Models and displays tephra transport through custom (windy, turbulent, heterogeneous) atmosphere over custom topography. Includes a Lagrangian (particle-tracking) tephra transport model and a function to save snapshots of model as png files.
A small language extension for succinct conditional assignment using `?` and `:`, emulating the conditional ternary operator syntax using in C, Java, JavaScript and other languages.
For natural language processing and analysis of qualitative text
    coding structures which provide a way to bind together text and text data
    are fundamental. The package provides such a structure and accompanying
    methods in form of R6 objects. The 'rtext' class allows for text handling
    and text coding (character or regex based) including data updates on
    text transformations as well as aggregation on various levels.
    Furthermore, the usage of R6 enables inheritance and passing by reference
    which should enable 'rtext' instances to be used as back-end for R based
    graphical text editors or text coding GUIs.
A machine learning package for automatic text classification 
	that makes it simple for novice users to get started with machine 
	learning, while allowing experienced users to easily experiment 
	with different settings and algorithm combinations. The package 
	includes eight algorithms for ensemble classification (svm, slda, 
	boosting, bagging, random forests, glmnet, decision trees, neural 
	networks), comprehensive analytics, and thorough documentation.
This package contains several functions for calculation of texture metrics for Grey Level Co-occurrence matrices
A set of R functions to output Rich Text Format (RTF) files with high resolution tables and graphics that may be edited with a standard word processor such as Microsoft Word.
Methods for comparing different regression algorithms for 
    describing the temporal dynamics of secondary tree growth (xylem and 
    phloem). Users can compare the accuracy of the most common fitting methods 
    usually used to analyse xylem and phloem data, i.e., Gompertz function and 
    General Additive Models (GAMs); and an algorithm newly introduced to the 
    field, i.e., Bayesian Regularised Neural Networks (brnn). The core function
    of the package is XPSgrowth(), while the results can be interpreted using 
    implemented generic S3 methods, such as plot() and summary().
Allow function for using 'TGStat Stat API' and
    'TGStat Search API', for more details see <https://api.tgstat.ru/docs/ru/start/intro.html>.
    'TGStat' provide telegram channel analytics data.
The goal of 'Rthingsboard' is to provide interaction with the API of 'ThingsBoard' (<https://thingsboard.io/>), an open-source IoT platform for device management, data collection, processing and visualization.
A suite of custom R Markdown formats and templates for
    authoring journal articles and conference submissions.
It provides functions for processing Articulate
    Assistant Advanced™ (AAA) export files and plot tongue contour data from any
    system.
Calculates tide heights based on tide station harmonics.  It
    includes the harmonics data for 637 US stations.  The harmonics data
    was converted from
    <https://github.com/poissonconsulting/rtide/blob/master/data-raw/harmonics-dwf-20151227-free.tar.bz2>,
    NOAA web site data processed by David Flater for 'XTide'.  The code to
    calculate tide heights from the harmonics is based on 'XTide'.
The name of this package grew out of our research on temporal interpersonal 
    emotion systems (TIES), hence 'rties'. It provides tools for using a set of models to 
    investigate temporal processes in bivariate (e.g., dyadic) systems. The general approach
    is to model, one dyad at a time, the dynamics of a variable that is assessed repeatedly 
    from both partners, extract the parameter estimates for each dyad, and then use those 
    parameter estimates as input to a latent profile analysis to extract groups of dyads with
    qualitatively distinct dynamics. Finally, the profile memberships can be used to either 
    predict, or be predicted by, another variable of interest. Currently, 2 models are supported:
    1) inertia-coordination, and 2) a coupled-oscillator. Extended documentation is provided 
    in vignettes. Theoretical background can be found in Butler (2011) <doi:10.1177/1088868311411164> 
    and Butler & Barnard (2019) <doi:10.1097/PSY.0000000000000703>.
Our method integrates information from all sequenced samples, thus avoiding loss of alleles due to low coverage. Moreover, it increases the statistical power to uncover sequencing or alignment errors.
R tools to measure and compare inequality, welfare and poverty using the EU statistics on income and living conditions surveys.
Color palettes from famous artists and paintings.
Rarefy data, calculate diversity and plot the results.
'STK++' <http://www.stkpp.org> is a collection of C++ classes
    for statistics, clustering, linear algebra, arrays (with an 'Eigen'-like API),
    regression, dimension reduction, etc. The integration of the library to 'R' is
    using 'Rcpp'. The 'rtkore' package includes the header files from the 'STK++'
    core library. All files contain only template classes and/or inline functions.
    'STK++' is licensed under the GNU LGPL version 2 or later. 'rtkore' (the 'stkpp'
    integration into 'R') is licensed under the GNU GPL version 2 or later. See file
    LICENSE.note for details.
A toolkit for Commodities 'analytics', risk management and
    trading professionals. Includes functions for API calls to
    'Morningstar Commodities' and 'Genscape'.
A set of tools to process and calculate 
             metrics on point clouds derived from terrestrial LiDAR 
             (Light Detection and Ranging; TLS). Its creation is based on key 
             aspects of the TLS application in forestry and ecology. 
             Currently, the main routines are based on filtering, neighboring 
             features of points, voxelization, canopy structure, 
             and the creation of artificial stands. It is written using 
             data.table and C++ language and in most of the functions it is 
             possible to use parallel processing to speed-up the routines.
Fit response-time extended multinomial processing tree (RT-MPT) models by Klauer and
			Kellen (2018) <doi:10.1016/j.jmp.2017.12.003>. The RT-MPT class not only incorporate
			frequencies like traditional multinomial processing tree (MPT) models, but also latencies. This enables it
			to estimate process completion times and encoding plus motor execution times next to the process probabilities
			of traditional MPTs. 'rtmpt' is a Bayesian framework and posterior samples are sampled using a Metropolis-Gibbs
			sampler like the one described in the Klauer and Kellen (2018), but with some modifications. Other than in
			the original C++ program we use the free and open source GNU Scientific Library (GSL). There is also the
			possibility to suppress single process completion times.
Truncated Newton function minimization with bounds constraints
	based on the 'Matlab'/'Octave' codes of Stephen Nash.
Allows you to interact with the API of the "Todoist" platform.
    'Todoist' <https://todoist.com/> provides an online task manager service for teams.
Aimed at seismic tomography, the package
  plots tomographic images, and allows one to interact and query
  three-dimensional tomographic models.
  Vertical cross-sectional cuts can be extracted by mouse click.
  Geographic information can be added easily.
Geostatistical interpolation of data with irregular spatial support such as runoff related data or data from administrative units.
'R' implementation and interface of the Machine Learning platform 
    'PyTorch' <https://pytorch.org/> developed in 'Python'. It requires a 'conda'
    environment with 'torch' and 'torchvision' Python packages to provide 
    'PyTorch' functions, methods and classes. The key object in 'PyTorch' is the 
    tensor which is in essence a multidimensional array. These tensors are fairly 
    flexible in performing calculations in CPUs as well as 'GPUs' to accelerate 
    tensor operations.
A toolkit for the analysis of paths from spatial tracking experiments (such as the Morris water maze) and calculation of goal-finding strategies. 
    This package is centered on an approach using machine learning for path classification.
Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) <doi:10.1515/snde-2012-0044> and Dimpfl and Peter (2014) <doi:10.1016/j.intfin.2014.03.004> for theory and applications to financial time series. Additional references can be found in the theory part of the vignette.
Provides datasets related to the Star Trek fictional universe and functions for working with the data.
    The package also provides access to real world datasets based on the televised series and other related licensed media productions.
    It interfaces with the Star Trek API (STAPI) (<http://stapi.co/>), 
    Memory Alpha (<https://memory-alpha.fandom.com/wiki/Portal:Main>), and Memory Beta (<https://memory-beta.fandom.com/wiki/Main_Page>) 
    to retrieve data, metadata and other information relating to Star Trek.
    It also contains several local datasets covering a variety of topics. 
    The package also provides functions for working with data from other Star Trek-related 
    R data packages containing larger datasets not stored in 'rtrek'.
The traditional linear regression trend, Modified Mann-Kendall (MK)
    non-parameter trend and bootstrap trend are included in this package. Linear 
    regression trend is rewritten by '.lm.fit'. MK trend is rewritten by 'Rcpp'.
    Finally, those functions are about 10 times faster than previous version 
    in R.
    Reference:
    Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for 
    autocorrelated data. Journal of hydrology, 204(1-4), 182-196. 
    <doi:10.1016/S0022-1694(97)00125-X>.
Analyze download logs from the CRAN RStudio mirror 
            (<http://cran.rstudio.com/>). 
            This CRAN mirror is the default one used in RStudio.
            The available data is the result of parsed and anonymised raw log data from
            that CRAN mirror.
This is a port of Jonathan Shewchuk's Triangle library to
    R. From his description: "Triangle generates exact Delaunay
    triangulations, constrained Delaunay triangulations, conforming
    Delaunay triangulations, Voronoi diagrams, and high-quality
    triangular meshes. The latter can be generated with no small or
    large angles, and are thus suitable for finite element analysis."
The TRIM model is widely used for estimating growth and decline of
    animal populations based on (possibly sparsely available) count data. The
    current package is a reimplementation of the original TRIM software developed
    at Statistics Netherlands by Jeroen Pannekoek. See
    <https://www.cbs.nl/en-gb/society/nature-and-environment/indices-and-trends%2d%2dtrim%2d%2d>
    for more information about TRIM.
Embeds sources and headers from Tina's Random
    Number Generator ('TRNG') C++ library. Exposes some functionality for
    easier access, testing and benchmarking into R. Provides examples of
    how to use parallel RNG with 'RcppParallel'. The methods and
    techniques behind 'TRNG' are illustrated in the package vignettes and
    examples. Full documentation is available in Bauke (2021)
    <https://github.com/rabauke/trng4/blob/v4.23.1/doc/trng.pdf>.
Process phylogenetic trees with tropical support vector machine and principal component analysis defined with tropical geometry. Details about tropical support vector machine are available in : Tang, X., Wang, H. & Yoshida, R. (2020) <arXiv:2003.00677>. Details about tropical principle component analysis are available in : Page, R., Yoshida, R. & Zhang L. (2020) <doi:10.1093/bioinformatics/btaa564> and Yoshida, R., Zhang, L. & Zhang, X. (2019) <doi:10.1007/s11538-018-0493-4>.
Designed to support the application of plant trait data providing easy applicable functions
    for the basic steps of data preprocessing, e.g. data import, data exploration, selection of columns
    and rows, excluding trait data according to different attributes, geocoding, long- to wide-table
    transformation, and data export. 'rtry' was initially developed as part of the TRY R project to
    preprocess trait data received via the TRY database.
This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).
Supports modelling real-time case data to facilitate the real-time
    surveillance of infectious disease. A simple grid class structure is provided to generate a computational grid over
    an area of interest with methods to map covariates between geographies. An approximate log-Gaussian Cox Process 
    model is fit using 'rstan' or 'cmdstanr' and provides output and analysis as 'sf' objects for simple visualisation.
    'cmdstanr' can be downloaded at <https://mc-stan.org/cmdstanr/>. Log-Gaussian Cox Processes are described by 
    Diggle et al. (2013) <doi:10.1214/13-STS441> and we use the low-rank approximation  for Gaussian processes 
    described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and Riutort-Mayol (2020) <arXiv:2004.11408>.
A tool that allows to download and save historical time series data 
	for future use offline. The intelligent updating functionality will only download 
	the new available information; thus, saving you time and Internet bandwidth. 
	It will only re-download the full data-set if any inconsistencies are detected. 
	This package supports following data provides: 'Yahoo' (<https://finance.yahoo.com>), 
	'FRED' (<https://fred.stlouisfed.org>), 'Quandl' (<https://data.nasdaq.com>), 
	'AlphaVantage' (<https://www.alphavantage.co>), 'Tiingo' (<https://www.tiingo.com>).
An R wrapper around the fast T-distributed Stochastic
    Neighbor Embedding implementation by Van der Maaten  (see <https://github.com/lvdmaaten/bhtsne/> for more information on the original implementation).
TSON, short for Typed JSON, is a binary-encoded serialization of
    JSON like document that support JavaScript typed data (https://github.com/tercen/TSON).
A fast and elegant time series visualization package. In addition to the 
	standard R plot types, this package supports candle sticks, open-high-low-close, and
	volume plots. Useful for visualizing any time series data, e.g., stock prices and 
	technical indicators.
A lightweight 'R' package to visualize large raster time series, building on a fast temporal interpolation core.
Contains the program 'ttf2pt1', for use with the
    'extrafont' package. This product includes software developed by the 'TTF2PT1'
    Project and its contributors.
R Web Client to 'TickTrader' platform. Provides you access to 'TickTrader' platform through Web API <https://ttlivewebapi.fxopen.net:8443/api/doc/index>.
Contains Shiny apps for Plotting and Exploratory Analysis. 
    The plotting app provides an automated interface for generating plots using the 'ggplot2' package.
    Current version of this app supports 10 different plot types along with options to manipulate specific
    aesthetics and controls related to each plot type. Exploratory Analysis app helps generates an Exploratory
    analysis report (in PowerPoint format) comprising of Univariate and Bivariate plots & related summary tables.
The 't-walk' is a general-purpose MCMC sampler for
      arbitrary continuous distributions that requires no tuning.
An implementation of calls designed to collect and organize Twitter data via Twitter's REST and stream Application Program Interfaces (API), which can be found at the following URL: <https://developer.twitter.com/en/docs>.
 This package has been peer-reviewed by rOpenSci (v. 0.6.9).
A strong type system for R which supports
    symbol declaration and assignment with type checking
    and condition checking.
An R interface to the 'typeform'
    <https://typeform.com> application program interface.  Also provides
    functions for downloading your results.
Implements Bayesian inference for the conditional genetic 
    stock identification model.  It allows inference of mixed fisheries and also 
    simulation of mixtures to predict accuracy.  A full description of the underlying
    methods is available in a recently published article in the
    Canadian Journal of Fisheries and Aquatic Sciences: <doi:10.1139/cjfas-2018-0016>.
Unobserved Components Models (introduced in Harvey, A. (1989),
    Forecasting, structural time series models and the Kalman filter, Cambridge
    New York: Cambridge University Press) decomposes a time series into
    components such as trend, seasonal, cycle, and the regression effects due
    to predictor series which captures the salient features of the series to
    predict its behavior.
R bindings for functions from the UCR Suite by Rakthanmanon et al. (2012) <DOI:10.1145/2339530.2339576>, which enables ultrafast subsequence
      search for a best match under Dynamic Time Warping and Euclidean Distance.
ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.
Wraps the 'StarSpace' library <https://github.com/facebookresearch/StarSpace> 
    allowing users to calculate word, sentence, article, document, webpage, link and entity 'embeddings'. 
    By using the 'embeddings', you can perform text based multi-label classification, 
    find similarities between texts and categories, do collaborative-filtering based recommendation 
    as well as content-based recommendation, find out relations between entities, calculate 
    graph 'embeddings' as well as perform semi-supervised learning and multi-task learning on plain text. 
    The techniques are explained in detail in the paper: 'StarSpace: Embed All The Things!' by Wu et al. (2017), available at <arXiv:1709.03856>.
A (not yet exhaustive) collection of common models of risk
    processes in actuarial science, represented as formal S4 classes. Each class
    (risk model) has a simulator of its path, and a plotting function. Further, 
    a Monte-Carlo estimator of a ruin probability for a finite time is
    implemented, using a parallel computation. Currently, the package extends
    two classical risk models Cramer-Lundberg and Sparre Andersen models by
    including capital injections, that are positive jumps (see Breuer L. and
    Badescu A.L. (2014) <doi:10.1080/03461238.2011.636969>). The intent of the
    package is to provide a user-friendly interface for ruin processes'
    simulators, as well as a solid and extensible structure for future
    extensions.
Tools for creating data validation pipelines and
    tidy reports. This package offers a framework for exploring and
    validating data frame like objects using 'dplyr' grammar of data
    manipulation.
Bindings for additional models for use with the 'parsnip'
    package.  Models include prediction rule ensembles (Friedman and
    Popescu, 2008) <doi:10.1214/07-AOAS148>, C5.0 rules (Quinlan, 1992
    ISBN: 1558602380), and Cubist (Kuhn and Johnson, 2013)
    <doi:10.1007/978-1-4614-6849-3>.
This holds some r markdown templates and a template to create a
    research project in RStudio.
Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components to a variety of GARCH and MEM (Engle (2002) <doi:10.1002/jae.683>) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. 'rumidas' also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.
Plots multiple run charts, finds successive signals of 
    improvement, and revises medians when each signal occurs. Finds runs
    above, below, or on both sides of the median, and returns a plot and
    a data.table summarising original medians and any revisions, for all
    groups within the supplied data.
Convert a string of text characters to Elder Futhark Runes <https://en.wikipedia.org/wiki/Elder_Futhark>.
Implements two methods of estimating runs scored in a softball 
    scenario: (1) theoretical expectation using discrete Markov chains and (2) empirical
    distribution using multinomial random simulation.  Scores are based on player-specific input 
    probabilities (out, single, double, triple, walk, and homerun).  Optional inputs include probability
    of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether
    a player is "fast" (e.g. the player could stretch home).  These probabilities may be 
    calculated from common player statistics that are publicly available on team's webpages. 
    Scores are evaluated based on a nine-player lineup and may be used to compare lineups, 
    evaluate base scenarios, and compare the offensive potential of individual players.  
    Manuscript forthcoming.  See Bukiet & Harold (1997) <doi:10.1287/opre.45.1.14> for 
    implementation of discrete Markov chains.
R functions implementing a standard Unit Testing
        framework, with additional code inspection and report
        generation tools.
This package contains some functions for converting R
        objects to Java style variables and XML. Generated Java code is
        interpretable by dynamic Java libraries such as Beanshell.
        Calling R externally and handling the Java or XML output is an
        other way to call R from other languages without native
        interfaces. For a Java implementation of this approach visit
        http://www.mhsatman.com/rcaller.php and
        http://stdioe.blogspot.com/search/label/rcaller
User-friendly interface utilities for MCMC models via
    Just Another Gibbs Sampler (JAGS), facilitating the use of parallel
    (or distributed) processors for multiple chains, automated control
    of convergence and sample length diagnostics, and evaluation of the
    performance of a model using drop-k validation or against simulated
    data. Template model specifications can be generated using a standard
    lme4-style formula interface to assist users less familiar with the
    BUGS syntax.  A JAGS extension module provides additional distributions
    including the Pareto family of distributions, the DuMouchel prior and
    the half-Cauchy prior.
Lightweight library for rolling windows operations. Package enables
  full control over the window length, window lag and a time indices. With a runner 
  one can apply any R function on a rolling windows. The package eases work with 
  equally and unequally spaced time series.
Package 'runonce' helps automating the saving of long-running code
    to help running the same code multiple times. If you run some long-running 
    code once, it saves the result in a file on disk. Then, if the result 
    already exists, i.e. if the code has already been run and its output has 
    already been saved, it just reads the result from the stored file instead 
    of running the code again.
Provides methods for fast computation of running sample 
    statistics for time series. These include: (1) mean, (2) 
    standard deviation, and (3) variance over a fixed-length window 
    of time-series, (4) correlation, (5) covariance, and (6) 
    Euclidean distance (L2 norm) between short-time pattern and 
    time-series. Implemented methods utilize Convolution Theorem to 
    compute convolutions via Fast Fourier Transform (FFT).
Interface to the 'UNU.RAN' library for Universal Non-Uniform RANdom variate generators. 
	     Thus it allows to build non-uniform random number generators from quite arbitrary
	     distributions. In particular, it provides an algorithm for fast numerical inversion
	     for distribution with given density function.
	     In addition, the package contains densities, distribution functions and quantiles
	     from a couple of distributions.
An interface to the web service methods provided by the United States Department of Agriculture (USDA). The Agricultural Research Service (ARS) provides a large set of databases. The current version of the package holds interfaces to the Systematic Mycology and Microbiology Laboratory (SMML), which consists of four databases: Fungus-Host Distributions, Specimens, Literature and the Nomenclature database. It provides functions for querying these databases. The main function is \code{associations}, which allows searching for fungus-host combinations.
By placing on a circle 10 points numbered from 1 to 10, and connecting them by a straight line to the point corresponding to its multiplication by 2. (1 must be connected to 1 * 2 = 2, point 2 must be set to 2 * 2 = 4, point 3 to 3 * 2 = 6 and so on). You will obtain an amazing geometric figure that complicates and beautifies itself by varying the number of points and the multiplication table you use.
Uses the generalized ratio-of-uniforms (RU) method to simulate
    from univariate and (low-dimensional) multivariate continuous distributions.
    The user specifies the log-density, up to an additive constant. The RU
    algorithm is applied after relocation of mode of the density to zero, and
    the user can choose a tuning parameter r. For details see Wakefield, Gelfand
    and Smith (1991) <DOI:10.1007/BF01889987>, Efficient generation of random
    variates via the ratio-of-uniforms method, Statistics and Computing (1991)
    1, 129-133.  A Box-Cox variable transformation can be used to make the input
    density suitable for the RU method and to improve efficiency.  In the
    multivariate case rotation of axes can also be used to improve efficiency.
    From version 1.2.0 the 'Rcpp' package 
    <https://cran.r-project.org/package=Rcpp> can be used to improve efficiency.
Implementation of several unsupervised neural networks,
    from building their architecture to their training and evaluation. Available
    networks are auto-encoders including their main variants: sparse, contractive,
    denoising, robust and variational, as described in Charte et al. (2018)
    <doi:10.1016/j.inffus.2017.12.007>.
A RUT (Rol Unico Tributario) is an unique and personal identification number implemented in Chile to identify citizens and taxpayers. Rutifier allows to validate if a RUT exist or not and change between the different formats a RUT can have.
Implements the 'RUV' (Remove Unwanted Variation) algorithms.  These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data.  The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well.  These algorithms were proposed in Gagnon-Bartsch and Speed (2012) <doi:10.1093/nar/gkz433>, Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) <doi:10.1093/nar/gkz433>.  The algorithms require the user to specify a set of negative control variables, as described in the references.  The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms.
Variations of Remove Unwanted Variation-III (RUV-III) known as RUV-III-C (RUV-III Complete). RUV-III performs normalisation using negative control variables and replication. RUV-III-C extends this method to cases where the data contains missing values, by applying RUV-III to complete subsets of the data. Originally designed for SWATH-MS proteomics datasets. 
    Poulos et al. (2020) <doi:10.1038/s41467-020-17641-3>.
Implements a simulation-based random variable class and a suite of
  methods for extracting parts of random vectors, calculating extremes of random
  vectors, and generating random vectors under a variety of distributions 
  following Kerman and Gelman (2007) <doi:10.1007/s11222-007-9020-4>.
Automate downstream visualization & pathway analysis in RNAseq analysis. 'RVA' is a collection of functions that efficiently visualize RNAseq differential expression analysis result from summary statistics tables. It also utilize the Fisher's exact test to evaluate gene set or pathway enrichment in a convenient and efficient manner.
Contains miscellaneous functions useful in biostatistics, mostly univariate and multivariate testing procedures with a special emphasis on permutation tests. Many functions intend to simplify user's life by shortening existing procedures or by implementing plotting functions that can be used with as many methods from different packages as possible.
A collection of functions for computing "r-values" from various
    kinds of user input such as MCMC output or a list of effect size estimates
    and associated standard errors. Given a large collection of measurement units,
    the r-value, r, of a particular unit is a reported percentile that may be
    interpreted as the smallest percentile at which the unit should be placed in the
    top r-fraction of units.
Operations on triangular meshes based on 'VCGLIB'. This package
    integrates nicely with the R-package 'rgl' to render the meshes processed by
    'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is
    an open source portable C++ templated library for manipulation, processing
    and displaying with OpenGL of triangle and tetrahedral meshes. The library,
    composed by more than 100k lines of code, is released under the GPL license,
    and it is the base of most of the software tools of the Visual Computing Lab of
    the Italian National Research Council Institute ISTI <http://vcg.isti.cnr.it>,
    like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk
    <https://github.com/cnr-isti-vclab/vcglib> and patched to work with options
    determined by the configure script as well as to work with the header files
    included by 'RcppEigen'.
Check latest release version of R and R package (both in 'CRAN', 'Bioconductor' or 'Github').
A framework with tools to compare two random variables, and determine which of takes produces lower values. See the README.md at <https://github.com/EtorArza/RVCompare> for a quick start guide. It can compute the Cp and Cd of two probability distributions, as explained in E. Arza (2021) <https://github.com/EtorArza/RVCompare-paper/releases>. Given the observed samples of two random variables X_A and X_B, it can compute the cumulative difference-plot (see E. Arza (2021) <https://github.com/EtorArza/RVCompare-paper/releases> for details). Uses bootstrap and DKW-bounds to compute the confidence bands of the cumulative distributions. These two methods are described in B. Efron. (1979) <doi:10.1214/aos/1176344552> and P. Massart (1990) <doi:10.1214/aop/1176990746>.
Set operations for many sets. The base functions for set operations
    in R can be used for only two sets. This package uses 'purr' to find the
    union, intersection and difference of three or more sets. This package also
    provides functions for pairwise set operations among several sets. Further, 
    based on 'ggplot2' and 'ggforce', a Venn diagram can be drawn for two or 
    three sets. For bigger data sets, a clustered heatmap showing presence/absence 
    of the elements of the sets can be drawn based on the 'pheatmap' package. 
    Finally, enrichment test can be applied to two sets whether an overlap is 
    statistically significant or not.
Build regular expressions using grammar and functionality inspired 
    by <https://github.com/VerbalExpressions>. Usage of the %>% is encouraged to
    build expressions in a chain-like fashion.
Query the main 'R' 'SVN' repository to find the
    versions 'r-release' and 'r-oldrel' refer to, and also all previous
    'R' versions and their release dates.
Retrieve, map and summarize data from the 'VertNet.org' 
    archives (<http://vertnet.org/>).  Functions allow searching by many 
    parameters, including 'taxonomic' names, places, and dates. In addition, 
    there is an interface for conducting spatially delimited searches, and 
    another for requesting large 'datasets' via email.
Wrappers around the 'xml2' and 'httr' packages to
    make it easy to download, then manipulate, HTML and XML.
The RVFam package provides functions to perform single SNP association analyses and gene-based tests for continuous, binary and survival traits against sequencing data (e.g. exome chip) using family data.
Vector Graphics devices for Microsoft 
  PowerPoint and Excel. Functions extending package 'officer' are provided to 
  embed 'DrawingML' graphics into 'Microsoft PowerPoint' presentations and 
  'Microsoft Excel' workbooks.
To detecting rare variants for binary traits using general pedigrees, the pedigree disequilibrium tests are proposed by collapsing rare haplotypes/variants with/without weights. To run the test, MERLIN is needed in Linux for haplotyping.
Play Video Poker with R, complete with a graphical user
        interface. So far, only "Jacks or Better" is implemented.
'ViennaCL' is a free open-source linear algebra library 
  for computations on many-core architectures (GPUs, MIC) and 
  multi-core CPUs. The library is written in C++ and supports 'CUDA', 
  'OpenCL', and 'OpenMP' (including switches at runtime). 
  I have placed these libraries in this package as a more efficient 
  distribution system for CRAN. The idea is that you can write a package 
  that depends on the 'ViennaCL' library and yet you do not need to 
  distribute a copy of this code with your package.
Provides 'Java' graphical user interfaces 
    for viewing, manipulating and plotting graphs. 
    Graphs may be directed or undirected.
Provides an interface to 'vinecopulib', a C++ library for vine 
 copula modeling. The 'rvinecopulib' package implements the core features of the
 popular 'VineCopula' package, in particular inference algorithms for both vine 
 copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker 
 and more modern API, improved performances, especially in high dimensions, 
 nonparametric and multi-parameter families, and the ability to model discrete 
 variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only 
 C++ library (currently version 0.6.1). Thus users do not need to install 
 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial 
 releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' 
 is licensed under the GNU GPL version 3.
The Regional Vulnerability Index (RVI), a statistical measure of brain structural abnormality, quantifies an individual's similarity to the expected pattern (effect size) of deficits in schizophrenia (Kochunov P, Fan F, Ryan MC, et al. (2020) <doi:10.1002/hbm.25045>).
Load data from vk.com api about your communiti users and views,
    ads performance, post on user wall and etc.	For more information 
    see API Documentation <https://vk.com/dev/first_guide>.
Has various functions designed to implement the 
    Hermite-Gaussian Radial Velocity (HGRV) estimation approach of 
    Holzer et al. (2020) <arXiv:2005.14083>, which is a particular application of the radial 
    velocity method for detecting exoplanets. The overall approach consists 
    of four sequential steps, each of which has a function in this package: 
    (1) estimate the template spectrum with the function estimate_template(), 
    (2) find absorption features in the estimated template with the function 
    findabsorptionfeatures(), (3) fit Gaussians to the absorption features 
    with the function Gaussfit(), (4) apply the HGRV with simple linear 
    regression by calling the function hgrv(). This package is meant to be 
    open source. But please cite the paper Holzer et al. (2020) <arXiv:2005.14083> when 
    publishing results that use this package.
Variable metric nonlinear function minimization with bounds constraints.
Designed to calculate the distance between each voter in a voter file -- given lat/long coordinates -- and many potential (early) polling or vote by mail drop box locations, then return the minimum distance.
The 'Vowpal Wabbit' project is a fast out-of-core learning
 system sponsored by Microsoft Research (having started at Yahoo! Research)
 and written by John Langford along with a number of contributors. This R
 package does not include the distributed computing implementation of the
 cluster/ directory of the upstream sources. Use of the software as a network
 service is also not directly supported as the aim is a simpler direct call
 from R for validation and comparison. Note that this package contains an
 embedded older version of 'Vowpal Wabbit'. The package 'rvw' at the GitHub
 repo <https://github.com/rvw-org/rvw-legacy> can provide an alternative using 
 an external 'Vowpal Wabbit' library installation.
Used to compute population controls weighted rare variants transmission distortion test
Use multiple regression methods to test rare variants
        association with disease traits.
Perform a Relative Weights Analysis (RWA) (a.k.a. Key Drivers Analysis) as per the method described 
    in Tonidandel & LeBreton (2015) <DOI:10.1007/s10869-014-9351-z>, with its original roots in Johnson (2000) <DOI:10.1207/S15327906MBR3501_1>. In essence, RWA decomposes
    the total variance predicted in a regression model into weights that accurately reflect the proportional 
    contribution of the predictor variables, which addresses the issue of multi-collinearity. In typical scenarios,
    RWA returns similar results to Shapley regression, but with a significant advantage on computational performance.
Provides API to Melbourne pedestrian and weather data
    <https://data.melbourne.vic.gov.au> in tidy data form.
A set of R functions which provide an
        environment for the Time-Frequency analysis of 1-D signals (and
        especially for the wavelet and Gabor transforms of noisy
        signals). It was originally written for Splus by Rene Carmona,
        Bruno Torresani, and Wen L. Hwang, first at the University of
        California at Irvine and then at Princeton University.  Credit
        should also be given to Andrea Wang whose functions on the
        dyadic wavelet transform are included. Rwave is based on the
        book: "Practical Time-Frequency Analysis: Gabor and Wavelet
        Transforms with an Implementation in S", by Rene Carmona, Wen
        L. Hwang and Bruno Torresani (1998, eBook ISBN:978008053942), Academic Press.
Perform wavelet analysis (orthogonal,translation invariant, tensorial, 1-2-3d transforms, thresholding, block thresholding, linear,...) with applications to data compression or denoising/regression. The core of the code is a port of 'MATLAB' Wavelab toolbox written by D. Donoho, A. Maleki and M. Shahram (<https://statweb.stanford.edu/~wavelab/>).
This package will download model predictions from 15 different global circulation models in 20 year intervals from the world bank.  Users can also access historical data, and create maps at 2 different spatial scales.
a Bipartite graph and is constructed based on the spatial and/or non-spatial attributes of the spatial objects in the dataset. Secondly, RW techniques are utilized on the graphs to compute the outlierness for each point (the differences between spatial objects and their spatial neighbours). The top k objects with higher outlierness are recognized as outliers.
Code to facilitate simulation and inference when connectivity is defined by underlying random walks. Methods for spatially-correlated pairwise distance data are especially considered. This provides core code to conduct analyses similar to that in Hanks and Hooten (2013) <doi:10.1080/01621459.2012.724647>.
A tool to read and manipulate data generated from 'RiverWare'(TM) 
    <http://www.riverware.org/> simulations. 'RiverWare' and 'RiverSMART' 
    generate data in "rdf", "csv", and "nc" format. This package provides an 
    interface to read, aggregate, and summarize data from one or more 
    simulations in a 'dplyr' pipeline.
Weave and tangle drivers for Sweave extending the
  standard drivers with additional code chunk options. Currently,
  these are only options to completely ignore, or skip, code chunks on
  weaving, tangling, or both. Chunks ignored on weaving are not parsed
  and are written out verbatim on tangling. Chunks ignored on tangling
  are processed as usual on weaving, but completely left out of the
  tangled scripts.
RWebLogo is a wrapper for the WebLogo python package
    that allows generating of customised sequence logos. Sequence logos are
    graphical representations of the sequence conservation of nucleotides (in a
    strand of DNA/RNA) or amino acids (in protein sequences). Each logo
    consists of stacks of symbols, one stack for each position in the sequence.
    The overall height of the stack indicates the sequence conservation at that
    position, while the height of symbols within the stack indicates the
    relative frequency of each amino or nucleic acid at that position. In
    general, a sequence logo provides a richer and more precise description of,
    for example, a binding site, than would a consensus sequence.
Access the Webstat API, download data and metadata from more than 35000 time series
    from the Banque de France statistics web portal. Access requires a free client ID easily available
    from the API portal <https://developer.webstat.banque-france.fr/>.
An R interface to Weka (Version 3.9.3).
   Weka is a collection of machine learning algorithms for data mining
   tasks written in Java, containing tools for data pre-processing,
   classification, regression, clustering, association rules, and
   visualization.  Package 'RWeka' contains the interface code, the
   Weka jar is in a separate package 'RWekajars'.  For more information
   on Weka see <http://www.cs.waikato.ac.nz/ml/weka/>.
External jars required for package 'RWeka'.
Communications simulation package supporting forward error correction.
A straightforward, easy-to-use and robust parsing package which aims to
    digest history files from the popular messenger service 'WhatsApp' in all locales
    and from all devices.
Provides functions to simulate and analyze data for a regression model with an interval censored covariate, as described in Morrison et al. (2021) <doi:10.1111/biom.13472>.
Provides Wiener process distribution functions,
  namely the Wiener first passage time density, CDF, quantile and random
  functions. Additionally supplies a modelling function (wdm) and further
  methods for the resulting object.
Provides an interface with the 'Wildbook' mark-recapture ecological database framework. It 
    helps users to pull data from the 'Wildbook' framework and format data for further analysis
    with mark-recapture applications like 'Program MARK' (which can be accessed via the 'RMark' package in 'R').
    Further information on the 'Wildbook' framework is available at: <http://www.wildbook.org/doku.php>.
Tools for download and manage surface wind and sea currents data from the Global Forecasting System <https://www.ncei.noaa.gov/products/weather-climate-models/global-forecast> and to compute connectivity between locations.
A plug in for using 'WinEdt' as an editor for R.
An expansion of R's 'stats' random wishart matrix generation. 
  This package allows the user to generate singular, Uhlig and Harald (1994) 
  <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) 
  <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate 
  wishart matrices with fractional degrees of freedom, Adhikari (2008) 
  <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility 
  modeling. Users can also use this package to create random covariance matrices.
Contains convenience functions for working with spatial data across
    multiple UTM zones, raster-vector operations common in the analysis of 
    conflict data, and converting degrees, minutes, and seconds latitude and
    longitude coordinates to decimal degrees.
An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) <doi:10.1016/j.agsy.2018.06.018> for a recent review of the history and use of the model.
Provides interfaces and useful tools for Chinese word segmentation. Implements a segmentation algorithm based on Hidden Markov Model (HMM) in native R codes. Methods for HHMM-Based Chinese lexical analyzer are as described in : Hua-Ping Zhang et al., (2003) <doi:10.3115/1119250.1119280>.
Enables mapping of country level and gridded user datasets.
High resolution vector country boundaries derived from
        Natural Earth data, can be plotted in rworldmap.
Search by keywords in R packages, task views, CRAN, the web and display the results in the console or in txt, html or pdf files. Download the package documentation (html index, README, NEWS, pdf manual, vignettes, source code, binaries) with a single instruction. Visualize the package dependencies and CRAN checks. Compare the package versions, unload and install the packages and their dependencies in a safe order. Explore CRAN archives. Use the above functions for task view maintenance. Access web search engines from the console thanks to 80+ bookmarks. All functions accept standard and non-standard evaluation.
It contains Chinese character frequency data based on 
    news data from 2017 to 2019. Source of these news include Sina, China daily and Tencent.
Provides a set of functions for performing digital signal
    processing.
Allows remote access to satellite image time 
    series provided by the web time series service (WTSS) available 
    at servers such as <https://brazildatacube.dpi.inpe.br/wtss/>. 
    The functions include listing the data sets available in WTSS servers, 
    describing the contents of a data set, and retrieving a time series 
    based on spatial location and temporal filters.
Implements various tests, visualizations, and metrics
    for diagnosing convergence of MCMC chains in phylogenetics.  It implements
    and automates many of the functions of the AWTY package in the R
    environment.
Tools for getting historical weather information and forecasts 
    from wunderground.com. Historical weather and forecast data includes, but 
    is not limited to, temperature, humidity, windchill, wind speed, dew point, 
    heat index. Additionally, the weather underground weather API also includes 
    information on sunrise/sunset, tidal conditions, satellite/webcam imagery, 
    weather alerts, hurricane alerts and historical high/low temperatures.
Fits the R x C inference model described in Greiner and
        Quinn (2009) <DOI:10.1111/j.1467-985X.2008.00551.x> and Greiner and
        Quinn (2010) <DOI:10.1214/10-AOAS353>.
	Allows incorporation of survey results.
Visualize your favorite XKCD comic strip directly from
        R. XKCD <https://xkcd.com> web comic content is provided under the Creative
        Commons Attribution-NonCommercial 2.5 License.
Functions which allow to read many XMCDA tags and transform them into R variables which are then usable in MCDA algorithms written in R. It also allows to write certain R variables into XML files respecting the XMCDA standard.
Methods to compute chemical similarity between two or more reactions and molecules. Allows masking of chemical substructures for weighted similarity computations. Uses packages 'rCDK' and 'fingerprint' for cheminformatics functionality.
Facilities for running simulations from ordinary
    differential equation ('ODE') models, such as pharmacometrics and other
    compartmental models.  A compilation manager translates the ODE model
    into C, compiles it, and dynamically loads the object code into R for
    improved computational efficiency.  An event table object facilitates
    the specification of complex dosing regimens (optional) and sampling
    schedules.  NB: The use of this package requires both C and
    Fortran compilers, for details on their use with R please see
    Section 6.3, Appendix A, and Appendix D in the "R Administration and
    Installation" manual. Also the code is mostly released under GPL.  The
    'VODE' and 'LSODA' are in the public domain.  The information is available
    in the inst/COPYRIGHTS.
Analysis of combined total and allele specific reads from the reciprocal cross study with RNA-seq data.
Functions are provided to calculate and display ridge TRACE Diagnostics for a 
  variety of alternative Shrinkage Paths. While all methods focus on Maximum Likelihood 
  estimation of unknown true effects under Normal-distribution theory, some estimates are 
  modified to be Unbiased or to have "Correct Range" when estimating either [1] the noncentrality 
  of the F-ratio for testing that true Beta coefficients are Zeros or [2] the "relative" MSE 
  Risk (i.e. MSE divided by true sigma-square, where the "relative" variance of OLS is known.) 
  The eff.ridge() function implements the "Efficient Shrinkage Path" introduced in Obenchain
  (2022) <Open Statistics>. This new "p-Parameter" Shrinkage-Path always passes through the
  vector of regression coefficient estimates Most-Likely to achieve the overall Optimal
  Variance-Bias Trade-Off and is the shortest Path with this property. Functions eff.aug() and
  eff.biv() augment the calculations made by eff.ridge() to provide plots of the bivariate
  confidence ellipses corresponding to any of the p*(p-1) possible ordered pairs of shrunken
  regression coefficients.
Provides access to the 'xylib' C library for to import xy 
  data from powder diffraction, spectroscopy and other experimental methods.
Interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).
A legacy version of 'Ryacas', an interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).
Load data from 'Yandex Direct' API V5 
    <https://tech.yandex.ru/direct/doc/dg/concepts/about-docpage/> into R.
	Provide function for load lists of campaings, ads, keywords and other 
	objects from 'Yandex Direct' account. Also you can load statistic from
	API 'Reports Service' <https://tech.yandex.ru/direct/doc/reports/reports-docpage/>.
	And allows keyword bids management.
'Yandex Translate' (https://translate.yandex.com/) is a statistical machine translation system.
	The system translates separate words, complete texts, and webpages.
	This package can be used to detect language from text and to translate it to supported target language.
	For more info: https://tech.yandex.com/translate/doc/dg/concepts/About-docpage/ .
Allows work with 'Management API' for load counters, segments, filters,
	user permissions and goals list from Yandex Metrica, 'Reporting API' allows you to get 
	information about the statistics of site visits and other data without
	using the web interface, 'Logs API' allows to receive non-aggregated data and 
	'Compatible with Google Analytics Core Reporting API v3' allows 
	receive information about site traffic and other data using field names 
	from Google Analytics Core API.	For more information see official 
	documents <https://yandex.ru/dev/metrika/doc/api2/concept/about-docpage>.
You can use this package to translate thousands of words. The Youdao translation open API is applied in this package. But, it just translates less than 1000 English words into Chinese.
Package contains some data and functions that 
    are used in my forthcoming "R you ready?" book.
A programmatic interface to web-services of YouTheria. YouTheria is
    an online database of mammalian trait data <http://www.utheria.org/>.
Provide function for get data from 'YouTube Data API' 
    <https://developers.google.com/youtube/v3/docs/>, 'YouTube Analytics API' 
    <https://developers.google.com/youtube/analytics/reference/> and 
    'YouTube Reporting API' <https://developers.google.com/youtube/reporting/v1/reports>.
R interface to the 'Zabbix API' data <https://www.zabbix.com/documentation/3.0/manual/api/reference>. Enables easy and direct communication with 'Zabbix API' from 'R'.
Provides functionality to read settings, statuses and readings of
  weather stations from the 'ZENTRA Cloud' API 
  <https://zentracloud.com/api/v1/guide#APIGuidelines>.
Implements the Zig-Zag algorithm (Bierkens, Fearnhead, Roberts, 2016) <arXiv:1607.03188> applied and Bouncy Particle Sampler <arXiv:1510.02451> for a Gaussian target and Student distribution.
Interface to the 'ZeroMQ' lightweight messaging kernel (see <https://zeromq.org/> for more information).
Functions to identify Homozygous-by-Descent (HBD) segments associated with runs of homozygosity (ROH) and to
    estimate individual autozygosity (or inbreeding coefficient). HBD segments and autozygosity are assigned to multiple HBD classes
    with a model-based approach relying on a mixture of exponential distributions. The rate of the exponential distribution is distinct
    for each HBD class and defines the expected length of the HBD segments. These HBD classes are therefore related to the age of the
    segments (longer segments and smaller rates for recent autozygosity / recent common ancestor). The functions allow to estimate the
    parameters of the model (rates of the exponential distributions, mixing proportions), to estimate global and local autozygosity
    probabilities and to identify HBD segments with the Viterbi decoding. The method is fully described in Druet and Gautier (2017)
    <doi:10.1111/mec.14324>.
Provides R bindings for Google's s2 library for geometric calculations on
    the sphere. High-performance constructors and exporters provide high compatibility
    with existing spatial packages, transformers construct new geometries from existing
    geometries, predicates provide a means to select geometries based on spatial 
    relationships, and accessors extract information about geometries.
A set of functions used in teaching STATS 201/208 Data Analysis at
    the University of Auckland. The functions are designed to make parts of R more
    accessible to a large undergraduate population who are mostly not statistics
    majors.
The advanced version of package 's2dverification'. It is 
    intended for 'seasonal to decadal' (s2d) climate forecast verification, but 
    it can also be used in other kinds of forecasts or general climate analysis.  
    This package is specially designed for the comparison between the experimental 
    and observational datasets. The functionality of the included functions covers 
    from data retrieval, data post-processing, skill scores against observation, 
    to visualization. Compared to 's2dverification', 's2dv' is more compatible 
    with the package 'startR', able to use multiple cores for computation and 
    handle multi-dimensional arrays with a higher flexibility.
Set of tools to verify forecasts through the computation of typical
    prediction scores against one or more observational datasets or reanalyses (a
    reanalysis being a physical extrapolation of observations that relies on the
    equations from a model, not a pure observational dataset). Intended for seasonal
    to decadal climate forecasts although can be useful to verify other kinds of
    forecasts. The package can be helpful in climate sciences for other purposes
    than forecasting. To find more details, see the review paper Manubens, N.et al. (2018)
    <doi:10.1016/j.envsoft.2018.01.018>.
Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models.  We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.
Fit a spatial instrumental-variable regression by two-stage least squares.
The main function s4vd() performs a biclustering via sparse
        singular value decomposition with a nested stability selection.
        The results is an biclust object and thus all methods of the
        biclust package can be applied.
Perform joint segmentation on two signal dimensions derived from 
    total read depth (intensity) and allele specific read depth (intensity) for 
    whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data.
Implements a new approach 'SABARSI' described in Wang et al., "A Statistical Approach of Background Removal and Spectrum Identification for SERS Data" (Unpublished). 
	Sabarsi forms a pipeline for SERS (surface-enhanced Raman scattering) data analysis including background removal, signal detection, signal integration, and cross-experiment comparison. The background removal algorithm, the very first step of SERS data analysis, takes into account the change of background shape.
Calculates a degree of spatial association between regionalizations 
    or categorical maps using the information-theoretical V-measure 
    (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>). It also
    offers an R implementation of the MapCurve method 
    (Hargrove et al. (2006) <doi:10.1007/s10109-006-0025-x>).
Semiparametric empirical likelihood ratio
    based tests of change-point with one-change or epidemic alternatives
    with data-based model diagnostic are contained.
Computes the Exposure-At-Default based on  the standardized approach
    of CRR2 (SA-CCR). The simplified version of SA-CCR has been included, as well as the OEM methodology.
	Multiple trade types of all the five major asset classes are being supported including the 'Other' Exposure and, given the inheritance-
    based structure of the application, the addition of further trade types
    is straightforward. The application returns a list of trees per Counterparty and CSA after
    automatically separating the trades based on the Counterparty, the CSAs, the hedging sets, the
    netting sets and the risk factors. The basis and volatility transactions are
    also identified and treated in specific hedging sets whereby the corresponding 
    penalty factors are applied. All the examples appearing on the
    regulatory papers (both for the margined and the un-margined workflow) have been
    implemented including the latest CRR2 developments.
Performs surrogate-assisted optimization for expensive black-box constrained problems.
Implementation of the wavelet-based spatial verification method of Buschow and Friederichs "SAD: Verifying the Scale, Anisotropy and Direction of precipitation forecasts" (2020, submitted to QJRMS). Forecasts and Observations are transformed by a decimated or redundant dual-tree complex wavelet transform to analyze the spatial scale, degree of anisotropy and preferred direction in each field. These structural attributes are compared by a series of scores. An experimental algorithm for the correction of these errors is included as well.
We analyzed the nucleotide composition of genes with a special emphasis on stability of DNA sequences. Besides, in a variety of different organisms unequal use of synonymous codons, or codon usage bias, occurs which also show variation among genes in the same genome. Seemingly, codon usage bias is affected by both selective constraints and mutation bias which allows and enables us to examine and detect changes in these two evolutionary forces between genomes or along one genome. Therefore, we determined the codon adaptation index (CAI), effective number of codons (ENC) and codon usage analysis with calculation of the relative synonymous codon usage (RSCU), and subsequently predicted the translation efficiency and accuracy through GC-rich codon usages. Furthermore, we estimated the relative stability of the DNA sequence following calculation of the average free energy (Delta G) and Dimer base-stacking energy level.
Computes the probability of a set of species abundances of a single or multiple samples of individuals with one or more guilds under a mainland-island model. One must specify the mainland (metacommunity) model and the island (local) community model. It assumes that species fluctuate independently. The package also contains functions to simulate under this model. See Haegeman, B. & R.S. Etienne (2017). A general sampling formula for community structure data. Methods in Ecology & Evolution 8: 1506-1519 <doi:10.1111/2041-210X.12807>.
Provides the density, distribution, quantile and generation functions of some obscure probability 
    distributions, including the doubly non-central t, F, Beta, and Eta distributions; 
    the lambda-prime and K-prime; the upsilon distribution; the (weighted) sum of 
    non-central chi-squares to a power; the (weighted) sum of log non-central chi-squares;
    the product of non-central chi-squares to powers; the product of doubly non-central
    F variables; the product of independent normals.
Maximum likelihood tools to fit and compare models of species
    abundance distributions and of species rank-abundance distributions.
Select best combination of auxiliary variables with certain criterion.
Provides small area estimation for count data type and gives option whether to use covariates in the estimation or not. By implementing Empirical Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean squared error (MSE) estimators for each area. The EB estimators without covariates are obtained using the model proposed by Clayton & Kaldor (1987) <doi:10.2307/2532003>, the EB estimators with covariates are obtained using the model proposed by Wakefield (2006) <doi:10.1093/biostatistics/kxl008> and the MSE estimators are obtained using Jackknife method by Jiang et. al. (2002) <doi:10.1214/aos/1043351257>.
Provides several functions for area level of small area estimation using hierarchical Bayesian (HB) method with several univariate distributions for variable of interest. The dataset that used in every function is generated accordingly in the Example. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.
We designed this package to provide function for area level of Small Area Estimation using Hierarchical Bayesian (HB) method under Generalized Poisson Distribution. This package provides model using Univariate Generalized Poisson Distribution for variable of interest. Some datasets simulated by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Wang (2021) <doi:10.1016/j.ecoinf.2021.101301> and Ntzoufras (2009) <doi:10.1002/9780470434567>.
We design this package to provide a function for area level of small area estimation using Hierarchical Bayesian (HB) method under Hurdle Negative Binomial Distribution. This package provides model using Univariate Hurdle Negative Binomial Distribution for variable of interest. This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the Hierarchical Bayes estimators which include the mean and the variation of mean. For references, see Hilbe (2011) <doi:10.1017/CBO9780511973420> and Rao (2015) <doi:10.1002/9781118735855>.
Implementation of small area estimation using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error. The 'rjags' package is employed to obtain parameter estimates. For the references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).
We designed this package to provide several functions for area level of small area estimation using hierarchical Bayesian (HB) method. This package provides model using panel data for variable interest.This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015).
Provides several functions and datasets for area level of Small Area Estimation under Spatial SAR Model using Hierarchical Bayesian (HB) Method.
We designed this package to provides several functions for area and subarea level of small area estimation under Twofold Subarea Level Model using hierarchical Bayesian (HB) method with Univariate Normal distribution for variables of interest. Some dataset simulated by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates using Gibbs Sampling algorithm. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Torabi and Rao (2014) <doi:10.1016/j.jmva.2014.02.001>, Leyla Mohadjer et al.(2007) <http://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000559.pdf>, and Erciulescu et al.(2019) <doi:10.1111/rssa.12390>.
Provides function for area level of small area estimation using hierarchical Bayesian (HB) method with Zero-Inflated Binomial (ZIB) distribution for variables of interest. Some dataset produced by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For references, see Wiley (2013) <doi:10.1111/cdoe.12044>.
We designed this package to provide a function for area level of small area estimation using Hierarchical Bayesian (HB) method under Zero Inflated Negative Binomial Distribution. This package provides model using Univariate Zero Inflated Negative Binomial Distribution for variable of interest. This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean, and the quantile. For the reference, see Rao,J.N.K & Molina (2015) <doi:10.1002/9781118735855>.
Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) <http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf>.
A set of functions and datasets implementation of small area estimation when auxiliary variable is measured with error. These functions provide a empirical best linear unbiased prediction (EBLUP) estimator and mean squared error (MSE) estimator of the EBLUP. These models were developed by Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>.
The 'saemix' package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.
We describe a new R package entitled 'saeMSPE' for the well-known Fay Herriot model and nested error regression model in small area estimation. Based on this package, it is possible to easily compute various common mean squared predictive error (MSPE) estimators, as well as several existing variance component predictors as a byproduct, for these two models.
Methods to fit robust alternatives to commonly used models used in
    Small Area Estimation. The methods here used are based on best linear
    unbiased predictions and linear mixed models. At this time available models
    include area level models incorporating spatial and temporal correlation in
    the random effects.
A complete set of functions to calculate several EBLUP (Empirical Best Linear Unbiased Predictor) estimators and their mean squared errors. All estimators are based on an area-level linear mixed model introduced by Rao and Yu in 1994 (see documentation). The REML method is used for fitting this model.
Tools for the simulation of data in the context of small area
    estimation. Combine all steps of your simulation - from data generation
    over drawing samples to model fitting - in one object. This enables easy
    modification and combination of different scenarios. You can store your
    results in a folder or start the simulation in parallel.
Allows users to produce diagnostic procedures and graphic tools for the evaluation of Small Area estimators.
Provides functionality for image processing and shape analysis in 
    the context of reconstructed medical images generated by deep learning-based
    methods or standard image processing algorithms and produced from different
    medical imaging types, such as X-ray, Computational Tomography (CT),
    Magnetic Resonance Imaging (MRI), and pathology imaging. Specifically,
    offers tools to segment regions of interest and to extract quantitative
    shape descriptors for applications in signal processing,
    statistical analysis and modeling, and machine learning.
The aim of the package is to provide some basic functions
        for doing statistics with one dimensional Fuzzy Data (in the
        form of polygonal fuzzy numbers). In particular, the package
        contains functions for the basic operations on the class of
        fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference) 
        as well as for calculating (Bertoluzza) distance and sample variance. 
        Moreover a function to simulate fuzzy random variables and bootstrap tests 
        for the equality of means is included. Version 2.1 fixes some bugs
        of previous versions.
Overloads the glm function in the stats package so that
  a test for the existence of the maximum likelihood estimate is included
  in the fitting procedure for binary regression models.
The goal of 'safejoin' is to guarantee that when performing joins extra rows are not added to your data. 'safejoin' provides a wrapper around 'dplyr::left_join' that will raise an error when extra rows are unexpectedly added to your data. This can be useful when working with data where you expect there to be a many to one relationship but you are not certain the relationship holds.
A consistent interface to encrypt and decrypt strings, R objects and files using symmetric and asymmetric key encryption.
Functions to design and apply tests that are anytime valid. The
  functions can be used to design hypothesis tests in the prospective/randomised
  control trial setting or in the observational/retrospective setting. The
  resulting tests remain valid under both optional stopping and optional
  continuation. The current version includes safe t-tests and safe tests of
  two proportions. For details on the theory of safe tests, see
  Grunwald, de Heide and Koolen (2019) "Safe Testing" <arXiv:1906.07801>, 
  for details on safe logrank tests see ter Schure, Perez-Ortiz, Ly and Grunwald
  (2020) "The Safe Logrank Test: Error Control under Continuous Monitoring with 
  Unlimited Horizon" <arXiv:2011.06931v3> and Turner, Ly and Grunwald (2021)
  "Safe Tests and Always-Valid Confidence Intervals for contingency tables and 
  beyond" <arXiv:2106.02693> for details on safe contingency table tests.
Contains chart code for monitoring clinical trial safety. Charts can be used as standalone output, but are also designed for use with the 'safetyGraphics' package, which makes it easy to load data and customize the charts using an interactive web-based interface created with Shiny.
Example clinical trial data sets formatted for easy use in R.
A framework for evaluation of clinical trial safety. Users can interactively explore their data using the included 'Shiny' application.
Datasets and functions for the book "Statistiques pour l’économie et la gestion", "Théorie et applications en entreprise", F. Bertrand, Ch. Derquenne, G. Dufrénot, F. Jawadi and M. Maumy, C. Borsenberger editor, (2021, ISBN:9782807319448, De Boeck Supérieur, Louvain-la-Neuve). 
    The first chapter of the book is dedicated to an introduction to statistics and their world. 
    The second chapter deals with univariate exploratory statistics and graphics. 
    The third chapter deals with bivariate and multivariate exploratory statistics and graphics. 
    The fourth chapter is dedicated to data exploration with Principal Component Analysis. 
    The fifth chapter is dedicated to data exploration with Correspondance Analysis.
    The sixth chapter is dedicated to data exploration with Multiple Correspondance Analysis. 
    The seventh chapter is dedicated to data exploration with automatic clustering. 
    The eighth chapter is dedicated to an introduction to probability theory and classical probability distributions.
    The ninth chapter is dedicated to an estimation theory, one-sample and two-sample tests.
    The tenth chapter is dedicated to an Gaussian linear model.
    The eleventh chapter is dedicated to an introduction to time series.
    The twelfth chapter is dedicated to an introduction to probit and logit models.
    Various example datasets are shipped with the package as well as some new functions.
Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) <doi:10.1201/9780429446177>. It also provides some test data generation and plotting functionality to assist with this process.
Highest posterior model is widely accepted as a good model among available models. In terms of variable selection highest posterior model is often the true model. Our stochastic search process SAHPM based on simulated annealing maximization method tries to find the highest posterior model by maximizing the model space with respect to the posterior probabilities of the models. This package currently contains the SAHPM method only for linear models. The codes for GLM will be added in future.
Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.
Sparse additive interaction learning with the strong heredity property, i.e., 
    an interaction is selected only if its corresponding main effects are also included. 
    Fits a linear model with non-linear interactions via penalized maximum likelihood. 
    Interactions are limited to a single exposure or environment variable. For more information, 
    see the website below and the accompanying paper: Bhatnagar et al., "A sparse additive model for 
    high-dimensional interactions with an exposure variable", 2019, <DOI:10.1101/445304>.
A new diagram for the verification of vector variables (wind, current, etc) generated by multiple models against a set of observations is presented in this package. It has been designed as a generalization of the Taylor diagram to two dimensional quantities. It is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. The matrix is divided into the part corresponding to the relative rotation and the bias of the empirical orthogonal functions of the data. The full set of diagnostics produced by the analysis of the errors between model and observational vector datasets comprises the errors in the means, the analysis of the total variance of both datasets, the rotation matrix corresponding to the principal components in observation and model, the angle of rotation of model-derived empirical orthogonal functions respect to the ones from observations, the standard deviation of model and observations, the root mean squared error between both datasets and the squared two-dimensional correlation coefficient. See the output of function UVError() in this package.
A coordinate descent algorithm for computing the solution path of the sparse and coupled sparse asymmetric least squares, including the elastic net and (adaptive) Lasso penalized SALES and COSALES regressions.
Functions connecting to the 'Salesforce' Platform APIs (REST, SOAP, 
    Bulk 1.0, Bulk 2.0, Metadata, Reports and Dashboards) 
    <https://trailhead.salesforce.com/en/content/learn/modules/api_basics/api_basics_overview>. 
    "API" is an acronym for "application programming interface". Most all calls 
    from these APIs are supported as they use CSV, XML or JSON data that can be 
    parsed into R data structures. For more details please see the 'Salesforce' 
    API documentation and this package's website 
    <https://stevenmmortimer.github.io/salesforcer/> for more information, 
    documentation, and examples.
The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2021) <arXiv:2105.04451>.
The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC)
    sampling of random variables on a simplex. A Self-Adjusting Logit Transform
    (SALT) proposal is used so that sampling is still efficient even in difficult
    cases, such as those in high dimensions or with parameters that differ by orders
    of magnitude. Special care is also taken to maintain accuracy even when some
    coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are
    included in the package, enabling easy assessment of the convergence and mixing
    of the chain within the constrained space.
Take real or simulated data and salt it with errors commonly 
    found in the wild, such as pseudo-OCR errors, Unicode problems, numeric 
    fields with nonsensical punctuation, bad dates, etc.
Computationally efficient tools for high dimensional predictive
        modeling (regression and classification). SAM is short for sparse 
        additive modeling, and adopts the computationally efficient basis 
        spline technique. We solve  the optimization problems by various 
        computational algorithms including the block coordinate descent 
        algorithm, fast iterative soft-thresholding algorithm, and newton method. 
        The computation is further accelerated by warm-start and active-set tricks.
Health research using data from electronic health records (EHR) has gained
    popularity, but misclassification of EHR-derived disease status and lack of
    representativeness of the study sample can result in substantial bias in
    effect estimates and can impact power and type I error for association
    tests. Here, the assumed target of inference is the relationship between
    binary disease status and predictors modeled using a logistic regression
    model. 'SAMBA' implements several methods for obtaining bias-corrected
    point estimates along with valid standard errors as proposed in Beesley and
    Mukherjee (2020) <doi:10.1101/2019.12.26.19015859>, currently under review.
A collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) <doi:10.1155/2017/7847531>. The methods may be used for further purposes where weighting and generation of new observations is needed.
Implements functions for working with absorbing Markov chains. The
    implementation is based on the framework described in "Toward a unified
    framework for connectivity that disentangles movement and mortality in space
    and time" by Fletcher et al. (2019) <doi:10.1111/ele.13333>, which applies
    them to spatial ecology. This framework incorporates both resistance and 
    absorption with spatial absorbing Markov chains (SAMC) to provide several
    short-term and long-term predictions for metrics related to connectivity in 
    landscapes. Despite the ecological context of the framework, this package
    can be used in any application of absorbing Markov chains.
A novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data.
This program can be used to fit Gaussian linear mixed models (LMM). Univariate and multivariate response models, multiple variance components, as well as, certain correlation and covariance structures are supported. In many occasions, the user can pick one of the several mixed model fitting algorithms, which are explained further in the details section. Some algorithms are specific to certain types of models (univariate or multivariate, diagonal or non-diagonal residual, one or multiple variance components, etc,...).
In a clinical trial with repeated measures designs, outcomes are often taken from subjects at fixed time-points.  The focus of the trial may be to compare the mean outcome in two or more groups at some pre-specified time after enrollment. In the presence of missing data auxiliary assumptions are necessary to perform such comparisons.  One commonly employed assumption is the missing at random assumption (MAR).   The 'samon' package allows the user to perform a (parameterized) sensitivity analysis of this assumption.  In particular it can be used to examine the sensitivity of tests in the difference in outcomes to violations of the MAR assumption.  The sensitivity analysis can be performed under two scenarios, a) where the data exhibit a monotone missing data pattern (see the samon() function), and, b) where in addition to a monotone missing data pattern the data exhibit intermittent missing values (see the samonIM() function).
Computes the required sample size using the optimal designs with multiple constraints proposed in Mayo et al.(2010). This optimal method is designed for two-arm, randomized phase II clinical trials, and the required sample size can be optimized either using fixed or flexible randomization allocation ratios.
Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs.
A framework for running sets of MCMC samplers on sets of distributions with a variety of tuning parameters, along with plotting functions to visualize the results of those simulations.
Two-step
   and maximum likelihood estimation
   of Heckman-type sample selection models:
   standard sample selection models (Tobit-2),
   endogenous switching regression models (Tobit-5),
   sample selection models with binary dependent outcome variable,
   interval regression with sample selection (only ML estimation),
   and endogenous treatment effects models.
   These methods are described in the three vignettes
   that are included in this package 
   and in econometric textbooks such as
   Greene (2011, Econometric Analysis, 7th edition, Pearson).
Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test for categorical data. The t-test function allows paired and unpaired (balanced / unbalanced) designs as well as homogeneous and heterogeneous variances. The Wilcoxon function allows for ties.
There are four categories of Phase III clinical trials according to different research goals, including (1) Testing for equality, (2) Superiority trial, (3) Non-inferiority trial, and (4) Equivalence trial. This package aims to help researchers to calculate sample size when comparing means or proportions in Phase III clinical trials with different research goals.
Computes the required sample size for estimation of totals, means
    and proportions under complex sampling designs.
Calculates the power and sample size for Cochran-Mantel-Haenszel tests. 
    There are also several helper functions for working with probability,
    odds, relative risk, and odds ratio values.
To determine sample size or power for case-control studies to be analyzed using logistic regression.
A set of R functions for calculating sample size
        requirements using three different Bayesian criteria in the
        context of designing an experiment to estimate a normal mean or
        the difference between two normal means.  Functions for
        calculation of required sample sizes for the Average Length
        Criterion, the Average Coverage Criterion and the Worst Outcome
        Criterion in the context of normal means are provided.
        Functions for both the fully Bayesian and the mixed
        Bayesian/likelihood approaches are provided.
Affords researchers the ability to draw stratified samples from the U.S. Department of Veteran's Affairs/Department of Defense Identity Repository (VADIR) database according to a variety of population characteristics. The VADIR database contains information for all veterans who were separated from the military after 1980. The central utility of the present package is to integrate data cleaning and formatting for the VADIR database with the stratification methods described by Mahto (2019) <https://CRAN.R-project.org/package=splitstackshape>. Data from VADIR are not provided as part of this package.
Functions to draw random samples using different sampling schemes are available. Functions are also provided to obtain (generalized) calibration weights, different estimators, as well some variance estimators.
Select sampling methods for probability samples using large data sets.  This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer.
Sampling procedures from the book 'Stichproben - Methoden und praktische Umsetzung mit R' by Goeran Kauermann and Helmut Kuechenhoff (2010).
Package provides the possibility to sampling complete datasets 
  from a normal distribution to simulate cluster randomized trails for different study designs.
Functions to estimate from survey data. This package is a user-friendly wrapper of the samplingVarEst package. It considers that the user is more familiar with practical survey data rather than with research on survey sampling (variance estimation). More functionalities are on the way.
In the field of stratified sampling design, this package offers an approach for the determination of the best stratification of a sampling frame, the one that ensures the minimum sample cost under the condition to satisfy precision constraints in a multivariate and multidomain case. This approach is based on the use of the genetic algorithm: each solution (i.e. a particular partition in strata of the sampling frame) is considered as an individual in a population; the fitness of all individuals is evaluated applying the Bethel-Chromy algorithm to calculate the sampling size satisfying precision constraints on the target estimates. Functions in the package allows to: (a) analyse the obtained results of the optimisation step; (b) assign the new strata labels to the sampling frame; (c) select a sample from the new frame accordingly to the best allocation. Functions for the execution of the genetic algorithm are a modified version of the functions in the 'genalg' package. M.Ballin, G.Barcaroli (2020) <arXiv:2004.09366> "R package SamplingStrata: new developments and extension to Spatial Sampling".
Functions to calculate some point estimators and estimating their variance under unequal probability sampling without replacement. Single and two stage sampling designs are considered. Some approximations for the second order inclusion probabilities (joint inclusion probabilities) are available (sample and population based). A variety of Jackknife variance estimators are implemented. Almost every function is written in C (compiled) code for faster results. The functions incorporate some performance improvements for faster results with large datasets.
Estimation of the required sample size to validate a risk model for binary outcomes, based on the sample size equations proposed by Pavlou et al. (2021) <doi:10.1177/09622802211007522>. For precision-based sample size calculations, the user is required to enter the anticipated values of the C-statistic and outcome prevalence, which can be obtained from a previous study. The user also needs to specify the required precision (standard error) for the C-statistic, the calibration slope and the calibration in the large. The calculations are valid under the assumption of marginal normality for the distribution of the linear predictor.
Sampling surface simulation is useful in the comparison of different areal sampling methods
             in forestry, ecology and natural resources. The sampSurf package allows the simulation 
			 of numerous sampling methods for standing trees and downed woody debris in a spatial context.
			 It also provides an S4 class and method structure that facilitates the addition of new sampling
			 methods.
Significance Analysis of Microarrays for differential expression analysis, RNAseq data and related problems.
Markov chain Monte Carlo samplers for posterior simulations of conjugate Bayesian nonparametric
    mixture models. Functionality is provided for Gibbs sampling as in Algorithm 3 of Neal (2000)
    <DOI:10.1080/10618600.2000.10474879>, restricted Gibbs merge-split sampling as described in Jain & Neal
    (2004) <DOI:10.1198/1061860043001>, and sequentially-allocated merge-split sampling, as well as
    summary and utility functions.
Simulation tools for closed-loop simulation are provided for the 'MSEtool' operating model to inform data-rich fisheries. 
  'SAMtool' provides a conditioning model, assessment models of varying complexity with standardized reporting, 
  model-based management procedures, and diagnostic tools for evaluating assessments inside closed-loop simulation.
A sensitivity analysis approach for unmeasured confounding in observational data with multiple treatments and a binary outcome. This approach derives the general bias formula and provides adjusted causal effect estimates in response to various assumptions about the degree of unmeasured confounding. Nested multiple imputation is embedded within the Bayesian framework to integrate   uncertainty about the sensitivity parameters and sampling variability.  Bayesian Additive Regression Model (BART) is used for outcome modeling. The causal estimands are the conditional average treatment effects (CATE) based on the risk difference.  For more details, see paper: Hu L et al. (2020) A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data <arXiv:2012.06093>.
This package contains R functions to gauge the impact of unpublished studies upon the meta-analytic summary effect of a set of published studies. (Credits: The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 282574.)
Provides a variety of original and flexible user-friendly 
    statistical latent variable models and unsupervised learning algorithms to 
    segment and represent time-series data (univariate or multivariate), and 
    more generally, longitudinal data, which include regime changes. 
    'samurais' is built upon the following packages, each of them is an 
    autonomous time-series segmentation approach: Regression with Hidden 
    Logistic Process ('RHLP'), Hidden Markov Model Regression ('HMMR'), 
    Multivariate 'RHLP' ('MRHLP'), Multivariate 'HMMR' ('MHMMR'), Piece-Wise 
    regression ('PWR'). For the advantages/differences of each of them, the 
    user is referred to our mentioned paper references.
Data sets and code blocks for the book 'Statistical Analysis of 
  Network Data with R, 2nd Edition'.
A flexible framework for definition and application of time/depth-
    based rules for sets of parameters for single grains that can be used to 
    create artificial sediment profiles. Such profiles can be used for virtual 
    sample preparation and synthetic, for instance, luminescence measurements.
Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic 
             robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC)
	     covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC)
	     covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators);
	     clustered covariances (one-way and multi-way); panel and panel-corrected covariances;
	     outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are
	     applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted
	     to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>,
	     Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.
Spatial interpolation is a common practice in social and environmental science. This package enables the implementation of SSH-based spatial interpolation proposed by Wang et al. (2013) <doi:10.1068/a44710>. It provides functions to (1) evaluate stratification schemes, (2) interpolate sampling data over user-defined reporting units, (3) assess interpolation uncertainties, and (4) evaluate overall accuracy using the k-fold cross-validation estimate.
Downloads and tidies the San Francisco Public Utilities Commission Beach Water Quality Monitoring Program data. Data sets can be downloaded per beach, or the raw data can be downloaded. See <https://sfwater.org/cfapps/lims/beachmain1.cfm>.
Created for population health analytics and monitoring.
    The functions in this package work best when working with patient level Master Patient Index-like datasets .
    Built to be used by  NHS bodies and other health service providers.
Routines for solving large systems of linear equations in R.
  Direct and iterative solvers from the Eigen C++ library are made available.
  Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate
  Gradient, BiCGSTAB). Both dense and sparse problems are supported.
Recent gcc and clang compiler versions provide functionality to
 memory violations and other undefined behaviour; this is often referred to as
 "Address Sanitizer" (or SAN) and "Undefined Behaviour Sanitizer" (UBSAN).
 The Writing R Extension manual describes this in some detail in Section 4.9.

    This feature has to be enabled in the corresponding binary, eg in R, which
 is somewhat involved as it also required a current compiler toolchain which 
 is not yet widely available, or in the case of Windows, not available at all
 (via the common Rtools mechanism).

    As an alternative, the pre-built Docker containers available via the Docker Hub
 at https://registry.hub.docker.com/u/eddelbuettel/docker-debian-r/ can be used
 on Linux, and via boot2docker on Windows and OS X.

    This package then provides a means of testing the compiler setup as the
 known code failures provides in the sample code here should be detected
 correctly, whereas a default build of R will let the package pass.

    The code samples are based on the examples from the Address Sanitizer
 Wiki at https://code.google.com/p/address-sanitizer/wiki/AddressSanitizer.
During the preparation of data set(s) one usually performs
    some sanity checks. The idea is that irrespective of where the
    checks are performed, they are centralized by this package in order
    to list all at once with examples if a check failed.
Plots that illustrate the flow of information or material.
By binding R functions and the 'Highcharts' <http://www.highcharts.com/> charting library, 'sankeywheel' package provides a simple way to draw dependency wheels and sankey diagrams.
There are several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.
A graphical and automated pipeline for the analysis 
		of short time-series in R ('santaR'). This approach is designed to accommodate asynchronous 
		time sampling (i.e. different time points for different individuals), 
		inter-individual variability, noisy measurements and large numbers of variables. 
		Based on a smoothing splines functional model, 'santaR' is able to detect variables
		highlighting significantly different temporal trajectories between study groups.
		Designed initially for metabolic phenotyping, 'santaR' is also suited for other Systems Biology 
		disciplines. Command line and graphical analysis (via a 'shiny' application) enable fast and
		parallel automated analysis and reporting, intuitive visualisation and comprehensive plotting
		options for non-specialist users.
A tool for cutting data into intervals. Allows singleton intervals.
  Always includes the whole range of data by default. Flexible labelling. 
  Convenience functions for cutting by quantiles etc. Handles dates, times, units
  and other vectors.
Inspired by the art and color research of Sanzo Wada (1883-1967), 
    his "Dictionary Of Color Combinations" (2011, ISBN:978-4861522475), and the 
    interactive site by Dain M. Blodorn Kim <https://github.com/dblodorn/sanzo-wada>, 
    this package brings Wada's color combinations to R for easy use in data 
    visualizations. This package honors 60 of Wada's color combinations: 
    20 duos, 20 trios, and 20 quads.
This analytic is an in initial foray into sentiment analysis.  
    This analytic will allow a user to access the Twitter API (once they create 
    their own developer account), ingest tweets of their interest, clean / tidy 
    data, perform topic modeling if interested, compute sentiment scores 
    utilizing the Bing Lexicon, and output visualizations.
Implementation of SAPEVO-M, a Group Ordinal Method for Multiple Criteria Decision-Making (MCDM). SAPEVO-M is an acronym for Simple Aggregation of Preferences Expressed by Ordinal Vectors Group Decision Making. This method provides alternatives ranking given decision makers' preferences: criteria preferences and alternatives preferences for each criterion.This method is described in Gomes et al. (2020) <doi: 10.1590/0101-7438.2020.040.00226524 >.
Access, modify, aggregate and plot data from the 'Sapfluxnet' project
  (<http://sapfluxnet.creaf.cat>), the first global database of sap flow measurements.
Functions for statistical analysis of point processes.
A collection of tools to access prepared air quality monitoring
    data files from web servers with ease and speed. Air quality data are 
    sourced from open and publicly accessible repositories and can be found in 
    these locations: 
    <https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8> 
    and <https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm>. The web 
    server space has been provided by Ricardo Energy & Environment.
'Smart Adaptive Recommendations' (SAR) is the name of a fast, scalable, adaptive algorithm for personalized recommendations based on user transactions and item descriptions. It produces easily explainable/interpretable recommendations and handles "cold item" and "semi-cold user" scenarios. This package provides two implementations of 'SAR': a standalone implementation, and an interface to a web service in Microsoft's 'Azure' cloud: <https://github.com/Microsoft/Product-Recommendations/blob/master/doc/sar.md>. The former allows fast and easy experimentation, and the latter provides robust scalability and extra features for production use.
A Graphical user interface to calculate the rainfall-runoff relation using the Natural Resources Conservation Service - Curve Number method (NRCS-CN method) but include modifications by Hawkins et al., (2002) about the Initial Abstraction. This GUI follows the programming logic of a previously published software (Hernandez-Guzman et al., 2011)<doi:10.1016/j.envsoft.2011.07.006>. It is a raster-based GIS tool that outputs runoff estimates from Land use/land cover and hydrologic soil group maps.
    This package has already been published in Journal of Hydroinformatics (Hernandez-Guzman et al., 2021)<doi:10.2166/hydro.2020.087> but it is under constant development at the Institute about Natural Resources Research (INIRENA) from the Universidad Michoacana de San Nicolas de Hidalgo and represents a collaborative effort between the Hydro-Geomatic Lab (INIRENA) with the Environmental Management Lab (CIAD, A.C.).
Functions, classes and methods for time series modelling with ARIMA
    and related models. The aim of the package is to provide consistent
    interface for the user. For example, a single function autocorrelations()
    computes various kinds of theoretical and sample autocorrelations. This is
    work in progress, see the documentation and vignettes for the current
    functionality.  Function sarima() fits extended multiplicative seasonal
    ARIMA models with trends, exogenous variables and arbitrary roots on the
    unit circle, which can be fixed or estimated.
Provides a set of functions to interpret changes in
 compositional data based on a network representation of all pairwise ratio
 comparisons: computation of all pairwise ratio, construction of a
 p-value matrix of all pairwise tests of these ratios between
 conditions, conversion of this matrix to a network.
Provides a set of basic functions for creating Moodle XML
 output files suited for importing questions in Moodle (a learning
 management system, see <https://moodle.org/> for more information).
Analysis and plotting tools for snow profile data produced from manual snowpack 
  observations and physical snowpack models. The functions in this package support snowpack 
  and avalanche research by reading various formats of data (including CAAML, SMET,
  generic csv, and outputs from the snow cover model SNOWPACK), manipulate the data, and 
  produce graphics such as stratigraphy and time series profiles. Package developed by 
  the Simon Fraser University Avalanche Research Program <http://www.avalancheresearch.ca>. 
  Graphics apply visualization concepts from Horton, Nowak, and Haegeli (2020, 
  <doi:10.5194/nhess-20-1557-2020>).
Snow profiles describe the vertical (1D) stratigraphy of layered 
    snow with different layer characteristics, such as grain type, hardness, 
    deposition date, and many more. Hence, they represent a data format similar 
    to multivariate time series containing categorical, ordinal, and numerical 
    data types. Use this package to align snow profiles by matching their 
    individual layers based on Dynamic Time Warping (DTW). The aligned profiles 
    can then be assessed with an independent, global similarity measure that is 
    geared towards avalanche hazard assessment. Finally, through exploiting data
    aggregation and clustering methods, the similarity measure provides the
    foundation for grouping and summarizing snow profiles according to similar
    hazard conditions. In particular, this package allows for averaging large
    numbers of snow profiles with DTW Barycenter Averaging and thereby 
    facilitates the computation of individual layer distributions and summary 
    statistics that are relevant for avalanche forecasting purposes. 
    For more background information refer to Herla, Horton, Mair,
    and Haegeli (2021) <doi:10.5194/gmd-14-239-2021>, and Herla, Mair, and Haegeli 
    (2022, in review) <doi:10.5194/tc-2022-29>.
Implements the basic elements of the multi-model
    inference paradigm for up to twenty species-area relationship models (SAR), using simple
    R list-objects and functions, as in Triantis et al. 2012 <DOI:10.1111/j.1365-2699.2011.02652.x>.
    The package is scalable and users can easily create their own model and data objects. Additional
    SAR related functions are provided.
A toolkit for Partially Observed Markov Decision Processes (POMDP). Provides
    bindings to C++ libraries implementing the algorithm SARSOP (Successive Approximations
    of the Reachable Space under Optimal Policies) and described in Kurniawati et al (2008),
    <doi:10.15607/RSS.2008.IV.009>.  This package also provides a high-level interface
    for generating, solving and simulating POMDP problems and their solutions.
Read SAS files in the sas7bdat data format.
Using any importation code designed for SAS users to read
        ASCII files into sas7bdat files, the SAScii package parses
        through the INPUT block of a (.sas) syntax file to design the
        parameters needed for a read.fwf function call.  This allows
        the user to specify the location of the ASCII (often a .dat)
        file and the location of the .sas syntax file, and then load
        the data frame directly into R in just one step.
Implements the sparse and smooth functional clustering (SaS-Funclust) method (Centofanti et al. (2021) <arXiv:2103.15224>) that aims to classify a sample of curves into homogeneous groups while jointly detecting the most informative portions of domain.
This is a core implementation of 'SAS' procedures for linear models - GLM, REG, and ANOVA. Some R packages provide type II and type III SS. However, the results of nested and complex designs are often different from those of 'SAS.' Different results does not necessarily mean incorrectness. However, many wants the same results to SAS. This package aims to achieve that. 
             Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).
A static code analysis tool for 'SAS' scripts. It is designed to load, count, extract, remove, and summarise components of 'SAS' code.
Settings and functions to extend the 'knitr' 'SAS' engine.
Data sets and sample lmer analyses corresponding
  to the examples in Littell, Milliken, Stroup and Wolfinger
  (1996), "SAS System for Mixed Models", SAS Institute.
An 'SCSS' compiler, powered by the 'LibSass' library. With this,
    R developers can use variables, inheritance, and functions to generate
    dynamic style sheets. The package uses the 'Sass CSS' extension language,
    which is stable, powerful, and CSS compatible.
A meta-package that aims to make 'R' easier for 'SAS®' programmers.
    This set of packages brings many familiar concepts to 'R', including
    data libraries, data dictionaries, formats 
    and format catalogs, a data step, and a traceable log.  The 'flagship'
    package is a reporting package that can output in text, rich text, 'PDF' and 
    'HTML' file formats.
Bundles functions used to analyze the harmfulness of trial errors in criminal trials.
    Functions in the Scientific Analysis of Trial Errors ('SATE') package help users estimate the 
    probability that a jury will find a defendant guilty given jurors' preferences for a guilty 
    verdict and the uncertainty of that estimate. Users can also compare actual and hypothetical 
    trial conditions to conduct harmful error analysis. The relationship between individual jurors' 
    verdict preferences and the probability that a jury returns a guilty verdict has been studied 
    by Davis (1973) <doi:10.1037/h0033951>; MacCoun & Kerr (1988) <doi:10.1037/0022-3514.54.1.21>, 
    and Devine et el. (2001) <doi:10.1037/1076-8971.7.3.622>, among others.
Herein, we provide a broad variety of functions which are useful
    for handling, manipulating, and visualizing satellite-based remote sensing 
    data. These operations range from mere data import and layer handling (eg 
    subsetting), over Raster* typical data wrangling (eg crop, extend), to more 
    sophisticated (pre-)processing tasks typically applied to satellite imagery 
    (eg atmospheric and topographic correction). This functionality is 
    complemented by a full access to the satellite layers' metadata at any 
    stage and the documentation of performed actions in a separate log file. 
    Currently available sensors include Landsat 4-5 (TM), 7 (ETM+), and 8 
    (OLI/TIRS Combined), and additional compatibility is ensured for the Landsat 
    Global Land Survey data set.
With 'satin' functions, visualisation, data extraction and further analysis like producing climatologies from several images, and anomalies of satellite derived ocean data can be easily done.  Reading functions can import a user defined geographical extent of data stored in netCDF files.  Currently supported ocean data sources include NASA's Oceancolor web page <https://oceancolor.gsfc.nasa.gov/>, sensors VIIRS-SNPP; MODIS-Terra; MODIS-Aqua; and SeaWiFS.  Available variables from this source includes chlorophyll concentration, sea surface temperature (SST), and several others.  Data sources specific for SST that can be imported too includes Pathfinder AVHRR <https://www.ncei.noaa.gov/products/avhrr-pathfinder-sst> and GHRSST <https://www.ghrsst.org/>.  In addition, ocean productivity data produced by Oregon State University <http://sites.science.oregonstate.edu/ocean.productivity/> can also be handled previous conversion from HDF4 to HDF5 format.  Many other ocean variables can be processed by importing netCDF data files from two European Union's Copernicus Marine Service databases <https://marine.copernicus.eu/>, namely Global Ocean Physical Reanalysis and Global Ocean Biogeochemistry Hindcast.
Supports the generation of maps based on the results from 
     'SaTScan' (TM) cluster analysis.
     The package handles mapping of Spatial and Spatial-Time analysis using
     the discrete Poisson, Bernoulli, and exponential models of case data generating
     cluster and location ('GIS') records containing observed, expected and observed/expected
     ratio for U. S. states (and DC), counties or census tracts of individual 
     states based on the U. S. 'FIPS' codes for state, county and census tracts 
     (locations) using 2000 or 2010 Census areas, 'FIPS' codes, and boundary data.
     'satscanMapper' uses the 'SeerMapper' package for the boundary data and 
     mapping of locations.  Not all of the 'SaTScan' (TM) analysis and models generate
     the observed, expected and observed/expected ratio values for the clusters and 
     locations.
     The user can map the observed/expected ratios for locations 
     (states, counties, or census tracts) for each cluster with a p-value less than 0.05 
     or a user specified p-value.  
     The locations are categorized and colored based on either the cluster's Observed/Expected 
     ratio or the locations' Observed/Expected ratio. 
     The place names are provided for each census tract using data from 'NCI', the 'HUD' crossover 
     tables (Tract to Zip code) as of December, 2013, the USPS Zip code 5 database for 1999, 
     and manual look ups on the USPS.gov web site.
Bayesian inference of graphical model structures using spanning trees.
Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.
An implementation of a regularized regression prediction and 
    empirical Bayes method to recover the true gene expression profile in 
    noisy and sparse single-cell RNA-seq data. See Huang M, et al (2018) 
    <doi:10.1038/s41592-018-0033-z> for more details.
The purpose of this package is to be able to save and load only
    the needed variables/columns of a dataframe in special binary files (tar
    archives) - which seems to be a lot faster method than loading the whole
    binary object (RData files) via load() function, or than loading columns
    from SQLite/MySQL databases via SQL commands (see vignettes). Performance
    gain on SSD drives is a lot more sensible compared to basic load()
    function. The performance improvement gained by loading only the chosen
    variables in binary format can be useful in some special cases (e.g. where
    merging data tables is not an option and very different datasets are needed
    for reporting), but be sure if using this package that you really need
    this, as non-standard file formats are used!
Provides a toolbox that allows the user to implement accessibility related concepts.
The SAWNUTI algorithm performs sequence comparison for finite sequences of discrete events with non-uniform time intervals. Further description of the algorithm can be found in the paper: A. Murph, A. Flynt, B. R. King (2021). Comparing finite sequences of discrete events with non-uniform time intervals, Sequential Analysis, 40(3), 291-313. <doi:10.1080/07474946.2021.1940491>.
Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.
Spectral and Average Autocorrelation Zero Distance Density
    ('sazed') is a method for estimating the season length of a 
    seasonal time series. 'sazed' is aimed at practitioners, as it employs only 
    domain-agnostic preprocessing and does not depend on parameter tuning or 
    empirical constants. The computation of 'sazed' relies on the efficient 
    autocorrelation computation methods suggested by Thibauld Nion (2012, URL: 
    <https://etudes.tibonihoo.net/literate_musing/autocorrelations.html>) and by 
    Bob Carpenter (2012, URL: 
    <https://lingpipe-blog.com/2012/06/08/autocorrelation-fft-kiss-eigen/>).
Get the most appropriate autoregressive integrated moving average, generalized auto-regressive conditional heteroscedasticity and Markov switching GARCH model. For method details see Haas M, Mittnik S, Paolella MS (2004). <doi:10.1093/jjfinec/nbh020>, Bollerslev T (1986). <doi:10.1016/0304-4076(86)90063-1>.
Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. Parast, L and Griffin, BA (2020). "Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476 <doi: 10.1002/sim.8549>.
Smooth Backfitting for additive models using
        Nadaraya-Watson estimator
An MCMC algorithm for simultaneous feature selection and classification, 
    and visualization of the selected features and feature interactions. 
    An implementation of SBFC by Krakovna, Du and Liu (2015), <arXiv:1506.02371>.
Estimation and inference for parameters in a Gaussian copula model,
        treating the univariate marginal distributions as nuisance
        parameters as described in Hoff (2007) <doi:10.1214/07-AOAS107>. 
        This package also provides a
        semiparametric imputation procedure for missing multivariate
        data.
Computes the sBIC for various singular model collections including:
    binomial mixtures, factor analysis models, Gaussian mixtures,
    latent forests, latent class analyses, and reduced rank regressions.
This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review).
   In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. 
   Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.
sbioPN is a package suited to perform simulation of deterministic and stochastic systems of biochemical reaction
  networks with spatial effects.
  Models are defined using a subset of Petri Nets, in a way that is close at how chemical reactions are defined.
  For deterministic solutions, sbioPN creates the associated system of differential equations "on the fly", and
  solves it with a Runge Kutta Dormand Prince 45 explicit algorithm.
  For stochastic solutions, sbioPN offers two variants of Gillespie algorithm, or SSA.
  For hybrid deterministic/stochastic,
  it employs the Haseltine and Rawlings algorithm, that partitions the system in fast and slow reactions.
  sbioPN algorithms are developed in C to achieve adequate performance.
Implements sparse Bayesian learning method for QTL mapping and genome-wide association studies.
A collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). 
  Supports at the moment Simple, Bipartite, 'Multipartite' and Multiplex SBM (undirected or directed with Bernoulli,
  Poisson or Gaussian emission laws on the edges, and possibly covariate for Simple and Bipartite SBM).
  See Léger (2016) <arxiv:1602.07587>, 'Barbillon et al.' (2020) <doi:10.1111/rssa.12193> and 
  'Bar-Hen et al.' (2020) <arxiv:1807.10138>.
Median-of-means is a generic yet powerful framework for scalable and robust estimation. A framework for Bayesian analysis is called M-posterior, which estimates a median of subset posterior measures. For general exposition to the topic, see the paper by Minsker (2015) <doi:10.3150/14-BEJ645>.
An ADMM implementation of SDP-1, a semidefinite programming relaxation of the maximum likelihood estimator for fitting a block model. SDP-1 has a tendency to produce equal-sized blocks and is ideal for producing a form of network histogram approximating a nonparametric graphon model. Alternatively, it can be used for community detection. (This is experimental code, proceed with caution.)
Inference in a Bayesian framework for a generalised stochastic block model. The generalised stochastic block model (SBM) can capture group structure in network data without requiring conjugate priors on the edge-states. Two sampling methods are provided to perform inference on edge parameters and block structure: a split-merge Markov chain Monte Carlo algorithm and a Dirichlet process sampler. Green, Richardson (2001) <doi:10.1111/1467-9469.00242>; Neal (2000) <doi:10.1080/10618600.2000.10474879>; Ludkin (2019) <arXiv:1909.09421>.
Generate Stochastic Branching Networks ('SBNs'). Used to model the branching structure of rivers.
Utilities for training and evaluating text predictors based on Stupid Back-Off N-gram models (Brants et al., 2007, <https://www.aclweb.org/anthology/D07-1090/>).
Creates classifier for binary outcomes using Adaptive Boosting 
    (AdaBoost) algorithm on decision stumps with a fast C++ implementation. 
    For a description of AdaBoost, see Freund and Schapire (1997) 
    <doi:10.1006/jcss.1997.1504>. This type of classifier is nonlinear, but
    easy to interpret and visualize. Feature vectors may be a combination of
    continuous (numeric) and categorical (string, factor) elements. Methods 
    for classifier assessment, predictions, and cross-validation also included.
The package uses fitting axes-aligned rectangles to a time series in order to find structural breaks. The algorithm enclose the time series in a number of axes-aligned rectangles and tries to minimize their area and number. As these are conflicting aims, the user has to specify a parameter alpha in [0.0,1.0]. Values close to 0 result in more breakpoints, values close to 1 in fewer. The left edges of the rectangles are the breakpoints. The package supplies two  methods, computeBreakPoints(series,alpha) which returns the indices of the break points and computeRectangles(series,alpha) which returns the rectangles. The algorithm is randomised; it uses a genetic algorithm. Therefore, the break point sequence found can be different in different executions of the method on the same data, especially when used on  longer series of some thousand observations. The algorithm uses a range-tree as background data structure which makes i very fast and suited to analyse series with millions of observations. A detailed description can be found in Paul Fischer, Astrid Hilbert, Fast detection  of structural breaks, Proceedings of Compstat 2014.
Calculates a Satorra-Bentler scaled chi-squared difference test between nested models that were estimated using maximum likelihood (ML) with robust standard errors, which cannot be calculated the traditional way. For details see Satorra & Bentler (2001) <doi:10.1007/bf02296192> and Satorra & Bentler (2010) <doi:10.1007/s11336-009-9135-y>. This package may be particularly helpful when used in conjunction with 'Mplus' software, specifically when implementing the complex survey option. In such cases, the model estimator in 'Mplus' defaults to ML with robust standard errors.
Tools for interacting with U.S. Geological Survey ScienceBase
    <https://www.sciencebase.gov> interfaces. ScienceBase is a data cataloging and
    collaborative data management platform. Functions included for querying
    ScienceBase, and creating and fetching datasets.
Implements the Stable Balancing Weights by Zubizarreta (2015) <DOI:10.1080/01621459.2015.1023805>. These are the weights of minimum variance that approximately balance the empirical distribution of the observed covariates. For an overview, see Chattopadhyay, Hase and Zubizarreta (2020) <DOI:10.1002/(ISSN)1097-0258>. To solve the optimization problem in 'sbw', the default solver is 'quadprog', which is readily available through CRAN. The solver 'osqp' is also posted on CRAN. To enhance the performance of 'sbw', users are encouraged to install other solvers such as 'gurobi' and 'Rmosek', which require special installation. For the installation of gurobi and pogs, please follow the instructions at <https://www.gurobi.com/documentation/9.1/quickstart_mac/r_ins_the_r_package.html> and <http://foges.github.io/pogs/stp/r>.
Spatial clustering with hidden markov random field fitted via EM algorithm, details of which can be found in Yi Yang (2021) <doi:10.1101/2021.06.05.447181>. It is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well.
A wrapper for Blizzard's Starcraft II (a 2010 real-time strategy game) Application Programming Interface (API). All documented API calls are implemented in an easy-to-use and consistent manner.
Comprehensive set of tools for scaffolding R
  interfaces to modules, classes, functions, and documentations
  written in other programming languages, such as 'Python'.
Calculates graph theoretic scagnostics. Scagnostics describe various measures of interest for pairs of variables, based on their appearance on a scatterplot.  They are useful tool for discovering interesting or unusual scatterplots from a scatterplot matrix, without having to look at every individual plot.
Scale alignment is a new procedure for rescaling dimensions of 
    between-items multidimensional Rasch family models so that dimensions scores
    can be compared directly (Feuerstahler & Wilson, 2019; under review) 
	<doi:10.1111/jedm.12209>. This package includes functions for implementing 
    delta-dimensional alignment (DDA) and logistic regression alignment (LRA) 
    for dichotomous or polytomous data. This function also includes a wrapper 
	for models fit using the 'TAM' package.
Calculating approximately unbiased (AU) p-values
	from multiscale bootstrap probabilities. See
	Shimodaira (2004) <doi:10.1214/009053604000000823>,
	Shimodaira (2008) <doi:10.1016/j.jspi.2007.04.001>, 
	Terada ans Shimodaira (2017) <arXiv:1711.00949>, and
	Shimodaira and Terada (2019) <doi.org/10.3389/fevo.2019.00174>.
Perform a 'probabilistic' linkage of two data files using a scaling procedure using the methods described in Goldstein, H., Harron, K. and Cortina-Borja, M. (2017) <doi:10.1002/sim.7287>.
Graphical scales map data to aesthetics, and
    provide methods for automatically determining breaks and labels for
    axes and legends.
Identifies the locations of neurons, and estimates their calcium concentrations over time using the SCALPEL method proposed in Petersen, Ashley; Simon, Noah; Witten, Daniela. SCALPEL: Extracting neurons from calcium imaging data. Ann. Appl. Stat. 12 (2018), no. 4, 2430--2456. <doi:10.1214/18-AOAS1159>. <https://projecteuclid.org/euclid.aoas/1542078051>.
Algorithms for fitting scaled sparse linear regression and estimating precision matrices.
Routines for generalized additive modelling under shape
        constraints on the component functions of the linear predictor
        (Pya and Wood, 2015) <doi:10.1007/s11222-013-9448-7>.
        Models can contain multiple shape constrained (univariate
        and/or bivariate) and unconstrained terms. The routines of gam() 
        in package 'mgcv' are used for setting up the model matrix,  
        printing and plotting the results.  Penalized likelihood
        maximization based on Newton-Raphson method is used to fit a
        model with multiple smoothing parameter selection by GCV or
        UBRE/AIC.
A collection of procedures for analysing, visualising, 
  and managing single-case data. These include piecewise linear regression 
  models, multilevel models, overlap indices (PND, PEM, PAND, PET, tauU, 
  baseline corrected tau, CDC), and randomization tests. Data preparation functions 
  support outlier detection, handling missing values, scaling, truncating, 
  rank transformation, and smoothing. An exporting function helps to generate 
  html and latex tables in a publication friendly style. More details can be 
  found at <https://jazznbass.github.io/scan-Book/>.
Import, plot, and diagnose results from statistical
  catch-at-age models, used in fisheries stock assessment.
Series of algorithms to translate  users' mental models of seascapes, 
  landscapes and, more generally, of geographic features into computer representations 
  (classifications). Spaces and geographic objects are classified with user-defined 
  rules taking into account spatial data as well as spatial relationships among 
  different classes and objects.
This package computes the maximum likelihood estimator of the generalised additive and index regression with shape constraints. Each additive component function is assumed to obey one of the nine possible shape restrictions: linear, increasing, decreasing, convex, convex increasing, convex decreasing, concave, concave increasing, or concave decreasing.
A port of the Scarabee toolkit originally written as a 
  Matlab-based application. scaRabee provides a framework for simulation and optimization 
  of pharmacokinetic-pharmacodynamic models at the individual and population level.
  It is built on top of the neldermead package, which provides the direct search 
  algorithm proposed by Nelder and Mead for model optimization.
Conditional association test based on summary data from genome-wide association study (GWAS). SCAT adjusts for heterogeneity in SNP coverage that exists in summary data if SNPs are not present in all of the participating studies of a GWAS meta-analysis. This commonly happens when different reference panels are used in participating studies for genotype imputation. This could happen when ones simply do not have data for some SNPs (e.g. different array, or imputated data is not available). Without properly adjusting for this kind of heterogeneity leads to inflated false positive rate. SCAT can also be used to conduct conventional conditional analysis when coverage heterogeneity is absent. For more details, refer to Zhang et al. (2018) Brief Bioinform. 19(6):1337-1343. <doi: 10.1093/bib/bbx072>.
Allows you to make clean, good-looking scatter plots with the option to 
    easily add marginal density or box plots on the axes. It is also available as a module for 'jamovi'
    (see <https://www.jamovi.org> for more information). 'Scatr' is based on the 
    'cowplot' package by Claus O. Wilke and the 'ggplot2' package by Hadley Wickham.
Creates 'D3' 'JavaScript' scatterplots from 'R' with interactive
    features : panning, zooming, tooltips, etc.
C-based conversion of large scatterplot data to rasters. Speeds up
             plotting of data with millions of points.
Creates scatterpie plots, especially useful for plotting pies on a map.
Plots a three dimensional (3D) point cloud.
Create a scatter plot matrix, using 'htmlwidgets' package and 'd3.js'.
Identifies a bicluster, a submatrix of the data such that the features and observations within the submatrix differ from those not contained in submatrix, using a two-step method. In the first step, observations in the bicluster are  identified to maximize the sum of weighted between cluster feature differences. The observations are identified in a similar fashion as in Witten and Tibshirani (2010) <doi:10.1198/jasa.2010.tm09415> except with a modified objective function and no feature sparsity constraint. In the second step, features in the bicluster are identified based on their contribution to the clustering of the observations. The cluster significance test of Liu, Hayes, Nobel, and Marron (2008): <doi:10.1198/016214508000000454> can then be used to test the strength of the identified bicluster. 'SCBiclust' can be used to identify biclusters which differ based on feature means, feature variances, or more general differences.
Cellular population mapping (CPM) a deconvolution algorithm in which single-cell genomics is required in only one or a few samples, where in other samples of the same tissue, only bulk genomics is measured and the underlying fine resolution cellular heterogeneity is inferred.
Statistical methods for estimating and inferring the mean of functional data. The methods include simultaneous confidence bands, local polynomial fitting,  bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions.
Provides tools to import and export from several existing pieces of ion-channel analysis software such as 'TAC', 'QUB', 'SCAN', and 'Clampfit', implements procedures such as dwell-time correction and defining bursts with a critical time, and provides tools for analysis of bursts, such as tools for sorting and plotting.
A single-cell Clustering method using 'Autoencoder' and Network fusion ('scCAN') for segregating the cells from the high-dimensional 'scRNA-Seq' data. The software automatically determines the optimal number of clusters and then partitions the cells in a way such that the results are robust to noise and dropouts. 'scCAN' is fast and it supports Windows, Linux, and Mac OS.
An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) <doi:10.1016/j.isci.2020.100882> for more details.
An efficient implementation of SCCI using 'Rcpp'. SCCI is short for the Stochastic Complexity-based Conditional Independence criterium (Marx and Vreeken, 2019). SCCI is an asymptotically unbiased and L2 consistent estimator of (conditional) mutual information for discrete data.
Provides wrappers for 'scclust', a C library for computationally efficient
    size-constrained clustering with near-optimal performance.
    See <https://github.com/fsavje/scclust> for more information.
Core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with differential expression (DE) matrices and count matrices, a collection of functions for manipulating and plotting data via 'ggplot2', and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP <doi:10.21105/joss.00861>, collapsing vertices of each cluster in the graph, and propagating graph labels.
The SC-SR Algorithm is used to calculate fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible making of the failure cause in a competing risks environment. In the version 2.0 the function creating the probability matrix from double-censored data is added.
Various self-controlled case series models used to investigate 
        associations between time-varying exposures such as vaccines or
        other drugs or non drug exposures and an adverse event can be
        fitted. Detailed information on the self-controlled case series
        method and its extensions with more examples can be found in 
        Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y.
        (2018, ISBN: 978-1-4987-8159-6. Self-controlled Case Series studies:
        A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press) 
        and <https://sccs-studies.info/index.html>.
Implements methods for obtaining kernel density estimates
    subject to a variety of shape constraints (unimodality, bimodality, 
    symmetry, tail monotonicity, bounds, and constraints on the number of 
    inflection points). Enforcing constraints can eliminate unwanted waves or 
    kinks in the estimate, which improves its subjective appearance and can 
    also improve statistical performance. The main function scdensity() is 
    very similar to the density() function in 'stats', allowing 
    shape-restricted estimates to be obtained with little effort. The 
    methods implemented in this package are described in Wolters and Braun 
    (2017) <doi:10.1080/03610918.2017.1288247>, Wolters (2012) 
    <doi:10.18637/jss.v047.i06>, and Hall and Huang (2002) 
    <http://www3.stat.sinica.edu.tw/statistica/j12n4/j12n41/j12n41.htm>.  
    See the scdensity() help for for full citations.
Provides a fast and accurate pipeline for single-cell analyses. 
    The 'scDHA' software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) <DOI:10.1038/s41467-021-21312-2>).
Provides a set of tools for estimating hierarchical linear
    models and effect sizes based on data from single-case designs. 
    Functions are provided for calculating standardized mean difference effect sizes that 
    are directly comparable to standardized mean differences estimated from between-subjects randomized experiments,
    as described in Hedges, Pustejovsky, and Shadish (2012) <DOI:10.1002/jrsm.1052>; 
    Hedges, Pustejovsky, and Shadish (2013) <DOI:10.1002/jrsm.1086>; and 
    Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>. 
    Includes an interactive web interface.
Analysis tools to investigate changes in intercellular
    communication from scRNA-seq data. Using a Seurat object as input,
    the package infers which cell-cell interactions are present in the dataset
    and how these interactions change between two conditions of interest
    (e.g. young vs old). It relies on an internal database of ligand-receptor
    interactions (available for both human and mouse) that have been gathered
    from several published studies. Detection and differential analyses
    rely on permutation tests. The package also contains several tools
    to perform over-representation analysis and visualize the results. See
    Lagger, C. et al. (2021) <doi:10.1101/2021.08.13.456238> for a full 
    description of the methodology.
Detection of item-wise Differential Item Functioning (DIF) 
             in fitted 'mirt', 'multipleGroup' or 'bfactor' models  
             using score-based structural change tests. Under the hood 
             the sctest() function from the 'strucchange' package is used.
We introduce improved methods for statistically assessing birth seasonality and intra-annual variation. The first method we propose is a new idea that uses a nonparametric clustering procedure to group individuals with similar time series data and estimate birth seasonality based on the clusters. One can use the function SCEM() to implement this method. The second method estimates input parameters for use with a previously-developed parametric approach (Tornero et al., 2013). The relevant code for this approach is makeFits_OLS(), while makeFits_initial() is the code to implement the same method but with given initial conditions for two parameters. The latter can be used to show the disadvantage of the existing approach. One can use the function makeFits() to generate parametric birth seasonality estimates using either initialization. Detailed description can be found here: Chazin Hannah, Soudeep Deb, Joshua Falk, and Arun Srinivasan. (2019) "New Statistical Approaches to Intra-Individual Isotopic Analysis and Modeling Birth Seasonality in Studies of Herd Animals." <doi:10.1111/arcm.12432>.
Uses the neural gas algorithm to construct
    a scenario tree for use in multi-stage stochastic programming.
    The primary input is a set of initial scenarios or realizations
    of a disturbance. The scenario tree nodal structure must be
    predefined using a scenario tree nodal partition matrix.
Analyse single cell RNA sequencing data using entropy to
    calculate heterogeneity and homogeneity of genes amongst the cell
    population. From the work of Michael J. Casey, Ruben J. Sanchez-Garcia 
    and Ben D. MacArthur.
A pipeline for estimating the stellar
        age, mass, and radius given observational 
        effective temperature, [Fe/H], and astroseismic
	parameters. The results are obtained adopting a maximum likelihood
	technique over a grid of pre-computed stellar models, as
	described in Valle et al. (2014) <doi:10.1051/0004-6361/201322210>.
SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models.
An extension of the Fisher Scoring Algorithm to combine PLS regression with GLM 
    estimation in the multivariate context. Covariates can also be grouped in themes.
Fast and regularized version of GWR for large dataset, detailed in Murakami, Tsutsumida, Yoshida, Nakaya, and Lu (2019) <arXiv:1905.00266>.
A fast and adaptable tool to convert photos and images into usable colour schemes for data visualisation. Contains functionality to extract colour palettes from images, as well for the conversion of images between colour spaces.
Functions for creating and manipulating 12-tone (i.e., dodecaphonic) musical matrices using Arnold Schoenberg's (1923) serialism technique. This package can generate random 12-tone matrices and can generate matrices using a pre-determined sequence of notes.
Provides functions to extract citation data from Google
    Scholar.  Convenience functions are also provided for comparing
    multiple scholars and predicting future h-index values.
This package contains functions and datasets for math
        taught in school. A main focus is set to prime-calculation
Offers a helping hand to psychologists and other behavioral scientists who routinely deal with experimental data from factorial experiments. It includes several functions to format output from other R functions according to the style guidelines of the APA (American Psychological Association). This formatted output can be copied directly into manuscripts to facilitate data reporting. These features are backed up by a toolkit of several small helper functions, e.g., offering out-of-the-box outlier removal. The package lends its name to Georg "Schorsch" Schuessler, ingenious technician at the Department of Psychology III, University of Wuerzburg. For details on the implemented methods, see Roland Pfister and Markus Janczyk (2016) <doi: 10.20982/tqmp.12.2.p147>.
The complete scripts from the American version of
    the Office television show in tibble format. Use this package to
    analyze and have fun with text from the best series of all time.
A collection of useful functions and example code created and
    used by the Schloss Lab for reproducible microbiome research. Perform
    common tasks like read files created by mothur <https://mothur.org/>,
    tidy up your microbiome data, and format R Markdown documents for
    publication.  See the website <http://www.schlosslab.org/schtools/>
    for more information, documentation, and examples.
This is a shape preserving spline <doi:10.1137/0720057>
    which is guaranteed to be monotonic and concave or convex if the
    data is monotonic and concave or convex. It does not use any 
    optimisation and is therefore quick and smoothly converges to a 
    fixed point in economic dynamics problems including value function 
    iteration. It also automatically gives the first two derivatives 
    of the spline and options for determining behaviour when evaluated 
    outside the interpolation domain.
Functions for generating Standardized Climate Indices (SCI). 
		SCI is a transformation of (smoothed) climate (or environmental)
		time series that  removes seasonality and forces the data to
		take values of the standard normal distribution. SCI was 
		originally developed for precipitation. In this case it is 
		known as the Standardized Precipitation Index (SPI).
Colour choice in information visualisation is important in order to
    avoid being mislead by inherent bias in the used colour palette. The 'scico'
    package provides access to the perceptually uniform and colour-blindness 
    friendly palettes developed by Fabio Crameri and released under the 
    "Scientific Colour-Maps" moniker. The package contains 24 different palettes 
    and includes both diverging and sequential types.
It involves bibliometric indicators calculation from bibliometric data.It also deals pattern analysis using the text part of bibliometric data.The bibliometric data are obtained from mainly Web of Science and Scopus.
Users may specify what fundamental qualities of a new study have 
             or have not changed in an attempt to reproduce or replicate an 
             original study. A comparison of the differences is visualized. 
             Visualization approach follows 'Patil', 'Peng', and 
             'Leek' (2016) <doi:10.1101/066803>.
Statistics and visualization of the distribution, diversity and composition of clonotypes, the abundance and length distribution of CDR3, the respective abundance distribution of V and J genes, and the abundance of V-J gene pairs are the basic requirements for single-cell immune group analysis. 'scImmuneGraph' is designed to process data from 10x Genomics Chromium Immune Profiling for T cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows.
An automatic cell type detection and assignment algorithm for single cell RNA-Seq and Cytof/FACS data. 'SCINA' is capable of assigning cell type identities to a pool of cells profiled by scRNA-Seq or Cytof/FACS data with prior knowledge of markers, such as genes and protein symbols that are highly or lowly expressed in each category. See Zhang Z, et al (2019) <doi:10.3390/genes10070531> for more details.
We develop a novel matrix factorization tool named 'scINSIGHT' to jointly analyze multiple single-cell gene expression samples from biologically heterogeneous sources, such as different disease phases, treatment groups, or developmental stages. Given multiple gene expression samples from different biological conditions, 'scINSIGHT' simultaneously identifies common and condition-specific gene modules and quantify their expression levels in each sample in a lower-dimensional space. With the factorized results, the inferred expression levels and memberships of common gene modules can be used to cluster cells and detect cell identities, and the condition-specific gene modules can help compare functional differences in transcriptomes from distinct conditions.
A collection of functions that creates graphs with error
             bars for data collected from one-way or higher factorial
             designs.
Create and format tables and APA statistics for
    scientific publication. This includes making a 'Table 1'
    to summarize demographics across groups, correlation tables
    with significance indicated by stars, and extracting formatted
    statistical summarizes from simple tests for in-text notation.
    The package also includes functions for Winsorizing data based
    on a Z-statistic cutoff.
Single-cell Interpretable Tensor Decomposition (scITD) employs the 
    Tucker tensor decomposition to extract multicell-type gene expression patterns 
    that vary across donors/individuals. This tool is geared for use with single-cell
    RNA-sequencing datasets consisting of many source donors. The method has a wide
    range of potential applications, including the study of inter-individual variation
    at the population-level, patient sub-grouping/stratification, and the analysis
    of sample-level batch effects. Each "multicellular process" that is extracted 
    consists of (A) a multi cell type gene loadings matrix and (B) a
    corresponding donor scores vector indicating the level at which the corresponding
    loadings matrix is expressed in each donor. Additional methods are implemented
    to aid in selecting an appropriate number of factors and to evaluate stability
    of the decomposition. Additional tools are provided for downstream analysis,
    including integration of gene set enrichment analysis and ligand-receptor analysis. 
    Tucker, L.R. (1966) <doi:10.1007/BF02289464>. Unkel, S., Hannachi, A., Trendafilov, N. T., & Jolliffe, I. T. (2011) <doi:10.1007/s13253-011-0055-9>. Zhou, G., & Cichocki, A. (2012) <doi:10.2478/v10175-012-0051-4>.
Functions to install SciViews additions to R, and more
        tools.
Uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data.
Maximum likelihood estimation of the scaled logit model parameters
    proposed in Dunning (2006) <doi:10.1002/sim.2282>.
Toolbox containing a variety of spectral clustering tools functions. Among the tools available are the hierarchical spectral clustering algorithm, the Shi and Malik clustering algorithm, the Perona and Freeman algorithm, the non-normalized clustering, the Von Luxburg algorithm, the Partition Around Medoids clustering algorithm, a multi-level clustering algorithm, recursive clustering and the fast method for all clustering algorithm. As well as other tools needed to run these algorithms or useful for unsupervised spectral clustering. This toolbox aims to gather the main tools for unsupervised spectral classification. See <http://mawenzi.univ-littoral.fr/> for more information and documentation.
Perform meta-analysis of single-case experiments, including calculating various effect size measures (SMD, PND, PEM and NAP) and probability combining (additive and multiplicative method), as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.
The single cell mapper (scMappR) R package contains a suite of bioinformatic tools that provide experimentally relevant cell-type specific information to a list of differentially expressed genes (DEG). The function "scMappR_and_pathway_analysis" reranks DEGs to generate cell-type specificity scores called cell-weighted fold-changes. Users input a list of DEGs, normalized counts, and a signature matrix into this function. scMappR then re-weights bulk DEGs by cell-type specific expression from the signature matrix, cell-type proportions from RNA-seq deconvolution and the ratio of cell-type proportions between the two conditions to account for changes in cell-type proportion. With cwFold-changes calculated, scMappR uses two approaches to utilize cwFold-changes to complete cell-type specific pathway analysis. The "process_dgTMatrix_lists" function in the scMappR package contains an automated scRNA-seq processing pipeline where users input scRNA-seq count data, which is made compatible for scMappR and other R packages that analyze scRNA-seq data. We further used this to store hundreds up regularly updating signature matrices. The functions "tissue_by_celltype_enrichment", "tissue_scMappR_internal", and "tissue_scMappR_custom" combine these consistently processed scRNAseq count data with gene-set enrichment tools to allow for cell-type marker enrichment of a generic gene list (e.g. GWAS hits). Reference: Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H., Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2021) Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes. NAR Genomics and Bioinformatics. 3(1). Iqab011. <doi:10.1093/nargab/lqab011>.
Provides functions for fitting discrete distribution models to count data.
  Included are the Poisson, the negative binomial, the Poisson-inverse gaussian and, most importantly,
  a new implementation of the Poisson-beta distribution (density, distribution and quantile
  functions, and random number generator) together with a needed new implementation of
  Kummer's function (also: confluent hypergeometric function of the first kind). Three
  different implementations of the Gillespie algorithm allow data simulation based on the
  basic, switching or bursting mRNA generating processes. Moreover, likelihood functions for
  four variants of each of the three aforementioned distributions are also available.
  The variants include one population and two population mixtures, both with and without
  zero-inflation. The package depends on the 'MPFR' libraries (<https://www.mpfr.org/>) which need to be installed separately 
  (see description at <https://github.com/fuchslab/scModels>).
  This package is supplement to the paper "A mechanistic model for the negative binomial distribution of single-cell mRNA counts" 
  by Lisa Amrhein, Kumar Harsha and Christiane Fuchs (2019) <doi:10.1101/657619> available on bioRxiv.
Provides a computational framework for identification of B cell clones from 
             Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data. Three main 
             functions are included (identicalClones, hierarchicalClones, and spectralClones) 
             that perform clustering among sequences of BCRs/IGs (B cell receptors/immunoglobulins) 
             which share the same V gene, J gene and junction length.
             Nouri N and Kleinstein SH (2018) <doi: 10.1093/bioinformatics/bty235>.
             Nouri N and Kleinstein SH (2019) <doi: 10.1101/788620>.
             Gupta NT, et al. (2017) <doi: 10.4049/jimmunol.1601850>.
Handling of behavioural data from the Ethoscope platform 
    (Geissmann, Garcia Rodriguez, Beckwith, French, Jamasb and Gilestro (2017) <DOI:10.1371/journal.pbio.2003026>).
    Ethoscopes (<http://gilestrolab.github.io/ethoscope/>) are an open source/open hardware framework made of 
    interconnected raspberry pis (<https://www.raspberrypi.org>) designed to quantify the behaviour of multiple 
    small animals in a distributed and real-time fashion. The default tracking algorithm records primary variables
    such as xy coordinates, dimensions and speed.
    This package is part of the rethomics framework <http://rethomics.github.io/>.
A non convex optimization package that optimizes any function under the criterion, combination of variables are on the surface of a unit sphere, as described in the paper : Das et al. (2019) <arXiv:1909.04024> .
Provides routines for scoring behavioral questionnaires. Includes scoring procedures for the 'International Physical Activity Questionnaire (IPAQ)' <http://www.ipaq.ki.se>. Compares physical functional performance to the age- and gender-specific normal ranges.
The `scorecard` package makes the development of credit risk scorecard 
  easier and efficient by providing functions for some common tasks, 
  such as data partition, variable selection, woe binning, scorecard scaling,
  performance evaluation and report generation. These functions can also used
  in the development of machine learning models.
    The references including: 
  1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: 
  Development and Implementation Using SAS. 
  2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. 
  Developing and Implementing Intelligent Credit Scoring.
Provides infrastructure functionalities such as missing value treatment, information value calculation, GINI calculation etc. which are used for developing a traditional credit scorecard as well as a machine learning based model. The functionalities defined are standard steps for any credit underwriting scorecard development, extensively used in financial domain.
Perform association test within linear mixed model framework using score test integrated with Empirical Bayes for genome-wide association study. Firstly, score test was conducted for each marker under linear mixed model framework, taking into account the genetic relatedness and population structure. And then all the potentially associated markers were selected with a less stringent criterion. Finally, all the selected markers were placed into a multi-locus model to identify the true quantitative trait nucleotide.
Estimate GGUM Person Parameters Using Pre-Calibrated Item Parameters and Binary or Graded Disagree-Agree Responses
Provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) <https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.
Implementation of community detection algorithm SCORE in the paper J. Jin (2015) <arXiv:1211.5803>, and SCORE+ in J. Jin, Z. Ke and S. Luo (2018) <arXiv:1811.05927>. Membership estimation algorithm called Mixed-SCORE in J. Jin, Z. Ke and S. Luo (2017) <arXiv:1708.07852>.
This package contains the functions necessary to run the SCORER 2.0 algorithm. SCORER 2.0 can be used to differentiate between parallel dimeric and trimeric coiled-coil sequence, which are the two most more frequent coiled-coil structures observed naturally. As such, SCORER 2.0 is particularly useful for researchers looking to characterize novel coiled-coil sequences. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences. Also included in this package are functions that allows the user to retrain the SCORER 2.0 algorithm using user-defined training data.
Evaluating probabilistic forecasts via proper scoring rules.  scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families.  Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score.  For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.
Dictionary-like reference for computing scoring rules in a wide
    range of situations. Covers both parametric forecast distributions (such as
    mixtures of Gaussians) and distributions generated via simulation.
Grouping essential tools for credit scoring. These statistical tools may be useful for other use-cases as well but were primarily designed for it. First, there are Reject Inference methods (Ehrhardt et al. (2017) <arXiv:1903.10855>). Second, we build upon the already CRAN-available package 'discretization' to automate discretization of continuous features.
Combines a collection of metrics and proper scoring rules 
    (Tilmann Gneiting & Adrian E Raftery (2007) 
    <doi:10.1198/016214506000001437>) with an easy to
    use wrapper that can be used to automatically evaluate predictions. 
    Apart from proper scoring rules functions are provided to assess bias, 
    sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski, 
    Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) 
    <doi:10.1371/journal.pcbi.1006785>) of forecasts. 
    Several types of predictions can be evaluated: 
    probabilistic forecasts (generally 
    predictive samples generated by Markov Chain Monte Carlo procedures), 
    quantile forecasts or point forecasts. Observed values and predictions
    can be either continuous, integer, or binary. Users can either choose 
    to apply these rules separately in a vector / matrix format that can 
    be flexibly used within other packages, or they can choose to do an 
    automatic evaluation of their forecasts. This is implemented with
    'data.table' and provides a consistent and very efficient framework for 
    evaluating various types of predictions.
A consistent, semi-supervised, non-parametric survival curve estimator optimized for efficient use of Electronic Health Record (EHR) data with a limited number of current status labels. See van der Laan and Robins (1997) <doi:10.2307/2670119>.
An accurate and easy tool for performing linear trajectory inference on
  single cells using single-cell RNA sequencing data. In addition, SCORPIUS
  provides functions for discovering the most important genes with respect to
  the reconstructed trajectory, as well as nice visualisation tools.
  Cannoodt et al. (2016) <doi:10.1101/079509>.
Perform the balanced (Scott and Knott, 1974) and unbalanced <doi:10.1590/1984-70332017v17n1a1> Scott & Knott algorithm.
The Scott-Knott Effect Size Difference (ESD) test is a mean comparison approach that leverages a hierarchical clustering to partition the set of treatment means (e.g., means of variable importance scores, means of model performance) into statistically distinct groups with non-negligible difference [Tantithamthavorn et al., (2018) <doi:10.1109/TSE.2018.2794977>].
Implements the Scout method for regression, described in "Covariance-regularized regression and classification for high-dimensional problems", by Witten and Tibshirani (2008),  Journal of the Royal Statistical Society, Series B 71(3): 615-636.
Using principal component analysis as a base model, 'SCOUTer' 
    offers a new approach to simulate outliers in a simple and precise way. 
    The user can generate new observations defining them by a pair of well-known 
    statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2) 
    statistics. Just by introducing the target values of the SPE and T^2, 'SCOUTer' 
    returns a new set of observations with the desired target properties. 
    Authors: Alba González, Abel Folch-Fortuny, Francisco Arteaga and 
    Alberto Ferrer (2020).
Implements different inventory models, the bullwhip effect and other supply chain performance variables. Marchena Marlene (2010) <arXiv:1009.3977>.
Implementation of estimation and inference procedures for Synthetic Control methods using least square, lasso, ridge, or simplex-type constraints. Uncertainty is quantified with prediction intervals as developed in Cattaneo, Feng, and Titiunik (2021) <https://cattaneo.princeton.edu/papers/Cattaneo-Feng-Titiunik_2021_JASA.pdf>.
Group of functions for spatial smoothing using cubic splines and variogram maximum likelihood estimation. Also allow the inclusion of linear parametric terms and change-points for segmented smoothing splines models.
Estimates a penalized precision matrix via an augmented ADMM algorithm. This package is an implementation of the methods described in "Shrinking Characteristics of Precision Matrix Estimators" by Aaron J. Molstad, PhD and Adam J. Rothman, PhD. The manuscript can be found here: <doi:10.1093/biomet/asy023> .
Evaluate batch effect correction algorithms for scRNA-seq using multiple established methods, including the Adjusted Rand Index, 
    Normalized Mutual Information, Local Inverse Simpson Index, and Silhouette Width. Methods for aggregating and weighing multiple metrics 
    together are also included. For further explanation of methods, see Swamy et al. (2021)<doi:10.1101/2021.03.26.437190> .
Functions to implement the stability controlled 
    quasi-experiment (SCQE) approach to study the effects of 
    newly adopted treatments that were not assigned at random. This package 
    contains tools to help users avoid making statistical assumptions that rely 
    on infeasible assumptions. Methods developed in Hazlett (2019) 
    <doi:10.1002/sim.8717>.
Given bincount data from single-cell copy number profiling (segmented or unsegmented), estimates ploidy, and uses the ploidy estimate to scale the data to absolute copy numbers. Uses the modular quantogram proposed by Kendall (1986) <doi:10.1002/0471667196.ess2129.pub2>, modified by weighting segments according to confidence, and quantifying confidence in the estimate using a theoretical quantogram. Includes optional fused-lasso segmentation with the algorithm in Johnson (2013) <doi:10.1080/10618600.2012.681238>, using the implementation from glmgen by Arnold, Sadhanala, and Tibshirani.
Tools for scraping information from webpages and other XML contents, using XPath or CSS selectors.
A group of functions to scrape data from different websites, for 
    academic purposes.
Routines for a collection of screen-and-clean type
        variable selection procedures, including UPS and GS.
Tools for the analysis of high-dimensional data developed/implemented
  at the group "Statistical Complexity Reduction In Molecular Epidemiology" (SCRIME).
  Main focus is on SNP data. But most of the functions can also be applied to other
  types of categorical data.
We provide a comprehensive scheme that is capable of simulating Single Cell RNA Sequencing data for various parameters of Biological Coefficient of Variation,
    busting kinetics, differential expression (DE), cell or sample groups, cell trajectory, batch effect and other experimental designs.
    'SCRIP' proposed and compared two frameworks with Gamma-Poisson and Beta-Gamma-Poisson models for simulating Single Cell RNA Sequencing data.
	Other reference is available in Zappia et al. (2017) <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1305-0>.
Run complex native scripts with a single command, similar to system commands.
Displays the content of a R script into the 'Cytoscape' network-visualization app <https://cytoscape.org/>.
A small set of functions wrapping up the call stack and command
  line inspection needed to determine a running script's filename from
  within the script itself.
Full text, in data frames containing one row per verse, of the 
    Standard Works of The Church of Jesus Christ of Latter-day Saints (LDS). 
    These are the Old Testament, (KJV), the New Testament (KJV), the Book of 
    Mormon, the Doctrine and Covenants, and the Pearl of Great Price.
A coalescent simulator that allows the rapid simulation of
    biological sequences under neutral models of evolution. Different to other
    coalescent based simulations, it has an optional approximation parameter that
    allows for high accuracy while maintaining a linear run time cost for long
    sequences. It is optimized for simulating massive data sets as produced by Next-
    Generation Sequencing technologies for up to several thousand sequences.
A pipeline that can process single or multiple Single Cell RNAseq 
    samples primarily specializes in Clustering and Dimensionality Reduction. 
    Meanwhile we use common cell type marker genes for T cells, B cells, Myeloid cells, 
    Epithelial cells, and stromal cells (Fiboblast, Endothelial cells, Pericyte,
    Smooth muscle cells) to visualize the Seurat clusters, to facilitate labeling 
    them by biological names. Once users named each cluster, they can evaluate the 
    quality of them again and find the de novo marker genes also.
We integrated the common analysis methods utilized in single cell RNA sequencing data, which included cluster method, principal components analysis (PCA), the filter of differentially expressed genes, pathway enrichment analysis and correlated analysis methods.
'Last.fm'<https://www.last.fm> is a music platform focussed on building a 
    detailed profile of a users listening habits. It does this by 'scrobbling' (recording) 
    every track you listen to on other platforms ('spotify', 'youtube', 'soundcloud' etc)
    and transferring them to your 'Last.fm' database. This allows 'Last.fm' to act as a 
    complete record of your entire listening history. 'scrobbler' provides helper functions
    to download and analyse your listening history in R.
Allows the user to animate 'shiny' elements when scrolling to view them.
    The animations are activated using the 'scrollrevealjs' library. See <https://scrollrevealjs.org/> for more information.
Contains four functions used in the DIC-tau_g procedure. SCRSELECT() and SCRSELECTRUN() uses Stochastic Search Variable Selection to select important
    covariates in the three hazard functions of a semi-competing risks model. These functions perform the Gibbs sampler for variable selection and a Metropolis-Hastings-Green sampler for the number of split points and parameters for the
    three baseline hazard function. The function SCRSELECT() returns the posterior sample of all quantities sampled in the Gibbs sampler after a burn-in period to a desired
    file location, while the function SCRSELECTRUN() returns posterior values of important quantities to the DIC-Tau_g procedure in a list.
    The function DICTAUG() returns a list containing the DIC values for the unique models visited by the DIC-Tau_g grid search.
    The function ReturnModel() uses SCRSELECTRUN() and DICTAUG() to return a summary of the posterior coefficient vectors for the optimal model along with saving this posterior sample to a desired path location.
Design single-case phase, alternation and multiple-baseline experiments, and conduct randomization tests on data gathered by means of such designs, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.
Functions for working with the scrypt key derivation functions
    originally described by Colin Percival
    <https://www.tarsnap.com/scrypt/scrypt.pdf> and in Percival and Josefsson
    (2016) <doi:10.17487/RFC7914>. Scrypt is a password-based key derivation
    function created by Colin Percival. The algorithm was specifically designed
    to make it costly to perform large-scale custom hardware attacks by
    requiring large amounts of memory.
A simple, light, and robust interface between R and
    the 'Scryfall' card data API <https://scryfall.com/docs/api>.
Solves convex cone programs via operator splitting. Can solve:
    linear programs ('LPs'), second-order cone programs ('SOCPs'), semidefinite programs
    ('SDPs'), exponential cone programs ('ECPs'), and power cone programs ('PCPs'), or
    problems with any combination of those cones. 'SCS' uses 'AMD' (a set of routines for permuting sparse matrices prior to factorization) and 'LDL' (a sparse 'LDL' factorization and solve package) from 'SuiteSparse' (<https://people.engr.tamu.edu/davis/suitesparse.html>).
Implements the algorithm described in
	Guo, H., and Li, J., "scSorter: assigning cells to known cell types according to known marker genes". 
	Cluster cells to known cell types based on marker genes specified for each cell type.
A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input.  First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells.
A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.
Extensions to the synthetic controls analyses 
    performed by the package 'Synth' as detailed in Abadie, Diamond, and Hainmueller (2011) <doi: 10.18637/jss.v042.i13>. Includes generating 
    and plotting placebos, post/pre-MSPE (Mean Squared Prediction Error) significance tests and plots, 
    and calculating average treatment effects for multiple treated units. 
    This package represents an implementation of those methods 
    suggested in 
    Abadie, Diamond,and Hainmueller (2010) <doi:10.1198/jasa.2009.ap08746> for
    use in Synthetic Control Analysis.
A normalization method for single-cell UMI count data using a 
  variance stabilizing transformation. The transformation is based on a 
  negative binomial regression model with regularized parameters. As part of the
  same regression framework, this package also provides functions for
  batch correction, and data correction. See Hafemeister and Satija (2019)
  <doi:10.1186/s13059-019-1874-1>, and Choudhary and Satija (2021) <doi:10.1101/2021.07.07.451498>
  for more details.
Code for describing and manipulating scuba diving profiles 
	(depth-time curves) and decompression models, 
        for calculating the predictions of decompression models,
	for calculating maximum no-decompression time and decompression tables,
	and for performing mixed gas calculations.
Analysis of single-cell RNA sequencing data can be simple and
    clear with the right utility functions. This package collects such functions,
    aiming to fulfill the following criteria:
    code clarity over performance (i.e. plain R code instead of C code),
    most important analysis steps over completeness
    (analysis 'by hand', not automated integration etc.),
    emphasis on quantitative visualization (intensity-coded color scale, etc.).
Imbalanced training datasets impede many popular classifiers. To balance training data, a combination of oversampling minority classes and undersampling majority classes is useful. This package implements the SCUT (SMOTE and Cluster-based Undersampling Technique) algorithm as described in Agrawal et. al. (2015) <doi:10.5220/0005595502260234>. Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package.
Make graphical representations of single case data and transform graphical displays back to raw data, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>. The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend.
Provides an efficient framework for 
   high-dimensional linear and diagonal discriminant analysis with 
   variable selection.  The classifier is trained using James-Stein-type 
   shrinkage estimators and predictor variables are ranked using 
   correlation-adjusted t-scores (CAT scores).  Variable selection error 
   is controlled using false non-discovery rates or higher criticism.
Functions and Datasets from Lohr, S. (1999), Sampling:
        Design and Analysis, Duxbury.
We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2020), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", <arXiv:2002.11992>.
Provides a computationally efficient discrete approximation to log-Gaussian Cox process model for spatially aggregated disease count data. It uses Monte Carlo Maximum Likelihood for model parameter estimation as proposed by Christensen (2004) <doi: 10.1198/106186004X2525> and delivers prediction of spatially discrete and continuous relative risk. It performs inference for static spatial and spatio-temporal dataset. The details of the methods are provided in Johnson et al (2019) <doi:10.1002/sim.8339>.
Data sets and functions to support the books "Statistics: Data 
        analysis and modelling" by Speekenbrink, M. (2021) 
        <https://mspeekenbrink.github.io/sdam-book/> and "An R companion to 
        Statistics: data analysis and modelling" by Speekenbrink, M. (2021) 
        <https://mspeekenbrink.github.io/sdam-r-companion/>. All datasets 
        analysed in these books are provided in this package. In addition, the 
        package provides functions to compute sample statistics (variance, 
        standard deviation, mode), create raincloud and enhanced Q-Q plots, and 
        expand Anova results into omnibus tests and tests of individual 
        contrasts.
A fast, consistent tool for plotting and facilitating the analysis of stratigraphic
    and sedimentological data. Taking advantage of the flexible plotting tools available in R, 
    'SDAR' uses stratigraphic and sedimentological data to produce detailed graphic logs for 
    outcrop sections and borehole logs. These logs can include multiple features (e.g., bed thickness,
    lithology, samples, sedimentary structures, colors, fossil content, bioturbation index, 
    gamma ray logs) (Johnson, 1992, <ISSN 0037-0738>).
Includes all the datasets of 'Sampling: Design and Analysis'
    (3rd edition by Sharon Lohr) in R format and additional functions for
    analyzing and graphing probability samples.
Test the global null in linear models using marginal approach.
Provides functionality to generate, (interactively) modify (by adding, removing and renaming nodes) and convert nested hierarchies between different formats.
  These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control.
Tools for researchers to explicitly show that their results
    comply to rules for statistical disclosure control imposed by research
    data centers. These tools help in checking descriptive statistics and
    models and in calculating extreme values that are not individual data.
    Also included is a simple function to create log files. The methods
    used here are described in the "Guidelines for the checking of output
    based on microdata research" by Bond, Brandt, and de Wolf (2015)
    <https://ec.europa.eu/eurostat/cros/system/files/dwb_standalone-document_output-checking-guidelines.pdf>.
Data from statistical agencies and other institutions are mostly
    confidential. This package (see also Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v067.i04>) can be used for the generation of anonymized
    (micro)data, i.e. for the creation of public- and scientific-use files.
    The theoretical basis for the methods implemented can be found in Templ (2017) <doi:10.1007/978-3-319-50272-4>.
    Various risk estimation and anonymisation methods are included. Note that the package
    includes a graphical user interface (Meindl and Templ, 2019 <doi:10.3390/a12090191>) that allows to use various methods of this
    package.
Tools for calculating disclosure risk measures for microdata,
    including record-level and file-level measures. The record-level disclosure
    risk is estimated primarily using exhaustive tabulation. The file-level
    disclosure risk is estimated by fitting loglinear models on the observed 
    sample counts in cells formed by key variables and their interactions. 
    Funded by the National Center for Education Statistics. See Skinner and
    Shlomo (2008) <doi:10.1198/016214507000001328> for a description of the
    file-level risk measures and the loglinear model approach.
Privacy protected raster maps 
  can be created from spatial point data. Protection
  methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) 
  <doi:10.1007/978-3-319-45381-1_9>, continuous variables by de Wolf and 
  de Jonge (2018) <doi:10.1007/978-3-319-99771-1_23>, suppressing 
  revealing values and a generalization of the quad tree method by 
  Suñé, Rovira, Ibáñez and Farré (2017) <doi:10.2901/EUROSTAT.C2017.001>.
Methods for statistical disclosure control in
    tabular data such as primary and secondary cell suppression as described for example
    in Hundepol et al. (2012) <doi:10.1002/9781118348239> are covered in this package.
Classes and methods to calculate and evaluate target matrices for
    statistical disclosure control.
Allows for  computing (and by default plotting) different types of serial dependence diagrams.
Compares the evolution of an original network X to an augmented network Y by counting the number of Shortcuts, Detours, Dead Ends (SDDE), equal paths and disconnected nodes.
Companion package to the book Simulation and Inference for
        Stochastic Differential Equations With R Examples, ISBN
        978-0-387-75838-1, Springer, NY.
Performs two tests to evaluate if the
        experiments are associated and returns a list of interesting
        features common to all the experiments.
Implementation of evolutionary fuzzy systems for the data mining task called
    "subgroup discovery". In particular, the algorithms presented in this package are:
    M. J. del Jesus, P. Gonzalez, F. Herrera, M. Mesonero (2007) <doi:10.1109/TFUZZ.2006.890662>
    M. J. del Jesus, P. Gonzalez, F. Herrera (2007) <doi:10.1109/MCDM.2007.369416>
    C. J. Carmona, P. Gonzalez, M. J. del Jesus, F. Herrera (2010) <doi:10.1109/TFUZZ.2010.2060200>
    C. J. Carmona, V. Ruiz-Rodado, M. J. del Jesus, A. Weber, M. Grootveld, P. González, D. Elizondo (2015) <doi:10.1016/j.ins.2014.11.030>
    It also provide a Shiny App to ease the analysis. The algorithms work with data sets provided in
    KEEL, ARFF and CSV format and also with data.frame objects.
Implementation of statistical methods for the estimation of
    toroidal diffusions. Several diffusive models are provided, most of them
    belonging to the Langevin family of diffusions on the torus. Specifically,
    the wrapped normal and von Mises processes are included, which can be seen
    as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of
    methods for approximate maximum likelihood estimation, organized in four
    blocks, is given: (i) based on the exact transition probability density,
    obtained as the numerical solution to the Fokker-Plank equation; (ii) based
    on wrapped pseudo-likelihoods; (iii) based on specific analytic
    approximations by wrapped processes; (iv) based on maximum likelihood of
    the stationary densities. The package allows the reproducibility of the
    results in García-Portugués et al. (2019) <doi:10.1007/s11222-017-9790-2>.
Functions to filter GPS/Argos locations, as well as assessing the sample size for the analysis of animal distributions. The filters remove temporal and spatial duplicates, fixes located at a given height from estimated high tide line, and locations with high error as described in Shimada et al. (2012) <doi:10.3354/meps09747> and Shimada et al. (2016) <doi:10.1007/s00227-015-2771-0>. Sample size for the analysis of animal distributions can be assessed by the conventional area-based approach or the alternative probability-based approach as described in Shimada et al. (2021) <doi:10.1111/2041-210X.13506>.
An extensible framework for developing species distribution
    models using individual and community-based approaches, generate ensembles of
    models, evaluate the models, and predict species potential distributions in
    space and time. For more information, please check the following paper:
    Naimi, B., Araujo, M.B. (2016) <doi:10.1111/ecog.01881>.
A 'shiny' 
    application that allows non-expert 'R' users to easily model
    species distribution. It offers a reproducible work flow for
    species distribution modeling into a single and user friendly environment.
    'sdmApp' takes 'raster' data (in format supported by the 'raster package') 
    and species occurrence data (several format supported) as input argument.
    It provides an interactive graphical user interface (GUI).
Species distribution modelling (SDM) has been developed for several years to address conservation issues, assess the direct impact of human activities on ecosystems and predict the potential distribution shifts of invasive species (see Elith et al. 2006, Pearson 2007, Elith and Leathwick 2009). SDM relates species occurrences with environmental information and can predict species distribution on their entire occupied space. This approach has been increasingly applied to Southern Ocean case studies, but requires corrections in such a context, due to the broad scale area, the limited number of presence records available and the spatial and temporal aggregations of these datasets. SDMPlay is a pedagogic package that will allow you to compute SDMs, to understand the overall method, and to produce model outputs. The package, along with its associated vignettes, highlights the different steps of model calibration and describes how to choose the best methods to generate accurate and relevant outputs. SDMPlay proposes codes to apply a popular machine learning approach, BRT (Boosted Regression Trees) and introduces MaxEnt (Maximum Entropy). It contains occurrences of marine species and environmental descriptors datasets as examples associated to several vignette tutorials available at <https://github.com/charleneguillaumot/THESIS/tree/master/SDMPLAY_R_PACKAGE>.
Terrestrial and marine predictors for species distribution modelling
    from multiple sources, including WorldClim <https://www.worldclim.org/>,,
    ENVIREM <https://envirem.github.io/>, Bio-ORACLE <https://bio-oracle.org/>
    and MARSPEC <http://www.marspec.org/>.
User-friendly framework that enables the training and the
    evaluation of species distribution models (SDMs). The package implements
    functions for data driven variable selection and model tuning and includes
    numerous utilities to display the results. All the functions used to select
    variables or to tune model hyperparameters have an interactive real-time
    chart displayed in the 'RStudio' viewer pane during their execution.
A software package help user to create virtual species for species distribution modelling. It includes
    several methods to help user to create virtual species distribution map.
    Those maps can be used for Species Distribution Modelling (SDM) study. SDM use
    environmental data for sites of occurrence of a species to predict all the sites
    where the environmental conditions are suitable for the species to persist, and
    may be expected to occur.
M-estimator for threshold and non-threshold spatial dynamic panel data model. Yang, Z (2018) <doi:10.1016/j.jeconom.2017.08.019>. Wu, J., Matsuda, Y (2021) <doi:10.1007/s43071-021-00008-1>.
Utility functions for scale-dependent and alternative hyperpriors. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend
  on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. Hyperpriors for all effects can be elicitated within the package. Including complex tensor product interaction terms and variable selection priors. The basic model is explained in in Klein and Kneib (2016) <doi:10.1214/15-BA983>.
Based on the compound Poisson risk process that is perturbed by
    a Brownian motion, saddlepoint approximations to some measures of risk are
    provided. Various approximation methods for the probability of ruin are
    also included. Furthermore, exact values of both the risk measures as well
    as the probability of ruin are available if the individual claims follow
    a hypo-exponential distribution (i. e., if it can be represented as a sum
    of independent exponentially distributed random variables with different
    rate parameters). For more details see Gatto and Baumgartner (2014)
    <doi:10.1007/s11009-012-9316-5>.
Solves the general Semi-Definite Linear Programming formulation using an R implementation of SDPT3 (K.C. Toh, M.J. Todd, and R.H. Tutuncu (1999) <doi:10.1080/10556789908805762>). This includes problems such as the nearest correlation matrix problem (Higham (2002) <doi:10.1093/imanum/22.3.329>), D-optimal experimental design (Smith (1918) <doi:10.2307/2331929>), Distance Weighted Discrimination (Marron and Todd (2012) <doi:10.1198/016214507000001120>), as well as graph theory problems including the maximum cut problem. Technical details surrounding SDPT3 can be found in R.H Tutuncu, K.C. Toh, and M.J. Todd (2003) <doi:10.1007/s10107-002-0347-5>.
Functions for post processing the outcomes of ecological niche models. It further include methods to build stability maps based on species distribution models under climate change projections and to reduce environmental data.
Functions for self-determination motivation theory (SDT) to compute
    measures of motivation internalization, motivation simplex structure, and of
    the original and adjusted self-determination or relative autonomy index. SDT
    was introduced by Deci and Ryan (1985) <doi:10.1007/978-1-4899-2271-7>. See
    package?SDT for an overview.
Implements algorithms to help with scenario discovery - currently only modified version of the the Patient Rule Induction Method.
Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties. More details about the methodology SDWD is seen on Wang and Zou (2016) (<doi:10.1080/10618600.2015.1049700>).
Implements the SE-test for equivalence
    according to Hoffelder et al. (2015) <DOI:10.1080/10543406.2014.920344>.
    The SE-test for equivalence is a multivariate two-sample equivalence test. 
    Distance measure of the test is the sum of standardized differences
    between the expected values or in other words: the sum of effect sizes (SE)
    of all components of the two multivariate samples. 
    The test is an asymptotically valid test for normally distributed data 
    (see Hoffelder et al.,2015).
    The function SE.EQ() implements the SE-test for equivalence 
    according to Hoffelder et al. (2015).
    The function SE.EQ.dissolution.profiles() implements a variant 
    of the SE-test for equivalence for similarity analyses of dissolution 
    profiles as mentioned in Suarez-Sharp et al.(2020) 
    <DOI:10.1208/s12248-020-00458-9>). The equivalence margin used in 
    SE.EQ.dissolution.profiles() is analogically defined as for the T2EQ
    approach according to Hoffelder (2019) <DOI:10.1002/bimj.201700257>) 
    by means of a systematic shift in location
    of 10 [\% of label claim] of both dissolution profile populations. 
    SE.EQ.dissolution.profiles() checks whether the weighted mean of the 
    differences of the expected values of both dissolution profile populations 
    is statistically significantly smaller than 10 [\% of label claim]. The 
    weights are built up by the inverse variances.
A few major genes and a series of polygene are responsive for each quantitative trait. Major genes are individually identified while polygene is collectively detected. This is mixed major genes plus polygene inheritance analysis or segregation analysis (SEA). In the SEA, phenotypes from a single or multiple bi-parental segregation populations along with their parents are used to fit all the possible models and the best model of the trait for population phenotypic distributions is viewed as the model of the trait. There are fourteen types of population combinations available. Zhang Yuan-Ming, Gai Jun-Yi, Yang Yong-Hua (2003, <doi:10.1017/S0016672303006141>).
Calculates parameters of the seawater carbonate system and assists the design of ocean acidification perturbation experiments.
The explosion of biobank data offers immediate opportunities for 
    gene-environment (GxE) interaction studies of complex diseases because of the 
    large sample sizes and rich collection in genetic and non-genetic information. 
    However, the extremely large sample size also introduces new computational 
    challenges in GxE assessment, especially for set-based GxE variance component (VC) 
    tests, a widely used strategy to boost overall GxE signals and to evaluate the 
    joint GxE effect of multiple variants from a biologically meaningful unit 
    (e.g., gene). 
    We present 'SEAGLE', a Scalable Exact AlGorithm for Large-scale Set-based 
    GxE tests, to permit GxE VC test scalable to biobank data. 'SEAGLE' employs modern 
    matrix computations to achieve the same “exact” results as the original GxE VC 
    tests, and does not impose additional assumptions nor relies on approximations. 
    'SEAGLE' can easily accommodate sample sizes in the order of 10^5, is implementable 
    on standard laptops, and does not require specialized equipment. 
    The accompanying manuscript for this package can be found at Chi, Ipsen, Hsiao, 
    Lin, Wang, Lee, Lu, and Tzeng. (2021+) <arXiv:2105.03228>.
Proximal gradient descent solver for the operators lasso, (fitted)
    group lasso, and (fitted) sparse-group lasso. The implementation involves
    backtracking line search and warm starts. Input data needs to be clustered/
    grouped for each group lasso variant before calling these algorithms.
Standard error adjusted adaptive lasso (SEA-lasso) is a version of the adaptive lasso, which incorporates OLS standard error to the L1 penalty weight. This method is intended for variable selection under linear regression settings (n > p). This new weight assignment strategy is especially useful when the collinearity of the design matrix is a concern.
Provides an interface with the Google Search Console,
    formally called Google Webmaster Tools.
Provides a search interface to look up terms
    on 'Google', 'Bing', 'DuckDuckGo', 'Startpage', 'Ecosia', 'rseek',
    'Twitter', 'StackOverflow', 'RStudio Community', 'GitHub', and 'BitBucket'.
    Upon searching, a browser window will open with the aforementioned search
    results.
This package provides an implementation of the QuadTree
        data structure. It uses this to implement fast k-Nearest
        Neighbor and Rectangular range lookups in 2 dimenions. The
        primary target is high performance interactive graphics.
Capable of deriving seasonal statistics, such as "normals", and
  analysis of seasonal data, such as departures. This package also has
  graphics capabilities for representing seasonal data, including boxplots for
  seasonal parameters, and bars for summed normals. There are many specific
  functions related to climatology, including precipitation normals,
  temperature normals, cumulative precipitation departures and precipitation
  interarrivals. However, this package is designed to represent any
  time-varying parameter with a discernible seasonal signal, such as found
  in hydrology and ecology.
This package conducts Score Equity Assessment (SEA; Dorans, 2004) by calculating and plotting multiple SEA indices as introduced by a variety of authors and summarized by Huggins and Penfield (2012).
Routines for the seasonal analysis of health data, including regression models, time-stratified case-crossover, plotting functions and residual checks, see Barnett and Dobson (2010) ISBN 978-3-642-10748-1. Thanks to Yuming Guo for checking the case-crossover code.
Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment
    software by the US Census Bureau. It offers full access to almost all
    options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model
    search, outlier detection and support for user defined holiday variables,
    such as Chinese New Year or Indian Diwali. A graphical user interface can be
    used through the 'seasonalview' package. Uses the X-13-binaries from the
    'x13binary' package.
Compiles a set of functions and dummy data that simplify reconstructions of seasonal temperature
	 variability in the geological past from stable isotope and clumped isotope records in sub–annually resolved
	 carbonate archives (e.g. mollusk shells, corals and speleothems). For more information, see
	 de Winter et al., 2020 (Climate of the Past Discussions, <doi:10.5194/cp-2020-118>).
The price action at any given time is determined by investor 
  sentiment and market conditions. Although there is no established principle, 
  over a long period of time, things often move with a certain periodicity.
  This is sometimes referred to as anomaly. 
  The seasonPlot() function in this package calculates and visualizes the 
  average value of price movements over a year for any given period. 
  In addition, the monthly increase or decrease in price movement is 
  represented with a colored background. 
  This seasonPlot() function can use the same symbols as the 'quantmod' package 
  (e.g. ^IXIC, ^DJI, SPY, BTC-USD, and ETH-USD etc).
A graphical user interface to the 'seasonal' package and
  'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software. 
  Unifies the code base of <http://www.seasonal.website> and the GUI in the
  'seasonal' package.
An overall test for seasonality of a given time
    series in addition to a set of individual seasonality tests as
    described by Ollech and Webel (forthcoming): An overall seasonality
    test. Bundesbank Discussion Paper.
A model and utilities for analyzing trends in chemical concentrations in streams with a seasonal wave (seawave) and adjustment for streamflow (Q) and other ancillary variables. See Ryberg and York, 2020, <doi:10.3133/ofr20201082>.
These functions were developed within SECFISH project (Strengthening regional cooperation in the area of fisheries  data collection-Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level). They are aimed at identifying  correlations between costs and transversal variables by metier using individual vessel data and for disaggregating variable costs from  fleet segment to metier  level.
Displays state-event charts, for graphical presentation of longitudinal data.
Density, distribution function, quantile function, random
    generation and survival function for the Secant Kumaraswamy Weibull Distribution
    as defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
SECP package provides functionality for estimating
        parameters of site clusters on 2D & 3D square lattice with
        various lattice sizes, relative fractions of accessible sites
        (occupation probability), iso- & anisotropy, von Neumann &
        Moore (1,d)-neighborhoods
Functions to estimate the density and size of a spatially distributed animal population sampled with an array of passive detectors, such as traps, or by searching polygons or transects. Models incorporating distance-dependent detection are fitted by maximizing the likelihood. Tools are included for data manipulation and model selection.
Tools for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package 'secr'. Extensions in version 2.5.0 include costing and evaluation of detector spacing.
Allow sharing sensitive information, for example passwords,
    'API' keys, etc., in R packages, using public key cryptography.
Encrypt text using a simple shifting substitution cipher with setcode(), providing two numeric keys used to define the encryption algorithm. The resulting text can be decoded using decode() function and the two numeric keys specified during encryption.
Tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package 'secr'.
Simultaneously infers state-dependent diversification across
 two or more states of a single or multiple traits while accounting for the
 role of a possible concealed trait. See Herrera-Alsina et al. 2019
 Systematic Biology 68: 317-328 <DOI:10.1093/sysbio/syy057>.
Sequential factor extraction via co-sparse unit-rank estimation (SeCURE).
Seamless and standardized interaction with data
    exported from the clinical data management system (CDMS)
    'secuTrial'<https://www.secutrial.com>.
    The primary data export the package works with is a standard non-rectangular export.
Proxy forward modelling for sediment archived climate proxies such 
  as Mg/Ca, d18O or Alkenones. The user provides a hypothesised "true" past climate, 
  such as output from a climate model, and details of the sedimentation rate and 
  sampling scheme of a sediment core. Sedproxy returns simulated proxy records. 
  Implements the methods described in Dolman and Laepple (2018) 
  <doi:10.5194/cp-14-1851-2018>.
Provides plotting utilities supporting easystats-packages
    (<https://github.com/easystats/easystats>) and some extra themes,
    geoms, and scales for 'ggplot2'. Color scales are based on
    <https://materialui.co/colors>.
Provides a wrapper to access data from the SeeClickFix
  web API for R. SeeClickFix is a central platform employed by many cities
  that allows citizens to request their city's services. This package
  creates several functions to work with all the built-in calls to the
  SeeClickFix API. Allows users to download service request data from
  numerous locations in easy-to-use dataframe format manipulable in
  standard R functions.
Output colors used in literal vectors, palettes and plot objects (ggplot).
Functions to calculate seed germination and seedling emergence and growth indexes. The main indexes for germination and seedling emergence, considering the time for seed germinate are: T10, T50 and T90, in Farooq et al. (2005) <10.1111/j.1744-7909.2005.00031.x>; and MGT, in Labouriau (1983). Considering the germination speed are: Germination Speed Index, in Maguire (1962), Mean Germination Rate, in Labouriau (1983); considering the homogeneity of germination are: Coefficient of Variation of the Germination Time, in Carvalho et al. (2005) <10.1590/S0100-84042005000300018>, and Variance of Germination, in Labouriau (1983); Uncertainty, in Labouriau and Valadares (1976) <ISSN:0001-3765>; and Synchrony, in Primack (1980). The main seedling indexes are Growth, in Sako (2001), Uniformity, in Sako (2001) and Castan et al. (2018) <doi:10.1590/1678-992x-2016-0401>; and Vigour, in Medeiros and Pereira (2018) <doi:10.1590/1983-40632018v4852340>.
Functions for dimension reduction through the seeded canonical correlation analysis are provided. A classical canonical correlation analysis (CCA) is one of useful statistical methods in multivariate data analysis, but it is limited in use due to the matrix inversion for large p small n data. To overcome this, a seeded CCA has been proposed in Im, Gang and Yoo (2015) <DOI:10.1002/cem.2691>. The seeded CCA is a two-step procedure. The sets of variables are initially reduced by successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and cov(Y), respectively, without loss of information on canonical correlation analysis, following Cook, Li and Chiaromonte (2007) <DOI:10.1093/biomet/asm038> and Lee and Yoo (2014) <DOI:10.1111/anzs.12057>. Then, the canonical correlation is finalized with the initially-reduced two sets of variables.
Implements the seeded-LDA model (Lu, Ott, Cardie & Tsou 2010) <doi:10.1109/ICDMW.2011.125> using the quanteda package and the GibbsLDA++ library for semisupervised topic modeling. 
    Seeded-LDA allows users to pre-define topics with keywords to perform theory-driven analysis of textual data in social sciences and humanities (Watanabe & Zhou 2020) <doi:10.1177/0894439320907027>.
Analysis of seed germination data
    using the physiological time modelling approach. Includes functions
    to fit hydrotime and thermal-time models with the traditional approaches
    of Bradford (1990) <doi:10.1104/pp.94.2.840>
    and Garcia-Huidobro (1982) <doi:10.1093/jxb/33.2.288>. 
    Allows to fit models to grouped datasets,
    i.e. datasets containing multiple species, seedlots or experiments.
Regression analysis using common models in seed temperature studies, such as the Gaussian model (Martins, JF, Barroso, AAM, & Alves, PLCA (2017) <doi:10.1590/s0100-83582017350100039>), quadratic (Nunes, AL, Sossmeier, S, Gotz, AP, & Bispo, NB (2018) <doi: 10.17265/2161-6264/2018.06.002>) and others with potential for use, such as those implemented in the 'drc' package (Ritz, C, Baty, F, Streibig, JC, & Gerhard, D (2015). <doi:10.1371/journal.pone.0146021>), in the estimation of the ideal and cardinal temperature for the occurrence of plant seed germination. The functions return graphs with the equations automatically.
Algorithms to calculate the hidden inputs of systems of differential equations. 
  These hidden inputs can be interpreted as a control that tries to minimize the
  discrepancies between a given model and taken measurements. The idea is 
  also called the Dynamic Elastic Net, as proposed in the paper "Learning (from) the errors of a systems biology model" 
  (Engelhardt, Froelich, Kschischo 2016) <doi:10.1038/srep20772>.
  To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: <https://bioconductor.org/packages/release/bioc/html/rsbml.html>.
A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.
Creates SEER (Surveillance, Epidemiology and End Results) and 
    A-bomb data binaries from ASCII sources and provides tools for estimating
    SEER second cancer risks. Methods are described in <doi:10.1038/leu.2015.258>.
Provides an easy way to map seer registry area rate data on a U.S. map.  
   The U.S. data may be mapped at the state or state/county.
   U.S. Seer registry data may be mapped at the Seer registry area, Seer Registry 
   area/county or Seer Registry area/county/census tract level. The function uses 
   a calculated default categorization breakpoint list for 5 categories, 
   when not breakpoint list is provided or the number of categories is specified 
   by the user. A user provided break point list is limited to containing 5 values.  
   The number of calculated categories may be from 3 to 11. The user provide the p-value 
   for each area and request hatching over any areas with a p-value > 0.05. 
   Other types of comparisons can be specified.  If states or 
   state/counties are used, the area identifier is the U.S. FIPS codes for states and 
   counties, 2 digits and 5 digits respectfully.  If the data is for U.S. Seer Registry 
   areas, the Seer Registry area identifier used to link the data to the geographical 
   area is a Seer Registry area abbreviation.  See documentation for the list of 
   acceptable Seer Registry area names and abbreviations. The state boundaries are 
   overlaid all rate maps. The package contains the boundary data for state, county, 
   Seer Registry areas, and census tracts for counties within a Seer registry area.   
   The package support boundary data from the 2000 and 2010 U.S. Census.  
   The SeerMapper package version contains the U.S. Census 2000 and 2010 boundary data
   for the regional, state, Seer Registry, and county levels.  Six supplement packages 
   contain the census tract boundary data.
   Copyrighted 2014, 2015, 2016, 2017, 2018 and 2019 by Pearson and Pickle.
Provides supplemental 2010 census tract boundary package for 20 states
   without Seer Registries that are east of the Mississippi river 
   for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U.S. Census data and is in public domain.
Provides supplemental 2010 census tract boundaries of the 19 states 
   containing Seer Registries for use with the 'SeerMapper' package.
   The data contained in this 
   package is derived from U.S. 2010 Census data and is in public domain.
Provides supplemental 2010 census tract boundaries for the 13 states
   without Seer Registries that are west of the Mississippi river 
   for use with the 'SeerMapper' package.
   The data contained in this 
   package is derived from U.S. 2010 Census data and is in public domain.
Provides supplemental 2000 census tract boundaries for the 20 states, district and territory
   without Seer Registries that are east of the Mississippi river 
   for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U.S. Census data and is in the public domain.
Provides supplemental 2000 census tract boundaries for the 19 states
   containing Seer Registries for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U.S. Census data and is in the public domain.
Provides supplemental 2000 census tract boundaries for the 13 states
   without Seer Registries that are west of the Mississippi river
   for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U.S. 2000 Census data and is in the public domain. 
   Updated to Alber Equal Area projection.
Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound).  This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses. See Sueur et al. (2008) <doi:10.1080/09524622.2008.9753600> and Sueur (2018) <doi:10.1007/978-3-319-77647-7>.
Measuring spatial segregation. The methods implemented in this 
        package include White's P index (1983) <doi:10.1086/227768>, 
        Morrill's D(adj) (1991), Wong's D(w) and D(s) (1993) 
        <doi:10.1080/00420989320080551>, and Reardon and O'Sullivan's set of 
        spatial segregation measures (2004) 
        <doi:10.1111/j.0081-1750.2004.00150.x>.
Provides two methods for segmentation and joint segmentation/clustering of
    bivariate time-series. Originally intended for ecological segmentation
    (home-range and behavioural modes) but easily applied on other series,
    the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'.
    The segmentation method is a bivariate extension of  Lavielle's method available in 'adehabitatLT' 
    (Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>).
    This method rely on dynamic programming for efficient segmentation.
    The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm.
    This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method 
    (formerly available in 'cghseg' package) to the bivariate case.
    The method is fully described in Patin et al (2018) <doi:10.1101/444794>.
Performs correlation matrix segmentation and applies a test
    procedure to detect highly correlated regions in gene expression.
A set of segregation-based indices and randomization methods to 
            make robust environmental inequality assessments, as described in 
            Schaeffer and Tivadar (2019) "Measuring Environmental Inequalities:
            Insights from the Residential Segregation Literature" 
            <doi:10.1016/j.ecolecon.2019.05.009>.
Contains functions that help to determine event
    boundaries in event segmentation experiments by bootstrapping a critical
    segmentation magnitude under the null hypothesis that all key presses were
    randomly distributed across the experiment. Segmentation magnitude is
    defined as the sum of Gaussians centered at the times of the segmentation
    key presses performed by the participants. Within a participant, the maximum
    of the overlaid Gaussians is used to prevent an excessive influence of a
    single participant on the overall outcome (e.g. if a participant is pressing
    the key multiple times in succession). Further functions are included, such
    as plotting the results.
Given a regression model, segmented `updates' it by adding one or more segmented (i.e., piece-wise linear) relationships. 
  Several variables with multiple breakpoints are allowed. The estimation method is discussed in Muggeo (2003, <doi:10.1002/sim.1545>) 
  and illustrated in Muggeo (2008, <https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf>). An approach for hypothesis testing is presented 
  in Muggeo (2016, <doi:10.1080/00949655.2016.1149855>), and interval estimation for the breakpoint is discussed 
  in Muggeo (2017, <doi:10.1111/anzs.12200>).
A dynamic programming solution to segmentation based on
        maximization of arbitrary similarity measures within segments.
	The general idea, theory and this implementation are described in
	Machne, Murray & Stadler (2017) <doi:10.1038/s41598-017-12401-8>.
	In addition to the core algorithm, the package provides time-series
	processing and clustering functions as described in the publication.
	These are generally applicable where a `k-means` clustering yields
	meaningful results, and have been specifically developed for
	clustering of the Discrete Fourier Transform of periodic gene
	expression data (`circadian' or `yeast metabolic oscillations').
	This clustering approach is outlined in the supplemental material of
	Machne & Murray (2012) <doi:10.1371/journal.pone.0037906>), and here
	is used as a basis of segment similarity measures. Notably, the
	time-series processing and clustering functions can also be used as
	stand-alone tools, independent of segmentation, e.g., for 
        transcriptome data already mapped to genes.
Given a likelihood provided by the user, this package applies it
    to a given matrix dataset in order to find change points in the data that
    maximize the sum of the likelihoods of all the segments. This package provides
    a handful of algorithms with different time complexities and assumption compromises
    so the user is able to choose the best one for the problem at hand. The implementation
    of the segmentation algorithms in this package are based on the paper by Bruno M. de Castro,
	Florencia Leonardi (2018) <arXiv:1501.01756>. The Berlin
	weather sample dataset was provided by Deutscher Wetterdienst <https://dwd.de/>.
	You can find all the references in the Acknowledgments section of this package's
	repository via the URL below.
A system that computes metrics to assess the segmentation 
    accuracy of geospatial data. These metrics calculate the discrepancy 
    between segmented and reference objects, and indicate the segmentation 
    accuracy. For more details on choosing evaluation metrics, we 
    suggest seeing Costa et al. (2018) <doi:10.1016/j.rse.2017.11.024> and 
    Jozdani et al. (2020) <doi:10.1016/j.isprsjprs.2020.01.002>.
Implements a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes. It simultaneously segments GARCH processes by identifying 'common' change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure.
Tools for modeling non-continuous linear responses of ecological communities to environmental data. The package is straightforward through three steps: (1) data ordering (function OrdData()), (2) split-moving-window analysis (function SMW()) and (3) piecewise redundancy analysis (function pwRDA()). Relevant references include Cornelius and Reynolds (1991) <doi:10.2307/1941559> and Legendre and Legendre (2012, ISBN: 9780444538697).
Computes segregation indices, including the Index of Dissimilarity,
    as well as the information-theoretic indices developed by
    Theil (1971) <isbn:978-0471858454>, namely
    the Mutual Information Index (M) and Theil's Information Index (H).
    The M, further described by Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x>
    and Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008>,
    is a measure of segregation that is highly decomposable. The package provides
    tools to decompose the index by units and groups (local segregation),
    and by within and between terms. The package also provides a method to decompose
    differences in segregation as described by Elbers (2021) <doi:10.1177/0049124121986204>.
    The package includes standard error estimation by bootstrapping, which also corrects for
    small sample bias.
An implementation of the full-likelihood Bayes factor (FLB) 
    for evaluating segregation evidence in clinical medical genetics. The method 
    was introduced by Thompson et al. (2003) <doi:10.1086/378100>, and further 
    popularised by Bayrak-Toydemir et al. (2008) <doi:10.1016/j.yexmp.2008.03.006>.
    This implementation allows custom penetrance values and liability classes, and 
    includes specialised pedigree visualisations.
A very nice interface to Princeton's 'WordNet' without 'rJava' dependency. 'WordNet' data is not included. Princeton University makes 'WordNet' available to research and commercial users free of charge provided the terms of their license (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed, and proper reference is made to the project using an appropriate citation (<https://wordnet.princeton.edu/citing-wordnet>).
Extended Susceptible-Exposed-Infected-Recovery Model for 
    handling high false negative rate and symptom based administration of 
    diagnostic tests. <doi:10.1101/2020.09.24.20200238>.
An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See http://snap.stanford.edu/seismic/ for more information and datasets.
Fast versions of seismic analysis functions that 'roll' over a
    vector of values. See the 'RcppRoll' package for alternative
    versions of basic statistical functions such as rolling mean,
    median, etc.
Set of functions that access information about deputies and votings
    in Polish diet from webpage <http://www.sejm.gov.pl>. The package was developed
    as a result of an internship in MI2 Group - <http://mi2.mini.pw.edu.pl>, Faculty
    of Mathematics and Information Science, Warsaw University of Technology.
Sejong(http://www.sejong.or.kr/) corpus and
        Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum)
        dictionaries for KoNLP
Implements a method for fitting a bounded probability
        distribution to quantiles (for example stated by an expert),
        see Bornkamp and Ickstadt (2009) for details.  For this
        purpose B-splines are used, and the density is obtained by
        penalized least squares based on a Brier entropy penalty.  The
        package provides methods for fitting the distribution as well
        as methods for evaluating the underlying density and cdf. In
        addition methods for plotting the distribution, drawing random
        numbers and calculating quantiles of the obtained distribution
        are provided.
Calculates (unconditional) post-selection confidence intervals
    and p-values for the coefficients of (generalized) linear models.
The objective of these functions is to derive a species assemblage
    that satisfies a functional trait profile. Restoring resilient ecosystems
    requires a flexible framework for selecting assemblages that are based on the
    functional traits of species. However, current trait-based models have been
    limited to algorithms that can only select species by optimising specific trait
    values, and could not elegantly accommodate the common desire among restoration
    ecologists to produce functionally diverse assemblages. We have solved this
    problem by applying a non-linear optimisation algorithm that optimises Rao Q,
    a closed-form functional trait diversity index that incorporates species
    abundances, subject to other linear constraints. This framework generalises
    previous models that only optimised the entropy of the community, and can
    optimise both functional diversity and entropy simultaneously. This package
    can also be used to generate experimental assemblages to test the effects
    of community-level traits on community dynamics and ecosystem function. The
    method is based on theory discussed in Laughlin (2014, Ecology Letters)
    <doi.org/10.1111/ele.12288>.
Provides indices such as Manly's alpha, foraging ratio, and Ivlev's selectivity to allow for analysis of dietary selectivity and preference. Can accommodate multiple experimental designs such as constant prey number of prey depletion. Please contact the package maintainer with any publications making use of this package in an effort to maintain a repository of dietary selections studies.
An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.
The aim of most plant breeding programmes is simultaneous improvement of several characters. An objective method involving simultaneous selection for several attributes then becomes necessary. It has been recognised that most rapid improvements in the economic value is expected from selection applied simultaneously to all the characters which determine the economic value of a plant, and appropriate assigned weights to each character according to their economic importance, heritability and correlations between characters. So the selection for economic value is a complex matter. If the component characters are combined together into an index in such a way that when selection is applied to the index, as if index is the character to be improved, most rapid improvement of economic value is expected. Such an index was first proposed by Smith (1937 <doi:10.1111/j.1469-1809.1936.tb02143.x>) based on the Fisher's (1936 <doi:10.1111/j.1469-1809.1936.tb02137.x>) "discriminant function" Dabholkar (1999 <https://books.google.co.in/books?id=mlFtumAXQ0oC&lpg=PA4&ots=Xgxp1qLuxS&dq=elements%20of%20biometrical%20genetics&lr&pg=PP1#v=onepage&q&f=false>). In this package selection index is calculated based on the Smith (1937) selection index method.
Multi-stage selection is practiced in numerous fields of life
    and social sciences and particularly in breeding. A special characteristic of
    multi-stage selection is that candidates are evaluated in successive stages
    with increasing intensity and effort, and only a fraction of the superior
    candidates is selected and promoted to the next stage. For the optimum design
    of such selection programs, the selection gain plays a crucial role. It can be
    calculated by integration of a truncated multivariate normal (MVN) distribution.
    While mathematical formulas for calculating the selection gain and the variance
    among selected candidates were developed long time ago, solutions for numerical
    calculation were not available. This package can also be used for optimizing
    multi-stage selection programs for a given total budget and different costs of
    evaluating the candidates in each stage.
New tools for post-selection inference, for use with forward
    stepwise regression, least angle regression, the lasso, and the many means
    problem. The lasso function implements Gaussian, logistic and Cox survival
    models.
Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).
Translates a CSS3 selector into an equivalent XPath
  expression. This allows us to use CSS selectors when working with
  the XML package as it can only evaluate XPath expressions. Also
  provided are convenience functions useful for using CSS selectors on
  XML nodes. This package is a port of the Python package 'cssselect'
  (<https://cssselect.readthedocs.io/>).
Fit and selects point pattern models based on minimum contrast, AIC and and goodness of fit.
Detection of outliers and influential errors using a latent variable model.
The W3C WebDriver specification defines a way for out-of-process
    programs to remotely instruct the behaviour of web browsers. It is detailed
    at <https://w3c.github.io/webdriver/webdriver-spec.html>. This package provides
    an R client implementing the W3C specification.
Provides the SELF criteria to learn causal structure. Please cite "Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao. SELF: Structural Equational Embedded Likelihood Framework for Causal Discovery. AAAI. 2018."
Functions using Cohen's effect sizes (Cohen, Jacob. Statistical power analysis for the behavioral sciences. Academic press, 2013) are provided for reliable feature selection in biology data analysis.  In addition to Cohen's effect sizes, p-values are calculated and adjusted from quasi-Poisson GLM, negative binomial GLM and Normal distribution ANOVA.  Significant features (genes, RNAs or proteins) are selected by adjusted p-value and minimum Cohen's effect sizes, calculated to keep certain level of statistical power of biology data analysis given p-value threshold and sample size.
A probability tree allows to compute probabilities of
	     complex events, such as genotype probabilities in intermediate generations of inbreeding
	     through recurrent self-fertilization (selfing). This package implements functionality to compute
	     probability trees for two- and three-marker genotypes in the F2 to F7 selfing
	     generations. The conditional probabilities are derived automatically
	     and in symbolic form. The package also provides functionality to
	     extract and evaluate the relevant probabilities.
Performs a regularization approach to variable selection in the
             model-based clustering and classification frameworks.
             First, the variables are arranged in order with a lasso-like procedure. 
             Second, the method of Maugis, Celeux, and Martin-Magniette (2009, 2011)
             <doi:10.1016/j.csda.2009.04.013>, <doi:10.1016/j.jmva.2011.05.004> 
             is adapted to define the role of variables in the two frameworks.
Functions for fitting general linear structural
    equation models (with observed and latent variables) using the RAM approach, 
    and for fitting structural equations in observed-variable models by two-stage least squares.
It offers functions for creating dashboard with Fomantic UI.
We present a rank-based Mercer kernel to compute a pair-wise similarity metric corresponding to informative representation of data. We tailor the development of a kernel to encode our prior knowledge about the data distribution over a probability space. The philosophical concept behind our construction is that objects whose feature values fall on the extreme of that feature’s probability mass distribution are more similar to each other, than objects whose feature values lie closer to the mean. Semblance emphasizes features whose values lie far away from the mean of their probability distribution. The kernel relies on properties empirically determined from the data and does not assume an underlying distribution. The use of feature ranks on a probability space ensures that Semblance is computational efficacious, robust to outliers, and statistically stable, thus making it widely applicable algorithm for pattern analysis. The output from the kernel is a square, symmetric matrix that gives proximity values between pairs of observations.
Outlier and leverage diagnostics for SEM.
Interactive 'shiny' application for working with Structural Equation Modelling technique. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/semwebappk/> .
Fits a structural equation multidimensional scaling (SEMDS) model for asymmetric and three-way input dissimilarities. It assumes that the dissimilarities are measured with errors. The latent dissimilarities are estimated as factor scores within an SEM framework while the objects are represented in a low-dimensional space as in MDS.
Automatically calculate direct, indirect, and total effects for 
    piecewise structural equation models, comprising lists of fitted models 
    representing structured equations (Lefcheck 2016 <doi:10/f8s8rb>). 
    Confidence intervals are provided via bootstrapping.
Estimate networks and causal relationships in complex systems through
  Structural Equation Modeling. This package also includes functions to import,
  weight, manipulate, and fit biological network models within the
  Structural Equation Modeling framework described in
  Palluzzi and Grassi (2021) <arXiv:2103.08332>.
Contains methods to generate and evaluate semi-artificial data sets. 
 Based on a given data set different methods learn data properties using machine learning algorithms and
 generate new data with the same properties.
 The package currently includes the following data generators:
  i) a RBF network based generator using rbfDDA() from package 'RSNNS',
  ii) a Random Forest based generator for both classification and regression problems
  iii) a density forest based generator for unsupervised data
 Data evaluation support tools include:
  a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance
  b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM
  c) evaluation based on classification performance with various learning models, e.g., random forests.
Use inverse probability weighting methods to estimate treatment effect under marginal structure model for the transition hazard of semi competing risk data, i.e. illness death model. We implement two specific such models, the usual Markov illness death structural model and the general Markov illness death structural model. We also provide the estimates of three cumulative incidence function of the potential outcomes.
Hierarchical multistate models are considered to perform the analysis of independent/clustered semi-competing risks data. The package allows to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions and cluster-specific random effects distribution; a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation approach for several parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.
Provides routines to check identifiability or non-identifiability
    of linear structural equation models as described in Drton, Foygel, and
    Sullivant (2011) <doi:10.1214/10-AOS859>, Foygel, Draisma, and Drton (2012) 
    <doi:10.1214/12-AOS1012>, and other works. The routines are based on the graphical 
    representation of structural equation models.
Semi-parametric estimation problem can be solved by two-step Newton-Raphson iteration. The implicit profiling method<arXiv:2108.07928> is an improved method of two-step NR iteration especially for the implicit-bundled type of the parametric part and non-parametric part. This package provides a function semislv() supporting the above two methods and numeric derivative approximation for unprovided Jacobian matrix.
Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model. Reference: A. Krol and P. Saint-Pierre (2015) 	<doi:10.18637/jss.v066.i06>.
A powerful, easy to syntax for specifying and estimating complex 
    Structural Equation Models. Models can be estimated using Partial 
    Least Squares Path Modeling or Covariance-Based Structural Equation 
    Modeling or covariance based Confirmatory Factor Analysis. Methods described in Ray, Danks, and Valdez (2021).
Functions for semiparametric regression analysis, to
        complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J.
        (2003). Semiparametric Regression. Cambridge University Press.
Provides Markov Chain Monte Carlo (MCMC) routine for the 
             structural equation modelling described in 
             Maity et. al. (2020) <doi:10.1093/bioinformatics/btaa286>. This MCMC sampler is 
             useful when one attempts to perform an integrative survival analysis for multiple 
             platforms of the Omics data where the response is time to event and the 
             predictors are different omics expressions for different platforms.
Provides methods for constructing and maintaining a database of presentations in R. The presentations are either ones that the user gives or gave or presentations at a particular event or event series. The package also provides a plot method for the interactive mapping of the presentations using 'leaflet' by grouping them according to country, city, year and other presentation attributes. The markers on the map come with popups providing presentation details (title, institution, event, links to materials and events, and so on).
Implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 <doi:10.3389/fnhum.2014.00407>), random walk simulation (Kenett & Austerweil, 2016 <http://alab.psych.wisc.edu/papers/files/Kenett16CreativityRW.pdf>), and a function to compute global network measures. Significance tests and plotting features are also implemented.
Implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the 'SemNetDictionaries' package to make the cleaning process more accurate, efficient, and reproducible.
Implements dictionaries that can be used in the 'SemNetCleaner' package. Also includes several functions aimed at facilitating the text cleaning analysis in the 'SemNetCleaner' package. This package is designed to integrate and update word lists and dictionaries based on each user's individual needs by allowing users to store and save their own dictionaries. Dictionaries can be added to the 'SemNetDictionaries' package by submitting user-defined dictionaries to <https://github.com/AlexChristensen/SemNetDictionaries>.
Latent repeated measures ANOVA (L-RM-ANOVA) is a structural 
    equation modeling based alternative to traditional repeated measures ANOVA.
    L-RM-ANOVA extends the latent growth components approach by 
    Mayer et al. (2012) <doi:10.1080/10705511.2012.713242> and introduces
    latent variables to repeated measures analysis.
Path diagrams and visual analysis of various SEM packages' output.
Provides a-priori, post-hoc, and compromise power-analyses
    for structural equation models (SEM). Moshagen & Erdfelder (2016)
    <doi:10.1080/10705511.2014.950896>.
Most function focus on specific ways to customize a graph. 
             They use a 'qgraph' output  as the first argument, and return a
             modified 'qgraph' object. This allows the functions to be chained
             by a pipe operator.
Implements methods for querying SEO (Search Engine Optimization) and SEM (Search Engine Marketing) data from 'SEMrush' using its API (<https://fr.semrush.com/api-documentation/>).
      'SEMrush' API uses a basic authentication with an API key.
Perform sensitivity analysis in structural equation modeling using 
    meta-heuristic optimization methods (e.g., ant colony optimization and others). 
    The references for the proposed methods are: 
    (1) Leite, W., & Shen, Z., Marcoulides, K., Fish, C., & Harring, J. (in press). 
    <doi:10.1080/10705511.2021.1881786>
    (2) Harring, J. R., McNeish, D. M., & Hancock, G. R. (2017)
    <doi:10.1080/10705511.2018.1506925>;
    (3) Socha, K., & Dorigo, M. (2008) <doi:10.1016/j.ejor.2006.06.046>.
    We also thank Dr. Krzysztof Socha for sharing his research on ant colony optimization
    algorithm with continuous domains and associated R code,
    which provided the base for the development of this package.
Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments.
For confirmatory factor analysis ('CFA') and structural equation
             models ('SEM') estimated with the 'lavaan' package,
             this package provides functions to create model summary tables and model
             comparison tables for hypothesis testing. Tables can be produced in
             'LaTeX', 'HTML', or comma separated variables ('CSV').
Provides tools for structural equation modeling, many of which extend the 'lavaan' package; for example, to pool results from multiple imputations, probe latent interactions, or test measurement invariance.
SEM Trees and SEM Forests -- an extension of model-based decision
    trees and forests to Structural Equation Models (SEM). SEM trees hierarchically
    split empirical data into homogeneous groups each sharing similar data patterns
    with respect to a SEM by recursively selecting optimal predictors of these
    differences. SEM forests are an extension of SEM trees. They are ensembles of
    SEM trees each built on a random sample of the original data. By aggregating
    over a forest, we obtain measures of variable importance that are more robust
    than measures from single trees. A description of the method was published by
    Brandmaier, von Oertzen, McArdle, & Lindenberger (2013; <doi:10.1037/a0030001>) 
    and Arnold, Voelkle, & Brandmaier (2020; <doi:10.3389/fpsyg.2020.564403>).
Tools and functions for parsing, rendering and operating on
    semantic version strings. Semantic versioning is a simple set of rules
    and requirements that dictate how version numbers are assigned and
    incremented as outlined at <http://semver.org>.
Semantic Versions allow for standardized management versions. 
    This package implements semantic versioning handling in R. using R6 to 
    create a mutable object that can handle deciphering and checking versions.
Functions to download Sentinel-2 optical images
 and perform preliminary processing operations.
 'sen2r' provides the instruments required to easily perform
 (and eventually automate) the steps necessary to build a complete
 Sentinel-2 processing chain.
 A Graphical User Interface to facilitate data processing is also provided.
 For additional documentation refer to the following article: 
 Ranghetti et al. (2020) <doi:10.1016/j.cageo.2020.104473>.
Send email using 'Sendgrid' <https://sendgrid.com/> 
            mail API(v3) <https://docs.sendgrid.com/api-reference/how-to-use-the-sendgrid-v3-api/authentication>.
Package contains a simple SMTP client which provides a
        portable solution for sending email, including attachment, from
        within R.
Stacked ensemble for regression tasks based on 'mlr3' framework with a pipeline for preprocessing numeric and factor features and hyper-parameter tuning using grid or random search.
Implements a suite of sensitivity analysis tools 
  that extends the traditional omitted variable bias framework and makes it easier 
  to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.
Provides an interface to the 'Sensibo Sky' API which allows to remotely control non-smart air conditioning units.
  See <https://sensibo.com> for more informations.
An implementation of sensitivity analysis for phylogenetic comparative
 methods. The package is an umbrella of statistical and graphical methods that 
 estimate and report different types of uncertainty in PCM:
 (i) Species Sampling uncertainty (sample size; influential species and clades).
 (ii) Phylogenetic uncertainty (different topologies and/or branch lengths).
 (iii) Data uncertainty (intraspecific variation and measurement error).
A collection of functions for factor screening, global sensitivity analysis and robustness analysis. Most of the functions have to be applied on model with scalar output, but several functions support multi-dimensional outputs.
Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.
Implements the calibrated sensitivity analysis approach for matched observational studies. Our sensitivity analysis framework views matched sets as drawn from a super-population. The unmeasured confounder is modeled as a random variable. We combine matching and model-based covariate-adjustment methods to estimate the treatment effect. The hypothesized unmeasured confounder enters the picture as a missing covariate. We adopt a state-of-art Expectation Maximization (EM) algorithm to handle this missing covariate problem in generalized linear models (GLMs). As our method also estimates the effect of each observed covariate on the outcome and treatment assignment, we are able to calibrate the unmeasured confounder to observed covariates.
      Zhang, B., Small, D. S. (2018). <arXiv:1812.00215>.
This package performs sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition.  The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468).  The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al.  The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al.  The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test.  The other functions in the package are internal functions.
Sensitivity to unmeasured biases in an observational study that is a full match.  Function senfm() performs tests and function senfmCI() creates confidence intervals.  The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) <DOI:10.1111/j.1541-0420.2006.00717.x>.
Sensitivity analysis for multiple outcomes in observational studies.  For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) <doi:10.1214/16-AOAS942>.  Alternatively, attention may focus on a few principal components in the principal() function.  The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.
The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean.  The main function in the package is senmv(), but amplify() and truncatedP() are also useful.  The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.
Sensitivity analysis for tests, confidence intervals and estimates in matched observational studies with one or more controls using weighted or unweighted Huber-Maritz M-tests (including the permutational t-test).   The method is from Rosenbaum (2014) Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls JASA, 109(507), 1145-1158 <doi:10.1080/01621459.2013.879261>.
This package provides functions to perform principal stratification sensitivity analyses on datasets.
We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) <doi:10.1002/sim.7620>. The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships.
It allows to rapidly compute, bootstrap and plot up to fourth-order Sobol'-based sensitivity indices using several state-of-the-art first and total-order estimators. Sobol' indices can be computed either for models that yield a scalar as a model output or for systems of differential equations. The package also provides a suit of benchmark tests functions and several options to obtain publication-ready figures of the model output uncertainty and sensitivity-related analysis. An overview of Sobol'-based sensitivity indices can be found in Saltelli et al. (2008, ISBN:9780470059975) and in Puy, Lo Piano, Saltelli, and Levin (2021) <arXiv:2101.10103>.
Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008) <DOI:10.1111/j.1745-459X.2007.00137.x>.
Contains the function CUUimpute() which performs model-based clustering and imputation simultaneously.
Collection of datasets from Sen & Srivastava: "Regression
        Analysis, Theory, Methods and Applications", Springer.  Sources
        for individual data files are more fully documented in the
        book.
Sensitivity analysis in unmatched observational studies, with or without strata.  The main functions are sen2sample() and senstrat().  See Rosenbaum, P. R. and Krieger, A. M. (1990), JASA, 85, 493-498, <doi:10.1080/01621459.1990.10476226> and Gastwirth, Krieger and Rosenbaum (2000), JRSS-B, 62, 545–555 <doi:10.1111/1467-9868.00249> .
Provides access and analytic functions for Sensus data.
Unsupervised text tokenizer allowing to perform byte pair encoding and unigram modelling. 
    Wraps the 'sentencepiece' library <https://github.com/google/sentencepiece> which provides a language independent tokenizer to split text in words and smaller subword units. 
    The techniques are explained in the paper "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing" by Taku Kudo and John Richardson (2018) <doi:10.18653/v1/D18-2012>.
    Provides as well straightforward access to pretrained byte pair encoding models and subword embeddings trained on Wikipedia using 'word2vec', 
    as described in "BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages" by Benjamin Heinzerling and Michael Strube (2018) <http://www.lrec-conf.org/proceedings/lrec2018/pdf/1049.pdf>.
Sentiment Analysis via deep learning and gradient boosting models with a lot of the underlying hassle taken care of to make the process as simple as possible. 
  In addition to out-performing traditional, lexicon-based sentiment analysis (see <https://benwiseman.github.io/sentiment.ai/#Benchmarks>),
  it also allows the user to create embedding vectors for text which can be used in other analyses.
  GPU acceleration is supported on Windows and Linux.
Performs a sentiment analysis of textual contents in R. This implementation
    utilizes various existing dictionaries, such as Harvard IV, or finance-specific 
    dictionaries. Furthermore, it can also create customized dictionaries. The latter 
    uses LASSO regularization as a statistical approach to select relevant terms based on 
    an exogenous response variable.
Calculate text polarity sentiment at the sentence level and
         optionally aggregate by rows or grouping variable(s).
Implements parsimonious mixtures of MSEN and MTIN distributions via expectation-
    maximization based algorithms for model-based clustering. For each mixture
    component, parsimony is reached via the eigen-decomposition of the scale 
    matrices and by imposing a constraint on the tailedness parameter. This produces
    a family of 28 parsimonious mixture models for each distribution.
Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) <doi:10.18637/jss.v099.i02>.
A framework that joins topic modeling and sentiment analysis of
  textual data. The package implements a fast Gibbs sampling estimation of
  Latent Dirichlet Allocation (Griffiths and Steyvers (2004)
  <doi:10.1073/pnas.0307752101>) and Joint Sentiment/Topic Model (Lin, He,
  Everson and Ruger (2012) <doi:10.1109/TKDE.2011.48>). It offers a variety of
  helpers and visualizations to analyze the result of topic modeling. The
  framework also allows enriching topic models with dates and externally
  computed sentiment measures. A flexible aggregation scheme enables the
  creation of time series of sentiment or topical proportions from the enriched
  topic models. Moreover, a novel method jointly aggregates topic proportions
  and sentiment measures to derive time series of topical sentiment.
Unofficial client for 'Sentry' <https://sentry.io>,
  a self-hosted or cloud-based error-monitoring service. It will inform about
  errors in real-time, and includes integration with the 'Plumber' package.
To calculate the standard error of measurement (SEM) to assess the observer variability (inter- and intra-observer variation).
    The methods used in this package are referenced from Zoran B. Popović (2017) <doi:10.21037/cdt.2017.03.12>.
Maximum likelihood estimation of the parameters
    of matrix and 3rd-order tensor normal distributions with unstructured
    factor variance covariance matrices, two procedures, and for unbiased
    modified likelihood ratio testing of simple and double separability
    for variance-covariance structures, two procedures.
Visual representations of model fit or predictive success in the form of "separation plots."  See Greenhill, Brian, Michael D. Ward, and Audrey Sacks. "The separation plot: A new visual method for evaluating the fit of binary models." American Journal of Political Science 55.4 (2011): 991-1002.
The 'seplyr' (standard evaluation plying) package supplies improved
    standard evaluation adapter methods for important common 'dplyr' data manipulation tasks.
    In addition the 'seplyr' package supplies several new "key operations
    bound together" methods.  These include 'group_summarize()' (which
    combines grouping, arranging and calculation in an atomic unit),
    'add_group_summaries()' (which joins grouped summaries into a 'data.frame'
    in a well documented manner), 'add_group_indices()' (which adds
    per-group identifiers to a 'data.frame' without depending on row-order),
    'partition_mutate_qt()' (which optimizes mutate sequences), and 'if_else_device()'
    (which simulates per-row if-else blocks in expression sequences).
Potential randomization schemes are prospectively evaluated when
    units are assigned to treatment arms upon entry into the experiment. The schemes
    are evaluated for balance on covariates and on predictability (i.e., how well
    could a site worker guess the treatment of the next unit enrolled).
This is a method for DNA Copy Number Profiling using
        Next-Generation Sequencing. It has new model and test
        statistics based on non-homogeneous Poisson Processes with
        change point models. It uses an adaptation of Circular Binary
        Segmentation. Also included are methods for point-wise Bayesian
        Confidence Interval and model selection method for the
        change-point model. A case and a control sample reads (normal
        and tumor) are required.
A modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.
Sequence detector in this package contains a specific automaton model that can be used to learn and detect data and process sequences.
    Automaton model in this package is capable of learning and tracing sequences. Automaton model can be found in Krleža, Vrdoljak, Brčić (2019) <doi:10.1109/ACCESS.2019.2955245>.
    This research has been partly supported under Competitiveness and Cohesion Operational Programme from the European Regional and Development Fund, as part of the Integrated Anti-Fraud System project no. KK.01.2.1.01.0041. This research has also been partly supported by the European Regional Development Fund under the grant KK.01.1.1.01.0009.
Sequential method to solve the the binary classification problem 
    by Wang (2019) <arXiv:arXiv:1901.10079>, multi-class 
    classification problem by Li (2020) <doi:10.1016/j.csda.2020.106911>
    and the highly stratified multiple-response problem by Chen (2019)
    <doi:10.1111/biom.13160>.
Generates the following sequential two-arm experimental designs:
    (1) completely randomized (Bernoulli)
    (2) balanced completely randomized
    (3) Efron's (1971) Biased Coin
    (4) Atkinson's (1982) Covariate-Adjusted Biased Coin
    (5) Kapelner and Krieger's (2014) Covariate-Adjusted Matching on the Fly
    (6) Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights (Naive)
    (7) Kapelner and Krieger's (2021) CARA Matching on the Fly with Differential Covariate Weights (Stepwise)
    and also provides the following types of inference:
    (1) estimation (with both Z-style estimators and OLS estimators), 
    (2) frequentist testing (via asymptotic distribution results and via employing the nonparametric randomization test) and
    (3) frequentist confidence intervals (only under the superpopulation sampling assumption currently). Details can be found
    in our publication: Kapelner and Krieger "A Matching Procedure for Sequential Experiments that Iteratively Learns which 
    Covariates Improve Power" (2020) <arXiv:2010.05980>.
Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.
Generates/modifies RNA-seq data for use in simulations. We provide
    a suite of functions that will add a known amount of signal to a real 
    RNA-seq dataset. The advantage of using this approach over simulating under
    a theoretical distribution is that common/annoying aspects of the data
    are more preserved, giving a more realistic evaluation of your method. 
    The main functions are select_counts(), thin_diff(), thin_lib(), 
    thin_gene(), thin_2group(), thin_all(), and effective_cor(). See
    Gerard (2020) <doi:10.1186/s12859-020-3450-9> for details on the
    implemented methods.
It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) <doi:10.1177/0049124115591013>), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) <doi:10.1177/0081175015570976>), smoothing sequences for index plots (see Piccarreta (2012) <doi:10.1177/0049124112452394>), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) <doi:10.1111/j.1467-985X.2009.00606.x>), symmetrical (or canonical) Partial Least Squares (see Bry (1996)).
Designed for fitting hidden (latent) Markov models and mixture
    hidden Markov models for social sequence data and other categorical time series.
    Also some more restricted versions of these type of models are available: Markov
    models, mixture Markov models, and latent class models. The package supports
    models for one or multiple subjects with one or multiple parallel sequences
    (channels). External covariates can be added to explain cluster membership in
    mixture models. The package provides functions for evaluating and comparing
    models, as well as functions for visualizing of multichannel sequence data and
    hidden Markov models. Models are estimated using maximum likelihood via the EM
    algorithm and/or direct numerical maximization with analytical gradients. All
    main algorithms are written in C++ with support for parallel computation. 
    Documentation is available via several vignettes in this page, and the  
    paper by Helske and Helske (2019, <doi:10.18637/jss.v088.i03>).
Contains an implementation of invariant causal prediction for sequential data. The main function in the package is 'seqICP', which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method 'seqICPnl', which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines 'seqICP.s' and 'seqICPnl.s' corresponding to the respective main methods.
Exploratory data analysis and data visualization
  for biological sequence (DNA and protein) data. Seqinr includes
  utilities for sequence data management under the ACNUC system
  described in Gouy, M. et al. (1984) Nucleic Acids Res.
  12:121-127 <doi:10.1093/nar/12.1Part1.121>.
Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 <doi:10.1101/287839>.
A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.
Supports reading and writing sequences for different formats (currently interleaved and sequential formats for 'FASTA' and 'PHYLIP'), file conversion, and manipulation (e.g. filter sequences that contain specify pattern, export consensus sequence from an alignment).
Integrate sequencing data (Variant call format, e.g. VCF or BCF) or meta-analysis results in R. This package can help you (1) read VCF/BCF/BGEN files by chromosomal ranges (e.g. 1:100-200); (2) read RareMETAL summary statistics files; (3) read tables from a tabix-indexed files; (4) annotate VCF/BCF files; (5) create customized workflow based on Makefile.
S4 class object for creating and managing group sequential designs. It calculates the efficacy and futility boundaries at each look. It allows modifying the design and tracking the design update history.
Methods to generate random gene-gene association networks and simulate RNA-seq data from them, as described in Grimes and Datta (2021) <doi:10.18637/jss.v098.i12>. Includes functions to generate random networks of any size and perturb them to obtain differential networks. Network objects are built from individual, overlapping modules that represent pathways. The resulting network has various topological properties that are characteristic of gene regulatory networks. RNA-seq data can be generated such that the association among gene expression profiles reflect the underlying network. A reference RNA-seq dataset can be provided to model realistic marginal distributions. Plotting functions are available to visualize a network, compare two networks, and compare the expression of two genes across multiple networks.
A fast implementation of k-mer (subsequences of length k) counting with the support for all types of biological sequences and k-mer variants.
This package includes functions for handling DNA
        sequences, especially simulated RFLP and TRFLP pattern based on
        selected restriction enzyme and DNA sequences.
Sequential triangular test for the arithmetic mean in one- and two-
    samples, proportions in one- and two-samples, and the Pearson's correlation
    coefficient.
Pierre, J. S. and Kasper, C.,(1990), "The Design of Ethological Flow-Charts on Factorial Analysis Representations - an Application to the Study of the Male Mole-Cricket Sexual Courtship." Biology of Behaviour 15(3-4): 125-151.(in french). Includes the calculation of transition matrices, their manipulation (symmetrization and diagonal loading), their analysis by CA (Correspondence Analysis), their comparison by likelihood ratio tests, the graphic plot of flow charts on the factorial axes of the CA.
Educational package used in R courses to illustrate
       	     object-oriented programming and package
       	     development. Using biological sequences (DNA and RNA) as
       	     a working example.
Contains fast functions to calculate the exact Bayes posterior
    for the Sparse Normal Sequence Model, implementing the algorithms
    described in Van Erven and Szabo (2021,
    <doi:10.1214/20-BA1227>). For general hierarchical
    priors, sample sizes up to 10,000 are feasible within half an hour
    on a standard laptop. For beta-binomial spike-and-slab priors, a
    faster algorithm is provided, which can handle sample sizes of
    100,000 in half an hour. In the implementation, special care has
    been taken to assure numerical stability of the methods even for
    such large sample sizes.
Functions to calculate exact critical values, statistical power, expected time to signal, and required sample sizes for performing exact sequential analysis. All these	calculations can be done for either Poisson or binomial data, for continuous or group sequential analyses, and for different types of rejection boundaries. In case of group sequential analyses, the group sizes do not have to be specified in advance and the alpha spending can be arbitrarily settled.
Functions to be used in conjunction with the 'Sequential' package that allows for planning of observational database studies that will be analyzed with exact sequential analysis. This package supports Poisson- and binomial-based data. The primary function, seq_wrapper(...), accepts parameters for simulation of a simple exposure pattern and for the 'Sequential' package setup and analysis functions. The exposure matrix is used to simulate the true and false positive and negative populations (Green (1983) <doi:10.1093/oxfordjournals.aje.a113521>, Brenner (1993) <doi:10.1093/oxfordjournals.aje.a116805>). Functions are then run from the 'Sequential' package on these populations, which allows for the exploration of outcome misclassification in data.
Tools to analyze genomic sequencing data from
 paired normal-tumor samples, including cellularity and ploidy estimation; mutation
 and copy number (allele-specific and total copy number) detection, quantification
 and visualization.
Apache Drill is a low-latency distributed query engine designed to enable 
    data exploration and analysis on both relational and non-relational data stores, 
    scaling to petabytes of data. Methods are provided that enable working with Apache 
    Drill instances via the REST API, DBI methods
    and using 'dplyr'/'dbplyr' idioms. Helper functions are included to facilitate
    using official Drill Docker images/containers.
Enables reading and writing binary and ASCII data to 
             RS232/RS422/RS485 or any other virtual serial interface of the
             computer.
Infrastructure for ordering objects with an implementation of several
    seriation/sequencing/ordination techniques to reorder matrices, dissimilarity
    matrices, and dendrograms. Also provides (optimally) reordered heatmaps,
    color images and clustering visualizations like dissimilarity plots, and
    visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) <doi:10.18637/jss.v025.i03>.
Calculate and compare lower confidence bounds for binomial series system reliability. The R 'shiny' application, launched by the function launch_app(), weaves together a workflow of customized simulations and delta coverage calculations to output recommended lower confidence bound methods.
Translates antibody levels measured in a (cross-sectional)
  population sample into an estimate of the frequency with which
  seroconversions (infections) occur in the sampled population.
A regularization method for the cumulative link
    models.  The smooth-effect-on-response penalty (SERP) provides
    flexible modelling of the ordinal model by enabling the smooth
    transition from the general cumulative link model to a coarser form of
    the same model. In other words, as the tuning parameter goes from zero
    to infinity, the subject-specific effects associated with each
    variable in the model tend to a unique global effect. The parameter
    estimates of the general cumulative model are mostly unidentifiable or
    at least only identifiable within a range of the entire parameter
    space. Thus, by maximizing a penalized rather than the usual
    non-penalized log-likelihood, this and other numerical problems common
    with the general model are to a large extent eliminated. Fitting is
    via a modified Newton's method. Several standard model performance and
    descriptive methods are also available. For more details on the penalty
    implemented here, see, Ugba (2021) <doi:10.21105/joss.03705> and 
    Ugba et al. (2021) <doi:10.3390/stats4030037>.
The primary goal of 'Serpstat' API <https://serpstat.com/api/> 
    is to reduce manual SEO (search engine optimization) and PPC (pay-per-click) 
    tasks. You can automate your keywords research or competitors analysis 
    with this API wrapper.
Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive
    mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS),
    using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate
    observations of SERRS are highly collinear and lend themselves to a reduced-rank
    representation. The GAMM separates the SERRS signal into three components: a
    sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline;
    and additive white noise. The parameters of each component of the model are estimated
    iteratively using SMC. The posterior distributions of the parameters given the observed
    spectra are represented as a population of weighted particles.
Functions that facilitate and speed up the analysis of data
    produced by a Syntech servosphere <http://www.ockenfels-syntech.com/products/locomotion-compensation/>,
    which is equipment for studying the movement behavior of arthropods.
    This package is designed to make working with data produced from a 
    servosphere easy for someone new to or unfamiliar with R. The functions
    provided in this package fall into three broad-use categories: functions for
    cleaning raw data produced by the servosphere software, functions for
    deriving movement variables based on position data, and functions for 
    summarizing movement variables for easier analysis. These functions are
    built with functions from the tidyverse package to work efficiently, as a
    single servosphere file may consist of hundreds of thousands of rows of data
    and a user may wish to analyze hundreds of files at a time. Many of the 
    movement variables derivable through this package are described in the 
    following papers:
    Otálora-Luna, Fernando; Dickens, Joseph C. (2011) <doi:10.1371/journal.pone.0020990>
    Party, Virginie; Hanot, Christophe; Busser, Daniela Schmidt; Rochat, Didier; Renou, Michel (2013) <doi:10.1371/journal.pone.0052897>
    Bell, William J.; Kramer, Ernest (1980) <doi:10.1007/BF01402908>
    Becher, Paul G; Guerin, Patrick M. (2009) <doi:10.1016/j.jinsphys.2009.01.006>.
Start an HTTP server in R to serve static files, or dynamic
    documents that can be converted to HTML files (e.g., R Markdown) under a
    given directory.
Structural equation modeling is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with inter-correlated dependent and independent variables. Here we implement a simple method for spatially explicit structural equation modeling based on the analysis of variance co-variance matrices calculated across a range of lag distances. This method provides readily interpreted plots of the change in path coefficients across scale.
Tools to compute and analyze the set of statistically-equivalent (Gaussian, linear) path models which generate the input precision or (partial) correlation matrix. 
             This procedure is useful for understanding how statistical network models such as the Gaussian Graphical Model (GGM) perform as causal discovery tools. 
             The statistical-equivalence set of a given GGM expresses the uncertainty we have about the sign, size and direction of directed relationships based on the weights matrix of the GGM alone. 
             The derivation of the equivalence set and its use for understanding GGMs as causal discovery tools is described by Ryan, O., Bringmann, L.F., & Schuurman, N.K. (2022) <doi: 10.31234/osf.io/ryg69>.
Utility functions for interacting with R processes from
        external programs. This package includes functions to save and
        restore session information (including loaded packages, and
        attached data objects), as well as functions to evaluate
        strings containing R commands and return the printed results or
        an execution transcript.
Query and print information about the current R
    session.  It is similar to 'utils::sessionInfo()', but includes more
    information about packages, and where they were installed from.
More easy to get intersection, union or complementary set and 
    combinations.
An object-oriented package for mathematical sets, upgrading the current gold-standard {sets}. Many forms of mathematical sets are implemented, including (countably finite) sets, tuples, intervals (countably infinite or uncountable), and fuzzy variants. Wrappers extend functionality by allowing symbolic representations of complex operations on sets, including unions, (cartesian) products, exponentiation, and differences (asymmetric and symmetric).
Functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the 	examples in the books by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", Cambridge University Press.
Tests gene expression data from a biological pathway for biologically meaningful differences in the eigenstructure between two classes.  Specifically, it tests the null hypothesis that the two classes' leading eigenvalues and sums of eigenvalues are equal.  A pathway's leading eigenvalue arguably represents the total variability due to variability in pathway activity, while the sum of all its eigenvalues represents the variability due to pathway activity and to other, unregulated causes. Implementation of the method described in Danaher (2015), "Covariance-based analyses of biological pathways".
Implements an algorithm to conduct advanced
    gene set enrichment analysis on the results of genomics experiments.
Provides utilities to help set and record the setting of
	the seed and the uniform and normal generators used when a random
	experiment is run. The utilities can be used in other functions 
	that do random experiments to simplify recording and/or setting all the 
	necessary information for reproducibility. 
	See the vignette and reference manual for examples.
Data structures and basic operations for ordinary sets,
             generalizations such as fuzzy sets, multisets, and
             fuzzy multisets, customizable sets, and intervals.
Mutators to set attributes of variables, that work well in a pipe
    (much like stats::setNames()).
It provides cumulative distribution function (CDF),
    quantile, p-value, statistical power calculator and random number generator
    for a collection of group-testing procedures, including the Higher Criticism
    tests, the one-sided Kolmogorov-Smirnov tests, the one-sided Berk-Jones tests,
    the one-sided phi-divergence tests, etc. The input are a group of p-values.
    The null hypothesis is that they are i.i.d. Uniform(0,1). In the context of
    signal detection, the null hypothesis means no signals. In the context of the
    goodness-of-fit testing, which contrasts a group of i.i.d. random variables to
    a given continuous distribution, the input p-values can be obtained by the CDF
    transformation. The null hypothesis means that these random variables follow the
    given distribution. For reference, see Hong Zhang, Jiashun Jin and Zheyang Wu. 
    "Distributions and Statistical Power of Optimal Signal-Detection Methods In Finite Cases",
    submitted.
Provides option settings management that goes
    beyond R's default 'options' function. With this package, users can define
    their own option settings manager holding option names, default values and 
    (if so desired) ranges or sets of allowed option values that will be 
    automatically checked. Settings can then be retrieved, altered and reset 
    to defaults with ease. For R programmers and package developers it offers 
    cloning and merging functionality which allows for conveniently defining 
    global and local options, possibly in a multilevel options hierarchy. See 
    the package vignette for some examples concerning functions, S4 classes, 
    and reference classes. There are convenience functions to reset par() 
    and options() to their 'factory defaults'.
A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
Defines S4 classes for single-cell genomic data and associated
  information, such as dimensionality reduction embeddings, nearest-neighbor
  graphs, and spatially-resolved coordinates. Provides data access methods and
  R-native hooks to ensure the Seurat object is familiar to other R users. See
  Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>,
  Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>,
  and Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031> for
  more details.
Customise 'Shiny' disconnected screens as well as sanitize error messages to make them clearer and friendlier to the user.
This package contains functions for calculating severity
        and generating severity curves. Specifically, the simple case
        of the one-parameter Normal distribution (i.e., with known
        variance) is considered.
Support for simple features, a standardized way to
    encode spatial vector data. Binds to 'GDAL' for reading and writing
    data, to 'GEOS' for geometrical operations, and to 'PROJ' for
    projection conversions and datum transformations. Uses by default the 's2'
    package for spherical geometry operations on ellipsoidal (long/lat) coordinates.
Stochastic Frontier Analysis
  introduced by Aigner, Lovell and Schmidt (1976)
  and Battese and Coelli (1992, 1995).
Stochastic frontier analysis with advanced methods.
 In particular, it applies the approach proposed by Latruffe et al. (2017) 
 <DOI:10.1093/ajae/aaw077> to estimate a stochastic frontier with technical 
 inefficiency effects when one input is endogenous.
Maximum likelihood estimation for stochastic frontier analysis (SFA) of production 
    (profit) and cost functions. 
    The package includes several distributions for the one-sided error term (i.e. Rayleigh, 
    Gamma, Weibull, lognormal, uniform, generalized exponential and truncated skewed Laplace) 
    as well as the latent class stochastic frontier model (LCM) as described in Dakpo et al. (2021) 
 <doi:10.1111/1477-9552.12422>. 
    Several possibilities in terms of optimization algorithms are proposed.
Support for reading/writing simple feature ('sf') spatial objects from/to 'Parquet' files. 'Parquet' files are an open-source, column-oriented data storage format from Apache (<https://parquet.apache.org/>), now popular across programming languages. This implementation converts simple feature list geometries into well-known binary format for use by 'arrow', and coordinate reference system information is maintained in a standard metadata format.
Provides a function sfc() to compute the substance flow
    with the input files --- "data" and "model". If sample.size is
    set more than 1, uncertainty analysis will be executed while
    the distributions and parameters are supplied in the file "data".
Routines to write, simulate, and validate stock-flow consistent (SFC) models. The accounting structure of SFC models are described in Godley and Lavoie (2007, ISBN:978-1-137-08599-3). The algorithms implemented to solve the models (Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010) <doi:10.2139/ssrn.1729205> and Peressini and Sullivan (1988, ISBN:0-387-96614-5).
Build a constrained Delaunay triangulation from simple features
    objects, applying constraints based on input line segments, and triangle
    properties including maximum area, minimum internal angle. The triangulation code
    in 'RTriangle' uses the method of Cheng, Dey and Shewchuk (2012, ISBN:9781584887300). 
    For a low-dependency alternative with low-quality path-based constrained 
    triangulation see <https://CRAN.R-project.org/package=decido> and for high-quality configurable
    triangulation see <https://CRAN.R-project.org/package=anglr>.
Gives design points from a sequential full factorial-based
 Latin hypercube design, as described in Duan, Ankenman, Sanchez,
 and Sanchez (2015, Technometrics,
 <doi:10.1080/00401706.2015.1108233>).
Converts between R and Simple Feature 'sf' objects, without depending
  on the Simple Feature library. Conversion functions are available at both the R level, 
  and through 'Rcpp'.
Identify and understand clusters of points (typically representing 
    the locations of places or events) stored in simple-features (SF) objects.
    This is useful for analysing, for example, hot-spots of crime events. The 
    package emphasises producing results from point SF data in a single step 
    using reasonable default values for all other arguments, to aid rapid data 
    analysis by users who are starting out. Functions available include kernel
    density estimation (for details, see Yip (2020) 
    <doi:10.22224/gistbok/2020.1.12>), analysis of spatial association (Getis
    and Ord (1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>) and hot-spot
    classification (Chainey (2020) ISBN:158948584X).
The straightforward filtering index (SFINX) identifies true positive
    protein interactions in a fast, user-friendly, and highly accurate way.
    It is not only useful for the filtering of affinity purification -
    mass spectrometry (AP-MS) data, but also for similar types of data
    resulting from other co-complex interactomics technologies, such as TAP-MS,
    Virotrap and BioID. SFINX can also be used via the website interface at
    <http://sfinx.ugent.be>.
Provides a tidy approach to spatial network
    analysis, in the form of classes and functions that enable a seamless
    interaction between the network analysis package 'tidygraph' and the
    spatial analysis package 'sf'.
Provides monthly statistics on the number of monthly air passengers at SFO airport such as operating airline, terminal, geo, etc.
             Data source: San Francisco data portal (DataSF) <https://data.sfgov.org/Transportation/Air-Traffic-Passenger-Statistics/rkru-6vcg>.
An implementation of the Similarity-First Search algorithm (SFS), a combinatorial algorithm which can be used to solve the seriation problem and to recognize some structured weighted graphs. The SFS algorithm represents a generalization to weighted graphs of the graph search algorithm Lexicographic Breadth-First Search (Lex-BFS), a variant of Breadth-First Search. The SFS algorithm reduces to Lex-BFS when applied to binary matrices (or, equivalently, unweighted graphs). Hence this library can be also considered for Lex-BFS applications such as recognition of graph classes like chordal or unit interval graphs. In fact, the SFS seriation algorithm implemented in this package is a multisweep algorithm, which consists in repeating a finite number of SFS iterations (at most n sweeps for a matrix of size n). If the data matrix has a Robinsonian structure, then the ranking returned by the multistep SFS algorithm is a Robinson ordering of the input matrix. Otherwise the algorithm can be used as a heuristic to return a ranking partially satisfying the Robinson property.
Here we provide tools for the estimation of coefficients in 
     penalized regressions when the (co)variance matrix of predictors 
     and the covariance vector between predictors and response, are 
     provided. These methods are extended to the context of a Selection Index
     (commonly used for breeding value prediction). The approaches offer 
     opportunities such as the integration of high-throughput traits in genetic 
     evaluations ('Lopez-Cruz et al., 2020') <doi:10.1038/s41598-020-65011-2> 
     and solutions for training set optimization in Genomic Prediction 
     ('Lopez-Cruz & de los Campos, 2021') <doi:10.1093/genetics/iyab030>.
Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich,
   some of which were ported from S-plus in the 1990s.
 For graphics, have pretty (Log-scale) axes, an enhanced Tukey-Anscombe
   plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()',
   pretty arrows, etc.
 For robustness, have a robust F test and robust range().
 For system support, notably on Linux, provides 'Sys.*()' functions with
   more access to system and CPU information.
 Finally, miscellaneous utilities such as simple efficient prime numbers,
   integer codes, Duplicated(), toLatex.numeric() and is.whole().
A series of tools for analyzing Systems Factorial Technology data.  This includes functions for plotting and statistically testing capacity coefficient functions and survivor interaction contrast functions.  Houpt, Blaha, McIntire, Havig, and Townsend (2013) <doi:10.3758/s13428-013-0377-3> provide a basic introduction to Systems Factorial Technology along with examples using the sft R package.
Classes and methods for spatial objects that have a registered time 
    column, in particular for irregular spatiotemporal data. The time column can 
    be of any type, but needs to be ordinal. Regularly laid out spatiotemporal 
    data (vector or raster data cubes) are handled by package 'stars'.
Contains space filling based tools for
    machine learning and data mining. Some functions offer
    several computational techniques and deal with the out of
    memory for large big data by using the ff package.
Modern classes for tracking and movement data, building
    on 'sf' spatial infrastructure, and early theoretical work from
    Turchin (1998, ISBN: 9780878938476), and Calenge et al. (2009)
    <doi:10.1016/j.ecoinf.2008.10.002>. Tracking data are series of
    locations with at least 2-dimensional spatial coordinates (x,y), a
    time index (t), and individual identification (id) of the object
    being monitored; movement data are made of trajectories, i.e. the
    line representation of the path, composed by steps (the
    straight-line segments connecting successive locations). 'sftrack'
    is designed to handle movement of both living organisms and
    inanimate objects.
Once you've identified a real life place, such as a shop, a restaurant, a bar, etc. use this package to simulate a Google search and retrieve its "Popular Times" and geographic location information and save them in Comma-Separated Values files. This package also downloads a list of restaurants and bars of Ushuaia city, Argentina.
Main properties and regression procedures using a generalization of the Dirichlet distribution called Simplicial Generalized Beta distribution. It is a new distribution on the simplex (i.e. on the space of compositions or positive vectors with sum of components equal to 1). The Dirichlet distribution can be constructed from a random vector of independent Gamma variables divided by their sum. The SGB follows the same construction with generalized Gamma instead of Gamma variables. The Dirichlet exponents are supplemented by an overall shape parameter and a vector of scales. The scale vector is itself a composition and can be modeled with auxiliary variables through a log-ratio transformation. Graf, M. (2017, ISBN: 978-84-947240-0-8). See also the vignette enclosed in the package.
Implements an extension of the Generalized Berk-Jones (GBJ) statistic for
    survival data, sGBJ. It computes the sGBJ statistic and its p-value for testing 
    the association between a gene set and a time-to-event outcome with possible 
    adjustment on additional covariates. Detailed method is available at Villain L, Ferte T, 
    Thiebaut R and Hejblum BP (2021) <doi:10.1101/2021.09.07.459329>.
A fast and flexible set of tools for large scale estimation. It
    features many stochastic gradient methods, built-in models, visualization
    tools, automated hyperparameter tuning, model checking, interval estimation,
    and convergence diagnostics.
Stagewise techniques implemented with Generalized Estimating Equations to handle individual, group, bi-level, and interaction selection. Stagewise approaches start with an empty model and slowly build the model over several iterations, which yields a 'path' of candidate models from which model selection can be performed. This 'slow brewing' approach gives stagewise techniques a unique flexibility that allows simple incorporation of Generalized Estimating Equations; see Vaughan, G., Aseltine, R., Chen, K., Yan, J., (2017) <doi:10.1111/biom.12669> for details.
An Object-oriented Framework for Geostatistical Modeling in S+ 
  containing functions for variogram estimation, variogram fitting and kriging
  as well as some plot functions. Written entirely in S, therefore works only
  for small data sets in acceptable computing time.
Fit a regularized generalized linear model via penalized
        maximum likelihood.  The model is fit for a path of values of
        the penalty parameter. Fits linear, logistic and Cox models.
RCON(V, E) models are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. 'sglasso' package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm.
Set of tools to fit a linear multiple or semi-parametric regression
    models with the possibility of non-informative random right-censoring. 
    Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression
    or semi-parametric functions, whose non-parametric components may be approximated
    by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes
    the generalized extreme value and standard normal distributions as important special cases. 
    Also, some numerical and graphical devices for diagnostic of the fitted models are offered.
Fast generic solver for sparse group lasso optimization
        problems. The loss (objective) function must be defined in a
        C++ module. The optimization problem is solved using a
        coordinate gradient descent algorithm. Convergence of the
        algorithm is established (see reference) and the algorithm is
        applicable to a broad class of loss functions. 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.
Functions for computing power and boundaries for pre-licensure vaccine trials using the Generalized Likelihood Ratio tests proposed by Shih, Lai, Heyse and Chen
Provides functions that performs popular stochastic gradient Markov chain Monte Carlo (SGMCMC) methods on user specified models. The required gradients are automatically calculated using 'TensorFlow' <https://www.tensorflow.org/>, an efficient library for numerical computation. This means only the log likelihood and log prior functions need to be specified. The methods implemented include stochastic gradient Langevin dynamics (SGLD), stochastic gradient Hamiltonian Monte Carlo (SGHMC), stochastic gradient Nose-Hoover thermostat (SGNHT) and their respective control variate versions for increased efficiency. References: M. Welling, Y. W. Teh (2011) <http://www.icml-2011.org/papers/398_icmlpaper.pdf>; T. Chen, E. B. Fox, C. E. Guestrin (2014) <arXiv:1402.4102>; N. Ding, Y. Fang, R. Babbush, C. Chen, R. D. Skeel, H. Neven (2014) <https://papers.nips.cc/paper/5592-bayesian-sampling-using-stochastic-gradient-thermostats>; J. Baker, P. Fearnhead, E. B. Fox, C. Nemeth (2017) <arXiv:1706.05439>. For more details see <doi:10.18637/jss.v091.i03>.
It computes the solutions to a generic stochastic growth model for a given set of user supplied parameters. It includes
    the solutions to the model, plots of the solution, 	
    a summary of the features of the model, a function that covers different types of consumption preferences,
    and a function that computes the moments of a Markov process.	
	Merton, Robert C (1971) <doi:10.1016/0022-0531(71)90038-X>,
	Tauchen, George (1986) <doi:10.1016/0165-1765(86)90168-0>,
	Wickham, Hadley (2009, ISBN:978-0-387-98140-6 ).
An implementation of sparse Gaussian Markov random field mixtures 
  presented by Ide et al. (2016) <doi:10.1109/ICDM.2016.0119>.
  It provides a novel anomaly detection method for multivariate noisy sensor data.
  It can automatically handle multiple operational modes.
  And it can also compute variable-wise anomaly scores.
Methods focused in performing the OSGB36/ETRS89 transformation 
  (Great Britain and the Isle of Man only) by using the Ordnance Survey's 
  OSTN15/OSGM15 transformation model. Calculation of distances and areas from 
  sets of points defined in any of the supported Coordinated Systems is also
  available.
Seven different methods for multiple testing problems. The SGoF-type methods (see for example, Carvajal Rodríguez et al., 2009 <doi:10.1186/1471-2105-10-209>;  de Uña Álvarez, 2012 <doi:10.1515/1544-6115.1812>; Castro Conde et al., 2015 <doi:10.1177/0962280215597580>) and the BH and BY false discovery rate controlling procedures.
An analytic framework for the calculation of norm- and criterion-referenced academic growth estimates using large scale, longitudinal education assessment data as developed in Betebenner (2009) <doi:10.1111/j.1745-3992.2009.00161.x>.
Data sets utilized by the 'SGP' package as exemplars for users to conduct their own student growth percentiles (SGP) analyses.
Regularized version of partial least square approaches providing sparse, group, and sparse group versions of partial least square regression models (Liquet, B., Lafaye de Micheaux, P., Hejblum B., Thiebaut, R. (2016) <doi:10.1093/bioinformatics/btv535>). Version of PLS Discriminant analysis is also provided.
Computation of second-generation p-values as described in Blume et al. (2018) <doi:10.1371/journal.pone.0188299> and Blume et al. (2019) <doi:10.1080/00031305.2018.1537893>. There are additional functions which provide power and type I error calculations, create graphs (particularly suited for large-scale inference usage), and a function to estimate false discovery rates based on second-generation p-value inference.
The package for Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.
A set of function that implements for seasonal multivariate time series analysis based on Seasonal Generalized Space
            Time Autoregressive with Seemingly Unrelated Regression (S-GSTAR-SUR) Model by Setiawan(2016)<https://www.researchgate.net/publication/316517889_S-GSTAR-SUR_model_for_seasonal_spatio_temporal_data_forecasting>.
Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation.
Functions for easily manipulating colours, creating colour scales and calculating colour distances.
Functions for calculating: (1) shadow height, (2) logical shadow flag, (3) shadow footprint, (4) Sky View Factor and (5) radiation load. Basic required inputs include a polygonal layer of obstacle outlines along with their heights (i.e. "extruded polygons"), sun azimuth and sun elevation. The package also provides functions for related preliminary calculations: breaking polygons into line segments, determining azimuth of line segments, shifting segments by azimuth and distance, constructing the footprint of a line-of-sight between an observer and the sun, and creating a 3D grid covering the surface area of extruded polygons.
Implement shadowtextGrob() for 'grid' and geom_shadowtext() layer for 'ggplot2'.
             These functions create/draw text grob with background shadow.
Implementations are provided for the models described in the paper D. B. Dahl, R. Day, J. Tsai (2017) <DOI:10.1080/01621459.2016.1165103>. The Ewens, Ewens-Pitman, Ewens attraction, Ewens-Pitman attraction, and ddCRP distributions are available for prior and posterior simulation. Posterior simulation is based on a user-supplied likelihood. Supporting functions for partition estimation and plotting are also provided.
Functions for plotting graphical shapes
  such as ellipses, circles, cylinders, arrows, ...
In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrap confidence interval for the change-point is incorporated in the main routine.
Functions to read and write ESRI shapefiles
M-estimators of location and shape following the power family 
    (Frahm, Nordhausen, Oja (2020) <doi:10.1016/j.jmva.2019.104569>) are 
    provided in the case of complete data and also when observations have 
    missing values together with functions aiding their visualization.
This is an evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. This collection provides tools for: (1) the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking; the spectra of results are returned in graphic and tabular formats (Remmel 2015) <doi:10.1111/cag.12222>, (2) simulating landscape patterns, (3) provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps (Remmel and Fortin 2013) <doi:10.1007/s10980-013-9905-x>, (4) counting the number of each first-order pattern element and converting that information into both frequency and empirical probability vectors (Remmel 2020) <doi:10.3390/e22040420>, and (5) computing the porosity of raster patches <doi:10.3390/su10103413>. NOTE: This is a consolidation of existing packages ('PatternClass', 'ShapePattern') to begin warehousing all shape and pattern code in a common package. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented.  Note that all future developments will appear in this package and that 'PatternClass' will eventually be archived.
Studies otolith shape variation among fish populations. 
  Otoliths are calcified structures found in the inner ear of teleost fish and their shape has 
  been known to vary among several fish populations and stocks, making them very useful in taxonomy, 
  species identification and to study geographic variations. The package extends previously described 
  software used for otolith shape analysis by allowing the user to automatically extract closed 
  contour outlines from a large number of images, perform smoothing to eliminate pixel noise, 
  choose from conducting either a Fourier or wavelet transform to the outlines and visualize 
  the mean shape. The output of the package are independent Fourier or wavelet coefficients 
  which can be directly imported into a wide range of statistical packages in R. The package 
  might prove useful in studies of any two dimensional objects.
Here we describe a simple geometric rigid rotation approach that removes the effect of random translation and rotation, enabling the morphological analysis of 3D articulated structures. Our method is based on Cartesian coordinates in 3D space so it can be applied to any morphometric problem that also uses 3D coordinates. See Vidal-García, M., Bandara, L., Keogh, J.S. (2018) <doi:10.1002/ece3.4018>.
Routines for the statistical analysis of landmark 
  shapes, including Procrustes analysis, graphical displays, principal
  components analysis, permutation and bootstrap tests, thin-plate 
  spline transformation grids and comparing covariance matrices. 
     See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, 
     with Applications in R (2nd Edition), John Wiley and Sons.
Landsat satellites collect important data about global forest conditions. Documentation about Landsat's role in forest disturbance estimation is available at the site <http://landsat.gsfc.nasa.gov/?p=9513>. By constrained quadratic B-splines, this package delivers an optimal shape-restricted trajectory to a time series of Landsat imagery for the purpose of modeling annual forest disturbance dynamics to behave in an ecologically sensible manner assuming one of seven possible "shapes", namely, flat, decreasing, one-jump (decreasing, jump up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then increasing), linear increasing, and double-jump (decreasing, jump up, decreasing, jump up, decreasing). The main routine selects the best shape according to the minimum Bayes information criterion (BIC) or the cone information criterion (CIC), which is defined as the log of the estimated predictive squared error. The package also provides parameters summarizing the temporal pattern including year(s) of inflection, magnitude of change, pre- and post-inflection rates of growth or recovery. In addition, it contains routines for converting a flat map of disturbance agents to time-series disturbance maps and a graphical routine displaying the fitted trajectory of Landsat imagery.
Aid in visual data investigations
 using SHAP (SHapley Additive exPlanation) visualization plots for 'XGBoost' and 'LightGBM'. 
 It provides summary plot, dependence plot, interaction plot, and force plot and relies on
 the SHAP implementation provided by 'XGBoost' and 'LightGBM'.
 Please refer to 'slundberg/shap' for the original implementation of SHAP in 'Python'.
Shapley Value Regression for calculating the relative importance of independent variables in linear regression with avoiding the collinearity.
Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) <arXiv:1705.07874> The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'.
Complex machine learning models are often hard to interpret. However, in 
  many situations it is crucial to understand and explain why a model made a specific 
  prediction. Shapley values is the only method for such prediction explanation framework 
  with a solid theoretical foundation. Previously known methods for estimating the Shapley 
  values do, however, assume feature independence. This package implements the method 
  described in Aas, Jullum and Løland (2019) <arXiv:1903.10464>, which accounts for any feature 
  dependence, and thereby produces more accurate estimates of the true Shapley values.
Analyse species-habitat associations in R. Therefore, information about the location 
  of the species (as a point pattern) is needed together with environmental conditions 
  (as a categorical raster). To test for significance habitat associations, one of 
  the two components is randomized. Methods are mainly based on Plotkin et al. (2000) 
  <doi:10.1006/jtbi.2000.2158> and Harms et al. (2001) <doi:10.1111/j.1365-2745.2001.00615.x>.
Functions and data sets inspired by data sharpening -
             data perturbation to achieve improved performance in
             nonparametric estimation, as described in Choi, E., Hall, P.
             and Rousson, V. (2000).  
             Capabilities for enhanced local linear regression function
             and derivative estimation are included, as well as an
             asymptotically correct iterated data sharpening estimator
             for any degree of local polynomial regression estimation.
             A cross-validation-based bandwidth selector is included which,
             in concert with the iterated sharpener, will often provide
             superior performance, according to a median integrated squared
             error criterion.  Sample data sets are provided to illustrate
             function usage.
A collection of tools for analyzing significance of assets,
    funds, and trading strategies, based on the Sharpe ratio and overfit 
    of the same. Provides density, distribution, quantile and random generation 
    of the Sharpe ratio distribution based on normal returns, as well
    as the optimal Sharpe ratio over multiple assets. Computes confidence intervals
    on the Sharpe and provides a test of equality of Sharpe ratios based on 
    the Delta method. The statistical foundations of the Sharpe can be found in
    the author's Short Sharpe Course  <doi:10.2139/ssrn.3036276>.
An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see <arXiv:1505.01333>).
Functions and data sets for penalized data sharpening.
    Nonparametric regressions are computed subject to smoothness
    and other kinds of penalties.
An algorithm for identifying high-resolution driver elements for datasets from a high-definition reporter assay library. Xinchen Wang, Liang He, Sarah Goggin, Alham Saadat, Li Wang, Melina Claussnitzer, Manolis Kellis (2017) <doi:10.1101/193136>.
Miscellaneous soil data management, summary, visualization, and conversion utilities to support soil survey.
Hierarchical models for the analysis of species-area 
  relationships (SARs) by combining several data sets and covariates; 
  with a global data set combining individual SAR studies; 
  as described in Solymos and Lele 
  (2012, Global Ecology and Biogeography 21, 109-120).
The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is selected from its search space bias F. This formal result depends on the Shattering coefficient function N(F,2n) to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of F, including its under and overfitting abilities while addressing specific target problems. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. As main contributions, one can use our approach to study the complexity of the search space bias F, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. Reference: de Mello, R.F. (2019) "On the Shattering Coefficient of Supervised Learning Algorithms" <arXiv:1911.05461>; de Mello, R.F., Ponti, M.A. (2018, ISBN: 978-3319949888) "Machine Learning: A Practical Approach on the Statistical Learning Theory".
Provides a computational framework for analyzing mutations in
    immunoglobulin (Ig) sequences. Includes methods for Bayesian estimation of
    antigen-driven selection pressure, mutational load quantification, building of
    somatic hypermutation (SHM) models, and model-dependent distance calculations.
    Also includes empirically derived models of SHM for both mice and humans.
    Citations: 
    Gupta and Vander Heiden, et al (2015) <doi:10.1093/bioinformatics/btv359>, 
    Yaari, et al (2012) <doi:10.1093/nar/gks457>, 
    Yaari, et al (2013) <doi:10.3389/fimmu.2013.00358>, 
    Cui, et al (2016) <doi:10.4049/jimmunol.1502263>.
Uses C++ via the 'Rcpp' package to parse modern Excel files ('.xlsx').
    Memory usage is kept minimal by decompressing only parts of the file at a time,
    while employing multiple threads to achieve significant runtime reduction.
    Uses <https://github.com/richgel999/miniz>, <https://github.com/ebiggers/libdeflate>,
    and <https://github.com/lemire/fast_double_parser>.
Implements various methods for eliciting a probability distribution
    for a single parameter from an expert or a group of experts. The expert
    provides a small number of probability judgements, corresponding
    to points on his or her cumulative distribution function. A range of parametric
    distributions can then be fitted and displayed, with feedback provided in the
    form of fitted probabilities and percentiles. For multiple experts, a weighted
    linear pool can be calculated. Also includes functions for eliciting beliefs
    about population distributions, eliciting multivariate distributions using a
    Gaussian copula, eliciting a Dirichlet distribution, and eliciting distributions 
    for variance parameters in a random effects meta-analysis model. R Shiny apps  
    for most of the methods are included.
Takes as input a stable oxygen isotope (d18O) profile measured in growth direction (D)
	through a shell + uncertainties in both variables (d18O_err & D_err). It then models the seasonality
	in the d18O record by fitting a combination of a growth and temperature sine wave to year-length chunks of
	the data (see Judd et al., (2018) <doi:10.1016/j.palaeo.2017.09.034>). This modeling is carried out along a sliding window through the data and yields estimates of
	the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modeling
	routine and the data itself are propagated and pooled to obtain a confidence envelope around the age of
	each data point in the shell. The end result is a shell chronology consisting of estimated ages of shell
	formation relative to the annual cycle with their uncertainties. All formulae in the package serve this
	purpose, but the user can customize the model (e.g. number of days in a year and the mineralogy of the
	shell carbonate) through input parameters.
Contains all the formulae of the growth and trace element uptake model described in the equally-named
    Geoscientific Model Development paper (de Winter, 2017, <doi:10.5194/gmd-2017-137>). The model takes as input a file with X- and Y-coordinates of digitized
    growth increments recognized on a longitudinal cross section through the bivalve shell, as well as a BMP file of an elemental map of the
    cross section surface with chemically distinct phases separated by phase analysis. It proceeds by a step-by-step process described in
    the paper, by which digitized growth increments are used to calculate changes in shell height, shell thickness, shell volume, shell mass
    and shell growth rate through the bivalve's life time. Then, results of this growth modelling are combined with the trace element mapping
    results to trace the incorporation of trace elements into the bivalve shell. Results of various modelling parameters can be exported in
    the form of XLSX files.
A template system based on 'AdminLTE3' 
    (<https://adminlte.io/themes/v3/>)
    theme. Comes with default theme that can be easily customized. 
    Developers can upload modified templates on 'Github', and users can 
    easily download templates with 'RStudio' project wizard.
    The key features of the default template include light and dark theme 
    switcher, resizing graphs, synchronizing inputs across sessions, 
    new notification system, fancy progress bars, and card-like flip 
    panels with back sides, as well as various of 'HTML' tool widgets.
An exact method for computing the Poisson-Binomial Distribution (PBD). The package provides a function for generating a random sample from the PBD, as well as two distinct approaches for computing the density, distribution, and quantile functions of the PBD. The first method uses direct-convolution, or a dynamic-programming approach which is numerically stable but can be slow for a large input due to its quadratic complexity. The second method is much faster on large inputs thanks to its use of Fast Fourier Transform (FFT) based convolutions. Notably in this case the package uses an exponential shift to practically guarantee the relative accuracy of the computation of an arbitrarily small tail of the PBD -- something that FFT-based methods often struggle with. This ShiftConvolvePoiBin method is described in Peres, Lee and Keich (2020) <arXiv:2004.07429> where it is also shown to be competitive with the fastest implementations for exactly computing the entire Poisson-Binomial distribution.
Fast enrichment analysis for locally correlated statistics
        via circular permutations.
        The analysis can be performed at multiple significance thresholds
        for both primary and auxiliary data sets with
        efficient correction for multiple testing.
Provides confidence intervals in least-squares regressions when the
  variable of interest has a shift-share structure, and in instrumental
  variables regressions when the instrument has a shift-share structure. The
  confidence intervals implement the AKM and AKM0 methods developed in Adão,
  Kolesár, and Morales (2019) <doi:10.1093/qje/qjz025>.
Prototype your 'shiny' apps quickly with these
    Lorem-Ipsum helper functions. Generate random elements
    for 'shiny' outputs that can be used as placeholder in your application.
Makes it incredibly easy to build interactive web
    applications with R. Automatic "reactive" binding between inputs and
    outputs and extensive prebuilt widgets make it possible to build
    beautiful, responsive, and powerful applications with minimal effort.
It provides easy internationalization of Shiny
    applications. It can be used as standalone translation package
    to translate reports, interactive visualizations or
    graphical elements as well.
Displays simple diagnostic information of the 'shiny' project in the user interface of the app.
Adds Progressive Web App support for Shiny apps, including
  desktop and mobile installations.
A toolbox for defining React component wrappers which can be used seamlessly in Shiny apps.
RegLog system provides a set of shiny modules to handle register
   procedure for your users, alongside with login, edit credentials and
   password reset functionality. 
   It provides support for popular SQL databases
   and optionally googlesheet-based database for easy setup. For email sending
   it provides support for 'emayili' and 'gmailr' backends. Architecture makes 
   customizing usability pretty straightforward.
   The authentication system created 
   with shiny.reglog is designed to be optional: user don't need to be logged-in 
   to access your application, but when logged-in the user data can be used 
   to read from and write to relational databases.
It is a simple router for your Shiny apps.
    The router allows you to create dynamic web applications with real-time
    User Interface and easily share url to pages within your Shiny apps.
Creating a great user interface for your Shiny apps
    can be a hassle, especially if you want to work purely in R
    and don't want to use, for instance HTML templates. This
    package adds support for a powerful UI library Fomantic UI -
    <https://fomantic-ui.com/> (before Semantic). It also supports
    universal UI input binding that works with various DOM elements.
It allows you to delegate heavy computation tasks
    to a separate process, such that it does not freeze your Shiny app.
Ace editor bindings to enable a rich text editing environment
    within Shiny.
Make R data available in Web-based virtual reality experiences
  for immersive, cross-platform data visualizations. Includes the 'gg-aframe'
  JavaScript package for a Grammar of Graphics declarative HTML syntax to create
  3-dimensional data visualizations with 'Mozilla A-Frame' <https://aframe.io>.
Easily create pretty popup messages (modals) in 'Shiny'. A modal can
    contain text, images, OK/Cancel buttons, an input to get a response from the
    user, and many more customizable options.
An extension of 'animate.css' that allows user to easily add animations to any UI element in 'shiny' app using the elements id.
Add in-app user authentication to 'shiny', 
    allowing you to secure publicly hosted apps and 
    build dynamic user interfaces from user information.
Provides Bootstrap 2 web components for use with the Shiny
    package. With versions of Shiny prior to 0.11, these Bootstrap 2 components
    were included as part of the package. Later versions of Shiny include
    Bootstrap 3, so the Bootstrap 2 components have been moved into this
    package for those uses who rely on features specific to Bootstrap 2.
A graphical user interface (GUI) for fitting Bayesian regression
    models using the package 'brms' which in turn relies on 'Stan'
    (<https://mc-stan.org/>). The 'shinybrms' GUI is a 'shiny' app.
Adds additional Twitter Bootstrap components to Shiny.
Add indicators (spinner, progress bar, gif) in your 'shiny'
  applications to show the user that the server is busy. And other tools to let
  your users know something is happening (send notifications, reports, ...).
Provides a combined slider and numeric input for usage in a 'Shiny' app. The slider and the numeric input are linked together: each one is updated when the other one changes. Many styling properties are customizable (e.g. colors and size).
Makes the 'React' library 'Chakra UI' usable in 'Shiny' apps. 'Chakra UI' components include alert dialogs, drawers (sliding panels), menus, modals, popovers, sliders, and more.
When a 'Shiny' output (such as a plot, table, map, etc.) is recalculating, it remains 
    visible but gets greyed out. Using 'shinycssloaders', you can add a loading animation ("spinner")
    to outputs instead. By wrapping a 'Shiny' output in 'withSpinner()', a spinner will automatically
    appear while the output is recalculating. See the demo online at
    <https://daattali.com/shiny/shinycssloaders-demo/>.
A custom css/html or gif/image file for the loading screen in R 'shiny'. It also can use the marquee to have custom text loading screen.
Create Interactive Graph (Network) Visualizations. 
  'shinyCyJS' can be used in 'Shiny' apps or viewed from 'Rstudio' Viewer.
  'shinyCyJS' includes API to build Graph model like node or edge with customized attributes for R. 
  'shinyCyJS' is built with 'cytoscape.js' and 'htmlwidgets' R package.
Create dashboards with 'Shiny'. This package provides
    a theme on top of 'Shiny', making it easy to create attractive dashboards.
Extend 'shinydashboard' with 'AdminLTE2' components. 
             'AdminLTE2' is a free 'Bootstrap 3' dashboard template available
             at <https://adminlte.io>. Customize boxes, add timelines and a lot more.
A 'Shiny' app can disconnect for a variety of reasons: an unrecoverable error occurred in
    the app, the server went down, the user lost internet connection, or any other reason
    that might cause the 'Shiny' app to lose connection to its server. With 'shinydisconnect', you can
    call disonnectMessage() anywhere in a Shiny app's UI to add a nice message when this happens. 
    Works locally (running Shiny apps within 'RStudio') and on Shiny servers (such as shinyapps.io,
    'RStudio Connect', 'Shiny Server Open Source', 'Shiny Server Pro'). See demo online at 
    <https://daattali.com/shiny/shinydisconnect-demo/>.
Add a download button to a 'shiny' plot or 'plotly' that appears when
  the plot is hovered. A tooltip, styled to resemble 'plotly' buttons, is displayed 
  on hover of the download button. The download button can be used to allow users
  to download the dataset used for a plot.
Add functionality to create drag and drop div elements in shiny.
Shiny module for easily sharing files between users. Admin can add, remove, edit and download file.
    User can only download file. It's also possible to manage files using R functions directly.
Add fancy CSS effects to your 'shinydashboards' or 'shiny' apps.
             100% compatible with 'shinydashboardPlus' and 'bs4Dash'.
Easily display user feedback in Shiny apps.
Provides functionality for client-side navigation of
    the server side file system in shiny apps. In case the app is running
    locally this gives the user direct access to the file system without the
    need to "download" files to a temporary location. Both file and folder
    selection as well as file saving is available.
Allows to connect 'selectizeInputs' widgets as filters to a 'reactable' table. 
  As known from spreadsheet applications, column filters are interdependent, so each 
  filter only shows the values that are really available at the moment based on 
  the current selection in other filters. Filter values currently not available 
  (and also those being available) can be shown via popovers or tooltips.
In 'Shiny' apps, it is sometimes useful to see a plot or a
    table in full screen. Using 'Shinyfullscreen', you can easily designate
    the 'HTML' elements that can be displayed on fullscreen and use buttons to 
    trigger the fullscreen view.
Insert Glide JavaScript component into Shiny applications for
    carousel or assistant-like user interfaces.
Collection of 'shiny' application styling that are the based on 
  the GOV.UK Design System.  See 
  <https://design-system.service.gov.uk/components/> for details.
Access functionality of the 'heatmaply' package through 'Shiny UI'.
Creates a lightweight way to add markdown helpfiles to 'shiny' apps,
    using modal dialog boxes, with no need to observe each help button separately.
Modifies the progress() function from 'httr' package to let it 
      send output to progressBar() function from 'shinyWidgets' package. 
      It is just a tweak at the original functions from 'httr' package to 
      make it smooth for 'shiny' developers.
Standard imaging operations, e.g. crop and contrast
  adjustment, but with ability to go back and forth through sequence of
  changes, with records being persistent.  Optional Shiny interface.
  Useful to help with the research reproducibility problem, and as a
  teaching tool.
Performing Item Response Theory analysis such as parameter estimation, ability estimation, item and model fit analyse, local independence assumption, dimensionality assumption, characteristic and information curves under various models with a user friendly 'shiny' interface.
Package including functions and interactive shiny application
    for the psychometric analysis of educational tests, psychological
    assessments, health-related and other types of multi-item
    measurements, or ratings from multiple raters.
An extension to shiny that brings interactions and animation effects from
    'jQuery UI' library.
Perform common useful JavaScript operations in Shiny apps that will
    greatly improve your apps without having to know any JavaScript. Examples
    include: hiding an element, disabling an input, resetting an input back to
    its original value, delaying code execution by a few seconds, and many more
    useful functions for both the end user and the developer. 'shinyjs' can also
    be used to easily call your own custom JavaScript functions from R.
An interactive Shiny application to perform fast parameter inference on
    dynamical systems (described by ordinary differential equations) using gradient matching.
    Please see the project page for more details.
A collection of highly configurable, touch-enabled knob input controls for 'shiny'. These components can be styled to fit in perfectly in any app, and allow users to set precise values through many input modalities. Users can touch-and-drag, click-and-drag, scroll their mouse wheel, double click, or use keyboard input.
Assesses the number of concurrent users 'shiny'
  applications are capable of supporting, and for directing application changes
  in order to support a higher number of users. Provides facilities for recording
  'shiny' application sessions, playing recorded sessions against a target
  server at load, and analyzing the resulting metrics.
Track and record the use of applications and the user's interactions with 'Shiny' inputs.
  Allows to trace the inputs with which the user interacts, the outputs generated, 
  as well as the errors displayed in the interface.
Provides functions that wrap HTML Bootstrap
    components code to enable the design and layout of informative landing home
    pages for Shiny applications. This can lead to a better user experience for
    the users and writing less HTML for the developer.
Simple and secure authentification mechanism for single 'Shiny' applications.
    Credentials are stored in an encrypted 'SQLite' database. Source code of main application
    is protected until authentication is successful.
Allows shiny developers to incorporate UI elements based on Google's Material design. See <https://material.io/guidelines/> for more information.
A 'Shiny' app allowing to compare and merge two files, with syntax highlighting for several coding languages.
Provides tools for capturing logic in a Shiny app and exposing it as code that can be run outside of Shiny (e.g., from an R console). It also provides tools for bundling both the code and results to the end user.
Implementation of a shiny app to easily compare supervised machine learning model performances. 
    You provide the data and configure each model parameter directly on the shiny app.  
    Different supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression and classification tasks.
    Implementation of available machine learning models on R has been done by Lantz (2013, ISBN:9781782162148).
Develop outstanding 'shiny' apps for 'iOS', 'Android', desktop as well as beautiful 'shiny' gadgets.
    'shinyMobile' is built on top of the latest 'Framework7' template <https://framework7.io>.
    Discover 14 new input widgets (sliders, vertical sliders, stepper, 
    grouped action buttons, toggles, picker, smart select, ...), 2 themes (light and dark), 
    12 new widgets (expandable cards, badges, chips, timelines, gauges, progress bars, ...) 
    combined with the power of server-side notifications such as alerts, modals, toasts,
    action sheets, sheets (and more) as well as 3 layouts (single, tabs and split).
Launch a 'shiny' application for 'tidymodels' results. For
    classification or regression models, the app can be used to determine
    if there is lack of fit or poorly predicted points.
Interactive visualization of 'RDML' files via 'shiny' apps. 
  Package provides (1) PCR plate interface with ability to select
  individual tubes and (2) amplification/melting plots with fast hiding and 
  highlighting individual curves.
A 'Shiny' app including the 'Monaco' editor. The 'Monaco' editor is the code editor which powers 'VS Code'. It is particularly well developed for 'JavaScript'. In addition to the 'Monaco' editor features, the app provides prettifiers and minifiers for multiple languages, 'SCSS' and 'TypeScript' compilers, code checking for 'C' and 'C++' (requires 'cppcheck').
An enterprise-targeted scalable and customizable 'shiny' module providing an easy way to incorporate free-form note taking or discussion boards into applications.
    The package includes a 'shiny' module that can be included in any 'shiny' application to create a panel containing searchable, editable text broken down by section headers.
    Can be used with a local 'SQLite' database, or a compatible remote database of choice.
Troubleshooting reactive data in 'shiny' can be difficult. These functions will convert reactive data frames into functions and load all assigned objects into your local environment. If you create a dummy input object, as the function will suggest, you will be able to test your server and ui functions interactively.
Add shiny inputs with one or more inline buttons that grow and shrink with inputs. 
 Also add tool tips to input buttons and styling and messages for input validation.
Create 'Shiny Apps' with collapsible vertical panels. 
    This package provides a new visual arrangement for elements on top of 'Shiny'. 
    Use the expand and collapse capabilities to leverage web applications with
    many elements to focus the user attention on the panel of interest.
Shiny Module to create, visualize, customize and export Excel-like pivot table.
Creates 'shiny' application ('app.R') for making predictions based on lm(), glm(), or coxph() models.
This 'RStudio' addin makes the creation of 'Shiny' and 'ShinyDashboard' apps more efficient.  Besides the necessary folder structure, entire apps can be created using a drag and drop interface and customized with respect to a specific use case. The addin allows the export of the required user interface and server code at any time. By allowing the creation of modules, the addin can be used throughout the entire app development process.
It builds dynamic R shiny based dashboards to analyze any CSV files. It provides simple dashboard design to subset the data, perform exploratory data analysis and preliminary machine learning (supervised and unsupervised). It also provides filters based on columns of interest.
An input controller for R Shiny: a matrix with radio buttons, 
    where only one option per row can be selected.
Implements user interfaces for log-linear models, Bayesian model averaging and 
    Bayesian Dirichlet process mixture models.
This gadget allows you to use the 'recipes' package belonging to 'tidymodels' to carry 
    out the data preprocessing tasks in an interactive way. Build your 'recipe' by dragging the variables, 
    visually analyze your data to decide which steps to use, add those steps and preprocess your data.
Allows to create modular, reusable 'HTML'
    forms which can be embedded in your 'shiny' app with minimal effort.
    Features include conditional code execution on form submission,
    automatic input validation and input tooltips.
Shiny wrappers for the RGL package. This package exposes RGL's
    ability to export WebGL visualization in a shiny-friendly format.
Capture screenshots in 'Shiny' applications. Screenshots can either be
    of the entire viewable page, or a specific section of the page. The captured
    image is automatically downloaded as a PNG image, or it can also be saved on
    the server. Powered by the 'html2canvas' JavaScript library.
Add a searchbar widget to your 'Shiny' application. The widget
  quickly integrates with any existing element containing text to highlight
  matches. Highlighting is done with the 'JavaScript' library 'mark.js'. The
  widget includes buttons to cycle through multiple instances of the match and
  automatically scroll to the matches in an overflow element (or window). The
  widget also displays the total number of matches and which match is currently
  being cycled through. The widget is structured as a 'Bootstrap 3' input
  group.
Provides a select control widget for 'Shiny'. It is easily customizable, and one can easily use HTML in the items and KaTeX to type mathematics.
Create in-app purchasing and subscriptions through 'Servicebot' payment using the 'Stripe' framework.
Provides function shinyShortcut() that, 
    when given the base directory of a shiny application, will produce an
    executable file that runs the shiny app directly in the user's
    default browser. Tested on both windows and unix machines. Inspired
    by and borrowing from 
    <http://www.mango-solutions.com/wp/2017/03/shiny-based-tablet-or-desktop-app/>.
Provides interactive plotting for mathematical models of infectious disease spread. Users can choose from a variety of common built-in ordinary differential equation (ODE) models (such as the SIR, SIRS, and SIS models), or create their own. This latter flexibility allows 'shinySIR' to be applied to simple ODEs from any discipline. The package is a useful teaching tool as students can visualize how changing different parameters can impact model dynamics, with minimal knowledge of coding in R. The built-in models are inspired by those featured in Keeling and Rohani (2008) <doi:10.2307/j.ctvcm4gk0> and Bjornstad (2018) <doi:10.1007/978-3-319-97487-3>.
A graphical user interface for interactive Markov chain Monte
    Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a
    posterior sample. The interface is powered by the 'Shiny' web
    application framework from 'RStudio' and works with the output of MCMC 
    programs written in any programming language (and has extended 
    functionality for 'Stan' models fit using the 'rstan' and 'rstanarm' 
    packages).
Easily create and deploy surveys in 'Shiny'. This package includes
    a minimalistic framework similar to 'Google Forms' that allows for url-based
    user tracking, customizable submit actions, easy survey-theming, and more.
For automated testing of Shiny applications, using
    a headless browser, driven through 'WebDriver'.
It's my experience that working with 'shiny' is intuitive once you're
    into it, but can be quite daunting at first. Several common mistakes are fairly
    predictable, and therefore we can control for these. The functions in this
    package help match up the assets listed in the UI and the SERVER files, and
    Visualize the ad hoc structure of the 'shiny' App.
Themes for use with Shiny. Includes several Bootstrap themes
    from <https://bootswatch.com/>, which are packaged for use with Shiny
    applications.
Provides a time input widget for Shiny. This widget allows intuitive time input in the
    '[hh]:[mm]:[ss]' or '[hh]:[mm]' (24H) format by using a separate numeric input for each time
    component. The interface with R uses date-time objects. See the project page for more
    information and examples.
Enables the ability to change or flash the title of the browser window during a 'shiny' session.
This is a wrapper of the 'React' library 'React-Toastify'. It allows to show some notifications (toasts) in 'Shiny' applications. There are options for the style, the position, the transition effect, and more.
Browser notifications in 'Shiny' apps, using
    'toastr': <https://github.com/CodeSeven/toastr#readme>.
Exposes bindings to jsTree -- a JavaScript library
    that supports interactive trees -- to enable a rich, editable trees in
    Shiny.
Improves the user experience of Shiny apps by helping to
    provide feedback when required inputs are missing, or input values
    are not valid.
Collection of custom input controls and user interface components for 'Shiny' applications. 
  Give your applications a unique and colorful style !
The SHIP-package allows the estimation of various types of
        shrinkage covariance matrices. These types differ in terms of
        the so-called covariance target (to be chosen by the user), the
        highly structured matrix which the standard unbiased sample
        covariance matrix is shrunken towards and which optionally
        incorporates prior biological knowledge extracted from the
        database KEGG. The shrinkage intensity is obtained via an
        analytical procedure.
A collection of functions for data manipulation, plotting and statistical computing,
 to use separately or with the book "Visual Statistics. Use R!":
 Shipunov (2020) <http://ashipunov.info/shipunov/software/r/r-en.htm>.
 Most useful functions:
 Bclust(), Jclust() and BootA() which bootstrap hierarchical clustering;
 Recode() which does multiple recoding in a fast, simple and flexible way;
 Misclass() which outputs confusion matrix even if classes are not concerted;
 Overlap() which measures group separation on any projection;
 Biarrows() which converts any scatterplot into biplot;
 and Pleiad() which is fast and flexible correlogram.
Block-diagonal covariance selection for high dimensional Gaussian
    graphical models. The selection procedure is based on the slope heuristics.
An interface to the Admin API of the E-commerce service Shopify,
    (<https://help.shopify.com/en/api/reference>).
Integrates clipboard copied data in R Studio, loads and installs libraries within a R script and returns all valid arguments of a selected function.
Performs automatic creation of short forms of scales with an 
    ant colony optimization algorithm and a Tabu search. As implemented in the 
    package, the ant colony algorithm randomly selects items to build a model of 
    a specified length, then updates the probability of item selection according 
    to the fit of the best model within each set of searches. The algorithm 
    continues until the same items are selected by multiple ants a given number 
    of times in a row. On the other hand, the Tabu search changes one parameter at
    a time to be either free, constrained, or fixed while keeping track of the
    changes made and putting changes that result in worse fit in a "tabu" list
    so that the algorithm does not revisit them for some number of searches. 
    See Leite, Huang, & Marcoulides (2008) <doi:10.1080/00273170802285743> for
    an applied example of the ant colony algorithm, and Marcoulides & Falk (2018)
    <doi:10.1080/10705511.2017.1409074> for an applied example of the Tabu search.
Create short sprint (<6sec) profiles using the split times or the radar gun data.
    Mono-exponential equation is used to estimate maximal sprinting speed (MSS), relative acceleration (TAU),
    and other parameters such us maximal acceleration (MAC) and maximal relative power (PMAX). These parameters 
    can be used to predict kinematic and kinetics variables and to compare individuals. The modeling method utilized
    in this package is based on the works of Chelly SM, Denis C. (2001) <doi: 10.1097/00005768-200102000-00024>,
    Clark KP, Rieger RH, Bruno RF, Stearne DJ. (2017) <doi: 10.1519/JSC.0000000000002081>, 
    Furusawa K, Hill AV, Parkinson JL (1927) <doi: 10.1098/rspb.1927.0035>, 
    Greene PR. (1986) <doi: 10.1016/0025-5564(86)90063-5>, and 
    Samozino P. (2018) <doi: 10.1007/978-3-319-05633-3_11>.
Analyzes shooting data with respect to group shape,
        precision, and accuracy. This includes graphical methods,
        descriptive statistics, and inference tests using standard,
        but also non-parametric and robust statistical methods.
        Implements distributions for radial error in bivariate normal
        variables. Works with files exported by 'OnTarget PC/TDS',
        'Silver Mountain' e-target, 'ShotMarker' e-target, or 'Taran',
        as well as with custom data files in text format.
        Supports inference from range statistics like extreme spread.
        Includes a set of web-based graphical user interfaces.
Sometimes it is handy to be able to view an image file on an
    'R' graphics device. This package just does that. Currently it supports
    'PNG' files.
Making it easy to use various types of fonts ('TrueType',
    'OpenType', Type 1, web fonts, etc.) in R graphs, and supporting most output
    formats of R graphics including PNG, PDF and SVG. Text glyphs will be converted
    into polygons or raster images, hence after the plot has been created, it no
    longer relies on the font files. No external software such as 'Ghostscript' is
    needed to use this package.
Providing font files that can be used by the 'showtext' package.
Functions for converting network data from a
        SpatialLinesDataFrame object to an 'igraph'-Class object.
The predictive value of a statistical model can often be improved
    by applying shrinkage methods. This can be achieved, e.g., by regularized
    regression or empirical Bayes approaches. Various types of shrinkage factors can
    also be estimated after a maximum likelihood. While global shrinkage modifies
    all regression coefficients by the same factor, parameterwise shrinkage factors
    differ between regression coefficients. With variables which are either highly
    correlated or associated with regard to contents, such as several columns of a
    design matrix describing a nonlinear effect, parameterwise shrinkage factors are
    not interpretable and a compromise between global and parameterwise shrinkage,
    termed 'joint shrinkage', is a useful extension. A computational shortcut to
    resampling-based shrinkage factor estimation based on DFBETA residuals can be
    applied. Global, parameterwise and joint shrinkage for models fitted by lm(),
    glm(), coxph(), or mfp() is available.
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.
Efficient Markov chain Monte Carlo (MCMC) algorithms for fully 
    Bayesian estimation of dynamic survival models with shrinkage priors. 
    Details on the algorithms used are provided in Wagner (2011) <doi:10.1007/s11222-009-9164-5>, 
    Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and
    Cadonna et al. (2020) <doi:10.3390/econometrics8020020>.
Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and  
  Cadonna et al. (2020) <doi:10.3390/econometrics8020020>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>.
We provide a collection of statistical hypothesis testing procedures ranging from classical to modern methods for non-trivial settings such as high-dimensional scenario. For the general treatment of statistical hypothesis testing, see the book by Lehmann and Romano (2005) <doi:10.1007/0-387-27605-X>.
Implementation of the shuffle estimator, a non-parametric estimator for signal and noise variance under mild noise correlations.
Scripts and exercises that use card shuffling to teach confidence interval comparisons for different estimators.
Shows the scatter plot along with the fitted regression lines. It 
  depicts min, max, the three quartiles, mean, and sd for each variable.
  It also depicts sd-line, sd-box, r, r-square, prediction boundaries, and regression outliers.
Access 'Shutterstock' API from R. The 'Shutterstock' API presents
    access to search, view, license and download the media and information
    from the 'Shutterstock's library <https://api-reference.shutterstock.com/>.
An implementation of four stochastic methods of integrating in R, including:
    1. Stochastic Point Method (or Monte Carlo Method); 
    2. Mean Value Method; 
    3. Important Sampling Method; 
    4. Stratified Sampling Method.
    It can be used to estimate one-dimension or multi-dimension integration by Monte Carlo methods. And the estimated variance (precision) is given.
    Reference: Caflisch, R. E. (1998) <doi:10.1017/S0962492900002804>.
This package takes data on organism isotopes and fits a
        Bayesian model to their dietary habits based upon a Gaussian
        likelihood with a mixture dirichlet-distributed prior on the
        mean. It also includes SiBER metrics. See siardemo() for an
        example. Version 4.1.2 contains bug fixes to allow more than
        isotope numbers other than 2. Version 4.2 fixes a bug that
        stopped siar working on 64-bit systems
Fits bi-variate ellipses to stable isotope data using Bayesian
    inference with the aim being to describe and compare their isotopic
    niche.
Provides models to identify bimodally expressed genes from
 RNAseq data based on the Bimodality Index. SIBERG models the  RNAseq data in
 the finite mixture modeling framework and incorporates mechanisms  for
 dealing with RNAseq normalization. Three types of mixture models are
 implemented,  namely, the mixture of log normal, negative binomial, or 
 generalized Poisson distribution. See Tong et al. (2013)
 <doi:10.1093/bioinformatics/bts713>.
Aims to quantify time intensity data by using sigmoidal and
    double sigmoidal curves. It fits straight lines, sigmoidal, 
    and double sigmoidal curves on to time vs intensity data. 
    Then all the fits are used to make decision on which model 
    best describes the data. This method was first developed 
    in the context of single-cell viral growth analysis (for
    details, see Caglar et al. (2018) <doi:10.7717/peerj.4251>),
    and the package name stands for "SIngle CEll Growth Analysis in R".
Access tax and accounting data of Brazilian states 
    and municipalities provided by the Brazilian Public Sector 
    Accounting and Tax Information System.
Fetch data on targeted public investments from Plataforma +Brasil (SICONV) <http://plataformamaisbrasil.gov.br/>, 
    the responsible system for requests, execution, and monitoring of federal discretionary transfers in Brazil.
The code computes the structural intervention distance (SID) between a true directed acyclic graph (DAG) and an estimated DAG. Definition and details about the implementation can be found in J. Peters and P. Bühlmann: "Structural intervention distance (SID) for evaluating causal graphs", Neural Computation 27, pages 771-799, 2015.
This package has many purposes: first, it gives to the
        community an R implementation of each known side channel attack
        and countermeasures as well as data to test it, second it
        allows to implement a side channel attack quickly and easily.
Provides function to apply "Subgroup Identification based on Differential Effect Search" (SIDES) method proposed by Lipkovich et al. (2011) <doi:10.1002/sim.4289>.
Evolutionary reconstruction based on substitutions and insertion-deletion (indels) analyses in a distance-based framework.
Allows the user to connect with IBGE's (Instituto Brasileiro de 
    Geografia e Estatistica, see <https://www.ibge.gov.br/> for more information)
    SIDRA API in a flexible way. SIDRA is the acronym to "Sistema IBGE de 
    Recuperacao Automatica" and is the system where IBGE turns available 
    aggregate data from their researches.
Allows to retrieve time series of all indicators available in the Bank of Mexico's Economic Information System (<http://www.banxico.org.mx/SieInternet/>).
Methods for regression with high-dimensional predictors and  univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.
Implements semiparametric estimation and testing procedures for a continuous, possibly multivariate, mark-specific hazard ratio (treatment/placebo) of an event of interest in a randomized treatment efficacy trial with a time-to-event endpoint, as described in Juraska M and Gilbert PB (2013), Mark-specific hazard ratio model with multivariate continuous marks: an application to vaccine efficacy. Biometrics 69(2):328 337 <doi:10.1111/biom.12016>, and in Juraska M and Gilbert PB (2015), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4): 606-25 <doi:10.1007/s10985-015-9353-9>. The former considers continuous multivariate marks fully observed in all subjects who experience the event of interest, whereas the latter extends the previous work to allow multivariate marks that are subject to missingness-at-random. For models with missing marks, two estimators are implemented based on (i) inverse probability weighting (IPW) of complete cases, and (ii) augmentation of the IPW estimating functions by leveraging correlations between the mark and auxiliary data to 'impute' the expected profile score vectors for subjects with missing marks. The augmented IPW estimator is doubly robust and recommended for use with incomplete mark data. The methods make two key assumptions: (i) the time-to-event is assumed to be conditionally independent of the mark given treatment, and (ii) the weight function in the semiparametric density ratio/biased sampling model is assumed to be exponential. Diagnostic testing procedures for evaluating validity of both assumptions are implemented. Summary and plotting functions are provided for estimation and inferential results.
Functions for making particle-size analysis. Sieve tests are widely used to obtain particle-size distribution of powders or granular materials.
Functions for estimating catalytic constant and Michaelis-Menten constant (MM constant) of 
             stochastic Michaelis-Menten enzyme kinetics model are provided. The likelihood functions 
             based on stochastic simulation approximation (SSA), diffusion approximation (DA), and 
             Gaussian processes (GP) are provided to construct posterior functions for the Bayesian 
             estimation. All functions utilize Markov Chain Monte Carlo (MCMC) methods with Metropolis-
             Hastings algorithm with random walk chain and robust adaptive Metropolis-Hastings 
             algorithm based on Bayesian framework.
Facilitate extraction of key information from common datasets.
Print function signatures and find overly complicated code.
SigClust is a statistical method for testing the
        significance of clustering results. SigClust can be applied to
        assess the statistical significance of splitting a data set
        into two clusters. For more than two clusters, SigClust can be
        used iteratively.
Uses logistic regression to model the probability of detection as a function of covariates. 
             This model is then used with observational survey data to estimate population size, while
             accounting for uncertain detection.  See Steinhorst and Samuel (1989).
Provides pseudo-likelihood methods for empirically analyzing common signaling games in international relations as described in Crisman-Cox and Gibilisco (2019) <doi:10.1017/psrm.2019.58>.
Interface to 'sigma.js' graph visualization library including animations, plugins and shiny proxies.
Create interactive graph visualizations using 'Sigma.js' <http://sigmajs.org/>.  This package is meant to be used in conjunction
    with 'igraph', replacing the (somewhat underwhelming) plotting features of the package.  The idea is to quickly render
    graphs, regardless of their size, in a way that allows for easy, iterative modification of aesthetics.  Because 
    'Sigma.js' is a 'javascript' library, the visualizations are inherently interactive and are well suited for integration
    with 'Shiny' apps.  While there are several 'htmlwidgets' focused on network visualization, they tend to underperform on 
    medium to large sized graphs.  'Sigma.js' was designed for larger network visualizations and this package aims to 
    make those strengths available to 'R' users.
Genomic alterations including single nucleotide
    substitution, copy number alteration, etc. are the major force for
    cancer initialization and development. Due to the specificity of
    molecular lesions caused by genomic alterations, we can generate
    characteristic alteration spectra, called 'signature' (Wang, Shixiang,
    et al.  (2020) <DOI:10.1371/journal.pgen.1009557> & Alexandrov, Ludmil
    B., et al. (2020) <DOI:10.1038/s41586-020-1943-3>).  This package helps
    users to extract, analyze and visualize signatures from genomic
    alteration records, thus providing new insight into cancer study.
Several different sigmoid functions are implemented, including a wrapper function, SoftMax preprocessing and inverse functions.
Provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways.
A framework for the analysis and exploration of single-cell chromatin data.
    The 'Signac' package contains functions for quantifying single-cell chromatin data,
    computing per-cell quality control metrics, dimension reduction
    and normalization, visualization, and DNA sequence motif analysis.
    Reference: Stuart et al. (2021) <doi:10.1038/s41592-021-01282-5>.
An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.
A set of signal processing functions originally written for 'Matlab' and 'Octave'.
  Includes filter generation utilities, filtering functions,
  resampling routines, and visualization of filter models. It also
  includes interpolation functions.
Predicts the presence of signal peptides in eukaryotic protein
    using hidden semi-Markov models. The implemented algorithm can be accessed from
    both the command line and GUI.
Extract the signed backbones of intrinsically dense weighted networks based on the significance filter and vigor filter as described in the following paper. Please cite it if you find this software useful in your work.	
	Furkan Gursoy and Bertan Badur. "Extracting the signed backbone of intrinsically dense weighted networks." Journal of Complex Networks. <arXiv:2012.05216>.
Add significance marks to any R Boxplot, including a given significance niveau.
Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.
Perform sign test on one-sample data, which is one of the oldest non-parametric statistical methods. Assume that X comes from a continuous distribution with median = v ( unknown ). Test the null hypothesis H0: median of X v = mu ( mu is the location parameter and is given in the test ) v.s. the alternative hypothesis H1: v > mu ( or v < mu or v != mu ) and calculate the p-value. When the sample size is large, perform the asymptotic sign test. In both ways, calculate the R-estimate of location of X and the distribution free confidence interval for mu.
Methods for the analysis of signed networks. This includes several measures for structural balance as introduced by Cartwright and Harary (1956) <doi:10.1037/h0046049>, blockmodeling algorithms from Doreian (2008) <doi:10.1016/j.socnet.2008.03.005>, various centrality indices, and projections of signed two-mode networks introduced by Schoch (2020) <doi:10.1080/0022250X.2019.1711376>.
Provides convenience functions to replace hyphen-minuses (ASCII 45)
    with proper minus signs (Unicode character 2212). The true minus matches
    the plus symbol in width, line thickness, and height above the baseline.
    It was designed for mathematics, looks better in presentation,
    and is understood properly by screen readers.
Interfaces with the 'SigOpt' API. More info at <https://sigopt.com>.
Pathway Analysis is statistically linking observations on 
     the molecular level to biological processes or pathways on 
     the systems(i.e., organism, organ, tissue, cell) level.  
     Traditionally, pathway analysis methods regard pathways 
     as collections of single genes and treat all genes in a pathway
     as equally informative. However, this can lead to identifying
     spurious pathways as statistically significant since components
     are often shared amongst pathways. SIGORA seeks to avoid this
     pitfall by focusing on genes or gene pairs that are (as a combination)
     specific to a single pathway.
     In relying on such pathway gene-pair signatures (Pathway-GPS),
     SIGORA inherently uses the status of other genes in the
     experimental context to identify the most relevant pathways.
     The current version allows for pathway analysis of human
     and mouse datasets. In addition, it contains pre-computed
     Pathway-GPS data for pathways in the KEGG and  Reactome
     pathway repositories and mechanisms for extracting GPS
     for user-supplied repositories.
Succinctly and correctly format statistical summaries of
    various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance). 
    This package also includes empirical tests, such as Monte Carlo and bootstrap distribution estimates.
Provides tools to identify and visualize branches in a phylogenetic tree that are significantly responsive to some intervention, taking as primary inputs a phylogenetic tree (of class phylo) and a data frame (or matrix) of corresponding tip (OTU) labels and p-values.
Inference procedures in the high-dimensional setting for (1) linear functionals and quadratic functionals in linear regression ('Cai et al.' (2019) <arXiv:1904.12891>, 'Guo et al.' (2019) <arXiv:1909.01503>), (2) linear functional in logistic regression ('Guo et al.' <arXiv:2012.07133>), (3) individual treatment effects in linear and logistic regression, (4) single regression coefficient in binary outcome regression.
Method to estimate the spatial influence scales of landscape variables on a response variable. The method is based on Chandler and Hepinstall-Cymerman (2016) Estimating the spatial scales of landscape effects on abundance, Landscape ecology, 31: 1383-1394, <doi:10.1007/s10980-016-0380-z>.
Provides a general framework to perform statistical inference of each gene pair 
        and global inference of whole-scale gene pairs in gene networks using the well known 
        Gaussian graphical model (GGM) in a time-efficient manner. We focus on the high-dimensional 
        settings where p (the number of genes) is allowed to be far larger than n (the number of subjects). 
        Four main approaches are supported in this package: (1) the bivariate nodewise scaled Lasso 
        (Ren et al (2015) <doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled Lasso 
        (Jankova and van de Geer (2017) <doi:10.1007/s11749-016-0503-5>) (3) the de-sparsified 
        graphical Lasso (Jankova and van de Geer (2015) <doi:10.1214/15-EJS1031>) (4) the GGM 
        estimation with false discovery rate control (FDR) using scaled Lasso or Lasso 
        (Liu (2013) <doi:10.1214/13-AOS1169>). Windows users should install 'Rtools' before the 
        installation of this package.
Generate common data forms for complex data suitable for conversions and
 transmission by decomposition as paths or primitives. Paths are sequentially-linked records, 
 primitives are basic atomic elements and both can model many forms and be grouped into hierarchical 
 structures.  The universal models 'SC0' (structural) and 'SC' (labelled, relational) are composed of 
 edges and can represent any hierarchical form. Specialist models 'PATH', 'ARC' and 'TRI' provide the 
 most common intermediate forms used for converting from one form to another. The methods are
 inspired by the simplicial complex <https://en.wikipedia.org/wiki/Simplicial_complex> and 
 provide intermediate forms that relate spatial data structures to this mathematical construct.
Simultaneous inference procedures for high-dimensional linear models as described by Zhang, X., and Cheng, G. (2017) <doi:10.1080/01621459.2016.1166114>.
It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs <doi:10.18637/jss.v096.i02>. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.
The definition of fuzzy random variable and the methods of simulation from fuzzy random variables are two challenging statistical problems in three recent decades. This package is organized based on a special definition of fuzzy random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy Numbers (PLFNs); see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for details about PLFNs. Some important statistical functions are considered for obtaining the membership function of main statistics, such as mean, variance, summation, standard deviation and coefficient of variance. Some of applied advantages of 'Sim.PLFN' package are:  (1) Easily generating / simulation a random sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or the membership function of the statistical result, and (3) Considering the simulated PLFNs for arithmetic operation or importing into some statistical computation. Finally, it must be mentioned that 'Sim.PLFN' package works on the basis of 'FuzzyNumbers' package.
Generates realistic simulated genetic data in families or unrelated individuals.
Provides tools for simulating spatially dependent predictors (continuous or binary),
    which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous
    predictors are generated using traditional multivariate normal distributions or Gauss Markov random
    fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288>
    and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using
    a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors 
	exhibiting spatial clustering can also be easily specified by the user.
Provides some code to run simulations of state-space models, and then
             use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) 
             algorithm of Toni et al. (2009) <doi:10.1098/rsif.2008.0172> and a bootstrap particle
             filter based particle Markov chain Monte Carlo (PMCMC) algorithm 
             (Andrieu et al., 2010 <doi:10.1111/j.1467-9868.2009.00736.x>). 
             Also provides functions to plot and summarise the outputs.
Provides estimation of simultaneous bootstrap and asymptotic confidence intervals for diversity indices, namely the Shannon and the Simpson index. Several pre--specified multiple comparison types are available to choose. Further user--defined contrast matrices are applicable. In addition, simboot estimates adjusted as well as unadjusted p--values for two of the three proposed bootstrap methods. Further simboot allows for comparing biological diversities of two or more groups while simultaneously testing a user-defined selection of Hill numbers of orders q, which are considered as appropriate and useful indices for measuring diversity.
Provides efficient R and 'C++' routines to simulate cognitive diagnostic
    model data for Deterministic Input, Noisy "And" Gate ('DINA') and
    reduced Reparameterized Unified Model ('rRUM') from 
    Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>,
    Culpepper (2015) <doi:10.3102/1076998615595403>, and
    de la Torre (2009) <doi:10.3102/1076998607309474>.
Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 <doi:10.2202/1557-4679.1258>) or ratios (Hasler and Hothorn, 2012 <doi:10.1080/19466315.2011.633868>) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 <doi:10.1002/bimj.200710466>) or covariance matrices (Hasler, 2014 <doi:10.1515/ijb-2012-0015>).
Provides a framework to generating random variates from
             arbitrary multivariate copulae, while concentrating on
             (bivariate) extreme value copulae.  Particularly useful if
             the multivariate copulae are not available in closed
             form.
Simulates correlated multinomial responses conditional on a marginal model specification.
Simulate and plot general experimental crosses. The focus is on simulating genotypes with an aim towards flexibility rather than speed. Meiosis is simulated following the Stahl model, in which chiasma locations are the superposition of two processes: a proportion p coming from a process exhibiting no interference, and the remainder coming from a process following the chi-square model.
Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures.
Provides tools to safely and efficiently organize and execute 
    Monte Carlo simulation experiments in R.
    The package controls the structure and back-end of Monte Carlo simulation experiments
    by utilizing a generate-analyse-summarise workflow. The workflow safeguards against 
    common simulation coding issues, such as automatically re-simulating non-convergent results, 
    prevents inadvertently overwriting simulation files, catches error and warning messages
    during execution, and implicitly supports parallel processing.
    For a pedagogical introduction to the package see
    Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of 
    the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.
Implementation of a model-based bootstrap approach for testing whether two formulations are similar. The package provides a function for fitting a pharmacokinetic model to time-concentration data and comparing the results for all five candidate models regarding the Residual Sum of Squares (RSS). The candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a logistic model. The assessment of similarity implemented in this package is performed regarding the maximum deviation of the profiles. See Moellenhoff et al. (2018) <doi:10.1002/sim.7689> for details.
Assessment of the distributions of baseline continuous and categorical 
    variables in randomised trials. This method is based on the Carlisle-Stouffer 
    method with Monte Carlo simulations. It calculates p-values for each trial 
    baseline variable, as well as combined p-values for each trial - these p-values 
    measure how compatible are distributions of trials baseline variables with 
    random sampling. This package also allows for graphically plotting the 
    cumulative frequencies of computed p-values.
    Please note that code was partly adapted from Carlisle JB, Loadsman JA. 
    (2017) <doi:10.1111/anae.13650>.
An object oriented framework to simulate
  ecological (and other) dynamic systems. It can be used for
  differential equations, individual-based (or agent-based) and other
  models as well. It supports structuring of simulation scenarios (to avoid copy
  and paste) and aims to improve readability and re-usability of code.
Contains various functions to be used for simulation education,
    including simple Monte Carlo simulation functions, queueing simulation
    functions, variate generation functions capable of producing independent
    streams and antithetic variates, functions for illustrating random variate
    generation for various discrete and continuous distributions, and functions
    to compute time-persistent statistics.  Also contains functions for 
    visualizing: event-driven details of a single-server queue model; a Lehmer
    random number generator; variate generation via acceptance-rejection; and
    of generating a non-homogeneous Poisson process via thinning.  Also
    contains two queueing data sets (one fabricated, one real-world) to
    facilitate input modeling.
An open-source R package for structuring, maintaining, running, and debugging statistical simulations on both local and cluster-based computing environments. Emphasis is placed on thorough documentation and scalability. See full documentation at <https://avi-kenny.github.io/SimEngine/>.
Estimation of function and index vector in single index model with and without shape constraints including different smoothness conditions.
Monte Carlo simulations of a game-theoretic model for the 
	legal exemption system of the European cartel law are implemented
	in order to estimate the (mean) deterrent effect of this system.
	The input and output parameters of the simulated cartel 
	opportunities can be visualized by three-dimensional projections.
	A description of the model is given in Moritz et al. (2018)
	<doi:10.1515/bejeap-2017-0235>.
Simulate the evolution of enzyme concentrations under selection for increased flux in a metabolic pathway, with cellular constraints.
  Create graphics for the simulation results.
  Compute evolutionary equilibrium and Range of Neutral Variations of enzyme concentrations.
  This package is part of "Coton, C., Talbot, G., Le Louarn, M., Dillmann, C., de Vienne, D. (2021) <bioRxiv:10.1101/2021.05.04.442631>".
  Version 2.0.0 and more takes account of regulation groups, and is part of a second article "Coton, C., Dillmann, C., de Vienne, D. (in progress)".
Implementation of the SIMEX-Algorithm by Cook & Stefanski (1994) <doi:10.1080/01621459.1994.10476871> and
    MCSIMEX by Küchenhoff, Mwalili & Lesaffre (2006) <doi:10.1111/j.1541-0420.2005.00396.x>.
Implement of the Simulation-Extrapolation (SIMEX) algorithm for the accelerated failure time (AFT) with covariates subject to measurement error.
Generates binary test data based on Item Response Theory using the two-parameter logistic model (Lord, 1980 <doi:10.4324/9780203056615>). Useful functions for test equating are included, e.g. functions for generating internal and external common items between test forms and a function to create a linkage plans between those forms. Ancillary functions for generating true item and person parameters as well as for calculating the probability of a person correctly answering an item are also included.
Implementation of the boosting procedure with the simulation and extrapolation approach to address variable selection and estimation for (ultra)high-dimensional data subject to measurement error in predictors. Some relevant references include Chen, L.-P. and Yi, G. Y. (2021) <doi:10.1111/biom.13331>, Hastie, T., Tibshirani, R. and Friedman, J. (2008, ISBN:978-0387848570), and Yi, G. Y. (2017, ISBN:978-1493966387).
The focus is on simulating and modeling families with founders drawn from a structured population (for example, with different ancestries or other potentially non-family relatedness), in contrast to traditional pedigree analysis that treats all founders as equally unrelated.  Main function simulates a random pedigree for many generations, avoiding close relatives, pairing closest individuals according to a 1D geography and their randomly-drawn sex, and with variable children sizes to result in a target population size per generation.  Auxiliary functions calculate kinship matrices, admixture matrices, and draw random genotypes across arbitrary pedigree structures starting from the corresponding founder values.  The code is built around the plink FAM table format for pedigrees.  Partially described in Yao and Ochoa (2019) <doi:10.1101/858399>.
Through simfinapi, you can intuitively access the 'SimFin'
    Web-API (<https://simfin.com/>) to make 'SimFin' data easily available
    in R. To obtain an 'SimFin' API key (and thus to use this package),
    you need to register at <https://simfin.com/login>.
Simulates data from model objects (e.g., from lm(), glm()),
    and plots this along with the original data to compare how well the
    simulated data matches the original data to determine model fit.
A general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort.  The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results.  It follows a clear object-oriented design and supports parallel computing to increase computational performance.
Simulates regression models,
    including both simple regression and generalized linear mixed
    models with up to three level of nesting. Power simulations that are
    flexible allowing the specification of missing data, unbalanced designs,
    and different random error distributions are built into the package.
Routine that allows the user to run several goodness-of-fit tests.  
    It also combines the tests and returns a properly adjusted family-wise p value.
    Details can be found in <arXiv:2007.04727>.
Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.
Calculates performance criteria measures and associated Monte Carlo standard errors for simulation results. Includes functions to help run simulation studies. Our derivation and explanation of formulas and our general simulation workflow is closely aligned with the approach described by Morris, White, and Crowther (2019) <DOI: 10.1002/sim.8086>.
An implementation of a novel method to quantify the similarity
    of the code-base of R functions by means of program dependence graphs.
    Possible use cases include detection of code clones for improving
    software quality and of plagiarism amongst students' assignments.
Functions to run and assist four
  different similarity measures. The similarity
  measures included are: longest common
  subsequence (LCSS), Frechet distance, edit distance
  and dynamic time warping (DTW). Each of these
  similarity measures can be calculated from two
  n-dimensional trajectories, both in matrix form.
Allows a Simile model saved as a compiled binary to be
  loaded, parameterized, executed and interrogated. This version works 
  with Simile v5.97 on.
Provides an efficient and very flexible framework to
    conduct data-driven epidemiological modeling in realistic large
    scale disease spread simulations. The framework integrates
    infection dynamics in subpopulations as continuous-time Markov
    chains using the Gillespie stochastic simulation algorithm and
    incorporates available data such as births, deaths and movements
    as scheduled events at predefined time-points. Using C code for
    the numerical solvers and 'OpenMP' (if available) to divide work
    over multiple processors ensures high performance when simulating
    a sample outcome. One of our design goals was to make the package
    extendable and enable usage of the numerical solvers from other R
    extension packages in order to facilitate complex epidemiological
    research. The package contains template models and can be extended
    with user-defined models. For more details see the paper by
    Widgren, Bauer, Eriksson and Engblom (2019)
    <doi:10.18637/jss.v091.i12>. The package also provides
    functionality to fit models to time series data using the
    Approximate Bayesian Computation Sequential Monte Carlo
    ('ABC-SMC') algorithm of Toni and others (2009)
    <doi:10.1098/rsif.2008.0172>.
Provides tools for the stochastic simulation of effectiveness scores to mitigate
  data-related limitations of Information Retrieval evaluation research, as described in Urbano and
  Nagler (2018) <doi:10.1145/3209978.3210043>. These tools include: fitting, selection and plotting
  distributions to model system effectiveness, transformation towards a prespecified expected value,
  proxy to fitting of copula models based on these distributions, and simulation of new evaluation
  data from these distributions and copula models.
Uses simulation to create prediction intervals for
    post-policy outcomes in interrupted time series (ITS) designs,
    following Miratrix (2020) <arXiv:2002.05746>. This package provides
    methods for fitting ITS models with lagged outcomes and variables to
    account for temporal dependencies.  It then conducts inference via
    simulation, simulating a set of plausible counterfactual post-policy
    series to compare to the observed post-policy series. This package
    also provides methods to visualize such data, and also to incorporate
    seasonality models and smoothing and aggregation/summarization.  This
    work partially funded by Arnold Ventures in collaboration with
    MDRC.
Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach <doi:10.1080/03610918208812265> and the Ruscio-Kaczetow iteration (2008) <doi:10.1080/00273170802285693>. Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator from <https://www.pcg-random.org>.
A toolbox for constructing potential landscapes for dynamical systems using Monte Carlo simulation. 
    The method is based on the potential landscape definition by Wang et al. (2008) <doi:10.1073/pnas.0800579105> 
    (also see Zhou & Li, 2016 <doi:10.1063/1.4943096> for further mathematical discussions) and can be used for 
    a large variety of models.
A process-oriented and trajectory-based Discrete-Event Simulation
    (DES) package for R. It is designed as a generic yet powerful framework. The
    architecture encloses a robust and fast simulation core written in 'C++' with
    automatic monitoring capabilities. It provides a rich and flexible R API that
    revolves around the concept of trajectory, a common path in the simulation
    model for entities of the same type. Documentation about 'simmer' is provided
    by several vignettes included in this package, via the paper by Ucar, Smeets
    & Azcorra (2019, <doi:10.18637/jss.v090.i02>), and the paper by Ucar,
    Hernández, Serrano & Azcorra (2018, <doi:10.1109/MCOM.2018.1700960>);
    see 'citation("simmer")' for details.
Provides wrappers for common activity patterns in 'simmer' trajectories.
A set of plotting methods for 'simmer' trajectories and simulations.
Allows users to quickly apply individual or multiple metrics to evaluate Monte Carlo simulation studies.
A major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients' data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1016/j.jspi.2019.05.008> and Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml().
Simulates somatic single base substitutions carried in cancer genomes. By only providing a human reference genome, substitutions that result from mutational processes operative in every cancer genome can be generated.
Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term replacement to the previous widely-used package SIAR. SIMMs are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load and simmr_mcmc. The two vignettes contain a quick start and a full listing of all the features. The methods used are detailed in the papers Parnell et al 2010 <doi:10.1371/journal.pone.0009672>, and Parnell et al 2013 <doi:10.1002/env.2221>.
Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.
Implements statistical inference for systems of ordinary differential equations,
  that uses the integral-matching criterion and takes advantage of the separability of parameters,
  in order to obtain initial parameter estimates for nonlinear least squares optimization. 
  Dattner & Yaari (2018) <arXiv:1807.04202>. 
  Dattner et al. (2017) <doi:10.1098/rsif.2016.0525>.
  Dattner & Klaassen (2015) <doi:10.1214/15-EJS1053>.
Simulate data from a Gaussian graphical model or a Gaussian Bayesian network in two conditions. Given a covariance matrix of a reference condition simulate plausible disregulations. See Salviato et al. (2017) <doi:10.1093/bioinformatics/btw642>.
Simulates and plots quantities of interest (relative
    hazards, first differences, and hazard ratios) for linear coefficients,
    multiplicative interactions, polynomials, penalised splines, and
    non-proportional hazards, as well as stratified survival curves from Cox
    Proportional Hazard models. It also simulates and plots marginal effects
    for multiplicative interactions. Methods described in Gandrud (2015)
    <doi:10.18637/jss.v065.i03>.
Provides functions to simulate single or multiple, independent or correlated phenotype(s) with additive, dominance effects and their interactions. Also includes functions to generate phenotype(s) with specific heritability. Flexible and user-friendly options for simulation.
A tool for simulating rhythmic data: transcriptome data using
  Gaussian or negative binomial distributions, and behavioral activity data
  using Bernoulli or Poisson distributions. See Singer et al. (2019)
  <doi:10.7717/peerj.6985>.
Provides SPSS- and SAS-like output for least squares multiple 
    regression and moderated regression, as well as interaction plots and 
    Johnson-Neyman regions of significance for interactions. The output 
    includes standardized coefficients, partial and semi-partial correlations, 
    collinearity diagnostics, plots of residuals, and detailed information 
    about simple slopes for interactions. There are numerous options for 
    designing interaction plots, including plots of interactions for both lm
    and lme models.
Provides intuitive functions for caching R objects, encouraging
    reproducible, restartable, and distributed R analysis. The user selects a
    location to store caches, and then provides  nothing more than a cache name
    and instructions (R code) for how to produce the R object. Also
    provides some advanced options like environment assignments, recreating or
    reloading caches, and cluster compute bindings (using the 'batchtools'
    package) making it flexible enough for use in large-scale data analysis
    projects.
A curated set of colors that are called using
    a standardized syntax: saturation + hue + lightness. For example, 
    "brightblue4" and "mutedred2". Functions exists to return individual colors 
    by name or to build palettes across or within hues. Most functions allow you 
    to visualize the palettes in addition to returning the desired hex codes.
Using the adjustment method from Benjamini & Hochberg (1995) <doi:10.1111/j.2517-6161.1995.tb02031.x>, this package determines which variables are significant under repeated testing with a given dataframe of p values and an user defined "q" threshold.  It then returns the original dataframe along with a significance column where an asterisk denotes a significant p value after FDR calculation, and NA denotes all other p values. This package uses the Benjamini & Hochberg method specifically as described in Lee, S., & Lee, D. K. (2018) <doi:10.4097/kja.d.18.00242>.
Simple classic graph algorithms for simple graph classes.
    Graphs may possess vertex and edge attributes. 'simplegraph' has
    so dependencies and it is written entirely in R, so it is easy to
    install.
This is a graph database in 'SQLite'.  It is inspired by Denis Papathanasiou's Python simple-graph project on 'GitHub'.
A very bare-bones interface to use the Metropolis-Hastings Monte 
    Carlo Markov Chain algorithm. It is suitable for teaching and testing 
    purposes.
Create and train a multilayer perceptron, a type of feedforward, 
    fully connected neural network. Features 2 ReLU hidden layers. Learn more 
    about about the activation functions and backpropagation used by this 
    network in Goodfellow et al. (2016, ISBN: 9780262035613) "Deep Learning".
Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.
The number of studies involving correlated traits and the availability of tools to handle this type of data has increased considerably in the last decade. With such a demand, we need tools for testing hypotheses related to single and multi-trait (correlated) phenotypes based on many genetic settings. Thus, we implemented various options for simulation of pleiotropy and Linkage Disequilibrium under additive, dominance and epistatic models. The simulation currently takes a marker data set as an input and then uses it for simulating multiple traits as described in Fernandes and Lipka (2020) <doi:10.1186/s12859-020-03804-y>.
Simple result caching in R based on R.cache. The global environment is not 
    considered when caching results simplifying moving files between multiple instances 
    of R. Relies on more base functions than R.cache (e.g. cached results are saved using 
    saveRDS() and readRDS()).
Runs R-code present in a pandoc markdown file and 
  includes the resulting output in the resulting markdown file. This
  file can then be converted into any of the output formats 
  supported by pandoc. The package can also be used as an engine
  for writing package vignettes.
When working across multiple machines and, similarly for
    reproducible research, it can be time consuming to ensure that you have
    all of the needed packages installed and loaded and that the correct working
    directory is set. 'simpleSetup' provides simple functions for making these
    tasks more straightforward.
Wrapper functions to make 'ggplot2' and 'leaflet' visualisation easier with less brainpower required.
Simplex density, distribution, quantile functions as well as random variable
   	generation of the simplex distribution are given. Regression analysis of proportional data
   	using various kinds of simplex models is available. In addition, GEE method can be applied 
	to longitudinal data to model the correlation. Residual analysis is also involved. Some 
	subroutines are written in C with GNU Scientific Library (GSL) so as to facilitate the 
	computation.
Provides an interface to a Simplex Tree data structure, which is 
  a data structure aimed at enabling efficient manipulation of simplicial complexes 
  of any dimension. The Simplex Tree data structure was originally introduced by 
  Jean-Daniel Boissonnat and Clément Maria (2014) <doi:10.1007/s00453-014-9887-3>.
Provides methods to integrate functions over m-dimensional simplices
 in n-dimensional Euclidean space.  There are exact methods for polynomials and
 adaptive methods for integrating an arbitrary function.
Pairwise group comparisons are often performed. While there are many packages that can perform these analyses, often it is the case that only a subset of comparisons are desired. 'SimplifyStats' performs pairwise comparisons and returns the results in a tidy fashion.
Reliability and agreement analyses often have limited software support. Therefore, this package was created to make agreement and reliability analyses easier for the average researcher. The functions within this package include simple tests of agreement, agreement analysis for nested and replicate data, and provide robust analyses of reliability. In addition, this package contains a set of functions to help when planning studies looking to assess measurement agreement. For robust analyses of agreement, limits of agreement through a bootstrap method can also be calculated.
Tools and methods to simulate populations for surveys based
    on auxiliary data. The tools include model-based methods, calibration and
    combinatorial optimization algorithms, see Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v079.i10>) and
    Templ (2017) <doi:10.1007/978-3-319-50272-4>. The package was developed with support of
    the International Household Survey Network, DFID Trust Fund TF011722 and funds
    from the World bank.
A general, 'tidyverse'-friendly framework 
  for simulation studies, design analysis, and power analysis. 
  Specify data generation, define varying parameters, generate data, 
  fit models, and tidy model results in a single pipeline, without 
  needing loops or custom functions.
Easy to use interfaces to a number of imputation methods
        that fit in the not-a-pipe operator of the 'magrittr' package.
Calculate power for generalised linear mixed models, using
    simulation. Designed to work with models fit using the 'lme4' package.
    Described in Green and MacLeod, 2016 <doi:10.1111/2041-210X.12504>.
Provides a number of functions to simulate restriction enzyme digestion, library construction and fragments size selection to predict the number of loci expected from most of the Restriction site Associated DNA (RAD) and Genotyping By Sequencing (GBS) approaches. SimRAD estimates the number of loci expected from a particular genome depending on the protocol type and parameters allowing to assess feasibility, multiplexing capacity and the amount of sequencing required.
Simulation of recurrent event data for non-constant baseline
    hazard in the total time model with risk-free intervals and possibly a competing event.
    Possibility to cut the data to an interim data set. Data can be plotted.
    Details about the method can be found in Jahn-Eimermacher, A. et al. (2015) <doi:10.1186/s12874-015-0005-2>.
Similarity regression,
    evaluating the probability of association between sets of ontological terms
    and binary response vector. A no-association model is compared with one in which
    the log odds of a true response is linked to the semantic similarity
    between terms and a latent characteristic ontological profile - 'Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases', Greene et al 2016 <doi:10.1016/j.ajhg.2016.01.008>.
Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts.
    This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an 'RStudio' gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) <doi:10.1016/j.chemolab.2015.05.012> and Rimal, R., Almøy, T., Sæbø, S. (2018) <doi:10.1016/j.chemolab.2018.02.009>.
Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers,
  heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on
  resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to
  linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be
  highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the
  landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in
  homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using
  the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input
  parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as
  mechanistic null models (Potts & Lewis 2014, <DOI:10.1098/rspb.2014.0231>) to test a variety of 'Movement Ecology'
  hypotheses (Nathan et al. 2008, <DOI:10.1073/pnas.0800375105>), including landscape effects (e.g. resources, 
  infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk,
  dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as
  those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species
  spread, and population dynamics.
Routines to simulate and manipulate pedigrees ascertained to
    contain multiple family members affected by a rare disease. 
    Christina Nieuwoudt, Samantha J Jones, Angela Brooks-Wilson, 
    and Jinko Graham (2018) <doi:10.1101/234153>.
Generates data from R or 'JAGS' code for use in simulation
    studies.  The data are returned as an 'nlist::nlists' object and/or
    saved to file as individual '.rds' files.  Parallelization is
    implemented using the 'future' package.  Progress is reported using
    the 'progressr' package.
Tools for setting up ("design"), conducting, and evaluating
  large-scale simulation studies with graphics and tables, including
  parallel computations.
Contains two functions for simulating survival data from piecewise exponential hazards with a proportional hazards adjustment for covariates. The first function SimUNIVPiecewise simulates univariate survival data based on a piecewise exponential hazard, covariate matrix and true regression vector. The second function SimSCRPiecewise semi-competing risks data based on three piecewise exponential hazards, three true regression vectors and three matrices of patient covariates (which can be different or the same). This simulates from the Semi-Markov model of Lee et al (2015) given patient covariates, regression parameters, patient frailties and baseline hazard functions.
Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.
RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. Users control the proportion of genes simulated to be differentially expressed (DE) and can provide a vector of weights to control the distribution of effect sizes. The algorithm requires a matrix of RNA-seq read counts with large sample sizes in at least two treatment groups. Many datasets are available that fit this standard.
An implementation of a single-index regression for optimizing individualized dose rules from an observational study. To model interaction effects between baseline covariates and a treatment variable defined on a continuum, we employ two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear combination of the covariates (a single-index). An unspecified main effect for the covariates is allowed, which can also be modeled through a parametric model. A unique contribution of this work is in the parsimonious single-index parametrization specifically defined for the interaction effect term. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (for the case of a discrete treatment) and Park, Petkova, Tarpey, and Ogden (2021) "A single-index model with a surface-link for optimizing individualized dose rules" <arXiv:2006.00267v2> for detail of the method. The model can take a member of the exponential family as a response variable and can also take an ordinal categorical response. The main function of this package is simsl().
Creates simulated data from structural equation models with standardized loading. Data generation methods are described in Schneider (2013) <doi:10.1177/0734282913478046>.
Simulates data sets in order to explore modeling
    techniques or better understand data generating processes. The user
    specifies a set of relationships between covariates, and generates
    data based on these specifications. The final data sets can represent
    data from randomized control trials, repeated measure (longitudinal)
    designs, and cluster randomized trials. Missingness can be generated
    using various mechanisms (MCAR, MAR, NMAR).
Simulate survival times from standard parametric survival
    distributions (exponential, Weibull, Gompertz), 2-component mixture
    distributions, or a user-defined hazard, log hazard, cumulative hazard,
    or log cumulative hazard function. Baseline covariates can be included
    under a proportional hazards assumption.
    Time dependent effects (i.e. non-proportional hazards) can be included by
    interacting covariates with linear time or a user-defined function of time.
    Clustered event times are also accommodated.
    The 2-component mixture distributions can allow for a variety of flexible
    baseline hazard functions reflecting those seen in practice.
    If the user wishes to provide a user-defined
    hazard or log hazard function then this is possible, and the resulting
    cumulative hazard function does not need to have a closed-form solution.
    For details see the supporting paper <doi:10.18637/jss.v097.i03>.
    Note that this package is modelled on the 'survsim' package available in
    the 'Stata' software (see Crowther and Lambert (2012)
    <https://www.stata-journal.com/sjpdf.html?articlenum=st0275> or
    Crowther and Lambert (2013) <doi:10.1002/sim.5823>).
Simulate age-structured populations that vary in space and time and 
    explore the efficacy of a range of built-in or user-defined sampling 
    protocols to reproduce the population parameters of the known population. 
    (See Regular et al. (2020) <doi:10.1371/journal.pone.0232822> for more
    details).
Provides functions to simulate from joint survival and marker 
    models. The user can specific all basis functions of time, random or 
    deterministic covariates, random or deterministic left-truncation and 
    right-censoring times, and model parameters.
Transforms or simulates data with a target empirical covariance matrix supplied
             by the user. The method to obtain the data with the target empirical covariance
             matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019)
             <arXiv:1812.05678>.
Handles datetimes as integers for the usage inside
        Discrete-Event Simulations (DES). The conversion is made using
        the internally generic function as.numeric() of the base
        package. DES is described in Simulation Modeling and Analysis
        by Averill Law and David Kelton (1999) <doi:10.2307/2288169>.
Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. <arXiv:1709.10074> for methodological details.
Tool for statistical simulations that have two components. 
    One component generates the data and the other one
    analyzes the data. The main aims of the package are the reduction
    of the administrative source code (mainly loops and management code for the
    results) and a simple applicability of the package that allows the user to
    quickly learn how to work with it. Parallel computing is
    also supported. Finally, convenient functions are provided to summarize the
    simulation results.
Simulate complex traits given a SNP genotype matrix and model parameters (the desired heritability, number of causal loci, and either the true ancestral allele frequencies used to generate the genotypes or the mean kinship for a real dataset).  Emphasis on avoiding common biases due to the use of estimated allele frequencies.  The code selects random loci to be causal, constructs coefficients for these loci and random independent non-genetic effects.  Traits can follow three models: random coefficients, fixed effect sizes, and infinitesimal (multivariate normal).  GWAS method benchmarking functions are also provided.  Partially described in Yao and Ochoa (2019) <doi:10.1101/858399>.
A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.
Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample. Duong (2017) <doi:10.18637/jss.v021.i07>, Christian P. Robert and George Casella (2010 ISBN:978-1-4419-1575-7) <doi:10.1007/978-1-4419-1576-4>.
A set of tools developed at Simularia for Simularia, to help
    preprocessing and post-processing of meteorological and air quality data.
This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) <DOI:10.1007/s10994-017-5635-7>.
Simulation tools for planning Vitamin D studies. Individual vitamin D status 
             profiles are simulated, modelling population heterogeneity in trial arms. 
             Exposures to infectious agents are generated, with infection depending on vitamin D status.
This package provides routines to perform SIN model selection
  as described in Drton & Perlman (2004, 2008).  The selected models are
  represented in the format of the 'ggm' package, which allows in
  particular parameter estimation in the selected model.
The sinaplot is a data visualization chart suitable for plotting
    any single variable in a multiclass data set. It is an enhanced jitter strip
    chart, where the width of the jitter is controlled by the density
    distribution of the data within each class.
Implementation of the Conditional Least Square (CLS) estimates and
    its covariance matrix for the first-order spatial integer-valued
    autoregressive model (SINAR(1,1)) proposed by Ghodsi (2012)
    <doi:10.1080/03610926.2011.560739>.
This implements the Brunton et al (2016; PNAS <doi:10.1073/pnas.1517384113>) sparse
    identification algorithm for finding ordinary differential equations for a measured 
    system from raw data (SINDy). The package includes a set of additional tools for 
    working with raw data, with an emphasis on cognitive science applications (Dale 
    and Bhat, in press <doi:10.1016/j.cogsys.2018.06.020>).
Manage package documentation and namespaces from the command line. 
             Programmatically attach namespaces in R and Rmd script, populates 
             'Roxygen2' skeletons with information scraped from within functions and 
             populate the Imports field of the DESCRIPTION file.
When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) <doi:10.1076/clin.12.4.482.7241>, Crawford and Garthwaite (2005) <doi:10.1037/0894-4105.19.3.318>, Crawford and Garthwaite (2007) <doi:10.1080/02643290701290146> and Crawford, Garthwaite and Ryan (2011) <doi:10.1016/j.cortex.2011.02.017>. The package is also equipped with power calculators for each method.
Provides R functions for calculating basic effect size indices for 
  single-case designs, including several non-overlap measures and parametric 
  effect size measures, and for estimating the gradual effects model developed 
  by Swan and Pustejovsky (2018) <DOI:10.1080/00273171.2018.1466681>. 
  Standard errors and confidence intervals (based on the assumption that the outcome 
  measurements are mutually independent) are provided for the subset of effect sizes 
  indices with known sampling distributions.
Identification of differentially expressed genes (DEGs) is a key step in single-cell 
    transcriptomics data analysis. 'singleCellHaystack' predicts DEGs without relying on 
    clustering of cells into arbitrary clusters. Single-cell RNA-seq (scRNA-seq) data is 
    often processed to fewer dimensions using Principal Component Analysis (PCA) and 
    represented in 2-dimensional plots (e.g. t-SNE or UMAP plots). 'singleCellHaystack' 
    uses Kullback-Leibler divergence to find genes that are expressed in subsets of cells 
    that are non-randomly positioned in a these multi-dimensional spaces or 2D representations. 
    For the theoretical background of 'singleCellHaystack' we refer to 
    Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3>.
Density, distribution function, quantile function 
	and random generation for the sum of independent non-identical
	binomial distribution with parameters \code{size} and \code{prob}.
Density, distribution function, quantile function, random
    generation and survival function for the Sine Inverse Weibull Distribution as
    defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
Provides access to packages developed for downloading, reading and analyzing microdata 
  from household surveys conducted by Brazilian Institute of Geography and Statistics - IBGE.
	More information can be obtained from the official website <https://www.ibge.gov.br/>.
A platform for computing competition indices and experimenting
    with spatially explicit individual-based vegetation models.
Single-Index Quantile Regression is effective in some scenarios. We provides functions that allow users to fit Single-Index Quantile Regression model. It also provides functions to do prediction, estimate standard errors of the single-index coefficients via bootstrap, and visualize the estimated univariate function. Please see W., Y., Y. (2010) <doi:10.1016/j.jmva.2010.02.003> for details.
Calculating daily global solar radiation at horizontal surface using several well-known models (i.e. Angstrom-Prescott, Supit-Van Kappel, Hargreaves, Bristow and Campbell, and Mahmood-Hubbard), and model calibration based on ground-truth data, and (3) model auto-calibration. The FAO Penmann-Monteith equation to calculate evapotranspiration is also included.
Provides two main functionalities.
    1 - Given a system of simultaneous equation,
    it decomposes  the matrix of coefficients weighting the endogenous variables 
    into three submatrices: one includes the subset of coefficients that have a causal nature
    in the model, two include the subset of coefficients that have a interdependent nature
    in the model, either at systematic level or induced by the correlation between error terms.
    2 - Given a decomposed model,
    it tests for the significance of the interdependent relationships acting in the system, 
    via Maximum likelihood and Wald test, which can be built starting from the function output.
    For theoretical reference see Faliva (1992) <doi:10.1007/BF02589085> and 
    Faliva and Zoia (1994) <doi:10.1007/BF02589041>.
Performs hybrid multi-stage factor analytic procedure for controlling acquiescence in restricted solutions (Ferrando & Lorenzo-Seva, 2000 <https://www.uv.es/revispsi/articulos3.00/ferran7.pdf>).
We build an Susceptible-Infectious-Recovered (SIR) model where the rate of infection is the sum of the household rate and the community rate. We estimate the posterior distribution of the parameters using the Metropolis algorithm. Further details may be found in: F Scott Dahlgren, Ivo M Foppa, Melissa S Stockwell, Celibell Y Vargas, Philip LaRussa, Carrie Reed (2021) "Household transmission of influenza A and B within a prospective cohort during the 2013-2014 and 2014-2015 seasons" <doi:10.1002/sim.9181>.
Supplementary functions for item response models aiming
    to complement existing R packages. The functionality includes among others
    multidimensional compensatory and noncompensatory IRT models
    (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), 
    MCMC for hierarchical IRT models and testlet models
    (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), 
    NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), 
    Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>;
    Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>),
    faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011,
    <doi:10.1111/j.1745-3984.2011.00143.x>),
    ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), 
    DETECT statistic (Stout, Habing, Douglas & Kim, 1996, 
    <doi:10.1177/014662169602000403>), local structural equation modeling 
    (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016,
    <doi:10.1080/00273171.2016.1142856>).
A regression and classification algorithm based on random forests, which takes the form of a short list of rules. SIRUS combines the simplicity of decision trees with a predictivity close to random forests. The core aggregation principle of random forests is kept, but instead of aggregating predictions, SIRUS aggregates the forest structure: the most frequent nodes of the forest are selected to form a stable rule ensemble model. The algorithm is fully described in the following articles: Benard C., Biau G., da Veiga S., Scornet E. (2021), Electron. J. Statist., 15:427-505 <DOI:10.1214/20-EJS1792> for classification, and Benard C., Biau G., da Veiga S., Scornet E. (2020) <arXiv:2004.14841> for regression. This R package is a fork from the project ranger (<https://github.com/imbs-hl/ranger>).
Variable selection techniques are essential tools for model
    selection and estimation in high-dimensional statistical models. Through this
    publicly available package, we provide a unified environment to carry out
    variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all
    of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards
    model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).
Implements the SISAL algorithm by Tikka and Hollmén. It is
        a sequential backward selection algorithm which uses a linear
        model in a cross-validation setting. Starting from the full
        model, one variable at a time is removed based on the
        regression coefficients. From this set of models, a
        parsimonious (sparse) model is found by choosing the model with
        the smallest number of variables among those models where the
        validation error is smaller than a threshold. Also implements
        extensions which explore larger parts of the search space
        and/or use ridge regression instead of ordinary least squares.
An interval fusion procedure for functional data in the
             semiparametric framework of SIR, as described in 
             <doi:10.1007/s11222-018-9806-6>. Standard ridge and sparse SIR are 
             also included in the package.
Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 979-8-68239-420-3, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 979-8-59347-027-0, "Adäquates Maschinelles Lernen").
The Brazilian system for diploma registration and validation on technical and superior
    courses are managing by 'Sistec' platform, see <https://sistec.mec.gov.br/>. This package provides 
    tools for Brazilian institutions to update the student's registration and make data analysis 
    about their situation, retention and drop out.
Functions common to members of the SISTM team.
Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.
Functions for fitting and plotting SITAR (Super
    Imposition by Translation And Rotation) growth curve models. SITAR is
    a shape-invariant model with a regression B-spline mean curve and
    subject-specific random effects on both the measurement and age
    scales.  The model was first described by Lindstrom (1995)
    <doi:10.1002/sim.4780141807> and developed as the SITAR method by Cole
    et al (2010) <doi:10.1093/ije/dyq115>.
The SiteAdapt procedure improves the accuracy of modeled solar irradiance series
    through site-adaptation with coincident ground-based measurements relying on the use of a
    regression preprocessing followed by an empirical quantile mapping (eQM) correction.
    Fernandez-Peruchena et al (2020) <doi:10.3390/rs12132127>.
Within the area of movement ecology, it can be useful to identify an animal's sites of interest. These will be places that an animal uses a lot and so will spend a proportionately longer time there. The aim of this package is to identify both the position and size of these sites of interest from high resolution (sub-second) positional data.
The goal of 'siteymlgen' is to make it easy to organise
  the building of your 'R Markdown' website.
  The init() function placed within the first code chunk of the index.Rmd
  file of an 'R' project directory will initiate the generation of an automatically
  written _site.yml file. 'siteymlgen' recommends a specific naming
  convention for your 'R Markdown' files. This naming will ensure that
  your navbar layout is ordered according to a hierarchy.
Implements a three-dimensional stochastic model of cancer growth and mutation similar to the one described in Waclaw et al. (2015) <doi:10.1038/nature14971>. Allows for interactive 3D visualizations of the simulated tumor. Provides a comprehensive summary of the spatial distribution of mutants within the tumor. Contains functions which create synthetic sequencing datasets from the generated tumor.
Provided within are two high quality and fast PPRNGs that may be
    used in an 'OpenMP' parallel environment. In addition, there is a generator
    for one dimensional low-discrepancy sequence. The objective of this library
    to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and
    'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the
    header files inside of 'sitmo' instead of including a copy of each engine
    within their individual package. Lastly, the package contains example
    implementations using the 'sitmo' package and three accompanying vignette that
    provide additional information.
Format a number (or a list of numbers) to a string (or a
        list of strings) with SI prefix. Use SI prefixes as constants
        like (4 * milli)^2
Framework to build an individual tree simulator.
Provides extensions for package 'sitree' for allometric variables, growth, mortality, recruitment, management, tree removal and external modifiers functions.
An iterative feature selection method (manuscript submitted) that
    internally utilizes various Machine Learning methods that have embedded
    feature reduction in order to shrink down the feature space into a small
    and yet robust set.
Functions and utilities to perform
    Statistical Analyses in the Six Sigma way.
    Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control),
    you can manage several Quality Management studies:
    Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis,
    etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2]
    and "Quality Control with R" [ISBN 978-3-319-24046-6], 
    are also included in the package.
This function develops an algorithm for presenting a Bayesian hierarchical model for estimating the sizes of local and national drug injected populations in Bangladesh. The model incorporates multiple commonly used data sources including mapping data, surveys, interventions, capture-recapture data, estimates or guesstimates from organizations, and expert opinion.
Estimate morphometric and gonadal size at sexual maturity for organisms, usually fish and invertebrates. It includes methods for classification based on relative growth (using principal components analysis, hierarchical clustering, discriminant analysis), logistic regression (Frequentist or Bayes), parameters estimation and some basic plots.
Calculates and plots the SiZer map for scatterplot data.  A 
  SiZer map is a way of examining when the p-th derivative of a 
  scatterplot-smoother is significantly negative, possibly zero or 
  significantly positive across a range of smoothing bandwidths.
Provides a database-independent JDBC interface.
Collection of functions dealing with labelled data, like reading and 
    writing data between R and other statistical software packages like 'SPSS',
    'SAS' or 'Stata', and working with labelled data. This includes easy ways 
    to get, set or change value and variable label attributes, to convert 
    labelled vectors into factors or numeric (and vice versa), or to deal with 
    multiple declared missing values.
Collection of miscellaneous utility functions, supporting data 
    transformation tasks like recoding, dichotomizing or grouping variables, 
    setting and replacing missing values. The data transformation functions 
    also support labelled data, and all integrate seamlessly into a 
    'tidyverse'-workflow.
Collection of plotting and table output functions for data
    visualization. Results of various statistical analyses (that are commonly used
    in social sciences) can be visualized using this package, including simple and
    cross tabulated frequencies, histograms, box plots, (generalized) linear models,
    mixed effects models, principal component analysis and correlation matrices, 
    cluster analyses, scatter plots, stacked scales, effects plots of regression 
    models (including interaction terms) and much more. This package supports
    labelled data.
A scalable method to estimate joint Species Distribution Models (jSDMs) for big community datasets based on a Monte Carlo approximation of the joint likelihood.  The numerical approximation is based on 'PyTorch' and 'reticulate', and can be run on CPUs and GPUs alike. The method is described in Pichler & Hartig (2021) <doi:10.1111/2041-210X.13687>. The package contains various extensions, including support for different response families, ability to account for spatial autocorrelation, and deep neural networks instead of the linear predictor in jSDMs.
Collection of convenient functions for common statistical computations,
             which are not directly provided by R's base or stats packages.
             This package aims at providing, first, shortcuts for statistical measures, 
             which otherwise could only be calculated with additional effort 
             (like Cramer's V, Phi, or effect size statistics like Eta or Omega squared), 
             or for which currently no functions available. Second, another focus 
             lies on weighted variants of common statistical measures and tests 
             like weighted standard error, mean, t-test, correlation, and more.
Segment-based Kriging methods, including segment-based ordinary 
             Kriging (SOK) and segment-based regression Kriging (SRK) for 
             spatial prediction of line segment spatial data as described in 
             Yongze Song (2018) <doi:10.1109/TITS.2018.2805817>. Includes the 
             spatial prediction and spatial visualisation. The descriptions of 
             the methods and case datasets refer to the citation information 
             below.
Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values. Methods  available in this package include SKAT described in Micheal Wu, Seunggeun Lee et al. (2011) <doi:10.1016/j.ajhg.2011.05.029>, SKAT-O in Seunggeun Lee et al. (2012) <doi:10.1093/biostatistics/kxs014>, combined test of rare and common variants in Iuliana Ionita-Laza and Seunggeun Lee et al. (2013) <doi:10.1016/j.ajhg.2013.04.015>, efficient resampling for binary traits in Seunggeun Lee et al. (2016) <doi:10.1093/biostatistics/kxv033>, and robust test for binary traits in Zhangchen Zhao et al. (2020) <doi:10.1016/j.ajhg.2019.11.012>.
Utilities for single nucleotide polymorphism (SNP) based kinship analysis
    testing and evaluation. The 'skater' package contains functions for importing, parsing, 
    and analyzing pedigree data, performing relationship degree inference, benchmarking 
    relationship degree classification, and summarizing identity by descent (IBD) segment data.
    Package functions and methods are described in Turner et al. (2021) "skater: An R package 
    for SNP-based Kinship Analysis, Testing, and Evaluation" <doi:10.1101/2021.07.21.453083>.
Sparse (Multicategory) Kernel Discriminant Analysis does variable selection for nonparametric classification
A tool for bootstrapping new packages with useful defaults,
    including a test suite outline that passes checks and helpers for running
    tests, checking test coverage, building vignettes, and more. Package
    skeletons it creates are set up for pushing your package to
    'GitHub' and using other hosted services for building and test automation.
Functions for the Skellam distribution, including: density
    (pmf), cdf, quantiles and regression.
Creates static / animated / interactive visualisations embeddable
    in R Markdown documents. It implements an R-to-JavaScript transpiler and
    enables users to write JavaScript applications using the syntax of R.
An implementation of image processing effects that convert a photo into a line drawing image. 
    For details, please refer to Tsuda, H. (2020). sketcher: An R package for converting a photo into a sketch style image. 
    <doi:10.31234/osf.io/svmw5>.
Provides functions to create and manage research compendiums for data analysis. Research compendiums are a standard and intuitive folder structure for organizing the digital materials of a research project, which can significantly improve reproducibility. The package offers several compendium structure options that fit different research project as well as the ability of duplicating the folder structure of existing projects or implementing custom structures. It also simplifies the use of version control.
Implementations of models which follow the Kolmogorov Birth-Death process framework and functions which utilize these Kolmogorov Birth-Death process models for analysis of skewed distribution functions.
Functions are provided for the density function,
	     distribution function, quantiles and random number
	     generation for the skew hyperbolic
	     t-distribution. There are also functions that fit
	     the distribution to data. There are functions for the
	     mean, variance, skewness, kurtosis and mode of a given
	     distribution and to calculate moments of any order about
	     any centre. To assess goodness of fit, there are
	     functions to generate a Q-Q plot, a P-P plot and a tail plot.
It fits scale mixture of skew-normal linear mixed models using an expectation–maximization (EM) type algorithm, including some possibilities for modeling the within-subject dependence. Details can be found in Schumacher, Lachos and Matos (2021) <doi:10.1002/sim.8870>.
Provide data generation and estimation tools for the multivariate scale mixtures of normal 
             presented in Lange and Sinsheimer (1993) <doi:10.2307/1390698>, the multivariate scale 
             mixtures of skew-normal presented in Zeller, Lachos and Vilca (2011) 
             <doi:10.1080/02664760903406504>, the multivariate skew scale mixtures of normal 
             presented in Louredo, Zeller and Ferreira (2021) <doi:10.1007/s13571-021-00257-y>
             and the multivariate scale mixtures of skew-normal-Cauchy presented in Kahrari et al. (2020)
	     <doi:10.1080/03610918.2020.1804582>.
Estimate necessary sample sizes for comparing the location
    of data from two groups or categories when the distribution of the 
    data is skewed. The package 
    offers a non-parametric method for a Wilcoxon Mann-Whitney test of 
    location shift as well as methods for several generalized linear 
    models, for instance, Gamma regression.
Density, distribution function, quantile function and
        random generation for the skewed t distribution of Fernandez
        and Steel.
A generalization of the statistic used in Friedman's ANOVA method and in Durbin's rank test. This nonparametric statistical test is useful for the data obtained from block designs with missing observations occurring randomly. A resulting p-value is based on the chi-squared distribution and Monte Carlo method.
Functions that simplify calls to the 'Skilljar' API. See 
    <https://api.skilljar.com/docs/> for documentation on the 'Skilljar' API.
    This package is not supported by 'Skilljar'.
A simple to use summary function that can be used with pipes
    and displays nicely in the console. The default summary statistics may
    be modified by the user as can the default formatting.  Support for
    data frames and vectors is included, and users can implement their own
    skim methods for specific object types as described in a vignette.
    Default summaries include support for inline spark graphs.
    Instructions for managing these on specific operating systems are
    given in the "Using skimr" vignette and the README.
Provides tools for applying Sklar's Omega (Hughes, 2018) <arXiv:1803.02734>
    methodology to nominal scores, ordinal scores, percentages, counts, amounts (i.e.,
    non-negative real numbers), and balances (i.e., any real number). The framework can
    accommodate any number of units, any number of coders, and missingness; and
    can be used to measure agreement with a gold standard, intra-coder agreement,
    and/or inter-coder agreement. Frequentist inference is supported for all levels
    of measurement. Bayesian inference is supported for continuous scores only.
Algorithms for solving selective k-means problem,
    which is defined as finding k rows in an m x n matrix such that 
    the sum of each column minimal is minimized. 
    In the scenario when m == n and each cell value in matrix is a 
    valid distance metric, this is equivalent to a k-means problem. 
    The selective k-means extends the k-means problem in the sense 
    that it is possible to have m != n, often the case m < n which 
    implies the search is limited within a small subset of rows. 
    Also, the selective k-means extends the k-means problem in the 
    sense that the instance in row set can be instance not seen in 
    the column set, e.g., select 2 from 3 internet service provider
    (row) for 5 houses (column) such that minimize the overall cost 
    (cell value) - overall cost is the sum of the column minimal of
    the selected 2 service provider.
Algorithms to compute spherical k-means partitions.
  Features several methods, including a genetic and a fixed-point
  algorithm and an interface to the CLUTO vcluster program.
Extension of the 'modeltime' ecosystem. In
    addition. Allows fitting of multiple models over multiple time series.
    It also provides a bridge for using the 'workflowsets' package with
    'modeltime'. It includes some functionalities for spatial data and
    visualization.
It's a Super K-Nearest Neighbor classification method with using kernel density to describe weight of the distance between a training observation and the testing sample.
Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) <doi:10.18637/jss.v099.i01>.
Provides an alternative to manually process hemispherical pictures. The algorithm processes each picture one by one to determine the proportion of sky pixel. The algorithm uses the Ridler and Calvard method (Ridler and Calvard 1978).
A flexible tool that allows generating bespoke
    air transport statistics for urban studies based on publicly available
    data from the Bureau of Transport Statistics (BTS) in the United States
    <https://www.transtats.bts.gov/databases.asp?Mode_ID=1&Mode_Desc=Aviation&Subject_ID2=0>.
Data reduction, visualization and statistical analysis of measurements of orientation of archaeological structures, following Silva (2020) <doi:10.1016/j.jas.2020.105138>.
'Slack' <https://slack.com/> provides a service for teams to
    collaborate by sharing messages, images, links, files and more.
    Functions are provided that make it possible to interact with the
    'Slack' platform 'API'. When you need to share information or data
    from R, rather than resort to copy/ paste in e-mails or other services
    like 'Skype' <https://www.skype.com/en/>, you can use this package to
    send well-formatted output from multiple R objects and expressions to
    all teammates at the same time with little effort. You can also send
    images from the current graphics device, R objects, and upload files.
Data structures and algorithms for sparse arrays and matrices,
  based on index arrays and simple triplet representations, respectively.
Slanted matrices and ordered clustering for better visualization of similarity data.
A self-contained hypothesis is tested for a given pathway of longitudinal omics. 'SlaPMEG' is a two-step procedure. First, a shared latent process mixed model is fitted over the longitudinal measures of omics in a pathway. This shared model allows deviation from the shared process at subject level (a random intercept, slope, or both per subject) and also at omic level (a random effect per omic). These random effects summarize the longitudinal trend of the observations which can be used to test for group differences using 'Globaltest' in the second step. If the pathway is large or the shared effect is small, the package fits a series of pairwise models and estimates the shared random effects based on them.
Implements the smooth LASSO estimator for the function-on-function linear regression model described in Centofanti et al. (2020) <arXiv:2007.00529>.
Estimates the slope and level change present in data after
        removing phase A trend. Represents graphically the original and
        the detrended data.
The skew logistic distribution is a quantile-defined generalisation
 of the logistic distribution (van Staden and King 2015).  Provides random 
 numbers, quantiles, probabilities, densities and density quantiles for the distribution.
 It provides Quantile-Quantile plots and method of L-Moments estimation 
 (including asymptotic standard errors) for the distribution.
Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for concentration of infectious units from serial limiting dilution assays. This package uses the likelihood equation, exact goodness of fit p-values, and exact confidence intervals described in Meyers et al. (1994) <http://jcm.asm.org/content/32/3/732.full.pdf>. This software is also implemented as a web application through the Shiny R package <https://iupm.shinyapps.io/sldassay/>.
Compute Time series Resistant Smooth 4253H, twice smoothing method.
Sleep cycles are largely detected according to the originally proposed criteria by Feinberg & Floyd (1979) <doi:10.1111/j.1469-8986.1979.tb02991.x> as described in Blume & Cajochen (2021) <doi:10.1016/j.mex.2021.101318>.
Use behavioural variables to score activity and infer sleep from bouts of immobility. 
    It is primarily designed to score sleep in fruit flies from Drosophila Activity Monitor (TriKinetics) and Ethoscope data.
    It implements sleep scoring using the "five-minute rule" (Hendricks et al. (2000) <DOI:10.1016/S0896-6273(00)80877-6>),
    activity classification for Ethoscopes (Geissmann et al. (2017) <DOI:10.1371/journal.pbio.2003026>) 
    and a new algorithm to detect when animals are dead.
A tool to interactively explore the
  embeddings created by dimension reduction methods such as 
  Principal Components Analysis (PCA), Multidimensional Scaling (MDS), 
  T-distributed Stochastic Neighbour Embedding (t-SNE),
  Uniform Manifold Approximation and Projection (UMAP) or any other.
Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset.
The implementation of the algorithm for estimation of mutual information and channel capacity from experimental data by classification procedures (logistic regression). Technically, it allows to estimate information-theoretic measures between finite-state input and multivariate, continuous output. Method described in Jetka et al. (2019) <doi:10.1371/journal.pcbi.1007132>.
Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (2nd
    ed)", Duxbury.
Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (3rd
    ed)", Cengage Learning.
Set of tools to find coherent patterns in gene expression (microarray) data using a Bayesian Sparse Latent Factor Model (SLFM) <DOI:10.1007/978-3-319-12454-4_15>. Considerable effort has been put to build a fast and memory efficient package, which makes this proposal an interesting and computationally convenient alternative to study patterns of gene expressions exhibited in matrices. The package contains the implementation of two versions of the model based on different mixture priors for the loadings: one relies on a degenerate component at zero and the other uses a small variance normal distribution for the spike part of the mixture.
Implementation for sparse logistic functional principal component analysis (SLFPCA). SLFPCA is specifically developed for functional binary data, and the estimated eigenfunction can be strictly zero on some sub-intervals, which is helpful for interpretation. The crucial function of this package is SLFPCA().
Implements the Bayesian model selection method with suspected latent 
    grouping factor methodology of Metzger and Franck (2020), 
    <doi:10.1080/00401706.2020.1739561>. SLGF detects latent 
    heteroscedasticity or group-based regression effects based on the levels of a 
    user-specified categorical predictor. We encourage you to review examples in 
    vignette("slgf_vignette", "slgf").
Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".
Provides an implementation of SLICER, an algorithm for inferring cellular trajectories from single cell RNA sequencing data. See Welch, JD, Hartemink AJ, Prins JF (2016) <doi:10.1186/s13059-016-0975-3>.
Create and customize interactive carousels
      using the 'Slick' 'JavaScript' library and the
      'htmlwidgets' package. The carousels can contain
      plots produced in R, images, 'iframes', videos and
      other 'htmlwidgets'.  These carousels can be created
      directly from the R console, and viewed in the 'RStudio' 
      internal viewer, in 'Shiny' apps and R Markdown documents.
This statistical method uses the nearest neighbor algorithm to estimate absolute distances between single cells 
	based on a chosen constellation of surface proteins, with these distances being a measure of the similarity between the 
	two cells being compared. Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015) <DOI:10.1016/j.ymeth.2015.07.008>.
Provides type-stable rolling window functions over any R data
    type. Cumulative and expanding windows are also supported. For more
    advanced usage, an index can be used as a secondary vector that
    defines how sliding windows are to be created.
Create a side-by-side view of raster(image)s with an interactive 
    slider to switch between regions of the images. This can be especially useful
    for image comparison of the same region at different time stamps.
A collection of functions to perform Detrended Fluctuation Analysis (DFA exponent), GUEDES et al. (2019) <doi:10.1016/j.physa.2019.04.132> , Detrended cross-correlation coefficient (RHODCCA), GUEDES & ZEBENDE (2019) <doi:10.1016/j.physa.2019.121286>, DMCA cross-correlation coefficient and Detrended multiple cross-correlation coefficient (DMC), GUEDES & SILVA-FILHO & ZEBENDE (2018) <doi:10.1016/j.physa.2021.125990>, both with sliding windows approach.
Fits singular linear models to longitudinal data. Singular linear
    models are useful when the number, or timing, of longitudinal observations
    may be informative about the observations themselves. They are described
    in Farewell (2010) <doi:10.1093/biomet/asp068>, and are extensions of the
    linear increments model <doi:10.1111/j.1467-9876.2007.00590.x> to general
    longitudinal data.
Sparse Linear Method(SLIM) predicts ratings and top-n
    recommendations suited for sparse implicit positive feedback systems. SLIM
    is decomposed into multiple elasticnet optimization problems which are solved
    in parallel over multiple cores. The package is based on "SLIM: Sparse Linear
    Methods for Top-N Recommender Systems" by Xia Ning and George Karypis <doi:10.1109/ICDM.2011.134>.
Provides functions for performing common tasks when working with
  slippy map tile service APIs e.g. Google maps, Open Street Map, Mapbox, Stamen,
  among others. Functionality includes converting from latitude and longitude to
  tile numbers, determining tile bounding boxes, and compositing tiles to a
  georeferenced raster image.
Provides statistical procedures for linear regression in the general context where the errors are assumed to be correlated. Different ways to estimate the asymptotic covariance matrix of the least squares estimators are available. Starting from this estimation of the covariance matrix, the confidence intervals and the usual tests on the parameters are modified. The functions of this package are very similar to those of 'lm': it contains methods such as summary(), plot(), confint() and predict(). The 'slm' package is described in the paper by E. Caron, J. Dedecker and B. Michel (2019), "Linear regression with stationary errors: the R package slm", arXiv preprint <arXiv:1906.06583>.
Stepwise models for the optimal linear combination of continuous variables in binary classification problems under Youden Index optimisation. Information on the models implemented can be found at Aznar-Gimeno et al. (2021) <doi:10.3390/math9192497>.
A collection of helper functions designed to help
    you to better understand object oriented programming in R,
    particularly using 'S3'.
Efficient implementations for Sorted L-One Penalized Estimation
    (SLOPE): generalized linear models regularized with the sorted L1-norm
    (Bogdan et al. (2015) <doi:10/gfgwzt>). Supported models include ordinary
    least-squares regression, binomial regression, multinomial regression, and
    Poisson regression. Both dense and sparse  predictor matrices are supported.
    In addition, the package features predictor screening rules that enable fast
    and efficient solutions to high-dimensional problems.
Optimal partitioning algorithm for change-in-slope problem with continuity constraint and a finite number of states. Some constraints can be enforced in the inference: isotonic, unimodal or smoothing. With the function slopeSN() (segment neighborhood) the number of segments to infer is fixed by the user and does not depend on a penalty value.
An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.
A mostly pure-R implementation of the RAKE algorithm (Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010) <doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from documents without any training data.
Interface for creation of 'slp' class smoother objects for 
             use in Generalized Additive Models (as implemented by packages 
             'gam' and 'mgcv').
Conduct latent trajectory class analysis with longitudinal data. Our method supports longitudinal continuous, binary and count data. For more methodological details, please refer to Hart, K.R., Fei, T. and Hanfelt, J.J. (2020), Scalable and robust latent trajectory class analysis using artificial likelihood. Biometrics <doi:10.1111/biom.13366>.
'Slurm', Simple Linux Utility for Resource Management
          <https://slurm.schedmd.com/>, is a popular 'Linux' based software used to 
          schedule jobs in 'HPC' (High Performance Computing) clusters. This R package
          provides a specialized lightweight wrapper of 'Slurm' with a syntax similar to
          that found in the 'parallel' R package. The package also includes a method for
          creating socket cluster objects spanning multiple nodes that can be used with
          the 'parallel' package.
This is software linked to the book
  'Applied Smoothing Techniques for Data Analysis -
  The Kernel Approach with S-Plus Illustrations' Oxford University Press.
Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods.
Implements the following approaches for multidimensional scaling (MDS) based on stress minimization using majorization (smacof): ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with external constraints on the configuration, individual differences scaling (idioscal, indscal), MDS with spherical restrictions, and ratio/interval/ordinal/spline unfolding (circular restrictions, row-conditional). Various tools and extensions like jackknife MDS, bootstrap MDS, permutation tests, MDS biplots, gravity models, unidimensional scaling, drift vectors (asymmetric MDS), classical scaling, and Procrustes are implemented as well.
Statistical methods for analyzing case-control point data.  Methods include the ratio of kernel densities, the difference in K Functions, the spatial scan statistic, and q nearest neighbors of cases.
Inference techniques for Fay Herriot Model.
A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data>.
Animal movement models including moving-resting process
    with embedded Brownian motion according to
    Yan et al. (2014) <doi:10.1007/s10144-013-0428-8>,
    Pozdnyakov et al. (2017) <doi:10.1007/s11009-017-9547-6>,
    Brownian motion with measurement error according to
    Pozdnyakov et al. (2014) <doi:10.1890/13-0532.1>,
    and moving-resting-handling process with embedded Brownian motion,
    Pozdnyakov et al. (2018) <arXiv:1806.00849>.
Facilitates programmatic access to NASA Soil Moisture Active
    Passive (SMAP) data with R. It includes functions to search for, acquire,
    and extract SMAP data.
Primary data analysis for sequential multiple assignment randomization trial (SMART) and calibration tools for clinical trial planning purposes. \n 
             The methods used for this package include:  \n
             (1) Likelihood-based global test (hypothesis test, power calculation) by in Zhong X., Cheng, B., Qian M., Cheung Y.K. (2019) <doi: 10.1016/j.cct.2019.105830>. \n
             (2) IPWE-based global test (hypotehsis test, power calculation) by Ogbagaber S.B., Karp J., Wahed A.S. (2016) <doi:10.1002/sim.6747>. \n
             (3) G estimates (pairwise comparison, power calculation) by  Lavori R., Dawson P.W. (2012) <doi: 10.1093/biostatistics/kxr016>. \n
             (4) IPW estimates (pairwise comparison, power calculation) by Murphy S.A. (2005) <doi: 10.1002/sim.2022>. \n
             (5) SAMRT with adaptive randomization by Cheung Y.K. (2015) <doi: 10.1111/biom.12258>.
Permits determination of a set of 
    optimal dynamic treatment regimes and sample size for a SMART design 
    in the Bayesian setting with binary outcomes. Please see Artman (2020) <arXiv:2008.02341>.
SMART trial design, as described by He, J., McClish, D., Sabo, R. (2021)  <doi:10.1080/19466315.2021.1883472>, includes multiple stages of randomization, where participants are randomized to an initial treatment in the first stage and then subsequently re-randomized between treatments in the following stage.
Exploratory analysis on any input data describing the structure and the relationships present in the data. The package automatically select the variable and does related descriptive statistics. Analyzing information value, weight of evidence, custom tables, summary statistics, graphical techniques will be performed for both numeric and categorical predictors.
Tools for accessing and processing datasets prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.
Preview spatial data as 'leaflet' maps with minimal
  effort. smartmap is optimized for interactive use and distinguishes itself 
  from similar packages because it does not need real spatial ('sp' or 'sf')
  objects an input; instead, it tries to automatically coerce everything that 
  looks like spatial data to sf objects or leaflet maps. It - for example -  
  supports direct mapping of: a vector containing a single coordinate pair,
  a two column matrix, a data.frame with longitude and latitude columns, or
  the path or URL to a (possibly compressed) 'shapefile'.
Methods for analysis of energy consumption data (electricity, gas, 
    water) at different data measurement intervals. The package provides feature extraction 
    methods and algorithms to prepare data for data mining and machine learning 
    applications. Deatiled descriptions of the methods and their application can be found 
    in Hopf (2019, ISBN:978-3-86309-669-4) "Predictive Analytics for Energy Efficiency and 
    Energy Retailing" <doi:10.20378/irbo-54833> and Hopf et al. (2016) <doi:10.1007/s12525-018-0290-9> 
    "Enhancing energy efficiency in the residential sector with smart meter data analytics".
Sample size calculation to detect dynamic treatment regime (DTR) effects based on change in clinical attachment level (CAL) outcomes from a non-surgical chronic periodontitis treatments study. The experiment is performed under a Sequential Multiple Assignment Randomized Trial (SMART) design. The clustered tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and non-randomly missing. The implemented algorithm is available in Xu et al. (2019+) <arXiv:1902.09386>.
A set of tools for determining the necessary sample size
    in order to identify the optimal dynamic treatment regime
    in a sequential, multiple assignment, randomized trial (SMART). 
    Utilizes multiple comparisons with the best methodology 
    to adjust for multiple comparisons.
    Designed for an arbitrary SMART design. Please see Artman (2018) <doi:10.1093/biostatistics/kxy064> for more details.
Fast computation of multivariate analyses of small (10s to 100s markers) to big (1000s to 100000s) genotype data. Runs Principal Component Analysis allowing for centering, z-score standardization and scaling for genetic drift, projection of ancient samples to modern genetic space and multivariate tests for differences in group location (Permutation-Based Multivariate Analysis of Variance) and dispersion (Permutation-Based Multivariate Analysis of Dispersion).
Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional data sets. The algorithm is built on the 'irwsva.build' function of the 'sva' package and proposes a revision on it that achieves an order of magnitude faster running time while trading no accuracy loss in return.
Methods for fitting bivariate lines in
    allometry using the major axis (MA) or standardised major axis (SMA), and
    for making inferences about such lines. The available methods of inference
    include confidence intervals and one-sample tests for slope and elevation,
    testing for a common slope or elevation amongst several allometric lines,
    constructing a confidence interval for a common slope or elevation, and
    testing for no shift along a common axis, amongst several samples. 
    See Warton et al. 2012 <doi:10.1111/j.2041-210X.2011.00153.x> for methods description.
A set of functions to build a scoring model from beginning to end, leading the user
	to follow an efficient and organized development process, reducing significantly the time
	spent on data exploration, variable selection, feature engineering, binning and model selection 
	among other recurrent tasks. 
	The package also incorporates monotonic and customized binning, scaling capabilities that 
	transforms logistic coefficients into points for a better business understanding and 
	calculates and visualizes classic performance metrics of a classification model.
A canonical correlation based framework for constructing phenotype-specific multi-omics networks by integrating multiple omics data types and a quantitative phenotype of interest.
Implements multiple imputation of missing covariates by
    Substantive Model Compatible Fully Conditional Specification.
    This is a modification of the popular FCS/chained equations
    multiple imputation approach, and allows imputation of missing
    covariate values from models which are compatible with the user
    specified substantive model.
This package is for optimizing non-linear complex
        functions based on Monte Carlo random sampling.
Data Sets for Kumar and Petersen (2012).
    Statistical Methods in Customer Relationship Management,
    Wiley: New York.
An R-package for Estimating Semiparametric PH and AFT
        Mixture Cure Models
Computes standardized mean differences and confidence intervals for 
    multiple data types based on Yang, D., & Dalton, J. E. (2012) 
    <http://www.lerner.ccf.org/qhs/software/lib/stddiff.pdf>.
Contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.
A computing tool is developed to automated identify somatic mutation-driven immune cells. The operation modes including: i) inferring the relative abundance matrix of tumor-infiltrating immune cells and integrating it with a particular gene mutation status, ii) detecting differential immune cells with respect to the gene mutation status and converting the abundance matrix of significant differential immune cell into two binary matrices (one for up-regulated and one for down-regulated), iii) identifying somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and iv) visualization of immune cell abundance of samples in different mutation status..
Symbolic multidimensional scaling for interval-valued dissimilarities. The hypersphere model and the hyperbox model are available.
Implements statistical methods for analyzing the counts of areal data,
    with a focus on the detection of spatial clusters and clustering.  The package
    has a heavy emphasis on spatial scan methods, which were first introduced
    by Kulldorff and Nagarwalla (1995) <doi:10.1002/sim.4780140809> and
    Kulldorff (1997) <doi:10.1080/03610929708831995>.
R functions and data from Chapter 5 of 'Statistical
        Methods for Financial Engineering', by Bruno Remillard, CRC
        Press, (2013).
Provides the filtering algorithms for the state space models on the Stiefel manifold as well as the corresponding sampling algorithms for uniform, vector Langevin-Bingham and matrix Langevin-Bingham distributions on the Stiefel manifold.
Discovery of spatial patterns with Hidden Markov Random Field. This package is designed for spatial transcriptomic data and single molecule fluorescent in situ hybridization (FISH) data such as sequential fluorescence in situ hybridization (seqFISH) and multiplexed error-robust fluorescence in situ hybridization (MERFISH). The methods implemented in this package are described in Zhu et al. (2018) <doi:10.1038/nbt.4260>.
Code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second and third editions of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press.
Functions that provide statistical methods for interval-censored (grouped) data. The package supports the estimation of linear and linear mixed regression models with interval-censored dependent variables. Parameter estimates are obtained by a stochastic expectation maximization algorithm. Furthermore, the package enables the direct (without covariates) estimation of statistical indicators from interval-censored data via an iterative kernel density algorithm. Survey and Organisation for Economic Co-operation and Development (OECD) weights can be included into the direct estimation (see, Walter, P. (2019) <doi:10.17169/refubium-1621>).
This tiny package contains one function smirnov() which
        calculates two scaled taxonomic coefficients, Txy (coefficient
        of similarity) and Txx (coefficient of originality). These two
        characteristics may be used for the analysis of similarities
        between any number of taxonomic groups, and also for assessing
        uniqueness of giving taxon. It is possible to use smirnov()
        output as a distance measure: convert it to distance by
        "as.dist(1 - smirnov(x))".
Constructs a yield curve by the Smith-Wilson method from a
        table of LIBOR and SWAP rates
Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data (SMITID).
  It allow sequence-space-time host and viral population data storage, indexation and querying.
Visualisation tools for 'SMITIDstruct' package.
  Allow to visualize host timeline, transmission tree, index diversities
  and variant graph using 'HTMLwidgets'. It mainly using 'D3JS' javascript framework.
Feature screening is a powerful tool in processing ultrahigh dimensional data. It attempts to screen out most irrelevant features in preparation for a more elaborate analysis. Xu and Chen (2014)<doi:10.1080/01621459.2013.879531> proposed an effective screening method SMLE, which naturally incorporates the joint effects among features in the screening process. This package provides an efficient implementation of SMLE-screening for high-dimensional linear, logistic, and Poisson models. The package also provides a function for conducting accurate post-screening feature selection based on an iterative hard-thresholding procedure and a user-specified selection criterion.
Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see <doi:10.1145/342009.335388>.
Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. 
	For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. 
	Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and 
	Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. 
	Estimation and simulation of discrete-time k-th order Markov chains are also considered.
Efficient design matrix free procedure for solving a soft maximin problem for  large scale array-tensor structured models, see Lund, Mogensen and Hansen (2019) <arXiv:1805.02407>. Currently Lasso and SCAD penalized estimation is implemented.
Efficient procedure for solving the soft maximin problem for large scale heterogeneous data, see Lund, Mogensen and Hansen (2021) <arXiv:1805.02407>. Currently Lasso and SCAD penalized estimation is implemented. Note this package subsumes and replaces the SMMA package.
Performs parametric and non-parametric estimation and simulation 
    for multi-state discrete-time semi-Markov processes. For the parametric 
    estimation, several discrete distributions are considered for the sojourn 
    times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial.
    The non-parametric estimation concerns the sojourn time distributions, 
    where no assumptions are done on the shape of distributions. Moreover, the 
    estimation can be done on the basis of one or several sample paths, with or
    without censoring at the beginning or/and at the end of the sample paths. 
    Reliability indicators such as reliability, maintainability, availability, 
    BMP-failure rate, RG-failure rate, mean time to failure and mean time to 
    repair are available as well. The implemented methods are described in 
    Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, 
    Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and 
    Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>.
    Estimation and simulation of discrete-time k-th order Markov chains are 
    also considered.
In Switzerland, the landscape of municipalities is changing rapidly
  mainly due to mergers. The Swiss Municipal Data Merger Tool 
  automatically detects these mutations and maps municipalities over time, i.e. municipalities of an old state
  to municipalities of a new state. This functionality is helpful when working 
  with datasets that are based on different spatial references. The package's idea and use 
  case is discussed in the following article: <https://onlinelibrary.wiley.com/doi/full/10.1111/spsr.12487>.
Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions
Fits flexible additive models to data on stream networks, taking account of flow-connectivity of the network.  Models are fitted using penalised least squares.
Fits a linear non-penalized phenotype (demographic) variables 
    and penalized groups of prognostic effect and predictive effect, by satisfying such
    hierarchy structures that if a predictive effect exists, its prognostic effect must 
    also exist. This package can deal with continuous, binomial or multinomial, and survival
    response variables, underlying the assumption of Gaussian, binomial (multinomial), and 
    Cox proportional hazard models, respectively. It is implemented by combining the iterative
    shrinkage-thresholding algorithm and the alternating direction method of multipliers
    algorithms. The main method is built in C++, and the complementary methods are
    written in R.
Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics between biomolecules. BLI signals are small and noisy when small molecules are investigated as ligands (analytes). We develop this package to process and analyze the BLI data acquired on Octet Red96 from Fortebio more accurately. 
    Sun Q., Li X., et al (2020) <doi:10.1038/s41467-019-14238-3>. 
    In this new version, we organize the BLI experiment data and analysis methods into a S4 class with self-explaining structure.
Provides generators for a high number of both single- and multi-
    objective test functions which are frequently used for the benchmarking of
    (numerical) optimization algorithms. Moreover, it offers a set of convenient
    functions to generate, plot and work with objective functions.
Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting.
             The package includes ADAM (Svetunkov, 2021, <https://openforecast.org/adam/>),
             Exponential Smoothing (Hyndman et al., 2008, <doi: 10.1007/978-3-540-71918-2>),
             SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>),
             Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi: 10.13140/RG.2.2.24986.29123>),
             Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi: 10.1080/00207543.2017.1380326>)
             and several simulation functions. It also allows dealing with intermittent demand based on the
             iETS framework (Svetunkov & Boylan, 2019, <doi: 10.13140/RG.2.2.35897.06242>).
The implemented method uses for smoothing bivariate thin plate
    splines, bivariate lasso-type regularization, and allows for both period
    and cohort effects. Thus the mortality rates are modelled as the sum of four
    components: a smooth bivariate function of age and time, smooth one-dimensional
    cohort effects, smooth one-dimensional period effects and random errors.
We provide full functionality to smooth L1 penalized regression operators and to compute regression estimates thereof. For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) <doi:10.1007/s10107-004-0552-5>), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) <doi:10.1101/2020.09.17.301788>). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates. A function to perform cross validation for selection of the regularization parameter is provided.
A collection of methods for smoothing numerical data, commencing with a port of the Matlab gaussian window smoothing function. In addition, several functions typically used in smoothing of financial data are included.
Estimation of two-state (survival) models and irreversible illness-
    death models with possibly interval-censored,left-truncated and right-censored
    data. Proportional intensities regression models can be specified to allow for
    covariates effects separately for each transition. We use either a parametric
    approach with Weibull baseline intensities or a semi-parametric approach with
    M-splines approximation of baseline intensities in order to obtain smooth
    estimates of the hazard functions. Parameter estimates are obtained by maximum
    likelihood in the parametric approach and by penalized maximum likelihood in the
    semi-parametric approach.
Provides flexible hazard ratio curves allowing non-linear
  relationships between continuous predictors and survival.
  To better understand the effects that each continuous covariate
  has on the outcome, results are ex pressed in terms of hazard
  ratio curves, taking a specific covariate value as reference.
  Confidence bands for these curves are also derived.
Implementation of the SIC epsilon-telescope method, either
    using single or multi-parameter regression. This package contains the data
    analyses from O'Neill and Burke (2021). "Variable Selection Using a Smooth
    Information Criterion for Multi-Parameter Regression Models". <arXiv:2110.02643>.
Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).
Some M-estimators for 1-dimensional location (Bisquare, ML
        for the Cauchy distribution, and the estimators from
        application of the smoothing principle introduced in Hampel,
        Hennig and Ronchetti (2011) to the above, the Huber
        M-estimator, and the median, main function is smoothm), and
        Pitman estimator.
Tools for smoothing and tidying spatial features
    (i.e. lines and polygons) to make them more aesthetically pleasing.
    Smooth curves, fill holes, and remove small fragments from lines and
    polygons.
Computes smooth estimations for the Cumulative/Dynamic and Incident/Dynamic ROC curves, in presence of right censorship, based on the bivariate kernel density estimation of the joint distribution function of the Marker and Time-to-event variables.
Contains, as a main contribution, a function to fit
             a regression model with possibly right, left or interval
             censored observations and with the error distribution
             expressed as a mixture of G-splines. Core part
             of the computation is done in compiled C++ written
             using the Scythe Statistical Library Version 0.3.
Given independent and identically distributed observations X(1), ..., X(n) from a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers several estimates to compute estimates of gamma. The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0].
A list of methods for estimating a smooth tensor with an unknown permutation. It also contains several multi-variate functions for generating permuted signal tensors and corresponding observed tensors. For a detailed introduction for the model and estimation techniques, see the paper by Chanwoo Lee and Miaoyan Wang (2021) "Smooth tensor estimation with unknown permutations" <arXiv:2111.04681>.
The main function in the package utilizes a windowing function in the form of an exponential weighting function to linear models. The bandwidth and sharpness of the window are controlled by two parameters. Then, a series of tests are used to identify the right parameters of the window (see Hamed Haselimashhadi et al (2019) <https://www.biorxiv.org/content/10.1101/656678v1>).
The nonparametric trend and its derivatives in equidistant time 
    series (TS) with short-memory stationary errors can be estimated. The 
    estimation is conducted via local polynomial regression using an 
    automatically selected bandwidth obtained by a built-in iterative plug-in 
    algorithm or a bandwidth fixed by the user. A Nadaraya-Watson kernel 
    smoother is also built-in as a comparison. With version 1.1.0, a linearity 
    test for the trend function, forecasting methods and backtesting 
    approaches are implemented as well.
    The smoothing methods of the package are described in Feng, Y., Gries, T., 
    and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598>.
A collection of various oversampling techniques developed from SMOTE is provided. SMOTE is a oversampling technique which synthesizes a new minority instance between a pair of one minority instance and one of its K nearest neighbor. (see <https://www.jair.org/media/953/live-953-2037-jair.pdf> for more information) Other techniques adopt this concept with other criteria in order to generate balanced dataset for class imbalance problem.
Provides movies to help students to understand statistical 
  concepts.  The 'rpanel' package  <https://cran.r-project.org/package=rpanel> 
  is used to create interactive plots that move to illustrate key statistical 
  ideas and methods.  There are movies to: visualise probability distributions
  (including user-supplied ones); illustrate sampling distributions of the
  sample mean (central limit theorem), the median, the sample maximum 
  (extremal types theorem) and (the Fisher transformation of the) 
  product moment correlation coefficient; examine the influence of an 
  individual observation in simple linear regression; illustrate key concepts 
  in statistical hypothesis testing. Also provided are dpqr functions for the 
  distribution of the Fisher transformation of the correlation coefficient 
  under sampling from a bivariate normal distribution.
Creates images that are the proper size for social media. Beautiful
    plots, charts and graphs wither and die if they are not shared. Social media 
    is perfect for this but every platform has its own image dimensions. With 
    'smpic' you can easily save your plots with the exact dimensions needed for 
    the different platforms.
Contains the datasets and a few functions for use with 
        the practicals outlined in Appendix A of the book
        Statistical Models (Davison, 2003, Cambridge University Press).
        The practicals themselves can be found at
        <http://statwww.epfl.ch/davison/SM/>.
Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers
Produce small area population estimates by fitting census data to
    survey data.
Datasets used in "Statistical Methods for the Social Sciences"
    (SMSS) by Alan Agresti and Barbara Finlay.
Implementation of the SMuRF algorithm of Devriendt et al. (2021) <doi:10.1016/j.insmatheco.2020.11.010> to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood.
Testing the mediation effect of multiple SNPs on an outcome through a mediator.
Implements the structural model for variances in order to
        detect differentially expressed genes from gene expression data
Mosaic diagram, scatterplot matrix, Andrews curves, parallel coordinate diagram, radar diagram, 
   and Chernoff plots as a Shiny app, which allow the order of variables to be changed interactively. 
   The apps are intended as teaching examples.
Build and manipulate probability distributions of the skew-normal 
  family and some related ones, notably the skew-t and the SUN families. 
  For the skew-normal and the skew-t distributions, statistical methods are 
  provided for data fitting and model diagnostics, in the univariate and the 
  multivariate case.
A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.
'RStudio' addin which provides a GUI to visualize and analyse networks. 
    After finishing a session, the code to produce the plot is inserted in the current script.
    Alternatively, the function SNAhelperGadget() can be used directly from the console.
    Additional addins include the Netreader() for reading network files, Netbuilder() to create
    small networks via point and click, and the Componentlayouter() to layout networks with many components manually.
Implements snake in R as a programming example, see <https://en.wikipedia.org/wiki/Snake_(video_game_genre)>.
A consistent, flexible and easy to use tool to parse and convert strings into cases like snake or camel among others.
Plays the game of Snakes and Ladders and has tools for analyses. The tools included allow you to find the average moves to win, frequency of each square, importance of the snakes and the ladders, the most common square and the plotting of the game played.
A simple wrapper to easily design vanilla deep neural networks using 'Tensorflow'/'Keras' backend for regression, classification and multi-label tasks, with some tweaks and tricks (skip shortcuts, embedding, feature selection and anomaly detection).
Compression and decompression with 'Snappy'.
Functions for reading and writing Gadget N-body snapshots. The Gadget code is popular in astronomy for running N-body / hydrodynamical cosmological and merger simulations. To find out more about Gadget see the main distribution page at www.mpa-garching.mpg.de/gadget/
Efficient estimation of multivariate skew-normal distribution in closed form.
Fitting of non-parametric production frontiers for use in efficiency analysis.
    Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a 
    non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for 
    multiple inputs and a single output using constrained kernel smoothing as in 
    Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints 
    on the estimated frontier.
Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.
Implementation of robust and non-robust Standard Normal Homogeneity
    Test (SNHT) for changepoint detection. This test statistic is equal sided,
    as proposed in "Homogenization of Radiosonde Temperature Time Series Using
    Innovation Statistics" by Haimberger, L., 2007. However, the statistic contains
    an estimate of sigma^2 in the denominator instead of sigma, which seems to
    be a more appropriate value (based on the paper "Homogenization of Swedish
    temperature data. Part I: Homogeneity test for linear trends." by Alexandersson,
    H., and A. Moberg, 1997).
Implement K-nearest neighbor classifier, weighted nearest neighbor classifier, bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier and stabilized nearest neighbor classifier, and perform model selection via 5 fold cross-validation for them. This package also provides functions for computing the classification error and classification instability of a classification procedure.
Programmatic interface to the 'SNOTEL' snow data
  (<https://www.wcc.nrcs.usda.gov/snow/>). Provides easy downloads of snow 
  data into your R work space or a local directory. Additional post-processing 
  routines to extract snow season indexes are provided.
Support for simple parallel computing in R.
An R interface to the C 'libstemmer' library that implements
  Porter's word stemming algorithm for collapsing words to a common
  root to aid comparison of vocabulary. Currently supported languages are
  Danish, Dutch, English, Finnish, French, German, Hungarian, Italian,
  Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish
  and Turkish.
Functions for analysis of network objects, which are imported or simulated by the package. The non-parametric methods of analysis center on snowball and bootstrap sampling for estimating functions of network degree distribution. For other parameters of interest, see, e.g., 'bootnet' package.
Usability wrapper around snow for easier development of
        parallel R programs. This package offers e.g. extended error
        checks, and additional functions. All functions work in
        sequential mode, too, if no cluster is present or wished.
        Package is also designed as connector to the cluster management
        tool sfCluster, but can also used without it.
The function generates and plots random snowflakes. Each snowflake is defined by a given diameter, width of the crystal, color, and random seed. Snowflakes are plotted in such way that they always remain round, no matter what the aspect ratio of the plot is. Snowflakes can be created using transparent colors, which creates a more interesting, somewhat realistic, image. Images of the snowflakes can be separately saved as svg files and used in websites as static or animated images.
Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed. Dynamic cluster size is also available.
Creates plots of p-values using single SNP and/or haplotype data.
    Main features of the package include options to display a linkage
    disequilibrium (LD) plot and the ability to plot multiple datasets
    simultaneously. Plots can be created using global and/or individual
    haplotype p-values along with single SNP p-values. Images are created as
    either PDF/EPS files.
To investigate the functional characteristics of selected SNPs and their vicinity genomic region. Linked SNPs in moderate to high linkage disequilibrium (e.g. r2>0.50) with the corresponding index SNPs will be selected for further analysis.
contains several supplementary non-parametric statistics methods including quantile test, Cox-Stuart trend test, runs test, normal score test, kernel PDF and CDF estimation, kernel regression estimation and kernel Kolmogorov-Smirnov test.
Functions to perform most of the common analysis in genome association studies are implemented. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated. The methods are described in Gonzalez JR et al., 2007 <doi: 10.1093/bioinformatics/btm025>.
Implements classes and methods for large scale SNP enrichment analysis (e.g. SNPs associated with genes expression in a GWAS signal).
Is designed to interactively and reproducibly visualize and filter SNP
	(single-nucleotide polymorphism) datasets. This R-based implementation of SNP
	and genotype filters facilitates 
	an interactive and iterative SNP filtering pipeline, which can be documented
	reproducibly via Rmarkdown. 'SNPfiltR' contains functions for visualizing 
	various quality and missing data metrics for a SNP dataset, and then filtering
	the dataset based on user specified cutoffs.
	All functions take 'vcfR' objects as input, which can easily be
	generated by reading standard vcf (variant call format) files into R using 
	the R package 'vcfR' (Knaus and Grünwald) (<doi:10.1111/1755-0998.12549>). 
	Each 'SNPfiltR' function can return a newly filtered vcfR object, which can then be
	written to a local directory in standard vcf format using the 'vcfR' package,
	for downstream population genetic and phylogenetic analyses.
Generate knockoffs for genetic data and hidden Markov models.
  For more information, see the website below and the accompanying papers: 
  "Gene hunting with hidden Markov model knockoffs", Sesia et al., Biometrika, 2019, (<doi:10.1093/biomet/asy033>).
  "Multi-resolution localization of causal variants across the genome", Sesia et al., bioRxiv, 2019, (<doi:10.1101/631390>).
A set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example to export to a PLINK set.
Tools for performing variable selection in three-way data using N-PLS 
    in combination with L1 penalization, Selectivity Ratio and VIP scores. 
    The N-PLS model (Rasmus Bro, 1996 <DOI:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) 
    is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries 
    to maximize the covariance between X and Y data arrays. The package also adds
    variable selection through L1 penalization, Selectivity Ratio and VIP scores.
Bivariate Gaussian genotype clustering and calling for Illumina
    microarrays, building on the package 'mclust'. Pronounced snip-em-clust.
Three functions to clean, summarize and prepare genomic datasets to Genome
    Selection and Genome Association analysis and to estimate population genetic parameters.
The goal of 'snpsettest' is to provide simple tools that perform
             set-based association tests (e.g., gene-based association tests)
             using GWAS (genome-wide association study) summary statistics. A
             set-based association test in this package is based on the
             statistical model described in VEGAS (versatile gene-based
             association study), which combines the effects of a set of SNPs
             accounting for linkage disequilibrium between markers. This package
             uses a different approach from the original VEGAS implementation to
             compute set-level p values more efficiently, as described in
             <https://github.com/HimesGroup/snpsettest/wiki/Statistical-test-in-snpsettest>.
Write snpStats objects to disk in formats suitable for reading by
    snphap, phase, mach, IMPUTE, beagle, and (almost) anything else that
    expects a rectangular format.
Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided.
Scan statistics applied in social network data can be used to test the cluster characteristics among a social network.
The methods discussed in this package are new non-parametric methods
    based on sequential normal scores 'SNS' (Conover et al (2017)
    <doi:10.1080/07474946.2017.1360091>), designed for sequences of observations,
    usually time series data, which may occur singly or in batches,
    and may be univariate or multivariate. These methods are designed
    to detect changes in the process, which may occur as changes in location
    (mean or median), changes in scale (standard deviation, or variance), or
    other changes of interest in the distribution of the observations,
    over the time observed. They usually apply to large data sets,
    so computations need to be simple enough to be done in a reasonable
    time on a computer, and easily updated as each new observation
    (or batch of observations) becomes available. Some examples and more detail
    in 'SNS' is presented in the work by Conover et al (2019) <arXiv:1901.04443>.
Contains functions to perform various models and
    methods for test equating. It currently implements the traditional
    mean, linear and equipercentile equating methods. Both IRT observed-score 
    and true-score equating are also supported, as well as the mean-mean, 
    mean-sigma, Haebara and Stocking-Lord IRT linking methods.
    It also supports newest methods such that local equating, kernel
    equating (using Gaussian, logistic, Epanechnikov, uniform and adaptive 
    kernels) with presmoothing, and IRT parameter linking methods based on 
    asymmetric item characteristic functions. Functions to obtain both 
    standard error of equating (SEE) and standard error of equating differences 
    between two equating functions (SEED) are also implemented for the kernel method of
    equating.
Allows objects to be stored on disc and automatically
       		recalled into memory, as required, by delayed assignment.
Creates stratum orthogonal arrays (also known as strong orthogonal arrays). These are arrays with more levels per column than the typical orthogonal array, and whose low order projections behave like orthogonal arrays, when collapsing levels to coarser strata. Details are described in Groemping (2021) "A unified implementation of stratum (aka strong) orthogonal arrays" <http://www1.bht-berlin.de/FB_II/reports/Report-2021-001.pdf>.
Algorithm to estimate the Sobol indices using a non-parametric fit of the regression curve. The bandwidth is estimated using bootstrap to reduce the finite-sample bias. The package is based on the paper Solís, M. (2018) <arXiv:1803.03333>.
R implementation of S. Joe and F. Y. Kuo(2008)
        <DOI:10.1137/070709359>.
        The implementation is based on the data file new-joe-kuo-6.21201
        <http://web.maths.unsw.edu.au/~fkuo/sobol/>.
Specific and class specific multiple correspondence analysis on
    survey-like data. Soc.ca is optimized to the needs of the social scientist and
    presents easily interpretable results in near publication ready quality.
Functions for evaluating tournament predictions, simulating results from individual soccer matches and tournaments. See <http://sandsynligvis.dk/2018/08/03/world-cup-prediction-winners/> for more information.
A set of functions to quantify and visualise 
    social autocorrelation.
Tools developed to facilitate the establishment of the rank and social hierarchy 
             for gregarious animals by the Si method developed by 
             Kondo & Hurnik (1990)<doi:10.1016/0168-1591(90)90125-W>. It is
             also possible to determine the number of agonistic interactions between two 
             individuals, sociometric and dyadics matrix from dataset obtained through 
             electronic bins.
Provides methods for sampling contact matrices from diary data for use in infectious disease modelling, as discussed in Mossong et al. (2008) <doi:10.1371/journal.pmed.0050074>.
Generates social networks using either of two
    approaches: using either pairwise distances or territorial area intersections.
Provides to sociologists (and related scientists) a toolbox to facilitate the construction of social position indicators from survey data. Social position indicators refer to what is commonly known as social class and social status. There exists in the sociological literature many theoretical conceptualisation and empirical operationalization of social class and social status. This first version of the package offers tools to construct the International Socio-Economic Index of Occupational Status (ISEI) and the Oesch social class schema. It also provides tools to convert several occupational classifications (PCS82, PCS03, and ISCO08) into a common one (ISCO88) to facilitate data harmonisation work, and tools to collapse (i.e. group) modalities of social position indicators.
The notion of power index has been widely used in literature to evaluate the influence of individual players (e.g., voters, political parties, nations, stockholders, etc.) involved in a collective decision situation like an electoral system, a parliament, a council, a management board, etc., where players may form coalitions. Traditionally this ranking is determined through numerical evaluation. More often than not however only ordinal data between coalitions is known. The package 'socialranking' offers a set of solutions to rank players based on a transitive ranking between coalitions, including through CP-Majority, ordinal Banzhaf or lexicographic excellence solution summarized by Tahar Allouche, Bruno Escoffier, Stefano Moretti and Meltem Öztürk (2020, <doi:10.24963/ijcai.2020/3>).
Social risks are increasingly becoming a critical component of health
    care research. One of the most common ways to identify social needs is by using
    ICD-10-CM "Z-codes." This package identifies social risks using varying taxonomies
    of ICD-10-CM Z-codes from administrative health care data. The conceptual
    taxonomies come from:
    Centers for Medicare and Medicaid Services (2021) <https://www.cms.gov/files/document/zcodes-infographic.pdf>,
    Reidhead (2018) <https://www.mhanet.com/mhaimages/Policy_Briefs/PolicyBrief_SDOH.pdf>,
    A Arons, S DeSilvey, C Fichtenberg, L Gottlieb (2018) <https://sirenetwork.ucsf.edu/tools-resources/resources/compendium-medical-terminology-codes-social-risk-factors>.
Accesses raw data via API and calculates social
    determinants of health measures for user-specified locations in the
    US, returning them in tidyverse- and sf-compatible data frames.
Supporting materials for a course and book on data visualization. It contains utility functions for graphs and several sample data sets. See Healy (2019) <ISBN 978-0691181622>.
Functions, examples and other software related to the book
        "Software for Data Analysis: Programming with R". See
        package?SoDA for an overview.
Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.
Bindings to 'libsodium': a modern, easy-to-use software library for
    encryption, decryption, signatures, password hashing and more. Sodium uses
    curve25519, a state-of-the-art Diffie-Hellman function by Daniel Bernstein,
    which has become very popular after it was discovered that the NSA had
    backdoored Dual EC DRBG.
Provides an interface to the 'NoSQL' database 'CouchDB'
    (<http://couchdb.apache.org>). Methods are provided for managing
    databases within 'CouchDB', including creating/deleting/updating/transferring,
    and managing documents within databases. One can connect with a local
    'CouchDB' instance, or a remote 'CouchDB' databases such as 'Cloudant'. 
    Documents can be inserted directly from vectors, lists, data.frames, 
    and 'JSON'. Targeted at 'CouchDB' v2 or greater.
Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos.
Software that leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. Circos is a novel software that addresses the challenges in visualizing genetic data by creating circular ideograms composed of tracks of heatmaps, scatter plots, line plots, histograms, links between common markers, glyphs, text, and etc. Please see <http://www.circos.ca>.
An extension of sensitivity, specificity, positive and negative
    predictive value to continuous predicted and reference memberships in
    [0, 1].
It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.
Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an 'EM' flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components).
Sparsity Oriented Importance Learning (SOIL) provides a new variable importance measure for high dimensional linear regression and logistic regression from a sparse penalization perspective, by taking into account the variable selection uncertainty via the use of a sensible model weighting. The package is an implementation of Ye, C., Yang, Y., and Yang, Y. (2017+).
Soil assessment builds information for improved decision in soil management. It analyzes soil conditions with regard to agriculture 
        crop suitability requirements [such as those given by FAO <https://www.fao.org/land-water/databases-and-software/crop-information/en/>] soil fertility classes, 
        soil erosion models and soil salinity classification. Suitability requirements are for crops  grouped into cereal crops, nuts, legumes, fruits, vegetables, 
        industrial crops, and root crops.
A collection of functions for reading data from USDA-NCSS soil databases.
Provides functions for (1) soil water retention (SWC) and unsaturated hydraulic conductivity (Ku) (van Genuchten-Mualem (vGM or vG) [1, 2], Peters-Durner-Iden (PDI) [3, 4, 5], Brooks and Corey (bc) [8]), (2) fitting of parameter for SWC  and/or Ku using Shuffled Complex Evolution (SCE) optimisation and (3) calculation of soil hydraulic properties (Ku and soil water contents) based on the simplified evaporation method (SEM) [6, 7].
    Main references:
    [1] van Genuchten (1980) <doi:10.2136/sssaj1980.03615995004400050002x>, 
    [2] Mualem (1976) <doi:10.1029/WR012i003p00513>, 
    [3] Peters (2013) <doi:10.1002/wrcr.20548>, 
    [4] Iden and Durner (2013) <doi:10.1002/2014WR015937>, 
    [5] Peters (2014) <doi:10.1002/2014WR015937>,
    [6] Wind G. P. (1966),
    [7] Peters and Durner (2008) <doi:10.1016/j.jhydrol.2008.04.016> and
    [8] Brooks and Corey (1964).
Provides functions for efficiently estimating properties of the Van Genuchten-Mualem model for soil hydraulic parameters from possibly sparse soil water retention and hydraulic conductivity data by multi-response parameter estimation methods (Stewart, W.E., Caracotsios, M. Soerensen, J.P. (1992) "Parameter estimation from multi-response data" <doi:10.1002/aic.690380502>). Parameter estimation is simplified by exploiting the fact that residual and saturated water contents and saturated conductivity are conditionally linear parameters (Bates, D. M.  and Watts, D. G. (1988) "Nonlinear Regression Analysis and Its Applications" <doi:10.1002/9780470316757>). Estimated parameters are optionally constrained by the evaporation characteristic length (Lehmann, P., Bickel, S., Wei, Z. and Or, D. (2020) "Physical Constraints for Improved Soil Hydraulic Parameter Estimation by Pedotransfer Functions" <doi:10.1029/2019WR025963>) to ensure that the estimated parameters are physically valid. Common S3 methods and further utility functions allow to process, explore and visualise estimation results.
Basic and model-based soil physical analyses.
Functions for modeling Soil Organic Matter decomposition
        in terrestrial ecosystems with linear and nonlinear models.
Taxonomic dictionaries, formative element lists, and functions related to the maintenance, development and application of U.S. Soil Taxonomy. 
   Data and functionality are based on official U.S. Department of Agriculture sources including the latest edition of the Keys to Soil Taxonomy. Descriptions and metadata are obtained from the National Soil Information System or Soil Survey Geographic databases. Other sources are referenced in the data documentation. 
   Provides tools for understanding and interacting with concepts in the U.S. Soil Taxonomic System. Most of the current utilities are for working with taxonomic concepts at the "higher" taxonomic levels: Order, Suborder, Great Group, and Subgroup.
"The Soil Texture Wizard" is a set of R functions designed to produce texture triangles (also called texture plots, texture diagrams, texture ternary plots), classify and transform soil textures data. These functions virtually allows to plot any soil texture triangle (classification) into any triangle geometry (isosceles, right-angled triangles, etc.). This set of function is expected to be useful to people using soil textures data from different soil texture classification or different particle size systems. Many (> 15) texture triangles from all around the world are predefined in the package. A simple text based graphical user interface is provided: soiltexture_gui().
It implements parametric formulas of soil
    water retention or conductivity curve. At the moment, only Van Genuchten
    (for soil water retention curve) and Mualem (for hydraulic conductivity)
    were implemented. 
    See reference (<http://en.wikipedia.org/wiki/Water_retention_curve>).
Provides a simple way for utilizing Sojourn methods for
    accelerometer processing, as detailed in Lyden K, Keadle S,
    Staudenmayer J, & Freedson P (2014) <doi:10.1249/MSS.0b013e3182a42a2d>,
    Ellingson LD, Schwabacher IJ, Kim Y, Welk GJ, & Cook DB (2016)
    <doi:10.1249/MSS.0000000000000915>, and Hibbing PR, Ellingson LD,
    Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.
Stores objects (e.g. neural networks) that are needed for
    using Sojourn accelerometer methods. For more information, see
    Lyden K, Keadle S, Staudenmayer J, & Freedson P (2014)
    <doi:10.1249/MSS.0b013e3182a42a2d>, Ellingson LD, Schwabacher IJ,
    Kim Y, Welk GJ, & Cook DB (2016) <doi:10.1249/MSS.0000000000000915>,
    and Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018)
    <doi:10.1249/MSS.0000000000001486>.
Interactively play a game of sokoban ,which has nine game levels.Sokoban is a type of transport puzzle, in which the player pushes boxes or crates around in a warehouse, trying to get them to storage locations.
Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.
Calculation of solar zenith and azimuth angles.
Provide utilities to work with solar time, 
  i.e. where noon is exactly when sun culminates.
  Provides functions for computing sun position and times of sunrise and sunset.
Isolation forest is anomaly detection method introduced by the paper Isolation based Anomaly Detection (Liu, Ting and Zhou <doi:10.1145/2133360.2133363>).
For surface energy models and estimation of solar positions and components with varying topography, time and locations. The functions calculate solar top-of-atmosphere, open, diffuse and direct components, atmospheric transmittance and diffuse factors, day length, sunrise and sunset, solar azimuth, zenith, altitude, incidence, and hour angles, earth declination angle, equation of time, and solar constant. Details about the methods and equations are explained in Seyednasrollah, Bijan, Mukesh Kumar, and Timothy E. Link. 'On the role of vegetation density on net snow cover radiation at the forest floor.' Journal of Geophysical Research: Atmospheres 118.15 (2013): 8359-8374, <doi:10.1002/jgrd.50575>.
Provides a set of functions for querying and parsing data
    from 'Solr' (<https://solr.apache.org/>) 'endpoints' (local and
    remote), including search, 'faceting', 'highlighting', 'stats', and
    'more like this'. In addition, some functionality is included for
    creating, deleting, and updating documents in a 'Solr' 'database'.
R language bindings for SolveBio's API.
    SolveBio is a biomedical knowledge hub that enables life science
    organizations to collect and harmonize the complex, disparate
    "multi-omic" data essential for today's R&D and BI needs.
    For more information, visit <https://www.solvebio.com>.
Given a symmetric positive definite matrix Q and a non-singular matrix L, find symmetric positive definite solution X such that X = Q + L (X inv) L^T.
        Reference: Benner, P., Faßbender, H. On the Solution of the Rational Matrix Equation.
        Benner, Faßbender (2007) <doi:10.1155/2007/21850>.
Universal and robust algorithm for solving the total alkalinity-pH equation presented in G. Munhoven (2013) <doi:10.5194/gmd-6-1367-2013> and G. Munhoven (2021) <doi:10.5194/gmd-2020-447>. The total alkalinity-pH equation relates total alkalinity and pH for a given set of acid-base concentrations in a given water sample, among which carbonic acid. This package is particularly useful in marine chemistry involving dissolved inorganic carbon. Original package in Fortran can be found at <doi:10.5281/zenodo.4328965>.
Self-Organizing Map (with application in gene clustering).
A topological version of k-NN: An abstract model is build
             as 2-dimensional self-organising map. Samples of unknown
             class are predicted by mapping them on the SOM and analysing
             class membership of neurons in the neighbourhood.
This package provides an R implementation of the Self-Organising Migrating Algorithm, a general-purpose, stochastic optimisation algorithm. The approach is similar to that of genetic algorithms, although it is based on the idea of a series of ``migrations'' by a fixed set of individuals, rather than the development of successive generations. It can be applied to any cost-minimisation problem with a bounded parameter space, and is robust to local minima.
The stochastic (also called on-line) version of the Self-Organising
             Map (SOM) algorithm is provided. Different versions of the
             algorithm are implemented, for numeric and relational data and for
             contingency tables as described, respectively, in Kohonen (2001)
             <isbn:3-540-67921-9>, Olteanu & Villa-Vialaneix (2005)
             <doi:10.1016/j.neucom.2013.11.047> and Cottrell et al (2004)
             <doi:10.1016/j.neunet.2004.07.010>. The package also contains many
             plotting features (to help the user interpret the results), can 
             handle (and impute) missing values and is delivered with a 
             graphical user interface based on 'shiny'.
some Brownian motions simulation functions
This package collects some procedures controlling the
        Generalized Familywise Error Rate.
It's a collection of functions for Multiplicity Correction and Multiple Testing.
Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on 'kohonen' and 'openair' packages implemented by functions embedding Vesanto et al. (2001) <http://www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf>  heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) <doi:10.4209/aaqr.2019.08.0414> are provided.
Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms. Designed for genomic prediction and genome wide association studies (GWAS), particularly focused in the p > n problem (more coefficients to estimate than observations). Spatial models can also be fitted using the two-dimensional spline functionality available in sommer.
The package provides the plot function som.plot() to
        create high quality visualisations of hexagonal Kohonen maps
        (self-organising maps).
Formulas for calculating sound velocity, water pressure, depth, 
    density, absorption and sonar equations.
Simulates the cultural evolution of quantitative traits of bird song. 'SongEvo' is an individual- (agent-) based model. 'SongEvo' is spatially-explicit and can be parameterized with, and tested against, measured song data. Functions are available for model implementation, sensitivity analyses, parameter optimization, model validation, and hypothesis testing.
Estimate the abundance of cell clones from the
	     distribution of lengths of DNA fragments (as created by
	     sonication, whence `sonicLength').  The algorithm in
	     "Estimating abundances of retroviral insertion sites from
	     DNA fragment length data" by Berry CC, Gillet NA, Melamed
	     A, Gormley N, Bangham CR, Bushman FD. Bioinformatics;
	     2012 Mar 15;28(6):755-62 is implemented.  The
	     experimental setting and estimation details are described
	     in detail there. Briefly, integration of new DNA in a
	     host genome (due to retroviral infection or gene therapy)
	     can be tracked using DNA sequencing, potentially allowing
	     characterization of the abundance of individual cell
	     clones bearing distinct integration sites. The locations
	     of integration sites can be determined by fragmenting the
	     host DNA (via sonication or fragmentase), breaking the
	     newly integrated DNA at a known sequence, amplifying the
	     fragments containing both host and integrated DNA,
	     sequencing those amplicons, then mapping the host
	     sequences to positions on the reference genome. The
	     relative number of fragments containing a given position
	     in the host genome estimates the relative abundance of
	     cells hosting the corresponding integration site, but
	     that number is not available and the count of amplicons
	     per fragment varies widely.  However, the expected number
	     of distinct fragment lengths is a function of the
	     abundance of cells hosting an integration site at a given
	     position and a certain nuisance parameter. The algorithm
	     implicitly estimates that function to estimate the
	     relative abundance.
Provides basic tools for manipulating sound files for bioacoustic analysis, and preparing analyses these for publication. The package validates that values are physically possible wherever feasible.
Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired.
Collection of different single objective test functions
        useful for benchmarks and algorithm development.
Generalised additive P-spline regression models estimation using the separation of overlapping precision matrices (SOP) method. Estimation is based on the equivalence between P-splines and linear mixed models, and variance/smoothing parameters are estimated based on restricted maximum likelihood (REML). The package enables users to estimate P-spline models with overlapping penalties. Based on the work described in Rodriguez-Alvarez et al. (2015) <doi:10.1007/s11222-014-9464-2>; Rodriguez-Alvarez et al. (2019) <doi:10.1007/s11222-018-9818-2>, and Eilers and Marx (1996) <doi:10.1214/ss/1038425655>.
The sparse online principal component can not only process the real-time updated data set and stream data set, but also obtain the sparse solution of the updated data set. The philosophy of the package is described in Guo G. (2018) <doi:10.1080/10485252.2018.1531130>.
Provides functions to non-parametrically estimate the off-pulse interval of a source
    function originating from a pulsar. The technique is based on a sequential application of P-values
    obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov,
    Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.
Computes sequential A-, MV-, D- and E-optimal or near-optimal block and row-column designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly.
Used to construct the URLs and parameters of 'Socrata Open Data API' <https://dev.socrata.com> calls, using the API's 'SoQL' parameter format. Has method-chained and sensical syntax. Plays well with pipes.
Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique.  Includes support for binary, count, and continuous data.  The first regression is a logistic regression, which uses a known ratio (the probability of being sampled given that the subject/observation was referred divided by the probability of being sampled given that the subject/observation was no referred) as an offset to estimate the probability of being referred given outcome and covariates.  The second regression uses this estimated probability to calculate the mean population response given covariates.
Can be used to model the fate of soil organic carbon and soil organic nitrogen and to calculate N mineralisation rates. Provides a framework that numerically solves differential equations of soil organic carbon models based on first-order kinetics and extends these models to include the nitrogen component. The name 'sorcering' is an acronym for 'Soil ORganic Carbon & CN Ratio drIven Nitrogen modellinG framework'.
A Bayesian semiparametric Dirichlet process mixtures to estimate correlated receiver operating characteristic (ROC) surfaces and the associated volume under the surface (VUS) with stochastic order constraints. The reference paper is:Zhen Chen, Beom Seuk Hwang, (2018) "A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints". Biometrics, 75, 539-550. <doi:10.1111/biom.12997>.
Provides tools to efficiently analyze and visualize laboratory data from aqueous static adsorption experiments. The package provides functions to plot Langmuir, Freundlich, and Temkin isotherms and functions to determine the statistical conformity of data points to the Langmuir, Freundlich, and Temkin adsorption models through statistical characterization of the isothermic least squares regressions lines. Scientific Reference: Dada, A.O, Olalekan, A., Olatunya, A. (2012) <doi:10.9790/5736-0313845>.
Enables drag-and-drop behaviour in Shiny apps, by exposing the 
    functionality of the 'SortableJS' <https://sortablejs.github.io/Sortable/> 
    JavaScript library as an 'htmlwidget'. 
    You can use this in Shiny apps and widgets, 'learnr' tutorials as well as 
    R Markdown. In addition, provides a custom 'learnr' question type - 
    'question_rank()' - that allows ranking questions with drag-and-drop.
Implements the estimation and inference methods for sorted causal effects and 
    classification analysis as in Chernozhukov, Fernandez-Val and Luo (2018) <doi:10.3982/ECTA14415>.
Algorithms for Finnish open government data.
Search contributed R packages, sort by package.
Access statistical information on welfare and health in Finland 
    from the Sotkanet open data portal <https://sotkanet.fi/sotkanet/fi/index>.
The President of the United States is constitutionally obligated to provide
  a report known as the 'State of the Union'. The report summarizes the current challenges
  facing the country and the president's upcoming legislative agenda. While historically
  the State of the Union was often a written document, in recent decades it has always
  taken the form of an oral address to a joint session of the United States Congress.
  This package provides the raw text from every such address with the intention of
  being used for meaningful examples of text analysis in R. The corpus is well suited
  to the task as it is historically important, includes material intended to be read
  and material intended to be spoken, and it falls in the public domain. As the corpus
  spans over two centuries it is also a good test of how well various methods hold up
  to the idiosyncrasies of historical texts. Associated data about each address, such
  as the year, president, party, and format, are also included.
Basic functions for dealing with wav files and sound samples.
Provides an all-in-one solution for automatic classification of 
    sound events using convolutional neural networks (CNN). The main purpose 
    is to provide a sound classification workflow, from annotating sound events
    in recordings to training and automating model usage in real-life
    situations. Using the package requires a pre-compiled collection of 
    recordings with sound events of interest and it can be employed for: 
    1) Annotation: create a database of annotated recordings, 
    2) Training: prepare train data from annotated recordings and fit CNN models, 
    3) Classification: automate the use of the fitted model for classifying 
    new recordings. By using automatic feature selection and a user-friendly GUI
    for managing data and training/deploying models, this package is intended 
    to be used by a broad audience as it does not require specific expertise in 
    statistics, programming or sound analysis. Please refer to the vignette for
    further information.
    Gibb, R., et al. (2019) <doi:10.1111/2041-210X.13101>
    Mac Aodha, O., et al. (2018) <doi:10.1371/journal.pcbi.1005995>
    Stowell, D., et al. (2019) <doi:10.1111/2041-210X.13103>
    LeCun, Y., et al. (2012) <doi:10.1007/978-3-642-35289-8_3>.
A set of tools that can be used in computer-aided analysis of
    sound correspondences between languages, plus several helper functions.
    Analytic functions range from purely qualitative analysis, through
    statistic methods yielding qualitative results, to an entirely
    quantitative approach.
Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings.
The SoundexBR package provides an algorithm for decoding names
    into phonetic codes, as pronounced in Portuguese. The goal is for
    homophones to be encoded to the same representation so that they can be
    matched despite minor differences in spelling. The algorithm mainly encodes
    consonants; a vowel will not be encoded unless it is the first letter. The
    soundex code resultant consists of a four digits long string composed by
    one letter followed by three numerical digits: the letter is the first
    letter of the name, and the digits encode the remaining consonants.
Performs parametric synthesis of sounds with harmonic and noise 
    components such as animal vocalizations or human voice. Also offers tools 
    for audio manipulation and acoustic analysis, including pitch tracking, 
    spectral analysis, audio segmentation, pitch and formant shifting, etc. 
    Includes four interactive web apps for synthesizing and annotating audio, 
    manually correcting pitch contours, and measuring formant frequencies. 
    Reference: Anikin (2019) <doi:10.3758/s13428-018-1095-7>.
Implement a promising, and yet little explored protocol for bioacoustical analysis, the eigensound method by MacLeod, Krieger and Jones (2013) <doi:10.4404/hystrix-24.1-6299>. Eigensound is a multidisciplinary method focused on the direct comparison between stereotyped sounds from different species. 'SoundShape', in turn, provide the tools required for anyone to go from sound waves to Principal Components Analysis, using tools extracted from traditional bioacoustics (i.e. 'tuneR' and 'seewave' packages), geometric morphometrics (i.e. 'geomorph' package) and multivariate analysis (e.g. 'stats' package). For more information, please see Rocha and Romano (2021) and check 'SoundShape' repository on GitHub for news and updates <https://github.com/p-rocha/SoundShape>.
Construct a ranking of a set of treatments/groups by
    gathering together information coming from several response variables.
    It can be used with both balanced and unbalanced experiments
    (with almost all test statistics) as well as in presence of either
    continuous covariates or a stratifying (categorical) variable.
Quantify, profile and remove ambient mRNA contamination (the "soup") from droplet based single cell RNA-seq experiments.  Implements the method described in Young et al. (2018) <doi:10.1101/303727>.
Implements a non-parametric source attribution model to attribute
    cases of disease to sources in Bayesian framework with source and type effects.
    Type effects are clustered using a Dirichlet Process. Multiple times and
    locations are supported.
The algorithm pursues the identification of the set of variables driving the differences in two different experimental conditions (i.e., the primary genes) within a graphical model context. It uses the idea of simultaneously looking for the differences between two multivariate normal distributions in all marginal and conditional distributions associated with a decomposable graph, which represents the pathway under exam. The implementation accommodates genomics specific issues (low sample size and multiple testing issues) and provides a number of functions offering numerical and visual summaries to help the user interpret the obtained results. In order to use the (optional) 'Cytoscape' functionalities, the suggested 'r2cytoscape' package must be installed from the 'GitHub' repository ('devtools::install_github('cytoscape/r2cytoscape')').
Tools for the reading and tokenization of R code. The
    'sourcetools' package provides both an R and C++ interface for the tokenization
    of R code, and helpers for interacting with the tokenized representation of R
    code.
A set of tools for state-dependent 
  empirical analysis through both VAR- and local projection-based 
  state-dependent forecasts, impulse response functions, 
  historical decompositions, and forecast error variance decompositions.
Genomic and multi-environmental soybean data. Soybean Nested
    Association Mapping (SoyNAM) project dataset funded by the United Soybean Board
    (USB). BLUP function formats data for genome-wide prediction and association analysis.
Classes and methods for spatial
  data; the classes document where the spatial location information
  resides, for 2D or 3D data. Utility functions are provided, e.g. for
  plotting data as maps, spatial selection, as well as methods for
  retrieving coordinates, for subsetting, print, summary, etc.
A programmatic interface to <http://sp2000.org.cn>, re-written based on an accompanying 'Species 2000' API. Access tables describing catalogue of the Chinese known species of animals, plants, fungi, micro-organisms, and more. This package also supports access to catalogue of life global <http://catalogueoflife.org>, China animal scientific database <http://zoology.especies.cn> and catalogue of life Taiwan <https://taibnet.sinica.edu.tw/home_eng.php>. The development of 'SP2000' package were supported by Biodiversity Survey and Assessment Project of the Ministry of Ecology and Environment, China <2019HJ2096001006>,Yunnan University's "Double First Class" Project <C176240405> and Yunnan University's Research Innovation Fund for Graduate Students <2019227>.
Provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.
Miscellaneous functions for analysing species association
        and niche overlap.
Genomic Region Detection via Spatial Convex Clustering. See <https://arxiv.org/abs/1611.04696> for details.
Partial correlation estimation with joint sparse
        regression model.
Generates random and quasi-random space-filling sequences. Supports the following sequences: 'Halton', 'Sobol', 'Owen'-scrambled 'Sobol',  'Owen'-scrambled 'Sobol' with errors distributed as blue noise, progressive jittered, progressive multi-jittered ('PMJ'), 'PMJ' with blue noise, 'PMJ02', and 'PMJ02' with blue noise. Includes a 'C++' 'API'. Methods derived from "Constructing Sobol sequences with better two-dimensional projections" (2012) <doi:10.1137/070709359> S. Joe and F. Y. Kuo, "Progressive Multi-Jittered Sample Sequences" (2018) <https://graphics.pixar.com/library/ProgressiveMultiJitteredSampling/paper.pdf> Christensen, P., Kensler, A. and Kilpatrick, C., and "A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space" (2019) E. Heitz, B. Laurent, O. Victor, C. David and I. Jean-Claude, <doi:10.1145/3306307.3328191>.
Social network analysis is becoming commonplace in many social 
    science disciplines, but access to useful network data, especially 
    among marginalized populations, still remains a formidable challenge. 
    This package mitigates that problem by providing tools to simulate spatial 
    Bernoulli networks as proposed in Carter T. Butts 
    (2002, ISBN:978-0-493-72676-2), "Spatial models of large-scale interpersonal            
    networks." Using this package, network analysts can simulate a spatial point 
    process or sequence with a given number of nodes inside a geographical 
    boundary and estimate the probability of a tie formation between all node 
    pairs. When simulating a network, an analyst can choose between five spatial
    interaction functions. The package also enables quick comparison of summary             
    statistics for simulated networks and provides simple to use plotting 
    methods for its classes that return plots which can be further refined with 
    the 'ggplot2' package.
Latent space models for multivariate networks (multiplex) estimated via MCMC algorithm. See D Angelo et al. (2018) <arXiv:1803.07166> and D Angelo et al. (2018) <arXiv:1807.03874>.
Standard RGB spaces included are sRGB, 'Adobe' RGB, 'ProPhoto' RGB, BT.709, and others.  User-defined RGB spaces are also possible.  There is partial support for ACES Color workflows.
Functions for converting among CIE XYZ, xyY, Lab, and Luv.
      Calculate Correlated Color Temperature (CCT) and the Planckian and daylight loci.  
      The XYZs of some standard illuminants and some standard linear chromatic adaptation transforms (CATs) are included.
      Three standard color difference metrics are included.
Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal matching or aggregation, retrieving coordinates, print, summary, etc.
One of the remaining pain points in making beautiful maps via 
    packages like 'rayshader' is both obtaining and processing spatial data
    to build from. 'spacey' aims to make it easier to obtain and use this data
    for locations within the United States, providing utilities to download 
    'USGS' and 'ESRI' geospatial data and quickly turn it into maps.
An R wrapper to the 'Python' 'spaCy' 'NLP' library,
    from <http://spacy.io>.
Provides easy to use functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems including on-the-fly system conversion. It supports any type of spherical projection to the plane defined by the 'mapproj' package.
A set of RStudio addins that are designed to be used in
             combination with user-defined RStudio keyboard shortcuts. These
             addins either:
             1) insert text at a cursor position (e.g. insert
             operators %>%, <<-, %$%, etc.),
             2) replace symbols in selected pieces of text (e.g., convert
             backslashes to forward slashes which results in stings like
             "c:\data\" converted into "c:/data/") or
             3) enclose text with special symbols (e.g., converts "bold" into
             "**bold**") which is convenient for editing R Markdown files.
Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.
Metapackage for implementing a variety of event-based models, with
    a focus on spatially explicit models. These include raster-based,
    event-based, and agent-based models. The core simulation components
    (provided by 'SpaDES.core') are built upon a discrete event simulation (DES;
    see Matloff (2011) ch 7.8.3 <https://nostarch.com/artofr.htm>)
    framework that facilitates modularity, and easily enables the user to
    include additional functionality by running user-built simulation modules
    (see also 'SpaDES.tools'). Included are numerous tools to visualize rasters
    and other maps (via 'quickPlot'), and caching methods for reproducible
    simulations (via 'reproducible'). Tools for running simulation experiments are
    provided by 'SpaDES.experiment'. Additional functionality is provided by
    the 'SpaDES.addins' and 'SpaDES.shiny' packages.
Provides 'RStudio' addins for 'SpaDES' packages and 'SpaDES' module
    development. See '?SpaDES.addins' for an overview of the tools provided.
Provides the core framework for a discrete event system (DES) to 
    implement a complete data-to-decisions, reproducible workflow.
    The core DES components facilitate modularity, and easily enable the user
    to include additional functionality by running user-built modules.
    Includes conditional scheduling, restart after interruption, packaging of
    reusable modules, tools for developing arbitrary automated workflows,
    automated interweaving of modules of different temporal resolution,
    and tools for visualizing and understanding the DES project.
Provides GIS and map utilities, plus additional modeling tools for
    developing cellular automata, dynamic raster models, and agent based models
    in 'SpaDES'.
    Included are various methods for spatial spreading, spatial agents, GIS
    operations, random map generation, and others.
    See '?SpaDES.tools' for an categorized overview of these additional tools.
Implements methods for anticipating the emergence and eradication
    of infectious diseases from surveillance time series. Also provides support
    for computational experiments testing the performance of such methods.
A utility package containing some simple tools to design and generate density functions on bounded regions in space and space-time, and simulate independent, identically distributed data therefrom. See Davies & Lawson (2019) <doi:10.1080/00949655.2019.1575066> for example.
Set of functions for sparse matrix algebra.
    Differences with other sparse matrix packages are:
    (1) we only support (essentially) one sparse matrix format,
    (2) based on transparent and simple structure(s),
    (3) tailored for MCMC calculations within G(M)RF.
    (4) and it is fast and scalable (with the extension package spam64).
    Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>; see 'citation("spam")' for details.
Provides the Fortran code of the R package 'spam'
    with 64-bit integers. Loading this package together with the R package
    spam enables the sparse matrix class spam to handle huge sparse matrices
    with more than 2^31-1 non-zero elements.
    Documentation is provided in Gerber, Moesinger and Furrer (2017) <doi:10.1016/j.cageo.2016.11.015>.
Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted  likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.
Fits multivariate Bayesian spatial regression models for large datasets using Spatial Multivariate Trees (SpamTrees). 
    The methods in this package are detailed in Peruzzi and Dunson (2020) <arXiv:2012.00943>. 
    Funded by ERC grant 856506 and NIH grant R01ES028804.
Fit the spatial panel data models: the fixed effects, random
    effects and between models.
Character vector to numerical translation in Euros from Spanish
    spelled monetary quantities. Reverse translation from integer to Spanish.
    Upper limit is up to the millions range. Geocoding via Cadastral web site.
Tools for reading, visualising and processing Magnetic Resonance
    Spectroscopy data. The package includes methods for spectral fitting: Wilson
    (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018)
    <DOI:10.1002/mrm.27605>.
This package performs robust nonparametric tests for rare variants
          association analysis using summation of partition approaches that incorporate
          gene-gene and gene-environmental interactions
Implements the sparse clustering methods of Witten and
        Tibshirani (2010): "A framework for feature selection in
        clustering"; published in Journal of the American Statistical
        Association 105(490): 713-726.
Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark' from R. 'Apache Spark' is an open source cluster computing framework available at <http://spark.apache.org>. This R package uses the 'spark-sas7bdat' 'Spark' package (<https://spark-packages.org/package/saurfang/spark-sas7bdat>) to import and process 'SAS' data in parallel using 'Spark'. Hereby allowing to execute 'dplyr' statements in parallel on top of 'SAS' data.
Load Avro Files into 'Apache Spark' using 'sparklyr'. This
    allows to read files from 'Apache Avro' <https://avro.apache.org/>.
A 'sparklyr' extension package providing an integration with Google 'BigQuery'.
  It supports direct import/export where records are directly streamed from/to 'BigQuery'.
  In addition, data may be imported/exported via intermediate data extracts on Google 'Cloud Storage'.
'Hail' is an open-source, general-purpose, 'python' based data analysis
    tool with additional data types and methods for working with genomic data, see
    <https://hail.is/>. 'Hail' is built to scale and has first-class support for 
    multi-dimensional structured data, like the genomic data in a genome-wide 
    association study (GWAS). 'Hail' is exposed as a 'python' library, using primitives
    for distributed queries and linear algebra implemented in 'scala', 'spark', and 
    increasingly 'C++'. The 'sparkhail' is an R extension using 'sparklyr' package. 
    The idea is to help R users to use 'hail' functionalities with the well-know
    'tidyverse' syntax, see <https://www.tidyverse.org/>.
Include interactive sparkline charts
    <http://omnipotent.net/jquery.sparkline> in 
    all R contexts with the convenience of 'htmlwidgets'.
R interface to Apache Spark, a fast and general
    engine for big data processing, see <https://spark.apache.org/>. This
    package supports connecting to local and remote Apache Spark clusters,
    provides a 'dplyr' compatible back-end, and provides an interface to
    Spark's built-in machine learning algorithms.
This sparklyr extension makes 'Flint' time series
    library functionalities (<https://github.com/twosigma/flint>) easily
    accessible through R.
A 'sparklyr' extension adding the capability to work easily with nested data.
Generate syntax for use with the sparklines package for
        LaTeX.
A 'sparklyr' extension that enables reading and writing 'TensorFlow'
  TFRecord files via 'Apache Spark'.
Load WARC (Web ARChive) files into Apache Spark using 'sparklyr'. This
    allows to read files from the Common Crawl project <http://commoncrawl.org/>.
A 'sparklyr' <https://spark.rstudio.com/> extension that provides an R
  interface for 'XGBoost' <https://github.com/dmlc/xgboost> on 'Apache Spark'. 'XGBoost' is an
  optimized distributed gradient boosting library.
Use SPARQL to pose SELECT or UPDATE queries to an end-point.
Provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) <DOI:10.1002/sim.7577>.
Calculate the statistical power to detect clusters using kernel-based 
        spatial relative risk functions that are estimated using the 'sparr' package.
        Details about the 'sparr' package methods can be found in the tutorial: Davies
        et al. (2018) <doi:10.1002/sim.7577>.  Details about kernel density estimation 
        can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>.  More 
        information about relative risk functions using kernel density estimation can 
        be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Implements the sparse biclustering proposal of Tan and Witten (2014), Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics  23(4):985-1008.
'HJ-Biplot' is a multivariate method that allow represent multivariate data on a subspace of low dimension, in such a way that most of the variability of the information is captured in a few dimensions. This package implements three new techniques and constructs in each case the 'HJ-Biplot', adapting restrictions to reduce weights and / or produce zero weights in the dimensions, based on the regularization theories. It implements three methods of regularization: Ridge, LASSO and Elastic Net.
A set of tools for representing and estimating sparse Bayesian networks from continuous and discrete data, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>.
Implements the algorithm described in 
    Barron, M., Zhang, S. and Li, J. 2017, "A sparse differential
    clustering algorithm for tracing cell type changes via single-cell
    RNA-sequencing data", Nucleic Acids Research, gkx1113,
    <doi:10.1093/nar/gkx1113>. This algorithm clusters samples from two
    different populations, links the clusters across the conditions and
    identifies marker genes for these changes. The package was designed for
    scRNA-Seq data but is also applicable to many other data types, just
    replace cells with samples and genes with variables. The package also
    contains functions for estimating the parameters for SparseDC as outlined
    in the paper. We recommend that users further select their marker genes
    using the magnitude of the cluster centers.
A collection of sparse and regularized discriminant analysis
    methods intended for small-sample, high-dimensional data sets. The package
    features the High-Dimensional Regularized Discriminant Analysis classifier
    from Ramey et al. (2017) <arXiv:1602.01182>. Other classifiers include
    those from Dudoit et al. (2002) <doi:10.1198/016214502753479248>, Pang et
    al. (2009) <doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012)
    <doi:10.1093/bioinformatics/btr690>.
Computation of sparse eigenvectors of a matrix (aka sparse PCA)
    with running time 2-3 orders of magnitude lower than existing methods and
    better final performance in terms of recovery of sparsity pattern and 
    estimation of numerical values. Can handle covariance matrices as well as 
    data matrices with real or complex-valued entries. Different levels of 
    sparsity can be specified for each individual ordered eigenvector and the 
    method is robust in parameter selection. See vignette for a detailed 
    documentation and comparison, with several illustrative examples. 
    The package is based on the paper:
    K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA 
    and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on 
    Signal Processing <doi:10.1109/TSP.2016.2605073>.
Multidimensional scaling provides a means of uncovering a latent structure underlying observed data, while estimating the number of latent dimensions.  This package presents a means for scaling binary and count data, for example the votes and word counts for legislators.  Future work will include an EM implementation and extend this work to ordinal and continuous data.
Estimation of functional linear mixed models for irregularly or
    sparsely sampled data based on functional principal component analysis.
Fits sparse frequentist GAMs (SF-GAM) for continuous and discrete responses in the exponential dispersion family with the group lasso, group smoothly clipped absolute deviation (SCAD), and group minimax concave (MCP) penalties <doi:10.1007/s11222-013-9424-2>. Also fits sparse Bayesian generalized additive models (SB-GAM) with the spike-and-slab group lasso (SSGL) penalty of Bai et al. (2021) <doi:10.1080/01621459.2020.1765784>. B-spline basis functions are used to model the sparse additive functions. Stand-alone functions for group-regularized negative binomial regression, group-regularized gamma regression, and group-regularized regression in the exponential dispersion family with the SSGL penalty are also provided.
Efficient implementation of sparse group lasso with optional bound 
  constraints on the coefficients. It supports the use of a sparse design matrix 
  as well as 
  returning coefficient estimates in a sparse matrix. Furthermore, it correctly
  calculates the degrees of freedom to allow for information criteria rather
  than cross-validation with very large data. Finally, the interface to 
  compiled code avoids unnecessary copies and allows for the use of
  long integers.
SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de
Estimates Hessian of a scalar-valued function, and returns it
    in a sparse Matrix format. The sparsity pattern must be known in advance. The
    algorithm is especially efficient for hierarchical models with a large number of
    heterogeneous units.  See Braun, M. (2017) <doi:10.18637/jss.v082.i10>.
Computation of sparse portfolios for financial index tracking, i.e., joint
    selection of a subset of the assets that compose the index and computation
    of their relative weights (capital allocation). The level of sparsity of the
    portfolios, i.e., the number of selected assets, is controlled through a
    regularization parameter. Different tracking measures are available, namely,
    the empirical tracking error (ETE), downside risk (DR), Huber empirical
    tracking error (HETE), and Huber downside risk (HDR). See vignette for a
    detailed documentation and comparison, with several illustrative examples.
    The package is based on the paper:
    K. Benidis, Y. Feng, and D. P. Palomar, "Sparse Portfolios for High-Dimensional
    Financial Index Tracking," IEEE Trans. on Signal Processing, vol. 66, no. 1,
    pp. 155-170, Jan. 2018. <doi:10.1109/TSP.2017.2762286>.
Creates a wrapper for the 'SuiteSparse' routines 
    that execute the Takahashi equations. These equations compute the
    elements of the inverse of a sparse matrix at locations where the
    its Cholesky factor is structurally non-zero. The resulting matrix is known as a 
	sparse inverse subset. Some helper functions are also implemented. 
	Support for spam matrices is currently limited and will be implemented 
	in the future. See Rue and Martino (2007) <doi:10.1016/j.jspi.2006.07.016> 
	and Zammit-Mangion and Rougier (2018) <doi:10.1016/j.csda.2018.02.001> for the 
	application of these equations to statistics.
Performs sparse linear discriminant analysis for Gaussians and mixture of Gaussian models.
Models the nonnegative entries of a rectangular adjacency matrix using a sparse latent position model, as illustrated in Rastelli, R. (2018) "The Sparse Latent Position Model for nonnegative weighted networks" <arXiv:1808.09262>.
Provides an S4 class for representing and
    interacting with sparse plus rank matrices. At the moment the
    implementation is quite spare, but the plan is eventually subclass
    Matrix objects.
Use RcppEigen to fit least trimmed squares
    regression models with an L1 penalty in order to obtain
    sparse models.
Some basic linear algebra functionality for sparse matrices is
  provided:  including Cholesky decomposition and backsolving as well as 
  standard R subsetting and Kronecker products.
The 'sparseMatEst' package provides functions for estimating sparse 
  covariance and precision matrices with error control. A false positive
  rate is fixed corresponding to the probability of falsely including a matrix 
  entry in the support of the estimator.  It uses the binary search
  method outlined in Kashlak and Kong (2019) <arXiv:1705.02679> and in 
  Kashlak (2019) <arXiv:1903.10988>.
Implements the algorithm described in
    Barron, M., and Li, J. (Not yet published). This algorithm clusters
    samples from multiple ordered populations, links the clusters across the
    conditions and identifies marker genes for these changes. The package was
    designed for scRNA-Seq data but is also applicable to many other data
    types, just replace cells with samples and genes with variables. The
    package also contains functions for estimating the parameters for SparseMDC
    as outlined in the paper. We recommend that users further select their
    marker genes using the magnitude of the cluster centers.
Implementation of spatially-explicit, stochastic disease models with customizable time windows that describe how parameter values fluctuate during outbreaks (e.g., in response to public health or conservation interventions).
Implements the routines and algorithms developed and analysed in "Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges when there are Non-Overlapping Lists" Chan, L, Silverman, B. W., Vincent, K (2019) <arXiv:1902.05156>. This package explicitly handles situations where there are pairs of lists which have no observed individuals in common.  It deals correctly with parameters whose estimated values can be considered as being negative infinity.  It also addresses other possible issues of non-existence and non-identifiability of maximum likelihood estimates.
Computes multivariate normal (MVN) densities, and
    samples from MVN distributions, when the covariance or
    precision matrix is sparse.
Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <DOI: 10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) <arXiv:1804.00341>.
Provides interface to 'sparsepp' - fast, memory efficient hash map. 
    It is derived from Google's excellent 'sparsehash' implementation.
    We believe 'sparsepp' provides an unparalleled combination of performance and memory usage, 
    and will outperform your compiler's unordered_map on both counts. 
    Only Google's 'dense_hash_map' is consistently faster, at the cost of much greater 
    memory usage (especially when the final size of the map is not known in advance).
Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects.  This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots.  The method can implement sparse linear regression and sparse probit regression.  Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to  models with truncated outcomes, propensity score, and instrumental variable analysis.
Sparse-aware maximum likelihood for structural equation models in inferring gene regulatory networks
Implements the SparseStep model for solving regression
    problems with a sparsity constraint on the parameters. The SparseStep
    regression model was proposed in Van den Burg, Groenen, and Alfons (2017)
    <arXiv:1701.06967>. In the model, a regularization term is added to the
    regression problem which approximates the counting norm of the parameters.
    By iteratively improving the approximation a sparse solution to the
    regression problem can be obtained.  In this package both the standard
    SparseStep algorithm is implemented as well as a path algorithm which uses
    golden section search to determine solutions with different values for the
    regularization parameter.
Wrapper around the 'SVDLIBC' library for (truncated) singular value decomposition of a sparse matrix.
             Currently, only sparse real matrices in Matrix package format are supported.
Fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization.
Computes sparse vector autoregressive 				coefficients and precision matrices for time series 		chain graphical models. Fentaw Abegaz and Ernst Wit (2013)	<doi:10.1093/biostatistics/kxt005>.
A wrapper for sparse VAR/VECM time series models estimation
             using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped 
             Absolute Deviation) and MCP (Minimax Concave Penalty). 
             Based on the work of Sumanta Basu and George Michailidis 
             <doi:10.1214/15-AOS1315>.
Implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. Features include a novel prioritized updating scheme, which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large, more relevant, coefficients. Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs. By default, the heavier-tailed Laplace density is used. Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo (2020) <doi:10.1080/01621459.2020.1847121> and Kolyan Ray, Botond Szabo, and Gabriel Clara (2020) <arXiv:2010.11665>.
Fast 'SVMlight' reader and writer. 
    'SVMlight' is most commonly used format for storing 
    sparse matrices (possibly with some target variable) on disk.
    For additional information about 'SVMlight' format see <http://svmlight.joachims.org/>.
Fast Multiplication and Marginalization of Sparse Tables.
Statistical pattern recognition and dating using archaeological artefacts assemblages.
    Package of statistical tools for archaeology.
    hclustcompro(perioclust): Bellanger Lise, Coulon Arthur, Husi Philippe (2020, ISBN:978-3-030-60103-4).
    mapclust: Bellanger Lise, Coulon Arthur, Husi Philippe (2021) <doi:10.1016/j.jas.2021.105431>.
    seriograph: Desachy Bruno (2004) <doi:10.3406/pica.2004.2396>.
The Stratified-Petersen Analysis System (SPAS) is designed
    to estimate abundance in two-sample capture-recapture experiments 
    where the capture and recaptures are stratified. This is a generalization
    of the simple Lincoln-Petersen estimator.
    Strata may be defined in time or in space or both, 
    and the s strata in which marking takes place 
    may differ from the t strata in which recoveries take place.
    When s=t, SPAS reduces to the method described by 
    Darroch (1961) <https://www.jstor.org/stable/2332748>.
    When s<t, SPAS implements the methods described in
    Plante, Rivest, and Tremblay (1988) 
    <https://www.jstor.org/stable/2533994>.
    Schwarz and Taylor (1998) <doi:10.1139/f97-238> describe
    the use of SPAS in estimating return of salmon stratified by
    time and geography. 
    A related package, BTSPAS, deals with temporal stratification where 
    a spline is used to model the distribution of the population 
    over time as it passes the second capture location.
    This is the R-version of the (now obsolete) standalone Windows 
    program available at <http://www.cs.umanitoba.ca/~popan/spas/spas_home.html>.
Sample size estimation and blinded sample size reestimation in Adaptive Study Design.
The heterogeneity of spatial data presenting a finite number of categories can be measured via computation of spatial entropy. Functions are available for the computation of the main entropy and spatial entropy measures in the literature. They include the traditional version of Shannon's entropy (Shannon, 1948 <doi:10.1002/j.1538-7305.1948.tb01338.x>), Batty's spatial entropy (Batty, 1974 <doi:10.1111/j.1538-4632.1974.tb01014.x>), O'Neill's entropy (O'Neill et al., 1998 <doi:10.1007/BF00162741>), Li and Reynolds' contagion index (Li and Reynolds, 1993 <doi:10.1007/BF00125347>), Karlstrom and Ceccato's entropy (Karlstrom and Ceccato, 2002 <urn:nbn:se:kth:diva-61351>), Leibovici's entropy (Leibovici, 2009 <doi:10.1007/978-3-642-03832-7_24>), Parresol and Edwards' entropy (Parresol and Edwards, 2014 <doi:10.3390/e16041842>) and Altieri's entropy (Altieri et al., 2018, <doi:10.1007/s10651-017-0383-1>). Full references for all measures can be found under the topic 'SpatEntropy'. The package is able to work with lattice and point data. The updated version works with the updated 'spatstat' package (>= 2.3-0). The speed of most functions has also been substantially increased.
Performs score test using saddlepoint approximation to estimate the null distribution. Also prepares summary statistics for meta-analysis and performs meta-analysis to combine multiple association results. For the latest version, please check <https://github.com/leeshawn/SPAtest>.
Graphs (or networks) and graph component
        calculations for spatial locations in 1D, 2D, 3D etc.
A generic tool for manifold analysis. It allows to infer a relevant transition or evolutionary path which can highlights the features involved in a specific process. 'spathial' can be useful in all the scenarios where the temporal (or pseudo-temporal) evolution is the main problem (e.g. tumor progression). The algorithm for finding the principal path is described in: Ferrarotti et al., (2019) <doi:10.1109/TNNLS.2018.2884792>."
Functions for kriging and point pattern analysis.
Provides a set of spatial accessibility measures from a set of locations 
             (demand) to another set of locations (supply). It aims, among others, 
             to support research on spatial accessibility to health care facilities. 
             Includes the locations and some characteristics of major public hospitals in Greece.
Creates offensive and defensive shot charts for teams, 
    players and seasons, and more comprehensively for spatial 
    analysis of NBA data. Includes data from the 2016-17 NBA 
    season extracted from <http://stats.nba.com>.
Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) <doi:10.1093/biomet/asz079>.
Functions that compute the spatial covariance matrix for the matern and power classes of spatial models, for data that arise on rectangular units.  This code can also be used for the change of support problem and for spatial data that arise on irregularly shaped regions like counties or zipcodes by laying a fine grid of rectangles and aggregating the integrals in a form of Riemann integration.
Utilities to support spatial data manipulation, query, sampling
    and modelling. Functions include models for species population density, download
    utilities for climate and global deforestation spatial products, spatial
    smoothing, multivariate separability, point process model for creating pseudo-
    absences and sub-sampling, polygon and point-distance landscape metrics,
    auto-logistic model, sampling models, cluster optimization, statistical
    exploratory tools and raster-based metrics.
Methods and data for cluster detection and disease mapping.
Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.
Multivariate modelling of geostatistical (point), lattice (areal) and point pattern data in a unifying spatial fusion framework. Details are given in Wang and Furrer (2021) <doi:10.1016/j.csda.2021.107240>. Model inference is done using either 'Stan' <https://mc-stan.org/> or 'INLA' <https://www.r-inla.org/>.
Fit latent variable models with the GEV distribution as the data likelihood and the GEV parameters following latent Gaussian processes. The models in this package are built using the template model builder 'TMB' in R, which has the fast ability to integrate out the latent variables using Laplace approximation. This package allows the users to choose in the fit function which GEV parameter(s) is considered as a spatially varying random effect following a Gaussian process, so the users can fit spatial GEV models with different complexities to their dataset without having to write the models in 'TMB' by themselves. This package also offers methods to sample from both fixed and random effects posteriors as well as the posterior predictive distributions at different spatial locations. Methods for fitting this class of models are described in Chen, Ramezan, and Lysy (2021) <arXiv:2110.07051>.
Provision of the S4 SpatialGraph class built on top of objects provided by 'igraph' and 'sp' packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg().
Calculate Kernel Density Estimation (KDE) for spatial data. 
  The algorithm is inspired by the tool 'Heatmap' from 'QGIS'. The method is described by:
  Hart, T., Zandbergen, P. (2014) <doi:10.1108/PIJPSM-04-2013-0039>, 
  Nelson, T. A., Boots, B. (2008) <doi:10.1111/j.0906-7590.2008.05548.x>,
  Chainey, S., Tompson, L., Uhlig, S.(2008) <doi:10.1057/palgrave.sj.8350066>.
Contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances between pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020), <doi:10.1137/19M1261195>). All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm. The package has four main helper functions: compareOneToOne() (to compare two spatial maps), compareOneToMany() (to compare a reference map with a list of other maps), compareAll() (to compute a matrix of distances between a list of maps), and focusArea() (to compute the KWD distance within a focus area). In non-convex maps, the helper functions first build the convex-hull of the input bins and pad the weights with zeros.
Implements a spatial extension of the random forest algorithm 
             (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>). 
             Future updates include more local machine learning methods 
             as well as a geographically weighted random forest.
Network based diffusion analysis (NBDA) allows inference on
             the asocial and social transmission of information.  This may involve
             the social transmission of a particular behaviour such as tool use, for example.
            For the NBDA, the key parameters estimated are the social effect and baseline rate 
            parameters.  The baseline rate parameter gives the rate at which the behaviour
            is first performed (or acquired) asocially amongst the individuals in a given population.
            The social effect parameter quantifies the effect of the social associations amongst 
            the individuals on the rate at which each individual first performs or displays
            the behaviour.  Spatial NBDA involves incorporating spatial information in the analysis.  
            This is done by incorporating social networks derived from 
            spatial point patterns (of the home bases of the individuals under study).  In addition, 
            a spatial covariate such as vegetation cover, or slope may be included in the modelling
            process.
Test and estimates of location, tests of independence, tests of sphericity and several estimates of shape all based on spatial signs, symmetrized signs, ranks and signed ranks. For details, see Oja and Randles (2004) <doi:10.1214/088342304000000558> and Oja (2010) <doi:10.1007/978-1-4419-0468-3>.
Tools to assess the association between two spatial processes. Currently,
  several methodologies are implemented: A modified t-test to perform hypothesis testing
  about the independence between the processes, a suitable nonparametric correlation
  coefficient, the codispersion coefficient, and an F test for assessing the multiple
  correlation between one spatial process and several others. Functions for image
  processing and computing the spatial association between images are also provided.
A spatial population can be generated based on spatially varying regression model under the assumption that observations are collected from a uniform two-dimensional grid consist of (m * m) lattice points with unit distance between any two neighbouring points. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).<DOI:10.1080/10485252.2018.1499907>.  This spatially generated data can be used to test different issues related to the statistical analysis of spatial data.
 This generated spatial data can be utilized in geographically weighted regression analysis for studying the spatially varying relationships among the variables.
Computes spatial position models: the potential model as defined
    by Stewart (1941) <doi:10.1126/science.93.2404.89> and catchment areas as
    defined by Reilly (1931) or Huff (1964) <doi:10.2307/1249154>.
Bayesian Estimation of Spatial Probit and Tobit Models.
A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep', 'sphet' and 'spse'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) <doi:10.1080/01621459.1975.10480272>. The models are further described by 'Anselin' (1988) <doi:10.1007/978-94-015-7799-1>. Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) <doi:10.1023/A:1007707430416> and (1999) <doi:10.1111/1468-2354.00027> are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) <doi:10.1201/9781420064254> are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) <doi:10.1111/gean.12008>, and model fitting methods by 'Bivand' and 'Piras' (2015) <doi:10.18637/jss.v063.i18>; both of these articles include extensive lists of references. 'spatialreg' >= 1.1-* correspond to 'spdep' >= 1.1-1, in which the model fitting functions are deprecated and pass through to 'spatialreg', but will mask those in 'spatialreg'. From versions 1.2-*, the functions will be made defunct in 'spdep'.
A collection of functions for estimating spatial regimes, aggregations of neighboring spatial units that are homogeneous in functional terms. The term spatial regime, therefore, should not be understood as a synonym for cluster. More precisely, the term cluster does not presuppose any functional relationship between the variables considered, while the term regime is linked to a regressive relationship underlying the spatial process.
Automatic generation and selection of spatial predictors for spatial regression with Random Forest. Spatial predictors are surrogates of variables driving the spatial structure of a response variable. The package offers two methods to generate spatial predictors from a distance matrix among training cases: 1) Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>): computed as the eigenvectors of a weighted matrix of distances; 2) RFsp (Hengl et al. <DOI:10.7717/peerj.5518>): columns of the distance matrix used as spatial predictors. Spatial predictors help minimize the spatial autocorrelation of the model residuals and facilitate an honest assessment of the importance scores of the non-spatial predictors. Additionally, functions to reduce multicollinearity, identify relevant variable interactions, tune random forest hyperparameters, assess model transferability via spatial cross-validation, and explore model results via partial dependence curves and interaction surfaces are included in the package. The modelling functions are built around the highly efficient 'ranger' package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>).
Methods for spatial risk calculations. It offers an efficient approach to determine the sum of all observations within a 
     circle of a certain radius. This might be beneficial for insurers who are required (by a recent European Commission regulation) to determine 
     the maximum value of insured fire risk policies of all buildings that are partly or fully located within a circle of a radius of 200m. See 
     Church (1974) <doi:10.1007/BF01942293> for a description of the problem.
Provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).
Functions and classes for spatial resampling to use with the 
    'rsample' package, such as spatial cross-validation (Brenning, 2012) 
    <doi:10.1109/IGARSS.2012.6352393>. The scope of 'rsample' and 
    'spatialsample' is to provide the basic building blocks for creating and
    analyzing resamples of a spatial data set, but neither package includes
    functions for modeling or computing statistics. The resampled spatial data
    sets created by 'spatialsample' do not contain much overhead in memory.
Visualization and analysis  of Vectra Immunoflourescent
    data. Options for calculating both the univariate and bivariate Ripley's K
    are included. Calculations are performed using a permutation-based 
    approach presented by Wilson et al.  <doi:10.1101/2021.04.27.21256104>.
Tools for spatial data analysis.  Emphasis on kriging.  Provides functions for prediction and simulation.  Intended to be relatively straightforward, fast, and flexible.
Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, <arXiv:1809.06418>). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006.
Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field.  Most of the functions here require the forecast and observed fields to be gridded and on the same grid. For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) <doi: 10.1175/2009WAF2222269.1>.
Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation on raster data sets. EWS are metrics derived from the observed spatial structure of an ecosystem -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) <doi:10.1111/2041-210X.13058>).
Many packages use 'htmlwidgets' <https://CRAN.R-project.org/package=htmlwidgets>
    for interactive plotting of spatial data.
    This package provides functions for converting R objects, such as simple features, 
    into structures suitable for use in 'htmlwidgets' mapping libraries.
Mixture model is used to achieve the clustering goal. Each component is itself a mixture model of polynomial autoregressive regressions whose the logistic weights consider the spatial and temporal information.
Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. To do so, it relies on and extends a number of facilities that the 'pomp' package provides for inference on time series data using partially-observed Markov process (POMP) models. Implemented methods include filtering and inference methods in Park and Ionides (2020) <doi:10.1007/s11222-020-09957-3>, Rebeschini and van Handel (2015) <doi:10.1214/14-AAP1061>, Evensen and van Leeuwen (1996) <doi:10.1029/94JC00572> and Ionides et al. (2021) <arXiv:2002.05211v2>. Pre-print statistical software article: Asfaw et al. (2021) <arXiv:2101.01157>.
Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models.
Detects spatial and temporal groups in GPS relocations 
    (Robitaille et al. (2020) <doi:10.1111/2041-210X.13215>). 
    It can be used to convert GPS relocations to 
    gambit-of-the-group format to build proximity-based social networks 
    In addition, the randomizations function provides data-stream 
    randomization methods suitable for GPS data.
Contains all the datasets for the 'spatstat' family of packages.
Defines spatial data types and supports geometrical operations
	     on them. Data types include point patterns, windows (domains),
	     pixel images, line segment patterns, tessellations and hyperframes.
	     Capabilities include creation and manipulation of data
	     (using command line or graphical interaction),
	     plotting, geometrical operations (rotation, shift, rescale,
	     affine transformation), convex hull, discretisation and
	     pixellation, Dirichlet tessellation, Delaunay triangulation,
	     pairwise distances, nearest-neighbour distances,
	     distance transform, morphological operations
	     (erosion, dilation, closing, opening), quadrat counting,
	     geometrical measurement, geometrical covariance,
	     colour maps, calculus on spatial domains,
	     Gaussian blur, level sets of images, transects of images,
	     intersections between objects, minimum distance matching.
	     (Excludes spatial data on a network, which are supported by
	     the package 'spatstat.linnet'.)
Extension to the 'spatstat' package, containing
	     interactive graphics capabilities.
Extension to the 'spatstat' family of packages, for analysing
	     large datasets of spatial points on a network. The geometrically-
	     corrected K function is computed using a memory-efficient
	     tree-based algorithm described by Rakshit, Baddeley and Nair (2019).
Defines types of spatial data on a linear network
	     and provides functionality for geometrical operations,
	     data analysis and modelling of data on a linear network,
	     in the 'spatstat' family of packages.
	     Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks.
	     Data types defined on a network include point patterns, pixel images, functions, and tessellations.
	     Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
	Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood.
	Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
	Random point patterns on a network can be generated using a variety of models.
Extension to the 'spatstat' package, enabling the user
	     to fit point process models to point pattern data
	     by local composite likelihood ('geographically weighted
	     regression').
Functionality for random generation of spatial data in the 'spatstat' family of packages.
	     Generates random spatial patterns of points according to many simple rules (complete spatial randomness,
	     Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns
	     (thinning, random shift, jittering),  simulated realisations of random point processes
	     (simple sequential inhibition, Matern inhibition models, Matern cluster process,
	     Neyman-Scott cluster processes, log-Gaussian Cox processes, product shot noise cluster processes)
	     and simulation of Gibbs point processes (Metropolis-Hastings birth-death-shift algorithm,
	     alternating Gibbs sampler). Also generates random spatial patterns of line segments,
	     random tessellations, and random images (random noise, random mosaics).
	     Excludes random generation on a linear network,
	     which is covered by the separate package 'spatstat.linnet'.
Defines sparse three-dimensional arrays
	     and supports standard operations on them.
	     The package also includes utility functions for
	     matrix calculations that are common in
	     statistics, such as quadratic forms.
Contains utility functions for the 'spatstat' family of packages
             which may also be useful for other purposes.
Bayesian inference for parametric proportional hazards spatial
    survival models; flexible spatial survival models. See Benjamin M. Taylor, Barry S. Rowlingson (2017) <doi:10.18637/jss.v077.i04>.
Tools to convert from specific formats to more general forms of 
    spatial data. Using tables to store the actual entities present in spatial
    data provides flexibility, and the functions here deliberately 
    minimize the level of interpretation applied, leaving that for specific 
    applications. Includes support for simple features,  round-trip for 'Spatial' classes and long-form 
    tables, analogous to 'ggplot2::fortify'. There is also a more 'normal form' representation
    that decomposes simple features and their kin to tables of objects, parts, and unique coordinates.
Fits univariate and multivariate spatio-temporal
        random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley, Banerjee, and Cook (2014) <doi:10.1111/2041-210X.12189>.
Provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.
Implements a spatial Bayesian non-parametric factor analysis model 
    with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). 
    Spatial correlation is introduced in the columns of the factor loadings 
    matrix using a Bayesian non-parametric prior, the probit stick-breaking 
    process. Areal spatial data is modeled using a conditional autoregressive 
    (CAR) prior and point-referenced spatial data is treated using a Gaussian 
    process. The response variable can be modeled as Gaussian, probit, Tobit, or
    Binomial (using Polya-Gamma augmentation). Temporal correlation is 
    introduced for the latent factors through a hierarchical structure and can 
    be specified as exponential or first-order autoregressive. Full details of 
    the package can be found in the accompanying vignette. Furthermore, the 
    details of the package can be found in "Bayesian Non-Parametric Factor 
    Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019), 
    <arXiv:1911.04337>. The paper is in press at the journal Bayesian Analysis.
Selection of spatially balanced samples. In particular, the implemented sampling designs allow to select probability samples well spread over the population of interest, in any dimension and using any distance function (e.g. Euclidean distance, Manhattan distance). For more details, Benedetti R and Piersimoni F (2017) <doi:10.1002/bimj.201600194> and Benedetti R and Piersimoni F (2017) <arXiv:1710.09116>. The implementation has been done in C++ through the use of 'Rcpp' and 'RcppArmadillo'.
Evaluation of control charts by means of
        the zero-state, steady-state ARL (Average Run Length) and RL quantiles.
        Setting up control charts for given in-control ARL. The control charts
        under consideration are one- and two-sided EWMA, CUSUM, and
        Shiryaev-Roberts schemes for monitoring the mean or variance of normally
        distributed independent data. ARL calculation of the same set of schemes under drift (in the mean) are added.
        Eventually, all ARL measures for the multivariate EWMA (MEWMA) are provided.
Provides statistical process control tools for stochastic 
    textured surfaces. The current version supports the following tools:
    (1) generic modeling of stochastic textured surfaces.
    (2) local defect monitoring and diagnostics in stochastic 
        textured surfaces, which was proposed by Bui and Apley (2018a) 
        <doi:10.1080/00401706.2017.1302362>.
    (3) global change monitoring in the nature of stochastic 
        textured surfaces, which was proposed by Bui and Apley (2018b) 
        <doi:10.1080/00224065.2018.1507559>.
    (4) computation of dissimilarity matrix of stochastic textured 
        surface images, which was proposed by Bui and Apley (2019b)
        <doi:10.1016/j.csda.2019.01.019>.
Calibration of thresholds of control charts such as
    CUSUM charts based on past data, taking estimation error into account.
A new reduced-rank LDA method which works for high dimensional multi-class data.
Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2018) <arXiv:1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.
Programs to find the sample size or power of studies using the 
    Sequential Parallel Comparison Design (SPCD) and programs to analyze 
    such studies. This is a clinical trial design where 
    patients initially on placebo who did not respond are re-randomized between 
    placebo and active drug in a second phase and the results of the two phases are 
    pooled. The method of analyzing binary data with this design is described in 
    Fava,Evins, Dorer and Schoenfeld(2003) <doi:10.1159/000069738>, and the 
    method of analyzing continuous data is described in Chen, Yang, 
    Hung and Wang (2011) <doi:10.1016/j.cct.2011.04.006>.
Fast computation of the required sample size or the
    achieved power, for GWAS studies with different types of covariate
    effects and different types of covariate-gene dependency structure.
    For the detailed description of the methodology, see Zhang (2022)
    "Power and Sample Size Computation for Genetic Association Studies
     of Binary Traits: Accounting for Covariate Effects" <arXiv:2203.15641>.
Spatial coverage sampling and random sampling from compact
        geographical strata created by k-means. See Walvoort et al. (2010) 
        <doi:10.1016/j.cageo.2010.04.005> for details.
Provides a covariance estimator for multivariate normal
        data that is sparse and positive definite.  Implements the
        majorize-minimize algorithm described in Bien, J., and
        Tibshirani, R. (2011), "Sparse Estimation of a Covariance
        Matrix," Biometrika. 98(4). 807--820.
Implements a spatially varying change point model with 
    unique intercepts, slopes, variance intercepts and slopes, and 
    change points at each location. Inference is within the 
    Bayesian setting using Markov chain Monte Carlo (MCMC). The 
    response variable can be modeled as Gaussian (no nugget), 
    probit or Tobit link and the five spatially varying parameter
    are modeled jointly using a multivariate conditional 
    autoregressive (MCAR) prior. The MCAR is a unique process that
    allows for a dissimilarity metric to dictate the local spatial 
    dependencies. Full details of the package can be found in the accompanying vignette.
    Furthermore, the details of the package can be found in the corresponding paper on arXiv
    by Berchuck et al (2018): "A spatially varying change points model for monitoring glaucoma 
    progression using visual field data", <arXiv:1811.11038>.
The sparse principal component regression is computed. The regularization parameters are optimized by cross-validation.
The Semi Parametric Piecewise Distribution blends the Generalized Pareto Distribution for the tails with a kernel based interior.
Diverse spatial datasets for demonstrating, benchmarking and teaching spatial data analysis. 
    It includes R data of class sf (defined by the package 'sf'), Spatial ('sp'), and nb ('spdep').
    Unlike other spatial data packages such as 'rnaturalearth' and 'maps', 
    it also contains data stored in a range of file formats including GeoJSON, ESRI Shapefile and GeoPackage. 
    Some of the datasets are designed to illustrate specific analysis techniques.
    cycle_hire() and cycle_hire_osm(), for example, is designed to illustrate point pattern analysis techniques.
Inspired by space-time regressions often performed to assess
    the expansion of the Neolithic from the Near East to Europe (Pinhasi et
    al. 2005 <doi:10.1371/journal.pbio.0030410>). Test for significant
    correlations between the (earliest) radiocarbon dates of archaeological
    sites and their respective distances from a hypothetical center of origin.
    Distances can be either great-circle or least-cost paths calculated from
    a friction surface. Both ordinary least squares (OLS) and reduced major
    axis (RMA) methods are supported (Russell et al. 2014
    <doi:10.1371/journal.pone.0087854>). It is also possible to iterate over
    many sites to identify the most likely origin.
A collection of functions to create spatial weights matrix
  objects from polygon 'contiguities', from point patterns by distance and
  tessellations, for summarizing these objects, and for permitting their
  use in spatial data analysis, including regional aggregation by minimum
  spanning tree; a collection of tests for spatial 'autocorrelation',
  including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord'
  (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel'
  general cross product statistic, Empirical Bayes estimates and
  'Assunção/Reis' (1999) <doi:10.1002/(SICI)1097-0258(19990830)18:16%3C2147::AID-SIM179%3E3.0.CO;2-I> Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992)
  <doi:10.1111/j.1538-4632.1992.tb00261.x> and multicoloured
  join count statistics, 'APLE' ('Li 'et al.' )
  <doi:10.1111/j.1538-4632.2007.00708.x>, local 'Moran's I', 'Gearys C' 
  ('Anselin' 1995) <doi:10.1111/j.1538-4632.1995.tb00338.x> and
  'Getis/Ord' G ('Ord' and 'Getis' 1995)
  <doi:10.1111/j.1538-4632.1995.tb00912.x>,
  'saddlepoint' approximations ('Tiefelsdorf' 2002)
  <doi:10.1111/j.1538-4632.2002.tb01084.x> and exact tests
  for global and local 'Moran's I' ('Bivand et al.' 2009)
  <doi:10.1016/j.csda.2008.07.021> and 'LOSH' local indicators
  of spatial heteroscedasticity ('Ord' and 'Getis')
  <doi:10.1007/s00168-011-0492-y>. The implementation of most of
  the measures is described in 'Bivand' and 'Wong' (2018)
  <doi:10.1007/s11749-018-0599-x>, with further extensions in 'Bivand' (2022)
  <doi:10.1111/gean.12319>.
  From 'spdep' and 'spatialreg' versions >= 1.2-1, the model fitting functions
  previously present in this package are defunct in 'spdep' and may be found
  in 'spatialreg'.
Spatial downscaling of climate data (Global Circulation Models/Regional Climate Models) using quantile-quantile bias correction technique.
Methods for 'dplyr' verbs for 'sp' 'Spatial' classes. The basic 
    verbs that modify data attributes, remove or re-arrange rows are supported
    and provide complete 'Spatial' analogues of the input data. The group by
    and summarize work flow returns a non-topological spatial union. There is 
    limited support for joins, with left and inner to copy attributes from 
    another table.
An implementation of split-population duration regression models. 
    Unlike regular duration models, split-population duration models are
    mixture models that accommodate the presence of a sub-population that is 
    not at risk for failure, e.g. cancer patients who have been cured by 
    treatment. This package implements Weibull and Loglogistic forms for the 
    duration component, and focuses on data with time-varying covariates. 
    These models were originally formulated in Boag (1949) 
    <http://www.jstor.org/stable/2983694> and Berkson and Gage (1952) 
    <http://www.jstor.org/stable/2281318>, and extended in Schmidt and Witte 
    (1989) <doi:10.1016/0304-4076(89)90034-1>.
A spatio-dynamic modelling package that focuses on three
    characteristic wetland plant communities in a semiarid Mediterranean
    wetland in response to hydrological pressures from the catchment. The
    package includes the data on watershed hydrological pressure and the
    initial raster maps of plant communities but also allows for random initial
    distribution of plant communities. For more detailed info see: Martinez-Lopez et al. (2015) <doi:10.1016/j.ecolmodel.2014.11.024>.
It allows running 'Praat' scripts from R and it provides some
    wrappers for basic plotting. It also adds support for literate markdown
    tangling. The package is designed to bring reproducible phonetic research
    into R.
Makes Nuclear Magnetic Resonance spectroscopy (NMR spectroscopy) data analysis as easy as possible by only requiring a small set of functions to perform an entire analysis. 'speaq' offers the possibility of raw spectra alignment and quantitation but also an analysis based on features whereby the spectra are converted to peaks which are then grouped and turned into features. These features can be processed with any number of statistical tools either included in 'speaq' or available elsewhere on CRAN. More details can be found in Vu et al. (2011) <doi:10.1186/1471-2105-12-405> and Beirnaert et al. (2018) <doi:10.1371/journal.pcbi.1006018>.
Functions for conducting jackknife Euclidean / empirical likelihood inference for Spearman's rho (de Carvalho and Marques (2012) <10.1080/10920277.2012.10597644>).
Creates a data specification that describes the columns of a 
 table (data.frame). Provides methods to read, write, and update the 
 specification. Checks whether a table matches its specification. See
 specification.data.frame(),read.spec(), write.spec(), as.csv.spec(),
 respecify.character(), and %matches%.data.frame().
Alternative to the kernlab::specc function. Includes a spectral clustering implementation, a locally adapted kernel function akin to what is already proposed in kernlab, and an optional procedure that automatically estimates the optimal number of clusters. Several sample data sets are also included.
Calculate change point based on spectral clustering with the option to automatically calculate the number of clusters if this information is not available.
Utility functions for spectroscopy. 1. Functions to simulate
    spectra for use in teaching or testing. 2. Functions to process files created by
    'LoggerPro' and 'SpectraSuite' software.
A set of functions for obtaining positional parameters and magnitude difference between components of binary and multiple stellar systems from series of speckle images.
Provides utilities for conducting specification curve analyses (Simonsohn, Simmons & Nelson (2015, <doi: 10.2139/ssrn.2694998>) or multiverse analyses (Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016, <doi: 10.1177/1745691616658637>) including functions to setup, run, evaluate, and plot all specifications.
Implementation of SPECS, your favourite Single-Equation Penalized Error-Correction Selector developed in
    Smeekes and Wijler (2020) <arXiv:1809.08889>. SPECS provides a fully automated estimation procedure for large and potentially
    (co)integrated datasets. The dataset in levels is converted to a conditional error-correction model, either by the user or
    by means of the functions included in this package, and various specialised forms of penalized regression can be applied to
    the model. Automated options for initializing and selecting a sequence of penalties, as well as the construction of penalty
    weights via an initial estimator, are available. Moreover, the user may choose from a number of pre-specified deterministic
    configurations to further simplify the model building process.
A collection of forecast verification routines developed for the SPECS
    FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate.
Stores and eases the manipulation of spectra and associated data,
    with dedicated classes for spatial and soil-related data.
Provides interface to the 'Spectator Earth' API <https://api.spectator.earth/>, 
    mainly for obtaining the acquisition plans and satellite overpasses for Sentinel-1, 
    Sentinel-2, Landsat-8 and Landsat-9 satellites. Current position and trajectory can 
    also be obtained for a much larger set of satellites. It is also possible to search 
    the archive for available images over the area of interest for a given (past) period, 
    get the URL links to download the whole image tiles, or alternatively to download the 
    image for just the area of interest based on selected spectral bands.
Provides a consistent interface to use various methods to calculate
  the periodogram and estimate the period of a rhythmic time-course. Methods
  include Lomb-Scargle, fast Fourier transform, and three versions of the
  chi-square periodogram. See Tackenberg and Hughey (2021)
  <doi:10.1371/journal.pcbi.1008567>.
On discrete data spectral analysis is performed by Fourier and Hilbert
    transforms as well as with model based analysis called Lomb-Scargle method.
    Fragmented and irregularly spaced data can be processed in almost all methods. Both,
    FFT as well as LOMB methods take multivariate data and return standardized PSD. 
    For didactic reasons an analytical approach for deconvolution of noise spectra and 
    sampling function is provided.
    A user friendly interface helps to interpret the results.
Infrared, near-infrared and Raman spectroscopic data measured during chemical reactions, provide structural fingerprints by which molecules can be identified and quantified. The application of these spectroscopic techniques as inline process analytical tools (PAT), provides the (pharma-)chemical industry with novel tools, allowing to monitor their chemical processes, resulting in a better process understanding through insight in reaction rates, mechanistics, stability, etc.
    Data can be read into R via the generic spc-format, which is generally supported by spectrometer vendor software. Versatile pre-processing functions are available to perform baseline correction by linking to the 'baseline' package; noise reduction via the 'signal' package; as well as time alignment, normalization, differentiation, integration and interpolation. Implementation based on the S4 object system allows storing a pre-processing pipeline as part of a spectral data object, and easily transferring it to other datasets. Interactive plotting tools are provided based on the 'plotly' package. 
    Non-negative matrix factorization (NMF) has been implemented to perform multivariate analyses on individual spectral datasets or on multiple datasets at once. NMF provides a parts-based representation of the spectral data in terms of spectral signatures of the chemical compounds and their relative proportions.
    The functionality to read in spc-files was adapted from the 'hyperSpec' package.
Routines for creating, manipulating, and performing 
 Bayesian inference about Gaussian processes in 
 one and two dimensions using the Fourier basis approximation: 
 simulation and plotting of processes, calculation of 
 coefficient variances, calculation of process density, 
 coefficient proposals (for use in MCMC).  It uses R environments to
 store GP objects as references/pointers.
In the era of big data and hyperconnectivity, learning
    high-dimensional structures such as graphs from data has become a prominent
    task in machine learning and has found applications in many fields such as
    finance, health care, and networks. 'spectralGraphTopology' is an open source,
    documented, and well-tested R package for learning graphs from data. It
    provides implementations of state of the art algorithms such as Combinatorial
    Graph Laplacian Learning (CGL), Spectral Graph Learning (SGL), Graph Estimation
    based on Majorization-Minimization (GLE-MM), and Graph Estimation based on
    Alternating Direction Method of Multipliers (GLE-ADMM). In addition, graph
    learning has been widely employed for clustering, where specific algorithms
    are available in the literature. To this end, we provide an implementation of
    the Constrained Laplacian Rank (CLR) algorithm.
Implements the diffusion map method of dimensionality reduction and spectral method of combining multiple diffusion maps, including creation of the spectra and visualization of maps.
Predict regional community composition at a fine spatial 
    resolution using only sparse biological and environmental data. The package 
    is based on the DynamicFOAM algorithm described 
    in Mokany et al. (2011) <doi:10.1111/j.1461-0248.2011.01675.x>.
Spectra viewer, organizer, data preparation and property blocks
        from within R or stand-alone. Binary (application) part is 
        installed separately using spnInstallApp() from spectrino package.
Input/Output, processing and visualization of spectra taken with different spectrometers, including SVC (Spectra Vista), ASD and PSR (Spectral Evolution). Implements an S3 class 'spectra' that other packages can build on. Provides methods to access, plot, manipulate, splice sensor overlap, vector normalize and smooth spectra.
A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.
Do multi-gene descent probabilities
   (Thompson, 1983, <doi:10.1098/rspb.1983.0072>)
   and special cases thereof
   (Thompson, 1986, <doi:10.1002/zoo.1430050210>)
   including inbreeding and kinship coefficients.  But does much more:
   probabilities of any set of genes descending from any other
   set of genes.
From output files obtained from the software 'ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.
Converts the floor speeches of Uruguayan legislators, extracted from the 
    parliamentary minutes, to tidy data.frame where each observation is the intervention of a single legislator.
Scrap speech text and speaker informations of speeches of House of 
          Representatives of Brazil, and transform in a cleaned tibble.
Fitting linear models and generalized linear models to large data sets by updating algorithms.
Label, recode, rename, and convert datasets and ASCII files more efficiently. 'speedycode' automates the code necessary for labeling variables with the 'labelled' package, recoding and renaming variables with 'dplyr' syntax, and converting ASCII files with the 'readroper' package. Most functions require only the name of the dataset and the code will be automatically written. Some convenience functions useful for converting ASCII files are also included.
Functions for fitting semiparametric regression models for
        panel count survival data. An overview of the package can be found 
        in Wang and Yan (2011) <doi:10.1016/j.cmpb.2010.10.005> and
	Chiou et al. (2018) <doi:10.1111/insr.12271>.
The package performs estimation and testing of the
        treatment effect in a 2-group randomized clinical trial with a
        quantitative, dichotomous, or right-censored time-to-event
        endpoint. The method improves efficiency by leveraging baseline
        predictors of the endpoint. The inverse probability weighting
        technique of Robins, Rotnitzky, and Zhao (JASA, 1994) is used
        to provide unbiased estimation when the endpoint is missing at
        random.
A set of functions for computing potential evapotranspiration and several widely used drought indices including the Standardized Precipitation-Evapotranspiration Index (SPEI).
Corrects the spelling of a given word in English 
    using a modification of Peter Norvig's spell correct 
    algorithm (see <http://norvig.com/spell-correct.html>) 
    which handles up to three edits. The algorithm tries to 
    find the spelling with maximum probability of intended
    correction out of all possible candidate corrections from
    the original word.
Spell checking common document formats including latex, markdown, manual pages,
    and description files. Includes utilities to automate checking of documentation and 
    vignettes as a unit test during 'R CMD check'. Both British and American English are 
    supported out of the box and other languages can be added. In addition, packages may
    define a 'wordlist' to allow custom terminology without having to abuse punctuation.
This implementation of the Empirical Mode Decomposition (EMD) works in 2 dimensions simultaneously, and 
    can be applied on spatial data. It can handle both gridded or un-gridded datasets.
Provides some easy-to-use functions to interpolate species range based on species occurrences and to estimate centers of biodiversity.
Implements spatial error estimation and
    permutation-based variable importance measures for predictive models
    using spatial cross-validation and spatial block bootstrap.
Functions to produce a fully fledged 'geo-spatial' object extent as a
    'SpatialPolygonsDataFrame'. Also included are functions to generate polygons
    from raster data using 'quadmesh' techniques, a round number buffered extent, and
    general spatial-extent and 'raster-like' extent helpers missing from the originating
    packages. Some latitude-based tools for polar maps are included.
Implements a group-bridge penalized function-on-scalar regression
    model proposed by Wang et al. (2020) <arXiv:2006.10163>, to simultaneously
    estimate functional coefficient and recover the local sparsity.
Tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.
Efficient estimation of spatial econometric models of origin-destination flows, which may exhibit spatial autocorrelation in the dependent variable, the explanatory variables or both.
    The model is the one proposed by LeSage and Pace (2008) <doi:10.1111/j.1467-9787.2008.00573.x>, who develop a matrix formulation that exploits the relational structure of flow data.
    The estimation procedures follow most closely those outlined by Dargel (2021) (preprint available at <https://www.tse-fr.eu/fr/publications/revisiting-estimation-methods-spatial-econometric-interaction-models>).
An implementation of feature selection and ranking via simultaneous perturbation
    stochastic approximation (SPSA-FSR) based on works by V. Aksakalli and M. Malekipirbazari 
    (2015) <arXiv:1508.07630> and Zeren D. Yenice and et al. (2018) <arXiv:1804.05589>.
    The SPSA-FSR algorithm searches for a locally optimal set of features that yield the best 
    predictive performance using a specified error measure such as mean squared error (for 
    regression problems) and accuracy rate (for classification problems). This package requires 
    an object of class 'task' and an object of class 'Learner' from the 'mlr' package.
Functions to manipulate, pre-process and analyze spectra in the mid-infrared region. The pre-processing of the mid-infrared spectra is a transcendental step in the spectral analysis. Preprocessing of the spectra includes smoothing, offset, baseline correction, and normalization,  is performed before the analysis of the spectra and is essential to obtain conclusive results in subsequent quantitative or qualitative analysis. This package was supported by FONDECYT 3150630, and CIPA Conicyt-Regional R08C1002 is gratefully acknowledged.
Estimation and Prediction Functions Using Bayesian Hierarchical Spatial Finlay-Wilkinson Model for Analysis of Multi-Environment Field Trials.
A collection of functions to deal with spatial and spatiotemporal autoregressive conditional heteroscedasticity (spatial ARCH and GARCH models) by Otto, Schmid, Garthoff (2018, Spatial Statistics) <arXiv:1609.00711>: simulation of spatial ARCH-type processes (spARCH, exponential spARCH, complex spARCH); quasi-maximum-likelihood estimation of the parameters of spARCH models and spatial autoregressive models with spARCH disturbances, diagnostic checks, visualizations.
Interpreted interface between GRASS 6+ geographical 
    information system and R, based on starting R from within the GRASS 
    environment, or running free-standing R in a temporary GRASS location;
    the package provides facilities for using all GRASS commands from the 
    R command line. This package may not be used for GRASS 7, for which
    rgrass7 should be used.
A collection of statistical hypothesis tests and other 
	techniques for identifying certain spatial relationships/phenomena in 
	DNA sequences. In particular, it provides tests and graphical methods for determining 
	whether or not DNA sequences comply with Chargaff's second parity rule 
	or exhibit purine-pyrimidine parity. In addition, there are functions for 
	efficiently simulating discrete state space Markov chains and testing 
	arbitrary symbolic sequences of symbols for the presence of first-order 
	Markovianness.
	Also, it has functions for counting words/k-mers (and cylinder patterns) in 
	arbitrary symbolic sequences. Functions which take a DNA sequence as input 
	can handle sequences stored as SeqFastadna objects from the 'seqinr' package.
Functions for computing geographically weighted
  regressions are provided, based on work by Chris
  Brunsdon, Martin Charlton and Stewart Fotheringham.
A simple tool for numerical optimization on the unit sphere. This is achieved by combining the spherical coordinating system with L-BFGS-B optimization. This algorithm is implemented in Kolkiewicz, A., Rice, G., & Xie, Y. (2020) <doi:10.1016/j.jspi.2020.07.001>.
Fitting dimension reduction methods to data lying on two-dimensional sphere.
    This package provides principal geodesic analysis, principal circle, principal curves 
    proposed by Hauberg, and spherical principal curves. Moreover, it offers the method of locally   
    defined principal geodesics which is underway. The detailed procedures 
    are described in Lee, J., Kim, J.-H. and Oh, H.-S. (2021) <doi:10.1109/TPAMI.2020.3025327>. 
    Also see Kim, J.-H., Lee, J. and Oh, H.-S. (2020) <arXiv:2003.02578>.
Various functions for creating spherical coordinate system plots via extensions to rgl.
Provides several methods to integrate functions over the unit
 sphere and ball in n-dimensional Euclidean space.  Routines for converting to/from
 multivariate polar/spherical coordinates are also provided.
Spherical K-function for point-pattern analysis on the sphere.
Functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables.
Implementation of uniformity tests on the circle and
    (hyper)sphere. The main function of the package is unif_test(), which
    conveniently collects more than 30 tests for assessing uniformity on
    S^{p-1}={x in R^p : ||x||=1}, p >= 2. The test statistics are implemented
    in the unif_stat() function, which allows computing several statistics to
    several samples within a single call, thus facilitating Monte Carlo
    experiments. Furthermore, the unif_stat_MC() function allows
    parallelizing them in a simple way. The asymptotic null distributions of
    the statistics are available through the function unif_stat_distr(). The
    core of 'sphunif' is coded in C++ by relying on the 'Rcpp' package.
    The package also provides several novel datasets and gives the
    reproducibility for the data application in García-Portugués,
    Navarro-Esteban and Cuesta-Albertos (2020) <arXiv:2008.09897>.
The SNP Panel Identification Assay (SPIA) is a package that enables an accurate determination of cell line identity from the genotype of single nucleotide polymorphisms (SNPs). The SPIA test allows to discern when two cell lines are close enough to be called similar and when they are not. Details about the method are available at "Demichelis et al. (2008) SNP panel identification assay (SPIA): a genetic-based assay for the identification of cell lines. Nucleic Acids Res., 3, 2446-2456".
A set of functions allowing to implement the 'SpiceFP' approach 
  which is iterative. It involves transformation of functional 
  predictors into several candidate explanatory matrices (based on contingency 
  tables), to which relative edge matrices with contiguity constraints are 
  associated. Generalized Fused Lasso regression are performed in order to 
  identify the best candidate matrix, the best class intervals and related 
  coefficients at each iteration. The approach is stopped when the maximal number
  of iterations is reached or when retained coefficients are zeros. Supplementary 
  functions allow to get coefficients of any candidate matrix or mean of 
  coefficients of many candidates. <https://hal.archives-ouvertes.fr/hal-03298977>.
Analysis of species limits and DNA barcoding data. Included are functions for generating important summary statistics from DNA barcode data, assessing specimen identification efficacy, testing and optimizing divergence threshold limits, assessment of diagnostic nucleotides, and calculation of the probability of reciprocal monophyly. Additionally, a sliding window function offers opportunities to analyse information across a gene, often used for marker design in degraded DNA studies. Further information on the package has been published in Brown et al (2012) <doi:10.1111/j.1755-0998.2011.03108.x>.
The 'Robots Exclusion Protocol' <https://www.robotstxt.org/orig.html> documents
    a set of standards for allowing or excluding robot/spider crawling of different areas of
    site content. Tools are provided which wrap The 'rep-cpp' <https://github.com/seomoz/rep-cpp>
    C++ library for processing these 'robots.txt' files.
Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML).  For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.
Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud.
Spike and slab for prediction and variable selection in
        linear regression models. Uses a generalized elastic net for
        variable selection.
Bayesian variable selection, model choice, and regularized
    estimation for (spatial) generalized additive mixed regression models
    via stochastic search variable selection with spike-and-slab priors.
A user-friendly tool for estimating both total and directional volatility spillovers based on Diebold and Yilmaz (2009, 2012). It also provides the user with rolling estimation for total and net indices. User can find both orthogonalized and generalized versions for each kind of measures. See Diebold and Yilmaz (2009, 2012) find them at  <doi:10.1111/j.1468-0297.2008.02208.x> and <doi:10.1016/j.ijforecast.2011.02.006>.
An optimal weighting strategy to compute
        simulation-efficient shortest probability intervals (spins).
Calculates constant structure parameters of endocrine homeostatic systems from equilibrium hormone concentrations. Methods and equations have been described in Dietrich et al. (2012) <doi:10.1155/2012/351864> and Dietrich et al. (2016) <doi:10.3389/fendo.2016.00057>.
Many complex diseases are known to be affected by the interactions between 
   genetic variants and environmental exposures beyond the main genetic and environmental 
   effects. Existing Bayesian methods for gene-environment (G×E) interaction studies are 
   challenged by the high-dimensional nature of the study and the complexity of environmental 
   influences. We have developed a novel and powerful semi-parametric Bayesian variable 
   selection method that can accommodate linear and nonlinear G×E interactions simultaneously 
   (Ren et al. (2019) <arXiv:1906.01057>). Furthermore, the proposed method can conduct 
   structural identification by distinguishing nonlinear interactions from main effects only 
   case within Bayesian framework. Spike-and-slab priors are incorporated on both individual 
   and group level to shrink coefficients corresponding to irrelevant main and interaction 
   effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and 
   alternative methods are  efficiently implemented in C++.
Functions for spatial methods based on generalized estimating equations (GEE) and
  wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR),
  conducting multi-model inference, and stepwise model selection. Further, contains functions 
  for spatially corrected model accuracy measures.
Data visualization tours animates linear projection 
  of multivariate data as its basis (ie. orientation) changes. The 'spinifex' 
  packages generates paths for manual tours by manipulating the contribution of 
  a single variable at a time Cook & Buja (1997) 
  <doi:10.1080/10618600.1997.10474754>. Other types of tours, such as grand 
  (random walk) and guided (optimizing some objective function) are available 
  in the 'tourr' package Wickham et al. <doi:10.18637/jss.v040.i02>. 
  'spinifex' builds on 'tourr' and can render tours with 'gganimate' and 
  'plotly' graphics, and allows for exporting as an .html widget and as an .gif, 
  respectively. This work is fully discussed in Spyrison & Cook (2020) 
  <doi:10.32614/RJ-2020-027>.
Implements a generative model that uses a
    spike-and-slab like prior distribution obtained by multiplying a
    deterministic binary vector. Such a model allows an EM algorithm,
    optimizing a type-II log-likelihood.
In 2015, The 17 United Nations' Sustainable Development Goals were adopted. 'spiR' is a wrapper of several open datasets published by the Social Progress Imperative (<https://www.socialprogress.org/>), including the Social Progress Index (a synthetic measure of human development across the world). 'spiR''s goal is to provide data to help policymakers and researchers prioritize actions that accelerate social progress across the world in the context of the Sustainable Development Goals. Please cite: Warin, Th. (2019) "spiR: An R Package for the Social Progress Index", <doi:10.6084/m9.figshare.11421573.v2>.
It visualizes data along an Archimedean spiral <https://en.wikipedia.org/wiki/Archimedean_spiral>, makes so-called spiral graph or spiral chart. 
    It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high 
    resolution. 2. It is efficient for time series data to reveal periodic patterns.
Contains an R Markdown template for a clinical trial 
    protocol adhering to the SPIRIT statement. The SPIRIT (Standard Protocol 
    Items for Interventional Trials) statement outlines recommendations for a 
    minimum set of elements to be addressed in a  clinical trial protocol. 
    Also contains functions to create a xml document from the template and 
    upload it to clinicaltrials.gov<https://www.clinicaltrials.gov/> for 
    trial registration.
The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".
This program calculates bioclimatic indices and fluxes (radiation, 
    evapotranspiration, soil moisture) for use in studies of ecosystem function, 
    species distribution, and vegetation dynamics under changing climate 
    scenarios. Predictions are based on a minimum of required inputs: latitude, 
    precipitation, air temperature, and cloudiness. 
    Davis et al. (2017) <doi:10.5194/gmd-10-689-2017>.
'Splash' <https://github.com/scrapinghub/splash> is a 'JavaScript' rendering service.
    It is a lightweight web browser with an 'HTTP' API, implemented in 'Python' using 'Twisted' 
    and 'QT' and provides some of the core functionality of the 'RSelenium' or 'seleniumPipes'
    R packages in a lightweight footprint. Some of 'Splash' features include the ability to process 
    multiple web pages in parallel; retrieving 'HTML' results and/or take screen shots; disabling 
    images or use 'Adblock Plus' rules to make rendering faster; executing custom 'JavaScript' in 
    page context; getting detailed rendering info in 'HAR' format.
The current version of this package estimates spatial autoregressive models for binary dependent variables using GMM estimators. It supports one-step (Pinkse and Slade, 1998) <doi:10.1016/S0304-4076(97)00097-3> and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) <doi:10.1198/073500107000000188>. It also allows for either Probit or Logit model and compute the average marginal effects.
An extension to the individual claim simulator called 'SynthETIC'
    (on CRAN), to simulate the evolution of case estimates of incurred losses
    through the lifetime of an insurance claim. The transactional simulation
    output now comprises key dates, and both claim payments and revisions of
    estimated incurred losses. An initial set of test parameters, designed to
    mirror the experience of a real insurance portfolio, were set up and applied
    by default to generate a realistic test data set of incurred histories (see
    vignette). However, the distributional assumptions used to generate this
    data set can be easily modified by users to match their experiences.
    Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss
    and Incurred Cost Experience Simulator" <arXiv:2109.04058>.
Constructs basis matrix of B-splines, M-splines,
    I-splines, convex splines (C-splines), periodic M-splines,
    natural cubic splines, generalized Bernstein polynomials,
    and their integrals (except C-splines) and derivatives
    of given order by close-form recursive formulas.
    It also contains a C++ head-only library integrated with Rcpp.
    See Wang and Yan (2021) <doi:10.6339/21-JDS1020> for details.
Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) <doi:10.1080/10618600.1999.10474847>. This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories.
Splines are efficiently represented through their Taylor expansion at the knots. The representation accounts for the support sets and is thus suitable for sparse functional data. The B-splines and orthogonal bases of splines that reside on small total support are implemented. The orthogonal bases and are utilized for functional data analysis. Random spline generator is implemented as well as all fundamental algebraic and calculus operations on splines. The optimal, in the least square sense, functional fit to data consisting of sampled values of functions as well as splines build over another set of knots is obtained. Podgórski, K. (2021)  <arXiv:2102.00733>.
Procedure to optimally split a dataset for training and testing. 
    'SPlit' is based on the method of support points, which is independent of modeling methods.
    Please see Joseph and Vakayil (2021) <doi:10.1080/00401706.2021.1921037> for details.
    This work is supported by U.S. National Science Foundation grant DMREF-1921873.
An implementation of the majorization-minimization (MM) algorithm introduced by
    Xu, Chi, Yang, and Lange (2017) <arXiv:1612.05614> for solving multi-set split feasibility problems. In the multi-set split feasibility problem, we seek to find a point x
    in the intersection of multiple closed sets and whose image under a mapping also must fall in the intersection of several closed sets.
Some R functions, such as optim(), require a function
  its gradient passed as separate arguments. When these are
  expensive to calculate it may be much faster to calculate
  the function (fn) and gradient (gr) together since they often share
  many calculations (chain rule). This package allows the user
  to pass in a single function that returns both the function
  and gradient, then splits (hence 'splitfngr') them
  so the results can be accessed
  separately. The functions provided allow this to be done with
  any number of functions/values, not just for functions and gradients.
Functions to compute split generalized linear models. The approach fits 
             generalized linear models that split the covariates into groups. The 
             optimal split of the variables into groups and the regularized estimation 
             of the coefficients are performed by minimizing an objective function 
             that encourages sparsity within each group and diversity among them.
Estimate the internal consistency of your tasks with a permutation based split-half reliability approach.
    Unofficial release name: "Kitten Mittens".
Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires. The 'splithalfr' supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, and the option to sub-sample data.
Split Knockoff is a data adaptive variable selection framework for controlling the
             (directional) false discovery rate (FDR) in structural sparsity, where variable 
             selection on linear transformation of parameters is of concern. This proposed scheme
             relaxes the linear subspace constraint to its neighborhood, often known as variable
             splitting in optimization.
             Simulation experiments can be reproduced following the Vignette. We include data
             (both .mat and .csv format) and application with our method of Alzheimer's Disease 
             study in this package.
             'Split Knockoffs' is first defined in Cao et al. (2021) <arXiv:2103.16159>.
Functions for computing split regularized estimators defined in Christidis, Lakshmanan, 
             Smucler and Zamar (2019) <arXiv:1712.03561>. The approach fits linear regression models that
             split the set of covariates into groups. The optimal split of the variables into groups and the 
             regularized estimation of the regression coefficients are performed by minimizing  an objective 
             function that encourages sparsity within each group and diversity among them. 
             The estimated coefficients are then pooled together to form the final fit.
Functions to generate or sample from all possible splits of features or variables
             into a number of specified groups. Also computes the best split selection estimator 
             (for low-dimensional data) as defined in Christidis, Van Aelst and Zamar (2019) 
             <arXiv:1812.05678>.
Allows to produce and use classification trees with soft (probability) splits,
	as described in: Dvořák, J. (2019), <doi:10.1007/s00180-019-00867-1>.
Online data collection tools like Google Forms often export
    multiple-response questions with data concatenated in cells. The
    concat.split (cSplit) family of functions splits such data into separate 
    cells. The package also includes functions to stack groups of columns and 
    to reshape wide data, even when the data are "unbalanced"---something 
    which reshape (from base R) does not handle, and which melt and dcast from 
    reshape2 do not easily handle.
Fast, lightweight toolkit for data splitting. Data sets can
    be partitioned into disjoint groups (e.g. into training, validation,
    and test) or into (repeated) k-folds for subsequent cross-validation.
    Besides basic splits, the package supports stratified, grouped as well
    as blocked splitting. Furthermore, cross-validation folds for time
    series data can be created. See e.g. Hastie et al. (2001)
    <doi:10.1007/978-0-387-84858-7> for the basic background on data
    partitioning and cross-validation.
ML and GM estimation and diagnostic testing of econometric models for spatial panel data.
Contains functions that fit linear mixed-effects models
        for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection. 
        The details of the algorithm can be found in Luoying Yang PhD thesis (Yang and Wu 2020). The algorithm implementation
        is based on the R package 'lmmlasso'. 
        Reference: Yang L, Wu TT (2020). Model-Based Clustering of Longitudinal Data in High-Dimensionality. Unpublished thesis.
Automates common plotting tasks to ease data exploration.
  Makes density plots (potentially overlaid on histograms),
  scatter plots with prediction lines, or bar or line plots with error bars.
  For each type, y, or x and y variables can be plotted at levels of other variables,
  all with minimal specification.
Provides functions for fitting a sparse
        partial least squares (SPLS) regression and classification
        (Chun and Keles (2010) <doi:10.1111/j.1467-9868.2009.00723.x>).
Currently there are many functions in S-PLUS that are
  missing in R. To facilitate the conversion of S-PLUS packages to R packages,
  this package provides some missing S-PLUS functionality in R.
A collection of classes and methods for working with
  times and dates. The code was originally available in S-PLUS.
A collection of classes and methods for working with time series.
  The code was originally available in S-PLUS.
Introduction to some novel accurate hybrid methods of geostatistical and machine learning methods for spatial predictive modelling. It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method. For details please see: Li, J., Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010) <https:www.ga.gov.au/metadata-gateway/metadata/record/gcat_71407>
  Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) <doi:10.1016/j.csr.2011.05.015>
  Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) <doi:10.1016/j.envsoft.2011.07.004>
  Li, J., Potter, A., Huang, Z. and Heap, A. (2012) <https:www.ga.gov.au/metadata-gateway/metadata/record/74030>.
Installs 'SPM12' to the R library directory and has 
    associated functions for 'fMRI' and general imaging utilities, 
    called through 'MATLAB'.
An updated and extended version of 'spm' package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.
Build custom Europe SpatialPolygonsDataFrame, if you don't know what is
    a SpatialPolygonsDataFrame see SpatialPolygons() in 'sp', by example for mapLayout() in 'antaresViz'. 
    Antares is a powerful software developed by RTE to simulate and study electric power systems 
    (more information about 'Antares' here: <https://antares-simulator.org/>).
A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields.
Implements the method of general semiparametric maximum likelihood estimation for logistic models in case-mother control-mother designs.
Functions for estimating spatial varying coefficient models, mixed models, and other spatial regression models for Gaussian and non-Gaussian data. Moran eigenvectors are used to an approximate Gaussian process modeling which is interpretable in terms of the Moran coefficient. The GP is used for modeling the spatial processes in residuals and regression coefficients. For details see Murakami (2021) <arXiv:1703.04467>.
Identify statistically significant flow clusters using the local spatial network autocorrelation statistic G_ij* 
    proposed by 'Berglund' and 'Karlström' (1999) <doi:10.1007/s101090050013>. 
    The metric, an extended statistic of 'Getis/Ord' G ('Getis' and 'Ord' 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>, 
    detects a group of flows having similar traits in terms of directionality. 
    You provide OD data and the associated polygon to get results 
    with several parameters, some of which are defined by spdep package.
Facilitates the rendering of networks for which nodes have a specific position on a map (cities, participants in a political debate, etc.). Map data and network data are stored together in a single object which handles the match between network nodes and their respective position on the map. The plot method renders both the map and the network data. Several networks can be plot simultaneously. The graphic is highly customisable and the legend is automatically printed. Map data have to be supplied as 'SpatialPolygons' objects (from the 'sp' package) and network data as 'named matrix'.
Launches a Shiny application that allows users to carry out some of the steps that are required to curate both a linear network object based on a road structure and a point pattern that lies on such a network, becoming two previous steps to the performance of a spatial statistics analysis.
Non-negative Matrix Factorization(NMF) is a powerful tool for identifying the key features of microbial communities and a dimension-reduction method. When we are interested in the differences between the structures of two groups of communities, supervised NMF(Yun Cai, Hong Gu and Tobby Kenney (2017),<doi:10.1186/s40168-017-0323-1>) provides a better way to do this, while retaining all the advantages of NMF -- such as interpretability, and being based on a simple biological intuition.
Scale invariant version of the original PNN proposed by Specht (1990) <doi:10.1016/0893-6080(90)90049-q> with the added functionality of allowing for smoothing along multiple dimensions while accounting for covariances within the data set. It is written in the R statistical programming language. Given a data set with categorical variables, we use this algorithm to estimate the probabilities of a new observation vector belonging to a specific category. This type of neural network provides the benefits of fast training time relative to backpropagation and statistical generalization with only a small set of known observations.
Fits univariate Bayesian spatial regression models for large datasets using Nearest Neighbor Gaussian Processes (NNGP) detailed in Finley, Datta, Banerjee (2020) <arXiv:2001.09111>, and Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) <doi:10.1080/10618600.2018.1537924> and Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091>.
A programmatic interface to many species occurrence data sources,
    including Global Biodiversity Information Facility ('GBIF'), 'USGSs'
    Biodiversity Information Serving Our Nation ('BISON'), 'iNaturalist',
    'eBird', Integrated Digitized
    'Biocollections' ('iDigBio'), 'VertNet', Ocean 'Biogeographic' Information
    System ('OBIS'), and Atlas of Living Australia ('ALA'). Includes
    functionality for retrieving species occurrence data, and combining
    those data.
Fits single-species, multi-species, and integrated non-spatial and spatial occupancy models using Markov Chain Monte Carlo (MCMC). Models are fit using Polya-Gamma data augmentation detailed in Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>. Spatial models are fit using either Gaussian processes or Nearest Neighbor Gaussian Processes (NNGP) for large spatial datasets. Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2020) <arXiv:2001.09111>. Provides functionality for data integration of multiple single-species occupancy data sets using a joint likelihood framework. Details on data integration are given in Miller, Pacifici, Sanderlin, and Reich (2019) <doi:10.1111/2041-210X.13110>. Details on single-species and multi-species models are found in MacKenzie, Nichols, Lachman, Droege, Royle, and Langtimm (2002) <doi:10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2> and Dorazio and Royle <doi:10.1198/016214505000000015>, respectively.
SPODT is a spatial partitioning method based on oblique decision trees, in order to classify study area into zones of different risks, determining their boundaries
It can be useful to temporarily hide some text or other HTML elements 
    in 'Shiny' applications. Building on 'Spoiler-Alert.js', it is possible to select the
    elements to hide at startup, to partially reveal them by hovering them, and to 
    completely show them when clicking on them.
Convert text (and text in R objects) to Mocking SpongeBob case 
             <https://knowyourmeme.com/memes/mocking-spongebob> and show them 
             off in fun ways. 
             CoNVErT TexT (AnD TeXt In r ObJeCtS) To MOCkINg SpoNgebOb CAsE
             <https://knowyourmeme.com/memes/mocking-spongebob> aND shOw tHem 
             OFf IN Fun WayS.
Transform a Movie into a Synthetic Picture. A frame every 10 seconds is summarized into one colour, then every generated colors are stacked together.
The 'spork' syntax describes
 label formatting concisely, supporting
 mixed nesting of subscripts and superscripts
 to arbitrary depth. It intends to be easy
 to read and write in plain text, and easy
 to convert to equivalent presentations
 in 'plotmath', 'latex', and 'html'.  Greek symbols
 and a multiplication symbol are explicitly
 supported. See ?as_spork and ?as_previews.
Calculates ratings for two-player or 
  multi-player challenges. Methods included in package such as are able to 
  estimate ratings (players strengths) and their evolution in time, also able to 
  predict output of challenge. Algorithms are based on Bayesian Approximation 
  Method, and they don't involve any matrix inversions nor likelihood estimation. 
  Parameters are updated sequentially, and computation doesn't require any 
  additional RAM to make estimation feasible. Additionally, base of the package 
  is written in C++ what makes sport computation even faster. Methods used in the 
  package refers to Mark E. Glickman (1999) 
  <http://www.glicko.net/research/glicko.pdf>; 
  Mark E. Glickman (2001) <doi:10.1080/02664760120059219>; 
  Ruby C. Weng, Chih-Jen Lin (2011) 
  <http://jmlr.csail.mit.edu/papers/volume12/weng11a/weng11a.pdf>; 
  W. Penny, Stephen J. Roberts (1999) <doi:10.1109/IJCNN.1999.832603>.
The aim of this package is to provide infrastructure for
        sports analysis. Anyway, currently it is a selection of data
        sets, functions to fetch sports data, examples, and demos --
        with the ambition to develop bit by bit a set of classes to
        represent general concepts of sports analysis.
A time series causal inference model for Randomized Controlled Trial (RCT) under spillover effect. 'SPORTSCausal' (Spillover Time Series Causal Inference) separates treatment effect and spillover effect from given responses of experiment group and control group by predicting the response without treatment. It reports both effects by fitting the Bayesian Structural Time Series (BSTS) model based on 'CausalImpact', as described in Brodersen et al. (2015) <doi:10.1214/14-AOAS788>.
Use of Knock Out and Round Robin Techniques in preparing tournament fixtures as discussed in the Book Health and Physical Education by 'Dr. V K Sharma'(2018,ISBN:978-93-5272-134-4).
Create scaled 'ggplot' representations of playing surfaces.
    Playing surfaces are drawn pursuant to rule-book specifications.
    This package should be used as a baseline plot for displaying player
    tracking data.
A set of tools for model-based optimization and tuning of
    algorithms (hyperparameter tuning respectively hyperparameter optimization). It includes surrogate models, optimizers, and design of experiment
    approaches. The main interface is spot, which uses sequentially updated
    surrogate models for the purpose of efficient optimization. The main goal is
    to ease the burden of objective function evaluations, when a single evaluation
    requires a significant amount of resources.
A graphical user interface for the Sequential Parameter Optimization Toolbox (package 'SPOT').
    It includes a quick, graphical setup for spot, interactive 3D plots, export possibilities and more.
Providing convenience functions to connect R with the 'Spotify' application programming interface ('API'). At first it aims to help setting up 
  the OAuth2.0 Authentication flow. The default output of the get_*() functions is tidy, but optionally the functions could return the raw response from the 
  'API' as well. The search_*() and get_*() functions can be combined. See the vignette for more information and examples and the official Spotify for 
  Developers website <https://developer.spotify.com/documentation/web-api/> for information about the Web 'API'.
An R wrapper for pulling data from the 'Spotify' Web API 
  <https://developer.spotify.com/documentation/web-api/> in bulk, or post items on a
  'Spotify' user's playlist.
Implements additional models, simulation tools, and interfaces as extensions to 'SPOT'.
    It provides tools for hyperparameter tuning via 'keras/tensorflow', interfacing 'mlr', for performing Markov chain simulations, 
    and for sensitivity analysis based on sequential bifurcation methods as described in Bettonvil and Kleijnen (1996). 
    Furthermore, additional plotting functions for output from 'SPOT' runs are implemented.
    Bartz-Beielstein T, Lasarczyk C W G, Preuss M (2005) <doi:10.1109/CEC.2005.1554761>.
    Bartz-Beielstein T, Zaefferer M, Rehbach F (2021) <arXiv:1712.04076>.
    Bartz-Beielstein T, Rehbach F, Sen A, Zaefferer M <arXiv:2105.14625>.
    Bettonvil, B, Kleijnen JPC (1996) <doi:10.1016/S0377-2217(96)00156-7>.
An algorithm to cluster satellite hot spot data spatially and temporally.
In spatial data, information of two neighboring units are generally very similar. 
  For spatial sampling, it is therefore more efficient to select samples that are well spread out in space. Often, the interest lies not only in estimating a measure at one point in time, but rather in estimating several points in time to also study the evolution. 
  Three new methods called Orfs (Optimal Rotation with Fixed sample Size), Orsp (Optimal Rotation with Spread sample), and Spot (Spatial and Optimally Temporal Sampling) are implemented in this package. Orfs allows to select temporal samples with fixed size. Orsp selects spatio-temporal samples with random size that are well spread out in space at each point in time. And Spot generates spread sample with fixed sample size at each wave. These methods provide an optimal time rotation of the selected units using the systematic sampling.
Analysis of ChIP-seq and other functional sequencing data [Kharchenko PV (2008) <DOI:10.1038/nbt.1508>].
We propose a novel two-step procedure to combine epidemiological
    data obtained from diverse sources with the aim to quantify risk factors
    affecting the probability that an individual develops certain disease such as
    cancer. See  Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford,
    Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach
    for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical
    Association, 109:505, 11-23,  <doi:10.1080/01621459.2013.870904>.
Testing for Spatial Dependence of Qualitative Data in Cross Section. The list of functions includes join-count tests, Q test, spatial scan test, similarity test and spatial runs test. The methodology of these models can be found in <doi:10.1007/s10109-009-0100-1> and <doi:10.1080/13658816.2011.586327>.
Sparse arrays interpreted as multivariate polynomials.
The notion of spreading activation is a prevalent metaphor in the cognitive sciences. This package provides the tools for cognitive scientists and psychologists to conduct computer simulations that implement spreading activation in a network representation. The algorithmic method implemented in 'spreadr' subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (2011, Frontiers), who viewed activation as a fixed cognitive resource that could spread among nodes that were connected to each other via edges or connections (i.e., a network). See Vitevitch, M. S., Ercal, G., & Adagarla, B. (2011). Simulating retrieval from a highly clustered network: Implications for spoken word recognition. Frontiers in Psychology, 2, 369. <doi:10.3389/fpsyg.2011.00369> and Siew, C. S. Q. (2019). spreadr: A R package to simulate spreading activation in a network. Behavior Research Methods, 51, 910-929. <doi: 10.3758/s13428-018-1186-5>.
The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.
Provides a function for the estimation of parameters 
		in a binary regression with the skew-probit link function. 
		Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented,
		as described in DongHyuk Lee and Samiran Sinha (2019+) <doi:10.1080/00949655.2019.1590579>.
Processing and analysis of field collected or simulated sprinkler 
    system catch data (depths) to characterize irrigation uniformity and efficiency using
    standard and other measures. Standard measures include the Christiansen coefficient
    of uniformity (CU) as found in Christiansen, J.E.(1942, ISBN:0138779295,
    "Irrigation by Sprinkling"); and distribution uniformity (DU), potential
    efficiency of the low quarter (PELQ), and application efficiency of the low quarter (AELQ)
    that are implementations of measures of the same notation in Keller, J. and Merriam, 
    J.L. (1978) "Farm Irrigation System Evaluation: A Guide for Management"
    <https://pdf.usaid.gov/pdf_docs/PNAAG745.pdf>. spreval::DU.lh is similar to spreval::DU
    but is the distribution uniformity of the low half instead of low quarter as in DU.
    spreval::PELQT is a version of spreval::PELQ adapted for traveling systems instead
    of lateral move or solid-set sprinkler systems. The function spreval::eff is
    analogous to the method used to compute application efficiency for furrow irrigation
    presented in Walker, W. and Skogerboe, G.V. (1987,ISBN:0138779295, "Surface
    Irrigation: Theory and Practice"),that uses piecewise integration of infiltrated
    depth compared against soil-moisture deficit (SMD), when the argument "target"
    is set equal to SMD.  The other functions contained in the package provide 
    graphical representation of sprinkler system uniformity, and other standard
    univariate parametric and non-parametric statistical measures as applied to
    sprinkler system catch depths. A sample data set of field test data spreval::catchcan
    (catch depths) is provided and is used in examples and vignettes. Agricultural systems
    emphasized, but this package can be used for landscape irrigation evaluation, and a
    landscape (turf) vignette is included as an example application.
Calculate species richness functions for rarefaction and
    extrapolation.
Recently, regularized variable selection has emerged as a powerful tool to identify and dissect gene-environment interactions. Nevertheless, in longitudinal studies with high dimensional genetic factors, regularization methods for G×E interactions have not been systematically developed. In this package, we provide the implementation of sparse group variable selection, based on both the quadratic inference function (QIF) and generalized estimating equation (GEE), to accommodate the bi-level selection for longitudinal G×E studies with high dimensional genomic features. Alternative methods conducting only the group or individual level selection have also been included. The core modules of the package have been developed in C++.
Computes the extended spring indices (SI-x) and false spring 
    exposure indices (FSEI). The SI-x indices are standard indices used for 
    analysis in spring phenology studies. In addition, the FSEI is also from 
    research on the climatology of false springs and adjusted to include an 
    early and late false spring exposure index. The indices  include the 
    first leaf index, first bloom index, and false spring exposure indices, 
    along with all calculations for all functions needed to calculate 
    each index. The main function returns all indices, but each function can 
    also be run separately. 
    Allstadt et al. (2015) <doi: 10.1088/1748-9326/10/10/104008> 
    Ault et al. (2015) <doi: 10.1016/j.cageo.2015.06.015> 
    Peterson and Abatzoglou (2014) <doi: 10.1002/2014GL059266>
    Schwarz et al. (2006) <doi: 10.1111/j.1365-2486.2005.01097.x> 
    Schwarz et al. (2013) <doi: 10.1002/joc.3625>.
An R data package containing setlists from all Bruce Springsteen concerts over 1973-2021. 
    Also includes all his song details such as lyrics and albums. Data extracted from: 
    <http://brucebase.wikidot.com/>.
An implementation of a computationally efficient method to fit large-scale interaction models based on the reluctant interaction selection principle. The method and its properties are described in greater depth in Yu, G., Bien, J., and Tibshirani, R.J. (2019) "Reluctant interaction modeling", which is available at <arXiv:1907.08414>.
Robust dimension reduction methods for regression and discriminant analysis are implemented that yield estimates with a partial least squares alike interpretability. Partial robust M regression (PRM) is robust to both vertical outliers and leverage points. Sparse partial robust M regression (SPRM) is a related robust method with sparse coefficient estimate, and therefore with intrinsic variable selection. For binary classification related discriminant methods are PRM-DA and SPRM-DA.
R functions to mine sparse models from data.
Perform Wald's Sequential Probability Ratio Test on variables with a Normal, Bernoulli, Exponential and Poisson distribution. Plot acceptance and continuation regions, or create your own with the help of closures.
The seq_ttest() function is the implementation of Abraham
    Wald’s (1947) <doi:10.2134/agronj1947.00021962003900070011x> Sequential Probability Ratio Test (SPRT) for the test of
    a normal mean (difference) with unknown variance in R (R Core Team, 2018).
    It performs sequential t tests developed by Rushton (1950) <doi:10.2307/2332385>, Rushton (1952) <doi:10.2307/2334026> and
    Hajnal (1961) <doi:10.2307/2333131>, based on the SPRT. Specifically, seq_ttest() performs
    one-sample, two-sample, and paired t tests for testing one- and
    two-sided hypotheses.  The test is to be applied to the data during
    the sampling process, ideally after each observation. At any stage, it
    will return a decision to either continue sampling or terminate and
    accept one of the specified hypotheses. For more information on the
    SPRT t test, see Schnuerch & Erdfelder (2019) <doi:10.1037/met0000234>.
Allows for identification of cell sub-populations within tissue samples using
  Bayesian multivariate mixture models with spatial random effects to account for a wide range of 
  spatial gene expression patterns, as described in Allen et. al, 2021 <doi:10.1101/2021.06.23.449615>.
  Bayesian inference is conducted using efficient Gibbs sampling implemented using 'Rcpp'.
Sequential Poisson sampling is a method for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998, ISSN:0282-423X), and generate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012, <doi:10.1111/j.1751-5823.2011.00166.x>).
The systemPipeShiny (SPS) framework comes with many UI and server components. However, installing the whole framework is heavy and takes some time. If you would like to use UI and server components from SPS in your own Shiny apps, do not hesitate to try this package.
Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models.  All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale.
Estimates model parameters of soil hydraulic property functions by inverting measured data. A wide range of hydraulic models, weighting schemes,
    global optimization algorithms, Markov chain Monte Carlo samplers, and extended statistical analyses of results are provided.
	Prediction of soil hydraulic property model parameters and common soil properties using pedotransfer functions is facilitated. 
	Parameter estimation is based on identically and independentally distributed (weighted) model residuals, and simple model selection criteria (Hoege, M., Woehling, T., and Nowak, W. (2018) <doi:10.1002/2017WR021902>) can be calculated.
	The included models are the van Genuchten-Mualem in its unimodal, bimodal and trimodal form, the the Kosugi 2 parametric-Mualem model, and the Fredlund-Xing model.
	All models can be extended to account for non-capillary water storage and conductivity (Weber, T.K.D., Durner, W., Streck, T. and Diamantopoulos, E. (2019) <doi:10.1029/2018WR024584>. 
	The isothermal vapour conductivity (Saito, H., Simunek, J. and Mohanty, B.P. (2006) <doi:10.2136/vzj2006.0007>) is calculated based on 
	volumetric air space and a selection of different tortuosity models: 
	(Grable, A.R., Siemer, E.G. (1968) <doi:10.2136/sssaj1968.03615995003200020011x>, Lai, S.H., Tiedje J.M., Erickson, E. (1976) <doi:10.2136/sssaj1976.03615995004000010006x>,
	Moldrup, P., Olesen, T., Rolston, D.E., and Yamaguchi, T. (1997) <doi:10.1097/00010694-199709000-00004>, Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2004) <doi:10.2136/sssaj2004.7500>, 
	Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2005) <doi:10.1097/01.ss.0000196768.44165.1f>, Millington, R.J., Quirk, J.P. (1961) <doi:10.1039/TF9615701200>,
	Penman, H.L. (1940) <doi:10.1017/S0021859600048164>, and Xu, X, Nieber, J.L. Gupta, S.C. (1992) <doi:10.2136/sssaj1992.03615995005600060014x>).
Program uses method of polynomial of variable degrees to interpolate gridded data preserving monotonicity and/or convexity or none. Method is implemented for univariate and bivariate cases. If values of derivatives are provided, spline will fix them,if not program will estimate them numerically. Package written purely in R.
Provides basic functionality for labeling 
        iso- & anisotropic percolation clusters on 2D & 3D square lattices 
        with various lattice sizes, occupation probabilities, von Neumann & 
        Moore (1,d)-neighborhoods, and random variables weighting 
        the percolation lattice sites.
An implementation of the feature Selection procedure by Partitioning the entire Solution Paths
            (namely SPSP) to identify the relevant features rather than using a single tuning parameter. 
            By utilizing the entire solution paths, this procedure can obtain better selection accuracy than 
            the commonly used approach of selecting only one tuning parameter based on existing criteria, 
            cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) 
            <doi:10.1214/18-EJS1434>). It is more stable and accurate (low false positive and 
            false negative rates) than other variable selection approaches. In addition, it can be flexibly 
            coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized 
            estimators.
A collection of functions to test and estimate Seemingly 
    Unrelated Regression (usually called SUR) models, with spatial structure, by maximum 
    likelihood and three-stage least squares. The package estimates the 
    most common spatial specifications, that is, SUR with Spatial Lag of 
    X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), 
    SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), 
    SUR with Spatial Durbin Error Model (called SUR-SDEM), 
    SUR with Spatial Autoregressive terms and Spatial Autoregressive 
    Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X 
    regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM).
    The methodology of these models can be found in next references
    Mur, J., Lopez, F., and Herrera, M. (2010) <doi:10.1080/17421772.2010.516443> 
    Lopez, F.A., Mur, J., and Angulo, A. (2014) <doi:10.1007/s00168-014-0624-2>.
A set of reliable routines to ease semiparametric survival regression modeling based on Bernstein polynomials. 'spsurv' includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) <arXiv:2003.10548>.
A design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more.
The systemPipeShiny (SPS) framework comes with many useful utility functions. However, installing the whole framework is heavy and takes some time. If you like only a few useful utility functions from SPS, install this package is enough.
A collection of algorithms related to Sierpinski 
  pedal triangle (SPT).
Fits, spatially predicts, and temporally forecasts space-time data using Gaussian Process (GP): (1) spatially varying coefficient process models and (2) spatio-temporal dynamic linear models. Bakar et al., (2016) <doi:10.1080/00949655.2015.1038267>. Bakar et al., (2015) <doi:10.1111/rssc.12103>.
A set of functions that can be used to spatially thin
    species occurrence data. The resulting thinned data can be used in ecological
    modeling, such as ecological niche modeling.
Fits, spatially predicts and temporally forecasts large amounts of space-time data using  [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. Bakar and Sahu (2015) <doi:10.18637/jss.v063.i15>.
Implements semiparametric transformation model two-phase estimation using calibration weights. The method in Fong and Gilbert (2015) Calibration weighted estimation of semiparametric transformation models for two-phase sampling. Statistics in Medicine <DOI:10.1002/sim.6439>.
Performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) <doi:10.1007/s10651-007-0035-y>. The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package.
Uncertainty propagation analysis in spatial environmental  modelling following methodology
         described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951> 
         and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions
         for examining the uncertainty propagation starting from input data and model parameters,
         via the environmental model onto model outputs. The functions include uncertainty model specification,
         stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques.
         Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled.
         Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for.
         The MC realizations may be used as input to the environmental models called from R, or externally.
Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson.  2014.  An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.
A set of tools for the peak filtering of mass spectrometry
  imaging data (MSI or IMS) based on spatial distribution of signal. Given a 
  region-of-interest (ROI), representing the spatial region where the informative
  signal is expected to be localized, a series of filters determine which peak
  signals are characterized by an implausible spatial distribution. The filters
  reduce the dataset dimensionality and increase its information vs noise ratio,
  improving the quality of the unsupervised analysis results, reducing data
  dimensionality and simplifying the chemical interpretation.
Data on the Spy vs. Spy comic strip of Mad magazine, created and
   written by Antonio Prohias.
Methodology: Remove one observation. Training the rest of data that are sampled without replacement and given this observation's input, predict the response back. Replicate this N times and for each response, take a sample from these replicates with replacement. Average each responses of sample and again replicate this step N time for each observation. Approximate these N new responses by using bootstrap method and generate another N responses y'. Training these y' and predict to have N responses of each testing observation. The average of N is the final prediction. Each observation will do the same.
Sparse Quadratic Discriminant Analysis (SQDA) can be performed. In SQDA, the covariance matrix are assumed to be block-diagonal.And, for each block, sparsity assumption is imposed on the covariance matrix. It is useful in high-dimensional setting.
The sqldf() function is typically passed a single argument which 
	is an SQL select statement where the table names are ordinary R data 
	frame names.  sqldf() transparently sets up a database, imports the 
	data frames into that database, performs the SQL select or other
	statement and returns the result using a heuristic to determine which 
	class to assign to each column of the returned data frame.  The sqldf() 
	or read.csv.sql() functions can also be used to read filtered files 
	into R even if the original files are larger than R itself can handle.
	'RSQLite', 'RH2', 'RMySQL' and 'RPostgreSQL' backends are supported.
sqliter helps users, mainly data munging practioneers, to organize
    their sql calls in a clean structure. It simplifies the process of
    extracting and transforming data into useful formats.
A tool for working with 'SQLite' databases. 'SQLite' has some idiosyncrasies and limitations that impose some hurdles to the R developer who is using this database as a repository. For instance, 'SQLite' doesn't have a date type and 'sqliteutils' has some functions to deal with that.
Wrapper for the non-validating 'SQL' parser 'Python' module 'sqlparse' <https://github.com/andialbrecht/sqlparse>. It allows parsing, splitting, and formatting 'SQL' statements.
A rendering tool for parameterized SQL that also translates into
  different SQL dialects.  These dialects include 'Microsoft Sql Server', 'Oracle', 
  'PostgreSql', 'Amazon RedShift', 'Apache Impala', 'IBM Netezza', 'Google BigQuery', 'Microsoft PDW', 'Apache Spark', and 'SQLite'.
Provides utilities for generating SQL queries (particularly CREATE
    TABLE statements) from R model objects. The most important use case is
    generating SQL to score a generalized linear model or related model
    represented as an R object, in which case the package handles parsing
    formula operators and including the model's response function.
This package provides utilities for working with a library of SQL
    files.
Normalization based a subset of negative control probes as
        described in 'Subset quantile normalization using negative
        control features'. Wu Z, Aryee MJ, J Comput Biol. 2010
        Oct;17(10):1385-95 [PMID 20976876].
Facilitates interaction with ODBC data sources, by remembering communication settings and supporting parameterised multi-statement SQL with embedded R.
A subgroup identification method for precision medicine based on 
    quantitative objectives. This method can handle continuous, binary and 
    survival endpoint for both prognostic and predictive case. For the 
    predictive case, the method aims at identifying a subgroup for which 
    treatment is better than control by at least a pre-specified or 
    auto-selected constant. For the prognostic case, the method aims at 
    identifying a subgroup that is at least better than a 
    pre-specified/auto-selected constant. The derived signature is a linear 
    combination of predictors, and the selected subgroup are subjects with 
    the signature > 0. The false discover rate when no true subgroup exists
    is strictly controlled at a user-specified level.
Algorithms for accelerating the convergence of slow,
        monotone sequences from smooth, contraction mapping such as the
        EM algorithm. It can be used to accelerate any smooth, linearly
        convergent acceleration scheme.  A tutorial style introduction
        to this package is available in a vignette on the CRAN download
        page or, when the package is loaded in an R session, with
        vignette("SQUAREM"). Refer to the J Stat Software article: <doi:10.18637/jss.v092.i07>.
Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps.  Lower-level functions  map numeric values to colors, display a matrix as an array of colors, and draw color keys.  Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.
Scrape SquashInfo <http://www.squashinfo.com/> for data on the Professional Squash Association World Tour and other squash events. 'squashinformr' functions scrape, parse, and clean data associated with players, tournaments, and rankings.
Fit linear and logistic regression models penalised with group-adaptive elastic net penalties.
  The group penalties correspond to groups of covariates defined by a co-data group set.
  The method accommodates inclusion of unpenalised covariates and overlapping groups.
  See Van Nee et al. (2021) <arXiv:2101.03875>.
A simulation-based tool made to help researchers to become familiar with
    multilevel variations, and to build up sampling designs for their study. 
    This tool has two main objectives: First, it provides an educational tool useful for students, 
    teachers and researchers who want to learn to use mixed-effects models. 
    Users can experience how the mixed-effects model framework can be used to understand 
    distinct biological phenomena by interactively exploring simulated multilevel data. 
    Second, it offers research opportunities to those who are already familiar with 
    mixed-effects models, as it enables the generation of data sets that users may download 
    and use for a range of simulation-based statistical analyses such as power 
    and sensitivity analysis of multilevel and multivariate data [Allegue, H., Araya-Ajoy, Y.G., Dingemanse, 
    N.J., Dochtermann N.A., Garamszegi, L.Z., Nakagawa, S., Reale, D., Schielzeth, H. and Westneat, D.F. (2016) 
    <doi: 10.1111/2041-210X.12659>].
Artificial selection through selective breeding is an efficient way to induce changes in traits of interest in experimental populations. This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models.
Connecting to databases requires boilerplate code to specify
    connection parameters and to set up sessions properly with the DBMS.
    This package provides a simple tool to fill two purposes: abstracting
    connection details, including secret credentials, out of your source
    code and managing configuration for frequently-used database connections
    in a persistent and flexible way, while minimizing requirements on the
    runtime environment.
Implementation of the SRCS method for a color-based visualization of the
    results of multiple pairwise tests on a large number of problem configurations, proposed in: 
    I.G. del Amo, D.A. Pelta. SRCS: a technique for comparing multiple algorithms under several
    factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry
    (Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in
    Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012.
Sparse redundancy analysis
	for high dimensional (biomedical) data. Directional multivariate analysis 
	to express the maximum variance in the predicted data set by a linear 
	combination of variables of the predictive data set. Implemented in a 
	partial least squares framework, for more details see Csala et al. (2017) <doi:10.1093/bioinformatics/btx374>.
Provides you with easy, programmatic access to SRDP data.
It is a framework to fit semiparametric regression estimators for the total parameter of a finite population
    when the interest variable is asymmetric distributed. The main references for this package are: Sarndal C.E., Swensson B., and Wretman J. (2003,ISBN: 978-0-387-40620-6, "Model Assisted Survey Sampling." Springer-Verlag) 
    and Cardozo C.A and Alonso-Malaver C.E. (2021). "Semi-parametric model assisted estimation in finite populations." In preparation.
Provides functionality for structural equation modeling for
    the social relations model (Kenny & La Voie, 1984;
    <doi:10.1016/S0065-2601(08)60144-6>; Warner, Kenny, & Soto, 1979,
    <doi:10.1037/0022-3514.37.10.1742>). Maximum likelihood
    estimation (Gill & Swartz, 2001, <doi:10.2307/3316080>;
    Nestler, 2018, <doi:10.3102/1076998617741106>) and
    least squares estimation is supported (Bond & Malloy, 2018,
    <doi:10.1016/B978-0-12-811967-9.00014-X>).
The most important function of the R package is the genetic effects analysis of small RNA in hybrid plants via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the small RNA in progeny with the expression level in the parent species.
Performs the change-point detection in regression coefficients of linear model 
    by partitioning the regression coefficients into two classes of smoothness. The change-point and
    the regression coefficients are jointly estimated.
Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint  (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional  delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. See Beule & Karlovsky (2020) <doi:10.7717/peerj.9593> for details.
Read 'SubRip'
    <https://sourceforge.net/projects/subrip/> subtitle files as data
    frames for easy text analysis or manipulation.  Easily shift numeric
    timings and export subtitles back into valid 'SubRip' timestamp format
    to sync subtitles and audio.
Srt file is a common subtitle format for videos, it contains subtitle and when the subtitle showed.
    This package is for align time of srt file, and also change color, style and position of subtitle in videos,
	the srt file will be read as a vector into R, and can be write into srt file after modified using this package.
Use piping, verbs like 'group_by' and 'summarize', and other
    'dplyr' inspired syntactic style when calculating summary statistics on survey
    data using functions from the 'survey' package.
Develops a framework for fisheries stock assessment simulation
    testing with Stock Synthesis (SS) as described in Anderson et al.
    (2014) <doi:10.1371/journal.pone.0092725>.
Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2021); Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).
A set of functions to calculate sample size for two-sample difference in means tests. Does adjustments for either nonadherence or variability that comes from using data to estimate parameters.
Functions used by other packages from Statistics Norway are gathered. General data manipulation functions, and functions for hierarchical computations are included (Langsrud, 2020) <doi:10.13140/RG.2.2.27313.61283>. The hierarchy specification functions are useful within statistical disclosure control.
Provides a collection of self-labeled techniques for semi-supervised 
    classification. In semi-supervised classification, both labeled and unlabeled
    data are used to train a classifier. This learning paradigm has obtained promising
    results, specifically in the presence of a reduced set of labeled examples. 
    This package implements a collection of self-labeled techniques to construct a
    classification model. This family of techniques enlarges the original labeled set 
	using the most confident predictions to classify unlabeled data. The techniques 
	implemented can be applied to classification problems in several domains by the 
	specification of a supervised base classifier. At low ratios of labeled data, it 
	can be shown to perform better than classical supervised classifiers.
Provides the spatial sign correlation and the two-stage spatial sign correlation as well as a one-sample test for the correlation coefficient.
ssd calculates the sample size needed to detect the differences between two sets of unordered categorical data.
Reference data sets of species sensitivities to compare the 
    results of fitting species sensitivity distributions 
    using software such as 'ssdtools' and 'Burrlioz'. 
    It consists of 17 primary data sets from
    four different Australian and Canadian organizations as well as five
    datasets from anonymous sources. It also includes a data set of the
    results of fitting various distributions using different software.
Functions to visually and statistically analyze single system data.
Toolbox for learning a dictionary from large-scale data collection
  using the Sketched Stochastic Dictionary Learning method (see Permiakova O, Burger T. 
 "Sketched Stochastic Dictionary Learning for large-scale data and application to large-scale 
 mass spectrometry data", 2021). It includes the routines for the dictionary initialization.
Allows to map species richness and endemism based on stacked
    species distribution models (SSDM). Individuals SDMs can be created using a
    single or multiple algorithms (ensemble SDMs). For each species, an SDM can
    yield a habitat suitability map, a binary map, a between-algorithm variance
    map, and can assess variable importance, algorithm accuracy, and between-
    algorithm correlation. Methods to stack individual SDMs include summing
    individual probabilities and thresholding then summing. Thresholding can be
    based on a specific evaluation metric or by drawing repeatedly from a Bernoulli
    distribution. The SSDM package also provides a user-friendly interface.
Performs structured OLS (sOLS) and structured SIR (sSIR).
Species sensitivity distributions are cumulative probability
    distributions which are fitted to toxicity concentrations for
    different species as described by Posthuma et al.(2001)
    <isbn:9781566705783>.  The ssdtools package uses Maximum Likelihood to
    fit distributions such as the gamma, log-logistic, log-normal and
    Weibull to censored and/or weighted data.  Multiple distributions can
    be averaged using Akaike Information Criteria.  Confidence intervals
    on hazard concentrations and proportions are produced by parametric
    bootstrapping.
Provides functions to evaluate user-defined power functions for a parameter range, and draws a sensitivity plot. It also provides a resampling procedure for semi-parametric sample size estimation and methods for adding information to a Sweave report.
Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided.
Spatial Stochastic Frontier Analysis (SSFA) is an original method for controlling the spatial heterogeneity in Stochastic Frontier Analysis (SFA) models, for cross-sectional data, by splitting the inefficiency term into three terms: the first one related to spatial peculiarities of the territory in which each single unit operates, the second one related to the specific production features and the third one representing the error term.
Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods.
Bayesian estimation for undirected graphical models using spike-and-slab priors. The package handles continuous, discrete, and mixed data.
Connect to a remote server over SSH to transfer files via SCP, 
    setup a secure tunnel, or run a command or script on the host while 
    streaming stdout and stderr directly to the client.
This package provides utility functions for system command
    execution, both locally and remotely using ssh/scp. The command
    output is captured and provided to the caller. This functionality is
    intended to streamline calling shell commands from R, retrieving and
    using their output, while instrumenting the calls with appropriate
    error handling. NOTE: this first version is limited to unix with local
    and remote systems running bash as the default shell.
Processes amino acid alignments produced by the 'IPD-IMGT/HLA (Immuno Polymorphism-ImMunoGeneTics/Human Leukocyte Antigen) Database' to identify user-defined amino acid residue motifs shared across HLA alleles, and calculates the frequencies of those motifs based on HLA allele frequency data. 'SSHAARP' (Searching Shared HLA Amino Acid Residue Prevalence) uses 'Generic Mapping Tools (GMT)' software and the 'GMT' R package to generate global frequency heat maps that illustrate the distribution of each user-defined map around the globe. 'SSHAARP' analyzes the allele frequency data described by Solberg et al. (2008) <doi:10.1016/j.humimm.2008.05.001>, a global set of 497 population samples from 185 published datasets, representing 66,800 individuals total.
Estimation of an S-shaped function and its corresponding inflection point via a least squares approach. A sequential mixed primal-dual based algorithm is implemented for the fast computation. Details can be found in Feng et al. (2021)  <arXiv:2107.07257>.
Convenient tools for exchanging files securely from within R. By
    encrypting the content safe passage of files (shipment) can be provided by
    common but insecure carriers such as ftp and email. Based on asymmetric
    cryptography no management of shared secrets is needed to make a secure
    shipment as long as authentic public keys are available. Public keys used
    for secure shipments may also be obtained from external providers as part of
    the overall process. Transportation of files will require that relevant
    services such as ftp and email servers are available.
Parse Standard Schedules Information file (types 2 and 3) into a Data Frame.
    Can also expand schedules into flights.
This package contains a set of functions that calculates 
 appropriate sample sizes for one-sample t-tests, two-sample t-tests, 
 and F-tests for microarray experiments based on desired power while 
 controlling for false discovery rates. For all tests, the standard
 deviations (variances) among genes can be assumed fixed or random. 
 This is also true for effect sizes among genes in one-sample and two
 sample experiments. Functions also output a chart of power versus sample
 size, a table of power at different sample sizes, and a table of critical
 test values at different sample sizes.
We propose a procedure for sample size calculation while
    controlling false discovery rate for RNA-seq experimental design. Our
    procedure depends on the Voom method proposed for RNA-seq data analysis
    by Law et al. (2014) <DOI:10.1186/gb-2014-15-2-r29> and the sample size 
    calculation method proposed for microarray experiments by Liu and Hwang 
    (2007) <DOI:10.1093/bioinformatics/btl664>. We develop a set of functions
    that calculates appropriate sample sizes for two-sample t-test for RNA-seq
    experiments with fixed or varied set of parameters. The outputs also contain a
    plot of power versus sample size, a table of power at different sample sizes,
    and a table of critical test values at different sample sizes. 
    To install this package, please use 
    'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'. 
    For R version 3.5 or greater, please use  
    'if(!requireNamespace("BiocManager", quietly = TRUE)){install.packages("BiocManager")}; BiocManager::install("ssizeRNA")'.
Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO of Rockova and George (2018) <doi:10.1080/01621459.2016.1260469>.
Providing a collection of techniques for semi-supervised 
    classification, regression and clustering. In semi-supervised problem, both labeled and unlabeled
    data are used to train a classifier. The package includes a collection of 
    semi-supervised learning techniques: self-training, co-training, democratic, 
    decision tree, random forest, 'S3VM' ... etc, with a fairly intuitive interface 
    that is easy to use.
Creates an S4 class "SSM" and defines functions for fitting smooth
    supersaturated models, a polynomial model with spline-like behaviour.
    Functions are defined for the computation of Sobol indices for sensitivity
    analysis and plotting the main effects using FANOVA methods. It also
    implements the estimation of the SSM metamodel error using a GP model with
    a variety of defined correlation functions.
Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.
Estimates previously compiled state-space modeling for mouse-tracking experiments using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation.
Package provides a set of tools for robust estimation and inference for models with sample selectivity and endogenous treatment model. For details, see Zhelonkin and Ronchetti (2021) <doi:10.18637/jss.v099.i04>.
It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) <doi:10.1007/s00180-016-0691-1>.
Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., 2010. <DOI:10.1198/jasa.2009.ap08248>.) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood.  Mapping and other graphical functions are included.
Fits Bayesian spatio-temporal models and makes predictions on stream networks using the approach by Santos-Fernandez, Edgar, et al. (2021)."Bayesian spatio-temporal models for stream networks" <arXiv:2103.03538>. In these models, spatial dependence is captured using stream distance and flow connectivity, while temporal autocorrelation is modelled using vector autoregression methods.
Soft-margin support vector machines (SVMs) are a common class of classification models. The training of SVMs usually requires that the data be available all at once in a single batch, however the Stochastic majorization-minimization (SMM) algorithm framework allows for the training of SVMs on streamed data instead Nguyen, Jones & McLachlan(2018)<doi:10.1007/s42081-018-0001-y>. This package utilizes the SMM framework to provide functions for training SVMs with hinge loss, squared-hinge loss, and logistic loss.
Simulation-based sampling protocol (SSP) is an R package design to estimate sampling effort in studies of
    ecological communities based on the definition of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon, 2015) <doi:10.1111/ele.12385> and simulation
    of ecological data. The theoretical background is described in Guerra-Castro et al. (2020) <doi:10.1101/2020.03.19.996991>.
R package for computing the spherical smoothing splines
Estimate the size of a networked population based on
    respondent-driven sampling data. The package is part of the "RDS Analyst"
    suite of packages for the analysis of respondent-driven sampling data.
    See Handcock, Gile and Mar (2014) <doi:10.1214/14-EJS923> and
    Handcock, Gile and Mar (2015) <doi:10.1111/biom.12255>.
An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) <doi:10.1007/978-3-642-04274-4_13>. Users can define which set of regressors to use as base models from the 'caret' package, other packages, or custom functions.
Takeya Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA)
             for polytomous items for examining whether each pair of items has a sequential or equal
             relation. Package includes functions for generating a sequential relation table and a
             treegram to visualize sequential or equal relations between pairs of items.
A set of functions for two-dimensional sociometric status
    determination with rating scales. For each person assessed, SSrat computes
    probability distributions of the total scores for `Sympathy' (S),
    `Antipathy' (A), social `Preference' (P) and social `Impact' (I), and
    applies a set of criteria for sociometric status categorization.
Provides the necessary sample size for a longitudinal study with binary outcome in order to attain a pre-specified power while strictly maintaining the Type I error rate. 
 Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ, Bhaumik D (2014) <doi:10.1002/sim.6203>.
Calculates the power and sample size based on the difference in Restricted Mean Survival Time.
Implement the algorithm provided in scan for estimating the 
  transmission route on railway network using passenger volume. It is a 
  generalization of the scan statistic approach for railway network to identify
  the hot railway route for transmitting infectious diseases.
This is a collection of functions to calculate stop-signal reaction time (SSRT). Includes functions for both "integration" and "mean" methods; both fixed and adaptive stop-signal delays are supported (see appropriate functions). Calculation is based on Verbruggen et al. (2019) <doi:10.7554/eLife.46323.001> and Verbruggen et al. (2013) <doi:10.1177/0956797612457390>.
Tools to import survey files in the '.sss' (triple-s) format. 
    The package provides the function 'read.sss()' that reads the '.asc' 
    (or '.csv') and '.sss' files of a triple-s survey data file. 
    See also <https://www.triple-s.org/>.
Imports Variant Calling Format file into R. It can detect
             whether a sample contains contaminant from the same species.
             In the first stage of the approach, a change-point detection
             method is used to identify copy number variations for filtering.
             Next, features are extracted from the data for a support vector
             machine model. For log-likelihood calculation, the deviation
             parameter is estimated by maximum likelihood method. Using
             a radial basis function kernel support vector machine, the
             contamination of a sample can be detected.
Generates and predicts a set of linearly stacked Random Forest models using bootstrap sampling. Individual datasets may be heterogeneous (not all samples have full sets of features). Contains support for parallelization but the user should register their cores before running. This is an extension of the method found in Matlock (2018) <doi:10.1186/s12859-018-2060-2>.
Framework for the implementation of solvency related computations based on 
             standard models for the Swiss Solvency Test (SST), a risk-based capital standard for Swiss 
             insurance companies. Allows Monte Carlo simulation of market risk, some insurance risks and 
             their aggregation. Additional toolbox for preprocessing computations. Convenient 'shiny' GUI 
             combined with a parser for an input 'excel' (.xlsx) template to simplify model configuration, 
             data fill-in and results visualization.
Fast iterative thresholding sparse SVD, together with an initialization algorithm
Functions for performing stochastic search variable selection (SSVS) 
    for binary and continuous outcomes and visualizing the results. 
    SSVS is a Bayesian variable selection method used to estimate the probability 
    that individual predictors should be included in a regression model. 
    Using MCMC estimation, the method samples thousands of regression models 
    in order to characterize the model uncertainty regarding both the predictor 
    set and the regression parameters.
Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions.
Implements the "shrinkage t" statistic 
   introduced in Opgen-Rhein and Strimmer (2007) <DOI:10.2202/1544-6115.1252> and 
   a shrinkage estimate of the "correlation-adjusted t-score" (CAT score) described 
   in Zuber and Strimmer (2009) <DOI:10.1093/bioinformatics/btp460>.  
   It also offers a convenient interface  to a number of other regularized 
   t-statistics commonly employed in high-dimensional case-control studies.
Efficiently estimate shape parameters of periodic time series 
    imagery (raster stacks) with which  a statistical seasonal trend analysis (STA) 
    is subsequently performed. STA output can be exported in conventional raster formats. 
    Methods to visualize STA output are also implemented as well as the calculation 
    of additional basic statistics. STA is based on (R. Eastman, F. Sangermano, 
    B. Ghimire, H. Zhu, H. Chen, N. Neeti, Y. Cai, E. Machado and S. Crema, 2009) <doi:10.1080/01431160902755338>.
A stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) <doi:10.1038/s41598-020-79317-8>, Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.
Functionalities to perform Stability Analysis of Genotype by Environment Interaction (GEI) to identify superior and stable genotypes under diverse environments. It performs Eberhart & Russel's ANOVA (1966) (<doi:10.2135/cropsci1966.0011183X000600010011x>), Finlay and Wilkinson (1963) Joint Linear Regression (<doi:10.1071/AR9630742>), Wricke (1962, 1964) Ecovalence, Shukla's stability variance parameter (1972) (<doi:10.1038/hdy.1972.87>)  and Kang's (1991) (<doi:10.2134/agronj1991.00021962008300010037x>) simultaneous selection for high yielding and stable parameter.
Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2019) <arXiv:1911.01850>. The procedure uses averaging to estimate a regression of a set of predictors X on a response variable Y by enforcing stability with respect to a given environment variable. The resulting regression leads to a variable selection procedure which allows to distinguish between stable and unstable predictors. The package further implements a visualization technique which illustrates the trade-off between stability and predictiveness of individual predictors.
Density, distribution, quantile and hazard functions of a
    stable variate; generalized regression models for the parameters
    of a stable distribution. See the README for how to make equivalent calls
    to those of 'stabledist' (i.e., Nolan's 0-parameterization and 
    1-parameterization as detailed in Nolan (2020)). 
    See github for Lambert and Lindsey 1999 JRSS-C journal article, 
    which details the parameterization of the Buck (1995) stable.   
    See the Details section of the `?dstable` help file for context and 
    references.
Density, Probability and Quantile functions, and random number
  generation for (skew) stable distributions, using the parametrizations of
  Nolan.
Estimate the four parameters of stable laws using maximum
             likelihood method, generalised method of moments with
             finite and continuum number of points, iterative
             Koutrouvelis regression and Kogon-McCulloch method.  The
             asymptotic properties of the estimators (covariance
             matrix, confidence intervals) are also provided.
Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC) samplers to sample from a posterior distribution. This package determines whether the MCMC sample is large enough   to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected  and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and  Knudson (2018) <arXiv:1812:09384>.
Graphical and computational methods that can be used to assess the 
  stability of results from supervised statistical learning.
An exploratory and heuristic approach for specification search in
    Structural Equation Modeling. The basic idea is to subsample the original data
    and then search for optimal models on each subset. Optimality is defined through
    two objectives: model fit and parsimony. As these objectives are conflicting,
    we apply a multi-objective optimization methods, specifically NSGA-II, to obtain
    optimal models for the whole range of model complexities. From these optimal
    models, we consider only the relevant model specifications (structures), i.e.,
    those that are both stable (occur frequently) and parsimonious and use those to
    infer a causal model.
An implementation of many measures for the
    assessment of the stability of feature selection. Both simple measures
    and measures which take into account the similarities between features
    are available, see Bommert (2020) <doi:10.17877/DE290R-21906>.
Efficient regression for heavy-tailed and skewed data following a stable distribution. Generalized regression where the skewness and tail parameter of residuals are dependent on regressors is also available. Includes fast calculation of stable densities. Calculation of densities is based on efficient numerical methods from Ament and O'Neil (2017) <doi:10.1007/s11222-017-9725-y>. Parts of the code have been ported to C from Ament's 'Matlab' code available at <https://gitlab.com/s_ament/qastable>.
Resampling procedures to assess the stability of selected variables
    with additional finite sample error control for high-dimensional variable
    selection procedures such as Lasso or boosting. Both, standard stability
    selection (Meinshausen & Buhlmann, 2010, <doi:10.1111/j.1467-9868.2010.00740.x>) 
    and complementary pairs stability selection with improved error bounds 
    (Shah & Samworth, 2013, <doi:10.1111/j.1467-9868.2011.01034.x>) are
    implemented. The package can be combined with arbitrary user specified
    variable selection approaches.
Provides methods for inference using stacked multiple 
    imputations augmented with weights. The vignette provides example R code for 
    implementation in general multiple imputation settings. For additional details 
    about the estimation algorithm, we refer the reader to Beesley, Lauren J and 
    Taylor, Jeremy M G (2020) “A stacked approach for chained equations multiple 
    imputation incorporating the substantive model” <doi:10.1111/biom.13372>, 
    and Beesley, Lauren J and Taylor, Jeremy M G (2021) “Accounting for not-at-random 
    missingness through imputation stacking” <arXiv:2101.07954>.
Helper functions collected from StackOverflow.com, a
    question and answer site for professional and enthusiast programmers.
Model stacking is an ensemble technique
    that involves training a model to combine the outputs of many 
    diverse statistical models, and has been shown to improve 
    predictive performance in a variety of settings. 'stacks' 
    implements a grammar for 'tidymodels'-aligned model stacking.
S4 class wrappers for the 'ODBC' and Pool DBI connection, also provides some 
    utilities to paste small datasets to clipboard, rename columns. It is used by the package 'stacomiR' for
    connections to the database. Development versions of 'stacomiR' are available in R-forge.
Creates and fits staged event tree probability models, 
             which are probabilistic graphical models capable of representing 
             asymmetric conditional independence statements 
             for categorical variables. 
             Includes functions to create, plot and fit staged 
             event trees from data, as well as many efficient structure 
             learning algorithms.
             References:
             Collazo R. A., Görgen C. and Smith J. Q. 
             (2018, ISBN:9781498729604).
             Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. (2018) 
             <arXiv:1705.09457>.
             Thwaites P. A., Smith, J. Q. (2017) <arXiv:1510.00186>.
             Barclay L. M., Hutton J. L. and Smith J. Q. (2013) 
             <doi:10.1016/j.ijar.2013.05.006>.
             Smith J. Q. and Anderson P. E. (2008) 
             <doi:10.1016/j.artint.2007.05.004>.
Provides facilities to implement and run population models of
    stage-structured species...
Efficiently estimates treatment effects in settings with randomized staggered rollouts, using tools 
    proposed by Roth and Sant'Anna (2021) <arXiv:2102.01291>.
Proposes an original instrument for measuring stakeholder influence on the development of an infrastructure project that is carried through by a municipality, drawing on stakeholder classifications (Mitchell, Agle, & Wood, 1997) and input-output modelling (Hester & Adams, 2013). Mitchell R., Agle B.R., & Wood D.J. <doi:10.2307/259247> Hester, P.T., & Adams, K.M. (2013) <doi:10.1016/j.procs.2013.09.282>.
Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation
 Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single
 ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values
 around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to  
 utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects 
 allowing integration with other packages such adegenet.
 Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. <doi:10.1111/1755-0998.12129> for the appropriate citation and user manual. Thank you in advance.
Perform spatial temporal analysis of moving polygons; a
    longstanding analysis problem in Geographic Information Systems. Facilitates
    directional analysis, shape analysis, and some other simple functionality for
    examining spatial-temporal patterns of moving polygons.
Fits semiparametric linear and multilevel models with non-parametric additive Bayesian additive regression tree (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) components and Stan (Stan Development Team (2021) <https://mc-stan.org/>) sampled parametric ones. Multilevel models can be expressed using 'lme4' syntax (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).
Provides functions for the analysis of
        occupational and environmental data with non-detects. Maximum
        likelihood (ML) methods for censored log-normal data and
        non-parametric methods based on the product limit estimate (PLE)
        for left censored data are used to calculate all of the
        statistics recommended by the American Industrial Hygiene
        Association (AIHA) for the complete data case. Functions for
        the analysis of complete samples using exact methods are also
        provided for the lognormal model. Revised from 2007-11-05
        'survfit~1'.
Tools which allow regression variables to be placed on similar
    scales, offering computational benefits as well as easing interpretation of
    regression output.
Standardizes text according to a template; particularly
        useful for country names.
The <http://standartox.uni-landau.de> database offers cleaned,
    harmonized and aggregated ecotoxicological test data, which can
    be used for assessing effects and risks of chemical concentrations
    found in the environment.
The C++ header files of the Stan project are provided by this package, but it contains little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is primarily useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.
Implementation of popular mortality models using the 'rstan' 
    package, which provides the R interface to the 'Stan' C++ library for 
    Bayesian estimation. The package supports well-known models proposed in the 
    actuarial and demographic literature including the Lee-Carter (1992) 
    <doi:10.1080/01621459.1992.10475265> and the Cairns-Blake-Dowd (2006) 
    <doi:10.1111/j.1539-6975.2006.00195.x> models. By a simple call, the user 
    inputs deaths and exposures and the package outputs the MCMC simulations for
    each parameter, the log likelihoods and predictions. Moreover, the package 
    includes tools for model selection and Bayesian model averaging by leave 
    future-out validation.
An implementation of Simultaneous Truth and 
    Performance Level Estimation (STAPLE) <doi:10.1109/TMI.2004.828354>.  This
    method is used when there are multiple raters for an object, typically an
    image, and this method fuses these ratings into one rating.  It uses an
    expectation-maximization method to estimate this rating and the individual
    specificity/sensitivity for each rater.
Provides function to manipulate PDF files: 
    fill out PDF forms;
    merge multiple PDF files into one; 
    remove selected pages from a file;
    rename multiple files in a directory;
    rotate entire pdf document; 
    rotate selected pages of a pdf file;
    Select pages from a file;
    splits single input PDF document into individual pages;
    splits single input PDF document into parts from given points.
    'staplr' requires Java 8 installed on your system.
Functions to analyze neuronal spike trains from a single
        neuron or from several neurons recorded simultaneously.
This is a user-friendly way to run a parallel factor (PARAFAC) analysis (Harshman, 1971) <doi:10.1121/1.1977523> on excitation emission matrix (EEM) data from dissolved organic matter (DOM) samples (Murphy et al., 2013) <doi:10.1039/c3ay41160e>. The analysis includes profound methods for model validation. Some additional functions allow the calculation of absorbance slope parameters and create beautiful plots.
Produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold 
    regression analysis results from several models side-by-side, as well as summary
    statistics.
Estimates the coefficients of the two-time centered autologistic regression model based on Gegout-Petit A., Guerin-Dubrana L., Li S. "A new centered spatio-temporal autologistic regression model. Application to local spread of plant diseases." 2019. <arXiv:1811.06782>, using a grid of binary variables to estimate the spread of a disease on the grid over the years.
Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.
Implements stacked elastic net regression (Rauschenberger 2020, <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005, <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).
Reading, manipulating, writing and plotting
    spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL'
    bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'.
Data in multidimensional systems is obtained from 
    operational systems and is transformed to adapt it to the new 
    structure. Frequently, the operations to be performed aim to 
    transform a flat table into a star schema. Transformations can 
    be carried out using professional ETL (extract, transform and 
    load) tools or tools intended for data transformation for end 
    users. With the tools mentioned, this transformation can be 
    carried out, but it requires a lot of work. The main objective 
    this package is to define transformations that allow obtaining 
    star schemas from flat tables easily. In addition, it includes 
    basic data cleaning, dimension enrichment, incremental data 
    refresh and query operations, adapted to this context.
Miscellaneous functions for working with 'stars' objects, mainly single-band rasters. Currently includes functions for: (1) focal filtering, (2) detrending of Digital Elevation Models, (3) calculating flow length, (4) calculating the Convergence Index, (5) calculating topographic aspect and topographic slope.
Makes it possible to serve map tiles for web maps (e.g. leaflet) based on a function or a stars object without having to render them in advance. This enables parallelization of the rendering, separating the data source and visualization location and to provide web services.
The package is used for calibrating the design parameters for single-to-double arm transition design proposed by Shi and Yin (2017). The calibration is performed via numerical enumeration to find the optimal design that satisfies the constraints on the type I and II error rates.
Get started with new projects by dropping a skeleton of a new
    project into a new or existing directory, initialise git repositories,
    and create reproducible environments with the 'renv' package. The
    package allows for dynamically named files, folders, file content, as
    well as the functionality to drop individual template files into
    existing projects.
Tool to automatically fetch, transform and arrange subsets of
    multi- dimensional data sets (collections of files) stored in local and/or
    remote file systems or servers, using multicore capabilities where possible.
    The tool provides an interface to perceive a collection of data sets as a single
    large multidimensional data array, and enables the user to request for automatic
    retrieval, processing and arrangement of subsets of the large array. Wrapper
    functions to add support for custom file formats can be plugged in/out, making
    the tool suitable for any research field where large multidimensional data sets
    are involved.
Contains functions for estimating the STARTS model of
    Kenny and Zautra (1995, 2001) <DOI:10.1037/0022-006X.63.1.52>,
    <DOI:10.1037/10409-008>. Penalized maximum likelihood
    estimation and Markov Chain Monte Carlo estimation are
    also provided, see Luedtke, Robitzsch and Wagner (2018) 
    <DOI:10.1037/met0000155>.
Adds support for R startup configuration via '.Renviron.d' and '.Rprofile.d' directories in addition to '.Renviron' and '.Rprofile' files.  This makes it possible to keep private / secret environment variables separate from other environment variables.  It also makes it easier to share specific startup settings by simply copying a file to a directory.
Provides utilities to create or suppress start-up messages.
Allows the user to estimate a vector logistic smooth transition autoregressive model via maximum log-likelihood or nonlinear least squares. It further permits to test for linearity in the multivariate framework against a vector logistic smooth transition autoregressive model with a single transition variable. The estimation method is discussed in Terasvirta and Yang (2014, <doi:10.1108/S0731-9053(2013)0000031008>). Also, realized covariances can be constructed from stock market prices or returns, as explained in Andersen et al. (2001, <doi:10.1016/S0304-405X(01)00055-1>).
Performance analysis workflow that combines the power of the R
    language (and the tidyverse realm) and many auxiliary tools to
    provide a consistent, flexible, extensible, fast, and versatile
    framework for the performance analysis of task-based applications
    that run on top of the StarPU runtime (with its MPI (Message
    Passing Interface) layer for multi-node support).  Its goal is to
    provide a fruitful prototypical environment to conduct performance
    analysis hypothesis-checking for task-based applications that run
    on heterogeneous (multi-GPU, multi-core) multi-node HPC
    (High-performance computing) platforms.
Provides data about the 'Star Wars' movie franchise
    in a set of relational tables or as a complete 'DuckDB' database. All
    data was collected from the open source 'Star Wars' API
    <https://swapi.dev/>.
An interactive document on  the topic of basic statistical analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/StatisticsPrimer/>.
Highest Density Regions are the smallest set in the support of a
    probability distribution with the specified coverage probability. 'HDRs'
    may contain disjoint intervals, but can be calculated efficiently using
    iterative methods. One can similarly construct optimal (i.e., shortest)
    confidence intervals for some basic inferential problems, including for
    population means, variances, or proportion parameters.
Datasets for the textbook Stat2: Modeling with Regression and ANOVA (second edition). 
    The package also includes data for the first edition, Stat2: Building Models for a World of Data
    and a few functions for plotting diagnostics.
A set of tools inspired by 'Stata' to explore data.frames ('summarize',
    'tabulate', 'xtile', 'pctile', 'binscatter', elapsed quarters/month, lead/lag).
Basic statistical methods with some modifications for the course Statistical Methods at Federal University of Bahia (Brazil). All methods in this packages are explained in the text book of Montgomery and Runger (2010) <ISBN: 978-1-119-74635-5>.
An easy connection with R to Statistics Canada's Web Data Service. Open economic data (formerly known as CANSIM tables, now identified by Product IDs (PID)) are accessible as a data frame, directly in the user's R environment.
    Warin, Le Duc (2019) <doi:10.6084/m9.figshare.10544735>.
Extract statistics from articles and recompute p values.
An implementation of local and global statistical complexity measures (aka Information Theory Quantifiers, ITQ) for time series analysis based on ordinal statistics (Bandt and Pompe (2002) <DOI:10.1103/PhysRevLett.88.174102>). Several distance measures that operate on ordinal pattern distributions, auxiliary functions for ordinal pattern analysis, and generating functions for stochastic and deterministic-chaotic processes for ITQ testing are provided.
Several tools are provided for the statistical analysis of environmental data.
Support for reading and writing files in StatDataML---an XML-based data exchange format.
The 'cartogram' heatmaps generated by the included methods 
    are an alternative to choropleth maps for the United States
    and are based on work by the Washington Post graphics department in their report
    on "The states most threatened by trade" 
    (<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>).
    "State bins" preserve as much of the geographic placement of the states as 
    possible but have the look and feel of a traditional heatmap. Functions are 
    provided that allow for use of a binned, discrete scale, a continuous scale 
    or manually specified colors depending on what is needed for the underlying data.
Create panel data consisting of independent states from 1816 to
    the present. The package includes the Gleditsch & Ward (G&W) and Correlates
    of War (COW) lists of independent states, as well as helper functions for 
    working with state panel data and standardizing other data sources to 
    create country-year/month/etc. data.
A tool that makes estimating models in state space form 
    a breeze. See "Time Series Analysis by State Space Methods" by 
    Durbin and Koopman (2012, ISBN: 978-0-19-964117-8) for details 
    about the algorithms implemented.
Fast single trait Genome Wide Association Studies (GWAS) following 
    the method described in Kang et al. (2010), <doi:10.1038/ng.548>.        
    One of a series of statistical genetic packages for streamlining the 
    analysis of typical plant breeding experiments developed by Biometris.
Analysis of multi environment data of plant breeding experiments
    following the analyses described in Malosetti, Ribaut, 
    and van Eeuwijk (2013), <doi:10.3389/fphys.2013.00044>.
    One of a series of statistical genetic packages for streamlining the analysis of 
    typical plant breeding experiments developed by Biometris.
    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 (<https://www.vsni.co.uk/software/asreml-r>).
Phenotypic analysis of data coming from high throughput 
    phenotyping (HTP) platforms, including different types of outlier detection,
    spatial analysis, and parameter estimation. The package is being developed
    within the EPPN2020 project (<https://eppn2020.plant-phenotyping.eu/>).
    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 (<https://www.vsni.co.uk/software/asreml>).
For biparental, three and four-way crosses Identity by Descent 
    (IBD) probabilities can be calculated using Hidden Markov Models and 
    inheritance vectors following Lander and Green
    (<https://www.jstor.org/stable/29713>) and Huang
    (<doi:10.1073/pnas.1100465108>). One of a series of statistical genetic 
    packages for streamlining the analysis of typical plant breeding experiments
    developed by Biometris.
For Multi Parent Populations (MPP) Identity By Descend (IBD) 
    probabilities are computed using Hidden Markov Models. These probabilities 
    are then used in a mixed model approach for QTL Mapping as described in 
    Li et al. (<doi:10.1007/s00122-021-03919-7>).
Phenotypic analysis of field trials using mixed models with and 
    without spatial components. One of a series of statistical genetic packages 
    for streamlining the analysis of typical plant breeding experiments developed
    by Biometris.    
    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 (<https://www.vsni.co.uk/software/asreml-r>).
Contains statistical methods to analyze graphs, such as graph 
      parameter estimation, model selection based on the GIC (Graph Information 
      Criterion), statistical tests to discriminate two or more populations of 
      graphs (ANOGVA - Analysis of Graph Variability), correlation between 
      graphs, and clustering of graphs. 
      References: Takahashi et al. (2012) <doi:10.1371/journal.pone.0049949>, 
      Futija et al. (2017) <doi:10.3389/fnins.2017.00066>, 
      Fujita et al. (2017) <doi:10.1016/j.csda.2016.11.016>, 
      Tang et al. (2017) <doi:10.3150/15-BEJ789>, 
      Tang et al. (2017) <doi:10.1080/10618600.2016.1193505>, 
      Ghoshdastidar et al. (2017) <arXiv:1705.06168>,  
      Ghoshdastidar et al. (2017) <arXiv:1707.00833>, 
      Cerqueira et al. (2017) <doi:10.1109/TNSE.2017.2674026>, 
      Fraiman and Fraiman (2018) <doi:10.1038/s41598-018-23152-5>, 
      Fujita et al. (2019) <doi:10.1093/comnet/cnz028>.
Acquire hourly meteorological data from stations located all over
    the world. There is a wealth of data available, with historic weather data
    accessible from nearly 30,000 stations. The available data is automatically
    downloaded from a data repository and processed into a 'tibble' for the
    exact range of years requested. A relative humidity approximation is
    provided using the 'August-Roche-Magnus' formula, which was adapted from
    Alduchov and Eskridge (1996) <doi:10.1175%2F1520-0450%281996%29035%3C0601%3AIMFAOS%3E2.0.CO%3B2>.
Templates, guides, and scripts for
    writing documents in 'LaTeX' and 'R markdown' to produce
    guides, slides, and reports. Special care is taken to
    illustrate use of templates and customization opportunities.
    Challenges and opportunities of 'HTML' output from 'R markdown'
    receive special attention. Includes several vignettes
    to assist new users of literate programming.
A collection of miscellaneous statistical functions for 
    probability distributions: 'dbern()', 'pbern()', 'qbern()', 'rbern()' for 
    the Bernoulli distribution, and 'distr2name()', 'name2distr()' for 
    distribution names; 
    probability density estimation: 'densityfun()'; 
    most frequent value estimation: 'mfv()', 'mfv1()'; 
    other statistical measures of location: 'cv()' (coefficient of variation),
    'midhinge()', 'midrange()', 'trimean()'; 
    construction of histograms: 'histo()', 'find_breaks()'; 
    calculation of the Hellinger distance: 'hellinger()'; 
    use of classical kernels: 'kernelfun()', 'kernel_properties()'; 
    univariate piecewise-constant regression: 'picor()'.
A collection of statistical and geometrical tools
	including the aligned rank transform (ART; Higgins et al.
	1990 <doi:10.4148/2475-7772.1443>; Peterson 2002
	<doi:10.22237/jmasm/1020255240>; Wobbrock et al. 2011
	<doi:10.1145/1978942.1978963>), 2-D histograms and
	histograms with overlapping bins, a function for making all
	possible formulae within a set of constraints, amongst others.
Integration of two data sources referred to the same target population which share a number of variables. Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing  with data from complex sample surveys are available too.
Offers useful functions to perform day-to-day data manipulation 
    operations, data quality checks and post modelling statistical checks.
    One can effortlessly change class of a number of variables to factor, 
    remove duplicate observations from the data, create deciles of a 
    variable, perform data quality checks for continuous (integer or numeric), 
    categorical (factor) and date variables, and compute goodness of fit 
    measures such as auc for statistical models. The functions are consistent 
    for objects of class 'data.frame' and 'data.table', which is an enhanced 
    'data.frame' implemented in the package 'data.table'.
A collection of algorithms and functions to aid statistical modeling. Includes limiting dilution analysis (aka ELDA), growth curve comparisons, mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models. Also includes advanced generalized linear model functions including Tweedie and Digamma distributional families and a secure convergence algorithm.
Statnet is a collection of packages for statistical network analysis that are 
  designed to work together because they share common data representations and 'API' 
  design.  They provide an integrated set of tools for the representation, 
  visualization, analysis, and simulation of many different forms of network data.  
  This package is designed to make it easy to install and load the 
  key 'statnet' packages in a single step.  Learn more about 'statnet' 
  at <http://www.statnet.org>.  Tutorials for many packages can be found 
  at <https://github.com/statnet/Workshops/wiki>.  For an introduction to functions in this package, 
  type help(package='statnet').
Non-statistical utilities used by the software developed by the Statnet Project. They may also be of use to others.
A graphical user interface for network modeling with the 'statnet'
    software <https://github.com/statnet>.
Get programmatic access to data from the Czech public
    budgeting and accounting database, Státní pokladna
    <https://monitor.statnipokladna.cz/>.
Statistical performance measures used in the econometric literature to evaluate conditional covariance/correlation matrix estimates (MSE, MAE, Euclidean distance, Frobenius distance, Stein distance, asymmetric loss function, eigenvalue loss function and the loss function defined in Eq. (4.6) of Engle et al. (2016) <doi:10.2139/ssrn.2814555>). Additionally, compute Eq. (3.1) and (4.2) of Li et al. (2016) <doi:10.1080/07350015.2015.1092975> to compare the factor loading matrix. The statistical performance measures implemented have been previously used in, for instance, Laurent et al. (2012) <doi:10.1002/jae.1248>,  Amendola et al. (2015) <doi:10.1002/for.2322> and  Becker et al. (2015) <doi:10.1016/j.ijforecast.2013.11.007>.
A small collection of data on graduate statistics programs from the United States.
Implements confidence interval and sample size methods that are especially useful in psychological research. The methods can be applied in 1-group, 2-group, paired-samples, and multiple-group designs and to a variety of parameters including means, medians, proportions, slopes, standardized mean differences, standardized linear contrasts of means, plus several measures of correlation and association. The confidence intervals and sample size functions are applicable to single parameters as well as differences, ratios, and linear contrasts of parameters.  The sample size functions can be used to approximate the sample size needed to estimate a parameter or function of parameters with desired confidence interval precision or to perform a variety of hypothesis tests (directional two-sided, equivalence, superiority, noninferiority) with desired power. For details, see: <https://csass.ucsc.edu/self-study%20courses/statistical_methods.html>.
Generates a random quotation from a data base of quotes on topics
    in statistics, data visualization and science. Other functions allow searching
    the quotes database or creating a word cloud.
A set of methods to implement Generalized Method of Moments and Maximal
    Likelihood methods for Random Utility Models. These methods are meant to
    provide inference on rank comparison data. These methods accept full,
    partial, and pairwise rankings, and provides methods to break down full or
    partial rankings into their pairwise components. Please see Generalized
    Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of
    some of our methods.
Tools to help download, process and analyse the UK road collision data collected using the
  'STATS19' form. The data are provided as 'CSV' files with detailed road safety data about the
  circumstances of car crashes and other incidents on the roads resulting in 
  casualties in Great Britain from 1979, the types
  (including make and model) of vehicles involved and the consequential casualties.  The
  statistics relate only to personal casualties on public roads that are reported
  to the police, and subsequently recorded, using the 'STATS19' accident reporting form. See
  the Department for Transport website 
  <https://data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data> for more
  information on these data.
Pull data from the 'STAT Search Analytics' API <https://help.getstat.com/knowledgebase/api-services/>. It was developed by the Search Discovery team to help analyze keyword ranking data.
Utilities for producing dataframes with rich details for the
    most common types of statistical approaches and tests: parametric,
    nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation
    analyses, contingency table analyses, and meta-analyses. The
    functions are pipe-friendly and provide a consistent syntax to work
    with tidy data. These dataframes additionally contain expressions with
    statistical details, and can be used in graphing packages. This
    package also forms the statistical processing backend for
    'ggstatsplot'.
Shiny apps for automated data analysis, annotated outputs and human-readable interpretation in natural language. Designed especially for learners and applied researchers. Currently available methods: EDA, EDA with Python, Correlation Analysis, Principal Components Analysis, Confirmatory Factor Analysis.
Data and functions to support Bayesian and frequentist inference and decision making 
            for the Coursera Specialization "Statistics with R".
            See <https://github.com/StatsWithR/statsr> for more information.
Download data from 'StatsWales' into R. Removes the need for the user to
    write their own loops when parsing data from the 'StatsWales' API. Provides functions
    for datasets (<http://open.statswales.gov.wales/en-gb/dataset>) and metadata
    (<http://open.statswales.gov.wales/en-gb/discover/metadata>) endpoints.
Visualization functions in the applications of translational medicine (TM) and biomarker (BM) development to compare groups by statistically visualizing data and/or results of analyses, such as visualizing data by displaying in one figure different groups' histograms, boxplots, densities, scatter plots, error-bar plots, or trajectory plots, by displaying scatter plots of top principal components or dendrograms with data points colored based on group information, or visualizing volcano plots to check the results of whole genome analyses for gene differential expression.
Provides an implementation of simultaneous tolerance bounds (STB), useful for checking whether a numeric vector fits to a hypothetical null-distribution or not.
             Furthermore, there are functions for computing STB (bands, intervals) for random variates of linear mixed models fitted with package 'VCA'. All kinds of, possibly transformed 
             (studentized, standardized, Pearson-type transformed) random variates (residuals, random effects), can be assessed employing STB-methodology.
Spatio-temporal change of support (STCOS) methods are designed for statistical inference
	on geographic and time domains which differ from those on which the data were observed. In
	particular, a parsimonious class of STCOS models supporting Gaussian outcomes was introduced
	by Bradley, Wikle, and Holan <doi:10.1002/sta4.94>. The 'stcos' package contains tools which
	facilitate use of STCOS models.
Estimates a covariance matrix using Stein's isotonized covariance
    estimator, or a related estimator suggested by Haff.
Contains three main functions including
  stddiff.numeric(), stddiff.binary() and stddiff.category().
  These are used to calculate the standardized difference between two groups.
  It is especially used to evaluate the balance between two groups
  before and after propensity score matching.
Contains functionality for regression standardization. Four general classes of models are allowed; generalized linear models, conditional generalized estimating equation models, Cox proportional hazards models and shared frailty gamma-Weibull models. Sjolander, A. (2016) <doi:10.1007/s10654-016-0157-3>.
Allows the creation and manipulation of C++ std::vector's in R.
Functions related to multivariate measures of independence and ICA:
 -estimate independent components by minimizing distance covariance;
 -conduct a test of mutual independence based on distance covariance; 
 -estimate independent components via infomax (a popular method but generally performs poorer than mdcovica, ProDenICA, and/or fastICA, but is useful for comparisons);
 -order indepedent components by skewness;
 -match independent components from multiple estimates;
 -other functions useful in ICA.
steepness is a package that computes steepness as a
        property of dominance hierarchies. Steepness is defined as the
        absolute slope of the straight line fitted to the normalized
        David's scores. The normalized David's scores can be obtained
        on the basis of dyadic dominance indices corrected for chance
        or by means of proportions of wins. Given an observed
        sociomatrix, it computes hierarchy's steepness and estimates
        statistical significance by means of a randomization test.
A set of functions for finding and analysing Steiner trees. It has applications in
    biological pathway network analysis. Sadeghi (2013) <doi:10.1186/1471-2105-14-144>.
Routines for computing different types of linear estimators, based on instrumental variables (IVs), including the semi-parametric Stein-like (SPS) estimator, originally introduced by Judge and Mittelhammer (2004)  <DOI:10.1198/016214504000000430>.
Manages and display stellar tracks and isochrones from 
	     Pisa low-mass database. Includes tools for isochrones
	     construction and tracks interpolation.
Estimation of the parameters of a spatio-temporal model
        using the EM algorithm, estimation of the parameter standard
        errors using a spatio-temporal parametric bootstrap, spatial
        mapping.
Explore and analyse the genealogy of textual or musical traditions, from their variants, with various stemmatological methods, mainly the disagreement-based algorithms suggested by Camps and Cafiero (2015) <doi:10.1484/M.LECTIO-EB.5.102565>.
It estimates the parameters of a censored or missing data in spatio-temporal models using the SAEM algorithm (Delyon et al., 1999). This algorithm is a stochastic approximation of the widely used EM algorithm and an important tool for models in which the E-step does not have an analytic form. Besides the expressions obtained to estimate the parameters to the proposed model, we include the calculations for the observed information matrix using the method developed by Louis (1982). To examine the performance of the fitted model, case-deletion measure are provided.
Provides R bindings for the Stencila Schema <https://schema.stenci.la>. This package is primarily aimed at R developers wanting to programmatically generate, or modify, executable documents.
Starting from a Beta Regression Model, it provides a stepwise 
              procedure to select the linear predictor.
Collection of model estimation, and model plotting functions 
             related to the STEPCAM family of community assembly models. 
             STEPCAM is a STEPwise Community Assembly Model that infers 
             the relative contribution of Dispersal Assembly, Habitat Filtering 
             and Limiting Similarity from a dataset consisting of the 
             combination of trait and abundance data. See also <http://onlinelibrary.wiley.com/wol1/doi/10.1890/14-0454.1/abstract> for more information.
Provides a step-down procedure for controlling the False
  Discovery Proportion (FDP) in a competition-based setup, implementing 
  Dong et al. (2020) <arXiv:2011.11939>. Such setups include target-decoy 
  competition (TDC) in computational mass spectrometry and the knockoff 
  construction in linear regression.
An introduction to a couple of novel predictive variable selection methods for generalised boosted regression modeling (gbm). They are based on various variable influence methods (i.e., relative variable influence (RVI) and knowledge informed RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as AVI, KIAVI and KIAVI2 in the 'steprf' package, and also based on predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <DOI: 10.13140/RG.2.2.27686.22085>.
A Package for selecting variables for the joint modeling of mean and dispersion (including models for mixture experiments) based on hypothesis testing and the quality of model's fit. In each iteration of the selection process, a criterion for checking the goodness of fit is used as a filter for choosing the terms that will be evaluated by a hypothesis test. Pinto & Pereira (2021) <arXiv:2109.07978>.
A method to explore the treatment-covariate interactions in survival or generalized 
	linear model (GLM) for continuous, binomial and count data arising from two or more treatment 
	arms of a clinical trial. A permutation distribution approach to inference is implemented, 
	based on permuting the covariate values within each treatment group. 
	Bonetti M, Gelber RD (2004) <DOI:10.1093/biostatistics/5.3.465>.
	Marco Bonetti, David Zahrieh, Bernard F. Cole, and Richard D. Gelber (2009) <doi:10.1002/sim.3524>.
	Ann A. Lazar, Bernard F. Cole, Marco Bonetti, and Richard D. Gelber (2010) <doi:10.1200/JCO.2009.27.9182>.
	Lazar AA,Bonetti M,Cole BF,Yip WK,Gelber RD (2016) <doi:10.1177/1740774515609106>.
 	Wai-Ki Yip,Marco Bonetti,Bernard F Cole,William Barcella,Xin Victoria Wang,Ann Lazar,and Richard D Gelber (2016) <doi:10.1177/1740774516643297>.
	Wang XV, Cole B, Bonetti M, Gelber RD (2016) <doi:10.1002/sim.6958>.
	Wai-Ki Yip (2017, ISBN:978-3-319-48846-2).
Tools for power and sample size 
    calculation as well as design diagnostics for 
    longitudinal mixed model settings, with a focus on stepped wedge designs.
    All calculations are oracle estimates i.e. assume random effect variances 
    to be known (or guessed) in advance.  
    The method is introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>,
    extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.
Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms.
L2 penalized logistic regression for both continuous and discrete predictors, with forward stagewise/forward stepwise variable selection procedure.
Allows fitting of step-functions to univariate serial data where neither the number of jumps nor their positions is known by implementing the multiscale regression estimators SMUCE (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.
Three most common types of stepwise regression including linear regression, logistic regression and cox proportional hazard regression can be performed to select best model with methods of forward selection, backward elimination, bidirectional selection and best subset selection. A widely used selection criteria are available for variable selection.
An introduction to several novel predictive variable selection methods for random forest. They are based on various variable importance methods (i.e., averaged variable importance (AVI), and knowledge informed AVI (i.e., KIAVI, and KIAVI2)) and predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <DOI: 10.13140/RG.2.2.27686.22085>.
Software to simulate population change across space and time.
Provides function to estimate multiple change points
    using marginal likelihood method. See the Manual file in data folder for
    a detailed description of all functions, and a walk through tutorial.
    For more information of the method, please see Du, Kao and Kou (2016) 
    <doi:10.1080/01621459.2015.1006365>.
Functions to perform stepwise split regularized regression. The approach first 
             uses a stepwise algorithm to split the variables into the models with a goodness
             of fit criterion, and then regularization is applied to each model. The weights 
             of the models in the ensemble are determined based on a criterion selected by 
             the user.
Collection of stepwise procedures to conduct multiple hypotheses testing. The details of the stepwise algorithm can be found in Romano and Wolf (2007) <DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014) <DOI:10.1093/jjfinec/nbu014>.
Functions for the collection of 3D points and curves using a stereo camera setup.
This is a collection of various kinds of data with broad uses for teaching. 
    My students, and academics like me who teach the same topics I teach, should find 
    this useful if their teaching workflow is also built around the R programming 
    language. The applications are multiple but mostly cluster on topics of statistical
    methodology, international relations, and political economy.
Work with containers over the Docker API.  Rather than
    using system calls to interact with a docker client, using the
    API directly means that we can receive richer information from
    docker.  The interface in the package is automatically generated
    using the 'OpenAPI' (a.k.a., 'swagger') specification, and all
    return values are checked in order to make them type stable.
These are miscellaneous functions that I find useful for my research and teaching.
    The contents include themes for plots, functions for simulating
    quantities of interest from regression models, functions for simulating various 
    forms of fake data for instructional/research purposes, and many more. All told, the functions
    provided here are broadly useful for data organization, data presentation, data recoding, 
    and data simulation.
These are my collection of 'R Markdown' templates, mostly for compilation to PDF.
    These are useful for all things academic and professional, if you are using 'R Markdown'
    for things like your CV or your articles and manuscripts.
A general spatiotemporal satellite image imputation method based on sparse functional data analytic techniques. The imputation method applies and extends the Functional Principal Analysis by Conditional Estimation (PACE). The underlying idea for the proposed procedure is to impute a missing pixel by borrowing information from temporally and spatially contiguous pixels based on the best linear unbiased prediction.
A collection of statistical hypothesis tests of functional time series. While it will include more tests when the related literature are enriched, this package contains the following key tests: functional stationarity test, functional trend stationarity test, functional unit root test, to name a few.
Genomic Selection (GS) is a latest development in animal and plant breeding where whole genome markers information is used to predict genetic merit of an individuals in a practical breeding programme. GS is one of the promising tool for improving genetic gain in animal and plants in today’s scenario. This package is basically developed for genomic predictions by estimating marker effects. These marker effects further used for calculation of genotypic merit of individual i.e. genome estimated breeding values (GEBVs). Genomic selection may be based on single trait or multi traits information. This package performs genomic selection only for single traits hence named as STGS i.e. single trait genomic selection. STGS is a comprehensive package which gives single step solution for genomic selection based on most commonly used statistical methods.
A set of functions for computing the Standardized Temperature Index (STI).
Download and use R hex stickers. Stickers made 
  available in standardized locations in GitHub repositories, as well as those in the 
  <https://github.com/rstudio/hex-stickers> repository will be available.
In base R, object attributes are lost when objects are modified by
    common data operations such as subset, filter, slice, append, extract
    etc. This packages allows objects to be marked as 'sticky' and have
    attributes persisted during these operations or when inserted
    into or extracted from list-like or table-like objects.
Functions for simplified emulation of time series computer model output in model parameter space using Gaussian processes. Stilt can be used more generally for Kriging of spatio-temporal fields. There are functions to predict at new parameter settings, to test the emulator using cross-validation (which includes information on 95% confidence interval empirical coverage), and to produce contour plots over 2D slices in model parameter space.
Regression trunk model estimation proposed by Dusseldorp and Meulman (2004) <doi:10.1007/bf02295641>
             and Dusseldorp, Conversano, Van Os (2010) <doi:10.1198/jcgs.2010.06089>, integrating a regression tree
             and a multiple regression model.
A consistently well behaved method of interpolation based on piecewise rational functions using Stineman's algorithm.
Univariate time series forecasting with STL decomposition based auto regressive integrated moving average (ARIMA) hybrid  model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.
The Citizens' Service Bureau of the City of St. Louis is a clearing house for 
    non-emergency service requests. This package provides functions for downloading, 
    categorizing problem requests, cleaning and subsetting CSB data, and projecting the data
    using the x and y coordinates included with CSB data releases.
Univariate time series forecasting with STL decomposition based Extreme Learning Machine hybrid  model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.
Statistical analysis of spatio-temporal point processes on linear networks. This packages provides tools to visualise and analyse spatio-temporal point patterns on linear networks using first- and second-order summary statistics.
Decompose a time series into seasonal, trend, and remainder
    components using an implementation of Seasonal Decomposition of Time
    Series by Loess (STL) that provides several enhancements over the STL
    method in the stats package.  These enhancements include handling missing
    values, providing higher order (quadratic) loess smoothing with automated
    parameter choices, frequency component smoothing beyond the seasonal and
    trend components, and some basic plot methods for diagnostics.
Implementation of hybrid STL decomposition based time delay neural network model for univariate time series forecasting. For method details see Jha G K, Sinha, K (2014). <doi:10.1007/s00521-012-1264-z>, Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.
The Structural Topic Model (STM) allows researchers 
  to estimate topic models with document-level covariates. 
  The package also includes tools for model selection, visualization,
  and estimation of topic-covariate regressions. Methods developed in
  Roberts et. al. (2014) <doi:10.1111/ajps.12103> and 
  Roberts et. al. (2016) <doi:10.1080/01621459.2016.1141684>. Vignette
  is Roberts et. al. (2019) <doi:10.18637/jss.v091.i02>.
Generates an interactive visualization of topic correlations/
    hierarchy in a Structural Topic Model (STM) of Roberts, Stewart, and Tingley.
    The package performs a hierarchical clustering of topics which are then exported
    to a JSON object and visualized using D3.
Analyses spatio-temporal data, decomposing data in n-dimensional arrays and using the median polish technique.
Rapidly build accurate genetic prediction models for genome-wide association or whole-genome sequencing study data by smooth-threshold multivariate genetic prediction (STMGP) method. Variable selection is performed using marginal association test p-values with an optimal p-value cutoff selected by Cp-type criterion. Quantitative and binary traits are modeled respectively via linear and logistic regression models. A function that works through PLINK software (Purcell et al. 2007 <DOI:10.1086/519795>, Chang et al. 2015 <DOI:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is provided. Covariates can be included in regression model.
Provides an application that acts as a GUI for the 'stm' text analysis package.
This app enables interactive validation, interpretation and visualization of structural topic models from the 'stm' package by Roberts and others (2014) <doi:10.1111/ajps.12103>. It also includes helper functions for model diagnostics and extracting data from effect estimates.
Implementation of the family of generalised age-period-cohort
    stochastic mortality models. This family of models encompasses many models
    proposed in the actuarial and demographic literature including the 
    Lee-Carter (1992) <doi:10.2307/2290201> and
    the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. 
    It includes functions for fitting mortality models, analysing their 
    goodness-of-fit and performing mortality projections and simulations.
Plots a QQ-Norm Plot with several Gaussian simulations.
Allow to identify motifs in spatial-time series. A motif is a previously unknown subsequence of a (spatial) time series with relevant number of occurrences. For this purpose, the Combined Series Approach (CSA) is used.
Strength training prescription using percent-based approach requires
    numerous computations and assumptions. 'STMr' package allow users to estimate 
    individual reps-max relationships, implement various progression tables, and
    create numerous set and rep schemes. The 'STMr' package is originally created as
    a tool to help writing Jovanović M. (2020) Strength Training Manual
    <ISBN:979-8604459898>.
Fit a spatial-temporal occupancy models using
    a probit formulation instead of a traditional logit
    model.
New Version of the R package originally accompanying the paper 
  "Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles" 
  by Sameer S Bajikar, Christiane Fuchs, Andreas Roller, Fabian J Theis and Kevin A Janes 
  (PNAS 2014, 111(5), E626-635 <doi:10.1073/pnas.1311647111>). In this paper, we measure expression profiles 
  from small heterogeneous populations of cells, where each cell is assumed to be from a mixture of 
  lognormal distributions. We perform maximum likelihood estimation in order to infer the mixture ratio and 
  the parameters of these lognormal distributions from the cumulated expression measurements.
  The main difference of this new package version to the previous one is that it is now possible to use 
  different n's, i.e. a dataset where each tissue sample originates from a different number of cells.  
  We used this on pheno-seq data, see: Tirier, S.M., Park, J., Preusser, F. et al. Pheno-seq - linking visual 
  features and gene expression in 3D cell culture systems. Sci Rep 9, 12367 (2019) 
  <doi:10.1038/s41598-019-48771-4>).
Implementations of stochastic, limited-memory quasi-Newton optimizers,
	similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm,
	for smooth stochastic optimization. Implements the following methods:
	oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 <http://proceedings.mlr.press/v2/schraudolph07a.html>),
	SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <arXiv:1401.7020>),
	adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <arXiv:1511.01169>).
	Provides functions for easily creating R objects
	with partial_fit/predict methods from some given objective/gradient/predict functions.
	Includes an example stochastic logistic regression using these optimizers.
	Provides header files and registered C routines for using it directly from C/C++.
Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Hosszejni and Kastner (2021) <doi:10.18637/jss.v100.i12> and Kastner (2016) <doi:10.18637/jss.v069.i05> and the package examples.
Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is 
    integrated out of the likelihood using the Laplace approximation. The models are fitted via 'TMB' (Template Model
    Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) <doi:10.18637/jss.v070.i05>).
An implementation of the UCI open communication protocol that
    ships with 'Stockfish' <https://stockfishchess.org/>, a very popular,
    open source, powerful chess engine written in C++.
Provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after find good starting values via a K-means clustering of the genetic data. Details can be found in Foster, Feutry, Grewe, Berry, Hui, Davies (2019) Reliably Discriminating Stock Structure with Genetic Markers: Mixture Models with Robust and Fast Computation. Molecular Ecology Resources.
Functions for analyzing stocks or other investments. Main features are loading and aligning historical data for ticker symbols, calculating performance metrics for individual funds or portfolios (e.g. annualized growth, maximum drawdown, Sharpe/Sortino ratio), and creating graphs. C++ code is used to improve processing speed where possible.
Given a list of substance compositions, a list of
        substances involved in a process, and a list of constraints in
        addition to mass conservation of elementary constituents, the
        package contains functions to build the substance composition
        matrix, to analyze the uniqueness of process stoichiometry, and
        to calculate stoichiometric coefficients if process
        stoichiometry is unique.  (See Reichert, P. and Schuwirth, N.,
        A generic framework for deriving process stoichiometry in
        enviromental models, Environmental Modelling and Software 25,
        1241-1251, 2010 for more details.)
Provides functionality for working with tensors, alternating
           tensors, wedge products, Stokes's theorem, and related concepts
	   from the exterior calculus.  Functionality for Grassman algebra
	   is provided.  The canonical reference would be:
	   M. Spivak (1965, ISBN:0-8053-9021-9) "Calculus on Manifolds".
A variable selection procedure for low to moderate size linear regressions models. This method repeatedly splits the data into two sets, one for estimation and one for validation, to obtain an empirically optimized threshold which is then used to screen for variables to include in the final model.
Provides multiple sources of stopwords, for use in text analysis and natural language processing.
Storm is a distributed real-time computation system. Similar to how
 Hadoop provides a set of general primitives for doing batch processing, Storm
 provides a set of general primitives for doing real-time computation.

    Storm includes a "Multi-Language" (or "Multilang") Protocol to allow
 implementation of Bolts and Spouts in languages other than Java.  This R
 extension provides implementations of utility functions to allow an application
 developer to focus on application-specific functionality rather than Storm/R
 communications plumbing.
Creates and manages simple key-value stores.  These can
    use a variety of approaches for storing the data.  This package
    implements the base methods and support for file system, in-memory
    and DBI-based database stores.
Download, explore, and analyze Literary Theme Ontology themes
    and thematically annotated story data. To learn more about the project
    visit <https://github.com/theme-ontology/theming> and
    <https://themeontology.org>.
An interface to explore trends in Twitter data using the 
    'Storywrangler' Application Programming Interface (API), which can be found
    here: <https://github.com/janeadams/storywrangler>.
A toolkit for Reliability Availability and Maintainability (RAM) modeling of industrial process systems.
Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.
Tools for transport planning with an emphasis on spatial transport
    data and non-motorized modes. Enables common transport planning tasks including:
    downloading and cleaning transport datasets; creating geographic "desire lines"
    from origin-destination (OD) data; route assignment, locally and via
    interfaces to routing services such as <https://cyclestreets.net/> and
    calculation of route segment attributes such as bearing.
    The package implements the 'travel flow aggregration' method
    described in Morgan and Lovelace (2020) <doi:10.1177/2399808320942779>.
    Further information on the package's aim and scope can be found
    in the vignettes and in a paper in the R Journal
    (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053>.
    This package Suggests the 'pct' package which at the time of writing
    is unavailable on CRAN. You can install it from the repository
    'itsleeds/pct' on GitHub.
Utilities to estimate parameters of the models with survival functions 
             induced by stochastic covariates. Miscellaneous functions for data preparation 
             and simulation are also provided. For more information, see: 
             (i)"Stochastic model for analysis of longitudinal data on aging and mortality" 
             by Yashin A. et al. (2007), 
             Mathematical Biosciences, 208(2), 538-551, <DOI:10.1016/j.mbs.2006.11.006>;
             (ii) "Health decline, aging and mortality: how are they related?" 
             by Yashin A. et al. (2007), 
             Biogerontology 8(3), 291(302), <DOI:10.1007/s10522-006-9073-3>.
Many of the models encountered in applications of point process methods to the study of spatio-temporal phenomena are covered in 'stpp'. This package provides statistical tools for analyzing the global and local second-order properties of spatio-temporal point processes, including estimators of the space-time inhomogeneous K-function and pair correlation function. It also includes tools to get static and dynamic display of spatio-temporal point patterns. See Gabriel et al (2013) <doi:10.18637/jss.v053.i02>.
Generates artificial spatiotemporal (ST) point patterns 
  through the integration of microsimulation 
  (Holm, E., (2017)<doi:10.1002/9781118786352.wbieg0320>) 
  and agent-based models 
  (Bonabeau, E., (2002)<doi:10.1073/pnas.082080899>). 
  Allows a user to define the behaviours of a set of 'walkers' (agents, 
  objects, persons, etc.) whose interactions with the spatial (landscape) 
  (Quaglietta, L. and Porto, M., (2019)<doi:10.1186/s40462-019-0154-8>)
  and the temporal domains produce new point events. The resulting ST 
  patterns from the point cloud can be measured and utilized for spatial 
  and/or temporal model testings and evaluations. Application: With 
  increasingly limited availability of fine-grained spatially and 
  temporally stamped point data, the package provides an alternative 
  source of data for a wide range of research in social and life sciences.
Methods for decomposing seasonal data: STR (a Seasonal-Trend decomposition procedure based on Regression) and Robust STR. In some ways, STR is similar to Ridge Regression and Robust STR can be related to LASSO. They allow for multiple seasonal components, multiple linear covariates with constant, flexible and seasonal influence. Seasonal patterns (for both seasonal components and seasonal covariates) can be fractional and flexible over time; moreover they can be either strictly periodic or have a more complex topology. The methods provide confidence intervals for the estimated components. The methods can be used for forecasting.
Offers a suite of functions for converting to and from
    (atomic) vectors, matrices, data.frames, and (3D+) arrays as well
    as lists of these objects. It is an alternative to the base R
    as.<str>.<method>() functions (e.g., as.data.frame.array()) that
    provides more useful and/or flexible restructuring of R objects.
    To do so, it only works with common structuring of R objects (e.g.,
    data.frames with only atomic vector columns).
Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics, using demographic and genetic data sampled in the curse of the epidemics. This package also includes the GMCPIC test.
Provides a framework for performing discrete (share-level) simulations of
  investment strategies. Simulated portfolios optimize exposure to an input signal subject
  to constraints such as position size and factor exposure. For background see L. Chincarini
  and D. Kim (2010, ISBN:978-0-07-145939-6) "Quantitative Equity Portfolio Management".
Functions for the stratigraphic analysis of phylogenetic trees.
An implementation of the stratification index proposed by Zhou (2012) <DOI:10.1177/0081175012452207>.
  The package provides two functions, srank, which returns stratum-specific
  information, including population share and average percentile rank; and strat,
  which returns the stratification index and its approximate standard error.
  When a grouping factor is specified, strat also provides a detailed decomposition
  of the overall stratification into between-group and within-group components.
Functions in this package provide solution to classical problem in survey methodology - 
  an optimum sample allocation in stratified sampling scheme with simple random sampling without replacement design in each stratum. 
  In this context, the optimal allocation is in the classical Tschuprow-Neyman's sense,
  and it satisfies additional upper bounds restrictions imposed on sample sizes in strata. 
  There are four different algorithms available to use, and one of them is Neyman optimal allocation applied in a recursive way.
  All the algorithms are described in detail in "Wojciak W. Optimal allocation in stratified sampling schemes. 
  Master's diploma thesis, Warsaw University of Technology. 2019", 
  available on-line at <http://home.elka.pw.edu.pl/~wwojciak/msc_optimal_allocation.pdf>.
A pilot matching design to automatically 
    stratify and match large datasets.  The manual_stratify() function allows
    users to manually stratify a dataset based on categorical variables of 
    interest, while the auto_stratify() function does automatically by
    allocating a held-aside (pilot) data set, fitting a prognostic score  
    (see Hansen (2008) <doi:10.1093/biomet/asn004>) on the pilot set, and stratifying the data set based
    on prognostic score quantiles.  The strata_match() function then does optimal
    matching of the data set in parallel within strata.
An Optimization Algorithm Applied to
    Stratification Problem.This function aims
    at constructing optimal strata with an optimization algorithm
    based on a global optimisation technique called Biased
    Random Key Genetic Algorithms.
Identifies individuals in a social network who should be the intervention
    subjects for a network intervention in which you have a group of targets, a
    group of avoiders, and a group that is neither.
Users can build and test customized quantitative trading strategies. Some quantitative trading strategies are already implemented, e.g. various moving-average filters with trend following approaches.
    The implemented class called "Strategy" allows users to access several methods to analyze performance figures, plots and backtest the strategies.
    Furthermore, custom strategies can be added, a generic template is available. The custom strategies require a certain input and output so they can be called from the Strategy-constructor.
Variants of strategy estimation (Dal Bo & Frechette, 2011, <doi:10.1257/aer.101.1.411>), including the model with parameters for the choice probabilities of the strategies (Breitmoser, 2015, <doi:10.1257/aer.20130675>), and the model with individual level covariates for the selection of strategies by individuals (Dvorak & Fehrler, 2018, <doi:10.2139/ssrn.2986445>).
A dynamic model of 
    the big-picture, whole ecosystem effects of hydrodynamics, 
	temperature, nutrients, and fishing on continental shelf marine 
	food webs. The package is described in: Heath, M.R., 
	Speirs, D.C., Thurlbeck, I. and Wilson, R.J. (2020) 
	<doi:10.1111/2041-210X.13510> StrathE2E2: An R package 
	for modelling the dynamics of marine food webs and 
	fisheries. 8pp.
Univariate stratification of survey populations with a generalization of the 
  Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account 
  a discrepancy between the stratification variable and the survey variable. The determination 
  of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all 
  stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure 
  that some specific units are in the sample. The well known cumulative root frequency rule of 
  Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented.
Performs Stratified Covariate Balancing with Markov blanket feature selection and use of synthetic cases. See Alemi et al. (2016) <DOI:10.1111/1475-6773.12628>.
A toolkit for stratified medicine, subgroup identification, and precision medicine.
    Current tools include (1) filtering models (reduce covariate space), (2) patient-level estimate
    models (counterfactual patient-level quantities, such as the conditional average treatment effect), 
    (3) subgroup identification models (find subsets of patients with similar treatment effects), 
    and (4) treatment effect estimation and inference (for the overall population and discovered 
    subgroups). These tools can be customized and are directly used in PRISM 
    (patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019
    <arXiv:1912.03337>. This package is in beta and will be continually updated.
Random Forest-like tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees.
    Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing One-Hot encoding in the processing). Can also be used to create a regular (non-stratified) Random Forest-like model, but made up of C5.0 trees and with some additional control options.
    As it's built with C5.0 trees, it works only for classification (not for regression).
Integrating a stratified structure in the population in a sampling design can considerably reduce the variance of the Horvitz-Thompson estimator. We propose in this package different methods to handle the selection of a balanced sample in stratified population. For more details see Raphaël Jauslin, Esther Eustache and Yves Tillé (2021) <arXiv:2101.05568>. The package propose also a method based on optimal transport and balanced sampling, see Raphaël Jauslin and Yves Tillé <arXiv:2105.08379>.
The stratification of univariate populations under stratified sampling designs is implemented according to Khan et al. (2002) <doi:10.1177/0008068320020518> and Khan et al. (2015) <doi:10.1080/02664763.2015.1018674> in this library. It determines the Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for the study variable, y, using the best-fit frequency distribution of a survey variable (if data is available) or a hypothetical distribution (if data is not available). The method formulates the problem of determining the OSB as mathematical programming problem which is solved by using a dynamic programming technique. If a dataset of the population is available to the surveyor, the method estimates its best-fit distribution and determines the OSB and OSS under Neyman allocation directly. When the dataset is not available, stratification is made based on the assumption that the values of the study variable, y, are available as hypothetical realizations of proxy values of y from recent surveys. Thus, it requires certain distributional assumptions about the study variable. At present, it handles stratification for the populations where the study variable follows a continuous distribution, namely, Pareto, Triangular, Right-triangular, Weibull, Gamma, Exponential, Uniform, Normal, Log-normal and Cauchy distributions.
Includes bases for litholog generation: graphical functions
    based on R base graphics, interval management functions and svg importation 
    functions among others. Also include stereographic projection functions, 
    and other functions made to deal with large datasets while keeping options
    to get into the details of the data.
    When using for publication please cite 
    Wouters, S., Da Silva, A.-C., Boulvain, F., and Devleeschouwer, X.. In press.
    StratigrapheR: Concepts for Litholog Generation in R. The R Journal. 
    The palaeomagnetism functions are based on:
    Tauxe, L., 2010. Essentials of Paleomagnetism. University of California 
    Press. <https://earthref.org/MagIC/books/Tauxe/Essentials/>;
    Allmendinger, R. W., Cardozo, N. C., and Fisher, D., 2013, Structural 
    Geology Algorithms: Vectors & Tensors: Cambridge, England, Cambridge
    University Press, 289 pp.;
    Cardozo, N., and Allmendinger, R. W., 2013, Spherical projections
    with OSXStereonet: Computers & Geosciences, v. 51, no. 0, p. 193 - 205,
    <doi: 10.1016/j.cageo.2012.07.021>.
Provides functions to estimate a strategic selection estimator. A strategic selection estimator is an agent error model in which the two random components are not assumed to be orthogonal. In addition this package provides generic functions to print and plot objects of its class as well as the necessary functions to create tables for LaTeX. There is also a function to create dyadic data sets.
For a single, known pathogen phylogeny, provides functions for enumeration of the set of compatible epidemic transmission trees, and for uniform sampling from that set. Optional arguments allow for incomplete sampling with a known number of missing individuals, multiple sampling, and known infection time limits. Always assumed are a complete transmission bottleneck and no superinfection or reinfection. See Hall and Colijn (2019) <doi:10.1093/molbev/msz058> for methodology.
An Optimization Algorithm Applied to
    Stratification Problem.This function aims
    at constructing optimal strata with an optimization algorithm
    based on a global optimisation technique called vns.
The main function is icweib(), which fits a stratified Weibull
    proportional hazards model for left censored, right censored, interval censored,
    and non-censored survival data. We parameterize the Weibull regression model
    so that it allows a stratum-specific baseline hazard function, but where the
    effects of other covariates are assumed to be constant across strata. 
    Please refer to Xiangdong Gu, David Shapiro, Michael D. Hughes and
    Raji Balasubramanian (2014) <doi:10.32614/RJ-2014-003> for more details.
API for fast data extraction for .hic files that provides programmatic access to the matrices. It doesn't store the pointer data for all the matrices, only the one queried, and currently we are only supporting matrices (not vectors).
This is a modification of 'HDoutliers' package. The 'HDoutliers' algorithm is a powerful 
    unsupervised algorithm for detecting anomalies in high-dimensional data, with a 
    strong theoretical foundation. However, it suffers from some limitations that 
    significantly hinder its performance level, under certain circumstances. This package 
    implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) 
    <arXiv:1908.04000>  for detecting anomalies in high-dimensional data
    that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority
    with a large distance gap. An approach based on extreme value theory is used 
    for the anomalous threshold calculation.
A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893 and NIH R21HG005912. Hahsler et al (2017) <doi:10.18637/jss.v076.i14>.
Numerically solve and plot solutions of a parametric ordinary
        differential equations model of growth, death, and respiration of
        macroinvertebrate and algae taxa dependent on pre-defined environmental
        factors. The model (version 1.0) is introduced in Schuwirth, N. and
        Reichert, P., (2013) <DOI:10.1890/12-0591.1>. This package includes
        model extensions and the core functions introduced and used in
        Schuwirth, N. et al. (2016) <DOI:10.1111/1365-2435.12605>,
        Kattwinkel, M. et al. (2016) <DOI:10.1021/acs.est.5b04068>,
        Mondy, C. P., and Schuwirth, N. (2017) <DOI:10.1002/eap.1530>,
        and Paillex, A. et al. (2017) <DOI:10.1111/fwb.12927>.
Implementation of analytical models for estimating streamflow 
    depletion due to groundwater pumping, and other related tools. Functions
    are broadly split into two groups: (1) analytical streamflow depletion
    models, which estimate streamflow depletion for a single stream reach
    resulting from groundwater pumping; and (2) depletion apportionment 
    equations, which distribute estimated streamflow depletion among multiple
    stream reaches within a stream network. See Zipper et al. (2018) <doi:10.1029/2018WR022707>
    for more information on depletion apportionment equations and Zipper et
    al. (2019) <doi:10.1029/2018WR024403> for more information on analytical
    depletion functions, which combine analytical models and depletion apportionment
    equations.
I provide functions to calculate Gross Primary Productivity, Net Ecosystem Production, and Ecosystem Respiration from single station diurnal Oxygen curves.
Interface for data stream clustering algorithms implemented in the MOA (Massive Online Analysis) framework (Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010). MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604).
Functions to access Twitter's filter, sample, and user streams, and to
    parse the output into data frames.
The stress addition approach is an alternative to the traditional
    concentration addition or effect addition models. It allows the modelling
    of tri-phasic concentration-response relationships either as single toxicant
    experiments, in combination with an environmental stressor or as mixtures of
    two toxicants. See Liess et al. (2019) <doi:10.1038/s41598-019-51645-4>
    and Liess et al. (2020) <doi:10.1186/s12302-020-00394-7>.
Forms queries to submit to the Cleveland Federal Reserve Bank web
    site's financial stress index data site.  Provides query functions for both
    the composite stress index and the components data. By default the download
    includes daily time series data starting September 25, 1991.  The functions
    return a class of either type easing or cfsi which contain a list of items
    related to the query and its graphical presentation.  The list includes the
    time series data as an xts object.  The package provides four lattice time
    series plots to render the time series data in a manner similar to the
    bank's own presentation.
Reliability of (normal) stress-strength models and for building two-sided or one-sided confidence intervals according to different approximate procedures.
There are some things that I wish were easier with
    the 'stringr' or 'stringi' packages. The foremost of these is the
    extraction of numbers from strings. 'stringr' and 'stringi' make you
    figure out the regular expression for yourself; 'strex' takes care of
    this for you. There are many other handy functionalities in 'strex'.
    Contributions to this package are encouraged: it is intended as a
    miscellany of string manipulation functions that cannot be found in
    'stringi' or 'stringr'.
The strided iterator adapts multidimensional buffers to work with 
  the C++ standard library and range-based for-loops. Given a pointer or iterator
  into a multidimensional data buffer, one can generate an iterator range using
  make_strided to construct strided versions of the standard library's begin and
  end. For constructing range-based for-loops, a strided_range class is provided.
  These help authors to avoid integer-based indexing, which in some cases can impede
  algorithm performance and introduce indexing errors. This library exists
  primarily to expose the header file to other R projects.
Takes a list of character strings and forms an adjacency matrix for
    the times the specified characters appear together in the strings provided. For
    use in social network analysis and data wrangling. Simple package, comprised of
    three functions.
Extract glyph information from a font file, and translate the
    outline curves to flattened paths or tessellated polygons. The converted
    data is returned as a 'data.frame' in easy-to-plot format.
Base R already ships with string handling capabilities 'out-
    of-the-box' but lacks streamlined function names and workflow. The
    'stringi' ('stringr') package on the other hand has well named functions,
    extensive Unicode support and allows for a streamlined workflow. On the other
    hand it adds dependencies and regular expression interpretation between base R
    functions and 'stringi' functions might differ. This packages aims at providing
    a solution to the use case of unwanted dependencies on the one hand but the need
    for streamlined text processing on the other. The packages' functions are solely
    based on wrapping base R functions into 'stringr'/'stringi' like function names.
    Along the way it adds one or two extra functions and last but not least provides
    all functions as generics, therefore allowing for adding methods for other text
    structures besides plain character vectors.
Implements an approximate string matching version of R's native
    'match' function. Also offers fuzzy text search based on various string
     distance measures. Can calculate various string distances based on edits
    (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q-
    gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An
    implementation of soundex is provided as well. Distances can be computed between
    character vectors while taking proper care of encoding or between integer
    vectors representing generic sequences. This package is built for speed and
    runs in parallel by using 'openMP'. An API for C or C++ is exposed as well.
    Reference: MPJ van der Loo (2014) <doi:10.32614/RJ-2014-011>.
Provides an extendable, performant and multithreaded 'alt-string' implementation backed by 'C++' vectors and strings.
Pass named and unnamed character vectors into specified positions
    in strings. This represents an attempt to replicate some of python's string
    formatting.
A collection of character string/text/natural language
    processing tools for pattern searching (e.g., with 'Java'-like regular
    expressions or the 'Unicode' collation algorithm), random string generation,
    case mapping, string transliteration, concatenation, sorting, padding,
    wrapping, Unicode normalisation, date-time formatting and parsing,
    and many more. They are fast, consistent, convenient, and -
    thanks to 'ICU' (International Components for Unicode) -
    portable across all locales and platforms.
A consistent, simple and easy to use set of
    wrappers around the fantastic 'stringi' package. All function and
    argument names (and positions) are consistent, all functions deal with
    "NA"'s and zero length vectors in the same way, and the output from
    one function is easy to feed into the input of another.
English is the native language for only 5% of the World population.
    Also, only 17% of us can understand this text. Moreover, the Latin alphabet
    is the main one for merely 36% of the total.
    The early computer era, now a very long time ago, was dominated by the US.
    Due to the proliferation of the internet, smartphones, social media, and
    other technologies and communication platforms, this is no longer the case.
    This package replaces base R string functions (such as grep(),
    tolower(), sprintf(), and strptime()) with ones that fully
    support the Unicode standards related to natural language and
    date-time processing. It also fixes some long-standing inconsistencies,
    and introduces some new, useful features.
    Thanks to 'ICU' (International Components for Unicode) and 'stringi',
    they are fast, reliable, and portable across different platforms.
The strip function deletes components of R model outputs that are useless for specific purposes, such as predict[ing], print[ing], summary[izing], etc.
For making Trellis-type conditioning plots without strip labels.
    This is useful for displaying the structure of results from factorial designs
    and other studies when many conditioning variables would clutter the display
    with layers of redundant strip labels. Settings of the variables are encoded by
    layout and spacing in the trellis array and decoded by a separate legend. The
    functionality is implemented by a single S3 generic strucplot() function that
    is a wrapper for the Lattice package's xyplot() function. This allows access to
    all Lattice graphics capabilities in the usual way.
Extracts plain text from RTF (Rich Text Format) file.
Implements a spatio-temporal regression model based on Chi, G. and Zhu, J. (2019) Spatial Regression Models for the Social Sciences <isbn:9781544302072>. The approach here fits a spatial error model while incorporating a temporally lagged response variable and temporally lagged explanatory variables. This package builds on the errorsarlm() function from the spatialreg package.
Loading, identifying, aggregating, manipulating, and analysing short tandem repeat regions of massively parallel sequencing data in forensic genetics. 'STRMPS' can work with the package 'STRaitRazoR' (an R interface to the 'STRaitRazor' commandline tool) for added speed. 'STRaitRazoR' only works on linux and can found at <https://github.com/svilsen/STRaitRazoR>. The analyses and framework implemented in this package relies on the papers of Vilsen et al. (2017) <doi:10.1016/j.fsigen.2017.01.017> and Vilsen et al. (2018) <doi:10.1016/j.fsigen.2018.04.003>. Lastly, note that the parallelisation in the package relies on mclapply() and, thus, speed-ups will only be seen on UNIX based systems.
R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications, CRC Press.
Testing, monitoring and dating structural changes in (linear)
             regression models. strucchange features tests/methods from
	     the generalized fluctuation test framework as well as from
	     the F test (Chow test) framework. This includes methods to
	     fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM,
	     recursive/moving estimates) and F statistics, respectively.
             It is possible to monitor incoming data online using
             fluctuation processes.
             Finally, the breakpoints in regression models with structural
             changes can be estimated together with confidence intervals.
             Emphasis is always given to methods for visualizing the data.
A fast implementation with additional experimental features for
             testing, monitoring and dating structural changes in (linear)
             regression models. 'strucchangeRcpp' features tests/methods from
	     the generalized fluctuation test framework as well as from
	     the F test (Chow test) framework. This includes methods to
             fit, plot and test fluctuation processes (e.g. cumulative/moving
             sum, recursive/moving estimates) and F statistics, respectively.
             These methods are described in Zeileis et al. (2002)
             <doi:10.18637/jss.v007.i02>.
             Finally, the breakpoints in regression models with structural
             changes can be estimated together with confidence intervals,
             and their magnitude as well as the model fit can be evaluated
             using a variety of statistical measures.
Perform more powerful false discovery control (FDR) for microbiome data, taking into account the prior phylogenetic relationship among bacteria species.  As a general methodology, it is applicable to any type of (genomic) data with prior structure information.
Tree-structured modelling of categorical predictors (Tutz and Berger (2018), <doi:10.1007/s11634-017-0298-6>) or measurement
    units (Berger and Tutz (2018), <doi:10.1080/10618600.2017.1371030>).
Provides an efficient method to recover the missing block of an approximately low-rank matrix. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design [Cai T, Cai TT, Zhang A (2016) <doi:10.1080/01621459.2015.1021005>]. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. The main function in our package, smc.FUN(), is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21.
An open source platform for validation and process control.
    Tools to analyze data from internal validation of forensic short tandem
    repeat (STR) kits are provided. The tools are developed to provide
    the necessary data to conform with guidelines for internal validation
    issued by the European Network of Forensic Science Institutes (ENFSI)
    DNA Working Group, and the Scientific Working Group on DNA Analysis Methods
    (SWGDAM). A front-end graphical user interface is provided.
    More information about each function can be found in the
    respective help documentation.
Fit the basic structural time series model by maximum likelihood.
Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling.
  Schultze (2017) <doi:10.17169/refubium-622>.
Create stubs of functions for use while testing.
Download, navigate and analyse the Student-Life dataset. 
    The Student-Life dataset contains passive and automatic sensing data 
    from the phones of a class of 48 Dartmouth college students. 
    It was collected over a 10 week term. Additionally, the dataset contains ecological 
    momentary assessment results along with pre-study and post-study mental  
    health surveys. The intended use is to assess 
    mental health, academic performance and behavioral trends. 
    The raw dataset and additional information is 
    available at <https://studentlife.cs.dartmouth.edu/>.
Implements multi-study learning algorithms such as 
		merging, the study-specific ensemble (trained-on-observed-studies ensemble) the study strap, 
		the covariate-matched study strap, covariate-profile similarity weighting, and stacking weights. 
		Embedded within the 'caret' framework, this package allows for a wide range of 
		single-study learners (e.g., neural networks, lasso, random forests). 
		The package offers over 20 default similarity measures and allows for specification of custom 
		similarity measures for covariate-profile similarity weighting and an accept/reject step. 
		This implements methods described in Loewinger, Kishida, Patil, and Parmigiani. (2019)
		<doi:10.1101/856385>.
Implementations of the Single Transferable Vote counting 
    system. By default, it uses the Cambridge method for surplus allocation
    and Droop method for quota calculation.  Fractional surplus allocation
    and the Hare quota are available as options.
Estimation of
 multivariate Student's t dynamic regression models for a given degrees of freedom and lag length. Users can also specify the trends and dummies of any kind in matrix form.
Pretty-prints R code without changing the user's formatting
    intent.
Estimates distinctiveness in speakers' (authors') style. Fits models that can be used for predicting speakers of new texts. Methods developed in Huang et al (2020) <doi:10.1017/pan.2019.49>.
Supervised and unsupervised multivariate methods, supplemented by GUI and some visualizations, to perform various analyses in the field of computational stylistics, authorship attribution, etc. For further reference, see Eder et al. (2016), <https://journal.r-project.org/archive/2016/RJ-2016-007/index.html>. You are also encouraged to visit the Computational Stylistics Group's website <https://computationalstylistics.github.io/>, where a reasonable amount of information about the package and related projects are provided.
Calculate empirical subcopula and dependence measures from a given bivariate sample, and Bernstein copula approximations.
The three functions in the package compute the maximum likelihood estimates of the informants' competence scores, tests for two answer keys with known groups, and finds "best" split of the informants into sub-culture groups.
A test for the existence of a subgroup with enhanced treatment effect. And, a sample size calculation procedure for the subgroup detection test.
A formula 'sub' is a subformula of 'formula' if all the terms
    on the right hand side of 'sub' are terms of 'formula' and their left hand 
    sides are identical. This package aids in the creation of subformulas.
Produces various measures of expected treatment effect heterogeneity under an assumption of homogeneity across subgroups. Graphical presentations are created to compare these expected differences with the observed differences.
Provides functions for obtaining a variety of 
    graphical displays that may be useful in the subgroup analysis setting. 
    An example with a prostate cancer dataset is provided.
    The graphical techniques considered include level plots, mosaic plots,
    contour plots, bar charts, Venn diagrams, tree plots, forest plots, 
    Galbraith plots, L'Abbé plots, the subpopulation treatment effect pattern 
    plot, alluvial plots, circle plots and UpSet plots.
Classical methods for combining summary data from genome-wide
    association studies (GWAS) only use marginal genetic effects and power can
    be compromised in the presence of heterogeneity. 'subgxe' is a R package
    that implements p-value assisted subset testing for association (pASTA),
    a method developed by Yu et al. (2019) <doi:10.1159/000496867>. pASTA
    generalizes association analysis based on subsets by incorporating
    gene-environment interactions into the testing procedure.
Effect modification occurs if a treatment effect is larger or more stable in certain subgroups defined by observed covariates.  The submax or subgroup-maximum method of Lee et al. (2017) <arXiv:1702.00525> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.
Complementary indexes calculation to the Outlying Mean Index analysis to explore niche shift of a community and biological constraint within an Euclidean space, with graphical displays. For details see Karasiewicz et al. (2017) <doi:10.7717/Peerj.3364>.
Routines for identifying metabolic subpathways mediated by microRNAs (miRNAs) through topologically locating miRNAs and genes within reconstructed Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway graphs embedded by miRNAs. (1) This package can obtain the reconstructed KEGG metabolic pathway graphs with genes and miRNAs as nodes, through converting KEGG metabolic pathways to graphs with genes as nodes and compounds as edges, and then integrating miRNA-target interactions verified by low-throughput experiments from four databases (TarBase, miRecords, mirTarBase and miR2Disease) into converted pathway graphs. (2) This package can locate metabolic subpathways mediated by miRNAs by topologically analyzing the "lenient distance" of miRNAs and genes within reconstructed KEGG metabolic pathway graphs.(3) This package can identify significantly enriched miRNA-mediated metabolic subpathways based on located subpathways by hypergenomic test. (4) This package can support six species for metabolic subpathway identification, such as caenorhabditis elegans, drosophila melanogaster, danio rerio, homo sapiens, mus musculus and rattus norvegicus, and user only need to update interested organism-specific environment variables.
Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies.
The subplex algorithm for unconstrained optimization, developed by Tom Rowan <http://www.netlib.org/opt/subplex.tgz>.
Estimation of copula using ranks and subsampling. The main feature of this method is that simulation studies show a low sensitivity to dimension, on realistic cases.
This subsample winner algorithm (SWA) for regression with a large-p data (X, Y) selects the important variables (or features) among the p features X in explaining the response Y.  The SWA first uses a base procedure,  here a linear regression, on each of subsamples randomly drawn from the p variables, and then computes the scores of all features, i.e., the p variables,  according to the performance of these features collected in each of the subsample analyses. It then obtains the 'semifinalist' of the features based on the resulting scores and determines the 'finalists', i.e., the important features, from the 'semifinalist'.  Fan, Sun and Qiao (2017) <http://sr2c.case.edu/swa-reg/>.
Functions for computing subscores for a test using different
    methods in both classical test theory (CTT) and item response theory (IRT). This
    package enables three sets of subscoring methods within the framework of CTT
    and IRT: Wainer's augmentation method, Haberman's three subscoring methods, and
    Yen's objective performance index (OPI). The package also includes the function
    to compute Proportional Reduction of Mean Squared Errors (PRMSEs) in Haberman's
    methods which are used to examine whether test subscores are of added value.
A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria. Theoretical support for the heuristic search methods and exploratory data analysis criteria is in Cadima, Cerdeira, Minhoto (2003, <doi:10.1016/j.csda.2003.11.001>). Theoretical support for the leap and bounds algorithm and the criteria for the general multivariate linear model is in Duarte Silva (2001, <doi:10.1006/jmva.2000.1920>). There is a package vignette "subselect", which includes additional references.
The Subsemble algorithm is a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a unique form of k-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble. The paper, "Subsemble: An ensemble method for combining subset-specific algorithm fits" is authored by Stephanie Sapp, Mark J. van der Laan & John Canny (2014) <doi:10.1080/02664763.2013.864263>.
An interface to 'OpenSubspace', an open source framework for
  evaluation and exploration of subspace clustering algorithms in WEKA 
  (see <http://dme.rwth-aachen.de/de/opensubspace> for more
  information).  Also performs visualization.
Naive and adjusted treatment effect estimation for subgroups. Model 
  averaging (Bornkamp et.al, 2016 <doi:10.1002/pst.1796>) and bagging 
  (Rosenkranz, 2016 <doi:10.1002/bimj.201500147>) are proposed to address the
  problem of selection bias in treatment effect estimates for subgroups. 
  The package can be used for all commonly encountered type of outcomes in
  clinical trials (continuous, binary, survival, count). Additional functions
  are provided to build the subgroup variables to be used and to plot the 
  results using forest plots. For details, see Ballarini et.al. (2021) <doi:10.18637/jss.v099.i14>.
Chooses subgroup specific optimal doses in a phase I dose finding clinical trial allowing for subgroup combination and simulates clinical trials under the subgroup specific time to event continual reassessment method.  Chapple, A.G., Thall, P.F. (2018) <doi:10.1002/pst.1891>.
subtype performs a biclustering procedure on a input
        dataset and assess whether resulting clusters are promising
        subtypes. Note that the R-package rsmooth should be installed
        before implementing subtype. rsmooth can be downloaded from
        http://www.meb.ki.se/~yudpaw.
A systematic biology tool was developed to prioritize cancer subtype-specific drugs by integrating genetic perturbation, drug action, biological pathway, and cancer subtype. 
    The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially 
    expressed subpathways based on cultured human cells treated with drugs in the 'cMap' (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease 
    reverse association score based on subpathway, and visualization of results (Castelo (2013) <doi:10.1186/1471-2105-14-7>; Han et al (2019) <doi:10.1093/bioinformatics/btz894>; Lamb and Justin (2006) <doi:10.1126/science.1132939>). Please cite using <doi:10.1093/bioinformatics/btab011>.
Substitution matrices are important parameters in protein alignment algorithms. These matrices represent the likelihood that an amino acid will be substituted for another during mutation. This tool allows users to apply predefined and custom matrices and then explore the resulting alignments with interactive visualizations.  'SubVis' requires the availability of a web browser.
Interface to 'Sudachi' <https://github.com/WorksApplications/Sudachi>,
    a Japanese morphological analyzer. This is a port of what is available in Python.
Identify sudden gains based on the three criteria outlined by Tang and DeRubeis (1999) <doi:10.1037/0022-006X.67.6.894> to a selection of repeated measures. Sudden losses, defined as the opposite of sudden gains can also be identified. Two different datasets can be created, one including all sudden gains/losses and one including one selected sudden gain/loss for each case. It can extract scores around sudden gains/losses. It can plot the average change around sudden gains/losses and trajectories of individual cases.
Generates, plays, and solves Sudoku puzzles.  The GUI
        playSudoku() needs package "tkrplot" if you are not on Windows.
Tools for making, retrieving, displaying and solving sudoku games.
    This package is an alternative to the earlier sudoku-solver package,
    'sudoku'.  The present package uses a slightly different algorithm, has a
    simpler coding and presents a few more sugar tools, such as plot and print
    methods.  Solved sudoku games are of some interest in Experimental Design
    as examples of Latin Square designs with additional balance constraints.
This is a package for the subsampling method of robust
        estimation of linear regression models
Generates region-specific Suess and Laws corrections for 
    stable carbon isotope data from marine organisms collected between 1850 and 2020. Version
    0.1.3 of 'SuessR' contains four built-in regions: the Bering Sea ('Bering Sea'), the
    Aleutian archipelago ('Aleutians Islands'), the Gulf of Alaska ('Gulf of Alaska'), and the 
    subpolar North Atlantic ('Subpolar North Atlantic'). Users can supply their own environmental 
    data for regions currently not built into the package to generate corrections for those regions.
Create a hexagon tile map display from spatial polygons. Each 
    polygon is represented by a hexagon tile, placed as close to it's original
    centroid as possible, with a focus on maintaining spatial relationship to
    a focal point. Developed to aid visualisation and analysis of spatial 
    distributions across Australia, which can be challenging due to the 
    concentration of the population on the coast and wide open interior.
Provides 'ggplot2' graphics for analysing time
    series data. It aims to fit into the 'tidyverse' and grammar of
    graphics framework for handling temporal data.
An adaptation of classical region/gene-based association analysis
    techniques to the use of summary statistics (P values and effect sizes)
    and correlations between genetic variants as input. It is a tool to perform
    the most popular and efficient gene-based tests using the results of genome-wide
    association (meta-)analyses without having the original genotypes and
    phenotypes at hand.
Functions to speed up the exploratory analysis of simple
    datasets using 'dplyr'. Functions are provided to do the 
    common tasks of calculating confidence intervals.
Shrinkage estimator for polygenic risk prediction models based on summary statistics of genome-wide association studies.
Data frame summaries, cross-tabulations,
  weight-enabled frequency tables and common descriptive
  (univariate) statistics in concise tables available in a
  variety of formats (plain ASCII, Markdown and HTML). A good 
  point-of-entry for exploring data, both for experienced
  and new R users.
Provides methods for spatial and spatio-temporal smoothing of demographic and health indicators using survey data, with particular focus on estimating and projecting under-five mortality rates, described in Mercer et al. (2015) <doi:10.1214/15-AOAS872>, Li et al. (2019) <doi:10.1371/journal.pone.0210645> and Li et al. (2020) <arXiv:2007.05117>.
Application of theoretical results which ensure that the summation
  of an infinite discrete series is within an arbitrary margin of error of its
  true value. The C code under the hood is shared through header files to allow
  users to sum their own low level functions as well. Based on the paper by
  Braden (1992) <doi: 10.2307/2324995>.
It allows to quickly perform permutation-based closed testing by sum-based global tests, and construct lower confidence bounds for the TDP, simultaneously over all subsets of hypotheses. As a main feature, it produces simultaneous lower confidence bounds for the proportion of active voxels in different clusters for fMRI cluster analysis. Details may be found in Vesely, Finos, and Goeman (2020) <arXiv:2102.11759>.
Make interactive 'd3.js' sequence sunburst diagrams in R with the
    convenience and infrastructure of an 'htmlwidget'.
Get sun position, sunlight phases (times for sunrise, sunset, dusk, etc.),
        moon position and lunar phase for the given location and time. Most calculations are based on the 
        formulas given in Astronomy Answers articles about position of the sun and the planets : 
        <https://www.aa.quae.nl/en/reken/zonpositie.html>.
Survival analysis for unbalanced clusters using Archimedean copulas (Prenen et al. (2016) <DOI:10.1111/rssb.12174>).
Quantification of the effect of geographic versus environmental isolation on genetic differentiation
Provides a way to call the functions in 'SUNDIALS' C ODE solving library (<https://computing.llnl.gov/projects/sundials>). Currently the serial version of ODE solver, 'CVODE', sensitivity calculator 'CVODES' and differential algebraic solver 'IDA' from the 'SUNDIALS' library are implemented. The package requires ODE to be written as an 'R' or 'Rcpp' function and does not require the 'SUNDIALS' library to be installed on the local machine.
Tools for integrating spatially-misaligned GIS datasets. Part of the Sub-National Geospatial Data Archive System.
Suns-Voc (or Isc-Voc) curves can provide the current-voltage (I-V) characteristics of the
    diode of photovoltaic cells without the effect of series resistance.
    Here, Suns-Voc curves can be constructed with outdoor time-series I-V
    curves [1,2,3] of full-size photovoltaic (PV) modules instead of having to be measured in the lab.
    Time series of four different power loss modes can be calculated based on obtained Isc-Voc curves.
    This material is based upon work supported by the U.S. Department of
    Energy's Office of Energy Efficiency and Renewable Energy (EERE) under
    Solar Energy Technologies Office (SETO) Agreement Number DE-EE0008172. 
    Jennifer L. Braid is supported by the U.S. Department of Energy (DOE) Office of 
    Energy Efficiency and Renewable Energy administered by the Oak Ridge 
    Institute for Science and Education (ORISE) for the DOE. 
    ORISE is managed by Oak Ridge Associated Universities (ORAU) under DOE
    contract number DE-SC0014664. 
    [1] Wang, M. et al, 2018. 
    <doi:10.1109/PVSC.2018.8547772>. 
    [2] Walters et al, 2018
    <doi:10.1109/PVSC.2018.8548187>. 
    [3] Guo, S. et al, 2016. 
    <doi:10.1117/12.2236939>.
Functions for drawing samples according to Sunter's
        sampling design, and for computing first and second order
        inclusion probabilities
Implements the self-updating process clustering algorithms proposed
    in Shiu and Chen (2016) <doi:10.1080/00949655.2015.1049605>.
Methodology for supervised grouping aka "clustering" of
   potentially many predictor variables, such as genes etc, implementing
   algorithms 'PELORA' and 'WILMA'.
Clusters features under the assumption that each cluster has a
 random effect and there is an outcome variable that is related to the random 
 effects by a linear regression. In this way the cluster analysis is 
 ``supervised'' by the outcome variable. An alternate specification is that 
 features in each cluster have the same compound symmetric normal distribution, 
 and the conditional distribution of the outcome given the features
 has the same coefficient for each feature in a cluster.
Computes standard error and confidence interval of various descriptive statistics under 
    various designs and sampling schemes. The main function, superbPlot(), can either return a plot 
    or a dataframe with the statistic and its precision interval so that other plotting package
    can be used. See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> 
    or Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau (2005)
    <doi:10.20982/tqmp.01.1.p042>, Morey (2008) <doi:10.20982/tqmp.04.2.p061>, Baguley (2012)
    <doi:10.3758/s13428-011-0123-7>, Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>,
    Cousineau & O'Brien (2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding 
    <doi:10.20982/tqmp.15.1.p001> for specific references.
Biclusters are submatrices in the data matrix which
        satisfy certain conditions of homogeneity. Package contains
        functions for generating robust biclusters with respect to the
        initialization parameters for a given bicluster solution
        contained in a bicluster set in data, the procedure is also
        known as ensemble biclustering. The set of biclusters is
        evaluated based on the similarity of its elements (the
        overlap), and afterwards the hierarchical tree is constructed
        to obtain cut-off points for the classes of robust biclusters.
        The result is a number of robust (or super) biclusters with
        none or low overlap.
Creates superpixels based on input spatial data. 
  This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters).
  It is based on the SLIC algorithm (Achanta et al. (2012) <doi:10.1109/TPAMI.2012.120>), and readapts it to work with arbitrary dissimilarity measures.
A supervised compression method that incorporates the response for reducing big data to a carefully selected subset. Please see Joseph and Mak (2021) <doi:10.1002/sam.11508>. This research is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646.
The 'superdiag' package provides a comprehensive test suite for testing
  Markov Chain nonconvergence. It integrates five standard empirical MCMC convergence
  diagnostics (Gelman-Rubin, Geweke, Heidelberger-Welch, Raftery-Lewis, and Hellinger
  distance) and plotting functions for trace plots and density histograms. The functions
  of the package can be used to present all diagnostic statistics and graphs at once
  for conveniently checking MCMC nonconvergence.
Identification of sets of objects with shared features is a common operation in all disciplines. Analysis of intersections among multiple sets is fundamental for in-depth understanding of their complex relationships. This package implements a theoretical framework for efficient computation of statistical distributions of multi-set intersections based upon combinatorial theory, and provides multiple scalable techniques for visualizing the intersection statistics. The statistical algorithm behind this package was published in Wang et al. (2015) <doi:10.1038/srep16923>.
Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations.  This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300.  Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo.  The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R.
A system for generating extendable and customizable heatmaps for exploring complex datasets, including big data and data with multiple data types.
Implements the super learner prediction method and contains a
    library of prediction algorithms to be used in the super learner.
Witten and Tibshirani (2011) Supervised multidimensional scaling for visualization, classification, and bipartite ranking. Computational Statistics and Data Analysis 55(1): 789-801.
Adds a Super Learner ensemble model method (using the 'SuperLearner' package)  
    to the 'mice' package. Laqueur, H. S., Shev, A. B., Kagawa, R. M. C. (2021) <doi:10.1093/aje/kwab271>.
The idea is to provide a standard interface 
             to users who use both R and Python for building machine learning models. 
             This package provides a scikit-learn's fit, predict interface to 
             train machine learning models in R.
Produces ANOVA tables in the format used by Judd, McClelland, and Ryan 
    (2017, ISBN: 978-1138819832) in their introductory textbook, Data Analysis. This includes 
    proportional reduction in error and formatting to improve ease the transition between the book 
    and R.
Does prediction in the case of a censored survival outcome, or a regression outcome, using the "supervised principal component" approach. 'Superpc' is especially useful for high-dimensional data when the number of features p dominates the number of samples n (p >> n paradigm), as generated, for instance, by high-throughput technologies.
Dimension reduction of complex data with supervision from auxiliary information. The package contains a series of methods for different data types (e.g., multi-view or multi-way data) including the supervised singular value decomposition (SupSVD), supervised sparse and functional principal component (SupSFPC), supervised integrated factor analysis (SIFA) and supervised PARAFAC/CANDECOMP factorization (SupCP). When auxiliary data are available and potentially affect the intrinsic structure of the data of interest, the methods will accurately recover the underlying low-rank structure by taking into account the supervision from the auxiliary data. For more details, see the paper by Gen Li, <DOI:10.1111/biom.12698>.
Image Segmentation using Superpixels, Affinity Propagation and Kmeans Clustering. The R code is based primarily on the article "Image Segmentation using SLIC Superpixels and Affinity Propagation Clustering, Bao Zhou, International Journal of Science and Research (IJSR), 2013" <https://www.ijsr.net/archive/v4i4/SUB152869.pdf>.
Functions to perform simulations of ANOVA designs of up to three factors. Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2021). "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs". <doi:10.1177/2515245920951503>.
Tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap.
The Patient Rule Induction Method (PRIM) is typically
  used for "bump hunting" data mining to identify regions with abnormally
  high concentrations of data with large or small values. This package
  extends this methodology so that it can be applied to binary classification
  problems and used for prediction.
Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (<DOI: 10.1080/03610926.2016.1235200>).
Downloads data supplementary materials from manuscripts,
    using papers' DOIs as references. Facilitates open, reproducible
    research workflows: scientists re-analyzing published datasets can
    work with them as easily as if they were stored on their own
    computer, and others can track their analysis workflow
    painlessly. The main function suppdata() returns a (temporary)
    location on the user's computer where the file is stored, making
    it simple to use suppdata() with standard functions like
    read.csv().
Ten distributions supplementing those built into R.
  Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi
  squared, Spearman's rho, maximum F ratio, the Pearson product
  moment correlation coefficient, Johnson distributions, normal
  scores and generalized hypergeometric distributions.
Provides functions sp() and sp_seq() for computing the support points in Mak and Joseph (2018) <DOI:10.1214/17-AOS1629>. Support points can be used as a representative sample of a desired distribution, or a representative reduction of a big dataset (e.g., an "optimal" thinning of Markov-chain Monte Carlo sample chains). This work was supported by USARO grant W911NF-14-1-0024 and NSF DMS grant 1712642.
Provides basic functions that support an implementation of object case (Case 1) best-worst scaling: a function for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; two functions for creating a data set suitable for analysis; a function for calculating count-based scores; a function for calculating shares of preference; and a function for generating artificial responses to questions. For details on best-worst scaling, see Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
Provides three basic functions that support an implementation of Case 2 (profile case) best-worst scaling. The first is to convert an orthogonal main-effect design into questions, the second is to create a dataset suitable for analysis, and the third is to calculate count-based scores. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.
Provides basic functions that support an implementation of multi-profile case (Case 3) best-worst scaling (BWS). Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for the analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
Provides basic functions that support an implementation of (discrete) choice experiments (CEs). CEs is a question-based survey method measuring people's preferences for goods/services and their characteristics. Refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831> for details on CEs, and Aizaki (2012) <doi:10.18637/jss.v050.c02> for the package.
Calculates likelihood based support intervals for
    several common data types including binomial, Poisson, normal, lm(), and
    glm(). For the binomial, Poisson, and normal data likelihood intervals are
    calculated via root finding algorithm. Additional parameters allow the
    user to specify whether they would like to receive a parametric bootstrap
    estimate of the confidence level of said support interval. For lm() and glm(),
    the function returns profile likelihoods for each coefficient in the model.
A modeling tool helping users better structure 'Shiny'
  applications developed with 'Shiny' modules. Users are able to: 1. Visualize
  relationship of modules in existing applications 2. Design new applications
  from scratch.
Access to the datasets and many of the functions used in "Statistics Using R: An Integrative Approach". These datasets include a subset of the National Education Longitudinal Study, the Framingham Heart Study, as well as several simulated datasets used in the examples throughout the textbook. The functions included in the package reproduce some of the functionality of 'Stata' that is not directly available in 'R'.  The package also contains a tutorial on basic data frame management, including how to handle missing data.
Implementation of the direct Monte Carlo approach of 
             Zellner and Ando (2010) <doi:10.1016/j.jeconom.2010.04.005>
             to sample from posterior of 
             Seemingly Unrelated Regression (SUR) models. In 
             addition, a Gibbs sampler is implemented that allows 
             the user to analyze SUR models using the power prior.
An implementation of the surrogate approach to residuals and 
  diagnostics for ordinal and general regression models; for details, see Liu 
  and Zhang (2017) <doi:10.1080/01621459.2017.1292915>. These residuals can be 
  used to construct standard residual plots for model diagnostics (e.g., 
  residual-vs-fitted value plots, residual-vs-covariate plots, Q-Q plots, etc.). 
  The package also provides an 'autoplot' function for producing standard 
  diagnostic plots using 'ggplot2' graphics. The package currently supports 
  cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM'. 
  Support for binary regression models using the standard 'glm' function is also 
  available.
A statistical learning method to simultaneously predict a range of target phenotypes using codified and natural language processing (NLP)-derived Electronic Health Record (EHR) data. See Ahuja et al (2020) JAMIA <doi:10.1093/jamia/ocaa079> for details.
Estimation of gross flows under non-response and complex sampling designs, using Gutiérrez, Nascimento Silva and Trujillo (2014) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2014002/article/14113-eng.pdf> complex sampling extension of the non-response model developed by Stasny (1987) <https://www.scb.se/contentassets/ca21efb41fee47d293bbee5bf7be7fb3/some-markov-chain-models-for-nonresponse-in-estimating-gross-labor-force-flows.pdf>. 
    It estimates the gross flows process under non-response by modelling the observable cross-tabulation counts as a two-stage Markov Chain process, combining (1) the unobservable Markov Chain describing the transition of states; and (2) the non-response process, given by the initial response probabilities and the response/non-response transition probabilities.
This data-driven phylogenetic comparative method fits stabilizing selection models to continuous trait data, building on the 'ouch' methodology of Butler and King (2004) <doi:10.1086/426002>. The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak. For more information see Ingram and Mahler (2013) <doi:10.1111/2041-210X.12034>.
Create sampling designs using the surface reconstruction algorithm.
  Original method by: Olsson, D. 2002. A method to optimize soil sampling from 
  ancillary data. Poster presenterad at: NJF seminar no. 336, 
  Implementation of Precision Farming in Practical Agriculture, 10-12 
  June 2002, Skara, Sweden.
In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
Provides functions to estimate the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, Tian, Cai (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1>. The main functions are (1) R.q.event() which calculates the proportion of the treatment effect (the difference in restricted mean survival time at time t) explained by surrogate outcome information observed up to a selected landmark time, (2) R.t.estimate() which calculates the proportion of the treatment effect explained by primary outcome information only observed up to a selected landmark time, and (3) IV.event() which calculates the incremental value of the surrogate outcome information.
Performs estimation and inference on a partially missing target outcome while borrowing information from a correlated surrogate outcome to increase estimation precision and improve power. The target and surrogate outcomes are jointly modeled within a bivariate outcome regression framework. Unobserved values of either outcome are regarded as missing data. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization algorithm. A flexible association test is provided for evaluating hypotheses about the target regression parameters. See McCaw ZR, Gaynor SM, Sun R, Lin X; “Cross-tissue eQTL mapping in the presence of missing data via surrogate outcome analysis” <doi:10.1101/2020.11.29.403063>.
Provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2019) ``Using a Surrogate Marker for Early Testing of a Treatment Effect" Biometrics, 75(4):1253-1263. <doi:10.1111/biom.13067>.
Provides functions for the evaluation of
    surrogate endpoints when both the surrogate and the true endpoint are failure
    time variables. The approaches implemented are:
    (1) the two-step approach (Burzykowski et al, 2001) <DOI:10.1111/1467-9876.00244> with a copula model (Clayton, Plackett, Hougaard) at
    the first step and either a linear regression of log-hazard ratios at the second
    step (either adjusted or not for measurement error);
    (2) mixed proportional hazard models estimated via mixed Poisson GLM
    (Rotolo et al, 2019 <DOI:10.1177/0962280217718582>).
Nonparametric and semiparametric estimations of the time-dependent ROC curve for an incomplete failure time data with surrogate failure time endpoints.
suRtex was designed for easy descriptive statistic reporting of categorical survey data (e.g., Likert scales) in LaTeX. suRtex takes a matrix or data frame and produces the LaTeX code necessary for a sideways table creation. Mean, median, standard deviation, and sample size are optional.
Performs inference of several model-free group contrast measures, which include difference/ratio of cumulative incidence rates at given time points, quantiles, and restricted mean survival times (RMST). Two kinds of covariate adjustment procedures (i.e., regression and augmentation) for inference of the metrics based on RMST are also included.
The package provides a variety of functions to estimate
        time-dependent true/false positive rates and AUC curves from a
        set of censored survival data.
Tests for equality of two survival functions based on integrated weighted differences of two Kaplan-Meier curves.
Three new methods to perform outlier detection in a survival context. In total there are six methods provided, the first three methods are traditional residual-based outlier detection methods, the second three are the concordance-based. Package developed during the work on the two following publications: Pinto J., Carvalho A. and Vinga S. (2015) <doi:10.5220/0005225300750082>; Pinto J.D., Carvalho A.M., Vinga S. (2015) <doi:10.1007/978-3-319-27926-8_22>.
Performs inference for C of risk prediction models with censored survival data, using the method proposed by Uno et al. (2011) <doi:10.1002/sim.4154>. Inference for the difference in C between two competing prediction models is also implemented.
Estimates correlation coefficients with associated
   confidence limits 
   for bivariate, partially censored survival times. Uses
   the iterative multiple imputation approach proposed
   by Schemper, Kaider, Wakounig and Heinze, Statistics
   in Medicine 2013. Provides a scatterplot function to visualize the bivariate 
   distribution, either on the original time scale or as copula.
Plots survival models from the 'survival' package. Additionally, it
    plots curves of multistate models from the 'mstate' package. Typically, a
    plot is drawn by the sequence survplot(), confIntArea(), survCurve() and
    nrAtRisk(). The separation of the plot in this 4 functions allows for great
    flexibility to make a custom plot for publication.
Various functions for discrete time survival analysis and longitudinal analysis. SIMEX method for correcting for bias for errors-in-variables
  in a mixed effects model. Asymptotic mean and variance of different proportional hazards test statistics using different ties methods given two
  survival curves and censoring distributions. Score test and Wald test for regression analysis of grouped survival data. Calculation of survival
  curves for events defined by the response variable in a mixed effects model crossing a threshold with or without confirmation.
Fits time series models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. Calculates Theil's index to measure inequality among multiple groups, and can be extended to measure inequality across multiple groups nested within geographies. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. References: Brandt and Williams (2007, ISBN:978-1-4129-0656-2); Clayton (1996, ISBN-13:978-0-412-05551-5); Stan Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).
Computing the one-sided/two-sided integrated/maximally selected EL statistics for simultaneous testing, the one-sided/two-sided EL tests for pointwise testing, and an initial test that precedes one-sided testing to exclude the possibility of crossings or alternative orderings among the survival functions.
Relative survival, AER and SMR based on French death rates
Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement.
Tools for using different kinds of bootstrap
    for estimating sampling variation using complex survey
    data.
Functions to generate K-fold cross validation (CV) folds
    and CV test error estimates that take into account
    how a survey dataset's sampling design was constructed
    (SRS, clustering, stratification, and/or unequal sampling weights).
    You can input linear and logistic regression models, along with data and a 
    type of survey design in order to get an output that can help you determine
    which model best fits the data using K-fold cross validation.
    Our paper on "K-Fold Cross-Validation for Complex Sample Surveys"
    by Wieczorek, Guerin, and McMahon (2022)
    <doi:10.1002/sta4.454>
    explains why differing how we take folds based on survey design is useful.
Data obtained from surveys contains information not only about the
    survey responses, but also the survey metadata, e.g. the original survey
    questions and the answer options. The 'surveydata' package makes it easy to
    keep track of this metadata, and to easily extract columns with
    specific questions.
Help generate slides for surveys or experiments.
 The resulted slides allow the subject to respond with the use of the mouse (usual keyboard input is 
 replaced with clicking on a virtual keyboard on the slide). Subjects' responses are saved to the user-
 specified location in the form of R-readable text file. To allow flexibility, each function in 
 this package generates a particular type of slides thus general R function writing skills are 
 required to compile these edited slides.
At present, the only functionality is the calculation of optimal one-sided winsorizing cutoffs. The main function is optimal.onesided.cutoff.bygroup. It calculates the optimal tuning parameter for one-sided winsorisation, and so calculates winsorised values for a variable of interest. See the help file for this function for more details and an example.
Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation.
Calculate point estimates and their standard errors in complex household surveys using bootstrap replicates. Bootstrapping considers survey design with a rotating panel. A comprehensive description of the methodology can be found under <https://statistikat.github.io/surveysd/articles/methodology.html>.
Contains a suite of functions for survival analysis in health economics. 
    These can be used to run survival models under a frequentist (based on maximum likelihood) 
    or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian 
    Monte Carlo). The user can specify a set of parametric models using a common notation and 
    select the preferred mode of inference. The results can also be post-processed to produce 
    probabilistic sensitivity analysis and can be used to export the output to an Excel 
    file (e.g. for a Markov model, as often done by modellers and practitioners). <doi:10.18637/jss.v095.i14>.
High dimensional time to events data analysis with variable selection technique.
             Currently support LASSO, clustering and Bonferroni's correction.
Performs inference for a class of measures to compare
        competing risk prediction models with censored survival data.
        The class includes the integrated discrimination improvement
        index (IDI) and category-less net reclassification index (NRI).
Newly developed methods for the estimation of several probabilities
      in an illness-death model. The package can be used to obtain nonparametric and 
      semiparametric estimates for: transition probabilities, occupation probabilities, 
      cumulative incidence function and the sojourn time distributions. 
      Additionally, it is possible to fit proportional hazards regression models
      in each transition of the Illness-Death Model. Several auxiliary 
      functions are also provided which can be used for marginal 
      estimation of the survival functions.
High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). 
              Currently support frailty data analysis. Allows for Weibull and 
              Exponential distribution. Includes function for interval censored data.
Contains the core survival analysis routines, including
	     definition of Surv objects, 
	     Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
	     and parametric accelerated failure time models.
Implementation of methodology designed to perform: (i) variable 
    selection, (ii) effect estimation, and (iii) uncertainty quantification, 
    for high-dimensional survival data. Our method uses a spike-and-slab prior 
    with Laplace slab and Dirac spike and approximates the corresponding 
    posterior using variational inference, a popular method in machine learning 
    for scalable conditional inference. Although approximate, the variational 
    posterior provides excellent point estimates and good control of the false 
    discovery rate. For more information see Komodromos et al. (2021) 
    <arXiv:2112.10270>.
Functions can be used for batch survival analysis, 
	but not only for it. Most importantly, it can verify any
 	P-value calculated according to the gene expression level and 
	eliminate the influence of co-expression genes.
A high-level interface to perform survival analysis, 
    including Kaplan-Meier analysis and log-rank tests and Cox regression.
    Aims at providing a clear and elegant syntax, support for use in a pipeline, structured output and plotting.
    Builds upon the 'survminer' package for Kaplan-Meier plots and provides
    a customizable implementation for forest plots.
    Kaplan & Meier (1958) <doi:10.1080/01621459.1958.10501452>
    Cox (1972) <JSTOR:2985181>
    Peto & Peto (1972) <JSTOR:2344317>.
Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk, survival probabilities, or survival distributions with 'distr6' <https://CRAN.R-project.org/package=distr6>. Models are either implemented from 'Python' via 'reticulate' <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using 'Rcpp' <https://CRAN.R-project.org/package=Rcpp>. Novel machine learning survival models wil be included in the package in near-future updates. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/pycox> and are detailed by Kvamme et al. (2019) <https://jmlr.org/papers/v20/18-424.html>. The 'Akritas' estimator is defined in Akritas (1994) <doi:10.1214/aos/1176325630>. 'DNNSurv' is defined in Zhao and Feng (2020) <arXiv:1908.02337>.
Estimate the regression coefficients and the baseline hazard 
      of proportional hazard Cox models with left, right or interval censored survival data 
      using maximum penalised likelihood. A 'non-parametric' smooth estimate of the baseline hazard
      function is provided.
Fitting Cox proportional hazard model under dependent right censoring using copula and maximum penalised likelihood methods.
Provides estimates for the bivariate and trivariate distribution 
             functions and bivariate and trivariate survival functions for 
             censored gap times. Two approaches, using existing methodologies, 
             are considered: (i) the Lin's estimator, which is based on the 
             extension the Kaplan-Meier estimator of the distribution function 
             for the first event time and the Inverse Probability of Censoring 
             Weights for the second time (Lin DY, Sun W, Ying Z (1999) 
             <doi:10.1093/biomet/86.1.59> and (ii) another estimator
             based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008)
             <https://w3.math.uminho.pt/~lmachado/Biometria_conference.pdf>). 
             The proposed methods are the landmark estimators based on 
             subsampling approach, and the estimator based on weighted cumulative
             hazard estimator. The package also provides nonparametric estimator
             conditional to a given continuous covariate. All these methods have
             been submitted to be published.
Compute time-dependent ROC curve from censored survival
        data using Kaplan-Meier (KM) or Nearest Neighbor Estimation
        (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No
        2, 2000, PP 337-344)
Performs support vectors analysis for data sets with survival
    outcome. Three approaches are available in the package: The regression approach
    takes censoring into account when formulating the inequality constraints of
    the support vector problem. In the ranking approach, the inequality constraints
    set the objective to maximize the concordance index for comparable pairs
    of observations. The hybrid approach combines the regression and ranking
    constraints in the same model.
Several datasets which detail the results and events of each season of Survivor. This includes 
  details on the cast, voting history, immunity and reward challenges, jury votes and viewers. This data is 
  useful for practicing data wrangling, graph analytics and analysing how each season of Survivor played out. 
  Includes 'ggplot2' scales and colour palettes for visualisation.
Microarray gene expression data can be analyzed individually or jointly using merging methods or meta-analysis to predict patients' survival and risk assessment.
Three breast cancer gene expression data sets that can be used for package 'survJamda'. This package contains the gene expression and phenotype data of GSE1992, GSE3143 and GSE4335.
Provides kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates. Cao H., Churpek M. M., Zeng D., and Fine J. P. (2015) <doi:10.1080/01621459.2014.957289>.
An implementation of popular evaluation metrics that are commonly used in survival prediction 
  including Concordance Index, Brier Score, Integrated Brier Score, 
  Integrated Square Error, Integrated Absolute Error and Mean Absolute Error.
  For a detailed information, see (Ishwaran H, Kogalur UB, Blackstone EH and Lauer MS (2008) <doi:10.1214/08-AOAS169>) and
  (Moradian H, Larocque D and Bellavance F (2017) <doi:10.1007/s10985-016-9372-1>) for different evaluation metrics.
In clinical trials, endpoints are sometimes evaluated with uncertainty. Adjudication is commonly adopted to ensure the study integrity. We propose to use multiple imputation (MI) introduced by Robin (1987) <doi:10.1002/9780470316696> to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) <doi:10.1016/S0197-2456(00)00053-2> were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods.
Contains the function 'ggsurvplot()' for drawing easily beautiful
    and 'ready-to-publish' survival curves with the 'number at risk' table
    and 'censoring count plot'. Other functions are also available to plot 
    adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions.
A collection of functions to help in the analysis of
    right-censored survival data. These extend the methods available in
    package:survival.
Sample size and effect size calculations for survival endpoints based on mixture survival-by-response model. The methods implemented can be found in  Bofill, Shen & Gómez (2021) <arXiv:2008.12887>.
Package enables the data simulation from different survival models (Cox, AFT, and AH models). The simulated data will have various levels of complexity according to the survival model considered. The implemented methods for the Cox model are described in Ralf Bender, Thomas Augustin, Maria Blettner (2004) <doi:10.1002/sim.2059>.
Perform survival simulation with parametric survival model generated from 'survreg' function in 'survival' package.
    In each simulation coefficients are resampled from variance-covariance matrix of parameter estimates to 
    capture uncertainty in model parameters.
    Prediction intervals of Kaplan-Meier estimates and hazard ratio of treatment effect can be further calculated using simulated survival data.
Fits hazard and excess hazard models with multidimensional penalized splines allowing for 
        time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for 
        multidimensional penalized hazard and excess hazard models. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics. 
	See Fauvernier et al. (2019a) <doi:10.21105/joss.01434> for an overview of the package and Fauvernier et al. (2019b) <doi:10.1111/rssc.12368> for the method.
Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data. For details, see Lopez-de-Ullibarri and Jacome (2013) <doi:10.18637/jss.v054.i11>.
The function SurvRegCens() of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions. Hubeaux, S. and Rufibach, K. (2014) <arXiv:1402.0432>.
Performs two-sample comparisons using the restricted mean survival time (RMST) as a summary measure of the survival time distribution. Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed. It performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures.
Performs the procedure proposed by Horiguchi et al. (2018) <doi:10.1002/sim.7661>. The method specifies a set of truncation time points tau's for calculating restricted mean survival times (RMST), performs testing for equality, and estimates the difference in RMST between two groups at the specified tau's. Multiplicity by specifying several tau's is taken into account in this procedure.
Performs the permutation test using difference in the restricted mean survival time (RMST) between groups as a summary measure of the survival time distribution. When the sample size is less than 50 per group, it has been shown that there is non-negligible inflation of the type I error rate in the commonly used asymptotic test for the RMST comparison. Generally, permutation tests can be useful in such a situation. However, when we apply the permutation test for the RMST comparison, particularly in small sample situations, there are some cases where the survival function in either group cannot be defined due to censoring in the permutation process. Horiguchi and Uno (2020) <doi:10.1002/sim.8565> have examined six workable solutions to handle this numerical issue. It performs permutation tests with implementation of the six methods outlined in the paper when the numerical issue arises during the permutation process. The result of the asymptotic test is also provided for a reference.
Performs a dual-parameter sensitivity analysis of treatment effect to unmeasured confounding in observational studies with either survival or competing risks outcomes. Huang, R., Xu, R. and Dulai, P.S.(2019) <arXiv:1908.01444>.
Simulation of simple and complex survival data including recurrent and multiple events and competing risks. See Moriña D, Navarro A. (2014) <doi:10.18637/jss.v059.i02> and Moriña D, Navarro A. (2017) <doi:10.1080/03610918.2016.1175621>.
Conduct asymptotic and empirical power and sample size calculations for Single-Nucleotide Polymorphism (SNP) association studies with right censored time to event outcomes.
Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021), Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival Data, Biometrics (under revision). The package also provides functions that visualize bivariate survival data and bivariate probability mass function.
Targeted estimates of marginal cumulative incidence in survival
    settings with and without competing risks, including estimators that respect
    bounds (Benkeser, Carone, and Gilbert. Statistics in Medicine, 2017.
    <doi:10.1002/sim.7337>).
Package performs Cox regression and survival distribution function estimation when the survival times are subject to double truncation. The estimation procedure for each method involves inverse probability weighting, where the weights correspond to the inverse of the selection probabilities and are estimated using the survival times and truncation times only. Both methods require that the survival and truncation times are quasi-independent. A test for checking this independence assumption is also included in this package. The functions available in this package for Cox regression, survival distribution function estimation, and testing independence under double truncation are based on the following methods, respectively: Rennert and Xie (2017) <doi:10.1111/biom.12809>, Shen (2010) <doi:10.1007/s10463-008-0192-2>, Martin and Betensky (2005) <doi:10.1198/016214504000001538>.
Implements methods for variable selection in linear
    regression based on the "Sum of Single Effects" (SuSiE) model, as
    described in Wang et al (2020) <DOI:10.1101/501114> and Zou et al
    (2021) <DOI:10.1101/2021.11.03.467167>. These methods provide
    simple summaries, called "Credible Sets", for accurately
    quantifying uncertainty in which variables should be selected.
    The methods are motivated by genetic fine-mapping applications,
    and are particularly well-suited to settings where variables are
    highly correlated and detectable effects are sparse. The fitting
    algorithm, a Bayesian analogue of stepwise selection methods
    called "Iterative Bayesian Stepwise Selection" (IBSS), is simple
    and fast, allowing the SuSiE model be fit to large data sets
    (thousands of samples and hundreds of thousands of variables).
The alpha-Sutte indicator (alpha-Sutte) was originally from developed of Sutte indicator. Sutte indicator can using to predict the movement of stocks. As the development of science, then Sutte indicator developed to predict not only the movement of stocks but also can forecast data on financial, insurance, and others time series data. Ahmar, A.S. (2017) <doi:10.17605/osf.io/rknsv>.
Implements the s-values proposed by Ed. Leamer.
    It provides a context-minimal approach for sensitivity analysis using extreme
    bounds to assess the sturdiness of regression coefficients.
Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. 
             Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural 
             impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>),  patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>),
             independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), 
             smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).
R bindings to SVD and eigensolvers (PROPACK, nuTRLan).
Quickly construct standard dialog boxes for your GUI, including 
  message boxes, input boxes, list, file or directory selection, ... In case R
  cannot display GUI dialog boxes, a simpler command line version of these
  interactive elements is also provided as fallback solution.
Reimplementation of the svDialogs dialog boxes in Tcl/Tk
Some visualization tools based on Singular Value Decomposition
Obtain parameters of Svensson's Method, including percentage agreement, 
    systematic change and individual change. Also, the contingency table can be generated. 
    Svensson's Method is a rank-invariant nonparametric method for the analysis of ordered scales 
    which measures the level of change both from systematic and individual aspects. For the details, 
    please refer to Svensson E. Analysis of systematic and random differences between paired ordinal 
    categorical data [dissertation]. Stockholm: Almqvist & Wiksell International; 1993.
A graphics device for R that produces 'Scalable Vector Graphics'.
  'svglite' is a fork of the older 'RSvgDevice' package.
This 'htmlwidget' provides pan and zoom interactivity to R
    graphics, including 'base', 'lattice', and 'ggplot2'. The interactivity is
    provided through the 'svg-pan-zoom.js' library. Various options to the widget
    can tailor the pan and zoom experience to nearly any user desire.
The purpose of this package is to manipulate SVG files that are templates of charts the user wants to produce. 
    In vector graphics one copes with x-/y-coordinates of elements (e.g. lines, rectangles, text). Their scale is often dependent on the program that is used to produce the graphics. 
    In applied statistics one usually has numeric values on a fixed scale (e.g. percentage values between 0 and 100) to show in a chart. 
    Basically, 'svgtools' transforms the statistical values into coordinates and widths/heights of the vector graphics.
    This is done by stackedBar() for bar charts, by linesSymbols() for charts with lines and/or symbols (dot markers) and scatterSymbols() for scatterplots.
The 'SciViews' 'svGUI' package eases the management of Graphical
  User Interfaces (GUI) in R. It is independent from any particular GUI widgets
  ('Tk', 'Gtk2', native, ...). It centralizes info about GUI elements currently
  used, and it dispatches GUI calls to the particular toolkits in use in
  function of the context (is R run at the terminal, within a 'Tk' application,
  a HTML page?).
Creates 3D animated, interactive visualizations that can be viewed in a web browser.
Implements a simple HTTP server allowing to connect GUI clients to R
Function for the GUI API to interact with external IDE/code editors.
Functions to manage the GUI client, like Komodo with the
  SciViews-K extension
Miscellaneous functions for 'SciViews' or general use: manage a
  temporary environment attached to the search path for temporary variables you
  do not want to save() or load(), test if 'Aqua', 'Mac', 'Win', ... Show
  progress bar, etc.
Implements the SVM-Maj algorithm to train data with support vector machine
  as described in Groenen et al. (2008) <doi:10.1007/s11634-008-0020-9>.
  This algorithm uses two efficient updates, one for linear kernel and one 
  for the nonlinear kernel.
Causal effect estimation in observational data often requires identifying a set of untreated observations that are comparable to some treated group of interest.  This package provides a suite of functions for identifying such a set of observations and for implementing standard and new diagnostics tools.  The primary function, svmmatch(), uses support vector machines to identify a region of common support between treatment and control groups.  A sensitivity analysis, balance checking, and assessment of the region of overlap between treated and control groups is included.  The Bayesian implementation allows for recovery of uncertainty estimates for the treatment effect and all other parameters.
Computes the entire regularization path for the two-class svm classifier
		with essentially the same cost as a single SVM fit.
Implementation of Support Vector Machines Plus (SVM+) for classification problems. See (Vladimir et. al, 2009, <doi:10.1016/j.neunet.2009.06.042>) for theoretical details and see (Li et. al, 2016, <https://github.com/okbalefthanded/svmplus_matlab>) for implementation details in 'MATLAB'.
Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>.
Two versions of sample variance plots, Sv-plot1 and Sv-plot2, will be provided illustrating 
             the squared deviations from sample variance. Besides indicating the contribution of squared 
             deviations for the sample variability, these plots are capable of detecting characteristics of the
             distribution such as symmetry, skewness and outliers. A remarkable graphical method based on 
             Sv-plot2 can determine the decision on testing hypotheses over one or two population means. 
             In sum, Sv-plots will be appealing visualization tools. Complete description of this methodology  
             can be found in the article, Wijesuriya (2020) <doi:10.1080/03610918.2020.1851716>.
Provides tools for creating and working with survey replicate weights,
  extending functionality of the 'survey' package from Lumley (2004) <doi:10.18637/jss.v009.i08>.
  Methods are provided for applying nonresponse adjustments to
  both full-sample and replicate weights as suggested by 
  Rust and Rao (1996) <doi:10.1177/096228029600500305> in order to account for
  the impact of these adjustments on sampling variances.
  Diagnostic functions are included to compare weights and weighted estimates
  from different sets of replicate weights.
Computes the entire solution paths for Support Vector Regression(SVR) with respect to the regularization parameter, lambda and epsilon in epsilon-intensive loss function, efficiently. We call each path algorithm svrpath and epspath. See Wang, G. et al (2008) <doi:10.1109/TNN.2008.2002077> for details regarding the method.
Various tools for semantic vector spaces, such as
    correspondence analysis (simple, multiple and discriminant), latent
    semantic analysis, probabilistic latent semantic analysis, non-negative
    matrix factorization, latent class analysis, EM clustering, logratio
	analysis and log-multiplicative (association) analysis. Furthermore,
    there are specialized distance measures, plotting functions and some helper
    functions.
Implements a socket server allowing to connect clients to R.
Supporting functions for the GUI API (Sweave functions)
Set of tools aimed at wrapping some of the functionalities of the
  packages tools, utils and codetools into a nicer format so that an IDE can use
  them.
A complete unit test system and functions to implement its GUI part.
High level management of widgets, windows and other graphical resources.
Contains functions for computing diagnostics for fixed effects linear regression models fitted with survey data. Extensions of standard diagnostics to complex survey data are included: standardized residuals, leverages, Cook's D, dfbetas, dffits, condition indexes, and variance inflation factors.
Builds, evaluates and validates a nomogram with survey data and right-censored outcomes.
Provides inference based on the survey package for the wide range of parametric models in the 'VGAM' package.
This algorithm conducts variable selection in the classification setting. It repeatedly subsamples variables and runs linear discriminant analysis (LDA) on the subsampled variables. Variables are scored based on the AUC and the t-statistics. Variables then enter a competition and the semi-finalist variables will be evaluated in a final round of LDA classification. The algorithm then outputs a list of variable selected. Qiao, Sun and Fan (2017) <http://people.math.binghamton.edu/qiao/swa.html>.
An algorithm that trains a meta-learning procedure that combines 
    screening and wrapper methods to find a set of extremely low-dimensional attribute 
    combinations. This package works on top of the 'caret' package and proceeds in a 
    forward-step manner. More specifically, it builds and tests learners starting 
    from very few attributes until it includes a maximal number of attributes by 
    increasing the number of attributes at each step. Hence, for each fixed number
    of attributes, the algorithm tests various (randomly selected) learners and 
    picks those with the best performance in terms of training error. Throughout,
    the algorithm uses the information coming from the best learners at the previous
    step to build and test learners in the following step. In the end, it outputs
    a set of strong low-dimensional learners.
A collection of 'HTML', 'JavaScript', and 'CSS' assets that
  dynamically generate beautiful documentation from a 'Swagger' compliant API:
  <https://swagger.io/specification/>.
Collection of functions to connect the structure of the data with the information on the samples. Three types of associations are covered: 1. linear model of principal components. 2. hierarchical clustering analysis. 3. distribution of features-sample annotation associations. Additionally, the inter-relation between sample annotations can be analyzed. Simple methods are provided for the correction of batch effects and removal of principal components.
Three ensemble learning algorithms based on support vector machines. 
    They all train support vector machines on subset of data and combine the result.
There are numerous places to create and download color
    palettes. These are usually shared in 'Adobe' swatch file formats of
    some kind. There is also often the need to use standard palettes
    developed within an organization to ensure that aesthetics are carried over
    into all projects and output. Now there is a way to read these swatch
    files in R and avoid transcribing or converting color values by hand or
    or with other programs. This package provides functions to read and
    inspect 'Adobe Color' ('ACO'), 'Adobe Swatch Exchange' ('ASE'), 'GIMP Palette' ('GPL'),
    'OpenOffice' palette ('SOC') files and 'KDE Palette' ('colors') files. Detailed
    descriptions of 'Adobe Color' and 'Swatch Exchange' file formats as well as other
    swatch file formats can be found at
    <http://www.selapa.net/swatches/colors/fileformats.php>.
A set of tools for examining the design and analysis aspects of stepped wedge cluster randomized trials (SW CRT) based on a repeated cross-sectional sampling scheme (Hussey MA and Hughes JP (2007) Contemporary Clinical Trials 28:182-191. <doi:10.1016/j.cct.2006.05.007>).
Implements the Sliding Window Discrete Fourier Transform (SWDFT). Also provides 
             statistical methods based on the SWDFT, and graphical tools to display the outputs.
To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) <doi:10.1093/biostatistics/kxy031>, Li et al. (2018) <doi:10.1111/biom.12918>. Supplementary documents can be found at: <https://ysph.yale.edu/cmips/research/software/swdpwr/>. The Shiny app for swdpwr can be accessed at: <https://jiachenchen322.shinyapps.io/swdpwr_shinyapp/>. The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively.
Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), <doi:10.1126/science.aal4230>, Relative Norm Distance (Garg et al., 2018), <doi:10.1073/pnas.1720347115>, Mean Average Cosine Similarity (Mazini et al., 2019) <arXiv:1904.04047>, SemAxis (An et al., 2018) <arXiv:1806.05521>, Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) <doi:10.18653/v1/P19-1162>, and Embedding Coherence Test (Dev & Phillips, 2019) <arXiv:1901.07656>.
Tidies up the forecasting modeling and prediction work flow, 
    extends the 'broom' package 
    with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions 
    for various forecasting models,
    and enables converting 'forecast' objects to 
    "tidy" data frames with 'sw_sweep'.
Structural handling of identity numbers used in the Swedish
    administration such as personal identity numbers ('personnummer') and
    organizational identity numbers ('organisationsnummer').
The Swiss Ephemeris (version 2.08) is a high precision ephemeris based upon the
    DE431 ephemerides from NASA's JPL. It covers the time range 13201 BCE to
    17191 CE. This package uses the semi-analytic theory by Steve Moshier.
    For faster and more accurate calculations, the compressed Swiss Ephemeris
    data is available in the 'swephRdata' package. To access this data package,
    run 'install.packages("swephRdata", repos = "https://rstub.github.io/drat/",
    type = "source")'. The size of the 'swephRdata' package is approximately
    115 MB. The user can also use the original JPL DE431 data.
Process and summarize aerial survey 'DAS' data (AirDAS) 
    <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-185.PDF>
    collected using an aerial survey program from the 
    Southwest Fisheries Science Center (SWFSC) 
    <https://www.fisheries.noaa.gov/west-coast/science-data/california-current-marine-mammal-assessment-program>.
    PDF files detailing the relevant AirDAS data formats are included in this package.
Process and summarize shipboard 
    'DAS' <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF> data
    produced by the Southwest Fisheries Science Center (SWFSC) program 'WinCruz' 
    <https://www.fisheries.noaa.gov/west-coast/science-data/california-current-marine-mammal-assessment-program>.
    This package standardizes and streamlines basic DAS data processing,
    and includes a PDF with the DAS data format requirements.
Collection of conversion, analytical, geodesic, mapping, and
    plotting functions. Used to support packages and code written by
    researchers at the Southwest Fisheries Science Center of the National
    Oceanic and Atmospheric Administration.
Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. (2008) <doi:10.1093/biostatistics/kxm054>; Cook, J. R. and Stefanski, L. A. (1994) <doi:10.1080/01621459.1994.10476871>; Little, R. J. A. and Rubin, D. B. (2002, ISBN:978-0-471-18386-0).
An efficient sensitivity analysis for stochastic models based on 
    Monte Carlo samples. Provides weights on simulated scenarios from a 
    stochastic model, such that stressed random variables fulfil given 
    probabilistic constraints (e.g. specified values for risk measures), 
    under the new scenario weights. Scenario weights are selected by 
    constrained minimisation of the relative entropy to the baseline model. 
    The 'SWIM' package is based on Pesenti S.M., Millossovich P., Tsanakas A. (2019)
    "Reverse Sensitivity Testing: What does it take to break the model" 
    <openaccess.city.ac.uk/id/eprint/18896/> and Pesenti S.M. (2021) 
    "Reverse Sensitivity Analysis for Risk Modelling" <https://www.ssrn.com/abstract=3878879>.
The goal of the 'SwimmeR' package is to provide means of acquiring, and then analyzing, data from swimming (and diving) competitions.  To that end 'SwimmeR' allows results to be read in from .html sources, like 'Hy-Tek' real time results pages, '.pdf' files, 'ISL' results, 'Omega' results, and (on a development basis) '.hy3' files.  Once read in, 'SwimmeR' can convert swimming times (performances) between the computationally useful format of seconds reported to the '100ths' place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community.  'SwimmeR' can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', 'SCM', 'SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data.  This is a developmental package, not yet mature.
Used for creating swimmers plots with functions to customize the bars, add points, add lines, add text, and add arrows.
Use the R console as an interactive learning
    environment. Users receive immediate feedback as they are guided through
    self-paced lessons in data science and R programming.
A set of tools for writing and sharing interactive courses
    to be used with swirl.
Ozone, NOx (= Sum of nitrogen monoxide and nitrogen dioxide), nitrogen monoxide, ambient temperature, dew point, wind speed and wind direction at 3 sites around lake of Lucerne in Central Switzerland in 30 min time resolution for year 2004.
Provides a number of useful functions to employ MrP for small area prediction in Switzerland. Based on a hierarchical model and survey data one can derive cantonal preference measures. The package allows to automatize the prediction and post-stratification steps. It further provides adequate print, summary, map, and plot functions for objects of its class.
Retrieves the most important data on parliamentary activities of the Swiss Federal Assembly via 
    an open, machine-readable interface (see <https://ws.parlament.ch/odata.svc/>).
An unsorted collection of visualization widgets rendered in 'Tcl/Tk'<https://www.tcl.tk/> to generate agile dashboards for your iterative simulations. Widgets include progress bars, counters, eavesdroppers, injectors, switches, and sliders for dynamic manipulation and visualization of simulation parameters.
Provides a switch-case construct for 'R', as it is known from other programming languages. It allows to test multiple, similar conditions in an efficient, easy-to-read manner, so nested if-else constructs can be avoided. The switch-case construct is designed as an 'R' function that allows to return values depending on which condition is met and lets the programmer flexibly decide whether or not to leave the switch-case construct after a case block has been executed.
Functions for estimating the parameters from the latent
  state process and the functions corresponding to the J states as
  proposed by De Souza and Heckman (2013).
Provides an abstraction for managing, installing,
    and switching between sets of installed R packages. This allows users to
    maintain multiple package libraries simultaneously, e.g. to maintain
    strict, package-version-specific reproducibility of many analyses, or
    work within a development/production release paradigm. Introduces a
    generalized package installation process which supports multiple repository
    and non-repository sources and tracks package provenance.
Provides a simple plugin to the switchr
	     framework which allows users to publish package and
	     session manifests as gists.
Functions to connect the widely used Storm Water Management Model (SWMM)
  of the United States Environmental Protection Agency (US EPA) 
  <https://www.epa.gov/water-research/storm-water-management-model-swmm> to R with
  currently two main goals: (1) Run a SWMM simulation from R and (2) provide fast 
  access to simulation results, i.e. SWMM's binary '.out'-files. High performance is achieved
  with help of Rcpp. Additionally, reading SWMM's '.inp' and '.rpt' files is supported to 
  glance model structures and to get direct access to simulation summaries.
Tools for retrieving, organizing, and analyzing environmental
    data from the System Wide Monitoring Program of the National Estuarine
    Research Reserve System <http://cdmo.baruch.sc.edu/>. These tools
    address common challenges associated with continuous time series data
    for environmental decision making.
Tools for performing routine analysis and plotting tasks with environmental
    data from the System Wide Monitoring Program of the National Estuarine
    Research Reserve System <http://cdmo.baruch.sc.edu/>. This package builds
    on the functionality of the 'SWMPr' package <https://cran.r-project.org/package=SWMPr>,
    which is used to retrieve and organize the data. The combined set of tools
    address common challenges associated with continuous time series data
    for environmental decision making, and are intended for use in annual reporting activities.
    References:
    Beck, Marcus W. (2016) <ISSN 2073-4859><https://journal.r-project.org/archive/2016-1/beck.pdf>
    Rudis, Bob (2014) <https://rud.is/b/2014/11/16/moving-the-earth-well-alaska-hawaii-with-r/>.
    United States Environmental Protection Agency (2015) <https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=OWOW&dirEntryId=327030>.
    United States Environmental Protection Agency (2012) <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.646.1973&rep=rep1&type=pdf>.
Functions to speed up work flow for hydrological analysis. 
    Focused on Australian climate data (SILO climate data), hydrological models (eWater Source) and in particular South Australia (<https://water.data.sa.gov.au> hydrological data).
This Systems Biology Package (Gelius-Dietrich et. al. (2012) <doi:10.1186/1752-0509-7-125>) implements algorithms for constraint based analyses of metabolic networks, e.g. flux-balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on/off minimization (ROOM), robustness analysis and flux variability analysis. The package is easily extendable for additional algorithms. Most of the current LP/MILP solvers are supported via additional packages.
Different techniques of cost constrained flux balance analysis.
        1- MetabOlic Modeling with ENzyme kineTics 'MOMENT' which uses enzyme kinetic data and enzyme molecular 
         weights to constrain flux balance analysis(FBA) and it is described in Adadi, R., Volkmer, B., Milo, R., 
         Heinemann, M., & Shlomi, T. (2012). Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network 
           with Kinetic Parameters, 8(7). <doi:10.1371/journal.pcbi.1002575>. 
        2- an improvement of 'MOMENT' that considers multi-functional enzymes 'ccFBA'. 
         Described in Desouki, Abdelmoneim. Algorithms for improving the predictive power of flux balance analysis. PhD dissertation, 2016.
         FBA is a mathematical technique to find fluxes in metabolic models at steady state. 
         It is described in Orth, J.D., Thiele, I. and Palsson, B.O.  What is flux balance analysis? Nat. Biotech. 28, 245-248(2010).
Implement cycle-Free flux balance analysis (CycleFreeFlux), cycle-free flux variability, and Random Sampling of solution space.
      Desouki, A. A., Jarre, F., Gelius-Dietrich, G., & Lercher, M. J. (2015). CycleFreeFlux: efficient removal of thermodynamically
      infeasible loops from flux distributions. Bioinformatics, 31(13), 2159-2165. <doi:10.1093/bioinformatics/btv096>. 
      Flux balance analysis is a technique to find fluxes in metabolic models at steady state. 
      It is described in Orth, J.D., Thiele, I. and Palsson, B.O. What is flux balance analysis? Nat. Biotech. 28, 245-248 (2010).
Implements dynamic FBA technique proposed by Varma et al 1994.
An English language syllable counter, plus readability score
    measure-er. For readability, we support 'Flesch' Reading Ease and
    'Flesch-Kincaid' Grade Level ('Kincaid' 'et al'. 1975)
    <https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1055&context=istlibrary>,
    Automated Readability Index ('Senter' and Smith 1967)
    <https://apps.dtic.mil/sti/citations/AD0667273>,
    Simple Measure of Gobbledygook (McLaughlin 1969)
    <https://www.semanticscholar.org/paper/SMOG-Grading-A-New-Readability-Formula.-Laughlin/5fccb74c14769762b3de010c5e8a1a7ce700d17a>,
    and 'Coleman-Liau' (Coleman and 'Liau' 1975) <doi:10.1037/h0076540>. The
    package has been carefully optimized and should be very efficient, both in
    terms of run time performance and memory consumption. The main methods are
    'vectorized' by document, and scores for multiple documents are computed in
    parallel via 'OpenMP'.
Implements tidy syllabification of transcription. 
  Based on @kylebgorman's 'python' implementation <https://github.com/kylebgorman/syllabify>.
Provides the hyphenation algorithm used for 'TeX'/'LaTeX' and similar software, as proposed by Liang (1983, <https://tug.org/docs/liang/>). Mainly contains the
                    function hyphen() to be used for hyphenation/syllable counting of text objects. It was originally developed for and part of the 'koRpus' package, but later
                    released as a separate package so it's lighter to have this particular functionality available for other packages. Support for various languages needs be added
                    on-the-fly or by plugin packages (<https://undocumeantit.github.io/repos/>); this package does not include any language specific data. 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 'sylly'
        package. Due to some restrictions on CRAN, the full package
        sources are only available from the project homepage. To ask
        for help, report bugs, suggest feature improvements, or discuss
        the global development of the package, please consider
        subscribing to the koRpus-dev mailing list
        (<http://korpusml.reaktanz.de>).
Functions for fitting the Autoregressive and Moving Average Symmetric Model for univariate time series introduced by Maior and Cysneiros (2018), <doi:10.1007/s00362-016-0753-z>. Fitting method: conditional maximum likelihood estimation. For details see: Wei (2006), Time Series Analysis: Univariate and Multivariate Methods, Section 7.2.
Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation.
 (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>,
 Breiman, L. (1996), <doi:10.1007/BF00058655>,
 Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>,
 Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>,
 Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>,
 Calinski, T., Harabasz, J. (1974) <doi:10.1080/03610927408827101>,
 Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>,
 Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>,
 Walesiak, M., Dudek, A. (2008) <doi:10.1007/978-3-540-78246-9_11>,
 Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).
Creates a numeric guide for writing the formula for the determinant of a square matrix (a detguide) as a function of the elements of the matrix and writes out that formula, the symbolic representation.
Package that implements various symbol plots (bars,
        profiles, stars, Chernoff faces, color icons, stick figures).
A matrix-like class to represent a symmetric matrix partitioned
    into file-backed blocks.
Provides an R interface to 'SymEngine' <https://github.com/symengine/>,
    a standalone 'C++' library for fast symbolic manipulation. The package has functionalities
    for symbolic computation like calculating exact mathematical expressions, solving
    systems of linear equations and code generation.
Implementations of a large number of tests for symmetry and their 
    bootstrap variants, which can be used for testing the symmetry of random
    samples around a known or unknown mean. Functions are also there for testing
    the symmetry of model residuals around zero. Currently, the supported models
    are linear models and generalized autoregressive conditional
    heteroskedasticity (GARCH) models (fitted with the 'fGarch' package). All
    tests are implemented using the 'Rcpp' package which ensures great
    performance of the code.
Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.
Implements the Symphony single-cell reference building and query mapping algorithms and additional functions described in Kang et al <https://www.biorxiv.org/content/10.1101/2020.11.18.389189v2>.
A collection of functions for symbolic computation using 'Ryacas'
  package for structural equation models. This package includes
  functions to calculate model-implied covariance (and correlation)
  matrix and sampling covariance matrix of functions of variables using
  the first-order Taylor approximation. Reference: McArdle and McDonald (1984) <doi:10.1111/j.2044-8317.1984.tb00802.x>.
Contains methods for simulation and for evaluating the pdf, cdf, and quantile functions for symmetric stable, symmetric classical tempered stable, and symmetric power tempered stable distributions.
Generates synonyms from a given word drawing from a synonym list
    from the 'moby' project <http://moby-thesaurus.org/>.
Methods for computing spatial, temporal, and spatiotemporal
    statistics as described in Gouhier and Guichard (2014) 
    <doi:10.1111/2041-210X.12188>. These methods include 
    empirical univariate, bivariate and multivariate
    variograms; fitting variogram models; phase locking and synchrony analysis;
    generating autocorrelated and cross-correlated matrices.
This package carries out synchrosqueezed wavelet transform. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.
The function 'syncSubsample' subsamples temporal data of different entities so that the result only contains synchronal events. The function 'mci' calculates the Movement Coordination Index (MCI, see reference on help page for function 'mci') of a data set created with the function 'syncSubsample'.
Random number generation designed for cross-language usage.
Analysis of metacommunities based on functional traits and
    phylogeny of the community components. The functions that are offered here
    implement for the R environment methods that have been available in the
    SYNCSA application written in C++ (by Valerio Pillar, available at 
    <http://ecoqua.ecologia.ufrgs.br/SYNCSA.html>).
Provides a flexible and simple tool to synthesize regular or irregular functional data and snippets to facilitate research or analysis of functional data.
Framework to perform synthetic likelihood inference
    for models where the likelihood function is unavailable or
    intractable.
Set of functions to compute different types of synoptic classification methods and for analysing their effect on environmental variables. More information about the methods used in Lemus-Canovas et al. 2019 <DOI:10.1016/j.atmosres.2019.01.018>,  Martin-Vide et al. 2008 <DOI:10.5194/asr-2-99-2008>, Jenkinson and Collison 1977.
Explore synesthesia
  consistency test data, calculate consistency scores, 
  and classify participant data as valid or invalid.
It implements various estimators of mutual information, such as
	the maximum likelihood and the Millow-Madow estimator, various Bayesian
	estimators, the shrinkage estimator, and the Chao-Shen estimator. It also
	offers wrappers to the kNN and kernel density estimators. Furthermore, it
	provides various index of performance evaluation such as precision, recall,
	FPR, F-Score, ROC-PR Curves and so on. Lastly, it provides a brand new way
	of generating synthetic RNA-Seq Network with known dependence structure.
A set of functions for generating 'SPSS' syntax files from the R environment.
Implements the synthetic control group method for comparative case studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2011, 2014). The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone.
Provides access to curated American Community Survey (ACS) base tables via a wrapper
  to library(acs). Builds synthetic micro-datasets at any user-specified geographic level with
  ten default attributes; and, conducts spatial microsimulation modeling (SMSM) via simulated
  annealing.  SMSM is conducted in parallel by default. Lastly, we provide functionality for
  data-extensibility of micro-datasets.
Not a new method implementation.
    Usage of the Synthetic Control Method, see Abadie et al. (2011) <doi:10.18637/jss.v042.i13>,
    as an ad-hoc approach to forecast series with panel in a specific context. The context being: There are units in different stages of a certain journey, there the assumption that the units’ behavior throw out the journey are similar is valid and there are not enough data to use traditional forecasting methods. 
    For a usage example see the package home page documentation.
Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) <doi:10.1016/j.advwatres.2019.103430> and Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962> associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>.
A critical first step in systematic literature reviews
  and mining of academic texts is to identify relevant texts from a range
  of sources, particularly databases such as 'Web of Science' or 'Scopus'.
  These databases often export in different formats or with different metadata
  tags. 'synthesisr' expands on the tools outlined by Westgate (2019)
  <doi:10.1002/jrsm.1374> to import bibliographic data from a range of formats
  (such as 'bibtex', 'ris', or 'ciw') in a standard way, and allows merging
  and deduplication of the resulting dataset.
Creation of an individual claims simulator which generates various
    features of non-life insurance claims. An initial set of test parameters,
    designed to mirror the experience of an Auto Liability portfolio, were set
    up and applied by default to generate a realistic test data set of
    individual claims (see vignette). The simulated data set then allows
    practitioners to back-test the validity of various reserving models and to
    prove and/or disprove certain actuarial assumptions made in claims
    modelling. The distributional assumptions used to generate this data set can
    be easily modified by users to match their experiences. Reference: Avanzi B,
    Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim
    simulator with feature control" <arXiv:2008.05693>.
A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. For a description of the implemented method see Nowok, Raab and Dibben (2016) <doi:10.18637/jss.v074.i11>.
A set of functions to support experimentation in the utility of partially synthetic data sets.  All functions compare an observed data set to one or a set of partially synthetic data sets derived from the observed data to (1) check that data sets have identical attributes, (2) calculate overall and specific variable perturbation rates, (3) check for potential logical inconsistencies, and (4) calculate confidence intervals and standard errors of desired variables in multiple imputed data sets. Confidence interval and standard error formulas have options for either synthetic data sets or multiple imputed data sets. For more information on the formulas and methods used, see Reiter & Raghunathan (2007) <doi:10.1198/016214507000000932>.
Drop-in replacements for the base system2() function with fine control
    and consistent behavior across platforms. Supports clean interruption, timeout, 
    background tasks, and streaming STDIN / STDOUT / STDERR over binary or text 
    connections. Arguments on Windows automatically get encoded and quoted to work 
    on different locales.
Quasi-Monte-Carlo algorithm for systematic generation of shock scenarios from an arbitrary multivariate elliptical distribution. The algorithm selects a systematic mesh of arbitrary fineness that approximately evenly covers an isoprobability ellipsoid in d dimensions (Flood, Mark D. & Korenko, George G. (2013) <doi:10.1080/14697688.2014.926018>).
  This package is the 'R' analogy to the 'Matlab' code published by Flood & Korenko in above-mentioned paper.
Loading system fonts and Google Fonts
    <https://fonts.google.com/> into R, in order to
    support other packages such as 'R2SWF' and 'showtext'.
Provides functions for constructing mathematical models of dynamical systems from measured input-output data.
Send 'syslog' protocol messages to a remote 'syslog' server
    specified by host name and TCP network port.
Econometric estimation of simultaneous
 systems of linear and nonlinear equations using Ordinary Least
 Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated
 Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted
 Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS)
 as suggested, e.g., by Zellner (1962) <doi:10.2307/2281644>,
 Zellner and Theil (1962) <doi:10.2307/1911287>, and
 Schmidt (1990) <doi:10.1016/0304-4076(90)90127-F>.
Provides system native access to the font catalogue. As font
    handling varies between systems it is difficult to correctly locate 
    installed fonts across different operating systems. The 'systemfonts' 
    package provides bindings to the native libraries on Windows, macOS and 
    Linux for finding font files that can then be used further by e.g. graphic
    devices. The main use is intended to be from compiled code but 'systemfonts'
    also provides access from R.
The past decade has demonstrated an increased need to better understand risks leading to systemic crises. This framework offers scholars, practitioners and policymakers a useful toolbox to explore such risks in financial systems. Specifically, this framework provides popular econometric and network measures to monitor systemic risk and to measure the consequences of regulatory decisions. These systemic risk measures are based on the frameworks of Adrian and Brunnermeier (2016) <doi:10.1257/aer.20120555> and Billio, Getmansky, Lo and Pelizzon (2012) <doi:10.1016/j.jfineco.2011.12.010>.
A toolbox for systemic risk based on liabilities matrices. Contains
    a Gibbs sampler for liabilities matrices where only row and column sums of the
    liabilities matrix as well as some other fixed entries are observed. Includes models 
    for power law distribution on the degree distribution.
Deals with standard Young tableaux (field of combinatorics). Performs enumeration, counting, random generation, the Robinson-Schensted correspondence, and conversion to and from paths on the Young lattice.
Extracts sentiment and sentiment-derived plot arcs
    from text using a variety of sentiment dictionaries conveniently
    packaged for consumption by R users.  Implemented dictionaries include
    "syuzhet" (default) developed in the Nebraska Literary Lab
    "afinn" developed by Finn Årup Nielsen, "bing" developed by Minqing Hu
    and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D.
    Applicable references are available in README.md and in the documentation
    for the "get_sentiment" function.  The package also provides a hack for
    implementing Stanford's coreNLP sentiment parser. The package provides
    several methods for plot arc normalization.
Contains functions for applying the T^2-test for equivalence.
    The T^2-test for equivalence is a multivariate two-sample equivalence test. 
    Distance measure of the test is the Mahalanobis distance.
    For multivariate normally distributed data the T^2-test for equivalence 
    is exact and UMPI.
    The function T2EQ() implements the T^2-test for equivalence 
    according to Wellek (2010) <DOI:10.1201/ebk1439808184>.
    The function T2EQ.dissolution.profiles.hoffelder() implements a variant 
    of the T^2-test for equivalence according to Hoffelder (2016) 
    <http://www.ecv.de/suse_item.php?suseId=Z|pi|8430> for the 
    equivalence comparison of highly variable dissolution profiles.
Cluster analysis is one of the most fundamental problems in data science. We provide a variety of algorithms from clustering to the learning on the space of partitions. See Hennig, Meila, and Rocci (2016, ISBN:9781466551886) for general exposition to cluster analysis.
Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) <doi:10.1561/2200000073> for the general exposition to the study of computational optimal transport.
Contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.
Calculates the robust Taba linear, Taba rank (monotonic), TabWil, and TabWil rank 
    correlations. Test statistics as well as one sided or two sided p-values are provided 
    for all correlations. Multiple correlations and p-values can be calculated 
    simultaneously across multiple variables. In addition, users will have the option to use 
    the partial, semipartial, and generalized partial correlations; where the partial and 
    semipartial correlations use linear, logistic, or Poisson regression to modify the specified
    variable.
Functions for tabulating and summarising categorical variables. 
    Most functions are designed to work with dataframes, and use the 'tidyverse' 
    idiom of taking the dataframe as the first argument so they work within 
    pipelines. Equivalent functions that operate directly on vectors are also 
    provided where it makes sense. This package aims to make exploratory data 
    analysis involving categorical variables quicker, simpler and more robust.
Sometimes you need to split your data and work on the two chunks independently before bringing them back together. 'Taber' allows you to do that with its two functions.
Simple tabulation should be dead simple. 
    This package is an opinionated approach to easy tabulations while also 
    providing exact numbers and allowing for re-usability. 
    This is achieved by providing tabulations as data.frames with columns for 
    values, optional variable names, frequency counts including and excluding 
    NAs and percentages for counts including and excluding NAs. Also values are
    automatically sorted by in decreasing order of frequency counts to allow for 
    fast skimming of the most important information.
For writing tables with custom formats in a Excel file ready to be distributed.
A specialization of 'dplyr' data manipulation verbs that parse and build expressions
    which are ultimately evaluated by 'data.table', letting it handle all optimizations. A set of
    additional verbs is also provided to facilitate some common operations on a subset of the data.
Translate double and integer valued data into
    character values formatted for tabulation in manuscripts
    or other types of academic reports.
Create HTML tables of descriptive statistics, as one would expect
    to see as the first table (i.e. "Table 1") in a medical/epidemiological journal
    article.
Table 1 is the classical way to describe the patients in a
    clinical study. The amount of splits in the data in such a table is
    limited. Table1Heatmap draws a heatmap of all crosstables that can be
    generated with the data. Users can choose between showing the actual
    crosstables or direction of effect of associations, and highlight
    associations by number of patients or p-values.
A collection of time-saving wrappers for reproducible export of
    summary tables commonly used in scientific articles, to .xls/.xlsx multi-tab spreadsheets, while controlling spreadsheet layout. Powered by 'XLConnect'/'rJava' utilities.
Creates some tables of clinical study. 'Table 1' is created by table1() to describe baseline characteristics, which is essential in every clinical study. Created by table2(), the function of 'Table 2' is to explore influence factors. And 'Table 3' created by table3() is able to make stratified analysis.
An easy-to-use tool for drawing paper-quality tables with double-information encoded in grobs shapes and colors.
Higher Criticism (HC) test between two frequency tables. Test is based on an adaptation of the Tukey-Donoho-Jin HC statistic to testing frequency tables described in Kipnis (2019) <arXiv:1911.01208>.
A tool to create and style HTML tables with CSS. These can
    be exported and used in any application that accepts HTML (e.g. 'shiny',
    'rmarkdown', 'PowerPoint'). It also provides functions to create CSS files
    (which also work with shiny).
Provides two classes extending 'data.table' class. Simple
    'tableList' class wraps 'data.table' and any additional structures together.
    More complex 'tableMatrix' class combines 'data.table' and
    'matrix'. See <http://github.com/InferenceTechnologies/tableMatrix> for more
    information and examples.
Provides a user friendly interface to 
  generation of booktab style tables using 'xtable'.
Creates 'Table 1', i.e., description of baseline patient
    characteristics, which is essential in every medical research.
    Supports both continuous and categorical variables, as well as
    p-values and standardized mean differences. Weighted data are
    supported via the 'survey' package.
'R' interface to the 'Tabler' HTML template. See more here <https://tabler.io>. 
    'tablerDash' is a light 'Bootstrap 4' dashboard template. There are different 
     layouts available such as a one page dashboard or a multi page template,
     where the navigation menu is contained in the navigation bar. A fancy example
     is available at <https://dgranjon.shinyapps.io/shinyMons/>.
Computes and displays complex tables of summary statistics.
  Output may be in LaTeX, HTML, plain text, or an R
  matrix for further processing.
Allows to work with 'Table Schema' (<http://specs.frictionlessdata.io/table-schema/>). 'Table Schema' is well suited for use cases around handling and validating tabular data in text formats such as 'csv', but its utility extends well beyond this core usage, towards a range of applications where data benefits from a portable schema format. The 'tableschema.r' package can load and validate any table schema descriptor, allow the creation and modification of descriptors, expose methods for reading and streaming data that conforms to a 'Table Schema' via the 'Tabular Data Resource' abstraction.
Presentation-quality tables are displayed as plots on an R 
  graphics device.  Although there are other packages that format tables 
  for display, this package is unique in combining two features: (a) It is 
  aware of the logical structure of the table being presented, and makes 
  use of that for automatic layout and styling of the table.  This avoids 
  the need for most manual adjustments to achieve an attractive result. 
  (b) It displays tables using 'ggplot2' graphics.  Therefore a table can 
  be presented anywhere a graph could be, with no more effort.  External 
  software such as LaTeX or HTML or their viewers is not required.  The 
  package provides a full set of tools to control the style and appearance 
  of tables, including titles, footnotes and reference marks, horizontal 
  and vertical rules, and spacing of rows and columns.  Methods are included 
  to display matrices; data frames; tables created by R's ftable(), table(), 
  and xtabs() functions; and tables created by the 'tables' and 'xtable' 
  packages.  Methods can be added to display other table-like objects.  A 
  vignette is included that illustrates usage and options available in the 
  package.
Creates a table of descriptive statistics
 for factor and numeric columns in a data frame. Displays
 these by groups, if any. Highly customizable, with support
 for 'html' and 'pdf' provided by 'kableExtra'. Respects
 original column order, column labels, and factor level order.
 See ?tablet.data.frame and vignettes.
A wrapper to a set of algorithms designed to recognise positional cues present in hierarchical for-human Tables (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into machine-readable LongForm Dataframes.
Implements the 'TabNet' model by Sercan O. Arik et al (2019) <arXiv:1908.07442>
    and provides a consistent interface for fitting and creating predictions. It's
    also fully compatible with the 'tidymodels' ecosystem.
Helps the user to build and register schema descriptions of 
    disorganised (messy) tables. Disorganised tables are tables that are 
    not in a topologically coherent form, where packages such as 'tidyr' could 
    be used for reshaping. The schema description documents the arrangement of 
    input tables and is used to reshape them into a standardised (tidy) output 
    format.
An easy way to examine archaeological count data. This
    package provides a convenient and reproducible toolkit for relative
    dating by matrix seriation (reciprocal ranking, CA-based seriation).
    It also provides several tests and measures of diversity:
    heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.),
    richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and
    similarity (Brainerd-Robinson, etc.). The package make it easy to
    visualize count data and statistical thresholds: rank vs abundance
    plots, heatmaps, Ford (1962) and Bertin (1977) diagrams.
Facilities to work with vector and raster data in efficient 
 repeatable and systematic work flow. Missing functionality in existing packages 
 is included here to allow extraction from raster data with 'simple features' and 
 'Spatial' types and to make extraction consistent and straightforward. Extract cell 
 numbers from raster data and  return the cells as a data frame 
 rather than as lists of matrices or vectors. The functions here allow spatial data 
 to be used without special handling for the format currently in use.
The 'tabularmap' is one of the visualization methods for 
    efficiently displaying data consisting of multiple elements by tiling them.
    When dealing with geospatial, it corrects for differences in visibility 
    between areas.
The maximum likelihood classifier (MLC) is one of the most common classifiers used for 
  remote sensing imagery. 
  This package uses 'RcppArmadillo' to provide a fast implementation of the MLC to train 
  and predict over tabular data (data.frame).
  The algorithms were based on Mather (1985) <doi:10.1080/01431168508948456> method.
Generates pretty console output for tables allowing for full customization
  of cell colors, font type, borders and many others attributes. It also supports 'multibyte'
  characters and nested tables.
Efficient tabulation with Stata-like output.
	For each unique value of the variable, it shows the number of 
	observations with that value, proportion of observations with that
	value, and cumulative proportion, in descending order of frequency.
	Accepts data.table, tibble, or data.frame as input. 
	Efficient with big data: if you give it a data.table, 
	tab() uses data.table syntax.
Convert semi-structured log files (such as 'Apache' access.log files)
    into a tabular format (data.frame) using a standard template system.
Tabu search algorithm for binary configurations. A basic version of the algorithm as described by Fouskakis and Draper (2007) <doi:10.1111/j.1751-5823.2002.tb00174.x>.
Make it easy to deal with multiple cross-tables in data 
    exploration, by creating them, manipulating them, and adding color 
    helpers to highlight important informations. All functions are "tidy",
    pipe-friendly, and render data frames which can be easily manipulated.
    Tables can be exported to Excel and in html with formats and colors.
To facilitate the analysis of positron emission tomography (PET) 
    time activity curve (TAC) data, and to encourage open science and 
    replicability, this package supports data loading and analysis of multiple 
    TAC file formats. Functions are available to analyze loaded TAC data for 
    individual participants or in batches. Major functionality includes weighted
    TAC merging by region of interest (ROI), calculating models including 
    standardized uptake value ratio (SUVR) and distribution volume ratio (DVR, 
    Logan et al. 1996 <doi:10.1097/00004647-199609000-00008>), basic plotting 
    functions and calculation of cut-off values (Aizenstein et al. 2008
    <doi:10.1001/archneur.65.11.1509>). Please see the walkthrough vignette for
    a detailed overview of 'tacmagic' functions.
Extensions to 'lattice', providing new high-level
  functions, methods for existing functions, panel functions, and a theme.
Implement the transformed additive Gaussian (TAG) process and the  transformed approximately additive Gaussian (TAAG) process proposed in Lin and Joseph (2020) <DOI:10.1080/00401706.2019.1665592>. These functions can be used to model deterministic computer experiments, obtain predictions at new inputs, and quantify the uncertainty of the predictions. This research is supported by a U.S. National Science Foundation grant DMS-1712642 and a U.S. Army Research Office grant W911NF-17-1-0007.
Function TailClassifier() in this package is a Tail-Classifier 
    function. The function suggests one of the following types of tail for
    your discrete data: 1) Power decaying tail; 2) Sub-exponential decaying 
    tail; and 3) Near-exponential decaying tail.
Provides functions implementing minimal distance estimation methods for parametric tail dependence models, as proposed in  
	 Einmahl, J.H.J., Kiriliouk, A., Krajina, A., and Segers, J. (2016) <doi:10.1111/rssb.12114> and 
	 Einmahl, J.H.J., Kiriliouk, A., and Segers, J. (2018) <doi:10.1007/s10687-017-0303-7>.
Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.
Implements the tail-rank statistic for selecting biomarkers
  from a microarray data set, an efficient nonparametric test focused
  on the distributional tails. See
  <http://bioinformatics.mdanderson.org/TailRank/tolstoy-new.pdf>.
When plotting treated-minus-control differences, after-minus-before changes, or difference-in-differences, the ttrans() function symmetrically transforms the positive and negative tails to aid plotting.  The package includes an observational study with three control groups and an unaffected outcome; see Rosenbaum (2020) <doi:10.1111/biom.13558>.
A tool to help create shiny apps for selecting and annotating 
    elements of images. Users must supply images, questions, and answer
    choices. The user interface is a dynamic shiny app, that displays the images
    and questions and answer choices. The data generated can be saved to a
    file that can be used for subsequent analysis. The original purpose was to
    annotate still images from tennis video for face recognition and emotion 
    detection purposes.
It includes functions for applying methodologies utilized for single-process kinetic analysis of solid-state processes were recently summarized and described in the Recommendation of ICTAC Kinetic Committee. These methods work with the basic kinetic equation. The Methodologies included refers to  Avrami, Friedman, Kissinger, Ozawa, OFM, Mo, Starink, isoconversional methodology (Vyazovkin) according to ICATAC Kinetics Committee recommendations as reported in Vyazovkin S, Chrissafis K, Di Lorenzo ML, et al. ICTAC Kinetics Committee recommendations for collecting experimental thermal analysis data for kinetic computations. Thermochim Acta. 2014;590:1-23. <doi:10.1016/J.TCA.2014.05.036> .
Includes marginal maximum likelihood estimation and joint maximum
    likelihood estimation for unidimensional and multidimensional 
    item response models. The package functionality covers the 
    Rasch model, 2PL model, 3PL model, generalized partial credit model, 
    multi-faceted Rasch model, nominal item response model, 
    structured latent class model, mixture distribution IRT models, 
    and located latent class models. Latent regression models and 
    plausible value imputation are also supported. For details see
    Adams, Wilson and Wang, 1997 <doi:10.1177/0146621697211001>,
    Adams, Wilson and Wu, 1997 <doi:10.3102/10769986022001047>,
    Formann, 1982 <doi:10.1002/bimj.4710240209>,
    Formann, 1992 <doi:10.1080/01621459.1992.10475229>.
The Tanaka method enhances the representation of topography on
    a map using shaded contour lines. In this simplified implementation of
    the method, north-west white contours represent illuminated topography
    and south-east black contours represent shaded topography. See Tanaka
    (1950) <doi:10.2307/211219>.
Density, distribution function, quantile function, random
    generation and survival function for the Tangent Burr Type XII Distribution as
    defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics 
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
A two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, <doi:10.1093/bioinformatics/btw449>), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.
Spatial data anonymization preserves confidentiality. Using methods described in Zandbergen (2014) <doi:10.1155/2014/567049>, spatial data anonymization is achieved by dithering original spatial coordinates with combinations of randomized vertical, horizontal and rotational shifts. This can apply to non-grid spatial point patterns and raster objects, and the methods preserve the same spatial characteristics and relationships of the original data. Unique hash keying enables data subjected to anonymization sequences to be re-identified where required.
Provides an extensible formula system to quickly and easily create
    production quality tables. The steps of the process are formula parser,
    statistical content generation from data, to rendering. Each step of the process
    is separate and user definable thus creating a set of building blocks for
    highly extensible table generation. A user is not limited by any of the 
    choices of the package creator other than the formula grammar. For example,
    one could chose to add a different S3 rendering function and output a format
    not provided in the default package. Or possibly one would rather have Gini
    coefficients for their statistical content. Routines to achieve New England
    Journal of Medicine style, Lancet style and Hmisc::summaryM() statistics are
    provided. The package contains rendering for HTML5, Rmarkdown and an indexing
    format for use in tracing and tracking are provided.
Builds tables with customizable rows. Users can specify the type
             of data to use for each row, as well as how to handle missing
             data and the types of comparison tests to run on the table
             columns.
Implementation of functions for fitting taper curves (a semiparametric 
  linear mixed effects taper model) to diameter measurements along stems. Further 
  functions are provided to estimate the uncertainty around the predicted curves, 
  to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. 
  For cases where tree heights are not measured, methods for estimating
  additional variance in volume predictions resulting from uncertainties in
  tree height models (tariffs) are provided.  The example data include the taper 
  curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees 
  and a subset of section-wise diameter measurements of these trees. The functions 
  implemented here are detailed in Kublin, E., Breidenbach, J., Kaendler, G. (2013)
  <doi:10.1007/s10342-013-0715-0>.
Wrapper for using 'tapkee' command line utility,
 it allows to run it from inside R and catch the results for further analysis and plotting.
 'Tapkee' is a program for fast dimension reduction, see 'package?tapkee' and <http://tapkee.lisitsyn.me/>
 for installation and other details.
Functions to produce, fit and predict from bipartite networks with abundance, trait and phylogenetic information. Its methods are described in detail in Benadi, G., Dormann, C.F., Fruend, J., Stephan, R. & Vazquez, D.P. (2021) Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny. The American Naturalist, in press.
Manager of tick-by-tick transaction data that performs 'cleaning', 'aggregation' and 'import' in an efficient and fast way. The package engine, written in C++, exploits the 'zlib' and 'gzstream' libraries to handle gzipped data without need to uncompress them. 'Cleaning' and 'aggregation' are performed according to Brownlees and Gallo (2006) <DOI:10.1016/j.csda.2006.09.030>. Currently, TAQMNGR processes raw data from WRDS (Wharton Research Data Service, <https://wrds-web.wharton.upenn.edu/wrds/>).
Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <DOI:10.1081/STA-200054435>.
Function-oriented Make-like declarative workflows for
  Statistics and data science are supported in the 'targets' R package.
  As an extension to 'targets', the 'tarchetypes' package provides
  convenient user-side functions to make 'targets' easier to use.
  By establishing reusable archetypes for common kinds of
  targets and pipelines, these functions help express complicated
  reproducible workflows concisely and compactly.
  The methods in this package were influenced by the 'drake' R package
  by Will Landau (2018) <doi:10.21105/joss.00550>.
Various methods for targeted and semiparametric inference including
	     augmented inverse probability weighted estimators for missing data and
	     causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>)
	     and estimators for risk differences and relative risks (Richardson et al. (2017)
	     <doi:10.1080/01621459.2016.1192546>).
As a pipeline toolkit for Statistics and data science in R,
  the 'targets' package brings together function-oriented programming and
  'Make'-like declarative workflows.
  It analyzes the dependency relationships among the tasks of a workflow,
  skips steps that are already up to date, runs the necessary
  computation with optional parallel workers, abstracts files as
  R objects, and provides tangible evidence that the results match
  the underlying code and data. The methodology in this package
  borrows from GNU 'Make' (2015, ISBN:978-9881443519)
  and 'drake' (2018, <doi:10.21105/joss.00550>).
Implement the Tariff algorithm for coding cause-of-death from verbal autopsies. The Tariff method was originally proposed in James et al (2011) <DOI:10.1186/1478-7954-9-31> and later refined as Tariff 2.0 in Serina, et al. (2015) <DOI:10.1186/s12916-015-0527-9>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist between the this implementation and the implementation available from IHME.
Interactive shiny application for working with textmining and text analytics. Various visualizations  are provided.
Provides functions for analyzing citizens' bicycle usage pattern and predicting rental amount on specific conditions.
    Functions on this package interacts with data on 'tashudata' package, a 'drat' repository.
    'tashudata' package contains rental/return history on public bicycle system('Tashu'), weather for 3 years and bicycle station information. 
    To install this data package, see the instructions at <https://github.com/zeee1/Tashu_Rpackage>.
    top10_stations(), top10_paths() function visualizes image showing the most used top 10 stations and paths.
    daily_bike_rental() and monthly_bike_rental() shows daily, monthly amount of bicycle rental.
    create_train_dataset(), create_test_dataset() is data processing function for prediction.
    Bicycle rental history from 2013 to 2014 is used to create training dataset and that on 2015 is for test dataset.
    Users can make random-forest prediction model by using create_train_model() 
    and predict amount of bicycle rental in 2015 by using predict_bike_rental().
Schedule R scripts/processes with the Windows task scheduler. This
    allows R users to automate R processes on specific time points from R itself.
You only need to type 'why pie charts are bad' on Google to find
    thousands of articles full of (valid) reasons why other types of charts 
    should be preferred over this one.
    Therefore, because of the little use due to the reasons already mentioned,
    making pie charts (and related) in R is not straightforward, so other 
    functions are needed to simplify things.
    In this R package there are useful functions to make 'tasty' pie charts 
    immediately by exploiting the many cool templates provided.
The ta-test is a modified two-sample or two-group t-test of Gosset (1908). In small samples with less than 15 replicates,the ta-test significantly reduces type I error rate but has almost the same power with the t-test and hence can greatly enhance reliability or reproducibility of discoveries in biology and medicine. The ta-test can test single null hypothesis or multiple null hypotheses without needing to correct p-values.
Functions to combine data.frames in ways that require additional effort in 
  base R, and to add metadata (id, title, ...) that can be used for printing and 
  xlsx export. The 'Tatoo_report' class is provided as a 
  convenient helper to write several such tables to a workbook, one table per 
  worksheet. Tatoo is built on top of 'openxlsx', but intimate knowledge of 
  that package is not required to use tatoo.
Evaluates traveltimes and ray paths using predefined Earth
        (or other planet) models.  Includes phase plotting routines.
        The IASP91 (Kennett and Engdahl, 1991 <doi:10.1111/j.1365-246X.1991.tb06724.x>)
	and AK135 (Kennett et al., 1995 <doi:10.1111/j.1365-246X.1995.tb03540.x>)
	Earth models are included, and most important arrival phases can be
	evaluated.
Computes the t* statistic corresponding to the tau* population
          coefficient introduced by Bergsma and Dassios (2014) <DOI:10.3150/13-BEJ514>
          and does so in O(n^2) time following the algorithm of Heller and
          Heller (2016) <arXiv:1605.08732> building off of the work of Weihs,
          Drton, and Leung (2016) <DOI:10.1007/s00180-015-0639-x>. Also allows for
          independence testing using the asymptotic distribution of t* as described by
          Nandy, Weihs, and Drton (2016) <arXiv:1602.04387>.
'Tautulli' (<http://tautulli.com>) is a monitoring application for 'Plex' Media Servers 
  (<https://www.plex.tv>) which collects a lot of data about media items and server 
  usage such as play counts. This package interacts with the 'Tautulli' API of any 
  specified server to get said data into R. The 'Tautulli' API documentation is available
  at <https://github.com/Tautulli/Tautulli/blob/master/API.md>.
Provides classes for storing and manipulating taxonomic data. 
 Most of the classes can be treated like base R vectors (e.g. can be used 
 in tables as columns and can be named). Vectorized classes can store taxon names
 and authorities, taxon IDs from databases, taxon ranks, and other types of
 information. More complex classes are provided to store taxonomic trees and
 user-defined data associated with them.
Creates a local database of many commonly used taxonomic authorities
             and provides functions that can quickly query this data.
Creates a local Lightning Memory-Mapped Database ('LMDB') 
             of many commonly used taxonomic authorities
             and provides functions that can quickly query this data.
             Supported taxonomic authorities include 
             the Integrated Taxonomic Information System ('ITIS'),
             National Center for Biotechnology Information ('NCBI'),
             Global Biodiversity Information Facility ('GBIF'), 
             Catalogue of Life ('COL'), and Open Tree Taxonomy ('OTT'). 
             Name and identifier resolution using 'LMDB' can
             be hundreds of times faster than either relational databases or
             internet-based queries. Precise data provenance information for
             data derived from naming providers is also included.
Computation and visualization of Taxicab Correspondence Analysis, Choulakian (2006) <doi:10.1007/s11336-004-1231-4>.  Classical correspondence analysis (CA) is a statistical method to analyse 2-dimensional tables of positive numbers and is typically applied to contingency tables (Benzecri, J.-P. (1973). L'Analyse des Donnees. Volume II. L'Analyse des Correspondances. Paris, France: Dunod).  Classical CA is based on the Euclidean distance.  Taxicab CA is like classical CA but is based on the Taxicab or Manhattan distance.  For some tables, Taxicab CA gives more informative results than classical CA.
Interacts with a suite of web 'APIs' for taxonomic tasks,
    such as getting database specific taxonomic identifiers, verifying
    species names, getting taxonomic hierarchies, fetching downstream and
    upstream taxonomic names, getting taxonomic synonyms, converting
    scientific to common names and vice versa, and more.
Tools for working with 'taxonomic' databases, including
    utilities for downloading databases, loading them into various
    'SQL' databases, cleaning up files, and providing a 'SQL' connection
    that can be used to do 'SQL' queries directly or used in 'dplyr'.
Handling taxonomic lists through objects of class 'taxlist'.
    This package provides functions to import species lists from 'Turboveg'
    (<https://www.synbiosys.alterra.nl/turboveg/>) and the possibility to create
    backups from resulting R-objects.
    Also quick displays are implemented as summary-methods.
The NCBI taxonomy is a popular resource for taxonomic studies but it only contains
    data on species with sequence data whereas the GBIF has a more extensive coverage of
    extinct species. Taxonbridge is useful for the creation and analysis of custom taxonomies
    based on the NCBI taxonomy and GBIF backbone taxonomy.
Functions for assigning taxonomy to NCBI accession numbers and taxon IDs based on NCBI's accession2taxid and taxdump files. This package allows the user to downloads NCBI data dumps and create a local database for fast and local taxonomic assignment.
Automated standardization of taxonomic names and removal of orthographic errors in plant species names using 'The Plant List' website (www.theplantlist.org).
Tools include matching and merging taxonomic lists, casting and 
  melting scientific names, managing taxonomic lists from GBIF and ITIS, 
  harvesting names from wikipedia and fuzzy matching.
A comprehensive resource for data on Taylor Swift songs. Data is
    included for all officially released studio albums, extended plays (EPs),
    and individual singles are included. Data comes from
    'Genius' (lyrics) and 'Spotify' (song characteristics). Additional functions
    are included for easily creating data visualizations with color palettes
    inspired by Taylor Swift's album covers.
For when your colors absolutely should not be excluded from the narrative.
Solves Teitz and Bart's p-median problem - given a set of
    points attempts to find subset of size p such that summed distances of any
    point in the set to the nearest point in p is minimised.  Although
    generally effective, this algorithm does not guarantee that a globally
    optimal subset is found.
Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data.
Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.
Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) <doi:10.1214/14-STS510>] and for the Cox model [Held et al. (2016) <doi:10.1002/sim.7089>].
Facilitate the movement between data frames to 'xts'. Particularly
    useful when moving from 'tidyverse' to the widely used 'xts' package, which is
    the input format of choice to various other packages. It also allows the user 
    to use a 'spread_by' argument for a character column 'xts' conversion.
Boosting the likelihood of conditional and shift transformation models as introduced in \doi{10.1007/s11222-019-09870-4}.
We provide a forecasting model for time series forecasting problems with predictors. The offered model, which is based on a submitted research and called tree-based moving average (TBMA),  is based on the integration of the moving average approach to tree-based ensemble approach.  The tree-based ensemble models can capture the complex correlations between the predictors and response variable but lack in modelling time series components. The integration of the moving average approach to the tree-based ensemble approach helps the TBMA model to handle both correlations and autocorrelations in time series data. This package provides a tbma() forecasting function that utilizes the ranger() function from the 'ranger' package. With the help of the ranger() function, various types of tree-based ensemble models, such as extremely randomized trees and random forests, can be used in the TBMA model.
Creates simulated clinical trial data with realistic correlation structures and assumed efficacy levels by using a tilted bootstrap resampling approach. Samples are drawn from observed data with some samples appearing more frequently than others. May also be used for simulating from a joint Bayesian distribution along with clinical trials based on the Bayesian distribution.
Fast calculation of the Subtree Prune and Regraft (SPR),
  Tree Bisection and Reconnection (TBR) and Replug distances between 
  unrooted trees, using the algorithms of Whidden and 
  Matsen (2017) <arxiv:1511.07529>.
Provides rolling statistical functions based
    on date and time windows instead of n-lagged observations.
Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of heterogeneous sources into a three-dimensional matrix of signals (features by observations by sources). The TCA framework further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects; particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a three-dimensional tensor of cell-type-specific methylation levels for each individual (i.e. methylation sites by individuals by cell types) and it allows to detect cell-type-specific statistical relations (associations) with phenotypes. For more details see Rahmani et al. (2019) <DOI:10.1038/s41467-019-11052-9>.
Maximum likelihood estimation (MLE) of parameters assuming an underlying left truncated normal distribution with left censoring described in Williams, J, Kim, H, and Crespi, C. (2020) <doi:10.1186/s12874-020-01032-9>. Censoring is assumed to occur above the truncation threshold meaning that only censored observations are observed. Additional maximum likelihood estimation procedures are implemented to solve left censored only and left truncated only problems.
The classical two-sample t-test works well for the normally distributed data or 
    data with large sample size. The tcfu() and tt() tests implemented in this package provide 
    better type-I-error control with more accurate power when testing the equality of two-sample 
    means for skewed populations having unequal variances. These tests are especially useful 
    when the sample sizes are moderate. The tcfu() uses the Cornish-Fisher expansion to achieve 
    a better approximation to the true percentiles. The tt() provides transformations of the Welch's 
    t-statistic so that the sampling distribution become more symmetric. For more technical details, 
    please refer to Zhang (2019) <http://hdl.handle.net/2097/40235>.
The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. Several TCGA Datasets are available online. 'TCGAretriever' helps accessing and downloading TCGA data hosted on 'cBioPortal' via its Web Interface (see <http://www.cbioportal.org/> for more information). 'TCGAretriever' is easy to use (get all the TCGA data you need with a few lines of code), enforces reliable data download (via 'httr'), and is suitable for downloading large volumes of data.
Implementation of Time-course Gene Set Analysis (TcGSA), a method for 
	analyzing longitudinal gene-expression data at the gene set level. Method is
	detailed in: Hejblum, Skinner & Thiebaut (2015) <doi: 10.1371/journal.pcbi.1004310>.
Implementation of target-controlled infusion algorithms for compartmental pharmacokinetic and pharmacokinetic-pharmacodynamic models. Jacobs (1990) <doi:10.1109/10.43622>; Marsh et al. (1991) <doi:10.1093/bja/67.1.41>; Shafer and Gregg (1993) <doi:10.1007/BF01070999>; Schnider et al. (1998) <doi:10.1097/00000542-199805000-00006>; Abuhelwa, Foster, and Upton (2015) <doi:10.1016/j.vascn.2015.03.004>; Eleveld et al. (2018) <doi:10.1016/j.bja.2018.01.018>.
A wrapper for The Cancer Imaging Archive's REST API. The Cancer
    Imaging Archive (TCIA) hosts de-identified medical images of cancer
    available for public download, as well as rich metadata for each image
    series. TCIA provides a REST API for programmatic access to the data.
    This package provides simple functions to access each API endpoint.
    For more information, see <https://github.com/pamelarussell/TCIApathfinder>
    and TCIA's website.
An implementation of hypothesis testing in an extended Rasch modelling framework,
    including sample size planning procedures and power computations. Provides 4 statistical tests, 
    i.e., gradient test (GR), likelihood ratio test (LR), Rao score or Lagrange multiplier test (RS), 
    and Wald test, for testing a number of hypotheses referring to the Rasch model (RM), linear 
    logistic test model (LLTM), rating scale model (RSM), and partial credit model (PCM). Three 
    types of functions for power and sample size computations are provided. Firstly, functions to 
    compute the sample size given a user-specified (predetermined) deviation from the hypothesis 
    to be tested, the level alpha, and the power of the test. Secondly, functions to evaluate the 
    power of the tests given a user-specified (predetermined) deviation from the hypothesis to be 
    tested, the level alpha of the test, and the sample size. Thirdly, functions to evaluate the 
    so-called post hoc power of the tests. This is the power of the tests given the observed 
    deviation of the data from the hypothesis to be tested and a user-specified level alpha of the
    test. Power and sample size computations are based on a Monte Carlo simulation approach. It is
    computationally very efficient. The variance of the random error in computing power and sample
    size arising from the simulation approach is analytically derived by using the delta method. 
    Draxler, C., & Alexandrowicz, R. W. (2015), <doi:10.1007/s11336-015-9472-y>.
A series of additional Tcl commands and Tk widgets with style
  and various functions (under Windows: DDE exchange, access to the
  registry and icon manipulation) to supplement the tcltk package
Provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) <doi:10.1214/07-AOS515>, Fritz et al. (2012) <doi:10.18637/jss.v047.i12>, Garcia-Escudero et al. (2011)  <doi:10.1007/s11222-010-9194-z> and others.
The 1311 time series from the tourism forecasting competition conducted in 2010 and described in Athanasopoulos et al. (2011) <DOI:10.1016/j.ijforecast.2010.04.009>.
A set of tools for processing and modeling high-throughput and
    high-content chemical screening data. The package was developed for the
    the chemical screening data generated by the US EPA ToxCast program, but
    can be used for diverse chemical screening efforts.
Performs the basic concentration response curve fitting used 
    in the 'tcpl' package. It is a substitute for the original tcplFit() function 
    (and sub-functions) and allows a wider variety of concentration-response models. 
    All of the models included in the 'BMDExpress' package are now part of this package,
    and the output includes a calculation of the bmd (Benchmark Dose or concentration) 
    value.
R functions for Tinkoff Investments API <https://tinkoffcreditsystems.github.io/invest-openapi/>. Using this package, analysts and traders can interact with account and market data from within R.  Clients for both REST and Streaming protocols implemented.
The 'twelvedata' REST service offers access to current and historical
 data on stocks, standard as well as digital 'crypto' currencies, and other financial
 assets covering a wide variety of course and time spans. See <https://twelvedata.com/>
 for details, to create an account, and to request an API key for free-but-capped access
 to the data.
Tools for the statistical analysis of persistent homology and for
    density clustering. For that, this package provides an R interface for the
    efficient algorithms of the C++ libraries 'GUDHI' <https://project.inria.fr/gudhi/software/>, 'Dionysus' <https://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements the methods in Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2014) <doi:10.1145/2582112.2582128>  for analyzing the statistical significance of persistent homology features.
Topological data analysis studies structure and shape of the data using topological features. We provide a variety of algorithms to learn with persistent homology of the data based on functional summaries for clustering, hypothesis testing, visualization, and others. We refer to Wasserman (2018) <doi:10.1146/annurev-statistics-031017-100045> for a statistical perspective on the topic.
Topological Data Analysis using Mapper (discrete Morse theory).
    Generate a 1-dimensional simplicial complex from a filter 
    function defined on the data:  1. Define a filter function (lens) on the 
    data.  2. Perform clustering within within each level set and generate 
    one node (vertex) for each cluster.  3. For each pair of clusters in 
    adjacent level sets with a nonempty intersection, generate one edge 
    between vertices.  The function mapper1D uses a filter function with
    codomain R, while the the function mapper2D uses a filter function with
    codomain R^2.
A comprehensive toolset for any
    useR conducting topological data analysis, specifically via the
    calculation of persistent homology in a Vietoris-Rips complex.
    The tools this package currently provides can be conveniently split
    into three main sections: (1) calculating persistent homology; (2)
    conducting statistical inference on persistent homology calculations;
    (3) visualizing persistent homology and statistical inference.
    The published form of TDAstats can be found in Wadhwa et al. (2018)
    <doi:10.21105/joss.00860>.   
    For a general background on computing persistent homology for
    topological data analysis, see Otter et al. (2017)
    <doi:10.1140/epjds/s13688-017-0109-5>.
    To learn more about how the permutation test is used for
    nonparametric statistical inference in topological data analysis,
    read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>.
    To learn more about how TDAstats calculates persistent homology,
    you can visit the GitHub repository for Ripser, the software that
    works behind the scenes at <https://github.com/Ripser/ripser>.
    This package has been published as Wadhwa et al. (2018)
    <doi:10.21105/joss.00860>.
Uniform random samples from simple manifolds, sometimes with noise,
    are commonly used to test topological data analytic (TDA) tools.
    This package includes samplers powered by two techniques: analytic
    volume-preserving parameterizations, as employed by Arvo (1995)
    <doi:10.1145/218380.218500>, and rejection sampling, as employed by
    Diaconis, Holmes, and Shahshahani (2013) <doi:10.1214/12-IMSCOLL1006>.
The companion package that provides all the datasets used in the book
 "Data Integration, Manipulation and Visualization of Phylogenetic Trees" by Guangchuang Yu (2022, ISBN:9781032233574).
A boosted Tweedie compound Poisson model using the gradient boosting. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing interactions among predictors.
The Time-Delay Correlation algorithm (TDCor) reconstructs the topology of a gene regulatory network (GRN) from time-series transcriptomic data.  The algorithm is described in details in Lavenus et al., Plant Cell, 2015.  It was initially developed to infer the topology of the GRN controlling lateral root formation in Arabidopsis thaliana.  The time-series transcriptomic dataset which was used in this study is included in the package to illustrate how to use it.
Deconvolution of instrument responses from seismic traces and seismogram lists from RSEIS.  Includes pre-calculated instrument responses for several common instruments.
The t-Digest construction algorithm, by 
    Dunning et al., (2019) <arXiv:1902.04023v1>, uses a variant of 1-dimensional 
    k-means clustering to produce a very compact data structure that allows 
    accurate estimation of quantiles. This t-Digest data structure can be used 
    to estimate quantiles, compute other rank statistics or even to estimate 
    related measures like trimmed means. The advantage of the t-Digest over 
    previous digests for this purpose is that the t-Digest handles data with 
    full floating point resolution. The accuracy of quantile estimates produced 
    by t-Digests can be orders of magnitude more accurate than those produced 
    by previous digest algorithms. Methods are provided to create and update 
    t-Digests and retrieve quantiles from the accumulated distributions.
Tuned Data Mining in R ('TDMR') performs the complete tuning of a 
    data mining task (predictive analytics, that is classification and regression). 
    Preprocessing parameters and modeling parameters can be tuned simultaneously. 
    It incorporates a variety of tuners (among them 'SPOT' and 'CMA' with package 'rCMA') and 
    allows integration of additional tuners. Noise handling in the data mining optimization  
    process is supported, see Koch et al. (2015) <doi:10.1016/j.asoc.2015.01.005>.
Set of functions designed to help in the
    analysis of TDP sensors. Features includes dates and time conversion, weather
    data interpolation, daily maximum of tension analysis and calculations required
    to convert sap flow density data to sap flow rates at the tree and plot scale (For more information see : Granier (1985) <DOI:10.1051/forest:19850204> & Granier (1987) <DOI:10.1093/treephys/3.4.309>).
Implementation of target diagrams using 'lattice' and 'ggplot2' graphics. Target diagrams provide a graphical overview of the respective contributions of the unbiased RMSE and MBE to the total RMSE (Jolliff, J. et al., 2009. "Summary Diagrams for Coupled Hydrodynamic-Ecosystem Model Skill Assessment." Journal of Marine Systems 76: 64–82.)
Compute time-dependent ROC curve from censored survival data using
    nonparametric weight adjustments.
Functions for performing time domain signal coding as used in Chesmore (2001) <doi:10.1016/S0003-682X(01)00009-3>, and related tasks. This package creates the standard S-matrix and A-matrix (with variable lag), has tools to convert coding matrices into distributed matrices, provides published codebooks and allows for extraction of code sequences.
Functions and examples are provided for Transmission/disequilibrium tests
             for extended marker haplotypes, as in
             Clayton, D. and Jones, H. (1999) "Transmission/disequilibrium tests
             for extended marker haplotypes". Amer. J. Hum. Genet., 65:1161-1169,
             <doi:10.1086/302566>.
Provides functions to estimate the insertion and deletion rates of transposable element (TE) families. The estimation of insertion rate consists of an improved estimate of the age distribution that takes into account random mutations, and an adjustment by the deletion rate. A hypothesis test for a uniform insertion rate is also implemented. This package implements the methods proposed in Dai et al (2018).
Different approaches for selecting the threshold in generalized Pareto distributions. Most of them are based on minimizing the AMSE-criterion or at least by reducing the bias of the assumed GPD-model. Others are heuristically motivated by searching for stable sample paths, i.e. a nearly constant region of the tail index estimator with respect to k, which is the number of data in the tail. The third class is motivated by graphical inspection. In addition, a sequential testing procedure for GPD-GoF-tests is also implemented here.
Statistics students often have problems understanding 
    the relation between a random variable's true scale and its z-values. 
    To allow instructors to better better visualize histograms for these 
    students, the package provides histograms with two horizontal 
    axis containing z-values and the true scale of the variable. 
    The function TeachHistDens() provides a density histogram with two axis.  
    TeachHistCounts() and TeachHistRelFreq() are variations for count and 
    relative frequency histograms, respectively. TeachConfInterv() and 
    TeachHypTest() help instructors to visualize confidence levels 
    and the results of hypothesis tests.
Contains apps and gadgets for teaching data analysis and 
    statistics concepts along with how to implement them in R.  Includes 
    tools to make app development easier and faster by nesting apps together.
Demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming.
Allows the user to draw probabilistic samples and make inferences from a finite population based on several sampling designs.
Can fit neural networks with up to two hidden layer and two different error functions. Also able to handle a weight decay. But just able to compute one output neuron and very slow.
Implements models of leaf temperature using energy balance. It uses units to ensure that parameters are properly specified and transformed before calculations. It allows separate lower and upper surface conductances to heat and water vapour, so sensible and latent heat loss are calculated for each surface separately  as in Foster and Smith (1986) <doi:10.1111/j.1365-3040.1986.tb02108.x>. It's straightforward to model leaf temperature over environmental gradients such as light, air temperature, humidity, and wind. It can also model leaf temperature over trait gradients such as leaf size or stomatal conductance. Other references are Monteith and Unsworth (2013, ISBN:9780123869104), Nobel (2009, ISBN:9780123741431), and Okajima et al. (2012) <doi:10.1007/s11284-011-0905-5>.
An implementation of the TEAM algorithm to identify local differences 
    between two (e.g. case and control) independent, univariate distributions, as 
    described in J Pura, C Chan, and J Xie (2019) <arXiv:1906.07757>. The algorithm 
    is based on embedding a multiple-testing procedure on a hierarchical structure 
    to identify high-resolution differences between two distributions. The 
    hierarchical structure is designed to identify strong, short-range differences 
    at lower layers and weaker, but long-range differences at increasing layers. 
    TEAM yields consistent layer-specific and overall false discovery rate control.
Provides color palettes corresponding to professional and amateur, 
    sports teams. These can be useful in creating data graphics that are themed 
    for particular teams.
Package of wrapper functions using R6 class to send requests to 
  Microsoft 'Teams' <https://products.office.com/en-us/microsoft-teams/group-chat-software> through webhooks.
  When you need to share information or data from R to 'Teams', rather than copying/pasting, you
  can use this package to send well-formatted output from multiple R objects.
The typicality and eccentricity data analysis (TEDA) framework was
    put forward by Angelov (2013) <DOI:10.14313/JAMRIS_2-2014/16>. It has been further developed into multiple
    different techniques since, and provides a non-parametric way of determining how
    similar an observation, from a process that is not purely random, is to other
    observations generated by the process. This package provides code to use the
    batch and recursive TEDA methods that have been published.
Fits mixtures of multivariate t-distributions (with eigen-decomposed covariance structure) via the expectation conditional-maximization algorithm under a clustering or classification paradigm.
Functions for interacting directly with the Taiwan Economic Journal API to offer data in R. For more information go to <https://api.tej.com.tw>.
Implementation of the remote effects spatial process (RESP) model for teleconnection.  The RESP model is a geostatistical model that allows a spatially-referenced variable (like average precipitation) to be influenced by covariates defined on a remote domain (like sea surface temperatures).  The RESP model is introduced in Hewitt et al. (2018) <doi:10.1002/env.2523>.  Sample code for working with the RESP model is available at <https://jmhewitt.github.io/research/resp_example>. This material is based upon work supported by the National Science Foundation under grant number AGS 1419558. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
R wrapper around the Telegram Bot API (http://core.telegram.org/bots/api) to access Telegram's messaging facilities with ease (e.g. you send messages, images, files from R to your smartphone).
Provides a pure interface for the 'Telegram Bot API'
    <http://core.telegram.org/bots/api>. In addition to the pure API
    implementation, it features a number of tools to make the development of
    'Telegram' bots with R easy and straightforward, providing an easy-to-use
    interface that takes some work off the programmer.
An R interface to the TELEMAC suite for modelling
    of free surface flow. This includes methods for model initialisation, simulation,
    and visualisation. So far only the TELEMAC-2D module for 2-dimensional hydrodynamic
    modelling is implemented.
Using The Free Evocation of Words Technique method with some functions, this package will make a
    social representation and other analysis. The Free Evocation of Words Technique consists of collecting a number of words evoked by a subject facing exposure to an inducer term. The purpose of this technique is to understand the relationships created between words evoked by the individual and the inducer term. This technique is included in the theory of social representations, therefore, on the information transmitted by an individual, seeks to create a profile that define a social group.
Method to estimate the effect of the trend in predictor variables on the observed trend
 of the response variable using mixed models with temporal autocorrelation. See Fernández-Martínez et
 al. (2017 and 2019) <doi:10.1038/s41598-017-08755-8> <doi:10.1038/s41558-018-0367-7>.
Temporal disaggregation methods are used to disaggregate and
    interpolate a low frequency time series to a higher frequency series, where
    either the sum, the mean, the first or the last value of the resulting
    high frequency series is consistent with the low frequency series. Temporal
    disaggregation can be performed with or without one or more high frequency
    indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso,
    Fernandez, Litterman, Denton and Denton-Cholette, summarized in Sax and
    Steiner (2013) <doi:10.32614/RJ-2013-028>. Supports most R time series
    classes.
Fits temperature response models to rate measurements taken at different temperatures. Etienne Low-Decarie,Tobias G. Boatman, Noah Bennett,Will Passfield,Antonio Gavalas-Olea,Philipp Siegel, Richard J. Geider (2017) <doi:10.1002/ece3.3576> .
Functions implementing the template ICA model proposed in Mejia et
    al. (2019) <doi:10.1080/01621459.2019.1679638> and the spatial template ICA
    model proposed in proposed in Mejia et al. (2020+). For both models, 
    subject-level brain networks are estimated as deviations from known 
    population-level networks, which can be estimated using standard ICA 
    algorithms. Both models employ an expectation-maximization algorithm 
    for estimation of the latent brain networks and unknown model parameters.
Provides tools to work with template code and text in R. It aims to
    provide a simple substitution mechanism for R-expressions inside these
    templates. Templates can be written in other languages like 'SQL', can
    simply be represented by characters in R, or can themselves be R-expressions
    or functions.
TEMPO (TEmporal Modeling of Pathway Outliers) is a pathway-based outlier detection approach for finding pathways showing 
    significant changes in temporal expression patterns across conditions.  Given a gene expression data set where each sample is characterized by 
    an age or time point as well as a phenotype (e.g. control or disease), and a collection of gene sets or pathways, TEMPO ranks each pathway
    by a score that characterizes how well a partial least squares regression (PLSR) model can predict age as a function of gene expression in the controls
    and how poorly that same model performs in the disease. TEMPO v1.0.3 is described in Pietras (2018) <doi:10.1145/3233547.3233559>.
Performs maximum likelihood based estimation and inference on time to event data, possibly subject to non-informative right censoring. FitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. CompParaSurv() compares the mean, median, and restricted mean survival experiences of two treatment groups. Candidate distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull.
Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS).
Compute the coordinates to produce a tendril plot. 
    In the tendril plot, each tendril (branch) represents a type of events, 
    and the direction of the tendril is dictated by on which treatment arm the 
    event is occurring. If an event is occurring on the first of the two 
    specified treatment arms, the tendril bends in a clockwise direction. 
    If an event is occurring on the second of the treatment arms, the
    tendril bends in an anti-clockwise direction. 
    Ref: Karpefors, M and Weatherall, J., "The Tendril Plot - a novel visual summary 
    of the incidence, significance and temporal aspects of adverse events in 
    clinical trials" - JAMIA 2018; 25(8): 1069-1073 <doi:10.1093/jamia/ocy016>.
Implementation of ZENIT-POLAR substitution cipher method of encryption using by default the TENIS-POLAR cipher. This last cipher of encryption became famous through the collection of Brazilian books "Os Karas" by the author Pedro Bandeira. For more details, see "A Cryptographic Dictionary" (GC&CS, 1944).
The tensor product of two arrays is notionally an outer
        product of the arrays collapsed in specific extents by summing
        along the appropriate diagonals.
Provides convenience functions for advanced
        linear algebra with tensors and computation with data sets of
        tensors on a higher level abstraction. It includes Einstein and
        Riemann summing conventions, dragging, co- and contravariate
        indices, parallel computations on sequences of tensors.
Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.
Contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: 'tPCA', 'tFOBI', 'tJADE', k-tJADE', 'tgFOBI', 'tgJADE', 'tSOBI', 'tNSS.SD', 'tNSS.JD', 'tNSS.TD.JD', 'tPP' and 'tTUCKER'.
Performs model-based tensor clustering methods including Tensor Gaussian Mixture Model (TGMM), Tensor Envelope Mixture Model (TEMM) by Deng and Zhang (2021) <DOI: 10.1111/biom.13486>, Doubly-Enhanced EM (DEEM) algorithm by Mai, Zhang, Pan and Deng (2021) <DOI: 10.1080/01621459.2021.1904959>.
Efficient algorithms for tensor noise reduction and completion. This package includes a suite of parametric and nonparametric tools for estimating tensor signals from noisy, possibly incomplete observations. The methods allow a broad range of data types, including continuous, binary, and ordinal-valued tensor entries. The algorithms employ the alternating optimization. The detailed algorithm description can be found in the following three references.
Interface to 'TensorFlow' <https://www.tensorflow.org/>,
  an open source software library for numerical computation using data
  flow graphs. Nodes in the graph represent mathematical operations,
  while the graph edges represent the multidimensional data arrays
  (tensors) communicated between them. The flexible architecture allows
  you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop,
  server, or mobile device with a single 'API'. 'TensorFlow' was originally
  developed by researchers and engineers working on the Google Brain Team
  within Google's Machine Intelligence research organization for the
  purposes of conducting machine learning and deep neural networks research,
  but the system is general enough to be applicable in a wide variety
  of other domains as well.
Provides methods to manipulate and store sparse tensors. Tensors 
  are multidimensional generalizations of matrices (two dimensional) and 
  vectors (one dimensional).
Implement the alternating algorithm for supervised tensor decomposition with interactive side information.
Implements the multiway sparse clustering approach of M. Wang and Y. Zeng, "Multiway clustering via tensor block models". Advances in Neural Information Processing System 32 (NeurIPS), 715-725, 2019.
An implementation of fitting generalized linear models on
    second-order tensor type data. The functions within this package mainly focus on
    parameter estimation, including parameter coefficients and standard deviation.
Factor and autoregressive models for matrix and tensor valued time series. We provide functions for estimation, simulation and prediction. The models are discussed in Li et al (2021) <arXiv:2110.00928>, Chen et al (2020) <DOI:10.1016/j.jeconom.2020.07.015>, Chen et al (2020) <arXiv:1905.07530>, and Xiao et al (2020) <arXiv:2006.02611>.
A collection of functions for Kronecker structured covariance
    estimation and testing under the array normal model. For estimation,
    maximum likelihood and Bayesian equivariant estimation procedures are
    implemented. For testing, a likelihood ratio testing procedure is
    available. This package also contains additional functions for manipulating
    and decomposing tensor data sets. This work was partially supported by NSF
    grant DMS-1505136. Details of the methods are described in
    Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and
    Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.
The TEQR package contains software to calculate the operating characteristics for the TEQR and the ACT designs.The TEQR (toxicity equivalence range) design is a toxicity based cumulative cohort design with added safety rules. The ACT (Activity constrained for toxicity) design  is also a cumulative cohort design with additional safety rules. The unique feature of this design is that dose is escalated based on lack of activity rather than on lack of toxicity and is de-escalated only if an unacceptable level of toxicity is experienced.
This package is for the comparison of various types of A+B
        escalation rules for dose finding trials.
An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis. See Krivitsky and Handcock (2014) <doi:10.1111/rssb.12014> and Carnegie, Krivitsky, Hunter, and Goodreau (2015) <doi:10.1080/10618600.2014.903087>.
Provides functions for the computationally efficient simulation of 
    dynamic networks estimated with the statistical framework of temporal 
    exponential random graph models, implemented in the 'tergm' package.
Creates, manipulates, queries and repairs vectors of
    parameter terms.  Parameter terms are the labels used to reference
    values in vectors, matrices and arrays. They represent the names in
    coefficient tables and the column names in 'mcmc' and 'mcmc.list'
    objects.
Plots ternary diagrams (simplex plots / Gibbs triangles) and
  Holdridge life zone plots <doi:10.1126/science.105.2727.367> using the
  standard graphics functions.
  An alternative to 'ggtern', which uses the 'ggplot2' family of plotting 
  functions.
  Includes a 'Shiny' user interface for point-and-click ternary plotting.
A suite of functions for visualising ternary probabilistic forecasts, as discussed in the paper by Jupp (2012) <doi:10.1098/rsta.2011.0350>.
Methods for spatial data analysis with raster and vector data. Raster methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/terra/> to get started. 'terra' is very similar to the 'raster' package; but 'terra' can do more, is easier to use, and it is faster.
Provides triangulations of regular height fields, based on the methods described in "Fast Polygonal Approximation of Terrains and Height Fields" Michael Garland and Paul S. Heckbert (1995) <https://www.mgarland.org/files/papers/scape.pdf> using code from the 'hmm' library written by Michael Fogleman <https://www.github.com/fogleman/hmm>.
Functions for the retrieval, manipulation, and visualization of 
    'geospatial' data, with an aim towards producing '3D' landscape 
    visualizations in the 'Unity' '3D' rendering engine. Functions are also 
    provided for retrieving elevation data and base map tiles from the 'USGS' 
    National Map <https://apps.nationalmap.gov/services/>.
Calculate the failure probability of civil engineering problems with Level I up to Level III Methods. Have fun and enjoy. References: Spaethe (1991, ISBN:3-211-82348-4) "Die Sicherheit tragender Baukonstruktionen", AU,BECK (2001) "Estimation of small failure probabilities in high dimensions by subset simulation." <doi:10.1016/S0266-8920(01)00019-4>, Breitung (1989) "Asymptotic approximations for probability integrals." <doi:10.1016/0266-8920(89)90024-6>.
Simulation of reconstructed phylogenetic trees under tree-wide time-heterogeneous birth-death processes and estimation of diversification parameters under the same model. Speciation and extinction rates can be any function of time and mass-extinction events at specific times can be provided. Trees can be simulated either conditioned on the number of species, the time of the process, or both. Additionally, the likelihood equations are implemented for convenience and can be used for Maximum Likelihood (ML) estimation and Bayesian inference.
Delaunay and Voronoï tessellations, with emphasis on the two-dimensional and the three-dimensional cases (the package provides functions to plot the tessellations for these cases). Delaunay tessellations are computed in C with the help of the 'Qhull' library <http://www.qhull.org/>.
Bindings to 'Tesseract' <https://opensource.google/projects/tesseract>: 
     a powerful optical character recognition (OCR) engine that supports over 100 languages.
     The engine is highly configurable in order to tune the detection algorithms and
     obtain the best possible results.
Package test2norm contains functions to generate formulas for 
    normative standards applied to cognitive tests. It takes raw test scores 
    (e.g., number of correct responses) and converts them to scaled scores and 
    demographically adjusted scores, using methods described in Heaton et al. 
    (2003) <doi:10.1016/B978-012703570-3/50010-9> & Heaton et al. (2009, 
    ISBN:9780199702800). The scaled scores are calculated as quantiles of the 
    raw test scores, scaled to have the mean of 10 and standard deviation of 3, 
    such that higher values always correspond to better performance on the test. 
    The demographically adjusted scores are calculated from the residuals of a 
    model that regresses scaled scores on demographic predictors (e.g., age). 
    The norming procedure makes use of the mfp() function from the 'mfp' package 
    to explore nonlinear associations between cognition and demographic 
    variables.
Finding the best values for user-specified arguments of a prediction algorithm can be difficult, particularly if there is an interaction between argument levels. This package automates the testing of any user-defined prediction algorithm over an arbitrary number of arguments. It includes functions for testing the algorithm over the given arguments with respect to an arbitrary number of user-defined diagnostics, visualising the results of these tests, and finding the optimal argument combinations with respect to each diagnostic.
A common way of validating a biological assay for is through a
    procedure, where m levels of an analyte are measured with n replicates at each
    level, and if all m estimates of the coefficient of variation (CV) are less
    than some prespecified level, then the assay is declared validated for precision
    within the range of the m analyte levels. Two limitations of this procedure are:
    there is no clear statistical statement of precision upon passing, and it is
    unclear how to modify the procedure for assays with constant standard deviation.
    We provide tools to convert such a procedure into a set of m hypothesis tests.
    This reframing motivates the m:n:q procedure, which upon completion delivers
    a 100q% upper confidence limit on the CV. Additionally, for a post-validation
    assay output of y, the method gives an ``effective standard deviation interval''
    of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where
    mu is the expected value of the assay output for that sample. Further, the m:n:q
    procedure can be straightforwardly applied to constant standard deviation assays.
    We illustrate these tools by applying them to a growth inhibition assay. This is
    an implementation of the methods described in Fay, Sachs, and Miura (2018) 
    <doi:10.1002/sim.7528>.
Different multiple testing procedures for correlation tests are implemented. These procedures were shown to theoretically control asymptotically the Family Wise Error Rate (Roux (2018) <https://tel.archives-ouvertes.fr/tel-01971574v1>) or the False Discovery Rate (Cai & Liu (2016) <doi:10.1080/01621459.2014.999157>). The package gather four test statistics used in correlation testing, four FWER procedures with either single step or stepdown versions, and four FDR procedures.
Computes the test statistics for examining the significance of autocorrelation in univariate time series, cross-correlation in bivariate time series, Pearson correlations in multivariate series and test statistics for i.i.d. property of univariate series given in Dalla, Giraitis and Phillips (2020), <https://cowles.yale.edu/sites/default/files/files/pub/d21/d2194-r.pdf>.
Test your data! An extension of the 'testthat' unit testing
    framework with a family of functions and reporting tools for checking
    and validating data frames.
Functions for imputing missing item responses for dichotomous and
    polytomous test and assessment data. This package enables missing imputation
    methods that are suitable for test and assessment data, including: 
    listwise (LW) deletion (see De Ayala et al. 2001 <doi:10.1111/j.1745-3984.2001.tb01124.x>), 
    treating as incorrect (IN, see Lord, 1974 <doi: 10.1111/j.1745-3984.1974.tb00996.x>; 
    Mislevy & Wu, 1996 <doi: 10.1002/j.2333-8504.1996.tb01708.x>; 
    Pohl et al., 2014 <doi: 10.1177/0013164413504926>), person mean imputation (PM), 
    item mean imputation (IM), two-way (TW) and response function (RF) imputation,
    (see Sijtsma & van der Ark, 2003 <doi: 10.1207/s15327906mbr3804_4>),
    logistic regression (LR) imputation, predictive mean matching (PMM), and expectation–maximization (EM) 
    imputation (see Finch, 2008 <doi: 10.1111/j.1745-3984.2008.00062.x>).
Uses the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and
    van der Linden (2018) <doi:10.1201/9781315117430> to construct fixed, adaptive, and parallel tests.
    Supports the following mixed-integer programming (MIP) solver packages: 'lpsymphony', 'Rsymphony',
    'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see <https://www.gurobi.com/downloads/>.
Offers a solution for the unavailability of raw data in most anthropological studies by facilitating the calculations of several sexual dimorphism related analyses using the published summary statistics of metric data (mean, standard deviation and sex specific sample size) as illustrated by the works of  Greene, D. L. (1989) <doi:10.1002/ajpa.1330790113> and Konigsberg, L. W. (1991) <doi:10.1002/ajpa.1330840110>.
Provides data sets for teaching statistics and data science courses. 
    It includes a sample of data from John Edmund Kerrich's famous 
    coinflip experiment. These are data that I use for teaching SOC 4015 / SOC 
    5050 at Saint Louis University (SLU). The package also contains an R Markdown
    template with the required formatting for assignments in my courses 
    SOC 4015, SOC 4650, SOC 5050, and SOC 5650 at SLU.
Tests the hypothesis  that variances are homogeneous or not using bootstrap. The procedure uses a variance-based statistic, and is derived from a normal-theory test.
             The test equivalently expressed the hypothesis as a function of the log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis. See Cahoy (2010) <doi:10.1016/j.csda.2010.04.012>.
tester allows you to test characteristics of common R objects.
Provides test statistics, p-value, and confidence intervals based on 9 hypothesis tests for dependence.
Test functions are often used to test computer code.
  They are used in optimization to test algorithms and in
  metamodeling to evaluate model predictions. This package provides
  test functions that can be used for any purpose.
  Some functions are taken from <https://www.sfu.ca/~ssurjano>, but
  their R code is not used.
Develop, evaluate, and score multiple choice examinations, 
    psychological scales, questionnaires, and similar types of data involving
    sequences of choices among one or more sets of answers.
    Using the package does not require any formal statistical knowledge 
    beyond what would be provided by a first course in statistics in a 
    social science department.  There the user would encounter the concept 
    of probability and how it is used to model data and make decisions, 
    and would become familiar with basic mathematical and statistical notation.
    The essential aspects of each display were designed to be self-explanatory, 
    although more statistically sophisticated users will also find information 
    that they may find helpful. Most of the output is in graphical form. Two
    recent papers on the methodology are
    Ramsay, James; Li, Juan; Wiberg, Marie (2020) <doi:10.3390/psych2040026> and
    Ramsay, James; Wiberg, Marie; Li, Juan (2019) <doi:10.3102/1076998619885636>.
Provides a bootstrap test which decides whether two dose response curves can be assumed as equal concerning their maximum absolute deviation. A plenty of choices for the model types are available, which can be found in the 'DoseFinding' package, which is used for the fitting of the models. See <doi:10.1080/01621459.2017.1281813> for details.
Provides two convenience functions assert() and test_pkg() to
    facilitate testing R packages.
GUI for entering test items and obtaining raw
   and transformed scores. The results are shown on the
   console and can be saved to a tabular text file for further
   statistical analysis. The user can define his own tests and
   scoring procedures through a GUI.
Software testing is important, but, in part because it is
    frustrating and boring, many of us avoid it. 'testthat' is a testing
    framework for R that is easy to learn and use, and integrates with
    your existing 'workflow'.
Utility functions and 'RStudio' addins for writing,
    running and organizing automated tests. Integrates tightly with the
    packages 'testthat', 'devtools' and 'usethis'.  Hotkeys can be
    assigned to the 'RStudio' addins for running tests in a single file or
    to switch between a source file and the associated test file. In
    addition, testthis provides function to manage and run tests in
    subdirectories of the test/testthat directory.
Tests one hypothesis with several test statistics, correcting for multiple testing.  The central function in the package is testtwice().  In a sensitivity analysis, the method has the largest design sensitivity of its component tests.  The package implements the method and examples in Rosenbaum, P. R. (2012) <doi:10.1093/biomet/ass032> Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774.
The satisfaction Analysis using the tetraclasse model from Sylvie Llosa. Llosa (1997) <http://www.jstor.org/stable/40592578>.
Each sequence is predicted by expanding the distance matrix. The compact set of hyper-parameters is tuned via grid or random search.
Checks LaTeX documents and .bib files for typing errors, such as spelling errors, incorrect quotation marks. Also provides useful functions for parsing and linting bibliography files.
Randomizing exams with 'LaTeX'.
    If you can compile your main document with 'LaTeX', the program should be able to compile the randomized
    versions without much extra effort when creating the document.
Statistical extreme value modelling of threshold excesses, maxima
    and multivariate extremes. Univariate models for threshold excesses and maxima
    are the Generalised Pareto, and Generalised Extreme Value model respectively.
    These models may be fitted by using maximum (optionally penalised-)likelihood,
    or Bayesian estimation, and both classes of models may be fitted with covariates
    in any/all model parameters. Model diagnostics support the fitting process.
    Graphical output for visualising fitted models and return level estimates is
    provided. For serially dependent sequences, the intervals declustering algorithm
    of Ferro and Segers (2003) <doi:10.1111/1467-9868.00401> is provided, with
    diagnostic support to aid selection of threshold and declustering horizon.
    Multivariate modelling is performed via the conditional approach of Heffernan
    and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>, with graphical tools for
    threshold selection and to diagnose estimation convergence.
An extension of ExPosition for two table analyses, specifically, discriminant analyses.
Compile snippets of 'LaTeX' directly into images
    from the R console to view in the 'RStudio' viewer pane, Shiny apps
    and 'RMarkdown' documents.
Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft <doi:10.18637/jss.v055.i08>. (If the Zelig package, which this package enhances, cannot be found on CRAN, you can find it at <https://github.com/IQSS/Zelig>. If the mnlogit package, which this package enhances, cannot be found on CRAN, you can find an old version in the CRAN Archive at <https://cran.r-project.org/src/contrib/Archive/mnlogit/>.)
Transforms text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see  <https://www.r-text.org>.
Find similarities between texts using the Smith-Waterman algorithm. The algorithm performs local sequence alignment and determines similar regions between two strings.
    The Smith-Waterman algorithm is explained in the paper: "Identification of common molecular subsequences" by T.F.Smith and M.S.Waterman (1981), available at <doi:10.1016/0022-2836(81)90087-5>. 
    This package implements the same logic for sequences of words and letters instead of molecular sequences.
This is a collection of functions optimized for working with
             with various kinds of text matrices. Focusing on 
             the text matrix as the primary object - represented 
             either as a base R dense matrix or a 'Matrix' package sparse 
             matrix - allows for a consistent and intuitive interface 
             that stays close to the underlying mathematical foundation 
             of computational text analysis. In particular, the package
             includes functions for working with word embeddings, 
             text networks, and document-term matrices. Methods developed in 
             Stoltz and Taylor (2019) <doi:10.1007/s42001-019-00048-6>, 
             Taylor and Stoltz (2020) <doi:10.1007/s42001-020-00075-8>, 
             Taylor and Stoltz (2020) <doi:10.15195/v7.a23>, and
             Stoltz and Taylor (2021) <doi:10.1016/j.poetic.2021.101567>.
The United Nations’ Sustainable Development Goals (SDGs) have become an important guideline for organisations to monitor and plan their contributions to social, economic, and environmental transformations. The 'text2sdg' package is an open-source analysis package that identifies SDGs in text using scientifically developed query systems, opening up the opportunity to monitor any type of text-based data, such as scientific output or corporate publications.
Unifies different text to speech engines, such as
    Google, Microsoft, and Amazon.  Text synthesis can be done
    in any engine with a simple switch of an argument denoting
    the service requested.  The 'aws.polly' package has been
    orphaned and can be found from the CRAN archives.
Fast and memory-friendly tools for text vectorization, topic
    modeling (LDA, LSA), word embeddings (GloVe), similarities. This package
    provides a source-agnostic streaming API, which allows researchers to perform
    analysis of collections of documents which are larger than available RAM. All
    core functions are parallelized to benefit from multicore machines.
Tools to clean and process text.  Tools are geared at checking for substrings that
          are not optimal for analysis and replacing or removing them (normalizing) with more
          analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards
          (2001) <doi:10.1006/csla.2001.0169>) or extracting them into new variables. For
          example, emoticons are often used in text but not always easily handled by analysis
          algorithms.  The replace_emoticon() function replaces emoticons with word
          equivalents.
Provides a framework to download, parse, and store text datasets
    on the disk and load them when needed. Includes various sentiment lexicons
    and labeled text data sets for classification and analysis.
Implements the approach described in Fong and Grimmer (2016) <https://aclweb.org/anthology/P/P16/P16-1151.pdf> for 
	automatically discovering latent treatments from a corpus and estimating the average marginal component effect (AMCE) of 
	each treatment.  The data is divided into a training and test set.  The supervised Indian Buffet Process (sibp) is used 
	to discover latent treatments in the training set.  The fitted model is then applied to the test set to infer the values 
	of the latent treatments in the test set.  Finally, Y is regressed on the latent treatments in the test set to estimate 
	the causal effect of each treatment.
Implement text and sentiment analysis with 'texter'. 
             Generate sentiment scores on text data and also visualize sentiments.
             'texter' allows you to quickly generate insights on your data.
             It includes support for lexicons such as 'NRC' and 'Bing'.
A tool for extracting some generic features (e.g., number of
    words, line breaks, characters per word, URLs, lower case, upper case,
    commas, periods, exclamation points, etc.) from strings of text.
Provides functionalities based on the paper "Time Varying Dictionary and the Predictive Power of FED Minutes" (Lima, 2018) <doi:10.2139/ssrn.3312483>. It selects the most predictive terms, that we call time-varying dictionary using supervised machine learning techniques as lasso and elastic net.
The software application Praat can be used to annotate
    waveform data (e.g., to mark intervals of interest or to label events).
    (See <http://www.fon.hum.uva.nl/praat/> for more information about Praat.)
    These annotations are stored in a Praat TextGrid object, which consists of
    a number of interval tiers and point tiers. An interval tier consists of
    sequential (i.e., not overlapping) labeled intervals. A point tier consists
    of labeled events that have no duration. The 'textgRid' package provides
    S4 classes, generics, and methods for accessing information that is stored
    in Praat TextGrid objects.
Contains real images of the same textile material 
    with/without local defects, which were used in 
    Bui and Apley (2018) <doi:10.1080/00401706.2017.1302362>.
Multinomial (inverse) regression inference for text documents and associated attributes.  For details see: Taddy (2013 JASA) Multinomial Inverse Regression for Text Analysis <arXiv:1012.2098> and Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>. A minimalist partial least squares routine is also included.  Note that the topic modeling capability of earlier 'textir' is now a separate package, 'maptpx'.
An aid for text mining in R, with a syntax that
    should be familiar to experienced R users. Provides a wrapper for several 
    topic models that take similarly-formatted input and give similarly-formatted
    output. Has additional functionality for analyzing and diagnostics for
    topic models.
Graphic interface for text analysis, implement a few methods such as biplots, correspondence analysis, co-occurrence, clustering, topic models, correlations and sentiments.
Statistical exploration of textual corpora using several methods
    from French 'Textometrie' (new name of 'Lexicometrie') and French 'Data Analysis' schools.
    It includes methods for exploring irregularity of distribution of lexicon features across
    text sets or parts of texts (Specificity analysis); multi-dimensional exploration (Factorial analysis), etc. 
    Those methods are used in the TXM software.
Visualise complex relations in texts. This is done by providing functionalities for displaying 
    text co-occurrence networks, text correlation networks, dependency relationships as well as text clustering and semantic text 'embeddings'. 
    Feel free to join the effort of providing interesting text visualisations.
The 'textrank' algorithm is an extension of the 'Pagerank' algorithm for text. The algorithm allows to summarize text by calculating how sentences are related to one another. This is done by looking at overlapping terminology used in sentences in order to set up links between sentences. The resulting sentence network is next plugged into the 'Pagerank' algorithm which identifies the most important sentences in your text and ranks them. 
    In a similar way 'textrank' can also be used to extract keywords. A word network is constructed by looking if words are following one another. On top of that network the 'Pagerank' algorithm is applied to extract relevant words after which relevant words which are following one another are combined to get keywords.  
    More information can be found in the paper from Mihalcea, Rada & Tarau, Paul (2004) <https://www.aclweb.org/anthology/W04-3252/>.
A small collection of convenience tools for reading text documents into R.
Converting text to numerical features requires specifically
    created procedures, which are implemented as steps according to the
    'recipes' package. These steps allows for tokenization, filtering,
    counting (tf and tfidf) and feature hashing.
Function for sparse regression on raw text, regressing a labeling
    vector onto a feature space consisting of all possible phrases.
Tools for measuring similarity among documents and detecting
    passages which have been reused. Implements shingled n-gram, skip n-gram,
    and other tokenizers; similarity/dissimilarity functions; pairwise
    comparisons; minhash and locality sensitive hashing algorithms; and a
    version of the Smith-Waterman local alignment algorithm suitable for
    natural language.
Tools that can be used to reshape and restructure text data.
Provides access to the text shaping functionality in the 'HarfBuzz'
    library and the bidirectional algorithm in the 'Fribidi' library. 
    'textshaping' is a low-level utility package mainly for graphic devices that 
    expands upon the font tool-set provided by the 'systemfonts' package.
Tools that stem and lemmatize text.  Stemming is a process that removes
         endings such as affixes.  Lemmatization is the process of grouping inflected
         forms together as a single base form.
It offers functions for splitting, parsing, tokenizing and creating a vocabulary for big text data files. Moreover, it includes functions for building a document-term matrix and extracting information from those (term-associations, most frequent terms). It also embodies functions for calculating token statistics (collocations, look-up tables, string dissimilarities) and functions to work with sparse matrices. Lastly, it includes functions for Word Vector Representations (i.e. 'GloVe', 'fasttext') and incorporates functions for the calculation of (pairwise) text document dissimilarities. The source code is based on 'C++11' and exported in R through the 'Rcpp', 'RcppArmadillo' and 'BH' packages.
A framework for text cleansing and analysis. Conveniently prepare and process large amounts of text for analysis. 
  Includes various metrics for word counts/frequencies that scale efficiently. Quickly 
  analyze large amounts of text data using a text.table (a data.table created with one word (or unit of text analysis) per row, similar to the tidytext format). 
  Offers flexibility to efficiently work with text data stored in vectors as well as text data formatted as a text.table.
Utilities for handling character vectors
  that store human-readable text (either plain or with
  markup, such as HTML or LaTeX). The package provides,
  in particular, functions that help with the
  preparation of plain-text reports, e.g. for expanding
  and aligning strings that form the lines of such
  reports. The package also provides generic functions for
  transforming R objects to HTML and to plain text.
'TensorFlow SIG Addons' <https://www.tensorflow.org/addons> is a repository 
             of community contributions that conform to well-established API patterns, 
             but implement new functionality not available in core 'TensorFlow'. 
             'TensorFlow' natively supports a large number of operators, layers, metrics, 
             losses, optimizers, and more. However, in a fast moving field like Machine Learning, 
             there are many interesting new developments that cannot be integrated into 
             core 'TensorFlow' (because their broad applicability is not yet clear, or 
             it is mostly used by a smaller subset of the community).
Building customized transfer function and ARIMA models with multiple operators and parameter restrictions. Functions for model identification, model estimation (exact or conditional maximum likelihood), model diagnostic checking, automatic outlier detection, calendar effects, forecasting and seasonal adjustment. See Bell and Hillmer (1983) <doi:10.1080/01621459.1983.10478005>, Box, Jenkins, Reinsel and Ljung <ISBN:978-1-118-67502-1>, Box, Pierce and Newbold (1987) <doi:10.1080/01621459.1987.10478430>, Box and Tiao (1975) <doi:10.1080/01621459.1975.10480264>, Chen and Liu (1993) <doi:10.1080/01621459.1993.10594321>.
Translate R control flow expressions into 'Tensorflow' graphs.
In Cox's proportional hazard model, covariates are modeled as linear function and may not be flexible. This package implements additive trend filtering Cox proportional hazards model as proposed in Jiacheng Wu & Daniela Witten (2019) "Flexible and Interpretable Models for Survival Data", Journal of Computational and Graphical Statistics, <DOI:10.1080/10618600.2019.1592758>. The fitted functions are piecewise polynomial with adaptively chosen knots.
Interface to 'TensorFlow' Datasets, a high-level library for
    building complex input pipelines from simple, re-usable pieces.
    See <https://www.tensorflow.org/guide> for additional
    details.
Tools to deploy 'TensorFlow' <https://www.tensorflow.org/> models across 
  multiple services. Currently, it provides a local server for testing 'cloudml' 
  compatible services.
Statistical interpretation of forensic glass transfer (Simulation of the probability distribution of recovered glass fragments).
Interface to 'TensorFlow' Estimators 
    <https://www.tensorflow.org/guide/estimator>, a high-level 
    API that provides implementations of many different model types 
    including linear models and deep neural networks.
'TensorFlow' Hub is a library for the publication, discovery, and
    consumption of reusable parts of machine learning models. A module is a 
    self-contained piece of a 'TensorFlow' graph, along with its weights and 
    assets, that can be reused across different tasks in a process known as
    transfer learning. Transfer learning train a model with a smaller dataset,
    improve generalization, and speed up training.
Interface to 'TensorFlow IO', Datasets and filesystem extensions maintained by `TensorFlow SIG-IO` <https://github.com/tensorflow/community/blob/master/sigs/io/CHARTER.md>.
We provide the cumulative distribution function (CDF),
    quantile, and statistical power calculator for a collection of 
    thresholding Fisher's p-value combination methods, including Fisher's 
    p-value combination method, truncated product method and, in particular, soft-thresholding
    Fisher's p-value combination method which is proven to be optimal in some
    context of signal detection. The p-value calculator for the omnibus version
    of these tests are also included. For reference, please see Hong Zhang and Zheyang Wu. "TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values", submitted.
In putative Transcription Factor Binding Sites (TFBSs) 
             identification from sequence/alignments,
             we are interested in the significance of certain match score.
             TFMPvalue provides the accurate calculation of P-value with 
             score threshold for Position Weight Matrices, 
             or the score with given P-value. 
             It is an interface to code originally made available by 
             Helene Touzet and Jean-Stephane Varre, 2007, 
             Algorithms Mol Biol:2, 15.
             Touzet and Varre (2007) <DOI:10.1186/1748-7188-2-15>.
Utilities for simple manipulation and quick  	
	plotting of time series data. These utilities use the 'tframe' package
	which provides a programming kernel for time series. Extensions to
	'tframe' provided in 'tframePlus' can also be used. See the Guide
	vignette for examples.
Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow'
    that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU').
    'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers,
    variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.
A kernel of functions for programming 
	time series methods in a way that is relatively independently of the 
	representation of time. Also provides plotting, time windowing, 
	and some
	other utility functions which are specifically intended for time series.
	See the Guide distributed as a vignette, or ?tframe.Intro for more
	details. (User utilities are in package tfplot.)
Extensions and additional 'tframe' utilities.
Create and manage unique directories for each 'TensorFlow' 
  training run. Provides a unique, time stamped directory for each run
  along with functions to retrieve the directory of the latest run or 
  latest several runs.
A collection of useful tools for programming and writing-scripts. 
    Several functions are simple wrappers around base R functions that extend 
    their functionality while also providing some convenient properties–regular
    expression functions that automatically detect look-ahead and look-behind 
    statements, a read-line function that suppresses incomplete-final-line 
    warnings and automatically opens and closes connections, a version of 
    substrings that starts from the end of strings, etc. Other functions are 
    useful for checking whether packages are installed, omitting 
    missing data, and showing in-use connections.
Connects R to TrueFX(tm) for free streaming real-time and
        historical tick-by-tick market data for dealable interbank
        foreign exchange rates with millisecond detail.
Theme and colour palettes for The Globe and Mail's graphics. Includes colour and fill scale functions, colour palette helpers and a Globe-styled 'ggplot2' theme object.
Deconvolving thermoluminescence glow curves according to various 
        kinetic models (first-order, second-order, general-order, and mixed-order) using 
        a modified Levenberg-Marquardt algorithm (More, 1978) <DOI:10.1007/BFb0067700>. 
        It provides the possibility of setting constraints or fixing any of parameters. 
        It offers an interactive way to initialize parameters by clicking with a mouse on 
        a plot at positions where peak maxima should be located. The optimal estimate is 
        obtained by "trial-and-error". It also provides routines for simulating first-order, 
        second-order, and general-order glow peaks.
Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).
Bayesian nonstationary, semiparametric nonlinear regression 
 and design by treed Gaussian processes (GPs) with jumps to the limiting 
 linear model (LLM).  Special cases also implemented include Bayesian 
 linear models, CART, treed linear models, stationary separable and 
 isotropic GPs, and GP single-index models.  Provides 1-d and 2-d plotting functions 
 (with projection and slice capabilities) and tree drawing, designed for 
 visualization of tgp-class output.  Sensitivity analysis and 
 multi-resolution models are supported. Sequential experimental 
 design and adaptive sampling functions are also provided, including ALM, 
 ALC, and expected improvement.  The latter supports derivative-free
 optimization of noisy black-box functions.  For details and tutorials, 
 see Gramacy (2007) <doi:10.18637/jss.v019.i09> and Gramacy & Taddy (2010) 
 <doi:10.18637/jss.v033.i06>.
Functions to compute and plot tracheidograms, as in De Soto et al. (2011) <doi:10.1139/x11-045>.
Rapid advancements in high-throughput gene sequencing
    technologies have resulted in genome-scale time-series datasets. 
    Uncovering the underlying temporal sequence of gene regulatory events 
    in the form of time-varying gene regulatory networks demands 
    accurate and computationally efficient algorithms. Such an
    algorithm is 'TGS'. It is proposed in Saptarshi Pyne, Alok Ranjan 
    Kumar, and Ashish Anand. Rapid reconstruction of time-varying 
    gene regulatory networks. IEEE/ACM Transactions on Computational 
    Biology and Bioinformatics, 17(1):278{291, Jan-Feb 2020. The TGS 
    algorithm is shown to consume only 29 minutes for a microarray 
    dataset with 4028 genes. This package provides an implementation 
    of the TGS algorithm and its variants.
Functions for implementing the targeted gold standard (GS) testing. You provide the true disease or treatment failure status and the risk score, tell 'TGST' the availability of GS tests and which method to use, and it returns the optimal tripartite rules. Please refer to Liu et al. (2013) <doi:10.1080/01621459.2013.810149> for more details.
A collection of high performance utilities to compute distance,
    correlation, auto correlation, clustering and other tasks.
    Contains graph clustering algorithm described in "MetaCell: analysis of
    single-cell RNA-seq data using K-nn graph partitions" (Yael Baran,
    Akhiad Bercovich, Arnau Sebe-Pedros, Yaniv Lubling, Amir Giladi,
    Elad Chomsky, Zohar Meir, Michael Hoichman, Aviezer Lifshitz & Amos Tanay,
    2019 <doi:10.1186/s13059-019-1812-2>).
Turing Geovisualization Engine R package for geospatial visualization and analysis.
Contains data sets used in other packages Torsten Hothorn
  maintains.
Find out who maintains the packages you use in
             your current session or in your package library and
             maybe say 'thank you'.
A tool for starring GitHub repositories.
Provides a simple interface to search available data provided by
    Theia (<https://theia.cnes.fr>), download it, and manage it. Data can be downloaded
    based on a search result or from a cart file downloaded from Theia website.
Theme 'ggplot2', 'lattice', and 'base' graphics based on a few choices, including foreground color, background color, accent color,
  and font family. Fonts that aren't available on the system, but are available via download on 'Google Fonts',
  can be automatically downloaded, cached, and registered for use with the 'showtext' and 'ragg' packages.
A dataset with an uneven number of cases in each class is
    said to be unbalanced. Many models produce a subpar performance on
    unbalanced datasets. A dataset can be balanced by increasing the
    number of minority cases using SMOTE 2011 <arXiv:1106.1813>,
    BorderlineSMOTE 2005 <doi:10.1007/11538059_91> and ADASYN 2008
    <https://ieeexplore.ieee.org/document/4633969>. Or by decreasing the
    number of majority cases using NearMiss 2003
    <https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek
    link removal 1976 <https://ieeexplore.ieee.org/document/4309452>.
We present a range of simulations to aid researchers in determining appropriate sample sizes when performing critical
    thermal limits studies (e.g. CTmin/CTmin experiments). A number of wrapper functions are provided for plotting and summarising outputs
    from these simulations. These simulations are presented in Owen, C., Sutton, G., Martin, G., van Steenderen, C., and Coetzee, J. Sample size determination for critical thermal
    limits studies. 2022. Physiological Entomology. Under review. The GUI version of this package is available on the R Shiny online server at: 
    <https://clarkevansteenderen.shinyapps.io/ThermalSampleR_Shiny/> , or it is accessible via GitHub at <https://github.com/clarkevansteenderen/ThermalSampleR_Shiny/>. We would like
    to thank Grant Duffy (University of Otago, Dundedin, New Zealand) for granting us permission to use the source code for the Test of Total Equivalency function.
A collection of functions and routines for inputting thermal
    image video files, plotting and converting binary raw data into estimates of
    temperature.  First published 2015-03-26.  Written primarily for research purposes
    in biological applications of thermal images.  v1 included the base calculations 
    for converting thermal image binary values to temperatures. v2 included additional
    equations for providing heat transfer calculations and an import function for thermal
    image files (v2.2.3 fixed error importing thermal image to windows OS). v3. Added numerous
    functions for converting thermal image, videos, rewriting and exporting.  
    v3.1. Added new functions to convert files. v3.2.  Fixed the various functions related to finding frame times.
    v4.0. fixed an error in atmospheric attenuation constants, affecting raw2temp and temp2raw functions.
    Recommend update for use with long distance calculations. v.4.1.3 changed to frameLocates to reflect change to as.character() to format().
Calculates several thermal comfort indexes using temperature, wind speed and relative humidity values, calculating indexes such as Humidex, windchill, Discomfort Index and others.
Temperature measurement data, equations and methods for thermocouples,
    wire RTD, thermistors, IC thermometers, bimetallic strips and the ITS-90.
A user-friendly R data package that is intended to make Turkish higher education statistics more accessible.
Plot your data using color palettes for 'ggplot2' inspired by the TV show, book series and video games "The Witcher".
A step-up test for genetic rare variants in a gene or in a pathway. The method determines an optimal grouping of rare variants analytically. The method has been described in Hoffmann TJ, Marini NJ, and Witte JS (2010) <doi:10.1371/journal.pone.0013584>.
Methods and tools for generating forecasts at different temporal
    frequencies using a hierarchical time series approach.
This package interacts directly with the Thinknum API to offer data
    in a number of formats usable in R
Some tools for cleaning up messy 'Excel' files to
    be suitable for R. People who have been working with 'Excel' for years
    built more or less complicated sheets with names, characters, formats
    that are not homogeneous. To be able to use them in R nowadays, we
    built a set of functions that will avoid the majority of importation
    problems and keep all the data at best.
Determine the full path of the executing script. Works when 
    running a line or selection from a script in 'RStudio' and 'Rgui', when 
    using 'source', 'sys.source', 'debugSource' in 'RStudio', and 
    'testthat::source_file', and when running R from a shell.
Key-value store, implemented as a wrapper around 'LMDB';
    the "lightning memory-mapped database" <https://symas.com/lmdb/>.
    'LMDB' is a transactional key value store that uses a memory map
    for efficient access.  This package wraps the entire 'LMDB'
    interface (except duplicated keys), and provides objects for
    transactions and cursors.
Creates some 'WebGL' shaders. They can be used as the background of a 'Shiny' app. They also can be visualized in the 'RStudio' viewer pane or included in 'Rmd' documents, but this is pretty useless, besides contemplating them.
Provides a crawler for programmatically navigating THREDDS Data Server (<https://www.unidata.ucar.edu/software/thredds/current/tds/TDS.html>) 
  catalogs, and access dataset metadata and resources.
Design and analyze three-arm non-inferiority or superiority trials
    which follow a gold-standard design, i.e. trials with an experimental treatment,
    an active, and a placebo control. Method for the following distributions are implemented:
    Poisson (Mielke and Munk (2009) <arXiv:0912.4169>), negative binomial (Muetze et al. (2016) <doi:10.1002/sim.6738>),
    normal (Pigeot et al. (2003) <doi:10.1002/sim.1450>; Hasler et al. (2009) <doi:10.1002/sim.3052>), 
    binary (Friede and Kieser (2007) <doi:10.1002/sim.2543>), nonparametric (Muetze et al. (2017) <doi:10.1002/sim.7176>),
    exponential (Mielke and Munk (2009) <arXiv:0912.4169>).
This package implements a thresholded version of the EEBoost
    algorithm described in [Wolfson (2011, JASA)]. EEBoost is a general-purpose
    method for variable selection which can be applied whenever inference would
    be based on an estimating equation. The package currently implements
    variable selection based on the Generalized Estimating Equations, but can
    also accommodate user-provided estimating functions. Thresholded EEBoost is
    a generalization which allows multiple variables to enter the model at each
    boosting step.
A fast, interactive cross-platform, and easy to share 
    'WebGL'-based 3D brain viewer that visualizes 'FreeSurfer' and/or
    'AFNI/SUMA' surfaces. The viewer widget can be either standalone or 
    embedded into 'R-shiny' applications. The standalone version only require
    a web browser with 'WebGL2' support (for example, 'Chrome'). It can be
    inserted into any websites; see <https://dipterix.org/project/threebrain/>).
    as an example. The 'R-shiny' support allows the 3D viewer to be dynamically 
    generated from reactive user inputs. This feature has been fully adopted by
    'RAVE' <https://openwetware.org/wiki/RAVE>, an interactive toolbox to 
    analyze 'iEEG/eCoG' data. Documentation about 'threeBrain' is provided
    by <https://dipterix.org/threeBrain/> and several vignettes included 
    in this package. To cite the package, please check our 'NeuroImage' paper 
    by Magnotti, Wang, and Beauchamp (2020, <doi:10.1016/j.neuroimage.2020.117341>),
    or see 'citation("threeBrain")' for details.
Implements the Maximum Likelihood estimator for baseline, placebo, and treatment groups (three-group) experiments with non-compliance proposed by Gerber, Green, Kaplan, and Kern (2010).
Create interactive 3D scatter plots, network plots, and
    globes using the 'three.js' visualization library (<https://threejs.org>).
Access open data from <https://www.threesixtygiving.org>, a 
    database of charitable grant giving in the UK operated by '360Giving'.
    The package provides functions to search and retrieve data on charitable 
    grant giving, and process that data into tidy formats. It relies on the 
    '360Giving' data standard, described at 
    <https://standard.threesixtygiving.org/>.
Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.
Does nothing useful, but perhaps does that nothing in an entertaining or informative fashion.
A connector to the 'What3Words' (http://what3words.com/) service, which represents each 3m by 3m square on earth
             with a unique trio of English-language words.
Fit a threshold regression model based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Fit a threshold regression model for Interval Censored Data based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Defines the classes used to identify
  outliers (threshing) and compute the number of significant principal
  components and number of clusters (reaping) in a joint application
  of PCA and hierarchical clustering. See Wang et al., 2018,
  <doi:10.1186/s12859-017-1998-9>.
Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. We also provide functions for sample size determination and estimation of diagnostic accuracy measures. We also include graphical tools. The statistical methodology used here can be found in Perez-Jaume et al (2017) <doi:10.18637/jss.v082.i04>.
Provides functions for the selection of thresholds for use in 
    extreme value models, based mainly on the methodology in 
    Northrop, Attalides and Jonathan (2017) <doi:10.1111/rssc.12159>.
    It also performs predictive inferences about future extreme values, 
    based either on a single threshold or on a weighted average of inferences 
    from multiple thresholds, using the 'revdbayes' package 
    <https://cran.r-project.org/package=revdbayes>.   
    At the moment only the case where the data can be treated as 
    independent identically distributed observations is considered.
Fit the Simultaneous Systems of Linear Equations using Three-stage Least Squares.
Collection of functions for rapid computation and visualisation of convective parameters commonly used in the operational prediction of severe convective storms. Core algorithm is based on a highly optimized 'C++' code linked into 'R' via 'Rcpp'. Highly efficient engine allows to derive thermodynamic and kinematic parameters from large numerical datasets such as reanalyses or operational Numerical Weather Prediction models in a reasonable amount of time. Package has been developed since 2017 by research meteorologists specializing in severe thunderstorms.
Fit Thurstonian Item Response Theory (IRT) models in R. This 
  package supports fitting Thurstonian IRT models and its extensions using 
  'Stan', 'lavaan', or 'Mplus' for the model estimation. Functionality for 
  extracting results, making predictions, and simulating data is provided as 
  well. References: 
  Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>;
  Bürkner et al. (2019) <doi:10.1177/0013164419832063>.
Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the traditional
    data frame.
Built on top of the 'tibble' package, 'tibbletime' is an extension
  that allows for the creation of time aware tibbles. Some immediate
  advantages of this include: the ability to perform time-based subsetting
  on tibbles, quickly summarising and aggregating results by time periods,
  and creating columns that can be used as 'dplyr' time-based groups.
A tool to rectangle a nested list, that is to convert it into a
    tibble. This is done automatically or according to a given specification.
    A common use case is for nested lists coming from parsing JSON files or
    the JSON response of REST APIs. It is supported by the 'vctrs' package
    and therefore offers a wide support of vector types.
Functions to execute orders in backtesting using tick data. A testing platform was established by the four major execution functions, namely 'LimitBuy', 'LimitSell', 'MarketBuy' and 'MarketSell', which enclosed all tedious aspects (such as queueing for order executions and calculate actual executed volumes) for order execution using tick data. Such that one can focus on the logic of strategies, rather than its execution.
Implements tic-tac-toe game to play on console, either with human or AI players.
  Various levels of AI players are trained through the Q-learning algorithm.
Provides the timing functions 'tic' and 'toc' that
    can be nested. One can record all timings while a complex script is
    running, and examine the values later. It is also possible to instrument
    the timing calls with custom callbacks. In addition, this package provides
    class 'Stack', implemented as a vector, and class 'List', implemented as a
    list, both of which support operations 'push', 'pop', 'first', 'last' and
    'clear'.
Utilities to generate wiki reports in TiddlyWiki format.
Tidal analysis of evenly spaced observed time series (time step 1 to 60 min) with or
    without shorter gaps using the harmonic representation of inequalities.
    The analysis should preferably cover an observation period of at least 19 years.
    For shorter periods low frequency constituents are not taken into account, in accordance with the Rayleigh-Criterion.
    The main objective of this package is to synthesize or predict a tidal time series.
Implements harmonic analysis of tidal and sea-level data.
  Over 400 harmonic tidal constituents can be estimated, all with 
  daily nodal corrections. Time-varying mean sea-levels can also
  be used.
Calculate Characteristics of Quasi-Periodic Time Series, e.g. Estuarine Water Levels.
Tide analysis and prediction of predominantly semi-diurnal tides
    with two high waters and two low waters during one lunar day (~24.842 hours,
    ~1.035 days). The analysis should preferably cover an observation period of at
    least 19 years. For shorter periods, for example, the nodal cycle can not be
    taken into account, which particularly affects the height calculation. The main
    objective of this package is to produce tide tables.
Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models
    ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided
    to help extract tidy data frames of draws from Bayesian models and that generate point
    summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for
    common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus
    densities), and fit curves with multiple, arbitrary uncertainty bands.
Tools to download data series from 'Banco de España' ('BdE')
    on 'tibble' format. 'Banco de España' is the national central bank
    and, within the framework of the Single Supervisory Mechanism ('SSM'),
    the supervisor of the Spanish banking system along with the European
    Central Bank. This package is in no way sponsored endorsed or
    administered by 'Banco de España'.
Multiple ways to bin numeric columns with a tidy output. Wraps a variety of existing binning methods into one function, and includes a new method for binning by equal value, which is useful for sales data. Provides a function to automatically summarize the properties of the binned columns.
Compute arbitrary non-parametric bootstrap statistics on data in
    tidy data frames.
Create additional rows and columns on broom::tidy() output to allow for easier control on categorical parameter estimates.
An integrated R interface to several United States Census Bureau 
    APIs (<https://www.census.gov/data/developers/data-sets.html>) and the US Census Bureau's 
    geographic boundary files. Allows R users to return Census and ACS data as 
    tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping 
    and spatial analysis.
There is a wide range of R packages created for data visualization, but still, there was no simple and easily accessible way to create clean and transparent charts - up to now. The 'tidycharts' package enables the user to generate charts compliant with International Business Communication Standards ('IBCS').
    It means unified bar widths, colors, chart sizes, etc. Creating homogeneous reports has never been that easy! Additionally, users can apply semantic notation to indicate different data scenarios (plan, budget, forecast). What's more, it is possible to customize the charts by creating a personal color pallet with the possibility of switching to default options after the experiments.
    We wanted the package to be helpful in writing reports, so we also made joining charts in a one, clear image possible.
    All charts are generated in SVG format and can be shown in the 'RStudio' viewer pane or exported to HTML output of 'knitr'/'markdown'.
Provides an intuitive interface for working with the
    competing risk endpoints. The package wraps the 'cmprsk' package, and
    exports functions for univariate cumulative incidence estimates and
    competing risk regression. Methods follow those introduced in Fine and
    Gray (1999) <doi:10.1002/sim.7501>.
Analyze lines of R code using tidy principles. This allows you to 
    input lines of R code and output a data frame with one row per function 
    included. Additionally, it facilitates code classification via included lexicons.
Provides convenience functions for common data
    modification and analysis tasks in communication research. This
    includes functions for univariate and bivariate data analysis, index
    generation and reliability computation, and intercoder reliability
    tests. All functions follow the style and syntax of the tidyverse, and
    are construed to perform their computations on multiple variables at
    once. Functions for univariate and bivariate data analysis comprise
    summary statistics for continuous and categorical variables, as well
    as several tests of bivariate association including effect sizes.
    Functions for data modification comprise index generation and
    automated reliability analysis of index variables. Functions for
    intercoder reliability comprise tests of several intercoder
    reliability estimates, including simple and mean pairwise percent
    agreement, Krippendorff's Alpha (Krippendorff 2004, ISBN:
    9780761915454), and various Kappa coefficients (Brennan & Prediger
    1981 <doi: 10.1177/001316448104100307>; Cohen 1960 <doi:
    10.1177/001316446002000104>; Fleiss 1971 <doi: 10.1037/h0031619>).
Loads the 5 packages in the Tidy Consultant Universe. This collection of packages is useful for anyone doing data science,
    data analysis, or quantitative consulting. The functions in these packages range from data cleaning, data validation, data binning, statistical modeling, and file exporting.
Core parts of the C API of R are wrapped in a C++ namespace via a set
 of inline functions giving a tidier representation of the underlying data structures
 and functionality using a header-only implementation without additional dependencies.
The Common Workflow Language <https://www.commonwl.org/> is an
    open standard for describing data analysis workflows. This package takes
    the raw Common Workflow Language workflows encoded in JSON or 'YAML'
    and turns the workflow elements into tidy data frames or lists.
    A graph representation for the workflow can be constructed and visualized
    with the parsed workflow inputs, outputs, and steps. Users can embed the
    visualizations in their 'Shiny' applications, and export them
    as HTML files or static images.
Visualize your 'Tidyverse' data analysis pipelines via the 
    'Tidy Data Tutor'(<https://tidydatatutor.com/>) web application.
To make it easy to generate random numbers based upon the underlying stats 
    distribution functions. All data is returned in a tidy and structured
    format making working with the data simple and straight forward. Given that the
    data is returned in a tidy 'tibble' it lends itself to working with the rest of the
    'tidyverse'.
Utils for basic statistical experiments, that can be used for teaching 
    introductory statistics. Each experiment generates a tibble.
    Dice rolls and coin flips are simulated using sample().
    The properties of the dice can be changed, like the number of sides.
    A coin flip is simulated using a two sided dice.
    Experiments can be combined with the pipe-operator.
Dimensionality reduction (DR) is widely used in many domain for analyzing and visualizing high-dimensional data. 'tidydr' provides uniform output and is compatible with multiple methods, including 'prcomp', 'mds', 'Rtsne'. etc.
Unicodes are not friendly to work with, and not all Unicodes are
    Emoji per se, making obtaining Emoji statistics a difficult task. This
    tool can help your experience of working with Emoji as smooth as possible,
    as it has the tidyverse style.
The 'ESTIMATE' package infers tumor purity from expression data as a 
  function of immune and stromal infiltrate, but requires writing of intermediate 
  files, is un-pipeable, and performs poorly when presented with modern datasets 
  with current gene symbols. 'tidyestimate' a fast, tidy, modern reimagination of
  'ESTIMATE' (2013) <doi:10.1038/ncomms3612>.
Tidying functions built on 'data.table'
    to provide quick and efficient data manipulation with
    minimal overhead.
A toolkit of tidy data manipulation verbs with 'data.table' as the backend.
  Combining the merits of syntax elegance from 'dplyr' and computing performance from 'data.table', 
  'tidyfst' intends to provide users with state-of-the-art data manipulation tools with least pain.
  This package is an extension of 'data.table'. While enjoying a tidy syntax, 
  it also wraps combinations of efficient functions to facilitate frequently-used data operations.
Tidy syntax for 'data.table', using modification by reference whenever possible.
 This toolkit is designed for big data analysis in high-performance desktop or laptop computers.
 The syntax of the package is similar or identical to 'tidyverse'.
 It is user friendly, memory efficient and time saving. For more information,
 check its ancestor package 'tidyfst'.
A toolset that allows you to easily import and tidy data sheets retrieved
    from Gapminder data web tools. It will therefore contribute to reduce the time
    used in data cleaning of Gapminder indicator data sheets as they are very messy.
It interactively or programmatically label points within custom gates on two dimensions <https://github.com/stemangiola/tidygate>. 
    The information is added to your tibble. It is based on the package 'gatepoints' from Wajid Jawaid (who is also author of this package). The code of 'gatepoints' was nto integrated in 'tidygate'. 
    The benefits are (i) in interactive mode you can draw your gates on extensive 'ggplot'-like scatter plots; 
    (ii) you can draw multiple gates; and (iii) you can save your gates and apply the programmatically.
Handle genomic data within data frames just as you would with 'GRanges'.
    This packages provides method to deal with genomic intervals the "tidy-way" which makes
    it simpler to integrate in the the general data munging process. The API is inspired by the
    popular 'bedtools' and the genome_join() method from the 'fuzzyjoin' package.
An intuitive interface for getting data from geocoding services.
In order to easily integrate geoRSS data into analysis,
    'tidygeoRSS' parses 'geo' feeds and returns 
    tidy simple features data frames.
A graph, while not "tidy" in itself, can be thought of as two tidy
    data frames describing node and edge data respectively. 'tidygraph'
    provides an approach to manipulate these two virtual data frames using the
    API defined in the 'dplyr' package, as well as provides tidy interfaces to 
    a lot of common graph algorithms.
This is a tidy implementation for heatmap.  At the
    moment it is based on the (great) package 'ComplexHeatmap'.  The goal
    of this package is to interface a tidy data frame with this powerful
    tool.  Some of the advantages are: Row and/or columns colour
    annotations are easy to integrate just specifying one parameter
    (column names).  Custom grouping of rows is easy to specify providing
    a grouped tbl. For example: df %>% group_by(...).  Labels size
    adjusted by row and column total number.  Default use of Brewer and
    Viridis palettes.
Provides functions to access historical and real-time national 'hydrometric'
    data from Water Survey of Canada data sources (<https://dd.weather.gc.ca/hydrometric/csv/> and
    <https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>) and then applies tidy data principles.
Turn complex 'JSON' data into tidy data frames.
Selection of packages designed to be loaded in concert. The
    'tidylab' package collection revolves around project development and
    deployment. The purpose of this is to make it easy to install and
    subsequently load those packages.
Implements an algorithm for Latent Dirichlet
    Allocation (LDA), Blei et at. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>,
    using style conventions from the 'tidyverse',
    Wickham et al. (2019)<doi:10.21105/joss.01686>,
    and 'tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>.
    Fitting is done via collapsed Gibbs sampling.
    Also implements several novel features for LDA such as guided models and
    transfer learning based on ongoing and, as yet, unpublished research.
How can we measure how the usage or frequency of some
    feature, such as words, differs across some group or set, such as
    documents? One option is to use the log odds ratio, but the log odds
    ratio alone does not account for sampling variability; we haven't
    counted every feature the same number of times so how do we know which
    differences are meaningful? Enter the weighted log odds, which
    'tidylo' provides an implementation for, using tidy data principles.
    In particular, here we use the method outlined in Monroe, Colaresi,
    and Quinn (2008) <doi:10.1093/pan/mpn018> to weight the log odds ratio
    by a prior. By default, the prior is estimated from the data itself,
    an empirical Bayes approach, but an uninformative prior is also
    available.
Provides feedback about 'dplyr' and 'tidyr' operations.
An interface to the 'mclust' package to easily
    carry out latent profile analysis ("LPA"). Provides functionality to
    estimate commonly-specified models. Follows a tidy approach, in that
    output is in the form of a data frame that can subsequently be
    computed on. Also has functions to interface to the commercial 'MPlus'
    software via the 'MplusAutomation' package.
The tidy modeling "verse" is a collection of packages for
    modeling and statistical analysis that share the underlying design
    philosophy, grammar, and data structures of the tidyverse.
Provides the means to convert 'multiqc_data.json' files,
    produced by the wonderful 'MultiQC' tool, into tidy data frames for downstream
    analysis in R. This analysis might involve cohort analysis, quality control visualisation,
    change-point detection, statistical process control, clustering, or any other
    type of quality analysis.
Provides functions for visualising generalised
    additive models and getting predicted values using tidy tools from the 'tidyverse' packages.
Tidy tools for 'NetCDF' data sources. Explore the contents of a 
 'NetCDF' source (file or URL) presented as variables organized by grid with a 
 database-like interface. The hyper_filter() interactive function translates the 
 filter value or index expressions to array-slicing form. No data is read until 
 explicitly requested, as a data frame or list of arrays via hyper_tibble() or 
 hyper_array().
The goal is to simplify routine analysis of the Nigeria National  Data Repository (NDR) <https://ndr.phis3project.org.ng> using the PEPFAR Monitoring, Evaluation, and Reporting (MER) indicators (see <https://datim.zendesk.com/hc/en-us/articles/360000084446-MER-Indicator-Reference-Guides>). It is designed to import in to R patient-level line-list downloaded as 'csv' file from the front-end of the NDR.
Provides a set of functions with a common framework for age-depth model management, 
  stratigraphic visualization, and common statistical transformations. The focus of the
  package is stratigraphic visualization, for which 'ggplot2' components are provided
  to reproduce the scales, geometries, facets, and theme elements commonly used in
  publication-quality stratigraphic diagrams. Helpers are also provided to reproduce
  the exploratory statistical summaries that are frequently included on
  stratigraphic diagrams. See Dunnington et al. (2021) <doi:10.18637/jss.v101.i07>.
Parse XML documents from the Open Access subset of Europe PubMed Central <https://europepmc.org>
    including section paragraphs, tables, captions and references.
Bayesian analysis used here to answer the question: "when looking at resampling results, are the differences between models 'real'?" To answer this, a model can be created were the performance statistic is the resampling statistics (e.g. accuracy or RMSE). These values are explained by the model types. In doing this, we can get parameter estimates for each model's affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) <https://jmlr.org/papers/v18/16-305.html>.
It parses a fitted 'R' model object, and returns a formula
    in 'Tidy Eval' code that calculates the predictions.
    It works with several databases back-ends because it leverages 'dplyr'
    and 'dbplyr' for the final 'SQL' translation of the algorithm. It currently
    supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(),
    cubist(), and ctree() models.
Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' 
    package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' 
    and 'PerformanceAnalytics' package 
    functions and returns the objects in the tidy 'tibble' format. The main 
    advantage is being able to use quantitative functions with the 'tidyverse'
    functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See 
    the 'tidyquant' website for more information, documentation and examples.
Use 'SQL' 'SELECT' statements to query 'R' data
    frames.
The purpose of this package is to access the 
    United States Census Bureau's Quarterly Workforce Indicator data. Additionally, 
    the data will be retrieved in a tidy format for further manipulation with full variable
    descriptions added if desired. Information about the United States Census Bureau's 
    Quarterly Workforce Indicator is available at 
    <https://www.census.gov/data/developers/data-sets/qwi.html>.
Tools to help to create tidy data, where each column is a
    variable, each row is an observation, and each cell contains a single
    value.  'tidyr' contains tools for changing the shape (pivoting) and
    hierarchy (nesting and 'unnesting') of a dataset, turning deeply
    nested lists into rectangular data frames ('rectangling'), and
    extracting values out of string columns. It also includes tools for
    working with missing values (both implicit and explicit).
Helper functions for processing REDCap data in R. 'REDCap' (Research
    Electronic Data CAPture; <https://projectredcap.org>) is a web-enabled
    application for building and managing surveys and databases developed at
    Vanderbilt University.
With the objective of including data from RSS feeds into your analysis, 
    'tidyRSS' parses RSS, Atom and JSON feeds and returns a tidy data frame.
Utility to convert text based summary of rule based models to a tidy dataframe (where each row represents a rule) with related metrics such as support, confidence and lift. Rule based models from these packages are supported: 'C5.0', 'rpart' and 'Cubist'.
A backend for the selecting functions of the 'tidyverse'.
    It makes it easy to implement select-like functions in your own
    packages in a way that is consistent with other 'tidyverse'
    interfaces for selection.
A tidy workflow for generating, estimating, reporting,
    and plotting structural equation models using 'lavaan', 'OpenMx', or
    'Mplus'. Throughout this workflow, elements of syntax, results, and graphs
    are represented as 'tidy' data, making them easy to customize.
It creates an invisible layer that allow to see the 'Seurat' object 
    as tibble and interact seamlessly with the tidyverse.
Tidy standardized mean differences ('SMDs'). 'tidysmd' uses
    the 'smd' package to calculate standardized mean differences for
    variables in a data frame, returning the results in a tidy format.
A tidy approach to analysis of biological sequences. All processing and data-storage functions are heavily optimized to allow the fastest and most efficient data storage.
Save the output of statistical tests in an organized file that can 
  be shared with others or used to report statistics in scientific papers.
Functions to generate stop-word lists in 110 languages, in a way consistent across all the languages supported. The generated lists are based on the morphological tagset from the Universal Dependencies.
Calculation of string distance following the tidy
    data principles. Built on top of the 'stringdist' package.
A synthetic control offers a way of evaluating the effect of an intervention in comparative case studies. The package makes a number of improvements when implementing the method in R. These improvements allow users to inspect, visualize, and tune the synthetic control more easily. A key benefit of a tidy implementation is that the entire preparation process for building the synthetic control can be accomplished in a single pipe. For more information on the synthetic control method, see Abadie et al. (2003) <doi:10.1257/000282803321455188>.
A tidy interface to 'data.table' that is 'rlang' compatible,
  giving users the speed of 'data.table' with the clean syntax of the tidyverse.
Using tidy data principles can make many text mining tasks
    easier, more effective, and consistent with tools already in wide use.
    Much of the infrastructure needed for text mining with tidy data
    frames already exists in packages like 'dplyr', 'broom', 'tidyr', and
    'ggplot2'. In this package, we provide functions and supporting data
    sets to allow conversion of text to and from tidy formats, and to
    switch seamlessly between tidy tools and existing text mining
    packages.
A selection of various tools to extend a data analysis workflow based on the 'tidyverse' packages.
  This includes high-level data frame editing methods (in the style of 'mutate'/'mutate_at'), some methods in the style of
  'purrr' and 'forcats', 'lookup' methods for dict-like lists, a generic method for lumping a data frame by a given count,
  various low-level methods for special treatment of 'NA' values, 'python'-style tuple-assignment and 'truthy'/'falsy' checks,
  saving to PDF and PNG from a pipe and various small utilities.
Read General Transit Feed Specification (GTFS) zipfiles into a list of R dataframes. Perform validation of the data structure against the specification. Analyze the headways and frequencies at routes and stops. Create maps and perform spatial analysis on the routes and stops. Please see the GTFS documentation here for more detail: <https://gtfs.org/>.
Functions for extracting tidy data from Bayesian treatment effect models, in particular BART, but extensions are possible. Functionality includes extracting tidy posterior summaries as in 'tidybayes' <https://github.com/mjskay/tidybayes>, estimating (average) treatment effects, common support calculations, and plotting useful summaries of these.
Phylogenetic tree generally contains multiple components including node, edge, branch and associated data. 'tidytree' provides an approach to convert tree object to tidy data frame as well as provides tidy interfaces to manipulate tree data.
'TidyTuesday' is a project by the 'R4DS Online Learning Community' in which they 
  post a weekly dataset onto post a weekly dataset in a public data repository 
  (<https://github.com/rfordatascience/tidytuesday>) for people to
  analyze and visualize. This package provides the tools to easily download this data and the 
  description of the source.