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.
A 'Shiny' web application for energy industry analytics.
      Take an overview of the industry, measure Key Performance Indicators,
      identify changes in the industry over time, and discover new relationships in the data.
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.)
Downloads a satellite image via Google Maps/Earth (these are
    originally from a variety of aerial photography sources), 
    translates the image into a perceptually uniform color space,
    runs one of a few different clustering algorithms on the colors in the image 
    searching for a user-supplied number of colors,
    and returns the resulting color palette.
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.
Descriptive analysis is essential for publishing medical articles.
  This package provides an easy way to conduct the descriptive analysis.
  1. Both numeric and factor variables can be handled. For numeric variables, normality test will be applied to choose the parametric and nonparametric test.
  2. Both two or more groups can be handled. For groups more than two, the post hoc test will be applied, 'Tukey' for the numeric variables and 'FDR' for the factor variables.
  3. ANOVA or Fisher test can be forced to apply.
Builds format strings that can be used with strptime() and sprintf().
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
Easily load and install multiple packages from different sources, 
    including CRAN and GitHub. The libraries function allows you to load or attach 
    multiple packages in the same function call. The packages function will load one 
    or more packages, and install any packages that are not installed on your system 
    (after prompting you). Also included is a from_import function that allows you 
    to import specific functions from a package into the global environment.
Power analysis is used in the estimation of sample sizes for
    experimental designs. Most programs and R packages will only output the highest
    recommended sample size to the user. Often the user input can be complicated
    and computing multiple power analyses for different treatment comparisons can
    be time consuming. This package simplifies the user input and allows the user
    to view all of the sample size recommendations or just the ones they want to see.
    The calculations used to calculate the recommended sample sizes are from the
    'pwr' package.
Query NCBI Entrez and retrieve PubMed records in XML format. A large number of records can be easily downloaded via this simple-to-use interface to the NCBI PubMed API.
Performs analysis of regression in simple designs with quantitative treatments, 
             including mixed models and non linear models. Plot graphics (equations and data).
The main function, ProtectTable(), performs table suppression according to a frequency rule with a data set as the only required input. Within this function, protectTable() or protectLinkedTables() in package 'sdcTable' is called. Lists of level-hierarchy (parameter 'dimList') and other required input to these functions are created automatically.
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.
Set of tools to simplify application of atomic forecast
    verification metrics for (comparative) verification of ensemble forecasts
    to large data sets. The forecast metrics are imported from the
    'SpecsVerification' package, and additional forecast metrics are provided
    with this package. Alternatively, new user-defined forecast scores can be
    implemented using the example scores provided and applied using the
    functionality of this package.
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.
We propose a new sample size calculation method for trial-based
    cost-effectiveness analyses. Our strategy is based on the value of perfect
    information that would remain after the completion of the study.
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.
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.
Provides empirical Bayesian lasso and elastic net algorithms for variable selection and effect estimation. Key features include sparse variable selection and effect estimation via generalized linear regression models, high dimensionality with p>>n, and significance test for nonzero effects. This package outperforms other popular methods such as lasso and elastic net methods in terms of power of detection, false discovery rate, and power of detecting grouping effects.
Functions for querying the EBI Metagenomics Portal (https://www.ebi.ac.uk/metagenomics/). The current main focus is on taxa abundance data, but the intention is that this package should evolve into a general purpose package for working with EBI Metagenomics data using R.
Empirical Bayes ranking applicable to parallel-estimation settings where the estimated parameters are asymptotically unbiased and normal, with known standard errors.  A mixture normal prior for each parameter is estimated using Empirical Bayes methods, subsequentially ranks for each parameter are simulated from the resulting joint posterior over all parameters (The marginal posterior densities for each parameter are assumed independent). Finally, experiments are ordered by expected posterior rank, although computations minimizing other plausible rank-loss functions are also given.
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.
Provides an interface to the European Central Bank's Statistical
    Data Warehouse API, allowing for programmatic retrieval of a vast quantity
    of statistical data.
Draw sample paths for endpoint-conditioned continuous time Markov chains via modified rejection sampling or uniformization.
An implementation of the univariate elliptic distribution, and
    lambda option pricing model. It provides detailed functionality and data
    sets for the distribution and modelling. Especially, it contains functions
    for the computation of density, probability, quantile, fitting procedures,
    option prices, volatility smile. It also comes with sample financial data,
    and plotting routines.
Computes and plots a transformed empirical CDF (ecdf) as a
    diagnostic for heavy tailed data, specifically data with power law decay on the
    tails.  Routines for annotating the plot, comparing data to a model, fitting a
    nonparametric model, and some multivariate extensions are given.
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 data sets in Ecdat and to create,
          manipulate, plot and analyze those and similar data sets.
With this package, users can embed interactive charts to their Shiny applications. These charts will be generated by ECharts library developed by Baidu (http://echarts.baidu.com/). Current version supports line chart, bar chart, pie chart, scatter plot, gauge, word cloud, radar chart, tree map, and heat map.
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.
Functions for easy building of error correction models (ECM) for time series regression.
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.
Tools for general properties including price, quantity, elasticity, convexity, marginal revenue and manifold of various economics demand systems including Linear, Translog, CES, LES and CREMR.
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.
Creates an R interface to the Bacen <http://api.bcb.gov.br/> API and IPEA data <http://www.ipeadata.gov.br/Default.aspx>.
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 interface to the Embedded COnic Solver (ECOS), an efficient
	     and robust C library for convex problems. Conic and equality
	     constraints can be specified in addition to integer and
	     boolean variable constraints for mixed-integer problems. This
	     R interface is inspired by the python interface and has
	     similar calling conventions.
Implements stochastic simulations of community assembly (ecological
    diversification) using customizable ecospace frameworks (functional trait
    spaces). Provides a wrapper to calculate common ecological disparity and
    functional ecology statistical dynamics as a function of species richness.
    Functions are written so they will work in a parallel-computing environment.
Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre-, core and post- modelling analyses of species distribution, niche quantification and community assembly. 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.
Implementation of the EPA's Ecological Exposure Research Division (EERD) tools (discontinued in 1999) for Probit and Trimmed Spearman-Karber Analysis.
    Probit and Spearman-Karber methods from Finney's book "Probit analysis a statistical treatment of the sigmoid response curve" with options for most accurate results or identical results to the book.
    Probit and all the tables from Finney's book (code-generated, not copied) with the generating functions included.
    Control correction: Abbott, Schneider-Orelli, Henderson-Tilton, Sun-Shepard.
    Toxicity scales: Horsfall-Barratt, Archer, Gauhl-Stover, Fullerton-Olsen, etc.
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.
Computer simulations of classical ecological models as a
    learning resource.
Implements various procedures for finding 
	     multiple change-points. Two methods make use of dynamic 
	     programming and probabilistic pruning, with no distributional 
	     assumptions other than the existence of certain absolute 
	     moments in one method. Hierarchical and exact search methods 
	     are included. All methods return the set of estimated change-
	     points as well as other summary information.
Provides a powerful framework for evolutionary
    computing in R. The user can easily construct powerful evolutionary algorithms
    for tackling both single- and multi-objective problems by plugging in different
    predefined evolutionary building blocks, e. g., operators for mutation,
    recombination and selection with just a few lines of code. Your problem
    cannot be easily solved with a standard EA which works on real-valued vectors,
    permutations or binary strings? No problem, 'ecr' has been developed with that in
    mind. Extending the framework with own operators is also possible. Additionally
    there are various comfort functions, like monitoring, logging and more.
Functions useful for exploratory data analysis
    using random forests which can be used to compute multivariate partial
    dependence, observation, class, and variable-wise marginal and joint permutation
    importance as well as observation-specific measures of distance 
    (supervised or unsupervised). All of the aforementioned functions are
    accompanied by 'ggplot2' plotting functions.
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.
Detection of edge points in images based on the difference
        of two asymmetric M-kernel estimators. Linear and circular
        regression clustering based on redescending M-estimators.
        Detection of linear edges in images.
Functions for exploratory and descriptive analysis of event based
    data. Can be used to import and export Xes-files, the IEEE eXtensible Event
    Stream standard (see <http://standards.ieee.org/findstds/standard/1849-2016.html> for more info). Provides methods for describing and selecting process data,
    and for preparing event log data for process mining.
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.
An implementation of maximum entropy sampling for spatial
  data is provided. An exact branch-and-bound algorithm as well as greedy and
  dual greedy heuristics are included.
Import physiologic data stored in
    the European Data Format (EDF and EDF+) into R.
    Both EDF and EDF+ files are supported. Discontinuous
    EDF+ files are not yet supported.
Functions for reading in data sets of prey and predator isotopic measurements and producing estimates for discrimination factors.
Reads European Data Format files EDF and EDF+, see http://www.edfplus.info,
    BioSemi Data Format files BDF, see http://www.biosemi.com/faq/file_format.htm,
    and BDF+ files, see http://www.teuniz.net/edfbrowser/bdfplus%20format%20description.html.
    The files are read in two steps: first the header is read
    and then the signals (using the header object as a parameter).
Easily creating empirical distribution functions from data: 'dfun', 'pfun',
    'qfun' and 'rfun'.
In the USA, firms file different forms with the U.S. Securities and
    Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis,
    and Retrieval system). EDGAR automated system collects all the different
    necessary filings and make it publicly available. Secondly, it validates
    collected filings, then perform indexing and accepting of these submitted
    forms. Investors, regulators, and researchers often require these forms for
    various purposes. This package helps in data gathering, management, and 
	visualization in this regard. It downloads SEC EDGAR quarterly master
    indexes, daily master indexes, and filings from SEC.org site and perform 
    sentiment analysis of these filing.
Generates interactive circle plots with the nodes around the
    circumference and linkages between the connected nodes using hierarchical
    edge bundling via the D3 JavaScript library. See <http://d3js.org/> for more
    information on D3.
Facilitates basic spatial edge correction to point pattern data.
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 as described in Kim et al. (2015) <DOI:10.1080/01621459.2015.1040881>. In this approach, 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.
Perform Dynamic Model Averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.
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"
Provides convenience functions and pre-programmed Stan models
    related to item response theory. Its purpose is to make fitting
    common item response theory models using Stan easy.
Data from the 2013 Australian Federal Election (House of 
    Representatives) and the 2011 Australian Census. Includes tools for 
    visualizing and analysing the data. This package incorporates 
    data that is copyright Commonwealth of Australia (Australian 
    Electoral Commission) 2016.
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.
Compute the extended empirical log likelihood ratio (Tsao & Wu, 2014) for the mean and parameters defined by estimating equations.
Read raw EEM data and prepares them for further analysis.
Provides various tools to calculate EEM fluorescence metrics and
    preprocess EEM for PARAFAC analysis.
Collection of convenience functions to make working with
    administrative records easier and more consistent. Includes functions to
    clean strings, identify cut points, and quickly combine shapefiles and
    data frames for plotting. Also includes three example data sets of 
    administrative education records for learning how to process records with 
    errors.
A number of utility function for exploratory
    factor analysis are included in this package. In particular, it computes standard errors for parameter estimates and factor correlations under a variety of conditions.
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.
Variable selection and Bayesian effect fusion for categorical predictors in linear regression models. Effect fusion aims at the question which categories have a similar effect on the response and therefore can be fused to obtain a sparser representation of the model. Effect fusion and variable selection can be obtained either with a prior that has an interpretation as spike and slab prior on the level effect differences or with a sparse finite mixture prior on the level effects. The regression coefficients are estimated with a flat uninformative prior after model selection or model averaged. For posterior inference, an MCMC sampling scheme is used that involves only Gibbs sampling steps.
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.
Notice: The package EffectStars2 provides a more up-to-date implementation of effect stars! EffectStars provides functions to visualize regression models with categorical response. The effects of the variables are plotted with star plots in order to allow for an optical impression of the fitted model.
Provides functions for the method of effect star. Effect stars can be used to visualize estimates of parameters corresponding to different groups, for example in multinomial logit models. Beside the main function 'effectstars' there exist methods for special objects, for example for 'vglm' objects from the 'VGAM' package.
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.
An implementation for using efficient initials to compute the
    maximal eigenpair in R. It provides two algorithms to find the efficient
    initials under two cases: the tridiagonal matrix case and the general matrix
    case. Besides, it also provides algorithms for the next to the maximal eigenpair under
    these two cases.
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.
The European Eddy Fluxes Database Cluster distributes fluxes of different Green House Gases measured mainly using the eddy covariance technique acquired in sites involved in EU projects but also single sites in Europe, Africa and others continents that decided to share their measurements in the database (cit. http://gaia.agraria.unitus.it ). The package provides two functions to load and row-wise bind CSV files distributed by the database. Currently only L3 and L4 (L=Level), half-hourly and daily (aggregation) files are supported.
Provides a function to check the
  importance of a feature based on a dependent classification
  variable. An ensemble of feature selection methods
  is used to determine the normalized importance value of
  all features. Combining these methods in one function
  (building the cumulative importance values) provides a 
  stable feature selection tool. This selection
  can also be viewed in a barplot using the barplot_fs() function
  and proved using the evaluation function efs_eval().
Functions for assigning Clarke or Parkes (Consensus) error grid
    zones to blood glucose values, and for plotting both types of error grids.
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 using Stan.
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).
Collection of functions to evaluate uncertainty of results from
    water quality analysis using the Weighted Regressions on Time Discharge and
    Season (WRTDS) method. This package is an add-on to the EGRET package that
    performs the WRTDS analysis.
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 et al. 1993 JVS <doi:10.1111/jvs.12050>) models. Response curves along one-dimensional gradients including no response, monotone, plateau, unimodal and bimodal models.
Software accompanying Gary King's book: A Solution to the Ecological Inference Problem. (1997). Princeton University Press. ISBN 978-0691012407.
An R wrapper to allow the user to query categories and Series IDs, and import data, from the EIA's API.
Compares estimates from three ecological inferences routines, based on King et. al.'s approach.
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.
Performs some enhanced variable selection algorithms 
  based on the least absolute shrinkage and selection operator for regression model.
Connect to 'Elasticsearch', a 'NoSQL' database built on the 'Java'
    Virtual Machine. Interacts with the 'Elasticsearch' 'HTTP' 'API'
    (<https://www.elastic.co/products/elasticsearch>), 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.
Description: Uses k-fold cross-validation and elastic-net regularization to estimate the
              Ising model on binary data. Produces 3D plots of the cost function as a function
              of the tuning parameter in addition to the optimal network structure.
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.
A lightweight R interface to 'Elasticsearch' - a NoSQL search-engine and 
    column store database (see <https://www.elastic.co/products/elasticsearch> for more 
    information). This package implements a simple Domain-Specific Language (DSL) for indexing, 
    deleting, querying, sorting and aggregating data using 'Elasticsearch'.
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.
Offers a set of functions that automatically downloads and aggregates
    election data from Brazil, directly from the Superior Electoral Court website.
    Among others, there are data available on local and federal elections for all positions
    (city councillor, mayor, state deputy, federal deputy, governor, and president)
    disaggregated by state of the Federation, city, zone, and polling stations.
    In addition, data on the verification (blank votes, null votes, abstention),
    candidates' and voters' backgrounds are also available.
Aims to facilitate the reduction of elemental microchemistry data from solid-phase LA-ICPMS analysis (laser ablation inductive coupled plasma mass spectrometry). The elementR package provides a reactive and user friendly interface for conducting all steps needed for an optimal data reduction while leaving maximum control for user.
Useful when reading the book above mentioned, in the
        documentation referred to as `the book'.
Provides R access to election results data. Wraps elex (https://github.com/newsdev/elex/), a Python package and command line tool for fetching and parsing Associated Press election results.
A tool to draw samples from a Empirical Likelihood Bayesian posterior
   of parameters using Hamiltonian Monte Carlo.
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.
Training and prediction functions are provided for the Extreme Learning Machine algorithm (ELM). The ELM use a Single Hidden Layer Feedforward Neural Network (SLFN) with random generated weights and no gradient-based backpropagation. The training time is very short and the online version allows to update the model using small chunk of the training set at each iteration. The only parameter to tune is the hidden layer size and the learning function.
Allows calculating global scores for characteristics of visual stimuli. Stimuli are presented as sequence of pairwise comparisons ('contests'), during each of which a rater expresses  preference for one stimulus over the other. The algorithm for calculating global scores is based on Elo rating, which updates individual scores after each single pairwise contest. Elo rating is widely used to rank chess players according to their performance. Its core feature is that dyadic contests with expected outcomes lead to smaller changes of participants' scores than outcomes that were unexpected. As such, Elo rating is an efficient tool to rate individual stimuli when a large number of such stimuli are paired against each other in the context of experiments where the goal is to rank stimuli according to some characteristic of interest.
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
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, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory movement analysis yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation").
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
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).
Functions that support estimating, assessing and mapping regional disaggregated indicators. So far, estimation methods comprise the model-based approach Empirical Best Prediction (see "Small area estimation of poverty indicators" by Molina and Rao (2010)<doi:10.1002/cjs.10051>), as well as their precision estimates. The assessment of the used model is supported by a summary and diagnostic plots. For a suitable presentation of estimates, map plots can be easily created. Furthermore, results can easily be exported to excel.
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.  Subsequent edits
				also include variational network and text scaling models.
The EmiStatR provides a fast and parallelised calculator to estimate combined wastewater emissions. 
    It supports the planning and design of urban drainage systems, without the requirement of 
    extensive simulation tools. The EmiStatR package implements modular R methods. This enables 
    to add new functionalities through the R framework. Furthermore, EmiStatR was implemented 
    with an interactive user interface with sliders and input data exploration.
Parse and serialize Ecological Metadata Language ('EML', see
  <https://en.wikipedia.org/wiki/Ecological_Metadata_Language> for
  more information) files into S4 objects.
Log-linear modeling is a popular method for the analysis of contingency table data. When the table is sparse, the data can fall on the boundary of the convex support, and we say that "the MLE does not exist" in the sense that some parameters cannot be estimated. However, an extended MLE always exists, and a subset of the original parameters will be estimable. The 'eMLEloglin' package determines which sampling zeros contribute to the non-existence of the MLE. These problematic zero cells can be removed from the contingency table and the model can then be fit (as far as is possible) using the glm() function.
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.
End-member modelling analysis of grain-size data.
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
Fit models of modularity to morphological landmarks. Perform model 
  selection on results. Fit models with a single within-module correlation or
  with separate within-module correlations fitted to each module.
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 implementation of using emoji and fontawesome for using in both base and 'ggplot2' graphics.
Statistical tools for environmental and ecological surveys.
    Simulation-based power and precision analysis; detection probabilities from
    different survey designs; visual fast count estimation.
Fixation and saccade detection in eye movement recordings. 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).
Routines for performing empirical calibration of observational
    study estimates. By using a set of negative control hypotheses we can
    estimate the empirical null distribution of a particular observational
    study setup. This empirical null distribution can be used to compute a
    calibrated p-value, which reflects the probability of observing an
    estimated effect size when the null hypothesis is true taking both random
    and systematic error into account.
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 analysis of variance table including the expected mean squares (EMS) for various types of experimental design is provided. When some variables are random effects or we use special experimental design such as nested design, repeated-measures design, or split-plot design, it is not easy to find the appropriate test, especially denominator for F-statistic which depends on EMS.
Background correction of spectral like data. Handles variations in
  scaling, polynomial baselines, interferents, constituents and replicate variation.
  Parameters for corrections are stored for further analysis, and spectra are corrected
  accordingly.
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.
Provides the next iteration of the EMU Speech 
    Database Management System (EMU-SDMS) with database management, data 
    extraction, data preparation and data visualization facilities.
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.
Likelihood-based approaches to estimate linear regression parameters and treatment effects in the presence of endogeneity. Specifically, this package includes James Heckman's classical simultaneous equation models-the sample selection model for outcome selection bias and hybrid model with structural shift for endogenous treatment. For more information, see the seminal paper of Heckman (1978) <DOI:10.3386/w0177> in which the details of these models are provided. This package accommodates repeated measures on subjects with a working independence approach. The hybrid model further accommodates treatment effect modification.
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.
Computes the expectation of the number of transmissions and receptions considering an End-to-End transport model with limited number of retransmissions per packet. It provides theoretical results and also estimated values based on Monte Carlo simulations.
E-statistics (energy) tests and statistics for multivariate and univariate inference,
             including distance correlation, one-sample, two-sample, and multi-sample tests for
             comparing multivariate distributions, are implemented. Measuring and testing
             multivariate independence based on distance correlation, partial distance correlation,
             multivariate goodness-of-fit tests, clustering based on energy distance, testing for
             multivariate normality, distance components (disco) for non-parametric analysis of
             structured data, and other energy statistics/methods are implemented.
Data published by the United States Federal Energy Regulatory Commission including
    electric company financial data, natural gas company financial data, 
    hydropower plant data, liquified natural gas plant data, oil company financial data
    natural gas company financial data, and natural gas storage field data.
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.
Analyzing English & European soccer results data from 1871-2016.
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'.
A set of tools for the analysis of ecological niche of species and calculation of habitat suitability maps.
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)<doi:10.1016/j.ecolmodel.2005.03.026>.
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.
An algorithmic framework for measuring feature importance,
    outlier detection, model applicability domain evaluation,
    and ensemble predictive modeling with (sparse)
    partial least squares regressions.
An R package for joint statistical modelling of ChIP-seq data, accounting for technical/biological replicates, multiple conditions and different ChIP efficiencies of the individual experiments.
These programs are used for calculating enrichment
        factors, drawing enrichment curves to evaluate virtual
        screening approaches.
Provides the "enrich" method to enrich list-like R objects with new, relevant components. The current version has methods for enriching objects of class 'family', 'link-glm' and 'glm'. The resulting objects preserve their class, so all methods associated with them still apply. The package can also be used to produce customisable source code templates for the structured implementation of methods to compute new components.
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, K-Nearest Neighbors, Penalized Regression (L1/L2), and Bayesian Additive Regression Trees. 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.
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.
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.
Functions to use ensembles of several machine learning models
    specified in caret 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.
Tools for automatic model selection and diagnostics for Climate and Environmental data.  In particular the envcpt() function does automatic model selection between a variety of trend, changepoint and autocorrelation models.  The envcpt() function should be your first port of call.
A collection of R functions of common use in
   the Kostas Lab for microbial ecology.
Gas Chromatography-Mass Spectrometer(GC-MS) Data Analysis for Environmental Science. This package covered topics such as raw data process, molecular isotope ratio, matrix effects, etc. in environmental analysis.
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.
Generation of bioclimatic rasters that will be particularly 
	useful for species distribution modeling.
Functions and datasets to support the book by Nhu D Le and James V Zidek, Springer (2006).
Envelope methodology and aster modeling are combined to provide users with precise estimation of expected Darwinian fitness.
A plot overlying the niche of multiple species is obtained: 1) to determine the niche conditions which favor a higher species richness, 2) to create a box plot with the range of environmental variables of the species, 3) to obtain a list of species in an area of the niche selected by the user and, 4) to estimate niche overlap among the species.
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.
Analyzing censored variables usually requires the use of optimization algorithms. This package provides an alternative algebraic approach to the task of determining the expected value of a random censored variable with a known censoring point. Likewise this approach allows for the determination of the censoring point if the expected value is known. These results are derived under the assumption that the variable follows an Epanechnikov kernel distribution with known mean and range prior to censoring. Statistical functions related to the uncensored Epanechnikov distribution are also provided by this package.
Reads water network simulation data in Epanet's text-based
    '.inp' and '.rpt' formats into R. Also reads results from Epanet-msx.
    Provides basic summary information and plots. 
    The README file has a quick introduction. See http://www2.epa.gov/
    water-research/epanet for more information on the Epanet software for modeling
    hydraulic and water quality behavior of water piping systems.
The main functions compute the expectation propagation approximation of a Bayesian probit/logit models with Gaussian prior. More information can be found in Chopin and Ridgway (2015). More models and priors should follow.
Functions for demographic and epidemiological analysis in
  the Lexis diagram, i.e. register and cohort follow-up data, in
  particular representation, manipulation and simulation of multistate 
  data - the Lexis suite of functions, which includes interfaces to
  'mstate', 'etm' and 'cmprsk' packages. 
  Also contains functions for Age-Period-Cohort and Lee-Carter
  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.
The Economic Policy Institute (<http://www.epi.org/>) provides
    researchers, media, and the public with easily accessible, up-to-date, and
    comprehensive historical data on the American labor force. It is compiled
    from Economic Policy Institute analysis of government data sources. Use
    it to research wages, inequality, and other economic indicators over time
    and among demographic groups. Data is usually updated monthly.
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.
Provides flexible model fitting used in epidemiological data
    analysis by a unified model specification, along with some data manipulation
    functions. This package covers fitting of variety models including Cox
    regression models, linear regression models, Poisson regression models, logistic
    models and others whose likelihood is expressed in negative binomial, gamma and
    Weibull distributions.
Tools for simulating mathematical models of infectious disease. 
    Epidemic model classes include deterministic compartmental models, stochastic 
    agent-based models, and stochastic network models. Network models use the
    robust statistical methods of exponential-family random graph models (ERGMs) 
    from the Statnet suite of software packages in R. Standard templates for epidemic 
    modeling include SI, SIR, and SIS disease types. EpiModel features 
    an easy API for extending these templates to address novel scientific research aims.
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.
Tools 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).
A collection of R functions supporting the text book
  Modern Epidemiology, Second Edition, by Kenneth J.Rothman and Sander Greenland.
  ISBN 13: 978-0781755641  See <http://www.krothman.org/> for more information.
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.
An efficient multi-core package to reconstruct an underlying network of
             genomic signatures of high-dimensional epistatic selection from 
             partially observed genotype data. The phenotype that we consider is viability. 
			 The network captures the conditional dependent short- and long-range linkage 
			 disequilibrium structure of genomes and thus reveals aberrant marker-marker 
			 associations that are due to epistatic selection. We target on high-dimensional
			 genotype data where number of variables (markers) is larger than number of 
			 sample sizes (p >> n). The computations is memory-optimized using the sparse 
			 matrix output.
EpiTools: R Package for Epidemiologic Data and Graphics
Users can easily derive the calendar dates from epidemiological weeks, and vice versa.
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.
Computation of direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items. Test scoring can be performed by true score equating and observed score equating methods.
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.
Automated compound deconvolution, alignment across samples, and identification of metabolites by spectral library matching in Gas Chromatography - Mass spectrometry (GC-MS) untargeted metabolomics. Outputs a table with compound names, matching scores and the integrated area of the compound for each sample.
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. 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 (ERGMs). '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.
Utilities for managing egocentrically sampled network data and a wrapper around the 'ergm' package to facilitate ERGM inference and simulation from such data.
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 set of extensions for the 'ergm' package to fit weighted networks whose edge weights are ranks.
A template package to demonstrate the use of user-specified statistics for use in "ergm" models as part of the "statnet" suite of packages.
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.
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, 2016) and the Guthrie and Buchwald (1991) test.
A set of user-friendly functions to aid in organizing, plotting 
    and analyzing event-related potential (ERP) data.  Provides an easy-to-learn 
    method to explore ERP data. Should be useful to those without a background 
    in computer programming, and to those who are new to ERPs (or new to the 
    more advanced ERP software available). Emphasis has been placed on highly 
    automated processes using functions with as few arguments as possible. 
    Expects processed (cleaned) data.
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.
Build and analyze error intervals for a particular model predictions assuming different distributions for noise in the data.
Provides a function to convert existing R functions into "errorized" versions 
    with added logging and handling functionality when encountering errors or warnings. 
    The errorize function accepts an existing R function as its first argument and 
    returns a R function with the exact same arguments and functionality. However, 
    if an error or warning occurs when running that "errorized" R function, it will save a 
    .Rds file to the current working directory with the relevant objects and information 
    required to immediately recreate the error.
Errors in data can be located and removed using validation rules from package 'validate'.
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).
Tools for fitting the Extended Empirical Saddlepoint (EES) density.
Implementation of the web-based 'Practical Meta-Analysis Effect Size
    Calculator' from David B. Wilson in R. Based on the input, the effect size
    can be returned as standardized mean difference, Hedges' g, correlation
    coefficient r or Fisher's transformation z, odds ratio or log odds effect size.
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++.
Functions for classification and group membership probability estimation are given. 
             The issue of non-informative features in the data is addressed by utilizing the ensemble method. 
             A few optimal models are selected in the ensemble from an initially large set of base k-nearest neighbours (KNN) models, generated on subset of features from the training data.
             A two stage assessment  is applied in selection of optimal models for the ensemble in the training function. 
             The prediction functions for classification and class membership probability estimation returns class outcomes and class membership probability estimates for the test data. 
             The package includes measure of classification error and brier score, for classification and probability estimation tasks respectively.
Misc functions programmed by Eduard Szöcs. 
    Provides read_regnie() to read gridded precipitation data from German Weather 
    Service  (DWD, see <http://www.dwd.de/> for more information).
Provides an interface to e-Stat API, the one-stop service for official statistics of the Japanese government.
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.
Fits the space-time Epidemic Type Aftershock Sequence
    (ETAS) model to earthquake catalogs using a stochastic declustering 
    approach. The ETAS model is a spatio-temporal marked point process
    model and a special case of the Hawkes process. The package is based 
    on a Fortran program by Jiancang Zhuang
    (available at <http://bemlar.ism.ac.jp/zhuang/software.html>),
    which is modified and translated into C++ and C such that it 
    can be called from R.
Estimation of the components of an ETAS (Epidemic Type Aftershock Sequence) 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.
A framework for loading medium-sized data from
    the Internet to a local or remote relational database management system.
    This package itself doesn't do much more than provide a toy example and set up
    the method structure. Packages that depend on this package will facilitate the
    construction and maintenance of their respective databases.
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
Traditional meta-regression based method has been developed for using meta-analysis data, but it faced the challenge of inconsistent estimates. This package purpose a new statistical method to detect epistasis using incomplete information summary, and have proven it not only successfully let consistency of evidence, but also increase the power compared with traditional method (Detailed tutorial is shown in website).
Computes moments of univariate truncated t distribution.
    There is only one exported function, e_trunct(), which should be seen for details.
Client to interact with the 'etcd' 'key-value' data store
    <https://github.com/coreos/etcd>. Functions included for managing
    directories, keys, nodes, and getting statistics.
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.
If possible, generates exactly area-proportional Euler diagrams,
    or otherwise approximately proportional diagrams using numerical
    optimization. A Euler diagram is a generalization of a Venn diagram,
    relaxing the criterion that all interactions need to be represented.
Calculations for DNA mixtures accounting for possibly inbred pedigrees (simulations with conditioning, LR). Calculation of exact p-values.
An R Client for the Europe PubMed Central RESTful Web Service. It
    gives access to both metadata on life science literature and open access
    full texts. Europe PMC indexes all PubMed content and other literature
    sources including Agricola, a bibliographic database of citations to the
    agricultural literature, or Biological Patents. In addition to bibliographic
    metadata, the client allows users to fetch citations and reference lists.
    Links between life-science literature and other EBI databases, including
    ENA, PDB or ChEMBL are also accessible. No registration or API key is
    required. See the vignettes for usage examples.
Frequentist and Bayesian single arm trial design and sample size software. 
	Designs cover one and two binary endpoints with both single and multi-stage 
	methodology. The research leading to these results has received funding from the 
	European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement 
	number 278742 (Eurosarc).
Tools to download data from the Eurostat database
    <http://ec.europa.eu/eurostat> together with search and manipulation
    utilities.
Goodness-of-fit tests for selection of r in the r-largest order
    statistics (GEVr) model. Goodness-of-fit tests for threshold selection in the
    Generalized Pareto distribution (GPD). Random number generation and density functions
    for the GEVr distribution. Profile likelihood for return level estimation
    using the GEVr and Generalized Pareto distributions. P-value adjustments for
    sequential, multiple testing error control. Non-stationary fitting of GEVr and
    GPD.
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.
Provides Some of the most important evaluation measures for evaluating a model. Just by giving the real and predicted class, measures such as accuracy, sensitivity, specificity, ppv, npv, fmeasure, mcc and ... will be returned.
Uses data and constants to calculate potential evapotranspiration (PET) and actual evapotranspiration (AET) from 21 different formulations including Penman, Penman-Monteith FAO 56, Priestley-Taylor and Morton formulations.
Different evidential distance-based classifiers, which provide
    outputs in the form of Dempster-Shafer mass functions. The methods are: the
    evidential K-nearest neighbor rule and the evidential neural network.
Various clustering algorithms that produce a credal partition,
    i.e., a set of Dempster-Shafer mass functions representing the membership of objects
    to clusters. The mass functions quantify the cluster-membership uncertainty of the objects.
    The algorithms are: Evidential c-Means (ECM), Relational Evidential c-Means (RECM),
    Constrained Evidential c-Means (CECM), EVCLUS and EK-NNclus.
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.
Compare 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 setting up and analyzing event history data.
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
Integrated Sensitivity Analysis of Evidence Factors in Observational Studies.
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.
Comparative analysis of continuous traits influencing discrete states, and utility tools to facilitate comparative analyses.  Implementations of ABBA/BABA type statistics to test for introgression in genomic data. Wright-Fisher, phylogenetic tree, and statistical distribution Shiny interactive simulations for use in teaching.
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).
Evolutionary process simulation using geometric morphometric data. Manipulation of landmark data files (TPS), shape plotting and distances plotting functions.
The EvoPER, Evolutionary Parameter Estimation for 'Repast Simphony'
    Agent-Based framework (<https://repast.github.io/>), provides optimization
    driven parameter estimation methods based on evolutionary computation
    techniques which could be more efficient and require, in some cases,
    fewer model evaluations than other alternatives relying on experimental design.
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.
Automatic generation of exams based on exercises in Markdown or LaTeX format,
	possibly including R code for dynamic generation of exercise elements.
	Exercise types include single- and multiple-choice questions, arithmetic problems,
	string questions, and combinations thereof (cloze). Output formats include
	PDF, HTML, Moodle XML, QTI 1.2 (for OLAT/OpenOLAT), QTI 2.1, Blackboard, ARSnova, and TCExam.
	In addition to fully customizable PDF exams, a standardized PDF format is provided that can be
	printed, scanned, and automatically evaluated.
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.
Extracts Exchangeable Image File Format (EXIF) metadata, such as camera make and model, ISO speed and the date-time
             the picture was taken on, from JPEG images. Incorporates the 'easyexif' (https://github.com/mayanklahiri/easyexif)
             library.
Reads EXIF data using ExifTool <http://www.sno.phy.queensu.ca/~phil/exiftool/>
    and returns results as a data frame.
    ExifTool is a platform-independent Perl library plus a command-line
    application for reading, writing and editing meta information in a wide variety
    of files. ExifTool supports many different metadata formats including EXIF,
    GPS, IPTC, XMP, JFIF, GeoTIFF, ICC Profile, Photoshop IRB, FlashPix, AFCP and
    ID3, as well as the maker notes of many digital cameras by Canon, Casio, FLIR,
    FujiFilm, GE, HP, JVC/Victor, Kodak, Leaf, Minolta/Konica-Minolta, Motorola, Nikon,
    Nintendo, Olympus/Epson, Panasonic/Leica, Pentax/Asahi, Phase One, Reconyx, Ricoh,
    Samsung, Sanyo, Sigma/Foveon and Sony.
Calls copy number variants (CNVs) from targeted sequence data, typically exome sequencing experiments designed to identify the genetic basis of Mendelian disorders.
For a given metabolic model with well formed Gene-Protein-Reaction (GPR) associations and an expressionSet with their associated gene expression values, this package converts gene expression values to the FBA boundaries for each reaction based in the boolean rules described in its associated GPR.
Generates feature matrix outputs from R object inputs
    using a variety of expansion functions.  The generated
    feature matrices have applications as inputs
    for a variety of machine learning algorithms.
    The expansion functions are based on coercing the input
    to a matrix, treating the columns as features and
    converting individual columns or combinations into blocks of
    columns.
    Currently these include expansion of columns by
    efficient sparse embedding by vectors of lags,
    quadratic expansion into squares and unique products,
    powers by vectors of degree,
    vectors of orthogonal polynomials functions,
    and block random affine projection transformations (RAPTs).
    The transformations are
    magrittr- and cbind-friendly, and can be used in a
    building block fashion.  For instance, taking the cos() of
    the output of the RAPT transformation generates a
    stationary kernel expansion via Bochner's theorem, and this
    expansion can then be cbind-ed with other features.
    Additionally, there are utilities for replacing features,
    removing rows with NAs,
    creating matrix samples of a given distribution,
    a simple wrapper for LASSO with CV,
    a Freeman-Tukey transform,
    generalizations of the outer function,
    matrix size-preserving discrete difference by row,
    plotting, etc.
Modular implementation of the Differential Evolution algorithm for
    experimenting with different types of operators.
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.
The exponential integrals E_1(x), E_2(x), E_n(x) and
  Ei(x), and the incomplete gamma function G(a, x) defined for
  negative values of its first argument. The package also gives easy
  access to the underlying C routines through an API; see the package
  vignette for details. A test package included in sub-directory
  example_API provides an implementation. C routines derived from the
  GNU Scientific Library <https://www.gnu.org/software/gsl/>.
Package contains methods to generate explanations for individual predictions of 
 classification and regression models. Weighted averages of individual explanations form explanation of the whole model. 
 The package extends 'CORElearn' package, but other prediction models can also be explained using a wrapper.
Shiny interfaces and graphical functions for multivariate analysis results exploration.
Simplifies some complicated and labor intensive processes involved in exploring and explaining data.  Allows you to quickly and efficiently visualize the interaction between variables and simplifies the process of discovering covariation in your data.  Also includes some convenience features designed to remove as much redundant typing as possible.
Computation of the matrix exponential, logarithm, 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"
Supervised machine learning has an increasingly important role in biological
    studies. However, the sheer complexity of classification pipelines poses a significant
    barrier to the expert biologist unfamiliar with machine learning. Moreover,
    many biologists lack the time or technical skills necessary to establish their own
    pipelines. This package introduces a framework for the rapid implementation of
    high-throughput supervised machine learning built with the biologist user in mind.
    Written by biologists, for biologists, this package provides a user-friendly interface
    that empowers investigators to execute state-of-the-art binary and multi-class
    classification, including deep learning, with minimal programming
    experience necessary.
Data sets from the book "Forecasting with exponential smoothing: the state space approach" by 
	Hyndman, Koehler, Ord and Snyder (Springer, 2008).
Package implements several popular functions from Excel ('COUNTIF', 'VLOOKUP', etc.) and 'SPSS' Statistics ('RECODE', 'COUNT', etc.). Also there are functions for basic tables with value labels/variable labels support. Package aimed to help people to move data processing from Excel/'SPSS' to R.
Tests for the composite hypothesis of exponentiality
Analysis of experimental results and automatic report generation in both interactive HTML and LaTeX. This package ships with a rich interface for data modeling and built in functions for the rapid application of statistical tests and generation of common plots and tables with publish-ready quality.
Estimation for high conditional quantiles based on quantile regression.
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.
If one treated group is matched to one control reservoir in two different ways to produce two sets of treated-control matched pairs, then the two control groups may be entwined, in the sense that some control individuals are in both control groups.  The exterior match is used to compare the two control groups.
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.
Categorical Data Analysis and Visualization.
Density, distribution function, quantile function
    and random generation for a number of univariate
    and multivariate distributions. This package implements the
    following distributions: Bernoulli, beta-binomial, beta-negative
    binomial, beta prime, Bhattacharjee, Birnbaum-Saunders,
    bivariate normal, bivariate Poisson, categorical, Dirichlet,
    Dirichlet-multinomial, discrete Laplace, discrete normal,
    discrete uniform, discrete Weibull, Frechet, gamma-Poisson,
    generalized extreme value, Gompertz, generalized Pareto,
    Gumbel, half-Cauchy, half-normal, half-t, Huber density, 
    inverse chi-squared, inverse-gamma, Kumaraswamy, Laplace,
    logarithmic, Lomax, multivariate hypergeometric, multinomial,
    non-standard t, non-standard beta, normal mixture,
    Poisson mixture, Pareto, power, reparametrized beta,
    Rayleigh, Skellam, slash, triangular, truncated normal,
    truncated Poisson, Tukey lambda, Wald, zero-inflated binomial,
    zero-inflated negative binomial, zero-inflated Poisson.
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.
Extreme value theory, nonparametric kernel estimation, tail
    conditional probabilities, extreme conditional quantile, adaptive estimation,
    quantile regression, survival probabilities.
Functions for performing extreme value analysis.
Code to fit, plot and compare several (extreme value)
    distribution functions. Can also compute (truncated) distribution quantile estimates and
    draw a plot with return periods on a linear scale.
Detect outliers in one-dimensional data.
Estimation of the sample univariate, cross and return time extremograms. The package can also adds empirical confidence bands to each of the extremogram plots via a permutation procedure under the assumption that the data are independent. Finally, the stationary bootstrap allows us to construct credible confidence bands for the extremograms.
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.
Eyelink eye trackers output a horrible mess, typically under
    the form of a '.asc' file. The file in question is an assorted collection of
    messages, events and raw data. This R package will attempt to make sense of it.
Misc function for working with eyetracking data
A set of tools that address tasks along the pipeline from raw
    data to analysis and visualization for eye-tracking data. Offers several
    popular types of analyses, including linear and growth curve time analyses,
    onset-contingent reaction time analyses, as well as several non-parametric
    bootstrapping approaches.
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.
Because fungicide resistance is an important phenotypic trait for
    fungi and oomycetes, it is necessary to have a standardized method of
    statistically analyzing the Effective Concentration (EC) values. This
    package is designed for those who are not terribly familiar with R to be
    able to analyze and plot an entire set of isolates using the 'drc' package.
This package implements a simplified version of least
        squares, and logistic regression for efficiently selecting the
        significant non-additive interactions between two variables.
An extension of 'knitr' that adds flexibility in several
    ways. One common source of frustration with 'knitr' is that it assumes
    the directory where the source file lives should be the working directory,
    which is often not true. 'ezknitr' addresses this problem by giving you
    complete control over where all the inputs and outputs are, and adds several
    other convenient features to make rendering markdown/HTML documents easier.
ezsim provides a handy way to run simulation and examine its result
Functions that simplify the process of generating print-ready data summary using 'dplyr' syntax.
Frequentist assisted by Bayes (FAB) confidence interval
    construction. See 'Adaptive multigroup confidence intervals with constant
    coverage' by Yu and Hoff <https://arxiv.org/abs/1612.08287>.
Fast covariance estimation for sparse functional data or longitudinal data.
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
Perform Maximum Likelihood Factor analysis on a covariance matrix or data matrix.
Multivariate exploration of a data table with factorial
        analysis and cluster methods.
Provides some easy-to-use functions to extract and visualize the
    output of multivariate data analyses, including 'PCA' (Principal Component
    Analysis), 'CA' (Correspondence Analysis), 'MCA' (Multiple Correspondence
    Analysis), 'FAMD' (Factor Analysis of Mixed Data), 'MFA' (Multiple Factor Analysis) and 'HMFA' (Hierarchical Multiple
    Factor Analysis) functions from different R packages. It contains also functions
    for simplifying some clustering analysis steps and provides 'ggplot2' - based
    elegant data visualization.
Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis.
Identifies change-points in the common and the idiosyncratic components via factor modelling.
Implementation of three machine learning approaches: Support Vector Machines (SVM) with a linear kernel, second-order Factorization Machines (FM), and higher-order Factorization Machines (HoFM).
Methods to 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.
Markov chain Monte Carlo (MCMC) sampler for fully Bayesian
             estimation of latent factor stochastic volatility models.
             Sparsity can be achieved through the usage of Normal-Gamma priors
             on the factor loading 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 a decorrelation 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 R data or source files
    in a key-value fashion.
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.
Generates fake data from a dataset of different variable types.
    The package contains the functions simulate_dataset and simulate_dataset_ts 
    to simulate time-independent and time-dependent data. It randomly samples 
    character and factor variables from contingency tables and  numeric and 
    ordered factors from a multivariate normal distribution. It currently supports the 
    simulation of stationary and zero-inflated count time series.
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.
This is a method for Allele-specific DNA Copy Number profiling for whole-Exome sequencing data.  Given the allele-specific coverage and site biases 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, as well as the site biases. For detection of somatic mutations, the case and control samples can be the tumor and normal sample from the same individual.
Simulates age-at-onset traits associated with a segregating major gene in family data 
     obtained from population-based, clinic-based, or multi-stage designs. Appropriate ascertainment 
     correction is utilized to estimate age-dependent penetrance functions either parametrically from 
     the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm 
     can infer missing genotypes and carrier probabilities estimated from family's genotype and
     phenotype information or from a fitted model. Plot functions include pedigrees of simulated 
     families and predicted penetrance curves based on specified parameter values.
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.
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.
Provides the function fancycut() which is like cut() except
  you can mix left open and right open intervals with point values,
  intervals that are closed on both ends and intervals that are open on both ends.
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, ISBN 9781439887332, and "Extending the Linear Model with R" published by CRC press in 1st Ed. December 2005 and 2nd Ed. March 2016, ISBN 9781584884248.
Reads and writes 'ARFF' files. 'ARFF' (Attribute-Relation File Format) files are like 'CSV' files, with a little bit of added meta information in a header and standardized NA values. They are quite often used for machine learning data sets and were introduced for the 'WEKA' machine learning 'Java' toolbox. See <http://weka.wikispaces.com/ARFF> for further info on 'ARFF' and for <http://www.cs.waikato.ac.nz/ml/weka/> for more info on 'WEKA'. 'farff' gets rid of the 'Java' dependency that 'RWeka' enforces, and it is at least a faster reader (for bigger files). It uses 'readr' as parser back-end for the data section of the 'ARFF' file. Consistency with 'RWeka' is tested on 'Github' and 'Travis CI' with hundreds of 'ARFF' files from 'OpenML'. Note that the 'OpenML' package is currently only available from 'Github' at: <https://github.com/openml/openml-r>.
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.
Implements Adaboost based on C++ backend code.
             This is blazingly fast and especially useful for large, in memory data sets. 
             The package uses decision trees as weak classifiers. Once the classifiers
             have been trained, they can be used to predict new data. 
             Currently, we support only binary classification tasks.
             The package implements the Adaboost.M1 algorithm and the real
             Adaboost(SAMME.R) algorithm.
Fast and numerically stable estimation of a covariance matrix by banding the Cholesky factor using a modified Gram-Schmidt algorithm implemented in RcppArmadilo. See <http://stat.umn.edu/~molst029> for details on the algorithm.
Provides a method of recovering the precision matrix efficiently 
        and solving for the dantzig selector by applying the parametric 
        simplex method.  The computation is based on a linear optimization
        solver. It also contains a generic LP solver and a parameterized LP 
        solver using 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.
A method which uses the Cochran-Mantel-Haenszel test with significant pattern mining to detect intervals in binary genotype data which are significantly associated with a particular phenotype, while accounting for categorical covariates.
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.
Provides an R interface to Bob Jenkin's streaming, 
 non-cryptographic 'SpookyHash' hash algorithm for use in digest-based 
 comparisons of R objects. 'fastdigest' plugs directly into R's internal 
 serialization machinery, allowing digests of all R objects the serialize() 
 function supports, including reference-style objects via custom hooks. Speed is
 high and scales linearly by object size; memory usage is constant and 
 negligible.
If there is a need to recognise edges on a raster image or a bitmap or any kind of a matrix, one can find packages
    that does only 90 degrees vectorization. Typically the nature of artefact images is linear and can be vectorized in much more
    efficient way than draw a series of 90 degrees lines. The fasteraster package does recognition of lines using only one pass.
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).
Provides functionality to produce graphs of probability density functions and cumulative distribution functions with few keystrokes, allows shading under the curve of the probability density function to illustrate concepts such as p-values and critical values, and fits a simple linear regression line on a scatter plot with the equation as the main title.
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.
The elliptical slice sampler for Bayesian shrinkage linear regression, such as horseshoe, double-exponential and user specific priors.
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'. FastImputation()
    function uses this 'FastImputationPatterns' object to impute (make
    a good guess at) missing data in a single line or a whole data frame
    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.
A computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software 'SKAT' and 'coxKM'. 'coxKM' can be obtained from http://www.hsph.harvard.edu/xlin/software.html.
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.
An efficient algorithm to apply the Benjamini–Hochberg Linear Step Up FDR controlling procedure in huge-scale testing problems (proposed in Vered Madar and Sandra Batista(2016) <DOI:10.1093/bioinformatics/btw029>). Unlike "BH" method, the package does not require any p value ordering. Besides, it permits separating p values arbitrarily into computationally feasible chunks  of arbitrary size and produces the same results as those from applying linear step up BH procedure to the entire set of tests.
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.
We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics.
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 K1, K2, K3, K4 and K7 to characterize
    distributions with left and right, symmetric or asymmetric fat tails in market
    finance, neuroscience and other disciplines. Two algorithms to estimate with
    a high accuracy distribution parameters, quantiles, value-at-risk and expected
    shortfall. Include power hyperbolas and power hyperbolic functions.
HTTP error helpers. Methods included for general purpose HTTP 
    error handling, as well as individual methods for every HTTP status
    code, both via status code numbers as well as their descriptive names.
    Supports ability to adjust behavior to stop, message or warning.
    Includes ability to use custom whisker template to have any configuration
    of status code, short description, and verbose message. Currently 
    supports integration with 'crul', 'curl', and 'httr'.
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".
This is a simple package for Flux Balance Analysis and related 
    metabolic modelling techniques. Functions are provided for: parsing 
    models in tabular format, converting parsed metabolic models to input 
    formats for common linear programming solvers, and 
    evaluating and applying gene-protein-reaction mappings. In addition, there 
    are wrappers to parse a model, select a solver, find the metabolic fluxes, 
    and return the results applied to the original model. Compared to other 
    packages in this field, this package puts a much heavier focus on 
    providing reusable components that can be used in the design of new 
    implementation of new techniques, in particular those that involve large 
    parameter sweeps.
Environment for teaching 
	"Financial Engineering and Computational Finance".
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 graph (DAG) models. The algorithm proposed makes use of moment fractional Bayes factors (MFBF) and thus it is suitable for learning sparse graph. The algorithm is implemented by using Armadillo: an open-source C++ linear algebra library.
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.
Wrapper functions around the Facebook Marketing 'API' to create, read, update and delete custom audiences, images, campaigns, ad sets, ads and related content.
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) and the partial area under the curve as well as 
    the true and false positive rate. The analysis of paired receiver operating curves is supported as well,
    so that a comparison of two predictors is possible. You can also plot the
    results and calculate confidence intervals. On a typical desktop computer the time needed for 
    the calculation of 100000 bootstrap replicates given 500 observations requires time on the
    order of magnitude of 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").
Fatigue Crack Growth in Reliability estimates the distribution
             of material lifetime due to mechanical fatigue efforts. The FCGR
             package provides simultaneous crack growth curves fitting to 
             different specimens in materials under mechanical stress efforts. 
             Linear mixed-effects models (LME) with smoothing B-Splines 
             and the linearized Paris-Erdogan law are applied. Once defined
             the fail for a determined crack length, the distribution function
             of failure times to fatigue is obtained. The density function is
             estimated by applying nonparametric binned kernel density estimate 
             (bkde) and the kernel estimator of the distribution function (kde). 
             The results of Pinheiro and Bates method based on nonlinear 
             mixed-effects regression (nlme) can be also retrieved. The package 
             contains the crack.growth, PLOT.cg, IB.F, and Alea.A (database) functions.
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.
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, simulated annealing, stochastic
    gradient) 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, reordering inputs, merging networks). Networks can be
    exported to C functions in order to integrate them into virtually any software
    solution.
Environment for teaching
	"Financial Engineering and Computational Finance".
A fold change rank based 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.
Provides implementation of various methods of Functional Data Analysis (FDA) and Empirical Dynamics. The core of this package is Functional Principal Component Analysis (FPCA), a key technique for functional data analysis, for sparsely or densely sampled random trajectories and time courses, via the Principal Analysis by Conditional Estimation (PACE) algorithm or numerical integration. PACE is useful for the analysis of data that have been generated by a sample of underlying (but usually not fully observed) random trajectories. It does not rely on pre-smoothing of trajectories, which is problematic if functional data are sparsely sampled. PACE provides options for functional regression and correlation, for Longitudinal Data Analysis, the analysis of stochastic processes from samples of realized trajectories, and for the analysis of underlying dynamics. The core computational algorithms are implemented using the 'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
An implementation of regression models with partial differential regularizations, making use of the Finite Element Method. The models efficiently handle data distributed over irregularly shaped domains and can comply with various conditions at the boundaries of the domain. A priori information about the spatial structure of the phenomenon under study can be incorporated in the model via the differential regularization.
Functions to support 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
    by a component-wise gradient boosting algorithm.
Provides a variety of tools for the analysis of covariance operators.
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).
Contains methods of analysis of forest databases, the purpose of the analyzes is to evaluate 
    the quality of the data present in the databases focusing on the dimensions of consistency, punctuality 
    and completeness. Databases can range from forest inventory data to growth model data. The package has 
    methods to work with large volumes of data quickly, in addition in certain analyzes it is possible to 
    generate the graphs for a better understanding of the analysis and reporting of the analyzed analysis.
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.
Defines a collection of functions to compute average power and sample size for studies that use the false discovery rate as the final measure of statistical significance.
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.
Read and write feather files, a lightweight binary columnar
    data store designed for maximum speed.
Local inferential 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.  User can compute Fechnerian distances among objects representing
  subjective dissimilarities, and other related information.  See
  package?fechner for an overview.
Calculates fishing effort following the DG MARE Ad-Hoc Workshops on Transversal Variables in Zagreb (2015) and Nicosia (2016).
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; the Daymet gridded estimates of daily weather parameters 
    for North America, version 3, available from the Oak Ridge National Laboratory's
    Distributed Active Archive Center (DAAC); and the International Tree Ring Data Bank.
    Additional data sources are in the works, global soils (HWSD), 
    MODIS satellite data products, the National Atlas (US), Natural Earth, and WorldClim.
Access data from the Federal Register API <https://www.federalregister.gov/developers/api/v1>.
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.
Efficient estimation of fixed-effect maximum likelihood models with, possibly, non-linear right hand sides.
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.
An implementation of the adaptive forgetting factor scheme described in Bodenham and Adams (2016) <doi:10.1007/s11222-016-9684-8> which adaptively estimates the mean and variance of a stream in order to detect multiple changepoints in streaming data. The implementation is in C++ and uses Rcpp. Additionally, implementations of the fixed forgetting factor scheme from the same paper, as well as the classic CUSUM and EWMA methods, are included.
Contains several functions that allow you
    to create, visualise, and test Fast and Frugal Trees (FFTrees). FFTrees are very simple decision trees for
    classifying cases (i.e.; breast cancer patients) into one of two classes (e.g.;
    no cancer vs. true cancer) based on a small number of cues (e.g.; test results). FFTrees can be preferable to more complex algorithms (such as
    logistic regression) because they are easy to communicate and implement, and are
    robust against noisy data.
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.
Environment for teaching "Financial Engineering and
        Computational Finance".
Growth models and forest production require existing data
    manipulation and the creation of new data, structured from basic forest
    inventory data. The purpose of this package is provide functions to support
    these activities.
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
Useful functions for fMRI preprocessing.
Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities.
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
 supported by functions that can determine the smoothing parameter
 (nugget and sill variance) and other covariance parameters by cross
 validation and also by restricted maximum likelihood. For Kriging
 there is an easy to use function that also estimates the correlation
 scale (range parameter).  A major feature is that any covariance function
 implemented in R and following a simple 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 package source code "tarball"
 (ending in tar.gz)
 and looking in the R subdirectory. Please cite fields along with its
 DOI in your publications!
Tools for random fields and bridges simulations.
A very flexible framework for building server side logic in R. The 
    framework is unopinionated when it comes to how HTTP requests and WebSocket
    messages are handled and supports all levels of app complexity; from serving
    static content to full-blown dynamic web-apps. Fiery does not hold your hand
    as much as e.g. the shiny package does, but instead sets you free to create
    your web app the way you want.
A collection of functions that assist in data
    manipulation analysis and plotting.
A simple data package to ease the process of creating choropleths
    in 'ggplot2' with all fifty U.S. states and Washington D.C.,
    including Alaska and Hawaii as insets.
Implements a simple key-value style database where character string keys
  are associated with data values that are stored on the disk. A simple interface is provided for inserting,
  retrieving, and deleting data from the database. Utilities are provided that allow 'filehash' databases to be
  treated much like environments and lists are already used in R. These utilities are provided to encourage
  interactive and exploratory analysis on large datasets. Three different file formats for representing the
  database are currently available and new formats can easily be incorporated by third parties for use in the
  'filehash' framework.
Simple key-value database using SQLite as the backend
Interface for working with large matrices stored in files,
  not in computer memory. Supports multiple non-character
  data types (double, integer, logical and raw) of
  various sizes (e.g. 8 and 4 byte real values).
  Access to parts of the matrix is done by indexing, 
  exactly as with usual R matrices.
  Supports very large matrices.
  Tested on multi-terabyte matrices.
  Allows for more than 2^32 rows or columns.
  Allows for quick addition of extra columns to a filematrix.
  Cross-platform as the package has R code only.
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.
Functions for printing the contents of a folder as columns in a ragged-bottom data.frame and for
  viewing the details (size, time created, time modified, etc.) of a folder's top level contents.
Functions for regression analysis and financial modeling, including batch graphs generation, beta calculation, descriptive statistics.
Time value of money, cash flows and other financial
        functions.
Infrastructure for defining meta-data and
    relationships for financial instruments.
Contains financial math functions and introductory derivative functions included in the Society of Actuaries and Casualty Actuarial Society 'Financial Mathematics' exam, and some topics in the 'Models for Financial Economics' exam.
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.
Parse and create Darwin Core (<http://rs.tdwg.org/dwc/>) Simple 
    and Archives. Functionality includes reading and parsing all the 
    files in a Darwin Core Archive, including the datasets and metadata; 
    read and parse simple Darwin Core files; and validation of Darwin 
    Core Archives.
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)<DOI: 10.1214/12-AOAS593>.  
        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, CausalANOVA, which estimates 
	the average marginal interaction effects by a regularized ANOVA as 
	proposed by Egami and Imai (2016+).
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.
A tool to explore wide data sets, by detecting, ranking
    and plotting groups of statistically dependent columns.
Statistical tool set for population genetics. The package provides empirical Bayes estimator of Fst, Nei's Gst, Nei & Chesser's Gst, and Weir & Cockerham's theta adapted for pairwise comparison. It also provides interfaces to read and manipulate 'GENEPOP' format data files and allele/haplotype frequency format files.
A S4 class to represent binary 'fingerprints' and methods to manipulate 
 fingerprint objects. Internally, the 'fingerprint' class models a binary fingerprint
 as 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.
Download and display company financial data from the U.S. Securities
    and Exchange Commission's EDGAR database. It contains a suite of functions with
    web scraping and XBRL parsing capabilities that allows users to extract data from EDGAR 
    in an automated and scalable manner. See <https://www.sec.gov/edgar/searchedgar/companysearch.html>
    for more information.
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.
Dataset of 302 measurements of 11 fish species to accompany the
    manuscript "Length-weight relationships of six freshwater fish species
    from lake Kirkkojarvi, Finland".
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 (G)LMs.
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.)
Provides functionality for constructing
    statistical models of transcriptomic dynamics in field conditions.
    It further offers the function to predict expression of a gene given 
    the attributes of samples and meteorological data.
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 Fitbit <http://www.fitbit.com>. 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 <http://www.fitbit.com>.
Fitbit R API <https://dev.fitbit.com/> that provides fitbit coach functionality by analyzing your data obtained via fitbit API calls, and by giving personalized recommendations for the rest of the day based on your behavior.
A pure R package for decoding activity files written in the FIT ("Flexible and Interoperable Data Transfer") format. A format that is fast becoming the standard for recording running and cycling data. Details of the FIT protocol can be found at <https://www.thisisant.com/resources/fit>.
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 (MLE), the package provides moment matching (MME), quantile matching (QME) and maximum goodness-of-fit estimation (MGE) methods (available only for non-censored data). Weighted versions of MLE, MME and QME are available.
Fits Density Ratio Classes to elicited
        probability-quantile points or intervals
Fits the Weibull equation to percent loss conductivity (plc) curves as a function of plant water potential, computes SE of parameter estimates and location of curve with bootstrap or parametric methods, and provides convenient plotting methods.
Utilities to read and write files in the FITS (Flexible
  Image Transport System) format, a standard format in astronomy (see
  e.g. <https://en.wikipedia.org/wiki/FITS> for more information).
  Present low-level routines allow: reading, parsing, and modifying
  FITS headers; reading FITS images (multi-dimensional arrays);
  reading FITS binary and ASCII 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 table; reading tables into
  data frames; generating vectors for image array axes; scaling and
  writing images as 16-bit integers.  Known incompletenesses are
  reading 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.
Data and code behind the stories and interactives at 'FiveThirtyEight'
    <https://github.com/fivethirtyeight/data>.
Distribution functions and test for over-representation of short
    distances in the Liland distribution. Simulation functions are included for
    comparison.
Several generalized / directional Fixed Sequence Multiple Testing
    Procedures (FSMTPs) are developed for testing a sequence of pre-ordered
    hypotheses while controlling the FWER, FDR and Directional Error (mdFWER).
    All three FWER controlling generalized FSMTPs are designed under arbitrary
    dependence, which allow any number of acceptances. Two FDR controlling
    generalized FSMTPs are respectively designed under arbitrary dependence and
    independence, which allow more but a given number of acceptances. Two mdFWER
    controlling directional FSMTPs are respectively designed under arbitrary
    dependence and independence, which can also make directional decisions based
    on the signs of the test statistics. The main functions for each proposed
    generalized / directional FSMTPs are designed to calculate adjusted p-values
    and critical values, respectively. For users' convenience, the functions also
    provide the output option for printing decision rules.
An implementation of the Fizz Buzz algorithm, as defined e.g. in <https://en.wikipedia.org/wiki/Fizz_buzz>. 
    It provides the standard algorithm with 3 replaced by Fizz and 5 replaced by Buzz, with the option of specifying start 
    and end numbers, step size and the numbers being replaced by fizz and buzz, respectively. This package gives 
    interviewers the optional answer of "I use fizzbuzzR::fizzbuzz()" when interviewing rather than having to write an algorithm
    themselves.
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.
Contains tools and features, which can be used for an exploratory
    landscape analysis of continuous optimization problems. Those are able to
    quantify rather complex properties, such as the global structure,
    separability, etc., of continuous optimization problems.
Implements the fused lasso additive model as proposed in Petersen, A., Witten, D., and Simon, N. (2015). Fused Lasso Additive Model. To appear in the Journal of Computational and Graphical Statistics.
Tools for fluctuations analysis of mutant cells counts.
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.
Variable selection algorithm for functional linear regression with scalar response variable and mixed scalar/functional predictors.
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.
Format for converting an R Markdown document to a grid oriented
  dashboard. The dashboard flexibly adapts the size of it's components to the
  containing web page.
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.
Perform relative survival analyses using approaches described in 
	     Remontet et al. (2007) <DOI:10.1002/sim.2656> and 
	     Mahboubi et al. (2011) <DOI:10.1002/sim.4208>.
	     It implements non-linear, non-proportional effects and both non proportional and non linear effects 
	     using splines (B-spline and truncated power basis).
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.
Estimate positions of animal from data collected by solar geolocation archival tags.
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.
The lipid scrambling activity of protein extracts and purified
    scramblases is often determined using a fluorescence-based assay involving
    many manual steps. flippant offers an integrated solution for the analysis
    and publication-grade graphical presentation of dithionite scramblase
    assays, as well as a platform for review, dissemination and extension of the
    strategies it employs. The package's name derives from a play on the fact
    that lipid scrambling is also sometimes referred to as 'flipping'.
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.
Implements synchronization between R processes (spawned by using the "parallel" package for instance) using file locks. Supports both exclusive and shared locking.
Tools to quickly compile taxonomic and distribution data from
    the Brazilian Flora 2020 at http://floradobrasil.jbrj.gov.br/.
Concatenates some 'flowWorkspace', 'flowCore', 'vegan' and 'gdata' packages functionalities to import 'FlowJo' workspaces and calculates ecological diversity indices for gated populations, based on two dimensions cytograms.
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.
This framework allows you to design and implement complex
    pipelines, and deploy them on your institution's computing cluster. This has
    been built keeping in mind the needs of bioinformatics workflows. However, it is
    easily extendable to any field where a series of steps (shell commands) are to
    be executed in a (work)flow.
Selections on flow matrices, statistics on selected flows, map and
    graph visualisations.
Screens daily streamflow time series for temporal trends and 
  change-points. This package has been primarily developed for assessing 
  the quality of daily streamflow time series. It also contains tools for 
  plotting and calculating many different streamflow metrics. The package can be 
  used to produce summary screening plots showing change-points and significant 
  temporal trends for high flow, low flow, and/or baseflow statistics, or it can 
  be used to perform more detailed hydrological time series analyses. The 
  package was designed for screening daily streamflow time series from Water 
  Survey Canada and the United States Geological Survey but will also work 
  with streamflow time series from many other agencies.
This package implements a path algorithm for the Fused
        Lasso Signal Approximator. For more details see the help files
A novel algorithm for solving the subset sum problem with bounded error in multidimensional real domain and its application to the general-purpose knapsack problem.
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).
Implements common date calculations relevant for specifying
    the economic nature of financial market contracts that are typically defined 
    by International Swap Dealer Association (ISDA, http://www2.isda.org) legal 
    documentation. This includes methods to check whether dates are business 
    days in certain locales, functions to adjust and shift dates and time length
    (or day counter) calculations.
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.
Estimates Filtered Monotonic Polynomial IRT Models as described by Liang and Browne (2015) <DOI:10.3102/1076998614556816>.
Contains R-functions to perform an fMRI analysis as described in
             Tabelow et al. (2006) <DOI:10.1016/j.neuroimage.2006.06.029>,
             Polzehl et al. (2010) <DOI:10.1016/j.neuroimage.2010.04.241>,
             Tabelow and Polzehl (2011) <DOI:10.18637/jss.v044.i11>.
Provides parameter estimation as well as variable selection in
    Finite Mixture of Accelerated Failure Time Regression and Finite
    Mixture of Regression Models.
    Furthermore, this package provides Ridge Regression and Elastic Net.
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.
Fit linear regression models where the random errors follow a finite mixture of of Skew Heavy-Tailed Errors.
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".
Provides fonts licenced under the Bitstream Vera Fonts licence
    for the fontquiver package.
Computer Modern font for use with extrafont package
A placeholder for the Liberation fontset intended for the
    `fontquiver` package. This fontset covers the 12 combinations of
    families (sans, serif, mono) and faces (plain, bold, italic, bold
    italic) supported in R graphics devices.
Provides a set of fonts with permissive licences. This is
    useful when you want to avoid system fonts to make sure your
    outputs are reproducible.
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.
Environment for teaching 
	"Financial Engineering and Computational Finance".
	Pricing and Evaluating Basic Options.
SHE, FORAM Index and ABC Method analyses and custom plot
        functions for community data.
Helpers for reordering factor levels (including moving
    specified levels to front, ordering by first appearance, reversing, and
    randomly shuffling), and tools for modifying factor levels (including
    collapsing rare levels into other, 'anonymising', and manually 'recoding').
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.
Aim: Supports the most frequently used methods to combine forecasts. Among others: Simple average, Ordinary Least Squares, Least Absolute Deviation, Constrained Least Squares, Variance-based, Best Individual model, Complete subset regressions and Information-theoretic (information criteria based).
Convenient functions for ensemble forecasts in R combining
    approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(),
    nnetar(), stlm(), and tbats() can be combined with equal weights or weights based on
    in-sample errors. Future methods such as cross validation are planned.
Methods to compute linear h-step ahead prediction coefficients based
    on localised and iterated Yule-Walker estimates and empirical mean squared
    prediction errors for the resulting predictors. Also, functions to compute
    autocovariances for AR(p) processes, to simulate tvARMA(p,q) time series,
    and to verify an assumption from a paper by the authors.
Routines for forecasting univariate time series using Theta Models. Contains several cross-validation routines.
The implemented algorithm performs a floating-point genetic algorithm search with a statistical forecasting operator that generates offspring which probably will be generated in future generations. Use of this operator enhances the search capabilities of floating-point genetic algorithms because offspring generated by usual genetic operators rapidly forecasted before performing more generations.
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
Form visualizations of high dimensional mapping structures of random forests and feature contributions.
Extensive global and small-area estimation procedures for multiphase
    forest inventories under the design-based Monte-Carlo approach are provided.
    The implementation includes estimators for simple and cluster sampling
    published by Daniel Mandallaz in 2007 (<DOI:10.1201/9781584889779>),
    2013 (<DOI:10.1139/cjfr-2012-0381>, <DOI:10.1139/cjfr-2013-0181>,
    <DOI:10.1139/cjfr-2013-0449>, <DOI:10.3929/ethz-a-009990020>)
    and 2016 (<DOI:10.3929/ethz-a-010579388>). It provides point estimates,
    their external- and design-based variances as well as confidence intervals.
    The procedures have also been optimized for the use of remote sensing data
    as auxiliary information.
Produces forest plots using 'ggplot2' from models produced by functions
    such as stats::lm(), stats::glm() and survival::coxph().
A forest plot that 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()).
Provides functions to create formattable vectors and data frames.
    'Formattable' vectors are printed with text formatting, and formattable
    data frames are printed with multiple types of formatting in HTML
    to improve the readability of data presented in tabular form rendered in
    web pages.
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.
A collection of fortunes from the R community.
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.
Computes Fourier integrals of functions of one and two variables using the Fast Fourier transform. The Fourier transforms must be evaluated on a regular grid.
Estimate Barton & Lord's four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2015).
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.
Spatio-temporal Fixation Pattern Analysis (FPA) is a new method of analyzing eye 
  movement data, developed by Mr. Jinlu Cao under the supervision of Prof. Chen Hsuan-Chih at 
  The Chinese University of Hong Kong, and Prof. Wang Suiping at the South China Normal 
  Univeristy. The package "fpa" is a R implementation which makes FPA analysis much easier. 
  There are four major functions in the package: ft2fp(), get_pattern(), plot_pattern(), and 
  lineplot(). The function ft2fp() is the core function, which can complete all the preprocessing 
  within moments. The other three functions are supportive functions which visualize the eye 
  fixation patterns.
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
Compute the two dimension functional principal component scores for a series of two dimension images.
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 works on high dimensional datasets.
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.
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 main function of this package allows numerical vector objects to
  be displayed with their values in vulgar fractional form.  This is convenient if
  patterns can then be more easily detected.  In some cases replacing the components
  of a numeric vector by a rational approximation can also be expected to remove
  some component of round-off error.  The main functions form a re-implementation
  of the functions 'fractions' and 'rational' of the MASS package, but using a
  radically improved programming strategy.
Implements and extends the fragility index calculation for
    dichotomous results as described in Walsh, Srinathan, McAuley,
    Mrkobrada, Levine, Ribic, Molnar, Dattani, Burke, Guyatt,
    Thabane, Walter, Pogue, and Devereaux (2014)
    <DOI:10.1016/j.jclinepi.2013.10.019>.
Performs fragment analysis using genetic data coming from capillary electrophoresis machines. These are files with FSA extension which stands for FASTA-type file, and .txt files from Beckman CEQ 8000 system, both contain DNA fragment intensities read by machinery. In addition to visualization, it performs automatic scoring of SSRs (Sample Sequence Repeats; a type of genetic marker very common across the genome) and other type of PCR markers (standing for Polymerase Chain Reaction) in biparental populations such as F1, F2, BC (backcross), and diversity panels (collection of genetic diversity).
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.
Simulates and fits semiparametric shared frailty models under a
    wide range of frailty distributions using a consistent and
    asymptotically-normal estimator. Currently supports: gamma, power variance
    function, log-normal, and inverse Gaussian frailty models.
Tools to support sensible statistics 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.
With no external dependencies and
    support for 335 languages; all languages spoken by
    more than one million speakers. 'Franc' is a port
    of the 'JavaScript' project of the same name,
    see <https://github.com/wooorm/franc>.
Accompanying package of the book 'Financial Risk Modelling
        and Portfolio Optimisation with R', second edition. 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.
Despike, ustar filtering, plotting, footprint modelling and general post-processing of a standard EddyPro full output file (LI-COR Inc 2011-2015 <https://www.licor.com/env/products/eddy_covariance/eddypro.html>).
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.
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.
Wrapper functions that interface with 'Freesurfer' (https://
    surfer.nmr.mgh.harvard.edu/), a powerful and commonly-used 'neuroimaging'
    software, using system commands. The goal is to be able to interface with
    'Freesurfer' completely in R, where you pass R objects of class 'nifti',
    implemented by package 'oro.nifti', and the function executes an 'Freesurfer'
    command and returns an R object of class 'nifti' or necessary output.
Fast regional association analysis of quantitative traits for family-based and population studies.
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.
Generates a frequency distribution. The frequency
    distribution includes raw frequencies, percentages in each category, and
    cumulative frequencies. The frequency distribution can be stored as a data
    frame.
Methods for the analysis of multivariate time series using frequency domain techniques. Implementations of dynamic principle components analysis (DPCA) and estimators of operators in lagged regression. Examples of usage in functional data analysis setup.
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.
Tools for generating an informative type of line graph, the frequency profile, 
    which allows single behaviors, multiple behaviors, or the specific behavioral patterns 
    of individual subjects to be graphed from occurrence/nonoccurrence behavioral data.
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.
Regular and non-regular Fractional Factorial 2-level designs 
        can be created. 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).
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.
Fast computation of moments via 'Rcpp'. Supports computation on
   vectors and matrices, and Monoidal append of moments.
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.
A variety of simple fish stock assessment methods.
    Detailed vignettes are available on the fishR website <http://derekogle.com/fishR/>.
The datasets to support the Fish Stock Assessment ('FSA') package.
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.
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, scoring using weights,the computation and the graphical representation of the frequencies of the responses to each item and the report of the responses of 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 objects of class 'nifti', implemented by
    package 'oro.nifti', and the function executes an 'FSL' command and returns an R
    object of class 'nifti' 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.
Read and write data frames at high speed. Compress your data with fast and efficient type-optimized algorithms that allow for random access of stored data frames (columns and rows).
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.
Implementation of Follow The Regularized Leader (FTRL) Proximal algorithm, proposed by McMahan et al. (2013) <doi:10.1145/2487575.2488200>, used for online training of large scale regression models using a mixture of L1 and L2 regularization.
Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.
Functions for estimating spectral density operator of functional
    time series (FTS) and comparing the spectral density operator of two
    functional time series, in a way that allows detection of differences of
    the spectral density operator in frequencies and along the curve length.
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.
We facilitate the analysis of full factorial mating designs with mixed-effects models. The observed data functions extract the variance explained by random and fixed effects and provide their significance. We then calculate the additive genetic, nonadditive genetic, and maternal variance components explaining the phenotype. In particular, we integrate nonnormal error structures for estimating these components for nonnormal data types. The resampled data functions are used to produce bootstrap confidence intervals, which can then be plotted using a simple function. This package will facilitate the analyses of full factorial mating designs in R, especially for the analysis of binary, proportion, and/or count data types and for the ability to incorporate additional random and fixed effects and power analyses. The paper associated with the package including worked examples is: Houde ALS, Pitcher TE (2016) <doi:10.1002/ece3.1943>.
Provides a single interface to many sources of full text
    'scholarly' data, including 'Biomed Central', Public Library of
    Science, 'Pubmed Central', 'eLife', 'F1000Research', 'PeerJ',
    'Pensoft', 'Hindawi', 'arXiv' 'preprints', and more. Functionality
    included for searching for articles, downloading full or partial
    text, downloading supplementary materials, converting to various
    data formats used in and outside of R.
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 using asymptotic chi-square or exact statistics. These tests were motivated to reveal evidence for causality based on functional dependencies. They include asymptotic functional chi-square tests, an exact functional test, a comparative functional chi-square test, and also a comparative chi-square test. The normalized non-constant functional chi-square test was used by Best Performer NMSUSongLab in HPN-DREAM (DREAM8) Breast Cancer Network Inference Challenges. For continuous data, these tests offer an advantage over regression analysis when a parametric functional form cannot be assumed; for categorical data, they provide a novel means to assess directional dependencies not possible with symmetrical Pearson's chi-square or Fisher's exact tests.
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.
Extends functional programming in R by
    providing support to the usual higher order functional
    suspects (Map, Reduce, Filter, etc.).
Unified framework to cluster functional data according to one of
    seven models. All models are based on the projection of the curves onto a basis.
    The main function funcit() calls wrapper functions for the existing algorithms,
    so that input parameters are the same. A list is returned with each entry
    representing the same or extended output for the corresponding method. Method
    specific as well as general visualization tools are available.
S4 classes for univariate and multivariate functional data with
    utility functions.
The funFEM algorithm (Bouveyron et al., 2014) allows to cluster functional data by modeling the curves within a common and discriminative functional subspace.
Computes fungible coefficients and Monte Carlo data.  
    Underlying theory for these functions is described in the following publications:
    Waller, N. (2008). Fungible Weights in Multiple Regression. Psychometrika, 73(4), 691-703, <DOI10.1007/s11336-008-9066-z>. 
    Waller, N. & Jones, J. (2009). Locating the Extrema of Fungible Regression Weights. 
    Psychometrika, 74(4), 589-602, <DOI10.1007/s11336-008-9087-7>.
    Waller, N. G. (2016). Fungible Correlation Matrices: 
    A Method for Generating Nonsingular, Singular, and Improper Correlation Matrices for 
    Monte Carlo Research. Multivariate Behavioral Research, 51(4), 554-568, <DOI10.1080/00273171.2016.1178566>. 
    Jones, J. A. & Waller, N. G. (2015). The normal-theory and asymptotic distribution-free (ADF) 
    covariance matrix of standardized regression coefficients: theoretical extensions 
    and finite sample behavior. Psychometrika, 80, 365-378, <DOI10.1007/s11336-013-9380-y>.
The package provides the funHDDC algorithm (Bouveyron & Jacques, 2011) which allows to cluster functional data by modeling each group within a specific functional subspace.
Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book <http://livebook.datascienceheroes.com/> are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
A small utility which wraps Rscript and provides access to all R
    functions from the shell.
Computes functional rarity indices as proposed by Violle et al (accepted).
    Various indices can be computed using both regional and local information.
    Functional Rarity combines both the functional aspect of rarity as well as
    the extent aspect of rarity.
Performs functional regression, and some related
    approaches, for intensive longitudinal data (see the book by Walls & Schafer, 
    2006, Models for Intensive Longitudinal Data, Oxford) when such data 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)<DOI:10.1198/jcgs.2010.10007> 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.
Computes the functional tangential angle pseudo-depth and its robustified version from the paper by Kuhnt and Rehage (2016). See Kuhnt, S.; Rehage, A. (2016): An angle-based multivariate functional pseudo-depth for shape outlier detection, JMVA 146, 325-340, <doi:10.1016/j.jmva.2015.10.016> for details.
Includes non-parametric estimators and tests for time series analysis. The functions are 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.
Contains two main functions (i.e., two pieces of furniture):
    table1() which produces a well-formatted table of descriptives common as Table 1
    in research articles and washer() which is helpful in cleaning up your data. 
    In addition, tableM() provides a descriptive table to understand patterns of 
    missing values in the stratifying variable. These furniture functions
    are designed to simplify common tasks in quantitative analysis.
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. Used primarily to identify the cutoff point
    for the noise portion of the eigenvalue spectrum.
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. 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, e.g.
    'f <- future({ mandelbrot(-0.75, 0, side=3) })' and 'v <- value(f)'. The
    'resolved()' function can be used to check if a future is resolved or
    not. An infix assignment operator '%<-%' exists for creating futures whose
    values are accessible by the assigned variables (as promises), e.g. 'v %<-%
    { mandelbrot(-0.75, 0, side=3) }'. This package implements synchronous "lazy"
    and "eager" futures, and asynchronous "multicore", "multisession" and ad hoc
    "cluster" futures. Globals variables and functions are automatically identified
    and exported. Required packages are attached in external R sessions whenever
    needed. All types of futures are designed to behave the same such that the
    exact same code work regardless of futures used or number of cores, background
    sessions or cluster nodes available. Additional types of futures are provided by
    other packages enhancing this package.
Simple parallel and distributed processing using futures
    that utilizes the 'BatchJobs' framework,
    e.g. 'fit %<-% { lm(y ~ x) }'.
    This package implements the Future API of the 'future' package.
Inputs a directory of climate projection files and, for each,
    identifies and characterizes heat waves for specified study locations. The
    definition used to identify heat waves can be customized. Heat wave
    characterizations include several metrics of heat wave length, intensity,
    and timing in the year. The heat waves that are identified can be
    explored using a function to apply user-created functions across all
    generated heat wave files.This work was supported in part by grants from
    the National Institute of Environmental Health Sciences (R00ES022631), the
    National Science Foundation (1331399), and the Colorado State University
    Vice President for Research.
Test function arguments with a wide array of inputs, and produce
  reports summarizing messages, warnings, errors, and returned values.
Calculation of AHP (Analytic Hierarchy Process - 
    <http://en.wikipedia.org/wiki/Analytic_hierarchy_process>) 
    with classic and fuzzy weights based on Saaty's pairwise
    comparison method for determination of weights.
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).
Fuzzy forests, a new algorithm based on random forests,
    is designed to reduce the bias seen in random forest feature selection
    caused by the presence of correlated features.  Fuzzy forests uses
    recursive feature elimination random forests to select
    features from separate blocks of correlated features where the
    correlation within each block of features is high
    and the correlation between blocks of features is low.
    One final random forest is fit using the surviving features.
    This package fits random forests using the 'randomForest' package and
    allows for easy use of 'WGCNA' to split features into distinct blocks.
Join tables together based not on whether columns
  match exactly, but whether they are similar by some comparison.
  Implementations include string distance and regular expression
  matching.
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.
Implementation of several MCDM methods for fuzzy data (triangular
    fuzzy numbers) for decision making problems. The methods that are implemented in
    this package are Fuzzy TOPSIS (with two normalization procedures), Fuzzy VIKOR,
    Fuzzy Multi-MOORA and Fuzzy WASPAS. In addition, function MetaRanking() calculates
    a new ranking from the sum of the rankings calculated, as well as an aggregated ranking.
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.
The main goal of this package is drawing the membership function of the fuzzy p-value which is defined as a fuzzy set on the unit interval for three following problems: (1) testing crisp hypotheses based on fuzzy data, (2) testing fuzzy hypotheses based on crisp data, and (3) testing fuzzy hypotheses based on fuzzy data. In all cases, the fuzziness of data or/and the fuzziness of the boundary of null fuzzy hypothesis transported via the p-value function and causes to produce the fuzzy p-value. If the p-value is fuzzy, it is more appropriate to consider a fuzzy significance level for the problem. Therefore, the comparison of the fuzzy p-value and the fuzzy significance level is evaluated by a fuzzy ranking method in this package.
Design and simulate fuzzy logic systems using Type 1 Fuzzy Logic. This toolkit includes with graphical user interface (GUI) and an adaptive neuro-fuzzy inference system (ANFIS). This toolkit is a continuation from the previous package ('FuzzyToolkitUoN'). Produced by the Intelligent Modelling & Analysis Group, University of Nottingham.
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).
Provides three 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.
For a given metabolic network, this package finds the gaps (metabolites not produced or not consumed in any other reaction), and fills it from the stoichiometric reactions of a reference metabolic reconstruction using a weighting function. Also the option to download all the set of gene-associated stoichiometric reactions for a specific organism from the KEGG database <http://www.genome.jp/kegg/> is available.
Full descriptive statistics, physical description of sediment,
            metric or phi sieves.
An R package for optimisation 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. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.
This is a Genetic Algorithm package for the determination
        of the stratum boundaries and sample sizes of each stratum in
        stratified sampling
Scale abbreviation using Genetic Algorithms that maximally capture the variance in the original data.
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.
A group of sample points are evaluated against a
        user-defined expression, the sample points are lists of
        parameters with values that may be substituted into that
        expression. The genetic algorithm attempts to make the result
        of the expression as low as possible (usually this would be the
        sum of residuals squared).
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.
Fits the gambin distribution to species-abundance distributions from 
  ecological data. 'gambin' is short for 'gamma-binomial'. 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
Functions and data are provided that support a course that emphasizes statistical
  issues of inference and generalizability.  Attention is restricted to a relatively small
  number of methods, often (misleadingly in my view) referred to as algorithms.
Ensemble classifiers based upon generalized additive models for binary
    classification (De Bock et al. (2010) <DOI:10.1016/j.csda.2009.12.013>). The ensembles
    implement Bagging (Breiman (1996) <DOI:10.1023/A:1018054314350>), the Random Subspace Method (Ho (1998) <DOI:10.1109/34.709601>), or
    both, and use Hastie and Tibshirani's (1990) generalized additive models (GAMs)
    as base classifiers. Once an ensemble classifier has been trained, it can
    be used for predictions on new data. A function for cross validation is also
    included.
Provides estimation and analysis functions for
    strategic statistical models.
Implementation of a common set of punctual solutions  for Cooperative Game Theory.
Many situations can be modeled as game theoretic situations. Some procedures are included in this package to calculate the most important allocations rules in Game Theory: Shapley value, Owen value or nucleolus, among other. First, we must define as an argument the value of the unions of the envolved agents with the characteristic function.
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.
Interface 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 Gaussian Markov Random Field (GMRF) 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.
Estimate generalized additive mixed models via a version of
         function gamm() from 'mgcv', 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.
Robust regression via gamma-divergence with L1, elastic net and ridge.
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.
Tools to fill missing values in satellite data and to develop new
    gap-fill algorithms. The methods are tailored to data (images) observed
    at equally-spaced points in time. The package is illustrated with MODIS
    NDVI data.
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.
Simulate, estimate and forecast using univariate and multivariate GAS models.
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.
Functions for greenhouse gas flux calculation from chamber
    measurements.
Manipulation of genetic data (SNPs), computation of Genetic Relationship Matrix, Linkage Disequilibrium, etc. Efficient algorithms for Linear Mixed Model (AIREML, diagonalization trick).
Allows user to choose/gate a region on the plot and returns points
    within it.
Fits a Gaussian process model to data. Gaussian processes
 are commonly used in computer experiments to fit an interpolating model.
 The model is stored as an 'R6' object and can be easily updated with new 
 data. There are options to run in parallel (not for Windows), and 'Rcpp'
 has been used to speed up calculations. Other R packages that perform
 similar calculations include 'laGP', 'DiceKriging', 'GPfit', and 'mlegp'.
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.
Display a random fact about Carl Friedrich Gauss
 based the on collection curated by Mike Cavers via the
 <http://gaussfacts.com> site.
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.
Functions for inference of ploidy from (Genotyping-by-sequencing) GBS data, including a function to infer allelic ratios and allelic proportions in a Bayesian framework.
An implementation of hyperparameter optimization for Gradient
    Boosted Trees on binary classification and regression problems. The current
    version provides two optimization methods: Bayesian optimization and random
    search.
Simulate real and complex numbers from distributions of
       their magnitude and arguments. Optionally, the magnitudes and/or
       arguments may be fixed in almost arbitrary ways.  Small
       programming utilities: check if an object is identical to NA,
       count positional arguments in a call, set intersection of more
       than two sets, check if an argument is unnamed.
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 un-accelerated 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 multi-threaded
  - 'Intel MKL' which is 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.
Generalized competing event model based on Cox PH model and Fine-Gray model.
    This function is designed to develop optimized risk-stratification methods for competing
    risks data, such as described in:
    1. Carmona R, Gulaya S, Murphy JD, Rose BS, Wu J, Noticewala S,McHale MT, Yashar CM, Vaida F,
    and Mell LK.(2014) <DOI:10.1016/j.ijrobp.2014.03.047>. Validated competing event model for thestage I-II
    endometrial cancer population. Int J Radiat Oncol Biol Phys.89:888-98.
    2. Carmona R, Zakeri K, Green G, Hwang L, Gulaya S, Xu B, Verma R, Williamson CW, Triplett DP, Rose
    BS, Shen H, Vaida F, Murphy JD, and Mell LK.(2016) <DOI:10.1200/JCO.2015.65.0739>. Improved method to stratify
    elderly cancer patients at risk for competing events. J Clin Oncol.in press.
Provides a variety of functions to analyze and model
    geostatistical count data with Gaussian copulas, including
 1) data simulation and visualization; 
 2) correlation structure assessment (here also known as the NORTA); 
 3) calculate multivariate normal rectangle probabilities; 
 4) likelihood inference and parallel prediction at unsampled locations.
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.
A set of psychometric tools for cognitive diagnostic analyses for both dichotomous and polytomous responses. Various cognitive diagnosis models (CDMs) can be estimated, include the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7>, the sequential G-DINA model by Ma and de la Torre (2016)<DOI:10.1111/bmsp.12070>, and many other models they subsume. Joint attribute distribution can be saturated, higher-order or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided.
Calculate distances and routes on geographic grids.
A toolkit with functions to fit, plot, and summarize Generalized Dissimilarity Models (Ferrier et al. 2007, <doi:10.1111/j.1472-4642.2007.00341.x>).
Manage and analyze high-dimensional SNP data from chips with multiple densities.
To address the problem of insecurity of UDP-based DNS requests,
    Google Public DNS offers DNS resolution over an encrypted HTTPS
    connection. DNS-over-HTTPS greatly enhances privacy and security
    between a client and a recursive resolver, and complements DNSSEC
    to provide end-to-end authenticated DNS lookups. Functions that enable
    querying individual requests that bulk requests that return detailed
    responses and bulk requests are both provided. Support for reverse
    lookups is also provided. See <https://developers.google.com/speed/public-dns/docs/dns-over-https> 
    for more information.
Functions to compute the Generalized Dynamic Principal Components
    introduced in Peña and Yohai (2016) <DOI:10.1080/01621459.2015.1072542>.
Contains a function called gds() which accepts three input
    parameters like lower limits, upper limits and the frequencies of the
    corresponding classes. The gds() function calculate and return the values
    of mean ('gmean'), median ('gmedian'), mode ('gmode'), variance ('gvar'), standard
    deviation ('gstdev'), coefficient of variance ('gcv'), quartiles ('gq1', 'gq2', 'gq3'),
    inter-quartile range ('gIQR'), skewness ('g1'), and kurtosis ('g2') which facilitate
    effective data analysis. For skewness and kurtosis calculations we use moments.
Useful tools for writing vector graphics devices.
Implements common geostatistical methods in a clean,
    straightforward, efficient manner. A quasi reboot of the SpatialTools R package.
Provides adaptive association tests for SNP level, gene level and pathway level analyses.
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.
Causal mediation analysis for a single exposure/treatment and a single mediator, 
  both allowed to be either continuous or binary. The package implements the difference method 
  and provide point and interval estimates as well as testing for the natural direct and 
  indirect effects and the mediation proportion.
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,
             using Lapack routines.
Methods for fitting macroevolutionary models to phylogenetic trees.
First major functionality is to compute the bias in misspecified linear gene-environment interaction models. The most 
	generalized function for this objective is GE_bias().  However GE_bias() requires specification of many
	higher order moments of covariates in the model.  If users are unsure about how to calculate/estimate
	these higher order moments, it may be easier to use GE_bias_normal_squaredmis().  This function places
	many more assumptions on the covariates (most notably that they are all jointly generated from a multivariate
	normal distribution) and is thus able to automatically calculate many of the higher order moments automatically,
	necessitating only that the user specify some covariances.  There are also functions to solve for the bias 
	through simulation and non-linear equation solvers, these can be used to check your work. Second major functionality
	is to implement the Bootstrap Inference with Correct Sandwich (BICS) testing procedure, which we have found to provide better finite-sample
	performance than other inference procedures for testing GxE interaction.  More details on these functions 
	are available in Sun, Carroll, Christiani, and Lin, "Testing for Gene-Environment Interaction Under Exposure Misspecification"
	(Submitted).
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.
Reads data files from the 'Gem infrasound logger' for analysis and converts to segy format (which is convenient for reading with traditional seismic analysis software). The Gem infrasound logger is an in-development low-cost, lightweight, low-power instrument for recording infrasound in field campaigns; email the maintainer for more information.
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 using JAGS. 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, Nunez S, Reed E, Reilly MP, Foulkes AS (2016) <DOI:10.1371/journal.pone.0148218> A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci. PLoS ONE 11(2): e0148218".
Engines for cross-validation of many types of regression and class prediction models are provided. These engines include built-in support for 'glmnet', 'lars', 'plus', 'MASS', 'rpart', 'C50' and 'randomforest'. It is easy for the user to add other regression or classification algorithms. The 'parallel' package is used to improve speed. Several data generation algorithms for problems in regression and classification are provided.
Set of functions to create datasets using a correlation matrix.
Encodes gender based on names and dates of birth using historical
    datasets. By using these datasets 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' Application Programming Interface 
    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.
Computes the probability density function (pdf), cumulative distribution function (cdf), quantile function (qf) and generates random values (rg) for the following general models : mixture models, composite models, folded models, skewed symmetric models and arc tan models.
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.
Using simple input, this package creates publication worthy plots of gene models. Users can create plots of alternatively spliced gene variants and the positions of mutations and other gene features.
Analyzes 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).
Belief propagation methods in genotype-phenotype networks (Conditional Gaussian and Discrete Bayesian Networks) to propagate phenotypic evidence through the network.
Functions for Genetic Epi Methods Developed at MSKCC
Asymmetric generalized correlations r*(x|y) measure strength of the
    dependence of x on y. If |r*(x|y)|> |r*(y|x)| it suggests that y is more likely
    the "kernel cause" of x. There are at least two additional ways of comparing
    two kernel regressions helping identify the `cause'. 
    In simultaneous equation
    models where endogeneity of regressors is feared, we can use Prof. Koopmans' method
    to ignore endogeneity problems when it kernel causes the dependent variable. 
    The usual partial correlations can be generalized for the asymmetric matrix of r*'s.
    Partial correlations help asses effect of x on y after removing the effect of a
    set of variables. 
    The package provides additional tools for causal assessment,
    for printing the causal detections in a clear, comprehensive compact summary form,
    for matrix algebra, for outlier detection, and for numerical integration by the
    trapezoidal rule, stochastic dominance, etc. 
    The package has functions for bootstrap-based statistical inference and one 
    for a heuristic t-test.
Functions to assess the goodness of fit of binary, multinomial and ordinal logistic models.
	Included are the Hosmer-Lemeshow tests (binary, multinomial and ordinal) and the Lipsitz and 
	Pulkstenis-Robinson tests (ordinal).
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.
Perform the Blinder-Oaxaca decomposition for generalized linear 
   model with bootstrapped standard errors. The twofold and threefold 
   decomposition are given, even the generalized linear model output in each group.
A general tree data structure implementation in R.
Allows users to quickly and easily generate fake data containing
    Personally Identifiable Information (PII) through convenience functions.
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.
Genome-wide association study (GWAS) performed with SLOPE,
    short for Sorted L-One Penalized Estimation, a
    method for estimating the vector of coefficients in linear model.
    In the first step of GWAS, SNPs are clumped according to their correlations and
    distances. Then, SLOPE is performed on data where each clump has
    one representative.
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 Heterozygosity, 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.
Implementation of spatial graph-theoretic genetic gravity models.
    The model framework is applicable for other types of spatial flow questions.
    Includes functions for constructing spatial graphs, sampling and summarizing
    associated raster variables and building unconstrained and singly constrained
    gravity models.
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').
A new hierarchical clustering linkage criterion:
    the Genie algorithm links two clusters in such a way that a chosen
    economic inequity measure (e.g., the Gini index) of the cluster
    sizes does not increase drastically above a given threshold. Benchmarks
    indicate a high practical usefulness of the introduced method:
    it most often outperforms the Ward or average linkage in terms of
    the clustering quality while retaining the single linkage speed,
    see (Gagolewski et al. 2016 <DOI:10.1016/j.ins.2016.05.003>)
    for more details.
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.
Plot values of markers(SNPs, expression, genes, RNA,...) for all chromosomes.
Circular genomic permutation approach uses GWAS results to establish the significance of pathway/gene-set associations whilst accounting for genomic structure. All SNPs in the GWAS are placed in a 'circular genome' according to their location. Then the complete set of SNP association p-values are permuted by rotation with respect to the SNPs' genomic locations. Two testing frameworks are available: permutations at the gene level, and permutations at the SNP level. The permutation at the gene level uses fisher's combination test to calculate a single gene p-value, followed by the hypergeometric test. The SNP count methodology maps each SNP to pathways/gene-sets and calculates the proportion of SNPs for the real and the permutated datasets above a pre-defined threshold. Genomicper requires a matrix of GWAS association p-values. The SNPs annotation and pathways annotations can be performed within the package or provided by the user.
A loose collection of tools for the analysis of expression and genotype data, currently with the main focus on (e)QTL and MDR analysis.
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 for generating samples from discrete random variables with prescribed 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.
Define and compute with generalized spherical distributions - multivariate probability
 laws that are specified by a star shaped contour (directional behavior) and a radial component.
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.
Split 'geospatial' objects into pieces. Includes
    support for some spatial object inputs, 'Well-Known Text', and
    'GeoJSON'.
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.
Make geographic box plot as detailed in Willmott et al. (2007).
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.
Visualizes two-dimensional geoelectric resistivity measurement profiles in three dimensions.
Kriging based methods are used for predicting functional data 
             (curves) with spatial dependence.
A model building procedure to select a sparse geoadditive model from a large number of covariates. Continuous, binary and ordered categorical responses are supported. The model building is based on component wise gradient boosting with linear effects and smoothing splines. The resulting covariate set after gradient boosting is further reduced through cross validated backward selection and aggregation of factor levels. The package provides a model based bootstrap method to simulate prediction intervals for point predictions. A test data set of a soil mapping case study is provided.
Quantification of the effect of geographic versus environmental isolation on genetic differentiation
Provides tools to encode lat/long pairs into geohashes, decode those geohashes,
             and identify their neighbours.
Classes for 'GeoJSON' to make working with 'GeoJSON' easier.
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'.
Tools for linting 'GeoJSON'. Includes tools for interacting with the
    online tool <http://geojsonlint.com>, the 'Javascript' library 'geojsonhint'
    (<https://www.npmjs.com/package/geojsonhint>), and validating against a
    'GeoJSON' schema via the 'Javascript' library
    (<https://www.npmjs.com/package/is-my-json-valid>). Some tools work locally
    while others require an internet connection.
Processes gridded datasets found on the U.S. Geological Survey
    Geo Data Portal web application or elsewhere, using a web-enabled workflow
    that eliminates the need to download and store large datasets that are reliably
    hosted on the Internet. The package provides access to several data subset and
    summarization algorithms that are available on remote web processing servers.
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
Provides eigenvector-based (geometric) forecast
    combination methods; also includes simple approaches (simple average,
    median, trimmed and winsorized mean, inverse rank method) and regression-based
    combination. Tools for data pre-processing are available in order to deal with 
    common problems in forecast combination (missingness, collinearity).
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.
Network visualization in the 'ggplot2' framework. Network
    functionality is provided in a single 'ggplot2' layer by calling the geom 'net'.
    Layouts are calculated using the 'sna' package, example networks are included.
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
A wrapper for the Geoparser.io API version 0.4.0 (see <https://geoparser.io/>), which is a web service
    that identifies places mentioned in text, disambiguates those places, and
    returns detailed data about the places found in the text. Basic, limited
    API access is free with paid plans to accommodate larger workloads.
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.
Provides functions for efficiently fitting linear models with 
         spatially correlated errors by robust and Gaussian (Restricted) 
         Maximum Likelihood and for computing robust and customary point 
         and block external-drift Kriging predictions, along with utility
         functions for variogram modelling in ad hoc geostatistical analyses, 
         model building, model evaluation by 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.
A compilation of functions for performing calculations and
    creating plots that commonly arise in geotechnical engineering and soil
    mechanics. The types of calculations that are currently included are:
    (1) phase diagrams and index parameters, (2) grain-size distributions, 
    (3) plasticity, (4) soil classification, (5) compaction, (6) groundwater,
    (7) subsurface stresses (geostatic and induced), (8) Mohr circle analyses,
    (9) consolidation settlement and rate, (10) shear strength, (11) bearing
    capacity, (12) lateral earth pressures, (13) slope stability, and (14)
    subsurface explorations. Geotechnical engineering students, educators,
    researchers, and practitioners will find this package useful.
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 https://github.com/geotopmodel 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.
Geometric objects defined in 'geozoo' can be simulated or displayed in the R package 'tourr'.
Encode and decode the Google Encoded Polyline Algorithm Format (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).
Estimation and diagnosis of the convergence of Generalized
    Exponential Random Graph Models via Gibbs sampling or Metropolis
    Hastings with exponential down weighting.
Different types of seed indexes, rates and visualization techniques are used to provide a robust approach for germination data analysis. The package aims to make available germination seed indexes and graphical functions to analyze germination seed data.
Fit a variety of component-based structural equation models.
Implements the gene-based segregation test(GESE) and the weighted GESE test for identifying genes with causal variants of large effects for family-based sequencing data. The methods are described in Qiao, D. Lange, C., Laird, N.M., Won, S., Hobbs, B., et al. 2016. Gene-based segregation method for identifying rare variants for family-based sequencing studies. More details can be found at <http://scholar.harvard.edu/dqiao/gese>.
Provides programmatic access to the GESIS - Leibniz-Institute for
    the Social Sciences Data Catalogue/Datenbestandkatalog (DBK), which
    maintains a large repository of data sets related to the social sciences.
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.
Provides functions that automate downloading and importing
    University of East Anglia Climate Research Unit (CRU) CL2.0 climatology data
    into R, facilitates the calculation of minimum temperature and maximum
    temperature and formats the data into a tidy data frame or a list of raster
    stack objects for use in an R session. CRU CL2.0 data are a gridded
    climatology of 1961-1990 monthly means released in 2002 and cover all land
    areas (excluding Antarctica) at 10 arc-second resolution. For more
    information see the description of the data provided by the University of
    East Anglia Climate Research Unit,
    <http://www.cru.uea.ac.uk/cru/data/hrg/tmc/readme.txt>.
Downloads and aggregates high frequency trading data for Brazilian instruments directly from Bovespa ftp site <ftp://ftp.bmf.com.br/MarketData/>.
Get Landsat 8 Data from Amazon Web Services ('AWS')
    public data sets (<https://aws.amazon.com/public-data-sets/landsat/>).
    Includes functions for listing images and fetching them, and handles
    caching to prevent unnecessary additional requests.
Hydrologic models often require users to collect and format input meteorological data. This package contains functions for sourcing, formatting, and
    editing meteorological data for hydrologic models.
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.
A micro-package for reading "passwords", i.e.  reading
    user input with masking, so that the input is not displayed as it 
    is typed.  Currently we have support for 'RStudio', the command line
    (every OS), and any platform where 'tcltk' is present.
The GetR package calculates Guttman error trees, which can
        be used to find homogeneous subgroups regarding Guttman errors.
Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean.
Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto <http://www.tesouro.fazenda.gov.br/tesouro-direto-balanco-e-estatisticas>.
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.
A function to retrieve the system timezone on Unix systems
 which has been found to find an answer when 'Sys.timezone()' has failed.
 It is based on an answer by Duane McCully posted on 'StackOverflow', and
 adapted to be callable from R. The package also builds on Windows, but
 just returns NULL.
Implements a flexible nonlinear modelling framework for nonstationary
        generalized extreme value analysis in hydroclimatology.
Package for simulation and estimation of ARMA-GARCH/APARCH models with GEV and stable 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
Factor analysis implementation for multiple data sources, i.e., for groups of variables. The whole data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The model group factor analysis (GFA) is inferred with Gibbs sampling.
The gfcanalysis package supports analyses using the Global
    Forest Change dataset released by Hansen et al. gfcanalysis was
    written for 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.
    The forest change data and more information on the product is available at
    http://earthenginepartners.appspot.com.
Implemented are the Wald-type statistic,
    a permuted version thereof as well as the ANOVA-type statistic
    for general factorial designs, even with non-normal error terms
    and/or heteroscedastic variances, for crossed designs with an
    arbitrary number of factors and nested designs with up to three factors.
Focuses on data collecting and analyzing in green finance and environmental 
  risk research and analysis. Main function includes environmental data collecting from 
  official websites like MEP (Ministry of Environmental Protection of China, <http://www.mep.gov.cn>), water 
  related projects identification and environmental data visualization.
The R package 'ggplot2' is a plotting system based on the grammar of graphics.
    'GGally' extends 'ggplot2' by adding several functions
    to reduce the complexity of combining geometric objects with transformed data.
    Some of these functions include a pairwise plot matrix, a two group pairwise plot
    matrix, a parallel coordinates plot, a survival plot, and several functions to
    plot networks.
A compendium of 'geoms', 'coords' and 'stats' for 'ggplot2', including
    splines, 1d and 2d densities, univariate average shifted histograms and
    a new map coordinate system based on the 'PROJ.4'-library.
Provides two methods of plotting categorical scatter plots such
    that the arrangement of points within a category reflects the density of
    data at that region, and avoids over-plotting.
We provide two primary resources in 'ggCompNet'. The first is a function to compare the speed of network drawing using several different packages. The second is the vignette folder which contains two vignettes that provide code for reproducing examples comparing the three network visualization packages 'geomnet', 'ggnetwork', and the ggnet2() function from the 'GGally' package.
The 'ggcorrplot' package can be used to visualize easily a
    correlation matrix using 'ggplot2'. It provides a solution for reordering the
    correlation matrix and displays the significance level on the plot. It also
    includes a function for computing a matrix of correlation p-values.
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. The package
    provides implementations for tree, rpart, as well as diana and agnes
    cluster diagrams.
A fast engine for computing hierarchical Bayesian model
    implemented in the Dynamic Model of Choice.
Create biplots for GGE (genotype plus genotype-by-environment) and
    GGB (genotype plus genotype-by-block-of-environments) models.
Description: 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. Includes interactive genealogy visualization tools. Provides parsing and calculation methods for variables in descendant branches of interest. 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.
The aim of 'ggplot2' is to aid in visual data investigations. This
    focus has led to a lack of facilities for composing specialised plots.
    'ggforce' aims to be a collection of mainly new stats and geoms that fills
    this gap. All additional functionality is aimed to come through the official
    extension system so using 'ggforce' should be a stable experience.
Unified plotting tools for statistics commonly used, such as GLM,
    time series, PCA families, clustering and survival analysis. The package offers
    a single plotting interface for these analysis results and plots in a unified
    style using 'ggplot2'.
Creates a reproducible 'ggplot2' object by storing the data and calls.
Utilities for Creating Guitar Tablature using tidyverse packages.
Reproduce the halfnorm() function found in the 'faraway' package 
    using the 'ggplot2' API.
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.
Create interactive 'ggplot2' graphics using 'htmlwidgets'.
Collection of functions to enhance 'ggplot2' and 'ggiraph'. Provides functions for exploratory plots.
    All plot can be a 'static' plot or an 'interactive' plot using 'ggiraph'.
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.
Visualize sequences in (modified) logo plots. The design choices
    used by these logo plots allow sequencing data to be more easily analyzed.
    Because it is integrated into the 'ggplot2' geom framework, these logo plots
    support native features such as faceting.
Pass a data frame and mapping aesthetics to ggloop() in order
    to create a list of 'ggplot2' plots. The way x-y and dots are paired together
    is controlled by the remapping arguments. Geoms, themes, facets, and other
    features can be added with the special %L+% (L-plus) operator.
Functions and datasets for maximum likelihood fitting of some classes of graphical Markov models.
A collection of functions to visualize spatial data and models
    on top of static maps from various online sources (e.g Google Maps and Stamen
    Maps). It includes tools common to those tasks, including functions for
    geolocation and routing.
Tools 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.
Mosaic plots in the 'ggplot2' framework. Mosaic plot functionality
    is provided in a single 'ggplot2' layer by calling the geom 'mosaic'.
Estimation of partial correlation matrix using ridge penalty followed by thresholding and reestimation. Under multivariate Gaussian assumption, the matrix constitutes an Gaussian graphical model (GGM).
Graph estimation in Gaussian Graphical Models. The main functions return the adjacency matrix of an undirected graph estimated from a data matrix.
Geometries to plot network objects with 'ggplot2'.
Create hammock plots, parallel sets, and common angle plots
    with 'ggplot2'.
A system for 'declaratively' creating graphics,
    based on "The Grammar of Graphics". You provide the data, tell 'ggplot2'
    how to map variables to aesthetics, what graphical primitives to use,
    and it takes care of the details.
A dataset about movies. This was previously contained in ggplot2,
   but has been moved its own package to reduce the download size of ggplot2.
Extensions to 'ggplot2' respecting the grammar of graphics
    paradigm. Provides new statistics to locate and tag peaks and valleys in 2D
    plots, a statistics to add a label with the equation of a polynomial fitted
    with lm(), or R^2 or adjusted R^2 or information criteria for any model
    fitted with function lm(). Additional statistics give access to functions
    in package 'broom'. Provides a function for flexibly converting time
    series to data frames suitable for plotting with ggplot(). In addition
    provides statistics and ggplot geometries useful for diagnosing what data
    are passed to compute_group() and compute_panel() functions and to
    geometries.
Tools for working with polygons with holes in 'ggplot2', with a
    new 'geom' for drawing a 'polypath' applying the 'evenodd' or 'winding'
    rules.
'ggplot2' is an excellent and flexible package for elegant data
    visualization in R. However the default generated plots requires some formatting
    before we can send them for publication. Furthermore, to customize a 'ggplot',
    the syntax is opaque and this raises the level of difficulty for researchers
    with no advanced R programming skills. 'ggpubr' provides some easy-to-use
    functions for creating and customizing 'ggplot2'- based publication ready plots.
Graphic elements for exploring Random Forests using the 'randomForest' or
    'randomForestSRC' package for survival, regression and classification forests and
    'ggplot2' package plotting.
Intended for both technical and non-technical users to create
    interactive data visualizations through a web browser GUI without writing any
    code.
Provides text and label geoms for 'ggplot2' that help to avoid overlapping
    text labels. Labels repel away from each other and away from the data
    points.
A collection of 'ggplot2' color palettes inspired by
    scientific journals, data visualization libraries, and
    science fiction TV shows.
Provides 'ggplot2' 'stats' that estimate seasonally adjusted series 
    and rolling summaries such as rolling average on the fly for time series.
Adds north symbols (18 options) and scale bars in kilometers to
    maps in geographic or metric coordinates created with 'ggplot2' or 'ggmap'.
Additional annotations, stats and scales for plotting "light"
    spectra with 'ggplot2', together with specializations of ggplot()
    and plot() methods for spectral data stored in objects of the classes
    defined in package 'photobiology' and a plot() method for objects of
    class "waveband", also defined in package 'photobiology'.
A 'ggplot2' extension that provides flipped components:
    horizontal versions of 'Stats' and 'Geoms', and vertical versions
    of 'Positions'.
Offers various swiss maps as data frames and '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.
Rstudio add-in that delivers a graphical interface for editing 'ggplot2' theme elements.
Some extra themes, geoms, and scales for 'ggplot2'.
    Provides 'ggplot2' themes and scales that replicate the look of plots
    by Edward Tufte, Stephen Few, 'Fivethirtyeight', 'The Economist', 'Stata',
    'Excel', and 'The Wall Street Journal', among others.
    Provides 'geoms' for Tufte's box plot and range frame.
An implementation of an interactive grammar of graphics, taking the
    best parts of 'ggplot2', combining them with the reactive framework of
    'shiny' and drawing web graphics using 'vega'.
A lightweight, vectorized drop-in replacement for
    'devtools::install_github()' that uses native git and R methods to clone and
    install a package from GitHub.
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.
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.
Toolbox for various enrichment analysis methods and quantification of uncertainty of gene sets.
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.
We provide a set of functions to retrieve information about GIMMS NDVI3g files currently available online; download (and re-arrange, in the case of NDVI3g.v0) the half-monthly data sets; import downloaded files from ENVI binary (NDVI3g.v0) or NetCDF format (NDVI3g.v1) directly into R based on the widespread 'raster' package; conduct quality control; and generate monthly composites (e.g., maximum values) from the half-monthly input data. As a special gimmick, a method is included to conveniently apply the Mann-Kendall trend test upon 'Raster*' images, optionally featuring trend-free pre-whitening to account for lag-1 autocorrelation.
Performs image segmentation in fruit or seeds pictures in order to measure physical features in a high-throughput manner for genome-wide association (GWAS) and genomic selection programs.
Gini-based coefficients and plot of the ordinary and generalized curve of maximum inequality in the presence of weighted and negative attributes.
Maximum likelihood estimation under relational models, with or without the overall effect.
Allows calculation on, and
 sampling from Gibbs Random Fields, and more precisely general 
 homogeneous Potts model. The primary tool is the exact computation of
 the intractable normalising constant for small rectangular lattices. 
 Beside the latter function, it contains method that give exact sample from the likelihood
 for small enough rectangular lattices or approximate sample from the 
 likelihood using MCMC samplers for large lattices.
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.
An Rstudio addin for version control that allows users to clone
    repositories, create and delete branches, and sync forks on GitHub, GitLab, etc.
    Furthermore, the addin uses the GitLab API to allow instructors to create
    forks and merge requests for all students/teams with one click of a button.
Provides an helpful way to install packages hosted on GitHub.
Provides R functions to access the API of the project and
    repository management web application gitlab. For many common tasks (repository
    file access, issue assignment and status, commenting) convenience wrappers
    are provided, and in addition the full API can be used by specifying request
    locations. Gitlab is open-source software and can be self-hosted or used on
    gitlab.com.
The goal of this package is to allow users to robustly
    and quickly grid and quantify sizes of pinned colonies in plate images.
    gitter works by first finding the grid of colonies from a preprocessed image and then locating the bounds of each colony separately.
    It includes several image pre-processing techniques, such
    as autorotation of plates, noise removal, contrast adjustment and image
    resizing.
Functions to assess the calibration of logistic regression models with the GiViTI (Gruppo Italiano per la Valutazione degli interventi in Terapia Intensiva, Italian Group for the Evaluation of the Interventions in Intensive Care Units - see <http://www.giviti.marionegri.it/>) approach. The approach consists in a graphical tool, namely the GiViTI calibration belt, and in the associated statistical test. These tools can be used both to evaluate the internal calibration (i.e. the goodness of fit) and to assess the validity of an externally developed model.
`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.
Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling.
Implementations of the treatment effect estimators for hybrid (self-selection) experiments, as developed by Brian J. Gaines and James H. Kuklinski, (2011), "Experimental Estimation of Heterogeneous Treatment Effects Related to Self-Selection," American Journal of Political Science 55(3): 724-736.
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.
Efficient design matrix free procedure for penalized estimation
    in large scale 2 and 3-dimensional generalized linear array models. Currently
    either Lasso or SCAD penalized estimation is possible for the followings models:
    The Gaussian model with identity link, the Binomial model with logit link, the
    Poisson model with log link and the Gamma model with log link.
Functions are provided 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). 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 Generalised Lambda Distributions (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 2015, Volume 24, Issue 3). Michael Lim & Trevor Hastie (2015) <DOI:10.1080/10618600.2014.938812>.
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
Several tests for high dimensional generalized linear models have been proposed recently. In this package, we implemented a new test called adaptive  sum of powered score (aSPU) for high dimensional generalized linear models, which is often more powerful than the existing methods in a wide scenarios. We also implemented permutation based version of several existing methods for research purpose. We recommend users use the aSPU test for their real testing problem. You can learn more about the tests implemented in the package via the following papers: 1. Pan, W., Kim, J., Zhang, Y., Shen, X. and Wei, P. (2014) <DOI:10.1534/genetics.114.165035> A powerful and adaptive association test for rare variants, Genetics, 197(4). 2. Guo, B., and Chen, S. X. (2016) <DOI:10.1111/rssb.12152>. Tests for high dimensional generalized linear models. Journal of the Royal Statistical Society: Series B. 3. Goeman, J. J., Van Houwelingen, H. C., and Finos, L. (2011) <DOI:10.1093/biomet/asr016>. Testing against a high-dimensional alternative in the generalized linear model: asymptotic type I error control. Biometrika, 98(2).
Find all hierarchical models of specified generalized linear
    model with information criterion (AIC, BIC, or AICc) within specified
    cutoff of minimum value.  Alternatively, find all such graphical models.
    Use branch and bound algorithm so we do not have to fit all models.
Implements the Bayesian paradigm
    for fractional polynomials in generalized linear
    models. See package 'bfp' for the treatment of normal
    models.
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.
Distributed training and prediction of generalized linear models using 'ddR' (Distributed Data Structures) API in the 'ddR' package.
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.
Automates running Generalized Linear Mixed Models, including
    spatial models, with WinBUGS, OpenBUGS and JAGS. Models are specified with
    formulas, with the package writings model files, arranging unbalanced data
    in ragged arrays, and creating starting values. The model is re-parameterized,
    and functions are provided for converting model outputs to the original
    parameterization.
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.
Generalized Linear Mixed Model (GLMM) for Binary Randomized Response Data.
    Includes Cauchit, Compl. Log-Log, Logistic, and Probit link functions for Bernoulli Distributed RR data.
    RR Designs: Warner, Forced Response, Unrelated Question, Kuk, Crosswise, and Triangular.
Conduct inference about generalized linear mixed models, with a
    choice about which method to use to approximate the likelihood. In addition
    to the Laplace and adaptive Gaussian quadrature approximations, which are
    borrowed from 'lme4', the likelihood may be approximated by the sequential
    reduction approximation, or an importance sampling approximation. These
    methods provide an accurate approximation to the likelihood in some
    situations where it is not possible to use adaptive Gaussian quadrature.
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.
Functions to calculate predicted values and the difference between
    the two cases with confidence interval for glm(), glm.nb(), polr() and multinom().
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.
Basic functions for plotting 2D and 3D views of a sphere, by default the Earth with its major coastline, and additional lines and points.
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
Implements the likelihood ratio test for genome-wide association
    under genetic heterogeneity as described in Qian and Shao (2013) <doi:10.1111/ahg.12005>.
Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
Useful utilities in N-glycan data analysis. This package tries
    to fill the gap in N-glycan data analysis by providing easy
    to use functions for basic operations on data
    (see https://en.wikipedia.org/wiki/Glycomics for more
    details on Glycomics). At the moment glycanr is mostly oriented
    to data obtained by UPLC and LCMS analysis of Plasma and IgG glycome.
An interface to the Gmail RESTful API.  Allows access to your
    Gmail messages, threads, drafts and labels.
Get distance and travel time between two points from Google Maps.
    Four possible modes of transportation (bicycling, walking, driving and
    public transportation).
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.
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.
A template for a geometallurgical database and a fast and easy
    interface for accessing it is provided in this package.
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 make short term forecasting for a univariate time series.
Fast algorithms  for robust estimation with large samples of multivariate observations. Estimation of the geometric median, robust k-Gmedian clustering, and robust PCA based on the Gmedian covariation matrix.
An implementation of an all-in-one function for a wide range of meta-analysis problems. It contains a single function gmeta() that unifies all standard meta-analysis methods and also several newly developed ones under a framework of combining confidence distributions (CDs). Specifically, the package can perform classical p-value combination methods (such as methods of Fisher, Stouffer, Tippett, etc.), fit meta-analysis fixed-effect and random-effects models, and synthesizes 2x2 tables. Furthermore, it can perform robust meta-analysis, which provides protection against model-misspecifications, and limits the impact of any unknown outlying studies. In addition, the package implements two exact meta-analysis methods from synthesizing 2x2 tables with rare events (e.g., zero total event). A plot function to visualize individual and combined CDs through extended forest plots is also available.
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.
Direct R interface to Support Vector Machine libraries ('LIBSVM' and 'SVMLight') and efficient C++ implementations of Growing Neural Gas and models developed by 'GMUM' group (Cross Entropy Clustering and 2eSVM).
Generalized Method of Wavelet Moments (GMWM) is an estimation
    technique for the parameters of time series models. It uses the wavelet variance
    in a moment matching approach that makes it particularly suitable for the
    estimation of certain state-space models. Furthermore, there exists a robust
    implementation of GMWM, which allows the robust estimation of some state-space
    models and ARIMA models. Lastly, the package provides the ability to quickly
    generate time series data, perform different wavelet decompositions, and
    visualizations.
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.
A variety of functions to fit linear and nonlinear
    regression with a large selection of distributions.
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).
A collection of functions useful in (vegetation) community analyses and ordinations, mainly to facilitate plotting and interpretation. Includes automatic species selection for ordination diagrams, species response curves and rank-abundance curves.
Implementation of model-checking techniques for generalized linear
    models and linear structural equation models based on cumulative residuals
Harness the power of 'quanteda', 'data.table' & 'stringi'
        to quickly generate 'tm' DocumentTermMatrix and
        TermDocumentMatrix data structures.
Several GoF tests for Copulae are provided. A new hybrid test is implemented which supports all of the individual tests. Estimation methods for the margins are provided. All the tests support parameter estimation and predefined values. The parameters are estimated by pseudo maximum likelihood but if it fails the estimation switches automatically to inversion of Kendall's tau. All the tests support automatized parallelization of the bootstrapping tasks.
Tests of goodness-of-fit based on a kernel smoothing of the data.
Consider a goodness-of-fit(GOF) problem of testing whether a random sample comes from one sample location-scale model where location and scale parameters are unknown. It is well known that Khmaladze martingale transformation method provides asymptotic distribution free test for the GOF problem. This package contains one function: KhmaladzeTrans(). In this version, KhmaladzeTrans() provides test statistic and critical value of GOF test for normal, Cauchy, and logistic distributions.
Goodness-of-fit metrics, such as R-Squared, RMSE, etc., share a sensitivity to noise, dependent on the degrees of freedom.  Some metrics, such as R-Squared, decrease with increasing dof and some, such as RMSE, increase with increasing dof.  This package calculates the noise baseline (ceiling) by random sampling, calculating the metric’s value for each sample and counting the number of samples below a desired level, 95% by default. If one’s measure is above (below) the calculation corresponding to the desired level, then the measurement is distinguishable from noise.  In addition, the ratio of the measurement to the calculated level provides a way to compare measurements of different degrees of freedom.
Goodness-of-fit tests for  gamma, inverse Gaussian, lognormal, Weibull, Frechet, Gumbel, univariate normal, multivariate normal, Cauchy, Laplace or double exponential, exponential and generalized Pareto distributions. Parameter estimators for gamma, inverse Gaussian and generalized Pareto distributions.
Cramer-Von Mises and Anderson-Darling tests of goodness-of-fit
	     for continuous univariate distributions, using
	     efficient algorithms.
Easy and flexible interface for manipulating go game (weiqi, baduk) data.
    The package features a reader function for SGF (smart go format) text files,
    and a set of plotting functions that draw go board images.
Accounting for genes' functional-non-equivalence within pathways in classical Gene-set-analysis.
Implementation of the GO-GARCH model class
A tool for identifying multiple word key terms in free text with application to Gene Ontology labels.
Association analysis between categorical
    variables using the Goodman and Kruskal tau measure. This asymmetric association
    measure allows the detection of asymmetric relations between categorical
    variables (e.g., one variable obtained by re-grouping another).
R library for interacting with the Google Analytics 
  Reporting API v3 and v4.
Create R functions that interact with OAuth2 Google APIs easily,
    with auto-refresh and Shiny compatibility.
Interact with Google Cloud Storage API in R. Part of the 'cloudyr' project.
Interact with the Google Compute Engine API in R. Lets you create, 
  start and stop instances in the Google Cloud.  Support for preconfigured instances, 
  with templates for common R needs.
GET and POST data to Google Forms; an API to Google Forms,
    allowing users to POST data securely to Google Forms without needing authentication or permissioning.
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's chart tools, allowing users
    to create interactive charts based on data frames. Charts
    are displayed locally via the R HTTP help server. A modern
    browser with an Internet connection is required and for some
    charts a Flash player. The data remains local and is not
    uploaded to Google.
Retrieves data from six Google Maps APIs: directions, distances, elevation, geocode, reverse geocode and timezone. Also able to decode encoded location data.
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, heatmap).
Generalized Odds Rate Mixture Cure (GORMC) model is a flexible model of fitting survival data with a cure fraction, including the Proportional Hazards Mixture Cure (PHMC) model and the Proportional Odds Mixture Cure Model as special cases. This package fit the GORMC model with interval censored data.
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/)
Compute Gower's distance (or similarity) coefficient between records. Compute 
    the top-n matches between records. Core algorithms are executed in parallel on systems
    supporting OpenMP.
Produces a generalized pairs (gpairs) plot.
Gaussian process regression models, a.k.a. Kriging models, are
    applied to global multi-objective optimization of black-box functions.
    Multi-objective Expected Improvement and Step-wise 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.
Functions implement collocation-inference for
    stochastic process driven by distributed delay differential equations.
    They also provide tools for selecting the lags for distributed delay
    using shrinkage methods, estimating time-varying coefficients,
    and tools for inference and prediction.
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.
Bindings to GnuPG for working with OpenGPG (RFC4880) cryptographic methods.
    Includes utilities for public key encryption, creating and verifying digital signatures,
    and managing your local keyring. Note that some functionality depends on the version of 
    GnuPG that is installed on the system. On Windows this package can be used together with
    'GPG4Win' which provides a GUI for managing keys and entering passphrases.
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.
Provides functions for estimating a generalized partial
	     linear model, a semiparametric variant of the generalized linear model
	     (GLM) which replaces the linear predictor by the sum of a linear
	     and a nonparametric function.
Various R programming tools for plotting data, including:
  - calculating and plotting locally smoothed summary function as
    ('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 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.
Implements a Gaussian process (GP)-based ranking method
    which can be used to rank multiple time series according to their
    temporal activity levels. An example is the case when expression
    levels of all genes are measured over a time course and the main
    concern is to identify the most active genes, i.e. genes which
    show significant non-random variation in their expression levels.
    This is achieved by computing Bayes factors for each time series
    by comparing the marginal likelihoods under time-dependent and
    time-independent GP models. Additional variance information from
    pre-processing of the observations is incorporated into the GP
    models, which makes the ranking more robust against model
    overfitting. The package supports exporting the results to
    'tigreBrowser' for visualisation, filtering or ranking.
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.
Provides GPU enabled functions for R objects in a simple and
    approachable manner.  New gpu* and vcl* classes have been provided to
    wrap typical R objects (e.g. vector, matrix), in both host and device
    spaces, to mirror typical R syntax without the need to know OpenCL.
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.
Use the graph-constrained estimation (Grace) procedure to estimate graph-guided linear regression coefficients and use the Grace/GraceI/GraceR tests to perform graph-guided hypothesis tests on the association between the response and the predictors.
An implementation of various learning algorithms based on Gradient Descent for dealing with regression tasks. 
	The variants of gradient descent algorithm are :
	Mini-Batch Gradient Descent (MBGD), an optimization to use training data partially to reduce the computation load.
	Stochastic Gradient Descent (SGD), an optimization to use a random data in learning to reduce the computation load drastically.
	Stochastic Average Gradient (SAG), a SGD-based algorithm to minimize stochastic step to average.
	Momentum Gradient Descent (MGD), an optimization to speed-up gradient descent learning.
	Accelerated Gradient Descent (AGD), an optimization to accelerate gradient descent learning.
	Adagrad, a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning.
	Adadelta, a gradient-descent-based algorithm that use hessian approximation to do adaptive learning.
	RMSprop, a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability.
	Adam, a gradient-descent-based algorithm that mean and variance moment to do adaptive learning.
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.
Probability propagation in graphical independence networks, also
    known as Bayesian networks or probabilistic expert systems.
A native R implementation of grammatical evolution (GE). GE facilitates the discovery of programs that can achieve a desired goal. This is done by performing an evolutionary optimisation over a population of R expressions generated via a user-defined context-free grammar (CFG) and cost function.
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.
Gene-Ranking Analysis of Pathway Expression (GRAPE) is a tool for
   summarizing the consensus behavior of biological pathways in the form of a
   template, and for quantifying the extent to which individual samples deviate
   from the template. GRAPE templates are based only on the relative rankings
   of the genes within the pathway and can be used for classification of tissue
   types or disease subtypes. GRAPE can be used to represent gene-expression
   samples as vectors of pathway scores, where each pathway score indicates the
   departure from a given collection of reference samples. The resulting pathway-
   space representation can be used as the feature set for various applications,
   including survival analysis and drug-response prediction.
   ----------------------------------------------------------------------------------------------
       GRAPE is a generalization and extension of DIRAC, originally implemented in:
   Eddy, J.A., et al. (2010) <DOI:10.1371/journal.pcbi.1000792>. As a result, some
   of the software below may have been previously published in Matlab by the DIRAC
   authors and can be found at:
   https://price.systemsbiology.org/pricelab-resources/software/.
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.
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.
A Network Implementation of Fuzzy Sets: Build Network Objects from Multivariate Flat Files. For more information on fuzzy sets, refer to: Zadeh, L.A. (1964) <DOI:10.1016/S0019-9958(65)90241-X>.
Estimates within and between time point interactions in experience sampling data, using the Graphical VAR model in combination with LASSO and EBIC.
Functions to generate
             graphics files, compare them with "model" files,
             and report the results, including visual and textual
             diffs of any differences.
Bindings to the 'libgraphqlparser' C++ library. Currently parses
    GraphQL and exports the AST in JSON format.
Multiple scan statistic with variable window for one dimension data and scan statistic based on connected components in 2D or 3D.
Allows building an edge table from data frame of tweets, 
  also provides function to build nodes and another create a temporal graph.
Optimizing a function F(U), where U is a semi-orthogonal matrix and F is invariant under an orthogonal transformation of U
Create graticule lines and labels for maps. Control the creation
    of lines by setting their placement (at particular meridians and parallels)
    and extent (along parallels and meridians). Labels are created independently of
    lines.
The 'gRbase' package provides general features
    which are used by other graphical modelling packages, in particular
    by the packages 'gRain', 'gRim' and 'gRc'.
    'gRbase' contains several data sets relevant for use in connection with
    graphical models. Almost all data sets 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) junction tree.
    'gRbase' facilitates array operations,
    'gRbase' implements functions for testing for conditional independence.
    'gRbase' illustrates how hierarchical log-linear models may be
    implemented and describes concept of 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.
    NOTICE Proper functionality of 'gRbase' requires that the packages graph,
    'Rgraphviz' and 'RBGL' are installed from 'bioconductor'; for
    installation instructions please refer to the web page given below.
Estimation, model selection and other aspects of
        statistical inference in Graphical Gaussian models with edge
        and vertex symmetries (Graphical Gaussian models with colours).
Computes experimental designs for a
    two-arm experiment with covariates by greedily optimizing a
    balance objective function. This optimization provides lower
    variance for the treatment effect estimator (and higher power) 
    while preserving a design that is close to complete randomization.
    We return all iterations of the designs for use in a permutation test.
Methods for manipulating regression models and for describing these in a style adapted for medical journals. 
  Contains functions for generating an HTML table with crude and adjusted estimates, plotting hazard ratio, plotting model 
  estimates and confidence intervals using forest plots, extending this to comparing multiple models in a single forest plots. 
  In addition to the descriptives methods, there are addons for the robust covariance matrix provided by the sandwich
  package, a function for adding non-linearities to a model, and a wrapper around the Epi package's Lexis functions for
  time-spliting a dataset when modeling non-proportional hazards in Cox regressions.
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.
Convert 'Ensembl' gene identifiers from Genotype-Tissue Expression (GTEx) data to identifiers in other annotation systems, including 'Entrez', 'HGNC', and 'UniProt'.
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.
Multi-stage cluster household surveys are commonly performed by 
	governments and programs to monitor population demographic, social, 
	economic, and health outcomes. In these surveys, communities are sampled 
	in a first  stage of sampling from within subpopulations of interest 
	(or strata), households are sampled in a second stage of sampling, and 
	sometimes individuals are listed and further sampled within households. 
	The first stage of sampling, where communities of sample populations are 
	defined, are called Primary Sampling Units (PSUs) while the households 
	are secondary sampling units (SSUs). Census data are typically used to 
	select PSUs within strata. If census data are outdated, inaccurate, or 
	not available at fine enough scale, however, gridded population data can 
	be used instead. This tool selects PSUs within user-defined strata using 
	gridded population data, given desired numbers of sampled households 
	within each PSU. The population densities used to create PSUs are drawn 
	from rasters such as the population data from the WorldPop Project 
	(http://www.worldpop.org.uk). PSUs are defined within a stratum using a 
	serpentine sampling method, and can be set to have a certain ratio of 
	urban and rural PSUs, or to be evenly distributed across a coarse, 
	user-defined grid.
Simulation tool to facilitate determination of
    required sample size to achieve category saturation
    for studies using multiple repertory grids in conjunction with
    content analysis.
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.
Contains functions for a two-stage multiple testing procedure for grouped hypothesis, aiming at controlling both the total posterior false discovery rate and within-group false discovery rate.
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.
Functions for fitting various normal theory (growth
    curve) and elliptically-contoured repeated measurements models
    with ARMA and random effects dependence.
This is a simple package that fits the logistic equation to
    microbial growth curve data (e.g., repeated absorbance measurements
    taken from a plate reader over time). From this fit, a variety of
    metrics are provided, including the maximum growth rate,
    the doubling time, the carrying capacity, the area under the logistic
    curve, and the time to the inflection point.
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.
A collection of methods to determine growth rates from
    experimental data, in particular from batch experiments and
    plate reader trials.
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.
Fit the regularization path of linear, logistic or Cox models with 
	overlapping grouped covariates based on the latent group lasso approach. Latent 
	group MCP/SCAD as well as bi-level selection methods, namely the group exponential 
	lasso and the composite MCP are also available. This package serves as an 
	extension of R package 'grpreg' (by Dr. Patrick Breheny <patrick-breheny@uiowa.edu>)
	for grouped variable selection involving overlaps between groups.
Group SLOPE is a penalized linear regression method that is used
    for adaptive selection of groups of significant predictors in a
    high-dimensional linear model.
    The Group SLOPE method can control the (group) false discovery rate at a
    user-specified level (i.e., control the expected proportion of irrelevant
    among all selected groups of predictors).
Contains the tools to screen grouped variables, and select screened grouped variables afterwards. The main function grpss() can perform the grouped variables screening as well as selection for ultra-high dimensional data with group structure. The screening step is primarily used to reduce the dimensions of data so that the selection procedure can easily handle the moderate or low dimensions instead of ultra-high dimensions.
Methods include converting series of event names to strings,
    discovering common patterns in a group of strings, discovering "unique" patterns
    when comparing two groups of strings as well as the number and starting position
    of each "unique" pattern in each string, finding the transition information, and
    statistically comparing the difference between two groups of strings.
Alternative implementations of some base R functions, including sort, order, and match.  Functions are simplified but can be faster or have other advantages.
Computational resources for test proposed by Gibbons, Ross, Shanken (1989)<DOI:10.2307/1913625>.
Functions to generate and analyze data for psychology
        experiments based on the General Recognition Theory.
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.
Computation of Quantitative Trait Loci hits in the selected gene set. Performing gene set validation with Quantitative Trait Loci information. Performing gene set enrichment analysis with available Quantitative Trait Loci data and computation of statistical significance value from gene set analysis. Obtaining the list of Quantitative Trait Loci hit genes along with their overlapped Quantitative Trait Loci names.
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.
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.
R-interface to C++ implementation of the rank/score permutation based GSEA test (Subramanian et al 2005 <doi: 10.1073/pnas.0506580102>).
Provides function to apply "Group sequential enrichment design incorporating subgroup selection" (GSED) method proposed by Magnusson and Turnbull (2013) <doi:10.1002/sim.5738>.
Using an approach based on similarity graph to estimate change-point(s) and the corresponding p-values.  Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.
Conducts a semi-gSEM statistical analysis (semi-supervised generalized structural equation modeling) on a data frame of coincident observations of multiple predictive or intermediate variables and a final continuous, outcome variable, via two functions sgSEMp1() and sgSEMp2(), representing fittings based on two statistical principles. Principle 1 determines all sensible univariate relationships in the spirit of the Markovian process. The relationship between each pair of variables, including predictors and the final outcome variable, is determined with the Markovian property that the value of the current predictor is sufficient in relating to the next level variable, i.e., the relationship is independent of the specific value of the preceding-level variables to the current predictor, given the current value. Principle 2 resembles the multiple regression principle in the way multiple predictors are considered simultaneously. Specifically, the relationship of the first-level predictors (such as Time and irradiance etc) to the outcome variable (such as, module degradation or yellowing)  is fit by a supervised additive model. Then each significant intermediate variable is taken as the new outcome variable and the other variables (except the final outcome variable) as the predictors in investigating the next-level multivariate relationship by a supervised additive model. This fitting process is continued until all sensible models are investigated.
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
Estimating trait heritability and handling overfitting. This package includes a collection of functions for (1) estimating genetic variance-covariances and calculate trait heritability; and (2) handling overfitting by calculating the variance components and the heritability through cross validation.
Pipeline to read and analyze raw SNP array data.
    The data is preprocessed and normalized.
    Genotypes and CNVs are called.
    Synteny blocks are calculated and translocations detected.
    The results can be plotted with special functions.
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.
We propose a fully efficient sieve maximum likelihood method to estimate genotype-specific distribution of time-to-event outcomes under a nonparametric model. We can handle missing genotypes in pedigrees.  We estimate the time-dependent hazard ratio between two genetic mutation groups using B-splines, while applying nonparametric maximum likelihood estimation to the reference baseline hazard function.  The estimators are calculated via an expectation-maximization algorithm.
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).
Estimation of the cutpoint defined by the Generalized Symmetry point in a binary classification setting based on a continuous diagnostic test or marker. Two methods have been implemented to construct confidence intervals for this optimal cutpoint, one based on the Generalized Pivotal Quantity and the other based on Empirical Likelihood. Numerical and graphical outputs for these two methods are easily obtained.
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).
Three graph-based tests are provided for testing whether two samples are from the same distribution.
Estimates variance components, generalizability coefficients,
    universe scores, and standard errors when observed scores contain variation from
    one or more measurement facets (e.g., items and raters).
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.
An interface for retrieving and displaying the information returned
    online by Google Trends is provided. Trends (number of hits) over the time as
    well as geographic representation of the results can be displayed.
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.
Adjust Estimates of Learning for Guessing. The package provides 
    standard guessing correction, and a latent class model that leverages
    informative pre-post transitions. For details of the latent class model,
    see <http://gsood.com/research/papers/guess.pdf>.
A nice GUI for financial DErivatives in R.
A collection 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).
Show graphically the results of profiling R functions 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.
Statistical analysis of monthly background checks of gun purchases for the New York Times 
 story "What Drives Gun Sales: Terrorism, Obama and Calls for Restrictions" at 
 <http://www.nytimes.com/interactive/2015/12/10/us/gun-sales-terrorism-obama-restrictions.html?> is provided.
Download and process public domain works in the Project
    Gutenberg collection. Includes metadata for all Project Gutenberg works,
    so that they can be searched and retrieved.
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 is a Shiny application intended to provide better understanding of how geometrically-weighted degree terms function in exponential random graph models of networks. It contains just one user function, gwdegree(), which launches the Shiny application.
This package provides functions to calculate the
        significance threshold for controlling the type I error rate in
        mixed-model association mapping analyses.
Performs Geographically Weighted Fractal Analysis (GWFA) to calculate the local fractal dimension of a set of points. GWFA mixes the Sandbox multifractal algorithm and the Geographically Weighted Regression. Unlike fractal box-counting algorithm, the sandbox algorithm avoids border effects because the boxes are adjusted on the set of points. The Geographically Weighted approach consists in applying a kernel that describes the way the neighbourhood of each estimated point is taken into account to estimate its fractal dimension. GWFA can be used to discriminate built patterns of a city, a region, or a whole country.
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 the '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'.
Implements the 'gWidgets2' API for 'RGtk2.'
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.
Fits Weighted Quantile Sum (WQS) regressions for continuous or binomial outcomes.
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.
The correlations and linkage disequilibrium between tests can vary as a function of minor allele frequency thresholds used to filter variants, and also varies with different choices of test statistic for region-based tests. Appropriate genome-wide significance thresholds can be estimated empirically through permutation on only a small proportion of the whole genome.
Quantifying and testing gene-by-measured-environment interaction in behavior genetic designs.
OpenAI Gym is a open-source Python toolkit for developing and comparing
    reinforcement learning algorithms. This is a wrapper for the OpenAI Gym API,
    and enables access to an ever-growing variety of environments.
    For more details on OpenAI Gym, please see here: <https://github.com/openai/gym>.
    For more details on the OpenAI Gym API specification, please see here:
    <https://github.com/openai/gym-http-api>.
SNP-set association testing for censored phenotypes in the presence of intrafamilial correlation.
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.
Read data from the City of Portland's 'HYDRA' <http://or.water.usgs.gov/precip/> rainfall datasets within R.
Various functions and algorithms are provided here for solving optimal matching tasks in the context of preclinical cancer studies. Further, various helper and plotting functions are provided for unsupervised and supervised machine learning as well as longitudinal mixed-effects modeling of tumor growth response patterns.
An S4 implementation of Eq. (3) and Eq. (7) by David J. Hand and 
    Robert J. Till (2001) <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.
Provides functions to download data from the <http://www.data.parliament.uk/> APIs. Because of the structure of the API, there is a named function for each type of available data for ease of use. Functions for each new API will be added as and when they become available on the API.
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. A GxE analysis allows testing interactions between environment and all estimated genetic effects.
'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.
A set of utilities for querying 
             Haploreg <http://archive.broadinstitute.org/mammals/haploreg/haploreg.php> 
             and RegulomeDB <http://www.regulomedb.org> web-based tools. The package connects to 
             Haploreg or RegulomeDB, searches and downloads results, without 
             opening web pages, directly from R environment. 
             Results are stored in a data frame that can be directly used in various 
             kinds of downstream analyses.
Reconstruction of founder haplotype blocks from time series data.
Simulate haplotypes through meioses. Allows specification
        of population parameters.
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(), and haplo.power(); all of which have detailed examples in the vignette.
Function to  identify  haplotypes
  within QTL (Quantitative Trait Loci). One haplotype is a combination of SNP
  (Single Nucleotide Polymorphisms) within the QTL. This function groups
  together all individuals of a population with the same haplotype.
  Each group contains individual with the same allele in each SNP,
  whether or not missing data. Thus, haplotyper groups individuals,
  that to be imputed, have a non-zero probability of having the same alleles
  in the entire sequence of SNP's. Moreover, haplotyper calculates such
  probability from relative frequencies.
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.
  Routines for dealing with markers on the X-chromosome are included.
  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.
The goal of 'HARtools' is to provide a simple set of functions
    to read/parse, write and visualise HTTP Archive ('HAR') files in R.
Functions for easy and reproducible simulation.
Aimed at applying the Harvest classification tree algorithm, modified algorithm of classic classification tree.The harvested tree has advantage of deleting redundant rules in trees, leading to a simplify and more efficient tree model.It was firstly used in drug discovery field, but it also performs well in other kinds of data, especially when the region of a class is disconnected. This package also improves the basic harvest classification tree algorithm by extending the field of data of algorithm to both continuous and categorical variables. To learn more about the harvest classification tree 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.
An R port of the hashids library.  hashids generates YouTube-like hashes from integers or vector of integers.  Hashes generated from integers are relatively short, unique and non-seqential.  hashids can be used to generate unique ids for URLs and hide database row numbers from the user.  By default hashids will avoid generating common English cursewords by preventing certain letters being next to each other.  hashids are not one-way: it is easy to encode an integer to a hashid and decode a hashid back into an integer.
Provides a hash table class for fast
    key-value storage of atomic vector types.
    Internally, hashmap makes extensive use of Rcpp, boost::variant,
    and boost::unordered_map to achieve high performance, type-safety,
    and versatility, while maintaining compliance with the C++98 standard.
Apply the SuperFastHash algorithm to any R object. Hash whole R objects or, 
     for vectors or lists, hash R objects to obtain a set of hash values that is stored 
     in a structure equivalent to the input.
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).
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.
Fit an array of decision-making tasks with computational models in a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysis of various computational models with a single line of coding.
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.
Determine the sample size requirement to achieve the target probability of correct classification (PCC) for studies employing high-dimensional features.  The package implements functions to 1) determine the asymptotic feasibility of the classification problem; 2) compute the upper bounds of the PCC for any linear classifier; 3) estimate the PCC of three design methods given design assumptions; 4) determine the sample size requirement to achieve the target PCC for three design methods.
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.
Test the significance of coefficients in high dimensional generalized linear models.
Implementation of multiple approaches to perform inference in high-dimensional models.
A generic function and a set of methods to calculate highest density intervals for a variety of classes of objects which can specify a probability density distribution, including MCMC output, fitted density objects, and functions.
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.
Implementation of selected high-dimensional statistical and
    econometric methods for estimation and inference. Efficient estimators and
    uniformly valid confidence intervals for various low-dimensional causal/
    structural parameters are provided which appear in high-dimensional
    approximately sparse models. Including functions for fitting heteroscedastic
    robust Lasso regressions with non-Gaussian errors and for instrumental variable
    (IV) and treatment effect estimation in a high-dimensional setting. Moreover,
    the methods enable valid post-selection inference and rely on a theoretically
    grounded, data-driven choice of the penalty.
Creates nomogram visualizations for penalized Cox regression
    models, with the support of reproducible survival model building,
    validation, calibration, and comparison for high-dimensional data.
An implementation of an algorithm for outlier detection that can handle a) data with a mixed categorical and continuous variables, b) many columns of data, c) many rows of data, d) outliers that mask other outliers, and e) both unidimensional and multidimensional datasets. Unlike ad hoc methods found in many machine learning papers, HDoutliers is based on a distributional model that uses probabilities to determine outliers. See <https://www.cs.uic.edu/~wilkinson/Publications/outliers.pdf>.
In high-dimensional settings:
	Estimate the number of distant spikes based on the Generalized Spiked Population (GSP) model.
	Estimate the population eigenvalues, angles between the sample and population eigenvectors, correlations between the sample and population PC scores, and the asymptotic shrinkage factors.
	Adjust the shrinkage bias in the predicted PC scores.
Algorithms for lasso and fused-lasso problems: implementation of
    the lars algorithm for lasso and fusion penalization and EM-based
    algorithms for (logistic) lasso  and fused-lasso penalization.
Provides a complete interface to the United Nations
    Development Programme Human Development Report API (<http://hdr.undp.org>). The API
    includes a large amount of human development data, including all the series used
    to compute the Human Development Index (HDI), as well as the HDI itself.
Computation of highest density regions in one and two dimensions,
    kernel estimation of univariate density functions conditional on one covariate,
    and multimodal regression.
Functions for calculating the hazard discrimination summary and its
    standard errors, as described in Liang and Heagerty (2016) <doi:10.1111/biom.12628>.
High dimensional testing procedures on mean, covariance and white noises.
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 described in Esarey and Pierce (2012) <DOI:10.1093/pan/mps026>.
Create interactive 'heatmaps' 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 or drag a rectangle to
    zoom. A 'heatmap' is a popular graphical method for visualizing high-dimensional
    data, in which a table of numbers are encoded as a grid of colored cells. The
    rows and columns of the matrix are ordered to highlight patterns and are often
    accompanied by 'dendrograms'. 'Heatmaps' are used in many fields for visualizing
    observations, correlations, missing values patterns, and more. Interactive
    'heatmaps' allow the inspection of specific value by hovering the mouse over a
    cell, as well as zooming into a region of the 'heatmap' by dragging a rectangle
    around the relevant area. This work is based on the 'ggplot2' and 'plotly.js'
    engine. It produces similar 'heatmaps' as 'd3heatmap', with the advantage of speed
    ('plotly.js' is able to handle larger size matrix), and the ability to zoom from
    the 'dendrogram' panes.
Allows heatmap matrix to have non-identical X- and
        Y-dimensions.  Allows multiple tracks of annotation for
        RowSideColors and ColSideColors.
Functions to perform robust estimation considering heavy-tailed distributions.
  Currently, the package includes linear regression, linear mixed-effect models, multivariate
  location and scatter estimation, multivariate regression, penalized splines and random
  variate generation.
Health Economic Evaluation Modelling.
  Markov models (decision trees, homogeneous and non-homogeneous)
  for health economic evaluation. Model comparison,
  sensitivity and probabilistic analysis.
Base R's default setting for 'stringsAsFactors' within
    'data.frame()' and 'as.data.frame()' is supposedly the most often complained
    about piece of code in the R infrastructure. The 'hellno' package provides
    an explicit solution without changing R itself or having to mess around with
    options. It tries to solve this problem by providing alternative
    'data.frame()' and 'as.data.frame()' functions that are in fact simple
    wrappers around base R's 'data.frame()' and 'as.data.frame()' with
    'stringsAsFactors' option set to 'HELLNO' ( which in turn equals FALSE )
    by default.
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 and other functions for visualizing hypothesis
    tests in multivariate linear models. HE plots represent sums-of-squares-and-
    products matrices for linear hypotheses and for error using ellipses (in two
    dimensions) and ellipsoids (in three dimensions). The related 'candisc' package
    provides visualizations in a reduced-rank canonical discriminant space when
    there are more than a few response variables.
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.
Statistical framework to analyze heritability of gene expression 
    based on next-generation sequencing data and simulating sequencing reads. 
    Variance partition coefficients (VPC) are computed using linear mixed effects 
    and generalized linear mixed effects models. Compound Poisson and negative 
    binomial models are included.
Probability functions and other utilities for the generalized Hermite distribution.
Assess the presence of statistical heterogeneity and quantify its impact in the context of meta-analysis. It includes test for heterogeneity as well as other statistical measures (R_b, I^2, R_I).
Functions for the fitting and summarizing of heteroscedastic t-regression.
An implementation of White's Test for Heteroskedasticity
        as outlined in Doornik (1996).
Implements various heuristics like Take The Best and
    unit-weight linear, which do two-alternative choice: which of
    two objects will have a higher criterion?  Also offers functions
    to assess performance, e.g. percent correct across all row pairs
    in a data set and finding row pairs where models disagree.
    New models can be added by implementing a fit and predict function--
    see vignette.
Binning and plotting functions for hexagonal bins. Now
        uses and relies on grid graphics and formal (S4) classes and
        methods.
Display hexagonally binned scatterplots for multi-class data, using coloured triangles to show class proportions.
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.
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 (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) 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.
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.
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.
Utilising the 'Have I been pwned?' API (see <https://haveibeenpwned.com/API/v2>
    for more information), check whether email addresses and/or user names have been present
    in a publicly disclosed data breach.
We propose a multiple logistic regression model to assess the influences of genomic features such as DNA-binding proteins and functional elements on topological domain borders.
We propose a generalized linear regression with higher-order interaction terms to assess the influences of genomic features such as DNA-binding proteins and functional elements on long-range contacts from Hi-C experiments.
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 ACMIF test and Bonferroni-adjusted p-value of interaction in two-factor studies.  Produces corresponding interaction plot and analysis of variance tables 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.
Pure set data visualization approaches are often limited in
    scalability due to the combinatorial explosion of distinct set families as
    the number of sets under investigation increases. hierarchicalSets applies
    a set centric hierarchical clustering of the sets under investigation and
    uses this hierarchy as a basis for a range of scalable visual
    representations. hierarchicalSets is especially well suited for collections
    of sets that describe comparable comparable entities as it relies on the
    sets to have a meaningful relational structure.
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.
Determine paths and states that social networks develop over
    time to form social hierarchies. Based upon algorithms described in
    W. Brent Lindquist & Ivan D. Chase (2009) <DOI:10.1007/s11538-008-9371-9>.
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; 
    <DOI:10.2307/2411227>. Functions are also given to test via randomisations the significance of each F and variance components,  
	using the likelihood-ratio statistics G.
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.
A wrapper for the 'Highcharts' library including
  shortcut functions to plot R objects. 'Highcharts' 
  <http://www.highcharts.com/> is a charting library offering
  numerous chart types with a simple configuration syntax.
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.
A tool to highlight specific cells in an HTML table or more 
    generally text from an HTML document. This may be helpful for those 
    using markdown to create reproducible documents. In addition, the ability
    to compile directly from R markdown files is also possible using the 'knitr' 
    package.
Provides various tests for comparing high-dimensional mean vectors in two sample populations.
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).
Can be used to carry out extraction, normalization, quality control (QC), candidate hits identification and visualization for plate based essays, in drug discovery. This project was funded by the Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract No. HHSN272201400054C entitled "Adjuvant Discovery For Vaccines Against West Nile Virus and Influenza", awarded to Duke University and lead by Drs. Herman Staats and Soman Abraham.
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.
H(x) is the h-index for the past x years. Here, the h(x) of a scientist/department/etc. can be calculated using the exported excel file from a Web of Science citation report of a search. Also calculated is the year of first publication, total number of publications, and sum of times cited for the specified period. Therefore, for h-10: the date of first publication, total number of publications, and sum of times cited in the past 10 years are calculated. Note: the excel file has to first be saved in a .csv format.
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 (2010).
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.
Hierarchical inference testing (HIT) for (generalized) linear models with 
    correlated covariates applicable to high-dimensional settings.
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)
This is a collection of functions for converting coordinates between WGS84UTM, WGS84GEO, HK80UTM, HK80GEO and HK1980GRID Coordinate Systems used in Hong Kong SAR, based on the algorithms described in Explanatory Notes on Geodetic Datums in Hong Kong by Survey and Mapping Office Lands Department, Hong Kong Government (1995).
Implements an ensemble algorithm for clustering combining a k-means and a hierarchical clustering approach.
Several procedures around a particular hierarchical model for extreme value: the HKEVP of Reich and Shaby (2012) <DOI:10.1214/12-AOAS591>. Simulation, estimation and spatial extrapolation of this model are available for extreme value data. A special case of this process is also handled: the Latent Variable Model of Davison et al. (2012) <DOI:10.1214/11-STS376>.
A set of functions helps to retrieve data from HKEX (Hong Kong Stock Exchange), see <https://www.hkex.com.hk/> for more information. In addition, a function generates insert SQL statements from a dataframe.
Methods to make inference about the Hurst-Kolmogorov and the AR(1) process.
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.
Implements Hierarchical Latent Space Network Model (HLSM) for ensemble of networks as described in Sweet et. al. (2012). <DOI:10.3102/1076998612458702>.
Utilities for reading data from the Human Mortality Database (<www.mortality.org>), Human Fertility Database (<www.humanfertility.org>), and similar databases from the web or locally into an R session as data.frame objects. These are the two most widely used sources of demographic data to study basic demographic change, trends, and develop new demographic methods. Other supported databases at this time include the Human Fertility Collection (<http://www.fertilitydata.org/>), The Japanese Mortality Database (<http://www.ipss.go.jp/p-toukei/JMD>), and the Canadian Human Mortality Database (<http://www.bdlc.umontreal.ca/chmd/>). Arguments and data are standardized.
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 and html 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.
Using Dirichlet-Multinomial distribution to provide several functions for formal hypothesis testing, power and sample size calculations for human microbiome experiments.
Tools to model, compare, and visualize populations of taxonomic tree objects.
Statistical analysis of static chamber concentration data for trace gas flux estimation.
Implements an S3 class for storing and formatting time-of-day
    values, based on the 'difftime' class.
Perform association test between a group of variable and the outcome.
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).
Cross-species identification of novel gene candidates using the NCBI web service is provided. Further, sets of miRNA target genes can be identified by using the targetscan.org API.
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 simple implementation of doughnut plots - pie charts with a blank center. The package is named after Homer Simpson - arguably the best-known lover of doughnuts.
Homomorphic computations in R for privacy-preserving applications. Currently only
             the Paillier Scheme is implemented.
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.
Computes the expectation of the number of transmissions and receptions considering a Hop-by-Hop transport model with limited number of retransmissions per packet. It provides the theoretical results shown in Palma et. al.(2016) <DOI:10.1109/TLA.2016.7555237> and also estimated values based on Monte Carlo simulations.
Calculates horizon elevation angle and sky view factor from a digital terrain model.
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.
Contains functions for applying the horseshoe prior to high-
    dimensional linear regression, yielding the posterior mean and credible
    intervals, amongst other things. The key parameter tau can be equipped with
    a prior or estimated via maximum marginal likelihood estimation (MMLE).
    The main function, horseshoe, is for linear regression. In addition, there
    are functions specifically for the sparse normal means problem, allowing
    for faster computation of for example the posterior mean and posterior
    variance. Finally, there is a function available to perform variable
    selection, using either a form of thresholding, or credible intervals.
Performs multiple hot-deck imputation of categorical and continuous variables in a data frame.
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
Contains data for software hotspot analysis, along with a function performing the analysis itself.
Calculates a hot spot cutoff and associated analyses for
        statistical populations
Monthly median home listing, sale price per square foot, and number of units sold for 2984 counties in the contiguous United States From 2008 to January 2016.  Additional data sets containing geographical information and links to Wikipedia are also included.
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 regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss.
Methods for testing main and interaction effects in possibly
    high-dimensional repeated measures in factorial designs. The observations
    of the subjects are assumed to be multivariate normal.
    It is possible to use up to 2 whole- and 2 subplot factors.
Print beautiful horizontal rules in your R scripts.
A library of the Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm.
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.
Implementation of the block coordinate descent procedure for
    solving the proximal function of latent group Lasso, highlighted by
    decomposing a DAG into several non-overlapping path graphs, and getting
    closed-form solution for each path graph. The procedure was introduced
    as Algorithm 4 in Yan and Bien (2015) <https://arxiv.org/abs/1512.01631>
    "Hierarchical Sparse Modeling: A Choice of Two Regularizers", and the
    closed-form solution for each path graph is solved in Algorithm 3 of
    that paper.
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.
Provides bindings to the National Geodetic Survey (NGS) Horizontal
 Time Dependent Positioning (HTDP) utility, v3.2.5, written by Richard Snay,
 Chris Pearson, and Jarir Saleh of NGS. HTDP is a utility that allows users to
 transform positional coordinates across time and between spatial reference
 frames. See <https://www.ngs.noaa.gov/TOOLS/Htdp/Htdp.shtml> for more
 information.
HTML tables are a valuable data source but extracting and recasting
    these data into a useful format can be tedious. This package allows to collect
    structured information from HTML tables. It is similar to readHTMLTable()
    of the XML package but provides three 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, including semantic header information
    that appear throughout the body. Third, the function preprocesses table code,
    corrects common types of malformations, 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'.
HTML documents can be beautiful and pristine. They can also be
    wretched, evil, malformed demon-spawn. Now, you can tidy up that HTML and XHTML
    before processing it with your favorite angle-bracket crunching tools, going beyond
    the limited tidying that 'libxml2' affords in the 'XML' and 'xml2' packages and
    taming even the ugliest HTML code generated by the likes of Google Docs and Microsoft
    Word. It's also possible to use the functions provided to format or "pretty print"
    HTML content as it is being tidied. Utilities are also included that make it  
    possible to view formatted and "pretty printed" HTML/XML
    content from HTML/XML document objects, nodes, node sets and plain character HTML/XML
    using 'vkbeautify' (by Vadim Kiryukhin) and 'highlight.js' (by Ivan Sagalaev).
    Also (optionally) enables filtering of nodes via XPath or viewing an HTML/XML document
    in "tree" view using 'xml-viewer' (by Julian Gruber). See
    <https://github.com/vkiryukhin/vkBeautify> and 
    <https://github.com/juliangruber/xml-viewer> for more information about 'vkbeautify'
    and 'xml-viewer', respectively.
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.
A Poisson mixture model is implemented to cluster genes from high-
    throughput transcriptome sequencing (RNA-seq) 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).
Perform HTS-SIP analyses such as high resolution stable isotope probing (HR-SIP),
    multi-window high resolution stable isotope probing (MW-HR-SIP), and quantitative stable
    isotope probing (q-SIP). More information on HR-SIP, MW-HR-SIP, and q-SIP can be
    found in:
    1) Pepe-Ranney, Charles, Ashley N. Campbell, Chantal N. Koechli, Sean Berthrong, and
    Daniel H. Buckley. 2016. "Unearthing the Ecology of Soil Microorganisms Using a
    High Resolution DNA-SIP Approach to Explore Cellulose and Xylose Metabolism in
    Soil." Terrestrial Microbiology, 703. <doi:10.3389/fmicb.2016.00703>
    2) Youngblut, Nicholas D., and Daniel H. Buckley. "Evaluating the Accuracy of DNA
    Stable Isotope Probing". in prep.
    3) Hungate, Bruce A., Rebecca L. Mau, Egbert Schwartz, J. Gregory Caporaso,
    Paul Dijkstra, Natasja van Gestel, Benjamin J. Koch, et al. 2015. "Quantitative
    Microbial Ecology Through Stable Isotope Probing." Applied and Environmental
    Microbiology, August, AEM.02280-15. <doi:10.1128/AEM.02280-15>
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").
In order to improve performance for HTTP API clients, 'httpcache'
    provides simple tools for caching and invalidating cache. It includes the
    HTTP verb functions GET, PUT, PATCH, POST, and DELETE, which are drop-in
    replacements for those in the 'httr' package. These functions are cache-aware
    and provide default settings for cache invalidation suitable for RESTful
    APIs; the package also enables custom cache-management strategies.
    Finally, 'httpcache' includes a basic logging framework to facilitate the
    measurement of HTTP request time and cache performance.
Find and explain the meaning of 'HTTP' status codes.
    Functions included for searching for codes by full or partial number,
    by message, and get appropriate dog and cat images for many
    status codes.
A suite of functions to ping 'URLs' and to time
    'HTTP' 'requests'. Designed to work with 'httr'.
HTTP Request protocols. Implements the GET, POST and multipart POST request.
Testing code and packages that communicate with remote servers can
    be painful. Dealing with authentication, bootstrapping server state,
    cleaning up objects that may get created during the test run, network
    flakiness, and other complications can make testing seem too costly to
    bother with. But it doesn't need to be that hard. This package enables one
    to test all of the logic on the R sides of the API in your package without
    requiring access to the remote service. Importantly, it provides three test
    contexts that mock the network connection in different ways, and it offers
    additional expectations to assert that HTTP requests were--or were
    not--made. Using these tools, one can test that code is making the intended
    requests and that it handles the expected responses correctly, all without
    depending on a connection to a remote API.
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).
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.
Human names are complicated and nonstandard things. Humaniformat,
    which is based on Anthony Ettinger's 'humanparser' project (https://github.com/
    chovy/humanparser) provides functions for parsing human names, making a best-
    guess attempt to distinguish sub-components such as prefixes, suffixes, middle
    names and salutations.
Utilises GRanges, data.frame or IRanges objects. Integrates gene annotation for ImmunoChip (or your custom chip) with function calls. Intuitive wrappers for annotation lookup (gene lists, exon ranges, etc) and conversion (e.g, between build 36 and 37 coordinates). Conversion between ensembl and HGNC gene ids, chip ids to rs-ids for SNP-arrays. Retrieval of chromosome and position for gene, band or SNP-ids, or reverse lookup. Simulation functions for ranges objects.
Vapor pressure, absolute humidity, specific humidity, and mixing ratio are commonly used water vapor measures in meteorology. This R package provides functions for calculating saturation vapor pressure (hPa), partial water vapor pressure (Pa), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and relative humidity (%).
A spell checker and morphological analyzer library designed for
    languages with rich morphology and complex word compounding or character
    encoding. The package can check and analyze individual words as well as
    search for incorrect words within a text, latex, html or xml document. Use
    the 'devtools' package to spell check R documentation with 'hunspell'.
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 Pearson's 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".
Simulates stochastic hybrid models for transmission of infectious
    diseases in dynamic networks.
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.
Focuses on data processing and visualization in hydrology and
    climate forecasting. Main function includes data extraction, data downscaling, data 
    resampling, gap filler of precipitation, bias correction of forecasting data, flexible
    time series plot, and spatial map generation. It is a good pre-processing and 
    post-processing tool for hydrological and hydraulic modellers.
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
Includes two main high level codes for hyperplane fitting (hyper.fit) and visualising (hyper.plot2d / hyper.plot3d). In simple terms this allows the user to produce robust 1D linear fits for 2D x vs y type data, and robust 2D plane fits to 3D x vs y vs z type data. This hyperplane fitting works generically for any N-1 hyperplane model being fit to a N dimension dataset. All fits include intrinsic scatter in the generative model orthogonal to the hyperplane.
Performs (exact) hypergeometric tests on IxJ and 2x2x2 contingency tables using parallelised C code.
The Gaussian hypergeometric function for complex numbers.
Machine learning supervised method to learn rare genomic features in imbalanced genetic data sets. This method can be also applied to classify or rank examples characterized by a high imbalance between the minority and majority class. hyperSMURF adopts a hyper-ensemble (ensemble of ensembles) approach, undersampling of the majority class and oversampling of the minority class to learn highly imbalanced data. Both single-core and parallel multi-core version of hyperSMURF are implemented.
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.
Identifying hyphenation points in strings can be useful for both
    text processing and display functions. The 'Hunspell' hyphenation library
    <https://github.com/hunspell/hyphen> provides tools to perform hyphenation
    using custom language rule dictionaries. Many hyphenation rules
    dictionaries are included. Words can be hyphenated directly or split into
    hyphenated component strings for further processing.
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.
A Multi-Hypothesis CSV Parser. Stresses your computer not you.
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.
Interact with the application programming interface for the web
    annotation service 'Hypothes.is' (See <http://hypothes.is> for more
    information.) Allows users to download data about public annotations, and
    create, retrieve, update, and delete their own annotations.
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.
Synthesizes information across collaborating research. Created specifically for Integrative Analysis of Longitudinal Studies of Aging (IALSA).
Functions for Water and Steam Properties based on the IAPWS Formulation
    1995 for the Thermodynamic Properties of Ordinary Water Substance for General and
    Scientific Use and on the releases for viscosity, conductivity, surface tension and
    melting pressure.
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 unimodal or bimodal.
Implements the standard D-Scoring algorithm
    (Greenwald, Banaji, & Nosek, 2003) for Implicit Association Test (IAT)
    data and includes plotting capabilities for exploring raw 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.
Implementation of some (simple) Individual Based Models and methods
  to create new ones, particularly for population dynamics models (reproduction, 
  mortality and movement). The basic operations for the simulations are 
  implemented in Rcpp for speed.
It provides a generic set of tools for initializing a synthetic
         population with each individual in specific disease states, and
         making transitions between those disease states according to the rates
         calculated on each timestep. The new version 1.0.0 has C++ code 
         integration to make the functions run faster. It has also a higher level
         function to actually run the transitions for the number of timesteps
         that users specify. Additional functions will follow for changing
         attributes on demographic, health belief and movement.
Functionality required to efficiently use R with
        IBM DB2(C) for Linux, Unix and Windows, IBM dashDB(C) as well as 
        DB2 for z/OS (C) in conjunction with IBM DB2 Analytics Accelerator (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.
Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines.
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.
Implementation of 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.
Finding locally D-optimal, minimax D-optimal, standardized maximin D-optimal, optim-on-the-average and multiple objective optimal designs for nonlinear models. Different Fisher information matrices can also be set by user. There are also  useful functions for verifying the optimality of the designs with respect to different criteria by equivalence theorem. ICA is a meta-heuristic evolutionary algorithm inspired from the socio-political process of humans. See Masoudi et al. (2016) <doi:10.1016/j.csda.2016.06.014>.
Implements mixed ICA/PCA model for blind source separation, potentially with inclusion of Gaussian sources
Provides user-friendly tools for calibration in survey sampling.
    The package is production-oriented, and its interface is inspired by the famous
    popular macro 'Calmar' for SAS, so that 'Calmar' users can quickly get used to
    'icarus'. In addition to calibration (with linear, raking and logit methods),
    'icarus' features functions for calibration on tight bounds and penalized
    calibration.
Contains functions to fit Bayesian semiparametric regression survival models (proportional hazards model, proportional odds model, and probit model) to interval-censored time-to-event data.
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.
Assists in generating binary clustered data, estimates of Intracluster Correlation coefficient (ICC) for binary response in 14 different methods, and 4 different types of confidence intervals.
Provides functions to calculate the requisite sample size for studies where ICC is 
    the primary outcome. Can also be used for calculation of power. In both cases it
    allows the user to test the impact of changing input variables by calculating the outcome
    for several different values of input variables. Based off the work of Zou.
    Zou, G. Y. (2012). Sample size formulas for estimating intraclass correlation coefficients with
    precision and assurance. Statistics in medicine, 31(29), 3972-3981.
Calculate comorbidities, Charlson scores, perform fast and accurate
    validation, conversion, manipulation, filtering and comparison of ICD-9 and
    ICD-10 codes. Common ambiguities and code formats are handled. This
    package enables a work flow from raw lists of ICD codes in hospital
    billing databases to comorbidities. ICD-9 and ICD-10 comorbidity mappings
    from Quan (Deyo and Elixhauser versions), Elixhauser and AHRQ included. This
    package replaces icd9, which can now be uninstalled.
Obsolete: 'icd9' is replaced by CRAN package 'icd'.
    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.
Provides an estimator for generalized linear models with incomplete
    data for discrete covariates. The estimation is based on the EM algorithm by the
    method of weights by Ibrahim (1990) <DOI:10.2307/2290013>.
Kernel Estimators for Interval-Censored Data
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, proportional odds, and accelerated failure time models. Allows for
    both semi and fully parametric models (parametric only for accelerated failure
    time models). Includes functions for easy visual diagnostics of model fits and
    imputation of censored data.
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.
Functions that are related to the advisory work of ICES
  (International Council for the Exploration of the Sea).
R interface to access the web services of the ICES (International
             Council for the Exploration of the Sea) DATRAS trawl survey
             database <https://datras.ices.dk/WebServices/Webservices.aspx>.
R interface to access the web services of the ICES (International
             Council for the Exploration of the Sea) Stock Assessment Graphs
             database <http://ices.dk/marine-data/tools/Pages/stock-assessment-graphs.aspx>.
R interface to access the RECO POX web services of the ICES (International
             Council for the Exploration of the Sea) Vocabularies database <http://vocab.ices.dk/services/POX.aspx>.
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.
Generalized Odds Rate Hazards (GORH) model is a flexible model of fitting survival data, including the Proportional Hazards (PH) model and the Proportional Odds (PO) Model as special cases. This package fit the GORH model with interval censored data.
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.
A GUI designed to support the analysis of financial-economic time
    series data.
Integrative clustering of multiple genomic data types
        using a joint latent variable model.
Reproducible, programmatic retrieval of datasets from the
    Inter-university Consortium for Political and Social Research archive.
Implements a modification to the Random Survival Forests algorithm for obtaining variable importance in high dimensional datasets. The proposed algorithm is appropriate for settings in which a silent event is observed through sequentially administered, error-prone self-reports or laboratory based diagnostic tests.  The modified algorithm incorporates a formal likelihood framework that accommodates sequentially administered, error-prone self-reports or laboratory based diagnostic tests. The original Random Survival Forests algorithm is modified by the introduction of a new splitting criterion based on a likelihood ratio test statistic.
Implementation of Tyler, Critchley, Duembgen and Oja's (JRSS B, 2009,
            <doi:10.1111/j.1467-9868.2009.00706.x>) and Oja, Sirkia and Eriksson's
            (AJS, 2006, <http://www.ajs.or.at/index.php/ajs/article/view/vol35,%20no2%263%20-%207>) 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 are implemented.
Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components.
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.
For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided.
Use R to make requests to the US Census Bureau's International Data Base API.
             Results are returned as R data frames.  For more information about the IDB API, visit
             <http://www.census.gov/data/developers/data-sets/international-database.html>.
In randomized studies involving severely ill patients, functional
    outcomes are often unobserved due to missed clinic visits, premature
    withdrawal or death. It is well known that if these unobserved functional
    outcomes are not handled properly, biased treatment comparisons can be
    produced. In this package, we implement a procedure for comparing treatments
    that is based on the composite endpoint of both the functional outcome and
    survival. The procedure considers missing data imputation with a sensitivity
    analysis strategy to handle the unobserved functional outcomes not due to
    death.
Interactive dendrogram that enables the user to select and
    color clusters, to zoom and pan the dendrogram, and to visualize
    the clustered data not only in a built-in heat map, but also in
    'GGobi' interactive plots and user-supplied plots. 
    This is a backport of Qt-based 'idendro' 
    (https://github.com/tsieger/idendro) to base R graphics and Tcl/Tk GUI.
Calculate identity coefficients, based on Mark Abney's C
        code.
Incremental Multiple Correspondence Analysis and Principal
    Component Analysis.
Contains techniques for mining large high-dimensional data sets 
             by using the concept of Intrinsic Dimension (ID). Here the ID is 
             not necessarily integer. It is extended to fractal dimensions. And 
             the Morisita estimator is used for the ID estimation, but other 
             tools are included as well.
Integrate molecular profiles to discover candidate oncogenic drivers.
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.
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
Generate random or human readable and pronounceable identifiers.
Implements various novel and standard
    clustering statistics and other analyses useful for understanding the
    spread of infectious disease.
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 collection of Irucka Embry's miscellaneous USGS functions
    (processing .exp and .psf files, statistical error functions,
    "+" dyadic operator for use with NA, creating ADAPS and QW
    spreadsheet files, calculating saturated enthalpy). Irucka created these
    functions while a Cherokee Nation Technology Solutions (CNTS) United States
    Geological Survey (USGS) Contractor and/or USGS employee.
A collection of Irucka Embry's miscellaneous USGS data sets (USGS
    Parameter codes with fixed values, USGS global time zone codes, and US Air
    Force Global Engineering Weather Data). Irucka created these data sets
    while a Cherokee Nation Technology Solutions (CNTS) United States
    Geological Survey (USGS) Contractor and/or USGS employee.
A pair of functions for getting and setting the IEEE
        rounding mode for floating point computations.
A collection of Irucka Embry's miscellaneous functions
    (Engineering Economics, Civil & Environmental/Water Resources Engineering,
    Geometry, Statistics, GNU Octave length functions, Trigonometric functions
    in degrees, etc.).
Miscellaneous data sets [Engineering Economics, Environmental/
    Water Resources Engineering, US Presidential Elections].
The eclectic collection includes the following written pieces:
    "The War Prayer" by Mark Twain, "War Is A Racket" by Major General
    Smedley Butler, "The Mask of Anarchy: Written on the Occasion of the
    Massacre at Manchester" by Percy Bysshe Shelley, "Connect the D.O.T.S." by
    Obiora Embry, "Untitled: Climate Strange" by Irucka Ajani Embry, and
    "Untitled: Us versus Them or People Screwing over Other People (as we all
    live on one Earth and there is no "us versus them" in the actual Ultimate
    Reality}" by Irucka Ajani Embry.
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').
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 for genetic association studies.
Software tools developed by the Institute for Genomic Medicine, Columbia University, for the characterization of neuronal networks as recorded on Multi-Electrode Arrays (MEAs).
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.
Using 'shiny' to demo 'igraph' package makes learning graph theory easy and fun.
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.
Simulate an inhomogeneous self-exciting process (IHSEP), or Hawkes process, with a given (possibly time-varying) baseline intensity and an excitation function. Calculate the likelihood of an IHSEP with given baseline intensity and excitation functions for an (increasing) sequence of event times. Calculate the point process residuals (integral transforms of the original event times). Calculate the mean intensity process.
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.
Improved Laplace approximation for integrals of unimodal functions.
    The method requires user-supplied R functions for: the integrand, its gradient
    and its Hessian matrix. The computations are run in parallel.
Fitting a class of Lee-Carter mortality models using iterative fitting algorithm.
It performs interlaboratory studies (ILS) to detect those laboratories that provide non-consistent results when comparing to others.
  It permits to work simultaneously with various testing materials, from standard univariate, and functional data analysis (FDA) perspectives.
  The univariate approach based on ASTM E691-08 consist of estimating the Mandel's h and k statistics to identify those laboratories
  that provide more significant different results, testing also the presence of outliers by Cochran and Grubbs tests, Analysis of variance (ANOVA) 
  techniques are provided (F and Tuckey tests) to test differences in means corresponding to different laboratories per each material.
  Taking into account the functional nature of data retrieved in analytical chemistry, applied physics and engineering (spectra, thermograms, etc.).
  ILS package provides a FDA approach for finding the Mandel's k and h statistics distribution by smoothing bootstrap resampling.
Compute moments of images and perform reconstruction from
        moments.
Extracts traits from imaging data produced using a Lemna-Tec Scananalyzer 
    (see <http://www.lemnatec.com/products/hardware-solutions/scanalyzer-3d> for more 
    information). Growth rates between successive imagings are obtained and those for 
    a nominated set of intervals can also be calculated. Profile or longitudinal plots 
    of the traits and growth rates can be produced. These allow one to check for 
    anomalous data and to explore growth patterns in the data.
Fast image processing for images in up to 4 dimensions (two spatial
    dimensions, one time/depth dimension, one colour dimension). Provides most
    traditional image processing tools (filtering, morphology, transformations,
    etc.) as well as various functions for easily analysing image data using R. The package wraps CImg, <http://cimg.eu>, a simple, modern C++ library for image processing.
Provides fast application of image filters to data matrices,
    using R and C++ algorithms.
This is an Automatic Item Generator for Psychological Testing. It is recommended for research purposes only.
Zoom in and out of maps or any supplied lines or points,
        with control for color, poly fill, and aspect.
Search, extract and formulate IMF's datasets.
Explore and download data from the International Monetary Fund's
    data API <http://data.imf.org/>.
A complete API client for the image hosting service Imgur.com, including the an imgur graphics device, enabling the easy upload and sharing of plots.
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.
Implements some item response models for multiple
    ratings, including the hierarchical rater model, 
    conditional maximum likelihood estimation of linear 
    logistic partial credit model and a wrapper function
    to the commercial FACETS program.
Contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).
Functions to analyse missing value mechanisms and to impute data sets in the context of bottom-up MS-based proteomics.
Implement a multivariate analysis of the impact of items to identify a bias in the questionnaire validation of Likert-type scale variables. The items requires considering a null value (category doesn't have tendency). Offering frequency, importance and impact of the items.
In this package, you can find two functions proposed in
        Ding, Geng and Zhou (2011) to estimate direct and indirect
        causal effects with randomization and multiple-component
        intervention using instrumental variable method.
A collection of tools for conducting an imprecise inference is provided.   Imprecise prior is used for this inference, and imprecise probability theory introduced by Peter Walley (1991) is its underlying theoretical foundation.   The package is developed based on the PhD thesis work of Lee (2014).  Poisson and zero-truncated Poisson sampling models are mainly studied with two types of prior distributions.
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.
Import and export data from the most common statistical formats by using 
	     R functions that guarantee the least loss of the data information, giving special
	     attention to the date variables and the labelled ones.
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
Compute missing values on a training data set and impute them on a new data set. Current available options are median/mode and random forest.
Implements imputation methods using EM and Data Augmentation for
    multinomial data following the work of Schafer 1997 <ISBN: 978-0-412-04061-0>.
Imputes the missing values in time series data with PSF algorithm based approach.
    The details about PSF algorithm are available at:
    <https://cran.r-project.org/package=PSF>.
Provides a test bench for the comparison of missing data imputation 
    methods in univariate time series. Imputation methods are compared using RMSE, 
    MAE or MAPE error metrics. Proposed imputation methods and alternative error 
    metrics can be used.
Imputation (replacement) of missing values in univariate time
    series.
Method brings less bias and more efficient estimates for AFT models.
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).
A framework for analysing inbreeding and heterozygosity-fitness
    correlations (HFCs) based on microsatellite and SNP markers.
Specific functions are provided for rounding real weights to
    integers and performing an integer programming algorithm for calibration
    problems. They are useful for census-weights adjustments, or for performing
    linear regression with integer parameters.
Provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class 'incidence' is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to 'incidence' objects using 'fit'. This package is part of the RECON (<http://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
Tools for estimating incidence from biomarker data in cross-
    sectional surveys and for calibrating tests for recent infection.
Package to import data generated by the Incucyte Zoom from Essen Biosciences and use this to fit dose response curves using the drc package.
Genome-wide gene insertion and deletion rates can be modelled in a maximum 
    likelihood framework with the additional flexibility of modelling potential missing 
    data using the models included within. These models simultaneously estimate insertion 
    and deletion (indel) rates of gene families and proportions of "missing" data for 
    (multiple) taxa of interest. The likelihood framework is utilized for parameter 
    estimation. A phylogenetic tree of the taxa and gene presence/absence patterns 
    (with data ordered by the tips of the tree) are required. For more details, see 
    Utkarsh J. Dang, Alison M. Devault, Tatum D. Mortimer, Caitlin S. Pepperell, 
    Hendrik N. Poinar, G. Brian Golding (2016). Gene insertion deletion analysis 
    while accounting for possible missing data. Genetics (accepted).
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.
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.
Several parametric and non-parametric tests  and measures to check independence between two or more (homogeneous or nonhomogeneous) point processes  in time are provided. Tools for simulating point processes in one dimension with different types of dependence are also implemented.
Provides functions to download and parse information from INEGI
    (Official Mexican statistics agency).
Inequality, concentration, and poverty measures. Lorenz curves (empirical and theoretical).
Provides simple functions to compute and plot two
    types (sample-size- and coverage-based) rarefaction and extrapolation of species
    diversity (Hill numbers) for individual-based (abundance) data or sampling-unit-
    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.
Provides functionality to compute various node centrality measures on networks.
    Included are functions to compute betweenness centrality (by utilizing Madduri and Bader's
    SNAP library), implementations of Burt's constraint and effective
    network size (ENS) metrics, Borgatti's algorithm to identify key players, and Valente's
    bridging metric. On Unix systems, the betweenness, Key Players, and
    bridging implementations are parallelized with OpenMP, which may run
    faster on systems which have OpenMP configured.
A set of tools for evaluating several measures of case influence for structural equation models. Functions are based on the approach of J Pek and RC MacCallum (Sensitivity analysis in structural equation models: cases and their influence, Multivariate Behavioral Research, 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.
Performs exploratory data analysis and variable screening for
    binary classification models using weight-of-evidence (WOE) and information
    value (IV). In order to make the package as efficient as possible, aggregations
    are done in data.table and creation of WOE vectors can be distributed across
    multiple cores. The package also supports exploration for uplift models (NWOE
    and NIV).
Provides companion function for analysing the performance of
    classification models. Also, provides function to optimise probability cut-
    off score based on used specified objectives, Plot 'ROC' Curve in 'ggplot2',
    'AUROC', 'IV', 'WOE' Calculation, 'KS Statistic' etc to aid accuracy improvement
    in binary classification models.
Multiple Imputation for Informative Censoring.
    This package implements two methods. Gamma Imputation
    from Jackson et al. (2014) <DOI:10.1002/sim.6274> and Risk Score Imputation
    from Hsu et al. (2009) <DOI:10.1002/sim.3480>.
Aids in creating sequence statistics for Butts's 'relevent' software.
This package implements various measures of information theory based on several entropy estimators.
Estimates the probability of informed trading (PIN) initially introduced by Easley et. al. (1996) <doi:10.1111/j.1540-6261.1996.tb04074.x> . Contribution of the package is that it uses likelihood factorizations of Easley et. al. (2010) <doi:10.1017/S0022109010000074> (EHO factorization) and Lin and Ke (2011) <doi:10.1016/j.finmar.2011.03.001> (LK factorization). Moreover, the package uses different estimation algorithms. Specifically, the grid-search algorithm proposed by Yan and Zhang (2012) <doi: 10.1016/j.jbankfin.2011.08.003> and hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> .
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
Facilitates the generation of input files for infraFDTD and processes snapshot output. infraFDTD is a finite-difference model written by Keehoon Kim for simulating infrasound that considers topography and a 1-D atmosphere (see Kim et al., 2015 <doi:10.1002/2015GL064466>).
Replace parameters in strings and/or text files with specified
    values.
Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated.
Calculation of information utility (i.e., Lindley
        information) quantities for item response models.
Parse simple '.ini' configuration files to an structured list. Users
  can manipulate this resulting list with lapply() functions. This same
  structured list can be used to write back to file after modifications.
R dependency injection framework. Dependency injection allows
    a program design to follow the dependency inversion principle. The user
    delegates to external code (the injector) the responsibility of providing its
    dependencies. This separates the responsibilities of use and construction.
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.
A collection of functions for creating high-level graphics,
    performing raster-based analysis, processing MODFLOW-based models, and
    overlaying multi-polygon objects. Used to support packages and scripts written
    by researchers at the United States Geological Survey (USGS)
    Idaho National Laboratory (INL) Project Office.
A set of functions and a graphical user interface
	to analyse and compare texts, using classical text mining
	functions, as well as those from theoretical ecology.
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.
Computes individual causes of death and population cause-specific mortality fractions using the 'InSilicoVA' algorithm from McCormick et al (2016) <DOI:10.1080/01621459.2016.1152191>. It uses data derived from verbal autopsy (VA) interviews, in a format similar to the input of the widely used 'InterVA4' method. This package provides general model fitting and customization for 'InSilicoVA' algorithm and basic graphical visualization of the output.
Functions to compute insolation on complex terrain.
Provides a data-driven projection-based method for estimating changepoints in high-dimensional time series. Multiple changepoints are estimated using a (wild) binary segmentation scheme.
A method to infer modules of co-expressed genes and the
    dependencies among the modules from multiple expression datasets that may
    contain different sets of genes. Please refer to: Extracting a low-dimensional
    description of multiple gene expression datasets reveals a potential driver for
    tumor-associated stroma in ovarian cancer, Safiye Celik, Benjamin A. Logsdon,
    Stephanie Battle, Charles W. Drescher, Mara Rendi, R. David Hawkins and Su-In
    Lee (2016) <DOI:10.1186/s13073-016-0319-7>.
The function `install_load` 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 Rstudio-sponsored CRAN mirror and/or
    the USGS GRAN Repository. Lastly, the chosen package(s)
    is/are loaded. The function `load_package` simply loads the provided
    packages.
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.
Provides an interface to the Instagram API <https://instagram.com/
    developer/>, which allows R users to download public pictures filtered by
    hashtag, popularity, user or location, and to access public users' profile data.
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.
Provides classes and methods for automated
    spatial interpolation.
A package that provides  additional functionality for spatial interpolation in the intamap package.
Several integrative data methods in which information of objects from different data sources can be combined are included in the IntClust package. As a single data source is limited in its point of view, this provides more insight and the opportunity to investigate how the variables are interconnected. Clustering techniques are to be applied to the combined information. For now, only agglomerative hierarchical clustering is implemented. Further, differential gene expression and pathway analysis can be conducted on the clusters. Plotting functions are available to visualize and compare results of the different methods.
Offers modelling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis.
An implementation of a framework for drug sensitivity prediction from various genetic characterizations using ensemble approaches. Random Forests or Multivariate Random Forest predictive models can be generated from each genetic characterization that are then combined using a Least Square Regression approach. It also provides options for the use of different error estimation approaches of Leave-one-out, Bootstrap, N-fold cross validation and 0.632+Bootstrap along with generation of prediction confidence interval using Jackknife-after-Bootstrap approach.
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
Implements the procedures suggested in Esarey and Sumner (2015) for controlling the false discovery rate or familywise error rate when constructing marginal effects plots for models with interaction terms.
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.
Implementations of semiparametric cure rate estimators for interval
    censored data in R. The algorithms are based on the promotion time and
    frailty models, all for interval censoring. For the frailty model,
    there is also a implementation contemplating clustered data.
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).
A deconvoluted mass spectrum (mass*intensity pairs) from Tri-Methyl-Silyl-derivatized GC-APCI-MS (Gas Chromatography-Atmospheric Pressure Chemical Ionization-Mass Spectrometry) data can be read, evaluated and plotted in an informative fashion. The main purpose is to identify the causal metabolite or more precisely the sum formula of the molecular peak within the spectrum.
Compute permutation- based performance measures and create partial
    dependence plots for (cross-validated) 'randomForest' and 'ada' models.
Generates three inter-related genomic datasets : methylation, gene expression and protein expression.
Provides an R version of the 'InterVA4' software (<http://www.interva.net>) 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.
Carries out integrative clustering analysis using multiple types of genomic dataset.
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.
Evaluating if values 
  of vectors are within different open/closed intervals
  (`x %[]% c(a, b)`), or if two closed
  intervals overlap (`c(a1, b1) %[o]% c(a2, b2)`).
  Operators for negation and directional relations also implemented.
Provides tools for importing, merging, and analysing data from 
  international assessment studies (TIMSS, PIRLS, PISA. ICILS, and PIAAC).
Interface to popular R functions with formulas and data,
  such as 'lm', so they can be included painlessly in data
  science pipelines implemented by 'magrittr'
  with the operator %>%.
Enum-type representation of vectors and representation
  of intervals, including a method of coercing variables in data frames.
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 (e.g., calibration) in
    linear, generalized linear, nonlinear, and (linear) mixed-effects 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.
Light weight implementation of the standard distribution
  functions for the inverse gamma distribution, wrapping those for the gamma
  distribution in the stats package.
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.
Provides two functions for the numerical inversion of Laplace-Transformed functions, returning the value of the standard (time) domain function at a specified value.  The first algorithm is the first optimum contour algorithm described by Evans and Chung (2000)[1].
    The second algorithm uses the Bromwich contour as per the definition of the inverse Laplace Transform.  The latter is unstable for numerical inversion and mainly included for comparison or interest.  There are also some additional functions provided for utility, including plotting and some simple Laplace Transform examples, for which there are known analytical solutions.  Polar-cartesian conversion functions are included in this package and are used by the inversion functions.
    [1] Evans & Chung, 2000: Laplace transform inversions using optimal contours in the complex plane; International Journal of Computer Mathematics v73 pp531-543.
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.
Method for the calculation of copy numbers and calling of copy number alterations. The algorithm uses coverage data from amplicon sequencing of a sample cohort as input. The method includes significance assessment, correction for multiple testing and does not depend on normal DNA controls.
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.
Provides item exclusion procedure, which is a formal method to 
    test 'Indifference Of iNdicator' (ION). When a latent personality 
    trait-outcome association is assumed, then the association strength 
    should not depend on which subset of indicators (i.e. items) has been 
    chosen to reflect the trait. Personality traits are  often measured 
    (reflected) by  a sum-score of a certain set of indicators. 
    Item exclusion procedure randomly excludes items from a sum-score and 
    tests, whether the sum-score - outcome correlation changes. ION has been 
    achieved, when any item can be excluded from the sum-score without the 
    sum-score - outcome correlation substantially changing . For more details, 
    see Vainik, Mottus et. al, (2015) "Are Trait-Outcome Associations Caused
    by Scales or Particular Items? Example Analysis of Personality Facets and
    BMI",European Journal of Personality  DOI: <10.1002/per.2009> .
Collection of functions for IO Psychologists.
Density, spectral density, and regression estimation using infinite
    order flat-top 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.
The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen by cross-validation.
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.
Provides methods to perform and analyse I-prior regression models.
    Estimation is mainly done via an EM algorithm, but there is flexibility in
    using any optimiser.
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.
Tools for identifying the origins of IP addresses. Includes functions for converting IP addresses
  to country names, location details (region, city, zip, latitude, longitude), IP codes, binary values, as well
  as a function for plotting IP locations on a world map. This product includes IP2Location LITE data available
  from <http://www.ip2location.com> and is is available by Creative Commons Attribution-ShareAlike 4.0 Interational
  license (CC-BY-SA 4.0).
A toolkit for manipulating, validating and testing 'IP' addresses and
    ranges, along with datasets relating to 'IP' addresses. Tools are also provided
    to map 'IPv4' blocks to country codes. While it primarily has support for the 'IPv4'
    address space, more extensive 'IPv6' support is intended.
Functions to estimate the probability to receive the observed treatment, based on
    individual characteristics. The inverse of these probabilities can be used as weights when
	estimating causal effects from observational data via marginal structural models. Both point
	treatment situations and longitudinal studies can be analysed. The same functions can be used to
	correct for informative censoring.
Builds statistical control charts with exact limits for
        univariate and multivariate cases.
Estimate an optimal dynamic treatment regime using Interactive Q-learning.
Generate chemical structures possibly satisfying desired properties
 using the inverse QSPR model. It has three reference classes. ENgram is a class for learning the grammar 
 structure of existing chemical strings using an extended N-gram model. QSPRpred contains 
 a simple Bayes regression model to predict properties from structures. SmcChem is a class of the generator of
 chemical strings from the Inverse-QSPR model. This class has ENgram and QSPRpred class objects inside. 
 The generator is implemented by the Sequential Monte Carlo sampler.
Iterated race is an extension of the Iterated F-race method for
             the automatic configuration of optimization algorithms, that is,
             (offline) tuning their parameters by finding the most appropriate
             settings given a set of instances of an optimization problem.
Provides a flexible integrative algorithm that allows information from prior data, such as protein protein interactions and gene knock-down, to be jointly considered for gene regulatory network inference.
A R interface to setup, run and read IRATE model runs to assess band recovery (conventional tagging) data (i.e. age-dependent or independent fishing and natural mortality rates).
An interface to the rich display capabilities of 'Jupyter' front-ends (e.g. 'Jupyter Notebook') <https://jupyter.org>.
    Designed to be used from a running 'IRkernel' session <https://irkernel.github.io>.
"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.
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.
This data package contains the boards' compositions of companies quoted in the Irish Stock Exchange at the end of each year from 2003 to 2013. The data have been first analysed in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.
Classes and functions for metrics calculation as part of the
             'IRIS DMC MUSTANG' project. The functionality in this package 
             builds upon the base classes of the 'IRISSeismic' package.
             Metrics include basic statistics as well as higher level
             'health' metrics that can help identify problematic seismometers.
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 <https://github.com/obsypy/obspy>. Additional classes and 
             methods support data returned by web services provided by the 'IRIS DMC'
             <http://service.iris.edu/>.
Fast and memory efficient methods for truncated singular and
    eigenvalue decompositions and principal component analysis of large sparse or
    dense matrices.
Coefficients of Interrater Reliability and Agreement for
        quantitative, ordinal and nominal data: ICC, Finn-Coefficient,
        Robinson'A, Kendall's W, Cohen's Kappa, ...
Includes a collection of shiny applications to demonstrate
  or to explore fundamental item response theory (IRT) concepts
  such as estimation, scoring, and multidimensional IRT models.
A collection of functions useful in learning and practicing IRT,
    which can be combined into larger programs. Provides basic CTT analysis,
    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), ability estimation (MLE, BME, EAP, WLE, plausible 
    values), item and person fit statistics, scaling methods (MM, MS, Stocking-Lord,
    and the complete Hebaera method), and a rich array of parametric and 
    non-parametric (kernel) plots. Estimates and plots Haberman's interaction model
    when all items are dichotomously scored.
An implementation of the IRT paradigm for the scoring of different
    instruments measuring latent traits (a.k.a Abilities) and estimating item
    parameters for a variety of models, this package is highly optimized using
    Rcpp and carefully written R for the rest of the package, it aims to expand IRT
    applications to those applications that require faster and more robust estimation
    procedures. See the IRTpp documentation and github site for more information and
    examples.
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.
Tools for parsing 'NOAA' Integrated Surface Data ('ISD') files,
    described at <https://www.ncdc.noaa.gov/isd>. Data includes for example,
    wind speed and direction, temperature, cloud data, sea level pressure,
    and more. Includes data from approximately 35,000 stations worldwide, 
    though best coverage is in North America/Europe/Australia. Data is stored 
    as variable length ASCII character strings, with most fields optional.
    Included are tools for parsing entire files, or individual lines of data.
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.The model explains the physics of spin glasses and magnetic materials, and cooperative phenomenon in general, for example phase transitions and neural networks.This package provides utilities to simulate one dimensional Ising Model with Metropolis and Glauber Monte Carlo with single flip dynamics in periodic boundary conditions. Utility functions for exact solutions are provided.
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.
Tools to develop stochastic models based on the Theory of Island
    Biogeography (TIB) of MacArthur and Wilson (1967) <DOI:10.1023/A:1016393430551>
    and extensions. The package implements methods to estimate colonization and
    extinction rates (including environmental variables) given presence-absence
    data, simulate community assembly, and perform model selection.
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.
A bunch of functions to deal with circular data under order restrictions.
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.
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.
Nonparametric estimation of an isotonic covariate effect for proprotional hazards model.
An R implementation of Ken Ludwig's popular Isoplot add-in to Microsoft Excel. Currently plots U-Pb data on Wetherill and Tera-Wasserburg concordia diagrams. Calculates concordia and discordia ages. Performs linear regression of measurements with correlated errors using the 'York' approach. Generates Kernel Density Estimates (KDEs) and Cumulative Age Distributions (CADs). Produces Multidimensional Scaling (MDS) configurations and Shepard plots of multi-sample detrital datasets using the Kolmogorov-Smirnov distance as a dissimilarity measure. Calculates 40Ar/39Ar ages, isochrons, and age spectra. Computes weighted means accounting for overdispersion. Calculates U-Th-He (single grain and central) ages, logratio plots and ternary diagrams. Processes fission track data using the external detector method and LA-ICP-MS, calculates central ages and plots fission track and other data on radial (a.k.a. 'Galbraith' plots). Constructs Re-Os, Sm-Nd and Rb-Sr isochrons. Future versions will include functionality for Lu-Hf and U-series dating methods. A graphical user interface is provided as an RStudio Shiny app at <http://isoplotr.london-geochron.com>. Offline access to this interface will be provided at a later point in time.
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.
Building isoscapes using mixed models and inferring the geographic origin of organisms based on their isotopic ratios. This package is essentially a simplified interface to several other packages. It uses 'spaMM' for fitting and predicting isoscapes, and assigning an organism's origin depending on its isotopic ratio. 'IsoriX' also relies heavily on the package 'rasterVis' for plotting the maps using lattice.
IsoSpec is a fine structure calculator used for obtaining the most
    probable masses of a chemical compound given the frequencies of the composing
    isotopes and their masses. It finds the smallest set of isotopologues with
    a given probability. The probability is assumed to be that of the product of
    multinomial distributions, each corresponding to one particular element and
    parametrized by the frequencies of finding these elements in nature. These
    numbers are supplied by IUPAC - the International Union of Pure and Applied
    Chemistry.
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.
Performs multivariate normal imputation through iterative sequential 
  regression.  Conditional dependency structure between imputed variables can be 
  specified a priori to accelerate imputation.
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.
Two methods for Individual Tree Crowns (ITCs) delineation on remote sensing data: one is based on LiDAR data in x,y,z format and one on imagery data in raster format.
Runs classical item analysis for multiple-choice test items and polytomous items (e.g., rating scales).
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.
A collection of iterators for generating permutations and combinations with or
    without replacement; with distinct items or non-distinct items (multiset).
    The generated sequences are in lexicographical order (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.
Individual tree model is an instrument to support the decision with
    regard to forest management. This package provides functions that let you work
    with data for this model. Also other support functions and extension related to
    this model are available.
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.
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
A powerful system for estimating an improved wavelet-based index
    of magnetic storm activity, storm activity preindex (from individual station) and SQ variations.
    It also serves as a flexible visualization tool.
An estimation method that can use computer simulations to
    approximate maximum-likelihood estimates even when the likelihood function can not
    be evaluated directly. It can be applied whenever it is feasible to conduct many
    simulations, but works best when the data is approximately Poisson distributed.
    It was originally designed for demographic inference in evolutionary
    biology. It has optional support for conducting coalescent simulation using
    the 'coala' package.
Computing the original and modified jackknife estimates of Kaplan-Meier estimators.
Significance test for association between variables
	and their estimated latent variables.
	Latent variables may be estimated by principal component analysis (PCA),
	logistic factor analysis (LFA), and other techniques.
Implements the classical Jacobi (1846) algorithm for the
    eigenvalues and eigenvectors of a real symmetric matrix, both in 
    pure R and in C++ using Rcpp. Mainly as a programming example 
    for teaching purposes.
Uses the Jaccard similarity index to account for population
    structure in sequencing studies. This method was specifically
    designed to detect population stratification based on rare variants, hence it
    will be especially useful in rare variant analysis.
Cardoso's JADE algorithm as well as his functions for joint diagonalization are ported to R. Also several other blind source separation (BSS) methods, like AMUSE and SOBI, and some criteria for performance evaluation of BSS algorithms, are given.
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).
Full texts for Jane Austen's 6 completed novels, ready for text
    analysis. These novels are "Sense and Sensibility", "Pride and Prejudice",
    "Mansfield Park", "Emma", "Northanger Abbey", and "Persuasion".
The main janitor functions can: perfectly format data.frame column
    names; isolate duplicate records; and provide quick one- and two-variable
    tabulations (i.e., frequency tables and crosstabs). Other janitor functions
    nicely format the results of these tabulations. These tabulate-and-report
    functions approximate popular features of SPSS and Microsoft Excel. This package
    follows the principles of the "tidyverse" and works well with the pipe function
    %>%. janitor was built with beginning-to-intermediate R users in mind and is
    optimized for user-friendliness. Advanced R users can already do everything
    covered here, but with janitor they can do it faster and save their thinking for
    the fun stuff.
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.
Fits two different joint generalized estimating equation models to multivariate longitudinal data.
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.
Fit joint mean-covariance models for longitudinal data. The models
    and their components are represented using S4 classes and methods. The core
    computational algorithms are implemented using the 'Armadillo' C++ library
    for numerical linear algebra and 'RcppArmadillo' glue.
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.
Implements time series z-normalization, SAX, HOT-SAX, VSM, SAX-VSM, RePair, and RRA
    algorithms facilitating time series motif (i.e., recurrent pattern), discord (i.e., anomaly),
    and characteristic pattern discovery along with interpretable time series classification.
Performs a permutation test on the difference between two location parameters, a permutation correlation test, a permutation F-test, the Siegel-Tukey test, a ratio mean deviance test.  Also performs some graphing techniques, such as for confidence intervals, vector addition, and Fourier analysis; and includes functions related to the Laplace (double exponential) and triangular distributions.  Performs power calculations for the binomial test.
'jamovi' is a framework for creating rich interactive statistical
    analyses (see <https://www.jamovi.org> for more information). This package
    represents the core libraries which jamovi analyses written in R depend
    upon.
Computing and plotting joint confidence regions and intervals. Regions include classical ellipsoids, minimum-volume or minimum-length regions, and an empirical Bayes region. Intervals include the TOST procedure with ordinary or expanded intervals and a fixed-sequence procedure. Such regions and intervals are useful e.g., for the assessment of multi-parameter (bio-)equivalence. Joint confidence regions for the mean and variance of a normal distribution are available as well.
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.
Fits the joint model proposed by Henderson and colleagues (2000) 
    <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple 
    continuous longitudinal measures. The time-to-event data is modelled using a 
    Cox proportional hazards regression model with time-varying covariates. The 
    multiple longitudinal outcomes are modelled using a multivariate version of the 
    Laird and Ware linear mixed model. The association is captured by a multivariate
    latent Gaussian process. The model is estimated using a Monte Carlo Expectation 
    Maximization algorithm. This project is funded by the Medical Research Council 
    (Grant number MR/M013227/1).
Perform the Cox regression and dynamic prediction under the joint frailty-copula model between tumour progression and death for meta-analysis.
 The method is applicable for meta-analytic data combining several studies. The data should have information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression).
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.
Joint fit of a semiparametric regression model for longitudinal responses and a semiparametric transformation model for time-to-event data.
Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.
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.
Performs Joins and Minus Queries on 'Excel' Files
    fulljoinXL() Merges all rows of 2 'Excel' files based upon a common column in the files.
    innerjoinXL() Merges all rows from base file and join file when the join condition is met.
    leftjoinXL() Merges all rows from the base file, and all rows from the join file
    if the join condition is met.
    rightjoinXL() Merges all rows from the join file, and all rows from the base file if the join
    condition is met.
    minusXL() Performs 2 operations source-minus-target and target-minus-source
    If the files are identical all output files will be empty.
    Choose two 'Excel' files via a dialog box, and then follow prompts at the console to
    choose a base or source file and columns to merge or minus on.
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).
Implementation of some unit level EBLUP and GREG estimators as well as the estimate of their variances to further document the publication of Breidenbach and Astrup (2011). The vignette further explains the use of the implemented functions.
A collection of specifications to securely transfer claims such as
    authorization information between parties. A JSON Web Token (JWT) contains
    claims used by systems to apply access control rules to its resources. One
    potential use case of the JWT is authentication and authorization for a
    system that exposes resources through OAuth 2.0.
Implements under/oversampling for probability estimation.  To be
    used with machine learning methods such as adaBoost, random forests, etc.
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.
A Joint PENalty Estimation of Covariance and Inverse Covariance Matrices.
Helpful functions for using mesh code (80km to 250m) data in Japan. Visualize mesh code using 'ggplot2' and 'leaflet', etc.
Utilizing the data that Japanese administration area provided 
    by the National Land Numerical Information download service (<http://nlftp.mlit.go.jp/ksj/index.html>).
Functions, methods, and datasets for cancer survival
        analysis, including the proportional hazard relative survival
        model, the join point relative survival model.
Client for 'jq', a JSON processor (<http://stedolan.github.io/jq/>), written
    in C. 'jq' allows the following with JSON data: index into, parse, do calculations,
    cut up and filter, change key names and values, perform conditionals and comparisons,
    and more.
Simultaneous estimation of multiple related networks.
These functions calculate the taxonomic measures presented in Miranda-Esquivel (2016). 
   The package introduces Jack-knife resampling in evolutionary distinctiveness prioritization analysis, 
   as a way to evaluate the support of the ranking in area prioritization, and the persistence of a given area 
   in a conservation analysis.
   The algorithm is described in: Miranda-Esquivel, D (2016) <DOI:10.1007/978-3-319-22461-9_11>.
Implements the basic financial analysis
    functions similar to (but not identical to) what
    is available in most spreadsheet software. This
    includes finding the IRR and NPV of regularly
    spaced cash flows and annuities. Bond pricing and
    YTM calculations are included. In addition, Black
    Scholes option pricing and Greeks are also
    provided.
A set of utility functions for working with JavaScript in R.
    Currently includes functions to compile, validate, reformat, optimize
    and analyze JavaScript code.
Maximum likelihood estimation for the semiparametric joint modeling of 
             survival and longitudinal data. Log-transforms and PRES procedures.
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.
JSON-LD is a light-weight syntax for expressing linked data. It is primarily
    intended for web-based programming environments, interoperable web services and for 
    storing linked data in JSON-based databases. This package provides bindings to the 
    JavaScript library for converting, expanding and compacting JSON-LD documents.
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 a web API. The implementation is
    based on the mapping described in the vignette (Ooms, 2014). In addition to
    converting JSON data from/to R objects, '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.
Uses the node library 'is-my-json-valid' to validate 'JSON' against
    a 'JSON' schema.
The core of this 'Rcpp' based package is a function to compute standardized Jonckheere-Terpstra test statistics for large numbers of dependent and independent variables, e.g., genome-wide analysis. It implements 'OpenMP', allowing the option of computing on multiple threads. Supporting functions are also provided to calculate p-values and summarize results.
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.
A set of functions to ease the building of web APIs.
Generates image data for fractals (Julia and Mandelbrot
        sets) on the complex plane in the given region and resolution.
The Junar API is a commercial platform to organize and publish data
    <http://junar.com>. It has been used in a number of national and local
    government Open Data initiatives in Latin America and the USA. This package
    is a wrapper to make it easier to access data made public through the Junar
    API.
Many method to compute, predict and back-test VaR. For more detail, see the report: Value at Risk <researchgate.net>.
A collection of miscellaneous tools and functions,
    such as tools to generate descriptive statistics tables,
    format output, visualize relations among variables or check
    distributions.
This is a set of simple utilities for various data manipulation and testing tasks.
    The goal is to use base tools well, without bringing in many
    dependencies. Main areas of interest are semi-automated data frame manipulation, such as
    converting factors in multiple binary indicator columns. There are testing
    functions which provide 'testthat' expectations to permute arguments to
    function calls. There are functions and data to test extreme numbers, dates,
    and bad input of various kinds which should allow testing failure and corner
    cases, which can be used for fuzzing your functions. The test suite has many examples of usage.
Implements methods for clustering mixed-type data,
  specifically combinations of continuous and nominal data. Special attention
  is paid to the often-overlooked problem of equitably balancing the
  contribution of the continuous and categorical variables. This package
  implements KAMILA clustering, a novel method for clustering
  mixed-type data in the spirit of k-means clustering. It does not require
  dummy coding of variables, and is efficient enough to scale to rather large
  data sets. Also implemented is Modha-Spangler clustering, which uses a
  brute-force strategy to maximize the cluster separation simultaneously in the
  continuous and categorical variables.
Identify and sort genes overexpressed and associated to transmembrane protein in Affymetrix expression set or any other results of microarray experiment.
Computes the Kantorovich distance between two probability measures on a finite set.
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.
Attempts to remove vocals from a stereo '.wav' recording of a song.
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.
Provides arrays with flexible control over dimension dropping when subscripting.
Collection of utility functions used in the KEHRA project (see http://www.brunel.ac.uk/ife/britishcouncil). It refers to the multidimensional analysis of air pollution, weather and health data.
Uses Bessel functions to calculate the 
  fundamental and complementary analytic solutions to the
  Kelvin differential equation.
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.
An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.
Gaussian Process models with customised covariance kernels.
Contains functions for estimation and model selection of kernel
    deep stacking networks. The model selection includes direct optimization or
    model based alternatives with arbitrary loss functions.
A novel implementation that solves the DWD and the kernel DWD which can be hundred times faster than the existing implementation of DWD based on the second-order-cone programming (SOCP). The package also handles some other general loss functions including large-margin unified machines (LUM).
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 anonymised 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 as well as data aggregated on areas is supported.
Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.
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.
Kernel Machine Score Test for Pathway Analysis in the Presence of Semi-Competing Risks.
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.
Computes group centrality scores and identifies the most central group of players in a network.
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.
Decrypts passwords stored in the Gnome Keyring, OSX Keychain and
    strings encrypted with the Windows Data Protection API.
State space modelling is an efficient and flexible method for 
    statistical inference of a broad class of time series and other data. KFAS 
    includes fast functions for Kalman filtering, smoothing, forecasting, and 
    simulation of multivariate exponential family state space models, with 
    observations from Gaussian, Poisson, binomial, negative binomial, and gamma 
    distributions.
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: Pretty intervals, 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 N. 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.
A graph based regression model from flat unstructured dataset.
             Each line in the input data set is treated as a node from which an edge to another line (node) can be formed.
             In the training process, a model is created which contains sparse graph adjacency matrix.
             This model is then used for prediction by taking a predictor and the model as inputs and outputs
             a prediction which is an average of the most similar node and its neighbours in the model graph.
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.
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.
Linearly constrained Kaplan-Meier estimator for right censored data. This version does empirical likelihood ratio test with right censored data with linear type constraint and hypothesis testing for coefficients in accelerated failure time model.
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.
Distributed k-means clustering algorithm written using 'ddR' (Distributed Data Structures) API in the 'ddR' package.
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).
K-means for longitudinal data using shape-respecting distance and shape-respecting means.
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.
The sampl.mcmc() function creates samples of the feasible region of a knapsack problem with both equalities and inequalities constraints.
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. The package is primarily aimed at authoring in the R 'markdown'
    format, and can provide outputs for web-based authoring such as linked
    text for 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.
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.
Makes nice pictures of knots using Bezier curves and
  numerical optimization.
It uses species accumulation curves and diverse estimators to assess, at the same time, the levels of survey coverage in multiple geographic cells of a size defined by the user. It also enables the geographical depiction of observed species richness, survey effort and completeness values including a background with administrative areas.
Retrieve data from 'kintone' (<https://www.kintone.com/>) via its API.
    'kintone' is an enterprise application platform.
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.
An unsupervised and semi-supervised 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 interface to Kokudo Suuchi API, the GIS data service of the Japanese government. See <http://nlftp.mlit.go.jp/ksj-e/index.html> for more information.
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.
POS Tagger and Morphological Analyzer for Korean text based research. It provides tools for corpus linguistics research such as Keystroke converter, Hangul automata, Concordance, and Mutual Information. It also provides a convenient interface for users to apply, edit and add morphological dictionary selectively.
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. Support for
    additional languages can be added on-the-fly or by plugin packages. 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. To ask for help, report bugs, suggest
    feature improvements, or discuss the global development of the package, please
    subscribe to the koRpus-dev mailing list (https://ml06.ispgateway.de/mailman/
    listinfo/korpus-dev_r.reaktanz.de).
Consider linear regression model and autoregressive model of
    order q where errors in the linear regression model and innovations in the
    autoregression model are independent and symmetrically distributed. Hira L.
    Koul (1986) <DOI:10.1214/aos/1176350059> proposed a nonparametric minimum 
    distance estimation method by minimizing L2-type distance between certain 
    weighted residual empirical processes. He also proposed a simpler version of 
    the loss function by using symmetry of the integrating measure in the distance. 
    This package contains three functions: KoulLrMde(), KoulArMde(), and Koul2StageMde(). 
    The former two provide minimum distance estimators for linear regression model 
    and autoregression model, respectively, where both are based on Koul's method. 
    These two functions take much less time for the computation than those based on parametric minimum
    distance estimation methods. Koul2StageMde() provides estimators for regression
    and autoregressive coefficients of linear regression model with autoregressive
    errors through minimum distant method of two stages.
Cubic spline fitting along with knot selection, includes support for additional variables.
The kpodclustr package implements the k-POD method for clustering
    partially observed data.
Implements a quantified approach to the Kraljic Matrix (Kraljic, 1983, <https://hbr.org/1983/09/purchasing-must-become-supply-management>) 
    for strategically analyzing a firm’s purchasing portfolio. It combines multi-objective decision analysis to measure purchasing characteristics and 
    uses this information to place products and services within the Kraljic Matrix.
Provides basic functions for Continuation-Passing Style development.
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, Steel's multiple comparison test, and the 
        Jonckheere-Terpstra (JT) 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 and the JT 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 compares tail probabilities
	obtained under asymptotic approximation with those obtained via simulation or exact 
	calculations.
Implements the Lilliefors-corrected Kolmogorov-Smirnoff test for use
    in goodness-of-fit tests, suitable when population parameters are unknown and
    must be estimated by sample statistics. P-values are estimated by simulation.
    Can be used with a variety of continuous distributions, including normal,
    lognormal, univariate mixtures of normals, uniform, loguniform, exponential,
    gamma, and Weibull distributions. Functions to generate random numbers and
    calculate density, distribution, and quantile functions are provided for use
    with the log uniform and mixture distributions.
An adaptation of Kernelized Stein Discrepancy, this package provides a goodness-of-fit test of whether a given i.i.d. sample is drawn from a given distribution. It works for any distribution once its score function (the derivative of log-density) can be provided. This method is based on "A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation" by Liu, Lee, and Jordan, available at <http://arxiv.org/abs/1602.03253>.
Selection of k in k-means clustering based on Pham et al. paper
    ``Selection of k in k-means clustering''.
Using this package you can combine known kinase substrate relationships with experimental data and determine active kinases and their substrates.
Knowledge Space Theory is a set-theoretical framework, which 
   proposes mathematical formalisms to operationalize knowledge structures in a 
   particular domain. The kst-package provides basic functionalities to 
   generate, handle, and manipulate knowledge structures and knowledge spaces.
Provides a function called COTUCKER3() (Co-Inertia Analysis
    + Tucker3 method) which performs a Co-Tucker3 analysis of two sequences of
    matrices, as well as other functions called PCA() (Principal Component Analysis)
    and BGA() (Between-Groups Analysis), which perform analysis of one matrix,
    COIA() (Co-Inertia Analysis), which performs analysis of two matrices, PTA()
    (Partial Triadic Analysis) and TUCKER3(), which perform analysis of a sequence
    of matrices, and BGCOIA() (Between-Groups Co-Inertia Analysis), STATICO()
    (STATIS method + Co-Inertia Analysis), COSTATIS() (Co-Inertia Analysis + STATIS
    method), which also perform analysis of two sequences of matrices.
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
Tools for data importation, recoding, and inspection that
    are used at the University of Kansas Center for Research Methods
    and Data Analysis. There are functions to create new project
    folders, R code templates, create uniquely named output
    directories, and to quickly obtain a visual summary for each
    variable in a data frame.  The main feature here is the systematic
    implementation of the "variable key" framework for data
    importation and recoding.  We are eager to have community feedback
    about the variable key and the vignette about it.
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.
Separation of wave motions in different scales and directions based on
     Kolmogorov-Zurbenko 2D periodograms and directional periodograms.
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.
An efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. Adaptive ridge algorithms are used to fit the models.
L1 estimation for linear regression and random number generation for
    the multivariate Laplace distribution.
Efficient implementation of Friedman's boosting algorithm
        with l2-loss function and coordinate direction (design matrix
        columns) basis functions.
A variety of ordination and community analyses
   useful in analysis of data sets in community ecology.  
   Includes many of the common ordination methods, with 
   graphical routines to facilitate their interpretation, 
   as well as several novel analyses.
Support labeled loop and escape from nested loop
Provides a range of axis labeling algorithms
Work with labelled data imported from
    'SPSS' or 'Stata' with 'haven' or 'foreign'.
An implementation of distance-based ranking algorithms to predict rankings of labels.  Two common algorithms are included: the naive Bayes and the nearest neighbor algorithms.
The Bayesian estimation of mixture models (and more general hidden Markov models) suffers from the label switching phenomenon, making the MCMC output non-identifiable. This package can be used in order to deal with this problem using various relabelling algorithms.
The package offers a dendrogram representation of series
        of labels, this is specially needed in Markov chain Monte Carlo
        clustering. If you have a dendrogram in your mind you can
        easily put series of meaningful labels in a matrix and heights
        in a vector, then convert them to a dendrogram abject.
Produces Labour Market Areas from commuting flows and provides tools for automatic tuning based on spatial contiguity. It also allows for analyses and visualisation of the new functional geography.
Insieme di funzioni di supporto al volume "Laboratorio di
        Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia, 2006.
        This package contains sets of functions defined in "Laboratorio
        di Statistica con R", Iacus-Masarotto, MacGraw-Hill Italia,
        2006. Function names and docs are in italian as well.
Contains data sets to accompany the book: Lazic SE
    (2016). "Experimental Design for Laboratory Biologists: Maximising Information
    and Improving Reproducibility". Cambridge University Press.
Estimation of indicators on social exclusion and poverty, as well
    as Pareto tail modeling for empirical income distributions.
The package contains functions to compare and group means.
Methods for fast access to large ASCII files.  Currently the
    following file formats are supported: comma separated format (CSV) and fixed
    width format. It is assumed that the files are too large to fit into memory,
    although the package can also be used to efficiently access files that do
    fit into memory. Methods are provided to access and process files blockwise.
    Furthermore, an opened file can be accessed as one would an ordinary
    data.frame. The LaF vignette gives an overview of the functionality
    provided.
Performs approximate GP regression for large computer experiments and spatial datasets.  The approximation is based on finding small local designs for prediction (independently) at particular inputs. OpenMP and SNOW parallelization are supported for prediction over a vast out-of-sample testing set; GPU acceleration is also supported for an important subroutine.  OpenMP and GPU features may require special compilation.  An interface to lower-level (full) GP inference and prediction is also provided, as are associated wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration.
Provides the tables from the 'Sean Lahman 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 2015, as recorded in the 2016
    version of the database.
A collection of tools for the calculation of freewater metabolism.
Lake morphometry metrics are used by limnologists to understand,
    among other things, the ecological processes in a lake. Traditionally, these
    metrics are calculated by hand, with planimeters, and increasingly with
    commercial GIS products. All of these methods work; however, they are either
    outdated, difficult to reproduce, or require expensive licenses to use. The
    lakemorpho package provides the tools to calculate a typical suite
    of these metrics from an input elevation model and lake polygon. The metrics
    currently supported are: fetch, major axis, minor axis,  maximum length, 
    maximum width, mean width,maximum depth, mean depth, shoreline development, 
    shoreline length, surface area, and volume.
Lake temperature records, metadata, and climate drivers for 291 global lakes during the time period 1985-2009. Temperature observations were collected using satellite and in situ methods. Climatic drivers and geomorphometric characteristics were also compiled and are included for each lake. Data are part of the associated publication from the Global Lake Temperature Collaboration project (http://www.laketemperature.org). See citation('laketemps') for dataset attribution.
Currently the package includes 14 methods for calculating internal
    consistency reliability but is still growing.  The package allows users
    access to whichever reliability estimator is deemed most appropriate for
    their situation.
A language extension to efficiently write functional programs in R. Syntax extensions include multi-part function definitions, pattern matching, guard statements, built-in (optional) type safety.
Provides tools that manipulate and transform data using methods
    and techniques consistent with functional programming. The idea is that
    through the use of these tools, a program can be reasoned about insomuch
    that the implementation can be proven to be equivalent to the mathematical
    model.
Lambert W x F distributions are a generalized framework to analyze
    skewed, heavy-tailed data. It is based on an input/output system, where the
    output random variable (RV) Y is a non-linearly transformed version of an input
    RV X ~ F with similar properties as X, but slightly skewed (heavy-tailed).
    The transformed RV Y has a Lambert W x F distribution. This package contains
    functions to model and analyze skewed, heavy-tailed data the Lambert Way:
    simulate random samples, estimate parameters, compute quantiles, and plot/
    print results nicely. Probably the most important function is 'Gaussianize',
    which works similarly to 'scale', but actually makes the data Gaussian.
    A do-it-yourself toolkit allows users to define their own Lambert W x
    'MyFavoriteDistribution' and use it in their analysis right away.
Implements both real-valued branches of the Lambert-W function, also known as the product logarithm, without the need for installing the entire GSL.
Using Liquid Association for Network Dynamics Detection.
Provides functions to estimate survival and a treatment effect using a landmark estimation approach.
This package includes functions for landmark prediction of a survival outcome incorporating covariate and short-term event information. For more information about landmark prediction please see: Parast, Layla, Su-Chun Cheng, and Tianxi Cai. Incorporating short-term outcome information to predict long-term survival with discrete markers. Biometrical Journal 53.2 (2011): 294-307.
Processing of Landsat or other multispectral satellite
        imagery. Includes relative normalization, image-based
        radiometric correction, and topographic correction options.
Functions for converted Landsat 8 multispectral satellite imagery
             rescaled to the top of atmosphere (TOA) reflectance, radiance
             and/or at satellite brightness temperature using radiometric
             rescaling coefficients provided in the metadata file (MTL file).
Simulates categorical maps on actual geographical realms, starting from either empty landscapes or landscapes provided by the user (e.g. land use maps). Allows to tweak or create landscapes while retaining a high degree of control on its features, without the hassle of specifying each location attribute. In this it differs from other tools which generate null or neutral landscapes in a theoretical space. The basic algorithm currently implemented uses a simple agent style/cellular automata growth model, with no rules (apart from areas of exclusion) and von Neumann neighbourhood (four cells, aka Rook case). Outputs are raster dataset exportable to any common GIS format.
Estimate drift and diffusion functions from time series and
    generate synthetic time series from given drift and diffusion coefficients.
Improve your text analysis with languagelayer
        <https://languagelayer.com>, a powerful language detection
        API.
Data sets exemplifying statistical methods, and some
        facilitatory utility functions used in "Analyzing Linguistic
        Data: A practical introduction to statistics using R",
        Cambridge University Press, 2008.
Solves the problem of Laplace deconvolution with noisy discrete
    non-equally spaced observations on a finite time interval based on expansions
    of the convolution kernel, the unknown function and the observed signal over
    Laguerre functions basis. It implements the methodology proposed in the paper
    "Laplace deconvolution on the basis of time domain data and its application to
    Dynamic Contrast Enhanced imaging" by F. Comte, C-A. Cuenod, M. Pensky and Y.
    Rozenholc in ArXiv (http://arxiv.org/abs/1405.7107).
Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview). The README describes the history of the package development process.
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.
Functions to write or append a R list to a file, as well as read, remove, modify elements from it without restoring the whole list.
Implements the largeVis algorithm (see Tang, et al. (2016)  <https://arxiv.org/abs/1602.00370>) for visualizing very large high-dimensional datasets. Also very fast search for approximate nearest neighbors; outlier detection; and optimized implementations of the HDBSCAN*, DBSCAN and OPTICS clustering algorithms; plotting functions for visualizing the above.
Efficient procedures for fitting an entire lasso sequence
        with the cost of a single least squares fit. Least angle
        regression and infinitesimal forward stagewise regression are
        related to the lasso, as described in the paper below.
laser implements maximum likelihood methods based on the
        birth-death process to test whether diversification rates have
        changed over time and whether rates vary among lineages.
Routines and documentation for solving regression problems
  while imposing an L1 constraint on the estimates, based on
  the algorithm of Osborne et al. (1998)
Implementation of the algorithm introduced in "Shah, R. D.
    (2016) Modelling interactions in high-dimensional data with Backtracking, JMLR,
    to appear". Data with thousands of predictors can be handled. The algorithm
    performs sequential Lasso (Tibshirani, 1996) fits on design matrices containing
    increasing sets of candidate interactions. Previous fits are used to greatly
    speed up subsequent fits so the algorithm is very efficient.
Use the lasso regression method to perform approximate inference
    in high dimensions, by penalizing the effects of nuisance parameters.
L1 regularized regression (Lasso) solver using the Cyclic
        Coordinate Descent algorithm aka Lasso Shooting is fast. This
        implementation can choose which coefficients to penalize. It
        support coefficient-specific penalities and it can take X'X and
        X'y instead of X and y.
This is a simple wrapper for the LASVM Solver (see http://leon.bottou.org/projects/lasvm). LASVM is basically an online variant of the SMO solver.
A graph
  proposed by Rosenbaum is useful
  for checking some properties of various
  sorts of latent scale, this program generates commands
  to obtain the graph using 'dot' from 'graphviz'.
Fit and simulate latent position and cluster models for statistical networks.
Provides a C++ implementation of the Multidimensional Item Response Theory (MIRT) capable of performing parameter and traits estimations. It also provides a list of options to perform an optimal analysis and obtain useful information about the resulting model. This package is a work of SICS Research Group, Universidad Nacional de Colombia.
Calculates and plots Handedness index (HI), absolute HI, mean HI and z-score which are commonly used indexes for the study of hand preference (laterality) in non-human primates.
Parses and converts LaTeX math formulas to R's plotmath
    expressions, used to enter mathematical formulas and symbols to be rendered as
    text, axis labels, etc. throughout R's plotting system.
A powerful and elegant high-level data visualization
  system inspired by Trellis graphics, with an emphasis on
  multivariate data. Lattice is sufficient for typical graphics needs,
  and is also flexible enough to handle most nonstandard requirements.
  See ?Lattice for an introduction.
This package contains functions that compute the
  lattice-based density estimator of Barry and McIntyre, which
  accounts for point processes in two-dimensional regions with 
  irregular boundaries and holes.  The package also implements
  two-dimensional non-parametric regression for similar regions.
Building on the infrastructure provided by the lattice
	     package, this package provides several new high-level
	     functions and methods, as well as additional utilities
	     such as panel and axis annotation functions.
Methods for the interpolation of large spatial
  datasets. This package follows a "fixed rank Kriging" approach using
  a large number of basis functions and provides spatial estimates
  that are comparable to standard families of covariance functions.
  Using a large number of basis functions allows for estimates that
  can come close to interpolating the observations (a spatial model
  with a small nugget variance.)  Moreover, the covariance model for
  this method can approximate the Matern covariance family but also
  allows for a multi-resolution model and supports efficient
  computation of the profile likelihood for estimating covariance
  parameters. This is accomplished through compactly supported basis
  functions and a Markov random field model for the basis
  coefficients. These features lead to sparse matrices for the
  computations and this package makes of the R spam package for this.
  An extension of this version over previous ones ( < 5.4 ) is the
  support for different geometries besides a rectangular domain. The
  Markov random field approach combined with a basis function
  representation makes the implementation of different geometries
  simple where only a few specific functions need to be added with
  most of the computation and evaluation done by generic routines that
  have been tuned to be efficient.  One benefit of this package's
  model/approach is the facility to do unconditional and conditional
  simulation of the field for large numbers of arbitrary points. There
  is also the flexibility for estimating non-stationary covariances
  and also the case when the observations are a linear combination
  (e.g. an integral) of the spatial process.  Included are generic
  methods for prediction, standard errors for prediction, plotting of
  the estimated surface and conditional and unconditional simulation.
Estimation and simulation of latent variable models.
Fit a variety of latent variable models, including confirmatory
   factor analysis, structural equation modeling and latent growth curve models.
Interactive shiny application for working with different kinds of
    latent variable analysis, with the 'lavaan' package. Graphical output for models
    are provided and different estimators are supported.
Fit structural equation models (SEM) including factor analysis,
    multivariate regression models with latent variables and many other latent
    variable models while correcting estimates, standard errors, and
    chi-square-derived fit measures for a complex sampling design. 
    Incorporate clustering, stratification, sampling weights, and 
    finite population corrections into a SEM analysis.
    Wrapper around packages lavaan and survey.
lava plugin allowing combinations of left and right
        censored and binary outcomes
Client for 'Turfjs' (<http://turfjs.org>) for
    'geospatial' analysis. The package revolves around using 'GeoJSON'
    data. Functions are included for creating 'GeoJSON' data objects,
    measuring aspects of 'GeoJSON', and combining, transforming,
    and creating random 'GeoJSON' data objects.
Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, 
 measures of relative variability etc, the package contains new robust tests of symmetry, 
 omnibus and directional tests of normality, and their graphical counterparts such as 
 Robust QQ plot; a robust trend tests for variances etc. All implemented tests and methods 
 are illustrated by simulations and real-life examples from legal statistics, economics, 
 and biostatistics.
By combining constant, linear, and quadratic local models,
        lazy estimates the value of an unknown multivariate function on
        the basis of a set of possibly noisy samples of the function
        itself.  This implementation of lazy learning automatically
        adjusts the bandwidth on a query-by-query basis through a
        leave-one-out cross-validation.
Supplies a LazyData facility for packages which have data
		sets but do not provide LazyData: true.  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'.
An alternative approach to non-standard evaluation using
    formulas. Provides a full implementation of LISP style 'quasiquotation',
    making it easier to generate code with other code.
An R Markdown html document format that provides the ability to lazily
    load plot outputs as the user scrolls over them.  This is useful for large R
    Markdown documents with many plots, as it allows for a fast initial page load and
    defers loading of individual graphics to the time that the user navigates near them.
Helper functions to build SQL statements
    for dbGetQuery or dbSendQuery under program control.
    They are intended to increase speed of coding and
    to reduce coding errors. Arguments are carefully checked,
    in particular SQL identifiers such as names of tables or columns.
    More patterns will be added as required.
Provides the functionality to write LaTeX code from within R
    without having to learn LaTeX.  Functionality also exists to create HTML
    and Markdown code.  While the functionality still exists to write
    complete documents with lazyWeave, it is generally easier to do so with
    with markdown and knitr.  lazyWeave's main strength now is the ability
    to design custom and complex tables for reporting results.
Latent budget analysis is a method for the analysis of a two-way
    contingency table with an exploratory variable and a response variable. It is
    specially designed for compositional data.
A wrapper built around the libLBFGS optimization library by Naoaki Okazaki. The lbfgs package implements both the Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and the Orthant-Wise Quasi-Newton Limited-Memory (OWL-QN) optimization algorithms. The L-BFGS algorithm solves the problem of minimizing an objective, given its gradient, by iteratively computing approximations of the inverse Hessian matrix. The OWL-QN algorithm finds the optimum of an objective plus the L1-norm of the problem's parameters. The package offers a fast and memory-efficient implementation of these optimization routines, which is particularly suited for high-dimensional problems.
Interfacing to Nocedal et al. L-BFGS-B.3.0 (2011) limited
	memory BFGS minimizer with bounds on parameters.
The package offers various length-bias corrections to
        survival curve estimation.
Maximum likelihood estimation of log-binomial regression with special functionality when the MLE is on (or close to) the boundary of the parameter space.
Simulate expected equilibrium length composition, YPR, and
    SPR using the LBSPR model. Fit the LBSPR model to length data to estimate
    selectivity, relative fishing mortality, and spawning potential ratio for 
	data-limited fisheries.
Performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset.
This package performs a Latent Class Analysis of
        phenotypic measurements in pedigrees and a model selection
        based on one of two methods: likelihood-based cross-validation
        and Bayesian Information Criterion. It computes also individual
        and triplet child-parents weights in a pedigree using an
        upward-downward algorithm. It takes into account the familial
        dependence defined by the pedigree structure by considering
        that a class of a child depends on his parents classes via
        triplet-transition probabilities of the classes. The package
        handles the case where measurements are available on all
        subjects and the case where measurements are available only on
        symptomatic (i.e. affected) subjects. Distributions for
        discrete (or ordinal) and continuous data are currently
        implemented. The package can deal with missing data.
Local Discrimination via Latent Class Models
This package contains (1) event-related brain potential data recorded from 10 participants at electrodes Fz, Cz, Pz, and Oz (0--300 ms) in the context of Antoine Tremblay's PhD thesis (Tremblay, 2009); (2) ERP amplitudes at electrode Fz restricted to the 100 to 175 millisecond time window; and (3) plotting data generated from a linear mixed-effects model.
The core of this 'Rcpp' based package is several functions to estimate the baseline hazard, frailty variance, and fixed effect parameter for a discrete-time shared frailty model with random effects. The functions are designed to analyze grouped time-to-event data accounting for family structure of related individuals (i.e., trios). The core functions include two processes: (1) evaluate the multivariable integration to compute the exact proportional hazards model based likelihood and (2) estimate the desired parameters using maximum likelihood estimation. The integration is evaluated by the 'Cuhre' algorithm from the 'Cuba' library (Hahn, T., Cuba-a library for multidimensional numerical integration, Comput. Phys. Commun. 168, 2005, 78-95 <doi:10.1016/j.cpc.2005.01.010>), and the source files of the 'Cuhre' function are included in this package. The maximization process is carried out using Brent's algorithm, with the 'C++' code file from John Burkardt and John Denker (Brent, R.,Algorithms for Minimization without Derivatives, Dover, 2002, ISBN 0-486-41998-3).
Bayesian population size estimation using non parametric latent-class models.
Estimation of various extensions of the mixed models including latent class mixed models, joint latent latent class mixed models and mixed models for curvilinear univariate or multivariate longitudinal outcomes using a maximum likelihood estimation method.
Collections of functions allowing random number generations and
    estimation of Liouville copulas.
The main purpose of lctools is to provide researchers and educators with easy-to-learn
    user friendly tools for calculating key spatial statistics and to apply simple as well as
    advanced methods of spatial analysis in real data. These include: Local Pearson and  
    Geographically Weighted Pearson Correlation Coefficients, Spatial Inequality Measures
    (Gini, Spatial Gini, LQ, Focal LQ), Spatial Autocorrelation (Global and Local Moran's I), 
    several Geographically Weighted Regression techniques and other Spatial Analysis tools 
    (other geographically weighted statistics). This package also contains functions for 
    measuring the significance of each statistic calculated, mainly based on Monte Carlo simulations.
Implements latent Dirichlet allocation (LDA)
	     and related models.  This includes (but is not limited
	     to) sLDA, corrLDA, and the mixed-membership stochastic
	     blockmodel.  Inference for all of these models is
	     implemented via a fast collapsed Gibbs sampler written
	     in C.  Utility functions for reading/writing data
	     typically used in topic models, as well as tools for
	     examining posterior distributions are also included.
Select statistically similar research groups by backward selection using various robust algorithms, including a heuristic based on linear discriminant analysis, multiple heuristics based on the test statistic, and parallelized exhaustive search.
For this first version only metrics to estimate the best fitting
    number of topics are implemented.
Tools to create an interactive web-based visualization of a
    topic model that has been fit to a corpus of text data using
    Latent Dirichlet Allocation (LDA). Given the estimated parameters of
    the topic model, it computes various summary statistics as input to
    an interactive visualization built with D3.js that is accessed via
    a browser. The goal is to help users interpret the topics in their
    LDA topic model.
Flexible procedures to compute local density-based outlier scores for ranking outliers.
  Both exact and approximate nearest neighbor search can be implemented, while also accommodating
  multiple neighborhood sizes and four different local density-based methods. It allows for
  referencing a random subsample of the input data or a user specified reference data set
  to compute outlier scores against, so both unsupervised and semi-supervised outlier
  detection can be implemented.
Computations related to group sequential boundaries.
             Includes calculation of bounds using the Lan-DeMets
             alpha spending function approach.
The package provides a set of functions which aim is to
        propose four measures of linkage disequilibrium: the usual r^2
        measure, the r^2_S measure (r^2 corrected by the structure
        sample), the r^2_V (r^2 corrected by the relatedness of
        genotyped individuals), the r^2_VS measure (r^2 corrected by
        both the relatedness of genotyped individuals and the structure
        of the sample).
Produces a graphical display, as a heat map, of measures
        of pairwise linkage disequilibria between SNPs. Users may
        optionally include the physical locations or genetic map
        distances of each SNP on the plot.
ldlasso requires data be of class gwaa.data from the the
        package GenABEL
this package provides functions for Finding Locally
        D-optimal designs for Logistic, Negative Binomial, Poisson,
        Michaelis-Menten, Exponential, Log-Linear, Emax, Richards,
        Weibull and Inverse Quadratic regression models and also
        functions for auto-constructing Fisher information matrix and
        Frechet derivative based on some input variables and without
        user-interfere.
Implementation of most popular approaches to PD (probability of default) calibration: Quasi Moment Matching algorithm (D. Tasche), algorithm proposed by M. van der Burgt, K. Pluto and D. Tasche's most prudent estimation methodology.
Functions, methods, and data sets for fitting likelihood-based dimension reduction in regression, using principal fitted components (pfc), likelihood acquired directions (lad), covariance reducing models (core).
Linear dimension reduction subspaces can be uniquely defined using orthogonal projection matrices. This package provides tools to compute distances between such subspaces and to compute the average subspace.
Statistical methods related 
	to the estimation and testing of multiple correlation, partial correlation and regression coefficient matrices when data is high-dimensional.
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.
An R gallery for China and other geojson choropleth map in leaflet. Contains the geojson data for provinces, cities in China.
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.
Implements the method described in "Network-based analysis of omics data: The LEAN method" [Gwinner Boulday (2016) <DOI:10.1093/bioinformatics/btw676>]
 Given a protein interaction network and a list of p-values describing a measure of interest (as e.g. differential gene expression) this method
 computes an enrichment p-value for the protein neighborhood of each gene and compares it to a background distribution of randomly drawn p-values.
 The resulting scores are corrected for multiple testing and significant hits are returned in tabular format.
Advances in sequencing technology now allow researchers to capture the expression profiles of individual cells. Several algorithms have been developed to attempt to account for these effects by determining a cell's so-called `pseudotime', or relative biological state of transition. By applying these algorithms to single-cell sequencing data, we can sort cells into their pseudotemporal ordering based on gene expression. LEAP (Lag-based Expression Association for Pseudotime-series) then applies a time-series inspired lag-based correlation analysis to reveal linearly dependent genetic associations.
Contains LEAPFrOG Gradient Optimisation and Expectation Maximisation functions for inferring parental admixture proportions from an offspring with SNP genotypes.
These functions take a gene expression value matrix, a
        primary covariate vector, an additional known covariates
        matrix.  A two stage analysis is applied to counter the effects
        of latent variables on the rankings of hypotheses.  The
        estimation and adjustment of latent effects are proposed by
        Sun, Zhang and Owen (2011).  "leapp" is developed in the
        context of microarray experiments, but may be used as a general
        tool for high throughput data sets where dependence may be
        involved.
Regression subset selection, including exhaustive search.
LearnBayes contains a collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.
An implementation of Crawford's and Wright's learning curve production functions. It provides unit and cumulative block estimates for time (or cost) of units along with an aggregate learning curve. It also provides delta and error functions and some basic learning curve plotting functions.
Crabs in the English channel, deer skulls, English
    monarchs, half-caste Manga characters, Jamaican cities,
    Shakespeare's The Tempest, drugged up cyclists and sexually
    transmitted diseases.
Implementations of several basic neural network concepts in R, as based on posts on \url{http://qua.st/}.
Allows students to use R as an interactive educational environment
    for statistical concepts, ranging from p-values to confidence intervals
	to stability in time series.
Utilizing phylogenetic and functional information for the analyses of ecological datasets. The analyses include methods for quantifying the phylogenetic and functional diversity of assemblages.
Estimate the slope and intercept of a bivariate
	linear relationship by calculating a posterior density
	that is invariant to interchange and scaling of the
	coordinates.
This package provides functions and datasets for the
        reader of the book "Le logiciel R: Maitriser le langage,
        Effectuer des analyse statistiques". The documentation and help
        pages are written in french.
Functions to access Lending Club's API and assist the investor manage 
	their account. Lending Club is a peer-to-peer lending service where loans are 
	broken up into $25 notes that investors buy with the expectation of earning a 
	return on the interest. You can learn more about the API here:
	<http://www.lendingclub.com/developers/lc-api.action>.
Each function accomplishes the work of several or more standard R functions. For example, two function calls, Read() and CountAll(), read the data and generate summary statistics for all variables in the data frame, plus histograms and bar charts as appropriate.  Other functions provide for descriptive statistics, a comprehensive regression analysis, analysis of variance and t-test, plotting, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function call, variable labels, color themes, and a built-in help system. A confirmatory factor analysis of multiple indicator measurement models is available, as are pedagogical routines for data simulation such as for the Central Limit Theorem. Compatible with 'RStudio' and 'knitr' including generation of R markdown instructions for interpretative output.
This package contains some simple objects and functions to do 
             statistics using linear models and a Bayesian framework.
R functions for handling, processing, and analyzing geographic
    data on species' distributions and environmental variables as well as species'
    description and conservation information.
Utilities for formatting strings and character
    vectors to for capitalization, word break and white space. Supported formats
    are: snake_case, spine-case, camelCase, PascalCase, Title Case, UPPERCASE,
    lowercase, Sentence case or combinations thereof. 'lettercase' strives to
    provide a simple, consistent, intuitive and high performing interface.
A collection of lexical hash tables, dictionaries, and
        word lists.
Functions to plot Lexis Diagrams for Demographic purposes.
An R implementation of the LexRank algorithm described by G. Erkan and D. R. Radev (2004) <DOI:10.1613/jair.1523>.
Provides an extension to factors called 'lfactor' that are similar
    to factors but allow users to refer to 'lfactor' levels by either the level or
    the label.
Functions for performing and visualizing Local Fisher Discriminant
    Analysis(LFDA), Kernel Fisher Discriminant Analysis(KLFDA), and Semi-supervised
    Local Fisher Discriminant Analysis(SELF).
Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE).
Transforms away factors with many levels prior to doing an OLS.
    Useful for estimating linear models with multiple group fixed effects, and for
    estimating linear models which uses factors with many levels as pure control
    variables. Includes support for instrumental variables, conditional F statistics
    for weak instruments, robust and multi-way clustered standard errors, as well as
    limited mobility bias correction.
Various algorithms related to linguistic fuzzy logic: mining for linguistic fuzzy association
    rules, composition of fuzzy relations, performing perception-based logical deduction (PbLD), 
    and forecasting time-series using fuzzy rule-based ensemble (FRBE).
The "Manual on Low-flow Estimation and Prediction", published by
    the World Meteorological Organisation (WMO), gives a comprehensive summary on
    how to analyse stream flow data focusing on low-flows. This packages provides
    functions to compute the described statistics and produces plots similar to the
    ones in the manual.
Tools for linear grouping analysis. Three user-level
        functions: gap, rlga and lga.
Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.
Spatial and spatio-temporal modelling of point patterns using the
    log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal,
    multivariate and aggregated point processes using Markov chain Monte Carlo.
Functions for testing the genetic association/gene-environment interaction in longitudinal gene-environment-wide interaction studies. Generalized score type tests are used for set based analyses. Then GEE based score tests are applied to all single variants within the defined set.
Functions for the longitudinal genetic random field method (He et al., 2015, <doi:10.1111/biom.12310>) to test the association between a longitudinally measured quantitative outcome and a set of genetic variants in a gene/region.
A very simple implementation of a class for 
	     longitudinal data.
Provides a number of methods for creating and augmenting Latin Hypercube Samples.
R interface to the open-source, ANSI C library 'libamtrack' (http://libamtrack.dkfz.org). 'libamtrack' provides computational routines for the prediction of detector response and radiobiological efficiency in heavy charged particle beams. It is designed for research in proton and ion dosimetry and radiotherapy. 'libamtrack' also includes many auxiliary physics routines for proton and ion beams. Original package and C-to-R conversion routines developed by Felix A. Klein.
Basic infrastructure for linear test statistics and permutation
  inference in the framework of Strasser and Weber (1999) <http://epub.wu.ac.at/102/>. 
  This package must not be used by end-users. CRAN package 'coin' implements all 
  user interfaces and is ready to be used by anyone.
A wrapper around the LIBLINEAR C/C++ library for
    machine learning (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear).
    LIBLINEAR is a simple library for solving large-scale regularized linear
    classification and regression. It currently supports L2-regularized
    classification (such as logistic regression, L2-loss linear SVM and L1-loss
    linear SVM) as well as L1-regularized classification (such as L2-loss linear
    SVM and logistic regression) and L2-regularized support vector regression
    (with L1- or L2-loss). The main features of LiblineaR include multi-class
    classification (one-vs-the rest, and Crammer & Singer method), cross
    validation for model selection, probability estimates (logistic regression
    only) or weights for unbalanced data. The estimation of the models is
    particularly fast as compared to other libraries.
Solving the linear SVM problem with coordinate descent
    is very efficient and is implemented in one of the most often used packages,
    'LIBLINEAR' (available at http://www.csie.ntu.edu.tw/~cjlin/liblinear).
    It has been shown that the uniform selection of coordinates can be
    accelerated by using an online adaptation of coordinate frequencies (ACF).
    This package implements ACF and is based on 'LIBLINEAR' as well as
    the 'LiblineaR' package (<https://cran.r-project.org/package=LiblineaR>).
    It currently supports L2-regularized L1-loss as well as L2-loss linear SVM.
    Similar to 'LIBLINEAR' multi-class classification (one-vs-the rest, and
    Crammer & Singer method) and cross validation for model selection is
    supported. The training of the models based on ACF is much faster than
    standard 'LIBLINEAR' on many problems.
Efficient procedures for fitting the regularization path
    for linear, binomial, multinomial, Ising and Potts models with lasso,
    group lasso or column lasso(only for multinomial) penalty.
    The package uses Linearized Bregman Algorithm to solve the
    regularization path through iterations. Bregman Inverse Scale Space Differential
    Inclusion solver is also provided for linear model with lasso penalty.
Estimates predictive states from spatio-temporal data and
    consequently can provide provably optimal forecasts.
    Currently this implementation
    supports an N-dimensional spatial grid observed over equally spaced time
    intervals. E.g. a video is a 2D spatial systems observed over time. This
    package implements mixed LICORS, has plotting tools (for (1+1)D and (2+1)D
    systems), and methods for optimal forecasting.  Due to memory limitations
    it is recommend to only analyze (1+1)D systems.
This package presents a general method for assessing the
        local influence of minor perturbations of case weight for the
        linear regression models.
Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write 
    'las' and 'laz' files, computation of metrics in area based approach, point filtering, artificial point 
    reduction, classification from geographic data, normalization and other manipulations.
Classes and methods that allow the user to manage life table,
    actuarial tables (also multiple decrement tables). Moreover, functions to easily
    perform demographic, financial and actuarial mathematics on life contingencies
    insurances calculations are contained therein.
Provides in built datasets and three functions.
  These functions are mobility_index, nonStanTest and linkedLives.  The mobility_index
  function facilitates the calculation of lifecourse fluidity, whilst the nonStanTest and the 
  linkedLives functions allow the user to determine the probability that the observed sequence data 
  was due to chance.  The linkedLives function acknowledges the fact that some individuals may
  have identical sequences.
  The datasets available provide sequence data on marital status(maritalData) 
  and mobility (mydata) for a selected group of individuals from the British Household Panel Study
  (BHPS).  In addition, personal and house ID's for 100 individuals are provided in a 
  third dataset (myHouseID) from the BHPS.
Likelihood-based estimation of individual growth and sexual maturity models for organisms, usually fish and invertebrates. It includes methods for data organization, plotting standard exploratory and analytical plots, predictions.
Functions supplied in this package will implement
        discriminant analysis to select an appropriate life table
        family, select an appropriate alpha level based on a desired
        life expectancy at birth, produce a model mortality pattern
        based on family and level as well as plot the results.
Compute the top decile lift and plot the lift curve. Cumulative lift curves are also supported.
Implementations of Hurst exponent estimators based on the relationship between wavelet lifting scales and wavelet energy.
Dockerize R Markdown documents with support for Rabix.
The purpose of this package is to enable usage of lightningviz server to be accessible from R. The
   server itself can be found at http://lightning-viz.org/ and is required to work with this package. Package
   by itself cannot and will not create any visualizations.
Lights Out is a puzzle game consisting of a grid of lights
    that are either on or off. Pressing any light will toggle it and its
    adjacent lights. The goal of the game is to switch all the lights off. This
    package provides an interface to play the game on different board sizes,
    both through the command line or with a visual application. Puzzles can
    also be solved using the automatic solver included. View a demo
    online at http://daattali.com/shiny/lightsout/.
A Poisson Subordinated Distribution to capture major
        leptokurtic features in log-return time series of financial
        data.
Tools for maximum likelihood estimation of parameters 
  of scientific models.
Functions for computing the r and r* statistics for inference on an arbitrary scalar function of model parameters, plus some code for the (modified) profile likelihood.
Tools to determine DNA profile Weight of Evidence. 
            For further information see the 'likeLTD' guide provided, 
            or Balding, D.J. (2013) <DOI:10.1073/pnas.1219739110>.
An approach to analyzing Likert response items, with an emphasis on visualizations. 
    The stacked bar plot is the preferred method for presenting Likert results. Tabular results
    are also implemented along with density plots to assist researchers in determining whether 
    Likert responses can be used quantitatively instead of qualitatively. See the likert(), 
    summary.likert(), and plot.likert() functions to get started.
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.
Computes  the  probability density,  the  cumulative  distribution, the quantile and
   the hazard rate functions and generates random deviates from the discrete and continuous
   Lindley  distribution  as  well  as  for 19 of  its  modifications. It  also  generates censored
   random deviates from any probability  distribution available in R.
Train and predict using fast prototype-based Linearized
    Support-Vector Regression methods.
Consider linear regression model Y = Xb + error where the distribution function of errors is unknown, but errors are independent and symmetrically distributed. The package contains a function named LRMDE which takes Y and X as input and returns minimum distance estimator of parameter b in the model.
Provides tools to manipulate formulas, such as getting x, y or contrasts from the model/formula, and functions to evaluate and check the marginal effects of a linear model.
Fits a linear excess relative risk model by maximum likelihood, possibly including several variables and allowing for lagged exposures.
Tools for detecting and correcting sample mix-ups between two sets
    of measurements, such as between gene expression data on two tissues.
Provides R with the Glottolog database <http://glottolog.org> and some more abilities for purposes of linguistic cartography. The Glottolog database contains the catalogue of languages of the world. This package helps researchers to make a linguistic maps, using philosophy of the Cross-Linguistic Linked Data project <http://clld.org/>, which allows for while at the same time facilitating uniform access to the data across publications. A tutorial for this package is available on GitHub wiki <https://github.com/agricolamz/lingtypology/wiki/>.
Link communities reveal the nested and overlapping structure in networks, and uncover the key nodes that form connections to multiple communities. linkcomm provides a set of tools for generating, visualizing, and analysing link communities in networks of arbitrary size and type. The linkcomm package also includes tools for generating, visualizing, and analysing Overlapping Cluster Generator (OCG) communities.
Matrices implemented as collections of matrix-like nodes, linked by
    columns or rows.
A linkage information based method for imputing missing diploid genotypes
Creates kinematic and static force models of 3D levers and linkage mechanisms, with particular application to the fields of engineering and biomechanics.
This package implements the methodology of
        Likelihood-based Imprecise Regression (LIR) for the case of
        linear regression with interval data.
This package can be used to solve Linear Programming /
        Linear Optimization problems by using the simplex algorithm.
Interactive visualization of effects, response functions 
    and marginal effects for different kinds of regression models. In this version 
    linear regression models, generalized linear models, generalized additive
    models and linear mixed-effects models are supported.  
    Major features are the interactive approach and the handling of the effects of categorical covariates: 
    if two or more factors are used as covariates every combination of the levels of each 
    factor is treated separately. The automatic calculation of 
    marginal effects and a number of possibilities to customize the graphical output 
    are useful features as well.
Estimate the mean of a Gaussian vector, by choosing among a large collection of estimators. In particular it solves the problem of variable selection by choosing the best predictor among  predictors emanating from different methods as lasso, elastic-net, adaptive lasso, pls, randomForest. Moreover, it can be applied for choosing the tuning parameter in a Gauss-lasso procedure.
Variable elimination (Gaussian elimination, Fourier-Motzkin elimination), 
    Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution,  
    projecting a vector on the convex polytope described by a system of (in)equations, 
    simplify systems by removing spurious columns and rows and collapse implied equalities, 
    test if a matrix is totally unimodilar, compute variable ranges implied by linear
    (in)equalities.
Checks adherence to a given style, syntax errors and possible
    semantic issues.  Supports on the fly checking of R code edited with Emacs,
    Vim and Sublime Text.
Provides three classes: Queue, PriorityQueue and Stack. Queue is just a
    "plain vanilla" FIFO queue; PriorityQueue orders items according to priority. Stack implements LIFO.
Performs Bayesian linear regression in astronomy. The method accounts for heteroscedastic errors in both the independent and the dependent variables, intrinsic scatters (in both variables), time evolution of slopes, normalization and scatters, Malmquist and Eddington bias, and break of linearity. The posterior distribution of the regression parameters  is sampled with a Gibbs method exploiting the JAGS library.
Fits lasso (total variation) penalised additive isotone models
Though SRFI-1 scopes both list-processing and higher-order
        programming, we'll save some list-orthogonal functions for the
        `functional' package; this is freely a mixture of
        implementation and API.
This is an unofficial package aimed at automating the
        import of LISREL output in R.  This package or its maintainer
        is not in any way affiliated with the creators of LISREL and
        SSI, Inc.
Allows researchers to conduct multivariate
    statistical analyses of survey data with list experiments. This
    survey methodology is also known as the item count technique or
    the unmatched count technique and is an alternative to the commonly
    used randomized response method. The package implements the methods
    developed by Imai (2011) <doi:10.1198/jasa.2011.ap10415>, 
    Blair and Imai (2012) <doi:10.1093/pan/mpr048>, 
    Blair, Imai, and Lyall (2013) <doi:10.1111/ajps.12086>, 
    Imai, Park, and Greene (2014) <doi:10.1093/pan/mpu017>,
    Aronow, Coppock, Crawford, and Green (2015) <doi:10.1093/jssam/smu023>, 
    and Chou, Imai, and Rosenfeld (2016) 
    <http://imai.princeton.edu/research/files/auxiliary.pdf>. 
    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.
Construction of list-based rules, i.e. a list of if-then clauses, to estimate the optimal dynamic treatment regime.
List environments are environments that have list-like properties.  For instance, the elements of a list environment are ordered and can be accessed and iterated over using index subsetting, e.g. 'x <- listenv(a=1, b=2); for (i in seq_along(x)) x[[i]] <- x[[i]]^2; y <- as.list(x)'.
Tests for independence between X and Y computed from a paired sample (x1,y1),...(xn,yn) of (X,Y), using one of the following statistics (a) the Longest Increasing Subsequence (Ln), (b) JLn, a Jackknife version of Ln or (c) JLMn, a Jackknife version of the longest monotonic subsequence. This family of tests can be applied under the assumption of continuity of X and Y.
A lightweight utility for converting lists to tidy data frames.
R lists, especially nested lists, can be very difficult to
    visualize or represent. Sometimes 'str()' is not enough, so this suite of
    htmlwidgets is designed to help see, understand, and maybe even modify your R
    lists.  The function 'reactjson()' requires a non-CRAN package
    'reactR' that can be installed from <https://github.com/timelyportfolio/reactR>.
Provides a function that, as an alternative to base::list, allows
    default values to be inherited from another list.
A scripting and command-line front-end
 is provided by 'r' (aka 'littler') as a lightweight binary wrapper around
 the GNU R language and environment for statistical computing and graphics.
 While R can be used in batch mode, the r binary adds full support for
 both 'shebang'-style scripting (i.e. using a  hash-mark-exclamation-path
 expression as the first line in scripts) as well as command-line use in
 standard Unix pipelines. In other words, r provides the R language without
 the environment.
Provides a wrapper around LiveChat's API. The R functions allow for
    one to extract chat sessions, raw text of chats between agents and customers and
    events.
Fits and tests logistic joinpoint models.
Provides functionality to train and evaluate algorithm selection models for portfolios.
LLE is a non-linear algorithm for mapping high-dimensional
        data into a lower dimensional (intrinsic) space. This package
        provides the main functions to performs the LLE alogrithm
        including some enhancements like subset selection, calculation
        of the intrinsic dimension etc.
Applies local log-linear capture-recapture models (LLLMs) for
    closed populations, as described in the doctoral thesis of Zachary Kurtz.
    The method is relevant when there are 3-5 capture occasions, with auxiliary
    covariates available for all capture occasions.  As part of estimating the
    number of missing population units, the method estimates the "rate of
    missingness" as it varies over the covariate space.  In addition,
    user-friendly functions are provided to recreate (approximately) the method
    of Zwane and van der Heijden (2004), which applied the VGAM package
    in a way that is closely related to LLLMs.
Analyses experimental data from liquid-liquid phase diagrams and
    provide a simple way to obtain its parameters and a simplified report. Designed
    initially to analyse Aqueous Two-Phases Systems, the package will include (every
    other update) new functions in order to comprise useful tools in liquid-liquid
    analysis.
Adds standardized regression coefficients to objects created by lm. Also extends the S3 methods print, summary and coef with additional boolean argument standardized.
Exact significance tests for a changepoint in linear or multiple linear regression.  
  Confidence regions with exact coverage probabilities for the changepoint.
Fit linear and generalized linear mixed-effects models.
    The models and their components are represented using S4 classes and
    methods.  The core computational algorithms are implemented using the
    'Eigen' C++ library for numerical linear algebra and 'RcppEigen' "glue".
This package includes a function to fit a linear
        mixed-effects model in the formulation described in Laird and
        Ware (1982) but allowing for censored normal responses. In this
        version, the with-in group errors are assumed independent and
        identically distributed.
Performs Genome-Wide Association analysis for diverse populations and
             for multi-environment and multi-trait analysis using linear mixed models.
Performs QTL mapping analysis for balanced and for
  multi-environment and multi-trait analysis using mixed models.
  Balanced population, single trait, single environment QTL mapping is
  performed through marker-regression (Haley and Knott (1992) <DOI:10.1038/hdy.1992.131>,
  Martinez and Curnow (1992) <DOI:10.1007/BF00222330>,
  while multi-environment and multi-trait QTL
  mapping is performed through linear mixed models.
  These functions could use any of the following populations: double haploid,
  F2, recombinant inbred lines, back-cross, and 4-way crosses.
  Performs a Single Marker Analysis, a Single Interval Mapping,
  or a Composite Interval Mapping analysis, and then constructs a final model
  with all of the relevant QTL.
The functions in this package implement the safety monitoring procedures proposed in the paper titled "Detection of unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Zhao, Y., Li, D.K.B., Petkau, A.J., Riddehough, A., Traboulsee, A., published in Journal of the American Statistical Association in 2013. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which can be modelled with a gamma or log-normal distributions. One can also choose the semi-parametric option which does not assume any distribution for the random effect. These mixed-effect models could be useful beyond the application of the safety monitoring. The maximum likelihood methods are used to estimate the unknown fixed effect parameters of the model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNBBayes, which contains the functions to compute the Personalized Activity Index in Bayesian framework.
The functions in this package implement the safety monitoring procedures proposed in the paper titled "A flexible mixed effect negative binomial regression model for detecting unusual increases in MRI lesion counts in individual multiple sclerosis patients" by Kondo, Y., Zhao, Y. and Petkau, A.J. The procedure first models longitudinally collected count variables with a negative binomial mixed-effect regression model. To account for the correlation among repeated measures from the same patient, the model has subject-specific random intercept, which is modelled with the infinite mixture of Beta distributions, very flexible distribution that theoretically allows any form. The package also has the option of a single beta distribution for random effects. These mixed-effect models could be useful beyond the application of the safety monitoring. The inference is based on MCMC samples and this package contains a Gibbs sampler to sample from the posterior distribution of the negative binomial mixed-effect regression model. Based on the fitted model, the personalized activity index is computed for each patient. Lastly, this package is companion to R package lmeNB, which contains the functions to compute the Personalized Activity Index in the frequentist framework.
Contains functions to estimate model parameters and filter, smooth and forecast random effects coefficients for mixed models with stationary and non-stationary stochastic processes under multivariate normal and t response distributions, diagnostic checks, bootstrap standard error calculation, etc.
The main function of the package is to perform backward selection of fixed effects, forward fitting of the random effects, and post-hoc analysis using parallel capabilities. Other functionality includes the computation of ANOVAs with upper- or lower-bound p-values and R-squared values for each model term, model criticism plots, data trimming on model residuals, and data visualization. The data to run examples is contained in package LCF_data.
Bootstrap routines for nested linear mixed effects models fit using
    either 'lme4' or 'nlme'. The provided 'bootstrap()' function implements the
    parametric, semi-parametric (i.e., CGR), residual, cases, and random effect
    block (REB) bootstrap procedures.
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 for different purposes related to Forest biometrics, including illustrative graphics, numerical computation, modeling height-diameter relationships, prediction of tree volumes, and modelling of diameter distributions. Datasets on tree height-diameter relationship,  light response of moss photosynthesis, and productivity of stump-lifting machines 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 L-moments (LMs) and probability-weighted moments (PWMs), parameter estimation for distributions, LM computation for distributions, and L-moment ratio diagrams. Maximum likelihood and maximum product of spacings estimation are also available. LMs for right-tail and left-tail censoring by known or unknown threshold and by indicator variable are available. Asymmetric (asy.) trimmed LMs (TL-moments, TLMs) are supported. LMs of residual (resid.) and reversed (rev.) resid. life are implemented along with 13 quantile function operators for reliability and survival analyses.  Exact analytical bootstrap estimates of order statistics, LMs, and variances-covariances of LMs are provided. The Harri-Coble Tau34-squared Normality Test is available. Distribution support with "L" (LMs), "TL" (TLMs) and added (+) support for right-tail censoring (RC) encompasses: 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 Resid. 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]. Multivariate sample L-comoments (LCMs) are implemented to measure asymmetric association.
Contains functions to estimate
        L-moments and trimmed L-moments from the data. Also
        contains functions to estimate the parameters of the normal
        polynomial quantile mixture and the Cauchy polynomial quantile
        mixture from L-moments and trimmed L-moments.
Functions for regional frequency analysis using the methods
  of J. R. M. Hosking and J. R. Wallis (1997), "Regional frequency analysis:
  an approach based on L-moments".
Linear model functions using permutation tests.
Linear ridge regression coefficient's estimation and testing with
    different ridge related measures such as MSE, R-squared etc.
Accompanies Markus Brauer, Bas Rokers, and John Curtin's two-course
    graduate series in General, Generalized, and Multi-level Linear Models (PSY
    610/710).
A collection of tests, data sets, and examples
 for diagnostic checking in linear regression models. Furthermore,
 some generic tools for inference in parametric models are provided.
Predict modulators regulating the ability of effectors to regulate their targets and produce
    modulator-effector-target triplets followed by goterm functional enrichment and survival analysis.This
	is mainly applied to long non-coding RNAs (lncRNAs) as candidate modulators regulating the ability of 
	transcription factors (TFs) to regulate their corresponding targets.
Identifies pathways synergisticly regulated by the interested lncRNA(long non-coding RNA) sets based on a lncRNA-mRNA(messenger RNA) interaction network. 1) The lncRNA-mRNA interaction network was built from the protein-protein interactions and the lncRNA-mRNA co-expression relationships in 28 RNA-Seq data sets. 2) The interested lncRNAs can be mapped into networks as seed nodes and a random walk strategy will be performed to evaluate the rate of each coding genes influenced by the seed lncRNAs. 3) Pathways regulated by the lncRNA set will be evaluated by a weighted Kolmogorov-Smirnov statistic as an ES Score. 4) The p value and false discovery rate value will also be calculated through a permutation analysis. 5) The running score of each pathway can be plotted and the heat map of each pathway can also be plotted if an expression profile is provided. 6) The rank and scores of the gene list of each pathway can be printed.
Prioritizing the disease lncRNAs based on random walking on a composite Multi-level network.
Allows the simultaneous analysis of responses and response times in an Item Response Theory (IRT) modelling framework. Parameter estimation is done with a MCMC algorithm. LNIRT replaces the package CIRT, which was written by Rinke Klein Entink. For reference, see the paper by Fox, Klein Entink and Van der Linden (2007), "Modeling of Responses and Response Times with the Package cirt", Journal of Statistical Software, <doi:10.18637/jss.v020.i07>.
Various plots and functions that make use of the lattice/trellis plotting framework. 
   The plots (which include 'loaPlot', 'GoogleMap' and 'trianglePlot') use panelPal(), a function that 
   extends 'lattice' and 'hexbin' package methods to automate plot subscript and panel-to-panel 
   and panel-to-key synchronization/management.
Simplicial, Mahalanobis and Ellipsoid Local and Global Depth
Computational routines for estimating 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 <DOI:10.1016/j.jeconom.2012.08.001>.
The package converts R data onto input and data for LocalSolver,
    executes optimization and exposes optimization results as R data.
    LocalSolver (http://www.localsolver.com/) is an optimization engine
    developed by Innovation24 (http://www.innovation24.fr/). It is designed to
    solve large-scale mixed-variable non-convex optimization problems.  The
    localsolver package is developed and maintained by WLOG Solutions
    (http://www.wlogsolutions.com/en/) in collaboration with Decision Support
    and Analysis Division at Warsaw School of Economics
    (http://www.sgh.waw.pl/en/).
Computation of local false discovery rates.
The main idea of the Local FDR algorithm is to estimate both proportion of null observations and the ratio of null and alternative densities. In the case that there are many null observations, this can be done reliably, through maximum likelihood or generalized linear models. This package implements this in the case that the null and alternative densities are Poisson.
Local regression, likelihood and density estimation.
Provides test of second-order stationarity for time
	series (for dyadic and arbitrary-n length data). Provides
	localized autocovariance, with confidence intervals,
	for locally stationary (nonstationary) time series.
Datasets for "Statistics:Unlocking the Power of Data" by
        Lock, Lock, Lock, Lock and Lock
Data sets and other utilities for 
    'Statistics: Unlocking the Power of Data'
    by Lock, Lock, Lock, Lock and Lock 
    (ISBN : 978-0-470-60187-7, http://lock5stat.com/).
Genome-wide association (GWAS) analyses
  of a biomarker that account for the limit of detection.
Local Ordinal embedding (LOE) is one of graph embedding methods for unweighted graphs.
logr4 provides an object-oriented logging system that uses an API
    roughly equivalent to log4j and its related variants.
Methods for fitting log-link GLMs and GAMs to binomial data,
    including EM-type algorithms with more stable convergence properties than standard methods.
Computes a log-concave (maximum likelihood) estimator for
        i.i.d. data in any number of dimensions.
Based on right or interval censored data, compute the maximum likelihood estimator of a (sub)probability density under the assumption that it is log-concave. For further information see Duembgen, Rufibach, and Schuhmacher (2011, preprint).
Given independent and identically distributed observations X(1), ..., X(n), compute the maximum likelihood estimator (MLE) of a density as well as a smoothed version of it under the assumption that the density is log-concave, see Rufibach (2007) and Duembgen and Rufibach (2009). The main function of the package is 'logConDens' that allows computation of the log-concave MLE and its smoothed version. In addition, we provide functions to compute (1) the value of the density and distribution function estimates (MLE and smoothed) at a given point (2) the characterizing functions of the estimator, (3) to sample from the estimated distribution, (5) to compute a two-sample permutation test based on log-concave densities, (6) the ROC curve based on log-concave estimates within cases and controls, including confidence intervals for given values of false positive fractions (7) computation of a confidence interval for the value of the true density at a fixed point. Finally, three datasets that have been used to illustrate log-concave density estimation are made available.
Computes maximum likelihood estimate of a log-concave density  with fixed and known location of the mode. Performs inference about the mode via a likelihood ratio test. Extension of the logcondens package.
Given independent and identically distributed observations X(1), ..., X(n), allows to compute the maximum likelihood estimator (MLE) of probability mass function (pmf) under the assumption that it is log-concave, see Weyermann (2007) and Balabdaoui, Jankowski, Rufibach, and Pavlides (2012). The main functions of the package are 'logConDiscrMLE' that allows computation of the log-concave MLE, 'logConDiscrCI' that computes pointwise confidence bands for the MLE, and 'kInflatedLogConDiscr' that computes a mixture of a log-concave PMF and a point mass at k.
Computes a cox PH model with a log concave baseline distribution. If no covariates are provided, estimates the log concave NPMLE. Built specifically for interval censored data, where data is a n by 2 matrix with [i, 1] as the left side of the interval for subject i and [i,2] as right side. Uncensored data can be entered by setting [i,1] = [i,2]. Alternatively, this can also handle uncensored data. If all the data is uncensored, you may enter data as a length(n) vector.
logging is a pure R package that implements the ubiquitous
        log4j package.
Two classification ensemble methods based on logic regression models.  LogForest uses a bagging approach to construct an ensemble of logic regression models.  LBoost uses a combination of boosting and cross-validation to construct an ensemble of logic regression models.  Both methods are used for classification of binary responses based on binary predictors and for identification of important variables and variable interactions predictive of a binary outcome.
This package contains the functions necessary to run the LOGICOIL algorithm. LOGICOIL can be used to differentiate between antiparallel dimers, parallel dimers, trimers and higher-order coiled-coil sequence. By covering >90 percent of the known coiled-coil structures, LOGICOIL is a net improvement compared with other existing methods, which achieve a predictive coverage of around 31 percent of this population. As such, LOGICOIL is particularly useful for researchers looking to characterize novel coiled-coil sequences or studying coiled-coil containing protein assemblies. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences.
Access to powerful logic minimization algorithms and data structures that operate on a sum-of-products truth table.  The core algorithms are built on Espresso Version 2.3 developed at UC Berkeley for digital logic synthesis purposes.  Enhancements have been made to integrate within the R framework and support additional logic optimization use cases such as those needed by Qualitative Comparative Analysis (QCA) and Genetic Programming.  There are no expressed or implied warranties.
Routines for fitting Logic Regression models.
Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys 
	prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal
	solution to the problem of separation in logistic regression. If needed, the bias reduction can be turned off such that ordinary
	maximum likelihood logistic regression is obtained.
Diagnostic tests and plots for GLMs (generalized linear models)
    with binomial/ binary outcomes, particularly logistic regression.
Dimensionality reduction techniques for binary data including
    logistic PCA.
Functions, data and code for Hilbe, J.M. 2015. Practical Guide to Logistic Regression, by Chapman and Hall/CRC.
Fits linear discrete logit choice models with l2 regularization. To handle reasonably sized datasets, we employ an accelerated version of generalized gradient descent.
LogitNet is developed for inferring network of binary
        variables under the high-dimension-low-sample-size setting
Density, distribution, quantile and random generation function for the logitnormal distribution. Estimation of the mode and the first two moments. Estimation of distribution parameters.
r,d,p,q functions for the double log normal distribution
Functions to fit log-multiplicative models using gnm, with
  support for convenient printing, plots, and jackknife/bootstrap
  standard errors. For complex survey data, models can be fitted from
  design objects from the 'survey' package. Currently supported models
  include UNIDIFF (Erikson & Goldthorpe), a.k.a. log-multiplicative
  layer effect model (Xie), and several association models: Goodman's
  row-column association models of the RC(M) and RC(M)-L families
  with one or several dimensions; two skew-symmetric association
  models proposed by Yamaguchi and by van der Heijden & Mooijaart.
LogrankA provides a logrank test across unlimited groups with the
 possibility to input aggregated survival data.
Routines for the logspline density estimation. oldlogspline()
  uses the same algorithm as the logspline 1.0.x package - the Kooperberg
  and Stone (1992) <DOI: 10.2307/1390786> algorithm (with an improved interface).
  The recommended routine logspline() uses an algorithm from Stone et al (1997). 
  <DOI: 10.1214/aos/1031594728>
Kernel regression smoothing with adaptive local or global plug-in
	     bandwidth selection.
Computes the Lomb-Scargle Periodogram for unevenly sampled time series. Includes a randomization procedure to obtain reliable p-values.
Methods for plotting categorical longitudinal and time-series data by mapping individuals to the vertical space (each horizontal line represents a participant), time (or repeated measures) to the horizontal space, categorical (or discrete) states as facets using color or shade, and events to points using plotting characters. Sorting individuals in the vertical space and (or) stratifying them by groups can reveal patterns in the changes over time.
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.
Tools expand vectors of short URLs into long URLs. No API services are used,
    which may mean that this operates more slowly than API services do (since they usually
    cache results of expansions every user of the service performs).
Efficient approximate leave-one-out cross-validation (LOO)
    using Pareto smoothed importance sampling (PSIS), a new procedure for
    regularizing importance weights. As a byproduct of the calculations, we also
    obtain approximate standard errors for estimated predictive errors and for
    the comparison of predictive errors between models. We also compute the
    widely applicable information criterion (WAIC).
Fits look-up tables by filling entries with the mean or median values of observations
            fall in partitions of the feature space. Partitions can be determined by user of the
            package using input argument feature.boundaries, and dimensions of the feature space
            can be any combination of continuous and categorical features provided by the data set.
            A Predict function directly fetches corresponding entry value, and a default value is
            defined as the mean or median of all available observations.
            The table and other components are represented using the S4 class lookupTable.
The program can perform loop analysis and plot network
        structure (especially for food webs),including minimum spanning
        tree, loop decomposition of weighted directed graphs, and other
        network properties which may be related to food chain
        properties in ecology.
Loop analysis makes qualitative predictions of variable change in a system of causally interdependent variables, where "qualitative" means sign only (i.e. increases, decreases, non change, and ambiguous). This implementation includes output support for graphs in .dot file format for use with visualization software such as graphviz (graphviz.org). 'LoopAnalyst' provides tools for the construction and output of community matrices, computation and output of community effect matrices, tables of correlations, adjoint, absolute feedback, weighted feedback and weighted prediction matrices, change in life expectancy matrices, and feedback, path and loop enumeration tools.
Remedies a common problem in piping: not having access to
    intermediate outputs of the pipe. Within a "loop", a piping intermediate
    is  stored in a stack archive, data is processed, and then both the
    stored intermediate and the current output are reintegrated using an
    "ending" function. Two special ending functions are provided: amend and
    insert. However, any ending function can be specified, including merge
    functions, join functions, setNames(), etc. This framework allows the
    following work-flow: focus on a particular aspect or section of a
    data set, conduct specific operations, and then reintegrate changes into
    the whole.
Analysis of Differential Item Functioning (DIF) for
        dichotomous and polytomous items using an iterative hybrid of
        ordinal logistic regression and item response theory (IRT).
Estimate covariance matrices that contain low rank and sparse components
Functions for simulating missing morphometric
	data randomly, with taxonomic bias and with anatomical bias. LOST also 
	includes functions for estimating linear and geometric morphometric data.
Running Lotka's Law following Pao (1985)(DOI: 10.1016/0306-4573(85)90055-X). The Law is based around the proof that the number of authors making n contributions is about 1/n^{a} of those making one contribution.
Performs the trimmed k-means clustering algorithm with lower memory use. It also provides a number of utility functions such as BIC calculations.
This package contains routines and documentation for
        solving quadratic programming problems where the hessian is
        represented as the product of two matrices.
Optimization of bipartite modularity using LP-BRIM (Label propagation
  followed by Bipartite Recursively Induced Modularity).
Implements the LPC method of Witten&Tibshirani(Annals of Applied Statistics 2008) for identification of significant genes in a microarray experiment.
Fitting multivariate data patterns with local principal curves; including simple tools for data compression (projection), bandwidth selection, and measuring goodness-of-fit.
Estimates the intensity function, or its derivative of a given order, of a multiplicative counting process using the local polynomial method
Apply Univariate Long Memory Models, 
    Multivariate Short Memory Models To Hydrological Dataset.
Two local polynomial estimators for solving the errors-in-variables problem: one is in Delaigle, Fan, and Carroll (2009), and one is in Huang and Zhou (2014+). The SIMEX bandwidth selection method is also provided for both estimators.
LPmerge creates a consensus genetic map by merging linkage maps from different populations.  The software uses linear programming (LP) to efficiently minimize the mean absolute error between the consensus map and the linkage maps.  This minimization is performed subject to linear inequality constraints that ensure the ordering of the markers in the linkage maps is preserved.  When marker order is inconsistent between linkage maps, a minimum set of ordinal constraints is deleted to resolve the conflicts.
lpmodeler is a set of user-friendly functions to simplify the modelling of linear programs (LP) and mixed integer programs (MIP). It provides a unified interface compatible with optimization packages: Rsymphony.
Contains a function called "LPR" to estimate coefficients using Lasso and Partial Ridge method and to calculate confidence intervals through bootstrap.
Local Polynomial Regression with Ridging.
An implementation of the Linear Predictor Score approach, as initiated by Radmacher et al. (J Comput Biol 2001) and enhanced by Wright et al. (PNAS 2003) for gene expression signatures. Several tools for unsupervised clustering of gene expression data are also provided.
Lp_solve is freely available (under LGPL 2) software for
        solving linear, integer and mixed integer programs. In this
        implementation we supply a "wrapper" function in C and some R
        functions that solve general linear/integer problems,
        assignment problems, and transportation problems. This version
        calls lp_solve version 5.5.
The lpSolveAPI package provides an R interface to 'lp_solve',
    a Mixed Integer Linear Programming (MILP) solver with support for pure
    linear, (mixed) integer/binary, semi-continuous and special ordered sets
    (SOS) models.
Learned Pattern Similarity (LPS) for time series. 
			Implements a novel approach to model the dependency structure 
			in time series that generalizes the concept of autoregression to local 
			auto-patterns. Generates a pattern-based representation of time series
			along with a similarity measure called Learned Pattern Similarity (LPS).
			Introduces a generalized autoregressive kernel.This package is based on the 
			'randomForest' package by Andy Liaw.
Specially designed rank transform based Legendre Polynomial-like (LP) orthonormal transformations are implemented for non-linear signal processing.
This package provides some basic infrastructure and tools
        to fit Generalized Linear Models (GLMs) via penalized
        likelihood inference. Estimating procedures already implemented
        are the LQA algorithm (that is where its name come from),
        P-IRLS, RidgeBoost, GBlockBoost and ForwardBoost.
This is a collection of functions to fit quantile regression models for independent and hierarchical data.
It fits a robust linear quantile regression model using a new
    family of zero-quantile distributions for the error term. This family of
    distribution includes skewed versions of the Normal, Student's t, Laplace, Slash
    and Contaminated Normal distribution. It also performs logistic quantile regression for bounded responses
    as shown in Bottai et.al.(2009) <doi:10.1002/sim.3781>. It provides estimates and full inference.
    It also provides envelopes plots for assessing the fit and confidences bands
    when several quantiles are provided simultaneously.
Provides functions for calculating test statistics, simulating quantiles 
	and simulating p-values of likelihood ratio contrast tests in regression models 
	with a lack of identifiability.
In the fashion of 'node.js' <https://nodejs.org/>, requires a file,
  sourcing into the current environment only the variables explicitly specified
  in the module.exports or exports list variable. If the file was already sourced,
  the result of the earlier sourcing is returned to the caller.
Implements a Gibbs sampler to do linear regression with multiple covariates, multiple responses, Gaussian measurement errors on covariates and responses, Gaussian intrinsic scatter, and a covariate prior distribution which is given by either a Gaussian mixture of specified size or a Dirichlet process with a Gaussian base distribution.
When multicollinearity exists among predictor variables of the linear model, least square estimators does not provide a better solution for estimating parameters. To deal with multicollinearity several estimators are proposed in the literature. Some of these estimators are Ordinary Least Square Estimator (OLSE), Ordinary Generalized Ordinary Least Square Estimator (OGOLSE), Ordinary Ridge Regression Estimator (ORRE), Ordinary Generalized Ridge Regression Estimator (OGRRE), Restricted Least Square Estimator (RLSE), Ordinary Generalized Restricted Least Square Estimator (OGRLSE), Ordinary Mixed Regression Estimator (OMRE), Ordinary Generalized Mixed Regression Estimator (OGMRE), Liu Estimator (LE), Ordinary Generalized Liu Estimator (OGLE), Restricted Liu Estimator (RLE), Ordinary Generalized Restricted Liu Estimator (OGRLE), Stochastic Restricted Liu Estimator (SRLE), Ordinary Generalized Stochastic Restricted Liu Estimator (OGSRLE), Type (1),(2),(3) Liu Estimator (Type-1,2,3 LTE), Ordinary Generalized Type (1),(2),(3) Liu Estimator (Type-1,2,3 OGLTE), Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 ALTE), Ordinary Generalized Type (1),(2),(3) Adjusted Liu Estimator (Type-1,2,3 OGALTE), Almost Unbiased Ridge Estimator (AURE), Ordinary Generalized Almost Unbiased Ridge Estimator (OGAURE), Almost Unbiased Liu Estimator (AULE), Ordinary Generalized Almost Unbiased Liu Estimator (OGAULE), Stochastic Restricted Ridge Estimator (SRRE), Ordinary Generalized Stochastic Restricted Ridge Estimator (OGSRRE), Restricted Ridge Regression Estimator (RRRE) and Ordinary Generalized Restricted Ridge Regression Estimator (OGRRRE). To select the best estimator in a practical situation the Mean Square Error (MSE) is used. Using this package scalar MSE value of all the above estimators and Prediction Sum of Square (PRESS) values of some of the estimators can be obtained, and the variation of the MSE and PRESS values for the relevant estimators can be shown graphically.
R code of a likelihood ratio test for genome-wide association under genetic
    heterogeneity.
Estimates two-dimensional local wavelet spectra
Wavelet-based methods for testing stationarity and quadtree segmenting of images
The basic idea of latent semantic analysis (LSA) is, 
  that text do have a higher order (=latent semantic) structure which, 
  however, is obscured by word usage (e.g. through the use of synonyms 
  or polysemy). By using conceptual indices that are derived statistically 
  via a truncated singular value decomposition (a two-mode factor analysis) 
  over a given document-term matrix, this variability problem can be overcome.
Provides functions that allow for convenient working
    with Latent Semantic Analysis. For actually building an LSA space, use the
    package 'lsa' or other specialized software.
Dieses R-Paket stellt Zusatzmaterial in Form von Daten, Funktionen
             und R-Hilfe-Seiten für den Herausgeberband Breit, S. und Schreiner, 
             C. (Hrsg.). (2016). "Large-Scale Assessment mit R: Methodische Grundlagen 
             der österreichischen Bildungsstandardüberprüfung." Wien: facultas. 
             (ISBN: 978-3-7089-1343-8, <https://www.bifie.at/node/3770>) zur 
             Verfügung.
Functions for performing least-squares bilinear clustering of
    three-way data. The method uses the bilinear decomposition (or biadditive
    model) to model two-way matrix slices while clustering over the third way.
    Up to four different types of clusters are included, one for each term of the
    bilinear decomposition. In this way, matrices are clustered simultaneously on
    (a subset of) their overall means, row margins, column margins and row-column
    interactions. The orthogonality of the bilinear model results in separability of
    the joint clustering problem into four separate ones. Three of these subproblems
    are specific k-means problems, while a special algorithm is implemented for the
    interactions. Plotting methods are provided, including biplots for the low-rank
    approximations of the interactions.
Estimators and visualization for local statistical complexity of
    (N+1)D fields. In particular for 0, 1 and 2 dimensional space this package
    provides useful visualization.
Create lots of colorful plots in a plethora of variations (try the LSD demotour() )
Interfaces R with LSD. Reads object-oriented data in results files (.res) produced by LSD and creates appropriate multi-dimensional arrays in R. Also provides functions to extract basic information and statistics from those files. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente (documentation and downloads available at <http://labsimdev.org>).
Fit a least square dummy variable regression
It contains functions that solve least squares linear
	     regression problems under linear equality/inequality
	     constraints. It is developed based on the 'Fortran' program of
	     Lawson and Hanson (1974, 1995), which is public domain and
	     available at http://www.netlib.org/lawson-hanson.
Linear multiple output using sparse group lasso. The algorithm
    finds the sparse group lasso penalized maximum likelihood estimator. This
    result in feature and parameter selection, and parameter estimation. Use of
    parallel computing for cross validation and subsampling is supported through the
    'foreach' and 'doParallel' packages. Development version is on GitHub, please
    report package issues on GitHub.
Calculates the (localised) length of the shorth and
        supplies corresponding diagnostic plots.
Conduct structural equation modeling via penalized likelihood.
Obtain least-squares means for many linear, generalized linear, 
  and mixed models. Compute contrasts or linear functions of least-squares
  means, and comparisons of slopes. Plots and compact letter displays.
The package compiles functions for calculating prices of American put options with Least Squares Monte Carlo method. The option types are plain vanilla American put, Asian American put, and Quanto American put. The pricing algorithms include variance reduction techniques such as Antithetic Variates and Control Variates. Additional functions are given to derive "price surfaces" at different volatilities and strikes, create 3-D plots, quickly generate Geometric Brownian motion, and calculate prices of European options with Black & Scholes analytical solution.
Creates plots of peptides from shotgun proteomics analysis of secretome and lysate samples. These plots contain associated protein features and scores for potential secretion and truncation.
Implements the LS-PLS (least squares - partial least
        squares) method described in for instance Jørgensen, K.,
        Segtnan, V. H., Thyholt, K., Næs, T. (2004) A Comparison of
        Methods for Analysing Regression Models with Both Spectral and
        Designed Variables.  Journal of Chemometrics, 18(10), 451--464.
A collection of tools intended to make introductory statistics easier to teach, including wrappers for common hypothesis tests and basic data manipulation. It accompanies Navarro, D. J. (2015). Learning Statistics with R: A Tutorial for Psychology Students and Other Beginners, Version 0.5. [Lecture notes] School of Psychology, University of Adelaide, Adelaide, Australia. ISBN: 978-1-326-18972-3. URL: http://health.adelaide.edu.au/psychology/ccs/teaching/lsr/.
Due to lack of proper inference procedure and software,
        the ordinary linear regression model is seldom used in practice
        for the analysis of right censored data. This paper presents an
        S-Plus/R program that implements a recently developed inference
        procedure (Jin, Lin and Ying, 2006)\cite{Jin} for the
        accelerated failure time model based on the least-squares
        principle.
Has a set of functions that allows stationary analysis and locally stationary time series analysis.
Functions for simulating realizations from the posterior distribution of
	a latent trait of an item response model. Distributions are conditional on one or
	a subset of response patterns (e.g., sum scores). Functions for computing likelihoods,
	Fisher and observed information, posterior modes, and profile likelihood confidence
	intervals are also included. These functions are designed to be easily amenable to 
	user-specified models.
Analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.
Targeted Maximum Likelihood Estimation (TMLE) of
    treatment/censoring specific mean outcome or marginal structural model for
    point-treatment and longitudinal data.
Calculation of rectifying LTPD and AOQL plans for sampling inspection by variables which minimize mean inspection cost per lot of process average quality.
A set of functions to execute the linear-transformation of
        replicate (LTR) algorithm for preprocessing of microarray data
Recursive partition algorithms designed for fitting survival tree
    with left-truncated and right censored (LTRC) data. Such LTRC trees can also
    be used to fit survival tree with time-varying covariates.
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.
Sparklines are small plots (about one line of text high),
  made popular by Edward Tufte.  This package is the interface from R
  to the LaTeX package sparklines by Andreas Loeffer and Dan Luecking
  (<http://www.ctan.org/pkg/sparklines>).  It can work with Sweave or
  knitr or other engines that produce TeX.  The package can be used to
  plot vectors, matrices, data frames, time series (in ts or zoo format).
Functions to work with date-times and time-spans: fast and user
    friendly parsing of date-time data, extraction and updating of components of
    a date-time (years, months, days, hours, minutes, and seconds), algebraic
    manipulation on date-time and time-span objects. The 'lubridate' package has
    a consistent and memorable syntax that makes working with dates easy and
    fun.
Likelihood inference in case-control studies of a rare
        disease under independence or simple dependence of genetic and
        non-genetic covariates
Print vectors (and data frames) of floating point numbers
    using a non-scientific format optimized for human readers.  Vectors
    of numbers are rounded using significant digits, aligned at the
    decimal point, and all zeros trailing the decimal point are dropped.
Reformat currency-based data as numeric values (or numeric values
    as currency-based data) and convert between currencies.
Probabilistic record linkage without direct identifiers using only diagnosis codes.
Classes and methods for spatially explicit land use change
    modelling in R.
A simple client for the Lumen Database <https://lumendatabase.org/> (formerly Chilling Effects) API.
A collection of various R functions for the purpose of Luminescence
    dating data analysis. This includes, amongst others, data import, export,
    application of age models, curve deconvolution, sequence analysis and
    plotting of equivalent dose distributions.
Provides functions to calculate lunar and other environmental
    covariates.
Provides flexible but lightweight logging facilities for R scripts.
    Supports priority levels for logs and messages, flagging messages, capturing
    script output, switching logs, and logging to files or connections.
Latent variable models for network data using fast inferential
    procedures.
A model for mixed ordinal and continuous responses is
        presented where the heteroscedasticity of the variance of the
        continuous response is also modeled. In this model ordinal
        response can be dependent on the continuous response. The aim
        is to use an approach similar to that of Heckman (1978) for the
        joint modelling of the ordinal and continuous responses. With
        this model, the dependence between responses can be taken into
        account by the correlation between errors in the models for
        continuous and ordinal responses
Estimate, fit and compare Structural Equation Models (SEM) and network models (Gaussian Graphical Models; GGM) using OpenMx. Allows for two possible generalizations to include GGMs in SEM: GGMs can be used between latent variables (latent network modeling; LNM) or between residuals (residual network modeling; RNM).
Implements the letter value 'boxplot' which extends the standard
    'boxplot' to deal with both larger and smaller number of data points
    by dynamically selecting the appropriate number of letter values to display.
The manual approach of Litchfield and Wilcoxon (1949)
    <http://jpet.aspetjournals.org/content/96/2/99.abstract>
    for evaluating dose-effect experiments
    is automated so that the computer can do the work.
Functions to quickly read LXB parameter data.
We provide a clonality score estimator that takes full advantage of the multi-biological-replicate structure of modern sequencing experiments; it specifically takes into account the reality that, typically, the clonal coverage is well below 0.1%.
This package contains functions to read in and manipulate
        air quality model output from Models3-formatted files.  This
        format is used by the Community Multiscale Air Quaility (CMAQ)
        model.
The 10.000 time series from the M4 time series forecasting
    competition, including print and plot functions.
Provides binomial tree models for European, American and Asian
    Options as well as Interest Rates. Monte Carlo Simulation and Methods for
    Solving Differential Equations are also included.
Performs binary and multiclass boosting in maximum-margin, sparse, smooth and normal settings
 as described in "A Boosting Framework on Grounds of Online Learning" by T. Naghibi and B. Pfister, (2014). 
 For further information regarding the algorithms, please refer to http://arxiv.org/abs/1409.7202
This is an integrated meta-analysis package for conducting
        a correlational research synthesis. One of the unique features
        of this package is in its integration of user-friendly
        functions to facilitate statistical analyses at each stage in a
        meta-analysis with correlations. It uses recommended procedures
        as described in The Handbook of Research Synthesis and
        Meta-Analysis (Cooper, Hedges, & Valentine, 2009).
Performs causal mediation analysis under confounding or correlated errors. This package includes a single level mediation model, a two-level mediation model, and a three-level mediation model for data with hierarchical structures. Under the two/three-level mediation model, the correlation parameter is identifiable and is estimated based on a hierarchical-likelihood, a marginal-likelihood or a two-stage method. See reference for details (Zhao, Y., & Luo, X. (2014). Estimating Mediation Effects under Correlated Errors with an Application to fMRI. arXiv preprint arXiv:1410.7217. <https://arxiv.org/abs/1410.7217>).
Connects to <https://machi.na/> and allows the creation
    of time series, and running backtests on selected strategy if requested.
Performs Quality Analysis on Machina algebraic indicators 'sma' (simple moving average), 'wavg' (weighted average),'xavg' (exponential moving average), 'hma' (Hull moving average), 'adma' (adaptive moving average), 'tsi' (true strength index), 'rsi' (relative strength index), 'gauss' (Gaussian elimination), 'momo' (momentum), 't3' (triple exponential moving average), 'macd' (moving average convergence divergence). Machina is a strategy creation and backtesting engine for quants and financial professionals (see <https://machi.na/> for more information).
Download data from the Ada and Archibald MacLeish Field 
    Station in Whately, MA. The Ada 
    and Archibald MacLeish Field Station is a 260-acre patchwork of 
    forest and farmland located in West Whately, MA that provides opportunities 
    for faculty and students to pursue environmental research, outdoor 
    education, and low-impact recreation 
    (see <http://www.smith.edu/ceeds/macleish.php> for more information). 
    This package contains 
    weather data over several years, and spatial data on various man-made and 
    natural structures.
'Maclinical' implements class prediction using both
        microarray data and clinical parameters. It addresses the
        question of the additional predictive value of microarray data.
        Class prediction is performed using a two-step method combining
        (pre-validated) PLS dimension reduction and random forests.
A collection of functions for conducting a meta-analysis with mean differences data.  It uses recommended procedures as	described in The 	Handbook of Research Synthesis and Meta-Analysis	(Cooper, Hedges, & Valentine, 2009).
Provides functions for diagnostic meta-analysis. Next to basic analysis and visualization the bivariate Model of Reitsma et al. (2005) that is equivalent to the HSROC of Rutter&Gatsonis (2001) can be fitted. A new approach based to diagnostic meta-analysis of Holling et al. (2012) is also available. Standard methods like summary, plot and so on are provided.
Contains the Maddison Project database, which provides estimates of
    GDP per capita for all countries in the world between AD 1 and 2010. See
    http://www.ggdc.net/maddison for more information.
An object that supports automatic differentiation
    of matrix- and multidimensional-valued functions with 
    respect to multidimensional independent variables. 
    Automatic differentiation is via 'forward accumulation'.
Estimates average treatment effects using model average double robust (MA-DR) estimation. The MA-DR estimator is defined as weighted average of double robust estimators, where each double robust estimator corresponds to a specific choice of the outcome model and the propensity score model. The MA-DR estimator extend the desirable double robustness property by achieving consistency under the much weaker assumption that either the true propensity score model or the true outcome model be within a specified, possibly large, class of models.
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.
Fits several forecast models available from the forecast package
    and selects the best one according to an error metric. Its main function
    is select_forecast().
Data class for increased interoperability working with spatial-temporal data together with corresponding functions and methods (conversions, basic calculations and basic data manipulation). The class distinguishes between spatial, temporal and other dimensions to facilitate the development and interoperability of tools build for it. Additional features are name-based addressing of data and internal consistency checks (e.g. checking for the right data order in calculations).
a collection of efficient, vectorized algorithms for the
 creation and investigation of magic squares and hypercubes, including
 a variety of functions for the manipulation and analysis of
 arbitrarily dimensioned arrays.  The package includes methods for
 creating normal magic squares of any order greater than 2.  The
 ultimate intention is for the package to be a computerized embodiment
 all magic square knowledge, including direct numerical verification
 of properties of magic squares (such as recent results on the
 determinant of odd-ordered semimagic squares).  Some antimagic
 functionality is included.  The package also
 serves as a rebuttal to the often-heard comment "I thought R
 was just for statistics".
Functions to make useful (and pretty) plots for scientific plotting. Additional plotting features are added for base plotting, with particular emphasis on making attractive log axis plots.
Magic functions to obtain results from for loops.
Bindings to ImageMagick: the most comprehensive open-source image
    processing library available. Supports many common formats (png, jpeg, tiff,
    pdf, etc) and manipulations (rotate, scale, crop, trim, flip, blur, etc).
    All operations are vectorized via the Magick++ STL meaning they operate either
    on a single frame or a series of frames for working with layers, collages,
    or animation. In RStudio images are automatically previewed when printed to
    the console, resulting in an interactive editing environment.
Constructs the normalized magnetic Laplacian Matrix of a square matrix, returns the eigenvectors and visualization of magnetic eigenmap.
Implements an interface to the legacy Fortran code from O'Connell and Dobson (1984) <DOI:10.2307/2531148>. Implements Fortran 77 code for the methods developed by Schouten (1982) <DOI:10.1111/j.1467-9574.1982.tb00774.x>. Includes estimates of average agreement for each observer and average agreement for each subject.
Provides a mechanism for chaining commands with a
    new forward-pipe operator, %>%. This operator will forward a
    value, or the result of an expression, into the next function
    call/expression. There is flexible support for the type
    of right-hand side expressions. For more information, see
    package vignette.
    To quote Rene Magritte, "Ceci n'est pas un pipe."
Provides a Graphical User Interface for Analysing DNA Microarray Data. It performs functional enrichment on genes of interest, identifies gene symbols and also builds co-expression network.
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 by Normal
    and Skew-Normal distributions, considering appropriate parameterizations of
    the variance-covariance matrix that takes into account the intrinsic nature of
    interval data, and lead to four different possible configuration structures.
    The Skew-Normal parameters can be estimated by maximum likelihood, while Normal
    parameters may be estimated by maximum likelihood or robust trimmed maximum
    likelihood methods.
Create dummy variables from categorical data.
    This package can convert categorical data (factor and ordered) into
    dummy variables and handle multiple columns simultaneously.
    This package enables to select whether a dummy variable for base group
    is included (for principal component analysis/factor analysis) or
    excluded (for regression analysis) by an option.
A user-friendly interface for the construction of
        'Makefiles'.
A user-friendly tool for visualizing categorical or group movement.
This package creates an empty framework of files and
        directories for the "Load, Clean, Func, Do" structure described
        by Josh Reich.
Find dark genes. These genes are often disregarded due to no detected mutation or differential expression, but are important in coordinating the functionality in cancer networks.
A complete analysis pipeline for matrix-assisted laser
        desorption/ionization-time-of-flight (MALDI-TOF) and other
        two-dimensional mass spectrometry data. In addition to commonly
        used plotting and processing methods it includes distinctive
        features, namely baseline subtraction methods such as
        morphological filters (TopHat) or the statistics-sensitive
        non-linear iterative peak-clipping algorithm (SNIP), peak
        alignment using warping functions, handling of replicated
        measurements as well as allowing spectra with different
        resolutions.
Functions for reading (tab, csv, Bruker fid, Ciphergen
    XML, mzXML, mzML, imzML, Analyze 7.5, CDF, mMass MSD) and
    writing (tab, csv, mMass MSD, mzML, imzML) different file
    formats of mass spectrometry data into/from MALDIquant objects.
This package allows you to train topic models in mallet and load results directly into R.
Implementation of methods for microarray meta-analysis.
Designing multi-arm multi-stage studies with normal endpoints and known variance.
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. The package
        includes functions for the MAMSE weighted Kaplan-Meier estimate
        and for MAMSE weighted ROC curves.
This will allow easier management of a CRAN-style repository on
    local networks (i.e. not on CRAN). This might be necessary where hosted
    packages contain intellectual property owned by a corporation.
High-dimensional data integration is a critical but difficult problem in genomics research because of potential biases from high-throughput experiments. We present MANCIE, a computational method for integrating two genomic data sets with homogenous dimensions from different sources based on a PCA procedure as an approximation to a Bayesian approach.
Datasets designed to be used in conjunction with Mango Solutions
    training materials and the book SAMS Teach Yourself R in 24 Hours (ISBN: 978-0-672-33848-9).
Create interactive Q-Q, manhattan and volcano plots 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 point to show details or drag a rectangle to
    zoom. A manhattan plot is a popular graphical method for visualizing results
    from high-dimensional data analysis such as a (epi)genome wide association study
    (GWAS or EWAS), in which p-values, Z-scores, test statistics are plotted on a scatter
    plot against their genomic position. Manhattan plots are used for visualizing
    potential regions of interest in the genome that are associated with a phenotype.
    Interactive manhattan plots allow the inspection of specific value (e.g. rs number or
    gene name) by hovering the mouse over a cell, as well as zooming into a region of the
    genome (e.g. a chromosome) by dragging a rectangle around the relevant area.
    This work is based on the 'qqman' package by Stephen Turner and the 'plotly.js'
    engine. It produces similar manhattan and Q-Q plots as the 'manhattan' and 'qq'
    functions in the 'qqman' package, with the advantage of including extra annotation 
    information and interactive web-based visualizations directly from R. 
    Once uploaded to a 'plotly' account, 'plotly' graphs (and the data behind them) 
    can be viewed and modified in a web browser.
Provides access to coded election programmes from the Manifesto
    Corpus and to the Manifesto Project's Main Dataset and routines to analyse this
    data. The Manifesto Project (https://manifesto-project.wzb.eu) collects and
    analyses election programmes across time and space to measure the political
    preferences of parties. The Manifesto Corpus contains the collected and
    annotated election programmes in the Corpus format of the package 'tm' to enable
    easy use of text processing and text mining functionality. Specific functions
    for scaling of coded political texts are included.
An R interface to the 'ROPTLIB' optimization library (see <http://www.math.fsu.edu/~whuang2> for more information). Optimize real-valued functions over manifolds such as Stiefel, Grassmann, and Symmetric Positive Definite matrices.
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.
Like package 'manipulate' does for static graphics, this package
    helps to easily add controls like sliders, pickers, checkboxes, etc. that 
    can be used to modify the input data or the parameters of an interactive 
    chart created with htmlwidgets.
The utility of this package includes finite mixture modeling and model-based clustering through Manly mixture models by Zhu and Melnykov (2016) <DOI:10.1016/j.csda.2016.01.015>. It also provides capabilities for forward and backward model selection procedures.
Implemented are various tests for semi-parametric repeated measures
    and general MANOVA designs that do neither assume multivariate normality nor
    covariance homogeneity, i.e., the procedures are applicable for a wide range
    of general multivariate factorial designs.
Performs the multiple testing procedures of Cox (2011) <doi:10.5170/CERN-2011-006> and Wong and Cox (2007) <doi:10.1080/02664760701240014>.
Allows the user to generate a list of features (gene, pseudo, RNA, 
    CDS, and/or UTR) directly from NCBI database for any species with a current 
    build available. Option to save downloaded and formatted files is available, 
    and the user can prioritize the feature list based on type and assembly builds 
    present in the current build used. The user can then use the list of features 
    generated or provide a list to map a set of markers (designed for SNP markers 
    with a single base pair position available) to the closest feature based on 
    the map build. This function does require map positions of the markers to be 
    provided and the positions should be based on the build being queried through 
    NCBI.
Functions and wrappers for using the Multiple Aggregation Prediction Algorithm (MAPA) for time series forecasting. MAPA models and forecasts time series at multiple temporal aggregation levels, thus strengthening and attenuating the various time series components for better holistic estimation of its structure.
Supplement to maps package, providing the larger and/or
	higher-resolution databases.
Estimation methods for phase-type
  distribution (PH) and Markovian arrival process (MAP) from
  empirical data (point and grouped data) and density function.
Contains functions for mapping odds ratios, hazard ratios, or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) or Cox models for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting linearly for confounding variables, using methods described by Kelsall and Diggle (1998), Webster at al. (2006), and Bai et al. (submitted).  Includes convenient functions for mapping point estimates and confidence intervals, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).
MAPLES is a general method for the estimation of age
        profiles that uses standard micro-level demographic survey
        data. The aim is to estimate smoothed age profiles and relative
        risks for time-fixed and time-varying covariates.
A minimal, light-weight set of tools for producing nice looking maps in R, with support for map projections.
Create simple maps; add sub-plots like pie plots to a map or any other plot; format, plot and export gridded data. The package was developed for displaying fisheries data but most functions can be used for more generic data visualisation.
Converts latitude/longitude into projected coordinates.
Utilities for visualizing species occurrence data. Includes
    functions to visualize occurrence data from 'spocc', 'rgbif',
    and other packages. Mapping options included for base R plots, 'ggplot2',
    'ggmap', 'leaflet' and 'GitHub' 'gists'.
Display of maps.  Projection code and larger maps are in
             separate packages ('mapproj' and 'mapdata').
Automated calculation and visualization of survey data statistics on a color-coded map.
Set of tools for manipulating and reading geographic data, in particular 'ESRI Shapefiles'; C code used from 'shapelib'. It includes binary access to 'GSHHG' shoreline files. The package also provides interface wrappers for exchanging spatial objects with packages such as 'PBSmapping', 'spatstat', 'maps', 'RArcInfo', 'Stata tmap', 'WinBUGS', 'Mondrian', and others.
Posterior maximization for topic models (LDA) in text analysis,
	as described in Taddy (2012) `on estimation and selection for topic models'.  Previous versions of this code were included as part of the textir package.  If you want to take advantage of openmp parallelization, uncomment the relevant flags in src/MAKEVARS before compiling.
Functions with example data for graphing, pruning, and
        mapping models from hierarchical clustering, and classification
        and regression trees.
Methods to view spatial objects interactively.
R functions for multivariate autoregressive analysis
The MAR1 package provides basic tools for preparing
        ecological community time-series data for MAR modeling,
        building MAR-1 models via model selection and bootstrapping,
        and visualizing and exporting model results. It is intended to
        make MAR analysis (sensu Ives et al. (2003) Analysis of
        community stability and ecological interactions from
        time-series data) a more accessible tool for anyone studying
        community dynamics.  The user need not necessarily be familiar
        with time-series modeling or command-based statistics programs
        such as R.
Multiplicative AR(1) with Seasonal is a stochastic
  process model built on top of AR(1). The package provides the
  following procedures for MAR(1)S processes: fit, compose, decompose,
  advanced simulate and predict.
Computation of various Markovian models for categorical data
    including homogeneous Markov chains of any order, MTD models, Hidden Markov
    models, and Double Chain Markov Models.
Datasets, constants, conversion factors, and utilities for 'MArine', 'Riverine',
             'Estuarine', 'LAcustrine' and 'Coastal' science. 
             The package contains among others: (1) chemical and
             physical constants and datasets, e.g. atomic weights, gas
             constants, the earths bathymetry; (2) conversion factors
             (e.g. gram to mol to liter, barometric units,
             temperature, salinity); (3) physical functions, e.g. to
             estimate concentrations of conservative substances, gas
             transfer and diffusion coefficients, the Coriolis force
             and gravity; (4) thermophysical properties of the
             seawater, as from the UNESCO polynomial or from the more
             recent derivation based on a Gibbs function.
Local recombination rates are graphically estimated across a genome using Marey maps.
Likelihood inference based on higher order approximations 
  for linear nonnormal regression models
Multivariate ARIMA and ARIMA-X estimation using Spliid's algorithm
    algorithm (marima()) and simulation (marima.sim()).
Provides R bindings to the 'Sundown' 'Markdown' rendering library
   (https://github.com/vmg/sundown). 'Markdown' is a plain-text formatting
   syntax that can be converted to 'XHTML' or other formats. See
   http://en.wikipedia.org/wiki/Markdown for more information about 'Markdown'.
Functions for fitting various models to capture-recapture data
    including fixed and mixed-effects Cormack-Jolly-Seber(CJS) for survival
    estimation and POPAN structured Jolly-Seber models for abundance
    estimation. Includes a CJS models that concurrently estimates and corrects
    for tag loss. Hidden Markov model (HMM) implementations of CJS and
    multistate models with and without state uncertainty.
maRketSim is a market simulator for R.  It was initially designed 
	around the bond market, with plans to expand to stocks.  maRketSim is
	built around the idea of portfolios of fundamental objects.  Therefore
	it is slow in its current incarnation, but allows you the flexibility of
	seeing exactly what is in your final results, since the objects are retained.
Automatic marking of R assignments for students and teachers based
    on 'testthat' test suites.
Allows for fitting of maximum likelihood models using Markov chains
    on phylogenetic trees for analysis of discrete character data. Examples of such
    discrete character data include restriction sites, gene family presence/absence,
    intron presence/absence, and gene family size data. Hypothesis-driven user-
    specified substitution rate matrices can be estimated. Allows for biologically
    realistic models combining constrained substitution rate matrices, site rate
    variation, site partitioning, branch-specific rates, allowing for non-stationary
    prior root probabilities, correcting for sampling bias, etc.
Functions and S4 methods to create and manage discrete time Markov
    chains (DTMC) more easily. In addition functions to perform statistical (fitting
    and drawing random variates) and probabilistic (analysis of DTMC proprieties)
    analysis are provided.
A collection of tools for analyzing significance of
    Markowitz portfolios.
Functions provided allow data simulation; construction of weighted relative likelihood functions; clustering and principal component analysis based on weighted relative likelihood functions.
Import xyz data from the NOAA (National Oceanic and Atmospheric Administration, <http://www.noaa.gov>), GEBCO (General Bathymetric Chart of the Oceans, <http://www.gebco.net>) and other sources, plot xyz data to prepare publication-ready figures, analyze xyz data to extract transects, get depth / altitude based on geographical coordinates, or calculate z-constrained least-cost paths.
This algorithm provides a numerical solution to the
        problem of minimizing a function. This is more efficient than
        the Gauss-Newton-like algorithm when starting from points very
        far from the final minimum. A new convergence test is
        implemented (RDM) in addition to the usual stopping criterion :
        stopping rule is when the gradients are small enough in the
        parameters metric (GH-1G).
The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models fit to multivariate time-series data.  Fitting is primarily via an Expectation-Maximization (EM) algorithm, although fitting via the BFGS algorithm (using the optim function) is also provided.  MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model.  Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, bootstrap model selection criteria (AICb), confidences intervals via the Hessian approximation and via bootstrapping and calculation of auxiliary residuals for detecting outliers and shocks.  The user guide shows examples of using MARSS for parameter estimation for a variety of applications, model selection, dynamic factor analysis, outlier and shock detection, and addition of covariates.  Type RShowDoc("UserGuide", package="MARSS") at the R command line to open the MARSS user guide.  Online workshops (lectures and computer labs) at http://faculty.washington.edu/eeholmes/workshops.shtml  See the NEWS file for update information.
An S4 implementation of the unbiased extension of the model-
    assisted synthetic-regression estimator proposed by 
    Mandallaz (2013) <DOI:10.1139/cjfr-2012-0381>, 
    Mandallaz et al. (2013) <DOI:10.1139/cjfr-2013-0181> and 
    Mandallaz (2014) <DOI:10.1139/cjfr-2013-0449>. 
    It yields smaller variances than the standard bias correction, 
    the generalised regression estimator.
Use a consistent syntax to create data structures of common
    statistical techniques that can be continued in a pipe chain.
    Design the analysis, add settings and variables, construct the results, and
    polish the final structure. Rinse and repeat for any number of statistical
    techniques.
Functions and datasets to support Venables and Ripley,
  "Modern Applied Statistics with S" (4th edition, 2002).
Estimation equations are from a variety of sources but are, in general, based on regressions between skeletal measurements (e.g., femoral circumference) and body mass in living taxa.
The primary aim of MasterBayes is to use MCMC techniques to integrate over uncertainty in pedigree configurations estimated from molecular markers and phenotypic data.  Emphasis is put on the marginal distribution of parameters that relate the phenotypic data to the pedigree. All simulation is done in compiled C++ for efficiency.
Calculates Model-Averaged Tail Area Wald (MATA-Wald) confidence
    intervals, which are constructed using single-model estimators and model
    weights.
Provides functions for multivariate and propensity score matching 
             and for finding optimal balance based on a genetic search algorithm. 
             A variety of univariate and multivariate metrics to
             determine if balance has been obtained are also provided.
Returns the subset of the data with the minimum imbalance for 
	     every possible subset size (N - 1, N - 2, ...), down to the data set with the 
	     minimum possible imbalance. Also includes tools for the estimation
	     of causal effects for each subset size, functions for visualization
	     and data export, and functions for calculating
	     model dependence as proposed by Athey and Imbens.
Implements structural estimators to correct for
    the sample selection bias from observed outcomes in matching
    markets. This includes one-sided matching of agents into
    groups as well as two-sided matching of students to schools.
    The package also contains algorithms to find stable matchings 
    in the three most common matching problems: the stable roommates 
    problem, the college admissions problem, and the house 
    allocation problem.
Computes matching algorithms quickly using Rcpp.
    Implements the Gale-Shapley Algorithm to compute the stable
    matching for two-sided markets, such as the stable marriage
    problem and the college-admissions problem. Implements Irving's
    Algorithm for the stable roommate problem. Implements the top
    trading cycle algorithm for the indivisible goods trading problem.
MatchIt selects matched samples of the the original
        treated and control groups with similar covariate distributions
        -- can be used to match exactly on covariates, to match on
        propensity scores, or perform a variety of other matching
        procedures.
Contains various methods for Standard Error estimation for 'MatchIt' objects.
Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies.
Performs multilevel matches for data with cluster-level treatments and individual-level outcomes using a network optimization algorithm.  Functions for checking balance at the cluster and individual levels are also provided, as are methods for permutation-inference-based outcome analysis.
Transferring over a code base from Matlab to R is often a repetitive
    and inefficient use of time. This package provides a translator for Matlab /
    Octave code into R code. It does some syntax changes, but most of the heavy
    lifting is in the function changes since the languages are so similar.
    Options for different data structures and the functions that can be changed
    are given. The Matlab code should be mostly in adherence to the standard
    style guide but some effort has been made to accommodate different number of
    spaces and other small syntax issues. This will not make the code more R
    friendly and may not even run afterwards. However, the rudimentary syntax,
    base function and data structure conversion is done quickly so that the
    maintainer can focus on changes to the design structure.
Tools to simulate, manage, visualize, and analyze
    spatially and temporally explicit datasets of mating potential.
    Implements methods to calculate synchrony, proximity, and compatibility.
Simple tools for constructing and manipulating objects of
        class mathgraph from the book "S Poetry", available at
        http://www.burns-stat.com/pages/spoetry.html
Uses a ratio of weighted distributions to estimate association between variables in a data set.
Provides users to call MATLAB from using the "system" command.
    Allows users to submit lines of code or MATLAB m files.
    This is in comparison to 'R.matlab', which creates a MATLAB server.
A collection of matrix functions for teaching and learning matrix
    linear algebra as used in multivariate statistical methods. These functions are
    mainly for tutorial purposes in learning matrix algebra ideas using R. In some
    cases, functions are provided for concepts available elsewhere in R, but where
    the function call or name is not obvious. In other cases, functions are provided
    to show or demonstrate an algorithm. In addition, a collection of functions are
    provided for drawing vector diagrams in 2D and 3D.
A general framework for computing powers of matrices.  A
   key feature is the capability for users to write callback functions,
   called after each iteration, thus enabling customization for specific
   applications.  Diverse types of matrix classes/matrix multiplication
   are accommodated.  If the multiplication type computes in parallel,
   then the package computation is also parallel.
An analysis platform for metagenomics combining 
 specialized tools and workflows, easy handling of the BIOM 
 format, and transparent access to MG-RAST resources.  matR integrates 
 easily with other R packages and non-R software.
Classes and methods for dense and sparse matrices and
    operations on them using 'LAPACK' and 'SuiteSparse'.
A collection of functions to support matrix calculations
        for probability, econometric and numerical analysis. There are
        additional functions that are comparable to APL functions which
        are useful for actuarial models such as pension mathematics.
        This package is used for teaching and research purposes at the
        Department of Finance and Risk Engineering, New York
        University, Polytechnic Institute, Brooklyn, NY 11201.
Computation and visualization of matrix correlation coefficients.
    The main method is the Similarity of Matrices Index, while various related
    measures like r1, r2, r3, r4, Yanai's GCD, RV, RV2 and adjusted RV are included
    for comparison.
Matrix eQTL is designed for fast eQTL analysis on large datasets.
	Matrix eQTL can test for association between genotype and gene expression using linear regression 
	with either additive or ANOVA genotype effects.
	The models can include covariates to account for factors 
	as population stratification, gender, and clinical variables. 
	It also supports models with heteroscedastic and/or correlated errors,
	false discovery rate estimation and separate treatment of local (cis) and distant (trans) eQTLs.
Constructs the normalized Laplacian matrix of a square matrix, returns the eigenvectors (singular vectors) and visualization of normalized Laplacian map.
Fits the penalized matrix-normal model to be used for linear discriminant analysis with matrix-valued predictors.
Modelling with sparse and dense 'Matrix' matrices, using
  modular prediction and response module classes.
Partial Least Squares Path Modeling
    algorithm and related algorithms. The algorithm implementations aim for
    computational efficiency using matrix algebra and covariance data. The
    package is designed toward Monte Carlo simulations and includes functions
    to perform simple Monte Carlo simulations.
High-performing functions operating on rows and columns of matrices, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds().  Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized.  There are also optimized vector-based methods, e.g. binMeans(), madDiff() and weightedMedian().
Implements data manipulation methods such as cast, aggregate, and merge/join for Matrix and matrix-like objects.
This package in includes functions for receiver operating
        characteristic (ROC) analyses as well as Monte Carlo
        simulation.  It includes specific graphical functions for
        interpreting the output of these techniques.
Interactive shiny application for running a meta-analysis,
    provides support for both random effects and fixed effects models with the 'metafor' package.
    Additional support is included for calculating effect sizes plus
    support for single case designs, graphical output, and detecting publication bias.
This package is a gene set analysis function for one-sided test (OLS), two-sided test (multivariate analysis of variance).
             If the experimental conditions are equal to 2, the p-value for Hotelling's t^2 test is calculated.
             If the experimental conditions are great than 2, the p-value for Wilks' Lambda is determined and post-hoc test is reported too.
             Three multiple comparison procedures, Dunnett, Tukey, and sequential pairwise comparison, are implemented.
             The program computes the p-values and FDR (false discovery rate) q-values for all gene sets.
             The p-values for individual genes in a significant gene set are also listed.
             MAVTgsa generates two visualization output: a p-value plot of gene sets (GSA plot) and a GST-plot of the empirical distribution function of the ranked test statistics of a given gene set.
             A Random Forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes.
Perform exact MAX3 or MAX2 test for one-locus genetic
        association analysis and trend test for dominant, recessive and
        additive models. It can also calculate approximated p-value
        with the normal approximation method.
maxent is an R package with tools for low-memory
        multinomial logistic regression, also known as maximum entropy.
        The focus of this maximum entropy classifier is to minimize
        memory consumption on very large datasets, particularly sparse
        document-term matrices represented by the tm package. The
        classifier is based on an efficient C++ implementation written
        by Dr. Yoshimasa Tsuruoka.
Complex niche models show low performance in identifying
    the most important range-limiting environmental variables and in
    transferring habitat suitability to novel environmental conditions
    (Warren and Seifert, 2011; Warren et al., 2014). This package helps to
    identify the most important set of uncorrelated variables and to
    fine-tune Maxent's regularization multiplier. In combination, this
    allows to constrain complexity and increase performance of Maxent niche
    models (assessed by information criteria, such as AICc (Akaike, 1974) ,
    and by the area under the receiver operating characteristic (AUC)
    (Fielding and Bell, 1997). Users of this package should be familiar with
    Maxent niche modelling.
Functions for Maximum Likelihood (ML) estimation and non-linear
   optimization, and related tools.  It includes a unified way to call
   different optimizers, and classes and methods to handle the results from
   the ML viewpoint.  It also includes a number of convenience tools for testing
   and developing your own models.
Provides a likelihood-based approach to modeling species distributions using presence-only data. In contrast to the popular software program MAXENT, this approach yields estimates of the probability of occurrence, which is a natural descriptor of a species' distribution.
Procedures to fit species distributions models from occurrence records and environmental variables, using 'glmnet' for model fitting. Model structure is the same as for the 'Maxent' Java package, version 3.4.0, with the same feature types and regularization options.  See the 'Maxent' website <http://biodiversityinformatics.amnh.org/open_source/maxent> for more details.
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.
It finds Orthogonal Data Projections with Maximal Skewness. The first data projection in the output is the most skewed among all linear data projections. The second data projection in the output is the most skewed among all data projections orthogonal to the first one, and so on.
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.)
A maze generator that creates the Elithorn Maze (HTML file) and the functions to calculate the associated maze parameters (i.e. Difficulty and Ability).
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.
Calibrate and apply multivariate bias correction algorithms
    for climate model simulations of multiple climate variables. Three methods
    by Cannon (2016, submitted) are supported: MBCp, MBCr, and MBCn.
Cluster genes based on Poisson or Negative-Binomial model
        for RNA-Seq or other digital gene expression (DGE) data
Perform clusterwise multiblock analyses (clusterwise multiblock Partial Least Squares, clusterwise multiblock Redundancy Analysis or a regularized method between the two latter ones) associated with a F-fold cross-validation procedure to select the optimal number of clusters and dimensions.
Implements methods that useful in designing research and analyzing data, with 
	particular emphasis on methods that are developed for or used within the behavioral, 
	educational, and social sciences (broadly defined). That being said, many of the methods 
	implemented within MBESS are applicable to a wide variety of disciplines. MBESS has a 
	suite of functions for a variety of related topics, such as effect sizes, confidence intervals 
	for effect sizes (including standardized effect sizes and noncentral effect sizes), sample size
	planning (from the accuracy in parameter estimation [AIPE], power analytic, equivalence, and 
	minimum-risk point estimation perspectives), mediation analysis, various properties of 
	distributions, and a variety of utility functions. MBESS (pronounced 'em-bes') was originally 
	an acronym for 'Methods for the Behavioral, Educational, and Social Sciences,' but at this 
	point MBESS contains methods applicable and used in a wide variety of fields and is an 
	orphan acronym, in the sense that what was an acronym is now literally its name. MBESS has 
	greatly benefited from others, see <http://nd.edu/~kkelley/site/MBESS.html> for a detailed 
	list of those that have contributed and other details.
Implements methods from the paper
     "Fast Moment-Based Estimation for Hierarchical Models," by Perry (2016).
Provides spatially balanced designs from a set of (contiguous) potential sampling locations in a study region. Accommodates , without detrimental effects on spatial balance, sites that the researcher wishes to include in the survey for reasons other than the current randomisation (legacy sites).
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.
An implementation of a Bayesian sparse group model using spike and slab priors in a regression context. It is designed for regression with a multivariate response variable, but also provides an implementation for univariate response.
Access to the MBTA API for R. Creates an easy-to-use bundle of
  functions to work with all the built-in calls to the MBTA API. Allows users
  to download realtime tracking data in dataframe format that is manipulable
  in standard R analytics functions.
Bayesian variable selection methods for data with continuous multivariate responses and multiple covariates.
A complete framework to build and study Two-Dimensional Monte-Carlo simulations, aka Second-Order Monte-Carlo simulations. Also includes various distributions (pert, triangular, Bernoulli, empirical discrete and continuous).
Post processes MC2 output, especially for use by Path or ST-Sim.  MC2 (short for "MC1 version 2") is a dynamic global vegetation model (en.wikipedia.org/wiki/DGVM).  Path (essa.com/tools/path) and ST-Sim (www.apexrms.com) are state-and-transition model (STM) engines. MC2 has a user website at sites.google.com/site/mc1dgvmusers.  Since 2001, MC1 has been used to simulate changes in natural vegetation due to climate change at scales from regional to global.  In 2012, MC1 was reimplemented in C++ to make it faster and to reduce storage requirements.  This newer version is referred to as MC2, an abbreviation of "MC1 version 2".  Beginning in 2011, output from MC1 and MC2 has been used to inform regional state-and-transition model simulations by the U.S. Forest Service and the Washington State Department of Natural Resources.  Projects to date have involved study areas in central Oregon, the Olympic Peninsula, the Blue Mountains ecoregion, southwestern Oregon, and southeastern Oregon.  In the first of this series of projects, the netCDF output files from MC2 were manually post-processed, mostly in Excel, to produce input .csv files for the STM engines. Beginning with the second project, R scripts were used to automate the post-processing work.  These R scripts have been collected into the MC2toPath R-package.
Weather and air pollution data, risk estimates, and other
        information from the Medicare Air Pollution Study (MCAPS) of
        204 U.S. counties, 1999--2002
Provides two variants of multiple correspondence analysis (ca):
                     multiple ca and ordered multiple ca via orthogonal polynomials of Emerson.
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.
Implementation of several MCDM methos for crisp data for decision
    making problems. The methods that are implemented in this package are RIM,
    TOPSIS (with two normalization procedures), VIKOR, Multi-MOORA and WASPAS.
    In addition, MetaRanking function calculates a new ranking from the sum 
    of the rankings calculated, as well as an aggregated ranking.
Maximum likelihood estimation for generalized linear mixed models via Monte Carlo EM.
Machine coded genetic algorithm (MCGA) is a fast tool for
    real-valued optimization problems. It uses the byte
    representation of variables rather than real-values. It
    performs the classical crossover operations (uniform) on these
    byte representations. Mutation operator is also similar to
    classical mutation operator, which is to say, it changes a
    randomly selected byte value of a chromosome by +1 or -1 with
    probability 1/2. In MCGAs there is no need for
    encoding-decoding process and the classical operators are
    directly applicable on real-values. It is fast and can handle a
    wide range of a search space with high precision. Using a
    256-unary alphabet is the main disadvantage of this algorithm
    but a moderate size population is convenient for many problems.
    Package also includes multi_mcga function for multi objective
    optimization problems. This function sorts the chromosomes
    using their ranks calculated from the non-dominated sorting
    algorithm.
Performs clustering and classification using the Mixtures of Contaminated Gaussian Factor Analyzers model. Allows for automatic detection of outliers and noise.
'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.
Fitting multivariate covariance generalized linear
     models (McGLMs) to data.  McGLMs is a general framework for non-normal
     multivariate data analysis, designed to handle multivariate response
     variables, along with a wide range of temporal and spatial correlation
     structures defined in terms of a covariance link function combined
     with a matrix linear predictor involving known matrices.
     The models take non-normality into account in the conventional way
     by means of a variance function, and the mean structure is modelled
     by means of a link function and a linear predictor.
     The models are fitted using an efficient Newton scoring algorithm
     based on quasi-likelihood and Pearson estimating functions, using
     only second-moment assumptions. This provides a unified approach to
     a wide variety of different types of response variables and covariance
     structures, including multivariate extensions of repeated measures,
     time series, longitudinal, spatial and spatio-temporal structures.
     The package offers a user-friendly interface for fitting McGLMs
     similar to the glm() R function.
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.
Market area models are used to analyze and predict store choices and market areas concerning retail and service locations. This package implements two market area models (Huff Model, Multiplicative Competitive Interaction Model) into R, while the emphases lie on 1.) fitting these models based on empirical data via OLS regression and nonlinear techniques and 2.) data preparation and processing (esp. interaction matrices and data preparation for the MCI Model).
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.
The likelihood of direct CAR models and Binomial and Poisson GLM with latent CAR variables are approximated by the Monte Carlo likelihood. The Maximum Monte Carlo likelihood estimator is found either by an iterative procedure of directly maximising the Monte Carlo approximation or by a response surface design method.
Maximum likelihood estimation using a Monte Carlo local likelihood (MCLL) method
Specification and estimation of conditional logit models of binary responses and multinomial counts is provided,
  with or without alternative-specific random effects (random intercepts only, no random slopes yet).
  The current implementation of the estimator for random effects variances uses a Laplace approximation (or PQL) 
  approach and thus should be used only if groups sizes are large.
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
Simulates continuous distributions of random vectors using
    Markov chain Monte Carlo (MCMC).  Users specify the distribution by an
    R function that evaluates the log unnormalized density.  Algorithms
    are random walk Metropolis algorithm (function metrop), simulated
    tempering (function temper), and morphometric random walk Metropolis
    (Johnson and Geyer, Annals of Statistics, 2012, function morph.metrop),
    which achieves geometric ergodicity by change of variable.
Provides some function to perform posterior estimation for some distribution, with emphasis to extreme value distributions. It contains some extreme datasets, and functions that perform the runs of posterior points of the GPD and GEV distribution. The package calculate some important extreme measures like return level for each t periods of time, and some plots as the predictive distribution, and return level plots.
Poisson-lognormal generalized linear mixed model analysis of multivariate counts data using MCMC, aiming to infer the changes in relative proportions of individual variables. The package was originally designed for sequence-based analysis of microbial communities ("metabarcoding", variables = operational taxonomic units, OTUs), but can be used for other types of multivariate counts, such as in ecological applications (variables = species). The results are summarized and plotted using 'ggplot2' functions. Includes functions to remove sample and variable outliers and reformat counts into normalized log-transformed values for correlation and principal component/coordinate analysis. Walkthrough and examples: http://www.bio.utexas.edu/research/matz_lab/matzlab/Methods_files/walkthroughExample_mcmcOTU_R.txt.
Contains functions to perform Bayesian
        inference using posterior simulation for a number of
        statistical models. Most simulation is done in compiled C++
        written in the Scythe Statistical Library Version 1.0.3. All
        models return coda mcmc objects that can then be summarized
        using the coda package. Some useful
        utility functions such as density functions, 
	pseudo-random number generators for statistical
        distributions, a general purpose Metropolis sampling algorithm, 
        and tools for visualization are provided.
Functions for convenient plotting and viewing of MCMC output.
Quantitative RT-PCR data are analyzed using generalized linear mixed models based on lognormal-Poisson error distribution, fitted using MCMC. Control genes are not required but can be incorporated as Bayesian priors or, when template abundances correlate with conditions, as trackers of global effects (common to all genes). The package also implements a lognormal model for higher-abundance data and a "classic" model involving multi-gene normalization on a by-sample basis. Several plotting functions are included to extract and visualize results. The detailed tutorial is available here: <http://bit.ly/1Nwo4CB>.
Provides tools for computing Monte Carlo standard
        errors (MCSE) in Markov chain Monte Carlo (MCMC) settings. MCSE
        computation for expectation and quantile estimators is
        supported as well as multivariate estimations. The package also provides 
	functions for computing effective sample size and for plotting
	Monte Carlo estimates versus sample size.
Performs key functions for MCMC analysis using minimal code - visualizes, manipulates, and summarizes MCMC output. Functions support simple and straightforward subsetting of model parameters within the calls, and produce presentable and 'publication-ready' output. MCMC output may be derived from Bayesian model output fit with JAGS, Stan, or other MCMC samplers.
Functions for multiple criteria optimization using genetic
        algorithms and related test problems
The 1001 time series from the M-competition (Makridakis et
        al. 1982) and the 3003 time series from the IJF-M3 competition
        (Makridakis and Hibon, 2000).
The package aids in creating a Conjoint Analysis design
        with extra cards.  Unlike traditional "holdout" cards these
        cards are used to create a set of "good" (balanced and low
        correlation) designs. Each of these designs is analyzed and the
        average calculated.
A Markov chain Monte Carlo method is provided to estimate the preferential attachment function from a single network snapshot. Conventional methods require the complete information about the appearance order of all nodes and edges in the network. This package incorporates the appearance order into the state space and estimates it together with the preferential attachment function. Auxiliary variables are introduced to facilitate fast Gibbs sampling.
Multiple contrast tests and simultaneous confidence
 intervals based on normal approximation. With implementations for
 binomial proportions in a 2xk setting (risk difference and odds ratio),
 poly-3-adjusted tumour rates, biodiversity indices (multinomial data) 
 and expected values under lognormal assumption. Approximative power 
 calculation for multiple contrast tests of binomial and Gaussian data.
Provides a function that wraps 
    mcparallel() and mccollect() from 'parallel' with temporary variables and a 
    task handler.  Wrapped in this way the results of an mcparallel() call 
    can be returned to the R session when the fork is complete 
    without explicitly issuing a specific mccollect() to retrieve the value.
    Outside of top-level tasks, multiple mcparallel() jobs can be retrieved with 
    a single call to mcparallelDoCheck().
A Monte Carlo permutation method for multiple test
        correlation.
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, <doi: 10.1111/j.1541-0420.2005.00344.x>).
             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 MCP-Mod methodology.
             Please 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.
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
Package computes popular and widely used multicollinearity diagnostic measures. Package also indicates which regressors may be the reason of collinearity among regressors.
Transition matrices (probabilities or counts) estimation for discrete Markov Chains of order n (1 <= n <= 5).
Selects bandwidth for the kernel density estimator with minimum distance method as proposed by Devroye and Lugosi (1996). The minimum distance method directly selects the optimal kernel density estimator from countably infinite kernel density estimators and indirectly selects the optimal bandwidth. This package selects the optimal bandwidth from finite kernel density estimators.
Mixture and flexible discriminant analysis, multivariate
        adaptive regression splines (MARS), BRUTO, ...
Package implements projection based methods for preprocessing,
    exploring and analysis of multivariate data used in chemometrics.
Provides a function for measuring the difference between two independent or non-independent empirical distributions and returning a significance level of the difference.
Allows various models for multivariate response variables where each response is assumed to follow double hierarchical generalized linear models. In double hierarchical generalized linear models, the mean, dispersion parameters for variance of random effects, and residual variance can be further modeled as random-effect models.
Normalize data to minimize the difference between sample plates 
    (batch effects). For given data in a matrix and grouping variable (or
	plate), the function 'normn_MA' normalizes the data on MA coordinates. 
	More details are in the citation. The primary method is 'Multi-MA'. Other 
	fitting functions on MA coordinates can also be employed e.g. loess.
The multinomial diversity model is a toolbox for relating diversity to complex predictors. It is based on (1) Shannon diversity; (2) the multinomial logit model, and (3) the link between Shannon diversity and the log-likelihood of the MLM.
Allows a user to conduct multivariate distance matrix regression using analytic p-values and compute measures of effect size.
Performs graph-constrained regularization in which regularization parameters are selected with the use of a known fact of equivalence between penalized regression and Linear Mixed Model solutions. Provides implementation of three different regression methods where graph-constraints among coefficients are accounted for. 'crPEER' (Partially Empirical Eigenvectors for Regression with Constant Ridge, Constant Ridge PEER) method utilizes additional Ridge term to handle the non-invertibility of a graph Laplacian matrix. 'vrPEER' (Variable Reduction PEER) method performs variable-reduction procedure to handle the non-invertibility of a graph Laplacian matrix. Finally, 'RidgePEER' method employs a penalty term being a linear combination of graph-originated and Ridge-originated penalty terms, whose two regularization parameters are ML estimators from corresponding Linear Mixed Model solution. Notably, in 'RidgePEER' method a graph-originated penalty term allows imposing similarity between coefficients based on graph information given whereas additional Ridge-originated penalty term facilitates parameters estimation: it reduces computational issues arising from singularity in a graph-originated penalty matrix and yields plausible results in situations when graph information is not informative or when it is unclear whether connectivities represented by a graph reflect similarities among corresponding coefficients.
Provides automatization for plot generation succeeding common molecular dynamics analyses.
             This includes straightforward plots, such as RMSD (Root-Mean-Square-Deviation) and
             RMSF (Root-Mean-Square-Fluctuation) but also more sophisticated ones such as
             dihedral angle maps, hydrogen bonds, cluster bar plots and
             DSSP (Definition of Secondary Structure of Proteins) analysis.
The Markov Decision Processes (MDP) toolbox proposes functions related to the resolution of discrete-time Markov Decision Processes: finite horizon, value iteration, policy iteration, linear programming algorithms with some variants and also proposes some functions related to Reinforcement Learning.
Performs multifactor dimensionality reduction (MDR) to
        detect potential gene-gene interactions in case-control
        studies.
A set of functions to obtain modified score test for generalized linear models.
Tools associated with General
    Recognition Theory (Townsend & Ashby, 1986), including Gaussian model fitting of 2x2 and more
    general designs, associated plotting and model comparison tools,
    and tests of marginal response invariance and report independence.
A graphical user interface (GUI) for performing Multidimensional Scaling applications and interactively analysing the results all within the GUI environment. The MDS-GUI provides means of performing Classical Scaling, Least Squares Scaling, Metric SMACOF, Non-Metric SMACOF, Kruskal's Analysis and Sammon Mapping with animated optimisation.
A complement to *Modern Data
    Science with R* (ISBN: 978-1498724487, publisher URL: 
    <https://www.crcpress.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9781498724487>). 
    This package contains all of the data and code necessary to
    complete exercises and reproduce examples from the text. It also 
    facilitates connections to the SQL database server used in the book.
Clustering of vector data and functional data using the mean shift algorithm (multi-core processing is supported) or its blurring version.
Performs mean shift classification using linear and 
  k-d tree based nearest neighbor implementations for the Gaussian
  kernel.
Collection of tools to make working with physical measurements
		easier. Convert between metric and imperial units, or calculate a dimension's
		unknown value from other dimensions' measurements.
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.
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.
Fits the mixture of experts latent position cluster model to network data to cluster nodes into subgroups, while incorporating covariate information, in a mixture of experts model setting.
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.
Functions to simulate forest dynamics using cohort-based description of vegetation.
Run flexible mediation analyses using natural
    effect models as described in Lange, Vansteelandt and Bekaert (2012),
    Vansteelandt, Bekaert and Lange (2012) and Loeys, Moerkerke, De Smet,
    Buysse, Steen and Vansteelandt (2013).
Calculates MeDiA_K (means Mean Distance Association by K-nearest neighbor) in order to detect nonlinear associations.
Provides a general framework for clinical trial simulations based
    on the Clinical Scenario Evaluation (CSE) approach. The package supports a
    broad class of data models (including clinical trials with continuous, binary,
    survival-type and count-type endpoints as well as multivariate outcomes that are
    based on combinations of different endpoints), analysis strategies and commonly
    used evaluation criteria.
We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010), Imai, Keele and Tingley (2010), Imai, Tingley and Yamamoto (2013), Imai and Yamamoto (2013) and Yamamoto (2013). In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.
Generates risk estimates and comorbidity flags from ICD-9-CM
    codes available in administrative medical datasets. The package supports
    the Charlson Comorbidity Index, the Elixhauser Comorbidity
    classification, the Revised Cardiac Risk Index, and the Risk Stratification
    Index.  Methods are table-based, fast, and use the 'plyr' package, so
    parallelization is possible for large jobs. Also includes a sample of
    real ICD-9 data for 100 patients from a publicly available dataset.
Publicly available data from Medicare frequently requires extensive
    initial effort to extract desired variables and merge them; this package
    formalizes the techniques I've found work best. More information on the 
    Medicare program, as well as guidance for the publicly available data this package 
    targets, can be found on CMS's website covering publicly available data. See <https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems.html>.
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.
Co-Expression Network Analysis by adopting network embedding technique.
Exploratory model analysis.  Fit and graphical
    explore ensembles of linear models.
Provides a graphical interface that allows the user to easily view 
    and classify output from 'MELODIC', a part of the 'FSL' neuroimaging analysis
    software suite that performs independent component analysis (ICA; see 
    <https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC/> for more information). The 
    user categorizes a component as signal or noise based on its spatial and 
    temporal characteristics and can then save a text file of these 
    classifications in the format required by 'ICA+FIX', an automatic noise 
    removal tool (<https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIX>).
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
        R users who deal with survey data sets. It provides an
        infrastructure for the management of survey data including
        value labels, definable missing values, recoding of variables,
        production of code books, and import of (subsets of) 'SPSS' and
        'Stata' files. Further, it provides functionality to produce
        tables and data frames of arbitrary descriptive statistics and
        (almost) publication-ready tables of regression model
        estimates, which can be exported to 'LaTeX' and HTML.
A simple in-memory, LRU cache that can be wrapped
    around any function to memoize it. The cache can be keyed on a hash of the
    input data (using 'digest') or on pointer equivalence.
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.
Encodes several methods for performing Mendelian randomization analyses with summarized data. Summarized data on genetic associations with the exposure and with the outcome can be obtained from large consortia. These data can be used for obtaining causal estimates using instrumental variable methods.
Set of configurable menus built with GTK+ to graphically interface new functions.
A GUI for merging datasets in R using gWidgets.
N>=3 methods are used to measure each of n items. 
 The data are used to estimate simultaneously systematic error (bias)
 and random error (imprecision). Observed measurements for each method
 or device are assumed to be linear functions of the unknown true values
 and the errors are assumed normally distributed. Maximum likelihood 
 estimation is used for the imprecision standard deviation estimates. 
 Pairwise calibration curves and plots can be easily generated.
Provides methods for extracting results from mixed-effect model
  objects fit with the 'lme4' package. Allows construction of prediction intervals
  efficiently from large scale linear and generalized linear mixed-effects models.
A mixed collection of useful and semi-useful diverse
    statistical functions, some of which may even be referenced in
    The R Primer book.
User-friendly general package providing standard methods for meta-analysis and supporting Schwarzer, Carpenter, and Rücker <DOI:10.1007/978-3-319-21416-0>, "Meta-Analysis with R" (2015):
 - fixed effect and random effects meta-analysis;
 - several plots (forest, funnel, Galbraith / radial, L'Abbe, Baujat, bubble);
 - statistical tests and trim-and-fill method to evaluate bias in meta-analysis;
 - import data from 'RevMan 5';
 - prediction interval, Hartung-Knapp and Paule-Mandel method for random effects model;
 - cumulative meta-analysis and leave-one-out meta-analysis;
 - meta-regression (if R package 'metafor' is installed);
 - generalised linear mixed models (if R packages 'metafor', 'lme4', 'numDeriv', and 'BiasedUrn' are installed).
Bayesian inference analysis for bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximation with INLA. A purpose built graphic user interface is available. The installation of R package INLA is compulsory for successful usage. The INLA package can be obtained from <http://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: install.packages("INLA", repos="http://www.math.ntnu.no/inla/R/testing").
An interactive application to visualise meta-analysis data as a
    physical weighing machine. The interface is based on the Shiny web application
    framework, though can be run locally and with the user's own data.
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.
Automatic metabolite annotation from Liquid Chromatography-Mass Spectrometry (LC-MS and LC-MS/MS) data from .mzXML files, providing an inclusion list of metabolites/fragments (Only the ion mass). The function returns the identification and quantification of the peaks presented in the sample, as well as the non-identified metabolites/fragments.
A collection of functions to aid in the statistical analysis of metabolomic data
Takes QC signal for each day and normalize metabolomic
    data that has been acquired in a certain period of time. At least
    three QC per day are required.
A set of tools for parsing, manipulating, and graphing data classified by a hierarchy (e.g. a taxonomy).
Functions to analyze coherence, boundary clumping, and turnover
    following the pattern-based metacommunity analysis of Leibold and Mikkelson
    2002. The package also includes functions to visualize ecological networks, and
    to calculate modularity as a replacement to boundary clumping.
Implement the DerSimonian-Laird (DSL) and Olkin-Pratt (OP)
        meta-analytical approaches with correlation coefficients as
        effect sizes.
Provides two functions-meta2d and meta3d for detecting 
    rhythmic signals from time-series datasets. For analyzing
    time-series datasets without individual information, 'meta2d' is 
    suggested, which could incorporates multiple methods from ARSER, 
    JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For 
    analyzing time-series datasets with individual information, 'meta3d' is 
    suggested, which takes use of any one of these three methods to analyze 
	time-series data individual by individual and gives out integrated values 
    based on analysis result of each individual.
MetaDE package implements 12 major meta-analysis methods
        for differential expression analysis.
The metafolio R package is a tool to simulate salmon
    metapopulations and apply financial portfolio optimization concepts. The
    package accompanies the paper 'Portfolio conservation of metapopulations
    under climate change'. See citation("metafolio").
A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted.
Fused lasso method to cluster and estimate regression coefficients
    of the same covariate across different data sets when a large number of
    independent data sets are combined. Package supports Gaussian, binomial,
    Poisson and Cox PH models.
Functionalities for facilitating systematic reviews, data
    extractions, and meta-analyses. It includes a GUI (graphical user interface)
    to help screen the abstracts and titles of bibliographic data; tools to assign
    screening effort across multiple collaborators/reviewers and to assess inter-
    reviewer reliability; tools to help automate the download and retrieval of
    journal PDF articles from online databases; figure and image extractions 
    from PDFs; web scraping of citations; automated and manual data extraction 
    from scatter-plot and bar-plot images; PRISMA (Preferred Reporting Items for
    Systematic Reviews and Meta-Analyses) flow diagrams; simple imputation tools
    to fill gaps in incomplete or missing study parameters; generation of random
    effects sizes for Hedges' d, log response ratio, odds ratio, and correlation
    coefficients for Monte Carlo experiments; covariance equations for modelling
    dependencies among multiple effect sizes (e.g., effect sizes with a common 
    control); and finally summaries that replicate analyses and outputs from 
    widely used but no longer updated meta-analysis software. Funding for this 
    package was supported by National Science Foundation (NSF) grants 
    DBI-1262545 and DEB-1451031.
Provides methods for making inference in the random effects meta
    regression model such as point estimates and confidence intervals for the
    heterogeneity parameter and the regression coefficients vector.  Inference
    methods are based on different approaches to statistical inference.
    Methods from three different schools are included: methods based on the
    method of moments approach, methods based on likelihood, and methods based
    on generalised inference.  The package also includes tools to run extensive
    simulation studies in parallel on high performance clusters in a modular
    way.  This allows extensive testing of custom inferential methods with all
    implemented state-of-the-art methods in a standardised way.  Tools for
    evaluating the performance of both point and interval estimates are
    provided.  Also, a large collection of different pre-defined plotting
    functions is implemented in a ready-to-use fashion.
Automation of preprocessing often requires computationally costly
    preprocessing combinations. This package helps to find near-best combinations
    faster. Metaheuristics supported are taboo search, simulated annealing, reheating
    and late acceptance. Start conditions include random and grid starts. End conditions
    include all iteration rounds completed, objective threshold reached and convergence.
    Metaheuristics, start and end conditions can be hybridized and hyperparameters optimized.
    Parallel computations are supported. The package is intended to be used with package
    'preprocomb' and takes its 'GridClass' object as input.
A nature-inspired metaheuristics algorithm based on the pollination
    process of flowers. This R package makes it easy to implement the standard
    flower pollination algorithm for every user. The algorithm was first developed
    by Xin-She Yang in 2012 (<DOI:10.1007/978-3-642-32894-7_27>).
A pipeline for the meta-analysis  of gene expression data. We have
	assembled several analysis and plot functions to
    perform integrated multi-cohort analysis of gene expression data (meta-
    analysis). Methodology described in:
	<http://biorxiv.org/content/early/2016/08/25/071514>.
Tools to generate random landscape graphs, evaluate species
    occurrence in dynamic landscapes, simulate future landscape occupation and
    evaluate range expansion when new empty patches are available (e.g. as a
    result of climate change).
First- and higher-order likelihood inference in
        meta-analysis and meta-regression models.
Combines either p-values or modified effect sizes from different
    studies to find differentially expressed genes
This package provides functions for diagnostic and
        prognostic meta-analyses. It estimates univariate, bivariate
        and multivariate models, and allows the aggregation of
        previously published prediction models with new data.
Resolves complex metagenomic mixtures by analysing
    deep sequencing data, using a mixture model based approach.
    The use of parallel Monte Carlo Markov chains for the exploration
    of the species space enables the identification of the set
    of species more likely to contribute to the mixture.
A novel meta-analytic method that allows an unbiased inclusion of studies with Non Statistically-Significant Unreported Effects (NSUEs). Briefly, the method first calculates the interval where the unreported effects (e.g. t-values) should be according to the threshold of statistical significance used in each study. Afterwards, maximizing likelihood techniques are used to impute the expected effect size of each study with NSUEs, accounting for between-study heterogeneity and potential covariates. Multiple imputations of the NSUEs are then randomly created based on the expected value, variance and statistical significance bounds. Finally, a restricted-maximum likelihood random-effects meta-analysis is separately conducted for each set of imputations, and estimations from these meta-analyses are pooled. Please read the reference in 'meta.nsue' for details of the procedure.
The canonical way to perform meta-analysis
   involves using effect sizes. When they are not available
   this package provides a number of methods for
   meta-analysis of significance values, a
   number of data-sets to replicate published results, and a
   routine for graphical display.
Perform the Meta-analysis for Pathway Enrichment (MAPE) 
		methods introduced by Shen and Tseng (2010). It includes functions to
        automatically perform MAPE_G (integrating multiple studies at
        gene level), MAPE_P (integrating multiple studies at pathway
        level) and MAPE_I (a hybrid method integrating MAEP_G and
        MAPE_P methods). In the simulation and real data analyses in
        the paper, MAPE_G and MAPE_P have complementary advantages and
        detection power depending on the data structure. In general,
        the integrative form of MAPE_I is recommended to use. In the
        case that MAPE_G (or MAPE_P) detects almost none pathway, the
        integrative MAPE_I does not improve performance and MAPE_P (or
        MAPE_G) should be used. Reference: Shen, Kui, and George C
        Tseng. Meta-analysis for pathway enrichment analysis when
        combining multiple microarray studies.Bioinformatics (Oxford,
        England) 26, no. 10 (April 2010): 1316-1323.
        doi:10.1093/bioinformatics/btq148.
        http://www.ncbi.nlm.nih.gov/pubmed/20410053.
MetaPCA implements simultaneous dimension reduction using
        PCA when multiple studies are combined. We propose two basic
        ideas to find a common PC subspace by eigenvalue maximization
        approach and angle minimization approach, and we extend the
        concept to incorporate Robust PCA and Sparse PCA in the
        meta-analysis realm.
Creates crosshairs plots to summarize and analyse
    meta-analysis results. In due time this package will contain code
    that will create other kind of meta-analysis graphs.
Performs meta-analysis and meta-regression using standard and robust methods with confidence intervals based on the profile likelihood. Robust methods are based on alternative distributions for the random effect, either the t-distribution (Lee and Thompson, 2008 <doi:10.1002/sim.2897> or Baker and Jackson, 2008 <doi:10.1007/s10729-007-9041-8>) or mixtures of normals (Beath, 2014 <doi:10.1002/jrsm.1114>).
MetaQC implements our proposed quantitative quality
        control measures: (1) internal homogeneity of co-expression
        structure among studies (internal quality control; IQC); (2)
        external consistency of co-expression structure correlating
        with pathway database (external quality control; EQC); (3)
        accuracy of differentially expressed gene detection (accuracy
        quality control; AQCg) or pathway identification (AQCp); (4)
        consistency of differential expression ranking in genes
        (consistency quality control; CQCg) or pathways (CQCp). (See
        the reference for detailed explanation.) For each quality
        control index, the p-values from statistical hypothesis testing
        are minus log transformed and PCA biplots were applied to
        assist visualization and decision. Results generate systematic
        suggestions to exclude problematic studies in microarray
        meta-analysis and potentially can be extended to GWAS or other
        types of genomic meta-analysis. The identified problematic
        studies can be scrutinized to identify technical and biological
        causes (e.g. sample size, platform, tissue collection,
        preprocessing etc) of their bad quality or irreproducibility
        for final inclusion/exclusion decision.
Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.
A collection of functions for conducting meta-analysis using a
             structural equation modeling (SEM) approach via the 'OpenMx' package.
             It also implements the two-stage SEM approach to conduct meta-analytic
             structural equation modeling on correlation and covariance matrices.
The following methods are implemented to evaluate how sensitive the results of a meta-analysis are to potential bias in meta-analysis and to support Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 5 "Small-Study Effects in Meta-Analysis":
 - Copas selection model described in Copas & Shi (2001) <DOI:10.1177/096228020101000402>;
 - limit meta-analysis by Rücker et al. (2011) <DOI:10.1093/biostatistics/kxq046>;
 - upper bound for outcome reporting bias by Copas & Jackson (2004) <DOI:10.1111/j.0006-341X.2004.00161.x>.
Functions for Meta-analysis Burden test, SKAT and SKAT-O. These methods use summary-level score statistics to carry out gene-based meta-analysis for rare variants.
This package fits meta regression models and generates a
        number of statistics: next to t- and z-tests, the likelihood
        ratio, bartlett corrected likelihood ratio and permutation
        tests are performed on the model coefficients.
This package allows doing meta-analysis for primary studies with classification outcomes in order to evaluate systematically the accuracies of classifiers, namely, the diagnostic tests. It provides functions to fit the bivariate model of Reitsma et al.(2005). Moreover, if the reference employed in the classification process isn't a gold standard, its deficit can be detected and its influence to the underestimation of the diagnostic test's accuracy can be corrected, as described in Botella et al.(2013).
Spatio-temporal geostatistical mapping of meteorological data. Global spatio-temporal models calculated using publicly available data are stored in package.
Access to several Numerical Weather Prediction services both in raster format and as a time series for a location. Currently it works with GFS, MeteoGalicia, NAM, and RAP.
meteogram is a collection of programs for plotting
        meteograms for meteorological data such as atmospheric cross
        section, temperatures plots.
Functions to estimate weather variables at any position of a landscape.
Fit and plot macroecological patterns predicted by the Maximum
    Entropy Theory of Ecology (METE).
Functions for selection of visual meteor data, calculations of Zenithal Hourly Rate (ZHR) and population index, graphics of population index, ZHR and magnitude distribution.
Meth27QC is a tool for analyzing Illumina Infinium
        HumanMethylation27 BeadChip Data and generating QC reports.
        This package allows users quickly assess data quality of the
        Assay. Users can evaluate the data quality in the way that
        Illumina GenomeStudio/BeadStudio recommended based on the
        control probes. The package reads files exported from
        GenomeStudio/BeadStudio software, generating intensity and
        standard deviation plots grouped by the types of the control
        probes. Meth27 carries 40 control probes for staining,
        hybridization, target removal, extension, bisulfite conversion,
        specificity, negative and non-polymorphic controls. Details of
        those control probes can be found in the Infinium Assay for
        Methylation Protocol Guide from Illumina.We also used the other
        non-control probes to plot intensity of detected genes, signal
        average for green and red. Outliers can be identified.
Methods (standard and advanced) for analysis of agreement
        between measurement methods.
Implementation of the methodology from the paper titled
    "Effective plots to assess bias and precision in method comparison studies"
    published in Statistical Methods in Medical Research, P. Taffe (2016) <doi:10.1177/0962280216666667>.
It plots the output from Methpup (https://github.com/XinYang6699/Methpup)
A full Bayesian partition model is implemented to identify
    differentially methylated loci from single-nucleotide resolution sequencing
    data.
Provides a univariate and several high dimensional multivariate test statistics for detecting differentially methylated regions based on MethylCap-seq data.
Metabolomics data are inevitably subject to a component of unwanted variation, due to factors such as batch effects, matrix effects, and confounding biological variation. This package contains a collection of R functions which can be used to remove unwanted variation and obtain normalized metabolomics data.
Split an untargeted metabolomics data set into a set of likely true 
    metabolites and a set of likely measurement artifacts. This process involves 
    comparing missing rates of pooled plasma samples and biological samples. The 
    functions assume a fixed injection order of samples where biological samples are 
    randomized and processed between intermittent pooled plasma samples. By comparing 
    patterns of missing data across injection order, metabolites that appear in blocks
    and are likely artifacts can be separated from metabolites that seem to have 
    random dispersion of missing data. The two main metrics used are: 1. the number of 
    consecutive blocks of samples with present data and 2. the correlation of missing rates 
    between biological samples and flanking pooled plasma samples.
Metrics is a set of evaluation metrics that is commonly
        used in supervised machine learning.
Provides an 'htmlwidgets' interface to the
    'MetricsGraphics.js' ('D3'-based) charting library which is geared towards
    displaying time-series data. Chart types include line charts, scatterplots,
    histograms and rudimentary bar charts. Support for laying out multiple charts
    into a grid layout is also provided. All charts are interactive and many
    have an option for line, label and region annotations.
Explore the behavior and performance of phylogenetic metrics and
    null models.
Provides classes and calculation and plotting functions 
 for metrology applications, including measurement uncertainty estimation
 and inter-laboratory metrology comparison studies.
Implementation of various statistical models for multivariate
    event history data. Including multivariate cumulative incidence models, and
    bivariate random effects probit models (Liability models). Also contains
    two-stage binomial modelling that can do pairwise odds-ratio dependence
    modelling based marginal logistic regression models. This is an alternative
    to the alternating logistic regression approach (ALR).
An easy to use Graphical User Interface for estimating sample sizes required for metabolomic experiments even when experimental pilot data is not available.
A diverse collection of metabolomics related statistical tools.
Exact simulation from max-stable processes and multivariate extreme value distributions for various parametric models. Threshold selection methods.
Computes the average of a sequence of random vectors
    in a moving expanding window using a fixed amount of storage
Fit flexible (excess) hazard regression models with a random effect defined at the cluster level.
MExPosition is for descriptive (i.e., fixed-effects)
        multi-table multivariate analysis the singular value
        decomposition.
Calculate MF (mitigated fraction) with clustering and bootstrap options. See http://goo.gl/pcXYVr for definition of MF.
Performs Multiple Factor Analysis method for quantitative, categorical, frequency and mixed data, in addition to generating a lot of graphics, also has other useful functions.
Multivariate functional halfspace depth and median for two-dimensional functional data.
The package implements several time series filters useful
        for smoothing and extracting trend and cyclical components of a
        time series. The routines are commonly used in economics and
        finance, however they should also be interest to other areas.
        Currently, Christiano-Fitzgerald, Baxter-King,
        Hodrick-Prescott, Butterworth, and trigonometric regression
        filters are included in the package.
Fractional polynomials are used to represent curvature in regression models. A key reference is Royston and Altman, 1994.
Calculate a multivariate functional principal component analysis
    for data observed on different dimensional domains. The estimation algorithm
    relies on univariate basis expansions for each element of the multivariate
    functional data. Multivariate and univariate functional data objects are
    represented by S4 classes for this type of data implemented in the package
    'funData'.
Convenience functions for multivariate MCMC using univariate samplers including:
  slice sampler with stepout and shrinkage (Neal (2003) <DOI:10.1214/aos/1056562461>),
  adaptive rejection sampler (Gilks and Wild (1992) <DOI:10.2307/2347565>),
  adaptive rejection Metropolis (Gilks et al (1995) <DOI:10.2307/2986138>), and
  univariate Metropolis with Gaussian proposal.
Estimates probit, logit, Poisson, negative binomial, and beta regression models, returning their marginal effects, odds ratios, or incidence rate ratios as an output.
Procedures to simulate, estimate and diagnose MGARCH
    processes of BEKK and multivariate GJR (bivariate asymmetric GARCH
    model) specification.
GAMs, GAMMs and other generalized ridge regression with 
             multiple smoothing parameter estimation by GCV, REML or UBRE/AIC. 
             Includes a gam() function, a wide variety of smoothers, JAGS 
             support and distributions beyond the exponential family.
Implement algorithms to recover multiple networks by pursuit of both sparseness and cluster.
An aggressive dimensionality reduction and network estimation
    technique for a high-dimensional Gaussian graphical model (GGM). Please
    refer to: Efficient Dimensionality Reduction for High-Dimensional Network
    Estimation, Safiye Celik, Benjamin A. Logsdon, Su-In Lee, Proceedings of
    The 31st International Conference on Machine Learning, 2014, p. 1953--1961.
Provides functions that (1) fit multivariate discrete distributions, (2) generate random numbers from multivariate discrete distributions, and (3) run regression and penalized regression on the multivariate categorical response data.  Implemented models include: multinomial logit model, Dirichlet multinomial model, generalized Dirichlet multinomial model, and negative multinomial model. Making the best of the minorization-maximization (MM) algorithm and Newton-Raphson method, we derive and implement stable and efficient algorithms to find the maximum likelihood estimates. On a multi-core machine, multi-threading is supported.
Tools for univariate and multivariate generalized linear models with model averaging and null model technique.
Estimation of time-varying Mixed Graphical models and mixed VAR models using L1-regularized neighborhood regression.
Extends distribution and density functions to parametric
        multivariate generalized Pareto distributions (MGPD of Type
        II), and provides fitting functions which calculate maximum
        likelihood estimates for bivariate and trivariate models. (Help
        is under progress)
Each function in the package performs three main functions
        i) it reads spatial data and produces basic graphs including
        pie chart, bar chart, box plots, histogram, scatter plots, and
        lines ii)it reads non-spatial data such as "csv", "txt", "dat"
        data and produces basic graphs and iii) it plots map(s) of the
        input attribute(s) of spatial data by setting "type" parameter
        to "map"
Convenience Functions for R Language Access to the v.1 API of the MG-RAST Metagenome Annotation Server, part of the US Department of Energy (DOE) Systems Biology Knowledge Base (KBase).
Implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2015), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA, to appear,[DOI:10.1080/01621459.2015.1034318].
Performs gene set analysis based on GSZ scoring function and asymptotic p-value. It is different from GSZ in that it implements asymptotic p-values instead of empirical p-values. Asymptotic p-values are calculated by fitting suitable distribution model to the null distribution. Unlike empirical p-values, resolution of asymptotic p-values are independent of the number of permutations and hence requires considerably fewer permutations. In addition, this package allows gene set analysis with seven other popular gene set analysis methods.
Performs general Metropolis-Hastings Markov Chain Monte
        Carlo sampling of a user defined function which returns the
        un-normalized value (likelihood times prior) of a Bayesian
        model.  The proposal variance-covariance structure is updated
        adaptively for efficient mixing when the structure of the
        target distribution is unknown.  The package also provides some
        functions for Bayesian inference including Bayesian Credible
        Intervals (BCI) and Deviance Information Criterion (DIC)
        calculation.
Implementation of a goodness-of-fit test for normality using the Minimum Hellinger Distance.
Runs a minimum-hypergeometric (mHG) test as described in: Eden, E. (2007). Discovering Motifs in Ranked Lists of DNA Sequences. Haifa.
Parameter estimation and prediction for hidden Markov and semi-Markov models for data with multiple observation sequences.  Suitable for equidistant time series data, with multivariate and/or missing data. Allows user defined emission distributions.
Multiple Hypothesis Testing For Variable Selection in high dimensional linear models. This package performs variable selection with multiple hypothesis testing, either for ordered variable selection or non-ordered variable selection. In both cases, a sequential procedure is performed. It starts to test the null hypothesis "no variable is relevant"; if this hypothesis is rejected, it then tests "only the first variable is relevant", and so on until the null hypothesis is accepted.
A framework for multiple hypothesis testing based on distribution
    of p values. It is well known that the p values come from different
    distribution for null and alternatives, in this package we provide
    functions to detect that change. We provide a method for using the change
    in distribution of p values as a way to detect the true signals in the
    data.
A Comprehensive tool for almost all existing multiple testing
    methods for discrete data. The package also provides some novel multiple testing
    procedures controlling FWER/FDR for discrete data. Given discrete p-values
    and their domains, the [method].p.adjust function returns adjusted p-values,
    which can be used to compare with the nominal significant level alpha and make
    decisions.
Spontaneous adverse event reports have a high potential for detecting adverse drug reactions. However, due to their dimension, the analysis of such databases requires statistical methods. We propose to use a logistic regression whose sparsity is viewed as a model selection challenge. Since the model space is huge, a Metropolis-Hastings algorithm carries out the model selection by maximizing the BIC criterion.
Estimation of models with zero left-censored variables. 
             Null values may be caused by a selection process, insufficient 
             resources or infrequency of purchase.
The mi package provides functions for data manipulation, imputing missing values in an approximate Bayesian framework, diagnostics of the models used to generate the imputations, confidence-building mechanisms to validate some of the assumptions of the imputation algorithm, and functions to analyze multiply imputed data sets with the appropriate degree of sampling uncertainty.
Tools for training, selecting, and evaluating maximum entropy
    (Maxent) distribution models. This package provides tools for user-
    controlled transformation of explanatory variables, selection of variables
    by nested model comparison,  and flexible model evaluation and projection.
    It is based on the strict maximum likelihood interpretation of maximum
    entropy modelling.
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.
Tools for microeconomic analysis and microeconomic modelling
Demand analysis with the Almost Ideal Demand System (AIDS)
   suggested by Deaton and Muellbauer (1980)
Tools for economic analysis and economic modelling
   with a Constant Elasticity of Substitution (CES) function
Production analysis with the Symmetric Normalized Quadratic (SNQ) profit function
A procedure for comparing multivariate samples associated with
    different groups. It uses principal component analysis to convert
    multivariate observations into a set of linearly uncorrelated statistical
    measures, which are then compared using a number of statistical methods. The
    procedure is independent of the distributional properties of samples and
    automatically selects features that best explain their differences, avoiding
    manual selection of specific points or summary statistics. It is appropriate
    for comparing samples of time series, images, spectrometric measures or
    similar multivariate observations.
Fits Semiparametric Promotion Time Cure Models, taking into account (using a 
			 corrected score approach or the SIMEX algorithm) or not the measurement error
			 in the covariates, using a backfitting approach to maximize the likelihood.
A nature-inspired metaheuristic algorithm based on the echolocation behavior of microbats that uses frequency tuning to optimize problems in both continuous and discrete dimensions. This R package makes it easy to implement the standard bat algorithm on any user-supplied function. The algorithm was first developed by Xin-She Yang in 2010 (<DOI:10.1007/978-3-642-12538-6_6>, <DOI:10.1109/CINTI.2014.7028669>).
Provides infrastructure to accurately measure and compare
        the execution time of R expressions.
Functions for assigning 16S sequence data to a
    taxonomic level in the tree-of-life for prokaryotes.
The consensus taxonomy for prokaryotes is a set of data-sets for
    best possible taxonomic classification based on 16S rRNA sequence data.
Provides utilities for reading and processing microdata from Spanish official statistics with R.
This group of functions simplifies the creation of linked micromap
    plots.
Provides the users with the ability to quickly create Linked 
     Micromap plots for a collection of geographic areas.  
     Linked Micromaps are visualizations of georeferenced data that link statistical
     graphics to an organized series of small maps or graphic images. 
     The Help description contains examples of how to use the micromapST function.
     Contained in this package are border group datasets to support creating micromaps for the 
     50 U.S. states and District of Columbia (51 areas), the U. S. 20 Seer Registries, 
     the 105 counties in the state of Kansas, the 62 counties of New York,
     the 24 counties of Maryland, the 29 counties of Utah, the 32 administrative areas 
     in China, the 218 administrative areas in the UK and Ireland (for testing only), 
     the 25 districts in the city of Seoul South Korea, and the 52 counties on the Africa 
     continent.
     A border group dataset contains the boundaries related to the data level areas, 
     a second layer boundaries, a top or third layer boundary, a parameter list of 
     run options, and a cross indexing table between area names, abbreviations, 
     numeric identification and alias matching strings for the specific geographic
     area.  By specifying a border group, the package create micromaps for any
     geographic region.  The user can create and provide their own border group dataset 
     for any area beyond the areas contained within the package.
     Copyrighted 2013, 2014, 2015 and 2016 by Carr, Pearson and Pickle.
A collection of functions for computations and visualizations of
    microbial pan-genomes.
Prepare lists of R graphics files to be used as
	     microplots (sparklines) in tables in either LaTeX or HTML files.  For LaTeX use
	     the Hmisc::latex() function or xtable::xtable() with
	     Sweave, knitr, rmarkdown, or Emacs org-mode to construct
	     latex tabular environments which include the graphs.  For HTML files use
	     either Emacs org-mode or the htmlTable::htmlTable()
	     function to construct an HTML file containing tables which
	     include the graphs.  Examples are shown with lattice
	     graphics, base graphics, and ggplot2 graphics.  Examples
	     for LaTeX include Sweave (both LaTeX-style and
	     Noweb-style), knitr, emacs org-mode,
	     and rmarkdown input files and their pdf
	     output files.  Examples for HTML include org-mode
	     and Rmd input files and their webarchive
	     HTML output files.  In addition, the as.orgtable function
	     can display a data.frame in an org-mode document.
Basic functions for microbial sequence data analysis. In some cases the functions use the corresponding functions in the 'Biostrings' package from Bioconductor <http://www.bioconductor.org/>, but provide an easier interface.
Deploys your R Analytic to MicroStrategy
This entry-level toolkit allows performing continuous-time microsimulation for a wide range of demographic applications. Individual life-courses are specified by a continuous-time multi-state model.
Methods and tools for mixed frequency time series data analysis.
    Allows estimation, model selection and forecasting for MIDAS regressions.
Contains the function mice.impute.midastouch(). Technically this function is to be run from within the 'mice' package (van Buuren et al. 2011), type ??mice. It substitutes the method 'pmm' within mice by 'midastouch'. The authors have shown that 'midastouch' is superior to default 'pmm'. Many ideas are based on Siddique / Belin 2008's  MIDAS.
Implementation of the mid-n algorithms presented in 
             Wellek S (2015) <DOI:10.1111/stan.12063> Statistica Neerlandica 69, 358-373 for exact 
             sample size calculation for superiority trials with binary outcome.
Apply tests of multiple comparisons based on studentized midrange
    and range distributions. The tests are: Tukey Midrange test, Student-Newman-
    Keuls Midrange test, Skott-Knott Midrange test and Skott-Knott Range test.
Functions for implementing species dispersal into projections
             of species distribution models (e.g. under climate change scenarios).
Indirect methods for estimating bilateral migration flows in the presence of partial or missing data. Methods might be relevant to other categorical data situations on non-migration data, where for example, marginal totals are known and only auxiliary bilateral data is available.
This package provides various indices, like Crude Migration Rate,
    different Gini indices or the Coefficient of Variation among others, to
    show the (un)equality of migration.
This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.
Implements multiple imputation for proportional hazards regression
    with interval censored data or proportional sub-distribution hazards
    regression for interval censored competing risks data. The main functions
    allow to estimate survival function, cumulative incidence function, Cox
    and Fine & Gray regression coefficients and associated variance-covariance
    matrix. 'MIICD' functions call 'Surv', 'survfit' and 'coxph' from the
    'survival' package, 'crprep' from the 'mstate' package, and 'mvrnorm' from
    the 'MASS' package.
Functions for estimating structural equation models using model-
    implied instrumental variables.
The MILC package is designed to predict individual trajectories using the continuous time microsimulation model MILC, that describes the natural history of lung cancer.
Multiple imputation for multivariate binary outcome by using Gibbs' Sampler on all potential profiles.
The multiple instance data set consists of many independent subjects 
    (called bags) and each subject is composed of several components (called instances). 
    The outcomes of such data set are binary or multinomial, and, we can only observe 
    the subject-level outcomes. For example, in manufactory processes, a subject is labeled 
    as "defective" if at least one of its own components is defective, and otherwise, is 
    labeled as "non-defective".  The 'milr' package focuses on the predictive model for the 
    multiple instance data set with binary outcomes and performs the maximum likelihood 
    estimation with the Expectation-Maximization algorithm under the framework of logistic 
    regression.  Moreover, the LASSO penalty is attached to the likelihood function for 
    simultaneous parameter estimation and variable selection.
Guesses the MIME type from a filename extension using the data
    derived from /etc/mime.types in UNIX-type systems.
Tools to combine results for multiply-imputed data using
        mixture approximations
66 data sets that were imported using read.table() where appropriate but more commonly after converting to a csv file for importing via read.csv().
R wrapper for 'cmine' implementation of Maximal
        Information-based Nonparametric Exploration statistics (MIC and
        MINE family). Detailed information of the ANSI C implementation 'cmine'
	can be found at 'http://mpba.fbk.eu/cmine'.
This package implements the core idea of games known as
        'Minesweeper' on Microsoft Windows or 'KMines' for KDE on
        Unix-like operating systems.
Makes it possible to create an internally consistent
    repository consisting of selected packages from CRAN-like repositories.
    The user specifies a set of desired packages, and miniCRAN recursively
    reads the dependency tree for these packages, then downloads only this
    subset. The user can then install packages from this repository directly,
    rather than from CRAN.  This is useful in production settings, e.g. server
    behind a firewall, or remote locations with slow broadband access.
quick and simple tktcl miniGUI to call functions.
Generate central composite designs (CCD)with full as well 
    as fractional factorial points (half replicate) and Box Behnken 
    designs (BBD) with minimally changed run sequence.
Create tile grid maps, which are like choropleth maps except each
  region is represented with equal visual space.
The minimax family of distributions is a two-parameter
        family like the beta family, but computationally a lot more
        tractible.
Provides two main functions: mMcPSO() and
    miniMaxPro(), which generates minimax designs and minimax projection designs using
    a hybrid clustering - particle swarm optimization (PSO) algorithm. These designs can be used
    in a variety of settings, e.g., as space-filling designs for computer experiments or
    sensor allocation designs. A detailed description of the two designs and the employed
    algorithms can be found in Mak and Joseph (2016) <https://arxiv.org/abs/1602.03938>.
A binding to the minimist JavaScript library. This module implements
    the guts of optimist's argument parser without all the fanciful decoration.
Provides UI widget and layout functions for writing Shiny apps
    that work well on small screens.
The nls.lm function provides an R interface to lmder and lmdif from the MINPACK library, for solving nonlinear least-squares problems by a modification of the Levenberg-Marquardt algorithm, with support for lower and upper parameter bounds.  The implementation can be used via nls-like calls using the nlsLM function.
Package minPtest is designed for estimating a gene region-level summary for SNP data from case-control studies using a permutation-based resampling method, called min P test, allowing execution on a compute cluster or multicore computer.
Derivative-free optimization by quadratic approximation
        based on an interface to Fortran implementations by M. J. D.
        Powell.
This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures.
Learns direct microbe-microbe interaction networks using a Poisson
    multivariate-normal hierarchical model with an L1 penalized precision
    matrix. Optimization is carried out using an iterative conditional modes
    algorithm.
For a given set of stoichiometric reactions, this package
    evaluates the mass and charge balance, extracts all reactants, products, orphan
    metabolites, metabolite names and compartments. Also are included some options
    to write models in TSV and SBML formats.
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 multidimensional). 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.
An adaptive normalization algorithm that selects housekeeping genes
    based on the sample level variability in the data. This is suitable for any data
    obtained from RT-qPCR assays.  A manuscript describing the method is submitted 
    to Genome Biology under ``MiRA-norm: An Adaptive Method for the Normalization of MicroRNA Array Data``, Yuda Zhu et al.
The tools for 'MicroRNA Set Enrichment Analysis' can identify risk pathways(or prior gene sets) regulated by microRNA set in the context of microRNA expression data. (1) This package constructs a correlation profile of microRNA and pathways by the hypergeometric statistic test. The gene sets of pathways derived from the three public databases (Kyoto Encyclopedia of Genes and Genomes ('KEGG'); 'Reactome'; 'Biocarta') and the target gene sets of microRNA are provided by four databases('TarBaseV6.0'; 'mir2Disease'; 'miRecords'; 'miRTarBase';). (2) This package can quantify the change of correlation between microRNA for each pathway(or prior gene set) based on a microRNA expression data with cases and controls. (3) This package uses the weighted Kolmogorov-Smirnov statistic to calculate an enrichment score (ES) of a microRNA set that co-regulate to a pathway , which reflects the degree to which a given pathway is associated with the specific phenotype. (4) This package can provide the visualization of the results.
Analysis of dichotomous and polytomous response data using
    unidimensional and multidimensional latent trait models under the Item
    Response Theory paradigm. Exploratory and confirmatory models can be
    estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory
    bi-factor and two-tier analyses are available for modeling item testlets.
    Multiple group analysis and mixed effects designs also are available for
    detecting differential item and test functioning as well as modelling
    item and person covariates. Finally, latent class models such as the DINA,
    DINO, multidimensional latent class, and several other discrete latent
    variable models are supported.
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 (CAT) using item response theory methodology and for
    creating simple questionnaires forms to collect response data directly in R.
    Additionally, optimal test designs (e.g., "shadow testing") are supported
    for tests which contain a large number of item selection constraints.
    Finally, package contains tools useful for performing Monte Carlo simulations 
    for studying the behavior of computerized adaptive test banks.
A collection of miscellaneous 3d plots, including
        isosurfaces.
Various functions for random number generation, density 
             estimation, classification, curve fitting, and spatial 
             data analysis.
Implementing various things including functions for LaTeX tables,
    the Kalman filter, web scraping, development tools, relative risk and odds
    ratio.
Estimates models that extend the standard GLM to take
    misclassification into account. The models require side information from a secondary data set
    on the misclassification process, i.e. some sort of misclassification
    probabilities conditional on some common covariates.
    A detailed description of the algorithm can be found in
    Dlugosz, Mammen and Wilke (2015) \url{http://www.zew.de/PU70410}.
Statistical test for the product-moment correlation coefficient based on H0: rho = rho0
             including sample size computation, statistical test for comparing the product-moment
             correlation coefficient in independent and dependent samples, partial and semipartial
             correlation, sequential triangular test for the product-moment correlation coefficient,
             and simulation of bivariate normal and non-normal distribution with a specified correlation.
A collection of miscellaneous methods to simplify various tasks,
  including plotting, data.frame and matrix transformations, environment
  functions, regular expression methods, and string and logical operations, as
  well as numerical and statistical tools. Most of the methods are simple but
  useful wrappers of common base R functions, which extend S3 generics or
  provide default values for important parameters.
Miscellaneous small tools and utilities.
   Many of them facilitate the work with matrices,
   e.g. inserting rows or columns, creating symmetric matrices,
   or checking for semidefiniteness.
   Other tools facilitate the work with regression models,
   e.g. extracting the standard errors,
   obtaining the number of (estimated) parameters,
   or calculating R-squared values.
Clears the workspace. Useful for the beginnings of R scripts, to
    avoid potential problems with accidentally using information from variables
    or functions from previous script evaluations, too many figure windows open
    at the same time, packages that you don't need any more, or a cluttered
    console.  Uses code from various StackOverflow users.  See help(mise) for
    pointers to the relevant StackOverflow pages.
There is an increasing interest in investigating how the compositions of microbial communities are associated with human health and disease. In this package, we present a novel global testing method called aMiSPU, that is highly adaptive and thus high powered across various scenarios, alleviating the issue with the choice of a phylogenetic distance. Our simulations and real data analysis demonstrated that aMiSPU test was often more powerful than several competing methods while correctly controlling type I error rates.
Enables investigation of the predictors of misreporting on sensitive survey questions through a multivariate list experiment regression method. The method permits researchers to model whether a survey respondent's answer to the sensitive item in a list experiment is different from his or her answer to an analogous direct question.
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.
Provide all the data sets and statistical analysis applications used in "Modern Industrial Statistics: with applications in R, MINITAB and JMP" by R.S. Kenett and S. Zacks with contributions by D. Amberti, John Wiley and Sons, 2013, which is a second revised and expanded revision of "Modern Industrial Statistics: Design and Control of Quality and Reliability", R. Kenett and S. Zacks, Duxbury/Wadsworth Publishing, 1998.
Various reliability analysis methods for rare event inference: 
    1) computing failure probability (probability that the output of a numerical model exceeds a threshold),
    2) computing quantiles of low or high-order,
    3) Wilks formula to compute quantile(s) from a sample or the size of the required i.i.d. sample.
Flexible multivariate function approximation using adapted
        Mixture of Student t Distributions. Mixture of t distribution
        is obtained using Importance Sampling weighted Expectation
        Maximization algorithm.
Provides tools for multiple imputation of missing data in multilevel
 modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo',
 and several functions for visualization, data management and the analysis 
 of multiply imputed data sets.
Tools to perform analyses and combine results from
        multiple-imputation datasets.
Estimation/multiple imputation programs for mixed categorical
    and continuous data.
Contains a mixture of statistical methods including the MCMC methods to analyze normal mixtures. Additionally, model based clustering methods are implemented to perform classification based on (multivariate) longitudinal (or otherwise correlated) data. The basis for such clustering is a mixture of multivariate generalized linear mixed models.
Algorithms and methods for estimating parametric mixture models for
    mixed data and with missing data.
Mixed effects cumulative and baseline logit link models
        for the analysis of ordinal or nominal responses, with
        non-parametric distribution for the random effects
This package contains functions for fitting finite mixture
        distribution models to grouped data and conditional data by the
        method of maximum likelihood using a combination of a
        Newton-type algorithm and the EM algorithm.
Missing data imputation for continuous and categorical data, using nonparametric Bayesian joint models (specifically the hierarchically coupled mixture model with local dependence described in Murray and Reiter (2015); see 'citation("MixedDataImpute")' or http://arxiv.org/abs/1410.0438). See '?hcmm_impute' for example usage.
Fits mixed membership models with discrete multivariate data (with
    or without repeated measures) following the general framework of Erosheva et al
    (2004). This package uses a Variational EM approach by approximating the
    posterior distribution of latent memberships and selecting hyperparameters
    through a pseudo-MLE procedure. Currently supported data types are
    Bernoulli, multinomial and rank (Plackett-Luce). The extended GoM model with fixed stayers from Erosheva et al     (2007) is now also supported. See Airoldi et al (2014) for other examples of mixed membership models.
The estimation of the parameters in mixed Poisson models.
Inference on stochastic differential models Ornstein-Uhlenbeck or
    Cox-Ingersoll-Ross, with one or two random effects in the drift function.
We provide detailed functions for univariate Mixed Tempered Stable distribution.
Routines for the analysis (unsupervised clustering) of
        networks using MIXtures of Erdos-Renyi random graphs
Functions for creating designs for mixture experiments, making ternary contour plots, and making mixture effect plots.
Evaluation and optimization of the Fisher Information Matrix in NonLinear Mixed Effect Models using Markov Chains Monte Carlo for continuous and discrete data.
Carries out model-based clustering, classification and discriminant analysis using five different models. The models are all based on the  generalized hyperbolic distribution.The first model 'MGHD' is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD', mixture of  multiple scaled generalized hyperbolic distributions. The 'cMSGHD' is a 'MSGHD' with convex contour plots. The 'MCGHD', mixture of  coalesced generalized hyperbolic distributions is a new more flexible model.
The Mixture Link model <http://arxiv.org/abs/1612.03302> is a proposed extension to generalized linear models, where the outcome distribution is a finite mixture of J > 1 densities. This package supports Mixture Link computations for Poisson and Binomial outcomes. This includes the distribution functions, numerical maximum likelihood estimation, Bayesian analysis, and quantile residuals to assess model fit.
The main functions perform mixed models analysis by least squares
    or REML by adding the function r() to formulas of lm and glm. A collection of
    text-book statistics for higher education is also included, e.g. modifications
    of the functions lm, glm and associated summaries from the package stats.
A collection of functions to implement the MixMAP algorithm, which performs gene
    level tests of association using data from a previous GWAS or data from a
    meta-analysis of several GWAS.  Conceptually, genes are detected as
    significant if the collection of p-values within a gene are determined to
    be collectively smaller than would be observed by chance.
Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: horizontal integration with regularised Generalised Canonical Correlation Analysis and vertical integration with multi-group Partial Least Squares.
Provides the function 'mixord' for fitting a mixed-effects ordinal and binary response models and associated methods for printing, summarizing, extracting estimated coefficients and variance-covariance matrix, and estimating contrasts for the fitted models.
A collection of tools implemented to facilitate the analysis of the components of a finite mixture distributions. The package has some functions to generate random samples coming from a finite mixture. The package provides a C++ implementation for the construction of a hierarchy over the components of a given finite mixture.
Fits multiple variable mixtures of various parametric proportional hazard models using the EM-Algorithm. Proportionality restrictions can be imposed on the latent groups and/or on the variables. Several survival distributions can be specified. Missing values and censored values are allowed. Independence is assumed over the single variables.
Estimates Rasch models and mixture Rasch models, including the dichotomous Rasch model, the rating scale model, and the partial credit model.
Fits mixtures of (possibly multivariate) regressions
	(which has been described as doing ANCOVA when you don't
	know the levels).
It offers random-forest-based functions to impute clustered
    incomplete data. The package is tailored for but not limited to imputing
    multitissue expression data, in which a gene's expression is measured on the
    collected tissues of an individual but missing on the uncollected tissues.
Separates DNA mixtures using a statistical model within a
        greedy algorithm with a useful tcl/tk GUI.
Creates and runs Bayesian mixing models to analyze
    biotracer data (i.e. stable isotopes, fatty acids), which estimate the
    proportions of source (prey) contributions to a mixture (consumer). 'MixSIAR'
    is not one model, but a framework that allows a user to create a mixing model
    based on their data structure and research questions, via options for fixed/
    random effects, source data types, priors, and error terms. 'MixSIAR' incorporates
    several years of advances since 'MixSIR' and 'SIAR', and includes both GUI
    (graphical user interface) and script versions.
The utility of this package is in simulating mixtures of Gaussian
        distributions with different levels of overlap between mixture
        components.  Pairwise overlap, defined as a sum of two
        misclassification probabilities, measures the degree of
        interaction between components and can be readily employed to
        control the clustering complexity of datasets simulated from
        mixtures. These datasets can then be used for systematic
        performance investigation of clustering and finite mixture
        modeling algorithms. Among other capabilities of 'MixSim', there
        are computing the exact overlap for Gaussian mixtures,
        simulating Gaussian and non-Gaussian data, simulating outliers
        and noise variables, calculating various measures of agreement
        between two partitionings, and constructing parallel
        distribution plots for the graphical display of finite mixture
        models.
Functions to fit finite mixture of scale mixture of
        skew-normal (FM-SMSN) distributions.
Differential expression analysis of RNA-Seq data when replicates under two conditions are available is performed. First, mixtures of Negative Binomial distributions are fitted on the data in order to estimate the dispersions, then the Wald test is computed.
A collection of R functions for analyzing finite mixture models.  This package is based upon work supported by the National Science Foundation under Grant No. SES-0518772.
Curve Fitting for monotonic(sigmoidal) & non-monotonic(J-shaped) 
 concentration-response data. Prediction of mixture toxicity based on reference 
 models such as 'concentration addition', 'independent action', and 'generalized 
 concentration addition'.
An implementation of all 14 Gaussian parsimonious
        clustering models (GPCMs) for model-based clustering and
        model-based classification.
Functions for computing the penalized maximum likelihood estimate (PMLE) or maximum likelihood estimate (MLE), testing the order of a finite mixture model using EM-test, drawing histogram of observations and the fitted density or probability mass function of the mixture model.
A set of classes and methods to set up and run multispecies, trait
    based and community size spectrum ecological models, focussed on the marine
    environment.
Provides functions to compute and visualize movement-based kernel density estimates (MKDEs) for animal utilization distributions in 2 or 3 spatial dimensions.
Calculation routines based on the FOCUS Kinetics Report (2006,
  2014).  Includes a function for conveniently defining differential equation
  models, model solution based on eigenvalues if possible or using numerical
  solvers and a choice of the optimisation methods made available by the 'FME'
  package.  If a C compiler (on windows: 'Rtools') is installed, differential
  equation models are solved using compiled C functions.  Please note that no
  warranty is implied for correctness of results or fitness for a particular
  purpose.
Package for fast computation of the maximum kernel
        likelihood estimator (mkle)
Contains several functions for statistical data analysis; e.g. for sample size and power calculations, computation of confidence intervals, and generation of similarity matrices.
mkssd is a package that generates efficient balanced
        non-aliased multi-level k-circulant supersaturated designs by
        interchanging the elements of the generator vector. The package
        tries to generate a supersaturated design that has chisquare
        efficiency more than user specified efficiency level (mef). The
        package also displays the progress of generation of an
        efficient multi-level k-circulant design through a progress
        bar. The progress of 100% means that one full round of
        interchange is completed. More than one full round (typically
        4-5 rounds) of interchange may be required for larger designs.
A collection of artificial and real-world machine learning
        benchmark problems, including, e.g., several data sets from the
        UCI repository.
Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version together with possibility of constraints on all model parameters.
Conjoint measurement is a psychophysical procedure in which stimulus pairs are presented that vary along 2 or more dimensions and the observer is required to compare the stimuli along one of them.  This package contains functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact.
Functions necessary to perform the machine learning-based
        differential network analysis of transcriptome data.
Exploratory data analysis and manipulation functions for multi-
    label data sets along with an interactive Shiny application to ease their use.
Large collection of multilabel datasets along with the functions
    needed to export them to several formats, to make partitions, and to obtain
    bibliographic information.
Difference scaling is a method for scaling perceived 
  supra-threshold differences.  The package contains functions that
  allow the user to design and run a difference scaling experiment, 
  to fit the resulting data by maximum likelihood and test the
  internal validity of the estimated scale.
This package provides a unified interface to various
        machine learning algorithms. Confusion matrices are provided
        too.
This package contains functions to compute the
        nonparametric maximum likelihood estimator (MLE) for the
        bivariate distribution of (X,Y), when realizations of (X,Y)
        cannot be observed directly.  To be more precise, we consider
        the situation where we observe a set of rectangles that are
        known to contain the unobservable realizations of (X,Y). We
        compute the MLE based on such a set of rectangles.  The methods
        can also be used for univariate censored data (see data set
        'cosmesis'), and for censored data with competing risks (see
        data set 'menopause').  We also provide functions to visualize
        the observed data and the MLE.
Maximum likelihood Gaussian process modeling for
        univariate and multi-dimensional outputs with diagnostic plots.
        Contact the maintainer for a package version that implements
        sensitivity analysis functionality.
Provides functions for unit root testing using MLE method
Processing and analysis of high throughput (Roche 454)
        sequences generated from multiple loci and multiple biological
        samples. Sequences are assigned to their locus and sample of
        origin, aligned and trimmed. Where possible, genotypes are
        called and variants mapped to known alleles.
An R code implementation of the maximum likelihood (fixed
        point) algorithm of Hyvaerinen, Karhuna, and Oja for
        independent component analysis.
Do multilevel mediation analysis with generalized additive multilevel models.
An implementation of Multi-level Monte Carlo for R.  This package
    builds on the original 'Matlab' and C++ implementations by Mike Giles to provide
    a full MLMC driver and example level samplers.  Multi-core parallel sampling
    of levels is provided built-in.
A collection of evaluation metrics, including loss, score and
    utility functions, that measure regression, classification and ranking performance.
Computational strategies for multivariate linear
        mixed-effects models with missing values, Schafer and Yucel
        (2002), Journal of Computational and Graphical Statistics, 11,
        421-442.
Data and examples from a multilevel modelling software review
  as well as other well-known data sets from the multilevel modelling
  literature.
Estimation of the multinomial logit model
Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL.
Phase haplotypes from genotypes based on a list of known
        haplotypes. Suited to highly diverse loci such as HLA.
An implementation of classifier chains (CC's) for multi-label
    prediction. Users can employ an external package (e.g. 'randomForest',
    'C50'), or supply their own. The package can train a single set of CC's or
    train an ensemble of CC's -- in parallel if running in a multi-core
    environment. New observations are classified using a Gibbs sampler since
    each unobserved label is conditioned on the others. The package includes
    methods for evaluating the predictions for accuracy and aggregating across
    iterations and models to produce binary or probabilistic classifications.
Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There is
    also an experimental extension for survival analysis, clustering and
    general, example-specific cost-sensitive learning. Generic resampling,
    including cross-validation, bootstrapping and subsampling. Hyperparameter
    tuning with modern optimization techniques, for single- and multi-objective
    problems. Filter and wrapper methods for feature selection. Extension of
    basic learners with additional operations common in machine learning, also
    allowing for easy nested resampling. Most operations can be parallelized.
This package provides Multilinear Regression Model Population 
             Analysis to build a pool of models between quantitative activity and chemical 
			 descriptors. Also some useful model validation functions. Contains all 
			 molecular descriptors of 101 organic compounds and acticity dataset.
Generate a stream of pseudo-random numbers generated using the MLS 
    Junk Generator algorithm. Functions exist to generate single pseudo-random 
    numbers as well as a vector, data frame, or matrix of pseudo-random numbers.
Likelihood-based estimation of conditional transformation
  models via the most likely transformation approach.
Additional documentation, a package vignette and 
  regression tests for package mlt.
A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase.
    Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data() 
    function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and 
    empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions.
Estimates the multi-level vector autoregression model on time-series data.
             Three network structures are obtained: temporal networks, contemporaneous
             networks and between-subjects networks.
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) <doi:10.4172/2155-6180.1000189> "General Multiple Mediation Analysis With an Application to Explore Racial Disparity in Breast Cancer Survival", published on Journal of Biometrics & Biostatistics, 5(2):189.
Provides tools for performing mathematical morphology operations,
    such as erosion and dilation, on data of arbitrary dimensionality. Can also
    be used for finding connected components, resampling, filtering, smoothing
    and other image processing-style operations.
R interface to POSIX mmap and Window's MapViewOfFile
Provides routines for multivariate measurement error correction. Includes procedures for linear, logistic and Cox regression models. Bootstrapped standard errors and confidence intervals can be obtained for corrected estimates.
An implementation of modified maximum contrast methods
    and the maximum contrast method: Functions 'mmcm.mvt' and
    'mcm.mvt' give P-value by using randomized quasi-Monte Carlo
    method with 'pmvt' function of package 'mvtnorm', and
    'mmcm.resamp' gives P-value by using a permutation method.
Fit incomplete multinomial data by mixture multinomial distribution
    approximation (Dunson and Xing (2009) <DOI:10.1198/jasa.2009.tm08439>). Estimate latent joint distribution by
    slice Gibbs sampler (Walker (2007) <DOI:10.1080/03610910601096262>). Perform efficient missing data imputation and
    other statistical inference based on joint distribution estimation.
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.
Marginalizes prediction functions using Monte-Carlo integration and computes permutation importance.
Compute similarities and distances between marked point processes.
Time-series of count data occur in many different contexts. A
    Markov-modulated Poisson process provides a framework for detecting
    anomalous events using an unsupervised learning approach.
Fixed effects Selection in Linear Mixed Models
Fits a family of mixtures of multivariate t-distributions under a continuous t-distributed latent variable structure for the purpose of clustering or classification. The alternating expectation-conditional maximization algorithm is used for parameter estimation.
The first day of any MMWR week is Sunday.
    MMWR week numbering is sequential beginning with 1
    and incrementing with each week to a maximum of 52
    or 53. MMWR week #1 of an MMWR year is the first week
    of the year that has at least four days in the calendar
    year. This package provides functionality to convert
    Dates to MMWR day, week, and year and the reverse.
An API package for the Members' Name Information Service operated by the UK parliament. Documentation for the API itself can be found here: <http://data.parliament.uk/membersdataplatform/default.aspx>.
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.
Multivariate tests, estimates and methods based on the identity score, spatial sign score and spatial rank score are provided. The methods include one and c-sample problems, shape estimation and testing, linear regression and principal components.
Computes integral of f(x)*x_i^k on a product of intervals,
 where f is the density of a gaussian law.
 This a is small alteration of the mnormt code from A. Genz and A. Azzalini.
Functions are provided for computing the density and the
  distribution function of multivariate normal and "t" random variables,
  and for generating random vectors sampled from these distributions.  
  Probabilities are computed via non-Monte Carlo methods; different routines 
  are used in the case d=1, d=2, d>2, if d denotes the number of dimensions.
MNP is a publicly available R package that fits the
        Bayesian multinomial probit model via Markov chain Monte Carlo.
        The multinomial probit model is often used to analyze the
        discrete choices made by individuals recorded in survey data.
        Examples where the multinomial probit model may be useful
        include the analysis of product choice by consumers in market
        research and the analysis of candidate or party choice by
        voters in electoral studies.  The MNP software can also fit the
        model with different choice sets for each individual, and
        complete or partial individual choice orderings of the
        available alternatives from the choice set. The estimation is
        based on the efficient marginal data augmentation algorithm
        that is developed by Imai and van Dyk (2005). ``A Bayesian
        Analysis of the Multinomial Probit Model Using the Data
        Augmentation,'' Journal of Econometrics, Vol. 124, No. 2
        (February), pp. 311-334. Detailed examples are given in Imai
        and van Dyk (2005). ``MNP: R Package for Fitting the
        Multinomial Probit Model.'' Journal of Statistical Software,
        Vol. 14, No. 3 (May), pp. 1-32.
An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.
Some canned plots and functions designed for the mobilize project.
    Designed to be called remotely.
This package provides methods to analyze cluster
        alternatives based on multi-objective optimization of cluster
        validation indices.
The two main functionalities of this package are creating mock
    objects (functions) and selectively intercepting calls to a given
    function that originate in some other function. It can be used
    with any testing framework available for R. Mock objects can
    be injected with either this package's own stub() function or a
    similar with_mock() facility present in the testthat package.
Package for processing downloaded MODIS Surface reflectance
    Product HDF files. Specifically, MOD09 surface reflectance product files, and
    the associated MOD03 geolocation files (for MODIS-TERRA). 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.
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.
Creates sophisticated models of training data and validates the models with an independent test set, cross validation, or in the case of Random Forest Models, with Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data.
Collection of metrics for evaluating models written in C++ using 'Rcpp'.
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.
Functions for modelling that help you seamlessly integrate
    modelling into a pipeline of data manipulation and visualisation.
A collection of tools to deal with statistical models. 
  The functionality is experimental and the user interface is likely to
  change in the future. The documentation is rather terse, but packages `coin'
  and `party' have some working examples. However, if you find the
  implemented ideas interesting we would be very interested in a discussion
  of this proposal. Contributions are more than welcome!
Designed with a dual purpose of
    accurately estimating the mode (or modes) as well as characterizing
    the modality of data. The specific application area includes complex
    or mixture distributions particularly in a big data environment.
    The heterogeneous nature of (big) data may require deep introspective
    statistical and machine learning techniques, but these statistical tools
    often fail when applied without first understanding the data. In small
    datasets, this often isn't a big issue, but when dealing with large scale
    data analysis or big data thoroughly inspecting each dimension
    typically yields an O(n^n-1) problem. As such, dealing with big data
    require an alternative toolkit. This package not only identifies the
    mode or modes for various data types, it also provides a programmatic
    way of understanding the modality (i.e. unimodal, bimodal, etc.) of
    a dataset (whether it's big data or not). See
    <http://www.sdeevi.com/modes_package> for examples and discussion.
Analyses species distribution models and evaluates their performance. It includes functions for performing variation partitioning, calculating several measures of model discrimination and calibration, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots.
Download and processing functionalities for the Moderate Resolution
    Imaging Spectroradiometer (MODIS). The package provides automated access to the
    global online data archives (LPDAAC and LAADS) and processing capabilities such
    as file conversion, mosaicking, subsetting and time series filtering.
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.
Package for downloading Moderate-resolution Imaging Spectroradiometer (MODIS) snow cover data. Global daily snow cover at 500 m resolution derived from MODIS is made available by the National Snow and Ice Center Data Center <http://nsidc.org/>.
Provides several functions for downloading, storing and processing 
		subsets of MODIS Land Processes data as a batch process.
The algorithms implemented here are used to detect the community structure of a network. 
            These algorithms follow different approaches, but are all based on the concept of modularity maximization.
Contains basic tools for performing 
             multiple-output quantile regression and computing 
             regression quantile contours by means of directional 
             regression quantiles. In the location case, one can thus 
             obtain halfspace depth contours in two to six dimensions.
Fits a Constrained Segmented Distributed Lag regression model 
	to epidemiological time series of mortality, temperature, and other confounders.
Methods for color labeling, calculation of eigengenes, merging of closely related modules.
Provides modules as an organizational unit for source code. Modules
    enforce to be more rigorous when defining dependencies and have
    a local search path. They can be used as a sub unit within packages
    or in scripts.
RMSD and Internal Energy analysis of NAMD and YASARA Molecular Dynamics output files. Allows to comparison of different dynamics per different complexes. Input files have to be ASCII files tab separated.
Statistical utilities for the analysis of data by means of the Marshall-Olkin Extended Zipf distribution. By plotting the probabilities in log-log scale, this two parameter distribution allows a concave as well as a convex behavior of the function at the beginning of the distribution, maintaining the linearity in the tail. The model contains the Zipf model as a particular case.
Functions for exploring the best subsets in regression with a genetic algorithm. The package is much faster than methods relying on complete enumeration, and is suitable for datasets with large number of variables.
A package for analysis between rare variants and
        quantitative traits by CMC (the combined multivariate and
        collapsing method).
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.
Multi-Objective optimization based on the Kriging metamodel.
  Important functions: mkm, VMPF, MEGO and HEGO.
Surface topography calculations of Dirichlet's normal energy,
    relief index, surface slope, and orientation patch count for teeth using scans of
    enamel caps.
    Importantly, for the relief index and orientation patch count calculations to
    work, the scanned tooth files must be oriented with the occlusal plane parallel
    to the x and y axes, and perpendicular to the z axis. The files should also be
    simplified, and smoothed in some other software prior to uploading into R.
Model selection and parameter estimation based on non-local and Zellner priors. Bayes factors, marginal densities and variable selection in regression setups. Routines to sample, evaluate prior densities, distribution functions and quantiles are included.
A collection of moment-matching methods for computing the cumulative distribution function of a positively-weighted sum of chi-squared random variables. Methods include the Satterthwaite-Welch method, Hall-Buckley-Eagleson method, Wood's F method, and the Lindsay-Pilla-Basak method.
Functions to calculate: moments, Pearson's kurtosis,
        Geary's kurtosis and skewness; tests related to them
        (Anscombe-Glynn, D'Agostino, Bonett-Seier).
A complete toolkit for morphometrics, from data extraction to
        multivariate analyses. Most common 2D morphometrics approaches are
        included: outlines, open outlines, configurations of landmarks,
        traditional morphometrics, and facilities for data preparation,
        manipulation and visualization with a consistent grammar throughout.
        Momocs allows reproducible, complex morphometric analyses, paves the
        way for a pure open-source workflow in R, and other morphometrics
        approaches should be easy to plug in, or develop from, on top of this
        canvas.
'MetaOMineR' suite is a set of R packages that offers many functions and modules needed for the analyses 
    of quantitative metagenomics data. 'momr' is the core package and contains routines for biomarker identification and exploration.
    Developed since the beginning of field, 'momr' has evolved and is structured around the different modules 
    such as preprocessing, analysis, vizualisation, etc. See package help for more information.
Keep track of dates in terms of months.
  Model dates as at close of business.
  Perform date arithmetic in units of "months" and "years" (multiples of months).
  Allow "infinite" dates to model "ultimate" time spans.
The unique function of this package allows representing in a single graph the relative occurrence and co-occurrence of events measured in a sample. 
  As examples, the package was applied to describe the occurrence and co-occurrence of different species of bacterial or viral symbionts infecting arthropods at the individual level. The graphics allows determining the prevalence of each symbiont and the patterns of multiple infections (i.e. how different symbionts share or not the same individual hosts). 
  We named the package after the famous painter as the graphical output recalls Mondrian’s paintings.
An in-process version of MonetDB, a relational database focused on analytical tasks. Similar to SQLite, the database runs entirely inside the R shell, with the main difference that queries complete much faster thanks to MonetDB's columnar architecture.
Allows to pull data from MonetDB into R. Includes a DBI implementation and a dplyr backend.
High-performance MongoDB client based on 'libmongoc' and 'jsonlite'.
    Includes support for aggregation, indexing, map-reduce, streaming, SSL
    encryption and SASL authentication. The vignette gives a brief overview
    of the available methods in the package.
Acoustic template detection and monitoring database interface. Create, modify, save, and use templates for detection of animal vocalizations. View, verify, and extract results. Upload a MySQL schema to a existing instance, manage survey metadata, write and read templates and detections locally or to the database.
Allows using some services of Monkeylearn <http://monkeylearn.com/> which is
    a Machine Learning platform on the cloud for text analysis (classification and extraction).
Train and make predictions from a multi-layer perceptron neural
        network with partial monotonicity constraints.
Geometric morphometric and evolutionary biology analyses of anchor shape from four-anchored monogeneans.
Contains functions intended to facilitate the production of plant taxonomic monographs. The package includes functions to convert tables into taxonomic descriptions, lists of collectors, examined specimens, and can generate a monograph skeleton. Additionally, wrapper functions to batch the production of phenology charts and distributional and diversity maps are also available.
Various imputation methods are utilized in this package, where one can flag and impute non-monotonic data that is outside of a prespecified range.
Estimation of multivariate normal and student-t data of 
             arbitrary dimension where the pattern of missing data is monotone.
             Through the use of parsimonious/shrinkage regressions 
             (plsr, pcr, lasso, ridge,  etc.), where standard regressions fail, 
             the package can handle a nearly arbitrary amount of missing data. 
             The current version supports maximum likelihood inference and 
	     a full Bayesian approach employing scale-mixtures for Gibbs sampling.
	     Monotone data augmentation extends this 
	     Bayesian approach to arbitrary missingness patterns.  
	     A fully functional standalone interface to the Bayesian lasso 
	     (from Park & Casella), Normal-Gamma (from Griffin & Brown),
             Horseshoe (from Carvalho, Polson, & Scott), and ridge regression 
             with model selection via Reversible Jump, and student-t errors 
             (from Geweke) is also provided.
Requires rooted resolved phylogeny as input and creates a table of genera, their monophyly-status, which taxa cause problems in monophyly etc. Different information can be extracted from the output and a plot function allows visualization of the results in a number of ways.
Functions for fitting monotone polynomials to data.
Estimates monotone regression and variance functions in a
        nonparametric model.
Several analysis-related functions for the book entitled "R
    statistics and graph for medical articles" (written in Korean), version 1,
    by Keon-Woong Moon with Korean demographic data with several plot
    functions.
A collection of basic astronomical routines for R based on
        "Practical astronomy with your calculator" by Peter
        Duffet-Smith.
A multi-objective optimization solver based on particle
        swarm optimization with crowding distance.
MOrder provide functions to check time homogeneity and order
    of markov chain by using chi-squared test, AIC value and BIC value.
This package is a port of the MATLAB colourmap functions
    accompanying the paper M. Geissbuehler and T. Lasser, "How to display data
    by color schemes compatible with red-green color perception deficiencies,"
    Opt. Express 21, 9862-9874 (2013) to R.
A toolset for Geometric Morphometrics and mesh processing. This
    includes (among other stuff) mesh deformations based on reference points,
    permutation tests, detection of outliers, processing of sliding
    semi-landmarks and semi-automated surface landmark placement.
Tools for ecotoxicologists and regulators dedicated to the
    mathematical and statistical modelling of bioassay data. They use advanced and
    innovative methods for a valuable quantitative environmental risk assessment.
MorseGen is a program for generating raw data based on
        user-specified summary (descriptive) statistics. Samples based
        on the supplied statistics are drawn from a normal distribution
        (or, in some cases, an exponential distribution) and scaled to
        match the desired descriptive statistics. Intended uses include
        creating raw data that fits desired characteristics or to
        replicate the results in a published study.
Smoothing one- and two-dimensional Poisson counts with
             P-splines specifically tailored to mortality data.  
             Extra-Poisson variation can be accounted as well as forecasting.
             Collection of mortality data and a specific function for
	     selecting those data by country, sex, age and years.
Fit diverse models of mortality graduation, parametric or not, in
    order to isolate the hump that is often observed in the age-specific death rates
    during adolescence and early adulthood in human populations. Can handle either
    all-cause or cause-specific data.
Data sets and utilities from Project MOSAIC (http://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 (http://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.
With the provision of several tools and templates the MOSAIC project (DFG-Grant Number HO 1937/2-1) supports the implementation of a central data management in epidemiological research projects. The 'mosaicQA' package enables epidemiologists with none or low experience in R to generate basic data quality reports for a wide range of application scenarios. See <https://mosaic-greifswald.de/> for more information.
Learning, manipulation and  evaluation of mixtures of  truncated basis  functions 
  (MoTBFs),  which include mixtures of  polynomials (MOPs) and  mixtures of truncated 
  exponentials (MTEs). MoTBFs are a flexible framework for modelling hybrid Bayesian
  networks. The  package provides  functionality for learning  univariate, multivariate and
  conditional  densities, with the  possibility of incorporating prior  knowledge. Structural
  learning of hybrid Bayesian  networks is also provided. A set of useful tools is provided,
  including  plotting, printing  and likelihood  evaluation. This  package  makes use of  S3 
  objects, with two new classes called 'motbf' and 'jointmotbf'.
Statistics to quantify tracks of moving things (x-y-z-t data),
    such as cells, bacteria or animals. Available measures include mean square
    displacement, confinement ratio, autocorrelation, straightness, turning angle,
    and fractal dimension.
Functions to estimate start and duration of moult from moult 
             data, based on models developed in Underhill 
             and Zucchini (1988, 1990).
Lattice functions for drawing folded empirical cumulative
    distribution plots, or mountain plots. A mountain plot is similar
    to an empirical CDF plot, except that the curve increases from
    0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at
    the right side.  See: Monti (1995), Folded empirical distribution
    function curves-mountain plots.  The American Statistician, 49, 342-345.
Extract from two-dimensional x-y coordinates of an arm-reaching trajectory, several dependent measures such as area under the curve, latency to start the movement, x-flips, etc.; which characterize the action-dynamics of the response. Mainly developed to analyze data coming from mouse-tracking experiments.
Mouse-tracking, the analysis of mouse movements in computerized
    experiments, is a method that is becoming increasingly popular in the
    cognitive sciences. The mousetrap package offers functions for importing,
    preprocessing, analyzing, aggregating, and visualizing mouse-tracking data.
Contains functions to access movement data stored in 'movebank.org'
    as well as tools to visualize and statistically analyze animal movement data,
    among others functions to calculate dynamic Brownian Bridge Movement Models.
    Move helps addressing movement ecology questions.
Provides tools for animal movement modelling using hidden Markov
    models. These include processing of tracking data, fitting hidden Markov models
    to movement data, visualization of data and fitted model, decoding of the state
    process...
Estimating wind speed form trajectories of individually tracked birds using a maximum likelihood approach.
Fit and simulate mixtures of von Mises-Fisher distributions.
Multidimensional projection techniques are used to create two
    dimensional representations of multidimensional data sets.
Preprocess and analysis of genomic data. MPAgenomics
    provides wrappers from commonly used packages to streamline their repeated
    manipulation, offering an easy-to-use pipeline. The segmentation of
    successive multiple profiles is performed with an automatic choice of
    parameters involved in the wrapped packages. Considering multiple profiles
    in the same time, MPAgenomics wraps efficient penalized regression methods
    to select relevant markers associated with a given outcome.
Algorithms for fitting model-based penalized coefficient paths. Currently the models include penalized Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial regression models. The penalties include least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP), and each possibly combining with L_2 penalty.
Fits Multinomial Probit Bayesian Additive Regression Trees.
It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.
Multivariate process capability analysis using the multivariate process capability vector. Allows to analyze a multivariate process with both normally and non-normally distributed and also with dependent and independent quality characteristics.
Data sets and scripts for Modeling Psychophysical Data in R (Springer).
A fast approach to compute approximate persistent homology using a multiscale approach
(1) Our system provides a network-based strategies for metabolite pathway identification.(2) The MPINet can support the identification of pathways using Hypergeometric test based on metabolite set. (3)MPINet can support pathways from multiple databases.
Computing modified profile likelihood estimates for Weibull Shape and Regression Parameters. Modified likelihood estimates are provided.
Model stability and variable inclusion plots [Mueller and Welsh
    (2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller
    (2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al.
    (2008, <doi:10.1214/07-AOS517>); Jiang et al. 
    (2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.
Leverages 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, this package
    provides routines for creating related groups of models, running batches of
    models, and extracting and tabulating model parameters and fit statistics.
Exploratory graphical analysis of multivariate data,
    specifically gene expression data with different projection
    methods: principal component analysis, correspondence analysis,
    spectral map analysis.
Tools for constructing linkage maps, reconstructing
        haplotypes, estimating linkage disequilibrium and QTL mapping
        in multi-parent RIL designs (e.g. MAGIC)
Functions to compute and plot multivariate (partial)
        Mantel correlograms.
Uses a kernel smoothing approach to calculate Mutual Information
    for comparisons between all types of variables including continuous vs
    continuous, continuous vs discrete and discrete vs discrete. Uses a
    nonparametric bias correction giving Bias Corrected Mutual Information
    (BCMI). Implemented efficiently in Fortran 95 with OpenMP and suited to
    large genomic datasets.
Symbolic computing with multivariate polynomials in R.
Mposterior package provides a general framework for estimating a
    median of subset posterior measures (M-posterior). Each subset posterior measure
    is represented as a matrix of posterior samples; rows represent the atoms and 
    columns index the dimensions. All subset measures are represented as list of 
    matrices; length of the list equals the number of subsets (or machines). The        
    distance between subset posterior measures and M-posterior is measured using the RBF kernel.
    M-posterior is represented as a weighted combination of empirical measures based on 
    subsets measures. These weights are estimated using Weiszfeld algorithm implemented
    in this package.
A procedure to test for dependence between point processes on the real line, e.g. causal dependence, correlation, inhibition or anti-correlation. The package also provides a number of utilities for plotting simultaneous point processes, and combining p-values.
Package for fitting Multi-Parameter Regression (MPR) models to right-censored survival data. These are flexible parametric regression models which extend standard models, for example, proportional hazards.
Computational tools to represent phylogenetic signals using adapted eigenvector maps.
Fitting and testing multinomial processing tree (MPT) models, a
  class of statistical models for categorical data.  The parameters are the
  link probabilities of a tree-like graph and represent the latent cognitive
  processing steps executed to arrive at observable response categories
  (Batchelder & Riefer, 1999 <doi:10.3758/bf03210812>; Erdfelder et al., 2009
  <doi:10.1027/0044-3409.217.3.108>; Riefer & Batchelder, 1988
  <doi:10.1037/0033-295x.95.3.318>).
Provides a user-friendly way for the analysis of multinomial processing tree (MPT) models (e.g.,  Riefer, D. M., and Batchelder, W. H. [1988]. Multinomial modeling and the measurement of cognitive processes. Psychological Review, 95, 318-339) for single and multiple datasets. The main functions perform model fitting and model selection. Model selection can be done using AIC, BIC, or the Fisher Information Approximation (FIA) a measure based on the Minimum Description Length (MDL) framework. The model and restrictions can be specified in external files or within an R script in an intuitive syntax or using the context-free language for MPTs. The 'classical' .EQN file format for model files is also supported. Besides MPTs, this package can fit a wide variety of other cognitive models such as SDT models (see fit.model). It also supports multicore fitting and FIA calculation (using the snowfall package), can generate or bootstrap data for simulations, and plot predicted versus observed data.
'RAMAS Metapop' <https://www.ramas.com/ramas.htm#metapop> is a 
    popular software package for performing spatially-explicit population 
    viability analysis. It is primarily GUI-driven, but can benefit from 
    integration into an R workflow, wherein model results can be subjected to 
    further analysis. 'RAMAS Metapop' stores metapopulation model parameter 
    settings and population dynamics simulation results in plain text files 
    (.mp files). This package facilitates access, summary and visualisation of 
    'RAMAS Metapop 5' outputs in order to better integrate 'RAMAS' analyses into 
    an R workflow.
Most of this package consists of data sets from the 
             textbook Introduction
             to Linear Regression Analysis (3rd ed), by Montgomery et al.
             Some additional data sets and functions useful in an
             undergraduate regression course are included.
mQTL provides a complete QTL analysis pipeline for metabolomic data. 
        Distinctive features include normalisation using PQN approach, peak alignment  
        using RSPA approach, dimensionality reduction using SRV approach and finally 
        QTL mapping using R/qtl package.
Analysis of mark-recapture data using covariates. Models: CJS open population; Huggin's closed population. Link functions: logit, sine, hazard.  Model selection and model averaging routines. Plot methods. Simulation routine. CJS methods produce estimates of population size using the Horvitz-Thompson estimator.
With this package, it is possible to perform
        (simultaneous) inferences for ratios of linear combinations of
        coefficients in the general linear model. In particular, tests
        and confidence interval estimations for ratios of treatment
        means in the normal one-way layout and confidence interval
        estimations like in (multiple) slope ratio and parallel line
        assays can be carried out. Moreover, it is possible to
        calculate the sample sizes required in comparisons with a
        control based on relative margins. For the simple two-sample
        problem, functions for a t-test for ratio-formatted hypotheses
        and the corresponding Fieller-type confidence interval are
        provided assuming homogeneous or heterogeneous group variances.
Compute and select tuning parameters for the MRCE estimator proposed by Rothman, Levina, and Zhu (2010) <doi:10.1198/jcgs.2010.09188>.  This estimator fits the multiple output linear regression model with a sparse estimator of the error precision matrix and a sparse estimator of the regression coefficient matrix.
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
Tools to get marine regions data from <http://
    www.marineregions.org/>. Includes tools to get region metadata, as well as
    data in 'GeoJSON' format, as well as Shape files. Use cases include using 
    data downstream to visualize 'geospatial' data by marine region, mapping 
    variation among different regions, and more.
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.
Various methods for MRI tissue classification.
Conditional maximum likelihood estimation via the EM algorithm and information-criterion-based model selection in binary mixed Rasch models.
Conduct multi-locus GWAS and multi-locus QTL mapping under the framework of random-SNP-effect mixed linear model (mrMLM). First, each position (or marker) on the genome is scanned by mrMLM algorithm. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers (or QTL) that are potentially associated with the trait are included in a multi-locus model, their effects are estimated by empirical Bayes and true QTN or QTL are identified by likelihood ratio test.
Non-life runoff reserves may be analyzed using linear models. This generalizes the special cases of multiplicative chain ladder and
    the additive model. In addition, the package provides visual and statistical diagnostics to assess the quality of modeled link ratios.
This package contains a set of function to compute mutual information matrices from continuous, categorical and survival variables. It also contains function to perform feature selection with mRMR and a new ensemble mRMR technique.
We can calculate directly used this package the Minimal Clinically Important Difference by applying the Anchor-based method and the Response shift effect by applying the Then-Test method.
An implementation of the MRS algorithm for comparison across distributions. 
  The model is based on a nonparametric process taking the form of a Markov model 
  that transitions between a "null" and an "alternative" state 
  on a multi-resolution partition tree of the sample space. 
  MRS effectively detects and characterizes a variety of underlying differences. 
  These differences can be visualized using several plotting functions.
Fits regularized multinomial response models using penalized loglikelihood methods with structured penalty terms.
This package contains functions, data and examples to compute a multiplicative-regression model for relative survival.
The Bayesian procedure starts with one warplet in the model and uses the posterior distributions as priors for a more extended model with one more warplet. The model is built with adding one warplet at a time and allows for amplitude variations.
Multi-step adaptive elastic-net (MSAENet) algorithm for
    feature selection in high-dimensional regressions proposed in
    Xiao and Xu (2015) <DOI:10.1080/00949655.2015.1016944>,
    with additional support for multi-step adaptive MCP-Net
    (MSAMNet) and multi-step adaptive SCAD-Net (MSASNet) methods.
The new methodology "moving subset analysis" provides functions to investigate the effect of environmental conditions on the CO2 fertilization effect within longterm free air carbon enrichment (FACE)  experiments. In general, the functionality is applicable to derive the influence of a third variable (forcing experiment-support variable) on the relation between a dependent and an independent variable.
Statistical Analyses of Methylation-sensitive Amplification Polymorphism (MSAP) assays.
Visualises multiple sequence alignments dynamically within the
    Shiny web application framework.
The output of an affinity-purification mass spectrometry
 experiment is typically a list of proteins that were observed
 in the experiment, identified by UniProt identifiers
 (http://www.uniprot.org/).  This package takes as input a list of
 UniProt identifiers, and the associated Mascot scores from the
 experiment (which indicate the likelihood that the protein has
 been correctly identified), clusters them by gene ontology
 category (http://geneontology.org/), then draws diagrams
 showing the results in hierarchical clusters by category, with lines
 for individual proteins representing the associated Mascot score.
 The results are in general not publication-ready, but will rather
 require editing via a graphics editor that can interpret SVG (scalable
 vector graphics) format.  As an alternative representation, the
 package will also generate tag clouds based on the Mascot scores.
Performs Bayesian nonparametric multiscale density estimation and multiscale testing of group differences with multiscale Bernstein polynomials (msBP) mixtures as in Canale and Dunson (2016).
Provides methods for estimating frequentist and
  Bayesian Vector Autoregression (VAR) models and Markov-switching
  Bayesian VAR (MSBVAR).  Functions for reduced
  form and structural VAR models are also available. Includes
  methods for the generating posterior inferences for these models,
  forecasts, impulse responses (using likelihood-based error bands),
  and forecast error decompositions.  Also includes utility functions
  for plotting forecasts and impulse responses, and generating draws
  from Wishart and singular multivariate normal densities.  Current
  version includes functionality to build and evaluate models with
  Markov switching.
Multivariate Synthetic Control Method Using Time Series. 
    Two generalizations of the synthetic control method (which has already an 
    implementation in package 'Synth') are implemented: first, 'MSCMT' allows 
    for using multiple outcome variables, second, time series can be supplied as 
    economic predictors. 
    Much effort has been taken to make the implementation as stable as possible 
    (including edge cases) without losing computational efficiency.
Find common entities detected in both positive and negative
    ionization mode, delete this entity in the less sensible mode and combine both
    matrices.
R Client for the Microsoft Cognitive Services Text Analytics
    REST API, including Sentiment Analysis, Topic Detection, Language Detection,
    and Key Phrase Extraction. An account MUST be registered at the Microsoft
    Cognitive Services website <https://www.microsoft.com/cognitive-services/>
    in order to obtain a (free) API key. Without an API key, this package will
    not work properly.
R Client for the Microsoft Cognitive Services Web Language Model
    REST API, including Break Into Words, Calculate Conditional
    Probability, Calculate Joint Probability, Generate Next Words, and List
    Available Models. A valid account MUST be registered at the Microsoft
    Cognitive Services website <https://www.microsoft.com/cognitive-services/>
    in order to obtain a (free) API key. Without an API key, this package will
    not work properly.
Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously.
This package provides functions for metabolite set
        enrichment analysis (MSEA) and principal component analysis
        (PCA), and converting metabolite set list from your own csv
        files or KEGG's tar.gz files to XML documents.  This package is
        suitable for computation of MSEA for factor loading in PCA.
Package for the detection of molecules in complex mixtures
        of compounds. It creates an initial_DATA matrix from several
        GC-MS analyses by collecting and assembling the information
        from chromatograms and mass spectra (MS.DataCreation), It can
        read several format (ASCII, CDF, mzML, mzXML or mzData).It
        tests for the best unsupervised clustering method to group
        similar mass spectra into molecules (MS.test.clust).It runs the
        optimal unsupervised clustering method on the initial_DATA
        matrix, identifies the optimal number of clusters, produces
        different files for facilitating the quality control and
        identification of putative molecules, and returns
        fingerprinting or profiling matrices (MS.clust).It converts
        output files from MS.clust for NIST mass spectral library
        search and ARISTO webtool search
A Tcl/Tk GUI for some basic functions in the MSeasy
        package
A companion to the Chinese book ``Modern Statistical Graphics''.
The MSGARCH package offers methods to fit (by Maximum Likelihood or Bayesian), simulate, and forecast various Markov-Switching GARCH processes.
Multinomial logistic regression with sparse group lasso penalty.
    Simultaneous feature selection and parameter estimation for classification.
    Suitable for high dimensional multiclass classification with many classes. The
    algorithm computes the sparse group lasso penalized maximum likelihood estimate.
    Use of parallel computing for cross validation and subsampling is supported
    through the 'foreach' and 'doParallel' packages. Development version is on
    GitHub, please report package issues on GitHub.
For fitting multivariate response and multiple predictor linear regressions with an arbitrary group structure assigned on the regression coefficient matrix, using the multivariate sparse group lasso and the mixed coordinate descent algorithm.
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.
A number of utilities for developing and maintaining error, warning,
    and other messages in R packages, including checking for consistency across
    messages, spell-checking messages, and building message translations into
    various languages for purposes of localization.
An R package for dimension reduction based on finite Gaussian mixture modeling of inverse regression.
A decision tree classifier for detecting microsatellite instability (MSI) in somatic mutation data from whole exome sequencing. MSI is detected based on different mutation rates in all sites as well as in simple sequence repeats. This mechanism can also be applied to sequence data of targeted gene panels with shorter sequence length.
Analysis of annual average ocean water level time series
    from long (minimum length 80 years) individual records, providing improved
    estimates of trend (mean sea level) and associated real-time velocities and
    accelerations. Improved trend estimates are based on Singular Spectrum Analysis
    methods. Various gap-filling options are included to accommodate incomplete time
    series records. The package also contains a forecasting module to consider the
    implication of user defined quantum of sea level rise between the end of the
    available historical record and the year 2100. A wide range of screen and pdf
    plotting options are available in the package.
Functions for fitting continuous-time Markov and hidden
 Markov multi-state models to longitudinal data.  Designed for
 processes observed at arbitrary times in continuous time (panel data)
 but some other observation schemes are supported. Both Markov
 transition rates and the hidden Markov output process can be modelled
 in terms of covariates, which may be constant or piecewise-constant
 in time.
There are several functions to implement the method for analysis in a multiblock multivariable data. If the input is only a matrix, then the principal components analysis (PCA) is implemented. If the input is a list of matrices, then the multiblock PCA is implemented. If the input is two matrices for exploratory and objective variables, then the partial least squares (PLS) analysis is implemented. If the input is two list of matrices for exploratory and objective variables, then the multiblock PLS analysis is implemented. Moreover, if the extra outcome variable is specified, then the supervised version for the methods above is implemented. For each methods, the sparse modeling is also incorporated. Functions to select the number of components and the regularized parameters are also provided.
This package provides functions and datasets from the book "Methods of Statistical Model Estimation".
A fast and general method for restructuring classical longitudinal data into
    augmented ones. The reason for this is to facilitate the modeling of longitudinal data under
    a multi-state framework using the 'msm' package.
Contains necessary Multivariate Analysis methods and Datasets used in STAT 571 at the University of Illinois at Urbana-Champaign
This is a toolkit for multivariate process monitoring. It computes several multivariate control charts e.g. Hotelling, Chi-squared, MEWMA, MCUSUM and Generalized Variance. Ten didactic datasets are included. It includes some techniques for assessing multivariate normality e.g. Mardia's, Royston's and Henze-Zirkler's tests. Please, see the NEWS file for the latest changes in the package.
Discrete Morse-Smale complex approximation based on kNN graph. The Morse-Smale complex provides a decomposition of the domain. This package provides methods to compute a hierarchical sequence of Morse-Smale complicies and tools that exploit this domain decomposition for regression and visualization of scalar functions.
Calculates ARMSS (age related multiple sclerosis severity), and both local and global MSSS (multiple sclerosis severity score).
Nonparametric estimation for right censored, left truncated time to event data in multistate models.
Constructs trees for multivariate survival data using marginal and frailty models.
Contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models.
Univariate Autoregressive Markov Switching Models for Linear and Generalized Models
Read data in the 'MTConnect'<http://www.mtconnect.org/> standard.
    You can use the package to read data from historical 'MTConnect logs' along
    with the 'devices.xml' describing
    the device. The data is organised into a 'MTConnectDevice' S4 data structure
    and some convenience methods are also provided for basic read/view operations.
    The package also includes some functions for analysis of 'MTConnect' data. This includes
    functions to simulate data (primarily position data, feed rate and velocities) 
    based on the G code and visualisation functions to compare the actual and simulated data.
The Mass Transportation Distance rank histogram was developed to assess the reliability of scenarios with equal or different probabilities of occurrence <doi:10.1002/we.1872>.
MTK (Mexico ToolKit) is a generic platform for the sensitivity and uncertainty analysis of complex models. It provides functions and facilities for experimental design, model simulation, sensitivity and uncertainty analysis, methods integration and data reporting, etc.
Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis.  (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models.  (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics.  (c) Finally, the MTS package also performs forecasting using diffusion index, transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.
This is an EM algorithm based method for imputation of
        missing values in multivariate normal time series. The
        imputation algorithm accounts for both spatial and temporal
        correlation structures. Temporal patterns can be modelled using
        an ARIMA(p,d,q), optionally with seasonal components, a
        non-parametric cubic spline or generalised additive models with
        exogenous covariates. This algorithm is specially tailored for
        climate data with missing measurements from several monitors
        along a given region.
Provides programmatic access to the Amazon Mechanical Turk (MTurk) Requester API.
A graphical user interface (GUI) for the MTurkR package.
Functions for downloading flight data from
        http://www.munich-airport.de and for analyzing flight patterns.
A data package containing public domain information on requests made by the
             'MuckRock' (https://www.muckrock.com/) project under the United States
             Freedom of Information Act.
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.
Generates Muller plot from parental/genealogy/phylogeny information and population/abundance/frequency dynamics data.
    Muller plots are plots which combine information about succession of different OTUs (genotypes, phenotypes, species, ...) and information about dynamics of their abundances (populations or frequencies) over time. They are powerful and fascinating tools to visualize evolutionary dynamics. They may be employed also in study of diversity and its dynamics, i.e. how diversity emerges and how changes over time. They are called Muller plots in honor of Hermann Joseph Muller which used them to explain his idea of Muller's ratchet (Muller, 1932, American Naturalist).
    A big difference between Muller plots and normal box plots of abundances is that a Muller plot depicts not only the relative abundances but also succession of OTUs based on their genealogy/phylogeny/parental relation. In a Muller plot, horizontal axis is time/generations and vertical axis represents relative abundances of OTUs at the corresponding times/generations. Different OTUs are usually shown with polygons with different colors and each OTU originates somewhere in the middle of its parent area in order to illustrate their succession in evolutionary process.
    To generate a Muller plot one needs the genealogy/phylogeny/parental relation of OTUs and their abundances over time.
    MullerPlot package has the tools to generate Muller plots which clearly depict the origin of successors of OTUs.
Integer Programming Formulations Applied to Univariate and Multivariate Allocation Problems.
Simultaneous tests and confidence intervals
  for general linear hypotheses in parametric models, including 
  linear, generalized linear, linear mixed effects, and survival models.
  The package includes demos reproducing analyzes presented
  in the book "Multiple Comparisons Using R" (Bretz, Hothorn, 
  Westfall, 2010, CRC Press).
Convert a logical vector or a vector of
    p-values or a correlation, difference, or distance
    matrix into a display identifying the pairs for
    which the differences were not significantly
    different.  Designed for use in conjunction with
    the output of functions like TukeyHSD, dist{stats},
    simint, simtest, csimint, csimtest{multcomp},
    friedmanmc, kruskalmc{pgirmess}.
Equivalence tests and related confidence intervals for the
        comparison of two treatments, simultaneously for one or many
        normally distributed, primary response variables (endpoints).
        The step-up procedure of Quan et al. (2001) is both applied for
        differences and extended to ratios of means. A related
        single-step procedure is also available.
GEE solver for correlated nominal or ordinal multinomial responses using a local odds ratios parameterization.
Efficient finite difference method for valuing European and American multi-asset options.
Algorithms for performing joint parameter estimation in
    astronomical survey data acquired in multiple bands.
Computationally efficient functions to provide direct likelihood-based
    inference for partially-observed multivariate birth-death processes.  Such processes
    range from a simple Yule model to the complex susceptible-infectious-removed model
    in disease dynamics.  Efficient likelihood evaluation facilitates maximum likelihood
    estimation and Bayesian inference.
A GUI with which users can construct and interact
        with Multibiplot Analysis and provides inferential results by using Bootstrap Methods.
Calculate the polygenic and major gene models for quantitative trait
  linkage analysis using variance components approach. The 0.2.2
  release includes bug fixes that allow 'multic' to run properly
  on 64-bit systems. The 0.3.0 release includes a fully implemented
  sw2mloci() function. As of 0.3.8, Splus version is no longer
  supported. Additional documentation and examples can be found
  in the inst directory.
Implements a generalization of the Cochran-Armitage trend test to
    multinomial data. In addition to an overall test, multiple testing adjusted
    p-values for trend in individual outcomes and power calculation is
    available.
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. (2009)  <DOI:10.1145/1496770.1496877> Loopless Generation of Multiset Permutations by Prefix Shifts. 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://tamivox.org/dave/multinomial/code.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/tag/integer-partitions.html>. The C++ code and Python code are distributed without conditions.
Code to support a systems biology research program from inception through publication.  The methods focus 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.
Read a table of fixed width formatted data of different types into
    a data.frame for each type.
Uses a transformed, rotated and optionally adapted n-dimensional
    grid of quadrature points to calculate the numerical integral of n multivariate
    normal distributed parameters.
Functions to plot and manipulate multigraphs and bipartite graphs with different layout options.
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.
Traditional and spatial 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 non-spatial or spatial 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).
Tools to create a layout for figures made of multiple panels, and
    to fill the panels with base, lattice and ggplot2 plots, grobs, and PNG,
    JPEG, and TIFF images.
Performs genetic association tests between SNPs
        (one-at-a-time) and multiple phenotypes (separately or in joint
        model).
Performs variable importance analysis using a causal inference approach. This is done by fitting Population Intervention Models. The default is to use a Targeted Maximum Likelihood Estimator (TMLE). The other available estimators are Inverse Probability of Censoring Weighted (IPCW), Double-Robust IPCW (DR-IPCW), and Graphical Computation (G-COMP) estimators. Inference can be obtained from the influence curve (plug-in) or by bootstrapping.
Fit Cox proportional hazard models with a weighted 
  partial likelihood. It handles one or multiple endpoints, additional matching 
  and makes it possible to reuse controls for other endpoints.
Algebraic procedures for the analysis of multiple social networks are delivered with 
	    this package. Among other things, it makes 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. It also provides an algebraic approach for two-mode networks 
	    through Galois derivations between families of the pairs of subsets in the two domains.
Provides a new form of data frame backed by shared memory matrices
    and a way to manipulate them. Upon creation these data frames are shared across
    multiple local nodes to allow for simple parallel processing.
Various utilities to manipulate multivariate polynomials
Functions to calculate Unique Trait Combinations (UTC) and scaled
    Unique Trait Combinations (sUTC) as measures of multivariate richness. The
    package can also calculate beta-diversity for trait richness and can
    partition this into nestedness-related and turnover components. The code
    will also calculate several measures of overlap.
Calculates bias, precision, and power for multi-level random regressions. Random regressions are types of hierarchical models in which data are structured in groups and (regression) coefficients can vary by groups. Tools to estimate model performance are designed mostly for scenarios where (regression) coefficients vary at just one level. 'MultiRR' provides simulation and analytical tools (based on 'lme4') to study model performance for random regressions that vary at more than one level (multi-level random regressions), allowing researchers to determine optimal sampling designs.
Functions to perform sensitivity analysis on a model with multivariate output.
Implements two versions of the algorithm namely: stochastic and batch. The package determines also the best number of clusters and offers to the user the best clustering scheme from different results.
The multispatial convergent cross mapping algorithm can be used as a test for causal associations between pairs of processes represented by time series. This is a combination of convergent cross mapping (CCM), described in Sugihara et al., 2012, Science, 338, 496-500, and dew-drop regression, described in Hsieh et al., 2008, American Naturalist, 171, 71–80. The algorithm allows CCM to be implemented on data that are not from a single long time series. Instead, data can come from many short time series, which are stitched together using bootstrapping.
MultiSV is an R package for identification of structural
    variations in multiple populations based on whole genome resequencing. It
    fits linear mixed model and identifies structural variations in multiple
    populations using whole genome sequencing data. It could also be
    manipulated to use on RNA-seq data for differential gene expression
    (implementation in future releases). Main steps for analysis include
    generating read depth in bins using ComputeBinCounts. conversion of bins to
    MultiSV format using Bin2MultiSV. Finally, identification of structural
    variations using CallMultiSV.
Implements multitaper spectral analysis using discrete prolate spheroidal sequences (Slepians) and sine tapers. It includes an adaptive weighted multitaper spectral estimate, a coherence estimate, Thomson's Harmonic F-test, and complex demodulation. The Slepians sequences are generated efficiently using a tridiagonal matrix solution, and jackknifed confidence intervals are available for most estimates.
Models and predicts Multiple Features in single random forest considering the linear relation among the features.
A multivariate generalization of the emulator package
Computation of an estimation of the long-memory parameters and
     the long-run covariance matrix using a multivariate model
	 (Lobato (1999) <doi:10.1016/S0304-4076(98)00038-4>; Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>). Two semi-parametric methods are
	 implemented: a Fourier based approach (Shimotsu (2007) <doi:10.1016/j.jeconom.2006.01.003>) and a wavelet based
	 approach (Achard and Gannaz (2016) <doi:10.1111/jtsa.12170>).
Fits multi-way component models via alternating least squares algorithms with optional constraints (orthogonality, non-negativity, and structural). Fit models include Individual Differences Scaling, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
Exports two functions implementing
    multi-way clustering using the method suggested by Cameron, Gelbach, &
    Miller (2011) and cluster (or block)
    bootstrapping for estimating variance-covariance matrices. Normal one and
    two-way clustering matches the results of other common statistical
    packages.  Missing values are handled transparently and rudimentary
    parallelization support is provided.
A collection of multivariate nonparametric methods, selected in
    part to support an MS level course in nonparametric statistical methods. Methods
    include adjustments for multiple comparisons, implementation of multivariate
    Mann-Whitney-Wilcoxon testing, inversion of these tests to produce a confidence
    region, some permutation tests for linear models, and some algorithms for
    calculating exact probabilities associated with one- and two- stage testing
    involving Mann-Whitney-Wilcoxon statistics.
Implementation of commonly used p-value-based and
        parametric multiple testing procedures (computation of adjusted
        p-values and simultaneous confidence intervals) and parallel
        gatekeeping procedures based on the methodology presented in
        the book "Multiple Testing Problems in Pharmaceutical
        Statistics" (edited by Alex Dmitrienko, Ajit C. Tamhane and
        Frank Bretz) published by Chapman and Hall/CRC Press 2009.
Preprocessing of high-throughput data (normalization and
        scalings); Principal Component Analysis with help tool for
        choosing best-separating principal components and automatic
        testing for outliers; automatic univariate analysis for
        parametric and non-parametric data, with generation of specific
        reports (volcano and box plots); partial least square
        discriminant analysis (PLS-DA); orthogonal partial least square
        discriminant analysis (OPLS-DA); Statistical Total Correlation
        Spectroscopy (STOCSY); Ratio Analysis Nuclear Magnetic
        Resonance (NMR) Spectroscopy (RANSY).
Model selection and model averaging based on information criteria
             (AICc and alike).
Multidimensional unfolding using Schoenemann's algorithm for metric
   and Procrustes rotation of unfolding results.
Provides easy access to, and manipulation of, the Munsell 
    colours. Provides a mapping between Munsell's 
    original notation (e.g. "5R 5/10") and hexadecimal strings suitable 
    for use directly in R graphics. Also provides utilities 
    to explore slices through the Munsell colour tree, to transform 
    Munsell colours and display colour palettes.
Methods for interpolating data in the Munsell color system following the ASTM D-1535 standard. Hues and chromas with decimal values can be interpolated and converted to/from the Munsell color system and CIE xyY, CIE XYZ, sRGB, CIE Lab or CIE Luv. Chromas can be odd and hue steps can be real numbers. Based on the work by Paul Centore, "The Munsell and Kubelka-Munk Toolbox".
Provides mailmerge methods for reading spreadsheets of addresses and other relevant information to create standardized but customizable letters.  Provides a method for mapping US ZIP codes, including those of letter recipients.  Provides a method for parsing and processing html code from online job postings of the American Political Science Association.
Data and code for the paper by Ehm, Gneiting, Jordan and Krueger ('Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings', 2015).
Provides functions allowing for (1) easy aggregation of multivariate time series into custom time scales, (2) comparison of statistical summaries between different data sets at multiple time scales (e.g. observed and bias-corrected data), (3) comparison of relations between variables and/or different data sets at multiple time scales (e.g. correlation of precipitation and temperature in control and scenario simulation) and (4) transformation of time series at custom time scales.
This package is a collection of function for converting and modifying mono dimensional nuclear magnetic resonance spectra.
Performs Wilcox rank sum test, Kruskal rank sum test,
        Friedman rank sum test and McNemar test.
The Mutoss package and accompanying mutossGUI package are
    designed to ease the application and comparison of multiple
    hypothesis testing procedures.
The mutossGUI package provides a graphical user interface for the MuToss Project.
An ensemble classifier for multiclass classification. This is a novel classifier that natively works as an ensemble. It projects data on a large number of matrices, and uses very simple classifiers on each of these projections. The results are then combined, ideally via Dempster-Shafer Calculus.
Functions, data sets, analyses and examples from the book 
  `An Introduction to Applied Multivariate Analysis with R' 
  (Brian S. Everitt and Torsten Hothorn, Springer, 2011).
A set of tools for displaying, modeling and analysing
        multivariate abundance data in community ecology. See
        'mvabund-package.Rd' for details of overall package organization.
        The package is implemented with the Gnu Scientific Library
        (http://www.gnu.org/software/gsl/) and Rcpp
        (http://dirk.eddelbuettel.com/code/rcpp.html) R / C++ classes.
Pacote para analise multivariada, que possui funcoes que executam analise de correspondencia simples (CA) e multipla (MCA), analise de componentes principais (PCA), analise de correlacao canonica (CCA), analise fatorial (FA), escalonamento multidimensional (MDS), analise de cluster hierarquico e nao hierarquico, regressao linear, analise de multiplos fatores (MFA) para dados quantitativos, qualitativos, de frequencia (MFACT) e dados mistos. Tambem possui outras funcoes uteis para a analise multivariada.
Fit log-linear model for multivariate Bernoulli
        distribution with mixed effect models and LASSO
Modelling Multivariate Binary Data with Blocks of Specific One-Factor Distribution. Variables are grouped into independent blocks. Each variable is described by two continuous parameters (its marginal probability and its dependency strength with the other block variables), and one binary parameter (positive or negative dependency). Model selection consists in the estimation of the repartition of the variables into blocks. It is carried out by the maximization of the BIC criterion by a deterministic (faster) algorithm or by a stochastic (more time consuming but optimal) algorithm. Tool functions facilitate the model interpretation.
Hierarchical workspace tree, code editing and backup, easy
        package prep, editing of packages while loaded, per-object
        lazy-loading, easy documentation, macro functions, and
        miscellaneous utilities. Needed by debug package.
An implementation of Multi-View Clustering (Bickel and Scheffer, 2004). Documents are generated by drawing word values from a categorical distribution for each word, given the cluster. This means words are not counted (multinomial, as in the paper), but words take on different values from a finite set of values (categorical). Thus, it implements Mixture of Categoricals EM (as opposed to Mixture of Multinomials developed in the paper), and Spherical k-Means. The latter represents documents as vectors in the categorical space.
Implementation of multi-view bi-clustering algorithms. When a sample is characterized by two or more sets of input features, it creates multiple data matrices for the same set of examples, each corresponding to a view. For instance, individuals who are diagnosed with a disorder can be described by their clinical symptoms (one view) and their genomic markers (another view). Rows of a data matrix correspond to examples and columns correspond to features. A multi-view bi-clustering algorithm groups examples (rows) consistently across the views and simultaneously identifies the subset of features (columns) in each view that are associated with the row groups. This mvcluster package includes three such methods. (1) MVSVDL1: multi-view bi-clustering based on singular value decomposition where the left singular vectors are used to identify row clusters and the right singular vectors are used to identify features (columns) for each row cluster. Each singular vector is regularized by the L1 vector norm.  (2) MVLRRL0: multi-view bi-clustering based on sparse low rank representation (i.e., matrix approximation) where the decomposed components are regularized by the so-called L0 vector norm (which is not really a vector norm). (3) MVLRRL1: multi-view bi-clustering based on sparse low rank representation (i.e., matrix approximation) where the decomposed components are regularized by the L1 vector norm.
Permutation tests for variance components for 2-level, 3-level and 4-level data with univariate or multivariate responses.
This package computes the continuous wavelet transform of
    irregularly sampled time series.
An open-source project implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools , and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users).   Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.
Maximum likelihood estimates are obtained via an EM algorithm with either a first-order or a fully exponential Laplace approximation.
Computes regression deletion diagnostics for multivariate linear models and provides some associated
	diagnostic plots.  The diagnostic measures include hat-values (leverages), generalized Cook's distance, and
	generalized squared 'studentized' residuals.  Several types of plots to detect influential observations are
	provided.
Allows a user to conduct multivariate multiple regression using analytic p-values rather than classic approximate F-tests.
Tools for analysing multivariate time series with wavelets. This includes: simulation of a multivariate locally stationary wavelet (mvLSW) process from a multivatriate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence; and, estimation of the asymptotic variance for mvEWS elements. See Park, Eckley and Ombao (2014) <doi:10.1109/TSP.2014.2343937> for details.
Define, manipulate and plot meshes on simplices, spheres, balls, rectangles and tubes.
 Directional and other multivariate histograms are provided.
Collection of functions to perform fixed and random-effects multivariate and univariate meta-analysis and meta-regression.
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series.
Performs multivariate normality tests and graphical approaches and implements multivariate outlier detection and univariate normality of marginal distributions through plots and tests.
This package computes the Nelson-Aalen estimator of the cumulative transition hazard for multistate models.
Provides computationally efficient tools related to
    the multivariate normal and Student's t distributions. The main functionalities are:
    simulating multivariate random vectors, evaluating multivariate
    normal or Student's t densities and Mahalanobis distances. These tools are very efficient
    thanks to the use of C++ code and of the OpenMP API.
Package for  moving grid adjustment 
	      in plant breeding field trials.
Finds the maximum likelihood estimate of the mean vector
        and variance-covariance matrix for multivariate normal data
        with missing values.
Generalization of shapiro-wilk test for multivariate
        variables.
Given a vector of multivariate normal data, a matrix of
    covariates and the data covariance matrix, generate new multivariate normal
    samples that have the same covariance matrix based on permutations of
    the transformed data residuals.
Routines for assessing multivariate normality. Implements three Wald's type chi-squared tests; non-parametric Anderson-Darling and Cramer-von Mises tests; Doornik-Hansen test,  Royston test and Henze-Zirkler test.
various methods for multivariate outlier detection.
Tools for high-dimensional peaks-over-threshold inference and simulation
  of spatial extremal processes.
Tools for estimating multivariate probit models,
   calculating conditional and unconditional expectations,
   and calculating marginal effects on conditional and unconditional
   expectations.
Computes orthant probabilities multivariate normal distribution.
Provides methods to construct multivariate grids, which can be used
    for multivariate quadrature. This grids can be based on different quadrature
    rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss
    quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the
    multidimensional grid the product-rule or the combination- technique can be
    applied.
This is a non-parametric method for joint adaptive mean-variance regularization and variance stabilization of high-dimensional data. It is suited for handling difficult problems posed by high-dimensional multivariate datasets (p >> n paradigm). Among those are that the variance is often a function of the mean, variable-specific estimators of variances are not reliable, and tests statistics have low powers due to a lack of degrees of freedom. Key features include:
            (i) Normalization and/or variance stabilization of the data,
            (ii) Computation of mean-variance-regularized t-statistics (F-statistics to follow),
            (iii) Generation of diverse diagnostic plots,
            (iv) Computationally efficient implementation using C/C++ interfacing and an option for parallel computing to enjoy a faster and easier experience in the R environment.
Mean, variance, and random variates for left/right truncated normal distributions.
Generalization of the Shapiro-Francia test for
        multivariate variables.
This package implements the generalization of the Shapiro-Wilk test
        for multivariate normality proposed by Villasenor-Alva and
        Gonzalez-Estrada (2009).
Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history.
Estimates the multivariate skew-t and nested models, as described in the articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal model: a population Monte Carlo approach. Comput. Statist. Data Anal. <doi:10.1016/j.csda.2013.02.007> and in Parisi, A., Liseo, B. Objective Bayesian analysis for the multivariate skew-t model (to appear).
Routines to perform estimation and inference under the multivariate t-distribution.
  Currently, the following methodologies are implemented: multivariate mean and covariance
  estimation, hypothesis testing about the mean, equicorrelation and homogeneity of variances,
  the Wilson-Hilferty transformation, QQ-plots with envelopes and random variate
  generation. Some auxiliary functions are also provided.
Fits a multivariate model of decision trees for multiple, continuous outcome variables. A model for each outcome variable is fit separately, selecting predictors that explain covariance in the outcomes.  Built on top of 'gbm', which fits an ensemble of decision trees to univariate outcomes.
This package contains functions to run fixed effects or
        random effects multivariate meta-analysis.
Computes multivariate normal and t probabilities, quantiles,
  random deviates and densities.
A function for plotting multivariate time series data
Matched Wake Analysis (mwa) grants insights into causal relationships in spatiotemporal event data.
Computes the Wavelet deconvolution estimate of a common signal
    present in multiple channels that have possible different levels of blur
    and long memory additive error.
The Moving-Window Lasso (MWLasso) method for genome-wide association studies. A window scans the design matrix. For predictors in the same window, their coefficients estimates are smoothed.
A two stage moving-window Ridge method for coefficients estimation and model prediction. In the first stage, moving-window penalty and L1 penalty are applied. In the second stage, ridge regression is applied.
mxkssd is a package that generates efficient balanced
        mixed-level k-circulant supersaturated designs by interchanging
        the elements of the generator vector. The package tries to
        generate a supersaturated design that has EfNOD efficiency more
        than user specified efficiency level (mef). The package also
        displays the progress of generation of an efficient mixed-level
        k-circulant design through a progress bar. The progress of 100
        per cent means that one full round of interchange is completed.
        More than one full round (typically 4-5 rounds) of interchange
        may be required for larger designs.
Feature selection methods for identifying minimal, statistically-equivalent and equally-predictive feature subsets. Bayesian network algorithms and related functions are also included. The package name 'MXM' stands for "Mens eX Machina", meaning "Mind from the Machine" in Latin.
The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.
Perform correlation and linear regression test
    among the numeric columns in a data frame automatically
    and make plots using pairs or lattice::parallelplot.
Useful functions for accessing MyEpisodes feeds and
        episode information as well as other tv episode related actions
        through www.myepisodes.com
Recommendation engine based on Myrrix. Myrrix is a complete,
    real-time, scalable clustering and recommender system, evolved from Apache
    Mahout. It uses Alternating Least Squares to build a recommendation engine.
External jars required for package Myrrix. Myrrix is a
    recommendation engine
Investigate the evolution of biological processes by capturing evolutionary signatures in transcriptomes. The aim of this tool is to provide a transcriptome analysis environment for answering questions regarding the evolution of biological processes.
Linear and logistic regression models for quantitative genetic analysis of data from monozygotic twins.
An R wrapper for 'libnabo', an exact or approximate k nearest
    neighbour library which is optimised for low dimensional spaces (e.g. 3D).
    'libnabo' has speed and space advantages over the 'ANN' library wrapped by
    package 'RANN'. 'nabor' includes a knn function that is designed as a 
    drop-in replacement for 'RANN' function nn2. In addition, objects which 
    include the k-d tree search structure can be returned to speed up repeated 
    queries of the same set of target points.
Contains methods described by Dennis R. Helsel in 
             his book "Nondetects And Data Analysis: Statistics 
             for Censored Environmental Data"
Constructs (non)additive genetic relationship matrices, and their inverses, from a pedigree to be used in linear mixed effect models (A.K.A. the 'animal model'). Also includes other functions to facilitate the use of animal models. Some functions have been created to be used in conjunction with the R package 'asreml' for the 'ASReml' software, which can be obtained upon purchase from 'VSN' international (http://www.vsni.co.uk/software/asreml).
Contains a sample of the 2005 Grade 8 Mathematics data from the National Assessment of Educational Progress (NAEP). This data set is called the NAEP Primer.
High performance implementation of the Naive Bayes algorithm.
Designed for association studies in nested association mapping (NAM) panels, also handling experimental and random panels. It includes tools for genome-wide associations of multiple populations, marker quality control, population genetics analysis, genome-wide prediction, solving mixed models and finding variance components through likelihood and Bayesian methods.
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.
Full 64-bit resolution date and time support with resolution up
 to nanosecond granularity is provided, with easy transition to and from the
 standard 'POSIXct' type.
Enables the processing and normalisation of the standard mRNA data output from the Nanostring nCounter software.
Provides a near drop-in replacement for base::Sys.sleep() that allows more types of input
    to produce delays in the execution of code and can silence/prevent typical sources of error.
Provides functions to query and manipulate arrays
    of arbitrary dimensions.
Provides functions to access NASA's Earth Imagery and Assets API
    and the Earth Observatory Natural Event Tracker (EONET) webservice.
This package contains tidied data from the ASA 2006 data expo,
    as well as a number of useful other related data sets.
NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D
    biological image data, especially traced neurons. Reads and writes 3D images
    in NRRD and 'Amira' AmiraMesh formats and reads surfaces in 'Amira' hxsurf
    format. Traced neurons can be imported from and written to SWC and 'Amira'
    LineSet and SkeletonGraph formats. These data can then be visualised in 3D
    via 'rgl', manipulated including applying calculated registrations, e.g.
    using the 'CMTK' registration suite, and analysed. There is also a simple
    representation for neurons that have been subjected to 3D skeletonisation
    but not formally traced; this allows morphological comparison between
    neurons including searches and clustering (via the 'nat.nblast' extension
    package).
Extends package 'nat' (NeuroAnatomy Toolbox) by providing a
    collection of NBLAST-related functions.
Interface to 'NatureServe' (<http://www.natureserve.org>).
    Includes methods to get data, image metadata, search taxonomic names,
    and make maps.
Extends package 'nat' (NeuroAnatomy Toolbox) by providing objects
    and functions for handling template brains.
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.
An implementation of the Naive Bayes Classifier (NBC) algorithm used for Verbal Autopsy (VA) built on code from Miasnikof et al (2015) <DOI:10.1186/s12916-015-0521-2>.
It provides 30 indexes for determining the optimal number of clusters in a data set and offers the best clustering scheme from different results to the user.
Calls the 'Jupyter'/'IPython' script 'nbconvert' to create vignettes from notebooks.
  Those notebooks ('.ipynb' files) are files containing rich text, code, and its output.
  Code cells can be edited and evaluated interactively.
  See <http://ipython.org/notebook.html> for more information.
The Dirichlet (aka NBD-Dirichlet) model describes the
        purchase incidence and brand choice of consumer products.  We
        estimate the model and summarize various theoretical quantities
        of interest to marketing researchers. Also provides functions
        for making tables that compare observed and theoretical
        statistics.
Perform non-bipartite matching and matched randomization. A
    "bipartite" matching utilizes two separate groups, e.g. smokers being
    matched to nonsmokers or cases being matched to controls. A "non-bipartite"
    matching creates mates from one big group, e.g. 100 hospitals being
    randomized for a two-arm cluster randomized trial or 5000 children who
    have been exposed to various levels of secondhand smoke and are being
    paired to form a greater exposure vs. lesser exposure comparison. At the
    core of a non-bipartite matching is a N x N distance matrix for N potential
    mates. The distance between two units expresses a measure of similarity or
    quality as mates (the lower the better). The 'gendistance()' and
    'distancematrix()' functions assist in creating this. The 'nonbimatch()'
    function creates the matching that minimizes the total sum of distances
    between mates; hence, it is referred to as an "optimal" matching. The
    'assign.grp()' function aids in performing a matched randomization. Note
    bipartite matching can be performed using the prevent option in
    'gendistance()'.
Negative Binomial (NB) models for two-group comparisons and
    regression inferences from RNA-Sequencing Data.
Performs a Necessary Condition Analysis (NCA). (Dul, J. 2016. Necessary Condition Analysis (NCA). ''Logic and Methodology of 'Necessary but not Sufficient' causality." Organizational Research Methods 19(1), 10-52)
  NCA identifies necessary (but not sufficient) conditions in datasets. Instead of drawing a regression line ''through the middle of the data'' in an xy-plot, NCA draws the ceiling line. The ceiling line y = f(x) separates the area with observations from the area without observations.
  (Nearly) all observations are below the ceiling line: y <= f(x). The empty zone is in the upper left hand corner of the xy-plot (with the convention that the x-axis is ''horizontal'' and the y-axis is ''vertical'' and that values increase ''upwards'' and ''to the right''). The ceiling line is a (piecewise) linear non-decreasing line: a linear step function or a straight line. It indicates which level of x (e.g. an effort or input) is necessary but not sufficient for a (desired) level of y (e.g. good performance or output).
Performs nonlinear calibration and curve fitting for data from Luminex, RT-PCR, ELISA, RPPA etc. Its precursor is Ruminex.
A flexible tool is presented here that can perform
    (i) traditional non-compartmental analysis (NCA) and
    (ii) simulation-based posterior predictive checks for a population
    pharmacokinetic (PK) model using NCA metrics.
making NCBI taxonomic data locally available and
        searchable as an R object
Provides a high-level R interface to 
 data files written using Unidata's
 netCDF library (version 4 or earlier), which are binary data files that are portable 
 across platforms and include metadata information in addition to the data sets.  
 Using this package, netCDF files (either version 4 
 or "classic" version 3) can be opened and data sets read in easily.
 It is also easy to create new netCDF dimensions, variables, and files,
 in either version 3 or 4 format, and manipulate existing netCDF files.
 This package replaces the former ncdf package, which only worked with
 netcdf version 3 files.  For various reasons the names of the functions
 have had to be changed from the names in the ncdf package.  The old ncdf package is still
 available at the URL given below, if you need to have backward compatibility.
 It should be possible to have both the ncdf and ncdf4 packages installed
 simultaneously without a problem.
 However, the ncdf package does not provide an interface for netcdf version 4 files.
This package contains a collection of helper functions for dealing
    with NetCDF files opened using ncdf4.
Set of tools to simplify the handling of 'NetCDF' files with the 'RNetCDF' package. 
 Most functions are wrappers of basic functions from the 'RNetCDF' package to easily run combinations of these 
 functions for frequently encountered tasks.
Computes the noncentral Dunnett's test distribution (pdf, cdf and quantile) and generates random numbers.
R functions for analyzing spatial (cross-)covariance: the
        nonparametric (cross-)covariance, the spline correlogram, the
        nonparametric phase coherence function, and related.
Computes the noncentral gamma function: pdf, cdf, quantile
        function and inverse for the noncentrality parameter.
A set of handy functions. Includes a versatile one line progress bar, one 
 line function timer with detailed output, time delay function, text histogram, object 
 preview, CRAN package search, simpler package installer, Linux command install check, 
 a flexible Mode function, top function, simulation of correlated data, and more.
Efficient algorithms for fitting regularization paths for linear or
 logistic regression models penalized by MCP or SCAD, with optional additional
 L2 penalty.
Streaming 'JSON' ('ndjson') has one 'JSON' record per-line and many modern
    'ndjson' files contain large numbers of records. These constructs may not be
    columnar in nature, but it's often useful to read in these files and "flatten"
    the structure out to work in an R data.frame-like context. Functions are provided that
    make it possible to read in plain 'ndjson' files or compressed ('gz') 'ndjson'
    files and either validate the format of the records or create "flat" data.table
    ('tbl_dt') structures from them.
Naive discriminative learning implements learning and
    classification models based on the Rescorla-Wagner equations and their
    equilibrium equations.
Renders dynamic network data from 'networkDynamic' objects as movies, interactive animations, or other representations of changing relational structures and attributes.
Performs network enrichment analysis against functional gene sets.
    Benchmarks networks. Renders raw gene profile matrices of dimensionality 'Ngenes
    x Nsamples' into the space of gene set (typically pathway) enrichment scores of
    dimensionality 'Npathways x Nsamples'.
Near-far matching is a study design technique for preprocessing observational data to mimic a pair-randomized trial.  Individuals are matched to be near on measured confounders and far on levels of an instrumental variable.
This package implements a path algorithm for Near-Isotonic
        Regression. For more details see the help files
Includes functions and examples to compute NEAT, a network-based test for gene enrichment analysis (Signorelli, Vinciotti and Wit, 2016, <DOI:10.1186/s12859-016-1203-6>).
NeatMap is a package to create heatmap like plots in 2 and
        3 dimensions, without the need for cluster analysis. Like the
        heatmap, the plots created by NeatMap display both a
        dimensionally reduced representation of the data as well as the
        data itself. They are intended to be used in conjunction with
        dimensional reduction techniques such as PCA.
A simple function for easier package loading and auto-installation.
needy is a small utility library designed to make testing function
    inputs less difficult. R is a dynamically typed language, but larger
    projects need input checking for scalabity. needy offers a single
    function, require_a( ), which lets you specify the traits an input object
    should have, such as class, size, numerical properties or number of
    parameters, while reducing boilerplate code and aiding debugging.
Effective population sizes (often abbreviated as "Neff") are essential in biodiversity monitoring and conservation. For the first time, calculating effective sizes with data obtained within less than a generation but considering demographic parameters is possible. This individual based model uses demographic parameters of a population to calculate annual effective sizes and effective population sizes (per generation). A defined number of alleles and loci will be used to simulate the genotypes of the individuals. Stepwise mutation rates can be included. Variations in life history parameters (sex ratio, sex-specific survival, recruitment rate, reproductive skew) are possible. These results will help managers to define existing populations as viable or not.
The Negative Binomial regression with mean and shape modeling and mean and variance modeling and Beta Binomial regression with mean and dispersion modeling.
Estimating the number of essential genes in a genome on the basis of data from a random transposon mutagenesis experiment, through the use of a Gibbs sampler.
Provides several direct search optimization algorithms based on the
  simplex method. The provided algorithms are direct search algorithms, i.e.
  algorithms which do not use the derivative of the cost function. They are
  based on the update of a simplex. The following algorithms are available: the
  fixed shape simplex method of Spendley, Hext and Himsworth (unconstrained
  optimization with a fixed shape simplex), the variable shape simplex method of
  Nelder and Mead (unconstrained optimization with a variable shape simplex
  made), and Box's complex method (constrained optimization with a variable
  shape simplex).
Access paleoecological datasets from the Neotoma Paleoecological
    Database using the published API (http://api.neotomadb.org/).  The functions
    in this package access various pre-built API functions and attempt to return
    the results from Neotoma in a usable format for researchers and the public.
Set of functions to estimate the Glomerular Filtration Rate (GFR) using different biomarkers and other phenotypes of interest for nephrology.
Package for a Network assisted algorithm for Epigenetic studies using mean and variance Combined signals: NEpiC. NEpiC combines both signals in mean and variance differences in methylation level between case and control groups searching for differentially methylated sub-networks (modules) using the protein-protein interaction network.
This tool set provides a set of functions to fit the nested Dirichlet process mixture of products of multinomial distributions (NDPMPM) model for nested categorical household data in the presence of impossible combinations. It has direct applications in generating synthetic nested household data.
Estimate hazard ratios, survival curves and attributable
        risks for cohorts with missing covariates, using Cox models or
        Kaplan-Meier estimated for strata.  This handles studies nested
        within cohorts, such as case-cohort studies with stratified
        sampling.  See
        http://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf
Calculate a Mann-Whitney-Wilcoxon test for a difference between treatment levels using nested ranks.  This test can be used when observations are structured into several groups and each group has received both treatment levels. The p-value is determined via bootstrapping.  The nested ranks test is intended to be one possible mixed-model extension of the Mann-Whitney-Wilcoxon test, for which treatment is a fixed effect and group membership is a random effect.
Infers species associations from community matrices. Uses local and (optional) regional-scale co-occurrence data by comparing observed partial correlation coefficients between species to those estimated from regional species distributions. Extends Gaussian graphical models to a null modeling framework. Provides interface to a variety of inverse covariance matrix estimation methods.
netClass is an R package for network-based feature (gene)
        selection for biomarkers discovery via integrating biological
        information.  This package adapts the following 5 algorithms
        for classifying and predicting gene expression data using prior
        knowledge: 1) average gene expression of pathway (aep); 2)
        pathway activities classification (PAC); 3) Hub network
        Classification (hubc); 4) filter via top ranked genes (FrSVM);
        5) network smoothed t-statistic (stSVM).
Facilitates network clustering and evaluation of cluster
        configurations.
Model fitting procedures for regression with network cohesion effects, when a network connecting sample individuals is available in a regression problem. In the future, other commonly used statistical models will be added, such as gaussian graphical model.
Create interactive networked coincidences. It joins the data analysis power of R to study coincidences and the visualization libraries of JavaScript in one package.
This package contains functions to carry out high
        throughput data analysis and to conduct data set comparisons.
        Similarity matrices from high throughput phenotypic data
        containing uninformative (e.g. wild type) or missing data can
        be calculated to report similarity of response.  A suite of
        graph comparisons using an adjacency or correlation matrix
        format are included to facilitate quick network analysis.
This package contains all data needed for Dan McFarland's
        SNA R labs.
Empirical statistical analysis, visualization and simulation
    of diffusion and contagion processes on networks. The package implements
    algorithms for calculating network diffusion statistics such as transmission
    rate, hazard rates, exposure models, network threshold levels, infectiousness
    (contagion), and susceptibility. The package is inspired by work published in
    Valente, et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995)
    <ISBN:9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others
    (2011) <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among
    others.
Methods for the generation of a wide range of network geographies,
    e.g., grid networks or clustered networks. Useful for the generation of
    benchmarking instances for the investigation of, e.g., Vehicle-Routing-Problems
    or Travelling Salesperson Problems.
Carry 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.
A comprehensive set of functions providing frequentist methods for network meta-analysis and supporting Schwarzer et al. (2015) <DOI:10.1007/978-3-319-21416-0>, Chapter 8 "Network Meta-Analysis":
 - frequentist network meta-analysis following Rücker (2012) <DOI:10.1002/jrsm.1058>;
 - net heat plot and design-based decomposition of Cochran's Q according to Krahn et al. (2013) <DOI:10.1186/1471-2288-13-35>;
 - measures characterizing the flow of evidence between two treatments by König et al. (2013) <DOI:10.1002/sim.6001>;
 - ranking of treatments (frequentist analogue of SUCRA) according to Rücker & Schwarzer (2015) <DOI:10.1186/s12874-015-0060-8>;
 - automated drawing of network graphs described in Rücker & Schwarzer (2016) <DOI:10.1002/jrsm.1143>.
Performs network-based source estimation. Different approaches are available: effective distance median, recursive backtracking, and centrality-based source estimation. Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.
Package for the pre-processing and normalization of graphs.
Functions for assessing the replication/preservation of a network 
  module's topology across datasets through permutation testing.
NetSim allows to combine and simulate a variety of micro-models to research their impact on the macro-features of social networks.
A set of functions for studying network robustness, resilience and vulnerability.
A collection of network inference methods for co-expression networks, quantitative network distances and a novel framework for network stability analysis.
Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.
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.
This permutation based hypothesis test, suited for Gaussian and
    binary data, assesses the difference between two networks based on several
    invariance measures (network structure invariance, global strength invariance,
    edge invariance). Network structures are estimated with l1-regularized partial
    correlations (Gaussian data) or with l1-regularized logistic regression (eLasso,
    binary data). Suited for comparison of independent and dependent samples
    (currently, only for one group measured twice).
Creates 'D3' 'JavaScript' network, tree, dendrogram, and Sankey
    graphs from 'R'.
Simple interface routines to facilitate the handling of network objects with complex intertemporal data. This is a part of the "statnet" suite of packages for network analysis.
A collection of dynamic network data sets from various sources and multiple authors represented as 'networkDynamic'-formatted objects.
Functions useful
    for producing estimates from data that were collected using network
    reporting techniques like network scale-up, indirect sampling,
    network reporting, and sibling history.
Implements some risk measures for (financial) networks, such as DebtRank, Impact Susceptibility, Impact Diffusion and Impact Fluidity.
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.
Base package for 'Neuroconductor', which includes many helper functions 
    that interact with objects of class 'nifti', implemented by
    package 'oro.nifti', for reading/writing and also other manipulation functions.
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.
Contains free datasets and statistical functions useful in psychology, neuropsychology and neuroscience.
The package allows users to generate fMRI time series or 4D data. Some high-level functions are created for fast data generation with only a few arguments and a diversity of functions to define activation and noise. For more advanced users it is possible to use the low-level functions and manipulate the arguments.
Computes the probability density function, cumulative distribution function, quantile function, random numbers and measures of inference for the following general families of  distributions (each family defined in terms of an arbitrary cdf G): Marshall Olkin G distributions, exponentiated G distributions, beta G distributions, gamma G distributions, Kumaraswamy G distributions, generalized beta G distributions, beta extended G distributions, gamma G distributions, gamma uniform G distributions, beta exponential G distributions, Weibull G distributions, log gamma G I distributions, log gamma G II distributions, exponentiated generalized G distributions, exponentiated Kumaraswamy G distributions, geometric exponential Poisson G distributions, truncated-exponential skew-symmetric G distributions, modified beta G distributions, and exponentiated exponential Poisson G distributions.
Indices, heuristics and strategies to help determine the number of factors/components to retain:
                1. Acceleration factor (af with or without Parallel Analysis);
                2. Optimal Coordinates (noc with or without Parallel Analysis);
                3. Parallel analysis (components, factors and bootstrap);
                4. lambda > mean(lambda) (Kaiser, CFA and related);
                5. Cattell-Nelson-Gorsuch (CNG);
                6. Zoski and Jurs multiple regression (b, t and p);
                7. Zoski and Jurs standard error of the regression coeffcient (sescree);
                8. Nelson R2;
                9. Bartlett khi-2;
               10. Anderson khi-2;
               11. Lawley khi-2 and
               12. Bentler-Yuan khi-2.
Numerical Formal Concept Analysis (nFCA) is a modern unsupervised learning tool for analyzing general numerical data. Given input data, this R package nFCA outputs two nFCA graphs: a H-graph and an I-graph that reveal systematic, hierarchical clustering and inherent structure of the data.
An implementation of the network fingerprint framework that introduced 
  in paper "Network fingerprint: a knowledge-based characterization of biomedical 
  networks" (Cui, 2015) <doi:10.1038/srep13286>. This method worked by making 
  systematic comparisons to a set of well-studied "basic networks", measuring 
  both the functional and topological similarity.  A biological could be
  characterized as a spectrum-like vector consisting of similarities to basic 
  networks. It shows great potential in biological network study.
An n-gram is a sequence of n "words" taken from a
    body of text in order.  This package offers utilities for creating,
    displaying, summarizing, and "babbling" n-grams.  The
    'tokenization' and "babbling" are handled by very efficient C
    code, which can even be build as its own standalone library.
    The babbler is a simple Markov chain.  The package also offers
    a vignette with complete example 'workflows' and information about
    the utilities offered in the package.
A simple n-gram (contiguous sequences of n items from a
    given sequence of text) tokenizer to be used with the 'tm' package with no
    'rJava'/'RWeka' dependency.
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) <DOI:10.1111/j.1467-9868.2012.01041.x> and the centered
	autologistic model of Caragea and Kaiser (2009) <DOI:10.1198/jabes.2009.07032>.
Body Shape and related measurements from the US National Health
    and Nutrition Examination Survey (NHANES, 1999-2004).  See
    http://www.cdc.gov/nchs/nhanes.htm for details.
Utility to retrieve data from the National Health and Nutrition
    Examination Survey (NHANES).
NHEMOtree performs cost-sensitive classification by
        solving the two-objective optimization problem of minimizing
        misclassification rate and minimizing total costs for
        classification. The three methods comprised in NHEMOtree are
        based on EMOAs with either tree representation or bitstring
        representation with an enclosed classification tree algorithm.
Compiling the NHL Real Time Scoring System Database for easy use in R
Bayesian HMM and NHMM modeling for multiple time series. The
    emission distribution can be mixtures of Gammas, Poissons, Normals and zero
    inflation is possible.
Calibration, simulation, validation of (non-)homogeneous Markov switching autoregressive models with Gaussian or von Mises innovations.  Penalization methods are implemented for Markov Switching Vector Autoregressive Models of order 1 only. Most functions of the package handle missing values.
Tools for modelling, ML estimation, validation analysis and simulation of non homogeneous Poisson processes in time.
Illustrate graphically the most common Null Hypothesis Significance Testing procedures. More specifically, this package provides functions to plot Chi-Squared, F, t (one- and two-tailed) and z (one- and two-tailed) tests, by plotting the probability density under the null hypothesis as a function of the different test statistic values. Although highly flexible (color theme, fonts, etc.), only the minimal number of arguments (observed test statistic, degrees of freedom) are necessary for a clear and useful graph to be plotted, with the observed test statistic and the p value, as well as their corresponding value labels. The axes are automatically scaled to present the relevant part and the overall shape of the probability density function. This package is especially intended for education purposes, as it provides a helpful support to help explain the Null Hypothesis Significance Testing process, its use and/or shortcomings.
Get or set UNIX priority (niceness) of running R process.
This package uses a probabilistic method to calculate niche
    regions and pairwise niche overlap using multidimensional niche indicator
    data (e.g., stable isotopes, environmental variables, etc.). The niche
    region is defined as the joint probability density function of the
    multidimensional niche indicators at a user-defined probability alpha
    (e.g., 95%).  Uncertainty is accounted for in a Bayesian framework, and the
    method can be extended to three or more indicator dimensions.  It provides
    directional estimates of niche overlap, accounts for species-specific
    distributions in multivariate niche space, and produces unique and
    consistent bivariate projections of the multivariate niche region.  A
    forthcoming article by Swanson et al. (Ecology, 2014) provides a detailed
    description of the methodology.  See the package vignette for a worked
    example using fish stable isotope data.
Computes and plots the boundary between night and day.
Miscellaneous R functions developed over the course of statistical
	     research. These include utilities that supplement the existing
	     idiosyncrasies of R; extend plotting functionality and aesthetics; 
	     provide alternative presentations of matrix decompositions; extend 
	     types of random variables supported for simulation; extend access
	     to command line tools and system information, making work on remote
	     systems easier.
Flexible application of algorithms to models specified in the BUGS
    language. Algorithms can be written in the NIMBLE language and made available to
    any model.
Japan-specific data is sometimes too unhandy for R users to manage. The utility functions and data in this package disencumber us from such an unnecessary burden.
Fast and Accurate Trisomy Prediction in Non-Invasive Prenatal
    Testing.
Datasets for testing nonlinear regression routines.
Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for 
    SI (International System of Units) and non-SI units, plus unit conversions
    Based on the data from NIST (National Institute of Standards and Technology, USA)
Noninferiority tests for difference in failure rates at a prespecified control rate or prespecified time.
Optimization for nonlinear objective and constraint functions.  Linear or nonlinear equality and inequality constraints are allowed.  It accepts the input parameters as a constrained matrix.
Solve a system of nonlinear equations using a Broyden or a Newton method
             with a choice of global strategies such as line search and trust region.
             There are options for using a numerical or user supplied Jacobian,
             for specifying a banded numerical Jacobian and for allowing
             a singular or ill-conditioned Jacobian.
Fit and compare Gaussian linear and nonlinear mixed-effects models.
This package combines the odesolve and nlme packages for
        mixed-effects modelling using differential equations.
nlmeU: Datasets and utility  functions enhancing functionality of nlme package. Datasets, functions and scripts are described in book titled 'Linear Mixed-Effects Models:
 A Step-by-Step Approach' by Galecki and Burzykowski (2013). Package is under development.
Replacement for nls() tools for working with nonlinear least squares problems.
      The calling structure is similar to, but much simpler than, that of the nls()
      function. Moreover, where nls() specifically does NOT deal with small or zero
      residual problems, nlmrt is quite happy to solve them. It also attempts to be
      more robust in finding solutions, thereby avoiding 'singular gradient' messages
      that arise in the Gauss-Newton method within nls(). The Marquardt-Nash approach
      in nlmrt generally works more reliably to get a solution, though this may be 
      one of a set of possibilities, and may also be statistically unsatisfactory.
      Added print and summary as of August 28, 2012.
It includes three methods: K-profiles clustering, non-linear network reconstruction, and non-linear hierarchical clustering.
nloptr is an R interface to NLopt. NLopt is a free/open-source library for
    nonlinear optimization, providing a common interface for a number of
    different free optimization routines available online as well as original
    implementations of various other algorithms.
    See http://ab-initio.mit.edu/wiki/index.php/NLopt_Introduction for more
    information on the available algorithms. During installation on Unix the
    NLopt code is downloaded and compiled from the NLopt website.
Basic classes and methods for Natural Language Processing.
Utilities for Natural Language Processing.
Likelihood inference based on higher order approximations 
  for nonlinear models with possibly non constant variance
This is a package which can help you search for the root
        of a equation.
Estimate the non-linear odds ratio and plot it against a continuous exposure.
Adds brute force and multiple starting values to nls.
Estimation of structural equation models with nonlinear effects
  and underlying nonnormal distributions.
Non-linear shrinkage estimation of population eigenvalues and covariance
    matrices, based on publications by Ledoit and Wolf (2004, 2015, 2016).
Data sets and nonlinear regression models dedicated to predictive microbiology
Fit univariate non-linear scale mixture of
        skew-normal(NL-SMSN) regression.
Fit biologically meaningful distribution functions to
  time-sequence data (phenology), estimate parameters to draw the cumulative
  distribution function and probability density function and calculate standard
  statistical moments and percentiles.
Several tools for assessing the quality of fit of a
        gaussian nonlinear model are provided.
Utilities and kinship information for behavior genetics and
    developmental research using the National Longitudinal Survey of Youth
    (NLSY; <http://www.bls.gov/nls/>).
Uses a modified lifting algorithm on which it builds the
        nondecimated lifting transform. It has applications in wavelet
        shrinkage.
R functions for (non)linear time series analysis. A core
        topic is order estimation through cross-validation.
Provides functions to calculate the normalised Lineage-Through-
    Time (nLTT) statistic, given two phylogenetic trees. The nLTT statistic measures
    the difference between two Lineage-Through-Time curves, where each curve is
    normalised both in time and in number of lineages.
Wald Test for nonlinear restrictions on model parameters and confidence
             intervals for nonlinear functions  of parameters using delta-method.  Applicable
             after ANY model, provided parameters estimates and their covariance matrix are
             available.
Without the need to know the true number of change-points in advance,
             this package provides a function for detecting multiple change-points
             using  a non-parametric maximum likelihood approach.
Provides a framework to perform Non-negative Matrix
    Factorization (NMF). The package implements a set of already published algorithms
    and seeding methods, and provides a framework to test, develop and plug
    new/custom algorithms. Most of the built-in algorithms have been optimized
    in C++, and the main interface function provides an easy way of performing
    parallel computations on multicore machines.
Wrapper package for the nmfgpu library, which implements several
    Non-negative Matrix Factorization (NMF) algorithms for CUDA platforms.
    By using the acceleration of GPGPU computing, the NMF can be used for
    real-world problems inside the R environment. All CUDA devices starting with
    Kepler architecture are supported by the library.
Calculates the normalized mutual information (NMI) of two community structures in network analysis.
Functions, examples and data from the book
 "Numerical Methods and Optimization in Finance" by M.
 'Gilli', D. 'Maringer' and E. Schumann (2011), ISBN
 978-0123756626.  The package provides implementations of
 several optimisation heuristics, such as Differential
 Evolution, Genetic Algorithms and Threshold Accepting.
 There are also functions for the valuation of financial
 instruments, such as bonds and options, and functions that
 help with stochastic simulations.
Software for feed-forward neural networks with a single
  hidden layer, and for multinomial log-linear models.
Computing prediction intervals of neural network models (e.g.backpropagation) at certain confidence level. It can take the output from models trained by other packages like 'nnet', 'neuralnet', 'RSNNS', etc.
Estimates of coefficients of lasso penalized linear regression and generalized linear models subject to non-negativity constraints on the parameters using multiplicative iterative algorithm. Entire regularization path for a sequence of lambda values can be obtained. Functions are available for creating plots of regularization path, cross validation and estimating coefficients at a given lambda value. There is also provision for obtaining standard error of coefficient estimates.
This is a package for Non-Negative Linear Models (NNLM). It implements
    fast sequential coordinate descent algorithms for non-negative linear regression
    and non-negative matrix factorization (NMF). It supports mean square error and Kullback-Leibler divergence loss.
    Many other features are also implemented, including missing value imputation, domain knowledge integration,
    designable W and H matrices and multiple forms of regularizations.
An R interface to the Lawson-Hanson implementation of an
        algorithm for non-negative least squares (NNLS).  Also allows
        the combination of non-negative and non-positive constraints.
Nonlinear nonparametric statistics using partial moments.  Partial moments are the elements of variance and asymptotically approximate the area of f(x).  These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences.  
    All routines based on ( Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995) ).
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).
Functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186.  Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements.  <http://arxiv.org/abs/1601.03334>.
An extensive implementation of state-of-the-art and classical
    algorithms to preprocess label noise in classification problems.
Digital nomads are people that use technology in order to work remotely and live an independent and nomadic lifestyle. Nomad List finds the best cities in the world for digital nomads and offers an API, see https://nomadlist.com/faq#api for more information. This package provides an interface for that API.
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 nomogram can not be easily applied,
    because it is difficult to calculate the points or even the survival probability.
  The package, including a function of nomogramEx(),
    is to extract the polynomial equations to calculate the points of each variable,
    and the survival probability corresponding to the total points.
A collection of various regional information determined by the
    U.S. Census Bureau along with demographic data.
Conduct a noncompartmental analysis as closely as possible to the most widely used commercial software for pharmacokinetic analysis, i.e. 'Phoenix(R) WinNonlin(R)' <https://www.certara.com/software/pkpd-modeling-and-simulation/phoenix-winnonlin/>.
             Some features include:
             1) Use CDISC SDTM PP domain terms.
             2) Automatic slope selection with the same criterion of WinNonlin(R)
             3) Support both 'linear-up linear-down' and 'linear-up log-down' method
             4) Calculate partial(interval) AUC with 'linear' or 'log' interpolation method
             For more details on noncompartmental analysis, see the reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107)
             *) Acknowledgement: Author thanks for the careful review and valuable input of Dr. Jee Eun Lee.
A finite-population significance test of the 'sharp' causal null hypothesis that
    treatment exposure X has no effect on final outcome Y, within the principal stratum of Compliers.
    A generalized likelihood ratio test statistic is used, and the resulting p-value is exact.
    Currently, it is assumed that there are only Compliers and Never Takers in the population.
Functions for nonlinear time series analysis. This package permits 
             the computation of the  most-used nonlinear statistics/algorithms
             including generalized correlation dimension, information dimension,
             largest Lyapunov exponent, sample entropy and Recurrence 
             Quantification Analysis (RQA), among others. Basic routines
             for surrogate data testing are also included. Part of this work 
             was based on the  book "Nonlinear time series analysis" by 
             Holger Kantz and Thomas Schreiber (ISBN: 9780521529020).
Loading NONMEM (NONlinear Mixed-Effect Modeling, <http://www.iconplc.com/innovation/solutions/nonmem/> ) output files and simulate with parameter uncertainty.
Testing non-nested models via theory supplied by Vuong (1989) <DOI:10.2307/1912557>.
    Includes tests of model distinguishability and of model fit that can be applied
    to both nested and non-nested models. Also includes functionality to obtain
    confidence intervals associated with AIC and BIC. This material is based on work
    supported by the National Science Foundation under Grant Number SES-1061334.
This package contains functions for measuring efficiency
        and productivity of decision making units (DMUs) under the
        framework of Data Envelopment Analysis (DEA) and its
        variations.
Provides tests of significance for covariates (or groups of covariates) in a fully nonparametric regression model and a variable (or group) selection procedure based on False Discovery Rate. In addition, it provides a function for local polynomial regression for any number of dimensions, using a bandwidth specified by the user or automatically chosen by cross validation or an adaptive procedure.
This package offers a comprehensive data analysis if
        stratification and matching by the propensity score is done.
        Several functions are implemented, starting from the selection
        of the propensity score model up to estimating propensity score
        based treatment or exposure effects. All functions can be
        applied separately as well as combined.
Screening a HRMS data set for peaks related by (1) isotope patterns, (2) different adducts of the same molecule and/or (3) homologue series. The resulting isotopic pattern and adduct groups can then be combined to so-called components, with homologue series information attached. Also allows plotting and filtering HRMS data for mass defects, frequent m/z distances and components vs. non-components.
Data sets for isotope pattern grouping of LC-HRMS peaks with package nontarget. Based on a vast set of unique PubChem molecular formulas, quantized (a) m/z, (b) m/z differences, (c) intensity ratios and (d) marker centroids of simulated centroid pairs are listed for different instrument resolutions.
Estimation of party/candidate ideological positions
             that correspond to a Nash equilibrium along a 
             one-dimensional space.
Onedimensional Normal Mixture Models Classes, for, e.g.,
   density estimation or clustering algorithms research and teaching;
   providing the widely used Marron-Wand densities.  Now fitting to data
   by ML (Maximum Likelihood) or EM estimation.
The Nordklim dataset 1.0 is a unique and useful achievement for climate 
    analysis. It includes observations of twelve different climate elements from 
    more than 100 stations in the Nordic region, in time span over 100 years.
    The project contractors were NORDKLIM/NORDMET on behalf of the National 
    meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI), 
    Norway (DNMI) and Sweden (SMHI).
Analysis of multivariate normal datasets with missing
        values
Functions for parameter estimation, Bayesian posterior simulation
        and multiple imputation from incomplete multivariate data under a
        normal model.
The functions proposed in this package compute the density of the sum of a Gaussian and a gamma random variables, estimate the parameters and correct the noise effect in a gamma-signal and Gaussian-noise model. This package has been used to implement the background correction method for Illumina microarray data presented in Plancade S., Rozenholc Y. and Lund E. "Generalization of the normal-exponential model : exploration of a more accurate parameterization for the signal distribution on Illumina BeadArrays", BMC Bioinfo 2012, 13(329).
This package provides functions for the normal Laplace
        distribution. It is currently under development and provides
        only limited functionality. Density, distribution and quantile
        functions, random number generation, and moments are provided.
Collection of utilities referred to Exponential Power distribution, 
  also known as General Error Distribution (see Mineo, A.M. and Ruggieri, M. (2005), A software Tool for the Exponential Power Distribution: The normalp package. In Journal of Statistical Software, Vol. 12, Issue 4)
The robustness of many of the statistical techniques, such as factor analysis, applied in 
          the social sciences rests upon the assumption of item-level normality. However, when dealing 
          with real data, these assumptions are often not met. The Box-Cox transformation (Box & Cox, 1964)
          <http://www.jstor.org/stable/2984418> provides an optimal transformation for non-normal variables. Yet, for 
          large datasets of continuous variables, its application in current software programs is cumbersome
          with analysts having to take several steps to normalise each variable. We present an R package 
          'normalr' that enables researchers to make convenient optimal transformations of multiple variables
          in datasets. This R package enables users to quickly and accurately: (1) anchor all of their 
          variables at 1.00, (2) select the desired precision with which the optimal lambda is estimated, 
          (3) apply each unique exponent to its variable, (4) rescale resultant values to within their 
          original X1 and X(n) ranges, and (5) provide original and transformed estimates of skewness, 
          kurtosis, and other inferential assessments of normality.
Functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided.
Evaluates the probability density function of the sum of
        the Gaussian and Student's t density on 3 degrees of freedom.
        Evaluates the p.d.f. of the sphered Student's t density
        function.  Also evaluates the erf, and erfc functions on
        complex-valued arguments. Thanks to Krishna Myneni the function
        is calculates the Faddeeva function also!
Tests for the composite hypothesis of normality
Includes Omnibus Univariate and Multivariate Normality
        Tests (See Doornik and Hansen (1994)). One variation allows for
        the possibility of weak dependence rather than independence in
        the variable(s). Also included is an univariate white noise
        test where the null hypothesis is "white noise" rather than
        strict "white noise".
CIPW Norm (acronym from the surnames of the authors: Cross, Iddings, Pirrson and Washington)
             is the most commonly used calculation algorithm to estimate the standard mineral assemblages
             for igneous rocks from its geochemical composition. NORRRM (acronym from noRm, R language and
             Renee) is the highly consistent program to calculate the CIPW Norm.
An implementation of a specific method for generating
    n-dimensional random vectors with given marginal distributions and
    correlation matrix. The method uses the NORTA (NORmal To Anything)
    approach which generates a standard normal random vector and then
    transforms it into a random vector with specified marginal distributions
    and the RA (Retrospective Approximation) algorithm which is a generic
    stochastic root-finding algorithm. The marginals can be continuous or
    discrete. See the vignette of package for more details.
Five omnibus tests for testing the composite hypothesis of
        normality.
Tests the goodness-of-fit to the Normal distribution for the errors of an ARMA model.
The nose package consists of a collection of three
        functions for classifying sparseness in typical 2 x 2 data sets
        with at least one cell should have zero count. These functions
        are based on the three widely applied summary measures for 2 x
        2 categorical data viz, Risk Difference (RD), Relative Risk
        (RR), Odds Ratio (OR). This package helps to identify suitable
        continuity correction for zero cells when a multi centre
        analysis or a meta analysis is carried out. Further, it can be
        considered as a tool for sensitivity analysis for adding a
        continuity correction and to identify the presence of Simpson's
        paradox.
Provides functions to produce advanced ascii graphics, directly to the terminal window. This package utilizes the txtplot() function from the 'txtplot' package, to produce text-based histograms, empirical cumulative distribution function plots, scatterplots with fitted and regression lines, quantile plots, density plots, image plots, and contour plots.
Provides efficient implementation of the Narrowest-Over-Threshold methodology for detecting an unknown number of change-points occurring at unknown locations in one-dimensional data following 'deterministic signal + noise' model. Currently implemented scenarios are: piecewise-constant signal, piecewise-constant signal with a heavy-tailed noise, piecewise-linear signal, piecewise-quadratic signal, piecewise-constant signal and with piecewise-constant variance of the noise.
Functions to flash your hue lights, or text yourself, from R. Designed to be used with long running scripts.
This Package provides a connection to the pushover.net API
        to send push notification to your smartphone directly from R.
        (ACCOUNT REQUIRED!)
Estimate Large correlation and covariance matrices and their inverses using 
 integration of the sample and thresholded correlation and covariance estimators.
The noweb system for source code, implemented in R.
The Nozzle package provides an API to generate HTML
        reports with dynamic user interface elements based on
        JavaScript and CSS (Cascading Style Sheets). Nozzle was
        designed to facilitate summarization and rapid browsing of
        complex results in data analysis pipelines where multiple
        analyses are performed frequently on big data sets. The package
        can be applied to any project where user-friendly reports need
        to be created.
This package provides a variety of nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC:www.nserc.ca), the Social Sciences and Humanities Research Council of Canada (SSHRC:www.sshrc.ca), and the Shared Hierarchical Academic Research Computing Network (SHARCNET:www.sharcnet.ca).
Computes interdaily stability (IS), intradaily variability (IV) & the relative amplitude (RA) from actigraphy data as described in Blume et al. (2016) <doi:10.1016/j.mex.2016.05.006> and van Someren et al. (1999) <doi:10.3109/07420529908998724>. Additionally, it also computes L5 (i.e. the 5 hours with lowest average actigraphy amplitude) and M10 (the 10 hours with highest average amplitude) as well as the respective start times. The flex versions will also compute the L-value for a user-defined number of minutes. IS describes the strength of coupling of a rhythm to supposedly stable zeitgebers. It varies between 0 (Gaussian Noise) and 1 for perfect IS. IV describes the fragmentation of a rhythm, i.e. the frequency and extent of transitions between rest and activity. It is near 0 for a perfect sine wave, about 2 for Gaussian noise and may be even higher when a definite ultradian period of about 2 hrs is present. RA is the relative amplitude of a rhythm. Note that to obtain reliable results, actigraphy data should cover a reasonable number of days.
With this package, it is possible to compute nonparametric simultaneous confidence intervals for relative contrast effects in the unbalanced one way layout. Moreover, it computes simultaneous p-values. The simultaneous confidence intervals can be computed using multivariate normal distribution, multivariate t-distribution with a Satterthwaite Approximation of the degree of freedom or using multivariate range preserving transformations with Logit or Probit as transformation function. 2 sample comparisons can be performed with the same methods described above. There is no assumption on the underlying distribution function, only that the data have to be at least ordinal numbers.
The package "nparLD" is designed to perform nonparametric
        analysis of longitudinal data in factorial experiments.
        Longitudinal data are those which are collected from the same
        subjects over time, and they frequently arise in biological
        sciences. Nonparametric methods do not require distributional
        assumptions, and are applicable to a variety of data types
        (continuous, discrete, purely ordinal, and dichotomous).  Such
        methods are also robust with respect to outliers and for small
        sample sizes.
These routines create multiple imputations of missing at random categorical data, with or without structural zeros. Imputations are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling.
A variety of functions for the best known and most innovative approaches to nonparametric boundary estimation. The selected methods are concerned with empirical, smoothed, unrestricted as well as constrained fits under both separate and multiple shape constraints. They cover robust approaches to outliers  as well as data envelopment techniques based on piecewise polynomials, splines, local linear fitting, extreme values and kernel smoothing. The package also seamlessly allows for Monte Carlo comparisons among these different estimation methods.  Its use is illustrated via a number of empirical applications and simulated examples.
An implementation of nonparametric combination of hypothesis tests.
    This package performs nonparametric combination (Pesarin and Salmaso 2010),
    a permutation-based procedure for jointly testing multiple hypotheses. The
    tests are conducted under the global "sharp" null hypothesis of no effects,
    and the component tests are combined on the metric of their p-values. A
    key feature of nonparametric combination is that it accounts for the
    dependence among tests under the null hypothesis. In addition to the
    "NPC" function, which performs nonparametric combination itself, the
    package also contains a number of helper functions, many of which calculate
    a test statistic given an input of data.
An array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model.