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.
Provides additional display mediums for time series visualisations, such as calendar heat map, steamgraph, marimekko, etc.
An implementation of the generalized graded unfolding model (GGUM) in R, see Roberts, Donoghue, and Laughlin (2000) <doi:10.1177/01466216000241001>). It allows to simulate data sets based on the GGUM. It fits the GGUM and the GUM, and it retrieves item and person parameter estimates. Several plotting functions are available (item and test information functions; item and test characteristic curves; item category response curves). Additionally, there are some functions that facilitate the communication between R and 'GGUM2004'. Finally, a model-fit checking utility, MODFIT(), is also available.
A collection of datasets for the upcoming book "Graficas versatiles con ggplot: Analisis visuales de datos", by Raymond L. Tremblay and Julian Hernandez-Serano.
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'.
Easy creation and manipulation of Voronoi diagrams using 'deldir' with visualization in 'ggplot2'.
Provides a word cloud text geom for 'ggplot2'. Texts
    are placed so that they do not overlap as in 'ggrepel'.  The algorithm
    used is a variation around the one of 'wordcloud2.js'.
Minimal client to access the 'GitHub' 'API'.
Colour palettes inspired by Studio Ghibli films, ported to R for your enjoyment.
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.
Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) <arXiv:1707.06661>. The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) <arXiv:1508.03884>. Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) <doi:10.1214/12-BA729>.
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.
Build a map of path-based geometry, this is a simple description of the number
 of parts in an object and their basic structure. Translation and restructuring operations for 
 planar shapes and other hierarchical types require a data model with a record of the underlying
 relationships between elements. The gibble() function creates a geometry map, a simple record of 
 the underlying structure in path-based hierarchical types. There are methods for the planar shape 
 types in the 'sf' and 'sp' packages and for types in the the 'trip' and in-development 'silicate' packages.
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.
Gibbs sampler for one-way linear mixed-effects models
        (ANOVA, ANCOVA) with homoscedasticity of errors and uniform
        priors.
Implements categorical principal component analysis ('PRINCALS'), multiple correspondence analysis ('HOMALS'). It replaces the 'homals' package.
Multi-threaded GIF encoder written in Rust: <https://gif.ski/>. 
    Converts images to GIF animations using pngquant's efficient cross-frame 
    palettes and temporal dithering with thousands of colors per frame.
Functions to read in the geometry format under the 
    'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called 
    'GIFTI' <https://www.nitrc.org/projects/gifti/>. 
    These files contain surfaces of brain imaging data.
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.
Implements the generalized integration model, which integrates individual-level data and summary statistics under a generalized linear model framework. It supports continuous and binary outcomes to be modeled by the linear and logistic regression models.
Automated identification and estimation of group- and
    individual-level relations in time series data from within a structural
    equation modeling framework.
Supplemental tools for the 'gimme' R package. It contains an interactive graphical user interface, allowing for the flexible specification of a variety of both basic and advanced options. It will expand to include a variety of tools for navigating output.
This is 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 from 
    <https://ecocast.arc.nasa.gov/data/pub/gimms/>; 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.
An interface to the 'API' of 'Giphy', a popular index-based search 
    engine for 'GIFs' and animated stickers (see <http://giphy.com/faq> and 
    <https://github.com/Giphy/GiphyAPI> for more information about 'Giphy' and 
    its 'API') . This package also provides a 'RStudio Addin', which can help 
    users easily search and download 'GIFs' and insert them to a 'rmarkdown' 
    presentation.
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.
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.  Full model and computation details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.
Routines for fitting various joint (and univariate) regression models, with several types of covariate effects, in the presence of equations' errors association, endogeneity, non-random sample selection or partial observability.
Functions for the g-and-k and generalised g-and-h distributions.
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 <http://www.beerlab.org/gkmsvm/>
    It also uses the 'kernlab' library (separate R package by different authors) for various SVM algorithms.
A fully distributed glacier surface mass balance model developed for the simulation of accumulation and ablation processes on debris-free as well as debris-covered glaciers.
Functions capable of performing  efficient design matrix free  penalized estimation in large scale 2 and 3-dimensional generalized linear array model framework. The generic glamlasso() function solves the penalized maximum likelihood estimation (PMLE) problem in a pure generalized linear array model (GLAM) as well as in a GLAM containing a non-tensor component. Currently Lasso or Smoothly Clipped Absolute Deviation (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. Furthermore this package also contains two  functions  that can be used to fit special cases of GLAMs, see glamlassoRR() and glamlassoS(). The procedure underlying these functions is based on the gdpg algorithm from Lund et al. (2017) <doi:10.1080/10618600.2017.1279548>.
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.
Interacts with the 'Glassdoor' API
    <https://www.glassdoor.com/developer/index.htm>. Allows the user to
    search job statistics, employer statistics, and job progression,
    where 'Glassdoor' provides a breakdown of other jobs a person did
    after their current one.
Estimation of a sparse inverse covariance matrix using a lasso (L1)
             penalty. Facilities are provided for estimates along a path of values
	     for the regularization parameter.
A fast and improved implementation of the graphical LASSO.
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.
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 Kolmogorov-Smirnov tests and data driven smooth 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.
Fits a generalized linear density ratio model (GLDRM).
    A GLDRM is a semiparametric generalized linear model.
    In contrast to a GLM, which assumes a particular exponential family distribution, 
    the GLDRM uses a semiparametric likelihood to estimate the reference distribution. 
    The reference distribution may be any discrete, continuous, or mixed exponential 
    family distribution. The model parameters, which include both the regression 
    coefficients and the cdf of the unspecified reference distribution, are estimated 
    by maximizing a semiparametric likelihood. Regression coefficients are estimated 
    with no loss of efficiency, i.e. the asymptotic variance is the same as if the 
    true exponential family distribution were known.
    Huang (2014) <doi:10.1080/01621459.2013.824892>.
    Huang and Rathouz (2012) <doi:10.1093/biomet/asr075>.
    Rathouz and Gao (2008) <doi:10.1093/biostatistics/kxn030>.
Functions evaluate global and individual tests for direct effects in Mendelian randomization studies.
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. See Espeland and Hui (1987)
             <doi:10.2307/2531553> for general approach.
Analysis of multivariate data using generalized linear latent variable models (gllvm). 
      Estimation is performed using either Laplace approximation method or variational approximation method implemented via TMB (Kristensen et al., (2016), <doi:10.18637/jss.v070.i05>). 
      Details for gllvm, see Hui et al. (2015) <doi:10.1111/2041-210X.12236> 
      and (2017) <doi:10.1080/10618600.2016.1164708> and Niku et al. (2017) 
      <doi:10.1007/s13253-017-0304-7>.
Provides two functions that generate source code implementing the predict 
  function of fitted glm objects. In this version, code can be generated for either 'C' 
  or 'Java'. The idea is to provide a tool for the easy and fast deployment of glm 
  predictive models into production. The source code generated by this package implements 
  two function/methods. One of such functions implements the equivalent to 
  predict(type="response"), while the second implements predict(type="link"). Source code 
  is written to disk as a .c or .java file in the specified path. In the case of c, an .h 
  file is also generated.
Functions to calculate predicted values and the difference between
    the two cases with confidence interval for glm(), glm.nb(), polr() and multinom().
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, described by Held, Gravestock, Sabanes Bove (2015) <doi:10.1214/14-STS510>.
    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.
This package contains functions to perform generalized
        linear mixed Dirichlet models using posterior simulation.
Recursive partitioning based on (generalized) linear mixed models
    (GLMMs) combining lmer()/glmer() from lme4 and lmtree()/glmtree() from partykit.
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.
Fits generalized linear mixed models for a single grouping factor under
    maximum likelihood approximating the integrals over the random effects with an 
    adaptive Gaussian quadrature rule; Jose C. Pinheiro and Douglas M. Bates (1995) 
    <doi:10.1080/10618600.1995.10474663>.
Performs bootstrap resampling for most models that update() works for. There
    are two main functions: BootGlmm() performs block resampling if random effects
    are present, and case resampling if not; BootCI() converts output from
    bootstrap model runs into confidence intervals and p-values. By default,
    BootGlmm() calls BootCI(). 
    Package motivated by Humphrey and Swingley (2018) <arXiv:1805.08670>.
Automates running Generalised 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.
Approximate frequentist inference for generalized linear mixed model analysis with expectation propagation used to circumvent the need for multivariate integration. In this version, the random effects can be any reasonable dimension. However, only probit mixed models with one level of nesting are supported. The methodology is described in Hall, Johnstone, Ormerod, Wand and Yu (2018) <arXiv:1805.08423v1>.
Implements Bayesian spatial and spatiotemporal models that
    optionally allow for extreme spatial deviations through time. 'glmmfields'
    uses a predictive process approach with random fields implemented through
    a multivariate-t distribution instead of the usual multivariate normal.
    Sampling is conducted with 'Stan'. References: Anderson and Ward (2018)
    <doi:10.1002/ecy.2403>.
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.
Fit linear and generalized linear mixed models with various
    extensions, including zero-inflation. The models are fitted using maximum
    likelihood estimation via 'TMB' (Template Model Builder). Random effects are
    assumed to be Gaussian on the scale of the linear predictor and are integrated
    out using the Laplace approximation. Gradients are calculated using automatic
    differentiation.
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 regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the paper linked to via the URL below.
Penalized methods are useful for fitting over-parameterized models. This package 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. The glmnet fitting algorithm is used to fit the continuation ratio model after data restructuring.
Provides a formula interface for the 'glmnet' package for
    elasticnet regression, a method for cross-validating the alpha parameter,
    and other quality-of-life tools.
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.
Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.
It provides an extremely efficient procedure for fitting the entire truncated lasso regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. The algorithm uses the difference of convex technique. The detail of the algorithm is described in Shen, Pan and Zhu (2012) <doi:10.1080/01621459.2011.645783>. The package is inherited from a popular R package 'glmnet' and many functions in 'glmnet' can be directly used in 'glmtlp'. You can learn more details by the online manual (<http://wuchong.org/glmtlp.html>).
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 configurations 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" or "free") 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, depends on GLPK Version >= 4.42.
Functions to conduct four generalized logrank tests and a score test under a proportional hazards model
Likelihood ratio tests for genome-wide association and genome-wide linkage analysis under heterogeneity.
The Graphical Least Square Estimation 'GLSE' package consists of several functions. The first function (called GLSE()), which is the main function in this package, is for estimating linear regression via graphical models, especially when the number of observations is smaller than the number of variables. This function gives unbiased estimated regression models under some conditions on the predictors. Moreover, this function aims to deal with studies where all covariates need to be kept in the model (such as portfolio optimisation). The second function provides an estimation of the bootstrap standard errors of the 'GLSE' parameters. The third function generates data from multivariate normal distribution for regression purposes, and the fourth function generates data from multivariate t-distribution for the same purpose. Generating an undirected decomposable graph is carried out by the fifth function and its  decomposability is tested in the sixth function. The seventh function gives a perfect sequence of the cliques and separators of the given decomposable graph. Finally, the last function gives the plot of the decomposale graph. The package supports both serial and parallel computations for estimating the graph.The 'GLSE' package is an implementation of the method introduced in Aldahmani, S., and Dai, H. (2015) <doi:10.5539/ijsp.v4n3p61>.
Performs linear regression with correlated predictors, responses and correlated measurement errors in predictors and responses, correcting for biased caused by these.
An implementation of interpreted string literals, inspired by
  Python's Literal String Interpolation <https://www.python.org/dev/peps/pep-0498/> and Docstrings
  <https://www.python.org/dev/peps/pep-0257/> and Julia's Triple-Quoted String Literals
  <https://docs.julialang.org/en/stable/manual/strings/#triple-quoted-string-literals>.
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 (Ultra Performance Liquid Chromatography)
    and LCMS (Liquid chromatography–mass spectrometry)
    analysis of Plasma and IgG glycome.
Performs Granger mediation analysis (GMA) for time series. This package includes a single level GMA model and a two-level GMA model, for time series with hierarchically nested structure. The single level GMA model for the time series of a single participant performs the causal mediation analysis which integrates the structural equation modeling and the Granger causality frameworks. A vector autoregressive model of order p is employed to account for the spatiotemporal dependencies in the data. Meanwhile, the model introduces the unmeasured confounding effect through a nonzero correlation parameter. Under the two-level model, by leveraging the variabilities across participants, the parameters are identifiable and consistently estimated based on a full conditional likelihood or a two-stage method. See Zhao, Y., & Luo, X. (2017), Granger Mediation Analysis of Multiple Time Series with an Application to fMRI, <arXiv:1709.05328> for details.
Performs genomic mediation
    analysis with adaptive confounding adjustment (GMAC) proposed by Yang et al. (2017) <doi:10.1101/078683>. It implements large scale
    mediation analysis and adaptively selects potential confounding variables to
    adjust for each mediation test from a pool of candidate confounders. The package
    is tailored for but not limited to genomic mediation analysis (e.g., cis-gene
    mediating trans-gene regulation pattern where an eQTL, its cis-linking gene
    transcript, and its trans-gene transcript play the roles as treatment, mediator
    and the outcome, respectively), restricting to scenarios with the presence of
    cis-association (i.e., treatment-mediator association) and random eQTL (i.e.,
    treatment).
An interface to the Gmail RESTful API.  Allows access to your
    Gmail messages, threads, drafts and labels.
Contains many functions useful for univariate outlier detection, permutation-based t-test, permutation-based chi-square test, visualization of residuals, and bootstrap Cramer's V, plotting of the results of the Mann-Whitney and Kruskall-Wallis test, calculation of Brainerd-Robinson similarity coefficient and subsequent clustering, validation of logistic regression models, optimism-corrected AUC, robust Bland-Altman plot, calculation of posterior probability for different chronological relationships between two Bayesian radiocarbon phases, point pattern analysis, landform classification, clustering of spatial features.
Get distance and travel time between two points from Google Maps.
    Four possible modes of transportation (bicycling, walking, driving and
    public transportation).
Simulation of positive definite matrices constrained by an undirected or acyclic directed graph structure.
Unsupervised Clustering and Meta-analysis using Gaussian Mixture
    Copula Models.
Functions and a graphical user interface for graphical described
    multiple test procedures.
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.
Performs binary classification via Group Method of Data Handling (GMDH) - type neural network algorithms. There exist two main algorithms available in GMDH() and dceGMDH() functions. GMDH() performs classification via GMDH algorithm for a binary response and returns important variables. dceGMDH() performs classification via diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. Also, the package produces a well-formatted table of descriptives for a binary response. Moreover, it produces confusion matrix, its related statistics and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. All 'GMDH2' functions are designed for a binary response. See Dag O. and Yozgatligil C. (2016, ISSN:2073-4859) and Kondo T. and Ueno J. (2016, ISSN:1349-4198) for the details of GMDH algorithms.
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.
Current version of this R package conducts mediation path analysis for multiple mediators in two stages.
An implementation of an all-in-one function for a wide range of meta-analysis problems. It contains three functions. The gmeta() function 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). The np.gmeta() function summarizes information obtained from multiple studies and makes inference for study-level parameters with no distributional assumption. Specifically, it can construct confidence intervals for unknown, fixed study-level parameters via confidence distribution. Furthermore, it can perform  estimation via asymptotic confidence distribution whether tie or near tie condition exist or not. The plot.gmeta() function to visualize individual and combined CDs through extended forest plots is also available. Compared to version 2.2-6, version 2.3-0 contains a new function np.gmeta().
Some methods for the inference and clustering of univariate and 
             multivariate functional data, using a generalization of Mahalanobis
             distance, along with some functions useful for the analysis of functional data.
             For further details, see Martino A., Ghiglietti, A., Ieva, F. and Paganoni A. M. (2017) <arXiv:1708.00386>.
Tools for making the descriptive "Table 1" used in medical
    articles, a transition plot for showing changes between categories, 
    flow charts by extending the grid package, 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 (Hansen 1982; <doi:10.2307/1912775>), the iterated GMM and continuous
        updated estimator (Hansen, Eaton and Yaron 1996; <doi:10.2307/1392442>) and several
        methods that belong to the Generalized Empirical Likelihood
        family of estimators (Smith 1997; <doi:10.1111/j.0013-0133.1997.174.x>,
        Kitamura 1997; <doi:10.1214/aos/1069362388>, Newey and Smith 2004; <doi:10.1111/j.1468-0262.2004.00482.x>,
	and Anatolyev 2005 <doi:10.1111/j.1468-0262.2005.00601.x>).
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.
Make 2D plots of the polyeder of a LP or IP problem, including
    integer points and iso profit curve. Can also make a plot of a bi-objective
    criterion space.
Multiple Precision Arithmetic (big integers and rationals,
 prime number tests, matrix computation), "arithmetic without limitations"
 using the C library GMP (GNU Multiple Precision Arithmetic).
Integrates game theory and ecological theory to construct 
    social-ecological models that simulate the management of populations and 
    stakeholder actions. These models build off of a previously developed 
    management strategy evaluation (MSE) framework to simulate all aspects of 
    management: population dynamics, manager observation of populations, manager
    decision making, and stakeholder responses to management decisions. The 
    newly developed generalised management strategy evaluation (GMSE) 
    framework uses genetic algorithms to mimic the decision-making process of 
    managers and stakeholders under conditions of change, uncertainty, and 
    conflict. Simulations can be run using gmse(), gmse_apply(), and
    gmse_gui() functions.
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.
Maximum likelihood estimation of Gaussian Mixture Vector Autoregressive (GMVAR)
    model, quantile residual tests, graphical diagnostics, forecasting and simulations. Applying
    general linear constraints to the autoregressive parameters is supported.
    Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>.
Generalized Mann-Whitney type tests based on probabilistic
        indices and new diagnostic plots.
Simulation of, and fitting models for, Generalised Network Autoregressive (GNAR) time series models which take account of network structure.  Such models are described in Knight et al. (2016), see <arXiv:1603.03221>.
Provide functions to compute standard and generalized Nash Equilibria. 
 Optimization methods are available nonsmooth reformulation, fixed-point formulation, 
 minimization problem and constrained-equation reformulation. 
 See e.g. Kanzow and Facchinei (2010), <doi:10.1007/s10479-009-0653-x>.
Computes the test statistic and p-value of the Cramer-von Mises and Anderson-Darling test for some continuous distribution functions proposed by Chen and Balakrishnan (1995) <http://asq.org/qic/display-item/index.html?item=11407>. In addition to our classic distribution functions here, we  calculate the Goodness of Fit (GoF) test to dataset which follows the extreme value distribution function, without remembering the formula of distribution/density functions. Calculates the Value at Risk (VaR) and Average VaR are another important risk factors which are estimated by using well-known distribution functions. Pflug and Romisch (2007, ISBN: 9812707409) is a good reference to study the properties of risk measures.
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.
Functions for obtaining generalized normal/exponential power distribution probabilities, quantiles, densities and random deviates. The generalized normal/exponential power distribution was introduced by Subbotin (1923) and rediscovered by Nadarajah (2005). The parametrization given by Nadarajah (2005) <doi:10.1080/02664760500079464> is used.
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. Includes automatic species selection for ordination diagrams, species response curves and rank-abundance curves as well as calculation and sorting of synoptic tables.
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, logistic, Gamma, Weibull, Gumbel, and Rayleigh 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, log-normal, 'Weibull', 'Frechet', Gumbel, 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.
Extension of 'gof' package to survival models.
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.
A zero-inflated quasi-Poisson factor model to display similarity between samples visually in a low (2 or 3) dimensional space.
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).
Give advice about good practices when building R packages.
    Advice includes functions and syntax to avoid, package structure,
    code complexity, code formatting, etc.
Interact with the Google Analytics 
  APIs <https://developers.google.com/analytics/>, including 
  the Core Reporting API (v3 and v4), Management API, and Multi-Channel
  Funnel API.
Create R functions that interact with OAuth2 Google APIs 
    <https://developers.google.com/apis-explorer/> easily,
    with auto-refresh and Shiny compatibility.
Interact with Google Cloud Storage <https://cloud.google.com/storage/> 
  API in R. Part of the 'cloudyr' <https://cloudyr.github.io/> 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.
Allows you to retrieve information from the 'Google Knowledge Graph' API <https://www.google.com/intl/bn/insidesearch/features/search/knowledge.html> and process it in R in various forms. The 'Knowledge Graph Search' API lets you find entities in the 'Google Knowledge Graph'. The API uses standard 'schema.org' types and is compliant with the 'JSON-LD' specification.
Call 'Google Cloud' machine learning APIs for text and speech tasks.
  Call the 'Cloud Translation' API <https://cloud.google.com/translate/> for detection 
  and translation of text, the 'Natural Language' API <https://cloud.google.com/natural-language/> to 
  analyse text for sentiment, entities or syntax, the 'Cloud Speech' API 
  <https://cloud.google.com/speech/> to transcribe sound files to text and 
  the 'Cloud Text-to-Speech' API <https://cloud.google.com/text-to-speech/> to turn text 
  into sound files.
Interact with Google's Cloud Natural Language API
    <https://cloud.google.com/natural-language/> (v1) via R. The API has
    four main features, all of which are available through this
    R package: syntax analysis and part-of-speech tagging, entity
    analysis, sentiment analysis, and language identification.
Encodes simple feature ('sf') objects and coordinates, and decodes polylines 
    using the 'Google' polyline encoding algorithm (<https://developers.google.com/maps/documentation/utilities/polylinealgorithm>).
Allows printing documents from R through 'Google Cloud Print' API.
  See <https://developers.google.com/cloud-print/docs/overview> for more information
  about 'Google Cloud Print'.
Provides a collection of functions to set up 'Google Public Data Explorer'
  <https://www.google.com/publicdata/> data visualization tool with your own data,
  building automatically the corresponding DataSet Publishing Language file, or
  DSPL (XML), metadata file jointly with the CSV files. All zip-up and ready to
  be published in '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.
Provides a mechanism to plot a 'Google Map' from 'R' and overlay
    it with shapes and markers. Also provides access to 'Google Maps' APIs,
    including places, directions, roads, distances, geocoding, elevation and
    timezone.
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 linear models and generalised linear 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.
Functions that compute the distribution functions for the Generalized Poisson Binomial distribution, which provides the cdf, pmf, quantile function, and random number generation for the distribution.
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
Provides a function to detect Differential Item Functioning (DIF) in Generalized Partial Credit Models (GPCM) and special cases of the GPCM. A joint model is set up where DIF is explicitly parametrized and penalized likelihood estimation is used for parameter selection. The big advantage of the method called GPCMlasso is that several variables can be treated simultaneously and that both continuous and categorical variables can be used to detect DIF.
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.
Sequential strategies for finding a game equilibrium are proposed in a black-box setting (expensive pay-off evaluations, no derivatives). The algorithm handles noiseless or noisy evaluations. Two acquisition functions are available. Graphical outputs can be generated automatically.
Functions for reordering
    input locations, finding ordered nearest neighbors
    (with help from 'FNN' package), grouping operations, 
    approximate likelihood evaluations, profile likelihoods,
    Gaussian process predictions, and conditional simulations.
    Covariance functions for spatial and spatial-temporal data
    on Euclidean domains and spheres are provided. The original 
    approximation is due to Vecchia (1988) 
    <http://www.jstor.org/stable/2345768>, and the reordering and
    grouping methods are from Guinness (2018)
    <doi:10.1080/00401706.2018.1437476>.
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.
A general and efficient package for modeling (possibly noisy) datasets via Gaussian processes.
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}]
Platform dedicated to the Global Modelling technique. Its aim is to
    obtain ordinary differential equations of polynomial form directly from
    time series. It can be applied to single or multiple time series under various
    conditions of noise, time series lengths, sampling, etc. This platform
    is developped at the Centre d'Etudes Spatiales de la Biosphere (CESBIO), UMR 5126
    UPS/CNRS/CNES/IRD, 18 av. Edouard Belin, 31401 TOULOUSE, FRANCE.
    The developments were funded by the French program Les Enveloppes
    Fluides et l'Environnement (LEFE, MANU, projets GloMo, SpatioGloMo and MoMu).
    The French program Defi InFiNiTi (CNRS) and PNTS are also acknowledged
    (projects Crops'IChaos and Musc & SlowFast).
Provides an implementation of Gaussian process panel modeling (GPPM). 
              GPPM is described in Karch (2016; <DOI:10.18452/17641>) and Karch, Brandmaier & Voelkle (2018; <DOI:10.17605/OSF.IO/KVW5Y>).
              Essentially, GPPM is Gaussian process based modeling of longitudinal panel data.
              'gppm' also supports regular Gaussian process regression (with a focus on flexible model specification), and multi-task learning.
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.
A Bayesian statistical model for estimating child (under-five age group) and adult (15-60 age group) mortality.  The main challenge is how to combine and integrate these different time series and how to produce unified estimates of mortality rates during a specified time span. GPR is a Bayesian statistical model for estimating child and adult mortality rates which its data likelihood is mortality rates from different data sources such as: Death Registration System, Censuses or surveys. There are also various hyper-parameters for completeness of DRS, mean, covariance functions and variances as priors. This function produces estimations and uncertainty (95% or any desirable percentiles) based on sampling and non-sampling errors due to variation in data sources. The GP model utilizes Bayesian inference to update predicted mortality rates as a posterior in Bayes rule by combining data and a prior probability distribution over parameters in mean, covariance function, and the regression model. This package uses Markov Chain Monte Carlo (MCMC) to sample from posterior probability distribution by 'rstan' package in R. Details are given in Wang H, Dwyer-Lindgren L, Lofgren KT, et al. (2012) <doi:10.1016/S0140-6736(12)61719-X>, Wang H, Liddell CA, Coates MM, et al. (2014) <doi:10.1016/S0140-6736(14)60497-9> and Mohammadi, Parsaeian, Mehdipour et al. (2017) <doi:10.1016/S2214-109X(17)30105-5>.
Functional enrichment analysis, gene identifier conversion and
    mapping homologous genes across related organisms via the 'g:Profiler' toolkit
    (<https://biit.cs.ut.ee/gprofiler/>).
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.
An EM algorithm, Karl et al. (2013) <doi:10.1016/j.csda.2012.10.004>, is used to estimate the generalized, variable, and complete persistence models, Mariano et al. (2010) <doi:10.3102/1076998609346967>. These are multiple-membership linear mixed models with teachers modeled as "G-side" effects and students modeled with either "G-side" or "R-side" effects.
Server implementation of 'GraphQL' <http://facebook.github.io/graphql/>,
    a query language created by Facebook for describing data requirements on complex application
    data models.  Visit <http://graphql.org> to learn more about 'GraphQL'.
Genomic biology is not limited to the confines of the canonical B-
    forming DNA duplex, but includes over ten different types of other secondary
    structures that are collectively termed non-B DNA structures. Of these non-B
    DNA structures, the G-quadruplexes are highly stable four-stranded structures
    that are recognized by distinct subsets of nuclear factors. This package
    provide functions for predicting intramolecular G quadruplexes. In addition, 
    functions for predicting other intramolecular nonB DNA structures are included.
Use the graph-constrained estimation (Grace) procedure (Zhao and Shojaie, 2016 <doi:10.1111/biom.12418>) 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), which is an optimization to use training data partially to reduce the computation load.
	Stochastic Gradient Descent (SGD), which is an optimization to use a random data in learning to reduce the computation load drastically.
	Stochastic Average Gradient (SAG), which is a SGD-based algorithm to minimize stochastic step to average.
	Momentum Gradient Descent (MGD), which is an optimization to speed-up gradient descent learning.
	Accelerated Gradient Descent (AGD), which is an optimization to accelerate gradient descent learning.
	Adagrad, which is a gradient-descent-based algorithm that accumulate previous cost to do adaptive learning.
	Adadelta, which is a gradient-descent-based algorithm that use hessian approximation to do adaptive learning.
	RMSprop, which is a gradient-descent-based algorithm that combine Adagrad and Adadelta adaptive learning ability.
	Adam, which is a gradient-descent-based algorithm that mean and variance moment to do adaptive learning.
	Stochastic Variance Reduce Gradient (SVRG), which is an optimization SGD-based algorithm to accelerates the process toward converging by reducing the gradient.
	Semi Stochastic Gradient Descent (SSGD),which is a SGD-based algorithm that combine GD and SGD to accelerates the process toward converging by choosing one of the gradients at a time.
	Stochastic Recursive Gradient Algorithm (SARAH), which is an optimization algorithm similarly SVRG to accelerates the process toward converging by accumulated stochastic information.
	Stochastic Recursive Gradient Algorithm+ (SARAHPlus), which is a SARAH practical variant algorithm to accelerates the process toward converging provides a possibility of earlier termination.
Provides functions for matching student-answers to teacher answers for a variety of data types.
Widget for an interactive selection and modification of a color gradient. 'gradientPickerD3' allows addition, removement and replacement of color ticks. List of numeric values will automatically translate in their corresponding tick position within the numeric range. App returns a data.frame containing tick values, colors and the positions in percent (0.0 to 1.0) for each color tick in the gradient. The original JS 'jquery-gradient-picker' was implemented by Matt Crinklaw-Vogt (nick: tantaman) <https://github.com/tantaman/>. Widget and JS modifications were done by CD. Peikert.
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.
Provides the classes and methods for GRANRepository
    objects that are used within the 'GRAN' build framework for R packages.
    This is primarily used by the 'GRANBase' package and repositories
    that are created by it.
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/.
Turn arbitrary functions into binary operators.
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.
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.
Set of functions for step-wise generation of (weighted) graphs. Aimed for research in the field of single- and multi-objective combinatorial optimization. Graphs are generated adding nodes, edges and weights. Each step may be repeated multiple times with different predefined and custom generators resulting in high flexibility regarding the graph topology and structure of edge weights.
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>.
A 'sparklyr' <https://spark.rstudio.com/> extension that provides an R 
  interface for 'GraphFrames' <https://graphframes.github.io/>. 'GraphFrames' is a package
  for 'Apache Spark' that provides a DataFrame-based API for working with graphs. Functionality
  includes motif finding and common graph algorithms, such as PageRank and Breadth-first
  search.
Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.
Functions to generate
             graphics files, compare them with "model" files,
             and report the results, including visual and textual
             diffs of any differences.
A fast C++ implementation for computing various graph kernels including (1) simple kernels between vertex and/or edge label histograms, (2) graphlet kernels, (3) random walk kernels (popular baselines), and (4) the Weisfeiler-Lehman graph kernel (state-of-the-art).
Provides a not-so-comprehensive list of methods for estimating graphon,
    a symmetric measurable function, from a single or multiple of observed networks. 
    For a detailed introduction on graphon and popular estimation techniques, 
    see the paper by Orbanz, P. and Roy, D.M.(2014) <doi:10.1109/TPAMI.2014.2334607>.
    It also contains several auxiliary functions for generating sample networks
    using various network models and graphons.
Histogram principal components analysis is the generalization of the PCA. Histogram data are adapted to design complex and big data which histograms used as variables (big data adapter). Functions implemented provides numerical and graphical tools of an extension of PCA. Sun Makosso Kallyth (2016) <doi:10.1002/sam.11270>. Sun Makosso Kallyth and Edwin Diday (2012) <doi:10.1007/s11634-012-0108-0>.
Bindings to the 'libgraphqlparser' C++ library. Parses GraphQL syntax
    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.
Utilities for costing and evaluating Australian tax policy, including high-performance tax and transfer calculators, a fast method of projecting tax collections, and an interface to common indices from the Australian Bureau of Statistics.  Written to support Grattan Institute's Australian Perspectives program. For access to the 'taxstats' package, please run
 install.packages("taxstats", repos = "https://hughparsonage.github.io/tax-drat/", type = "source"). 
 N.B. The 'taxstats' package is approximately 50 MB.
A wrapper of different standard estimation methods for gravity models. 
  This package provides estimation methods for log-log models and multiplicative models.
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.
We implement two main functions.
    The first function uses a given grouped and/or
    right-censored grouping scheme and empirical data to infer parameters,
    and implements chi-square goodness-of-fit tests.
    The second function searches for the global optimal grouping
    scheme of grouped and/or right-censored count responses in surveys.
Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.
Summarises a collection of partitions into a single optimal partition. The objective function is the expected posterior loss, and the minimisation is performed through a greedy algorithm described in Rastelli, R. and Friel, N. (2016) "Optimal Bayesian estimators for latent variable cluster models" <arXiv:1607.02325>.
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.
    Additional functionality includes using branch and bound optimization 
    (via Gurobi) and exhaustive enumeration.
These are GreedyExperimentalDesign Java dependency libraries. Note: this package has no functionality of its own and should not be installed as a standalone package without GreedyExperimentalDesign.
Performs clustering on the nodes of an undirected binary dynamic network, by maximising the exact integrated complete likelihood. The greedy algorithm used is described in Rastelli, R. (2017) "Exact integrated completed likelihood maximisation in a stochastic block transition model for dynamic networks" <arXiv:1710.03551>.
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 descriptive methods, there are add-ons 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-splitting a dataset when modeling non-proportional hazards in Cox regressions.
Fit linear mixed-effects models using restricted (or residual)
    maximum likelihood (REML) and with generalized inverse matrices to specify
    covariance structures for random effects. In particular, the package is suited
    to fit quantitative genetic mixed models, often referred to as 'animal models' 
    (Kruuk. 2004 <DOI: 10.1098/rstb.2003.1437>). Implements the average
    information algorithm as the main tool to maximize the restricted likelihood,
    but with other algorithms available (Meyer. 1997. Genet Sel Evol 29:97; Meyer
    and Smith. 1998. Genet Sel Evol 28:23.).
Allows the user to incorporate multiple sources of co-data 	
	(e.g., 	previously obtained p-values, published gene lists, and annotation) in the	estimation of a logistic regression model to enhance predictive performance and 	feature selection, as described in Münch, Peeters, van der Vaart, and van de Wiel 	(2018) <arXiv:1805.00389>.
An R based pipeline to download and process Gene Expression Omnibus (GEO) RNA-seq data. 
    For a given GEO series accession ID, this pipeline generates metadata, 
    both gene and transcript level counts, and quality control (QC) report. 
	This package is mainly developed to process the GEO RNA-seq datasets 
	for the web platform GREIN. More details about GREP2 and GREIN can be 
	found here <doi:10.1101/326223>.
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.
Write statistical models in R and fit them by MCMC on CPUs and GPUs, using Google TensorFlow (see <https://greta-dev.github.io/greta> for more information).
Convert 'Ensembl' gene identifiers from Genotype-Tissue
    Expression (GTEx) data to identifiers in other annotation systems,
    including 'Entrez', 'HGNC', and 'UniProt'.
Implements functions and instruments for regression model building and its
             application to forecasting. The main scope of the package is in variables selection
             and models specification for cases of time series data. This includes promotional
             modelling, selection between different dynamic regressions with non-standard
             distributions of errors, selection based on cross validation, solutions to the fat
             regression model problem and more. Models developed in the package are tailored
             specifically for forecasting purposes. So as a results there are several methods
             that allow producing forecasts from these models and visualising them.
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.
A pluggable package for forest-based statistical estimation and inference.
    GRF currently provides methods for non-parametric least-squares regression,
    quantile regression, and treatment effect estimation (optionally using instrumental
    variables). This package is currently in beta, and we expect to make continual
    improvements to its performance and usability.
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 surveys of households are commonly performed by
  governments and programmes to monitor population-level demographic, social,
  economic, and health outcomes. Generally, communities are sampled from
  subpopulations (strata) in a first stage, and then households are listed and
  sampled in a second stage. In this typical two-stage design, sampled
  communities are the Primary Sampling Units (PSUs) and households are the
  Secondary Sampling Units (SSUs). Census data typically serve as the sample
  frame from which PSUs are selected. However, if census data are outdated
  inaccurate, or too geographically course, gridded population data (such as
  <http://www.worldpop.org.uk>) can be used as a sample frame instead.
  GridSample (<doi:10.1186/s12942-017-0098-4>) generates PSUs from
  gridded population data according to user-specified complex survey design
  characteristics and household sample size. In gridded population sampling,
  like census sampling, PSUs are selected within each stratum using a
  serpentine sampling method, and can be oversampled in urban or rural areas to
  ensure a minimum sample size in each of these important sub-domains.
  Furthermore, because grid cells are uniform in size and shape, gridded
  population sampling allows for samples to be representative of both the
  population and of space, which is not possible with a census sample frame.
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.
Provides the following types of 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.
Functions for importing external vector images and
  drawing them as part of 'R' plots.  This package is different from the
  'grImport' package because, where that package imports 'PostScript'
  format images, this package imports 'SVG' format images.  Furthermore,
  this package imports a specific subset of 'SVG', so external images
  must be preprocessed using a package like 'rsvg' to produce 'SVG'
  that this package can import.  'SVG' features that are not supported
  by 'R' graphics, e.g., gradient fills, can be imported and then
  exported via the 'gridSVG' package.
Calculates optical parameters of crystals like the optical
    axes, the axis angle 2V, and the direction of the principal axes of
    the indicatrix from extinction angles measured on a spindle stage 
    mounted on a polarisation microscope stage. Details of the method can be found in Dufey (2017) <arXiv:1703.00070>.
The program GRNN implements the algorithm proposed by
        Specht (1991).
Workbench for testing genomic regression accuracy on (optionally noisy) phenotypes.
Robust multiple or multivariate linear regression, nonparametric regression on orthogonal components, classical or robust partial least squares models.
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.
Receives two vectors, computes appropriate function for group comparison (i.e., t-test, Mann-Whitney; equality of variances), and reports the findings (mean/median, standard deviation, test statistic, p-value, effect size) in APA format (Fay, M.P., & Proschan, M.A. (2010)<DOI: 10.1214/09-SS051>).
Subsetting methods for balanced cross-validation, time series windowing,
    and general grouping and splitting of data.
Regression models for grouped and coarse data, under the
        Coarsened At Random assumption.
Collection of functions to run statistical tests across all levels
  of multiple grouping variables.
These 'Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the 'Cuba' library (Hahn, 2005), and the source files 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, 2002).
Contains an implementation of an independent component analysis (ICA) for grouped data. The main function groupICA() performs a blind source separation, by maximizing an independence across sources and allows to adjust for varying confounding for user-specified groups. Additionally, the package contains the function uwedge() which can be used to approximately jointly diagonalize a list of matrices. For more details see the project website <https://sweichwald.de/groupICA/>.
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.
Group subset selection for linear regression models is provided in this package. Given response variable, and explanatory variables, which are organised in groups, group subset selection selects a small number of groups to explain response variable linearly using least squares.
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.
Functional denoising and functional ANOVA through wavelet-domain 
  Markov groves. Fore more details see: Ma L. and Soriano J. (2016) 
  Efficient functional ANOVA through wavelet-domain Markov groves. 
  <arXiv:1602.03990v2 [stat.ME]>.
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.
Efficient algorithms for fitting the regularization path of linear
  regression, GLM, and Cox 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, finding common patterns
    in a group of strings, discovering featured patterns when comparing two groups of strings as well
    as the number and starting position of each pattern in each string, obtaining transition matrix, 
    computing transition entropy, statistically comparing the difference between two groups of strings,
    and clustering string groups. Event names can be any action names or labels such as events in log
    files or areas of interest (AOIs) in eye tracking research.
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>.
	It also has the functions for the power analysis and the choice of the optimal level of significance.
	The optimal level is determined by minimizing the expected loss from hypothesis testing.
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.
Provides the p-value for a joint test of association between a phenotype and a set of genetic variants (SNPs) by combining marginal p-values using the Fisher method. See Fisher, R.A. (1925,ISBN:0-05-002170-2) Statistical Methods for Research Workers.
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.
Design and analysis of group sequential designs with negative
    binomial outcomes, as described by T Muetze, E Glimm, H Schmidli, T Friede (2017) <arXiv:1707.04612>.
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.
Contains published data sets for global benthic d18O data for 
    0-5.3 Myr <doi:10.1029/2004PA001071> and global sea levels based 
    on marine sediment core data for 0-800 ka <doi:10.5194/cp-12-1-2016>.
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.
Provides automated downloading, parsing, cleaning, unit conversion
    and formatting of Global Surface Summary of the Day ('GSOD') weather data
    from the from the USA National Centers for Environmental Information
    ('NCEI') for use in R.  Units are converted from from United States
    Customary System ('USCS') units to International System of Units ('SI').
    Stations may be individually checked for number of missing days defined by
    the user, where stations with too many missing observations are omitted.
    Only stations with valid reported latitude and longitude values are
    permitted in the final data.  Additional useful elements, saturation vapour
    pressure ('es'), actual vapour pressure ('ea') and relative humidity are
    calculated from the original data and included in the final data set.  The
    resulting data include station identification information, state, country,
    latitude, longitude, elevation, weather observations and associated flags.
    Additional data are included with this R package: a list of elevation values
    for stations between -60 and 60 degrees latitude derived from the Shuttle
    Radar Topography Measuring Mission ('SRTM').  For information on the 'GSOD'
    data from 'NCEI', please see the 'GSOD' 'readme.txt' file available from,
    <http://www1.ncdc.noaa.gov/pub/data/gsod/readme.txt>.
Approaches a group sparse solution of an underdetermined linear system. It implements the proximal gradient algorithm to solve a lower regularization model of group sparse learning. For details, please refer to the paper "Y. Hu, C. Li, K. Meng, J. Qin and X. Yang. Group sparse optimization via l_{p,q} regularization. Journal of Machine Learning Research, to appear, 2017".
A gate-keeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks. Computations related to group sequential primary and secondary boundaries. Refined secondary boundaries are calculated for a gate-keeping test on a primary and a secondary endpoint in a group sequential design with multiple interim looks. The choices include both the standard boundaries and the boundaries using error spending functions. Version 1.0.0 was released on April 12, 2017. See Tamhane et al. (2017+) "A gatekeeping procedure to test a primary and a secondary endpoint in a group sequential design with multiple interim looks", Biometrics, to appear.
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.
Uses an approach based on k-nearest neighbor information to sequentially detect change-points. Offers analytic approximations for false discovery control given user-specified average run length.  Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available.
The gsubfn function 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.
   There is also a facility for returning multiple objects from functions
   and a version of transform that allows the RHS to refer to LHS used in
   the same transform.
Provides an interface to the Gibbs 'SeaWater' ('TEOS-10') C
    library, version 3.05-4 (commit '5b4d959e54031f9e972f3e863f63e67fa4f5bfec', dated 2017-08-07,
    available at <https://github.com/TEOS-10/GSW-C>, which stems from
    'Matlab' and other code written by members of 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.
Provides causal inference with interactive fixed-effect models. It imputes counterfactuals for each treated unit using control group information based on a linear interactive fixed effects model that incorporates unit-specific intercepts interacted with time-varying coefficients. This method generalizes the synthetic control method to the case of multiple treated units and variable treatment periods, and improves efficiency and interpretability. This version supports unbalanced panels and implements the matrix completion method. Main reference: Yiqing Xu (2017) <doi:10.1017/pan.2016.2>.
Provides tools to measure the reliability of an Information Retrieval test collection.
  It allows users to estimate reliability using Generalizability Theory and map those estimates onto
  well-known indicators such as Kendall tau correlation or sensitivity.
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).
Four graph-based tests are provided for testing whether two samples are from the same distribution.  It works for both continuous data and discrete data.
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 ('asc', 'chr'),
  - convert character vector to ASCII representation ('ASCIIfy').
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.
Quantitative trait loci mapping and genome wide association analysis are used to find candidate molecular marker or region associated with phenotype based on linkage analysis and linkage disequilibrium. Gene expression quantitative trait loci mapping is used to find candidate molecular marker or region associated with gene expression. In this package, we applied the method in Liu W. (2011) <doi:10.1007/s00122-011-1631-7> and Gusev A. (2016) <doi:10.1038/ng.3506> to genome and transcriptome wide association study, which is aimed at revealing the association relationship between phenotype and molecular markers, expression levels, molecular markers nested within different related expression effect and expression effect nested within different related molecular marker effect. F test based on full and reduced model are performed to obtain p value or likelihood ratio statistic. The best linear model can be obtained by stepwise regression analysis.
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 graphical user interface for the R package Gems.  
    Apart from the functionality of Gems package in the Graphical User interface, GUIgems
    allows adding states to a defined model, merging states for the analysis and plotting 
    progression paths between states based on the simulated cohort.
    There is also a module in the GUIgems which allows to compare costs and QALYs between different cohorts.
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 distances 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.
Provides a toolkit for analytical variance estimation in survey sampling. Apart from the implementation of standard variance estimators, its main feature is to help the sampling expert produce easy-to-use variance estimation "wrappers", where systematic operations (linearization, domain estimation) are handled in a consistent and transparent way.
Download and process public domain works in the Project
    Gutenberg collection <http://www.gutenberg.org/>. 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.
Functionalities to compute model based genetic components i.e. genotypic variance, phenotypic variance and heritability for given traits of different genotypes from replicated data using methodology explained by Burton, G. W. & Devane, E. H. (1953) (<doi:10.2134/agronj1953.00021962004500100005x>) and Allard, R.W. (2010, ISBN:8126524154).
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).
Performs variable selection with data from Genome-wide association studies (GWAS) combining, in an iterative variable selection framework, the computational efficiency of the screen-and-select approach based on some association learning and the parsimonious uncertainty quantification provided by the use of nonlocal priors, as described in Sanyal et al. (2018).
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 RGtk2 toolkit.
Port of the gWidgets API to the tcltk package. Requires Tk 8.5 or greater.
Fit a geographically weighted logistic elastic net regression. Detailed explanations can be found in Yoneoka et al. (2016): New algorithm for constructing area-based index with geographical heterogeneities and variable selection: An application to gastric cancer screening <doi:10.1038/srep26582>.
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).
Computes the sample probability value (p-value)
    for the estimated coefficient from a standard
    genome-wide univariate regression.  It computes the exact
    finite-sample p-value under the assumption that the measured
    phenotype (the dependent variable in the regression) has
    a known Bernoulli-normal mixture distribution.
    Finite-sample genome-wide regression p-values (Gwrpv) with
    a non-normally distributed phenotype (Gregory Connor and
    Michael O'Neill, bioRxiv 204727 <doi:10.1101/204727>).
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.
Shiny application for the analysis of groundwater monitoring data, designed to work with simple time-series data for solute concentration and ground water elevation, but can also plot non-aqueous phase liquid (NAPL) thickness if required. Also provides the import of a site basemap in GIS shapefile format.
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 interface for 'H2O', the scalable open source machine learning
    platform that offers parallelized implementations of many supervised and
    unsupervised machine learning algorithms such as Generalized Linear
    Models, Gradient Boosting Machines (including XGBoost), Random Forests,
    Deep Neural Networks (Deep Learning), Stacked Ensembles, Naive Bayes, Cox
    Proportional Hazards, K-Means, PCA, Word2Vec, as well as a fully automatic
    machine learning algorithm (AutoML).
Interface to 'H2O4GPU' <https://github.com/h2oai/h2o4gpu>, a collection of 'GPU' solvers for machine learning algorithms.
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
Simple tools for converting columns to new data types. Intuitive summary functions.
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.
There are growing concerns on flow data in diverse fields including trade, migration, knowledge diffusion, disease spread, and transportation. The package is an effective visual support to learn the pattern of flow which is called halfcircle diagram. The flow between two nodes placed on the center line of a circle is represented using a half circle drawn from the origin to the destination in a clockwise direction. Through changing the order of nodes, the halfcircle diagram enables users to examine the complex relationship between bidirectional flow and each potential determinants. Furthermore, the halfmeancenter function, which calculates (un) weighted mean center of half circles, makes the comparison easier.
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, 
  as well as some functions designed to retrieve specific pieces of commonly 
  used data. Functions for each new API will be added as and when they become
  available.
Functions to perform ROR for sequence-based association
        analysis
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).
Inference of chromosome-length haplotypes using a few haploid 
	gametes of an individual. The gamete genotype data may be generated from various platforms 
	including genotyping arrays and sequencing even with low-coverage. Hapi simply takes 
	genotype data of known hetSNPs in single gamete cells as input and report the high-resolution 
	haplotypes as well as confidence of each phased hetSNPs. The package also includes a module 
	allowing downstream analyses and visualization of identified crossovers in the gametes.
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. The models were originally described in Gjessing, HK and Lie, RT (2006) <doi:10.1111/j.1529-8817.2005.00218.x>.
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.
A set of utilities for querying 
             'HaploReg' <http://archive.broadinstitute.org/mammals/haploreg/haploreg.php>, 
             'RegulomeDB' <http://www.regulomedb.org>, and LDlink <https://analysistools.nci.nih.gov/LDlink>
             web-based tools. The package connects to 
             'HaploReg', 'RegulomeDB' and 'LDlink', 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.
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.
Software creation is a pretty big deal!  A repository's initial commit date can be thought of as its birthday.  Next time, drop in and wish a developer (any Github username) a happy birthday of their repo(s).  Or maybe just toast to the upcoming anniversary of your own software!  The software life cycle is too short not to celebrate!
There are two interesting games in this package, one is 2048 games(for windows), using up and down to control the direction until there is a 2048 figure. And the other is 'what to eat today',preparing for people who choose difficulties, including most of the delicious Cantonese cuisine.
Package for haplotype-based genotype simulations. Haplotypes are
        generated such that their allele frequencies and linkage
        disequilibrium coefficients match those estimated from an input
        data set.
Contains tools for exploring Hardy-Weinberg equilibrium (Hardy, 1908;  Weinberg, 1908) <doi:10.1126/science.28.706.49> for bi and multi-allelic genetic marker data. All classical tests (chi-square, exact, likelihood-ratio and permutation tests) with bi-allelic variants 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 (Graffelman & Weir, 2016) <doi: 10.1038/hdy.2016.20>, including Bayesian procedures. Some exact and permutation procedures also work with multi-allelic variants. Special test procedures that jointly address Hardy-Weinberg equilibrium and equality of allele frequencies in both sexes are supplied, for the bi and multi-allelic case. 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 bi-allelic markers: ternary plots with acceptance regions, log-ratio plots and Q-Q plots.
Estimation, simulation, and forecasting using the HAR model from Corsi(2009) <DOI:10.1093/jjfinec/nbp001>.
The harmonic mean p-value (HMP) test simply and instantly combines p-values and corrects for multiple testing while controlling the family-wise error rate in a way that is more powerful than common alternatives including Bonferroni and Simes procedures, more stringent than controlling the false discovery rate, and is robust to positive correlations between tests and unequal weights. It is a multi-level test in the sense that a superset of one or more significant tests is almost certain to be significant and conversely when the superset is non-significant, the constituent tests are almost certain to be non-significant. It is based on MAMML (model averaging by mean maximum likelihood), a frequentist analogue to Bayesian model averaging, and is theoretically grounded in generalized central limit theorem.
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.
Harriet was Charles Darwin's pet tortoise (possibly). 'harrietr'
    implements some function to manipulate distance matrices and phylogenetic trees
    to make it easier to plot with 'ggplot2' and to manipulate using 'tidyverse'
    tools.
Implementation of the characteristic palette present in all Harry Potter movies. Colours obtained by averaging the mean colour from every scene of each movie and from the main colours of each house of Hogwarts.
The goal of 'HARtools' is to provide a simple set of functions
    to read/parse, write and visualise HTTP Archive ('HAR') files in R.
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.
Functions for easy and reproducible simulation.
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.
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.
Provides a set of functions to estimate haziness of an image based on RGB bands. It returns a haze factor, varying from 0 to 1, a metric for fogginess and cloudiness. The package also presents additional functions to estimate brightness, darkness and contrast rasters of the RGB image. This package can be used for several applications such as inference of weather quality data and performing environmental studies from interpreting digital images.
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.
Implements our hierarchical Bayesian design for randomized clinical trials.
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.
Software for performing the reduction, exploratory and model selection phases of the procedure proposed by Cox, D.R. and Battey, H.S. (2017) <doi:10.1073/pnas.1703764114> for sparse regression when the number of potential explanatory variables far exceeds the sample size. The software supports linear regression, likelihood-based fitting of generalized linear regression models and the proportional hazards model fitted by partial likelihood.
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.
This code provides a method to fit the hidden compact representation model as well as to identify the causal direction on discrete data. 
    We implement an effective solution to recover the above hidden compact representation under the likelihood framework. 
    Please see the Causal Discovery from Discrete Data using Hidden Compact Representation from NIPS 2018 by Ruichu Cai, Jie Qiao, Kun Zhang, Zhenjie Zhang and Zhifeng Hao (2018) <https://nips.cc/Conferences/2018/Schedule?showEvent=11274> for a description of some of our methods.
Given a database of previous treatment/placebo estimates, their standard errors and sample sizes,
      the program calculates a significance criteria and power estimate that takes into account the among
      trial variation.
Functions to perform dimensionality reduction for classification if the covariance matrices of the classes are unequal.
Binary segmentation methods for detecting and estimating 
	multiple change-points in the mean or second-order structure 
	of high-dimensional time series as described in Cho and Fryzlewicz (2014) <doi:10.1111/rssb.12079> and Cho (2016) <doi:10.1214/16-EJS1155>.
Fits regression models on high dimensional data to estimate coefficients and use bootstrap method to obtain confidence intervals. Choices for regression models are Lasso, Lasso+OLS, Lasso partial ridge, Lasso+OLS partial ridge.
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.
Clustering of high dimensional data with Hidden Markov Model on Variable Blocks (HMM-VB) fitted via Baum-Welch algorithm. Clustering is performed by the Modal Baum-Welch algorithm (MBW), which finds modes of the density function. Lin Lin and Jia Li (2017) <http://jmlr.org/papers/v18/16-342.html>.
Objective: Implement new methods for detecting change points in high-dimensional time series data. These new methods can be applied to non-Gaussian data, account for spatial and temporal dependence, and detect a wide variety of change-point configurations, including changes near the boundary and changes in close proximity. Additionally, this package helps address the “small n, large p” problem, which occurs in many research contexts. This problem arises when a dataset contains changes that are visually evident but do not rise to the level of statistical significance due to the small number of observations and large number of parameters. The problem is overcome by treating the dimensions as a whole and scaling the test statistics only by its standard deviation, rather than scaling each dimension individually. Due to the computational complexity of the functions, the package runs best on datasets with a relatively large number of attributes but no more than a few hundred observations.
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 are intended for didactic use. The  package implements,
  and extends, methods described in J.H. Maindonald and C.J. Burden (2005)
  <https://journal.austms.org.au/V46/CTAC2004/Main/home.html>.
Facilitates discovery and handling of hydrological data, access to catalogues and databases.
'HDF5' is a data model, library and file format for storing 
    and managing large amounts of data. This package provides a nearly
    feature complete, object oriented  wrapper for the 'HDF5' API
    <https://support.hdfgroup.org/HDF5/doc/RM/RM_H5Front.html> using R6 classes. 
    Additionally, functionality is added so that 'HDF5' objects behave very 
    similar to their corresponding R counterparts.
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.
    Chernozhukov, Hansen, Spindler (2016) <arXiv:1603.01700>.
Penalized regression for generalized linear models for
  measurement error problems (aka. errors-in-variables). The package
  contains a version of the lasso (L1-penalization) which corrects
  for measurement error (Sorensen et al. (2015) <doi:10.5705/ss.2013.180>). 
  It also contains an implementation of the Generalized Matrix Uncertainty 
  Selector, which is a version the (Generalized) Dantzig Selector for the 
  case of measurement error (Sorensen et al. (2018) <doi:10.1080/10618600.2018.1425626>).
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.
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.
A machine learning toolbox tailored to healthcare data.
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.
Allows heatmap matrix to have non-identical X- and
        Y-dimensions.  Allows multiple tracks of annotation for
        RowSideColors and ColSideColors.
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 cluster 'heatmaps' that can be saved as a stand-
    alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and
    available in the 'RStudio' viewer pane. 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 'heatmap.2' or 'd3heatmap', with the advantage of speed
    ('plotly.js' is able to handle larger size matrix), the ability to zoom from
    the 'dendrogram' panes, and the placing of factor variables in the sides of the
    'heatmap'.
The different methods of defining and detecting extreme events, known as heatwaves or cold-spells in both air and water temperature data are encompassed within this package. These detection algorithms may be used on non-temperature data as well however, this is not catered for explicitly here as no use of this technique in the literature currently exists.
Functions to perform robust estimation considering heavy-tailed distributions.
    Currently, the package includes routines for linear regression, linear mixed-effect
    models, multivariate location and scatter estimation, multivariate regression,
    penalized splines, random variate generation and some support functions.
Hedgehog will eat all your bugs.
  'Hedgehog' is a property-based testing package in the spirit
  of 'QuickCheck'. With 'Hedgehog', one can test properties
  of their programs against randomly generated input, providing
  far superior test coverage compared to unit testing. One of the
  key benefits of 'Hedgehog' is integrated shrinking of
  counterexamples, which allows one to quickly find the cause of
  bugs, given salient examples when incorrect behaviour occurs.
An implementation of the modelling and
    reporting features described in reference textbooks
    and guidelines: deterministic and probabilistic
    sensitivity analysis, heterogeneity analysis, time
    dependency on state-time and model-time (semi-Markov
    and non-homogeneous Markov models), etc.
Decode elements of the Australian Higher Education Information Management System (HEIMS) data for clarity and performance. HEIMS is the record system of the Department of Education, Australia to record enrolments and completions in Australia's higher education system, as well as a range of relevant information. For more information, including the source of the data dictionary, see <http://heimshelp.education.gov.au/sites/heimshelp/dictionary/pages/data-element-dictionary>.
Create publication-quality, 2-dimensional visualizations of alpha-helical peptide
  sequences. Specifically, allows the user to programmatically generate
  helical wheels and wenxiang diagrams to provide a bird's eye, top-down view of
  alpha-helical oligopeptides. See Wadhwa RR, et al. (2018)
  <doi:10.21105/joss.01008> for more information.
Empirical value of the Hellinger correlation, which is a new measure of dependence between
	     two continuous random variables that satisfies a set of 7 axioms that
	     any valid measure of dependence should satisfy. More details can be found in Geenens and Lafaye De Micheaux (2018) <arXiv:1810.10276>.
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.
Access to large host-parasite data is often hampered by the 
  availability of data and difficulty in obtaining it in a programmatic way
  to encourage analyses. 'helminthR' provides a programmatic interface to the 
  London Natural History Museum's host-parasite database, one of the largest 
  host-parasite databases existing currently <http://www.nhm.ac.uk/research-curation/scientific-resources/taxonomy-systematics/host-parasites/>. The package allows the user
  to query by host species, parasite species, and geographic location.
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 (<http://ptp.hel.fi/avoindata/>),
    Service Map API (<http://api.hel.fi/servicemap/v1/>),
    Linked Events API (<http://api.hel.fi/linkedevents/v0.1/>),
    Helsinki Region Infoshare statistics API (<https://dev.hel.fi/stats/>).
An implementation of Hierarchical Ensemble Methods for Directed Acyclic Graphs (DAGs). The 'HEMDAG' package can be used to enhance the predictions of virtually any flat learning methods, by taking into account the hierarchical nature of the classes of a bio-ontology. 'HEMDAG' is specifically designed for exploiting the hierarchical relationships of DAG-structured taxonomies, such as the Human Phenotype Ontology (HPO) or the Gene Ontology (GO), but it can be also safely applied to tree-structured taxonomies (as FunCat), since trees are DAGs. 'HEMDAG' scale nicely both in terms of the complexity of the taxonomy and in the cardinality of the examples. (Marco Notaro, Max Schubach, Peter N. Robinson and Giorgio Valentini (2017) <doi:10.1186/s12859-017-1854-y>).
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.
Constructs paths to your project's files.
    The 'here()' function uses a reasonable heuristics to find your project's
    files, based on the current working directory at the time when the package
    is loaded. Use it as a drop-in replacement for 'file.path()', it will always
    locate the files relative to your project root.
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.
Data and data-generating functions related to the paper Fast Bivariate Penalized Splines by Xiao et al. (2012) <doi:10.1111/rssb.12007>.    A hero is a specific type of sandwich.  Dictionary.com (2018) <https://www.dictionary.com> describes a hero as: a large sandwich, usually consisting of a small loaf of bread or long roll cut in half lengthwise and containing a variety of ingredients, as meat, cheese, lettuce, and tomatoes.
Functionality for developing and analyzing the output of health-economic 
    simulation models. Contains random sampling functions for conducting probabilistic  
    sensitivity analyses (Claxton et al. 2005) <doi:10.1002/hec.985> and 
    individual patient simulations (Brennan et al. 2006) <doi:10.1002/hec.1148>. 
    Individualized cost-effectiveness analysis (Basu and Meltzer 2007, Ioannidis and Garber 2011) 
    <doi:10.1177/0272989X06297393>, <doi:10.1371/journal.pmed.1001058> 
    can be performed on simulation output and used to 
    summarize a probabilistic sensitivity analysis at the subgroup or individual 
    level. Core functions are written in C++ to facilitate computationally 
    intensive modeling.
An implementation of White's Test for Heteroskedasticity
        as outlined in Doornik (1996).
Performs Gaussian process regression with heteroskedastic noise following Binois, M., Gramacy, R., Ludkovski, M. (2016) <arXiv:1611.05902>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.
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.
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.
Helper functions for creating reproducible hexagon sticker purely in R.
Simple and integrated tool that automatically extracts and folds all hairpin sequences from raw genome-wide data. It predicts the secondary structure of several overlapped segments, with longer length than the mean length of sequences of interest for the species under processing, ensuring that no one is lost nor inappropriately cut.
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.
Implemented here are procedures for fitting hierarchical generalized linear models (HGLM). It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model. As statistical models, HGLMs were initially developed by Lee and Nelder (1996) <https://www.jstor.org/stable/2346105?seq=1>. We provide an implementation (Ronnegard, Alam and Shen 2010) <https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Roennegaard~et~al.pdf> following Lee, Nelder and Pawitan (2006) <ISBN: 9781420011340> with algorithms extended for spatial modeling (Alam, Ronnegard and Shen 2015) <https://journal.r-project.org/archive/2015/RJ-2015-017/RJ-2015-017.pdf>.
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 human gene symbols and MGI mouse gene symbols 
 which have been converted to date format by Excel, withdrawn, or aliased.
 Also contains functions for reversibly converting between HGNC
 symbols and valid R names.
Estimation of high-dimensional multi-response regression with heterogeneous noises under Heterogeneous group square-root Lasso penalty. For details see: Ren, Z., Kang, Y., Fan, Y. and Lv, J. (2018)<arXiv:1606.03803>.
A handy collection of utility functions designed to aid in package development, plotting and scientific research.
    Package development functionalities includes among others tools such as cross-referencing package imports with the description file,
    analysis of redundant package imports, editing of the description file and the creation of package badges for GitHub.
    Some of the other functionalities include automatic package installation and loading, plotting points without overlap,
    creating nice breaks for plots, overview tables and many more handy utility functions.
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.
Meyer and Held (2017) <doi:10.1093/biostatistics/kxw051> present an
    age-structured spatio-temporal model for infectious disease counts. The
    approach is illustrated in a case study on norovirus gastroenteritis in
    Berlin, 2011-2015, by age group, city district and week, using additional
    contact data from the POLYMOD survey. This package contains the data and
    code to reproduce the results from the paper, see 'demo("hhh4contacts")'.
Based on the aggregated shares retained by individual firms or actors within a market or space, the Herfindahl-Hirschman Index (HHI) measures the level of concentration in a space. This package allows for intuitive and straightforward computation of HHI scores, requiring placement of objects of interest directly into the function. The package also includes a plot function for quick visual display of an HHI time series using any measure of time (year, quarter, month, etc.). For usage, please cite the Journal of Open Source Software paper associated with the package: Waggoner, Philip D. (2018) <doi:10.21105/joss.00828>.
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.
Here we propose a model to systematically analyze the roles of architectural proteins and functional elements in blocking long-range contacts between loci. The proposed model does not rely on topologically associating domain (TAD) mapping from Hi-C data. Instead of testing the enrichment or influence of protein binding at TAD borders, the model directly estimates the blocking effect of proteins on long-range contacts between flanking loci, making the model intuitive and biologically meaningful.
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.
Variance partition of a multivariate data set
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.
Miscellaneous R functions for calculating and decomposing multiple hierarchical diversity metrics, including hierarchical allele richness (q=1), hierarchical exponential Shannon entropy (q=1), hierarchical heterozygosity (q=2) and genetic differentiation (Fst and Delta D). This package allows you to analyse spatial structured genetic data or species data under a unifying framework (Gaggiotti, O. E. et al, 2018, Evol Appl, 11:1176-1193; <DOI:10.1111/eva.12593>), which partitions diversity and differentiation into any hierarchical levels. It helps you easily structure and format your data. It produces diversity profiles (q=0,1,2), hierarchical diversities and differentiation, as well as the the correlation between geographic distance and genetic differentiation.
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.
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, detect price jumps 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 format R markdown with CSS ids for HTML output. 
    The tool may be most helpful for those using markdown to create reproducible
    documents. The biggest limitations in formatting is the knowledge of CSS
    by the document authors.
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) <http://highriskzone.r-forge.r-project.org/Bachelorarbeit.pdf> 
    and 'Determining high-risk zones for unexploded World War II bombs by using point 
    process methodology', Mahling et al. (2013) <doi:10.1111/j.1467-9876.2012.01055.x>.
Can be used to carry out extraction, normalization, quality control (QC), candidate hits identification and visualization for plate based assays, 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.
Implements the Hierarchical Incremental GRAdient Descent (HiGrad) algorithm,
    a first-order algorithm for finding the minimizer of a function in online learning just like stochastic gradient descent (SGD).
    In addition, this method attaches a confidence interval to assess the uncertainty of its predictions.
    See Su and Zhu (2018) <arXiv:1802.04876> for details.
Generate occupancy patterns based on arrival and departure timestamps
Calculate taxonomic, functional and phylogenetic diversity measures 
    through Hill Numbers proposed by Chao, Chiu and Jost (2014) 
    <doi:10.1146/annurev-ecolsys-120213-091540>.
Estimation of heritability with confidence intervals in linear mixed models.
Allows to estimate and test high-dimensional mediation effects based on sure independent screening and minimax concave penalty techniques. A joint significance test is used for mediation effect. Haixiang Zhang, Yinan Zheng, Zhou Zhang, Tao Gao, Brian Joyce, Grace Yoon, Wei Zhang, Joel Schwartz, Allan Just, Elena Colicino, Pantel Vokonas, Lihui Zhao, Jinchi Lv, Andrea Baccarelli, Lifang Hou, Lei Liu (2016) <doi:10.1093/bioinformatics/btw351>.
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.
Based on hierarchical modeling, this package provides a
        few practical functions to find and present the optimal designs
        for a pooled NGS design.
Read hierarchical fixed width files like those commonly used by 
    many census data providers. Also allows for reading of data in chunks,
    and reading 'gzipped' files without storing the full file in memory.
Identifying labeled compounds in a 13C-tracer experiment in non-targeted fashion is a cumbersome process. This package facilitates such type of analyses
    by providing high level quality control plots, deconvoluting and evaluating spectra and performing a multitude of tests in an automatic fashion.
    The main idea is to use changing intensity ratios of ion pairs from peak list generated with 'xcms' as candidates and evaluate those against
    base peak chromatograms and spectra information within the raw measurement data automatically.
Implementation of a class of hierarchical item response
  theory (IRT) models where both the mean and the variance of latent preferences
  (ability parameters) may depend on observed covariates. The current
  implementation includes both the two-parameter latent trait model for binary data and the
  graded response model for ordinal data. Both are fitted via the Expectation-Maximization (EM)
  algorithm. Asymptotic standard errors are derived from the observed information
  matrix.
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. Beaulieu and O'Meara (2016) <doi:10.1093/sysbio/syw022>.
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., the 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. An introducing paper is Irpino A. Verde R. (2015) <doi:10.1007/s11634-014-0176-4>.
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.
Automatically tracks and makes programmatically available code
    evaluation history in R sessions and dynamic documents.
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.
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)
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.
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>.
For estimation of a variable of interest using two sources of auxiliary information available in a nested structure. For reference see Saarela et al. (2016)<doi:10.1007/s13595-016-0590-1> and Saarela et al. (2018) <doi:10.3390/rs10111832>.
Utilities for reading data from the Human Mortality Database (<https://www.mortality.org>), Human Fertility Database (<https://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.
Runs single level and multilevel imputation models. The user just has to pass the data to the main function and, optionally, his analysis model. Basically the package then translates this analysis model into commands to impute the data according to it with functions from 'mice', 'MCMCglmm' or routines build for this package.
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
Fits hidden Markov models with discrete non-parametric 
	observation distributions to data sets.  The observations may
	be univariate or bivariate. 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.  Auxiliary predictors are accommodated in the
        univariate setting.
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.
R functions to estimate and perform goodness of fit test for several
    Markov regime switching and mixture bivariate copula models. 
    The goodness of fit test is based on a Cramer von Mises statistic and 
    uses the Rosenblatt transform and parametric bootstrap to estimate the p-value. 
    The estimation of the copula parameters are based on the pseudo-maximum likelihood
    method using pseudo-observations defined as normalized ranks.
Consider the linear mixed model with normal random effects. A typical method to solve Henderson's Mixed Model Equations (HMME) is recursive estimation of the fixed effects and random effects. We provide a fast, stable, and scalable solver to the HMME without computing matrix inverse. See Kim (2017) <arXiv:1710.09663> for more details.
Contains functions for hidden Markov models with observations having extra zeros as defined in the following two publications, Wang, T., Zhuang, J., Obara, K. and Tsuruoka, H. (2016) <doi:10.1111/rssc.12194>; Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) <doi:10.1029/2017JB015360>. The observed response variable is either univariate or bivariate Gaussian conditioning on presence of events, and extra zeros mean that the response variable takes on the value zero if nothing is happening. Hence the response is modelled as a mixture distribution of a Bernoulli variable and a continuous variable. That is, if the Bernoulli variable takes on the value 1, then the response variable is Gaussian, and if the Bernoulli variable takes on the value 0, then the response is zero too. This package includes functions for simulation, parameter estimation, goodness-of-fit, the Viterbi algorithm, and plotting the classified 2-D data. Some of the functions in the package are based on those of the R package 'HiddenMarkov' by David Harte.
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.
Improved analysis of marine animal movement data by implementing
    a state-space hidden Markov model (HMM) to improve position estimates. Position
    estimates are derived by comparing electronic tag data (from tags deployed on
    marine animals, typically fish) to three-dimensional oceanographic data.
Analysing time-series accelerometer data to quantify length and 
             intensity of physical activity using hidden Markov models. 
             It also contains the traditional cut-off point method.
             Witowski V, Foraita R, Pitsiladis Y, Pigeot I, 
             Wirsik N (2014)<doi:10.1371/journal.pone.0114089>.
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.
Hierarchical and single-level non-negative matrix factorization. Several NMF algorithms are available.
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.
Suite of tools for managing cached files, targeting
    use in other R packages. Uses 'rappdirs' for cross-platform paths.
    Provides utilities to manage cache directories, including targeting
    files by path or by key; cached directories can be compressed and
    uncompressed easily to save disk space.
Implementation of higher order generalized singular value decomposition (HO GSVD). Based on Ponnapalli, Saunders, etal (2011) <doi:10.1371/journal.pone.0028072>.
Simulates hands and tournaments of Texas Holdem, 
	the most popular form of poker.
	For examples of probability problems involving Texas Holdem 
	and a brief explanation of the game see Schoenberg, F. (2011).
	An Introduction to Probability with Texas Hold'em Examples.
	Taylor and Francis, New York, ISBN-13: 978-1439827680.
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.
Provides methods for closed testing using Simes local tests. In particular, calculates adjusted p-values for Hommel's multiple testing method, and provides lower confidence bounds for true discovery proportions. A robust but more conservative variant of the closed testing procedure that does not require the assumption of Simes inequality is also implemented. The methods have been described in detail in Meijer et al (2016) <arXiv:1611.06739>.
A wrapper for the homologene database by the National Center for
    Biotechnology Information ('NCBI'). It allows searching for gene homologs across 
    species. Data in this package can be found at <ftp://ftp.ncbi.nih.gov/pub/HomoloGene/build68/>.
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 (Dozier and Frew, 1990).
Supplement for the book "Handbook of Regression Methods" by D. S. Young.  Some datasets used in the book are included and documented.  Wrapper functions are included that simplify the examples in the textbook, such as code for constructing a regressogram and expanding ANOVA tables to reflect the total sum of squares.
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.
Implementation of the HorseRule model a flexible tree based Bayesian regression method for linear and nonlinear regression described in Nalenz & Villani (2017) <arXiv:1702.05008>.
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 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.
The hotspots package is designed to look within a set of measured values of a variable and identify values that are disproportionately high based on both the deviance of any given value from a statistical distribution and its similarity to other values. Because this relative magnitude of each value is taken into account, a value that is a statistical outlier may not always be a hot spot if other values are similarly large.
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.
Compute house price indexes and series using a variety of different methods and
    models common through the real estate literature.  Evaluate index 'goodness' based
    on accuracy, volatility and revision statistics. Background on basic model construction
    for repeat sales models can be found at: Case and Quigley (1991) <doi:10.2307/2109686> and 
    for hedonic pricing models at: Bourassa et al (2006) <doi:10.1016/j.jhe.2006.03.001>.
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.
A compilation of extra 'ggplot2' themes, scales and utilities, including a 
    spell check function for plot label fields and an overall emphasis on typography. 
    A copy of the 'Google' font 'Roboto Condensed' <https://github.com/google/roboto/> 
    is also included along with a copy of the 'IBM' 'Plex Sans' <https://github.com/IBM/type>
    and 'Titillium Web' <https://fonts.google.com/specimen/Titillium+Web> fonts
    are also included to support their respective typography-oriented themes.
Estimates the log hazard ratio associated with a binary exposure using a Cox PH model weighted by the propensity score. 
  Propensity model is estimated using a simple logistic regression. 
  Variance estimation takes into account the propensity score estimation step with the method proposed by Hajage et al. (2018) <doi:10.1002/bimj.201700330>. 
  Both the average treatment effect on the overall (ATE) or the treated (ATT) population can be estimated. 
  For the ATE estimation, both unstabilized and stabilized weights can be used.
Methods for testing main and interaction effects in possibly
    high-dimensional parametric or nonparametric repeated measures in factorial designs.
    The observations of the subjects are assumed to be multivariate normal if using the parametric test.
    The nonparametric version tests with regard to nonparametric relative effects (based on pseudo-ranks).
    It is possible to use up to 2 whole- and 3 subplot factors.
Offers tools and modelling approaches for binomial data with overdispersion, with particular interest in Health Related Quality of Life (HRQoL) questionnaires regression analysis.
A Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm ( Dong and Harris (2014) <doi:10.1111/gean.12049> ). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.
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). The publishers web page is 
  '<http://www.crcpress.com/product/isbn/9781482204582>'.
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.
We explore sparsity and homogeneity of regression coefficients incorporating prior constraint information. A general pairwise fusion approach is proposed to deal with the sparsity and homogeneity detection when combining prior convex constraints. We develop an modified alternating direction method of multipliers algorithm (ADMM) to obtain the estimators.
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 (2017) <doi:10.1214/17-STS622>
    "Hierarchical Sparse Modeling: A Choice of Two Group Lasso Formulations", 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.
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.
Convert a html document to simple plain texts by removing all html tags.  This package utilizes regular expressions to strip off html tags. It also offers gettxt() and browse() function, which enables you to get or browse texts at a certain web page.
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.
Historical regression trees are an extension of standard trees, 
	producing a non-parametric estimate of how the response depends on 
	all of its prior realizations as well as that of any time-varying predictor 
	variables. The method applies equally to regularly as well as irregularly 
	sampled data. The package implements random forest and boosting ensembles 
	based on historical regression trees, suitable for longitudinal data. 
	Standard error estimation and Z-score variable importance is also implemented.
Provides methods for analysing and forecasting hierarchical and 
    grouped time series. The available forecast methods include bottom-up,
    top-down, optimal combination reconciliation (Hyndman et al. 2011) 
    <doi:10.1016/j.csda.2011.03.006>, and trace minimization reconciliation
    (Wickramasuriya et al. 2018) <doi:10.1080/01621459.2018.1448825>.
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).
Functions for analyzing high throughput sequencing 
    stable isotope probing (HTS-SIP) data.
    Analyses include high resolution stable isotope probing (HR-SIP),
    multi-window high resolution stable isotope probing (MW-HR-SIP), 
    and quantitative stable isotope probing (q-SIP).
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 and documenting code that communicates with remote servers
    can be painful. Dealing with authentication, server state,
    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
    contexts that mock the network connection in different ways, as well as
    testing functions to assert that HTTP requests were---or were
    not---made. It also allows one to safely record real API responses to use as
    test fixtures. The ability to save responses and load them offline also
    enables one to write vignettes and other dynamic documents that can be
    distributed without access to a live server.
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).
Creating effective colour palettes for figures is 
    challenging. This package generates and plot palettes of optimally 
    distinct colours in perceptually uniform colour space, based on 
    'iwanthue' <http://tools.medialab.sciences-po.fr/iwanthue/>. 
    This is done through k-means clustering of CIE Lab colour space, 
    according to user-selected constraints on hue, chroma, and 
    lightness.
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.
An almost direct port of the 'python' 'humanize' package <https://github.com/jmoiron/humanize>.
  This package contains utilities to convert values into human readable forms.
Generates high-entropy integer synthetic populations from marginal and (optionally) seed data using quasirandom sampling, 
  in arbitrary dimensionality (Smith, Lovelace and Birkin (2017) <doi:10.18564/jasss.3550>). 
  The package also provides an implementation of the Iterative Proportional Fitting (IPF) algorithm (Zaloznik (2011) <doi:10.13140/2.1.2480.9923>).
Vapor pressure, relative humidity, 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), relative humidity (%), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and dew point (K). Conversion functions between humidity measures are also provided.
Low level spell checker and morphological analyzer based on the 
    famous 'hunspell' library <https://hunspell.github.io>. The package can analyze
    or check individual words as well as parse text, latex, html or xml documents.
    For a more user-friendly interface use the 'spelling' package which builds on
    this package to automate checking of files, documentation and vignettes in all
    common formats.
Scraped dataset of the Hurricane Research Division's Hurricane 
    Re-Analysis Project known as HURDAT. Storm details are available for most 
    known hurricanes and tropical storms for the Atlantic and northeastern 
    Pacific ocean (northwestern hemisphere). See <http://www.aoml.noaa.gov/hrd/hurdat/Data_Storm.html> 
    for more information.
When considering count data, it is often the case that many more zero counts than would be expected of some given distribution are observed. It is well established that data such as this can be reliably modelled using zero-inflated or hurdle distributions, both of which may be applied using the functions in this package. Bayesian analysis methods are used to best model problematic count data that cannot be fit to any typical distribution. The package functions are flexible and versatile, and can be applied to varying count distributions, parameter estimation with or without explanatory variable information, and are able to allow for multiple hurdles as it is also not uncommon that count data have an abundance of large-number observations which would be considered outliers of the typical distribution. In lieu of throwing out data or misspecifying the typical distribution, these extreme observations can be applied to a second, extreme distribution. With the given functions of this package, such a two-hurdle model may be easily specified in order to best manage data that is both zero-inflated and over-dispersed.
Allows users to create time series of tropical storm
    exposure histories for chosen counties for a number of hazard metrics (wind, 
    rain, distance from the storm, etc.). This package interacts with data available 
    through the 'hurricaneexposuredata' package, which is available in a 'drat'
    repository. To access this data package, run 'install.packages("hurricaneexposuredata", 
    repos = "https://geanders.github.io/drat/", type = "source")'. The size of the 
    'hurricaneexposuredata' package is approximately 25 MB. This work was supported in 
    part by grants from the National Institute of Environmental Health Sciences 
    (R00ES022631), the National Science Foundation (1331399), and a NASA Applied 
    Sciences Program/Public Health Program Grant (NNX09AV81G).
Provides utility functions for, and drawing on, the 'data.table' package. The package also collates useful miscellaneous functions extending base R not available elsewhere. The name is a portmanteau of 'utils' and the author.
Like 'xtable', creates styled tables. Export to HTML, LaTeX, 'Word', 
  'Excel', 'PowerPoint' and RTF. Simple, modern interface to manipulate 
  borders, size, position, captions, colours, text styles and number formatting.
  Table cells can span multiple rows and/or columns.
  Includes  a 'huxreg' function for creation of regression tables, and 'quick_*' 
  one-liners to print data to a new document.
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.
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.
Easy-to-use and versatile functions to output R objects in
        HTML format
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.
A hybrid method of feature selection which combines both filter and wrapper methods. The first level involves feature reduction based on some of the important filter methods while the second level involves feature subset selection as in a wrapper method. Comparative analysis with the existing feature selection packages shows this package results in higher classification accuracy, reduced processing time and improved data handling capacity.
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. It is a metapopulation model in which each
    node in the network is a sub-population and disease spreads within nodes
    and among them, combining two approaches: stochastic simulation algorithm
    or its approximations (Gillespie DT (2007)
    <doi:10.1146/annurev.physchem.58.032806.104637>) and individual-based
    approach, respectively. Movement among nodes are data based and can be
    irregular. Equations that models spread within nodes are customizable and
    there are two link types among nodes: migration and influence (commuting).
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.
Tools to link geographic data with hydrologic network, including lakes, streams and rivers. 
    Includes automated download of U.S. National Hydrography Network and other hydrolayers.
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
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). See Zambrano-Bigiarini and Rojas (2013) <doi:10.1016/j.envsoft.2013.01.004> for more details.
R interface to the Greek National Data Bank for Hydrological and 
    Meteorological Information <http://www.hydroscope.gr/>. It covers 
    Hydroscope's data sources and provides functions to transliterate, 
    translate and download them into tidy dataframes (tibbles).
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.
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.
A suite of routines for the hyperdirichlet distribution; supersedes the 'hyperdirichlet' package for most purposes.
This package provides functions for the hyperbolic and
        related distributions. Density, distribution and quantile
        functions and random number generation are provided for the
        hyperbolic distribution, the generalized hyperbolic
        distribution, the generalized inverse Gaussian distribution and
        the skew-Laplace distribution. Additional functionality is
        provided for the hyperbolic distribution, including fitting of
        the hyperbolic to data.
Provides methods for organizing data in a hypercube
  (i.e. a multi-dimensional cube). Cubes are generated from molten data frames.
  Each cube can be manipulated with five operations: rotation (changeDimensionOrder()),
  dicing and slicing (add.selection(), remove.selection()), drilling down (add.aggregation()),
  and rolling up (remove.aggregation()).
Given a high-dimensional dataset that typically represents a cytometry dataset, and a subset of the datapoints, this algorithm outputs an hyperrectangle so that datapoints within the hyperrectangle best correspond to the specified subset. In essence, this allows the conversion of clustering algorithms' outputs to gating strategies outputs.
Performs (exact) hypergeometric tests on IxJ and 2x2x2 contingency tables using parallelized C code.
The Gaussian hypergeometric function for complex numbers.
Implements an algorithm for efficient and exact calculation of hypergeometric 
    and binomial probabilities using Chebyshev polynomials, while other algorithm use an 
    approximation when N is large. A useful applications is also considered in this package 
    for the construction of attribute sampling plans which is an important field of statistical
    quality control. The quantile, and the confidence limit for the attribute sampling plan are
    also implemented in this package. The hypergeometric distribution can be represented in 
    terms of Chebyshev polynomials. This representation is particularly useful in the calculation
    of exact values of hypergeometric variables.
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.
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, support vector machine delineation, and convex hull generation. Applications include modeling trait and niche hypervolumes and 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.
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.
Methods for addressing uncertainty in risk assessments using hybrid representations of uncertainty (probability distributions, fuzzy numbers, intervals, probability distributions with imprecise parameters). The uncertainty propagation procedure combines random sampling using Monte Carlo method with fuzzy interval analysis of Baudrit et al. (2007) <doi:10.1109/TFUZZ.2006.876720>. The sensitivity analysis is based on the pinching method of Ferson and Tucker (2006) <doi:10.1016/j.ress.2005.11.052>.
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.
In randomized-controlled trials, interim analyses are often planned for possible early 
   trial termination to claim superiority or futility of a new therapy. Blinded data also have 
   information about the potential treatment difference between the groups. We developed a blinded 
   data monitoring tool that enables investigators to predict whether they observe such an unblinded 
   interim analysis results that supports early termination of the trial. Investigators may skip 
   some of the planned interim analyses if an early termination is unlikely. 
   This tool will provide reference information about N: Sample size at interim analysis, and T: Total 
   number of responders at interim analysis for decision on performing an interim analysis.
Calculate false ring proportions from data frames of intra annual 
  density fluctuations.
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.
Quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>.    The output of this function is a data frame that consists of four rows containing the following information: (1) the overall IAT effect size for the participant's dataset, (2) the effect size calculated for odd trials only, (3) the effect size calculated for even trials only, and (4) the proportion of trials with reaction times under 300ms (which is important for exclusion purposes). Items (2) and (3) allow for a measure of the internal consistency of the IAT. Specifically, you can use the subsetted IAT effect sizes for odd and even trials to calculate Cronbach's alpha across participants in the sample.    The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 220, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent).    Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds).    A sample dataset (titled 'sampledata') is included in this package to practice with.
This minimalist package is designed to quickly score raw data outputted from an Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) <doi:10.1037/0022-3514.74.6.1464>. IAT scores are calculated as specified by Greenwald, Nosek, and Banaji (2003) <doi:10.1037/0022-3514.85.2.197>. Outputted values can be interpreted as effect sizes. The input function consists of three arguments. First, indicate the name of the dataset to be analyzed. This is the only required input. Second, indicate the number of trials in your entire IAT (the default is set to 219, which is typical for most IATs). Last, indicate whether congruent trials (e.g., flowers and pleasant) or incongruent trials (e.g., guns and pleasant) were presented first for this participant (the default is set to congruent). The script will tell you how long it took to run the code, the effect size for the participant, and whether that participant should be excluded based on the criteria outlined by Greenwald et al. (2003). Data files should consist of six columns organized in order as follows: Block (0-6), trial (0-19 for training blocks, 0-39 for test blocks), category (dependent on your IAT), the type of item within that category (dependent on your IAT), a dummy variable indicating whether the participant was correct or incorrect on that trial (0=correct, 1=incorrect), and the participant’s reaction time (in milliseconds). Three sample datasets are included in this package (labeled 'IAT', 'TooFastIAT', and 'BriefIAT') to practice with.
Compute several variations of the Implicit Association Test (IAT) scores, including the D scores (Greenwald, Nosek, Banaji, 2003, <doi:10.1037/0022-3514.85.2.197>) and the new scores that were developed using robust statistics (Richetin, Costantini, Perugini, and Schonbrodt, 2015, <doi:10.1371/journal.pone.0129601>).
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.
Implements the item based collaborative filtering (IBCF) method for continues phenotypes in the context of plant breeding where data are collected for various traits that were studied in various environments proposed by Montesinos-López et al. (2017) <doi:10.1534/g3.117.300309>.
A collection of several utility functions related to binary incomplete block designs. The package contains function to generate A- and D-efficient binary incomplete block designs with given numbers of treatments, number of 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 estimated marginal 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.
Simulation of segments shared identical-by-descent (IBD) by 
    pedigree members. Using sex specific recombination rates along the human
    genome (Kong et. al (2010) <doi:10.1038/nature09525>), phased chromosomes
    are simulated for all pedigree members, either by unconditional gene 
    dropping or conditional on a specified IBD pattern. Additional functions 
    provide summaries and further analysis of the simulated genomes.
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 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(R) Db2(R) 
        Warehouse offerings (formerly IBM dashDB(R)) and IBM Db2 for z/OS(R) in 
        conjunction with IBM Db2 Analytics Accelerator for z/OS. 
        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.
Generates Personality Insights sunburst diagrams
    based on 'IBM Watson' Personality Insights service output.
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.
Calculate B-spline basis functions with a given set of knots and order, or a B-spline function with a given set of knots and order and set of de Boor points (coefficients), or the integral of a B-spline function.
Fit a bagging survival tree on a mixture of population (susceptible and nonsusceptible)
             using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is 
             evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance
             are computed for variable selection. The thresholds values for variable selection are 
             computed using a nonparametric permutation test.
Implements inequality constrained inference. This includes parameter estimation in normal (linear) models under linear equality and inequality constraints, as well as 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.
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.
A simple wrapper around the 'ical.js' library executing 
    'Javascript' code via 'V8' (the 'Javascript' engine driving the 'Chrome' 
    browser and 'Node.js' and accessible via the 'V8' R package). 
    This package enables users to parse 'iCalendar' files ('.ics', '.ifb', 
    '.iCal', '.iFBf') into lists and 'data.frames' to ultimately do statistics
    on events, meetings, schedules, birthdays, and the like.
Provides R functions 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 (Zhu, X., & Hunter, D.R., 2016 <DOI:10.1080/10485252.2016.1225049> and Zhu, X., & Hunter, D.R., 2015 <arXiv:1510.08178>). This is a new tool for unsupervised clustering.
Finds optimal designs for nonlinear models using a metaheuristic algorithm called imperialist competitive algorithm ICA. See, for details, Masoudi et al. (2017) <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.
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.
Calibration and risk-set calibration methods for fitting Cox proportional hazard model when a binary covariate is measured intermittently. Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) <arXiv:1801.01529>.
A function and vignettes for computing an intraclass correlation
    described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>.
    This package 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 16 different methods, and 5 different types of confidence intervals.
Calculate comorbidities, Charlson and van Walraven
    scores, perform fast and accurate validation, conversion,
    manipulation, filtering and comparison of ICD-9 and ICD-10 codes. This
    package enables a work flow from raw lists of ICD codes in hospital
    databases to comorbidities.  ICD-9 and ICD-10 comorbidity mappings
    from Quan (Deyo and Elixhauser versions), Elixhauser and AHRQ
    included.  Common ambiguities and code formats are handled.
Data from the United States Center for Medicare and Medicaid
    Services (CMS) is included in this package. There are ICD-9 and ICD-10
    diagnostic and procedure codes, and lists of the chapter and sub-chapter 
    headings and the ranges of ICD codes they encompass. There are also two 
    sample datasets. These data are used by the 'icd' package for finding
    comorbidities.
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.
Tools for modeling and correcting biases in sea ice predictions obtained from dynamical models. This package depends on the 'ncdf4' and 'rgeos' R packages. These packages respectively require installing externally from R Unidata's 'NetCDF' library and Geometry Engine - Open Source ('GEOS'). (See the 'rgeos' and 'ncdf4' packages for details.) References, Bivand and Rundel (2017) <https://CRAN.R-project.org/package=rgeos>, Open Source Geospatial Foundation (2017) <https://trac.osgeo.org/geos>, Pierce (2017) <https://CRAN.R-project.org/package=ncdf4>, Unidata (2017) <https://www.unidata.ucar.edu/software/netcdf/>.
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 either gamma or eta parameters.
Regression models for interval censored data. Currently supports
    Cox-PH, proportional odds, and accelerated failure time models. Allows for
    semi and fully parametric models (parametric only for accelerated failure
    time models) and Bayesian parametric models. Includes functions for easy visual
    diagnostics of model fits and imputation of censored data.
Contains datasets and several smaller functions suitable for analysis of interval-censored data. The package complements the book Bogaerts, Komárek and Lesaffre (2017). "Survival Analysis with Interval-Censored Data: A Practical Approach." ISBN 978-1-4200-7747-6, published by Chapman and Hall/CRC. Full R code related to the examples presented in the book can be found at <https://ibiostat.be/online-resources/icbook/supplemental>. Packages mentioned in the "Suggests" section are used in those examples.
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 ICES advisory process.
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 Stock Assessment
             Graphs database <http://sg.ices.dk>.
Functions to support the ICES Transparent Assessment Framework
  <http://taf.ices.dk> to organize data, methods, and results used in ICES
  assessments. ICES is an organization facilitating international collaboration
  in marine science.
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.
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.
Carries out empirical Bayes variable selection via ICM/M algorithm. The basic problem is to fit high-dimensional regression which most coefficients are assumed to be zero. This package allows incorporating the Ising prior to capture structure of predictors in the modeling process. The current version of this package can handle the normal, binary logistic, and Cox's regression (Pungpapong et. al. (2015) <doi:10.1214/15-EJS1034>, Pungpapong et. al. (2017) <arXiv:1707.08298>).
Employs triangular tessellation to refine icosahedra
    defined in 3d space. The procedures can be set to provide a grid with a
    custom resolution. Both the primary triangular and their inverted penta-
    hexagonal grids are available for implementation. Additional functions
    are provided to position points (latitude-longitude data) on the grids,
    to allow 2D and 3D plotting, use raster data and shapefiles.
Reproducible, programmatic retrieval of datasets from the
    Inter-university Consortium for Political and Social Research archive.
Provides functions to compute and plot Krippendorff's inter-coder 
    reliability coefficient alpha and bootstrapped uncertainty estimates 
    (Krippendorff 2004, ISBN:0761915443). The bootstrap routines are set up to
    make use of parallel threads where supported.
Algorithms to construct confidence intervals for the ranks of centers mu_1,...,mu_n based on an independent Gaussian sample using multiple testing techniques.
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.
Performs Invariant Coordinate Selection (ICS) (Tyler, Critchley, Duembgen and Oja (2009) <doi:10.1111/j.1467-9868.2009.00706.x>) and especially ICS for multivariate outlier detection with application to quality control (Archimbaud, Nordhausen, Ruiz-Gazen (2016) <arXiv:1612.06118>) using a shiny app.
Currently using the proportional hazards (PH) model. More methods under other semiparametric regression models will be included in later versions.
Nonparametric and semiparametric estimations of the time-dependent ROC curve for the interval-censored failure time data.
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.
Functions for computing the global and local Gaussian density estimates based on the ICV bandwidth. See the article of Savchuk, O.Y., Hart, J.D., Sheather, S.J. (2010). Indirect cross-validation for density estimation. Journal of the American Statistical Association, 105(489), 415-423 <doi:10.1198/jasa.2010.tm08532>.
Computes and tests individual (species, phylogenetic and functional) diversity-area relationships, i.e., how species-, phylogenetic- and functional-diversity varies with spatial scale around the individuals of some species in a community. See applications of these methods in Wiegand et al. (2007) <doi:10.1073/pnas.0705621104> or Chacon-Labella et al. (2016) <doi:10.1007/s00442-016-3547-z>.
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>.
The digits of the old version (before 2000 year) of 'Chinese ID Card Number' is 15, this package aims to update to the current version of 18 digits. Besides, this package can help check whether the given 'ID' is right or not.
The Integro-Difference Equation model is a linear, dynamical model used to model
   phenomena that evolve in space and in time; see, for example, Cressie and Wikle (2011,
   ISBN:978-0-471-69274-4) or Dewar et al. (2009) <doi:10.1109/TSP.2008.2005091>. At the
   heart of the model is the kernel, which dictates how the process evolves from one time
   point to the next. Both process and parameter reduction are used to facilitate computation,
   and spatially-varying kernels are allowed. Data used to estimate the parameters are assumed
   to be readings of the process corrupted by Gaussian measurement error. Parameters are fitted
   by maximum likelihood, and estimation is carried out using an evolution algorithm.
Offers item-response theory (IRT) ideal-point estimation for binary, ordinal, counts and continuous responses with time-varying and missing-data inference. Full and approximate Bayesian sampling with 'Stan' (www.mc-stan.org).
Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Crabbe M, Akinc D and Vandebroek M (2014) <doi:10.1016/j.trb.2013.11.008>.
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 was proposed in Wang et al. (2016) <DOI:10.1111/biom.12594>.
    It 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.
An implementation of various methods for estimating intrinsic
    dimension of vector-valued dataset or distance matrix. Most methods implemented
    are based on different notion of fractal dimension such as the capacity
    dimension, the box-counting dimension, and the information dimension.
Provides efficient implementation of the Isolate-Detect
    methodology for the consistent estimation of the number and location of multiple 
    change-points in one-dimensional data sequences from the "deterministic 
    + noise" model. For details on the Isolate-Detect methodology, please see Anastasiou and
    Fryzlewicz (2018) <https://docs.wixstatic.com/ugd/24cdcc_6a0866c574654163b8255e272bc0001b.pdf>.
    Currently implemented scenarios are: piecewise-constant signal with Gaussian
    noise, piecewise-constant signal with heavy-tailed noise, continuous piecewise-linear 
    signal with Gaussian noise, continuous piecewise-linear signal with heavy-tailed noise.
Intensity-duration-frequency (IDF) curves are a widely used analysis-tool in hydrology to assess extreme values of precipitation [e.g. Mailhot et al., 2007, <doi:10.1016/j.jhydrol.2007.09.019>]. The package 'IDF' provides a function to read precipitation data from German weather service (DWD) 'webwerdis' <http://www.dwd.de/EN/ourservices/webwerdis/webwerdis.html> files and Berlin station data from 'Stadtmessnetz' <http://www.geo.fu-berlin.de/en/met/service/stadtmessnetz/index.html> files, and additionally IDF parameters can be estimated also from a given data.frame containing a precipitation time series. The data is aggregated to given levels yearly intensity maxima are calculated either for the whole year or given months. From these intensity maxima IDF parameters are estimated on the basis of a duration-dependent generalised extreme value distribution [Koutsoyannis et al., 1998, <doi:10.1016/S0022-1694(98)00097-3>]. IDF curves based on these estimated parameters can be plotted.
Fits covariate dependent partial correlation matrices for integrative models to identify differential networks between two groups. The method is described in Ha et. al., (2015) <doi:10.1093/bioinformatics/btv406>.
Incremental Multiple Correspondence Analysis and Principal
    Component Analysis.
Contains techniques for mining large and high-dimensional data
    sets by using the concept of Intrinsic Dimension (ID). Here the ID is
    not necessarily an integer. It is extended to fractal dimensions. And
    the Morisita estimator is used for the ID estimation, but other
    tools are included as well.
Modeling of regression effects for transition probabilities in a 
   progressive illness-death model. Azarang, Scheike, and de Una-Alvarez (2017) <doi:10.1002/sim.7245>.
Integrate molecular profiles to discover candidate oncogenic drivers.
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.
		 Mangili, F., Benavoli, A., de Campos, C.P., Zaffalon, M. (2015) <doi:10.1002/bimj.201500062>.
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.
Convert files to and from IDX format to vectors, matrices and arrays.
    IDX is a very simple file format designed for storing vectors and multidimensional matrices in
    binary format. The format is described on the website from Yann LeCun
    <http://yann.lecun.com/exdb/mnist/>.
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.
Functions for single-variant and region-based tests with external control samples. These methods use external study samples as control samples with adjusting for possible batch effects.
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 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/>.
A collection of statistical tests for genetic association studies.
Implements Bayesian linear regression, normal means estimation, and variable selection using the inverse gamma-gamma prior, as introduced by Bai and Ghosh (2018) <arXiv:1710.04369>.
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.
Creates a Gaussian process model using the specified package. 
    Makes it easy to try different packages in same code, only the
    package argument needs to be changed.
    It is essentially a wrapper for the other Gaussian process
    software packages.
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
To integrate multiple GSEA studies, we propose a hybrid strategy,
    iGSEA-AT, for choosing random effects (RE) versus fixed effect (FE) models,
    with an attempt to achieve the potential maximum statistical efficiency as 
    well as stability in performance in various practical situations. In addition
    to iGSEA-AT, this package also provides options to perform integrative GSEA
    with testing based on a FE model (iGSEA-FE) and testing based on a RE model
    (iGSEA-RE). The approaches account for different set sizes when testing a
    database of gene sets. The function is easy to use, and the three approaches
    can be applied to both binary and continuous phenotypes.
Make complex, interactive heatmaps. 'iheatmapr' includes a modular 
    system for iteratively building up complex heatmaps, as well as the 
    iheatmap() function for making relatively standard heatmaps.
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.
Efficient algorithms for fitting linear / logistic regression model with Independently Interpretable Lasso.
  Takada, M., Suzuki, T., & Fujisawa, H. (2018). Independently Interpretable Lasso: A New Regularizer for Sparse Regression with Uncorrelated Variables. AISTATS.
  <http://proceedings.mlr.press/v84/takada18a/takada18a.pdf>.
These datasets and functions accompany Wolfe and Schneider (2017) - Intuitive Introductory Statistics (ISBN:  978-3-319-56070-0) <http://www.springer.com/us/book/9783319560700>. They are used in the examples throughout the text and in the end-of-chapter exercises. The datasets are meant to cover a broad range of topics in order to appeal to the diverse set of interests and backgrounds typically present in an introductory Statistics class.
General purpose TIFF file I/O for R users. Currently the only such 
    package with read and write support for TIFF files with floating point 
    (real-numbered) pixels, and the only package that can correctly import TIFF 
    files that were saved from 'ImageJ' and write TIFF files than can be 
    correctly read by 'ImageJ' <https://imagej.nih.gov/ij/>.
    Also supports text image I/O.
The package offers an S4 infrastructure to store climatological
  station data of various temporal aggregation scales. In-built quality
  control and homogeneity tests follow the methodology from the European
  Climate Assessment & Dataset project. Wrappers for climate indices
  defined by the Expert Team on Climate Change Detection and Indices
  (ETCCDI), a quick summary of important climate statistics and climate
  diagram plots provide a fast overview of climatological
  characteristics of the station.
Fitting a class of Lee-Carter mortality models using iterative fitting algorithm.
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.
Providing advanced functions for image processing based on the package 'imager'.
Simulate general insurance policies, losses and loss emergence. The package contemplates 
  deterministic and stochastic policy retention and growth scenarios. Retention and growth rates are percentages relative
  to the expiring portfolio. Claims are simulated for each policy. This is accomplished either be assuming a frequency
  distribution per development lag or by generating random wait times until claim emergence and settlement. Loss simulation 
  uses standard loss distributions for claim amounts.
Provides fast application of image filters to data matrices,
    using R and C++ algorithms.
The pearl oyster, Pinctada margaritifera (Linnaeus, 1758), represents the second economic resource of French Polynesia. It is one of the only bivalves expressing a large varied range of inner shell color, & by correlation, of pearl color. This phenotypic variability is partly under genetic control, but also under environmental influence. With ImaginR, it's now possible to delimit the color phenotype of the pearl oyster's inner shell and to characterize their color variations (by the HSV color code system) with pictures.
This is an Automatic Item Generator for Psychological Assessment. Items created with the 'IMak' package should not be used in applied settings as part of the working protocol without ensuring first that the items meet the required psychometric quality standards (see Blum & Holling, 2018) <DOI:10.3389/fpsyg.2018.01286>.
Zoom in and out of maps or any supplied lines or points,
        with control for color, poly fill, and aspect.
Class imbalance usually damages the performance of classifiers. Thus, it is
             important to treat data before applying a classifier algorithm. This package
             includes recent resampling algorithms in the literature: (Barua et al. 2014)
             <doi:10.1109/tkde.2012.232>; (Das et al. 2015) <doi:10.1109/tkde.2014.2324567>,
             (Zhang et al. 2014) <doi:10.1016/j.inffus.2013.12.003>; (Gao et al. 2014)
             <doi:10.1016/j.neucom.2014.02.006>; (Almogahed et al. 2014)
             <doi:10.1007/s00500-014-1484-5>. It also includes an useful interface to
             perform oversampling.
Provides API access to the <http://imdbapi.net> which maintains metadata
             about movies, games and television shows through a public API.
Implements likelihood based methods for mediation analysis.
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.
Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2017) <arXiv:1701.07010v4>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, and quantifying uncertainty.
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.
Interpretability methods to analyze the behavior and predictions of
 any machine learning model.
 Implemented methods are:
 Feature importance described by Fisher et al. (2018) <arXiv:1801.01489>,
 accumulated local effects plots described by Apley (2018) <arXiv:1612.08468>,
 partial dependence plots described by Friedman (2001) <http://www.jstor.org/stable/2699986>,
 individual conditional expectation ('ice') plots described by Goldstein et al. (2013) <doi:10.1080/10618600.2014.907095>,
 local models (variant of 'lime') described by Ribeiro et. al (2016) <arXiv:1602.04938>,
 the Shapley Value described by Strumbelj et. al (2014) <doi:10.1007/s10115-013-0679-x>, 
 feature interactions described by Friedman et. al <doi:10.1214/07-AOAS148> and
 tree surrogate models.
Send emails using the 'mailgun' api. To use this package you will need an account from <https://www.mailgun.com> .
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. See Robitzsch and
    Steinfeld (2018) for a description of the functionality
    of the package.
Based on large margin principle, this package performs feature selection methods: "IM4E"(Iterative Margin-Maximization under Max-Min Entropy Algorithm); "imIM4E"(imbalance Iterative Margin-Maximization under Max-Min Entropy Algorithm); "Immigrate"(Iterative Max-Min Entropy Margin-Maximization with Interaction Terms Algorithm); "Simba"(Iterative Search Margin Based Algorithm); "LFE"(Local Feature Extraction Algorithm). This package also performs prediction for the above feature selection methods.
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.
Imputing blockwise missing data by imprecise imputation,
    featuring a domain-based, variable-wise, and case-wise strategy. 
    Furthermore, the estimation of lower and upper bounds for 
    unconditional and conditional probabilities based on the obtained
    imprecise data is implemented.
    Additionally, two utility functions are supplied: one to check 
    whether variables in a data set contain set-valued observations;
    and another to merge two already imprecisely imputed data. 
    The method is described in a technical report by Endres, Fink and
    Augustin (2018, <doi:10.5282/ubm/epub.42423>).
'SQL' back-end to 'dplyr' for Apache Impala, the massively
    parallel processing query engine for Apache 'Hadoop'. Impala enables
    low-latency 'SQL' queries on data stored in the 'Hadoop' Distributed
    File System '(HDFS)', Apache 'HBase', Apache 'Kudu', Amazon Simple 
    Storage Service '(S3)', Microsoft Azure Data Lake Store '(ADLS)', 
    and Dell 'EMC' 'Isilon'. See <https://impala.apache.org> for more
    information about Impala.
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.
Enables 'Python'-like importing/loading of packages or functions
    with aliasing to prevent namespace conflicts.
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.
Creation of imprecise classification trees. They rely on
    probability estimation within each node by means of either the
    imprecise Dirichlet model or the nonparametric predictive
    inference approach. The splitting variable is selected by the
    strategy presented in Fink and Crossman (2013)
    <http://www.sipta.org/isipta13/index.php?id=paper&paper=014.html>,
    but also the original imprecise information gain of Abellan and
    Moral (2003) <doi:10.1002/int.10143> is covered.
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>.
Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.
Provides new imputation methods for the 'mice' package based on generalized additive models for location, scale, and shape (GAMLSS) as described in de Jong, van Buuren and Spiess <doi:10.1080/03610918.2014.911894>.
Provides a test bench for the comparison of missing data imputation 
    methods in uni-variate time series. Imputation methods are compared using 
    different error metrics. Proposed imputation methods and alternative error 
    metrics can be used.
Imputation (replacement) of missing values 
             in univariate time series. 
             Offers several imputation functions
             and missing data plots. 
             Available imputation algorithms include: 
            'Mean', 'LOCF', 'Interpolation', 
            'Moving Average', 'Seasonal Decomposition', 
            'Kalman Smoothing on Structural Time Series models',
            'Kalman Smoothing on ARIMA models'.
Method brings less bias and more efficient estimates for AFT models.
Do Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers)
  evaluate patient images (cases). What does this mean? ... Many imaging studies are designed
  so that every reader reads every case in all modalities, a fully-crossed study. In this case, the
  data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects.
  An MRMC analysis accounts for the variability and correlations from the readers and cases when
  estimating variances, confidence intervals, and p-values. The functions in this package can treat
  arbitrary study designs and studies with missing data, not just fully-crossed study designs.
  The initial package analyzes the reader-average area under the receiver operating characteristic (ROC)
  curve with U-statistics according to Gallas, Bandos, Samuelson,
  and Wagner 2009 <doi:10.1080/03610920802610084>.
  Additional functions analyze other endpoints with U-statistics (binary performance
  and score differences) following the work by Gallas, Pennello, and Myers 2007
  <doi:10.1364/JOSAA.24.000B70>.
  Package development and documentation is at <https://github.com/DIDSR/iMRMC/tree/master>.
Estimate the relative abundance of tissue-infiltrating immune subpopulations abundances using gene expression data.
Implementation of the information matrix test for generalized partial credit models.
Graphical User Interface (GUI) to some of the functions in the package extRemes version >= 2.0 are included.
Partitioning clustering algorithms divide data sets into k subsets or partitions so-called clusters. They require some initialization procedures for starting to partition the data sets. Initialization of cluster prototypes is one of such kind of procedures for most of the partitioning algorithms. Cluster prototypes are the data elements, i.e. centroids or medoids, representing the clusters in a data set. In order to initialize cluster prototypes, the package 'inaparc' contains a set of the functions that are the implementations of several linear time-complexity and loglinear time-complexity methods in addition to some novel techniques. Initialization of fuzzy membership degrees matrices is another important task for starting the probabilistic and possibilistic partitioning algorithms. In order to initialize membership degrees matrices required by these algorithms, a number of functions based on some traditional and novel initialization techniques are also available in the package 'inaparc'.
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.
Handle data in formats used by cancer centers in Sweden, both from 'INCA' 
  (the current register platform, (see <http://rcc.incanet.se> for more 
  information) and 
  by the older register platform 'Rockan' (used in the Western and Northern part 
  of the country). 
  All variables are coerced to suitable classes based on their 
  format. 
  Dates (from various formats such as with missing month or day, with or 
  without century prefix or with just a week number) are all recognized as
  dates and coerced to the ISO 8601 standard (Y-m-d).
  Boolean variables (internally stored either as 0/1 or "True"/"False"/blanks 
  when exported) are coerced to logical. 
  Variable names ending in '_Beskrivning' and '_Varde' will be character, 
  and 'PERSNR' will be coerced (if possible) to a valid personal identification 
  number 'pin' (by the 'sweidnumbr' package).
  The package also allow the user to interactively choose if a variable should 
  be coerced into a potential format even though not all of its values might 
  conform to the recognized pattern.
  It also contain a caching mechanism in order to temporarily store data sets 
  with its newly decided formats in order to not rerun the identification 
  process each time. 
  And finally, it also include a mechanism to aid the documentation process 
  connected to projects build on data from 'INCA'.
Data analysis, normalisation and differential expression for Translatome studies by microarrays (T Sbarrato et al. RNA. 2017 Aug 25; <DOI:10.1261/rna.060525.116>).
The Dynamic Time Warping (DTW) distance for time series allows non-linear alignments of time series to match  similar patterns in time series of different lengths and or different speeds. Beside the traditional implementation of the DTW algorithm, the specialities of this package are, (1) the incremental calculation, which is specifically useful for life data streams due to computationally efficiency, (2) the vector based implementation of the traditional DTW algorithm which is faster because no matrices are allocated and is especially useful for computing distance matrices of pairwise DTW distances for many time series  and (3) the combination of incremental and vector-based calculation. C++ in the heart. For details about DTW see the original paper "Dynamic programming algorithm optimization for spoken word recognition" by Sakoe and Chiba (1978) <DOI:10.1109/TASSP.1978.1163055>.
An efficient and incremental approach for calculating 
    the differences in orbit counts when performing single edge modifications 
    in a network. Calculating the differences in orbit counts is much more efficient than
    recalculating all orbit counts from scratch for each time point.
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.
Suite of functions to study animal incubation.
    At the core of incR 
    lies an algorithm that allows for the scoring of 
    incubation behaviour. Additionally, several functions 
    extract biologically relevant metrics of incubation such as off-bout number 
    and off-bout duration - for a review of avian incubation studies, 
    see Nests, Eggs, and Incubation: New ideas about avian reproduction (2015) 
    edited by D. Charles Deeming and S. James Reynolds <doi:10.1093/acprof:oso/9780198718666.001.0001>.
Tools for estimating incidence from biomarker data in cross-
    sectional surveys, and for calibrating tests for recent infection. 
    Implements and extends the method of Kassanjee et al. (2012)
    <doi:10.1097/EDE.0b013e3182576c07>.
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. See Dang et al.
    (2016) <doi:10.1534/genetics.116.191973> for more details.
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.
Implementations of the weighted Kozachenko-Leonenko entropy estimator and independence tests based on this estimator, (Kozachenko and Leonenko (1987) <http://mi.mathnet.ru/eng/ppi797>). Also includes a goodness-of-fit test for a linear model which is an independence test between covariates and errors.
Computes bilateral and multilateral index numbers. 
    It has support for several standard bilateral indices as well as
    the GEKS multilateral index number methods 
    (see Ivancic, Diewert and Fox (2011) <doi:10.1016/j.jeconom.2010.09.003>) . 
    It also supports updating of GEKS indexes using several splicing methods.
Calculate Indian Income Tax liability for Financial years of Individual resident aged below 60 years,Senior Citizen,Super Senior Citizen, Firm, Local Authority, Any Non Resident Individual / Hindu Undivided Family / Association of Persons /Body of Individuals / Artificial Judicial Person, Co-operative Society.
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.
Functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.
An implementation of the 7-step approach suggested by Otto et al. 
    (2018) <doi:10.1016/j.ecolind.2017.05.045> to validate ecological state indicators
    and to select a suite of complimentary and well performing indicators. 
    This suite can be then used to assess the current state of the system 
    in comparison to a reference period. However, the tools in this package 
    are very generic and can be used to test any type of indicator (e.g. social 
    or economic indicators).
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). To learn more about the API, see <http://www.inegi.org.mx/desarrolladores/default.aspx>.
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.
Interpolation and extrapolation for phylogenetic diversity.
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.
The objective of this package is to perform inference using an expressive statistical grammar that coheres with the tidy design framework.
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 described in  B. Saul and M. Hugdens (2017) <doi:10.18637/jss.v082.i02>. Currently it implements the inverse-probability weighted (IPW) estimators proposed by E.J. Tchetgen Tchetgen and T.J. Vanderweele (2012) <doi:10.1177/0962280210386779>.
Select set of parametric and non-parametric statistical tests. 'inferr' builds upon the solid set of
    statistical tests provided in 'stats' package by including additional data types as inputs, expanding and
    restructuring the test results. The tests included are t tests, variance tests, proportion tests, chi square tests, Levene's test, McNemar Test, Cochran's Q test and Runs test.
The proportion of cancer cells in solid tumor sample, known as the tumor purity, has adverse impact on a variety of data analyses if not properly accounted for. We develop 'InfiniumPurify', which is a comprehensive R package for estimating and accounting for tumor purity based on DNA methylation Infinium 450k array data. 'InfiniumPurify' provides functionalities for tumor purity estimation. In addition, it can perform differential methylation detection and tumor sample clustering with the consideration of tumor purities.
Provides access to core inflation functions. Four different core inflation 
 functions are provided. The well known trimmed means, exclusion and double weighing methods, 
 alongside the new Triple Filter method introduced in Ferreira et al. (2016) <https://goo.gl/UYLhcj>.
Implementation of methods Extremum Surface Estimator (ESE) and 
 Extremum Distance Estimator (EDE) to identify the inflection point of a 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 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 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.
A set of tools for evaluating several measures of case influence for structural equation models.
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.
An R interface to the InfluxDB time series database <https://www.influxdata.com>. This package allows you to fetch and write time series data from/to an InfluxDB server. Additionally, handy wrappers for the Influx Query Language (IQL) to manage and explore a remote database are provided.
The main purpose of this package is to generate the structure of the analysis of variance 
            (ANOVA) table of the two-phase experiments. The user only need to input the design and the 
              relationships of the random and fixed factors using the Wilkinson-Rogers' syntax, 
              this package can then quickly generate the structure of the ANOVA table with the 
              coefficients of the variance components for the expected mean squares.
              Thus, the balanced incomplete block design and provides the efficiency
              factors of the fixed effects can also be studied and compared much easily.
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> , hierarchical agglomerative clustering approach proposed by Gan et. al. (2015) <doi:10.1080/14697688.2015.1023336> and later extended by Ersan and Alici (2016) <doi:10.1016/j.intfin.2016.04.001> .
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>).
This light-weight templating package provides flexible and 
    user-friendly value substitution in string templates or template files
    (e.g. SQL files) at runtime.
Implements functions for simulation-based inference. In particular, implements functions to perform likelihood inference from data summaries whose distributions are simulated (Rousset et al. 2017 <doi:10.1111/1755-0998.12627>).
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.
Calculate the injury severity score (ISS)
    based on the dictionary in 'ICDPIC' from <https://ideas.repec.org/c/boc/bocode/s457028.html>. The original code was written in 
    'STATA 11'. The original 'STATA' code was written by David Clark, 
    Turner Osler and David Hahn. I implement the same logic for easier access.
    Ref: David E. Clark & Turner M. Osler & David R. Hahn, 2009. 
    "ICDPIC: Stata module to provide methods for translating International 
    Classification of Diseases (Ninth Revision) diagnosis codes into standard injury 
    categories and/or scores," Statistical Software Components S457028, 
    Boston College Department of Economics, revised 29 Oct 2010.
Fit Spatial Econometrics models using Bayesian model averaging 
  on models fitted with INLA. The INLA package can be obtained from 
  <http://www.r-inla.org>.
Facilitates spatial modeling using integrated nested Laplace approximation via the 
  INLA package (<http://www.r-inla.org>). Additionally, implements a log Gaussian Cox process likelihood for 
  modeling univariate and spatial point processes based on ecological survey data. See Yuan Yuan, 
  Fabian E. Bachl, Finn Lindgren, David L. Borchers, Janine B. Illian, Stephen T. Buckland, Havard Rue, 
  Tim Gerrodette (2017), <arXiv:1604.06013>.
Functionality to dynamically define R functions and S4 methods
 with 'inlined' C, C++ or Fortran code supporting the .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,
    selecting subsets using a genetic algorithm, creating interactive web maps,
    accessing color palettes, etc. Used to support packages and scripts written
    by researchers at the United States Geological Survey (USGS)
    Idaho National Laboratory (INL) Project Office.
Integrated joint analysis of multiple platform genomic data across biological gene sets or pathways using powerful variance-component based testing procedures.
A set of functions to analyse and compare texts, using classical 
  text mining	functions, as well as those from theoretical ecology.
It provides in-place operators for R 
    that are equivalent to '+=', '-=', '*=', '/=' in C++. 
    Those can be applied on integer|double vectors|matrices.
    You have also access to sweep operations (in-place).
Non-parametric resampling-based inference tests for ExPosition.
Provides tools for probabilistic taxon assignment with informatic sequence classification trees. See Wilkinson et al (2018) <doi:10.7287/peerj.preprints.26812v1>.
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.
Check one column or multiple columns of a dataframe
    using the preset basic checks or your own functions. Enables
    checks without knowledge of lapply() or sapply().
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.
A consistent API for estimating linear and generalized linear models with instrumental variables.
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.
Includes additional functionality for spatial interpolation in the intamap package, such as bias correction and network optimization.
Semiparametric regression models on the cumulative incidence function with interval-censored competing risks data as described in Bakoyannis, Yu, & Yiannoutsos (2017) <doi:10.1002/sim.7350>. The main function fits the proportional subdistribution hazards model (Fine-Gray model), the proportional odds model, and other models that belong to the class of semiparametric generalized odds rate transformation models.
The kernel of this 'Rcpp' based package is an efficient implementation of the generalized gradient projection method for spline function based constrained maximum likelihood estimator for interval censored survival data (Wu, Yuan; Zhang, Ying. Partially monotone tensor spline estimation of the joint distribution function with bivariate current status data. Ann. Statist. 40, 2012, 1609-1636 <doi:10.1214/12-AOS1016>). The key function computes the density function of the joint distribution of event time and the marker and returns the receiver operating characteristic (ROC) curve for the interval censored survival data as well as area under the curve (AUC).
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.
Provides a systematic framework for
 integrating multiple modalities of assays profiled on the same set of
 samples. The goal is to identify genes that are altered in cancer
 either marginally or consistently across different assays. The
 heterogeneity among different platforms and different samples are
 automatically adjusted so that the overall alteration magnitude can
 be accurately inferred. See Tong and Coombes (2012) 
 <doi:10.1093/bioinformatics/bts561>.
Offers modeling 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 (2017) <http://justinesarey.com/interaction-overconfidence.pdf> for controlling the false discovery 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.
Extensive penalized variable selection methods have been developed in the past two decades for analyzing high dimensional omics data, such as gene expressions, single nucleotide polymorphisms (SNPs), copy number variations (CNVs) and others. However, lipidomics data have been rarely investigated by using high dimensional variable selection methods. This package incorporates our recently developed penalization procedures to conduct interaction analysis for high dimensional lipidomics data with repeated measurements. The core module of this package is developed in C++. The development of this software package and the associated statistical methods have been partially supported by an Innovative Research Award from Johnson Cancer Research Center, Kansas State University.
Graphical User Interface allowing to determine the concentration in the sample in CFU per mL or in number of copies per mL provided to qPCR results after with or without PMA treatment. This package is simply to use because no knowledge in R commands is necessary. A graphic represents the standard curve, and a table containing the result for each sample is created.
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.
Performs diagnostic tests of multiplicative interaction models and plots non-linear marginal effects of a treatment on an outcome across different values of a moderator.
Computes interference color tables and plots customized Michel-Levy or Raith-Sorensen charts. Automatic interpretation of polarized-light microscopy images is still under development and will come soon.
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.
Allows the simulation of both the recruitment and treatment phase of a clinical trial. Based on these simulations, the timing of interim analyses can be assessed.
Interlinearized glossed texts (IGT) are used in descriptive linguistics for
 representing a morphological analysis of a text through a morpheme-by-morpheme gloss.
 'InterlineaR' provide a set of functions that targets several popular formats of IGT
 ('SIL Toolbox', 'EMELD XML') and that turns an IGT into a set of data frames following
 a relational model (the tables represent the different linguistic units: texts,
 sentences, word, morphems).
 The same pieces of software ('SIL FLEX', 'SIL Toolbox') typically produce dictionaries
 of the morphemes used in the glosses. 'InterlineaR' provide a function for turning
 the LIFT XML dictionary format into a set of data frames following a relational model
 in order to represent the dictionary entries, the sense(s) attached to the entries,
 the example(s) attached to senses, etc.
Search the Internet Archive, retrieve metadata, and download
    files.
Bivariate data interpolation on regular and irregular
  grids, either linear or using splines are the main part of this
  package.  It is intended to provide FOSS replacement functions for
  the ACM licensed akima::interp and tripack::tri.mesh functions.
  Currently the piecewise linear interpolation part of akima::interp
  (and also akima::interpp) is implemented in interp::interp, this
  corresponds to the call akima::interp(..., linear=TRUE) which is the
  default setting and covers most of akima::interp use cases in
  depending packages.  A re-implementation of Akimas spline
  interpolation (akima::interp(..., linear=FALSE)) is currently under
  development and will complete this package in a later
  version. Estimators for partial derivatives are already available,
  these are a prerequisite for the spline interpolation.  The basic
  part is currently a GPLed triangulation algorithm (sweep hull
  algorithm by David Sinclair) providing the starting point for the
  piecewise linear interpolator. As side effect this algorithm is also
  used to provide replacements for the basic functions of the tripack
  package which also suffer from the ACM restrictions.  All functions
  are designed to be backward compatible with their akima / tripack
  counterparts.
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).
Annotate and interpret deconvoluted mass spectra (mass*intensity pairs) from high resolution mass spectrometry devices.
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.
Provides an R version of the 'InterVA5' 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.
Functions for manipulating integer-bounded intervals including finding overlaps, piling and merging.
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 using integrative Non-negative Matrix factorization.
Implements %$% and %$%<- attribute operators and an object.info() function for printing objects with attributes, succinctly but informatively.
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 Integrated Regression method. Works for several 
  different fitting techniques.
Download financial data from the free 'Intrinio Stock API' (<https://intrinio.com/>).
    'Intrinio' offers a REST API which provides financial markets data 
    including intra-day stock prices, historical stock prices, technical indicators, company fundamentals, and more. 
    Complete documentation for the 'Intrinio Stock API' is available here: 
    <https://intrinio.com/documentation/api/>. To access the 'Intrinio Stock API',
    simply create a free account <https://intrinio.com/>.
A variety of methods for estimating intrinsic dimension of data sets (i.e the manifold or Hausdorff dimension of the support of the distribution that generated the data) as reviewed in Johnsson, K. (2016, ISBN:978-91-7623-921-6) and Johnsson, K., Soneson, C. and Fontes, M. (2015) <doi:10.1109/TPAMI.2014.2343220>. Furthermore, to evaluate the performance of these estimators, functions for generating data sets with given intrinsic dimensions are provided.
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.
Calculates event rates and compares means and variances of groups of interval data corrected for missed arrival observations.
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.
The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.
Determination of the optimal policy in inventory problems from a game-theoretic perspective.
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.
Calculates fundamental IO matrices (Leontief, Wassily W. (1951) <doi:10.1038/scientificamerican1051-15>, Ghosh, A. (1958) <doi:10.2307/2550694>); within period analysis via various rankings and coefficients (Sonis and Hewings (2006) <doi:10.1080/09535319200000013>, Blair and Miller (2009) <ISBN:978-0-521-73902-3>, Antras et al (2012) <doi:10.3386/w17819>, Hummels, Ishii, and Yi (2001) <doi:10.1016/S0022-1996(00)00093-3>); across period analysis with impact analysis (Dietzenbacher, van der Linden, and Steenge (2006) <doi:10.1080/09535319300000017>, Sonis, Hewings, and Guo (2006) <doi:10.1080/09535319600000002>); and a variety of table operators.
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. Budczies (2016 Mar 15) <doi:10.18632/oncotarget.7451>.
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.
Pre-processing tasks related to working with Eurostat's symmetric input-output
    tables and provide basic input-output economics calculations. The package is 
    a part of rOpenGov <http://ropengov.github.io/> to open source open government initiatives.
Basic I/O tools for streaming and data parsing.
Provides tools for passing messages between R processes. 
    Shiny Examples are provided showing how to perform useful tasks such as: 
    updating reactive values from within a future, progress bars for long running 
    async tasks, and interrupting async tasks based on user input.
An unsupervised clustering algorithm based on iterative pruning is for capturing population structure. This version supports ordinal data which can be applied directly to SNP data to identify fine-level population structure and it is built on the iterative pruning Principal Component Analysis ('ipPCA') algorithm as explained in Intarapanich et al. (2009) <doi:10.1186/1471-2105-10-382>. The 'IPCAPS' involves an iterative process using multiple splits based on multivariate Gaussian mixture modeling of principal components and 'Expectation-Maximization' clustering as explained in Lebret et al. (2015) <doi:10.18637/jss.v067.i06>. In each iteration, rough clusters and outliers are also identified using the function rubikclust() from the R package 'KRIS'.
Contains functions for formatting clinical trials data and implementing inverse probability of censoring weights to handle treatment switches when estimating causal treatment effect in randomized clinical trials.
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 geo-referenced point data via
    Inverse Path Distance Weighting. Useful for coastal marine applications where
    barriers in the landscape preclude interpolation with Euclidean distances.
Access data from IPEA (Instituto de Pesquisa Economica Aplicada) hosted
             at <http://www.ipeadata.gov.br>. It gets metadata and values for all series on 
             the website.
Calculates the RMS intrinsic and parameter-effects curvatures of a nonlinear regression model.
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.
Algorithms and utility functions for indoor positioning using fingerprinting techniques. 
    These functions are designed for manipulation of RSSI (Received Signal Strength Intensity) data 
    sets, estimation of positions,comparison of the performance of different models, and graphical 
    visualization of data. Machine learning algorithms and methods such as k-nearest neighbors or
    probabilistic fingerprinting are implemented in this package to perform analysis
    and estimations over RSSI data sets.
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).
Computes IPM for assessing variable importance for random forests. See details at I. Epifanio (2017) <DOI:10.1186/s12859-017-1650-8>.
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 done either via direct optimisation of the log-likelihood or 
    an EM algorithm.
This package provides functions that wrap popular phylogenetic software for sequence alignment, masking of sequence alignments, and estimation of phylogenies and ancestral character states.
An introductory probability and statistics textbook, alongside other supplementary materials.  The book is released under the GNU Free Documentation License.
Provides an R interface to the 'iPTMnet' database REST API, which can be used to retrieve 
    Post Translational Modification (PTM) data in systems biology context. This package handles all the aspects
    of communicating with the API, which involve sending the request, checking the error codes and parsing the
    response in a format that is ready to integrate into existing workflows.
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.
An easy way to import census, survey and geographic data provided by 'IPUMS'
    into R plus tools to help use the associated metadata to make analysis easier. 'IPUMS'
    data describing 1.4 billion individuals drawn from over 750 censuses and surveys is
    available free of charge from our website <https://ipums.org>.
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.
An implementation of the correction methods proposed by Shu and Yi (2017) <doi:10.1177/0962280217743777> for the inverse probability weighting (IPW) estimation of average treatment effect (ATE) with misclassified outcomes. Logistic regression model is assumed for treatment model for all implemented correction methods, and is assumed for the outcome model for the implemented doubly robust correction method.
In observational studies, the presence of confounding factors is common and the comparison of different groups of subjects requires adjustment. In this package, we propose simple functions to estimate adjusted survival curves and log-rank test based on inverse probability weighting (IPW).
Builds statistical control charts with exact limits for
        univariate and multivariate cases.
Estimate an optimal dynamic treatment regime using Interactive Q-learning.
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).
Provides implementation of various correlation coefficients of common use in
  Information Retrieval. In particular, it includes Kendall (1970, isbn:0852641990) tau coefficient
  as well as tau_a and tau_b for the treatment of ties. It also includes Yilmaz et al. (2008)
  <doi:10.1145/1390334.1390435> tauAP correlation coefficient, and versions tauAP_a and tauAP_b
  developed by Urbano and Marrero (2017) <doi:10.1145/3121050.3121106> to cope with ties.
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.
Calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements (Kovacic and Varnai (2014) <doi:10.1097/EDE.0000000000000139>). 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.
Iteratively grows feature weighted random forests and finds high-order feature interactions in a stable fashion.
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/>.
The R kernel for the 'Jupyter' environment executes R code which the front-end
    ('Jupyter Notebook' or other front-ends) submits to the kernel via the network.
Fast and memory efficient methods for truncated singular value
    decomposition and principal components analysis of large sparse and dense matrices.
Missing data are frequently encountered in high-dimensional data analysis, but they are usually difficult to deal with using standard algorithms, such as the EM algorithm and its variants. This package provides a general algorithm, the so-called Imputation Regularized Optimization (IRO) algorithm, for high-dimensional missing data problems. You can refer to Liang, F., Jia, B., Xue, J., Li, Q. and Luo, Y. (2018) at <arXiv:1802.02251> for detail.
Coefficients of Interrater Reliability and Agreement for
        quantitative, ordinal and nominal data: ICC, Finn-Coefficient,
        Robinson'A, Kendall's W, Cohen's Kappa, ...
Simulation and density evaluation of irregularly sampled stationary
  AR(1) processes with Gaussian errors using the algorithms described in
  Allévius (2018) <arXiv:1801.03791>.
Provides coefficients of interrater reliability, that are generalized to cope with randomly incomplete (i.e. unbalanced) datasets without any imputation of missing values or any (row-wise or column-wise) omissions of actually available data. Applied to complete (balanced) datasets, these generalizations yield the same results as the common procedures, namely the Intraclass Correlation according to McGraw & Wong (1996) <doi:10.1037/1082-989X.1.1.30> and the Coefficient of Concordance according to Kendall & Babington Smith (1939) <doi:10.1214/aoms/1177732186>.
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.
IRT-based methods are used to estimate IRT linking coefficients
    and to perform IRT equating under the common item non-equivalent group
    design. For the IRT linking procedure, the Mean/Mean, Mean/Sigma, Haebara, 
    and Stocking-Lord methods are available. For IRT test score equating, 
    the true score equating and observed score equating methods are available. 
    This package supports both dichotomous IRT models (1PL, 2PL and 3PL) and 
    polytomous IRT models (graded response model, partial credit model, 
    and generalized partial credit model). In addition to IRT linking and 
    equating capabilities, there are several useful functions such as importing 
    item and/or ability parameters from IRT software, generating simulated data, 
    estimating the standard error of equating due to the random selection of common items, 
    computing the conditional distribution of observed scores using the Lord-Wingersky
    recursion formula, computing item and test information functions, computing 
    item and test characteristic curve functions, and plotting item and test 
    characteristic curves and item and test information functions as well as scatter plots.
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.
Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.
Interactive shiny application for running Item Response Theory
    analysis. Provides graphics for characteristic and information curves.
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.
Reads the output of the 'PerkinElmer InForm' software <http://www.perkinelmer.com/product/inform-cell-analysis-one-seat-cls135781>. In addition to cell-density count, it can derive statistics of intercellular spatial distance for each cell-type.
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.
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.
Provides datasets for Introduction to Statistical Data Analysis for the Life Sciences
Functions for predicting and mapping potential and realized distributions of invasive species within the invaded range.
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.
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.
The development of ISM was made by Warfield in 1974. 
      ISM is the process of collaborating distinct or related essentials into a simplified and an organized format. Hence, ISM is a methodology that seeks the interrelationships among the various elements considered and endows with a hierarchical and multilevel structure.
      To run this package user needs to provide a matrix (VAXO) converted into 0's and 1's.
      Warfield,J.N. (1974) <doi:10.1109/TSMC.1974.5408524>
      Warfield,J.N. (1974, E-ISSN:2168-2909).
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.
The current version provides functions to compute, print and summarize the Index of Sensitivity to Nonignorability (ISNI) in the generalized linear model for independent data, and in the marginal multivariate Gaussian model and the linear mixed model for longitudinal/clustered data. It allows for arbitrary patterns of missingness in the regression outcomes  caused by dropout and/or intermittent missingness. One can compute the sensitivity index without estimating any nonignorable models or positing specific magnitude of nonignorability. Thus ISNI provides a simple quantitative assessment of how robust the standard estimates assuming missing at random  is with respect to the assumption of ignorability. For a tutorial, download at <http://huixie.people.uic.edu/Research/ISNI_R_tutorial.pdf>.	For more details, see Troxel Ma and Heitjan (2004) and Xie and Heitjan (2004) <doi:10.1191/1740774504cn005oa> and Ma Troxel and Heitjan (2005) <doi:10.1002/sim.2107> and  Xie (2008) <doi:10.1002/sim.3117> and  Xie (2012) <doi:10.1016/j.csda.2010.11.021> and Xie and Qian (2012) <doi:10.1002/jae.1157>.
Check if an 'externalptr' is a null pointer.
    R does currently not have a native function for that purpose.
    This package contains a C function that returns TRUE in case of a null pointer.
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 proportional hazards model.
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 'York', 'Titterington' and 'Ludwig' approaches. 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 total Pb-U, Pb-Pb, K-Ca, Re-Os, Sm-Nd, Lu-Hf, Rb-Sr and 230Th-U isochrons as well as 230Th-U evolution plots.
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 samples based on their isotopic ratios. This package is essentially a
  simplified interface to several other packages which implements a new
  statistical framework based on mixed models. 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 produced with 'raster' 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.
Accomplish high performance simulations in quantitative genetics. 
    The molecular genetic components are represented by R6/C++ classes and methods. 
    Mimic the meiosis recombination and de novo genetic variability by means a 
    count-location process (Karlin & Liberman, 1978) <doi:10.1073/pnas.75.12.6332>. 
    The core computational algorithm is implemented using 'Boost' dynamic bitsets 
    (Schaling, 2014) [ISBN:978-1937434366]. A mix between low and high level interfaces 
    provides great flexibility and allows user defined extensions and a wide range of 
    applications.
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 (see Teschendorff AE et al 2011, <doi: 10.1093/bioinformatics/btr171>).
Data sets and scripts for text examples and exercises in 
  P. Dalgaard (2008), `Introductory Statistics with R', 2nd ed., Springer Verlag, ISBN 978-0387790534.
Provides two record linkage data sets on the Italian Survey on Household and Wealth, 2008 and 2010, a sample survey conducted by the Bank of Italy every two years. The 2010 survey covered 13,702 individuals, while the 2008 survey covered 13,734 individuals. The following categorical variables are included in this data set: year of birth, working status, employment status, branch of activity, town size, geographical area of birth, sex, whether or not Italian national, and highest educational level obtained. Unique identifiers are available to assess the accuracy of one’s method. Please see Steorts (2015) <DOI:10.1214/15-BA965SI> to find more details about the data set.
Functions for analyzing multiple choice items. These analyses include the convertion of student response into binaty data (correct/incorrect), the computation of the number of corrected responses and grade for each subject, the calculation of item difficulty and discrimination, the computation of the frecuency and point-biserial correlation for each distractor and the graphical analysis of each item.
Three 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). The statistics reported in this package can be found in any measurement textbook such as Crocker and Algina (2006, ISBN:9780495395911).
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.
Iterator for generating permutations and combinations. They
    can be either drawn with or without replacement, or with distinct/ 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 performance. Note: 'iterpc' is no longer
    being maintained. Users are recommended to switch to 'arrangements'.
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.
Functions to clean and process international trade data into an international trade network (ITN) are provided. It then provides a set a functions to undertake analysis and plots of the ITN (extract the backbone, centrality, blockmodels, clustering). Examining the key players in the ITN and regional trade patterns.
Infers a topology of relationships between different datasets, such as multi-omics and phenotypic data recorded on the same samples. We based this methodology on the RV coefficient (Robert & Escoufier, 1976, <doi:10.2307/2347233>), a measure of matrix correlation, which we have extended for partial matrix correlations and binary data (Aben et al., 2018, <doi:10.1101/293993>).
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.
Maximin-projection learning (MPL, Shi, et al., 2018) is implemented for 
	recommending a meaningful and reliable individualized treatment regime for future 
	groups of patients based on the observed data from different populations with
	heterogeneity in individualized decision making. Q-learning and A-learning are
	implemented for estimating the groupwise contrast function that shares the same
	marginal treatment effects. The packages contains classical Q-learning and A-learning
	algorithms for a single stage study as a byproduct. More functions will be added
	at later versions.
Sequential advantage selection (SAS, Fan, Lu and Song, 2016) <arXiv:1405.5239> 
	and penalized A-learning (PAL, Shi, et al., 2018) methods are implement for selecting 
	important variables involved in optimal individualized (dynamic) treatment regime in 
	both single-stage or multi-stage studies.
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).
Provides functions for modeling and forecasting time series data. Forecasting is based on the innovations algorithm. A description of the innovations algorithm can be found in the textbook "Introduction to Time Series and Forecasting" by Peter J. Brockwell and Richard A. Davis. <http://www.springer.com/us/book/9781475777505>.
To enable 'iOS' App Developers to access iTunes App Store Ratings and Reviews using R to extract Basic App Information and Reviews submitted by their App users, Since Apple Store does not provide this straightforward.
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.
Compute the instrumental variables probit model using  the Amemiya's Generalized Least Squares estimators (Amemiya, Takeshi, (1978) <doi: 10.2307/1911443>).
Allows you to create an evidence factor (EX analysis) in an 
	instrumental variables regression model. Additionally, performs Sensitivity 
	analysis for OLS analysis, 2SLS analysis and EX analysis with interpretable 
	plotting and printing features.
Contains tools for instrumental variables estimation. Currently, non-parametric bounds, two-stage estimation and G-estimation are implemented. Balke, A. and Pearl, J. (1997) <doi:10.2307/2965583>, Vansteelandt S., Bowden J., Babanezhad M., Goetghebeur E. (2011) <doi:10.1214/11-STS360>.
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.
Calculate statistical significance of Jaccard/Tanimoto similarity
    coefficients for binary data.
Computing the original and modified jackknife estimates of Kaplan-Meier estimators.
Test for association between the observed data
	and their systematic patterns of variations.
	Systematic patterns may be captured by latent variables using principal component analysis (PCA), factor analysis (FA), and related methods. The jackstraw enables statistical testing for association between observed variables and latent variables, as captured by PCs or other estimates. Similarly, unsupervised clustering, such as K-means clustering, partition around medoids (PAM), and others, finds subpopulations among the observed variables. The jackstraw estimates statistical significance of cluster membership, including unsupervised evaluation of cell identities in single cell RNA-seq. P-values and posterior probabilities allows one to rigorously evaluate the strength of cluster membership assignments.
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. The package is described in Miettinen, Nordhausen and Taskinen (2017) <doi:10.18637/jss.v076.i02>.
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; provide quick counts of variable combinations (i.e., frequency
    tables and crosstabs); and isolate duplicate records. Other janitor functions
    nicely format the tabulation results. 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.
Client for the Directory of Open Access Journals ('DOAJ')
    (<https://doaj.org/>). 'API' documentation at
    <https://doaj.org/api/v1/docs>. Methods included for working with
    all 'DOAJ' 'API' routes: fetch article information by identifier,
    search for articles, fetch journal information by identifier,
    and search for journals.
Provides a new class of Bayesian meta-analysis models that we called "The Hierarchical Meta-Regression" (HMR). The aim of HMR is to incorporate into the meta-analysis, the data collection process, which results in a model for the internal and external validity bias. In this way, it is possible to combine studies of different types. For example, we can combine the results of randomized control trials (RCTs) with the results of observational studies (OS). The statistical methods and their applications are described in Verde, Ohmann, Morbach and Icks (2016) <doi:10.1002/sim.6809> and in Verde (2017) <doi:10.5772/intechopen.70231>.
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').
Provides a gridded classification of weather types by applying the Jenkinson and Collison classification. For a given region (it can be either local region or the whole map),it computes at each grid the 11 weather 
             types during the period considered for the analysis. See Otero et al., (2017) <doi:10.1007/s00382-017-3705-y> for more information.
Contains a selection of color palettes and 'ggplot2' themes designed by the package author.
Black-Scholes model [Black (1973) <doi:10.1086/260062>] is important to calculate option prices in the stock market and a variety of improved models are studied. In this package, I propose methods in order to calculate both Black-Scholes model and Jump diffusion model [Kou (2002) <doi:10.1287/mnsc.48.8.1086.166>] by Monte Carlo methods. This package can be used for computational finance.
Simplifies and extends data exchange between 'R' and 'Java'.
Provides a fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse Gaussian Graphical Models (JEEK). The JEEK algorithm can be used to fast estimate multiple related precision matrices in a large-scale. For instance, it can identify multiple gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogeneous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(jeek) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi "A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models" (ICML 2018) <arXiv:1806.00548>.
Manage project dependencies from your DESCRIPTION file. Create a reproducible virtual environment with minimal additional files in your project. Provides tools to add, remove, and update dependencies as well as install existing dependencies with a single function.
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.
Support the decision analysis of international assets selection and portfolio backtesting.
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. Reference: Danaher P, Wang P, Witten DM. (2013) 
        <doi:10.1111/rssb.12033>.
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.
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.
Approximate joint-inclusion probabilities in Unequal Probability Sampling, or compute Monte Carlo approximations of the first and second-order inclusion probabilities of a general sampling design as in Fattorini (2006) <doi:10.1093/biomet/93.2.269>.
Set of common functions used for manipulating colors,
    detecting and interacting with 'RStudio', modeling, formatting, determining
    users' operating system, feature scaling, and more!
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; Dimitris Rizopoulos (2016) <doi:10.18637/jss.v072.i07>.
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.
Fit joint models of continuous or ordinal longitudinal data and time-to-event data with competing risks. For a detailed information, see Robert Elashoff, Gang li and Ning Li (2016, ISBN:9781439807828) ; Robert M. Elashoff,Gang Li and Ning Li (2008) <doi:10.1111/j.1541-0420.2007.00952.x> ; Ning Li, Robert Elashoff, Gang Li and Jeffrey Saver (2010) <doi:10.1002/sim.3798> .
Joint mean and dispersion effects models fit the mean and dispersion parameters of a response variable by two separate linear models, the mean and dispersion submodels, simultaneously. It also allows the users to choose either the deviance or the Pearson residuals as the response variable of the dispersion submodel. Furthermore, the package provides the possibility to nest the submodels in one another, if one of the parameters has significant explanatory power on the other. Wu & Li (2016) <doi:10.1016/j.csda.2016.04.015>.
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.
Fit joint mean-correlation models for discrete longitudinal data (Tang CY,Zhang W,
    Leng C, 2017 <doi:10.5705/ss.202016.0435>).
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.
Computes the Jackknife Mutual Information (JMI) between two random vectors and provides the p-value for dependence tests. See Zeng, X., Xia, Y. and Tong, H. (2018) <doi:10.1073/pnas.1715593115>.
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.
A suite of common statistical methods such as descriptives,
    t-tests, ANOVAs, regression, correlation matrices, proportion tests,
    contingency tables, and factor analysis. This package is also useable from
    the 'jamovi' statistical spreadsheet (see <https://www.jamovi.org> for more
    information).
Methods to access data sets from the 'jamovi' statistical
  spreadsheet (see <https://www.jamovi.org> for more information) from R.
A framework for creating rich interactive analyses for the jamovi
    platform (see <https://www.jamovi.org> for more information).
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.
Analysis of repeated measurements and time-to-event data via random
    effects joint models. Fits the joint models proposed by Henderson and colleagues
    <doi:10.1093/biostatistics/1.4.465> (single event time) and by Williamson and
    colleagues (2008) <doi:10.1002/sim.3451> (competing risks events time) to a
    single continuous repeated measure. The time-to-event data is modelled using a 
    (cause-specific) Cox proportional hazards regression model with time-varying 
    covariates. The longitudinal outcome is modelled using a linear mixed effects
    model. The association is captured by a latent Gaussian process. The model is 
    estimated using am Expectation Maximization algorithm. Some plotting functions 
    and the variogram are also included. This project is funded by the Medical 
    Research Council (Grant numbers G0400615 and MR/M013227/1).
Fits joint models of the type proposed by Henderson and colleagues 
    (2000) <doi:10.1093/biostatistics/1.4.465>, but extends to the multi-study, 
    meta-analytic case. Functions for meta-analysis of a single longitudinal and 
    a single  time-to-event outcome from multiple studies using joint models.  
    Options to produce plots for multi study joint data, to pool joint model 
    fits from 'JM' and 'joineR' packages in a two stage meta-analysis, and to 
    model multi-study joint data in a one stage meta-analysis.
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 methods under
 the joint frailty-copula model between tumour progression and death for meta-analysis.
 A penalized likelihood is employed for estimating model parameters, where the baseline hazard functions are approximated by smoothing splines.
 The methods are applicable for meta-analytic data combining several studies.
 The methods can analyze data having information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression).
 See Emura et al. (2015) <doi:10.1177/0962280215604510> and
 Emura et al. (2017) <doi:10.1177/0962280216688032> for details.
 Survival data from ovarian cancer patients are also available.
Provides joint analysis and imputation of (generalized) 
    linear regression models, (generalized) linear mixed models and parametric
    survival models with incomplete (covariate) data in the Bayesian framework.
    The package performs some preprocessing of the data and creates a 'JAGS'
    model, which will then automatically be passed to 'JAGS' 
    <http://mcmc-jags.sourceforge.net> with the help of 
    the package 'rjags'.
    It also provides summary and plotting functions for the output.
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.
Methods for fast segmentation of multivariate
    signals into piecewise constant profiles and for generating realistic
    copy-number profiles. A typical application is the joint segmentation of total
    DNA copy numbers and allelic ratios obtained from Single Nucleotide Polymorphism
    (SNP) microarrays in cancer studies. The methods are described in Pierre-Jean, 
    Rigaill and Neuvial (2015) <doi:10.1093/bib/bbu026>.
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 (Carpenter and Kenward, 2013) <doi: 10.1002/9781119942283>.
 Novel aspects of 'jomo' are the possibility of handling binary and categorical data through latent normal variables, the option to use cluster-specific covariance matrices and to impute compatibly with the substantive model.
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 and area level EBLUP estimators as well as the estimators of their MSE also under heteroscedasticity. The package further documents the publications Breidenbach and Astrup (2012) <DOI:10.1007/s10342-012-0596-7>, Breidenbach et al. (2016) <DOI:10.1016/j.rse.2015.07.026> and Breidenbach et al. (2018 in press). The vignette further explains the use of the implemented functions.
Read and write JSON Web Keys (JWK, rfc7517), generate and verify JSON
    Web Signatures (JWS, rfc7515) and encode/decode JSON Web Tokens (JWT, rfc7519).
    These standards provide modern signing and encryption formats that are the basis
    for services like OAuth 2.0 or LetsEncrypt and are natively supported by browsers
    via the JavaScript WebCryptoAPI.
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 125m) 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>). 
    This package provide map data is based on the Digital Map 25000 (Map Image) published 
    by Geospatial Information Authority of Japan (Approval No.603FY2017 information usage <http://www.gsi.go.jp>).
Client for 'jq', a 'JSON' processor (<https://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 utilities for working with JavaScript syntax in R.
    Includes tools to parse, tokenize, compile, validate, reformat, optimize 
    and analyze JavaScript code.
Maximum likelihood estimation for the semiparametric joint modeling of survival and longitudinal data.
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.
Converts 'R' objects into Javascript Object Notation (JSON) using 
    the 'rapidjsonr' library <https://CRAN.R-project.org/package=rapidjsonr>.
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.
Interface to 'JSON-stat' <https://json-stat.org/>,
  a simple lightweight 'JSON' format for data dissemination.
Uses the node library 'is-my-json-valid' to validate 'JSON' against
    a 'JSON' schema.
Provides a high-level integration for the 'Java' platform that makes 'Java' objects easy to use from within 'R'; provides a unified interface to integrate 'R' with several programming languages; and features extensive data exchange between 'R' and 'Java'. The 'jsr223'-supported programming languages include 'Groovy', 'JavaScript', 'JRuby' ('Ruby'), 'Jython' ('Python'), and 'Kotlin'. Any of these languages can use and extend 'Java' classes in natural syntax. Furthermore, solutions developed in any of the 'jsr223'-supported languages are also accessible to 'R' developers. The 'jsr223' package also features callbacks, script compiling, and string interpolation. In all, 'jsr223' significantly extends the computing capabilities of the 'R' software environment.
Functions and helpers to import metadata, ngrams and full-texts 
    delivered by Data for Research by JSTOR.
Create and customize interactive trees using the 'jQuery' 'jsTree' <https://www.jstree.com/>
    plugin library and the 'htmlwidgets' package. These trees can be used
    directly from the R console, from 'RStudio', in Shiny apps and R Markdown documents.
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.
This is a collection of tools that the author (Jacob) has written
  for the purpose of more efficiently understanding and sharing the results of
  (primarily) regression analyses. There are a number of functions focused
  specifically on the interpretation and presentation of interactions.
  Just about everything supports models from the survey package.
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 long-term forecast model called "Jubilee-Tectonic model" is implemented to forecast future returns of the U.S. stock market, Treasury yield, and gold price. The five-factor model can forecast the 10-year and 20-year future equity returns with high R-squared above 80 percent. It is based on linear growth and mean reversion characteristics in the U.S. stock market. In addition, this model enhances the CAPE model by introducing the hypothesis that there are fault lines in the historical CAPE, which can be calibrated and corrected through statistical learning.
jug is a web framework aimed at easily building APIs. It is mostly aimed at exposing R functions, 
    models and visualizations to third-parties by way of http requests.
Generates image data for fractals (Julia and Mandelbrot
        sets) on the complex plane in the given region and resolution.
Provides an R interface to 'Julia',
    which is a high-level, high-performance dynamic programming language
    for numerical computing, see <https://julialang.org/> for more information.
    It provides a high-level interface as well as a low-level interface.
    Using the high level interface, you could call any 'Julia' function just like
    any R function with automatic type conversion. Using the low level interface,
    you could deal with C-level SEXP directly while enjoying the convenience of
    using a high-level programming language like 'Julia'.
A fast simulation on stochastic volatility model, with jump tests, p-values pooling, and FDR adjustments.
Individual based simulations of hybridizing populations, where the accumulation of junctions is tracked. Furthermore, mathematical equations are provided to verify simulation outcomes. Both simulations and mathematical equations are based on Janzen (2018) <doi:10.1101/058107>.
Provides a full implementation of the 'Jupyter' <http://jupyter.org/> messaging protocol in C++ by leveraging 'Rcpp' and 'Xeus' <https://github.com/QuantStack/xeus>.
             'Jupyter' supplies an interactive computing environment and a messaging protocol defined over 'ZeroMQ' for multiple programming languages. This package implements
             the 'Jupyter' kernel interface so that 'R' is exposed to this interactive computing environment. 'ZeroMQ' functionality is provided by the 'pbdZMQ' package.
             'Xeus' is a C++ library that facilitates the implementation of kernels for 'Jupyter'. Additionally, 'Xeus' provides an interface to libraries that exist in the 'Jupyter' ecosystem for building widgets,
             plotting, and more <https://blog.jupyter.org/interactive-workflows-for-c-with-jupyter-fe9b54227d92>. 'JuniperKernel' uses 'Xeus' as a library for the 'Jupyter' messaging protocol.
The 'Junar' API is a commercial platform to organize and publish data
    <http://www.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 core R 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.
Build complex HTML or 'LaTeX' tables using 'kable()' from 'knitr' 
    and the piping syntax from 'magrittr'. Function 'kable()' is a light weight 
    table generator coming from 'knitr'. This package simplifies the way to 
    manipulate the HTML or 'LaTeX' codes generated by 'kable()' and allows 
    users to construct complex tables and customize styles using a readable 
    syntax.
Implementation of various kernel adaptive methods in nonparametric curve
    estimation like density estimation as introduced in Stute and Srihera (2011)
    <doi:10.1016/j.spl.2011.01.013> and Eichner and Stute (2013)
    <doi:10.1016/j.jspi.2012.03.011> for pointwise estimation, and like regression
    as described in Eichner and Stute (2012) <doi:10.1080/10485252.2012.760737>.
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.
Methods to extract information on pathways, genes and SNPs from
    online databases. It provides functions for data preparation and evaluation
    of genetic influence on a binary outcome using the logistic kernel machine
    test (LKMT). Three different kernel functions are offered to analyze genotype
    information in this variance component test: A linear kernel, a size-adjusted
    kernel and a network based kernel.
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.
Tests the homogeneity of intraclass kappa statistics obtained from independent studies or a stratified study with binary results. It is desired to compare the kappa statistics obtained in multi-center studies or in a single stratified study to give a common or summary kappa using all available information. If the homogeneity test of these kappa statistics is not rejected, then it is possible to make inferences over a single kappa statistic that summarizes all the studies. Jun-mo Nam (2003) <doi:10.1111/j.0006-341X.2003.00118.x> Jun-mo Nam (2005) <doi:10.1002/sim.2321>Mousumi Banerjee, Michelle Capozzoli, Laura McSweeney,Debajyoti Sinha (1999) <doi:10.2307/3315487> Allan Donner, Michael Eliasziw, Neil Klar (1996) <doi:10.2307/2533154>.
Offers a graphical user interface for the evaluation of inter-rater agreement with Cohen's and Fleiss' Kappa. The calculation of kappa statistics is done using the R package 'irr', so that 'KappaGUI' is essentially a Shiny front-end for 'irr'.
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.
Contains basic tools for sample size estimation in studies of interobserver/interrater agreement (reliability).  Includes 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.
An R graphical user interface for microclimate time series, with an emphasis on underground environments. 'KarsTS' provides linear and nonlinear methods, including recurrence analysis (Marwan et al. (2007) <10.1016/j.physrep.2006.11.001>) and filling methods (Moffat et al. (2007) <doi:10.1016/j.agrformet.2007.08.011>), as well as tools to manipulate easily time series and gap sets.
Many data science problems reduce to operations on very tall,
    skinny matrices.  However, sometimes these matrices can be so tall that they
    are difficult to work with, or do not even fit into main memory.  One
    strategy to deal with such objects is to distribute their rows across
    several processors.  To this end, we offer an 'S4' class for tall, skinny,
    distributed matrices, called the 'shaq'.  We also provide many useful
    numerical methods and statistics operations for operating on these
    distributed objects.  The naming is a bit "tongue-in-cheek", with the class
    a play on the fact that 'Shaquille' 'ONeal' ('Shaq') is very tall, and he
    starred in the film 'Kazaam'.
Create, Simulate, Fit, Solve k-Cube Thurstonian IRT Models
Provides an efficient implementation of univariate local polynomial
    kernel density estimators that can handle bounded and discrete data. See 
    Geenens (2014) <arXiv:1303.4121>, 
    Geenens and Wang (2018) <arXiv:1602.04862>, 
    Nagler (2018a) <arXiv:1704.07457>, 
    Nagler (2018b) <arXiv:1705.05431>.
Provides fast implementations of kernel smoothing techniques for
    bivariate copula densities, in particular density estimation and resampling,
    see Nagler (2018) <doi:10.18637/jss.v084.i07>.
Handles univariate non-parametric density estimation with 
    parametric starts and asymmetric kernels in a simple and flexible way. 
    Kernel density estimation with parametric starts involves fitting a
    parametric density to the data before making a correction with kernel 
    density estimation, see Hjort & Glad (1995) <doi:10.1214/aos/1176324627>.
    Asymmetric kernels make kernel density estimation more efficient on bounded
    intervals such as (0, 1) and the positive half-line. Supported asymmetric 
    kernels are the gamma kernel of Chen (2000) <doi:10.1023/A:1004165218295>,
    the beta kernel of Chen (1999) <doi:10.1016/S0167-9473(99)00010-9>, and the
    copula kernel of Jones & Henderson (2007) <doi:10.1093/biomet/asm068>.
    User-supplied kernels, parametric starts, and bandwidths are supported.
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.
Implements the vine copula based kernel density estimator of
    Nagler and Czado (2016) <doi:10.1016/j.jmva.2016.07.003>. The estimator does
    not suffer from the curse of dimensionality and is therefore well suited for
    high-dimensional applications.
Density, distribution function, quantile function and random generation
             for the K-distribution. A plotting function that plots data on Weibull
             paper and another function to draw additional lines. See results from package in T Lamont-Smith (2018), submitted J. R. Stat. Soc.
Provides various tools for working with multidimensional
  data in R and C++, including extremely fast nearest-neighbor- and range-
  queries without the overhead of linked tree nodes.
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.
Kendall random walks are a continuous-space Markov chains generated
             by the Kendall generalized convolution. This package provides tools
             for simulating these random walks and studying distributions
             related to them. For more information about Kendall random walks see Jasiulis-Gołdyn (2014) <arXiv:1412.0220>.
Calculate the kernel-smoothed nonparametric estimator for the exposure distribution in presence of detection limits.
Demo and datasets accompanying the book "Knowledge Synthesis: From Experimental Network to Meta-Analysis" (Makowski, Piraux and Brun, in preparation for December 2017).
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.
Interface to 'Keras' <https://keras.io>, a high-level neural
  networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation,
  supports both convolution based networks and recurrent networks (as well as
  combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
Adds a high-level interface for 'keras' neural nets. kms() fits neural net and accepts R formulas to aid data munging and hyperparameter selection. kms() can optionally accept a compiled keras_sequential_model() from 'keras'. 
    kms() accepts a number of parameters (like loss and optimizer) and splits the data into (optionally sparse) test and training matrices. kms() facilitates setting advanced hyperparameters (e.g., regularization). kms() returns a single object with predictions, a confusion matrix, and function call details.
Provides a consistent interface to the 'Keras' Deep Learning Library
  directly from within R. 'Keras' provides specifications for describing dense
  neural networks, convolution neural networks (CNN) and recurrent neural networks
  (RNN) running on top of either 'TensorFlow' or 'Theano'. Type conversions between
  Python and R are automatically handled correctly, even when the default
  choices would otherwise lead to errors. Includes complete R documentation
  and many working examples.
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.
A novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination.
Smoothed bootstrap and functions for random generation from
             univariate and multivariate kernel densities. It does not
             estimate kernel densities.
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: Gross, M. and Rendtel, U. (2016) (<doi:10.1093/jssam/smw011>).
    Additionally, bivariate non-parametric density estimation for rounded data, Gross, M. et al. (2016) (<doi:10.1111/rssa.12179>),
    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.
Provides wind energy practitioners with an effective machine learning-based
    tool that estimates a multivariate power curve and predicts the wind power output
    for a specific environmental condition.
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.
Implements functions from 'EXPOKIT' 
			 (<https://www.maths.uq.edu.au/expokit/>) to calculate 
			 matrix exponentials, Sidje RB, (1998) <doi:10.1145/285861.285868>. 
			 Includes functions for small dense matrices along with functions 
			 for large sparse matrices. The functions for large sparse matrices 
			 implement Krylov subspace methods which help minimise the 
			 computational complexity for matrix exponentials. 'Kexpmv' can be 
			 utilised to calculate both the matrix exponential in isolation 
			 along with the product of the matrix exponential and a vector.
Control your keyboard and mouse with R code, simulate key press and mouse click.
Tools for keeping track of information, named "keys", about rows of
    data frame like objects. This is done by creating special attribute "keys"
    which is updated after every change in rows (subsetting, ordering, etc.).
    This package is designed to work tightly with 'dplyr' package.
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.
Platform independent 'API' to access the operating system's
    credential store. Currently supports: 'Keychain' on 'macOS', Credential
    Store on 'Windows', the Secret Service 'API' on 'Linux', and a simple,
    platform independent store implemented with environment variables.
    Additional storage back-ends can be added easily.
Decrypts passwords stored in the Gnome Keyring, macOS Keychain and
    strings encrypted with the Windows Data Protection API.
State space modelling is an efficient and flexible framework for 
    statistical inference of a broad class of time series and other data. KFAS 
    includes computationally efficient 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. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.
Kernel Fisher Discriminant Analysis (KFDA) is performed using Kernel Principal Component Analysis (KPCA) and Fisher Discriminant Analysis (FDA).
    There are some similar packages. First, 'lfda' is a package that performs Local Fisher Discriminant Analysis (LFDA) and performs other functions.
    In particular, 'lfda' seems to be impossible to test because it needs the label information of the data in the function argument. Also, the 'ks' package has a limited dimension, which makes it difficult to analyze properly.
    This package is a simple and practical package for KFDA based on the paper of Yang, J., Jin, Z., Yang, J. Y., Zhang, D., and Frangi, A. F. (2004) <DOI:10.1016/j.patcog.2003.10.015>.
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.
Aids in identifying the Koeppen-Geiger (KG) climatic zone for 
    a given location. The Koeppen-Geiger climate zones were first published in 1884, as a system
    to classify regions of the earth by their relative heat and humidity through the year, for 
    the benefit of human health, plant and agriculture and other human activity [1]. This climate
    zone classification system, applicable to all of the earths surface, has continued to be 
    developed by scientists up to the present day.  Recently one of use (FZ) has published updated,
    higher accuracy KG climate zone definitions [2]. In this package we use these updated 
    high-resolution maps as the data source [3]. We provide functions that return the KG climate zone 
    for a given longitude and lattitude, or for a given United States zip code. In addition
    the CZUncertainty() function will check climate zones nearby to check if the given location
    is near a climate zone boundary. In addition an interactive shiny app is provided to define 
    the KG climate zone for a given longitude and lattitude, or United States zip code. 
    Digital data, as well as animated maps, showing the shift of the climate zones are provided 
    on the following website <http://koeppen-geiger.vu-wien.ac.at>.
    This work was supported by the DOE-EERE SunShot award DE-EE-0007140.
     [1] W. Koeppen, (2011) <doi:10.1127/0941-2948/2011/105>.
     [2] F. Rubel and M. Kottek, (2010) <doi:10.1127/0941-2948/2010/0430>.
     [3] F. Rubel, K. Brugger, K. Haslinger, and I. Auer, (2016) <doi:10.1127/metz/2016/0816>.
The kernel ridge regression and the gradient matching algorithm proposed in Niu et al. (2016) <http://jmlr.org/proceedings/papers/v48/niu16.html> and the warping algorithm proposed in Niu et al. (2017) <DOI:10.1007/s00180-017-0753-z> are implemented for parameter inference in differential equations. Four schemes are provided for improving parameter estimation in odes by using the odes regularisation and warping.
Restore underlining numeric data from rating history graph of 
    KGS (an online platform of the game of go, <http://www.gokgs.com/>). 
    A shiny application is also provided.
Colour schemes for archaeological data visualization. Provides 
    Paul Tol's colour schemes and several thematic palettes (geologic timescale, 
    etc.) with scales for 'ggplot2'.
Contains kidney care oriented functions.
			 Current version contains only function for calculation of Kidney Donor Risk Index and Kidney Donor Profile Index for kidney transplant donors by Rao et al. (2009) <doi:10.1097/TP.0b013e3181ac620b>.
			 Citation: Bikbov B. R open source programming code for calculation of the Kidney Donor Profile Index and Kidney Donor Risk Index. Kidney Diseases (2018) <doi:10.1159/000492427>.
A collection of useful functions not found anywhere else,
        mainly for programming: Pretty intervals, generalized lagged
        differences, checking containment in an interval, 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.
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.
Multi-modal magnetic resonance imaging ('MRI')
    data from the 'Kirby21' 'reproducibility' study
    <https://www.nitrc.org/projects/multimodal/>, including functional
    and structural imaging.
Functional magnetic resonance imaging ('fMRI')
       data from the 'Kirby21' 'reproducibility' study
       <doi:10.1016/j.neuroimage.2010.11.047>.
Structural T1 magnetic resonance imaging ('MRI')
       data from the 'Kirby21' 'reproducibility' study
       <doi:10.1016/j.neuroimage.2010.11.047>.
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, or analyze
    measured responses. There are
    two classes of models here: (1) for sealed wells, based on the model of 
    Kitagawa et al (2011, <doi:10.1029/2010JB007794>), 
    and (2) for open wells, based on the models of
    Cooper et al (1965, <doi:10.1029/JZ070i016p03915>), 
    Hsieh et al (1987, <doi:10.1029/WR023i010p01824>), 
    Rojstaczer (1988, <doi:10.1029/JB093iB11p13619>), and 
    Liu et al (1989, <doi:10.1029/JB094iB07p09453>). 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,
	 e.g. regularized discriminant analysis, sknn() kernel-density naive Bayes, 
	 an interface to 'svmlight' and stepclass() wrapper variable selection 
   	 for supervised classification, partimat() visualization of classification rules 
         and shardsplot() of cluster results as well as kmodes() clustering for categorical data, 
	 corclust() variable clustering, variable extraction from different variable clustering models 
         and weight of evidence preprocessing.
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.
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
K-means implementation is based on "Yinyang K-Means: A Drop-In Replacement of 
	the Classic K-Means with Consistent Speedup". While it introduces some overhead and many 
	conditional clauses which are bad for CUDA, it still shows 1.6-2x speedup against the Lloyd 
	algorithm. K-nearest neighbors employ the same triangle inequality idea and require 
	precalculated centroids and cluster assignments, similar to the flattened ball tree.
Compute p-values of metabolite differential expression analysis using the kernel-based approach.
Algorithms of distance-based k-medoids clustering: 
  simple and fast k-medoids (Park and Jun, 2009) <doi:10.1016/j.eswa.2008.01.039>,
  ranked k-medoids (Zadegan et al., 2013) <doi:10.1016/j.knosys.2012.10.012>, and
  step k-medoids (Yu et al., 2018) <doi:10.1016/j.eswa.2017.09.052>.
  Calculate distances for mixed variable data such as 
  Gower (1971) <doi:10.2307/2528823>,
  Wishart (2003) <doi:10.1007/978-3-642-55721-7_23>, 
  Podani (1999) <doi:10.2307/1224438>,
  Huang (1997) <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.94.9984&rep=rep1&type=pdf>,
  Harikumar and PV (2015) <doi:10.1016/j.procs.2015.10.077>, and
  Ahmad and Dey (2007) <doi:10.1016/j.datak.2007.03.016>.
  Cluster validation applies bootstrap procedure producing a heatmap with a flexible
  reordering matrix algorithm such as complete, ward, or average linkages.
Contains tools for rapidly computing distance matrices 
    and clustering large sequence datasets using fast alignment-free 
    k-mer counting and recursive k-means partitioning. 
    See Vinga and Almeida (2003) <doi:10.1093/bioinformatics/btg005> 
    for a review of k-mer counting methods and applications for 
    biological sequence analysis.
Contains tools to calculate similarity score matrix for DNA k-mers. The pairwise
            similarity score is calculated using PAM or BLOSUM substitution matrix. The 
            results are evaluated by similarity score calculated by Needleman-Wunsch 
            (1970) <doi:10.1016/0022-2836(70)90057-4> global or Smith-Waterman 
            (1981) <doi:10.1016/0022-2836(81)90087-5> local alignment. Higher similarity
            score indicates more similar sequences for BLOSUM and less similar sequences
            for PAM matrix; 30, 40, 70, 120, 250 and 62, 45, 50, 62, 80, 100 matrix 
            versions are available for PAM and BLOSUM, respectively.
Gene based association test between a group of SNPs and traits
    including familial (both continuous and binary) traits, multivariate
    continuous (both independent and familial) traits, longitudinal continuous
    traits and survival traits. Part of methods were previously published in
    Maity et al (2012) <doi:10.1002/gepi.21663>, Chen et al (2013)
    <doi:10.1002/gepi.21703>, Chen et al (2014) <doi:10.1002/gepi.21791>, Yan
    et al (2015) <doi:10.1159/000375409>, Yan et al (2015)
    <doi:10.1534/genetics.115.178590> and Yan et al (2015)
    <doi:10.1159/000445057>.
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 (Allignol and Beyersmann, 2010 <doi:10.1093/biostatistics/kxq018>).
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 <http://getbootstrap.com/> HTML reports from 'knitr'
    'rmarkdown'.
Provides a progress bar similar to 'dplyr' that can write progress out to a 
    variety of locations, including stdout(), stderr(), or from file(). Useful when using 'knitr' or 'rmarkdown',
    and you still want to see progress of calculations in the terminal.
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.
Two main functionalities are provided. One of them is predicting values with 
    k-nearest neighbors algorithm and the other is optimizing the parameters k and d of the algorithm.
    These are carried out in parallel using multiple threads.
The knockoff filter is a general procedure for controlling the false discovery rate (FDR)
  when performing variable selection. 
  For more information, see the website below and the accompanying paper: Candes et al., 
  "Panning for Gold: Model-X Knockoffs for High-dimensional Controlled Variable Selection", 
  2016, <arXiv:1610.02351>.
Using this package, users can access to the largest collection of public data and statistics on the Internet featuring about 2.5 billion time series from thousands of sources collected in 'Knoema' repository and use rich R calculations in order to analyze the data. Because data in 'Knoema' is time series data, 'Knoema' function offers data in a number of formats usable in R such as 'ts', 'xts' or 'zoo'. For more information about 'Knoema' API go to <https://knoema.com/dev/docs>.
The k-means 'NUMA' Optimized Routine library or 'knor' is a highly optimized and fast library for computing k-means in parallel with accelerations for Non-Uniform Memory Access ('NUMA') architectures.
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 or polygons. 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.
KODAMA algorithm is an unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. The algorithm was published by Cacciatore et al. 2014 <DOI:10.1073/pnas.1220873111>. Addition functions was introduced by Cacciatore et al. 2017 <DOI:10.1093/bioinformatics/btw705> to facilitate the identification of key features associated with the generated output and are easily interpretable for the user. Cross-validated techniques are also included in this package.
Read Swiss time series data from the 'KOF Datenservice' API, <https://datenservice.kof.ethz.ch>. The API provides macroeconomic survey data, business cycle and further macro economic time series about Switzerland. The package itself is a set of wrappers around the 'KOF Datenservice' API. The 'kofdata' package is able to consume public information as well as data that requires an API token.
Provides a function that uses a genetic algorithm to search for a subset
 of 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. Population objective function values may 
 optionally be evaluated in parallel.
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 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.
R Markdown templates based on the 'KOMA-Script' classes for
  LaTeX, additionally offering cross-referencing via the 'bookdown' package.
An R Markdown template for writing beautiful yet versatile letters,
  using the 'KOMA-Script' letter class 'scrlttr2' and an adaptation of the
  'pandoc-letter' template. 'scrlttr2' provides layouts for many different
  window envelope types and the possibility to define your own.
Statistical methods that quantify the conditions necessary to alter
    inferences, also known as sensitivity analysis, are becoming increasingly
    important to a variety of quantitative sciences. A series of recent works,
    including Frank (2000) <doi:10.1177/0049124100029002001> and Frank et al.
    (2013) <doi:10.3102/0162373713493129> extend previous sensitivity analyses
    by considering the characteristics of omitted variables or unobserved cases
    that would change an inference if such variables or cases were observed. These
    analyses generate statements such as "an omitted variable would have to be
    correlated at xx with the predictor of interest (e.g., treatment) and outcome
    to invalidate an inference of a treatment effect". Or "one would have to replace
    pp percent of the observed data with null hypothesis cases to invalidate the
    inference". We implement these recent developments of sensitivity analysis and
    provide modules to calculate these two robustness indices and generate such
    statements in R. In particular, the functions konfound(), pkonfound() and 
    mkonfound() allow users to calculate the robustness of inferences for a user's 
    own model, a single published study and multiple studies respectively.
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. Note: For full functionality a local installation of
        TreeTagger is recommended. It is also recommended to not load
        this package directly, but by loading one of the available
        language support packages from the 'l10n' repository
        <https://undocumeantit.github.io/repos/l10n>. '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, request
        features, or discuss the development of the package, please
        subscribe to the koRpus-dev mailing list
        (<http://korpusml.reaktanz.de>).
Adds support for the English language to the 'koRpus'
        package. To ask for help, report bugs, suggest feature
        improvements, or discuss the global development of the package,
        please consider subscribing to the koRpus-dev mailing list
        (<http://korpusml.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. Kim
    (2018) <DOI:10.1080/00949655.2017.1392527> proposed a fast computational method
    which enables practitioners to compute the minimum distance estimator of the vector
    of general multiple regression parameters for several integrating measures. 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.
    The new version is written in Rcpp and dramatically reduces computational time.
Cubic spline fitting along with knot selection, includes support for additional variables.
Kernel PC (kPC) algorithm for causal structure learning and causal inference using graphical models. kPC is a version of PC algorithm that uses kernel based independence criteria in order to be able to deal with non-linear relationships and non-Gaussian noise.
The input argument k which is the number of clusters is needed to start all of the partitioning clustering algorithms. In unsupervised learning applications, an optimal value of this argument is widely determined by using the internal validity indexes. Since these indexes suggest a k value which is computed on the clustering results after several runs of a clustering algorithm they are computationally expensive. On the contrary, 'kpeaks' enables to estimate k before running any clustering algorithm. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in a data set.
Functions that implement the known population median test.
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.
Implements different methods for spatial statistics, in particular focused in
    Kriging based models. We count with different implemented models, simple, ordinary and 
    universal forms of Kriging, co-Kriging and regression Kriging models. Includes, multivariate 
    sensitivity analysis under an approximation designed over reproducing kernel Hilbert spaces and 
    computation of Sobol indexes under this framework.
Estimates kriging models for geographical point-referenced data. Method is described in Monogan and Gill (2016) <doi:10.1017/psrm.2015.5>.
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.
Provides useful functions which are needed for bioinformatic analysis such as calculating linear principal components from numeric data and Single-nucleotide polymorphism (SNP) dataset, calculating fixation index (Fst) using Hudson method, creating scatter plots in 3 views, handling with PLINK binary file format, detecting rough structures and outliers using unsupervised clustering, and calculating matrix multiplication in the faster way for big data.
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.
Solves kernel ridge regression, within the the mixed model framework, for the linear, polynomial, Gaussian, Laplacian and ANOVA kernels. The model components (i.e. fixed and random effects) and variance parameters are estimated using the expectation-maximization (EM) algorithm. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available. The kernel ridge mixed model (KRMM) is described in Jacquin L, Cao T-V and Ahmadi N (2016) A Unified and Comprehensible View of Parametric and Kernel Methods for Genomic Prediction with Application to Rice. Front. Genet. 7:145. <doi:10.3389/fgene.2016.00145>.
Kernel smoothers for univariate and multivariate data, including densities, density derivatives, cumulative distributions, clustering, classification, density ridges, significant modal regions, and two-sample hypothesis tests. Duong (2017) <doi:10.18637/jss.v021.i07>.
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.
Predicts a protein's residue contact map, based on the estimation of the corresponding knob-socket list. For more details, please refer to our paper:  Q. Li, D. B. Dahl, M. Vannucci, H. Joo, J. W. Tsai (2016), KScons: A Bayesian Approach for Protein Residue Contact Prediction using the Knob-socket Model of Protein Tertiary Structure, Bioinformatics, 32(24): 3774-3781 <doi:10.1093/bioinformatics/btw553>.
Implements the Lilliefors-corrected Kolmogorov-Smirnov 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>.
Infers relative kinase activity from phosphoproteomics data
    using the method described by Casado et al. (2013) <doi:10.1126/scisignal.2003573>.
Selection of k in k-means clustering based on Pham et al. paper
    ``Selection of k in k-means clustering''.
Computes a p-value of the one-sample two-sided (or one-sided, as a special case) Kolmogorov-Smirnov (KS) statistic, for any fixed critical level, and an arbitrary, possibly large sample size for a pre-specified purely discrete, mixed or continuous cumulative distribution function (cdf) under the null hypothesis. If a data sample is supplied, 'KSgeneral' computes the p-value corresponding to the value of the KS test statistic computed based on the user provided data sample. The package 'KSgeneral' implements a novel, accurate and efficient method named Exact-KS-FFT, expressing the p-value as a double-boundary non-crossing probability for a homogeneous Poisson process, which is then efficiently computed using Fast Fourier Transform (FFT). The package can also be used to compute and plot the complementary cdf of the KS statistic which is known to depend on the hypothesized distribution when the latter is discontinuous (i.e. purely discrete or mixed).
Using this package you can combine known kinase substrate relationships with experimental data and determine active kinases and their substrates.
Knowledge space theory by Doignon and Falmagne (1999) 
   <doi:10.1007/978-3-642-58625-5> is a set- and order-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.
Knowledge space theory by Doignon and Falmagne (1999) 
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework which proposes mathematical formalisms to operationalize 
   knowledge structures in a particular domain.  The 'kstIO' package 
   provides basic functionalities to read and write KST data from/to 
   files to be used together with the 'kst', 'kstMatrix', 'pks' or 
   'DAKS' packages.
Knowledge space theory by Doignon and Falmagne (1999)
   <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical
   framework, which proposes mathematical formalisms to operationalize
   knowledge structures in a particular domain. The 'kstMatrix' package 
   provides basic functionalities to generate, handle, and manipulate 
   knowledge structures and knowledge spaces. Opposed to the 'kst'
   package, 'kstMatrix' uses matrix representations for knowledge
   structures. Furthermore, 'kstMatrix' contains several knowledge spaces
   developed by the research group around Cornelia Dowling through
   querying experts.
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.
The format KVH is a lightweight format that can be read/written both by humans and machines.
   It can be useful in situations where XML or alike formats seem to be an overkill.
   We provide an ability to parse KVH files in R pretty fast due to 'Rcpp' use.
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).
Helper functions for creating formatted summary of regression models, writing publication-ready tables to latex files, and running Monte Carlo experiments.
Time Series Analysis including break detection, spectral analysis, KZ Fourier Transforms.
Separation of wave motions in different scales and directions based on
     Kolmogorov-Zurbenko Periodograms and Kolmogorov-Zurbenko Fourier Transform.
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.
Highly optimized toolkit for approximately solving L0-regularized learning problems (aka best subset selection).
    The algorithms are based on coordinate descent and local combinatorial search.
    For more details, check the paper by Hazimeh and Mazumder (2018) <arXiv:1803.01454>;
    the link is provided in the URL field below.
LINCS L1000 is a high-throughput technology that allows the gene expression measurement in a large number of assays. However, to fit the measurements of ~1000 genes in the ~500 color channels of LINCS L1000, every two landmark genes are designed to share a single channel. Thus, a deconvolution step is required to infer the expression values of each gene. Any errors in this step can be propagated adversely to the downstream analyses. We present a LINCS L1000 data peak calling R package l1kdeconv based on a new outlier detection method and an aggregate Gaussian mixture model. Upon the remove of outliers and the borrowing information among similar samples, l1kdeconv shows more stable and better performance than methods commonly used in LINCS L1000 data deconvolution.
L1 estimation for linear regression, density, distribution function,
    quantile function and random number generation for univariate and 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.
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.
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'. This package
    provides useful functions to deal with "haven_labelled" and
    "haven_labelled_spss" classes introduced by 'haven' package.
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.
Labels are a common construct in statistical software providing a 
  human readable description of a variable. While variable names are succinct,
  quick to type, and follow a language's naming conventions, labels may 
  be more illustrative and may use plain text and spaces. R does not provide
  native support for labels. Some packages, however, have made this feature
  available.  Most notably, the 'Hmisc' package provides labelling methods
  for a number of different object. Due to design decisions, these methods
  are not all exported, and so are unavailable for use in package development.
  The 'labelVector' package supports labels for atomic vectors in a light-weight
  design that is suitable for use in other packages.
Produces Labour Market Areas from commuting flows available at elementary territorial units. It provides tools for automatic tuning based on spatial contiguity. It also allows for statistical 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.
Perform pairwise likelihood inference in latent autoregressive count models. See Pedeli and Varin (2018) <arXiv:1805.10865> for details.
LAD (Least Absolute Deviations) estimation for linear regression, confidence intervals, tests of hypotheses, methods for outliers detection, measures of leverage, methods of diagnostics for LAD regression, special diagnostics graphs and measures of leverage. The algorithms are based in Dielman (2005)  <doi:10.1080/0094965042000223680>, Elian et al. (2000) <doi:10.1080/03610920008832518> and Dodge (1997) <doi:10.1006/jmva.1997.1666>. This package also has two datasets "houses" and "pollution", respectively, from Narula and Wellington (1977) <doi:10.2307/1268628> and Santos et al. (2016) <doi:10.1371/journal.pone.0163225>.
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.
Provides classes and methods for lagged objects.
Client for programmatic access to the Lake
    Multi-scaled Geospatial and Temporal database <https://lagoslakes.org>, with functions
    for accessing lake water quality and ecological context data for the US.
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 provided. Wrapper routines for blackbox optimization under mixed equality and inequality constraints via an augmented Lagrangian scheme, and for large scale computer model calibration, are also provided.
Model-based linear model trees adjusting for spatial correlation using a
             simultaneous autoregressive spatial lag.
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, major/minor axis 
    ratio, 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.
Includes some procedures for latent variable modeling with a 
    particular focus on multilevel data.
    The 'LAM' package contains mean and covariance structure modelling
    for multivariate normally distributed data (mlnormal(); Longford, 1987;
    <doi:10.1093/biomet/74.4.817>), a general Metropolis-Hastings algorithm 
    (amh(); Roberts & Rosenthal, 2001, <doi:10.1214/ss/1015346320>) and 
    penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland, 
    2014; <doi:10.1093/aje/kwt245>).
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.
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.
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.
Log-analytic methods intended for testing multiplicative effects.
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).
Calculates landscape metrics for categorical landscape patterns in 
    a tidy workflow. 'landscapemetrics' reimplements the most common metrics from
    'FRAGSTATS' (<https://www.umass.edu/landeco/research/fragstats/fragstats.html>) 
    and new ones from the current literature on landscape metrics.
    This package supports 'raster' spatial objects and takes 
    RasterLayer, RasterStacks, RasterBricks or lists of RasterLayer from the
    'raster' package as input arguments. It further provides utility functions
    to visualize patches, select metrics and building blocks to develop new 
    metrics.
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.
Provides utility functions for some of the less-glamorous tasks involved
    in landscape analysis. It includes functions to coerce raster data to the
    common tibble format and vice versa, it helps with flexible reclassification
    tasks of raster data and it provides a function to merge multiple raster.
    Furthermore, 'landscapetools' helps landscape scientists to visualize their
    data by providing optional themes and utility functions to plot single
    landscapes, rasterstacks, -bricks and lists of raster.
A spatio-temporal stochastic model to assess resistance deployment strategies 
    against plant pathogens. The model is based on stochastic geometry for describing the 
    landscape and the resistant hosts, a dispersal kernel for the dissemination of the 
    pathogen, and a SEIR (Susceptible-Exposed-Infectious-Removed) architecture to simulate 
    plant response to disease.
    Loup Rimbaud, Julien Papaïx, Jean-François Rey, Luke G Barrett, 
    Peter H Thrall (2018) <doi:10.1371/journal.pcbi.1006067>.
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.
An implementation of the Language Server Protocol for R. The Language Server protocol is used by an editor client to integrate features like auto completion. See <https://microsoft.github.io/language-server-protocol> for details.
R interface for working with nanometer scale secondary ion mass 
    spectrometry (NanoSIMS) data exported from Look at NanoSIMS.
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.
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.
Model selection for the (possibly high-dimensional) linear regression model 
    with Lasso-Zero, an L1-based methodology relying on the repeated use of noise 
    dictionaries, as described by Descloux, P. and Sardy, S. (2018) <arXiv:1805.05133>.
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) <http://www.jmlr.org/papers/volume17/13-515/13-515.pdf>.
    Data with thousands of predictors can be handled. The algorithm
    performs sequential Lasso 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.
Estimate the p-values for predictors x against target variable y in lasso regression, using the regularization strength when each predictor enters the active set of regularization path for the first time as the statistic. This is based on the assumption that predictors (of the same variance) that (first) become active earlier tend to be more significant. Three null distributions are supported: normal and spherical, which are computed separately for each predictor and analytically under approximation, which aims at efficiency and accuracy for small p-values.
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.
Estimate the sufficient dimension reduction space using sparsed sliced inverse regression via Lasso (Lasso-SIR) introduced in Lin, Zhao, and Liu (2017) <arxiv:1611.06655>. The Lasso-SIR is consistent and achieve the optimal convergence rate under certain sparsity conditions for the multiple index models.
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.
Executes arbitrary R or C functions some time after the current
    time, after the R execution stack has emptied.
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.
Converts table-like objects to stand-alone PDF or PNG.
 Can be used to embed tables and arbitrary content in PDF or Word
 documents. Provides a low-level R interface for creating 'LaTeX'
 code, e.g. command() and a high-level interface for creating PDF
 documents, e.g. as.pdf.data.frame(). Extensive customization is
 available via mid-level functions, e.g. as.tabular(). See also 
 'package?latexpdf'. Support for PNG is experimental; see
 'as.png.data.frame'. Adapted from 'metrumrg' 
 <http://r-forge.r-project.org/R/?group_id=1215>.
 Requires a compatible installation of 'pdflatex',
 e.g. <https://miktex.org/>.
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.
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.
Lattice-based space-filling designs with fill or separation distance properties, including interleaved lattice-based maximin distance designs proposed in Xu He (2018) <arXiv:1807.02289v1>, 
  (sliced) rotated sphere packing designs proposed in Xu He (2017) <doi:10.1080/01621459.2016.1222289> 
  and Xu He (2018) <doi:10.1080/00401706.2018.1458655>, and densest packing-based maximum projections designs proposed in Xu He (2018) <arXiv:1709.02062v2>.
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.
A general implementation of Structural Equation Models
	with latent variables (MLE, 2SLS, and composite likelihood
	estimators) with both continuous, censored, and ordinal
	outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>).
	Mixture latent variable models and non-linear latent variable models
	(Holst and Budtz-Joergensen (2018) <arXiv:1803.06578>).
	The package also provides methods for graph exploration (d-separation,
	back-door criterion), simulation of general non-linear latent variable
	models, and estimation of influence functions for a broad range of
	statistical models.
Lava plugin allowing combinations of left and right censored and
    binary outcomes.
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.
Plots path diagrams from models in lavaan using the plotting
    functionality from the DiagrammeR package. DiagrammeR provides nice path diagrams 
    via Graphviz, and these functions make it easy to generate these diagrams from a
    lavaan path model without having to write the DOT language graph specification.
Tools for model specification in the latent variable framework
    (add-on to the 'lava' package). The package contains three main functionalities:
    Wald tests/F-tests with improved control of the type 1 error in small samples,
    adjustment for multiple comparisons when searching for local dependencies,
    and adjustment for multiple comparisons when doing inference for multiple latent variable models.
The lava estimation is a new technique to recover signals that is the sum of a sparse and dense signals. The post-lava method corrects the shrinkage bias of lava. For more information on the lava estimation, see Chernozhukov, Hansen, and Liao (2017) <doi:10.1214/16-AOS1434>.
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.
Interfacing to Nocedal et al. L-BFGS-B.3.0 (2011 <doi:10.1145/2049662.2049669>) limited
	memory BFGS minimizer with bounds on parameters. This is a fork of 'lbfgsb3'.  This
	registers a 'R' compatible 'C' interface to L-BFGS-B.3.0 that uses the same function types
	and optimization as the optim() function (see writing 'R' extensions and source for
	details).  Ths package also adds more stopping criterion as well as allows adjusting more tolerances.
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 the boundary of the parameter space.
Simulate expected equilibrium length composition, yield-per-recruit, and
    the spawning potential ratio (SPR) using the length-based SPR (LBSPR) model. Fit the LBSPR
    model to length data to estimate  selectivity, relative apical fishing mortality, and
    the spawning potential ratio for data-limited fisheries.
    See Hordyk et al (2016) <doi:10.1139/cjfas-2015-0422> for more information about the
    LBSPR assessment method.
Performs model fitting and significance estimation for Localised Co-Dependency between pairs of features of a numeric dataset.
Latent Class Analysis of
        phenotypic measurements in pedigrees and model selection
        based on one of two methods: likelihood-based cross-validation
        and Bayesian Information Criterion. Computation of individual
        and triplet child-parents weights in a pedigree is performed using an
        upward-downward algorithm. The model 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.
Variable selection for latent class analysis for model-based clustering of multivariate categorical data. The package implements a general framework for selecting the subset of variables with relevant clustering information and discard those that are redundant and/or not informative. The variable selection method is based on the approach of Fop et al. (2017) <doi:10.1214/17-AOAS1061> and Dean and Raftery (2010) <doi:10.1007/s10463-009-0258-9>. Different algorithms are available to perform the selection: stepwise, swap-stepwise and evolutionary stochastic search. Concomitant covariates used to predict the class membership probabilities can also be included in the latent class analysis model. The selection procedure can be run in parallel on multiple cores machines.
Estimates the longitudinal concordance correlation to access the longitudinal agreement profile. The estimation approach implemented is variance components approach based on polynomial mixed effects regression model, as proposed by Oliveira, Hinde and Zocchi (2018) <doi:10.1007/s13253-018-0321-1>.  In addition, non-parametric confidence intervals were implemented using percentile method or normal-approximation  based on Fisher Z-transformation.
Local Discrimination via Latent Class Models
Baseline correction, normalization and linear combination fitting (LCF) 
    of X-ray absorption near edge structure (XANES) spectra.
    The package includes data loading of .xmu files exported from 'ATHENA' (Ravel and Newville, 2005) <doi:10.1107/S0909049505012719>. 
    Loaded spectra can be background corrected and all standards can be fitted at once.
    Two linear combination fitting functions can be used:
    (1) fit_athena(): Simply fitting combinations of standards as in ATHENA, 
    (2) fit_float(): Fitting all standards with changing baseline correction and edge-step normalization parameters.
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.
Longitudinal genomics data and survival outcome are common in biomedical studies. It is of great interest to select genes related to the survival outcome. LCox is a computationally efficient tool for selecting genes related to the survival outcome using the longitudinal genomics data. LCox is powerful to detect different forms of dependence between the longitudinal biomarkers and the survival outcome.
Provides 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.
Analysis of stock data ups and downs trend, the stock technical analysis indicators function have trend line, reversal pattern and market trend.
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.
Tools for working with vectors and data sets that are too large
    to keep in memory. Extends the basic functionality provided in the 'lvec'
    package. Provides basis statistical functionality of 'lvec' objects, such
    as arithmetic operations and calculating means and sums. Also implements
    'data.frame'-like objects storing its data in 'lvec' objects.
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.
Four measures of linkage disequilibrium are provided: 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.
Hidden Markov Model (HMM) based on symmetric lambda distribution
    framework is implemented for the study of return time-series in the financial
    market. Major features in the S&P500 index, such as regime identification,
    volatility clustering, and anti-correlation between return and volatility,
    can be extracted from HMM cleanly. Univariate symmetric lambda distribution
    is essentially a location-scale family of exponential power distribution.
    Such distribution is suitable for describing highly leptokurtic time series
    obtained from the financial market. It provides a theoretically solid foundation
    to explore such data where the normal distribution is not adequate. The HMM
    implementation follows closely the book: "Hidden Markov Models for Time Series",
    by Zucchini, MacDonald, Langrock (2016).
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. For details see Liski, E.Nordhausen K., Oja H., Ruiz-Gazen A. (2016) Combining Linear Dimension Reduction Subspaces <doi:10.1007/978-81-322-3643-6_7>.
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 algorithm Leabra (local error driven and associative
    biologically realistic algorithm) allows for the construction of artificial
    neural networks that are biologically realistic and balance supervised and
    unsupervised learning within a single framework. This package is based on
    the 'MATLAB' version by Sergio Verduzco-Flores, which in turn was based on
    the description of the algorithm by Randall O'Reilly (1996)
    <ftp://grey.colorado.edu/pub/oreilly/thesis/oreilly_thesis.all.pdf>. For
    more general (not 'R' specific) information on the algorithm Leabra see
    <https://grey.colorado.edu/emergent/index.php/Leabra>.
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.
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 applications and R Markdown
    documents.
An add-on package to the 'leaflet' package, which provides bindings for 'ESRI' services. This package allows a user to add 'ESRI' provided services such as 'MapService', 'ImageMapService', 'TiledMapService' etc. to a 'leaflet' map.
The 'leaflet' JavaScript library provides many plugins some of which
    are available in the core 'leaflet' package, but there are many more. It is not
    possible to support them all in the core 'leaflet' package. This package serves
    as an add-on to the 'leaflet' package by providing extra functionality via 'leaflet'
    plugins.
Add and modify small charts on an interactive map created with 
    package 'leaflet'. These charts can be used to represent at same time multiple 
    variables on a single map.
Extends the 'leaflet' R package with the 'Leaflet.OpacityControls' JavaScript 
    plugin. Adds controls to the leaflet map for adjusting the 
    opacity of a layer.
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.
Implementation of trigonometric functions to calculate the exposure of flat, tilted surfaces, such as leaves and slopes, to direct solar radiation. It implements the equations in A.G. Escribano-Rocafort, A. Ventre-Lespiaucq, C. Granado-Yela, et al. (2014) <doi:10.1111/2041-210X.12141> in a few user-friendly R functions. All functions handle data obtained with 'Ahmes' 1.0 for Android, as well as more traditional data sources (compass, protractor, inclinometer). The main function (star()) calculates the potential exposure of flat, tilted surfaces to direct solar radiation (silhouette to area ratio, STAR). It is equivalent to the ratio of the leaf projected area to total leaf area, but instead of using area data it uses spatial position angles, such as pitch, roll and course, and information on the geographical coordinates, hour, and date. The package includes additional functions to recalculate STAR with custom settings of location and time, to calculate the tilt angle of a surface, and the minimum angle between two non-orthogonal planes.
Provides access to the 'Leanpub' API
    <https://leanpub.com/help/api> for gathering information about 
    publications and submissions to the 'Leanpub' platform.
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.
Provides methods for estimating borders of uniform distribution on
  the interval (one-dimensional) and on the elliptical domain (two-dimensional)
  under measurement errors. For one-dimensional case, it also estimates the
  length of underlying uniform domain and tests the hypothesized length against
  two-sided or one-sided alternatives. For two-dimensional case, it estimates
  the area of underlying uniform domain. It works with numerical inputs as well
  as with pictures in JPG format.
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.
Contains some functions to learn and teach basic plane Geometry at undergraduate level with the aim of being helpful to young students with little programming skills.
Implements common learning curve production functions. It incorporates
    Crawford's and Wright's learning curve functions to compute 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.along with functions to compute aggregated learning curves, 
    error rates, and to visualize learning curves.
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.
Aids in learning statistical functions incorporating the result of calculus done with each function and how they are obtained, that is, which equations and variables are used. Also for all these equations and their related variables detailed explanations and interactive exercises are also included. All these characteristics allow to the package user to improve the learning of statistics basics by means of their use.
Implementations of several basic neural network concepts in R, as based on posts on \url{http://qua.st/}.
Conducts various numerical analyses and simulations in population genetics and evolutionary theory, primarily for the purpose of teaching (and learning about) key concepts in population & quantitative genetics, and evolutionary theory.
Create interactive tutorials using R Markdown. Use a combination 
  of narrative, figures, videos, exercises, and quizzes to create self-paced
  tutorials for learning about R and R packages.
Datasets used in the book "Learn R ...as you learnt your mother
    tongue" by Pedro J. Aphalo (2017) <https://leanpub.com/learnr>.
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.
Utilities for querying plain text accounting files from 'Ledger', 'HLedger', and 'Beancount'.
Functions to download data from the SIE, which is the Statistical Information System (Sistema de Información Estadística) in the Statistical Portal of the Government of Castilla y León (Spain) <https://estadistica.jcyl.es>.
Utilizing phylogenetic and functional information for the analyses of ecological datasets. The analyses include methods for quantifying the phylogenetic and functional diversity of assemblages.
Constructs genotype x environment interaction (GxE) models where
    G is a weighted sum of genetic variants (genetic score) and E is a weighted
    sum of environments (environmental score) using the alternating optimization algorithm 
    by Jolicoeur-Martineau et al. (2017) <arXiv:1703.08111>. This approach has greatly 
    enhanced predictive power over traditional GxE models which include only a single 
    genetic variant and a single environmental exposure. Although this approach was 
    originally made for GxE modelling, it is flexible and does not require the use of 
    genetic and environmental variables. It can also handle more than 2 latent variables 
    (rather than just G and E) and 3-way interactions or more. The LEGIT model produces 
    highly interpretable results and is very parameter-efficient thus it can even be 
    used with small sample sizes (n < 250). Tools to determine the type of interaction
    (vantage sensitivity, diathesis-stress or differential susceptibility), with any 
    number of genetic variants or environments, are available <arXiv:1712.04058>.
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.
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 for working with legends and axis lines of 'ggplot2',
    facets that repeat axis lines on all panels, and some 'knitr' extensions.
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>.
Generates quotes from 'Lero Lero', a database for meaningless sentences filled with corporate buzzwords, intended to be used as corporate lorem ipsum (see <http://www.lerolero.com/> for more information). Unfortunately, quotes are currently portuguese-only.
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 including the introduced here Violin/Box/Scatter plot for a numerical variable, bar chart, histogram, box plot, density curves, calibrated power curve, reading multiple data formats with the same function call, variable labels, color themes, Trellis graphics and a built-in help system. Also includes a confirmatory factor analysis of multiple indicator measurement models, pedagogical routines for data simulation such as for the Central Limit Theorem, and generation of R markdown instructions for interpretative output.
Functions for vectorised conditional recoding of variables. case_when()
  enables you to vectorise multiple if and else statements (like 'CASE WHEN'
  in 'SQL'). if_else() is a stricter and more predictable
  version of ifelse() in 'base' that preserves attributes. These functions are 
  forked from 'dplyr' with all package dependencies removed and behave
  identically to the originals.
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.
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.
Provides easy access for sentiment lexicons for those who want to do text analysis in Portuguese texts.
    As of now, two Portuguese lexicons are available: 'SentiLex-PT02' and 'OpLexicon' (v2.1 and v3.0).
My PhD supervisor once told me that everyone doing newspaper
    analysis starts by writing code to read in files from the 'LexisNexis' newspaper
    archive (retrieved e.g., from <http://www.nexis.com/> or any of the partner
    sites). However, while this is a nice exercise I do recommend, not everyone has
    the time. This package takes TXT files downloaded from the newspaper archive of
    'LexisNexis', reads them into R and offers functions for further processing.
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 allows 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).
New empirical Bayes methods aiming at analyzing the association of single nucleotide polymorphisms (SNPs) to some particular disease are implemented in this package. The package uses local false discovery rate (LFDR) estimates of SNPs within a sample population defined as a  "reference class" and discovers if SNPs are associated with the corresponding disease. Although SNPs are used throughout this document, other biological data such as protein data and other gene data can be used. Karimnezhad, Ali and Bickel, D. R. (2016) <http://hdl.handle.net/10393/34889>.
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) - see Burda (2015) <doi:10.1109/FUZZ-IEEE.2015.7337826>.
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.
An implementation of locally Gaussian distributions. It provides methods for
    implementing the locally Gaussian density estimator (LGDE) by Otneim and Tjøstheim (2017) 
    <doi:10.1007/s11222-016-9706-6>, as well as the corresponding estimator for conditional 
    density functions by Otneim and Tjøstheim (2018) <doi:10.1007/s11222-017-9732-z>.
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 genome-wide association studies (GWAS)/gene-environment-wide interaction studies (GEWIS) with longitudinal outcomes and exposures. He et al. (2017) "Set-Based Tests for Gene-Environment Interaction in Longitudinal Studies" and He et al. (2017) "Rare-variant association tests in longitudinal studies, with an application to the Multi-Ethnic Study of Atherosclerosis (MESA)".
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.
Fits sex-specific life-history models for fish and other taxa where some of the individuals have unknown sex.
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.
Automatically install, update, and load 'CRAN' and 'GitHub' packages in a 
    single function call. By accepting bare unquoted names for packages, it's easy to add 
    or remove packages from the list.
Generate a local library copy with relevant packages. 
    All packages currently found within the search path - except base packages - 
    will be copied to the directory provided and can be used later on with the 
    .libPaths() function.
Handle 'PharmML' (Pharmacometrics Markup Language) standard output (SO) XML files.
    SO files can be created, read, manipulated and written through a
    data binding from the XML structure to a tree structure of R objects.
Tools for fast and accurate evaluation of skew stable distributions (CDF, PDF 
  and quantile functions), random number generation and parameter estimation.
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.
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, individual tree segmentation
    and other manipulations.
Classes and methods that allow the user to manage life table,
    actuarial tables (also multiple decrements 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.
Provides a framework for combining self-data (exercise, sleep, etc.) from multiple sources (fitbit, Apple Health), creating visualizations, and experimenting on onself.
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 of Knight et al (2017) <doi:10.1007/s11222-016-9698-2>.
Persistent reproducible reporting by containerization of R Markdown documents.
Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states. The original algorithm is detailed in Subramanian et al. with 'Java' implementations available through the Broad Institute (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>). The 'liger' package provides a lightweight R implementation of this enrichment test on a list of values (Fan et al., 2017 <doi:10.5281/zenodo.887386>). Given a list of values, such as p-values or log-fold changes derived from differential expression analysis or other analyses comparing biological states, this package enables you to test a priori defined set of genes for enrichment to enable interpretability of highly significant or high fold-change genes.
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.
Provides likelihood functions as defined by Fisher (1922)
    <doi:10.1098/rsta.1922.0009> and a function that creates likelihood 
    functions from density functions. The functions are meant to aid in 
    education of likelihood based methods.
Tools to determine DNA profile Weight of Evidence. 
            For further information see the 'likeLTD' guide provided, 
            Balding, D.J. (2013) <DOI:10.1073/pnas.1219739110>,
	    or Steele, C.D. et al. (2016) <DOI:10.1515/sagmb-2016-0038>.
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.
Computes the pathway deregulation score for a given set of metabolites, selects the pathways with the highest mutual information and then uses them to build a classifier. F. Alakwaa, S. Huang, and L. Garmire (2018) <doi:10.1101/283408>.
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.
When building complex models, it is often difficult to explain why
    the model should be trusted. While global measures such as accuracy are
    useful, they cannot be used for explaining why a model made a specific
    prediction. 'lime' (a port of the 'lime' 'Python' package) is a method for
    explaining the outcome of black box models by fitting a local model around
    the point in question an perturbations of this point. The approach is
    described in more detail in the article by Ribeiro et al. (2016) 
    <arXiv:1602.04938>.
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(a_i*x_i)
  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. It 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.
Provides a set of streamlined functions that allow
    easy generation of linear regression diagnostic plots necessarily 
    for checking linear model assumptions.
    This package is meant for easy scheming of linear 
    regression diagnostics, while preserving merits of 
    "The Grammar of Graphics" as implemented in 'ggplot2'.
    See the 'ggplot2' website for more information regarding the
    specific capability of graphics.
Computes the probability density function, the cumulative distribution function, the hazard rate function, the quantile function and random generation for Lindley Power Series distributions, see Nadarajah and Si (2018) <doi:10.1007/s13171-018-0150-x>.
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.
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.
It is a linear algorithm to simulate quantiles of multiscale statistics 
            under hull hypothesis for multiscale change-point segmentation. 
            The reference is in preparation.
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.
Create maps made of lines. The package contains two functions: linemap() and getgrid(). linemap() displays a map made of lines using a data frame of gridded data. getgrid() transforms a set of polygons (sf objects) into a suitable data frame for linemap().
Gaussian processes regression models with linear inequality constraints
            (Lopez-Lopera et al., 2017) <arXiv:1710.07453>.
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 mapping. 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 pages <https://ropensci.github.io/lingtypology/> and package vignette. Maps created by this package can be used both for the investigation and linguistic teaching. In addition, package provides an ability to download data from typological databases such as WALS, AUTOTYP and some others and to create your own database website.
Provides 'Shiny gadgets' to search, type, and insert IPA symbols
    into documents or scripts, requiring only knowledge about phonetics or 
    'X-SAMPA'. Also provides functions to facilitate the rendering of IPA
    symbols in 'LaTeX' and PDF format, making IPA symbols properly rendered
    in all output formats. A minimal R Markdown template for authoring 
    Linguistics related documents is also bundled with the package.
Functions to simplify the linking of open source GIS and remote sensing related command line interfaces.
Produces high resolution, publication ready linkage maps
    and quantitative trait loci maps. Input can be output from 'R/qtl',
    simple text or comma delimited files. Output is currently
    a portable document file.
Link communities reveal the nested and overlapping structure in networks, and uncover the key nodes that form connections to multiple communities. linkcomm provides a set of tools for generating, visualizing, and analysing link communities in networks of arbitrary size and type. The linkcomm package also includes tools for generating, visualizing, and analysing Overlapping Cluster Generator (OCG) communities.
A class that links matrix-like objects (nodes) by rows or by
    columns while behaving similarly to a base R matrix. Very large matrices
    are supported if the nodes are memory-mapped objects.
A linkage information based method for imputing missing diploid genotypes
Implementations of most of the existing proximity-based methods of 
  link prediction in graphs. Among the 20 implemented methods are e.g.:
  Adamic L. and Adar E. (2003) <doi:10.1016/S0378-8733(03)00009-1>,
  Leicht E., Holme P., Newman M. (2006) <doi:10.1103/PhysRevE.73.026120>,
  Zhou T. and Zhang Y (2009) <doi:10.1140/epjb/e2009-00335-8>, and
  Fouss F., Pirotte A., Renders J., and Saerens M. (2007) <doi:10.1109/TKDE.2007.46>.
Creates kinematic and static force models of 3D levers and linkage mechanisms, with particular application to the fields of engineering and biomechanics.
Compute and visualize using the 'visNetwork' package all the bivariate correlations of a dataframe.
  Several and different types of correlation coefficients (Pearson's r, Spearman's rho, Kendall's tau,
  distance correlation, maximal information coefficient and 
  equal-freq discretization-based maximal normalized mutual information) are used according to 
  the variable couple type (quantitative vs categorical, quantitative vs quantitative, categorical vs categorical).
A 'LaTeX' Letter class for 'rmarkdown', using the
 'pandoc-letter' template adapted for use with 'markdown'.
This package implements the methodology of
        Likelihood-based Imprecise Regression (LIR) for the case of
        linear regression with interval data.
Generate concentration-time profiles from linear pharmacokinetic
  (PK) systems, possibly with first-order absorption or zero-order infusion,
  possibly with one or more peripheral compartments, and possibly under
  steady-state conditions. Single or multiple doses may be specified. Secondary
  (derived) PK parameters (e.g. Cmax, Ctrough, AUC, Tmax, half-life, etc.) are
  computed.
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 unimodular, 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 'RStudio IDE', 'Emacs',
    'Vim', 'Sublime Text' and 'Atom'.
Lipid annotation in untargeted liquid chromatography-data independent acquisition-mass spectrometry lipidomics based on fragmentation and intensity rules.
Provides three classes: Queue, PriorityQueue and Stack. Queue is just a
    "plain vanilla" FIFO queue; PriorityQueue orders items according to priority. Stack implements LIFO.
Support vector machines (SVMs) and related kernel-based learning
    algorithms are a well-known class of machine learning algorithms, for
    non-parametric classification and regression.
    liquidSVM is an implementation of SVMs whose key features are:
    fully integrated hyper-parameter selection,
    extreme speed on both small and large data sets,
    full flexibility for experts, and
    inclusion of a variety of different learning scenarios:
    multi-class classification, ROC, and Neyman-Pearson learning, and
    least-squares, quantile, and expectile regression.
Performs Bayesian linear regression and forecasting in astronomy. The method accounts for heteroscedastic errors in both the independent and the dependent variables, intrinsic scatters (in both variables) and scatter correlation, time evolution of slopes, normalization, scatters, Malmquist and Eddington bias, upper limits and break of linearity. The posterior distribution of the regression parameters is sampled with a Gibbs method exploiting the JAGS library.
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>, 
    Chou, Imai, and Rosenfeld (2017) <doi:10.1177/0049124117729711>, and 
    Blair, Chou, and Imai (2018) <https://imai.fas.harvard.edu/research/files/listerror.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, a method for combining 
    list experiments with direct questions, and methods for diagnosing and
    adjusting for response error. 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.
A toolbox for R arrays. Flexibly split, bind, reshape, modify, 
    subset and name arrays.
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 package
    'reactR' that can be installed from CRAN or <https://github.com/timelyportfolio/reactR>.
Provides a function that, as an alternative to base::list, allows
    default values to be inherited from another list.
Temporary and permanent message queues for R. Built on top of
    'SQLite' databases. 'SQLite' provides locking, and makes it possible
    to detect crashed consumers. Crashed jobs can be automatically marked
    as "failed", or put in the queue again, potentially a limited number of times.
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.
Linear Liu regression coefficient's estimation and testing with different Liu related measures such as MSE, R-squared etc.
  REFERENCES
  i.   Akdeniz and Kaciranlar (1995) <doi:10.1080/03610929508831585>
  ii.  Druilhet and Mom (2008) <doi:10.1016/j.jmva.2006.06.011>
  iii. Imdadullah, Aslam, and Saima (2017)
  iv.  Liu (1993) <doi:10.1080/03610929308831027>
  v.   Liu (2001) <doi:10.1016/j.jspi.2010.05.030>.
Interpretability of complex machine learning models is a growing concern.
    This package helps to understand key factors that drive the 
    decision made by complicated predictive model (so called black box model). 
    This is achieved through local approximations that are either based on 
    additive regression like model or CART like model that allows for 
    higher interactions. The methodology is based on Tulio Ribeiro, Singh, Guestrin (2016) <doi:10.1145/2939672.2939778>.
    More details can be found in Staniak, Biecek (2018) <arXiv:1804.01955>.
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.
Fits the Logit Leaf Model, makes predictions and visualizes the output. (De Caigny et al., (2018) <DOI:10.1016/j.ejor.2018.02.009>).
Density, distribution function, quantile function and random generation for the L-Logistic distribution with parameters m and b. The parameter m is the median of the distribution.
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.  The method is from
  Knowles, Siegmund and Zhang (1991) <doi:10.1093/biomet/78.1.15>.
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 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.
Provides p-values in type I, II or III anova and summary tables
    for lmer model fits (cf. lme4) via Satterthwaite's degrees of freedom method. A
    Kenward-Roger method is also available via the pbkrtest package. Model selection
    methods include step, drop1 and anova-like tables for random effects (ranova).
    Methods for Least-Square means (LS-means) and tests of linear contrasts of fixed
    effects are also available.
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.
  Details can be found at Zhang et al (2016) <doi:10.1002/cjs.11295>.
This R package provide methods for estimation and statistical
               inference on directional and fluctuating selection in age-structured
               populations.
We present a method based on filtering algorithms to estimate the parameters of linear regressions, i.e. the coefficients and the variance of the error term. The proposed algorithms make use of Particle Filters following Ristic, B., Arulampalam, S., Gordon, N. (2004, ISBN: 158053631X) resampling methods.
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.
It implements Expectation/Conditional Maximization Either (ECME)
             and rapidly converging algorithms as well as
             Bayesian inference for linear mixed models, 
             which is described in Schafer, J.L. (1998)
             "Some improved procedures for linear mixed models".
             Dept. of Statistics, The Pennsylvania State University.
Fits (Gaussian) linear mixed-effects models
        for high-dimensional data (n<<p) using the linear mixed model elastic-net penalty.
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.
Embarrassingly Parallel Linear Mixed Model calculations spread across local cores which repeat until convergence.
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 associations.
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.
It is an extension of 'lmom' R package: 'pel','cdf',qua' function
    families are lumped and called from one function per each family respectively in order to
    create robust automatic tools to fit data  with different probability
    distributions and then to estimate probability values and return periods.  The implemented functions are able to manage time series with constant and/or missing values without stopping
    the execution with error messages. The package also contains tools to  calculate several  indices based on variability (e.g. 'SPI' , Standardized
    Precipitation Index, see <https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-index-spi> and <http://spei.csic.es/>) for multiple time series or spatio-temporal gridded values.
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.
Implementation based on Zhang, Jie & Huang, Kun (2014) <doi:10.4137/CIN.S14021> Normalized ImQCM: An Algorithm for Detecting Weak Quasi-Cliques in Weighted Graph with Applications in Gene Co-Expression Module Discovery in Cancers. Cancer informatics, 13, CIN-S14021.
Linear ridge regression coefficient's estimation and testing with different ridge related measures such as MSE, R-squared etc.
  REFERENCES
  i.   Hoerl and Kennard (1970) <doi:10.2307/1267351>
  ii.  Halawa and El-Bassiouni (2000) <doi:10.1080/00949650008812006>
  iii. Imdadullah, Aslam, and Saima (2017)
  iv.  Marquardt (1970) <doi:10.2307/1267205>.
Exact and approximation algorithms for variable-subset
  selection in ordinary linear regression models.  Either compute all
  submodels with the lowest residual sum of squares, or determine the
  single-best submodel according to a pre-determined statistical
  criterion.  Hofmann, Gatu, Kontoghiorghes, Colubi, Zeileis (2018,
  submitted).
Provides tools and a consistent interface to support analyses using General, Generalized, and Multi-level Linear Models.
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.
Runs a linear-like regression with in which both the expected value and the variance can vary per observation. The expected values mu follows the standard linear model mu = X_mu * beta_mu. The standard deviation sigma follows the model log(sigma) = X_sigma * beta_sigma. The package comes with two vignettes: 'Intro' gives an introduction, 'Math' gives mathematical details.
Contains three shiny applications.
    Two are meant to explore linear model inference feature through simulation.
    The third is a game to learn interpreting diagnostic plots.
Construct the simultaneous confidence intervals for
             ratios of means of Log-normal populations with zeros. It also has a Python module that do the same thing,
             and can be applied to multiple comparisons of parameters of any k mixture distributions. And we provide four methods,
             the method based on generalized pivotal quantity with order statistics and the quantity based on Wilson by Li et al. (2009) <doi:10.1016/j.spl.2009.03.004> (GPQW), and the methods based on 
             generalized pivotal quantity with order statistics and the quantity based on Hannig (2009) <doi:10.1093/biomet/asp050> (GPQH). The other two methods are 
             based on two-step MOVER intervals by Amany H, Abdel K (2015) <doi:10.1080/03610918.2013.767911>. We deduce Fiducial generalized pivotal two-step MOVER intervals based on 
             Wilson quantity (FMW) and based on Hannig's quantity (FMWH). All these approach you can find in the paper of us which it has been submitted.
Long non-coding RNAs identification and analysis. Default models are trained with human, mouse and wheat datasets by employing SVM. Features are based on intrinsic composition of sequence, EIIP value (electron-ion interaction pseudopotential), and secondary structure. This package can also extract other classic features and build new classifiers. Reference: Han SY., Liang YC., Li Y., et al. (2018) <doi:10.1093/bib/bby065>.
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.
Allows the simultaneous analysis of responses and response times in an Item Response Theory (IRT) modelling framework. Supports covariates for item and person (random) parameters. 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.
A set of tools for inspecting and understanding R data
    structures inspired by str(). Includes ast() for visualizing abstract 
    syntax trees, ref() for showing shared references, cst() for showing 
    call stack trees, and obj_size() for computing object sizes.
Implements novel nonparametric approaches to address
    biases and confounding when comparing treatments or exposures in
    observational studies of outcomes. While designed and appropriate for use
    in studies involving medicine and the life sciences, the package can be
    used in other situations involving outcomes with multiple confounders.
    The package implements a family of methods for nonparametric bias
    correction when comparing treatments in cross-sectional, case-control,
    and survival analysis settings, including competing risks with censoring.
    The approach extends to bias-corrected personalized predictions of
    treatment outcome differences, and analysis of heterogeneity of treatment
    effect-sizes across patient subgroups.
Especially when cross-sectional data are observational, effects of treatment
  selection bias and confounding are revealed by using the unsupervised, nonparametric
  preprocessing methods central to Local Control (LC) Strategy. The LC objective is to
  estimate the "effect-size distribution" that best quantifies a potentially causal
  relationship between a numeric y-Outcome variable and a t-Treatment variable. This
  t-variable may be either binary {1 = "new" vs 0 = "control"} or a numeric measure of
  Exposure level. LC Strategy starts by CLUSTERING experimental units (patients) on
  baseline x-Covariates, forming mutually exclusive and exhaustive BLOCKS of relatively
  well-matched units. The implicit statistical model for LC is thus simple one-way ANOVA.
  The Within-Block measures of effect-size are Local Rank Correlations (LRCs) when Exposure
  is numeric with more than two levels. Otherwise, Treatment choice is Nested within
  BLOCKS, and effect-sizes are Local Treatment Differences (LTDs) between within-cluster
  y-Outcome Means ["new" minus "control"]. An Instrumental Variable (IV) method is also
  provided so that Local Average y-Outcomes (LAOs) within BLOCKS may also contribute
  information for effect-size inferences ...assuming that x-Covariates influence only
  Treatment choice or Exposure level and otherwise have no direct effects on y-Outcome.
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>.
In the generalized Roy model, the marginal treatment effect (MTE) can be used as
  a building block for constructing conventional causal parameters such as the average treatment
  effect (ATE) and the average treatment effect on the treated (ATT) (Heckman, Urzua, and Vytlacil 2006
  <doi:10.1162/rest.88.3.389>). Given a treatment selection model and an outcome model, the function mte()
  estimates the MTE via local instrumental variables (or via a normal selection model) and also the
  projection of MTE onto the 2-dimensional space of the propensity score and a latent variable representing
  unobserved resistance to treatment (Zhou and Xie 2018 <https://scholar.harvard.edu/files/xzhou/files/zhou-xie_mte2.pdf>).
  The object returned by mte() can be used to estimate conventional parameters such as ATE and ATT
  (via average()) or marginal policy-relevant treatment effects (via mprte()).
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^5
    Datasets for the first and second editions of the book.  Older editions of revised data often have an extra 1e in the name.
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/).
Computes local polynomial estimators for 
  the regression and also density. It comprises several 
  different utilities to handle kernel estimators.
Read and write access to PNG image files using the LodePNG
    library. The package has no external dependencies.
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.
Software for computing a log-concave (maximum likelihood) estimator for i.i.d. data in any number of dimensions. For a detailed description of the method see Cule, Samworth and Stewart (2010, Journal of Royal Statistical Society Series B, <doi:10.1111/j.1467-9868.2010.00753.x>).
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.
Pure R implementation of the ubiquitous log4j package. It offers hierarchic 
  loggers, multiple handlers per logger, level based filtering, space handling in messages 
  and custom formatting.
A very simple and easy-to-use set of suspiciously-familiar functions. 'loggit'
    provides a set of wrappings for base R's message(), warning(), and stop()
    functions that maintain identical functionality, but also log the handler
    message to a 'JSON' log file. While mostly automatic, powerful custom logging
    is available via these handlers' logging function, loggit(), which is also
    exported for use. No change in existing code is necessary to use this
    package.
Provides a set of methods that learn time-varying graphical models based on data measured over a temporal grid. The underlying statistical model is motivated by the needs to describe and understand evolving interacting relationships among a set of random variables in many real applications, for instance the study of how stocks interact with each other and how such interactions change over time. The time-varying graphical models are estimated under the assumption that the graph topology changes gradually over time. For more details on estimating time-varying graphical models, please refer to: Yang, J. & Peng, J. (2018) <arXiv:1804.03811>.
Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) <doi:10.1198/106186006X133933>.
This package contains the functions necessary to run the LOGICOIL algorithm. LOGICOIL can be used to differentiate between antiparallel dimers, parallel dimers, trimers and higher-order coiled-coil sequence. By covering >90 percent of the known coiled-coil structures, LOGICOIL is a net improvement compared with other existing methods, which achieve a predictive coverage of around 31 percent of this population. As such, LOGICOIL is particularly useful for researchers looking to characterize novel coiled-coil sequences or studying coiled-coil containing protein assemblies. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences.
Routines for fitting Logic Regression models.
Provides histograms, boxplots and dotplots as alternatives to scatterplots of data when plotting fitted logistic regressions.
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.
Error in a binary dependent variable, also known as misclassification, has not drawn much attention in psychology. Ignoring misclassification in logistic regression can result in misleading parameter estimates and statistical inference. This package conducts logistic regression analysis with misspecification in outcome variables.
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.
Adjusted odds ratio conditional on potential confounders can be directly obtained from logistic regression. However, those adjusted odds ratios have been widely incorrectly interpreted as a relative risk. As relative risk is often of interest in public health, we provide a simple code to return adjusted relative risks from logistic regression model under potential confounders.
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.
Computes log-transformed kernel density estimates for positive data using a variety of kernels. It follows the methods described in Jones, Nguyen and McLachlan (2018) <doi:10.21105/joss.00870>.
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.
  Functions allow computing the intrinsic association coefficient
  (and therefore the Altham index), including via the Bayes shrinkage
  estimator proposed by Zhou; and the RAS/IPF/Deming-Stephan algorithm.
The lognormal distribution  
  (Limpert et al. (2001) <doi:10.1641/0006-3568(2001)051[0341:lndats]2.0.co;2>)
  can characterize uncertainty that is bounded by zero.
  This package provides estimation of distribution parameters, computation of
  moments and other basic statistics, and an approximation of the distribution
  of the sum of several correlated lognormally distributed variables 
  (Lo 2013 <doi:10.12988/ams.2013.39511>).
Functions to fits simple linear regression models with log normal errors 
		and identity link (taking the responses on the original scale). See
		Muggeo (2018) <doi:10.13140/RG.2.2.18118.16965>.
Uses approximations to compute the natural logarithm of the Gamma
    function for large values.
LogrankA provides a logrank test across unlimited groups with the
 possibility to input aggregated survival data.
Contains routines for logspline density estimation.
	The function oldlogspline() uses the same algorithm as the logspline package
	version 1.0.x; i.e. 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.
Estimation of Latent Order Logistic (LOLOG) Models for Networks.
    LOLOGs are a flexible and fully general class of statistical graph models. 
    This package provides functions for performing MOM, GMM and variational 
    inference. Visual diagnostics and goodness of fit metrics are provided. 
    See Fellows (2018) <arXiv:1804.04583> for a detailed description of the methods.
Supervised learning techniques designed for the situation when the dimensionality exceeds the sample size have a tendency to overfit as the dimensionality of the data increases. To remedy this High dimensionality; low sample size (HDLSS) situation, we attempt to learn a lower-dimensional representation of the data before learning a classifier. That is, we project the data to a situation where the dimensionality is more manageable, and then are able to better apply standard classification or clustering techniques since we will have fewer dimensions to overfit. A number of previous works have focused on how to strategically reduce dimensionality in the unsupervised case, yet in the supervised HDLSS regime, few works have attempted to devise dimensionality reduction techniques that leverage the labels associated with the data. In this package and the associated manuscript Vogelstein et al. (2017) <arXiv:1709.01233>, we provide several methods for feature extraction, some utilizing labels and some not, along with easily extensible utilities to simplify cross-validative efforts to identify the best feature extraction method. Additionally, we include a series of adaptable benchmark simulations to serve as a standard for future investigative efforts into supervised HDLSS. Finally, we produce a comprehensive comparison of the included algorithms across a range of benchmark simulations and real data applications.
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
  'Jan Beran' (1994). Statistics for Long-Memory Processes; Chapman & Hall.
  Estimation of Hurst (and more) parameters for fractional Gaussian noise,
  'fARIMA' and 'FEXP' models.
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.
Time-dependent Receiver Operating Characteristic curves, Area Under the Curve, and Net Reclassification Indexes for repeated measures. It is based on methods in Barbati and Farcomeni (2017) <doi:10.1007/s10260-017-0410-2>.
Performs recursive partitioning of linear and nonlinear mixed effects models,
    specifically for longitudinal data. The package is an
    extension of the original 'longRPart' package by 
    Stewart and Abdolell (2013) <https://cran.r-project.org/package=longRPart>.
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)
    for Bayesian models fit using Markov chain Monte Carlo. The approximation
    uses 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. The package also 
    provides methods for using stacking and other model weighting techniques 
    to average Bayesian predictive distributions.
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.
An extendable toolkit for interactive data visualization and exploration.
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' (<http://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.
Collection of functions to improve work-flow in survival analysis and data science.
             The package features include: the generation of balanced datasets, live retrieval of 
             protein coding genes from two public databases, generation of random 
             matrix based on covariance matrix, cache function to store function results.
             This work was supported by two grants from the Portuguese Foundation for Science
             and technology, and the EU Commission under SOUND project.
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
Addition of velocities in special relativity is not
 straightforward: the parallelogram addition law does not apply because
 velocity addition is neither commutative nor associative.  This
 package furnishes functionality to work with Lorentz transforms
 and the gyrogroup structure of three-velocities.  For general
 relativity, see the 'schwarzschild' package.
Analysis of count data with covariates. LORI uses a log-linear model where main row and column effects are decomposed as regression terms on known covariates. A residual low-rank interaction term is also fitted.
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.
Solves quadratic programming problems where the Hessian is represented as the product of two matrices.
Implementation of Low Walsh Figure of Merit (WAFOM) sequence
        based on Niederreiter-Xing sequence <DOI:10.1007/978-3-642-56046-0_30>.
Implementation of Low Walsh Figure of Merit (WAFOM) sequence
        based on Sobol sequence.
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.
Without imposing stringent distributional assumptions or shape restrictions, nonparametric density estimation has been popular in economics and other social sciences for counterfactual analysis, program evaluation, and policy recommendations. This package implements a novel density estimator based on local polynomial regression, documented in Cattaneo, Jansson and Ma (2017a): lpdensity() to construct local polynomial based density (and derivatives) estimator; lpbwdensity() to perform data-driven bandwidth selection; and lpdensity.plot() for density plot with robust confidence interval.
A nonparametric method to approximate Laplacian graph spectra of a network with 
    ordered vertices. This provides a computationally efficient algorithm for obtaining an 
	accurate and smooth estimate of the graph Laplacian basis. The approximation results can 
	then be used for tasks like change point detection, k-sample testing, and so on. The 
	primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).
Estimates the intensity function, or its derivative of a given order, of a multiplicative counting process using the local polynomial method
Provides functions to estimate and plot linear as well as nonlinear impulse 
             responses based on local projections by Jordà (2005) <doi:10.1257/0002828053828518>.
LP nonparametric high-dimensional K-sample comparison method that includes 
    (i) confirmatory test, (ii) exploratory analysis, and (iii) options to output a data-driven 
    LP-transformed matrix for classification. 
    The primary reference is Mukhopadhyay, S. and Wang, K. (2018, Technical Report).
Provides 'dplyr' verbs for lists and other useful 
    verbs for manipulation of data frames. In particular, it includes a 
    mutate_which() function that mutates columns for a specific subset of 
    rows defined by a condition, and fuse() which is a more flexible version 
    of 'tidyr' unite() function.
Apply Univariate Long Memory Models,
    Apply Multivariate Short Memory Models To Hydrological Dataset,
    Estimate Intensity Duration Frequency curve to rainfall series.
Provide nonparametric methods for mean regression model, modal regression and conditional density estimation in the presence/absence of measurement error. Bandwidth selection is also provided for each method.
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.
Computes power, or sample size or the detectable difference for a repeated measures model with attrition. It requires the variance covariance matrix of the observations but can compute this matrix for several common random effects models. See Diggle, P, Liang, KY and Zeger, SL (1994, ISBN:9780198522843).
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.
Computes a time series distance measure for clustering based on weighted correlation and introduction of lags. The lags capture delayed responses in a time series dataset. The timepoints must be specified. T. Chandereng, A. Gitter (2018) <doi:10.1101/292615>.
Functions to fit quantile regression models for hierarchical
    data (2-level nested designs) as described in Geraci and
    Bottai (2014, Statistics and Computing) <doi:10.1007/s11222-013-9381-9>.
    A vignette is given in Geraci (2014, Journal of Statistical Software)
    <doi:10.18637/jss.v057.i13> and included in the package documents.
    The packages also provides functions to fit quantile models for
    independent	data and for count responses.
It fits a robust linear quantile regression model using a new
    family of zero-quantile distributions for the error term as in Galarza et.al.(2017) <doi:10.1002/sta4.140>. 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, see Taylor et al (2014) <doi:10.1080/00401706.2013.823890>.
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 (LSA) and other vector space models 
	of semantics. For actually building a vector semantic space, use 
	the package 'lsa' or other specialized software. Downloadable 
	semantic spaces can be found here: <https://sites.google.com/site/fritzgntr/software-resources>
	A description of the LSA algorithm can be found in Landauer and Dumais (1997) 
	<doi:10.1037/0033-295X.104.2.211> .
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.
Provides functions to simulate data from large-scale educational 
  assessments, including background questionnaire data and cognitive item 
  responses that adhere to a multiple-matrix sampled design.
Functions for performing least-squares bilinear clustering of
    three-way data. The method uses the bilinear decomposition (or bi-additive
    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 sub-problems
    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.
Bandwidth selection for kernel density estimators of 2-d
    level sets and highest density regions. It applies a plug-in
    strategy to estimate the asymptotic risk function and minimize to
    get the optimal bandwidth matrix. See Doss and Weng (2018)
    <arXiv:1806.00731> for more detail.
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. Supports multiple core parallelization of multi-file data reading for increased performance. Also provides functions to extract basic information and statistics from data files. LSD (Laboratory for Simulation Development) is free software developed by Marco Valente (documentation and downloads available at <http://labsimdev.org>).
Tools for sensitivity analysis of LSD simulation models. Reads object-oriented data produced by LSD simulation models and performs screening and global sensitivity analysis (Sobol decomposition method, Saltelli et al. (2008) ISBN:9780470725177). A Kriging or polynomial meta-model (Kleijnen (2009) <doi:10.1016/j.ejor.2007.10.013>) is estimated using the simulation data to provide the data required by the Sobol decomposition. 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. Functions for solving quadratic programming
	     problems are also available, which transform such problems
	     into least squares ones first. 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>.
Calculates the (localised) length of the shorth and
        supplies corresponding diagnostic plots.
Fits structural equation modeling via penalized likelihood.
Fits semi-confirmatory structural equation modeling (SEM) via penalized likelihood (PL) with LASSO or minimax concave penalty (MCP).
When the values of the outcome variable Y are either 0 or 1, the function lsm() calculates the estimation of the log likelihood in the saturated model. This model is characterized by Llinas (2006, ISSN:2389-8976) in section 2.3 through the assumptions 1 and 2. The function LogLik() works (almost perfectly) when the number of independent variables K is high, but for small K it calculates wrong values in some cases. For this reason, when Y is dichotomous and the data are grouped in J populations, it is recommended to use the function lsm() because it works very well for all K.
Obtain least-squares means for linear, generalized linear, 
    and mixed models. Compute contrasts or linear functions of 
    least-squares means, and comparisons of slopes. 
    Plots and compact letter displays. Least-squares means were proposed in
    Harvey, W (1960) "Least-squares analysis of data with unequal subclass numbers",
    Tech Report ARS-20-8, USDA National Agricultural Library, and discussed
    further in Searle, Speed, and Milliken (1980) "Population marginal means 
    in the linear model: An alternative to least squares means", 
    The American Statistician 34(4), 216-221 <doi:10.1080/00031305.1980.10483031>.
    NOTE: lsmeans now relies primarily on code in the 'emmeans' package.
    'lsmeans' will be archived in the near future.
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.
Tools for statistical analysis using partitioning-based least squares regression as described in Cattaneo, Farrell and Feng (2018) <arXiv:1804.04916>. lsprobust() for nonparametric point estimation of regression functions and derivatives thereof, and for robust bias-corrected (pointwise and uniform) inference procedures. lspkselect() for data-driven procedure for selecting the IMSE-optimal number of knots. lsprobust.plot() for regression plots with robust confidence intervals and confidence bands. lsplincom() for estimation and inference for linear combinations of regression functions from different groups.
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.
Linear splines with convenient parametrisations such that 
  (1) coefficients are slopes of consecutive segments or (2) coefficients are 
  slope changes at consecutive knots. Knots can be set manually or at break points
  of equal-frequency or equal-width intervals covering the range of 'x'.
  The implementation follows Greene (2003), chapter 7.2.5.
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, <doi:10.1002/cem.890>.
Fit logistic regression models using LS-PLS approaches 
	to analyse both clinical and genomic data. (C. Bazzoli and 
	S. Lambert-Lacroix. (2017) Classification using LS-PLS with 
	logistic regression based on both clinical and gene expression
	variables <https://hal.archives-ouvertes.fr/hal-01405101>).
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/.
Rapid satellite data streams in operational applications
    have clear benefits for monitoring land cover, 
    especially when information can be delivered as fast as changing
    surface conditions. Over the past decade,
    remote sensing has become a key tool for monitoring and predicting
    environmental variables by using satellite data. 
    This package presents the main applications in remote sensing for 
    land surface monitoring and land cover mapping (soil, vegetation, water...).
     Tomlinson, C.J., Chapman, L., Thornes, E., Baker, C (2011) <doi:10.1002/met.287>.
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.
Constructs tables of counts and proportions out of data sets. It has simplified syntax appealing for novice and even for advanced user under time pressure. It is particularly suitable for exploratory data analysis or presentation to single out most appropriate pieces of tabulated information. The other important feature is possibility to insert tables to Excel and Word documents.
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, as well as interval-censored data.
    The 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.
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.
    Parts of the 'CCTZ' source code, released under the Apache 2.0 License,
    are included in this package. See <https://github.com/google/cctz> for more
    details.
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.
    See: Wright (2016). Lucid: An R Package for Pretty-Printing Floating Point
    Numbers. In JSM Proceedings, Statistical Computing Section. Alexandria,
    VA: American Statistical Association. 2270-2279.
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.
Contains LUE_BIOMASS(),LUE_BIOMASS_VPD(), LUE_YIELD() and LUE_YIELD_VPD() to estimate aboveground biomass and crop yield firstly by calculating the Absorbed Photosynthetically Active Radiation (APAR) and secondly the actual values of light use efficiency with and without vapour presure deficit Shi et al.(2007) <doi:10.2134/agronj2006.0260>.
Classes and methods for spatially explicit land use change
    modelling in R.
A function composition ('pipe') operator and extensible 
    framework that allows for easy logging of changes in data.
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.
A series of functions to estimate the detection windows of a luminescence reader based on the filters and the photomultiplier (PMT) selected. These functions also allow to simulate a luminescence experiment based on the thermoluminesce (TL) or the optically stimulated luminescence (OSL) properties of a material.
Provides functions to calculate lunar and other environmental
    covariates.
Input latitude and longitude values or an 'sf/sfc' POINT 
    object and get back the timezone in which they exist. Two methods are implemented. 
    One is very fast and uses the 'V8' package to access the 'tz-lookup.js' 'Javascript' library
    (<https://github.com/darkskyapp/tz-lookup/>). This method also works outside of countries' 
    borders and in international waters, however speed comes at the cost of accuracy - near time 
    zone borders away from populated centres there is a chance that it will return the incorrect
    time zone. The other method is slower but more accurate - it uses the sf package to intersect 
    points with a detailed map of time zones from here: 
    <https://github.com/evansiroky/timezone-boundary-builder/>.
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.
Core functionality for working with vectors (numeric, integer,
    logical and character) that are too large to keep in memory. The vectors are
    kept (partially) on disk using memory mapping. This package contains the
    basic functionality for working with these memory mapped vectors (e.g.
    creating, indexing, ordering and sorting) and provides C++ headers which can
    be used by other packages to extend the functionality provided in this
    package.
Fit response surfaces for datasets with latent-variable Gaussian process modeling, predict responses for new inputs, and plot latent variables locations in the latent space (only 1D or 2D).
    The input variables of the datasets can be quantitative, qualitative/categorical or mixed.
    The output variable of the datasets is a scalar (quantitative).
    The optimization of the likelihood function is done using a successive approximation/relaxation algorithm similar to another GP modeling package "GPM".
    The modeling method is published in "A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors"
    by Yichi Zhang, Siyu Tao, Wei Chen, and Daniel W. Apley (2018) <arXiv:1806.07504>. The package is developed in IDEAL of Northwestern University.
Latent variable models for network data using fast inferential
    procedures.
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). For details, see Epskamp, Rhemtulla and Borsboom (2017) <doi:10.1007/s11336-017-9557-x>.
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.
Access to selected functions found in 'liblwgeom' <https://github.com/postgis/postgis/tree/svn-trunk/liblwgeom>, the light-weight geometry library used by 'PostGIS' <http://postgis.net/>.
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%.
An implementation of algorithms described in Jewell and Witten (2017) <arXiv:1703.08644>.
Prediction of behaviour from movement 
	characteristics using observation and random forest for the analyses of movement
	data in ecology.
	From movement information (speed, bearing...) the model predicts the
	observed behaviour (movement, foraging...) using random forest. The
	model can then extrapolate behavioural information to movement data
	without direct observation of behaviours.
	The specificity of this method relies on the derivation of multiple predictor variables from the
	movement data over a range of temporal windows. This procedure allows to capture
	as much information as possible on the changes and variations of movement and
	ensures the use of the random forest algorithm to its best capacity. The method
	is very generic, applicable to any set of data providing movement data together with
	observation of behaviour.
Persistent interface to Macaulay2 (<http://www.math.uiuc.edu/Macaulay2/>)
	and front-end tools facilitating its use in the R ecosystem.
A new method to implement clustering from multiple modality data of certain samples, 
    the function M2SMF() jointly factorizes multiple similarity matrices into a shared sub-matrix 
    and several modality private sub-matrices, which is further used for clustering. Along with 
    this method, we also provide function to calculate the similarity matrix and function to 
    evaluate the best cluster number from the original data.
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.
Fit raw or grouped continuous data from a population with a smooth density on unit interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author this package published in the Journal of Nonparametric Statistics: Guan (2016) <doi:10.1080/10485252.2016.1163349> and Guan (2017) <doi:10.1080/10485252.2017.1374384>.
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 Zhao, Y., & Luo, X. (2014), Estimating Mediation Effects under Correlated Errors with an Application to fMRI, <arXiv:1410.7217> for details.
Connects to <https://machi.na/> and allows the creation
    of time series, and running backtests on selected strategy if requested.
Meta-package for statistical and machine learning with a common interface for model fitting, prediction, performance assessment, and presentation of results.  Supports predictive modeling of numerical, categorical, and censored time-to-event outcomes and resample (bootstrap and cross-validation) estimation of model performance.
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.
Package provides pipe-style interface for 'data.table'. It preserves all
    'data.table' features without significant impact on performance. 'let'
    and 'take' functions are simplified interfaces for most common data manipulation
    tasks. For example, you can write 'mtcars %>% take(mean(mpg), by = am)' for 
    aggregation or 'mtcars %>% let(hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg)' for modification.
    Use 'take_if/let_if' for conditional aggregation/modification. 'query_if' function
    translates its arguments one-to-one to '[.data.table' method. Additionally there are
    some conveniences such as automatic 'data.frame' conversion to 'data.table'.
An object that supports automatic differentiation
    of matrix- and multidimensional-valued functions with 
    respect to multidimensional independent variables. 
    Automatic differentiation is via 'forward accumulation'.
Tools for the analysis of population differences
    using the Major Histocompatibility Complex (MHC) genotypes of samples
    having a variable number of alleles (1-4) recorded for each
    individual.  A hierarchical Dirichlet-Multinomial model on the
    genotype counts is used to pool small samples from multiple
    populations for pairwise tests of equality.  Bayesian inference is
    implemented via the 'rstan' package.  Bootstrapped and posterior
    p-values are provided for chi-squared and likelihood ratio tests of
    equal genotype probabilities.
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.
Provides a framework which should improve reproducibility and transparency in data processing. It provides functionality such as automatic meta data creation and management, rudimentary quality management, data caching, work-flow management and data aggregation.
    * The title is a wish not a promise. By no means we expect this package to deliver everything what is needed to achieve full reproducibility and transparency, but we believe that it supports efforts in this direction.
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. The latest 
    version of the package includes a native graphics device for creating 
    in-memory graphics or drawing onto images using pixel coordinates.
Set of utility functions to interact with 'WeMo Switch', a smart 
    plug that can be remotely controlled via wifi. The provided functions make 
    it possible to turn one or more 'WeMo Switch' plugs on and off in a 
    scriptable fashion. More information about 'WeMo Switch' can be found at 
    <http://www.belkin.com/us/p/P-F7C027/>.
Correlates variation within the meta-genome to target species
    phenotype variations in meta-genome with association studies. Follows
    the pipeline described in Chaston, J.M. et al. (2014) <doi:10.1128/mBio.01631-14>.
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."
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.
Implements methods for querying backlink data from 'Majestic' using its API (<https://developer-support.majestic.com/api/>).
      'Majestic' API uses a basic authentication with an API key.
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.
Writing parallel R code can be difficult, particularly for code
    that is not "embarrassingly parallel".
    This experimental package automates the transformation of serial R code
    into more efficient parallel versions. It identifies task parallelism by
    statically analyzing entire scripts to detect dependencies between
    statements. It implements an extensible system for scheduling
    and generating new code. It includes a reference implementation of the
    'List Scheduling' approach to the general task scheduling problem of scheduling 
    statements on multiple processors.
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 suite of tools to allow you to download all 
  publicly available parasite rate survey points, mosquito occurrence points and raster surfaces from 
  the 'Malaria Atlas Project' <https://map.ox.ac.uk/> servers as well as utility functions for plotting
  the downloaded data.
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.
Provides methods for quality control and robust pre-processing and analysis of MALDI mass spectrometry data.
This package allows you to train topic models in mallet and load results directly into R.
Designing multi-arm multi-stage studies with (asymptotically) 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.
Estimates membership for the Mandelbrot set.
Mixture model with overlapping clusters for binary actor-event data. Parameters are estimated in a Bayesian framework. Model and inference are described in Ranciati, Vinciotti, Wit (2017) Modelling actor-event network data via a mixture model under overlapping clusters. Submitted.
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).
Methods for performing genetic risk
        prediction from genotype data.  You can use it to perform risk
        prediction for individuals, or for families with missing data.
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 version 0.3 of 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 package '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. Furthermore, post-hoc comparisons 
    are provided for the multivariate analyses.
    Friedrich, S., Konietschke, F. and Pauly, M. (2018) <arXiv:1801.08002>.
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. For details see Kourentzes et al. (2014) <doi:10.1016/j.ijforecast.2013.09.006>.
Supplement to maps package, providing the larger and/or
	higher-resolution databases.
Provides a mechanism to plot an interactive map using 'Mapbox GL' 
		(<https://www.mapbox.com/mapbox-gl-js/api/>), a javascript library for interactive maps,
		and 'Deck.gl' (<http://deck.gl/#/>), a javascript library which uses 'WebGL' for 
		visualising large data sets.
Suite of interactive functions and helpers for selecting and editing
    geospatial data.
Estimation methods for phase-type
  distribution (PH) and Markovian arrival process (MAP) from
  empirical data (point and grouped data) and density function.
Construct consensus genetic maps with LPmerge, see Endelman and Plomion (2014) <doi:10.1093/bioinformatics/btu091> and 
             model the relationship between physical distance and genetic distance using thin-plate regression splines, 
             see Wood (2003) <doi:10.1111/1467-9868.00374>. Perform quality control on input data and visualise intermediate steps.
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).
Mapping Averaged Pairwise Information (MAPI) is an exploratory method providing graphical representations summarizing the spatial variation of pairwise metrics (eg. distance, similarity coefficient, ...) computed between georeferenced samples. Reference: Piry et al. (2016) <DOI:10.1111/2041-210X.12616>.
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').
Interface to the 'Google Maps' APIs: (1) routing directions based on the 'Directions' API, returned as 'sf' objects, either as single feature per alternative route, or a single feature per segment per alternative route; (2) travel distance or time matrices based on the 'Distance Matrix' API; (3) geocoded locations based on the 'Geocode' API, returned as 'sf' objects, either points or bounds.
Local adaptation and evaluation of digital soil maps in raster 
    format by use of point location soil property data.
Automated calculation and visualization of survey data statistics on a color-coded map.
Set of tools for manipulating geographic data. 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.
Quickly and conveniently create interactive
    visualisations of spatial data with or without background maps.
    Attributes of displayed features are fully queryable via pop-up
    windows. Additional functionality includes methods to visualise true-
    and false-color raster images, bounding boxes, small multiples and 3D
    raster data cubes.
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.
A set of tools for likelihood-based estimation, model selection and testing of two- and three-range shift and migration models for animal movement data as described in Gurarie et al. (2017) <doi: 10.1111/1365-2656.12674>.  Provided movement data (X, Y and Time), including irregularly sampled data, functions estimate the time, duration and location of one or two range shifts, as well as the ranging area and auto-correlation structure of the movment.  Tests assess, for example, whether the shift was "significant", and whether a two-shift migration was a true return migration.
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.
Fits joint marginal conditional models for multivariate longitudinal data, as in Proudfoot, Faig, Natarajan, and Xu (2018) <doi:10.1002/sim.7552>. Development of this package was supported by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Provides the ability to perform "Marginal Mediation"--mediation
   wherein the indirect and direct effects are in terms of the average marginal effects 
   (Bartus, 2005, <https://EconPapers.repec.org/RePEc:tsj:stataj:v:5:y:2005:i:3:p:309-329>). 
   The style of the average marginal effects stems from Thomas Leeper's work on the "margins" package.
   This framework allows the use of categorical mediators and outcomes with little change in interpretation
   from the continuous mediators/outcomes. See <doi:10.13140/RG.2.2.18465.92001> for more details 
   on the method (peer reviewed articles forthcoming).
An R port of Stata's 'margins' command, which can be used to
    calculate marginal (or partial) effects from model objects.
Multivariate ARIMA and ARIMA-X estimation using Spliid's 
    algorithm (marima()) and simulation (marima.sim()).
A collection of marine species benchmark data sets and functions
    for species distribution modelling (ecological niche modelling).
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 mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and
    the multi-variate state model structure for survival
    estimation and POPAN structured Jolly-Seber models for abundance estimation.
    There are also Hidden Markov model (HMM) implementations of CJS and multistate
    models with and without state uncertainty and a simulation capability for HMM
    models.
For a given test market find the best control markets using time series matching and analyze the impact of an intervention. The intervention could be be a marketing event or some other local business tactic that is being tested. The workflow implemented in MarketMatching utilizes dynamic time warping (the dtw package) to do the matching and the CausalImpact package to analyze the causal impact. In fact, this package can be considered a "workflow wrapper" for those two packages.
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. See Dang and Golding 
    (2016) <doi:10.1093/bioinformatics/btv541> for more details.
Functions and S4 methods to create and manage discrete time Markov
    chains more easily. In addition functions to perform statistical (fitting
    and drawing random variates) and probabilistic (analysis of their structural
    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 <https://nwfsc-timeseries.github.io/>  See the NEWS file for update information.
Simulate, estimate (by t-MLE), select and forecast mixed causal-noncausal autoregressive models with possibly exogenous regressors, using methods proposed in Lanne and Saikkonen (2011) <doi:10.2202/1941-1928.1080> and Hecq et al. (2016) <doi:10.15609/annaeconstat2009.123-124.0307>.
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.
A set of model-assisted survey estimators and corresponding
    variance estimators for single stage, unequal probability, without replacement
    sampling designs.  All of the estimators can be written as a generalized 
    regression estimator with the Horvitz-Thompson, ratio, post-stratified, and 
    regression estimators summarized by Sarndal et al. (1992, ISBN:978-0-387-40620-6).
    Two of the estimators employ a statistical learning model as the assisting model:
    the elastic net regression estimator, which is an extension of the lasso regression
    estimator given by McConville et al. (2017) <doi:10.1093/jssam/smw041>, and the 
    regression tree estimator described in McConville and Toth (2017) <arXiv:1712.05708>. 
    The variance estimators which approximate the joint inclusion probabilities can
    be found in Berger and Tille (2009) <doi:10.1016/S0169-7161(08)00002-3> and the
    bootstrap variance estimator is presented in Mashreghi et al. (2016) 
    <doi:10.1214/16-SS113>.
Three key functionalities are present. It is able to mask confidential data using multiplicative noise. It is able to unmask this data while still preserving confidentiality. It is able to calculate the numerical joint density function of the original data from the unmasked data, as well as obtaining a sample from the marginal density functions of the unmasked data. The final results are a reasonable approximation to the original data for the purposes of analysis (Lin et al. (2018) <http://www.tdp.cat/issues16/abs.a271a17.php>).
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).
Constructing matrices for quick prototyping can be a nuisance, 
    requiring the user to think about how to fill the matrix with values using 
    the matrix() function. The %<-% operator solves that issue by allowing the 
    user to construct matrices using code that shows the actual matrices.
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 a wrapper for the some basic functionality around
    the 'Matchbook' <http://www.matchbook.com> REST API. It features calls to get events, markets and runners
    in data frame format. It features functions for bet placement and position
    management and also allows reporting of settled bet transactions. Note: this
    package uses the back-lay format. The default for odds type, currency and
    language are set according to those of the registered user account.
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.
Selects matched samples of 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.  The
    package also implements a series of recommendations offered in
    Ho, Imai, King, and Stuart (2007) <DOI:10.1093/pan/mpl013>.
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.  Details in Pimentel et al. (2017+), forthcoming in the Annals of Applied Statistics.
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>.
Given an image of a formula (typeset or handwritten) this package
    provides calls to the 'Mathpix' service to produce the 'LaTeX' code which should
    generate that image, and pastes it into a (e.g. an 'rmarkdown') document. 
    See <https://docs.mathpix.com/> for full details. 'Mathpix' is an external service 
    and use of the API is subject to their terms and conditions.
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.
Matrix clustering with finite mixture models.
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.  Integrates 
 easily with other R packages and non-R software.
A rich hierarchy of matrix classes, including triangular,
   symmetric, and diagonal matrices, both dense and sparse and with
   pattern, logical and numeric entries.   Numerous methods for and
   operations on these matrices, using 'LAPACK' and 'SuiteSparse' libraries.
Implements data manipulation methods such as cast, aggregate, and merge/join for Matrix and matrix-like objects.
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. For a description of the method, see Molstad and Rothman (2018) <doi:10.1080/10618600.2018.1476249>.
Modelling with sparse and dense 'Matrix' matrices, using
  modular prediction and response module classes.
Computes densities, probabilities, and random deviates of the Matrix Normal (Iranmanesh et.al. (2010) <doi:10.7508/ijmsi.2010.02.004>). Also includes simple but useful matrix functions. See R package help file for more information.
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.
A simple and the early stage package for matrix profile based on the paper of Chin-Chia Michael Yeh, Yan Zhu, Liudmila Ulanova, Nurjahan Begum, Yifei Ding, Hoang Anh Dau, Diego Furtado Silva, Abdullah Mueen, and Eamonn Keogh (2016) <DOI:10.1109/ICDM.2016.0179>. This package calculates all-pairs-similarity for a given window size for time series data.
Provides samplers for various matrix variate distributions: Wishart, inverse-Wishart, normal, t, inverted-t, Beta type I and Beta type II. Allows to simulate the noncentral Wishart distribution without the integer restriction on the degrees of freedom.
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().
Functions to perform fast statistical hypothesis tests on rows/columns of matrices.
  The main goals are: 1) speed via vectorization, 2) output that is detailed and easy to use,
  3) compatibility with tests implemented in R (like those available in the 'stats' package).
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.
Provides functions for the creation, evaluation and test of decision models based in
    Multi Attribute Utility Theory (MAUT). Can process and evaluate local risk aversion utilities
    for a set of indexes, compute utilities and weights for the whole decision tree defining the
    decision model and simulate weights employing Dirichlet distributions under addition constraints 
    in weights.
Functions for dimension reduction, using MAVE (Minimum Average Variance Estimation), OPG (Outer Product of Gradient) and KSIR (sliced inverse regression of kernel version). Methods for selecting the best dimension are also included.
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.
Fits a linear combination of predictors by maximizing a smooth approximation to the estimated covariate-adjusted area under the receiver operating characteristic curve (AUC) for a discrete covariate. (Meisner, A, Parikh, CR, and Kerr, KF (2017) <http://biostats.bepress.com/uwbiostat/paper421/>.)
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 <DOI:10.1890/10-1171.1>; Warren et al.,
    2014 <DOI:10.1111/ddi.12160>). 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
    <DOI:10.1109/TAC.1974.1100705>), and by the area under the receiver
    operating characteristic (AUC) (Fielding and Bell, 1997
    <DOI:10.1017/S0376892997000088>). Users of this package should be
    familiar with Maxent niche modelling.
Builds up sequential space-filling design under the criterion of maximin distance. Both discrete and continuous versions are provided.
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.
Computes the maximum matching for unweighted graph and maximum
    matching for (un)weighted bipartite graph efficiently.
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 maximum projection (MaxPro) designs for quantitative and/or qualitative factors. Details of the MaxPro criterion can be found in: (1) Joseph, Gul, and Ba. (2015) "Maximum Projection Designs for Computer Experiments", Biometrika, 102, 371-380, and (2) Joseph, Gul, and Ba. (2018) "Designing Computer Experiments with Multiple Types of Factors: The MaxPro Approach", Journal of Quality Technology, to appear.
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.
Estimates a linear combination of predictors by maximizing a smooth approximation to the estimated true positive rate (TPR; sensitivity) while constraining a smooth approximation to the estimated false positive rate (FPR; 1-specificity) at a user-specified level.
A suite of utility functions providing functionality commonly
    needed for production level projects such as logging, error handling,
    and cache management.
A suite of conversion scripts to create internally standardized
    spatial polygons data frames. Utility scripts use these data sets to return
    values such as country, state, timezone, watershed, etc. associated with a
    set of longitude/latitude pairs. (They also make cool maps.)
A suite of utility functions providing standardized functionality
    often needed in web services including: logging, cache management
    and parsing of http request headers.
Supports the generation of parallelogram, equilateral
    triangle, regular hexagon, isosceles trapezoid, Koch snowflake,
    'hexaflake', Sierpinski triangle, Sierpinski carpet and Sierpinski
    trapezoid mazes via 'TurtleGraphics'. Mazes are generated by the recursive method:
    the domain is divided into sub-domains in which mazes are generated,
    then dividing lines with holes are drawn between them, see
    J. Buck, Recursive Division, <http://weblog.jamisbuck.org/2011/1/12/maze-generation-recursive-division-algorithm>.
A maze generator that creates the Elithorn Maze (HTML file) and the functions to calculate the associated maze parameters (i.e. Difficulty and Ability).
Methods for calculating and testing the significance of
  pairwise monotonic association from and based on the work of
  Pimentel (2009) <doi:10.4135/9781412985291.n2>. Computation of association of vectors from one
  or multiple sets can be performed in parallel thanks to the
  packages 'foreach' and 'doMC'.
Functions to interpolate irregularly and regularly spaced data using Multilevel B-spline Approximation (MBA). Functions call portions of the SINTEF Multilevel B-spline Library written by Øyvind Hjelle which implements methods developed by Lee, Wolberg and Shin (1997; <doi:10.1109/2945.620490>).
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
    described by Cannon (2016) <doi:10.1175/JCLI-D-15-0679.1> and 
    Cannon (2018) <doi:10.1007/s00382-017-3580-6> are implemented:
    (i) MBC Pearson correlation (MBCp), (ii) MBC rank correlation (MBCr),
    and (iii) MBC N-dimensional PDF transform (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 studies 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.
Fast moment-based hierarchical model fitting. Implements
    methods from the papers
    "Fast Moment-Based Estimation for Hierarchical Models," by Perry (2017)
    and
    "Fitting a Deeply Nested Hierarchical Model to a Large Book Review
    Dataset Using a Moment-Based Estimator," by Zhang, Schmaus, and Perry
    (2018).
Measure based exploratory data analysis. Some of the functions call interactive apps programmed with the package shiny to provide flexible selection options.
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
Allows practitioners and researchers a wholesale approach for deriving magnitude-based inferences from raw data. A major goal of 'mbir' is to programmatically detect appropriate statistical tests to run in lieu of relying on practitioners to determine correct stepwise procedures independently.
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.
Fit generalized linear models with binomial responses using  a median modified score approach (Kenne Pagui et al., 2016, <https://arxiv.org/abs/1604.04768>) to median bias reduction. This method respects equivariance under reparameterizations for each parameter component and also solves the infinite estimates problem (data separation).
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.
Implements a sparse Bayesian multivariate linear regression model using shrinkage priors from the three parameter beta normal family. The method is described in Bai and Ghosh (2018) <arXiv:1711.07635>.
Access to the MBTA's performance API for R. Creates an easy-to-use
  bundle of functions to work with all the built-in calls to the MBTA performance 
  API, and some to work with the GTFS-compatible API V3 which replaced some 
  functionality from the old performance API. Allows users to download realtime 
  tracking data in dataframe format that is manipulable in standard R analytics 
  functions. Pulls performance statistics in both realtime and for historical 
  dates and trips (after July 2015).
Bayesian variable selection methods for data with multivariate responses and multiple covariates. The package contains implementations of multivariate Bayesian variable selection methods for continuous data and zero-inflated count data.
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.
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.
It contains six common multi-category classification accuracy evaluation measures:
 Hypervolume Under Manifold (HUM), described in
 Li and Fine (2008) <doi:10.1093/biostatistics/kxm050>.
 Correct Classification Percentage (CCP), Integrated Discrimination Improvement (IDI), Net Reclassification Improvement (NRI), R-Squared Value (RSQ), described in
 Li, Jiang and Fine (2013) <doi:10.1093/biostatistics/kxs047>.
 Polytomous Discrimination Index (PDI), described in
 Van Calster et al. (2012) <doi:10.1007/s10654-012-9733-3>.
 Li et al. (2018) <doi:10.1177/0962280217692830>.
The MCC-F1 analysis is a method to evaluate the performance of binary classifications. 
    The MCC-F1 curve is more reliable than the Receiver Operating Characteristic (ROC) curve and the Precision-Recall (PR)curve under imbalanced ground truth.
    The MCC-F1 analysis also provides the MCC-F1 metric that integrates classifier performance over varying thresholds, and the best threshold of binary classification.
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.
Applying the methodology from Manuel, Wang, and Sinha (2018), estimates for the 
    parameters for matched case control data with a mismeasured exposure are calculated 
    through the use of user supplied instrumental variables.
The Matthews correlation coefficient (MCC) score is calculated (Matthews BW  (1975) <DOI:10.1016/0005-2795(75)90109-9>).
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.
Fits the Multivariate Cluster Elastic Net (MCEN) presented in Price & Sherwood (2018) <arXiv:1707.03530>. The MCEN model simultaneously estimates regression coefficients and a clustering of the responses for a multivariate response model. Currently accommodates the Gaussian and binomial likelihood.
Machine coded genetic algorithm (MCGA) is a fast tool for
    real-valued optimization problems. It uses the byte
    representation of variables rather than real-values. It
    performs the classical crossover operations (uniform) on these
    byte representations. Mutation operator is also similar to
    classical mutation operator, which is to say, it changes a
    randomly selected byte value of a chromosome by +1 or -1 with
    probability 1/2. In MCGAs there is no need for
    encoding-decoding process and the classical operators are
    directly applicable on real-values. It is fast and can handle a
    wide range of a search space with high precision. Using a
    256-unary alphabet is the main disadvantage of this algorithm
    but a moderate size population is convenient for many problems.
    Package also includes multi_mcga function for multi objective
    optimization problems. This function sorts the chromosomes
    using their ranks calculated from the non-dominated sorting
    algorithm.
'mcgibbsit' provides an implementation of Warnes & Raftery's
  MCGibbsit run-length diagnostic for a set of (not-necessarily
  independent) MCMC samplers.  It combines the estimate error-bounding
  approach of the Raftery and Lewis MCMC run length diagnostic with
  the between verses within chain approach of the Gelman and
  Rubin MCMC convergence diagnostic.
Fitting multivariate covariance generalized linear
     models (McGLMs) to data.  McGLM 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. 
     See Bonat (2018) <doi:10.18637/jss.v084.i04>, for more information 
     and examples.
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.
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).
Market area models are used to analyze and predict store choices and market areas concerning retail and service locations. This package is a more user-friendly wrapper of the functions in the package 'MCI' (Wieland 2017) providing market area analysis using the Huff Model or the Multiplicative Competitive Interaction (MCI) Model. In 'MCI2', also a function for creating transport costs matrices is provided.
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.Reference for the method can be found in the DPhil thesis in Z. Sha (2016). For application a good reference is R.Bivand et.al (2017) <doi:10.1016/j.spasta.2017.01.002>.
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
    random effects. 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.
Given a set of data points, a clustering is defined as a disjoint partition
    where each pair of sets in a partition has no overlapping elements. 
    This package provides 25 methods that play a role somewhat similar to 
    distance or metric that measures similarity of two clusterings - or partitions.
    For a more detailed description, see Meila, M. (2005) <doi:10.1145/1102351.1102424>.
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, 2012, <https://doi.org/10.1214/12-AOS1048>,
    function morph.metrop),
    which achieves geometric ergodicity by change of variable.
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.
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 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.
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.
Estimates the precision of transdimensional Markov chain Monte Carlo 
    (MCMC) output, which is often used for Bayesian analysis of models with different 
    dimensionality (e.g., model selection). Transdimensional MCMC (e.g., reversible 
    jump MCMC) relies on sampling a discrete model-indicator variable to estimate 
    the posterior model probabilities. If only few switches occur between the models, 
    precision may be low and assessment based on the assumption of independent 
    samples misleading. Based on the observed transition matrix of the indicator 
    variable, the method of Heck, Overstall, Gronau, & Wagenmakers (2018, 
    Statistics & Computing) <doi:10.1007/s11222-018-9828-0> draws posterior samples 
    of the stationary distribution to (a) assess the uncertainty in the estimated 
    posterior model probabilities and (b) estimate the effective sample size of 
    the MCMC output.
Functions and classes to store, manipulate and summarise 
   Monte Carlo Markov Chain (MCMC) samples.
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.
Algorithms to approximate the Pareto-front of multi-criteria minimum spanning tree problems. Additionally, a modular toolbox for the generation of multi-objective benchmark graph problems is included.
Functions for multiple criteria optimization using genetic
        algorithms and related test problems
The 1001 time series from the M-competition (Makridakis et al. 1982) <DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.
Provides a class for multi-companion matrices with methods for
    arithmetic and factorization.  A method for generation of
    multi-companion matrices with prespecified spectral properties is
    provided, as well as some utilities for periodically correlated and
    multivariate time series models. See Boshnakov (2002)
    <doi:10.1016/j.laa.2007.02.010> and Boshnakov & Iqelan (2009)
    <doi:10.1111/j.1467-9892.2009.00617.x>.
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.
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) <doi:10.3982/ECTA5771>.
Identifies the optimal number of clusters by calculating the similarity between
             two clustering methods at the same number of clusters using the corrected indices of Rand and
             Jaccard as described in Albatineh and Niewiadomska-Bugaj (2011). The number of clusters at
             which the index attain its maximum more frequently is a candidate for being the optimal
             number of clusters.
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.
Produces clean and neat Markdown log file
    and also provide an argument to include the function call inside the Markdown log.
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.
A collection of methods for agglomerative hierarchical clustering
  strategies on a matrix of distances, implemented using the variable-group
  approach introduced in Fernandez and Gomez (2008)
  <doi:10.1007/s00357-008-9004-x>. Descriptive measures to analyze the resulting
  hierarchical trees are also provided. In addition to the usual clustering
  methods, two parameterized methods are provided to explore an infinite family
  of hierarchical clustering strategies. When there are ties in proximity
  values, the hierarchical trees obtained are unique and independent of the
  order of the elements in the input matrix.
Functions for MultiDimensional Feature Selection (MDFS):
 calculating multidimensional information gains, scoring variables,
 finding important variables, plotting selection results.
 This package includes an optional CUDA implementation that speeds up
 information gain calculation using NVIDIA GPGPUs.
'MTrackJ' is an 'ImageJ' plugin for motion tracking and analysis (see 
    <https://imagescience.org/meijering/software/mtrackj/>). This package reads 
    and writes 'MTrackJ Data Files' ('.mdf', see 
    <https://imagescience.org/meijering/software/mtrackj/format/>). It supports
    2D data and read/writes cluster, point, and channel information. If desired, 
    generates track identifiers that are unique over the clusters.
    See the project page for more information and examples.
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.
Implemented are the one-sided and two-sided 
  multiple-direction logrank test for two-sample right 
  censored data. In addition to the statistics p-values are calculated: 
  1. For the one-sided testing problem one p-value based on a
   wild bootstrap approach is determined. 2. In the two-sided case
   one p-value based on a chi-squared approximation and 
   a second p-values based on a permutation approach are calculated.
 Ditzhaus, M. and Friedrich, S. (2018) <arXiv:1807.05504>.
 Ditzhaus, M. and Pauly, M. (2018) <arXiv:1808.05627>.
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.
Contains model-based treatment of missing data for regression 
    models with missing values in covariates or the dependent 
    variable using maximum likelihood or Bayesian estimation 
    (Ibrahim et al., 2005; <doi:10.1198/016214504000001844>).
    The regression model can be nonlinear (e.g., interaction 
    effects, quadratic effects or B-spline functions). 
    Multilevel models with missing data in predictors are
    available for Bayesian estimation. Substantive-model compatible 
    multiple imputation can be also conducted.
Allows users to conduct multivariate distance matrix regression using analytic p-values and compute measures of effect size. For details on the method, see McArtor, Lubke, & Bergeman (2017) <doi:10.1007/s11336-016-9527-8>.
Provides graph-constrained regression methods in which
    regularization parameters are selected automatically via estimation of
    equivalent Linear Mixed Model formulation. 'riPEER' (ridgified Partially
    Empirical Eigenvectors for Regression) 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; 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. 'riPEERc' (ridgified Partially Empirical Eigenvectors
    for Regression with constant) method utilizes addition of a diagonal matrix
    multiplied by a predefined (small) scalar to handle the non-invertibility of
    a graph Laplacian matrix. 'vrPEER' (variable reducted PEER) method performs
    variable-reduction procedure to handle the non-invertibility of a graph
    Laplacian matrix.
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. Currently able to load
             GROMOS, GROMACS and AMBER formats, respectively.
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.
A set of core functions for handling medical device event data in
    the context of post-market surveillance, pharmacovigilance, signal detection
    and trending, and regulatory reporting. Primary inputs are data on events by
    device and data on exposures by device. Outputs include: standardized
    device-event and exposure datasets, defined analyses, and time series.
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.
Estimate genetic linkage maps for markers on a single chromosome (or in a single linkage group) from pairwise recombination fractions or intermarker distances using weighted metric multidimensional scaling. The methods are suitable for autotetraploid as well as diploid populations. Options for assessing the fit to a known map are also provided. Methods are discussed in detail in Preedy and Hackett (2016) <doi:10.1007/s00122-016-2761-8>.
Selecting the optimal multidimensional scaling (MDS) procedure for metric data via metric MDS (ratio, interval, mspline) and nonmetric MDS (ordinal). Selecting the optimal multidimensional scaling (MDS) procedure for interval-valued data via metric MDS (ratio, interval, mspline).Selecting the optimal multidimensional scaling procedure for interval-valued data by varying all combinations of normalization and optimization methods.Selecting the optimal MDS procedure for statistical data referring to the evaluation of tourist attractiveness of Lower Silesian counties.
 (Borg, I., Groenen, P.J.F., Mair, P. (2013) <doi:10.1007/978-3-642-31848-1>, 
 Groenen, P.J.F., Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>,
 Walesiak, M. (2016) <doi:10.15611/ekt.2016.2.01>, 
 Walesiak, M. (2017) <doi:10.15611/ekt.2017.3.01>).
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.
A collection of common statistical algorithms used in active
    surveillance of medical device events. Context includes post-market
    surveillance, pharmacovigilance, signal detection and trending, and
    regulatory reporting. Primary inputs are device-event time series. Outputs
    include trending results with the ability to run multiple algorithms at 
    once. This package works well with the 'mds' package, but does not require 
    it.
Dimension-reduction methods aim at defining a score that maximizes signal diversity. Two approaches, namely maximum entropy weights and maximum variance weights, are provided.
Sentiment analysis is a popular technique in text mining.  Roughly
    speaking, the technique is an attempt to determine the overall emotional
    attitude of a piece of text (i.e., positive or negative).  We provide a new
    implementation of a common method for computing sentiment, whereby words are
    scored as positive or negative according to a "dictionary", and then an
    sum of those scores for the document is produced.  We use the 'Hu' and 'Liu'
    sentiment dictionary for determining sentiment.  The scoring function is
    'vectorized' by document, and scores for multiple documents are computed in
    parallel via 'OpenMP'.
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,
  Epanechnikov, and biweight product kernels.
Core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments, published by Gelfman et al (2017) at <https://github.com/igm-team/meaRtools/>.
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.
Provides the biggest amount of statistical measures in the whole R world. Includes measures of regression, (multiclass) classification and multilabel classification. The measures come mainly from the 'mlr' package and were programed by several 'mlr' developers.
Identification of ring borders on scanned image sections from dendrochronological samples. Processing of image reflectances to produce gray matrices and 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 devices, 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. Most of the in-package routines can be recursively implemented using the multiDetect() function.
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.
Nonparametric estimation and inference for natural direct and indirect effects by Chan, Imai, Yam and Zhang (2016) <arXiv:1601.03501>.
Multi Calculator of different scores to measure adherence to Mediterranean Diet, to compute them in nutriepidemiological data. Additionally, a sample dataset of this kind of data is provided, and some other minor tools useful in epidemiological studies.
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) <DOI:10.1093/aje/kwr525>, 
  Vansteelandt, Bekaert and Lange (2012) <DOI:10.1515/2161-962X.1014> 
  and Loeys, Moerkerke, De Smet, Buysse, Steen and Vansteelandt (2013) <DOI:10.1080/00273171.2013.832132>.
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) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, Imai and Yamamoto (2013) <doi:10.1093/pan/mps040> and Yamamoto (2013) <http://web.mit.edu/teppei/www/research/IVmediate.pdf>. 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 toolbox allows you to do simple mediation and moderation 
    analysis. It is also available as a module for 'jamovi' 
    (see <https://www.jamovi.org> for more information). 'Medmod' is based 
    on the 'lavaan' package by Yves Rosseel. You can find an in depth tutorial 
    on the 'lavaan' model syntax used for this package on 
    <http://lavaan.ugent.be/tutorial/index.html>.
This package contains the functions used to perform some
        confidence statistics based in population median.
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).
Allows management of 'Meetup' groups via the <https:www.meetup.com/meetup_api/>.
    Provided are a set of functions that enable fetching information of joined meetups, attendance,
    and members. This package requires the use of an API key.
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.
Uses as input genetic data that have been reformatted
     and stored in a 'SQLite' database; this database is initially
     created by the standalone 'mega2' C++ program (available freely
     from <https://watson.hgen.pitt.edu/register/>). Loads and
     manipulates data frames containing genotype, phenotype, and family
     information from the input 'SQLite' database, and decompresses
     needed subsets of the genotype data, on the fly, in a memory
     efficient manner.  We have also created several more functions that
     illustrate how to use the data frames as well as perform useful
     tasks: these permit one to run the 'pedgene' package to carry out
     gene-based association tests on family data using selected marker
     subsets, to run the 'SKAT' package to carry out gene-based
     association tests using selected marker subsets, to output the
     'Mega2R' data as a VCF file and related files (for phenotype and
     family data), and to convert the data frames into 
     CoreArray Genomic Data Structure (GDS) format.
Co-Expression Network Analysis by adopting network embedding technique. Song W.-M., Zhang B. (2015) Multiscale Embedded Gene Co-expression Network Analysis. PLoS Comput Biol 11(11): e1004574. <doi: 10.1371/journal.pcbi.1004574>.
Exploratory model analysis.  Fit and graphical
    explore ensembles of linear models.
Create variable width bar charts i.e.
    "bar mekko" charts to include important quantitative context.
    Closely related to mosaic, spine (or spinogram), matrix,
    submarine, olympic, Mondrian or product plots and tree maps.
Framework for merging and disambiguating event data based on spatiotemporal co-occurrence and secondary event characteristics. It can account for intrinsic "fuzziness" in the coding of events, varying event taxonomies and different geo-precision codes.
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>).
The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) <doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week.
The Moving Epidemic Method, created by T Vega and JE Lozano (2012, 2015) <doi:10.1111/j.1750-2659.2012.00422.x>, <doi:10.1111/irv.12330>, allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems. Allows the comparison of different epidemic indicators, timing and shape with past epidemics and across different regions or countries with different surveillance systems. Also, it gives a measure of the performance of the method in terms of sensitivity and specificity of the alert week. 'memapp' is a web application created in the Shiny framework for the 'mem' R package.
The word 'Meme' was originated from the book, 'The Selfish Gene', authored by Richard Dawkins (1976).
             It is a unit of culture that is passed from one generation to another and correlates to the gene, the unit of physical heredity.
             The internet memes are captioned photos that are intended to be funny, ridiculous.
             Memes behave like infectious viruses and travel from person to person quickly through social media.
             The 'meme' package allows users to make custom memes.
Generates internet memes that optionally include a superimposed inset plot and other atypical features, 
    combining the visual impact of an attention-grabbing meme with graphic results of data analysis.
    The package differs from related packages that focus on imitating and reproducing standard memes.
    Some packages do this by interfacing with online meme generators whereas others achieve this natively.
    This package takes the latter approach. It does not interface with online meme generators or require any authentication with external websites.
    It reads images directly from local files or via URL and meme generation is done by the package.
    While this is similar to the 'meme' package available on CRAN, it differs in that the focus is on 
    allowing for non-standard meme layouts and hybrids of memes mixed with graphs.
    While this package can be used to make basic memes like an online meme generator would produce, 
    it caters primarily to hybrid graph-meme plots where the meme presentation can be seen as a backdrop highlighting 
    foreground graphs of data analysis results.
    The package also provides support for an arbitrary number of meme text labels with arbitrary size, position and other attributes 
    rather than restricting to the standard top and/or bottom text placement. 
    This is useful for proper aesthetic interleaving of plots of data between meme image backgrounds and overlain text labels.
    The package offers a selection of templates for graph placement and appearance with respect to the underlying meme.
    Graph templates also permit additional template-specific customization.
    Animated gif support is provided but this is optional and functional only if the 'magick' package is installed. 
    'magick' is not required unless gif functionality is desired.
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.
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 is provided. Further, the package allows 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.
Efficient implementations of network science tools 
	to facilitate research into human (semantic) memory. In its current version, the package 
	contains several methods to infer networks from verbal fluency data, various network 
	growth models, diverse (switcher-) random walk processes, and tools to analyze and 
	visualize networks. To deliver maximum performance the majority of the code is written
	in C++. For an application see: Wulff, D. U., Hills, T., & Mata, R. (2018) 
	<doi:10.31234/osf.io/s73dp>.
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.
A 'rmarkdown' template that supports company logo, contact info, 
    watermarks and more. Currently restricted to 'Latex'/'Markdown'; a similar 
    'HTML' theme will be added in the future.
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.
Based on the input data an n-dimensional cube with sub cells of user specified side length is created.
    The number of sample points which fall in each sub cube is counted, and with the cell volume and overall sample
    size an empirical probability can be computed. A number of cubes of higher resolution can be superimposed. The
    basic method stems from J.L. Bentley in "Multidimensional Divide and Conquer".
    J. L. Bentley (1980) <doi:10.1145/358841.358850>.
    Furthermore a simple kernel density estimation method is made available, as well as an expansion of Bentleys
    method, which offers a kernel approach for the grid method.
Compute analytic case-wise and cluster-wise derivative for 
  mixed effects models with respect to fixed effects parameter, random effect (co)variances, 
  and residual variance.
A GUI for merging datasets in R using gWidgets.
Fits linear, non-linear, and user-defined mixed effects regression models following 
  the framework developed by Crowther (2017) <arXiv:1710.02223>. 'merlin' can fit multivariate 
  outcome models of any type, each of which could be repeatedly measured (longitudinal), with 
  any number of levels, and with any number of random effects at each level. Standard 
  distributions/models available include the Bernoulli, Gaussian, Poisson, beta, 
  negative-binomial, and time-to-event/survival models include the exponential, 
  Gompertz, Royston-Parmar, Weibull and general hazard model. 'merlin' provides a 
  flexible predictor syntax, allowing the user to define variables, random effects, 
  spline and fractional polynomial functions, functions of 
  other outcome models, and any interaction between each of them. Non-linear and 
  time-dependent effects are seamlessly incorporated into the predictor. 'merlin' 
  allows multivariate normal random effects, which are integrated out using Gaussian 
  quadrature or Monte-Carlo integration. Note, 'merlin' is based on the 'Stata' package 
  of the same name, described in Crowther (2018) <arXiv:1806.01615>.
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.
Provides tools for creating and issuing nicely-formatted
    text within R diagnostic messages and those messages given during
    warnings and errors. The formatting of the messages can be
    customized using templating features. Issues with singular and
    plural forms can be handled through specialized syntax.
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);
 - produce forest plot summarising several (subgroup) meta-analyses.
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=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/testing"), dep=TRUE).
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
The sample mean and standard deviation are two commonly used statistics in meta-analyses, 
    but some trials use other summary statistics such as the median and quartiles to report the results. 
    Therefore, researchers need to transform those information back to the sample mean and 
    standard deviation. This package implemented sample mean estimators by Luo et al. (2016) <arXiv:1505.05687>, sample standard deviation estimators by Wan et al. (2014) <arXiv:1407.8038>, and the best linear unbiased estimators (BLUEs) of location and scale parameters by Yang et al. (2018, submitted) based on sample quantiles derived summaries in a meta-analysis.
Computes the posterior model probabilities for four meta-analysis models 
    (null model vs. alternative model assuming either fixed- or random-effects, respectively).
    These posterior probabilities are used to estimate the overall mean effect size 
    as the weighted average of the mean effect size estimates of the random- and 
    fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, & 
    Wagenmakers (2017, <doi:10.1080/23743603.2017.1326760>). The user can define 
    a wide range of noninformative or informative priors for the mean effect size 
    and the heterogeneity coefficient. Funding for this research was provided by 
    the Berkeley Initiative for Transparency in the Social Sciences, a program of 
    the Center for Effective Global Action (CEGA), with support from the Laura and 
    John Arnold Foundation.
Integrates metabolic networks and RNA-seq data to construct condition-specific series of metabolic sub-networks and applies to gene set enrichment analysis (Tran et al. (2018) <doi:10.1093/bioinformatics/bty929>).
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
A set of functions to investigate raw data from (metabol)omics experiments intended to be used on a raw data matrix, i.e. following peak picking and signal deconvolution. Functions can be used to normalize data, detect biomarkers and perform sample classification.
Simulating dynamic (longitudinal, time-resolved) metabolomics data based on an underlying biological network. The network is initiating with certain concentrations and evolves over a simulated time period. Optionally external influxes (concentration drivers) can be added.
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.
Meta-CART integrates classification and regression trees (CART) into meta-analysis. Meta-CART is a flexible approach to identify interaction effects between moderators in meta-analysis. The method is described in Dusseldorp et al. (2014) <doi:10.1037/hea0000018> and Li et al. (2017) <doi:10.1111/bmsp.12088>.
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  <doi:10.1034/j.1600-0706.2002.970210.x>. The package also includes 
		functions to visualize ecological networks, and to calculate modularity 
		as a replacement to boundary clumping.
Implements routines for metagenome sample taxonomy assignments collection, 
    aggregation, and visualization. Accepts the EDGE-formatted output from GOTTCHA/GOTTCHA2, 
    BWA, Kraken, MetaPhlAn, DIAMOND, and Pangia. Produces SVG and PDF heatmap-like plots 
    comparing taxa abundances across projects.
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.
High-throughput, flexible and reproducible extraction of data from figures in primary research papers. metaDigitise() can extract data and / or automatically calculate summary statistics for users from box plots, bar plots (e.g., mean and errors), scatter plots and histograms.
Metadynamics is a state of the art biomolecular simulation technique.
  'Plumed' Tribello, G.A. et al. (2014) <doi:10.1016/j.cpc.2013.09.018> program makes
  it possible to perform metadynamics using various simulation codes. The results of
  metadynamics done in 'Plumed' can be analyzed by 'metadynminer'. The package
  'metadynminer' reads 1D and 2D metadynamics hills files from 'Plumed' package.
  It uses a fast algorithm by Hosek, P. and Spiwok, V. (2016) <doi:10.1016/j.cpc.2015.08.037>
  to calculate a free energy surface from hills. Minima can be located and plotted on
  the free energy surface. Transition states can be analyzed by Nudged Elastic Band
  method by Henkelman, G. and Jonsson, H. (2000) <doi:10.1063/1.1323224>. Free energy
  surfaces, minima and transition paths can be plotted to produce publication quality
  images.
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.
Conduct random forests-based meta-analysis, obtain partial dependence plots for metaforest and classic meta-analyses, and cross-validate and tune metaforest- and classic meta-analyses in conjunction with the caret package. A requirement of classic meta-analysis is that the studies being aggregated are conceptually similar, and ideally, close replications. However, in many fields, there is substantial heterogeneity between studies on the same topic. Classic meta-analysis lacks the power to assess more than a handful of univariate moderators. MetaForest, by contrast, has substantial power to explore heterogeneity in meta-analysis. It can identify important moderators from a larger set of potential candidates, even with as little as 20 studies (Van Lissa, in preparation). This is an appealing quality, because many meta-analyses have small sample sizes. Moreover, MetaForest yields a measure of variable importance which can be used to identify important moderators, and offers partial prediction plots to explore the shape of the marginal relationship between moderators and effect size.
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>).
An implementation of metaheuristic algorithms for continuous
	optimization. Currently, the package contains the implementations of the following algorithms: particle swarm optimization (Kennedy and Eberhart, 1995), ant lion optimizer (Mirjalili, 2015 <doi:10.1016/j.advengsoft.2015.01.010>), grey wolf optimizer (Mirjalili et al., 2014 <doi:10.1016/j.advengsoft.2013.12.007>), dragonfly algorithm (Mirjalili, 2015 <doi:10.1007/s00521-015-1920-1>), firefly algorithm (Yang, 2009 <doi:10.1007/978-3-642-04944-6_14>), genetic algorithm (Holland, 1992, ISBN:978-0262581110), grasshopper optimisation algorithm (Saremi et al., 2017 <doi:10.1016/j.advengsoft.2017.01.004>), harmony search algorithm (Mahdavi et al., 2007 <doi:10.1016/j.amc.2006.11.033>), moth flame optimizer (Mirjalili, 2015 <doi:10.1016/j.knosys.2015.07.006>, sine cosine algorithm (Mirjalili, 2016 <doi:10.1016/j.knosys.2015.12.022>) and whale optimization algorithm (Mirjalili and Lewis, 2016 <doi:10.1016/j.advengsoft.2016.01.008>).
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.
Identify time intervals of differentially abundant metagenomics features in longitudinal studies.
Combines either p-values or modified effect sizes from different
    studies to find differentially expressed genes
Functions for path based queries against 'Omics' <https://github.com/etheleon/omics-neo4j-container>, a 'NEO4J' <https://neo4j.com/> database populated with data from metagenomic surveys and their accompanying metabolic and taxonomic annotations.
Implements several methods to meta-analyze one-group or two-group 
    studies that report the median of the outcome. In the one-group context,
    the methods of McGrath et al. (2018a) <doi:10.1002/sim.8013> estimate 
    the pooled median. In the two-group context, the methods of McGrath et al. 
    (2018b) <arXiv:1809.01278> estimate the pooled raw difference of medians 
    across groups.
Meta-analysis of diagnostic and prognostic modeling studies. Summarize estimates of prognostic factors, diagnostic test accuracy and prediction model performance. Validate, update and combine published prediction models. Develop new prediction models with data from multiple studies.
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.
Novel method to unbiasedly include studies with Non Statistically-Significant Unreported Effects (NSUEs) in a meta-analysis <doi:10.1001/jamapsychiatry.2015.2196>. 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, maximum 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 'metansue' 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 including the methods of Edgington, Fisher,
   Lancaster, Stouffer, Tippett, and Wilkinson; 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.
Designs plots in terms of core structure.  See 'example(metaplot)'.
 Primary arguments are (unquoted) column names; order and type (numeric or not)
 dictate the resulting plot.  Specify any y variables, x variable, any groups variable,
 and any conditioning variables to metaplot() to generate density plots, boxplots, 
 mosaic plots, scatterplots, scatterplot matrices, or conditioned plots. Use multiplot() 
 to arrange plots in grids. Wherever present, scalar column attributes 'label' and 'guide' 
 are honored, producing fully annotated plots with minimal effort. Attribute 'guide' 
 is typically units, but may be encoded() to provide interpretations of categorical 
 values (see '?encode').  Utility unpack() transforms scalar column attributes to row 
 values and pack() does the reverse, supporting tool-neutral storage of metadata along 
 with primary data. The package supports customizable aesthetics such as such as reference 
 lines, unity lines, smooths, log transformation, and linear fits. The user may choose
 between trellis and ggplot output. Compact syntax and integrated metadata promote workflow 
 scalability.
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' and
             'lavaan' packages. It also implements various procedures to
			 perform meta-analytic structural equation modeling on the
             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.
Performs Bayesian meta-analysis using 'Stan'. 
             Includes binomial-normal hierarchical models and option to use weakly informative priors for the
             heterogeneity parameter and the treatment effect parameter which are described in 
             Guenhan, Roever, and Friede (2018) <arXiv:1809.04407>.
If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. The validation cohort needs to be independent from the meta-Genome-Wide-Association-Study (meta-GWAS) results. 'MetaSubtract' will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. It can also handle different meta-analysis methods. It can be used for whole GWAS, but also for a limited set of genetic markers.
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).
Compute variance-covariance matrix for multivariate meta-analysis. Effect sizes include correlation (r), mean difference (MD), standardized mean difference (SMD), log odds ratio (logOR), log risk ratio (logRR), and risk difference (RD).
A compilation of functions to plot meta-analytic data using 'ggplot2'. Currently allows to 
    create forest plots, funnel plots, and many of their variants, such as rainforest plots and 
    additional evidence contour enhanced funnel plots. In addition, functionalities for visual funnel 
    plot inference are provided. In the near future, the 'metaviz' package will be extended by further, 
    established as well as novel, plotting options for visualizing meta-analytic data.
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 [De Caceres et al. (2018) <doi:10.1016/j.envsoft.2018.08.003>].
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)
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.
Many useful functions and extensions for dealing
    with meteorological data in the tidy data framework. Extends 'ggplot2'
    for better plotting of scalar and vector fields and provides commonly
    used analysis methods in the atmospheric sciences.
An implementation of evaluation metrics in R that are commonly
             used in supervised machine learning. It implements metrics for
             regression, time series, binary classification, classification,
             and information retrieval problems. It has zero dependencies and
             a consistent, simple interface for all functions.
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 statistical performance of 13 pre-defined
	phylogenetic metrics and 11 null models, and of user-defined metrics
	and null models, as detailed in Miller et al. (2017) <doi:10.1111/ecog.02070>.
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 <doi:10.1007/s10985-013-9244-x>. Including multivariate
    cumulative incidence models <doi:10.1002/sim.6016>, and  bivariate random
    effects probit models (Liability models) <doi:10.1016/j.csda.2015.01.014>.
    Also contains two-stage binomial modelling that can do pairwise odds-ratio
    dependence modelling based marginal logistic regression models. This is an
    alternative to the alternating logistic regression approach (ALR).
A tool for locating, mapping, and gathering environmental data and metadata, worldwide.  Users can search for and filter metadata from > 107,000 environmental monitoring stations among 219 countries/territories and >20 networks/organizations via elevation, location, active dates, elements measured (e.g., temperature, precipitation), country, network, and/or known identifier. Future updates to the package will allow the user to obtain datasets from stations within the database.
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.
Provides an interface with the Meteo France Synop data API 
    (see <https://donneespubliques.meteofrance.fr/?fond=produit&id_produit=90&id_rubrique=32> 
    for more information). 
    The Meteo France Synop data are made of meteorological data recorded 
    every three hours on 62 French meteorological stations.
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 the possibility of including non-proportional effects of covariables and of adding a random effect at the cluster level (corresponding to a shared frailty).
The method 'm:Explorer' associates a given list of target genes (e.g. those involved in a biological process) to gene regulators such as transcription factors. Transcription factors that bind DNA near significantly many target genes or correlate with target genes in transcriptional (microarray or RNAseq data) are selected. Selection of candidate master regulators is carried out using multinomial regression models, likelihood ratio tests and multiple testing correction. Reference: m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence. Juri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman and Nicholas M Luscombe. Genome Biology (2012) 13:R55 <doi:10.1186/gb-2012-13-6-r55>.
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.
Applies the MultiFractal Detrended Fluctuation Analysis (MFDFA) to time
  series. The MFDFA() function proposed in this package was used in Laib et al.
  (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>). See references 
  for more information.
Extracts meta-features from datasets to support the design of 
  recommendation systems based on Meta-Learning. The meta-features, also called 
  characterization measures, are able to characterize the complexity of datasets
  and to provide estimates of algorithm performance. The package contains not 
  only the standard characterization measures, but also more recent 
  characterization measures. By making available a large set of meta-feature 
  extraction functions, tasks like comprehensive data characterization, deep 
  data exploration and large number of Meta-Learning based data analysis can be
  performed. These concepts are described in the paper: Fabio Pinto, Carlos 
  Soares, and Joao Mendes-Moreira. Towards automatic generation of metafeatures.
  In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 
  pages 215 - 226, 2016, <doi:10.1007/978-3-319-31753-3_18>.
Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2018, <doi:10.2139/ssrn.2752354>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.
Multivariate functional halfspace depth and median for two-dimensional functional data.
The mFilter 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.
Model-free knockoffs are a general procedure for controlling the false
  discovery rate (FDR) when performing variable selection. For more information,
  see the website below and the accompanying paper: Candes et al., 
  "Panning for Gold: Model-free Knockoffs for High-dimensional Controlled Variable Selection", 
  2016, <arXiv:1610.02351>.
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  (Happ & Greven, 2018) <doi:10.1080/01621459.2016.1273115>. Multivariate and univariate functional data objects are
    represented by S4 classes for this type of data implemented in the package
    'funData'. For more details on the general concepts of both packages and a case study, see Happ (2018) <arXiv:1707.02129>.
Provides statistical tests and algorithms for the detection of change points in time series and point processes - particularly for changes in the mean in time series and for changes in the rate and in the variance in point processes. References - Michael Messer, Marietta Kirchner, Julia Schiemann, Jochen Roeper, Ralph Neininger and Gaby Schneider (2014) <doi:10.1214/14-AOAS782>, Stefan Albert, Michael Messer, Julia Schiemann, Jochen Roeper, Gaby Schneider (2017) <doi:10.1111/jtsa.12254>, Michael Messer, Kaue M. Costa, Jochen Roeper and Gaby Schneider (2017) <doi:10.1007/s10827-016-0635-3>.
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.
Multiscale Graph Correlation (MGC) is a framework developed by Shen et al. (2017) <arXiv:1609.05148> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.
Generalized additive (mixed) models, some of their extensions and 
             other generalized ridge regression with multiple smoothing 
             parameter estimation by (Restricted) Marginal Likelihood, 
             Generalized Cross Validation and similar. Includes a gam() 
             function, a wide variety of smoothers, JAGS support and 
             distributions beyond the exponential family.
Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models (GAMs) that exploit the additive structure of GAMs, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.
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.
Tools to download and organize large-scale, publicly available genomic studies on a candidate gene scale. Includes functions to integrate these data sources and compare features across candidate genes.
Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see linked paper.
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].
Designed to enable simultaneous substitution in strings in a safe fashion.
    Safe means it does not rely on placeholders (which can cause errors in same length matches).
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.
Functions for computing (Mixed) Geographycally Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) <doi:10.1016/j.regsciurbeco.2017.04.001>.
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.
Ten tools for analysis of major histocompatibility complex (MHC) data in non-
    model species. The functions are tailored for amplicon data sets that have been 
    filtered using the 'dada2' method (for more information visit 
    <https://benjjneb.github.io/dada2>), but even other data sets can be analysed, 
    if the data tables are formatted according to the description in each function.
    The ReplMatch() function matches replicates in data sets in order to evaluate 
    genotyping success.
    The GetReplTable() and GetReplStats() functions perform such an evaluation.
    The HpltFind() function infers putative haplotypes from families in the data 
    set. 
    The GetHpltTable() and GetHpltStats() functions evaluate the accuracy of 
    the haplotype inference.
    The PapaDiv() function compares parent pairs in the data set and calculate their 
    joint MHC diversity, taking into account sequence variants that occur in both 
    parents.
    The MeanPdist() function calculates the mean p-distance from pairwise 
    comparisons sequences in each sample in a data set. The function includes the 
    option to specify which codons to compare.
    The CreateFas() function creates a fasta file with all the sequences in the data 
    set.
    The CreateSamplesFas() function creates a fasta file for each sample in the data 
    set.
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.
Implements tests controlling the false discovery rate (FDR) / family-wise error rate (FWER) for some copula models.
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. For users' convenience, the functions also provide the output option 
    for printing decision rules.
A Comprehensive tool for almost all existing multiple testing
    methods for multiple families. The package summarizes the existing methods for multiple families multiple testing procedures (MTPs) such as double FDR, group Benjamini-Hochberg (GBH) procedure and average FDR controlling procedure. The package also provides some novel multiple testing procedures using selective inference idea.
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 (Cragg
             (1971) <doi:10.2307/1909582>), insufficient resources
             (Tobin (1958) <doi:10.2307/1907382>) or infrequency of
             purchase (Deaton and Irish (1984)
             <doi:10.1016/0047-2727(84)90067-7>).
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
    (and standard logistic regression) 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 follows principles based on the maximum-
    likelihood interpretation of maximum entropy modeling, and uses infinitely-
    weighted logistic regression for model fitting.
Multiple imputation using Fully Conditional Specification (FCS)
    implemented by the MICE algorithm as described in Van Buuren and
    Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. 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 functions for multiple imputation which
    complements existing functionality in R. 
    In particular, several imputation methods for the 
    mice package (van Buuren & Groothuis-Oudshoorn, 2011,
    <doi:10.18637/jss.v045.i03>) are included.
    Main features of the miceadds package include	
    plausible value imputation (Mislevy, 1991, 
    <doi:10.1007/BF02294457>), multilevel imputation for
    variables at any level or with any number of hierarchical
    and non-hierarchical levels (Grund, Luedtke & Robitzsch,
    2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, 
    Ch.7, <doi:10.1201/9780429492259>), imputation using 
    partial least squares (PLS) for high dimensional 
    predictors (Robitzsch, Pham & Yanagida, 2016), 
    and nested multiple imputation (Rubin, 2003, 
    <doi:10.1111/1467-9574.00217>).
Various tools for microeconomic analysis and microeconomic modelling,
   e.g. estimating quadratic, Cobb-Douglas and Translog functions,
   calculating partial derivatives and elasticities of these functions,
   and calculating Hessian matrices, checking curvature
   and preparing restrictions for imposing monotonicity of Translog functions.
Functions and tools
   for analysing consumer demand
   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
Tools for calculating Laspeyres, Paasche, and Fisher
  price and quantity indices.
Production analysis with the Symmetric Normalized Quadratic (SNQ) profit function
Extends and builds on the 'mice' package by
	adding a functionality to perform multivariate predictive mean matching on
	imputed data as well as new functionalities to perform predictive mean
	matching on factor variables.
Fast imputations under the object-oriented programming paradigm.
	There was used quantitative models with a closed-form solution. Thus package is based on linear algebra operations.
	The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used.
	A single evaluation of a quantitative model for the multiple imputations is another major enhancement.
	Moreover there are offered a few functions built to work with popular R packages such as 'data.table'.
Addons for the 'mice' package to perform multiple imputation using chained equations with two-level data. Includes imputation methods dedicated to sporadically and systematically missing values. Imputation of continuous, binary or count variables are available. Following the recommendations of Audigier, V. et al (2018) <doi:10.1214/18-STS646>, the choice of the imputation method for each variable can be facilitated by a default choice tuned according to the structure of the incomplete dataset. Allows parallel calculation and overimputation for 'mice'.
Provides imputation models and functions for binary or continuous Missing Not At Random (MNAR) outcomes through the use of the 'mice' package. The mice.impute.hecknorm() function provides imputation model for continuous outcome based on Heckman's model also named sample selection model as described in Galimard et al (2018) and Galimard et al (2016) <doi:10.1002/sim.6902>. The mice.impute.heckprob() function provides imputation model for binary outcome based on bivariate probit model as described in Galimard et al (2018).
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.
The 'Microsoft Academic Knowledge' API provides programmatic access
	to scholarly articles in the 'Microsoft Academic Graph'
	(<https://academic.microsoft.com/>). Includes methods matching all 'Microsoft
	Academic' API routes, including search, graph search, text similarity, and
	interpret natural language query string.
Most multilevel methodologies can only model macro-micro
    multilevel situations in an unbiased way, wherein group-level predictors
    (e.g., city temperature) are used to predict an individual-level
    outcome variable (e.g., citizen personality). In contrast,
    this R package enables researchers to model micro-macro situations, wherein
    individual-level (micro) predictors (and other group-level predictors) are
    used to predict a group-level (macro) outcome variable in an unbiased way.
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.
The microplot function writes a set of R graphics files to be used as
  microplots (sparklines) in tables in either 'LaTeX', 'HTML', 'Word',
  or 'Excel' files.  For 'LaTeX', we provide methods for the
  Hmisc::latex() generic function to construct 'latex' tabular
  environments which include the graphs.  These can be used directly
  with the operating system 'pdflatex' or 'latex' command, or by using
  one of 'Sweave', 'knitr', 'rmarkdown', or 'Emacs org-mode' as an
  intermediary.  For 'MS Word', the msWord() function uses the
  'flextable' package to construct 'Word' tables which include the
  graphs.  There are several distinct approaches for constructing HTML
  files.  The simplest is to use the msWord() function with argument
  filetype="html".  Alternatively, use either 'Emacs org-mode' or the
  htmlTable::htmlTable() function to construct an 'HTML' file
  containing tables which include the graphs.  See the documentation
  for our as.htmlimg() function.  For 'Excel' use on 'Windows', the
  file examples/irisExcel.xls includes 'VBA' code which brings the
  individual panels into individual cells in the spreadsheet.
  Examples in the examples and demo subdirectories are shown with
  'lattice' graphics, 'ggplot2' graphics, and 'base' 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.  The examples for 'MS Word'
  (with either filetype="docx" or filetype="html") work with all
  operating systems.  The package does not require the installation of
  'LaTeX' or 'MS Word' to be able to write '.tex' or '.docx' files.
Modelling interacting microbial populations - example applications
    include human gut microbiota, rumen microbiota and phytoplankton. Solves a
    system of ordinary differential equations to simulate microbial growth and
    resource uptake over time.
Find optimal microsampling designs for non-compartmental pharacokinetic analysis using a  general simulation methodology:
  Algorithm III of Barnett, Helen, Helena Geys, Tom Jacobs, and Thomas Jaki. (2017) "Optimal Designs for Non-Compartmental
  Analysis of Pharmacokinetic Studies. (currently unpublished)"
  This methodology consist of (1) specifying a pharmacokinetic model
  including variability among animals; (2) generating possible sampling times; (3)
  evaluating performance of each time point choice on simulated data; (4)
  generating possible schemes given a time point choice and additional constraints
  and finally (5) evaluating scheme performance on simulated data. The default
  settings differ from the article of Barnett and others, in the default pharmacokinetic model used and
  the parameterization of variability among animals. Details can be found in the package vignette. A 'shiny'
  web application is included, which guides users from model parametrization to
  optimal microsampling scheme.
Basic functions for microbial sequence data analysis. The idea is to use the basic R data structures as much as possible, without building complex data types.
Deploys your R Analytic to MicroStrategy
A generalization of the 'Synth' package that is designed
 for data at a more granular level (e.g., micro-level). Provides
 functions to construct weights (including propensity score-type
 weights) and run analyses for synthetic control methods with
 micro- and meso-level data; see Robbins, Saunders, and Kilmer 
 (2017) <doi:10.1080/01621459.2016.1213634>.
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.
Contains functions for converting existing HTML/JavaScript
   source into equivalent 'shiny' functions. Bootstraps the process of making
   new 'shiny' functions by allowing us to turn HTML snippets directly into 
   R functions.
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.
We report an information-theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. This approach can be applied on a wide range of datasets and provide new biological insights on regulatory networks from single cell expression data, genomic alterations during tumor development and co-evolving residues in protein structures. For more information you can refer to: Verny et al. Plos Comput Biol. (2017) <doi:10.1371/journal.pcbi.1005662>.
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 
    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.
A variety of association tests for microbiome data analysis including Quasi-Conditional Association Tests (QCAT) described in Tang Z.-Z. et al.(2017) <doi:10.1093/bioinformatics/btw804> and Zero-Inflated Generalized Dirichlet Multinomial (ZIGDM) tests described in Tang Z.-Z. & Chen G. (2017, submitted).
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 categorical responses,
    and, we can only observe the subject-level outcomes. For example, in manufacturing
    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().
Convert Markdown ('.md') or Rmarkdown ('.Rmd') files into FreeMind mindmap ('.mm') files, and vice versa. FreeMind mindmap ('.mm') files can be opened by or imported to common mindmap software such as 'FreeMind' (<http://freemind.sourceforge.net/wiki/index.php/Main_Page>) and 'XMind' (<http://www.xmind.net>).
Citrus is a computational technique developed for the analysis
    of high dimensional cytometry data sets. This package extracts, statistically
    analyzes, and visualizes marker expression from 'citrus' data. This code was used to 
    generate data for Figures 3 and 4 in the forthcoming manuscript: Throm et al. 
    “Identification of Enhanced Interferon-Gamma Signaling in Polyarticular Juvenile 
    Idiopathic Arthritis with Mass Cytometry”, JCI-Insight. For more information on Citrus,
    please see: Bruggner et al. (2014) <doi:10.1073/pnas.1408792111>. To download
    the 'citrus' package, please see <https://github.com/nolanlab/citrus>.
Wrapper for 'minepy' implementation of Maximal
        Information-based Nonparametric Exploration statistics (MIC and
        MINE family). Detailed information of the ANSI C implementation of
	'minepy' can be found at <http://minepy.readthedocs.io/en/latest>.
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 (or zero) Internet access.
Quick and simple Tcl/Tk Graphical User Interface 
  to call functions. Also comprises a very simple experimental 
  GUI framework.
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 (2017) <DOI:10.1080/10618600.2017.1302881>.
A binding to the minimist JavaScript library. This module implements
    the guts of optimist's argument parser without all the fanciful decoration.
Randomization schedules are generated in the schemes with k (k>=2) treatment groups and any allocation ratios by minimization algorithms.
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 characterize and 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.
Test for overall association between microbiome composition data with a continuous or dichotomous outcome via phylogenetic kernels. The phenotype can be univariate continuous or binary phenotypes (Zhao et al. (2015) <doi:10.1016/j.ajhg.2015.04.003>), survival outcomes (Plantinga et al. (2017) <doi:10.1186/s40168-017-0239-9>), multivariate (Zhan et al. (2017) <doi:10.1002/gepi.22030>) and structured phenotypes (Zhan et al. (2017) <doi:10.1111/biom.12684>). For all these effect, the microbiome community effect was modeled nonparametrically through a kernel function, which can incorporate the phylogenetic tree information.
Implements a variable selection and prediction method for high-dimensional data with missing entries following the paper Liu et al. (2016) <doi:10.1214/15-AOAS899>. It deals with missingness by multiple imputation and produces a selection probability for each variable following stability selection. The user can further choose a threshold for the selection probability to select a final set of variables. The threshold can be picked by cross validation or the user can define a practical threshold for selection probability. If you find this work useful for your application, please cite the method paper.
Machine learning method specifically designed for
    pre-miRNA prediction. It takes advantage of unlabeled sequences to improve
    the prediction rates even when there are just a few positive examples, when
    the negative examples are unreliable or are not good representatives of
    its class. Furthermore, the method can automatically search for negative
    examples if the user is unable to provide them. MiRNAss can find a good
    boundary to divide the pre-miRNAs from other groups of sequences; it
    automatically optimizes the threshold that defines the classes boundaries,
    and thus, it is robust to high class imbalance. Each step of the method is
    scalable and can handle large volumes of data.
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 (Chalmers (2012) <doi:10.18637/jss.v048.i06>). 
    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 modeling
    item and person covariates. Finally, latent class models such as the DINA,
    DINO, multidimensional latent class, and several other discrete latent
    variable models, including mixture and zero-inflated response models, 
    are supported.
Provides tools to generate an HTML interface for creating adaptive
    and non-adaptive educational and psychological tests using the shiny
    package (Chalmers (2016) <doi:10.18637/jss.v071.i05>). 
    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.
Estimate parameters of logistic regression with missing data and perform model selection, using algorithm Stochastic Approximation EM.
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 ad dependent samples, sequential triangular
             test for the product-moment correlation coefficient, partial and semipartial correlation,
             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.
An efficient approach for fishing out the dual (or multiple)
    isotope labeled analytes using dual labeling of metabolites for 
    metabolome analysis (DLEMMA) approach, described in Liron (2018) <doi:10.1021/acs.analchem.8b01644>.
Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.
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. Also includes a powerful set of
    object oriented survival data simulation functions.
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 or MCA.
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/.
Alternative implementation of the beautiful 'MissForest' algorithm used to impute mixed-type data sets by chaining tree ensembles, introduced by Stekhoven, D.J. and Buehlmann, P. (2012) <doi:10.1093/bioinformatics/btr597>. Under the hood, it uses the lightning fast random jungle package 'ranger'. Between the iterative model fitting, we offer the option of using predictive mean matching. This firstly avoids imputation with values not already present in the original data (like a value 0.3334 in 0-1 coded variable). Secondly, predictive mean matching tries to raise the variance in the resulting conditional distributions to a realistic level. This would allow e.g. to do multiple imputation when repeating the call to missRanger().
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.
Implements the Mittag-Leffler function, distribution,
  random variate generation, and estimation. Based on the Laplace-Inversion
  algorithm by Garrappa, R. (2015) <doi:10.1137/140971191>.
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 model-based clustering and classification.
 It supports various types of data: continuous, categorical and counting
 and can handle mixed data of these types. It can fit Gaussian (with diagonal
 covariance structure), gamma, categorical and Poisson models. The algorithms
 also support missing values. This package can be used as an independent alternative to the
 (not free) 'mixtcomp' software available at <https://massiccc.lille.inria.fr/>.
Deconvolution of thermal decay curves allows you to quantify proportions 
    of biomass components in plant litter. Thermal decay curves derived from 
    thermogravimetric analysis (TGA) are imported, modified, and then modelled in a 
    three- or four- part  mixture model using the Fraser-Suzuki function. The output 
    is estimates for weights of pseudo-components corresponding to hemicellulose, 
    cellulose, and lignin. For more information see: Müller-Hagedorn, M. and Bockhorn, 
    H. (2007) <doi:10.1016/j.jaap.2006.12.008>, Órfão, J. J. M. and Figueiredo, J. L. 
    (2001) <doi:10.1016/S0040-6031(01)00634-7>, and Yang, H. and Yan, R. and 
    Chen, H. and Zheng, C. and Lee, D. H. and Liang, D. T. (2006) <doi:10.1021/ef0580117>.
Scalable Bayesian clustering of categorical datasets. The package implements a hierarchical Dirichlet 
    (Process) mixture  of multinomial distributions. It is thus a probabilistic latent class model (LCM) and can be used
    to reduce the  dimensionality of hierarchical data and cluster individuals into latent classes. It can automatically
    infer an appropriate number of latent classes or find k classes, as defined by the user.  The model is based on a
    paper by Dunson and Xing (2009) <doi:10.1198/jasa.2009.tm08439>, but implements a scalable variational inference algorithm so that it is
    applicable to large datasets.
Fit finite mixture distribution models to grouped data and conditional data by 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.
Tools for the analysis of psychophysical data. This package allows to estimate the Point of Subjective Equivalence (PSE) 
    and the Just Noticeable Difference (JND), either from a psychometric function or from a Generalized Linear Mixed Model (GLMM). 
    Additionally, the package allows plotting the fitted models and the response data, simulating psychometric functions of different shapes, and simulating data sets.
    For a description of the use of GLMMs applied to psychophysical data, refer to Moscatelli et al. (2012), <doi:10.1167/12.11.26>.
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.
Contains functions for estimating a mixed-effects model for
             clustered data (or batch-processed data) with cluster-level (or batch-
             level) missing values in the outcome, i.e., the outcomes of some 
             clusters are either all observed or missing altogether. The model is 
             developed for analyzing incomplete data from labeling-based quantitative 
             proteomics experiments but is not limited to this type of data. 
             We used an expectation conditional maximization (ECM) algorithm for model 
             estimation. The cluster-level missingness may depend on the average 
             value of the outcome in the cluster (missing not at random).
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.
Utilizing model-based clustering (unsupervised)
        for functional magnetic resonance imaging (fMRI) data.
        The developed methods (Chen and Maitra (2018, manuscript)) include
        2D and 3D clustering analyses (for p-values with voxel locations) and
        segmentation analyses (for p-values alone) for fMRI data where p-values
        indicate significant level of activation responding to stimulate
        of interesting. The analyses are mainly identifying active
        voxel/signal associated with normal brain behaviors.
        Analysis pipelines (R scripts) utilizing this package
        (see examples in 'inst/workflow/') is also implemented with high
        performance techniques.
Mixtures of Gaussian graphical models for model-based clustering with sparse covariance and concentration matrices. See Fop, Murphy, and Scrucca (2018) <doi:10.1007/s11222-018-9838-y>.
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' (Browne and McNicholas (2015)<doi:10.1002/cjs.11246>) is the classical mixture of generalized hyperbolic distributions. The 'MGHFA' (Tortora et al. (2016)<doi:10.1007/s11634-015-0204-z>) is the mixture of generalized hyperbolic factor analyzers for high dimensional data sets. The 'MSGHD'(Tortora et al. (2016)<arXiv:1403.2332v7>), mixture of  multiple scaled generalized hyperbolic distributions. The 'cMSGHD' (Tortora et al. (2016)<arXiv:1403.2332v7>) is a 'MSGHD' with convex contour plots. The 'MCGHD' (Tortora et al. (2016)<arXiv:1403.2332v7>), mixture of  coalesced generalized hyperbolic distributions is a new more flexible model.
Kernel-based methods are powerful methods for integrating 
    heterogeneous types of data. mixKernel aims at providing methods to combine
    kernel for unsupervised exploratory analysis. Different solutions are 
    provided to compute a meta-kernel, in a consensus way or in a way that 
    best preserves the original topology of the data. mixKernel also integrates
    kernel PCA to visualize similarities between samples in a non linear space
    and from the multiple source point of view. Functions to assess and display
    important variables are also provided in the package. Jerome Mariette and 
    Nathalie Villa-Vialaneix (2017) <doi:10.1093/bioinformatics/btx682>.
The Mixture Link model <arXiv: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 variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares.
Provides the function 'mixor' 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.
Performs maximum likelihood estimation for finite mixture models for families including Normal, Weibull, Gamma and Lognormal by using EM algorithm, together with Newton-Raphson algorithm or bisection method when necessary. It also conducts mixture model selection by using information criteria or bootstrap likelihood ratio test. The data used for mixture model fitting can be raw data or binned data. The model fitting process is accelerated by using R package 'Rcpp'.
Estimates Rasch models and mixture Rasch models, including the dichotomous Rasch model, the rating scale model, and the partial credit model.
Provides supplemental functions for the 'mixRasch'
    package (Willse, 2014), <https://cran.r-project.org/package=mixRasch/mixRasch.pdf> including a 
    plotting function to compare item parameters for multiple
    class models and a function that provides average theta values for each class in
    a mixture 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.
The current version of the 'MixSAL' package allows users to generate data from a multivariate SAL distribution or a mixture of multivariate SAL distributions, evaluate the probability density function of a multivariate SAL distribution or a mixture of multivariate SAL distributions, and fit a mixture of multivariate SAL distributions using the Expectation-Maximization (EM) algorithm (see Franczak et. al, 2014, <doi:10.1109/TPAMI.2013.216>, for details).
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
    biological tracer 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.
Provides optimization algorithms based on sequential
  quadratic programming (SQP) for maximum likelihood estimation of the
  mixture proportions in a finite mixture model where the component
  densities are known. The algorithms are expected to obtain solutions
  that are at least as accurate as the state-of-the-art MOSEK
  interior-point solver (called by function "KWDual" in the 'REBayes'
  package), and they are expected to arrive at solutions more quickly
  in large data sets. The algorithms are described in Y. Kim,
  P. Carbonetto, M. Stephens & M. Anitescu (2012) <arXiv:1806.01412>.
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.
Analyzes finite mixture models for various parametric and semiparametric settings.  This includes mixtures of parametric distributions (normal, multivariate normal, multinomial, gamma), various Reliability Mixture Models (RMMs), mixtures-of-regressions settings (linear regression, logistic regression, Poisson regression, linear regression with changepoints, predictor-dependent mixing proportions, random effects regressions, hierarchical mixtures-of-experts), and tools for selecting the number of components (bootstrapping the likelihood ratio test statistic and model selection criteria).  Bayesian estimation of mixtures-of-linear-regressions models is available as well as a novel data depth method for obtaining credible bands.  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.
Fit mixture regression models with nonsusceptibility/cure for left-truncated and interval-censored (LTIC) data (see Chen et al. (2013) <doi:10.1002/sim.5845>). This package also provides the nonparametric maximum likelihood estimator (NPMLE) for the survival/event curves with LTIC data.
Optimization algorithms implemented in R, including
    conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the
    limited memory BFGS (L-BFGS) methods. Most internal parameters can be set 
    through the call interface. The solvers hold up quite well for 
    higher-dimensional problems.
A set of classes and methods to set up and run multi-species, trait
    based and community size spectrum ecological models, focused 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.
Package solves multiple knapsack optimisation problem. 
    Given a set of items, each with volume and value, 
    it will allocate them to knapsacks of a given size in a way that
    value of top N knapsacks is as large as possible.
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.
Inference of a multi-states birth-death model from a phylogeny, comprising a number of states N, birth and death rates for each state and on which edges each state appears. Inference is done using a hybrid approach: states are progressively added in a greedy approach. For a fixed number of states N the best model is selected via maximum likelihood. Reference: J. Barido-Sottani and T. Stadler (2017) <doi:10.1101/215491>.
Provides 'R6' abstract classes for building machine learning models 
    with 'scikit-learn' like API. <http://scikit-learn.org/> is a popular module 
    for 'Python' programming language which design became de facto a standard 
    in industry for machine learning tasks.
Hyper parameter tuning using Bayesian
    optimization (Shahriari et al. <doi:10.1109/JPROC.2015.2494218>) for support vector machine,
    random forest, and extreme gradient boosting (Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>). 
    Unlike already existing packages (e.g. 'mlr', 'rBayesianOptimization', or 'xgboost'), there is no need to change in accordance with the package or method of machine learning.
    You just prepare a data frame with feature vectors and the label column that has any class ('character', 'factor', 'integer').
    Moreover, to write a optimization function, you have only to specify the data and the column name of the label to classify.
A collection of artificial and real-world machine learning
        benchmark problems, including, e.g., several data sets from the
        UCI repository.
Multi-core processing of Gameday data from Major League Baseball Advanced Media <http://gd2.mlb.com/components/game/mlb/>. Additional tools to parallelize large data sets and write them to a database.
Computational functions for player metrics in major league baseball including batting, pitching, fielding, base-running, and overall player statistics. This package is actively maintained with new metrics being added as they are developed.
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.
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.
Calculates the expected/observed Fisher information and the bias-corrected maximum likelihood estimate(s) via Cox-Snell Methodology.
A 'sparklyr' <https://spark.rstudio.com> extension that provides
  an interface to 'MLeap' <https://github.com/combust/mleap>, an open source library
  that enables exporting and serving of 'Apache Spark' pipelines.
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 
        following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>.
        Contact the maintainer for a package version that includes 
        sensitivity analysis.
Estimates exponential-family random graph models for multilevel network data, assuming the multilevel structure is observed. The scope, at present, covers multilevel models where the set of nodes is nested within known blocks. The estimation method uses Monte-Carlo maximum likelihood estimation (MCMLE) methods to estimate a variety of canonical or curved exponential family models for binary random graphs. MCMLE methods for curved exponential-family random graph models can be found in Hunter and Handcock (2006) <DOI: 10.1198/106186006X133069>. The package supports parallel computing, and provides methods for assessing goodness-of-fit of models and visualization of networks.
Provides functions for unit root testing using MLE method
Offers a gentle introduction to machine learning concepts for practitioners with a statistical pedigree: decomposition of model error (bias-variance trade-off), nonlinear correlations, information theory and functional permutation/bootstrap simulations. Székely GJ, Rizzo ML, Bakirov NK. (2007). <doi:10.1214/009053607000000505>. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC. (2011). <doi:10.1126/science.1205438>.
R interface to 'MLflow', open source platform for the complete machine
  learning life cycle, see <https://mlflow.org/>. This package supports installing
  'MLflow', tracking experiments, creating and running projects, and saving and
  serving models.
It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2018) <https://hal.inria.fr/hal-01857242>).
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.
Tools and functions to fit a multilevel index of dissimilarity.
Provides an R API wrapper for 'mljar.com', a web service allowing for on-line training for machine learning models (see <https://mljar.com> for more information).
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.
Maximum likelihood estimates (MLE) of the proportions
    of 5-mC and 5-hmC in the DNA using information from BS-conversion,
    TAB-conversion, and oxBS-conversion methods. One can use information from all three methods 
    or any combination of two of them. Estimates are based on Binomial model by
    Qu et al. (2013) <doi:10.1093/bioinformatics/btt459> and
    Kiihl et al. (2018, to appear).
Pipeline for Genome-Wide Association Study using Multi-Locus Mixed Model from Segura V, Vilhjálmsson BJ et al. (2012) <doi:10.1038/ng.2314>. The pipeline include detection of associated SNPs with MLMM, model selection by lowest eBIC, estimation of the effects of the SNPs in the selected model and graphical functions.
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.
Maximum Likelihood estimation of random utility discrete choice models (logit and probit).
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.
Toolset that enriches 'mlr' with a diverse set of preprocessing
    operators. Composable Preprocessing Operators ("CPO"s) are first-class
    R objects that can be applied to data.frames and 'mlr' "Task"s to modify
    data, can be attached to 'mlr' "Learner"s to add preprocessing to machine
    learning algorithms, and can be composed to form preprocessing pipelines.
Flexible and comprehensive R toolbox for model-based optimization
    ('MBO'), also known as Bayesian optimization. It implements the Efficient
    Global Optimization Algorithm and is designed for both single- and multi-
    objective optimization with mixed continuous, categorical and conditional
    parameters. The machine learning toolbox 'mlr' provide dozens of regression
    learners to model the performance of the target algorithm with respect to
    the parameter settings. It provides many different infill criteria to guide
    the search process. Additional features include multi-point batch proposal,
    parallel execution as well as visualization and sophisticated logging
    mechanisms, which is especially useful for teaching and understanding of
    algorithm behavior. 'mlrMBO' is implemented in a modular fashion, such that
    single components can be easily replaced or adapted by the user for specific
    use cases.
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 described in
  Hothorn et al. (2018) <DOI:10.1111/sjos.12291>.
Additional documentation, a package vignette and 
  regression tests for package mlt.
A fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix.
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.
Estimation functions and diagnostic tools for mean length-based total mortality estimators based on Gedamke and Hoenig (2006) <doi:10.1577/T05-153.1>.
Various utilities for the Multiplicative Multinomial distribution.
The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Hunter, D. R., & Lange, K. (2004) <doi:10.1198/0003130042836>).
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; and Yu et al.(2017) <DOI:10.1016/j.sste.2017.02.001> "Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors", published on Spatial and Spatio-temporal Epidemiology, 21, 13-23.
Used for general multiple mediation analysis with big data sets.
Contains the data sets for the textbook "Mathematical Modeling and Applied Calculus" by Joel Kilty and Alex M. McAllister. The book will be published by Oxford University Press in 2018 with ISBN-13: 978-019882472.
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.
Uses memory-mapping to enable the random access of elements of
  a text file of characters separated by characters as if it was a simple
  R(cpp) matrix.
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 (Sato et al. (2009) <doi:10.1038/tpj.2008.17>;
  Nagashima et al. (2011) <doi:10.2202/1544-6115.1560>) and the maximum contrast method (Yoshimura et al. (1997)
  <doi:10.1177/009286159703100213>): 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.
Missingness in categorical data is a common problem in various real applications. 
  Traditional approaches either utilize only the complete observations or impute the missing data by some ad hoc methods rather than the true conditional distribution of the missing data, 
  thus losing or distorting the rich information in the partial observations. 
  This package develops a Bayesian nonparametric approach, the Dirichlet Process Mixture of Collapsed Product-Multinomials (DPMCPM, Wang et al. (2017) <arXiv:1712.02214v1>), 
  to model the full data jointly and compute the model efficiently. 
  By fitting an infinite mixture of product-multinomial distributions, 
  DPMCPM is applicable for any categorical data regardless of the true distribution, 
  which may contain complex association among variables. 
  Under the framework of latent class analysis, we show that DPMCPM can model general missing mechanisms by creating an extra category to denote missingness, 
  which implicitly integrates out the missing part with regard to their true conditional distribution.
This library implements mixture model distance sampling
        methods. See Miller and Thomas (in prep.).
Gene selection based on variance using the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions. Please see the reference: Li X, Fu Y, Wang X, DeMeo DL, Tantisira K, Weiss ST, Qiu W. (2018) <doi:10.1155/2018/6591634>.
Fit multivariate mixture of normal distribution using
        covariance structure.
Produces parallel coordinate plots of probability transition matrices from Markov, hidden Markov, and mixture transition distribution models.
fits multivariate marginal models for multivariate longitudinal data for both continuous and discrete responses
Fits multivariate marginal models with shared regression parameters for discrete and continuous responses
Provides global hypothesis tests, multiple testing procedures and simultaneous confidence intervals for multiple linear contrasts of regression
	coefficients in a single generalized estimating equation (GEE) model or across multiple GEE models. GEE models are fit by a modified version of the 'geeM' package.
The package implements a multi-marker molecular signature (MMMS) approach
             for treatment-specific subgroup identification.
Provides functions for measuring
    population divergence from genotypic data.
To determine the number of quantitative assays needed for a sample 
    of data using pooled testing methods, which include mini-pooling (MP), MP 
    with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number 
    of assays needed, the package also provides a tool to conduct Monte Carlo (MC) 
    to simulate different orders in which the sample would be collected to form pools. 
    Using MC avoids the dependence of the estimated number of assays on any specific 
    ordering of the samples to form pools.
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.
Efficient implementation of the Gibbs sampler by Fearnheard and Sherlock (2006) <DOI:10.1111/j.1467-9868.2006.00566.x> for the Markov modulated Poisson process that uses 'C++' via the 'Rcpp' interface. Fearnheard and Sherlock proposed an exact Gibbs-sampler for performing Bayesian inference on Markov Modulated Poisson processes. This package is an efficient implementation of their proposal for binned data. Furthermore, the package contains an efficient implementation of the hierarchical MMPP framework, proposed by Clausen, Adams, and Briers (2017) <https://github.com/hc2116/MMPPsampler/blob/master/Master_thesis_Henry.pdf>, that is tailored towards inference on network flow arrival data and extends Fearnheard and Sherlock's Gibbs sampler. Both frameworks harvests greatly from routines that are optimised for this specific problem in order to remain scalable and efficient for large amounts of input data. These optimised routines include  matrix exponentiation and multiplication, and endpoint-conditioned Markov process sampling. Both implementations require an input vector that contains the binned observations, the length of a binning interval, the number of states of the hidden Markov process, and lose prior hyperparameters. As a return, the user receives the desired number of sample trajectories of the hidden Markov process as well as the likelihood of each trajectory.
Perform Fixed effects Selection in Linear Mixed Models using a multicycle ECM algorithm; see F. Rohart & al, 2014, <doi:10.1016/j.csda.2014.06.022>.
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.
An implementation of multiple maps t-distributed stochastic
    neighbor embedding (t-SNE). Multiple maps t-SNE is a method for
    projecting high-dimensional data into several low-dimensional maps such that
    non-metric space properties are better preserved than they would be by a single
    map. Multiple maps t-SNE with only one map is equivalent to standard t-SNE.
    When projecting onto more than one map, multiple maps t-SNE estimates a set
    of latent weights that allow each point to contribute to one or more maps
    depending on similarity relationships in the original data. This
    implementation is a port of the original 'Matlab' library by Laurens van der
    Maaten. 
    See Van der Maaten and Hinton (2012) <doi:10.1007/s10994-011-5273-4>.
    This material is based upon work supported by the United States Air Force 
    and Defense Advanced Research Project Agency (DARPA) under Contract No. 
    FA8750-17-C-0020.
    Any opinions, findings and conclusions or recommendations expressed in this
    material are those of the author(s) and do not necessarily reflect the views 
    of the United States Air Force and Defense Advanced Research Projects Agency.
    Distribution Statement A: Approved for Public Release; Distribution Unlimited.
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. The methodology is described in Oja (2010) <doi:10.1007/978-1-4419-0468-3> and Nordhausen and Oja (2011) <doi:10.18637/jss.v043.i05>.
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.
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 package 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. 
 <DOI:10.1016/j.jeconom.2004.02.002>  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. <DOI:10.18637/jss.v014.i03>.
Allows to analyze the reading data obtained with the MNREAD Acuity 
    Chart, a continuous-text reading acuity chart for normal and low vision. 
    Provides the necessary functions to plot the MNREAD curve and estimate 
    automatically the four MNREAD parameters: Maximum Reading Speed, 
    Critical Print Size, Reading Acuity and Reading Accessibility Index.
    Parameters can be estimated either with the standard method 
    or with a nonlinear mixed-effects (NLME) modeling. 
    See Calabrese et al. 2018 for more details <doi.org/10.1167/18.1.8>.
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.
Functions to implements random forest method for model based
    recursive partitioning. The mob() function, developed by Zeileis et al. (2008),
    within 'party' package, is modified to construct model-based decision trees based
    on random forests methodology. The main input function mobforest.analysis() takes
    all input parameters to construct trees, compute out-of-bag errors, predictions,
    and overall accuracy of forest. The algorithm performs parallel computation
    using cluster functions within 'parallel' package.
Some canned plots and functions designed for the mobilize project.
    Designed to be called remotely.
Tools for the simulation, analysis and sampling of spatial
    biodiversity data (May et al. 2017) <doi:10.1101/209502>.
    In the simulation tools user define the numbers of
    species and individuals, the species abundance distribution and species
    aggregation. Functions for analysis include species rarefaction 
    and accumulation curves, species-area relationships and the distance
    decay of similarity.
Provides methods to analyze cluster alternatives based on multi-objective optimization of cluster validation indices. For details see Kraus et al. (2011) <doi:10.1007/s00180-011-0244-6>.
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.
Provides a means to mock a package function, i.e., temporarily substitute it for testing. Designed as a drop-in replacement for 'testthat::with_mock()', which may break in R 3.4.0 and later.
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.
Fits a modified version of the Connor-Mosimann distribution (Connor & Mosimann (1969)<doi:10.2307/2283728>), 
    a Connor-Mosimann distribution or Dirichlet distribution (e.g. Gelman, Carlin, Stern & Rubin Chapter 3.5 (2004, <ISBN:1-58488-388-X>) to elicited
    quantiles of a multinomial distribution. Code is also provided to 
    sample from the distributions, generating inputs suitable for a 
    probabilistic sensitivity analysis / Monte Carlo simulation in a decision model.
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).
Model-based trees for subgroup analyses in clinical trials and
  model-based forests for the estimation and prediction of personalised
  treatment effects (personalised models). Currently partitioning of linear
  models, lm(), generalised linear models, glm(), and Weibull models,
  survreg(), is supported.  Advanced plotting functionality is supported for
  the trees and a test for parameter heterogeneity is provided for the
  personalised models. For details on model-based trees for subgroup analyses
  see Seibold, Zeileis and Hothorn (2016) <doi:10.1515/ijb-2015-0032>; for
  details on model-based forests for estimation of individual treatment effects
  see Seibold, Zeileis and Hothorn (2017) <doi:10.1177/0962280217693034>.
Provides two important functions for producing Gain chart and Lift chart for any classification model.
Uses 'dplyr' and 'tidyeval' to fit statistical models inside the database. It currently supports KMeans and linear regression models.
Defines classes and methods to learn models and use them
  to predict binary outcomes.  These are generic tools, but we also
  include specific examples for many common classifiers.
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.
A minimalistic but flexible framework that facilitates the creation,
    management and training of multiple 'caret' models. A model grid consists of
    two components: (1) a set of settings that is shared by all models by 
    default, and (2) specifications that apply only to the individual models.
    When the model grid is trained, model and training specifications are first 
    consolidated from the shared and the model specific settings into complete
    'caret' model configurations. These models are then trained with the 'train' 
    function from the 'caret' package.
Creates sophisticated models of training data and validates the models with an independent test set, cross validation, or 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'. Popular metrics include area under the curve, log loss, root mean square error, etc.
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!
Makes a word cloud of text, sized by the frequency of the word, and colored either by user-specified colors or colored by the strength of the coefficient of that text derived from a regression model.
Datasets and wrapper functions for tidyverse-friendly introductory linear regression, used in ModernDive: An Introduction to Statistical and Data Sciences via R available at <http://moderndive.com/> and DataCamp's Modeling with Data in the Tidyverse available at <https://www.datacamp.com/courses/modeling-with-data-in-the-tidyverse>.
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.
User-friendly Shiny apps for designing and evaluating phase I cancer clinical trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel drug, using a Bayesian decision procedure based on logistic regression.
Compute important quantities when we consider stochastic systems that are observed continuously. Such as, Cost model, Limiting distribution, Transition matrix, Transition distribution and Occupancy matrix. The methods are described, for example, Ross S. (2014), Introduction to Probability Models. Eleven Edition. Academic Press.
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.
Algorithms for multivariate outlier detection when missing values occur.
    Algorithms are based on Mahalanobis distance or data depth. Imputation is based
    on the multivariate normal model or uses nearest neighbour donors. The algorithms 
    take sample designs, in particular weighting, into account. The methods are 
    described in Bill and Hulliger (2016) <doi:10.17713/ajs.v45i1.86>.
Power of non-parametric Mann-Kendall test and Spearman’s Rho test is highly influenced by serially correlated data. To address this issue, trend tests may applied on the modified versions of the time series data by  Block Bootstrapping (BBS), Prewhitening (PW) , Trend Free Prewhitening (TFPW), Bias Corrected Prewhitening and Variance Correction Approach by calculating effective sample size.
    Mann, H. B. (1945).<doi:10.1017/CBO9781107415324.004>.
    Kendall, M. (1975). Multivariate analysis. Charles Griffin&Company Ltd,. 
    sen, P. K. (1968).<doi:10.2307/2285891>.
    Önöz, B., & Bayazit, M. (2012) <doi:10.1002/hyp.8438>.
    Hamed, K. H. (2009).<doi:10.1016/j.jhydrol.2009.01.040>.
    Yue, S., & Wang, C. Y. (2002) <doi:10.1029/2001WR000861>.
    Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002) <doi:10.1002/hyp.1095>.
    Hamed, K. H., & Ramachandra Rao, A. (1998) <doi:10.1016/S0022-1694(97)00125-X>.
    Yue, S., & Wang, C. Y. (2004) <doi:10.1023/B:WARM.0000043140.61082.60>.
Download and processing functionalities for the Moderate Resolution
    Imaging Spectroradiometer (MODIS). The package provides automated access to the
    global online data archives LP DAAC (<https://lpdaac.usgs.gov/>), LAADS 
    (<https://ladsweb.modaps.eosdis.nasa.gov/>) and NSIDC (<https://nsidc.org/>) 
    as well as 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/>.
Programmatic interface to the 'MODIS Land Products Subsets'
    web services (<https://modis.ornl.gov/data/modis_webservice.html>).
    Allows for easy downloads of 'MODIS' time series directly to your R 
    workspace or your computer.
Allows automating the creation of time series of rasters derived
    from MODIS Satellite Land Products data. It performs several typical
    preprocessing steps such as download, mosaicking, reprojection and resize
    of data acquired on a specified time period. All processing parameters
    can be set using a user-friendly GUI. Users can select which layers of
    the original MODIS HDF files they want to process, which additional
    Quality Indicators should be extracted from aggregated MODIS Quality
    Assurance layers and, in the case of Surface Reflectance products
    , which Spectral Indexes should be computed from the original reflectance
    bands. For each output layer, outputs are saved as single-band raster
    files corresponding to each available acquisition date. Virtual files
    allowing access to the entire time series as a single file are also created.
    Command-line execution exploiting a previously saved processing options
    file is also possible, allowing to automatically update time series
    related to a MODIS product whenever a new image is available.
Calculate predicted levels and marginal effects,
    using the delta method to calculate standard errors. This is an R-based
    version of the 'margins' command from Stata.
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.
'MatLab'-Style Modeling of Optimization Problems with 'R'. This package provides a set of convenience functions to transform a 'MatLab'-style optimization modeling structure to its 'ROI' equivalent.
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.
Turning point method is a method proposed by Choi (1990) <doi:10.2307/2531453> to 
    estimate 50 percent effective dose (ED50) in the study of drug sensitivity. The method has 
    its own advantages for that it can provide robust ED50 estimation. This package contains 
    the modified function of Choi's turning point method.
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.
Modular implementation of Multiobjective Evolutionary Algorithms 
              based on Decomposition (MOEA/D) [Zhang and Li (2007), 
              <DOI:10.1109/TEVC.2007.892759>] for quick assembling and 
              testing of new algorithmic components, as well as easy 
              replication of published MOEA/D proposals.
Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2017) <arXiv:1711.05632>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations via the EM algorithm. Visualisation of the results of such models using generalised pairs plots is also facilitated.
Statistical utilities for the analysis of data by means of the Marshall-Olkin Extended Zipf distribution are presented. The distribution is a two-parameter extension of the widely used Zipf model. By plotting the probabilities in log-log scale, this two-parameter extension allows a concave as well as a convex behavior of the function at the beginning of the distribution, maintaining the linearity, associated to the Zipf model, in the tail.
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 data sets with large number of variables. For more information, see Sinha, Malo & Kuosmanen (2015) <doi:10.1080/10618600.2014.899236>.
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 (e.g., Sijtsma and Van der Ark, 2017, <doi:10.1111/bmsp.12078>).
   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 for simulating language evolution in terms of cultural evolution (Smith & Kirby (2008) <DOI:10.1098/rstb.2008.0145>; Deacon 1997). The focus is on the emergence of argument-marking systems (Dowty (1991) <DOI:10.1353/lan.1991.0021>, Van Valin 1999, Dryer 2002, Lestrade 2015a), i.e. noun marking (Aristar (1997) <DOI:10.1075/sl.21.2.04ari>, Lestrade (2010) <DOI:10.7282/T3ZG6R4S>), person indexing (Ariel 1999, Dahl (2000) <DOI:10.1075/fol.7.1.03dah>, Bhat 2004), and word order (Dryer 2013), but extensions are foreseen. Agents start out with a protolanguage (a language without grammar; Bickerton (1981) <DOI:10.17169/langsci.b91.109>, Jackendoff 2002, Arbib (2015) <DOI:10.1002/9781118346136.ch27>) and interact through language games (Steels 1997). Over time, grammatical constructions emerge that may or may not become obligatory (for which the tolerance principle is assumed; Yang 2016). Throughout the simulation, uniformitarianism of principles is assumed (Hopper (1987) <DOI:10.3765/bls.v13i0.1834>, Givon (1995) <DOI:10.1075/z.74>, Croft (2000), Saffran (2001) <DOI:10.1111/1467-8721.01243>, Heine & Kuteva 2007), in which maximal psychological validity is aimed at (Grice (1975) <DOI:10.1057/9780230005853_5>, Levelt 1989, Gaerdenfors 2000) and language representation is usage based (Tomasello 2003, Bybee 2010). In Lestrade (2015b) <DOI:10.15496/publikation-8640>, Lestrade (2015c) <DOI:10.1075/avt.32.08les>, and Lestrade (2016) <DOI:10.17617/2.2248195>), which reported on the results of preliminary versions, this package was announced as WDWTW (for who does what to whom), but for reasons of pronunciation and generalization the title was changed.
Generate the optimal maximin distance, minimax distance (only for low dimensions), and maximum projection designs within the class of Latin hypercube designs efficiently for computer experiments. Generate Pareto front optimal designs for each two of the three criteria and all the three criteria within the class of Latin hypercube designs efficiently. Provide criterion computing functions. References of this package can be found in Morris, M. D. and Mitchell, T. J. (1995) <doi:10.1016/0378-3758(94)00035-T>, Lu Lu and Christine M. Anderson-CookTimothy J. Robinson (2011) <doi:10.1198/Tech.2011.10087>, Joseph, V. R., Gul, E., and Ba, S. (2015) <doi:10.1093/biomet/asv002>.
Model selection and parameter estimation for non-local and some conjugate priors. Bayes factors for regression and mixtures. Routines to sample, evaluate prior densities, distributions and quantiles.
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).
Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.
The goal of Momocs is to provide a complete, convenient, 
       reproducible and open-source toolkit for 2D morphometrics.
       It includes most common 2D morphometrics approaches on outlines, open outlines, configurations of landmarks, traditional morphometrics, and facilities for data preparation, manipulation and visualization with a consistent grammar throughout.
       It allows reproducible, complex morphometric analyses 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.
It computes the raw moments for the folded and truncated multivariate normal and Student's t-distribution. It also offers specific functions to compute the mean and variance-covariance matrix as well as the cumulative distribution function (cdf) for the folded normal and folded t-distribution. Algorithms are extensions based on Kan, R., & Robotti, C. (2017) <doi:10.1080/10618600.2017.1322092>.
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.
Allows to pull data from MonetDB into R. Includes a DBI implementation and a dplyr backend.
An in-process version of 'MonetDB', a SQL database designed for analytical tasks. Similar to 'SQLite', the database runs entirely inside the 'R' shell.
High-performance MongoDB client based on 'mongo-c-driver' and 'jsonlite'.
    Includes support for aggregation, indexing, map-reduce, streaming, encryption,
    enterprise authentication, and GridFS. The online user manual provides an overview 
    of the available methods in the package: <https://jeroen.github.io/mongolite/>.
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 optional partial monotonicity constraints.
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.
             Detailed discussion of the methodologies used can be
             found in Murray, Mueller and Turlach (2013)
             <doi:10.1007/s00180-012-0390-5> and Murray, Mueller and
             Turlach (2016) <doi:10.1080/00949655.2016.1139582>.
An extended version of the nonparametric Bayesian monotonic regression procedure described in Saarela & Arjas (2011) <DOI:10.1111/j.1467-9469.2010.00716.x>, allowing for multiple additive monotonic components in the linear predictor, and time-to-event outcomes through case-base sampling. The extension and its applications, including estimation of absolute risks, are described in Saarela & Arjas (2015) <DOI:10.1111/sjos.12125>.
Test for monotonicity in financial variables sorted by portfolios. It is conventional practice in empirical research to form portfolios of assets ranked by a certain sort variable. A t-test is then used to consider the mean return spread between the portfolios with the highest and lowest values of the sort variable. Yet comparing only the average returns on the top and bottom portfolios does not provide a sufficient way to test for a monotonic relation between expected returns and the sort variable. This package provides nonparametric tests for the full set of monotonic patterns by Patton, A. and Timmermann, A. (2010) <doi:10.1016/0304-4076(89)90094-8> and compares the proposed results with extant alternatives such as t-tests, Bonferroni bounds, and multivariate inequality tests through empirical applications and simulations.
Estimates monotone regression and variance functions in a
        nonparametric model.
Simplifies Monte Carlo simulation studies by automatically 
             setting up loops to run over parameter grids and parallelising
             the Monte Carlo repetitions. It also generates LaTeX tables.
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.
Tools for transferable species distribution modeling and pseudo-absence 
    data generation allowing the straightforward design of relatively complex experiments 
    with multiple factors affecting the uncertainty (variability) of SDM outputs 
    (pseudo-absence sample, climate projection, modeling algorithm, etc.), and the 
    quantification of the contribution of different factors to the final variability 
    following the method described in Deque el al. (2010) <doi:10.1007/s00382-011-1053-x>. 
    Multiple methods for pseudo-absence data generation can be applied, including the novel 
    Three-step method as described in Iturbide et al. (2015) <doi:10.1016/j.ecolmodel.2015.05.018>.
    Additionally, a function for niche overlap calculation is provided, considering the metrics 
    described in Warren et al. (2008) <10.1111/j.1558-5646.2008.00482.x> and in
    Pianka (1973) <10.1146/annurev.es.04.110173.000413>.
A multi-objective optimization solver based on particle
        swarm optimization with crowding distance.
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 'MOQA' 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. Please read and cite the corresponding open access publication (using the former package-name) in METHODS OF INFORMATION IN MEDICINE by M. Bialke, H. Rau, T. Schwaneberg, R. Walk, T. Bahls and W. Hoffmann (2017) <doi:10.3414/ME16-01-0123>. <https://methods.schattauer.de/en/contents/most-recent-articles/issue/2483/issue/special/manuscript/27573/show.html>.
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.
Mixture of logistic regressions parameters (H)estimation with
    (U)spectral methods. The main methods take d-dimensional inputs and a vector
    of binary outputs, and return parameters according to the GLMs mixture model
    (General Linear Model). For more details see chapter 3 in the PhD thesis of
		Mor-Absa Loum: <http://www.theses.fr/s156435>, available here
		<https://www.math.u-psud.fr/~loum/IMG/pdf/these.compressed-2.pdf>.
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 toxicity test 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.
A collection of functions for the analysis of archaeological mortality
    data (on the topic see e.g. Chamberlain 2006 
    <https://books.google.de/books?id=nG5FoO_becAC&lpg=PA27&ots=LG0b_xrx6O&dq=life%20table%20archaeology&pg=PA27#v=onepage&q&f=false>). 
    It takes demographic data in different formats and displays the result in a standard life table
    as well as plots the relevant indices (percentage of deaths, survivorship, probability of death, life expectancy, percentage of population).
Life expectancy is highly correlated over time among countries and 
  between males and females. These associations can be used to improve forecasts. 
  Here we have implemented a method for forecasting female life expectancy based on 
  analysis of the gap between female life expectancy in a country compared with
  the record level of female life expectancy in the world. Second, to forecast 
  male life expectancy, the gap between male life expectancy and female life 
  expectancy in a country is analysed. We named this method the Double-Gap model.
  For a detailed description of the method see Pascariu et al. (2017). 
  <doi:10.1016/j.insmatheco.2017.09.011>.
Fit the most popular human mortality 'laws', and construct 
  full and abridge life tables given various input indices. A mortality
  law is a parametric function that describes the dying-out process of 
  individuals in a population during a significant portion of their 
  life spans. For a comprehensive review of the most important mortality 
  laws see Tabeau (2001) <doi:10.1007/0-306-47562-6_1>. 
  An elegant function for downloading data from Human Mortality 
  Database <https://www.mortality.org> is provided as well.
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.
Classes to implement and plot cohort life tables
    for actuarial calculations. In particular, birth-year dependent mortality
    tables using a yearly trend to extrapolate from a base year are implemented,
    as well as period life table, cohort life tables using an age shift, and
    merged life tables.
Age-specific mortality rates are estimated and projected using 
    the Kannisto, Lee-Carter and related methods as described in Sevcikova et al. (2016) <doi:10.1007/978-3-319-26603-9_15>.
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.
Part of the Project MOSAIC (<http://mosaic-web.org/>)
    suite that provides utility functions for doing calculus 
    (differentiation and integration) in R. The main differentiation and 
    antidifferentiation operators are described using formulas and return functions 
    rather than numerical values. Numerical values can be obtained by evaluating
    these functions.
Common utilities used in other MOSAIC-family packages are 
    collected here.
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.
Provides functions for evaluating, displaying, and interpreting statistical models. The goal is to abstract the operations on models from the particular architecture of the model. For instance, calculating effect sizes rather than looking at coefficients. The package includes interfaces to both regression and classification architectures, including lm(), glm(), rlm() in 'MASS', random forests and recursive partitioning, k-nearest neighbors, linear and quadratic discriminant analysis, and models produced by the 'caret' package's train(). It's straightforward to add in other other model architectures.
Provides functions similar to the 'SAS' macros previously provided
              to accompany Collins, Dziak, and Li (2009) <DOI:10.1037/a0015826>
              and Dziak, Nahum-Shani, and Collins (2012) <DOI:10.1037/a0026972>, papers
              which outline practical benefits and challenges of factorial
              and fractional factorial experiments for scientists interested
              in developing biological and/or behavioral interventions, especially
              in the context of the multiphase optimization strategy
              (see Collins, Kugler & Gwadz 2016) <DOI:10.1007/s10461-015-1145-4>.  The package
              currently contains three functions. First, RelativeCosts1() draws a graph
              of the relative cost of complete and reduced factorial designs versus
              other alternatives. Second, RandomAssignmentGenerator() returns a dataframe
              which contains a list of random numbers that can be used to conveniently
              assign participants to conditions in an experiment with
              many conditions. Third, FactorialPowerPlan() estimates the power, detectable effect
              size, or required sample size of a factorial or fractional factorial
              experiment, for main effects or interactions, given several possible choices
              of effect size metric, and allowing pretests and clustering.
Implementations of MOSUM-based statistical procedures and algorithms for detecting multiple changes in the mean. This comprises the MOSUM procedure for estimating multiple mean changes from Eichinger and Kirch (2018) <doi:10.3150/16-BEJ887> and the multiscale algorithmic extensions from Cho and Kirch (2018+).
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 for fitting models of trait evolution
        on phylogenies for continuous traits. The majority of functions
        described in Thomas and Freckleton (2011) <doi:10.1111/j.2041-210X.2011.00132.x> 	and include functions that allow for tests of variation in the rates of trait 		evolution.
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.
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 the facility to calculate accumulated cost surface and least-cost paths using a number of human-movement-related cost functions that can be selected by the user. It just requires a Digital Terrain model, a start location and (optionally) destination locations.
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...
Tools to visualize movement data (e.g. from GPS tracking) and temporal changes of environmental data (e.g. from remote sensing) by creating video animations.
Estimating wind speed from trajectories of individually tracked birds using a maximum likelihood approach.
Fit and simulate mixtures of von Mises-Fisher distributions.
Weekly notified dengue cases in 25 districts of Sri Lanka from 2008/ week-52 to 2014/ week-21.
Multidimensional projection techniques are used to create two
    dimensional representations of multidimensional data sets.
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. See Wang et al. (2014) <doi:10.1002/sim.6314>, Wang et al. (2015) <doi:10.1002/bimj.201400143>, Wang et al. (2016) <doi:10.1177/0962280214530608>.
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).
Functions for calculating sample size and power for clinical trials
    with multiple (co-)primary endpoints.
(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.
A matrix discrete model having the form
    'M[i+1] = (I + Q)*M[i]'.
    The calculation of the values of 'M[i]' only for
    pre-selected values of 'i'. The method of calculation
    is presented in the vignette 'Fundament' ('Base'). Maybe it`s own
    idea of the author of the package. A weakness is that the method
    gives information only in selected steps of the process.
    It mainly refers to cases with matrices that are not Markov chain.
    If 'Q' is Markov transition matrix, then MUPkL() may be
    used to calculate the steady-state distribution 'p' for
    'p = Q*p'.
    Matrix power of non integer (matrix.powerni()) gives
    the same results as a mpower() from package 'matlib'.
    References:
    "Markov chains",
    (<https://en.wikipedia.org/wiki/Markov_chain#Expected_number_of_visits>).
    Donald R. Burleson, Ph.D. (2005), 
    "ON NON-INTEGER POWERS OF A SQUARE MATRIX", 
    (<http://www.blackmesapress.com/Eigenvalues.htm>).
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 (<http://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.
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.
Calculates the Most Probable Number for serial dilutions to
    quantify the concentration (density) of microbes in a laboratory sample.
    Currently implements the confidence interval approach described in Jarvis
    (2010) <doi:10.1111/j.1365-2672.2010.04792.x>.
Symbolic computing with multivariate polynomials in R.
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.
Analysis of experimental multi-parent populations to detect
             regions of the genome (called quantitative trait loci, QTLs)
             influencing phenotypic traits. The population must be composed of crosses
             between a set of at least three parents (e.g. factorial design,
             'diallel', or nested association mapping). The functions cover data
             processing, QTL detection, and results visualization. The implemented
             methodology is described by Garin, Wimmer, Mezmouk, Malosetti and
             van Eeuwijk (2017) <doi:10.1007/s00122-017-2923-3>.
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.
Infer parental genotypes based on low-coverage population sequencing data and 
		thus can genotype mapping populations and construct ultra-high density linkage map 
		in a parent-independent manner. Weibo Xie et al. (2010) <doi:10.1073/pnas.1005931107>.
Developed for computing the probability density function, computing the cumulative distribution function, computing the quantile function, random generation, and estimating the parameters of 24 G-family of statistical distributions via the maximum product spacing approach introduced in <https://www.jstor.org/stable/2345411>. The set of families contains: beta G distribution, beta exponential G distribution, beta extended G distribution, exponentiated G distribution, exponentiated exponential Poisson G distribution, exponentiated generalized G distribution, exponentiated Kumaraswamy G distribution, gamma type I G distribution, gamma type II G distribution, gamma uniform G distribution, gamma-X generated of log-logistic family of G distribution, gamma-X family of modified beta exponential G distribution, geometric exponential Poisson G distribution, generalized beta G distribution, generalized transmuted G distribution, Kumaraswamy G distribution, log gamma type I G distribution, log gamma type II G distribution, Marshall Olkin G distribution, Marshall Olkin Kumaraswamy G distribution, modified beta G distribution, odd log-logistic G distribution, truncated-exponential skew-symmetric G distribution, and Weibull G distribution.
Computational tools to represent phylogenetic signals using adapted eigenvector maps.
Supplementary materials and datasets for the book "Modern Psychometrics With R" (Mair, 2018, Springer useR! series).
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.
Statistical or cognitive modeling usually requires a number of more or less 
    arbitrary choices creating one specific path through a 'garden of forking paths'. 
    The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016, 
    <doi:10.1177/1745691616658637>) offers a principled alternative in which results 
    for all possible combinations of reasonable modeling choices are reported. 
    MPTmultiverse performs a multiverse analysis for multinomial processing tree 
    (MPT, Riefer & Batchelder, 1988, <doi:10.1037/0033-295X.95.3.318>) models combining 
    maximum-likelihood/frequentist and Bayesian estimation approaches with 
    different levels of pooling (i.e., data aggregation). For the 
    frequentist approaches, no pooling (with and without parametric or nonparametric 
    bootstrap) and complete pooling are implemented using 
    MPTinR <https://cran.r-project.org/package=MPTinR>. 
    For the Bayesian approaches, no pooling, complete pooling, and three different 
    variants of partial pooling are implemented using 
    TreeBUGS <https://cran.r-project.org/package=TreeBUGS>. The main function is 
    fit_mpt() who performs the multiverse analysis in one call.
'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, Peck
             and Vining.
             Some additional data sets and functions are included.
Parametric modeling of M-quantile regression coefficient functions.
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.
Mendelian randomization is a method of identifying and estimating a confounded causal effect using genetic instrumental variables. This packages implements methods for two-sample Mendelian randomization with summary statistics by using Robust Adjusted Profile Score (RAPS). References: Qingyuan Zhao, Jingshu Wang, Jack Bowden, Dylan S. Small. Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. <arXiv:1801.09652>.
Accomplishes mark-recapture analysis with covariates. Models available include the Cormack-Jolly-Seber open population (Cormack (1972) <doi:10.2307/2556151>; Jolly (1965) <doi:10.2307/2333826>; Seber (1965) <doi:10.2307/2333827>) and Huggin's (1989) <doi:10.2307/2336377> closed population. 
	Link functions include logit, sine, and hazard.  Model selection, model averaging, plot, and simulation routines included. Open population size by the Horvitz-Thompson (1959) <doi:10.2307/2280784> estimator.
Performs (simultaneous) inferences for ratios of linear combinations of coefficients in the general linear model. Multiple comparisons and simultaneous confidence interval estimations can be performed for ratios of treatment means in the normal one-way layout with homogeneous and heterogeneous treatment variances, according to Dilba et al. (2007) <https://cran.r-project.org/doc/Rnews/Rnews_2007-1.pdf> and Hasler and Hothorn (2008) <doi:10.1002/bimj.200710466>. Confidence interval estimations for ratios of linear combinations of linear model parameters 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 confidence interval are provided assuming homogeneous or heterogeneous group variances.
A method for the multiresolution analysis of spatial fields and images to capture scale-dependent features.
    mrbsizeR is based on scale space smoothing and uses differences of smooths at neighbouring scales for finding features on different scales.
    To infer which of the captured features are credible, Bayesian analysis is used.
    The scale space multiresolution analysis has three steps: (1) Bayesian signal reconstruction.
    (2) Using differences of smooths, scale-dependent features of the reconstructed signal can be found.
    (3) Posterior credibility analysis of the differences of smooths created.
    The method has first been proposed by Holmstrom, Pasanen, Furrer, Sain (2011) <DOI:10.1016/j.csda.2011.04.011>.
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.
Scrape season results, estimate win probabilities, and find a
  competitive bracket for your office pool. Additional utilities include:
  scraping population picks; simulating tournament results; and testing your
  bracket in simulation.
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.
Performs the MRFA approach proposed by Sung et al. (2017+) <arXiv:1709.07064> to fit
    and predict nonlinear regression problems, particularly for large-scale and
    high-dimensional problems. The application includes deterministic or stochastic
    computer experiments, spatial datasets, and so on.
Approximate node interaction parameters of Markov Random Fields 
    graphical networks. Models can incorporate additional covariates, allowing users to estimate
    how interactions between nodes in the graph are predicted to change across
    covariate gradients. The general methods implemented in this package are described 
    in Clark et al. (2018) <doi:10.1002/ecy.2221>.
Tools to compute depth measures and implementations of related 
             tasks such as outlier detection, data exploration and 
            classification of multivariate, regression and functional data.
Facilitates simulation from hierarchical, ordinary
    differential equation (ODE) based models typically employed in drug development.
    A model specification file is created consisting of R and C++ code that
    is parsed, compiled, and dynamically loaded into the R session. Input data are
    passed in and simulated data are returned as R objects.  A dosing event engine
    allows interventions (bolus and infusion) to be managed separately from the 
    model code. Differential equations are solved with the 'DLSODA' routine 
    in 'ODEPACK' (<https://computation.llnl.gov/casc/odepack/>).
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.
Simulate a (bivariate) multivariate renewal Hawkes (MRHawkes) 
    self-exciting process, with given immigrant hazard rate functions and 
    offspring density function. Calculate the likelihood of a MRHawkes process 
    with given hazard rate functions and offspring density function for an 
    (increasing) sequence of event times. Calculate the Rosenblatt residuals of the 
    event times. Predict future event times based on observed event times up to a 
    given time. For details see Stindl and Chen (2018) <doi:10.1016/j.csda.2018.01.021>.
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 genome-wide association study under the framework of random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus model, their effects are estimated by empirical Bayes and true QTN are identified by likelihood ratio test. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) <doi:10.1093/bib/bbw145>.
Conduct multi-locus genome-wide association study under the framework of random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus model, their effects are estimated by empirical Bayes and true Quantitative Trait Nucleotides (QTN) are identified by likelihood ratio test. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) <doi:10.1093/bib/bbw145>.
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.
Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique.
Computes multiple correlation coefficient when the data matrix is given and tests its significance.
Infer causal graphs using the principle of Mendelian randomization-based PC (MRPC) algorithm. See Badsha and Fu (2018) <doi:10.1101/171348>,Badsha et al. (2018) <arXiv:1806.01899>.
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,
  as described in Jacopo Soriano, Li Ma (2016) <doi:10.1111/rssb.12180>. 
  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.
Provide a sample size calculator for micro-randomized
    trials (MRTs) based on methodology developed in Sample Size Calculations for
    Micro-randomized Trials in mHealth by Liao et al. (2016) <DOI:10.1002/sim.6847>.
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.
Calculates ARMSS (age related multiple sclerosis severity), and both local and global MSSS (multiple sclerosis severity score).
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 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.
Common mass spectrometry tools described in John Roboz (2013) <doi:10.1201/b15436>. It allows checking element
 isotopes, calculating (isotope labelled) exact monoisitopic mass, m/z values and mass accuracy, and inspecting possible contaminant mass peaks,
 examining possible adducts in electrospray ionization (ESI) and matrix-assisted laser desorption ionization (MALDI)
 ion sources.
Performs Bayesian nonparametric multiscale density estimation and multiscale testing of group differences with multiscale Bernstein polynomials (msBP) mixtures as in Canale and Dunson (2016).
Create native charts for 'Microsoft PowerPoint' and 'Microsoft Word' documents. 
 These can then be edited and annotated. Functions are provided to let users create charts, modify 
 and format their content. The chart's underlying data is automatically saved within the 
 'Word' document or 'PowerPoint' presentation. It extends package 'officer' that does 
 not contain any feature for 'Microsoft' native charts production.
Three 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, and third, a well-defined 
    cross-validation approach can be used.
    Much effort has been taken to make the implementation as stable as possible 
    (including edge cases) without losing computational efficiency.
    A detailed description of the main algorithms is given in 
    Becker and Klößner (2018) <doi:10.1016/j.ecosta.2017.08.002>.
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 
  'Text-to-Speech' REST API, including voice synthesis. 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.
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.
Implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components.  The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with 'OpenMP'.
Parametric estimation in stochastic differential equations with random effects in the drift, or in the diffusion or both. Approximate maximum likelihood methods are used. M. Delattre, V. Genon-Catalot and A. Samson (2012) <doi:10.1111/j.1467-9469.2012.00813.x> M. Delattre, V. Genon-Catalot and A. Samson (2015) <doi:10.1051/ps/2015006> M. Delattre, V. Genon-Catalot and A. Samson (2016) <doi:10.1016/j.jspi.2015.12.003>.
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
Simulation tools for management strategy evaluation are provided for the 'DLMtool' operating model to inform data-rich fisheries. 
  'MSEtool' provides complementary assessment models of varying complexity with standardized reporting, diagnostic tools for evaluating 
  assessment models within closed-loop simulation, and helper functions for building more complex operating models and management procedures.
A companion to the Chinese book ``Modern Statistical Graphics''.
Fit (by Maximum Likelihood or MCMC/Bayesian), simulate, and forecast various Markov-Switching GARCH models as described in Ardia et al. (2017) <https://ssrn.com/abstract=2845809>.
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.
A fast C-based encoder and streaming decoder for the
             'messagepack' data format. 'Messagepack' is similar in structure
             to 'JSON' but uses a more compact binary encoding. Based on the
             CWPack C library.
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.
Provides the 'Molecular Signatures Database' (MSigDB) gene sets
    typically used with the 'Gene Set Enrichment Analysis' (GSEA) software
    (Subramanian et al. 2005 <doi:10.1073/pnas.0506580102>, Liberzon et al. 2015
    <doi:10.1016/j.cels.2015.12.004>) in a standard R data frame with key-value
    pairs. Included are the original human gene symbols and Entrez IDs as well
    as the equivalents for various frequently studied model organisms such as
    mouse, rat, pig, fly, and yeast.
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.
Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.
Functions and datasets from Hilbe, J.M., and Robinson, A.P. 2013. Methods of Statistical Model Estimation. Chapman & Hall / CRC.
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.
Data sets in the book entitled "Multivariate Statistical Methods with R Applications", H.Bulut (2018). 
             The book will be published in Turkish and the original name of this book will be "R Uygulamalari ile Cok Degiskenli Istatistiksel Yontemler".
Contains necessary Multivariate Analysis methods and data sets used
    in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>.
    This is also serves as a companion package for the 
    STAT 571: Multivariate Analysis course at the University of Illinois
    at Urbana-Champaign (UIUC).
A modified SPRT (MSPRT) can be designed and implemented with the help of this package. In a
             MSPRT design, the maximum sample size of an experiment is fixed prior to the start of an
             experiment, the alternative hypothesis used to define the rejection region of the test is
             derived from the size of the test (Type I error), the maximum available sample size (N), and
             the targeted Type 2 error (equal to 1 minus the power) is also prespecified. Given these values,
             the MSPRT is defined in a manner very similar to Wald's initial proposal. This test can reduce
             the average sample size required to perform statistical hypothesis tests at the specified levels
             of significance and power. This package facilitates the carrying out of one sample Z tests, T Tests
             and tests of binomial success probabilities. A user guidance for this software package is provided
             here and also in the supplemental information.
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.
Can be used to query data from data from Microsoft SQL Server (MSSQL, see <http://www.microsoft.com/sqlserver/> for more information). Based on the concepts of Entity Framework, the package allows querying data from MSSQL Database.
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.
    Grows, prunes, and selects the best-sized tree.
Contains functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models, see Putter, Fiocco, Geskus (2007) <doi:10.1002/sim.2712>.
Software to aid in modeling and analyzing mass-spectrometry-based
    proteome melting data. Quantitative data is imported and normalized and
    thermal behavior is modeled at the protein level. Methods exist for
    normalization, modeling, visualization, and export of results. For a
    general introduction to MS-based thermal profiling, see Savitski et al.
    (2014) <doi:10.1126/science.1255784>.
Generation of response patterns under dichotomous and polytomous computerized multistage testing (MST) framework. It holds various item response theory (IRT) and score-based methods to select the next module and estimate ability levels (Magis, Yan and von Davier (2017, ISBN:978-3-319-69218-0)).
Interface for creating data sets and extracting data through the 'MicroStrategy' REST API. Access the demo API at <https://demo.microstrategy.com/MicroStrategyLibrary/api-docs/index.html>.
Estimators for multivariate symmetrical uncertainty based
    on the work of Gustavo Sosa et al. (2016) <arXiv:1709.08730>,
    total correlation, information gain and symmetrical uncertainty of
    categorical variables.
Estimation, inference and diagnostics for Univariate Autoregressive Markov Switching Models for Linear and Generalized Models. Distributions for the series include gaussian, Poisson, binomial and gamma cases. The EM algorithm is used for estimation (see Perlin (2012) <doi:10.2139/ssrn.1714016>).
Build multiscalar territorial analysis based on various contexts.
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>.
Provides several robust estimators for linear regression and variable selection. They are Maximum tangent likelihood estimator (Qin, et al. (2017) <arXiv:1708.05439>), least absolute deviance estimator, and Huber loss. The penalized version of each of these estimator incorporates L1 penalty function, i.e., LASSO and Adaptive Lasso. They are able to produce consistent estimates for both fixed and high-dimensional settings.
Calculate Sample Size and Post-Hoc Power of
    Association Studies Involving Mitochondrial DNA Haplogroups.
    Based on formulae by Samuels et al. AJHG, 2006. 78(4):713-720. <DOI:10.1086/502682>.
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.
Use morphological image processing and edge detection algorithms to automatically identify tree-ring boundaries on digital images. Tree-ring boundaries are determined by the changes of light reflectance from late wood to early wood. Two geometric models are provided to calibrate the errors resulted from inclined rings. The package provides a Shiny-based application, allowing R beginners to easily analyze tree-ring images and export ring-width series in standard file formats.
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 modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region.
Mahalanobis-Taguchi (MT) system is a collection of multivariate
    analysis methods developed for the field of quality engineering. MT system
    consists of two families depending on their purpose. One is a family of
    Mahalanobis-Taguchi (MT) methods (in the broad sense) for diagnosis (see
    Woodall, W. H., Koudelik, R., Tsui, K. L., Kim, S. B., Stoumbos, Z. G., and
    Carvounis, C. P. (2003) <doi:10.1198/004017002188618626>) and the other is a
    family of Taguchi (T) methods for forecasting (see Kawada, H., and Nagata, Y.
    (2015) <doi:10.17929/tqs.1.12>). The MT package contains three basic methods
    for the family of MT methods and one basic method for the family of T
    methods. The MT method (in the narrow sense), the Mahalanobis-Taguchi
    Adjoint (MTA) methods, and the Recognition-Taguchi (RT) method are for the
    MT method and the two-sided Taguchi (T1) method is for the family of T
    methods. In addition, the Ta and Tb methods, which are the improved versions
    of the T1 method, are included.
Provides programmatic access to the Amazon Mechanical Turk (MTurk) <https://www.mturk.com/mturk/welcome> 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.
Nonparametric approach to estimate the location of block boundaries (change-points) of 
    non-overlapping blocks in a random symmetric matrix which consists of random variables whose 
    distribution changes from block to block.
    BRAULT Vincent, OUADAH Sarah, SANSONNET Laure and LEVY-LEDUC Celine (2017) <doi:10.1016/j.jmva.2017.12.005>.
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.
Formatting and structuring multi-parameter spatiotemporal data
  is often a time-consuming task. This package offers functions and data structures 
  designed to easily organize and visualize these data for applications in geology, 
  paleolimnology, dendrochronology, and paleoclimate. See Dunnington and Spooner (2018)
  <doi:10.1139/facets-2017-0026>.
Compute a density estimate from a vector of right-censored survival time using kernel functions.
Nonparametric item response theory (IRT) model for nonmonotonic IRFs, which is fruitful for scale analysis of proximity items.
Implements an estimator for relative risk based on the median unbiased estimator. The relative risk estimator is well defined and performs satisfactorily for a wide range of data configurations. The details of the method are available in Carter et al (2010) <doi:10.1111/j.1467-9876.2010.00711.x>.
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.
Constructing hierarchical Voronoi tessellations for a given data set and overlay
    heatmap for variables at various levels of the tessellations for in-depth data analysis. See <https://en.wikipedia.org/wiki/Voronoi_diagram> for more information. Credits to Mu Sigma for their continuous support throughout the development of the package.
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}.
Allows to perform the multivariate version of the Diebold-Mariano test for equal predictive ability of multiple forecast comparison. Main reference: Mariano, R.S., Preve, D. (2012) <doi:10.1016/j.jeconom.2012.01.014>.
Multiregression Dynamic Models for directed dynamic functional brain network analysis.
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.
Calculates exact hypothesis tests to compare a treatment and a reference group with respect to multiple binary endpoints.
    The tested null hypothesis is an identical multidimensional distribution of successes and failures in both groups. The alternative
    hypothesis is a larger success proportion in the treatment group in at least one endpoint. The tests are based on the multivariate
    permutation distribution of subjects between the two groups. For this permutation distribution, rejection regions are calculated 
    that satisfy one of different possible optimization criteria. In particular, regions with maximal exhaustion of the nominal
    significance level, maximal power under a specified alternative or maximal number of elements can be found. Optimization is achieved
    by a branch-and-bound algorithm. By application of the closed testing principle, the global hypothesis tests are extended to multiple
    testing procedures.
GEE solver for correlated nominal or ordinal multinomial responses using a local odds ratios parameterization.
The base apply function and its variants, as well as the related
    functions in the 'plyr' package, typically apply user-defined functions to a
    single argument (or a list of vectorized arguments in the case of mapply). The
    'multiApply' package extends this paradigm with its only function, Apply, which
    efficiently applies functions taking one or a list of multiple unidimensional
    or multidimensional numeric arrays (or combinations thereof) as input. The input
    arrays can have different numbers of dimensions as well as different dimension
    lengths, and the applied function can return one or a list of unidimensional or
    multidimensional arrays as output. This saves development time by preventing the
    R user from writing often error-prone and memory-inefficient loops dealing with
    multiple complex arrays. Also, a remarkable feature of Apply is the transparent
    use of multi-core through its parameter 'ncores'. In contrast to the base apply
    function, this package suggests the use of 'target dimensions' as opposite
    to the 'margins' for specifying the dimensions relevant to the function to be
    applied.
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.
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.
Provides a basic interface for accessing annotation data from
   the Multi-CAST collection, a database of spoken natural language texts
   edited by Geoffrey Haig and Stefan Schnell. The collection draws from a
   diverse set of languages and has been annotated across multiple levels.
   Annotation data is downloaded on request from the servers of the
   Language Archive Cologne. See the Multi-CAST website
   <https://lac.uni-koeln.de/multicast/> for more information and a list of
   related publications.
Given a set of models for which a measure of model (mis)fit and model complexity is provided, CHull(), developed by Ceulemans and Kiers (2006) <doi:10.1348/000711005X64817>, determines the models that are located on the boundary of the convex hull and selects an optimal model by means of the scree test values.
A toolkit containing statistical analysis models motivated by multivariate forms of the Conway-Maxwell-Poisson (COM-Poisson) distribution for flexible modeling of multivariate count data, especially in the presence of data dispersion. Currently the package only supports bivariate data, via the bivariate COM-Poisson distribution described in Sellers et al. (2016) <doi:10.1016/j.jmva.2016.04.007>. Future development will extend the package to higher-dimensional data.
This package provides a tool for analysis of multiple CNV.
Add multiple colors to text that is printed to the console.
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.
Test for independence of two random vectors, learn and report the dependency structure. For more information, see Gorsky and Ma (2018) <arXiv:1806.06777>.
Deals with several images of a same object, constituted of different zones. Each image constitutes a variable for a given pixel. The user can interactively select different zones of an image. Then, multivariate analysis (PCA) can be run in order to characterize the different selected zones, according to the different images. Hotelling (Hotelling, 1931, <doi:10.1214/aoms/1177732979>) and Srivastava (Srivastava, 2009, <doi:10.1016/j.jmva.2006.11.002>) tests can be run to detect multivariate differences between the zones.
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, signed and weighted multigraphs, bipartite graphs, and Cayley graphs with different layout options (devel version).
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.
Wrapper around the Unix join facility which is more efficient than the built-in R routine merge().
 The package enables the joining of multiple files on disk at once. 
 The files can be compressed and various filters can be deployed before joining.
 Compiles only under Unix.
Computing transitive (and non-transitive) index numbers (Coelli et al., 2005 <doi:10.1007/b136381>) for cross-sections and panel data. For the calculation of transitive indexes, the EKS (Coelli et al., 2005 <doi:10.1007/b136381>; Rao et al., 2002 <doi:10.1007/978-1-4615-0851-9_4>) and Minimum spanning tree (Hill, 2004 <doi:10.1257/0002828043052178>) methods are implemented. Traditional fixed-base and chained indexes, and their growth rates, can also be derived using the Paasche, Laspeyres, Fisher and Tornqvist formulas.
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.
Conducts and visualizes propensity score analysis for multilevel, 
  or clustered data. Bryer & Pruzek (2011) <doi:10.1080/00273171.2011.636693>.
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.
Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2018 <DOI:10.1007/s11749-018-0611-5>) and exploring (including the mode tree, mode forest and SiZer) the number of modes using nonparametric techniques.
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.
Functions for the creation/generation and analysis of multilayer social networks.
Analyze multilevel networks as described in Lazega et al (2008)
    <doi:10.1016/j.socnet.2008.02.001> and in Lazega and Snijders 
    (2016, ISBN:978-3-319-24520-1). The package was developed essentially as an 
    extension to 'igraph'.
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', 'ggplot2' and 'ComplexHeatmap'
    plots, grobs, as well as content from all image formats supported by
    ImageMagick (accessed through 'magick').
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.
Provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) <doi:10.1111/iere.12131>. Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.
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.
Functions are provided for calculating efficiency using multiplier DEA (Data Envelopment Analysis): Measuring the efficiency of decision making units (Charnes et al., 1978 <doi:10.1016/0377-2217(78)90138-8>) and cross efficiency using single and two-phase approach. In addition, it includes some datasets for calculating efficiency and cross efficiency.
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.
Pseudo-random number generation for 11 multivariate distributions: Normal, t, Uniform, Bernoulli, Hypergeometric, Beta (Dirichlet), Multinomial, Dirichlet-Multinomial, Laplace, Wishart, and Inverted Wishart.
Tools to solve real-world problems with multiple classes classifications by computing the areas under ROC and PR curve via micro-averaging and macro-averaging. The vignettes of this package can be found via <https://github.com/WandeRum/multiROC>. The methodology is described in V. Van Asch (2013) <https://www.clips.uantwerpen.be/~vincent/pdf/microaverage.pdf> and Pedregosa et al. (2011) <http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html>.
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.
Uses multiple AUCs to select a combination of predictors when the outcome has multiple (ordered) levels and the focus is discriminating one particular level from the others. This method is most naturally applied to settings where the outcome has three levels. (Meisner, A, Parikh, CR, and Kerr, KF (2017) <http://biostats.bepress.com/uwbiostat/paper423/>.)
Functions to perform sensitivity analysis on a model with multivariate output.
We implement multisite causal mediation analysis using the methods proposed by Qin and Hong (2017) <doi:10.3102/1076998617694879> and Qin, Hong, Deutsch, and Bein (under review). It enables causal mediation analysis in multisite trials, in which individuals are assigned to a treatment or a control group at each site. It allows for estimation and hypothesis testing for not only the population average but also the between-site variance of direct and indirect effects. This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. This package also provides a function that can further incorporate a sample weight and a nonresponse weight for multisite causal mediation analysis in the presence of complex sample and survey designs and non-random nonresponse, to enhance both the internal validity and external validity. Because the identification assumptions are not always warranted, the package also provides a weighting-based balance checking function for assessing the remaining overt bias, as well as a weighting-based sensitivity analysis function for further evaluating the potential bias related to omitted confounding or to propensity score model misspecification.
Computes the third multivariate cumulant of either the raw, centered or standardized data. Computes the main measures of multivariate skewness, together with their bootstrap distributions. Finally, computes the least skewed linear projections of the data.
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.
Medical researchers are often interested in investigating the relationship between explicative variables and multiple times-to-event. Time-inhomogeneous Markov models consist of modelling the probabilities of transitions according to the chronological times (times since the baseline of the study). Semi-Markov (SM) models consist of modelling the probabilities of transitions according to the times spent in states. In this package, we propose functions implementing such 3-state and 4-state multivariable and multistate models. The user can introduce multiple covariates to estimate conditional (subject-specific) effects. We also propose to adjust for possible confounding factors by using the Inverse Probability Weighting (IPW). When a state is patient death, the user can consider to take into account the mortality of the general population (relative survival approach). Finally, in the particular situation of one initial transient state and two competing and absorbing states, this package allows for estimating mixture models.
Provides functions for working with multi-state modelling, 
    such as efficient simulation routines for estimating transition probabilities and length of stay.
    It is designed as an extension to multi-state modelling capabilities provided with the 'flexsurv' 
    package (see Jackson (2016) <doi:10.18637/jss.v070.i08>).
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.
Distance multivariance is a measure of dependence which can be used to detect 
    and quantify dependence. The necessary functions are implemented in this packages, 
    and examples are given. For the theoretic background we refer to the papers:
    B. Böttcher, M. Keller-Ressel, R.L. Schilling, Detecting independence of random vectors I. Generalized distance covariance and Gaussian covariance. Preprint 2017, <arXiv:1711.07778>.
    B. Böttcher, M. Keller-Ressel, R.L. Schilling, Detecting independence of random vectors II. Distance multivariance and Gaussian multivariance. Preprint 2017, <arXiv:1711.07775>.
    B. Böttcher, Dependence Structures - Estimation and Visualization Using Distance Multivariance. Preprint 2017, <arXiv:1712.06532>.
    G. Berschneider, B. Böttcher, On complex Gaussian random fields, Gaussian quadratic forms and sample distance multivariance. Preprint 2018, <arXiv:1808.07280>.
Models and predicts multiple output features in single random forest considering the 
    linear relation among the output features, see details in Rahman et al (2017)<doi:10.1093/bioinformatics/btw765>.
Fully parametric Bayesian multiple imputation framework for massive multivariate data of different variable types as seen in Demirtas, H. (2017) <doi:10.1007/978-981-10-3307-0_8>.
It performs variable selection in a multivariate linear model by estimating the covariance matrix 
 of the residuals then use it to remove the dependence that may exist among the responses and eventually performs variable selection by using the Lasso criterion.
 The method is described in the paper Perrot-Dockès et al. (2017) <arXiv:1704.00076>.
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. Fit models include Individual Differences Scaling, Multiway Covariates Regression, 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).
Tools for performing model selection and model averaging. Automated
             model selection through subsetting the maximum model, with optional
             constraints for model inclusion. Model parameter and prediction
             averaging based on model weights derived from information criteria
             (AICc and alike) or custom model weighting schemes.
Fit multiplicative mixed models using maximum likelihood estimation via the Template
  Model Builder (TMB), Kristensen K, Nielsen A, Berg CW, Skaug H, Bell BM (2016) <doi:10.18637/jss.v070.i05>.
  One version of the multiplicative mixed model is applied in Piepho (1999) <doi:10.1111/j.0006-341X.1999.01120.x>.
  The package provides functions for calculating confidence intervals for the model parameters
  and for performing likelihood ratio tests.
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, CIE Lab, CIE Luv, or RGB.  Includes ISCC-NBS color block lookup.  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).
Sampling and evaluation methods to apply Monetary Unit Sampling (or in older literature Dollar Unit Sampling) during an audit of financial statements.
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.
Main function LR2 calculates likelihood ratio for non-inbred relationships accounting for mutation, silent alleles and theta correction. Egeland, Pinto and Amorim (2017) <DOI:10.1016/j.fsigen.2017.04.018>.
Designed to ease the application and comparison of multiple
    hypothesis testing procedures for FWER, gFWER, FDR and FDX. Methods are 
    standardized and usable by the accompanying 'mutossGUI'.
The mutossGUI package provides a graphical user interface for the MuToss Project.
Cancer cells accumulate DNA mutations as result of DNA damage and DNA repair processes. This computational framework is aimed at deciphering DNA mutational signatures operating in cancer. The input is a numeric matrix of DNA mutation counts detected in a panel of cancer samples. The framework performs Non-negative Matrix Factorization to extract the most likely signatures explaining the observed set of DNA mutations. The framework relies on parallelization and is optimized for use on multi-core systems. This framework is an R-based implementation of the original MATLAB WTSI framework by Alexandrov LB et al (2013) <DOI:10.1016/j.celrep.2012.12.008>.
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.
Package for multivariate analysis, having functions that perform simple correspondence analysis (CA) and multiple correspondence analysis (MCA), principal components analysis (PCA), canonical correlation analysis (CCA), factorial analysis (FA), multidimensional scaling (MDS), hierarchical and non-hierarchical cluster analysis, linear regression, multiple factor analysis (MFA) for quantitative, qualitative, frequency (MFACT) and mixed data, projection pursuit (PP), grant tour method and other useful functions for the multivariate analysis.
Pacote para analise multivariada, tendo 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, projection pursuit (PP), grant tour e 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 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.
Pearson and Spearman correlation coefficients are commonly used to quantify the strength
	of bivariate associations of genomic variables.  For example, correlations of gene-level 
	DNA copy number and gene expression measurements may be used to assess the impact of 
	DNA copy number changes on gene expression in tumor tissue.  'MVisAGe' enables users to 
	quickly compute and visualize the correlations in order to assess the effect of regional 
	genomic events such as changes in DNA copy number or DNA methylation level.  Please see
	Walter V, Du Y, Danilova L, Hayward MC, Hayes DN, 2018. Cancer Research 
	<doi:10.1158/0008-5472.CAN-17-3464>.
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 multivariate evolutionary wavelet spectrum (mvEWS); estimation of the mvEWS, local coherence and local partial coherence. 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.
Offers a general framework of multivariate mixed-effects
        models for the joint analysis of multiple correlated outcomes with clustered 
        data structures and potential missingness proposed by Wang et al. (2018) <doi:10.1093/biostatistics/kxy022>. The missingness of outcome values may 
        depend on the values themselves (missing not at random and non-ignorable), 
        or may depend on only the covariates (missing at random and ignorable), or both.
        This package provides functions for two models: 1) mvMISE_b() 
        allows correlated outcome-specific random intercepts with a factor-analytic 
        structure, and 2) mvMISE_e() allows the correlated outcome-specific 
        error terms with a graphical lasso penalty on the error precision matrix. Both functions 
        are motivated by the multivariate data analysis on data with clustered structures 
        from labelling-based quantitative proteomic studies. These models and functions 
        can also be applied to univariate and multivariate analyses of clustered data 
        with balanced or unbalanced design and no missingness.
Use multi-state splitting to apply Adaptive-Dynamic PCA (ADPCA) to
    data generated from a continuous-time multivariate industrial or natural
    process. Employ PCA-based dimension reduction to extract linear combinations
    of relevant features, reducing computational burdens. For a description of 
    ADPCA, see <doi:10.1007/s00477-016-1246-2>, the 2016 paper from Kazor et al.
    The multi-state application of ADPCA is from a manuscript under current
    revision entitled "Multi-State Multivariate Statistical Process Control" by
    Odom, Newhart, Cath, and Hering, and is  expected to appear in Q1 of 2018.
Fits multivariate (Brownian Motion, Early Burst, ACDC, Ornstein-Uhlenbeck and Shifts) models of continuous traits evolution on trees and time series. 'mvMORPH' also proposes high-dimensional multivariate comparative tools (linear models using Generalized Least Squares) based on penalized likelihood.  See
    Clavel et al. (2015) <DOI:10.1111/2041-210X.12420> and Clavel et al. (2018) <DOI:10.1093/sysbio/syy045>.
Performs multivariate normality tests and graphical approaches and
    implements multivariate outlier detection and univariate normality of marginal
    distributions through plots and tests (Korkmaz et al, (2014), <https://journal.r-project.org/archive/2014-2/korkmaz-goksuluk-zararsiz.pdf>).
Computes the Nelson-Aalen estimator of the cumulative transition hazard for arbitrary Markov multistate models <ISBN:978-0-387-68560-1>.
Tools of Bayesian analysis framework using the method
  suggested by Berger (1985) <doi:10.1007/978-1-4757-4286-2> for
  multivariate normal (MVN) distribution and multivariate normal
  mixture (MixMVN) distribution:
  a) calculating Bayesian posteriori of (Mix)MVN distribution;
  b) generating random vectors of (Mix)MVN distribution;
  c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution.
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.
A flexible framework for fitting multivariate
    ordinal regression models with composite likelihood methods.
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. (2017). Objective Bayesian
    analysis for the multivariate skew-t model. Statistical Methods & Applications
    <doi: 10.1007/s10260-017-0404-0>.
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.
Many feature selection methods for a wide range of response variables, including 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. Reference: Feature Selection with the R Package MXM: Discovering Statistically Equivalent Feature Subsets, Lagani, V. and Athineou, G. and Farcomeni, A. and Tsagris, M. and Tsamardinos, I. (2017). Journal of Statistical Software, 80(7). <doi:10.18637/jss.v080.i07>.
The stepwise variable selection procedure (with iterations
 between the 'forward' and 'backward' steps) can be used to obtain
 the best candidate final regression model in regression analysis.
 All the relevant covariates are put on the 'variable list' to be
 selected. The significance levels for entry (SLE) and for stay
 (SLS) are usually set to 0.15 (or larger) for being conservative.
 Then, with the aid of substantive knowledge, the best candidate
 final regression model is identified manually by dropping the
 covariates with p value > 0.05 one at a time until all regression
 coefficients are significantly different from 0 at the chosen alpha
 level of 0.05.
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 fields 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 (Drost et al. (2017) <doi:10.1093/bioinformatics/btx835>). The aim of this tool is to provide a transcriptome analysis environment for answering questions regarding the evolution of biological processes (Drost et al. (2016) <doi:10.1101/051565>).
Linear and logistic regression models for quantitative genetic analysis of data from monozygotic twins.
Provides 'Scilab' 'n1qn1', or Quasi-Newton BFGS
      "qn" without constraints and 'qnbd' or Quasi-Newton BFGS with constraints.
       This takes more memory than traditional L-BFGS.  The n1qn1 routine is useful since it allows prespecification of a Hessian.
       If the Hessian is near enough the truth in optimization it can speed up the optimization problem. Both algorithms are described in the
       'Scilab' optimization documentation located at 
       <http://www.scilab.org/content/download/250/1714/file/optimization_in_scilab.pdf>.
This comprehensive toolkit provide a consistent and 
    extensible framework for working with missing values in vectors. The 
    companion package 'tidyimpute' provides similar functionality for list-like 
    and table-like structures).
    Functions exist for detection, removal, replacement, imputation, 
    recollection, etc. of 'NAs'.
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 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.
In empirical studies, instrumental variable (IV) regression is the signature method to solve the endogeneity problem. If we enforce the exogeneity condition of the IV, it is likely that we end up with a large set of IVs without knowing which ones are good. This package uses adaptive group lasso and B-spline methods to select the nonparametric components of the IV function, with the linear function being a special case. The package incorporates two stage least squares estimator (2SLS), generalized method of moment (GMM), generalized empirical likelihood (GEL) methods post instrument selection. It is nonparametric version of 'ivregress' in 'Stata' with IV selection and high dimensional features. The package is based on the paper "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective" (2017) published online in Journal of Business & Economic Statistics <doi:10.1080/07350015.2016.1180991>.
Designed for association studies in nested association mapping (NAM) panels, experimental and random panels. The method is described by Xavier et al. (2015) <doi:10.1093/bioinformatics/btv448>. 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.
User-friendly wrappers for 
 named capture regular expressions.
The Needleman-Wunsch global alignment algorithm can be
  used to find approximate matches between sample names in different
  data sets. See Wang et al. (2010) <doi:10.4137/CIN.S5613>.
It names the 'R Markdown' chunks of files based on the filename.
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)
Calculation of molecular number and brightness from 
    fluorescence microscopy image series. The software was published in a 2016
    paper <doi:10.1093/bioinformatics/btx434>. The seminal paper for the 
    technique is Digman et al. 2008 <doi:10.1529/biophysj.107.114645>. A review
    of the technique was published in 2017 <doi:10.1016/j.ymeth.2017.12.001>.
Missing values are ubiquitous in data and need to be explored and
    handled in the initial stages of analysis. 'naniar' provides data structures 
    and functions that facilitate the plotting of missing values and examination 
    of imputations. This allows missing data dependencies to be explored with 
    minimal deviation from the common work patterns of 'ggplot2' and tidy data.
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.
Computes the nonlinear cointegrating autoregressive distributed lag model with p lags of the dependent variables and q lags of independent variables proposed by (Shin, Yu & Greenwood-Nimmo, 2014 <doi:10.1007/978-1-4899-8008-3_9>).
Stacking arrays according to dimension names, subset-aware
    splitting and mapping of functions, intersecting along arbitrary
    dimensions, converting to and from data.frames, and many other helper
    functions.
Provides functions to access NASA's Earth Imagery and Assets API
    and the Earth Observatory Natural Event Tracker (EONET) webservice.
Client for 'NASA' 'POWER' global meteorology, surface solar
    energy and climatology data 'API'.  'POWER' (Prediction Of Worldwide Energy
    Resource) data are freely available global meteorology and surface solar
    energy climatology data for download with a resolution of 1/2 by 1/2 arc
    degree longitude and latitude and are funded through the 'NASA' Earth
    Science Directorate Applied Science Program.  For more on the data
    themselves, a web-based data viewer and web access, please see
    <https://power.larc.nasa.gov/>.
This package contains tidied data from the ASA 2006 data expo,
    as well as a number of useful other related data sets.
NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D
    biological image data, especially traced neurons. Reads and writes 3D images
    in NRRD and 'Amira' AmiraMesh formats and reads surfaces in 'Amira' hxsurf
    format. Traced neurons can be imported from and written to SWC and 'Amira'
    LineSet and SkeletonGraph formats. These data can then be visualised in 3D
    via 'rgl', manipulated including applying calculated registrations, e.g.
    using the 'CMTK' registration suite, and analysed. There is also a simple
    representation for neurons that have been subjected to 3D skeletonisation
    but not formally traced; this allows morphological comparison between
    neurons including searches and clustering (via the 'nat.nblast' extension
    package).
Extends package 'nat' (NeuroAnatomy Toolbox) by providing a
    collection of NBLAST-related functions.
Extends package 'nat' (NeuroAnatomy Toolbox) by providing objects
    and functions for handling template brains.
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.
Interface to 'NatureServe' (<http://www.natureserve.org>).
    Includes methods to get data, image metadata, search taxonomic names,
    and make maps.
Implementation of the two error variance estimation methods in high-dimensional linear models of Yu, Bien (2017) <arXiv:1712.02412>.
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.
Collection of example animal sounds for bioacoustic analysis.
Estimate the effective population size of a closed population using genetic data collected from two or more data points.
Model-based clustering of high-dimensional non-negative
             data that follow Generalized Negative Binomial distribution. All functions 
             in this package applies to either continuous or integer data. Correlation
            between variables are allowed, while samples are assumed to be independent.
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.
Calculates various functions needed for design and monitoring clinical trials with negative binomial endpoint
    with variable follow-up.
We proposed a package for classification task which uses Negative Binomial distribution within Linear Discriminant Analysis (NBLDA). It is basically an extension of 'PoiClaClu' package to Negative Binomial distribution. The classification algorithms are based on the papers Dong et al. (2016, ISSN: 1471-2105) and Witten, DM (2011, ISSN: 1932-6157) for NBLDA and PLDA respectively. Although PLDA is a sparse algorithm and can be used for variable selection, the algorithm proposed by Dong et. al. is not sparse, hence, it uses all variables in the classifier. Here, we extent Dong et. al.'s algorithm to sparse case by shrinking overdispersion towards 0 (Yu et. al., 2013, ISSN: 1367-4803) and offset parameter towards 1 (as proposed by Witten DM, 2011). We support only the classification task with this version. However, the clustering task will be included with the following versions.
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,
  <http://journals.sagepub.com/doi/abs/10.1177/1094428115584005>).
  NCA identifies necessary (but not sufficient) conditions in datasets, where x causes (e.g. precedes) y. 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). A quick start guide for using this package can be found here: <http://repub.eur.nl/pub/78323/> or <https://ssrn.com/abstract=2624981>.
Performs nonlinear calibration and curve fitting for data from Luminex, RT-PCR, ELISA, RPPA etc. Its precursor is Ruminex. This package is described in nCal: a R package for nonlinear calibration. Bioinformatics, <DOI:10.1093/bioinformatics/btt456>.
A flexible tool that can perform
    (i) traditional non-compartmental analysis (NCA) and
    (ii) Simulation-based posterior predictive checks for population
    pharmacokinetic (PK) and/or pharmacodynamic (PKPD) models using NCA metrics.
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 are
             1) CDISC SDTM terms
             2) Automatic slope selection with the same criterion of WinNonlin(R)
             3) Supporting both 'linear-up linear-down' and 'linear-up log-down' method
             4) Interval(partial) AUCs with 'linear' or 'log' interpolation method
             5) Produce pdf, rtf, text report files.
             * Reference: Gabrielsson J, Weiner D. Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications. 5th ed. 2016. (ISBN:9198299107).
making NCBI taxonomic data locally available and
        searchable as an R object
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.
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.
Tools for handling 'NetCDF' metadata in data frames. The metadata is provided
    as relations in tabular form, to avoid having to scan printed header output or to navigate 
    nested lists of raw metadata.
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 function, the spline correlogram, the
        nonparametric phase coherence function, local indicators of spatial 
        association (LISA), (Mantel) correlogram, (Partial) Mantel test.
Computes the noncentral gamma function: pdf, cdf, quantile
        function and inverse for the noncentrality parameter.
Extract metadata from 'NetCDF' data sources, these can be files, file handles or
 servers. This package leverages and extends the lower level functions of the 'RNetCDF' package 
 providing a consistent set of functions that all return data frames. We introduce named concepts 
 of 'grid', 'axis' and 'source' which are all meaningful entities without formal definition in the 
 'NetCDF' library <https://www.unidata.ucar.edu/software/netcdf/>. 'RNetCDF' matches the library 
 itself with only the named concepts of 'variables', 'dimensions' and 'attributes'. 'ncmeta'
 provides a required framework for the in-development 'tidync' project <https://github.com/hypertidy/tidync>.
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.
A set of techniques that can be used to develop, validate, and implement automated classifiers. A powerful tool for transforming raw data into meaningful information, 'ncodeR' (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is designed specifically for working with big data: large document collections, logfiles, and other text data.
Construct and manipulate hierarchical Archimedean copulas with multivariate compound distributions. The model used is the one of Cossette et al. (2017) <doi:10.1016/j.insmatheco.2017.06.001>.
An efficient unified nonconvex penalized estimation algorithm for
    Gaussian (linear), binomial Logit (logistic), Poisson, multinomial Logit,
    and Cox proportional hazard regression models.
    The unified algorithm is implemented based on the convex concave procedure and
    the algorithm can be applied to most of the existing nonconvex penalties.
    The algorithm also supports convex penalty:
    least absolute shrinkage and selection operator (LASSO).
    Supported nonconvex penalties include
    smoothly clipped absolute deviation (SCAD),
    minimax concave penalty (MCP), truncated LASSO penalty (TLP),
    clipped LASSO (CLASSO), sparse ridge (SRIDGE),
    modified bridge (MBRIDGE) and modified log (MLOG).
    For high-dimensional data (data set with many variables),
    the algorithm selects relevant variables producing a parsimonious regression model.
    Kim, D., Lee, S. and Kwon, S. (2018) <arXiv:1811.05061>,
    Lee, S., Kwon, S. and Kim, Y. (2016) <doi:10.1016/j.csda.2015.08.019>,
    Kwon, S., Lee, S. and Kim, Y. (2015) <doi:10.1016/j.csda.2015.07.001>.
    (This research is funded by Julian Virtue Professorship from Center for Applied Research at Pepperdine
    Graziadio Business School and the National Research Foundation of Korea.)
Provides functionality for performing Nearest Centroid (NC) Sampling. The NC sampling procedure was developed for forestry applications and selects plots for ground measurement so as to maximize the efficiency of imputation estimates. It uses multiple auxiliary variables and multivariate clustering to search for an optimal sample. Further details are given in Melville G. & Stone C. (2016) <doi:10.1080/00049158.2016.1218265>.
Omics data come in different forms: gene expression, methylation, copy number, protein measurements and more.
    'NCutYX' allows clustering of variables, of samples, and both variables and samples (biclustering),
    while incorporating the dependencies across multiple types of Omics data.
    (SJ Teran Hidalgo et al (2017), <doi:10.1186/s12864-017-3990-1>).
Fits regularization paths for linear regression, GLM, and Cox regression models using lasso or nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty, with options for additional L2 penalties (the "elastic net" idea).  Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, inference, and prediction are also provided.
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 is often useful to read in these files
    and "flatten" the structure out to enable working with the data 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' 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 "N genes
    x N samples" into the space of gene set (typically pathway) enrichment scores of
    dimensionality "N pathways x N samples".
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.