and the local Bayesian approach for Binomial kernel. Note that DiracDU is used for categorical data, Binomial kernel is appropriate for count data with small or moderate sample sizes, and Discrete Triangular kernel is recommended for count data with large sample sizes.
Discrete Laplace exponential family for models such as a generalized linear model
Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).
DiscML performs rate estimation using maximum likelihood with the
options to correct for unobservable data, to implement a Gamma-distribution
for rate variation, and to estimate the prior root probabilities from the
empirical data.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the discrete inverse Weibull distribution
Probability mass function, distribution function, quantile function, random generation and estimation for the skew discrete Laplace distributions.
Multiple testing procedures for discrete test statistics,
that use the known discrete null distribution of the p-values
for simultaneous inference.
Create, manipulate, transform, and simulate from discrete random
variables. The syntax is modeled after that which is used in mathematical
statistics and probability courses, but with powerful support for more
advanced probability calculations. This includes the creation of joint
random variables, and the derivation and manipulation of their conditional
and marginal distributions.
Probability mass function, distribution function, quantile function, random generation and parameter estimation for the type I and III discrete Weibull distributions.
This package is a collection of supervised discretization
algorithms. It can also be grouped in terms of top-down or
bottom-up, implementing the discretization algorithms.
'discrimARTs' discriminates with explicit confidence the alternative reproductive tactics (ARTs) in dimorphic systems with bimodal traits by computing the maximum likelihood estimate of a mixture of distributions of a measured ARTs trait. Supported distributions include a mixture of 2 normal distributions and a mixture of 2 facing gamma distributions.
Functions for Discriminant Analysis and Classification purposes
covering various methods such as descriptive, geometric, linear, quadratic,
PLS, as well as qualitative discriminant analyses
Provides data transformations, estimation utilities,
predictive evaluation measures and simulation functions for discrete time
survival analysis.
Formatting of population and case data, calculation of Standardized Incidence Ratios, and fitting the BYM model using INLA.
Functions for species distribution modeling, that is, predicting entire geographic distributions form occurrences at a number of sites.
An implementation of two exact algorithms for testing the
Hausdorff and simplex dispersion orderings
Disparity filter is a network reduction algorithm to extract the backbone structure of both directed and undirected weighted networks. Disparity filter can reduce the network without destroying the multi-scale nature of the network. The algorithm has been developed by M. Angeles Serrano, Marian Boguna, and Alessandro Vespignani in Extracting the multiscale backbone of complex weighted networks, Proceedings of the national academy of sciences 106 (16).
A package containing R functions for displaying data and
results from high-throughput screening experiments.
Description: Functions for modelling dispersion in GLM.
Create disposable R packages for testing.
You can create, install and load multiple R packages with a single
function call, and then unload, uninstall and destroy them with another
function call. This is handy when testing how some R code or an R package
behaves with respect to other packages.
This package has extensible C++ code for computing dissimilarities between vectors. It also has a number of C++ functions for assembling collections of dissimilarities. In particular, it lets you find a matrix of dissimilarities between the rows of two input matrices. There are also functions for finding the nearest neighbors of each row of a matrix, either within the matrix itself or within another matrix.
A simple way of fitting detection functions to distance sampling
data for both line and point transects. Adjustment term selection, left and
right truncation as well as monotonicity constraints and binning are
supported. Abundance and density estimates can also be calculated (via a
Horvitz-Thompson-like estimator) if survey area information is provided.
Implement DiSTATIS and CovSTATIS (three-way multidimensional scaling). For the  analysis of  multiple distance/covariance matrices collected on the same set of observations
Implementing algorithms and fitting models when sites (possibly remote) share
computation summaries rather than actual data over HTTP with a master R process (using
'opencpu', for example). A stratified Cox model and a singular value decomposition are
provided. The former makes direct use of code from the R 'survival' package. (That is,
the underlying Cox model code is derived from that in the R 'survival' package.)
Sites may provide data via several means: CSV files, Redcap API, etc. An extensible
design allows for new methods to be added in the future. Web applications are provided
(via 'shiny') for the implemented methods to help in designing and deploying the
computations.
An R package that is developed for constructing confidence
regions without the need to know the sampling distribution of
bivariate data.
Some very simple method functions for confidence interval calculation and to distill pertinent information from a potentially complex object; primarily used in common with packages extRemes and SpatialVx.
Geodesic distance between phylogenetic trees and
associated functions.
S4 Classes and Methods for distributions
provides documentation in form of a common vignette to packages distr, distrEx,
distrMod, distrSim, distrTEst, distrTeach, and distrEllipse
Distribution (S4-)classes for elliptically contoured distributions (based on
package distr)
Extensions of package distr and some additional functionality
This package contains utilities which are of use in the
packages I have developed for dealing with distributions.
Currently these packages are GeneralizedHyperbolic,
VarianceGamma, and SkewHyperbolic and NormalLaplace. Each of
these packages requires DistributionUtils. Functionality
includes sample skewness and kurtosis, log-histogram, tail
plots, moments by integration, changing the point about which a
moment is calculated, functions for testing distributions using
inversion tests and the Massart inequality. Also includes an
implementation of the incomplete Bessel K function.
Object oriented implementation of probability models based on packages 'distr' and
'distrEx'
Estimation for a multinomial logistic regression factorized into independent  Poisson log regressions.  See the textir package for applications in multinomial inverse regression analysis of text.
(S4-)distribution classes based on package distr for distributions from packages
fBasics and fGarch
Simulation (S4-)classes based on package distr
Evaluation (S4-)classes based on package distr for evaluating procedures
(estimators/tests) at data/simulation in a unified way.
Provides tools for quality checks of georeferenced plant
species accessions.
R-package DivE contains functions for the DivE estimator (Laydon, D. et al., Quantification of HTLV-1 clonality and TCR diversity, PLOS Comput. Biol. 2014). The DivE estimator is a heuristic approach to estimate the number of classes or the number of species (species richness) in a population.
Utilities to represent, visualize, filter, analyse, and summarize
time-depth recorder (TDR) data.  Miscellaneous functions for
handling location data are also provided.
Contains a number of comparative phylogenetic methods,
mostly focusing on analysing diversification and character
evolution.  Contains implementations of BiSSE (Binary State
Speciation and Extinction; Maddison et al. 2007: Syst. Biol. 56:
701) and its unresolved tree extensions (FitzJohn et al. 2009:
Syst. Biol. 58: 595), MuSSE (Multiple State Speciation and
Extinction), QuaSSE (Quantitative State Speciation and Extinction;
FitzJohn 2010: Syst. Biol. 59: 619), GeoSSE (Geographic State
Speciation and Extinction; Goldberg et al. 2011: Syst. Biol. 60:
451), and BiSSE-ness (Magnuson-Ford and Otto 2012: Am. Nat.)
Other included methods include Markov models of discrete and
continuous trait evolution and constant rate speciation and
extinction.
Allows the calculation of both genetic diversity partition
statistics,genetic differentiation statistics and locus
informativeness for ancestry assignment. It also provides users
with various option to calculate bootstrapped 95% confidence
intervals both across loci, for pairwise population comparisons
and to plot these results interactively. V1.2 and up provides
parallel computing capabilities and pairwise results without
bootstrapping. Weir and Cockerham's 1984 F-statistics are now
also calculated. Various plotting features are also provided.
Chi-square tests of genetic heterogeneity are also provided.
Functionality for the calculation of various diversity
parameters is also possible for RAD-seq derived SNP data sets
containing thousands of marker loci. A shiny application for
the development of microsatellite multiplexes is also available
There are two functions in this package, which can be used
together to estimate the Shannon's Diversity index at different
levels of sample size. A Monte-Carlo procedure is used to
re-sample a given observation at each level of sampling. The
expectation being that the mean of the re-sampling will
approach Shannon's diversity index at that sample level.
This package has tools for analyzing DNA melting data to
generate HRM scores, the DNA diversity measure output of the
HRM Diversity Assay.  For additional documentation visit
http://code.google.com/p/divmelt/.
A set of tools for empirical analysis of diversity (a number and frequency of different types in population) and similarity (a number and frequency of shared types in two populations) in biological or ecological systems.
Function to test spatial segregation and association based
in contingency table analysis of nearest neighbour counts
following Dixon (2002). Some fortran code has been included to
the original dixon2002 function of the ecespa package to
improve speed.
Compute diffusion kernels on DNA polymorphisms, including SNP and bi-allelic genotypes.
Maximum likelihood, Kalman filtering and smoothing, and Bayesian
analysis of Normal linear State Space models, also known as
Dynamic Linear Models
QTL mapping in a mixed model framework with separate
detection and localization stages. The first stage detects the
number of QTL on each chromosome based on the genetic variation
due to grouped markers on the chromosome; the second stage uses
this information to determine the most likely QTL positions.
The mixed model can accommodate general fixed and random
effects, including spatial effects in field trials and pedigree
effects. Applicable to backcrosses, doubled haploids,
recombinant inbred lines, F2 intercrosses, and association
mapping populations.
dlmodeler is a set of user-friendly functions to simplify the state-space modelling, fitting, analysis and forecasting of Generalized Dynamic Linear Models (DLMs). It includes functions to name and extract individual components of a DLM, build classical seasonal time-series models (monthly, quarterly, yearly, etc. with calendar adjustments) and provides a unified interface compatible with other state-space packages including: dlm, FKF and KFAS.
Simulation testing and implementation of data-limited fishery stock assessment methods.
The package dlnm contains functions to specify and run distributed lag linear and non-linear models.
Dynamic model averaging for binary and continuous
outcomes.
Dyadic mixed model analysis with multi-trait responses and
pedigree-based partitioning of individual variation into a range of
environmental and genetic variance components for individual and
maternal effects.
The framework provides functions to generate ODEs of reaction networks, parameter transformations, observation functions, residual functions, etc. The framework follows the paradigm that derivative information should be used for optimization whenever possible. Therefore, all major functions produce and can handle expressions for symbolic derivatives.
A backward selection procedure called delete or merge
regressors (DMR) combines deleting continuous variables with
merging levels of factors. The method assumes greedy search
among linear models with set of constraints of two types:
either a parameter for a continuous variable is set to zero or
parameters corresponding to two levels of a factor are
compared. DMR is a stepwise regression procedure, where in each
step a new constraint is added according to ranking of the
hypotheses based on squared t-statistics. As a result a nested
family of linear models is obtained and the final decision is
made according to minimization of the generalized information
criterion (GIC, default BIC). The main function of the package
is DMR, which is based on hierarchical clustering. Moreover,
other functions for extensions of DMR method are given, such as
stepDMR which is based on recalculation of t-statistics in each
step and function DMR4glm for generalized linear models.
This package includes functions and data accompanying the book
"Data Mining with R, learning with case studies" by Luis Torgo, CRC Press 2010.
Package for conducting differential network analysis from
microarray data.
Methods for studying forensic DNA profiling, in particular familial database searches.
Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic
is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also
called F-designation) is implemented. Dedicated functions ease plotting the results.
The DnE package involves functions to analyse the distribution of a set of given data. The basic idea of the analysis is chi-squared test. Functions which have the form as "is.xxdistribution" are used to analyse whether the data obeys the xxdistrbution. If you do not know which distribution to judge, use function is.dt().
There lacks an open-source tool for high-throughput omics data in an integrative manner in terms of network, evolution and ontology. To meet this need, dnet intends to analyse the biological network whose nodes/genes are associated with digitised information such as expression levels across samples. To help make sense of identified networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific gene age information in major organisms including human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. Add-on functionalities are supports for semantic similarity calculation between ontology terms (and genes), and network affinity calculation using Random Walk with Restart; both can be done via high-performance parallel computing. In summary, dnet aims to deliver an eye-intuitive tool with rich visuals but less inputs.
Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.
Frequentist (EM) and Bayesian  (MCMC) Methods for Inference of  Birth-Death-Immigration Markov Chains
doBy originally contained facilities to 'do' something to data stratified 'by' some variables. doBy contains a
variety of utilities including:

1) Facilities for groupwise computations of summary statistics and
other facilities for working with grouped data.

2) LSmeans (least-squares means), general linear contrasts.

3) Miscellaneous other utilities.
Define a command-line interface by just giving it
a description in the specific format.
Production of R packages from tagged comments introduced within the code
and a minimum of additional documentation files.
Various sampling plans are able to be compared using evaluations of their OC, AOQ, ATI etc.
Package \pkg{DoE.base} creates full factorial experimental designs and designs based on orthogonal arrays for (industrial) experiments. Additionally, it provides utility functions for the class \code{design}, which is also used by other packages for designed experiments.
This package creates various kinds of designs for
(industrial) experiments. It uses, and sometimes enhances,
design generation routines from other packages.
So far, response surface designs from package rsm, latin hypercube
samples from packages lhs and DiceDesign, and
D-optimal designs from package AlgDesign have been implemented.
Provides a parallel backend for the %dopar% function using
the multicore functionality of the parallel package..
The package can calculate ADI (Average Dominance Index) and can build social network graphs with dual directions, can build a Music Notation Graph
A wrapper on top of the 'Domino Command-Line Client'. It lets you run 'Domino' commands (e.g., "run", "upload", "download") directly from your R environment. Under the hood, it uses R's system function to run the 'Domino' executable, which must be installed as a prerequisite. 'Domino' is a service that makes it easy to run your code on scalable hardware, with integrated version control and collaboration features designed for analytical workflows (see <http://www.dominoup.com> for more information).
Provides a parallel backend for the %dopar% function using
the Rmpi package.
Provides a parallel backend for the %dopar% function using
the parallel package.
A Redis parallel backend for the %dopar% function
This package provides functions to perform
reproducible parallel foreach loops, using independent
random streams as generated by L'Ecuyer's combined
multiple-recursive generator [L'Ecuyer (1999)]. It
enables to easily convert standard %dopar% loops into
fully reproducible loops, independently of the number
of workers, the task scheduling strategy, or the chosen
parallel environment and associated foreach backend.
The DoseFinding package provides functions for the design and analysis
of dose-finding experiments (with focus on pharmaceutical Phase
II clinical trials). It provides functions for: multiple contrast
tests, fitting non-linear dose-response models (using Bayesian and
non-Bayesian estimation), calculating optimal designs and an
implementation of the MCPMod methodology.
Provides a parallel backend for the %dopar% function using
Luke Tierney's snow package.
It estimates a dose-response relation from either a single or
multiple summarized data. The trend estimation takes into account the
correlation among sets of log relative risks and use it to efficiently
estimate the dose-response relation. To obtain a pooled functional
relation, the study-specific trends are combined according to principles of
multivariate random-effects meta-analysis.
A small package containing helper utilities for creating function
for computing statistics.
Load configuration from a .env file, that is
in the current working directory, into environment variables.
Quick and easy dot-and-whisker plots of regression models saved in tidy data frames.
Performs hypothesis tests concerning a regression function in a least-squares model, where the null is a parametric function, and the alternative is the union of large-dimensional convex polyhedral cones.
Differential Exon Usage Test for RNA-Seq data via Empirical Bayes Shrinkage of the Dispersion Parameter
Local linear estimator for the univariate hazard (hazard rate) and bandwidth parameter selection using the do-validation method and the standard least squares cross-validation method.
Provides a wrapper for the download.file function,
making it possible to download files over HTTPS on Windows, Mac OS X, and
other Unix-like platforms. The 'RCurl' package provides this functionality
(and much more) but can be difficult to install because it must be compiled
with external dependencies. This package has no external dependencies, so
it is much easier to install.
A GUI or command-line operated data analysis tool, for
analyzing time-dependent simulation data in which multiple
instantaneous or time-lagged relations are assumed. This
package uses Structural Equation Modeling (the sem package). It
is aimed to deal with time-dependent data and estimate whether
a causal diagram fits data from an (agent-based) simulation
model.
Analysis, visualisation and simulation of digital PCR experiments.
fits the primal graphical lasso, via one-at-a-time
block-coordinate descent.
Perform tree-ring analyses such as detrending, chronology
building, and cross dating.  Read and write standard file formats
used in dendrochronology.
The concordance method is a non-parametric method based on bootstrapping that is used to test the hypothesis that two subsets of time series are similar in terms of mean, variance or both. This method was developed to address a concern within dendroclimatology that young trees may produce a differing climate response to older more established trees. Details of this method are available in  Pirie, M. (2013). The Climate of New Zealand reconstructed from kauri tree rings: Enhancement through the use of novel statistical methodology. PhD. Dissertation, School of Environment and Department of Statistics, University of Auckland, New Zealand. This package also produces a figure with 3 panels, each panel is for a different climate variable. An example of this figure in included in "On the influence of tree size on the climate - growth relationship of New Zealand kauri (Agathis australis): insights from annual, monthly and daily growth patterns. J Wunder, AM Fowler, ER Cook, M Pirie, SPJ McCloskey. Trees 27 (4), 937-948". For further R functions for loading your own dendroclimatology datasets and performing dendrochronology analysis refer to the R package "dplR: Dendrochronology Program Library in R". The concordance procedure is intended to add to the standard dendrochronology techniques provided in "dplR".
A fast, consistent tool for working with data frame like objects,
both in memory and out of memory.
The package implements a Dirichlet Process Mixture (DPM)
model for clustering and image segmentation.  The DPM model is
a Bayesian nonparametric methodology that relies on MCMC
simulations for exploring mixture models with an unknown number
of components.  The code implements conjugate models with
normal structure (conjugate normal-normal DP mixture model).
The package's applications are oriented towards the
classification of magnetic resonance images according to tissue
type or region of interest.
Create, install, and summarise data packages that follow
the Open Knowledge Foundation's Data Package Protocol.
This package contains functions to perform inference via
simulation from the posterior distributions for Bayesian
nonparametric and semiparametric models. Although the name of
the package was motivated by the Dirichlet Process prior, the
package considers and will consider other priors on functional
spaces. So far, DPpackage includes models considering Dirichlet
Processes, Dependent Dirichlet Processes, Dependent Poisson-
Dirichlet Processes, Hierarchical Dirichlet Processes, Polya
Trees, Linear Dependent Tailfree Processes, Mixtures of
Triangular distributions, Random Bernstein polynomials priors
and Dependent Bernstein Polynomials.  The package also includes
models considering Penalized B-Splines. Currently the package
includes semiparametric models for marginal and conditional
density estimation, ROC curve analysis, interval censored data,
binary regression models, generalized linear mixed models, IRT
type models, and generalized additive models. The package also
contains functions to compute Pseudo-Bayes factors for model
comparison, and to elicitate the precision parameter of the
Dirichlet Process. To maximize computational efficiency, the
actual sampling for each model is done in compiled FORTRAN. The
functions return objects which can be subsequently analyzed
with functions provided in the coda package.
Implement the Bayesian subset selection procedure proposed in the paper titled "Bayesian Subset Selection Procedures with an Application to Lumber Strength Properties" by Yumi Kondo, James V. Zidek, Carolyn Taylor, and Constance van Eeden. This procedure models the lumber strengths with Dirichlet processes (DP) with the Weibull components and finds, with pre-specified certainty, the smallest subset that contains those species having the smallest alpha-th quantile of strength (i.e., the weakest species). We consider two models for the strengths: [1] A semi-parametric model which assumes that strengths are from the DP mixture of Weibulls; [2] the nonparametric model which assumes that probability measures of strengths are from DP with Weibull base distribution. Either model can be used to estimate the posterior probability that a subset contains the weakest species.
Functions, methods, and datasets for fitting dimension
reduction regression, using slicing (methods SAVE and SIR), Principal
Hessian Directions (phd, using residuals and the response), and an
iterative IRE.  Partial methods, that condition on categorical
predictors are also available.  A variety of tests, and stepwise
deletion of predictors, is also included.  Also included is
code for computing permutation tests of dimension.  Adding additional
methods of estimating dimension is straightforward.
For documentation, see the vignette in the package.   With version 3.0.4,
the arguments for dr.step have been modified.
Creation and Use of R Repositories via two helper functions
to insert packages into a repository, and to add repository information
to the current R session. Two primary types of repositories are support:
gh-pages at GitHub, as well as local repositories on either the same machine
or a local network. Drat is a recursive acronym: Drat R Archive Template.
Graphical display of R expression, showing the
interpretation of an expression by R and the various kind of R
data structure. The steps of the interpretation of an
expression are obtained through the parsed tree.
Analysis of dose-response data is made available through a suite of flexible and versatile model fitting and after-fitting functions.
drfit provides basic and easy-to-use functions for fitting
dose-response curves to dose-response data, calculating some
(eco)toxicological parameters and plotting the results. Functions that are
fitted are the cumulative density function of the lognormal distribution
(probit fit), of the logistic distribution (logit fit), of the weibull
distribution (weibull fit) and a linear-logistic model ("linlogit" fit),
derived from the latter, which is used to describe data showing stimulation
at low doses (hormesis). In addition, functions checking, plotting and
retrieving dose-response data retrieved from a database accessed via RODBC
are included. As an alternative to the original fitting methods, the
algorithms from the drc package can be used.
Fit restricted mean models for the conditional association
between an exposure and an outcome, given covariates. Three methods
are implemented: O-estimation, where a nuisance model for the
association between the covariates and the outcome is used;
E-estimation where a nuisance model for the association
between the covariates and the exposure is used, and doubly robust (DR)
estimation where both nuisance models are used. In DR-estimation,
the estimates will be consistent when at least one of the nuisance
models is correctly specified, not necessarily both.
Likelihood-based marginal regression and association
modelling for repeated, or otherwise clustered, categorical
responses using dependence ratio as a measure of the
association
Dual empirical likelihood (DEL) inference under semiparametric density ratio models (DRM) in the presence of multiple samples, including population cumulative distribution function estimation, quantile estimation and comparison, density estimation, composite hypothesis testing for DRM parameters which encompasses testing for changes in population distribution functions as a special case, etc.
Drop out analysis for psychologists in a R based web application.
Shiny is used to visualize and analyze drop outs tailored to the methods of
online survey methodology. Concept and app presented at the SCIP Conference
in Long Beach, California.
Drsmooth provides tools for assessing the shape of a dose-response
curve by testing linearity and non-linearity at user-defined cut-offs. It
also provides two methods of estimating a threshold dose, or the dose at
which the dose-response function transitions to significantly increasing:
bi-linear (based on pkg:segmented) and smoothed with splines (based on
pkg:mgcv).
The package performs various analyzes of descriptive statistics, including correlations
Two discretization-based Monte Carlo algorithms, namely the Fu-Wang algorithm and the Wang-Lee algorithm, are provided for random sample generation from a high dimensional distribution of complex structure.  The normalizing constant of the target distribution needs not to be known.
Calculate posterior modes and credible intervals of parameters of the Dixon-Simon model for subgroup analysis (with binary covariates) in clinical trials.
Package dse provides tools for multivariate, linear, time-invariant,
time series models. It includes ARMA and state-space representations,
and methods for converting between them. It also includes simulation
methods and several estimation functions. The package has functions
for looking at model roots, stability, and forecasts at different
horizons. The ARMA model representation is general, so that VAR, VARX,
ARIMA, ARMAX, ARIMAX can all be considered to be special cases. Kalman
filter and smoother estimates can be obtained from the state space
model, and state-space model reduction techniques are implemented.
An introduction and User's Guide is available in a vignette.
An abstract DList class helps storing large list-type objects in a distributed manner. Corresponding high-level functions and methods for handling distributed storage (DStorage) and lists allows for processing such DLists on distributed systems efficiently. In doing so it uses a well defined storage backend implemented based on the DStorage class.
Dynamic slicing is a method designed for dependency detection between a categorical variable and a continuous variable. It could be applied for non-parametric hypothesis testing and gene set enrichment analysis.
Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.
Fits inhomogeneous Poisson process spatial models
to line transect sampling data and provides estimate of
abundance within a region.
Performs distance sampling simulations. It repeatedly
generates instances of a user defined population within a given survey
region, generates realisations of a survey design (currently these must
be pre-generated using Distance software) and simulates the detection
process. The data are then analysed so that the results can be compared for
accuracy and precision across all replications. This will allow users to
select survey designs which will give them the best accuracy and precision
given their expectations about population distribution. Any uncertainty in
population distribution or population parameters can be included by running
the different survey designs for a number of different population
descriptions. An example simulation can be found in the help file
for make.simulation.
This package allows you to make basic probability assignments on a set of
possibilities (events) and combine these events with Dempster's rule of combination.
Building discrete-time survival trees and bagged trees based on
the functionalities of the rpart package. Splitting criterion maximizes the
likelihood of a covariate-free logistic discrete time hazard model.
Elucidation of human disease similarities has emerged as an active research area, which is highly relevant to etiology, disease classification, and drug repositioning. This package was designed and implemented for identifying disease similarities. It contains five functions which are 'DCEA', 'DCpathway', 'DS', 'comDCGL' and 'comDCGLplot'. In 'DCEA' function, differentially co-expressed genes and differentially co-expressed links are extracted from disease vs. health samples. Then 'DCpathway' function assigns differential co-expression values of pathways to be the average differential co-expression value of their component genes. Then 'DS' employs partial correlation coefficient of pathways as the disease similarity for each disease pairs. And 'DS' contains a permutation process for evaluating the statistical significant of observed disease partial correlation coefficients. At last, 'comDCGL' and 'comDCGLplot' sort out shared differentially co-expressed genes and differentially co-expressed links with regulation information and visualize them.
Data objects in R can be rendered as HTML tables using the
JavaScript library 'DataTables' (typically via R Markdown or Shiny). The
'DataTables' library has been included in this R package. The package name
'DT' is an abbreviation of 'DataTables'.
This package contains functions to compare the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.
This package implements different algorithms for analyzing
randomly truncated data, one-sided and two-sided (i.e. doubly)
truncated data. Two real data sets are included.
Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging
modality, that measures diffusion of water in tissues like the human
brain. The package contains R-functions to process diffusion-weighted
data. The functionality includes diffusion tensor imaging (DTI),
diffusion kurtosis imaging (DKI), modeling for high angular resolution
diffusion weighted imaging (HARDI) using Q-ball-reconstruction and
tensor mixture models, several methods for structural adaptive
smoothing including POAS and msPOAS, and a streamline fiber tracking
for tensor and tensor mixture models.
The package provides functionality to manipulate and visualize results
in 2D and 3D.
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 Dunnett modified Tukey-Kramer test (a.k.a. the
T3 Procedure), create two vectors: one for your observations
and one for the factor level of each observation. The function,
gl.unequal, provides a means to more conveniently produce a
factor vector with unequal sample sizes. Next, use the DTK.test
function to conduct the test and save the output as an object
to input into the DTK.plot function, which produces a
confidence interval plot for each of the pairwise comparisons.
Lastly, the function TK.test conducts the original Tukey-Kramer
test.
A series of functions which aid in both simulating and
determining the properties of finite, discrete-time, discrete
state markov chains.  Two functions (DTMC, MultDTMC) produce n
iterations of a Markov Chain(s) based on transition
probabilities and an initial distribution.  The function FPTime
determines the first passage time into each state.  The
function statdistr determines the stationary distribution of a
Markov Chain.
Estimation and comparison of survival distributions of dynamic treatment regimes (DTRs) from sequentially randomized clinical trials.
This package provides functions for 1D and 2D Discrete
Cosine Transform (DCT), Discrete Sine Transform (DST) and
Discrete Hartley Transform (DHT).
A comprehensive implementation of dynamic time warping (DTW) algorithms in R.  DTW computes the optimal (least cumulative distance) alignment between points of two time series.  Common DTW variants covered include local (slope) and global (window) constraints, subsequence matches, arbitrary distance definitions, normalizations, minimum variance matching, and so on.  Provides cumulative distances, alignments, specialized plot styles, etc.
Functions to analyze multiple choice data using Dual Scaling
Expands factors, characters and other eligible classes
into dummy/indicator variables.
Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).
For the implementation of the step-down or step-up Dunnett testing procedures, the package includes R functions to calculate critical constants and R functions to calculate adjusted P-values of the test statistics. In addition, the package also contains functions to evaluate testing powers and hence the necessary sample sizes specially for the classical problem of comparisons of several treatments with a control.
Computes Dunn's test (1964) for stochastic dominance and reports the results among multiple pairwise comparisons after a Kruskal-Wallis test for stochastic dominance among k groups (Kruskal and Wallis, 1952). The interpretation of stochastic dominance requires an assumption that the CDF of one group does not cross the CDF of the other. 'dunn.test' makes k(k-1)/2 multiple pairwise comparisons based on Dunn's z-test-statistic approximations to the actual rank statistics. The null hypothesis for each pairwise comparison is that the probability of observing a randomly selected value from the first group that is larger than a randomly selected value from the second group equals one half; this null hypothesis corresponds to that of the Wilcoxon-Mann-Whitney rank-sum test. Like the rank-sum test, if the data can be assumed to be continuous, and the distributions are assumed identical except for a difference in location, Dunn's test may be understood as a test for median difference. 'dunn.test' accounts for tied ranks.
Inference of population sizes using a binomial likelihood and least informative discrete uniform priors.
Hurst exponent estimation of a fractional Brownian motion
by using discrete variations methods in presence of outliers
and/or an additive noise
Functionality for analyzing dose-volume histograms (DVH)
in radiation oncology: Read DVH text files,
calculate DVH metrics, gEUD, BED, EQD2, NTCP, TCP, show DVH
diagrams, check and visualize quality assurance constraints
for the DVH. Includes web-based graphical user interface.
Provides access to The Dataverse Network APIs via the RCurl package, enabling access to archived data (and metadata), and the ability to create and manipulate studies in a user's dataverse(s).
An R interface to the 'dygraphs' JavaScript charting library
(a copy of which is included in the package). Provides rich facilities
for charting time-series data in R, including highly configurable
series- and axis-display and interactive features like zoom/pan and
series/point highlighting.
Add a "Did You Mean" feature to the R interactive.
With this package, error messages for misspelled input of variable names or package names
suggest what you really want to do in addition to notification of the mistake.
Time series regression.  The dyn class interfaces ts,
irts, its, zoo and zooreg time series classes to lm, glm,
loess, quantreg::rq, MASS::rlm, MCMCpack::MCMCregress,
quantreg::rq, randomForest::randomForest and other regression
functions allowing those functions to be used with time series
including specifications that may contain lags, diffs and
missing values.
The package is aimed at dynamically visualizing probability
distributions and their moments and all the commonly used distributions are
included.
Interactive graphical tool for manipulating graphs
Contains methods for detection of clusters in hierarchical clustering dendrograms.
a native R implementation and enhancement of Dynamic TOPMODEL, Beven and Freers' (2001) extension to the semi-distributed hydrological model TOPMODEL (Beven and Kirkby, 1979).
Inference by sequential Monte Carlo for
dynamic tree regression and classification models
with hooks provided for sequential design and optimization,
fully online learning with drift, variable selection, and
sensitivity analysis of inputs.  Illustrative
examples from the original dynamic trees paper are facilitated
by demos in the package; see demo(package="dynaTree")
A GUI to solve dynamic biplots and classical biplot. Try matrices of 2-way and 3-way. The GUI can be run in multiple languages.
DynClust is a two-stage procedure for the denoising and clustering of stack of noisy images acquired over time. Clustering only assumes that the data contain an unknown but small number of dynamic features. The method first denoises the signals using local spatial and full temporal information. The clustering step uses the previous output to aggregate voxels based on the knowledge of their spatial neighborhood. Both steps use a single keytool based on the statistical comparison of the difference of two signals with the null signal. No assumption is therefore required on the shape of the signals. The data are assumed to be normally distributed (or at least follow a symmetric distribution) with a known constant variance. Working pixelwise, the method can be time-consuming depending on the size of the data-array but harnesses the power of multicore cpus.
Computes dynamical correlation estimates and percentile
bootstrap confidence intervals for pairs of longitudinal
responses, including consideration of lags and derivatives.
Fit dynamic intervention model using the arima() function.
Dynamic linear models and time series regression.
The DynNom function makes it possible to present the results of an
lm or glm model object as a dynamic nomogram that can be displayed in an R
Studio panel or web browser.
The dynpred package contains functions for dynamic prediction in survival analysis.
Improves the concept of multivariate range boxes, which is highly susceptible for outliers and does not consider the distribution of the data. The package uses dynamic range boxes to overcome these problems.
Dynamic simulations and graphical depictions of autoregressive relationships.
Functions to fit time-varying coefficient models for interval
censored and right censored survival data. Three major
approaches are implemented: 1) Bayesian Cox model with
time-independent, time-varying or dynamic coefficients for
right censored and interval censored data; 2) Spline based
time-varying coefficient Cox model for right censored data;
3) Transformation model with time-varying coefficients for
right censored data using estimating equations.
A comprehensive toolkit for estimating Dynamic Treatment Regimes. Available methods include Interactive Q-Learning, Q-Learning, and value-search methods based on Augmented Inverse Probability Weighted estimators and Inverse Probability Weighted estimators.
Functions for latent class analysis, short time Fourier
transform, fuzzy clustering, support vector machines,
shortest path computation, bagged clustering, naive Bayes
classifier, ...
Plots of the empirical attainment function for two objectives.
The Early-Warning-Signals Toolbox provides methods for estimating
statistical changes in timeseries that can be used for identifying nearby
critical transitions. Based on Dakos et al (2012) Methods for Detecting
Early Warnings of Critical Transitions in Time Series Illustrated Using
Simulated Ecological Data. PLoS ONE 7(7):e41010
Build regression models using the techniques in Friedman's
papers "Fast MARS" and "Multivariate Adaptive Regression
Splines". (The term "MARS" is trademarked and thus not used in
the name of the package.)
Enables launching a series of simulations of a computer code from the R session, and to retrieve the simulation outputs in an appropriate format for post-processing treatments. Five sequential sampling schemes and three coupled-to-MCMC schemes are implemented.
Perform analysis of variance and other important complementary analyzes. The functions are easy to use. Performs analysis in various designs, with balanced and unbalanced data.
It is a package that can be used to send HTML reports easily.
Contains a few utility functions for working with capture-history data, a function for simulating capture-history data, and a function to fit this data, using a Gibbs sampler.
The package fit and plot some nonlinear models
This is a pipelining tool that facilitates
evaluation and visualisation of stratified genome-wide
association meta-analyses (GWAMAs) results data. It
provides (i) statistical methods to test and to account
for between-strata difference and to clump genome-wide
results into independent loci and (ii) extended graphical
features (e.g., Manhattan, Miami and QQ plots) tailored
for stratified GWAMA results.
Fitting and testing multi-attribute probabilistic choice
models, especially the Bradley-Terry-Luce (BTL) model (Bradley
& Terry, 1952; Luce, 1959), elimination-by-aspects (EBA) models
(Tversky, 1972), and preference tree (Pretree) models (Tversky
& Sattath, 1979).
Package implements entropy balancing, a data preprocessing procedure that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of user specified moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.
This package carries out Empirical Bayes thresholding
using the methods developed by I. M. Johnstone and B. W.
Silverman. The basic problem is to estimate a mean vector given
a vector of observations of the mean vector plus white noise,
taking advantage of possible sparsity in the mean vector.
Within a Bayesian formulation, the elements of the mean vector
are modelled as having, independently, a distribution that is a
mixture of an atom of probability at zero and a suitable
heavy-tailed distribution.  The mixing parameter can be
estimated by a marginal maximum likelihood approach.  This
leads to an adaptive thresholding approach on the original
data.  Extensions of the basic method, in particular to wavelet
thresholding, are also implemented within the package.
This package is used to infer the adjacency matrix of a
network from time course data using an empirical Bayes
estimation procedure based on Dynamic Bayesian Networks.
Provides the Empirical Bayesian Elastic Net for handling multicollinearity in generalized linear regression models.  As a special case of the 'EBglmnet'
package (also available on CRAN), this package encourages a grouping effects to select relevant variables and estimate the corresponding non-zero effects.
Genotyping the population using next generation sequencing data is essentially important for the rare variant detection. In order to distinguish the genomic structural variation from sequencing error, we propose a statistical model which involves the genotype effect through a latent variable to depict the distribution of non-reference allele frequency data among different samples and different genome loci, while decomposing the sequencing error into sample effect and positional effect. An ECM algorithm is implemented to estimate the model parameters, and then the genotypes and SNPs are inferred based on the empirical Bayes method.
Empirical Bayesian Lasso and Elastic Net algorithms are variable selection and effect estimation methods that outperform other popular methods such as Lasso and Elastic Net methods.
This package performs an exact Bayesian segmentation on data and returns the probabilities of breakpoints, an ICL criteria, comparison of change-point location, etc
Genotyping and SNP calling tool for single-sample next generation sequencing data analysis using an empirical Bayes method.
Some wrappers, functions and data sets for for spatial point pattern analysis (mainly based on spatstat), used in the book "Introduccion al Analisis Espacial de Datos en Ecologia y Ciencias Ambientales: Metodos y Aplicaciones".
Functions to update datasets in Ecdat and to create,
manipulate, plot and analyze those and similar datasets.
Provides a function that quickly computes the fine structure
isotope patterns of a set of chemical formulas to a given degree of
accuracy (up to the limit set by errors in floating point arithmetic). A
data-set comprising the masses and isotopic abundances of individual
elements is also provided.
We implement the Bayesian and likelihood methods proposed
in Imai, Lu, and Strauss (2008, 2011) for ecological inference in 2
by 2 tables as well as the method of bounds introduced by Duncan and
Davis (1953).  The package fits both parametric and nonparametric
models using either the Expectation-Maximization algorithms (for
likelihood models) or the Markov chain Monte Carlo algorithms (for
Bayesian models).  For all models, the individual-level data can be
directly incorporated into the estimation whenever such data are available.
Along with in-sample and out-of-sample predictions, the package also
provides a functionality which allows one to quantify the effect of data
aggregation on parameter estimation and hypothesis testing under the
parametric likelihood models.
Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community data.
The ecoengine provides access to more than 5 million georeferenced specimen records from the University of California, Berkeley's Natural History Museums.
Geostatistical tools for analyzing spatial patterns in
population biology. Easy integration of information from multiple sources
with "ecogen" objects.
This package provides a flexible foundation for scientists,
engineers, and policy makers to base teaching exercises as well as for
more applied use to model complex eco-hydrological interactions.
Figures, data sets and examples from the book "A practical guide to ecological modelling - using R as a simulation platform" by Karline Soetaert and Peter MJ Herman (2009). Springer.
All figures from chapter x can be generated by "demo(chapx)", where x = 1 to 11.
The R-scripts of the model examples discussed in the book are in subdirectory "examples", ordered per chapter.
Solutions to model projects are in the same subdirectories.
Estimating individual-level covariate-outcome associations
using aggregate data ("ecological inference") or a combination
of aggregate and individual-level data ("hierarchical related
regression").
Provides an R interface to the EcoData Retriever
(http://ecodataretriever.org/) via the EcoData Retriever's
command line interface. The EcoData Retriever automates the
tasks of finding, downloading, and cleaning ecological datasets,
and then stores them in a local database.
Classes and methods for implementing aquatic ecosystem models,
for running these models, and for visualizing their results.
Given a site by species interaction matrix, users can make inferences about species interactions by performance hypothesis comparing test statistics against a null distribution. The current package provides algorithms and metrics for niche-overlap, body size ratios and species co-occurrence. Users can also integrate their own algorithms and metrics within these frameworks or completely novel null models. Detailed explanations about the underlying assumptions of null model analysis in ecology can be found at http://ecosimr.org.
R package for the support of spatial analyses and modelling of species niches and distributions, written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) & Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland.
EcoTroph is an approach and software for modelling marine and freshwater ecosystems. It is articulated entirely around trophic levels. EcoTroph's key displays are bivariate plots, with trophic levels as the abscissa, and biomass flows or related quantities as ordinates. Thus, trophic ecosystem functioning can be modelled as a continuous flow of biomass surging up the food web, from lower to higher trophic levels, due to predation and ontogenic processes. Such an approach, wherein species as such disappear, may be viewed as the ultimate stage in the use of the trophic level metric for ecosystem modelling, providing a simplified but potentially useful caricature of ecosystem functioning and impacts of fishing. This version contains catch trophic spectrum analysis (CTSA) function and corrected versions of the mf.diagnosis and create.ETmain functions.
Functions for evaluating and visualizing
ecological assessment procedures for surface waters
containing physical, chemical and biological assessments
in the form of value functions.
A package for simulations of classical ecological models as a learning resource.
Implements various procedures for finding
multiple change-points through the use of U-statistics.
The procedures do not make any distributional assumptions
other than the existence of certain absolute moments. Hierarchical
and exact search methods are included. All methods return the
set of estimated change-points as well as other summary information.
This package provides a unified approach for Economic
Design of Control Charts. The main purpose of this package is
to find out the optimal parameters to minimize the ECH
(Expected Cost per Hour) of the process.
This package will allow to connect with email server through Internet Message Access Protocol (IMAP) and extract header information e.g. from, to, cc, subject, date and time. User will supply their email address and password along with other options. Initially this package is developed only for Gmail. To run the functions from this package user have to have IMAP enabled Gmail account.
Functions for reading in data sets of prey and predator isotopic measurements and producing estimates for discrimination factors.
Extends edgeR functionality by improving on exactTest using an unconditional exact test of negative binomial means.
Package EDISON (Estimation of Directed Interactions from
Sequences Of Non-homogeneous gene expression) runs an MCMC
simulation to reconstruct networks from time series data, using
a non-homogeneous, time-varying dynamic Bayesian network.
Networks segments and changepoints are inferred concurrently,
and information sharing priors provide a reduction of the
inference uncertainty.
An integrated editing and imputation method for continuous microdata under linear constraints is implemented. It relies on a Bayesian nonparametric hierarchical modeling approach in which the joint distribution of the data is estimated by a flexible joint probability model. The generated edit-imputed data are guaranteed to satisfy all imposed edit rules, whose types include ratio edits, balance edits and range restrictions.
Facilitates reading and manipulating (multivariate) data restrictions
(edit rules) on numerical and categorical data. Rules can be defined with common R syntax
and parsed to an internal (matrix-like format). Rules can be manipulated with
variable elimination and value substitution methods, allowing for feasibility checks
and more. Data can be tested against the rules and erroneous fields can be found based
on Fellegi and Holt's generalized principle. Rules dependencies can be visualized with
using the igraph package.
Optimized DMR analysis based on bimodal normal distribution
model and cost function for regional methylation analysis optimization.
It captures the regional methylation modification by taking the spatial
distribution of CpGs into account for the enrichment DNA methylation sequencing
data so as to optimize the definition of the empirical regions. Combined with
the dependent adjustment for regional p-value combination and DMR annotation.
The library contains R-functions to estimate the effective
dimension reduction space in multi-index regression models.
This package comes to illustrate the articles "A graphical
tool for selecting the number of slices and the dimension of
the model in SIR and SAVE approaches" and "Comparison of sliced
inverse regression approaches for underdetermined cases"
Package with tools for classification of electroencephalography (EEG) data. Feature extraction techniques such as Fourier Transform and Continuous Wavelet Transform (CWT) are available. Support Vector Machines (SVM) can be used to classify the extracted features. An algorithm using Analysis of Variance (ANOVA), False Discovery Rate (FDR), and SVM is available to feature selection. Additionally, the package contains functions to plot data and features.
Analysis and visualization tools for electroencephalography (EEG) data. Includes functions for plotting (a) EEG caps, (b) single- and multi-channel EEG time courses, and (c) EEG spatial maps. Also includes smoothing and Independent Component Analysis functions for EEG data analysis, and a function for simulating event-related potential EEG data.
Contains the example EEG data used in the package eegkit. Also contains code for easily creating larger EEG datasets from the EEG Database on the UCI Machine Learning Repository.
Collection of convenience functions to make working with
administrative records easier and more consistent. Includes functions to
clean strings, identify cutpoints, and quickly combine shapefiles and
dataframes for plotting. Includes four alternative themes for ggplot2 as
well as a wrapper for exporting graphics for inclusion in MS Office
products. Also includes three example datasets of administrative education
records for learning how to process records with errors.
Enhanced False Discovery Rate (EFDR) is a tool to detect anomalies
in an image. The image is first transformed into the wavelet domain in
order to decorrelate any noise components, following which the coefficients
at each resolution are standardised. Statistical tests (in a multiple
hypothesis testing setting) are then carried out to find the anomalies. The
power of EFDR exceeds that of standard FDR, which would carry out tests on
every wavelet coefficient: EFDR choose which wavelets to test based on a
criterion described in Shen et al. (2002). The package also provides
elementary tools to interpolate spatially irregular data onto a grid of the
required size. The work is based on Shen, X., Huang, H.-C., and Cressie, N.
'Nonparametric hypothesis testing for a spatial signal.' Journal of the
American Statistical Association 97.460 (2002): 1122-1140.
Use structural equation modeling to estimate average and conditional effects of a treatment variable
on an outcome variable, taking into account multiple continuous and categorical covariates.
Graphical and tabular effect displays, e.g., of interactions, for
various statistical models with linear predictors.
Functions to test for changes of a response to a stimulus grouping relative
to a background or baseline response.
The package provides functions to visualize regression models with categorical response. The effects of the covariates are plotted with star plots in order to allow for an optical impression of the fitted model.
In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. 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.
Fitting a causal loglinear model and calculating the causal effects for a causal loglinear model with the multiplicative interaction or without the multiplicative interaction, obtaining the natural direct, indirect and the total effect. It calculates also the cell effect, which is a new interaction effect.
A collection of functions to compute the standardized
effect sizes for experiments (Cohen d, Hedges g, Cliff delta, Vargha-Delaney A).
The computation algorithms have been optimized to allow efficient computation even
with very large data sets.
Functions for assigning Clarke or Parkes (Consensus) error grid
zones to blood glucose values, and for plotting both types of error grids.
This package provides an easy-to-use implementation of the Engle-Granger
two-step procedure for identifying pairs of cointegrated series.  It is geared towards
the analysis of pairs of securities.  Summary and plot functions are provided,
and the package is able to fetch closing prices of securities from Yahoo.
A variety of unit root tests are supported, and an improved unit root test is included.
An implementation of hierarchical models
for faecal egg count data to assess anthelmintic
efficacy. Bayesian inference is done via MCMC sampling.
A small tool for Social Network Analysis, dealing with
ego-centric network measures, including Burt's effective size
and aggregate constraint and an import code suitable for a
large number of adjacency matrices. A free web application is
also available on http://www.egonet.associazionerospo.org
Statistics and graphics for streamflow history,
water quality trends, and the statistical modeling algorithm: Weighted
Regressions on Time, Discharge, and Season (WRTDS).
Sampling of risk sets in Cox regression, selections in
the Lexis diagram, bootstrapping. Parametric proportional
hazards fitting with left truncation and right censoring for
common families of distributions, piecewise constant hazards,
and discrete models. AFT regression for left truncated and
right censored data. Binary and Poisson regression for
clustered data, fixed and random effects with bootstrapping.
Extended and enhanced hierarchical logistic regression models (called Huisman-Olff-Fresco in biology, see Huisman eta l. 1993 JVS) models. Response curves along one-dimensional gradients including no response, monotone, plateau, unimodal and bimodal models.
An R wrapper to allow the user to query categories and Series IDs, and import data, from the EIA's API.
Solves the ``inverse eigenvalue problem'' which is to
generate a real-valued matrix that has the specified real
eigenvalue spectrum.  It can generate infinitely many dense
matrices, symmetric or asymmetric, with the given set of
eigenvalues.  Algorithm can also generate stochastic and doubly
stochastic matrices.
This package estimates the parameters of a model for
symmetric relational data (e.g., the above-diagonal part of a
square matrix), using a model-based eigenvalue decomposition
and regression. Missing data is accomodated, and a posterior
mean for missing data is calculated under the assumption that
the data are missing at random. The marginal distribution of
the relational data can be arbitrary, and is fit with an
ordered probit specification.
Computes confidence intervals for the proportion explained by the first 1,2,k principal components, and computes confidence intervals for each eigenvalue. Both computations are done via nonparametric bootstrap.
Implementation of Efficient Inference of Local Ancestry
using fused quantile regression and k-means classifier
Provides methods for analyzing RxC ecological contingency
tables using the extreme case analysis, ecological regression,
and Multinomial-Dirichlet ecological inference models.  Also
provides tools for manipulating higher-dimension data objects.
EIVE performs a compact genetic algorithm search to reduce errors-in-variables bias in linear regression.
This package allows to use the hybrid Multinomial-Dirichlet-Model
of Ecological Inference for estimating inner Cells of RxC-Tables. This was
already implemented in the eiPack-package. eiwild-package now has the
possibility to use individual level data to support the aggregate level
data and using different Hyperpriori-Distributions.
Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves.
Connect to 'Elasticsearch', a 'NoSQL' database built on the 'Java' Virtual Machine.
Interacts with the 'Elasticsearch' 'HTTP' 'API' (http://elasticsearch.org), including
functions for setting connection details to 'Elasticsearch' instances, loading bulk
data, searching for documents with both 'HTTP' query variables and 'JSON' based body
requests. In addition, 'elastic' provides functions for interacting with 'APIs' for
'indices', documents, nodes, clusters, an interface to the cat 'API', and more.
This package provides functions for fitting the entire
solution path of the Elastic-Net and also provides functions
for estimating sparse Principal Components. The Lasso solution
paths can be computed by the same function. First version:
2005-10.
This is a bizzare collection of functions written to do
various sorts of statistical election audits.  There are also
functions to generate simulated voting data, and simulated
"truth" so as to do simulations to check charactaristics of
these methods.
An extension of the elec package intended for use on
election audits using stratified random samples.  Includes
functions to obtain conservative and exact p-values, and
functions that give sample sizes that may make election audits
more efficient.
Useful when reading the book above mentioned, in the
documentation referred to as `the book'.
Correlation chart of two set (x and y) of data.
Using Quantiles.  Visualize the effect of factor.
This package contains various routines for drawing
ellipses and ellipse-like confidence regions, implementing the
plots described in Murdoch and Chow (1996), A graphical display
of large correlation matrices, The American Statistician 50,
178-180. There are also routines implementing the profile plots
described in Bates and Watts (1988), Nonlinear Regression
Analysis and its Applications.
A suite of elliptic and related functions including Weierstrass and
Jacobi forms.  Also includes various tools for manipulating and
visualizing complex functions.
Training and predict functions for SLFN ( Single
Hidden-layer Feedforward Neural Networks ) using the ELM
algorithm. ELM algorithm differs from the traditional
gradient-based algorithms for very short training times ( it
doesn't need any iterative tuning, this makes learning time
very fast ) and there is no need to set any other parameters
like learning rate, momentum, epochs, etc.
Calculate Elo ratings as means to describe animal dominance hierarchies
elrm implements a Markov Chain Monte Carlo algorithm to
approximate exact conditional inference for logistic regression
models. Exact conditional inference is based on the
distribution of the sufficient statistics for the parameters of
interest given the sufficient statistics for the remaining
nuisance parameters. Using model formula notation, users
specify a logistic model and model terms of interest for exact
inference.
Flexible implementation of Elston-Stewart algorithm
The package is meant to help actuaries to build experience life tables. It contains functions to calculate exposure and number of deaths, smoothing following four different methods, and validation following three criteria.
Empirical likelihood ratio tests for the Yang and Prentice (short/long term hazards ratio) models.
Empirical likelihood tests within a Cox model, for parameters defined via
both baseline hazard function and regression parameters.
We propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.
Unsupervised, multivariate, clustering algorithm yielding a meaningful binary clustering taking into account the uncertainty in the data. A specific constructor for trajectory movement analysis yields behavioural annotation of the tracks based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator.
Tools to analyze the embryo growth and the sexualisation thermal reaction norms.
random walk Metropolis, Metropolis Hasting, parallel tempering, evolutionary Monte Carlo, temperature ladder construction and placement
evolutionary Monte Carlo methods for clustering,
temperature ladder construction and placement. For mclust see
http://cran.r-project.org/web/packages/mclust/LICENSE
EMCluster provides EM algorithms and several efficient
initialization methods for model-based clustering of finite
mixture Gaussian distribution with unstructured dispersion
in both of unsupervised and semi-supervised learning.
This package carries out empirical mode decomposition and Hilbert spectral
analysis. For usage of EMD, see Kim and Oh, 2009 (Kim, D and Oh, H.-S. (2009) EMD: A Package for Empirical
Mode Decomposition and Hilbert Spectrum, The R Journal, 1, 40-46).
Auxiliary functions and data sets for "Ecological Models and Data", a book presenting maximum likelihood estimation and related topics for ecologists (ISBN 978-0-691-12522-0).
Package providing calculation of Earth Mover's Distance
(EMD).
Provides basic distribution functions for a mixture model of a Gaussian and exponential distribution.
A toolbox for designing and evaluating predictive models with
resampling methods. The aim of this package is to provide a simple and
efficient general framework for working with any type of prediction
problem, be it classification, regression or survival analysis, that is
easy to extend and adapt to your specific setting. Some commonly used
methods for classification, regression and survival analysis are included.
Various Expectation-Maximization (EM) algorithms are implemented for item response theory
(IRT) models. The current implementation includes IRT models for binary and ordinal
responses, along with dynamic and hierarchical IRT models with binary responses. The
latter two models are derived and implemented using variational EM.
The evolutionary model-based multiresponse approach (EMMA)
is a novel methodology to process optimisation and product
improvement. The approach is suitable to contexts in which the
experimental cost and/or time limit the number of implementable
trials.
This package provides a set of functions for convenient end-member modelling analysis of grain-size data (EMMAgeo).
This package includes functions to read and write to an
EMME/2 databank
For forming contrasts in the mixed effects for mixtures of linear mixed models fitted to the gene profiles.
EM algorithm for Mixture of Multivariate Skew Normal/T Distributions
Functions to fit finite mixture of unrestricted multivariate skew t (FM-uMST) model, random sample generation, discriminant analysis, 2D and 3D contour plots
The main functions are 'emmreml', and 'emmremlMultiKernel'. 'emmreml' solves a mixed model with known covariance structure using the 'EMMA' algorithm.  'emmremlMultiKernel' is a wrapper for 'emmreml' to handle multiple random components with known covariance structures. The function 'emmremlMultivariate' solves a multivariate gaussian mixed model with known covariance structure using the 'ECM' algorithm.
Collection of building blocks for the design and analysis
of evolutionary multiobjective optimization algorithms.
An R package for fixation and saccade detection in eye movement recordings. Eye movements consist of alternating saccades and fixations. This package implements a dispersion-based algorithm (I-DT) proposed by Salvucci & Goldberg (2000) which detects fixation duration and position.
Functions for estimating EMP (Expected Maximum Profit Measure) in Credit Risk Scoring and Customer Churn Prediction, according to Verbraken et al (2013, 2014).
Auxiliary functions for the DESeq2 package to simulate read counts according to the null hypothesis (i.e., with empirical sample size factors, per-gene total counts and dispersions, but without effects of predictor variables) and to compute the empirical false discovery rate.
Empirical likelihood ratio tests for means/quantiles/hazards
from possibly censored and/or truncated data. Now does regression too.
This version contains some C code.
Calculates the p-value for a mean-type hypothesis (or multiple mean-type hypotheses) based on two samples with censored data.
The package provides functions to carry out a
Goodness-of-fit test for discrete multivariate data. It is
tested if a given observation is likely to have occurred under
the assumption of an ab-initio model. A p-value can be
calculated using different distance measures between observed
and expected frequencies. A Monte Carlo method is provided to
make the package capable of solving high-dimensional problems.
This package allows one to estimate the output of a computer program,
as a function of the input parameters, without actually running it.
The computer program is assumed to be a Gaussian process, whose
parameters are estimated using Bayesian techniques that give a PDF of
expected program output.  This PDF is conditional on a ``training set''
of runs, each consisting of a point in parameter space and the model
output at that point.  The emphasis is on complex codes that take
weeks or months to run, and that have a large number of undetermined
input parameters; many climate prediction models fall into this
class.  The emulator essentially determines Bayesian posterior
estimates of the PDF of the output of a model, conditioned on results
from previous runs and a user-specified prior linear model.  A
working example is given in the help page for function `interpolant()',
which should be the first point of reference.
Contains logic for the data-driven optimization of annotations via minimization of the entropy of variable group members over discrete variable clusters.
Provides algorithms for the analysis of ecological networks.
endogMNP is an R package that fits a Bayesian multinomial
probit model with endogenous selection, which is sometimes
called an endogenous switching model.  This can be used to
model discrete choice data when respondents select themselves
into one of several groups.  This package is based on the MNP
package by Kosuke Imai and David A. van Dyk.  This package
modifies their code.
This R package implements the statistical model proposed by Bullock, Imai, and Shapiro (2011; Political Analysis) to analyze endorsement experiments.  Endorsement experiments are a survey methodology for eliciting truthful responses to sensitive questions.  This methodology is helpful when measuring support for socially sensitive political actors such as militant groups.  The model is fitted with the Markov chain Monte Carlo algorithm and produces the output containing draws from the posterior distribution.
E-statistics (energy) tests and statistics for comparing
distributions: multivariate normality, multivariate distance
components and k-sample test for equal distributions,
hierarchical clustering by e-distances, multivariate
independence tests, distance correlation, goodness-of-fit
tests. Energy- statistics concept based on a generalization of
Newton's potential energy is due to Gabor J. Szekely.
Allow numbers to be presented in an English language
version, one, two, three, ...
Datasets from Nelson, Coffin and Copeland "Introductory
Statistics for Engineering Experimentation" (Elsevier, 2003)
with sample code.
The company 'Enigma' (https://enigma.io) holds many public 'datasets' from
governments, companies, universities, and organizations. 'Enigma' provides an
'API' for data, 'metadata', and statistics on each of the 'datasets'. 'enigma' is
a client to interact with the 'Enigma' 'API', including getting the data
and 'metadata' for 'datasets' in 'Enigma', as well as collecting statistics on
'datasets'. In addition, you can download a 'gzipped' 'csv' file of a 'dataset'
if you want the whole 'dataset'. An 'API' key from 'Enigma' is required to use
'enigma'.
The package allows to perform the Ecological Niche Factor Analysis, calculate habitat suitability maps and classify the habitat in suitability classes. Computations are executed in a throw-away GRASS environment from R in order to be able to perform analysis with large data sets.
Automatically partitions data into evaluation bins, executes ecological niche models across a range of settings, and calculates a variety of evaluation statistics.  Current version only implements ENMs with Maxent (Phillips et al. 2006).
The ENmisc package contains utility function for different
purposes: mtapply and mlapply (multivariate version of tapply
and lapply), wtd.boxplot (a boxplot with weights), and a visual
interface to restructuring mosaic plots.
R package for ensemble partial least squares regression, a unified
framework for feature selection, outlier detection, and ensemble learning.
These programs are used for calculating enrichment
factors, drawing enrichment curves to evaluate virtual
screening approaches.
Extensible S4 classes and methods for batch training of regression and classification algorithms such as Random Forest, Gradient Boosting Machine, Neural Network, Support Vector Machines, and K-Nearest Neighbors. These algorithms constitute a set of 'base learners', which can subsequently be combined together to form ensemble predictions. This package provides cross-validation wrappers to allow for downstream application of ensemble integration techniques, including best-error selection. All base learner estimation objects are retained, allowing for repeated prediction calls without the need for re-training. For large problems, an option is provided to save estimation objects to disk, along with prediction methods that utilize these objects. This allows users to train and predict with large ensembles of base learners without being constrained by system RAM.
Bayesian Model Averaging to create probabilistic forecasts
from ensemble forecasts and weather observations.
This package extends the base classes and methods of EnsembleBase package for cross-validation-based integration of base learners. Default implementation calculates average of repeated CV errors, and selects the base learner / configuration with minimum average error. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. The package can be extended, e.g. by adding variants of the current implementation.
Ensemble Model Output Statistics to create probabilistic
forecasts from ensemble forecasts and weather observations.
This package extends the base classes and methods of EnsembleBase package for Principal-Components-Regression-based (PCR) integration of base learners. Default implementation uses cross-validation error to choose the optimal number of PC components for the final predictor. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in EnsembleBase package as well as this package.
Extending the base classes and methods of EnsembleBase package for Penalized-Regression-based (Ridge and Lasso) integration of base learners. Default implementation uses cross-validation error to choose the optimal lambda (shrinkage parameter) for the final predictor. The package takes advantage of the file method provided in EnsembleBase package for writing estimation objects to disk in order to circumvent RAM bottleneck. Special save and load methods are provided to allow estimation objects to be saved to permanent files on disk, and to be loaded again into temporary files in a later R session. Users and developers can extend the package by extending the generic methods and classes provided in EnsembleBase package as well as this package.
Add simple runtime contracts to R values. These ensure that values
fulfil certain conditions and will raise appropriate errors if they do not.
Measurement and partitioning of diversity, based on Tsallis entropy.
This package implements various estimators of entropy, such
as the shrinkage estimator by Hausser and Strimmer, the maximum likelihood
and the Millow-Madow estimator, various Bayesian estimators, and the
Chao-Shen estimator.  It also offers an R interface to the NSB estimator.
Furthermore, it provides functions for estimating Kullback-Leibler divergence,
chi-squared, mutual information, and chi-squared statistic of independence.
In addition there are functions for discretizing continuous random variables.
Contains methods for the estimation of Shannon's entropy, variants of Renyi's entropy, mutual information, Kullback-Leibler divergence, and generalized Simpson's indices. The estimators used have a bias that decays exponentially fast.
Rows of two matrices are compared for Shannon entropy,
coefficient of variation, and expression. P-values can be requested for all metrics.
Fast and very memory-efficient calculation of isotope patterns,
subsequent convolution to theoretical envelopes (profiles) plus valley
detection and centroidization or intensoid calculation. Batch processing,
resolution interpolation, wrapper, adduct calculations and molecular
formula parsing.
Sequential partitioning, clustering and peak detection of
centroided LC-MS mass spectrometry data (.mzXML). Interactive result and raw
data plot.
Functions and datasets to support the book by Nhu D Le and James V Zidek, Springer (2006).
Plot environmental conditions with a high number of species, create boxplots with a range of environmental variables under different environmental conditions selected by the user, list species under the different environmental conditions selected by the user and create a summary of the environmental conditions, using an output file of ModestR (www.ipez.es/ModestR).
Graphical and statistical analyses of environmental data, with
focus on analyzing chemical concentrations and physical parameters, usually in
the context of mandated environmental monitoring.  Major environmental
statistical methods found in the literature and regulatory guidance documents,
with extensive help that explains what these methods do, how to use them,
and where to find them in the literature.  Numerous built-in data sets from
regulatory guidance documents and environmental statistics literature.  Includes
scripts reproducing analyses presented in the book "EnvStats:  An R Package for
Environmental Statistics" (Millard, 2013, Springer).
A collection of nice plotting functions directly from a
data.frame with limited customisation possibilities.
Functions for demographic and epidemiological analysis in
the Lexis diagram, i.e. register and cohort follow-up data, in
particular representation and manipulation of multistate data - the
Lexis suite of functions.
Includes interfaces to mstate, etm and cmprsk packages.
Also contains functions for Age-Period-Cohort modeling and a
function for interval censored data and some useful functions for
tabulation and plotting, as well some epidemiological datasets.
This package contains elementary tools for analysis of
common epidemiological problems, ranging from sample size
estimation, through 2x2 contingency table analysis and basic
measures of agreement (kappa, sensitivity/specificity).
Appropriate print and summary statements are also written to
facilitate interpretation wherever possible.  Source code is
commented throughout to facilitate modification.  The target
audience includes advanced undergraduate and graduate students
in epidemiology or biostatistics courses, and clinical
researchers.
Hierarchical Bayesian models for use in disease
freedom and disease prevalence studies, designed
with epidemiological applications in mind. The models
themselves are in the spirit of those presented in Branscum
et al. (2006) (see package documentation for full
reference). The helper functions and methods were designed
to make implementation and processing of the complex model
output relatively simple in application.
Routines for epidemiological contact tracing
and visualisation of network of contacts.
Package for data exploration and result presentation.
Full 'epicalc' package with data management functions is available
at the author's repository.
Mathematical models of infectious diseases in humans and animals.
Both, deterministic and stochastic models can be simulated and plotted.
This package provides tools to quantify transmissibility
throughout an epidemic from the analysis of time series of
incidence.
Tools for simulating mathematical models of infectious disease.
Epidemic model classes include deterministic compartmental models,
stochastic individual contact models, and stochastic network models.
Disease types include SI, SIR, and SIS epidemics with and without
demography, with tools available for expansion to model complex epidemic
processes.
A collection of epidemic/network-related tools. Simulates transmission of diseases through contact networks. Performs Bayesian inference on network and epidemic parameters, given epidemic data.
An R package for the analysis of epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.
Basic sensitivity analysis of the observed relative risks adjusting
for unmeasured confounding and misclassification of the
exposure/outcome, or both. It follows the bias analysis methods and
examples from the book by Lash T.L, Fox M.P, and Fink A.K. Applying
Quantitative Bias Analysis to Epidemiologic Data, Springer, 2009.
Produce one-dimensional density estimates using
exponential epi-splines. The user may incorporate soft information, by
imposing constraints that (i) require unimodality; (ii) require that the
density be monotone non-increase or non-decreasing; (iii) put upper bounds
on first or second moments; (iv) bound the density's values at mesh points;
(v) require that the estimate be continuous or continuously differentiable;
and more.
EpiTools: R Package for Epidemiologic Data and Graphics
Aim: Adjust the graphical parameters to create nicer
longitudinal series plots. The default set of graphical parameters is very
general, and can be improved upon when we are interested in plotting data
points observed over time. Functions facilitate plotting those kind of
series, univariate plots, bivariate plots (with vertical axis on both left
and right hand sides), multivariate plots and plots which allow to examine
whether a new observation is 'unusual' via construction and visualization
of prediction intervals around it.
Estimates sparse matrices A or G using fast lasso regression from mRNA transcript levels Y and CNA profiles U. Two models are provided, EPoC A where
AY + U + R = 0
and EPoC G where
Y = GU + E,
the matrices R and E are so far treated as noise. For details see the reference and the manual page of `lassoshooting'.
The package performs analysis of polynomial regression in simple designs with quantitative treatments
Computation of the EQL for a given family of variance
functions, Saddlepoint-approximations and related auxiliary
functions (e.g. Hermite polynomials)
Transitioning from EQS to R for structural equation modeling (SEM)
is made easier with a set of functions to convert .out files into R code.
The EQS output can be converted into lavaan syntax and run in the R
environment.  Other functions parse descriptive statistics and the covariance matrix
from an EQS .out file.  A heat map plot of a covariance matrix is also
included.
Analysis of experimental crosses to identify genes (called
quantitative trait loci, QTLs) contributing to variation in
quantitative traits.
Contains methods for observed-score linking
and equating under the single-group, equivalent-groups,
and nonequivalent-groups with anchor test(s) designs.
Equating types include identity, mean, linear, general
linear, equipercentile, circle-arc, and composites of
these. Equating methods include synthetic, nominal
weights, Tucker, Levine observed score, Levine true
score, Braun/Holland, frequency estimation, and chained
equating. Plotting and summary methods, and methods for
multivariate presmoothing and bootstrap error estimation
are also provided.
This package computes direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items.
Provides statistical tests and graphics for assessing tests
of equivalence.  Such tests have similarity as the alternative
hypothesis instead of the null.  Sample data sets are included.
Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression.
Functions, datasets, and sample codes related to the book of 'Empirical Research in Economics: Growing up with R' by Dr. Changyou Sun are included. The book is still under preparation as of 2014. Marginal effects for binary or ordered choice models can be calculated. Static and dynamic Almost Ideal Demand System (AIDS) models can be estimated. A typical event analysis in finance can be conducted with several functions included.
An integrated set of tools to analyze and simulate networks based on exponential-family random graph models (ERGM). "ergm" is a part of the "statnet" suite of packages for network analysis.
A set of extensions for the 'ergm' package to fit weighted networks whose edge weights are counts.
Integrates graphlet statistics based model terms for use in exponential-family random graph models ('ergm') as part of the 'statnet' suite of packages.
Data for use with the Sage Introduction to Exponential
Random Graph Modeling text by Jenine K. Harris.  Network data
set consists of 1283 local health departments and the
communication links among them along with several attributes.
A template package to demonstrate the use of user-specified statistics for use in "ergm" models as part of the "statnet" suite of packages.
eRm fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM).  Missing values are allowed in the data matrix.  Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Löf-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, various ICC and related plots, automated stepwise item elimination, simulation module for various binary data matrices.  An eRm platform is provided at R-forge (see URL).
The functions provided in the package ERP are designed for the significance analysis of ERP data in a linear model framework. The possible procedures are either the collection of FDR or FWER controlling methods available in the generic function p.adjust, the same collection combined with a factor modeling of the time dependence among tests (see Sheu, Perthame, Lee and Causeur, 2014) and the Guthrie and Buchwald (1991) test.
This package is dedicated to the analysis of event-related potentials (ERPs). Event-related potentials are the measured brain responses associated with a specific sensory, cognitive, or motor event and are obtained from electroencephalographic (EEG) signal. The erpR package contains a series of functions for importing ERP data, computing traditional ERP measures, exploratory ERP analyses and plotting.
Implementation of the Edge Selection Algorithm
These functions estimate the latent factors of a given matrix, no matter it is high-dimensional or not. It tries to first estimate the number of factors using bi-cross-validation and then estimate the latent factor matrix and the noise variances. For more information about the method, see Art B. Owen and Jingshu Wang 2015 archived article on factor model (http://arxiv.org/abs/1503.03515).
The package can identify the dysregulated canonical pathways by investigating the changes of biological relationships of pathways in the context of gene expression data. (1) The ESEA package constructs a background set of edges by extracting pathway structure (e.g. interaction, regulation, modification, and binding etc.) from the seven public databases (KEGG; Reactome; Biocarta; NCI; SPIKE; HumanCyc; Panther) and the edge sets of pathways for each of the above databases. (2) The ESEA package can can quantify the change of correlation between genes for each edge based on gene expression data with cases and controls. (3) The ESEA package uses the weighted Kolmogorov-Smirnov statistic to calculate an edge enrichment score (EES), which reflects the degree to which a given pathway is associated the specific phenotype. (4) The ESEA package can provide the visualization of the results.
The package presents a "Scenarios" class containing
general parameters, risk parameters and projection results.
Risk parameters are gathered together into a ParamsScenarios
sub-object. The general process for using this package is to
set all needed parameters in a Scenarios object, use the
customPathsGeneration method to proceed to the projection, then
use xxx_PriceDistribution() methods to get asset prices.
Toolkit for Monte Carlo simulations of financial assets and
interest rates models, involved in an Economic Scenario Generator (ESG).
The underlying simulation loops have been implemented in C++.
Estimates item and person parameters for the Samejima's Continuous Response Model (CRM), computes item fit residual statistics, draws empirical 3D item category response curves, draws theoretical 3D item category response curves, and generates data under the CRM for simulation studies.
Our method is a variable selection method to select active components in sparse linear mixed models in order to estimate the heritability. The selection allows us to reduce the size of the data sets which improves the accuracy of the estimations. Our package also provides a confidence interval for the estimated heritability.
Provides tools for determining estimability of linear functions of regression coefficients,
and 'epredict' methods that handle non-estimable cases correctly.
This package is intended to speedup the process of
creating model-comparing tables common in Macroeconomics. The
function collection stores the estimates of several models and
formats it to a table of the form estimate starred and std.err.
below. The default output is LaTeX but output to CSV for later
editing in a spreadsheet tool is possible as well. It works for
linear models (lm) and panel models from the "plm"-package
(plm). Two further implemented functions "descsto" and
"desctab" enable you to export descriptive statistics of
data-frames and single variables to LaTeX and CSV.
This package deals with the estimation of the jump rate for piecewise-deterministic Markov processes (PDMPs), from only one observation of the process within a long time. The main functions provide an estimate of this function. The state space may be discrete or continuous. The associated paper has been published in Scandinavian Journal of Statistics and is given in references. Other functions provide a method to simulate random variables from their (conditional) hazard rate, and then to simulate PDMPs.
A table function for descriptive statistics in tabular format, using variables in a data.frame. You can create simple or highly customized tables.
A package for fitting the ETAS model to earthquake
catalogoues ETAS model is a spatio-temporal marked point
process model and a special case of the Hawcks process
Estimation of the components of an ETAS model for earthquake description. Non-parametric background seismicity can be estimated through FLP (Forward Likelihood Predictive), while parametric components are estimated through maximum likelihood. The two estimation steps are alternated until convergence is obtained. For each event the probability of being a background event is estimated and used as a weight for declustering steps. Many options to control the estimation process are present, together with some diagnostic tools. Some descriptive functions for earthquakes catalogs are  present; also plot, print, summary, profile methods are defined for main output (objects of class 'etasclass').
The package allows selecting those treatments of a one-way layout
being equivalent to a control. Bonferroni adjusted "two one-sided t-tests"
(TOST) and related simultaneous confidence intervals are given for both
differences or ratios of means of normally distributed data. For the case of
equal variances and balanced sample sizes for the treatment groups, the
single-step procedure of Bofinger and Bofinger (1995) can be chosen. For
non-normal data, the Wilcoxon test is applied.
Provides functions to facilitate the use of the 'ff' package in
interaction with big data in 'SQL' databases (e.g. in
'Oracle', 'MySQL', 'PostgreSQL', 'Hive') by allowing easy importing directly into 'ffdf'
objects using 'DBI', 'RODBC' and 'RJDBC'. Also contains some basic utility
functions to do fast left outer join merging based on 'match', factorisation of data and a basic
function for re-coding vectors.
Matrix of transition probabilities for any time-inhomogeneous multistate model with finite state space
An eulerian path is a path in a graph which visits every edge exactly once. This package provides methods to handle eulerian paths or cycles.
Tools to download data from the Eurostat database
http://ec.europa.eu/eurostat/ together with search and manipulation
utilities.
Provides functions for evaluating (time series) model
estimation methods. These facilitate Monte Carlo experiments of repeated
simulations and estimations. Also provides methods for
looking at the distribution of the results from these experiments,
including model roots (which are an equivalence class invariant).
Parsing and evaluation tools that make it easy to recreate the
command line behaviour of R.
Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 17 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.
Package for combining pieces of evidence
Extends simulation, distribution, quantile and density
functions to univariate and multivariate parametric extreme
value distributions, and provides fitting functions which
calculate maximum likelihood estimates for univariate and
bivariate maxima models, and for univariate and bivariate
threshold models.
Provides functions for the bayesian analysis of extreme
value models, using MCMC methods.
Compute a matrix comparison of lists (from 2 to infinite) and plot the results in a Venn diagram if (N<=4) with regulation details. It allows to produce a complete annotated file, merging the annotations of the compared lists. It is also possible to compute an overlaps table to show the overlaps proportions of all the couples of lists and draw proportional Venn diagrams.
Functions for analysis of rate changes in sequential events
Stores, manipulates, aggregates and otherwise messes with event
data from KEDS/TABARI or any other extraction tool with similar output
Implementation of short and long term event study
methodology
Functions for extreme value theory, which may be divided
into the following groups; exploratory data analysis, block
maxima, peaks over thresholds (univariate and bivariate), point
processes, gev/gpd distributions.
The usual distribution functions, maximum likelihood inference and
model diagnostics for univariate stationary extreme value mixture models
are provided. Kernel density estimation including various boundary
corrected kernel density estimation methods and a wide choice of kernels,
with cross-validation likelihood based bandwidth estimator.
Reasonable consistency with the base functions in the 'evd' package is
provided, so that users can safely interchange most code.
evobiR is a collection of tools for use in evolutionary biology
Provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools.
An implementation of the evolvability parameters defined in Hansen and Houle (2008).
Uses maximum likelihood to estimate rates of trait evolution across environmental gradients.
Computes extreme value index (EVI) estimate for heavy tailed models by Mean of order p (MOP)
and peaks over random threshold (PORT) Hill methodologies.
Besides, also computes moment, generalised Hill and mixed moment estimates for EVI.
Compute high quantile or value-at-risk (VaR) based on above EVI estimates.
Commonly used classification and regression tree methods like the CART algorithm
are recursive partitioning methods that build the model in a forward stepwise search.
Although this approach is known to be an efficient heuristic, the results of recursive
tree methods are only locally optimal, as splits are chosen to maximize homogeneity at
the next step only. An alternative way to search over the parameter space of trees is
to use global optimization methods like evolutionary algorithms. The evtree package
implements an evolutionary algorithm for learning globally optimal classification and
regression trees in R. CPU and memory-intensive tasks are fully computed in C++ while
the partykit package is leveraged to represent the resulting trees in R, providing
unified infrastructure for summaries, visualizations, and predictions.
An implementation of a large number of the goodness-of-fit tests for the exponential and Weibull distributions classified into families: the tests based on the empirical distribution function, the tests based on the probability plot, the tests based on the normalized spacings, the tests based on the Laplace transform and the likelihood-based tests.
Performs unconditional exact tests and power calculations for 2x2 contingency
tables. Unconditional exact tests are often more powerful than conditional exact tests and asymptotic tests.
Calculates Fisher's exact test, Blaker's exact test, or the exact McNemar's test with appropriate matching confidence intervals. Provides power and sample size calculations. Also gives melded confidence intervals for the binomial case.
Calculates exact tests and confidence intervals for one-sample binomial and one- or two-sample Poisson cases.
This is a package for exact Confidence Intervals for the
difference between two independent or dependent proportions.
Monte Carlo and MCMC goodness of fit tests for log-linear
models
Perform exact fixed effect meta analysis for rare events data without the need of artificial continuity correction.
ExactPath implements an algorithm for exact LASSO
solution. Two methods are provided to print and visualize the
whole solution paths. Use ?ExactPath to see an introduction.
Packages ncvreg and lars are required so that their data sets
can be used in examples.
Computes exact conditional p-values and quantiles using an
implementation of the Shift-Algorithm by Streitberg & Roehmel.
Sweave-based automatic generation of exams including multiple-choice
questions and arithmetic problems. Exams can be produced in various formats, including
PDF, HTML, Moodle XML, QTI 1.2 (for OLAT/OpenOLAT), QTI 2.1.
Tools for constructing confidence regions for exceedance regions
and contour lines.
Allows access to data in running instance of Microsoft Excel
(e. g. 'xl[a1] = xl[b2]*3' and so on). Graphics can be transferred with
'xl[a1] = current.graphics()'. There is an Excel workbook with examples of
calling R from Excel in the 'doc' folder. It tries to keep things as
simple as possible - there are no needs in any additional
installations besides R, only 'VBA' code in the Excel workbook.
Microsoft Excel is required for this package.
Interactive tools to explore topographic-like data
sets.  Such data sets take the form of a matrix in which the rows and
columns provide location/frequency information, and the matrix elements
contain altitude/response information.  Such data is found in cartography,
2D spectroscopy and chemometrics.  The functions in this package create
interactive web pages showing the contoured data, possibly with
slices from the original matrix parallel to each dimension. The interactive
behavior is created using the D3.js 'JavaScript' library by Mike Bostock.
Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package.
Calls copy number variants (CNVs) from targeted sequence data, typically exome sequencing experiments designed to identify the genetic basis of Mendelian disorders.
Expanding Ploidy and Allele Frequency on Nested Subpopulations (ExPANdS) characterizes coexisting subpopulations in a tumor using copy number and allele frequencies derived from exome- or whole genome sequencing input data (http://www.ncbi.nlm.nih.gov/pubmed/24177718). The model amplifies the statistical power to detect coexisting genotypes, by exploiting run-specific tradeoffs between depth of coverage and breadth of coverage. ExPANdS predicts the number of clonal expansions, the size of the resulting subpopulations in the tumor bulk, the mutations specific to each subpopulation and tumor purity. The main function runExPANdS provides the complete functionality needed to predict coexisting subpopulations from single nucleotide variations (SNVs) and associated copy numbers.  The robustness of the subpopulation predictions by ExPANdS increases with the number of mutations provided. It is recommended that at least 200 mutations are used as an input to obtain stable results. Updates include: (1) Additional optional parameter "min_CellFreq" provided for function runExPANdS, specifying the minimum cellular prevalence of mutations to be included for subpopulation predictions.(2) Filtered loci with high-level amplifications, according to max_PM setting. This reduces unnecessary processing time, as assignment of mutations within amplified regions to subpopulations is not successful. (3) Additional function "buildMultiSamplePhylo" available, which integrates the subpopulations predicted in multiple, geographically distinct tumor samples into one common phylogeny.
Package for analysis of simple experimental designs (CRD,
RBD and LSD), experiments in double factorial schemes (in CRD
and RBD), experiments in a split plot in time schemes (in CRD
and RBD), experiments in double factorial schemes with an
additional treatment (in CRD and RBD), experiments in triple
factorial scheme (in CRD and RBD) and experiments in triple
factorial schemes with an additional treatment (in CRD and
RBD), performing the analysis of variance and means comparison
by fitting regression models until the third power
(quantitative treatments) or by a multiple comparison test,
Tukey test, test of Student-Newman-Keuls (SNK), Scott-Knott,
Duncan test, t test (LSD) and Bonferroni t test (protected LSD)
- for qualitative treatments.
Pacote destinado a analise de delineamentos experimentais
simples (DIC, DBC e DQL), experimentos em esquema de fatorial
duplo (em DIC e DBC), experimentos em esquema de parcelas
subdivididas no tempo (em DIC e DBC), experimentos em esquema
de fatorial duplo com um tratamento adicional (em DIC e DBC),
experimentos em esquema de fatorial triplo (em DIC e DBC) e
experimentos em esquema de fatorial triplo com um tratamento
adicional (em DIC e DBC); realizando a analise de variancia e
comparacao de medias pelo ajuste de modelos de regressao ate o
terceiro grau (tratamentos quantitativos) ou por testes de
comparacao multipla: teste de Tukey, teste de
Student-Newman-Keuls (SNK), teste de Scott-Knott, teste de
Duncan, teste t (LSD), teste t de Bonferroni (LSD protegido) e
teste Bootstrap - tratamentos qualitativos.
Expectile and quantile regression of models with nonlinear effects
e.g. spatial, random, ridge using least asymmetric weighed squares / absolutes
as well as boosting; also supplies expectiles for common distributions.
The package provides various statistical methods for
designing and analyzing randomized experiments. One main
functionality of the package is the implementation of
randomized-block and matched-pair designs based on possibly
multivariate pre-treatment covariates. The package also
provides the tools to analyze various randomized experiments
including cluster randomized experiments, randomized
experiments with noncompliance, and randomized experiments with
missing data.
Expert opinion (or judgment) is a body of techniques to
estimate the distribution of a random variable when data is scarce
or unavailable. Opinions on the quantiles of the distribution are
sought from experts in the field and aggregated into a final
estimate. The package supports aggregation by means of the Cooke,
Mendel-Sheridan and predefined weights models.
Computation of the matrix exponential and related quantities.
An R-interface to the Fortran package Expokit.
ExPosition is for descriptive (i.e., fixed-effects) multivariate analysis with the singular value decomposition.
Calculates the density dependent likelihood of a phylogenetic tree. It takes branching and sampling times as an argument and integrates the likelihood function over the whole tree.
Tools and data to accompany Schlicht, Valcu and Kempenaers "Spatial patterns of extra-pair paternity: beyond paternity gains and losses"
Data sets from the book "Forecasting with exponential smoothing: the state space approach" by
Hyndman, Koehler, Ord and Snyder (Springer, 2008).
Tests for the composite hypothesis of exponentiality
The package provides tools for botanists, plant taxonomists,
curators of plant genebanks and perhaps other biological collections.
A consistent, unified and extensible
framework for estimation of parameters for probability distributions, including
parameter estimation procedures that allow for weighted samples; the current set of distributions included are: the standard beta, The four-parameter beta, Burr, gamma, Gumbel, Johnson SB and SU, Laplace, logistic, normal, symmetric truncated normal, truncated normal, symmetric-reflected truncated beta, standard symmetric-reflected truncated beta, triangular, uniform, and Weibull distributions; decision criteria and selections based on these decision criteria.
This is a package containing the function extfunnel()
which produces a funnel plot including additional augmentations
such as statistical significance contours and heterogeneity
contours.
The package estimates coefficients of extended LASSO penalized linear regression and generalized linear models. Currently lasso and elastic net penalized linear regression and generalized linear models are considered. The package currently utilizes an accurate approximation of L1 penalty and then a modified Jacobi algorithm to estimate the coefficients. There is provision for plotting of the solutions and predictions of coefficients at given values of lambda. The package also contains functions for cross validation to select a suitable lambda value given the data. The package also provides a function for estimation in fused lasso penalized linear regression.
This package tests for differences in minor allele
frequency between groups and is based on an extra-binomial
variation model for pooled sequencing data.
Tools to using fonts other than the standard PostScript fonts.
This package makes it easy to use system TrueType fonts and with PDF or
PostScript output files, and with bitmap output files in Windows. extrafont
can also be used with fonts packaged specifically to be used with, such as
the fontcm package, which has Computer Modern PostScript fonts with math
symbols. See https://github.com/wch/extrafont for instructions and
examples.
Package for holding the database for the extrafont package
Classification and regression based on an ensemble of decision trees. The package also provides extensions of ExtraTrees to multi-task learning and quantile regression. Uses Java implementation of the method.
An implementation of Extreme Bounds Analysis (EBA), a global sensitivity analysis that examines the robustness of determinants in regression models. The package supports both Leamer's and Sala-i-Martin's versions of EBA, and allows users to customize all aspects of the analysis.
Functions for performing extreme value analysis.
Detect outliers in one-dimensional data.
It implements the subjectively censored Weibull MLE and censored Weibull mixture methods for the lower quantile estimation. Quantile estimates from these two methods are robust to model misspecification in the lower tail. It also includes functions to evaluation the standard error of the resulting quantile estimates. Also, the methods here can be used to fit the Weibull or Weibull mixture for the Type-I or Type-II right censored data.
Misc function for working with eyetracking data
This package facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design.
This package implements a simplified version of least
squares, and logistic regression for efficiently selecting the
significant non-additive interactions between two variables.
ezsim provides a handy way to run simulation and examine its result
This method tries to explain the gene-wise treatment response ratios in terms of the latent pathways. It uses bayesian sparse factor modeling to infer the loadings (weights) of each pathway on its associated probesets as well as the latent factor activity levels for each treatment.
The package estimates Factor Mixture Analysis via the EM
algorithm
Multivariate exploration of a data table with factorial
analysis and cluster methods.
Exploratory data analysis methods such as principal component methods and clustering.
Calculate, print, summarize and plot pairwise differences from GLMs, GLHT or Multinomial Logit models
Package to fit Bayesian quantile regression models that
assume a factor structure for at least part of the design
matrix.
Perform factorial analysis with a menu and draw graphs interactively thanks to FactoMineR and a Shiny application.
Provides functions to score the Functional Assessment of Cancer
Therapy (FACT) and Functional Assessment of Chronic Illness Therapy (FACIT)
family of patient-reported outcome (PRO) measures.  The questionnaires
themselves can be downloaded from www.FACIT.org.  For most of the FACIT
questionnaires, FACIT.org provides scoring syntax for use with commercial
statistical software (SAS and SPSS).  The FACTscorer R package is intended
to serve as a free, reliable alternative for those without access to SAS or
SPSS.  Additionally, it will allow R users to both score and analyze the
FACT and FACIT scales in R, avoiding the time-consuming and and error-prone
process of transferring data back-and-forth between statistical software.
Finally, use of the FACTscorer package will prevent many sources of scoring
error common when using SAS and/or SPSS syntax (e.g., copy-paste errors and
other accidental modifications to the syntax).
Per the Factual.com website, "Factual is a platform where
anyone can share and mash open, living data on any subject."
The data is in the form of tables and is accessible via REST
API.  The factualR package is a thin wrapper around the
Factual.com API, to make it even easier for people working with
R to explore Factual.com data sets.
The functions provided in the FADA (Factor Adjusted Discriminant Analysis) package aim at performing supervised classification of high-dimensional and correlated profiles. The procedure combines adecorrelation step based on a
factor modeling of the dependence among covariates and a classification method. The available methods are Lasso regularized logistic model
(see Friedman et al. (2010)), sparse linear discriminant analysis (see
Clemmensen et al. (2011)), shrinkage linear and diagonal discriminant
analysis (see M. Ahdesmaki et al. (2010)). More methods of classification can be used on the decorrelated data provided by the package FADA.
Probability distributions that are sometimes useful in hydrology.
Data and functions for the book "Multivariate Statistical
Modelling Based on Generalized Linear Models", first edition, by
Ludwig Fahrmeir and Gerhard Tutz.  Useful when using the book.
More comfortable interface to work with a directory of R data or
source files
This package estimates factor analysis models using a
genetic algorithm, which permits a general mechanism for
restricted optimization with arbitrary restrictions that are
chosen at run time with the help of a GUI. Importantly,
inequality restrictions can be imposed on functions of multiple
parameters, which provides a new avenues for testing and
generating theories with factor analysis models. This package
also includes an entirely new estimator of the common factor
analysis model called semi-exploratory factor analysis, which
is a general alternative to exploratory and confirmatory factor
analysis. Finally, this package integrates a lot of other
packages that estimate sample covariance matrices and thus
provides a lot of alternatives to the traditional sample
covariance calculation. Note that you need to have the Gtk run
time library installed on your system to use this package; see
the URL below for detailed installation instructions. Most
users would only need to understand the first twenty-four pages
of the PDF manual.
It is used for systematic analysis of decisions based on attributes and its levels.
This is a method for Allele-specific DNA Copy Number Profiling using Next-Generation Sequencing.  Given the allele-specific coverage at the variant loci, this program segments the genome into regions of homogeneous allele-specific copy number.  It requires, as input, the read counts for each variant allele in a pair of case and control samples. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.
A value is falsy if it is NULL, FALSE, the empty string,
zero, or an empty vector or list. Other values are truthy. The new
%&&% and %||% operators work with falsy and truthy values and
can be used for concise conditional execution.
An interface to the core Familias functions (www.familias.name),
which are programmed in C++.
Fits penalized linear and logistic regression models with pairwise interaction terms.
Estimate parameters of univariate probability distributions
with maximum likelihood and Bayesian methods.
The method proposed in this package takes into account the impact of dependence on the multiple testing procedures for high-throughput data as proposed by Friguet et al. (2009). The common information shared by all the variables is modeled by a factor analysis structure. The number of factors considered in the model is chosen to reduce the false discoveries variance in multiple tests. The model parameters are estimated thanks to an EM algorithm. Adjusted tests statistics are derived, as well as the associated p-values. The proportion of true null hypotheses (an important parameter when controlling the false discovery rate) is also estimated from the FAMT model. Graphics are proposed to interpret and describe the factors.
An R package "fanc" computes the penalized maximum
likelihood estimates of factor loadings and unique variances
for various tuning parameters. The pathwise coordinate descent
along with EM algorithm is used.  This package also includes a
new graphical tool which outputs path diagram, goodness-of-fit
indices and model selection criteria for each regularization
parameter. The user can change the regularization parameter by
manipulating scrollbars, which is helpful to find a suitable
value of regularization parameter.
This package contains a collection of R functions to
perform nonparametric analysis of covariance for regression
curves or surfaces. Testing the equality or parallelism of
nonparametric curves or surfaces is equivalent to analysis of
variance (ANOVA) or analysis of covariance (ANCOVA) for
one-sample functional data. Three different testing methods are
available in the package, including one based on L-2 distance,
one based on an ANOVA statistic, and one based on variance
estimators.
Estimation and plotting of a function's FANOVA graph to identify the interaction structure and fitting, prediction and simulation of a kriging model modified by the identified structure. The interactive function plotManipulate can only be run on the RStudio IDE with RStudio's package manipulate loaded. RStudio is freely available (www.rstudio.org), and includes package manipulate. The equivalent function plotTk bases on CRAN Repository packages only.
Visualise sequential distributions using a range of plotting
styles. Sequential distribution data can be input as either simulations or
values corresponding to percentiles over time. Plots are added to
existing graphic devices using the fan function. Users can choose from four
different styles, including fan chart type plots, where a set of coloured
polygon, with shadings corresponding to the percentile values are layered
to represent different uncertainty levels.
A list of functions to download statistics from FAOSTAT (database
of the Food and Agricultural Organization of the United Nations) and WDI
(database of the World Bank), and to perform some harmonization operations.
Tools for detecting and summarize influential cases that
can affect exploratory and confirmatory factor analysis models as well as
structural equation models more generally.
Modelizations and previsions functions for
Functional AutoRegressive processes using
nonparametric methods: functional kernel,
estimation of the covariance operator in
a subspace, ...
Books are "Practical Regression and ANOVA in R" on CRAN, "Linear Models with R" published 1st Ed. August 2004, 2nd Ed. July 2014 by CRC press and "Extending the Linear Model with R" published by CRC press in December 2005.
Environment for teaching "Financial Engineering and
Computational Finance"
Allow numbers to be presented in an persian language
version
Environment for teaching
"Financial Engineering and Computational Finance".
The Fourier Amplitude Sensitivity Test (FAST) is a method to determine global sensitivities of a model on parameter changes with relatively few model runs. This package implements this sensitivity analysis method.
An efficient method of recovering precision matrices
by applying the parametric simplex method is provided in this package.
The computation is based on a linear optimization solver.
It also contains a generic Linear Programming solver and a
parameterized Linear Programming solver based on the parametric simplex method.
This is a two-in-one package which provides interfaces to
both R and Python. It implements fast hierarchical, agglomerative
clustering routines. Part of the functionality is designed as drop-in
replacement for existing routines: "linkage" in the SciPy package
"scipy.cluster.hierarchy", "hclust" in R's "stats" package, and the
"flashClust" package. It provides the same functionality with the
benefit of a much faster implementation. Moreover, there are
memory-saving routines for clustering of vector data, which go beyond
what the existing packages provide. For information on how to install
the Python files, see the file INSTALL in the source distribution.
We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model.
Contains Rcpp and RcppEigen implementations of matrix operations useful for Gaussian process models, such as the inversion of a symmetric Toeplitz matrix, sampling from multivariate normal distributions, evaluation of the log-density of a multivariate normal vector, and Bayesian inference for latent variable Gaussian process models with elliptical slice sampling (Murray, Adams, and MacKay 2010).
The FastHCS algorithm of Schmitt and Vakili (2014) for high-dimensional, robust PCA modelling and associated outlier detection and diagnostic tools.
It implements HICA (Hierarchical Independent Component Analysis) algorithm. This approach, obtained through the integration between treelets and Independent Component Analysis, is able to provide a multi-scale non-orthogonal data-driven basis, whose elements have a phenomenological interpretation according to the problem under study.
Implementation of FastICA algorithm to perform Independent
Component Analysis (ICA) and Projection Pursuit.
TrainFastImputation uses training data to describe a
multivariate normal distribution that the data approximates or
can be transformed into approximating and stores this information
as an object of class FastImputationPatterns. The FastImputation
function uses this FastImputationPatterns object to impute (make
a good guess at) missing data in a single line or a whole dataframe
of data.  This approximates the process used by Amelia
[http://gking.harvard.edu/amelia/] but is much faster when
filling in values for a single line of data.
Compute labels for a test set according to the k-Nearest Neighbors classification. This is a fast way to do k-Nearest Neighbors classification because the distance matrix -between the features of the observations- is an input to the function rather than being calculated in the function itself every time.
The package implements the new algorithm for fast computation of M-scatter matrices using a partial Newton-Raphson procedure for several estimators.
Package providing a fast match() replacement for cases
that require repeated look-ups. It is slightly faster that R's
built-in match() function on first match against a table, but
extremely fast on any subsequent lookup as it keeps the hash
table in memory.
The FastPCS algorithm of Vakili and Schmitt (2014) for robust estimation of multivariate location and scatter and multivariate outliers detection.
Computes pseudo-observations for survival analysis on right-censored data based on restricted mean survival time.
Data sets and utilities to accompany
"Foundations and Applications of Statistics: an Introduction
using R" (R Pruim, published by AMS, 2011), a text covering
topics from probability and mathematical statistics at an advanced
undergraduate level.  R is integrated throughout, and access to all
the R code in the book is provided via the snippet function.
The FastRCS algorithm of Vakili and Schmitt (2014) for robust fit of the multivariable linear regression model and outliers detection.
Infrastrcture for creating rich, dynamic web content using R scripts while maintaining very fast response time.
Functions for computing spillover measures, especially
spillover tables and spillover indices, as well as their
average, minimal, and maximal values.
Fast functions for timestamp
manipulation that avoid system calls and take shortcuts
to facilitate operations on very large data.
Performs family-based association tests with a polytomous outcome under 2-locus and 1-locus models defined by some design matrix.
Kiener distributions of type I, II, III and IV tailored to
distributions with left and right fat tails like those that occur
in financial markets. These distributions can be used to estimate
with a high accuracy market risks and value-at-risk. Also include power
hyperbolas and power hyperbolic functions.
A dataset of favourite numbers, selected from an online poll of over 30,000 people by Alex Bellos
(http://pages.bloomsbury.com/favouritenumber).
Provides functions and datasets from the book "Forest Analytics with R".
Does family-based gene by environment interaction tests, joint gene, gene-environment interaction test, and a test of a set of genes conditional on another set of genes.
We propose an objective Bayesian algorithm for searching
the space of Gaussian directed acyclic graphical models when
the variables are assumed to satisfy a given ordering. The
approach used is based on non-local parameter priors and thus
it is suitable for learning sparse graphs. The algorithm is
implemented in C++ using the open-source library Armadillo.
Normalizes the data from a file containing the raw values
of the SNP probes of microarrray data by using the FISH probes
and their corresponding CNs.
This package uses time dependent Poisson regression and a record of goals scored in matches to rank teams via estimated attack and defense strengths.  The statistical model is based on Dixon and Coles (1997) Modeling Association Football Scores and Inefficiencies in the Football Betting Market, Applied Statistics, Volume 46, Issue 2, 265-280.  The package has a some webscrapers to assist in the development and updating of a match database.  If the match database contains unconnected clusters (i.e. sets of teams that have only played each other and not played teams from other sets), each cluster is ranked separately relative to the median team strength in the cluster.  The package contains functions for predicting and simulating tournaments and leagues from estimated models. The package allows fitting via the glm(), speedglm(), and glmnet() functions.  The latter allows fast and efficient fitting of very large numbers of teams.  The fitting algorithm will analyze the match data and determine which teams form a cluster (a set of teams where there is a path of matches connecting every team) and fit each cluster separately.
Implements a very fast C++ algorithm to quickly bootstrap
receiver operating characteristics (ROC) curves and derived performance metrics, including the area under the
curve (AUC) as well as the true and false positive rate.
You can also plot the results and calculate confidence intervals.
Currently the calculation of 100000 bootstrap replicates for 500 observations
takes about one second.
Efficient procedures for community detection in network studies, especially for sparse networks with not very obvious community structure. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
Collection of pricing by duplication methods for popular structured products ("Zertifikate").
Algorithms for fuzzy clustering, cluster validity indices and plots for cluster validity and visualizing fuzzy clustering results.
Provides several functions to create and manipulate fuzzy cognitive maps. It is based on FCMapper for Excel, distributed at http://www.fcmappers.net/joomla/, developed by Michael Bachhofer and Martin Wildenberg. Maps are inputted as adjacency matrices. Attributes of the maps and the equilibrium values of the concepts (including with user-defined constrained values) can be calculated. The maps can be graphed with a function that calls "igraph". Multiple maps with shared concepts can be aggregated.
The FCNN4R package provides an interface to kernel routines
from the FCNN C++ library. FCNN is based on a completely new
Artificial Neural Network representation that offers unmatched
efficiency, modularity, and extensibility. FCNN4R provides
standard teaching (backpropagation, Rprop) and pruning
algorithms (minimum magnitude, Optimal Brain Surgeon), but it is
first and foremost an efficient computational engine. Users
can easily implement their algorithms by taking advantage
of fast gradient computing routines, as well as network
reconstruction functionality (removing weights and redundant
neurons).
A fold change rank ordering statistics method is presented to search for
differentially expressed genes and to detect recurrent chromosomal copy number aberrations.
This method may be useful for high-throughput biological data (microarray, sequencing, ...).
Probabilities are associated with genes/probes in the dataset and there is no problem of
multiple tests when using this method. For aCGH data, segmentation results are obtained by
merging the significant probes detected.
FD is a package to compute different multidimensional FD indices. It implements a distance-based framework to measure FD that allows any number and type of functional traits, and can also consider species relative abundances. It also contains other useful tools for functional ecology.
These functions were developed to support functional data
analysis as described in Ramsay, J. O. and Silverman, B. W.
(2005) Functional Data Analysis. New York: Springer.  They were
ported from earlier versions in Matlab and S-PLUS.  An
introduction appears in Ramsay, J. O., Hooker, Giles, and
Graves, Spencer (2009) Functional Data Analysis with R and
Matlab (Springer). The package includes data sets and script
files working many examples including all but one of the 76
figures in this latter book.  Matlab versions of the code and
sample analyses are no longer distributed through CRAN, as they
were when the book was published.  For those, ftp from
http://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/
There you find a set of .zip files containing the functions and
sample analyses, as well as two .txt files giving instructions for
installation and some additional information.
The changes from Version 2.4.1 are fixes of bugs in density.fd and
removal of functions create.polynomial.basis, polynompen, and
polynomial. These were deleted because the monomial basis
does the same thing and because there were errors in the code.
It performs simultaneously clustering and alignment of a multidimensional or unidimensional functional dataset by means of k-mean alignment.
Likelihood based analysis of 1-dimension functional data
in a mixed-effects model framework. Matrix computation are
approximated by semi-explicit operator equivalents with linear
computational complexity.
A library for functional data analysis using the square root
velocity framework
Implementation of the Interval Testing Procedure for functional data in different frameworks (i.e., one or two-population frameworks, functional linear models) by means of different basis expansions (i.e., B-spline, Fourier, and phase-amplitude Fourier). The current version of the package requires functional data evaluated on a uniform grid; it automatically projects each function on a chosen functional basis; it performs the entire family of multivariate tests; and, finally, it provides the matrix of the p-values of the previous tests and the vector of the corrected p-values. The functional basis, the coupled or uncoupled scenario, and the kind of test can be chosen by the user. The package provides also a plotting function creating a graphical output of the procedure: the p-value heat-map, the plot of the corrected p-values, and the plot of the functional data.
Routines for exploratory and descriptive analysis of functional data such as depth measurements, atypical curves detection, regression models, supervised classification, unsupervised classification and functional analysis of variance.
Regression models for functional data, i.e. scalar-on-function,
function-on-scalar and function-on-function regression models are fitted
using a component-wise gradient boosting algorithm.
FDG copulas are a class of copulas featuring an interesting balance between flexibility and tractability. This package provides tools to construct, calculate the pairwise dependence coefficients of, simulate from, and fit FDG copulas. The acronym FDG stands for 'one-Factor with Durante Generators', as an FDG copula is a one-factor copula -- that is, the variables are independent given a latent factor -- whose linking copulas belong to the Durante class of bivariate copulas (also referred to as exchangeable Marshall-Olkin or semilinear copulas).
FDR functions for permutation-based estimators, including pi0 as well as FDR
confidence intervals. The confidence intervals account for dependencies between
tests by the incorporation of an overdispersion parameter, which is estimated
from the permuted data.
It is known that current false discovery rate (FDR) procedures can be very conservative
when applied to p-values (and test statistics) with discrete (and heterogeneous) null distributions.
This package implements the more powerful weighted generalized FDR procedure that adapts to these two features of the discrete paradigm for multiple testing.
The package takes in the original data set rather than the p-values in order to carry out the adjustments needed for multiple testing in this paradigm. The methodology applies also to multiple testing where the null p-values are uniformly distributed.
The package implements the method for three types of test statistics and their p-values:
(a) binomial test on if two independent Poisson distributions have the same means, (b) Fisher's exact test on if the conditional distribution is the same as the marginal distribution for two binomial distributions,
(c) the exact negative binomial test on if two independent negative binomial distributions with the same size parameter have the same means.
It depends on the R packages ``MCMCpack'' to use its function ``dnoncenhypergeom'' for hypergeometric distributions, and edgeR to uses its normalization techniques for data that follow negative binomial distributions.
Tools for FDR problems, including false discovery rate regression.
See corresponding paper: "False discovery rate regression: application to
neural synchrony detection in primary visual cortex."  James G. Scott, Ryan
C. Kelly, Matthew A. Smith, Robert E. Kass.
Estimates both tail area-based false
discovery rates (Fdr) as well as local false discovery rates (fdr) for a
variety of null models (p-values, z-scores, correlation coefficients,
t-scores).  The proportion of null values and the parameters of the null
distribution are adaptively estimated from the data.  In addition, the package
contains functions for non-parametric density estimation (Grenander estimator),
for monotone regression (isotonic regression and antitonic regression with weights),
for computing the greatest convex minorant (GCM) and the least concave majorant (LCM),
for the half-normal and correlation distributions, and for computing
empirical higher criticism (HC) scores and the corresponding decision threshold.
Perform frequency distribution tables, associated histograms
and polygons from vector, data.frame and matrix objects for
numerical and categorical variables.
For each feature, a score is computed that can be useful
for feature selection. Several random subsets are sampled from
the input data and for each random subset, various linear
models are fitted using lars method. A score is assigned to
each feature based on the tendency of LASSO in including that
feature in the models.Finally, the average score and the models
are returned as the output. The features with relatively low
scores are recommended to be ignored because they can lead to
overfitting of the model to the training data. Moreover, for
each random subset, the best set of features in terms of global
error is returned. They are useful for applying Bolasso, the
alternative feature selection method that recommends the
intersection of features subsets.
Feature significance for multivariate kernel density estimation
Feature hashing, also called as the hashing trick, is a method to transform
features of a instance to a vector. Thus, it is a method to transform a real dataset to a matrix.
Without looking up the indices in an associative array,
it applies a hash function to the features and uses their hash values as indices directly.
The method of feature hashing in this package was proposed in Weinberger et al. (2009).
The hashing algorithm is the murmurhash3 from the digest package.
Please see the README in https://github.com/wush978/FeatureHashing for more information.
Discretely-sampled function is first smoothed.  Features
of the smoothed function are then extracted.  Some of the key
features include mean value, first and second derivatives,
critical points (i.e. local maxima and minima), curvature of
cunction at critical points, wiggliness of the function, noise
in data, and outliers in data.
Functions and example datasets for Fechnerian scaling of
discrete object sets.  It computes Fechnerian distances among
objects representing subjective dissimilarities, and other
related information.  See package?fechner for an overview.
Functions to automate downloading geospatial data available from several federated data sources (mainly sources maintained by the US Federal government). Currently, the package allows for retrieval of five datasets: The National Elevation Dataset digital elevation models (1 and 1/3 arc-second; USGS); The National Hydrography Dataset (USGS); The Soil Survey Geographic (SSURGO) database from the National Cooperative Soil Survey (NCSS), which is led by the Natural Resources Conservation Service (NRCS) under the USDA; the Global Historical Climatology Network (GHCN), coordinated by National Climatic Data Center at NOAA; and the International Tree Ring Data Bank. Additional data sources are in the works, including global DEM resources (ETOPO1, ETOPO5, ETOPO30, SRTM), global soils (HWSD), MODIS satellite data products, the National Atlas (US), Natural Earth, PRISM, and WorldClim.
Access data from the Federal Register API
Analysis of fragmented time directionality to investigate feedback in time series. Tools provided by the package allow the analysis of feedback for a single time series and the analysis of feedback for a set of time series collected across a spatial domain.
Data from various catalogs of astrophysical gamma-ray sources detected by NASA's Large Area Telescope
(The Astrophysical Journal, 697, 1071, 2009 June 1), on board the Fermi gamma-ray satellite. More information on
Fermi and its data products is available from the Fermi Science Support Center (http://fermi.gsfc.nasa.gov/ssc/).
Collection of pricing by duplication and Monte Carlo methods for Express Certificates products (also known as Autocallables)
Extends the out of memory vectors of 'ff' with
statistical functions and other utilities to ease their usage.
Functions, S4 classes/methods and a graphical user interface (GUI) to design surveys to substantiate freedom from disease using a modified hypergeometric function (see Cameron and Baldock, 1997). Herd sensitivities are computed according to sampling strategies "individual sampling" or "limited sampling" (see M. Ziller, T. Selhorst, J. Teuffert, M. Kramer and H. Schlueter, 2002). Methods to compute the a-posteriori alpha-error are implemented. Risk-based targeted sampling is supported.
Force field simulation of interaction of set of points.
Very useful for placing text labels on graphs, such as
scatterplots.
This package performs general linear modeling with
multiple responses (MANCOVA).  An overall p-value for each
model term is calculated by the 50-50 MANOVA method, which
handles collinear responses.  Rotation testing is used to
compute adjusted single response p-values according to
familywise error rates and false discovery rates.
Provides a simple and efficient wrapper around the fastest
Fourier transform in the west (FFTW) library.
Provides a wrapper for several FFTW functions. This package provides access to the two-dimensional FFT, the multivariate FFT, and the one-dimensional real to complex FFT using FFTW3. The package includes the functions fftw and mvfftw which are designed to mimic the functionality of the R functions fft and mvfft. The FFT functions have a parameter that allows them to not return the redundant complex conjugate when the input is real data.
Bi-variate data fitting is done by two stochastic
components: the marginal distributions and the dependency
structure. The dependency structure is modeled through a
copula. An algorithm was implemented considering seven families
of copulas (Generalized Archimedean Copulas), the best fitting
can be obtained looking all copula's options (totally positive
of order 2 and stochastically increasing models).
This is a package for implementation of Flury-Gautschi
algorithms.
Exact MLE and Whittle MLE estimation for power law decay models.
Goodness-of-fit test with multiplier or parametric
bootstrap.
A permutation technique to explore and control for spatial autocorrelation. This package contains low level functions for performing permutations and calculating statistics as well as higher level functions. Higher level functions are an easy to use function for performing spatially restricted permutation tests and summarize and plot results.
Implement algorithms for feature grouping and selection over an undirected graph, solves problems like graph fused lasso, graph OSCAR and so on.
Rapidly create a GUI interface for a function you created
by automatically creating widgets for arguments of the
function. Automatically parses help routines for
context-sensative help to these arguments. The interface
essentially a wrapper to some tcltk routines to both simplify
and facilitate GUI creation. More advanced tcltk routines/GUI
objects can be incorporated into the interface for greater
customization for the more experienced.
R function to plot high quality, elegant heatmap using 'ggplot2' graphics .
Some of the important features of this package are, coloring of row/column side tree with
respect to the number of user defined cuts in the cluster, add annotations to both columns
and rows, option to input annotation palette for tree and column annotations and multiple
parameters to modify aesthetics (style, color, font) of texts in the plot.
Functions to compare two or more survival curves with:
a) The Fleming-Harrington test for right-censored data based on permutations and on counting processes.
b) An extension of the Fleming-Harrington test for interval-censored data based on a permutation distribution and on a score vector distribution.
Provide functions for forest inventory calculations.
Common volumetric equations (Smalian, Newton and Huber) as well
stacking factor and form
Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities.
Fields is for curve, surface and function
fitting with an emphasis on splines, spatial data and spatial
statistics. The major methods include cubic, and thin plate splines,
Kriging and compact covariances for large data sets. The splines and
Kriging methods are supporting by functions that can determine the
smoothing parameter (nugget and sill variance) by cross validation
and also by restricted maximum likelihood. For Kriging there is
an easy to function that also estimates the correlation scale (range).
A major feature is that
any covariance function implemented in R and following a simple fields
format can be used for spatial prediction. There are also many useful
functions for plotting and
working with spatial data as images. This package also contains an
implementation of sparse matrix methods for large spatial data sets and
currently requires the sparse matrix (spam) package.
Use help(fields) to get started and for an
overview.  The fields source code is deliberately commented and
provides useful explanations of numerical details in addition to the
manual pages. The commented source code can be viewed by expanding the
source code .tar.gz file and looking in R subdirectory.
Tools for random fields and bridges simulations.
A collection of functions that assist in data
manipulation analysis and plotting.
Simple key-value database using SQLite as the backend
Interface for working with large matrices stored in files, not in computer memory.
Supports multiple data types (double, integer, logical and raw) of different sizes (e.g. 4, 2, or 1 byte integers).
Access to parts of the matrix is done by indexing, exactly as with usual R matrices.
Supports very large matrices (tested on 1 terabyte matrix), allowing for more than 2^32 rows or columns.
Cross-platform as the package has R code only, no C/C++.
Create descriptive file names with ease. New file names are
automatically (but optionally) time stamped and placed in date stamped
directories. Streamline your analysis pipeline with input and output file
names that have informative tags and proper file extensions.
Time value of money, cash flows and other financial
functions.
Infrastructure for defining meta-data and
relationships for financial instruments.
This package accomplishes two tasks: a) it classifies
implicit trading activity from quotes in OTC markets using the
algorithm of Lee and Ready (1991); b) based on information for
trade initiation, the package computes the probability of
informed trading of Easley and O'Hara (1987).
Package for time value of money calculation, time series analysis
and computational finance
Estimation and regularization for covariance matrix of asset returns. For covariance matrix estimation,
three major types of factor models are included: macroeconomic factor model, fundamental factor model and statistical
factor model. For covariance matrix regularization, four regularized estimators are included: banding, tapering,
hard-thresholding and soft-thresholding. The tuning parameters of these regularized estimators are selected via
cross-validation.
Find all root(s) of the equation,including complex
roots;Find root(s) of the equation by dichotomy.Besides,in
dichotomy, more than one interval can be given at a time.
The heterogeneous treatment effect estimation procedure
proposed by Imai and Ratkovic (2013).
The proposed method is applicable, for
example, when selecting a small number of most (or least)
efficacious treatments from a large number of alternative
treatments as well as when identifying subsets of the
population who benefit (or are harmed by) a treatment of
interest. The method adapts the Support Vector Machine
classifier by placing separate LASSO constraints over the
pre-treatment parameters and causal heterogeneity parameters of
interest. This allows for the qualitative distinction between
causal and other parameters, thereby making the variable
selection suitable for the exploration of causal heterogeneity.
The package also contains the function, INT, which estimates
the average marginal treatment effect, the average treatment
combination effect, and the average marginal treatment interaction
effect proposed by Egami and Imai (2015).
Creates models from all combinations of a list of variables and sorts by minimum IC (information criterion).
Package designed to find an acceptable python binary.
This package contains functions to manipulate binary fingerprints
of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint'
which is internally represented a vector of integers, such
that each element represents the position in the fingerprint that is set to 1.
The bitwise logical functions in R are overridden so that they can be used directly
with 'fingerprint' objects. A number of distance metrics are also
available (many contributed by Michael Fadock). Fingerprints
can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and
can also be folded using OR.  Arbitrary fingerprint formats can be handled via line
handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.
In the Cramér–Lundberg risk process perturbed by a Wiener
process, this packages provides approximations to the probability of
ruin within a finite time horizon.  Currently, there are three methods
implemented: The first one uses saddlepoint approximation (two
variants are provided), the second one uses importance sampling and
the third one is based on the simulation of a dual process.  This last
method is not very accurate and only given here for completeness.
R companion to Tsay (2005)
Analysis of Financial Time Series, 2nd ed. (Wiley).
Includes data sets, functions and script files
required to work some of the examples.  Version 0.3-x
includes R objects for all data files used in the text
and script files to recreate most of the analyses in
chapters 1-3 and 9 plus parts of chapters 4 and 11.
The FisherEM package provides an efficient algorithm for
the unsupervised classification of high-dimensional data. This
FisherEM algorithm models and clusters the data in a
discriminative and low-dimensional latent subspace. It also
provides a low-dimensional representation of the clustered
data. A sparse version of Fisher-EM algorithm is also provided.
fisheyeR provides tools for creating Interactive Data
Visualizations by implementing ideas from Furnas, Munzner,
Costa and Venturini.
FisHiCal integrates Hi-C and FISH data, offering a modular and easy-to-use tool for chromosomal spatial analysis.
Fishery science methods and models from published literature and contributions from colleagues.
Fits models to catch and effort data. Single-species models are 1) delta log-normal, 2) Tweedie, or 3) Poisson-gamma (GL)Ms.
Functions to predict fish movement parameters plotting leptokurtic fish dispersal kernels (see Radinger and Wolter, 2014: Patterns and predictors of fish dispersal in rivers. Fish and Fisheries. 15:456-473.)
Comprehensive model building function for identification,
estimation and diagnostic checking for AR and subset AR models.
Two types of subset AR models are supported.  One family of
subset AR models, denoted by ARp, is formed by taking subet of
the original AR coefficients and in the other, denoted by ARz,
subsets of the partial autocorrelations are used. The main
advantage of the ARz model is its applicability to very large
order models.
Implements fast maximum likelihood algorithm for fitting ARMA time series. Uses S3 methods print, summary, fitted, residuals. Fast exact Gaussian ARMA simulation.
Scrapes data from www.fitbit.com, the website that contains data
for the popular fitness tracker. This does not use the official API,
but instead uses the API that the web dashboard uses to generate the graphs
displayed on the dashboard after login at www.fitbit.com.
Extends the fitdistr function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation method the package provides moment matching, quantile matching and maximum goodness-of-fit estimation methods (available only for non-censored data).
Fits Density Ratio Classes to elicited
probability-quantile points or intervals
Utilities to read and write files in the FITS (Flexible
Image Transport System) format, a standard format in astronomy.
Present low-level routines allow: reading, parsing, and modifying
FITS headers; reading FITS images (multi-dimensional arrays);
reading FITS binary tables; and writing FITS images
(multi-dimensional arrays).  Higher-level functions allow: reading
files composed of one or more headers and a single (perhaps
multidimensional) image or single bintable; reading bintables into
data frames; generating vectors for image array axes; scaling and
writing images as 16-bit integers.  Known incompletenesses are
reading FITS ASCII table and random group extensions, as well as
bit, complex, and array descriptor data types in binary tables.
Package fitTetra contains three functions that can be used
to assign genotypes to a collection of tetraploid samples based
on bialleleic marker assays. Functions fitTetra (to fit several
models for one marker from the data and select the best
fitting) or saveMarkerModels (calls fitTetra for multiple
markers and saves the results to files) will probably be the
most convenient to use. Function CodomMarker offers more
control and fits one specified model for a given marker.
This is a fast and flexible implementation of the Kalman
filter, which can deal with NAs. It is entirely written in C
and relies fully on linear algebra subroutines contained in
BLAS and LAPACK. Due to the speed of the filter, the fitting of
high-dimensional linear state space models to large datasets
becomes possible. This package also contains a plot function
for the visualization of the state vector and graphical
diagnostics of the residuals.
Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2014). Fused Lasso Additive Model. arXiv preprint arXiv:1409.5391.
The package "flare" provides the implementation of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear model. We adopt the alternating direction method of multipliers and convert the original optimization problem into a sequential L1 penalized least square minimization problem, which can be efficiently solved by linearization algorithm. A multi-stage screening approach is adopted for further acceleration. Besides the sparse linear model estimation, we also provide the extension of these Lasso variants to sparse Gaussian graphical model estimation including TIGER and CLIME using either L1 or adaptive penalty. Missing values can be tolerated for Dantzig selector and CLIME. The computation is memory-optimized using the sparse matrix output.
Fast implementation of hierarchical clustering
The main function kcca implements a general framework for
k-centroids cluster analysis supporting arbitrary distance
measures and centroid computation. Further cluster methods
include hard competitive learning, neural gas, and QT
clustering. There are numerous visualization methods for
cluster results (neighborhood graphs, convex cluster hulls,
barcharts of centroids, ...), and bootstrap methods for the
analysis of cluster stability.
Allows for maximum likelihood fitting of cluster-weighted models, a class of mixtures of regression models with random covariates.
FlexMix implements a general framework for finite
mixtures of regression models using the EM algorithm.
FlexMix provides the E-step and all data handling, while the M-step can be
supplied by the user to easily define new models. Existing drivers
implement mixtures of standard linear models, generalized linear
models and model-based clustering.
Model selection tools and 'selfStart' functions to fit parametric curves in 'nls', 'nlsList' and 'nlme' frameworks.
Estimation of flexible parametric models for survival data.
Flexible parametric models for time-to-event data, including the Royston-Parmar spline model, generalized gamma and generalized F distributions.   Any user-defined parametric distribution can be fitted, given at least an R function defining the probability density or hazard.  There are also tools for fitting and predicting from fully parametric multi-state models.
FLIM fits linear models for the observed increments in a longitudinal dataset, and imputes missing values according to the models.
It implements many univariate and multivariate permutation (and rotation) tests. Allowed tests: the t one and two samples, ANOVA, linear models, Chi Squared test, rank tests (i.e. Wilcoxon, Mann-Whitney, Kruskal-Wallis), Sign test and McNemar. Test on Linear Models are performed also in presence of covariates (i.e. nuisance parameters). The permutation and the rotation methods to get the null distribution of the test statistics are available. It also implements methods for multiplicity control such as Westfall-Young minP procedure and Closed Testing (Marcus, 1976) and k-FWER. Moreover, it allows to test for fixed effects in mixed effects models.
Fits the Fused Lasso Latent Feature model, which is used for modeling multi-sample aCGH data to identify regions of copy number variation (CNV).  Produces a set of features that describe the patterns of CNV and a set of weights that describe the composition of each sample.  Also provides functions for choosing the optimal tuning parameters and the appropriate number of features, and for estimating the false discovery rate.
Tools to quickly compile taxonomic and distribution data from
the Brazilian Flora Checklist.
Flowering is an important life history trait of flowering plants. It has been mainly analyzed with respect to flowering onset and duration of flowering. This tools provide some functions to compute the temporal distribution of an flowering individual related to other population members. fCV() measures the temporal variation in flowering. RIind() measures the rank order of flowering for individual plants within a population. SI(), SI2(), SI3(), and SI4() calculate flowering synchrony with different methods.
Flow field forecasting draws information from an interpolated flow field of the observed time series to incrementally build a forecast.
Selections on flow matrices, statistics on selected flows, map and graph visualisations.
This package implements a path algorithm for the Fused
Lasso Signal Approximator. For more details see the help files
Solve an approximate Subset Sum Problem via a recursive compression algorithm
Simulate and analyse fluorescence data sets obtained from
calcium imaging experiments in living cells (such as neurons).
The package provide tools to estimate background-subtracted
fluorescence transients and build meaningful confidence
intervals on these estimations. This covers both the case of
homogeneous background fluorescence and that of Regions Of
Interest (ROI) displaying background inhomogeneities.
Background-subtracted fluorescence transients can be fitted
with monoexponential models or estimated pointwise.
Contains data sets from Bernard Flury (1997) A First
Course in Multivariate Statistics, Springer NY
Functions for the calculation of greenhouse gas flux rates
from closed chamber concentration measurements. The package follows
a modular concept: Fluxes can be calculated in just two simple steps
or in several steps if more control in details is wanted. Additionally
plot and preparation functions as well as functions for modelling
gpp and reco are provided.
All data sets from "Forecasting: methods and applications"
by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998).
Provides functions to help in fitting models to data, to
perform Monte Carlo, sensitivity and identifiability analysis. It is
intended to work with models be written as a set of differential
equations that are solved either by an integration routine from
package deSolve, or a steady-state solver from package
rootSolve. However, the methods can also be used with other types of
functions.
The package contains R-functions to perform an fmri analysis
as described in
K. Tabelow, J. Polzehl, H.U. Voss, and V. Spokoiny,
Analysing fMRI experiments with structure adaptive smoothing procedures,
NeuroImage, 33:55-62 (2006),
J. Polzehl, H.U. Voss, K. Tabelow, Structural adaptive segmentation
for statistical parametric mapping, NeuroImage, 52:515-523 (2010)
and
K. Tabelow, J. Polzehl, Statistical Parametric Maps for
Functional MRI Experiments in {R}: The Package {fmri}},
Journal of Statistical Software, 44(11):1--21 (2011).
Several utility functions for the book entitled
"Practices of Medical and Health Data Analysis using R"
(Pearson Education Japan, 2007) with Japanese demographic
data and some demographic analysis related functions.
This package implements some basic procedures for dealing
with log maximally skew stable distributions, which are also
called finite moment log stable distributions.
This package computes posterior residual variances to be
used in the denominator of a moderated t-statistic from a
linear model analysis of microarray data.  It is an extension
of the moderated t-statistic original proposed by Smyth (2004).
LOESS local regression and empirical Bayesian method are used
to estimate gene specific prior degrees of freedom and prior
variance based on average gene intensity level. The posterior
residual variance in the denominator is a weighted average of
prior and residual variance and the weights are prior degrees
of freedom and residual variance degrees of freedom. The
degrees of freedom of the moderated t-statistic is simply the
sum of prior and residual variance degrees of freedom.
Environment for teaching
"Financial Engineering and Computational Finance"
Cover-tree and kd-tree fast k-nearest neighbor search algorithms and related applications
including KNN classification, regression and information measures are implemented.
foba is a package that implements forward, backward, and foba sparse learning algorithms for ridge regression, described in the paper "Adaptive Forward-Backward Greedy Algorithm for Learning Sparse Representations".
Computer Modern font for use with extrafont package
Calculates twelve commonly-used, basic measures of food
web network structure from binary, predator-prey matrices:
species richness, connectance, total number of links, link
density, number of trophic positions, predator:prey ratio, and
fraction of carnivores, herbivores, top species and
intermediate species.  Employs food web language in the code
and output, translates between a couple of common food web
formats, can handle food webs consisting of multiple levels,
and can automate the analysis for a large number of webs. The
program produces 3-dimensional graphs of high quality that can
be customized by the user.
SHE, FORAM Index and ABC Method analyses and custom plot
functions for community data.
Support for the foreach looping construct.  Foreach is an
idiom that allows for iterating over elements in a collection,
without the use of an explicit loop counter.  This package in
particular is intended to be used for its return value, rather
than for its side effects.  In that sense, it is similar to the
standard lapply function, but doesn't require the evaluation
of a function.  Using foreach without side effects also
facilitates executing the loop in parallel.
Implementation of Forecastable Component Analysis ('ForeCA'),
including main algorithms and auxiliary function (summary, plotting, etc.)
to apply 'ForeCA' to multivariate time series data.
'ForeCA' is a novel dimension reduction (DR) technique for temporally
dependent signals. Contrary to other popular DR methods, such as 'PCA' or 'ICA',
'ForeCA' takes time dependency explicitly into account and searches for the
most ''forecastable'' signal.  The measure of forecastability is based on
the Shannon entropy of the spectral density of the transformed signal.
Methods and tools for displaying and analysing
univariate time series forecasts including exponential smoothing
via state space models and automatic ARIMA modelling.
Routines for forecasting univariate time series using Theta Method and Optimised Theta Method (Fioruci et al, 2015). Contains two cross-validation routines of Tashman (2000).
Functions for reading and writing data stored by some versions of
Epi Info, Minitab, S, SAS, SPSS, Stata, Systat and Weka
and for reading and writing some dBase files.
The statistical evaluation of DNA mixtures, DNA profile
match probability
Statistical methods and simulation tools for the
interpretation of forensic DNA mixtures
Enables users to form appropriate visualizations of high dimensional mapping curvatures of random forests.
The plot allows for multiple confidence intervals per row,
custom fonts for each text element,
custom confidence intervals,
text mixed with expressions, and more.
The aim is to extend the use of forest plots beyond meta-analyses.
This is a more general version of the original 'rmeta' package's forestplot
function and relies heavily on the 'grid' package.
Imputation of missing values in datasets of ordinal variables through a forward imputation algorithm
This package provides estimates of tree volume and biomass from
Italian NFI models
Implemented, addition, subtracking, multiplication,
division in formal series rings of any number of variables
(except division is only to 3 variables). Also are available
"[" "[<-" operators.
Provides a function tidy_source() to format R source code. Spaces
and indent will be added to the code automatically, and comments will be
preserved under certain conditions, so that R code will be more
human-readable and tidy. There is also a Shiny app as a user interface in
this package (see tidy_app()).
Infrastructure for extended formulas with multiple parts on the
right-hand side and/or multiple responses on the left-hand side.
These utilities facilitate the programmatic manipulations of
formulas, expressions, calls, names, symbols and other objects.
Forward search approach to robust analysis in linear and
generalized linear regression models.
Forward Search analysis of time series regressions. Implements the asymptotic theory developed in Johansen and Nielsen (2013, 2014).
A set of analytical tools useful in analysing ecological
and geographical data sets, both ancient and modern. The
package includes functions for estimating species richness
(Chao 1 and 2, ACE, ICE, Jacknife), shared species/beta
diversity, species area curves and geographic distances and
areas.
A game for two players: Who gets first four in a row
(horizontal, vertical or diagonal) wins. Published by Milton
Bradley, designed by Howard Wexler and Ned Strongin.
Various methods for clustering and cluster validation.
Fixed point clustering. Linear regression clustering. Clustering by
merging Gaussian mixture components. Symmetric
and asymmetric discriminant projections for visualisation of the
separation of groupings. Cluster validation statistics
for distance based clustering including corrected Rand index.
Cluster-wise cluster stability assessment. Methods for estimation of
the number of clusters: Calinski-Harabasz, Tibshirani and Walther's
prediction strength, Fang and Wang's bootstrap stability.
Gaussian/multinomial mixture fitting for mixed
continuous/categorical variables. Variable-wise statistics for cluster
interpretation. DBSCAN clustering. Interface functions for many
clustering methods implemented in R, including estimating the number of
clusters with kmeans, pam and clara. Modality diagnosis for Gaussian
mixtures. For an overview see package?fpc.
A geometric approach to MLE for functional principal
components
Comparisons of floating point numbers are problematic due to errors
associated with the binary representation of decimal numbers.
Despite being aware of these problems, people still use numerical methods
that fail to account for these and other rounding errors (this pitfall is
the first to be highlighted in Circle 1 of Burns (2012)
[The R Inferno](http://www.burns-stat.com/pages/Tutor/R_inferno.pdf)).
This package provides new relational operators useful for performing
floating point number comparisons with a set tolerance.
Probabilistic distance clustering (PD-clustering) is an iterative, distribution free, probabilistic clustering method. PD-clustering assigns units to a cluster according to their probability of membership, under the constraint that the product of the probability and the distance of each point to any cluster centre is a constant. PD-clustering is a flexible method that can be used with non-spherical clusters, outliers, or noisy data. Facto PD-clustering (FPDC) is a recently proposed factor clustering method that involves a linear transformation of variables and a cluster optimizing the PD-clustering criterion. It allows clustering of high dimensional data sets.
Returns the noncentrality parameter of the noncentral F
distribution if probability of type I and type II error,
degrees of freedom of the numerator and the denominator are
given.  It may be useful for computing minimal detectable
differences for general ANOVA models.  This program is
documented in the paper of A. Baharev, S. Kemeny, On the
computation of the noncentral F and noncentral beta
distribution; Statistics and Computing, 2008, 18 (3), 333-340.
All data sets required for the examples and exercises in
the book "Forecasting: principles and practice" by Rob J
Hyndman and George Athanasopoulos. All packages required to run
the examples are also loaded.
This package contains several functions for an efficient
approximation of first-passage-time densities for diffusion
processes based on the First-Passage-Time Location (FPTL)
function.
Maximum likelihood estimation of the parameters of a
fractionally differenced ARIMA(p,d,q) model (Haslett and
Raftery, Appl.Statistics, 1989).
Functions for fitting data to a quiescent growth model,
i.e. a growth process that involves members of the population
who stop dividing or propagating.
Stochastic fractal and deterministic chaotic time series
analysis.
Implements various methods for estimating fractal dimension of time series and 2-dimensional data.
The parameters p and q are estimated with the aid of randomized Sierpinski Carpet which is built on [p-p-p-q]-model. Thereby, for three times simulation with p-value and once with q-value is assumed. Hence these parameters are estimated and displayed.
The basic principle driving fractal generation of time
series is that data is generated iteratively based on
increasing levels of resolution.  The initial series is defined
by a so-called initiator pattern and then generators are used
to replace each segment of the initial pattern. Regular,
repeatable patterns can be produced by using the same seed and
generators.  By using a set of generators, non-repeatable time
series can be produced.  This technique is the basis of the
fractal time series process in this package.
This is the package which can help you turn
numeric,dataframe,matrix into fraction form.
The frailtyHL package implements the h-likelihood
estimation procedures for frailty models. The package fits
Cox's proportional hazards models with random effects (or
frailties). For the frailty distribution lognormal and gamma
are allowed. The h-likelihood uses the Laplace approximation
when the numerical integration is intractable, giving a
statistically efficient estimation in frailty models.
Frailtypack fits several classes of frailty models using a penalized likelihood
estimation on the hazard function but also a parametric estimation.
1) A shared frailty model (with gamma or log-normal frailty distribution) and Cox
proportional hazard model. Clustered and recurrent survival times can be studied.
2) Additive frailty models for proportional hazard models with two correlated
random effects (intercept random effect with random slope).
3) Nested frailty models for hierarchically clustered data (with 2 levels of
clustering) by including two iid gamma random effects.
4) Joint frailty models in the context of joint modelling for recurrent events
with terminal event for clustered data or not.
Prediction values are available. Left-truncated (not for Joint model),
right-censored data, interval-censored data (only for Cox proportional hazard
and shared frailty model) and strata are allowed. In each model,
the random effects have a gamma distribution, but you can switch to a log-normal
in shared and joint models. Now, you can also consider time-varying covariates
effects in Cox, shared and joint models. The package includes concordance measures
for Cox proportional hazards models and for shared frailty models.
A package for functional response analysis.
Point and interval estimation in dual frame surveys. In contrast
to classic sampling theory, where only one sampling frame is considered,
dual frame methodology assumes that there are two frames available for
sampling and that, overall, they cover the entire target population. Then,
two probability samples (one from each frame) are drawn and information
collected is suitably combined to get estimators of the parameter of
interest.
Accompanying package of the book 'Financial Risk Modelling
and Portfolio Optimisation with R'. The data sets used in the
book are contained in this package.
This package performs robust inference based on applying
Fast and Robust Bootstrap on robust estimators. Available
methods are multivariate regression, PCA and Hotelling tests.
An implementation of various learning algorithms based on fuzzy rule-based systems (FRBSs) for dealing with classification and regression tasks. Moreover, it allows to construct an FRBS model defined by human experts.
FRBSs are based on the concept of fuzzy sets, proposed by Zadeh in 1965, which aims at
representing the reasoning of human experts in a set of IF-THEN rules, to
handle real-life problems in, e.g., control, prediction and inference, data
mining, bioinformatics data processing, and robotics. FRBSs are also known
as fuzzy inference systems and fuzzy models. During the modeling of an
FRBS, there are two important steps that need to be conducted: structure
identification and parameter estimation. Nowadays, there exists a wide
variety of algorithms to generate fuzzy IF-THEN rules automatically from
numerical data, covering both steps. Approaches that have been used in the
past are, e.g., heuristic procedures, neuro-fuzzy techniques, clustering
methods, genetic algorithms, squares methods, etc. Furthermore, in this
version we provide a universal framework named 'frbsPMML', which is adopted
from the Predictive Model Markup Language (PMML), for representing FRBS
models. PMML is an XML-based language to provide a standard for describing
models produced by data mining and machine learning algorithms. Therefore,
we are allowed to export and import an FRBS model to/from 'frbsPMML'.
Finally, this package aims to implement the most widely used standard
procedures, thus offering a standard package for FRBS modeling to the R
community.
This package implements the functions associated with Fast
Regularized Canonical Correlation Analysis.
This package is for fitting free-knot splines for data
with one independent variable and one dependent variable.  Four
free-knot spline algorithms are provided for the case where the
number of knots is known in advance.  A knot-search algorithm
is provided for the case where the number of knots is not known
in advance.  In addition, methods are available to compute the
fitted values, the residuals, and the coefficients of the
splines, and to plot the results, along with a method to
summarize the results.
The package FreeSortR provides tools for describing and analysing free sorting data. Main methods are computation of consensus partition and factorial analysis of the dissimilarity matrix between stimuli (using multidimensional scaling approach).
A collections of useful statistical functions used in Columbia
course W4240/W4400.
Real capture frequencies will be fitted to various distributions which provide the basis of estimating population sizes, their standard error, and symmetric as well as asymmetric confidence intervalls.
A frequency moving average plot (MAP) is estimated from a
multinomial data and a continuous covariate. The frequency MAP
is a moving average estimate of category frequencies, where
frequency means and posterior bounds are estimated. Comparisons
of two frequency MAPs as well as odds ratios can be plotted.
New approaches to parallel coordinates plots for
multivariate data visualization, including applications to clustering,
outlier hunting and regression diagnostics.  Includes general functions
for multivariate nonparametric density and regression estimation,
using parallel computation.
The frequency of a particular data value is the number of times it
occurs. A frequency table is a table of values with their corresponding
frequencies. Frequency weights are integer numbers that indicate how many
cases each case represents. This package provides some functions to work
with such type of collected data.
Contains a set of utilities for building and testing formula-based models (linear, logistic or COX) for Computer Aided Diagnosis/Prognosis applications. Utilities include data adjustment, univariate analysis, model building, model-validation, longitudinal analysis, reporting and visualization.
This package creates regular and non-regular Fractional
Factorial designs. Furthermore, analysis tools for Fractional
Factorial designs with 2-level factors are offered (main
effects and interaction plots for all factors simultaneously,
cube plot for looking at the simultaneous effects of three
factors, full or half normal plot, alias structure in a more
readable format than with the built-in function alias). The
package is still under development. While much of the intended
functionality is already available, some changes and
improvements are still to be expected. Suggestions are welcome.
This package provides catalogues of resolution IV regular
fractional factorial designs in 128 runs for up to 33 2-level
factors. The catalogues are complete, excluding resolution IV
designs without 5-letter words, because these do not add value
for a search for clear designs. The previous package version
1.0 with complete catalogues up to 24 runs (24 runs and a
namespace added later) can be downloaded from the authors
website.
Estimation and specification analysis of one- and two-part fractional regression models and calculation of partial effects.
Estimation and specification analysis of fractional regression models with neglected heterogeneity and/or endogenous covariates.
Estimation of panel data regression models for fractional responses.
A collection of R and C++ functions to work with the
generalized hyperbolic distribution, related distributions and
their applications in financial risk management and
quantitative analysis.
Maximum Likelihood Estimation of
Stochastic Frontier Production and Cost Functions.
Two specifications are available:
the error components specification with time-varying efficiencies
(Battese and Coelli, 1992)
and a model specification in which the firm effects are directly
influenced by a number of variables (Battese and Coelli, 1995).
It calculates the alpha-quantile and order-m efficiency score in multi-dimension and computes several summaries and representation of the associated frontiers in 2d and 3d.
Automated feature selection using variety of models
provided by 'caret' package.
This work was funded by Poland-Singapore bilateral cooperation
project no 2/3/POL-SIN/2012.
This package provides functions for selecting attributes
from a given dataset. Attribute subset selection is the process
of identifying and removing as much of the irrelevant and
redundant information as possible.
Import data of tests and questionnaires from FormScanner. FormScanner is an open source software that converts scanned images to data using optical mark recognition (OMR) and it can be downloaded from http://sourceforge.net/projects/formscanner/. The spreadsheet file created by FormScanner is imported in a convenient format to perform the analyses provided by the package. These analyses include the conversion of multiple responses to binary (correct/incorrect) data, the computation of the number of corrected responses for each subject or item, the computation and the graphical representation of the frequencies of the responses to each item and the report of the responses of one or a few persons.
Performs fast detection of interactions in large-scale data using the
method of random intersection trees introduced in
``Shah, R. D. and Meinshausen, N. (2014) Random Intersection Trees''.  The algorithm
finds potentially high-order interactions in high-dimensional binary
two-class classification data, without requiring lower order interactions
to be informative.  The search is particularly fast when the matrices of
predictors are sparse.  It can also be used to perform market basket analysis
when supplied with a single binary data matrix.  Here it will find collections
of columns which for many rows contain all 1's.
Wrapper functions that interface with FSL
(http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/), a powerful and commonly-used
neuroimaging software, using system commands. The goal is to be able to
interface with FSL completely in R, where you pass R nifti objects and the
function executes an FSL command and returns an R nifti object if desired.
Fuzzy set ordination is a multivariate analysis used in
ecology to relate the composition of samples to possible
explanatory variables.  While differing in theory and method,
in practice, the use is similar to 'constrained ordination.'
The package contains plotting and summary functions as well as
the analyses
Social Relations Analysis with roles ("Family
SRM") are computed, using a structural equation modeling approach. Groups ranging from
three members up to an unlimited number of members are supported and the
mean structure can be computed. Means and variances can be compared between
different groups of families and between roles.
This package was developed partially with funding from the
NIH Training Program in Biomolecular Technology
(2-T32-GM08799).
The package contains R-functions to perform the methods in
nonparametric regression and density estimation, described in
Davies, P. L. and Kovac, A. (2001) Local Extremes, Runs,
Strings and Multiresolution (with discussion) Annals of
Statistics. 29. p1-65 Davies, P. L. and Kovac, A. (2004)
Densities, Spectral Densities and Modality Annals of
Statistics. Annals of Statistics. 32. p1093-1136 Kovac, A.
(2006) Smooth functions and local extreme values. Computational
Statistics and Data Analysis (to appear) D\"umbgen, L. and
Kovac, A. (2006) Extensions of smoothing via taut strings
Davies, P. L. (1995) Data features. Statistica Neerlandica
49,185-245.
Functions for displaying and analyzing functional time series.
Fuel economy data from the EPA, 1985-2015, conveniently
packaged for consumption by R users.
This is an evolutionary algorithm for fuzzy systems, a
genetic algorithm is used to construct a fuzzy system able to
fit the given training data.  This fuzzy system can then be
used as a prediction model, it's composed of fuzzy logic rules
that provide a good lingustic representation.
This is a collection of R games and other funny stuff,
such as the classical Mine sweeper and sliding puzzles.
Statistical hypothesis testing methods for non-parametric functional dependencies based on asymptotic chi-square and exact statistics. These tests were designed to reveal causality based on evidence from functional dependencies. They include an asymptotic non-constant functional chi-square test, an exact functional test, and a comparative functional chi-square test. The normalized functional chi-square test was used by the Best Performer NMSUSongLab in DREAM8 Breast Cancer Network Inference Challenges. These tests offer an advantage over regression analysis when a parametric functional form cannot be assumed.
FunCluster performs a functional analysis of microarray
expression data based on Gene Ontology & KEGG functional
annotations. From expression data and functional annotations
FunCluster builds classes of putatively co-regulated biological
processes through a specially designed clustering procedure.
This packages proposes a model-based clustering algorithm for
multivariate functional data. The parametric mixture model, based on the
assumption of normality of the principal components resulting from a
multivariate functional PCA, is estimated by an EM-like algorithm. The main
advantage of the proposed algorithm is its ability to take into account the
dependence among curves.
Analyzes the function calls in an R package and creates a hive plot of the calls, dividing them among functions that only make outgoing calls (sources), functions that have only incoming calls (sinks), and those that have both incoming calls and make outgoing calls (managers).  Function calls can be mapped by their absolute numbers, their normalized absolute numbers, or their rank.  FuncMap should be useful for comparing packages at a high level for their overall design.  Plus, it's just plain fun.  The hive plot concept was developed by Martin Krzywinski (www.hiveplot.com) and inspired this package.  Note: this package is maintained for historical reasons. HiveR is a full package for creating hive plots.
Curry, Compose, and other higher-order functions
R package providing functions to perform gene and gene set network inference.
Extending functional programming in R by
providing support to the usual higher order functional
suspects (Map, Reduce, Filter, etc.).
The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.
The package provides the funHDDC algorithm (Bouveyron & Jacques, 2011) which allows to cluster functional data by modeling each group within a specific functional subspace.
This package performs functional regression, and some related
approaches, for intensive longitudinal data (see Walls & Schafer, 2006)
which is not necessarily observed on an equally spaced grid of times.  The
approach generally follows the ideas of Goldsmith, Bobb, Crainiceanu,
Caffo, and Reich (2011) and the approach taken in their sample code, but
with some modifications to make it more feasible to use with long rather
than wide, non-rectangular longitudinal datasets with unequal and
potentially random measurement times.  It also allows easy plotting of the
correlation between the smoothed covariate and the outcome as a function of
time, which can add additional insights on how to interpret a functional
regression.  Additionally, it also provides several permutation tests for
the significance of the functional predictor.  The heuristic interpretation
of ``time'' is used to describe the index of the functional predictor, but
the same methods can equally be used for another unidimensional continuous
index, such as space along a north-south axis.  The development of this
package was part of a research project supported by Award R03 CA171809-01
from the National Cancer Institute and Award P50 DA010075 from the National
Institute on Drug Abuse. The content is solely the responsibility of the
authors and does not necessarily represent the official views of the
National Institute on Drug Abuse, the National Cancer Institute, or the
National Institutes of Health.
Includes non-parametric estimators and tests for time series analysis. The functions allow to test for presence of possibly non-monotonic trends and for synchronism of trends in multiple time series, using modern bootstrap techniques and robust non-parametric difference-based estimators.
Efficient procedures for community detection in network studies, especially for sparse networks. The algorithms impose penalties on the differences of the coordinates which represent the community labels of the nodes.
This utility package provides polymorphism over common operations and is now subsumed by lambda.tools.
Provides a simple yet powerful logging utility. Based loosely on
log4j, futile.logger takes advantage of R idioms to make logging a
convenient and easy to use replacement for cat and print statements.
A collection of functions for manipulating matrices and generating ensembles of random matrices
Provides dispatching implementations suitable for
functional programming paradigms. The framework provides a
mechanism for attaching guards to functions similar to Erlang,
while also providing the safety of assertions reminiscent of
Eiffel.
A Future API for R is provided.  In programming, a future is an abstraction for a value that may be available at some point in the future.  The state of a future can either be unresolved or resolved.  As soon as it is resolved, the value is available.  Futures are useful constructs in for instance concurrent evaluation, e.g. multicore parallel processing and distributed processing on compute clusters.  The purpose of this package is to provide a lightweight interface for using futures in R.  Functions 'future()' and 'value()' exist for creating futures and requesting their values.  An infix assignment operator '%<=%' exists for creating futures whose values are accessible by the assigned variables (as promises).  This package implements the synchronous "lazy" and "eager" futures, and the asynchronous "multicore" future (not on Windows).  Additional types of futures are provided by other packages enhancing this package.
Exact calculation of fuzzy decision rules for multiple
testing. Choose to control FDR (false discovery rate) using the
Benjamini and Hochberg method, or FWER (family wise error rate)
using the Bonferroni method. Kulinsakaya and Lewin (2007).
Methods to solve Fuzzy Linear Programming Problems with
fuzzy constraints (by Verdegay, Zimmermann, Werner, Tanaka),
fuzzy costs (multiobjective, interval arithmetic, stratified piecewise reduction,
defuzzification-based), and fuzzy technological matrix.
Implements a fuzzy-logic-based map-matching algorithm used to match GPS
trajectories to the OpenStreetMap digital road network.
S4 classes and methods
to deal with fuzzy numbers. With them you can compute any arithmetic
operations (e.g. by using the Zadeh extension principle),
perform approximation of arbitrary FNs by trapezoidal and piecewise
linear FNs, prepare plots of FNs for publications, calculate
possibility and necessity values for comparisons, etc.
Does fuzzy tests and confidence intervals (following Geyer
and Meeden, Statistical Science, 2005) for sign test and Wilcoxon
signed rank and rank sum tests.
An implementation of a method for computing fuzzy numbers representing stationary probabilities of an unknown Markov chain,
from which a sequence of observations along time has been obtained. The algorithm is based on the proposal presented by James Buckley
in his book on Fuzzy probabilities (Springer, 2005), chapter 6. Package FuzzyNumbers is used to represent the output probabilities.
A custom framework for working with Type 1 Fuzzy Logic,
produced by the University of Nottingham IMA Group.
fwdmsa performs the Forward Search for Mokken scale
analysis. It detects outliers, it produces several types of
diagnostic plots.
A simple method
to select the best model or best subset of variables using
different types of data (binary, Gaussian or Poisson) and
applying it in different contexts (parametric or non-parametric).
This package provides three new functions to calculate the outputs of the two main components of the Canadian Forest Fire Danger Rating System (CFFDRS): the Fire Weather Index (FWI) System and the Fire Behaviour Prediction (FBP) System.
Simulates a population under the Fisher-Wright model (fixed or stochastic population size) with a one-step neutral mutation process (stepwise mutation model, logistic mutation model and exponential mutation model supported). The stochastic population sizes are random Poisson distributed and different kinds of population growth are supported. For the stepwise mutation model, it is possible to specify locus and direction specific mutation rate (in terms of upwards and downwards mutation rate). Intermediate generations can be saved in order to study e.g. drift.
Exchange rate regression and structural change tools
for estimating, testing, dating, and monitoring
(de facto) exchange rate regimes.
G1DBN performs DBN inference using 1st order conditional
dependencies.
Full descriptive statistics, physical description of sediment,
metric or phi sieves.
An R package for optimization using genetic algorithms. The package provides a flexible general-purpose set of tools for implementing genetic algorithms search in both the continuous and discrete case, whether constrained or not. Users can easily define their own objective function depending on the problem at hand. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. GAs can be run sequentially or in parallel.
This is a Genetic Algorithm package for the determination
of the stratum boundaries and sample sizes of each stratum in
stratified sampling
The GAabbreviate uses Genetic Algorithms as an optimization tool to create abbreviated forms of lengthy questionnaires (or other measures) that maximally capture the variance in the original data of the long form of the measure.
Generalized subspace pattern mining in data arrays, using a genetic algorithm framework.
This package analyses complex ANOVA models with any
combination of orthogonal/nested and fixed/random factors, as
described by Underwood (1997). There are two restrictions: (i)
data must be balanced; (ii) fixed nested factors are not
allowed. Homogeneity of variances is checked using Cochran's C
test and 'a posteriori' comparisons of means are done using
Student-Newman-Keuls (SNK) procedure.
In this package we consider Gaussian Diffusion processes and smooth thresholds. After evaluating the mean of the process to check the subthreshold regimen hypothesis, the FPT density function is reconstructed via the numerical quadrature of the integral equation in (Buonocore 1987); first passage times are also generated by the method in (Buonocore 2014) and results are compared. The timestep of the simulations can iteratively be refined. User should provide the functional form for the drift and the infinitesimal variance in the script 'userfunc.R' and for the threshold in the script 'userthresh.R'. All the parameters required by the implementation are to be set in the script 'userparam.R'. Example scripts for common drifts and thresholds are given.
This package constructs gains tables and lift charts for prediction algorithms. Gains tables and lift charts are commonly used in direct marketing applications.
GAIPE implements graphical representation of accuracy in
parameter estimation (AIPE) on RMSEA for sample size planning
in structural equation modeling. Sample sizes suggested by
RMSEA with AIPE method and power analysis approach can also be
obtained separately using the provided functions.
This package includes the ga.lts function that estimates
LTS (Least Trimmed Squares) parameters using genetic algorithms
and C-steps. ga.lts() constructs a genetic algorithm to form a
basic subset and iterates C-steps as defined in Rousseeuw and
van-Driessen (2006) to calculate the cost value of the LTS
criterion. OLS(Ordinary Least Squares) regression is known to
be sensitive to outliers. A single outlying observation can
change the values of estimated parameters. LTS is a resistant
estimator even the number of outliers is up to half of the
data. This package is for estimating the LTS parameters with
lower bias and variance in a reasonable time. Version 1.3
included the function medmad for fast outlier detection in
linear regression.
Functions for fitting and working with generalized
additive models, as described in chapter 7 of "Statistical Models in
S" (Chambers and Hastie (eds), 1991), and "Generalized Additive
Models" (Hastie and Tibshirani, 1990).
Data sets and scripts used in the book "Generalized
Additive Models: An Introduction with R", Wood (2006) CRC.
This package provides functions for fitting the gambin distribution to
species-abundance distributions from ecological data. The main function is fitGambin, which
estimates the 'alpha' parameter of the gambin distribution using maximum likelihood. Functions
are also provided to generate the gambin distribution and for calculating likelihood
statistics.
This package provides routines for fitting generalized
linear and and generalized additive models by likelihood based
boosting, using penalized B-splines
Boosting models for fitting generalized additive models for
location, shape and scale (GAMLSS) to potentially high dimensional
data.
Provides features to use function gamboost() from  package mboost for estimation of multistate models
Modern regression extends to include classification and multivariate exploration
Provides estimation and analysis functions for
strategic statistical models.
Implementation of a common set of punctual solutions  for Cooperative Game Theory.
The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms.  As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms).  For details see: Taddy (2015), One-Step Estimator Paths for Concave Regularization, http://arxiv.org/abs/1308.5623.
Functions for fitting, displaying and checking GAMLSS Models.
Interfaces for extra smooth functions including tensor products, neural networks and decision trees.
This is an add-on package to GAMLSS. The purpose of this
package is to allow users to fit interval response variables in
GAMLSS models. The main function gen.cens() generates a
censored version of an existing GAMLSS family distribution.
Demos for smoothing and gamlss.family distributions.
The different distributions used for the response variables in GAMLSS
modelling.
The main purpose of this package is to allow fitting of
mixture distributions with GAMLSS models.
This is an add on package to GAMLSS. It allows one extra
method for fitting GAMLSS models. The main function nlgamlss()
can fit any parametric (up to four parameter) GAMLSS
distribution.
It allows us to fit Markov Random Field (MRF) within the GAMLSS algorithms.
This is an add on package to GAMLSS. The purpose of this
package is to allow users to defined truncated distributions in
GAMLSS models. The main function gen.trun() generates truncated
version of an existing GAMLSS family distribution.
Extra Functions For GAMLSS And Others Models
Fit generalized additive mixed models via a version of
mgcv's gamm function, using lme4 for estimation.
This package performs gamma regression, where both mean and shape parameters follows lineal regression structures.
Uses a slice sampling-based Markov chain Monte Carlo to
conduct Bayesian fitting and inference for generalized additive
mixed models (GAMM).  Generalized linear mixed models and
generalized additive models are also handled as special cases
of GAMM.
Using overlap grouped lasso penalties, gamsel selects whether a term in a gam is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families.
This package implements a network-based gene weighting
algorithm for pathways, as well as a gene-weighted gene set
analysis approach for microarray data pathway analysis.
This is a dataset package for GANPA, which implements a
network-based gene weighting approach to pathway analysis. This
package includes data useful for GANPA, such as a functional
association network, pathways, an expression dataset and
multi-subunit proteins.
Performs a Genetic Algorithm Optimization, given a
real-based or permutation-based function and the associated
search space.
It is designed as an integrated package for genetic data
analysis of both population and family data. Currently, it
contains functions for sample size calculations of both
population-based and family-based designs, probability of
familial disease aggregation, kinship calculation, statistics
in linkage analysis, and association analysis involving genetic
markers including haplotype analysis with or without environmental
covariates.
The gap encodes the distance between clusters and improves
interpretation of cluster heatmaps. The gaps can be of the same
distance based on a height threshold to cut the dendrogram. Another
option is to vary the size of gaps based on the distance between
clusters.
An excerpt of the data available at Gapminder.org. For each of 142
countries, the package provides values for life expectancy, GDP per capita,
and population, every five years, from 1952 to 2007.
The functions included are used to obtain initial authentication with Google Analytics as well as simple and organized data retrieval from the API. Allows for retrieval from multiple profiles at once.
Provides a genetic algorithm for finding variable
subsets in high dimensional data with high prediction performance. The
genetic algorithm can use ordinary least squares (OLS) regression models or
partial least squares (PLS) regression models to evaluate the prediction
power of variable subsets. By supporting different cross-validation
schemes, the user can fine-tune the tradeoff between speed and quality of
the solution.
A collection difference measures for multivariate Gaussian
probability density functions, such as the Euclidea mean, the
Mahalanobis distance, the Kullback-Leibler divergence, the
J-Coefficient, the Minkowski L2-distance, the Chi-square
divergence and the Hellinger Coefficient.
A collection of functions to perform Gaussian quadrature
with different weight functions corresponding to the orthogonal
polynomials in package orthopolynom.  Examples verify the
orthogonality and inner products of the polynomials.
Eye-tracking data must be transformed into fixations and saccades before it can be analyzed. This package provides a non-parametric speed-based approach to do this on a trial basis. The method is especially useful when there are large differences in data quality, as the thresholds are adjusted accordingly. The same pre-processing procedure can be applied to all participants, while accounting for individual differences in data quality.
This package collects algorithms and functions for fitting data to a generalized lambda distribution via moment matching methods, and generalized bootstrapping.
Package GB2 explores the Generalized Beta distribution of the second kind. Density, cumulative distribution function, quantiles and moments of the distributions are given. Functions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formulas for various indicators of inequality and poverty under the GB2 are implemented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation using the full and profile log-likelihood, and non-linear least squares estimation of the model parameters. Various plots for the visualization and analysis of the results are provided. Variance estimation of the parameters is provided for the method of maximum pseudo-likelihood estimation. A mixture distribution based on the compounding property of the GB2 is presented (denoted as "compound" in the documentation). This mixture distribution is based on the discretization of the distribution of the underlying random scale parameter. The discretization can be left or right tail. Density, cumulative distribution function, moments and quantiles for the mixture distribution are provided. The compound mixture distribution is fitted using the method of maximum pseudo-likelihood estimation. The fit can also incorporate the use of auxiliary information. In this new version of the package, the mixture case is complemented with new functions for variance estimation by linearization and comparative density plots.
An implementation of extensions to Freund and
Schapire's AdaBoost algorithm and Friedman's gradient boosting
machine. Includes regression methods for least squares,
absolute loss, t-distribution loss, quantile regression,
logistic, multinomial logistic, Poisson, Cox proportional
hazards partial likelihood, AdaBoost exponential loss,
Huberized hinge loss, and Learning to Rank measures
(LambdaMart).
Writes SAS code to get predicted values from every tree of a gbm.object.
Provides utilities for processing Rd objects and files.
Extract argument descriptions and other parts of the help pages
of functions.
Many inherited biases and effects exists in RNA-seq due to both biological and technical effects. We observed the biological variance of testing target transcripts can influence the yield of sequencing reads, which might indicate a resource competition existing in RNA-seq. We developed this package to capture the bias depending on local sequence and perform the correction of this type of bias by borrowing information from spike-in measurement.
These are two-sample tests for categorical data utilizing similarity information among the categories.  They are useful when there is underlying structure on the categories.
GPU/CPU Benchmarking on Debian-package based systems
This package benchmarks performance of a few standard linear algebra
operations (such as a matrix product and QR, SVD and LU decompositions)
across a number of different BLAS libraries as well as a GPU implementation.

To do so, it takes advantage of the ability to 'plug and play' different
BLAS implementations easily on a Debian and/or Ubuntu system.  The current
version supports
- reference blas (refblas) which are unaccelerated as a baseline
- Atlas which are tuned but typically configure single-threaded
- Atlas39 which are tuned and configured for multi-threaded mode
- Goto Blas which are accelerated and multithreaded
- Intel MKL which are a commercial accelerated and multithreaded version.
As for GPU computing, we use the CRAN package
- gputools

For Goto Blas, the gotoblas2-helper script from the ISM in Tokyo can be
used. For Intel MKL we use the Revolution R packages from Ubuntu 9.10.
Contains the Global Charcoal database data. Data include charcoal
series (age, depth, charcoal quantity, associated units and methods) and
informations on sedimentary sites (localisation, depositional environment, biome, etc.).
This package implements a generalized coordinate descent (GCD) algorithm for computing the solution path of the hybrid Huberized support vector machine (HHSVM) and its generalization, including the elastic net penalized least squares, the elastic net penalized SVM with the squared hinge loss and the elastic net penalized logistic regression.
Orders panels in scatterplot matrices and parallel
coordinate displays by some merit index. Package contains
various indices of merit, ordering functions, and enhanced
versions of pairs and parcoord which color panels according to
their merit level.
Likelihood inference in Gaussian copula marginal
regression models.
Estimates a sparse inverse covariance matrix from a convex
pseudo-likelihood function with L1 penalty
This package contains data sets used in the book "R
Graphics Cookbook" by Winston Chang, published by O'Reilly
Media.
Analyze the default risk of credit portfolios. Commonly known models,
like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings.
The portfolio loss distribution can be achieved either by simulation or analytically
in case of the classic CreditRisk+ model. Models are only implemented to respect losses
caused by defaults, i.e. migration risk is not included. The package structure is kept
flexible especially with respect to distributional assumptions in order to quantify the
sensitivity of risk figures with respect to several assumptions. Therefore the package
can be used to determine the credit risk of a given portfolio as well as to quantify
model sensitivities.
Datasets used in the book 'Graphical Data Analysis with R' (Antony Unwin, CRC Press 2015).
Wrappers for the Geospatial Data Abstraction Library (GDAL)
Utilities
Various R programming tools for data manipulation, including:
- medical unit conversions ('ConvertMedUnits', 'MedUnits'),
- combining objects ('bindData', 'cbindX', 'combine', 'interleave'),
- character vector operations ('centerText', 'startsWith', 'trim'),
- factor manipulation ('levels', 'reorder.factor', 'mapLevels'),
- obtaining information about R objects ('object.size', 'elem', 'env',
'humanReadable', 'is.what', 'll', 'keep', 'ls.funs',
'Args','nPairs', 'nobs'),
- manipulating MS-Excel formatted files ('read.xls',
'installXLSXsupport', 'sheetCount', 'xlsFormats'),
- generating fixed-width format files ('write.fwf'),
- extricating components of date & time objects ('getYear', 'getMonth',
'getDay', 'getHour', 'getMin', 'getSec'),
- operations on columns of data frames  ('matchcols', 'rename.vars'),
- matrix operations ('unmatrix', 'upperTriangle', 'lowerTriangle'),
- operations on vectors ('case', 'unknownToNA', 'duplicated2', 'trimSum'),
- operations on data frames ('frameApply', 'wideByFactor'),
- value of last evaluated expression ('ans'), and
- wrapper for 'sample' that ensures consistent behavior for both
scalar and vector arguments ('resample').
Create and maintain delayed-data packages (ddp's).  Data stored in
a ddp are available on demand, but do not take up memory until requested.
You attach a ddp with g.data.attach(), then read from it and assign to it in
a manner similar to S-PLUS, except that you must run g.data.save() to
actually commit to disk.
This package contains functions for 'specific' MCA (Multiple Correspondence Analysis),
'class specific' MCA, computing and plotting structuring factors and concentration ellipses,
'standardized' MCA, inductive tests and others tools for Geometric Data Analysis. It also provides functions
for the translation of logit models coefficients into percentages (forthcoming), weighted contingency tables and an association
measure - i.e. Percentages of Maximum Deviation from Independence (PEM).
The GDELT data set is over 60 GB now and growing 100 MB a month.
The number of source articles has increased over time and unevenly across
countries. This package makes it easy to download a subset of that data,
then normalize that data to facilitate valid time series analysis.
Diffusion anisotropy has been used to characterize
white matter neuronal pathways in the human brain, and infer global
connectivity in the central nervous system. The package implements
algorithms to estimate and visualize the orientation of neuronal
pathways in model-free methods (q-space imaging methods).
For estimating fibre orientations two methods have been
implemented.  One method implements fibre orientation detection
through local maxima extraction. A second more robust method
is based on directional statistical clustering of ODF voxel data.
Fibre orientations in multiple fibre voxels are estimated using
a mixture of von Mises-Fisher (vMF) distributions. This statistical
estimation procedure is used to resolve crossing fibre
configurations.
Reconstruction of orientation distribution function (ODF)
profiles may be performed using the standard generalized
q-sampling imaging (GQI) approach, Garyfallidis' GQI (GQI2)
approach, or Aganj's variant of the Q-ball imaging (CSA-QBI)
approach. Procedures for the visualization of RGB-maps,
line-maps and glyph-maps of real diffusion magnetic resonance
imaging (dMRI) data-sets are included in the package.
Calculate distances and routes on geographic grids.
A toolkit with functions to fit, plot, and summarize Generalized Dissimilarity Models.
GEE estimation of the parameters in mean structures with possible correlation between the outcomes. User-specified mean link and variance functions are allowed, along with observation weighting.  The "M" in the name "geeM" is meant to emphasize the use of the Matrix package, which allows for an implementation based fully in R.
Generalized estimating equations solver for parameters in
mean, scale, and correlation structures, through mean link,
scale link, and correlation link. Can also handle clustered
categorical responses.
Generalized estimating equations with the original sandwich variance estimator proposed by Liang and Zeger (1986), and eight types of more recent modified variance estimators for improving the finite small-sample performance.
Functions to compute generalized eigenvalues and eigenvectors,
the generalized Schur decomposition and
the generalized singular value decomposition of a matrix pair, utilizing Lapack routines.
macroevolutionary simulation and parameter estimation
related to diversification from comparative
phylogenetic data
Implements several extensions of the elastic net regularization scheme. These extensions include individual feature penalties for the L1 term, feature-feature penalties for the L2 term, as well as translation coefficients for the latter.
Simulate and analyze multistate models with general hazard functions. gems provides functionality for the preparation of hazard functions and parameters, simulation from a general multistate model and predicting future events. The multistate model is not required to be a Markov model and may take the history of previous events into account. In the basic version, it allows to simulate from transition-specific hazard function, whose parameters are multivariable normally distributed.
Network meta-analyses (mixed treatment comparisons) in the Bayesian framework. You need either BUGS or JAGS to use this package, preferably JAGS (through the rjags package). Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations.
An R package providing the Java JAR for the gemtc package
a package for genome-wide association analysis between
quantitative or binary traits and single-nucleotide
polymorphisms (SNPs).
GenABEL.data package consists of a data set used by GenABEL functions
R based genetic algorithm for binary and floating point
chromosomes.
genasis package contains methods for air pollution assessment. Concerned on persistent organic pollutants, the package allows to compute trends of their concentrations, compare different datasets and estimate relations between values from active and passive air samplers.
Density, distribution function, quantile function and random generation for the Generalized Binomial Distribution. Functions to compute the Clopper-Pearson Confidence Interval and the required sample size. Enhanced model for burn-in studies, where failures are tackled by countermeasures.
Implementation of the genetic class level association testing (GenCAT) method from SNP level association data. Refer to: "Qian, J., Reed, E., Nunez, S., Qu, L., Reilly, M., Foulkes, A. (2015) Testing class-level genetic associations using single-element summary statistics. (Under review).".
Set of functions to create datasets using a correlation matrix.
Encodes gender based on names and dates of birth, using either the
Social Security Administration's data set of first names by year of birth
or the Census Bureau data from 1789 to 1940, both from the United States of
America. By using these data sets instead of lists of male and female names,
this package is able to more accurately guess the gender of a name, and it
is able to report the probability that a name was male or female.
Utilizes the 'genderize.io' API to predict gender from first
names extracted from a text vector. The accuracy of prediction could be
controlled by two parameters: counts of a first name in the database and
probability of prediction.
Functions and analytics for GENEA-compatible accelerometer
data into R objects. See topic 'GENEAread' for an introduction
to the package.
The GeneCycle package implements the approaches of Wichert
et al. (2004), Ahdesmaki et al. (2005) and Ahdesmaki et al.
(2007) for detecting periodically expressed genes from gene
expression time series data.
This package implements several generalized F-statistics.
The current version includes a generalized F-statistic based on
the flexible isotonic/monotonic regression or order restricted
hypothesis testing.
GeneFeST is a genome scan method to detect outlier loci (genes) which are under balancing or directional selection.
Stochastic simulation and MCMC inference of structure from genetic data.
"geneListPie" package is for mapping a gene list to
function categories defined in GOSlim or Kegg. The results can
be plotted as a pie chart to provide a quick view of the genes
distribution of the gene list among the function categories.
The gene list must contain a list of gene symbols. The package
contains a set of pre-processed gene sets obtained from Gene
Ontology and MSigDB including human, mouse, rat and yeast. To
provide a high level concise view, only GO slim and kegg are
provided. The gene sets are regulared updated. User can also
use customized gene sets. User can use the R Pie() or Pie3D()
function for plotting the pie chart. Users can also choose to
output the gene function mapping results and use external
software such as Excel(R) for ploting.
GeneNet is a package for analyzing gene expression
(time series) data with focus on the inference of gene networks.
In particular, GeneNet implements the methods of Schaefer and
Strimmer (2005a,b,c) and Opgen-Rhein and Strimmer (2006, 2007)
for learning large-scale gene association networks (including
assignment of putative directions).
Functions for Genetic Epi Methods Developed at MSKCC
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, normal inverse
Gaussian distribution and generalized inverse Gaussian
distribution, including fitting of these distributions to data.
Linear models with hyperbolic errors may be fitted using
hyperblmFit.
GeneReg is an R package for inferring time delay gene
regulatory network using time course gene expression profiles.
The main idea of time delay linear model is to fit a linear
regression model using a set of putative regulators to estimate
the transcription pattern of a specific target gene.
A tool for finding an ensemble gene-signature by a steepest ascending algorithm partially supervised by survival time data.
Classes and methods for handling genetic data. Includes
classes to represent genotypes and haplotypes at single markers
up to multiple markers on multiple chromosomes. Function
include allele frequencies, flagging homo/heterozygotes,
flagging carriers of certain alleles, estimating and testing
for Hardy-Weinberg disequilibrium, estimating and testing for
linkage disequilibrium, ...
Finds subsets of sets of genotypes with a high mean Polymorphism Information Content (PIC), and Mean of Transformed Kinships (MTK), measures that can indicate a subset would be beneficial for rare-trait discovery and genome-wide association scanning, respectively.
A loose collection of tools for the analysis of gene expression and genotype data, currently with main focus on eQTL and MDR analysis.
Two methods based on the Forward Imputation approach are implemented for the imputation of quantitative missing data. One method alternates Nearest Neighbour Imputation and Principal Component Analysis (function 'ForImp.PCA'), the other uses Nearest Neighbour Imputation with the Mahalanobis distance (function 'ForImp.Mahala').
This package computes the solution path for generalized lasso problems. Important use cases are the fused lasso over an arbitrary graph, and trend fitting of any given polynomial order. Specialized implementations for the latter two subproblems are given to improve stability and speed.
Genealogical data analysis including descriptive statistics (e.g., kinship and inbreeding coefficients) and gene-dropping simulations.
Implements the Mode Oriented Stochastic Search (MOSS) algorithm as well as a simple moving window approach to look for combinations of SNPs that are associated with a response.
This is a genMOSS package with additional datafile preprocessing functions. Performs genome-wide analysis of dense SNP array data using the mode oriented stochastic search (MOSS) algorithm in a case-control design. The MOSS algorithm is a Bayesian variable selection procedure that is applicable to GWAS data. It identifies combinations of the best predictive SNPs associated with the response. It also performs a hierarchical log-linear model search to identify the most relevant associations among the resulting subsets of SNPs. This package also includes preprocessing of the data from Plink format to the format required by the MOSS algorithm.
Draws gene or genome maps and comparisons between these, in a
publication-grade manner. Starting from simple, common files, it will
draw postscript or pdf files that can be sent as such to journals.
A gaussian copula based procedure is implemented for generating samples from ordinal/discrete random variables with pre-specified correlation matrix and marginal distributions.
genpathmox provides a very interesting solution for handling
segmentation variables in complex statistical methodology. It contains en
extended version of the PATHMOX algorithm in the context of partial least
square path modeling (Sanchez, 2009) including the F-block test (to detect
the responsible latent endogenous equations of the difference), the
F-coefficient (to detect the path coefficients responsible of the
difference) and the invariance test (to realize a comparison between the
sub-models' latent variables). Furthermore, the package contains a
generalized version of the PATHMOX algorithm to approach different
methodologies: linear regression and least absolute regression models.
The genridge package introduces generalizations of the standard univariate
ridge trace plot used in ridge regression and related methods.  These graphical methods
show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated
coefficients, rather than just the estimates themselves.  2D and 3D plotting methods are provided,
both in the space of the predictor variables and in the transformed space of the PCA/SVD of the
predictors.
Performs search for global minimum of a very complex non-linear objective function with a very large number of optima.
Generalized ensemble methods allowing arbitrary underlying
models to be used. Currently only bagging is supported.
Generation of survival data with one (binary)
time-dependent covariate.  Generation of survival data arising
from a progressive illness-death model.
Defines window or bin boundaries for the analysis of genomic data.
Boundaries are based on the inflection points of a cubic smoothing spline
fitted to the raw data. Along with defining boundaries, a technique to
evaluate results obtained from unequally-sized windows is provided.
Applications are particularly pertinent for, though not limited to, genome
scans for selection based on variability between populations (e.g. using
Wright's fixations index, Fst, which measures variability in subpopulations
relative to the total population).
Used by Hafro staff to draw maps showing the distribution of
fishing intensity and catches, and of survey data for Icelandic fish stocks.
Potentially useful for others.
Functions to fit geostatistical data. The data can be
continuous, binary or count data and the models implemented are
flexible. Conjugate priors are assumed on some parameters while
inference on the other parameters can be done through a full
Bayesian analysis of by empirical Bayes methods.
Wrapper for Nokia's HERE geocoding API. See http://here.com/ for
more information on HERE and https://developer.here.com/geocoder for more
information on the HERE geocoding API.
This package provides a variety of functions to analyze and model geostatistical count data with generalized linear spatial models, including
1) simulate and visualize the data;
2) posterior sampling with robust MCMC algorithms (in serial or parallel way);
3) perform prediction for unsampled locations;
4) conduct Bayesian model checking procedure to evaluate the goodness of fitting;
5) conduct transformed residual checking procedure.
In the package, seamlessly embedded C++ programs and parallel computing techniques are implemented to speed up the computing processes.
Given expression data this package calculate a multivariate
geometrical characterization of the differential expression and can also
perform gene-set enrichment.
Quantification of the effect of geographic versus environmental isolation on genetic differentiation
Convert data to 'geoJSON' or 'topoJSON' from various R classes,
including vectors, lists, data frames, shape files, and spatial
classes. 'geojsonio' does not aim to replace packages like 'sp', 'rgdal', 'rgeos',
but rather aims to be a high level client to simplify conversions of data
from and to 'geoJSON' and 'topoJSON'.
The package provides basic functions for global
positioning based on light intensity measurements over time.
Positioning process includes the determination of sun events, a
discrimination of residency and movement periods, the
calibration of period-specific data and, finally, the
calculation of positions.
Set of routines for making Map Projections (forward and inverse), Topographic Maps, Perspective plots, Geological Maps, geological map symbols, geological databases, interactive plotting and selection of focus regions.
Set of data for use in package GEOmap.  Includes world
map, USA map, Coso map, Japan Map, ETOPO5
This package makes the qhull library (www.qhull.org)
available in R, in a similar manner as in Octave and MATLAB. Qhull
computes convex hulls, Delaunay triangulations, halfspace
intersections about a point, Voronoi diagrams, furthest-site
Delaunay triangulations, and furthest-site Voronoi diagrams. It
runs in 2-d, 3-d, 4-d, and higher dimensions. It implements the
Quickhull algorithm for computing the convex hull. Qhull does not
support constrained Delaunay triangulations, or mesh generation of
non-convex objects, but the package does include some R functions
that allow for this. Currently the package only gives access to
Delaunay triangulation and convex hull computation.
Read, manipulate, and digitize landmark data, generate shape
variables via Procrustes analysis for points, curves and surfaces, perform
shape analyses, and provide graphical depictions of shapes and patterns of
shape variation.
Code for querying the web service at www.geonames.org
Codes for analyzing various problems of Geophysics, Continuum Mechanics and  Mogi Models.
Geostatistical analysis including traditional, likelihood-based and Bayesian methods.
Functions for inference in generalised linear spatial models. The posterior and predictive inference is based on Markov chain Monte Carlo methods. Package geoRglm is an extension to the package geoR, which must be installed first.
The georob package provides functions for fitting linear models
with spatially correlated errors by robust and Gaussian Restricted
Maximum Likelihood and for computing robust and customary point
and block kriging predictions, along with utility functions for
cross-validation and for unbiased back-transformation of kriging
predictions of log-transformed data.
Function for adding the geological timescale to bivariate plots.
Generates distance matrices from shape files and represents spatially weighted multilevel analysis results (see 'spacom')
Spherical trigonometry for geographic applications. That is, compute distances and related measures for angular (longitude/latitude) locations.
Estimation of the variogram through trimmed mean, radial basis
functions (optimization, prediction and cross-validation), summary
statistics from cross-validation, pocket plot, and design of
optimal sampling networks through sequential and simultaneous
points methods.
Spatio-temporal: Inverse Distance Weighting (IDW) and radial basis functions; optimization, prediction, summary statistics from leave-one-out cross-validation, adjusting distance-based linear regression model and generation of the principal coordinates of a new individual from Gower's distance.
Geostatistical modelling facilities using Raster and SpatialPoints objects are provided.  Non-Gaussian models are fit using INLA, and Gaussian geostatistical models use Maximum Likelihood Estimation.
It analyzes raster maps and other information as input/output
files from the Hydrological Distributed Model GEOtop. It contains functions
and methods to import maps and other keywords from geotop.inpts file. Some
examples with simulation cases of GEOtop 2.0 are presented in the package.
Any information about the GEOtop Distributed Hydrological Model source code
is available on http://code.google.com/p/geotop/ or
https://github.com/se27xx/GEOtop. Technical details about the model are
available in Endrizzi et al, 2014
(http://www.geosci-model-dev.net/7/2831/2014/gmd-7-2831-2014.html).
GeoXp is a tool for researchers in spatial statistics,
spatial econometrics, geography, ecology etc allowing to link
dynamically statistical plots with elementary maps. This
coupling consists in the fact that the selection of a zone on
the map results in the automatic highlighting of the
corresponding points on the statistical graph or reversely the
selection of a portion of the graph results in the automatic
highlighting of the corresponding points on the map. GeoXp
includes tools from different areas of spatial statistics
including geostatistics as well as spatial econometrics and
point processes. Besides elementary plots like boxplots,
histograms or simple scatterplot, GeoXp also couples with maps
Moran scatterplots, variogram cloud, Lorentz Curves,...In order
to make the most of the multidimensionality of the data, GeoXp
includes some dimension reduction techniques such as PCA.
The package allows geometric objects defined in geozoo to be
displayed in the R package tourr
The Gene Expression State Transformation (GESTr) models
the states of expression of genes across a compendium of
samples in order to provide a universal scale of gene
expression for all genes. TranSAM is a modification of the SAM
approach designed to utilise GESTr-transformed gene expression
data.
Package designed to be used with Rscript to write
``#!'' shebang scripts that accept short and long flags/options.
Many users will prefer using instead the packages optparse or argparse
which add extra features like automatically generated help option and usage,
support for default values, positional argument support, etc.
This is yet another command-line argument parser which wraps the
powerful Perl module Getopt::Long and with some adaptation for easier use
in R. It also provides a simple way for variable interpolation in R.
The GetR package calculates Guttman error trees, which can
be used to find homogeneous subgroups regarding Guttman errors.
Automated multi-path General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting structural breaks in the mean. The mean can be specified as an autoregressive model with covariates (an 'AR-X' model), and the variance can be specified as a log-variance model with covariates (a 'log-ARCH-X' model). The four main functions of the package are arx, getsm, getsv and isat. The first function, arx, estimates an AR-X model with log-ARCH-X errors. The second function, getsm, undertakes GETS model selection of the mean specification of an arx object. The third function, getsv, undertakes GETS model selection of the log-variance specification of an arx object. The fourth function, isat, undertakes GETS model selection of an indicator saturated mean specification.
Getting to the Bottom accompanies the "Getting to
the Bottom" optimization methods series at Statisticsviews.com.  It
contains data and code to reproduce the examples in the articles.
Implements a flexible nonlinear modelling framework for nonstationary
generalized extreme value analysis in hydroclimatology.
Package for ARMA-GARCH or ARMA-APARCH modelling with GEV and stable conditional distributions.
Perform statistical tests to unveil genomic clusters,
produces garphical interpretations of the statistical results
in pdf files, perform a Gene Ontology analysis and produces
graphic results in pdf files
The gfcanalysis package supports analyses using the Global
Forest Change dataset released by Hansen et al. 2013. gfcanalysis was
written by the Tropical Ecology Assessment and Monitoring (TEAM) Network
(http://www.teamnetwork.org). For additional details on the Global Forest
Change dataset, see: Hansen, M. et al. 2013.  "High-Resolution Global Maps
of 21st-Century Forest Cover Change." Science 342 (15 November): 850-53.
Data available on-line from:
http://earthenginepartners.appspot.com/science-2013-global-forest
GGally is designed to be a helper to ggplot2. It contains
templates for different plots to be combined into a plot matrix, a parallel
coordinate plot function, as well as a function for making a network plot.
This is a set of tools for dendrograms and
tree plots using ggplot2.  The ggplot2 philosophy is to
clearly separate data from the presentation.
Unfortunately the plot method for dendrograms plots
directly to a plot device without exposing the data.
The ggdendro package resolves this by making available
functions that extract the dendrogram plot data.
A GUI with which to construct and interact with GGE biplots.
Methods for searching through genealogical data and displaying the results. Plotting algorithms assist with data exploration and publication-quality image generation. Uses the Grammar of Graphics.
Collection of functions and layers to enhance 'ggplot2'. The main
function is ggMarginal(), which can be used to add marginal
histograms/boxplots/density plots to 'ggplot2' scatterplots.
A tool to process and analyse data collected with wearable raw acceleration sensors. The package has been developed and tested for binary data from GENEActiv and GENEA devices and .csv-export data from  Actigraph devices. These devices are currently widely used in research on human daily physical activity.
This package implements a unified algorithm, blockwise-majorization-decent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM.
Functions and datasets for maximum likelihood fitting of some classes of graphical Markov models.
Easily visualize of spatial data and models on top of Google Maps,
OpenStreetMaps, Stamen Maps, or CloudMade Maps with ggplot2.
Tool for assessing and diagnosing convergence of
Markov Chain Monte Carlo simulations, as well as for graphically display
results from full MCMC analysis. The package also facilitates the graphical
interpretation of models by providing flexible functions to plot the
results against observed variables.
GGMselect is a package dedicated to graph estimation in Gaussian Graphical Models. The main functions return the adjacency matrix of an undirected graph estimated from a data matrix.
R package for creating hammock plots, parallel sets, and
common angle plots using the ggplot2 framework.
An implementation of the grammar of graphics
in R. It combines the advantages of both base and
lattice graphics: conditioning and shared axes are
handled automatically, and you can still build up a
plot step by step from multiple data sources. It also
implements a sophisticated multidimensional
conditioning system and a consistent interface to map
data to aesthetic attributes. See http://ggplot2.org
for more information, documentation and examples.
Graphic elements for exploring Random Forests using the randomForestSRC package for survival, regression and
classification forests and ggplot2 package plotting.
ggsubplot makes it easy to embed customized
subplots within larger graphics. Subplots may be used
as a geom to explore interaction effects, spatial data,
and hierarchical data. Subplots can also be used to
explore big data without overplotting.
Offers various swiss maps as ggplot2 objects and gives the
possibility to add layers of data on the maps. Data are publicly available
from the swiss federal statistical office.
Extends the functionality of ggplot2, providing the capability to plot ternary diagrams for (subset of) the ggplot2 geometries. Additionally, ggtern has implemented several NEW geometries which are unavailable to the standard ggplot2 release. For further examples and documentation, please proceed to the ggtern website.
Some extra themes, geoms, and scales for `ggplot2`.
An implementation of an interactive grammar of graphics, taking the
best parts of 'ggplot2', combining them with the reactive framework from
'shiny' and web graphics from 'vega'.
The GhcnDaily package provides the core functions required
to download and format the GHCN Daily Data (TMax and Tmin) into
Monthly datasets. It is currently restricted to operating with
temperature data from the daily files.
The GHQ function can be used to obtain the quadrature points and weights to approximate an integral in two or more dimensions. This function uses the pruning approach to eliminate that points that do not contribute to the approximation of the integral and increases computational cost. The advantage to conducting this elimination of points is the decrease in the number of times that the function of interest is evaluated. This advantage is crucial in mixed models in which we must address several integrations within an iterative process to obtain model parameters.
This package provides detailed functionality for working
with the univariate and multivariate Generalized Hyperbolic
distribution and its special cases (Hyperbolic (hyp), Normal
Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t
and Gaussian distribution). Especially, it contains fitting
procedures, an AIC-based model selection routine, and functions
for the computation of density, quantile, probability, random
variates, expected shortfall and some portfolio optimization
and plotting routines as well as the likelihood ratio test. In
addition, it contains the Generalized Inverse Gaussian
distribution.
Gibbs sampler for one-way linear mixed-effects models
(ANOVA, ANCOVA) with homoscedasticity of errors and uniform
priors.
This package provides two generic functions for performing
Markov chain sampling in a naive way for a user-defined target
distribution, which involves only continuous variables. The
function "gibbs_met" performs Gibbs sampling with each
1-dimensional distribution sampled with Metropolis update using
Gaussian proposal distribution centered at the previous state.
The function "met_gaussian" updates the whole state with
Metropolis method using independent Gaussian proposal
distribution centered at the previous state. The sampling is
carried out without considering any special tricks for
improving efficiency. This package is aimed at only routine
applications of MCMC in moderate-dimensional problems.
Generator and density function for the
Generalized Inverse Gaussian (GIG) distribution.
GillespieSSA provides a simple to use, intuitive, and
extensible interface to several stochastic simulation
algorithms for generating simulated trajectories of finite
population continuous-time model. Currently it implements
Gillespie's exact stochastic simulation algorithm (Direct
method) and several approximate methods (Explicit tau-leap,
Binomial tau-leap, and Optimized tau-leap). The package also
contains a library of template models that can be run as demo
models and can easily be customized and extended. Currently the
following models are included, decaying-dimerization reaction
set, linear chain system, logistic growth model, Lotka
predator-prey model, Rosenzweig-MacArthur predator-prey model,
Kermack-McKendrick SIR model, and a metapopulation SIRS model.
Automated identification and estimation of group- and
individual-level relations in time series data from within a structural
equation modeling framework.
Maximum likelihood estimation under relational models, with or without the overall effect.
Supply classes and methods to represent and manipulate
graphs
Some mapping and spatial data manipulation tools - in particular
drawing choropleth maps with nice looking legends,  and aggregation of point
data to polygons.
Work with 'GitHub' 'gists' from 'R' (e.g.,
http://en.wikipedia.org/wiki/GitHub#Gist,
https://help.github.com/articles/about-gists/). A 'gist' is simply one or
more files with code/text/images/etc. This package allows the user to
create new 'gists', update 'gists' with new files, rename files, delete files,
get and delete 'gists', star and 'un-star' 'gists', fork 'gists', open a 'gist' in
your default browser, get embed code for a 'gist', list 'gist' 'commits', and
get rate limit information when 'authenticated'. Some requests require
authentication and some do not. 'Gists' website: https://gist.github.com/.
Interface to the 'libgit2' library, which is a pure C
implementation of the 'Git' core methods. Provides access to 'Git'
repositories to extract data and running some basic 'Git'
commands.
gitter is a image analysis package for R. Its primary goal is to allow users to robustly and quickly grid and quantify biological images of pinned colonies. It includes several image pre-processing techniques, such as autorotation of plates, noise removal, contrast adjustment and image resizing.
`Giza' offers a simple way of creating multiple pyramid
plots in one graphics window, exploiting the power of the
lattice package. It is a handy way of visualizing longitudinal
grouped (i.e.: age- and education-structured) data.
Imports the 'gkmSVM' v2.0 functionalities into R (www.beerlab.org/gkmsvm). It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms.
This package provides functions for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables.  These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.
Analyses response times and accuracies from psychological experiments with the linear ballistic accumulator (LBA) model from Brown and Heathcote (2008). The LBA model is optionally fitted with explanatory variables on the parameters such as the drift rate, the boundary and the starting point parameters. A log-link function on the linear predictors can be used to ensure that parameters remain positive when needed.
Enables calculation of image textures derived from grey-level
co-occurrence matrices (GLCMs) in R. Supports processing images that cannot
fit in memory.
The  generalised lambda distribution, or Tukey lambda distribution, provides a wide variety of shapes with one functional form.
This package provides random numbers, quantiles, probabilities, densities and density quantiles for four different parameterisations of the distribution.
It provides the density function, distribution function, and Quantile-Quantile plots.
It implements a variety of estimation methods for the distribution, including diagnostic plots.
Estimation methods include the starship (all 4 parameterisations) and a number of methods for only the FKML parameterisation.
These include maximum likelihood, maximum product of spacings, Titterington's method, Moments, L-Moments, Trimmed L-Moments and Distributional Least Absolutes.
The fitting algorithms considered in this package have two major objectives. One is to provide a smoothing device to fit distributions to data using the weight and unweighted discretised approach based on the bin width of the histogram. The other is to provide a definitive fit to the data set using the maximum likelihood and quantile matching estimation. Other methods such as moment matching, starship method, L moment matching are also provided. Diagnostics on goodness of fit can be done via qqplots, KS-resample tests and comparing mean, variance, skewness and kurtosis of the data with the fitted distribution.
The generalized lambda distribution (GLD) is a versatile
distribution that can accommodate a wide range of shapes,
including fat-tailed and asymmetric distributions. This package
implements a more intuitive parameterization of the GLD that
expresses the location and scale parameters directly as the
median and inter-quartile range of the distribution. The
remaining two shape parameters characterize the asymmetry and
steepness of the distribution respectively. The fitting of the
GLD to empirical data can be reduced to a two-parameter
estimation problem where the location and scale parameters are
estimated by their robust sample estimators. Moreover, the
parameterization can be used to compare data sets in a
convenient asymmetry and steepness shape plot. The underline C
routines are written such that compilers that support
vectorized mathematical operations can automatically vectorize
the most time consuming loops (tested with icc 12.1.0). The
package includes the usual distribution functions, fitting
routines and a shape plot function.
Owing to the rich shapes of GLDs, GLD standard/quantile regression is a competitive flexible model compared to standard/quantile regression.  The proposed method has some major advantages: 1) it provides a reference line which is very robust to outliers with the attractive property of zero mean residuals and 2) it gives a unified, elegant quantile regression model from the reference line with smooth regression coefficients across different quantiles. The goodness of fit of the proposed model can be assessed via QQ plots and the Kolmogorov-Smirnov test, to ensure the appropriateness of the statistical inference under consideration. Statistical distributions of coefficients of the GLD regression line are obtained using simulation, and interval estimates are obtained directly from simulated data.
Group-Lasso INTERaction-NET. Fits linear pairwise-interaction models that satisfy strong hierarchy: if an interaction coefficient is estimated to be nonzero, then its two associated main effects also have nonzero estimated coefficients. Accommodates categorical variables (factors) with arbitrary numbers of levels, continuous variables, and combinations thereof. Implements the machinery described in the paper "Learning interactions via hierarchical group-lasso regularization" (JCGS 2014).
Routines for log-linear models of incomplete contingency tables,
including some latent class models, via EM and Fisher scoring
approaches.  Allows bootstrapping.
Fits generalized linear models using the same model specification as glm in the stats package, but with a modified default fitting method that provides greater stability for models that may fail to converge using glm
Fits generalized linear models where the parameters are
subject to linear constraints. The model is specified by giving
a symbolic description of the linear predictor, a description
of the error distribution, and a matrix of constraints on the
parameters.
This package contains functions to perform generalized
linear mixed Dirichlet models using posterior simulation.
We propose to use sparse regression model to achieve variable selection while accounting for graph-constraints among coefficients. Different linear combination of a sparsity penalty(L1) and a smoothness(MCP) penalty has been used, which induces both sparsity of the solution and certain smoothness on the linear coefficients.
Efficient algorithms for fitting regularization paths for
linear or logistic regression models penalized by LEP.
Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.
Write bugs model files for hierarchical and spatial models, arranges unbalanced data in ragged arrays, and creates starting values.
Solve Generalized Linear Mixed Models using Gauss-Seidel
algorithm
A variable selection approach for generalized linear mixed models by L1-penalized estimation is provided.
Binomial and Poisson regression for clustered data, fixed
and random effects with bootstrapping.
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below.
This packages includes functions for restructuring an ordinal response dataset for fitting continuation ratio models for datasets
where the number of covariates exceeds the sample size or when there is collinearity among the covariates. This package uses the glmnet package
fitting algorithm.
A path-following algorithm for L1 regularized generalized
linear models and Cox proportional hazards model
Provides a function for fitting a penalized constrained continuation ratio model using the glmpath algorithm and methods for extracting coefficient estimates, predicted class, class probabilities, and plots.
Automated model selection and model-averaging. Provides a
wrapper for glm and other functions, automatically generating
all possible models (under constraints set by the user) with
the specified response and explanatory variables, and finding
the best models in terms of some Information Criterion (AIC,
AICc or BIC). Can handle very large numbers of candidate
models. Features a Genetic Algorithm to find the best models
when an exhaustive screening of the candidates is not feasible.
Variable selection deviation (VSD) measures and instability tests for high-dimensional model selection methods such as LASSO, SCAD and MCP, etc., to decide whether the sparse patterns identified by those methods are reliable.
Extended techniques for generalized linear models (GLMs), especially for binary responses,
including parametric links and heteroskedastic latent variables.
'globalboosttest' implements a permutation-based testing
procedure to globally test the (additional) predictive value of
a large set of predictors given that a small set of predictors
is already available. Currently, 'globalboosttest' supports
binary outcomes (via logistic regression) and survival outcomes
(via Cox regression). It is based on boosting regression as
implemented in the package 'mboost'.
permutation based global test with deviance as test statistic
Initial metabolic networks often inaccurately predict in-silico growth or non-growth if compared to in-vivo data. This package refines metabolic network models by making networks changes (i.e., removing, adding, changing reversibility of reactions; adding and removing biomass metabolites) and simultaneously matching sets of experimental growth and non-growth data (e.g., KO-mutants, mutants grown under different media conditions,...)
Implementation of three different Gene set analysis (GSA) algorithms for combining the individual pvalues of a set of genetic variats (SNPs) in a gene level pvalue. The implementation includes the selection of the best inheritance model for each SNP.
It provides more controls on the option values such as validation
and filtering on the values, making options invisible or private.
This package makes available 50 objective functions for benchmarking the performance of global optimization algorithms
Identifies global ("unknown") objects in R expressions by code inspection using various strategies, e.g. conservative or liberal.  The objective of this package is to make it as simple as possible to identify global objects for the purpose of exporting them in distributed compute environments.
Tools for the generalized logistic distribution (Type I,
also known as skew-logistic distribution), encompassing
basic distribution functions (p, q, d, r, score), maximum
likelihood estimation, and structural change methods.
R Interface to C API of GLPK, needs GLPK Version >= 4.42
Functions to conduct four generalized logrank tests and a score test under a proportional hazards model
Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
An interface to the Gmail RESTful API.  Allows access to your
Gmail messages, threads, drafts and labels.
This package implements a general framework for utilizing R to harness the power of NVIDIA GPU's. The "gmatrix" and "gvector" classes allow for easy management of the separate device and host memory spaces. Numerous numerical operations are implemented for these objects on the GPU. These operations include matrix multiplication, addition, subtraction, the kronecker product, the outer product, comparison operators, logical operators, trigonometric functions, indexing, sorting, random number generation and many more.
Unsupervised Clustering and Meta-analysis using Gaussian Mixture
Copula Models.
This package provides functions and a graphical user interface for graphical described multiple test procedures.
GMD is a package for non-parametric distance measurement between
two discrete frequency distributions.
Group method of data handling (GMDH) - type neural network algorithm is the heuristic self-organization method for modelling the complex systems. In this package, GMDH-type neural network algorithms are applied to predict and forecast a univariate time series.
Tools for making the descriptive "Table 1" used in medical articles,
a transition plot for showing changes between categories,
a method for variable selection based on the SVD,
Bézier lines with arrows complementing the ones in the 'grid' package,
and more.
It is a complete suite to estimate models based on moment
conditions. It includes the two step Generalized method of
moments (GMM) of Hansen(1982), the iterated GMM and continuous
updated estimator (CUE) of Hansen-Eaton-Yaron(1996) and several
methods that belong to the Generalized Empirical Likelihood
(GEL) family of estimators, as presented by Smith(1997),
Kitamura(1997), Newey-Smith(2004) and Anatolyev(2005).
Likelihood-based Boosting for Generalized mixed models
An implementation of maximum simulated likelihood method for the estimation of multinomial logit models with random coefficients. Specifically, it allows estimating models with continuous heterogeneity such as the mixed multinomial logit and the generalized multinomial logit. It also allows estimating models with discrete heterogeneity such as the latent class and the mixed-mixed multinomial logit model.
Various R programming tools for model fitting.
Multiple Precision Arithmetic (big integers and rationals,
prime number tests, matrix computation), "arithmetic without limitations"
using the C library GMP (GNU Multiple Precision Arithmetic).
Interface between the GMT map-making software and R,
enabling the user to manipulate geographic data within R and call GMT commands
to draw and annotate maps in postscript format. The 'gmt' package is about
interactive data analysis, rapidly visualizing subsets and summaries of
geographic data, while performing statistical analysis in the R console.
Generalized Mann-Whitney type tests based on probabilistic
indices and new diagnostic plots.
Provide functions to compute standard and generalized Nash Equilibria. Optimization methods are available nonsmooth reformulation, fixed-point formulation, minimization problem and constrained-equation reformulation.
Functions to specify and fit generalized nonlinear models, including models with multiplicative interaction terms such as the UNIDIFF model from sociology and the AMMI model from crop science, and many others.  Over-parameterized representations of models are used throughout; functions are provided for inference on estimable parameter combinations, as well as standard methods for diagnostics etc.
This package performs generalized non-negative matrix factorization based on Renyi divergence.
Read data files readable by gnumeric into R. Can read
whole sheet or a range, from several file formats, including
the native format of gnumeric. Reading is done by using
ssconvert (a file converter utility included in the gnumeric
distribution http://projects.gnome.org/gnumeric/) to convert
the requested part to CSV. From gnumeric files (but not other
formats) can list sheet names and sheet sizes or read all
sheets.
A collection of functions to solve weighted and lexicographical
goal programming problems as specified by Lee (1972) and Ignizio (1976).
Implementation of model-checking techniques for generalized linear
models and linear structural equation models based on cumulative residuals
Tests of goodness-of-fit based on kernel smoothing of the data
Tests of fit for the gamma, normal, multivariate normal and generalized Pareto distributions.
Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit
for continuous univariate distributions, using
efficient algorithms.
Accounting for genes' functional-non-equivalence within pathways in classical Gene-set-analysis.
Implementation of the GO-GARCH model class
Provides a collection of functions designed for working with 'Google Public Data Explorer'. Automatically builds up the corresponding DSPL (XML) metadata files and CSV files; compressing all the files and leaving them ready to be published on the 'Public Data Explorer'.
R interface to Google Charts API, allowing users
to create interactive charts based on data frames. Charts
are displayed locally via the R HTTP help server. A modern
browser with Internet connection is required and for some
charts a Flash player. The data remains local and is not
uploaded to Google.
Implementation of multilayered visualizations for enhanced graphical
representation of functional analysis data. It combines and integrates omics
data derived from expression and functional annotation enrichment analyses.
Its plotting functions have been developed with an hierarchical structure in
mind: starting from a general overview to identify the most enriched
categories (modified bar plot, bubble plot) to a more detailed one displaying
different types of relevant information for the molecules in a given set of
categories (circle plot, chord plot, cluster plot, Venn diagram).
Generalized Order-Restricted Information Criterion (GORIC) value for a set of hypotheses in multivariate regression models
This package purposes to deal with public survey data of
Japanese government via their Application Programming Interface
(http://statdb.nstac.go.jp/)
Produces a generalized pairs (gpairs) plot.
Gaussian process regression models, a.k.a. kriging models, are
applied to global multiobjective optimization of black-box functions.
Multiobjective Expected Improvement and Stepwise Uncertainty Reduction
sequential infill criteria are available. A quantification of uncertainty
on Pareto fronts is provided using conditional simulations.
Gradient Projection Algorithm Rotation for Factor Analysis. See ?GPArotation.Intro for more details.
A generalized polynomial chaos expansion of a model taking as input independent random variables is achieved. A statistical and a global sensitivity analysis of the model are also carried out.
This package implements guided principal components analysis for the detection of batch effects in high-throughput data.
General polygon clipping routines for R based on Alan
Murta's C library
This package implements an extension of principal component analysis (PCA) tailored to handle multiple data tables. It can handle Big Data in the sense that the variation in massive data can be described by intervals [a, b] and multiple tables.
This package computes the bootstrap goodness-of-fit test
for the generalized Pareto distribution by Villasenor-Alva and
Gonzalez-Estrada (2009). The null hypothesis includes heavy and
non-heavy tailed gPd's. A function for fitting the gPd to data
using the parameter estimation methods proposed in the same
article is also provided.
Use functional regression as the mean structure and Gaussian Process as the covariance structure.
A computationally stable approach of fitting a Gaussian Process (GP) model to a deterministic simulator.
Collection of datasets as prepared by Profs. A.P. Gore, S.A. Paranjape, and M.B. Kulkarni of Department of Statistics, Poona University, India. With their permission, first letter of their names forms the name of this package, the package has been built by me and made available for the benefit of R users. This collection requires a rich class of models and can be a very useful building block for a beginner.
Functions for estimating generalized partial linear models
Various R programming tools for plotting data, including:
- calculating and plotting locally smoothed summary functionals
('bandplot', 'wapply'),
- enhanced versions of standard plots ('barplot2', 'boxplot2',
'heatmap.2', 'smartlegend'),
- manipulating colors ('col2hex', 'colorpanel', 'redgreen',
'greenred', 'bluered', 'redblue', 'rich.colors'),
- calculating and plotting two-dimensional data summaries ('ci2d',
'hist2d'),
- enhanced regression diagnostic plots ('lmplot2', 'residplot'),
- formula-enabled interface to stats::lowess function ('lowess'),
- displaying textual data in plots ('textplot', 'sinkplot'),
- plotting a matrix where each cell contains a dot whose size
reflects the relative magnitude of the elements ('balloonplot'),
- plotting Venn diagrams ('venn'),
- displaying Open-Office style plots ('ooplot'),
- plotting multiple data sets on same region, with separate axes
('overplot'),
- plotting means and confidence intervals ('plotCI', 'plotmeans'),
- spacing points in an x-y plot so they don't overlap ('space').
Combining a generalized linear model with an additional tree part
on the same scale. A four-step procedure is proposed to fit the model and test
the joint effect of the selected tree part while adjusting on confounding factors.
We also proposed an ensemble procedure based on the bagging to improve prediction
accuracy and computed several scores of importance for variable selection.
This package contains tools for studying genotype-phenotype (GP) maps for bi-allelic loci underlying quantitative phenotypes. The 0.1 version is released in connection with the publication of Gjuvsland et al. (2003) and implements basic line plots and the monotonicity measures for GP maps presented in the paper. Reference: Gjuvsland AB, Wang Y, Plahte E and Omholt SW (2013) Monotonicity is a key feature of genotype-phenotype maps. Front. Genet. 4:216. doi: 10.3389/fgene.2013.00216 [\href{http://www.frontiersin.org/Journal/10.3389/fgene.2013.00216/full}{link}]
This package provides a minimalistic functionality necessary to apply Gaussian Process in R. They provide a selection of functionalities of GPML Matlab library.
Functional enrichment analysis, gene identifier conversion
and mapping homologous genes across related organisms via the
'g:Profiler' toolkit (http://biit.cs.ut.ee/gprofiler/).
Some functions for modeling sequence read counts as a
generalized poisson model and to use this model for detecting
differentially expressed genes in different conditions and
differentially spliced exons.
The gptk package implements a general-purpose toolkit for Gaussian
process regression with a variety of covariance functions (e.g. RBF, Mattern, polynomial, etc).
Based on a MATLAB implementation by Neil D. Lawrence. See inst/doc/index.html for more details.
This package provides R interfaces to a handful of common
functions implemented using the Nvidia CUDA toolkit. Some of
the functions require at least GPU Compute Capability 1.3.
Thanks to Craig Stark at UC Irvine for donating time on his
lab's Mac.
Maximum likelihood estimators are obtained via an EM algorithm.
Nucleic acid sequences that are rich in guanine are capable of forming highly stable four-stranded structures known as G quadruplex motifs. This package provide functions for predicting G quadruplex motif(s) in nucleotide sequences.
Provides functions for matching student-answers to teacher answers for a variety of data types.
Functions to fit, visualise and compare Gaussian random field species distribution models.
A package for probability propagation in graphical independence networks, also known as probabilistic expert systems or Bayesian networks.
Context Free Grammar and Grammatical Evolution for R.
Represents metagenomic samples on the Euclidean space to examine similarity amongst samples by studying clusters in the model. Given the matrix of metagenomic counts for samples, this package (1) quantifies dissimilarity between samples using Kendall's tau-distance, (2) constructs multidimensional models of different dimension, and (3) plots the models for visualization and comparison.
Repository based tools for department and analysis level
reproducibility. 'GRANBase' allows creation of custom branched,
continuous integration-ready R repositories, including incremental testing of only packages which have changed versions since the last repository build.
gRapfa is for modelling discrete longitudinal data using acyclic probabilistic finite automata (APFA). The package contains functions for constructing APFA models from a given data using penalized likelihood methods. For graphical display of APFA models, gRapfa depends on 'igraph package'. gRapfa also contains an interface function to Beagle software that implements an efficient model selection algorithm.
gRapHD is designed for efficient selection of high-dimensional undirected
graphical models. The package provides tools for selecting trees, forests
and decomposable models minimizing information criteria such as AIC or BIC,
and for displaying the independence graphs of the models. It has also some
useful tools for analysing graphical structures. It supports the use of
discrete, continuous, or both types of variables.
GrapheR is a multi-platform user interface for drawing highly customizable graphs in R. It aims to be a valuable help to quickly draw publishable graphs without any knowledge of R commands. Six kinds of graph are available: histogram, box-and-whisker plot, bar plot, pie chart, curve and scatter plot.
Estimates within and between time point interactions in experience sampling data, using the Graphical VAR model in combination with LASSO and EBIC.
The package provides functions to generate
graphics files, compare them with ``model'' files,
and report the results.
This package implements a histogram principal components analysis. It provides numerical and graphical tools to deal with big data.
Multiple scan statistic with variable window for one dimension data and scan statistic based on connected components in 2D or 3D.
Optimizing a function F(U), where U is a semi-orthogonal matrix and F is invariant under an orthogonal transformation of U
The gRbase package provides certain general constructs
which are used by other graphical modelling packages, in particular
by the packages gRain, gRim and gRc.

gRbase contains several datasets relevant for use in connection with
graphical models. Almost all datasets used in the book Graphical
Models with R (2012) are contained in gRbase.

gRbase implements several graph algorithms (based mainly on
representing graphs as adjacency matrices - either in the form
of a standard matrix or a sparse matrix). Some graph
algorithms are:
(i) maximum cardinality search (for marked and unmarked graphs).
(ii) moralize.
(iii) triangulate.
(iv) junctionTree.

gRbase facilities for array operations,

gRbase implements functions for testing for conditional independence.

gRbase illustrates how hierarchical log-linear models (hllm) may be
implemented and describes concept of gmData (graphical meta
data). These features, however, are not maintained anymore and
remains in gRbase only because there exists a paper describing
these facilities: A Common Platform for Graphical Models in R:
The gRbase Package, Journal of Statistical Software, Vol 14,
No 17, 2005.
Estimation, model selection and other aspects of
statistical inference in Graphical Gaussian models with edge
and vertex symmetries (Graphical Gaussian models with colours)
Contains many functions useful for
monitoring and reporting the results of clinical trials and other
experiments in which treatments are compared.  LaTeX is
used to typeset the resulting reports, recommended to be in the
context of 'knitr'. The 'Hmisc', 'ggplot2', and 'lattice' packages are used
by 'greport' for high-level graphics.
Allows one to classify patients into low, intermediate, and high risk groups for disease progression based on a continuous marker that is associated with progression-free survival. It uses a latent class model to link the marker and survival outcome and produces two cutoffs for the marker to divide patients into three groups. See the References section for more details.
Converts a spatial object from class SpatialGridDataFrame to
SpatialPolygonsDataFrame.
Functions for drawing scene trees representing scenes that
have been drawn using grid graphics.
Provides a number of user-level functions to work with "grid" graphics, notably to arrange multiple grid-based plots on a page, and draw tables.
Functions to convert a page of plots drawn with the
graphics package into identical output drawn with the grid package.
The result looks like the original graphics-based plot, but consists
of grid grobs and viewports that can then be manipulated with
grid functions (e.g., edit grobs and revisit viewports).
Functions for drawing node-and-edge graphs that have been
laid out by graphviz.  This provides an alternative
rendering to that provided by the 'Rgraphviz' package, with
two main advantages:  the rendering provided by 'gridGraphviz'
should be more similar to what 'graphviz' itself would draw;
and rendering with 'grid' allows for post-hoc customisations
using the named viewports and grobs that 'gridGraphviz'
produces.
Functions to export graphics drawn with package grid to SVG
format.  Additional functions provide access to SVG features that
are not available in standard R graphics, such as hyperlinks,
animation, filters, masks, clipping paths, and gradient and pattern fills.
The function GriegSmith accepts either quadrat count data,
a point process object(ppp) or a matrix of x and y coordinates.
The function calculates a nested analysis of variance and
simulation envelopes.
gRaphical Interaction Models:

Models for for contingency tables (i.e. log-linear models)

Graphical Gaussian models for multivariate normal data (i.e. covariance selection
models)

Mixed interaction models
Functions for converting, importing, and drawing PostScript
pictures in R plots.
The program GRNN implements the algorithm proposed by
Specht (1991).
Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models.
The package was developed to fit fit many growth curves
obtained under different conditions in order to derive a
conclusive dose-response curve, for instance for a compound
that potentially affects growth. grofit fits data to different
parametric models (function gcFitModel) and in addition
provides a model free spline fit (function gcFitSpline) to
circumvent systematic errors that might occur within
application of parametric methods.
Computing Gromov-Hausdorff type l^p distances for labeled metric spaces. These distances were introduced in V.Liebscher, Gromov meets Phylogenetics - new Animals for the Zoo of Metrics on Tree Space. preprint  arXiv:1504.05795,  for phylogenetic trees but may apply to much more situations.
Regression models for grouped and coarse data, under the
Coarsened At Random assumption.
An implementation of the GroupRemMap penalty for fitting regularized multivariate response regression models under the high-dimension-low-sample-size setting. When the predictors naturally fall into groups, the GroupRemMap penalty encourages procedure to select groups of predictors, while control for the overall sparsity of the final model.
A graphical user interface to compute group sequential designs
based on normally distributed test statistics, particularly critical
boundaries, power, drift, and confidence intervals of such designs. All
computations are based on the alpha spending approach by Lan-DeMets with
various alpha spending functions being available to choose among.
Employs a non-parametric formulation for by-subject random effect
parameters to borrow strength over a constrained number of repeated
measurement waves in a fashion that permits multiple effects per subject.
One class of models employs a Dirichlet process (DP) prior for the subject
random effects and includes an additional set of random effects that
utilize a different grouping factor and are mapped back to clients through
a multiple membership weight matrix; e.g. treatment(s) exposure or dosage.
A second class of models employs a dependent DP (DDP) prior for the subject
random effects that directly incorporates the multiple membership pattern.
Estimates a collection of time-indexed functions under
either of Gaussian process (GP) or intrinsic Gaussian Markov
random field (iGMRF) prior formulations where a Dirichlet process
mixture allows sub-groupings of the functions to share the same
covariance or precision parameters.  The GP and iGMRF formulations
both support any number of additive covariance or precision terms,
respectively, expressing either or both of multiple trend and
seasonality.
A compilation of nonlinear growth models used in many areas
A nonparametric empirical Bayes method for recovering
gradients (or growth velocities) from observations of smooth
functions (e.g., growth curves) at isolated time points.
Fits user specified (GLM-) models with Group Lasso penalty
The package implements the concave 1-norm and 2-norm group penalty in linear and logistic regression.
The concave 1-norm group penalty includes 1-norm group SCAD and 1-norm group MCP.
The concave 1-norm group penalty has bi-level selection features. That is it selects variables at group and individual levels with proper tuning parameters.
The concave 1-norm group penalty is robust to mis-specified group information.
The concave 2-norm group penalty includes 2-norm group SCAD and 2-norm group MCP. The concave 2-norm group penalty select variable at group level only.
The package can also fit group Lasso, which is a special case of concave 2-norm group penalty when the regularization parameter kappa equals zero.
The highly efficient (block) coordinate descent algorithm (CDA) is used to compute the solutions for both penalties in linear models.
The highly stable and efficient (block) CDA and minimization-majorization approach are used to compute the solution for both penalties in logistic models.
In the computation of solution surface, the solution path along kappa is implemented.
This provides a better solution path compared to the solution path along lambda.
The package also provides a tuning parameter selection method based on cross-validation for both linear and logistic models.
Efficient algorithms for fitting the regularization path of
linear or logistic regression models with grouped penalties.  This
includes group selection methods such as group lasso, group MCP, and
group SCAD as well as bi-level selection methods such as the group
exponential lasso, the composite MCP, and the group bridge.
Functions to generate and analyze data for psychology
experiments based on the General Recognition Theory.
This package offers functions for the comparison of Gutenberg-Richter
b-values. Several functions in GRTo are helpful for the assessment of the
quality of seismicity catalogs.
GSAgm is an R package that completes a self-contained gene set analysis (GSA) for RNA-seq and SNP data using the Gamma Method.
This package contains utility functions used by the Genome Analysis Toolkit (GATK) to load tables and plot data. The GATK is a toolkit for variant discovery in high-throughput sequencing data.
Write SARIMA models in (finite) AR representation and simulate
generalized multiplicative seasonal autoregressive moving average (time) series
with Normal / Gaussian, Poisson or negative binomial distribution.
Group Sequential Operating Characteristics for Clinical,
Bayesian two-arm Trials with known Sigma and Normal Endpoints.
gsDesign is a package that derives group sequential designs and describes their properties.
Robust Estimation of Multivariate Location and Scatter in the
Presence of Cellwise and Casewise Contamination and Missing Data.
This package provides an estimate of the change-point(s) and the corresponding p-values through graph-based approach.
calculate equivalence and futility boundaries based on the exact bivariate $t$ test statistics for group sequential designs in studies with equivalence hypotheses.
gsg (gam selection gradients) provides a unified approach to the regression analysis of selection from longitudinal data collected from natural populations.
Simple package to download Google Sheets using just the sharing link. Spreadsheets can be downloaded as a data frame, or as plain text to parse manually. Google Sheets is the new name for Google Docs Spreadsheets.
Global Soil Information Facilities - tools (standards and functions) and sample datasets for global soil mapping.
Family based association test via GEE Kernel Machine score test
An R wrapper for the special functions and quasi random number
generators of the Gnu Scientific Library
(http://www.gnu.org/software/gsl/).  See gsl-package.Rd for details of
overall package organization, and Misc.Rd for some functions that are
widely used in the package, and some tips on installation.
Implementation of a Bayesian approach for estimating a mixture of gamma distributions in which the mixing occurs over the shape parameter. This family provides a flexible and novel approach for modeling heavy-tailed distributions, it is computationally efficient, and it only requires to specify a prior distribution for a single parameter.
Tools rewritten in C for various smoothing tasks
A comprehensive package for structural multivariate
function estimation using smoothing splines.
A collection of routines for the estimation of copula density
and 2-D hazard function using smoothing splines.
Variogram modelling; simple, ordinary and universal point or block (co)kriging; spatio-temporal kriging; sequential Gaussian or indicator (co)simulation; variogram and variogram map plotting utility functions.
gsubfn is like gsub but can take a replacement function
or certain other objects instead of the replacement string.
Matches and back references are input to the replacement function and
replaced by the function output.   gsubfn can be used to split strings
based on content rather than delimiters and for quasi-perl-style string
interpolation. The package also has facilities for translating formulas
to functions and allowing such formulas in function calls instead of
functions.  This can be used with R functions such as apply, sapply,
lapply, optim, integrate, xyplot, Filter and any other function that
expects another function as an input argument or functions like cat
or sql calls that may involve strings where substitution is desirable.
Provides an interface to the Gibbs SeaWater (TEOS-10) C library, which derives from Matlab and other code written by WG127 (Working Group 127) of SCOR/IAPSO (Scientific Committee on Oceanic Research / International Association for the Physical Sciences of the Oceans).
Tools to make it easier to work with "tables" of grobs.
This package provides functions to calculate the
efficiencies (expected tests per unit) of hierarchical and
matrix group testing procedures.  Efficiencies can be
calculated in the presense of correlated responses under
multiple arrangements of clustesrs.  Efficiencies can also be
evaluated in the presense of test error.
Generalized Turnbull's estimator proposed by Dehghan and Duchesne
(2011).
Functions to assist in R programming, including:
- assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion',
'getDependencies', 'keywords', 'scat'),
- calculate the logit and inverse logit transformations ('logit', 'inv.logit'),
- test if a value is missing, empty or contains only NA and NULL values ('invalid'),
- manipulate R's .Last function ('addLast'),
- define macros ('defmacro'),
- detect odd and even integers ('odd', 'even'),
- convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'),
- perform a binary search ('binsearch'),
- sort strings containing both numeric and character components ('mixedsort'),
- create a factor variable from the quantiles of a continuous variable ('quantcut'),
- enumerate permutations and combinations ('combinations', 'permutation'),
- calculate and convert between fold-change and log-ratio ('foldchange',
'logratio2foldchange', 'foldchange2logratio'),
- calculate probabilities and generate random numbers from Dirichlet distributions
('rdirichlet', 'ddirichlet'),
- apply a function over adjacent subsets of a vector ('running'),
- modify the TCP\_NODELAY ('de-Nagle') flag for socket objects,
- efficient 'rbind' of data frames, even if the column names don't match ('smartbind'),
- generate significance stars from p-values ('stars.pval'),
- convert characters to/from ASCII codes.
In hierarchical time series (HTS) forecasting, the hierarchical relation between multiple time series is exploited to make better forecasts. This hierarchical relation implies one or more aggregate consistency constraints that the series are known to satisfy. Many existing approaches, like for example bottom-up or top-down forecasting, therefore attempt to achieve this goal in a way that guarantees that the forecasts will also be aggregate consistent. This package provides with an implementation of the Game-Theoretically OPtimal (GTOP) reconciliation method proposed in van Erven and Cugliari (2015), which is guaranteed to only improve any given set of forecasts. This opens up new possibilities  for constructing the forecasts. For example, it is not necessary to assume that bottom-level forecasts are unbiased, and aggregate forecasts may be constructed by regressing both on bottom-level forecasts and on other covariates that may only be available at the aggregate level.
Assorted tools for genetic association analyses.  The
current focus is on implementing (either exactly or
approximately) regression analyses using summary statistics
instead of using subject-specific data.  So far, functions
exist to support multi-SNP risk score analyses, multi-SNP
conditional regression analyses, and multi-phenotype analyses,
using summary statistics.  There are helper functions for
reading and manipulating subject-specific genotype data, which
provide a platform for calculating the summary statistics, or
for using R to conduct other analyses not supported by specific
GWAS analysis tools.
Provides an interface to the Open Platform's Content API of the Guardian Media Group. It retrieves content from news outlets The Observer, The Guardian, and guardian.co.uk from 1999 to current day
This package comprises maps of France in 1830, multivariate data from A.-M. Guerry and others, and statistical and
graphic methods related to Guerry's "Moral Statistics of France". The goal is to facilitate the exploration and
development of statistical and graphic methods for multivariate data in a geo-spatial context of historical interest.
The GUILDS package combines a range of sampling formulas for the unified neutral model of biogeography and biodiversity. Alongside the sampling formulas, it includes methods to perform maximum likelihood optimization of the sampling formulas, methods to generate data given the neutral model, and methods to estimate the expected species abundance distribution. Sampling formulas included in the GUILDS package are the Etienne Sampling Formula (Etienne 2005), the guild sampling formula, where guilds are assumed to differ in dispersal ability (Janzen et al. 2015), and  the guilds sampling formula conditioned on guild size (Janzen et al. 2015).
GUIProfiler shows graphically the results of profiling R programs by tracking their execution time.
Provides probability functions (cumulative distribution and density functions), simulation function (Gumbel copula multivariate simulation) and estimation functions (Maximum Likelihood Estimation, Inference For Margins, Moment Based Estimation and Canonical Maximum Likelihood).
Generalized UniFrac distance for comparing microbial
communities. Permutational multivariate analysis of variance
using multiple distance matrices.
Given exposure and survival time series as well as parameter values, GUTS allows for the fast calculation of the survival probabilities as well as the logarithm of the corresponding likelihood.
Several tools for Global Value Chain (GVC) analysis are implemented.
Generalized structured regression models with regularized categorical effects, categorical effect modifiers, continuous effects and smooth effects.
Methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.
Functions for genome-wide association/interaction analysis and rare variant analysis on a continuous/dichotomous trait using family data, and for making genome-wide p-value plot and QQ plot.
This package contains a function to do exact
Hardy-Weinburg testing (using Fisher's test) for SNP genotypes
as typically obtained in a Genome Wide Association Study
(GWAS).
This package provides functions to calculate the
significance threshold for controlling the type I error rate in
mixed-model association mapping analyses.
Based on calculations of 758 women this package calculates
positive predictive values (PPV) and negative predictive values
(NPV) for inadequate and excessive gestational weight gain
(GWG) for different prevalences for different BMI categories.
gWidgets provides a toolkit-independent API for building interactive GUIs. At least one of the 'gWidgetsXXX packages', such as gWidgetstcltk, needs to be installed. Some icons are on loan from the scigraphica project http://scigraphica.sourceforge.net.
Re-implementation of gWidgets API. The API is defined in this
package. A second, toolkit-specific package is required to use it. There
are three in development: 'gWidgets2RGtk2', 'gWidgets2Qt', and 'gWidgets2tcltk'.
Port of the gWidgets2 API for the tcltk
package.
Port of the gWidgets API to the tcltk package. Requires Tk 8.5 or greater.
Fit a geographically weighted logistic elastic net regression.
In GWmodel, we introduce techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. GWmodel includes functions to calibrate: GW summary statistics, GW principal components analysis, GW discriminant analysis and various forms of GW regression; some of which are provided in basic and robust (outlier resistant) forms.
Statistical functions to fit, validate and describe a Generalized
Waring Regression Model (GWRM).
Fits geographically weighted regression (GWR) models and
has tools to diagnose and remediate collinearity in the GWR
models. Also fits geographically weighted ridge regression
(GWRR) and geographically weighted lasso (GWL) models.
Estimate genome-wide significance using extrapolation approach
Quantifying and testing gene-by-measured-environment interaction in behavior genetic designs.
R scripting functionality for H2O, the open source
math engine for big data that computes parallel distributed
machine learning algorithms such as generalized linear models,
gradient boosting machines, random forests, and neural networks
(deep learning) within various cluster environments.
S4 Interface to the 'HDF5' library supporting fast storage and
retrieval of R-objects like vectors, matrices and arrays to binary files in
a language independent format. The 'HDF5' format can therefore be used as
an alternative to R's save/load mechanism. Since h5 is able to access only
subsets of stored data it can also handle data sets which do not fit into
memory.
A Haar-Fisz algorithm for Poisson intensity estimation
Package provides the estimation of the structure and the parameters, sampling methods and structural plots of Hierarchical Archimedean Copulae (HAC).
Provides a framework for writing map/reduce scripts for
use in Hadoop Streaming. Also facilitates operating on data in
a streaming fashion, without Hadoop.
This package offers helpful functions and algorithms for solving optimal matching tasks in the context of preclinical cancer studies. Furthermore, various helper and plotting functions are provided for unsupervised and supervised machine learning and longitudinal modeling of tumor response patterns.
An S4 implementation of Eq. (3) and Eq. (7)
by David J. Hand and Robert J. Till (2001). A Simple Generalisation of
the Area Under the ROC Curve for Multiple Class Classification Problems.
Machine Learning, 45 (2), p. 171--186.  DOI:10.1023/A:1010920819831.
Provides an R routine for a Cramer-von Mises type two sample test which is based on empirical Hankel transforms of the non-negative sample variables. The test is non-parametric and not distribution free. The exact value of the test statistic for univariate data as well as the p-value and the critical value are computed.
The following R functions are used for inference of trait
associations with haplotypes and other covariates in
generalized linear models.  The functions are developed
primarily for data collected in cohort or cross-sectional
studies. They can accommodate uncertain haplotype phase and
handle missing genotypes at some SNPs.
The multi-locus stepwise regression (MSR) combines the advantages of stepwise regression and haplotype-based analysis. The MSR can be used to identify informative combinations of single nucleotide polymorphisms (SNPs) from unlinked SNPs (allele combinations) or SNPs within a chromosomal region (haplotypes).
The package provides a set of functions whose aim is to
propose 4 methods of QTL detection. HAPimLD is an
interval-mapping method designed for unrelated individuals with
no family information that makes use of linkage disequilibrium.
HAPimLDL is an interval-mapping method for design of half-sib
families. It combines linkage analysis and linkage
disequilibrium. HaploMax is based on an analysis of variance
with a dose haplotype effect. HaploMaxHS is based on an
analysis of variance with a sire effect and a dose haplotype
effect in half-sib family design.  Fundings for the package
development were provided to the LDLmapQTL project by the ANR
GENANIMAL program and APIS-GENE.
Performs genetic association analyses of case-parent triad (trio) data with multiple markers. It can also incorporate complete or incomplete control triads, for instance independent control children. Estimation is based on haplotypes, for instance SNP haplotypes, even though phase is not known from the genetic data. Haplin estimates relative risk (RR + conf.int.) and p-value associated with each haplotype. It uses maximum likelihood estimation to make optimal use of data from triads with missing genotypic data, for instance if some SNPs has not been typed for some individuals. Haplin also allows estimation of effects of maternal haplotypes and parent-of-origin effects, particularly appropriate in perinatal epidemiology. Haplin allows special models, like X-inactivation, to be fitted on the X-chromosome.
'haplo.ccs' estimates haplotype and covariate relative
risks in case-control data by weighted logistic regression.
Diplotype probabilities, which are estimated by EM computation
with progressive insertion of loci, are utilized as weights.
Simulate haplotypes through meioses. Allows specification
of population parameters.
A suite of R routines for the analysis of indirectly measured haplotypes. The statistical methods assume that all subjects are unrelated and that haplotypes are ambiguous (due to unknown linkage phase of the genetic markers). The main functions are: haplo.em, haplo.glm, haplo.score, haplo.power, and seqhap; all of which have detailed examples in doc/manualHaploStats.pdf.
Provides S4 classes and methods for reading and manipulating aligned DNA sequences, supporting an indel coding methods (only simple indel coding method is available in the current version), showing base substitutions and indels, calculating absolute pairwise distances between DNA sequences, and inferring haplotypes from DNA sequences or user provided absolute character difference matrix.  This package also includes S4 classes and methods for estimating genealogical relationships among haplotypes using statistical parsimony.
Functions to perform ROR for sequence-based association
analysis
Contains tools for exploring Hardy-Weinberg equilibrium for
diallelic genetic marker data. All classical tests (chi-square, exact,
likelihood-ratio and permutation tests) for Hardy-Weinberg equilibrium
are included in the package, as well as functions for power computation and
for the simulation of marker data under equilibrium and disequilibrium.
Functions for testing equilibrium in the presence of missing data by
using multiple imputation are also provided. Implements several graphics
for exploring the equilibrium status of a large set of diallelic markers:
ternary plots with acceptance regions, log-ratio plots and Q-Q plots.
Fits the first harmonics in a Fourier expansion to one or more time series. Trend elimination can be performed.  Computed values include estimates of amplitudes and phases, as well as confidence intervals and p-values for the null hypothesis of Gaussian noise.
Functions for easy simulation and reproducability.
Aimed at applying the Harvest classification tree algorithm, which is a modified algorithm of classic classification tree. It was firstly used in drug discovery field, but it also performs well in other kind of data, especially when active region is unrelated.To learn more about the harvest classification algorithm, you can go to http://www.stat.ubc.ca/Research/TechReports/techreports/220.pdf for more information.
This package implements a data structure similar to hashes
in Perl and dictionaries in Python but with a purposefully R
flavor.  For objects of appreciable size, access using hashes
outperforms native named lists and vectors.
This package provides common non-cryptographic hash
functions for R. For example, SpookyHash, Murmur3Hash, Google
CityHash.
Drawing Hasse diagram - visualization of transitive reduction of a finite partially ordered set.
Import foreign statistical formats into R via the embedded
ReadStat C library (https://github.com/WizardMac/ReadStat). Package includes
preliminary support for writing Stata and SPSS formats.
The package allows to simulate Hawkes process both in univariate and multivariate settings. It gives functions to compute different moments of the number of jumps of the process on a given interval, such as mean, variance or autocorrelation of process jumps on time intervals separated by a lag.
Damage Functions (DFs), also known as
Vulnerability Functions, associate the physical damage
to a building or a structure (and also its contents and
inventory) from natural disasters to financial damage.
The Federal Emergency Management Agency (FEMA) in USA
developed several thousand DFs and these serve as a
benchmark in natural catastrophe modeling, both in
academia and industry. However, these DFs and their
documentation are buried within the HAZUS software are
not easily accessible for analysis and visualization.
This package provides more than 1300 raw DFs used by FEMA's
HAZUS software and also functionality to extract and
visualize DFs specific to the flood hazard. The vignette
included with this package demonstrates its use.
Convenient and efficient functions for performing 2-level hierarchical Bayesian regression analysis for multi-group data. The lowest level may belong to the generalized linear model (GLM) family while the prior level, which effects pooling, allows for linear regression on lower level covariates. Constraints on all or part of the parameter set maybe specified with ease. A rich set of methods is included to visualize and analyze results.
Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for several component vaccine. Uses Hill models for each component which are combined under Bliss independence.
A package for building hierarchical block matrices from association matrices and for performing multi-scale analysis. It specifically targets chromatin contact maps, generated from high-throughput chromosome conformation capture data, such as 5C and Hi-C, and provides methods for detecting movements and for computing chain hierarchy and region communicability across scales.
Contains functions for fitting hierarchical versions of
EVSD, UVSD, DPSD, DPSD with d' restricted to be positive, and
our gamma signal detection model to recognition memory
confidence-ratings data.
Functions to compute small area estimates based on a basic
area or unit-level model. The model is fit using restricted
maximum likelihood, or in a hierarchical Bayesian way. In the
latter case numerical integration is used to average over the
posterior density for the between-area variance.  The output
includes the model fit, small area estimates and corresponding
MSEs, as well as some model selection measures. Additional
functions provide means to compute aggregate estimates and
MSEs, to minimally adjust the small area estimates to
benchmarks at a higher aggregation level, and to graphically
compare different sets of small area estimates.
This package fits Hierarchical Bayesian space-Time models for Gaussian data. Furthermore, its functions have been implemented for analysing the fitting qualities of those models.
A new diagnostic check for model adequacy in regression
and generalized linear models is implemented.
This package calculates the interval estimates for the parameters of
linear models heteroscedastic regression using bootstrap - (Wild Bootstrap) and double
bootstrap-t (Wild Bootstrap). It is also possible to calculate confidence intervals using
the percentile bootstrap and percentile bootstrap double. It is possible to calculate
consistent estimates of the covariance matrix of the parameters of linear regression models
with heteroskedasticity of unknown form. The package also provides function to calculate
consistently the covariance matrix of the parameters of linear models with heteroskedasticity
of unknown form.
Estimation of parameters in 3-segment (i.e. 2 change-point)
regression models with heteroscedastic variances is provided based on both
likelihood and hybrid Bayesian approaches, with and without continuity
constraints at the change points.
Functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.
Discriminant analysis and data clustering methods for high dimensional data, based on the assumption that high-dimensional data live in different subspaces with low dimensionality proposing a new parametrization of the Gaussian mixture model which combines the ideas of dimension reduction and constraints on the model.
Cross-validated linear discriminant calculations determine
the optimum number of features. Test and training scores from
successive cross-validation steps determine, via a principal
components calculation, a low-dimensional global space onto which test
scores are projected, in order to plot them. Further functions are
included that serve didactic purposes.
Facilitates discovery and handling of hydrological data,
non-programmatic access to catalogues and databases.
A flexible and hierarchical framework for comparing categorical map composition and configuration (spatial pattern) along spatial, thematic, or external grouping variables.  Comparisons are based on measures of mutual information between thematic classes (colours) and location (spatial partitioning).  Results are returned in textual, tabular, and graphical forms.
Implementation of multiple approaches to perform inference in
high-dimensional models
Mimics the lm function found in the package stats to fit
high dimensional regression models with point estimates,
standard errors, and p-values. Methods for printing and
summarizing the results are given.
Algorithms for lasso and fused-lasso
problems: implementation of the lars algorithm [1], for the
lasso and fusion penalization and EM-based algorithms for (logistic) lasso
and fused-lasso.
Computation of highest density regions in one and two dimensions,
kernel estimation of univariate density functions conditional on one covariate,
and multimodal regression.
This package implements a iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing the solution paths of the (grouped) lasso and the (grouped) elastic net for the Tweedie model.
Timeseries analysis is conducted using Korean mortality and environmental variables
The heatex package calculates heat storage in the body and
the components of heat exchange (conductive, convective,
radiative, and evaporative) between the body and the
environment during physical activity based on the principles of
partitional calorimetry. The program enables heat exchange
calculations for a range of environmental conditions when
wearing various clothing ensembles.
An improved heatmap package. Completely
compatible with the original R function 'heatmap',
and provides more powerful and convenient features.
Generates a fit plot for diagnosing misspecification in models of
binary dependent variables, and calculates the related heatmap fit
statistic (Esarey and Pierce, 2012).
Allows heatmap matrix to have non-identical X- and
Y-dimensions.  Allows multiple tracks of annotation for
RowSideColors and ColSideColors.
This package contains functions to perform robust estimation considering
heavy-tailed distributions. Currently, the package includes linear regression, linear
mixed-effect models, multivariate location and scatter estimation, penalized splines
and random variate generation.
A dummy package to demonstrate how to interface to a jar
file that resides inside an R package.
Contains many functions useful for managing 'NetCDF' files (see http://en.wikipedia.org/wiki/NetCDF), get tide levels on any point of the globe, get moon phase and time for sun rise and fall, analyse and reconstruct periodic time series of temperature with irregular sinusoidal pattern, show scales and wind rose in plot with change of color of text, Metropolis-Hastings algorithm for Bayesian MCMC analysis, plot graphs or boxplot with error bars, search files in disk by there names or their content, read the contents of all files from a folder at one time.
Tools for accessing various open data sources in the Helsinki
region in Finland. Current data sources include
the Real Estate Department (district maps),
the Environmental Services Authority,
Service Map API,
Linked Events API and
Helsinki Region Infoshare statistics API.
Provides HE plot functions for visualizing hypothesis tests in multivariate linear models. They represents sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions).
Estimate and simulate hierarchical exponential-family random graph models with local dependence.
Implements marker-based estimation of heritability when observations on genetically identical replicates are available. These can be either observations on individual plants or plot-level data in a field trial. Heritability can then be estimated using a mixed model for the individual plant or plot data. For comparison, also mixed-model based estimation using genotypic means and estimation of repeatability with ANOVA are implemented. For illustration the package contains several datasets for the model species Arabidopsis thaliana.
Probability functions and other utilities for the generalized Hermite distribution.
Functions for the fitting and summarizing of heteroscedastic t-regression.
An implementation of White's Test for Heteroskedasticity
as outlined in Doornik (1996).
Binning and plotting functions for hexagonal bins. Now
uses and relies on grid graphics and formal (S4) classes and
methods.
Functions to view files in raw binary form like in a hex editor.  Additional functions to specify and read arbitrary binary formats.
A data only package containing commercial domestic flights that
departed Houston (IAH and HOU) in 2011.
High-dimensional additive models as introduced by Meier,
van der Geer and Buehlmann (2009).
Implements the hub graphical lasso and hub covariance graph proposal by Tan, KM., London, P., Mohan, K., Lee, S-I., Fazel, M., and Witten, D. (2014). Learning graphical models with hubs. To appear in Journal of Machine Learning Research. arXiv.org/pdf/1402.7349.pdf.
Implemented here are procedures for fitting hierarchical generalized linear models (HGLM). It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model.
This data-only package was created for distributing data used in the examples of the hglm package.
The holonomic gradient method (HGM, hgm) gives a way to evaluate normalization
constants of unnormalized probability distributions by utilizing holonomic
systems of differential or difference equations. The holonomic gradient descent (HGD, hgd) gives a method
to find maximal likelihood estimates by utilizing the HGM.
Contains functions for
identifying and correcting HGNC gene symbols which have been converted
to date format by Excel, for reversibly converting between HGNC
symbols and valid R names, identifying invalid HGNC symbols and
correcting synonyms and outdated symbols which can be mapped to an
official symbol.
Support software for Statistical Analysis and Data Display (First Edition, Springer, ISBN 0-387-40270-5, 2004) and (Second Edition, Springer, ISBN 978-1-4939-2121-8, anticipated 2015) by Richard M. Heiberger and Burt Holland.  This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis.  The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print.  All chapters have exercises.  All functions introduced in the book are in the package.  R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.
Heller-Heller-Gorfine (HHG) tests are a set of powerful statistical tests of multivariate k-sample homogeneity and independence. For the univariate case, the package also offers implementations of the MinP DDP and MinP ADP tests, which are consistent against all continuous alternatives but are distribution-free, and are thus much faster to apply.
This package builds on the EMD package to provide additional tools for empirical mode decomposition (EMD) and Hilbert spectral analysis. It also implements the ensemble empirical decomposition (EEMD) and the complete ensemble empirical mode decomposition (CEEMD) methods to avoid mode mixing and intermittency problems found in EMD analysis.  The package comes with several plotting methods that can be used to view intrinsic mode functions, the HHT spectrum, and the Fourier spectrum. To see the version history and download the bleeding-edge version (at your own risk!), see the project website at code.google.com below.  See the other links for PDF files describing numerical and exact analytical methods for determining instantaneous frequency, some examples of signals processed with this package, and some examples of the ensemble empirical mode decomposition method.
Simulation from distributions supported by nested
hyperplanes, using the algorithm described in Petris &
Tardella, "A geometric approach to transdimensional Markov
chain Monte Carlo", Canadian Journal of Statistics, v.31, n.4,
(2003).  Also random direction multivariate Adaptive Rejection
Metropolis Sampling.
A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering.
It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical
clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution,
geographic masking (by continents, regions, or countries), data filtering by mean and/or variance
thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function
with preliminary big data support, different clustering methods, hybrid hierarchical clustering,
multi-variate clustering (MVC), cluster validation, and visualization of region maps.
This package allows you to detect domains in HiC data by rephrasing this problem as a two-dimensional segmentation issue.
Computes the F-test and Bonferroni-adjusted p-value for a test of interaction in two-factor studies.  Reports corresponding interaction plot and analysis of variance table and p-values from several other tests of non-additivity.
Contains functions for the analysis of Discrete Time Hidden Markov Models, Markov Modulated GLMs and the Markov Modulated Poisson Process. It includes functions for simulation, parameter estimation, and the Viterbi algorithm. See the topic "HiddenMarkov" for an introduction to the package, and "Change Log" for a list of recent changes. The algorithms are based of those of Walter Zucchini.
Performs linear discriminant analysis in high dimensional
problems based on reliable covariance estimators for problems
with (many) more variables than observations. Includes routines
for classifier training, prediction, cross-validation and
variable selection.
Inference of high dimensional max-stable
distributions, from the paper "Likelihood based inference for
high-dimensional extreme value distributions", by A. Bienvenüe
and C. Robert, arXiv:1403.0065 [stat.AP].
Functions for performing hierarchical analysis of distance
sampling data, with ability to use an areal spatial ICAR model on
top of user supplied covariates to get at variation in abundance
intensity.  The detection model can be specified as a function of
observer and individual covariates, where a parametric model is
supposed for the population level distribution of covariate values.
The model uses data augmentation and a reversible jump MCMC
algorithm to sample animals that were never observed.  Also
included is the ability to include point independence (increasing
correlation multiple observer's observations as a function of
distance, with independence assumed for distance=0 or first
distance bin), as well as the ability to model species
misclassification rates using a multinomial logit formulation on data
from double observers.  New in version 2.1 is the ability to
include zero inflation, but this is only recommended for cases where
sample sizes and spatial coverage of the survey are high.
Implementation of the convex banding procedure (using a
hierarchical group lasso penalty) for covariance estimation that is
introduced in Bien, Bunea, Xiao (2015) Convex Banding of the Covariance
Matrix. Accepted for publication in JASA.
Hierarchical group-wise partitioning of phenotypic diversity into
within-group (alpha), among-group (beta), and pooled-total (gamma)
components using Hill numbers.
Turnover and overlap are also calculated as standardized alternatives to
beta diversity. Hierarchical bootstrapping is used to approximate
uncertainty around each diversity component.
This R package allows the estimation of hierarchical F-statistics from haploid or diploid genetic data with any numbers of levels in the hierarchy, following the algorithm of Yang (Evolution, 1998, 52(4):950-956). Functions are also given to test via randomisations the significance of each F and variance components, using the likelihood-ratio statistics G -see Goudet etal (Genetics, 1996, 144(4): 1933-1940)
Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect.  For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.
HierO is a graphical user interface (GUI) tool for calculating optimal statistical power and sample size for hierarchical data structure. HierO constructs a user defined sample size optimization problem to GAMS (General Algebraic Modeling System)form and uses Rneos package to send the problem to NEOS server for solving.
Variance partition of a multivariate data set
Implementation of the convex hierarchical testing (CHT) procedure
introduced in Bien, Simon, and Tibshirani (2015) Convex Hierarchical Testing
of Interactions. Annals of Applied Statistics. Vol. 9, No. 1, 27-42.
Uses likelihood to estimate ancestry and heterozygosity.
Evaluates simple hybrid classifications (parentals, F1, F2,
backcrosses). Estimates genomic clines.
Performs the generalized component test from Gregory et al (2015), as well as the tests from Chen and Qin (2010), Srivastava and Kubokawa (2013), and Cai, Liu, and Xia (2014) for equality of two population mean vectors when the length of the vectors exceeds the sample size.
Three high-dimensional outlier detection algorithms and a outlier unification scheme are implemented in this package. The angle-based outlier detection (ABOD) algorithm is based on the work of Kriegel, Schubert, and Zimek [2008]. The subspace outlier detection (SOD) algorithm is based on the work of Kriegel, Kroger, Schubert, and Zimek [2009]. The feature bagging-based outlier detection (FBOD) algorithm is based on the work of Lazarevic and Kumar [2005]. The outlier unification scheme is based on the work of Kriegel, Kroger, Schubert, and Zimek [2011].
Provide functionality to manage, clean and match highfrequency
trades and quotes data, calculate various liquidity measures, estimate and
forecast volatility, and investigate microstructure noise and intraday
periodicity.
Syntax highlighter for R code based
on the results of the R parser. Rendering in HTML and latex
markup. Custom Sweave driver performing syntax highlighting
of R code chunks.
Provides syntax highlighting for R source code. Currently it
supports LaTeX and HTML output. Source code of other languages is supported
via Andre Simon's highlight package (http://www.andre-simon.de).
Functions for determining and evaluating high-risk zones and
simulating and thinning point process data, as described in 'Determining
high risk zones using point process methodology - Realization by building
an R package' (Seibold, 2012) and 'Determining high-risk zones for
unexploded World War II bombs by using point process methodology' (Mahling
et al., 2013).
Implements the method developed by Cao and Kosorok (2011) for the significance analysis of thousands of features in high-dimensional biological studies. It is an asymptotically valid data-driven procedure to find critical values for rejection regions controlling the k-familywise error rate, false discovery rate, and the tail probability of false discovery proportion.
Generate occupancy patterns based on arrival and departure timestamps
Estimation of heritability with confidence intervals in linear mixed models.
Hypergeometric Intersection distributions are a broad group of distributions that describe the probability of picking intersections when drawing independently from two (or more) urns containing variable numbers of balls belonging to the same n categories.
Provides multi-core or GPU support (or both if the system
has GPU and multi-core CPU) for the recommended R package,
Matrix.
Based on hierarchical modeling, this package provides a
few practical functions to find and present the optimal designs
for a pooled NGS design.
This package implements methods for hierarchical semiparametric regression models for test statistics. Specifically, test statistics given the null/alternative hypotheses are modeled parametrically, whereas the unobservable status of null/alternative hypotheses are modeled using nonparametric additive logistic regression over covariates.
Sets up and executes a HiSSE model (Hidden State Speciation and Extinction) on a phylogeny and character sets to test for hidden shifts in trait dependent rates of diversification.
The HistData package provides a collection of small data sets
that are interesting and important in the history of statistics and data visualization.
The goal of the package is to make these available, both for instructional use
and for historical research.  Some of these present interesting challenges for graphics
or analysis in R.
In the framework of Symbolic Data Analysis, a relatively new approach
to the statistical analysis of multi-valued data, we consider histogram-valued data, i.e.,
data described by univariate histograms. The  methods and the basic statistics for
histogram-valued data are mainly based
on the L2 Wasserstein metric between distributions, i.e., a Euclidean metric
between quantile functions. The package contains unsupervised classification techniques,
least square regression and tools for histogram-valued data and for histogram time series.
Using the MDL principle, it is possible to estimate
parameters for a histogram-like model. The package contains
the implementation of such an estimation method.
Automatic construction of regular and irregular histograms
as described in Rozenholc/Mildenberger/Gather (2009).
Provides a number of utility functions useful for manipulating large histograms.  This includes methods to trim, subset, merge buckets, merge histograms, convert to CDF, and calculate information loss due to binning.  It also provides a protocol buffer representations of the default R histogram class to allow histograms over large data sets to be computed and manipulated in a MapReduce environment.
These sample data sets are intended for historians
learning R. They include population, institutional, religious,
military, and prosopographical data suitable for mapping,
quantitative analysis, and network analysis.
The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints.
Hadoop InteractiVE facilitates distributed
computing via the MapReduce paradigm through R and Hadoop. An easy to use
interface to Hadoop, the Hadoop Distributed File System (HDFS),
and Hadoop Streaming is provided.
Creates and plots 2D and 3D hive plots. Hive plots are a unique method of displaying networks of many types in which node properties are mapped to axes using meaningful properties rather than being arbitrarily positioned.  The hive plot concept was invented by Martin Krzywinski at the Genome Science Center (www.hiveplot.net/).  Keywords: networks, food webs, linnet, systems biology, bioinformatics.
The functions in this package produce a complete set of mortality rates as a function of a combination of HIV prevalence and either life expectancy at birth (e0), child mortality (5q0), or child mortality with adult mortality (45q15)
Conversion and Reverse Conversion between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems. Algorithms follow Survey and Mapping Office Lands Department, Hong Kong Government (1995)
A suite of diagnostic tools for hierarchical
(multilevel) linear models. The tools include
not only leverage and traditional deletion diagnostics (Cook's
distance, covratio, covtrace, and MDFFITS) but also
convenience functions and graphics for residual analysis. Models
can be fit using either lmer in the 'lme4' package or lme in the 'nlme' package,
but only two-level models fit using lme are currently supported.
Hierarchical latent space network model for ensemble of networks
Scalar performance metrics, including the H-measure, based
on classification scores for several classifiers applied to the
same dataset.
Contains many functions useful for data
analysis, high-level graphics, utility operations, functions for
computing sample size and power, importing and annotating datasets,
imputing missing values, advanced table making, variable clustering,
character string manipulation, conversion of R objects to LaTeX code,
and recoding variables.
Easy to use library to setup, apply and make inference
with discrete time and discrete space Hidden Markov Models
The package includes the functions designed to analyse continuous observations processes with the Hidden Markov Model approach. They include Baum-Welch and Viterbi algorithms and additional visualisation functions. The observations are assumed to have Gaussian distribution and to be weakly stationary processes. The package was created for analyses of financial time series, but can also be applied to any continuous observations processes.
Fits hidden Markov models with discrete non-parametric
observation distributions to data sets.  Simulates data
from such models.  Finds most probable underlying hidden
states, the most probable sequences of such states, and the
log likelihood of a collection of observations given the
parameters of the model.
Functions for specifying and fitting marginal models for contingency tables proposed
by Bergsma and Rudas (2002) here called hierarchical multinomial marginal models (hmmm) and their extensions presented by Bartolucci et al.
(2007); multinomial Poisson homogeneous (mph) models and homogeneous linear predictor (hlp) models for contingency
tables proposed by Lang (2004) and (2005); hidden Markov models where the distribution of the observed variables
is described by a marginal model.
Inequality constraints on the parameters are allowed and can be tested.
Analysing time-series accelerometer data to quantify length and intensity of physical activity.
This package provides several functions to perform formal
hypothesis testing, and power and sample size calculations for
human microbiome experiments. Dirichlet-Multinomial
distribution is proposed to model species abundance and ranked
abundance data.
In this package, we introduce Object Oriented Data Analysis (OODA) methods to analyze Human Microbiome taxonomic trees directly. We provide tools to model, compare, and visualize populations of taxonomic tree objects.
Statistical analysis of static chamber concentration data for trace gas flux estimation.
Generates (half-)normal plots with simulation envelopes using different diagnostics from a range of different fitted models. A few example datasets are included.
Performs likelihood-based inference for a wide range of regression models. Provides higher-order approximations for inference based on extensions  of saddlepoint type arguments as discussed in the book Applied Asymptotics: Case Studies in Small-Sample Statistics by Brazzale, Davison, and Reid (2007).
Information retrieval from National Center for Biotechnology Information (NCBI) databases, with main focus on identifying genes in unannotated organisms via Blast similarity search in annotated organisms.
Simulates hands and tournaments of Texas Holdem.
Contains trading holiday and half-day data that is automatically registered when loaded.
Performs a homogeneity analysis (multiple correspondence analysis) and various extensions. Rank restrictions on the category quantifications can be imposed (nonlinear PCA). The categories are transformed by means of optimal scaling with options for nominal, ordinal, and numerical scale levels (for rank-1 restrictions). Variables can be grouped into sets, in order to emulate regression analysis and canonical correlation analysis.
A collection of functions useful for the analysis of
building physics experiments.
A theoretical model to simulate  radical polymerization. Material, energy and population balances are integrated for batch, semi batch and continuous process in a ideally mixed reactor. Limitations: single monomer (i.e.homo polymer), one phase (organic, aqueous). Datasets with chemical-physical data for the most common monomers is included. Some background in polymer science is suggested for its use. Graphical interface for a quick and friendly use is available.
A collection of homogeneity tests described in: Viglione
A., Laio F., Claps P. (2007) ``A comparison of homogeneity
tests for regional frequency analysis'', Water Resources
Research, 43, W03428, doi:10.1029/2006WR005095.  More on
Regional Frequency Analysis can be found in package nsRFA.
A stand-alone function that generates a user specified number of random datasets and computes eigenvalues using the random datasets (i.e., implements Horn's [1965, Psychometrika] parallel analysis). Users then compare the resulting eigenvalues (the mean or the specified percentile) from the random datasets (i.e., eigenvalues resulting from noise) to the eigenvalues generated with the user's data. Can be used for both principal components analysis (PCA) and common/exploratory factor analysis (EFA). The output table shows how large eigenvalues can be as a result of merely using randomly generated datasets. If the user's own dataset has  actual eigenvalues greater than the corresponding eigenvalues, that lends support to retain that factor/component. In other words, if the i(th) eigenvalue from the actual data was larger than the percentile of the (i)th eigenvalue generated using randomly generated data, empirical support is provided to retain that factor/component.
Horn, J. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185.
Performs multiple hot-deck imputation of categorical and continuous variables in a data frame.
This package provides hot deck imputation methods to resolve missing data.
A set of R functions and data sets which implements Hotelling's T^2 test, and some variants of it. Functions are also included for Aitchison's additive log ratio and centred log ratio transformations
Calculates a hot spot cutoff and associated analyses for
statistical populations
When testing multiple hypotheses simultaneously, this
package provides functionality to calculate a lower bound for
the number of correct rejections (as a function of the number
of rejected hypotheses), which holds simultaneously -with high
probability- for all possible number of rejections.  As a
special case, a lower bound for the total number of false null
hypotheses can be inferred.  Dependent test statistics can be
handled for multiple tests of associations. For independent
test statistics, it is sufficient to provide a list of
p-values.
This package provides all the functions necessary to
estimate the 8 parameters of the Heligman Pollard mortality
model using a Bayesian Melding procedure with IMIS as well as
to convert those parameters into age-specifc probabilities of
death and a corresponding life table
Probabilistic models describing the behavior
of workload and queue on a High Performance Cluster and computing GRID
under FIFO service discipline basing on modified Kiefer-Wolfowitz
recursion. Also sample data for inter-arrival times, service times,
number of cores per task and waiting times of HPC of Karelian
Research Centre are included, measurements took place from 06/03/2009 to 02/30/2011.
Functions provided to import/export workload traces in Standard Workload Format (swf).
Stability condition of the model may be verified either exactly, or approximately.
Collection of functions for manipulating sets of HPO terms and plotting them with a various options
This package contains some miscellaneous convenience functions,
to create a matrix of dummy columns from a factor,
to determine whether x lies in range [a,b],
to add a rectangular bracket to an existing plot,
and to convert frequencies between Hz, semitones, mel and Bark.
This package also contains an example data set of a stratified sample
of 80 talkers of Dutch.
Efficient algorithms for fitting entire regularization paths for Huber loss regression and quantile regression penalized by lasso or elastic-net.
Print beautiful horizontal rules in your R scripts.
Functions, data sets, analyses and examples from the book
`A Handbook of Statistical Analyses Using R' (Brian S. Everitt and Torsten
Hothorn, Chapman & Hall/CRC, 2006). The first chapter
of the book, which is entitled `An Introduction to R',
is completely included in this package, for all other chapters,
a vignette containing all data analyses is available.
Functions, data sets, analyses and examples from the
second edition of the book
`A Handbook of Statistical Analyses Using R' (Brian S. Everitt and Torsten
Hothorn, Chapman & Hall/CRC, 2008). The first chapter
of the book, which is entitled `An Introduction to R',
is completely included in this package, for all other chapters,
a vignette containing all data analyses is available. In addition,
the package contains Sweave code for producing slides for selected
chapters (see HSAUR2/inst/slides).
Functions, data sets, analyses and examples from the
third edition of the book
`A Handbook of Statistical Analyses Using R' (Torsten Hothorn and Brian S.
Everitt, Chapman & Hall/CRC, 2014). The first chapter
of the book, which is entitled `An Introduction to R',
is completely included in this package, for all other chapters,
a vignette containing all data analyses is available. In addition,
Sweave source code for slides of selected chapters is included in
this package (see HSAUR3/inst/slides).
Transformation of reflectance spectra, calculation of vegetation indices and red edge parameters, spectral resampling for hyperspectral remote sensing, simulation of reflectance and transmittance using the leaf reflectance model PROSPECT and the canopy reflectance model PROSAIL.
hSDM is an R package for estimating parameters of hierarchical Bayesian species distribution models. Such models allow interpreting the observations (occurrence and abundance of a species) as a result of several hierarchical processes including ecological processes (habitat suitability, spatial dependence and anthropogenic disturbance) and observation processes (species detectability). Hierarchical species distribution models are essential for accurately characterizing the environmental response of species, predicting their probability of occurrence, and assessing uncertainty in the model results.
Canonical correlation analysis that extracts nonlinear
correlation through the use of Hilbert Schmidt Independence
Criterion and Centered Kernel Target Alignment.
A package for computation of hidden semi markov models
Identification of recombination events, haplotype reconstruction, sire imputation and pedigree reconstruction using half-sib family SNP data
Implements a model for joint meta-analysis of sensitivity and specificity of the diagnostic test under evaluation, while taking into account the possibly imperfect sensitivity and specificity of the reference test. This hierarchical model accounts for both within and between study variability. Estimation is carried out using a Bayesian approach, implemented via a Gibbs sampler. The model can be applied in situations where more than one reference test is used in the selected studies.
A data mining tool for discovering subgroups of patients and genes that simultaneously display unusual levels of variability compared to other genes and patients. Based on sparse singular value decomposition (SSVD), the method can detect both mean and variance biclusters in the presence of heterogeneous residual variance.
HTML tables are a valuable data source but extracting and recasting
these data into a useful format can be tedious. htmltab is a package for
extracting structured information from HTML tables. It is similar to
readHTMLTable() of the XML package but provides two major advantages. First,
the function automatically expands row and column spans in the header and
body cells. Second, users are given more control over the identification of
header and body rows which will end up in the R table. Additionally, the
function preprocesses table code, removes unneeded parts and so helps to
alleviate the need for tedious post-processing.
Tables with state-of-the-art layout elements such as
row spanners, column spanners, table spanners, zebra striping,
and more. While allowing advanced layout, the underlying CSS structure is
simple in order to maximize compatibility with word processors such as
MS Word or LibreOffice.
The package also contains a few text formatting functions that help outputting
text compatible with HTML/LaTeX.
Facilitates automated HTML report creation, in particular
framed HTML pages and dynamically sortable tables.
A framework for creating HTML widgets that render in various
contexts including the R console, 'R Markdown' documents, and 'Shiny'
web applications.
Methods for analysing and forecasting hierarchical and grouped time series.
This package implements a Poisson mixture model to cluster observations (e.g., genes) in high throughput sequencing data. Parameter estimation is performed using either the EM or CEM algorithm, and the slope heuristics are used for model selection (i.e., to choose the number of clusters).
Functions and data tables for simulation and statistical analysis of chemical toxicokinetics ("TK") using data obtained from relatively high throughput, in vitro studies. Both physiologically-based ("PBTK") and empirical (e.g., one compartment) "TK" models can be parameterized for several hundred chemicals and multiple species. These models are solved efficiently, often using compiled (C-based) code. A Monte Carlo sampler is included for simulating biological variability and measurement limitations. Functions are also provided for exporting "PBTK" models to "SBML" and "JARNAC" for use with other simulation software. These functions and data provide a set of tools for in vitro-in vivo extrapolation ("IVIVE") of high throughput screening data (e.g., ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK").
HTTP Request protocols. Implements the GET, POST and multipart POST request.
httpuv provides low-level socket and protocol support for handling
HTTP and WebSocket requests directly from within R. It is primarily
intended as a building block for other packages, rather than making it
particularly easy to create complete web applications using httpuv alone.
httpuv is built on top of the libuv and http-parser C libraries, both of
which were developed by Joyent, Inc. (See LICENSE file for libuv and
http-parser license information.)
Useful tools for working with HTTP organised by HTTP verbs
(GET(), POST(), etc). Configuration functions make it easy to control
additional request components (authenticate(), add_headers() and so on).
The package "huge" provides a general framework for
high-dimensional undirected graph estimation. It integrates
data preprocessing, neighborhood screening, graph estimation,
and model selection techniques into a pipeline. In
preprocessing stage, the nonparanormal(npn) transformation is
applied to help relax the normality assumption. In the graph
estimation stage, the graph structure is estimated by
Meinshausen-Buhlmann graph estimation or the graphical lasso,
and both methods can be further accelerated by the lossy
screening rule preselecting the neighborhood of each variable
by correlation thresholding. We target on high-dimensional data
analysis usually d >> n, and the computation is
memory-optimized using the sparse matrix output. We also
provide a computationally efficient approach, correlation
thresholding graph estimation. Three
regularization/thresholding parameter selection methods are
included in this package: (1)stability approach for
regularization selection (2) rotation information criterion (3)
extended Bayesian information criterion which is only available
for the graphical lasso.
Tools for computing HUM (Hypervolume Under the Manifold) value to estimate features ability
to discriminate the class labels, visualizing the ROC curve for two or three class labels.
Format quantities of time or bytes into human-friendly strings.
Fits models for genotypic disequilibria, as described in
Huttley and Wilson (2000), Weir (1996) and Weir and Wilson (1986).
Contrast terms are available that account for first order interactions
between loci.
Also implements, for a single locus in a single population, a conditional
exact test for Hardy-Weinberg equilibrium
Estimation and testing of HWE using Bayesian methods.
Three models are currently considered: HWE, a model
parameterized in terms of the allele frequencies and a single
inbreeding coefficient f, and the saturated model. Testing is
based on Bayes factors.
General (multi-allelic) Hardy-Weinberg equilibrium problem from an objective Bayesian testing standpoint. This aim is achieved through the identification of a class of priors specifically designed for this testing problem. A class of intrinsic priors under the full model is considered. This class is indexed by a tuning quantity, the training sample size, as discussed in Consonni, Moreno and Venturini (2010). These priors are objective, satisfy Savage's continuity condition and have proved to behave extremely well for many statistical testing problems.
HW.pval calculates plain and fully conditional
root-mean-square, chi-square, and log likelihood-ratio P-values
for the user-provided genotypic counts to be consistent with
the Hardy-Weinberg equilibrium model.  For further information
on the Hardy-Weinberg equilibrium model and the pseudocode,
refer to the paper "Testing Hardy-Weinberg equilibrium with a
simple root-mean-square statistic" by Rachel Ward.
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.
Provides methods to test whether time series is consistent
with white noise.
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.
Functions to build and deploy a hybrid ensemble consisting of eight different sub-ensembles: bagged logistic regressions, random forest, stochastic boosting, kernel factory, bagged neural networks, bagged support vector machines, rotation forest, and bagged k-nearest neighbors. 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.  A mutual cluster is a set of points closer to each other than to all other points.  Mutual clusters are used to enrich top-down hierarchical clustering.
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".
Facilities for easy implementation of hybrid Bayesian networks
using R. Bayesian networks are directed acyclic graphs representing joint
probability distributions, where each node represents a random variable and
each edge represents conditionality. The full joint distribution is
therefore factorized as a product of conditional densities, where each node
is assumed to be independent of its non-descendents given information on its
parent nodes. Since exact, closed-form algorithms are computationally
burdensome for inference within hybrid networks that contain a combination
of continuous and discrete nodes, particle-based approximation techniques
like Markov Chain Monte Carlo are popular.  We provide a user-friendly
interface to constructing these networks and running inference using the 'rjags' package.
Econometric analyses (maximum expected utility under competing policies,
value of information) involving decision and utility nodes are also
supported.
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
Comfortable ways to work with hyperspectral data sets.
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 datasets and performs set operations: intersection / overlap, union, unique components, inclusion test, and hole detection. Uses stochastic geometry approach to high-dimensional kernel density estimation. Builds n-dimensional convex hulls (polytopes). 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.
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.
Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015).
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.
Functions to analyze, plot, and store the output of running IBD_Haplo software package. More information regarding IBD_Haplo can be found at http://www.stat.washington.edu/thompson/Genepi/pangaea.shtml.
Convert "label", "lexicographic", "jacquard" and "vec", full state description vector. All conversions are done to and from "label", as used in IBD_Haplo. More information regarding IBD_Haplo can be found at http://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).
Some basic functions to implement belief functions including:
transformation between belief functions using the method introduced by
Philippe Smets (arXiv:1304.1122 [cs.AI]), evidence combination, evidence
discounting, decision-making, and constructing masses. Currently, thirteen
combination rules and five decision rules are supported. It can also be
used to generate different types of random masses when working on belief
combination and conflict management.
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.
Functionality required to efficiently use R with
IBM DB2(C) for Linux, Unix and Windows as well as IBM dashDB(C).
Many basic and complex R operations are pushed down into the database, which removes
the main memory boundary of R and allows to make full use of parallel processing in
the underlying database.
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).
Imperialist Competitive Algorithm (ICA)
<http://en.wikipedia.org/wiki/Imperialist_competitive_algorithm>
is a computational method that is used to solve optimization
problems of different types and it is the mathematical model
and the computer simulation of human social evolution.
The package provides a minimum value for the cost function
and the best value for the optimization variables by
Imperialist Competitive Algorithm.
Users can easily define their own objective function
depending on the problem at hand.
This version has been successfully applied to solve
optimization problems, for continuous functions.
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 that currently integrates npEM and Fast-ICA for non-parametric estimation of ICA mixture models. We will continue to build in algorithms that interweave the EM step with other types of ICA techniques.
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
Assist in the estimation of the Intraclass Correlation Coefficient (ICC) from variance components of a one-way analysis of variance and also estimate the number of individuals or groups necessary to obtain an ICC estimate with a desired confidence interval width.
A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (in press). iccbeta quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes.
Calculate comorbidities, Charlson scores, perform fast and accurate
validation, conversion, manipulation, filtering and comparison of ICD-9-CM
(clinical modification) codes. ICD-9 codes appear numeric but leading and
trailing zeroes, and both decimal and non-decimal "short" format codes
exist. The package enables a work flow from raw lists of ICD-9 codes from
hospital billing databases to comorbidities. ICD-9 to comorbidity mappings
from Quan (Deyo and Elixhauser versions), Elixhauser and AHRQ included. Any
other mapping of codes, such as ICD-10, to comorbidities can be used.
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.
Regression models for interval censored data. Currently supports Cox-PH and proportional odds models. Allows for both semi and fully parametric models. Includes functions for easy visual diagnostics of model fits.
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.
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.
Provides the necessary tools to estimate average treatment effects with an instrumental variable by re-weighting observations using a model of compliance.
Calculate identity coefficients, based on Mark Abney's C
code.
Incremental Principal Component Analysis and Multiple Correspondence Analysis using incremental eigenvalue decomposition methods.
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
It is a method to identify individually turtles using the plastral biometries. It is also useful to detect errors on the identification of turtles as is explained in Valdeon & Longares 2015.
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.
Tools, tutorials, and demos of Item Factor Analysis using OpenMx.
Provides utility functions to deal with Italian fiscal code
('codice fiscale').
Incremental gene selection algorithm(iFes) expands the screening targets in each step of the forward stepwise algorithm, and proposes a novel feature selection algorithm iFes with both high accuracies and low feature numbers. Logistic regression classification algorithm accelerated by NVIDIA GPU is utilized to measure how good the currently chosen feature subset is.
A suite for identifying causal models using relative concordances and identifying causal polymorphisms in case-control genetic association data, especially with large controls re-sequenced data.
A collection of statistical tests and utilities for genetic association studies
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. Also includes functions to load
special pxp files produced by the 'Igor Pro' 'Neuromatic' and 'Nclamp'
packages for recording and analysing neuronal data. See
http://www.neuromatic.thinkrandom.com/ for details.
Routines for simple graphs and network analysis. It can
handle large graphs very well and provides functions for generating random
and regular graphs, graph visualization, centrality methods and much more.
A small collection of various network data sets,
to use with the 'igraph' package: the Enron email network, various food webs,
interactions in the immunoglobulin protein, the karate club network,
Koenigsberg's bridges, visuotactile brain areas of the macaque monkey,
UK faculty friendship network, domestic US flights network, etc.
This program facilitates exporting igraph graphs to the
SoNIA file format
A dataset of the top colours of photos from Instagram
taken in 2014 in the city of Vancouver, British Columbia, Canada.
It consists of: top colour and counts data. This data was
obtained using the Instagram API. Instagram is a web photo
sharing service. It can be found at: <https://instagram.com>.
The Instagram API is documented at: <https://instagram.com/developer/>.
Density, distribution function, quantile function and random generation for the inverse hyperbolic sine distribution. This package also provides a function that can fit data to the inverse hyperbolic sine distribution using maximum likelihood estimation.
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.
Fitting a class of Lee-Carter mortality models using iterative fitting algorithm.
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.
This is an alternative mechanism for importing
objects from packages. The syntax allows for importing multiple objects
from a package with a single command in an expressive way. The import
package bridges some of the gap between using library (or require) and
direct (single-object) imports. Furthermore the imported objects are not
placed in the current environment. It is also possible to import
objects from stand-alone .R files. For more information, refer to
the package vignette.
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. * New functionality has been added: a hybrid method that allows the imputation of missing values in a more complex scenario where the missing data are both MAR and MNAR.
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.
Imputation (replacement) of missing values in univariate time series.
Graphical User Interface (GUI) to some of the functions in the package extRemes version >= 2.0 are included.
Fits mixtures models for longitudinal data. Appropriate when the data are counts and when the correlation structure is assumed to be AR(1).
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.
R-based client for Machine Learning APIs at http://indico.io.
Provides wrappers for following APIs: Positive/Negative Sentiment Analysis,
Political Sentiment Analysis, Image Feature Extraction, Facial Emotion
Recognition, Facial Feature Extraction, Language Detection
Functions to assess the strength and statistical significance of the relationship between species occurrence/abundance and groups of sites. Also includes functions to measure species niche breadth using resource categories.
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
Interpolation and extrapolation for species diversity: a unified approach for both individual-based (abundance) data and sample-based (incidence) data.
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).
Provides methods for estimating causal effects in the presence of interference. Currently it implements the IPW estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele in "On causal inference in the presence of interference" (Statistical Methods in Medical Research, 21(1) 55-75).
Implementation of methods Extremum Surface Estimator and
Extremum Distance Estimator for identifying the inflection
point of a convex/concave curve.
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 measures in Structural Equation Models: 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.
Aids in creating sequence statistics for Butts's 'relevent' software.
This package implements various measures of information theory based on several entropy estimators.
Takes a data frame containing latitude and longitude coordinates and downloads images from map servers to determine their file size as a proxy of infrastructure
Replace parameters in strings and/or text files with specified values.
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 inlined 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.
Checks the local R library(ies) to see if the required
package(s) is/are installed or not. If the package(s) is/are
not installed, then the package(s) will be installed along with
the required dependency(ies). This function pulls source or
binary packages from the Revolution Analytics CRAN mirror and/or
the USGS GRAN Repository. Lastly, the chosen package(s)
is/are loaded.
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
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
Chi-square tests are computed with corrections.
Computes large sample confidence intervals of Liu and Hudgens (2014), exact confidence intervals of Tchetgen Tchetgen and VanderWeele (2012), and exact confidence intervals of Rigdon and Hudgens (2014) for treatment effects on a binary outcome in two-stage randomized experiments with interference.
Functions implemented in this package allow to coerce (i.e.
convert) network data between classes provided by other R packages.
Currently supported classes are those defined in packages: network and
igraph.
Search the Internet Archive, retrieve metadata, and download
files.
Plots the conditional coefficients ("marginal effects") of variables included in multiplicative interaction terms.
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).
Compute permutation- based performance measures and create partial
dependence plots for (cross-validated) 'randomForest' and 'ada' models.
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.  Supports both common and observation-specific boundaries.
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).
Confidence intervals for causal effects, using data collected in different experimental or environmental conditions. Hidden variables can be included in the model with a more experimental version.
This package describes the associated cost games to inventory situations.
Functions to facilitate inverse estimation/calibration in linear,
generalized 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.
One function to read files. One function to write files. One
function to direct plots to screen or file. Automatic file format inference
and directory structure creation.
Two methods for calculation of the number of required flows for semiconductor sequencing: 1. Using a simulation, the number of flows can be calculated for a concrete list of amplicons. 2. An exact combinatorial model is evaluated to calculate the number of flows for a random ensemble of sequences.
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.
Functions are provided to interpolate georeferenced point data via Inverse Path Distance Weighting. Useful for coastal marine applications where barriers in the landscape preclude interpolation with Euclidean distances.
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 provides functions that wrap popular phylogenetic software for sequence alignment, masking of sequence alignments, and estimation of phylogenies and ancestral character states.
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.
A toolkit for manipulating,
validating and testing IP addresses and ranges, along with
datasets relating to IP addresses. While it primarily has
support for the IPv4 address space, more extensive IPv6 support
is intended.
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.
Provides classes and methods for seismic data analysis. The
base classes and methods are inspired by the python code found in
the 'ObsPy' python toolbox <http://github.com/obsypy/obspy>. Additional classes and
methods support data returned by web services provided by the 'IRIS
DMC' <http://service.iris.edu/>.
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.
Interactive shiny application for running Item Response Theory
analysis.
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.
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.
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.
The package offers framework for testing for monotonic relationship between gene expression and doses in a microarray experiment. 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.
Deconvolution of mixed tumour profiles into normal and cancer for each patient, using
the ISOpure algorithm in Quon et al. Genome Medicine, 2013 5:29. Deconvolution requires
mixed tumour profiles and a set of unmatched "basis" normal profiles.
Contains two main functions: one for
solving general isotone regression problems using the
pool-adjacent-violators algorithm (PAVA); another one provides
a framework for active set methods for isotone optimization
problems with arbitrary order restrictions. Various types of
loss functions are prespecified.
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.
It 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. These iterative 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++ for faster performances.
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
GAMM (Generalized Additive Mixed Modeling; Lin & Zhang, 1999) as implemented in the R package mgcv (Wood, S.N., 2006; 2011) is a nonlinear regression analysis which is particularly useful for time course data such as EEG, pupil dilation, gaze data (eye tracking), and articulography recordings, but also for behavioral data such as reaction times and response data. As time course measures are sensitive to autocorrelation problems, GAMMs implements methods to reduce the autocorrelation problems. This package includes functions for the evaluation of GAMM models (e.g., model comparisons, determining regions of significance, inspection of autocorrelational structure in residuals) and interpreting of GAMMs (e.g., visualization of complex interactions, and contrasts).
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
GMM estimation of triangular systems using heteroscedasticity based instrumental variables as in Lewbel (2012)
Contains functions for carrying out instrumental variable estimation of causal effects, including power analysis, sensitivity analysis, and diagnostics.
This package contains functions for carrying out instrumental variable estimation of causal effects and power analyses for instrumental variable studies.
Fit the instrumental panel data models: the fixed effects, random
effects and between models.
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.
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.
A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags').  A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers.
Implements a novel score test that measures 1) the overall shift in the gene expression due to genotype (additive genetic effect), and 2) group-specific changes in gene expression due to genotype (interaction effect) in a mixed-effects model framework.
Analyze and visualize results of studies performed with the
analysis tools in 'JAMES', a modern object-oriented Java
framework for discrete optimization using local search
metaheuristics (see http://www.jamesframework.org).
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').
On Affymetrix gene expression microarrays, a single gene may be measured by multiple probe sets. This can present a mild conundrum when attempting to evaluate a gene "signature" that is defined by gene names rather than by specific probe sets. This package provides a one-to-one mapping from gene to "best" probe set for four Affymetrix human gene expression microarrays: hgu95av2, hgu133a, hgu133plus2, and u133x3p. This package also includes the pre-calculated probe set quality scores that were used to define the mapping.
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/
Chinese text segmentation, keyword extraction and speech tagging
For R.
jiebaR is a package for Chinese text segmentation, keyword extraction
and speech tagging. This package provides the data files required by jiebaR.
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.
Performs power calculations for joint modeling of longitudinal and survival data with k-th order trajectories when the variance-covariance matrix, Sigma_theta, is unknown.
The main purpose of this package is to make it easy for userR's to interact with 'jMetrik' an open source application for psychometric analysis. For example it allows useR's to write data frames to file in a format that can be used by 'jMetrik'. It also allows useR's to read *.jmetrik files (e.g. output from an analysis) for follow-up analysis in R. The *.jmetrik format is a flat file that includes a multiline header and the data as comma separated values. The header includes metadata about the file and one row per variable with the following information in each row: variable name, data type, item scoring, special data codes, and variable label.
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.
Perform regression analyses under the joint Cox proportional hazards model between tumour progression and death for meta-analysis.
The method is applicable for meta-analysis combining several studies or for cluster survival data.
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.
Similarly to Schafer's package pan, jomo is a package for multilevel joint modelling multiple imputation.
Novel aspects of jomo are the possibility of handling binary and categorical data through latent normal variables and the option to use cluster-specific covariance matrices.
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.
A set of utility functions for working with JavaScript in R.
Currently includes functions to compile, validate, reformat, optimize
and analyze JavaScript code.
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 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. The unit
tests included with the package verify that all edge cases are encoded and
decoded consistently for use with dynamic data in systems and applications.
Transforming univariate non-normal data to normality using Johnson
families of distributions. Johnson family is a comprehensive distribution
family that accommodates many kinds of non-normal distributions. A bunch of
distributions with various parameters will be fit and the corresponding
p-values under a user-specified normality test will be given. The final
transformation will be the one with the largest p-value under the given
normality test.
Generates image data for fractals (Julia and Mandelbrot
sets) on the complex plane in the given region and resolution.
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.
S4 tool box for capacity (or non-additive measure, fuzzy measure) and integral manipulation in 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 based 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 parititioning (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
Provides fast implementations of kernel smoothing techniques for bivariate copula densities, in particular density estimation and resampling.
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.
Gaussian Process models with customised covariance kernels.
Binary classification based on an ensemble of kernel machines ("Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), 2904-2913"). Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data.
In self-reported or anonymized data the user often encounters heaped data, i.e. data which are rounded (to a possibly different
degree of coarseness). While this is mostly a minor problem in parametric density estimation the bias can be very large
for non-parametric methods such as kernel density estimation. This package implements a partly Bayesian algorithm treating the true unknown values as additional
parameters and estimates the rounding parameters to give a corrected kernel density estimate. It supports various standard bandwidth selection methods. Varying rounding probabilities (depending on the true value) and asymmetric rounding is estimable as well. Additionally, bivariate non-parametric density estimation for rounded data is supported.
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.
Wait for a single key press at the 'R' prompt.
This works in terminals, but does not currently work
in the 'Windows' 'GUI', the 'OS X' 'GUI' ('R.app'),
in 'Emacs' 'ESS', in an 'Emacs' shell buffer or in
'R Studio'. In these cases 'keypress' stops with an
error message.
Functions for Kalman filtering, smoothing,
forecasting and simulation of multivariate exponential family state space
models with exact diffuse initialization and sequential processing.
A streamlined cross-referencing system for R Markdown documents
generated with 'knitr'. R Markdown is  an authoring format for generating
dynamic content from R. 'kfigr' provides a hook for anchoring code
chunks and a function to cross-reference document elements generated from
said chunks, e.g. figures and tables.
Naive implementation of the Kalman filter, smoother and disturbance
smoother for state space models.
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.
Data cleaning, processing, visualisation and analysis for manual (Goldmann) and automated (Octopus 900) kinetic visual field data.
The FOCUS Kinetics Report first published in 2006 describes mathematical
models and recommends statistical methods for the evaluation of
chemical degradation data. This package implements fitting the kinetic
models suitable for observations of the decline of a single chemical
compound (no metabolite formation/decline or multi-compartment kinetics).
Please note that no warranty is implied for correctness of results or
fitness for a particular purpose.  'kinfit' is maintained, but not
actively developed at the moment. Please check the 'mkin' package for an
actively developed package for kinetic evaluations of degradation data.
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.
Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, stochastic migration model. Providing accessibility for considering species migration in niche-based species distribution models.
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 for 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. klausuR 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.
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
Compute p-values of metabolite differential expression analysis using the kernel-based approach.
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.
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.
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).
An implementation of the 'k-means--' algorithm proposed by Chawla and Gionis, 2013 in their paper, "k-means-- : A unified approach to clustering and outlier detection. SIAM International Conference on Data Mining (SDM13)", and using 'ordering' described by Howe, 2013 in the thesis, "Clustering and anomaly detection in tropical cyclones". Useful for creating (potentially) tighter clusters than standard k-means and simultaneously finding outliers inexpensively in multidimensional space.
Data sets and functions for Klein and Moeschberger (1997),
"Survival Analysis, Techniques for Censored and Truncated
Data", Springer.
A tool which 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.
Provides several helper functions for working with 'knitr' and 'LaTeX'.
It includes 'xTab' for creating traditional 'LaTeX' tables, 'lTab' for generating
'longtable' environments, and 'sTab' for generating a 'supertabular' environment.
Additionally, this package contains a knitr_setup() function which fixes a
well-known bug in 'knitr', which distorts the 'results="asis"' command when used
in conjunction with user-defined commands; and a com command (<<com=TRUE>>=)
which renders the output from 'knitr' as a 'LaTeX' command.
Provides a general-purpose tool for dynamic report generation in R
using Literate Programming techniques.
A framework to create Bootstrap 3 HTML reports from knitr
Rmarkdown and RHTML.
Scale categorical variables in such a way as
to make NN classification as accurate as possible. The code also
handles continuous variables and prior probabilities, and does
intelligent variable selection and estimation of both error rates
and the right number of NN's.
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 knockoff filter is a procedure for controlling the false
discovery rate (FDR) when performing variable selection. For more information,
see the website below and the accompanying paper.
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.
KODAMA (KnOwledge Discovery by Accuracy MAximization) is an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data.
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.
Rank-based tests for enrichment of KOG (euKaryotic Orthologous Groups) classes with up- or down-regulated genes based on a continuous measure. The meta-analysis is based on correlation of KOG delta-ranks across datasets (delta-rank is the difference between mean rank of genes belonging to a KOG class and mean rank of all other genes). With binary measure (1 or 0 to indicate significant and non-significant genes), one-tailed Fisher's exact test for over-representation of each KOG class among significant genes will be performed.
Functions to train supervised and self-organising maps (SOMs). Also interrogation of the maps and prediction using trained maps are supported. The name of the package refers to Teuvo Kohonen, the inventor of the SOM.
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 graphical dialogs
for its basic features. The respective R package 'rkward' cannot be installed
directly from a repository, as it is a part of RKWard. To make full use of this
feature, please install RKWard from https://rkward.kde.org (plugins are detected
automatically). Due to some restrictions on CRAN, the full package sources are
only available from the project homepage.
The kpodclustr package implements the k-POD method for clustering
partially observed data.
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 with 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).
Implements several methods for testing the variance component parameter in regression models that contain kernel-based random effects, including a maximum of adjusted scores test. Several kernels are supported, including a profile hidden Markov model mutual information kernel for protein sequence.
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''.
Knowledge Space Theory is a set-theoretical framework, which
proposes mathematical formalisms to operationalize knowledge structures in a
particular domain. The kst-package provides basic functionalities to
generate, handle, and manipulate knowledge structures and knowledge spaces.
This function is designed for use with an arbitrary set of equations with
an arbitrary set of unknowns.
The user selects "fixed" values for enough unknowns to leave as many variables as
there are equations, which in most cases means the system is properly
defined and a unique solution exists. The function, the fixed values
and initial values for the remaining unknowns are fed to a nonlinear backsolver.
The original version of "TK!Solver" was the inspiration for this function.
These functions compute the k best pairs of genes used to classify samples based on the relative rank of the genes expression within each profile. A score based on the sensitivity and the specificity is calculated for every possible pair. The k pairs with the highest score will be selected with the restriction that a gene can appear in at most one pair. The value of k is either set as a parameter chosen by the user or computed through crossvalidation. Other functions related to the k-TSP are also available, for example the functions prediction, summary, plot, etc. can be found in the package.
Provides various functions and data sets from experiments at the Faculty of Life Sciences, University of Copenhagen. This package will be discontinued and archived, and the functions and datasets will be maintained and updated in the MESS package
Calculation groundwater mounding beneath an infiltration basin based on the Hantush (1967) equation (http://doi.org/10.1029/WR003i001p00227). The correct implementation is shown with a verification example based on a USGS report (page 25, http://pubs.usgs.gov/sir/2010/5102/support/sir2010-5102.pdf).
A miscellaneous set of functions for printing, plotting, kernels, etc. Additional contributors are acknowledged on individual function help pages.
Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.
A colletion of functions to implement Kolmogorov-Zurbenko
Fourier transform based periodograms and smoothing methods
A spatial smoothing algorithm based on convolutions of finite rectangular kernels that provides sharp resolution in the presence of high levels of noise.
L1 estimation for linear regression and random number generation for
the multivariate Laplace distribution.
Efficient implementation of Friedman's boosting algorithm
with l2-loss function and coordinate direction (design matrix
columns) basis functions.
A variety of ordination and community analyses
useful in analysis of data sets in community ecology.
Includes many of the common ordination methods, with
graphical routines to facilitate their interpretation,
as well as several novel analyses.
Support labeled loop and escape from nested loop
Provides a range of axis labeling algorithms
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, and large scale computer model calibration.
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.
Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. Climatic drivers and geomorphometric characteristics were also compiled and are included for each lake. Data are part of the associated publication from the Global Lake Temperature Collaboration project (http://www.laketemperature.org). See citation('laketemps') for dataset attribution.
Currently the package includes 14 methods for calculating internal
consistency reliability but is still growing.  The package allows users
access to whichever reliability estimator is deemed most appropriate for
their situation.
A language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety.
Provides tools that manipulate and transform data using methods
and techniques consistent with functional programming. The idea is that
through the use of these tools, a program can be reasoned about insomuch
that the implementation can be proven to be equivalent to the mathematical
model.
Lambert W x F distributions are a generalized framework to analyze
skewed, heavy-tailed data. They are based on an input/output system, where
the input random variable (RV) X ~ F, and the output Y is a non-linearly
transformed version of X with similar properties, but slightly skewed and/or
heavy-tailed. This transformed RV Y has a Lambert W x F distribution.
This package contains functions to model and analyze skewed, heavy-tailed
data the Lambert Way: simulate random samples, estimate parameters, compute
quantiles, and plot/print results nicely. Probably the most important
function is 'Gaussianize', which works similarly to 'scale', but actually
makes the data Gaussian.  A do-it-yourself toolkit allows users to define
their own Lambert W x 'MyFavoriteDistribution' and use it in their analysis
right away.
Implements both real-valued branches of the Lambert-W function, also known as the product logarithm, without the need for installing the entire GSL.
This package includes functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307.
Processing of Landsat or other multispectral satellite
imagery. Includes relative normalization, image-based
radiometric correction, and topographic correction options.
Functions for converted Landsat 8 multispectral satellite imagery
rescaled to the top of atmosphere (TOA) reflectance, radiance
and/or at satellite brightness temperature using radiometric
rescaling coefficients provided in the metadata file (MTL file).
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.
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
Fit and simulate latent position and cluster models for statistical networks.
Calculates and plots Handedness index (HI), absolute HI, mean HI and z-score which are commonly used indexes for the study of hand preference (laterality) in non-human primates.
Parses and converts LaTeX math formulas to R's plotmath expressions, used to enter mathematical formulas and symbols to be rendered as text, axis labels, etc. throughout R's plotting system.
A powerful and elegant high-level data visualization
system inspired by Trellis graphics, with an emphasis on
multivariate data. Lattice is sufficient for typical graphics needs,
and is also flexible enough to handle most nonstandard requirements.
See ?Lattice for an introduction.
This package contains functions that compute the
lattice-based density estimator of Barry and McIntyre, which
accounts for point processes in two-dimensional regions with
irregular boundaries and holes.  The package also implements
two-dimensional non-parametric regression for similar regions.
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
R client for 'Turfjs' (http://turfjs.org/) for
'geospatial' analysis. The package revolves around using 'GeoJSON'
data. Functions are included for creating 'GeoJSON' data objects,
measuring aspects of 'GeoJSON', and combining, transforming,
and creating random 'GeoJSON' data objects.
Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness,
measures of relative variability etc, the package contains new robust tests of symmetry,
omnibus and directional tests of normality, and their graphical counterparts such as
Robust QQ plot; a robust trend tests for variances etc. All implemented tests and methods
are illustrated by simulations and real-life examples from legal statistics, economics,
and biostatistics.
By combining constant, linear, and quadratic local models,
lazy estimates the value of an unknown multivariate function on
the basis of a set of possibly noisy samples of the function
itself.  This implementation of lazy learning automatically
adjusts the bandwidth on a query-by-query basis through a
leave-one-out cross-validation.
Supplies a LazyData facility for packages which have data
sets but do not provide LazyData.  A single function is
is included, requireData, which is a drop-in replacement for
base::require, but carrying the additional
functionality. By default, it suppresses package
startup messages as well.  See argument 'reallyQuitely'.
A disciplined approach to non-standard evaluation.
Provides the functionality to write LaTeX code from within R
without having to learn LaTeX.  Functionality also exists to create HTML
and Markdown code.  While the functionality still exists to write
complete documents with lazyWeave, it is generally easier to do so with
with markdown and knitr.  lazyWeave's main strength now is the ability
to design custom and complex tables for reporting results.
Latent budget analysis is a method for the analysis of a two-way contingency table with an exploratory variable and a response variable. It is specially designed for compositional data.
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.
Interfacing to Nocedal et al. L-BFGS-B.3.0 (2011) limited
memory BFGS minimizer with bounds on parameters.
The package offers various length-bias corrections to
survival curve estimation.
Performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset.
This package performs a Latent Class Analysis of
phenotypic measurements in pedigrees and a model selection
based on one of two methods: likelihood-based cross-validation
and Bayesian Information Criterion. It computes also individual
and triplet child-parents weights in a pedigree using an
upward-downward algorithm. It takes into account the familial
dependence defined by the pedigree structure by considering
that a class of a child depends on his parents classes via
triplet-transition probabilities of the classes. The package
handles the case where measurements are available on all
subjects and the case where measurements are available only on
symptomatic (i.e. affected) subjects. Distributions for
discrete (or ordinal) and continuous data are currently
implemented. The package can deal with missing data.
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.
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.
The main purpose of lctools is to assist researchers and educators to use user friendly tools for calculating key spatial statistics. These include: Local Pearson and Geographically Weighted Pearson Coefficients, Spatial Inequality Measures (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation (Global and Local Moran's I) and other Spatial Analysis tools (Geographically Weighted Statistics, Inferential Statistics). This package also contains functions for statistical inference for each statistic calculated.
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.
Tools to create an interactive web-based visualization of a
topic model that has been fit to a corpus of text data using
Latent Dirichlet Allocation (LDA). Given the estimated parameters of
the topic model, it computes various summary statistics as input to
an interactive visualization built with D3.js that is accessed via
a browser. The goal is to help users interpret the topics in their
LDA topic model.
Computations related to group sequential boundaries.
Includes calculation of bounds using the Lan-DeMets
alpha spending function approach.
The package provides a set of functions which aim is to
propose four measures of linkage disequilibrium: the usual r^2
measure, the r^2_S measure (r^2 corrected by the structure
sample), the r^2_V (r^2 corrected by the relatedness of
genotyped individuals), the r^2_VS measure (r^2 corrected by
both the relatedness of genotyped individuals and the structure
of the sample).
Produces a graphical display, as a heat map, of measures
of pairwise linkage disequilibria between SNPs. Users may
optionally include the physical locations or genetic map
distances of each SNP on the plot.
ldlasso requires data be of class gwaa.data from the the
package GenABEL
this package provides functions for Finding Locally
D-optimal designs for Logistic, Negative Binomial, Poisson,
Michaelis-Menten, Exponential, Log-Linear, Emax, Richards,
Weibull and Inverse Quadratic regression models and also
functions for auto-constructing Fisher information matrix and
Frechet derivative based on some input variables and without
user-interfere.
Implementation of most popular approaches to PD (probability of default) calibration: Quasi Moment Matching algorithm (D. Tasche), algorithm proposed by M. van der Burgt, K. Pluto and D. Tasche's most prudent estimation methodology.
Functions, methods, and data sets for fitting likelihood-based dimension reduction in regression, using principal fitted components (pfc), likelihood acquired directions (lad), covariance reducing models (core).
Linear dimension reduction 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)
The leader clustering algorithm provides
a means for clustering a set of data points. Unlike many other clustering
algorithms it does not require the user to specify the number of clusters,
but instead requires the approximate radius of a cluster as its primary
tuning parameter. The package provides a fast implementation of this
algorithm in n-dimensions using Lp-distances (with special cases for p=1,2,
and infinity) as well as for spatial data using the Haversine
formula, which takes latitude/longitude pairs as inputs and clusters
based on great circle distances.
A leaf angle distribution is a special distribution that is defined between 0 and 90 degrees, and a number of distributions are used to characterize the leaf angle distribution in real plant canopies. This package includes methods to fit distributions to data, visualize the fit, and compare fits of nine different distributions.
An interface for the image processing program ImageJ, which allows a rapid digital image analysis for particle sizes. This package includes function to write an ImageJ macro which is optimized for a leaf area analysis by default.
Create and customize interactive maps using the 'Leaflet' JavaScript
library and the 'htmlwidgets' package. These maps can be used directly from
the R console, from 'RStudio', in Shiny apps and R Markdown documents.
Display your spatial data on interactive web-maps using the open-source JavaScript library Leaflet. 'leafletR' provides basic web-mapping functionality to combine vector data and online map tiles from different sources. See <http://leafletjs.com> for more information on Leaflet.
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.
Allows students to use R as an interactive educational environment
for statistical concepts, ranging from p-values to confidence intervals
to stability in time series.
Utilizing phylogenetic and functional information for the analyses of ecological datasets. The analyses include methods for quantifying the phylogenetic and functional diversity of assemblages.
Estimate the slope and intercept of a bivariate
linear relationship by calculating a posterior density
that is invariant to interchange and scaling of the
coordinates.
This package provides functions and datasets for the
reader of the book "Le logiciel R: Maitriser le langage,
Effectuer des analyse statistiques". The documentation and help
pages are written in french.
Each function accomplishes the work of several or more standard R functions.  For example, two function calls, Read() and CountAll(), read the data and generate summary statistics for all variables in the data frame, plus histograms and bar charts as appropriate.  Other functions provide for descriptive statistics, a comprehensive regression analysis, analysis of variance and t-test, plotting, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function, variable labels, color themes, and a built-in help system. A confirmatory factor analysis of multiple indicator measurement models is also available as are pedagogical routines for data simulation such as for the Central Limit Theorem. Compatible with RStudio and knitr including generation of knitr instructions for interpretative output.
This package contains some simple objects and functions to do
statistics using linear models and a Bayesian framework.
R functions for handling, processing, and analyzing geographic data on
species' distributions and environmental variables as well as species' description
and conservation information.
Provides an extension to factors called 'lfactor' that are similar to factors but allow users to refer to 'lfactor' levels by either the level or the label.
Transforms away factors with many levels prior to doing an OLS.
Useful for estimating linear models with multiple group fixed effects, and for
estimating linear models which uses factors with many levels as pure control variables.
Includes support for instrumental variables, conditional F statistics for weak instruments,
robust and multi-way clustered standard errors, as well as limited mobility bias correction.
Various algorithms related to linguistic fuzzy logic: mining for linguistic fuzzy association
rules, performing perception-based logical deduction (PbLD), and forecasting time-series using fuzzy
rule-based ensemble (FRBE).
The "Manual on Low-flow Estimation and Prediction", published by the World Meteorological Organisation (WMO), gives a comprehensive summary on how to analyse stream flow data focusing on low-flows. This packages provides functions to compute the described statistics and 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. Bayesian inference for spatial, spatiotemporal,
multivariate and aggregated point processes using Markov chain Monte Carlo.
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
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.
A wrapper around the LIBLINEAR C/C++ library for
machine learning (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear).
LIBLINEAR is a simple library for solving large-scale regularized linear
classification and regression. It currently supports L2-regularized
classification (such as logistic regression, L2-loss linear SVM and L1-loss
linear SVM) as well as L1-regularized classification (such as L2-loss linear
SVM and logistic regression) and L2-regularized support vector regression
(with L1- or L2-loss). The main features of LiblineaR include multi-class
classification (one-vs-the rest, and Crammer & Singer method), cross
validation for model selection, probability estimates (logistic regression
only) or weights for unbalanced data. The estimation of the models is
particularly fast as compared to other libraries.
Estimates predictive states from spatio-temporal data and
consequently can provide provably optimal forecasts.
Currently this implementation
supports an N-dimensional spatial grid observed over equally spaced time
intervals. E.g. a video is a 2D spatial systems observed over time. This
package implements mixed LICORS, has plotting tools (for (1+1)D and (2+1)D
systems), and methods for optimal forecasting.  Due to memory limitations
it is recommend to only analyze (1+1)D systems.
This package presents a general method for assessing the
local influence of minor perturbations of case weight for the
linear regression models.
Classes and methods that allow the user to manage life table, actuarial tables (also multiple decrement tables). Moreover, functions to easily perform demographic, financial and actuarial mathematics on life contingencies insurances calculations are contained therein.
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.
Dockerize R Markdown documents with support for Rabix.
A Poisson Subordinated Distribution to capture major
leptokurtic features in log-return time series of financial
data.
Tools for maximum likelihood estimation of parameters
of scientific models.
Functions for computing the r and r* statistics for inference on an arbitrary scalar function of model parameters, plus some code for the (modified) profile likelihood.
Tools to determine DNA profile Weight of Evidence.
For further information see the likeLTD guide at the URL provided,
or the paper under citation.
Functions to analyze and visualize likert type items
Functions that read and solve linear inverse problems (food web problems, linear programming problems).
These problems find solutions to linear or quadratic functions:
min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(ai*xi)
subject to equality constraints Ex=f and inequality constraints Gx>=h. Uses package limSolve.
Values below a specified limit of detection are stacked in
rows in order to reduce overplotting and create a clear
graphical representation of your data.
Functions that (1.) Find the minimum/maximum of a linear or quadratic function:
min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(ai*xi)
subject to equality constraints Ex=f and/or inequality constraints Gx>=h.
(2.) Sample an underdetermined- or overdetermined system Ex=f subject to Gx>=h, and if applicable Ax~=b.
(3.) Solve a linear system Ax=B for the unknown x. Includes banded and tridiagonal linear systems.
The package calls Fortran functions from LINPACK
Short for 'linear binning', the linbin package provides functions for manipulating,
binning, and plotting linearly referenced data. Although developed for data collected on river
networks, it can be used with any interval or point data referenced to a 1-dimensional coordinate
system. Flexible bin generation and batch processing makes it easy to compute and visualize
variables at multiple scales, useful for identifying patterns within and between variables and
investigating the influence of scale of observation on data interpretation.
Estimate and confidence/credible intervals for an unknown
regressor x0 given an observed y0.
L-systems or Lindenmayer systems are parallel rewriting systems which can
be used to simulate biological forms and certain kinds of fractals.
Briefly, in an L-system a series of symbols in a string are replaced
iteratively according to rules to give a more complex string. Eventually,
the symbols are translated into turtle graphics for plotting. Wikipedia has
a very good introduction: en.wikipedia.org/wiki/L-system This package
provides basic functions for exploring L-systems.
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
Creates kinematic and static force models of 3D levers and linkage mechanisms, with particular application to the fields of engineering and biomechanics.
This package implements the methodology of
Likelihood-based Imprecise Regression (LIR) for the case of
linear regression with interval data.
This package can be used to solve Linear Programming /
Linear Optimization problems by using the simplex algorithm.
Interactive visualization of effects, response functions
and marginal effects for different kinds of regression models. In this version
linear regression models, generalized linear models, generalized additive
models and linear mixed-effects models are supported.
Major features are the interactive approach and the handling of the effects of categorical covariates:
if two or more factors are used as covariates every combination of the levels of each
factor is treated separately. The automatic calculation of
marginal effects and a number of possibilities to customize the graphical output
are useful features as well.
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.
Checks adherence to a given style, syntax errors and possible
semantic issues.  Supports on the fly checking of R code edited with Emacs,
Vim and Sublime Text.
Fits lasso (total variation) penalised additive isotone models
Though SRFI-1 scopes both list-processing and higher-order
programming, we'll save some list-orthogonal functions for the
`functional' package; this is freely a mixture of
implementation and API.
This is an unofficial package aimed at automating the
import of LISREL output in R.  This package or its maintainer
is not in any way affiliated with the creators of LISREL and
SSI, Inc.
Allows researchers to conduct multivariate statistical analyses of survey data with list experiments. This survey methodology is also known as the item count technique or the unmatched count technique and is an alternative to the commonly used randomized response method. The package implements the methods developed by Imai (2011), Blair and Imai (2012), Blair, Imai, and Lyall (2013), Imai, Park, and Greene (2014), and Aronow, Coppock, Crawford, and Green (2015). This includes a Bayesian MCMC implementation of regression for the standard and multiple sensitive item list experiment designs and a random effects setup, a Bayesian MCMC hierarchical regression model with up to three hierarchical groups, the combined list experiment and endorsement experiment regression model, a joint model of the list experiment that enables the analysis of the list experiment as a predictor in outcome regression models, and a method for combining list experiments with direct questions. In addition, the package implements the statistical test that is designed to detect certain failures of list experiments, and a placebo test for the list experiment using data from direct questions.
List Environments are environments that can be indexed similarly to lists, e.g. 'x <- listenv(); x[[2]] <- "b"; names(x)[2] <- "B"; print(x$B)'.
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) for
closed populations, as described in the doctoral thesis of Zachary Kurtz.
The method is relevant when there are 3-5 capture occasions, with auxiliary
covariates available for all capture occasions.  As part of estimating the
number of missing population units, the method estimates the "rate of
missingness" as it varies over the covariate space.  In addition,
user-friendly functions are provided to recreate (approximately) the method
of Zwane and van der Heijden (2004), which applied the VGAM package
in a way that is closely related to LLLMs.
Analyses experimental data from liquid-liquid phase diagrams and provide a simple way to obtain its parameters and a simplified report. Designed initially to analyse Aqueous Two-Phases Systems, the package will include (every other update) new functions in order to comprise useful tools in liquid-liquid analysis.
Adds standardized regression coefficients to objects created by lm. Also extends the S3 methods print, summary and coef with additional boolean argument standardized.
Exact significance tests for a changepoint in linear or multiple linear regression.
Confidence regions with exact coverage probabilities for the changepoint.
Fit linear and generalized linear mixed-effects models.
The models and their components are represented using S4 classes and
methods.  The core computational algorithms are implemented using the
'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
This package includes a function to fit a linear
mixed-effects model in the formulation described in Laird and
Ware (1982) but allowing for censored normal responses. In this
version, the with-in group errors are assumed independent and
identically distributed.
The functions in this package implement the safety monitoring procedures proposed in the paper titled "Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Zhao, Y., Li, D.K.B., Petkau, A.J., Riddehough, A., Traboulsee, A., published in Journal of the American Statistical Association in 2013. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which can be modelled with a gamma or log-normal distributions. One can also choose the semi-parametric option which does not assume any distribution for the random effect. These mixed-effect models could be useful beyond the application of the safety monitoring. The maximum likelihood methods are used to estimate the unknown fixed effect parameters of the model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNBBayes, which contains the functions to compute the Personalized Activity Index in Bayesian framework.
The functions in this package implement the safety monitoring procedures proposed in the paper titled "A flexible mixed effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Kondo, Y., Zhao, Y. and Petkau, A.J. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which is modelled with the infinite mixture of Beta distributions, very flexible distribution that theoretically allows any form. The package also has the option of a single beta distribution for random effects. These mixed-effect models could be useful beyond the application of the safety monitoring. The inference is based on MCMC samples and this package contains a Gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNB, which contains the functions to compute the Personalized Activity Index in the frequentist framework.
Contains functions to estimate model parameters and filter, smooth and forecast random effects coefficients for mixed models with stationary and non-stationary stochastic processes under multivariate normal and t response distributions, diagnostic checks, bootstrap standard error calculation, etc.
The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data.
Different kinds of tests for linear mixed effects models as implemented
in 'lme4' package are provided. The tests comprise types I - III F tests
for fixed effects, LR tests for random effects.
The package also provides the calculation of population means for fixed factors
with confidence intervals  and corresponding plots. Finally the backward
elimination of non-significant effects is implemented.
Add smoothing spline modelling capability to nlme. Fit
smoothing spline terms in Gaussian linear and nonlinear
mixed-effects models
Fit certain versions of the Latent Markov model for longitudinal categorical data.
Test zero variance components in linear mixed models and
test additivity in nonparametric regression
using the restricted likelihood ratio test and the generalized F-test.
This R package provide methods for estimation and statistical
inference on directional and fluctuating selection in age-structured
populations.
Functions related to forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, and recovery of tree diameter distributions. Datasets on tree height-diameter relationship and light response of moss photosynthesis are included.
Some improved procedures for linear mixed models.
This package fits (gaussian) linear mixed-effects models
for high-dimensional data (n<<p) using a Lasso-type approach
for the fixed-effects parameter.
Fit right censored Multiple Ordinal Tobit (MOT) model.
Linear Mixed effect Model Splines (lmms) implements linear mixed effect model splines for
modelling and differential expression for highly dimensional data sets:
investNoise for quality control and filterNoise for removing
non-informative trajectories; lmmSpline to model time course expression
profiles and lmmsDE performs differential expression analysis to identify
differential expression between groups, time and/or group x time
interaction.
Computes model II simple linear regression using ordinary
least squares (OLS), major axis (MA), standard major axis (SMA), and
ranged major axis (RMA).
Functions related to L-moments: computation of L-moments
and trimmed L-moments of distributions and data samples; parameter
estimation; L-moment ratio diagram; plot vs. quantiles of an
extreme-value distribution.
Extensive functions for the theory of L-moments (LMs) in addition to probability-weighted moments (PWMs), and parameter (p) estimation for numerous familiar and not-so-familiar distributions. Estimation of LMs for the same distributions is provided as are L-moment ratio diagrams. Estimation of LMs for right-tail and left-tail censoring by known or unknown threshold and also by indicator variable is available. Asymmetric trimmed LMs (TL-moments, TLMs) are supported as are numerical integrations to dynamically compute trajectories of select TLM ratios for TL-moment ratio diagram construction.  LMs of residual (resid.) and reversed (rev.) resid. life are implemented along with 13 operators on quantile functions that are useful in reliability, lifetime, and survival analysis.  Exact analytical bootstrap estimates of order statistics, LMs, and variances-covariances of LMs are provided. The Harri-Coble Tau34-squared Test for Normality is available. Distribution support with "L" (LMs), "TL" (TLMs) and added (+) support for right-tail censoring (RC) encompasses: Asymmetric (Asy.) Exponential (Exp.) Power [L], Asy. Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen.) Exp. Poisson [L], Gen. Extreme Value [L], Gen. Lambda [L,TL], Gen. Logistic [L), Gen. Normal [L], Gen. Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L], Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L], 3-p log-Normal [L], Pearson Type III [L], Rayleigh [L], Rev. Gumbel [L+RC], Rice/Rician (L), Slash [TL], 3-p Student t [L], Truncated Exponential [L], Wakeby [L], and Weibull [L]. LMs have multivariate analogs; the sample L-comoments (LCMs) are implemented and might have considerable application with copulas because LCMs measure asymmetric association and higher comoments or comovements of variables.
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.
Predict modulators regulating the ability of effectors to regulate their targets and produce
modulator-effector-target triplets followed by goterm functional enrichment and survival analysis.This
is mainly applied to long non-coding RNAs (lncRNAs) as candidate modulators regulating the ability of
transcription factors (TFs) to regulate their corresponding targets.
This package, Lattice Options and Add-ins (or loa), contains various plots and
functions that make use of the lattice/trellis plotting framework. The plots (which include
loaPlot, GoogleMap and trianglePlot) use panelPal, a function that extends lattice and hexbin
package methods to automate plot subscripting and panel-to-panel and panel-to key
synchronization/management. See ?loa for further details.
Simplicial, Mahalanobis and Ellipsoid Local and Global Depth
Computational routines for estimating and visualizing local Gaussian parameters. Local Gaussian parameters are useful for characterizing and testing for non-linear dependence within bivariate data. See e.g. Tjostheim and Hufthammer, Local Gaussian correlation: A new measure of dependence, Journal of Econometrics, 2013, Volume 172 (1), pages 33-48.
The package converts R data onto input and data for LocalSolver,
executes optimization and exposes optimization results as R data.
LocalSolver (http://www.localsolver.com/) is an optimization engine
developed by Innovation24 (http://www.innovation24.fr/). It is designed to
solve large-scale mixed-variable non-convex optimization problems.  The
localsolver package is developed and maintained by WLOG Solutions
(http://www.wlogsolutions.com/en/) in collaboration with Decision Support
and Analysis Division at Warsaw School of Economics
(http://www.sgh.waw.pl/en/).
Computation of local false discovery rates.
The main idea of the Local FDR algorithm is to estimate both proportion of null observations and the ratio of null and alternative densities. In the case that there are many null observations, this can be done reliably, through maximum likelihood or generalized linear models. This package implements this in the case that the null and alternative densities are Poisson.
Local regression, likelihood and density estimation.
Provides test of second-order stationarity for time
series. 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
Genome-wide association (GWAS) analyses
of a biomarker that account for the limit of detection.
Local Ordinal embedding (LOE) is one of graph embedding methods for unweighted graphs.
logr4 provides an object-oriented logging system that uses an API
roughly equivalent to log4j and its related variants.
Methods for fitting log-link GLMs and GAMs to binomial data,
using EM-type algorithms with more stable convergence properties than standard methods.
Computes a log-concave (maximum likelihood) estimator for
i.i.d. data in any number of dimensions.
Based on right or interval censored data, compute the maximum likelihood estimator of a (sub)probability density under the assumption that it is log-concave. For further information see Duembgen, Rufibach, and Schuhmacher (2011, preprint).
Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.
Computes maximum likelihood estimate of a log-concave density  with fixed and known location of the mode. Performs inference about the mode via a likelihood ratio test. Extension of the logcondens package.
Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k.
Computes a cox PH model with a log concave baseline distribution. If no covariates are provided, estimates the log concave NPMLE. Built specifically for interval censored data, where data is a n by 2 matrix with [i, 1] as the left side of the interval for subject i and [i,2] as right side. Uncensored data can be entered by setting [i,1] = [i,2]. Alternatively, this can also handle uncensored data. If all the data is uncensored, you may enter data as a length(n) vector.
logging is a pure R package that implements the ubiquitous
log4j package.
Two classification ensemble methods based on logic regression models.  LogForest uses a bagging approach to construct an ensemble of logic regression models.  LBoost uses a combination of boosting and cross-validation to construct an ensemble of logic regression models.  Both methods are used for classification of binary responses based on binary predictors and for identification of important variables and variable interactions predictive of a binary outcome.
This package contains the functions necessary to run the LOGICOIL algorithm. LOGICOIL can be used to differentiate between antiparallel dimers, parallel dimers, trimers and higher-order coiled-coil sequence. By covering >90 percent of the known coiled-coil structures, LOGICOIL is a net improvement compared with other existing methods, which achieve a predictive coverage of around 31 percent of this population. As such, LOGICOIL is particularly useful for researchers looking to characterize novel coiled-coil sequences or studying coiled-coil containing protein assemblies. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences.
Routines for fitting Logic Regression models.
Firth's bias reduced logistic regression approach with
penalized profile likelihood based confidence intervals for
parameter estimates.
Diagnostic tests and plots for GLMs (generalized linear models)
with binomial/ binary outcomes, particularly logistic regression.
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
Clustering or classification of longitudinal data based on a mixture of multivariate t or Gaussian distributions with a Cholesky-decomposed covariance structure.
Contains general data structures and
functions for longitudinal data with multiple variables,
repeated measurements, and irregularly spaced time points.
Also implements a shrinkage estimator of dynamical correlation
and dynamical covariance.
Tools for longitudinal data and joint longitudinal data (used by packages kml and kml3d).
Datasets and Functionality from the textbook Jan Beran
(1994). Statistics for Long-Memory Processes; Chapman & Hall.
The longpower package contains functions for computing
power and sample size for linear models of longitudinal data
based on the formula due to Liu and Liang (1997) and Diggle et
al (2002). Either formula is expressed in terms of marginal
model or Generalized Estimating Equations (GEE) parameters.
This package contains functions which translate pilot mixed
effect model parameters (e.g. random intercept and/or slope)
into marginal model parameters so that the formulas of Diggle
et al or Liu and Liang formula can be applied to produce sample
size calculations for two sample longitudinal designs assuming
known variance.
Interface to the 'LongURL' API to identify known URL shortener services
and expand vectors of short URLs with optional error checking and
URL validation. See <http://longurl.org/> for more information about
'LongURL'.
We efficiently approximate leave-one-out cross-validation (LOO)
using Pareto smoothed importance sampling (PSIS), a new procedure for
regularizing importance weights. As a byproduct of our calculations, we also
obtain approximate standard errors for estimated predictive errors, and for
the comparison of predictive errors between two models. We also compute the
widely applicable information criterion (WAIC).
The program can perform loop analysis and plot network
structure (especially for food webs),including minimum spanning
tree, loop decomposition of weighted directed graphs, and other
network properties which may be related to food chain
properties in ecology.
Loop analysis makes qualitative predictions of variable change in a system of causally interdependent variables, where "qualitative" means sign only (i.e. increases, decreases, non change, and ambiguous). This implementation includes output support for graphs in .dot file format for use with visualization software such as graphviz (graphviz.org). 'LoopAnalyst' provides tools for the construction and output of community matrices, computation and output of community effect matrices, tables of correlations, adjoint, absolute feedback, weighted feedback and weighted prediction matrices, change in life expectancy matrices, and feedback, path and loop enumeration tools.
Remedies a common problem in piping: not having access to
intermediate outputs of the pipe. Within a "loop", a piping intermediate
is  stored in a stack archive, data is processed, and then both the
stored intermediate and the current output are reintegrated using an
"ending" function. Two special ending functions are provided: amend and
insert. However, any ending function can be specified, including merge
functions, join functions, setNames(), etc. This framework allows the
following work-flow: focus on a particular aspect or section of a
data set, conduct specific operations, and then reintegrate changes into
the whole.
Analysis of Differential Item Functioning (DIF) for
dichotomous and polytomous items using an iterative hybrid of
(ordinal) logistic regression and item response theory (IRT)
Estimate covariance matrices that contain low rank and sparse components
LOST includes functions for simulating missing morphometric
data randomly, with taxonomic bias and with anatomical bias. LOST also
includes functions for estimating linear and geometric morphometric data.
This package contains routines and documentation for
solving quadratic programming problems where the hessian is
represented as the product of two matrices.
Optimization of bipartite modularity using LP-BRIM (Label propagation
followed by Bipartite Recursively Induced Modularity).
Implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.
Fitting multivariate data patterns with local principal curves; including simple tools for data compression (projection), bandwidth selection, and measuring goodness-of-fit.
Estimates the intensity function, or its derivative of a given order, of a multiplicative counting process using the local polynomial method
Apply Univariate Long Memory Models,
Multivariate Short Memory Models To Hydrological Dataset.
Two local polynomial estimators for solving the errors-in-variables problem: one is in Delaigle, Fan, and Carroll (2009), and one is in Huang and Zhou (2014+). The SIMEX bandwidth selection method is also provided for both estimators.
LPmerge creates a consensus genetic map by merging linkage maps from different populations.  The software uses linear programming (LP) to efficiently minimize the mean absolute error between the consensus map and the linkage maps.  This minimization is performed subject to linear inequality constraints that ensure the ordering of the markers in the linkage maps is preserved.  When marker order is inconsistent between linkage maps, a minimum set of ordinal constraints is deleted to resolve the conflicts.
lpmodeler is a set of user-friendly functions to simplify the modelling of linear programs (LP) and mixed integer programs (MIP). It provides a unified interface compatible with optimization packages: Rsymphony.
Local Polynomial Regression with Ridging.
An implementation of the Linear Predictor Score approach, as initiated by Radmacher et al. (J Comput Biol 2001) and enhanced by Wright et al. (PNAS 2003) for gene expression signatures. Several tools for unsupervised clustering of gene expression data are also provided.
Lp_solve is freely available (under LGPL 2) software for
solving linear, integer and mixed integer programs. In this
implementation we supply a "wrapper" function in C and some R
functions that solve general linear/integer problems,
assignment problems, and transportation problems. This version
calls lp_solve version 5.5.
The lpSolveAPI package provides an R interface 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.
Implements a novel approach to model the dependency structure
in time series that generalizes the concept of autoregression to local
auto-patterns. Generates a pattern-based representation of time series
along with a similarity measure called Learned Pattern Similarity (LPS).
Introduces a generalized autoregressive kernel.This package is based on the
'randomForest' package by Andy Liaw.
Specially designed rank transform based Legendre Polynomial-like (LP) orthonormal transformations are implemented for non-linear signal processing.
This package provides some basic infrastructure and tools
to fit Generalized Linear Models (GLMs) via penalized
likelihood inference. Estimating procedures already implemented
are the LQA algorithm (that is where its name come from),
P-IRLS, RidgeBoost, GBlockBoost and ForwardBoost.
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.
Provides functions that allow for convenient working
with Latent Semantic Analysis. For actually building an LSA space, use the
package 'lsa' or other specialized software.
Functions for performing least-squares bilinear clustering of three-way data. The
method uses the bilinear decomposition (or biadditive model) to model two-way matrix slices
while clustering over the third way. Up to four different types of clusters are included, one
for each term of the bilinear decomposition. In this way, matrices are clustered simultaneously
on (a subset of) their overall means, row margins, column margins and row-column interactions.
The orthogonality of the bilinear model results in separability of the joint clustering problem
into four separate ones. Three of these subproblems are specific k-means problems, while a
special algorithm is implemented for the interactions. Plotting methods are provided, including
biplots for the low-rank approximations of the interactions.
Estimators and visualization for local statistical complexity of
(N+1)D fields. In particular for 0, 1 and 2 dimensional space this package
provides useful visualization.
Create lots of colorful plots in a plethora of variations (try the LSD demotour() )
Interfaces R with LSD. Reads object-oriented data in results files (.res) produced by LSD and creates appropriate multi-dimensional arrays in R. Also provides functions to extract basic information and statistics from those files. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente (documentation and downloads available at <http://labsimdev.org>).
Fit a least square dummy variable regression
Linear multiple output using sparse group lasso
Calculates the (localised) length of the shorth and
supplies corresponding diagnostic plots.
Conduct latent structure learning methods, particularly
structural equation modeling via penalized likelihood.
Obtain least-squares means for many linear, generalized linear,
and mixed models. Compute contrasts or linear functions of least-squares
means, and comparisons of slopes. Plots and compact letter displays.
The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution.
Implements the LS-PLS (least squares - partial least
squares) method described in for instance Jørgensen, K.,
Segtnan, V. H., Thyholt, K., Næs, T. (2004) A Comparison of
Methods for Analysing Regression Models with Both Spectral and
Designed Variables.  Journal of Chemometrics, 18(10), 451--464.
A collection of tools intended to make introductory statistics easier to teach, including wrappers for common hypothesis tests and basic data manipulation. It accompanies Navarro, D. J. (2015). Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners, Version 0.5. [Lecture notes] School of Psychology, University of Adelaide, Adelaide, Australia. ISBN: 978-1-326-18972-3. URL: http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/.
Due to lack of proper inference procedure and software,
the ordinary linear regression model is seldom used in practice
for the analysis of right censored data. This paper presents an
S-Plus/R program that implements a recently developed inference
procedure (Jin, Lin and Ying, 2006)\cite{Jin} for the
accelerated failure time model based on the least-squares
principle.
Functions for simulating realizations from the posterior distribution of
a latent trait of an item response model. Distributions are conditional on one or
a subset of response patterns (e.g., sum scores). Functions for computing likelihoods,
Fisher and observed information, posterior modes, and profile likelihood confidence
intervals are also included. These functions are designed to be easily amenable to
user-specified models.
Analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.
Targeted Maximum Likelihood Estimation (TMLE) of
treatment/censoring specific mean outcome or marginal structural model for
point-treatment and longitudinal data.
Calculation of rectifying LTPD and AOQL plans for sampling inspection by variables which minimize mean inspection cost per lot of process average quality.
A set of functions to execute the linear-transformation of
replicate (LTR) algorithm for preprocessing of microarray data
Methods of developing linear time series modelling.
Methods are given for loglikelihood computation, forecasting
and simulation.
This is a new tool to estimate Ridge and Liu estimators based on LTS method in multiple linear regression analysis.
Implements local spatial and local spatiotemporal Kriging based on local spatial and local spatiotemporal variograms, respectively.
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
Print vectors (and data frames) of floating point numbers
using a non-scientific format optimized for human readers.  Vectors
of numbers are rounded using significant digits, aligned at the
decimal point, and all zeros trailing the decimal point are dropped.
A collection of various R functions for the purpose of Luminescence dating data analysis. This includes, amongst others, data import, export, application of age models, curve deconvolution, sequence analysis and plotting of equivalent dose distributions.
Provides functions to calculate lunar and other environmental
covariates.
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
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.
Performs binary and multiclass boosting in maximum-margin, sparse, smooth and normal settings
as described in "A Boosting Framework on Grounds of Online Learning" by T. Naghibi and B. Pfister, (2014).
For further information regarding the algorithms, please refer to http://arxiv.org/abs/1409.7202
This is an integrated meta-analysis package for conducting
a correlational research synthesis. One of the unique features
of this package is in its integration of user-friendly
functions to facilitate statistical analyses at each stage in a
meta-analysis with correlations. It uses recommended procedures
as described in The Handbook of Research Synthesis and
Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
'Maclinical' implements class prediction using both
microarray data and clinical parameters. It addresses the
question of the additional predictive value of microarray data.
Class prediction is performed using a two-step method combining
(pre-validated) PLS dimension reduction and random forests.
A collection of functions for conducting a meta-analysis with mean differences data.  It uses recommended procedures as	described in The 	Handbook of Research Synthesis and Meta-Analysis	(Cooper, Hedges, & Valentine, 2009).
Provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter&Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.
Performs distance sampling analyses on a number of species
accounting for unidentified sightings, model uncertainty and covariate
uncertainty.
This function allows to generate two biological conditions
synthetic microarray dataset which has similar behavior to
those currently observed with common platforms. User provides a
subset of parameters.  Available default parameters settings
can be modified.
A bundle of functions for modifying MAESTRA/MAESPA input files,reading output files, and visualizing the stand in 3D. Handy for running sensitivity analyses, scenario analyses, etc.
a collection of efficient, vectorized algorithms for the
creation and investigation of magic squares and hypercubes, including
a variety of functions for the manipulation and analysis of
arbitrarily dimensioned arrays.  The package includes methods for
creating normal magic squares of any order greater than 2.  The
ultimate intention is for the package to be a computerized embodiment
all magic square knowledge, including direct numerical verification
of properties of magic squares (such as recent results on the
determinant of odd-ordered semimagic squares).  Some antimagic
functionality is included.  The package also
serves as a rebuttal to the often-heard comment "I thought R
was just for statistics".
Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.
Provides a mechanism for chaining commands with a
new forward-pipe operator, %>%. This operator will forward a
value, or the result of an expression, into the next function
call/expression. There is flexible support for the type
of right-hand side expressions. For more information, see
package vignette.
To quote Rene Magritte, "Ceci n'est pas un pipe."
Easy to use package for sending email notifications with status information from R
Interface to Apache Commons Email to send emails
from R.
Implements methodologies for modelling interval data, considering appropriate parameterizations of the variance-covariance matrix that take into account the intrinsic nature of interval data, and lead to five different possible configuration structures.
This package creates an empty framework of files and
directories for the "Load, Clean, Func, Do" structure described
by Josh Reich.
A complete analysis pipeline for 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.
This will allow easier management of a CRAN-style repository on
local networks (i.e. not on CRAN). This might be necessary where hosted
packages contain intellectual property owned by a corporation.
Provides access to coded election programmes from
the Manifesto Corpus and to the Manifesto Project's Main Dataset. The Manifesto
Project (https://manifesto-project.wzb.eu) collects and analyses election
programmes across time and space to measure the political preferences
of parties. The Manifesto Corpus contains the collected
and annotated election programmes in the Corpus format of the package 'tm' to enable
easy use of text processing and text mining functionality. Specific functions
for scaling of coded political texts are included.
Interactive plotting functions for use within RStudio.
The manipulate function accepts a plotting expression and a set of
controls (e.g. slider, picker, checkbox, or button) which are used
to dynamically change values within the expression. When a value is
changed using its corresponding control the expression is
automatically re-executed and the plot is redrawn.
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 (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure.
Supplement to maps package, providing the larger and/or
higher-resolution databases.
Create static choropleth maps of Denmark using the ggplot2 package.
Estimation methods for phase-type
distribution (PH) and Markovian arrival process (MAP) from
empirical data (point and grouped data) and density function.
Contains functions for mapping odds ratios or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting for confounding variables, using methods described by Kelsall and Diggle (1998) and Webster at al. (2006).  Includes convenient functions for mapping, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).
MAPLES is a general method for the estimation of age
profiles that uses standard micro-level demographic survey
data. The aim is to estimate smoothed age profiles and relative
risks for time-fixed and time-varying covariates.
A minimal, light-weight set of tools for producing nice looking maps in R.
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).
Automated calculation and visualization of survey data statistics on a color-coded map.
Set of tools for manipulating and reading geographic data, in particular ESRI shapefiles; C code used from shapelib. It includes binary access to GSHHG shoreline files. The package also provides interface wrappers for exchanging spatial objects with packages such as PBSmapping, spatstat, maps, RArcInfo, Stata tmap, WinBUGS, Mondrian, and others.
Posterior maximization for topic models (LDA) in text analysis,
as described in Taddy (2012) `on estimation and selection for topic models'.  Previous versions of this code were included as part of the textir package.  If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.
Functions with example data for graphing, pruning, and
mapping models from hierarchical clustering, and classification
and regression trees.
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.
Local recombination rates are graphically estimated across a genome using marey maps.
Likelihood inference based on higher order approximations
for linear nonnormal regression models
Provides R bindings to the 'Sundown' 'Markdown' rendering library
(https://github.com/vmg/sundown). 'Markdown' is a plain-text formatting
syntax that can be converted to 'XHTML' or other formats. See
http://en.wikipedia.org/wiki/Markdown for more information about 'Markdown'.
Functions for fitting various models to capture-recapture data
including fixed and mixed-effects Cormack-Jolly-Seber(CJS) for survival
estimation and POPAN structured Jolly-Seber models for abundance
estimation. Includes a CJS models that concurrently estimates and corrects
for tag loss. Hidden Markov model (HMM) implementations of CJS and
multistate models with and without state uncertainty.
maRketSim is a market simulator for R.  It was initially designed
around the bond market, with plans to expand to stocks.  maRketSim is
built around the idea of portfolios of fundamental objects.  Therefore
it is slow in its current incarnation, but allows you the flexibility of
seeing exactly what is in your final results, since the objects are retained.
Allows for fitting of maximum likelihood models using Markov chains on phylogenetic trees for analysis of discrete character data. Examples of such discrete character data include restriction sites, gene family presence/absence, intron presence/absence, and gene family size data. Hypothesis-driven user-specified substitution rate matrices can be estimated. Allows for biologically realistic models combining constrained substitution rate matrices, site rate variation, site partitioning, branch-specific rates, allowing for non-stationary prior root probabilities, correcting for sampling bias, etc.
Functions and S4 methods to create and manage discrete time Markov chains (DTMC) more easily. In addition functions to perform statistical (fitting and drawing random variates) and probabilistic (analysis of DTMC proprieties) analysis are provided.
a collection of tools for analyzing significance of
Markowitz portfolios.
Functions provided allow data simulation; construction of weighted relative likelihood functions; clustering and principal component analysis based on weighted relative likelihood functions.
Import xyz data from the NOAA, GEBCO and other sources, plot xyz data to prepare publication-ready figures, analyze xyz data to extract transects, get depth / altitude based on geographical coordinates, or calculate z-constrained least-cost paths.
This algorithm provides a numerical solution to the
problem of minimizing a function. This is more efficient than
the Gauss-Newton-like algorithm when starting from points very
far from the final minimum. A new convergence test is
implemented (RDM) in addition to the usual stopping criterion :
stopping rule is when the gradients are small enough in the
parameters metric (GH-1G).
The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models fit to multivariate time-series data.  Fitting is primarily via an Expectation-Maximization (EM) algorithm, although fitting via the BFGS algorithm (using the optim function) is also provided.  MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model.  Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, bootstrap model selection criteria (AICb), confidences intervals via the Hessian approximation and via bootstrapping and calculation of auxiliary residuals for detecting outliers and shocks.  The user guide shows examples of using MARSS for parameter estimation for a variety of applications, model selection, dynamic factor analysis, outlier and shock detection, and addition of covariates.  Type RShowDoc("UserGuide", package="MARSS") at the R command line to open the MARSS user guide.  Online workshops (lectures and computer labs) at http://faculty.washington.edu/eeholmes/workshops.shtml  See the NEWS file for update information.
An S4 implementation of the unbiased extension of the model-assisted synthetic-regression estimator proposed by Mandallaz (2013), 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.
Calculates Model-Averaged Tail Area Wald (MATA-Wald) confidence
intervals, which are constructed using single-model estimators and model
weights.
Provides functions for multivariate and propensity score matching
and for finding optimal balance based on a genetic search algorithm.
A variety of univariate and multivariate metrics to
determine if balance has been obtained are also provided.
Returns the subset of the data with the minimum imbalance for
every possible subset size (N - 1, N - 2, ...), down to the data set with the
minimum possible imbalance. Also includes tools for the estimation
of causal effects for each subset size, functions for visualization
and data export, and functions for calculating
model dependence as proposed by Athey and Imbens.
Implements structural estimators to correct for the
sample selection bias from observed outcomes in matching markets. Also
contains R code for matching algorithms such as the deferred-acceptance
algorithm for college admissions, the top-trading-cycles algorithm for
house allocation and a partitioning linear program for the roommates
problem.
Computes the Gale-Shapley Algorithm efficiently using Rcpp. Provides algorithms to compute the stable matching for the marriage problem and for the college-admissions problem, i.e. the matching of students to colleges.
MatchIt selects matched samples of the the original
treated and control groups with similar covariate distributions
-- can be used to match exactly on covariates, to match on
propensity scores, or perform a variety of other matching
procedures.
Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies.
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.
An analysis platform for metagenomics combining
specialized tools and workflows, easy handling of the BIOM
format, and transparent access to MG-RAST resources.  matR integrates
easily with other R packages and non-R software.
Classes and methods for dense and sparse matrices and
operations on them using 'LAPACK' and 'SuiteSparse'.
A collection of functions to support matrix calculations
for probability, econometric and numerical analysis. There are
additional functions that are comparable to APL functions which
are useful for actuarial models such as pension mathematics.
This package is used for teaching and research purposes at the
Department of Finance and Risk Engineering, New York
University, Polytechnic Institute, Brooklyn, NY 11201.
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.
Methods operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds().  There are also some vector-based methods, e.g. binMeans(), madDiff() and weightedMedians().  All methods have been 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.
Interactive shiny application for running a meta-analysis,
provides support for both random effects and fixed effects models with the 'metafor' package.
Additional support is included for calculating effect sizes plus
support for single case designs, graphical output, and detecting publication bias.
This package is a gene set analysis function for one-sided test (OLS), two-sided test (multivariate analysis of variance).
If the experimental conditions are equal to 2, the p-value for Hotelling's t^2 test is calculated.
If the experimental conditions are great than 2, the p-value for Wilks' Lambda is determined and post-hoc test is reported too.
Three multiple comparison procedures, Dunnett, Tukey, and sequential pairwise comparison, are implemented.
The program computes the p-values and FDR (false discovery rate) q-values for all gene sets.
The p-values for individual genes in a significant gene set are also listed.
MAVTgsa generates two visualization output: a p-value plot of gene sets (GSA plot) and a GST-plot of the empirical distribution function of the ranked test statistics of a given gene set.
A Random Forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes.
Perform exact MAX3 or MAX2 test for one-locus genetic
association analysis and trend test for dominant, recessive and
additive models. It can also calculate approximated p-value
with the normal approximation method.
maxent is an R package with tools for low-memory
multinomial logistic regression, also known as maximum entropy.
The focus of this maximum entropy classifier is to minimize
memory consumption on very large datasets, particularly sparse
document-term matrices represented by the tm package. The
classifier is based on an efficient C++ implementation written
by Dr. Yoshimasa Tsuruoka.
Provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program MAXENT, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species' distribution.
Generate a maximum projection (MaxPro) design, a MaxPro Latin hypercube design or improve an initial design based on the MaxPro criterion. Details of the MaxPro criterion can be found in: Joseph, V. R., Gul, E., and Ba, S. (2015) "Maximum Projection Designs for Computer Experiments", Biometrika.
Maximally selected rank statistics with
several p-value approximations.
A suite of conversion scripts to create internally standardized
spatial polygons dataframes. Utility scripts use these datasets to return
values such as country, state, timezone, watershed, etc. associated with a
set of longitude/latitude pairs. (They also make cool maps.)
Scattered data interpolation with Multilevel B-Splines
Distributions that are typically used for exposure rating in
general insurance, in particular to price reinsurance contracts.
The vignettes show code snippets to fit the distribution to
empirical data.
Cluster genes based on Poisson or Negative-Binomial model
for RNA-Seq or other digital gene expression (DGE) data
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 (2015).
Over 20 multiple-site diversity indices can be calculated.
Later versions will include phylogenetic diversity
This package provides linear models based on Theil-Sen
single median and Siegel repeated medians. They are very robust
(29 or 50 percent breakdown point, respectively), and if no
outliers are present, the estimators are very similar to OLS.
The MBmca package provides data sets and lightweight utilities for
nucleic acid melting curve analysis and presentation on microbead surfaces
but also for reactions in solution (e.g., qPCR).
Model Based Multifactor Dimension Reduction proposed by
Calle et al. (2008) as a dimension reduction method for
exploring gene-gene interactions.
Functional gradient descent algorithm
(boosting) for optimizing general risk functions utilizing
component-wise (penalised) least squares estimates or regression
trees as base-learners for fitting generalized linear, additive
and interaction models to potentially high-dimensional data.
Access to the MBTA API for R. Creates an easy-to-use bundle of
functions to work with all the built-in calls to the MBTA API. Allows users
to download realtime tracking data in dataframe format that is manipulable
in standard R analytics functions.
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.
Functions which can be useful to support the analyst in the Multicriteria Decision Aiding (MCDA) process involving multiple, conflicting criteria.
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.
Contains the Markov cluster algorithm (MCL) for identifying clusters in networks and graphs. The algorithm simulates random walks on a (n x n) matrix as the adjacency matrix of a graph. It alternates an expansion step and an inflation step until an equilibrium state is reached.
Maximum likelihood estimation using a Monte Carlo local likelihood (MCLL) method
This packages provides a function to estimate parameters for
the conditional logit model (also with multinomial counts), and for the
mixed conditional logit model, or conditional logit with random effects
(random intercepts only, no random slopes yet).
The current implementation of random effects is limited to
the PQL technique, which requires large cluster sizes.
Normal Mixture Modelling fitted via EM algorithm for Model-Based Clustering, Classification, and Density Estimation, including Bayesian regularization.
Simulates continuous distributions of random vectors using
Markov chain Monte Carlo (MCMC).  Users specify the distribution by an
R function that evaluates the log unnormalized density.  Algorithms
are random walk Metropolis algorithm (function metrop), simulated
tempering (function temper), and morphometric random walk Metropolis
(Johnson and Geyer, Annals of Statistics, 2012, function morph.metrop),
which achieves geometric ergodicity by change of variable.
Provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions.It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots.
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.
Functions for convenient plotting and viewing of MCMC output.
Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: http://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.
A Monte Carlo permutation method for multiple test
correlation.
This package implements a methodology for the design and analysis of
dose-response studies that combines aspects of multiple comparison procedures
and modeling approaches (Bretz, Pinheiro and Branson, 2005, Biometrics 61, 738-748).
The package provides tools for the analysis of dose finding trials as well as a variety
of tools necessary to plan a trial to be conducted with the MCPMod methodology.
***NOTE*** The MCPMod package will not be further developed, all future development of
the MCP-Mod methodology will be done in the DoseFinding R-package. ***NOTE***
Calculation of signed root deviance profiles for linear combinations of parameters in a generalized linear model. Multiple tests and simultaneous confidence intervals are provided.
This package provides regression methods to quantify the relation between two measurement methods. In particular it addresses regression problems with errors in both variables and without repeated measurements. The package provides implementations of Deming regression, weighted Deming regression, and Passing-Bablok regression following the CLSI EP09-A3 recommendations for analytical method comparison and bias estimation using patient samples.
Perform the model confidence set procedure of Hansen et al (2011).
mcsm contains a collection of functions that allows the
reenactment of the R programs used in the book EnteR Monte
Carlo Methods without further programming. Programs being
available as well, they can be modified by the user to conduct
one's own simulations.
Locally weighted regression, semiparametric and
conditionally parametric regression, fourier and cubic spline
functions, GMM and linearized spatial logit and probit,
k-density functions and counterfactuals, nonparametric quantile
regression and conditional density functions, Machado-Mata
decomposition for quantile regressions, spatial AR model,
repeat sales models, conditionally parametric logit and probit
Transition matrices (probabilities or counts) estimation for discrete Markov Chains of order n (1 <= n <= 5).
Mixture and flexible discriminant analysis, multivariate
adaptive regression splines (MARS), BRUTO, ...
Package implements projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics.
Provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.
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.
A set of functions to obtain modified score test for generalized linear models.
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, Kruskal's Analysis and Sammon Mapping with animated optimisation.
Identification of ring borders on scanned image sections from dendrochronological samples. Processesing of image reflectances to produce gray matrices and a time series of smoothed gray values. Luminance data is plotted on segmented images for users to perform both: visual identification of ring borders, or control of automatic detection. Routines to visually include/exclude ring borders on the R graphical device, or automatically detect ring borders using a linear detection algorithm. This algorithm detects ring borders according to positive/negative extreme values in the smoothed time-series of gray values.
This package performs maximum entropy density based
dependent data bootstrap. An algorithm is provided to create a
population of time series (ensemble) without assuming
stationarity. The reference paper (Vinod, H.D., 2004) explains
how the algorithm satisfies the ergodic theorem and the central
limit theorem.
Two distinct but related statistical approaches to the problem of identifying the combinations of medication error characteristics that are more likely to result in harm are implemented in this package: 1) a Bayesian hierarchical model with optimal Bayesian ranking on the log odds of harm, and 2) an empirical Bayes model that estimates the ratio of the observed count of harm to the count that would be expected if error characteristics and harm were independent. In addition, for the Bayesian hierarchical model, the package provides functions to assess the sensitivity of results to different specifications of the random effects distributions.
Run flexible mediation analyses using natural
effect models as described in Lange, Vansteelandt and Bekaert (2012),
Vansteelandt, Bekaert and Lange (2012) and Loeys, Moerkerke, De Smet,
Buysse, Steen and Vansteelandt (2013).
Provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) approach. The package supports a broad class of data models (including clinical trials with continuous, binary, survival-type and count-type endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.
We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010), Imai, Keele and Tingley (2010), Imai, Tingley and Yamamoto (2013), Imai and Yamamoto (2013) and Yamamoto (2013). In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.
Generates risk estimates and comorbidity flags from ICD-9-CM
codes available in administrative medical datasets. The package supports
the Charlson Comorbidity Index, the Elixhauser Comorbidity
classification, the Revised Cardiac Risk Index, and the Risk Stratification
Index.  Methods are table-based, fast, and use the plyr package, so
parallelization is possible for large jobs. Also includes a sample of
real ICD-9 data for 100 patients from a publicly available dataset.
This package contains the functions used to perform some
confidence statistics based in population median.
This is a C++ implementation of Sparse Topical Coding
(STC), a model of discrete data which is fully described in Zhu
et al. (2011) (http://www.cs.cmu.edu/~junzhu/stc/stc.pdf). It
can be used for multi-class classification and describing
documents with underlying sparse topics.
MEET (Motif Elements Estimation Toolkit) is a R-package
that integrates a set of computational algorithms for the
detection of Transcription Factor Binding Sites (TFBS).
A framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa' is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa' objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples.
An S4 update of the 'mefa' package
using sparse matrices for enhanced efficiency.
Sparse array-like objects are supported via
lists of sparse matrices.
Given a set of taxa and marker, the goal is build a complete and correct sequence alignment for phylogenetic analysis.
Exploratory model analysis.  Fit and graphical
explore ensembles of linear models.
Modelization of influenza epidemics in order to monitor future activity.
Memgene can detect relatively weak spatial genetic patterns by using Moran's Eigenvector Maps (MEM) to extract only the spatial component of genetic variation.  Memgene has applications in landscape genetics where the movement and dispersal of organisms are studied using neutral genetic variation.
One of the aims of this package is to make life easier for
useRs who deal with survey data sets. It provides an
infrastructure for the management of survey data including
value labels, definable missing values, recoding of variables,
production of code books, and import of (subsets of) SPSS and
Stata files. Further, it provides functionality to produce
tables and data frames of arbitrary descriptive statistics and
(almost) publication-ready tables of regression model
estimates. 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?
How much ram is your current R session using? How much ram do you even have?
Learn the scintillating answer to these and many more such questions with
the 'memuse' package.
Set of configurable menus built with GTK+ to graphically interface new functions.
A GUI for merging datasets in R using gWidgets.
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.
A collection of functions to aid in the statistical analysis of metabolomic data
Functions to analyze coherence, boundary clumping, and turnover following the pattern-based metacommunity analysis of Leibold and Mikkelson 2002. The package also includes functions to visualize ecological networks, and to calculate modularity as a replacement to boundary clumping.
Implement the DerSimonian-Laird (DSL) and Olkin-Pratt (OP)
meta-analytical approaches with correlation coefficients as
effect sizes.
MetaDE package implements 12 major meta-analysis methods
for differential expression analysis.
The metafolio R package is a tool to simulate salmon
metapopulations and apply financial portfolio optimization concepts. The
package accompanies the paper 'Portfolio conservation of metapopulations
under climate change'. See citation("metafolio").
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects (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.
Functionalities for facilitating systematic reviews, data extractions, and meta-analyses.  It includes a GUI (graphical user interface) to help screen the abstracts and titles of bibliographic data; tools to assign screening effort across multiple collaborators/reviewers and to assess inter-reviewer reliability; tools to help automate the download and retrieval of journal PDF articles from online databases; automated data extractions from scatter-plots; PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools to fill gaps in incomplete or missing study parameters; generation of random effects sizes for Hedges' d, log response ratio, odds ratio, and correlation coefficients for Monte Carlo experiments; covariance equations for modelling dependencies among multiple effect sizes (e.g., effect sizes with a common control); and finally summaries that replicate analyses and outputs from widely used but no longer updated meta-analysis software.  Funding for this package was supported by National Science Foundation (NSF) grants DBI-1262545 and DEB-1451031.
Provides methods for making inference in the random effects meta
regression model such as point estimates and confidence intervals for the
heterogeneity parameter and the regression coefficients vector.  Inference
methods are based on different approaches to statistical inference.
Methods from three different schools are included: methods based on the
method of moments approach, methods based on likelihood, and methods based
on generalised inference.  The package also includes tools to run extensive
simulation studies in parallel on high performance clusters in a modular
way.  This allows extensive testing of custom inferential methods with all
implemented state-of-the-art methods in a standardised way.  Tools for
evaluating the performance of both point and interval estimates are
provided.  Also, a large collection of different pre-defined plotting
functions is implemented in a ready-to-use fashion.
Tools to generate random landscape graphs, evaluate species
occurrence in dynamic landscapes, simulate future landscape occupation and
evaluate range expansion when new empty patches are available (e.g. as a
result of climate change).
First- and higher-order likelihood inference in
meta-analysis and meta-regression models.
Combines either p-values or modified effect sizes from different
studies to find differentially expressed genes
This package provides functions for diagnostic and
prognostic meta-analyses. It estimates univariate, bivariate
and multivariate models, and allows the aggregation of
previously published prediction models with new data.
Resolves complex metagenomic mixtures by analysing
deep sequencing data, using a mixture model based approach.
The use of parallel Monte Carlo Markov chains for the exploration
of the species space enables the identification of the set
of species more likely to contribute to the mixture.
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 and meta-regression using standard and robust methods with confidence intervals from the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 doi:10.1002/sim.2897 or Baker and Jackson, 2008 doi:10.1007/s10729-007-9041-8) or mixtures of normals (Beath, 2014 doi:10.1002/jrsm.1114).
MetaQC implements our proposed quantitative quality
control measures: (1) internal homogeneity of co-expression
structure among studies (internal quality control; IQC); (2)
external consistency of co-expression structure correlating
with pathway database (external quality control; EQC); (3)
accuracy of differentially expressed gene detection (accuracy
quality control; AQCg) or pathway identification (AQCp); (4)
consistency of differential expression ranking in genes
(consistency quality control; CQCg) or pathways (CQCp). (See
the reference for detailed explanation.) For each quality
control index, the p-values from statistical hypothesis testing
are minus log transformed and PCA biplots were applied to
assist visualization and decision. Results generate systematic
suggestions to exclude problematic studies in microarray
meta-analysis and potentially can be extended to GWAS or other
types of genomic meta-analysis. The identified problematic
studies can be scrutinized to identify technical and biological
causes (e.g. sample size, platform, tissue collection,
preprocessing etc) of their bad quality or irreproducibility
for final inclusion/exclusion decision.
Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.
A collection of functions for conducting meta-analysis using a
structural equation modeling (SEM) approach via the 'OpenMx' package.
It also implements the two-stage SEM approach to conduct meta-analytic
structural equation modeling on correlation and covariance matrices.
The following methods to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis are implemented:
- 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).
Spatio-temporal geostatistical mapping of meteorological data. Global spatio-temporal models calculated using publicly available data are stored in package.
Access to several Numerical Weather Prediction services both in raster format and as a time series for a location. Currenty it works with GFS, MeteoGalicia, NAM, and RAP.
meteogram is a collection of programs for plotting
meteograms for meteorological data such as atmospheric cross
section, temperatures plots.
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)
Metabolomics data are inevitably subject to a component of unwanted variation, due to factors such as batch effects, matrix effects, and confounding biological variation. This package contains a collection of R functions which can be used to remove unwanted variation and obtain normalized metabolomics data.
Metrics is a set of evaluation metrics that is commonly
used in supervised machine learning.
Provides an 'htmlwidgets' interface to the 'MetricsGraphics.js' ('D3'-based) charting
library which is geared towards displaying time-series data.
metRology provides classes and calculation and plotting functions
for metrology applications, including measurement uncertainty estimation
and inter-laboratory metrology comparison studies.
Implementation of various statistical models for multivariate
event history data. Including multivariate cumulative incidence models, and
bivariate random effects probit models (Liability models). Also contains
two-stage binomial modelling that can do pairwise odds-ratio dependence
modelling based marginal logistic regression models. This is an alternative
to the alternating logistic regression approach (ALR).
An easy to use Graphical User Interface for estimating sample sizes required for metabolomic experiments even when experimental pilot data is not available.
A diverse collection of metabolomics related statistical tools.
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.
Multivariate functional halfspace depth and median for two-dimensional functional data.
The package implements several time series filters useful
for smoothing and extracting trend and cyclical components of a
time series. The routines are commonly used in economics and
finance, however they should also be interest to other areas.
Currently, Christiano-Fitzgerald, Baxter-King,
Hodrick-Prescott, Butterworth, and trigonometric regression
filters are included in the package.
Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994.
Convenience functions for multivariate MCMC using univariate samplers, including slice sampler with stepout and shrinkage (Neal, 2003), and adaptive rejection sampler (Gilks and Wild, 1992).
Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output.
GAMs, GAMMs and other generalized ridge regression with
multiple smoothing parameter estimation by GCV, REML or UBRE/AIC.
Includes a gam() function, a wide variety of smoothers, JAGS
support and distributions beyond the exponential family.
Tools for univariate and multivariate generalized linear models with model averaging and null model technique.
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.
Runs a minimum-hypergeometric (mHG) test as described in: Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa.
Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences.  Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.
Multiple Hypothesis Testing For Variable Selection in high dimensional linear models. This package performs variable selection with multiple hypothesis testing, either for ordered variable selection or non-ordered variable selection. In both cases, a sequential procedure is performed. It starts to test the null hypothesis "no variable is relevant"; if this hypothesis is rejected, it then tests "only the first variable is relevant", and so on until the null hypothesis is accepted.
Estimation of models with zero left-censored variables.
Null values may be caused by a selection process, insufficient
resources or infrequency of purchase.
The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
Multiple imputation using Fully Conditional Specification (FCS)
implemented by the MICE algorithm. Each variable has its own imputation
model. Built-in imputation models are provided for continuous data
(predictive mean matching, normal), binary data (logistic regression),
unordered categorical data (polytomous logistic regression) and ordered
categorical data (proportional odds). MICE can also impute continuous
two-level data (normal model, pan, second-level variables). Passive
imputation can be used to maintain consistency between variables. Various
diagnostic plots are available to inspect the quality of the imputations.
Contains some auxiliary functions for multiple imputation which
complements existing functionality in R.
In addition to some utility functions, main features include
plausible value imputation, multilevel imputation functions,
imputation using partial least squares (PLS) for high dimensional
predictors, nested multiple imputation, and two-way imputation.
Tools for microeconomic analysis and microeconomic modelling
Demand analysis with the Almost Ideal Demand System (AIDS)
suggested by Deaton and Muellbauer (1980)
Tools for economic analysis and economic modelling
with a Constant Elasticity of Substitution (CES) function
Production analysis with the Symmetric Normalized Quadratic (SNQ) profit function
Provides infrastructure to accurately measure and compare
the execution time of R expressions.
The MicroDatosEs package provides utilities for reading and processing microdata from Spanish official statistics with R
This group of functions simplifies the creation of linked micromap
plots.
This package provides the user with the ability to quickly create Linked
Micromap plots of the 50 U.S. states and District of Columbia (51 areas).
Linked Micromap plots are visualizations of geo-referenced data that link statistical
graphics to an organized series of small maps.
The Help description contains examples of how to use the micromapST function.
This package contains the US and State boundary files and U. S. FIPS code to State conversion tables
used by the micromapST function and several data sets used in the micromapST examples.
Copyrighted 2013, 2014 and 2015 by Carr, Pearson and Pickle.
A collection of functions for computations and visualizations of microbial pan-genomes.
Deploys your R Analytic to MicroStrategy
This entry-level toolkit allows performing generic continuous-time microsimulation for a wide range of demograhic applications.
Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.
Functions for implementing species dispersal into projections
of species distribution models (e.g. under climate change scenarios).
Indirect methods for estimating bilateral migration flows in the presence of partial or missing data. Methods might be relevant to other categorical data situations on non-migration data, where for example, marginal totals are known and only auxiliary bilateral data is available.
This package provides various indices, like Crude Migration Rate,
different Gini indices or the Coefficient of Variation among others, to
show the (un)equality of migration.
This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.
Implements data augmentation schemes with multiple imputation steps for proportional hazards regression with interval censored data or proportional sub-distribution hazards regression for interval censored competing risks data. The main functions allow to estimate survival function, cumulative incidence function, Cox and Fine & Gray regression coefficients and associated variance-covariance matrix. 'MIICD' functions call 'Surv', 'survfit' and 'coxph' from the 'survival' package, 'crprep' from the 'mstate' package, and 'mvrnorm' from the 'MASS' package.
Functions for estimating structural equation models
using a model-implied instrumental variable (MIIV) search
and two stage least squares (2SLS) estimator (MIIV-2SLS).
The MILC package is designed to predict individual trajectories using the continuous time microsimulation model MILC, that describes the natural history of lung cancer.
Guesses the MIME type from a filename extension using the data
derived from /etc/mime.types in UNIX-type systems.
Tools to combine results for multiply-imputed data using
mixture approximations
66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv().
R wrapper for cmine implementation of Maximal
Information-based Nonparametric Exploration statistics (MIC and
MINE family)
This package implements the core idea of games known as
'Minesweeper' on Microsoft Windows or 'KMines' for KDE on
Unix-like operating systems.
Makes it possible to create an internally consistent
repository consisting of selected packages from CRAN-like repositories.
The user specifies a set of desired packages, and miniCRAN recursively
reads the dependency tree for these packages, then downloads only this
subset. The user can then install packages from this repository directly,
rather than from CRAN.  This is useful in production settings, e.g. server
behind a firewall, or remote locations with slow broadband access.
quick and simple tktcl miniGUI to call functions.
The minimax family of distributions is a two-parameter
family like the beta family, but computationally a lot more
tractible.
A binding to the minimist JavaScript library. This module implements
the guts of optimist's argument parser without all the fanciful decoration.
The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds.  The implementation can be used via nls-like calls using the nlsLM function.
Package minPtest is designed for estimating a gene region-level summary for SNP data from case-control studies using a permutation-based resampling method, called min P test, allowing execution on a compute cluster or multicore computer.
Derivative-free optimization by quadratic approximation
based on an interface to Fortran implementations by M. J. D.
Powell.
This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures.
Learns direct microbe-microbe interaction networks using a Poisson
multivariate-normal hierarchical model with an L1 penalized precision
matrix. Optimization is carried out using an iterative conditional modes
algorithm.
This package implements entropy optimization distribution
under specified constraints. It also offers an R interface to
the MinxEnt and MaxEnt distributions.
An implementation of the iterative proportional fitting (IPFP),
maximum likelihood, minimum chi-square and weighted least squares procedures
for updating a N-dimensional array with respect to given target marginal
distributions (which, in turn can be multi-dimensional). The package also
provides an application of the IPFP to simulate multivariate Bernoulli
distributions.
This package contains functions to carry out processing of
high throughput data analysis and detection of putative
hits/mutants.  Contents include a function for post-hoc quality
control for removal of outlier sample sets, a median-based
normalization method for use in datasets where there are no
explicit controls and where most of the responses are of the
wildtype/no response class (see accompanying paper).  The
package also includes a way to prioritize individuals of
interest using am empirical cumulative distribution function.
Methods for generating synthetic data as well as data from the
Chloroplast 2010 project are included.
This package collects algorithms/functions developed for
microRNA profiling data analyses.  Analytical platforms include
traditional hybridization microarray, CGH, beads-based
microarray, and qRT-PCR array.
The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
Analysis of dichotomous and polytomous response data using
unidimensional and multidimensional latent trait models under the Item
Response Theory paradigm. Exploratory and confirmatory models can be
estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory
bi-factor and two-tier analyses are available for modeling item testlets.
Multiple group analysis and mixed effects designs also are available for
detecting differential item 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.
Implements two methods: a nonparametric risk adjustment and a data imputation method that use general population mortality tables to allow a correct analysis of time to disease recurrence.
The function 'missForest' in this package is used to
impute missing values particularly in the case of mixed-type
data. It uses a random forest trained on the observed values of
a data matrix to predict the missing values. It can be used to
impute continuous and/or categorical data including complex
interactions and non-linear relations. It yields an out-of-bag
(OOB) imputation error estimate without the need of a test set
or elaborate cross-validation. It can be run in parallel to
save computation time.
Provides numeric and graphical
summaries for the missing values from both categorical
and quantitative variables. A variety of imputation
methods are applied, including the univariate imputations
like fixed or random values, multivariate imputations
like the nearest neighbors and multiple imputations,
and imputations conditioned on a categorical variable.
Imputation of incomplete continuous or categorical datasets; Missing values are imputed with a principal component analysis (PCA), a multiple correspondence analysis (MCA) model or a multiple factor analysis (MFA) model; Perform multiple imputation with and in PCA.
To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR).
For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)," Journal of Statistical Software,  56(6), 1-31. URL http://www.jstatsoft.org/v56/i06/.
Test for association between a set of SNPS/genes and
continuous or binary outcomes by including variant
characteristic information and using (weighted) score
statistics.
This R package is providing all the data sets and statistical analysis applications used in "Modern Industrial Statistics: with applications in R, MINITAB and JMP" by R.S. Kenett and S. Zacks with contributions by D. Amberti, John Wiley and Sons, 2013, which is a second revised and expanded revision of "Modern Industrial Statistics: Design and Control of Quality and Reliability", R. Kenett and S. Zacks, Duxbury/Wadsworth Publishing, 1998.
Various reliability analysis methods for: 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.
Provides tools for multiple imputation of missing data in multilevel
modeling. Includes a user-friendly interface to the 'pan' package, and
several functions for visualization, data management and the analysis
of multiply imputed data sets.
Tools to perform analyses and combine results from
multiple-imputation datasets.
Estimation/multiple imputation programs for mixed categorical
and continuous data.
Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.
Algorithms and methods for estimating parametric mixture models with missing data.
Mixed effects cumulative and baseline logit link models
for the analysis of ordinal or nominal responses, with
non-parametric distribution for the random effects
This package contains functions for fitting finite mixture
distribution models to grouped data and conditional data by the
method of maximum likelihood using a combination of a
Newton-type algorithm and the EM algorithm.
Fits mixed membership models with discrete
multivariate data (with or without repeated measures) following the general
framework of Erosheva 2004. This package uses a Variational EM approach by
approximating the posterior distribution of latent memberships and
selecting hyperparameters through a pseudo-MLE procedure. Currently
supported data types are Bernoulli, multinomial and rank (Plackett-Luce).
We provide detailed functions for univariate Mixed Tempered Stable distribution.
Routines for the analysis (unsupervised clustering) of
networks using MIXtures of Erdos-Renyi random graphs
Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.
Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the  generalized hyperbolic distribution.The first model 'MGHD' is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD', mixture of  multiple scaled generalized hyperbolic distributions. The 'cMSGHD' is a 'MSGHD' with convex contour plots. The 'MCGHD', mixture of  coalesced generalized hyperbolic distributions is a new more flexible model.
The main functions perform mixed models analysis by least squares or REML by adding the function r() to formulas of lm and glm. A collection of text-book statistics for higher education is also included, e.g. modifications of the functions lm, glm and associated summaries from the package stats.
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.
We provide statistical integrative techniques and
variants to analyse highly dimensional data sets: regularized
Canonical Correlation Analysis ('rCCA') and sparse Partial Least Squares variants ('sPLS')
to unravel relationships between two heterogeneous data sets of size (n times p) and (n times q) where the p and
q variables are measured on the same samples or individuals n. These data may come from high throughput
technologies, such as 'omics' data (e.g. transcriptomics, metabolomics or proteomics
data) that require an integrative or joint analysis. However, 'mixOmics' can also be applied to any other
large data sets where p + q >> n. 'rCCA' is a regularized version of Canonical Correlation Analysis to deal with
the large number of variables. 'sPLS' allows variable selection in a one step procedure and two frameworks
are proposed: regression and canonical analysis. Numerous graphical outputs are provided to help interpreting
the results. Recent methodological developments include: sparse PLS-Discriminant Analysis ('sPLS-DA'), Independent
Principal Component Analysis ('IPCA'), multilevel analysis using variance decomposition of the data and integration
of multiple data sets with regularized Generalised Canonical Correlation Analysis ('rGCCA') and variants (sparse 'GCCA'). More details  can be found
on our website.
Provides the function 'mixord' for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.
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.
The utility of this package is in simulating mixtures of Gaussian
distributions with different levels of overlap between mixture
components.  Pairwise overlap, defined as a sum of two
misclassification probabilities, measures the degree of
interaction between components and can be readily employed to
control the clustering complexity of datasets simulated from
mixtures. These datasets can then be used for systematic
performance investigation of clustering and finite mixture
modeling algorithms. Among other capabilities of 'MixSim', there
are computing the exact overlap for Gaussian mixtures,
simulating Gaussian and non-Gaussian data, simulating outliers
and noise variables, calculating various measures of agreement
between two partitionings, and constructing parallel
distribution plots for the graphical display of finite mixture
models.
Functions to fit finite mixture of scale mixture of
skew-normal (FM-SMSN) distributions.
Differential expression analysis of RNA-Seq data when replicates under two conditions are available is performed. First, mixtures of Negative Binomial distributions are fitted on the data in order to estimate the dispersions, then the Wald test is computed.
A collection of R functions for analyzing finite mixture models.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.
Toxicity prediction of chemical mixtures using reference models such as
'concentration addition' and 'independent action' (Backhaus, T., Faust, M., Environmental
Science and Technology. 2012, 46, 2564-2573. doi:10.1021/es2034125).
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.
If a C compiler (on windows: 'Rtools') is installed, differential
equation models are solved using compiled C functions.
Please note that no warranty is implied for correctness of results or fitness
for a particular purpose.
Package for fast computation of the maximum kernel
likelihood estimator (mkle)
Contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals, and generation of similarity matrices.
mkssd is a package that generates efficient balanced
non-aliased multi-level k-circulant supersaturated designs by
interchanging the elements of the generator vector. The package
tries to generate a supersaturated design that has chisquare
efficiency more than user specified efficiency level (mef). The
package also displays the progress of generation of an
efficient multi-level k-circulant design through a progress
bar. The progress of 100% means that one full round of
interchange is completed. More than one full round (typically
4-5 rounds) of interchange may be required for larger designs.
A collection of artificial and real-world machine learning
benchmark problems, including, e.g., several data sets from the
UCI repository.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multi-dimensionality 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.
Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them.  This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact.
Functions necessary to perform the machine learning-based
differential network analysis of transcriptome data.
Exploratory data analysis and manipulation functions for
multi-label data sets along with an interactive Shiny application to ease their
use.
Difference scaling is a method for scaling perceived
supra-threshold differences.  The package contains functions that
allow the user to design and run a difference scaling experiment,
to fit the resulting data by maximum likelihood and test the
internal validity of the estimated scale.
This package provides a unified interface to various
machine learning algorithms. Confusion matrices are provided
too.
This package contains functions to compute the
nonparametric maximum likelihood estimator (MLE) for the
bivariate distribution of (X,Y), when realizations of (X,Y)
cannot be observed directly.  To be more precise, we consider
the situation where we observe a set of rectangles that are
known to contain the unobservable realizations of (X,Y). We
compute the MLE based on such a set of rectangles.  The methods
can also be used for univariate censored data (see data set
'cosmesis'), and for censored data with competing risks (see
data set 'menopause').  We also provide functions to visualize
the observed data and the MLE.
Maximum likelihood Gaussian process modeling for
univariate and multi-dimensional outputs with diagnostic plots.
Contact the maintainer for a package version that implements
sensitivity analysis functionality.
Provides functions for unit root testing using MLE method
Processing and analysis of high throughput (Roche 454)
sequences generated from multiple loci and multiple biological
samples. Sequences are assigned to their locus and sample of
origin, aligned and trimmed. Where possible, genotypes are
called and variants mapped to known alleles.
An R code implementation of the maximum likelihood (fixed
point) algorithm of Hyvaerinen, Karhuna, and Oja for
independent component analysis.
A collection of evaluation metrics, including loss, score and utility functions,
that measure regression and classification performance.
Computational strategies for multivariate linear
mixed-effects models with missing values, Schafer and Yucel
(2002), Journal of Computational and Graphical Statistics, 11,
421-442.
Data and examples from a multilevel modelling software review
as well as other well-known data sets from the multilevel modelling
literature.
Estimation of the multinomial logit model
Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL.
Phase haplotypes from genotypes based on a list of known
haplotypes. Suited to highly diverse loci such as HLA.
Interface to a large number of classification and regression
techniques, including machine-readable parameter descriptions. There is
also an experimental extension for survival analysis, clustering and
general, example-specific cost-sensitive learning. Generic resampling,
including cross-validation, bootstrapping and subsampling. Hyperparameter
tuning with modern optimization techniques, for single- and multi-objective
problems. Filter and wrapper methods for feature selection. Extension of
basic learners with additional operations common in machine learning, also
allowing for easy nested resampling. Most operations can be parallelized.
This package provides Multilinear Regression Model Population
Analysis to build a pool of models between quantitative activity and chemical
descriptors. Also some useful model validation functions. Contains all
molecular descriptors of 101 organic compounds and acticity dataset.
Compute estimates of the multivariate vector autoregression model as
used to (but not limited to) analyze experience sampling method data in
clinical psychology. The model can be extended through treatment effects,
covariates and pre- and post-assessment effects.
Simulation and visualization of complex
models for longitudinal data. The models are encoded using the model coding
language 'Mlxtran', automatically converted into C++ codes, compiled on the
fly and linked to R using the 'Rcpp' package. That allows one to implement
very easily complex ODE-based models and complex statistical models,
including mixed effects models, for continuous, count, categorical, and
time-to-event data.
Description: Various utilities for the Multiplicative
Multinomial distribution
Description: A single-sample classifier that generates Medulloblastoma (MB) subtype predictions for single-samples of human MB patients and model systems, including cell lines and mouse-models. The MM2S algorithm uses a systems-based methodology that facilitates application of the algorithm on samples irrespective of their platform or source of origin. MM2S demonstrates > 96% accuracy for patients of well-characterized normal cerebellum, Wingless (WNT), or Sonic hedgehog (SHH) subtypes, and the less-characterized Group4 (86%) and Group3 (78.2%). MM2S also enables classification of MB cell lines and mouse models into their human counterparts.This package contains function for implementing the classifier onto human data and mouse data, as well as graphical rendering of the results as PCA plots and heatmaps.
Gene Expression datasets for the 'MM2S' package. Contains normalized expression data for Human Medulloblastoma ('GSE37418') as well as Mouse Medulloblastoma models ('GSE36594').
Used for general multiple mediation analysis.
The analysis method is described in Yu et al. 2014, "General Multiple Mediation Analysis With an Application to Explore Racial Disparity in Breast Cancer Survival", published on Journal of Biometrics & Biostatistics, 5(2):189.
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.
fits multivariate marginal models for multivariate longitudinal data for both continuous and discrete responses
Fits multivariate marginal models with shared regression parameters for discrete and continuous responses
The package implements a multi-marker molecular signature (MMMS) approach
for treatment-specific subgroup identification.
Provides functions for measuring
population divergence from genotypic data.
Compute similarities and distances between marked point processes.
Fixed effects Selection in Linear Mixed Models
Fits a family of mixtures of multivariate t-distributions under a continuous t-distributed latent variable structure for the purpose of clustering or classification. The alternating expectation-conditional maximization algorithm is used for parameter estimation.
Time and memory efficient estimation of multinomial logit models using maximum likelihood method. Numerical optimization performed by Newton-Raphson method using an optimized, parallel C++ library to achieve fast computation of Hessian matrices. Motivated by large scale multiclass classification problems in econometrics and machine learning.
The package provides multivariate tests, estimates and methods based on the identity score, spatial sign score and spatial rank score. The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components.
Computes integral of f(x)*x_i^k on a product of intervals,
where f is the density of a gaussian law.
This a is small alteration of the mnormt code from A. Genz and A. Azzalini.
Functions are provided for computing the density and the
distribution function of multivariate normal and "t" random variables,
and for generating random vectors sampled from these distributions.
Probabilities are computed via 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.
Some canned plots and functions designed for the mobilize project.
Designed to be called remotely.
This package provides methods to analyze cluster
alternatives based on multi-objective optimization of cluster
validation indices.
Performs Modal Clustering (MAC) including Hierarchical Modal Clustering (HMAC) along with their parallel implementation (PHMAC) over several processors.  These model-based non-parametric clustering techniques can extract  clusters in very high dimensions with arbitrary density shapes. By default clustering is performed over several resolutions and the results are summarised as a hierarchical tree. Associated plot functions are also provided. There is a package vignette that provides many examples. This version adheres to CRAN policy of not spanning more than two child processes by default.
This package provides estimators of the mode of univariate
unimodal data or univariate unimodal distributions
Given independent and identically distributed observations X(1), ..., X(n) from a density f,
provides five methods to perform a multiscale analysis about f as well as the necessary critical
values. The first method, introduced in Duembgen and Walther (2008), provides simultaneous confidence statements
for the existence and location of local increases (or decreases) of f, based on all intervals I(all) spanned by
any two observations X(j), X(k). The second method approximates the latter approach by using only a subset of
I(all) and is therefore computationally much more efficient, but asymptotically equivalent. Omitting the additive
correction term Gamma in either method offers another two approaches which are more powerful on small scales and
less powerful on large scales, however, not asymptotically minimax optimal anymore. Finally, the block procedure is a
compromise between adding Gamma or not, having intermediate power properties. The latter is again asymptotically
equivalent to the first and was introduced in Rufibach and Walther (2010).
Local linear estimation of psychometric functions.
Provides functions for nonparametric estimation of a
psychometric function and for estimation of a derived threshold
and slope, and their standard deviations and confidence
intervals
Bootstrap cross-validation for ROC, AUC and Brier score to assess
and compare predictions of binary status responses.
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 utility library to facilitate the generalization of statistical methods built on a regression framework. Package developers can use 'modelObj' methods to initiate a regression analysis without concern for the details of the regression model and the method to be used to obtain parameter estimates. The specifics of the regression step are left to the user to define when calling the function. The user of a function developed within the 'modelObj' framework creates as input a 'modelObj' that contains the model and the R methods to be used to obtain parameter estimates and to obtain predictions.  In this way, a user can easily go from linear to non-linear models within the same package.
A collection of tools to deal with statistical models.
The functionality is experimental and the user interface is likely to
change in the future. The documentation is rather terse, but packages `coin'
and `party' have some working examples. However, if you find the
implemented ideas interesting we would be very interested in a discussion
of this proposal. Contributions are more than welcome!
Package for processing downloaded MODIS Cloud Product HDF
files and derived files. Specifically, MOD35_L2 cloud product
files, and the associated MOD03 geolocation files (for
MODIS-TERRA); and MYD35_L2 cloud product files, and the
associated MYD03 geolocation files (for MODIS-AQUA).  The
package will be most effective if the user installs MRTSwath
(MODIS Reprojection Tool for swath products;
https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath),
and adds the directory with the MRTSwath executable to the
default R PATH by editing ~/.Rprofile.
Provides several functions for downloading, storing and processing
subsets of MODIS Land Processes data as a batch process.
The algorithms implemented here are used to detect the community structure of a network.
These algorithms follow different approaches, but are all based on the concept of modularity maximization.
Contains basic tools for performing
multiple-output quantile regression and computing
regression quantile contours by means of directional
regression quantiles. In the location case, one can thus
obtain halfspace depth contours in two to six dimensions.
Fits a Constrained Segmented Distributed Lag regression model
to epidemiological time series of mortality, temperature, and other confounders.
Methods for color labeling, calculation of eigengenes, merging of closely related modules.
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.
Model selection and parameter estimation based on non-local priors. Routines are provided to compute Bayes factors, marginal densities and to perform variable selection in regression setups. Routines to evaluate prior densities, distribution functions, quantiles and modes are included.
A collection of moment-matching methods for computing the cumulative distribution function of a positively-weighted sum of chi-squared random variables. Methods include the Satterthwaite-Welch method, Hall-Buckley-Eagleson method, Wood's F method, and the Lindsay-Pilla-Basak method.
Functions to calculate: moments, Pearson's kurtosis,
Geary's kurtosis and skewness; tests related to them
(Anscombe-Glynn, D'Agostino, Bonett-Seier).
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.
'MetaOMineR' suite is a set of R packages that offers many functions and modules needed for the analyses
of quantitative metagenomics data. 'momr' is the core package and contains routines for biomarker identification and exploration.
Developed since the beginning of field, 'momr' has evolved and is structured around the different modules
such as preprocessing, analysis, vizualisation, etc. See package help for more information.
Keep track of dates in terms of months.
Model dates as at close of business.
Perform date arithmetic in units of "months" and "years" (multiples of months).
Allow "infinite" dates to model "ultimate" time spans.
Allows to pull data from MonetDB into R. Includes a DBI implementation and a dplyr backend.
High-level 'MongoDB' client based on 'jsonlite' and 'mongoc' driver.
Includes support for aggregation, indexing, map-reduce, streaming, 'TLS'
encryption and 'SASL' authentication.
Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.
Train and make predictions from a multi-layer perceptron neural
network with partial monotonicity constraints.
Geometric morphometric and evolutionary biology analyses of anchor shape from four-anchored monogeneans.
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.
Several analysis-related functions for the book entitled "R
statistics and graph for medical articles" (written in Korean), version 1,
by Keon-Woong Moon with Korean demographic data with several plot
functions.
A collection of basic astronomical routines for R based on
"Practical astronomy with your calculator" by Peter
Duffet-Smith.
A multi-objective optimization solver based on particle
swarm optimization with crowding distance.
MOrder provide functions to check time homogeneity and order
of markov chain by using chi-squared test, AIC value and BIC value.
This package is a port of the MATLAB colourmap functions
accompanying the paper M. Geissbuehler and T. Lasser, "How to display data
by color schemes compatible with red-green color perception deficiencies,"
Opt. Express 21, 9862-9874 (2013) to R.
A toolset for Geometric Morphometrics and mesh processing. This
includes (among other stuff) mesh deformations based on reference points,
permutation tests, detection of outliers, processing of sliding
semi-landmarks and semi-automated surface landmark placement.
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.
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.
Lattice functions for drawing folded empirical cumulative
distribution plots, or mountain plots. A mountain plot is similar
to an empirical CDF plot, except that the curve increases from
0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at
the right side.  See: Monti (1995), Folded empirical distribution
function curves-mountain plots.  The American Statistician, 49, 342-345.
Extract from two-dimensional x-y coordinates of an arm-reaching trajectory, several dependent measures such as area under the curve, latency to start the movement, x-flips, etc.; which characterize the action-dynamics of the response. Mainly developed to analyze data coming from mouse-tracking experiments.
Contains functions to access movement data stored in movebank.org as well as tools to visualize and statistically analyze animal movement data, among others functions to calculate dynamic Brownian Bridge Movement Models. Move helps addressing movement ecology questions.
Fit and simulate mixtures of von Mises-Fisher distributions.
Implementation of multidimensional projection techniques
Preprocess and analysis of genomic data. MPAgenomics
provides wrappers from commonly used packages to streamline their repeated
manipulation, offering an easy-to-use pipeline. The segmentation of
successive multiple profiles is performed with an automatic choice of
parameters involved in the wrapped packages. Considering multiple profiles
in the same time, MPAgenomics wraps efficient penalized regression methods
to select relevant markers associated with a given outcome.
Algorithms for fitting model-based penalized coefficient paths. Currently the models include penalized Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models. The penalties include least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP), and each possibly combining with L_2 penalty.
Multivariate process capability analysis using the multivariate process capability vector. Allows to analyze a multivariate process with both normally and non-normally distributed and also with dependent and independent quality characteristics.
Data sets and scripts for Modeling Psychophysical Data in R (Springer).
A fast approach to compute approximate persistent homology using a multiscale approach
(1) Our system provides a network-based strategies for metabolite pathway identification.(2) The MPINet can support the identification of pathways using Hypergeometric test based on metabolite set. (3)MPINet can support pathways from multiple databases.
Model stability and variable importance plots [Mueller and Welsh (2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller (2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al. (2008, doi:10.1214/07-AOS517>); Jiang et al. (2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.
The MplusAutomation package leverages the flexibility of the R
language to automate latent variable model estimation and interpretation
using Mplus, a powerful latent variable modeling program developed by
Muthen and Muthen (www.statmodel.com). Specifically, MplusAutomation
provides routines for creating related groups of models, running batches of
models, and extracting and tabulating model parameters and fit statistics.
Exploratory graphical analysis of multivariate data,
specifically gene expression data with different projection
methods: principal component analysis, correspondence analysis,
spectral map analysis.
Tools for constructing linkage maps, reconstructing
haplotypes, estimating linkage disequilibrium and QTL mapping
in multi-parent RIL designs (e.g. MAGIC)
Functions to compute and plot multivariate (partial)
Mantel correlograms.
Uses a kernel smoothing approach to calculate Mutual Information
for comparisons between all types of variables including continuous vs
continuous, continuous vs discrete and discrete vs discrete. Uses a
nonparametric bias correction giving Bias Corrected Mutual Information
(BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to
large genomic datasets.
Data structures, operations, and functions for the symbolic
manipulation of multivariate polynomials.
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).
Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g.,  Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.
Most of this package consists of data sets from the
textbook Introduction
to Linear Regression Analysis (3rd ed), by Montgomery et al.
Some additional data sets and functions useful in an
undergraduate regression course are included.
mQTL provides a complete QTL analysis pipeline for metabolomic data.
Distinctive features include normalisation using PQN approach, peak alignment
using RSPA approach, dimensionality reduction using SRV approach and finally
QTL mapping using R/qtl package.
Analysis of mark-recapture data using covariates. Models: CJS open population; Huggin's closed population. Link functions: logit, sine, hazard.  Model selection and model averaging routines. Plot methods. Simulation routine. CJS methods produce estimates of population size using the Horvitz-Thompson estimator.
With this package, it is possible to perform
(simultaneous) inferences for ratios of linear combinations of
coefficients in the general linear model. In particular, tests
and confidence interval estimations for ratios of treatment
means in the normal one-way layout and confidence interval
estimations like in (multiple) slope ratio and parallel line
assays can be carried out. Moreover, it is possible to
calculate the sample sizes required in comparisons with a
control based on relative margins. For the simple two-sample
problem, functions for a t-test for ratio-formatted hypotheses
and the corresponding Fieller-type confidence interval are
provided assuming homogeneous or heterogeneous group variances.
Multivariate regression with covariance estimation
The MRCV package provides functions for analyzing the association between
one single response categorical variable (SRCV) and one multiple response
categorical variable (MRCV), or between two or three MRCVs.  A modified Pearson
chi-square statistic can be used to test for marginal independence for the one or
two MRCV case, or a more general loglinear modeling approach can be used to examine
various other structures of association for the two or three MRCV case.  Bootstrap-
and asymptotic-based standardized residuals and model-predicted odds ratios are
available, in addition to other descriptive information.
Animal abundance estimation via conventional, multiple covariate
and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function
fitting is performed via maximum likelihood. Also included are diagnostics
and plotting for fitted detection functions. Abundance estimation is via a
Horvitz-Thompson-like estimator.
Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics
Used on survival data to jointly estimate the hazard rate and the effects of covariates on failure times. Can accommodate covariates under the proportional and non-proportional hazards setting, and is ideal for analysis of survival data with long-term follow-up.
Provides three Modified Rand Indices and three Modified Adjusted Rand Indices for comparing two partitions, which are usually obtained on two different sets of units, where one is a subset of another set of units. Splitting and merging of clusters have a different affects on the value of the indices.
Provide a compact storage for medical imaging data with access and display possibilities. Basic tools for processing brain imaging data are proposed like extraction of brain voxels, morphological image segmentation, and filtering / normalization of contrast parameters. Specific tools are also provided for blood perfusion imaging to calculate hypoperfusion and reperfusion volumes.
Various methods for MRI tissue classification.
Conditional maximum likelihood estimation via the EM
algorithm and information-criterion-based model selection in
binary mixed Rasch models.
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.
Fits regularized multinomial response models using penalized loglikelihood methods with structured penalty terms.
This package contains functions, data and examples to compute a multiplicative-regression model for relative survival.
The Bayesian procedure starts with one warplet in the model and uses the posterior distributions as priors for a more extended model with one more warplet. The model is built with adding one warplet at a time and allows for amplitude variations.
Statistical Analyses of Methylation-sensitive Amplification Polymorphism (MSAP) assays.
The output of an affinity-purification mass spectrometry
experiment is typically a list of proteins that were observed
in the experiment, identified by UniProt identifiers
(http://www.uniprot.org/).  This package takes as input a list of
UniProt identifiers, and the associated Mascot scores from the
experiment (which indicate the likelihood that the protein has
been correctly identified), clusters them by gene ontology
category (http://geneontology.org/), then draws diagrams
showing the results in hierarchical clusters by category, with lines
for individual proteins representing the associated Mascot score.
The results are in general not publication-ready, but will rather
require editing via a graphics editor that can interpret SVG (scalable
vector graphics) format.  As an alternative representation, the
package will also generate tag clouds based on the Mascot scores.
Performs Bayesian nonparametric multiscale density estimation and multiscale testing of group differences with multiscale Bernstein polynomials (msBP) mixtures as in Canale and Dunson (2014).
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
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.
Continuous-time Markov and hidden Markov multi-state
models for longitudinal data.  Designed for processes observed at
arbitrary times in continuous time (panel data) but some other
observation schemes are supported. Both Markov transition rates and
the hidden Markov output process can be modelled in terms of
covariates, which may be constant or piecewise-constant in time.
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 programmatic 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.
A simple tool allowing users to easily and dynamically explore or document a data set using a tree structure.
Simultaneous tests and confidence intervals
for general linear hypotheses in parametric models, including
linear, generalized linear, linear mixed effects, and survival models.
The package includes demos reproducing analyzes presented
in the book "Multiple Comparisons Using R" (Bretz, Hothorn,
Westfall, 2010, CRC Press).
Convert a logical vector or a vector of p-values or a
correlation, difference, or distance matrix into a display
identifying the pairs for which the differences were not
significantly different.  Designed for use in conjunction with
the output of functions like TukeyHSD, dist{stats}, simint,
simtest, csimint, csimtest{multcomp}, friedmanmc,
kruskalmc{pgirmess}.
Equivalence tests and related confidence intervals for the
comparison of two treatments, simultaneously for one or many
normally distributed, primary response variables (endpoints).
The step-up procedure of Quan et al. (2001) is both applied for
differences and extended to ratios of means. A related
single-step procedure is also available.
GEE solver for correlated nominal or ordinal multinomial responses using a local odds ratios parameterization.
Efficient finite difference method for valuing European and American multi-asset options.
Algorithms for performing joint parameter estimation in
astronomical survey data acquired in multiple bands.
A GUI with which users can construct and interact
with Multibiplot Analysis and provides inferential results by using Bootstrap Methods.
Calculate the polygenic and major gene models for
quantitative trait linkage analysis using variance components
approach.  The 0.2.2 release includes bug fixes that allow
multic to run properly on 64-bit systems.  The 0.3.0 release
includes a fully implemented sw2mloci function.  As of 0.3.8,
Splus version is no longer supported.
This package provides a tool for analysis of multiple CNV.
Includes functions designed to examine relationships among multivariate constructs (e.g., personality). This includes functions for profile (within-person) analysis, dealing with large numbers of analyses, lens model analyses, and structural summary methods for data with circumplex structure. The package also includes functions for graphically comparing and displaying group means.
A set of tools to permute multisets without loops or hash tables and to generate integer partitions. The permutation functions are based on C code from Aaron Williams. Cool-lex order is similar to colexicographical order. The algorithm is described in Williams, A. Loopless Generation of Multiset Permutations by Prefix Shifts. SODA 2009, Symposium on Discrete Algorithms, New York, United States. The permutation code is distributed without restrictions. The code for stable and efficient computation of multinomial coefficients comes from Dave Barber. The code can be download from http://home.comcast.net/~tamivox/dave/multinomial/index.html and is distributed without conditions. The package also generates the integer partitions of a positive, non-zero integer n. The C++ code for this is based on Python code from Jerome Kelleher which can be found here http://jeromekelleher.net/partitions.php. The C++ code and Python code are distributed without conditions.
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.
Several functions are presented in order to study data in a group structure,
where the same set of variables are measured on different groups of
individuals.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).
The functions in this package are designed to be used in
the analysis of multilevel data by applied psychologists.  The
package includes functions for estimating common within-group
agreement and reliability indices. The package also contains
basic data manipulation functions that facilitate the analysis
of multilevel and longitudinal data.
Functions to estimate and visualize propensity
score analysis for multilevel, or clustered, data.
Capture-mark-recapture analysis with multiple non-invasive marks. The models implemented in 'multimark' combine encounter history data arising from two different non-invasive ``marks'', such as images of left-sided and right-sided pelage patterns of bilaterally asymmetrical species, to estimate abundance and related demographic parameters while accounting for imperfect detection. Bayesian models are specified using simple formulae and fitted using Markov chain Monte Carlo. Addressing deficiencies in currently available software, 'multimark' also provides a user-friendly interface for performing Bayesian multimodel inference using capture-recapture data consisting of a single conventional mark or multiple non-invasive marks.
Allows running a meta-analysis of multivariate Genome Wide
Association Studies (GWAS) and easily visualizing results through custom
plotting functions. The multivariate setting implies that results for each
single nucleotide polymorphism (SNP) include several effect sizes (also
known as "beta coefficients", one for each trait), as well as related
variance values, but also covariance between the betas. The main goal of
the package is to provide combined beta coefficients across different
cohorts, together with the combined variance/covariance matrix. The method
is inverse-variance based, thus each beta is weighted by the inverse of its
variance-covariance matrix, before taking the average across all betas. The
default options of the main function \code{multi_meta} will work with files
obtained from GEMMA multivariate option for GWAS (Zhou & Stephens, 2014).
It will work with any other output, as soon as columns are formatted to
have the according names. The package also provides several plotting
functions for QQ-plots, Manhattan Plots and custom summary plots.
This is a likelihood approach for the regression analysis of overdispersed correlated count data with cluster varying covariates. The approach fits a multivariate negative binomial model by maximum likelihood and provides robust estimates of the regression coefficients.
An implementation of a method for building simultaneous
confidence intervals for the probabilities of a multinomial
distribution given a set of observations, proposed by Sison and
Glaz in their paper Sison, C.P and J. Glaz. Simultaneous
confidence intervals and sample size determination for
multinomial proportions. Journal of the American Statistical
Association, 90:366-369 (1995).  The method is an R translation
of the SAS code implemented by May and Johnson in their paper:
May, W.L. and W.D. Johnson. Constructing two-sided simultaneous
confidence intervals for multinomial proportions for small
counts in a large number of cells. Journal of Statistical
Software 5(6) (2000). Paper and code available at
http://www.jstatsoft.org/v05/i06
MNL and overdispersed multinomial regression using robust
(LQD and tanh) estimation
A method for multivariate ordinal data generation given marginal distributions and correlation matrix based on the methodology proposed by Demirtas (2006).
Performs genetic association tests between SNPs
(one-at-a-time) and multiple phenotypes (separately or in joint
model).
Performs variable importance analysis using a causal inference approach. This is done by fitting Population Intervention Models. The default is to use a Targeted Maximum Likelihood Estimator (TMLE). The other available estimators are Inverse Probability of Censoring Weighted (IPCW), Double-Robust IPCW (DR-IPCW), and Graphical Computation (G-COMP) estimators. Inference can be obtained from the influence curve (plug-in) or by bootstrapping.
Fits Cox proportional hazard models with a weighted partial
likelihood. It handles one and multiple endpoints, additional matching
and makes it possible to reuse controls for other endpoints.
Algebraic procedures for the analysis of multiple social networks
are delivered with this package. Among other things, it is possible to
create and manipulate multivariate network data with different formats,
and there are effective ways available to treat multiple networks with
routines that combine algebraic systems like the partially ordered
semigroup or the semiring structure together with the relational bundles
occurring in different types of multivariate network data sets. As well an
algebraic approach for two-mode networks is made through Galois
derivations between families of the pair of subsets.
Various utilities to manipulate multivariate polynomials
Functions to calculate Unique Trait Combinations (UTC) and
scaled Unique Trait Combinations (sUTC) as measures of multivariate
richness. The package can also calculate beta-diversity for trait richness and
can partition this into nestedness-related and turnover components. The
code will also calculate several measures of overlap.
Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.
Functions to perform sensitivity analysis on a model with multivariate output.
The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.
MultiSV is an R package for identification of structural
variations in multiple populations based on whole genome resequencing. It
fits linear mixed model and identifies structural variations in multiple
populations using whole genome sequencing data. It could also be
manipulated to use on RNA-seq data for differential gene expression
(implementation in future releases). Main steps for analysis include
generating read depth in bins using ComputeBinCounts. conversion of bins to
MultiSV format using Bin2MultiSV. Finally, identification of structural
variations using CallMultiSV.
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.
Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates.
A multivariate generalization of the emulator package
Fits multi-way component models via alternating least squares algorithms with optional constraints (orthogonality and non-negativity). Fit models include Individual Differences Scaling, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
Exports two functions implementing
multi-way clustering using the method suggested by Cameron, Gelbach, &
Miller (2011) and cluster (or block)
bootstrapping for estimating variance-covariance matrices. Normal one and
two-way clustering matches the results of other common statistical
packages.  Missing values are handled transparently and rudimentary
parallelization support is provided.
A collection of multivariate nonparametric methods, selected in part to support an MS level course in nonparametric statistical methods.   Methods include adjustments for multiple comparisons, implementation of multivariate Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence region, some permutation tests for linear models, and 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.
This package is a collection of function for converting and modifying mono dimensional nuclear magnetic resonance spectra.
Performs Wilcox rank sum test, Kruskal rank sum test,
Friedman rank sum test and McNemar test.
The Mutoss package and accompanying mutossGUI package are
designed to ease the application and comparison of multiple
hypothesis testing procedures.
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.
Pacote para Analise Multivariada, que possui funcoes que executam Analise de Correspondencia Simples (CA) e Multipla (MCA), Analise de Componentes Principais (PCA), Analise de Multiplos Fatores (MFA) para dados Quantitativos, Qualitativos, Frequencia (MFACT) e dados Mistos. Tambem possuim outras funcoes uteis para a Analise Multivariada.
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.
Define, manipulate and plot meshes on simplices, spheres, balls, and rectangles for use in
multivariate statistics.  Directional and other multivariate histograms are provided.
Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees.
Performs multivariate normality tests and graphical approaches and implements multivariate outlier detection and univariate normality of marginal distributions through plots and tests.
This package computes the Nelson-Aalen estimator of the cumulative transition hazard for multistate models.
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.
Given a vector of multivariate normal data, a matrix of
covariates and the data covariance matrix, generate new multivariate normal
samples that have the same covariance matrix based on permutations of
the transformed data residuals.
various methods for multivariate outlier detection.
Computes orthant probabilities multivariate normal distribution.
This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data.
It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm).
Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom.
Key features include:
(i) Normalization and/or variance stabilization of the data,
(ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
(iii) Generation of diverse diagnostic plots,
(iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.
Mean, variance, and random variates for left/right truncated normal distributions.
Generalization of the Shapiro-Francia test for
multivariate variables.
This package implements the generalization of the Shapiro-Wilk test
for multivariate normality proposed by Villasenor-Alva and
Gonzalez-Estrada (2009).
Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history.
Fits a multivariate model of decision trees for multiple,
continuous outcome variables. A model for each outcome variable is fit separately, selecting
predictors that explain that explain covariance in multiple outcomes.
Package is built on top of 'gbm'.
This package contains functions to run fixed effects or
random effects multivariate meta-analysis.
Computes multivariate normal and t probabilities, quantiles,
random deviates and densities.
A function for plotting multivariate time series data
Matched Wake Analysis (mwa) grants insights into causal relationships in spatiotemporal event data.
Computes the Wavelet deconvolution estimate of a common signal
present in multiple channels that have possible different levels of blur
and long memory additive error.
mxkssd is a package that generates efficient balanced
mixed-level k-circulant supersaturated designs by interchanging
the elements of the generator vector. The package tries to
generate a supersaturated design that has EfNOD efficiency more
than user specified efficiency level (mef). The package also
displays the progress of generation of an efficient mixed-level
k-circulant design through a progress bar. The progress of 100
per cent means that one full round of interchange is completed.
More than one full round (typically 4-5 rounds) of interchange
may be required for larger designs.
Feature selection methods for identifying minimal, statistically-equivalent and equally-predictive feature subsets. 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
A statistical framework allowing users interested in the evolution of biological processes to capture evolutionary signals in transcriptomes.
Linear and logistic regression models for quantitative genetic analysis of data from monozygotic twins.
An R wrapper for 'libnabo', an exact or approximate k nearest
neighbour library which is optimised for low dimensional spaces (e.g. 3D).
'libnabo' has speed and space advantages over the 'ANN' library wrapped by
package 'RANN'. 'nabor' includes a knn function that is designed as a
drop-in replacement for 'RANN' function nn2. In addition, objects which
include the k-d tree search structure can be returned to speed up repeated
queries of the same set of target points.
Contains methods described by Dennis R. Helsel in
his book "Nondetects And Data Analysis: Statistics
for Censored Environmental Data"
Constructs (non)additive genetic relationship matrices, and their inverses, from a pedigree to be used in linear mixed effect models (A.K.A. the 'animal model'). Also includes other functions to facilitate the use of animal models. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (http://www.vsni.co.uk/software/asreml).
Designed for association studies in nested association mapping (NAM) panels, also handling biparental and random panels. It includes functions for genome-wide associations mapping of multiple populations, marker quality control, solving mixed models and finding variance components through REML and Gibbs sampling.
This package provides user-level functions to manage
namespaces not (yet) available in base R: 'registerNamespace',
'unregisterNamespace', 'makeNamespace', and
'getRegisteredNamespace'

('makeNamespaces' is extracted from the R 'base' package source code:
src/library/base/R/namespace.R)
This software package implements functions to simulate spherical, ellipsoid and cubic polyatomic nanoparticles with arbitrary crystal structures and to calculate the associated pair-distribution function and X-ray/neutron total-scattering signals. It also provides a target function that can be used for simultaneous fitting of small- and wide-angle total scattering data in real and reciprocal spaces. The target function can be generated either as a sum of weighted residuals for individual datasets or as a vector of residuals suitable for optimization using multi-criteria algorithms (e.g. Pareto methods).
A set of tools for normalizing, diagnostics and visualization of NanoString nCounter data.
Enables the processing and normalisation of the standard mRNA data output from the Nanostring nCounter software.
This package contains tidied data from the ASA 2006 data expo,
as well as a number of useful other related data sets.
NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D
biological image data, especially traced neurons. Reads and writes 3D images
in NRRD and 'Amira' AmiraMesh formats and reads surfaces in 'Amira' hxsurf
format. Traced neurons can be imported from and written to SWC and 'Amira'
LineSet and SkeletonGraph formats. These data can then be visualised in 3D
via 'rgl', manipulated including applying calculated registrations, e.g.
using the 'CMTK' registration suite, and analysed. There is also a simple
representation for neurons that have been subjected to 3D skeletonisation
but not formally traced; this allows morphological comparison between
neurons including searches and clustering (via the 'nat.nblast' extension
package).
Extends package nat (NeuroAnatomy Toolbox) by providing a
collection of NBLAST-related functions.
Extends package nat (NeuroAnatomy Toolbox) by providing objects
and functions for handling template brains.
The package provides functions related to human natural ordering.
It handles adjacent digits in a character sequence as a number
so that natural sort function arranges a character vector by their numbers, not digit characters.
It is typically seen when operating systems lists file names.
For example, a sequence a-1.png, a-2.png, a-10.png looks naturally ordered
because 1 < 2 < 10 and natural sort algorithm arranges so whereas general sort algorithms arrange
it into a-1.png, a-10.png, a-2.png owing to their third and fourth characters.
Utility functions that may be of general interest but are
specifically required by the 'NeuroAnatomy Toolbox' ('nat'). Includes
functions to provide a basic make style system to update files based on
timestamp information, file locking and 'touch' utility. Convenience
functions for working with file paths include 'abs2rel', 'split_path'
and 'common_path'. Finally there are utility functions for working with
'zip' and 'gzip' files including integrity tests.
Estimate the effective population size of a closed population using genetic data collected from two or more data points.
It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.
Calls the 'Jupyter'/'IPython' script 'nbconvert' to create vignettes from notebooks.
Those notebooks ('.ipynb' files) are files containing rich text, code, and its output.
Code cells can be edited and evaluated interactively.
See <http://ipython.org/notebook.html> for more information.
The Dirichlet (aka NBD-Dirichlet) model describes the
purchase incidence and brand choice of consumer products.  We
estimate the model and summarize various theoretical quantities
of interest to marketing researchers. Also provides functions
for making tables that compare observed and theoretical
statistics.
Perform non-bipartite matching and matched randomization. A
"bipartite" matching utilizes two separate groups, e.g. smokers being
matched to nonsmokers or cases being matched to controls. A "non-bipartite"
matching creates mates from one big group, e.g. 100 hospitals being
randomized for a two-arm cluster randomized trial or 5000 children who
have been exposed to various levels of secondhand smoke and are being
paired to form a greater exposure vs. lesser exposure comparison. At the
core of a non-bipartite matching is a N x N distance matrix for N potential
mates. The distance between two units expresses a measure of similarity or
quality as mates (the lower the better). The 'gendistance()' and
'distancematrix()' functions assist in creating this. The 'nonbimatch()'
function creates the matching that minimizes the total sum of distances
between mates; hence, it is referred to as an "optimal" matching. The
'assign.grp()' function aids in performing a matched randomization. Note
bipartite matching can be performed using the prevent option in
'gendistance()'.
Negative Binomial (NB) models for two-group comparisons and
regression inferences from RNA-Sequencing Data.
Performs a Necessary Condition Analysis (NCA). (Dul, J. 2015. Necessary Condition Analysis (NCA). ''Logic and Methodology of 'Necessary but not Sufficient' causality." Organizational Research Methods)
NCA identifies necessary (but not sufficient) conditions in datasets. Instead of drawing a regression line ''through the middle of the data'' in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations.
(Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is ''horizontal'' and the y-axis is ''vertical'' and that values increase ''upwards'' and ''to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g. an effort or input) is necessary but not sufficient for a (desired) level of y (e.g. good performance or output).
Performs nonlinear calibration and curve fitting for data from Luminex, RT-PCR, ELISA, RPPA etc. Its precursor is Ruminex.
A flexible tool is presented here that can perform
(i) traditional non-compartmental analysis (NCA) and
(ii) simulation-based posterior predictive checks for a population
pharmacokinetic (PK) model using NCA metrics.
making NCBI taxonomic data locally available and
searchable as an R object
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 'NetCDF' files with the 'RNetCDF' package.
Most functions are wrappers of basic functions from the 'RNetCDF' package to easily run combinations of these
functions for frequently encountered tasks.
Computes the noncentral Dunnett's test distribution (pdf,
cdf and quantile) and generates random numbers
R functions for analyzing spatial (cross-)covariance: the
nonparametric (cross-)covariance, the spline correlogram, the
nonparametric phase coherence function, and related.
Computes the noncentral gamma function: pdf, cdf, quantile
function and inverse for the noncentrality parameter.
A set of handy functions. Includes a versatile one line progress bar, one
line function timer with detailed output, time delay function, text histogram, object
preview, CRAN package search, simpler package installer, Linux command install check,
a flexible Mode function, top function, simulation of correlated data, and more.
Efficient algorithms for fitting regularization paths for linear or logistic regression models penalized by MCP or SCAD, with optional additional L2 penalty.
Naive discriminative learning implements learning and classification models based on the Rescorla-Wagner equations and their equilibrium equations.
Renders dynamic network data from "networkDynamic" objects as 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.
Effective population sizes (often abbreviated as "Neff") are essential in biodiversity monitoring and conservation. For the first time, calculating effective sizes with data obtained within less than a generation but considering demographic parameters is possible. This individual based model uses demographic parameters of a population to calculate annual effective sizes and effective population sizes (per generation). A defined number of alleles and loci will be used to simulate the genotypes of the individuals. Stepwise mutation rates can be included. Variations in life history parameters (sex ratio, sex-specific survival, recruitment rate, reproductive skew) are possible. These results will help managers to define existing populations as viable or not.
Estimating the number of essential genes in a genome on
the basis of data from a random transposon mutagenesis
experiment, through the use of a Gibbs sampler.
Provides several direct search optimization algorithms based on the
simplex method. The provided algorithms are direct search algorithms, i.e.
algorithms which do not use the derivative of the cost function. They are
based on the update of a simplex. The following algorithms are available: the
fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained
optimization with a fixed shape simplex), the variable shape simplex method of
Nelder and Mead (unconstrained optimization with a variable shape simplex
made), and Box's complex method (constrained optimization with a variable
shape simplex).
Access paleoecological datasets from the Neotoma Paleoecological
Database using the published API (http://api.neotomadb.org/).  The functions
in this package access various pre-built API functions and attempt to return
the results from Neotoma in a usable format for researchers and the public.
Set of functions to estimate the Glomerular Filtration Rate (GFR) using different biomarkers and other phenotypes of interest for nephrology.
Estimate hazard ratios, survival curves and attributable
risks for cohorts with missing covariates, using Cox models or
Kaplan-Meier estimated for strata.  This handles studies nested
within cohorts, such as case-cohort studies with stratified
sampling.  See
http://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf
Calculate a Mann-Whitney-Wilcoxon test for a difference between treatment levels using nested ranks.  This test can be used when observations are structured into several groups and each group has received both treatment levels. The p-value is determined via bootstrapping.  The nested ranks test is intended to be one possible mixed-model extension of the Mann-Whitney-Wilcoxon test, for which treatment is a fixed effect and group membership is a random effect.
Infers species associations from local and regional-scale co-occurrence data (species x site matrices).
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.
Methods for the generation of a wide range of network geographies, e.g., grid networks or clustered networks. Useful for the generation of benchmarking instances for the investigation of, e.g., Vehicle-Routing-Problems or Travelling Salesperson Problems.
The netgsa-package contains functions for carrying out Network-based Gene Set Analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data.
Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs.
Network meta-analysis following methods by Rücker (2012) and Krahn et al. (2013).
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.
Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey
graphs from 'R'.
Simple interface routines to facilitate the handling of network objects with complex intertemporal data. "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.
Visualization and analysis tools to aid in the interpretation of
neural network models.  Functions are available for plotting,
quantifying variable importance, conducting a sensitivity analysis, and
obtaining a simple list of model weights.
Annotated neuroblastoma copy number profiles,
a benchmark data set for change-point detection algorithms.
A collection of data structures that represent
volumetric brain imaging data. The focus is on basic data handling for 3D
and 4D neuroimaging data. In addition, there are function to read and write
NIFTI files and limited support for reading AFNI files.
The package allows users to generate fMRI time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments.
Computes the probability density function, cumulative distribution function, quantile function, random numbers and measures of inference for the following general families of  distributions (each family defined in terms of an arbitrary cdf G): Marshall Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, and exponentiated exponential Poisson G distributions.
Indices, heuristics and strategies to help determine the number of factors/components to retain:
1. Acceleration factor (af with or without Parallel Analysis);
2. Optimal Coordinates (noc with or without Parallel Analysis);
3. Parallel analysis (components, factors and bootstrap);
4. lambda > mean(lambda) (Kaiser, CFA and related);
5. Cattell-Nelson-Gorsuch (CNG);
6. Zoski and Jurs multiple regression (b, t and p);
7. Zoski and Jurs standard error of the regression coeffcient (sescree);
8. Nelson R2;
9. Bartlett khi-2;
10. Anderson khi-2;
11. Lawley khi-2 and
12. Bentler-Yuan khi-2.
Numerical Formal Concept Analysis (nFCA) is a modern unsupervised learning tool for analyzing general numerical data. Given input data, this R package nFCA outputs two nFCA graphs: a H-graph and an I-graph that reveal systematic, hierarchical clustering and inherent structure of the data.
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
A simple n-gram (contiguous sequences of n items from a given sequence of text) tokenizer to be used with the 'tm' package with no 'rJava'/'RWeka' dependency.
ngspatial provides tools for analyzing spatial data, especially
non-Gaussian areal data. The current version supports the sparse spatial
generalized linear mixed model of Hughes and Haran (2013) and the centered
autologistic model of Caragea and Kaiser (2009).
Body Shape and related measurements from the US National Health
and Nutrition Examination Survey (NHANES, 1999-2004).  See
http://www.cdc.gov/nchs/nhanes.htm for details.
NHEMOtree performs cost-sensitive classification by
solving the two-objective optimization problem of minimizing
misclassification rate and minimizing total costs for
classification. The three methods comprised in NHEMOtree are
based on EMOAs with either tree representation or bitstring
representation with an enclosed classification tree algorithm.
Compiling the NHL Real Time Scoring System Database for easy use in R
Bayesian NHMM modeling for multiple time series. The emission distribution can be mixtures of Gammas, Poissons, Normals and zero inflation is possible.
Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations.  Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only.
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.
Japan-specific data is sometimes too unhandy for R users to manage. The utility functions and data in this package disencumber us from such an unnecessary burden.
Datasets for testing nonlinear regression routines.
Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for
SI (International System of Units) and non-SI, plus unit conversions
Based on the data from NIST (National Institute of Standards and Technology)
Solve a system of nonlinear equations using a Broyden or a Newton method
with a choice of global strategies such as line search and trust region.
There are options for using a numerical or user supplied Jacobian,
for specifying a banded numerical Jacobian and for allowing
a singular or ill-conditioned Jacobian.
Fit and compare Gaussian linear and nonlinear mixed-effects models.
This package combines the odesolve and nlme packages for
mixed-effects modelling using differential equations.
nlmeU: Datasets and utility  functions enhancing functionality of nlme package. Datasets, functions and scripts are described in book titled 'Linear Mixed-Effects Models:
A Step-by-Step Approach' by Galecki and Burzykowski (2013). Package is under development.
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 solution
may be one of a set of possibilities, and may also be statistically unsatisfactory.
Because of the intended aggressive policy to find solutions, the approach may
use additional and unnecessary function and Jacobian evaluations.
Note that the Jacobian function is developed using analytic expressions rather
than numerical approximations if this is possible.
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.
Adds brute force and multiple starting values to nls.
Estimation of structural equation models with nonlinear effects
and underlying nonnormal distributions.
Data sets and nonlinear regression models dedicated to predictive microbiology
Fit univariate non-linear scale mixture of
skew-normal(NL-SMSN) regression.
Several tools for assessing the quality of fit of a
gaussian nonlinear model are provided.
Utilities and kinship information for behavior genetics and
developmental research using the National Longitudinal Survey of Youth
(NLSY; http://www.bls.gov/nls/)
Uses a modified lifting algorithm on which it builds the
nondecimated lifting transform. It has applications in wavelet
shrinkage.
R functions for (non)linear time series analysis. A core
topic is order estimation through cross-validation.
Provides functions to calculate the normalised Lineage-Through-Time (nLTT) statistic, given two phylogenetic trees. The nLTT statistic measures the difference between two Lineage-Through-Time curves, where each curve is normalised both in time and in number of lineages.
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.
Provides a framework to perform Non-negative Matrix
Factorization (NMF). The package implements a set of already published algorithms
and seeding methods, and provides a framework to test, develop and plug
new/custom algorithms. Most of the built-in algorithms have been optimized
in C++, and the main interface function provides an easy way of performing
parallel computations on multicore machines.
Functions,  examples and  data  from the  book
'Numerical  Methods  and  Optimization  in Finance'  by  M.
Gilli, D.  Maringer and E.  Schumann.  The package provides
implementations of several optimisation heuristics, such as
Differential  Evolution, Genetic  Algorithms  and Threshold
Accepting.  There  are also functions for  the valuation of
financial  instruments,  such  as  bonds and  options,  and
functions that help with stochastic simulations.
Software for feed-forward neural networks with a single
hidden layer, and for multinomial log-linear models.
Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.
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.
Helps a clinical trial team  discuss
the clinical goals  of a well-defined biomarker with a diagnostic,
staging, prognostic, or predictive purpose.  From this discussion will
come a statistical plan for a (non-randomized) validation trial.
Both prospective and retrospective trials are supported. In a specific
focused discussion, investigators should determine the range of
"discomfort" for the NNT, number needed to treat.  The meaning of
the discomfort range, [NNTlower, NNTupper], is that within this range
most physicians would feel discomfort either in treating or withholding
treatment. A pair of NNT values bracketing that range, NNTpos and NNTneg,
become the targets of the study's design.  If the trial can demonstrate
that a positive biomarker test yields an NNT less than NNTlower,
and that a negative biomarker test yields an NNT less than NNTlower,
then the biomarker may be useful for patients. A highlight of the package
is visualization of a "contra-Bayes" theorem, which produces criteria for
retrospective case-controls studies.
Node harvest is a simple interpretable tree-like estimator for high-dimensional regression and classification. A few nodes are selected from an initially large ensemble of nodes, each associated with a positive weight. New observations can fall into one or several nodes and predictions are the weighted average response across all these groups. The package offers visualization of the estimator. Predictions can return the nodes a new observation fell into, along with the mean response of training observations in each node, offering a simple explanation of the prediction.
An implementation of the nodiv algorithm, (see Borregaard, M.K., Rahbek, C., Fjeldsaa, J., Parra, J.L., Whittaker, R.J. & Graham, C.H. 2014. Node-based analysis of species distributions. Methods in Ecology and Evolution 5(11): 1225-1235. DOI: 10.1111/2041-210X.12283. Package for phylogenetic analysis of species distributions. The main function goes through each node in the phylogeny, compares the distributions of the two descendant nodes, and compares the result to a null model. This highlights nodes where major distributional divergence have occurred. The distributional divergence for these nodes is mapped using the SOS statistic.
The NOIA model, as described extensively in Alvarez-Castro & Carlborg (2007), is a framework facilitating the estimation of genetic effects and genotype-to-phenotype maps. This package provides the basic tools to perform linear and multilinear regressions from real populations (provided the phenotype and the genotype of every individuals), estimating the genetic effects from different reference points, the genotypic values, and the decomposition of genetic variances in a multi-locus, 2 alleles system. This package is presented in Le Rouzic & Alvarez-Castro (2008).
Package for hierarchical clustering of objects characterized by nominal variables.
Analysis of a matrix of polytomous items using Nominal Logistic Biplots (NLB)
according to Hernandez-Sanchez and Vicente-Villardon (2013).
The NLB procedure extends the binary logistic biplot to nominal (polytomous) data.
The  individuals are represented as points on a plane and the  variables are represented
as convex prediction regions rather than vectors as in a classical or binary biplot.
Using the methods from Computational Geometry, the set of prediction regions is converted to a set of points
in such a way that the prediction for each individual is established by its closest
"category point". Then interpretation is based on distances rather than on projections.
In this package we implement the geometry of such a representation and construct computational algorithms
for the estimation of parameters and the calculation of prediction regions.
A collection of various regional information determined by the
U.S. Census Bureau along with demographic data.
Functions for nonlinear time series analysis. This package permits
the computation of the  most-used nonlinear statistics/algorithms
including generalized correlation dimension, information dimension,
largest Lyapunov exponent, sample entropy and Recurrence
Quantification Analysis (RQA), among others. Basic routines
for surrogate data testing are also included. Part of this work
was based on the  book "Nonlinear time series analysis" by
Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
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.
The Nordklim dataset 1.0 is a unique and useful achievement for climate
analysis. It includes observations of twelve different climate elements from
more than 100 stations in the Nordic region, in time span over 100 years.
The project contractors were NORDKLIM/NORDMET on behalf of the National
meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI),
Norway (DNMI) and Sweden (SMHI).
Analysis of multivariate normal datasets with missing
values
The functions proposed in this package compute the density of the sum of a Gaussian and a gamma random variables, estimate the parameters and correct the noise effect in a gamma-signal and Gaussian-noise model. This package has been used to implement the background correction method for Illumina microarray data presented in Plancade S., Rozenholc Y. and Lund E. "Generalization of the normal-exponential model : exploration of a more accurate parameterization for the signal distribution on Illumina BeadArrays", BMC Bioinfo 2012, 13(329).
This package provides functions for the normal Laplace
distribution. It is currently under development and provides
only limited functionality. Density, distribution and quantile
functions, random number generation, and moments are provided.
Collection of utilities referred to Exponential Power distribution,
also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4)
Functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided.
Evaluates the probability density function of the sum of
the Gaussian and Student's t density on 3 degrees of freedom.
Evaluates the p.d.f. of the sphered Student's t density
function.  Also evaluates the erf, and erfc functions on
complex-valued arguments. Thanks to Krishna Myneni the function
is calculates the Faddeeva function also!
Tests for the composite hypothesis of normality
Includes Omnibus Univariate and Multivariate Normality
Tests (See Doornik and Hansen (1994)). One variation allows for
the possibility of weak dependence rather than independence in
the variable(s). Also included is an univariate white noise
test where the null hypothesis is "white noise" rather than
strict "white noise".
CIPW Norm (acronym from the surnames of the authors: Cross, Iddings, Pirrson and Washington)
is the most commonly used calculation algorithm to estimate the standard mineral assemblages
for igneous rocks from its geochemical composition. NORRRM (acronym from noRm, R language and
Renee) is the highly consistent program to calculate the CIPW Norm.
An implementation of a specific method for generating
n-dimensional random vectors with given marginal distributions and
correlation matrix. The method uses the NORTA (NORmal To Anything)
approach which generates a standard normal random vector and then
transforms it into a random vector with specified marginal distributions
and the RA (Retrospective Approximation) algorithm which is a generic
stochastic root-finding algorithm. The marginals can be continuous or
discrete. See the vignette of package for more details.
Five omnibus tests for testing the composite hypothesis of
normality.
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!)
Estimate Large correlation and covariance matrices and their inverses using
integration of the sample and thresholded correlation and covariance estimators.
The noweb system for source code, implemented in R.
The Nozzle package provides an API to generate HTML
reports with dynamic user interface elements based on
JavaScript and CSS (Cascading Style Sheets). Nozzle was
designed to facilitate summarization and rapid browsing of
complex results in data analysis pipelines where multiple
analyses are performed frequently on big data sets. The package
can be applied to any project where user-friendly reports need
to be created.
This package provides a variety of nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC:www.nserc.ca), the Social Sciences and Humanities Research Council of Canada (SSHRC:www.sshrc.ca), and the Shared Hierarchical Academic Research Computing Network (SHARCNET:www.sharcnet.ca).
With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers.
The package "nparLD" is designed to perform nonparametric
analysis of longitudinal data in factorial experiments.
Longitudinal data are those which are collected from the same
subjects over time, and they frequently arise in biological
sciences. Nonparametric methods do not require distributional
assumptions, and are applicable to a variety of data types
(continuous, discrete, purely ordinal, and dichotomous).  Such
methods are also robust with respect to outliers and for small
sample sizes.
These routines create multiple imputations of missing at random categorical data, with or without structural zeros. Imputations are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling.
A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers  as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods.  Its use is illustrated via a number of empirical applications and simulated examples.
An implementation of nonparametric combination of hypothesis tests.
This package performs nonparametric combination (Pesarin and Salmaso 2010),
a permutation-based procedure for jointly testing multiple hypotheses. The
tests are conducted under the global "sharp" null hypothesis of no effects,
and the component tests are combined on the metric of their p-values. A
key feature of nonparametric combination is that it accounts for the
dependence among tests under the null hypothesis. In addition to the
"NPC" function, which performs nonparametric combination itself, the
package also contains a number of helper functions, many of which calculate
a test statistic given an input of data.
An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model.
This package implements nonparametric smoothing methods for circular data.
Provides nonparametric tests for assessing whether possibly serially dependent univariate or multivariate observations have the same c.d.f. or not. In addition to tests focusing directly on the c.d.f., the package contains tests designed to be particularly sensitive to changes in the underlying copula, Spearman's rho or certain quantities that can be estimated using one-sample U-statistics of order two such as the variance, Gini's mean difference or Kendall's tau. The latest addition is a nonparametric test for detecting changes in the distribution of independent block maxima.
Routines to compute normalised prediction distribution
errors, a metric designed to evaluate non-linear mixed effect
models such as those used in pharmacokinetics and
pharmacodynamics
An R-package for calculating sample size of a survival trial with or without cure fractions
Nonparametric aligned rank tests for interaction in two-way factorial designs, on data set with repeated measures in 'wide' format. There is a choice between three types of aligned ranks for the tests: (1) REGULAR, (2) FRIEDMAN, and (3) KOCH ranks.
This package provides routines for plotting
linkage and association results along a chromosome,
with marker names displayed along the top border.
There are also routines for generating BED and BedGraph
custom tracks for viewing in the UCSC genome browser.
The data reformatting program Mega2 uses this
package to plot output from a variety of
programs.
Performing drug response analyses and IC50 estimations using n-Parameter logistic regression. nplr can also be applied to proliferation analyses.
Given a failure type, the function computes covariate-specific probability of failure over time and covariate-specific conditional hazard rate based on possibly right-censored competing risk data. Specifically, it computes the non-parametric maximum-likelihood estimates of these quantities and their asymptotic variances in a semi-parametric mixture model for competing-risks data, as described in Chang et al. (2007a).
Nonparametric maximum likelihood estimation or Gaussian
quadrature for overdispersed generalized linear models and
variance component models
The main method npmpm calculates bacterial concentrations
during food manufacture after a contamination. Variability and
uncertainty are included by use of probability distributions
and Monte Carlo Simulation. The model aims at predicting
possible bacterial concentrations at one certain point in time
s, e.g. at the end of a process chain. The process steps of
this process chain are run through in linear order.
Experimental data that match current process step conditions
are gathered, and one deterministic primary model is fitted to
every series of measured values. From every fitted curve one
concentration of bacteria at time s is computed, yielding a set
of concentrations. This sample of possible contamination sizes
is assumed to follow a certain probability distribution. After
calculation of distribution parameters, one value is randomly
drawn from this probability distribution. This value may be
modified, and then serves as contamination for the next process
step.
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.
Small functions to make working with survey data in the context of
a Net Promoter programme easier. Specifically, data transformation methods,
some methods for examining the statistical properties of the NPS, such as
its variance and standard errors, and some simple inferential testing
procedures. Net Promoter and NPS are registered trademarks of Bain &
Company, Satmetrix Systems and Fred Reichheld.
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.
Post-normalization of total copy-number estimates.
This package implements two algorithms for canonical correlation
analysis (CCA) that are based on iterated regression
steps. By choosing the appropriate regression algorithm for each data
modality, it is possible to enforce sparsity, non-negativity or other kinds
of constraints on the projection vectors. Multiple canonical variables are
computed sequentially using a generalized deflation scheme, where the
additional correlation not explained by previous variables is maximized.
'nscancor' is used to analyze paired data from two domains, and has the same
interface as the 'cancor' function from the 'stats' package (plus some extra
parameters). 'mcancor' is appropriate for analyzing data from three or more
domains.
This package simulates and estimates spatial clustered point patterns of the Neyman-Scott model and their extensions.
This package provide functions for box-constrained
multiobjective optimization using the elitist non-dominated
sorting genetic algorithm - NSGA-II. Fast non-dominated
sorting, crowding distance, tournament selection, simulated
binary crossover, and polynomial mutation are called in the
main program, nsga2R, to complete the search.
A Gaussian process regression using a Gaussian kernel for both
one-sample and two-sample cases. Includes non-stationary Gaussian kernel
(exponential decay function) and several likelihood ratio tests for
differential testing along target points.
Designed to replace the tables which were in the back of the first two editions of Hollander and Wolfe - Nonparametric Statistical Methods.  Exact procedures are performed when computationally possible.  Monte Carlo and Asymptotic procedures are performed otherwise.  For those procedures included in the base packages, our code simply provides a wrapper to standardize the output with the other procedures in the package.
This package implements two methods for performing a constrained
principal component analysis (PCA), where non-negativity and/or sparsity
constraints are enforced on the principal axes (PAs). The function
'nsprcomp' computes one principal component (PC) after the other. Each PA
is optimized such that the corresponding PC has maximum additional variance
not explained by the previous components. In contrast, the function
'nscumcomp' jointly computes all PCs such that the cumulative variance is
maximal. Both functions have the same interface as the 'prcomp' function
from the 'stats' package (plus some extra parameters), and both return the
result of the analysis as an object of class 'nsprcomp', which inherits
from 'prcomp'.
A collection of statistical tools for objective (non-supervised) applications
of the Regional Frequency Analysis methods in hydrology.
The package refers to the index-value method and, more precisely, helps the
hydrologist to: (1) regionalize the index-value; (2) form homogeneous regions
with similar growth curves; (3) fit distribution functions to the
empirical regional growth curves.
A Bayesian framework for population group size estimation using the Network Scale Up Method (NSUM). Size estimates are based on a random degree model and include options to adjust for barrier and transmission effects.
Tools for visual inference. Generate null data sets
and null plots using permutation and simulation. Calculate distance metrics
for a lineup, and examine the distributions of metrics.
Provides some number-theoretic functions for R.
Methods for calculating (usually) accurate
numerical first and second order derivatives. Accurate calculations
are done using Richardson's extrapolation or, when applicable, a complex
step derivative is available. A simple difference
method is also provided. Simple difference is (usually) less accurate
but is much quicker than Richardson's extrapolation and provides a
useful cross-check.
Methods are provided for real scalar and vector valued functions.
Package for optimizing regular numeric problems in optically stimulated luminescence dating, such as: equivalent dose calculation, annual dose rate determination, growth curve fitting, decay curve decomposition, fast-component equivalent dose estimation, statistical age models optimization, and statistical plots visualization.
This package contains data sets used as examples in the
book "R in a Nutshell" from O'Reilly Media. For more
information on this book, see
http://shop.oreilly.com/product/0636920022008.do
This package contains the Audio Scrobbler data set used as
an example in the book "R in a Nutshell" from O'Reilly Media.
For more information about this book, see
http://shop.oreilly.com/product/0636920022008.do
This package contains the baseball databank data set used
as an example in the book "R in a Nutshell" from O'Reilly
Media. For more information about this book, see
http://shop.oreilly.com/product/0636920022008.do
Provides coordination and parallel execution facilities,
as well as limited cross-language data exchange, using the
netWorkSpaces server developed by REvolution Computing
Airline on-time data for all flights departing NYC in 2013.
Also includes useful metadata on airlines, airports, weather, and planes.
Harvest metadata using the Open Archives Initiative Protocol for Metadata
Harvesting (OAI-PMH) version 2.0.
Interface to D-optimal design generation code of the Orthogonal Array package. Can generate D-optimal designs with specified number of runs and factors. The optimality of the designs is defined in terms of a user specified optimization function based on the D-efficiency and Ds-efficiency.
Generalise the starting point of the array index
A set of indexes and tests for the analysis of social segregation.
An implementation of the Blinder-Oaxaca decomposition for linear regression models.
Supports the definition of sets of properties on objects. Observers can listen to changes on individual properties or the set as a whole. The properties are meant to be fully self-describing. In support of this, there is a framework for defining enumerated types, as well as other bounded types, as S4 classes.
A mutable Signal object can report changes to its state,
clients could register functions so that they are called whenever
the signal is emited. The signal could be emited, disconnected,
blocked, unblocked, and buffered.
This package conducts factor rotation techniques which
intentd to identify a simple and well-clustered structure in a
factor loading matrix.
Random forest with oblique decision trees for binary
classification tasks. Discriminative node models in the tree
are based on: ridge regression, partial least squares
regression, logistic regression, linear support vector
machines, or random coefficients.
Grows Oblique Trees to Classification Data.
The package includes two functions for measuring agreement. Raw Agreement Indices (RAI) to categorical data and Information-Based Measure of Disagreement (IBMD) to continuous data. It can be used for multiple raters and multiple readings cases.
Observational studies are limited in that there could be
an unmeasured variable related to both the response variable
and the primary predictor.  If this unmeasured variable were
included in the analysis it would change the relationship
(possibly changing the conclusions).  Sensitivity analysis is a
way to see how much of a relationship needs to exist with the
unmeasured variable before the conclusions change.  This
package provides tools for doing a sensitivity analysis for
regression (linear, logistic, and cox) style models.
Estimates Optimal Classification scores from roll call
votes supplied though a 'rollcall' object from package 'pscl'.
This package provides a generic function for estimating
positron emission tomography (PET) neuroreceptor occupancies
from the total volumes of distribution of a set of regions of
interest. Fittings methods include the simple 'reference
region' and 'ordinary least squares' (sometimes known as
occupancy plot) methods, as well as the more efficient
'restricted maximum likelihood estimation'.
Supports the analysis of Oceanographic data, including ADCP
measurements, CTD measurements, sectional data, sea-level time series,
coastline files, etc.  Handles dozens of specialized functions for calculating
seawater properties such as potential temperature and density, as well as
derived properties such as buoyancy frequency and dynamic height. Map projections
are calculated using PROJ.4 functions included within the oce.
This package contains a set of functions for manipulating and
visualizing output from numerical ocean models. Many of the routines are
targeted towards Lagrangian particle-tracking models with the data stored
in NetCDF files. This package may be useful to spatial marine ecologists
and biological or physical oceanographers.
Functions for transforming and viewing 2-D and 3-D (oceanographic) data and model output.
Several important and Oceanographic datasets are provided. These are particularly useful to the Oce package, but can be helpful in a general context, as well.
This package provides functions to create, connect, update
and query HSQL databases embedded in Open Document Databases
(.odb) files, as OpenOffice and LibreOffice do.
Conversion between the most common odds types for sports betting. Hong Kong odds, US odds, Decimal odds, Indonesian odds, Malaysian odds, and raw Probability are covered in this package.
Wraps the Boost 'odeint' library for integration of differential equations.
Sweave processing of Open Document Format (ODF) files
Provides methods for odfTable() for objects in the survey
package.
Transformation of 'ODM' (see http://www.cdisc.org/odm) files into R format, Microsoft Office format, 'CDA' format and vice versa. 'ODM' format is commonly used in clinical trials. Semantic annotation (such as 'UMLS', 'SNOMED' or 'LOINC' codes) are transformed accordingly.
Fit penalized regression with orthogonalizing EM algorithm
Ordered models such as ordered probit and ordered logit presume that the error variance is constant across observations. In the case that this assumption does not hold estimates of marginal effects are typically biased. This package allows for generalization of ordered probit and ordered logit models by allowing the user to specify a model for the variance. Furthermore, the package includes functions to calculate the marginal effects.
R Client for Ohmage 2 server. Implements basic R
functions to retrieve and process data.
A collection of data sets from several sources that may be
useful for teaching, practice, or other purposes. Functions
have also been included to assist in the retrieval of table
data from websites or in visualizing sample data.
Supplemental functions and data for the OpenIntro guide to the survival package in R.
The package provides functions for the Oja median, Oja
signs and ranks and methods based upon them.
okmesonet retrieves and summarizes Oklahoma (USA) Mesonet
climatological data provided by the Oklahoma Climatological Survey.
Measurements are recorded every five minutes at approximately 120 stations
throughout Oklahoma and are available in near real-time.
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.
Purpose of this package is it to plan, monitor and evaluate oncological phase II studies. In general this kind of studies are single-arm trials with planned interim analysis and binary endpoint. To meet the resulting requirements, the package provides functions to calculate and evaluate 'Simon's two-stage designs' and 'so-called' 'subset designs'. If you are unfamiliar with this package a good starting point is to take a closer look at the functions getSolutions() and getSolutionsSub1().
Analysis of molecular marker data from model (backcrosses,
F2 and recombinant inbred lines) and non-model systems (i. e.
outcrossing species). For the later, it allows statistical
analysis by simultaneously estimating linkage and linkage
phases (genetic map construction). All analysis are based on
multipoint approaches using hidden Markov models.
Provides a series of functions designed to enable users to easily search and interact with occupational data from the O*NET API <www.onetonline.org>. The package produces parsed and listed XML data for custom interactions, or pre-packaged functions for easy extraction of specific data (e.g., Knowledge, Skills, Abilities, Work Styles, etc.).
In this package, we introduce an R function
one_two_sample() which can deal with one and two (normal)
samples. For one normal sample x, the function reports
descriptive statistics, plot, interval estimation and test of
hypothesis of x. For two normal samples x and y, the function
reports descriptive statistics, plot, interval estimation and
test of hypothesis of x and y, respectively. It also reports
interval estimation and test of hypothesis of mu1-mu2 (the
difference of the means of x and y) and sigma1^2 / sigma2^2
(the ratio of the variances of x and y), tests whether x and y
are from the same population, finds the correlation coefficient
of x and y if x and y have the same length.
A collection of routines to manipulate and visualize quaternions and
octonions.
Online PCA for multivariate and functional data using perturbation, incremental, and stochastic gradient methods.
Orthogonal Nonlinear Least-Squares Regression using Levenberg-Marquardt minimization.
Contains tools for open assessment and modelling in Opasnet,
a wiki-based web site and workspace for societal decision making
(see <http://en.opasnet.org/w/Main_Page> for more information).
The core principle of the workspace is maximal openness and modularity.
Variables are defined on public wiki pages using wiki inputs/tables,
databases and R code. This package provides the functionality to download and use these
variables. It also contains health impact assessment tools such as
spatial methods for exposure modelling.
Companion package to the book Option Pricing and
Estimation of Financial Models in R, Wiley, Chichester. ISBN:
978-0-470-74584-7
Tools to analyse, interpret and understand air
pollution data. Data are typically hourly time series
and both monitoring data and dispersion model output
can be analysed.  Many functions can also be applied to
other data, including meteorological and traffic data.
This package provides an interface to OpenCL, allowing R
to leverage computing power of GPUs and other HPC accelerator
devices.
The OpenCPU system exposes an HTTP API for embedded scientific
computing with R. This provides scalable foundations for integrating R
based analysis and visualization modules into pipelines, web applications
or big data infrastructures. The OpenCPU server can run either as a
single-user development server within the interactive R session, or as a
high performance multi-user cloud server that builds on Linux, Nginx and
rApache. The current R package forms the core of the system. When loaded
in R, it automatically initiates the single-user server and displays the
web address in the console. Visit the OpenCPU website for detailed
information and documentation on the API.
This package is a supplement to OpenIntro Statistics,
which is a free textbook available at openintro.org (at cost
paperbacks are also available for under $10 on Amazon). The
package contains data sets used in the textbook along with
custom plotting functions for reproducing book figures. Note
that many functions and examples include color transparency.
Some plotting elements may not show up properly (or at all) in
some Windows versions.
The OpenMPController package provides a function
'omp_set_num_threads()' to set the number of OpenMP threads to
be used.  This may be useful, for example, when linking against
a vendor optimised BLAS/LAPACK library (e.g. the AMD Core Math
Library), since the defaults used by those libraries may not be
highly performant.
Facilitates treatment of statistical model specifications
as things that can be generated and manipulated programmatically.
Structural equation models may be specified with reticular action model matrices or paths,
linear structural relations matrices or paths, or
directly in matrix algebra.
Fit functions include full information maximum likelihood,
maximum likelihood, and weighted least squares.
Example models include confirmatory factor, multiple group, mixture
distribution, categorical threshold, modern test theory, differential
equations, state space, and many others.
An interface to the Apache OpenNLP tools (version 1.5.3).
The Apache OpenNLP library is a machine learning based toolkit for the
processing of natural language text written in Java.
It supports the most common NLP tasks, such as tokenization, sentence
segmentation, part-of-speech tagging, named entity extraction, chunking,
parsing, and coreference resolution.
See <http://opennlp.apache.org/> for more information.
Apache OpenNLP jars and basic English language models.
Interfaces to the OpenSSL libraries libssl and libcrypto.
Currently implements bindings to the OpenSSL secure random number
generator, base64 encoder and hashing functions.
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.
Simplifies the creation of Excel .xlsx files by providing a
high level interface to writing, styling and editing worksheets. Through
the use of Rcpp, read/write times are comparable to the xlsx and XLConnect
packages with the added benefit of removing the dependency on Java.
A set of binary operators for common tasks such as regex
manipulation.
These utilities allow for programatically working with
R's operators: translating between an operator and its underlying
function, inverting an operator, determining an operator's type, etc.
Implementation of the Open Perimetry Interface (OPI) for simulating and controlling visual field machines using R. The OPI is a standard for interfacing with visual field testing machines (perimeters). It specifies basic functions that allow many visual field tests to be constructed. As of December 2014 it is fully implemented on the Octopus 600 and Octopus 900 and partially on the Heidelberg Edge Perimeter and the Kowa AP 7000. It also has a cousin: the R package visualFields, which has tools for analysing and manipulating visual field data.
Comparison of data by Pearson product-moment correlation
coefficients is prone to outliers. The problem can be
alleviated by normalizing data with outliers before computing
the Pearson correlation coefficient. The sample provides such
normalization by optimal power space transformation.
Searches for optimal linear combination of multiple
diagnostic tests (markers) that maximizes the area under the
receiver operating characteristic curve (AUC); performs an
approximated cross-validation for estimating the AUC associated
with the estimated coefficients.
Estimates optimal number of biomarkers for two-group
classification based on microarray data
Cluster analysis using statistical and biological
validation measures for both transformed and count data.
Tools to assist in safely applying user-generated objective and
derivative functions to optimization programs. "Optimization" at the
time of writing refers primarily to function minimization methods with at most
bounds and masks on the parameters.
Tools are provided to check the basic computation of objective functions that
the user provides, along with proposed gradient and Hessian functions,
as well as to wrap such functions to avoid failures when inadmissible parameters
are provided. Checks on bounds and masks (fixed parameters) are available, as
are a check on scaling or optimality conditions. An axial search to seek lower
points on the objective function surface is included. Though numDeriv is
used as the primary tool for numerical derivative approximations, forward,
central and backward gradient approximation codes for convenience.
Functions to find near-optimal multi-stage designs for continuous outcomes.
Estimation of value and hedging strategy of call and put options, based on optimal hedging and Monte Carlo method, from Chapter 3 of 'Statistical Methods for Financial Engineering', by Bruno Remillard, CRC Press, (2013).
A single function 'options.ifunset(...)' is contained herewith, which allows the user to set a global option ONLY if it is not already set. By this token, for package maintainers this function can be used in preference to the standard 'options(...)' function, making provision for THEIR end user to place 'options(...)' directives within their '.Rprofile' file, which will not be overridden at the point when a package is loaded.
This package enables users to compute one or more optimal cutpoints for diagnostic tests or continuous markers. Various approaches for selecting optimal cutoffs have been implemented, including methods based on cost-benefit analysis and diagnostic test accuracy measures (Sensitivity/Specificity, Predictive Values and Diagnostic Likelihood Ratios). Numerical and graphical output for all methods is easily obtained.
Provides a set of commands to manage an abstract
optimization method. The goal is to provide a building block
for a large class of specialized optimization methods. This
package manages: the number of variables, the minimum and
maximum bounds, the number of non linear inequality
constraints, the cost function, the logging system, various
termination criteria, etc...
Provides a building block for optimization algorithms
based on a simplex. The optimsimplex package may be used in the
following optimization methods: the simplex method of Spendley
et al., the method of Nelder and Mead, Box's algorithm for
constrained optimization, the multi-dimensional search by
Torczon, etc...
Provides a replacement and extension of the optim()
function to unify and streamline optimization capabilities in R
for smooth, possibly box constrained functions of several or
many parameters. This is the CRAN version of the package.
Optimal two and three stage designs monitoring
time-to-event endpoints at a specified timepoint
Efficient Monte Carlo Algorithms for the price and the sensitivities of Asian and European Options under Geometric Brownian Motion.
This fills the gaps credit analysts and loan modellers at
Optimum Credit identify in the existing R code body.
It allows for the production of documentation with less coding,
replicates a number of Microsoft Excel functions useful for
modelling loans (without rounding), and other helpful functions
for producing charts and tables.  It also has some additional scales for
use, including a GBP scale.
Tools for performing an optimal scaling transformation on a data
vector
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.
Counts orbits of nodes and edges from edge matrix or data frame, or a
graph object from the graph package.
Computes various confidence intervals for the odds ratio of two independent binomial proportions.
Functions to perform subspace clustering and
classification.
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.
Ordered lasso and time-lag sparse regression. Ordered Lasso fits a
linear model and imposes an order constraint on the coefficients. It writes
the coefficients as positive and negative parts, and requires positive
parts and negative parts are non-increasing and positive. Time-Lag Lasso
generalizes the ordered Lasso to a general data matrix with multiple
predictors. For more details, see Suo, X.,Tibshirani, R., (2014) 'An
Ordered Lasso and Sparse Time-lagged Regression'.
In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.
Implementation of cumulative link (mixed) models also known
as ordered regression models, proportional odds models, proportional
hazards models for grouped survival times and ordered logit/probit/...
models. Estimation is via maximum likelihood and mixed models are fitted
with the Laplace approximation and adaptive Gauss-Hermite quadrature.
Multiple random effect terms are allowed and they may be nested, crossed or
partially nested/crossed. Restrictions of symmetry and equidistance can be
imposed on the thresholds (cut-points/intercepts). Standard model
methods are available (summary, anova, drop-methods, step,
confint, predict etc.) in addition to profile methods and slice
methods for visualizing the likelihood function and checking
convergence.
A regression framework for response variables which are continuous self-rating scales such as the Visual Analog Scale (VAS) used in pain assessment, or the Linear Analog Self-Assessment (LASA) scales in quality of life studies. These scales  measure subjects' perception of an intangible quantity, and cannot be handled as ratio variables because of their inherent nonlinearity. We treat them as ordinal variables, measured on a continuous scale. A function (the g function, currently the generalized logistic function) connects the scale with an underlying continuous latent  variable.  The link function is the inverse of the CDF of the assumed underlying distribution of the latent variable. Currently the logit link, which corresponds to a standard logistic distribution, is implemented.
This package provides a function for fitting cumulative link, adjacent category, forward and backward continuation ratio, and stereotype ordinal response models when the number of parameters exceeds the sample size, using the the generalized monotone incremental forward stagewise method.
Analysis of a matrix of polytomous items using Ordinal Logistic Biplots (OLB)
The OLB procedure extends the binary logistic biplot to ordinal (polytomous) data.
The  individuals are represented as points on a plane and the variables are represented
as lines rather than vectors as in a classical or binary biplot, specifying the points
for each of the categories of the variable.
The set of prediction regions is established by stripes perpendicular to the line
between the category points, in such a way that the prediction for each individual is given
by its projection into the line of the variable.
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.
Provides an alternative to R's built-in functionality for handling
regular expressions, based on the Oniguruma library. It offers first-class
compiled regex objects, partial matching and function-based substitutions,
amongst other features.
Organic/biological mass spectrometry data analysis.
Provides functionality to process text files created by Emacs' Org mode, and decompose the content to the smallest components (headlines, body, tag, clock entries etc).  Emacs is an extensible, customizable text editor and Org mode is for keeping notes, maintaining TODO lists, planning projects.  Allows users to analyze org files as data frames in R, e.g., to convieniently group tasks by tag into project and calculate total working hours.  Also provides some help functions like search.parent, gg.pie (visualise working hours in ggplot2) and tree.headlines (visualise headline stricture in tree format) to help user managing their complex org files.
ORIClust is a user-friendly R-based software package for
gene clustering. Clusters are given by genes matched to
prespecified profiles across various ordered treatment groups.
It is particularly useful for analyzing data obtained from
short time-course or dose-response microarray experiments.
Representations, conversions and display of orientation
SO(3) data.  See the orientlib help topic for details.
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 B-splines in a simple matrix
formulation that facilitates, taking integrals, derivatives, and
making orthogonal 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.
Implements basic tools for storing, handling and visualizing disease outbreak data, as well as simple analysis tools. OutbreakTools defines the new formal class obkData which can be used to store any case-base outbreak data, and provides summaries for these objects, alongside a range of functions for subsetting and data manipulation. It implements a range of graphics for visualising timelines, maps, contact networks and genetic analyses. It also includes a simple case-base outbreak simulation tool.
This package provides three algorithms to detect outlying observations for censored survival data.
Detecting outlying values such as genes, peptides or samples for multi-replicated high-throughput high-dimensional data
A collection of some tests commonly used for identifying
outliers.
Functions to process ''outranking'' ELECTRE methods existing in the literature. See, e.g.,
<http://en.wikipedia.org/wiki/ELECTRE> about the outranking approach and the foundations of ELECTRE methods.
Calculates and compares rate differences of continuous character evolution under Brownian motion and a new set of Ornstein-Uhlenbeck-based Hansen models that allow the strength of selection and stochastic motion to vary across selective regimes.
Provides functions to fit kernel density functions to
data on temporal activity patterns of animals; estimate coefficients
of overlapping of densities for two species; and calculate bootstrap
estimates of confidence intervals.
Functions for plotting Australia's coastline and state
boundaries.
Conducts posterior predictive checks of coalescent models using gene and species trees generated by 'BEAST' or '*BEAST'. The functionality of P2C2M can be extended via two third-party R packages that are available from the author websites: 'genealogicalSorting' and 'phybase'. To use these optional packages, the installation of the Python libraries 'NumPy' (>= 1.9.0) and 'DendroPy' (= 3.12.0) is required.
The welfare's synthetic indicator provides an ideal tool
for measuring multi-dimensional concepts such as welfare,
development, living standards, etc. It enables information from
the various indicators to be aggregated into a single synthetic
measure.
Analyzing survival data from illness-death model
A package that provides tools for conducting performance attribution for equity portfolios. The package uses two methods: the Brinson method and a regression-based analysis.
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.
Simple algorithm to arrange non-overlapping circles within a rectangle.
This package comes to illustrate the book "Petit Manuel de
Programmation Orientee Objet sous R"
packdep elucidates the dependencies
between user-contributed R packages and
identifies key packages according to social
network analysis metrics.
Various useful functions for statisticians: describe data, plot Kaplan-Meier curves with numbers of subjects at risk, compare data sets, display spaghetti-plot, build multi-contingency tables...
Manage the R packages your project depends
on in an isolated, portable, and reproducible way.
Illustration of the book "Petit Manuel de Programmation Orientee Objet sous R". The english version "A (Not so) Short Introduction to S4" is on CRAN, 'Contributed documentation'.
Tools to more conveniently perform tasks
associated with add-on packages.  pacman conveniently wraps library and
package related functions and names them in an intuitive and consistent
fashion.  It seeks to combine functionality from lower level functions which
can speed up workflow.
Procrustes analyses to infer co-phylogenetic
matching between pairs of (ultrametric) phylogenetic trees.
Given a vector of Taylor series coefficients of sufficient length as input, the function returns the numerator and denominator coefficients for the Padé approximant of appropriate order.
Calculate unadjusted/adjusted attributable fraction function of a set of covariates for a censored survival outcome from a Cox model using the method proposed by Chen, Lin and Zeng (Biometrika 97, 713-726., 2010).
A statistically sound method for estimating jointly the attachment function and node fitness in a temporal complex network by maximizing a suitable penalized log-likelihood function is implemented in this package.
The package can identify the dysregulated KEGG pathways
based on global influence from the internal effect of pathways
and crosstalk between pathways. (1) The PAGI package can
prioritize the pathways associated with two biological states
by statistical significance or FDR. (2) The PAGI package can
evaluated the global influence factor (GIF) score in the global
gene-gene network constructed based on the relationships of
genes extracted from each pathway in KEGG database and the
overlapped genes between pathways.
The two Bayesian hierarchical pathway analysis methods Sparse normal/Adaptive lasso and Normal/Bayes factors proposed by Evangelou et al (2013) are implemented. Further, the well-known Fisher's method is implemented
The package contains two functions: paired.Loc and
paired.Scale. A parametric and nonparametric confidence
interval can be computed for the ratio of locations
(paired.Loc) and the ratio of scales (paired.Scale). The
samples must be paired and expected values must be positive.
This package provides many datasets and a set of graphics
(based on ggplot2), statistics, effect sizes and hypothesis
tests for analysing paired data with S4 class.
A tool to compare two heatmaps and discover patterns
within and across groups. In the context of biology, group can
be defined based on gene ontology.
Creates an interactive scatterplot matrix using the D3 JavaScript library. See <http://d3js.org/> for more information on D3.
Eulerian tours and Hamiltonian decompositions of complete graphs are used to ameliorate order effects in statistical graphics.
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.
19 functions to wrap each endpoint of
the PaleobioDB API, plus 8 functions to visualize and process the fossil
data. The API documentation for the Paleobiology Database can be found in
<http://paleobiodb.org/data1.1/>
Tools to extract and analyse charcoal sedimentary data stored in
the Global Charcoal Database. Main functionalities includes data extraction
and sites selection, transformation and interpolation of the charcoal
records as well as compositing.
Transfer functions and statistical operations for
paleoecology
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.
Colour palettes for data, based on some well known public data sets.
Correspondence Analysis of Contingency Tables with Simple
and Double Structures Superimposed Representations, Intra
Blocks Correspondence Analysis (IBCA), Weighted Intra Blocks
Correspondence Analysis (WIBCA).
Simulation functions to assess or explore the power of a
dataset to estimates significant random effects (intercept or
slope) in a mixed model. The functions are based on the "lme4"
package.
Implements the Panel Data Approach Method for program evaluation as developed in Hsiao, Ching and Ki Wan (2012). pampe estimates the effect of an intervention by comparing the evolution of the outcome for a unit affected by an intervention or treatment to the evolution of the unit had it not been affected by the intervention.
Some functions for sample classification in microarrays
Multiple imputation for multivariate panel or clustered data.
PANDA (Preferential Attachment based common Neighbor Distribution derived Associations) is designed to perform the following tasks in PPI networks: (1) identify significantly functionally associated protein pairs, (2) predict GO terms and KEGG pathways for proteins, (3) make a cluster of proteins based on the significant protein pairs, (4) identify subclusters whose members are enriched in KEGG pathways. For other types of biological networks, (1) and (3) can still be performed.
Contains some functions catching all messages, stdout and other
useful information while evaluating R code and other helpers to return user
specified text elements (like: header, paragraph, table, image, lists etc.)
in pandoc's markdown or several type of R objects similarly automatically
transformed to markdown format. Also capable of exporting/converting (the
resulting) complex pandoc documents to e.g. HTML, PDF, docx or odt. This
latter reporting feature is supported in brew syntax or with a custom
reference class with a smarty caching backend.
Aggregate Business Tendency Survey Data (and other qualitative
surveys) to time series at various aggregation levels. Run aggregation of
survey data in a speedy, re-traceable and a easily deployable way.
Aggregation is substantially accelerated by use of data.table.
Panelaggregation aims at providing an interface that is more geared towards
survey researchers.
The package estimates linear models on panel data structures in the presence of AR(1)-type autocorrelation as well as panel heteroskedasticity and/or contemporaneous correlation. First, AR(1)-type autocorrelation is addressed via a two-step Prais-Winsten feasible generalized least squares (FGLS) procedure, where the autocorrelation coefficients may be panel-specific. A number of common estimators for the autocorrelation coefficient are supported. In case of panel heteroskedasticty, one can choose to use a sandwich-type robust standard error estimator with OLS or a panel weighted least squares estimator after the two-step Prais-Winsten estimator. Alternatively, if panels are both heteroskedastic and contemporaneously correlated, the package supports panel-corrected standard errors (PCSEs) as well as the Parks-Kmenta FGLS estimator.
Linear models for panel data: the fixed effect model and the
random effect model
This package contains a methodology that makes use of the factor
structure of large dimensional panels to understand the nature of
nonstationarity inherent in data. This is referred to as PANIC - Panel
Analysis of Nonstationarity in Idiosyncratic and Common Components. PANIC
(2004) includes valid pooling methods that allow panel unit root tests
tests to be constructed. PANIC (2004) can detect whether the
nonstationarity in a series is pervasive, or variable specific, or both.
PANIC (2010) includes the Panel Modified Sargan-Bhargava test, three models
for a Moon and Perron style test, and a bias correction for the
idiosyncratic unit root test of PANIC (2004). The PANIC model approximates
the number of factors based on Bai and Ng (2002).
A toolbox for writing 'knitr', 'Sweave' or other 'LaTeX'-based
reports and to prettify the output of various estimated models.
R package implementing random forest classification using parallel computing, built with Fortran and OpenMP in the backend.
Passing a given function name or a call to the
parallelize/parallelize_call functions analyses and executes
the code, if possible in parallel. Parallel code execution can
be performed locally or on remote batch queuing systems.
Unified parallelization framework for multiple back-end,
designed for internal package and interactive usage.
The main operation is a parallel "map" over lists.
Supports local, multicore, mpi and BatchJobs mode.
Allows "tagging" of the parallel operation
with a level name that can be later selected by the user to
switch on parallel execution for exactly this operation.
Recent Bayesian Markov chain Monto Carlo (MCMC) methods have been developed for big data sets that are too large to be analyzed using traditional statistical methods. These methods partition the data into non-overlapping subsets, and perform parallel independent Bayesian MCMC analyses on the data subsets, creating independent subposterior samples for each data subset. These independent subposterior samples are combined through four functions in this package, including averaging across subset samples, weighted averaging across subsets samples, and kernel smoothing across subset samples. The four functions assume the user has previously run the Bayesian analysis and has produced the independent subposterior samples outside of the package; the functions use as input the array of subposterior samples. The methods have been demonstrated to be useful for Bayesian MCMC models including Bayesian logistic regression, Bayesian Gaussian mixture models and Bayesian hierarchical Poisson-Gamma models. The methods are appropriate for Bayesian hierarchical models with hyperparameters, as long as data values in a single level of the hierarchy are not split into subsets.
By sampling your data, running the provided classifier on these samples in parallel on your own machine and letting your models vote on a prediction, we return much faster predictions than the regular machine learning algorithm and possibly even more accurate predictions.
By sampling your data, running the Support-Vector-Machine algorithm on these samples in parallel on your own machine and letting your models vote on a prediction, we return much faster predictions than the regular Support-Vector-Machine and possibly even more accurate predictions.
Functions for parameter descriptions and operations in black-box optimization, tuning
and machine learning. Parameters can be described (type, constraints, defaults, etc.),
combined to parameter sets and can in general be programmed on.
A useful OptPath object (archive) to log function evaluations is also provided.
Parametric linkage analysis and other likelihood-based pedigree analyses based on the Elston-Stewart algorithm, including singlepoint LOD scores, power analysis and computation of marker genotype distributions. Multipoint analysis is provided through a convenient MERLIN wrapper. Includes many utility functions for creating, manipulating and plotting pedigrees, markers and linkage results.
An interface to simplify organizing parameters used in a package, using external configuration files.
This attempts to provide a cleaner alternative to options().
paran is an implementation of Horn's technique for
numerically and graphically evaluating the components or
factors retained in a principle components analysis (PCA) or
common factor analysis (FA). Horn's method contrasts
eigenvalues produced through a PCA or FA on a number of random
data sets of uncorrelated variables with the same number of
variables and observations as the experimental or observational
data set to produce eigenvalues for components or factors that
are adjusted for the sample error-induced inflation. Components
with adjusted eigenvalues greater than one are retained. paran
may also be used to conduct parallel analysis following
Glorfeld's (1995) suggestions to reduce the likelihood of
over-retention.
Distributed gradient boosting based on the mboost package. The
parboost package is designed to scale up component-wise functional
gradient boosting in a distributed memory environment by splitting the
observations into disjoint subsets, or alternatively using bootstrap
samples (bagging). Each cluster node then fits a boosting model to its
subset of the data. These boosting models are combined in an ensemble,
either with equal weights, or by fitting a (penalized) regression
model on the predictions of the individual models on the complete
data.
The package estimates the matrix of partial correlations
based on different regularized regression methods: lasso,
adaptive lasso, PLS, and Ridge Regression. In addition, the
package provides model selection for lasso, adaptive lasso and
Ridge regression based on cross-validation.
Parallelized version of the "segment" function from Bioconductor package "DNAcopy", utilizing multi-core computation on host CPU
Conduct the Parent-Offspring Test Using Monomorphic SNP Markers. The similarity to the parents is computed for each offspring, and a plot of similarity for all offspring is produced. One can keep the offspring above some threshold for the similarity for further studies.
Statistical functions to describe a Pareto Positive Stable (PPS)
distribution and fit it to real data. Graphical and statistical tools to
validate the fits are included.
Fits Parametric Frailty Models by maximum marginal likelihood.
Possible baseline hazards: Weibull, inverse Weibull, exponential, Gompertz, lognormal and loglogistic.
Possible Frailty distributions: gamma, inverse Gaussian, positive stable and lognormal.
The package provides a number of easily parallelized
functions from the fossil package. This package is designed to
be used with some type of parallel computing backend, such as
multicore, snow or MPI.
Methods for Portfolio Optimization and Risk Management.
The package provides a parallel estimation of the mutual
information based on entropy estimates from k-nearest neighbors
distances and algorithms for the reconstruction of gene
regulatory networks.
Implements basic partial order tools for multidimensional poverty evaluation with ordinal variables. Its main goal is to provide socio-economic scholars with an integrated set of elementary functions for multidimensional poverty evaluation, based on ordinal information. The package is organized in four main parts. The first two comprise functions for data management and basic partial order analysis; the third and the fourth are devoted to evaluation and implement both the poset-based approach and a more classical counting procedure.
Parse dates automatically, without the need of
specifying a format. Currently it includes the git date parser.
It can also recognize and parse all ISO 8601 formats.
A novel tool for generating a piecewise
constant estimation list of increasingly complex predictors
based on an intensive and comprehensive search over the entire
covariate space.
Fits time series models which consist of a sum of a permanent and a transient component.
Computes Odds Ratio adjusted for a vector of possibly
continuous covariates
Low-dimensional embedding, using Random Forests for
multiclass classification
partitionMetric computes a distance between two partitions
of a set.
Additive partitions of integers.  Enumerates the
partitions, unequal partitions, and restricted partitions of an
integer; the three corresponding partition functions are also
given.  Set partitions are now included.
Miscellaneous utilities for the 'parallel' package,
cluster-oriented section; "Snowdoop" alternative to MapReduce;
file splitting and distributed operations such as sort and aggregate.
"Software Alchemy" method for parallelizing most statistical methods,
presented in N. Matloff, Parallel Computation for Data Science,
Chapman and Hall, 2015.  Includes a debugging aid.
This package performs basic functions to fit and predict periodic autoregressive time series models. These models are discussed in the book P.H. Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancement are expected, and the maintainer cannot provide any support.
A computational toolbox for recursive partitioning.
The core of the package is ctree(), an implementation of
conditional inference trees which embed tree-structured
regression models into a well defined theory of conditional
inference procedures. This non-parametric class of regression
trees is applicable to all kinds of regression problems, including
nominal, ordinal, numeric, censored as well as multivariate response
variables and arbitrary measurement scales of the covariates.
Based on conditional inference trees, cforest() provides an
implementation of Breiman's random forests. The function mob()
implements an algorithm for recursive partitioning based on
parametric models (e.g. linear models, GLMs or survival
regression) employing parameter instability tests for split
selection. Extensible functionality for visualizing tree-structured
regression models is available.
A toolkit with infrastructure for representing, summarizing, and
visualizing tree-structured regression and classification models. This
unified infrastructure can be used for reading/coercing tree models from
different sources (rpart, RWeka, PMML) yielding objects that share
functionality for print/plot/predict methods. Furthermore, new and improved
reimplementations of conditional inference trees (ctree) and model-based
recursive partitioning (mob) from the party package are provided based
on the new infrastructure.
Parviol combines parallel coordinates and violin plot
An R package for polygenic trait analysis
To implement two methods, Kappa and PASS, for selecting
tuning parameters in regularized procedures such as LASSO,
SCAD, adaptive LASSO, aiming for variable selection in
regularized regression
Regulation, decomposition and analysis of space-time series. The pastecs library is a PNEC-Art4 and IFREMER (Benoit Beliaeff <Benoit.Beliaeff@ifremer.fr>) initiative to bring PASSTEC 2000 (http://www.obs-vlfr.fr/~enseigne/anado/passtec/passtec.htm) functionalities to R.
A pre-processor for mrBayes that assimilates sequences, taxonomic
information and tree constraints as per xxx.  The main functions of
interest for most users will be pastis_simple, pastis_main and conch. The
main analysis is conducted with pastis_simple or pastis_main followed by a
manual execution of mrBayes (>3.2). The placement of taxa not contained in
the tree constraint can be investigated using conch.
Data and functions for the book PROBABILITY and STATISTICS
WITH R.
Functions and data sets for the text Probability and Statistics
with R, Second Edition
Functions to patch specials in .dvi files,
or entries in .synctex files.  Works with "concordance=TRUE"
in Sweave or knitr to link sources to previews.
Functions to generate scatterplots with images patches
instead of usual glyphs, with associated utilities.
This utility eases the debugging of literate documents
('noweb' files) by patching the synchronization information
(the '.synctex(.gz)' file) produced by 'pdflatex' with
concordance information produced by 'Sweave' or 'knitr' and
'Sweave' or 'knitr' ; this allows for bilateral communication
between a text editor (visualizing the 'noweb' source) and
a viewer (visualizing the resultant 'PDF'), thus bypassing
the intermediate 'TeX' file.
pathClass is a collection of classification methods that
use information about feature connectivity in a biological
network as an additional source of information. This additional
knowledge is incorporated into the classification a priori.
Several authors have shown that this approach significantly
increases the classification performance.
Implementation of simple functions to draw
basic path diagrams just for visualization purposes.
pathmox, the cousin package of plspm, provides a very
interesting solution for handling segmentation variables
in PLS Path Modeling: segmentation trees in PLS Path Modeling.
Utilities for paths, files and directories.
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.
Adds different kinds of brackets to a plot, including braces, chevrons, parentheses or square brackets.
R Support for ADMB (Automatic Differentiation Model Builder)
This package solves systems of delay differential equations.
by interfacing numerical routines written by Simon N. Wood , with
contributions by Benjamin J. Cairns. These numerical routines first
appeared in Simon Wood's solv95 program.
This package includes a vignette and a complete user's guide.
'PBSddesolve' originally appeared on CRAN under the name 'ddesolve'.
That version is no longer supported. The current name emphasizes a close
association with other PBS packages, particularly 'PBSmodelling'.
This software has evolved from fisheries research conducted at the
Pacific Biological Station (PBS) in `Nanaimo', British Columbia, Canada. It
extends the R language to include two-dimensional plotting features similar
to those commonly available in a Geographic Information System (GIS).
Embedded C code speeds algorithms from computational geometry, such as
finding polygons that contain specified point events or converting between
longitude-latitude and Universal Transverse Mercator (UTM) coordinates.
Additionally, we include `C++' code developed by Angus Johnson for the `Clipper'
library. Also included are data for a global shoreline and other
data sets in the public domain. The R directory `.../library/PBSmapping/doc'
offers a complete user's guide, which should be consulted to use package
functions effectively.
Provides software to facilitate the design, testing, and operation
of computer models. It focuses particularly on tools that make it easy to
construct and edit a customized graphical user interface (GUI). Although our
simplified GUI language depends heavily on the R interface to the Tcl/Tk
package, a user does not need to know Tcl/Tk. Examples illustrate models
built with other R packages, including PBSmapping, PBSddesolve, and BRugs.
A complete user's guide `PBSmodelling-UG.pdf' shows how to use this package
effectively.
Functions simplifying presentation of PCA models in a 3D interactive representation using 'rgl'.
Draws a 2D principal component plot using the first 2 principal
components from the original and bootstrapped data to give some sense of
variability.
Three implementations of principal component analysis methods
using the L1 norm.  The package depends on COIN-OR Clp version >=
1.12.0.  The methods implemented are PCA-L1 (Kwak 2008), L1-PCA (Ke and
Kanade 2003, 2005), and L1-PCA* (Brooks, Dula, and Boone 2013).
Functions for causal structure
learning and causal inference using graphical models. The main algorithms
for causal structure learning are PC (for observational data without hidden
variables), FCI and RFCI (for observational data with hidden variables),
and GIES (for a mix of data from observational studies
(i.e. observational data) and data from experiments
involving interventions (i.e. interventional data) without hidden
variables). For causal inference the IDA algorithm, the Generalized
Backdoor Criterion (GBC) and the Generalized Adjustment Criterion (GAC)
are implemented.
Principal Component Analysis, orthogonal rotation and multiple factor analysis for a mixture of quantitative and qualitative variables.
Semiparametric regression analysis of panel count data under the non-homogeneous Poisson
process model with and without Gamma frailty using monotone splines.
The package solves linear system of equations Ax=b by using Preconditioned Conjugate Gradient Algorithm where A is real symmetric positive definite matrix. A suitable preconditioner matrix may be provided by user. This can also be used to minimize quadratic function (x'Ax)/2-bx for unknown x.
Contains logic for computing the statistical association of variable groups, i.e., gene sets, with respect to the principal components of genomic data.
This package implements a work-alike to R's POSIXct class
which implements 360- and 365-day calendars in addition to the
gregorian calendar.
pcIRT estimates the multidimensional polytomous Rasch model
(Rasch, 1961) and the Continous Rating Scale model (Mueller, 1987).
Apply Partial Correlation coefficient with Information
Theory (PCIT) to a correlation matrix.
The PCIT algorithm identifies meaningful correlations to define
edges in a weighted network. The algorithm can be applied to
any correlation-based network including but not limited to gene
co-expression networks.
To reduce compute time by making use of multiple compute cores,
simply run PCIT on a computer with has multiple cores and also
has the Rmpi package installed. PCIT will then auto-detect the
multicore environment and run in parallel mode without the need
to rewrite your scripts. This makes scripts, using PCIT, portable
across single core (or no Rmpi package installed) computers
which will run in serial mode and multicore (with Rmpi package
installed) computers which will run in parallel mode.
Provides functions to perform arm-based network meta-analysis for datasets with binary, continuous, and count outcomes.
Computation of the Pedroni (1999) panel cointegration test statistics.  Reported are the empirical and the standardized values.
Analyzing regression data with many and/or highly collinear predictor variables, by simultaneously reducing the predictor variables to a limited number of components and regressing the criterion variables on these components. Several rotation options are provided in this package, as well as model selection options.
Set of functions for analysis of Principal Coordinates of Phylogenetic Structure (PCPS).
Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations?  This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.
This package contains a function to estimate
panel-corrected standard errors. Data may contain balanced or
unbalanced panels.
Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.
The package performs cluster analysis via nonparametric density
estimation. Operationally, the kernel method is used throughout to estimate
the density. Diagnostics methods for evaluating the quality of the clustering
are available. The package includes also a routine to estimate the
probability density function obtained by the kernel method, given a set of
data with arbitrary dimensions.
Download economic and financial time series from public sources.
Computes the euclidean distance between rows of a matrix X
and rows of another matrix Y.  Previously, this could be done
by binding the two matrices together and calling 'dist', but
this creates unnecessary computation by computing the distances
between a row of X and another row of X, and likewise for Y.
pdist strictly computes distances across the two matrices, not
within the same matrix, making computations significantly
faster for certain use cases.
This package provides a model of Pavlovian conditioning phenomena, such as response extinction and spontaneous recovery, and partial reinforcement extinction effects. Competing proximal and distal reward predictions, computed using fast and slow learning rates, combine according to their uncertainties and the recency of information. The resulting mean prediction drives the response rate.
A collection of tools for approximating the 'PDQ' functions (respectively, the
cumulative distribution, density, and quantile) of probability distributions via classical
expansions involving moments and cumulants.
Threshold model, panel version of Hylleberg et al. (1990) seasonal unit root tests, and panel unit root test of Chang (2002).
A package to compute and tune some positive definite and
sparse covariance estimators
Calculates the periodogram of a time series, maximum-likelihood fits an Ornstein-Uhlenbeck state space (OUSS) null model and evaluates the statistical significance of periodogram peaks against the OUSS null hypothesis. The OUSS is a parsimonious model for stochastically fluctuating variables with linear stabilizing forces, subject to uncorrelated measurement errors. Contrary to the classical white noise null model for detecting cyclicity, the OUSS model can account for temporal correlations typically occurring in ecological and geological time series.
Spectrum manipulation: background estimation, Markov
smoothing, deconvolution and peaks search functions. Ported
from ROOT/TSpectrum class.
Package for estimating periodic autoregressive models.
Datasets: monthly ozone and Fraser riverflow. Plots: periodic
versions of boxplot, auto/partial correlations, moving-average
expansion.
pearson7 supports maximum likelihood inference for the Pearson VII
distribution with shape parameter 3/2 and free location and scale
parameters. This distribution is relevant when estimating the velocity of
processive motor proteins with random detachment.
Implementation of the Pearson distribution system, including full
support for the (d,p,q,r)-family of functions for probability distributions
and fitting via method of moments and maximum likelihood method.
The Pearson-ICA algorithm is a mutual information-based
method for blind separation of statistically independent source
signals. It has been shown that the minimization of mutual
information leads to iterative use of score functions, i.e.
derivatives of log densities. The Pearson system allows
adaptive modeling of score functions. The flexibility of the
Pearson system makes it possible to model a wide range of
source distributions including asymmetric distributions. The
algorithm is designed especially for problems with asymmetric
sources but it works for symmetric sources as well.
Validation of risk predictions obtained from survival models and
competing risk models based on censored data using inverse weighting and
cross-validation.
Contains functions for sensitivity and power analysis, for calculating statistics describing pedigrees from wild populations, and for viewing pedigrees
An implementation for association analysis with CNV data in R. It
provides two methods for association study: first, the observed probe
intensity measurement can be directly used to detect the association of CNV
with phenotypes of interest. Second, the most probable copy number is
estimated with the proposed likelihood and the association of the most
probable copy number with phenotype is tested. This method can be applied
to both the independent and correlated population.
Gene-level association tests with disease status for pedigree data: kernel and burden association statistics.
Functions to employ many of the tools and
techniques used in the field of pedometrics (see
http://en.wikipedia.org/wiki/Pedometrics for definition of pedometrics).
These functions were developed to fulfil 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 (Universidade Federal Rural do
Rio de Janeiro, Brazil), Dr Gustavo M Vasques (Embrapa Solos, Brazil), and
Dr Gerard BM Heuvelink (ISRIC - World Soil Information, the Netherlands).
The project is supported by the CAPES, Ministério da Educação, Brasília -
DF 70040-020, Brazil, process ID BEX 11677/13-9. Several functions
simply 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 the use of this package as a
dependency for other packages is strongly discouraged.
Functions for reading, writing, plotting, analysing, and manipulating allelic and haplotypic data, and for the analysis of population nucleotide sequences and micro-satellites including coalescence analyses.
Several functions introduced in Aster et al.'s book on inverse theory. The functions are often translations of MATLAB code developed by the authors to illustrate concepts of inverse theory as applied to geophysics. Generalized inversion, tomographic inversion algorithms (conjugate gradients, 'ART' and 'SIRT'), non-linear least squares, first and second order Tikhonov regularization, roughness constraints, and procedures for estimating smoothing parameters are included. Includes a wrapper for the FORTRAN based 'LSQR' (Paige and Saunders) routine.
This package provides functions to perform multivariate Gaussian parameter estimation based on data with abundance-dependent missingness. It implements a penalized Expectation-Maximization (EM) algorithm. The package is tailored for but not limited to proteomics data applications, in which a large proportion of the data are often missing-not-at-random with lower values (or absolute values) more likely to be missing.
A package for fitting possibly high dimensional penalized
regression models. The penalty structure can be any combination
of an L1 penalty (lasso and fused lasso), an L2 penalty (ridge) and a
positivity constraint on the regression coefficients. The
supported regression models are linear, logistic and Poisson
regression and the Cox Proportional Hazards model.
Cross-validation routines allow optimization of the tuning
parameters.
Implements the penalized LDA proposal of "Witten and Tibshirani (2011), Penalized classification using Fisher's linear discriminant, to appear in Journal of the Royal Statistical Society, Series B".
This package provides feature selection SVM using penalty
functions. The smoothly clipped absolute deviation (SCAD),
'L1-norm', 'Elastic Net' ('L1-norm' and 'L2-norm') and 'Elastic
SCAD' (SCAD and 'L2-norm') penalties are availible. The tuning
parameters can be founf using either a fixed grid or a interval
search.
Flexible copula density estimation with penalized hierarchical B-Splines.
Estimation of univariate (conditional) densities using penalized B-splines with automatic selection of optimal smoothing parameter.
Flexible Pair-Copula Estimation in D-vines using Bivariate Penalized Splines.
Structured fusion Lasso penalized estimation of multi-state models with the penalty applied to absolute effects and absolute effect differences (i.e., effects on transition-type specific hazard rates).
Simulation of continuous, correlated high-dimensional data with time to event or binary response, and parallelized functions for Lasso, Ridge, and Elastic Net penalized regression with repeated starts and two-dimensional tuning of the Elastic Net.
Package peperr is designed for prediction error estimation
through resampling techniques, possibly accelerated by parallel
execution on a compute cluster. Newly developed model fitting
routines can be easily incorporated.
This package provides a variety of methods for dealing
with analysis of peptide library data, including clustering,
motif finding, and QSAR model fitting.
Amino acid exchange based on single nucleotide variant (SNV) information and tryptic digestion on peptide sequence. Searching for homologous by comparison of tryptic digested peptide sequences.
Evaluation of diversity in peptide libraries, including NNN, NNB,
NNK/S, and 20/20 schemes. Custom encoding schemes can also be defined.
Metrics for evaluation include expected coverage, relative efficiency, and
the functional diversity of the library. Peptide-level inclusion
probabilities are computable for both the native and custom encoding
schemes.
Calculate physicochemical properties and indices from amino-acid sequences of peptides and proteins. Include also the option to read and plot output files from the 'GROMACS' molecular dynamics package.
Moderated regression with mean and residual centering and simple slopes analysis.
The package includes procedures for identification, model
fitting and estimation for time series with periodic structure.
Additionally procedures for simulation of periodic processes
and real data sets are included.
Several person-fit statistics (PFSs) are offered. These statistics allow assessing whether
individual response patterns to tests or questionnaires are (im)plausible given
the other respondents in the sample or given a specified item response theory model. Some PFSs apply to
dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs
(personal biserial correlation, caution index, (normed) number of Guttman errors,
agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include
extensions of lz, U3, and (normed) number of Guttman errors.
Package that implements different performance measures for classification and ranking tasks. AUC, precision at a given recall, F-score for single and multiple classes are available.
Collection of econometric functions for
performance and risk analysis. This package aims to aid
practitioners and researchers in utilizing the latest
research in analysis of non-normal return streams.  In
general, it is most tested on return (rather than
price) data on a regular scale, but most functions will
work with irregular return data as well, and increasing
numbers of functions will work with P&L or price data
where possible.
An  infra-structure for estimating the predictive performance
of predictive models. In this context, it can also be used to compare and/or select among
different alternative ways of solving one or more predictive tasks. The main goal of the
package is to provide a generic infra-structure to estimate the values of different metrics
of predictive performance using different estimation procedures. These estimation tasks can be
applied to any solutions (workflows) to the predictive tasks. The package provides easy to use
standard workflows that allow the usage of any available R modeling algorithm together with
some pre-defined data pre-processing steps and also prediction post-processing methods. It also provides means for addressing issues related with the statistical significance of the observed differences.
Perform Exact or Asymptotic permutation tests
This version of the permutational algorithm generates a
dataset in which event and censoring times are conditional on
an user-specified list of covariates, some or all of which are
time-dependent.
Includes functions to work with the Mallows, 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 to 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
A set of restricted permutation designs for freely exchangeable, line transects (time series), and spatial grid designs plus permutation of blocks (groups of samples) is provided. 'permute' also allows split-plot designs, in which the whole-plots or split-plots or both can be freely-exchangeable or one of the restricted designs. The 'permute' package is modelled after the permutation schemes of 'Canoco 3.1' (and later) by Cajo ter Braak.
Tools that allow developers to write functions for
prediction error estimation with minimal programming effort and
assist users with model selection in regression problems.
Translate words from English to Persian (Over 67,000
words)
Provides a robust framework for analyzing the extent to which differential survival with respect to higher level trait variation is reducible to lower level variation. In addition to its primary test, it also provides functions for simulation-based power analysis, reading in common data set formats, and visualizing results. Temporarily contains an edited version of function hr.mcp() from package 'wild1', written by Glen Sargeant.
"perturb" evaluates collinearity by adding random noise to
selected variables. "colldiag" calculates condition numbers and
variance decomposition proportions to test for collinearity and
uncover its sources.
Single item and composite pesticide residue measurements
of fifteen commodity-pesticide combinations plus analytical
tools.
This package implements different analytic/direct and
iterative reconstruction methods of Peter Toft. It also offer
the possibility to simulate PET data.
Eco-phylogenetic and community phylogenetic analyses.
Keeps community ecological and phylogenetic data matched up and
comparable using 'comparative.comm' objects. Wrappers for common
community phylogenetic indices ('shape', 'evenness', 'dispersion',
and 'dissimilarity' metrics). Implementation of Cavender-Bares
(2004) correlation of phylogenetic and ecological matrices
('fingerprint.regression'). Phylogenetic Generalised Linear Mixed
Models (PGLMMs) following ('pglmm') following Ives & Helmus (2011)
and Rafferty & Ives (2013). Simulation of null assemblages,
traits, and phylogenies ('scape', 'sim.meta.comm').
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.
A Group ICA Algorithm that can run in parallel on an SGE platform or multi-core PCs
Miscellaneous functions for data analysis in ecology, with special emphasis on spatial data.
Estimation of panel models for glm-like models: this includes binomial models (logit and probit) count models (poisson and negbin) and ordered models (logit and probit)
Based on the Generalized Least Square model for
comparative Phylogenetics (ref).
Construction of balanced incomplete binary blocks designs (BIB) from a projective geometry defined on a Galois fields order 2, using to extract the RBIBD & the uniforms designs associated.
Carries out model-based clustering or classification using parsimonious Gaussian mixture models.
Provides functions to aid the identification of probable/possible
duplicates in Plant Genetic Resources (PGR) collections using
'passport databases' comprising of information records of each constituent
sample. These include methods for cleaning the data, creation of a
searchable Key Word in Context (KWIC) index of keywords associated with
sample records and the identification of nearly identical records with
similar information by fuzzy, phonetic and semantic matching of keywords.
Computation of mean squared errors of stereological predictors.
The phalen package contains (1) clustering and
partitioning algorithms; (2) penalty functions for numeric
vectors; (3) a ranking function; and (4) color palettes and
functions.
Phylogenetic analysis in R: Estimation of phylogenetic
trees and networks using Maximum Likelihood, Maximum Parsimony,
distance methods and Hadamard conjugation.
Contains a set of functions to perform large-scale analysis of pharmacogenomic data.
This package contains functions performing power calculations for mixed (sparse/dense sampled) pharmacokinetic study designs. The input data for these functions is tailored for NONMEM .phi files.
phaseR is an R package for the qualitative analysis of one and
two dimensional autonomous ODE systems, using phase plane methods. Programs
are available to identify and classify equilibrium points, plot the
direction field, and plot trajectories for multiple initial conditions. In
the one dimensional case, a program is also available to plot the phase
portrait. Whilst in the two dimensional case, additionally a program is
available to plot nullclines. Many example systems are provided for the
user.
Functions to perform Bayesian inference on absorption time
data for Phase-type distributions.  Plans to expand this to
include frequentist inference and simulation tools.
phcfM is an R package for modelling anthropogenic
deforestation. It was initially developed to obtain REDD+
baseline scenarios of deforestation for the "programme
holistique de conservation des forets a Madagascar" (from which
the package is named after). Parameter inference is done in a
hierarchical Bayesian framework. Markov chains Monte Carlo
(MCMC) are coded in C++ using the Scythe statistical library to
maximize computation efficiency.
Implementation of heatmaps that offers more control
over dimensions and appearance.
An alternative to carrying out phenotypic adaptability and stability analyses, taking into account nonparametric statistics. Can be used as a robust approach, less sensitive to departures from common genotypic, environmental, and GxE effects data assumptions (e.g., normal distribution of errors).
Provides some easy-to-use functions for
spatial analyses of (plant-) phenological data
sets and satellite observations of vegetation.
Provides functions to estimate the size-controlled phenotypic integration index, a novel method by Torices & Méndez (2014) to solve problems due to individual size when estimating integration (namely, larger individuals have larger components, which will drive a correlation between components only due to resource availability that might obscure the observed measures of integration). In addition, the package also provides the classical estimation by Wagner (1984), bootstrapping and jackknife methods to calculate confidence intervals and a significance test for both integration indices.
Provides functions to preprocess phenological data, for modelling and result handling.
Provides some easy-to-use functions for time series
analyses of (plant-) phenological data sets. These functions
mainly deal with the estimation of combined phenological time
series and are usually wrappers for functions that are already
implemented in other R packages adapted to the special
structure of phenological data and the needs of phenologists.
Some date conversion functions to handle Julian dates are also
provided.
High-throughput generation of genetic markers from molecular phenotypes for crosses between inbred strains. These markers can be use to saturate existing genetic map or creating a new one.
Functions used to fit and test the phenology of species based on counts.
This package provides tools for the evaluation of the goodness of fit and the predictive capacity of the proportional hazards model.
Analysis of terms in linear, generalized and mixed linear models,
on the basis of multiple comparisons of factor contrasts.  Specially suited
for the analysis of interaction terms.
Fits proportional hazards model incorporating random effects using
an EM algorithm using Markov Chain Monte Carlo at E-step.
Convert English letters to numbers or numbers to English letters as
on a telephone keypad. When converting letters to numbers, a character
vector is returned with "A," "B," or "C" becoming 2, "D," "E", or "F"
becoming 3, etc. When converting numbers to letters, a character vector is
returned with multiple elements (i.e., "2" becomes a vector of "A," "B," and
"C").
Tools for phoneticians and phonologists, including functions for normalization and plotting of vowels.
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.
PHREEQC (Version 3) 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.
Based on (but not identical to) the no-longer-maintained package 'phyext', provides enhancements to 'phylobase' classes, specifically for use by package 'SigTree'; provides classes and methods which help users manipulate branch-annotated trees (as in 'SigTree'); also provides support for a few other extra features.
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.
Tools for studying the evolution of function-valued traits (as well as other high-dimensional traits) including ancestral state reconstruction, estimating phylogenetic signal, and assessing correlated trait evolution. Visit http://www.phylocurve.org for more information.
Manipulation and analysis of phylogenetically simulated
data sets and phylogenetically based analyses using GLS.
Phyloland package models a space colonization process mapped onto a phylogeny, it aims at estimating limited dispersal and ecological competitive exclusion in a Bayesian MCMC statistical phylogeographic framework (please refer to phyloland-package help for details.)
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.
Given a phylogenetic tree T and a sample S of species represented as
a subset of tips in T, we want to compute a measure of the diversity
of the species in S with respect to T. The current package offers the
implementations of efficient algorithms for several such measures.
Most importantly, the package includes algorithms for computing exactly and
efficiently the expectation and the variance of these measures, which are
essential for null model comparisons. Efficient computation of
richness-standardized versions of these metrics, such as the net
relatedness index (NRI), nearest taxon index (NTI), phylogenetic
diversity index (PDI), and the corresponding indices of two-sample measures
are also available in this package. The package also introduces a new
single-sample measure, the Core Ancestor Cost (CAC); the package provides
functions for computing the value and the standardised index of the CAC and,
more than that, there is an extra function available that can compute exactly
any statistical moment of the measure.
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.
A variety of formulae are provided for estimation of height,
weight and fluid compartments of adults and children. Each formula is
referenced to the original publication. Warnings can be given for
estimation based on input data outside of normal ranges. Future functions
will cover more material with a focus on anaesthesia, critical
care and peri-operative medicine.
Fits PE and RLC data to one of a four published PE models.
Simulates incident irradiance as a function of time and space.
Calculates phytoplankton production by transposing modeled PE or RLC data
to a water column with a user-defined theoretical in-situ irradiance field.
Methods for estimating the proportion of true null hypotheses, i.e., the pi0, when a very large number of hypotheses are simultaneously tested, especially for the purpose of (local) false discovery rate control for microarray data. It also contains functions to estimate the distribution of noncentrality parameters from a large number of parametric tests.
Phylocom integration, community analyses, null-models, traits and evolution in R
Implement 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, such as group Lasso, group MCP and group SCAD, are also implemented for Sparse Linear Regression, Sparse Logistic Regression and Sparse Multivariate Regression.
A collection of scripts and data files for the statistics text:
"Process Improvement using Data". The package contains code for designed
experiments, data sets and other convenience functions used in the book.
Extension of the ARTP package for gene- and pathway-environment
interaction
Check if a remote computer is up. It can either
just call the system ping command, or check a specified
TCP port.
Implements the Product of Independent beta Probabilities dose Escalation (PIPE) design for dual-agent Phase I trials as described in Mander AP, Sweeting MJ. Statistics in Medicine 2015. doi: 10.1002/sim.6434.
Provides various styles of function chaining methods: Pipe
operator, Pipe object, and pipeline function, each representing a distinct
pipeline model yet sharing almost a common set of features: A value can be
piped to the first unnamed argument of a function and to dot symbol in an
enclosed expression. The syntax is designed to make the pipeline more
readable and friendly to a wide range of operations.
Generate Predicted Interval Plots.  Simulate and plot
confidence intervals of an effect estimate given observed data
and a hypothesis about the distribution of future data.
With pitchRx, one can easily obtain Major League Baseball Advanced
Media's Gameday data (as well as store it in a remote database). The
Gameday website hosts a wealth of data in XML format, but perhaps most
interesting is PITCHf/x. Among other things, PITCHf/x data can be used to
recreate a baseball's flight path from a pitcher's hand to home plate. With
pitchRx, one can easily create animations and interactive 3D scatterplots
of the baseball's flight path. PITCHf/x data is also commonly used to
generate a static plot of baseball locations at the moment they cross home
plate. These plots, sometimes called strike-zone plots, can also refer to a
plot of event probabilities over the same region. pitchRx provides an easy
and robust way to generate strike-zone plots using the ggplot2 package.
R interface of Pivotal Data Fabrics running on PostgreSQL
or Pivotal (Greenplum and HAWQ) database with parallel and
distributed computation ability for big data analytics.
PivotalR is a package that enables users of R to interact with
the Pivotal (Greenplum) Database as well as Pivotal HD/HAWQ for
Big Data analytics. It does so by providing an interface to the
operations on tables/views in the database. These operations
are almost the same as those of data.frame. Thus the users of R
do not need to learn SQL when they operate on the objects in
the database. It also provides a wrapper for MADlib, which is
an open-source library for parallel and scalable in-database
analytics.
Functions for import, export, plotting and other
manipulations of bitmapped images.
Estimation of pharmacokinetic parameters using
non-compartmental theory
Set configuration options on a per-package basis.
Options set by a given package only apply to that package,
other packages are unaffected.
Provides a function kitten() which creates cute little
packages which pass R package checks. This sets it apart from
package.skeleton() which it calls, and which leaves imperfect files
behind. As this is not exactly helpful for beginners, kitten() offers
an alternative.
This package provides some low-level utilities to use for package
development. It currently provides managers for multiple package specific
options and registries, vignette, unit test and bibtex related utilities.
It serves as a base package for packages like NMF, RcppOctave, doRNG, and
as an incubator package for other general purposes utilities, that will
eventually be packaged separately.
It is still under heavy development and changes in the interface(s) are
more than likely to happen.
PKgraph provides a graphical user interface for population
pharmacokinetic model diagnosis. It also provides an integrated
and comprehensive platform for the analysis of pharmacokinetic
data including exploratory data analysis, goodness of model
fit, model validation and model comparison. Results from a
variety of modeling fitting software, including NONMEM,
Monolix, SAS and R, can be used. PKgraph is programmed in R,
and uses the R packages lattice, ggplot2 for static graphics,
and rggobi for interactive graphics.
PKI functions such as verifying certificates, RSA encription and signing which can be used to build PKI infrastructure and perform cryptographic tasks.
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.
Supports the creation of burndown charts and gantt diagrams.
Solves the electromagnetic problem of reflection and transmission
of a plane wave or a gaussian beam, at a planar multilayer interface. Also
computed are the decay rates and emission profile for a dipolar emitter
near a multilayer structure.
Automatic generation of regular factorial designs, including fractional designs, orthogonal block designs, row-column designs and split-plots.
Coupled leaf gas exchange model, A-Ci curve simulation and
fitting, leaf energy balance using Penman-Monteith, Cowan-Farquhar
optimization, unit conversions.
Estimation, prediction, thresholding, and plotting for partially linear additive quantile regression.  Intuitive functions for fitting and plotting partially linear additive quantile regression models.  Uses and works with functions from the 'quantreg' package.
This package implements schemes for estimating player or
team skill based on dynamic updating. Implemented methods
include Elo, Glicko and Stephenson. Contains pdf documentation
of a reproducible analysis using approximately one million
chess matches.
A GTK+ graphical user interface for editing and
interacting with R plots.
The package can be used to estimate probabilistic latent feature models
with a disjunctive or a conjunctive mapping rule for two-way two-mode frequency data
Sequential Monte Carlo inference for fully Bayesian
Gaussian process (GP) regression and classification models by
particle learning (PL).  The sequential nature of inference
and the active learning (AL) hooks provided facilitate thrifty
sequential design (by entropy) and optimization
(by improvement) for classification and
regression models, respectively.
This package essentially provides a generic
PL interface, and functions (arguments to the interface) which
implement the GP models and AL heuristics.  Functions for
a special, linked, regression/classification GP model and
an integrated expected conditional improvement (IECI) statistic
is provides for optimization in the presence of unknown constraints.
Separable and isotropic Gaussian, and single-index correlation
functions are supported.
See the examples section of ?plgp and demo(package="plgp")
for an index of demos
PLIS is a multiple testing procedure for testing several
groups of hypotheses. Linear dependency is expected from the
hypotheses within the same group and is modeled by hidden
Markov Models. It is noted that, for PLIS, a smaller p value
does not necessarily imply more significance because of
dependency among the hypotheses. A typical application of PLIS
is to analyze genome wide association studies datasets, where
SNPs from the same chromosome are treated as a group and
exhibit strong linear genomic dependency.
A set of estimators and tests for panel data.
A set of tools for identifying genes whose differential
expression is associated with measurements of other covariates
on a continuous scale.  These methods rely on generalized
additive partially linear models which can be fitted
efficiently using a B-spline basis approximation.  Still under
development: methods for interfacing with objects extending the
eSet class and a function to pass linear models in edgeR and
DEseq format.
This package fits the partially linear mixed effects model
(semiparametric random intercept model) using kernel
regression, without distributional assumptions for the random
terms. Estimation procedure is an iterative generalized least
squares type. A nonparametric heteroskedastic variance function
is allowed for the regression error. Bootstrap resampling is
provided for inference. The package implements bandwidth
selection by an alternative cross validation for correlated
data.
Performs bivariate composite likelihood and full
information maximum likelihood estimation for polytomous
logit-normit (graded logistic) item response theory (IRT)
models.
Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likehood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function  is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.
A function to plot scatter points for two
groups of values.  It simultaneously calculates a P
value of t-test and incorporate the P value into the
plot.  Its advantage is jittering the adjacent
points side by side to avoid or reduce overlapping
in the plot.
Functions for viewing 2-D and 3-D data, including perspective plots, slice plots, surface plots, scatter plots, ... Includes data sets from oceanography
Provides an interactive plot device for handling the geographic data for web browsers, designed for the automatic creation of web maps as a combination of users' data and Google Maps layers.
Writes sp-class, spacetime-class, raster-class and similar spatial and spatio-temporal objects to KML following some basic cartographic rules.
Markov chain Monte Carlo diagnostic plots. The purpose of the
package is to combine existing tools from the 'coda' and 'lattice' packages,
and make it easy to adjust graphical details.
Plot a model's response when varying one or two predictors
while holding the other predictors constant.  A poor man's
partial dependence plot.  Also plot model residuals.
Plot principal component histograms around a bivariate
scatter plot.
Useful set of tools for plotting network diagrams in any kind of project.
Creates an envelope around a set of plotted points. The
envelope is compact with a boundary that is continuous, smooth
and convex. Each point is represented as a circle and the
circles and connecting lines are the solution to the multiple
pulley problem. This method can be used to highlight regions in
a two-dimensional space.
Lots of plots, various labeling, axis and color scaling functions.
Most ROC curve plots obscure the cutoff values and inhibit
interpretation and comparison of multiple curves. This attempts to address
those shortcomings by providing plotting and interactive tools. Functions
are provided to generate an interactive ROC curve plot for web use, and
print versions. A Shiny application implementing the functions is also
included.
Contains 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)
Principal Component Analysis (PCA) biplots, Covariance monoplots
and biplots, Partial Least Squares (PLS) biplots, Partial Least Squares for
Generalized Linear Model (PLS-GLM) biplots, Sparse Partial Least Squares
(SPLS) biplots and Sparse Partial Least Squares for Generalized Linear
Model (SPLS-GLM) biplots.
plsdepot contains different methods for PLS analysis of
one or two data tables such as Tucker's Inter-Battery, NIPALS,
SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis.
The main reference for this software is the awesome book (in
French) 'La Regression PLS: Theorie et Pratique' by Michel
Tenenhaus.
The plsdof package provides Degrees of Freedom estimates
for Partial Least Squares (PLS) Regression. Model selection for
PLS is based on various information criteria (aic, bic, gmdl)
or on cross-validation. Estimates for the mean and covariance
of the PLS regression coefficients are available. They allow
the construction of approximate confidence intervals and the
application of test procedures. Further, cross-validation
procedures for Ridge Regression and Principal Components
Regression are available.
Routines for PLS-based genomic analyses,
implementing PLS methods for classification with
microarray data and prediction of transcription factor
activities from combined ChIP-chip analysis. The >=1.2-1
versions include two new classification methods for microarray
data: GSIM and Ridge PLS. The >=1.3 versions includes a
new classification method combining variable selection and
compression in logistic regression context: RIRLS-SPLS; and
an adaptive version of the sparse PLS.
Partial Least Squares Path Modeling (PLS-PM)
analysis for both metric and
non-metric data, as well as REBUS analysis.
Provides Partial least squares Regression for (weighted) beta regression models and k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Provides Partial least squares Regression and various regular, sparse or kernel, techniques for fitting Cox models in high dimensional settings.
Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.
Kernel density estimation with global bandwidth selection
via "plug-in".
The base R data.frame, like any vector, is
copied upon modification. This behavior is at odds with
that of GUIs and interactive graphics. To rectify this,
plumbr provides a mutable, dynamic tabular data model.
Models may be chained together to form the complex
plumbing necessary for sophisticated graphical
interfaces. Also included is a
general framework for linking datasets; an typical
use case would be a linked brush.
plusser provides an API interface to Google+ so that posts,
profiles and pages can be automatically retrieved.
A set of tools that solves a common set of problems: you
need to break a big problem down into manageable pieces, operate on each
piece and then put all the pieces back together.  For example, you might
want to fit a model to each spatial location or time point in your study,
summarise data by panels or collapse high-dimensional arrays to simpler
summary statistics. The development of 'plyr' has been generously supported
by 'Becton Dickinson'.
Performs Penalized Multivariate Analysis: a penalized
matrix decomposition, sparse principal components analysis, and
sparse canonical correlation analysis, described in the
following papers: (1) Witten, Tibshirani and Hastie (2009) A
penalized matrix decomposition, with applications to sparse
principal components and canonical correlation analysis.
Biostatistics 10(3):515-534. (2) Witten and Tibshirani (2009)
Extensions of sparse canonical correlation analysis, with
applications to genomic data. Statistical Applications in
Genetics and Molecular Biology 8(1): Article 28.
Monte Carlo based model choice for applied phylogenetics of
continuous traits. Method described in  Carl Boettiger, Graham Coop,
Peter Ralph (2012) Is your phylogeny informative? Measuring
the power of comparative methods, Evolution 66 (7)
2240-51. doi:10.1111/j.1558-5646.2011.01574.x.
Parsimonious Mixtures of Contaminated Gaussian Distributions
The pmclust aims to utilize model-based clustering (unsupervised)
for high dimensional and ultra large data, especially in a distributed
manner. The package employs Rmpi to perform a
expectation-gathering-maximization (EGM) algorithm
for finite mixture Gaussian
models. The unstructured dispersion matrices are assumed in the Gaussian
models. The implementation is default in the single program multiple
data (SPMD) programming model. The code can be executed through Rmpi and
independent to most MPI applications. See the High Performance
Statistical Computing (HPSC) website for more information, documents
and examples.
The Kruskal and Wallis one-way analysis of variance by ranks
can be employed, if the data do not meet the assumptions
for one-way ANOVA. Provided that significant differences
were detected by the Kruskal-Wallis-Test, one may be interested
in applying post-hoc tests for pairwise multiple comparisons
of the ranked data. 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.
An R package for prime number generation, primality testing and integer factorization, currently, using simple methods.
This package provides an easy and simple way to read, write and display bitmap images stored in the PNG format. It can read and write both files and in-memory raw vectors.
An R package for Probit-Normal Marginalized Transition
Random Effects Models
The program pnn implements the algorithm proposed by
Specht (1990).  It is written in the R statistical language. It
solves a common problem in automatic learning. Knowing a set of
observations described by a vector of quantitative variables,
we classify them in a given number of groups. Then, the
algorithm is trained with this datasets and should guess
afterwards the group of any new observation. This neural
network has the main advantage to begin generalization
instantaneously even with a small set of known observations. It
is delivered with four functions (learn, smooth, perf and
guess) and a dataset. The functions are documented with
examples and provided with unit tests.
Provides functions to implement and simulate the partial order continual reassessment method (PO-CRM) for use in Phase I trials of combinations of agents. Provides a function for generating a set of initial guesses (skeleton) for the toxicity probabilities at each combination that correspond to the set of possible orderings of the toxicity probabilities specified by the user.
Bayesian variable selection for regression models of under-reported
count data as well as for Poisson and binomial logit regression models using
spike and slab priors.
The data sets used in the online course ,,PogromcyDanych''. You can process data in many ways. The course Data Crunchers will introduce you to this variety. For this reason we will work on datasets of different size (from several to several hundred thousand rows), with various level of complexity (from two to two thousand columns) and prepared in different formats (text data, quantitative data and qualitative data). All of these data sets were gathered in a single big package called PogromcyDanych to facilitate access to them. It contains all sorts of data sets such as data about offer prices of cars, results of opinion polls, information about changes in stock market indices, data about names given to newborn babies, ski jumping results or information about outcomes of breast cancer patients treatment.
This package implements both the exact and approximation
methods for computing the cdf of the Poisson binomial
distribution. It also provides the pmf, quantile function, and
random number generation for the Poisson binomial distribution.
Implements the methods described in the paper, Witten (2011) Classification and Clustering of Sequencing Data using a Poisson Model, Annals of Applied Statistics 5(4) 2493-2518.
Functions for obtaining the density, random deviates
and maximum likelihood estimates of the Poisson lognormal
distribution and the bivariate Poisson lognormal distribution.
For every spatial point in a list, calculates the density
and temporal tendency (average age) of points within a user defined
neighborhood; also supports point density and temporal tendency
visualization using 'ggmap'.
Functions to calculate and plot event and pointer years as well as components of resilience. Designed for dendroecological applications, but also suitable to analyze patterns in other ecological time series.
Generation of multiple count, binary and continuous variables simultaneously
given the marginal characteristics and association structure. Throughout the package,
the word 'Poisson' is used to imply count data under the assumption of Poisson distribution.
Generation of multiple count, binary and ordinal variables simultaneously
given the marginal characteristics and association structure. Throughout the package,
the word 'Poisson' is used to imply count data under the assumption of Poisson distribution.
Generation of a chosen number of count, binary, ordinal, and continuous (via Fleishman polynomials) random variables, with specified correlations and marginal properties.
Generation of multiple count, binary, ordinal and normal variables simultaneously given the marginal characteristics and association structure.
Functions to create confidence intervals for ratios of Poisson
rates under misclassification using double sampling.
This package generates count (assuming Poisson distribution) and continuous data (using Fleishman polynomials) simultaneously.
A package for simulating multivariate data with count and continuous variables with a pre-specified correlation matrix. The count and continuous variables are assumed to have Poisson and normal marginals, respectively. The data generation mechanism is a combination of the normal to anything principle and a connection between Poisson and normal correlations in the mixture.
High dimensional mixtures of Poisson Generalized Linear models with three different parameterizations of Poisson means are considered. Moreover, partitioning the response variables into a set of blocks is possible. The package estimates parameters via EM algorithm. For an efficient initialization, a random splitting small-EM is introduced.
This package implements a method for normalization,
testing, and false discovery rate estimation for RNA-sequencing
data. The description of the method is in Li J, Witten DM,
Johnstone I, Tibshirani R (2012). Normalization, testing, and
false discovery rate estimation for RNA-sequencing data.
Biostatistics 13(3): 523-38. We estimate the sequencing depths
of experiments using a new method based on Poisson
goodness-of-fit statistic, calculate a score statistic on the
basis of a Poisson log-linear model, and then estimate the
false discovery rate using a modified version of permutation
plug-in method. A more detailed instruction as well as sample
data is available at
http://www.stanford.edu/~junli07/research.html.  In this
version, we changed the way of calculating log foldchange for
two-class data. The FDR estimation part remains unchanged.
Latent class analysis and latent class regression models
for polytomous outcome variables.  Also known as latent structure analysis.
This package provides easy access to various political data APIs
directly from R. For example, you can access Google Civic Information API
(https://developers.google.com/civic-information/) or Sunlight Congress API
(https://sunlightlabs.github.io/congress/) for US Congress data, and POPONG
API (http://data.popong.com/) for South Korea National Assembly data.
Client for the HuffPost Pollster API, which provides
access to U.S. opinion polls since 2004.
Routines for the polynomial spline fitting routines
hazard regression, hazard estimation with flexible tails, logspline,
lspec, polyclass, and polymars, by C. Kooperberg and co-authors.
Functions for evaluating the mass density, cumulative distribution function, quantile function and random variate generation for the Polya-Aeppli distribution, also known as the geometric compound Poisson distribution.
Simulate via Markov chain Monte Carlo (hit-and-run algorithm)
a Dirichlet distribution conditioned to satisfy a finite set of linear
equality and inequality constraints (hence to lie in a convex polytope
that is a subset of the unit simplex).
R port of the Clipper library. Performs polygon clipping operations (intersection, union, set minus, set difference) for polygonal regions of arbitrary complexity, including holes. Also computes offset polygons (spatial buffer zones, morphological dilations, Minkowski dilations) for polygonal regions and polygonal lines.
Computes polychoric and polyserial correlations by quick
"two-step" methods or ML, optionally with standard errors;
tetrachoric and biserial correlations are special cases.
The following methods for cubature (numerical integration)
over polygonal domains are currently implemented:
the two-dimensional midpoint rule as a simple wrapper around
as.im.function() from package spatstat (Baddeley and Turner, 2005),
the product Gauss cubature by Sommariva and Vianello (2007),
an adaptive cubature for isotropic functions via line integrate()
along the boundary (Meyer and Held, 2014),
and quasi-exact methods specific to the integration of the
bivariate Gaussian density over polygonal and circular domains
(based on formulae from the Abramowitz and Stegun (1972) handbook).
For cubature over simple hypercubes, the packages "cubature" and
"R2Cuba" are more appropriate.
A collection of functions to implement a class for univariate
polynomial manipulations.
Implements univariate polynomial operations in R
Functions to perform paternity exclusion via allele
matching, in autopolyploid species having ploidy 4, 6, or 8. The
marker data data used can be genotypic (copy numbers known) or
'phenotypic' (copy numbers not known).
A collection of tools to handle microsatellite data of
any ploidy (and samples of mixed ploidy) where allele copy number is not
known in partially heterozygous genotypes.  It can import and export data in
ABI GeneMapper, Structure, ATetra, Tetrasat/Tetra, GenoDive, SPAGeDi,
POPDIST, STRand, and binary presence/absence formats.  It can calculate
pairwise distances between individuals using a stepwise mutation model or
infinite alleles model, with or without taking ploidies and allele frequencies
into account.  These distances can be used for the calculation of clonal
diversity statistics or used for further analysis in R.  Allelic diversity
statistics are also available.  polysat can assist the user in estimating the
ploidy of samples, and it can estimate allele frequencies in
populations, calculate pairwise Fst values based on those frequencies, and
export allele frequencies to SPAGeDi and adegenet.  Functions are also
included for assigning alleles to isoloci in cases where one pair of
microsatellite primers amplifies alleles from two or more independently
segregating isoloci.
Perform classic chi-squared tests and Ripol et al(1999)
binomial confidence interval approach for autopolyploid
dominant markers. Also, dominant markers may be generated
for families of offspring where either one or both of the
parents possess the marker. Missing values and
misclassified markers may be generated at random.
Fits Bayesian mixture models to estimate marker dosage for dominant markers on autopolyploids using JAGS (1.0 or greater) as outlined in Baker et al (2010). May be used in conjunction with polySegratio for simulation studies and comparison with standard methods.
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.
Inference methods for partially observed Markov processes (AKA stochastic dynamical systems, state-space models).
Finds equivalence classes corresponding to a symmetric relation
or undirected graph.  Finds total order consistent with partial order
or directed graph (so-called topological sort).
Construct and analyze projection matrix models from a
demography study of marked individuals classified by age or
stage. The package covers methods described in Matrix
Population Models by Caswell (2001) and Quantitative
Conservation Biology by Morris and Doak (2002).
popdemo provides a set of tools designed for modelling population demography
using population projection matrix (PPM) models.  The focus is mainly on indices
of transient dynamics, and the use of control theory (specifically transfer
functions).  However, popdemo includes a number of tools focussing on other
areas of demographic modelling.  Some tools may prove useful to anyone working
with projection matrix models of any type, or indeed matrices in general.
The package is designed to build on those tools already available in popbio.
Optimal experimental designs for both population and individual
studies based on nonlinear mixed-effect models.  Often this is based on a
computation of the Fisher Information Matrix (FIM). This package was developed
for pharmacometric problems, and examples and predefined models are available
for these types of systems.
A package that implements a variety of statistical and
population genetic methodology.
There are two main purposes to this package. The first is
to allow batch conversion of Genepop (Rousset 2008) input files
for use with Arlequin (Excoffier and Lischer 2010), which has a
simple GUI to analyze batch files. Two commonly used simulation
software, BottleSim (Kuo & Janzen 2003) and Easypop (Balloux
2001) produce Genepop output files that can be analyzed this
way. There are also functions to convert to and from BottleSim
format, to quickly produce allele frequency tables or to
convert a file directly for use in ordination analyses (e.g.
principal component analysis). This package also includes
functions to calculate allele rarefaction curves, confidence
intervals on heterozygosity and allelic richness with
resampling strategies (bootstrap and jackknife).
Provides efficient tools for population genomics data analysis,
able to process individual loci, large sets of loci, or whole genomes. PopGenome not only
implements a wide range of population genetics statistics, but also facilitates the easy
implementation of new algorithms by other researchers. PopGenome is optimized for speed via
the seamless integration of C code.
Provides beginner friendly framework to analyse population genetic data. It uses 'knitr' to create comprehensive reports on spatial genetic data. For detailed information how to use the package refer to the comprehensive tutorials or visit www.popgenreport.org.
This is a generic package that produces "Population Graphs"
a graph-theoretic topology based upon conditional genetic covariance.  This
functionality used to be within the gstudio package, but has been taken out
to focus on spatial integration of graph topologies with existing packages
like sp, raster, and ggplot2.
Provides a suite of tools for various methods of estimating
confidence intervals for the mean of selected populations.
Provides objects and accompanying methods which facilitates populating and querying SQLite databases.
Population genetic analyses for hierarchical analysis of partially
clonal populations built upon the architecture of the 'adegenet' package.
A wrapper for 'readxl' to read GenAlEx data directly into 'poppr'.
Implements the Bayesian hierarchical model described by Wheldon, Raftery, Clark and Gerland (see: http://www.csss.washington.edu/Papers/wp108.pdf) for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows, at the national level.
A set of routines which are useful in constructing
and evaluating self-organizing maps (SOMs).
The main attribute of 'PopVar' is the prediction of genetic variance in bi-parental populations, from which the package derives its name. 'PopVar' contains a set of functions that use phenotypic and genotypic data from a set of candidate parents to 1) predict the mean, genetic variance, and superior progeny value of all, or a defined set of pairwise bi-parental crosses, and 2) perform cross-validation to estimate genome-wide prediction accuracy of multiple statistical models. More details are available in Mohammadi, Tiede, and Smith (2015). Crop Sci. doi:10.2135/cropsci2015.01.0030. A dataset 'think_barley.rda' is included for reference and examples.
This package contains a set of portmanteau diagnostic
checks for univariate and multivariate time series.
Classes for analysing and implementing equity portfolios.
Portfolio optimization and analysis routines and graphics.
Classes that serve as a framework for designing equity
portfolio simulations.
Risk Attribution of a portfolio with Volatility Risk Analysis.
Does Markov chain Monte Carlo (MCMC) simulation of Potts models
and composite likelihood inference for Potts models.
A package including several functions related to the Potts models.
Optimizes a function using Powell's UObyQA algorithm.
Functions for the computation of power and level tables for hypothesis tests, in LaTeX format, functions to build explanatory graphs for studying power of test statistics.
Functions to obtain the operational characteristics of
bioequivalence studies with 2-stage designs via simulations
- Contains a function to calculate power and sample-size distribution
of 2-stage bioequivalence (BE) studies with a 2x2 crossover design
according to Potvin et al. / Montague et al. modified to include a
futility Nmax and modified to do the sample size estimation step
with PE and mse of stage 1.
- Contains further a function with the modifications according to
Karalis & Macheras which use both point estimate (PE) and mse from stage 1
also for the power monitoring steps.
- The third function power.2stage.fC() calculates power and sample-size
distribution of 2-stage BE studies with a futility criterion
for the PE or CI of T/R from stage 1.
- The fourth function power.2stage.GS() calculates power of non-adaptive
group sequential (2-stage) BE studies.
- The fifth function power.2stage.p() calculates power and sample size
distribution of 2-stage BE studies with 2 parallel groups.
This function has a sibling power.2stage.pAF(), which performs exactly as
described in A.Fuglsang 2014
- Another function power.2stage.ssr() allows the power calculation
for 2-stage studies with (blinded) interim sample size re-estimation.
Basic functions for power analysis and effect size calculation.
Analytical power calculations for GxE and GxG interactions for case-control studies of candidate genes and genome-wide association studies (GWAS). This includes power calculation for four two-step screening and testing procedures.  It can also calculate power for GxE and GxG without any screening.
An implementation of maximum likelihood estimators for a variety
of heavy tailed distributions, including both the discrete and continuous
power law distributions. Additionally, a goodness-of-fit based approach is
used to estimate the lower cut-off for the scaling region.
Functions to
calculate power and sample size for testing
(1) mediation effects;
(2) the slope in a simple linear regression;
(3) odds ratio in a simple logistic regression;
(4) mean change for longitudinal study with 2 time points;
(5) interaction effect in 2-way ANOVA; and
(6) the slope in a simple Poisson regression.
(1) To estimate the power of testing for linkage using an
affected sib pair design, as a function of the recurrence risk
ratios. We will use analytical power formulae as implemented in
R. These are based on a Mathematica notebook created by Martin
Farrall. (2) To examine how the power of the transmission
disequilibrium test (TDT) depends on the disease allele
frequency, the marker allele frequency, the strength of the
linkage disequilibrium, and the magnitude of the genetic
effect. We will use an R program that implements the power
formulae of Abel and Muller-Myhsok (1998). These formulae allow
one to quickly compute power of the TDT approach under a
variety of different conditions. This R program was modeled on
Martin Farrall's Mathematica notebook.
Functions to calculate power and
sample size for testing main effect or interaction effect in
the survival analysis of epidemiological studies
(non-randomized studies), taking into account the
correlation between the covariate of the
interest and other covariates. Some calculations also take
into account the competing risks and stratified analysis.
This package also includes
a set of functions to calculate power and sample size
for testing main effect in the survival analysis of
randomized clinical trials.
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 (EMA recommended) 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 / sample size 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.
Computing similarities between proteins based on their GO annotation, KEGG annotation and PPI network topology. It integrates seven features (TCSS, IntelliGO, Wang, KEGG, Jaccard, RA and AA) to predict PPIs using an SVM classifier. Some internal functions to calculate GO semantic similarities are re-used from R package GOSemSim authored by Guangchuang Yu.
This package contains linear and nonlinear regression
methods based on Partial Least Squares and Penalization
Techniques. Model parameters are selected via cross-validation,
and confidence intervals ans tests for the regression
coefficients can be conducted via jackknifing.
Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"). Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.
The pps package contains functions to select samples using
PPS (probability proportional to size) sampling. It also
includes a function for stratified simple random sampling, a
function to compute joint inclusion probabilities for
Sampford's method of PPS sampling, and a few utility functions.
The user's guide pps-ug.pdf is included.
Projection pursuit classification tree using LDA, Lr or PDA projection pursuit index
Tools for exploring projection pursuit classification tree using
various projection pursuit indices.
This package allows users to calculate the number of
under-five child deaths caused by consumption of poor quality
antimalarials across 39 sub-Saharan nations. The package supports one
function, that starts an interactive web tool created using
the shiny R package. The web tool runs locally on the user's machine.
The web tool allows users to set input parameters (prevalence of poor
quality antimalarials, case fatality rate of children who take poor
quality antimalarials, and sample size) which are then used to perform
an uncertainty analysis following the Latin hypercube
sampling scheme. Users can download the output figures as PDFs, and the
output data as CSVs. Users can also download their input parameters
for reference. This package was designed to accompany the analysis
presented in:
J. Patrick Renschler, Kelsey Walters, Paul Newton, Ramanan Laxminarayan
"Estimated under-five deaths associated with poor-quality
antimalarials in sub-Saharan Africa", 2014. Paper submitted.
Distance-based parametric bootstrap tests for clustering with
spatial neighborhood information. Some distance measures,
Clustering of presence-absence, abundance and multilocus genetical data
for species delimitation, nearest neighbor
based noise detection. Try package?prabclus for on overview.
Functions from numerical analysis and linear algebra, numerical
optimization, differential equations, plus some special functions.
Uses Matlab function names where appropriate to simplify porting.
Contains functions for sample size calculation for survey samples using stratified or clustered one-, two-, and three-stage sample designs. Other functions compute variance components for multistage designs and sample sizes in two-phase designs. A number of example datasets are included.
pragma allows for the use of pragma (also sometimes called
directives or keywords. These allow assigning arbitrary
functionality to a word without requiring the standard function
call syntax i.e. with parens.
The Prais-Winsten estimation procedure takes into account serial correlation of type AR(1) in a linear model. The procedure is an iterative method that recursively estimates the beta coefficients and the error autocorrelation of the specified model until convergence of rho, i.e. the AR(1) coefficient, is attained. All estimates are obtained by OLS.
Kasulikud funktsioonid kvantitatiivsete mudelite kursuse
(SHPH.00.004) jaoks
Estimation, prediction and testing for analyzing serial dilution assay data using paired response curve.
It contains functions to analyze the precipitation
intensity, concentration and anomaly.
PredictABEL includes functions to assess the performance of
risk models. The package contains functions for the various measures that are
used in empirical studies, including univariate and multivariate odds ratios
(OR) of the predictors, the c-statistic (or area under the receiver operating
characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test,
reclassification table, net reclassification improvement (NRI) and
integrated discrimination improvement (IDI). Also included are functions
to create plots, such as risk distributions, ROC curves, calibration plot,
discrimination box plot and predictiveness curves. In addition to functions
to assess the performance of risk models, the package includes functions to
obtain weighted and unweighted risk scores as well as predicted risks using
logistic regression analysis. These logistic regression functions are
specifically written for models that include genetic variables, but they
can also be applied to models that are based on non-genetic risk factors only.
Finally, the package includes function to construct a simulated dataset with
genotypes, genetic risks, and disease status for a hypothetical population, which
is used for the evaluation of genetic risk models.
Three prediction algorithms described in the paper
"On-line predictive linear regression" Annals of Statistics 37,
1566 - 1590 (2009)
This package provides functions to diagnose
and make inferences from various linear models, such as those obtained from 'aov',
'lm', 'glm', 'gls', 'lme', and 'lmer'. Inferences include predicted
means and standard errors, contrasts, multiple comparisons, permutation tests and graphs.
"train_predict_mix" predicts the binary response with
binary features
Generates design matrix for analysing real paired comparisons and derived paired comparison data (Likert type items/ratings or rankings) using a loglinear approach.  Fits loglinear Bradley-Terry model (LLBT) exploiting an eliminate feature.  Computes pattern models for paired comparisons, rankings, and ratings.  Some treatment of missing values (MCAR and MNAR). Fits latent class (mixture) models for paired comparison, rating and ranking patterns using a non-parametric ML approach.
Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically linking a response vector to covariate data through cluster membership. The package allows Bernoulli, Binomial, Poisson, Normal, survival and categorical response, as well as Normal and discrete covariates. It also allows for fixed effects in the response model, where a spatial CAR (conditional autoregressive) term can be also included. Additionally, predictions may be made for the response, and missing values for the covariates are handled. Several samplers and label switching moves are implemented along with diagnostic tools to assess convergence. A number of R functions for post-processing of the output are also provided. In addition to fitting mixtures, it may additionally be of interest to determine which covariates actively drive the mixture components. This is implemented in the package as variable selection.
This package provides a set of functions useful when
evaluating the results of presence-absence models. Package
includes functions for calculating threshold dependent measures
such as confusion matrices, pcc, sensitivity, specificity, and
Kappa, and produces plots of each measure as the threshold is
varied. It will calculate optimal threshold choice according to
a choice of optimization criteria. It also includes functions
to plot the threshold independent ROC curves along with the
associated AUC (area under the curve).
Makes output files from select PreSens Fiber Optic Oxygen Transmitters easier to work with in R. See <http://www.presens.de> for more information about PreSens (Precision Sensing GmbH).
Estimating species accumulation curves by rational function approximations to the non-parametric empirical Bayes estimator and by the zero-truncated Negative Binomial distribution.
Computes probability-scale residuals and residual correlations
for continuous, ordinal, binary, count, and time-to-event data.
prettyGraphs contains simple, crisp graphics. Graphics produced by prettyGraphs are publication-quality.
Functions for conventionally formatting descriptive stats,
reshaping data frames and formatting R output as HTML.
Pretty, human readable formatting of quantities.
Time intervals: 1337000 -> 15d 11h 23m 20s.
Vague time intervals: 2674000 -> about a month ago.
Bytes: 1337 -> 1.34 kB.
The prevalence package provides Frequentist and Bayesian methods for prevalence assessment studies. IMPORTANT: the truePrev functions in the prevalence package call on JAGS (Just Another Gibbs Sampler), which therefore has to be available on the user's system. JAGS can be downloaded from http://mcmc-jags.sourceforge.net/.
It provides functions for both likelihood-based
and Bayesian analysis of spatially referenced prevalence data, and is
also an extension of the 'geoR' package.
Spatial estimation of a prevalence surface
or a relative risks surface, using data from a Demographic and Health
Survey (DHS) or an analog survey.
Estimate False Discovery Rates (FDRs) for importance metrics from random forest runs.
PRIM for bump hunting in high-dimensional data
Functions are primarily functions for systems of ordinary
differential equations, difference equations, and eigenanalysis
and projection of demographic matrices; data are for examples.
Visually Assessing the Specificity and Informativeness of Primer
Pairs.
Functions to test whether a number is prime and generate the prime numbers within a specified range. Based around
an implementation of Wilson's theorem for testing for an integer's primality.
Performs a unified treatment of Bump Hunting by Patient Rule Induction Method (PRIM) in Survival, Regression and Classification settings (SRC). The current version is a development release that only implements the case of a survival response. New features will be added soon as they are available.
fits a principal curve to a data matrix in arbitrary
dimensions
Provides capabilities beyond principal components
analysis to focus on finding structure in low variability
subspaces. Constructs and plots simple basis vectors for
pre-defined and user-defined measures of simplicity.
The PRISMA package is capable of loading and processing huge text
corpora processed with the sally toolbox (http://www.mlsec.org/sally/).
sally acts as a very fast preprocessor which splits the text files into
tokens or n-grams. These output files can then be read with the PRISMA
package which applies testing-based token selection and has some
replicate-aware, highly tuned non-negative matrix factorization and
principal component analysis implementation which allows the processing of
very big data sets even on desktop machines.
PrivateLR implements two differentially private algorithms for
estimating L2-regularized logistic regression coefficients. A randomized
algorithm F is epsilon-differentially private (C. Dwork, Differential
Privacy, ICALP 2006), if
|log(P(F(D) in S)) - log(P(F(D') in S))| <= epsilon
for any pair D, D' of datasets that differ in exactly one element, any
set S, and the randomness is taken over the choices F makes.
Estimation of prevalence ratios using logistic models and confidence intervals with delta and bootstrap methods.
This package provides a framework for performing elementary probability
calculations on finite sample spaces, which may be represented by data frames
or lists.  Functionality includes setting up sample spaces, counting tools,
defining probability spaces, performing set algebra, calculating probability
and conditional probability, tools for simulation and checking the law of
large numbers, adding random variables, and finding marginal distributions.
Characteristic functions for all base R distributions are included.
Contains functions that are useful for probing moderation effects (or interactions) including techniques such as pick-a-point (also known as spotlight analysis) and Johnson-Neyman (also known as floodlight analysis). Plot function is also provided to facilitate visualization of results from each of these techniques.
Probabilistic Fisher discriminant analysis (pFDA) is a probabilistic version of the popular and powerful Fisher linear discriminant analysis for dimensionality reduction and classification.
The ProbForecastGOP package contains a main function,
called ProbForecastGOP and other functions, to produce
probabilistic weather forecasts of weather fields using the
Geostatistical Output Perturbation (GOP) method of Gel,
Raftery, and Gneiting (JASA, 2004).
This package provides multiclass conditional probability
estimation for the SVM, which is distributional assumption
free.
Tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). (Partial) area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.
A direct and flexible method for estimating an ICA model.
This approach estimates the densities for each component
directly via a tilted gaussian. The tilt functions are
estimated via a GAM poisson model. Details can be found in
"Elements of Statistical Learning (2nd Edition)" Section 14.7.4
Fast and user friendly implementation of nonparametric estimators
for censored event history (survival) analysis. Kaplan-Meier and
Aalen-Johansen method.
The package can prioritize the candidate disease metabolites based on the assumption
that functionally related metabolites tend to associate with the same or similar
diseases in the context of metabolic pathway. The PROFANCY package (1) prioritizes the
disease metabolites from global functional similarity and local modularity of the
metabolic network; (2) allows users to select default metabolites or input their
interested metabolites as seed nodes or candidate nodes (3) can prioritize the candidate
metabolites in KEGG or EHMN metabolic network.
This package facilitates profile inference (inference at
the posterior mode) for a class of product partition models
(PPM). The Dirichlet process mixture is currently the only
available member of this class. These methods search for the
maximum posterior (MAP) estimate for the data partition in a
PPM.
Programs to determine student grades and create
examinations from Question banks.  Programs will create numerous
multiple choice exams, randomly shuffled, for different versions of same question list.
This package provides profile likelihoods for a parameter of interest in commonly used statistical models. The models include linear models, generalized linear models, proportional odds models, linear mixed-effects models, and linear models for longitudinal responses fitted by generalized least squares. The package also provides plots for normalized profile likelihoods as well as the maximum profile likelihood estimates and the kth likelihood support intervals.
profileModel provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of *arbitrary* inference functions for *arbitrary* 'glm'-like fitted models with linear predictors.
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.
Terminal progress bars. They are
configurable, may include percentage, elapsed time, and/or
the estimated completion time. They work in the command line,
in Emacs, R Studio, Windows Rgui and Mac OSX R.app. The package
also provides a C++ API, that works with or without Rcpp.
A simple interface to lat/long projection and datum
transformation of the PROJ.4 cartographic projections library.
It allows transformation of geographic coordinates from one
projection and/or datum to another.
ProjectTemplate provides functions to
automatically build a directory structure for a new R
project. Using this structure, ProjectTemplate
automates data loading, preprocessing, library
importing and unit testing.
Propagation of uncertainty using higher-order Taylor expansion and Monte Carlo simulation.
Computes two-sample confidence intervals for single, paired and independent proportions
This package implements propensity clustering and
decomposition. Propensity decomposition can be viewed on the
one hand as a generalization of the eigenvector-based
approximation of correlation networks, and on the other hand as
a generalization of random multigraph models and
conformity-based decompositions.
The prop.comb.RR package analyzes combination of proportions and relative risk.
A package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.
This package contains functions to run propensity-biased allocation to balance covariate distributions in sequential trials and propensity-constrained randomization to balance covariate distributions in trials with known baseline covariates at time of randomization.  Currently this package only supports trials comparing two groups.
The package provides functions for pretreatment and sample
selection of visible and near infrared diffuse reflectance spectra
This package provides functions to identify the optimal
solution that maximizes numbers of detectable differentiated
proteins from a multi-stage clinical proteomic study.
Provides methods for making inference in isobaric labelled
LC-MS/MS experiments, i.e. iTRAQ experiments. It provides a function that
reasonably parses a CSV-export from Proteome Discoverer(TM) into a data
frame that can be easily handled in R. Functions and methods are provided
for quality control, filtering, norming, and the calculation of response
variables for further analysis. The merging of multiple iTRAQ experiments
with respect to a reference is also covered.
Method for protein quantification based on identified and
quantified peptides. protiq can be used for absolute and relative protein
quantification. Input peptide abundance scores can come from various
sources, including SRM transition areas and intensities or spectral counts
derived from shotgun experiments. The package is still being extended to
also include the model for protein identification, MIPGEM, presented in
Gerster, S., Qeli, E., Ahrens, C.H. and Buehlmann, P. (2010). Protein and
gene model inference based on statistical modeling in k-partite graphs.
Proceedings of the National Academy of Sciences 107(27):12101-12106.
An object oriented system using object-based, also called
prototype-based, rather than class-based object oriented ideas.
Greedy algorithm described in Bien and Tibshirani (2011)
Prototype Selection for Interpretable Classification. Annals of
Applied Statistics. 5(4). 2403-2424
Performs minimax linkage hierarchical clustering.  Every cluster
has an associated prototype element that represents that cluster as
described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering
with Prototypes via Minimax Linkage," accepted for publication in The
Journal of the American Statistical Association, DOI:
10.1198/jasa.2011.tm10183.
PROTOLIDAR package contains functions for analyze the
LIDAR scan of plants (grapevine) and make 3D maps in GRASS GIS.
Comprehensive toolkit for generating various numerical
representation schemes of protein sequence. The descriptors and similarity
scores included are extensively utilized in bioinformatics and
chemogenomics research. For full functionality, ncbi-blast+ is required.
This R package helps with quality checks, visualizations
and analysis of mass spectrometry data, coming from proteomics
experiments. The package is developed, tested and used at the Functional
Genomics Center Zurich. We use this package mainly for prototyping,
teaching, and having fun with proteomics data. But it can also be
used to do data analysis for small scale data sets.
Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov dissimilarity and Sircombe-Hazelton L2-norm. Categorical provenance proxies, such as mineralogical, petrographic or chemical compositions are compared with the Aitchison and Bray-Curtis distances.
Provides an extensible framework for the efficient calculation of auto- and cross-proximities, along with implementations of the most popular ones.
Computes the areas under the precision-recall (PR) and ROC curve for weighted (e.g., soft-labeled) and unweighted data. In contrast to other implementations, the interpolation between points of the PR curve is done by a non-linear piecewise function. In addition to the areas under the curves, the curves themselves can also be computed and plotted by a specific S3-method.
Test of periodicity in short time series using response surface regression
Useful tools to pry back the covers of R and understand the
language at a deeper level.
Bootstrapping 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.
The package provides algorithms for fitting penalized semiparametric Bayesian Cox (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.
Provides a number of functions to
simplify and automate the scoring, comparison, and evaluation of
different ways of creating composites of data.  It is particularly
aimed at facilitating the creation of physiological composites of
metabolic syndrome symptom score (MSSS) and allostatic load (AL).
Produces power spectral density estimates through iterative
refinement of the optimal number of sine-tapers at each frequency. This
optimization procedure is based on the method of Riedel and Sidorenko
(1995), which minimizes the Mean Square Error (sum of variance and bias)
at each frequency, but modified for computational stability.
This R package includes functions for gathering commonly used and
regularly maintained data set in political science. It also includes
functions for combining components from these data sets into variables that
have been suggested in the literature, but are not regularly maintained.
Functions for creating Latin Hypercubes with
prescribed correlations and performing parameter space exploration.
Also implements the PLUE method.
Based on the package sensitivity, by Gilles Pujol,
Bertrand Iooss & Alexandre Janon.
Various functions for computing pseudo-observations for
censored data regression
Implements projected sparse Gaussian process kriging for the intamap package
This package contains functions to calculate the Specificity Index statistic, which can be used for comparative quantitative analysis to identify genes enriched in specific cell populations across a large number of profiles, as well as perform numerous post-processing operations. NOTE:Supplementary data (human & mouse expression sets, calculated pSI datasets, etc.) can be found in pSI.data package located at the following URL: http://genetics.wustl.edu/jdlab/psi_package/
Makes it easy to build panel data in wide format from PSID
delivered raw data. Deals with data downloaded and pre-processed by Stata or SAS,
or can optionally download directly from the PSID server using the SAScii package.
`psidR` takes care of merging data from each wave onto a cross-period index file,
so that individuals can be followed over time. The user must specify which years
they are interested in, and the PSID variable names (e.g. ER21003) for each year
(they differ in each year). There are different panel data designs and
sample subsetting criteria implemented ("SRC", "SEO", "immigrant" and "latino"
samples).
This package provides functions for estimation of linear
and non-linear mixed-effects models using stochastic
differential equations. Moreover it provides functions for
finding smoothed estimates of model states and for simulation.
The package allows for any multivariate non-linear time-variant
model to be specified, and it also handles multidimensional
input, co-variates, missing observations and specification of
dosage regimen.
The package provides an implementation of PSO consistent
with the standard PSO 2007/2011 by Maurice Clerc et al.
Additionally a number of ancillary routines are provided for
easy testing and graphics.
Spearman's rank correlation test with precomputed exact
null distribution for n <= 22.
Smoothing splines with penalties on order m derivatives.
Estimates parameters of a piecewise exponential model for time to progression and time from progression to death with interval censoring of the time to progression and covariates for each distribution using proportional hazards.
Provides a framework for analysing state sequences with probabilistic suffix trees (PST), the construction that stores variable length Markov chains (VLMC). Besides functions for learning and optimizing VLMC models, the PST library includes many additional tools to analyse sequence data with these models: visualization tools, functions for sequence prediction and artificial sequences generation, as well as for context and pattern mining. The package is specifically adapted to the field of social sciences by allowing to learn VLMC models from sets of individual sequences possibly containing missing values, and by accounting for case weights. The  library also allows to compute probabilistic divergence between two models, and to fit segmented VLMC, where sub-models fitted to distinct strata of the learning sample are stored in a single PST. This software results from research work executed within the framework of the Swiss National Centre of Competence in Research LIVES, which is financed by the Swiss National Science Foundation. The authors are grateful to the Swiss National Science Foundation for its financial support.
Functions to simulate Poisson or Normally distributed responses relative to
a baseline and compute achieved significance level and powers for tests on the
simulated responses.
Kappa, ICC, Cronbach alpha, screeplot, mtmm
A general purpose toolbox for personality, psychometrics and experimental psychology.   Functions are primarily for multivariate analysis and scale construction using factor analysis, principal component analysis, cluster analysis and reliability analysis, although others provide basic descriptive statistics. Item Response Theory is done using  factor analysis of tetrachoric and polychoric correlations. Functions for analyzing data at multi-levels include within and between group statistics, including correlations and factor analysis.   Functions for simulating particular item and test structures are included. Several functions serve as a useful front end for structural equation modeling.  Graphical displays of path diagrams, factor analysis and structural equation models are created using basic graphics. Some of the functions are written to support a book on psychometrics as well as publications in personality research. For more information, see the personality-project.org/r webpage.
Contains functions useful for correlation theory,
meta-analysis (validity-generalization), reliability, item
analysis, inter-rater reliability, and classical utility
Psychometric mixture models based on 'flexmix' infrastructure. At the moment Rasch mixture models
with different parameterizations of the score distribution (saturated vs. mean/variance specification)
and Bradley-Terry mixture models are implemented. Both mixture models can be estimated with or without
concomitant variables. See vignette('raschmix', package = 'psychomix') for details on the Rasch mixture
models.
Infrastructure for psychometric modeling such as data classes
(for item response data and paired comparisons), basic model fitting
functions (for Bradley-Terry, Rasch, partial credit, rating scale,
multinomial processing tree models), extractor functions for different types
of parameters (item, person, threshold, discrimination), unified inference
and visualizations, and various datasets for illustration. Intended as a
common lightweight and efficient toolbox for psychometric modeling and a
common building block for fitting psychometric mixture models in package
"psychomix" and psychometric tree models in package "psychotree".
Recursive partitioning based on psychometric models,
employing the general MOB algorithm (from package partykit) to obtain
Bradley-Terry trees, Rasch trees, rating scale and partial credit trees, and
MPT trees.
An assortment of functions that could be useful in analyzing data from psychophysical experiments. It includes functions for calculating d' from several different experimental designs, links for m-alternative forced-choice (mafc) data to be used with the binomial family in glm (and possibly other contexts) and self-Start functions for estimating gamma values for CRT screen calibrations.
Psytabs produces well-formatted tables in .rtf or .html,
which largely conform to "psychological style".
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.
Fits and analyses time dependent marked point process models with an emphasis on earthquake modelling. For a more detailed introduction to the package, see the topic "PtProcess". A list of recent changes can be found in the topic "Change Log".
Parametric Time Warping aligns patterns, i.e. it aims to
put corresponding features at the same locations. The algorithm
searches for an optimal polynomial describing the warping. It
is possible to align one sample to a reference, several samples
to the same reference, or several samples to several
references. One can choose between calculating individual
warpings, or one global warping for a set of samples and one
reference. Two optimization criteria are implemented: RMS (Root
Mean Square error) and WCC (Weighted Cross Correlation). Both
warping of peak profiles and of peak lists are supported.
Text mining of PubMed Abstracts (text and XML) from http://www.ncbi.nlm.nih.gov/pubmed.
create a word cloud using the abstract of publications from PubMed
R interface to pullword service for Natural Language Processing
in Chinese. It enables users to extract valuable words from text by deep learning models.
For more details please visit the official site (in Chinese) http://pullword.com/.
R package to query and get data out of a Pumilio sound archive system (http://ljvillanueva.github.io/pumilio/).
PurBayes is an MCMC-based algorithm that uses
next-generation sequencing data to estimate tumor purity and
clonality for paired tumor-normal data.
A package for sending push notifications to mobile devices (iOS and
Android) and the desktop using Pushover
Likelihood based population viability analysis in the
presence of observation error and missing data.
The package can be used to fit, compare, predict,
and forecast various growth model types using data cloning.
The calculation of p-variation of the finite sample data
Computes nonparametric p-values for the potential class
memberships of new observations as well as cross-validated
p-values for the training data. The p-values are based on
permutation tests applied to an estimated Bayesian likelihood
ratio, using a plug-in statistic for the Gaussian model, 'k
nearest neighbors', 'weighted nearest neighbors' or
'penalized logistic regression'.
Additionally, it provides graphical displays and quantitative
analyses of the p-values.
An implementation of multiscale bootstrap resampling for
assessing the uncertainty in hierarchical cluster analysis.
It provides AU (approximately unbiased) p-value as well as
BP (bootstrap probability) value for each cluster in a dendrogram.
Computes rank correlations and their p-values with various options for tied ranks.
The pvsR package facilitates data retrieval from Project
Vote Smart's rich online data base on US politics via the Project Vote
Smart application programming interface (PVS API). The functions in this
package cover most PVS API classes and methods and return the
requested data in a data frame.
This R package contains open source implementations
of several p-value weighting methods, including Spjotvoll, exponential
and Bayes weights. These are methods for improving power in multiple testing
via the use of prior information.
Power analysis functions along the lines of Cohen (1988).
Tools the evaluation of interim analysis plans for sequentially
monitored trials on a survival endpoint; tools to construct efficacy and
futility boundaries, for deriving power of a sequential design at a specified
alternative, template for evaluating the performance of candidate plans at a
set of time varying alternatives.
The Penn World Table provides purchasing power parity and
national income accounts converted to international prices for
189 countries for some or all of the years 1950-2010.
The Penn World Table 8.x provides information on relative levels of
income, output, inputs, and productivity for 167 countries
between 1950 and 2011.
The pxR package provides a set of functions for reading and writing PC-Axis files, used by different statistical organizations around the globe for data dissemination.
Generic interface for the PX-Web/PC-Axis API. The PX-Web/PC-Axis
API is used by organizations such as Statistics Sweden and Statistics
Finland to disseminate data. The R package can interact with all
PX-Web/PC-Axis APIs to fetch information about the data hierarchy, extract
metadata and extract and parse statistics to R data.frame format. PX-Web is
a solution to disseminate PC-Axis data files in dynamic tables on the web.
Since 2013 PX-Web contains an API to disseminate PC-Axis files.
Given a SpatialPolygonsDataFrame and a set of populations for each polygon,
compute a population density estimate based on Tobler's pycnophylactic interpolation
algorithm. The result is a SpatialGridDataFrame.
Drawing population pyramid using (1) data.frame or (2) vectors.
The former is named as pyramid() and the latter pyramids(), as wrapper
function of pyramid().  pyramidf() is the function to draw population
pyramid within the specified frame.
String operations the Python way - a package for those of us who miss Python's string methods while we're working in R.
This package delivers some functions to provide a scientific quality assurance of meteorological data.
An extensive set of functions to perform Qualitative Comparative Analysis:
crisp sets (csQCA), temporal (tQCA), multi-value (mvQCA) and fuzzy sets (fsQCA).
QCA is a methodology that bridges the qualitative and quantitative divide
in social science research. It uses a boolean algorithm that results in a minimal
causal combination which explains a given phenomenon.
A set of functions for Qualitative Comparative Analysis (QCA). It can be used for various types of QCA (csQCA, mvQCA fsQCA and crip set TQCA  and time-serious QCA). It has methods for simplifying assumption, contradictory simplifying assumption.
Implements tests for Type I error in Qualitative Comparative Analysis (QCA) that take into account the multiple hypothesis tests inherent in the procedure. Tests can be carried out on three variants of QCA: crisp-set QCA (csQCA), multi-value QCA (mvQCA) and fuzzy-set QCA (fsQCA). For fsQCA, the fsQCApermTest() command implements a permutation test that provides 95% confidence intervals for the number of counterexamples and degree of consistency, respectively. The distributions of permuted values can be plotted against the observed values. For csQCA and mvQCA, simple binomial tests are implemented in csQCAbinTest() and mvQCAbinTest(), respectively.
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.
Helper functions for Qualitative Comparative Analysis: evaluate and plot Boolean formulae on fuzzy set score data, apply Boolean operations, compute consistency and coverage measures
Shewhart quality control charts for continuous, attribute and count data. Cusum and EWMA charts. Operating characteristic curves. Process capability analysis. Pareto chart and cause-and-effect chart. Multivariate control charts.
Tools for (automated and manual) quality control of
the results of Genome Wide Association Studies
Robust estimation of Gaussian mixture models fitted by modified EM algorithm, robust clustering and classification.
This package allows to generate Shewhart-type charts and to obtain
numerical results of interest to the quality control of a process
(involving  continuous, attribute or count data).
This package provides basic functionality for univariable and multivariable
quality control analysis, including: xbar, xbar-one, S, R, ewna, cusum,
mewna, mcusum  and T2 charts.
Automates many of the tasks associated with quantitative
discourse analysis of transcripts containing discourse
including frequency counts of sentence types, words, sentences,
turns of talk, syllables and other assorted analysis tasks. The
package provides parsing tools for preparing transcript data.
Many functions enable the user to aggregate data by any number
of grouping variables, providing analysis and seamless
integration with other R packages that undertake higher level
analysis and visualization of text.  This affords the user a
more efficient and targeted analysis.  'qdap' is designed for
transcript analysis, however, many functions are applicable to
other areas of Text Mining/Natural Language Processing.
A collection of dictionaries and word lists for use with
the 'qdap' package.
A collection of regular expression tools associated with the 'qdap'
package that may be useful outside of the context of discourse analysis. Tools
include removal/extraction/replacement of abbreviations, dates, dollar amounts,
email addresses, hash tags, numbers, percentages, citations, person tags, phone
numbers, times, and zip codes.
A collection of tools associated with the qdap package that may be
useful outside of the context of text analysis.
This package provides different specifications of a Quadrilateral
Dissimilarity Model which can be used to fit same-different judgments
in order to get a predicted matrix that satisfies regular minimality
[Colonius & Dzhafarov, 2006, Measurement and representations of
sensations, Erlbaum]. From such a matrix, Fechnerian distances can be
computed.
Option pricing (financial derivatives) techniques mainly following textbook 'Options, Futures and Other Derivatives', 9ed by John C.Hull, 2014. Prentice Hall. Implementations are via binomial tree option model (BOPM), Black-Scholes model, Monte Carlo simulations, etc.
This package is a result of Quantitative Financial Risk Management course (STAT 449 and STAT 649) at Rice University, Houston, TX, USA, taught by Oleg Melnikov, statistics PhD student, as of Spring 2015.
The qgraph package can be used to visualize data as networks as well as provides an interface for visualizing weighted graphical models.
Two linear mixed model approaches: REML(restricted maximum likelihood) and MINQUE (minimum norm quadratic unbiased estimation) approaches and several resampling techniques are integrated for various quantitative genetics analyses. With these two types of approaches, various unbalanced data structures, missing data, and any irregular genetic  mating designs can be analyzed and statistically tested. This package also offers fast computations for many large data sets. Other functions will be added to this R tool in the future.
Functions for making run charts and  basic Shewhart control
charts for measure and count data.
The main function, qic(), creates run and control charts and has a
simple interface with a rich set of options to control data analysis
and plotting, including options for automatic data aggregation by
subgroups, easy analysis of before-and-after data, exclusion of one
or more data points from analysis, and splitting charts into
sequential time periods.
Missing values and empty subgroups are handled gracefully.
Open QIIME output files in R, compute statistics, and
create plots from the data.
Extension of the functionality of the Matrix package for using sparse matrices. Some of the functions are very general, while other are highly specific for special data format as used for quantitative language comparison (QLC).
Functions to implement Q-learning for estimating optimal
dynamic treatment regimes from two stage sequentially
randomized trials, and to perform inference via m-out-of-n
bootstrap for parameters indexing the optimal regime.
Empirical adjustment of the distribution of variables originating from (regional) climate model simulations using quantile mapping.
Analysis of Q methodology, used to identify distinct perspectives existing within a group. This methodology is used across social, health and environmental sciences to understand diversity of attitudes, discourses, or decision-making styles (for more information, see http://qmethod.org).
A single function runs the full analysis. Each step can be run separately using the corresponding functions: for automatic flagging of Q-sorts (manual flagging is optional), for statement scores, for distinguishing and consensus statements, and for general characteristics of the factors.
Additional functions are available to import and export data, to print and plot, to import raw data from individual *.CSV files, and to make printable cards.
The package uses principal components and it allows manual or automatic flagging, a number of mathematical methods for rotation, and a number of correlation coefficients for the initial correlation matrix.
See further details in the package documentation, and in the web pages below, which include a cookbook, guidelines for more advanced analysis (how to perform manual flagging or change the sign of factors), data management, and a beta graphical user interface for online and offline use.
Basic functions for building parsers, with an application to PC-AXIS format files.
To generate the scores of the EORTC QLQ-C30 questionnaire and supplementary modules and to determine the time to health-related quality of life score deterioration in longitudinal analysis.
Model fitting, optimal model selection and calculation of various features that are essential in the analysis of quantitative real-time polymerase chain reaction (qPCR).
use 2^ddCT methods calculate the relative gene expression,
data file can be export from bio-rad qpcr machine, the results
can be plot with errorbar. ver 1.1 add GroupPlot function, can
plot all the groups once.
Provides the function qqtest which incorporates uncertainty in its
qqplot display(s) so that the user might have a better sense of the
evidence against the specified distributional hypothesis.  qqtest draws a
quantile quantile plot for visually assessing whether the data come from a
test distribution that has been defined in one of many ways.  The vertical
axis plots the data quantiles, the horizontal those of a test distribution.
The default behaviour generates 1000 samples from the test distribution and
overlays the plot with pointwise interval estimates for the ordered
quantiles from the test distribution.  A small number of independently
generated exemplar quantile plots are also overlaid.  Both the interval
estimates and the exemplars provide different comparative information to
assess the evidence provided by the qqplot for or against the hypothesis
that the data come from the test distribution (default is normal or
gaussian).  Finally, a visual test of significance (a lineup plot) can also
be displayed to test the null hypothesis that the data come from the test
distribution.
Tools that create D3 JavaScript force directed graph from R. D3 JavaScript was created by Michael Bostock. See http://d3js.org/ and, more specifically for Force Directed Graph https://github.com/mbostock/d3/wiki/Force-Layout.
The qrfactor package simultaneously runs both Q and R  mode factor analyses. The package contains only one function called qrfactor() that can perform PCA, R-mode Factor Analysis, Q-mode Factor Analysis, Simultaneous R- and Q-mode Factor Analysis, Principal Coordinate Analysis, as wells as Multidimensional Scaling  (MDS). Loadings and scores can easily be computed from the  simulation.The plot.qrfactor() function offers several annotated  biplots for all possible combinations of eigenvectors, loadings, and scores. Input data includes shapefiles, tables and dataframe
Joint estimation of quantile specific intercept and slope parameters in a linear regression setting.
Quantile regression (QR) for Linear
Mixed-Effects Models via the asymmetric Laplace distribution (ALD).
It uses the Stochastic Approximation of the EM (SAEM) algorithm for
deriving exact maximum likelihood estimates and full inference results
for the fixed-effects and variance components.
It also provides graphical summaries for assessing the algorithm
convergence and fitting results.
This package is designed to accompany the book
Quantitative Risk Management: Concepts, Techniques and Tools by
Alexander J. McNeil, Rudiger Frey, and Paul Embrechts.
Functions and data sets for reproducing selected results from
the book "Quantitative Risk Management: Concepts, Techniques and Tools".
Furthermore, new developments and auxiliary functions for Quantitative
Risk Management practice.
Functionality for generating (randomized) quasi-random numbers in
high dimensions.
Quantile regression (QR) for Nonlinear
Mixed-Effects Models via the asymmetric Laplace distribution (ALD).
It uses the Stochastic Approximation of the EM (SAEM) algorithm for
deriving exact maximum likelihood estimates and full inference results
for the fixed-effects and variance components.
It also provides graphical summaries for assessing the algorithm
convergence and fitting results.
Fit a quantile regression neural network with optional
left censoring using a variant of the finite smoothing
algorithm.
Molecular descriptors and outcomes for several public domain data sets
Dynamic bindings to the Qt library for calling Qt
methods and extending Qt classes from R. Other packages build upon 'qtbase'
to provide special-purpose high-level interfaces to specific parts of Qt.
Provides several methods for computing the Quantile Treatment Effect (QTE) and Quantile Treatment Effect on the Treated (QTET).  The main cases covered are (i) Treatment is randomly assigned, (ii) Treatment is as good as randomly assigned after conditioning on some covariates (also called conditional independence or selection on observables), (iii) Identification is based on a Difference in Differences assumption (several varieties are available in the package).
Analysis of experimental crosses to identify genes
(called quantitative trait loci, QTLs) contributing to variation in
quantitative traits.
Datasets for the book, A Guide to QTL Mapping with R/qtl
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.
Quantitative Analysis of Complex Networks. This package offers a set of topological network measures to analyze complex Networks structurally.
This package gives you the exact first four permutation moments for the most commonly used quadratic form statistics, which need not be positive definite. The extension of this work to quadratic forms greatly expands the utility of density approximations for these problems, including for high-dimensional applications, where the statistics must be extreme in order to exceed stringent testing thresholds. Approximate p-values are obtained by matching the exact moments to the Pearson family of distributions using the PearsonDS package.
This package contains routines and documentation for
solving quadratic programming problems.
This package fits classical sparse regression models with
efficient active set algorithms by solving quadratic problems. Also
provides a few methods for model selection purpose (cross-validation,
stability selection).
Exact one-sided p-values and confidence intervals for an outcome variable defined on an interval measurement scale with only qualitative and ordinal information available.
Used for testing for qualitative interactions between
treatment effects and patient subgroups. Here the term treatment effect
means a comparison result of two treatments within each patient subgroup.
Models included in this package are Gaussian, binomial and Cox models.
Methods included here are interval based graphical approach and Gail Simon
LRT.
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.
Functions for interacting directly with the Quandl API to offer data
in a number of formats usable in R, as well as the ability to 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).
A fast, flexible toolset for for the management, processing, and
quantitative analysis of textual data in R.
This package provides different functions for quantifying qualitative survey data. It supports the Carlson-Parkin method, the regression approach and the balance approach.
Estimation of conditional quantiles using optimal quantization.
Construction of an optimal grid of N quantizers, estimation of conditional
quantiles and data driven selection of the size N of the grid. Graphical
illustrations for the selection of N and of resulting estimated curves or
surfaces when the dimension of the covariate is one or two.
Specify, build, trade, and analyse quantitative financial trading strategies.
Contains functions useful for data screening, testing
moderation, mediation and estimating power.
Estimation and inference methods for models of conditional quantiles:
Linear and nonlinear parametric and non-parametric (total variation penalized) models
for conditional quantiles of a univariate response and several methods for handling
censored survival data.  Portfolio selection methods based on expected shortfall
risk are also included.
Quantile Regression Forests is a tree-based ensemble
method for estimation of conditional quantiles. It is
particularly well suited for high-dimensional data. Predictor
variables of mixed classes can be handled. The package is
dependent on the package randomForests, written by Andy Liaw.
Fits non-crossing regression quantiles as a function of linear covariates and a smooth terms via B-splines with difference penalties.
Methods to determine, smooth and plot quantile (i. e., Laplace or
copula) periodograms for univariate time series.
Using HTS data, clusters mutations in order to recreate putative clones from the data provided. It requires genotype at the location of the variant as well as the depth of coverage and number of reads supporting the mutation. Additional information may be provided, such as the contamination in the tumor sample. This package also provides a function QuantumCat() which simulates data obtained from tumor sequencing.
Identify differentially expressed genes in RNA-seq count data using quasi-Poisson or quasi-negative binomial models with 'QL', 'QLShrink' and 'QLSpline' methods (Lund, Nettleton, McCarthy, and Smyth, 2012).
Set of functions to make the processing and analysis of
surveys easier : interactive shiny apps for data recoding, contingency
tables, dataset metadata handling, and several convenience functions.
It provides versatile tools for analysis of birth and death based Markovian Queueing Models
and Single and Multiclass Product-Form Queueing Networks.
It implements M/M/1, M/M/c, M/M/Infinite, M/M/1/K, M/M/c/K, M/M/c/c, M/M/1/K/K, M/M/c/K/K, M/M/c/K/m, M/M/Infinite/K/K,
Multiple Channel Open Jackson Networks, Multiple Channel Closed Jackson Networks,
Single Channel Multiple Class Open Networks, Single Channel Multiple Class Closed Networks
and Single Channel Multiple Class Mixed Networks.
Also it provides a B-Erlang, C-Erlang and Engset calculators.
This work is dedicated to the memory of D. Sixto Rios Insua.
Use Newton's method and coordinate descent to solve the
regularized inverse covariance matrix estimation problem.
Please refer to: Sparse Inverse Covariance Matrix Estimation
Using Quadratic Approximation, Cho-Jui Hsieh, Matyas A. Sustik,
Inderjit S. Dhillon, Pradeep Ravikumar, Advances in Neural
Information Processing Systems 24, 2011, p. 2330--2338.
Quickly fits and plots psychometric functions (normal, logistic, Weibull or any or any function defined by the user) for multiple groups.
Grows a qualitative interaction tree. Quint is a tool for subgroup analysis, suitable for data from a two-arm randomized controlled trial.
Gene banks increasingly use molecular markers for routine
characterization of plant collections and farmer managed diversity. The
gene bank of the International Potato Center presently uses a
micro-satellite marker kit to produce molecular profiles for potato
accessions. We have been searching for a compact graphical representation
that shows both molecular diversity and accession characteristics - thus
permitting biologists and collection curators to have a simple way to
interpret molecular data. Inspired by the ancient Andean data recording
system we devised a graph that allows for standardized representation while
leaving room for updates of the marker kit and the collection of
accessions. The graph has been used in several catalogs of potatoes.
Functions to compute confidence statistics
for ordering population quantiles.
For a given data matrix A and cluster centers/prototypes collected in the matrix P, the functions described here select a subset of statistic variables Q that mostly explains/justifies P as prototypes. The functions are useful to reduce the data dimension for classification and to discard masking variables for clustering.
Functions to compute quasi variances and associated measures of approximation error
A collection of (wrapper) functions the creator found useful for
quickly placing data summaries and formatted regression results into .Rnw or
.Rmd files.  Functions for generating commonly used graphics, such as receiver
operating curves or Bland-Altman plots, are also provided by 'qwraps2'.
'qwraps2' is a updated version of a package 'qwraps'.  The original version
'qwraps' was never submitted to CRAN but can be found at
https://github.com/dewittpe/qwraps.  The implementation and limited scope of the
functions within 'qwraps2' (https://github.com/dewittpe/qwraps2) is fundamentally
different from 'qwraps'.
QZ package provides generalized eigenvalues and QZ
decomposition (generalized Schur form) for an N-by-N
non-symmetric matrix A or paired matrices (A,B) with
eigenvalues reordering mechanism. The package is mainly based
complex*16 and double precision of LAPACK library (version
3.4.2.)
Estimation of reproduction numbers for disease outbreak, based on
incidence data. The R0 package implements several documented methods. It is
therefore possible to compare estimations according to the methods used.
Depending on the methods requested by user, basic reproduction number
(commonly denoted as R0) or real-time reproduction number (referred to as
R(t)) is computed, along with a 95% Confidence Interval. Plotting outputs
will give different graphs depending on the methods requested : basic
reproductive number estimations will only show the epidemic curve
(collected data) and an adjusted model, whereas real-time methods will also
show the R(t) variations throughout the outbreak time period. Sensitivity
analysis tools are also provided, and allow for investigating effects of
varying Generation Time distribution or time window on estimates.
Utilities for sparse signal recovery suitable for compressed sensing. L1, L2 and TV penalties, DFT basis matrix, simple sparse signal generator, mutual cumulative coherence between two matrices and examples, Lp complex norm, scaling back regression coefficients.
A series of functions to call AD Model Builder (i.e.,
compile and run models) from within R, read the results back
into R as 'admb' objects, and provide standard accessors (i.e.
coef(), vcov(), etc.)
An R interface to estimate structured additive regression (STAR) models with BayesX.
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).
Providing wrapper functions to implement Bayesian analysis in JAGS.  Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.
generate univariate and bivariate analyses in LaTeX or
HTML formats
An R command interface to the 'MLwiN' multilevel
modelling software package.
Using this package,
it is possible to call a BUGS model, summarize inferences and
convergence in a table and graph, and save the simulations in arrays for easy access
in R.
R2PPT provides a simple set of wrappers to easily use rcom
or RDCOMClient for generating Microsoft PowerPoint
presentations.
R2STATS is a gWidgetsRGtk2 GUI for fitting and comparing GLM or GLMM (based on Douglas Bates' lme4 package) in R. It is designed to make comparisons between numerous models easy, both numerically and graphically, which may be useful for teaching. Relevant plots are automatically produced for each model family. R2STATS is *not* a generic graphical interface for R, but a GUI for statistical modelling in a model comparison approach.
r2stl, R package for visualizing data using a 3D printer
Package r2stl converts R data to STL (stereolithography) files
that can be used to feed a 3-dimensional printer.  The
3-dimensional output from an R function can be materialized
into a solid surface in a plastic material, therefore allowing
more detailed examination. There are many possible uses for
this new R tool, such as to examine mathematical expressions
with very irregular shapes, to aid teaching people with
impaired vision, to create raised relief maps from digital
elevation maps (DEMs), to bridge the gap between mathematical
tools and rapid prototyping, and many more.  Ian Walker created
the function "r2stl" and Jose' Gama assembled the package.
Using the Ming library
(http://www.libming.org/) to create Flash animations.
Users can either use the SWF device swf() to generate SWF file
directly through plotting functions like plot() and lines(),
or convert images of other formats (SVG, PNG, JPEG) into SWF.
This package uses either the statconnDCOM server (via the
rcom package) or the RDCOMClient to communicate with MS-Word
via the COM interface.
Invoke a 'BUGS' model in 'OpenBUGS' or 'WinBUGS', a class "bugs" for 'BUGS'
results and functions to work with that class.
Function write.model() allows a 'BUGS' model file to be written.
The class and auxiliary functions could be used with other MCMC programs, including 'JAGS'.
This is a collection of useful functions and data for
Stats 330
CDISC Dataset-XML and Define-XML are used as file format for clinical trial data and metadata respectively. This functions supports reading both Dataset-XML and Define-XML.
Provides a collection of functions for basic
database and document management operations such as add, get, list or
delete. Every cdbFunction() gets and returns a list() containing the
connection setup.
Such a list (in the documentation mostly called cdb) can be generated by
cdb <- cdbIni(). Then, the list cdb provides some functions respectively
functionality e.g cdb$baseUrl() or cdb$getDocRev().
This package provides an R interface with 2-part 4dfp MRI images
(.4dfp.ifh and .4dfp.img files.)
A collection of R functions for use with Stock Synthesis, a
fisheries stock assessment modeling platform written in ADMB by Dr. Richard
D.  Methot at the NMFS Northwest Fisheries Science Center. The functions
include tools for summarizing and plotting results, manipulating files,
visualizing model parameterizations, and various other tasks.
The R6 package allows the creation of classes with reference
semantics, similar to R's built-in reference classes. Compared to reference
classes, R6 classes are simpler and lighter-weight, and they are not built
on S4 classes so they do not require the methods package. These classes
allow public and private members, and they support inheritance, even when
the classes are defined in different packages.
Implementation of some racing methods for the empirical
selection of the best. If the R package `rpvm' is installed
(and if PVM is available, properly configured, and
initialized), the evaluation of the candidates are performed in
parallel on different hosts.
Fit a variety of models to Rank Abundance Data
This package implements import, export, manipulation, visualization, and downstream
(post-clustering) analysis of RADseq data, integrating with the pyRAD package by Deren Eaton.
Fundamental formulas for Radar, for attenuation, range, velocity,
effectiveness, power, scatter, doppler, geometry, radar equations, etc.
Based on Nick Guy's Python package PyRadarMet
A platform-independent browser-based interface for business analytics in R, based on the Shiny package.
RadioSonde is a collection of programs for reading and
plotting SKEW-T,log p diagrams and wind profiles for data
collected by radiosondes (the typical weather balloon-borne
instrument), which we will call "flights", "sondes", or
"profiles" throughout the associated documentation.  The raw
data files are in a common format that has a header followed by
specific variables.  Use "help(ExampleSonde)" for the full
explanation of the data files.
Radioactive doses estimation using individual chromosomal aberrations information. See Higueras M, Puig P, Ainsbury E, Rothkamm K. (2015). A new inverse regression model applied to radiation biodosimetry. Proceedings of the Royal Society A, 471
Designed for the import, analysis, and visualization of dosimetric and volumetric data in Radiation Oncology, the tools herein enable import of dose-volume histogram information from Varian's 'Eclipse' platform and 3D structural representations and dosimetric information from 'DICOM-RT' files.  These tools also enable subsequent visualization and statistical analysis of these data.
Contains 4 different functions for radon and soil gas transport in a porous medium.
Aims at loading Google Adwords data into R. Adwords is an online
advertising service that enables advertisers to display advertising copy to web users.
Therefore the package implements three main features. First, the package provides an
authentication process for R with the Google Adwords API via OAUTH2. Second, the
package offers an interface to apply the Adwords query language in R and
query the Adwords API with ad-hoc reports. Third, the received data are
transformed into suitable data formats for further data processing and data
analysis.
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.
Data fusion filters for Attitude Heading Reference System (AHRS) based on
Vlad Maximov's GyroLib AHRS library (quaternion based linearized/extended/unscented Kalman filter,
Euler based LKF, gyro-free with vector matching, SVD calibration and EKF calibration),
Sebastian O.H. Madgwick AHRS algorithms and Sebastian O.H. Madgwick implementation of Mayhony et al AHRS algorithm.
Functions and data sets for functional data display and outlier detection.
The Climate Prediction Center's (CPC) rainfall data for the
world (1979 to present, 50 km resolution) and the USA (1948 to
present, 25 km resolution), is one of the few high quality, long
term, observation based, daily rainfall products available for free.
Although raw data is available at CPC's ftp site, obtaining,
processing and visualizing the data is not straightforward. There are
more than 12,000 files for the world and about 24,000 files for the USA.
Moreover, file formats and file extensions have not been consistent.
This package provides functionality to download, process and visualize
over 35 years of global rainfall data and over 65 years of USA rainfall
data from CPC.
Estimates of rainfall at desired frequency (e.g., 1% annual
chance or 100-year return period) and desired duration (e.g.,
24-hour duration) are often required in the design of dams and other
hydraulic structures, catastrophe risk modeling, environmental
planning and management. One major source of such estimates for the
USA is the NOAA National Weather Service's (NWS) division of
Hydrometeorological Design Studies Center (HDSC). Raw data from
NWS-HDSC is available at 1-km resolution and comes as a huge number
of GIS files. This package provides functionality to easily access
and analyze the 1-km GIS files provided by NWS' PF Data Server for
the entire USA. This package also comes with datasets on record point
rainfall measurements provided by NWS-HDSC.
Programmatic interface to the Altmetric.com API.
Characterizing environmental microbiota diversity using amplicon-based next generation sequencing (NGS) data. Functions are developed to manipulate operational taxonomic unit (OTU) table, perform descriptive and inferential statistics, and generate publication-quality plots.
Estimate the parameters, the number of classes and cluster vertices of a random network into groups with homogeneous connection profiles. The clustering is performed for directed graphs with typed edges (edges are assumed to be drawn from multinomial distributions) for which a partition of the vertices is available.
A "define" function is provided that allows
for inclusion of external scripts without polluting
the global namespace and makes it easier to
write modular R code.
Additional matrix functionality for R including: (1) wrappers
for the base matrix function that allows matrices to be created from character
strings and lists (the former is especially useful for creating block
matrices), (ii) better printing of large matrices via a new generic function
for "pretty" printing, and (iii) a number of convenience functions for users
more familiar with other scientific languages like Julia, Matlab/Octave, or
Python+NumPy.
This package provides an efficient procedure for fitting the entire solution path for high-dimensional regularized generalized linear models with interactions effects under the strong heredity constraint.
RAMpath is a rewrite of RAMpath software developed by John McArdle and Steven Boker. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features.  First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variances and covariances into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system.
Bayesian geostatistical modeling of Gaussian processes using a reparameterized and marginalized posterior sampling (RAMPS) algorithm designed to lower autocorrelation in MCMC samples.  Package performance is tuned for large spatial datasets.
The deterministic part of the Fortuna cryptographic
pseudorandom number generator, described by Schneier & Ferguson
"Practical Cryptography"
Generates random names with additional information including fake SSNs, gender, location, zip, age, address, and nationality.
The true random number service provided by the RANDOM.ORG
website created by Mads Haahr samples atmospheric noise via radio tuned to
an unused broadcasting frequency together with a skew correction algorithm
due to John von Neumann.  More background is available in the included
vignette based on an essay by Mads Haahr.  In its current form, the package
offers functions to retrieve random integers, randomized sequences and
random strings.
Methods for the inference on and the simulation of Gaussian fields are provided, as well as methods for the simulation of extreme value random fields.
Various utilities are provided that might be used in spatial statistics and elsewhere. It delivers a method for solving linear equations that checks the sparsity of the matrix before any algorithm is used. Furthermore, it includes the Struve functions.
Classification and regression based on a forest of trees
using random inputs.
A unified treatment of Breiman's random forests for survival, regression and classification problems based on Ishwaran and Kogalur's random survival forests (RSF) package.  The package runs in both serial and parallel (OpenMP) modes.
The package implements a bagging predictor based on
general linear models
Allows the user to conduct randomization-based inference for a wide variety of experimental scenarios. The package leverages a potential outcomes framework to output randomization-based p-values and null intervals for test statistics geared toward any estimands of interest, according to the specified null and alternative hypotheses. Users can define custom randomization schemes so that the randomization distributions are accurate for their experimental settings. The package also creates visualizations of randomization distributions and can test multiple test statistics simultaneously.
Contains a function to randomize subjects, patients in groups of
sequences (treatment sequences).
If a blocksize is given, the randomization will be done within blocks.
The randomization may be controlled by a Wald-Wolfowitz runs test.
Functions to obtain the p-value of that test are included.
The package is mainly intended for randomization of bioequivalence studies
but may be used also for other clinical crossover studies.
Contains two helper functions sequences() and williams() to get the sequences
of commonly used designs in BE studies.
Generates random assignments for common experimental
designs: simple, complete, blocked, and clustered.
Fits random effects latent class models, as well as standard latent class models.
Function for generating random gender and ethnicity correct first and/or last names. Names are chosen proportionally based upon their probability of appearing in a large scale data base of real names.
The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis.
Ensemble model, for classification, regression
and unsupervised learning, based on a forest of unpruned
and randomized binary decision trees. Each tree is grown
by sampling, with replacement, a set of variables at each node.
Each cut-point is generated randomly, according to the continuous
Uniform distribution. For each tree, data are either bootstrapped
or subsampled. The unsupervised mode introduces clustering, dimension reduction
and variable importance, using a three-layer engine. Random Uniform Forests are mainly
aimed to lower correlation between trees (or trees residuals), to provide a deep analysis
of variable importance and to allow native distributed and incremental learning.
Several non parametric randomness tests for numeric sequences
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
Tools for easy generation of (life-history) traits maps based on species range (extent-of-occurrence) maps.
A fast implementation of Random Forests, particularly suited for high
dimensional data. Ensembles of classification, regression, survival and
probability prediction trees are supported. Data from genome-wide
association studies can be analyzed efficiently. In addition to data
frames, datasets of class 'gwaa.data' (R package GenABEL) can be directly
analyzed.
This package performs aggregation of ordered lists based
on the ranks using several different algorithms: Borda count,
Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a
brute force algorithm (for small problems)
This package proposes a model-based clustering algorithm for
ranking data. Multivariate rankings as well as partial rankings are taken
into account. This algorithm is based on an extension of the Insertion
Sorting Rank (ISR) model for ranking data, which is a meaningful and
effective model parametrized by a position parameter (the modal ranking,
quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity
of the rank population is modelled by a mixture of ISR, whereas conditional
independence assumption is considered for multivariate rankings.
Implements distance based probability models for ranking data.
The supported distance metrics include Kendall distance and Weighted Kendall distance.
Mixture models are also supported.
Rank-hazard plots (Karvanen and Harrell, Statistics in Medicine 2009) visualize the relative importance of covariates in a proportional hazards model. The key idea is to rank the covariate values and plot the relative hazard as a function of ranks scaled to interval [0,1]. The relative hazard is plotted in respect to the reference hazard, which can bee.g. the hazard related to the median of the covariate.
Methods for ranking responses of a single response question or a multiple response question