The 'Rcpp' package provides a C++ library to make it easier
    to use C++ with R. R and 'Rcpp' provide functions for a variety of
    statistical distributions. Several R packages make functions
    available to R for additional statistical distributions. However,
    to access these functions from C++ code, a costly call to the R
    functions must be made. 'RcppDist' provides a header-only C++ library
    with functions for additional statistical distributions that can be
    called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'.
    Functions are available that return a 'NumericVector' as well as
    doubles, and for multivariate or matrix distributions, 'Armadillo'
    vectors and matrices. 'RcppDist' provides functions for the following
    distributions: the four parameter beta distribution; the location-
    scale t distribution; the truncated normal distribution; the
    truncated t distribution; a truncated location-scale t distribution;
    the triangle distribution; the multivariate normal distribution*;
    the multivariate t distribution*; the Wishart distribution*; and
    the inverse Wishart distribution*. Distributions marked with an
    asterisk rely on 'RcppArmadillo'.
This package is based on the C++ code from Yusuke Sugomori,
             which implements basic machine learning methods with 
             many layers (deep learning), including dA (Denoising Autoencoder), 
             SdA (Stacked Denoising Autoencoder), RBM (Restricted Boltzmann machine) and 
             DBN (Deep Belief Nets).
Dynamic Programming implemented in 'Rcpp'.  Includes example partition and out of sample fitting applications.  Also supplies additional custom coders for the 'vtreat' package.
R and 'Eigen' integration using 'Rcpp'.
 'Eigen' is a C++ template library for linear algebra: matrices, vectors,
 numerical solvers and related algorithms.  It supports dense and sparse
 matrices on integer, floating point and complex numbers, decompositions of
 such matrices, and solutions of linear systems. Its performance on many
 algorithms is comparable with some of the best implementations based on
 'Lapack' and level-3 'BLAS'. The 'RcppEigen' package includes the header
 files from the 'Eigen' C++ template library (currently version 3.3.4). Thus
 users do not need to install 'Eigen' itself in order to use 'RcppEigen'.
 Since version 3.1.1, 'Eigen' is licensed under the Mozilla Public License
 (version 2); earlier version were licensed under the GNU LGPL version 3 or
 later. 'RcppEigen' (the 'Rcpp' bindings/bridge to 'Eigen') is licensed under
 the GNU GPL version 2 or later, as is the rest of 'Rcpp'.
Compiles 'C++' code using 'Rcpp', 'Eigen' and 'CppAD' to produce first and second order partial derivatives. Also provides an implementation of Faa' di Bruno's formula to combine the partial derivatives of composed functions, (see Hardy, M (2006) <arXiv:math/0601149v1>).
'Ensmallen' is a templated C++ mathematical optimization library 
 (by the 'MLPACK' team) that provides a simple set of abstractions for writing an
 objective function to optimize. Provided within are various standard and
 cutting-edge optimizers that include full-batch gradient descent techniques, 
 small-batch techniques, gradient-free optimizers, and constrained optimization.
 The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library
 and pairs the appropriate header files from 'armadillo' through the 
 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor
 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 
 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2,
 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under 
 the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under
 similar terms. Note that 'Ensmallen' requires a compiler that supports 
 'C++11' and 'Armadillo' 8.400 or later.
Examples for Seamless R and C++ integration
 The 'Rcpp' package contains a C++ library that facilitates the integration of
 R and C++ in various ways. This package provides some usage examples.
 Note that the documentation in this package currently does not cover all the
 features in the package. The site <http://gallery.rcpp.org> regroups a large
 number of examples for 'Rcpp'.
Access to a family of Gauss error functions for arbitrary complex arguments is provided via the 'Faddeeva' package by Steven G. Johnson (see <http://ab-initio.mit.edu/wiki/index.php/Faddeeva_Package> for more information).
The 'getconf' command-line tool provided by 'libc' allows
 querying of a large number of system variables. This package provides
 similar functionality.
A fast implementation of the greedy algorithm for the set cover problem using 'Rcpp'.
'Rcpp' integration for 'GNU GSL' vectors and matrices
 The 'GNU Scientific Library' (or 'GSL') is a collection of numerical routines for
 scientific computing. It is particularly useful for C and C++ programs as it
 provides a standard C interface to a wide range of mathematical routines. There
 are over 1000 functions in total with an extensive test suite. The 'RcppGSL'
 package provides an easy-to-use interface between 'GSL' data structures and
 R using concepts from 'Rcpp' which is itself a package that eases the
 interfaces between R and C++. This package also serves as a prime example of
 how to build a package that uses 'Rcpp' to connect to another third-party
 library. The 'autoconf' script, 'inline' plugin and example package can all
 be used as a stanza to  write a similar package against another library.
Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.
'Hnswlib' is a C++ library for Approximate Nearest Neighbors. This 
 package provides a minimal R interface by relying on the 'Rcpp' package. See 
 <https://github.com/nmslib/hnswlib> for more on 'hnswlib'. 'hnswlib' is 
 released under Version 2.0 of the Apache License.
Header library and R functions to solve minimum cost bipartite matching problem 
 using Huhn-Munkres algorithm (Hungarian algorithm; <https://en.wikipedia.org/wiki/Hungarian_algorithm>;
 Kuhn (1955) doi:10.1002/nav.3800020109). 
 This is a repackaging of code written by Cong Ma in the GitHub repo <https://github.com/mcximing/hungarian-algorithm-cpp>.
R package based on 'Rcpp' for 'MeCab': Yet Another Part-of-Speech and Morphological Analyzer. 
	The purpose of this package is providing a seamless developing and analyzing environment for CJK texts.
	This package utilizes parallel programming for providing highly efficient text preprocessing 'posParallel()' function.
	For installation, please refer to README.md file.
'MLPACK' is an intuitive, fast, scalable C++ machine learning
   library, meant to be a machine learning analog to 'LAPACK'. It
   aims to implement a wide array of machine learning methods
   and function as a Swiss army knife for machine learning
   researchers: 'MLPACK' is available from <http://www.mlpack.org/>;
   sources are included in the package.
'MsgPack' header files are provided for use by R packages, along 
 with the ability to access, create and alter 'MsgPack' objects directly from R.
 'MsgPack' is an efficient binary serialization format. It lets you exchange
 data among multiple languages like 'JSON' but it is faster and smaller.
 Small integers are encoded into a single byte, and typical short strings
 require only one extra byte in addition to the strings themselves. This
 package provides headers from the 'msgpack-c' implementation for C and
 C++(11) for use by R, particularly 'Rcpp'. The included 'msgpack-c' headers
 are licensed under the Boost Software License (Version 1.0); the code added
 by this package as well the R integration are licensed under the GPL (>= 2).
 See the files 'COPYRIGHTS' and 'AUTHORS' for a full list of  copyright holders
 and contributors to 'msgpack-c'.
An example package which shows use of 'NLopt' functionality from
 C++ via 'Rcpp' without requiring linking, and relying just on 'nloptr' thanks
 to the exporting API added there by Jelmer Ypma. This package is a fully
 functioning, updated, and expanded version of the initial example by
 Julien Chiquet at <https://github.com/jchiquet/RcppArmadilloNLoptExample>
 also containing a large earlier pull request of mine.
A collection of open source libraries for numerical computing
    (numerical integration, optimization, etc.) and their integration with
    'Rcpp'.
High level functions for parallel programming with 'Rcpp'.
    For example, the 'parallelFor()' function can be used to convert the work of
    a standard serial "for" loop into a parallel one and the 'parallelReduce()'
    function can be used for accumulating aggregate or other values.
Allows to display a progress bar in the R
    console for long running computations taking place in c++ code,
    and support for interrupting those computations even in multithreaded
    code, typically using OpenMP.
'QuantLib' bindings are provided for R using 'Rcpp' and the
 header-only 'Quantuccia' variant (put together by Peter Caspers) offering
 an essential subset of 'QuantLib'. See the included file 'AUTHORS' for a full
 list of contributors to both 'QuantLib' and 'Quantuccia'.
Connection to the 'Redis' key/value store using the
 C-language client library 'hiredis' (included as a fallback) with
 'MsgPack' encoding provided via 'RcppMsgPack' headers.
Provides fast and efficient routines for
    common rolling / windowed operations. Routines for the
    efficient computation of windowed mean, median,
    sum, product, minimum, maximum, standard deviation
    and variance are provided.
R access to the Sequential Monte Carlo Template Classes
 by Johansen <doi:10.18637/jss.v030.i06> is provided. At present, four
 additional examples have been added, and the first example from the JSS
 paper has been extended. Further integration and extensions are planned.
The 'Streamulus' (template, header-only) library by
 Irit Katriel (at <https://github.com/iritkatriel/streamulus>)
 provides a very powerful yet convenient framework for stream
 processing. This package connects 'Streamulus' to R by providing 
 both the header files and all examples.
Provides a C++11-style thread class and thread pool that can safely
    be interrupted from R.
R-level and C++-level functionality
    to generate random deviates from and calculate moments of a
    Truncated Normal distribution using the algorithm of Robert (1995) <DOI:10.1007/BF00143942>.
    In addition to RNG, functions for calculating moments, densities,
    and entropies are provided at both levels.
The configuration format defined by 'TOML' (which expands to
 "Tom's Obvious Markup Language") specifies an excellent format (described at
 <https://github.com/toml-lang/toml>) suitable for both human editing as well
 as the common uses of a machine-readable format. This package uses 'Rcpp' to
 connect the 'cpptoml' parser written by Chase Geigle (in modern C++11) to R.
Provides the means to compile user-supplied C++ functions with
  'Rcpp' and retrieve an 'XPtr' that can be passed to other C++ components.
This header-only library provides modern, portable C++ wrappers for SIMD
    intrinsics and parallelized, optimized math implementations (SSE, AVX, NEON, AVX512).
    By placing this library in this package, we offer an efficient distribution system for
    Xsimd <https://github.com/QuantStack/xsimd> for R packages using CRAN.
This package provides access to some of the C level
        functions of the xts package.

  In its current state, the package is mostly a proof-of-concept to
        support adding useful functions, and does not yet add any of
        its own.
The Ziggurat generator for normally distributed random
 numbers, originally proposed by Marsaglia and Tsang (2000, 
 <doi:10.18637/jss.v005.i08>) has been improved upon a few times
 starting with Leong et al (2005, <doi:10.18637/jss.v012.i07>).
 This package provides an aggregation in order to compare different
 implementations in order to provide an 'faster but good enough'
 alternative for use with R and C++ code.
Performs parallel web crawling and web scraping. It is designed to crawl, parse and store web pages to produce data that can be directly used for analysis application. For details see Khalil and Fakir (2017) <DOI:10.1016/j.softx.2017.04.004>.
Tools to write a list of credentials to an encrypted file and later read from that file into R. The goal is to have a useful alternative to including username/passwords as part of a script or even stored in the clear in a separate text file. Additional tools provided which are specific for connecting to a database.
Aims at loading Criteo online advertising campaign data into R.
    Criteo <http://www.criteo.com/> is an online advertising service that enables
    advertisers to display commercial ads to web users. The package provides
    an authentication process for R with the Criteo API <http://kb.criteo.com/
    advertising/content/5/27/en/api.html>. Moreover, the package features an
    interface to query campaign data from the Criteo API. The data can be downloaded
    and will be transformed into a R data frame.
Goldwin-Pierre correlogram. Research of critical periods in the past. Integrates a time series in a given window.
Fit a 2-parameter continual reassessment method (CRM) model (O'Quigley and Shen (1996), <doi: 10.2307/2532905>) regularized with L2 norm (Friedman et al. (2010), <doi: 10.18637/jss.v033.i01>) adjusted by the distance with the target dose limiting toxicity (DLT) rate.
'NanoString nCounter' is a medium-throughput platform that measures gene or microRNA expression levels. Here is a publication that introduces this platform: Malkov (2009) <doi:10.1186/1756-0500-2-80>. Here is the webpage of 'NanoString nCounter' where you can find detailed information about this platform <https://www.nanostring.com/scientific-content/technology-overview/ncounter-technology>. It has great clinical application, such as diagnosis and prognosis of cancer. Implements integrated system of random-coefficient hierarchical regression model to normalize data from 'NanoString nCounter' platform so that noise from various sources can be removed.
Client for various 'CrossRef' 'APIs', including 'metadata' search
    with their old and newer search 'APIs', get 'citations' in various formats
    (including 'bibtex', 'citeproc-json', 'rdf-xml', etc.), convert 'DOIs'
    to 'PMIDs', and 'vice versa', get citations for 'DOIs', and get links to
    full text of articles when available.
Contains methods for criterion-referenced test analyses as 
    described in Brown & Hudson (2002). This includes cut-score item discrimination 
    analyses and measures of dependability.
Provides easy symmetric file encryption using GPG with
    cryptographically strong defaults. Only symmetric encryption is 
    supported. GPG is pre-installed with most Linux distributions. 
    Windows users will need to install 'Gpg4win' (http://www.gpg4win.org/). 
    OS X users will need to install 'GPGTools' (https://gpgtools.org/).
Pulls current pricing and market cap info from the <https://coinmarketcap.com/api/> API and presents it in a dataframe, see function help documentation for headerdetails.
R interface to the CSDP semidefinite programming library. Installs version 6.1.1 of CSDP from the COIN-OR website if required. An existing installation of CSDP may be used by passing the proper configure arguments to the installation command. See the INSTALL file for further details.
Cloth Simulation Filter (CSF) is an airborne LiDAR (Light Detection and Ranging) ground 
    points filtering algorithm  which is based on cloth simulation. It tries to simulate the interactions
    between the cloth nodes and the corresponding LiDAR points, the locations of the cloth nodes can be
    determined to generate an approximation of the ground surface.
The numerical treatment of optimal switching problems in a finite time setting when the state evolves as a controlled Markov chain consisting of a uncontrolled continuous component following linear dynamics and a controlled Markov chain taking values in a finite set. The reward functions are assumed to be convex and Lipschitz continuous in the continuous state. The action set is finite.
Provides a means to style plots through cascading style sheets.
    This separates the aesthetics from the data crunching in plots and charts.
Provides simplified methods for managing classic Rubik's cubes and many other modifications of it (such as NxNxN size cubes, void cubes and 8-coloured cubes - so called octa cubes). Includes functions of handling special syntax for managing such cubes; and different approach to plotting 3D cubes without using external libraries (for example 'OpenGL').
Functions and objects for CUR matrix decomposition.
Implements robust cure models for survival analysis by incorporate
    a weakly informative prior in the logistic part of cure models. Estimates
    prognostic accuracy, i.e. AUC, k-index and c-index, with bootstrap confidence
    interval for cure models.
A wrapper for 'libcurl' <http://curl.haxx.se/libcurl/>
	Provides functions to allow one to compose general HTTP requests
        and provides convenient functions to fetch URIs, get & post
        forms, etc. and process the results returned by the Web server.
        This provides a great deal of control over the HTTP/FTP/...
        connection and the form of the request while providing a
        higher-level interface than is available just using R socket
        connections.  Additionally, the underlying implementation is
        robust and extensive, supporting FTP/FTPS/TFTP (uploads and
        downloads), SSL/HTTPS, telnet, dict, ldap, and also supports
        cookies, redirects, authentication, etc.
A collection of ranked choice voting data and functions to 
    manipulate, run elections with, and visualize this data and others. 
    It can bring in raw data, transform it into a ballot you can read, 
    and return election results for an RCV contest.
Administrative regions and other spatial objects of the Czech Republic.
Creation and manipulation of R documentation (Rd) as 
    R objects.  Also contains functions for checking consistency 
    of Rd and S4 generics and methods for converting  
    objects to Rd formatted output.
The native R functionalities only allow PDF exports of reference manuals. This shall be extended by converting the package documentation files into markdown files and combining them into a markdown version of the package reference manual.
Functions to convert Rd to 'roxygen' documentation. It can parse an
    Rd file to a list, create the 'roxygen' documentation and update the original
    R script (e.g. the one containing the definition of the function)
    accordingly. This package also provides utilities that can help developers
    build packages using 'roxygen' more easily. The 'formatR' package can be used
    to reformat the R code in the examples sections so that the code will be
    more readable.
Shrunken Centroids Regularized Discriminant Analysis for
        the classification purpose in high dimensional data.
Provides basic functionalities for writing a module
    for http://datacanvas.io. The http://datacanvas.io is a big data
    analytics platform that helps data scientists to build, manage
    and share data pipelines.
Client for the web service methods provided
    by 'DataCite' (<https://www.datacite.org/>), including functions to interface with
    their 'OAI-PMH' 'metadata' service, and a 'RESTful' search API. The API
    is backed by 'SOLR', allowing expressive queries, including faceting,
    statistics on variables, and 'more-like-this' queries.
Provides an R interface to the Data Retriever
    <http://data-retriever.org/> via the Data Retriever's
    command line interface. The Data Retriever automates the
    tasks of finding, downloading, and cleaning public datasets,
    and then stores them in a local database.
R access to hundreds of millions data series from DBnomics API
    (<https://db.nomics.world/>).
Provides the tools to undertake estimation in
    Regression Discontinuity Designs. Both sharp and fuzzy designs are
    supported. Estimation is accomplished using local linear regression.
    A provided function will utilize Imbens-Kalyanaraman optimal
    bandwidth calculation. A function is also included to test the
    assumption of no-sorting effects.
Estimation of both single- and multiple-assignment Regression Discontinuity Designs 
    (RDDs). Provides both parametric (global) and non-parametric (local) estimation choices for
    both sharp and fuzzy designs, along with power analysis and assumption checks. 
    Introductions to the underlying logic and analysis of RDDs are in 
    Thistlethwaite, D. L., Campbell, D. T. (1960) <doi:10.1037/h0044319> and
    Lee, D. S., Lemieux, T. (2010) <doi:10.1257/jel.48.2.281>.
Density discontinuity test (a.k.a. manipulation test) is commonly employed in regression discontinuity designs and other program evaluation settings to detect whether there is evidence of perfect self-selection (manipulation) around a cutoff where a treatment/policy assignment changes. This package provides tools for conducting the aforementioned statistical test: rddensity() to construct local polynomial based density discontinuity test given a prespecified cutoff, rdbwdensity() to perform bandwidth selection, and rdplotdensity() to construct density plot near the cutoff.
Set of functions for Regression Discontinuity Design ('RDD'), for
    data visualisation, estimation and testing.
Allows caching of raw data directly in R code. This allows R
  scripts and R Notebooks to be shared and re-run on a machine without access
  to the original data. Cached data is encoded into an ASCII string that can
  be pasted into R code. When the code is run, the data is automatically
  loaded from the cached version if the original data file is unavailable.
  Works best for small datasets (a few hundred observations).
Data Envelopment Analysis for R, estimating robust DEA scores without and with environmental variables and doing returns-to-scale tests.
The package provides functions for estimating the relevant
        dimension of a data set in feature spaces, applications to
        model selection, graphical illustrations and prediction.
The Resource Description Framework, or 'RDF' is a widely used
             data representation model that forms the cornerstone of the 
             Semantic Web. 'RDF' represents data as a graph rather than 
             the familiar data table or rectangle of relational databases.
             The 'rdflib' package provides a friendly and concise user interface
             for performing common tasks on 'RDF' data, such as reading, writing
             and converting between the various serializations of 'RDF' data,
             including 'rdfxml', 'turtle', 'nquads', 'ntriples', and 'json-ld';
             creating new 'RDF' graphs, and performing graph queries using 'SPARQL'.
             This package wraps the low level 'redland' R package which
             provides direct bindings to the 'redland' C library.  Additionally,
             the package supports the newer and more developer friendly
             'JSON-LD' format through the 'jsonld' package. The package
             interface takes inspiration from the Python 'rdflib' library.
Functions to interact with the 'Google DoubleClick for Publishers (DFP)' API 
    <https://developers.google.com/ad-manager/api/start> (recently renamed to 
    'Google Ad Manager'). This package is automatically compiled from the API WSDL 
    (Web Service Description Language) files to dictate how the API is structured. 
    Theoretically, all API actions are possible using this package; however, care 
    must be taken to format the inputs correctly and parse the outputs correctly. 
    Please see the 'Google Ad Manager' API reference <https://developers.google.com/ad-manager/api/rel_notes> 
    and this package's website <https://stevenmmortimer.github.io/rdfp/> for more 
    information, documentation, and examples.
Different Tensor Factorization techniques suitable for RDF Tensors.
            RDF Tensors are three-mode-tensors, binary tensors and usually very sparse.
            Currently implemented methods are 
              'RESCAL' Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel (2012) <doi:10.1145/2187836.2187874>, 
              'NMU' Daniel D. Lee and H. Sebastian Seung (1999) <doi:10.1038/44565>,
              'ALS', Alternating Least Squares
              'parCube' Papalexakis, Evangelos, C. Faloutsos, and N. Sidiropoulos (2012) <doi:10.1007/978-3-642-33460-3_39>,  
              'CP_APR' C. Chi and T. G. Kolda (2012) <doi:10.1137/110859063>.
            The code is mostly converted from MATLAB and Python implementations of these methods.
            The package also contains functions to get Boolean (Binary) transformation of the real-number-decompositions.
            These methods also are for general tensors, so with few modifications they can be applied for other types of tensor.
Provides a client for (1) querying the DHS API for survey indicators
  and metadata (<https://api.dhsprogram.com/#/index.html>), (2) identifying surveys
  and datasets for analysis, (3) downloading survey datasets from the DHS website,
  (4) loading datasets and associate metadata into R, and (5) extracting variables
  and combining datasets for pooled analysis.
Methods for calculation and visualization of the Repertoire
    Dissimilarity Index. Citation: Bolen and Rubelt, et al (2017) 
    <doi:10.1186/s12859-017-1556-5>.
A client library for 'The Guardian' (https://www.guardian.com/)
    and their API, this package allows users to search for Guardian articles and
    retrieve both the content and metadata.
A collection of functions to simulate
  dice rolls and the like. In particular, experiments and exercises can
  be performed looking at combinations and permutations of values in dice
  rolls and coin flips, together with the corresponding frequencies of
  occurrences. When applying each function, the user has to input the
  number of times (rolls, flips) to toss the dice. Needless to say, the more
  the tosses, the more the frequencies approximate the actual probabilities.
  Moreover, the package provides functions to generate non-transitive sets
  of dice (like Efron's) and to check whether a given set of dice is non-transitive
  with given probability.
The package has many function that helps to perform
        various quality check on the data.It basically provides many
        function that helps in performing Extrapolative data analysis.
The 'RDieHarder' package provides an R interface to 
 the 'DieHarder' suite of random number generators and tests that 
 was developed by Robert G. Brown and David Bauer, extending 
 earlier work by George Marsaglia and others. The 'DieHarder'
 library is included, but if a version is already installed
 it will be used instead.
We provide linear and nonlinear dimension reduction techniques.
	Intrinsic dimension estimation methods for exploratory analysis are also provided.
    For more details on dimensionality techniques, see the paper by
    Ma and Zhu (2013) <doi:10.1111/j.1751-5823.2012.00182.x> if you are interested in
    statistical approach, or Engel, Huttenberger, and Hamann (2012)
    <doi:10.4230/OASIcs.VLUDS.2011.135> for a broader multi-disciplinary overview.
A common framework for calculating distance matrices.
Distance-sampling is a popular method for estimating density and 
  abundance of organisms in ecology. Rdistance contains routines that  
  assist with analysis of
  distance-sampling data collected on point or line transects. 
  Distance models are specified using regression-like formula (similar 
  to lm, glm, etc.). Abundance routines
  perform automated bootstrapping and automated detection-function 
  selection. Overall (study area) and site-level (transect or point) 
  abundance estimates are available. A large suite of classical, 
  parametric detection functions are 
  included along with some uncommon parametric 
  functions (e.g., Gamma, negative exponential) and non-parametric
  smoothed distance functions. Custom (user-defined) detection functions
  are easily implemented (see vignette). 
  The help files and vignettes have been 
  vetted by multiple authors and tested in workshop 
  settings.
Provides a framework for the measurement and partitioning of
    the (similarity-sensitive) biodiversity of a metacommunity and its
    constituent subcommunities. Richard Reeve, et al. (2016) 
    <arXiv:1404.6520v3>.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. Under the local randomization approach, RD designs can be interpreted as randomized experiments inside a window around the cutoff. This package provides tools to perform randomization inference for RD designs under local randomization: rdrandinf() to perform hypothesis testing using randomization inference, rdwinselect() to select a window around the cutoff in which randomization is likely to hold, rdsensitivity() to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds() to construct Rosenbaum bounds for sensitivity to unobserved confounders. See Cattaneo, Titiunik and Vazquez-Bare (2016) <https://sites.google.com/site/rdpackages/rdlocrand/Cattaneo-Titiunik-VazquezBare_2016_Stata.pdf> for further methodological details.
Imports real-time thermo cycler (qPCR) data from Real-time PCR
    Data Markup Language (RDML) and transforms to the appropriate formats of
    the 'qpcR' and 'chipPCR' packages. Contains a dendrogram visualization 
    for the structure of RDML object and GUI for RDML editing.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdmulti' package provides tools to analyze RD designs with multiple cutoffs or scores: rdmc() estimates pooled and cutoff specific effects for multi-cutoff designs, rdmcplot() draws RD plots for multi-cutoff designs and rdms() estimates effects in cumulative cutoffs or multi-score designs. See Cattaneo, Titiunik and Vazquez-Bare (2019) <https://sites.google.com/site/rdpackages/rdmulti/Cattaneo-Titiunik-VazquezBare_2019_rdmulti.pdf> for further methodological details.
Comprehensive toolkit for generating various numerical                                 representation schemes of DNA sequence. The descriptors and similarity
    scores included are extensively used in bioinformatics and chemogenomics.
A wrapper for the 'Deutsche Nationalbibliothek (German National
    Library) API', available at <http://www.dnb.de>. The German National Library is
    the German central archival library, collecting, archiving, bibliographically
    classifying all German and German-language publications, foreign
    publications about Germany, translations of German works, and the works of
    German-speaking emigrants published abroad between 1933 and 1945. A personal
    access token is required for usage.
Extends tools::Rd2txt() by adding customisable text and colour formatting to R documentation 
    contents. If used from a terminal, output will be displayed via file.show() otherwise contents
    will be printed in sections. Also provides stand-in replacements for ?() and help().
Wraps around the default help functionality in R. Instead of plain documentation files, documentation will show up as it does on <https://www.rdocumentation.org>, a platform that shows R documentation from 'CRAN', 'GitHub' and 'Bioconductor', together with informative stats to assess the package quality.
An R API Client for Valve's Dota2. RDota2 can be easily used 
    to connect to the Steam API and retrieve data for Valve's popular video 
    game Dota2. You can find out more about Dota2 at 
    <http://store.steampowered.com/app/570/>.
Low-level interface to '.NET' virtual machine along the lines of the R C .call interface.  Can create '.NET' object, call methods, get or set properties, call static functions, etc.
Create doxygen documentation for source code in R packages. 
  Includes a RStudio Addin, that allows to trigger the doxygenize process.
Functions for manipulation of R documentation objects,
    including functions reprompt() and ereprompt() for updating 'Rd'
    documentation for functions, methods and classes; 'Rd' macros for
    citations and import of references from 'bibtex' files for use in
    'Rd' files and 'roxygen2' comments; 'Rd' macros for evaluating and
    inserting snippets of 'R' code and the results of its evaluation or
    creating graphics on the fly; and many functions for manipulation of
    references and Rd files.
Interact with the Digital Public Library of America
    <https://dp.la> ('DPLA') 'REST' 'API'
    <https://dp.la/info/developers/codex/> from R, including search
    and more.
The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The 'rdpower' package provides tools to perform power and sample size calculations in RD designs: rdpower() calculates the power of an RD design and rdsampsi() calculates the required sample size to achieve a desired power. See Cattaneo, Titiunik and Vazquez-Bare (2019) <https://sites.google.com/site/rdpackages/rdpower/Cattaneo-Titiunik-VazquezBare_2019_Stata.pdf> for further methodological details.
Regression-discontinuity (RD) designs are quasi-experimental research designs popular in social, behavioral and natural sciences. The RD design is usually employed to study the (local) causal effect of a treatment, intervention or policy. This package provides tools for data-driven graphical and analytical statistical inference in RD	designs: rdrobust() to construct local-polynomial point estimators and robust confidence intervals for average treatment effects at the 	cutoff in Sharp, Fuzzy and Kink RD settings, rdbwselect() to perform bandwidth selection for the different procedures implemented, and rdplot() to conduct exploratory data analysis (RD plots).
An interface for using the popular Java based Drools, which is a Business Rule Management System (See <https://www.drools.org> for more information). This package provides data scientists an intuitive interface to execute business rules on datasets for the purpose of analysis or designing intelligent systems, while leveraging the Drools rule engine. Rules written in DRL format accepted natively by Drools can also be executed through an R interface. Credits to Mu Sigma for their continued support throughout the development of the package.
External jars required for package 'Rdrools'.
Provides full programmatic access to the 'Dropbox' file hosting platform <https://dropbox.com>, including support for all standard file operations.
Interface to the Dryad "Solr" API, their "OAI-PMH" service, and
    fetch datasets. Dryad (<http://datadryad.org/>) is a curated host of
    data underlying scientific publications.
Provides functionality for carrying out estimation
    with data collected using Respondent-Driven Sampling. This includes
    Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential
    Sampling estimator. The package is part of the "RDS Analyst" suite of
    packages for the analysis of respondent-driven sampling data.
    See Gile and Handcock (2010) <doi:10.1111/j.1467-9531.2010.01223.x> and
    Gile and Handcock (2015) <doi:10.1111/rssa.12091>.
R interface to DSDP semidefinite programming library. The DSDP software is a free open source implementation of an interior-point method for semidefinite programming. It provides primal and dual solutions, exploits low-rank structure and sparsity in the data, and has relatively low memory requirements for an interior-point method.
Provides a threads-type programming environment for R.
   The package gives the R programmer the clearer, more concise
   shared memory world view, and in some cases gives superior
   performance as well.  In addition, it enables parallel processing on
   very large, out-of-core matrices.
This package provides an R interface to Pete Warden's Data
        Science Toolkit.  See www.datasciencetoolkit.org for more
        information.  The source code for this package can be found at
        github.com/rtelmore/RDSTK Happy hacking!
A tree bootstrap method for estimating uncertainty in respondent-driven samples (RDS). Quantiles are estimated by multilevel resampling in such a way that preserves the dependencies of and accounts for the high variability of the RDS process.
Defines functions that can be used to collect provenance as
  an R script executes or during a console session. The output is a text 
  file in PROV-JSON format.
Implementation of density tracking by quadrature (DTQ) algorithms for stochastic differential equations (SDEs).  DTQ algorithms numerically compute the density function of the solution of an SDE with user-specified drift and diffusion functions.  The calculation does not require generation of sample paths, but instead proceeds in a deterministic fashion by repeatedly applying quadrature to the Chapman-Kolmogorov equation associated with a discrete-time approximation of the SDE.  The DTQ algorithm is provably convergent.  For several practical problems of interest, we have found the DTQ algorithm to be fast, accurate, and easy to use.
Functions for connecting to and interfacing with an 'Arduino' or similar device. Functionality includes uploading of sketches, setting and reading digital and analog pins, and rudimentary servo control. This project is not affiliated with the 'Arduino' company, <https://www.arduino.cc/>.
Handle climate data from the 'DWD' ('Deutscher Wetterdienst', see 
             <https://www.dwd.de/EN/climate_environment/cdc/cdc.html> for more information).
             Choose files with 'selectDWD()', download and process data sets with 'dataDWD()' and 'readDWD()'.
RE2 <https://github.com/google/re2> is a primarily deterministic finite automaton based regular expression engine from Google that is very fast
    at matching large amounts of text.
Building interactive web applications with R is incredibly easy
  with 'shiny'. Behind the scenes, 'shiny' builds a reactive graph that can
  quickly become intertwined and difficult to debug. 'reactlog' 
  (Schloerke 2019) <doi:10.5281/zenodo.2591517> provides a visual insight into
  that black box of 'shiny' reactivity by constructing a directed dependency
  graph of the application's reactive state at any time point in a reactive
  recording.
Make it easy to use 'React' in R with 'htmlwidget' scaffolds,
              helper dependency functions, an embedded 'Babel' 'transpiler',
              and examples.
Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions.
  Includes transport routines in porous media, in estuaries, and in bodies with variable shape.
Functions for reading and decompressing the DBC (compressed DBF) files. Please note that this is the file format used by the Brazilian Ministry of Health (DATASUS) to publish healthcare datasets. It is not related to the FoxPro or CANdb DBC file formats.
Opens complete record(s) with .gb extension from the NCBI/GenBank Nucleotide database and returns a list containing shaped record(s). These kind of files contains detailed records of DNA samples (locus, organism, type of sequence, source of the sequence...). An example of record can be found at <https://www.ncbi.nlm.nih.gov/nuccore/HE799070>.
Loads Axon Binary Files (both 'ABF' and 'ABF2') created by Axon Instruments/Molecular Devices software such as 'pClamp'.
Downloads, imports, and tidies time series data from the 
    Australian Bureau of Statistics <https://www.abs.gov.au/>.
Identifies and reads Windows BMP, JPEG, PNG, and TIFF format bitmap
    images. Identification defaults to the use of the magic number embedded in
    the file rather than the file extension. Reading of JPEG and PNG image
    depends on libjpg and libpng libraries. See file INSTALL for details if
    necessary.
Reads data files acquired by Bruker Daltonics' matrix-assisted
    laser desorption/ionization-time-of-flight mass spectrometer of the *flex
    series.
Combine multiple data files from a common directory.
    The data files will be read into R and bound together, creating a
    single large data.frame. A general function is provided along with
    a specific function for data that was collected using the open-source
    experiment builder 'OpenSesame' <http://osdoc.cogsci.nl/>.
A set of functions to simplify reading data from files. The main function, reader(), should read most common R datafile types without needing any parameters except the filename. Other functions provide simple ways of handling file paths and extensions, and automatically detecting file format and structure.
Providing just one primary function, 'readit' uses a set of reasonable
    heuristics to apply the appropriate reader function to the given file path.
    As long as the data file has an extension, and the data is (or can be
    coerced to be) rectangular, readit() can probably read it.
Import data written in the JCAMP-DX format. This is an instrument-independent format used in the field of spectroscopy. Examples include IR, NMR, and Raman spectroscopy. See the vignette for background and supported formats.  The official JCAMP-DX site is <http://www.jcamp-dx.org/>.
Functions for reading data sets in different formats
  for testing machine learning tools are provided. This allows to run
  a loop over several data sets in their original form, for example
  if they are downloaded from UCI Machine Learning Repository.
  The data are not part of the package and have to be downloaded
  separately.
You can use the function Read.mnist() to read data and arrange them properly from MNIST dataset (the open handwriting digit database <http://yann.lecun.com/exdb/mnist/>). With this package, you can conveniently get all of necessary informations and then immediately start to check whether your machine learning algorithm works well. It can automatically recognize the type of dataset and returns the informations in corresponding structure.
Functions for reading mass spectrometry data in mzXML format.
Wraps 'tiny_obj_loader' C++ library for reading the 'Wavefront' OBJ
    3D file format including both mesh objects and materials files. The
    resultant R objects are either structured to match the 'tiny_obj_loader'
    internal data representation or in a form directly compatible with the 'rgl'
    package.
Import ODS (OpenDocument Spreadsheet) into R as a data frame. Also support writing data frame into ODS file.
Reads in text from 'unstructured' modern Microsoft Office files (XML based files) such as Word and PowerPoint.
    This does not read in structured data (from Excel or Access) as there are many other great packages to that do so already.
The goal of 'readr' is to provide a fast and friendly way to read
    rectangular data (like 'csv', 'tsv', and 'fwf'). It is designed to flexibly
    parse many types of data found in the wild, while still cleanly failing when
    data unexpectedly changes.
A convenient way to read fixed-width ASCII polling datasets from providers like the Roper Center <https://ropercenter.cornell.edu>.
Read Statistical Data and Metadata Exchange (SDMX) XML data. 
    This the main transmission format used in official statistics. Data can be imported from
    local SDMX-ML files or a SDMX web-service and will be read in 'as is' into a dataframe object.
    The 'RapidXML' C++ library <http://rapidxml.sourceforge.net> is used to parse the XML data.
Function to read and write the 'Stata' file format.
Functions for importing and handling text files and formatted text
    files with additional meta-data, such including '.csv', '.tab', '.json', '.xml',
    '.html', '.pdf', '.doc', '.docx', '.rtf', '.xls', '.xlsx', and others.
Quickly read contents from files or URLs. Functions provide a simple, consistent, and
    performant interface for reading (all at once or line-by-line) the contents of files or web pages.
Reads and writes data frames to 'SQLite' databases while 
  preserving time zones (for POSIXct columns), 
  projections (for 'sfc' columns), units (for 'units' columns), 
  levels (for factors and ordered factors) and classes for 
  logical, Date and 'hms' columns.
  It also logs changes to tables and provides more informative error messages.
Import excel files into R. Supports '.xls' via the
    embedded 'libxls' C library <https://github.com/libxls/libxls> and
    '.xlsx' via the embedded 'RapidXML' C++ library
    <http://rapidxml.sourceforge.net>.  Works on Windows, Mac and Linux
    without external dependencies.
Import data from 'Excel' binary (.xlsb) workbooks into R.
Fits a multivariate value-added model (VAM), see Broatch, Green, and Karl (2018) <doi:10.32614/RJ-2018-033> and Broatch and Lohr (2012) <doi:10.3102/1076998610396900>, with normally distributed test scores and a binary outcome indicator. A pseudo-likelihood approach, Wolfinger (1993) <doi:10.1080/00949659308811554>, is used for the estimation of this joint generalized linear mixed model.  The inner loop of the pseudo-likelihood routine (estimation of a linear mixed model) occurs in the framework of the EM algorithm presented by Karl, Yang, and Lohr (2013) <DOI:10.1016/j.csda.2012.10.004>. This material is based upon work supported by the National Science Foundation under grants DRL-1336027 and DRL-1336265.
Resampling methods for adaptive linear model selection.
        These can be thought of as extensions of the Akaike information
        criterion that account for searching among candidate models.
The rearrangement operator (Hardy,
        Littlewood, and Polya 1952) for univariate, bivariate, and
        trivariate point estimates of monotonic functions. The package
        additionally provides a function that creates simultaneous
        confidence intervals for univariate functions and applies the
        rearrangement operator to these confidence intervals.
Collection of models and analysis methods used in regional and urban economics and (quantitative) economic geography, e.g. measures of inequality, regional disparities and convergence, regional specialization as well as accessibility and spatial interaction models.
Kiefer-Wolfowitz maximum likelihood estimation for mixture models
    and some other density estimation and regression methods based on convex
    optimization.  See Koenker and Gu (2017) REBayes: An R Package for Empirical
    Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26, 
    <DOI:10.18637/jss.v082.i08>.
A programmatic client for the eBird database, including functions
    for searching for bird observations by geographic location (latitude,
    longitude), eBird hotspots, location identifiers, by notable sightings, by
    region, and by taxonomic name.
R functions for random univariate and multivariate finite mixture model generation, estimation, clustering, latent class analysis and classification. Variables can be continuous, discrete, independent or dependent and may follow normal, lognormal, Weibull, gamma, binomial, Poisson, Dirac or circular von Mises parametric families.
Build regular expressions piece by piece using human readable code.
    This package is designed for interactive use.  For package development, use
    the rebus.* dependencies.
Build regular expressions piece by piece using human readable code.
    This package contains core functionality, and is primarily intended to be
    used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains date and time functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains number-related functionality, and is primarily intended
    to be used by package developers.
Build regular expressions piece by piece using human readable code.
    This package contains Unicode functionality, and is primarily intended to be
    used by package developers.
Relevant Component Analysis (RCA) tries to find a linear
    transformation of the feature space such that the effect of irrelevant
    variability is reduced in the transformed space.
A toolset for 3D reconstruction and analysis of excavations. It provides methods to reconstruct natural and artificial surfaces based on field measurements. This allows to spatially contextualize documented subunits and features. Intended to be part of a 3D visualization workflow.
The 'Echo nest' <http://the.echonest.com> is the industry's leading
    music intelligence company, providing developer with deepest understanding of
    music content and music fans. This package can be used to access artist's data
    including songs, blogs, news, reviews etc. Song's data including audio summary,
    style, danceability, tempo etc can also be accessed.
Pathways in a database could have many redundancies among
        them. This package allows the user to set a maximum value for
        the proportion of these redundancies.
An extensible framework to create and preprocess 
    design matrices. Recipes consist of one or more data manipulation 
    and analysis "steps". Statistical parameters for the steps can 
    be estimated from an initial data set and then applied to 
    other data sets. The resulting design matrices can then be used 
    as inputs into statistical or machine learning models.
Functions to assist in performing probabilistic record linkage and
    deduplication: generating pairs, comparing records, em-algorithm for
    estimating m- and u-probabilities, forcing one-to-one matching. Can also be
    used for pre- and post-processing for machine learning methods for record
    linkage.
Beta-diversity indices provide dissimilarity matrices with particular distribution of data requiring specific treatment. For example the high frequency of ties and zero values in turnover indices produces hierarchical cluster dendrograms whose topology and bootstrap supports are affected by the order of rows in the original matrix. Moreover, biogeographical regionalization can be facilitated by a combination of hierarchical clustering and multi-dimensional scaling. The recluster package provides robust techniques to analyze pattern of similarity in species composition.
Provides an interface and a C++ implementation of the RecMap MP2
  construction heuristic (Panse C. (2018) <doi:10.18637/jss.v086.c01>). This algorithm
  draws maps according to a given statistical value (e.g., election results,
  population or epidemiological data). The basic idea of the RecMap algorithm is
  that each map region (e.g., different countries) is represented by a
  rectangle. The area of each rectangle represents the statistical value given
  as input (maintain zero cartographic error). Documentation about the usage
  of the recmap algorithm is provided by a vignette included in this package.
Simple, easy to use, and flexible functionality for recoding variables. It allows for simple piecewise definition of transformations.
Turns nested lists into data.frames in an orderly manner.
Provides a research infrastructure to test and develop
    recommender algorithms including UBCF, IBCF, FunkSVD and association
    rule-based algorithms.
Provides the Book-Crossing Dataset for the package recommenderlab.
Provides the Jester Dataset for package recommenderlab.
Implements solutions to canonical models of Economics such as Monopoly Profit Maximization, Cournot's Duopoly, Solow (1956, <doi:10.2307/1884513>) growth model and Mankiw, Romer and Weil (1992, <doi:10.2307/2118477>) growth model.
Functions to reconstruct sessions from web log or other user trace data
             and calculate various metrics around them, producing tabular,
             output that is compatible with 'dplyr' or 'data.table' centered processes.
A lightweight toolkit to validate new observations when computing
    their predictions with a predictive model. The validation process 
    consists of two steps: (1) record relevant statistics and meta data of the
    variables in the original training data for the predictive model and
    (2) use these data to run a set of basic validation tests on the new set of 
    observations.
Provides functions for linking and de-duplicating data sets.
  Methods based on a stochastic approach are implemented as well as 
  classification algorithms from the machine learning domain.
Functions for generating k-record values and k-record
        times
Statistical tools based on probabilistic properties of the occurrence of records 
   in a sequence X_t of independent and identically distributed random variables. 
   More details on the objective of the library can be found with help(RecordTest-package).
R wrapper of the 'libmf' library
    <http://www.csie.ntu.edu.tw/~cjlin/libmf/> for recommender
    system using matrix factorization. It is typically used to
    approximate an incomplete matrix using the product of two
    matrices in a latent space. Other common names for this task
    include "collaborative filtering", "matrix completion",
    "matrix recovery", etc. High performance multi-core parallel
    computing is supported in this package.
Computes revisitation metrics for trajectory data, such as the number of revisitations for each location as well as the time spent for that visit and the time since the previous visit. Also includes functions to plot data.
Includes algorithms to facilitate the assessment of extinction
    risk of species according to the IUCN (International Union for Conservation of
    Nature, see <http://www.iucn.org> for more information) red list criteria.
Contains implementations of recurrent event data analysis routines
    including (1) survival and recurrent event data simulation from
    stochastic process point of view by the thinning method
    proposed by Lewis and Shedler (1979) <doi:10.1002/nav.3800260304>
    and the inversion method introduced in Cinlar (1975, ISBN:978-0486497976),
    (2) the mean cumulative function (MCF) estimation by the
    Nelson-Aalen estimator of the cumulative hazard rate function,
    (3) two-sample recurrent event responses comparison with the pseudo-score
    tests proposed by Lawless and Nadeau (1995) <doi:10.2307/1269617>,
    (4) gamma frailty model with spline rate function following
    Fu, et al. (2016) <doi:10.1080/10543406.2014.992524>.
Provides functions used in the 'R: Einführung durch angewandte Statistik' (second edition).
Access data stored in 'REDCap' databases using the Application
    Programming Interface (API).  'REDCap' (Research Electronic Data CAPture;
    <https://projectredcap.org>) is
    a web application for building and managing online surveys and databases
    developed at Vanderbilt University.  The API allows users to access data
    and project meta data (such as the data dictionary) from the web
    programmatically.  The 'redcapAPI' package facilitates the process of
    accessing data with options to prepare an analysis-ready data set
    consistent with the definitions in a database's data dictionary.
Encapsulates functions to streamline calls from R to the REDCap
    API.  REDCap (Research Electronic Data CAPture) is a web application for
    building and managing online surveys and databases developed at Vanderbilt
    University.  The Application Programming Interface (API) offers an avenue
    to access and modify data programmatically, improving the capacity for
    literate and reproducible programming.
A collection of tools for extracting structured data from <https://www.reddit.com/>.
Computes quality control for daily precipitation datasets, 
    reconstructs the original series by estimating precipitation in missing values, 
    creates new series in a specified pair of coordinates and creates grids.
Standard and extensible Eddy-Covariance data post-processing 
  (Wutzler et al. (2018) <doi:10.5194/bg-15-5015-2018>)
  includes  
  uStar-filtering, gap-filling, and flux-partitioning.
  The Eddy-Covariance (EC)  micrometeorological technique quantifies continuous 
  exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere.
  It is important for understanding ecosystem dynamics and upscaling exchange fluxes.
  (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>).
  This package inputs pre-processed (half-)hourly data and supports further processing. 
  First, a quality-check and filtering is performed based on the relationship between 
  measured flux and friction
  velocity (uStar) to discard biased data 
  (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>).
  Second, gaps in the data are filled based on information from environmental conditions
  (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>).
  Third, the net flux of carbon dioxide is partitioned
  into its gross fluxes in and out of the ecosystem by night-time 
  based and day-time based approaches
  (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).
Extension to 'REddyProc' that allows reading data from netCDF files.
Enables researchers to sample redistricting plans from a pre-
    specified target distribution using a Markov Chain Monte Carlo algorithm.
    The package allows for the implementation of various constraints in the
    redistricting process such as geographic compactness and population parity
    requirements. The algorithm also can be used in combination with efficient
    simulation methods such as simulated and parallel tempering algorithms. Tools
    for analysis such as inverse probability reweighting and plotting functionality
    are included. The package implements methods described in Fifield, Higgins, Imai
    and Tarr (2016) ``A New Automated Redistricting Simulator Using Markov Chain
    Monte Carlo,'' working paper available at
    <https://imai.fas.harvard.edu/research/files/redist.pdf>.
Provides methods to parse, query and serialize information
    stored in the Resource Description Framework (RDF). RDF is described at <http://www.w3.org/TR/rdf-primer>.
    This package supports RDF by implementing an R interface to the Redland RDF C library,
    described at <http://librdf.org/docs/api/index.html>. In brief, RDF provides a structured graph
    consisting of Statements composed of Subject, Predicate, and Object Nodes.
A toolbox created by members of the International Union for
    Conservation of Nature (IUCN) Red List of Ecosystems Committee for
    Scientific Standards. Primarily, it is a set of tools suitable
    for calculating the metrics required for making assessments of species and
    ecosystems against the IUCN Red List of Threatened Species and the IUCN Red
    List of Ecosystems categories and criteria. See the IUCN website for
    detailed guidelines, the criteria, publications and other information.
A new implementation of EDM algorithms based on research software previously developed for internal use in the Sugihara Lab (UCSD/SIO). Contains C++ compiled objects that use time delay embedding to perform state-space reconstruction and nonlinear forecasting and an R interface to those objects using 'Rcpp'. It supports both the simplex projection method from Sugihara & May (1990) <DOI:10.1038/344734a0> and the S-map algorithm in Sugihara (1994) <DOI:10.1098/rsta.1994.0106>. In addition, this package implements convergent cross mapping as described in Sugihara et al. (2012) <DOI:10.1126/science.1227079> and multiview embedding as described in Ye & Sugihara (2016) <DOI:10.1126/science.aag0863>.
Provide color schemes for maps (and other graphics) based on the
    color palettes of several Microsoft(r) products. Forked from 'RColorBrewer' v1.1-2.
Regularization by Denoising uses a denoising engine to solve
  many image reconstruction ill-posed inverse problems. This is a R
  implementation of the algorithm developed by Romano et.al. (2016) <arXiv:1611.02862>. Currently,
  only the gradient descent optimization framework is implemented. Also,
  only the median filter is implemented as a denoiser engine. However,
  (almost) any denoiser engine can be plugged in. There are currently available
  3 reconstruction tasks: denoise, deblur and super-resolution. And again,
  any other task can be easily plugged into the main function 'RED'.
Efficiently upload data into an Amazon Redshift database using the approach recommended by Amazon <https://aws.amazon.com/es/redshift/>.
A 'hiredis' wrapper that includes support for
    transactions, pipelining, blocking subscription, serialisation of
    all keys and values, 'Redis' error handling with R errors.
    Includes an automatically generated 'R6' interface to the full
    'hiredis' 'API'.  Generated functions are faithful to the
    'hiredis' documentation while attempting to match R's argument
    semantics.  Serialisation must be explicitly done by the user, but
    both binary and text-mode serialisation is supported.
This package estimates regression trees with random
        effects as a way to use data mining techniques to describe
        longitudinal or panel data.
Implements an objective Bayes intrinsic conditional autoregressive 
    prior. This model provides an objective Bayesian approach 
    for modeling spatially correlated areal data using an intrinsic conditional autoregressive prior on a vector of 
    spatial random effects.
This is a collection of tools to allow the medical professional to
 calculate appropriate reference ranges (intervals) with confidence intervals around
 the limits for diagnostic purposes.
Reference-free method for conducting EWAS while deconvoluting DNA methylation 
    arising as mixtures of cell types.  The older method 
    (Houseman et al., 2014,<doi:10.1093/bioinformatics/btu029>) is similar to 
    surrogate variable analysis (SVA and ISVA), except that it makes additional use 
    of a biological mixture assumption.
    The newer method (Houseman et al., 2016, <doi:10.1186/s12859-016-1140-4>) is similar 
    to non-negative matrix factorization, with additional constraints 
    and additional utilities.
Contains functionalities for importing and managing downloaded genome annotation data from 'Ensembl' genome browser (European Bioinformatics Institute, <https://www.ensembl.org>) and from 'UCSC' genome browser (University of California, Santa Cruz, <https://genome.ucsc.edu/>) and annotation routines for genomic positions and splice site positions.
These functions take a character vector as input, identify and 
  cluster similar values, and then merge clusters together so their values 
  become identical. The functions are an implementation of the key collision 
  and ngram fingerprint algorithms from the open source tool Open Refine 
  <http://openrefine.org/>. More info on key collision and ngram fingerprint 
  can be found here <https://github.com/OpenRefine/OpenRefine/wiki/Clustering-In-Depth>.
Provides tools for importing and working with bibliographic
    references. It greatly enhances the 'bibentry' class by providing a class
    'BibEntry' which stores 'BibTeX' and 'BibLaTeX' references, supports 'UTF-8'
    encoding, and can be easily searched by any field, by date ranges, and by
    various formats for name lists (author by last names, translator by full names,
    etc.). Entries can be updated, combined, sorted, printed in a number of styles,
    and exported. 'BibTeX' and 'BibLaTeX' '.bib' files can be read into 'R' and
    converted to 'BibEntry' objects. Interfaces to 'NCBI Entrez', 'CrossRef', and
    'Zotero' are provided for importing references and references can be created
    from locally stored 'PDF' files using 'Poppler'. Includes functions for citing
    and generating a bibliography with hyperlinks for documents prepared with
    'RMarkdown' or 'RHTML'.
A tool for refining data frame with formulas.
Provides subsets with reference semantics, i.e. subsets
    which automatically reflect changes in the original object, and which
    optionally update the original object when they are changed.
Access the 'Refuge' API, a web-application for locating trans and 
    intersex-friendly restrooms, including unisex and accessible restrooms. 
    Includes data on the location of restrooms, along with directions, 
    comments, user ratings and amenities. Coverage is global, but data is 
    most comprehensive in the United States.
    See <https://www.refugerestrooms.org/api/docs/> for full API documentation.
Methods for regression for functional
    data, including function-on-scalar, scalar-on-function, and
    function-on-function regression. Some of the functions are applicable to
    image data.
Interactive plotting for functional data analyses.
Contains basic tools for visualizing, interpreting, and building regression models.  It has been designed for use with the book Introduction to Regression and Modeling with R by Adam Petrie, Cognella Publishers, ISBN: 978-1-63189-250-9 <https://titles.cognella.com/behavioral-and-social-sciences/general-social-sciences/introduction-to-regression-and-modeling-with-r-9781631892509.html>.
This package clusters regression coefficients using the methods of clustering through linear regression models (CLM) (Qin and Self 2006). Maximum likelihood approach is used to infer the parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to determine the number of clusters.
Detecting mislabeled samples in genetic data.
Produces population distribution of disease risk and statistical risk categories, and predicts risks for individuals with genotype information.
Provides wrappers around base::grep() where the first argument
    is standardized to take the data object. This makes it less of a pain to use
    regular expressions with 'magrittr' or other pipe operators.
'shiny' extension that adds regular expression filtering capabilities to 
  the choice vector of the select list.
A set of functions used to automate commonly used methods in
    regression analysis. This includes plotting interactions, calculating simple
    slopes, calculating standardized coefficients, etc. See the reghelper
    documentation for more information, documentation, and examples.
Provides a generic infrastructure for creating and using registries.
Regularized (polychotomous) logistic regression 
  by Gibbs sampling. The package implements subtly different 
  MCMC schemes with varying efficiency depending on the data type 
  (binary v. binomial, say) and the desired estimator (regularized maximum
  likelihood, or Bayesian maximum a posteriori/posterior mean, etc.) through a 
  unified interface.
Network-based regularization has achieved success in variable selection for 
    high-dimensional biological data due to its ability to incorporate correlations among 
    genomic features. This package provides procedures of network-based variable selection 
    for generalized linear models (Ren et al. (2017) <doi:10.1186/s12863-017-0495-5> and 
    Ren et al. (2019) <doi:10.1002/gepi.22194>). Two recent additions are the robust network 
    regularization for the survival response and the network regularization for continuous 
    response. Functions for other regularization methods will be included in the forthcoming 
    upgraded versions.
A function to plot a regression nomogram of coxph, lm and glm regression 
    objects. Covariate distributions are superimposed on nomogram scales and the plot
	is animated to allow on the fly changes to distribution representation and to 
	enable outcome calculation.
Tools are provided for
  (1) nonparametric regression (kernel, local linear),
  (2) semiparametric regression (single index, additive models), and
  (3) quantile regression (linear, kernel).
Functions to fit Gaussian linear model by maximising the
        residual log likelihood where the covariance structure can be
        written as a linear combination of known matrices.  Can be used
        for multivariate models and random effects models.  Easy
        straight forward manner to specify random effects models,
        including random interactions. Code now optimised to use
        Sherman Morrison Woodbury identities for matrix inversion in
        random effects models. We've added the ability to fit models
        using any kernel as well as a function to return the mean and
        covariance of random effects conditional on the data (BLUPs).
The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors' paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.
Perform a supervised data analysis on a database through a 'shiny' graphical interface. It includes methods such as linear regression, penalized regression, k-nearest neighbors, decision trees, ada boosting, extreme gradient boosting, random forest, neural networks, deep learning and support vector machines.
Compiling regression results into a publishable format, conducting post-hoc hypothesis testing, and plotting moderating effects (the effect of X on Y becomes stronger/weaker as Z increases).
Performs Random Subspace Method (RSM) for high-dimensional linear regression to obtain variable importance measures. The final model is chosen based on validation set or Generalized Information Criterion.
Implementation of the methods described in the paper with the above title: Langsrud, Ø. (2019) <doi:10.1007/s11222-018-9848-9>. Open view-only version at <https://rdcu.be/bfeWQ>. The package can be used to generate synthetic or hybrid continuous microdata, and the relationship to the original data can be controlled in several ways. A function for replacing suppressed tabular cell frequencies with decimal numbers is included.
Functions for fitting linear and generalized linear models with variable selection. The functions can automatically do Stepwise Regression, Lasso or Elastic Net as variable selection methods. Lasso and Elastic net are improved and handle factors better (they can either include or exclude all factor levels).
Uses both ridge and lasso penalties (and extensions) to penalize
    specific parameters in structural equation models. The package offers additional
    cost functions, cross validation, and other extensions beyond traditional structural
    equation models. Also contains a function to perform exploratory mediation (XMed).
Computes linear Bayesian spectral estimates from multirate
	data for second-order stationary time series. Provides credible
	intervals and methods for plotting various spectral estimates.
For a sequence of event occurence times, we are interested in
  finding subsequences in it that are too "regular". We define regular as being
  significantly different from a homogeneous Poisson process. The departure from
  the Poisson process is measured using a L1 distance. See Di and Perlman 2007
  for more details.
Functions for unary and binary regression tests
Tools for linear, nonlinear and nonparametric regression
             and classification.  Novel graphical methods for assessment 
             of parametric models using nonparametric methods. One 
             vs. All and All vs. All multiclass classification, optional
             class probabilities adjustment.  Nonparametric regression 
             (k-NN) for general dimension, local-linear option.  Nonlinear 
             regression with Eickert-White method for dealing with 
             heteroscedasticity.  Utilities for converting time series
             to rectangular form.  Utilities for conversion between
             factors and indicator variables.  Some code related to
             "Statistical Regression and Classification: from Linear
             Models to Machine Learning", N. Matloff, 2017, CRC,
             ISBN 9781498710916.
It performs regularized simultaneous component based data
    integration for multiblock data.
Population genetic data such as 'Single Nucleotide
        Polymorphisms' (SNPs) is often used to identify genomic regions
        that have been under recent natural or artificial selection
        and might provide clues about the molecular mechanisms of adaptation. 
        One approach, the concept of an 'Extended Haplotype Homozygosity' (EHH), 
        introduced by (Sabeti 2002) <doi:10.1038/nature01140>, has given rise to 
        several statistics designed for whole genome scans. 
        The package provides functions to compute three of these,
        namely: 'iHS' (Voight 2006) <doi:10.1371/journal.pbio.0040072> for
        detecting positive or 'Darwinian' selection within a single population as well as
        'Rsb' (Tang 2007) <doi:10.1371/journal.pbio.0050171> and 
        'XP-EHH' (Sabeti 2007) <doi:10.1038/nature06250>, targeted
        at differential selection between two populations. 
        Various plotting functions are also included to facilitate
        visualization and interpretation of these statistics. 
        Due to changes in the API, albeit mostly minor, 
        this version is not compatible with versions 2.0.X.
Contains example data for the 'rehh' package.
Facilitates replication of Twitter-based research by handling
    common programming tasks needed when downloading tweets.  Specifically, it ensures a 
    user does not exceed Twitter’s rate limits, and it saves tweets in moderately sized files.  
    While a user could perform these tasks in their own code, doing so may be beyond the 
    capabilities of many users.
Traditional risk prediction only utilizes baseline factors known 
    to be associated with the disease. Given that longitudinal information are
    routinely measured and documented for patients, it is worthwhile to make
    full use of these data. The available longitudinal biomarker data will 
    likely improve prediction. However, repeated biomarker collection could be 
    costly and inconvenient, and risk prediction for patients at a later time 
    could delay necessary medical decisions. Thus, there is a trade-off between
    high quality prediction and cost. This package implements a cost-effective 
    statistical procedure that recursively incorporates comprehensive 
    longitudinal information into the risk prediction model, taking into 
    account the cost of delaying the decision to a follow-up time when more 
    information is available. The statistical methods are described in the 
    following paper: Pan, Y., Laber, E., Smith, M., Zhao, Y. (2018). Reinforced 
    risk prediction with budget constraint: application to electronic health 
    records data. Manuscript submitted for publication.
Implements reinforcement learning environments and algorithms as described in Sutton & Barto (1998, ISBN:0262193981).
    The Q-Learning algorithm can be used with function approximation, 
    eligibility traces (Singh & Sutton (1996) <doi:10.1007/BF00114726>) 
    and experience replay (Mnih et al. (2013) <arXiv:1312.5602>).
Performs model-free reinforcement learning in R. This implementation enables the learning
    of an optimal policy based on sample sequences consisting of states, actions and rewards. In 
    addition, it supplies multiple predefined reinforcement learning algorithms, such as experience 
    replay. Methodological details can be found in Sutton and Barto (1998) <ISBN:0262039249>.
Functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470772689.html>.
Search R files for not installed packages and run install.packages.
Application of reinsurance treaties to claims portfolios. 
            The package creates a class Claims whose objective is to 
            store claims and premiums, on which different treaties can be applied.
            A statistical analysis can then be applied to measure the impact of
            reinsurance, producing a table or graphical output. This package can
            be used for estimating the impact of reinsurance on several portfolios
            or for pricing treaties through statistical analysis. Documentation
            for the implemented methods can be found in "Reinsurance: Actuarial
            and Statistical Aspects" by Hansjöerg Albrecher, Jan Beirlant,
            Jozef L. Teugels (2017, ISBN: 978-0-470-77268-3) and 
            "REINSURANCE: A Basic Guide to Facultative and Treaty Reinsurance"
            by Munich Re (2010) <https://www.munichre.com/site/mram/get/documents_E96160999/mram/assetpool.mr_america/PDFs/3_Publications/reinsurance_basic_guide.pdf>.
Derives point estimates with confidence intervals for Bennett et als S, Cohen's kappa, Conger's kappa, Fleiss' kappa, Gwet's AC, intraclass correlation coefficients, Krippendorff's alpha, Scott's pi, the standard error of measurement, and weighted kappa.
Item analysis with alpha standard error and principal axis
        factoring for continuous variable scales (with plots).
In confirmatory factor analysis (CFA), structural constraints
    typically ensure that the model is identified up to all possible reflections,
    i.e., column sign changes of the matrix of loadings. Such reflection invariance
    is problematic for Bayesian CFA when the reflection modes are not well separated
    in the posterior distribution. Imposing rotational constraints -- fixing
    some loadings to be zero or positive in order to pick a factor solution that
    corresponds to one reflection mode -- may not provide a satisfactory solution
    for Bayesian CFA. The function 'relabel' uses the relabeling algorithm of
    Erosheva and Curtis to correct for sign invariance in MCMC draws from CFA
    models. The MCMC draws should come from Bayesian CFA models that are fit without
    rotational constraints.
Provides several metrics for assessing relative importance in linear models. These can be printed, plotted and bootstrapped. The recommended metric is lmg, which provides a decomposition of the model explained variance into non-negative contributions. There is a version of this package available that additionally provides a new and also recommended metric called pmvd. If you are a non-US user, you can download this extended version from Ulrike Groempings web site.
Functions to safely map from a vector of keys to a vector of values, determine properties of a given relation, or ensure a relation conforms to a given type, such as many-to-many, one-to-many, injective, surjective, or bijective. Permits default return values for use similar to a vectorised switch statement, as well as safely handling large vectors, NAs, and duplicate mappings.
Inference of relatedness coefficients from a bi-allelic genotype matrix using a Maximum Likelihood estimation, Laporte, F., Charcosset, A. and Mary-Huard, T. (2017) <doi:10.1111/biom.12634>.
Data structures and algorithms for k-ary relations with
  arbitrary domains, featuring relational algebra, predicate functions,
  and fitters for consensus relations.
Extends the glmnet package with "relaxation", done by running glmnet once on the entire predictor matrix, then again on each different subset of variables from along the regularization path. Relaxation may lead to improved prediction accuracy for truly sparse data generating models, as well as fewer false positives (i.e. fewer noncontributing predictors in the final model). Penalty may be lasso (alpha = 1) or elastic net (0 < alpha < 1). For this version, family may be "gaussian" or "binomial" only. Takes advantage of fast FORTRAN code from the glmnet package.
Relaxed Lasso is a generalisation of the Lasso shrinkage
        technique for linear regression. Both variable selection and
        parameter estimation is achieved by regular Lasso, yet both
        steps do not necessarily use the same penalty parameter. The
        results include all standard Lasso solutions but allow often
        for sparser models while having similar or even slightly better
        predictive performance if many predictor variables are present.
        The package depends on the LARS package.
Tools for the comparison of distributions. This includes nonparametric estimation of the relative distribution PDF and CDF and numerical summaries as described in "Relative Distribution Methods in the Social Sciences" by Mark S. Handcock and Martina Morris, Springer-Verlag, 1999, Springer-Verlag, ISBN 0387987789.
This function computes the relative entropy (H) as an index for qualitative variation of a factor.
The purpose of 'Relexon' is to allow R users to pull data from the UK's Balancing Mechanism Reporting Service, otherwise known as 'Elexon', using the 'BMRS' API <https://www.bmreports.com/bmrs/?q=help/about-us>. This can range from different types of electricity generation, how much electricity is used in a 5-minute period, imports, temperatures and forecasts. Users will require an 'Elexon' key, which are freely available by registering at <https://www.elexonportal.co.uk>.
Functions for estimating parameters in software reliability models.
             Only infinite failure models are implemented so far.
A variety of tools useful for performing structural
        reliability analysis, such as with structure function and
        system signatures.  Plans to expand more widely.
A collection of utilities for some reliability
        models/probability distributions.
Functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets.
Performs Principal Components Analysis (also known as PCA) dimensionality reduction in the context of a linear regression. In most cases, PCA dimensionality reduction is performed independent of the response variable for a regression. This captures the majority of the variance of the model's predictors, but may not actually be the optimal dimensionality reduction solution for a regression against the response variable. An alternative method, optimized for a regression against the response variable, is to use both PCA and a relative importance measure. This package applies PCA to a given data frame of predictors, and then calculates the relative importance of each PCA factor against the response variable. It outputs ordered factors that are optimized for model fit. By performing dimensionality reduction with this method, an individual can achieve a the same r-squared value as performing just PCA, but with fewer PCA factors. References: Yuri Balasanov (2017) <https://ilykei.com>.
Makes relationship inference involving DNA mixtures with unknown profiles.
A set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations.
Contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting  and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables.
Classes and functions for analyzing the performance of portfolios relative to a benchmark.
Calculate endogenous network effects in event sequences and fit relational event models (REM): Using network event sequences (where each tie between a sender and a target in a network is time-stamped), REMs can measure how networks form and evolve over time. Endogenous patterns such as popularity effects, inertia, similarities, cycles or triads can be calculated and analyzed over time.
A small wrapper on 'regexpr' to extract the matches and
    captured groups from the match of a regular expression to a character
    vector.
Wrappers on 'regexpr' and 'gregexpr' to return the match
    results in tidy data frames.
An 'RStudio' addin providing shortcuts for writing in 'Markdown'. This package provides a series of 
             functions that allow the user to be more efficient when using 'Markdown'. For example, you can select 
             a word, and put it in bold or in italics, or change the alignment of elements inside you Rmd. The idea
             is to map all the functionalities from 'remedy' on keyboard shortcuts, so that it provides an interface 
             close to what you can find in any other text editor.
Insert/extract text "reminders" into/from function source code 
    comments or as the "comment" attribute of any object.  
    The former can be handy in development as reminders of e.g. argument
    requirements, expected objects in the calling environment, required options
    settings, etc. The latter can be used to provide information of the object and 
    as simple manual "tooltips" for users, among other things.
Implements TRACDS (Temporal Relationships 
    between Clusters for Data Streams), a generalization of 
    Extensible Markov Model (EMM). TRACDS adds a temporal or order model
    to data stream clustering by superimposing a dynamically adapting
    Markov Chain. Also provides an implementation of EMM (TRACDS on top of tNN 
    data stream clustering). Development of this 
    package was supported in part by NSF IIS-0948893 and R21HG005912 from 
    the National Human Genome Research Institute.
remMap is developed for fitting multivariate response regression models under the high-dimension-low-sample-size setting
Empirical orthogonal teleconnections in R.
    'remote' is short for 'R(-based) EMpirical Orthogonal TEleconnections'.
    It implements a collection of functions to facilitate empirical
    orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections
    (EOTs) denote a regression based approach to decompose spatio-temporal
    fields into a set of independent orthogonal patterns. They are quite
    similar to Empirical Orthogonal Functions (EOFs) with EOTs producing
    less abstract results. In contrast to EOFs, which are orthogonal in both
    space and time, EOT analysis produces patterns that are orthogonal in
    either space or time.
A set of utilities for client/server computing with R, controlling
    a remote R session (the server) from a local one (the client).  Simply set
    up a server (see package vignette for more details) and connect to it from
    your local R session ('RStudio', terminal, etc).  The client/server
    framework is a custom 'REPL' and runs entirely in your R session without the
    need for installing a custom environment on your system.  Network
    communication is handled by the 'ZeroMQ' library by way of the 'pbdZMQ'
    package.
Download and install R packages stored in 'GitHub',
    'BitBucket', or plain 'subversion' or 'git' repositories. This package
    provides the 'install_*' functions in 'devtools'.
    Indeed most of the code was copied over from 'devtools'.
ENA (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is a method used to identify meaningful and quantifiable patterns in discourse or reasoning. ENA moves beyond the traditional frequency-based assessments by examining the structure of the co-occurrence, or connections in coded data. Moreover, compared to other methodological approaches, ENA has the novelty of (1) modeling whole networks of connections and (2) affording both quantitative and qualitative comparisons between different network models.   Shaffer, D.W., Collier, W., & Ruis, A.R. (2016) <doi:10.18608/jla.2016.33.3>.
Fits linear models with endogenous regressor using latent instrumental variable approaches. 
    The methods included in the package are Lewbel's (1997) <doi:10.2307/2171884> higher moments approach as well as 
    Lewbel's (2012) <doi:10.1080/07350015.2012.643126> heteroscedasticity approach, Park and Gupta's (2012) <doi:10.1287/mksc.1120.0718> joint estimation method 
    that uses Gaussian copula and Kim and Frees's (2007) <doi:10.1007/s11336-007-9008-1> multilevel generalized
    method of moment approach that deals with endogeneity in a multilevel setting.
    These are statistical techniques to address the endogeneity problem where no external instrumental variables are needed.
    Note that with version 2.0.0 sweeping changes were introduced which greatly improve functionality and usability but break backwards compatibility.
Peaks Over Threshold (POT) or 'methode du renouvellement'. The distribution for the exceedances can be chosen, and heterogeneous data (including historical data or block data) can be used in a Maximum-Likelihood framework.
Supports calculations and visualization for renewable power systems and the environment. Analysis and graphical tools for DC and AC circuits and their use in electric power systems. Analysis and graphical tools for thermodynamic cycles and heat engines, supporting efficiency calculations in coal-fired power plants, gas-fired power plants. Calculations of carbon emissions and atmospheric CO2 dynamics. Analysis of power flow and demand for the grid, as well as power models for microgrids and off-grid systems. Provides resource and power generation for hydro power, wind power, and solar power.
Provides an R interface to the NCBI's 'EUtils' API, 
    allowing users to search databases like 'GenBank' 
    <https://www.ncbi.nlm.nih.gov/genbank/> and 'PubMed' 
    <https://www.ncbi.nlm.nih.gov/pubmed/>, process the 
    results of those searches and pull data into their R sessions.
A dependency management toolkit for R. Using 'renv', you can create
    and manage project-local R libraries, save the state of these libraries to
    a 'lockfile', and later restore your library as required. Together, these
    tools can help make your projects more isolated, portable, and reproducible.
Provides a general routine, envMU, which allows  estimation of the M envelope of span(U) given root n consistent estimators of M and U. The routine envMU does not presume a model.  This package implements response envelopes,  partial response envelopes,  envelopes in the predictor space,  heteroscedastic envelopes,  simultaneous envelopes,  scaled response envelopes,  scaled envelopes in the predictor space,  groupwise envelopes, weighted envelopes,  envelopes in logistic regression and envelopes in Poisson regression. For each of these model-based routines the package provides inference tools including bootstrap, cross validation, estimation and prediction, hypothesis testing on coefficients are included except for weighted envelopes. Tools for selection of dimension include AIC, BIC and likelihood ratio testing.   Background is available at Cook, R. D., Forzani, L. and Su, Z. (2016) <doi:10.1016/j.jmva.2016.05.006>. Optimization is based on a clockwise coordinate descent algorithm.
Tools for performing the leaf reordering for the dendrogram that preserves the hierarchical clustering result and at the same time tries to group instances from the same class together.
The function RepaymentPlan() calculates repayment schedule for repayment/mortgage plans.
Various functions to fit models for non-normal repeated
    measurements, such as Binary Random Effects Models with Two Levels of Nesting,
    Bivariate Beta-binomial Regression Models, Marginal Bivariate Binomial Regression Models,
    Cormack capture-recapture models, Continuous-time Hidden Markov Chain Models, 
    Discrete-time Hidden Markov Chain Models,
    Changepoint Location Models using a Continuous-time Two-state Hidden Markov Chain,
    generalized nonlinear autoregression models, multivariate Gaussian copula models,
    generalized non-linear mixed models with one random effect,  
    generalized non-linear mixed models using h-likelihood for one random effect, 
    Repeated Measurements Models for Counts with Frailty or Serial Dependence,
    Repeated Measurements Models for Continuous Variables with Frailty or Serial Dependence,
    Ordinal Random Effects Models with Dropouts, marginal homogeneity models for square
    contingency tables, correlated negative binomial models with Kalman update.
    References include Lindey's text books, 
    JK Lindsey (2001) <isbn-10:0198508123> and JK Lindsey (1999) <isbn-10:0198505590>.
Global tests for expression data of high-dimensional sets of
    molecular features.
Utilities for accessing RePEc (Research Papers in Economics)
    through a RESTful API. You can request a code and get detailed
    information at the following page: <https://ideas.repec.org/api.html>.
Estimation of Bayes and local Bayes false discovery rates for
      replicability analysis (Heller & Yekutieli, 2014 <doi:10.1214/13-AOAS697> ;
      Heller at al., 2015 <doi: 10.1093/bioinformatics/btu434>).
Create presentations and display them inside the R 'REPL'
    (Read-Eval-Print loop), aka the R console. Presentations can be written in
    'RMarkdown' or any other text format. A set of convenient navigation options
    as well as code evaluation during a presentation is provided. It is great
    for tech talks with live coding examples and tutorials. While this is not a
    replacement for standard presentation formats, it's old-school looks might
    just be what sets it apart. This project has been inspired by the
    'REPLesent' project for presentations in the 'Scala' 'REPL'.
For a multisite replication project, computes metrics and confidence intervals representing: (1) the probability that the original study would observe an estimated effect size as extreme or more extreme than it actually did, if in fact the original study is statistically consistent with the replications; (2) the probability of a true effect of scientifically meaningful size in the same direction as the estimate the original study; and (3) the probability of a true effect of meaningful size in the direction opposite the original study's estimate. Additionally computes older metrics used in replication projects (namely expected agreement in "statistical significance" between an original study and replication studies as well as prediction intervals for the replication estimates). See Mathur and VanderWeele (2017; <https://osf.io/apnjk/>) for details.
Performs comparative bioavailability-calculations for the EMA's Average Bioequivalence
  with Expanding Limits (ABEL). Implemented are 'Method A' and 'Method B', detection of outliers.
  If the design allows, assessment of the empiric Type I Error and iteratively adjusting alpha to
  control the consumer risk.
  Average Bioequivalence (ABE) - optionally with tighter (EMA: NTIDs) or wider limits (GCC: Cmax) -
  is implemented as well.
Test for effects of  both individual factors and their interaction on  replicated spatial patterns in a two factorial design, as explained in Ramon et al. (2016) <doi:10.1111/ecog.01848>.
Allows for the computation of a prior predictive p-value to test replication of relevant features of original studies. Relevant features are captured in informative hypotheses. The package also allows for the computation of power. The statistical underpinnings are described in Zondervan-Zwijnenburg (2019) <doi:10.31234/osf.io/uvh5s>.
A common problem faced by journal reviewers and authors is the question of
     whether the results of a replication study are consistent with the original
     published study. One solution to this problem is to examine the effect size
     from the original study and generate the range of effect sizes that could
     reasonably be obtained (due to random sampling) in a replication attempt
     (i.e., calculate a replication interval). If a replication effect size falls
     outside the replication interval, then that effect likely did not occur
     due to the effects of sampling error alone. Alternatively, if a replication
     effect size falls within the replication interval, then the replication
     effect could have reasonably occurred due to the effects of sampling error
     alone. This package has functions that calculate the replication interval for 
     the correlation (i.e., r), standardized mean difference (i.e., d-value), and mean. 
     The calculations used in version 2.0.0 and onward differ from past calculations 
     due to feedback during the journal review process. The new calculations allow 
     for a more precise interpretation of the replication interval.
Create, Plot and Compare Replication Timing Profiles. The method is described in Muller et al., (2014) <doi: 10.1093/nar/gkt878>.
Patches to use 'dplyr' on remote data sources ('SQL' databases,
   'Spark' 2.0.0 and above) in a reliable "generic" fashion (generic meaning
   user code works similarly on all such sources, without needing per-source
   adaption).  Due to the fluctuating nature of 'dplyr'/'dbplyr'/'rlang' 'APIs' this package
   is going into maintenance mode.  Most of the 'replyr' functions are now 
   done better by one of the non-monolithic replacement packages: 'wrapr', 'seplyr', 'rquery',
   or 'cdata'.
Tools to load 'R' packages
    and automatically generate BibTeX files citing them as well as load and
    cache plain-text and 'Excel' formatted data stored on 'GitHub', and
    from other sources.
A data manager meant to avoid manual storage/retrieval of
    data to/from the file system. It builds one (or more) centralized
    repository where R objects are stored with rich annotations,
    including corresponding code chunks, and easily searched and
    retrieved.
Generates a project and repo for easy initialization of a GitHub repo for R workshops. The repo includes a README with instructions to ensure that all users have the needed packages, an 'RStudio' project with the right directories and the proper data. The repo can then be used for hosting code taught during the workshop.
Fits linear models to repeated ordinal scores using GEE methodology.
Provides a system for reporting messages, which provides certain useful features over the standard R system, such as the incorporation of output consolidation, message filtering, assertions, expression substitution, automatic generation of stack traces for debugging, and conditional reporting based on the current "output level".
Provides an easy way to report the results of regression analysis, including:
    1. Proportional hazards regression from function 'coxph' of package 'survival';
    2. Conditional logistic regression from function 'clogit' of package 'survival';
    3. Ordered logistic regression from function 'polr' of package 'MASS';
    4. Binary logistic regression from function 'glm' of package 'stats';
    5. Linear regression from function 'lm' of package 'stats';
    6. Risk regression model for survival analysis with competing risks from function 'FGR' of package 'riskRegression';
    7. Multilevel model from function 'lme' of package 'nlme'.
Provides an easy way to report the results of ROC analysis, including:
  1. an ROC curve. 2. the value of Cutoff, AUC (Area Under Curve), ACC (accuracy),
  SEN (sensitivity), SPE (specificity),
  PLR (positive likelihood ratio), NLR (negative likelihood ratio),
  PPV (positive predictive value), NPV (negative predictive value).
reportRx is a set of tools that integrates with LaTeX and knitr to
    automatically generate generate reproducible clinical reports. Fuctions to
    automatically produce demoraphic tables, outcome summaries, univariate and
    multivariate analysis results and more are included.
Assists in writing reports and
        presentations by providing a frame work that brings together
        existing R, LaTeX/.docx and Pandoc tools. The package is
        designed to be used with RStudio, MiKTex/Tex Live/LibreOffice,
        knitr, knitcitations, Pandoc and pander. The user will want to
        download these free programs/packages to maximize the
        effectiveness of the reports package.  Functions with two
        letter names are general text formatting functions for copying
        text from articles for inclusion as a citation.
These functions are especially helpful when writing reports of data analysis using Sweave.
An R Interface to 'EPP-lab' v1.0. 'EPP-lab' is a Java program for
    projection pursuit using genetic algorithms written by Alain Berro and S. Larabi
    Marie-Sainte and is included in the package. The 'EPP-lab' sources are available
    under https://github.com/fischuu/EPP-lab.git.
String and binary representations of objects for several formats /
    mime types.
Contains workhorse function jrparams(), as well as two
        helper functions Mboxtest() and JRsMahaldist(), and four
        example data sets.
Reproducible research tools automates the creation of an analysis directory structure and work flow. There are R markdown
  skeletons which encapsulate typical analytic work flow steps. Functions will create appropriate modules which may
  pass data from one step to another.
Convenience wrapper that uses the 'rmarkdown' package to render
  small snippets of code to target formats that include both code and output.
  The goal is to encourage the sharing of small, reproducible, and runnable
  examples on code-oriented websites, such as <https://stackoverflow.com> and
  <https://github.com>, or in email. The user's clipboard is the default source
  of input code and the default target for rendered output. 'reprex' also
  extracts clean, runnable R code from various common formats, such as
  copy/paste from an R session.
Includes data analysis functions (e.g., to calculate effect sizes and 95% Confidence Intervals (CI) on Standardised Effect Sizes (d) for ABBA cross-over repeated-measures experimental designs), data presentation functions (e.g., density curve overlaid on histogram), and the data sets analyzed in different research papers in software engineering (e.g., related to software defect prediction or multi-site experiment concerning the extent to which structured abstracts were clearer and more complete than conventional abstracts) to streamline reproducible research in software engineering.
Collection of high-level, robust, machine- and OS-independent tools
    for making deeply reproducible and reusable content in R.
    This includes light weight package management (similar to 'packrat' and
    'checkpoint', but more flexible, lightweight, simpler yet less tested than both), 
    tools for caching, downloading and verifying or writing checksums, post-processing 
    of common spatial datasets, and accessing GitHub repositories. 
    Some features are still under active development.
Transform coordinates via 'PROJ' using the library directly, by 
 wrapping the 'proj4' package. The 'reproj' function handles the need for
 radian units for either source or target and allows removing an explicit 
 source definition in methods that extend the generic. The 'PROJ' library is available 
 at <https://proj4.org/>.
Predicting regulatory DNA elements based on epigenomic signatures. This package is more of a set of building blocks than a direct solution. REPTILE regulatory prediction pipeline is built on this R package. See <https://github.com/yupenghe/REPTILE> for more information.
Recursive lists in the form of R objects, 'JSON',
    and 'XML', for use in teaching and examples. Examples include color
    palettes, Game of Thrones characters, 'GitHub' users and repositories,
    music collections, and entities from the Star Wars universe. Data from
    the 'gapminder' package is also included, as a simple data frame and
    in nested and split forms.
In order to facilitate parsing of http requests and creating 
    appropriate responses this package provides two classes to handle a lot of
    the housekeeping involved in working with http exchanges. The infrastructure
    builds upon the 'rook' specification and is thus well suited to be combined
    with 'httpuv' based web servers.
This package contains the function run.eqs() which calls
        an EQS script file, executes the EQS estimation, and, finally,
        imports the results as R objects. These two steps can be
        performed separately: call.eqs() calls and executes EQS,
        whereas read.eqs() imports existing EQS outputs as objects into
        R. It requires EQS 6.2 (build 98 or higher).
High level and easy 'HTTP' client for 'R'. Provides functions for
    building 'HTTP' queries, including query parameters, body requests, headers,
    authentication, and more.
Modularizes source code. Keeps the global environment clean,
    explicifies interdependencies. Inspired by 'RequireJS'<http://requirejs.org/>.
General purpose R client for 'ERDDAP' servers. Includes
    functions to search for 'datasets', get summary information on
    'datasets', and fetch 'datasets', in either 'csv' or 'netCDF' format.
    'ERDDAP' information: 
    <https://upwell.pfeg.noaa.gov/erddap/information.html>.
Contains three functions that access
    environmental data from any 'ERDDAP' data web service. The rxtracto() function extracts
    data along a trajectory for a given "radius" around the point. The
    rxtracto_3D() function extracts data in a box. The rxtractogon() function
    extracts data in a polygon. All of those three function use the 'rerddap' package
    to extract the data, and should work with any 'ERDDAP' server.
    There are also two functions, plotBBox() and plotTrack() that use the 'plotdap'
    package to simplify the creation of maps of the data.
Executes a post-rotation algorithm that REorders and/or REflects FACTors (REREFACT) for each replication of a simulation study with exploratory factor analysis.
R-RerF (aka Randomer Forest (RerF) or Random Projection
  Forests) is an algorithm developed by Tomita (2016) <arXiv:1506.03410v2>
  which is similar to Random Forest - Random Combination (Forest-RC)
  developed by Breiman (2001) <doi:10.1023/A:1010933404324>.  Random
  Forests create axis-parallel, or orthogonal trees. That is, the feature
  space is recursively split along directions parallel to the axes of the
  feature space. Thus, in cases in which the classes seem inseparable
  along any single dimension, Random Forests may be suboptimal.  To
  address this, Breiman also proposed and characterized Forest-RC, which
  uses linear combinations of coordinates rather than individual
  coordinates, to split along.  This package, 'rerf', implements RerF
  which is similar to Forest-RC.  The difference between the two
  algorithms is where the random linear combinations occur: Forest-RC
  combines features at the per tree level whereas RerF takes linear
  combinations of coordinates at every node in the tree.
Fits a linear excess relative risk model by maximum likelihood, possibly including several variables and allowing for lagged exposures. Allow time dependent covariates.
Bootstrap, permutation tests, and other resampling functions,
	featuring easy-to-use syntax.
Package of data sets from "Mathematical Statistics
    with Resampling in R" (1st Ed. 2011, 2nd Ed. 2018) by Laura Chihara and Tim Hesterberg.
This package is the R implementation of the Autoencoder-based Residual Deep Network that is based on this paper (https://arxiv.org/abs/1812.11262).
Implementation of functions for spectral similarity/dissimilarity
    analysis and memory-based learning (MBL) for non-linear modeling
    in complex spectral datasets. In chemometrics MBL is also known as local modeling.
Measure single-storage water supply system performance using resilience,
    reliability, and vulnerability metrics; assess storage-yield-reliability
    relationships; determine no-fail storage with sequent peak analysis; optimize
    release decisions for water supply, hydropower, and multi-objective reservoirs
    using deterministic and stochastic dynamic programming; generate inflow
    replicates using parametric and non-parametric models; evaluate inflow
    persistence using the Hurst coefficient.
Flexibly restructure and aggregate data using 
  just two functions: melt and cast.
Flexibly restructure and aggregate data using just two
    functions: melt and 'dcast' (or 'acast').
A tool for learning how to use the functions, melt,
        acast/dcast, and ddply.
Electrical properties of resistor networks using matrix methods.
Resource Selection (Probability) Functions
  for use-availability wildlife data
  based on weighted distributions as described in
  Lele and Keim (2006) <doi:10.1890/0012-9658(2006)87%5B3021:WDAEOR%5D2.0.CO;2>,
  Lele (2009) <doi:10.2193/2007-535>,
  and Solymos & Lele (2016) <doi:10.1111/2041-210X.12432>.
Provides tools to enable the researcher to more precisely conduct
    respirometry experiments. Strong emphasis is on aquatic respirometry. Tools
    focus on helping the researcher setup and conduct experiments. Functions for
    analysis of resulting respirometry data are also provided. This package
    provides tools for intermittent, flow-through, and closed respirometry
    techniques.
Contains three functions that query AuriQ Systems' Essentia Database and return the results in R. 'essQuery' takes a single Essentia command and captures the output in R, where you can save the output to a dataframe or stream it directly into additional analysis. 'read.essentia' takes an Essentia script and captures the output csv data into R, where you can save the output to a dataframe or stream it directly into additional analysis. 'capture.essentia' takes a file containing any number of Essentia commands and captures the output of the specified statements into R dataframes. Essentia can be downloaded for free at http://www.auriq.com/documentation/source/install/index.html.
Contains easy scripts which can be used to quickly create GUI windows for 'Rcmdr' Plugins. No knowledge about Tcl/Tk is required to make use of these scripts (These scripts are a generalisation of the template scripts in the 'RcmdrPlugin.BiclustGUI' package).
Eurostat is the statistical office of the European Union and provides high quality statistics for Europe.
             Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/data/database>). 
             The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX <https://sdmx.org>) Web Services 
             (<https://ec.europa.eu/eurostat/web/sdmx-web-services/about-this-service>) to search and download data from 
             the Eurostat database using the SDMX standard.
Models a RESTful service as if it were a nested R list.
Provides the user with functions to develop their trading strategy,
    uncover actionable trading ideas, and monitor consensus shifts with
    crowdsourced earnings and economic estimate data directly from
    <www.estimize.com>. Further information regarding the web services this
    package invokes can be found at <www.estimize.com/api>.
The restlos package provides algorithms for robust estimation of location (mean and mode) and scatter based on minimum spanning trees (pMST), self-organizing maps (Flood Algorithm), Delaunay triangulations (RDELA), and nested minimum volume convex sets (MVCH). The functions are also suitable for outlier detection.
Debugging with restore points instead of break points. A restore
    point stores all local variables when called inside a function. The stored
    values can later be retrieved and evaluated in a modified R console that
    replicates the function's environment. To debug step by step, one can simply
    copy & paste the function body from the R script. Particularly convenient
    in combination with "RStudio". See the "Github" page inst/vignettes for a
    tutorial.
A fast Gibbs sampler for multivariate normal with affine constraints.
Allow for easy-to-use testing or evaluating of linear equality and inequality 
 restrictions about parameters and effects in (generalized) linear statistical models.
Using a database, LaTeX and R easily build attractive resumes.
Interface to 'Python' modules, classes, and functions. When calling
    into 'Python', R data types are automatically converted to their equivalent 'Python'
    types. When values are returned from 'Python' to R they are converted back to R
    types. Compatible with all versions of 'Python' >= 2.7.
Reaction time analysis by maximum likelihood
Reconstructs retinae by morphing a flat surface with cuts (a
    dissected flat-mount retina) onto a curvilinear surface (the standard retinal
    shape). It can estimate the position of a point on the intact adult retina
    to within 8 degrees of arc (3.6% of nasotemporal axis). The coordinates in
    reconstructed retinae can be transformed to visuotopic coordinates.
Using 'Digital Object Identifiers', check for retracted (or otherwise updated) articles using 'Open Retractions' <http://openretractions.com>.
Provides tools for working with Type S (Sign) and
    Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000)
    <doi.org/10.1007/s001800000040> and
    Gelman & Carlin (2014) <doi.org/10.1177/1745691614551642>.
    In addition to simply calculating the probability of
    Type S/M error, the package includes functions for calculating these errors
    across a variety of effect sizes for comparison, and recommended sample size
    given "tolerances" for Type S/M errors. To improve the speed of these
    calculations, closed forms solutions for the probability of a Type S/M error
    from Lu, Qiu, and Deng (2018) <doi.org/10.1111/bmsp.12132>
    are implemented. As of 1.0.0, this includes
    support only for simple research designs. See the
    package vignette for a fuller exposition on how Type S/M errors arise in
    research, and how to analyze them using the type of design analysis proposed
    in the above papers.
An interface to NCBI databases such as PubMed, GenBank, or GEO
    powered by the Entrez Programming Utilities (EUtils). The nine EUtils
    provide programmatic access to the NCBI Entrez query and database
    system for searching and retrieving biological data.
Simplified scenario testing and sensitivity analysis with R via a
    generalized function for one-factor-at-a-time (OFAT) sensitivity analysis,
    evaluation of parameter sets and (sampled) parameter permutations. Options
    for formatting output and parallel processing are also provided.
Provides functions for the Bayesian analysis of extreme value
    models.  The 'rust' package <https://cran.r-project.org/package=rust> is
    used to simulate a random sample from the required posterior distribution.
    The functionality of 'revdbayes' is similar to the 'evdbayes' package
    <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain
    Monte Carlo ('MCMC') methods for posterior simulation.  Also provided
    are functions for making inferences about the extremal index, using 
    the K-gaps model of Suveges and Davison (2010) <doi:10.1214/09-AOAS292>.
    Also provided are d,p,q,r functions for the Generalised Extreme Value 
    ('GEV') and Generalised Pareto ('GP') distributions that deal 
    appropriately with cases where the shape parameter is very close to zero.
Computation of (direct and indirect) revealed preferences, fast non-parametric tests of rationality axioms (WARP, SARP, GARP), simulation of axiom-consistent data, and detection of axiom-consistent subpopulations. Rationality tests follow Varian (1982) <doi:10.2307/1912771>, axiom-consistent subpopulations follow Crawford and Pendakur (2012) <doi:10.1111/j.1468-0297.2012.02545.x>.
R Markdown format for 'reveal.js' presentations, a framework 
  for easily creating beautiful presentations using HTML.
An implementation of the reverse ecology framework. Reverse ecology
    refers to the use of genomics to study ecology with no a priori assumptions
    about the organism(s) under consideration, linking organisms to their
    environment. It allows researchers to reconstruct the metabolic networks and
    study the ecology of poorly characterized microbial species from their
    genomic information, and has substantial potentials for microbial community
    ecological analysis.
Decoupled (e.g. separate averages) and censored (e.g. > 100 species) variables are continually reported by many well-established organizations (e.g. World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), World Bank, and various national censuses).  The challenge therefore is to infer what the original data could have been given summarized information.  We present an R package that reverse engineers decoupled and/or censored count data with two main functions.  The cnbinom.pars function estimates the average and dispersion parameter of a censored univariate frequency table.  The rec function reverse engineers summarized data into an uncensored bivariate table of probabilities.
Tests linear regressions for significance reversal through leave-one(multiple)-out and shifting/addition of response values. The paradigm of the package is loosely based on the somewhat forgotten "dfstat" criterion (Belsley, Kuh & Welsch 1980 <doi:10.1002/0471725153.ch2>), which tests influential values in linear models from their effect on statistical inference, i.e. changes in p-value.
Function revgeo() allows you to use the Photon geocoder for OpenStreetMap <http://photon.komoot.de>, Google Maps <http://maps.google.com>, and Bing <https://www.bingmapsportal.com> to reverse geocode coordinate pairs with minimal hassle.
Researchers commonly need to summarize scientific information, a process known as 'evidence synthesis'. The first stage of a synthesis process (such as a systematic review or meta-analysis) is to download a list of references from academic search engines such as 'Web of Knowledge' or 'Scopus'. The traditional approach to systematic review is then to sort these data manually, first by locating and removing duplicated entries, and then screening to remove irrelevant content by viewing titles and abstracts (in that order). 'revtools' provides interfaces for each of these tasks. An alternative approach, however, is to draw on tools from machine learning to visualise patterns in the corpus. In this case, you can use 'revtools' to render ordinations of text drawn from article titles, keywords and abstracts, and interactively select or exclude individual references, words or topics.
Adjusts the weights of survey respondents so that the
        marginal distributions of certain variables fit more closely to
        those from a more precise source (e.g. Census Bureau's data).
A friendly interface for the construction of regular expressions.
Provides convenience functions to communicate with
    an Experigen server: Experigen (<http://github.com/aquincum/experigen>)
    is an online framework for creating  linguistic experiments,
    and it stores the results on a dedicated server. This package can be
    used to retrieve the results from the server, and it is especially
    helpful with registered experiments, as authentication with the server
    has to happen.
Wraps some of the matrix exponentiation 
        utilities from EXPOKIT (<http://www.maths.uq.edu.au/expokit/>), 
        a FORTRAN library that is widely recommended for matrix 
        exponentiation (Sidje RB, 1998. "Expokit: A Software Package
        for Computing Matrix Exponentials." ACM Trans. Math. Softw.
        24(1): 130-156).  EXPOKIT includes functions for 
        exponentiating both small, dense matrices, and large, sparse
        matrices (in sparse matrices, most of the cells have value 0).
        Rapid matrix exponentiation is useful in phylogenetics when we 
        have a large number of states (as we do when we are inferring 
        the history of transitions between the possible geographic 
        ranges of a species), but is probably useful in other ways as 
        well.
Provides an interface to the Facebook API.
Load data by campaigns, ads, ad sets and insights, ad account and business manager 
    from Facebook Marketing API into R. For more details see official documents by Facebook 
	Marketing API <https://developers.facebook.com/docs/marketing-apis/>.
A collection of fast (utility) functions for data analysis. Column- and row- wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. Reference: Tsagris M, Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>.
A collection of fast statistical and utility functions for data analysis. Functions for regression, maximum likelihood, column-wise statistics and many more have been included. C++ has been utilized to speed up the functions.
Downloads and parses data from official site on Department
    of Federal Revenue in Brazil (Receita Federal do Brasil, RFB). Data are 
    collected from RFB site (<http://idg.receita.fazenda.gov.br>). Latest update 
    was in December 15, 2017. There are more than 9 million companies IDs and 
    17 million partner information.
This package facilitates searching for and downloading seismic time series in miniSEED format (a minimalist version of the Standard for the Exchange of Earthquake Data) from International Federation of Digital Seismograph Networks repositories. This package can also be used to gather information about seismic networks (stations, channels, locations, etc) and find historical earthquake data (origins, magnitudes, etc).
Provides the random ferns classifier by Ozuysal, Calonder, Lepetit and Fua (2009) <doi:10.1109/TPAMI.2009.23>, modified for generic and multi-label classification and featuring OOB error approximation and importance measure as introduced in Kursa (2014) <doi:10.18637/jss.v061.i10>.
RFGLS  uses a generalized least-squares method to perform single-marker association analysis, 
  in datasets of nuclear families containing parents, twins, and/or adoptees
Variable selection tools for groups of variables and functional data based on a new grouped variable importance with random forests.
The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatio-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted estimators to improve population, change, and ratio estimates.
An interface to 'figshare' (http://figshare.com), a scientific repository to archive and assign 'DOIs' to data, software, figures, and more.
Japanese stock market from Yahoo!-finance-Japan
An integrated package for constructing random forest prediction intervals using a fast implementation package 'ranger'. This package can apply the following three methods described in Haozhe Zhang, Joshua Zimmerman, Dan Nettleton, and Daniel J. Nordman (2019) <doi:10.1080/00031305.2019.1585288>: the out-of-bag prediction interval, the split conformal method, and the quantile regression forest.
A programmatic interface to <http://www.fishbase.org>, re-written
    based on an accompanying 'RESTful' API. Access tables describing over 30,000
    species of fish, their biology, ecology, morphology, and more. This package also
    supports experimental access to <http://www.sealifebase.org> data, which contains
    nearly 200,000 species records for all types of aquatic life not covered by
    'FishBase.'
Helps fisheries scientists collect measurements from calcified
    structures and back-calculate estimated lengths at previous ages using
    standard procedures and models. This is intended to replace much of the
    functionality provided by the now out-dated 'fishBC' software
    (<https://fisheries.org/bookstore/all-titles/software/70317/>).
A programmatic interface to 'openfisheries.org'. This package is
    part of the 'rOpenSci' suite (http://ropensci.org).
R estimation and inference for linear models.  Estimation
        is for general scores and a library of commonly used score
        functions is included.
Functions for the detection of spatial clusters using the flexible
    spatial scan statistic developed by Tango and Takahashi (2005) <doi:10.1186/1476-072X-4-11>.
    This package implements a wrapper for the C routine used in the FleXScan 3.1.2 
    <https://sites.google.com/site/flexscansoftware/home> developed by Takahashi,
    Yokoyama, and Tango.
Query plane tickets, from several airlines, using the 'Kiwi' API
    (similar to 'Google Flights').
    The API is documented at <https://docs.kiwi.com/>.
RFLPtools provides functions to analyse DNA fragment samples 
  (i.e. derived from RFLP-analysis) and standalone BLAST report files 
  (i.e. DNA sequence analysis).
Tools for RFM (recency, frequency and monetary value) analysis. 
    Generate RFM score from both transaction and customer level data. Visualize the
    relationship between recency, frequency and monetary value using heatmap, 
    histograms, bar charts and scatter plots. Includes a 'shiny' app for 
    interactive segmentation. References:
    i. Blattberg R.C., Kim BD., Neslin S.A (2008) <doi:10.1007/978-0-387-72579-6_12>.
A collection of tools for multivariate analysis of metabolomics
    data, which includes several preprocessing methods (normalization, scaling)
    and various exploration and data visualization techniques (Principal
    Components Analysis and Multi Dimensional Scaling). The core of the package
    is the Random Forest algorithm used for the construction, optimization and
    validation of classification models with the aim of identifying potentially
    relevant biomarkers.
Functionality required to efficiently use R with MarkLogic NoSQL Database Server, <http://www.marklogic.com/what-is-marklogic/>. Many basic and complex R operations are pushed down into the database, which removes the main memory boundary of R and allows to make full use of MarkLogic server. In order to use the package you need a MarkLogic Server version 8 or higher.
R access to the 'FOAAS' (F... Off As A Service) web service is provided.
Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps.  Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.
The basic algorithm to perform the folding test of unimodality. 
  Given a dataset X (d dimensional, n samples), the test checks whether the 
  distribution of the data are rather unimodal or rather multimodal. This 
  package stems from the following research publication: 
  Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. 
  "Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International 
  Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018.
  <doi:10.1145/3219819.3219994>.
Insert, update,
    retrieve, delete and bulk operate datasets with a SaaS based CRM
    Salesforce.com and a PaaS based application platform Force.com from R.
Contains all the code examples in the book "R for Dummies" (2nd
    edition) by Andrie de Vries and Joris Meys. You can view the table of 
    contents as well as the sample code for each chapter.
A modified version (with great help from Ian J. Wilson) of Ian J. Wilson's program BATWING for calculating forensic trace-suspect match probabilities.
The R Formatter formats R source code. It is very much based on
    formatR, but tries to improve it by heuristics. For example, spaces can be
    forced around the division operator "/".
Estimate significance of importance metrics
    for a Random Forest model by permuting the response
    variable. Produces null distribution of importance
    metrics for each predictor variable and p-value of
    observed. Provides summary and visualization functions for 'randomForest' 
    results.
An R interface to a modified version of the Free Evolutionary Algorithm Kit FrEAK. FrEAK is a toolkit written in Java to design and analyze evolutionary algorithms. Both the R interface and an extended version of FrEAK are contained in the RFreak package. For more information on FrEAK see http://sourceforge.net/projects/freak427/.
Assists in statistical model building to find optimal and semi-optimal higher order interactions
    and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible 
    solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained
    for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. The feasible solution 
    algorithm comes up with model forms of a specific type that can have fixed variables, higher order 
    interactions and their lower order terms.
Methods and tools for implementing functional singular spectrum analysis for functional time series
    as described in Haghbin H., Najibi, S.M., Mahmoudvand R., Trinka J., Maadooliat M. (2019). Functional singular spectrum Analysis. Manuscript submitted for publication.
Utilities for Random Forest model selection, class balance
    correction, significance test, cross validation and partial dependency
    plots.
Computes a novel variable importance for random forests: Impurity reduction importance scores for out-of-bag (OOB) data complementing the existing inbag Gini importance, see also Strobl et al (2007) <doi:10.1186/1471-2105-8-25>, Strobl et al (2007) <doi:10.1016/j.csda.2006.12.030> and Breiman (2001) <DOI:10.1023/A:1010933404324>. 
    The Gini impurities for inbag and OOB data are combined in three different ways, after which the information gain is computed at each split.
    This gain is aggregated for each split variable in a tree and averaged across trees.
An interactive data visualization and exploration toolkit
    that implements Breiman and Cutler's original random forest Java based        
    visualization tools in R, for supervised and unsupervised classification and 
    regression within the algorithm random forest.
Provides functions for accessing and retrieving data from the
    Google Analytics APIs (https://developers.google.com/analytics/). Supports
    OAuth 2.0 authorization. Package provides access to the Management, Core
    Reporting, Multi-Channel Funnels Reporting, Real Time Reporting and
    Metadata APIs. Access to all the Google Analytics accounts which the user
    has access to. Auto-pagination to return more than 10,000 rows of the
    results by combining multiple data requests. Also package provides
    shiny app to explore the core reporting API dimensions and metrics.
An Interface to the GA4GH API that allows users to easily GET responses and POST requests to
  GA4GH Servers. See <http://ga4gh.org> for more information about the GA4GH project.
This package was created to analyze multi-level one-way
        experimental designs. It is designed to handle vectorized
        observation and factor data where there are unequal sample
        sizes and population variance homogeneity can not be assumed.
        To conduct the Gabriel test, create two vectors: one for your 
        observations and one for the factor level of each observation. 
        The function, rgabriel, conduct the test and save the output as
        a vector to input into the gabriel.plot function, which produces 
        a confidence interval plot for Multiple Comparison.
This package implements a Gamma convolution model for
        background correction.
Classes and methods to efficiently handle (slice, annotate, draw ...) genomic features (such as genes or transcripts), and an interactive interface to browse them.
A programmatic interface to the Web Service methods
    provided by the Global Biodiversity Information Facility ('GBIF';
    <https://www.gbif.org/developer/summary>). 'GBIF' is a database
    of species occurrence records from sources all over the globe.
    'rgbif' includes functions for searching for taxonomic names,
    retrieving information on data providers, getting species occurrence
    records, getting counts of occurrence records, and using the 'GBIF'
    tile map service to make 'rasters' summarizing huge amounts of data.
Provides an implementation of Regularized LS-TreeBoost & LAD-TreeBoost algorithm for Regulatory Network inference from any type of expression data (Microarray/RNA-seq etc). See Mall et al (2017) <doi:10.1101/132670>.
We utilize  approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data.  The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce  posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.
Multiblock data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: (i) to study the relationships between blocks and (ii) to identify subsets of variables of each block which are active in their relationships with the other blocks.
Implementation of chemometrics analysis for bidimensional gas 
    chromatography data. This package can handle data for common scientific data
    format (netCDF) and fold it to 2D chromatogram. Then, it can perform
    preprocessing and multivariate analysis. In the preprocessing algorithms,
    baseline correction, smoothing, and peak alignment are available.
    While in multivariate analysis, multiway principal component analysis is
    incorporated.
Provides bindings to the 'Geospatial' Data Abstraction Library ('GDAL') (>= 1.11.4) and access to projection/transformation operations from the 'PROJ.4' library. The 'GDAL' and 'PROJ.4' libraries are external to the package, and, when installing the package from source, must be correctly installed first. From 'rgdal' 1.4.1, provision is made for 'PROJ6' accommodation, with 'PROJ6' functionality to follow; from 1.4.1 'rgdal' will build and function when 'PROJ' >= 6. Both 'GDAL' raster and 'OGR' vector map data can be imported into R, and 'GDAL' raster data and 'OGR' vector data exported. Use is made of classes defined in the 'sp' package. Windows and Mac Intel OS X binaries (including 'GDAL', 'PROJ.4' and 'Expat') are provided on 'CRAN'.
Allow access to both public and private end points to Coinbase Pro (erstwhile GDAX) 
    cryptocurrency exchange. For authenticated flow, users must have valid api, secret and passphrase to be able to connect.
Provides popular sampling distributions C++ routines based in 
    armadillo through a header file approach.
The GenDataSample() and GenDataPopulation() functions create, 
    respectively, a sample or population of multivariate nonnormal data 
    using methods described in Ruscio and Kaczetow (2008). Both of these 
    functions call a FactorAnalysis() function to reproduce a correlation 
    matrix. The EFACompData() function allows users to determine how many 
    factors to retain in an exploratory factor analysis of an empirical data
    set using a method described in Ruscio and Roche (2012). The latter 
    function uses populations of comparison data created by calling the 
    GenDataPopulation() function.
    <DOI: 10.1080/00273170802285693>.
    <DOI: 10.1037/a0025697>.
A method 'generate()' is implemented in this package for the random
    generation of vector time series according to models obtained by 'RMAWGEN',
    'vars' or other packages.  This package was created to generalize the
    algorithms of the 'RMAWGEN' package for the analysis and generation of any
    environmental vector time series.
The method 'generate()' is extended for spatial multi-site
    stochastic generation of daily precipitation. It generates precipitation
    occurrence in several sites using logit regression (Generalized Linear
    Models) and D.S. Wilks' approach (Journal of Hydrology, 1998).
A genetic algorithm plus derivative optimizer.
Provides the hybrid Bayesian method Geometric Density Estimation. On the one hand, it scales the dimension of our data, on the other it performs inference. The method is fully described in the paper "Scalable Geometric Density Estimation" by Y. Wang, A. Canale, D. Dunson (2016) <http://proceedings.mlr.press/v51/wang16e.pdf>.
Connectors to online and offline sources for taking IP addresses
    and geolocating them to country, city, timezone and other geographic ranges. For
    individual connectors, see the package index.
Supports analysis of spatial data processed with the 'GeoPAT' 2
    software <http://sil.uc.edu/cms/index.php?id=geopat2>. 
    Available features include creation of a grid based on the 'GeoPAT' 2
    grid header file and reading a 'GeoPAT' 2 text outputs.
Interface to Geometry Engine - Open Source ('GEOS') using the C 'API' for topology operations on geometries. The 'GEOS' library is external to the package, and, when installing the package from source, must be correctly installed first. Windows and Mac Intel OS X binaries are provided on 'CRAN'. ('rgeos' >= 0.5-1): Up to and including 'GEOS' 3.7.1, topological operations succeeded with some invalid geometries for which the same operations fail from and including 'GEOS' 3.7.2. The 'checkValidity=' argument defaults and structure have been changed, from default FALSE to integer default '0L' for 'GEOS' < 3.7.2 (no check), '1L' 'GEOS' >= 3.7.2 (check and warn). A value of '2L' is also provided that may be used, assigned globally using 'set_RGEOS_CheckValidity(2L)', or locally using the 'checkValidity=2L' argument, to attempt zero-width buffer repair if invalid geometries are found. The previous default (FALSE, now '0L') is fastest and used for 'GEOS' < 3.7.2, but will not warn users of possible problems before the failure of topological operations that previously succeeded.
Create, read and write GEXF (Graph Exchange XML Format) graph files (used in Gephi and others). Using the XML package, it allows the user to easily build/read graph files including attributes, GEXF viz attributes (such as color, size, and position), network dynamics (for both edges and nodes) and edge weighting. Users can build/handle graphs element-by-element or massively through data-frames, visualize the graph on a web browser through "sigmajs" (a javascript library) and interact with the igraph package.
Regularized Greedy Forest wrapper of the 'Regularized Greedy Forest' <https://github.com/RGF-team/rgf/tree/master/python-package> 'python' package, which also includes a Multi-core implementation (FastRGF) <https://github.com/RGF-team/rgf/tree/master/FastRGF>.
A command-line interface to 'GGobi', an interactive and dynamic
    graphics package. 'Rggobi' complements the graphical user interface of
    'GGobi' providing a way to fluidly transition between analysis and
    exploration, as well as automating common tasks.
This package provides some functions to create quizzes
  in the GIFT format. This format is used by several Virtual Learning
  Environments such as Moodle.
Downloading, customizing, and processing time series of satellite images for a region of interest. 'RGISTools' functions allow a unified access to multispectral images from Landsat, MODIS and Sentinel repositories. 'RGISTools' also offers capabilities for customizing satellite images, such as tile mosaicking and image cropping. Finally, 'RGISTools' covers the processing, including cloud masking, deriving new variables, and gap-filling/smoothing time series of images (Militino et al., 2018 <doi:10.3390/rs10030398> and Militino et al., 2019 <doi:10.1109/TGRS.2019.2904193>).
Provides medium to high level functions for 3D interactive graphics, including
    functions modelled on base graphics (plot3d(), etc.) as well as functions for
    constructing representations of geometric objects (cube3d(), etc.).  Output
    may be on screen using OpenGL, or to various standard 3D file formats including
    WebGL, PLY, OBJ, STL as well as 2D image formats, including PNG, Postscript, SVG, PGF.
A programmatic interface to the web service methods
    provided by Global Biotic Interactions (GloBI). GloBI provides 
    access to spatial-temporal species interaction records from 
    sources all over the world. rglobi provides methods to search 
    species interactions by location, interaction type, and 
    taxonomic name. In addition, it supports Cypher, a graph query
    language, to allow for executing custom queries on the GloBI 
    aggregate species interaction data set.
R interface to the GNU Linear Programming Kit.
  'GLPK' is open source software for solving large-scale linear programming (LP),
  mixed integer linear programming ('MILP') and other related problems.
The contents of this package have
  been merged into rgl, so it is no longer needed.
Interface for gnuplot
        Based on gnuplot_i version 1.11, the GPL code from Nicolas Devillard.
Export raw reports from 'GoodData' business intelligence platform 
    (see <http://www.gooddata.com> for more information).
Provides functions for accessing and retrieving data from the
    Google Analytics API.
It fires a query to the API to get the unsampled data in R for Google Analytics Premium Accounts. It retrieves data from the Google drive document and stores it into the local drive. The path to the excel file is returned by this package. The user can read data from the excel file into R using read.csv() function.
Provides interface to Google Fit REST API v1 (see <https://developers.google.com/fit/rest/v1/reference/>).
Serves two purposes: (i) Provide a
        comfortable R interface to query the Google server for static
        maps, and (ii) Use the map as a background image to overlay
        plots within R. This requires proper coordinate scaling.
Previously, when one is working with in the Google Ecosystem (Using Google Drive etc), there is hardly any good workflow of getting the values calculated from R and getting that into Google Slides. The normal and easy way out would be to just copy your work over but when you have a number of analysis to present with a lot of changes between each environment, it just becomes quite cumbersome.
Geological Survey of Canada (GSC) functions for exploratory data analysis with applied geochemical data, with special application to the estimation of background ranges and identification of outliers, 'anomalies', to support mineral exploration and environmental studies.  Additional functions are provided to support analytical data QA/QC, ANOVA for investigations of field sampling and analytical variability, and utility tasks.  NOTE: function caplot() for concentration-area plots employs package 'akima', however, 'akima' is only licensed for not-for-profit use.  Therefore, not-for-profit users of 'rgr' will have to independently make package 'akima' available through library(....); and use of function caplot() by for-profit users will fail.
Data and Functions from the book R Graphics, Second Edition. There is a function to produce each figure in the book, plus several functions, classes, and methods defined in Chapter 8.
Interpreted interface between 'GRASS' 7 geographical 
    information system and R, based on starting R from within the 'GRASS' 'GIS'
    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' 6, for which
    'spgrass6' should be used.
Fully 'DBI'-compliant interface to 'Greenplum' <https://greenplum.org/>,
    an open-source parallel database. This is an extension of the 'RPostgres'
    package <https://github.com/r-dbi/RPostgres>.
Integrates the Groovy scripting language with the R Project for Statistical Computing.
Functions to calculate Sample Number and Average Sample Number for Repetitive Group Sampling Plan Based on Cpk as given in Aslam et al. (2013) (<DOI:10.1080/00949655.2012.663374>).
Facilities in the R language for programming
  graphical interfaces using Gtk, the Gimp Tool Kit.
Implementation of the affine-invariant method of Goodman & Weare (2010) <DOI:10.2140/camcos.2010.5.65>, a method of producing Monte-Carlo samples from a target distribution.
DBI/RJDBC interface to h2 database. h2 version 1.3.175 is included.
An R interface to the 'Handsontable' JavaScript library, which is a
    minimalist Excel-like data grid editor.  See <https://handsontable.com/> for details.
Simulate a renewal Hawkes (RHawkes) self-exciting process,
    with a given immigrant hazard rate function and offspring density function.
    Calculate the likelihood of a RHawkes process with given hazard rate function
    and offspring density function for an (increasing) sequence of event times.
    Calculate the Rosenblatt residuals of the event times. Predict
    future event times based on observed event times up to a given time.
    For details see Chen and Stindl (2017) <doi:10.1080/10618600.2017.1341324>.
An R interface for the HealthData.gov data API.  For each data resource, you can filter results (server-side) to select subsets of data.
Complete access from 'R' to the FIS 'MarketMap 
    C-Toolkit' ('FAME C-HLI'). 'FAME' is a fully integrated software and database 
    management system from FIS that provides the following capabilities: 
    Time series and cross-sectional data management;
    Financial calculation, data analysis, econometrics, and forecasting;
    Table generation and detailed multicolor, presentation-quality report writing;
    Multicolor, presentation-quality graphics;
    "What-if" analysis;
    Application development and structured programming;
    Data transfer to and from other applications;
    Tools for building customized graphical user interfaces.
Implements simple Hamiltonian Monte Carlo routines in R for
             sampling from any desired target distribution which is continuous
             and smooth. See Neal (2017) <arXiv:1701.02434> for further details
             on Hamiltonian Monte Carlo. Automatic parameter selection is not
             supported.
'HMMER' is a profile hidden Markov model tool used primarily for sequence
    analysis in bioinformatics (<http://hmmer.org/>). 'rhmmer' provides
    utilities for parsing the 'HMMER' output into tidy data frames.
Hydrologic modelling system is an object oriented tool which enables R users to simulate and analyze hydrologic events. The package proposes functions and methods for construction, simulation, visualization, and calibration of hydrologic systems.
Performs the random heteroscedastic nested error regression model described in Kubokawa, Sugasawa, Ghosh and Chaudhuri (2016) <doi:10.5705/ss.202014.0070>.
Rho is used to test the generalization of inter rater reliability
    (IRR) statistics. Calculating rho starts by generating a large number of
    simulated, fully-coded data sets: a sizable collection of hypothetical
    populations, all of which have a kappa value below a given threshold -- which
    indicates unacceptable agreement. Then kappa is calculated on a sample from
    each of those sets in the collection to see if it is equal to or higher than
    the kappa in then real sample. If less than five percent of the distribution
    of samples from the simulated data sets is greater than actual observed kappa,
    the null hypothesis is rejected and one can conclude that if the two raters had
    coded the rest of the data, we would have acceptable agreement (kappa above the
    threshold).
Evaluating risk (that a patient arises a side effect) during hospitalization is the main purpose of this package. Several methods (Parametric, non parametric and De Vielder estimation) to estimate the risk constant (R) are implemented in this package. There are also functions to simulate the different models of this issue in order to quantify the previous estimators. It is necessary to read at least the first six pages of the report to understand the topic.
Function of apply style using 'MPI' provides better 'HPC' environment on R.
 And this package supports long vector, can deal with slightly big data.
Microbenchmarks for determining the run time
  performance of aspects of the R programming environment and packages
  relevant to high-performance computation.  The benchmarks are divided into
  three categories: dense matrix linear algebra kernels, sparse matrix linear
  algebra kernels, and machine learning functionality.
Control the number of threads on 'BLAS' (Aka 'GotoBLAS', 'OpenBLAS', 'ACML', 'BLIS' and 'MKL').
  And possible to control the number of threads in 'OpenMP'.
  Get a number of logical cores and physical cores if feasible.
rHpcc is an R package providing an Interface between R and
        HPCC.Familiarity with ECL (Enterprise Control Language) is a
        must to use this package.HPCC is a massive parallel-processing
        computing platform that solves Big Data problems.ECL is the
        Enterprise Control Language designed specifically for huge data
        projects using the HPCC platform.Its extreme scalability comes
        from a design that allows you to leverage every query you
        create for re-use in subsequent queries as needed. To do this,
        ECL takes a dictionary approach to building queries wherein
        each ECL definition defines an Attribute. Each previously
        defined Attribute can then be used in succeeding ECL Attribute
        definitions as the language extends itself as you use it.
Allows users to import data files containing heartbeat	positions in the most broadly used formats, to remove outliers or points with unacceptable physiological values present in the time series, to plot HRV data, and to perform time domain, frequency domain and nonlinear HRV analysis. See Garcia et al. (2017) <DOI:10.1007/978-3-319-65355-6>.
This package offers functions to perform regularized
        Hotelling's T-square test for pathway or gene set analysis. The
        package is tailored for but not limited to proteomics data, in
        which sample sizes are often small, a large proportion of the
        data are missing and/or correlations may be present.
Run 'R CMD check' on any of the 'R-hub' (<https://builder.r-hub.io/>)
    architectures, from the command line. The current architectures include
    'Windows', 'macOS', 'Solaris' and various 'Linux' distributions.
Wrapper for 'Datamuse' API to find rhyming and other associated words.
    This includes words of similar meaning, spelling, or other related words. Learn
    more about the 'Datamuse' API here <http://www.datamuse.com/api/>.
This package provides a set of tools for conducting exact
        or approximate randomization-based inference for experiments of
        arbitrary design. The primary functionality of the package is
        in the generation, manipulation and use of permutation matrices
        implied by given experimental designs. Among other features,
        the package facilitates estimation of average treatment
        effects, constant effects variance estimation, randomization
        inference for significance testing against sharp null
        hypotheses and visualization of data and results.
Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.
Computes attributable effects based confidence interval, permutation
    test confidence interval, or asymptotic confidence interval for the
    the average treatment effect on a binary outcome.
Radiomics image analysis toolbox for 2D and 3D radiological images. RIA supports DICOM, NIfTI and
             nrrd file formats. RIA calculates first-order, gray level co-occurrence matrix,
             gray level run length matrix and geometry-based statistics. Almost all calculations are done
             using vectorized formulas to optimize run speeds. Calculation of several thousands of parameters
             only takes minutes on a single core of a conventional PC.
Recursive algorithms for computing various relatedness coefficients, 
    including pairwise kinship, kappa and identity coefficients. Both autosomal 
    and X-linked coefficients are computed. Founders are allowed to be inbred. 
    In addition to the standard pairwise coefficients, ribd also computes a range
    of lesser-known coefficients, including generalised kinship coefficients 
    (Karigl (1981) <doi:10.1111/j.1469-1809.1981.tb00341.x>; Weeks and 
    Lange (1988) <https:www.ncbi.nlm.nih.gov/pmc/articles/PMC1715269>),  
    two-locus coefficients (Thompson (1988) <doi:10.1093/imammb/5.4.261>) and 
    multi-person coefficients. This package is part of the ped suite, 
    a collection of packages for pedigree analysis with 'pedtools' as the core 
    package for creating and handling pedigree objects.
People's Republic of China resident identity card contains a lot of personal information. 
    It can extract the location of the household's residence in the identity card, the date of birth, the sex, 
    the location of the registered police station, and can identify the authenticity of the ID card through the check code. 
    'Ricetl' provides graphical user interface to process the function of extracting the above effective private information in the ID card number,
    and provides a public data set related to the ID number.
The Diceware method can be used to generate strong passphrases.
  In short, you roll a 6-faced dice 5 times in a row, the number obtained is
  matched against a dictionary of easily remembered words. By combining together 7
  words thus generated, you obtain a password that is relatively easy to remember,
  but would take several millions years (on average) for a powerful computer to guess.
Computes rarefaction curves, cumulated and mean species
        richness. Compares these estimates by means of randomization
        tests.
It provides a list of genes associated to diseases (g2d$clean and g2d$complete) based on the following  4 publications (GS2D, Fontaine (2016) <doi:10.18547/gcb.2016.vol2.iss1.e33>, DisGeNET, Pinero (2016) <doi:10.1093/nar/gkw943> Berto2016, Berto (2016) <doi:10.3389/fgene.2016.00031> and  PsyGeNET, Sacristan (2015) <doi:10.1093/bioinformatics/btv301>). Those lists were combined and manually curated to have matching disease names.  When provided a list of gene names, it calculates the disease enrichment of the gene set. The enrichment is calculated using proportion test and Fisher's exact test. Adjusted fdr p-values are returned alongside with p-values combined using the Fisher's method.
For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data in idiograms with no restriction of species.
Linear and logistic ridge regression functions. Additionally includes special functions for 
            genome-wide single-nucleotide polymorphism (SNP) data.
This package implements ridge fusion methodology for inverse covariance matrix estimation for use in quadratic discriminant analysis.  The package also contains function for model based clustering using ridge fusion for inverse matrix estimation, as well as tuning parameter selection functions.  We have also implemented QDA using joint inverse covariance estimation.
An interface to iDigBio's search API that allows downloading
  specimen records. Searches are returned as a data.frame. Other functions
  such as the metadata end points return lists of information. iDigBio is a US
  project focused on digitizing and serving museum specimen collections on the
  web. See <https://www.idigbio.org> for information on iDigBio.
An extension of the Kruskal-Wallis Test that allow
        selection of arbitrary reference group. Also provide Mean Ridit
        for each group. Mean Ridit of a group is an estimate of
        probability a random observation from that group will be
        greater than or equal to a random observation from reference
        group.
Functions to compute ridit scores of vectors, compute mean ridits
  and their standard errors for vectors compared to a reference vector,
  as described in Fleiss (1981, ISBN:0-471-06428-9), and compute
  means/SEs for multiple groups in matrices. Data can be either
  counts or proportions. Emphasis is on ridit analysis of ordered categorical
  data such as Likert items and pain-rating scales.
Allows to get weather data from Automated Surface Observing System (ASOS) stations (airports) in the
    whole world thanks to the Iowa Environment Mesonet website.
We provide a number of algorithms to estimate fundamental statistics including Fréchet mean and geometric median for manifold-valued data. Also, C++ header files are contained that implement elementary operations on manifolds such as Sphere, Grassmann, and others. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> if you are interested in statistics on manifolds, and Absil et al (2007, ISBN:9780691132983) on computational aspects of optimization on matrix manifolds.
Retrieves efficiently and reliably Investors Exchange ('IEX') stock and market data using 'IEX Cloud API'. The platform is offered by Investors Exchange Group (IEX Group).
    Main goal is to leverage 'R' capabilities including existing packages to effectively provide financial and statistical analysis as well as visualization in support of fact-based decisions.
    In addition, continuously improve and enhance 'Riex' by applying best practices and being in tune with users' feedback and requirements.
    Please, make sure to review and acknowledge Investors Exchange Group (IEX Group) terms and conditions before using 'Riex' (<https://iexcloud.io/terms/>).
Client for 'Neuroscience' Information Framework ('NIF') 'APIs'
    (<https://neuinfo.org/>; <https://neuinfo.org/about/webservices>).
    Package includes functions for each 'API' route, and gives back data
    in tidy data.frame format.
Implements the algorithms for solving sparse generalized eigenvalue problem by Tan, et. al. (2018). Sparse Generalized Eigenvalue Problem: Optimal Statistical Rates via Truncated Rayleigh Flow.  To appear in Journal of the Royal Statistical Society: Series B. <arXiv:1604.08697>.
RIFS package provides functionality for generating &
        plotting prefractals in R^n with various protofractal sets and
        partition coefficient for iterative segments
Functionality to download stock prices, cryptocurrency data, and
   more from the 'Tiingo' API <https://api.tiingo.com/>.
Create colour palettes based on famous paintings. Using the 
    function rijksPalette(), you can search for any painting in the collection
    of the Dutch Rijksmuseum and generate a colour palette from it. This package 
    was developed using the fantastic Rijksmuseum API 
    <https://www.rijksmuseum.nl/api>.
Tools to download data from the ilostat database
    <https://ilostat.ilo.org> together with search and
    manipulation utilities.
Provides functions to read 'ImageJ' (http://imagej.nih.gov/ij/)
    Region of Interest (ROI) files, to plot the ROIs and to convert them to
    'spatstat' (http://spatstat.org/) spatial patterns.
A pure R implementation of the median cut algorithm.
    Extracts the dominant colors from an image, and turns them into
    a scale for use in plots or for fun!
The metrics() function calculates measures of scholarly impact. 
    These include conventional measures, such as the number of publications
    and the total citations to all publications, as well as modern and 
    robust metrics based on the vector of citations associated with each 
    publication, such as the h index and many of its variants or rivals.
    These methods are described in Ruscio et al. (2012) 
    <DOI: 10.1080/15366367.2012.711147>.
A programmatic interface to the API provided by the iNaturalist website <http://inaturalist.org> to download species occurrence data submitted by citizen scientists.
Circular / ring buffers in R and C.  There are a couple
    of different buffers here with different implementations that
    represent different trade-offs.
Installs shiny apps packaged as stand-alone Electron apps using Inno Setup, an open source software that builds installers for Windows programs <http://www.jrsoftware.org/ishelp/>.
C++ classes to embed R in C++ applications
 A C++ class providing the R interpreter is offered by this package
 making it easier to have "R inside" your C++ application. As R itself
 is embedded into your application, a shared library build of R is
 required. This works on Linux, OS X and even on Windows provided you
 use the same tools used to build R itself. d Numerous examples are
 provided in the eight subdirectories of the examples/ directory of
 the installed package: standard, 'mpi' (for parallel computing), 'qt'
 (showing how to embed 'RInside' inside a Qt GUI application), 'wt'
 (showing how to build a "web-application" using the Wt toolkit),
 'armadillo' (for 'RInside' use with 'RcppArmadillo') and 'eigen' (for
 'RInside' use with 'RcppEigen').  The examples use 'GNUmakefile(s)'
 with GNU extensions, so a GNU make is required (and will use the
 'GNUmakefile' automatically). 'Doxygen'-generated documentation of
 the C++ classes is available at the 'RInside' website as well.
Functions to calculate several ecological indices of individual and population niche width
   (Araujo's E, clustering and pairwise similarity among individuals, IS, Petraitis' W, and Roughgarden's
   WIC/TNW) to assess individual specialization based on data of resource use. Resource use can be
   quantified by counts of categories, measures of mass/lenght or proportions. Monte Carlo resampling
   procedures are available for hypothesis testing against multinomial null models.
Provides functions to use the 'Instagram' API to get feed and user 
    information, but also performs basic in-app functionality such as liking, 
    commenting, following, and blocking. Use of this package means that you will 
    not use it to spam, harass, or perform other nefarious acts. For more details 
    on how to use the API please see this package's website 
    <https://eric88tchong.github.io/Rinstapkg/> for more information, documentation, 
    and examples.
This tool can be used to build binary interval trees using real number inputs. 
    The tree supports queries of intervals overlapping a single number or an interval (start, end). 
    Intervals with same bounds but different names are treated as distinct intervals. 
    Insertion of intervals is also allowed. Deletion of intervals is not implemented at this point.
    See  Mark de Berg, Otfried Cheong, Marc van Kreveld, Mark Overmars (2008). Computational Geometry: Algorithms and Applications, for a reference.
A wrapper for the 'Intro.js' library (For more info: <http://www.introjs.com>). 
  This package makes it easy to include step-by-step introductions, and clickable hints in a 'Shiny' 
  application. It supports both static introductions in the UI, and programmatic introductions from 
  the server-side.
Streamlined data import and export by making assumptions that
    the user is probably willing to make: 'import()' and 'export()' determine
    the data structure from the file extension, reasonable defaults are used for
    data import and export (e.g., 'stringsAsFactors=FALSE'), web-based import is
    natively supported (including from SSL/HTTPS), compressed files can be read
    directly without explicit decompression, and fast import packages are used where
    appropriate. An additional convenience function, 'convert()', provides a simple
    method for converting between file types.
Constrained clustering, transfer functions, and other methods for analysing Quaternary science data.
Takes an array of IPs and the keys for the services the user wishes to use (IP Hub, IP Intel, and Proxycheck), 
    and passes these to all respective APIs. Returns a dataframe with the IP addresses (used for merging), 
    country, ISP, labels for non-US IP Addresses, VPS use, and recommendations for blocking. The package 
    also provides optional visualization tools for checking the distributions. Additional
    functions are provided to call each discrete API endpoint. The package and methods are detailed in the recent paper 
    Waggoner, Kennedy, and Clifford (2019) <doi:10.21105/joss.01285>.
Utility functions and S3 classes for IPv4 and IPv6 addresses, including 
             conversion to and from binary representation.
Parameter estimation, computation of probability, information, and 
    (log-)likelihood, and visualization of item/test characteristic curves and
    item/test information functions for three uni-dimensional item response theory
    models: the 3-parameter-logistic model, generalized partial credit model, 
    and graded response model. The full documentation and tutorials are at 
    <https://github.com/xluo11/Rirt>.
We propose numerous functions for cohort-based analyses, either for prediction or causal inference. For causal inference, it includes Inverse Probability Weighting and G-computation for marginal estimation of an exposure effect when confounders are expected. We deal with binary outcomes, times-to-events (Le Borgne, 2016, <doi:10.1002/sim.6777>), competing events (Trebern-Launay, 2018, <doi: 10.1007/s10654-017-0322-3>), and multi-state data (Gillaizeau, 2018, <doi: 10.1002/sim.7550>). For multistate data, semi-Markov model with interval censoring (Foucher, 2008, <doi: 10.1177/0962280208093889>) may be considered and we propose the possibility to consider the excess of mortality related to the disease compared to reference lifetime tables (Gillaizeau, 2017, <doi: 10.1177/0962280215586456>). For predictive studies, we propose a set of functions to estimate time-dependent receiver operating characteristic (ROC) curves with the possible consideration of right-censoring times-to-events or the presence of confounders (Le Borgne, 2018,  <doi: 10.1177/0962280217702416>). Finally, several functions are available to assess time-dependant ROC curves (Combescure, 2017, <doi: 10.1177/0962280212464542>) or survival curves (Combescure, 2014, <doi: 10.1002/sim.6111>) from aggregated data.
Implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) 
    <doi:10.19173/irrodl.v18i2.2952>.	Automates the process of identifying learning materials that are not effectively supporting student learning in 
    technology-mediated courses by synthesizing information about access to course content and performance on assessments.
Fits an Ising model to a binary dataset using L1 regularized
    logistic regression and extended BIC. Also includes a fast lasso logistic
    regression function for high-dimensional problems. Uses the 'libLBFGS'
    optimization library by Naoaki Okazaki.
Computes 26 financial risk measures for any continuous distribution.  The 26 financial risk measures  include value at risk, expected shortfall due to Artzner et al. (1999) <DOI:10.1007/s10957-011-9968-2>, tail conditional median due to Kou et al. (2013) <DOI:10.1287/moor.1120.0577>, expectiles due to Newey and Powell (1987) <DOI:10.2307/1911031>, beyond value at risk due to Longin (2001) <DOI:10.3905/jod.2001.319161>, expected proportional shortfall due to Belzunce et al. (2012) <DOI:10.1016/j.insmatheco.2012.05.003>, elementary risk measure due to Ahmadi-Javid (2012) <DOI:10.1007/s10957-011-9968-2>, omega due to Shadwick and Keating (2002), sortino ratio due to Rollinger and Hoffman (2013), kappa  due to Kaplan and Knowles  (2004), Wang (1998)'s <DOI:10.1080/10920277.1998.10595708> risk measures, Stone (1973)'s <DOI:10.2307/2978638> risk measures, Luce (1980)'s <DOI:10.1007/BF00135033> risk measures, Sarin (1987)'s <DOI:10.1007/BF00126387> risk measures, Bronshtein and Kurelenkova (2009)'s risk measures.
A collection of functions related to the study of etiologic heterogeneity both across disease subtypes and across individual disease markers. The included functions allow one to quantify the extent of etiologic heterogeneity in the context of a case-control study, and provide p-values to test for etiologic heterogeneity across individual risk factors. Begg CB, Zabor EC, Bernstein JL, Bernstein L, Press MF, Seshan VE (2013) <doi: 10.1002/sim.5902>.
Fast design of risk parity portfolios for financial investment.
    The goal of the risk parity portfolio formulation is to equalize or distribute
    the risk contributions of the different assets, which is missing if we simply
    consider the overall volatility of the portfolio as in the mean-variance
    Markowitz portfolio. In addition to the vanilla formulation, where the risk
    contributions are perfectly equalized subject to no shortselling and budget
    constraints, many other formulations are considered that allow for box
    constraints and shortselling, as well as the inclusion of additional
    objectives like the expected return and overall variance. See vignette for
    a detailed documentation and comparison, with several illustrative examples.
    The package is based on the papers:
    Y. Feng, and D. P. Palomar (2015). SCRIP: Successive Convex Optimization Methods
    for Risk Parity Portfolio Design. IEEE Trans. on Signal Processing, vol. 63,
    no. 19, pp. 5285-5300. <doi:10.1109/TSP.2015.2452219>.
    F. Spinu (2013), An Algorithm for Computing Risk Parity Weights.
    <doi:10.2139/ssrn.2297383>.
    T. Griveau-Billion, J. Richard, and T. Roncalli (2013). A fast algorithm for computing 
    High-dimensional risk parity portfolios. <arXiv:1311.4057>.
Collection of functions designed to compute risk-based portfolios as described 
    in Ardia et al. (2017) <doi:10.1007/s10479-017-2474-7> and Ardia et al. (2017) <doi:10.21105/joss.00171>.
Assessing and comparing risk prediction rules for clustered data. The method is based on the paper: Rosner B, Qiu W, and Lee MLT.(2013) <doi: 10.1007/s10985-012-9240-6>.
Computes risk measures from data, as well as performs risk management procedures.
Implementation of the following methods for event history analysis.
    Risk regression models for survival endpoints also in the presence of competing
    risks are fitted using binomial regression based on a time sequence of binary
    event status variables. A formula interface for the Fine-Gray regression model
    and an interface for the combination of cause-specific Cox regression models.
    A toolbox for assessing and comparing performance of risk predictions (risk
    markers and risk prediction models). Prediction performance is measured by the
    Brier score and the area under the ROC curve for binary possibly time-dependent
    outcome. Inverse probability of censoring weighting and pseudo values are used
    to deal with right censored data. Lists of risk markers and lists of risk models
    are assessed simultaneously. Cross-validation repeatedly splits the data, trains
    the risk prediction models on one part of each split and then summarizes and
    compares the performance across splits.
Compute time-dependent Incident/dynamic accuracy measures
        (ROC curve, AUC, integrated AUC )from censored survival data
        under proportional or non-proportional hazard assumption of
        Heagerty & Zheng (Biometrics, Vol 61 No 1, 2005, PP 92-105).
Implements efficient simulation procedures to estimate tail loss probabilities and conditional excess for a stock portfolio. The log-returns are assumed to follow a t-copula model with generalized hyperbolic or t marginals.
Risk-related information (like the prevalence of conditions and the sensitivity and specificity of diagnostic tests or treatment decisions) can be expressed in terms of probabilities or frequencies. By providing a toolbox of methods and metrics, 'riskyr' computes, translates, and visualizes risk-related information in a variety of ways. Offering multiple complementary perspectives on the interplay between key parameters renders teaching and training of risk literacy more transparent.
A set of tools to extract bibliographic content from the National
    Center for Biotechnology Information (NCBI) databases, including PubMed. The
    name RISmed is a portmanteau of RIS (for Research Information Systems, a common
    tag format for bibliographic data) and PubMed.
Implements the simulation and regression of
        integrated Isothermal Titration Calorimetry (ITC) data using
        the most commonly used one-to-one binding reaction model.
An interface to the Integrated Taxonomic Information System ('ITIS')
    (<https://www.itis.gov>). Includes functions to work with the 'ITIS' REST
    API methods (<https://www.itis.gov/ws_description.html>), as well as the
    'Solr' web service (<https://www.itis.gov/solr_documentation.html>).
Tools for randomization-based inference. Current focus is on the d^2 omnibus test of differences of means following Hansen and Bowers (2008) useful for assessing balance in matched observational studies or for analysis of outcomes in block-randomized experiments.
Finds a robust instrumental variables estimator using a
             high breakdown point S-estimator of multivariate location
             and scatter matrix.
Generates graphs, CSV files, and coordinates related to river valleys when calling the riverbuilder() function.
Reads river network shape files and computes network distances.
    Also included are a variety of computation and graphical tools designed 
    for fisheries telemetry research, such as minimum home range, kernel density 
    estimation, and clustering analysis using empirical k-functions with 
    a bootstrap envelope.  Tools are also provided for editing the river 
    networks, meaning there is no reliance on external software.
Implements several of the most popular load estimation procedures, including averaging methods, ratio estimators and regression methods. The package provides an easy-to-use tool to rapidly calculate the load for various compounds and to compare different methods. The package also supplies additional functions to easily organize and analyze the data.
Functions for reading, analysing and plotting river networks.
  For this package, river networks consist of sections and nodes with associated attributes, 
  e.g. to characterise their morphological, chemical and biological state.
  The package provides functions to read this data from text files, to analyse the network
  structure and network paths and regions consisting of sections and nodes that fulfill
  prescribed criteria, and to plot the river network and associated properties.
Sankey plots are a type of diagram that is convenient to
    illustrate how flow of information, resources etc. separates and joins,
    much like observing how rivers split and merge. For example, they can be
    used to compare different clusterings.
This R package is a flexible and efficient tool to 
    visualise both quantitative and qualitative data from river surveys. 
    It can be used to produce diagrams with the topological structure of 
    the river network.
This package is devoted to the IVIVC linear level A with
        numerical deconvolution method. The latter is working for
        inequal and incompatible timepoints between impulse and
        response curves. A numerical convolution method is also
        available. Application domains include pharamaceutical industry
        QA/QC and R&D together with academic research.
A tool for undergraduate and graduate courses in open-channel
    hydraulics. Provides functions for computing normal and critical depths,
    steady-state water surface profiles (e.g. backwater curves) and unsteady flow
    computations (e.g. flood wave routing).
Bayesian analysis of CGH microarrays fitting Hidden Markov
        Chain models. The selection of the number of states is made via
        their posterior probability computed by Reversible Jump Markov
        Chain Monte Carlo Methods. Also returns probabilistic common
        regions for gains/losses.
Jade is a high performance template engine heavily influenced by
    Haml and implemented with JavaScript for node and browsers.
Low-level interface to Java VM very much like .C/.Call and friends. Allows creation of objects, calling methods and accessing fields.
This is the official 'Jazz' client. 'Jazz' is a lightweight modular data processing
    framework, including a web server. It provides data persistence and computation capabilities
    accessible from 'R' and 'Python' and also through a REST API. <https://github.com/bbvadata/Jazz>
    See ?rjazz::rjazz to get a 'Jazz' server.
The RJDBC package is an implementation of R's DBI interface using JDBC as a back-end. This allows R to connect to any DBMS that has a JDBC driver.
Interface around 'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially
    recommended to the members of the European Statistical System (ESS) and the European System of Central Banks.
    It offers full access to all options and outputs of 'JDemetra+', including the two leading seasonal adjustment methods
    TRAMO/SEATS+ and X-12ARIMA/X-13ARIMA-SEATS.
Add-in to the 'RJDemetra' package on seasonal adjustments.
    It allows to produce quality assessments outputs (dashboards, quality report matrix, etc.).
A series of functions in some way considered useful to the author.  These include functions for subsetting tables and generating indices for arrays, conditioning and intervening in probability distributions, generating combinations and more...
Performs reversible-jump Markov chain Monte Carlo (Green, 1995)
    <doi:10.2307/2337340>, specifically the restriction introduced by 
    Barker & Link (2013) <doi:10.1080/00031305.2013.791644>. By utilising 
    a 'universal parameter' space, RJMCMC is treated as a Gibbs sampling 
    problem. Previously-calculated posterior distributions are used to 
    quickly estimate posterior model probabilities. Jacobian matrices are 
    found using automatic differentiation. For a detailed description of
    the package, see Gelling, Schofield & Barker (2019)
    <doi:10.1111/anzs.12263>.
R-interface to gene drop programs from Alun Thomas' Java Programs for Statistical Genetics and Computational Statistics (JPSGCS).
Provides functions to retrieve data and metadata from providers 
			 that disseminate data by means of SDMX web services. 
			 SDMX (Statistical Data and Metadata eXchange) is a standard that 
			 has been developed with the aim of simplifying the exchange of 
			 statistical information. 
			 More about the SDMX standard and the SDMX Web Services 
			 can be found at: <http://sdmx.org>.
Converts R object into JSON objects and vice-versa.
Consumer for APIs that Follow the JSON API Specification
    (<http://jsonapi.org/>). Package mostly consumes data - with experimental
    support for serving JSON API data.
This is a package that allows conversion to and from 
  data in Javascript object notation (JSON) format.
  This allows R objects to be inserted into Javascript/ECMAScript/ActionScript code
  and allows R programmers to read and convert JSON content to R objects.
  This is an alternative to rjson package. Originally, that was too slow for converting large R objects to JSON
  and was not extensible.  rjson's performance is now similar to this package, and perhaps slightly faster in some cases.
  This package uses methods and is readily extensible by defining methods for different classes, 
  vectorized operations, and C code and callbacks to R functions for deserializing JSON objects to R. 
  The two packages intentionally share the same basic interface. This package (RJSONIO) has many additional
  options to allow customizing the generation and processing of JSON content.
  This package uses libjson rather than implementing yet another JSON parser. The aim is to support
  other general projects by building on their work, providing feedback and benefit from their ongoing development.
Creates interactive graphs with 'R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package.
Estimates the Joint Sentiment Topic model and its reversed variety, 
    as described by Lin and He, 2009 <DOI:10.1145/1645953.1646003> and Lin, He, Everson and Ruger 
    (2012) <DOI:10.1109/TKDE.2011.48>.
Read and write the 'JSON-stat' format (http://json-stat.org) to and
    from (lists of) R data frames. Not all features are supported, especially
    the extensive metadata features of 'JSON-stat'.
'JDemetra+' (<https://github.com/jdemetra/jdemetra-app>) is the seasonal adjustment software officially recommended
  to the members of the European Statistical System and the European System of Central Banks. Seasonal adjustment models performed
  with 'JDemetra+' can be stored into workspaces. 'JWSACruncher' (<https://github.com/jdemetra/jwsacruncher/releases>) is a console tool that 
  re-estimates all the multi-processing defined in a workspace and to export the result. 'rjwsacruncher' allows to launch easily the 'JWSACruncher'.
R interface to Python via Jython allowing R to call python
        code.
Apache 'Kafka' is an open-source message broker project developed by the Apache Software Foundation which can be thought of as a distributed, partitioned, replicated commit log service.At a high level, producers send messages over the network to the 'Kafka' cluster which in turn serves them up to consumers.See <http://kafka.apache.org/> for more information.Functions included in this package enable:1.Creating 'Kafka' producer 2.Writing messages to a topic 3.Closing 'Kafka' producer 4.Creating 'Kafka' consumer 5.Reading messages from a topic 6.Closing 'Kafka' consumer. The jars required for this package are included in a separate package 'rkafkajars'.
The 'rkafkajars' package collects all the external jars required for the 'rkafka' package.
An R interface to KEA (Version 5.0).
  KEA (for Keyphrase Extraction Algorithm) allows for extracting
  keyphrases from text documents. It can be either used for free
  indexing or for indexing with a controlled vocabulary. For more
  information see <http://www.nzdl.org/Kea/>.
External jars required for package RKEA.
'KEEL' is a popular Java software for a large number of different knowledge data discovery tasks.
    This package takes the advantages of 'KEEL' and R, allowing to use 'KEEL' algorithms in simple R code.
    The implemented R code layer between R and 'KEEL' makes easy both using 'KEEL' algorithms in R as implementing new algorithms for 'RKEEL' in a very simple way.
    It includes more than 100 algorithms for classification, regression, preprocess, association rules and imbalance learning, which allows a more complete experimentation process.
    For more information about KEEL, see <http://www.keel.es/>.
'KEEL' is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, 'RKEEL' is a package with a R code layer between R and 'KEEL', for using 'KEEL' in R code. This package includes the datasets from 'KEEL' in .dat format for its use in 'RKEEL' package. For more information about 'KEEL', see <http://www.keel.es/>.
'KEEL' is a popular Java software for a large number of different knowledge data discovery tasks. Furthermore, 'RKEEL' is a package with a R code layer between R and 'KEEL', for using 'KEEL' in R code. This package downloads and install the .jar files necessary for 'RKEEL' algorithms execution. For more information about 'KEEL', see <http://www.keel.es/>.
Calculates the Kelly criterion (Kelly, J.L. (1956) <doi:10.1002/j.1538-7305.1956.tb03809.x>) for bets given quoted prices, model predictions and commissions.
    Additionally it contains helper functions to calculate the probabilities for wins and draws in multi-leg games.
Estimates the Hoeffding decomposition of an unknown function by solving ridge group sparse optimization problem based on reproducing kernel Hilbert spaces, and approximates its sensitivity indices (see Kamari, H., Huet, S. and Taupin, M.-L. (2019) <arXiv:1905.13695>).
Applies methods used to estimate animal homerange, but
    instead of geospatial coordinates, we use isotopic coordinates. The estimation
    methods include: 1) 2-dimensional bivariate normal kernel utilization density
    estimator, 2) bivariate normal ellipse estimator, and 3) minimum convex polygon
    estimator, all applied to stable isotope data. Additionally, functions to
    determine niche area, polygon overlap between groups and levels (confidence
    contours) and plotting capabilities.
An interface of R to Klout API v2. It fetches Klout Score for a Twitter Username/handle in real time. Klout is a website and mobile app that uses social media analytics to rank its users according to online social influence via the "Klout Score", which is a numerical value between 1 and 100.  In determining the user score, Klout measures the size of a user's social media network and correlates the content created to measure how other users interact with that content.
Hybrid Mortality Modelling (HMM) provides a framework in which mortality around "the accident hump" and at very old ages can be modelled under a single model. The graphics' codes necessary for visualization of the models' output are included here. Specifically, the graphics are based on the assumption that, the mortality rates can be expressed as a function of the area under the curve between the crude mortality rates plots and the tangential transform of the force of mortality.
Random knn classification and regression are implemented. Random knn based feature selection methods are also included. The approaches are mainly developed for high-dimensional data with small sample size.
Contains functions for the topological analysis of polymers, with a focus on protein structures.
Contains function rkt which computes the Mann-Kendall test (MK) and the Seasonal and the Regional Kendall Tests for trend (SKT and RKT) and  Theil-Sen's slope estimator.
Robust  kernel center matrix, robust  kernel cross-covariance operator for kernel unsupervised methods, kernel canonical correlation analysis, 
 influence function of identifying significant outliers or atypical objects from multimodal datasets. Alam, M. A,  Fukumizu, K., Wang  Y.-P. (2018) <doi:10.1016/j.neucom.2018.04.008>.
   Alam, M. A,  Calhoun, C. D.,  Wang  Y.-P. (2018) <doi:10.1016/j.csda.2018.03.013>.
This package provides functionality to read files
    containing observations which consist of arbitrary key/value
    pairs.
Functions and Datasets Required for ST370 class
The 'LabKey' client library for R makes it easy for R users to
    load live data from a 'LabKey' Server, <http://www.labkey.com/>,
    into the R environment for analysis, provided users have permissions
    to read the data. It also enables R users to insert, update, and
    delete records stored on a 'LabKey' Server, provided they have appropriate
    permissions to do so.
Standardized methods for calculating common important derived
    physical features of lakes including water density based based on
    temperature, thermal layers, thermocline depth, lake number, Wedderburn
    number, Schmidt stability and others.
A toolbox for working with base types, core R features
  like the condition system, and core 'Tidyverse' features like tidy
  evaluation.
Read and write 'las' and 'laz' binary file formats. The LAS file format is a public file format for the interchange of 3-dimensional point cloud data between data users. The LAS specifications are approved by the American Society for Photogrammetry and Remote Sensing <https://www.asprs.org/committee-general/laser-las-file-format-exchange-activities.html>. The LAZ file format is an open and lossless compression scheme for binary LAS format versions 1.0 to 1.3 <https://www.laszip.org/>.
An easy-to-use tool to employ interactivity in every-day exploratory analysis. It contains 
	a collection of most commonly used types of charts (such as scatter plots, line plots, heatmaps, bar charts),
	which can be linked to each other or to other interactive elements with just few lines of code.
Analyzes data from repeated low-dose challenge experiments and provide vaccine efficacy estimates. In addition, this package can provide guidance to design repeated low-dose challenge studies.
Estimates the Bayesian LDA model for mixed-membership clustering based on different types of data
    (i.e., Multinomial, Bernoulli, and Binomial entries). Albuquerque, Valle and Li (2019) <doi:10.1016/j.knosys.2018.10.024>.
Linguistic Descriptions of Complex Phenomena (LDCP) is an architecture and methodology that allows us to model complex phenomena, interpreting input data, and generating automatic text reports customized to the user needs (see <doi:10.1016/j.ins.2016.11.002> and <doi:10.1007/s00500-016-2430-5>). The proposed package contains a set of methods that facilitates the development of LDCP systems. It main goal is increasing the visibility and practical use of this research line.
Leaf angle distribution is described by a number of functions
    (e.g. ellipsoidal, Beta and rotated ellipsoidal). The parameters of leaf angle
    distributions functions are estimated through different empirical relationship.
    This package includes estimations of parameters of different leaf angle
    distribution function, plots and evaluates leaf angle distribution functions,
    calculates extinction coefficients given leaf angle distribution.
    Reference: Wang(2007)<doi:10.1016/j.agrformet.2006.12.003>.
Display spatial data with interactive maps powered by the 
  open-source JavaScript library 'Leaflet' (see <http://leafletjs.com/>). Maps can be rendered in a web browser or
  displayed in the HTML viewer pane of 'RStudio'. This package is designed to be easy to use and
  can create complex maps with vector and raster data, web served map tiles and interface elements.
Provides an interface to the C implementation of the
        random number generator with multiple independent streams
        developed by L'Ecuyer et al (2002). The main purpose of this
        package is to enable the use of this random number generator in
        parallel R applications.
Converts LESS to CSS. 
    It uses V8 engine, where LESS parser is run. Functions for
    LESS text, file or folder conversion are provided.
    This work was supported by a junior grant research project by 
    Czech Science Foundation 'GACR' no. 'GJ18-04150Y'.
Contains functions for simulating linear fractional stable motions, according to techniques developed by Stoev and Taqqu (2004) <doi:10.1142/S0218348X04002379>, as well as functions for computing important statistics used with these processes introduced by Mazur, Otryakhin and Podolskij (2018) <arXiv:1802.06373>, and also different quantities related to those statistics.
An implementation of a number of Global Trend models for time series forecasting 
    that are Bayesian generalizations and extensions of some Exponential Smoothing models. 
    The main differences/additions include 1) nonlinear global trend, 2) Student-t error 
    distribution, and 3) a function for the error size, so heteroscedasticity. The methods 
    are particularly useful for short time series. When tested on the well-known M3 dataset,
    they are able to outperform all classical time series algorithms. The models are fitted 
    with MCMC using the 'rstan' package.
An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>, 
    a C library of highly efficient parallelizable functions  for performing the ensemble empirical mode decomposition (EEMD), 
    its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). 
    Due to the possible portability issues CRAN version no longer supports OpenMP, you can install OpenMP-supported version 
    from GitHub: <https://github.com/helske/Rlibeemd/>.
Set of tools for reading, processing and visualizing small set of
    LiDAR (Light Detection and Ranging) data for forest inventory applications.
An interface to LINDO API. Supports Linear, Integer, Quadratic, Conic, General Nonlinear, Global, and Stochastic Programming models. To download the trial version LINDO API, please visit www.lindo.com/rlindo.
A series of functions that allow users
    to access the 'LinkedIn' API to get information about connections,
    search for people and jobs, share updates with their network,
    and create group discussions.  For more information about using
    the API please visit <https://developer.linkedin.com/>.
Solving a system of linear equations is one of the most fundamental
    computational problems for many fields of mathematical studies, such as
    regression problems from statistics or numerical partial differential equations.
    We provide basic stationary iterative solvers such as Jacobi, Gauss-Seidel,
    Successive Over-Relaxation and SSOR methods. Nonstationary, also known as
	Krylov subspace methods are also provided. Sparse matrix computation is also supported
    in that solving large and sparse linear systems can be manageable using 'Matrix' package
    along with 'RcppArmadillo'. For a more detailed description, see a book by Saad (2003)
    <doi:10.1137/1.9780898718003>.
Provides a set of functions for data manipulation with
    list objects, including mapping, filtering, grouping, sorting,
    updating, searching, and other useful functions. Most functions
    are designed to be pipeline friendly so that data processing with
    lists can be chained.
Robust fitting of linear model which can take response in matrix form.
Data driven approach for robust regression estimation in homoscedastic and heteroscedastic context. See Wang et al. (2007), <doi:10.1198/106186007X180156> regarding homoscedastic framework.
Estimates robust rank-based fixed effects and predicts robust
    random effects in two- and three- level random effects nested models.
    The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.
Utilities for writing to 'LibreOffice Writer'
  (see  <https://www.libreoffice.org/discover/writer/> for more information)
   documents using the 'Python-UNO' bridge.
R parallel implementation of Local Outlier Factor(LOF) which uses multiple CPUs to significantly speed up the LOF computation for large datasets. (Note: The overall performance depends on the computers especially the number of the cores).It also supports multiple k values to be calculated in parallel, as well as various distance measures in addition to the default Euclidean distance.
Processing logical operations such as AND/OR/NOT operations
    dynamically. It also handles nesting in the operations.
Rapid, simulation-based exact (restricted) likelihood ratio tests
    for testing the presence of variance components/nonparametric terms for
    models fit with nlme::lme(),lme4::lmer(), lmeTest::lmer(), gamm4::gamm4(),
    mgcv::gamm() and SemiPar::spm().
Random forest with a variety of additional features for regression, classification and survival analysis. The features include: parallel computing with OpenMP, embedded model for selecting the splitting variable (based on Zhu, Zeng & Kosorok, 2015), subject weight, variable weight, tracking subjects used in each tree, etc.
R interface to the 'LTP'-Cloud service for Natural Language Processing
    in Chinese (http://www.ltp-cloud.com/).
A collection of functions to simulate luminescence signals in quartz and Al2O3 based on published models.
A collection of 'shiny' applications for the R package
    'Luminescence'. These mainly, but not exclusively, include applications for
    plotting chronometric data from e.g. luminescence or radiocarbon dating. It
    further provides access to bootstraps tooltip and popover functionality and
    contains the 'jscolor.js' library with a custom 'shiny' output binding.
R implementation of the common parsing tools 'lex' and 'yacc'.
Rasch model and extensions for survey data, using Conditional Maximum likelihood (CML). Carlo Cafiero, Sara Viviani, Mark Nord (2018) <doi:10.1016/j.measurement.2017.10.065>.
RM2 is a simple package that implements functions used in
        revenue management and pricing environments.
Estimation the conditional covariance matrix using the RiskMetrics 2006 methodology of Zumbach (2007) <doi:10.2139/ssrn.1420185>.
Compute an exact CI for the population mean under a random effects model. The routines implement the algorithm described in Michael, Thronton, Xie, and Tian (2017) <https://haben-michael.github.io/research/Exact_Inference_Meta.pdf>.
Allows for production of Czekanowski's Diagrams. See A. Vasterlund (2019) Master thesis, Linkoping University.
Uses refined moving average filter based on the optimal and data-driven moving average lag q or smoothing spline to estimate trend and seasonal components, as well as irregularity (residuals) for univariate time series or data.
MAGIC (Markov affinity-based graph imputation of cells) is a method for addressing technical noise in single-cell data, including under-sampling of mRNA molecules, often termed "dropout" which can severely obscure important gene-gene relationships. MAGIC shares information across similar cells, via data diffusion, to denoise the cell count matrix and fill in missing transcripts. Read more: van Dijk et al. (2018) <DOI:10.1016/j.cell.2018.05.061>.
Creates and maintains a build process for complex analytic tasks in R.
  Package allows to easily generate Makefile for the (GNU) 'make' tool, which drives the build process
  by (in parallel) executing build commands in order to update results accordingly to given dependencies
  on changed data or updated source files.
An EM algorithm to fit Mallows' Models to full or partial
        rankings, with or without ties.
An implementation of an algorithm family for continuous
    optimization called memetic algorithms with local search chains
    (MA-LS-Chains). Memetic algorithms are hybridizations of genetic
    algorithms with local search methods. They are especially suited
    for continuous optimization.
An interface to the 'Mangal' database - a collection of ecological networks. This package includes functions to work with the 'Mangal RESTful API' methods (<https://mangal.io/doc/api>).
Edit and simplify 'geojson', 'Spatial', and 'sf' objects.
    This is wrapper around the 'mapshaper' 'JavaScript' library by Matthew Bloch 
    <https://github.com/mbloch/mapshaper/> to perform topologically-aware
    polygon simplification, as well as other operations such as clipping,
    erasing, dissolving, and converting 'multi-part' to 'single-part' geometries.
    It relies on the 'geojsonio' package for working with 'geojson' objects, the 'sf' 
    package for working with 'sf' objects, and the 'sp' and 'rgdal' packages for 
    working with 'Spatial' objects.
Provides an interface to 'Mapzen'-based APIs (including 
    geocode.earth, Nextzen, and NYC GeoSearch) for geographic search 
    and geocoding, isochrone calculation, and vector data to draw map tiles. 
    See <https://mapzen.com/documentation/> for more information. The original 
    Mapzen has gone out of business, but 'rmapzen' can be set up to work with 
    any provider who implements the Mapzen API.
Three robust marginal integration procedures for additive models based on local 
             polynomial kernel smoothers. As a preliminary estimator of the multivariate 
             function for the marginal integration procedure, a first approach uses local 
             constant M-estimators, a second one uses local polynomials of order 1 over all the
             components of covariates, and the third one uses M-estimators based on local 
             polynomials but only in the direction of interest. For this last approach, 
             estimators of the derivatives of the additive functions can be obtained. All three
             procedures can compute predictions for points outside the training set if desired. 
             See Boente and Martinez (2017) <doi:10.1007/s11749-016-0508-0> for details.
Implements a 'DBI'-compliant interface to 'MariaDB' (<https://mariadb.org/>) and 'MySQL' (<https://www.mysql.com/>) databases.
Given a time series of daily temperatures, the package provides tools
    to detect extreme thermal events, including marine heat waves, and to
    calculate the exceedances above or below specified threshold values.
    It outputs the properties of all detected events and exceedances.
An interface to the software package MARK that constructs input
    files for MARK and extracts the output. MARK was developed by Gary White
    and is freely available at <http://www.phidot.org/software/mark/downloads/>
    but is not open source.
Convert R Markdown documents into a variety of formats.
Read and write 'Matlab' MAT files from R. The 'rmatio'
    package supports reading MAT version 4, MAT version 5 and MAT
    compressed version 5. The 'rmatio' package can write version 5 MAT
    files and version 5 files with variable compression.
S3 and S4 functions are implemented for spatial multi-site
    stochastic generation of daily time series of temperature and
    precipitation. These tools make use of Vector AutoRegressive models (VARs).
    The weather generator model is then saved as an object and is calibrated by
    daily instrumental "Gaussianized" time series through the 'vars' package
    tools. Once obtained this model, it can it can be used for weather
    generations and be adapted to work with several climatic monthly time
    series.
MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, 'small n large p' transactional and biological data. M. Draminski, J. Koronacki (2018) <doi:10.18637/jss.v085.i12>.
Compute the repeated measures correlation, a statistical technique
    for determining the overall within-individual relationship among paired measures
    assessed on two or more occasions, first introduced by Bland and Altman (1995).
    Includes functions for diagnostics, p-value, effect size with confidence
    interval including optional bootstrapping, as well as graphing. Also includes
    several example datasets.
The 'rmd' package manages multiple R markdown packages. These R markdown packages include currently 'rmarkdown', 'knitr', 'bookdown', 'bookdownplus', 'blogdown', 'rticles',  'tinytex', 'xaringan', 'citr',  and 'mindr'. They can be installed and loaded in a single step with the 'rmd' package. The conflicts between these packages are evaluated as well.
Provides tools to evaluate the value of using a risk prediction instrument to decide treatment or intervention (versus no treatment or intervention).  Given one or more risk prediction instruments (risk models) that estimate the probability of a binary outcome, rmda provides functions to estimate and display decision curves and other figures that help assess the population impact of using a risk model for clinical decision making.   Here, "population" refers to the relevant patient population. Decision curves display estimates of the (standardized) net benefit over a range of probability thresholds used to categorize observations as 'high risk'. The curves help evaluate a treatment policy that recommends treatment for patients who are estimated to be 'high risk' by comparing the population impact of a risk-based policy to "treat all" and "treat none" intervention policies.  Curves can be estimated using data from a prospective cohort.  In addition, rmda can estimate decision curves using data from a case-control study if an estimate of the population outcome prevalence is available.  Version 1.4 of the package provides an alternative framing of the decision problem for situations where treatment is the standard-of-care and a risk model might be used to recommend that low-risk patients (i.e., patients below some risk threshold) opt out of treatment. Confidence intervals calculated using the bootstrap can be computed and displayed. A wrapper function to calculate cross-validated curves using k-fold cross-validation is also provided.
Estimates different multiple discrete-continuous extreme value
	(MDCEV) demand model specifications with observed and unobserved individual heterogeneity (Bhat (2008) <doi:10.1016/j.trb.2007.06.002>). Fixed parameter,
	latent class, and random parameter models can be estimated. These models are estimated using
	maximum likelihood or Bayesian estimation techniques and are implemented in 'Stan', which is a
	C++ package for performing full Bayesian inference (see Stan Development Team (2018) <http://mc-stan.org>). 
	The 'rmdcev' package also includes functions for demand simulation (Pinjari and Bhat (2011) <https://repositories.lib.utexas.edu/handle/2152/23880>) 
	and welfare simulation (Lloyd-Smith (2018) <doi:10.1016/j.jocm.2017.12.002>).
A collection of 'Lua' filters that extend the functionality
  of R Markdown templates (e.g., count words or post-process 'pandoc-citeproc'-
  citations).
HTML formats and templates for 'rmarkdown' documents, with some extra
    features such as automatic table of contents, lightboxed figures, dynamic
    crosstab helper.
A series of functions to aid in repeated tasks for Rmd documents. All details are to my personal preference, though I am happy to add flexibility if there are use cases I am missing. I will continue updating with new functions as I add utility functions for myself.
Formats for R Markdown that undo modifications by
    'pandoc' and 'rmarkdown' to original 'latex' templates, such as
    smaller margins, paragraph spacing, and compact titles. In addition, 
    enhancements such as author blocks with affiliations and
    headers and footers are introduced. All of this functionality is built 
    around plugins that modify the default 'pandoc' template without relying on 
    custom templates.
'R' 'Markdown' format for 'shower' presentations, see
   <https://github.com/shower/shower>.
A suite of tools useful to read, visualize and export bivariate motion energy time-series. Lagged synchrony between subjects can be analyzed through windowed cross-correlation. Surrogate data generation allows an estimation of pseudosynchrony that helps to estimate the effect size of the observed synchronization. Ramseyer & Tschacher (2011) <doi:10.1037/a0023419>.
An 'Rcpp' interface for Eunjeon project <http://eunjeon.blogspot.com/>.
    The 'mecab-ko' and 'mecab-ko-dic' is based on a C++ library,
    and part-of-speech tagging with them is useful when the spacing of source Korean text is not correct.
    This package provides part-of-speech tagging and tokenization function for Korean text.
We provide functions to compute confidence
    intervals (CIs) for a well-defined nonlinear function of the model
    parameters (e.g., product of k coefficients) in single--level and
    multilevel structural equation models.
Functions for simple fixed and random effects
        meta-analysis for two-sample comparisons and cumulative
        meta-analyses. Draws standard summary plots, funnel plots, and
        computes summaries and tests for association and heterogeneity.
Implementation of the metalog distribution in R.
    The metalog distribution is a modern, highly flexible, data-driven distribution. 
    Metalogs are developed by Keelin (2016) <doi:10.1287/deca.2016.0338>.
    This package provides functions to build these distributions from raw data. 
    Resulting metalog objects are then useful for exploratory and probabilistic analysis.
An interface between R and the metasim simulation engine.
    The simulation environment is documented in: "Strand, A.(2002) <DOI:10.1046/j.1471-8286.2002.00208.x> Metasim 1.0: an individual-based environment for simulating population genetics of 
    complex population dynamics. Mol. Ecol. Notes. 
    Please see the vignettes CreatingLandscapes and Simulating to get some ideas on how to use the packages.  
    See the rmetasim vignette to get an overview and to see important changes to the 
    code in the most recent version.
Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH. See Boudt, Galanos, Payseur and Zivot (2019) for a review of multivariate GARCH models <doi:10.1016/bs.host.2019.01.001>.
Provides mutual information estimators based on k-nearest neighbor estimators by A. Kraskov, et al. (2004) <doi:10.1103/PhysRevE.69.066138>, S. Gao, et al. (2015) <http://proceedings.mlr.press/v38/gao15.pdf> and local density estimators by W. Gao, et al. (2017) <doi:10.1109/ISIT.2017.8006749>.
Facilitates the use of data mining algorithms in classification and regression (including time series forecasting) tasks by presenting a short and coherent set of functions. Versions: 1.4.2 new NMAE metric, "xgboost" and "cv.glmnet" models (16 classification and 18 regression models); 1.4.1 new tutorial and more robust version; 1.4 - new classification and regression models/algorithms, with a total of 14 classification and 15 regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting; 1.3 and 1.3.1 - new classification and regression metrics (improved mmetric function); 1.2 - new input importance methods (improved Importance function); 1.0 - first version.
Provides header files of 'mio', a cross-platform C++11 header-only 
    library for memory mapped file IO <https://github.com/mandreyel/mio>.
The Rmisc library contains many functions useful for data analysis
    and utility operations.
Interface of 'MIXMOD' software for supervised, unsupervised and
    semi-supervised classification with mixture modelling.
The Rmixmod package provides model-based clustering by fitting a mixture model (e.g. Gaussian components for quantitative continuous data) to the data and identifying each cluster with one of its components. The number of components can be determined from the data, typically using the BIC criterion. In practice, however, individual clusters can be poorly fitted by Gaussian distributions, and in that case model-based clustering tends to represent one non-Gaussian cluster by a mixture of two or more Gaussian components. If the number of mixture components is interpreted as the number of clusters, this can lead to overestimation of the number of clusters. This is because BIC selects the number of mixture components needed to provide a good approximation to the density. This package, RmixmodCombi, according to \emph{Combining Mixture Components for Clustering} by J.P. Baudry, A.E. Raftery, G. Celeux, K. Lo, R. Gottardo, combines the components of the EM/BIC solution (provided by Rmixmod) hierarchically according to an entropy criterion. This yields a clustering for each number of clusters less than or equal to K. These clusterings can be compared on substantive grounds, and we also provide a way of selecting the number of clusters via a piecewise linear regression fit to the (possibly rescaled) entropy plot.
Provides an interface to many endpoints of Mixpanel's Data Export, Engage and JQL API. The R functions allow for event and profile data export as well as for segmentation, retention, funnel and addiction analysis. Results are always parsed into convenient R objects. Furthermore it is possible to load and update profiles.
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management. 
    It includes 8 models for real, categorical, counting, functional and ranking data.
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management. 
    It includes models for real, categorical, counting, functional and ranking data.
    This package contains the minimal R interface of the C++ 'MixtComp' library.
Mixture Composer <https://github.com/modal-inria/MixtComp> is a project to build mixture models with
    heterogeneous data sets and partially missing data management. This package contains graphical, getter and some utility 
    functions to facilitate the analysis of 'MixtComp' output.
Sundry discrete probability distributions and helper functions.
Provides R and C++ function that enable the user to conduct multiple 
 kernel learning (MKL) and cross validation for support vector machine (SVM) models. 
 Cross validation can be used to identify kernel shapes and hyperparameter 
 combinations that can be used as candidate kernels for MKL. There are three 
 implementations provided in this package, namely SimpleMKL Alain Rakotomamonjy et. al
 (2008), Simple and Efficient MKL Xu et. al (2010), and Dual augmented Lagrangian MKL
 Suzuki and Tomioka (2011) <doi:10.1007/s10994-011-5252-9>. These methods identify the convex combination of 
 candidate kernels to construct an optimal hyperplane.
Connect R with MOA (Massive Online Analysis -
    <http://moa.cms.waikato.ac.nz>) to build classification models and
    regression models on streaming data or out-of-RAM data.
    Also streaming recommendation models are made available.
External jars required for package RMOA. RMOA is a framework to
    build data stream models on top of MOA (Massive Online Analysis -
    <http://moa.cms.waikato.ac.nz>). The jar files are put in this R package, the modelling logic can be found in the RMOA package.
A monadic solution to pipeline analysis. All operations -- and the errors,
    warnings and messages they emit -- are merged into a directed graph. Infix
    binary operators mediate when values are stored, how exceptions are
    handled, and where pipelines branch and merge. The resulting structure may
    be queried for debugging or report generation. 'rmonad' complements, rather
    than competes with, non-monadic pipeline packages like 'magrittr' or
    'pipeR'. This work is funded by the NSF (award number 1546858).
This is a meta-package designed to support the
        installation of Rmosek (>= 6.0) and bring the optimization
        facilities of MOSEK (>= 6.0) to the R-language. The interface
        supports large-scale optimization of many kinds: Mixed-integer
        and continuous linear, second-order cone, exponential cone and
        power cone optimization, as well as continuous semidefinite
        optimization. Rmosek and the R-language are open-source
        projects. MOSEK is a proprietary product, but unrestricted
        trial and academic licenses are available.
Provides wrapper functions to the Java Robot class to automate user input, like mouse movements, clicks and keyboard input.
Arithmetic (via S4 classes and methods) for
  arbitrary precision floating point numbers, including transcendental
  ("special") functions.  To this end, the package interfaces to
  the 'LGPL' licensed 'MPFR' (Multiple Precision Floating-Point Reliable) Library
  which itself is based on the 'GMP' (GNU Multiple Precision) Library.
An interface (wrapper) to MPI. It also 
	     provides interactive R manager and worker environment.
We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998617749561>.
Analysis of oxygen consumption data generated by Loligo (R) Systems respirometry equipment. The package includes a function for loading data output by Loligo's 'AutoResp' software (get.witrox.data()), functions for calculating metabolic rates over user-specified time intervals, extracting critical points from data using broken stick regressions based on Yeager and Ultsch (<DOI:10.1086/physzool.62.4.30157935>), and easy functions for converting between different units of barometric pressure.
This package contains functions and S3 methods for spatial
    multi-site stochastic generation of daily precipitation. It generates
    precipitation occurrence in several sites using Wilks' Approach (1998).
    Bugs/comments/questions/collaboration of any kind are warmly welcomed.
Regression modeling, testing, estimation, validation,
	graphics, prediction, and typesetting by storing enhanced model design
	attributes in the fit.  'rms' is a collection of functions that
	assist with and streamline modeling.  It also contains functions for
	binary and ordinal logistic regression models, ordinal models for
  continuous Y with a variety of distribution families, and the Buckley-James
	multiple regression model for right-censored responses, and implements
	penalized maximum likelihood estimation for logistic and ordinary
	linear models.  'rms' works with almost any regression model, but it
	was especially written to work with binary or ordinal regression
	models, Cox regression, accelerated failure time models,
	ordinary linear models,	the Buckley-James model, generalized least
	squares for serially or spatially correlated observations, generalized
	linear models, and quantile regression.
This package can be used to test any simple null
        hypothesis using the root-mean-square goodness of fit test.
        Monte Carlo estimation is used to calculate the associated
        P-value.
Display a randomly selected quote about Richard M. Stallman
 based on the collection in the 'GNU Octave' function 'fact()' which was
 aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site
 stallmanfacts.com (which is accessible only via <http://archive.org>).
Contains several useful navigation helper functions, including easily building
    folder paths, quick viewing dataframes in 'Excel', creating date vectors and changing the
    console prompt to reflect time.
Assemble the panels of computerized adaptive multistage testing by the 
    bottom-up and the top-down approach, and simulate the administration of the assembled 
    panels. The full documentation and tutorials are at <https://github.com/xluo11/Rmst>.
    Reference: Luo and Kim (2018) <doi:10.1111/jedm.12174>.
An algorithm which can be used to determine an objective threshold for signal-noise separation in large random matrices (correlation matrices, mutual information matrices, network adjacency matrices) is provided. The package makes use of the results of Random Matrix Theory (RMT). The algorithm increments a suppositional threshold monotonically, thereby recording the eigenvalue spacing distribution of the matrix. According to RMT, that distribution undergoes a characteristic change when the threshold properly separates signal from noise. By using the algorithm, the modular structure of a matrix - or of the corresponding network - can be unraveled.
Efficient solvers for 10 regularized multi-task learning algorithms applicable for regression, classification, joint feature selection, task clustering, low-rank learning, sparse learning and network incorporation. Based on the accelerated gradient descent method, the algorithms feature a state-of-art computational complexity O(1/k^2). Sparse model structure is induced by the solving the proximal operator. The detail of the package is described in the paper of Han Cao and Emanuel Schwarz (2018) <doi:10.1093/bioinformatics/bty831>.
Functions for working with the Tracy-Widom laws and other distributions 
    related to the eigenvalues of large Wishart matrices.
    The tables for computing the Tracy-Widom densities and distribution
    functions were computed by Momar Dieng's MATLAB package "RMLab"
    (formerly available on his homepage at 
    http://math.arizona.edu/~momar/research.htm ).
    This package is part of a collaboration between Iain Johnstone, 
    Zongming Ma, Patrick Perry, and Morteza Shahram.  It will soon be
    replaced by a package with more accuracy and built-in support for
    relevant statistical tests.
Some basic features of MUMPS (Multifrontal Massively Parallel
         sparse direct Solver) are wrapped in a class whose methods can be used
         for sequentially solving a sparse linear system (symmetric or not)
         with one or many right hand sides (dense or sparse).
         There is a possibility to do separately symbolic analysis,
         LU (or LDL^t) factorization and system solving.
         Third part ordering libraries are included and can be used: PORD, METIS, SCOTCH.
         MUMPS method was first described in Amestoy et al. (2001) <doi:10.1137/S0895479899358194>
         and Amestoy et al. (2006) <doi:10.1016/j.parco.2005.07.004>.
A toolkit of functions for nonlinear regression and repeated
    measurements not to be used by itself but called by other Lindsey packages such
    as 'gnlm', 'stable', 'growth', 'repeated', and 'event' 
    (available at <http://www.commanster.eu/rcode.html>).
A memory-efficient, visualize-enhanced, parallel-accelerated Genome-Wide Association Study (GWAS) tool. It can
    (1) effectively process large data, 
    (2) rapidly evaluate population structure, 
    (3) efficiently estimate variance components several algorithms, 
    (4) implement parallel-accelerated association tests of markers three methods, 
    (5) globally efficient design on GWAS process computing, 
    (6) enhance visualization of related information. 
    'rMVP' contains three models GLM (Alkes Price (2006) <DOI:10.1038/ng1847>), MLM (Jianming Yu (2006) <DOI:10.1038/ng1702>) 
    and FarmCPU (Xiaolei Liu (2016) <doi:10.1371/journal.pgen.1005767>); variance components estimation methods EMMAX 
    (Hyunmin Kang (2008) <DOI:10.1534/genetics.107.080101>;), FaSTLMM (method: Christoph Lippert (2011) <DOI:10.1038/nmeth.1681>, 
    R implementation from 'GAPIT2': You Tang and Xiaolei Liu (2016) <DOI:10.1371/journal.pone.0107684> and 
    'SUPER': Qishan Wang and Feng Tian (2014) <DOI:10.1371/journal.pone.0107684>), and HE regression 
    (Xiang Zhou (2017) <DOI:10.1214/17-AOAS1052>).
An integrated set of tools to allow data users to conduct 
    meteorological normalisation on air quality data. This meteorological 
    normalisation technique uses predictive random forest models to remove 
    variation of pollutant concentrations so trends and interventions can be 
    explored in a robust way. For examples, see Grange et al. (2018) 
    <doi:10.5194/acp-18-6223-2018> and Grange and Carslaw (2019) 
    <doi:10.1016/j.scitotenv.2018.10.344>.
Legacy 'DBI' interface to 'MySQL' / 'MariaDB' based on old code
    ported from S-PLUS. A modern 'MySQL' client based on 'Rcpp' is available 
    from the 'RMariaDB' package.
Allows work with 'MyTarget API v2' 
	<https://target.my.com/doc/apiv2/ru/detailed.html> and
	load data by ads, campaigns, agency clients and statistic from
	your ads account.
Infer log-linear Poisson Graphical Model with an auxiliary data
    set. Hot-deck multiple imputation method is used to improve the reliability
    of the inference with an auxiliary dataset. Standard log-linear Poisson 
    graphical model can also be used for the inference and the Stability 
    Approach for Regularization Selection (StARS) is implemented to drive the 
    selection of the regularization parameter. The method is fully described in
    <doi:10.1093/bioinformatics/btx819>.
This package is designed to streamline several of the common steps for RNA-seq
              analysis, including differential expression and variant discovery.
              For the development build, or to contribute changes to this package,
              please see our repository at https://bitbucket.org/petersmp/rnaseqwrapper/
Provides function for RNA secondary structure plotting, comparison and module mining. Given a RNA secondary structure, you can obtain stem regions, hairpin loops, internal loops, bulge loops and multibranch loops of this RNA structure using this program. They are the basic modules of RNA secondary structure. For each module you get, you can use this program to label the RNA structure with a specific color. You can also use this program to compare two RNA secondary structures to get a score that represents similarity. Reference: Reuter JS, Mathews DH (2010) <doi:10.1186/1471-2105-11-129>.
Interface to access data via the United States Department of 
  Agriculture's National Agricultural Statistical Service (NASS) 'Quick Stats' 
  web API <https://quickstats.nass.usda.gov/api>. Convenience functions 
  facilitate building queries based on available parameters and valid parameter 
  values.
Functions in this program is designed for RNA secondary structure plotting, comparison and module mining. Given a RNA secondary structure, you can obtain stem regions, hairpin loops, internal loops, bulge loops and multibranch loops of this RNA structure using this program. They are the basic modules of RNA secondary structure. For each module you get, you can use this program to label the RNA structure with a specific color. You can also use this program to compare two RNA secondary structures to get a score that represents similarity.
Facilitates mapping by making natural earth map data from <http://www.naturalearthdata.com/> more easily available to R users.
Vector map data from <http://www.naturalearthdata.com/>. Access functions are provided in the accompanying package 'rnaturalearth'.
Image data used as examples in the loon R package.
Provides a set of functions to access a data visualization web service. For more information and a tutorial on how to use it, see https://navicell.curie.fr/pages/nav_web_service.html and https://github.com/sysbio-curie/RNaviCell.
Use the <http://api.nbp.pl/> API through R. Retrieve
    currency exchange rates and gold prices data published by the
    National Bank of Poland in form of convenient R objects.
Provides the libraries of the EUtils operations for the
        RNCBI package.
Contains functions to retrieve, organize, and visualize weather data from the NCEP/NCAR Reanalysis (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html) and NCEP/DOE Reanalysis II (http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis2.html) datasets.  Data are queried via the Internet and may be obtained for a specified spatial and temporal extent or interpolated to a point in space and time.  We also provide functions to visualize these weather data on a map.  There are also functions to simulate flight trajectories according to specified behavior using either NCEP wind data or data specified by the user.
An interface to the Nexus Class Library which allows parsing
    of NEXUS, Newick and other phylogenetic tree file formats. It provides
    elements of the file that can be used to build phylogenetic objects
    such as ape's 'phylo' or phylobase's 'phylo4(d)'. This functionality
    is demonstrated with 'read_newick_phylo()' and 'read_nexus_phylo()'.
Extract the implied risk neutral density from options using various methods.
Using as input a 'LaTeX' file with a multiple choice exam, this package will produce several versions with randomized contents of the same exam. Functions for grading are also available.
An implementation of a method based on information theory devised 
    for the identification of genes showing a significant variation of expression
    across multiple conditions. Given expression estimates from any number of 
    RNA-Seq samples and conditions it identifies genes or transcripts with a 
    significant variation of expression across all the conditions studied, 
    together with the samples in which they are over- or under-expressed. 
    Zambelli et al. (2018) <doi:10.1093/nar/gky055>.
Within this package the XML-RPC API to NEOS <https://neos-server.org/neos/> is implemented. This enables the user to pass optimization problems to NEOS and retrieve results within R.
It provides functions to compute the modularity and modularity-related roles in networks. It is a wrapper around the rgraph library (Guimera & Amaral, 2005, doi:10.1038/nature03288).
An interface to the 'NetCDF' file formats designed by Unidata
  for efficient storage of array-oriented scientific data and descriptions.
  Most capabilities of 'NetCDF' version 4 are supported. Optional conversions
  of time units are enabled by 'UDUNITS' version 2, also from Unidata.
Interface to use and access Wilensky's 'NetLogo' (Wilensky 1999) from R using either headless (no GUI) or interactive GUI mode. Provides functions to load models, execute commands, and get values from reporters. Mostly analogous to the 'NetLogo' 'Mathematica' Link <https://github.com/NetLogo/Mathematica-Link>.
Novel methods are needed to analyze the large amounts of antimicrobial resistance (AMR) data generated by AMR surveillance programs. This package is used to estimate resistance relationship networks, or 'Rnets', from empirical antimicrobial susceptibility data. These networks can be used to study relationships between antimicrobial resistances (typically measured using MICs) and genes in populations. The 'GitHub' for this package is available at <https://GitHub.com/EpidemiologyDVM/Rnets>. Bug reports and features requests should be directed to the same 'GitHub' site. The methods used in 'Rnets' are available in the following publications: An overview of the method in WJ Love, et al., "Markov Networks of Collateral Resistance: National Antimicrobial Resistance Monitoring System Surveillance Results from Escherichia coli Isolates, 2004-2012" (2016) <doi:10.1371/journal.pcbi.1005160>; The graphical LASSO for sparsity in J Friedman, T Hastie, R Tibshirani "Sparse inverse covariance estimation with the graphical lasso" (2007) <doi:10.1093/biostatistics/kxm045>; L1 penalty selection in H Liu, K Roeder, L Wasserman "Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models" (2010) <arXiv:1006.3316>; Modularity for graphs with negative edge weights in S Gomez, P Jensen, A Arenas. "Analysis of community structure in networks of correlated data" (2009) <doi:10.1103/PhysRevE.80.016114>.
A collection of tools for measuring the similarity of text messages and tracing the flow of messages over
    time and across media.
Provides access to phyloinformatic data in 'NeXML' format.  The
    package should add new functionality to R such as the possibility to
    manipulate 'NeXML' objects in more various and refined way and compatibility
    with 'ape' objects.
Generation of univariate and multivariate data that follow the generalized Poisson distribution. The details of the univariate part are explained in Demirtas (2017), and the multivariate part is an extension of the correlated Poisson data generation routine that was introduced in Yahav and Shmueli (2012).
A function setVectorSeed() is provided. Its argument
        is a numeric vector of an arbitrary nonzero length, whose
        components have integer values from [0, 2^32-1]. The input
        vector is transformed using AES (Advanced Encryption Standard)
        algorithm into an initial state of Mersenne-Twister random
        number generator. The function provides a better alternative
        to the R base function set.seed(), if the input vector is
        a single integer. Initializing a stream of random numbers
        with a vector is a convenient way to obtain several streams,
        each of which is identified by several integer indices.
Provides a set of functions for working with
    Random Number Generators (RNGs). In particular, a generic
    S4 framework is defined for getting/setting the current RNG, or RNG data
    that are embedded into objects for reproducibility.
    Notably, convenient default methods greatly facilitate the way current
    RNG settings can be changed.
It is a dedicated package to WELL pseudo random generators, which were introduced in Panneton et al. (2006), ``Improved Long-Period Generators Based on Linear Recurrences Modulo 2'', ACM Transactions on Mathematical Software. But this package is not intended to be used directly, you are strongly __encouraged__ to use the 'randtoolbox' package, which depends on this package.
Long period linear random number generator WELL19937a by
        F. Panneton, P. L'Ecuyer and M. Matsumoto. The initialization
        algorithm allows to seed the generator with a
        numeric vector of an arbitrary length and uses MRG32k5a by
        P. L'Ecuyer to achieve good quality of the initialization. The
        output function may be set to provide numbers from the interval
        (0,1) with 53 (the default) or 32 random bits. WELL19937a is of
        similar type as Mersenne Twister and has the same period.
        WELL19937a is slightly slower than Mersenne Twister, but has
        better equidistribution and "bit-mixing" properties and faster
        recovery from states with prevailing zeros than Mersenne
        Twister. All WELL generators with orders 512, 1024, 19937 and
        44497 can be found in randtoolbox package.
Tools for downloading and analyzing CDC NHANES data, with a focus
    on analytical laboratory data.
Provides very fast read and write access to images stored in the
    NIfTI-1 and ANALYZE-7.5 formats, with seamless synchronisation between
    compiled C and interpreted R code. Also provides a C/C++ API that can be
    used by other packages. Not to be confused with 'RNiftyReg', which performs
    image registration.
Provides an 'R' interface to the 'NiftyReg' image registration tools
    <http://sourceforge.net/projects/niftyreg/>. Linear and nonlinear registration
    are supported, in two and three dimensions.
An implementation of robust nonnegative matrix factorization (rNMF). The rNMF algorithm decomposes a nonnegative high dimension data matrix into the product of two low rank nonnegative matrices, while detecting and trimming outliers. The main function is rnmf(). The package also includes a visualization tool, see(), that arranges and prints vectorized images.
Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free induction decay (raw data) and based on a processing sequence (macro-command file). An additional file specifies all the spectra to be considered by associating their sample code as well as the levels of experimental factors to which they belong. More detail can be found in Jacob et al. (2017) <doi:10.1007/s11306-017-1178-y>.
Implementation of a Recurrent Neural Network architectures in native R, including Long Short-Term Memory (Hochreiter and Schmidhuber, <doi:10.1162/neco.1997.9.8.1735>), Gated Recurrent Unit (Chung et al., <arXiv:1412.3555>) and vanilla RNN.
Client for many 'NOAA' data sources including the 'NCDC' climate
    'API' at <https://www.ncdc.noaa.gov/cdo-web/webservices/v2>, with functions for
    each of the 'API' 'endpoints': data, data categories, data sets, data types,
    locations, location categories, and stations. In addition, we have an interface
    for 'NOAA' sea ice data, the 'NOAA' severe weather inventory, 'NOAA' Historical
    Observing 'Metadata' Repository ('HOMR') data, 'NOAA' storm data via 'IBTrACS',
    tornado data via the 'NOAA' storm prediction center, and more.
An interface to the National Oceanic and Atmospheric Administration's Operational Model Archive and Distribution System (NOMADS, see <http://nomads.ncep.noaa.gov/> for more information) that allows R users to quickly and efficiently download global and regional weather model data for processing.  rNOMADS currently supports a variety of models ranging from global weather data to an altitude of over 40 km, to high resolution regional weather models, to wave and sea ice models. It can also retrieve archived NOMADS models.  rNOMADS can retrieve binary data in grib format as well as import ascii data directly into R by interfacing with the GrADS-DODS system.
Genetic association tests that use the rank-based inverse normal transformation (INT). These tests are recommend for continuous traits with non-normally distributed residuals. INT-based tests robustly control the type I error in settings where standard linear regression does not. Moreover, INT-based tests dominate standard linear regression in terms of power. INT-based tests may be classified into two types: tests that directly transform the phenotype (D-INT) and tests that transform phenotypic residuals (I-INT). Our omnibus test (O-INT) adaptively combines D-INT and I-INT into a single robust and statistically powerful approach.
Programmatic interface to the
    Web Service methods provided by the National 'Phenology' Network
    (<https://usanpn.org/>), which includes data on various life history
    events that occur at specific times.
Apply sensitivity analysis for offline policy evaluation, as
    implemented in Jung et al. (2017) <arXiv:1702.04690> based
    on Rosenbaum and Rubin (1983) <http://www.jstor.org/stable/2345524>.
Downloads Natural Resources Conservation Service (NRCS) data for sites in the Soil Climate Analysis Network (SCAN) <https://www.wcc.nrcs.usda.gov/scan/>, and Snow Telemetry (SNOTEL and SNOLITE) <https://www.wcc.nrcs.usda.gov/snow/> networks. Metadata can be returned for all sites in the NRCS' Air and Water Data Base (AWDB) <https://www.wcc.nrcs.usda.gov/report_generator/AWDB_Network_Codes.pdf>.
Utility functions to retrieve data from the UK National River Flow Archive (<http://nrfa.ceh.ac.uk/>). The package contains R wrappers to the UK NRFA data temporary-API. There are functions to retrieve stations falling in a bounding box, to generate a map and extracting time series and general information.
Routines to interact with the Numerai Machine Learning Tournament
  API <https://numer.ai>. The functionality includes the ability to automatically download the
  current tournament data, submit predictions, and to get information for your
  user. General 'GraphQL' queries can also be executed.
This web client interfaces Unpaywall <https://unpaywall.org/products/api>, formerly
    oaDOI, a service finding free full-texts of academic papers by linking DOIs with 
    open access journals and repositories. It provides unified access to various data sources 
    for open access full-text links including Crossref and the Directory of Open Access 
    Journals (DOAJ). API usage is free and no registration is required.
A collection of methods for the robust analysis of univariate and
    multivariate functional data, possibly in high-dimensional cases, and hence
    with attention to computational efficiency and simplicity of use.
Provides an interface to the OAuth 1.0 specification
        allowing users to authenticate via OAuth to the
        server of their choice.
Base S4-classes and functions for robust asymptotic statistics.
Includes 'sysdata.rda' file for packages of the 'RobASt' - family of packages; is
        currently used by package 'RobExtremes' only.
Conditionally unbiased bounded influence estimates as described in 
  Kuensch et al. (1989) <doi:10.1080/01621459.1989.10478791> in three special cases of 
  the generalized linear model: Bernoulli, Binomial, and Poisson distributed responses.
Methods for analysis of compositional data including robust
    methods, imputation, methods to replace rounded zeros, (robust) outlier
    detection for compositional data, (robust) principal component analysis for
    compositional data, (robust) factor analysis for compositional data, (robust)
    discriminant analysis for compositional data (Fisher rule), robust regression
    with compositional predictors and (robust) Anderson-Darling normality tests for
    compositional data as well as popular log-ratio transformations (addLR, cenLR,
    isomLR, and their inverse transformations). In addition, visualisation and
    diagnostic tools are implemented as well as high and low-level plot functions
    for the ternary diagram.
Robust pairwise correlations based on estimates of scale,
  particularly on "FastQn" one-step M-estimate.
Provides robust methods to detect change-points in uni- or multivariate time series. They can cope with corrupted data and heavy tails. One can detect changes in location, scale and dependence structure of a possibly multivariate time series. Procedures are based on Huberized versions of CUSUM tests proposed in Duerre and Fried (2019) <arXiv:1905.06201>.
Locations problems, M-estimates of coefficients and scale
        in linear regression, Weights for bounded influence regression,
        Covariance matrix of the coefficient estimates, Asymptotic
        relative efficiency of regression M-estimates, Robust testing
        in linear models, High breakdown point regression, M-estimates
        of covariance matrices, M-estimates for discrete generalized
        linear models.
We provide an outlier robust alternative of the function ets() in the 'forecast' package of Hyndman and Khandakar (2008) <DOI:10.18637/jss.v027.i03>. For each method of a class of exponential smoothing variants we made a robust alternative. The class includes methods with a damped trend and/or seasonal components. The robust method is developed by robustifying every aspect of the original exponential smoothing variant. We provide robust forecasting equations, robust initial values, robust smoothing parameter estimation and a robust information criterion. The method is described in more detail in Crevits and Croux (2016) <DOI:10.13140/RG.2.2.11791.18080>.
Optimally robust estimation for extreme value distributions using S4 classes and
            methods (based on packages 'distr', 'distrEx', 'distrMod', 'RobAStBase', and
            'ROptEst').
A set of functions to filter time series based on concepts
        from robust statistics.
Contains a single function named robFitConGraph() which includes two algorithms for robust estimation of scatter matrices subject to zero-constraints in its inverse. The methodology is described in Vogel & Tyler (2014) <doi:10.1093/biomet/asu041>. See robFitConGraph() function documentation for further details.
Bootstrap forecast densities for GARCH (Generalized Autoregressive Conditional Heteroskedastic) returns and volatilities using the robust residual-based bootstrap procedure of Trucios, Hotta and Ruiz (2017) <DOI:10.1080/00949655.2017.1359601>.
Many community detection algorithms have been developed in network 
    analysis. However, their applications leave unaddressed the statistical 
    validation of the results, for this reason we developed ROBIN (ROBustness 
    In Network), a useful method for the validation of community detection. 
    It has a double aim, it studies the robustness of a single community 
    detection algorithm and compares two community detection algorithms to 
    understand which provides the best partition. Reference in Annamaria Carissimo, 
    Luisa Cutillo, Italia De Feis (2018) <doi:10.1016/j.csda.2017.10.006>.
Execute API calls to the RobinHood <https://robinhood.com> investing platform. Functionality includes accessing account data and current holdings, retrieving investment statistics and quotes, placing and canceling orders, getting market trading hours, searching investments by popular tag, and interacting with watch lists.
Client for the Ocean Biogeographic Information System (<https://obis.org>).
Functions for the determination of optimally robust influence curves and
            estimators in case of normal location and/or scale.
Functions for the determination of optimally robust influence curves and
            estimators for preprocessing omics data, in particular gene expression data.
Perform mediation analysis via a (fast and robust) bootstrap test.
Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) <doi:10.1080/02664763.2017.1414164>. This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model.
Generation of dynamic coappearance matrices for elements
             within a text along with utilities to aid in the generation
	     of Gephi dynamic networks.
Provides functions to download and parse 'robots.txt' files.
        Ultimately the package makes it easy to check if bots
        (spiders, crawler, scrapers, ...) are allowed to access specific
        resources on a domain.
Calculates periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). Three main functions are included: RobPer() calculates the periodogram. Outlying periodogram bars (indicating a period) can be detected with betaCvMfit(). Artificial light curves can be generated using the function tsgen(). For more details see the corresponding article: Thieler, Fried and Rathjens (2016), Journal of Statistical Software 69(9), 1-36, <doi:10.18637/jss.v069.i09>.
Three-step regression and inference for cellwise and casewise contamination.
We implement the algorithm estimating the parameters of the robust regression model with compositional covariates. The model simultaneously treats outliers and provides reliable parameter  estimates. Publication reference: Mishra, A., Mueller, C.,(2019) <arXiv:1909.04990>.
Functions for the determination of optimally robust influence curves in case of
            linear regression with unknown scale and standard normal distributed errors where
            the regressor is random.
This package provides the function to calculate SVD, regularized SVD, robust SVD and robust regularized SVD method. The robust SVD methods use alternating iteratively reweighted least squares methods. The regularized SVD uses generalized cross validation to choose the optimal smoothing parameters.
Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>.  This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.
Multiple functions to compute robust survey statistics. The package 
    supports the computations of robust means, totals, and ratios. Available 
    methods are Huber M-estimators, trimming, and winsorization. The package 
    'robsurvey' complements the 'survey' package. The package additionally 
    includes a weighted version of the resistant line function of base R (line()), 
    as well as two median based simple regression estimators. The methods are 
    described in Hulliger (1995) 
    <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X199500114407/>.
Functions for conducting robust variance estimation (RVE) meta-regression using both large and small sample RVE estimators under various weighting schemes. These methods are distribution free and provide valid point estimates, standard errors and hypothesis tests even when the degree and structure of dependence between effect sizes is unknown. Also included are functions for conducting sensitivity analyses under correlated effects weighting and producing RVE-based forest plots.
Methods for robust statistics, a state of the art in the early
  2000s, notably for robust regression and robust multivariate analysis.
R functions for the computation of the truncated maximum
	     likelihood and the robust accelerated failure time regression 
	     for gaussian and log-Weibull case.
Functions for fitting a linear regression model with ARIMA
  errors using a filtered tau-estimate.
"Essential" Robust Statistics.
 Tools allowing to analyze data with robust methods.  This includes
 regression methodology including model selections and multivariate
 statistics where we strive to cover the book "Robust Statistics,
 Theory and Methods" by 'Maronna, Martin and Yohai'; Wiley 2006.
Bayesian robust fitting of linear mixed effects models through weighted likelihood equations and approximate Bayesian computation as proposed by Ruli et al. (2017) <arXiv:1706.01752>.
Implements full Bayesian analysis for calibrating mathematical models with new methodology for modeling the discrepancy function. It allows for emulation, calibration and prediction using complex mathematical model outputs and experimental data. See the reference: Mengyang Gu and Long Wang (2017) <arXiv:1707.08215>.
Robust mixture discriminant analysis (RMDA, Bouveyron & Girard, 2009) allows to build a robust supervised classifier from learning data with label noise. The idea of the proposed method is to confront an unsupervised modeling of the data with the supervised information carried by the labels of the learning data in order to detect inconsistencies. The method is able afterward to build a robust classifier taking into account the detected inconsistencies into the labels.
Testing homogeneity for generalized exponential tilt model. This package includes a collection of functions for (1) implementing methods for testing homogeneity for generalized exponential tilt model; and (2) implementing existing methods under comparison.
An object oriented solution for robust factor analysis. In the solution, new S4 classes "Fa", "FaClassic", "FaRobust", "FaCov", "SummaryFa" are created.
This package provides robust estimation for generalized
        additive models. It implements a fast and stable algorithm in
        Wong, Yao and Lee (2013). The implementation also contains
        three automatic selection methods for smoothing parameter. They
        are designed to be robust to outliers. For more details, see
        Wong, Yao and Lee (2013).
Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.
Robust methods for high-dimensional data, in particular linear
    model selection techniques based on least angle regression and sparse
    regression.
A method to fit linear mixed effects models robustly.
    Robustness is achieved by modification of the scoring equations
    combined with the Design Adaptive Scale approach.
Robust estimation of the generalized log gamma model is provided using Quantile Tau estimator, Weighted Likelihood estimator and Truncated Maximum Likelihood estimator. Functions for regression and censored data are also available.
Implements two-sample tests for paired data with missing values (Fong, Huang, Lemos and McElrath 2018, Biostatics, <doi:10.1093/biostatistics/kxx039>) and modified Wilcoxon-Mann-Whitney two sample location test, also known as the Fligner-Policello test.
Methods for aggregating ranked lists, especially lists of
        genes. It implements the Robust Rank Aggregation (Kolde et. al
        in preparation) and some other simple algorithms for the task.
        RRA method uses a probabilistic model for aggregation that is
        robust to noise and also facilitates the calculation of
        significance probabilities for all the elements in the final
        ranking.
A collection of functions to compute the Rao-Stirling diversity index
	(Porter and Rafols, 2009) <DOI:10.1007/s11192-008-2197-2> and its extension to
	acknowledge missing data (i.e.,	uncategorized references) by calculating its
	interval of uncertainty using	mathematical optimization as proposed in Calatrava
	et al. (2016) <DOI:10.1007/s11192-016-1842-4>.
	The Rao-Stirling diversity index is a well-established bibliometric indicator
	to measure the interdisciplinarity of scientific publications. Apart from the
	obligatory dataset of publications with their respective references and	a
	taxonomy of disciplines that categorizes references as well as a measure of
	similarity between the disciplines, the Rao-Stirling diversity index requires
	a complete categorization of all references of a publication into disciplines.
	Thus, it fails for a incomplete categorization; in this case, the robust
	extension has to be used, which encodes the uncertainty caused by missing
	bibliographic data as an uncertainty interval.
	Classification / ACM - 2012: Information systems ~ Similarity measures,
	Theory of computation ~ Quadratic	programming, Applied computing ~ Digital
	libraries and archives.
Linear regression functions using Huber and bisquare psi functions. Optimal weights are calculated using IRLS algorithm.
Functions for Robust Bayesian Small Area Estimation.
Robust single cell clustering and comparison of population compositions across tissues and 
  experimental models via similarity analysis from Magen 2019 <doi:10.1101/543199>.
Robustness -- 'eXperimental', 'eXtraneous', or 'eXtraordinary'
  Functionality for Robust Statistics.  In other words, methods which are not
  yet well established, often related to methods in package 'robustbase'.
This package computes traditional ROC curves and time-dependent ROC curves using the cross-validation, the 0.632 and the 0.632+ estimators. The 0.632+ estimator of time-dependent ROC curve is useful to estimate the predictive accuracy of prognostic signature based on high-dimensional data. For instance, it outperforms the other approaches, especially the cross-validation solution which is often used. The 0.632+ estimators correct the area under the curve in order to adequately estimate the prognostic capacities regardless of the overfitting level. This package also allows for the construction of diagnostic or prognostic scoring systems (penalized regressions). The methodology is adapted to complete data (penalized logistic regression associated with ROC curve) or incomplete time-to-event data (penalized Cox model associated with time-dependent ROC curve).
Functions for a classification method based on receiver
        operating characteristics (ROC). Briefly, features are selected
        according to their ranked AUC value in the training set. The
        selected features are merged by the mean value to form a
        metagene. The samples are ranked by their metagene value and
        the metagene threshold that has the highest accuracy in
        splitting the training samples is determined. A new sample is
        classified by its metagene value relative to the threshold. In
        the first place, the package is aimed at two class problems in
        gene expression data, but might also apply to other problems.
Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data.
Sensitivity (or recall or true positive rate), false positive rate, specificity, precision (or positive predictive value), negative predictive value, misclassification rate, accuracy, F-score- these are popular metrics for assessing performance of binary classifier for certain threshold. These metrics are calculated at certain threshold values. Receiver operating characteristic (ROC) curve is a common tool for assessing overall diagnostic ability of the binary classifier. Unlike depending on a certain threshold, area under ROC curve (also known as AUC), is a summary statistic about how well a binary classifier performs overall for the classification task. ROCit package provides flexibility to easily evaluate threshold-bound metrics. Also, ROC curve, along with AUC, can be obtained using different methods, such as empirical, binormal and non-parametric. ROCit encompasses a wide variety of methods for constructing confidence interval of ROC curve and AUC. ROCit also features the option of constructing empirical gains table, which is a handy tool for direct marketing. The package offers options for commonly used visualization, such as, ROC curve, KS plot, lift plot. Along with in-built default graphics setting, there are rooms for manual tweak by providing the necessary values as function arguments. ROCit is a powerful tool offering a range of things, yet it is very easy to use.
The Reproducible Open Coding Kit ('ROCK', and this package, 'rock')
  was developed to facilitate reproducible and open coding, specifically
  geared towards qualitative research methods. Although it is a
  general-purpose toolkit, three specific applications have been
  implemented, specifically an interface to the 'rENA' package that
  implements Epistemic Network Analysis ('ENA'), means to process notes
  from Cognitive Interviews ('CIs'), and means to work with a decentralized
  construct taxonomy ('DCT').
A collection of functions for interpretation and presentation
    of regression analysis.  These functions are used
    to produce the statistics lectures in
    <http://pj.freefaculty.org/guides>. Includes regression
    diagnostics, regression tables, and plots of interactions and
    "moderator" variables. The emphasis is on "mean-centered" and
    "residual-centered" predictors. The vignette 'rockchalk' offers a
    fairly comprehensive overview.  The vignette 'Rstyle' has advice
    about coding in R.  The package title 'rockchalk' refers to our
    school motto, 'Rock Chalk Jayhawk, Go K.U.'.
Provides functions to complete three-dimensional rock fabric and strain analyses following the Rf Phi, Fry, and normalized Fry methods. Also allows for plotting of results and interactive 3D visualization functionality.
Non-inferiority test and diagnostic test are very important in clinical trails.
  This package is to get a p value from the non-inferiority test for ROC curves from diagnostic test.
Provides the robust gamma rank correlation coefficient as introduced by
	     Bodenhofer, Krone, and Klawonn (2013) <DOI:10.1016/j.ins.2012.11.026>
	     along with a permutation-based rank correlation test.
	     The rank correlation coefficient and the test are explicitly
	     designed for dealing with noisy numerical data.
ROC graphs, sensitivity/specificity curves, lift charts,
  and precision/recall plots are popular examples of trade-off
  visualizations for specific pairs of performance measures. ROCR is a
  flexible tool for creating cutoff-parameterized 2D performance curves
  by freely combining two from over 25 performance measures (new
  performance measures can be added using a standard interface).
  Curves from different cross-validation or bootstrapping runs can be
  averaged by different methods, and standard deviations, standard
  errors or box plots can be used to visualize the variability across
  the runs. The parameterization can be visualized by printing cutoff
  values at the corresponding curve positions, or by coloring the
  curve according to cutoff. All components of a performance plot can
  be quickly adjusted using a flexible parameter dispatching
  mechanism. Despite its flexibility, ROCR is easy to use, with only
  three commands and reasonable default values for all optional
  parameters.
Plots the Receiver Operating Characteristics Surface for high-throughput class-skewed data, calculates the Volume under the Surface (VUS) and the FDR-Controlled Area Under the Curve (FCAUC), and conducts tests to compare two ROC surfaces. Computes eROC curve and the corresponding AUC for imperfect reference standard.
We develop the entire solution paths for ROC-SVM presented by Rakotomamonjy. The ROC-SVM solution path algorithm greatly facilitates the tuning procedure for regularization parameter, lambda in ROC-SVM by avoiding grid search algorithm which may be computationally too intensive. For more information on the ROC-SVM, see the report in the ROC Analysis in AI workshop(ROCAI-2004) : Hernàndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>.
Contains functions in order to estimate diagnostic and prognostic capacities of continuous markers. More precisely, one function concerns the estimation of time-dependent ROC (ROCt) curve, as proposed by Heagerty et al. (2000) <doi:10.1111/j.0006-341X.2000.00337.x>. One function concerns the adaptation of the ROCt theory for studying the capacity of a marker to predict the excess of mortality of a specific population compared to the general population. This last part is based on additive relative survival models and the work of Pohar-Perme et al. (2012) <doi:10.1111/j.1541-0420.2011.01640.x>. We also propose two functions for cut-off estimation in medical decision making by maximizing time-dependent expected utility function. Finally, we propose confounder-adjusted estimators of ROC and ROCt curves by using the Inverse Probability Weighting (IPW) approach.  For the confounder-adjusted ROC curve (without censoring), we also proposed the implementation of the estimator based on placement values proposed by Pepe and Cai (2004) <doi:10.1111/j.0006-341X.2004.00200.x>.
Receiver Operating Characteristic (ROC)-guided survival trees and ensemble algorithms are implemented, providing a unified framework for tree-structured analysis with censored survival outcomes. A time-invariant partition scheme on the survivor population was considered to incorporate time-dependent covariates. Motivated by ideas of randomized tests, generalized time-dependent ROC curves were used to evaluate the performance of survival trees and establish the optimality of the target hazard/survival function. The optimality of the target hazard function motivates us to use a weighted average of the time-dependent area under the curve (AUC) on a set of time points to evaluate the prediction performance of survival trees and to guide splitting and pruning. A detailed description of the implemented methods can be found in Sun et al. (2019) <arXiv:1809.05627>.
Function to estimate the ROC Curve of a continuous-scaled
        diagnostic test with the help of a second imperfect diagnostic
        test with binary responses.
'ODAM' (Open Data for Access and Mining) is a framework that implements a simple way to make research data broadly accessible and fully available for reuse, including by a script language such as R. The main purpose is to make a data set accessible online with a minimal effort from the data provider, and to allow any scientists or bioinformaticians to be able to explore the data set and then extract a subpart or the totality of the data according to their needs. The Rodam package has only one class, 'odamws', that provides methods to allow you to retrieve online data using 'ODAM' Web Services. This obviously requires that data are implemented according the 'ODAM' approach , namely that the data subsets were deposited in the suitable data repository in the form of TSV files associated with  their metadata also described  in TSV files. See <http://www.slideshare.net/danieljacob771282/odam-open-data-access-and-mining>.
An implementation of R's DBI interface using ODBC package as a
    back-end. This allows R to connect to any DBMS that has a ODBC driver.
An extension for RODBC package adding support for parameterized
    queries.
A collection of functions for numerical construction of optimal discriminating designs. At the current moment T-optimal designs (which maximize the lower bound for the power of F-test for regression model discrimination), KL-optimal designs (for lognormal errors) and their robust analogues can be calculated with the package.
Show physics, math and engineering students how an ODE solver
    is made and how effective R classes can be for the construction of
    the equations that describe natural phenomena. Inspiration for this work 
    comes from the book on "Computer Simulations in Physics" 
    by Harvey Gould, Jan Tobochnik, and Wolfgang Christian. 
    Book link: <http://www.compadre.org/osp/items/detail.cfm?ID=7375>.
Provides an R6 class and several utility methods to
    facilitate the implementation of models based on ordinary
    differential equations. The heart of the package is a code generator
    that creates compiled 'Fortran' (or 'R') code which can be passed to
    a numerical solver. There is direct support for solvers contained
    in packages 'deSolve' and 'rootSolve'.
Fetch and process Hillary Rodham Clinton's "personal" emails.
This package implements an interface to Oracle Data Mining
        (ODM). It provides an ideal environment for rapid development
        of demos and proof of concept data mining studies. It
        facilitates the prototyping of vertical applications and makes
        ODM and the RDBMS environment easily accessible to
        statisticians and data analysts familiar with R but not fluent
        in SQL or familiar with the database environment. It also
        facilitates the benchmarking and testing of ODM functionality
        including the production of summary statistics, performance
        metrics and graphics. It enables the scripting and control of
        production data mining methodologies from a high-level
        environment. Oracle Data Mining (ODM) is an option of Oracle
        Relational Database Management System (RDBMS) Enterprise
        Edition (EE). It contains several data mining and data analysis
        algorithms for classification, prediction, regression,
        clustering, associations, feature selection, anomaly detection,
        feature extraction, and specialized analytics. It provides
        means for the creation, management and operational deployment
        of data mining models inside the database environment. For more
        information consult the entry for "Oracle Data Mining" in
        Wikipedia (en.wikipedia.org).
The R Optimization Infrastructure ('ROI') is a
    sophisticated framework for handling optimization problems in R.
	More information can be found on the 'ROI' homepage <http://roi.r-forge.r-project.org/>.
A collection of non-linear optimization problems with box bounds
             transformed into 'ROI' optimization problems.
             This package provides a wrapper around the 'globalOptTests'
             which provides a collection of global optimization problems.
             More information can be found in the 'README' file.
The mixed integer programming library 'MIPLIB' (see <http://miplib.zib.de/>) 
	is commonly used to compare the performance of mixed integer optimization solvers.
	This package provides functions to access 'MIPLIB' from the 
	'R' Optimization Infrastructure ('ROI'). More information about 'MIPLIB'
	can be found in the paper by Koch et al. available at
	<http://mpc.zib.de/index.php/MPC/article/viewFile/56/28>.
	The 'README.md' file illustrates how to use this package.
A collection of 'ROI' optimization problems based on the 'NETLIB-LP' collection.
  'Netlib' is a software repository, which amongst many other software for scientific computing contains a collection of linear programming problems. The purpose of this package is to make 
  this problems easily accessible from 'R' as 'ROI' optimization problems.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'alabama' solver for solving nonlinear optimization problems.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the COIN-OR Clp open-source solver from the COIN-OR suite <https://projects.coin-or.org/>.
	     It allows for solving linear programming with continuous objective variables 
	     keeping sparse constraints definition.
Enhances the R Optimization Infrastructure (ROI) package by registering
	     the 'CPLEX' commercial solver. It allows for solving mixed integer quadratically
	     constrained programming (MIQPQC) problems as well as all
	     variants/combinations of LP, QP, QCP, IP.
Enhances the R Optimization Infrastructure ('ROI') package
        	 with the 'DEoptim' and 'DEoptimR' package. 'DEoptim' is
        	 used for unconstrained optimization and 'DEoptimR' for
        	 constrained optimization.
Enhances the 'R' Optimization Infrastructure ('ROI') package
	     with the Embedded Conic Solver ('ECOS') for solving conic optimization problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package by registering
	     the free 'GLPK' solver. It allows for solving mixed integer linear programming ('MILP')
	     problems as well as all variants/combinations of 'LP', 'IP'.
Enhances the R Optimization Infrastructure ('ROI') package 
	     by registering the ipop solver from package 'kernlab'.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with the 'lp_solve' solver.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with the possibility to obtain multiple solutions for linear 
             problems with binary variables. The main function is copied 
             (with small modifications) from the relations package.
Enhances the 'R' Optimization Infrastructure ('ROI') package
             with a connection to the 'neos' server. 'ROI' optimization
             problems can be directly be sent to the 'neos' server
             and solution obtained in the typical 'ROI' style.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'NLopt' solver for solving nonlinear optimization problems.
Enhances the R Optimization Infrastructure ('ROI') package
        with the 'optimx' package.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the quadratic solver 'qpOASES'. More information about
         'qpOASES' can be found at <https://projects.coin-or.org/qpOASES/>.
Enhances the R Optimization Infrastructure ('ROI') package
	     by registering the 'quadprog' solver. It allows for solving quadratic programming (QP)
	     problems.
Enhances the 'R' Optimization Infrastructure ('ROI') package
         with the 'SCS' solver for solving convex cone problems.
Enhances the R Optimization Infrastructure ('ROI') package by registering
	     the 'SYMPHONY' open-source solver from the COIN-OR suite. It allows for
	     solving mixed integer linear programming (MILP) problems as well as all
	     variants/combinations of LP, IP.
Fast and efficient computation of rolling statistics for time-series data.
Reports errors and messages to Rollbar, the error tracking platform <https://rollbar.com>.
R Markdown output formats based on JavaScript libraries such as
    'Scrollama' (<https://github.com/russellgoldenberg/scrollama>) for storytelling.
Functions to perform propensity score matching on rolling entry interventions for which a suitable "entry" date is not observed for nonparticipants. For more details, please reference Witman, Beadles, Liu, Larsen, Kafali, Gandhi, Amico, and Hoerger (2018) <https://onlinelibrary.wiley.com/doi/abs/10.1111/1475-6773.13086>.
Methods for fast rolling and expanding linear regression models. That is, series of linear regression models estimated on either an expanding window of data or a moving window of data. The methods use rank-one updates and downdates of the upper triangular matrix from a QR decomposition (see Dongarra, Moler, Bunch, and Stewart (1979) <doi:10.1137/1.9781611971811>).
Functions to convert an R colour specification to a colour name. The user can select and create different lists of colour names and different colour metrics for the conversion.
A colour list and colour metric based on the ISCC-NBS System of Color Designation for use with the 'roloc' package for converting colour specifications to colour names.
Implements the rank-ordered logit (RO-logit) model for stratified 
    analysis of continuous outcomes introduced by 
    Tan et al. (2017) <doi:10.1177/0962280217747309>. Model diagnostics based on 
    the heuristic residuals and estimates in linear scales are available from 
    the package, and outcomes with ties are supported.
Provides fast procedures for exploring all pairs of
    cutpoints of a single covariate with respect to survival and determining
    optimal cutpoints using a hierarchical method and various ordered logrank tests.
Using enrichment of genome-wide association summary statistics to
  identify trait-relevant cellular functional annotations.
Provides the possibility to plot Lexis surface maps (heat maps) of rates of mortality improvement. Raw data to be plotted can be read from the Human Mortality Database using code originally written by Tim Riffe. The European Research Council has provided financial support under the European Community's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement no. 263744.
R on FHIR is an easy to use wrapper around the 'HL7 FHIR' REST API (STU 3). It provides tools to easily read and search resources on a FHIR server and brings the results into the R environment. R on FHIR is based on the FhirClient of the official 'HL7 FHIR .NET API', also made by Firely.
This package contains the Rook specification and
 convenience software for building and running Rook applications. To
 get started, be sure and read the 'Rook' help file first.
A set of tools to reconstruct ordered ontogenic trajectories from
    single cell RNAseq data.
Implementation of Taylor Regression Estimator (TRE), 
   Tulip Extreme Finding Estimator (TEFE), Bell Extreme Finding Estimator (BEFE),
   Integration Extreme Finding Estimator (IEFE) and 
   Integration Root Finding Estimator (IRFE) for roots, extrema and inflections of a curve .     
   Christopoulos, DT (2019) <doi:10.13140/RG.2.2.17158.32324> .
   Christopoulos, DT (2016) <doi:10.2139/ssrn.3043076> .
   Christopoulos, DT (2016) <https://veltech.edu.in/wp-content/uploads/2016/04/Paper-04-2016.pdf> .
   Christopoulos, DT (2014) <arXiv:1206.5478v2 [math.NA]> .
Routines to find the root of nonlinear functions, and to perform steady-state and equilibrium analysis of ordinary differential equations (ODE). 
  Includes routines that: (1) generate gradient and jacobian matrices (full and banded),
  (2) find roots of non-linear equations by the 'Newton-Raphson' method, 
  (3) estimate steady-state conditions of a system of (differential) equations in full, banded or sparse form, using the 'Newton-Raphson' method, or by dynamically running,
  (4) solve the steady-state conditions for uni-and multicomponent 1-D, 2-D, and 3-D partial differential equations, that have been converted to ordinary differential equations
    by numerical differencing (using the method-of-lines approach).
  Includes fortran code.
Functions for hypothesis testing in single and double Wishart
    settings, based on Roy's largest root. This test statistic is especially
    useful in multivariate analysis. The computations are based on results by
    Chiani (2014) <DOI:10.1016/j.jmva.2014.04.002> and Chiani (2016)
    <DOI:10.1016/j.jmva.2015.10.007>. They use the fact that the CDF is related
    to the Pfaffian of a matrix that can be computed in a finite number of
    iterations. This package takes advantage of the Boost and Eigen C++ libraries
    to perform multi-precision linear algebra.
Trees Classification and Regression using multivariate nodes calculated by an exhaustive numerical approach. We propose a new concept of decision tree, including multivariate knots and non hierarchical pathway. This package's model uses a multivariate nodes tree that calculates directly a risk score for each observation for the state Y observed. Nguyen JM, Gaultier A, Antonioli D (2015) <doi:10.1016/j.respe.2018.03.088> Castillo JM, Knol AC, Nguyen JM, Khammari A, Saint Jean M, Dreno B (2016) <doi:10.1684/ejd.2016.2826> Vildy S, Nguyen JM, Gaultier A, Khammari A, Dreno B (2017) <doi:10.1684/ejd.2016.2955> Nguyen JM, Gaultier A, Antonioli D (2018) <doi:10.1016/j.respe.2018.03.088>.
Selects one model with variable selection FDR controlled at a
    specified level. A q-value for each potential variable is also returned. The
    input, variable selection counts over many bootstraps for several levels of
    penalization, is modeled as coming from a beta-binomial mixture
    distribution.
Allows access to air quality data from the API of the OpenAQ
    platform <https://docs.openaq.org/>, with the different services the API offers
    (getting measurements for a given query, getting latest measurements, getting
    lists of available countries/cities/locations). This package has been peer-reviewed by rOpenSci (v. 0.1.0).
Installs 'OpenCV' for use by other packages. 'OpenCV' <https://opencv.org/> 
    is library of programming functions mainly aimed at real-time computer 
    vision. This 'Lite' version contains the stable base version of 'OpenCV' and 
    does not contain any of its externally contributed modules.
'Rapid7' collects 'cybersecurity' data and makes it available via
    their 'Open Data' <http://opendata.rapid7.com> portal which has an API. Tools are
    provided to assist in querying for available data sets and downloading any
    data set authorized to a free, registered account.
Provides a client for the API of OpenDota. OpenDota is a web service which is provide DOTA2 real time data. Data is collected through the Steam WebAPI. With ROpenDota you can easily grab the latest DOTA2 statistics in R programming such as latest match on official international competition, analyzing your or enemy performance to learn their strategies,etc. Please see <https://github.com/rosdyana/ROpenDota> for more information.
Provide a simple interface to Bloomberg's OpenFIGI API. Please
    see <https://openfigi.com> for API details and registration. You may be
    eligible to have an API key to accelerate your loading process.
An R package for the OpenSecrets.org web services API.
OpenWeatherMap (OWM) <http://openweathermap.org/api> is a service providing weather related data.
           This package can be used to access current weather data for one location or several locations.
           It can also be used to forecast weather for 5 days with data for every 3 hours.
Provides string arithmetic, reassignment operators, logical operators that handle missing values, and extra logical operators such as floating point equality and all or nothing. The intent is to allow R users to write code that is easier to read, write, and maintain while providing a friendlier experience to new R users from other language backgrounds (such as 'Python') who are used to concepts such as x += 1 and 'foo' + 'bar'.
Reproducible, programmatic retrieval of datasets from the
    Roper Center data archive.  The Roper Center for Public Opinion
    Research <https://ropercenter.cornell.edu> maintains the largest 
    archive of public opinion data in existence, but researchers using
    these datasets are caught in a bind.  The Center's terms and conditions
    bar redistribution of downloaded datasets, but to ensure that one's 
    work can be reproduced, assessed, and built upon by others, one must
    provide access to the raw data one employed.  The `ropercenter`
    package cuts this knot by providing registered users with programmatic,
    reproducible access to Roper Center datasets from within R.
Read the data from Origin(R) project files ('*.opj')
	<https://www.originlab.com/doc/User-Guide/Origin-File-Types>.
	No write support is planned. More object types may be available
	to be imported later.
A suite of classes and methods which provide low-level support for modeling provenance between in-memory R objects. This is an infrastructure package and is not intended to be used directly by end-users.
Optimally robust estimation in general smoothly parameterized models using S4
            classes and methods.
Optimally robust estimation using S4 classes and methods. Old version still
            needed for current versions of ROptRegTS and RobRex.
Perform general purpose optimization in R using C++. A unified 
    wrapper interface is provided to call C functions of the five optimization 
    algorithms ('Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B' and 'SANN') underlying 
    optim().
R package extracts optimizely test results and test information using Optimizely REST API. Only read functionality is supported for analysis and reporting.
Optimally robust estimation for regression-type models using S4 classes and
            methods.
Matrix reconstruction, also known as matrix completion, is
    the task of inferring missing entries of a partially observed matrix.
    This package provides a method called OptSpace,
    which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009)
    <doi:10.1109/ISIT.2009.5205567> for a case under low-rank assumption.
Oracle Database interface (DBI) driver for R.
   This is a DBI-compliant Oracle driver based on the OCI.
Client for the 'Orcid.org' API (<https://orcid.org/>).
    Functions included for searching for people, searching by 'DOI',
    and searching by 'Orcid' 'ID'.
Implementation of Robust Ordinal Regression for multiple criteria value-based sorting with preference information provided in form of possibly imprecise assignment examples, assignment-based pairwise comparisons, and desired class cardinalities [Kadzinski et al. 2015, <doi:10.1016/j.ejor.2014.09.050>].
The package provides functions to deal with binary classification
  problems in the presence of imbalanced classes. Synthetic balanced samples are  
  generated according to ROSE (Menardi and Torelli, 2013).  
  Functions that implement more traditional remedies to the class imbalance
  are also provided, as well as different metrics to evaluate a learner accuracy.
  These are estimated by holdout, bootstrap or cross-validation methods.
When teaching statistics, it can often be desirable to
  uncouple the content from specific software packages. To ease such
  efforts, the Rosetta Stats website (<https://rosettastats.com>) allows
  comparing analyses in different packages. This package is the companion
  to the Rosetta Stats website, aiming to provide functions that produce
  output that is similar to output from other statistical packages, thereby
  facilitating 'software-agnostic' teaching of statistics.
'Rosette' is an API for multilingual text analysis and information
    extraction. More information can be found at <https://developer.rosette.com>.
Download and plot Open Street Map <http://www.openstreetmap.org/>,
    Bing Maps <http://www.bing.com/maps> and other tiled map sources. Use to create 
    basemaps quickly and add hillshade to vector-based maps.
Implementation of robust sparse PCA using the ROSPCA algorithm 
             of Hubert et al. (2016) <DOI:10.1080/00401706.2015.1093962>.
Provides bindings to the 'OSQP' solver, which can solve sparse convex quadratic programming problems with optional equality and inequality constraints.
Creates reproducible academic projects with integrated academic elements, including datasets, references, codes, images, manuscripts, dissertations, slides and so on. These elements are well connected so that they can be easily synchronized and updated.
Implementation of the tests for rotational symmetry on the
     hypersphere proposed in García-Portugués, Paindaveine and Verdebout
     (2019) <arXiv:1706.05030>. The package also implements the proposed
     distributions on the hypersphere, based on the tangent-normal
     decomposition, and allows for the replication of the data
     application considered in the paper.
Fit and deploy rotation forest models ("Rodriguez, J.J., Kuncheva,
    L.I., 2006. Rotation forest: A new classifier ensemble method. IEEE Trans.
    Pattern Anal. Mach. Intell. 28, 1619-1630 <doi:10.1109/TPAMI.2006.211>") for binary classification.
    Rotation forest is an ensemble method where each base classifier (tree) is
    fit on the principal components of the variables of random partitions of
    the feature set.
Tools for working with rotational data, including
    simulation from the most commonly used distributions on SO(3),
    methods for different Bayes, mean and median type estimators for
    the central orientation of a sample, confidence/credible
    regions for the central orientation based on those estimators and
    a novel visualization technique for rotation data.  Most recently,
    functions to identify potentially discordant (outlying) values
    have been added.
R build of Rothermel's (1972) model for surface fire rate of spread with some additional utilities (uncertainty propagation, standard fuel model selection, fuel model optimization by genetic algorithm) and sample datasets.
An interface to the 'Open Tree of Life' API to retrieve
    phylogenetic trees, information about studies used to assemble the
    synthetic tree, and utilities to match taxonomic names to 'Open Tree
    identifiers'. The 'Open Tree of Life' aims at assembling a
    comprehensive phylogenetic tree for all named species.
Conditionally rotate or back-up files based on
    their size or the date of the last backup; inspired by the 'Linux'
    utility 'logrotate'.
A set of functions to support Xiong K, 'Roughened Random Forests for Binary Classification' (2014). The functions include RRFA, RRFB, RRFC1-RRFC7, RRFD and RRFE. RRFB and RRFC6 are usually recommended. RRFB is much faster than RRFC6.
Rough Sets were introduced by Zdzislaw Pawlak on his book "Rough Sets: Theoretical Aspects of Reasoning About Data". Rough Sets provide a formal method to approximate crisp sets when the set-element belonging relationship is either known or undetermined. This enables the use of Rough Sets for reasoning about incomplete or contradictory knowledge. A decision table is a prescription of the decisions to make given some conditions. Such decision tables can be reduced without losing prescription ability. This package provides the classes and methods for knowledge reduction from decision tables as presented in the chapter 7 of the aforementioned book. This package provides functions for calculating the both the discernibility matrix and the essential parts of decision tables.
Implementations of algorithms for data analysis
    based on the rough set theory (RST) and the fuzzy rough set theory (FRST). We
    not only provide implementations for the basic concepts of RST and FRST but also
    popular algorithms that derive from those theories. The methods included in the
    package can be divided into several categories based on their functionality:
    discretization, feature selection, instance selection, rule induction and classification
    based on nearest neighbors. RST was introduced by Zdzisław Pawlak in 1982
    as a sophisticated mathematical tool to
    model and process imprecise or incomplete information. By using
    the indiscernibility relation for objects/instances, RST does not require
    additional parameters to analyze the data. FRST is an extension of RST. The
    FRST combines concepts of vagueness and indiscernibility that are expressed
    with fuzzy sets (as proposed by Zadeh, in 1965) and RST.
Visualize the objects in orbits in 2D and 3D. The packages is under developing to plot the orbits of objects in polar coordinate system. See the examples in demo.
An R wrapper to the 'Internet Chuck Norris database' ('ICNDb') API for 
  generating random Chuck Norris facts.
Detecting outliers using robust methods, 
 	i.e. the Median Absolute Deviation (MAD) for univariate outliers; Leys, Ley, Klein, Bernard, & Licata (2013) <doi:10.1016/j.jesp.2013.03.013>
        and the Mahalanobis-Minimum Covariance Determinant (MMCD) for multivariate outliers; Leys, C., Klein, O., Dominicy, Y. & Ley, C. (2018) <doi:10.1016/j.jesp.2017.09.011>.
        There is also the more known but less robust Mahalanobis distance method, only for comparison purposes.
In order to make sure that web request ends up in the correct 
    handler function a router is often used. 'routr' is a package implementing a
    simple but powerful routing functionality for R based servers. It is a fully
    functional 'fiery' plugin, but can also be used with other 'httpuv' based
    servers.
Provides utilities which interact with all R objects as
    if they were arranged in rows.  It allows more consistent and predictable 
    output to common functions, and generalizes a number of utility functions to
    to be failsafe with any number and type of input objects.
Generate your Rd documentation, 'NAMESPACE' file, and collation 
    field using specially formatted comments. Writing documentation in-line
    with code makes it easier to keep your documentation up-to-date as your
    requirements change. 'Roxygen2' is inspired by the 'Doxygen' system for C++.
Converts elements of 'roxygen' documentation to
    'markdown'.
Performs a multivariate normality test based on Royston's H test
Analysis of corneal data obtained from a Placido disk corneal 
    topographer with calculation of irregularity indices. A corneal topographer is 
    an ophthalmic clinical device that obtains measurements in the cornea (the anterior 
    part of the eye). A Placido disk corneal topographer makes use of the Placido disk 
    (Rowsey et al. (1981), <doi:10.1001/archopht.1981.03930011093022>, Rand et al.
    (1997), <doi:10.1016/S0886-3350(99)00355-7>), which produce a circular pattern
    of measurement nodes. The raw information measured by such a topographer is 
    used by practitioners to analyze curvatures, to study optical aberrations, or
    to diagnose specific conditions of the eye. The rPACI package allows the 
    calculation of the corneal irregularity indices described in Ramos-Lopez et al.
    (2013), <doi:10.1097/OPX.0b013e3182843f2a>, and that were firstly introduced in
    Ramos-Lopez et al. (2011), <doi:10.1097/OPX.0b013e3182843f2a>. It provides a 
    simple interface to read corneal topography data files as exported by a typical
    Placido disk topographer, to compute the irregularity indices mentioned before,
    and to display summary plots that are easy to interpret for a clinician.
Packed bar charts are a variation of treemaps for visualizing skewed data.  The concept was introduced by Xan Gregg at 'JMP'.
Design and analysis of confirmatory adaptive clinical trials with continuous, binary, and survival endpoints according to the methods described in the monograph by Wassmer and Brannath (2016) <doi:10.1007/978-3-319-32562-0>. This includes classical group sequential as well as multi-stage adaptive hypotheses tests that are based on the combination testing principle.
A set of functions to build simple 
        GUI controls for R functions.  These are built on the 'tcltk' package. 
        Uses could include changing a parameter on a graph by animating it 
        with a slider or a "doublebutton", up to more sophisticated control 
        panels.
        Some functions for specific graphical tasks, referred to as 'cartoons',
        are provided.
Recursive partitioning for classification, 
  regression and survival trees.  An implementation of most of the 
  functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.
Plot 'rpart' models. Extends plot.rpart() and text.rpart()
             in the 'rpart' package.
This package contains additional tools for working with rpart
    objects.  Most importantly, it includes methods for converting rpart rules
    into a series of structured tables sufficient for executing the decision
    tree completely in SQL.
Provides algorithims for randomly sampling a feasible set defined
    by a given total and number of elements using integer partitioning.
This package contains functions that allow to build
        classification trees for ordinal responses within the CART
        framework. The trees are grown using the Generalized Gini
        impurity function, where the misclassification costs are given
        by the absolute or squared differences in scores assigned to
        the categories of the response. Pruning is based on the total
        misclassification rate or on the total misclassification cost.
Generating visual charting patterns and noise,
    smoothing to find a signal in noisy time series and enabling
    users to apply their findings to real life data.
Suppose we have a data matrix, which is the superposition of a low-rank component and a sparse component. Candes, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust principal component analysis?. Journal of the ACM (JACM), 58(3), 11. prove that we can recover each component individually under some suitable assumptions. It is possible to recover both the low-rank and the sparse components exactly by solving a very convenient convex program called Principal Component Pursuit; among all feasible decompositions, simply minimize a weighted combination of the nuclear norm and of the L1 norm. This package implements this decomposition algorithm resulting with Robust PCA approach.
Allows users to identify relevant clinical codes and
    automate the construction of clinical code lists for primary care database
    studies. This package is analogous to the Stata command pcdsearch.
This package implements the R-PCLR (Random-Penalized
        Conditional Logistic Regression) algorithm for obtaining
        variable importance. The algorithm is applicable for the
        analysis of high dimensional data from matched case-control
        studies.
Provides tools to read, write, visualize PDB files and perform some
    structural manipulations.
Monthly Pacific Decadal Oscillation (PDO) index
    values from January 1900 to present.
The quantitative measurement and detection of molecules in HPLC should be carried out by an accurate description of chromatographic peaks. In this package non-linear fitting using a modified Gaussian model with a parabolic variance (PVMG) has been implemented to obtain the retention time and height at the peak maximum. This package also includes the traditional Van Deemter approach and two alternatives approaches to characterize chromatographic column.
Implements the methodology proposed by Anderlucci, Fortunato and Montanari (2019) <arXiv:1909.10832> for high-dimensional unsupervised classification. The random projection ensemble clustering algorithm applies a Gaussian Mixture Model to different random projections of the high-dimensional data and selects a subset of solutions accordingly to the Bayesian Information Criterion, computed here as discussed in Raftery and Dean (2006) <doi:10.1198/016214506000000113>. The clustering results obtained on the selected projections are then aggregated via consensus to derive the final partition.
Implements the fast iterative shrinkage-thresholding algorithm
             (FISTA) algorithm to fit a Gamma distribution with an elastic net 
             penalty as described in Chen, Arakvin and Martin (2018) 
             <arxiv:1804.07780>. An implementation for the case of the 
             exponential distribution is also available, with details available 
             in Chen and Martin (2018) <https://papers.ssrn.com/abstract_id=3085672>.
Computes the influence functions time series of the returns for the risk and 
             performance measures as mentioned in Zhang and Martin (2017) 
             <https://ssrn.com/abstract=2747179> as well as Chen and Martin (2018) 
             <https://ssrn.com/abstract=3085672>. Also evaluates estimators influence
             functions at a set of parameter values and plots them to display the shapes of 
             the influence functions.
Implements the methodology of "Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Stat. Soc., Ser. B. (with discussion), 79, 959--1035". The random projection ensemble classifier is a general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment.
Estimates of standard errors of popular risk and performance 
             measures for asset or portfolio returns using methods as described in 
             Chen and Martin (2019) <https://ssrn.com/abstract=3085672>.
This reduced piecewise exponential survival software implements the likelihood ratio test and backward elimination procedure in Han, Schell, and Kim (2012 <doi:10.1080/19466315.2012.698945>, 2014 <doi:10.1002/sim.5915>), and Han et al. (2016 <doi:10.1111/biom.12590>). Inputs to the program can be either times when events/censoring occur or the vectors of total time on test and the number of events. Outputs of the programs are times and the corresponding p-values in the backward elimination. Details about the model and implementation are given in Han et al. 2014. This program can run in R version 3.2.2 and above.
The purpose of this package is to factor out logic and math common
    to Item Factor Analysis fitting, diagnostics, and analysis. It is
    envisioned as core support code suitable for more specialized IRT packages
    to build upon. Complete access to optimized C functions are made available
    with R_RegisterCCallable().
Allows ad hoc queries and reading and
    writing data frames to and from a database.
Provides an R interface to the 'PHAST'(<http://compgen.cshl.edu/phast/>) software
    (Phylogenetic Analysis with Space/Time Models).  It can be used for
    many types of analysis in comparative and evolutionary genomics,
    such as estimating models of evolution from sequence data, scoring
    alignments for conservation or acceleration, and predicting
    elements based on conservation or custom phylogenetic hidden Markov
    models.  It can also perform many basic operations on multiple
    sequence alignments and phylogenetic trees.
Rphylip provides an R interface for the PHYLIP package. All users
    of Rphylip will thus first have to install the PHYLIP phylogeny methods
    program package (Felsenstein 2013). See http://www.phylip.com for more 
	information about installing PHYLIP.
Tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data. Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny.
Work with 'Phylopic' web service (<http://phylopic.org/api/>) 
    to get 'silhouette' images of 'organisms', search names, and more.
    Includes functions for adding 'silhouettes' to both base plots and
    ggplot2 plots.
Bindings for the 'PicoSAT' solver to solve Boolean satisfiability problems (SAT).
             The boolean satisfiability problem asks the question if a given boolean formula can be TRUE; 
             i.e. does there exist an assignment of TRUE/FALSE for each variable such that the whole formula is TRUE?
             The package bundles 'PicoSAT' solver release 965 <http://www.fmv.jku.at/picosat/>.
Get information (boards, pins and
    users) from the Pinterest <http://www.pinterest.com>
    API.
R interaction with 'pipedrive.com API'. 
  All functions were created and documented according to <https://developers.pipedrive.com/docs/api/v1/>. 
  Created with the objective of offering integration and even the development of 'APIs'. 
  Making possible to create workflows and easily downloading databases for analysis.
Build powerful pivot tables (aka Pivot Grid, Pivot Chart, Cross-Tab)
    and dynamically slice & dice / drag 'n' drop your data. 'rpivotTable' is a
    wrapper of 'pivottable', a powerful open-source Pivot Table library implemented
    in 'JavaScript' by Nicolas Kruchten. Aligned to 'pivottable' v2.19.0.
A programmatic interface to the 'SOLR' based
    search API (<http://api.plos.org/>) provided by the Public
    Library of Science journals to search their articles.
    Functions are included for searching for articles, retrieving
    articles, making plots, doing 'faceted' searches,
    'highlight' searches, and viewing results of 'highlighted'
    searches in a browser.
Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary:
  (1) The graph parameters file, which name is passed to the function 'rplotengine()'.
    The user can specify the titles, choose the type of the graph, graph output formats
    (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend,
    whether to show or not a grid at the background, etc.
  (2) The data to be plotted, which name is specified as a parameter ('data_filename')
    in the previous file. This data file has a tabulated format, with a single character
    (e.g. tab) between each column, and a headers line located in the first row.
    Optionally, the file could include data columns for showing confidence intervals.
Really Poor Man's Graphical User Interface, used to create interactive R analysis sessions with simple R commands.
Recursively Partitioned Mixture Model for Beta and Gaussian Mixtures.  
    This is a model-based clustering algorithm that returns a hierarchy
    of classes, similar to hierarchical clustering, but also similar to
    finite mixture models.
Implements the P-model (Stocker et al., 2019 <doi:10.5194/gmd-2019-200>), predicting acclimated parameters of the enzyme kinetics of C3 photosynthesis, assimilation, and dark respiration rates as a function of the environment (temperature, CO2, vapour pressure deficit, light, atmospheric pressure).
Functions to allow users to build and analyze design consistent 
    tree and random forest models using survey data from a complex sample 
    design.  The algorithm can fit a linear model to survey data in each 
    node obtained by recursively partitioning the data.  The splitting 
    variables and selected splits are obtained using a randomized permutation 
    test procedure which adjusted for complex sample design features used to 
    obtain the data. Likewise the model fitting algorithm produces 
    design-consistent coefficients to any specified least squares linear model 
    between the dependent and independent variables used in the end nodes.
    The main functions return the resulting binary tree or random forest as 
    an object of "rpms" or "rpms_forest" type. The package also provides a number 
    of functions and methods available for use with these object types.
A set of functions to analyze and print the development of a
    commodity using the Point and Figure (P&F) approach. A P&F processor can be used
    to calculate daily statistics for the time series. These statistics can be used
    for deeper investigations as well as to create plots. Plots can be generated as
    well known X/O Plots in plain text format, and additionally in a more graphical
    format.
Wrapper for the 'PoetryDB' API <http://poetrydb.org> that allows for interaction and data extraction from the 
  database in an R interface. The 'PoetryDB' API is a database of poetry and poets implemented with 'MongoDB' to 
  enable developers and poets to easily access one of the most comprehensive poetry databases currently available.
A polyhedra database scraped from various sources as R6 objects and 'rgl' visualizing capabilities.
PDF tools based on the Poppler PDF rendering library.
  See <http://poppler.freedesktop.org/> for more information on Poppler.
A collection of tools used to generate
        various types of random portfolios.  The weights of these
        portfolios are random variables derived from truncated
        continuous random variables.
Provides an interface between R and 'PostGIS'-enabled
    'PostgreSQL' databases to transparently transfer spatial
    data. Both vector (points, lines, polygons) and raster data are
    supported in read and write modes. Also provides convenience
    functions to execute common procedures in 'PostgreSQL/PostGIS'.
Integrates R and the 'PostgreSQL/PostGIS' database 
    system to build and manage animal trajectory (movement) data sets. 
    The package relies on 'ltraj' objects from the R package 'adehabitatLT',
    building the analogous 'pgtraj' data structure in 'PostGIS'. Functions
    allow users to seamlessly transfer between 'ltraj' and 'pgtraj', as
    well as build new 'pgtraj' directly from location data stored in the 
    database.
Fully 'DBI'-compliant 'Rcpp'-backed interface to 'PostgreSQL' <https://www.postgresql.org/>,
    an open-source relational database.
Database interface and 'PostgreSQL' driver for 'R'.
 This package provides a Database Interface 'DBI' compliant 
 driver for 'R' to access 'PostgreSQL' database systems.  
 In order to build and install this package from source, 'PostgreSQL' 
 itself must be present your system to provide 'PostgreSQL' functionality 
 via its libraries and header files. These files are provided as
 'postgresql-devel' package under some Linux distributions.
 On 'macOS' and 'Microsoft Windows' system the attached 'libpq' library source will be used.
The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) <doi:10.1080/10543406.2013.860156>.
Using some association schemes to obtain a new series of resolvable partially pairwise balanced designs (RPPBD) and space-filling designs.
Reads in sample description and slide description files and
        annotates the expression values taken from GenePix results files
	(text file format used by many microarray scanner and software providers). 
	After normalization data can be visualized as boxplot, heatmap or dotplot.
Performs joint selection in Generalized Linear Mixed Models (GLMMs) using penalized likelihood methods. Specifically, the Penalized Quasi-Likelihood (PQL) is used as a loss function, and penalties are then augmented to perform simultaneous fixed and random effects selection. Regularized PQL avoids the need for integration (or approximations such as the Laplace's method) during the estimation process, and so the full solution path for model selection can be constructed relatively quickly.
Read, write and manipulate 'Praat' TextGrid, PitchTier, Pitch, IntensityTier, Formant, and Collection files <http://www.fon.hum.uva.nl/praat/>.
Routines to select and visualize the maxima for a given strict
    partial order. This especially includes the computation of the Pareto
    frontier, also known as (Top-k) Skyline operator (see Börzsönyi, et al. 
    (2001) <doi:10.1109/ICDE.2001.914855>), and some generalizations 
    known as database preferences (see Kießling (2002) 
    <doi:10.1016/B978-155860869-6/50035-4>).
Implements a 'DBI' compliant interface to Presto. Presto is
    an open source distributed SQL query engine for running interactive
    analytic queries against data sources of all sizes ranging from
    gigabytes to petabytes: <https://prestodb.io/>.
Estimates disease prevalence for a given index date, using existing
    registry data extended with Monte Carlo simulations.
'Eprime' is a set of programs for administering psychological
    experiments by computer. This package provides functions for loading,
    parsing, filtering and exporting data in the text files produced by
    'Eprime' experiments.
Provides a set of functions to facilitate building formatted strings
    under various replacement rules: C-style formatting, variable-based formatting,
    and number-based formatting. C-style formatting is basically identical to built-in
    function 'sprintf'. Variable-based formatting allows users to put variable names
    in a formatted string which will be replaced by variable values. Number-based
    formatting allows users to use index numbers to represent the corresponding
    argument value to appear in the string.
The A() function calculates the A statistic, a nonparametric
    measure of effect size for two independent groups that’s also known as
    the probability of superiority (Ruscio, 2008), along with its standard
    error and a confidence interval constructed using bootstrap methods 
    (Ruscio & Mullen, 2012). Optional arguments can be specified to 
    calculate variants of the A statistic developed for other research 
    designs (e.g., related samples, more than two independent groups or 
    related samples; Ruscio & Gera, 2013). 
    <DOI: 10.1037/1082-989X.13.1.19>.
    <DOI: 10.1080/00273171.2012.658329>.
    <DOI: 10.1080/00273171.2012.738184>.
Performs all steps in the credit scoring process. This package allows the user to follow all the necessary steps for building an effective scorecard. It provides the user functions for coarse binning of variables, Weights of Evidence (WOE) transformation, variable clustering, custom binning, visualization, and scaling of logistic regression coefficients. The results will generate a scorecard that can be used as an effective credit scoring tool to evaluate risk. For complete details on the credit scoring process, see Siddiqi (2005, ISBN:047175451X).
Robust, reliable and flexible paths to files below a
    project root. The 'root' of a project is defined as a directory
    that matches a certain criterion, e.g., it contains a certain
    regular file.
Protocol Buffers are a way of encoding structured data in an
 efficient yet extensible format. Google uses Protocol Buffers for almost all
 of its internal 'RPC' protocols and file formats.  Additional documentation
 is available in two included vignettes one of which corresponds to our 'JSS'
 paper (2016, <doi:10.18637/jss.v071.i02>. Either version 2 or 3 of the
 'Protocol Buffers' 'API' is supported.
Based on RPS tools, a rather complete resistant shape
    analysis of 2D and 3D datasets based on landmarks can be performed. In addition, 
    landmark-based resistant shape analysis of individual asymmetry in 2D for
    matching or object symmetric structures is also possible.
Implements methods described by the paper Robins and Tsiatis (1991) <DOI:10.1080/03610929108830654>. These use g-estimation to estimate the causal effect of a treatment in a two-armed randomised control trial where non-compliance exists and is measured, under an assumption of an accelerated failure time model and no unmeasured confounders.
An implementation of Recursive Partitioning Survival Trees via a node-splitting rule that builds decision tree models that reflected within-node and within-treatment responses. The algorithm aims to find the maximal difference in survival time among different treatments.
The rpsychi offers a number of functions for psychiatry, psychiatric nursing, clinical psychology. Functions are primarily for statistical significance testing using published work. For example, you can conduct a factorial analysis of variance (ANOVA), which requires only the mean, standard deviation, and sample size for each cell, rather than the individual data. This package covers fundamental statistical tests such as t-test, chi-square test, analysis of variance, and multiple regression analysis. With some exceptions, you can obtain effect size and its confidence interval. These functions help you to obtain effect size from published work, and then to conduct a priori power analysis or meta-analysis, even if a researcher do not report effect size in a published work.
Performs goodness of fits tests for both high and low-dimensional linear models.
    It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity.
    In addition one can test the significance of potentially large groups of variables, and also
    produce p-values for the significance of individual variables in high-dimensional linear
    regression.
Estimating repeatability (intra-class
    correlation) from Gaussian, binary, proportion and Poisson data.
Access PubChem data (compounds, substance, assays) using R.
 Structural information is provided in the form of SMILES strings. 
 It currently only provides access to a subset of the 
 precalculated data stored by PubChem. Bio-assay data can be accessed to 
 obtain descriptions as well as the actual data. It is also possible to search for assay ID's by keyword.
Client for accessing data journalism APIs from ProPublica <http://www.propublica.org/>.
An R interface to the Pushbullet messaging service which
 provides fast and efficient notifications (and file transfer) between
 computers, phones and tablets.  An account has to be registered at the 
 site <http://www.pushbullet.com> site to obtain a (free) API key.
Provides access to ArcGIS geoprocessing tools by building an 
             interface between R and the ArcPy Python side-package via the 
             reticulate package.
Run Python code, make function calls,  assign and retrieve variables, etc. from R.
Constructs robust quality control chart based on the median and Hodges-Lehmann estimators (location) and the median absolute deviation (MAD) and Shamos estimators (scale) which are unbiased with a sample of finite size. For more details, see Park, Kim and Wang (2019)<arXiv:1908.00462>. This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169).
Software for qualitative text analysis (Kuckartz, 2014, <doi:10.4135/9781446288719>). Current version only supports plain text, but it can import PDF highlights if package 'rjpod' (<https://r-forge.r-project.org/projects/rqda/>) is installed.
Implements the 'rquery' piped Codd-style query algebra using 'data.table'.  This allows
   for a high-speed in memory implementation of Codd-style data manipulation tools.
It computes the Schmidt decomposition of bipartite quantum systems, discrete or continuous,
    and their respective entanglement metrics. See Artur Ekert, Peter L. Knight (1995) <doi:10.1119/1.17904> 
    for more details.
Establishes an interface between R and 'QGIS', i.e. it allows
    the user to access 'QGIS' functionalities from the R console. It achieves this
    by using the 'QGIS' Python API via the command line. Hence, RQGIS extends R's
    statistical power by the incredible vast geo-functionality of 'QGIS' (including
    also 'GDAL', 'SAGA'- and 'GRASS'-GIS among other third-party providers).
    This in turn creates a powerful environment for advanced and innovative
    (geo-)statistical geocomputing. 'QGIS' is licensed under GPL version 2 or
    greater and is available from <http://www.qgis.org/en/site/>.
Performs penalized quantile regression for LASSO, SCAD and MCP functions including group penalties. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC.
Hypocenter estimation and analysis of seismic data collected continuously, or in trigger mode. The functions organize other functions from RSEIS and GEOmap to help researchers pick, locate, and store hypocenters for detailed seismic investigation.
The 'RQuantLib' package makes parts of 'QuantLib' accessible from R
 The 'QuantLib' project aims to provide a comprehensive software framework
 for quantitative finance. The goal is to provide a standard open source library
 for quantitative analysis, modeling, trading, and risk management of financial
 assets.
An implementation of the QUEFTS (Quantitative Evaluation of the Native Fertility of Tropical Soils) model. The model estimates nutrient requirements for crops to achieve a target yield given native soil fertility, as estimated from a few soil chemical properties. See Janssen et al. (1990) <doi:10.1016/0016-7061(90)90021-Z> for the technical details and Sattari et al. (2014) <doi:10.1016/j.fcr.2013.12.005> for a recent evaluation.
A piped query generator based on Edgar F. Codd's relational
    algebra, and on production experience using 'SQL' and 'dplyr' at big data
    scale.  The design represents an attempt to make 'SQL' more teachable by
    denoting composition by a sequential pipeline notation instead of nested
    queries or functions.   The implementation delivers reliable high 
    performance data processing on large data systems such as 'Spark',
    databases, and 'data.table'. Package features include: data processing trees
    or pipelines as observable objects (able to report both columns
    produced and columns used), optimized 'SQL' generation as an explicit
    user visible table modeling step, plus explicit query reasoning and checking.
Enables researchers to conduct multivariate statistical analyses
    of survey data with randomized response technique items from several designs,
    including mirrored question, forced question, and unrelated question. This
    includes regression with the randomized response as the outcome and logistic
    regression with the randomized response item as a predictor. In addition,
    tools for conducting power analysis for designing randomized response items
    are included. The package implements methods described in Blair, Imai, and Zhou
    (2015) ''Design and Analysis of the Randomized Response Technique,'' Journal
    of the American Statistical Association 
    <http://graemeblair.com/papers/randresp.pdf>.
Three methods to calculate R2 for models with correlated errors, 
    including Phylogenetic GLS, Phylogenetic Logistic Regression, Linear Mixed 
    Models (LMMs), and Generalized Linear Mixed Models (GLMMs). See details in 
    Ives 2018 <doi:10.1093/sysbio/syy060>.
Analyzes and predicts from matrix population models (Caswell 2006) <doi:10.1002/9781118445112.stat07481>.
An implementation of the Real-time Adaptive Penalization (RAP) algorithm through which to iteratively update a regularization parameter in a streaming context.
Implementation of Robust Regression tailored to deal with Asymmetric noise Distribution, which was originally proposed by Takeuchi & Bengio & Kanamori (2002) <doi:10.1162/08997660260293300>. In addition, this implementation is extended as introducing potential feature regularization by LASSO etc.
Replication Rate (RR) is the probability of replicating a statistically significant association in genome-wide association studies. This R-package provide the estimation method for replication rate which makes use of the summary statistics from the primary study. We can use the estimated RR to determine the sample size of the replication study, and to check the consistency between the results of the primary study and those of the replication study.
A tool to exchange data between R and 'Raven' sound analysis software (Cornell Lab of Ornithology). Functions work on data formats compatible with the R package 'warbleR'.
Provides a toolkit for manipulating arrays in a consistent, 
    powerful, and intuitive manner through the use of broadcasting and a new
    array class, the 'rray'.
Software for genomic prediction with the RR-BLUP mixed model (Endelman 2011, <doi:10.3835/plantgenome2011.08.0024>). One application is to estimate marker effects by ridge regression; alternatively, BLUPs can be calculated based on an additive relationship matrix or a Gaussian kernel.
Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point.
Provides methods for multiway data analysis by means of Parafac
    and Tucker 3 models. Robust versions (Engelen and Hubert (2011) <doi:10.1016/j.aca.2011.04.043>) and versions
    for compositional data are also provided (Gallo (2015) <doi:10.1080/03610926.2013.798664>, Di Palma et al. (in press)).
Robust multivariate methods for high dimensional data including
        outlier detection, PCA, PLS and classification.
Robust Location and Scatter Estimation and Robust
        Multivariate Analysis with High Breakdown Point for Incomplete
        Data (missing values) (Todorov et al. (2010) <doi:10.1007/s11634-010-0075-2>).
Makes it easy to import the data from a 'RRD' database 
   (<https://oss.oetiker.ch/rrdtool/>) directly into R data structures. The 
   resulting objects are 'tibble' objects or a list of 'tibble' objects, making
   it easy to manipulate the data.  The package uses `librrd` to import the 
   numerical data in a `RRD` database directly into R data structures without 
   using intermediate formats.
Make use of the hardware random number accessed by the 'RdRand'
 instruction in recent Intel CPUs (Ivy Bridge and later).
 'DRNG' is "Digital Random Number Generator".
Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.
R client interface to the "Redis" key-value database.
'IUCN' Red List (<http://apiv3.iucnredlist.org/api/v3/docs>) client.
    The 'IUCN' Red List is a global list of threatened and endangered species.
    Functions cover all of the Red List 'API' routes. An 'API' key is required.
Superclasses 'PostgreSQL' connection to help enable full 'dplyr' functionality on 'Redshift'.
'OpenRefine' (formerly 'Google Refine') is a popular, open source data cleaning software. This package enables users to programmatically trigger data transfer between R and 'OpenRefine'. Available functionality includes project import, export and deletion.
Assists for presentation and visualization of data from the Norwegian Health Quality Registries following the standardization based on the requirement specified by the National Service for Health Quality Registries. This requirement can be accessed from (<https://www.kvalitetsregistre.no/resultater-til-publisering-pa-nett>). Unfortunately the website is only available in Norwegian.
An R and Repast integration tool for running individual-based
    (IbM) simulation models developed using 'Repast Simphony' Agent-Based framework
    directly from R code supporting multicore execution. This package 
    integrates 'Repast Simphony' models within R environment, making easier 
    the tasks of running and analyzing model output data for 
    automated parameter calibration and for carrying out uncertainty and
    sensitivity analysis using the power of R environment.
Functions for studying realized genetic relatedness between people. Users will be able to simulate inheritance patterns given pedigree structures, generate SNP marker data given inheritance patterns, and estimate realized relatedness between pairs of individuals using SNP marker data. See Wang (2017) <doi:10.1534/genetics.116.197004>. This work was supported by National Institutes of Health grants R37 GM-046255.
Feature Selection with Regularized Random Forest. This
    package is based on the 'randomForest' package by Andy Liaw.
    The key difference is the RRF() function that builds a
    regularized random forest.
Testing and inference for regression models using residual randomization methods. The basis of inference is an invariance assumption on the regression errors. This includes cluster errors, and exchangeability within clusters or sign symmetry across clusters.
Collection of functions for fitting distributions to given data or
    by known quantiles. Two main functions fit.perc() and fit.cont() provide
    users a GUI that allows to choose a most appropriate distribution without
    any knowledge of the R syntax. Note, this package is a part of the 'rrisk'
    project.
This package offers methods to perform robust regularized
        linear discriminant analysis.
Functions for creating and manipulating RNA secondary structure plots.
Computation of the International Roughness Index (IRI) given a
    longitudinal road profile. The IRI can be calculated for a single road segment
    or for a sequence of segments with a fixed length (e. g. 100m). For the latter,
    an overlap of the segments can be selected. The IRI and likewise the algorithms
    for its determination are defined in Sayers, Michael W; Gillespie, Thomas D;
    Queiroz, Cesar A.V. 1986. The International Road Roughness Experiment (IRRE) :
    establishing correlation and a calibration standard for measurements. World
    Bank technical paper; no. WTP 45. Washington, DC : The World Bank. (ISBN
    0-8213-0589-1) available from <http://documents.worldbank.org/curated/en/326081468740204115>.
Fetches information from the 'SHERPA/RoMEO' API
  <http://www.sherpa.ac.uk/romeo/apimanual.php> which indexes policies of
  journal regarding the archival of scientific manuscripts before and/or after
  peer-review as well as formatted manuscripts.
Multivariate regression methodologies including
    reduced-rank regression (RRR) proposed by
    Chen et al. (2013) <doi:10.1093/biomet/ast036>,
    reduced-rank ridge regression (RRS) proposed by
    Mukherjee and Zhu (2011) <doi:10.1002/sam.10138>,
    robust reduced-rank regression (R4) proposed by
    She and Chen (2017) <doi:10.1093/biomet/asx032>,
    generalized/mixed-response reduced-rank regression (mRRR) proposed by
    Luo et al. (2018) <doi:10.1016/j.jmva.2018.04.011>,
    row-sparse reduced-rank regression (SRRR) proposed by
    Chen and Huang (2012) <doi:10.1080/01621459.2012.734178>,
    reduced-rank regression with a sparse singular value decomposition (RSSVD)
    proposed by Chen et al. (2012) <doi:10.1111/j.1467-9868.2011.01002.x>
    and sparse and orthogonal factor regression (SOFAR) proposed by
    Uematsu et al. (2019) <doi:10.1109/TIT.2019.2909889>.
Functions for phylogenetic analysis (Castiglione et al, 2017 <doi:10.1111/2041-210X.12954>). The functions perform the estimation of phenotypic evolutionary rates, identification of phenotypic evolutionary rate shifts, quantification of direction and size of evolutionary change in multivariate traits, and the computation of ontogenetic shape vectors.
Linear model calculations are made for many random versions of data.  
    Using residual randomization in a permutation procedure, sums of squares are 
    calculated over many permutations to generate empirical probability distributions 
    for evaluating model effects.  This packaged is described by 
    Collyer & Adams (2018) <doi:10.1111/2041-210X.13029>.  Additionally, coefficients, statistics, fitted values, and residuals generated over many 
    permutations can be used for various procedures including pairwise tests, prediction, classification, and
    model comparison.  This package should provide most tools one could need for the analysis of
    high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.
Reduced-rank regression, diagnostics and graphics.
Univariate and multivariate methods to analyze randomized response 
    (RR) survey designs (e.g., Warner, S. L. (1965). Randomized response: A 
    survey technique for eliminating evasive answer bias. Journal of the 
    American Statistical Association, 60, 63–69, <doi:10.2307/2283137>). 
    Besides univariate estimates of true proportions, RR variables can be used 
    for correlations, as dependent variable in a logistic regression (with or 
    without random effects), or as predictors in a linear regression
    (Heck, D. W., & Moshagen, M. (2018). RRreg: An R package for correlation and 
    regression analyses of randomized response data. Journal of Statistical 
    Software, 85(2), 1–29, <doi:10.18637/jss.v085.i02>). For simulations and 
    the estimation of statistical power, RR data can be generated according to 
    several models. The implemented methods also allow to test the link between 
    continuous covariates and dishonesty in cheating paradigms such as the 
    coin-toss or dice-roll task (Moshagen, M., & Hilbig, B. E. (2017). 
    The statistical analysis of cheating paradigms. Behavior Research Methods, 
    49, 724–732, <doi:10.3758/s13428-016-0729-x>).
Non-linear transformations of data to better discover latent effects. Applies a sequence of three transformations (1) a Gaussianizing transformation, (2) a Z-score transformation, and (3) an outlier removal transformation.
Makes documents containing plots and tables from a table of R codes. 
    Can make "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS powerpoint')" documents with or without R code.
    In the package, modularized 'shiny' app codes are provided. These modules are intended for reuse across applications.
Point and interval estimation of linear parameters with data
    obtained from complex surveys (including stratified and clustered samples)
    when randomization techniques are used. The randomized response technique
    was developed to obtain estimates that are more valid when studying
    sensitive topics. Estimators and variances for 14 randomized response
    methods for qualitative variables and 7 randomized response methods for
    quantitative variables are also implemented. In addition, some data sets
    from surveys with these randomization methods are included in the package.
Implementation of Gibbs sampling algorithm for Bayesian Estimation
    of the Reduced Reparameterized Unified Model ('rrum'), described by 
    Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.
Advanced response surface analysis. The main function RSA computes
    and compares several nested polynomial regression models (full polynomial,
    shifted and rotated squared differences, rising ridge surfaces, basic
    squared differences). The package provides plotting functions for 3d
    wireframe surfaces, interactive 3d plots, and contour plots. Calculates
    many surface parameters (a1 to a4, principal axes, stationary point,
    eigenvalues) and provides standard, robust, or bootstrapped standard errors
    and confidence intervals for them.
The package contains all the data sets related to the book
        written by the maintainer of the package.
Robust Small Area Estimation. Robust Basic Unit- and Area-Level Models
Provides access to geocomputing and terrain analysis functions
    of the geographical information system (GIS) 'SAGA' (System for Automated
    Geoscientific Analyses) from within R by running the command line version of
    SAGA. This package furthermore provides several R functions for handling ASCII
    grids, including a flexible framework for applying local functions (including
    predict methods of fitted models) and focal functions to multiple grids. SAGA
    GIS is available under GPLv2 / LGPLv2 licence from 
    <http://sourceforge.net/projects/saga-gis/>.
Provides an R scripting interface to the open-source 'SAGA-GIS' (System for Automated Geoscientific Analyses Geographical Information System) software. 'Rsagacmd' dynamically generates R functions for every 'SAGA-GIS' geoprocessing tool based on the user's currently installed 'SAGA-GIS' version. These functions are contained within an S3 object and are accessed as a named list of libraries and tools. This structure facilitates an easier scripting experience by organizing the large number of 'SAGA-GIS' geoprocessing tools (>700) by their respective library. Interactive scripting can fully take advantage of code autocompletion tools (e.g. in 'Rstudio'), allowing for each tools syntax to be quickly recognized. Furthermore, the most common types of spatial data (via the 'raster', 'sp', and 'sf' packages) along with non-spatial data are automatically passed from R to the 'SAGA-GIS' command line tool for geoprocessing operations, and the results are loaded as the appropriate R object. Outputs from individual 'SAGA-GIS' tools can also be chained using pipes from the 'magrittr' and 'dplyr' packages to combine complex geoprocessing operations together in a single statement. 'SAGA-GIS' is available under a GPLv2 / LGPLv2 licence from <https://sourceforge.net/projects/saga-gis/> including Windows x86/x84 binaries. SAGA-GIS is also included in Debian/Ubuntu default software repositories and is available for macOS using homebrew (<https://brew.sh/>) from the osgeo/osgeo4mac (<https://github.com/OSGeo/homebrew-osgeo4mac>) formula tap. Rsagacmd has currently been tested on 'SAGA-GIS' versions from 2.3.1 to 7.2.0 on Windows, Linux and macOS.
Assists in processing reflectance data, developing empirical models using stepwise regression and a generalized linear modeling approach, cross-
    validation, and analysis of trends in water quality conditions (specifically chl-a) and climate conditions using the Theil-Sen estimator.
Classes and functions to create and summarize different types of resampling objects (e.g. bootstrap, cross-validation).
Sample ancestral trees conditional on phased or unphased SNP genotype data. The actual tree sampling is done using a C++ program that is launched within R. The package also contains functions for specifying the tree-sampling settings (pre-processing) and for storing and manipulating the sampled trees (post-processing). More information about 'sampletrees' can be found at <http://stat.sfu.ca/statgen/research/sampletrees.html>.
Resampling Stats (http://www.resample.com) is an add-in for
    running randomization tests in Excel worksheets. The workflow is (1) to define
    a statistic of interest that can be calculated from a data table, (2) to
    randomize rows ad/or columns of a data table to simulate a null hypothesis
    and (3) and to score the value of the statistic from many randomizations. The
    relative frequency distribution of the statistic in the simulations is then
    used to infer the probability of the observed value be generated by the null
    process (probability of Type I error). This package intends to translate this
    logic for R for teaching purposes. Keeping the original workflow is favored over
    performance.
Implements the Gibbs sampling algorithm to randomly sample association rules with one pre-chosen item as the consequent from a transaction dataset. The Gibbs sampling algorithm was proposed in G. Qian, C.R. Rao, X. Sun and Y. Wu (2016) <DOI:10.1073/pnas.1604553113>.
SaTScan(TM) (http://www.satscan.org) is software for finding regions in 
    Time, Space, or Time-Space that have excess risk, based on scan statistics, and 
	uses Monte Carlo hypothesis testing to generate P-values for these regions.  The 
	rsatscan package provides functions for writing R data frames in 
	SaTScan-readable formats, for setting SaTScan parameters, for running SaTScan in 
	the OS, and for reading the files that SaTScan creates.
Retrieve, update, delete job information from <https://saucelabs.com/>. Poll the 'SauceLabs' services
    current status and access supported platforms. Send and retrieve files from 'SauceLabs' and manage tunnels associated
    with 'SauceConnect'.
A statistical tool for multivariate modeling and clustering using stepwise cluster analysis. The modeling output of rSCA is constructed as a cluster tree to represent the complicated relationships between multiple dependent and independent variables. A free tool (named rSCA Tree Generator) for visualizing the cluster tree from rSCA is also released and it can be downloaded at <https://rscatree.weebly.com/>.
'Scala' <http://www.scala-lang.org/> is embedded in 'R' and callbacks from 'Scala' to 'R' are available. Support is provided to write 'R' packages that access 'Scala'. After installation, please run 'rscala::scalaConfig()'.
As an advanced approach to computerized adaptive testing (CAT), 
  shadow testing (van der Linden(2005) <doi:10.1007/0-387-29054-0>) dynamically 
  assembles entire shadow tests as a part of 
  selecting items throughout the testing process.
  Selecting items from shadow tests guarantees the compliance of all content 
  constraints defined by the blueprint. 'RSCAT' is an R package for the 
  shadow-test approach to CAT. The objective of 
  'RSCAT' is twofold: 1) Enhancing the effectiveness of shadow-test CAT simulation;
  2) Contributing to the academic and scientific community for CAT research.
Scrapes data from scientific articles hosted on the Scientific 
    Electronic Library Online Platform <http://www.scielo.br/>. The data 
    information includes author's names, articles' metadata and contents, among others. 
    The package also provides additional functions to easily summarize the scraped data.
The SciMark 2.0 benchmark was originally developed in Java as a benchmark for numerical and scientific computational performance. It measures the performance of several computational kernels which are frequently occurring in scientific applications. This package is a simple wrapper around the ANSI C implementation of the benchmark.
Client for Rserve, allowing to connect to Rserve instances and issue commands.
Provides a tool for mass deployment of shiny apps to 'RStudio Connect' or 'Shiny Server'. Multiple user accounts and servers can be configured for deployment.
Programmatic deployment interface for 'RPubs', 'shinyapps.io', and
    'RStudio Connect'. Supported content types include R Markdown documents,
    Shiny applications, Plumber APIs, plots, and static web content.
Provides a generic implementation of the 'RStudio' connection contract to 
    make it easier for database connections, and other type of connections, opened 
    via R packages integrate with the connections pane inside the 'RStudio' interactive
    development environment (IDE).
Uses Elsevier 'Scopus' API
    <https://dev.elsevier.com/sc_apis.html> to download 
    information about authors and their citations.
A method to download Department of Education College
     Scorecard data using the public API
     <https://collegescorecard.ed.gov/data/documentation/>. It is based on
     the 'dplyr' model of piped commands to select and filter data in a
     single chained function call.  An API key from the U.S. Department of
     Education is required.
Symbolic Data Analysis (SDA) was proposed by professor Edwin Diday in 1987, the main purpose of SDA is to substitute the set of rows (cases) in the data table for  a concept (second order statistical unit). This package implements, to the symbolic case, certain techniques of automatic classification, as well as some linear models.
Ray Shooting Depth functions are provided for bivariate analysis.
Set of classes and methods to read data and metadata documents
  exchanged through the Statistical Data and Metadata Exchange (SDMX) framework,
  currently focusing on the SDMX XML standard format (SDMX-ML).
A Bayesian-weighted estimator and two unweighted estimators are
 developed to estimate the number of newly found rare species in additional
 ecological samples. Among these methods, the Bayesian-weighted estimator
 and an unweighted (Chao-derived) estimator are of high accuracy and
 recommended for practical applications.
 Technical details of the proposed estimators have been well described
 in the following paper: Shen TJ, Chen YH (2018) A Bayesian
 weighted approach to predicting the number of newly discovered
 rare species. Conservation Biology, In press.
SEA performs simultaneous feature-set testing for (gen)omics data. It tests the unified null hypothesis and controls the family-wise error rate for all possible pathways. The unified null hypothesis is defined as: "The proportion of true features in the set is less than or equal to a threshold." Family-wise error rate control is provided through use of closed testing with Simes test. There are some practical functions to play around with the pathways of interest.
Tools for stream editing:  manipulating text files with insertions, replacements, deletions, substitutions, and commenting.
An implementation of the analysis about seed components from Borenstein et.al. 2008.
Estimate the percentage of seeds in a seedlot that contain stacks
    of genetically modified traits.  Estimates are calculated using a
    multinomial group testing model with maximum likelihood estimation of the
    parameters.
Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms.  Includes general time-series tools, plotting, filtering, interactive display.
Provides a set of R bindings for the 'Selenium 2.0 WebDriver'
    (see <https://seleniumhq.github.io/docs/wd.html>
    for more information) using the 'JsonWireProtocol' (see
    <https://github.com/SeleniumHQ/selenium/wiki/JsonWireProtocol> for more
    information). 'Selenium 2.0 WebDriver' allows driving a web browser
    natively as a user would either locally or on a remote machine using
    the Selenium server it marks a leap forward in terms of web browser
    automation. Selenium automates web browsers (commonly referred to as
    browsers). Using RSelenium you can automate browsers locally or
    remotely.
A robust procedure is implemented to estimate means and covariance matrix of multiple variables with missing data using Huber weight and then to estimate a structural equation model.
Analyses sentiment of a sentence in English and assigns score to it. It can classify sentences to the following categories of sentiments:- Positive, Negative, very Positive, very negative, 
              Neutral. For a vector of sentences, it counts the number of sentences in each
              category of sentiment.In calculating the score, negation and various degrees
              of adjectives are taken into consideration. It deals only with English sentences.
Rserve acts as a socket server (TCP/IP or local sockets) 
	     which allows binary requests to be sent to R. Every
	     connection has a separate workspace and working
	     directory. Client-side implementations are available
	     for popular languages such as C/C++ and Java, allowing
	     any application to use facilities of R without the need of
	     linking to R code. Rserve supports remote connection,
	     user authentication and file transfer. A simple R client
	     is included in this package as well.
A report of statistical findings (RSF) project
    template is generated using a 'bookdown' format. 'YAML' fields can be
    further customized. Additional helper functions provide extra features
    to the RSF.
Slow Feature Analysis in R, ported to R based on the
    matlab versions SFA toolkit 1.0 by Pietro Berkes and SFA toolkit
    2.8 by Wolfgang Konen for matlab.
This package provides functions for calculating
        associations between two genes with five correlation
        methods(e.g., the Gini correlation coefficient [GCC], the
        Pearson's product moment correlation coefficient [PCC], the
        Kendall tau rank correlation coefficient [KCC], the Spearman's
        rank correlation coefficient [SCC] and the Tukey's biweight
        correlation coefficient [BiWt], and three non-correlation
        methods (e.g., mutual information [MI] and the maximal
        information-based nonparametric exploration [MINE], and the
        euclidean distance [ED]). It can also been implemented to
        perform the correlation and clustering analysis of
        transcriptomic data profiled by microarray and RNA-Seq
        technologies. Additionally, this package can be further applied
        to construct gene co-expression networks (GCNs).
Functions for estimating models using a Hierarchical Bayesian (HB) framework. The flexibility comes in allowing the user to specify the likelihood function directly instead of assuming predetermined model structures. Types of models that can be estimated with this code include the family of discrete choice models (Multinomial Logit, Mixed Logit, Nested Logit, Error Components Logit and Latent Class) as well ordered response models like ordered probit and ordered logit. In addition, the package allows for flexibility in specifying parameters as either fixed (non-varying across individuals) or random with continuous distributions. Parameter distributions supported include normal, positive/negative log-normal, positive/negative censored normal, and the Johnson SB distribution. Kenneth Train's Matlab and Gauss code for doing Hierarchical Bayesian estimation has served as the basis for a few of the functions included in this package. These Matlab/Gauss functions have been rewritten to be optimized within R. Considerable code has been added to increase the flexibility and usability of the code base. Train's original Gauss and Matlab code can be found here: <http://elsa.berkeley.edu/Software/abstracts/train1006mxlhb.html> See Train's chapter on HB in Discrete Choice with Simulation here: <http://elsa.berkeley.edu/books/choice2.html>; and his paper on using HB with non-normal distributions here: <http://eml.berkeley.edu//~train/trainsonnier.pdf>. The authors would also like to thank the invaluable contributions of Stephane Hess and the Choice Modelling Centre: <https://cmc.leeds.ac.uk/>.
The main purpose of this package is to perform simulation-based 
   estimation of stochastic actor-oriented models for longitudinal network 
   data collected as panel data. Dependent variables can be single or 
   multivariate networks, which can be directed, non-directed, or two-mode. 
   There are also functions for testing parameters and checking goodness of fit.
   An overview of these models is given in Tom A.B. Snijders (2017), Stochastic 
   Actor-Oriented Models for Network Dynamics, Annual Review of Statistics and 
   Its Application, 4, 343-363 <doi: 10.1146/annurev-statistics-060116-054035>.
Provides a way to transform an image into a mosaic composed from a set of smaller images (tiles). It also contains a simple function for creating the tiles from a folder of images directly through R, without the need of any external code. At this moment only the JPEG format is supported, either as input (image and tiles) or output (mosaic transformed image).
Summarise results from simulation studies and compute Monte Carlo
  standard errors of commonly used summary statistics. This package is modelled 
  on the 'simsum' user-written command in 'Stata' (White I.R., 2010 
  <http://www.stata-journal.com/article.html?article=st0200>), further extending
  it with additional functionality.
Easy-to-use functions for downloading air quality data from the 
    Mexican National Air Quality Information System (SINAICA).  Allows you to 
    query pollution and meteorological parameters from more than a hundred
    monitoring stations located throughout Mexico. See <https://sinaica.inecc.gob.mx> 
    for more information.
Makes  operations with raster images, such as map
    viewing in time series, export values in time series for specific, total or limited
    within a polygon locations. Makes data processing of remote sensing of climatic variables distributed in the space (maps 2D) and the time (time series).
Functions for interacting with the Adobe Analytics API V1.4
    (<https://api.omniture.com/admin/1.4/rest/>).
This RSKC package contains a function RSKC which runs the robust sparse K-means clustering algorithm.
Create custom keyboard shortcuts to examine code selected in the 'Rstudio' editor.
             F3 can for example yield 'str(selection)' and F7 open the source
             code of CRAN and base package functions on 'github'.
Provides users functions for sleep data management and analysis such as European Data Format (EDF) to Morpheo Data Format (MDF) conversion: P.Bouchequet, D.Jin, G.Solelhac, M.Chennaoui, D.Leger (2018) <doi:10.1016/j.msom.2018.01.130> "Morpheo Data Format (MDF), un nouveau format de donnees simple, robuste et performant pour stocker et analyser les enregistrements de sommeil". Provides hypnogram statistics computing and visualisation functions from the American Academy of Sleep Medicine (AASM) manual "The AASM Manual for the Scoring of Sleep and Associated Events" <https://aasm.org/clinical-resources/scoring-manual/>.
Implements the "Stemming Algorithm for the Portuguese Language" <DOI:10.1109/SPIRE.2001.10024>.
Functions that simplify submitting R scripts to a Slurm 
    workload manager, in part by automating the division of embarrassingly
    parallel calculations across cluster nodes.
Provides functions to generate response-surface designs, 
    fit first- and second-order response-surface models, 
    make surface plots, obtain the path of steepest ascent, 
    and do canonical analysis. A good reference on these methods 
    is Chapter 10 of Wu, C-F J and Hamada, M (2009) 
    "Experiments: Planning, Analysis, and Parameter Design Optimization"
    ISBN 978-0-471-69946-0.
Aims at loading Facebook and Instagram advertising data from
    'Smartly.io' into R. 'Smartly.io' is an online advertising service that enables
    advertisers to display commercial ads on social media networks (see <http://www.smartly.io/> for more information).
    The package offers an interface to query the 'Smartly.io' API and loads data directly into R for further data processing and data analysis.
Provides methods for model building and model evaluation of mixed effects models using 
    'Monolix' <http://monolix.lixoft.com>. 'Monolix' is a software tool for nonlinear mixed effects 
    modeling that must have been installed in order to use 'Rsmlx'. 
    Among other tasks, 'Rsmlx' provides a powerful tool for automatic PK model building, performs 
    statistical tests for model assessment, bootstrap simulation and likelihood profiling for 
    computing confidence intervals. 'Rsmlx' also proposes several automatic covariate search methods for 
    mixed effects models.
Tools for the guided selection of satellite data and environmental predictors, the combination of remote sensing and animal movement data and the mapping of resource suitabilitz. Based on the paper by Remelgado et al. (2015) <doi:10.1002/rse2.70>.
The Stuttgart Neural Network Simulator (SNNS) is a library
    containing many standard implementations of neural networks. This
    package wraps the SNNS functionality to make it available from
    within R. Using the 'RSNNS' low-level interface, all of the
    algorithmic functionality and flexibility of SNNS can be accessed.
    Furthermore, the package contains a convenient high-level
    interface, so that the most common neural network topologies and
    learning algorithms integrate seamlessly into R.
A programmatic interface to various 'SNP' 'datasets'
    on the web: 'OpenSNP' (<https://opensnp.org>), and 'NBCIs' 'dbSNP' database
    (<https://www.ncbi.nlm.nih.gov/projects/SNP>). Functions
    are included for searching for 'NCBI'. For 'OpenSNP', functions are included 
    for getting 'SNPs', and data for 'genotypes', 'phenotypes', annotations, 
    and bulk downloads of data by user.
An implementation of the use of efficient score statistics
  in genome-wide SNP set analysis with complex traits. Three standard score statistics
  (Cox, binomial, and Gaussian) are provided, but the package is easily extensible to
  include others. Code implementing the inferential procedure is primarily written in C++ and
  utilizes parallelization of the analysis to reduce runtime. A supporting function offers
  simple computation of observed, permutation, and FWER and FDR adjusted p-values.
Provides easier interaction with
    'Socrata' open data portals <http://dev.socrata.com>.
    Users can provide a 'Socrata' data set resource URL,
    or a 'Socrata' Open Data API (SoDA) web query,
    or a 'Socrata' "human-friendly" URL,
    returns an R data frame. Converts dates to 'POSIX'
    format and manages throttling by 'Socrata'.
    Users can upload data to 'Socrata' portals directly
    from R.
Downloads Southern Oscillation Index, Oceanic Nino
    Index, North Pacific Gyre Oscillation data, North Atlantic Oscillation
    and Arctic Oscillation. Data sources are described in the README file.
General Non-linear Optimization Using Augmented Lagrange Multiplier Method.
A comprehensive R API for querying Apache Solr databases.
             A Solr core is represented as a data frame or list that
             supports Solr-side filtering, sorting,
             transformation and aggregation, all through the familiar
             base R API. Queries are processed
             lazily, i.e., a query is only sent to the database when
             the data are required.
Somoclu is a massively parallel implementation of self-organizing maps.  It exploits multicore CPUs and it can be accelerated by CUDA. The topology of the map can be planar or toroid and the grid of neurons can be rectangular or hexagonal . Details refer to (Peter Wittek, et al (2017)) <doi:10.18637/jss.v078.i09>.
Minimally adjust the values of numerical records in a data.frame, such
    that each record satisfies a predefined set of equality and/or inequality
    constraints. The constraints can be defined using the 'validate' package. 
    The core algorithms have recently been moved to the 'lintools' package,
    refer to 'lintools' for a more basic interface and access to a version
    of the algorithm that works with sparse matrices.
To clean and analyze hospital data, and generate sets for statistical modeling.
An extension package for 'sparklyr' that provides an R interface to
    H2O Sparkling Water machine learning library (see <https://github.com/h2oai/sparkling-water> for more information).
Implements many algorithms for statistical learning on 
  sparse matrices - matrix factorizations, matrix completion, 
  elastic net regressions, factorization machines. 
  Also 'rsparse' enhances 'Matrix' package by providing methods for 
  multithreaded <sparse, dense> matrix products and native slicing of 
  the sparse matrices in Compressed Sparse Row (CSR) format.
  List of the algorithms for regression problems:
  1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL) 
  Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>)
  2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>)
  List of algorithms for matrix factorization and matrix completion:
  1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least 
  Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>)
  2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro 
  (2005, <doi:10.1145/1102351.1102441>)
  3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD, 
  Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder 
  et al. (2014, <arXiv:1410.2596>)
  4) Linear-Flow matrix factorization, from 'Practical linear models for 
  large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al 
  (2016, ISBN:978-1-57735-770-4)
  5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington, 
  Socher, Manning (2014, <https://www.aclweb.org/anthology/D14-1162>)
  Package is reasonably fast and memory efficient - it allows to work with large
  datasets - millions of rows and millions of columns. This is particularly useful 
  for practitioners working on recommender systems.
Implementation of Nelson rules for control charts in 'R'. The 'Rspc' implements some Statistical Process Control methods, namely Levey-Jennings type of I (individuals) chart, Shewhart C (count) chart and Nelson rules (as described in Montgomery, D. C. (2013) Introduction to statistical quality control. Hoboken, NJ: Wiley.). Typical workflow is taking the time series, specify the control limits, and list of Nelson rules you want to evaluate. There are several options how to modify the rules (one sided limits, numerical parameters of rules, etc.). Package is also capable of calculating the control limits from the data (so far only for i-chart and c-chart are implemented).
Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main reference for the methods is Bolin and Kirchner (2019) <arXiv:1711.04333>, which can be generated by the citation function in R.
R interface to the 'Spectra' library
    <https://spectralib.org/> for large-scale eigenvalue and SVD
    problems. It is typically used to compute a few
    eigenvalues/vectors of an n by n matrix, e.g., the k largest eigenvalues,
    which is usually more efficient than eigen() if k << n. This package
    provides the 'eigs()' function that does the similar job as in 'Matlab',
    'Octave', 'Python SciPy' and 'Julia'. It also provides the 'svds()' function
    to calculate the largest k singular values and corresponding
    singular vectors of a real matrix. The matrix to be computed on can be
    dense, sparse, or in the form of an operator defined by the user.
Conversion between attitude representations: DCM, Euler angles, Quaternions, and Euler vectors.
    Plus conversion between 2 Euler angle set types (xyx, yzy, zxz, xzx, yxy, zyz, xyz, yzx, zxy, xzy, yxz, zyx).
    Fully vectorized code, with warnings/errors for Euler angles (singularity, out of range, invalid angle order), 
    DCM (orthogonality, not proper, exceeded tolerance to unity determinant) and Euler vectors(not unity).
    Also quaternion and other useful functions.
    Based on SpinCalc by John Fuller and SpinConv by Paolo de Leva.
Provides an interface to the Spotify API <https://developer.spotify.com/documentation/web-api/>.
An implementation of functionalities to transform directed graphs that are bound to a set of
  known forbidden paths. There are several transformations, following the rules provided by Villeneuve 
  and Desaulniers (2005) <doi: 10.1016/j.ejor.2004.01.032>, and Hsu et al. (2009) <doi: 10.1007/978-3-642-03095-6_60>. 
  The resulting graph is generated in a data-frame format. See rsppfp website for more information, 
  documentation an examples.
Provides functions for estimating power or sample size for RNA-Seq studies. Empirical approach is used and the data is assumed to be count in nature. The underlying distribution of data is assumed to be Poisson or negative binomial. The package contains 6 function; 4 functions provide estimates of sample size or power for Poisson and Negative Binomial distribution; 2 functions provide plots of power for given sample size or sample size for given power.
Calculate generalized R-squared, partial R-squared, and partial correlation coefficients for generalized linear models (including quasi models with well defined variance functions).
Embeds the 'SQLite' database engine in R and
    provides an interface compliant with the 'DBI' package. The
    source for the 'SQLite' engine is included.
Conducts statistical downscaling of daily CMIP5 (Coupled Model Intercomparison Project 5) climate change scenario data at a station level using empirical quantile mapping method by Jaepil Cho et al. (2016) <doi:10.1002/ird.2035>.
Methods and tools for Singular Spectrum Analysis including decomposition, forecasting and gap-filling for univariate and multivariate time series.
Ranked set sampling (RSS) is introduced as an advanced method for data collection which is substantial for the statistical and methodological analysis in scientific studies by McIntyre (1952) (reprinted in 2005) <doi:10.1198/000313005X54180>. This package introduces the first package  that implements the RSS and its modified versions for sampling. With 'RSSampling', the researchers can sample with basic RSS and the modified versions, namely, Median RSS, Extreme RSS, Percentile RSS, Balanced groups RSS, Double RSS, L-RSS, Truncation-based RSS, Robust extreme RSS. The 'RSSampling' also allows imperfect ranking using an auxiliary variable (concomitant) which is widely used in the real life applications. Applicants can also use this package for parametric and nonparametric inference such as mean, median and variance estimation, regression analysis and some distribution-free tests where the the samples are obtained via basic RSS.
A collection of implementations of semi-supervised classifiers
    and methods to evaluate their performance. The package includes implementations
    of, among others, Implicitly Constrained Learning, Moment Constrained Learning,
    the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic
    Likelihood estimation, S4VM and WellSVM.
Reservoir Systems Standard Operation Policy. A system for simulation of supply reservoirs. It proposes functionalities for plotting and evaluation of supply reservoirs systems.
An extremely simple stack data type, implemented with 'R6'
    classes. The size of the stack increases as needed, and the amortized
    time complexity is O(1). The stack may contain arbitrary objects.
Provides fast, persistent (side-effect-free) stack, queue and
    deque (double-ended-queue) data structures. While deques include a superset
    of functionality provided by queues, in these implementations queues are
    more efficient in some specialized situations. See the documentation for
    rstack, rdeque, and rpqueue for details.
User-facing R functions are provided to parse, compile, test,
    estimate, and analyze Stan models by accessing the header-only Stan library
    provided by the 'StanHeaders' package. The Stan project develops a probabilistic
    programming language that implements full Bayesian statistical inference
    via Markov Chain Monte Carlo, rough Bayesian inference via 'variational'
    approximation, and (optionally penalized) maximum likelihood estimation via
    optimization. In all three cases, automatic differentiation is used to quickly
    and accurately evaluate gradients without burdening the user with the need to
    derive the partial derivatives.
Estimates previously compiled regression models using the 'rstan'
    package, which provides the R interface to the Stan C++ library for Bayesian
    estimation. Users specify models via the customary R syntax with a formula and
    data.frame plus some additional arguments for priors.
Perform sigmoidal Emax model fit using 'Stan' in a formula notation, without writing 'Stan' model code.
Provides various tools for developers of R packages interfacing
    with 'Stan' <https://mc-stan.org>, including functions to set up the required 
    package structure, S3 generics and default methods to unify function naming 
    across 'Stan'-based R packages, and vignettes with recommendations for 
    developers.
Estimates previously compiled stap regression models using the 'rstan' package. Users specify models via a custom R syntax with a formula and data.frame plus additional arguments for priors.
A simple R -> Stata interface allowing the user to
    execute Stata commands (both inline and from a .do file)
    from R.
Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, 
    for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. 
    The output of each test is automatically transformed into a tidy data frame to facilitate visualization. 
    Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix.  
    Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs 
    (repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs. 
    It's also possible to compute several effect size metrics, including "eta squared" for ANOVA, "Cohen's d" for t-test and 
    "Cramer's V" for the association between categorical variables. 
    The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.
R interface for china national data <http://data.stats.gov.cn/>, 
    some convenient functions for accessing the national data are provided.
Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate  Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" <doi:10.1198/jcgs.2009.07177>. The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" <doi:10.1007/s10107-012-0584-1>.
Toolbox for remote sensing image processing and analysis such as
    calculating spectral indices, principal component transformation, unsupervised
    and supervised classification or fractional cover analyses.
While streaming processing provides opportunities to deal with extremely large and ever growing data sets in (near) real time, the development of streaming algorithms for complex models is often cumbersome: the software packages that facilitate streaming processing in production environments do not provide statisticians with the simulation, estimation, and plotting tools they are used to. Developers of streaming algorithms would thus benefit from the flexibility of [R] to create, plot and compute data while developing streaming algorithms. Package RStorm implements a streaming architecture modeled on Storm for easy development and testing of streaming algorithms in [R]. RStorm is not intended as a production package, but rather a development tool for streaming algorithms.
R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.
Unified object oriented interface for multiple independent streams of random numbers from different sources.
A convenience interface for communicating with the Stripe payment processor to accept payments online. See <https://stripe.com> for more information.
Access the RStudio API (if available) and provide informative error
    messages when it's not.
A collection of efficient and effective tools and
	algorithms for subgroup discovery and analytics. The package
	integrates an R interface to the org.vikamine.kernel library
	of the VIKAMINE system (http://www.vikamine.org) implementing
	subgroup discovery, pattern mining and analytics in Java.
Supports safe and reproducible solutions development in R.
        It will help you with environment separation per project,
        dependency management, local packages creation and preparing
        deployment packs for your solutions.
Produces tables with the level of replication (number of replicates) and the
    experimental uncoded values of the quantitative factors to be used for rotatable Central
    Composite Design (CCD) experimentation and a 2-D contour plot of the corresponding
    variance of the predicted response according to
    Mead et al. (2012) <doi:10.1017/CBO9781139020879> design_ccd(), and analyzes
    CCD data with response surface methodology ccd_analysis(). A rotatable CCD
    provides values of the variance of the predicted response that are concentrically
    distributed around the average treatment combination used in the experimentation, which
    with uniform precision (implied by the use of several replicates at the average
    treatment combination) improves greatly the search and finding of an optimum response.
    These properties of a rotatable CCD represent undeniable advantages over the classical
    factorial design, as discussed by Panneton et al. (1999) <doi:10.13031/2013.13267> and
    Mead et al. (2012) <doi:10.1017/CBO9781139020879.018> among others.
The software suite, 'Freesurfer', is a open-source software suite involving the segmentation of brain MRIs (see <http://freesurfer.net/> for more information). This package provides functionality to import the data generated by 'Freesurfer'; functions to easily manipulate the data; and provides brain specific normalisation commonly used when studying structural brain MRIs. This package has been designed using an installation of and data generated from 'Freesurfer' version 5.3.
Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.
A geographic information system (GIS) graphical user interface (GUI) that
  provides data viewing, management, and analysis tools.
Low-rank matrix decompositions are fundamental tools and widely used for data
  analysis, dimension reduction, and data compression. Classically, highly accurate 
  deterministic matrix algorithms are used for this task. However, the emergence of 
  large-scale data has severely challenged our computational ability to analyze big data. 
  The concept of randomness has been demonstrated as an effective strategy to quickly produce
  approximate answers to familiar problems such as the singular value decomposition (SVD). 
  The rsvd package provides several randomized matrix algorithms such as the randomized 
  singular value decomposition (rsvd), randomized principal component analysis (rpca), 
  randomized robust principal component analysis (rrpca), randomized interpolative 
  decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot 
  functions are provided. The methods are discussed in detail by Erichson et al. (2016) <arXiv:1608.02148>.
Renders vector-based svg images into high-quality custom-size bitmap
    arrays using 'librsvg2'. The resulting bitmap can be written to e.g. png, jpeg
    or webp format. In addition, the package can convert images directly to various
    formats such as pdf or postscript.
A graphics device for R that uses the w3.org xml standard
        for Scalable Vector Graphics.
A graphics device for R that uses the w3.org xml standard
             for Scalable Vector Graphics.  This version supports
             tooltips with 1 to 3 lines, hyperlinks, and line styles.
Allows to link data to Swiss administrative divisions (municipalities,
    districts, cantons) and to plot and save customised maps thereof.  Furthermore, the
    package allows to generate tailored templates for data collection.  The used geodata
    is publicly available on the Swiss Federal Statistical Office website
    <https://www.bfs.admin.ch/bfs/de/home/dienstleistungen/geostat/geodaten-bundesstatistik.html>.
Interface to the web data portals of 'Symbiota'. Allows to query taxon natural history collections (herbarium specimens, etc.) from 41 portals including plants, animals and fungi. See the 'Symbiota' main page <http://symbiota.org/docs/> for more information.
An R interface to the SYMPHONY solver for mixed-integer linear programs.
Access SymPy computer algebra system from R via Jython.
Functions to write messages to the 'syslog' system logger API,
  available on all 'POSIX'-compatible operating systems. Features include
  tagging messages with a priority level and application type, as well as
  masking (hiding) messages below a given priority level.
Performs one-sample t-test based on robustified statistics using median/MAD (TA) and Hodges-Lehmann/Shamos (TB). For more details, see Park and Wang (2018)<arXiv:1807.02215>. This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169).
Play the classic game of tic-tac-toe (naughts and crosses).
Contains functions for random generation of R x C and 2 x 2 x K contingency tables. In addition to the generation of contingency tables over predetermined intraclass-correlated clusters, it is possible to generate contingency tables without intraclass correlations under product multinomial, multinomial, and Poisson sampling plans. It also consists of a function for generation of random data from a given discrete probability distribution function. See Demirhan (2016) <https://journal.r-project.org/archive/2016-1/demirhan.pdf> for more information.
Storing huge data in RData format causes problems because
        of the necessity to load the whole file to the memory in order
        to access and manipulate objects inside such file; rtape is a
        simple solution to this problem. The package contains several
        wrappers of R built-in serialize/unserialize mechanism allowing
        user to quickly append objects to a tape-like file and later
        iterate over them requiring only one copy of each stored object
        to reside in memory a time.
Discretize AR(1) process following Tauchen (1986) <http://www.sciencedirect.com/science/article/pii/0165176586901680>. A discrete Markov chain that approximates in the sense of weak convergence a continuous-valued univariate Autoregressive process of first order is generated. It is a popular method used in economics and in finance.
We provide functions to perform taxometric analyses. This package contains 44 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method.
Tools for analyzing sales report files of iTunes Connect.
Upload R data.frame to Arm Treasure Data, see <https://www.treasuredata.com/>. 
  You can execute database or table handling for resources on Arm Treasure Data.
Robust tail dependence estimation for bivariate models. This package is based on two papers by the authors:'Robust and bias-corrected estimation of the coefficient of tail dependence' and 'Robust and bias-corrected estimation of probabilities of extreme failure sets'. This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).
Provides response time distributions (density/PDF,
       distribution function/CDF, quantile function, and random
       generation): (a) Ratcliff diffusion model (Ratcliff &
       McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) based on C
       code by Andreas and Jochen Voss and (b) linear ballistic
       accumulator (LBA; Brown & Heathcote, 2008,
       <doi:10.1016/j.cogpsych.2007.12.002>) with different
       distributions underlying the drift rate.
Exploit controlled vocabularies organized on tematres servers.
A set of tools for creation, manipulation, and modeling
    of tensors with arbitrary number of modes. A tensor in the context of data
    analysis is a multidimensional array. rTensor does this by providing a S4
    class 'Tensor' that wraps around the base 'array' class. rTensor
    provides common tensor operations as methods, including matrix unfolding,
    summing/averaging across modes, calculating the Frobenius norm, and taking
    the inner product between two tensors. Familiar array operations are
    overloaded, such as index subsetting via '[' and element-wise operations.
    rTensor also implements various tensor decomposition, including CP, GLRAM,
    MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements
    transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some
    auxiliary functions include the Khatri-Rao product, Kronecker product, and
    the Hamadard product for a list of matrices.
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.
This provides a framework for R packages developed for
  a regulatory environment. It is based on the 'testthat' unit testing system and provides the adapter
    functionalities for XML-based test case definition as well as for standardized
    reporting of the test results.
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.
Reorder sentences for Korean text using TextRank algorithm.
Build a text summary by extracting relevant sentences from your text. The training dataset should consist of several documents, each document should have sentences separated by a period. While fitting the model, the 'term frequency - inverse document frequency' (TF-IDF) matrix that reflects how important a word is to a document is calculated first. Then vector representations for words are obtained from the 'global vectors for word representation' algorithm (GloVe). While applying the model on new data, the GloVe word vectors for each word are weighted by their TF-IDF weights and averaged to give a sentence vector or a document vector. The magnitude of this sentence vector gives the importance of that sentence within the document. Another way to obtain the importance of the sentence is to calculate cosine similarity between the sentence vector and the document vector. The output can either be at the sentence level (sentences and weights are returned) or at a document level (the summary for each document is returned). It is useful to first get a sentence level output and get quantiles of the sentence weights to determine a cutoff threshold for the weights. This threshold can then be used in the document level output. This method is a variation of the TF-IDF extractive summarization method mentioned in a review paper by Gupta (2010) <doi:10.4304/jetwi.2.3.258-268>.
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.
Identifies and scores possible Transcription Factor
    Binding Sites and allows for FDR analysis and pruning.  It supports
    splitting of sequences based on size or a specified GFF, grouping
    by G+C content, and specification of Markov model order.  The heavy
    lifting is done in C while all results are made available via R.
A suite of custom R Markdown formats and templates for
  authoring journal articles and conference submissions.
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'.
Reads and writes TIFF format images and
        returns them as a pixmap object. Because the resulting object
        can be very large for even modestly sized TIFF images, images
        can be reduced as they are read for improved performance.  This
        package is a wrapper around libtiff (www.libtiff.org), on which
        it depends (i.e. the libtiff shared library must be on your
        PATH for the binary to work, and tiffio.h must be on your
        system to build the package from source). By using libtiff's
        highlevel TIFFReadRGBAImage function, this package inherently
        supports a wide range of image formats and compression schemes.
        This package also provides an implementation of the Ridler
        Autothresholding algorithm for easy generation of binary masks as
        described in Ridler & Calvard (1978) <doi:10.1109/TSMC.1978.4310039>.
Extract text or metadata from over a thousand file types, using Apache Tika <https://tika.apache.org/>. Get either plain text or structured XHTML content.
Supports the input and reproducible analysis of RTI
    MicroPEM output files.
R tools to measure and compare inequality, welfare and poverty using the EU statistics on income and living conditions surveys.
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.
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.
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' 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 to perform
    calculations in CPUs as well as 'GPUs' to accelerate the process.
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.
A set of functions used to automate commonly used methods in credit risk to estimate migration (transition) matrices. The package includes multiple methods for bootstrapping default rates and forecasting/stress testing credit exposures migrations, via Econometric and Machine Learning approaches.  More information can be found at <https://analyticsrusers.blog>.
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 (<http://memory-alpha.wikia.com/>), and Memory Beta (<http://memory-beta.wikia.com/>) 
    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'.
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 (2018)
    <https://numbercrunch.de/trng/trng.pdf>.
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).
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://www.quandl.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.
Contains the program 'ttf2pt1', for use with the
    'extrafont' package. This product includes software developed by the 'TTF2PT1'
    Project and its contributors.
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>.
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.org/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.
Contains data and visualization functions for USA unemployment
    data. Data comes from the US Bureau of Labor Statistics (BLS). State data
    is in ?df_state_unemployment and covers 2000-2013. County data is in
    ?df_county_unemployment and covers 1990-2013. Choropleth maps of the data
    can be generated with ?state_unemployment_choropleth() and
    ?county_unemployment_choropleth() respectively.
R functions implementing a standard Unit Testing
        framework, with additional code inspection and report
        generation tools.
Automatically convert a file or package worth of 'RUnit' test
    functions into 'testthat' tests.
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.
Calculates running functions  with varying window size and missing handling options. 
  Package brings also running streak and running which, what extends beyond range of functions 
  already implemented in R packages. Calculating on windows depending on date also available.
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.
A GUI (Graphical User Interface) for the UNU.RAN random variate
   generators. Thus it allows to build non-uniform random number
   generators interactively for quite arbitrary distributions.
   In addition, R code for the required calls from package Runuran can
   be displayed and stored for later use. Some basic analysis like
   goodness-of-fit tests can be performed.
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>.
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.
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>.
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').
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.
Test suite for non-uniform pseudo-random number generators.
To detecting rare variants for binary traits using general pedigrees, the pedigree disequilibrium tests are proposed by collapsing rare haplotypes/variants with/without weights. To run the test, MERLIN is needed in Linux for haplotyping.
Play Video Poker with R, complete with a graphical user
        interface. So far, only "Jacks or Better" is implemented.
'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.
This is an 'R' interface to Alun Thomas's 'ViewGraph' 'Java' graph
    viewing program.
    It takes a graph specified as an incidence matrix, list of
    edges, or in 'igraph' format and runs a graphical user interface that shows
    an animation of a force directed algorithm positioning the vertices in two
    dimensions. It works well for graphs of various structure of up to a few
    thousand vertices. It's not fazed by graphs that comprise several
    components. The coordinates can be read as an 'igraph' style layout 
    matrix at any time. The user can mess with the layout using a mouse, 
    preferably one with 3 buttons, and some keyed commands.
    The 'Java' program 'ViewGraph' is contained in Alun Thomas's 'JPSGCS' 
    collection of 'Java' programs for statistical genetics and computational 
    statistics. The homepage for 'JPSGCS' is 
    <http://www-genepi.med.utah.edu/~alun/software/index.html>.
    The documentation page for 'ViewGraph' is at 
    <http://www-genepi.med.utah.edu/~alun/software/docs/ViewGraph.html>.
Provides an interface to 'vinecopulib', a C++ library for vine 
 copula modeling based on 'Boost' and 'Eigen'. 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. The 'rvinecopulib' package includes 'vinecopulib' as
 header-only C++ library (currently version 0.3.1). 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.
Load data from vk.com api about your communiti users and views,
    ads performance, post on user wall and etc. For more detail see 
    <https://vk.com/dev/first_guide>.
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/eddelbuettel/rvw> 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.
Provides API to Melbourne pedestrian data in tidy data form.
Provides functions to retrieve and reformat data from the 'Star Wars' API (SWAPI) <https://swapi.co/>.
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.
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 digests
    history files from the popular messenger service 'WhatsApp' in all locales and
    from all devices.
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.ncdc.noaa.gov/data-access/model-data/model-datasets/global-forcast-system-gfs> and to compute connectivity between locations.
A plug in for using 'WinEdt' as an editor for R.
Facilitates communication between R
        and the Rasch modeling software Winsteps. Currently
        includes functions for reading and writing command files,
        sending them to Winsteps, reading and writing data according to
        command file specifications, reading output into R, and
        plotting various results.
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.
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 console, txt, html or pdf pages. Download the whole documentation (html index, pdf manual, vignettes, source code, etc) with a single instruction. Visualize the package dependencies. Several functions for task view maintenance and exploration of CRAN archive. Quick links to more than 70 web search engines. Lazy evaluation of non-standard content is available throughout the package and eases the use of many functions.
Provides a set of functions for performing digital signal
    processing.
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).  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.
Allows communication with Extensible Neuroimaging Archive Toolkit <https://www.xnat.org>. 
  'Rxnat' is using the 'XNAT' REST API to perform data queries and download images.
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.
Identify and display TRACEs for a specified shrinkage path and determine
  the extent of shrinkage most likely, under normal distribution theory, to produce an
  optimal reduction in MSE Risk in estimates of regression (beta) coefficients. Alternative
  estimates are also provided when ill-conditioned (nearly multicollinear) models yield
  OLS estimates with "wrong" numerical signs.
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://tech.yandex.ru/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/>.
This package is a port to R of the SysGran program, written in Delphi by Camargo (2006). It contains functions for the analysis of grain size samples (in logarithmic (phi) and geometric (micrometers) scale) based on various methods, like Folk & Ward (1957) and Methods of Moments (Tanner, 1995), among others; textural classifications and distribution of unconsolidated sediments are shown in histograms, bivariated plots and ternary diagrams of Shepard (1954) and Pejrup (1988). English and Portuguese languages are supported in outputs
R 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'.
Interface to gather newspaper articles from 'DIE ZEIT' and 'ZEIT ONLINE', based on a multilevel query <http://developer.zeit.de/>. A personal API key is required for usage.
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 <http://www.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>.
R bindings for Google's s2 library for geometric calculations on
    the sphere.
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.
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.
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 test of changepoint with one-change or epidemic alternatives
    with data-based model diagnostic
Functions for detecting eye fixations in raw eye-tracking
    data.  The detection is done using a velocity-based algorithm for
    saccade detection proposed by Ralf Engbert and Reinhold Kliegl in
    2003.  The algorithm labels segments as saccades when the velocity of
    the eye movement exceeds a certain threshold.  Anything between two
    saccades is considered a fixation.  Thus the algorithm is not
    appropriate for data containing episodes of smooth pursuit eye
    movements.
Computes the Exposure-At-Default based on standardized approach
    of the Basel III Regulatory framework (SA-CCR). Currently, trade types of all
    the five major asset classes have been created and, given the inheritance-
    based structure of the application, the addition of further trade types
    is straightforward. The application returns a list of trees (one per CSA) after
    automatically separating the trades based on 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 paper (including the margined and the un-margined workflow) have been
    implemented including the latest FAQ enhancements.
Performs surrogate-assisted optimization for expensive black-box constrained problems.
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.
Time series models for small area estimation based on area-level models.
The SAEMIX package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. The SAEM algorithm: - computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, - provides standard errors for the maximum likelihood estimator - estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm. Several applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group (<http://group.monolix.org/>).
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.
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.
Functions for Generalized and Safe-Bayesian Ridge and Lasso Regression models with both fixed and varying variance.
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.
Tools for the statistical analysis and visualization of (relative
    and absolute) quantitative (LFQ,TMT,HRM) Proteomics data.
A consistent interface to encrypt and decrypt strings, R objects and files using symmetric and asymmetric key encryption.
A framework for evaluation of clinical trial safety. Users can interactively explore their data using the 'Shiny' application or create standalone 'htmlwidget' charts. Interactive charts are built using 'd3.js' and 'webcharts.js' 'JavaScript' libraries.
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.
Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.
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, and Metadata) <https://trailhead.salesforce.com/en/content/learn/modules/api_basics/api_basics_overview>. 
    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.
Point estimation for partition distributions using the sequentially-allocated latent structure optimization (SALSO) method to minimize the expectation of the Binder loss or the lower bound of the expectation of the variation of information loss. The SALSO method was presented at the workshop "Bayesian Nonparametric Inference: Dependence Structures and their Applications" in Oaxaca, Mexico on December 6, 2017. See <https://www.birs.ca/events/2017/5-day-workshops/17w5060/schedule>.
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.
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.
An implementation of 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>. 
    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.
Stochastic Approximation Monte Carlo (SAMC) is one of the celebrated Markov chain Monte Carlo (MCMC) algorithms. It is known to be capable of sampling from multimodal or doubly intractable distributions. We provide generic SAMC samplers for continuous distributions. User-specified densities in R and C++ are both supported. We also provide functions for specific problems that exploit SAMC computation. See Liang et al (2010) <doi:10.1002/9780470669723> for complete introduction to the method.
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. The ssc function can calculate the sample size with pre-specified type I error rate, statistical power and effect size.
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 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.
A set of R functions for calculating sample size
        requirements using three different Bayesian criteria in the
        context of designing an experiment to estimate the difference
        between two binomial proportions. Functions for calculation of
        required sample sizes for the Average Length Criterion, the
        Average Coverage Criterion and the Worst Outcome Criterion in
        the context of binomial observations are provided. In all
        cases, estimation of the difference between two binomial
        proportions is considered. Functions for both the fully
        Bayesian and the mixed Bayesian/likelihood approaches are
        provided.
Functions for drawing and calibrating samples.
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.
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.
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.
Augmenting a matched data set by generating multiple stochastic, matched samples from the data using a
  multi-dimensional histogram constructed from dropping the input matched data into a multi-dimensional grid built on
  the full data set. The resulting stochastic, matched sets will likely provide a collectively higher coverage of the full
  data set compared to the single matched set. Each stochastic match is without duplication, thus allowing downstream
  validation techniques such as cross-validation to be applied to each set without concern for overfitting.
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 for the book 'Statistical Analysis of 
  Network Data with R'.
Model-robust standard error estimators for cross-sectional, time series, clustered, panel, and longitudinal data.
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>.
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.
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 series of functions designed to aid in the creation of input files for the 'Circos' visualization platform <http://circos.ca/>. Included are two main function types - make_circos_x() and write_circos_x(). The "make" functions are designed to process an input data set, and return a data frame that can be manipulated and filtered to eliminate the need to write Perl scripts within the 'Circos' software platform for data filtering. The "write" functions take processed data frames from the "make" functions, and write .txt files compatible with the 'Circos' platform. In addition to these function types, there are other accessory functions that aid in the manipulation of the make_circos_x() returned data frames to enhance final visual appeal (such as color_circos_links()).
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 x  bing Lexicon, and output visualizations.
Software for the book Spectral Analysis for Physical
        Applications, Donald B. Percival and Andrew T. Walden,
        Cambridge University Press, 1993.
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 <http://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.
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).
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.
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.
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.
A statistical method for significant analysis of
        comparative proteomics based on LC-MS/MS Experiments
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.
Functions for reading, listing
    the contents of, and writing 'SAS' 'xport' format files.
    The functions support reading and writing of either
    individual data frames or sets of data frames.  Further,
    a mechanism has been provided for customizing how
    variables of different data types are stored.
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. Moreover, support for Terra and Aqua-MODIS as 
    well as PROBA-V is expected to arrive shortly.
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>.
Implements Bayesian statistical methodology for the 
		analysis of complex computer models.
		It allows for the emulation, calibration, and validation of computer models, 
		following methodology described in Bayarri et al 2007, Technometrics.
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!
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: 
    <http://www.tibonihoo.net/literate_musing/autocorrelations.html>) and by 
    Bob Carpenter (2012, URL: 
    <https://lingpipe-blog.com/2012/06/08/autocorrelation-fft-kiss-eigen/>).
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), <http://arxiv.org/abs/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.
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.
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.)
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.
Provides an API for analysing repetitive parameter estimations and simulations of
    mathematical models. Examples of mathematical models are Ordinary Differential equations (ODEs) 
    or Stochastic Differential Equations (SDEs) models. Among the analyses for parameter 
    estimation 'sbpiper' calculates statistics and generates plots for parameter density, PCA of the best 
    fits, parameter profile likelihood estimations (PLEs), and 2D parameter PLEs. These results can 
    be generated using all or a subset of the best computed parameter sets. Among the analyses 
    for model simulation 'sbpiper' calculates statistics and generates plots for deterministic 
    and stochastic time courses via cartesian and heatmap plots. Plots for the scan of one or two model 
    parameters can also be generated. This package is primarily used by the software 'SBpipe'. 
    Citation: Dalle Pezze P, Le Novère N. SBpipe: a collection of pipelines for automating 
    repetitive simulation and analysis tasks. BMC Systems Biology. 2017;11:46. <doi:10.1186/s12918-017-0423-3>.
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.
An implementation of Scalable Bayesian Rule Lists Algorithm.
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.
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.
Provides the Scale class and corresponding functions, in order to facilitate data input for scale construction. Reverse items and alternative orders of administration are dealt with by the program. Computes reliability statistics, confirmatory, single factor loadings. It suggests item deletions and produces basic text output in English, for incorporation in reports. Returns list objects of all relevant functions from other packages (see Depends).
Calculating approximately unbiased (AU) p-values
	from multiscale bootstrap probabilities. See
	Shimodaira (2004) <doi:10.1214/009053604000000823> and
	Shimodaira (2008) <doi:10.1016/j.jspi.2007.04.001>.
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, A., Simon, N., and Witten, D. SCALPEL: Extracting Neurons from Calcium Imaging Data <https://ajpetecom.files.wordpress.com/2017/12/scalpel_dec17.pdf>, which is to appear in the Annals of Applied Statistics.
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), and randomization tests. Data preparation functions 
  support outlier detection, handling missing values, scaling, truncating, 
  rank transformation, and smoothing. An exporting function help to generate 
  html and latex tables in a publication friendly style. More details can be 
  found at <https://jazznbass.github.io/scan-Book/>.
Detection of anomalous space-time clusters using the scan 
    statistics methodology. Focuses on prospective surveillance of data streams, 
    scanning for clusters with ongoing anomalies. Hypothesis testing is made 
    possible by Monte Carlo simulation.
Import, plot, and diagnose results from statistical
  catch-at-age models, used in fisheries stock assessment.
This package computes the maximum likelihood estimator of the generalised additive and index regression with shape constraints. Each additive component function is assumed to obey one of the nine possible shape restrictions: linear, increasing, decreasing, convex, convex increasing, convex decreasing, concave, concave increasing, or concave decreasing.
scaRabee is a port of the Scarabee toolkit originally
        written as a Matlab-based application. It provides a framework
        for simulation and optimization of pharmacokinetic-pharmacodynamic 
        models at the individual and population level. It is built on top of the
        neldermead package, which provides the direct search algorithm proposed 
        by Nelder and Mead for model optimization.
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.
Creates scatterpie plots, especially useful for plotting pies on a map.
Plots a three dimensional (3D) point cloud.
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.
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.
The SC-SR Algorithm is used to calculate the cause-deleted life expectancy improvement for left and right censored data. 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>.
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 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.
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.
SCEPtER 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.
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.
Estimates a fast and regularized version of GWR for large dataset, detailed in Murakami, Tsutsumida, Yoshida, Nakaya, and Lu (2019) <arXiv:1905.00266>.
Provides schema-on-read tools including a single function call (e.g., schemaOnRead('filename')) that reads text ('TXT'), comma separated value ('CSV'), raster image ('BMP', 'PNG', 'GIF', 'TIFF', and 'JPG'), R data ('RDS'), HDF5 ('H5'), NetCDF ('CS'), spreadsheet ('XLS', 'XLSX', 'ODS', and 'DIF'), Weka Attribute-Relation File Format ('ARFF'), Epi Info ('REC'), SPSS ('SAV'), Systat ('SYS'), and Stata ('DTA') files. It also recursively reads folders (e.g., schemaOnRead('folder')), returning a nested list of the contained elements.
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>.
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.
An R interface to the 'SciDB' array database <http://scidb.org>.
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>.
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.
Sparse Column-wise Inverse Operator for estimating the inverse covariance matrix.  Note that this is a preliminary version accompanying the arXiv paper (arXiv:1203.3896) in 2012.   This version contains only the minimal set of functions for estimation and cross validation.
A collection of functions that creates graphs with error
             bars for data collected from one-way or higher factorial
             designs.
Functions to install SciViews additions to R, and more
        tools.
Provides functions to measure growth patterns and align
    sampling spots in chronologically deposited materials. The package is
    intended for the fields of sclerochronology, dendrochronology and geology.
Maximum likelihood estimation of the scaled logit model parameters
    proposed in Dunning (2006) <doi:10.1002/sim.2282>.
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>.
Given a matrix with results of different algorithms for different
    problems, the package uses statistical tests and corrections to assess the
    differences between algorithms.
Provides functions for fitting discrete distribution models to count data.
  Included are the Poisson, the negative binomial 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 B cell clones identification 
             from adaptive immune receptor repertoire sequencing (AIRR-Seq) datasets. 
             Three models are included (identical, hierarchical, and spectral) 
             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>.
             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.
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>.
A set of tools for quickly scoring models in data science and
    machine learning. This toolset is written in C++ for blazing fast performance.
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.
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>.
Division of an ANOVA experiment treatment means into homogeneous distinct groups using the clustering method of Scott & Knott.
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.
Calculating the monthly conventional and self-calibrating Palmer
    Drought Severity Index (PDSI and scPDSI) using the precipitation and potential
    evapotranspiration data. The function to calculate PDSI is based on the C++ 
    source codes developed by Nathan Wells, Steve Goddard and Michael J. Hayes,
    University of Nebraska-Lincoln.
    Reference: Palmer W. (1965). Meteorological drought.
    U.s.department of Commerce Weather Bureau Research Paper,
    <https://www.ncdc.noaa.gov/temp-and-precip/drought/docs/palmer.pdf>;
    Wells N., Goddard S., Hayes M. J. (2004). A Self-Calibrating Palmer Drought Severity Index.
    Journal of Climate, 17(12):2335-2351, <DOI:10.1175/1520-0442(2004)017%3C2335:ASPDSI%3E2.0.CO;2>.
Implements different inventory models, the bullwhip effect and other supply chain performance variables. Marchena Marlene (2010) <arXiv:1009.3977>.
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> .
Given a word will produce that word's scrabble score. Unlike many naive implementations this package takes into consideration the distribution of letter in scrabble. So a word like 'zzz' will be scored '10' rather than '30'.
A utility to webscrape the in-house careers of members of the European parliament, from its website <http://www.europarl.europa.eu>.
Tools for Scraping Data from Web-Based Documents
Routines for a collection of screen-and-clean type
        variable selection procedures, including UPS and GS.
Tools for the analysis of high-dimensional data developed/implemented
  at the group "Statistical Complexity Reduction In Molecular Epidemiology" (SCRIME).
  Main focus is on SNP data. But most of the functions can also be applied to other
  types of categorical data.
Support for using .Rt (transcript) tests
  in the tests directory of a package.  Provides more
  convenience and features than the standard .R/.Rout.save
  tests.  Tests can be run under R CMD check and also
  interactively.  Provides source.pkg() for quickly loading
  code, DLLs, and data from a package for use in an
  edit/compile/test development cycle.
Run complex native scripts with a single command, similar to system commands.
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.
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.
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>.
Clean biological occurrence records. Includes functionality
    for cleaning based on various aspects of spatial coordinates,
    unlikely values due to political 'centroids', coordinates based on
    where collections of specimens are held, and more.
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.
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' (<http://www.suitesparse.com>).
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:10.1101/576827> 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.
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.
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.
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, 
    electrical logs) (Johnson, 1992, <ISSN 0037-0738>).
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.
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.
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.
The semidiscrete decomposition (SDD) approximates a matrix
        as a weighted sum of outer products formed by vectors with
        entries constrained to be in the set {-1, 0, 1}.
Companion package to the book Simulation and Inference for
        Stochastic Differential Equations With R Examples, ISBN
        978-0-387-75838-1, Springer, NY.
Performs two tests to evaluate if the
        experiments are associated and returns a list of interesting
        features common to all the experiments.
Functions to filter GPS and/or Argos locations. The provided
    filters remove temporal and spatial duplicates, fixes located at a given
    height from estimated high tide line, and locations with high error as
    proposed in Shimada et al. (2012) <doi:10.3354/meps09747> and 
    Shimada et al. (2016) <doi:10.1007/s00227-015-2771-0>.
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>.
Functions provided by this pedagogic package allow to compute models with two popular machine learning approaches, BRT (Boosted Regression Trees) and MaxEnt (Maximum Entropy) applied on sets of marine biological and environmental data. They include the possibility of managing the main parameters for the construction of the models. Classic tools to evaluate model performance are provided (Area Under the Curve, omission rate and confusion matrix, map standard deviation) and are completed with tools to perform null models. The biological dataset includes original occurrences of two species of the class Echinoidea (sea urchins) present on the Kerguelen Plateau and that show contrasted ecological niches. The environmental dataset includes the corresponding statistics for 15 abiotic and biotic descriptors summarized for the Kerguelen Plateau and for different periods in a raster format. The package can be used for practicals to teach and learn the basics of species distribution modelling. Maps of potential distribution can be produced based on the example data included in the package, which brings prior observations of the influence of spatial and temporal heterogeneities on modelling performances. The user can also provide his own datasets to use the modelling functions.
Terrestrial and marine predictors for species distribution modelling
    from multiple sources, including WorldClim <http://www.worldclim.org/>,,
    ENVIREM <http://envirem.github.io/>, Bio-ORACLE <http://bio-oracle.org/>
    and MARSPEC <http://www.marspec.org/>.
This packages provides a set of tools for post processing the
    outcomes of species distribution modeling exercises. It includes novel
    methods for comparing models and tracking changes in distributions through
    time. It further includes methods for visualizing outcomes, selecting
    thresholds, calculating measures of accuracy and landscape fragmentation
    statistics, etc.. This package was made possible in part by financial
    support from the Australian Research Council & ARC Research Network for
    Earth System Science.
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.
Fitting discrete Bayesian networks using soft-discretized data.  Soft-discretization is based on mixture of normal distributions.  Also implemented is a supervised Bayesian network learning employing Kullback-Leibler divergence. For more information see Balov (2013) <DOI:10.1186/1755-8794-6-S3-S1>.
Summaries of distributions on clusterings and feature allocations are provided. Specifically, point estimates are obtained by the sequentially-allocated latent structure optimization (SALSO) algorithm to minimize squared error loss, absolute error loss, Binder loss, or the lower bound of the variation of information loss. Clustering uncertainty can be assessed with the confidence calculations and the associated plot.
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>.
Routines for drawing samples from spatial objects, focused on spatially 
  balanced algorithms. Draws Halton Iterative Partition (HIP) 
  (Robertson et al., 2018; <doi:10.1007/s10651-018-0406-6>), 
  Balanced Acceptance Samples (BAS) (Robertson et al., 2013; <doi:10.1111/biom.12059>), 
  Generalized Random 
  Tessellation Stratified (GRTS) (Stevens and Olsen, 2004; <doi:10.1198/016214504000000250>), 
  Simple Systematic Samples (SSS) and 
  Simple Random Samples (SRS) from point, line, and polygon resources.  
  Frames are 'SpatialPoints', 'SpatialLines', or 'SpatialPolygons' 
  objects from package 'sp'.
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.
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 segregation populations along with their parents are used to fit all the possible models and the best model is viewed as the model of the trait. There are fourteen combinations of populations available. Zhang YM, Gai JY, Yang YH (2003) <doi:10.1017/S0016672303006141>.
Access Sea Around Us (<http://www.seaaroundus.org/>) fish 
    catch data, including tools for visualizing the data.
Calculates parameters of the seawater carbonate system and assists the design of ocean acidification perturbation experiments.
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 functionality for searching / subsetting and slicing named
    objects using 'stringr/i'-style modifiers by case (in)sensitivity,
    regular expressions or fixed expressions; searches uses the standard '['
    operator and allows specification of default search behavior to either the
    search target (named object) and/or the search pattern.
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', '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.
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 single seasonality tests as used in 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
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.
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).
When security risks in web services are discovered by independent
   security researchers who understand the severity of the risk, they
   often lack the channels to properly disclose them. As a result,
   security issues may be left unreported. The 'security.txt' 'Web Security Policies'
   specification defines an 'IETF' draft standard <https://tools.ietf.org/html/draft-foudil-securitytxt-00>
   to help organizations define the process for security researchers to securely 
   disclose security vulnerabilities. Tools are provided to help identify and 
   parse 'security.txt' files to enable analysis of the usage and adoption of these policies.
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://www.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.
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.
Bringing together tools for modeling drying and soaking (rehydration)
	kinetics of seeds. This package contains several widely used predictive models
	(e.g.: da Silva et al., 2018). As these are nonlinear, the functions are interactive-based 
	and easy-to-use. Least squares estimates are obtained with just a few visual adjustments 
	of the initial parameter values. 
	Reference: da Silva AR et al. (2018) <doi:10.2134/agronj2017.07.0373>.
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, U. S. NCI Seer Register, state/county 
   or census tract level. The function can categorize the data into "n" quantiles, where "n" is 3 to 11 or
   the caller can specify a cut point list for the categorizes.  
   The caller can also provide the data and the comparison operation to request
   hatching over any areas.  The default operation and value are > 0.05 (p-values).
   The location id provided in the data determines the geographic level of the mapping.
   If states, state/counties or census tracts are being mapped, the location ids 
   used must be the U.S. FIPS codes for states (2 digits), state/counties (5 digits)
   or state/county/census tracts (11 digits). If the location id references the U.S. Seer Registry 
   areas, the Seer Registry area identifier used to link the data to the geographical 
   areas, then the location id is the Seer Registry name or abbreviation.
   Additional parameters are used to provide control over the drawing of the boundaries
   at the data's boundary level and higher levels.
   The package uses modified boundary data from the 2000 and 2010 U. S. Census to reduce the 
   storage requirements and improve drawing speed.  
   The 'SeerMapper' package 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 (see manual for more details.)
Provides supplemental 2010 census tract boundary package for 23 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 15 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 14 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 23 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 the public domain.
Provides supplemental 2000 census tract boundaries for the 15 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 14 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. Census data and is in the public domain.
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.
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.
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.
Performs a fast exact segmentation on data and allows for use of various cost functions.
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.
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 entropy-based segregation indices, as developed by
    Theil (1971) <isbn:978-0471858454>, with a focus on
    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.
    Includes standard error estimation by bootstrapping.
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
This package implements a novel method for fitting a
        bounded probability distribution to quantiles (for example
        stated by an expert), see Bornkamp and Ickstadt (2009) for
        details.  For this purpose B-splines are used, and the density
        is obtained by penalized least squares based on a Brier entropy
        penalty.  The package provides methods for fitting the
        distribution as well as methods for evaluating the underlying
        density and cdf. In addition methods for plotting the
        distribution, drawing random numbers and calculating quantiles
        of the obtained distribution are provided.
Sets up and executes a SelAC model (Selection on Amino acids and codons) for testing the presence of selection in amino acid or codon among a set of genes on a fixed phylogeny. Beaulieu et al (2019) <doi:10.1093/molbev/msy222>.
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 (Aouadi et al. 2018, <arXiv:1810.01670>), 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.
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.
Basic functions for creating semantic UI dashboard.
  This package adds support for a powerful UI library semantic UI -
  <http://semantic-ui.com/> to your dashboard and enables you to
  stay compatible with 'shinydashboard' functionalities.
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.
Provides functionality to automatically calculate direct, indirect, 
    and total effects from piecewise structural equation models, comprising 
    lists of fitted models representing structured equations (Lefcheck 2016 
    <doi:10/f8s8rb>). Confidence intervals are provided via bootstrapping.
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.
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 by a path diagram/mixed graph.
Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model. Reference: A. Krol and P. Saint-Pierre (2015) 	<doi:10.18637/jss.v066.i06>.
A powerful, easy to write and easy to modify syntax for
    specifying and estimating Partial Least Squares (PLS) path models allowing for the latest estimation methods
    for Consistent PLS as per Dijkstra & Henseler (2015, MISQ 39(2): 297-316), adjusted interactions as per 
    Henseler & Chin (2010) <doi:10.1080/10705510903439003> and bootstrapping utilizing 
    parallel processing as per Hair et al. (2017, ISBN:978-1483377445).
Functions for semiparametric regression analysis, to
        complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J.
        (2003). Semiparametric Regression. Cambridge University Press.
Routine for fitting continuous or discrete response copula sample selection models with semi-parametric predictors, including linear and nonlinear effects.
Conduct tests of difference in fit for mean and covariance
        structure models as in structural equation modeling (SEM)
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 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>.
Path diagrams and visual analysis of various SEM packages' output.
Fits structural equation models using partial least
        squares (PLS). The PLS approach is referred to as
        'soft-modeling' technique requiring no distributional
        assumptions on the observed data.
Provides a-priori, post-hoc, and compromise power-analyses for structural equation models (SEM). Moshagen & Erdfelder (2016) <doi:10.1080/10705511.2014.950896>.
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.
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 useful tools for structural equation modeling.
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 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.
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>.
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.
Package contains a simple SMTP client which provides a
        portable solution for sending email, including attachment, from
        within R.
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 and Hazlett (2018) 
  <https://www.researchgate.net/publication/322509816_Making_Sense_of_Sensitivity_Extending_Omitted_Variable_Bias>.
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 reliability sensitivity 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 analysis in matched observational studies with multiple controls using weighted M-statistics to increase design sensitivity.
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.
Functions that facilitate analysis of 
              Sensory as well as Consumer data within a mixed effects model 
              framework. The so-called mixed assessor models, 
              that correct for the scaling effect are implemented.
              The generation of the d-tilde plots forms part of the package.
              The shiny application provides a GUI for the functionalities.
It allows to rapidly compute, bootstrap and plot up to third-order Sobol' indices
    using the estimators by Saltelli et al. 2010 <doi:10.1016/j.cpc.2009.09.018> and 
    Jansen 1999 <doi:10.1016/S0010-4655(98)00154-4>. The 'sensobol' package also implements
    the algorithm by Khorashadi Zadeh et al. 2017 <doi:10.1016/j.envsoft.2017.02.001> to 
    calculate the approximation error in the computation of Sobol' first and
    total indices, an approach that allows to robustly screen influential from non-influential
    model inputs. Finally, it also provides functions to obtain publication-ready figures
    of the model output uncertainty and sensitivity-related analysis.
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>.
Test sampling designs by several flexible cost functions, usually based on the simulations, and optimise sampling designs using different optimisation algorithms; load plume simulations (on lattice or points) even if they do not fit into memory.
Contains the function CUUimpute() which performs model-based clustering and imputation simultaneously.
Provides methods for sensory discrimination methods;
  duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different,
  2-AC and degree-of-difference.
  This enables the calculation of d-primes, standard errors of
  d-primes, sample size and power computations, and
  comparisons of different d-primes. Methods for profile likelihood
  confidence intervals and plotting are included.
Collection of datasets from Sen & Srivastava: "Regression
        Analysis, Theory, Methods and Applications", Springer.  Sources
        for individual data files are more fully documented in the
        book.
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.
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).
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. (2019) <doi:10.2139/ssrn.3067734>.
It combines maximum likelihood estimation of the parameters
    of matrix and 3rd-order tensor normal distributions with unstructured
    factor variance-covariance matrices, two procedures, and unbiased
    modified likelihood ratio testing of simple and double separability
    for variance-covariance structures, two procedures.
Functions to generate separation plots for evaluation of
        model fit.
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.
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 (2019) <doi:10.1101/758524> for details on the
    implemented methods.
The SeqGrapheR package provide interactive GUI for visualization of DNA sequence clusters. Details and principles of usage are described in user manual and (2010 BMC Bioinformatics 11:378). For full functionality installed NCBI blast is required.
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>.
A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.
Computes necessary information to meta analyze region-based
    tests for rare genetic variants (e.g. SKAT, T1) in individual studies, and
    performs meta analysis.
Integrate sequencing data (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. 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.
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.
Educational package used in R courses to illustrate
       	     object-oriented programming and package
       	     development. Using biological sequences (DNA and RNA) as
       	     a working example.
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.
Fast multi-generational pedigree inference from incomplete data on
    hundreds of SNPs, including parentage assignment and sibship clustering.
    See Huisman (2017) (DOI:10.1111/1755-0998.12665, citation('sequoia')) for 
    more information.
'Apache Drill' is a low-latency distributed query engine designed to enable 
    data exploration and 'analytics' on both relational and non-relational 'datastores', 
    scaling to petabytes of data. Methods are provided that enable working with 'Apache' 
    'Drill' instances via the 'REST' 'API', 'JDBC' interface (optional), 'DBI' 'methods'
    and using 'dplyr'/'dbplyr' idioms.
Enables reading and writing binary and ASCII data to 
             RS232/RS422/RS485 or any other virtual serial interfaces 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).
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.
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.
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.
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 book by C. Q. Schneider and C. Wagemann "Set Theoretic Methods for 	the Social Sciences", Cambridge University Press and in the online appendix.
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.
SetRNG provides utilities to help set and record the setting of
	the seed and the uniform and normal generators used when a random
	experiment is run. The utilities can be used in other functions 
	that do random experiments to simplify recording and/or setting all the 
	necessary information for reproducibility. 
	See the vignette and reference manual for examples.
Data structures and basic operations for ordinary sets,
             generalizations such as fuzzy sets, multisets, and
             fuzzy multisets, customizable sets, and intervals.
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>, and Butler A and Satija R (2017) <doi:10.1101/164889> for more details.
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.
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.
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".
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>.
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 R and 'Rcpp' objects to Simple Features 'sf', without depending
  on the Simple Feature library. Conversion functions are available at both the R level, 
  and through 'Rcpp'.
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>.
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.
Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich,
   some of which were ported from S-plus in the 1990's.
 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.
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.
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.
Graph based clustering summaries for spatial point patterns.
    Includes Connectivity function, Cumulative connectivity function and clustering
    function, plus the triangle/triplet intensity function T.
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 and non-informative right-censoring may be considered. 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 distribution as an important special case.
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.
Seven different methods for multiple testing problems. The SGoF-type methods and the BH and BY false discovery rate controlling procedures.
Functions to calculate student growth percentiles and percentile growth projections/trajectories for students using large scale,
        longitudinal assessment data.  Functions use quantile regression to estimate the conditional density associated
        with each student's achievement history.  Percentile growth projections/trajectories are calculated using the coefficient matrices derived from
	the quantile regression analyses and specify what percentile growth is required for students to reach future achievement targets.
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.
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.
Provides functions to implement sgRSEA (single-guide RNA Set Enrichment Analysis), which is a robust test for identification of essential genes from genetic screening data using CRISPR (clustered regularly interspaced short palindromic repeats) and Cas9 (CRISPR-associated nuclease 9) system.
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
An evolving and growing collection of tools for the quantification, assessment, and comparison of planar shape and pattern. The current flagship functionality is in 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. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented.
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.
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.
The aim of 'SHAPforxgboost' is to aid in visual data investigations
 using SHAP (SHapley Additive exPlanation) visualization plots for 'XGBoost'. 
 It provides summary plot, dependence plot, interaction plot, and force plot. 
 It relies on the 'dmlc/xgboost' package to produce SHAP values.
 Please refer to 'slundberg/shap' for the original implementation of SHAP in 'Python'.
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'.
Analyse species-habitat associations in R. Therefore, information about the 
  location of the species is needed and about the environmental conditions. 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) <doi:10.1214/aos/1015957396>.  
             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.
An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see <https://arxiv.org/abs/1505.01333>).
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>.
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).
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>.
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.
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.
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>.
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.
The minimal router for your Shiny apps.
    It allows you to create dynamic web applications with real-time user interface and easily share URLs 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 Semantic UI -
    <http://semantic-ui.com/>. It also supports universal UI input 
    binding that works with various DOM elements.
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.
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.
Adds additional Twitter Bootstrap components to Shiny.
Add a global indicator (spinner, progress bar, gif) in your 'shiny' applications to show the user that the server is busy.
Create a lightweight Shiny wrapper for the css-loaders created by Luke Hass <https://github.com/lukehaas/css-loaders>. Wrapping a Shiny output will automatically show a loader when the output is (re)calculating.
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 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.
Add functionality to create drag and drop div elements in shiny.
Add fancy CSS effects to your 'shinydashboards' or 'shiny' apps.
             100% compatible with 'shinydashboardPlus' and 'bs4Dash'.
Logging framework dedicated for complex shiny apps. 
    Different types of events can be logged 
    (value of a variable, multi-line output of a function,
    result of a unit test, custom error, warning, or diagnostic message).
    Each event can be logged with a list of parameters that are event-specific, 
    common for events within the same scope, session-specific, or app-wide.
    Logging can be done simultaneously to R console, 
    browser JavaScript console, a file log, and a database (MongoDB).
    Log data can be further analyzed with the help of process-mining techniques 
    from 'bupaR' package.
Easily display user feedback next to Shiny inputs.  The feedback message is displayed when the feedback condition evaluates to TRUE.
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.
Insert Glide JavaScript component into Shiny applications for
    carousel or assistant-like user interfaces.
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.
Interactive shiny application for analysis of educational tests and
    their items.
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.
Track and record the use of applications and the user's interactions with 'Shiny' inputs.
  Allow to save inputs clicked, output generated and eventually errors.
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.
Implementation of a shiny app to easily compare supervised regression model performances. 
    You provide the data and configure each model parameter directly on the shiny app.  
    Four main supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression problem on a given time series.
    Implementation of these time series forecasting methods on R has been done by Shmueli and Lichtendahl (2015, ISBN:0991576632).
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.
Implements user interfaces for log-linear models, Bayesian model averaging and 
    Bayesian Dirichlet process mixture models.
Shiny wrappers for the RGL package. This package exposes RGL's
    ability to export WebGL visualization in a shiny-friendly format.
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/>.
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).
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 <http://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.
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.
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 (2019) <http://ashipunov.info/shipunov/software/r/r-en.htm>.
 Most useful functions are probably Bclust(), Jclust() and BootA() which bootstrap hierarchical clustering; Recode...() which multiple recode in a fast, flexible and simple way; Misclass() which outputs confusion matrix even if classes are not concerted;
 Overlap() which calculates overlaps of convex hulls from any projection; and Pleiad() which is fast and flexible correlogram. In fact, there are much more useful functions, please see documentation.
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>).
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.
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 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>.
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.
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 (sigmoidal, double sigmoidal, no signal or ambiguous) best describes the data. No signal means the intensity does not reach a high enough point or does not change at all over time. Sigmoidal means intensity starts from a small number than climbs to a maximum. Double sigmoidal means intensity starts from a small number, climbs to a maximum then starts to decay. After the decision between those four options, the algorithm gives the sigmoidal (or double sigmoidal) associated parameter values that quantifies the time intensity curve. The origin of the package name came from "SIngle CEll Growth Analysis in R".
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 <http://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, and in Juraska M and Gilbert PB (2015), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4): 606-25. 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.
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).
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.
Contains functions for identification of  
  copy number signatures (Geoffrey et al. (2018) <doi:10.1038/s41588-018-0179-8>) and 
  mutation signatures (Alexandrov et al. (2013) <doi:10.1038/nature12477>) 
  by non-negative matrix factorization, signature analysis and visualization. 
  It can be used to capture signatures of genomic variation, compare genotype or
  phenotype features of different signatures and thus uncover the relationship 
  between the mechanism of genomic variation and phenotypes in cancer.
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 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.
Provide consistent significance controlled variable selection procedure with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, and p-value). The algorithm selects a final model with only significant variables based on a correction choice of False Discovery Rate, Bonferroni, or no correction.
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.
Provides convenience functions to replace hyphen-minuses (ASCII 45)
    with proper minus signs (Unicode character 2212). The minus sign is wider
    and slightly nicer for display than the hyphen-minus we use for computation.
Interfaces with the 'SigOpt' API. More info at <https://sigopt.com>.
Pathway Analysis is the process of 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. This 
    can lead to identification of 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 
    and contains pre-computed Pathway-GPS data for pathways in the KEGG and 
    Reactome pathway repositories as well as mechanisms for extracting GPS for 
    user supplied repositories.
Provides gene signature quality control metrics in publication ready plots. Namely, enables the visualization of properties such as expression, variability, correlation, and comparison of methods of standardisation and scoring metrics.
Succinctly and correctly format statistical summaries of
    various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance).  The main purpose is unified reporting 
    of experimental results, working around issue such as the difficulty of
    extracting model summary facts (such as with 'lm'/'glm').  This package also
    includes empirical tests, such as bootstrap 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.
This package calculates ANSI S3.5-1997 Speech Intelligibility Index
   (SII), a standard method for computing the intelligibility of
   speech from acoustical measurements of speech, noise, and hearing
   thresholds. This package includes data frames corresponding to
   Tables 1 - 4 in the ANSI standard as well as a function utilizing
   these tables and user-provided hearing threshold and noise level
   measurements to compute the SII score.  The methods implemented
   here extend the standard computations to allow calculation of SII
   when the measured frequencies do not match those required by the
   standard by applying interpolation to obtain values for the
   required frequencies
   -- 
   Development of this package was funded by the Center for Bioscience
   Education and Technology (CBET) of the Rochester Institute of
   Technology (RIT).
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 SDE's. 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.
Besides functions for the calculation of similarity and
        multiple plot similarity measures with binary data (for
        instance presence/absence species data) the package contains
        some simple wrapper functions for reshaping species lists into
        matrices and vice versa and some other functions for further
        processing of similarity data (Mantel-like permutation
        procedures) as well as some other useful stuff for vegetation
        analysis.
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.
A flexible tool for simulating complex longitudinal data using
    structural equations, with emphasis on problems in causal inference.
    Specify interventions and simulate from intervened data generating
    distributions. Define and evaluate treatment-specific means, the average
    treatment effects and coefficients from working marginal structural models.
    User interface designed to facilitate the conduct of transparent and
    reproducible simulation studies, and allows concise expression of complex
    functional dependencies for a large number of time-varying nodes. See the
    package vignette for more information, documentation and examples.
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.
Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures.
Provides tools to help safely and efficiently organize Monte Carlo simulations in R.
    The package controls the structure and back-end of Monte Carlo simulations
    by utilizing a general generate-analyse-summarise strategy. The functions provided control
    common simulation issues such as re-simulating non-convergent results, support parallel
    back-end and MPI distributed computations, save and restore temporary files,
    aggregate results across independent nodes, and provide native support for debugging.
    For a pedagogical introduction to the package refer to 
    Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>.
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 two queueing data sets
    (one fabricated, one real-world) to facilitate input modeling.
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.
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.
A general framework for statistical simulation.
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.
Simulating output from a case-control genome wide association study (GWAS) with a given causal model. Fortune and Wallace (2019) <doi:10.1093/bioinformatics/bty898>.
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.
An implementation of a novel method to quantify the similarity
    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 among students' homework 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.
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.
Simulate multivariate correlated data given nonparametric marginals and their covariance 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 outperform various CRAN packages to date by substantial margins. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. This heuristic demonstrated not only strong capability of cost reduction, but also good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow.
Provides methods for simulation and analysis of a very general fatigue
			 lifetime model for (metal matrix) composite materials.
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.
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 Petkova, Tarpey, Su, and Ogden (2017) <doi: 10.1093/biostatistics/kxw035> and "A constrained single-index model for estimating interactions between a treatment and covariates" (under review, 2019) for detail. 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>.
Algorithms to create prognostic biomarkers using biological genesets or networks.
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>.
Implements the inference of
    co-expression networks based on partial correlation
    coefficients from either steady-state or time-course
    transcriptomic data. Note that with both type of data this
    package can deal with samples collected in different
    experimental conditions and therefore not identically
    distributed. In this particular case, multiple but related
    networks are inferred on one simone run.
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.
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.
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.
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.
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.
Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.
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()).
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.
SimpleTable provides a series of methods to conduct
        Bayesian inference and sensitivity analysis for causal effects
        from 2 x 2 and 2 x 2 x K tables when unmeasured confounding is
        present or suspected.
Simplex density, distribution, quantile functions as well as random variable
   	generation of the simplex distribution are given. Regression analysis of proportional data
   	using various kinds of simplex models is available. In addition, GEE method can be applied 
	to longitudinal data to model the correlation. Residual analysis is also involved. Some 
	subroutines are written in C with GNU Scientific Library (GSL) so as to facilitate the 
	computation.
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.  Dirichlet probabilities
 are calculated in certain cases.
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.
Tools and methods to simulate populations for surveys based
    on auxiliary data. The tools include model-based methods, calibration and
    combinatorial optimization algorithms. The package was developed with support of
    the International Household Survey Network, DFID Trust Fund TF011722 and funds
    from the World bank.
This package detects instances of Simpson's Paradox in
        datasets. It examines subpopulations in the data, either
        user-defined or by means of cluster analysis, to test whether a
        regression at the level of the group is in the opposite
        direction at the level of subpopulations.
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.
Functions for performing Bayesian similarity regression,
    and evaluating the probability of association between sets of ontological terms
    and binary response vector. A random 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.
Simulate multivariate linear model data is useful in research and education weather 
    for comparison or create data with specific properties. This package lets user to simulate
    linear model data of wide range of properties with few tuning parameters.
    The package also consist of function to create plots for the simulation
    objects and A shiny app as RStudio gadget. It can be a handy tool for model comparison, 
    testing and many other purposes.
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>.
Methods to simulate genetic sequence data for pedigrees, with 
    functionality to simulate genetic heterogeneity among pedigrees.
    Christina Nieuwoudt, Angela Brooks-Wilson, 
    and Jinko Graham (2019) <doi:10.1101/534552>.
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. 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:10.1016/j.jspi.2019.05.008> (for the case of a discrete treatment) and Park, Petkova, Tarpey, and Ogden (2019) "A single-index model with a surface-link for optimizing individualized dose rules" (pre-print) for detail of the method. The main function of this package is simsl().
Creates simulated data from structural equation models with standardized loading.
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).
simSummary is a small utility package which eases the
        process of summarizing simulation results. Simulations often
        produce intermediate results - some focal statistics that need
        to be summarized over several scenarios and many replications.
        This step is in principle easy, but tedious. The package
        simSummary fills this niche by providing a generic way of
        summarizing the focal statistics of simulations. The useR must
        provide properly structured input, holding focal statistics,
        and then the summary step can be performed with one line of
        code, calling the simSummary function.
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. 
    Note that this package is modelled on the 'survsim' package available in 
    the 'Stata' software (see Crowther and Lambert (2012) 
    <http://www.stata-journal.com/sjpdf.html?articlenum=st0275> or 
    Crowther and Lambert (2013) <doi:10.1002/sim.5823>).
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.
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.
This package provides simulation process of 4 selection methods in predicting potent compounds
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 framework for performing simulations such as those common in
    methodological statistics papers.  The design principles of this package
    are described in greater depth in Bien, J. (2016) "The simulator: An Engine
    to Streamline Simulations," which is available at
    <http://faculty.bscb.cornell.edu/~bien/simulator.pdf>.
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 including
             approximation of power. Individual vitamin D status 
             profiles are simulated, modelling population heterogeneity. 
             Individuals experience random exposures to infectious 		agents. Development of infection considers a heterogeneous treatment effect. This is incorporated into the power approximation.
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.
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>).
Create 'roxygen2' skeleton populated with information scraped from the
         within the function script. Also creates field entries for imports in the
         'DESCRIPTION' and import in the 'NAMESPACE' files. Can be run from the R
         console or through the 'RStudio' 'addin' menu.
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.
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).
Displays a pseudorandom message from a database of quotations. It works as an advanced version of the package 'fortunes', while 'sinx' supports multi-byte languages such as Chinese. The databases of 'sinx' can be given in markdown format, which is easier and more friendly than spread sheets for users.
A platform for experimenting with spatially explicit individual-based vegetation models.
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>.
Allows the user to compare SIRI (Service Interface for Real Time Information) data sets to their GTFS (General Transit Feed Specification) counterparts, a "Request_id" column us needed for the SIRI data frame in order to subset parts of it for use.
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 classification algorithm based on random forests, which takes the form of a short list of rules. SIRUS combines the simplicity of decision trees with the predictivity of random forests for problems with low order interactions. The core aggregation principle of random forests is kept, but instead of aggregating predictions, SIRUS selects the most frequent nodes of the forest to form a stable rule ensemble model. The algorithm is fully described in the following article: Benard C., Biau G., da Veiga S., Scornet E. (2019) <arXiv:1908.06852>. 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) and all
    of its variants in generalized linear models and the Cox proportional hazards
    model.
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. Standard ridge and sparse SIR are 
             also included in the package.
A tool for the simulation of gene expression profiles for in silico regulatory networks. The package generates gene regulatory networks, which include protein-coding and noncoding genes linked via different types of regulation: regulation of transcription, translation, RNA or protein decay, and post-translational modifications. The effect of genetic mutations on the system behaviour is accounted for via the simulation of genetically different in silico individuals. The ploidy of the system is not restricted to the usual haploid or diploid situations, but is defined by the user. A choice of stochastic simulation algorithms allow us to simulate the expression profiles (RNA and if applicable protein abundance) of the genes in the in silico system for the different in silico individuals. A tutorial explaining how to use the package is available at <https://oliviaab.github.io/sismonr/>. Manuscript in preparation; see also Angelin-Bonnet O., Biggs P.J. and Vignes M. (2018) <doi:10.1109/BIBM.2018.8621131>.
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>.
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.
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
Provides extensions for package 'sitree' for allometric variables, growth, mortality, recruitment, management, tree removal and external modifiers functions.
Sensitivity indices with dependent correlated inputs, using a
    method based on PLS regression.
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", Cano et al. (2012) [ISBN 978-1-4614-3652-2]
    and "Quality Control with R", Cano et al. (2015) [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.
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.
Sparse (Multicategory) Kernel Discriminant Analysis does variable selection for nonparametric classification
A shiny interface and supporting tools to guide users in choosing
    appropriate simulations, setting parameters, calculating summary genetic
    statistics, and organizing data output, all within the R environment. In
    addition to supporting existing forward and reverse-time simulators, new
    simulators can be integrated into the environment relatively easily.
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.
Functions are provided for the density function,
	     distribution function, quantiles and random number
	     generation for the skew hyperbolic
	     t-distribution. There are also functions that fit
	     the distribution to data. There are functions for the
	     mean, variance, skewness, kurtosis and mode of a given
	     distribution and to calculate moments of any order about
	     any centre. To assess goodness of fit, there are
	     functions to generate a Q-Q plot, a P-P plot and a tail plot.
Density, distribution function, quantile function and
        random generation for the skewed t distribution of Fernandez
        and Steel.
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.
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, ordinal, interval, or ratio scores. 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 interval and ratio scores.
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.
Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of 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.
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>.
A toolset for data reduction, visualization and analysis in skyscape archaeology, archaeoastronomy and cultural astronomy.
Query and download satellite imagery and climate/atmospheric datasets using the SkyWatch API. 
     Search datasets by wavelength (band), cloud cover, resolution, location, date, etc.
     Get the query results as data frame and as HTML. To learn more about the SkyWatch API, see <https://github.com/skywatchspaceapps/api>.
'Slack' <http://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' <http://www.skype.com/>, 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.
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.
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.
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 microarray data using a Bayesian Sparse Latent Factor Model - SLFM; see Duarte and Mayrink (2015) <DOI:10.1007/978-3-319-12454-4_15>. Considerable effort has been put into making slfm fast and memory efficient, turning it an interesting alternative to simpler methods in terms of execution time. It implements versions of the SLFM based on two type of mixtures priors for the loadings: one relying on a degenerate component at zero and the other using a small variance normal distribution for the spike part of the mixture. It also implements additional functions to allow pre-processing procedures for the data and to fit the model for a large number of probesets or genes.
Provides access to soil and landscape grid of Australia raster datasets via existing open geospatial consortium web coverage services. See <http://www.csiro.au/soil-and-landscape-grid>.
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 used directly from
    the R console, from 'RStudio', 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>.
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.
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>.
A collection of helper functions designed to help
    you to better understand object oriented programming in R,
    particularly using 'S3'.
Efficient procedures for Sorted L1 Penalized Estimation (SLOPE).
    The sorted L1 norm is useful for statistical estimation and testing,
    particularly for variable selection in the linear model.
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').
This is software linked to the book
  'Applied Smoothing Techniques for Data Analysis -
  The Kernel Approach with S-Plus Illustrations' Oxford University Press.
Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods.
This package provides a solution path for L1-penalized angle-based classification. Three loss functions are implemented in smac, including the deviance loss in logistic regression, the exponential loss in boosting, and the proximal support vector machine loss.
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>.
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. 
             The methods used for this package include: 
             (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>.
             (2) IPWE-based global test (hypotehsis test, power calculation) by Ogbagaber S.B., Karp J., Wahed A.S. (2016) <doi:10.1002/sim.6747>. 
             (3) G estimates (pairwise comparison, power calculation) by  Lavori R., Dawson P.W. (2012) <doi:10.1093/biostatistics/kxr016>. 
             (4) IPW estimates (pairwise comparison, power calculation) by Murphy S.A. (2005) <doi:10.1002/sim.2022>. 
             (5) SAMRT with adaptive randomization by Cheung Y.K. (2015) <doi:10.1111/biom.12258>.
Eases data preprocessing tasks, providing a data flow based 
   on a pipe operator which eases cleansing, transformation, oversampling, 
   or instance/feature selection operations.
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.
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 tool for assessing bio-economic feedback in 
  different management scenarios. 'smartR' (Spatial Management and Assessment 
  of demersal Resources for Trawl fisheries) combines information from different
   tasks gathered within the European Data Collection Framework for the fishery
  sector. The 'smartR' package implements the SMART model (Russo et al., 2014 
  <doi:10.1371/journal.pone.0086222>), through the object-oriented 
  programming paradigm, and within this package it is possible to achieve the 
  complete set of analyses required by the SMART approach: from the editing and
   formatting of the raw data; the construction and maintenance of coherent 
  datasets; the numerical and visual inspection of the generated metadata; to 
  the final simulation of management scenarios and the forecast of their 
  effects. The interaction between the user and the application could take 
  place through invocation of methods via the command line or could be entirely
   committed to the graphical user interfaces (GUI).
Addressing the problem of outlier detection from the viewpoint of statistical learning theory. This method is proposed by Yamanishi, K., Takeuchi, J., Williams, G. et al. (2004) <DOI:10.1023/B:DAMI.0000023676.72185.7c>. It learns the probabilistic model (using a finite mixture model) through an on-line unsupervised process. After each datum is input, a score will be given with a high one indicating a high possibility of being a statistical outlier.
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) <arXiv:1804.04587> for more details.
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.
particle filtering, auxiliary particle filtering and sequential Monte Carlo algorithms
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
Contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.
This package provides similarity among documents.
Symbolic multidimensional scaling for interval-valued dissimilarities. The hypersphere model and the hyperbox model are available.
Fit smoothing-splines mixed-effects models to replicated functional data sets and visualise the results.
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, 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.
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, Groß, M., U. Rendtel, T. Schmid, S. Schmon, and N. Tzavidis (2017) <doi:10.1111/rssa.12179>).
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))".
A collection of functions for statistical computing and data manipulation in R. 
    Includes routines for data ingestion, operating on dataframes and matrices, conversion to and 
    from lists, converting factors, filename manipulation, programming utilities, parallelization, plotting, 
    statistical and mathematical operations, and time series.
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.
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. Currently Lasso and SCAD penalized estimation is implemented.
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.
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 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>),
             Vector Exponential Smoothing (de Silva et al., 2010, <doi: 10.1177/1471082X0901000401>) in state space forms,
             several simulation functions and intermittent demand state space models. It also allows dealing with
             intermittent demand based on the iETS framework (Svetunkov & Boylan, 2017, <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.
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.
Functions to smooth two-dimensional fields using FFT and the convolution theorem
Some M-estimators for 1-dimensional location (Bisquare, ML
        for the Cauchy distribution, and the estimators from
        application of the smoothing principle introduced in Hampel,
        Hennig and Ronchetti (2011) to the above, the Huber
        M-estimator, and the median, main function is smoothm), and
        Pitman estimator.
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].
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>).
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 sample maximum (extremal types 
  theorem) and (the Fisher transformation of the) Pearson 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. (2018) <arXiv:1810.03136> 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
Build and manipulate probability distributions of the skew-normal 
  family and some related ones, notably the skew-t family, and provide related
  statistical methods for data fitting and 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 and Netbuilder() to create
    small networks via point and click.
A consistent, flexible and easy to use tool to parse and convert strings into cases like snake or camel among others.
Run 'Python' code, make function calls, assign and retrieve variables, etc. from R.
    A fork from 'rPython' which uses 'jsonlite', 'Rcpp' and has several fixes and improvements.
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.
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).
Snipping methods optimally removing scattered cells for robust estimation and cluster analysis.
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.
Solve the problem of over-parameterization in neural networks for genomic selection. Daniel Gianola, Hayrettin OkutEmail, Kent A Weigel and Guilherme JM Rosa (2011) <doi:10.1186/1471-2156-12-87>.
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.
contains several supplementary non-parametric statistics methods including quantile test, Cox-Stuart trend test, runs test, normal score test, kernel PDF and CDF estimation, kernel regression estimation and kernel Kolmogorov-Smirnov test.
This package carries out most common analysis when performing whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated.
Implements classes and methods for large scale SNP enrichment analysis (e.g. SNPs associated with genes expression in a GWAS signal).
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 for use with the RSNPset package, or to export to a PLINK set.
Tools for performing variable selection in three-way data using N-PLS 
    in combination with L1 penalization. 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.
This package implements asymptotic methods related to
        maximally selected statistics, with applications to SNP data.
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.
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.
Allows objects to be stored on disc and automatically
       		recalled into memory, as required, by delayed assignment.
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.
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.
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.
To implement two clustering methods, ODC and SODC, for
        clustering datasets using optimal scoring, can also be used as
        an dimension reduction tool.
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'
    (<https://docs.cloudant.com>). 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)
Implementation of 'softmax' regression and classification models with multiple layer neural network. It can be used for many tasks like word embedding based document classification, 'MNIST' dataset handwritten digit recognition and so on. Multiple optimization algorithm including 'SGD', 'Adagrad', 'RMSprop', 'Moment', 'NAG', etc are also provided.
Performs random forests for soft decision trees for a 
	classification problem.  Current limitations are for a maximum depth of 5
	resulting in 16 terminal nodes.  Some data cleaning is required before
	input.  Final graphic output requires currently requires exporting to 'Microsoft
	Excel' for visualization.  Method based on Irsoy, Yildiz and Alpaydin (2012, ISBN: 978-4-9906441-1-6).
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 <http://ecocrop.fao.org/ecocrop/srv/en/home>] 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 tool for importing, visualizing, and analyzing the soil carbon database created by the Powell Center working group.
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).
Basic and model-based soil physical analyses.
This package contains functions for modeling Soil Organic
        Matter decomposition in terrestrial ecosystems.
"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.
SOLAR is the standard software program to perform linkage and
    association mappings of the quantitative trait loci (QTLs) in pedigrees of
    arbitrary size and complexity. This package allows the user to exploit the
    variance component methods implemented in SOLAR. It automates such routine
    operations as formatting pedigree and phenotype data. It also parses the model
    output and contains summary and plotting functions for exploration of the
    results. In addition, solarius enables parallel computing of the linkage and
    association analyses, that makes the calculation of genome-wide scans more
    efficient. See <http://solar.txbiomedgenetics.org/> for more information about
    SOLAR.
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://lucene.apache.org/solr>) '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>.
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) and 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.
Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects and unknown variance-covariance structures (i.e. heterogeneous and unstructured variance models) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>). ML/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 than observations) and dense known covariance structures for levels of random effects. Spatial models can also be fitted using i.e. 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).
Application of the Self-Organizing Maps technique for spatial classification of time series. The package uses spatial data, point or gridded, to create clusters with similar characteristics. The clusters can be further refined to a smaller number of regions by hierarchical clustering and their spatial dependencies can be presented as complex networks. Thus, meaningful maps can be created, representing the regional heterogeneity of a single variable. More information and an example of implementation can be found in Markonis and Strnad (2019).
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.
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.
Load Russian economic indicators from the Archive of Economic and Social Data <http://sophist.hse.ru/>.
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.
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>.
SortableHTMLTables writes a data frame to an HTML file
        that contains a sortable table. The sorting is done using the
        jQuery plugin Tablesorter. The appearance is controlled through
        a CSS file and several GIF's.
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.
A client for Sensor Observation Services (SOS, see 
        <https://www.opengeospatial.org/standards/sos>) as specified by the 
        Open Geospatial Consortium (OGC). With the package users can
        retrieve (meta)data from SOS instances and interactively 
        create requests for near real-time observation
        data based on the available sensors, phenomena, observations etc.
        using thematic, temporal, and spatial filtering.
Access data from the sotkanet open data portal
        <https://www.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.
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.
Tools for sound synthesis and acoustic analysis.
    Performs parametric synthesis of sounds with harmonic and noise components
    such as animal vocalizations or human voice. Also includes tools for
    spectral analysis, pitch tracking, audio segmentation, self-similarity
    matrices, morphing, etc.
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.
Analogous to source(), but works when given a Gist URL or
        ID.
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.
Genomic and multi-environmental soybean data. Soybean Nested
    Association Mapping (SoyNAM) project dataset funded by the United Soybean Board
    (USB). See <doi.org/10.1534/g3.118.200332> and <doi.org/10.1534/g3.117.300300>. 
    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.
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.
The package implements the model-based kernel machine
        method for detecting gene-centric gene-gene interactions of Li
        and Cui (2012).
Miscellaneous functions for analysing species association
        and niche overlap.
Genomic Region Detection via Spatial Convex Clustering. See <https://arxiv.org/abs/1611.04696> for details.
Partial correlation estimation with joint sparse
        regression model.
This package provides an extension of conditional
        independence (CIG) and directed acyclic graph (DAG) estimation
        to the case where conditional relationships are (non-linear)
        additive models.
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.
      The XYZs of some standard illuminants and some standard linear chromatic adaptation transforms (CATs) are included.
      Compute CCT and the Planckian and daylight loci.  Three different 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.
SPACoDi is primarily designed to characterise the
        structure and phylogenetic diversity of communities using
        abundance or presence-absence data of species among community
        plots.
Provides tools to construct and exploit spatially weighted context data. Spatial weights are derived by a Kernel function from a user-defined matrix of distances between contextual units. Spatial weights can then be applied either to precise contextual measures or to aggregate estimates based on micro-level survey data, to compute spatially weighted context data. Available aggregation functions include indicators of central tendency, dispersion, or inter-group variability, and take into account survey design weights. The package further allows combining the resulting spatially weighted context data with individual-level predictor and outcome variables, for the purposes of multilevel modelling. An ad hoc stratified bootstrap resampling procedure generates robust point estimates for multilevel regression coefficients and model fit indicators, and computes confidence intervals adjusted for measurement dependency and measurement error of aggregate estimates. As an additional feature, residual and explained spatial dependency can be estimated for the tested models.
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'). 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.
Provide the core discrete event simulation (DES) framework for
    implementing spatially explicit simulation models. The core DES components 
    facilitate modularity, and easily enable the user to include additional
    functionality by running user-built simulation modules. Now includes (experimental)
    conditional scheduling.
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 iid data therefrom. See Davies & Hazelton (2010) <doi:10.1002/sim.3995> 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 mixed-effect models, including generalized linear mixed models with spatial correlations and models with non-Gaussian random effects (e.g., Beta). Both classical geostatistical models, and Markov random field models on irregular grids, can be fitted. Variation in residual variance (heteroscedasticity) can itself be represented by a generalized linear mixed model. Various approximations of likelihood or restricted likelihood are implemented, in particular h-likelihood (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) and Laplace approximation.
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.
Perform analysis of variance when the experimental units are spatially correlated. There are two methods to deal with spatial dependence: Spatial autoregressive models (see Rossoni, D. F., & Lima, R. R. (2019) <doi:10.28951/rbb.v37i2.388>) and geostatistics (see Pontes, J. M., & Oliveira, M. S. D. (2004) <doi:10.1590/S1413-70542004000100018>). For both methods, there are three multicomparison procedure available: Tukey, multivariate T, and Scott-Knott.
Tools for reading, visualising and processing Magnetic Resonance
    Spectroscopy data.
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'.
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 <http://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.
A 'sparklyr' extension adding the capability to work easily with nested data.
Provides an R Front end for 'Apache Spark' <https://spark.apache.org>.
Create sparklines and graphical tables for documents and websites.
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 
  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>.
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.
Contains a set of functions that allow to represent multivariate on a subspace of low dimension, in such a way that most of the variability of the information is captured. This representation is carried out through the 'HJ Biplot' methodology. A first method performs the 'HJ Biplot'.Then, the package implements three new techniques and constructs in each case the 'HJ Biplot', adapting restrictions to contract and / or produce zero charges in the main components, using three methods of regularization: Ridge, LASSO and Elastic Net.
Fast methods for learning sparse Bayesian networks from high-dimensional data using sparse regularization, as described in Aragam, Gu, and Zhou (2017) <arXiv:1703.04025>. Designed to handle mixed experimental and observational data with thousands of variables with either continuous or discrete observations.
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.
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.
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>.
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.
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.
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) 
    <https://arxiv.org/abs/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.
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>.
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/>.
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>.
Functionality for spatio-temporal modeling of large data sets is provided. A Gaussian process in space and time is defined through a stochastic partial differential equation (SPDE). The SPDE is solved in the spectral space, and after discretizing in time and space, a linear Gaussian state space model is obtained. When doing inference, the main computational difficulty consists in evaluating the likelihood and in sampling from the full conditional of the spectral coefficients, or equivalently, the latent space-time process. In comparison to the traditional approach of using a spatio-temporal covariance function, the spectral SPDE approach is computationally advantageous. See Sigrist, Kuensch, and Stahel (2015) <doi:10.1111/rssb.12061> for more information on the methodology. This package aims at providing tools for two different modeling approaches. First, the SPDE based spatio-temporal model can be used as a component in a customized hierarchical Bayesian model (HBM). The functions of the package then provide parameterizations of the process part of the model as well as computationally efficient algorithms needed for doing inference with the HBM. Alternatively, the adaptive MCMC algorithm implemented in the package can be used as an algorithm for doing inference without any additional modeling. The MCMC algorithm supports data that follow a Gaussian or a censored distribution with point mass at zero. Covariates can be included in the model through a regression term.
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, Batty's spatial entropy, O'Neill's entropy, Li and Reynolds' contagion index, Karlstrom and Ceccato's entropy, Leibovici's entropy, Parresol and Edwards' entropy and Altieri's entropy. References for all measures can be found under the topic 'SpatEntropy'. The package is able to work with lattice and point data.
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.
Functions for kriging and point pattern analysis.
This package fits a hierarchical spatial model for the generalized extreme value distribution with the option of model averaging over the space of covariates.
Spatial functions meant to enhance the core functionality of the
    package "raster", including a parallel processing engine for use with
    rasters.
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>.
Perform Spatial Clustering Analysis using Fuzzy Geographically Weighted Clustering. Provide optimization using Gravitational Search Algorithm.
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.
Runs a Shiny web application that allows to visualize spatial and spatio-temporal disease data, estimate disease risk and detect clusters. The application allows to fit Bayesian disease models to obtain risk estimates and their uncertainty by using the 'R-INLA' package, <http://www.r-inla.org>, and to detect clusters by using the scan statistics implemented in 'SaTScan', <https://www.satscan.org>. The application allows user interaction and creates interactive visualizations such as maps supporting padding and zooming and tables that allow for filtering. It also enables the generation of reports containing the analyses performed.
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>.
Filter matrices or (three dimensional) array data using different convolution kernels.
Spatial floor simulation with exponential/Gaussian variance-covariance function (isotropic), with specification of distance function, nugget, sill, range. The methodology follows Nole A.C. Cressie (2015) <doi:10.1002/9781119115151>. The original release is 2017-08-29.
Multivariate modelling of geostatistical (point), lattice (areal) and point pattern data in a unifying spatial fusion framework. Details are given in Wang and Furrer (2019) <arXiv:1906.00364>. Model inference is done using either 'Stan' <https://mc-stan.org/> or 'INLA' <http://www.r-inla.org>.
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().
Edge-corrected kernel density estimation and binary kernel
        regression estimation for multivariate spatial point process
        data. For details, see Diggle, P.J., Zheng, P. and Durr, P. A. (2005)
        <doi:10.1111/j.1467-9876.2005.05373.x>.
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.
  SpatialPack gives methods to complement methodologies that are available in geoR
  for one spatial process.
Computes spatial position models: Stewart potentials, Reilly
    catchment areas, Huff catchment areas.
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'.
Methods for determining spatial risk, in particular calculating the maximum value of insured fire risk policies of all buildings that are partly or fully located within circle of a radius of 200m.
Summaries for measuring segregation/mingling in multitype spatial
    point patterns with graph based neighbourhood description. Included indices:
    Mingling, Shannon, Simpson (also the non-spatial) Included functionals:
    Mingling, Shannon, Simpson, ISAR, MCI. Included neighbourhoods: Geometric, k-
    nearest neighbours, Gabriel, Delaunay. Dixon's test.
Provides functions implementing the pairwise M-estimator for
    parametric models for stable tail dependence functions described in "An
    M-estimator of spatial tail dependence"
    (Einmahl, J.H.J., Kiriliouk, A., Krajina, A. and Segers, J., 2014).
    See http://arxiv.org/abs/1403.1975.
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 arose from verifying high-resolution forecasts, where coarser-resolution models generally are favored even when a human forecaster finds the higher-resolution model to be considerably better.  Most newly proposed methods, which largely come from image analysis, computer vision, and similar, are available, with more on the way.
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.
Utilities that estimate, predict and cross-validate the
    spatio-temporal model developed for MESA Air.
Provide regularized maximum covariance analysis incorporating smoothness,
  sparseness and orthogonality of couple patterns by using the alternating direction method
  of multipliers algorithm. The method can be applied to either regularly or irregularly
  spaced data,  including 1D, 2D, and 3D (Wang and Huang, 2017 <doi:10.1002/env.2481>).
Provide regularized principal component analysis incorporating smoothness,
  sparseness and orthogonality of eigenfunctions by using the alternating direction method of
  multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced
  data, including 1D, 2D, and 3D.
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. 
    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' package.
Extension to the 'spatstat' package, for analysing large datasets
	     of spatial points on a network. Provides a memory-efficient
	     algorithm for computing the geometrically-corrected K function,
             described in S. Rakshit, A. Baddeley and
	     G. Nair (2019) <doi:10.18637/jss.v090.i01>
Extension to the 'spatstat' package, enabling the user
	     to fit point process models to point pattern data
	     by local composite likelihood ('geographically weighted
	     regression').
Contains utility functions for the 'spatstat' package
             which may also be useful for other purposes.
Bayesian inference for parametric proportional hazards spatial
    survival models; flexible spatial survival models.
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).
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.
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>.
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.
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'
  ('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>.
  'spdep' >= 1.1-1 corresponds to 'spatialreg' >= 1.1-1, in which the model
  fitting functions are deprecated and pass through to 'spatialreg', but
  will mask those in 'spatialreg'. From versions 1.2-1, the functions will
  be made defunct in 'spdep'.
  For now 'spatialreg' only has functions from 'spdep', where they are shown
  as deprecated. 'spatialreg' only loads the namespace of 'spdep'; if you
  attach 'spdep', the same functions in the other package will be masked.
  Some feed through adequately, others do not.
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>.
Implements stochastic proximity embedding as described by
 Agrafiotis et al. in PNAS, 2002, 99, pg 15869 and J. Comput. Chem., 2003,24, pg 1215
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.
SPECIES is an R package for estimation of species richness
        or diversity.
Fitting Mixtures to Species distributions using BFGS and analytical derivatives.
A set of functions for obtaining positional parameters and magnitude difference between components of binary and multiple stellar systems from series of speckle images.
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.
Fourier and Hilbert transforms are utilized to perform several
    types of spectral analysis on the supplied data. Fragmented and irregularly
    spaced data can be processed in terms of Lomb-Scargle method. Both,
    FFT as well as LOMB methods take multivariate data. A user friendly
    interface helps to interpret the results.
Contains some implementations of Singular Spectrum Analysis (SSA) for the gapfilling and spectral decomposition of time series. It contains the code used by Buttlar et. al. (2014), Nonlinear Processes in Geophysics. In addition, the iterative SSA gapfilling method of Kondrashov and Ghil (2006) is implemented. All SSA calculations are done via the truncated and fast SSA algorithm of Korobeynikov (2010) (package 'Rssa').
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.
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.
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.
Fitting linear models and generalized linear models to large data sets by updating algorithms.
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.
A set of tools for estimation of various spatial specifications of
    stochastic frontier 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.
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.
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.
Generalized Method of Moment estimation of Cliff-Ord-type spatial autoregressive models with and without  Heteroscedasticity.
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".
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.
Fits and simulates data from our predator preferences model, <DOI:10.1007/s10651-016-0341-3>.
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.
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.
Projection pursuit is used to find interesting low-dimensional
    projections of high-dimensional data by optimizing an index over all
    possible projections. The 'spinebil' package contains methods to evaluate
    the performance of projection pursuit index functions using tour methods,
    as described in Laa & Cook (2019) <arXiv:1902.00181>.
Generates the path for manual tours
  ['Cook' & 'Buja' (1997) <doi:10.2307/1390747>]. Tours are generally available in the
  'tourr' package ['Wickham' 'et' 'al.' (2011) <doi:10.18637/jss.v040.i02>]. 
  The grand tour is an algorithm that shows all possible projections given sufficient time. 
  Guided uses projection pursuit to steer the tour towards interesting projections. 
  The 'spinifex' package implements manual control, where the contribution of a selected variable 
  can be adjusted between -1 to 1, to examine the sensitivity of structure in the data
  to that variable. 
  The result is an animation where the variable is toured into and out of the projection 
  completely, which can be rendered using the 'gganimate' and 'plotly' packages.
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.
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".
'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.
Constructs B-splines and its integral, monotone splines
    (M-splines) and its integral (I-splines), convex splines (C-splines),
    and their derivatives of given order. Piecewise constant basis is
    allowed for B-splines and M-splines. See
    De Boor (1978) <doi:10.1002/zamm.19800600129>,
    Ramsay (1988) <doi:10.1214/ss/1177012761>, and
    Meyer (2008) <doi:10.1214/08-AOAS167> for more information.
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.
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.
Estimate the internal consistency of your tasks with a permutation based split-half reliability approach.
    Unofficial release name: "Fight Milk - re-brand".
Calculates scores and estimates bootstrapped split-half reliabilities for reaction time tasks and questionnaires. The 'splithalfr' can be extended with custom scoring algorithms for user-provided datasets. For more information, see Parsons, Kruijt, & Fox (2018) <doi:10.31234/osf.io/6ka9z>.
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.
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.
ML and GM estimation and diagnostic testing of econometric models for spatial panel data.
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'.
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.rte-france.com>).
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 Moran's eigenvector-based
    spatial regression models.
    For details see Murakami (2019) <arXiv:1703.04467>.
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, 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',
    Berkeley 'Ecoinformatics' Engine, '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.
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
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.
R implementation of the methods described in "A rank-based
        empirical likelihood approach to two-sample proportional odds
        model and its goodness-of-fit" by Zhong Guan and Cheng Peng,
        Journal of Nonparametric Statistics, to appear.
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 set of tools for model based optimization and tuning of
    algorithms. 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.
An R wrapper for pulling data from the 'Spotify' Web API 
  <http://developer.spotify.com/web-api> in bulk. It allows you to enter 
  an artist's name and retrieve their entire discography in seconds, along 
  with audio features and lyrics from Genius Lyrics <https://www.genius.com>. 
  You can also pull song and playlist information for a given 'Spotify' user.
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>.
Implements classes and methods for modeling spatial point patterns using inhomogeneous Poisson point processes, where the intensity surface is assumed to be analogous to a finite additive mixture of normal components and the number of components is a finite, fixed or random integer. Extensions to the marked inhomogeneous Poisson point processes case are also presented. We provide an extensive suite of R functions that can be used to simulate, visualize and model point patterns, estimate the parameters of the models, assess convergence of the algorithms and perform model selection and checking in the proposed modeling context.
Sparse arrays interpreted as multivariate polynomials.
A stochastic SEIIaR (susceptible, exposed, infectious, infectious asymptomatic, recovered) metapopulation model that including commuting. Each location has a local infection system, while the locations are connected by people who commute each day. The model differentiates between day and night. During the day you can infect/be infected in the location where you work, while during the night you can infect/be infected in the location where you live. It is the same commuters who travel back and forth each day. At the start of a day, all commuters are sent to their work location, where they mix for 12 hours. The commuters are then sent to their respective home locations, where they mix for 12 hours. The model is loosely based upon a published model by Engebretsen (2019) <doi:10.1371/journal.pcbi.1006879>.
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>.
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.
The recent advancement of high-throughput technologies has led to frequent utilization of gene expression and other "omics" data for toxicological, diagnostic or prognostic studies in and clinical applications. Unlike in classical predictions where the number of samples is greater than the number of variables (n>p), the challenge faced with prediction using "omics" data is that the number of parameters greatly exceeds the number of samples (p>>n). To solve this curse of dimensionality problem, several predictive functions have been proposed for direct and probabilistic classification and survival predictions. Nevertheless, these predictive functions have been shown to perform differently across datasets. Comparing predictive functions and choosing the best is computationally intensive and leads to selection bias. Thus, the question which function should one choose for a given dataset is to be ascertained. This package implements the approach proposed by Jong et al., (2016) to address this question.
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>.
Calculate species richness functions for rarefaction and
    extrapolation.
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.
Methods to optimize sample configurations using spatial simulated annealing. Multiple objective 
    functions are implemented for various purposes, such as variogram estimation, spatial trend estimation 
    and spatial interpolation. A general purpose spatial simulated annealing function enables the user to 
    define his/her own objective function. Solutions for augmenting existing sample configurations and solving
    multi-objective optimization problems are available as well.
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.
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.
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) 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>.
These functions provide procedures for selecting sites for spatial
    surveys using spatially balanced algorithms applied to discrete points,
    linear networks, or polygons. The probability survey designs available
    include independent random samples, stratified random samples, and unequal
    probability random samples (categorical or probability proportional to
    size).  Design-based estimation based on the results from surveys is
    available for estimating totals, means, quantiles, CDFs, and linear models.
    The analyses rely on package survey for most results.  Variance estimation
    options include a local neighborhood variance estimator that is appropriate
    for spatially-balanced survey designs.  A reference for the survey design
    portion of the package is: D. L. Stevens, Jr. and A. R. Olsen (2004),
    "Spatially-balanced sampling of natural resources.", Journal of the American
    Statistical Association 99(465): 262-278, <DOI:10.1198/016214504000000250>.
    Additional helpful references for this package are A. R. Olsen, T. M.
    Kincaid, and Q. Payton (2012) and T. M. Kincaid and A. R. Olsen (2012), both
    of which are chapters in the book "Design and Analysis of Long-Term
    Ecological Monitoring Studies" (R. A. Gitzen, J. J. Millspaugh, A. B.
    Cooper, and D. S. Licht (eds.), Cambridge University Press, New York,
    <Online ISBN:9781139022422>).
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>.
The approach of constrained spatiotemporal mixed models is to make reliable estimation of air pollutant concentrations at high spatiotemporal
    resolution (Li, L., Zhang, J., Meng, X., Fang, Y., Ge, Y., Wang, J., Wang, C., Wu, J., Kan, H. (2018) <doi.org/10.1016/j.rse.2018.09.001>; 
    Li, L., Lurmann, F., Habre, R., Urman, R., Rappaport, E., Ritz, B., Chen, J., Gilliland, F., Wu, J., (2017) <doi:10.1021/acs.est.7b01864>).
    This package is an extensive tool for this modeling approach with support of block Kriging (Goovaerts, P. (1997) <http://www.gbv.de/dms/goettingen/229148123.pdf>)
    and uses the PM2.5 modeling as examples. It provides the following functionality:
    (1) Extraction of covariates from the satellite images such as GeoTiff and NC4 raster;
    (2) Generation of temporal basis functions to simulate the seasonal trends in the study regions;
    (3) Generation of the regional monthly or yearly means of air pollutant concentration;
    (4) Generation of Thiessen polygons and spatial effect modeling;
    (5) Ensemble modeling for spatiotemporal mixed models, supporting multi-core parallel computing;
    (6) Integrated predictions with or without weights of the model's performance, supporting multi-core parallel computing;
    (7) Constrained optimization to interpolate the missing values;
    (8) Generation of the grid surfaces of air pollutant concentration estimates at high resolution;
    (9) Block Kriging for regional mean estimation at multiple scales.
spThin is 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>.
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.
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', 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 managing communications on behalf of the user. Supports multi-statement SQL scripts, with or without embedded R.
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").
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.
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>].
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.
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 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>).
This package contains a collection of functions to perform
        nonparametric estimation of receiver operating characteristic
        (ROC) curves for continuous data.
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.
This package implements the SRRS method introduced in Phoa (2013) into a graphical user interface (GUI) R program.
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 3 (SS3) as described in Anderson et al.
    (2014) <doi:10.1371/journal.pone.0092725>.
Procedures for analyzing simultaneous signals, e.g., features that are simultaneously significant in two different studies. Includes methods for detecting simultaneous signals, for identifying them under false discovery rate control, and for leveraging them to improve prediction.
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. 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.
Functions to visually and statistically analyze single system data.
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 multiple species.
  The ssdtools package uses Maximum Likelihood to fit
  log-normal, log-logistic, log-Gumbel, Gompertz, gamma or Weibull distributions.
  Multiple distributions can be averaged using Information Criteria.
  Confidence intervals can be calculated for the fitted cumulative distribution 
  function or specific hazard concentrations (percentiles).
  Confidence intervals are currently produced by 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.
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>.
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.
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.
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.
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.
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.
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
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) and a shrinkage estimate
   of the "correlation-adjusted t-score" (CAT score) described in
   Zuber and Strimmer (2009).  It also offers a convenient interface 
   to a number of other regularized t-statistics commonly 
   employed in high-dimensional case-control studies.
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.
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'. See github for Journal article.
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.
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 et al. (2017) <doi:10.1155/2017/7907163>.
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.
Stacks rectangular datasets on top of each other, possibly
    performing several type coercions along the way. For large datasets,
    depends on the ff package. Provides an aggressive version of
    ffbase::compact for data that may appear be real-typed but is in fact
    int/short/byte. For many purposes plyr::rbind.fill may be more appropriate,
    but for some kinds of survey data, the rules here work better.
Helper functions collected from StackOverflow.com, a
    question and answer site for professional and enthusiast programmers.
Graphical outputs and treatment for a database of fish pass
    monitoring. It is a part of the 'STACOMI' open source project developed in
    France by the French Agency for Biodiversity (AFB) institute to centralize
    data obtained by fish pass monitoring. This version is available in French and
    English. See <http://stacomir.r-forge.r-project.org/> for more information on
    'STACOMI'.
S4 class wrappers for the 'ODBC' 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.
Analyzing a mixture of two-stage logistic regressions with fixed candidate knots. See Bruch, E., F. Feinberg, K. Lee (in press)<DOI:10.1073/pnas.1522494113>.
Creates and fits staged event tree probability models. 
             Staged event trees are probabilistic graphical models capable of 
             representing asymmetric conditional independence statements
             among categorical variables. 
             This package contains functions to create, plot and fit 
             staged event trees from data, moreover different structure learning 
             algorithms are available.
             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...
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>.
stam is an evolving package that target on the various
        methods to conduct Spatio-Temporal Analysis and
        Modelling,including Exploratory Spatio-Temporal Analysis and
        Inferred Spatio-Temporal Modelling.
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.
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 C++ header files of the Stan project are provided by this package, but it contains no R code or function documentation. There is a shared object containing part of the 'CVODES' library, but it is not accessible from R. 'StanHeaders' is only 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.
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.
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.
Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.
Data structures and methods for  manipulating output of genetic population structure clustering algorithms.
  'starmie' can parse output from 'STRUCTURE' (see <https://pritchardlab.stanford.edu/structure.html> for details) or
  'ADMIXTURE' (see <https://www.genetics.ucla.edu/software/admixture/> for details). 'starmie' performs model selection via
  information criterion, and provides functions for MCMC diagnostics, correcting label switching and visualisation of admixture coefficients.
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'.
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.
Tool to automatically fetch, transform and arrange subsets of multidimensional 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.
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/>.
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).
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.
Cartogram heatmaps are an alternative to choropleth maps for USA States
    and are based on work by the Washington Post graphics department in their report
    on "The states most threatened by trade". "State bins" preserve as much of the
    geographic placement of the states as possible but has the look and feel of a
    traditional heatmap. Functions are provided that allow for use of 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.
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(); 
    calculation of the Hellinger distance: hellinger(); 
    use of classical kernels: kernelfun(), kernel_properties(); 
    univariate piecewise-constant regression: picor().
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 a number of advanced generalized linear model functions including new Tweedie and Digamma glm 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>.
A collection of statistical tools for social network analysis, with strong emphasis on the analysis of discrete powerlaw distributions and statistical hypothesis tests.
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.
Generates a random quotation from a data base of quotes on topics
    in statistics, data visualization and science.
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 Transportation website 
  <https://data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-safety-data> for more
  information on these data.
Statistical processing backend for 'ggstatsplot',
    this package creates expressions with details from statistical tests.
    Currently, it supports only the most common types of statistical
    tests: parametric, nonparametric, robust, and bayesian versions of
    t-test/anova, correlation analyses, contingency table analysis.
Use this package to scrap Test Matches, One Day Internationals, Twenty-20 Internationals data of a player from ESPNCricinfo's Statsguru (<http://stats.espncricinfo.com/ci/engine/stats/index.html>) and then visualize their batting, bowling and fielding performances in the form of charts and graphs.
Provides functions and datasets to support inference with the open access book
            "An Introduction to Bayesian Thinking", available online
             <https://statswithr.github.io/book> and  online videos for the
             "Statistics with R Specialization" <https://www.coursera.org/specializations/statistics>.  
             which includes an introduction to Bayesian inference and decision
             making for one and two sample credible intervals and 
             hypothesis testing for Gaussian and Binomial data, in addition to
             frequentist inference using model-based and randomization-based methods.
             To help with understanding concepts, 'shiny' applications are used to aide 
             visualization of sampling distributions, credible intervals, hypothesis testing, 
             Lindley's and Bartlett's paradoxes. For development versions or to report issues, please visit
             <https://github.com/StatsWithR/statsr>.
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.
Steem is a blockchain-based social media platform (see <https://en.wikipedia.org/wiki/Steemit>). The Steem social activity data are saved in the Steem blockchain, the SteemDB database, the SteemSQL database, and so on. 'steemr' is an R package that downloads the Steem data from the SteemDB and SteemSQL servers, re-organizes the data in a user-friendly way, and visualizes the data for further analysis.
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>.
A package to manage and display stellar tracks and
        isochrones from Pisa low-mass database. Includes tools for
        isochrones construction and tracks interpolation.
Estimation of the parameters of a spatio-temporal model
        using the EM algorithm, estimation of the parameter standard
        errors using a spatio-temporal parametric bootstrap, spatial
        mapping.
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 <doi:10.1214/aos/1018031103>). 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) <https://www.jstor.org/stable/2345828>. To examine the performance of the fitted model, case-deletion measure are provided.
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.
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).
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.
Stepwise regression analysis for variable selection can be used to get the best candidate final regression model in univariate or multivariate regression analysis with the 'forward', 'backward' and 'bidirection' steps. Besides, best subset selection is included in this package. Procedure can use Akaike information criterion, corrected Akaike information criterion, Bayesian information criterion, Hannan and Quinn information criterion, corrected Hannan and Quinn information criterion, Schwarz criterion and significance levels as selection criteria. Multicollinearity detection in regression model are performed by checking tolerance value. Continuous variables nested within class effect and weighted stepwise regression are also considered.
Software to simulate population dynamics 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>.
A stepwise approach to identifying recombination
        breakpoints in a sequence alignment.
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.
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.
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.
Functions implementing the procedure of entropy comparison between two data samples after the renormalization of respective probability distributions with the algorithm designed by Klimontovich (Zeitschrift fur Physik B Condensed Matter. 1987, Volume 66, Issue 1, pp 125-127) and extended by Anishchenko (Proc. SPIE 2098, Computer Simulation in Nonlinear Optics. 1994, pp.130-136).  The package also includes data preparation tools which can also be used separately for various applications.
A set of functions for computing the Standardized Temperature Index (STI).
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 recursive (i.e. list- 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.
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.
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.
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.
Fit a spatial-temporal occupancy models using
    a probit formulation instead of a traditional logit
    model.
This is an R package accompanying the paper "Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles" by Sameer S Bajikar, Christiane Fuchs, Andreas Roller, Fabian J Theis and Kevin A Janes (PNAS 2014, 111(5), E626-635). In this paper, we measure expression profiles from small heterogeneous populations of cells, where each cell is assumed to be from a mixture of lognormal distributions. We perform maximum likelihood estimation in order to infer the mixture ratio and the parameters of these lognormal distributions from the cumulated expression measurements.
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 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>; the most common use cases are described in Kastner (2016) <doi:10.18637/jss.v069.i05>. Also incorporates SV with leverage.
With this package, users can obtain the real-time price and volume information of stocks in China market, as well as the information of the stock index. This package adopted the API from Sina Finance (http://finance.sina.com.cn/).
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 (in press) 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.)
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.
Allows users to input tracking data for a hurricane
    or other tropical storm, along with a data frame of grid points at which
    to model wind speeds. Functions in this package will then calculate wind
    speeds at each point based on wind model equations. This modeling framework
    is currently set up to model winds for North American locations with 
    Atlantic basin storms. This work was supported 
    in part by grants from the National Institute of Environmental Health 
    Sciences (R00ES022631), the National Science Foundation (1331399), and the 
    Department of Energy (DE-FG02-08ER64644).
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.
An implementation of 1) the hypergeometric test for over-representation of literary themes in a storyset (a list of stories) relative to a background list of stories, and 2) a recommendation system that takes a user-selected story as input and returns a ranked list of similar stories on the basis of shared themes. The package is currently implemented for the episodes of the Star Trek television franchise series The Original Series (TOS), The Animated Series (TAS), The Next Generation (TNG), and Voyager (VOY).
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 <http://cyclestreets.net/>;
    calculation of route segment attributes such as bearing and aggregate flow;
    and 'travel watershed' analysis.
    See Lovelace and Ellison (2018) <doi:10.32614/RJ-2018-053>.
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>.
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.
Searches for short tandem repeats (STR) in a specified region of any genome. This analysis can be expanded such that several regions (chromosomes) are studied. These STRs can be grouped into hotspot as well as flanking regions of user specified width. Hotspots are defined by the double strand break maps from Pratto et al. (2014) <doi:10.1126/science.1256442>. Moreover, the user can also search for a specified motif in a DNAStringSet-object, or a fasta-file, or a specified region of any genome. For an application of STR detections please see Heissl et al. (2018) <doi:10.1101/431841>.
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.
Analysis of biological data from stranded marine mammals: mortality-at-age (Heligman, L. and Pollard, J.H. 1980 <doi:10.1017/S0020268100040257>), life tables, Leslie matrices, etc.
Functions for the stratigraphic analysis of phylogenetic trees.
Create data frames with virtual groups that can be used with 
    'dplyr' to efficiently compute resampled statistics, generate the data for
    hypothetical outcome plots, and fit multiple models on resampled variations
    of the original data.
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.
A toolkit for analyzing stratified population genetic data. 
  Functions are provided for summarizing and checking loci 
  (haploid, diploid, and polyploid), single stranded DNA sequences,
  calculating most population subdivision metrics, and running external programs 
  such as structure and fastsimcoal. The package is further described in 
  Archer et al (2016) <doi:10.1111/1755-0998.12559>.
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 the strategy frequency estimation method by Dal Bo & Frechette (2011) <doi:10.1257/aer.101.1.411>, including the adaptation to estimate choice parameters of behavior strategies by Breitmoser (2015) <doi:10.1257/aer.20130675>, and the extension in the spirit of latent-class regression by Dvorak & Fehrler (2018) <doi:10.2139/ssrn.2986445>.
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, for example the individual treatment effect), 
    (3) subgroup identification models (find subsets of patients with similar treatment effects), 
    and (4) parameter estimation and inference (for the overall population and discovered subgroups).
    These tools can directly feed into stratified medicine algorithms including PRISM 
    (patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019 (in progress)). 
    PRISM is a flexible and general framework which accepts user-created models/functions. 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).
This implements the stratification of univariate populations under stratified sampling designs using the method of Khan et al. (2002) <doi:10.1177/0008068320020518>, Khan et al. (2008) (<http://www.statcan.gc.ca/pub/12-001-x/2008002/article/10761-eng.pdf>) and Khan et al. (2015) <doi:10.1080/02664763.2015.1018674>. 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. Crucifix, 
    M., Sinnesael, M., Zivanovic, M., Boulvain, F., Devleeschouwer, X., 2019,
    Litholog generation with the StratigrapheR package and signal decomposition 
    for cyclostratigraphic purposes. Geophysical Research Abstracts Vol. 21, 
    EGU2019-5520, 2019, EGU General Assembly 2019. 
    <http://hdl.handle.net/2268/234402>
    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.
  It is aims to delimit the population strata
  and defining the allocation of sample,considering
  the following objective: minimize the sample size given
  a fixed precision level. Exhaustive enumeration method
  is applied in small problems, while in problems with greater
  complexity the algorithm is based on metaheuristic Variable
  Neighborhood Decomposition Search with Path Relink.
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.
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:10.31223/osf.io/uqbd7> 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.
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.
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. 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.
Pass named and unnamed character vectors into specified positions
    in strings. This represents an attempt to replicate some of python's string
    formatting.
Fast, correct, consistent, portable,
    as well as convenient character string/text processing in every locale
    and any native encoding. Owing to the use of the 'ICU'
    (International Components for Unicode) library,
    the package provides 'R' users with platform-independent functions
    known to 'Java', 'Perl', 'Python', 'PHP', and 'Ruby' programmers. Available
    features include: pattern searching (e.g., with 'Java'-like regular
    expressions or the 'Unicode' collation algorithm), random string generation,
    case mapping, string transliteration, concatenation,
    Unicode normalization, date-time formatting and parsing, and many more.
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.
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.
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.
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>).
Performs multiple testing corrections that take specific structure of hypotheses into account.
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.
Implements a broad class of latent variable and structural equation models for general pedigree data.
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.
This package defines an S4 class for structural time series models 
  and provides some basic methods to work with it.
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/>.
Integrated environmental modelling requires coupling sub-models at 
    different spatial and temporal scales, thus accounting for change of support 
    procedures (aggregation and disaggregation). We contribute to state-of-the-art 
    open source tools that support uncertainty propagation analysis in temporal and 
    spatio-temporal domains. We implement the tool for uncertainty propagation in 
    environmental modelling, with examples in the urban water domain. 
    The functionalities of the class setup and the methods and functions MC.setup, 
    MC.sim, MC.analysis, MC.analysis_generic and Agg.t are contained, which are used for setting up, 
    running and analysing Monte Carlo uncertainty propagation simulations, and for 
    spatio-temporal aggregation. We also implement functionalities to model and predict 
    variables that vary in space and time. stUPscales takes uncertainty characterisation 
    and propagation a step further by including temporal and spatio-temporal auto- and
    cross-correlation, resulting in more realistic (spatio-)temporal series of environmental 
    variables. Due to its modularity, the package allows the implementation of additional 
    methods and functions for spatio-temporal disaggregation of model inputs and outputs, 
    when linking models across multiple space-time scales.
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 Spirling et al (2018) <doi:10.2139/ssrn.3235506> (working paper).
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.
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.
Developed to assist in discovering interesting subgroups in high-dimensional data. The PRIM implementation is based on the 1998 paper "Bump hunting in high-dimensional data" by Jerome H. Friedman and Nicholas I. Fisher. <doi:10.1023/A:1008894516817> PRIM involves finding a set of "rules" which combined imply unusually large (or small) values of some other target variable. Specifically one tries to find a set of sub regions in which the target variable is substantially larger than overall mean. The objective of bump hunting in general is to find regions in the input (attribute/feature) space with relatively high (low) values for the target variable. The regions are described by simple rules of the type if: condition-1 and ... and condition-n then: estimated target value. Given the data (or a subset of the data), the goal is to produce a box B within which the target mean is as large as possible. There are many problems where finding such regions is of considerable practical interest.  Often these are problems where a decision maker can in a sense choose or select the values of the input variables so as to optimize the value of the target variable. In bump hunting it is customary to follow a so-called covering strategy. This means that the same box construction (rule induction) algorithm is applied sequentially to subsets of the data.
Function Wrapper contains four algorithms for developing threshold-based multivariate (prognostic/predictive) biomarker signatures via bootstrapping and aggregating of thresholds from trees, Monte-Carlo variations of the Adaptive Indexing method and Patient Rule Induction Method. Variable selection is automatically built-in to these algorithms. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output.
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.
Creates probability maps of incident and enlarging lesion voxels
    from a baseline and followup magnetic resonance imaging study in 
    patients with multiple sclerosis.
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.
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>.
Create and handle multiple sub-processes in R, exchange
  data over standard input and output streams, control their life cycle.
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.
Identifying outcome relevant subgroups has now become as simple as possible! The formerly lengthy and tedious search for the needle in a haystack will be replaced by a single, comprehensive and coherent presentation.
 The central result of a subgroup screening is a diagram in which each single dot stands for a subgroup.
 The diagram may show thousands of them. The position of the dot in the diagram is determined by the
 sample size of the subgroup and the statistical measure of the treatment effect in that subgroup. The
 sample size is shown on the horizontal axis while the treatment effect is displayed on the vertical axis. Furthermore,
 the diagram shows the line of no effect and the overall study results. For small subgroups, which
 are found on the left side of the plot, larger random deviations from the mean study effect are expected,
 while for larger subgroups only small deviations from the study mean can be expected to be chance findings.
 So for a study with no conspicuous subgroup effects, the dots in the figure are expected to form a
 kind of funnel. Any deviations from this funnel shape hint to conspicuous subgroups.
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.
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.
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.
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.
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.
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
Create a hexagon tilegram 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' and 'ggplot2'. Functions are provided to do the 
    common tasks of calculating confidence intervals and visualising the 
    results.
Data frame summaries, cross-tabulations,
  weight-enabled frequency tables and common 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 estimating, projecting, and plotting spatio-temporal 
  under-five mortality rates, described in Mercer et al. (2015) <doi:10.1214/15-AOAS872>
  and Li et al. (2019) <doi:10.1371/journal.pone.0210645>.
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>).
Provides a way to call the functions in 'SUNDIALS' C ODE solving library (<https://computation.llnl.gov/projects/sundials>). Currently the serial version of ODE solver, 'CVODE' and sensitivity calculator 'CVODES' 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.
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.
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.
Biclusters are submatrices in the data matrix which
        satisfy certain conditions of homogeneity. Package contains
        functions for generating robust biclusters with respect to the
        initialization parameters for a given bicluster solution
        contained in a bicluster set in data, the procedure is also
        known as ensemble biclustering. The set of biclusters is
        evaluated based on the similarity of its elements (the
        overlap), and afterwards the hierarchical tree is constructed
        to obtain cut-off points for the classes of robust biclusters.
        The result is a number of robust (or super) biclusters with
        none or low overlap.
A Comprehensive Test Suite for Markov Chain
        Nonconvergence.
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.
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.
Supervised principal components for regression and
        survival analsysis. Especially useful for high-dimnesional
        data, including microarray data.
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://pdfs.semanticscholar.org/6533/654973054b742e725fd433265700c07b48a2.pdf>.
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. In
        addition two random number generators of George Marsaglia are
        included.
The 'support' package provides the 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 three basic functions that support an implementation of object case (Case 1) best-worst scaling: one for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; one for creating a data set suitable for analysis; and one for calculating count-based scores. 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.
Provides basic functions that support an implementation of Case 3 (multi-profile case) 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 Case 3 BWS analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, see Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
Provides seven basic functions that support an implementation of choice experiments.
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.
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.
SURFACE is a data-driven phylogenetic comparative method for fitting stabilizing selection models to continuous trait data, building on the ouch package. The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak.
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.
Multi-Objective optimization based on surrogate models.
  Important functions: build_surmodel, train_hego, train_mego, train_sme.
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 "Assessing the Value of a Censored Surrogate Outcome" by Parast L, Tian L, and Cai T which is currently in press at Lifetime Data Analysis. 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.
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, In press.
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.
Gradient boosting for optimizing loss functions of different types of survival data, 
  including stratified data. 
  Emily Morris, Kevin He, Yanming Li, Yi Li, Jian Kang (2018) <arXiv:1803.07715> see for 
  description of methods.
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). 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.
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.
Contains routines for 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.
Relative survival, AER and SMR based on French death rates
Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. Principal components, factor analysis.
Tools for using different kinds of bootstrap
    for estimating sampling variation using complex survey
    data.
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>.
The Gini concentration test for survival data is a nonparametric test based on the Gini index for testing the equality of two survival distributions from the point of view of concentration. The package compares different nonparametric tests (asymptotic Gini test, permutation Gini test, log-rank test, Gray-Tsiatis test and Wilcoxon test) and computes their p-values.
Sample size calculation utilizing the information fraction and the alpha spending function 
             in a group sequential clinical trial with censored survival data from underlying generalized 
             gamma survival distributions or log-logistic survival distributions.
             Hsu, C.-H., Chen, C.-H, Hsu, K.-N. and Lu, Y.-H. (2018) A useful design utilizing the 
             information fraction in a group sequential clinical trial with censored survival data. 
             To appear in Biometrics.
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).
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.
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.
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) <doi:10.2307/2344317>.
In routine practice, biomarker performance is calculated by 
    splitting a patient cohort at some arbitrary level, often by median gene 
    expression. The logic behind this is to divide patients into “high” or “low”
    expression groups that in turn correlate with either good or poor prognosis.
    However, this median-split approach assumes that the data set composition 
    adheres to a strict 1:1 proportion of high vs. low expression, that for 
    every one “low” there is an equivalent “high”. In reality, data sets are 
    often heterogeneous in their composition (Perou, CM et al., 2000 
    <doi:10.1038/35021093>)- i.e. this 1:1 relationship is unlikely to exist and
    the true relationship unknown. Given this limitation, it remains difficult 
    to determine where the most significant separation should be made. For 
    example, estrogen receptor (ER) status determined by immunohistochemistry is
    standard practice in predicting hormone therapy response, where ER is found 
    in an ~1:3 ratio (-:+) in the population (Selli, C et al., 2016 
    <doi:10.1186/s13058-016-0779-0>). We would expect therefore, upon dividing 
    patients by ER expression, 25% to be classified “low” and 75% “high”, and 
    an otherwise 50-50 split to incorrectly classify 25% of our patient cohort,
    rendering our survival estimate under powered. 'survivALL' is a data-driven 
    approach to calculate the relative survival estimates for all possible 
    points of separation - i.e. at all possible ratios of “high” vs. “low” - 
    allowing a measure’s relationship with survival to be more reliably 
    determined and quantified. We see this as a solution to a flaw in common 
    research practice, namely the failure of a true biomarker as part of a 
    meta-analysis.
Estimate the regression coefficients and the baseline hazard 
      of proportional hazard Cox models using maximum penalised likelihood.  
      A 'non-parametric' smooth estimate of the baseline hazard function 
      is provided.
Compute time-dependent ROC curve from censored survival
        data using Kaplan-Meier (KM) or Nearest Neighbor Estimation
        (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No
        2, 2000, PP 337-344)
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.
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.
Kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates.
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.
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.
The main function of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions.
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.
Simulation of simple and complex survival data including recurrent and multiple events and competing risks.
Conduct asymptotic and empirical power and sample size calculations for Single-Nucleotide Polymorphism (SNP) association studies with right censored time to event outcomes.
Implements functions to plot survival curves. Provides the capability to add numbers at risk tables, and allows for using the pipe operator to create more complex plots.
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>.
Functional programming principles to iteratively run Cox 
    regression and plot its results. The results are reported in tidy data 
    frames. Additional utility functions are available for working with 
    other aspects of survival analysis such as survival curves, C-statistics, 
    etc.
Survival models may have very different structures. This package contains functions 
  for creating a unified representation of a survival models, which can be further processed by various 
  survival explainers. Tools implemented in 'survxai' help to understand how input variables are used in 
  the model and what impact do they have on the final model prediction. Currently, four explanation methods are implemented. 
  We can divide them into two groups: local and global.
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.
Accurate identification of genes that are truly differentially expressed over two sample varieties, after adjusting for hidden subject-specific effects of residual heterogeneity.
Implements data-driven identification methods for structural vector autoregressive (SVAR) models. 
             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>)).
2d and 3d space-varying coefficient models are fitted to
        regular grid data using either a full B-spline tensor product
        approach or a sequential approximation. The latter one is
        computationally more efficient. Resolution increment is
        enabled.
R bindings to SVD and eigensolvers (PROPACK, nuTRLan).
Rapidly 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 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
Solve large-scale regularised linear/kernel classification by using ADMM and IADMM algorithms. This package provides linear L2-regularised primal classification (both ADMM and IADMM are available), kernel L2-regularised dual classification (IADMM) as well as L1-regularised primal classification (both ADMM and IADMM are available).
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>.
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 and EM clustering. Furthermore,
    there are specialized distance measures, plotting functions and some helper
    functions.
Implements a simple socket server allowing to connect GUI clients to R
Supporting functions for the GUI API (Sweave functions)
Set of tools aimed at wrapping some of the functionalities of the
  packages tools, utils and codetools into a nicer format so that an IDE can use
  them.
A complete unit test system and functions to implement its GUI part
High level management of widgets, windows and other graphical resources.
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.
This package deals with data which stem from survey designs including plausible values. This package has been created to handle data from Large Scale Assessments like PISA, PIAAC etc. which use  complex survey designs to draw the sample and plausible values to report person related estimates. Various functions/statistics (mean, quantile, GLM etc.) are provided to handle this kind of data.
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>.
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.
A null model randomizing semi-quantitative multi-classes (or ordinal) data by swapping sub-matrices while both the row and the column marginal sums are held constant.
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>.
The Soil and Water Assessment Tool is a river basin or
        watershed scale model developed by Dr. Jeff Arnold for the
        USDA-ARS.
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.
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.
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", 
    <doi:10.1016/j.ejor.2018.10.003>.
There are two goals for 'SwimmeR' as presently constructed.  First, it converts swimming times (performances) between the computationally useful 
    format of seconds, reported to the 100ths place (eg 95.37) and the conventional reporting format (1:35.37) used in the swimming community.
    Secondly 'SwimmeR' converts times between the various pool sizes used in competitive swimming, namely 50m length (LCM), 25m length (SCM)
    and 25y length (SCY).
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.
Builds upon the real time data service as well as the archive for national votes <https://opendata.swiss/api/3/action/package_show?id=echtzeitdaten-am-abstimmungstag-zu-eidgenoessischen-abstimmungsvorlagen> and cantonal votes <https://opendata.swiss/api/3/action/package_show?id=echtzeitdaten-am-abstimmungstag-zu-kantonalen-abstimmungsvorlagen>. It brings the results of Swiss popular votes, aggregated at the geographical level of choice, into R.
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.
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/web/packages/SWMPr/index.html>,
    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?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>.
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, flux variability, and Random Sampling of solution space. Flux balance analysis is a technique to find fluxes in metabolic models at steady state. It is described in Orth, J.D., Thiele, I. and Palsson, B.O. What is flux balance analysis? Nat. Biotech. 28, 245-248 (2010).
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)
    <http://www.dtic.mil/cgi-bin/GetTRDoc?AD=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>).
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.
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.
Analyze data from the 'Healthcare Social Graph' via access to the 'Symplur' API.
    The 'Healthcare Social Graph' contains billions of healthcare social media data points. Hundreds of published journal articles have leveraged data from the 'Healthcare Social Graph'.
    About the 'Symplur' API: <https://api.symplur.com/v1/docs/>.
    About 'Symplur' research: <https://www.symplur.com/healthcare-social-media-research/>.
    Credit to Professor Larry Chu, MD at Stanford University School of Medicine for the idea of the 'SympluR' package.
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.
A collection of functions required for genomic prediction which were developed within the Synbreed project for synergistic plant and animal breeding (<http://www.synbreed.tum.de>). This covers data processing, data visualization, and analysis. All functions are embedded within the framework of a single, unified data object. The implementation is flexible with respect to a wide range of data formats in plant and animal breeding. This research was funded by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr Synbreed - Synergistic plant and animal breeding (FKZ 0315528A).
Data sets for the 'synbreed' package with three data sets from cattle, maize and mice to illustrate the functions in the 'synbreed' R package. All data sets are stored in the gpData format introduced in the 'synbreed'
 package. This research was funded by the German Federal Ministry of Education and Research (BMBF) within the AgroClustEr Synbreed - Synergistic plant and animal breeding (FKZ 0315528A).
Methods for computing spatial, temporal, and spatiotemporal
    statistics including: empirical univariate, bivariate and multivariate
    variograms; fitting variogram models; phase locking and synchrony analysis;
    generating autocorrelated and cross-correlated matrices.
This package carries out synchrosqueezed wavelet transform. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.
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/ecoqua/SYNCSA.html>).
Infers sympatry matrices from distributional data and analyzes them in order to identify groups of species cohesively connected.
Framework to perform synthetic likelihood inference
    for models where the likelihood function is unavailable or
    intractable.
Computes a synoptic climate classification through daily reanalysis data and
    creates a climate or environmental regionalization based on the previous synoptic climate classification.
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.
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.
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.
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).
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.
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 {\AA}rup 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.
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.
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.
Some functions are included in this package for writing tables in Excel format suitable for distribution.
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.
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.
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. See 'github' for a screen cast.
    'tableone' was inspired by descriptive statistics functions in
    'Deducer' , a Java-based GUI package by Ian Fellows. This package
    does not require GUI or Java, and intended for command-line users.
'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.
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.
A tableplot is a visualisation of a (large) dataset with a dozen of variables, both numeric and categorical. Each column represents a variable and each row bin is an aggregate of a certain number of records. Numeric variables are visualized as bar charts, and categorical variables as stacked bar charts. Missing values are taken into account. Also supports large 'ffdf' datasets from the 'ff' package.
Provides a music notation syntax and a collection of music programming functions for 
    generating, manipulating, organizing and analyzing musical information in R.
    The music notation framework facilitates creating and analyzing music data in notation form.
    The package also provides API wrapper functions for transcribing musical representations in R into guitar tablature ("tabs") using the 'LilyPond' backend (<http://lilypond.org>).
    'LilyPond' is open source music engraving software for generating high quality sheet music based on markup syntax. 
    'tabr' generates 'LilyPond' files from R code and can pass them to 'LilyPond' to be rendered into sheet music pdf files from R.
    While 'LilyPond' caters to sheet music in general and 'tabr' can be used to create basic sheet music, 
    the transcription functions focus on leveraging 'LilyPond' specifically for creating quality guitar tablature.
    The package offers nominal MIDI file output support in conjunction with rendering tablature output. See the 'tuneR' package for more general use of MIDI files in R.
An easy way to examine archaeological count data. This package 
    provides a convenient and reproducible toolkit for relative and absolute 
    dating and analysis of (chronological) patterns. It includes functions for 
    matrix seriation (reciprocal ranking, CA-based seriation), chronological 
    modeling and dating of archaeological assemblages and/or objects. 
    Beyond these, the package 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, values and weights 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.
Bindings for the 'Tabula' <http://tabula.technology/> 'Java'
    library, which can extract tables from PDF documents. The 'tabulizerjars'
    package <https://github.com/ropensci/tabulizerjars> provides versioned
    'Java' .jar files, including all dependencies, aligned to releases of
    'Tabula'.
'Java' .jar files for the 'Tabula' <http://tabula.technology/>
    'Java' library, which are required by the 'tabulizer' R package. The version
    numbering of this package corresponds to versions of 'tabula-java' library
    releases <https://github.com/tabulapdf/tabula-java/releases/>.
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>.
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.
Contains functions for the easy display of statistical tests as well as
    some convenience functions for data cleanup. It is meant to ease existing workflows
    with packages like 'sjPlot', 'dplyr', and 'ggplot2'. The primary components are the functions
    prefixed with 'tadaa_', which are built to work in an interactive environment, but also print
    tidy markdown tables powered by 'pixiedust' for the creation of 'RMarkdown' reports.
Implement the transformed additive Gaussian (TAG) process and the  transformed approximately additive Gaussian (TAAG) process proposed in Lin and Joseph (2019+) <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.
Provides functions implementing minimal distance estimation methods for parametric tail dependence models.
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 meta-programming functions for automatically
    translating recursive functions into looping functions or trampolines.
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>.