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.
Description of scMappR R package adapted from pre-print. The single cell mapper (scMappR) R package contains a suite of bioinformatic tools that provide experimentally relevant cell-type specific information to a list of differentially expressed genes (DEG). The function "scMappR_and_pathway_analysis" reranks DEGs to generate cell-type specificity scores called cell-weighted fold-changes. Users input a list of DEGs, normalized counts, and a signature matrix into this function. scMappR then re-weights bulk DEGs by cell-type specific expression from the signature matrix, cell-type proportions from RNA-seq deconvolution and the ratio of cell-type proportions between the two conditions to account for changes in cell-type proportion. With cwFold-changes calculated, scMappR uses two approaches to utilize cwFold-changes to complete cell-type specific pathway analysis. The "process_dgTMatrix_lists" function in the scMappR package contains an automated scRNA-seq processing pipeline where users input scRNA-seq count data, which is made compatible for scMappR and other R packages that analyze scRNA-seq data. We further used this to store hundreds up regularly updating signature matrices. The functions "tissue_by_celltype_enrichment", "tissue_scMappR_internal", and "tissue_scMappR_custom" combine these consistently processed scRNAseq count data with gene-set enrichment tools to allow for cell-type marker enrichment of a generic gene list (e.g. GWAS hits). Reference: Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H., Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2020) Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes. BioRxiv, 10.1101/2020.08.24.265298.
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 identification of B cell clones from 
             Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data. Three main 
             functions are included (identicalClones, hierarchicalClones, and spectralClones) 
             that perform clustering among sequences of BCRs/IGs (B cell receptors/immunoglobulins) 
             which share the same V gene, J gene and junction length.
             Nouri N and Kleinstein SH (2018) <doi: 10.1093/bioinformatics/bty235>.
             Nouri N and Kleinstein SH (2019) <doi: 10.1101/788620>.
             Gupta NT, et al. (2017) <doi: 10.4049/jimmunol.1601850>.
Handling of behavioural data from the Ethoscope platform 
    (Geissmann, Garcia Rodriguez, Beckwith, French, Jamasb and Gilestro (2017) <DOI:10.1371/journal.pbio.2003026>).
    Ethoscopes (<http://gilestrolab.github.io/ethoscope/>) are an open source/open hardware framework made of 
    interconnected raspberry pis (<https://www.raspberrypi.org>) designed to quantify the behaviour of multiple 
    small animals in a distributed and real-time fashion. The default tracking algorithm records primary variables
    such as xy coordinates, dimensions and speed.
    This package is part of the rethomics framework <http://rethomics.github.io/>.
A non convex optimization package that optimizes any function under the criterion, combination of variables are on the surface of a unit sphere, as described in the paper : Das et al. (2019) <arXiv:1909.04024> .
Provides routines for scoring behavioral questionnaires. Includes scoring procedures for the 'International Physical Activity Questionnaire (IPAQ)' <http://www.ipaq.ki.se>. Compares physical functional performance to the age- and gender-specific normal ranges.
The `scorecard` package makes the development of credit risk scorecard 
  easier and efficient by providing functions for some common tasks, 
  such as data partition, variable selection, woe binning, scorecard scaling,
  performance evaluation and report generation. These functions can also used
  in the development of machine learning models.
    The references including: 
  1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: 
  Development and Implementation Using SAS. 
  2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. 
  Developing and Implementing Intelligent Credit Scoring.
Provides infrastructure functionalities such as missing value treatment, information value calculation, GINI calculation etc. which are used for developing a traditional credit scorecard as well as a machine learning based model. The functionalities defined are standard steps for any credit underwriting scorecard development, extensively used in financial domain.
Estimate GGUM Person Parameters Using Pre-Calibrated Item Parameters and Binary or Graded Disagree-Agree Responses
Provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) <https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.
Implementation of community detection algorithm SCORE in the paper J. Jin (2015) <arXiv:1211.5803>, and SCORE+ in J. Jin, Z. Ke and S. Luo (2018) <arXiv:1811.05927>. Membership estimation algorithm called Mixed-SCORE in J. Jin, Z. Ke and S. Luo (2017) <arXiv:1708.07852>.
This package contains the functions necessary to run the SCORER 2.0 algorithm. SCORER 2.0 can be used to differentiate between parallel dimeric and trimeric coiled-coil sequence, which are the two most more frequent coiled-coil structures observed naturally. As such, SCORER 2.0 is particularly useful for researchers looking to characterize novel coiled-coil sequences. It may also be used to assist in the structural characterization of synthetic coiled-coil sequences. Also included in this package are functions that allows the user to retrain the SCORER 2.0 algorithm using user-defined training data.
Evaluating probabilistic forecasts via proper scoring rules.  scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families.  Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score.  For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.
Dictionary-like reference for computing scoring rules in a wide
    range of situations. Covers both parametric forecast distributions (such as
    mixtures of Gaussians) and distributions generated via simulation.
Grouping essential tools for credit scoring. These statistical tools may be useful for other use-cases as well but were primarily designed for it. First, there are Reject Inference methods (Ehrhardt et al. (2017) <arXiv:1903.10855>). Second, we build upon the already CRAN-available package 'discretization' to automate discretization of continuous features.
Combines a collection of metrics and proper scoring rules 
    (Tilmann Gneiting & Adrian E Raftery (2007) 
    <doi:10.1198/016214506000001437>) with an easy to
    use wrapper that can be used to automatically evaluate predictions. 
    Apart from proper scoring rules functions are provided to assess bias, 
    sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski, 
    Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) 
    <doi:10.1371/journal.pcbi.1006785>) of forecasts. 
    Several types of predictions can be evaluated: 
    probabilistic forecasts (generally 
    predictive samples generated by Markov Chain Monte Carlo procedures), 
    quantile forecasts or point forecasts. Observed values and predictions
    can be either continuous, integer, or binary. Users can either choose 
    to apply these rules separately in a vector / matrix format that can 
    be flexibly used within other packages, or they can choose to do an 
    automatic evaluation of their forecasts. This is implemented with
    'data.table' and provides a consistent and very efficient framework for 
    evaluating various types of predictions.
A consistent, semi-supervised, non-parametric survival curve estimator optimized for efficient use of Electronic Health Record (EHR) data with a limited number of current status labels. See van der Laan and Robins (1997) <doi:10.2307/2670119>.
An accurate and easy tool for performing linear trajectory inference on
  single cells using single-cell RNA sequencing data. In addition, SCORPIUS
  provides functions for discovering the most important genes with respect to
  the reconstructed trajectory, as well as nice visualisation tools.
  Cannoodt et al. (2016) <doi:10.1101/079509>.
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.
Using principal component analysis as a base model, 'SCOUTer' 
    offers a new approach to simulate outliers in a simple and precise way. 
    The user can generate new observations defining them by a pair of well-known 
    statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2) 
    statistics. Just by introducing the target values of the SPE and T^2, 'SCOUTer' 
    returns a new set of observations with the desired target properties. 
    Authors: Alba González, Abel Folch-Fortuny, Francisco Arteaga and 
    Alberto Ferrer (2020).
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.
Displays the content of a R script into the 'Cytoscape' network-visualization app <https://cytoscape.org/>.
A small set of functions wrapping up the call stack and command
  line inspection needed to determine a running script's filename from
  within the script itself.
Full text, in data frames containing one row per verse, of the 
    Standard Works of The Church of Jesus Christ of Latter-day Saints (LDS). 
    These are the Old Testament, (KJV), the New Testament (KJV), the Book of 
    Mormon, the Doctrine and Covenants, and the Pearl of Great Price.
A coalescent simulator that allows the rapid simulation of
    biological sequences under neutral models of evolution. Different to other
    coalescent based simulations, it has an optional approximation parameter that
    allows for high accuracy while maintaining a linear run time cost for long
    sequences. It is optimized for simulating massive data sets as produced by Next-
    Generation Sequencing technologies for up to several thousand sequences.
We integrated the common analysis methods utilized in single cell RNA sequencing data, which included cluster method, principal components analysis (PCA), the filter of differentially expressed genes, pathway enrichment analysis and correlated analysis methods.
'Last.fm'<https://www.last.fm> is a music platform focussed on building a 
    detailed profile of a users listening habits. It does this by 'scrobbling' (recording) 
    every track you listen to on other platforms ('spotify', 'youtube', 'soundcloud' etc)
    and transferring them to your 'Last.fm' database. This allows 'Last.fm' to act as a 
    complete record of your entire listening history. 'scrobbler' provides helper functions
    to download and analyse your listening history in R.
Allows the user to animate 'shiny' elements when scrolling to view them.
    The animations are activated using the 'scrollrevealjs' library. See <https://scrollrevealjs.org/> for more information.
Contains four functions used in the DIC-tau_g procedure. SCRSELECT() and SCRSELECTRUN() uses Stochastic Search Variable Selection to select important
    covariates in the three hazard functions of a semi-competing risks model. These functions perform the Gibbs sampler for variable selection and a Metropolis-Hastings-Green sampler for the number of split points and parameters for the
    three baseline hazard function. The function SCRSELECT() returns the posterior sample of all quantities sampled in the Gibbs sampler after a burn-in period to a desired
    file location, while the function SCRSELECTRUN() returns posterior values of important quantities to the DIC-Tau_g procedure in a list.
    The function DICTAUG() returns a list containing the DIC values for the unique models visited by the DIC-Tau_g grid search.
    The function ReturnModel() uses SCRSELECTRUN() and DICTAUG() to return a summary of the posterior coefficient vectors for the optimal model along with saving this posterior sample to a desired path location.
Design single-case phase, alternation and multiple-baseline experiments, and conduct randomization tests on data gathered by means of such designs, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.
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>).
Implements the algorithm described in
	Guo, H., and Li, J. (Not yet published), "scSorter: assigning cells to known cell types according to known marker genes". 
	Clusters cells to known cell types based on marker genes specified for each cell type.
A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input.  First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells.
A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.
Extensions to the synthetic controls analyses 
    performed by the package 'Synth' as detailed in Abadie, Diamond, and Hainmueller (2011) <doi: 10.18637/jss.v042.i13>. Includes generating 
    and plotting placebos, post/pre-MSPE (Mean Squared Prediction Error) significance tests and plots, 
    and calculating average treatment effects for multiple treated units. 
    This package represents an implementation of those methods 
    suggested in 
    Abadie, Diamond,and Hainmueller (2010) <doi:10.1198/jasa.2009.ap08746> for
    use in Synthetic Control Analysis.
A normalization method for single-cell UMI count data using a 
  variance stabilizing transformation. The transformation is based on a 
  negative binomial regression model with regularized parameters. As part of the
  same regression framework, this package also provides functions for
  batch correction, and data correction. See Hafemeister and Satija 2019 
  <doi:10.1186/s13059-019-1874-1> for more details.
Code for describing and manipulating scuba diving profiles 
	(depth-time curves) and decompression models, 
        for calculating the predictions of decompression models,
	for calculating maximum no-decompression time and decompression tables,
	and for performing mixed gas calculations.
Analysis of single-cell RNA sequencing data can be simple and
    clear with the right utility functions. This package collects such functions,
    aiming to fulfill the following criteria:
    code clarity over performance (i.e. plain R code instead of C code),
    most important analysis steps over completeness
    (analysis 'by hand', not automated integration etc.),
    emphasis on quantitative visualization (intensity-coded color scale, etc.).
Make graphical representations of single case data and transform graphical displays back to raw data, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>. The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend.
Provides an efficient framework for 
   high-dimensional linear and diagonal discriminant analysis with 
   variable selection.  The classifier is trained using James-Stein-type 
   shrinkage estimators and predictor variables are ranked using 
   correlation-adjusted t-scores (CAT scores).  Variable selection error 
   is controlled using false non-discovery rates or higher criticism.
Functions and Datasets from Lohr, S. (1999), Sampling:
        Design and Analysis, Duxbury.
We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2020), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", <arXiv:2002.11992>.
Provides a computationally efficient discrete approximation to log-Gaussian Cox process model for spatially aggregated disease count data. It uses Monte Carlo Maximum Likelihood for model parameter estimation as proposed by Christensen (2004) <doi: 10.1198/106186004X2525> and delivers prediction of spatially discrete and continuous relative risk. It performs inference for static spatial and spatio-temporal dataset. The details of the methods are provided in Johnson et al (2019) <doi:10.1002/sim.8339>.
A fast, consistent tool for plotting and facilitating the analysis of stratigraphic
    and sedimentological data. Taking advantage of the flexible plotting tools available in R, 
    'SDAR' uses stratigraphic and sedimentological data to produce detailed graphic logs for 
    outcrop sections and borehole logs. These logs can include multiple features (e.g., bed thickness,
    lithology, samples, sedimentary structures, colors, fossil content, bioturbation index, 
    gamma ray logs) (Johnson, 1992, <ISSN 0037-0738>).
Test the global null in linear models using marginal approach.
Provides functionality to generate, (interactively) modify (by adding, removing and renaming nodes) and convert nested hierarchies between different formats.
  These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control.
Tools for researchers to explicitly show that their
    results comply to rules for statistical disclosure control imposed by
    research data centers. These tools help in checking descriptive
    statistics and models and in calculating extreme values that are not
    individual data. Also included is a simple function to create log
    files. The methods used here are described in the "Guidelines for the
    checking of output based on microdata research" by Bond, Brandt, and
    de Wolf (2015)
    <https://ec.europa.eu/eurostat/cros/system/files/dwb_standalone-document_output-checking-guidelines.pdf>.
Data from statistical agencies and other institutions are mostly
    confidential. This package (see also Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v067.i04>) can be used for the generation of anonymized
    (micro)data, i.e. for the creation of public- and scientific-use files.
    The theoretical basis for the methods implemented can be found in Templ (2017) <doi:10.1007/978-3-319-50272-4>.
    Various risk estimation and anonymisation methods are included. Note that the package
    includes a graphical user interface (Meindl and Templ, 2019 <doi:10.3390/a12090191>) that allows to use various methods of this
    package.
Tools for calculating disclosure risk measures, including 
    record-level measures primarily using exhaustive tabulation, as well as
    file-level measures using a loglinear model. Funded by the National Center 
    for Education Statistics. See El Emam (2011) <doi:10.1186/gm239> for a 
    description of risk measures.
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.
Implementation of evolutionary fuzzy systems for the data mining task called
    "subgroup discovery". In particular, the algorithms presented in this package are:
    M. J. del Jesus, P. Gonzalez, F. Herrera, M. Mesonero (2007) <doi:10.1109/TFUZZ.2006.890662>
    M. J. del Jesus, P. Gonzalez, F. Herrera (2007) <doi:10.1109/MCDM.2007.369416>
    C. J. Carmona, P. Gonzalez, M. J. del Jesus, F. Herrera (2010) <doi:10.1109/TFUZZ.2010.2060200>
    C. J. Carmona, V. Ruiz-Rodado, M. J. del Jesus, A. Weber, M. Grootveld, P. González, D. Elizondo (2015) <doi:10.1016/j.ins.2014.11.030>
    It also provide a Shiny App to ease the analysis. The algorithms work with data sets provided in
    KEEL, ARFF and CSV format and also with data.frame objects.
Implementation of statistical methods for the estimation of
     toroidal diffusions. Several diffusive models are provided, most of them
     belonging to the Langevin family of diffusions on the torus. Specifically,
     the wrapped normal and von Mises processes are included, which can be seen
     as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of
     methods for approximate maximum likelihood estimation, organized in four
     blocks, is given: (i) based on the exact transition probability density,
     obtained as the numerical solution to the Fokker-Plank equation; (ii) based
     on wrapped pseudo-likelihoods; (iii) based on specific analytic
     approximations by wrapped processes; (iv) based on maximum likelihood of
     the stationary densities. The package allows the reproducibility of the
     results in García-Portugués et al. (2019) <doi:10.1007/s11222-017-9790-2>.
A tool for synthetic data generation that can be used for linkage method 
    development, with elements of i) gold standard file with complete and accurate 
    information and ii) linkage files that are corrupted as we often see in raw dataset.
Functions to filter GPS/Argos locations, as well as assessing the sample size for the analysis of animal distributions. The filters remove temporal and spatial duplicates, fixes located at a given height from estimated high tide line, and locations with high error as described in Shimada et al. (2012) <doi:10.3354/meps09747> and Shimada et al. (2016) <doi:10.1007/s00227-015-2771-0>. Sample size for the analysis of animal distributions can be assessed by the conventional area-based approach or the alternative probability-based approach as described in Shimada et al. (under review).
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 <https://www.worldclim.org/>,,
    ENVIREM <https://envirem.github.io/>, Bio-ORACLE <https://bio-oracle.org/>
    and MARSPEC <http://www.marspec.org/>.
User-friendly framework that enables the training and the
    evaluation of species distribution models (SDMs). The package implements
    functions for data driven variable selection and model tuning and includes
    numerous utilities to display the results. All the functions used to select
    variables or to tune model hyperparameters have an interactive real-time
    chart displayed in the 'RStudio' viewer pane during their execution.
A software package help user to create virtual species for species distribution modelling. It includes
    several methods to help user to create virtual species distribution map.
    Those maps can be used for Species Distribution Modelling (SDM) study. SDM use
    environmental data for sites of occurrence of a species to predict all the sites
    where the environmental conditions are suitable for the species to persist, and
    may be expected to occur.
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. More details about the methodology SDWD is seen on Wang and Zou (2016) (<doi:10.1080/10618600.2015.1049700>).
Implements the SE-test for equivalence
    according to Hoffelder et al. (2015) <DOI:10.1080/10543406.2014.920344>.
    The SE-test for equivalence is a multivariate two-sample equivalence test. 
    Distance measure of the test is the sum of standardized differences
    between the expected values or in other words: the sum of effect sizes (SE)
    of all components of the two multivariate samples. 
    The test is an asymptotically valid test for normally distributed data 
    (see Hoffelder et al.,2015).
    The function SE.EQ() implements the SE-test for equivalence 
    according to Hoffelder et al. (2015).
    The function SE.EQ.dissolution.profiles() implements a variant 
    of the SE-test for equivalence for similarity analyses of dissolution 
    profiles as mentioned in Suarez-Sharp et al.(2020) 
    <DOI:10.1208/s12248-020-00458-9>). The equivalence margin used in 
    SE.EQ.dissolution.profiles() is analogically defined as for the T2EQ
    approach according to Hoffelder (2019) <DOI:10.1002/bimj.201700257>) 
    by means of a systematic shift in location
    of 10 [\% of label claim] of both dissolution profile populations. 
    SE.EQ.dissolution.profiles() checks whether the weighted mean of the 
    differences of the expected values of both dissolution profile populations 
    is statistically significantly smaller than 10 [\% of label claim]. The 
    weights are built up by the inverse variances.
A few major genes and a series of polygene are responsive for each quantitative trait. Major genes are individually identified while polygene is collectively detected. This is mixed major genes plus polygene inheritance analysis or segregation analysis (SEA). In the SEA, phenotypes from a single or multiple 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>.
Calculates parameters of the seawater carbonate system and assists the design of ocean acidification perturbation experiments.
Proximal gradient descent solver for the operators lasso, group
    lasso, and sparse-group lasso. The implementation involves backtracking line
    search and warm starts. Input data needs to be clustered/grouped for the
    (sparse-)group lasso before calling these algorithms.
Standard error adjusted adaptive lasso (SEA-lasso) is a version of the adaptive lasso, which incorporates OLS standard error to the L1 penalty weight. This method is intended for variable selection under linear regression settings (n > p). This new weight assignment strategy is especially useful when the collinearity of the design matrix is a concern.
Provides an interface with the Google Search Console,
    formally called Google Webmaster Tools.
Provides a search interface to look up terms
    on 'Google', 'Bing', 'DuckDuckGo', 'Startpage', 'Twitter', 'StackOverflow', 
    'RStudio Community', 'GitHub', and 'BitBucket'. Upon searching, a browser 
    window will open with the aforementioned search results.
This package provides an implementation of the QuadTree
        data structure. It uses this to implement fast k-Nearest
        Neighbor and Rectangular range lookups in 2 dimenions. The
        primary target is high performance interactive graphics.
Capable of deriving seasonal statistics, such as "normals", and
  analysis of seasonal data, such as departures. This package also has
  graphics capabilities for representing seasonal data, including boxplots for
  seasonal parameters, and bars for summed normals. There are many specific
  functions related to climatology, including precipitation normals,
  temperature normals, cumulative precipitation departures and precipitation
  interarrivals. However, this package is designed to represent any
  time-varying parameter with a discernible seasonal signal, such as found
  in hydrology and ecology.
This package conducts Score Equity Assessment (SEA; Dorans, 2004) by calculating and plotting multiple SEA indices as introduced by a variety of authors and summarized by Huggins and Penfield (2012).
Routines for the seasonal analysis of health data,
    including regression models, time-stratified case-crossover,
    plotting functions and residual checks, see Barnett and Dobson (2010) ISBN 978-3-642-10748-1. Thanks to Yuming Guo
    for checking the case-crossover code.
Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment
    software by the US Census Bureau. It offers full access to almost all
    options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model
    search, outlier detection and support for user defined holiday variables,
    such as Chinese New Year or Indian Diwali. A graphical user interface can be
    used through the 'seasonalview' package. Uses the X-13-binaries from the
    'x13binary' package.
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.
Encrypt text using a simple shifting substitution cipher with setcode(), providing two numeric keys used to define the encryption algorithm. The resulting text can be decoded using decode() function and the two numeric keys specified during encryption.
Tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package 'secr'.
Simultaneously infers state-dependent diversification across
 two or more states of a single or multiple traits while accounting for the
 role of a possible concealed trait. See Herrera-Alsina et al. 2019
 Systematic Biology 68: 317-328 <DOI:10.1093/sysbio/syy057>.
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.
Seamless and standardized interaction with data
    exported from the clinical data management system (CDMS)
    'secuTrial'<https://www.secutrial.com>.
    The primary data export the package works with is a standard non-rectangular export.
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.
Output colors used in literal vectors, palettes and plot objects (ggplot).
Functions to calculate seed germination and seedling emergence and growth indexes. The main indexes for germination and seedling emergence, considering the time for seed germinate are: T10, T50 and T90, in Farooq et al. (2005) <10.1111/j.1744-7909.2005.00031.x>; and MGT, in Labouriau (1983). Considering the germination speed are: Germination Speed Index, in Maguire (1962), Mean Germination Rate, in Labouriau (1983); considering the homogeneity of germination are: Coefficient of Variation of the Germination Time, in Carvalho et al. (2005) <10.1590/S0100-84042005000300018>, and Variance of Germination, in Labouriau (1983); Uncertainty, in Labouriau and Valadares (1976) <ISSN:0001-3765>; and Synchrony, in Primack (1980). The main seedling indexes are Growth, in Sako (2001), Uniformity, in Sako (2001) and Castan et al. (2018) <doi:10.1590/1678-992x-2016-0401>; and Vigour, in Medeiros and Pereira (2018) <doi:10.1590/1983-40632018v4852340>.
Functions for dimension reduction through the seeded canonical correlation analysis are provided. A classical canonical correlation analysis (CCA) is one of useful statistical methods in multivariate data analysis, but it is limited in use due to the matrix inversion for large p small n data. To overcome this, a seeded CCA has been proposed in Im, Gang and Yoo (2015) <DOI:10.1002/cem.2691>. The seeded CCA is a two-step procedure. The sets of variables are initially reduced by successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and cov(Y), respectively, without loss of information on canonical correlation analysis, following Cook, Li and Chiaromonte (2007) <DOI:10.1093/biomet/asm038> and Lee and Yoo (2014) <DOI:10.1111/anzs.12057>. Then, the canonical correlation is finalized with the initially-reduced two sets of variables.
Implements the seeded-LDA model (Lu, Ott, Cardie & Tsou 2010) <doi:10.1109/ICDMW.2011.125> using the quanteda package and the GibbsLDA++ library for semisupervised topic modeling. 
    Seeded-LDA allows users to pre-define topics with keywords to perform theory-driven analysis of textual data in social sciences and humanities (Watanabe & Zhou 2020) <doi:10.1177/0894439320907027>.
Analysis of seed germination data
    using the physiological time modelling approach. Includes functions
    to fit hydrotime and thermal-time models with the traditional approaches
    of Bradford (1990) <doi:10.1104/pp.94.2.840>
    and Garcia-Huidobro (1982) <doi:10.1093/jxb/33.2.288>. 
    Allows to fit models to grouped datasets,
    i.e. datasets containing multiple species, seedlots or experiments.
Algorithms to calculate the hidden inputs of systems of differential equations. 
  These hidden inputs can be interpreted as a control that tries to minimize the
  discrepancies between a given model and taken measurements. The idea is 
  also called the Dynamic Elastic Net, as proposed in the paper "Learning (from) the errors of a systems biology model" 
  (Engelhardt, Froelich, Kschischo 2016) <doi:10.1038/srep20772>.
  To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: <https://bioconductor.org/packages/release/bioc/html/rsbml.html>.
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>.
A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.
Creates SEER (Surveillance, Epidemiology and End Results) and 
    A-bomb data binaries from ASCII sources and provides tools for estimating
    SEER second cancer risks. Methods are described in <doi:10.1038/leu.2015.258>.
Provides an easy way to map seer registry area rate data on a U.S. map.  
   The U.S. data may be mapped at the state or state/county.
   U.S. Seer registry data may be mapped at the Seer registry area, Seer Registry 
   area/county or Seer Registry area/county/census tract level. The function uses 
   a calculated default categorization breakpoint list for 5 categories, 
   when not breakpoint list is provided or the number of categories is specified 
   by the user. A user provided break point list is limited to containing 5 values.  
   The number of calculated categories may be from 3 to 11. The user provide the p-value 
   for each area and request hatching over any areas with a p-value > 0.05. 
   Other types of comparisons can be specified.  If states or 
   state/counties are used, the area identifier is the U.S. FIPS codes for states and 
   counties, 2 digits and 5 digits respectfully.  If the data is for U.S. Seer Registry 
   areas, the Seer Registry area identifier used to link the data to the geographical 
   area is a Seer Registry area abbreviation.  See documentation for the list of 
   acceptable Seer Registry area names and abbreviations. The state boundaries are 
   overlaid all rate maps. The package contains the boundary data for state, county, 
   Seer Registry areas, and census tracts for counties within a Seer registry area.   
   The package support boundary data from the 2000 and 2010 U.S. Census.  
   The SeerMapper package version contains the U.S. Census 2000 and 2010 boundary data
   for the regional, state, Seer Registry, and county levels.  Six supplement packages 
   contain the census tract boundary data.
   Copyrighted 2014, 2015, 2016, 2017, 2018 and 2019 by Pearson and Pickle.
Provides supplemental 2010 census tract boundary package for 20 states
   without Seer Registries that are east of the Mississippi river 
   for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U.S. Census data and is in public domain.
Provides supplemental 2010 census tract boundaries of the 19 states 
   containing Seer Registries for use with the 'SeerMapper' package.
   The data contained in this 
   package is derived from U.S. 2010 Census data and is in public domain.
Provides supplemental 2010 census tract boundaries for the 13 states
   without Seer Registries that are west of the Mississippi river 
   for use with the 'SeerMapper' package.
   The data contained in this 
   package is derived from U.S. 2010 Census data and is in public domain.
Provides supplemental 2000 census tract boundaries for the 20 states, district and territory
   without Seer Registries that are east of the Mississippi river 
   for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U.S. Census data and is in the public domain.
Provides supplemental 2000 census tract boundaries for the 19 states
   containing Seer Registries for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U.S. Census data and is in the public domain.
Provides supplemental 2000 census tract boundaries for the 13 states
   without Seer Registries that are west of the Mississippi river
   for use with the 'SeerMapper' package.  
   The data contained in this 
   package is derived from U.S. 2000 Census data and is in the public domain. 
   Updated to Alber Equal Area projection.
Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound).  This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses.
Measuring spatial segregation. The methods implemented in this 
        package include White's P index (1983) <doi:10.1086/227768>, 
        Morrill's D(adj) (1991), Wong's D(w) and D(s) (1993) 
        <doi:10.1080/00420989320080551>, and Reardon and O'Sullivan's set of 
        spatial segregation measures (2004) 
        <doi:10.1111/j.0081-1750.2004.00150.x>.
Provides two methods for segmentation and joint segmentation/clustering of
    bivariate time-series. Originally intended for ecological segmentation
    (home-range and behavioural modes) but easily applied on other series,
    the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'.
    The segmentation method is a bivariate extension of  Lavielle's method available in 'adehabitatLT' 
    (Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>).
    This method rely on dynamic programming for efficient segmentation.
    The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm.
    This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method 
    (formerly available in 'cghseg' package) to the bivariate case.
    The method is fully described in Patin et al (2018) <doi:10.1101/444794>.
Performs correlation matrix segmentation and applies a test
    procedure to detect highly correlated regions in gene expression.
A set of segregation-based indices and randomization methods to 
            make robust environmental inequality assessments, as described in 
            Schaeffer and Tivadar (2019) "Measuring Environmental Inequalities:
            Insights from the Residential Segregation Literature" 
            <doi:10.1016/j.ecolecon.2019.05.009>.
Contains functions that help to determine event
    boundaries in event segmentation experiments by bootstrapping a critical
    segmentation magnitude under the null hypothesis that all key presses were
    randomly distributed across the experiment. Segmentation magnitude is
    defined as the sum of Gaussians centered at the times of the segmentation
    key presses performed by the participants. Within a participant, the maximum
    of the overlaid Gaussians is used to prevent an excessive influence of a
    single participant on the overall outcome (e.g. if a participant is pressing
    the key multiple times in succession). Further functions are included, such
    as plotting the results.
Given a regression model, segmented `updates' it by adding one or more segmented (i.e., piece-wise linear) relationships. Several variables with multiple breakpoints are allowed. The estimation method is discussed in Muggeo (2003, <doi:10.1002/sim.1545>) and illustrated in Muggeo (2008, <https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf>). An approach for hypothesis testing is presented in Muggeo (2016, <doi:10.1080/00949655.2016.1149855>), and interval estimation for the breakpoint is discussed in Muggeo (2017, <doi:10.1111/anzs.12200>).
A dynamic programming solution to segmentation based on
        maximization of arbitrary similarity measures within segments.
	The general idea, theory and this implementation are described in
	Machne, Murray & Stadler (2017) <doi:10.1038/s41598-017-12401-8>.
	In addition to the core algorithm, the package provides time-series
	processing and clustering functions as described in the publication.
	These are generally applicable where a `k-means` clustering yields
	meaningful results, and have been specifically developed for
	clustering of the Discrete Fourier Transform of periodic gene
	expression data (`circadian' or `yeast metabolic oscillations').
	This clustering approach is outlined in the supplemental material of
	Machne & Murray (2012) <doi:10.1371/journal.pone.0037906>), and here
	is used as a basis of segment similarity measures. Notably, the
	time-series processing and clustering functions can also be used as
	stand-alone tools, independent of segmentation, e.g., for 
        transcriptome data already mapped to genes.
Given a likelihood provided by the user, this package applies it
    to a given matrix dataset in order to find change points in the data that
    maximize the sum of the likelihoods of all the segments. This package provides
    a handful of algorithms with different time complexities and assumption compromises
    so the user is able to choose the best one for the problem at hand. The implementation
    of the segmentation algorithms in this package are based on the paper by Bruno M. de Castro,
	Florencia Leonardi (2018) <arXiv:1501.01756>. The Berlin
	weather sample dataset was provided by Deutscher Wetterdienst <https://dwd.de/>.
	You can find all the references in the Acknowledgments section of this package's
	repository via the URL below.
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.
Extended Susceptible-Exposed-Infected-Recovery Model for 
    handling high false negative rate and symptom based administration of 
    diagnostic tests. <doi:10.1101/2020.09.24.20200238>.
An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See http://snap.stanford.edu/seismic/ for more information and datasets.
Fast versions of seismic analysis functions that 'roll' over a
    vector of values. See the 'RcppRoll' package for alternative
    versions of basic statistical functions such as rolling mean,
    median, etc.
Set of functions that access information about deputies and votings
    in Polish diet from webpage <http://www.sejm.gov.pl>. The package was developed
    as a result of an internship in MI2 Group - <http://mi2.mini.pw.edu.pl>, Faculty
    of Mathematics and Information Science, Warsaw University of Technology.
Sejong(http://www.sejong.or.kr/) corpus and
        Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum)
        dictionaries for KoNLP
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 (Bertrand et al. 2020, <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.
It offers functions for creating dashboard with Fomantic UI.
We present a rank-based Mercer kernel to compute a pair-wise similarity metric corresponding to informative representation of data. We tailor the development of a kernel to encode our prior knowledge about the data distribution over a probability space. The philosophical concept behind our construction is that objects whose feature values fall on the extreme of that feature’s probability mass distribution are more similar to each other, than objects whose feature values lie closer to the mean. Semblance emphasizes features whose values lie far away from the mean of their probability distribution. The kernel relies on properties empirically determined from the data and does not assume an underlying distribution. The use of feature ranks on a probability space ensures that Semblance is computational efficacious, robust to outliers, and statistically stable, thus making it widely applicable algorithm for pattern analysis. The output from the kernel is a square, symmetric matrix that gives proximity values between pairs of observations.
Outlier and leverage diagnostics for SEM.
Interactive 'shiny' application for working with Structural Equation Modelling technique. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/semwebappk/> .
Fits a structural equation multidimensional scaling (SEMDS) model for asymmetric and three-way input dissimilarities. It assumes that the dissimilarities are measured with errors. The latent dissimilarities are estimated as factor scores within an SEM framework while the objects are represented in a low-dimensional space as in MDS.
Provides functionality to automatically calculate direct, indirect, 
    and total effects for piecewise structural equation models, comprising lists
    of fitted models representing structured equations (Lefcheck 2016 
    <doi:10/f8s8rb>). Confidence intervals are provided via bootstrapping.
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 syntax for specifying and estimating complex 
    Structural Equation Models. Models can be estimated using Partial 
    Least Squares Path Modeling or Covariance-Based Structural Equation 
    Modeling or covariance based Confirmatory Factor Analysis.
Functions for semiparametric regression analysis, to
        complement the book: Ruppert, D., Wand, M.P. and Carroll, R.J.
        (2003). Semiparametric Regression. Cambridge University Press.
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 different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 <doi:10.3389/fnhum.2014.00407>), random walk simulation (Kenett & Austerweil, 2016 <http://alab.psych.wisc.edu/papers/files/Kenett16CreativityRW.pdf>), and a function to compute global network measures. Significance tests and plotting features are also implemented.
Implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the 'SemNetDictionaries' package to make the cleaning process more accurate, efficient, and reproducible.
Implements dictionaries that can be used in the 'SemNetCleaner' package. Also includes several functions aimed at facilitating the text cleaning analysis in the 'SemNetCleaner' package. This package is designed to integrate and update word lists and dictionaries based on each user's individual needs by allowing users to store and save their own dictionaries. Dictionaries can be added to the 'SemNetDictionaries' package by submitting user-defined dictionaries to <https://github.com/AlexChristensen/SemNetDictionaries>.
Latent repeated measures ANOVA (L-RM-ANOVA) is a structural 
    equation modeling based alternative to traditional repeated measures ANOVA.
    L-RM-ANOVA extends the latent growth components approach by 
    Mayer et al. (2012) <doi:10.1080/10705511.2012.713242> and introduces
    latent variables to repeated measures analysis.
Path diagrams and visual analysis of various SEM packages' output.
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.
Perform sensitivity analysis in structural equation modeling using 
    meta-heuristic optimization methods (e.g., ant colony optimization and others). 
    The references for the proposed methods are: 
    (1) Harring, J. R., McNeish, D. M., & Hancock, G. R. (2017)
    <doi:10.1080/10705511.2018.1506925>;
    (2) Socha, K., & Dorigo, M. (2008) <doi:10.1016/j.ejor.2006.06.046>.
    We also thank Dr. Krzysztof Socha for sharing his thesis and R code,
    which provided the base for the development of this package.
Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments.
For confirmatory factor analysis ('CFA') and structural equation
             models ('SEM') estimated with the 'lavaan' package,
             this package provides functions to create model summary tables and model
             comparison tables for hypothesis testing. Tables can be produced in
             'LaTeX', 'HTML', or comma separated variables ('CSV').
Provides 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>.
Semantic Versions allow for standardized management versions. 
    This package implements semantic versioning handling in R. using R6 to 
    create a mutable object that can handle deciphering and checking versions.
Functions to download Sentinel-2 optical images
 and perform preliminary processing operations.
 'sen2r' provides the instruments required to easily perform
 (and eventually automate) the steps necessary to build a complete
 Sentinel-2 processing chain.
 A Graphical User Interface to facilitate data processing is also provided.
 For additional documentation refer to the following article: 
 Ranghetti et al. (2020) <doi:10.1016/j.cageo.2020.104473>.
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, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.
Provides an interface to the 'Sensibo Sky' API which allows to remotely control non-smart air conditioning units.
  See <https://sensibo.com> for more informations.
An implementation of sensitivity analysis for phylogenetic comparative
 methods. The package is an umbrella of statistical and graphical methods that 
 estimate and report different types of uncertainty in PCM:
 (i) Species Sampling uncertainty (sample size; influential species and clades).
 (ii) Phylogenetic uncertainty (different topologies and/or branch lengths).
 (iii) Data uncertainty (intraspecific variation and measurement error).
A collection of functions for factor screening, global sensitivity analysis and robustness analysis. Most of the functions have to be applied on model with scalar output, but several functions support multi-dimensional outputs.
Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.
Implements the calibrated sensitivity analysis approach for matched observational studies. Our sensitivity analysis framework views matched sets as drawn from a super-population. The unmeasured confounder is modeled as a random variable. We combine matching and model-based covariate-adjustment methods to estimate the treatment effect. The hypothesized unmeasured confounder enters the picture as a missing covariate. We adopt a state-of-art Expectation Maximization (EM) algorithm to handle this missing covariate problem in generalized linear models (GLMs). As our method also estimates the effect of each observed covariate on the outcome and treatment assignment, we are able to calibrate the unmeasured confounder to observed covariates.
      Zhang, B., Small, D. S. (2018). <arXiv:1812.00215>.
This package performs sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition.  The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468).  The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al.  The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al.  The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test.  The other functions in the package are internal functions.
Sensitivity to unmeasured biases in an observational study that is a full match.  Function senfm() performs tests and function senfmCI() creates confidence intervals.  The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) <DOI:10.1111/j.1541-0420.2006.00717.x>.
Sensitivity analysis for multiple outcomes in observational studies.  For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) <doi:10.1214/16-AOAS942>.  Alternatively, attention may focus on a few principal components in the principal() function.  The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.
The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean.  The main function in the package is senmv(), but amplify() and truncatedP() are also useful.  The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.
Sensitivity analysis 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.
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>.
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.
Unsupervised text tokenizer allowing to perform byte pair encoding and unigram modelling. 
    Wraps the 'sentencepiece' library <https://github.com/google/sentencepiece> which provides a language independent tokenizer to split text in words and smaller subword units. 
    The techniques are explained in the paper "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing" by Taku Kudo and John Richardson (2018) <doi:10.18653/v1/D18-2012>.
    Provides as well straightforward access to pretrained byte pair encoding models and subword embeddings trained on Wikipedia using 'word2vec', 
    as described in "BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages" by Benjamin Heinzerling and Michael Strube (2018) <http://www.lrec-conf.org/proceedings/lrec2018/pdf/1049.pdf>.
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. (2020) <doi:10.2139/ssrn.3067734>.
Unofficial client for 'Sentry' <https://sentry.io>,
  a self-hosted or cloud-based error-monitoring service. It will inform about
  errors in real-time, and includes integration with the 'Plumber' package.
Visual representations of model fit or predictive success in the form of "separation plots."  See Greenhill, Brian, Michael D. Ward, and Audrey Sacks. "The separation plot: A new visual method for evaluating the fit of binary models." American Journal of Political Science 55.4 (2011): 991-1002.
The 'seplyr' (standard evaluation plying) package supplies improved
    standard evaluation adapter methods for important common 'dplyr' data manipulation tasks.
    In addition the 'seplyr' package supplies several new "key operations
    bound together" methods.  These include 'group_summarize()' (which
    combines grouping, arranging and calculation in an atomic unit),
    'add_group_summaries()' (which joins grouped summaries into a 'data.frame'
    in a well documented manner), 'add_group_indices()' (which adds
    per-group identifiers to a 'data.frame' without depending on row-order),
    'partition_mutate_qt()' (which optimizes mutate sequences), and 'if_else_device()'
    (which simulates per-row if-else blocks in expression sequences).
Potential randomization schemes are prospectively evaluated when
    units are assigned to treatment arms upon entry into the experiment. The schemes
    are evaluated for balance on covariates and on predictability (i.e., how well
    could a site worker guess the treatment of the next unit enrolled).
This is a method for DNA Copy Number Profiling using
        Next-Generation Sequencing. It has new model and test
        statistics based on non-homogeneous Poisson Processes with
        change point models. It uses an adaptation of Circular Binary
        Segmentation. Also included are methods for point-wise Bayesian
        Confidence Interval and model selection method for the
        change-point model. A case and a control sample reads (normal
        and tumor) are required.
A modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.
Sequence detector in this package contains a specific automaton model that can be used to learn and detect data and process sequences.
    Automaton model in this package is capable of learning and tracing sequences. Automaton model can be found in Krleža, Vrdoljak, Brčić (2019) <doi:10.1109/ACCESS.2019.2955245>.
    This research has been partly supported under Competitiveness and Cohesion Operational Programme from the European Regional and Development Fund, as part of the Integrated Anti-Fraud System project no. KK.01.2.1.01.0041. This research has also been partly supported by the European Regional Development Fund under the grant KK.01.1.1.01.0009.
Sequential method to solve the the binary classification problem 
    by Wang (2019) <arXiv:arXiv:1901.10079>, multi-class 
    classification problem by Li (2020) <doi:10.1016/j.csda.2020.106911>
    and the highly stratified multiple-response problem by Chen (2019)
    <doi:10.1111/biom.13160>.
Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.
Generates/modifies RNA-seq data for use in simulations. We provide
    a suite of functions that will add a known amount of signal to a real 
    RNA-seq dataset. The advantage of using this approach over simulating under
    a theoretical distribution is that common/annoying aspects of the data
    are more preserved, giving a more realistic evaluation of your method. 
    The main functions are select_counts(), thin_diff(), thin_lib(), 
    thin_gene(), thin_2group(), thin_all(), and effective_cor(). See
    Gerard (2020) <doi:10.1186/s12859-020-3450-9> for details on the
    implemented methods.
It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) <doi:10.1177/0049124115591013>), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) <doi:10.1177/0081175015570976>), smoothing sequences for index plots (see Piccarreta (2012) <doi:10.1177/0049124112452394>), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) <doi:10.1111/j.1467-985X.2009.00606.x>), symmetrical (or canonical) Partial Least Squares (see Bry (1996)).
Designed for fitting hidden (latent) Markov models and mixture
    hidden Markov models for social sequence data and other categorical time series.
    Also some more restricted versions of these type of models are available: Markov
    models, mixture Markov models, and latent class models. The package supports
    models for one or multiple subjects with one or multiple parallel sequences
    (channels). External covariates can be added to explain cluster membership in
    mixture models. The package provides functions for evaluating and comparing
    models, as well as functions for visualizing of multichannel sequence data and
    hidden Markov models. Models are estimated using maximum likelihood via the EM
    algorithm and/or direct numerical maximization with analytical gradients. All
    main algorithms are written in C++ with support for parallel computation. 
    Documentation is available via several vignettes in this page, and the  
    paper by Helske and Helske (2019, <doi:10.18637/jss.v088.i03>).
Contains an implementation of invariant causal prediction for sequential data. The main function in the package is 'seqICP', which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method 'seqICPnl', which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines 'seqICP.s' and 'seqICPnl.s' corresponding to the respective main methods.
Exploratory data analysis and data visualization
  for biological sequence (DNA and protein) data. Seqinr includes 
  utilities for sequence data management under the ACNUC system
  described in Gouy, M. et al. (1984) Nucleic Acids Res.
  12:121-127 <doi:10.1093/nar/12.1Part1.121>.
Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 <doi:10.1101/287839>.
A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.
Supports reading and writing sequences for different formats (currently interleaved and sequential formats for 'FASTA' and 'PHYLIP'), file conversion, and manipulation (e.g. filter sequences that contain specify pattern, export consensus sequence from an alignment).
Integrate sequencing data (Variant call format, e.g. VCF or BCF) or meta-analysis results in R. This package can help you (1) read VCF/BCF/BGEN files by chromosomal ranges (e.g. 1:100-200); (2) read RareMETAL summary statistics files; (3) read tables from a tabix-indexed files; (4) annotate VCF/BCF files; (5) create customized workflow based on Makefile.
S4 class object for creating and managing group sequential designs. It calculates the efficacy and futility boundaries at each look. It allows modifying the design and tracking the design update history.
Methods to generate random gene-gene association networks and simulate RNA-seq data from them. 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.
Pierre, J. S. and Kasper, C.,(1990), "The Design of Ethological Flow-Charts on Factorial Analysis Representations - an Application to the Study of the Male Mole-Cricket Sexual Courtship." Biology of Behaviour 15(3-4): 125-151.(in french). Includes the calculation of transition matrices, their manipulation (symmetrization and diagonal loading), their analysis by CA (Correspondence Analysis), their comparison by likelihood ratio tests, the graphic plot of flow charts on the factorial axes of the CA.
Educational package used in R courses to illustrate
       	     object-oriented programming and package
       	     development. Using biological sequences (DNA and RNA) as
       	     a working example.
Contains fast functions to calculate the exact Bayes posterior
  for the Sparse Normal Sequence Model, which implement the algorithms described in
  Van Erven and Szabo (2018) <arXiv:1810.10883>. For general hierarchical priors,
  sample sizes up to 10,000 are feasible within half an hour on a standard laptop.
  For beta-binomial spike-and-slab priors, a faster algorithm is provided, which
  can handle sample sizes of 100,000 in half an hour. In the implementation,
  special care has been taken to assure numerical stability of the methods even for
  such large sample sizes.
Functions to calculate exact critical values, statistical power, expected time to signal, and required sample sizes for performing exact sequential analysis. All these	calculations can be done for either Poisson or binomial data, for continuous or group sequential analyses, and for different types of rejection boundaries. In case of group sequential analyses, the group sizes do not have to be specified in advance and the alpha spending can be arbitrarily settled.
Functions to be used in conjunction with the 'Sequential' package that allows for planning of observational database studies that will be analyzed with exact sequential analysis. This package supports Poisson- and binomial-based data. The primary function, seq_wrapper(...), accepts parameters for simulation of a simple exposure pattern and for the 'Sequential' package setup and analysis functions. The exposure matrix is used to simulate the true and false positive and negative populations (Green (1983) <doi:10.1093/oxfordjournals.aje.a113521>, Brenner (1993) <doi:10.1093/oxfordjournals.aje.a116805>). Functions are then run from the 'Sequential' package on these populations, which allows for the exploration of outcome misclassification in data.
Tools to analyze genomic sequencing data from
 paired normal-tumor samples, including cellularity and ploidy estimation; mutation
 and copy number (allele-specific and total copy number) detection, quantification
 and visualization.
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 analysis on both relational and non-relational data stores, 
    scaling to petabytes of data. Methods are provided that enable working with Apache 
    Drill instances via the REST API, DBI methods
    and using 'dplyr'/'dbplyr' idioms. Helper functions are included to facilitate
    using official Drill Docker images/containers.
Enables reading and writing binary and ASCII data to 
             RS232/RS422/RS485 or any other virtual serial interface of the
             computer.
Infrastructure for ordering objects with an implementation of several
    seriation/sequencing/ordination techniques to reorder matrices, dissimilarity
    matrices, and dendrograms. Also provides (optimally) reordered heatmaps,
    color images and clustering visualizations like dissimilarity plots, and
    visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) <doi:10.18637/jss.v025.i03>.
Calculate and compare lower confidence bounds for binomial series system reliability. The R 'shiny' application, launched by the function launch_app(), weaves together a workflow of customized simulations and delta coverage calculations to output recommended lower confidence bound methods.
Translates antibody levels measured in a (cross-sectional)
  population sample into an estimate of the frequency with which
  seroconversions (infections) occur in the sampled population.
The primary goal of 'Serpstat' API <https://serpstat.com/api/> 
    is to reduce manual SEO (search engine optimization) and PPC (pay-per-click) 
    tasks. You can automate your keywords research or competitors analysis 
    with this API wrapper.
Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive
    mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS),
    using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate
    observations of SERRS are highly collinear and lend themselves to a reduced-rank
    representation. The GAMM separates the SERRS signal into three components: a
    sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline;
    and additive white noise. The parameters of each component of the model are estimated
    iteratively using SMC. The posterior distributions of the parameters given the observed
    spectra are represented as a population of weighted particles.
Functions that facilitate and speed up the analysis of data
    produced by a Syntech servosphere <http://www.ockenfels-syntech.com/products/locomotion-compensation/>,
    which is equipment for studying the movement behavior of arthropods.
    This package is designed to make working with data produced from a 
    servosphere easy for someone new to or unfamiliar with R. The functions
    provided in this package fall into three broad-use categories: functions for
    cleaning raw data produced by the servosphere software, functions for
    deriving movement variables based on position data, and functions for 
    summarizing movement variables for easier analysis. These functions are
    built with functions from the tidyverse package to work efficiently, as a
    single servosphere file may consist of hundreds of thousands of rows of data
    and a user may wish to analyze hundreds of files at a time. Many of the 
    movement variables derivable through this package are described in the 
    following papers:
    Otálora-Luna, Fernando; Dickens, Joseph C. (2011) <doi:10.1371/journal.pone.0020990>
    Party, Virginie; Hanot, Christophe; Busser, Daniela Schmidt; Rochat, Didier; Renou, Michel (2013) <doi:10.1371/journal.pone.0052897>
    Bell, William J.; Kramer, Ernest (1980) <doi:10.1007/BF01402908>
    Becher, Paul G; Guerin, Patrick M. (2009) <doi:10.1016/j.jinsphys.2009.01.006>.
Start an HTTP server in R to serve static files, or dynamic
    documents that can be converted to HTML files (e.g., R Markdown) under a
    given directory.
Structural equation modeling is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with inter-correlated dependent and independent variables. Here we implement a simple method for spatially explicit structural equation modeling based on the analysis of variance co-variance matrices calculated across a range of lag distances. This method provides readily interpreted plots of the change in path coefficients across scale.
Utility functions for interacting with R processes from
        external programs. This package includes functions to save and
        restore session information (including loaded packages, and
        attached data objects), as well as functions to evaluate
        strings containing R commands and return the printed results or
        an execution transcript.
Query and print information about the current R session.
    It is similar to 'utils::sessionInfo()', but includes more information
    about packages, and where they were installed from.
More easy to get intersection, union or complementary set and combinations.
An object-oriented package for mathematical sets, upgrading the current gold-standard {sets}. Many forms of mathematical sets are implemented, including (countably finite) sets, tuples, intervals (countably infinite or uncountable), and fuzzy variants. Wrappers extend functionality by allowing symbolic representations of complex operations on sets, including unions, (cartesian) products, exponentiation, and differences (asymmetric and symmetric).
Functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the 	examples in the book by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Gentle Introduction. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social 	Sciences", Cambridge University Press.
Tests gene expression data from a biological pathway for biologically meaningful differences in the eigenstructure between two classes.  Specifically, it tests the null hypothesis that the two classes' leading eigenvalues and sums of eigenvalues are equal.  A pathway's leading eigenvalue arguably represents the total variability due to variability in pathway activity, while the sum of all its eigenvalues represents the variability due to pathway activity and to other, unregulated causes. Implementation of the method described in Danaher (2015), "Covariance-based analyses of biological pathways".
Implements an algorithm to conduct advanced
    gene set enrichment analysis on the results of genomics experiments.
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 Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031> for more details.
Customise 'Shiny' disconnected screens as well as sanitize error messages to make them clearer and friendlier to the user.
This package contains functions for calculating severity
        and generating severity curves. Specifically, the simple case
        of the one-parameter Normal distribution (i.e., with known
        variance) is considered.
Support for simple features, a standardized way to
    encode spatial vector data. Binds to 'GDAL' for reading and writing
    data, to 'GEOS' for geometrical operations, and to 'PROJ' for
    projection conversions and datum transformations. Optionally uses the 's2'
    package for spherical geometry operations on geographic coordinates.
Stochastic Frontier Analysis
  introduced by Aigner, Lovell and Schmidt (1976)
  and Battese and Coelli (1992, 1995).
Stochastic frontier analysis with advanced methods.
 In particular, it applies the approach proposed by Latruffe et al. (2017) 
 <DOI:10.1093/ajae/aaw077> to estimate a stochastic frontier with technical 
 inefficiency effects when one input is endogenous.
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 between R and Simple Feature 'sf' objects, without depending
  on the Simple Feature library. Conversion functions are available at both the R level, 
  and through 'Rcpp'.
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.
Modern classes for tracking and movement data, building
    on 'sf' spatial infrastructure, and early theoretical work from
    Turchin (1998, ISBN: 9780878938476), and Calenge et al. (2009)
    <doi:10.1016/j.ecoinf.2008.10.002>. Tracking data are series of
    locations with at least 2-dimensional spatial coordinates (x,y), a
    time index (t), and individual identification (id) of the object
    being monitored; movement data are made of trajectories, i.e. the
    line representation of the path, composed by steps (the
    straight-line segments connecting successive locations). 'sftrack'
    is designed to handle movement of both living organisms and
    inanimate objects.
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 and standard normal distributions as important special cases.
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 (see for example, Carvajal Rodríguez et al., 2009 <doi:10.1186/1471-2105-10-209>;  de Uña Álvarez, 2012 <doi:10.1515/1544-6115.1812>; Castro Conde et al., 2015 <doi:10.1177/0962280215597580>) and the BH and BY false discovery rate controlling procedures.
An analytic framework for the calculation of norm- and criterion-referenced academic growth estimates using large scale, longitudinal education assessment data as developed in Betebenner (2009) <doi:10.1111/j.1745-3992.2009.00161.x>.
Data sets utilized by the 'SGP' package as exemplars for users to conduct their own student growth percentiles (SGP) analyses.
Regularized version of partial least square approaches providing sparse, group, and sparse group versions of partial least square regression models (Liquet, B., Lafaye de Micheaux, P., Hejblum B., Thiebaut, R. (2016) <doi:10.1093/bioinformatics/btv535>). Version of PLS Discriminant analysis is also provided.
Computation of second-generation p-values as described in Blume et al. (2018) <doi:10.1371/journal.pone.0188299> and Blume et al. (2019) <doi:10.1080/00031305.2018.1537893>. There are additional functions which provide power and type I error calculations, create graphs (particularly suited for large-scale inference usage), and a function to estimate false discovery rates based on second-generation p-value inference.
The package for Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.
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
This is an evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. This collection provides tools for: (1) the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking; the spectra of results are returned in graphic and tabular formats (Remmel 2015) <doi:10.1111/cag.12222>, (2) simulating landscape patterns, (3) provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps (Remmel and Fortin 2013) <doi:10.1007/s10980-013-9905-x>, (4) counting the number of each first-order pattern element and converting that information into both frequency and empirical probability vectors (Remmel 2020) <doi:10.3390/e22040420>, and (5) computing the porosity of raster patches <doi:10.3390/su10103413>. NOTE: This is a consolidation of existing packages ('PatternClass', 'ShapePattern') to begin warehousing all shape and pattern code in a common package. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented.  Note that all future developments will appear in this package and that 'PatternClass' will eventually be archived.
Studies otolith shape variation among fish populations. 
  Otoliths are calcified structures found in the inner ear of teleost fish and their shape has 
  been known to vary among several fish populations and stocks, making them very useful in taxonomy, 
  species identification and to study geographic variations. The package extends previously described 
  software used for otolith shape analysis by allowing the user to automatically extract closed 
  contour outlines from a large number of images, perform smoothing to eliminate pixel noise, 
  choose from conducting either a Fourier or wavelet transform to the outlines and visualize 
  the mean shape. The output of the package are independent Fourier or wavelet coefficients 
  which can be directly imported into a wide range of statistical packages in R. The package 
  might prove useful in studies of any two dimensional objects.
Here we describe a simple geometric rigid rotation approach that removes the effect of random translation and rotation, enabling the morphological analysis of 3D articulated structures. Our method is based on Cartesian coordinates in 3D space so it can be applied to any morphometric problem that also uses 3D coordinates. See Vidal-García, M., Bandara, L., Keogh, J.S. (2018) <doi:10.1002/ece3.4018>.
Routines for the statistical analysis of landmark 
  shapes, including Procrustes analysis, graphical displays, principal
  components analysis, permutation and bootstrap tests, thin-plate 
  spline transformation grids and comparing covariance matrices. 
     See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, 
     with Applications in R (2nd Edition), John Wiley and Sons.
Landsat satellites collect important data about global forest conditions. Documentation about Landsat's role in forest disturbance estimation is available at the site <http://landsat.gsfc.nasa.gov/?p=9513>. By constrained quadratic B-splines, this package delivers an optimal shape-restricted trajectory to a time series of Landsat imagery for the purpose of modeling annual forest disturbance dynamics to behave in an ecologically sensible manner assuming one of seven possible "shapes", namely, flat, decreasing, one-jump (decreasing, jump up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then increasing), linear increasing, and double-jump (decreasing, jump up, decreasing, jump up, decreasing). The main routine selects the best shape according to the minimum Bayes information criterion (BIC) or the cone information criterion (CIC), which is defined as the log of the estimated predictive squared error. The package also provides parameters summarizing the temporal pattern including year(s) of inflection, magnitude of change, pre- and post-inflection rates of growth or recovery. In addition, it contains routines for converting a flat map of disturbance agents to time-series disturbance maps and a graphical routine displaying the fitted trajectory of Landsat imagery.
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'.
Complex machine learning models are often hard to interpret. However, in 
  many situations it is crucial to understand and explain why a model made a specific 
  prediction. Shapley values is the only method for such prediction explanation framework 
  with a solid theoretical foundation. Previously known methods for estimating the Shapley 
  values do, however, assume feature independence. This package implements the method 
  described in Aas, Jullum and Løland (2019) <arXiv:1903.10464>, which accounts for any feature 
  dependence, and thereby produces more accurate estimates of the true Shapley values.
Analyse species-habitat associations in R. Therefore, information about the 
  location of the species 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. The statistical foundations of the Sharpe can be found in
    the author's Short Sharpe Course  <doi:10.2139/ssrn.3036276>.
An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see <arXiv:1505.01333>).
An algorithm for identifying high-resolution driver elements for datasets from a high-definition reporter assay library. Xinchen Wang, Liang He, Sarah Goggin, Alham Saadat, Li Wang, Melina Claussnitzer, Manolis Kellis (2017) <doi:10.1101/193136>.
Miscellaneous soil data management, summary, visualization, and conversion utilities to support soil survey.
Hierarchical models for the analysis of species-area 
  relationships (SARs) by combining several data sets and covariates; 
  with a global data set combining individual SAR studies; 
  as described in Solymos and Lele 
  (2012, Global Ecology and Biogeography 21, 109-120).
The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is selected from its search space bias F. This formal result depends on the Shattering coefficient function N(F,2n) to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of F, including its under and overfitting abilities while addressing specific target problems. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. As main contributions, one can use our approach to study the complexity of the search space bias F, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. Reference: de Mello, R.F. (2019) "On the Shattering Coefficient of Supervised Learning Algorithms" <arXiv:1911.05461>; de Mello, R.F., Ponti, M.A. (2018, ISBN: 978-3319949888) "Machine Learning: A Practical Approach on the Statistical Learning Theory".
Provides a computational framework for analyzing mutations in
    immunoglobulin (Ig) sequences. Includes methods for Bayesian estimation of
    antigen-driven selection pressure, mutational load quantification, building of
    somatic hypermutation (SHM) models, and model-dependent distance calculations.
    Also includes empirically derived models of SHM for both mice and humans.
    Citations: 
    Gupta and Vander Heiden, et al (2015) <doi:10.1093/bioinformatics/btv359>, 
    Yaari, et al (2012) <doi:10.1093/nar/gks457>, 
    Yaari, et al (2013) <doi:10.3389/fimmu.2013.00358>, 
    Cui, et al (2016) <doi:10.4049/jimmunol.1502263>.
Implements various methods for eliciting a probability distribution
    for a single parameter from an expert or a group of experts. The expert
    provides a small number of probability judgements, corresponding
    to points on his or her cumulative distribution function. A range of parametric
    distributions can then be fitted and displayed, with feedback provided in the
    form of fitted probabilities and percentiles. For multiple experts, a weighted
    linear pool can be calculated. Also includes functions for eliciting beliefs
    about population distributions, eliciting multivariate distributions using a
    Gaussian copula, eliciting a Dirichlet distribution, and eliciting distributions 
    for variance parameters in a random effects meta-analysis model. R Shiny apps  
    for most of the methods are included.
Takes as input a stable oxygen isotope (d18O) profile measured in growth direction (D)
	through a shell + uncertainties in both variables (d18O_err & D_err). It then models the seasonality
	in the d18O record by fitting a combination of a growth and temperature sine wave to year-length chunks of
	the data (see Judd et al., (2018) <doi:10.1016/j.palaeo.2017.09.034>). This modelling is carried out along a sliding window through the data and yields estimates of
	the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modelling
	routine and the data itself are propagated and pooled to obtain a confidence envelope around the age of
	each data point in the shell. The end result is a shell chronology consisting of estimated ages of shell
	formation relative to the annual cycle with their uncertainties. All formulae in the package serve this
	purpose, but the user can customize the model (e.g. number of days in a year and the mineralogy of the
	shell carbonate) through input parameters.
Contains all the formulae of the growth and trace element uptake model described in the equally-named
    Geoscientific Model Development paper (de Winter, 2017, <doi:10.5194/gmd-2017-137>). The model takes as input a file with X- and Y-coordinates of digitized
    growth increments recognized on a longitudinal cross section through the bivalve shell, as well as a BMP file of an elemental map of the
    cross section surface with chemically distinct phases separated by phase analysis. It proceeds by a step-by-step process described in
    the paper, by which digitized growth increments are used to calculate changes in shell height, shell thickness, shell volume, shell mass
    and shell growth rate through the bivalve's life time. Then, results of this growth modelling are combined with the trace element mapping
    results to trace the incorporation of trace elements into the bivalve shell. Results of various modelling parameters can be exported in
    the form of XLSX files.
An exact method for computing the Poisson-Binomial Distribution (PBD). The package provides a function for generating a random sample from the PBD, as well as two distinct approaches for computing the density, distribution, and quantile functions of the PBD. The first method uses direct-convolution, or a dynamic-programming approach which is numerically stable but can be slow for a large input due to its quadratic complexity. The second method is much faster on large inputs thanks to its use of Fast Fourier Transform (FFT) based convolutions. Notably in this case the package uses an exponential shift to practically guarantee the relative accuracy of the computation of an arbitrarily small tail of the PBD -- something that FFT-based methods often struggle with. This ShiftConvolvePoiBin method is described in Peres, Lee and Keich (2020) <arXiv:2004.07429> where it is also shown to be competitive with the fastest implementations for exactly computing the entire Poisson-Binomial distribution.
Fast enrichment analysis for locally correlated statistics
        via circular permutations.
        The analysis can be performed at multiple significance thresholds
        for both primary and auxiliary data sets with
        efficient correction for multiple testing.
Provides confidence intervals in least-squares regressions when the
  variable of interest has a shift-share structure, and in instrumental
  variables regressions when the instrument has a shift-share structure. The
  confidence intervals implement the AKM and AKM0 methods developed in Adão,
  Kolesár, and Morales (2019) <doi:10.1093/qje/qjz025>.
Prototype your 'shiny' apps quickly with these
    Lorem-Ipsum helper functions. Generate random elements
    for 'shiny' outputs that can be used as placeholder in your application.
Makes it incredibly easy to build interactive web
    applications with R. Automatic "reactive" binding between inputs and
    outputs and extensive prebuilt widgets make it possible to build
    beautiful, responsive, and powerful applications with minimal effort.
It provides easy internationalization of Shiny
    applications. It can be used as standalone translation package
    to translate reports, interactive visualizations or
    graphical elements as well.
Displays simple diagnostic information of the 'shiny' project in the user interface of the app.
Adds Progressive Web App support for Shiny apps, including
  desktop and mobile installations.
It is a simple router for your Shiny apps.
    The router allows you to create dynamic web applications with real-time
    User Interface and easily share url to pages within your Shiny apps.
Creating a great user interface for your Shiny apps
    can be a hassle, especially if you want to work purely in R
    and don't want to use, for instance HTML templates. This
    package adds support for a powerful UI library 'FomanticUI' -
    <https://fomantic-ui.com/> (before: Semantic). 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.
A graphical user interface (GUI) for fitting Bayesian
    regression models using the package 'brms' which in turn relies on
    'Stan' (<https://mc-stan.org/>). The 'shinybrms' GUI is a 'shiny'
    app.
Adds additional Twitter Bootstrap components to Shiny.
Add indicators (spinner, progress bar, gif) in your 'shiny' applications to show the user that the server is busy.
Provides a combined slider and numeric input for usage in a 'Shiny' app. The slider and the numeric input are linked together: each one is updated when the other one changes. Many styling properties are customizable (e.g. colors and size).
When a 'Shiny' output (such as a plot, table, map, etc.) is recalculating, it remains 
    visible but gets greyed out. Using 'shinycssloaders', you can add a loading animation ("spinner")
    to outputs instead. By wrapping a 'Shiny' output in 'withSpinner()', a spinner will automatically
    appear while the output is recalculating. See the demo online at
    <https://daattali.com/shiny/shinycssloaders-demo/>.
A custom css/html or gif/image file for the loading screen in R 'shiny'. It also can use the marquee to have custom text loading screen.
Create Interactive Graph (Network) Visualizations. 
  'shinyCyJS' can be used in 'Shiny' apps or viewed from 'Rstudio' Viewer.
  'shinyCyJS' includes API to build Graph model like node or edge with customized attributes for R. 
  'shinyCyJS' is built with 'cytoscape.js' and 'htmlwidgets' R package.
Create dashboards with 'Shiny'. This package provides
    a theme on top of 'Shiny', making it easy to create attractive dashboards.
Extend 'shinydashboard' with 'AdminLTE2' components. 
             'AdminLTE2' is a free 'Bootstrap 3' dashboard template available
             at <https://adminlte.io>. Customize boxes, add timelines and a lot more.
A 'Shiny' app can disconnect for a variety of reasons: an unrecoverable error occurred in
    the app, the server went down, the user lost internet connection, or any other reason
    that might cause the 'Shiny' app to lose connection to its server. With 'shinydisconnect', you can
    call disonnectMessage() anywhere in a Shiny app's UI to add a nice message when this happens. 
    Works locally (running Shiny apps within 'RStudio') and on Shiny servers (such as shinyapps.io,
    'RStudio Connect', 'Shiny Server Open Source', 'Shiny Server Pro'). See demo online at 
    <https://daattali.com/shiny/shinydisconnect-demo/>.
Add 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'.
Easily display user feedback in Shiny apps.
Provides functionality for client-side navigation of
    the server side file system in shiny apps. In case the app is running
    locally this gives the user direct access to the file system without the
    need to "download" files to a temporary location. Both file and folder
    selection as well as file saving is available.
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.
A collection of highly configurable, touch-enabled knob input controls for 'shiny'. These components can be styled to fit in perfectly in any app, and allow users to set precise values through many input modalities. Users can touch-and-drag, click-and-drag, scroll their mouse wheel, double click, or use keyboard input.
Assesses the number of concurrent users 'shiny'
  applications are capable of supporting, and for directing application changes
  in order to support a higher number of users. Provides facilities for recording
  'shiny' application sessions, playing recorded sessions against a target
  server at load, and analyzing the resulting metrics.
Track and record the use of applications and the user's interactions with 'Shiny' inputs.
  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.
A 'Shiny' app allowing to compare and merge two files, with syntax highlighting for several coding languages.
Implementation of a shiny app to easily compare supervised machine learning model performances. 
    You provide the data and configure each model parameter directly on the shiny app.  
    Different supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression and classification tasks.
    Implementation of available machine learning models on R has been done by Lantz (2013, ISBN:9781782162148).
Develop outstanding 'shiny' apps for 'iOS', 'Android', desktop as well as beautiful 'shiny' gadgets.
    'shinyMobile' is built on top of the latest 'Framework7' template <https://framework7.io>.
    Discover 14 new input widgets (sliders, vertical sliders, stepper, 
    grouped action buttons, toggles, picker, smart select, ...), 2 themes (light and dark), 
    12 new widgets (expandable cards, badges, chips, timelines, gauges, progress bars, ...) 
    combined with the power of server-side notifications such as alerts, modals, toasts,
    action sheets, sheets (and more) as well as 3 layouts (single, tabs and split).
Interactive visualization of 'RDML' files via 'shiny' apps. 
  Package provides (1) PCR plate interface with ability to select
  individual tubes and (2) amplification/melting plots with fast hiding and 
  highlighting individual curves.
A 'Shiny' app including the 'Monaco' editor. The 'Monaco' editor is the code editor which powers 'VS Code'. It is particularly well developed for 'JavaScript'. In addition to the 'Monaco' editor features, the app provides prettifiers and minifiers for multiple languages, 'SCSS' and 'TypeScript' compilers, code checking for 'C' and 'C++' (requires 'cppcheck').
An enterprise-targeted scalable and customizable 'shiny' module providing an easy way to incorporate free-form note taking or discussion boards into applications.
    The package includes a 'shiny' module that can be included in any 'shiny' application to create a panel containing searchable, editable text broken down by section headers.
    Can be used with a local 'SQLite' database, or a compatible remote database of choice.
Troubleshooting reactive data in 'shiny' can be difficult. These functions will convert reactive data frames into functions and load all assigned objects into your local environment. If you create a dummy input object, as the function will suggest, you will be able to test your server and ui functions interactively.
Add shiny inputs with one or more inline buttons that grow and shrink with inputs. 
 Also add tool tips to input buttons and styling and messages for input validation.
Create 'Shiny Apps' with collapsible vertical panels. 
    This package provides a new visual arrangement for elements on top of 'Shiny'. 
    Use the expand and collapse capabilities to leverage web applications with
    many elements to focus the user attention on the panel of interest.
This 'RStudio' addin enables faster creation of the project structure and design of 'ShinyDashboard' apps. The graphical interface allows you to add certain code templates interactively. In addition to generating the basic project structure, the navigation in the app itself can also be defined or the app can be structured with modules.
It builds dynamic R shiny based dashboards to analyze any CSV files. It provides simple dashboard design to subset the data, perform exploratory data analysis and preliminary machine learning (supervised and unsupervised). It also provides filters based on columns of interest.
Implements user interfaces for log-linear models, Bayesian model averaging and 
    Bayesian Dirichlet process mixture models.
This gadget allows you to use the 'recipes' package belonging to 'tidymodels' to carry 
    out the data preprocessing tasks in an interactive way. Build your 'recipe' by dragging the variables, 
    visually analyze your data to decide which steps to use, add those steps and preprocess your data.
Allows to create modular, reusable 'HTML'
    forms which can be embedded in your 'shiny' app with minimal effort.
    Features include conditional code execution on form submission,
    automatic input validation and input tooltips.
Shiny wrappers for the RGL package. This package exposes RGL's
    ability to export WebGL visualization in a shiny-friendly format.
Capture screenshots in 'Shiny' applications. Screenshots can either be
    of the entire viewable page, or a specific section of the page. The captured
    image is automatically downloaded as a PNG image. Powered by the
    'html2canvas' JavaScript library.
Add a searchbar widget to your 'Shiny' application. The widget
  quickly integrates with any existing element containing text to highlight
  matches. Highlighting is done with the 'JavaScript' library 'mark.js'. The
  widget includes buttons to cycle through multiple instances of the match and
  automatically scroll to the matches in an overflow element (or window). The
  widget also displays the total number of matches and which match is currently
  being cycled through. The widget is structured as a 'Bootstrap 3' input
  group.
Create in-app purchasing and subscriptions through 'Servicebot' payment using the 'Stripe' framework.
Provides function shinyShortcut() that, 
    when given the base directory of a shiny application, will produce an
    executable file that runs the shiny app directly in the user's
    default browser. Tested on both windows and unix machines. Inspired
    by and borrowing from 
    <http://www.mango-solutions.com/wp/2017/03/shiny-based-tablet-or-desktop-app/>.
Provides interactive plotting for mathematical models of infectious disease spread. Users can choose from a variety of common built-in ordinary differential equation (ODE) models (such as the SIR, SIRS, and SIS models), or create their own. This latter flexibility allows 'shinySIR' to be applied to simple ODEs from any discipline. The package is a useful teaching tool as students can visualize how changing different parameters can impact model dynamics, with minimal knowledge of coding in R. The built-in models are inspired by those featured in Keeling and Rohani (2008) <doi:10.2307/j.ctvcm4gk0> and Bjornstad (2018) <doi:10.1007/978-3-319-97487-3>.
A graphical user interface for interactive Markov chain Monte
    Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a
    posterior sample. The interface is powered by the 'Shiny' web
    application framework from 'RStudio' and works with the output of MCMC 
    programs written in any programming language (and has extended 
    functionality for 'Stan' models fit using the 'rstan' and 'rstanarm' 
    packages).
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 !
A user interface to the 'ypr' R package. 'Ypr' implements equilibrium-based yield per recruit methods for estimating the optimal yield for a fish population.
The SHIP-package allows the estimation of various types of
        shrinkage covariance matrices. These types differ in terms of
        the so-called covariance target (to be chosen by the user), the
        highly structured matrix which the standard unbiased sample
        covariance matrix is shrunken towards and which optionally
        incorporates prior biological knowledge extracted from the
        database KEGG. The shrinkage intensity is obtained via an
        analytical procedure.
A collection of functions for data manipulation, plotting and statistical computing,
 to use separately or with the book "Visual Statistics. Use R!":
 Shipunov (2020) <http://ashipunov.info/shipunov/software/r/r-en.htm>.
 Most useful functions:
 Bclust(), Jclust() and BootA() which bootstrap hierarchical clustering;
 Recode() which does multiple recoding in a fast, simple and flexible way;
 Misclass() which outputs confusion matrix even if classes are not concerted;
 Overlap() which measures group separation on any projection;
 Biarrows() which converts any scatterplot into biplot;
 and Pleiad() which is fast and flexible correlogram.
Block-diagonal covariance selection for high dimensional Gaussian
    graphical models. The selection procedure is based on the slope heuristics.
An interface to the Admin API of the E-commerce service Shopify,
    (<https://help.shopify.com/en/api/reference>).
Integrates clipboard copied data in R Studio, loads and installs libraries within a R script and returns all valid arguments of a selected function.
Performs automatic creation of short forms of scales with an 
    ant colony optimization algorithm and a Tabu search. As implemented in the 
    package, the ant colony algorithm randomly selects items to build a model of 
    a specified length, then updates the probability of item selection according 
    to the fit of the best model within each set of searches. The algorithm 
    continues until the same items are selected by multiple ants a given number 
    of times in a row. On the other hand, the Tabu search changes one parameter at
    a time to be either free, constrained, or fixed while keeping track of the
    changes made and putting changes that result in worse fit in a "tabu" list
    so that the algorithm does not revisit them for some number of searches. 
    See Leite, Huang, & Marcoulides (2008) <doi:10.1080/00273170802285743> for
    an applied example of the ant colony algorithm, and Marcoulides & Falk (2018)
    <doi:10.1080/10705511.2017.1409074> for an applied example of the Tabu search.
Create short sprint (<6sec) profiles using the split times or the radar gun data.
    Mono-exponential equation is used to estimate maximal sprinting speed (MSS), relative acceleration (TAU),
    and other parameters such us maximal acceleration (MAC) and maximal relative power (PMAX). These parameters 
    can be used to predict kinematic and kinetics variables and to compare individuals. The modeling method utilized
    in this package is based on the works of Chelly SM, Denis C. (2001) <doi: 10.1097/00005768-200102000-00024>,
    Clark KP, Rieger RH, Bruno RF, Stearne DJ. (2017) <doi: 10.1519/JSC.0000000000002081>, 
    Furusawa K, Hill AV, Parkinson JL (1927) <doi: 10.1098/rspb.1927.0035>, 
    Greene PR. (1986) <doi: 10.1016/0025-5564(86)90063-5>, and 
    Samozino P. (2018) <doi: 10.1007/978-3-319-05633-3_11>.
Analyzes shooting data with respect to group shape,
        precision, and accuracy. This includes graphical methods,
        descriptive statistics, and inference tests using standard,
        but also non-parametric and robust statistical methods.
        Implements distributions for radial error in bivariate normal
        variables. Works with files exported by 'OnTarget PC/TDS',
        'Silver Mountain' e-target, 'ShotMarker' e-target, or 'Taran',
        as well as with custom data files in text format.
        Supports inference from range statistics like extreme spread.
        Includes a set of web-based graphical user interfaces.
Sometimes it is handy to be able to view an image file on an
    'R' graphics device. This package just does that. Currently it supports
    'PNG' files.
Making it easy to use various types of fonts ('TrueType',
    'OpenType', Type 1, web fonts, etc.) in R graphs, and supporting most output
    formats of R graphics including PNG, PDF and SVG. Text glyphs will be converted
    into polygons or raster images, hence after the plot has been created, it no
    longer relies on the font files. No external software such as 'Ghostscript' is
    needed to use this package.
Providing font files that can be used by the 'showtext' package.
Functions for converting network data from a
        SpatialLinesDataFrame object to an 'igraph'-Class object.
The predictive value of a statistical model can often be improved
    by applying shrinkage methods. This can be achieved, e.g., by regularized
    regression or empirical Bayes approaches. Various types of shrinkage factors can
    also be estimated after a maximum likelihood. While global shrinkage modifies
    all regression coefficients by the same factor, parameterwise shrinkage factors
    differ between regression coefficients. With variables which are either highly
    correlated or associated with regard to contents, such as several columns of a
    design matrix describing a nonlinear effect, parameterwise shrinkage factors are
    not interpretable and a compromise between global and parameterwise shrinkage,
    termed 'joint shrinkage', is a useful extension. A computational shortcut to
    resampling-based shrinkage factor estimation based on DFBETA residuals can be
    applied. Global, parameterwise and joint shrinkage for models fitted by lm(),
    glm(), coxph(), or mfp() is available.
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.
Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and  
  Cadonna et al. (2020) <doi:10.3390/econometrics8020020>.
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 <doi:10.1111/biom.12016>, and in Juraska M and Gilbert PB (2015), Mark-specific hazard ratio model with missing multivariate marks. Lifetime Data Analysis 22(4): 606-25 <doi:10.1007/s10985-015-9353-9>. The former considers continuous multivariate marks fully observed in all subjects who experience the event of interest, whereas the latter extends the previous work to allow multivariate marks that are subject to missingness-at-random. For models with missing marks, two estimators are implemented based on (i) inverse probability weighting (IPW) of complete cases, and (ii) augmentation of the IPW estimating functions by leveraging correlations between the mark and auxiliary data to 'impute' the expected profile score vectors for subjects with missing marks. The augmented IPW estimator is doubly robust and recommended for use with incomplete mark data. The methods make two key assumptions: (i) the time-to-event is assumed to be conditionally independent of the mark given treatment, and (ii) the weight function in the semiparametric density ratio/biased sampling model is assumed to be exponential. Diagnostic testing procedures for evaluating validity of both assumptions are implemented. Summary and plotting functions are provided for estimation and inferential results.
Functions for making particle-size analysis. Sieve tests are widely used to obtain particle-size distribution of powders or granular materials.
Functions for estimating catalytic constant and Michaelis-Menten constant (MM constant) of 
             stochastic Michaelis-Menten enzyme kinetics model are provided. The likelihood functions 
             based on stochastic simulation approximation (SSA), diffusion approximation (DA), and 
             Gaussian processes (GP) are provided to construct posterior functions for the Bayesian 
             estimation. All functions utilize Markov Chain Monte Carlo (MCMC) methods with Metropolis-
             Hastings algorithm with random walk chain and robust adaptive Metropolis-Hastings 
             algorithm based on Bayesian framework.
Print function signatures and find overly complicated code.
SigClust is a statistical method for testing the
        significance of clustering results. SigClust can be applied to
        assess the statistical significance of splitting a data set
        into two clusters. For more than two clusters, SigClust can be
        used iteratively.
Uses logistic regression to model the probability of detection as a function of covariates. 
             This model is then used with observational survey data to estimate population size, while
             accounting for uncertain detection.  See Steinhorst and Samuel (1989).
Provides pseudo-likelihood methods for empirically analyzing common signaling games in international relations as described in Crisman-Cox and Gibilisco (2019) <doi:10.1017/psrm.2019.58>.
Interface to 'sigma.js' graph visualization library including animations, plugins and shiny proxies.
Create interactive graph visualizations using 'Sigma.js' <http://sigmajs.org/>.  This package is meant to be used in conjunction
    with 'igraph', replacing the (somewhat underwhelming) plotting features of the package.  The idea is to quickly render
    graphs, regardless of their size, in a way that allows for easy, iterative modification of aesthetics.  Because 
    'Sigma.js' is a 'javascript' library, the visualizations are inherently interactive and are well suited for integration
    with 'Shiny' apps.  While there are several 'htmlwidgets' focused on network visualization, they tend to underperform on 
    medium to large sized graphs.  'Sigma.js' was designed for larger network visualizations and this package aims to 
    make those strengths available to 'R' users.
Genomic alterations including single nucleotide
    substitution, copy number alteration, etc. are the major force for
    cancer initialization and development. Due to the specificity of
    molecular lesions caused by genomic alterations, we can generate
    characteristic alteration spectra, called 'signature' (Wang, Shixiang,
    et al.  (2020) <DOI:10.1101/2020.04.27.20082404> & Alexandrov, Ludmil
    B., et al. (2020) <DOI:10.1038/s41586-020-1943-3> & Macintyre, Geoff,
    et al. (2018) <DOI:10.1038/s41588-018-0179-8>).  This package helps
    users to extract, analyze and visualize signatures from genomic
    alteration records, thus providing new insight into cancer study.
Several different sigmoid functions are implemented, including a wrapper function, SoftMax preprocessing and inverse functions.
Provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways.
A framework for the analysis and exploration of single-cell chromatin data.
    The 'Signac' package contains functions for quantifying single-cell chromatin data,
    computing per-cell quality control metrics, dimension reduction
    and normalization, visualization, and DNA sequence motif analysis.
    Reference: Stuart and Butler et al. (2019) <doi:10.1016/j.cell.2019.05.031>.
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.
Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables 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.
Methods for the analysis of signed networks. This includes several measures for structural balance as introduced by Cartwright and Harary (1956) <doi:10.1037/h0046049>, blockmodeling algorithms from Doreian (2008) <doi:10.1016/j.socnet.2008.03.005>, various centrality indices, and projections of signed two-mode networks introduced by Schoch (2020) <doi:10.1080/0022250X.2019.1711376>.
Provides convenience functions to replace hyphen-minuses (ASCII 45)
    with proper minus signs (Unicode character 2212). The true minus matches
    the plus symbol in width, line thickness, and height above the baseline.
    It was designed for mathematics, looks better in presentation,
    and is understood properly by screen readers.
Interfaces with the 'SigOpt' API. More info at <https://sigopt.com>.
Pathway Analysis is 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). 
    This package also includes empirical tests, such as Monte Carlo and bootstrap distribution estimates.
Provides tools to identify and visualize branches in a phylogenetic tree that are significantly responsive to some intervention, taking as primary inputs a phylogenetic tree (of class phylo) and a data frame (or matrix) of corresponding tip (OTU) labels and p-values.
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).
Functions to analyze the effect of landscape features on spatial observations (described in a GIS shapefile format). It simultaneously estimates the spatial scales and intensities of landscape variable effects without any information about the scale of effect, Carpentier and Martin (2019) <doi:10.1101/692566>.
Provides a general framework to perform statistical inference of each gene pair 
        and global inference of whole-scale gene pairs in gene networks using the well known 
        Gaussian graphical model (GGM) in a time-efficient manner. We focus on the high-dimensional 
        settings where p (the number of genes) is allowed to be far larger than n (the number of subjects). 
        Four main approaches are supported in this package: (1) the bivariate nodewise scaled Lasso 
        (Ren et al (2015) <doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled Lasso 
        (Jankova and van de Geer (2017) <doi:10.1007/s11749-016-0503-5>) (3) the de-sparsified 
        graphical Lasso (Jankova and van de Geer (2015) <doi:10.1214/15-EJS1031>) (4) the GGM 
        estimation with false discovery rate control (FDR) using scaled Lasso or Lasso 
        (Liu (2013) <doi:10.1214/13-AOS1169>). Windows users should install 'Rtools' before the 
        installation of this package.
Generate common data forms for complex data suitable for conversions and
 transmission by decomposition as paths or primitives. Paths are sequentially-linked records, 
 primitives are basic atomic elements and both can model many forms and be grouped into hierarchical 
 structures.  The universal models 'SC0' (structural) and 'SC' (labelled, relational) are composed of 
 edges and can represent any hierarchical form. Specialist models 'PATH', 'ARC' and 'TRI' provide the 
 most common intermediate forms used for converting from one form to another. The methods are
 inspired by the simplicial complex <https://en.wikipedia.org/wiki/Simplicial_complex> and 
 provide intermediate forms that relate spatial data structures to this mathematical construct.
Simultaneous inference procedures for high-dimensional linear models as described by Zhang, X., and Cheng, G. (2017) <doi:10.1080/01621459.2016.1166114>.
It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs <doi:10.18637/jss.v096.i02>. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.
The definition of fuzzy random variable and the methods of simulation from fuzzy random variables are two challenging statistical problems in three recent decades. This package is organized based on a special definition of fuzzy random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy Numbers (PLFNs); see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for details about PLFNs. Some important statistical functions are considered for obtaining the membership function of main statistics, such as mean, variance, summation, standard deviation and coefficient of variance. Some of applied advantages of 'Sim.PLFN' package are:  (1) Easily generating / simulation a random sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or the membership function of the statistical result, and (3) Considering the simulated PLFNs for arithmetic operation or importing into some statistical computation. Finally, it must be mentioned that 'Sim.PLFN' package works on the basis of 'FuzzyNumbers' package.
Generates realistic simulated genetic data in families or unrelated individuals.
Provides tools for simulating spatially dependent predictors (continuous or binary),
    which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous
    predictors are generated using traditional multivariate normal distributions or Gauss Markov random
    fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288>
    and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using
    a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors 
	exhibiting spatial clustering can also be easily specified by the user.
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.
Provides efficient R and 'C++' routines to simulate cognitive diagnostic
    model data for Deterministic Input, Noisy "And" Gate ('DINA') and
    reduced Reparameterized Unified Model ('rRUM') from 
    Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>,
    Culpepper (2015) <doi:10.3102/1076998615595403>, and
    de la Torre (2009) <doi:10.3102/1076998607309474>.
Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 <doi:10.2202/1557-4679.1258>) or ratios (Hasler and Hothorn, 2012 <doi:10.1080/19466315.2011.633868>) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 <doi:10.1002/bimj.200710466>) or covariance matrices (Hasler, 2014 <doi:10.1515/ijb-2012-0015>).
Provides a framework to generating random variates from
             arbitrary multivariate copulae, while concentrating on
             (bivariate) extreme value copulae.  Particularly useful if
             the multivariate copulae are not available in closed
             form.
Simulates correlated multinomial responses conditional on a marginal model specification.
Simulate and plot general experimental crosses. The focus is on simulating genotypes with an aim towards flexibility rather than speed. Meiosis is simulated following the Stahl model, in which chiasma locations are the superposition of two processes: a proportion p coming from a process exhibiting no interference, and the remainder coming from a process following the chi-square model.
Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures.
Provides tools to 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>, and for an in-depth overview of 
    the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.
Implementation of a model-based bootstrap approach for testing whether two formulations are similar. The package provides a function for fitting a pharmacokinetic model to time-concentration data and comparing the results for all five candidate models regarding the Residual Sum of Squares (RSS). The candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a logistic model. The assessment of similarity implemented in this package is performed regarding the maximum deviation of the profiles. See Moellenhoff et al. (2018) <doi:10.1002/sim.7689> for details.
Assessment of the distributions of baseline continuous and categorical 
    variables in randomised trials. This method is based on the Carlisle-Stouffer 
    method with Monte Carlo simulations. It calculates p-values for each trial 
    baseline variable, as well as combined p-values for each trial - these p-values 
    measure how compatible are distributions of trials baseline variables with 
    random sampling. This package also allows for graphically plotting the 
    cumulative frequencies of computed p-values.
    Please note that code was partly adapted from Carlisle JB, Loadsman JA. 
    (2017) <doi:10.1111/anae.13650>.
An object oriented framework to simulate
  ecological (and other) dynamic systems. It can be used for
  differential equations, individual-based (or agent-based) and other
  models as well. It supports structuring of simulation scenarios (to avoid copy
  and paste) and aims to improve readability and re-usability of code.
Contains various functions to be used for simulation education, 
    including simple Monte Carlo simulation functions, queueing simulation
    functions, variate generation functions capable of producing independent
    streams and antithetic variates, functions for illustrating random variate
    generation for various discrete and continuous distributions, and functions
    to compute time-persistent statistics.  Also contains 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.
Through simfinapi, you can intuitively access the
    'SimFin' Web-API (<https://simfin.com/>) to make 'SimFin' data easily
    available in R. To obtain an 'SimFin' API key (and thus to use this
    package), you need to register at <https://simfin.com/login>.
Uses the 'SimFin' (SIMmplifying FINnance) api at <https://simfin.com/data/access/api> to download financial data straight into your R session. 
    It includes financial statements -- balance sheet, cash flow and income statement -- and adjusted daily price of stocks.
    The available data is comprehensive, going back to 2005 and available for quarters (Q1, Q2, Q3, Q4) and years (FY).
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.
Generate power for the Cox proportional hazards model by simulating survival events data with time dependent exposure status for subjects. A dichotomous exposure variable is considered with a single transition from unexposed to exposed status during the subject's time on study.
Calculates performance criteria measures and associated Monte Carlo standard errors for simulation results. Includes functions to help run simulation studies. Our derivation and explanation of formulas and our general simulation workflow is closely aligned with the approach described by Morris, White, and Crowther (2019) <DOI: 10.1002/sim.8086>.
An implementation of a novel method to quantify the similarity
    of the code-base of R functions by means of program dependence graphs.
    Possible use cases include detection of code clones for improving
    software quality and of plagiarism amongst students' assignments.
Functions to run and assist four
  different similarity measures. The similarity
  measures included are: longest common
  subsequence (LCSS), Frechet distance, edit distance
  and dynamic time warping (DTW). Each of these
  similarity measures can be calculated from two
  n-dimensional trajectories, both in matrix form.
Allows a Simile model saved as a compiled binary to be
  loaded, parameterized, executed and interrogated. This version works 
  with Simile v5.97 on.
Provides an efficient and very flexible framework to
    conduct data-driven epidemiological modeling in realistic large
    scale disease spread simulations. The framework integrates
    infection dynamics in subpopulations as continuous-time Markov
    chains using the Gillespie stochastic simulation algorithm and
    incorporates available data such as births, deaths and movements
    as scheduled events at predefined time-points. Using C code for
    the numerical solvers and 'OpenMP' (if available) to divide work
    over multiple processors ensures high performance when simulating
    a sample outcome. One of our design goals was to make the package
    extendable and enable usage of the numerical solvers from other R
    extension packages in order to facilitate complex epidemiological
    research. The package contains template models and can be extended
    with user-defined models. For more details see the paper by
    Widgren, Bauer, Eriksson and Engblom (2019)
    <doi:10.18637/jss.v091.i12>.
Provides tools for the stochastic simulation of effectiveness scores to mitigate
  data-related limitations of Information Retrieval evaluation research, as described in Urbano and
  Nagler (2018) <doi:10.1145/3209978.3210043>. These tools include: fitting, selection and plotting
  distributions to model system effectiveness, transformation towards a prespecified expected value,
  proxy to fitting of copula models based on these distributions, and simulation of new evaluation
  data from these distributions and copula models.
Uses simulation to create prediction intervals for
    post-policy outcomes in interrupted time series (ITS) designs,
    following Miratrix (2020) <arXiv:2002.05746>. This package provides
    methods for fitting ITS models with lagged outcomes and variables to
    account for temporal dependencies.  It then conducts inference via
    simulation, simulating a set of plausible counterfactual post-policy
    series to compare to the observed post-policy series. This package
    also provides methods to visualize such data, and also to incorporate
    seasonality models and smoothing and aggregation/summarization.  This
    work partially funded by Arnold Ventures in collaboration with
    MDRC.
Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach <doi:10.1080/03610918208812265> and the Ruscio-Kaczetow iteration (2008) <doi:10.1080/00273170802285693>. Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator from <http://www.pcg-random.org>.
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>.
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.
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.
A curated set of colors that are called using
    a standardized syntax: saturation + hue + lightness. For example, 
    "brightblue4" and "mutedred2". Functions exists to return individual colors 
    by name or to build palettes across or within hues. Most functions allow you 
    to visualize the palettes in addition to returning the desired hex codes.
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.
The number of studies involving correlated traits and the availability of tools to handle this type of data has increased considerably in the last decade. With such a demand, we need tools for testing hypotheses related to single and multi-trait (correlated) phenotypes based on many genetic settings. Thus, we implemented various options for simulation of pleiotropy and Linkage Disequilibrium under additive, dominance and epistatic models. The simulation currently takes a marker data set as an input and then uses it for simulating multiple traits as described in Fernandes and Lipka (2020) <doi:10.1101/2020.01.11.902874>.
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.
Support effortless high quality 'ggplot2' and 'leaflet' visualisations for reports or interactive 'shiny' apps through wrapping functions.
Simplex density, distribution, quantile functions as well as random variable
   	generation of the simplex distribution are given. Regression analysis of proportional data
   	using various kinds of simplex models is available. In addition, GEE method can be applied 
	to longitudinal data to model the correlation. Residual analysis is also involved. Some 
	subroutines are written in C with GNU Scientific Library (GSL) so as to facilitate the 
	computation.
Provides an interface to a Simplex Tree data structure, which is 
  a data structure aimed at enabling efficient manipulation of simplicial complexes 
  of any dimension. The Simplex Tree data structure was originally introduced by 
  Jean-Daniel Boissonnat and Clément Maria (2014) <doi:10.1007/s00453-014-9887-3>.
Provides methods to integrate functions over m-dimensional simplices
 in n-dimensional Euclidean space.  There are exact methods for polynomials and
 adaptive methods for integrating an arbitrary function.  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, see Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v079.i10>) and
    Templ (2017) <doi:10.1007/978-3-319-50272-4>. The package was developed with support of
    the International Household Survey Network, DFID Trust Fund TF011722 and funds
    from the World bank.
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.
Simulation of recurrent event data for non-constant baseline
    hazard in the total time model with risk-free intervals and possibly a competing event.
    Possibility to cut the data to an interim data set. Data can be plotted.
    Details about the method can be found in Jahn-Eimermacher, A. et al. (2015) <doi:10.1186/s12874-015-0005-2>.
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. Data generation methods are described in Schneider (2013) <doi:10.1177/0734282913478046>.
Simulates data sets in order to explore modeling
    techniques or better understand data generating processes. The user
    specifies a set of relationships between covariates, and generates
    data based on these specifications. The final data sets can represent
    data from randomized control trials, repeated measure (longitudinal)
    designs, and cluster randomized trials. Missingness can be generated
    using various mechanisms (MCAR, MAR, NMAR).
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>).
Simulate age-structured populations that vary in space and time and 
    explore the efficacy of a range of built-in or user-defined sampling 
    protocols to reproduce the population parameters of the known population. 
    (See Regular et al. (2020) <doi.org/10.1371/journal.pone.0232822> for more
    details).
Provides functions to simulate from joint survival and marker 
    models. The user can specific all basis functions of time, random or 
    deterministic covariates, random or deterministic left-truncation and 
    right-censoring times, and model parameters.
Transforms or simulates data with a target empirical covariance matrix supplied
             by the user. The method to obtain the data with the target empirical covariance
             matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019)
             <arXiv:1812.05678>.
Handles datetimes as integers for the usage inside
        Discrete-Event Simulations (DES). The conversion is made using
        the internally generic function as.numeric() of the base
        package. DES is described in Simulation Modeling and Analysis
        by Averill Law and David Kelton (1999) <doi:10.2307/2288169>.
Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. <arXiv:1709.10074> for methodological details.
Tool for statistical simulations that have two components. 
    One component generates the data and the other one
    analyzes the data. The main aims of the package are the reduction
    of the administrative source code (mainly loops and management code for the
    results) and a simple applicability of the package that allows the user to
    quickly learn how to work with it. Parallel computing is
    also supported. Finally, convenient functions are provided to summarize the
    simulation results.
A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.
Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample. Duong (2017) <doi:10.18637/jss.v021.i07>, Christian P. Robert and George Casella (2010 ISBN:978-1-4419-1575-7) <doi:10.1007/978-1-4419-1576-4>.
A 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.
Implementation of the Conditional Least Square (CLS) estimates and
    its covariance matrix for the first-order spatial integer-valued
    autoregressive model (SINAR(1,1)) proposed by Ghodsi (2012)
    <doi:10.1080/03610926.2011.560739>.
This implements the Brunton et al (2016; PNAS <doi:10.1073/pnas.1517384113>) sparse
    identification algorithm for finding ordinary differential equations for a measured 
    system from raw data (SINDy). The package includes a set of additional tools for 
    working with raw data, with an emphasis on cognitive science applications (Dale 
    and Bhat, in press <doi:10.1016/j.cogsys.2018.06.020>).
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.
When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) <doi:10.1076/clin.12.4.482.7241>, Crawford and Garthwaite (2005) <doi:10.1037/0894-4105.19.3.318>, Crawford and Garthwaite (2007) <doi:10.1080/02643290701290146> and Crawford, Garthwaite and Ryan (2011) <doi:10.1016/j.cortex.2011.02.017>. The package is also equipped with power calculators for each method.
Provides R functions for calculating basic effect size indices for 
  single-case designs, including several non-overlap measures and parametric 
  effect size measures, and for estimating the gradual effects model developed 
  by Swan and Pustejovsky (2018) <DOI:10.1080/00273171.2018.1466681>. 
  Standard errors and confidence intervals (based on the assumption that the outcome 
  measurements are mutually independent) are provided for the subset of effect sizes 
  indices with known sampling distributions.
Identification of differentially expressed genes (DEGs) is a key step in single-cell 
    transcriptomics data analysis. 'singleCellHaystack' predicts DEGs without relying on 
    clustering of cells into arbitrary clusters. Single-cell RNA-seq (scRNA-seq) data is 
    often processed to fewer dimensions using Principal Component Analysis (PCA) and 
    represented in 2-dimensional plots (e.g. t-SNE or UMAP plots). 'singleCellHaystack' 
    uses Kullback-Leibler divergence to find genes that are expressed in subsets of cells 
    that are non-randomly positioned in a these multi-dimensional spaces or 2D representations. 
    For the theoretical background of 'singleCellHaystack' we refer to 
    Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3>.
Density, distribution function, quantile function 
	and random generation for the sum of independent non-identical
	binomial distribution with parameters \code{size} and \code{prob}.
Density, distribution function, quantile function, random
    generation and survival function for the Sine Inverse Weibull Distribution as
    defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics and Information, Federal Rural
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
A platform for computing competition indices and 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>.
Supplementary functions for item response models aiming
    to complement existing R packages. The functionality includes among others
    multidimensional compensatory and noncompensatory IRT models
    (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), 
    MCMC for hierarchical IRT models and testlet models
    (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), 
    NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), 
    Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>;
    Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>),
    faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011,
    <doi:10.1111/j.1745-3984.2011.00143.x>),
    ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), 
    DETECT statistic (Stout, Habing, Douglas & Kim, 1996, 
    <doi:10.1177/014662169602000403>), local structural equation modeling 
    (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016,
    <doi:10.1080/00273171.2016.1142856>).
A regression and classification algorithm based on random forests, which takes the form of a short list of rules. SIRUS combines the simplicity of decision trees with 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) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all
    of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards
    model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).
Implements the SISAL algorithm by Tikka and Hollmén. It is
        a sequential backward selection algorithm which uses a linear
        model in a cross-validation setting. Starting from the full
        model, one variable at a time is removed based on the
        regression coefficients. From this set of models, a
        parsimonious (sparse) model is found by choosing the model with
        the smallest number of variables among those models where the
        validation error is smaller than a threshold. Also implements
        extensions which explore larger parts of the search space
        and/or use ridge regression instead of ordinary least squares.
An interval fusion procedure for functional data in the
             semiparametric framework of SIR, as described in 
             <doi:10.1007/s11222-018-9806-6>. Standard ridge and sparse SIR are 
             also included in the package.
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>. Note that sismonr relies on Julia code called internally by the functions. No knowledge of Julia is required in order to use sismonr, but Julia must be installed on the computer (instructions can be found in the tutorial, the GitHub page or the vignette of the package).
The Brazilian system for diploma registration and validation on technical and superior
    courses are managing by 'Sistec' platform, see <https://sistec.mec.gov.br/>. This package provides 
    tools for Brazilian institutions to update the student's registration and make data analysis 
    about their situation, retention and drop out.
Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.
Functions for fitting and plotting SITAR (Super
    Imposition by Translation And Rotation) growth curve models. SITAR is
    a shape-invariant model with a regression B-spline mean curve and
    subject-specific random effects on both the measurement and age
    scales.  The model was first described by Lindstrom (1995)
    <doi:10.1002/sim.4780141807> and developed as the SITAR method by Cole
    et al (2010) <doi:10.1093/ije/dyq115>.
The SiteAdapt procedure improves the accuracy of modeled solar irradiance series
    through site-adaptation with coincident ground-based measurements relying on the use of a
    regression preprocessing followed by an empirical quantile mapping (eQM) correction.
    Fernandez-Peruchena et al (2020) <doi:10.3390/rs12132127>.
Within the area of movement ecology, it can be useful to identify an animal's sites of interest. These will be places that an animal uses a lot and so will spend a proportionately longer time there. The aim of this package is to identify both the position and size of these sites of interest from high resolution (sub-second) positional data.
The goal of 'siteymlgen' is to make it easy to organise
  the building of your 'R Markdown' website.
  The init() function placed within the first code chunk of the index.Rmd
  file of an 'R' project directory will initiate the generation of an automatically
  written _site.yml file. 'siteymlgen' recommends a specific naming
  convention for your 'R Markdown' files. This naming will ensure that
  your navbar layout is ordered according to a hierarchy.
Implements a three-dimensional stochastic model of cancer growth and mutation similar to the one described in Waclaw et al. (2015) <doi:10.1038/nature14971>. Allows for interactive 3D visualizations of the simulated tumor. Provides a comprehensive summary of the spatial distribution of mutants within the tumor. Contains functions which create synthetic sequencing datasets from the generated tumor.
Provided within are two high quality and fast PPRNGs that may be
    used in an 'OpenMP' parallel environment. In addition, there is a generator
    for one dimensional low-discrepancy sequence. The objective of this library
    to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and
    'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the
    header files inside of 'sitmo' instead of including a copy of each engine
    within their individual package. Lastly, the package contains example
    implementations using the 'sitmo' package and three accompanying vignette that
    provide additional information.
Format a number (or a list of numbers) to a string (or a
        list of strings) with SI prefix. Use SI prefixes as constants
        like (4 * milli)^2
Provides extensions for package 'sitree' for allometric variables, growth, mortality, recruitment, management, tree removal and external modifiers functions.
An iterative feature selection method (manuscript submitted) that
    internally utilizes various Machine Learning methods that have embedded
    feature reduction in order to shrink down the feature space into a small
    and yet robust set.
Functions and utilities to perform
    Statistical Analyses in the Six Sigma way.
    Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control),
    you can manage several Quality Management studies:
    Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis,
    etc. Data frames used in the books "Six Sigma with R", 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 tool for bootstrapping new packages with useful defaults,
    including a test suite outline that passes checks and helpers for running
    tests, checking test coverage, building vignettes, and more. Package
    skeletons it creates are set up for pushing your package to
    'GitHub' and using other hosted services for building and test automation.
Functions for the Skellam distribution, including: density
    (pmf), cdf, quantiles and regression.
Creates static / animated / interactive visualisations embeddable
    in R Markdown documents. It implements an R-to-JavaScript transpiler and
    enables users to write JavaScript applications using the syntax of R.
An implementation of image processing effects that convert a photo into a line drawing image. 
    For details, please refer to Tsuda, H. (2020). sketcher: An R package for converting a photo into a sketch style image. 
    <doi:10.31234/osf.io/svmw5>.
Functions are provided for the density function,
	     distribution function, quantiles and random number
	     generation for the skew hyperbolic
	     t-distribution. There are also functions that fit
	     the distribution to data. There are functions for the
	     mean, variance, skewness, kurtosis and mode of a given
	     distribution and to calculate moments of any order about
	     any centre. To assess goodness of fit, there are
	     functions to generate a Q-Q plot, a P-P plot and a tail plot.
It fits scale mixture of skew-normal linear mixed models using an expectation–maximization (EM) type algorithm, including some possibilities for modeling the within-subject dependence. Details can be found in Schumacher, Lachos and Matos (2020) <arXiv:2002.01040>.
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 buttskyle96@gmail.comwith 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 blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible.
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>.
'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.
Slanted matrices and ordered clustering for better visualization of similarity data.
A self-contained hypothesis is tested for a given pathway of longitudinal omics. 'SlaPMEG' is a two-step procedure. First, a shared latent process mixed model is fitted over the longitudinal measures of omics in a pathway. This shared model allows deviation from the shared process at subject level (a random intercept, slope, or both per subject) and also at omic level (a random effect per omic). These random effects summarize the longitudinal trend of the observations which can be used to test for group differences using 'Globaltest' in the second step. If the pathway is large or the shared effect is small, the package fits a series of pairwise models and estimates the shared random effects based on them.
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 gene expression (microarray) data using a Bayesian Sparse Latent Factor Model (SLFM) <DOI:10.1007/978-3-319-12454-4_15>. Considerable effort has been put to build a fast and memory efficient package, which makes this proposal an interesting and computationally convenient alternative to study patterns of gene expressions exhibited in matrices. The package contains the implementation of two versions of the model based on different mixture priors for the loadings: one relies on a degenerate component at zero and the other uses a small variance normal distribution for the spike part of the mixture.
Provides access to soil and landscape grid of Australia raster datasets via existing open geospatial consortium web coverage services. See <https://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 created
      directly from the R console, and viewed in the 'RStudio' 
      internal viewer, in Shiny apps and R Markdown documents.
This statistical method uses the nearest neighbor algorithm to estimate absolute distances between single cells 
	based on a chosen constellation of surface proteins, with these distances being a measure of the similarity between the 
	two cells being compared. Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015) <DOI:10.1016/j.ymeth.2015.07.008>.
Provides type-stable rolling window functions over
    any R data type. Cumulative and expanding windows are also supported.
    For more advanced usage, an index can be used as a secondary vector
    that defines how sliding windows are to be created.
Create a side-by-side view of raster(image)s with an interactive 
    slider to switch between regions of the images. This can be especially useful
    for image comparison of the same region at different time stamps.
A collection of functions to perform Detrended Fluctuation Analysis (DFA exponent), GUEDES et al. (2019) <doi:10.1016/j.physa.2019.04.132> , Detrended cross-correlation coefficient (RHODCCA), GUEDES & ZEBENDE (2019) <doi:10.1016/j.physa.2019.121286>, DMCA cross-correlation coefficient and Multiple Detrended cross-correlation coefficient (DMC), ZEBENDE & SILVA-FILHO (2018) <doi:10.1016/j.physa.2018.06.119>, both with sliding windows approach.
Fits singular linear models to longitudinal data. Singular linear
    models are useful when the number, or timing, of longitudinal observations
    may be informative about the observations themselves. They are described
    in Farewell (2010) <doi:10.1093/biomet/asp068>, and are extensions of the
    linear increments model <doi:10.1111/j.1467-9876.2007.00590.x> to general
    longitudinal data.
Sparse Linear Method(SLIM) predicts ratings and top-n
    recommendations suited for sparse implicit positive feedback systems. SLIM
    is decomposed into multiple elasticnet optimization problems which are solved
    in parallel over multiple cores. The package is based on "SLIM: Sparse Linear
    Methods for Top-N Recommender Systems" by Xia Ning and George Karypis <doi:10.1109/ICDM.2011.134>.
Provides functions for performing common tasks when working with
  slippy map tile service APIs e.g. Google maps, Open Street Map, Mapbox, Stamen,
  among others. Functionality includes converting from latitude and longitude to
  tile numbers, determining tile bounding boxes, and compositing tiles to a
  georeferenced raster image.
Provides statistical procedures for linear regression in the general context where the errors are assumed to be correlated. Different ways to estimate the asymptotic covariance matrix of the least squares estimators are available. Starting from this estimation of the covariance matrix, the confidence intervals and the usual tests on the parameters are modified. The functions of this package are very similar to those of 'lm': it contains methods such as summary(), plot(), confint() and predict(). The 'slm' package is described in the paper by E. Caron, J. Dedecker and B. Michel (2019), "Linear regression with stationary errors: the R package slm", arXiv preprint <arXiv:1906.06583>.
A collection of helper functions designed to help
    you to better understand object oriented programming in R,
    particularly using 'S3'.
Efficient implementations for Sorted L-One Penalized Estimation 
    (SLOPE): generalized linear models regularized with the sorted L1-norm 
    (Bogdan et al. (2015) <doi:10/gfgwzt>). Supported models include ordinary 
    least-squares regression, binomial regression, multinomial regression, and 
    Poisson regression. Both dense and sparse  predictor matrices are supported.
    In addition, the package features predictor screening rules that enable fast
    and efficient solutions to high-dimensional problems.
Optimal partitioning algorithm for change-in-slope problem with continuity constraint and a finite number of states. Some constraints can be enforced in the inference: isotonic, unimodal or smoothing. With the function slopeSN() (segment neighborhood) the number of segments to infer is fixed by the user and does not depend on a penalty value.
An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.
A mostly pure-R implementation of the RAKE algorithm (Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010) <doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from documents without any training data.
Interface for creation of 'slp' class smoother objects for 
             use in Generalized Additive Models (as implemented by packages 
             'gam' and 'mgcv').
Conduct latent trajectory class analysis with longitudinal data. Our method supports longitudinal continuous, binary and count data. For more methodological details, please refer to Hart, K.R., Fei, T. and Hanfelt, J.J. (2020), Scalable and robust latent trajectory class analysis using artificial likelihood. Biometrics <doi:10.1111/biom.13366>.
'Slurm', Simple Linux Utility for Resource Management
          <https://slurm.schedmd.com/>, is a popular 'Linux' based software used to 
          schedule jobs in 'HPC' (High Performance Computing) clusters. This R package
          provides a specialized lightweight wrapper of 'Slurm' with a syntax similar to
          that found in the 'parallel' R package. The package also includes a method for
          creating socket cluster objects spanning multiple nodes that can be used with
          the 'parallel' package.
This is software linked to the book
  'Applied Smoothing Techniques for Data Analysis -
  The Kernel Approach with S-Plus Illustrations' Oxford University Press.
Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods.
Implements the following approaches for multidimensional scaling (MDS) based on stress minimization using majorization (smacof): ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with external constraints on the configuration, individual differences scaling (idioscal, indscal), MDS with spherical restrictions, and ratio/interval/ordinal/spline unfolding (circular restrictions, row-conditional). Various tools and extensions like jackknife MDS, bootstrap MDS, permutation tests, MDS biplots, gravity models, unidimensional scaling, drift vectors (asymmetric MDS), classical scaling, and Procrustes are implemented as well.
Statistical methods for analyzing case-control point data.  Methods include the ratio of kernel densities, the difference in K Functions, the spatial scan statistic, and q nearest neighbors of cases.
Inference techniques for Fay Herriot Model.
A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data>.
Animal movement models including moving-resting process
    with embedded Brownian motion according to
    Yan et al. (2014) <doi:10.1007/s10144-013-0428-8>,
    Pozdnyakov et al. (2017) <doi:10.1007/s11009-017-9547-6>,
    Brownian motion with measurement error according to
    Pozdnyakov et al. (2014) <doi:10.1890/13-0532.1>,
    and moving-resting-handling process with embedded Brownian motion,
    Pozdnyakov et al. (2018) <arXiv:1806.00849>.
Facilitates programmatic access to NASA Soil Moisture Active
    Passive (SMAP) data with R. It includes functions to search for, acquire,
    and extract SMAP data.
Primary data analysis for sequential multiple assignment randomization trial (SMART) and calibration tools for clinical trial planning purposes. \n 
             The methods used for this package include:  \n
             (1) Likelihood-based global test (hypothesis test, power calculation) by in Zhong X., Cheng, B., Qian M., Cheung Y.K. (2019) <doi: 10.1016/j.cct.2019.105830>. \n
             (2) IPWE-based global test (hypotehsis test, power calculation) by Ogbagaber S.B., Karp J., Wahed A.S. (2016) <doi:10.1002/sim.6747>. \n
             (3) G estimates (pairwise comparison, power calculation) by  Lavori R., Dawson P.W. (2012) <doi: 10.1093/biostatistics/kxr016>. \n
             (4) IPW estimates (pairwise comparison, power calculation) by Murphy S.A. (2005) <doi: 10.1002/sim.2022>. \n
             (5) SAMRT with adaptive randomization by Cheung Y.K. (2015) <doi: 10.1111/biom.12258>.
Permits determination of a set of 
    optimal dynamic treatment regimes and sample size for a SMART design 
    in the Bayesian setting. Please see Artman (2020) <arXiv:2008.02341>.
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.
Preview spatial data as 'leaflet' maps with minimal
  effort. smartmap is optimized for interactive use and distinguishes itself 
  from similar packages because it does not need real spatial ('sp' or 'sf')
  objects an input; instead, it tries to automatically coerce everything that 
  looks like spatial data to sf objects or leaflet maps. It - for example -  
  supports direct mapping of: a vector containing a single coordinate pair,
  a two column matrix, a data.frame with longitude and latitude columns, or
  the path or URL to a (possibly compressed) 'shapefile'.
Methods for analysis of energy consumption data (electricity, gas, 
    water) at different data measurement intervals. The package provides feature extraction 
    methods and algorithms to prepare data for data mining and machine learning 
    applications. Deatiled descriptions of the methods and their application can be found 
    in Hopf (2019, ISBN:978-3-86309-669-4) "Predictive Analytics for Energy Efficiency and 
    Energy Retailing" <doi:10.20378/irbo-54833> and Hopf et al. (2016) <doi:10.1007/s12525-018-0290-9> 
    "Enhancing energy efficiency in the residential sector with smart meter data analytics".
Sample size calculation to detect dynamic treatment regime (DTR) effects based on change in clinical attachment level (CAL) outcomes from a non-surgical chronic periodontitis treatments study. The experiment is performed under a Sequential Multiple Assignment Randomized Trial (SMART) design. The clustered tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and non-randomly missing. The implemented algorithm is available in Xu et al. (2019+) <arXiv:1902.09386>.
A tool for assessing bio-economic feedback in 
  different management scenarios (D'Andrea et al., 2020 
  <doi:10.1111/2041-210X.13394>). '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) <10.1093/biostatistics/kxy064> for more details.
    The basic functionality of this R package has been implemented in a Shiny app <https://wilart.shinyapps.io/smartsizer-power-sample-size/>.
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
Computes standardized mean differences and confidence intervals for 
    multiple data types based on Yang, D., & Dalton, J. E. (2012) 
    <http://www.lerner.ccf.org/qhs/software/lib/stddiff.pdf>.
Contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.
A computing tool is developed to automated identify somatic mutation-driven immune cells. The operation modes including: i) inferring the relative abundance matrix of tumor-infiltrating immune cells and integrating it with a particular gene mutation status, ii) detecting differential immune cells with respect to the gene mutation status and converting the abundance matrix of significant differential immune cell into two binary matrices (one for up-regulated and one for down-regulated), iii) identifying somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and iv) visualization of immune cell abundance of samples in different mutation status..
Symbolic multidimensional scaling for interval-valued dissimilarities. The hypersphere model and the hyperbox model are available.
Implements statistical methods for analyzing the counts of areal data,
    with a focus on the detection of spatial clusters and clustering.  The package has a heavy emphasis on spatial scan methods, which were first introduced by Kulldorff and Nagarwalla (1995) <doi:10.1002/sim.4780140809> and Kulldorff (1997) <doi:10.1080/03610929708831995>.
R functions and data from Chapter 5 of 'Statistical
        Methods for Financial Engineering', by Bruno Remillard, CRC
        Press, (2013).
Provides the filtering algorithms for the state space models on the Stiefel manifold as well as the corresponding sampling algorithms for uniform, vector Langevin-Bingham and matrix Langevin-Bingham distributions on the Stiefel manifold.
Discovery of spatial patterns with Hidden Markov Random Field. This package is designed for spatial transcriptomic data and single molecule fluorescent in situ hybridization (FISH) data such as sequential fluorescence in situ hybridization (seqFISH) and multiplexed error-robust fluorescence in situ hybridization (MERFISH). The methods implemented in this package are described in Zhu et al. (2018) <doi:10.1038/nbt.4260>.
Code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second and third editions of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press.
Functions that provide statistical methods for interval-censored (grouped) data. The package supports the estimation of linear and linear mixed regression models with interval-censored dependent variables. Parameter estimates are obtained by a stochastic expectation maximization algorithm. Furthermore, the package enables the direct (without covariates) estimation of statistical indicators from interval-censored data via an iterative kernel density algorithm. Survey and Organisation for Economic Co-operation and Development (OECD) weights can be included into the direct estimation (see, Walter, P. (2019) <doi:10.17169/refubium-1621>).
This tiny package contains one function smirnov() which
        calculates two scaled taxonomic coefficients, Txy (coefficient
        of similarity) and Txx (coefficient of originality). These two
        characteristics may be used for the analysis of similarities
        between any number of taxonomic groups, and also for assessing
        uniqueness of giving taxon. It is possible to use smirnov()
        output as a distance measure: convert it to distance by
        "as.dist(1 - smirnov(x))".
Constructs a yield curve by the Smith-Wilson method from a
        table of LIBOR and SWAP rates
Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data (SMITID).
  It allow sequence-space-time host and viral population data storage, indexation and querying.
Visualisation tools for 'SMITIDstruct' package.
  Allow to visualize host timeline, transmission tree, index diversities
  and variant graph using 'HTMLwidgets'. It mainly using 'D3JS' javascript framework.
Variable selection techniques are essential tools for model selection and estimation in high-dimensional statistical models. Sparse Maximal Likelihood Estimator (SMLE) (Xu and Chen (2014)<doi:10.1080/01621459.2013.879531>) provides an efficient implementation for the joint feature screening method on high-dimensional generalized linear models. It also conducts a post-screening selection based on a user-specified selection criterion. The algorithm uses iterative hard thresholding along with parallel computing.
Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see <doi:10.1145/342009.335388>.
Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. 
	For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. 
	Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and 
	Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. 
	Estimation and simulation of discrete-time k-th order Markov chains are also considered.
Efficient design matrix free procedure for solving a soft maximin problem for  large scale array-tensor structured models, see Lund, Mogensen and Hansen (2019) <arXiv:1805.02407>. Currently Lasso and SCAD penalized estimation is implemented.
In Switzerland, the landscape of municipalities is changing rapidly
  mainly due to mergers. The Swiss Municipal Data Merger Tool 
  automatically detects these mutations and maps municipalities over time, i.e. municipalities of an old state
  to municipalities of a new state. This functionality is helpful when working 
  with datasets that are based on different spatial references. The spatial 
  reference in this context signifies a set of municipalities at a given point in time.
Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions
Fits flexible additive models to data on stream networks, taking account of flow-connectivity of the network.  Models are fitted using penalised least squares.
Fits a linear non-penalized phenotype (demographic) variables 
    and penalized groups of prognostic effect and predictive effect, by satisfying such
    hierarchy structures that if a predictive effect exists, its prognostic effect must 
    also exist. This package can deal with continuous, binomial or multinomial, and survival
    response variables, underlying the assumption of Gaussian, binomial (multinomial), and 
    Cox proportional hazard models, respectively. It is implemented by combining the iterative
    shrinkage-thresholding algorithm and the alternating direction method of multipliers
    algorithms. The main method is built in C++, and the complementary methods are
    written in R.
Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics between biomolecules. BLI signals are small and noisy when small molecules are investigated as ligands (analytes). We develop this package to process and analyze the BLI data acquired on Octet Red96 from Fortebio more accurately. 
    Sun Q., Li X., et al (2020) <doi:10.1038/s41467-019-14238-3>. 
    In this new version, we organize the BLI experiment data and analysis methods into a S4 class with self-explaining structure.
Provides generators for a high number of both single- and multi-
    objective test functions which are frequently used for the benchmarking of
    (numerical) optimization algorithms. Moreover, it offers a set of convenient
    functions to generate, plot and work with objective functions.
Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting.
             The package includes 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.
We provide full functionality to smooth L1 penalized regression operators and to compute regression estimates thereof. For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) <doi:10.1007/s10107-004-0552-5>), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) <doi:10.1101/2020.09.17.301788>). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates.
A 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>).
The nonparametric trend and its derivatives in equidistant time 
    series (TS) with short-memory stationary errors can be estimated. The 
    estimation is conducted via local polynomial regression using an 
    automatically selected bandwidth obtained by a built-in iterative plug-in 
    algorithm or a bandwidth fixed by the user. A Nadaraya-Watson kernel 
    smoother is also built-in as a comparison.
    The methods of the package are described in
    Feng, Y., and Gries, T., (2017) <http://groups.uni-paderborn.de/wp-wiwi/RePEc/pdf/ciepap/WP102.pdf>. 
    A current version of the paper that is also referred to in the 
    documentation of the functions is prepared for publication.
A collection of various oversampling techniques developed from SMOTE is provided. SMOTE is a oversampling technique which synthesizes a new minority instance between a pair of one minority instance and one of its K nearest neighbor. (see <https://www.jair.org/media/953/live-953-2037-jair.pdf> for more information) Other techniques adopt this concept with other criteria in order to generate balanced dataset for class imbalance problem.
Provides movies to help students to understand statistical 
  concepts.  The 'rpanel' package  <https://cran.r-project.org/package=rpanel> 
  is used to create interactive plots that move to illustrate key statistical 
  ideas and methods.  There are movies to: visualise probability distributions
  (including user-supplied ones); illustrate sampling distributions of the
  sample mean (central limit theorem), the median, the sample maximum 
  (extremal types theorem) and (the Fisher transformation of the) 
  product moment correlation coefficient; examine the influence of an 
  individual observation in simple linear regression; illustrate key concepts 
  in statistical hypothesis testing. Also provided are dpqr functions for the 
  distribution of the Fisher transformation of the correlation coefficient 
  under sampling from a bivariate normal distribution.
Creates images that are the proper size for social media. Beautiful
    plots, charts and graphs wither and die if they are not shared. Social media 
    is perfect for this but every platform has its own image dimensions. With 
    'smpic' you can easily save your plots with the exact dimensions needed for 
    the different platforms.
Contains the datasets and a few functions for use with 
        the practicals outlined in Appendix A of the book
        Statistical Models (Davison, 2003, Cambridge University Press).
        The practicals themselves can be found at
        <http://statwww.epfl.ch/davison/SM/>.
Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers
Produce small area population estimates by fitting census data to
    survey data.
Datasets used in "Statistical Methods for the Social Sciences"
    (SMSS) by Alan Agresti and Barbara Finlay.
Implementation of the SMuRF algorithm of Devriendt et al. (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, Netbuilder() to create
    small networks via point and click, and the Componentlayouter() to layout networks with many components manually.
Implements snake in R as a programming example, see <https://en.wikipedia.org/wiki/Snake_(video_game_genre)>.
A consistent, flexible and easy to use tool to parse and convert strings into cases like snake or camel among others.
Plays the game of Snakes and Ladders and has tools for analyses. The tools included allow you to find the average moves to win, frequency of each square, importance of the snakes and the ladders, the most common square and the plotting of the game played.
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.
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.
Functions to perform most of the common analysis in genome association studies are implemented. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated. The methods are described in Gonzalez JR et al. (2007) <doi: 10.1093/bioinformatics/btm025>.
Implements classes and methods for large scale SNP enrichment analysis (e.g. SNPs associated with genes expression in a GWAS signal).
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.
Contains functions to perform various models and
    methods for test equating. It currently implements the traditional
    mean, linear and equipercentile equating methods. Both IRT observed-score 
    and true-score equating are also supported, as well as the mean-mean, 
    mean-sigma, Haebara and Stocking-Lord IRT linking methods.
    It also supports newest methods such that local equating, kernel
    equating (using Gaussian, logistic, Epanechnikov, uniform and adaptive 
    kernels) with presmoothing, and IRT parameter linking methods based on 
    asymmetric item characteristic functions. Functions to obtain both 
    standard error of equating (SEE) and standard error of equating differences 
    between two equating functions (SEED) are also implemented for the kernel method of
    equating.
Allows objects to be stored on disc and automatically
       		recalled into memory, as required, by delayed assignment.
Separates significant boundary constraints in a dataset from illusions. A modified permutation test is applied using the no-data zone areas above the top-left, top-right, bottom-right and bottom-left boundary lines as test statistics. The observed no-data zone areas are compared to the distribution of the random permutations to calculate a p-value for each boundary line. An article detailing the methodologies and rationale is currently in preparation.
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.
Bindings to 'libsodium': a modern, easy-to-use software library for
    encryption, decryption, signatures, password hashing and more. Sodium uses
    curve25519, a state-of-the-art Diffie-Hellman function by Daniel Bernstein,
    which has become very popular after it was discovered that the NSA had
    backdoored Dual EC DRBG.
Provides an interface to the 'NoSQL' database 'CouchDB'
    (<http://couchdb.apache.org>). Methods are provided for managing
    databases within 'CouchDB', including creating/deleting/updating/transferring,
    and managing documents within databases. One can connect with a local
    'CouchDB' instance, or a remote 'CouchDB' databases such as 'Cloudant'. 
    Documents can be inserted directly from vectors, lists, data.frames, 
    and 'JSON'. Targeted at 'CouchDB' v2 or greater.
Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos.
Software that leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. Circos is a novel software that addresses the challenges in visualizing genetic data by creating circular ideograms composed of tracks of heatmaps, scatter plots, line plots, histograms, links between common markers, glyphs, text, and etc. Please see <http://www.circos.ca>.
An extension of sensitivity, specificity, positive and negative
    predictive value to continuous predicted and reference memberships in
    [0, 1].
It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.
Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an "EM" flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components)
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://www.fao.org/land-water/databases-and-software/crop-information/en/>] soil fertility classes, 
        soil erosion models and soil salinity classification. Suitability requirements are for crops  grouped into cereal crops, nuts, legumes, fruits, vegetables, 
        industrial crops, and root crops.
A tool for importing, visualizing, and analyzing the soil carbon database created by the Powell Center working group.
A collection of functions for reading data from USDA-NCSS soil databases.
Provides functions for (1) soil water retention (SWC) and unsaturated hydraulic conductivity (Ku) (van Genuchten-Mualem (vGM or vG) [1, 2], Peters-Durner-Iden (PDI) [3, 4, 5], Brooks and Corey (bc) [8]), (2) fitting of parameter for SWC  and/or Ku using Shuffled Complex Evolution (SCE) optimisation and (3) calculation of soil hydraulic properties (Ku and soil water contents) based on the simplified evaporation method (SEM) [6, 7].
    Main references:
    [1] van Genuchten (1980) <doi:10.2136/sssaj1980.03615995004400050002x>, 
    [2] Mualem (1976) <doi:10.1029/WR012i003p00513>, 
    [3] Peters (2013) <doi:10.1002/wrcr.20548>, 
    [4] Iden and Durner (2013) <doi:10.1002/2014WR015937>, 
    [5] Peters (2014) <doi:10.1002/2014WR015937>,
    [6] Wind G. P. (1966),
    [7] Peters and Durner (2008) <doi:10.1016/j.jhydrol.2008.04.016> and
    [8] Brooks and Corey (1964).
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.
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).
Formulas for calculating sound velocity, water pressure, depth, 
    density, absorption and sonar equations.
Simulates the cultural evolution of quantitative traits of bird song. 'SongEvo' is an individual- (agent-) based model. 'SongEvo' is spatially-explicit and can be parameterized with, and tested against, measured song data. Functions are available for model implementation, sensitivity analyses, parameter optimization, model validation, and hypothesis testing.
Estimate the abundance of cell clones from the
	     distribution of lengths of DNA fragments (as created by
	     sonication, whence `sonicLength').  The algorithm in
	     "Estimating abundances of retroviral insertion sites from
	     DNA fragment length data" by Berry CC, Gillet NA, Melamed
	     A, Gormley N, Bangham CR, Bushman FD. Bioinformatics;
	     2012 Mar 15;28(6):755-62 is implemented.  The
	     experimental setting and estimation details are described
	     in detail there. Briefly, integration of new DNA in a
	     host genome (due to retroviral infection or gene therapy)
	     can be tracked using DNA sequencing, potentially allowing
	     characterization of the abundance of individual cell
	     clones bearing distinct integration sites. The locations
	     of integration sites can be determined by fragmenting the
	     host DNA (via sonication or fragmentase), breaking the
	     newly integrated DNA at a known sequence, amplifying the
	     fragments containing both host and integrated DNA,
	     sequencing those amplicons, then mapping the host
	     sequences to positions on the reference genome. The
	     relative number of fragments containing a given position
	     in the host genome estimates the relative abundance of
	     cells hosting the corresponding integration site, but
	     that number is not available and the count of amplicons
	     per fragment varies widely.  However, the expected number
	     of distinct fragment lengths is a function of the
	     abundance of cells hosting an integration site at a given
	     position and a certain nuisance parameter. The algorithm
	     implicitly estimates that function to estimate the
	     relative abundance.
Provides basic tools for manipulating sound files for bioacoustic analysis, and preparing analyses these for publication. The package validates that values are physically possible wherever feasible.
Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired.
Collection of different single objective test functions
        useful for benchmarks and algorithm development.
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.
A Bayesian semiparametric Dirichlet process mixtures to estimate correlated receiver operating characteristic (ROC) surfaces and the associated volume under the surface (VUS) with stochastic order constraints. The reference paper is:Zhen Chen, Beom Seuk Hwang, (2018) "A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints". Biometrics, 75, 539-550. <doi:10.1111/biom.12997>.
Provides tools to efficiently analyze and visualize laboratory data from aqueous static adsorption experiments. The package provides functions to plot Langmuir, Freundlich, and Temkin isotherms and functions to determine the statistical conformity of data points to the Langmuir, Freundlich, and Temkin adsorption models through statistical characterization of the isothermic least squares regressions lines. Scientific Reference: Dada, A.O, Olalekan, A., Olatunya, A. (2012) <doi:10.9790/5736-0313845>.
Enables drag-and-drop behaviour in Shiny apps, by exposing the 
    functionality of the 'SortableJS' <https://sortablejs.github.io/Sortable/> 
    JavaScript library as an 'htmlwidget'. 
    You can use this in Shiny apps and widgets, 'learnr' tutorials as well as 
    R Markdown. In addition, provides a custom 'learnr' question type - 
    'question_rank()' - that allows ranking questions with drag-and-drop.
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.
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.
A set of tools that can be used in computer-aided analysis of
    sound correspondences between languages, plus several helper functions.
    Analytic functions range from purely qualitative analysis, through
    statistic methods yielding qualitative results, to an entirely
    quantitative approach.
Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings.
The SoundexBR package provides an algorithm for decoding names
    into phonetic codes, as pronounced in Portuguese. The goal is for
    homophones to be encoded to the same representation so that they can be
    matched despite minor differences in spelling. The algorithm mainly encodes
    consonants; a vowel will not be encoded unless it is the first letter. The
    soundex code resultant consists of a four digits long string composed by
    one letter followed by three numerical digits: the letter is the first
    letter of the name, and the digits encode the remaining consonants.
Performs parametric synthesis of sounds with harmonic and noise 
    components such as animal vocalizations or human voice. Also offers tools 
    for spectral analysis, audio segmentation, self-similarity matrices, 
    modulation spectra, morphing, etc., as well as interactive web apps for 
    manually corrected pitch tracking and formant measurement. Reference: 
    Anikin (2019) <doi:10.3758/s13428-018-1095-7>.
Implement a promising, and yet little explored protocol for bioacoustical analysis, the eigensound method by MacLeod, Krieger and Jones (2013) <doi:10.4404/hystrix-24.1-6299>. Eigensound is a multidisciplinary method focused on the direct comparison between stereotyped sounds from different species. 'SoundShape', in turn, provide the tools required for anyone to go from sound waves to Principal Components Analysis, using tools extracted from traditional bioacoustics (i.e. 'tuneR' and 'seewave' packages), geometric morphometrics (i.e. 'geomorph' package) and multivariate analysis (e.g. 'stats' package). For more information, please see Rocha and Romano (in prep) and check 'SoundShape' repository on GitHub for news and updates <https://github.com/p-rocha/SoundShape>.
Construct a ranking of a set of treatments/groups by
    gathering together information coming from several response variables.
    It can be used with both balanced and unbalanced experiments
    (with almost all test statistics) as well as in presence of either
    continuous covariates or a stratifying (categorical) variable.
Quantify, profile and remove ambient mRNA contamination (the "soup") from droplet based single cell RNA-seq experiments.  Implements the method described in Young et al. (2018) <doi:10.1101/303727>.
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.
A programmatic interface to <http://sp2000.org.cn>, re-written based on an accompanying 'Species 2000' API. Access tables describing catalogue of the Chinese known species of animals, plants, fungi, micro-organisms, and more. This package also supports access to catalogue of life global <http://catalogueoflife.org>, China animal scientific database <http://zoology.especies.cn> and catalogue of life Taiwan <https://taibnet.sinica.edu.tw/home_eng.php>. The development of 'SP2000' package were supported by Biodiversity Survey and Assessment Project of the Ministry of Ecology and Environment, China <2019HJ2096001006>,Yunnan University's "Double First Class" Project <C176240405> and Yunnan University's Research Innovation Fund for Graduate Students <2019227>.
Provides methods for generating, exploring and executing seamless Phase II-III designs of Lai, Lavori and Shih using generalized likelihood ratio statistics. Includes pdf and source files that describe the entire R implementation with the relevant mathematical details.
Miscellaneous functions for analysing species association
        and niche overlap.
Genomic Region Detection via Spatial Convex Clustering. See <https://arxiv.org/abs/1611.04696> for details.
Partial correlation estimation with joint sparse
        regression model.
Latent space models for multivariate networks (multiplex) estimated via MCMC algorithm. See D Angelo et al. (2018) <arXiv:1803.07166> and D Angelo et al. (2018) <arXiv:1807.03874>.
Standard RGB spaces included are sRGB, 'Adobe' RGB, 'ProPhoto' RGB, BT.709, and others.  User-defined RGB spaces are also possible.  There is partial support for ACES Color workflows.
Functions for converting among CIE XYZ, xyY, Lab, and Luv.
      Calculate Correlated Color Temperature (CCT) and the Planckian and daylight loci.  
      The XYZs of some standard illuminants and some standard linear chromatic adaptation transforms (CATs) are included.
      Three standard color difference metrics are included.
Classes and methods for spatio-temporal data, including space-time regular lattices, sparse lattices, irregular data, and trajectories; utility functions for plotting data as map sequences (lattice or animation) or multiple time series; methods for spatial and temporal selection and subsetting, as well as for spatial/temporal/spatio-temporal matching or aggregation, retrieving coordinates, print, summary, etc.
One of the remaining pain points in making beautiful maps via 
    packages like 'rayshader' is both obtaining and processing spatial data
    to build from. 'spacey' aims to make it easier to obtain and use this data
    for locations within the United States, providing utilities to download 
    'USGS' and 'ESRI' geospatial data and quickly turn it into maps.
An R wrapper to the 'Python' 'spaCy' 'NLP' library,
    from <http://spacy.io>.
Provides easy to use functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems including on-the-fly system conversion. It supports any type of spherical projection to the plane defined by the 'mapproj' package.
A set of RStudio addins that are designed to be used in
             combination with user-defined RStudio keyboard shortcuts. These
             addins either:
             1) insert text at a cursor position (e.g. insert
             operators %>%, <<-, %$%, etc.),
             2) replace symbols in selected pieces of text (e.g., convert
             backslashes to forward slashes which results in stings like
             "c:\data\" converted into "c:/data/") or
             3) enclose text with special symbols (e.g., converts "bold" into
             "**bold**") which is convenient for editing R Markdown files.
Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.
Metapackage for implementing a variety of event-based models, with
    a focus on spatially explicit models. These include raster-based,
    event-based, and agent-based models. The core simulation components
    (provided by 'SpaDES.core') are built upon a discrete event simulation (DES;
    see Matloff (2011) ch 7.8.3 <https://nostarch.com/artofr.htm>)
    framework that facilitates modularity, and easily enables the user to
    include additional functionality by running user-built simulation modules
    (see also 'SpaDES.tools'). Included are numerous tools to visualize rasters
    and other maps (via 'quickPlot'), and caching methods for reproducible
    simulations (via 'reproducible'). Additional functionality is provided by
    the 'SpaDES.addins' and 'SpaDES.shiny' packages.
Provides 'RStudio' addins for 'SpaDES' packages and 'SpaDES' module
    development. See '?SpaDES.addins' for an overview of the tools provided.
Provides the core framework for a discrete event system (DES) to 
    implement a complete data-to-decisions, reproducible workflow.
    The core DES components facilitate modularity, and easily enable the user
    to include additional functionality by running user-built modules.
    Includes conditional scheduling, restart after interruption, packaging of
    reusable modules, tools for developing arbitrary automated workflows,
    automated interweaving of modules of different temporal resolution,
    and tools for visualizing and understanding the DES project.
Provides GIS and map utilities, plus additional modeling tools for
    developing cellular automata, dynamic raster models,  and agent based models
    in 'SpaDES'.
    Included are various methods for spatial spreading, spatial agents, GIS
    operations, random map generation, and others.
    See '?SpaDES.tools' for an categorized overview of these additional tools.
Implements methods for anticipating the emergence and eradication
    of infectious diseases from surveillance time series. Also provides support
    for computational experiments testing the performance of such methods.
A utility package containing some simple tools to design and generate density functions on bounded regions in space and space-time, and simulate 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). Various approximations of likelihood or restricted likelihood are implemented, in particular Laplace approximation and h-likelihood (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>). Both classical geostatistical models, and Markov random field models on irregular grids (as considered in the 'INLA' package, <http://www.r-inla.org>), can be fitted. Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model.
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'.
'Hail' is an open-source, general-purpose, 'python' based data analysis
    tool with additional data types and methods for working with genomic data, see
    <https://hail.is/>. 'Hail' is built to scale and has first-class support for 
    multi-dimensional structured data, like the genomic data in a genome-wide 
    association study (GWAS). 'Hail' is exposed as a 'python' library, using primitives
    for distributed queries and linear algebra implemented in 'scala', 'spark', and 
    increasingly 'C++'. The 'sparkhail' is an R extension using 'sparklyr' package. 
    The idea is to help R users to use 'hail' functionalities with the well-know
    'tidyverse' syntax, see <https://www.tidyverse.org/>.
Include interactive sparkline charts
    <http://omnipotent.net/jquery.sparkline> in 
    all R contexts with the convenience of 'htmlwidgets'.
R interface to Apache Spark, a fast and general engine for big data
    processing, see <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.
This sparklyr extension makes 'Flint' time series library
    functionalities (<https://github.com/twosigma/flint>) easily accessible
    through R.
A 'sparklyr' extension adding the capability to work easily with nested data.
Generate syntax for use with the sparklines package for
        LaTeX.
A 'sparklyr' extension that enables reading and writing 'TensorFlow'
  TFRecord files via 'Apache Spark'.
Load WARC (Web ARChive) files into Apache Spark using 'sparklyr'. This
    allows to read files from the Common Crawl project <http://commoncrawl.org/>.
A 'sparklyr' <https://spark.rstudio.com/> extension that provides an 
  interface for 'XGBoost' <https://github.com/dmlc/xgboost> on 'Apache Spark'. 'XGBoost' is an 
  optimized distributed gradient boosting library.
Use SPARQL to pose SELECT or UPDATE queries to an end-point.
Provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) <DOI:10.1002/sim.7577>.
Calculate the statistical power to detect clusters using kernel-based 
        spatial relative risk functions that are estimated using the 'sparr' package.
        Details about the 'sparr' package methods can be found in the tutorial: Davies
        et al. (2018) <doi:10.1002/sim.7577>.  Details about kernel density estimation 
        can be found in J. F. Bethell (1990) <doi:10.1002/sim.4780090616>.  More 
        information about relative risk functions using kernel-density estimation can 
        be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
Implements the sparse biclustering proposal of Tan and Witten (2014), Sparse biclustering of transposable data. Journal of Computational and Graphical Statistics  23(4):985-1008.
'HJ-Biplot' is a multivariate method that allow represent multivariate data on a subspace of low dimension, in such a way that most of the variability of the information is captured in a few dimensions. This package implements three new techniques and constructs in each case the 'HJ-Biplot', adapting restrictions to reduce weights and / or produce zero weights in the dimensions, based on the regularization theories. It implements three methods of regularization: Ridge, LASSO and Elastic Net.
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.  It deals correctly with parameters whose estimated values can be considered as being negative infinity.  It also addresses other possible issues of non-existence and non-identifiability of maximum likelihood estimates.
Computes multivariate normal (MVN) densities, and
    samples from MVN distributions, when the covariance or
    precision matrix is sparse.
Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <DOI: 10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) <arXiv:1804.00341>.
Provides interface to 'sparsepp' - fast, memory efficient hash map. 
    It is derived from Google's excellent 'sparsehash' implementation.
    We believe 'sparsepp' provides an unparalleled combination of performance and memory usage, 
    and will outperform your compiler's unordered_map on both counts. 
    Only Google's 'dense_hash_map' is consistently faster, at the cost of much greater 
    memory usage (especially when the final size of the map is not known in advance).
Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects.  This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots.  The method can implement sparse linear regression and sparse probit regression.  Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to  models with truncated outcomes, propensity score, and instrumental variable analysis.
Sparse-aware maximum likelihood for structural equation models in inferring gene regulatory networks
Implements the SparseStep model for solving regression
    problems with a sparsity constraint on the parameters. The SparseStep 
    regression model was proposed in Van den Burg, Groenen, and Alfons (2017) 
    <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.
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/>.
Fast Multiplication and Marginalization of Sparse Tables.
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.
A generic tool for manifold analysis. It allows to infer a relevant transition or evolutionary path which can highlights the features involved in a specific process. 'spathial' can be useful in all the scenarios where the temporal (or pseudo-temporal) evolution is the main problem (e.g. tumor progression). The algorithm for finding the principal path is described in: Ferrarotti et al., (2019) <doi:10.1109/TNNLS.2018.2884792>."
Functions for kriging and point pattern analysis.
Provides a set of spatial accessibility measures from a set of locations 
             (demand) to another set of locations (supply). It aims, among others, 
             to support research on spatial accessibility to health care facilities. 
             Includes the locations and some characteristics of major public hospitals in Greece.
Creates offensive and defensive shot charts for teams, 
    players and seasons, and more comprehensively for spatial 
    analysis of NBA data. Includes data from the 2016-17 NBA 
    season extracted from <http://stats.nba.com>.
Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of local covariance matrices. This package is an implementation of the methods described in Bachoc, Genton, Nordhausen, Ruiz-Gazen and Virta (2020) <doi:10.1093/biomet/asz079>.
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>.
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().
Calculate Kernel Density Estimation (KDE) for spatial data. 
  The algorithm is inspired by the tool 'Heatmap' from 'QGIS'. The method is described by:
  Hart, T., Zandbergen, P. (2014) <doi:10.1108/PIJPSM-04-2013-0039>, 
  Nelson, T. A., Boots, B. (2008) <doi:10.1111/j.0906-7590.2008.05548.x>,
  Chainey, S., Tompson, L., Uhlig, S.(2008) <doi:10.1057/palgrave.sj.8350066>.
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.
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'.
A collection of functions for estimating spatial regimes, aggregations of neighboring spatial units that are homogeneous in functional terms. The term spatial regime, therefore, should not be understood as a synonym for cluster. More precisely, the term cluster does not presuppose any functional relationship between the variables considered, while the term regime is linked to a regressive relationship underlying the spatial process. For more information, please see Postiglione, P., Andreano, M.S., Benedetti R. (2013) <doi:10.1007/s10614-012-9325-z> , Andreano, M.S., Benedetti, R., and Postiglione, P. (2017) <doi:10.1007/s11135-016-0415-1> , Bille', A.G., Benedetti, R., and Postiglione, P. (2017) <doi:10.1080/17421772.2017.1286373>.
Methods for spatial risk calculations. It offers an efficient approach to determine the sum of all observations within a 
    circle of a certain radius. This might be beneficial for insurers who are required (by a recent European Commission regulation) to determine 
    the maximum value of insured fire risk policies of all buildings that are partly or fully located within a circle of a radius of 200m.
Provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).
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.
Tools for spatial data analysis.  Emphasis on kriging.  Provides functions for prediction and simulation.  Intended to be relatively straightforward, fast, and flexible.
Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, <arXiv:1809.06418>). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006.
Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field.  Most of the functions here require the forecast and observed fields to be gridded and on the same grid.  For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) <doi: 10.1175/2009WAF2222269.1>.
Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation on raster data sets. EWS are metrics derived from the observed spatial structure of an ecosystem -- e.g. spatial autocorrelation -- that increase before an ecosystem undergoes a non-linear transition (Genin et al. (2018) <doi:10.1111/2041-210X.13058>).
Many packages use 'htmlwidgets' <https://CRAN.R-project.org/package=htmlwidgets>
    for interactive plotting of spatial data.
    This package provides functions for converting R objects, such as simple features, 
    into structures suitable for use in 'htmlwidgets' mapping libraries.
Mixture model is used to achieve the clustering goal. Each component is itself a mixture model of polynomial autoregressive regressions whose the logistic weights consider the spatial and temporal information.
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 
    (Robitaille et al. (2020) <doi:10.1111/2041-210X.13215>). 
    It can be used to convert GPS relocations to 
    gambit-of-the-group format to build proximity-based social networks 
    In addition, the randomizations function provides data-stream 
    randomization methods suitable for GPS data.
Contains all the datasets for the 'spatstat' package.
Extension to the 'spatstat' package, containing
	     interactive graphics capabilities.
Extension to the 'spatstat' package, for analysing large datasets
	     of spatial points on a network. The geometrically-corrected
	     K function is computed using the memory-efficient tree-based
	     algorithm described by Rakshit, Baddeley and Nair (2019).
Extension to the 'spatstat' package, enabling the user
	     to fit point process models to point pattern data
	     by local composite likelihood ('geographically weighted
	     regression').
Defines sparse three-dimensional arrays
	     and supports standard operations on them.
	     The package also includes utility functions for
	     matrix calculations that are common in
	     statistics, such as quadratic forms.
Contains utility functions for the 'spatstat' package
             which may also be useful for other purposes.
Bayesian inference for parametric proportional hazards spatial
    survival models; flexible spatial survival models. See Benjamin M. Taylor, Barry S. Rowlingson (2017) <doi:10.18637/jss.v077.i04>.
Tools to convert from specific formats to more general forms of 
    spatial data. Using tables to store the actual entities present in spatial
    data provides flexibility, and the functions here deliberately 
    minimize the level of interpretation applied, leaving that for specific 
    applications. Includes support for simple features,  round-trip for 'Spatial' classes and long-form 
    tables, analogous to 'ggplot2::fortify'. There is also a more 'normal form' representation
    that decomposes simple features and their kin to tables of objects, parts, and unique coordinates.
Fits univariate and multivariate spatio-temporal
        random effects models for point-referenced data using Markov chain Monte Carlo (MCMC). Details are given in Finley, Banerjee, and Gelfand (2015) <doi:10.18637/jss.v063.i13> and Finley, Banerjee, and Cook (2014) <doi:10.1111/2041-210X.12189>.
Provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.
Implements a spatial Bayesian non-parametric factor analysis model with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). Spatial correlation is introduced in the columns of the factor loadings matrix using a Bayesian non-parametric prior, the probit stick-breaking process. Areal spatial data is modeled using a conditional autoregressive (CAR) prior and point-referenced spatial data is treated using a Gaussian process. The response variable can be modeled as Gaussian, probit, Tobit, or Binomial (using Polya-Gamma augmentation). Temporal correlation is introduced for the latent factors through a hierarchical structure and can be specified as exponential or first-order autoregressive.
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.
Inspired by space-time regressions often performed to assess
    the expansion of the Neolithic from the Near East to Europe (Pinhasi et
    al. 2005 <doi:10.1371/journal.pbio.0030410>). Test for significant
    correlations between the (earliest) radiocarbon dates of archaeological
    sites and their respective distances from a hypothetical center of origin.
    Distances can be either great-circle or least-cost paths calculated from
    a friction surface. Both ordinary least squares (OLS) and reduced major
    axis (RMA) methods are supported (Russell et al. 2014
    <doi:10.1371/journal.pone.0087854>). It is also possible to iterate over
    many sites to identify the most likely origin.
A collection of functions to create spatial weights matrix
  objects from polygon 'contiguities', from point patterns by distance and
  tessellations, for summarizing these objects, and for permitting their
  use in spatial data analysis, including regional aggregation by minimum
  spanning tree; a collection of tests for spatial 'autocorrelation',
  including global 'Morans I' and 'Gearys C' proposed by 'Cliff' and 'Ord'
  (1973, ISBN: 0850860369) and (1981, ISBN: 0850860814), 'Hubert/Mantel'
  general cross product statistic, Empirical Bayes estimates and
  'Assunção/Reis' (1999) <doi:10.1002/(SICI)1097-0258(19990830)18:16%3C2147::AID-SIM179%3E3.0.CO;2-I> Index, 'Getis/Ord' G ('Getis' and 'Ord' 1992)
  <doi:10.1111/j.1538-4632.1992.tb00261.x> and multicoloured
  join count statistics, 'APLE' ('Li 'et al.' )
  <doi:10.1111/j.1538-4632.2007.00708.x>, local 'Moran's I'
  ('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.
A set of functions for obtaining positional parameters and magnitude difference between components of binary and multiple stellar systems from series of speckle images.
Provides the data sets used to exemplify 'specmine'. The data sets were formerly distributed with 'specmine', however they exceed current CRAN policy for package size.
Provides utilities for conducting specification curve analyses (Simonsohn, Simmons & Nelson (2015, <doi: 10.2139/ssrn.2694998>) or multiverse analyses (Steegen, Tuerlinckx, Gelman & Vanpaemel, 2016, <doi: 10.1177/1745691616658637>) including functions to setup, run, evaluate, and plot all specifications.
Implementation of SPECS, your favourite Single-Equation Penalized Error-Correction Selector developed in
    Smeekes and Wijler (2020) <arXiv:1809.08889>. SPECS provides a fully automated estimation procedure for large and potentially
    (co)integrated datasets. The dataset in levels is converted to a conditional error-correction model, either by the user or
    by means of the functions included in this package, and various specialised forms of penalized regression can be applied to
    the model. Automated options for initializing and selecting a sequence of penalties, as well as the construction of penalty
    weights via an initial estimator, are available. Moreover, the user may choose from a number of pre-specified deterministic
    configurations to further simplify the model building process.
A collection of forecast verification routines developed for the SPECS
    FP7 project. The emphasis is on comparative verification of ensemble forecasts of weather and climate.
Stores and eases the manipulation of spectra and associated data,
    with dedicated classes for spatial and soil-related data.
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.
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.
Do multi-gene descent probabilities
   (Thompson, 1983, <doi:10.1098/rspb.1983.0072>)
   and special cases thereof
   (Thompson, 1986, <doi:10.1002/zoo.1430050210>)
   including inbreeding and kinship coefficients.  But does much more:
   probabilities of any set of genes descending from any other
   set of genes.
From output files obtained from the software 'ModestR', the relative contribution of factors to explain species distribution is depicted using several plots. A global geographic raster file for each environmental variable may be also obtained with the mean relative contribution, considering all species present in each raster cell, of the factor to explain species distribution. Finally, for each variable it is also possible to compare the frequencies of any variable obtained in the cells where the species is present with the frequencies of the same variable in the cells of the extent.
Converts the floor speeches of Uruguayan legislators, extracted from the 
    parliamentary minutes, to tidy data.frame where each observation is the intervention of a single legislator.
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.
Implements a group-bridge penalized function-on-scalar regression
    model proposed by Wang et al. (2020) <arXiv:2006.10163>, to simultaneously
    estimate functional coefficient and recover the local sparsity.
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.
A simple tool for numerical optimization on the unit sphere. This is achieved by combining the spherical coordinating system with L-BFGS-B optimization. This algorithm is implemented in Kolkiewicz, A., Rice, G., & Xie, Y. (2020) <doi:10.1016/j.jspi.2020.07.001>.
Fitting a principal curve to data lying in the spherical surface.
    This package provides principal circle, principal geodesic analysis, Hauberg's      
    principal curves, and spherical principal curves. Moreover, it offers locally   
    defined principal geodesics which are currently under study. The detailed procedures 
    are described in Jang-Hyun Kim, Jongmin Lee and Hee-Seok Oh (2020) <arXiv:2003.02578>.
Various functions for creating spherical coordinate system plots via extensions to rgl.
Provides several methods to integrate functions over the unit
 sphere and ball in n-dimensional Euclidean space.  Routines for converting to/from
 multivariate polar/spherical coordinates are also provided.
Spherical K-function for point-pattern analysis on the sphere.
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.
A user-friendly tool for estimating both total and directional volatility spillovers based on Diebold and Yilmaz (2009, 2012). It also provides the user with rolling estimation for total and net indices. User can find both orthogonalized and generalized versions for each kind of measures. See Diebold and Yilmaz (2009, 2012) find them at  <doi:10.1111/j.1468-0297.2008.02208.x> and <doi:10.1016/j.ijforecast.2011.02.006>.
An optimal weighting strategy to compute
        simulation-efficient shortest probability intervals (spins).
Calculates constant structure parameters of endocrine homeostatic systems from equilibrium hormone concentrations. Methods and equations have been described in Dietrich et al. (2012) <doi:10.1155/2012/351864> and Dietrich et al. (2016) <doi:10.3389/fendo.2016.00057>.
Many complex diseases are known to be affected by the interactions between 
   genetic variants and environmental exposures beyond the main genetic and environmental 
   effects. Existing Bayesian methods for gene-environment (G×E) interaction studies are 
   challenged by the high-dimensional nature of the study and the complexity of environmental 
   influences. We have developed a novel and powerful semi-parametric Bayesian variable 
   selection method that can accommodate linear and nonlinear G×E interactions simultaneously 
   (Ren et al. (2019) <arXiv:1906.01057>). Furthermore, the proposed method can conduct 
   structural identification by distinguishing nonlinear interactions from main effects only 
   case within Bayesian framework. Spike-and-slab priors are incorporated on both individual 
   and group level to shrink coefficients corresponding to irrelevant main and interaction 
   effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and 
   alternative methods are  efficiently implemented in C++.
Functions for spatial methods based on generalized estimating equations (GEE) and
  wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR),
  conducting multi-model inference, and stepwise model selection. Further, contains functions 
  for spatially corrected model accuracy measures.
Data visualization tours animates linear projection 
  of multivariate data as its basis (ie. orientation) changes. The 'spinifex' 
  packages generates paths for manual tours by manipulating the contribution of 
  a single variable at a time ['Cook' & 'Buja' (1997) <doi:10.2307/1390747>]. 
  Other types of tours, such as grand (random walk) and guided (optimizing some
  objective function) are available in the
  'tourr' package ['Wickham' 'et' 'al.' (2011) <doi:10.18637/jss.v040.i02>]. 
  'spinifex' builds on 'tourr' and can render tours with 'gganimate' and 
  'plotly' graphics, and allows for exporting as an .html widget and as an .gif, 
  respectively. This work is fully discussed at ['Spyrison' & 'Cook' (2020) 
  <doi:10.32614/RJ-2020-027>.
Implements a generative model that uses a
    spike-and-slab like prior distribution obtained by multiplying a
    deterministic binary vector. Such a model allows an EM algorithm,
    optimizing a type-II log-likelihood.
In 2015, The 17 United Nations' Sustainable Development Goals were adopted. 'spiR' is a wrapper of several open datasets published by the Social Progress Imperative (<https://www.socialprogress.org/>), including the Social Progress Index (a synthetic measure of human development across the world). 'spiR''s goal is to provide data to help policymakers and researchers prioritize actions that accelerate social progress across the world in the context of the Sustainable Development Goals. Please cite: Warin, Th. (2019) "spiR: An R Package for the Social Progress Index", <doi:10.6084/m9.figshare.11421573>.
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, M-splines
    and its integral (I-splines), convex splines (C-splines),
    generalized Bernstein polynomials, and their derivatives.
    It also contains a C++ head-only library integrated with Rcpp.
    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 about the spline basis.
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: "Kitten Mittens".
Estimates split-half reliabilities for scoring algorithms of reaction time (RT) tasks and questionnaires. The 'splithalfr' supports user-provided scoring algorithms. It provides four splitting techniques, split stratification, a number of reliability coefficients, and the option to sub-sample data.
Functions for computing split regularized estimators defined in Christidis, Lakshmanan, 
             Smucler and Zamar (2019) <arXiv:1712.03561>. The approach fits linear regression models that
             split the set of covariates into groups. The optimal split of the variables into groups and the 
             regularized estimation of the regression coefficients are performed by minimizing  an objective 
             function that encourages sparsity within each group and diversity among them. 
             The estimated coefficients are then pooled together to form the final fit.
Functions to generate or sample from all possible splits of features or variables
             into a number of specified groups. Also computes the best split selection estimator 
             (for low-dimensional data) as defined in Christidis, Van Aelst and Zamar (2019) 
             <arXiv:1812.05678>.
Allows to produce and use classification trees with soft (probability) splits,
	as described in: Dvořák, J. (2019), <doi:10.1007/s00180-019-00867-1>.
Online data collection tools like Google Forms often export
    multiple-response questions with data concatenated in cells. The
    concat.split (cSplit) family of functions splits such data into separate 
    cells. The package also includes functions to stack groups of columns and 
    to reshape wide data, even when the data are "unbalanced"---something 
    which reshape (from base R) does not handle, and which melt and dcast from 
    reshape2 do not easily handle.
Fast, lightweight toolkit for data splitting. Data
    sets can be partitioned into disjoint groups (e.g. into training,
    validation, and test) or into (repeated) k-folds for subsequent
    cross-validation. Besides basic splits, the package supports
    stratified, grouped as well as blocked splitting. Furthermore,
    cross-validation folds for time series data can be created. See e.g.
    Hastie et al. (2001) <doi:10.1007/978-0-387-84858-7> for the basic
    background on data partitioning and cross-validation.
ML and GM estimation and diagnostic testing of econometric models for spatial panel data.
Contains functions that fit linear mixed-effects models
        for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection. 
        The details of the algorithm can be found in Luoying Yang PhD thesis (Yang and Wu 2020). The algorithm implementation
        is based on the R package 'lmmlasso'. 
        Reference: Yang L, Wu TT (2020). Model-Based Clustering of Longitudinal Data in High-Dimensionality. Unpublished thesis.
Automates common plotting tasks to ease data exploration.
  Makes density plots (potentially overlaid on histograms),
  scatter plots with prediction lines, or bar or line plots with error bars.
  For each type, y, or x and y variables can be plotted at levels of other variables,
  all with minimal specification.
Provides functions for fitting a sparse
        partial least squares (SPLS) regression and classification
        (Chun and Keles (2010) <doi:10.1111/j.1467-9868.2009.00723.x>).
Currently there are many functions in S-PLUS that are
        missing in R. To facilitate the conversion of S-PLUS packages
        to R packages, this package provides some missing S-PLUS
        functionality in R.
A collection of classes and methods for working with
  times and dates. The code was originally available in S-PLUS.
A collection of classes and methods for working with time series.
  The code was originally available in S-PLUS.
Introduction to some novel accurate hybrid methods of geostatistical and machine learning methods for spatial predictive modelling. It contains two commonly used geostatistical methods, two machine learning methods, four hybrid methods and two averaging methods. For each method, two functions are provided. One function is for assessing the predictive errors and accuracy of the method based on cross-validation. The other one is for generating spatial predictions using the method. For details please see: Li, J., Potter, A., Huang, Z., Daniell, J. J. and Heap, A. (2010) <https:www.ga.gov.au/metadata-gateway/metadata/record/gcat_71407>
  Li, J., Heap, A. D., Potter, A., Huang, Z. and Daniell, J. (2011) <doi:10.1016/j.csr.2011.05.015>
  Li, J., Heap, A. D., Potter, A. and Daniell, J. (2011) <doi:10.1016/j.envsoft.2011.07.004>
  Li, J., Potter, A., Huang, Z. and Heap, A. (2012) <https:www.ga.gov.au/metadata-gateway/metadata/record/74030>.
Installs 'SPM12' to the R library directory and has 
    associated functions for 'fMRI' and general imaging utilities, 
    called through 'MATLAB'.
Build custom Europe SpatialPolygonsDataFrame, if you don't know what is
    a SpatialPolygonsDataFrame see SpatialPolygons() in 'sp', by example for mapLayout() in 'antaresViz'. 
    Antares is a powerful software developed by RTE to simulate and study electric power systems 
    (more information about 'Antares' here: <https://antares-simulator.org/>).
A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields.
Implements the method of general semiparametric maximum likelihood estimation for logistic models in case-mother control-mother designs.
Functions for estimating Moran eigenvector-based
    spatial additive mixed models, and other spatial regression models.
    For details see Murakami (2020) <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, Banerjee (2020) <arXiv:2001.09111>, and Finley, Datta, Cook, Morton, Andersen, and Banerjee (2019) <doi:10.1080/10618600.2018.1537924> and Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091>.
A programmatic interface to many species occurrence data sources,
    including Global Biodiversity Information Facility ('GBIF'), 'USGSs'
    Biodiversity Information Serving Our Nation ('BISON'), 'iNaturalist',
    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.
The 'spork' syntax describes
 label formatting concisely, supporting
 mixed nesting of subscripts and superscripts
 to arbitrary depth. It intends to be easy
 to read and write in plain text, and easy
 to convert to equivalent presentations
 in 'plotmath' and 'latex'.  Greek symbols
 and a multiplication symbol are explicitly
 supported. See ?as_spork and ?as_previews.
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.
In spatial data, information of two neighboring units are generally very similar. 
  For spatial sampling, it is therefore more efficient to select samples that are well spread out in space. Often, the interest lies not only in estimating a measure at one point in time, but rather in estimating several points in time to also study the evolution. 
  Three new methods called Orfs (Optimal Rotation with Fixed sample Size), Orsp (Optimal Rotation with Spread sample), and Spot (Spatial and Optimally Temporal Sampling) are implemented in this package. Orfs allows to select temporal samples with fixed size. Orsp selects spatio-temporal samples with random size that are well spread out in space at each point in time. And Spot generates spread sample with fixed sample size at each wave. These methods provide an optimal time rotation of the selected units using the systematic sampling.
Analysis of ChIP-seq and other functional sequencing data [Kharchenko PV (2008) <DOI:10.1038/nbt.1508>].
We propose a novel two-step procedure to combine epidemiological
    data obtained from diverse sources with the aim to quantify risk factors
    affecting the probability that an individual develops certain disease such as
    cancer. See  Hui Huang, Xiaomei Ma, Rasmus Waagepetersen, Theodore R. Holford,
    Rong Wang, Harvey Risch, Lloyd Mueller & Yongtao Guan (2014) A New Estimation Approach
    for Combining Epidemiological Data From Multiple Sources, Journal of the American Statistical
    Association, 109:505, 11-23,  <doi:10.1080/01621459.2013.870904>.
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.
Recently, regularized variable selection has emerged as a powerful tool to identify and dissect gene-environment interactions. Nevertheless, in longitudinal studies with high dimensional genetic factors, regularization methods for G×E interactions have not been systematically developed. In this package, we provide the implementation of sparse group variable selection, based on both the quadratic inference function (QIF) and generalized estimating equation (GEE), to accommodate the bi-level selection for longitudinal G×E studies with high dimensional genomic features. Alternative methods conducting only the group or individual level selection have also been included. The core modules of the package have been developed in C++.
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.
Estimates model parameters of soil hydraulic property functions by inverting measured data. A wide range of hydraulic models, weighting schemes,
    global optimization algorithms, Markov chain Monte Carlo samplers, and extended statistical analyses of results are provided.
	Prediction of soil hydraulic property model parameters and common soil properties using pedotransfer functions is facilitated. 
	Parameter estimation is based on identically and independentally distributed (weighted) model residuals, and simple model selection criteria (Hoege, M., Woehling, T., and Nowak, W. (2018) <doi:10.1002/2017WR021902>) can be calculated.
	The included models are the van Genuchten-Mualem in its unimodal, bimodal and trimodal form, the the Kosugi 2 parametric-Mualem model, and the Fredlund-Xing model.
	All models can be extended to account for non-capillary water storage and conductivity (Weber, T.K.D., Durner, W., Streck, T. and Diamantopoulos, E. (2019) <doi:10.1029/2018WR024584>. 
	The isothermal vapour conductivity (Saito, H., Simunek, J. and Mohanty, B.P. (2006) <doi:10.2136/vzj2006.0007>) is calculated based on 
	volumetric air space and a selection of different tortuosity models: 
	(Grable, A.R., Siemer, E.G. (1968) <doi:10.2136/sssaj1968.03615995003200020011x>, Lai, S.H., Tiedje J.M., Erickson, E. (1976) <doi:10.2136/sssaj1976.03615995004000010006x>,
	Moldrup, P., Olesen, T., Rolston, D.E., and Yamaguchi, T. (1997) <doi:10.1097/00010694-199709000-00004>, Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2004) <doi:10.2136/sssaj2004.7500>, 
	Moldrup, P., Olesen, T., Yoshikawa, S., Komatsu, T., and Rolston, D.E. (2005) <doi:10.1097/01.ss.0000196768.44165.1f>, Millington, R.J., Quirk, J.P. (1961) <doi:10.1039/TF9615701200>,
	Penman, H.L. (1940) <doi:10.1017/S0021859600048164>, and Xu, X, Nieber, J.L. Gupta, S.C. (1992) <doi:10.2136/sssaj1992.03615995005600060014x>).
Program uses method of polynomial of variable degrees to interpolate gridded data preserving monotonicity and/or convexity or none. Method is implemented for univariate and bivariate cases. If values of derivatives are provided, spline will fix them,if not program will estimate them numerically. Package written purely in R.
Provides basic functionality for labeling 
        iso- & anisotropic percolation clusters on 2D & 3D square lattices 
        with various lattice sizes, occupation probabilities, von Neumann & 
        Moore (1,d)-neighborhoods, and random variables weighting 
        the percolation lattice sites.
A collection of functions to test and estimate Seemingly 
    Unrelated Regression (usually called SUR) models, with spatial structure, by maximum 
    likelihood and three-stage least squares. The package estimates the 
    most common spatial specifications, that is, SUR with Spatial Lag of 
    X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), 
    SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), 
    SUR with Spatial Durbin Error Model (called SUR-SDEM), 
    SUR with Spatial Autoregressive terms and Spatial Autoregressive 
    Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X 
    regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM).
    The methodology of these models can be found in next references
    Mur, J., Lopez, F., and Herrera, M. (2010) <doi:10.1080/17421772.2010.516443> 
    Lopez, F.A., Mur, J., and Angulo, A. (2014) <doi:10.1007/s00168-014-0624-2>.
A set of reliable routines to ease semiparametric survival regression modeling based on Bernstein polynomials. 'spsurv' includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) <arXiv:2003.10548>.
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>.
A set of functions that can be used to spatially thin
    species occurrence data. The resulting thinned data can be used in ecological
    modeling, such as ecological niche modeling.
Fits, spatially predicts and temporally forecasts large amounts of space-time data using  [1] Bayesian Gaussian Process (GP) Models, [2] Bayesian Auto-Regressive (AR) Models, and [3] Bayesian Gaussian Predictive Processes (GPP) based AR Models for spatio-temporal big-n problems. Bakar and Sahu (2015) <doi:10.18637/jss.v063.i15>.
Implements semiparametric transformation model two-phase estimation using calibration weights. The method in Fong and Gilbert (2015) Calibration weighted estimation of semiparametric transformation models for two-phase sampling. Statistics in Medicine <DOI:10.1002/sim.6439>.
Performs predictions of totals and weighted sums, or finite population block kriging, on spatial data using the methods in Ver Hoef (2008) <doi:10.1007/s10651-007-0035-y>. The primary outputs are an estimate of the total, mean, or weighted sum in the region, an estimated prediction variance, and a plot of the predicted and observed values. This is useful primarily to users with ecological data that are counts or densities measured on some sites in a finite area of interest. Spatial prediction for the total count or average density in the entire region can then be done using the functions in this package.
Uncertainty propagation analysis in spatial environmental  modelling following methodology
         described in Heuvelink et al. (2007) <doi:10.1080/13658810601063951> 
         and Brown and Heuvelink (2007) <doi:10.1016/j.cageo.2006.06.015>. The package provides functions
         for examining the uncertainty propagation starting from input data and model parameters,
         via the environmental model onto model outputs. The functions include uncertainty model specification,
         stochastic simulation and propagation of uncertainty using Monte Carlo (MC) techniques.
         Uncertain variables are described by probability distributions. Both numerical and categorical data types are handled.
         Spatial auto-correlation within an attribute and cross-correlation between attributes is accommodated for.
         The MC realizations may be used as input to the environmental models called from R, or externally.
Provides functions and datasets from Jones, O.D., R. Maillardet, and A.P. Robinson.  2014.  An Introduction to Scientific Programming and Simulation, Using R. 2nd Ed. Chapman And Hall/CRC.
A set of tools for the peak filtering of mass spectrometry
  imaging data (MSI or IMS) based on spatial distribution of signal. Given a 
  region-of-interest (ROI), representing the spatial region where the informative
  signal is expected to be localized, a series of filters determine which peak
  signals are characterized by an implausible spatial distribution. The filters
  reduce the dataset dimensionality and increase its information vs noise ratio,
  improving the quality of the unsupervised analysis results, reducing data
  dimensionality and simplifying the chemical interpretation.
Data on the Spy vs. Spy comic strip of Mad magazine, created and
   written by Antonio Prohias.
Methodology: Remove one observation. Training the rest of data that are sampled without replacement and given this observation's input, predict the response back. Replicate this N times and for each response, take a sample from these replicates with replacement. Average each responses of sample and again replicate this step N time for each observation. Approximate these N new responses by using bootstrap method and generate another N responses y'. Training these y' and predict to have N responses of each testing observation. The average of N is the final prediction. Each observation will do the same.
Sparse Quadratic Discriminant Analysis (SQDA) can be performed. In SQDA, the covariance matrix are assumed to be block-diagonal.And, for each block, sparsity assumption is imposed on the covariance matrix. It is useful in high-dimensional setting.
The sqldf() function is typically passed a single argument which 
	is an SQL select statement where the table names are ordinary R data 
	frame names.  sqldf() transparently sets up a database, imports the 
	data frames into that database, performs the SQL select or other
	statement and returns the result using a heuristic to determine which 
	class to assign to each column of the returned data frame.  The sqldf() 
	or read.csv.sql() functions can also be used to read filtered files 
	into R even if the original files are larger than R itself can handle.
	'RSQLite', 'RH2', 'RMySQL' and 'RPostgreSQL' backends are supported.
sqliter helps users, mainly data munging practioneers, to organize
    their sql calls in a clean structure. It simplifies the process of
    extracting and transforming data into useful formats.
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].
Solving procedures for quadratic programming with optional equality and inequality constraints, which can be used for by sequential quadratic programming (SQP). Similar to Newton-Raphson methods in the unconstrained case, sequential quadratic programming solves non-linear constrained optimization problems by iteratively solving linear approximations of the optimality conditions of such a problem (cf. Powell (1978) <doi:10.1007/BFb0067703>; Nocedal and Wright (1999, ISBN: 978-0-387-98793-4)). The Hessian matrix in this strategy is commonly approximated by the BFGS method in its damped modification proposed by Powell (1978) <doi:10.1007/BFb0067703>. All methods are implemented in C++ as header-only library, such that it is easy to use in other packages.
Facilitates interaction with ODBC data sources, by remembering and hiding communication settings and supporting parameterised multi-statement SQL with 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"). Refer to the J Stat Software article: <doi:10.18637/jss.v092.i07>.
Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps.  Lower-level functions  map numeric values to colors, display a matrix as an array of colors, and draw color keys.  Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.
Scrape SquashInfo <http://www.squashinfo.com/> for data on the Professional Squash Association World Tour and other squash events. 'squashinformr' functions scrape, parse, and clean data associated with players, tournaments, and rankings.
A simulation-based tool made to help researchers to become familiar with
    multilevel variations, and to build up sampling designs for their study. 
    This tool has two main objectives: First, it provides an educational tool useful for students, 
    teachers and researchers who want to learn to use mixed-effects models. 
    Users can experience how the mixed-effects model framework can be used to understand 
    distinct biological phenomena by interactively exploring simulated multilevel data. 
    Second, it offers research opportunities to those who are already familiar with 
    mixed-effects models, as it enables the generation of data sets that users may download 
    and use for a range of simulation-based statistical analyses such as power 
    and sensitivity analysis of multilevel and multivariate data [Allegue, H., Araya-Ajoy, Y.G., Dingemanse, 
    N.J., Dochtermann N.A., Garamszegi, L.Z., Nakagawa, S., Reale, D., Schielzeth, H. and Westneat, D.F. (2016) 
    <doi: 10.1111/2041-210X.12659>].
Artificial selection through selective breeding is an efficient way to induce changes in traits of interest in experimental populations. This package (sra) provides a set of tools to analyse artificial-selection response datasets. The data typically feature for several generations the average value of a trait in a population, the variance of the trait, the population size and the average value of the parents that were chosen to breed. Sra implements two families of models aiming at describing the dynamics of the genetic architecture of the trait during the selection response. The first family relies on purely descriptive (phenomenological) models, based on an autoregressive framework. The second family provides different mechanistic models, accounting e.g. for inbreeding, mutations, genetic and environmental canalization, or epistasis. The parameters underlying the dynamics of the time series are estimated by maximum likelihood. The sra package thus provides (i) a wrapper for the R functions mle() and optim() aiming at fitting in a convenient way a predetermined set of models, and (ii) some functions to plot and analyze the output of the models.
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.
Analysis of species count data in ecology often requires normalization to an identical sample size. Rarefying (random subsampling without replacement), which is a popular method for normalization, has been widely criticized for its poor reproducibility and potential distortion of the community structure. In the context of microbiome count data, researchers explicitly advised against the use of rarefying. An alternative to rarefying is scaling with ranked subsampling (SRS). SRS consists of two steps. In the first step, the total counts for all OTUs (operational taxonomic units) or species in each sample are divided by a scaling factor chosen in such a way that the sum of the scaled counts Cscaled equals Cmin. In the second step, the non-integer Cscaled values are converted into integers by an algorithm that we dub ranked subsampling. The Cscaled value for each OTU or species is split into the integer part Cint  (Cint = floor(Cscaled)) and the fractional part Cfrac (Cfrac = Cscaled - Cints). Since the sum of Cint is smaller or equal to Cmin, additional  delta C = Cmin - the sum of Cint counts have to be added to the library to reach the total count of Cmin. This is achieved as follows. OTUs are ranked in the descending order of their Cfrac values. Beginning with the OTU of the highest rank, single count per OTU is added to the normalized library until the total number of added counts reaches delta C and the sum of all counts in the normalized library equals Cmin. When the lowest Cfrag involved in picking delta C counts is shared by several OTUs, the OTUs used for adding a single count to the library are selected in the order of their Cint values. This selection minimizes the effect of normalization on the relative frequencies of OTUs. OTUs with identical Cfrag as well as Cint are sampled randomly without replacement. See Beule & Karlovsky (2020) <doi:10.7717/peerj.9593> for details.
Read 'SubRip' <https://sourceforge.net/projects/subrip/> 
    subtitle files as data frames for easy text analysis or manipulation. 
    Easily shift numeric timings and export subtitles back into valid 'SubRip' 
    timestamp format to sync subtitles and audio.
Srt file is a common subtitle format for videos, it contains subtitle and when the subtitle showed.
    This package is for align time of srt file, and also change color, style and position of subtitle in videos,
	the srt file will be read as a vector into R, and can be write into srt file after modified using this package.
Use piping, verbs like 'group_by' and 'summarize', and other
    'dplyr' inspired syntactic style when calculating summary statistics on survey
    data using functions from the 'survey' package.
Develops a framework for fisheries stock assessment simulation
    testing with Stock Synthesis (SS) as described in Anderson et al.
    (2014) <doi:10.1371/journal.pone.0092725>.
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 different species as described by
  Posthuma et al.(2001) <isbn:9781566705783>.
  The ssdtools package uses Maximum Likelihood to fit distributions 
  such as the log-normal, gamma, log-logistic, 
  log-Gumbel, Gompertz and Weibull.
  The user can provide custom distributions.
  Multiple distributions can be averaged using Information Criteria.
  Confidence intervals on hazard concentrations and proportions are produced by 
  parametric bootstrapping.
Provides functions to evaluate user-defined power functions for a parameter range, and draws a sensitivity plot. It also provides a resampling procedure for semi-parametric sample size estimation and methods for adding information to a Sweave report.
Computes the optimal sample size for various 2-group designs (e.g., when comparing the means of two groups assuming equal variances, unequal variances, or comparing proportions) when the aim is to maximize the rewards over the full decision procedure of a) running a trial (with the computed sample size), and b) subsequently administering the winning treatment to the remaining N-n units in the population. Sample sizes and expected rewards for standard t- and z- tests are also provided.
Spatial Stochastic Frontier Analysis (SSFA) is an original method for controlling the spatial heterogeneity in Stochastic Frontier Analysis (SFA) models, for cross-sectional data, by splitting the inefficiency term into three terms: the first one related to spatial peculiarities of the territory in which each single unit operates, the second one related to the specific production features and the third one representing the error term.
Fits complex parametric models using the method proposed by Cox and Kartsonaki (2012) without likelihoods.
Bayesian estimation for undirected graphical models using spike-and-slab priors. The package handles continuous, discrete, and mixed data.
Connect to a remote server over SSH to transfer files via SCP, 
    setup a secure tunnel, or run a command or script on the host while 
    streaming stdout and stderr directly to the client.
This package provides utility functions for system command
    execution, both locally and remotely using ssh/scp. The command
    output is captured and provided to the caller. This functionality is
    intended to streamline calling shell commands from R, retrieving and
    using their output, while instrumenting the calls with appropriate
    error handling. NOTE: this first version is limited to unix with local
    and remote systems running bash as the default shell.
Processes amino acid alignments produced by the 'IPD-IMGT/HLA (Immuno Polymorphism-ImMunoGeneTics/Human Leukocyte Antigen) Database' to identify user-defined amino acid residue motifs shared across HLA alleles, and calculates the frequencies of those motifs based on HLA allele frequency data. 'SSHAARP' (Searching Shared HLA Amino Acid Residue Prevalence) uses 'Generic Mapping Tools (GMT)' software and the 'GMT' R package to generate global frequency heat maps that illustrate the distribution of each user-defined map around the globe. 'SSHAARP' analyzes the allele frequency data described by Solberg et al. (2008) <doi:10.1016/j.humimm.2008.05.001>, a global set of 497 population samples from 185 published datasets, representing 66,800 individuals total.
This package contains a set of functions that calculates 
 appropriate sample sizes for one-sample t-tests, two-sample t-tests, 
 and F-tests for microarray experiments based on desired power while 
 controlling for false discovery rates. For all tests, the standard
 deviations (variances) among genes can be assumed fixed or random. 
 This is also true for effect sizes among genes in one-sample and two
 sample experiments. Functions also output a chart of power versus sample
 size, a table of power at different sample sizes, and a table of critical
 test values at different sample sizes.
We propose a procedure for sample size calculation while
    controlling false discovery rate for RNA-seq experimental design. Our
    procedure depends on the Voom method proposed for RNA-seq data analysis
    by Law et al. (2014) <DOI:10.1186/gb-2014-15-2-r29> and the sample size 
    calculation method proposed for microarray experiments by Liu and Hwang 
    (2007) <DOI:10.1093/bioinformatics/btl664>. We develop a set of functions
    that calculates appropriate sample sizes for two-sample t-test for RNA-seq
    experiments with fixed or varied set of parameters. The outputs also contain a
    plot of power versus sample size, a table of power at different sample sizes,
    and a table of critical test values at different sample sizes. 
    To install this package, please use 
    'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'. 
    For R version 3.5 or greater, please use  
    'if(!requireNamespace("BiocManager", quietly = TRUE)){install.packages("BiocManager")}; BiocManager::install("ssizeRNA")'.
Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO of Rockova and George (2018) <doi:10.1080/01621459.2016.1260469>.
Providing a collection of techniques for semi-supervised 
    classification, regression and clustering. In semi-supervised problem, both labeled and unlabeled
    data are used to train a classifier. The package includes a collection of 
    semi-supervised learning techniques: self-training, co-training, democratic, 
    decision tree, random forest, 'S3VM' ... etc, with a fairly intuitive interface 
    that is easy to use.
Creates an S4 class "SSM" and defines functions for fitting smooth
    supersaturated models, a polynomial model with spline-like behaviour.
    Functions are defined for the computation of Sobol indices for sensitivity
    analysis and plotting the main effects using FANOVA methods. It also
    implements the estimation of the SSM metamodel error using a GP model with
    a variety of defined correlation functions.
Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.
Estimates previously compiled state-space modeling for mouse-tracking experiments using the 'rstan' package, which provides the R interface to the Stan C++ library for Bayesian estimation.
Package provides a set of tools for robust estimation and inference for models with sample selectivity.
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.
Simulation-based sampling protocol (SSP) is an R package design to estimate sampling effort in studies of
    ecological communities based on the definition of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon, 2015) <doi:10.1111/ele.12385> and simulation
    of ecological data. The theoretical background is described in Guerra-Castro et al. (2020) <doi:10.1101/2020.03.19.996991>.
R package for computing the spherical smoothing splines
Estimate the size of a networked population based on
    respondent-driven sampling data. The package is part of the "RDS Analyst"
    suite of packages for the analysis of respondent-driven sampling data.
    See Handcock, Gile and Mar (2014) <doi:10.1214/14-EJS923> and
    Handcock, Gile and Mar (2015) <doi:10.1111/biom.12255>.
An implementation of semi-supervised regression methods including self-learning and co-training by committee based on Hady, M. F. A., Schwenker, F., & Palm, G. (2009) <doi:10.1007/978-3-642-04274-4_13>. Users can define which set of regressors to use as base models from the 'caret' package, other packages, or custom functions.
Takeya Semantic Structure Analysis (TSSA) and Sakai Sequential Relation Analysis (SSRA)
             for polytomous items for examining whether each pair of items has a sequential or equal
             relation. Package includes functions for generating a sequential relation table and a
             treegram to visualize sequential or equal relations between pairs of items.
A set of functions for two-dimensional sociometric status
    determination with rating scales. For each person assessed, SSrat computes
    probability distributions of the total scores for `Sympathy' (S),
    `Antipathy' (A), social `Preference' (P) and social `Impact' (I), and
    applies a set of criteria for sociometric status categorization.
Provides the necessary sample size for a longitudinal study with binary outcome in order to attain a pre-specified power while strictly maintaining the Type I error rate. 
 Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ, Bhaumik D (2014) <doi:10.1002/sim.6203>.
Calculates the power and sample size based on the difference in Restricted Mean Survival Time.
Implement the algorithm provided in scan for estimating the 
  transmission route on railway network using passenger volume. It is a 
  generalization of the scan statistic approach for railway network to identify
  the hot railway route for transmitting infectious diseases.
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) <DOI:10.2202/1544-6115.1252> and 
   a shrinkage estimate of the "correlation-adjusted t-score" (CAT score) described 
   in Zuber and Strimmer (2009) <DOI:10.1093/bioinformatics/btp460>.  
   It also offers a convenient interface  to a number of other regularized 
   t-statistics commonly employed in high-dimensional case-control studies.
Functionalities to perform Stability Analysis of Genotype by Environment Interaction (GEI) to identify superior and stable genotypes under diverse environments. It performs Eberhart & Russel's ANOVA (1966) (<doi:10.2135/cropsci1966.0011183X000600010011x>), Finlay and Wilkinson (1963) Joint Linear Regression (<doi:10.1071/AR9630742>), Wricke (1962, 1964) Ecovalence, Shukla's stability variance parameter (1972) (<doi:10.1038/hdy.1972.87>)  and Kang's (1991) (<doi:10.2134/agronj1991.00021962008300010037x>) simultaneous selection for high yielding and stable parameter.
Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2019) <arXiv:1911.01850>. The procedure uses averaging to estimate a regression of a set of predictors X on a response variable Y by enforcing stability with respect to a given environment variable. The resulting regression leads to a variable selection procedure which allows to distinguish between stable and unstable predictors. The package further implements a visualization technique which illustrates the trade-off between stability and predictiveness of individual predictors.
Density, distribution, quantile and hazard functions of a
    stable variate; generalized regression models for the parameters
    of a stable distribution. See the README for how to make equivalent calls
    to those of 'stabledist'. 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.
Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC) samplers to sample from a posterior distribution. This package determines whether the MCMC sample is large enough   to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected  and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and  Knudson (2018) <arXiv:1812:09384>.
Graphical and computational methods that can be used to assess the 
  stability of results from supervised statistical learning.
An exploratory and heuristic approach for specification search in
    Structural Equation Modeling. The basic idea is to subsample the original data
    and then search for optimal models on each subset. Optimality is defined through
    two objectives: model fit and parsimony. As these objectives are conflicting,
    we apply a multi-objective optimization methods, specifically NSGA-II, to obtain
    optimal models for the whole range of model complexities. From these optimal
    models, we consider only the relevant model specifications (structures), i.e.,
    those that are both stable (occur frequently) and parsimonious and use those to
    infer a causal model.
An implementation of many measures for the
    assessment of the stability of feature selection. Both simple measures
    and measures which take into account the similarities between features
    are available, see Bommert 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.
Helper functions collected from StackOverflow.com, a
    question and answer site for professional and enthusiast programmers.
Model stacking is an ensemble technique
    that involves training a model to combine the outputs of many 
    diverse statistical models, and has been shown to improve 
    predictive performance in a variety of settings. 'stacks' 
    implements a grammar for 'tidymodels'-aligned model stacking.
S4 class wrappers for the 'ODBC' connection, also provides some 
    utilities to paste small datasets to clipboard, rename columns. It is used by the package 'stacomiR' for
    connections to the database. Development versions of 'stacomiR' are available in R-forge.
Creates and fits staged event tree probability models, 
             which are probabilistic graphical models capable of representing 
             asymmetric conditional independence statements 
             for categorical variables. 
             Includes functions to create, plot and fit staged 
             event trees from data, as well as many efficient structure 
             learning algorithms.
             References:
             Collazo R. A., Görgen C. and Smith J. Q. 
             (2018, ISBN:9781498729604).
             Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. (2018) 
             <arXiv:1705.09457>.
             Thwaites P. A., Smith, J. Q. (2017) <arXiv:1510.00186>.
             Barclay L. M., Hutton J. L. and Smith J. Q. (2013) 
             <doi:10.1016/j.ijar.2013.05.006>.
             Smith J. Q. and Anderson P. E. (2008) 
             <doi:10.1016/j.artint.2007.05.004>.
Provides facilities to implement and run population models of
    stage-structured species...
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 little R code or documentation. The main reference is the vignette. There is a shared object containing part of the 'CVODES' library, but its functionality is not accessible from R. 'StanHeaders' is primarily useful for developers who want to utilize the 'LinkingTo' directive of their package's DESCRIPTION file to build on the Stan library without incurring unnecessary dependencies. The Stan project develops a probabilistic programming language that implements full or approximate Bayesian statistical inference via Markov Chain Monte Carlo or 'variational' methods and implements (optionally penalized) maximum likelihood estimation via optimization. The Stan library includes an advanced automatic differentiation scheme, 'templated' statistical and linear algebra functions that can handle the automatically 'differentiable' scalar types (and doubles, 'ints', etc.), and a parser for the Stan language. The 'rstan' package provides user-facing R functions to parse, compile, test, estimate, and analyze Stan models.
An implementation of Simultaneous Truth and 
    Performance Level Estimation (STAPLE) <doi:10.1109/TMI.2004.828354>.  This
    method is used when there are multiple raters for an object, typically an
    image, and this method fuses these ratings into one rating.  It uses an
    expectation-maximization method to estimate this rating and the individual
    specificity/sensitivity for each rater.
Provides function to manipulate PDF files: 
    fill out PDF forms;
    merge multiple PDF files into one; 
    remove selected pages from a file;
    rename multiple files in a directory;
    rotate entire pdf document; 
    rotate selected pages of a pdf file;
    Select pages from a file;
    splits single input PDF document into individual pages;
    splits single input PDF document into parts from given points.
    'staplr' requires Java 8 installed on your system.
Functions to analyze neuronal spike trains from a single
        neuron or from several neurons recorded simultaneously.
This is a user-friendly way to run a parallel factor (PARAFAC) analysis (Harshman, 1971) <doi:10.1121/1.1977523> on excitation emission matrix (EEM) data from dissolved organic matter (DOM) samples (Murphy et al., 2013) <doi:10.1039/c3ay41160e>. The analysis includes profound methods for model validation. Some additional functions allow the calculation of absorbance slope parameters and create beautiful plots.
Produces LaTeX code, HTML/CSS code and ASCII text for well-formatted tables that hold 
    regression analysis results from several models side-by-side, as well as summary
    statistics.
Estimates the coefficients of the two-time centered autologistic regression model based on Gegout-Petit A., Guerin-Dubrana L., Li S. "A new centered spatio-temporal autologistic regression model. Application to local spread of plant diseases." 2019. <arXiv:1811.06782>, using a grid of binary variables to estimate the spread of a disease on the grid over the years.
Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.
Implements stacked elastic net regression (Rauschenberger 2020, <doi:10.1093/bioinformatics/btaa535>). The elastic net generalises ridge and lasso regularisation (Zou 2005, <doi:10.1111/j.1467-9868.2005.00503.x>). Instead of fixing or tuning the mixing parameter alpha, we combine multiple alpha by stacked generalisation (Wolpert 1992 <doi:10.1016/S0893-6080(05)80023-1>).
Reading, manipulating, writing and plotting
    spatiotemporal arrays (raster and vector data cubes) in 'R', using 'GDAL'
    bindings provided by 'sf', and 'NetCDF' bindings by 'ncmeta' and 'RNetCDF'.
Data in multidimensional systems is obtained from 
    operational systems and is transformed to adapt it to the new 
    structure. Frequently, the operations to be performed aim to 
    transform a flat table into a star schema. Transformations can 
    be carried out using professional ETL (extract, transform and 
    load) tools or tools intended for data transformation for end 
    users. With the tools mentioned, this transformation can be 
    carried out, but it requires a lot of work. The main objective 
    this package is to define transformations that allow obtaining 
    star schemas from flat tables easily. In addition, it includes 
    basic data cleaning, dimension enrichment, incremental data 
    refresh and query operations, adapted to this context.
Miscellaneous functions for working with 'stars' objects, mainly single-band rasters. Currently includes functions for: (1) focal filtering, (2) detrending of Digital Elevation Models, (3) calculating flow length, (4) calculating the Convergence Index, (5) calculating topographic aspect and topographic slope.
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
    multi- dimensional data sets (collections of files) stored in local and/or
    remote file systems or servers, using multicore capabilities where possible.
    The tool provides an interface to perceive a collection of data sets as a single
    large multidimensional data array, and enables the user to request for automatic
    retrieval, processing and arrangement of subsets of the large array. Wrapper
    functions to add support for custom file formats can be plugged in/out, making
    the tool suitable for any research field where large multidimensional data sets
    are involved.
Contains functions for estimating the STARTS model of
    Kenny and Zautra (1995, 2001) <DOI:10.1037/0022-006X.63.1.52>,
    <DOI:10.1037/10409-008>. Penalized maximum likelihood
    estimation and Markov Chain Monte Carlo estimation are
    also provided, see Luedtke, Robitzsch and Wagner (2018) 
    <DOI:10.1037/met0000155>.
Adds support for R startup configuration via '.Renviron.d' and '.Rprofile.d' directories in addition to '.Renviron' and '.Rprofile' files.  This makes it possible to keep private / secret environment variables separate from other environment variables.  It also makes it easier to share specific startup settings by simply copying a file to a directory.
Provides utilities to create or suppress start-up messages.
Allows the user to estimate a vector logistic smooth transition autoregressive model via maximum log-likelihood or nonlinear least squares. It further permits to test for linearity in the multivariate framework against a vector logistic smooth transition autoregressive model with a single transition variable. The estimation method is discussed in Terasvirta and Yang (2014, <doi:10.1108/S0731-9053(2013)0000031008>). Also, realized covariances can be constructed from stock market prices or returns, as explained in Andersen et al. (2001, <doi:10.1016/S0304-405X(01)00055-1>).
Performance analysis workflow that combines the power of the R
    language (and the tidyverse realm) and many auxiliary tools to
    provide a consistent, flexible, extensible, fast, and versatile
    framework for the performance analysis of task-based applications
    that run on top of the StarPU runtime (with its MPI (Message
    Passing Interface) layer for multi-node support).  Its goal is to
    provide a fruitful prototypical environment to conduct performance
    analysis hypothesis-checking for task-based applications that run
    on heterogeneous (multi-GPU, multi-core) multi-node HPC
    (High-performance computing) platforms.
Provides data about the 'Star Wars' movie franchise
    in a set of relational tables or as a complete 'DuckDB' database. All
    data was collected from the open source 'Star Wars' API
    <https://swapi.dev/>.
An interactive document on  the topic of basic statistical analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/StatisticsPrimer/>.
Highest Density Regions are the smallest set in the support of a
    probability distribution with the specified coverage probability. 'HDRs'
    may contain disjoint intervals, but can be calculated efficiently using
    iterative methods. One can similarly construct optimal (i.e., shortest)
    confidence intervals for some basic inferential problems, including for
    population means, variances, or proportion parameters.
Datasets for the textbook Stat2: Modeling with Regression and ANOVA (second edition). 
    The package also includes data for the first edition, Stat2: Building Models for a World of Data
    and a few functions for plotting diagnostics.
A set of tools inspired by 'Stata' to explore data.frames ('summarize',
    'tabulate', 'xtile', 'pctile', 'binscatter', elapsed quarters/month, lead/lag).
An easy connection with R to Statistics Canada's Web Data Service. Open economic data (formerly known as CANSIM tables, now identified by Product IDs (PID)) are accessible as a data frame, directly in the user's R environment.
    Warin, Le Duc (2019) <doi:10.6084/m9.figshare.10544735>.
A front end for the statistical analyses involved in the tier II endocrine 
	disruptor screening program. The analyses available to this package are: 
	Rao-Scott adjusted Cochran-Armitage test for trend By Slices (RSCABS), 
	a Standard Cochran-Armitage test for trend By Slices (SCABS), 
	mixed effects Cox proportional model, Jonckheere-Terpstra step down trend test 
	Dunn test, one way ANOVA, weighted ANOVA, mixed effects ANOVA, repeated 
	measures ANOVA, and Dunnett test.
Extract statistics from articles and recompute p values.
An implementation of local and global statistical complexity measures (aka Information Theory Quantifiers, ITQ) for time series analysis based on ordinal statistics (Bandt and Pompe (2002) <DOI:10.1103/PhysRevLett.88.174102>). Several distance measures that operate on ordinal pattern distributions, auxiliary functions for ordinal pattern analysis, and generating functions for stochastic and deterministic-chaotic processes for ITQ testing are provided.
Several tools are provided for the statistical analysis of environmental data.
Support for reading and writing files in StatDataML---an XML-based data exchange format.
The 'cartogram' heatmaps generated by the included methods 
    are an alternative to choropleth maps for the United States
    and are based on work by the Washington Post graphics department in their report
    on "The states most threatened by trade" 
    (<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>).
    "State bins" preserve as much of the geographic placement of the states as 
    possible but have the look and feel of a traditional heatmap. Functions are 
    provided that allow for use of a binned, discrete scale, a continuous scale 
    or manually specified colors depending on what is needed for the underlying data.
Create panel data consisting of independent states from 1816 to
    the present. The package includes the Gleditsch & Ward (G&W) and Correlates
    of War (COW) lists of independent states, as well as helper functions for 
    working with state panel data and standardizing other data sources to 
    create country-year/month/etc. data.
A tool that makes estimating models in state space form 
    a breeze. See "Time Series Analysis by State Space Methods" by 
    Durbin and Koopman (2012, ISBN: 978-0-19-964117-8) for details 
    about the algorithms implemented.
Fast single trait Genome Wide Association Studies (GWAS) following 
    the method described in Kang et al. (2010), <doi:10.1038/ng.548>.        
    One of a series of statistical genetic packages for streamlining the 
    analysis of typical plant breeding experiments developed by Biometris.
Analysis of multi environment data of plant breeding experiments
    following the analyses described in Malosetti, Ribaut, 
    and van Eeuwijk (2013), <doi:10.3389/fphys.2013.00044>.
    One of a series of statistical genetic packages for streamlining the analysis of 
    typical plant breeding experiments developed by Biometris.
    Some functions have been created to be used in conjunction with the R 
    package 'asreml' for the 'ASReml' software, which can be obtained upon 
    purchase from 'VSN' international (<http://www.vsni.co.uk/software/asreml-r>).
Phenotypic analysis of field trials using mixed models with and 
    without spatial components. One of a series of statistical genetic packages 
    for streamlining the analysis of typical plant breeding experiments developed
    by Biometris.    
    Some functions have been created to be used in conjunction with the R 
    package 'asreml' for the 'ASReml' software, which can be obtained upon 
    purchase from 'VSN' international (<http://www.vsni.co.uk/software/asreml-r>).
Contains statistical methods to analyze graphs, such as graph 
      parameter estimation, model selection based on the GIC (Graph Information 
      Criterion), statistical tests to discriminate two or more populations of 
      graphs (ANOGVA - Analysis of Graph Variability), correlation between 
      graphs, and clustering of graphs. 
      References: Takahashi et al. (2012) <doi:10.1371/journal.pone.0049949>, 
      Futija et al. (2017) <doi:10.3389/fnins.2017.00066>, 
      Fujita et al. (2017) <doi:10.1016/j.csda.2016.11.016>, 
      Tang et al. (2017) <doi:10.3150/15-BEJ789>, 
      Tang et al. (2017) <doi:10.1080/10618600.2016.1193505>, 
      Ghoshdastidar et al. (2017) <arXiv:1705.06168>,  
      Ghoshdastidar et al. (2017) <arXiv:1707.00833>, 
      Cerqueira et al. (2017) <doi:10.1109/TNSE.2017.2674026>, 
      Fraiman and Fraiman (2018) <doi:10.1038/s41598-018-23152-5>, 
      Fujita et al. (2019) <doi:10.1093/comnet/cnz028>.
Acquire hourly meteorological data from stations located all over
    the world. There is a wealth of data available, with historic weather data
    accessible from nearly 30,000 stations. The available data is automatically
    downloaded from a data repository and processed into a 'tibble' for the
    exact range of years requested. A relative humidity approximation is
    provided using the 'August-Roche-Magnus' formula, which was adapted from
    Alduchov and Eskridge (1996) <doi:10.1175%2F1520-0450%281996%29035%3C0601%3AIMFAOS%3E2.0.CO%3B2>.
Templates, guides, and scripts for
    writing documents in 'LaTeX' and 'R markdown' to produce
    guides, slides, and reports. Special care is taken to
    illustrate use of templates and customization opportunities.
    Challenges and opportunities of 'HTML' output from 'R markdown'
    receive special attention. Includes several vignettes
    to assist new users of literate programming.
A collection of miscellaneous statistical functions for 
    probability distributions: 'dbern()', 'pbern()', 'qbern()', 'rbern()' for 
    the Bernoulli distribution, and 'distr2name()', 'name2distr()' for 
    distribution names; 
    probability density estimation: 'densityfun()'; 
    most frequent value estimation: 'mfv()', 'mfv1()'; 
    other statistical measures of location: 'cv()' (coefficient of variation),
    'midhinge()', 'midrange()', 'trimean()'; 
    construction of histograms: 'histo()', 'find_breaks()'; 
    calculation of the Hellinger distance: 'hellinger()'; 
    use of classical kernels: 'kernelfun()', 'kernel_properties()'; 
    univariate piecewise-constant regression: 'picor()'.
Integration of two data sources referred to the same target population which share a number of variables. Some functions can also be used to impute missing values in data sets through hot deck imputation methods. Methods to perform statistical matching when dealing  with data from complex sample surveys are available too.
Offers useful functions to perform day-to-day data manipulation 
    operations, data quality checks and post modelling statistical checks.
    One can effortlessly change class of a number of variables to factor, 
    remove duplicate observations from the data, create deciles of a 
    variable, perform data quality checks for continuous (integer or numeric), 
    categorical (factor) and date variables, and compute goodness of fit 
    measures such as auc for statistical models. The functions are consistent 
    for objects of class 'data.frame' and 'data.table', which is an enhanced 
    'data.frame' implemented in the package 'data.table'.
A collection of algorithms and functions to aid statistical modeling. Includes limiting dilution analysis (aka ELDA), growth curve comparisons, mixed linear models, heteroscedastic regression, inverse-Gaussian probability calculations, Gauss quadrature and a secure convergence algorithm for nonlinear models. Also includes advanced generalized linear model functions including Tweedie and Digamma distributional families and a secure convergence algorithm.
Statnet is a collection of packages for statistical network analysis that are 
  designed to work together because they share common data representations and 'API' 
  design.  They provide an integrated set of tools for the representation, 
  visualization, analysis, and simulation of many different forms of network data.  
  This package is designed to make it easy to install and load the 
  key 'statnet' packages in a single step.  Learn more about 'statnet' 
  at <http://www.statnet.org>.  Tutorials for many packages can be found 
  at <https://github.com/statnet/Workshops/wiki>.  For an introduction to functions in this package, 
  type help(package='statnet').
Non-statistical utilities used by the software developed by the Statnet Project. They may also be of use to others.
A graphical user interface for network modeling with the 'statnet'
    software <https://github.com/statnet>.
Get programmatic access to data from the Czech public
    budgeting and accounting database, Státní pokladna
    <https://monitor.statnipokladna.cz/>.
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 Transport 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.
    It can additionally return dataframes with these results, which also
    make these functions a more pipe-friendly way to do statistical
    analysis. 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, and meta-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.
Data and functions to support Bayesian and frequentist inference and decision making 
            for the Coursera Specialization "Statistics with R".
            See <https://github.com/StatsWithR/statsr> for more information.
Download data from 'StatsWales' into R. Removes the need for the user to
    write their own loops when parsing data from the 'StatsWales' API. Provides functions
    for datasets (<http://open.statswales.gov.wales/en-gb/dataset>) and metadata
    (<http://open.statswales.gov.wales/en-gb/discover/metadata>) endpoints.
Visualization functions in the applications of translational medicine (TM) and biomarker (BM) development to compare groups by statistically visualizing data and/or results of analyses, such as visualizing data by displaying in one figure different groups' histograms, boxplots, densities, scatter plots, error-bar plots, or trajectory plots, by displaying scatter plots of top principal components or dendrograms with data points colored based on group information, or visualizing volcano plots to check the results of whole genome analyses for gene differential expression.
Provides an implementation of simultaneous tolerance bounds (STB), useful for checking whether a numeric vector fits to a hypothetical null-distribution or not.
             Furthermore, there are functions for computing STB (bands, intervals) for random variates of linear mixed models fitted with package 'VCA'. All kinds of, possibly transformed 
             (studentized, standardized, Pearson-type transformed) random variates (residuals, random effects), can be assessed employing STB-methodology.
Spatio-temporal change of support (STCOS) methods are designed for statistical inference
	on geographic and time domains which differ from those on which the data were observed. In
	particular, a parsimonious class of STCOS models supporting Gaussian outcomes was introduced
	by Bradley, Wikle, and Holan <doi:10.1002/sta4.94>. The 'stcos' package contains tools which
	facilitate use of STCOS models.
Estimates a covariance matrix using Stein's isotonized covariance
    estimator, or a related estimator suggested by Haff.
Contains three main functions including
  stddiff.numeric(), stddiff.binary() and stddiff.category().
  These are used to calculate the standardized difference between two groups.
  It is especially used to evaluate the balance between two groups
  before and after propensity score matching.
Contains functionality for regression standardization. Four general classes of models are allowed; generalized linear models, conditional generalized estimating equation models, Cox proportional hazards models and shared frailty gamma-Weibull models. Sjolander, A. (2016) <doi:10.1007/s10654-016-0157-3>.
Allows the creation and manipulation of C++ std::vector's in R.
Functions related to multivariate measures of independence and ICA:
 -estimate independent components by minimizing distance covariance;
 -conduct a test of mutual independence based on distance covariance; 
 -estimate independent components via infomax (a popular method but generally performs poorer than mdcovica, ProDenICA, and/or fastICA, but is useful for comparisons);
 -order indepedent components by skewness;
 -match independent components from multiple estimates;
 -other functions useful in ICA.
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). This algorithm is a stochastic approximation of the widely used EM algorithm and an important tool for models in which the E-step does not have an analytic form. Besides the expressions obtained to estimate the parameters to the proposed model, we include the calculations for the observed information matrix using the method developed by Louis (1982). To examine the performance of the fitted model, case-deletion measure are provided.
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.
Software to simulate population change across space and time.
Provides function to estimate multiple change points
    using marginal likelihood method. See the Manual file in data folder for
    a detailed description of all functions, and a walk through tutorial.
    For more information of the method, please see Du, Kao and Kou (2016) 
    <doi:10.1080/01621459.2015.1006365>.
Collection of stepwise procedures to conduct multiple hypotheses testing. The details of the stepwise algorithm can be found in Romano and Wolf (2007) <DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014) <DOI:10.1093/jjfinec/nbu014>.
Functions for the collection of 3D points and curves using a stereo camera setup.
This is an R package for all my toy data. Its main uses are for teaching. 
    My students, and academics like me who teach the same topics I teach, should find 
    this useful if their teaching workflow is also built around the R programming 
    language.
Work with containers over the Docker API.  Rather than
    using system calls to interact with a docker client, using the
    API directly means that we can receive richer information from
    docker.  The interface in the package is automatically generated
    using the 'OpenAPI' (a.k.a., 'swagger') specification, and all
    return values are checked in order to make them type stable.
These are my collection of 'R Markdown' templates, mostly for compilation to PDF.
    These are useful for all things academic and professional, if you are using 'R Markdown'
    for things like your CV or your articles and manuscripts.
A general spatiotemporal satellite image imputation method based on sparse functional data analytic techniques. The imputation method applies and extends the Functional Principal Analysis by Conditional Estimation (PACE). The underlying idea for the proposed procedure is to impute a missing pixel by borrowing information from temporally and spatially contiguous pixels based on the best linear unbiased prediction.
Genomic Selection (GS) is a latest development in animal and plant breeding where whole genome markers information is used to predict genetic merit of an individuals in a practical breeding programme. GS is one of the promising tool for improving genetic gain in animal and plants in today’s scenario. This package is basically developed for genomic predictions by estimating marker effects. These marker effects further used for calculation of genotypic merit of individual i.e. genome estimated breeding values (GEBVs). Genomic selection may be based on single trait or multi traits information. This package performs genomic selection only for single traits hence named as STGS i.e. single trait genomic selection. STGS is a comprehensive package which gives single step solution for genomic selection based on most commonly used statistical methods.
A set of functions for computing the Standardized Temperature Index (STI).
Download and use R hex stickers. Stickers made 
  available in standardized locations in GitHub repositories, as well as those in the 
  <https://github.com/rstudio/hex-stickers> repository will be available.
In base R, object attributes are lost when objects are modified by
    common data operations such as subset, filter, slice, append, extract
    etc. This packages allows objects to be marked as 'sticky' and have
    attributes persisted during these operations or when inserted
    into or extracted from list-like or table-like objects.
Functions for simplified emulation of time series computer model output in model parameter space using Gaussian processes. Stilt can be used more generally for Kriging of spatio-temporal fields. There are functions to predict at new parameter settings, to test the emulator using cross-validation (which includes information on 95% confidence interval empirical coverage), and to produce contour plots over 2D slices in model parameter space.
Regression trunk model estimation proposed by Dusseldorp and Meulman (2004) <doi:10.1007/bf02295641>
             and Dusseldorp, Conversano, Van Os (2010) <doi:10.1198/jcgs.2010.06089>, integrating a regression tree
             and a multiple regression model.
A consistently well behaved method of interpolation based on piecewise rational functions using Stineman's algorithm.
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.
Statistical analysis of spatio-temporal point processes on linear networks. This packages provides tools to visualise and analyse spatio-temporal point patterns on linear networks using first- and second-order summary statistics.
Decompose a time series into seasonal, trend, and remainder
    components using an implementation of Seasonal Decomposition of Time
    Series by Loess (STL) that provides several enhancements over the STL
    method in the stats package.  These enhancements include handling missing
    values, providing higher order (quadratic) loess smoothing with automated
    parameter choices, frequency component smoothing beyond the seasonal and
    trend components, and some basic plot methods for diagnostics.
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.
New Version of the R package originally accompanying the paper 
  "Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles" 
  by Sameer S Bajikar, Christiane Fuchs, Andreas Roller, Fabian J Theis and Kevin A Janes 
  (PNAS 2014, 111(5), E626-635 <doi:10.1073/pnas.1311647111>). In this paper, we measure expression profiles 
  from small heterogeneous populations of cells, where each cell is assumed to be from a mixture of 
  lognormal distributions. We perform maximum likelihood estimation in order to infer the mixture ratio and 
  the parameters of these lognormal distributions from the cumulated expression measurements.
  The main difference of this new package version to the previous one is that it is now possible to use 
  different n's, i.e. a dataset where each tissue sample originates from a different number of cells.  
  We used this on pheno-seq data, see: Tirier, S.M., Park, J., Preusser, F. et al. Pheno-seq - linking visual 
  features and gene expression in 3D cell culture systems. Sci Rep 9, 12367 (2019) 
  <doi:10.1038/s41598-019-48771-4>).
Implementations of stochastic, limited-memory quasi-Newton optimizers,
	similar in spirit to the LBFGS (Limited-memory Broyden-Fletcher-Goldfarb-Shanno) algorithm,
	for smooth stochastic optimization. Implements the following methods:
	oLBFGS (online LBFGS) (Schraudolph, N.N., Yu, J. and Guenter, S., 2007 <http://proceedings.mlr.press/v2/schraudolph07a.html>),
	SQN (stochastic quasi-Newton) (Byrd, R.H., Hansen, S.L., Nocedal, J. and Singer, Y., 2016 <arXiv:1401.7020>),
	adaQN (adaptive quasi-Newton) (Keskar, N.S., Berahas, A.S., 2016, <arXiv:1511.01169>).
	Provides functions for easily creating R objects
	with partial_fit/predict methods from some given objective/gradient/predict functions.
	Includes an example stochastic logistic regression using these optimizers.
	Provides header files and registered C routines for using it directly from C/C++.
Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Kastner (2016) <doi:10.18637/jss.v069.i05> and the package vignette.
Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is 
    integrated out of the likelihood using the Laplace approximation. The models are fitted via 'TMB' (Template Model
    Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) <doi:10.18637/jss.v070.i05>).
Provides a mixture model for clustering individuals (or sampling groups) into stocks based on their genetic profile. Here, sampling groups are individuals that are sure to come from the same stock (e.g. breeding adults or larvae). The mixture (log-)likelihood is maximised using the EM-algorithm after find good starting values via a K-means clustering of the genetic data. Details can be found in Foster, Feutry, Grewe, Berry, Hui, Davies (2019) Reliably Discriminating Stock Structure with Genetic Markers: Mixture Models with Robust and Fast Computation. Molecular Ecology Resources.
Functions for analyzing stocks or other investments. Main features are loading and aligning historical data for ticker symbols, calculating performance metrics for individual funds or portfolios (e.g. annualized growth, maximum drawdown, Sharpe/Sortino ratio), and creating graphs. C++ code is used to improve processing speed where possible.
Given a list of substance compositions, a list of
        substances involved in a process, and a list of constraints in
        addition to mass conservation of elementary constituents, the
        package contains functions to build the substance composition
        matrix, to analyze the uniqueness of process stoichiometry, and
        to calculate stoichiometric coefficients if process
        stoichiometry is unique.  (See Reichert, P. and Schuwirth, N.,
        A generic framework for deriving process stoichiometry in
        enviromental models, Environmental Modelling and Software 25,
        1241-1251, 2010 for more details.)
Provides functionality for working with differentials,
           k-forms, wedge products, Stokes's theorem, and related concepts
	   from the exterior calculus.  Functionality for Grassman algebra
	   is provided.  The canonical reference would be:
	   M. Spivak (1965, ISBN:0-8053-9021-9) "Calculus on Manifolds".
	   The 'stokes' package was formerly known as the 'wedge' package.
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 <https://cyclestreets.net/> and
    calculation of route segment attributes such as bearing.
    The package implements the 'travel flow aggregration' method
    described in Morgan and Lovelace (2020) <doi:10.1177/2399808320942779>.
    Further information on the package's aim and scope can be found
    in the vignettes and in a paper in the R Journal
    (Lovelace and Ellison 2018) <doi:10.32614/RJ-2018-053>.
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.
Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics, using demographic and genetic data sampled in the curse of the epidemics. This package also includes the GMCPIC test.
Provides a framework for performing discrete (share-level) simulations of
  investment strategies. Simulated portfolios optimize exposure to an input signal subject
  to constraints such as position size and factor exposure. For background see L. Chincarini
  and D. Kim (2010, ISBN:978-0-07-145939-6) "Quantitative Equity Portfolio Management".
Functions for the stratigraphic analysis of phylogenetic trees.
An implementation of the stratification index proposed by Zhou (2012) <DOI:10.1177/0081175012452207>.
  The package provides two functions, srank, which returns stratum-specific
  information, including population share and average percentile rank; and strat,
  which returns the stratification index and its approximate standard error.
  When a grouping factor is specified, strat also provides a detailed decomposition
  of the overall stratification into between-group and within-group components.
Functions in this package provide solution to classical problem in survey methodology - 
  an optimum sample allocation in stratified sampling scheme with simple random sampling without replacement design in each stratum. 
  In this context, the optimal allocation is in the classical Tschuprow-Neyman's sense,
  and it satisfies additional upper bounds restrictions imposed on sample sizes in strata. 
  There are four different algorithms available to use, and one of them is Neyman optimal allocation applied in a recursive way.
  All the algorithms are described in detail in "Wojciak W. Optimal allocation in stratified sampling schemes. 
  Master's diploma thesis, Warsaw University of Technology. 2019", 
  available on-line at <http://home.elka.pw.edu.pl/~wwojciak/msc_optimal_allocation.pdf>.
A pilot matching design to automatically 
    stratify and match large datasets.  The manual_stratify() function allows
    users to manually stratify a dataset based on categorical variables of 
    interest, while the auto_stratify() function does automatically by
    allocating a held-aside (pilot) data set, fitting a prognostic score  
    (see Hansen (2008) <doi:10.1093/biomet/asn004>) on the pilot set, and stratifying the data set based
    on prognostic score quantiles.  The strata_match() function then does optimal
    matching of the data set in parallel within strata.
An Optimization Algorithm Applied to
    Stratification Problem.This function aims
    at constructing optimal strata with an optimization algorithm
    based on a global optimisation technique called Biased
    Random Key Genetic Algorithms.
Identifies individuals in a social network who should be the intervention
    subjects for a network intervention in which you have a group of targets, a
    group of avoiders, and a group that is neither.
Users can build and test customized quantitative trading strategies. Some quantitative trading strategies are already implemented, e.g. various moving-average filters with trend following approaches.
    The implemented class called "Strategy" allows users to access several methods to analyze performance figures, plots and backtest the strategies.
    Furthermore, custom strategies can be added, a generic template is available. The custom strategies require a certain input and output so they can be called from the Strategy-constructor.
Variants of strategy estimation (Dal Bo & Frechette, 2011, <doi:10.1257/aer.101.1.411>), including the model with parameters for the choice probabilities of the strategies (Breitmoser, 2015, <doi:10.1257/aer.20130675>), and the model with individual level covariates for the selection of strategies by individuals (Dvorak & Fehrler, 2018, <doi:10.2139/ssrn.2986445>).
A dynamic model of 
    the big-picture, whole ecosystem effects of hydrodynamics, 
	temperature, nutrients, and fishing on continental shelf marine 
	food webs. The package has been developed from a prototype described 
	in: Heath, M.R. (2012) <doi:10.1016/j.pocean.2012.03.004> 
	Ecosystem limits to food web fluxes and fisheries yields in 
	the North Sea simulated with an end-to-end food web model. 
	Progress in Oceanography (Special issue: End-to-end modelling: 
	Towards Comparative Analysis of Marine Ecosystem Organisation) 
	102, 42-66.
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
    <arXiv:1912.03337>. 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.This function aims
    at constructing optimal strata with an optimization algorithm
    based on a global optimisation technique called vns.
The main function is icweib(), which fits a stratified Weibull
    proportional hazards model for left censored, right censored, interval censored,
    and non-censored survival data. We parameterize the Weibull regression model
    so that it allows a stratum-specific baseline hazard function, but where the
    effects of other covariates are assumed to be constant across strata. 
    Please refer to Xiangdong Gu, David Shapiro, Michael D. Hughes and
    Raji Balasubramanian (2014) <doi:10.32614/RJ-2014-003> for more details.
This is a modification of 'HDoutliers' package. The 'HDoutliers' algorithm is a powerful 
    unsupervised algorithm for detecting anomalies in high-dimensional data, with a 
    strong theoretical foundation. However, it suffers from some limitations that 
    significantly hinder its performance level, under certain circumstances. This package 
    implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) 
    <arXiv:1908.04000>  for detecting anomalies in high-dimensional data
    that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority
    with a large distance gap. An approach based on extreme value theory is used 
    for the anomalous threshold calculation.
A framework for data stream modeling and associated data mining tasks such as clustering and classification. The development of this package was supported in part by NSF IIS-0948893 and NIH R21HG005912. Hahsler et al (2017) <doi:10.18637/jss.v076.i14>.
Numerically solve and plot solutions of a parametric ordinary
        differential equations model of growth, death, and respiration of
        macroinvertebrate and algae taxa dependent on pre-defined environmental
        factors. The model (version 1.0) is introduced in Schuwirth, N. and
        Reichert, P., (2013) <DOI:10.1890/12-0591.1>. This package includes
        model extensions and the core functions introduced and used in
        Schuwirth, N. et al. (2016) <DOI:10.1111/1365-2435.12605>,
        Kattwinkel, M. et al. (2016) <DOI:10.1021/acs.est.5b04068>,
        Mondy, C. P., and Schuwirth, N. (2017) <DOI:10.1002/eap.1530>,
        and Paillex, A. et al. (2017) <DOI:10.1111/fwb.12927>.
Implementation of analytical models for estimating streamflow 
    depletion due to groundwater pumping, and other related tools. Functions
    are broadly split into two groups: (1) analytical streamflow depletion
    models, which estimate streamflow depletion for a single stream reach
    resulting from groundwater pumping; and (2) depletion apportionment 
    equations, which distribute estimated streamflow depletion among multiple
    stream reaches within a stream network. See Zipper et al. (2018) <doi:10.1029/2018WR022707>
    for more information on depletion apportionment equations and Zipper et
    al. (2019) <doi:10.1029/2018WR024403> for more information on analytical
    depletion functions, which combine analytical models and depletion apportionment
    equations.
I provide functions to calculate Gross Primary Productivity, Net Ecosystem Production, and Ecosystem Respiration from single station diurnal Oxygen curves.
Interface for data stream clustering algorithms implemented in the MOA (Massive Online Analysis) framework (Albert Bifet, Geoff Holmes, Richard Kirkby, Bernhard Pfahringer (2010). MOA: Massive Online Analysis, Journal of Machine Learning Research 11: 1601-1604).
Functions to access Twitter's filter, sample, and user streams, and to
    parse the output into data frames.
The stress addition approach is an alternative to the traditional
    concentration addition or effect addition models. It allows the modelling
    of tri-phasic concentration-response relationships either as single toxicant
    experiments, in combination with an environmental stressor or as mixtures of
    two toxicants. See Liess et al. (2019) <doi:10.1038/s41598-019-51645-4>
    and Liess et al. (2020) <doi:10.1186/s12302-020-00394-7>.
Forms queries to submit to the Cleveland Federal Reserve Bank web
    site's financial stress index data site.  Provides query functions for both
    the composite stress index and the components data. By default the download
    includes daily time series data starting September 25, 1991.  The functions
    return a class of either type easing or cfsi which contain a list of items
    related to the query and its graphical presentation.  The list includes the
    time series data as an xts object.  The package provides four lattice time
    series plots to render the time series data in a manner similar to the
    bank's own presentation.
Reliability of (normal) stress-strength models and for building two-sided or one-sided confidence intervals according to different approximate procedures.
There are some things that I wish were easier with
    the 'stringr' or 'stringi' packages. The foremost of these is the
    extraction of numbers from strings. 'stringr' and 'stringi' make you
    figure out the regular expression for yourself; 'strex' takes care of
    this for you. There are many other handy functionalities in 'strex'.
    Contributions to this package are encouraged: it is intended as a
    miscellany of string manipulation functions that cannot be found in
    'stringi' or 'stringr'.
The strided iterator adapts multidimensional buffers to work with 
  the C++ standard library and range-based for-loops. Given a pointer or iterator
  into a multidimensional data buffer, one can generate an iterator range using
  make_strided to construct strided versions of the standard library's begin and
  end. For constructing range-based for-loops, a strided_range class is provided.
  These help authors to avoid integer-based indexing, which in some cases can impede
  algorithm performance and introduce indexing errors. This library exists
  primarily to expose the header file to other R projects.
Takes a list of character strings and forms an adjacency matrix for
    the times the specified characters appear together in the strings provided. For
    use in social network analysis and data wrangling. Simple package, comprised of
    three functions.
Base R already ships with string handling capabilities 'out-
    of-the-box' but lacks streamlined function names and workflow. The
    'stringi' ('stringr') package on the other hand has well named functions,
    extensive Unicode support and allows for a streamlined workflow. On the other
    hand it adds dependencies and regular expression interpretation between base R
    functions and 'stringi' functions might differ. This packages aims at providing
    a solution to the use case of unwanted dependencies on the one hand but the need
    for streamlined text processing on the other. The packages' functions are solely
    based on wrapping base R functions into 'stringr'/'stringi' like function names.
    Along the way it adds one or two extra functions and last but not least provides
    all functions as generics, therefore allowing for adding methods for other text
    structures besides plain character vectors.
Implements an approximate string matching version of R's native
    'match' function. Also offers fuzzy text search based on various string
     distance measures. Can calculate various string distances based on edits
    (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q-
    gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An
    implementation of soundex is provided as well. Distances can be computed between
    character vectors while taking proper care of encoding or between integer
    vectors representing generic sequences. This package is built for speed and
    runs in parallel by using 'openMP'. An API for C or C++ is exposed as well.
    Reference: MPJ van der Loo (2014) <doi:10.32614/RJ-2014-011>.
Provides an extendable, performant and multithreaded 'alt-string' implementation backed by 'C++' vectors and strings.
Pass named and unnamed character vectors into specified positions
    in strings. This represents an attempt to replicate some of python's string
    formatting.
A multitude of character string/text/natural language
    processing tools: pattern searching (e.g., with 'Java'-like regular
    expressions or the 'Unicode' collation algorithm), random string generation,
    case mapping, string transliteration, concatenation, sorting, padding,
    wrapping, Unicode normalisation, date-time formatting and parsing,
    and many more. They are fast, consistent, convenient, and -
    owing to the use of the 'ICU' (International Components for Unicode)
    library - portable across all locales and platforms.
A consistent, simple and easy to use set of
    wrappers around the fantastic 'stringi' package. All function and
    argument names (and positions) are consistent, all functions deal with
    "NA"'s and zero length vectors in the same way, and the output from
    one function is easy to feed into the input of another.
The strip function deletes components of R model outputs that are useless for specific purposes, such as predict[ing], print[ing], summary[izing], etc.
For making Trellis-type conditioning plots without strip labels.
    This is useful for displaying the structure of results from factorial designs
    and other studies when many conditioning variables would clutter the display
    with layers of redundant strip labels. Settings of the variables are encoded by
    layout and spacing in the trellis array and decoded by a separate legend. The
    functionality is implemented by a single S3 generic strucplot() function that
    is a wrapper for the Lattice package's xyplot() function. This allows access to
    all Lattice graphics capabilities in the usual way.
Extracts plain text from RTF (Rich Text Format) file.
Implements a spatio-temporal regression model based on Chi, G. and Zhu, J. (2019) Spatial Regression Models for the Social Sciences <isbn:9781544302072>. The approach here fits a spatial error model while incorporating a temporally lagged response variable and temporally lagged explanatory variables. This package builds on the errorsarlm() function from the spatialreg package.
Loading, identifying, aggregating, manipulating, and analysing short tandem repeat regions of massively parallel sequencing data in forensic genetics. 'STRMPS' can work with the package 'STRaitRazoR' (an R interface to the 'STRaitRazor' commandline tool) for added speed. 'STRaitRazoR' only works on linux and can found at <https://github.com/svilsen/STRaitRazoR>. The analyses and framework implemented in this package relies on the papers of Vilsen et al. (2017) <doi:10.1016/j.fsigen.2017.01.017> and Vilsen et al. (2018) <doi:10.1016/j.fsigen.2018.04.003>. Lastly, note that the parallelisation in the package relies on mclapply() and, thus, speed-ups will only be seen on UNIX based systems.
R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models: Modern Concepts, Methods and Applications, CRC Press.
Testing, monitoring and dating structural changes in (linear)
             regression models. strucchange features tests/methods from
	     the generalized fluctuation test framework as well as from
	     the F test (Chow test) framework. This includes methods to
	     fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM,
	     recursive/moving estimates) and F statistics, respectively.
             It is possible to monitor incoming data online using
             fluctuation processes.
             Finally, the breakpoints in regression models with structural
             changes can be estimated together with confidence intervals.
             Emphasis is always given to methods for visualizing the data.
Perform more powerful false discovery control (FDR) for microbiome data, taking into account the prior phylogenetic relationship among bacteria species.  As a general methodology, it is applicable to any type of (genomic) data with prior structure information.
Tree-structured modelling of categorical predictors (Tutz and Berger (2018), <doi:10.1007/s11634-017-0298-6>) or measurement
    units (Berger and Tutz (2018), <doi:10.1080/10618600.2017.1371030>).
Provides an efficient method to recover the missing block of an approximately low-rank matrix. Current literature on matrix completion focuses primarily on independent sampling models under which the individual observed entries are sampled independently. Motivated by applications in genomic data integration, we propose a new framework of structured matrix completion (SMC) to treat structured missingness by design [Cai T, Cai TT, Zhang A (2016) <doi:10.1080/01621459.2015.1021005>]. Specifically, our proposed method aims at efficient matrix recovery when a subset of the rows and columns of an approximately low-rank matrix are observed. The main function in our package, smc.FUN(), is for recovery of the missing block A22 of an approximately low-rank matrix A given the other blocks A11, A12, A21.
An open source platform for validation and process control.
    Tools to analyze data from internal validation of forensic short tandem
    repeat (STR) kits are provided. The tools are developed to provide
    the necessary data to conform with guidelines for internal validation
    issued by the European Network of Forensic Science Institutes (ENFSI)
    DNA Working Group, and the Scientific Working Group on DNA Analysis Methods
    (SWGDAM). A front-end graphical user interface is provided.
    More information about each function can be found in the
    respective help documentation.
Fit the basic structural time series model by maximum likelihood.
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/>.
Implements multi-study learning algorithms such as 
		merging, the study-specific ensemble (trained-on-observed-studies ensemble) the study strap, 
		the covariate-matched study strap, covariate-profile similarity weighting, and stacking weights. 
		Embedded within the 'caret' framework, this package allows for a wide range of 
		single-study learners (e.g., neural networks, lasso, random forests). 
		The package offers over 20 default similarity measures and allows for specification of custom 
		similarity measures for covariate-profile similarity weighting and an accept/reject step. 
		This implements methods described in Loewinger, Kishida, Patil, and Parmigiani. (2019)
		<doi:10.1101/856385>.
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.
A formula 'sub' is a subformula of 'formula' if all the terms
    on the right hand side of 'sub' are terms of 'formula' and their left hand 
    sides are identical. This package aids in the creation of subformulas.
Produces various measures of expected treatment effect heterogeneity under an assumption of homogeneity across subgroups. Graphical presentations are created to compare these expected differences with the observed differences.
Provides functions for obtaining a variety of 
    graphical displays that may be useful in the subgroup analysis setting. 
    An example with a prostate cancer dataset is provided.
    The graphical techniques considered include level plots, mosaic plots,
    contour plots, bar charts, Venn diagrams, tree plots, forest plots, 
    Galbraith plots, L'Abbé plots, the subpopulation treatment effect pattern 
    plot, alluvial plots, circle plots and UpSet plots.
Classical methods for combining summary data from genome-wide
    association studies (GWAS) only use marginal genetic effects and power can
    be compromised in the presence of heterogeneity. 'subgxe' is a R package
    that implements p-value assisted subset testing for association (pASTA),
    a method developed by Yu et al. (2019) <doi:10.1159/000496867>. pASTA
    generalizes association analysis based on subsets by incorporating
    gene-environment interactions into the testing procedure.
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>.
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.
 This approach was presented in Muysers (2020) <doi:10.1007/s43441-019-00082-6> and referenced in Ballarini (2020) <doi:10.1002/pst.2012>.
 New to version 3 is the Automatic Screening of one- or MUlti-factorial Subgroups (ASMUS) for documentation of the structured review of subgroup findings.
A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria. Theoretical support for the heuristic search methods and exploratory data analysis criteria is in Cadima, Cerdeira, Minhoto (2003, <doi:10.1016/j.csda.2003.11.001>). Theoretical support for the leap and bounds algorithm and the criteria for the general multivariate linear model is in Duarte Silva (2001, <doi:10.1006/jmva.2000.1920>). There is a package vignette "subselect", which includes additional references.
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.  Chapple, A.G., Thall, P.F. (2018) <doi:10.1002/pst.1891>.
subtype performs a biclustering procedure on a input
        dataset and assess whether resulting clusters are promising
        subtypes. Note that the R-package rsmooth should be installed
        before implementing subtype. rsmooth can be downloaded from
        http://www.meb.ki.se/~yudpaw.
A systematic biology tool was developed to prioritize cancer subtype-specific drugs by integrating genetic perturbation, drug action, biological pathway, and cancer subtype. 
    The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially 
    expressed subpathways based on cultured human cells treated with drugs in the 'cMap' (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease 
    reverse association score based on subpathway, and visualization of results (Castelo (2013) <doi:10.1186/1471-2105-14-7>; Han et al (2019) <doi:10.1093/bioinformatics/btz894>; Lamb and Justin (2006) <DOI:10.1126/science.1132939>).
Substitution matrices are important parameters in protein alignment algorithms. These matrices represent the likelihood that an amino acid will be substituted for another during mutation. This tool allows users to apply predefined and custom matrices and then explore the resulting alignments with interactive visualizations.  'SubVis' requires the availability of a web browser.
Interface to 'Sudachi' <https://github.com/WorksApplications/Sudachi>,
    a Japanese morphological analyzer. This is a port of what is available in Python.
Identify sudden gains based on the three criteria outlined by Tang and DeRubeis (1999) <doi:10.1037/0022-006X.67.6.894> to a selection of repeated measures. Sudden losses, defined as the opposite of sudden gains can also be identified. Two different datasets can be created, one including all sudden gains/losses and one including one selected sudden gain/loss for each case. It can extract scores around sudden gains/losses. It can plot the average change around sudden gains/losses and trajectories of individual cases.
Generates, plays, and solves Sudoku puzzles.  The GUI
        playSudoku() needs package "tkrplot" if you are not on Windows.
Tools for making, retrieving, displaying and solving sudoku games.
    This package is an alternative to the earlier sudoku-solver package,
    'sudoku'.  The present package uses a slightly different algorithm, has a
    simpler coding and presents a few more sugar tools, such as plot and print
    methods.  Solved sudoku games are of some interest in Experimental Design
    as examples of Latin Square designs with additional balance constraints.
This is a package for the subsampling method of robust
        estimation of linear regression models
Generates region-specific Suess and Laws corrections for 
    stable carbon isotope data from marine organisms collected between 1850 and 2019. Version
    0.1.1 of 'SuessR' contains four built-in regions: the Bering Sea ('Bering Sea'), the
    Aleutian archipelago ('Aleutians Islands'), the Gulf of Alaska ('Gulf of Alaska'), and the 
    subpolar North Atlantic ('Subpolar North Atlantic'). Users can supply their own environmental 
    data for regions currently not built into the package to generate corrections for those regions.
Create a hexagon tile map display from spatial polygons. Each 
    polygon is represented by a hexagon tile, placed as close to it's original
    centroid as possible, with a focus on maintaining spatial relationship to
    a focal point. Developed to aid visualisation and analysis of spatial 
    distributions across Australia, which can be challenging due to the 
    concentration of the population on the coast and wide open interior.
Provides 'ggplot2' graphics for analysing time
    series data. It aims to fit into the 'tidyverse' and grammar of
    graphics framework for handling temporal data.
An adaptation of classical region/gene-based association analysis
    techniques to the use of summary statistics (P values and effect sizes)
    and correlations between genetic variants as input. It is a tool to perform
    the most popular and efficient gene-based tests using the results of genome-wide
    association (meta-)analyses without having the original genotypes and
    phenotypes at hand.
Functions to speed up the exploratory analysis of simple
    datasets using 'dplyr'. Functions are provided to do the 
    common tasks of calculating confidence intervals.
Shrinkage estimator for polygenic risk prediction models based on summary statistics of genome-wide association studies.
Data frame summaries, cross-tabulations,
  weight-enabled frequency tables and common descriptive
  (univariate) statistics in concise tables available in a
  variety of formats (plain ASCII, Markdown and HTML). A good 
  point-of-entry for exploring data, both for experienced
  and new R users.
Provides methods for spatial and spatio-temporal smoothing of demographic and health indicators using survey data, with particular focus on estimating and projecting under-five mortality rates, described in Mercer et al. (2015) <doi:10.1214/15-AOAS872>
  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>).
Quantification of the effect of geographic versus environmental isolation on genetic differentiation
Provides a way to call the functions in 'SUNDIALS' C ODE solving library (<https://computation.llnl.gov/projects/sundials>). Currently the serial version of ODE solver, 'CVODE', sensitivity calculator 'CVODES' and differential algebraic solver 'IDA' from the 'SUNDIALS' library are implemented. The package requires ODE to be written as an 'R' or 'Rcpp' function and does not require the 'SUNDIALS' library to be installed on the local machine.
Suns-Voc (or Isc-Voc) curves can provide the current-voltage (I-V) characteristics of the
    diode of photovoltaic cells without the effect of series resistance.
    Here, Suns-Voc curves can be constructed with outdoor time-series I-V
    curves [1,2,3] of full-size photovoltaic (PV) modules instead of having to be measured in the lab.
    Time series of four different power loss modes can be calculated based on obtained Isc-Voc curves.
    This material is based upon work supported by the U.S. Department of
    Energy's Office of Energy Efficiency and Renewable Energy (EERE) under
    Solar Energy Technologies Office (SETO) Agreement Number DE-EE0008172. 
    Jennifer L. Braid is supported by the U.S. Department of Energy (DOE) Office of 
    Energy Efficiency and Renewable Energy administered by the Oak Ridge 
    Institute for Science and Education (ORISE) for the DOE. 
    ORISE is managed by Oak Ridge Associated Universities (ORAU) under DOE
    contract number DE-SC0014664. 
    [1] Wang, M. et al, 2018. 
    <doi:10.1109/PVSC.2018.8547772>. 
    [2] Walters et al, 2018
    <doi:10.1109/PVSC.2018.8548187>. 
    [3] Guo, S. et al, 2016. 
    <doi:10.1117/12.2236939>.
Functions for drawing samples according to Sunter's
        sampling design, and for computing first and second order
        inclusion probabilities
Implements the self-updating process clustering algorithms proposed
    in Shiu and Chen (2016) <doi:10.1080/00949655.2015.1049605>.
Methodology for supervised grouping aka "clustering" of
   potentially many predictor variables, such as genes etc, implementing
   algorithms 'PELORA' and 'WILMA'.
Clusters features under the assumption that each cluster has a
 random effect and there is an outcome variable that is related to the random 
 effects by a linear regression. In this way the cluster analysis is 
 ``supervised'' by the outcome variable. An alternate specification is that 
 features in each cluster have the same compound symmetric normal distribution, 
 and the conditional distribution of the outcome given the features
 has the same coefficient for each feature in a cluster.
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.
Does prediction in the case of a censored survival outcome, or a regression outcome, using the "supervised principal component" approach. 'Superpc' is especially useful for high-dimensional data when the number of features p dominates the number of samples n (p >> n paradigm), as generated, for instance, by high-throughput technologies.
Dimension reduction of complex data with supervision from auxiliary information. The package contains a series of methods for different data types (e.g., multi-view or multi-way data) including the supervised singular value decomposition (SupSVD), supervised sparse and functional principal component (SupSFPC), supervised integrated factor analysis (SIFA) and supervised PARAFAC/CANDECOMP factorization (SupCP). When auxiliary data are available and potentially affect the intrinsic structure of the data of interest, the methods will accurately recover the underlying low-rank structure by taking into account the supervision from the auxiliary data. For more details, see the paper by Gen Li, <DOI:10.1111/biom.12698>.
Image Segmentation using Superpixels, Affinity Propagation and Kmeans Clustering. The R code is based primarily on the article "Image Segmentation using SLIC Superpixels and Affinity Propagation Clustering, Bao Zhou, International Journal of Science and Research (IJSR), 2013" <https://pdfs.semanticscholar.org/6533/654973054b742e725fd433265700c07b48a2.pdf>.
Functions to perform simulations of ANOVA designs of up to three factors. Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2019). "Simulation-Based Power-Analysis for Factorial ANOVA Designs". <doi:10.31234/osf.io/baxsf>.
Tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap.
The Patient Rule Induction Method (PRIM) is typically
  used for "bump hunting" data mining to identify regions with abnormally
  high concentrations of data with large or small values. This package
  extends this methodology so that it can be applied to binary classification
  problems and used for prediction.
Calculates the sup MZ value to detect the unknown structural break points under Heteroskedasticity as given in Ahmed et al. (2017) (<DOI: 10.1080/03610926.2016.1235200>).
Downloads data supplementary materials from manuscripts,
    using papers' DOIs as references. Facilitates open, reproducible
    research workflows: scientists re-analyzing published datasets can
    work with them as easily as if they were stored on their own
    computer, and others can track their analysis workflow
    painlessly. The main function suppdata() returns a (temporary)
    location on the user's computer where the file is stored, making
    it simple to use suppdata() with standard functions like
    read.csv().
Ten distributions supplementing those built into R.
  Inverse Gauss, Kruskal-Wallis, Kendall's Tau, Friedman's chi
  squared, Spearman's rho, maximum F ratio, the Pearson product
  moment correlation coefficient, Johnson distributions, normal
  scores and generalized hypergeometric distributions.
Provides basic functions that support an implementation of object case (Case 1) best-worst scaling: a function for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; two functions for creating a data set suitable for analysis; a function for calculating count-based scores; a function for calculating shares of preference; and a function for generating artificial responses to questions. For details on best-worst scaling, see Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
Provides three basic functions that support an implementation of Case 2 (profile case) best-worst scaling. The first is to convert an orthogonal main-effect design into questions, the second is to create a dataset suitable for analysis, and the third is to calculate count-based scores. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.
Provides basic functions that support an implementation of multi-profile case (Case 3) best-worst scaling (BWS). Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for the analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
Provides 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.
A modeling tool helping users better structure 'Shiny'
  applications developed with 'Shiny' modules. Users are able to: 1. Visualize
  relationship of modules in existing applications 2. Design new applications
  from scratch.
Access to the datasets and many of the functions used in "Statistics Using R: An Integrative Approach". These datasets include a subset of the National Education Longitudinal Study, the Framingham Heart Study, as well as several simulated datasets used in the examples throughout the textbook. The functions included in the package reproduce some of the functionality of 'Stata' that is not directly available in 'R'.  The package also contains a tutorial on basic data frame management, including how to handle missing data.
Implementation of the direct Monte Carlo approach of 
             Zellner and Ando (2010) <doi:10.1016/j.jeconom.2010.04.005>
             to sample from posterior of 
             Seemingly Unrelated Regression (SUR) models. In 
             addition, a Gibbs sampler is implemented that allows 
             the user to analyze SUR models using the power prior.
An implementation of the surrogate approach to residuals and 
  diagnostics for ordinal and general regression models; for details, see Liu 
  and Zhang (2017) <doi:10.1080/01621459.2017.1292915>. These residuals can be 
  used to construct standard residual plots for model diagnostics (e.g., 
  residual-vs-fitted value plots, residual-vs-covariate plots, Q-Q plots, etc.). 
  The package also provides an 'autoplot' function for producing standard 
  diagnostic plots using 'ggplot2' graphics. The package currently supports 
  cumulative link models from packages 'MASS', 'ordinal', 'rms', and 'VGAM'. 
  Support for binary regression models using the standard 'glm' function is also 
  available.
A statistical learning method to simultaneously predict a range of target phenotypes using codified and natural language processing (NLP)-derived Electronic Health Record (EHR) data. See Ahuja et al (2020) JAMIA <doi:10.1093/jamia/ocaa079> for details.
Create sampling designs using the surface reconstruction algorithm.
  Original method by: Olsson, D. 2002. A method to optimize soil sampling from 
  ancillary data. Poster presenterad at: NJF seminar no. 336, 
  Implementation of Precision Farming in Practical Agriculture, 10-12 
  June 2002, Skara, Sweden.
In a clinical trial, it frequently occurs that the most credible outcome to evaluate the effectiveness of a new therapy (the true endpoint) is difficult to measure. In such a situation, it can be an effective strategy to replace the true endpoint by a (bio)marker that is easier to measure and that allows for a prediction of the treatment effect on the true endpoint (a surrogate endpoint). The package 'Surrogate' allows for an evaluation of the appropriateness of a candidate surrogate endpoint based on the meta-analytic, information-theoretic, and causal-inference frameworks. Part of this software has been developed using funding provided from the European Union's Seventh Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
Provides functions to estimate the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in "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.
Plots survival models from the 'survival' package. Additionally, it
    plots curves of multistate models from the 'mstate' package. Typically, a
    plot is drawn by the sequence survplot(), confIntArea(), survCurve() and
    nrAtRisk(). The separation of the plot in this 4 functions allows for great
    flexibility to make a custom plot for publication.
Various functions for discrete time survival analysis and longitudinal analysis. SIMEX method for correcting for bias for errors-in-variables
  in a mixed effects model. Asymptotic mean and variance of different proportional hazards test statistics using different ties methods given two
  survival curves and censoring distributions. Score test and Wald test for regression analysis of grouped survival data. Calculation of survival
  curves for events defined by the response variable in a mixed effects model crossing a threshold with or without confirmation.
Computing the one-sided/two-sided integrated/maximally selected EL statistics for simultaneous testing, the one-sided/two-sided EL tests for pointwise testing, and an initial test that precedes one-sided testing to exclude the possibility of crossings or alternative orderings among the survival functions.
Relative survival, AER and SMR based on French death rates
Summary statistics, two-sample tests, rank tests, generalised linear models, cumulative link models, Cox models, loglinear models, and general maximum pseudolikelihood estimation for multistage stratified, cluster-sampled, unequally weighted survey samples. Variances by Taylor series linearisation or replicate weights. Post-stratification, calibration, and raking. Two-phase subsampling designs. Graphics. PPS sampling without replacement. 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). doi:10.18637/jss.v095.i14.
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) <JSTOR:2344317>.
Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk, survival probabilities, or survival distributions with 'distr6' <https://CRAN.R-project.org/package=distr6>. Models are either implemented from 'Python' via 'reticulate' <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using 'Rcpp' <https://CRAN.R-project.org/package=Rcpp>. Novel machine learning survival models wil be included in the package in near-future updates. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/pycox> and are detailed by Kvamme et al. (2019) <https://jmlr.org/papers/v20/18-424.html>. The 'Akritas' estimator is defined in Akritas (1994) <doi:10.1214/aos/1176325630>. 'DNNSurv' is defined in Zhao and Feng (2020) <arXiv:1908.02337>.
Estimate the regression coefficients and the baseline hazard 
      of proportional hazard Cox models using maximum penalised likelihood.  
      A 'non-parametric' smooth estimate of the baseline hazard function 
      is provided.
Fitting Cox proportional hazard model under dependent right censoring using copula and maximum penalised likelihood methods.
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.
Provides kernel weighting methods for estimation of proportional hazards models with intermittently observed longitudinal covariates. Cao H., Churpek M. M., Zeng D., and Fine J. P. (2015) <doi:10.1080/01621459.2014.957289>.
In clinical trials, endpoints are sometimes evaluated with uncertainty. Adjudication is commonly adopted to ensure the study integrity. We propose to use multiple imputation (MI) introduced by Robin (1987) <doi:10.1002/9780470316696> to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) <doi:10.1016/S0197-2456(00)00053-2> were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods.
Contains the function 'ggsurvplot()' for drawing easily beautiful
    and 'ready-to-publish' survival curves with the 'number at risk' table
    and 'censoring count plot'. Other functions are also available to plot 
    adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions.
A collection of functions to help in the analysis of
    right-censored survival data. These extend the methods available in
    package:survival.
Perform survival simulation with parametric survival model generated from 'survreg' function in 'survival' package.
    In each simulation coefficients are resampled from variance-covariance matrix of parameter estimates to 
    capture uncertainty in model parameters.
    Prediction intervals of Kaplan-Meier estimates and hazard ratio of treatment effect can be further calculated using simulated survival data.
Fits hazard and excess hazard models with multidimensional penalized splines allowing for 
        time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for 
        multidimensional penalized hazard and excess hazard models. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics. 
	See Fauvernier et al. (2019a) <doi:10.21105/joss.01434> for an overview of the package and Fauvernier et al. (2019b) <doi:10.1111/rssc.12368> for the method.
Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data.
The 'survRatio', provides numerical and graphical summaries for time to event data. In this release functions are provided to estimate and compare estimated survivor functions, the ratio of survivor functions and the difference of survivor functions in independent and paired time to event problems. Time intervals where the survival prospects may differ are identified using pointwise confidence bands. See "Survival ratio plots with permutation envelopes in survival data problems" by Newell J, et al. (2006) <doi:10.1016/j.compbiomed.2005.03.005>.
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.
Performs the permutation test using difference in the restricted mean survival time (RMST) between groups as a summary measure of the survival time distribution. When the sample size is less than 50 per group, it has been shown that there is non-negligible inflation of the type I error rate in the commonly used asymptotic test for the RMST comparison. Generally, permutation tests can be useful in such a situation. However, when we apply the permutation test for the RMST comparison, particularly in small sample situations, there are some cases where the survival function in either group cannot be defined due to censoring in the permutation process. Horiguchi and Uno (2020) <doi:10.1002/sim.8565> have examined six workable solutions to handle this numerical issue. It performs permutation tests with implementation of the six methods outlined in the paper when the numerical issue arises during the permutation process. The result of the asymptotic test is also provided for a reference.
Performs a dual-parameter sensitivity analysis of treatment effect to unmeasured confounding in observational studies with either survival or competing risks outcomes. Huang, R., Xu, R. and Dulai, P.S.(2019) <arXiv:1908.01444>.
Simulation of simple and complex survival data including recurrent and multiple events and competing risks.
Conduct asymptotic and empirical power and sample size calculations for Single-Nucleotide Polymorphism (SNP) association studies with right censored time to event outcomes.
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>.
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. Explanations of the methods can be found in Grudziaz et al.(2018) <doi:10.21105/joss.00961>.
The alpha-Sutte indicator (alpha-Sutte) was originally from developed of Sutte indicator. Sutte indicator can using to predict the movement of stocks. As the development of science, then Sutte indicator developed to predict not only the movement of stocks but also can forecast data on financial, insurance, and others time series data. Ahmar, A.S. (2017) <doi:10.17605/osf.io/rknsv>.
Implements the s-values proposed by Ed. Leamer.
    It provides a context-minimal approach for sensitivity analysis using extreme
    bounds to assess the sturdiness of regression coefficients.
Implements data-driven identification methods for structural vector autoregressive (SVAR) models. 
             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 purpose of this package is to manipulate SVG files that are templates of charts the user wants to produce. 
    In vector graphics one copes with x-/y-coordinates of elements (e.g. lines, rectangles, text). Their scale is often dependent on the program that is used to produce the graphics. 
    In applied statistics one usually has numeric values on a fixed scale (e.g. percentage values between 0 and 100) to show in a chart. 
    Basically, 'svgtools' transforms the statistical values into coordinates and widths/heights of the vector graphics.
    This is done by stackedBar() for bar charts, by linesSymbols() for charts with lines and/or symbols (dot markers) and scatterSymbols() for scatterplots.
The SciViews svGUI package eases the management of Graphical User
  Interfaces (GUI) in R. It is independent from any particular GUI widgets (Tk,
  Gtk2, native, ...). It centralizes info about GUI elements currently used,
  and it dispatches GUI calls to the particular toolkits in use in function of
  the context (is R run at the terminal, within a Tk application, a HTML page?).
Creates 3D animated, interactive visualizations that can be viewed in a web browser.
Implements a simple HTTP server allowing to connect GUI clients to R
Function for the GUI API to interact with external IDE/code editors.
Functions to manage the GUI client, like Komodo with the
  SciViews-K extension
Miscellaneous functions for SciViews or general use: manage a
  temporary environment attached to the search path for temporary variables you
  do not want to save() or load(), test if Aqua, Mac, Win, ... Show progress
  bar, etc.
Implements the SVM-Maj algorithm to train data with support vector machine
  as described in Groenen et al. (2008) <doi:10.1007/s11634-008-0020-9>.
  This algorithm uses two efficient updates, one for linear kernel and one 
  for the nonlinear kernel.
Causal effect estimation in observational data often requires identifying a set of untreated observations that are comparable to some treated group of interest.  This package provides a suite of functions for identifying such a set of observations and for implementing standard and new diagnostics tools.  The primary function, svmmatch(), uses support vector machines to identify a region of common support between treatment and control groups.  A sensitivity analysis, balance checking, and assessment of the region of overlap between treated and control groups is included.  The Bayesian implementation allows for recovery of uncertainty estimates for the treatment effect and all other parameters.
Computes the entire regularization path for the two-class svm classifier
		with essentially the same cost as a single SVM fit.
Implementation of Support Vector Machines Plus (SVM+) for classification problems. See (Vladimir et. al, 2009, <doi:10.1016/j.neunet.2009.06.042>) for theoretical details and see (Li et. al, 2016, <https://github.com/okbalefthanded/svmplus_matlab>) for implementation details in 'MATLAB'.
Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>.
Computes the entire solution paths for Support Vector Regression(SVR) with respect to the regularization parameter, lambda and epsilon in epsilon-intensive loss function, efficiently. We call each path algorithm svrpath and epspath. See Wang, G. et al (2008) <doi:10.1109/TNN.2008.2002077> for details regarding the method.
Various tools for semantic vector spaces, such as
    correspondence analysis (simple, multiple and discriminant), latent
    semantic analysis, probabilistic latent semantic analysis, non-negative
    matrix factorization, latent class analysis, EM clustering, logratio
	analysis and log-multiplicative (association) analysis. Furthermore,
    there are specialized distance measures, plotting functions and some helper
    functions.
Implements a socket server allowing to connect clients to R.
Supporting functions for the GUI API (Sweave functions)
Set of tools aimed at wrapping some of the functionalities of the
  packages tools, utils and codetools into a nicer format so that an IDE can use
  them.
A complete unit test system and functions to implement its GUI part.
High level management of widgets, windows and other graphical resources.
Contains functions for computing diagnostics for fixed effects linear regression models fitted with survey data. Extensions of standard diagnostics to complex survey data are included: standardized residuals, leverages, Cook's D, dfbetas, dffits, condition indexes, and variance inflation factors.
Builds, evaluates and validates a nomogram with survey data and right-censored outcomes.
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.
Provides design-based inference for the wide range of parametric models in the 'VGAM' package.
This algorithm conducts variable selection in the classification setting. It repeatedly subsamples variables and runs linear discriminant analysis (LDA) on the subsampled variables. Variables are scored based on the AUC and the t-statistics. Variables then enter a competition and the semi-finalist variables will be evaluated in a final round of LDA classification. The algorithm then outputs a list of variable selected. Qiao, Sun and Fan (2017) <http://people.math.binghamton.edu/qiao/swa.html>.
An algorithm that trains a meta-learning procedure that combines 
    screening and wrapper methods to find a set of extremely low-dimensional attribute 
    combinations. This package works on top of the 'caret' package and proceeds in a 
    forward-step manner. More specifically, it builds and tests learners starting 
    from very few attributes until it includes a maximal number of attributes by 
    increasing the number of attributes at each step. Hence, for each fixed number
    of attributes, the algorithm tests various (randomly selected) learners and 
    picks those with the best performance in terms of training error. Throughout,
    the algorithm uses the information coming from the best learners at the previous
    step to build and test learners in the following step. In the end, it outputs
    a set of strong low-dimensional learners.
A collection of 'HTML', 'JavaScript', and 'CSS' assets that
  dynamically generate beautiful documentation from a 'Swagger' compliant API:
  <https://swagger.io/specification/>.
Collection of functions to connect the structure of the data with the information on the samples. Three types of associations are covered: 1. linear model of principal components. 2. hierarchical clustering analysis. 3. distribution of features-sample annotation associations. Additionally, the inter-relation between sample annotations can be analyzed. Simple methods are provided for the correction of batch effects and removal of principal components.
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>.
A set of tools for examining the design and analysis aspects of stepped wedge cluster randomized trials (SW CRT) based on a repeated cross-sectional sampling scheme (Hussey MA and Hughes JP (2007) Contemporary Clinical Trials 28:182-191. <doi:10.1016/j.cct.2006.05.007>).
Implements the Sliding Window Discrete Fourier Transform (SWDFT). Also provides 
             statistical methods based on the SWDFT, and graphical tools to display the outputs.
To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cohort and cross-sectional settings, binary and continuous outcomes, marginal (GEE) and conditional (mixed effect model) methods,  different link functions (identity, log, logit links), with and without time effect of treatment, etc. The methods included in this package: Zhou et al. (2020) <doi:10.1093/biostatistics/kxy031>, Li et al. (2018) <doi:10.1111/biom.12918>. Supplementary documents can be found at: <https://publichealth.yale.edu/cmips/research/software/swdpwr/>. The Shiny app for swdpwr can be accessed at: <https://jiachenchen322.shinyapps.io/swdpwr_shinyapp/>.
Tidies up the forecasting modeling and prediction work flow, 
    extends the 'broom' package 
    with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions 
    for various forecasting models,
    and enables converting 'forecast' objects to 
    "tidy" data frames with 'sw_sweep'.
Structural handling of identity numbers used in the Swedish
    administration such as personal identity numbers ('personnummer') and
    organizational identity numbers ('organisationsnummer').
The Swiss Ephemeris (version 2.08) is a high precision ephemeris based upon the
    DE431 ephemerides from NASA's JPL. It covers the time range 13201 BCE to
    17191 CE. This package uses the semi-analytic theory by Steve Moshier.
    For faster and more accurate calculations, the compressed Swiss Ephemeris
    data is available in the 'swephRdata' package. To access this data package,
    run 'install.packages("swephRdata", repos = "https://rstub.github.io/drat/",
    type = "source")'. The size of the 'swephRdata' package is approximately
    115 MB. The user can also use the original JPL DE431 data.
Process and summarize aerial survey 'DAS' data (AirDAS) 
    <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-185.PDF>
    collected using an aerial survey program from the 
    Southwest Fisheries Science Center (SWFSC) 
    <https://www.fisheries.noaa.gov/west-coast/science-data/california-current-marine-mammal-assessment-program>.
    PDF files detailing the relevant AirDAS data formats are included in this package.
Process and summarize shipboard DAS data (data written to a .das file) 
    collected using the Southwest Fisheries Science Center (SWFSC) 'WinCruz' program.
    This package is intended to standardize and streamline basic DAS data processing.
    A PDF with the DAS data format requirements is included in this package.
Collection of conversion, analytical, geodesic, mapping, and
    plotting functions. Used to support packages and code written by
    researchers at the Southwest Fisheries Science Center of the National
    Oceanic and Atmospheric Administration.
Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. (2008) <doi:10.1093/biostatistics/kxm054>; Cook, J. R. and Stefanski, L. A. (1994) <doi:10.1080/01621459.1994.10476871>; Little, R. J. A. and Rubin, D. B. (2002, ISBN:978-0-471-18386-0).
An efficient sensitivity analysis for stochastic models based on 
    Monte Carlo samples. Provides weights on simulated scenarios from a 
    stochastic model, such that stressed random variables fulfil given 
    probabilistic constraints (e.g. specified values for risk measures), 
    under the new scenario weights. Scenario weights are selected by 
    constrained minimisation of the relative entropy to the baseline model. 
    The 'SWIM' package is based on Pesenti S.M, Millossovich P., Tsanakas A. 
    (2019) "Reverse Sensitivity Testing: What does it take to break the model", 
    <openaccess.city.ac.uk/id/eprint/18896/>.
The goal for of the 'SwimmeR' package is to provide means of acquiring, and then analyzing, data from swimming (and diving) competitions.  To that end 'SwimmeR' allows results to be read in from .html sources, like 'Hy-Tek' real time results pages, '.pdf' files, 'ISL' results, and (on a development basis) '.hy3' files.  Once read in, 'SwimmeR' can convert swimming times (performances) between the computationally useful format of seconds reported to the '100ths' place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community.  'SwimmeR' can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', 'SCM', 'SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data.  This is a developmental package, not yet mature.
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. Additionally, it allows to retrieve data from the Swissvotes-Database, one of the most comprehensive data platforms on Swiss referendums and initiatives <https://swissvotes.ch/page/dataset/swissvotes_dataset.csv>.
Provides a number of useful functions to employ MrP for small area prediction in Switzerland. Based on a hierarchical model and survey data one can derive cantonal preference measures. The package allows to automatize the prediction and post-stratification steps. It further provides adequate print, summary, map, and plot functions for objects of its class.
Retrieves the most important data on parliamentary activities of the Swiss Federal Assembly via 
    an open, machine-readable interface (see <https://ws.parlament.ch/odata.svc/>).
Provides a switch-case construct for 'R', as it is known from other programming languages. It allows to test multiple, similar conditions in an efficient, easy-to-read manner, so nested if-else constructs can be avoided. The switch-case construct is designed as an 'R' function that allows to return values depending on which condition is met and lets the programmer flexibly decide whether or not to leave the switch-case construct after a case block has been executed.
Functions for estimating the parameters from the latent
  state process and the functions corresponding to the J states as
  proposed by De Souza and Heckman (2013).
Provides an abstraction for managing, installing,
    and switching between sets of installed R packages. This allows users to
    maintain multiple package libraries simultaneously, e.g. to maintain
    strict, package-version-specific reproducibility of many analyses, or
    work within a development/production release paradigm. Introduces a
    generalized package installation process which supports multiple repository
    and non-repository sources and tracks package provenance.
Functions to connect the widely used Storm Water Management Model (SWMM)
  of the United States Environmental Protection Agency (US EPA) 
  <https://www.epa.gov/water-research/storm-water-management-model-swmm> to R with
  currently two main goals: (1) Run a SWMM simulation from R and (2) provide fast 
  access to simulation results, i.e. SWMM's binary '.out'-files. High performance is achieved
  with help of Rcpp. Additionally, reading SWMM's '.inp' and '.rpt' files is supported to 
  glance model structures and to get direct access to simulation summaries.
Tools for retrieving, organizing, and analyzing environmental
    data from the System Wide Monitoring Program of the National Estuarine
    Research Reserve System <http://cdmo.baruch.sc.edu/>. These tools
    address common challenges associated with continuous time series data
    for environmental decision making.
Tools for performing routine analysis and plotting tasks with environmental
    data from the System Wide Monitoring Program of the National Estuarine
    Research Reserve System <http://cdmo.baruch.sc.edu/>. This package builds
    on the functionality of the SWMPr package <https://cran.r-project.org/package=SWMPr>,
    which is used to retrieve and organize the data. The combined set of tools
    address common challenges associated with continuous time series data
    for environmental decision making, and are intended for use in annual reporting activities.
    References:
    Beck, Marcus W. (2016) <ISSN 2073-4859><https://journal.r-project.org/archive/2016-1/beck.pdf>
    Rudis, Bob (2014) <https://rud.is/b/2014/11/16/moving-the-earth-well-alaska-hawaii-with-r/>.
    United States Environmental Protection Agency (2015) <https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=OWOW&dirEntryId=327030>.
    United States Environmental Protection Agency (2012) <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.646.1973&rep=rep1&type=pdf>.
This Systems Biology Package (Gelius-Dietrich et. al. (2012) <doi:10.1186/1752-0509-7-125>) implements algorithms for constraint based analyses of metabolic networks, e.g. flux-balance analysis (FBA), minimization of metabolic adjustment (MOMA), regulatory on/off minimization (ROOM), robustness analysis and flux variability analysis. The package is easily extendable for additional algorithms. Most of the current LP/MILP solvers are supported via additional packages.
Different techniques of cost constrained flux balance analysis.
        1- MetabOlic Modeling with ENzyme kineTics 'MOMENT' which uses enzyme kinetic data and enzyme molecular 
         weights to constrain flux balance analysis(FBA) and it is described in Adadi, R., Volkmer, B., Milo, R., 
         Heinemann, M., & Shlomi, T. (2012). Prediction of Microbial Growth Rate versus Biomass Yield by a Metabolic Network 
           with Kinetic Parameters, 8(7). <doi:10.1371/journal.pcbi.1002575>. 
        2- an improvement of 'MOMENT' that considers multi-functional enzymes 'ccFBA'. 
         Described in Desouki, Abdelmoneim. Algorithms for improving the predictive power of flux balance analysis. PhD dissertation, 2016.
         FBA is a mathematical technique to find fluxes in metabolic models at steady state. 
         It is described in Orth, J.D., Thiele, I. and Palsson, B.O.  What is flux balance analysis? Nat. Biotech. 28, 245-248(2010).
Implement cycle-Free flux balance analysis (CycleFreeFlux), cycle-free flux variability, and Random Sampling of solution space.
      Desouki, A. A., Jarre, F., Gelius-Dietrich, G., & Lercher, M. J. (2015). CycleFreeFlux: efficient removal of thermodynamically
      infeasible loops from flux distributions. Bioinformatics, 31(13), 2159-2165. <doi.org/10.1093/bioinformatics/btv096>. 
      Flux balance analysis is a technique to find fluxes in metabolic models at steady state. 
      It is described in Orth, J.D., Thiele, I. and Palsson, B.O. What is flux balance analysis? Nat. Biotech. 28, 245-248 (2010).
Implements dynamic FBA technique proposed by Varma et al 1994.
'SBML' (Systems Biology Markup Language) with 'FBC' (Flux Balance Constraints) integration in 'sybil'. Many constraint based metabolic models are published in 'SBML' format ('*.xml'). Herewith is the ability to read, write, and check 'SBML' files in 'sybil' provided.
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>).
Creates a guide for writing the formula for the determinant of a square matrix (a detguide) as a function of the elements of the matrix and writes out that formula, the symbolic representation.
Package that implements various symbol plots (bars,
        profiles, stars, Chernoff faces, color icons, stick figures).
A matrix-like class to represent a symmetric matrix partitioned
    into file-backed blocks.
Provides an R interface to 'SymEngine' <https://github.com/symengine/>,
    a standalone 'C++' library for fast symbolic manipulation. The package has functionalities
    for symbolic computation like calculating exact mathematical expressions, solving
    systems of linear equations and code generation.
Implementations of a large number of tests for symmetry and their 
    bootstrap variants, which can be used for testing the symmetry of random
    samples around a known or unknown mean. Functions are also there for testing
    the symmetry of model residuals around zero. Currently, the supported models
    are linear models and generalized autoregressive conditional
    heteroskedasticity (GARCH) models (fitted with the 'fGarch' package). All
    tests are implemented using the 'Rcpp' package which ensures great
    performance of the code.
Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.
A collection of functions for symbolic computation using 'Ryacas'
  package for structural equation models. This package includes
  functions to calculate model-implied covariance (and correlation)
  matrix and sampling covariance matrix of functions of variables using
  the first-order Taylor approximation. Reference: McArdle and McDonald (1984) <doi:10.1111/j.2044-8317.1984.tb00802.x>.
Contains methods for simulation and for evaluating the pdf, cdf, and quantile functions for symmetric stable, symmetric classical tempered stable, and symmetric power tempered stable distributions.
Generates synonyms from a given word drawing from a synonym list
    from the 'moby' project <http://moby-thesaurus.org/>.
Methods for computing spatial, temporal, and spatiotemporal
    statistics as described in Gouhier and Guichard (2014) 
    <doi:10.1111/2041-210X.12188>. These methods include 
    empirical univariate, bivariate and multivariate
    variograms; fitting variogram models; phase locking and synchrony analysis;
    generating autocorrelated and cross-correlated matrices.
This package carries out synchrosqueezed wavelet transform. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.
The function 'syncSubsample' subsamples temporal data of different entities so that the result only contains synchronal events. The function 'mci' calculates the Movement Coordination Index (MCI, see reference on help page for function 'mci') of a data set created with the function 'syncSubsample'.
Random number generation designed for cross-language usage.
Analysis of metacommunities based on functional traits and
    phylogeny of the community components. The functions that are offered here
    implement for the R environment methods that have been available in the
    SYNCSA application written in C++ (by Valerio Pillar, available at 
    <http://ecoqua.ecologia.ufrgs.br/SYNCSA.html>).
Infers sympatry matrices from distributional data and analyzes them in order to identify groups of species cohesively connected.
Provides a flexible and simple tool to synthesize regular or irregular functional data and snippets to facilitate research or analysis of functional data.
Framework to perform synthetic likelihood inference
    for models where the likelihood function is unavailable or
    intractable.
Set of functions to compute different types of synoptic classification methods and for analysing their effect on environmental variables. More information about the methods used in Lemus-Canovas et al. 2019 <DOI:10.1016/j.atmosres.2019.01.018>,  Martin-Vide et al. 2008 <DOI:10.5194/asr-2-99-2008> and Jenkinson and Collison 1977.
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.
Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. An application to testing a new method can be found in Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962> associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>.
A critical first step in systematic literature reviews
  and mining of academic texts is to identify relevant texts from a range
  of sources, particularly databases such as 'Web of Science' or 'Scopus'.
  These databases often export in different formats or with different metadata
  tags. 'synthesisr' expands on the tools outlined by Westgate (2019)
  <doi:10.1002/jrsm.1374> to import bibliographic data from a range of formats
  (such as 'bibtex', 'ris', or 'ciw') in a standard way, and allows merging
  and deduplication of the resulting dataset.
Creation of an individual claims simulator which generates various
    features of non-life insurance claims. An initial set of test parameters,
    designed to mirror the experience of an Auto Liability portfolio, were set
    up and applied by default to generate a realistic test data set of
    individual claims (see vignette). The simulated data set then allows
    practitioners to back-test the validity of various reserving models and to
    prove and/or disprove certain actuarial assumptions made in claims
    modelling. The distributional assumptions used to generate this data set can
    be easily modified by users to match their experiences. Reference: Avanzi B,
    Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim
    simulator with feature control" <arXiv:2008.05693>.
A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. For a description of the implemented method see Nowok, Raab and Dibben (2016) <doi:10.18637/jss.v074.i11>.
A set of functions to support experimentation in the utility of partially synthetic data sets.  All functions compare an observed data set to one or a set of partially synthetic data sets derived from the observed data to (1) check that data sets have identical attributes, (2) calculate overall and specific variable perturbation rates, (3) check for potential logical inconsistencies, and (4) calculate confidence intervals and standard errors of desired variables in multiple imputed data sets. Confidence interval and standard error formulas have options for either synthetic data sets or multiple imputed data sets. For more information on the formulas and methods used, see Reiter & Raghunathan (2007) <doi:10.1198/016214507000000932>.
Drop-in replacements for the base system2() function with fine control
    and consistent behavior across platforms. Supports clean interruption, timeout, 
    background tasks, and streaming STDIN / STDOUT / STDERR over binary or text 
    connections. Arguments on Windows automatically get encoded and quoted to work 
    on different locales.
Quasi-Monte-Carlo algorithm for systematic generation of shock scenarios from an arbitrary multivariate elliptical distribution. The algorithm selects a systematic mesh of arbitrary fineness that approximately evenly covers an isoprobability ellipsoid in d dimensions (Flood, Mark D. & Korenko, George G. (2013) <doi:10.1080/14697688.2014.926018>).
  This package is the 'R' analogy to the 'Matlab' code published by Flood & Korenko in above-mentioned paper.
Loading system fonts and Google Fonts
    <https://fonts.google.com/> into R, in order to
    support other packages such as 'R2SWF' and 'showtext'.
Provides functions for constructing mathematical models of dynamical systems from measured input-output data.
Send 'syslog' protocol messages to a remote 'syslog' server
    specified by host name and TCP network port.
Econometric estimation of simultaneous
 systems of linear and nonlinear equations using Ordinary Least
 Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated
 Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted
 Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS)
 as suggested, e.g., by Zellner (1962) <doi:10.2307/2281644>,
 Zellner and Theil (1962) <doi:10.2307/1911287>, and
 Schmidt (1990) <doi:10.1016/0304-4076(90)90127-F>.
Provides system native access to the font catalogue. As font
    handling varies between systems it is difficult to correctly locate 
    installed fonts across different operating systems. The 'systemfonts' 
    package provides bindings to the native libraries on Windows, macOS and 
    Linux for finding font files that can then be used further by e.g. graphic
    devices. The main use is intended to be from compiled code but 'systemfonts'
    also provides access from R.
The past decade has demonstrated an increased need to better understand risks leading to systemic crises. This framework offers scholars, practitioners and policymakers a useful toolbox to explore such risks in financial systems. Specifically, this framework provides popular econometric and network measures to monitor systemic risk and to measure the consequences of regulatory decisions. These systemic risk measures are based on the frameworks of Adrian and Brunnermeier (2016) <doi:10.1257/aer.20120555> and Billio, Getmansky, Lo and Pelizzon (2012) <doi:10.1016/j.jfineco.2011.12.010>.
A toolbox for systemic risk based on liabilities matrices. Contains
    a Gibbs sampler for liabilities matrices where only row and column sums of the
    liabilities matrix as well as some other fixed entries are observed. Includes models 
    for power law distribution on the degree distribution.
Deals with standard Young tableaux (field of combinatorics). Performs enumeration, counting, random generation, the Robinson-Schensted correspondence, and conversion to and from paths on the Young lattice.
Extracts sentiment and sentiment-derived plot arcs
    from text using a variety of sentiment dictionaries conveniently
    packaged for consumption by R users.  Implemented dictionaries include
    "syuzhet" (default) developed in the Nebraska Literary Lab
    "afinn" developed by Finn Årup Nielsen, "bing" developed by Minqing Hu
    and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D.
    Applicable references are available in README.md and in the documentation
    for the "get_sentiment" function.  The package also provides a hack for
    implementing Stanford's coreNLP sentiment parser. The package provides
    several methods for plot arc normalization.
Contains functions for applying the T^2-test for equivalence.
    The T^2-test for equivalence is a multivariate two-sample equivalence test. 
    Distance measure of the test is the Mahalanobis distance.
    For multivariate normally distributed data the T^2-test for equivalence 
    is exact and UMPI.
    The function T2EQ() implements the T^2-test for equivalence 
    according to Wellek (2010) <DOI:10.1201/ebk1439808184>.
    The function T2EQ.dissolution.profiles.hoffelder() implements a variant 
    of the T^2-test for equivalence according to Hoffelder (2016) 
    <http://www.ecv.de/suse_item.php?suseId=Z|pi|8430> for the 
    equivalence comparison of highly variable dissolution profiles.
Cluster analysis is one of the most fundamental problems in data science. We provide a variety of algorithms from clustering to the learning on the space of partitions. See Hennig, Meila, and Rocci (2016, ISBN:9781466551886) for general exposition to cluster analysis.
Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) <doi:10.1561/2200000073> for the general exposition to the study of computational optimal transport.
Contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.
Calculates the robust Taba linear, Taba rank (monotonic), TabWil, and TabWil rank 
    correlations. Test statistics as well as one sided or two sided p-values are provided 
    for all correlations. Multiple correlations and p-values can be calculated 
    simultaneously across multiple variables. In addition, users will have the option to use 
    the partial, semipartial, and generalized partial correlations; where the partial and 
    semipartial correlations use linear, logistic, or Poisson regression to modify the specified
    variable.
Sometimes you need to split your data and work on the two chunks independently before bringing them back together. 'Taber' allows you to do that with its two functions.
Simple tabulation should be dead simple. 
    This package is an opinionated approach to easy tabulations while also 
    providing exact numbers and allowing for re-usability. 
    This is achieved by providing tabulations as data.frames with columns for 
    values, optional variable names, frequency counts including and excluding 
    NAs and percentages for counts including and excluding NAs. Also values are
    automatically sorted by in decreasing order of frequency counts to allow for 
    fast skimming of the most important information.
For writing tables with custom formats in a Excel file ready to be distributed.
A specialization of 'dplyr' data manipulation verbs that parse and build expressions
    which are ultimately evaluated by 'data.table', letting it handle all optimizations. A set of
    additional verbs is also provided to facilitate some common operations on a subset of the data.
Translate double and integer valued data into
    character values formatted for tabulation in manuscripts
    or other types of academic reports.
Create HTML tables of descriptive statistics, as one would expect
    to see as the first table (i.e. "Table 1") in a medical/epidemiological journal
    article.
Table 1 is the classical way to describe the patients in a
    clinical study. The amount of splits in the data in such a table is
    limited. Table1Heatmap draws a heatmap of all crosstables that can be
    generated with the data. Users can choose between showing the actual
    crosstables or direction of effect of associations, and highlight
    associations by number of patients or p-values.
A collection of time-saving wrappers for reproducible export of
    summary tables commonly used in scientific articles, to .xls/.xlsx multi-tab spreadsheets, while controlling spreadsheet layout. Powered by 'XLConnect'/'rJava' utilities.
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.
'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.
Helps the user to build and register schema descriptions of 
    disorganised (messy) tables. Disorganised tables are tables that are 
    not in a topologically coherent form, where packages such as 'tidyr' could 
    be used for reshaping. The schema description documents the arrangement of 
    input tables and is used to reshape them into a standardised (tidy) output 
    format.
The 'tabularmap' is one of the visualization methods for 
    efficiently displaying data consisting of multiple elements by tiling them.
    When dealing with geospatial, it corrects for differences in visibility 
    between areas.
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.
A teaching project for the display of statistical
    tests as well as some convenience functions for data cleanup. The
    primary components are the functions prefixed with 'tadaa_', which are
    built to work in an interactive environment, but also print markdown
    tables powered by 'pixiedust' for the creation of 'RMarkdown' reports.
    This package is no longer actively developed.
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. This research is supported by a U.S. National Science Foundation grant DMS-1712642 and a U.S. Army Research Office grant W911NF-17-1-0007.
Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.
Implements the tail-rank statistic for selecting biomarkers
  from a microarray data set, an efficient nonparametric test focused
  on the distributional tails. See
  <http://bioinformatics.mdanderson.org/TailRank/tolstoy-new.pdf>.
A tool to help create shiny apps for selecting and annotating 
    elements of images. Users must supply images, questions, and answer
    choices. The user interface is a dynamic shiny app, that displays the images
    and questions and answer choices. The data generated can be saved to a
    file that can be used for subsequent analysis. The original purpose was to
    annotate still images from tennis video for face recognition and emotion 
    detection purposes.
It includes functions for applying methodologies utilized for single-process kinetic analysis of solid-state processes were recently summarized and described in the Recommendation of ICTAC Kinetic Committee. These methods work with the basic kinetic equation. The Methodologies included refers to  Avrami, Friedman, Kissinger, Ozawa, OFM, Mo, Starink, isoconversional methodology (Vyazovkin) according to ICATAC Kinetics Committee recommendations as reported in Vyazovkin S, Chrissafis K, Di Lorenzo ML, et al. ICTAC Kinetics Committee recommendations for collecting experimental thermal analysis data for kinetic computations. Thermochim Acta. 2014;590:1-23. <doi:10.1016/J.TCA.2014.05.036> .
Includes marginal maximum likelihood estimation and joint maximum
    likelihood estimation for unidimensional and multidimensional 
    item response models. The package functionality covers the 
    Rasch model, 2PL model, 3PL model, generalized partial credit model, 
    multi-faceted Rasch model, nominal item response model, 
    structured latent class model, mixture distribution IRT models, 
    and located latent class models. Latent regression models and 
    plausible value imputation are also supported. For details see
    Adams, Wilson and Wang, 1997 <doi:10.1177/0146621697211001>,
    Adams, Wilson and Wu, 1997 <doi:10.3102/10769986022001047>,
    Formann, 1982 <doi:10.1002/bimj.4710240209>,
    Formann, 1992 <doi:10.1080/01621459.1992.10475229>.
The Tanaka method enhances the representation of topography on a map using shaded contour lines. In this simplified implementation of the method, north-west white contours represent illuminated topography and south-east black contours represent shaded topography. See Tanaka (1950) <doi:10.2307/211219>.
Density, distribution function, quantile function, random
    generation and survival function for the Tangent Burr Type XII Distribution as
    defined by SOUZA, L. New Trigonometric Class of Probabilistic Distributions.
    219 p. Thesis (Doctorate in Biometry and Applied Statistics) - Department of
    Statistics and Information, Federal Rural University of Pernambuco, Recife,
    Pernambuco, 2015 (available at <http://www.openthesis.org/documents/New-trigonometric-classes-probabilistic-distributions-602633.html>) and BRITO, C. C. R. Method Distributions generator and
    Probability Distributions Classes. 241 p. Thesis (Doctorate in Biometry and
    Applied Statistics) - Department of Statistics 
    University of Pernambuco, Recife, Pernambuco, 2014 (available upon request).
A two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, <doi:10.1093/bioinformatics/btw449>), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.
Spatial data anonymization preserves confidentiality. Using methods described in Zandbergen (2014) <doi:10.1155/2014/567049>, spatial data anonymization is achieved by dithering original spatial coordinates with combinations of randomized vertical, horizontal and rotational shifts. This can apply to non-grid spatial point patterns and raster objects, and the methods preserve the same spatial characteristics and relationships of the original data. Unique hash keying enables data subjected to anonymization sequences to be re-identified where required.
Provides an extensible formula system to quickly and easily create
    production quality tables. The steps of the process are formula parser,
    statistical content generation from data, to rendering. Each step of the process
    is separate and user definable thus creating a set of building blocks for
    highly extensible table generation. A user is not limited by any of the 
    choices of the package creator other than the formula grammar. For example,
    one could chose to add a different S3 rendering function and output a format
    not provided in the default package. Or possibly one would rather have Gini
    coefficients for their statistical content. Routines to achieve New England
    Journal of Medicine style, Lancet style and Hmisc::summaryM() statistics are
    provided. The package contains rendering for HTML5, Rmarkdown and an indexing
    format for use in tracing and tracking are provided.
Implementation of functions for fitting taper curves (a semiparametric linear mixed effects taper model) to diameter measurements along stems. Further functions are provided to estimate the uncertainty around the predicted curves, to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. For cases where tree
  heights are not measured, methods for estimating
  additional variance in volume predictions resulting from uncertainties in
  tree height models (tariffs) are provided.  The example data include the taper curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees and a subset of section-wise diameter measurements of these trees. The functions implemented here are detailed in the following publication: Kublin, E., Breidenbach, J., Kaendler, G. (2013) A flexible stem taper and volume prediction method based on mixed-effects B-spline regression, Eur J For Res, 132:983-997.
Wrapper for using 'tapkee' command line utility,
 it allows to run it from inside R and catch the results for further analysis and plotting.
 'Tapkee' is a program for fast dimension reduction, see 'package?tapkee' and <http://tapkee.lisitsyn.me/>
 for installation and other details.
Manager of tick-by-tick transaction data that performs 'cleaning', 'aggregation' and 'import' in an efficient and fast way. The package engine, written in C++, exploits the 'zlib' and 'gzstream' libraries to handle gzipped data without need to uncompress them. 'Cleaning' and 'aggregation' are performed according to Brownlees and Gallo (2006) <DOI:10.1016/j.csda.2006.09.030>. Currently, TAQMNGR processes raw data from WRDS (Wharton Research Data Service, <https://wrds-web.wharton.upenn.edu/wrds/>).
Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <DOI:10.1081/STA-200054435>.
Various methods for targeted and semiparametric inference including
	     augmented inverse probability weighted estimators for missing data and
	     causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>)
	     and estimators for risk differences and relative risks (Richardson et al. (2017)
	     <doi:10.1080/01621459.2016.1192546>).
Functions to provide an intuitive interface for retrieving travel time data from the
    'Targomo' API (see <https://targomo.com/developers/> for details). Provides support for retrieving
    isochrone polygons, travel routes, times and distances. Also includes functions for easily adding
    the data to 'leaflet' maps, and functions for using the 'Targomo' map tiles.
Implement the Tariff algorithm for coding cause-of-death from verbal autopsies. The Tariff method was originally proposed in James et al (2011) <DOI:10.1186/1478-7954-9-31> and later refined as Tariff 2.0 in Serina, et al. (2015) <DOI:10.1186/s12916-015-0527-9>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist between the this implementation and the implementation available from IHME.
A collection of various utility and convenience functions.
Interactive shiny application for working with textmining and text analytics. Various visualizations  are provided.
Provides functions for analyzing citizens' bicycle usage pattern and predicting rental amount on specific conditions.
    Functions on this package interacts with data on 'tashudata' package, a 'drat' repository.
    'tashudata' package contains rental/return history on public bicycle system('Tashu'), weather for 3 years and bicycle station information. 
    To install this data package, see the instructions at <https://github.com/zeee1/Tashu_Rpackage>.
    top10_stations(), top10_paths() function visualizes image showing the most used top 10 stations and paths.
    daily_bike_rental() and monthly_bike_rental() shows daily, monthly amount of bicycle rental.
    create_train_dataset(), create_test_dataset() is data processing function for prediction.
    Bicycle rental history from 2013 to 2014 is used to create training dataset and that on 2015 is for test dataset.
    Users can make random-forest prediction model by using create_train_model() 
    and predict amount of bicycle rental in 2015 by using predict_bike_rental().
Schedule R scripts/processes with the Windows task scheduler. This
    allows R users to automate R processes on specific time points from R itself.
The ta-test is a modified two-sample or two-group t-test of Gosset (1908). In small samples with less than 15 replicates,the ta-test significantly reduces type I error rate but has almost the same power with the t-test and hence can greatly enhance reliability or reproducibility of discoveries in biology and medicine. The ta-test can test single null hypothesis or multiple null hypotheses without needing to correct p-values.
Functions to combine data.frames in ways that require additional effort in 
  base R, and to add metadata (id, title, ...) that can be used for printing and 
  xlsx export. The 'Tatoo_report' class is provided as a 
  convenient helper to write several such tables to a workbook, one table per 
  worksheet. Tatoo is built on top of 'openxlsx', but intimate knowledge of 
  that package is not required to use tatoo.
Evaluates traveltimes and ray paths using predefined Earth
        (or other planet) models.  Includes phase plotting routines.
        The IASP91 (Kennett and Engdahl, 1991 <doi:10.1111/j.1365-246X.1991.tb06724.x>)
	and AK135 (Kennett et al., 1995 <doi:10.1111/j.1365-246X.1995.tb03540.x>)
	Earth models are included, and most important arrival phases can be
	evaluated.
Computes the t* statistic corresponding to the tau* population
          coefficient introduced by Bergsma and Dassios (2014) <DOI:10.3150/13-BEJ514>
          and does so in O(n^2) time following the algorithm of Heller and
          Heller (2016) <arXiv:1605.08732> building off of the work of Weihs,
          Drton, and Leung (2016) <DOI:10.1007/s00180-015-0639-x>. Also allows for
          independence testing using the asymptotic distribution of t* as described by
          Nandy, Weihs, and Drton (2016) <arXiv:1602.04387>.
'Tautulli' (<http://tautulli.com>) is a monitoring application for 'Plex' Media Servers 
  (<https://www.plex.tv>) which collects a lot of data about media items and server 
  usage such as play counts. This package interacts with the 'Tautulli' API of any 
  specified server to get said data into R. The 'Tautulli' API documentation is available
  at <https://github.com/Tautulli/Tautulli/blob/master/API.md>.
Provides taxonomic classes for
    groupings of taxonomic names without data, and those
    with data. Methods provided are "taxonomically aware", in
    that they know about ordering of ranks, and methods that
    filter based on taxonomy also filter associated data. 
    This package is described in the publication: "Taxa: An R 
    package implementing data standards and methods for 
    taxonomic data", Zachary S.L. Foster, Scott Chamberlain,  
    Niklaus J. Grünwald (2018) <doi:10.12688/f1000research.14013.2>.
Creates a local database of many commonly used taxonomic authorities
             and provides functions that can quickly query this data.
Computation and visualization of Taxicab Correspondence Analysis, Choulakian (2006) <doi:10.1007/s11336-004-1231-4>.  Classical correspondence analysis (CA) is a statistical method to analyse 2-dimensional tables of positive numbers and is typically applied to contingency tables (Benzecri, J.-P. (1973). L'Analyse des Donnees. Volume II. L'Analyse des Correspondances. Paris, France: Dunod).  Classical CA is based on the Euclidean distance.  Taxicab CA is like classical CA but is based on the Taxicab or Manhattan distance.  For some tables, Taxicab CA gives more informative results than classical CA.
Interacts with a suite of web 'APIs' for taxonomic tasks,
    such as getting database specific taxonomic identifiers, verifying
    species names, getting taxonomic hierarchies, fetching downstream and
    upstream taxonomic names, getting taxonomic synonyms, converting
    scientific to common names and vice versa, and more.
Tools for working with 'taxonomic' databases, including
    utilities for downloading databases, loading them into various
    'SQL' databases, cleaning up files, and providing a 'SQL' connection
    that can be used to do 'SQL' queries directly or used in 'dplyr'.
Handling taxonomic lists through objects of class 'taxlist'.
    This package provides functions to import species lists from 'Turboveg'
    (<https://www.synbiosys.alterra.nl/turboveg>) and the possibility to create
    backups from resulting R-objects.
    Also quick displays are implemented as summary-methods.
Functions for assigning taxonomy to NCBI accession numbers and taxon IDs based on NCBI's accession2taxid and taxdump files. This package allows the user to downloads NCBI data dumps and create a local database for fast and local taxonomic assignment.
Automated standardization of taxonomic names and removal of orthographic errors in plant species names using 'The Plant List' website (www.theplantlist.org).
Some tools to work with taxonomic name lists.
Solves Teitz and Bart's p-median problem - given a set of
    points attempts to find subset of size p such that summed distances of any
    point in the set to the nearest point in p is minimised.  Although
    generally effective, this algorithm does not guarantee that a globally
    optimal subset is found.
Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data.
This package provides functions to assist researchers
        working in the field of tuberculosis diagnostics.  Functions
        for the interpretation of two popular interferon-gamma release
        assays are provided, and additional functionality is planned.
Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.
Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) <doi:10.1214/14-STS510>] and for the Cox model [Held et al. (2016) <doi:10.1002/sim.7089>].
Facilitate the movement between data frames to 'xts'. Particularly
    useful when moving from 'tidyverse' to the widely used 'xts' package, which is
    the input format of choice to various other packages. It also allows the user 
    to use a 'spread_by' argument for a character column 'xts' conversion.
Boosting the likelihood of conditional and shift transformation models as introduced in \doi{10.1007/s11222-019-09870-4}.
We provide a forecasting model for time series forecasting problems with predictors. The offered model, which is based on a submitted research and called tree-based moving average (TBMA),  is based on the integration of the moving average approach to tree-based ensemble approach.  The tree-based ensemble models can capture the complex correlations between the predictors and response variable but lack in modelling time series components. The integration of the moving average approach to the tree-based ensemble approach helps the TBMA model to handle both correlations and autocorrelations in time series data. This package provides a tbma() forecasting function that utilizes the ranger() function from the 'ranger' package. With the help of the ranger() function, various types of tree-based ensemble models, such as extremely randomized trees and random forests, can be used in the TBMA model.
Fast calculation of the Subtree Prune and Regraft (SPR),
  Tree Bisection and Reconnection (TBR) and Replug distances between 
  unrooted trees, using the algorithms of Whidden and 
  Matsen (2017) <arxiv:1511.07529>.
Provides rolling statistical functions based
    on date and time windows instead of n-lagged observations.
Functions to perform the reliability/survival
  analysis using a parametric Transform-both-sides (TBS) model.
Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of sources into a three-dimensional matrix of signals (features by observations by sources). TCA further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects (TCA regression); particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a tensor of cell-type-specific methylation levels for each individual (methylation sites by individuals by cell types) and it allows to detect cell-type-specific relations (associations) with an outcome of interest. For more details see Rahmani et al. (2018) <DOI:10.1101/437368>.
Maximum likelihood estimation (MLE) of parameters assuming an underlying left truncated normal distribution with left censoring described in Williams, J, Kim, H, and Crespi, C. (2020) <doi:10.1186/s12874-020-01032-9>. Censoring is assumed to occur above the truncation threshold meaning that only censored observations are observed. Additional maximum likelihood estimation procedures are implemented to solve left censored only and left truncated only problems.
The classical two-sample t-test works well for the normally distributed data or 
    data with large sample size. The tcfu() and tt() tests implemented in this package provide 
    better type-I-error control with more accurate power when testing the equality of two-sample 
    means for skewed populations having unequal variances. These tests are especially useful 
    when the sample sizes are moderate. The tcfu() uses the Cornish-Fisher expansion to achieve 
    a better approximation to the true percentiles. The tt() provides transformations of the Welch's 
    t-statistic so that the sampling distribution become more symmetric. For more technical details, 
    please refer to Zhang (2019) <http://hdl.handle.net/2097/40235>.
The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. Several TCGA Datasets are available online. 'TCGAretriever' helps accessing and downloading TCGA data hosted on 'cBioPortal' via its Web Interface (see <http://www.cbioportal.org/> for more information). 'TCGAretriever' is easy to use (get all the TCGA data you need with a few lines of code), enforces reliable data download (via 'httr'), and is suitable for downloading large volumes of data.
Implementation of Time-course Gene Set Analysis (TcGSA), a method for 
	analyzing longitudinal gene-expression data at the gene set level. Method is
	detailed in: Hejblum, Skinner & Thiebaut (2015) <doi: 10.1371/journal.pcbi.1004310>.
Analyze RNA-seq data with variance component score test accounting 
    for data heteroscedasticity through precision weights. Perform both gene-wise and gene set
    analyses, and can deal with longitudinal data. Method is detailed in: Agniel & Hejblum 
    (2017) <doi: 10.1093/biostatistics/kxx005>, Variance component score test for 
    time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604.
A wrapper for The Cancer Imaging Archive's REST API. The Cancer
    Imaging Archive (TCIA) hosts de-identified medical images of cancer
    available for public download, as well as rich metadata for each image
    series. TCIA provides a REST API for programmatic access to the data.
    This package provides simple functions to access each API endpoint.
    For more information, see <https://github.com/pamelarussell/TCIApathfinder>
    and TCIA's website.
Provide the core functionality to transform longitudinal data to
    complex-time (kime) data using analytic and numerical techniques, visualize the original 
    time-series and reconstructed kime-surfaces, perform model based (e.g., tensor-linear regression)
    and model-free classification and clustering methods in the book Dinov, ID and Velev, MV. (2021)
    "Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics", De Gruyter STEM Series,
    ISBN 978-3-11-069780-3. <https://www.degruyter.com/view/title/576646>.
    The package includes 18 core functions which can be separated into three groups.
    1) draw longitudinal data, such as fMRI time-series, and forecast or transform the time-series data.
    2) simulate real-valued time-series data, e.g., fMRI time-courses, detect the activated areas,
    report the corresponding p-values, and visualize the p-values in the 3D brain space.
    3) Laplace transform and kimesurface reconstructions of the fMRI data.
An implementation of extended Rasch modeling hypothesis testing in R. 
    Provides 4 statistical tests, i.e. gradient test (GR), likelihood ratio test (LR), 
    Rao score or Lagrange multiplier test (RS), and Wald test, for testing a number of 
    hypotheses referring to the Rasch model (RM), linear logistic test model (LLTM), 
    rating scale model (RSM), and partial credit model (PCM).
A series of additional Tcl commands and Tk widgets with style
  and various functions (under Windows: DDE exchange, access to the
  registry and icon manipulation) to supplement the tcltk package
Provides functions for robust trimmed clustering. The methods are described in Garcia-Escudero (2008) <doi:10.1214/07-AOS515>, Fritz et al. (2012) <doi:10.18637/jss.v047.i12>, Garcia-Escudero et al. (2011)  <doi:10.1007/s11222-010-9194-z> and others.
The 1311 time series from the tourism forecasting competition conducted in 2010 and described in Athanasopoulos et al. (2011) <DOI:10.1016/j.ijforecast.2010.04.009>.
A set of tools for processing and modeling high-throughput and
    high-content chemical screening data. The package was developed for the
    the chemical screening data generated by the US EPA ToxCast program, but
    can be used for diverse chemical screening efforts.
Platform for the advanced analysis of T cell receptor and
    Immunoglobulin repertoires data and visualisation of the analysis results.
    Publication: Nazarov et al. (2015) <doi:10.1186/s12859-015-0613-1>.
Tools for the statistical analysis of persistent homology and for
    density clustering. For that, this package provides an R interface for the
    efficient algorithms of the C++ libraries 'GUDHI' <http://gudhi.gforge.inria.fr/>, 'Dionysus' <http://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements the methods in Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2014) <doi:10.1145/2582112.2582128>  for analyzing the statistical significance of persistent homology features.
Topological Data Analysis using Mapper (discrete Morse theory).
    Generate a 1-dimensional simplicial complex from a filter 
    function defined on the data:  1. Define a filter function (lens) on the 
    data.  2. Perform clustering within within each level set and generate 
    one node (vertex) for each cluster.  3. For each pair of clusters in 
    adjacent level sets with a nonempty intersection, generate one edge 
    between vertices.  The function mapper1D uses a filter function with
    codomain R, while the the function mapper2D uses a filter function with
    codomain R^2.
A comprehensive toolset for any
    useR conducting topological data analysis, specifically via the
    calculation of persistent homology in a Vietoris-Rips complex.
    The tools this package currently provides can be conveniently split
    into three main sections: (1) calculating persistent homology; (2)
    conducting statistical inference on persistent homology calculations;
    (3) visualizing persistent homology and statistical inference.
    The published form of TDAstats can be found in Wadhwa et al. (2018)
    <doi:10.21105/joss.00860>.   
    For a general background on computing persistent homology for
    topological data analysis, see Otter et al. (2017)
    <doi:10.1140/epjds/s13688-017-0109-5>.
    To learn more about how the permutation test is used for
    nonparametric statistical inference in topological data analysis,
    read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>.
    To learn more about how TDAstats calculates persistent homology,
    you can visit the GitHub repository for Ripser, the software that
    works behind the scenes at <https://github.com/Ripser/ripser>.
    This package has been published as Wadhwa et al. (2018)
    <doi:10.21105/joss.00860>.
Uniform random samples from simple manifolds, sometimes with noise,
    are commonly used to test topological data analytic (TDA) tools.
    This package includes samplers powered by two techniques: analytic
    volume-preserving parameterizations, as employed by Arvo (1995)
    <doi:10.1145/218380.218500>, and rejection sampling, as employed by
    Diaconis, Holmes, and Shahshahani (2013) <doi:10.1214/12-IMSCOLL1006>.
A boosted Tweedie compound Poisson model using the gradient boosting. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing interactions among predictors.
The Time-Delay Correlation algorithm (TDCor) reconstructs the topology of a gene regulatory network (GRN) from time-series transcriptomic data.  The algorithm is described in details in Lavenus et al., Plant Cell, 2015.  It was initially developed to infer the topology of the GRN controlling lateral root formation in Arabidopsis thaliana.  The time-series transcriptomic dataset which was used in this study is included in the package to illustrate how to use it.
Deconvolution of instrument responses from seismic traces and seismogram lists from RSEIS.  Includes pre-calculated instrument responses for several common instruments.
The t-Digest construction algorithm, by 
    Dunning et al., (2019) <arXiv:1902.04023v1>, uses a variant of 1-dimensional 
    k-means clustering to produce a very compact data structure that allows 
    accurate estimation of quantiles. This t-Digest data structure can be used 
    to estimate quantiles, compute other rank statistics or even to estimate 
    related measures like trimmed means. The advantage of the t-Digest over 
    previous digests for this purpose is that the t-Digest handles data with 
    full floating point resolution. The accuracy of quantile estimates produced 
    by t-Digests can be orders of magnitude more accurate than those produced 
    by previous digest algorithms. Methods are provided to create and update 
    t-Digests and retrieve quantiles from the accumulated distributions.
Tuned Data Mining in R ('TDMR') performs the complete tuning of a 
    data mining task (predictive analytics, that is classification and regression). 
    Preprocessing parameters and modeling parameters can be tuned simultaneously. 
    It incorporates a variety of tuners (among them 'SPOT' and 'CMA' with package 'rCMA') and 
    allows integration of additional tuners. Noise handling in the data mining optimization  
    process is supported, see Koch et al. (2015) <doi:10.1016/j.asoc.2015.01.005>.
Set of functions designed to help in the
    analysis of TDP sensors. Features includes dates and time conversion, weather
    data interpolation, daily maximum of tension analysis and calculations required
    to convert sap flow density data to sap flow rates at the tree and plot scale (For more information see : Granier (1985) <DOI:10.1051/forest:19850204> & Granier (1987) <DOI:10.1093/treephys/3.4.309>).
Implementation of target diagrams using 'lattice' and 'ggplot2' graphics. Target diagrams provide a graphical overview of the respective contributions of the unbiased RMSE and MBE to the total RMSE (Jolliff, J. et al., 2009. "Summary Diagrams for Coupled Hydrodynamic-Ecosystem Model Skill Assessment." Journal of Marine Systems 76: 64–82.)
Compute time-dependent ROC curve from censored survival data using
    nonparametric weight adjustments.
Functions for performing time domain signal coding as used in Chesmore (2001) <doi:10.1016/S0003-682X(01)00009-3>, and related tasks. This package creates the standard S-matrix and A-matrix (with variable lag), has tools to convert coding matrices into distributed matrices, provides published codebooks and allows for extraction of code sequences.
Functions and examples are provided for Transmission/disequilibrium tests
             for extended marker haplotypes, as in
             Clayton, D. and Jones, H. (1999) "Transmission/disequilibrium tests
             for extended marker haplotypes". Amer. J. Hum. Genet., 65:1161-1169,
             <doi:10.1086/302566>.
Provides functions to estimate the insertion and deletion rates of transposable element (TE) families. The estimation of insertion rate consists of an improved estimate of the age distribution that takes into account random mutations, and an adjustment by the deletion rate. A hypothesis test for a uniform insertion rate is also implemented. This package implements the methods proposed in Dai et al (2018).
Different approaches for selecting the threshold in generalized Pareto distributions. Most of them are based on minimizing the AMSE-criterion or at least by reducing the bias of the assumed GPD-model. Others are heuristically motivated by searching for stable sample paths, i.e. a nearly constant region of the tail index estimator with respect to k, which is the number of data in the tail. The third class is motivated by graphical inspection. In addition, a sequential testing procedure for GPD-GoF-tests is also implemented here.
Contains apps and gadgets for teaching data analysis and 
    statistics concepts along with how to implement them in R.  Includes 
    tools to make app development easier and faster by nesting apps together.
Demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming.
Allows the user to draw probabilistic samples and make inferences from a finite population based on several sampling designs.
Can fit neural networks with up to two hidden layer and two different error functions. Also able to handle a weight decay. But just able to compute one output neuron and very slow.
Implements models of leaf temperature using energy balance. It uses units to ensure that parameters are properly specified and transformed before calculations. It allows separate lower and upper surface conductances to heat and water vapour, so sensible and latent heat loss are calculated for each surface separately  as in Foster and Smith (1986) <doi:10.1111/j.1365-3040.1986.tb02108.x>. It's straightforward to model leaf temperature over environmental gradients such as light, air temperature, humidity, and wind. It can also model leaf temperature over trait gradients such as leaf size or stomatal conductance. Other references are Monteith and Unsworth (2013, ISBN:9780123869104), Nobel (2009, ISBN:9780123741431), and Okajima et al. (2012) <doi:10.1007/s11284-011-0905-5>.
An implementation of the TEAM algorithm to identify local differences 
    between two (e.g. case and control) independent, univariate distributions, as 
    described in J Pura, C Chan, and J Xie (2019) <arXiv:1906.07757>. The algorithm 
    is based on embedding a multiple-testing procedure on a hierarchical structure 
    to identify high-resolution differences between two distributions. The 
    hierarchical structure is designed to identify strong, short-range differences 
    at lower layers and weaker, but long-range differences at increasing layers. 
    TEAM yields consistent layer-specific and overall false discovery rate control.
Provides color palettes corresponding to professional and amateur, 
    sports teams. These can be useful in creating data graphics that are themed 
    for particular teams.
Package of wrapper functions using R6 class to send requests to 
  Microsoft 'Teams' <https://products.office.com/en-us/microsoft-teams/group-chat-software> through webhooks.
  When you need to share information or data from R to 'Teams', rather than copying/pasting, you
  can use this package to send well-formatted output from multiple R objects.
The typicality and eccentricity data analysis (TEDA) framework was
    put forward by Angelov (2013) <DOI:10.14313/JAMRIS_2-2014/16>. It has been further developed into multiple
    different techniques since, and provides a non-parametric way of determining how
    similar an observation, from a process that is not purely random, is to other
    observations generated by the process. This package provides code to use the
    batch and recursive TEDA methods that have been published.
Fits mixtures of multivariate t-distributions (with eigen-decomposed covariance structure) via the expectation conditional-maximization algorithm under a clustering or classification paradigm.
Functions for interacting directly with the Taiwan Economic Journal API to offer data in R. For more information go to <https://api.tej.com.tw>.
Implementation of the remote effects spatial process (RESP) model for teleconnection.  The RESP model is a geostatistical model that allows a spatially-referenced variable (like average precipitation) to be influenced by covariates defined on a remote domain (like sea surface temperatures).  The RESP model is introduced in Hewitt et al. (2018) <doi:10.1002/env.2523>.  Sample code for working with the RESP model is available at <https://jmhewitt.github.io/research/resp_example>. This material is based upon work supported by the National Science Foundation under grant number AGS 1419558. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
R wrapper around the Telegram Bot API (http://core.telegram.org/bots/api) to access Telegram's messaging facilities with ease (e.g. you send messages, images, files from R to your smartphone).
Provides a pure interface for the 'Telegram Bot API'
    <http://core.telegram.org/bots/api>. In addition to the pure API
    implementation, it features a number of tools to make the development of
    'Telegram' bots with R easy and straightforward, providing an easy-to-use
    interface that takes some work off the programmer.
Using The Free Evocation of Words Technique method with some functions, this package will make a
    social representation and other analysis. The Free Evocation of Words Technique consists of collecting a number of words evoked by a subject facing exposure to an inducer term. The purpose of this technique is to understand the relationships created between words evoked by the individual and the inducer term. This technique is included in the theory of social representations, therefore, on the information transmitted by an individual, seeks to create a profile that define a social group.
Method to estimate the effect of the trend in predictor variables on the observed trend
 of the response variable using mixed models with temporal autocorrelation. See Fernández-Martínez et
 al. (2017 and 2019) <doi:10.1038/s41598-017-08755-8> <doi:10.1038/s41558-018-0367-7>.
This is the data companion package to the package tempcycles.
    This package includes the metadata, linear, and cycling parameters from
    "Recent geographic convergence in diurnal and annual temperature cycling
    flattens global thermal profiles", Wang & Dillon, Nature Climate Change,
    4, 988-992 (2014). doi:10.1038/nclimate2378.
Temporal disaggregation methods are used to disaggregate and
    interpolate a low frequency time series to a higher frequency series, where
    either the sum, the mean, the first or the last value of the resulting
    high frequency series is consistent with the low frequency series. Temporal
    disaggregation can be performed with or without one or more high frequency
    indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso,
    Fernandez, Litterman, Denton and Denton-Cholette, summarized in Sax and
    Steiner (2013) <doi:10.32614/RJ-2013-028>. Supports most R time series
    classes.
Fits temperature response models to rate measurements taken at different temperatures. Etienne Low-Decarie,Tobias G. Boatman, Noah Bennett,Will Passfield,Antonio Gavalas-Olea,Philipp Siegel, Richard J. Geider (2017) <doi:10.1002/ece3.3576> .
Provides tools to work with template code and text in R. It aims to
    provide a simple substitution mechanism for R-expressions inside these
    templates. Templates can be written in other languages like 'SQL', can
    simply be represented by characters in R, or can themselves be R-expressions
    or functions.
TEMPO (TEmporal Modeling of Pathway Outliers) is a pathway-based outlier detection approach for finding pathways showing 
    significant changes in temporal expression patterns across conditions.  Given a gene expression data set where each sample is characterized by 
    an age or time point as well as a phenotype (e.g. control or disease), and a collection of gene sets or pathways, TEMPO ranks each pathway
    by a score that characterizes how well a partial least squares regression (PLSR) model can predict age as a function of gene expression in the controls
    and how poorly that same model performs in the disease. TEMPO v1.0.3 is described in Pietras (2018) <doi:10.1145/3233547.3233559>.
Performs maximum likelihood based estimation and inference on time to event data, possibly subject to non-informative right censoring. fitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. compParaSurv() compares the mean, median, and restricted mean survival experiences of two treatment groups. Candidate distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull.
Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS).
Compute the coordinates to produce a tendril plot. 
    In the tendril plot, each tendril (branch) represents a type of events, 
    and the direction of the tendril is dictated by on which treatment arm the 
    event is occurring. If an event is occurring on the first of the two 
    specified treatment arms, the tendril bends in a clockwise direction. 
    If an event is occurring on the second of the treatment arms, the
    tendril bends in an anti-clockwise direction. 
    Ref: Karpefors, M and Weatherall, J., "The Tendril Plot - a novel visual summary 
    of the incidence, significance and temporal aspects of adverse events in 
    clinical trials" - JAMIA 2018; 25(8): 1069-1073 <doi:10.1093/jamia/ocy016>.
Implementation of ZENIT-POLAR substitution cipher method of encryption using by default the TENIS-POLAR cipher. This last cipher of encryption became famous through the collection of Brazilian books "Os Karas" by the author Pedro Bandeira. For more details, see "A Cryptographic Dictionary" (GC&CS, 1944).
The tensor product of two arrays is notionally an outer
        product of the arrays collapsed in specific extents by summing
        along the appropriate diagonals.
Provides convenience functions for advanced
        linear algebra with tensors and computation with data sets of
        tensors on a higher level abstraction. It includes Einstein and
        Riemann summing conventions, dragging, co- and contravariate
        indices, parallel computations on sequences of tensors.
Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.
Contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: 'tPCA', 'tFOBI', 'tJADE', k-tJADE', 'tgFOBI', 'tgJADE', 'tSOBI', 'tNSS.SD', 'tNSS.JD', 'tNSS.TD.JD', 'tPP' and 'tTUCKER'.
Interface to 'TensorFlow' <https://www.tensorflow.org/>, 
  an open source software library for numerical computation using data
  flow graphs. Nodes in the graph represent mathematical operations, 
  while the graph edges represent the multidimensional data arrays 
  (tensors) communicated between them. The flexible architecture allows
  you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop, 
  server, or mobile device with a single 'API'. 'TensorFlow' was originally
  developed by researchers and engineers working on the Google Brain Team 
  within Google's Machine Intelligence research organization for the 
  purposes of conducting machine learning and deep neural networks research,
  but the system is general enough to be applicable in a wide variety
  of other domains as well.
A set of tools for noise reduction and completion from ordinal tensor data with possibly missing values. The algorithms can be found in Lee and Wang (2020) <arXiv:2002.06524>.
Provides methods to manipulate and store sparse tensors. Tensors 
  are multidimensional generalizations of matrices (two dimensional) and 
  vectors (one dimensional).
Implement the alternating algorithm for supervised tensor decomposition with interactive side information.
Implements the multiway sparse clustering approach of M. Wang and Y. Zeng, "Multiway clustering via tensor block models". Advances in Neural Information Processing System 32 (NeurIPS), 715-725, 2019.
An implementation of fitting generalized linear models on
    second-order tensor type data. The functions within this package mainly focus on
    parameter estimation, including parameter coefficients and standard deviation.
A collection of functions for Kronecker structured covariance
    estimation and testing under the array normal model. For estimation,
    maximum likelihood and Bayesian equivariant estimation procedures are
    implemented. For testing, a likelihood ratio testing procedure is
    available. This package also contains additional functions for manipulating
    and decomposing tensor data sets. This work was partially supported by NSF
    grant DMS-1505136. Details of the methods are described in
    Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and
    Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.
The TEQR package contains software to calculate the operating characteristics for the TEQR and the ACT designs.The TEQR (toxicity equivalence range) design is a toxicity based cumulative cohort design with added safety rules. The ACT (Activity constrained for toxicity) design  is also a cumulative cohort design with additional safety rules. The unique feature of this design is that dose is escalated based on lack of activity rather than on lack of toxicity and is de-escalated only if an unacceptable level of toxicity is experienced.
This package is for the comparison of various types of A+B
        escalation rules for dose finding trials.
An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis.
Provides functions for the computationally efficient simulation of 
    dynamic networks estimated with the statistical framework of temporal 
    exponential random graph models, implemented in the 'tergm' package.
Creates, manipulates, queries and repairs vectors
    of parameter terms.  Parameter terms are the labels used to reference
    values in vectors, matrices and arrays. They represent the names in
    coefficient tables and the column names in 'mcmc' and 'mcmc.list'
    objects.
Plots ternary diagrams (simplex plots / Gibbs triangles) using the
  standard graphics functions.
  An alternative to 'ggtern', which uses the 'ggplot2' family of plotting 
  functions.
  Includes a 'Shiny' user interface for point-and-click plotting.
A suite of functions for visualising ternary probabilistic forecasts, as discussed in the paper by Jupp (2012) <doi:10.1098/rsta.2011.0350>.
Methods for spatial data analysis, especially raster data. Methods allow for low-level data manipulation as well as high-level global, local, zonal, and focal computation. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/terra/> to get started. The package is very similar to the 'raster' package; but 'terra' is simpler and faster.
Provides triangulations of regular height fields, based on the methods described in "Fast Polygonal Approximation of Terrains and Height Fields" Michael Garland and Paul S. Heckbert (1995) <https://www.mgarland.org/files/papers/scape.pdf> using code from the 'hmm' library written by Michael Fogleman <https://www.github.com/fogleman/hmm>.
Simulation of reconstructed phylogenetic trees under tree-wide time-heterogeneous birth-death processes and estimation of diversification parameters under the same model. Speciation and extinction rates can be any function of time and mass-extinction events at specific times can be provided. Trees can be simulated either conditioned on the number of species, the time of the process, or both. Additionally, the likelihood equations are implemented for convenience and can be used for Maximum Likelihood (ML) estimation and Bayesian inference.
Bindings to 'Tesseract' <https://opensource.google.com/projects/tesseract>: 
     a powerful optical character recognition (OCR) engine that supports over 100 languages.
     The engine is highly configurable in order to tune the detection algorithms and
     obtain the best possible results.
Function test2norm() generates formulas for normative standards 
    applied to cognitive tests. It takes raw test scores (e.g., number of 
    correct responses) and converts them to scaled scores and demographically 
    adjusted scores, using methods described in Heaton et al. (2003) 
    <doi:10.1016/B978-012703570-3/50010-9> & Heaton et al. (2009, 
    ISBN:9780199702800). The scaled scores are calculated as quantiles of the 
    raw test scores, scaled to have the mean of 10 and standard deviation of 3, 
    such that higher values always correspond to better performance on the test. 
    The demographically adjusted scores are calculated from the residuals of a 
    model that regresses scaled scores on demographic predictors (e.g., age). 
    The norming procedure makes use of the mfp() function from the 'mfp' package 
    to explore nonlinear associations between cognition and demographic 
    variables.
A common way of validating a biological assay for is through a
    procedure, where m levels of an analyte are measured with n replicates at each
    level, and if all m estimates of the coefficient of variation (CV) are less
    than some prespecified level, then the assay is declared validated for precision
    within the range of the m analyte levels. Two limitations of this procedure are:
    there is no clear statistical statement of precision upon passing, and it is
    unclear how to modify the procedure for assays with constant standard deviation.
    We provide tools to convert such a procedure into a set of m hypothesis tests.
    This reframing motivates the m:n:q procedure, which upon completion delivers
    a 100q% upper confidence limit on the CV. Additionally, for a post-validation
    assay output of y, the method gives an ``effective standard deviation interval''
    of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where
    mu is the expected value of the assay output for that sample. Further, the m:n:q
    procedure can be straightforwardly applied to constant standard deviation assays.
    We illustrate these tools by applying them to a growth inhibition assay. This is
    an implementation of the methods described in Fay, Sachs, and Miura (2018) 
    <doi:10.1002/sim.7528>.
Different multiple testing procedures for correlation tests are implemented. These procedures were shown to theoretically control asymptotically the Family Wise Error Rate (Roux (2018) <https://tel.archives-ouvertes.fr/tel-01971574v1>) or the False Discovery Rate (Cai & Liu (2016) <doi:10.1080/01621459.2014.999157>). The package gather four test statistics used in correlation testing, four FWER procedures with either single step or stepdown versions, and four FDR procedures.
Computes the test statistics for examining the significance of autocorrelation in univariate time series, cross-correlation in bivariate time series, Pearson correlations in multivariate series and test statistics for i.i.d. property of univariate series given in Dalla, Giraitis and Phillips (2019), <https://cowles.yale.edu/sites/default/files/files/pub/d21/d2194.pdf>.
Functions for imputing missing item responses for dichotomous and
    polytomous test and assessment data. This package enables missing imputation
    methods that are suitable for test and assessment data, including: 
    listwise (LW) deletion (see De Ayala et al. 2001 <doi:10.1111/j.1745-3984.2001.tb01124.x>), 
    treating as incorrect (IN, see Lord, 1974 <doi: 10.1111/j.1745-3984.1974.tb00996.x>; 
    Mislevy & Wu, 1996 <doi: 10.1002/j.2333-8504.1996.tb01708.x>; 
    Pohl et al., 2014 <doi: 10.1177/0013164413504926>), person mean imputation (PM), item mean
    imputation (IM), two-way imputation (TW, see Sijtsma & van der Ark, 2003 <doi: 10.1207/s15327906mbr3804_4>),
    logistic regression imputation (LR), and expectation–maximization (EM) 
    imputation (see Finch, 2008 <doi: 10.1111/j.1745-3984.2008.00062.x>).
Use the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and
    van der Linden (2018) <doi:10.1201/9781315117430> in constructing fixed and adaptive tests. Supports the following
    mixed-integer programming (MIP) solver packages: 'lpsymphony', 'Rsymphony', 'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package
    is not available from CRAN; see <https://www.gurobi.com/downloads/>. See vignette for installing 'Rsymphony' package
    on Mac systems.
Provides two approaches of comparison; the univariate and the multivariate analysis in two or more populations. Since the main obstacle of performing systematic comparisons in anthropological studies is the absence of raw data, the current package offer a solution for this problem by allowing the use of published summary statistics of metric data (mean, standard deviation and sex specific sample size) as illustrated by the works of  Greene, D. L. (1989) <doi:10.1002/ajpa.1330790113> and Konigsberg, L. W. (1991) <doi:10.1002/ajpa.1330840110>.
Provides data sets for teaching statistics and data science courses. 
    It includes a sample of data from John Edmund Kerrich's famous 
    coinflip experiment. These are data that I use for teaching SOC 4015 / SOC 
    5050 at Saint Louis University (SLU). The package also contains an R Markdown
    template with the required formatting for assignments in my courses 
    SOC 4015, SOC 4650, SOC 5050, and SOC 5650 at SLU.
Tests the hypothesis  that variances are homogeneous or not using bootstrap. The procedure uses a variance-based statistic, and is derived from a normal-theory test.
             The test equivalently expressed the hypothesis as a function of the log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis. See Cahoy (2010) <doi:10.1016/j.csda.2010.04.012>.
tester allows you to test characteristics of common R objects.
A collection of testing enhancements and utilities.
    Including utilities for extracting inline test blocks from 
    package source files.
Provides test statistics, p-value, and confidence intervals based on 9 hypothesis tests for dependence.
Test functions are often used to test computer code.
  They are used in optimization to test algorithms and in
  metamodeling to evaluate model predictions. This package provides
  test functions that can be used for any purpose.
  Some functions are taken from <https://www.sfu.ca/~ssurjano>, but
  their R code is not used.
Provides a bootstrap test which decides whether two dose response curves can be assumed as equal concerning their maximum absolute deviation. A plenty of choices for the model types are available, which can be found in the 'DoseFinding' package, which is used for the fitting of the models. See <doi:10.1080/01621459.2017.1281813> for details.
Provides two convenience functions assert() and test_pkg() to
    facilitate testing R packages.
GUI for entering test items and obtaining raw
   and transformed scores. The results are shown on the
   console and can be saved to a tabular text file for further
   statistical analysis. The user can define his own tests and
   scoring procedures through a GUI.
Software testing is important, but, in part because
    it is frustrating and boring, many of us avoid it. 'testthat' is a
    testing framework for R that is easy to learn and use, and integrates
    with your existing 'workflow'.
Utility functions and 'RStudio' addins for writing,
    running and organizing automated tests. Integrates tightly with the
    packages 'testthat', 'devtools' and 'usethis'.  Hotkeys can be
    assigned to the 'RStudio' addins for running tests in a single file or
    to switch between a source file and the associated test file. In
    addition, testthis provides function to manage and run tests in
    subdirectories of the test/testthat directory.
Tests one hypothesis with several test statistics, correcting for multiple testing.  The central function in the package is testtwice().  In a sensitivity analysis, the method has the largest design sensitivity of its component tests.  The package implements the method and examples in Rosenbaum, P. R. (2012) <doi:10.1093/biomet/ass032> Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774.
The satisfaction Analysis using the tetraclasse model from Sylvie Llosa. Llosa (1997) <http://www.jstor.org/stable/40592578>.
Checks LaTeX documents and .bib files for typing errors, such as spelling errors, incorrect quotation marks. Also provides useful functions for parsing and linting bibliography files.
Randomizing exams with 'LaTeX'.
    If you can compile your main document with 'LaTeX', the program should be able to compile the randomized
    versions without much extra effort when creating the document.
Statistical extreme value modelling of threshold excesses, maxima
    and multivariate extremes. Univariate models for threshold excesses and maxima
    are the Generalised Pareto, and Generalised Extreme Value model respectively.
    These models may be fitted by using maximum (optionally penalised-)likelihood,
    or Bayesian estimation, and both classes of models may be fitted with covariates
    in any/all model parameters. Model diagnostics support the fitting process.
    Graphical output for visualising fitted models and return level estimates is
    provided. For serially dependent sequences, the intervals declustering algorithm
    of Ferro and Segers (2003) <doi:10.1111/1467-9868.00401> is provided, with
    diagnostic support to aid selection of threshold and declustering horizon.
    Multivariate modelling is performed via the conditional approach of Heffernan
    and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>, with graphical tools for
    threshold selection and to diagnose estimation convergence.
A collection of functions and data - mostly from Texas - is provided. 
  These are used as teaching tools for geo-spatial data analytics courses in the 
  GISciences program at The University of Texas at Dallas. In addition, several 
  vignettes illustrate geo-spatial data analytics practices, such as relative 
  risk kernel density estimations based on food store locations within Dallas 
  County or the identification of homogenous and spatially contiguous market areas 
  built on socio-economic, demographic and infrastructure census information. The 
  spatial resolution of the data-sets ranges from 1623 food store locations, over 
  geo-referenced areal data of 258 Texan counties, to 529 census tracts as well 
  as 1669 block groups in Dallas County. Cartographic, specialized regression and 
  data handling functions are provided.
An extension of ExPosition for two table analyses, specifically, discriminant analyses.
Compile snippets of 'LaTeX' directly into images
    from the R console to view in the 'RStudio' viewer pane, Shiny apps
    and 'RMarkdown' documents.
Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. (If the Zelig package, which this package enhances, cannot be found on CRAN, you can find it at <https://github.com/IQSS/Zelig>.)
Transforms text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see  <https://www.r-text.org>.
Find similarities between texts using the Smith-Waterman algorithm. The algorithm performs local sequence alignment and determines similar regions between two strings.
    The Smith-Waterman algorithm is explained in the paper: "Identification of common molecular subsequences" by T.F.Smith and M.S.Waterman (1981), available at <doi:10.1016/0022-2836(81)90087-5>. 
    This package implements the same logic for sequences of words and letters instead of molecular sequences.
Unifies different text to speech engines, such as
    Google, Microsoft, and Amazon.  Text synthesis can be done
    in any engine with a simple switch of an argument denoting
    the service requested.  The 'aws.polly' package has been
    orphaned and can be found from the CRAN archives.
Fast and memory-friendly tools for text vectorization, topic
    modeling (LDA, LSA), word embeddings (GloVe), similarities. This package
    provides a source-agnostic streaming API, which allows researchers to perform
    analysis of collections of documents which are larger than available RAM. All
    core functions are parallelized to benefit from multicore machines.
Tools to clean and process text.  Tools are geared at checking for substrings that
          are not optimal for analysis and replacing or removing them (normalizing) with more
          analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards
          (2001) <doi:10.1006/csla.2001.0169>) or extracting them into new variables. For
          example, emoticons are often used in text but not always easily handled by analysis
          algorithms.  The replace_emoticon() function replaces emoticons with word
          equivalents.
Provides a framework to download, parse, and store text datasets
    on the disk and load them when needed. Includes various sentiment lexicons
    and labeled text data sets for classification and analysis.
Implements the approach described in Fong and Grimmer (2016) <https://aclweb.org/anthology/P/P16/P16-1151.pdf> for 
	automatically discovering latent treatments from a corpus and estimating the average marginal component effect (AMCE) of 
	each treatment.  The data is divided into a training and test set.  The supervised Indian Buffet Process (sibp) is used 
	to discover latent treatments in the training set.  The fitted model is then applied to the test set to infer the values 
	of the latent treatments in the test set.  Finally, Y is regressed on the latent treatments in the test set to estimate 
	the causal effect of each treatment.
A tool for extracting some generic features (e.g., number of
    words, line breaks, characters per word, URLs, lower case, upper case,
    commas, periods, exclamation points, etc.) from strings of text.
Provides functionalities based on the paper "Time Varying Dictionary and the Predictive Power of FED Minutes" (Lima, 2018) <doi:10.2139/ssrn.3312483>. It selects the most predictive terms, that we call time-varying dictionary using supervised machine learning techniques as lasso and elastic net.
The software application Praat can be used to annotate
    waveform data (e.g., to mark intervals of interest or to label events).
    (See <http://www.fon.hum.uva.nl/praat/> for more information about Praat.)
    These annotations are stored in a Praat TextGrid object, which consists of
    a number of interval tiers and point tiers. An interval tier consists of
    sequential (i.e., not overlapping) labeled intervals. A point tier consists
    of labeled events that have no duration. The 'textgRid' package provides
    S4 classes, generics, and methods for accessing information that is stored
    in Praat TextGrid objects.
Contains real images of the same textile material 
    with/without local defects, which were used in 
    Bui and Apley (2017) <doi:10.1080/00401706.2017.1302362>.
Multinomial (inverse) regression inference for text documents and associated attributes.  For details see: Taddy (2013 JASA) Multinomial Inverse Regression for Text Analysis <arXiv:1012.2098> and Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>. A minimalist partial least squares routine is also included.  Note that the topic modeling capability of earlier 'textir' is now a separate package, 'maptpx'.
An aid for text mining in R, with a syntax that
    should be familiar to experienced R users. Provides a wrapper for several 
    topic models that take similarly-formatted input and give similarly-formatted
    output. Has additional functionality for analyzing and diagnostics for
    topic models.
Graphic interface for text analysis, implement a few methods such as biplots, correspondence analysis, co-occurrence, clustering, topic models, correlations and sentiments.
Statistical exploration of textual corpora using several methods
    from French 'Textometrie' (new name of 'Lexicometrie') and French 'Data Analysis' schools.
    It includes methods for exploring irregularity of distribution of lexicon features across
    text sets or parts of texts (Specificity analysis); multi-dimensional exploration (Factorial analysis), etc. 
    Those methods are used in the TXM software.
Visualise complex relations in texts. This is done by providing functionalities for displaying 
    text co-occurrence networks, text correlation networks, dependency relationships as well as text clustering. 
    Feel free to join the effort of providing interesting text visualisations.
The 'textrank' algorithm is an extension of the 'Pagerank' algorithm for text. The algorithm allows to summarize text by calculating how sentences are related to one another. This is done by looking at overlapping terminology used in sentences in order to set up links between sentences. The resulting sentence network is next plugged into the 'Pagerank' algorithm which identifies the most important sentences in your text and ranks them. 
    In a similar way 'textrank' can also be used to extract keywords. A word network is constructed by looking if words are following one another. On top of that network the 'Pagerank' algorithm is applied to extract relevant words after which relevant words which are following one another are combined to get keywords.  
    More information can be found in the paper from Mihalcea, Rada & Tarau, Paul (2004) <https://www.aclweb.org/anthology/W04-3252/>.
A small collection of convenience tools for reading text documents into R.
Converting text to numerical features requires
    specifically created procedures, which are implemented as steps
    according to the 'recipes' package. These steps allows for
    tokenization, filtering, counting (tf and tfidf) and feature hashing.
Function for sparse regression on raw text, regressing a labeling
    vector onto a feature space consisting of all possible phrases.
Tools for measuring similarity among documents and detecting
    passages which have been reused. Implements shingled n-gram, skip n-gram,
    and other tokenizers; similarity/dissimilarity functions; pairwise
    comparisons; minhash and locality sensitive hashing algorithms; and a
    version of the Smith-Waterman local alignment algorithm suitable for
    natural language.
Tools that can be used to reshape and restructure text data.
Provides access to the text shaping functionality in the 'HarfBuzz'
    library and the bidirectional algorithm in the 'Fribidi' library. 
    'textshaping' is a low-level utility package mainly for graphic devices that 
    expands upon the font tool-set provided by the 'systemfonts' package.
Tools that stem and lemmatize text.  Stemming is a process that removes
         endings such as affixes.  Lemmatization is the process of grouping inflected
         forms together as a single base form.
It offers functions for splitting, parsing, tokenizing and creating a vocabulary for big text data files. Moreover, it includes functions for building a document-term matrix and extracting information from those (term-associations, most frequent terms). It also embodies functions for calculating token statistics (collocations, look-up tables, string dissimilarities) and functions to work with sparse matrices. Lastly, it includes functions for Word Vector Representations (i.e. 'GloVe', 'fasttext') and incorporates functions for the calculation of (pairwise) text document dissimilarities. The source code is based on 'C++11' and exported in R through the 'Rcpp', 'RcppArmadillo' and 'BH' packages.
Utilities for handling character vectors
  that store human-readable text (either plain or with
  markup, such as HTML or LaTeX). The package provides,
  in particular, functions that help with the
  preparation of plain-text reports, e.g. for expanding
  and aligning strings that form the lines of such
  reports. The package also provides generic functions for
  transforming R objects to HTML and to plain text.
'TensorFlow SIG Addons' <https://www.tensorflow.org/addons> is a repository 
             of community contributions that conform to well-established API patterns, 
             but implement new functionality not available in core 'TensorFlow'. 
             'TensorFlow' natively supports a large number of operators, layers, metrics, 
             losses, optimizers, and more. However, in a fast moving field like Machine Learning, 
             there are many interesting new developments that cannot be integrated into 
             core 'TensorFlow' (because their broad applicability is not yet clear, or 
             it is mostly used by a smaller subset of the community).
Building customized transfer function and ARIMA models with multiple operators and parameter restrictions. Functions for model identification, model estimation (exact or conditional maximum likelihood), model diagnostic checking, automatic outlier detection, calendar effects, forecasting and seasonal adjustment. See Bell and Hillmer (1983) <doi:10.1080/01621459.1983.10478005>, Box, Jenkins, Reinsel and Ljung <ISBN:978-1-118-67502-1>, Box, Pierce and Newbold (1987) <doi:10.1080/01621459.1987.10478430>, Box and Tiao (1975) <doi:10.1080/01621459.1975.10480264>, Chen and Liu (1993) <doi:10.1080/01621459.1993.10594321>, Thompson and Tiao (1970) <http://old-www.stat.wisc.edu/sites/default/files/TR222.pdf>.
Translate R control flow expressions into 'Tensorflow' graphs.
In Cox's proportional hazard model, covariates are modeled as linear function and may not be flexible. This package implements additive trend filtering Cox proportional hazards model as proposed in Jiacheng Wu & Daniela Witten (2019) "Flexible and Interpretable Models for Survival Data", Journal of Computational and Graphical Statistics, <DOI:10.1080/10618600.2019.1592758>. The fitted functions are piecewise polynomial with adaptively chosen knots.
Interface to 'TensorFlow' Datasets, a high-level library for 
    building complex input pipelines from simple, re-usable pieces. 
    See <https://www.tensorflow.org/programmers_guide/datasets> for additional
    details.
Tools to deploy 'TensorFlow' <https://www.tensorflow.org/> models across 
  multiple services. Currently, it provides a local server for testing 'cloudml' 
  compatible services.
Statistical interpretation of forensic glass transfer (Simulation of the probability distribution of recovered glass fragments).
Interface to 'TensorFlow' Estimators 
    <https://www.tensorflow.org/programmers_guide/estimators>, a high-level 
    API that provides implementations of many different model types 
    including linear models and deep neural networks.
'TensorFlow' Hub is a library for the publication, discovery, and
    consumption of reusable parts of machine learning models. A module is a 
    self-contained piece of a 'TensorFlow' graph, along with its weights and 
    assets, that can be reused across different tasks in a process known as
    transfer learning. Transfer learning train a model with a smaller dataset,
    improve generalization, and speed up training.
Interface to 'TensorFlow IO', Datasets and filesystem extensions maintained by `TensorFlow SIG-IO` <https://github.com/tensorflow/community/blob/master/sigs/io/CHARTER.md>.
We provide the cumulative distribution function (CDF),
    quantile, and statistical power calculator for a collection of 
    thresholding Fisher's p-value combination methods, including Fisher's 
    p-value combination method, truncated product method and, in particular, soft-thresholding
    Fisher's p-value combination method which is proven to be optimal in some
    context of signal detection. The p-value calculator for the omnibus version
    of these tests are also included. For reference, please see Hong Zhang and Zheyang Wu. "TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values", submitted.
In putative Transcription Factor Binding Sites (TFBSs) 
             identification from sequence/alignments,
             we are interested in the significance of certain match score.
             TFMPvalue provides the accurate calculation of P-value with 
             score threshold for Position Weight Matrices, 
             or the score with given P-value. 
             It is an interface to code originally made available by 
             Helene Touzet and Jean-Stephane Varre, 2007, 
             Algorithms Mol Biol:2, 15.
             Touzet and Varre (2007) <DOI:10.1186/1748-7188-2-15>.
Utilities for simple manipulation and quick  	
	plotting of time series data. These utilities use the tframe package
	which provides a programming kernel for time series. Extensions to
	tframe provided in tframePlus can also be used. See the Guide vignette
	for examples.
Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow'
    that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU').
    'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers,
    variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.
A kernel of functions for programming 
	time series methods in a way that is relatively independently of the 
	representation of time. Also provides plotting, time windowing, 
	and some
	other utility functions which are specifically intended for time series.
	See the Guide distributed as a vignette, or ?tframe.Intro for more
	details. (User utilities are in package tfplot.)
Extensions and additional 'tframe' utilities.
Create and manage unique directories for each 'TensorFlow' 
  training run. Provides a unique, time stamped directory for each run
  along with functions to retrieve the directory of the latest run or 
  latest several runs.
A collection of useful tools for programming and writing-scripts. 
    Several functions are simple wrappers around base R functions that extend 
    their functionality while also providing some convenient properties–regular
    expression functions that automatically detect look-ahead and look-behind 
    statements, a read-line function that suppresses incomplete-final-line 
    warnings and automatically opens and closes connections, a version of 
    substrings that starts from the end of strings, etc. Other functions are 
    useful for checking whether packages are installed, omitting 
    missing data, and showing in-use connections.
Connects R to TrueFX(tm) for free streaming real-time and
        historical tick-by-tick market data for dealable interbank
        foreign exchange rates with millisecond detail.
Deconvolving thermoluminescence glow curves according to various 
        kinetic models (first-order, second-order, general-order, and mixed-order) using 
        a modified Levenberg-Marquardt algorithm (More, 1978) <DOI:10.1007/BFb0067700>. 
        It provides the possibility of setting constraints or fixing any of parameters. 
        It offers an interactive way to initialize parameters by clicking with a mouse on 
        a plot at positions where peak maxima should be located. The optimal estimate is 
        obtained by "trial-and-error". It also provides routines for simulating first-order, 
        second-order, and general-order glow peaks.
Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).
Bayesian nonstationary, semiparametric nonlinear regression 
 and design by treed Gaussian processes (GPs) with jumps to the limiting 
 linear model (LLM).  Special cases also implemented include Bayesian 
 linear models, CART, treed linear models, stationary separable and 
 isotropic GPs, and GP single-index models.  Provides 1-d and 2-d plotting functions 
 (with projection and slice capabilities) and tree drawing, designed for 
 visualization of tgp-class output.  Sensitivity analysis and 
 multi-resolution models are supported. Sequential experimental 
 design and adaptive sampling functions are also provided, including ALM, 
 ALC, and expected improvement.  The latter supports derivative-free
 optimization of noisy black-box functions.
Functions to compute and plot tracheidograms, as in De Soto et al. (2011) <doi:10.1139/x11-045>.
Rapid advancements in high-throughput gene sequencing
    technologies have resulted in genome-scale time-series datasets. 
    Uncovering the underlying temporal sequence of gene regulatory events 
    in the form of time-varying gene regulatory networks demands 
    accurate and computationally efficient algorithms. Such an
    algorithm is 'TGS'. It is proposed in Saptarshi Pyne, Alok Ranjan 
    Kumar, and Ashish Anand. Rapid reconstruction of time-varying 
    gene regulatory networks. IEEE/ACM Transactions on Computational 
    Biology and Bioinformatics, 17(1):278{291, Jan-Feb 2020. The TGS 
    algorithm is shown to consume only 29 minutes for a microarray 
    dataset with 4028 genes. This package provides an implementation 
    of the TGS algorithm and its variants.
Functions for implementing the targeted gold standard (GS) testing. You provide the true disease or treatment failure status and the risk score, tell 'TGST' the availability of GS tests and which method to use, and it returns the optimal tripartite rules. Please refer to Liu et al. (2013) <doi:10.1080/01621459.2013.810149> for more details.
A collection of high performance utilities to compute distance,
    correlation, auto correlation, clustering and other tasks.
    Contains graph clustering algorithm described in "MetaCell: analysis of
    single-cell RNA-seq data using K-nn graph partitions" (Yael Baran,
    Akhiad Bercovich, Arnau Sebe-Pedros, Yaniv Lubling, Amir Giladi,
    Elad Chomsky, Zohar Meir, Michael Hoichman, Aviezer Lifshitz & Amos Tanay,
    2019 <doi:10.1186/s13059-019-1812-2>).
Contains data sets used in other packages Torsten Hothorn
  maintains.
Find out who maintains the packages you use in
             your current session or in your package library and
             maybe say 'thank you'.
A tool for starring GitHub repositories.
A collection of tools for performing the second step of 
    Hermeneutic Content Analysis (HCA; see Bergman, 2010, 
    <doi:10.4135/9781506335193>). The current version 
    provides tools for implementing multidimensional scaling (MDS) within 
    an HCA framework. The tools offer assistance in data preparation, modeling, 
    and interpretation of MDS maps. The data preparation tools help 
    users to create similarity matrices based on co-occurrence or sorting 
    data. The modelling tools allow users to optimize the modelling process 
    by systematically calculating Stress values for a set of p points out of n 
    points. The tools assisting interpretation allow users to highlight the 
    highest similarities either per point or overall in an MDS representation.
Provides a simple interface to search available data provided by
    Theia (<https://theia.cnes.fr>), download it, and manage it. Data can be downloaded
    based on a search result or from a cart file downloaded from Theia website.
A means to explore the structure of 16S rRNA surveys using a Structural 
  Topic Model coupled with functional prediction. The user provides an abundance 
  table, sample metadata, and taxonomy information, and themetagenomics infers 
  associations between topics and sample features, as well as topics and predicted 
  functional content. Functional prediction can be accomplished via Tax4Fun (for 
  Silva references) and PICRUSt (for GreenGeenes references). See 
  <doi:10.1371/journal.pone.0219235>.
A dataset with an uneven number of cases in each
    class is said to be unbalanced. Many models produce a subpar
    performance on unbalanced datasets. A dataset can be balanced by
    increasing the number of minority cases using SMOTE 2011
    <arXiv:1106.1813>, BorderlineSMOTE 2005 <doi:10.1007/11538059_91> and
    ADASYN 2008 <https://ieeexplore.ieee.org/document/4633969>. Or by
    decreasing the number of majority cases using NearMiss 2003
    <https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek
    link removal 1976 <https://ieeexplore.ieee.org/document/4309452>.
A collection of functions and routines for inputting thermal
    image video files, plotting and converting binary raw data into estimates of
    temperature.  First published 2015-03-26.  Written primarily for research purposes
    in biological applications of thermal images.  v1 included the base calculations 
    for converting thermal image binary values to temperatures. v2 included additional
    equations for providing heat transfer calculations and an import function for thermal
    image files (v2.2.3 fixed error importing thermal image to windows OS). v3. Added numerous
    functions for converting thermal image, videos, rewriting and exporting.  
    v3.1. Added new functions to convert files. v3.2.  Fixed the various functions related to finding frame times.
    v4.0. fixed an error in atmospheric attenuation constants, affecting raw2temp and temp2raw functions.
    Recommend update for use with long distance calculations.
Calculates several thermal comfort indexes using temperature, wind speed and relative humidity values, calculating indexes such as Humidex, windchill, Discomfort Index and others.
Temperature measurement data, equations and methods for thermocouples,
    wire RTD, thermistors, IC thermometers, bimetallic strips and the ITS-90.
A step-up test for genetic rare variants in a gene or in a pathway. The method determines an optimal grouping of rare variants analytically. The method has been described in Hoffmann TJ, Marini NJ, and Witte JS (2010) <doi:10.1371/journal.pone.0013584>.
Conducts several closest matched pairs to strengthen a matched quantitative comparison of many pairs by thick description in observational studies.
    Rosenbaum, P. R. (2017). <doi:10.1080/10618600.2016.1152971>.
Methods and tools for generating forecasts at different temporal
    frequencies using a hierarchical time series approach.
This package interacts directly with the Thinknum API to offer data
    in a number of formats usable in R
Some tools for cleaning up messy 'Excel' files to
    be suitable for R. People who have been working with 'Excel' for years
    built more or less complicated sheets with names, characters, formats
    that are not homogeneous. To be able to use them in R nowadays, we
    built a set of functions that will avoid the majority of importation
    problems and keep all the data at best.
Determine the full path of the executing script. Works when running 
    a line or selection from an open R script using the Run button in 'RStudio', 
    when using 'source' and 'sys.source', and when running 'Rscript' from the command-line.
Key-value store, implemented as a wrapper around 'LMDB';
    the "lightning memory-mapped database" <https://symas.com/lmdb/>.
    'LMDB' is a transactional key value store that uses a memory map
    for efficient access.  This package wraps the entire 'LMDB'
    interface (except duplicated keys), and provides objects for
    transactions and cursors.
Creates some 'WebGL' shaders. They can be used as the background of a 'Shiny' app. They also can be visualized in the 'RStudio' viewer pane or included in 'Rmd' documents, but this is pretty useless, besides contemplating them.
Design and analyze three-arm non-inferiority or superiority trials
    which follow a gold-standard design, i.e. trials with an experimental treatment,
    an active, and a placebo control. Method for the following distributions are implemented:
    Poisson (Mielke and Munk (2009) <arXiv:0912.4169>), negative binomial (Muetze et al. (2016) <doi:10.1002/sim.6738>),
    normal (Pigeot et al. (2003) <doi:10.1002/sim.1450>; Hasler et al. (2009) <doi:10.1002/sim.3052>), 
    binary (Friede and Kieser (2007) <doi:10.1002/sim.2543>), nonparametric (Muetze et al. (2017) <doi:10.1002/sim.7176>),
    exponential (Mielke and Munk (2009) <arXiv:0912.4169>).
This package implements a thresholded version of the EEBoost
    algorithm described in [Wolfson (2011, JASA)]. EEBoost is a general-purpose
    method for variable selection which can be applied whenever inference would
    be based on an estimating equation. The package currently implements
    variable selection based on the Generalized Estimating Equations, but can
    also accommodate user-provided estimating functions. Thresholded EEBoost is
    a generalization which allows multiple variables to enter the model at each
    boosting step.
In neuroscience, 'AFNI/SUMA' is a great tool to visualize 3D brain. 
    However, it takes efforts to interact and share the viewer to others. In 
    addition, 'AFNI/SUMA' doesn't support Windows platform. In the 'EEG/iEEG' 
    field, it's hard to have multiple cortical electrodes mapped to a template 
    brain for group analysis. Therefore this package is written aimed at 
    providing a fast, stable, interactive and easy to share tool based on 'Three.js', 
    a 'WebGL' engine to render 3D objects in the web browser such that we can 
    display brain surfaces on webpage interactively. This package translates R 
    objects to JavaScript objects via 'JSON' format, and provides 'R-Shiny' interface
    to manipulate geometries interactively. The visualizations can also serve as 
    standalone widgets that can be easily shared across different platforms. 
    Along with 'rave', another package developed by Beauchamp's lab at Baylor 
    College Medicine, this package provides solutions to easily map surface 
    electrodes from multiple subjects to one template 141 brain.
Implements the Maximum Likelihood estimator for baseline, placebo, and treatment groups (three-group) experiments with non-compliance proposed by Gerber, Green, Kaplan, and Kern (2010).
Create interactive 3D scatter plots, network plots, and
    globes using the 'three.js' visualization library (<https://threejs.org>).
Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.
Does nothing useful, but perhaps does that nothing in an entertaining or informative fashion.
A connector to the 'What3Words' (http://what3words.com/) service, which represents each 3m by 3m square on earth
             with a unique trio of English-language words.
Fit a threshold regression model based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Fit a threshold regression model for Interval Censored Data based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Defines the classes used to identify
  outliers (threshing) and compute the number of significant principal
  components and number of clusters (reaping) in a joint application
  of PCA and hierarchical clustering. See Wang et al., 2018,
  <doi:10.1186/s12859-017-1998-9>.
Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. The package also provides functions for sample size determination and estimation of diagnostic accuracy measures. It also includes graphical tools.
Provides functions for the selection of thresholds for use in 
    extreme value models, based mainly on the methodology in 
    Northrop, Attalides and Jonathan (2017) <doi:10.1111/rssc.12159>.
    It also performs predictive inferences about future extreme values, 
    based either on a single threshold or on a weighted average of inferences 
    from multiple thresholds, using the 'revdbayes' package 
    <https://cran.r-project.org/package=revdbayes>.   
    At the moment only the case where the data can be treated as 
    independent identically distributed observations is considered.
Pure R implementation of Apache Thrift.
    This library doesn't require any code generation.
    To learn more about Thrift go to <https://thrift.apache.org>.
Fit the Simultaneous Systems of Linear Equations using Three-stage Least Squares.
Fit Thurstonian Item Response Theory (IRT) models in R. This 
  package supports fitting Thurstonian IRT models and its extensions using 
  'Stan', 'lavaan', or 'Mplus' for the model estimation. Functionality for 
  extracting results, making predictions, and simulating data is provided as 
  well. References: 
  Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>;
  Bürkner et al. (2019) <doi:10.1177/0013164419832063>.
Provides a 'tbl_df' class (the 'tibble') that
    provides stricter checking and better formatting than the traditional
    data frame.
Built on top of the 'tibble' package, 'tibbletime' is an extension
  that allows for the creation of time aware tibbles. Some immediate
  advantages of this include: the ability to perform time-based subsetting
  on tibbles, quickly summarising and aggregating results by time periods,
  and creating columns that can be used as 'dplyr' time-based groups.
A tool to rectangle a nested list, that is to convert it into a
    tibble. This is done automatically or according to a given specification.
    A common use case is for nested lists coming from parsing JSON files or
    the JSON response of REST APIs. It is supported by the 'vctrs' package
    and therefore offers a wide support of vector types.
Functions to execute orders in backtesting using tick data. A testing platform was established by the four major execution functions, namely 'LimitBuy', 'LimitSell', 'MarketBuy' and 'MarketSell', which enclosed all tedious aspects (such as queueing for order executions and calculate actual executed volumes) for order execution using tick data. Such that one can focus on the logic of strategies, rather than its execution.
Implements tic-tac-toe game to play on console, either with human or AI players.
  Various levels of AI players are trained through the Q-learning algorithm.
This package provides the timing functions 'tic' and 'toc' that
    can be nested. One can record all timings while a complex script is
    running, and examine the values later. It is also possible to instrument
    the timing calls with custom callbacks. In addition, this package provides
    class 'Stack', implemented as a vector, and class 'List', implemented as a
    list, both of which support operations 'push', 'pop', 'first', 'last' and
    'clear'.
Utilities to generate wiki reports in TiddlyWiki format.
Tidal analysis of evenly spaced observed time series (time step 1 to 60 min) with or
    without shorter gaps using the harmonic representation of inequalities.
    The analysis should preferably cover an observation period of at least 19 years.
    For shorter periods low frequency constituents are not taken into account, in accordance with the Rayleigh-Criterion.
    The main objective of this package is to synthesize or predict a tidal time series.
Implements harmonic analysis of tidal and sea-level data.
  Over 400 harmonic tidal constituents can be estimated, all with 
  daily nodal corrections. Time-varying mean sea-levels can also
  be used.
Calculate Characteristics of Quasi-Periodic Time Series, e.g. Estuarine Water Levels.
Tide analysis and prediction of predominantly semi-diurnal tides
    with two high waters and two low waters during one lunar day (~24.842 hours,
    ~1.035 days). The analysis should preferably cover an observation period of at
    least 19 years. For shorter periods, for example, the nodal cycle can not be
    taken into account, which particularly affects the height calculation. The main
    objective of this package is to produce tide tables.
Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models
    ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided
    to help extract tidy data frames of draws from Bayesian models and that generate point
    summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for
    common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus
    densities), and fit curves with multiple, arbitrary uncertainty bands.
Provides helper functions that make it easy to run
    'BayesFactor' package tests on a data which is in a tidy format.
    Additionally, it provides a more consistent syntax and by default
    returns a dataframe with rich details. These functions can also return
    expressions containing results from Bayes Factor tests that can then
    be displayed on custom plots. Posterior estimation is carried out using 
    the 'bayestestR' package.
Compute arbitrary non-parametric bootstrap statistics on data in
    tidy data frames.
Create additional rows and columns on broom::tidy() output to allow for easier control on categorical parameter estimates.
An integrated R interface to the decennial US Census and American Community Survey APIs and
    the US Census Bureau's geographic boundary files. Allows R users to return Census and ACS data as
    tidyverse-ready data frames, and optionally returns a list-column with feature geometry for all Census 
    geographies.
Analyze lines of R code using tidy principles. This allows you to 
    input lines of R code and output a data frame with one row per function 
    included. Additionally, it facilitates code classification via included lexicons.
Provides convenience functions for common data
    modification and analysis tasks in communication research. This
    includes functions for univariate and bivariate data analysis, index
    generation and reliability computation, and intercoder reliability
    tests. All functions follow the style and syntax of the tidyverse, and
    are construed to perform their computations on multiple variables at
    once. Functions for univariate and bivariate data analysis comprise
    summary statistics for continuous and categorical variables, as well
    as several tests of bivariate association including effect sizes.
    Functions for data modification comprise index generation and
    automated reliability analysis of index variables. Functions for
    intercoder reliability comprise tests of several intercoder
    reliability estimates, including simple and mean pairwise percent
    agreement, Krippendorff's Alpha (Krippendorff 2004, ISBN:
    9780761915454), and various Kappa coefficients (Brennan & Prediger
    1981 <doi: 10.1177/001316448104100307>; Cohen 1960 <doi:
    10.1177/001316446002000104>; Fleiss 1971 <doi: 10.1037/h0031619>).
Core parts of the C API of R are wrapped in a C++ namespace via a set
 of inline functions giving a tidier representation of the underlying data structures
 and functionality using a header-only implementation without additional dependencies.
The Common Workflow Language <https://www.commonwl.org/> is an
    open standard for describing data analysis workflows. This package takes
    the raw Common Workflow Language workflows encoded in JSON or 'YAML'
    and turns the workflow elements into tidy data frames or lists.
    A graph representation for the workflow can be constructed and visualized
    with the parsed workflow inputs, outputs, and steps. Users can embed the
    visualizations in their 'Shiny' applications, and export them
    as HTML files or static images.
Utils for basic statistical experiments, that can be used for teaching 
    introductory statistics. Each experiment generates a tibble.
    Dice rolls and coin flips are simulated using sample().
    The properties of the dice can be changed, like the number of sides.
    A coin flip is simulated using a two sided dice.
    Experiments can be combined with the pipe-operator.
Tidying functions built on 'data.table'
    to provide quick and efficient data manipulation with
    minimal overhead.
A toolkit of tidy data manipulation verbs with 'data.table' as the backend.
  Combining the merits of syntax elegance from 'dplyr' and computing performance from 'data.table', 
  'tidyfst' intends to provide users with state-of-the-art data manipulation tools with least pain.
  This package is an extension of 'data.table'. While enjoying a tidy syntax, 
  it also wraps combinations of efficient functions to facilitate frequently-used data operations.
Tidy syntax for 'data.table', using modification by reference whenever possible.
 This toolkit is designed for big data analysis in high-performance desktop or laptop computers.
 The syntax of the package is similar or identical to 'tidyverse'.
 It is user friendly, memory efficient and time saving. For more information,
 check its ancestor package 'tidyfst'.
A toolset that allows you to easily import and tidy data sheets retrieved
    from Gapminder data web tools. It will therefore contribute to reduce the time
    used in data cleaning of Gapminder indicator data sheets as they are very messy.
It interactively or programmatically label points within custom gates on two dimensions. 
    The information is added to your tibble. It is based on the package 'gatepoints' from Wajid Jawaid. 
    The benefits are (i) in interactive mode you can draw your gates on extensive 'ggplot'-like scatter plots; 
    (ii) you can draw multiple gates; and (iii) you can save your gates and apply the programmatically.
Handle genomic data within data frames just as you would with 'GRanges'.
    This packages provides method to deal with genomic intervals the "tidy-way" which makes
    it simpler to integrate in the the general data munging process. The API is inspired by the
    popular 'bedtools' and the genome_join() method from the 'fuzzyjoin' package.
An intuitive interface for getting data from geocoder services.
In order to easily integrate geoRSS data into analysis,
    'tidygeoRSS' parses 'geo' feeds and returns 
    tidy simple features data frames.
A graph, while not "tidy" in itself, can be thought of as two tidy
    data frames describing node and edge data respectively. 'tidygraph'
    provides an approach to manipulate these two virtual data frames using the
    API defined in the 'dplyr' package, as well as provides tidy interfaces to 
    a lot of common graph algorithms.
This is a tidy implementation for heatmap.  At the
    moment it is based on the (great) package 'ComplexHeatmap'.  The goal
    of this package is to interface a tidy data frame with this powerful
    tool.  Some of the advantages are: Row and/or columns colour
    annotations are easy to integrate just specifying one parameter
    (column names).  Custom grouping of rows is easy to specify providing
    a grouped tbl. For example: df %>% group_by(...).  Labels size
    adjusted by row and column total number.  Default use of Brewer and
    Viridis palettes.
Provides functions to access historical and real-time national 'hydrometric'
    data from Water Survey of Canada data sources (<https://dd.weather.gc.ca/hydrometric/csv/> and
    <https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>) and then applies tidy data principles.
Turn complex 'JSON' data into tidy data frames.
How can we measure how the usage or frequency of some feature, such 
    as words, differs across some group or set, such as documents? One option is 
    to use the log odds ratio, but the log odds ratio alone does not account for 
    sampling variability; we haven't counted every feature the same number of 
    times so how do we know which differences are meaningful? Enter the weighted 
    log odds, which 'tidylo' provides an implementation for, using tidy data 
    principles. In particular, here we use the method outlined in Monroe, 
    Colaresi, and Quinn (2008) <doi:10.1093/pan/mpn018> to weight the log odds 
    ratio by a prior. By default, the prior is estimated from the data itself, 
    an empirical Bayes approach, but an uninformative prior is also available.
Provides feedback about 'dplyr' and 'tidyr' operations.
An interface to the 'mclust' package to easily
    carry out latent profile analysis ("LPA"). Provides functionality to
    estimate commonly-specified models. Follows a tidy approach, in that
    output is in the form of a data frame that can subsequently be
    computed on. Also has functions to interface to the commercial 'MPlus'
    software via the 'MplusAutomation' package.
A reliable alternative to popular microbiome analysis R packages. We provide standard tools as well as novel extensions on standard analyses to improve interpretability and the analyst’s ability to communicate results, all while maintaining object malleability to encourage open source collaboration.
The tidy modeling "verse" is a collection of packages for 
    modeling and statistical analysis that share the underlying design
    philosophy, grammar, and data structures of the tidyverse.
Provides functions for visualising generalised
    additive models and getting predicted values using tidy tools from the 'tidyverse' packages.
Tidy tools for 'NetCDF' data sources. Explore the contents of a 
 'NetCDF' source (file or URL) presented as variables organized by grid with a 
 database-like interface. The hyper_filter() interactive function translates the 
 filter value or index expressions to array-slicing form. No data is read until 
 explicitly requested, as a data frame or list of arrays via hyper_tibble() or 
 hyper_array().
Parse XML documents from the Open Access subset of Europe PubMed Central <https://europepmc.org>
    including section paragraphs, tables, captions and references.
Bayesian analysis used here to answer the question: "when looking at resampling results, are the differences between models 'real'?" To answer this, a model can be created were the performance statistic is the resampling statistics (e.g. accuracy or RMSE). These values are explained by the model types. In doing this, we can get parameter estimates for each model's affect on performance and make statistical (and practical) comparisons between models. The methods included here are similar to Benavoli et al (2017) <http://jmlr.org/papers/v18/16-305.html>.
It parses a fitted 'R' model object, and returns a formula
    in 'Tidy Eval' code that calculates the predictions.
    It works with several databases back-ends because it leverages 'dplyr'
    and 'dbplyr' for the final 'SQL' translation of the algorithm. It currently
    supports lm(), glm(), randomForest(), ranger(), earth(), xgb.Booster.complete(),
    cubist(), and ctree() models.
Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' 
    package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' 
    and 'PerformanceAnalytics' package 
    functions and returns the objects in the tidy 'tibble' format. The main 
    advantage is being able to use quantitative functions with the 'tidyverse'
    functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See 
    the 'tidyquant' website for more information, documentation and examples.
Use 'SQL' 'SELECT' statements to query 'R' data frames.
The purpose of this package is to access the 
    United States Census Bureau's Quarterly Workforce Indicator data. Additionally, 
    the data will be retrieved in a tidy format for further manipulation with full variable
    descriptions added if desired. Information about the United States Census Bureau's 
    Quarterly Workforce Indicator is available at 
    <https://www.census.gov/data/developers/data-sets/qwi.html>.
Tools to help to create tidy data, where each column is a 
    variable, each row is an observation, and each cell contains a single value.  
    'tidyr' contains tools for changing the shape (pivoting) and hierarchy
    (nesting and 'unnesting') of a dataset, turning deeply nested lists
    into rectangular data frames ('rectangling'), and extracting values out 
    of string columns. It also includes tools for working with missing values 
    (both implicit and explicit).
Helper functions for processing REDCap data in R. 'REDCap' (Research
    Electronic Data CAPture; <https://projectredcap.org>) is a web-enabled
    application for building and managing surveys and databases developed at
    Vanderbilt University.
With the objective of including data from RSS feeds into your analysis, 
    'tidyRSS' parses RSS, Atom and JSON feeds and returns a tidy data frame.
Utility to convert text based summary of rule based models to a tidy dataframe (where each row represents a rule) with related metrics such as support, confidence and lift. Rule based models from these packages are supported: 'C5.0', 'rpart' and 'Cubist'.
A backend for the selecting functions of the 'tidyverse'.
    It makes it easy to implement select-like functions in your own
    packages in a way that is consistent with other 'tidyverse'
    interfaces for selection.
A tidy workflow for generating, estimating, reporting,
    and plotting structural equation models using 'lavaan' or 'Mplus'. 
    Throughout this workflow, elements of syntax, results, and graphs are 
    represented as 'tidy' data, making them easy to customize.
It creates an invisible layer that allow to see the 'Seurat' object 
    as tibble and interact seamlessly with the tidyverse.
Save the output of statistical tests in an organized file that can 
  be shared with others or used to report statistics in scientific papers.
Calculation of string distance following the tidy
    data principles. Built on top of the 'stringdist' package.
A tidy interface to 'data.table' that is 'rlang' compatible,
  giving users the speed of 'data.table' with the clean syntax of the tidyverse.
Using tidy data principles can make many text mining tasks easier, 
    more effective, and consistent with tools already in wide use. Much of the 
    infrastructure needed for text mining with tidy data frames already exists 
    in packages like 'dplyr', 'broom', 'tidyr', and 'ggplot2'. In this package, 
    we provide functions and supporting data sets to allow conversion of text 
    to and from tidy formats, and to switch seamlessly between tidy tools and 
    existing text mining packages.
A selection of various tools to extend a data analysis workflow based on the 'tidyverse' packages.
  This includes high-level data frame editing methods (in the style of 'mutate'/'mutate_at'), some methods in the style of
  'purrr' and 'forcats', 'lookup' methods for dict-like lists, a generic method for lumping a data frame by a given count,
  various low-level methods for special treatment of 'NA' values, 'python'-style tuple-assignment and 'truthy'/'falsy' checks,
  saving to PDF and PNG from a pipe and various small utilities.
Read General Transit Feed Specification (GTFS) zipfiles into a list of R dataframes. Perform validation of the data structure against the specification. Analyze the headways and frequencies at routes and stops. Create maps and perform spatial analysis on the routes and stops. Please see the GTFS documentation here for more detail: <http://gtfs.org/>.
Phylogenetic tree generally contains multiple components including node, edge, branch and associated data. 'tidytree' provides an approach to convert tree object to tidy data frame as well as provides tidy interfaces to manipulate tree data.
'TidyTuesday' is a project by the 'R4DS Online Learning Community' in which they 
  post a weekly dataset onto post a weekly dataset in a public data repository 
  (<https://github.com/rfordatascience/tidytuesday>) for people to
  analyze and visualize. This package provides the tools to easily download this data and the 
  description of the source.
Provides a consistent API to pull United States
    Department of Agriculture census and survey data from the National
    Agricultural Statistics Service (NASS) QuickStats service
    <https://quickstats.nass.usda.gov>.
The 'tidyverse' is a set of packages that work in
    harmony because they share common data representations and 'API'
    design. This package is designed to make it easy to install and load
    multiple 'tidyverse' packages in a single step. Learn more about the
    'tidyverse' at <https://tidyverse.org>.
Perform a Visual Predictive Check (VPC), while accounting for 
    stratification, censoring, and prediction correction. Using piping from 
    'magrittr', the intuitive syntax gives users a flexible and powerful method 
    to generate VPCs using both traditional binning and a new binless approach 
    Jamsen et al. (2018) <doi:10.1002/psp4.12319> with Additive Quantile 
    Regression (AQR) and Locally Estimated Scatterplot Smoothing (LOESS) 
    prediction correction.
Imports non-tabular from Excel files into R.  Exposes cell content,
    position and formatting in a tidy structure for further manipulation.
    Tokenizes Excel formulas.  Supports '.xlsx' and '.xlsm' via the embedded
    'RapidXML' C++ library <http://rapidxml.sourceforge.net>.  Does not support
    '.xlsb' or '.xls'.
This package provides an easy and simple way to read, write and display bitmap images stored in the TIFF format. It can read and write both files and in-memory raw vectors.
Temporally resolved groups of typical differences (errors) between two time series are determined and visualized
Pre-process for discrete time series data set which is not continuous at the column
    of 'date'. Refilling records of missing 'date' and other columns to the hollow data set so that
    final data set is able to be dealt with time series analysis.
A collection of data sets and functions that are useful in the
    teaching of statistics at an elementary level to students who may have
    little or no previous experience with the command line.  The functions for
    elementary inferential procedures follow a uniform interface for user
    input.  Some of the functions are instructional applets that can only be
    run on the R Studio integrated development environment with package
    'manipulate' installed.  Other instructional applets are Shiny apps
    that may be run locally. In teaching the package is used alongside of
    package 'mosaic', 'mosaicData' and 'abd', which are therefore listed as
    dependencies.
Infers the V genotype of an individual from immunoglobulin (Ig)
    repertoire sequencing data (AIRR-Seq, Rep-Seq). Includes detection of 
    any novel alleles. This information is then used to correct existing V 
    allele calls from among the sample sequences.
    Citations:
    Gadala-Maria, et al (2015) <doi:10.1073/pnas.1417683112>.
    Gadala-Maria, et al (2019) <doi:10.3389/fimmu.2019.00129>.
The functions needed to perform tight clustering Algorithm.
Write modelling results into a database for
    'tigreBrowser', a web-based tool for browsing figures and summary
    data of independent model fits, such as Gaussian process models
    fitted for each gene or other genomic element. The browser is
    available at <https://github.com/PROBIC/tigreBrowser>.
Download TIGER/Line shapefiles from the United States Census Bureau 
    (<https://www.census.gov/geo/maps-data/data/tiger-line.html>) and load into R as 'sf' objects.
Provides a graphics output device for R that records plots
        in a LaTeX-friendly format. The device transforms plotting
        commands issued by R functions into LaTeX code blocks. When
        included in a LaTeX document, these blocks are interpreted with
        the help of 'TikZ'---a graphics package for TeX and friends
        written by Till Tantau. Using the 'tikzDevice', the text of R
        plots can contain LaTeX commands such as mathematical formula.
        The device also allows arbitrary LaTeX code to be inserted into
        the output stream.
The data science storage engine 'TileDB' introduces a
 powerful on-disk format for multi-dimensional arrays. It supports
 dense and sparse arrays, dataframes and key-values stores, cloud
 storage ('S3', 'GCS', 'Azure'), chunked arrays, multiple compression,
 encryption and checksum filters, uses a fully multi-threaded
 implementation, supports parallel I/O, data versioning ('time
 travel'), metadata and groups. It is implemented as an embeddable
 cross-platform C++ library with APIs from several languages, and
 integrations.
R spatial objects for Tilegrams.
  Tilegrams are tiled maps where the region size is proportional to
  the certain characteristics of the dataset.
Tools for creating and detecting tiling schemes for geospatial datasets.
Implements an algorithm for generating maps, known as tile maps,
    in which each region is represented by a single tile of the same shape and
    size. The algorithm was first proposed in "Generating Tile Maps" by Graham
    McNeill and Scott Hale (2017) <doi:10.1111/cgf.13200>. Functions allow
    users to generate, plot, and compare square or hexagon tile maps.
Creates geographic map tiles from geospatial map files or non-geographic map tiles from simple image files. 
    This package provides a tile generator function for creating map tile sets for use with packages such as 'leaflet'. 
    In addition to generating map tiles based on a common raster layer source, it also handles the non-geographic edge case, producing map tiles from arbitrary images.
    These map tiles, which have a non-geographic, simple coordinate reference system (CRS), can also be used with 'leaflet' when applying the simple CRS option.
    Map tiles can be created from an input file with any of the following extensions: tif, grd and nc for spatial maps and png, jpg and bmp for basic images.
    This package requires 'Python' and the 'gdal' library for 'Python'. 
    'Windows' users are recommended to install 'OSGeo4W' (<https://trac.osgeo.org/osgeo4w/>) as an easy way to obtain the required 'gdal' support for 'Python'.
Implements an algorithm for variable selection in high-dimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a data-driven way.
Efficient routines for manipulation of date-time objects while
   accounting for time-zones and daylight saving times. The package includes
   utilities for updating of date-time components (year, month, day etc.),
   modification of time-zones, rounding of date-times, period addition and
   subtraction etc. Parts of the 'CCTZ' source code, released under the Apache
   2.0 License, are included in this package. See
   <https://github.com/google/cctz> for more details.
The 'timeDate' class fulfils the conventions of the ISO 8601 
	standard as well as of the ANSI C and POSIX standards. Beyond
	these standards it provides the "Financial Center" concept
	which allows to handle data records collected in different time 
	zones and mix them up to have always the proper time stamps with 
	respect to your personal financial center, or alternatively to the GMT
	reference time. It can thus also handle time stamps from historical 
	data records from the same time zone, even if the financial 
	centers changed day light saving times at different calendar
	dates.
We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for  irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands. See also Tak et al. (2017) <doi:10.1214/17-AOAS1027>, Tak et al. (2018) <doi:10.1080/10618600.2017.1415911>, Hu and Tak (2020) <arXiv:2005.08049>.
Infer constant and stochastic, time-dependent parameters to consider intrinsic stochasticity of a dynamic model and/or to analyze model structure modifications that could reduce model deficits.
  The concept is based on inferring time-dependent parameters as stochastic processes in the form of Ornstein-Uhlenbeck processes jointly with inferring constant model parameters and parameters of the Ornstein-Uhlenbeck processes.
  The package also contains functions to sample from and calculate densities of Ornstein-Uhlenbeck processes.
  References:
  Tomassini, L., Reichert, P., Kuensch, H.-R. Buser, C., Knutti, R. and Borsuk, M.E. (2009) "A smoothing algorithm for estimating stochastic, continuous-time model parameters and its application to a simple climate model." Journal of the Royal Statistical Society: Series C (Applied Statistics) 58, 679-704, <doi:10.1111/j.1467-9876.2009.00678.x>;
  Reichert, P., and Mieleitner, J. (2009) "Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters." Water Resources Research, 45, W10402, <doi:10.1029/2009WR007814>;
  Reichert, P., Ammann, L. and Fenicia, F. (2020) "Potential and challenges of investigating intrinsic uncertainty of hydrological models with stochastic, time-dependent parameters", in preparation;
  Reichert, P. (2020) "timedeppar: An R package for inferring stochastic, time-dependent model parameters", in preparation.
Helps to visualize multi-variate time-series having numeric and factor variables.
    You can use the package for visual analysis of data by plotting the data for each variable in the desired order and study
    interaction between a factor and a numeric variable by creating overlapping plots.
An easy tool for plotting annotated timelines, grouped timelines, and exploratory graphics (boxplot/histogram/density plot/scatter plot/line plot). Filter, summarize date data by duration and convert to calendar units.
Approaches for incorporating time into network analysis. Methods include: construction of time-ordered networks (temporal graphs); shortest-time and shortest-path-length analyses; resource spread calculations; data resampling and rarefaction for null model construction; reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks; vector clock latencies; and plotting functionalities. The package supports <doi:10.1371/journal.pone.0020298>.
Simple definition of time intervals for the current, previous, and next week, month, quarter and year.
Extract useful time components of a date object, such as
        day of week, weekend, holiday, day of month, etc, and put it in
        a data frame.  This can be used to create many predictor
        variables out of a single time variable, which can then be used
        in a regression or decision tree.  Also includes function
        plotCalendarHeatmap which draws a calendar and overlays a
        heatmap based on values.
Provides a 'timeR' class that makes timing codes easier. One can create 'timeR' objects and use them to record all timings, and extract recordings as data frame for later use.
Programs for Martinussen and Scheike (2006), `Dynamic Regression
    Models for Survival Data', Springer Verlag.  Plus more recent developments.
    Additive survival model, semiparametric proportional odds model, fast
    cumulative residuals, excess risk models and more. Flexible competing risks
    regression including GOF-tests. Two-stage frailty modelling. PLS for the
    additive risk model. Lasso in the 'ahaz' package.
Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed. See Blanche et al. (2013) <doi:10.1002/sim.5958> and references therein for the details of the methods implemented in the package.
Computes bootstrap CI for the sample ACF and periodogram
A negative binomial mixed-effects (NBME) model to detect
    nonparallel differential expression(NPDE) genes and parallel differential
    expression(PDE) genes in the time course RNA-seq data.
'S4' classes and various tools for financial time series:
  Basic functions such as scaling and sorting, subsetting,
  mathematical operations and statistical functions.
Estimate and return the needed parameters for visualizations designed for 'OpenBudgets.eu' <http://openbudgets.eu/> time series data. Calculate time series model and forecast parameters in budget time series data of municipalities across Europe, according to the 'OpenBudgets.eu' data model. There are functions for measuring deterministic and stochastic trend of the input time series data with 'ACF', 'PACF', 'Phillips Perron' test, 'Augmented Dickey Fuller (ADF)' test, 'Kwiatkowski-Phillips-Schmidt-Shin (KPSS)' test, 'Mann Kendall' test for monotonic trend and 'Cox and Stuart' trend test, decomposing with local regression models or 'stl' decomposition, fitting the appropriate 'arima' model and provide forecasts for the input 'OpenBudgets.eu' time series fiscal data. Also, can be used generally to extract visualization parameters convert them to 'JSON' format and use them as input in a different graphical interface.
Archive and manage times series data from official statistics. The 'timeseriesdb' package was designed to manage a large catalog of time series from official statistics which are typically published on a monthly, quarterly or yearly basis. Thus timeseriesdb is optimized to handle updates caused by data revision as well as elaborate, multi-lingual meta information.
Easy visualization, wrangling, and feature engineering of time series data for 
    forecasting and machine learning prediction. Consolidates and extends time series functionality 
    from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'.
Objects to manipulate sequential and seasonal time series. Sequential time series based on time instants and time durations are handled. Both can be regularly or unevenly spaced (overlapping durations are allowed). Only POSIX* format are used for dates and times. The following classes are provided : 'POSIXcti', 'POSIXctp', 'TimeIntervalDataFrame', 'TimeInstantDataFrame', 'SubtimeDataFrame' ; methods to switch from a class to another and to modify the time support of series (hourly time series to daily time series for instance) are also defined. Tools provided can be used for instance to handle environmental monitoring data (not always produced on a regular time base).
A interface to the TimeTree of Life Webpage (www.timetree.org). TimeTree is a public database for information on the evolutionary timescale of life. This package includes functions for searching divergence time for  taxa or all nodes of a phylogeny.
Create rich and fully interactive timeline visualizations.
    Timelines can be included in Shiny apps and R markdown documents, or viewed
    from the R console and 'RStudio' Viewer. 'timevis' includes an extensive API
    to manipulate a timeline after creation, and supports getting data out of
    the visualization into R. Based on the 'vis.js' Timeline module and the
    'htmlwidgets' R package.
Estimates time varying regression effects under Cox type models in
    survival data using classification and regression tree. The codes in this package were 
    originally written in S-Plus for the paper "Survival Analysis with Time-Varying Regression
    Effects Using a Tree-Based Approach," by Xu, R. and Adak, S. (2002) <doi:10.1111/j.0006-341X.2002.00305.x>, Biometrics, 58: 305-315.
    Development of this package was supported by NIH grants AG053983 and AG057707,
    and by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442.
    The content is solely the responsibility of the authors and does not necessarily
    represent the official views of the NIH.
    The example data are from the Honolulu Heart Program/Honolulu Asia Aging Study (HHP/HAAS).
Date sequence, relative date calculations, and date manipulation with business days
  and holidays.  Works with Date and POSIXt classes.
Prediction and ranking of drug combinations based on their drug-target interaction profiles and single-drug sensitivities in a given cancer cell line or patient-derived sample.
A problem-solving environment (PSE) for fitting
         separable nonlinear models to measurements arising in physics
         and chemistry experiments; has been extensively applied to
         time-resolved spectroscopy and FLIM-FRET data.
Functions for statistical analysis, prediction and control of time
 series based mainly on Akaike and Nakagawa (1988) <ISBN 978-90-277-2786-2>.
A universal non-uniform random number generator
  for quite arbitrary distributions with piecewise twice
  differentiable densities.
Non-parametric resampling-based inference tests for TExPosition.
Instead of nesting function calls, annotate and transform 
    functions using "#." comments.
A 'tufte'-alike style for 'rmarkdown'.
 A modern take on the 'Tufte' design for pdf and html vignettes,
 building on the 'tufte' package with additional contributions
 from the 'knitr' and 'ggtufte' package, and also acknowledging
 the key influence of 'envisioned css'.
Generate a palette of tints, shades or both from a single colour.
Assign, extract, or remove variable labels from R vectors.
  Lightweight and free of non-base dependencies.
Creates useful files and folders for data
  analysis  projects and provides functions to manage data,
  scripts and output files. Also provides a project
  template for 'Rstudio'.
Provides functions to get personal 'Google Scholar'
    profile data from web API and show it in table or figure format.
Provides a lightweight (zero-dependency) and easy to use 
    unit testing framework. Main features: install tests with 
    the package. Test results are treated as data that can be stored and 
    manipulated. Test files are R scripts interspersed with test commands, that
    can be programmed over. Fully automated build-install-test sequence for 
    packages. Skip tests when not run locally (e.g. on CRAN). Flexible and 
    configurable output printing. Compare computed output with output stored 
    with the package. Run tests in parallel. Extensible by other packages.
    Report side effects.
Helper functions to install and maintain the 'LaTeX' distribution
  named 'TinyTeX' (<https://yihui.org/tinytex/>), a lightweight, cross-platform,
  portable, and easy-to-maintain version of 'TeX Live'. This package also
  contains helper functions to compile 'LaTeX' documents, and install missing
  'LaTeX' packages automatically.
Assists performing tip-dating of phylogenetic trees with BEAST 
    BEAST is a popular software for phylogenetic analysis.
    The package assists the implementation of various phylogenetic tip-
	dating tests using BEAST. It contains two main functions.
	The first one allows preparing date randomization analyses, 
	which assess the temporal signal of a data set. 
	The second function allows performing leave-one-out analyses,
	which test for the consistency between independent calibration sequences
	and allow pinpointing those leading to potential bias.
	The included tutorial provides detailed step-by-step instructions.
	An expanded description of the package can be found in article:
	Rieux, A. and Khatchikian, C.E. (2017), 
	TIPDATINGBEAST: an R package to assist the implementation of phylogenetic
	tip-dating tests using BEAST. Molecular Ecology Resources, 17: 608-613.
	<onlinelibrary.wiley.com/doi/full/10.1111/1755-0998.12603>.
Provides access to the complete Pali Canon, or 
    Tipitaka, the canonical scripture for Theravadin Buddhists 
    worldwide. Based on the Chattha Sangayana Tipitaka version 
    4 (Vipassana Research Institute, 1990).
TIPOM is based on a methodology that was developed in the
  1960s by Bernardino Bagolini. The basic idea is to use
  the three simple dimensions of length, width and thickness
  of each lithic artefact to classify them in discrete
  groups and infer their function.
'Htmlwidget' of 'Tippyjs' to add tooltips to 'Shiny' apps and 'R markdown' documents.
The strength of evidence provided by epidemiological and observational 
            studies is inherently limited by the potential for unmeasured confounding. 
            We focus on three key quantities: the observed bound of the confidence interval
            closest to the null, a plausible residual effect size for an unmeasured continuous 
            or binary confounder, and a realistic mean difference or prevalence difference for 
            this hypothetical confounder. Building on the methods put forth by 
            Lin, Psaty, & Kronmal (1998) DOI:10.2307/2533848, we can use these quantities to 
            assess how an unmeasured confounder may tip our result to insignificance, rendering the
            study inconclusive.
Functions and S3 classes for time indexes and time indexed
        series, which are compatible with FAME frequencies.
Uses indicator species scores across binary partitions of
  a sample set to detect congruence in taxon-specific changes of abundance
  and occurrence frequency along an environmental gradient as evidence of
  an ecological community threshold.  Relevant references include: Baker,
  ME and RS King. 2010. A new method for detecting and interpreting
  biodiversity and ecological community thresholds.  Methods in Ecology and
  Evolution 1(1): 25:37. King, RS and ME Baker. 2010. Considerations for
  identifying and interpreting ecological community thresholds.  Journal
  of the North American Benthological Association 29(3):998-1008. Baker ME
  and RS King. 2013. Of TITAN and straw men: an appeal for greater
  understanding of community data. Freshwater Science 32(2):489-506.
This data set provides information on the fate of passengers on
    the fatal maiden voyage of the ocean liner "Titanic", summarized according
    to economic status (class), sex, age and survival. Whereas the base R
    Titanic data found by calling data("Titanic") is an array resulting from
    cross-tabulating 2201 observations, these data sets are the individual
    non-aggregated observations and formatted in a machine learning context
    with a training sample, a testing sample, and two additional data sets
    that can be used for deeper machine learning analysis. These data sets
    are also the data sets downloaded from the Kaggle competition and thus
    lowers the barrier to entry for users new to R or machine learing.
Functions to design phase 1 trials using an isotonic regression based design incorporating time-to-event information. Simulation and design functions are available, which incorporate information about followup and DLTs, and apply isotonic regression to devise estimates of DLT probability.
A collection of functions to plot acid/base titration 
    curves (pH vs. volume of titrant), complexation titration curves 
    (pMetal vs. volume of EDTA), redox titration curves (potential 
    vs.volume of titrant), and precipitation titration curves (either 
    pAnalyte or pTitrant vs. volume of titrant). Options include the 
    titration of mixtures, the ability to overlay two or more 
    titration curves, and the ability to show equivalence points.
Simple mechanism for placing R graphics in a Tk widget.
Tools for 'ecotoxicology' dedicated to the simulation of 
    'toxicokinetic-toxicodynamic' models. See 'Baudrot' and 'Charles'
    (2019) <doi:10.1038/s41598-019-47698-0>.
An optimal alternating optimization algorithm for estimation of precision matrices of sparse tensor graphical models, and an efficient inference procedure for support recovery of the precision matrices.
Contains functions for training and applying two-level random forest and hidden Markov models for human behavior classification from raw tri-axial accelerometer and/or GPS data. Includes functions for training a two-level model, applying the model to data, and computing performance.
TLE implements a general framework for robust fitting of
        finite mixture models. Parameter estimation is performed using
        the EM algorithm.
Computation of effects under linear, logistic and Poisson regression models with transformed variables. Logarithm and power transformations are allowed. Effects can be displayed both numerically and graphically in both the original and the transformed space of the variables.
Fit a linear mixed effects model for censored data with
        Student-t or normal distributions. The errors are assumed
        independent and identically distributed.
Calculates empirical TL-moments (trimmed L-moments) of arbitrary 
    order and trimming, and converts them to distribution parameters.
Implementation of the classic Genz algorithm and a novel tile-low-rank algorithm for computing 		relatively high-dimensional multivariate normal (MVN) and Student-t (MVT) probabilities. 
	References used for this package:
	Foley, James, Andries van Dam, Steven Feiner, and John Hughes. 
	"Computer Graphics: Principle and Practice". Addison-Wesley 
	Publishing Company. Reading, Massachusetts (1987, ISBN:0-201-84840-6 1);
	Genz, A., "Numerical computation of multivariate
	normal probabilities," Journal of Computational and
	Graphical Statistics, 1, 141-149 (1992) <doi:10.1080/10618600.1992.10477010>;
	Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "Exploiting Low
	Rank Covariance Structures for Computing High-Dimensional Normal and Student-
	t Probabilities" (2019) <https://marcgenton.github.io/2019.CGKT.manuscript.pdf>.
Functions for point and interval estimation in
	     error-in-variables models via total least squares or
	     generalized total least squares method. See Golub and Van
	     Loan (1980) <doi:10.1137/0717073>, Gleser (1981)
	     <https://www.jstor.org/stable/2240867>, Ivan Markovsky
	     and Huffel (2007) <doi:10.1016/j.sigpro.2007.04.004> for
	     more information.
An implementation of the blocking algorithm transitive locality-sensitive hashing (TLSH) in Steorts, Ventura, Sadinle, Fienberg (2014) <DOI:10.1007/978-3-319-11257-2_20>, which is a k-means variant of locality sensitive hashing. The method is illustrated with examples and a vignette.
A framework for text mining applications within R.
This package provides a tm Source to create corpora from
  a corpus prepared in the format used by the Alceste application (i.e.
  a single text file with inline meta-data). It is able to import both
  text contents and meta-data (starred) variables.
A plug-in for the text mining framework tm to support text mining 
             in a distributed way. The package provides a convenient interface for
             handling distributed corpus objects based on distributed list objects.
Provides a 'tm' Source to create corpora from
  articles exported from the 'Europresse' content provider as
  HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author and pages).
Provides a 'tm' Source to create corpora from
  articles exported from the Dow Jones 'Factiva' content provider as
  XML or HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author, subject,
  geographical coverage, company, industry, and various
  provider-specific fields).
Provides a 'tm' Source to create corpora from
  articles exported from the 'LexisNexis' content provider as
  HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author and pages).
  Note that the file format is highly unstable: there is no warranty
  that this package will work for your corpus, and you may have
  to adjust the code to adapt it to your particular format.
A plug-in for the tm text mining framework providing mail handling
  functionality.
Facilitate text retrieval from feed
    formats like XML (RSS, ATOM) and JSON. Also direct retrieval from
    HTML is supported. As most (news) feeds only incorporate small
    fractions of the original text tm.plugin.webmining even retrieves
    and extracts the text of the original text source.
Useful functions to connect to 'TM1' <https://www.ibm.com/uk-en/products/planning-and-analytics> instance from R via REST API. With the functions in the package, data can be imported from 'TM1' via mdx view or native view, data can be sent to 'TM1', processes and chores can be executed, and cube and dimension metadata information can be taken.
Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.
Set of tools for reading and processing spatial data. The aim is to supply the workflow to create thematic maps. This package also facilitates 'tmap', the package for visualizing thematic maps.
With this tool, a user should be able to quickly implement
    complex random effect models through simple C++ templates. The package combines
    'CppAD' (C++ automatic differentiation), 'Eigen' (templated matrix-vector
    library) and 'CHOLMOD' (sparse matrix routines available from R) to obtain
    an efficient implementation of the applied Laplace approximation with exact
    derivatives. Key features are: Automatic sparseness detection, parallelism
    through 'BLAS' and parallel user templates.
Enables all 'rstan' functionality for a 'TMB' model object, in particular MCMC sampling and chain visualization. Sampling can be performed with or without Laplace approximation for the random effects.
A Text mining toolkit for Chinese, which includes facilities for 
    Chinese string processing, Chinese NLP supporting, encoding detecting and 
    converting. Moreover, it provides some functions to support 'tm' package 
    in Chinese.
Provides an R-interface to the TMDb API (see TMDb API on <https://developers.themoviedb.org/3/getting-started/introduction>). The Movie Database (TMDb) is a popular user editable database for movies and TV shows (see <https://www.themoviedb.org>).
Random number generation of truncated multivariate Gaussian distributions using Hamiltonian Monte Carlo. The truncation is defined using linear and/or quadratic polynomials.
Predicting the topology of membrane proteins is part of
    the field of computational biology.
    'TMHMM' is a program to predict transmembrane helices in proteins. 
    'TMHMM' can be called from a website and/or locally using Bash. 
    This package allows to install and call TMHMM from R and parse its results.
Miscellaneous utility functions for data manipulation,
    data tidying, and working with gene expression data.
Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006.  This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC).  The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes.  Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values.  The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
Targeted minimum loss estimation (TMLE) of a non-parametric variable importance measure of a continuous exposure 'X' on an outcome 'Y', taking baseline covariates 'W' into account.
Methods and feature set definitions for feature or gene set
  enrichment analysis in transcriptional and metabolic profiling data.
  Package includes tests for enrichment based on ranked lists of features,
  functions for visualisation and multivariate functional analysis.
Trauma Mortality prediction for ICD-9, ICD-10, and AIS lexicons in
    long or wide format based on Dr. Alan Cook's tmpm mortality model.
Provides conditional maximum likelihood (CML) estimation of item parameters in multistage designs (Zwitser & Maris, 2013, <doi:10.1007/s11336-013-9369-6>) and CML estimation for conventional designs. Additional features are the likelihood ratio test (Andersen, 1973, <doi:10.1007/BF02291180>) and simulation of multistage designs.
Create, control, and capture 'tmux' sessions, windows, and panes
    using a pipeable API.
Efficient sampling of truncated multivariate (scale) mixtures of normals under linear inequality constraints is nontrivial due to the analytically intractable normalizing constant. Meanwhile, traditional methods may subject to numerical issues, especially when the dimension is high and dependence is strong.    Algorithms proposed by Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> are adopted for overcoming difficulties in simulating truncated distributions. Efficient rejection sampling for simulating truncated univariate normal distribution is included in the package, which shows superiority in terms of acceptance rate and numerical stability compared to existing methods and R packages. An efficient function for sampling from truncated multivariate normal distribution subject to convex polytope restriction regions based on Gibbs sampler for conditional truncated univariate distribution is provided. By extending the sampling method, a function for sampling truncated multivariate Student's t distribution is also developed.     Moreover, the proposed method and computation remain valid for high dimensional and strong dependence scenarios. Empirical results in Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> illustrated the superior performance in terms of various criteria (e.g. mixing and integrated auto-correlation time).
Importance sampling from the truncated multivariate normal using the GHK (Geweke-Hajivassiliou-Keane) simulator.
  Unlike Gibbs sampling which can get stuck in one truncation sub-region depending on initial values, this package allows 
  truncation based on disjoint regions that are created by truncation of absolute values. The GHK algorithm uses simple Cholesky
  transformation followed by recursive simulation of univariate truncated normals hence there are also no convergence issues. 
  Importance sample is returned along with sampling weights, based on which, one can calculate integrals over truncated regions
  for multivariate normals.
Random number generation for the truncated multivariate normal and Student t distribution. 
  Computes probabilities, quantiles and densities, 
  including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.
Node centrality measures for temporal networks. Available measures are temporal degree centrality, temporal closeness centrality and temporal betweenness centrality defined by Kim and Anderson (2012) <doi:10.1103/PhysRevE.85.026107>. Applying the REN algorithm by Hanke and Foraita (2017) <doi:10.1186/s12859-017-1677-x> when calculating the centrality measures keeps the computational running time linear in the number of graph snapshots. Further, all methods can run in parallel up to the number of nodes in the network.
Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.
Construction of the Total Operating Characteristic (TOC) Curve and the Receiver (aka Relative) Operating Characteristic (ROC) Curve for spatial and non-spatial data. The TOC method is a modification of the ROC method which measures the ability of an index variable to diagnose either presence or absence of a characteristic. The diagnosis depends on whether the value of an index variable is above a threshold. Each threshold generates a two-by-two contingency table, which contains four entries: hits (H), misses (M), false alarms (FA), and correct rejections (CR). While ROC shows for each threshold only two ratios, H/(H + M) and FA/(FA + CR), TOC reveals the size of every entry in the contingency table for each threshold (Pontius Jr., R.G., Si, K. 2014. <doi:10.1080/13658816.2013.862623>).
This is a simple addin to 'RStudio' that finds all 'TODO', 'FIX ME', 'CHANGED' etc. comments in your project and shows them as a markers list.
Use the <http://toggl.com> time tracker api through R.
Approximations of global p-values when testing hypothesis in presence of non-identifiable nuisance parameters. The method relies on the Euler characteristic heuristic and the expected Euler characteristic is efficiently computed by  in Algeri and van Dyk (2018) <arXiv:1803.03858>.
Create browsers for reading full texts from a token list format.
    Information obtained from text analyses (e.g., topic modeling, word scaling)
    can be used to annotate the texts.
Convert natural language text into tokens. Includes tokenizers for
    shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs,
    characters, shingled characters, lines, tweets, Penn Treebank, regular
    expressions, as well as functions for counting characters, words, and sentences,
    and a function for splitting longer texts into separate documents, each with
    the same number of words.  The tokenizers have a consistent interface, and
    the package is built on the 'stringi' and 'Rcpp' packages for  fast
    yet correct tokenization in 'UTF-8'.
Unsupervised text tokenizer focused on computational efficiency. Wraps the 'YouTokenToMe' library <https://github.com/VKCOM/YouTokenToMe> which is an implementation of fast Byte Pair Encoding (BPE) <https://www.aclweb.org/anthology/P16-1162>.
Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence.  This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals.  Visualizations are also available for most of these settings.
Several functions to allow comparisons of data across different geographies, in particular for Canadian census data from different censuses.
Implements two tests for same-source of toolmarks. The chumbley_non_random() test follows the paper "An Improved Version of a Tool Mark Comparison Algorithm" by Hadler and Morris (2017) <doi:10.1111/1556-4029.13640>. This is an extension of the Chumbley score as previously described in "Validation of Tool Mark Comparisons Obtained Using a Quantitative, Comparative, Statistical Algorithm" by Chumbley et al (2010) <doi:10.1111/j.1556-4029.2010.01424.x>. fixed_width_no_modeling() is based on correlation measures in a diamond shaped area of the toolmark as described in Hadler (2017).
Uplift modeling aims at predicting the causal effect of an action such as a medical
    treatment or a marketing campaign on a particular individual, by taking into consideration
    the response to a treatment. In order to simplify the task for practitioners in uplift modeling,
    we propose a combination of tools that can be separated into the following ingredients: 
    i) quantization, ii) visualization, iii) feature engineering, iv) feature selection and, 
    v) model validation. For more details, please read Belbahri et Al. (2019) 
    <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>.
Provides functions for multivariate analysis with compositional data. Includes a function for doing compositional canonical correlation analysis.  This analysis requires two data matrices of compositions, which can be adequately transformed and used as entries in a specialized program for canonical correlation analysis, that is able to deal with singular covariance matrices. The methodology is described in Graffelman et al. (2017) <doi:10.1101/144584>. A function for log-ratio principal component analysis with condition number computations has been added to the package.
An R wrapper for using 'TooManyCells', a command line program for clustering, visualizing, and quantifying cell clade relationships. See <https://gregoryschwartz.github.io/too-many-cells/> for more details.
Language specific cardinal to ordinal number conversion.
Calculates topic-specific diagnostics (e.g. mean token length, exclusivity) for 
    Latent Dirichlet Allocation and Correlated Topic Models fit using the 'topicmodels' package.
    For more details, see Chapter 12 in Airoldi et al. (2014, ISBN:9781466504080), 
    pp 262-272 Mimno et al. (2011, ISBN:9781937284114), and Bischof et al. (2014) <arXiv:1206.4631v1>.
Provides an interface to the C code for Latent Dirichlet
	     Allocation (LDA) models and Correlated Topics Models
	     (CTM) by David M. Blei and co-authors and the C++ code
	     for fitting LDA models using Gibbs sampling by Xuan-Hieu
	     Phan and co-authors.
Provides implementation of the "Topic SCORE" algorithm that is
	     proposed by Tracy Ke and Minzhe Wang. The singular value decomposition
	     step is optimized through the usage of svds() function in 'RSpectra'
	     package, on a 'dgRMatrix' sparse matrix. Also provides a column-wise
	     error measure in the word-topic matrix A, and an algorithm for
	     recovering the topic-document matrix W given A and D based on
	     quadratic programming.
		The details about the techniques are explained in the paper "A new SVD approach to optimal topic estimation" by Tracy Ke and Minzhe Wang (2017) <arXiv:1704.07016>.
For multiple ranked input lists (full or partial) representing the same set of N objects, the package TopKLists offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results.
Set of hydrological functions including an R
        implementation of the hydrological model TOPMODEL, which is
        based on the 1995 FORTRAN version by Keith Beven. From version
        0.7.0, the package is put into maintenance mode.
A toolkit for calculating topographic distances and identifying and plotting topographic paths. Topographic distances can be calculated along shortest topographic paths (Wang (2009) <doi:10.1111/j.1365-294X.2009.04338.x>), weighted topographic paths (Zhan et al. (1993) <doi:10.1007/3-540-57207-4_29>), and topographic least cost paths (Wang and Summers (2010) <doi:10.1111/j.1365-294X.2009.04465.x>). Functions can map topographic paths on colored or hill shade maps and plot topographic cross sections (elevation profiles) for the paths.
Using Gaussian graphical models we propose a novel approach to
             perform pathway analysis using gene expression. Given the
             structure of a graph (a pathway) we introduce two statistical
             tests to compare the mean and the concentration matrices between
             two groups. Specifically, these tests can be performed on the
             graph and on its connected components (cliques).
Estimate geometric sensitivity indices reconstructing the embedding manifold of the data. The reconstruction is done via a Vietoris Rips with a fixed radius. With the homology of order 2 we estimate symmetric reflections of the those manifold to determine a sensitivity index on of the input model. Detailed information of the methods can be found in <https://www.dropbox.com/s/0kcrjhhkl7899n1/article-symmetric-reflection.pdf> (WIP preprint).
Evaluation of alternatives based on multiple criteria using TOPSIS method.
The goal of tor (to-R) is to help you to import
    multiple files from a single directory at once, and to do so as
    quickly, flexibly, and simply as possible.
Provides functionality to define and train neural networks similar to 
    'PyTorch' by Paszke et al (2019) <arXiv:1912.01703> but written entirely in R 
    using the 'libtorch' library. Also supports low-level tensor operations and 
    'GPU' acceleration.
Provides access to datasets, models and preprocessing
    facilities for deep learning with images. Integrates seamlessly
    with the 'torch' package and it's 'API' borrows heavily from 
    'PyTorch' vision package.
A framework for statistical analysis in content analysis. In addition to a pipeline for preprocessing text corpora and linking to the latent Dirichlet allocation from the 'lda' package, plots are offered for the descriptive analysis of text corpora and topic models. In addition, an implementation of Chang's intruder words and intruder topics is provided.
Fit an Instrumental Variables Two Stage Least Squares model
Two one-sided tests (TOST) procedure to test equivalence for t-tests, correlations, differences between proportions, and meta-analyses, including power analysis for t-tests and correlations. Allows you to specify equivalence bounds in raw scale units or in terms of effect sizes. See: Lakens (2017) <doi:10.1177/1948550617697177>.
Download summary files from Census Bureau <https://www2.census.gov/> 
    and extract data, in particular high resolution data at 
    block, block group, and tract level, from decennial census and 
    American Community Survey 1-year and 5-year estimates.
For a given phylogenetic tree, calculates the Total Cophenetic Index.
  Reference: A. Mir, F. Rossello, L. A. Rotger (2013).
  A new balance index for phylogenetic trees.
  Math. Biosci. 241, 125-136 <doi:10.1016/j.mbs.2012.10.005>.
R implementation of the software tools developed in the H-CUP
    (Healthcare Cost and Utilization Project) <https://www.hcup-us.ahrq.gov>
    and AHRQ (Agency for Healthcare Research and Quality)
    <https://www.ahrq.gov>.  It currently contains functions for mapping ICD-9
    codes to the AHRQ comorbidity measures and translating ICD-9
    (resp. ICD-10) codes to ICD-10 (resp. ICD-9) codes based on GEM (General
    Equivalence Mappings) from CMS (Centers for Medicare and Medicaid
    Services).
Tools for analyzing count data with the Touchard model (Matsushita et al., 2018, Comm Stat Th Meth <doi:10.1080/03610926.2018.1444177>). It includes univariate estimation (ML and MM) and regression tools developed by Andrade et al. (submitted).
Contains two functions related to sports competitions. One to create league tables and one to create a match schedule.
Implements geodesic interpolation and basis
    generation functions that allow you to create new tour
    methods from R.
Data analysis package for estimating potential biological effects from chemical concentrations in environmental samples. Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays. A description of the analysis can be found in Blackwell et al. (2017) <doi:10.1021/acs.est.7b01613>.
Calculates sample size and dose allocation for binary toxicity 
 tests, using the Fish Embryo Toxicity Test as example. 
 An optimal test design is obtained by running 
 (i) spoD (calculate the number of individuals to test under control 
 conditions), (ii) setD (estimate the minimal sample size per treatment
 given the users precision requirements) and (iii) doseD (construct 
 an individual dose scheme).
Estimation of transition probabilities for the illness-death model. Both the Aalen-Johansen estimator for a Markov model and a novel non-Markovian estimator by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>, see also Balboa and de Una-Alvarez (2018) <doi:10.18637/jss.v083.i10>, are included.
Tools for estimating and inferring two-way partial area under receiver operating characteristic curves (two-way pAUC), partial area under receiver operating characteristic curves (pAUC), and partial area under ordinal dominance curves (pODC). Methods includes Mann-Whitney statistic and Jackknife, etc.
Tools to calculate trait probability density functions (TPD) at
    any scale (e.g. populations, species, communities). TPD functions are used to compute
    several indices of functional diversity, as well as its partition across scales.
    These indices constitute a unified framework that incorporates the underlying
    probabilistic nature of trait distributions into uni- or multidimensional
    functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for
    further information.
This package implements the greedy approximation for tree
        preserving embedding.
We described a novel Topology-based pathway enrichment analysis, which integrated the global position of the nodes and the topological property of the pathways in  Kyoto Encyclopedia of Genes and Genomes Database.
             We also provide some functions to obtain the latest information about pathways to finish pathway enrichment analysis using this method.
A bioinformatics tool for the estimation of the tumor purity from sequencing data. 
    It uses the set of putative clonal somatic single nucleotide variants within copy number neutral
    segments to call tumor cellularity.
Uses thresholded partial least squares algorithm to create a regression or classification model.
A tool created to simplify the data manipulation necessary to create clinical reports.
Estimation of transition probabilities for the
        illness-death model and or the three-state progressive model.
Regression models for temporal process responses with
        time-varying coefficient.
Plant ecologists often need to collect "traits" data
    about plant species which are often scattered among various
    databases: TR8 contains a set of tools which take care of
    automatically retrieving some of those functional traits data
    for plant species from publicly available databases (Biolflor,
    The Ecological Flora of the British Isles, LEDA traitbase, Ellenberg
    values for Italian Flora, Mycorrhizal intensity databases, Catminat, BROT,
    PLANTS, Jepson Flora Project).
    The TR8 name, inspired by "car plates" jokes, was chosen since
    it both reminds of the main object of the package and is
    extremely short to type.
Better looking call stacks after an error.
'BEAST2' (<https://www.beast2.org>) is a widely used
  Bayesian phylogenetic tool, that uses DNA/RNA/protein data
  and many model priors to create a posterior of jointly estimated 
  phylogenies and parameters.
  'Tracer' (<http://tree.bio.ed.ac.uk/software/tracer/>) is a GUI tool 
  to parse and analyze the files generated by 'BEAST2'.
  This package provides a way to parse and analyze 'BEAST2' input
  files without active user input, but using
  R function calls instead.
Contains functions to standardize tracheid profiles
    using the traditional method (Vaganov) and a new method to standardize
    tracheidograms based on the relative position of tracheids within tree rings.
Automatically stores objects in files on disk
  so that files are rewritten when objects are changed, and
  so that objects are accessible but do not occupy memory
  until they are accessed. Keeps track of times when objects
  are created and modified, and caches some basic
  characteristics of objects to allow for fast summaries of
  objects.  Also provides a command history mechanism that
  saves the last command to a history file after each
  command completes.
Obtain population density and body size structure, using video material or image sequences as input. Functions assist in the creation of image sequences from videos, background detection and subtraction, particle identification and tracking. An artificial neural network can be trained for noise filtering. The goal is to supply accurate estimates of population size, structure and/or individual behavior, for use in  evolutionary and ecological studies.
Data frame class for storing collective movement data (e.g. fish
    schools, ungulate herds, baboon troops) collected from GPS trackers or
    computer vision tracking software.
Provides infrastructure for handling running, cycling and swimming data from GPS-enabled tracking devices within R. The package provides methods to extract, clean and organise workout and competition data into session-based and unit-aware data objects of class 'trackeRdata' (S3 class). The information can then be visualised, summarised, and analysed through flexible and extensible methods. Frick and Kosmidis (2017) <doi: 10.18637/jss.v082.i07>, which is updated and maintained as one of the vignettes, provides detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality.
Provides an integrated user interface and workflow for
             the analysis of running, cycling and swimming data from GPS-enabled
             tracking devices through the 'trackeR' <https://CRAN.R-project.org/package=trackeR> R package.
Automatically annotates R-based artifacts
	     with relevant descriptive and provenance-related
    	     and provides a backend-agnostic storage and
	     discoverability system for organizing, retrieving, and
	     interrogating such artifacts.
Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data.  Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals.
Functions for working with magnetic resonance images. Reading and
    writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH);
    interactive and non-interactive visualisation; flexible image manipulation;
    metadata and sparse image handling.
A collection of tools for trade practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of tariffs and quotas under different competitive regimes. These tools are derived from Anderson et al. (2001) <doi:10.1016/S0047-2727(00)00085-2> and Froeb et al. (2003) <doi:10.1016/S0304-4076(02)00166-5>.
Tree Ring Analysis of Disturbance Events in R (TRADER) package provides only one way for disturbance reconstruction from tree-ring data.
Access 'Open Trade Statistics' API from R to download international trade data.
Contains performance analysis metrics of track records including entropy-based
            correlation and dynamic beta based on the Kalman filter. The normalized sample entropy method
            has been implemented which produces accurate entropy estimation even on smaller datasets while for
            the dynamic beta calculation the Kalman filter methodology has been utilized.
            On a separate stream, trades from the five major assets classes and also
            functionality to use pricing curves, rating tables, CSAs and add-on tables. The
            implementation follows an object oriented logic whereby each trade inherits from
            more abstract classes while also the curves/tables are objects. There is a lot
            of functionality focusing on the counterparty credit risk calculations however
            the package can be used for trading applications in general.
Estimate and return either the traffic speed or the car entries in the city of Thessaloniki using historical traffic data. It's used in transport pilot <http://trafficstatusprediction.imet.gr/> of the 'BigDataEurope' project <https://www.big-data-europe.eu/>. There are functions for processing these data, training a neural network, select the most appropriate model and predict the traffic speed or the car entries for a selected time date.
Estimation, selection and comparison of several families of
 transformations. The families of transformations included in the package are 
 the following: Bickel-Doksum (Bickel and Doksum 1981 <doi:10.2307/2287831>), 
 Box-Cox, Dual (Yang 2006 <doi:10.1016/j.econlet.2006.01.011>), 
 Glog (Durbin et al. 2002 <doi:10.1093/bioinformatics/18.suppl_1.S105>), 
 gpower (Kelmansky et al. 2013 <doi:10.1515/sagmb-2012-0030>), 
 Log, Log-shift opt (Feng et al. 2016 <doi:10.1002/sta4.104>),
 Manly, modulus (John and Draper 1980 <doi:10.2307/2986305>), 
 Neglog (Whittaker et al. 2005 <doi:10.1111/j.1467-9876.2005.00520.x>), 
 Reciprocal and Yeo-Johnson. The package simplifies to 
 compare linear models with untransformed and transformed dependent variable 
 as well as linear models where the dependent variable is transformed with 
 different transformations. Furthermore, the package employs maximum likelihood 
 approaches, moments optimization and divergence minimization to estimate the optimal 
 transformation parameter.
Methods to unify the different ways of creating predictive models and their different predictive formats. It includes 
       methods such as K-Nearest Neighbors, Decision Trees, ADA Boosting, Extreme Gradient Boosting, Random Forest, Neural Networks, Deep Learning,
       Support Vector Machines and Bayesian Methods.
A collection of commonly used tools for animal movement and other tracking 
 data. Variously distance, angle, bearing, distance-to, bearing-to and speed are 
 provided for geographic data that can be used directly or within 'tidyverse' 
 syntax. Distances and bearings are calculated using modern geodesic methods as 
 provided by Charles F. F. Karney (2013) <doi:10.1007/s00190-012-0578-z> 
 via the 'geodist' and 'geosphere' packages.
Assistance for handling ecological trait data and applying the 
    Ecological Trait-Data Standard terminology (Schneider et al. 2019
    <doi:10.1111/2041-210X.13288>). There are two major use cases: (1) preparation of
    own trait datasets for upload into public data bases, and (2) harmonizing
    trait datasets from different sources by re-formatting them into a unified
    format. See 'traitdataform' website for full documentation.
Species trait data from many different sources, including
    sequence data from 'NCBI' (<https://www.ncbi.nlm.nih.gov/>),
    plant trait data from 'BETYdb', data from 'EOL' 'Traitbank',
    'Birdlife' International, and more.
Functions for analysis of bulk data generated from experiments in Randomized block design as per Panse and Sukhatme (1954) <https://books.google.co.in/books?id=Efo9AAAAYAAJ>. Computes analysis of variance; Descriptive statistics parameter like Mean, Minimum, Maximum, CV, Standard error of mean, Standard Error of deviation, CD; Genetic parameter statistics Genotypic Coefficient of Variation, Phenotypic Coefficient of Variation, Heritability in broad sense, Genetic Advance and Genetic Advance per cent mean; Variance and Co-variance matrix of genotypic, phenotypic and environmental; Correlation of genotypic, phenotypic and environmental. Further includes directly publication ready tables.
Implements the three-step procedure proposed by Leffondree et al. (2004) to identify clusters of individual longitudinal trajectories. The procedure involves (1) calculating 24 measures describing the features of the trajectories; (2) using factor analysis to select a subset of the 24 measures and (3) using cluster analysis to identify clusters of trajectories, and classify each individual trajectory in one of the clusters.
Contains a set of methods for trajectory data preparation, such as filtering, compressing and clustering, and for trajectory pattern discovery.
Classes and methods for trajectory data, with support for nesting individual Track objects in track sets (Tracks) and track sets for different entities in collections of Tracks. Methods include selection, generalization, aggregation, intersection, simulation, and plotting.
A toolbox to assist with statistical analysis of 2-dimensional animal trajectories.
    It provides simple access to algorithms for calculating and assessing a variety of 
    characteristics such as speed and acceleration, as well as multiple measures of 
    straightness or tortuosity. McLean & Skowron Volponi (2018) <doi:10.1111/eth.12739>.
Formula-based user-interfaces to specific transformation models
  implemented in package 'mlt'. Available models include Cox models, some parametric 
  survival models (Weibull, etc.), models for ordered categorical variables,
  normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression
  (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory
  is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to
  transformation models for clustered data is provided (Hothorn, 2019, <arxiv:1910.09219>).
  Multivariate conditional transformation models (<arxiv:1906.03151>) can be fitted as well.
Toolbox for the manipulation, description and rendering of sequences, and more generally the mining of sequence data in the field of social sciences. Although the toolbox is primarily intended for analyzing state or event sequences that describe life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and simple functions for extracting the most frequent subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Collection of ancillary functions and utilities to be used in conjunction with the 'TraMineR' package for sequence data exploration. Most of the functions are in test phase, lack systematic consistency check of the arguments and are subject to changes. Once fully checked, some of the functions of this collection could be included in a next release of 'TraMineR'.
Likelihood-based estimation of mixed-effects transformation models using the Template
  Model Builder (TMB). The technical details of transformation models are given in Hothorn et al. (2018)
  <doi:10.1111/sjos.12291>. The random effects are assumed to be normally distributed on the scale of the
  transformation function, the marginal likelihood is evaluated using the Laplace approximation,
  and the gradients are calculated with automatic differentiation (AD).
Partially penalized versions of specific transformation models
    implemented in package 'mlt'. Available models include a fully parametric version
    of the Cox model, other parametric survival models (Weibull, etc.), models for
    binary and ordered categorical variables, normal and transformed-normal (Box-Cox type)
    linear models, and continuous outcome logistic regression. Hyperparameter tuning
    is facilitated through model-based optimization functionalities from package 'mlrMBO'.
    The accompanying vignette describes the methodology used in 'tramnet' in detail.
    Transformation models and model-based optimization are described in
    Hothorn et al. (2019) <doi:10.1111/sjos.12291> and
    Bischl et al. (2016) <arxiv:1703.03373>, respectively.
Matching terminal restriction fragment length
        polymorphism ('TRFLP') profiles between unknown samples and a
        database of known samples.  'TRAMPR' facilitates analysis of
        many unknown profiles at once, and provides tools for working
        directly with electrophoresis output through to generating
        summaries suitable for community analyses with R's rich set of
        statistical functions.  'TRAMPR' also resolves the issues of
        multiple 'TRFLP' profiles within a species, and shared 'TRFLP'
        profiles across species.
Transcribes audio to text with the HP IDOL API. Includes functions to upload files, 
	     retrieve transcriptions, and monitor jobs.
Implementation of the transformation of the Mean Opinion Scores (MOS) to be used before applying the rank based statistical techniques. The method and its necessity is described in: Babak Naderi, Sebastian Möller (2020) <arXiv:2004.11490>.
In order to smoothly animate the transformation of polygons and 
    paths, many aspects needs to be taken into account, such as differing number 
    of control points, changing center of rotation, etc. The 'transformr' 
    package provides an extensive framework for manipulating the shapes of 
    polygons and paths and can be seen as the spatial brother to the 'tweenr' 
    package.
Bindings for the Google Translate API v2
translateR provides easy access to the Google and Microsoft APIs. The package is easy to use with the related R package "stm" for the estimation of multilingual topic models.
R  version 2.1.0 and later support Korean translations of program messages.  The continuous efforts have been made by <http://developer.r-project.org/TranslationTeams.html>   The R Documentation files are licensed under the General Public License, version 2 or 3.   This means that the pilot project to translate them into Korean has permission to reproduce them and translate them.  This work is done with GNU 'gettext' utilities.  The portable object template is updated a weekly basis or whenever changes are necessary.  Comments and corrections via email to the maintainer is of course most welcome.   In order to voluntarily participate in or offer your help with this translation, please contact the maintainer.   To check the change and progress of Korean translation, please visit <http://www.openstatistics.net>.
Treatment and visualization of membrane (selective) transport 
    data. Transport profiles involving up to three species are produced as
    publication-ready plots and several membrane performance parameters 
    (e.g. separation factors as defined in Koros et al. (1996) 
    <doi:10.1351/pac199668071479> and non-linear regression parameters
    for the equations described in Rodriguez de San Miguel et al. (2014)
    <doi:10.1016/j.jhazmat.2014.03.052>) can be obtained. Many widely used 
    experimental setups (e.g. membrane physical aging) can be easily studied
    through the package's graphical representations.
A unified estimation procedure for the analysis of right censored data using linear transformation models.
Implementation of two transportation problem algorithms. 
             1. North West Corner Method 
             2. Minimum Cost Method or Least cost method.
             For more technical details about the algorithms please refer below URLs.
             <http://www.universalteacherpublications.com/univ/ebooks/or/Ch5/nw.htm>.
             <http://personal.maths.surrey.ac.uk/st/J.F/chapter7.pdf>.
Inference of transmission tree from a dated phylogeny. 
    Includes methods to simulate and analyse outbreaks.
    The methodology is described in
    Didelot et al. (2014) <doi:10.1093/molbev/msu121>,
    Didelot et al. (2017) <doi:10.1093/molbev/msw275>.
A set of vectorised functions to calculate medical equations used in transplantation, 
    focused mainly on transplantation of abdominal organs. These functions include donor and recipient
    risk indices as used by NHS Blood & Transplant, OPTN/UNOS and Eurotransplant, tools for 
    quantifying HLA mismatches, functions for calculating estimated glomerular filtration rate (eGFR), 
    a function to calculate the APRI (AST to platelet ratio) score used in initial screening of suitability to receive a 
    transplant from a hepatitis C seropositive donor and some biochemical unit converter functions. 
    All functions are designed to work with either US or international units.
    References for the equations are provided in the vignettes and function documentation.
Solve optimal transport problems. Compute Wasserstein distances (a.k.a. Kantorovitch, Fortet--Mourier, Mallows, Earth Mover's, or minimal L_p distances), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, (weighted) point patterns, and mass vectors.
A latent, quasi-independent truncation time is assumed to be linked with the observed dependent truncation time, the event time, and an unknown transformation parameter via a structural transformation model. The transformation parameter is chosen to minimize the conditional Kendall's tau (Martin and Betensky, 2005) <doi:10.1198/016214504000001538> or the regression coefficient estimates (Jones and Crowley, 1992) <doi:10.2307/2336782>. The marginal distribution for the truncation time and the event time are completely left unspecified. The methodology is applied to survival curve estimation and regression analysis.
Univariate time series operations that follow an opinionated design. 
    The main principle of 'transx' is to keep the number of observations the same. 
    Operations that reduce this number have to fill the observations gap.
The trapezoid package provides dtrapezoid, ptrapezoid,
        qtrapezoid, and rtrapezoid functions for the trapezoidal
        distribution.
Transmission Ratio Distortion (TRD) is a genetic phenomenon where the two alleles from either parent are not transmitted to the offspring at the expected 1:1 ratio under Mendelian inheritance, leading to spurious signals in genetic association studies. Functions in this package are developed to account for this phenomenon using loglinear model and Transmission Disequilibrium Test (TDT). Some population information can also be calculated.
A suite of descriptive and inferential methods designed to evaluate one or more biomarkers for their ability to guide patient treatment recommendations.  Package includes functions to assess the calibration of risk models; and plot, evaluate, and compare markers. Please see the reference Janes H, Brown MD, Huang Y, et al. (2014) <doi:10.1515/ijb-2012-0052> for further details.
An R re-implementation of the 'treeinterpreter' package on PyPI
        <https://pypi.org/project/treeinterpreter/>. Each prediction can be
        decomposed as 'prediction = bias + feature_1_contribution + ... +
        feature_n_contribution'. This decomposition is then used to calculate
        the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using
        out-of-bag samples (MDI-oob) feature importance measures based on the
        work of Li et al. (2019) <arXiv:1906.10845>.
Interface to the API for 'TreeBASE' <http://treebase.org>
    from 'R.' 'TreeBASE' is a repository of user-submitted phylogenetic
    trees (of species, population, or genes) and the data used to create
    them.
User-friendly analysis of hierarchical multinomial processing tree (MPT) 
    models that are often used in cognitive psychology. Implements the latent-trait 
    MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT 
    approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model 
    heterogeneity of participants. MPT models are conveniently specified by an
    .eqn-file as used by other MPT software and data are provided by a .csv-file 
    or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored 
    Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides 
    tests of heterogeneity and MPT-tailored summaries and plotting functions.
    A detailed documentation is available in Heck, Arnold, & Arnold (2018) 
    <DOI:10.3758/s13428-017-0869-7>.
Bootstrapped response and correlation functions,
    seasonal correlations and evaluation of reconstruction
    skills for use in dendroclimatology and dendroecology,
    see Zang and Biondi (2015) <doi:10.1111/ecog.01335>.
Create a measure of inter-point dissimilarity useful 
 for clustering mixed data, and, optionally, perform the clustering.
The centre of mass is a crucial data for arborists in order to
    consolidate a tree using steel or dynamic cables. Given field-recorded data
    on branchiness of a tree, the package: (i) computes and plots the centre of
    mass of the tree itself, (ii) computes branches slenderness coefficient in
    order to aid the arborist identify potentially dangerous branches, and
    (iii) computes the force acting on a ground plinth and its best position
    relating to the tree centre of mass, should the tree need to be stabilized
    by a steel cable.
Performs sparse discriminant analysis on a combination of
    node and leaf predictors when the predictor variables are
    structured according to a tree, as described in Fukuyama et
    al. (2017) <doi:10.1371/journal.pcbi.1005706>.
An implementation that combines trait data and a phylogenetic tree (or trees) into a 
    single object of class treedata.table. The resulting object can be easily 
    manipulated to simultaneously change the trait- and tree-level sampling.
    Currently implemented functions allow users to use a 'data.table' syntax when
    performing operations on the trait dataset within the treedata.table object.
Functions for estimating times of common ancestry and molecular clock rates of evolution using a variety of evolutionary models, parametric and nonparametric bootstrap confidence intervals, methods for detecting outlier lineages, root-to-tip regression, and a statistical test for selecting molecular clock models. The methods are described in Volz, E.M. and S.D.W. Frost (2017) <doi:10.1093/ve/vex025>.
The model estimates air pollution removal by dry deposition on trees. It also estimates or uses hourly values for aerodynamic resistance, boundary layer resistance, canopy resistance, stomatal resistance, cuticular resistance, mesophyll resistance, soil resistance, friction velocity and deposition velocity. It also allows plotting graphical results for a specific time period. The pollutants are nitrogen dioxide, ozone, sulphur dioxide, carbon monoxide and particulate matter. Baldocchi D (1994) <doi:10.1093/treephys/14.7-8-9.1069>. Farquhar GD, von Caemmerer S, Berry JA (1980) Planta 149: 78-90. Hirabayashi S, Kroll CN, Nowak DJ (2015) i-Tree Eco Dry Deposition Model. Nowak DJ, Crane DE, Stevens JC (2006) <doi:10.1016/j.ufug.2006.01.007>. US EPA (1999) PCRAMMET User's Guide. EPA-454/B-96-001. Weiss A, Norman JM (1985) Agricultural and Forest Meteorology 34: 205—213.
Visualizing cuts for either axis-align or non axis-align tree methods (e.g. decision tree, random tessellation process).
Implements measures of tree similarity, including 
  information-based generalized Robinson-Foulds distances
  (Phylogenetic Information Distance, Clustering Information Distance,
  Matching Split Information Distance; Smith, 2020)
  <doi:10.1093/bioinformatics/btaa614>; 
  Jaccard-Robinson-Foulds distances (Bocker et al. 2013)
  <doi:10.1007/978-3-642-40453-5_13>, 
  including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>;
  the Matching Split Distance (Bogdanowicz & Giaro 2012)
  <doi:10.1109/TCBB.2011.48>;
  Maximum Agreement Subtree distances;
  the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the
  Nearest Neighbour Interchange (NNI) distance, approximated per Li et al. 
  (1996) <doi:10.1007/3-540-61332-3_168>.
  Calculates the median of a set of trees under any distance metric.
Simulates nested phylogenetic trees (gene trees in species tree, 
    symbiont trees in host trees) using birth-death processes and transfers
    between lineages. Simulations of gene trees within species trees are performed
    using a three-tree model with species trees, locus trees, and gene trees.
    The cophylogenetic birth-death process is used to simulate sets of host and
    symbiont trees with extant associations between tips.  
    For more information about the three-tree model see:
    Mallo et al. (2015) <doi:10.1093/sysbio/syv082>, 
    Rasmussen and Kellis (2012) <doi:10.1101/gr.123901.111>.
Perform two types of analysis: 1) checking the
    goodness-of-fit of tree models to your single-cell gene expression
    data; and 2) deciding which tree best fits your data.
Creates interpretable decision tree visualizations 
    with the data represented as a heatmap at the tree's leaf nodes.
    'treeheatr' utilizes the customizable 'ggparty' package for 
    drawing decision trees.
Treelets provides a novel construction of multi-scale bases that extends
        wavelets to non-smooth signals. It returns a multi-scale orthonormal 
	basis, where the final computed basis functions are supported 
	on nested clusters in a hierarchical tree.  Both the tree and
	the basis, which are constructed simultaneously, reflect the 
	internal structure of the data.
High performance algorithms for manipulation of terrestrial 'LiDAR' (but not only) point clouds for use in research and forest monitoring applications, being fully compatible with the 'LAS' infrastructure of 'lidR'. For in depth descriptions of stem denoising and segmentation algorithms refer to Conto et al. (2017) <doi:10.1016/j.compag.2017.10.019>.
S4 class and methods for intuitive and efficient phylogenetic tree manipulation.
A treemap is a space-filling visualization of hierarchical
    structures. This package offers great flexibility to draw treemaps.
Provides 'ggplot2' geoms for drawing treemaps.
Provides functionality for creation and comparison of polynomials that uniquely
  describe trees as introduced in Liu (2019, <arXiv:1904.03332>). The core method
  converts rooted unlabeled phylo objects from 'ape' to the tree defining polynomials 
  described with coefficient matrices. Additionally, a conversion for rooted binary trees 
  with binary trait labels is also provided. Once the polynomials of trees are calculated 
  there are functions to calculate distances, distance matrices and plot different distance 
  trees from a target tree. Manipulation and conversion to the tree defining polynomials is 
  implemented in C++ with 'Rcpp' and 'RcppArmadillo'. Furthermore, parallel programming with 
  'RcppThread' is used to improve performance converting to polynomials and calculating distances.
(i) For a given species phylogeny on present day data which is calibrated to calendar-time, a method for estimating maximum likelihood speciation and extinction processes is provided. The method allows for non-constant rates. Rates may change (1) as a function of time, i.e. rate shifts at specified times or mass extinction events (likelihood implemented as LikShifts, optimization as bd.shifts.optim and visualized as bd.shifts.plot) or (2) as a function of the number of species, i.e. density-dependence (likelihood implemented as LikDD and optimization as bd.densdep.optim) or (3) extinction rate may be a function of species age (likelihood implemented as LikAge and optimization as bd.age.optim.matlab). Note that the methods take into account the whole phylogeny, in particular it accounts for the "pull of the present" effect. (1-3) can take into account incomplete species sampling, as long as each species has the same probability of being sampled. For a given phylogeny on higher taxa (i.e. all but one species per taxa are missing), where the number of species is known within each higher taxa, speciation and extinction rates can be estimated under model (1) (implemented within LikShifts and bd.shifts.optim with groups !=0). (ii) For a given phylogeny with sequentially sampled tips, e.g. a virus phylogeny, rates can be estimated under a model where rates vary across time using bdsky.stt.optim based on likelihood LikShiftsSTT (extending LikShifts and bd.shifts.optim). Furthermore, rates may vary as a function of host types using LikTypesSTT (multitype branching process extending functions in R package diversitree). This function can furthermore calculate the likelihood under an epidemiological model where infected individuals are first exposed and then infectious.
An implementation of permutation tests in R, supporting both exact and asymptotic K sample test of data locations. The p value of exact tests is found using tree algorithms. Tree algorithms treat permutations of input data as tree nodes and perform constraint depth-first searches for permutations that fall into the critical region of a test systematically. Pruning of tree search and optimisations at C level enable exact tests for certain large data sets.
Matches phylogenetic trees and trait data, and
    allows simultaneous manipulation of the tree and data using 'dplyr'.
Searches for phylogenetic trees that are optimal using a 
  user-defined criterion. 
  Handles inapplicable data using the algorithm of 
  Brazeau, Guillerme and Smith (2019) <doi:10.1093/sysbio/syy083>.
  Implements Profile Parsimony (Faith and Trueman, 2001) 
  <doi:10.1080/10635150118627>, and Successive Approximations (Farris, 1969) 
  <doi:10.2307/2412182>.
Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees.
Provides a flexible simulation tool for phylogenetic trees under a general model for speciation and extinction. Trees with a user-specified number of extant tips, or a user-specified stem age are simulated. It is possible to assume any probability distribution for the waiting time until speciation and extinction. Furthermore, the waiting times to speciation / extinction may be scaled in different parts of the tree, meaning we can simulate trees with clade-dependent diversification processes. At a speciation event, one species splits into two. We allow for two different modes at these splits: (i) symmetric, where for every speciation event new waiting times until speciation and extinction are drawn for both daughter lineages; and (ii) asymmetric, where a speciation event results in one species with new waiting times, and another that carries the extinction time and age of its ancestor. The symmetric mode can be seen as an vicariant or allopatric process where divided populations suffer equal evolutionary forces while the asymmetric mode could be seen as a peripatric speciation where a mother lineage continues to exist.  Reference: O. Hagen and T. Stadler (2017). TreeSimGM: Simulating phylogenetic trees under general Bellman Harris models with lineage-specific shifts of speciation and extinction in R. Methods in Ecology and Evolution. <doi:10.1111/2041-210X.12917>.
Tools for the exploration of distributions of phylogenetic trees.
    This package includes a 'shiny' interface which can be started from R using
    treespaceServer(). 
    For further details see Jombart et al. (2017) <DOI:10.1111/1755-0998.12676>.
Combine a list of taxa with a phylogeny to generate a starting tree for use in
    total evidence dating analyses.
Algorithms for detecting population structure from the history of coalescent events recorded in phylogenetic trees. This method classifies each tip and internal node of a tree into disjoint sets characterized by similar coalescent patterns. The methods are described in Volz, E., Wiuf, C., Grad, Y., Frost, S., Dennis, A., & Didelot, X. (2020) <doi:10.1093/sysbio/syaa009>.
Efficient implementations of functions for the creation, 
  modification and analysis of phylogenetic trees.
  Applications include:
  generation of trees with specified shapes;
  analysis of tree shape;
  rooting of trees and extraction of subtrees;
  calculation and depiction of node support;
  calculation of ancestor-descendant relationships;
  import and export of trees from Newick, Nexus (Maddison et al. 1997)
  <doi:10.1093/sysbio/46.4.590>,
  and TNT <http://www.lillo.org.ar/phylogeny/tnt/> formats;
  and analysis of splits and cladistic information.
Provides a dataset of predefined color palettes based on the Star Trek science fiction series, associated color palette functions, 
    and additional functions for generating customized palettes that are on theme. The package also offers functions for applying 
    the palettes to plots made using the 'ggplot2' package.
Provides a collection of true type and open type Star Trek-themed fonts.
Trelliscope is a scalable, flexible, interactive approach to visualizing data (Hafen, 2013 <doi:10.1109/LDAV.2013.6675164>). This package provides methods that make it easy to create a Trelliscope display specification for TrelliscopeJS. High-level functions are provided for creating displays from within 'tidyverse' or 'ggplot2' workflows. Low-level functions are also provided for creating new interfaces.
Provides access to Trello API (<https://developers.trello.com/>).
    A family of GET functions make it easy to retrieve cards, labels, members,
    teams and other data from both public and private boards. Server responses
    are formatted upon retrieval. Automated paging allows for large requests
    that exceed server limit. See <https://github.com/jchrom/trelloR> for more
    information.
The analysis of environmental data often requires
	     the detection of trends and change-points. 
	     This package includes tests for trend detection
	     (Cox-Stuart Trend Test, Mann-Kendall Trend Test, 
	     (correlated) Hirsch-Slack Test,
             partial Mann-Kendall Trend Test, multivariate (multisite)
	     Mann-Kendall Trend Test, (Seasonal) Sen's slope, 
	     partial Pearson and Spearman correlation trend test),
             change-point detection (Lanzante's test procedures, 
	     Pettitt's test, Buishand Range Test,
	     Buishand U Test, Standard Normal Homogeinity Test),
	     detection of non-randomness (Wallis-Moore Phase Frequency Test,
	     Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test)
	     and the two sample Robust Rank-Order Distributional Test.
Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series.
  Zekai, S. (2011). <doi:10.1061/(ASCE)HE.1943-5584.0000556>.
  Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. 
  Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp.
Provides a coherent interface for evaluating models fit with the
  trending package.  This package is part of the RECON
  (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
Provides a coherent interface to multiple modelling tools for
  fitting trends along with a standardised approach for generating confidence
  and prediction intervals.
Estimation of causal odds ratio and power calculation given trends in exposure prevalence    and outcome frequencies of stratified data.
Performs the detection of point anomalies and linear trend changes 
    for univariate time series by implementing the bottom-up unbalanced wavelet transformation 
    proposed by H. Maeng and P. Fryzlewicz (2019) <http://personal.lse.ac.uk/maengh/>. 
    The estimated number and locations of the change-points are returned with the 
    piecewise-linear estimator for signal.
Analysis of annual average ocean water level time series, providing
    improved estimates of trend (mean sea level) and associated real-time velocities and
    accelerations. Improved trend estimates are based on singular spectrum analysis
    methods. Various gap-filling options are included to accommodate incomplete time
    series records. The package also includes a range of diagnostic tools to inspect
    the components comprising the original time series which enables expert
    interpretation and selection of likely trend components. A wide range of
    screen and plot to file options are available in the package.
Access Google Trends information. This package provides a tidy wrapper to the 'gtrendsR' package.
    Use four spaces when indenting paragraphs within the Description.
Provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive. For details of TRR, see Li L, Zhang X (2017) <doi:10.1080/01621459.2016.1193022>. For details of TPR, see Zhang X, Li L (2017) <doi:10.1080/00401706.2016.1272495>. For details of 1D algorithm, see Cook RD, Zhang X (2016) <doi:10.1080/10618600.2015.1029577>. For details of ECD algorithm, see Cook RD, Zhang X (2018) <doi:10.5705/ss.202016.0037>.
Simulate genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage diequilibrium structure and allele frequency distribution. For studies of epistasis one can simulate models that involve specific SNPs at specific sets of loci, which we will refer to as "pathways". TriadSim generates genotype data by resampling triad genotypes from existing data. The details of the method is described in the manuscript under preparation "Simulating Autosomal Genotypes with Realistic Linkage Disequilibrium and a Spiked in Genetic Effect" Shi, M., Umbach, D.M., Wise A.S., Weinberg, C.R.
A collection of clinical trial designs and methods, implemented in 
    'rstan' and R, including: the Continual Reassessment Method by O'Quigley et 
    al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004) 
    <doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of
    Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the 
    Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and
    more. We provide functions to aid model-fitting and analysis. 
    The 'rstan' implementations may also serve as a cookbook to anyone looking 
    to extend or embellish these models. We hope that this package encourages 
    the use of Bayesian methods in clinical trials. There is a preponderance of 
    early phase trial designs because this is where Bayesian methods are used 
    most. If there is a method you would like implemented, please get in touch.
Functions and Examples in Sample Size Calculation in
        Clinical Research.
Provides the "r, q, p, and d" distribution functions for the triangle distribution.
Measuring angles between points in a landscape is much easier
    than measuring distances. When the location of three points is known the
    position of the observer can be determined based solely on the angles between
    these points as seen by the observer. This task (known as triangulation)
    however requires onerous calculations - these calculations are automated by this
    package.
A collection of high-performance functions for the triangular
    distribution that consists of the probability density function, cumulative
    distribution function, quantile function, random variate generator, moment
    generating function, characteristic function, and expected shortfall
    function. References: Samuel Kotz, Johan Ren Van Dorp (2004)
    <doi:10.1142/5720>, Melissa E. O'Neill (2014)
    <https://www.cs.hmc.edu/tr/hmc-cs-2014-0905.pdf>, David Blackman and
    Sebastiano Vigna (2018) <arXiv:1805.01407>, and Acerbi, Carlo and
    Tasche, Dirk. (2002) <doi:10.1111/1468-0300.00091>.
Functions to make manipulation of object attributes easier. 
    It also contains a few functions that extend the 'dplyr' package for data 
    manipulation, and it provides new pipe operators, including the pipe '%@>%' 
    similar to the 'magrittr' '%>%', but with the additional functionality to 
    enable attributes propagation.
A flexible color scale for ternary compositions with options for
  discretization, centering and scaling.
'Radix trees', or 'tries', are key-value data structures optimised for efficient lookups, similar in purpose
             to hash tables. 'triebeard' provides an implementation of 'radix trees' for use in R programming and in
             developing packages with 'Rcpp'.
A complete data set of historic GB trig points in British National Grid (OSGB36) coordinate reference system. Trig points (aka triangulation stations) are fixed survey points used to improve the accuracy of map making in Great Britain during the 20th Century. Trig points are typically located on hilltops so still serve as a useful navigational aid for walkers and hikers today.
Propensity score matching for non-binary treatments.
Trimmed k-means clustering. The method is described in Cuesta-Albertos et al. (1997) <doi:10.1214/aos/1031833664>.
Information on all of the TriMet stops in the Portland Metro Area. It includes information such as the longitude, latitude, cross street, and direction of the stop. TriMet has catalogued these stops, 6880 in total.
A lightweight toolkit to reduce the size of a list object. The
    object is minimized by recursively removing elements from the object 
    one-by-one. The process is constrained by a reference function call
    specified by the user, where the target object is given as an argument. 
    The procedure will not allow elements to be removed from the object, that 
    will cause results from the function call to diverge from the function 
    call with the original object.
Provides various commonly-used response time trimming
    methods, including the recursive / moving-criterion methods reported by
    Van Selst and Jolicoeur (1994). By passing trimming functions raw data files,
    the package will return trimmed data ready for inferential testing.
Creates point and probability forecasts from the trees in a random forest using a trimmed opinion pool.
Several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers.
The package contains functions that 1) estimates
        gene-environment interaction between a SNP and a continuous
        non-genetic attribute by fitting a generalized additive model
        to case-parent trio data, 2) produces graphical displays of
        estimated interaction, 3) performs permutation test of
        gene-environment interaction; 4) simulates informative
        case-parent trios.
Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) <doi:10.1080/10618600.2012.681250>.
Functions for accessing and manipulating spatial data for animal
    tracking, with straightforward coercion from and to other formats. Filter
    for speed and create time spent maps from animal track data. There are
    coercion methods to convert between 'trip' and 'ltraj' from 'adehabitatLT', 
    and between 'trip' and 'psp' and 'ppp' from 'spatstat'. Trip objects
    can be created from raw or grouped data frames, and from types in the 'sp', 
    'sf', 'amt', 'trackeR', 'mousetrap', and other packages.
A constrained two-dimensional Delaunay triangulation package
  providing both triangulation and generation of voronoi mosaics of 
  irregular spaced data.
Data handling and estimation functions for animal movement
    estimation from archival or satellite tags. Helper functions are included
    for making image summaries binned by time interval from Markov Chain Monte Carlo
    simulations.
Social Relation Model (SRM) analyses for single or multiple
    round-robin groups are performed. These analyses are either based on one
    manifest variable, one latent construct measured by two manifest variables,
    two manifest variables and their bivariate relations, or two latent
    constructs each measured by two manifest variables. Within-group t-tests
    for variance components and covariances are provided for single groups.
    For multiple groups two types of significance tests are provided:
    between-groups t-tests (as in SOREMO) and enhanced standard errors based on
    Lashley and Bond (1997) <DOI:10.1037/1082-989X.2.3.278>. Handling for missing values is provided.
Tools for exploring effects of correlated features in predictive 
    models. The predict_triplot() function delivers instance-level explanations 
    that calculate the importance of the groups of explanatory variables. The 
    model_triplot() function delivers data-level explanations. The generic plot 
    function visualises in a concise way importance of hierarchical groups of 
    predictors. All of the the tools are model agnostic, therefore works for any
    predictive machine learning models. Find more details in Biecek (2018) 
    <arXiv:1806.08915>.
Uses read counts for biallelic single nucleotide polymorphisms (SNPs)
    to compare the likelihoods for the observed read counts given that a sample is 
    either diploid or triploid. It allows parameters to be specified to account for 
    sequencing error rates and allelic bias. For details of the algorithm, please see
    Delomas (2019) <doi:10.1111/1755-0998.13073>.
Computing diversity measures on tripartite graphs. This package first implements a parametrized family of such diversity measures which apply on probability distributions. Sometimes called "True Diversity", this family contains famous measures such as the richness, the Shannon entropy, the Herfindahl-Hirschman index, and the Berger-Parker index. Second, the package allows to apply these measures on probability distributions resulting from random walks between the levels of tripartite graphs. By defining an initial distribution at a given level of the graph and a path to follow between the three levels, the probability of the walker's position within the final level is then computed, thus providing a particular instance of diversity to measure.
It includes functions like tropical addition, tropical multiplication for vectors and matrices. In tropical algebra, the tropical sum of two numbers is their minimum and the tropical product of two numbers is their ordinary sum. For more information see also I. Simon (1988) Recognizable sets with multiplicities in the tropical semi ring: Volume 324 Lecture Notes I Computer Science, pages 107-120 <doi: 10.1007/BFb0017135>.
A compilation of fish stock assessment methods for the
    analysis of length-frequency data in the context of data-poor
    fisheries. Includes methods and examples included in the FAO
    Manual by P. Sparre and S.C. Venema (1998), "Introduction to tropical fish
    stock assessment"
    (<http://www.fao.org/documents/card/en/c/9bb12a06-2f05-5dcb-a6ca-2d6dd3080f65/>),
    as well as other more recent methods.
Estimates the trophic position of a consumer relative 
    to a baseline species. It implements a Bayesian approach which combines an 
    interface to the 'JAGS' MCMC library of 'rjags' and stable isotopes. Users are
    encouraged to test the package and send bugs and/or errors to
    trophicposition-support@googlegroups.com.
Some of the basic tropical algebra functionality is provided for sparse matrices by applying sparse matrix storage techniques. Some of these are addition and multiplication of vectors and matrices, dot product of the vectors in tropical form and some general equations are also solved using tropical algebra.
Class definitions and constructors for pseudo-vectors containing
    all permutations, combinations and subsets of objects taken from a vector.
    Simplifies working with structures commonly encountered in combinatorics.
Recursive partytioning of transformation models with
  corresponding random forest for conditional transformation models 
  as described in 'Transformation Forests' (Hothorn and Zeileis, 2017, <arXiv:1701.02110>) 
  and 'Top-Down Transformation Choice' (Hothorn, 2018, <DOI:10.1177/1471082X17748081>).
Implements Bayesian methods, described in
    Hugh-Jones (2019) <doi:10.1007/s40881-019-00069-x>, for estimating the
    proportion of liars in coin flip-style experiments, where subjects
    report a random outcome and are paid for reporting a "good" outcome.
An implementation of the TrueSkill algorithm (Herbrich,
        R., Minka, T. and Grapel, T) in R; a Bayesian skill rating
        system with inference by approximate message passing on a
        factor graph. Used by Xbox to rank gamers and identify
        appropriate matches.
        http://research.microsoft.com/en-us/projects/trueskill/default.aspx
        Current version allows for one player per team. Will update as
        time permits.  Requires R version 3.0 as it is written with
        Reference Classes.  URL:
        https://github.com/bhoung/trueskill-in-r Acknowledgements to
        Doug Zongker and Heungsub Lee for their python implementations
        of the algorithm and for the liberal reuse of Doug's code
        comments (@dougz and @sublee on github).
A collection of functions to deal with the truncated univariate and multivariate normal and Student distributions, described in Botev (2017) <doi:10.1111/rssb.12162> and Botev and L'Ecuyer (2015) <doi:10.1109/WSC.2015.7408180>.
A collection of tools to evaluate probability density
        functions, cumulative distribution functions, quantile functions
        and random numbers for truncated random variables.  These functions are
        provided to also compute the expected value and variance. Nadarajah
        and Kotz (2006) developed most of the functions. QQ plots can be produced.
        All the probability functions in the stats, stats4 and evd
        packages are automatically available for truncation..
Goodness-of-fit tests and some adjusted exploratory tools
        allowing for left truncated data
Density, probability, quantile and random number
        generation functions for the truncated normal distribution.
Estimation of models for truncated Gaussian variables by maximum likelihood.
Semi-parametric estimation of truncated linear regression models
A wrapper around the 'Trundler' API, which gives
    access to historical retail product and pricing data, and can be found
    at <https://api.trundler.dev/>.
Does local optimization using two derivatives and trust regions.
    Guaranteed to converge to local minimum of objective function.
Trusted Timestamps (tts) are created by incorporating a hash of a file or dataset into a transaction on the decentralized blockchain (Stellar network). 
    The package makes use of a free service provided by <https://stellarapi.io>.
Trust region algorithm for nonlinear optimization. Efficient when
    the Hessian of the objective function is sparse (i.e., relatively few nonzero
    cross-partial derivatives). See Braun, M. (2014) <doi:10.18637/jss.v060.i04>.
Advanced tryCatch() and try() functions for better error handling
             (logging, stack trace with source code references and support for post-mortem analysis via dump files).
Stationary Gaussian ARMA processes and the stationary 'GARMA' distribution are fundamental in time series analysis. Here we give utilities to compute 
    the auto-covariance/auto-correlation for a stationary Gaussian ARMA process, as well as the probability functions (density, cumulative distribution, random 
    generation) for random vectors from this distribution.  We also give functions for the spectral intensity, and the permutation-spectrum test for testing 
    a time-series vector for the presence of a signal.
Contains R functions and datasets detailed in the book
        "Time Series Analysis with Applications in R (second edition)" by Jonathan Cryer and Kung-Sik Chan.
Tsallis distribution also known as the q-exponential family distribution. Provide distribution d, p, q, r functions, fitting and testing functions. Project initiated by Paul Higbie and based on Cosma Shalizi's code.
Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978>  "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.
Time series toolkit with identical behavior for all
  time series classes: 'ts','xts', 'data.frame', 'data.table', 'tibble', 'zoo',
  'timeSeries', 'tsibble', 'tis' or 'irts'. Also converts reliably between these classes.
Different estimators are provided to solve the blind source separation problem for multivariate time series with stochastic volatility (Matilainen, Nordhausen and Oja (2015) <doi:10.1016/j.spl.2015.04.033>; Matilainen, Miettinen, Nordhausen, Oja and Taskinen (2017) <doi:10.17713/ajs.v46i3-4.671>) and supervised dimension reduction problem for multivariate time series (Matilainen, Croux, Nordhausen and Oja (2017) <doi:10.1016/j.ecosta.2017.04.002>). Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace.
Performs the two-sample comparisons using the following exact test procedures: the exact likelihood-ratio test (LRT) for equality of two normal populations proposed in Zhang et al. (2012); the combined test based on the LRT and Shapiro-Wilk test for normality via the Bonferroni correction technique; the newly proposed density-based empirical likelihood (DBEL) ratio test. To calculate p-values of the DBEL procedures, three procedures are used: (a) the traditional Monte Carlo (MC) method implemented in C++, (b) a new interpolation method based on regression techniques to operate with tabulated critical values of the test statistic; (c) a Bayesian type method that uses the tabulated critical values as the prior information and  MC generated DBEL-test-statistic's values as data.
A set of measures of dissimilarity between time series to perform time series clustering. Metrics based on raw data, on generating models and on the forecast behavior are implemented. Some additional utilities related to time series clustering are also provided, such as clustering algorithms and cluster evaluation metrics.
Utilities for comparing the equality of series on two databases.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.
A set of functions to implement Time Series Cointegrated System (TSCS)
    spatial interpolation and relevant data visualization.
Illustrates the various 'TSdbi' packages with a vignette using time series 
	data from several sources. The vignette also illustrates some simple time series
	manipulation and plotting using packages 'tframe' and 'tfplot'.
A terribly-simple data base for numeric
  time series, written purely in R, so no external
  database-software is needed. Series are stored in
  plain-text files (the most-portable and enduring file
  type) in CSV format. Timestamps are encoded using R's
  native numeric representation for 'Date'/'POSIXct',
  which makes them fast to parse, but keeps them
  accessible with other software. The package provides
  tools for saving and updating series in this
  standardised format, for retrieving and joining data,
  for summarising files and directories, and for
  coercing series from and to other data types (such as
  'zoo' series).
Provides a common interface to time series databases. The
	objective is to define a standard interface so users can retrieve time 
	series data from various sources with a simple, common, set of 
	commands, and so programs can be written to be portable with respect 
	to the data source. The SQL implementations also provide a database 
	table design, so users needing to set up a time series database 
	have a reasonably complete way to do this easily. The interface 
	provides for a variety of options with respect to the representation 
	of time series in R. The interface, and the SQL implementations, also
	handle vintages of time series data (sometime called editions or 
	real-time data). There is also a (not yet well tested) mechanism to
	handle multilingual data documentation.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
ARIMA-model-based decomposition of quarterly and 
 monthly time series data.
 The methodology is developed and described, among others, in 
 Burman (1980) <DOI:10.2307/2982132> and 
 Hillmer and Tiao (1982) <DOI:10.2307/2287770>.
Calculate optimal Zhong's two-/three-stage Phase II designs (see Zhong (2012) <doi:10.1016/j.cct.2012.07.006>). Generate Target Toxicity decision table for Phase I dose-finding (two-/three-stage). This package also allows users to run dose-finding simulations based on customized decision table.
Determining training set for genomic selection using a genetic algorithm (Holland J.H. (1975) <DOI:10.1145/1216504.1216510>) or simple exchange algorithm (change an individual every iteration). Three different criteria are used in both algorithms, which are r-score (Ou J.H., Liao C.T. (2018) <DOI:10.6342/NTU201802290>), PEV-score (Akdemir D. et al. (2015) <DOI:10.1186/s12711-015-0116-6>) and CD-score (Laloe D. (1993) <DOI:10.1186/1297-9686-25-6-557>). Phenotypic data for candidate set is not necessary for all these methods. By using it, one may readily determine a training set that can be expected to provide a better training set comparing to random sampling.
Disaggregates low frequency time series data to higher frequency series. Implements the following methods for temporal disaggregation: Boot, Feibes and Lisman (1967) <DOI:10.2307/2985238>, Chow and Lin (1971) <DOI:10.2307/1928739>, Fernandez (1981) <DOI:10.2307/1924371> and Litterman (1983) <DOI:10.2307/1391858>.
A set of commonly used distance measures and some additional functions which, although initially not designed for this purpose, can be used to measure the dissimilarity between time series. These measures can be used to perform clustering, classification or other data mining tasks which require the definition of a distance measure between time series. U. Mori, A. Mendiburu and J.A. Lozano (2016), <doi:10.32614/RJ-2016-058>.
Implements a method for identifying subgroups with superior response relative to the overall sample.
Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).
Calculates total survey error (TSE) for one or more surveys, using common scale-dependent and/or scale-independent metrics.  On TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>.
Calculates total survey error (TSE) for one or more surveys, using both scale-dependent and scale-independent metrics.  Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTIND and its documentation), properly input column names (see functions documentation), and run your functions.  For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.
A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions 'predict()' and 'forecast()' to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as 'update_weights()' or 'update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017 <doi:10.1007/978-3-319-59153-7_62>.
Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance.
Time series analysis and computational finance.
Routines for the analysis of nonlinear time series. This
        work is largely inspired by the TISEAN project, by Rainer
        Hegger, Holger Kantz and Thomas Schreiber:
        <http://www.mpipks-dresden.mpg.de/~tisean/>.
Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes.
Multiscale multifractal analysis (MMA) (Gierałtowski et al.,
    2012)<DOI:10.1103/PhysRevE.85.021915> is a time series analysis method,
    designed to describe scaling properties of fluctuations within the signal
    analyzed. The main result of this procedure is the so called Hurst surface
    h(q,s) , which is a dependence of the local Hurst exponent h (fluctuation
    scaling exponent) on the multifractal parameter q and the scale of observation s
    (data window width).
Tools for downloading and organizing data from Tehran Stock Exchange (TSE) <http://new.tse.ir/en/>. It also performs some descriptive data analysis for assets.
Calculates total survey error (TSE) for a survey under multiple, different weighting schemes, using both scale-dependent and scale-independent metrics.  Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTWGT and its documentation), properly input column names (see functions documentation), and run your functions.  For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.
Forecasting of long memory time series in presence of structural break by using TSF algorithm by Papailias and Dias (2015) <doi:10.1016/j.ijforecast.2015.01.006>.
Extraction of Factors from Multivariate Time Series. See ?00tsfa-Intro for more details.
A 'fame' interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.
A general regression neural network (GRNN) is a variant of a
    Radial Basis Function Network characterized by a fast single-pass learning.
    'tsfgrnn' allows you to forecast time series using a GRNN model Francisco 
    Martinez et al. (2019) <doi:10.1007/978-3-030-20521-8_17> and Weizhong Yan
    (2012) <doi:10.1109/TNNLS.2012.2198074>. When the forecasting horizon
    is higher than 1, two multi-step ahead forecasting strategies can be used.
    The model built is autoregressive, that is, it is only based on the 
    observations of the time series. You can consult and plot how the
    prediction was done. It is also possible to assess the forecasting accuracy
    of the model using rolling origin evaluation.
Allows to forecast time series using nearest neighbors regression
    Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J.
    Rivera (2017) <doi:10.1007/s10462-017-9593-z>. When the forecasting horizon
    is higher than 1, two multi-step ahead forecasting strategies can be used.
    The model built is autoregressive, that is, it is only based on the 
    observations of the time series. The nearest neighbors used in a prediction
    can be consulted and plotted.
To provide a high dimensional grouped variable selection approach for detection of whole-genome SNP effects and SNP-SNP interactions, as described in Fang et al. (2017, under review).
This gui shows realisations of times series, currently ARMA and GARCH processes. It might be helpful for teaching and studying.
Two-stage procedure compares hazard rate functions,
    which may or may not cross each other.
Provides a 'tbl_ts' class (the 'tsibble') for
    temporal data in an data- and model-oriented format. The 'tsibble'
    provides tools to easily manipulate and analyse temporal data, such as
    filling in time gaps and aggregating over calendar periods.
Provides diverse datasets in the 'tsibble' data structure. These datasets are useful for learning and demonstrating how tidy temporal data can tidied, visualised, and forecasted.
A shared tsibble data easily communicates between
    htmlwidgets on both client and server sides, powered by 'crosstalk'. A
    shiny module is provided to visually explore periodic/aperiodic
    temporal patterns.
Functions for analysing and forecasting intermittent demand/slow moving items time series.
An implementation of the time-series Susceptible-Infected-Recovered (TSIR) model using a number of different fitting options for infectious disease time series data. The manuscript based on this package can be found here <https://doi:10.1371/0185528>. The method implemented here is described by Finkenstadt and Grenfell (2000) <DOI: 10.1111/1467-9876.00187>.
A novel and fast two stage method for simultaneous multiple change point detection and variable selection for piecewise stationary autoregressive (PSAR) processes and linear regression model. It also simultaneously performs variable selection for each autoregressive model and hence the order selection.
Implementations of a number of functions used to mine numeric time-series data. It covers the implementation of SAX transformation, univariate motif discovery (based on the random projection method), multivariate motif discovery (based on graph clustering), and several functions used for the ease of visualizing the motifs discovered. The details of SAX transformation can be found in J. Lin. E. Keogh, L. Wei, S. Lonardi, Experiencing SAX: A novel symbolic representation of time series, Data Mining and Knowledge Discovery 15 (2) (2007) 107-144. Details on univariate motif discovery method implemented can be found in B. Chiu, E. Keogh, S. Lonardi, Probabilistic discovery of time series motifs, ACM SIGKDD, Washington, DC, USA, 2003, pp. 493-498. Details on the multivariate motif discovery method implemented can be found in A. Vahdatpour, N. Amini, M. Sarrafzadeh, Towards unsupervised activity discovery using multi-dimensional motif detection in time series, IJCAI 2009 21st International Joint Conference on Artificial Intelligence.
Methods to 
	retrieve data from several different sources. This include historical
	quote data from 'Yahoo' and 'Oanda', economic data from 'FRED', and
	'xls' and 'csv' data from different sources.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Return the first four moments of the SMN distributions (Normal, Student-t, Pearson VII, Slash or Contaminated Normal).
Tools for specifying time series regression models
A toolkit implementing the Matrix Profile concept
    that was created by CS-UCR
    <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.
Return the first four moments, estimation of parameters and sample of the TSMSN distributions (Skew Normal, Skew t, Skew Slash or Skew Contaminated Normal).
A 'MySQL' interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the 'networkDynamic' format. This work was supported by grant R01HD68395 from the National Institute of Health.
A "pure R" implementation of the t-SNE algorithm.
An ODBC interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Detection of outliers in time series following the 
    Chen and Liu (1993) <DOI:10.2307/2290724> procedure. 
    Innovational outliers, additive outliers, level shifts, 
    temporary changes and seasonal level shifts are considered.
Basic infrastructure and some algorithms for the traveling
    salesperson problem (also traveling salesman problem; TSP).
    The package provides some simple algorithms and
    an interface to the Concorde TSP solver and its implementation of the
    Chained-Lin-Kernighan heuristic. The code for Concorde
    itself is not included in the package and has to be obtained separately.
    Hahsler and Hornik (2007) <doi:10.18637/jss.v023.i02>.
Adjusts for correlations among the rows and columns via
        the Transposable Sphering Algorithm when conducting large-scale
        inference on the rows of a data matrix.
Prediction intervals for ARIMA and structural time series
    models using importance sampling approach with uninformative priors for model
    parameters, leading to more accurate coverage probabilities in frequentist
    sense. Instead of sampling the future observations and hidden states of the
    state space representation of the model, only model parameters are sampled,
    and the method is based solving the equations corresponding to the conditional
    coverage probability of the prediction intervals. This makes method relatively
    fast compared to for example MCMC methods, and standard errors of prediction
    limits can also be computed straightforwardly.
Instance feature calculation and evolutionary instance generation
    for the traveling salesman problem. Also contains code to "morph" two TSP
    instances into each other. And the possibility to conveniently run a couple
    of solvers on TSP instances.
A 'PostgreSQL' interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Functions for time series preprocessing, decomposition, prediction and accuracy assessment using automatic linear modelling. The generated linear models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Time Series Qn is a package with applications of the Qn estimator of Rousseeuw and Croux (1993) <doi:10.1080/01621459.1993.10476408> to univariate and multivariate Time Series in time and frequency domains. More specifically, the robust estimation of autocorrelation or autocovariance matrix functions from Ma and Genton (2000, 2001) <doi:10.1111/1467-9892.00203>, <doi:10.1006/jmva.2000.1942> and Cotta (2017) <doi:10.13140/RG.2.2.14092.10883> are provided. The robust pseudo-periodogram of Molinares et. al. (2009) <doi:10.1016/j.jspi.2008.12.014> is also given. This packages also provides the M-estimator of the long-memory parameter d based on the robustification of the GPH estimator proposed by Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.
Methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining.
    Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson),
    and forecasting accuracy measures are implemented.
Additive hazards models with two stage residual inclusion method are fitted under either survival data or competing risks data. The estimator incorporates an instrumental variable and therefore can recover causal estimand in the presence of unmeasured confounding under some assumptions. A.Ying, R. Xu and J. Murphy. (2019) <doi:10.1002/sim.8071>.
Methods for handling the missing values outliers are introduced in
    this package. The recognized missing values and outliers are replaced 
    using a model-based approach. The model may consist of both autoregressive
    components and external regressors. The methods work robust and efficient,
    and they are fully tunable. The primary motivation for writing the package
    was preprocessing of the energy systems data, e.g. power plant production
    time series, but the package could be used with any time series data.
Time Series Segmented Residual Trends is a method for the automated detection of land degradation from remotely sensed vegetation and climate datasets. TSS-RESTREND incorporates aspects of two existing degradation detection methods: RESTREND which is used to control for climate variability, and BFAST which is used to look for structural changes in the ecosystem. The full details of the testing and justification of the TSS-RESTREND method (version 0.1.02) are published in Burrell et al., (2017). <doi:10.1016/j.rse.2017.05.018>. The changes to the method introduced in version 0.2.03 focus on the inclusion  of temperature as an additional climate variable. This allows for land  degradation assessment in temperature limited drylands. A paper that details this work is currently under review. There are also a number of bug fixes and speed improvements. Version 0.3.0 introduces additional attribution for eCO2,  climate change and climate variability the details of which are in press in Burrell et al., (2020).  The version under active development and additional example scripts showing  how the package can be applied can be found at <https://github.com/ArdenB/TSSRESTREND>.
Methods to 
	retrieve data in the Statistical Data and Metadata Exchange ('SDMX') format
	from several database. (For example,
	'EuroStat', the European Central Bank, the Organisation for Economic 
	Co-operation and Development, the 'Unesco' Institute for Statistics,
	and the International Labor Organization.)
	This is a wrapper for package 'RJSDMX'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Execution of various time series models and choosing the best one
    either by a specific error metric or by picking the best one by majority vote.
    The models are based on the "forecast" package, written by Prof. Rob Hyndman.
It performs the smoothing approach provided by penalized least squares for univariate and bivariate time series, as proposed by Guerrero (2007) and Gerrero et al. (2017). 
          This allows to estimate the time series trend by controlling the amount of resulting (joint) smoothness.
          ---
          Guerrero, V.M (2007)  <DOI:10.1016/j.spl.2007.03.006>.
          Guerrero, V.M; Islas-Camargo, A. and Ramirez-Ramirez, L.L. (2017) <DOI:10.1080/03610926.2015.1133826>.
Standard SQL query functions used by 
	SQL plugins packages for the 'TSdbi' interface to time series databases. 
	It will mainly be used by other packages rather than directly by end
	users. The one exception is the function 'TSquery' which can be used to
	construct a time series from a database containing observations over
	time (e.g. balance statements for multiple years), but where the database
	is not specifically designed to store time series (as with other 
	'TSdbi' SQL plugin packages).
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
An 'SQLite' interface for 'TSdbi'.
	Comprehensive examples of all the 'TS*' packages is provided in the
	vignette Guide.pdf with the 'TSdata' package.
Functions for statistical analysis, modeling and simulation of time
 series with state space model, based on the methodology in Kitagawa
 (1993, ISBN: 4-00-007703-1 and 2005, ISBN: 4-00-005455-4).
Provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.
Plot official statistics' time series conveniently: automatic legends, highlight windows, stacked bar chars with positive and negative contributions, sum-as-line option, two y-axes with automatic horizontal grids that fit both axes and other popular chart types. 'tstools' comes with a plethora of defaults to let you plot without setting an abundance of parameters first, but gives you the flexibility to tweak the defaults. In addition to charts, 'tstools' provides a super fast, 'data.table' backed time series I/O that allows the user to export / import long format, wide format and transposed wide format data to various file types.
A ternary search tree is a type of prefix tree with up to three children and the ability for incremental string search. The package uses this ability for word auto-completion and spell checking. Includes a dataset with the 10001 most frequent English words.
Interactive laboratory of Time Series based in Box-Jenkins methodology.
Includes: (i) tests and visualisations that can help the modeller explore time series components and perform decomposition; (ii) modelling shortcuts, such as functions to construct lagmatrices and seasonal dummy variables of various forms; (iii) an implementation of the Theta method; (iv) tools to facilitate the design of the forecasting process, such as ABC-XYZ analyses; and (v) "quality of life" functions, such as treating time series for trailing and leading values.
Fitting tree-structured varying coefficient models (Berger et al. (2018), <doi:10.1007/s11222-018-9804-8>). Simultaneous detection of covariates with varying coefficients and effect modifiers that induce varying coefficients if they are present.
An 'RStudio' add-in to visualize time series. Time series are searched in the global environment as data.frame objects with a column of type date and a column of type numeric. Interactive charts are produced using 'plotly' package.
Tools for timescale decomposition of the classic variance ratio of community ecology. Tools are as described in Zhao et al (in prep), extending commonly used methods introduced by Peterson et al (1975) <doi: 10.2307/1936306>.
Accompanies the text Applied Time Series Analysis with R, 2nd edition by Woodward, Gray, and Elliott. It is helpful for data analysis and for time series instruction.
Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <DOI:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.
This interface was created to develop a standard procedure 
 to analyse temporal trend in the framework of the OSPAR convention.
 The analysis process run through 4 successive steps : 1) manipulate your data, 2)
 select the parameters you want to analyse, 3) build your regulated 
 time series, 4) perform diagnosis and analysis and 5) read the results. 
 Statistical analysis call other package function such as Kendall tests
 or cusum() function.
Computes a point pattern in R^2 or on a graph that is representative of a collection of many data patterns. The result is an approximate barycenter (also known as Fréchet mean or prototype) based on a transport-transform metric. Possible choices include Optimal SubPattern Assignment (OSPA) and Spike Time metrics. Details can be found in Müller, Schuhmacher and Mateu (2019) <arXiv:1909.07266>.
U.S. Department of the Treasury, Alcohol and Tobacco Tax and
    Trade Bureau (TTB) collects data and reports on monthly beer
    industry production and operations. This data package includes
    a collection of 10 years (2006 - 2015) worth of data on materials
    used at U.S. breweries in pounds reported by the Brewer's Report
    of Operations and the Quarterly Brewer's Report of Operations
    forms, ready for data analysis. This package also includes historical
    tax rates on distilled spirits, wine, beer, champagne, and tobacco
    products as individual data sets.
The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Paper: Albrecht, Marco, et al. (2017)<DOI:10.1186/s12859-016-1440-8>.
The 'tinytest' package offers a light-weight zero-dependency unit-testing
 framework to which this package adds support of the 'diffobj' package for 'diff'-style
 comparison of R objects.
Interactive shiny application for working with ttest on data. Results for analysis are provided.
C source code and R wrappers for the tth/ttm TeX-to-HTML/MathML translators.
Computing the first two moments of the truncated multivariate t (TMVT) distribution under the double truncation. Appling the slice sampling algorithm to generate random variates from the TMVT distribution.
Several forms of likelihood ratio calculations are available.
A collection of over 50 technical indicators for creating technical trading rules. The package also provides fast implementations of common rolling-window functions, and several volatility calculations.
Time-Temperature Superposition analysis is often applied to frequency modulated data obtained by Dynamic
		Mechanic Analysis (DMA) and Rheometry in the analytical chemistry and physics
		areas. These techniques provide estimates of material mechanical properties
		(such as moduli) at different temperatures in a wider range of time. This
		package provides the Time-Temperature superposition Master Curve at a referred
		temperature by the three methods: the two wider used methods, Arrhenius based
		methods and WLF, and the newer methodology based on derivatives procedure.
		The Master Curve is smoothed by B-splines basis. The package output is composed
		of plots of experimental data, horizontal and vertical shifts, TTS data, and TTS
		data fitted using B-splines with bootstrap confidence intervals.
A screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects.
Tensor-train is a compact representation for higher-order tensors. Some algorithms for performing tensor-train decomposition are available such as TT-SVD, TT-WOPT, and TT-Cross. For the details of the algorithms, see I. V. Oseledets (2011) <doi:10.1137/090752286>, Yuan Longao, et al (2017) <arXiv:1709.02641>, I. V. Oseledets (2010) <doi:10.1016/j.laa.2009.07.024>.
Implementation of Time to Target plot based on the work 
             of Ribeiro and Rosseti (2015) <DOI:10.1007/s11590-014-0760-8>, 
             that describe a numerical method that gives the probability of 
             an algorithm A finds a solution at least as good as a given 
             target value in smaller computation time than algorithm B.
This package makes Travel To Work Area from a commuting flow data frame.
Get comments posted on YouTube videos, information on how many 
    times a video has been liked, search for videos with particular content, and 
    much more. You can also scrape captions from a few videos. To learn more about
    the YouTube API, see <https://developers.google.com/youtube/v3/>.
Get statistics and reports from YouTube. To learn more about
    the YouTube Analytics and Reporting API, see <https://developers.google.com/youtube/reporting/>.
Performs Three-Mode Principal Components Analysis,
    which carries out Tucker Models.
Provides R Markdown output formats to use Tufte styles for PDF and HTML output.
Custom template and output formats for use with rmarkdown. Produce
    Edward Tufte-style handouts in html formats with full support for rmarkdown
    features
Perform the conventional Tukey test from formula, lm, aov, aovlist and lmer objects.
Provides wrapper functions to the multiple marginal model function mmm() of package 'multcomp' to implement the trend test of Tukey, Ciminera and Heyse (1985) <DOI:10.2307/2530666> for general parametric models.
Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <arXiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.
Provides an R-interface to the Tumblr web API (see Tumblr v2 API on <https://www.tumblr.com/docs/en/api/v2>). Tumblr is a microblogging platform and social networking website (<https://www.tumblr.com>).
A tool to obtain tumor growth rates from clinical trial patient data.  Output includes individual and summary data for tumor growth rate estimates as well as optional plots of the observed and predicted tumor quantity over time.
The ability to tune models is important. 'tune' contains functions and
    classes to be used in conjunction with other 'tidymodels' packages for 
    finding reasonable values of hyper-parameters in models, pre-processing
    methods, and post-processing steps.
Generic methods for parameter tuning of classification algorithms using multiple scoring functions.
Analyze music and speech, extract features like MFCCs, handle wave files and their representation in various ways, read mp3, read midi, perform steps of a transcription, ...
        Also contains functions ported from the 'rastamat' 'Matlab' package.
Tuning random forest with one line. The package is mainly based on the packages 'ranger' and 'mlrMBO'.
Functions to find all matches or non-matches, orphans, and
    duplicate or other replicated elements.
Algorithms for accelerating the convergence of slow,
        monotone sequences from smooth, contraction mapping such as the
        EM and MM algorithms. 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("turboEM").
Package for analyzing TURF (Total Unduplicated Reach and Frequency) data in R. 
    No looping in TURF algorithm results in fast processing times. Allows for individual-level 
	weights, depth specification, and user-truncated combination set(s). Allows user to substitute 
	Monte Carlo simulated combination set(s) after set(s) exceed a user-specified limit.
Package designed for working with vectors and lists of vectors,
    mainly for turning them into other indexed data structures.
An implementation of turtle graphics
    <http://en.wikipedia.org/wiki/Turtle_graphics>.
    Turtle graphics comes from Papert's language Logo and has
    been used to teach concepts of computer programming.
Helps the R users to get data from 'Tushare Pro'<https://tushare.pro>.
    'Tushare Pro' is a platform as well as a community with a lot of staffs working in financial area.
    We support financial data such as stock price, financial report statements and digital coins data.
DataCamp Light (<https://github.com/datacamp/datacamp-light>) is a light-weight implementation of the DataCamp UI,
  that allows you to embed interactive exercises inside HTML documents. The tutorial package makes it easy to create these
  HTML files from R Markdown files. An extension to knitr, tutorial detects appropriately formatted code chunks and replaces them
  with DataCamp Light readable chunks in the resulting HTML file.
Models of time-uncertain time series addressing frequency and non-frequency behavior of continuous and discrete (counting) data.
The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. 
             The model can also be run in a semi-distributed fashion and with dual representation of soil layer.
             The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine. 
             See Parajka, J., R. Merz, G. Bloeschl (2007) <DOI:10.1002/hyp.6253> Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446.
Total Variation Denoising is a regularized denoising method which
    effectively removes noise from piecewise constant signals whilst preserving
    edges.  This package contains a C++ implementation of Condat's very fast 1D
    squared error loss TVD algorithm.  Additional methods and loss functions may
    be added in future versions.
Fits time-varying effect models (TVEM). These are a kind of application of varying-coefficient models in the context of longitudinal data, allowing the strength of linear, logistic, or Poisson regression relationships to change over time.  These models are described further in Tan, Shiyko, Li, Li & Dierker (2012) <doi:10.1037/a0025814>.  We thank Kaylee Litson, Patricia Berglund, and Yajnaseni Chakraborti for their valuable help with testing the package and documentation. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
Probability mass (d), distribution (p), quantile (q), and random 
    number generating (r and rt) functions for the time-varying right-truncated 
    geometric (tvgeom) distribution. Also provided are functions to calculate the first 
    and second central moments of the distribution. The tvgeom distribution
    is similar to the geometric distribution, but the probability 
    of success is allowed to vary at each time step, and there are a limited 
    number of trials. This distribution is essentially a Markov chain, and it
    is useful for modeling Markov chain systems with a set 
    number of time steps.
Functions for managing cashflows and interest rate curves.
This is a statistical tool interactive that provides multivariate statistical tests that are more powerful than traditional Hotelling T2 test and LRT (likelihood ratio test) for the vector of normal mean populations with and without contamination and non-normal populations (Henrique J. P. Alves & Daniel F. Ferreira (2019) <DOI: 10.1080/03610918.2019.1693596>).
Provides tools for denoising noisy signal and images via
    Total Variation Regularization. Reducing the total variation of
    the given signal is known to remove spurious detail while preserving
    essential structural details. For the seminal work on the topic,
    see Rudin et al (1992) <doi:10.1016/0167-2789(92)90242-F>.
Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques.
Contains various 'ggplot2' themes and color palettes based on TV shows 
    such as 'Game of Thrones', 'Brooklyn Nine-Nine', 'Avatar: The Last Airbender',
    'Spongebob Squarepants', and more.
Provides functions for propensity score
        estimating and weighting, nonresponse weighting, and diagnosis
        of the weights.
Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; <doi10.1007/s11222-005-4070-y>) and the Fourier inversion  (Dunn and Smyth, 2008; <doi:10.1007/s11222-007-9039-6>), and related methods.
In order to create smooth animation between states of data,
    tweening is necessary. This package provides a range of functions for
    creating tweened data that can be used as basis for animation. Furthermore 
    it adds a number of vectorized interpolaters for common R data 
    types such as numeric, date and colour.
This is an improved implementation of the package 'StreamR' to
    capture tweets and store it into R, SQLite, 'postGIS' data base or GIS format. The package
    performs a description of harvested data and performs space time exploratory analysis.
Generates a game of 2048 that can be played in the
    console.  Supports grids of arbitrary sizes, undoing the last move,
    and resuming a game that was exited during the current session.
An R wrapper around the API of TheyWorkForYou, a parliamentary 
    monitoring site that scrapes and repackages Hansard (the UK's parliamentary 
    record) and augments it with information from the Register of Members' 
    Interests, election results, and voting records to provide a unified 
    source of information about UK legislators and their activities. See 
    <http://www.theyworkforyou.com> for details.
Twiddler is an interactive tool that automatically creates
        a Tcl/Tk GUI for manipulating variables in any R expression.
        See the documentation of the function twiddle to get started.
The Twilio web service provides an API for computer programs
    to interact with telephony. The included functions wrap the SMS and MMS 
    portions of Twilio's API, allowing users to send and receive text messages
    from R. See <https://www.twilio.com/docs/> for more information.
Provides an interface to the Twitter web API.
Provides an integration to the 'metatrader 5'. 
    The functionalities carry out automated trading using
    sentiment indexes computed from 'twitter' and/or 'stockwits'. 
    The sentiment indexes are based on the ph.d. dissertation 
    "Essays on Economic Forecasting Models" (Godeiro,2018) <https://repositorio.ufpb.br/jspui/handle/123456789/15198>
    The integration between the 'R' and the 'metatrader 5' allows sending buy/sell orders to the brokerage.
Include the Twitter status widgets in HTML pages created
  using R markdown. The package uses the Twitter javascript APIs to
  embed in your document Twitter cards associated to specific statuses.
  The main targets are regular HTML pages or dashboards.
Implementation of a Bayesian two-way latent structure model for integrative genomic clustering.  The model clusters samples in relation to distinct data sources, with each subject-dataset receiving a latent cluster label, though cluster labels have across-dataset meaning because of the model formulation.  A common scaling across data sources is unneeded, and inference is obtained by a Gibbs Sampler.  The model can fit multivariate Gaussian distributed clusters or a heavier-tailed modification of a Gaussian density.  Uniquely among integrative clustering models, the formulation makes no nestedness assumptions of samples across data sources -- the user can still fit the model if a study subject only has information from one data source. The package provides a variety of post-processing functions for model examination including ones for quantifying observed alignment of clusterings across genomic data sources.  Run time is optimized so that analyses of datasets on the order of thousands of features on fewer than 5 datasets and hundreds of subjects can converge in 1 or 2 days on a single CPU.  See "Swanson DM, Lien T, Bergholtz H, Sorlie T, Frigessi A, Investigating Coordinated Architectures Across Clusters in Integrative Studies: a Bayesian Two-Way Latent Structure Model, 2018, <doi:10.1101/387076>, Cold Spring Harbor Laboratory" at <https://www.biorxiv.org/content/early/2018/08/07/387076.full.pdf> for model details.
The TWN-list (Taxa Waterbeheer Nederland) is the Dutch standard for naming 
    taxons in Dutch Watermanagement. This package makes it easier to use the 
    TWN-list for ecological analyses. It  consists of two parts. First it makes the 
    TWN-list itself available in R. Second, it has a few functions that make it 
    easy to perform some basic and often recurring tasks for checking and consulting
    taxonomic data from the TWN-list.
This package implements the nonparametric test of equality
        between two copulas proposed by Remillard and Scaillet in their
        2009 JMVA paper.
Estimation of gene-treatment interactions in randomized clinical trials exploiting gene-treatment independence.
Application of two-regression algorithms for wearable
    research devices. It provides an easy way for users to read in
    device data files and apply an appropriate two-regression
    algorithm. More information is available from
    Hibbing PR, LaMunion SR, Kaplan AS, & Crouter SE (2017)
    <doi:10.1249/MSS.0000000000001532>.
Fast randomization based two sample tests. 
  Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. 
  The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at <arXiv:2007.01360> or <https://codowd.com/public/DTS.pdf> for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others.
For high-dimensional data whose main feature is a large number, p, of variables but a small sample size, the null hypothesis that the marginal distributions of p variables are the same for two groups is tested. We propose a test statistic motivated by the simple idea of comparing, for each of the p variables, the empirical characteristic functions computed from the two samples. If one rejects this global null hypothesis of no differences in distributions between the two groups, a set of permutation p-values is reported to identify which variables are not equally distributed in both groups.
Implements a variety of non-parametric methods for computing one-stage and two-stage confidence intervals, as well as point estimates of threshold values.
Conditional logistic regression with longitudinal follow up and
    individual-level random coefficients: A stable and efficient
    two-step estimation method.
Carries out analyses of two-way tables with one observation per cell, together with graphical displays
    for an additive fit and
    a diagnostic plot for removable 'non-additivity' via a power transformation of the response.
    It implements Tukey's Exploratory Data Analysis (1973) <ISBN: 978-0201076165> methods, including a
    1-degree-of-freedom test for row*column 'non-additivity', linear in the row and column effects.
Performs two-way tests in independent groups designs; Parametric Bootstrap based Generalized Test and Generalized Pivotal Quantity based Generalized Test (Weerahandi and Krishnamoorthy, 2019) <doi:10.1080/03610926.2017.1419264>. The package performs descriptive statistics and graphical approaches. Moreover, it assesses variance homogeneity and normality of data in each group via tests and plots. All 'twowaytests' functions are designed for two-way layout.
Efficient estimation of the population-level causal effects of
    stochastic interventions on a continuous-valued exposure. Both one-step and
    targeted minimum loss estimators are implemented for a causal parameter
    defined as the counterfactual mean of an outcome of interest under a
    stochastic intervention that may depend on the natural value of the
    exposure (i.e., a modified treatment policy). To accommodate settings in
    which two-phase sampling is employed, procedures for making use of inverse
    probability of censoring weights are provided to facilitate construction of
    inefficient and efficient one-step and targeted minimum loss estimators.
    The causal parameter and estimation methodology were first described by
    Díaz and van der Laan (2013) <doi:10.1111/j.1541-0420.2011.01685.x>).
    Estimation of nuisance parameters may be enhanced through the Super Learner
    ensemble model in 'sl3', available for download from GitHub using
    'remotes::install_github("tlverse/sl3")'.
Provides functions to produce rudimentary ascii graphics
        directly in the terminal window. Provides a basic plotting
        function (and equivalents of curve, density, acf and barplot)
        as well as boxplot and image functions.
This queue is a data structure that lets
  parallel processes send and receive messages,
  and it can help coordinate the work
  of complicated parallel tasks.
  Processes can push new messages to the queue,
  pop old messages, and obtain a
  log of all the messages ever pushed. File locking
  preserves the integrity of the data even when
  multiple processes access the queue simultaneously.
Provides a simple type annotation for R that is usable in scripts,
    in the R console and in packages. It is intended as a convention to allow other
    packages to use the type information to provide error checking,
    automatic documentation or optimizations.
Download and compile any version of the
  IANA Time Zone Database (also known as Olson database)
  and make it current in your R session.
  Beware: on Windows 'Cygwin' is required!
Despite there being a section in RFC 7231
    <https://tools.ietf.org/html/rfc7231#section-5.5.3> defining a suggested
    structure for 'User-Agent' headers this data is notoriously difficult
    to parse consistently. Tools are provided that will take in user agent
    strings and return structured R objects. This is a 'V8'-backed package
    based on the 'ua-parser' project <https://github.com/ua-parser>.
The Unmanned Aerial Vehicle Mission Planner provides an easy to use work flow for planning autonomous obstacle avoiding surveys of (almost) ready to fly unmanned aerial vehicles to retrieve aerial or spot related data. It creates either intermediate flight control files for the DJI phantom series or ready to upload control files for the pixhawk based flight controller as used in the 3DR Solo. Additionally it contains some useful tools for digitizing and data manipulation.
Two Phase I designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method. Simulations tools are also available to estimate the operating characteristics of the methods with several user-dependent options.
Complete work flow for the analysis of pharmacokinetic pharmacodynamic (PKPD), physiologically-based pharmacokinetic (PBPK) and systems pharmacology models including: creation of ordinary differential equation-based models, pooled parameter estimation, individual/population based simulations, rule-based simulations for clinical trial design and modeling assays, deployment with a customizable 'Shiny' app, and non-compartmental analysis. System-specific analysis templates can be generated and each element includes integrated reporting with 'PowerPoint' and 'Word'.
Provides a set of functions that can be used to obtain better predictive performance on cost-sensitive and cost/benefits tasks (for both regression and classification). This includes re-sampling approaches that modify the original data set biasing it towards the user preferences.
This suite of packages is developed to predicts resistome for unknown chemical compounds. It utilizes two algorithms viz. deterministic model and stochastic model (manuscript under preparation) for the prediction of drug class. Once the drug class is predicted, the resistome profile of Escherichia coli and Pseudomonas aeruginosa for the drug class is fetched from the database.
This package contains latex, knitr and R Markdown templates that
    adhere to the UC Berkeley Graduate Division's thesis guidelines. The
    templates are located in the inst/ directory.
R bindings of the 'uchardet', encoding detector library from Mozilla
  (<https://www.freedesktop.org/wiki/Software/uchardet/>). It takes a sequence
  of bytes in an unknown character encoding and without any additional
  information, and attempts to get the encoding of the text. All return names of
  the encodings are iconv-compatible.
Clustering and classification inference for high dimension low sample size (HDLSS)
    data with U-statistics. The package contains implementations of nonparametric statistical
    tests for sample homogeneity, group separation, clustering, and classification of 
    multivariate data. The methods have high statistical power and are tailored for data
    in which the dimension L is much larger than sample size n. See Gabriela B. Cybis,
    Marcio Valk and Sílvia RC Lopes (2018) <doi:10.1080/00949655.2017.1374387> and Marcio 
    Valk and Gabriela B. Cybis (2018) <arXiv:1805.12179>.
An algorithm for general-purpose unconstrained non-linear optimization.
             The algorithm is of quasi-Newton type with BFGS updating of the inverse
             Hessian and soft line search with a trust region type monitoring of the
             input to the line search algorithm. The interface of 'ucminf' is
             designed for easy interchange with 'optim'.
Comprehensive analysis and forecasting 
             of univariate time series using automatic 
             unobserved components models and algorithms.
             Harvey, AC (1989) <doi:10.1017/CBO9781107049994>.
             Pedregal, DJ and Young PC (2002) <doi:10.1002/9780470996430>.
             Durbin J and Koopman SJ (2012) <doi:10.1093/acprof:oso/9780199641178.001.0001>.
Changes the column names of the inputted dataset to the correct
    names from the Uniform Crime Report codebook for the "Offenses Known and
    Clearance by Arrest" datasets from 1998-2014.
Provides a web app for downloading, analyzing and
    visualizing datasets from UCSC Xena (<http://xena.ucsc.edu/>), which is a collection of
    UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE,
    and others.
Download and explore datasets from UCSC Xena data hubs, which are
    a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.
    Databases are normalized so they can be combined, linked, filtered, explored and downloaded.
A client for the Urban Dictionary <http://www.urbandictionary.com/> API.
The udder quarter infection data set contains infection times of individual cow udder quarters with Corynebacterium bovis (Laevens et al. 1997 <DOI:10.3168/jds.S0022-0302(97)76295-7>). Obviously, the four udder quarters are clustered within a cow, and udder quarters are sampled only approximately monthly, generating interval-censored data. The data set contains both covariates that change within a cow (e.g., front and rear udder quarters) and covariates that change between cows (e.g., parity [the number of previous calvings]). The correlation between udder infection times within a cow also is of interest, because this is a measure of the infectivity of the agent causing the disease. Various models have been applied to address the problem of interdependence for right-censored event times. These models, as applied to this data set, can be found back in the publications found in the reference list.
This natural language processing toolkit provides language-agnostic
    'tokenization', 'parts of speech tagging', 'lemmatization' and 'dependency
    parsing' of raw text. Next to text parsing, the package also allows you to train
    annotation models based on data of 'treebanks' in 'CoNLL-U' format as provided
    at <https://universaldependencies.org/format.html>. The techniques are explained
    in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0
    with UDPipe', available at <doi:10.18653/v1/K17-3009>. 
    The toolkit also contains functionalities for commonly used data manipulations on texts 
    which are enriched with the output of the parser. Namely functionalities and algorithms 
    for collocations, token co-occurrence, document term matrix handling, 
    term frequency inverse document frequency calculations,
    information retrieval metrics (Okapi BM25), handling of multi-word expressions,
    keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) 
    sentiment scoring and semantic similarity analysis.
Provides simple bindings to Unidata's udunits library.
This is a new version of the 'userfriendlyscience' package,
    which has grown a bit unwieldy. Therefore, distinct functionalities
    are being 'consciously uncoupled' into different packages. This package
    contains the general-purpose tools and utilities (see the
    'behaviorchange' package, the 'rosetta' package, and the
    soon-to-be-released 'scd' package for other functionality), and
    is the most direct 'successor' of the original 'userfriendlyscience' package.
    For example, this package contains a number of basic functions to create
    higher level plots, such as diamond plots, to easily plot sampling
    distributions, to generate confidence intervals, to plan study sample sizes
    for confidence intervals, and to do some basic operations such as
    (dis)attenuate effect size estimates.
A set of tools to read, process, and summarize Agilent Cary 620 uFTIR Microscope hyperspectral images
    primarily intended for microplastic analysis.
Maximum likelihood estimation of univariate Gaussian Mixture Autoregressive (GMAR),
    Student's t Mixture Autoregressive (StMAR), and Gaussian and Student's t Mixture Autoregressive (G-StMAR) models, 
    quantile residual tests, graphical diagnostics, forecast and simulate from GMAR, StMAR and G-StMAR processes. 
    Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) <doi:10.1111/jtsa.12108>, 
    Mika Meitz, Daniel Preve, Pentti Saikkonen (2018) <arXiv:1805.04010>,
    Savi Virolainen (2020) <arXiv:2003.05221>.
Construct a Hidden Markov Model with states learnt by unsupervised classification.
Implements functions to derive uncertainty intervals for (i) regression (linear and probit) parameters when outcome is missing not at random (non-ignorable missingness) introduced in Genbaeck, M., Stanghellini, E., de Luna, X. (2015) <doi:10.1007/s00362-014-0610-x> and Genbaeck, M., Ng, N., Stanghellini, E., de Luna, X. (2018) <doi:10.1007/s10433-017-0448-x>; and (ii) double robust and outcome regression estimators of average causal effects (on the treated) with possibly unobserved confounding introduced in Genbaeck, M., de Luna, X. (2018) <doi:10.1111/biom.13001>.
A set of custom 'R' 'Markdown' templates for documents and
   presentations with the University of Illinois at Urbana-Champaign (UIUC)
   color scheme and identity standards.
Full listing of UK baby names occurring more than three times per year between 1996 and 2015, and rankings of baby name popularity by decade from 1904 to 1994.
A set of tools to create a UK Biobank <http://www.ukbiobank.ac.uk/> dataset from a UKB fileset (.tab, .r, .html),
    visualize primary demographic data for a sample subset, query ICD diagnoses,
    retrieve genetic metadata, read and write standard file formats for genetic analyses.
Functions to implement the methods of the Flood Estimation Handbook (FEH), associated updates and the revitalised flood hydrograph model (ReFH). Currently the packages uses NRFA peak flow dataset version 9. Aside from FEH functionality, further hydrological functions are available. Most of the methods implemented in this package are described in one or more of the following: "Flood Estimation Handbook", Centre for Ecology & Hydrology (1999, ISBN:0 948540 94 X). "Flood Estimation Handbook Supplementary Report No. 1", Kjeldsen (2007, ISBN:0 903741 15 7). "Regional Frequency Analysis - an approach based on L-moments", Hosking & Wallis (1997, ISBN: 978 0 521 01940 8). "Proposal of the ‘extreme rank plot’ for extreme value analysis: with an emphasis on flood frequency studies", Hammond (2019, <doi:10.2166/nh.2019.157>). "Making better use of local data in flood frequency estimation", Environment Agency (2017, ISBN: 978 1 84911 387 8). "Improving the FEH statistical procedures for flood frequency estimation", Environment Agency (2008, ISBN: 978 1 84432 920 5). "Low flow estimation in the United Kingdom", Institute of Hydrology (1992, ISBN 0 948540 45 1). Wallingford HydroSolutions, (2016, <http://software.hydrosolutions.co.uk/winfap4/Urban-Adjustment-Procedure-Technical-Note.pdf>). Data from the UK National River Flow Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions: <https://nrfa.ceh.ac.uk/costs-terms-and-conditions>.
Allows users to access live UK energy market information via various APIs.
A time series of the national grid demand (high-voltage electric power transmission network) in the UK since 2011.
Access data from Land Registry Open Data
    <http://landregistry.data.gov.uk/> through 'SPARQL' queries. 'uklr'
    supports the house price index, transaction and price paid data.
Downloads data from the 'UK Police' public data API, 
    the full docs of which are available at <https://data.police.uk/docs/>. 
    Includes data on police forces and police force areas, crime reports, 
    and the use of stop-and-search powers.
Universally unique identifiers ('UUIDs') can be suboptimal
        for many uses-cases because they aren't the most character
        efficient way of encoding 128 bits of randomness; v1/v2 versions
        are impractical in many environments, as they require access to a
        unique, stable MAC address; v3/v5 versions require a unique seed
        and produce randomly distributed IDs, which can cause
        fragmentation in many data structures; v4 provides no other
        information than randomness which can cause fragmentation in many
        data structures. 'ULIDs' (<https://github.com/ulid/spec>) have
        128-bit compatibility with 'UUID', 1.21e+24 unique 'ULIDs' per
        millisecond, are lexicographically sortable, canonically encoded
        as a 26 character string, as opposed to the 36 character 'UUID',
        use Crockford's 'base32' for better efficiency and readability (5
        bits per character), are case insensitive, have no special
        characters (i.e. are 'URL' safe) and have a onotonic sort order
        (correctly detects and handles the same millisecond).
A generic reference Bayesian analysis of unidimensional mixture distributions obtained by a location-scale parameterisation of the model is implemented. The including functions simulate and summarize posterior samples for location-scale mixture models using a weakly informative prior. There is no need to define priors for scale-location parameters except two hyperparameters in which are associated with a Dirichlet prior for weights and a simplex.
Uniform manifold approximation and projection is a technique
    for dimension reduction. The algorithm was described by McInnes and
    Healy (2018) in <arXiv:1802.03426>. This package provides an interface
    for two implementations. One is written from scratch, including components
    for nearest-neighbor search and for embedding. The second implementation
    is a wrapper for 'python' package 'umap-learn' (requires separate
    installation, see vignette for more details).
By gaining the property of emergence through self-organization, the enhancement of SOMs(self organizing maps) is called Emergent SOM (ESOM). The result of the projection by ESOM is a grid of neurons which can be visualised as a three dimensional landscape in form of the Umatrix. Further details can be found in the referenced publications (see url). This package offers tools for calculating and visualising the ESOM as well as Umatrix, Pmatrix and UStarMatrix. All the functionality is also available through graphical user interfaces implemented in 'shiny'.
Calculates one-sample unbiased central moment estimates and 
    two-sample pooled estimates up to 6th order, including estimates of 
    powers and products of central moments. Provides the machinery for 
    obtaining unbiased central moment estimators beyond 6th order by generating
    expressions for expectations of raw sample moments and their powers and 
    products. 
    Gerlovina and Hubbard (2019) <doi:10.1080/25742558.2019.1701917>.
Does uniformly most powerful (UMP) and uniformly most
    powerful unbiased (UMPU) tests.  At present only distribution implemented
    is binomial distribution.  Also does fuzzy tests and confidence intervals
    (following Geyer and Meeden, 2005, <doi:10.1214/088342305000000340>)
    for the binomial
    distribution (one-tailed procedures based on UMP test and two-tailed
    procedures based on UMPU test).
The Ultimate Microrray Prediction, Reality and Inference
  Engine (UMPIRE) is a package to facilitate the simulation of realistic
  microarray data sets with links to associated outcomes. See Zhang and
  Coombes (2012) <doi:10.1186/1471-2105-13-S13-S1>. Version 2.0 adds the
  ability to simulate realistic mixed-typed clinical data.
Unmatched regression refers to the regression setting where
    covariates and predictors are collected separately/independently and so are not paired
    together, as in the usual regression setting.   Balabdaoui, Doss, and Durot (2020) <arXiv:2007.00830>
    study the unmatched regression setting where the univariate regression function is known to
    be monotone.     This package implements (two) gradient    descent method(s) to
    compute the estimator developed in  Balabdaoui, Doss, and Durot (2020).
Quickly create, run, and report structural equation and twin models.
    See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS.
A dataset is said to be unbalanced when the class of interest (minority class) is much rarer than normal behaviour (majority class). The cost of missing a minority class is typically much higher that missing a majority class. Most learning systems are not prepared to cope with unbalanced data and several techniques have been proposed. This package implements some of most well-known techniques and propose a racing algorithm to select adaptively the most appropriate strategy for a given unbalanced task.
The package implements top-down and bottom-up algorithms
        for nonparametric function estimation in Gaussian noise using
        Unbalanced Haar wavelets.
If a procedure consists of several stages and there are several models that can be selected for each stage,
	uncertainty of the procedure can be decomposed by stages or models.
	This package includes the ANOVA-based method, the cumulative uncertainty-based method, and the balanced decomposition method.
	Yongdai Kim et al. (2019) <doi:10.1016/j.hydroa.2019.100024> is a related paper which is accessible via the URL below.
Functions for the determination of an uncertain interval, that is, a
    range of test scores that is inconclusive and does not allow a classification other
    than 'Uncertain' (Reference: J.A. Landsheer (2016) <doi:10.1371/journal.pone.0166007>).
Implements the Gaussian method of first and second order, the Kragten numerical method and the Monte Carlo simulation method for uncertainty estimation and analysis.
Consensus clustering by the unification of clustering results from multiple datasets using external specifications.
Dataset contains select attributes for each match result since 1949-1950 season for UNC men's basketball team.
Calculate several understandability metrics of BPMN models. BPMN stands for business process modelling notation and is a language for expressing business processes into business process diagrams. Examples of these understandability metrics are: average connector degree, maximum connector degree, sequentiality, cyclicity, diameter, depth, token split, control flow complexity, connector mismatch, connector heterogeneity, separability, structuredness and cross connectivity. See R documentation and paper on metric implementation included in this package for more information concerning the metrics.
Computes a universal numeric fingerprint (UNF) for an R data
    object. UNF is a cryptographic hash or signature that can be used to uniquely
    identify (a version of) a rectangular dataset, or a subset thereof. UNF can
    be used, in tandem with a DOI, to form a persistent citation to a versioned
    dataset.
Stereological unfolding of the joint size-shape-orientation distribution of spheroidal shaped particles.
Makes it easy to extract data from a survey. You can use an 
    explanatory variable and make multiple contingency tables for all variables
    in a data frame. You can also summarize response alternatives.
    URL: <https://en.wikipedia.org/wiki/Contingency_table>.
Use syntax inspired by the package 'glue' to extract matched substrings in a more intuitive and compact way than by using standard regular expressions.
Versatile method for ungrouping histograms (binned count data) 
 assuming that counts are Poisson distributed and that the underlying sequence 
 on a fine grid to be estimated is smooth. The method is based on the composite 
 link model and estimation is achieved by maximizing a penalized likelihood. 
 Smooth detailed sequences of counts and rates are so estimated from the binned 
 counts. Ungrouping binned data can be desirable for many reasons: Bins can be 
 too coarse to allow for accurate analysis; comparisons can be hindered when 
 different grouping approaches are used in different histograms; and the last 
 interval is often wide and open-ended and, thus, covers a lot of information 
 in the tail area. Age-at-death distributions grouped in age classes and 
 abridged life tables are examples of binned data. Because of modest assumptions, 
 the approach is suitable for many demographic and epidemiological applications. 
 For a detailed description of the method and applications see 
 Rizzi et al. (2015) <doi:10.1093/aje/kwv020>.
Verb-like functions to work with messy data, often derived from 
             spreadsheets or parsed PDF tables. Includes functions for unwrapping 
             values broken up across rows, relocating embedded grouping values, 
             and to annotate meaningful formatting in spreadsheet files.
Nonparametric estimation of a unimodal or U-shape covariate effect under additive hazards model.
Data from Unicode 13.0.0 and related utilities.
Efficient procedures for constructing uniform design of experiments under various space-filling criteria. It is based on a stochastic and adaptive threshold accepting algorithm with flexible initialization, adaptive threshold, and stochastic evolution. The package may also construct the augmented uniform designs in a sequential manner.
Uniform sampling of Directed Acyclic Graphs (DAG) using exact
 enumeration by relating each DAG to a sequence of outpoints (nodes with no
 incoming edges) and then to a composition of integers as suggested by
 Kuipers, J. and Moffa, G. (2015) <doi:10.1007/s11222-013-9428-y>.
Probability functions, family for glm() and Stan code for working with the unifed distribution (Quijano Xacur, 2019; <doi:10.1186/s40488-019-0102-6>).
In many phase I trials, the design goal is to find the dose associated with a certain target toxicity rate. In some trials, the goal can be to find the dose with a certain weighted sum of rates of various toxicity grades. For others, the goal is to find the dose with a certain mean value of a continuous response. This package provides the setup and calculations needed to run a dose-finding trial with non-binary endpoints and performs simulations to assess design’s operating characteristics under various scenarios. Three dose finding designs are included in this package: unified phase I design (Ivanova et al. (2009) <doi:10.1111/j.1541-0420.2008.01045.x>), Quasi-CRM/Robust-Quasi-CRM (Yuan et al. (2007) <doi:10.1111/j.1541-0420.2006.00666.x>, Pan et al. (2014) <doi:10.1371/journal.pone.0098147>) and generalized BOIN design (Mu et al. (2018) <doi:10.1111/rssc.12263>). The toxicity endpoints can be handled with these functions including equivalent toxicity score (ETS), total toxicity burden (TTB), general continuous toxicity endpoints, with incorporating ordinal grade toxicity information into dose-finding procedure. These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous toxicity score, and incorporate safety and/or stopping rules.
Uniform sampling on various geometric shapes, such as spheres, ellipsoids, simplices.
Goodness-of-fit tests for the uniform distribution.
Perform L1 or L2 isotonic and unimodal regression on 1D weighted or unweighted input vector and isotonic regression on 2D weighted or unweighted input vector. It also performs L infinity isotonic and unimodal regression on 1D unweighted input vector. Reference: Quentin F. Stout (2008) <doi:10.1016/j.csda.2008.08.005>. Spouge, J., Wan, H. & Wilbur, W.(2003) <doi:10.1023/A:1023901806339>. Q.F. Stout (2013) <doi:10.1007/s00453-012-9628-4>.
Define and use graphical elements of corporate design manuals in R. The 'unikn' package provides color functions (by defining dedicated colors and color palettes, and commands for changing, viewing, and using them) and styled text elements (e.g., for marking, underlining, or plotting colored titles). The pre-defined range of colors and text functions is based on the corporate design of the University of Konstanz <https://www.uni-konstanz.de/>, but can be adapted and extended for other institutions and purposes.
Implementation of "light" stemmers for French, German, Italian, Spanish, Portuguese, Finnish, Swedish. 
  They are based on the same work as the "light" stemmers found in 'SolR' <https://lucene.apache.org/solr/> or 'ElasticSearch' <https://www.elastic.co/fr/products/elasticsearch>. 
  A "light" stemmer consists in removing inflections only for noun and adjectives. 
  Indexing verbs for these languages is not of primary importance compared to nouns and adjectives. 
  The stemming procedure for French is described in (Savoy, 1999) <doi:10.1002/(SICI)1097-4571(1999)50:10%3C944::AID-ASI9%3E3.3.CO;2-H>.
Connect to Uniprot <https://www.uniprot.org/> to retrieve information about proteins using their accession number such information could be name or taxonomy information, For detailed information kindly read the publication <https://www.sciencedirect.com/science/article/pii/S1874391919303859>.
For each string in a set of strings, determine a unique tag that is a substring of fixed size k unique to that string, if it has one. If no such unique substring exists, the least frequent substring is used. If multiple unique substrings exist, the lexicographically smallest substring is used. This lexicographically smallest substring of size k is called the "UniqTag" of that string.
Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or
	self-defined penalties on the B-spline coefficients.
Provides the ability to read 'Unisens' data into R. 'Unisens' is a universal data format for multi sensor data.
The uc.check() function checks whether the roots of a given polynomial lie outside the Unit circle. You can also easily draw an unit circle.
United is a software tool which can be downloaded at the following
    website <http://www.schroepl.net/pbm/software/united/>. In general, it is
    a virtual manager game for football teams. This package contains helpful
    functions for determining an optimal formation for a virtual match in
    United. E.g. knowing that the opponent has a strong defensive it is
    advisable to beat him in the midfield. Furthermore, this package contains
    functions for computing the optimal usage of hardness in a game.
Simplifies regression tests by comparing objects produced by test
    code with earlier versions of those same objects.  If objects are unchanged
    the tests pass, otherwise execution stops with error details.  If in
    interactive mode, tests can be reviewed through the provided interactive
    environment.
Support for measurement units in R vectors, matrices
	and arrays: automatic propagation, conversion, derivation
	and simplification of units; raising errors in case of unit
	incompatibility. Compatible with the POSIXct, Date and difftime 
	classes. Uses the UNIDATA udunits library and unit database for 
	unit compatibility checking and conversion.
	Documentation about 'units' is provided in the paper by Pebesma, Mailund &
  Hiebert (2016, <doi:10.32614/RJ-2016-061>), included in this package as a
  vignette; see 'citation("units")' for details.
Concise TAP <http://testanything.org/> compliant unit testing package. Authored tests can be run using CMD check with minimal implementation overhead.
Assess essential unidimensionality using external validity
    information  using the procedure proposed by Ferrando & Lorenzo-Seva
    (2019) <doi:10.1177/0013164418824755>. Provides two indices for assessing
    differential  and incremental validity, both based on a second-order 
    modelling schema for the general factor.
User-friendly maximum likelihood estimation (Fisher (1921) 
    <doi:10.1098/rsta.1922.0009>) of univariate densities.
Provides S3 generic methods and some default implementations
    for Bayesian analyses that generate Markov Chain Monte Carlo (MCMC) samples.
    The purpose of 'universals' is to reduce package dependencies and conflicts.
    The 'nlist' package implements many of the methods for its 'nlist' class.
Well known outlier detection techniques in the univariate case. Methods to deal with skewed distribution are included too. The Hidiroglou-Berthelot (1986) method to search for outliers in ratios of historical data is implemented as well. When available, survey weights can be used in outliers detection.
Pseudo-random number generation of 17 univariate distributions proposed by Demirtas. (2005) <DOI:10.22237/jmasm/1114907220>.
Bindings to system utilities found in most Unix systems such as
    POSIX functions which are not part of the Standard C Library.
Separate a data frame in two based on key columns. The function
 unjoin() provides an inside-out version of a nested data frame. This is used
 to identify duplication and normalize it (in the database sense) by linking
 two tables with the redundancy removed. This is a basic requirement for
 detecting topology within spatial structures that has motivated the need for
 this package as a building block for workflows within more applied projects.
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates.
Fast flattening of hierarchical data structures (e.g. JSON and XML
             documents) into data.frames with a flexible spec language.
Assess the significance of identified clusters and estimates the true number of clusters by comparing the explained variation due to the clustering from the original data to that produced by clustering a unimodal reference distribution which preserves the covariance structure in the data. The reference distribution is generated using kernel density estimation and a Gaussian copula framework. A dimension reduction strategy and sparse covariance estimation optimize this method for the high-dimensional, low-sample size setting. This method is similar to them method described in Helgeson and Bair (2016) <arXiv:1610.01424> except a Gaussian copula approach is used to account for feature correlation.
Tools for converting data from complex or irregular layouts to a
    columnar structure.  For example, tables with multilevel column or row
    headers, or spreadsheets.  Header and data cells are selected by their
    contents and position, as well as formatting and comments where available,
    and are associated with one other by their proximity in given directions.
    Functions for data frames and HTML tables are provided.
Provides half-normal plots, reference plots, and Pareto plots
    of effects from an unreplicated experiment, along with various 
    pseudo-standard-error measures, simulated reference distributions, 
    and other tools. Many of these methods are described in 
    Daniel C. (1959) <doi:10.1080/00401706.1959.10489866> and/or
    Lenth R.V. (1989) <doi:10.1080/00401706.1989.10488595>, but some new
    approaches are added and integrated in one package.
Wraps the 'unrtf' utility to extract text from RTF files. Supports
    document conversion to HTML, LaTeX or plain text. Output in HTML is recommended
    because 'unrtf' has limited support for converting between character encodings.
Performs a test for second-order stationarity of time series based
    on unsystematic sub-samples.
Hubbell's Unified Neutral Theory of Biodiversity.
Historical voting data of the United Nations General Assembly. This
    includes votes for each country in each roll call, as well as descriptions and
    topic classifications for each vote.
Implements empirical Bayes approaches to genotype
       polyploids from next generation sequencing data while
       accounting for allele bias, overdispersion, and sequencing
       error. The main functions are flexdog() and multidog(), 
       which allow the specification
       of many different genotype distributions. Also provided are functions to
       simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as
       functions to calculate oracle genotyping error rates, oracle_mis(), and
       correlation with the true genotypes, oracle_cor(). These latter two
       functions are useful for read depth calculations. Run
       browseVignettes(package = "updog") in R for example usage. See
       Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and
       Gerard and Ferrao (2020) <doi:10.1093/bioinformatics/btz852> for details 
       on the implemented methods.
An integrated package for building and testing uplift models
Provides a variety of plots and metrics to evaluate
    uplift models including the 'R uplift' package's Qini metric and Qini plot,
    a port of the 'python pylift' module's plotting function, and
    an alternative plot (in beta) useful for continuous outcomes.
    Background: Radcliffe (2007) <https://pdfs.semanticscholar.org/147b/32f3d56566c8654a9999c5477dded233328e.pdf>.
An implementation of the UPMASK method for performing membership
    assignment in stellar clusters in R. It is prepared to use photometry and
    spatial positions, but it can take into account other types of data. The
    method is able to take into account arbitrary error models, and it is
    unsupervised, data-driven, physical-model-free and relies on as few
    assumptions as possible. The approach followed for membership assessment is
    based on an iterative process, dimensionality reduction, a clustering
    algorithm and a kernel density estimation.
'UpSet.js' is a re-implementation of 'UpSetR' to create interactive set visualizations for more than three sets.
  This is a 'htmlwidget' wrapper around the 'JavaScript' library 'UpSet.js'.
Creates visualizations of intersecting sets using a novel matrix
    design, along with visualizations of several common set, element and attribute
    related tasks (Conway 2017) <doi:10.1093/bioinformatics/btx364>.
Variance approximations for the 
    Horvitz-Thompson total estimator in Unequal Probability Sampling
    using only first-order inclusion probabilities. 
    See Matei and Tillé (2005) and Haziza, Mecatti and Rao (2008) for details.
'Elasticsearch' is an open-source, distributed, document-based datastore
    (<https://www.elastic.co/products/elasticsearch>).
    It provides an 'HTTP' 'API' for querying the database and extracting datasets, but that
    'API' was not designed for common data science workflows like pulling large batches of
    records and normalizing those documents into a data frame that can be used as a training
    dataset for statistical models. 'uptasticsearch' provides an interface for 'Elasticsearch'
    that is explicitly designed to make these data science workflows easy and fun.
Provide a set of wrappers to call all the endpoints of UptimeRobot API
  which includes various kind of ping, keep-alive and speed tests.
  See <https://uptimerobot.com/> for more information.
Estimation and Inference for Unconditional Quantile Regression for cross-sectional and panel data (see Firpo et al. (2009) <DOI:10.3982/ECTA6822>).
Unit root and cointegration tests encountered in applied 
 econometric analysis are implemented.
Allows using two URL shortening services, which also provide
    expanding and analytic functions. Specifically developed for 'Bit.ly' (which requires OAuth2) and 'is.gd' (no API key).
A toolkit for all URL-handling needs, including encoding and decoding,
    parsing, parameter extraction and modification. All functions are
    designed to be both fast and entirely vectorised. It is intended to be
    useful for people dealing with web-related datasets, such as server-side
    logs, although may be useful for other situations involving large sets of
    URLs.
Seasonal unit roots and seasonal stability tests.
    P-values based on response surface regressions are available for both tests.
    P-values based on bootstrap are available for seasonal unit root tests.
S3 classes and methods for manipulation with georeferenced raster data: reading/writing, processing, multi-panel visualization.
A simple interface to the Geographic Header information
  from the "2010 US Census Summary File 2".  The entire Summary File 2
  is described at
  <https://catalog.data.gov/dataset/census-2000-summary-file-2-sf2>,
  but note that this package only provides access to parts of the
  geographic header ('geoheader') of the file.  In particular, only
  the first 101 columns of the geoheader are included and, more
  importantly, only rows with summary levels (SUMLEVs) 010 through 050
  (nation down through county level) are included.  In addition to
  access to (part of) the geoheader, the package also provides a
  decode function that takes a column name and value and, for certain
  columns, returns "the meaning" of that column (i.e., a "SUMLEV"
  value of 40 means "State"); without a value, the decode function
  attempts to describe the column itself.
Updated versions of the 1970's "US State Facts and
    Figures" objects from the 'datasets' package included with R. The new
    data is compiled from a number of sources, primarily from United
    States Census Bureau or the relevant federal agency.
The boundaries for geographical units in the United States of
    America contained in this package include state, county, congressional
    district, and zip code tabulation area. Contemporary boundaries are provided
    by the U.S. Census Bureau (public domain). Historical boundaries for the
    years from 1629 to 2000 are provided form the Newberry Library's 'Atlas of
    Historical County Boundaries' (licensed CC BY-NC-SA). Additional  data is
    provided in the 'USAboundariesData' package; this package provides an
    interface to access that data.
This package contains functions to read cancer data from SEER (http://seer.cancer.gov/) and IARC (http://www.iarc.fr) to create datasets at the county level based on US census information.
US Census 2000 Designated Places shapefiles and additional
        demographic data from the SF1 100 percent files. This data set
        contains polygon files in lat/lon coordinates and the
        corresponding demographic data for a number of different
        variables.
US 2000 Census Tract shapefiles and additional demographic
        data from the SF1 100 percent files. This data set contains
        polygon files in lat/lon coordinates and the corresponding
        demographic data for a number of different variables.
US Census 2010 shape files and additional demographic data
        from the SF1 100 percent files. This package contains a number
        of helper functions for the UScensus2010blk,
        UScensus2010blkgrp, UScensus2010tract, UScensus2010cdp
        packages.
Interface to easily access data via the United States Department of Agriculture (USDA)'s Livestock Mandatory Reporting ('LMR')
  Data API at <https://mpr.datamart.ams.usda.gov/>. The downloaded data can be saved for later off-line use. 
  Also provide relevant information and metadata for each of the input variables needed for sending the data inquiry.
An alternative for downloading various United States Department of
    Agriculture (USDA) data from <https://quickstats.nass.usda.gov/> through R. 
    You must sign up for an API token from the mentioned website in order for 
    this package to work.
Demographic data on the United States at the county and state levels spanning multiple years.
This is a framework that aims to provide methods and tools for assessing the impact of different sources of uncertainties (e.g.positional uncertainty) on performance of species distribution models (SDMs).)
Functions to re-arrange, extract, and work with distances.
A set of little functions that have been found useful to do little
    odds and ends such as plotting the results of K-means clustering, substituting
    special text characters, viewing parts of a data.frame, constructing formulas
    from text and building design and response matrices.
A set of general functions that I have used in various 
  projects and in other R packages. They support some miscellaneous operations 
  on data frames, matrices and vectors like adding a row on a ternary (3-value)
  data.frame based on positive and negative vector-indicators, rearranging a 
  list of data.frames by rownames, pruning rows or columns of a data.frame 
  that contain only one specific value given by the user,
  pruning and reordering a vector according to the common elements between its 
  names and elements of another given vector, finding the non-common elements 
  between two vectors (outer-section), 
  normalization of a vector, matrix or data.frame's numeric values in a specified range, 
  pretty printing of vector names and values in an R Markdown document.
  Also included is a function that returns the statistics needed for plotting a ROC curve.
Code snippets to fit models using the tidymodels framework can be easily created for a given data set.
Contains a number of functions that serve
    two goals. First, to make R more accessible to people migrating
    from SPSS by adding a number of functions that behave roughly like
    their SPSS equivalents (also see <https://rosettastats.com>). Second,
    to make a number of slightly more advanced functions more user
    friendly to relatively novice users. The package also conveniently
    houses a number of additional functions that are intended to
    increase the quality of methodology and statistics in psychology,
    not by offering technical solutions, but by shifting perspectives,
    for example towards reasoning based on sampling distributions as
    opposed to on point estimates.
Automate package and project setup tasks that are otherwise
    performed manually. This includes setting up unit testing, test 
    coverage, continuous integration, Git, 'GitHub', licenses, 'Rcpp', 'RStudio' 
    projects, and more.
Compiled and cleaned the county-level estimates of fertilizer, 
    nitrogen and phosphorus, from 1945 to 2012 in United States of America (USA).  The commercial fertilizer data were originally 
    generated by USGS based on the sales data of commercial fertilizer. The manure data were estimated
    based on county-level population data of livestock, poultry, and other animals.
    See the user manual for detailed data sources and cleaning methods.  
    'usfertilizer' utilized the tidyverse to clean the original data and provide 
    user-friendly dataframe. Please note that USGS does not endorse this package. Also data from 1986 is not available for now.
Provides a set of regular time-series datasets, describing the US electricity grid. That includes the total demand and supply, and as well as the demand by energy source (coal, solar, wind, etc.). Source: US Energy Information Administration (Dec 2019) <https://www.eia.gov/>.
Analyzes longitudinal data of HIV decline in patients on antiretroviral therapy using the canonical biphasic exponential decay model (pioneered, for example, by work in Perelson et al. (1997) <doi:10.1038/387188a0>; and Wu and Ding (1999) <doi:10.1111/j.0006-341X.1999.00410.x>). Model fitting and parameter estimation are performed, with additional options to calculate the time to viral suppression. Plotting and summary tools are also provided for fast assessment of model results.
A collection of data sets to accompany the
    textbook "Using R for Introductory Statistics," second
    edition.
Implements mixtures of unrestricted skew-t factor analyzer models via the EM algorithm.
The Universal Scalability Law (Gunther 2007)
    <doi:10.1007/978-3-540-31010-5> is a model to predict hardware and
    software scalability. It uses system capacity as a function of load to
    forecast the scalability for the system.
Obtain United States map data frames of varying region types (e.g. county, 
    state). The map data frames include Alaska and Hawaii conveniently placed to the
    bottom left, as they appear in most maps of the US. Convenience functions for plotting
    choropleths and working with FIPS codes are also provided.
Implements various independence tests for discrete, continuous, and infinite-dimensional data. The tests are based on a U-statistic permutation test, the USP of Berrett, Kontoyiannis and Samworth (2020) <arXiv:2001.05513>, and shown to be minimax rate optimal in a wide range of settings. As the permutation principle is used, all tests have exact, non-asymptotic Type I error control at the nominal level.
A set of functions leading to multivariate response L1 regression. 
    This includes functions on computing Euclidean inner products and norms, 
    weighted least squares estimates on multivariate responses, function to compute 
    fitted values and residuals. This package is a companion to the book "U-Statistics,
    M-estimation and Resampling", by Arup Bose and Snigdhansu Chatterjee, to appear 
    in 2017 as part of the "Texts and Readings in Mathematics" (TRIM) series of 
    Hindustan Book Agency and Springer-Verlag.
Forms a query to submit for US Treasury yield curve data, posting
    this query to the US Treasury web site's data feed service.  By default the
    download includes data yield data for 12 products from January 1, 1990,
    some of which are NA during this span.  The caller can pass parameters to
    limit the query to a certain year or year and month, but the full download
    is not especially large.  The download data from the service is in XML
    format.  The package's main function transforms that XML data into a numeric 
    data frame with treasury product items (constant maturity yields for 12 kinds 
    of bills, notes, and bonds) as columns and dates as row names. The function 
    returns a list which includes an item for this data frame as well as query-related
    values for reference and the update date from the service.
Three functions are provided: first function changes time from local to UTC, other changes from UTC to local and third returns difference between local and UTC. %h+% operator is also provided it adds hours to a time.
Process and print 'UTF-8' encoded international text (Unicode). Input, validate, normalize, encode, format, and display.
A collection of functions to make building customized ready-to-export tables for publication purposes easier and creating summaries of large datasets for review a breeze.
A set of tools for preparing and summarizing data for publication purposes. Includes functions for tabulating models, means to produce human-readable summary statistics from raw data, macros for calculating duration of time, and simplistic hypothesis testing tools.
A small set of functions for making visuals for publication in ggplot2. Includes minimalist themes with transparent backgrounds and a suite of tools for building Kaplan-Meier curves with risk tables.
Construct and plot objective hierarchies and associated value and utility functions. 
  Evaluate the values and utilities and visualize the results as colored objective hierarchies or tables. 
  Visualize uncertainty by plotting median and quantile intervals within the nodes of objective hierarchies.
  Get numerical results of the evaluations in standard R data types for further processing.
Contains functions for simulating phase 12 trial designs described by Chapple and Thall (2019) including simulation and the EFF-TOX trial and simulation and implementation of the U12 trial. Functions for implementing the EFF-TOX trial are found in the package 'Phase123'.
Predicts a smooth and continuous (individual) utility function from utility points, and computes measures of intensity for risk and higher order risk measures (or any other measure computed with user-written function) based on this utility function and its derivatives according to the method introduced in Schneider (2017) <http://hdl.handle.net/11858/00-1735-0000-002E-E306-0>.
The most used functions on IPEA (Instituto de Pesquisa Economica Aplicada). 
             Most of functions deal with brazilian names. 
             It can guess the women single's name, extract prepositions or extract the first name.
Multi-label learning strategies and others procedures to support multi-
  label classification in R. The package provides a set of multi-label procedures such as
  sampling methods, transformation strategies, threshold functions, pre-processing 
  techniques and evaluation metrics. A complete overview of the matter can be seen in
  Zhang, M. and Zhou, Z. (2014) <doi:10.1109/TKDE.2013.39> and Gibaja, E. and 
  Ventura, S. (2015) A Tutorial on Multi-label Learning.
Tools for generating and handling of UUIDs (Universally Unique Identifiers).
A method for estimating log-normalizing constants (or free
        energies) and expectations from multiple distributions (such as
        multiple generalized ensembles).
Supports the course Introduction to Methodology (POLSCI 4419/9715) at the University of Western Ontario.
An implementation of the Uniform Manifold Approximation and 
    Projection dimensionality reduction by McInnes et al. (2018) 
    <arXiv:1802.03426>. It also provides means to transform new data and to 
    carry out supervised dimensionality reduction. An implementation of the 
    related LargeVis method of Tang et al. (2016) <arXiv:1602.00370> is also 
    provided. This is a complete re-implementation in R (and C++, via the 'Rcpp'
    package): no Python installation is required. See the uwot website 
    (<https://github.com/jlmelville/uwot>) for more documentation and examples.
An R interface to V8: Google's open source JavaScript and WebAssembly 
    engine. This package can be compiled either with V8 version 6 and up, a NodeJS
    shared library, or the legacy 3.14/3.15 branch of V8.
An implementation of three procedures developed by 
    John Tukey: FUNOP (FUll NOrmal Plot), FUNOR-FUNOM 
    (FUll NOrmal Rejection-FUll NOrmal Modification), and vacuum cleaner. 
    Combined, they provide a way to identify, treat, and analyze outliers 
    in two-way (i.e., contingency) tables, as described in his 
    landmark paper "The Future of Data Analysis", Tukey, John W. (1962) 
    <https://www.jstor.org/stable/2237638>.
A lexicon and rule-based sentiment analysis tool that is specifically 
    attuned to sentiments expressed in social media, and works well on texts from other 
    domains. Hutto & Gilbert (2014) <https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8109/8122>.
Non-Domestic VAERS vaccine data for 01/01/2016 - 06/14/2016. If
    you want to explore the full VAERS data for 1990 - Present (data, symptoms,
    and vaccines), then check out the 'vaersND' package from the URL below. The
    URL and BugReports below correspond to the 'vaersND' package, of which
    'vaersNDvax' is a small subset (2016 only). 'vaersND' is not hosted on CRAN
    due to the large size of the data set. To install the Suggested 'vaers' and
    'vaersND' packages, use the following R code:
    'devtools::install_git("https://gitlab.com/iembry/vaers.git",
    build_vignettes = TRUE)' and
    'devtools::install_git("https://gitlab.com/iembry/vaersND.git",
    build_vignettes = TRUE)'. "VAERS is a national vaccine safety
    surveillance program co-sponsored by the US Centers for Disease Control and
    Prevention (CDC) and the US Food and Drug Administration (FDA). VAERS is a
    post-marketing safety surveillance program, collecting information about
    adverse events (possible side effects) that occur after the administration
    of vaccines licensed for use in the United States." For more information
    about the data, visit <https://vaers.hhs.gov/index>. For information about
    vaccination/immunization hazards, visit
    <http://www.questionuniverse.com/rethink.html/#vaccine>.
US VAERS vaccine data for 01/01/2018 - 06/14/2018. If you want to
    explore the full VAERS data for 1990 - Present (data, symptoms, and
    vaccines), then check out the 'vaers' package from the URL below. The URL
    and BugReports below correspond to the 'vaers' package, of which 'vaersvax'
    is a small subset (2018 only). 'vaers' is not hosted on CRAN due to the
    large size of the data set. To install the Suggested 'vaers' and 'vaersND'
    packages, use the following R code:
    'devtools::install_git("<https://gitlab.com/iembry/vaers.git>",
    build_vignettes = TRUE)' and
    'devtools::install_git("<https://gitlab.com/iembry/vaersND.git>",
    build_vignettes = TRUE)'. "The Vaccine Adverse Event Reporting System (VAERS)
    is a national early warning system to detect possible safety problems in
    U.S.-licensed vaccines. VAERS is co-managed by the Centers for Disease Control
    and Prevention (CDC) and the U.S. Food and Drug Administration (FDA)." For
    more information about the data, visit <https://vaers.hhs.gov/>. For
    information about
    vaccination/immunization hazards, visit
    <http://www.questionuniverse.com/rethink.html#vaccine>.
Provides access to the 'Vagalume' API <https://api.vagalume.com.br>. 
    The data extracted is basically lyrics of songs and information about
    artists/bands.
Fits generalized additive models (GAMs) using a variational approximations (VA) framework. In brief, the VA framework provides a fully or at least closed to fully tractable lower bound approximation to the marginal likelihood of a GAM when it is parameterized as a mixed model (using penalized splines, say). In doing so, the VA framework aims offers both the stability and natural inference tools available in the mixed model approach to GAMs, while achieving computation times comparable to that of using the penalized likelihood approach to GAMs. See Hui et al. (2018) <doi:10.1080/01621459.2018.1518235>.
A set of basic tools to transform functions into functions with
    input validation checks, in a manner suitable for both programmatic and
    interactive use.
A binding for the 'valection' program which offers various ways to 
  sample the outputs of competing algorithms or parameterizations, and fairly 
  assess their performance against each other. The 'valection' C library is 
  required to use this package and can be downloaded from:
  <http://labs.oicr.on.ca/boutros-lab/software/valection>. Cooper CI, et al; Valection: Design Optimization for Validation and Verification Studies; Biorxiv 2018; <doi:10.1101/254839>.
Alternative splicing produces a variety of different protein products from a given gene. 'VALERIE' enables visualisation of alternative splicing events from high-throughput single-cell RNA-sequencing experiments. 'VALERIE' computes percent spliced-in (PSI) values for user-specified genomic coordinates corresponding to alternative splicing events. PSI is the proportion of sequencing reads supporting the included exon/intron as defined by Shiozawa (2018) <doi:10.1038/s41467-018-06063-x>. PSI are inferred from sequencing reads data based on specialised infrastructures for representing and computing annotated genomic ranges by Lawrence (2013) <doi:10.1371/journal.pcbi.1003118>. Computed PSI for each single cell are subsequently presented in the form of a heatmap implemented using the 'pheatmap' package by Kolde (2010) <https://CRAN.R-project.org/package=pheatmap>. Board overview of the mean PSI difference and associated p-values across different user-defined groups of single cells are presented in the form of a line graph using the 'ggplot2' package by Wickham (2007) <https://CRAN.R-project.org/package=ggplot2>.
Methods and tools for analysing and validating the outputs
    and modelled functions of artificial neural networks (ANNs) in terms
    of predictive, replicative and structural validity. Also provides a
    method for fitting feed-forward ANNs with a single hidden layer.
Declare data validation rules and data quality indicators;
        confront data with them and analyze or visualize the results.
        The package supports rules that are per-field, in-record,
        cross-record or cross-dataset. Rules can be automatically
        analyzed for rule type and connectivity. See also Van der Loo
        and De Jonge (2018) <doi:10.1002/9781118897126>,
        chapter 6.
An implementation of statistical tests for the validation of rating systems as described in the ECB Working paper ''Advances in multivariate back-testing for credit risk underestimation'', by F. Coppens, M. Mayer, L. Millischer, F.  Resch, S. Sauer, K. Schulze (ECB WP series, forthcoming).
Rule sets with validation rules may contain redundancies or contradictions. 
  Functions for finding redundancies and problematic rules are provided, 
  given a set a rules formulated with 'validate'.
The algorithm implemented in this package was
    designed to quickly estimates the distribution of the 
    log-rank especially for heavy unbalanced groups. VALORATE 
    estimates the null distribution and the p-value of the 
    log-rank test based on a recent formulation. For a given 
    number of alterations that define the size of survival 
    groups, the estimation involves a weighted sum of 
    distributions that are conditional on a co-occurrence term 
    where mutations and events are both present. The estimation 
    of conditional distributions is quite fast allowing the 
    analysis of large datasets in few minutes 
    <http://bioinformatica.mty.itesm.mx/valorate>.
Historical results for the state of Virginia lottery draw games. Data were downloaded from https://www.valottery.com/.
Read and manipulate genome intervals and signals. Provides
             functionality similar to command-line tool suites within R,
             enabling interactive analysis and visualization of genome-scale data. 
             Riemondy et al. (2017) <doi:10.12688/f1000research.11997.1>.
EQ-5D is a standard instrument (<https://euroqol.org/eq-5d-instruments/>) that measures the quality of life 
    often used in clinical and economic evaluations of health care technologies. Both adult versions of EQ-5D (EQ-5D-3L and EQ-5D-5L)
    contain a descriptive system and visual analog scale. The descriptive system measures the patient's health in 5 dimensions: 
    the 5L versions has 5 levels and 3L version has 3 levels. The descriptive system scores are usually converted to index values 
    using country specific values sets (that incorporates the country preferences). This package allows the calculation of both descriptive system
    scores to the index value scores. The value sets for EQ-5D-3L are from the references mentioned in the website <https://euroqol.org/eq-5d-instruments/eq-5d-3l-about/valuation/>
    The value sets for EQ-5D-3L for a total of 31 countries are used for the valuation (see the user guide for a complete list of references).
    The value sets for EQ-5D-5L are obtained from references mentioned in the <https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/valuation-standard-value-sets/> and other sources.
    The value sets for EQ-5D-5L for a total of 17 countries are used for the valuation (see the user guide for a complete list of references).
    The package can also be used to map 5L scores to 3L index values for 10 countries: Denmark, France, Germany, Japan, Netherlands, Spain, Thailand, UK, USA, and Zimbabwe. 
    The value set and method for mapping are obtained from Van Hout et al (2012) <doi: 10.1016/j.jval.2012.02.008>.
Pricing of variable annuity life insurance
    contracts by means of Monte Carlo methods. Monte Carlo is used to price
    the contract in case the policyholder cannot surrender while
    Least Squares Monte Carlo is used if the insured can surrender.
    This package implements the pricing framework and algorithm described in
    Bacinello et al. (2011) <doi:10.1016/j.insmatheco.2011.05.003>.
    It also implements the state-dependent fee structure
    discussed in Bernard et al. (2014) <doi:10.1017/asb.2014.13> as well as 
    a function which prices the contract by resolving the partial differential equation 
    described in MacKay et al. (2017) <doi:10.1111/jori.12094>.
Implementation of a Monte Carlo simulation engine for valuing synthetic portfolios of 
    variable annuities, which reflect realistic features of common annuity contracts in practice. 
    It aims to facilitate the development and dissemination of research related to the efficient 
    valuation of a portfolio of large variable annuities. The main valuation methodology was 
    proposed by Gan (2017) <doi:10.1515/demo-2017-0021>.
Vampirize the response biases from a dataset! Performs 
    factor analysis controlling the effects of social desirability
    and acquiescence using the method described in Ferrando, 
    Lorenzo-Seva & Chico (2009) <doi:10.1080/10705510902751374>.
Wrapper around the 'City of Vancouver' Open Data API <https://opendata.vancouver.ca/api/v2/console> to simplify and standardize access to 'City of Vancouver' open data. 
  Functionality to list the data catalogue and access data and geographic records.
Identify and analyze conserved waters within crystallographic 
  protein structures and molecular dynamics simulation trajectories. Statistical 
  parameters for each water cluster, informative graphs, and a PyMOL session 
  file to visually explore the conserved waters and protein are returned. 
  Hydrophilicity is the propensity of waters to congregate near specific protein 
  atoms and is related to conserved waters. An informatics derived set of 
  hydrophilicity values are provided based on a large, high-quality X-ray 
  protein structure dataset.
Provides low-level access to 'GDAL' functionality for R packages. The aim is to minimize
  the level of interpretation put on the 'GDAL' facilities, to enable direct use of it for a variety of purposes. 
  'GDAL' is the 'Geospatial Data Abstraction Library' a translator for raster and vector geospatial data formats 
  that presents a single raster abstract data model and single vector abstract data model to the calling application 
  for all supported formats <http://gdal.org/>. Other available packages 'rgdal' and 'sf' also provide access to 
  the 'GDAL' library, but neither can be used for these lower level tasks, and both do many other tasks.
Estimation, Hypothesis Testing, Prediction for Stationary Vector Autoregressive Models
Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at <https://arxiv.org/abs/1604.07451>.
Tool for easy and efficient discretization of continuous and categorical data. 
    The package calculates the most optimal binning of a given explanatory variable with respect to a 
    user-specified target variable. The purpose is to assign a unique Weight-of-Evidence value 
    to each of the calculated binpoints in order to recode the original variable. 
    The package allows users to impose certain restrictions on the functional form on the resulting 
    binning while maximizing the overall information value in the original data. 
    The package is well suited for logistic scoring models where input variables may be subject to 
    restrictions such as linearity by e.g. regulatory authorities. An excellent source describing in 
    detail the development of scorecards, and the role of Weight-of-Evidence coding in credit scoring 
    is (Siddiqi 2006, ISBN: 978–0-471–75451–0). The package utilizes the discrete nature of decision trees and 
    Isotonic Regression to accommodate the trade-off between flexible functional forms and maximum 
    information value.
Easily create list-like structures with constant, read-only 
    variables. Combines the flexibility of lists with read-only fields.
    Supports defensive programming by throwing errors on field assignment,
    helping mitigate the introduction of logical runtime bugs.
Fast algorithms for fitting Bayesian variable selection
    models and computing Bayes factors, in which the outcome (or
    response variable) is modeled using a linear regression or a
    logistic regression. The algorithms are based on the variational
    approximations described in "Scalable variational inference for
    Bayesian variable selection in regression, and its accuracy in
    genetic association studies" (P. Carbonetto & M. Stephens, 2012,
    <DOI:10.1214/12-BA703>). This software has been applied to large
    data sets with over a million variables and thousands of samples.
Performs clustering of quantitative variables,
    assuming that clusters lie in low-dimensional subspaces. Segmentation of
    variables, number of clusters and their dimensions are selected based on
    BIC. Candidate models are identified based on many runs of K-means
    algorithm with different random initializations of cluster centers.
Generation of domain variables, linearization of several non-linear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, Sample Survey Methods And Theory, vol. I: Methods and Applications; vol. II: Theory. 1953, New York: John Wiley and Sons), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y. G., 2015, <doi:10.1111/rssa.12116>). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.
Generating functions for both optimal and ordinary difference sequences, and the difference-based estimation functions.
Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions,  including all commonly known distributions.  Also computed are the corresponding probability density function and cumulative distribution function.
Error variance estimation in ultrahigh dimensional datasets with four different methods, viz. Refitted cross validation, k-fold refitted cross validation, Bootstrap-refitted cross validation, Ensemble method.
Captures and compiles the genes and variants related to a disease, a phenotype or a clinical feature from the public databases including HPO (Human Phenotype Ontology, <http://human-phenotype-ontology.github.io/about.html>), Orphanet <http://www.orpha.net/consor/cgi-bin/index.php>, OMIM (Online Mendelian Inheritance in Man, <http://www.omim.org>), ClinVar <http://www.ncbi.nlm.nih.gov/clinvar>, and UniProt (Universal Protein Resource, <http://www.uniprot.org>) and PubMed abstracts. HPO provides a standardized vocabulary of phenotypic abnormalities encountered in human disease. HPO currently contains approximately 11,000 terms and over 115,000 annotations to hereditary diseases. Orphanet is the reference portal for information on rare diseases and orphan drugs, whose aim is to help improve the diagnosis, care and treatment of patients with rare diseases. OMIM is a continuously updated catalog of human genes and genetic disorders and traits, with particular focus on the molecular relationship between genetic variation and phenotypic expression. ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence. UniProt focuses on amino acid altering variants imported from Ensembl Variation databases. For Homo sapiens, the variants including human polymorphisms and disease mutations in the UniProt are manually curated  from UniProtKB/Swiss-Prot. Additionally, PubMed provides the primary and latest source of the information. Text mining was employed to capture the information from PubMed abstracts.
Variables are the fundamental parts of each programming language but handling them efficiently might be frustrating for programmers. This package contains some functions to help user (especially data explorers) to make more sense of their variables and take the most out of variables and hardware resources. These functions are written, collected and crafted over 7 years of experience in statistical data analysis on high-dimensional data, and for each of them there was a need. Functions in this package are suppose to be efficient and easy to use, hence they will be frequently updated to make them more convenient.
Performs analysis of various genetic parameters like genotypic and phenotypic coefficient of variance, heritability, genetic advance, genetic advance as a percentage of mean. The package also has functions for genotypic and phenotypic covariance, correlation and path analysis. Dataset has been added to facilitate example. For more information refer Singh, R.K. and Chaudhary, B.D. (1977, ISBN:81766330709788176633079).
Abstract descriptions of (yet) unobserved variables.
Implements variable screening techniques for ultra-high
    dimensional regression settings.  Techniques for independent (iid) data,
    varying-coefficient models, and longitudinal data are implemented. The package
     currently contains three screen functions: screenIID(), screenLD() and screenVCM(),
     and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(),
     simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of
    Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) <DOI:10.1198/jasa.2011.tm10563>,
    Runze LI, Wei ZHONG, & Liping ZHU (2012) <DOI:10.1080/01621459.2012.695654>,
    Jingyuan LIU, Runze LI, & Rongling WU (2014) <DOI:10.1080/01621459.2013.850086>
    Hengjian CUI, Runze LI, & Wei ZHONG (2015) <DOI:10.1080/01621459.2014.920256>, and
    Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) <DOI:10.1214/16-AOAS912>.
    Special thanks are due to Ling Zhang for providing detailed testing and proposing
    a method for speed improvement on the simulation of data with AR-1 structure.
Uses a Bayesian model to
    estimate the variability in a repeated
    measure outcome and use that as an outcome or a predictor
    in a second stage model.
Provides functions for the variance gamma
        distribution. Density, distribution and quantile functions.
        Functions for random number generation and fitting of the
        variance gamma to data. Also, functions for computing moments
        of the variance gamma distribution of any order about any
        location. In addition, there are functions for checking the
        validity of parameters and to interchange different sets of
        parameterizations for the variance gamma distribution.
This is a 'sparklyr' extension integrating 'VariantSpark' and R. 'VariantSpark' is 
  a framework based on 'scala' and 'spark' to analyze genome datasets, 
  see <https://bioinformatics.csiro.au/>. It was tested on datasets with 3000 samples 
  each one containing 80 million features in either unsupervised clustering approaches 
  and supervised applications, like classification and regression. The genome datasets
  are usually writing in VCF, a specific text file format used 
  in bioinformatics for storing gene sequence variations. So, 'VariantSpark' is a great 
  tool for genome research, because it is able to read VCF files, run analyses and return 
  the output in a 'spark' data frame.
Computes the random forest variable importance (VIMP) for the conditional inference random forest (cforest) of the 'party' package. Includes a function (varImp) that computes the VIMP for arbitrary measures from the 'measures' package. For calculating the VIMP regarding the measures accuracy and AUC two extra functions exist (varImpACC and varImpAUC).
Applying Monte Carlo permutation to generate pointwise variogram envelope and checking for spatial dependence at different scales using permutation test. Empirical Brown's method and Fisher's method are used to compute overall p-value for hypothesis test.
Multi-caller variant analysis pipeline for targeted analysis sequencing (TAS) data. Features a modular, automated workflow that can start with raw reads and produces a user-friendly PDF summary and a spreadsheet containing consensus variant information.
The goal of the package is to equip the 'jmcm' package (current version 0.2.1) 
    with estimations of the covariance of estimated parameters. Two methods are provided. 
    The first method is to use the inverse of estimated Fisher's information matrix, see 
    M. Pourahmadi (2000) <doi:10.1093/biomet/87.2.425>, M. Maadooliat, M. Pourahmadi and 
    J. Z. Huang (2013) <doi:10.1007/s11222-011-9284-6>, and W. Zhang, C. Leng, C. Tang (2015) 
    <doi:10.1111/rssb.12065>. The second method is bootstrap based, see Liu, R.Y. (1988) 
    <doi:10.1214/aos/1176351062> for reference.
A computational toolbox of heuristics approaches for performing variable ranking and feature selection based on mutual information well adapted for multivariate system epidemiology datasets. The core function is a general implementation of the minimum redundancy maximum relevance model. R. Battiti (1994) <doi:10.1109/72.298224>. Continuous variables are discretized using a large choice of rule. Variables ranking can be learned with a sequential forward/backward search algorithm. The two main problems that can be addressed by this package is the selection of the most representative variable within a group of variables of interest (i.e. dimension reduction) and variable ranking with respect to a set of features of interest.
Methods for fitting semi-parametric mean and variance models, with normal or censored data. Also extended to allow a regression in the location, scale and shape parameters.
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
An algorithm which identifies the morphometric features that significantly discriminate two taxa and validates the morphological distinctness between them via a Monte-Carlo test, polar coordinates and overlap of the area under the density curve.
Feature selection using Sequential Forward Floating feature Selection and Jeffries-Matusita distance. It returns a suboptimal set of features to use for image classification. Reference: Dalponte, M., Oerka, H.O., Gobakken, T., Gianelle, D. & Naesset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51, 2632-2645, <DOI:10.1109/TGRS.2012.2216272>.
Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here <doi:10.1007/s11222-016-9670-1>). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.
Variable selection from random forests using both
        backwards variable elimination (for the selection of small sets
        of non-redundant variables) and selection based on the
        importance spectrum (somewhat similar to scree plots; for the
        selection of large, potentially highly-correlated variables).
        Main applications in high-dimensional data (e.g., microarray
        data, and other genomics and proteomics applications).
Creates a summary dataframe that can be used in 'RStudio' similar to the variable
    explorer in 'Stata', but which also includes the summary statistics. By default the result 
    is shown in the 'RStudio' Viewer Pane as a searchable data table. This is useful particularly 
    if you have a large dataset with a very large number of labelled variables with hard to 
    remember names. Can also be used to generate a table of summary statistics.
Vector autoregressive (VAR) model is a fundamental and effective approach
    for multivariate time series analysis. Shrinkage estimation methods can be
    applied to high-dimensional VAR models with dimensionality greater than
    the number of observations, contrary to the standard ordinary least squares
    method. This package is an integrative package delivering nonparametric,
    parametric, and semiparametric methods in a unified and consistent manner,
    such as the multivariate ridge regression in Golub, Heath, and Wahba (1979)
    <doi:10.2307/1268518>, a James-Stein type nonparametric shrinkage method in
    Opgen-Rhein and Strimmer (2007) <doi:10.1186/1471-2105-8-S2-S3>, and
    Bayesian estimation methods using noninformative and informative priors
    in Lee, Choi, and S.-H. Kim (2016) <doi:10.1016/j.csda.2016.03.007> and
    Ni and Sun (2005) <doi:10.1198/073500104000000622>.
Provides routines for identifying structural shocks in vector autoregressions (VARs) using sign restrictions.
Computes a portfolio of European options that replicates
        the cost of capturing the realised variance of an equity index.
An implementation of the Likelihood ratio Test (LRT) for testing that,
    in a (non)linear mixed effects model, the variances of a subset of the random
    effects are equal to zero. There is no restriction on the subset of variances
    that can be tested: for example, it is possible to test that all the variances
    are equal to zero. Note that the implemented test is asymptotic.
    This package should be used on model fits from packages 'nlme', 'lmer', and 'saemix'.
    Charlotte Baey, Paul-Henry Cournède and Estelle Kuhn (2019) <doi:10.1016/j.csda.2019.01.014>.
Implements the Wild bootstrap tests for autocorrelation in vector autoregressive models of Ahlgren, N. & Catani, P. 
	(2016, <doi:10.1007/s00362-016-0744-0>), the Combined LM test for ARCH in VAR models of Catani, P. & Ahlgren, N. 
	(2016, <doi:10.1016/j.ecosta.2016.10.006>), and Bootstrap determination of the co-integration rank (Cavaliere, G., Rahbek, A.,
	& Taylor, A. M. R., 2012, 2014).
Implements a maximum likelihood estimation (MLE)
    method for estimation and prediction in spatially varying coefficient
    (SVC) models (Dambon et al. (2020) <arXiv:2001.08089>). Covariance
    tapering (Furrer et al. (2006) <doi:10.1198/106186006X132178>) can be
    applied such that the method scales to large data.
Provide a collection of miscellaneous R functions
    related to the Vasicek distribution with the intent to make
    the lives of risk modelers easier.
Provides an interface to a 'HashiCorp' vault server over
  its http API (typically these are self-hosted; see
  <https://www.vaultproject.io>).  This allows for secure storage and
  retrieval of secrets over a network, such as tokens, passwords and
  certificates.  Authentication with vault is supported through
  several backends including user name/password and authentication via
  'GitHub'.
Efficient algorithm for solving discrete mixture
		regression model for rare variant association analysis.
		Uses variational Bayes algorithm to efficiently search over
		model space.  Outputs an approximate likelihood ratio test
		as well as variant level posterior probabilities of
		association.
Efficient algorithm for solving ultra-sparse
		regularized regression models using a variational
		Bayes algorithm with a spike (l0) prior.  Algorithm
		is solved on a path, with coordinate updates, and is
		capable of generating very sparse models.  There are
		very general model diagnostics for controling type-1
		error included in this package.
Provides a new data structure, vector binary tree, to make
  your data visiting and management more efficient. if your data has
  very structurized column names with specific connecting pattern, it can 
  read, split, and factorize these names, then build the mapping from all
  string objects to an array or tensor, through vector binary tree, by
  which the batched data processing can be implemented easily. The methods
  of array and tensor are also applicable.
Provides new classes for (rotated) BB1, BB6, BB7, BB8, and 
  Tawn copulas, extends the existing Gumbel and Clayton families with 
  rotations, and allows to set up a vine copula model using the 'copula' API.
  Corresponding objects from the 'VineCopula' API can easily be converted.
ANOVA and REML estimation of linear mixed models is implemented, once following
 Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package.
 The primary objective of this package is to perform a variance component analysis (VCA)
 according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement
 Procedures" (2014). There are plotting methods for visualization of an experimental design,
 plotting random effects and residuals. For ANOVA type estimation two methods for computing
 ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of 
 variance components can be derived, which is used in estimating confidence intervals. Linear
 hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific
 values of covariables and with custom weighting schemes for factor variables. See ?VCA for a
 more comprehensive description of the features.
Visualization techniques, data sets, summary and inference
        procedures aimed particularly at categorical data. Special
        emphasis is given to highly extensible grid graphics. The
        package was package was originally inspired by the book 
	"Visualizing Categorical Data" by Michael Friendly and is 
	now the main support package for a new book, 
	"Discrete Data Analysis with R" by Michael Friendly and 
	David Meyer (2015).
Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data
    and the 'gnm' package for Generalized Nonlinear Models.
	In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and
	adds a 3D version in 'mosaic3d'.  Additionally, methods are provided for comparing and visualizing lists of
	'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by
  Michael Friendly and David Meyer.
Facilitates easy manipulation of variant call format (VCF) data.
    Functions are provided to rapidly read from and write to VCF files. Once
    VCF data is read into R a parser function extracts matrices of data. This
    information can then be used for quality control or other purposes. Additional
    functions provide visualization of genomic data. Once processing is complete
    data may be written to a VCF file (*.vcf.gz). It also may be converted into
    other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between
    VCF data and familiar R software.
Methods for faster extraction (about 5x faster in a few test cases) of variance-covariance matrices and standard errors from models. Methods in the 'stats' package tend to rely on the summary method, which may waste time computing other summary statistics which are summarily ignored.
Method to perform penalized variance component analysis.
Record test suite 'HTTP' requests and replays them during
    future runs. A port of the Ruby gem of the same name
    (<https://github.com/vcr/vcr/>). Works by hooking into the 'webmockr'
    R package for matching 'HTTP' requests by various rules ('HTTP' method,
    'URL', query parameters, headers, body, etc.), and then caching
    real 'HTTP' responses on disk in 'cassettes'. Subsequent 'HTTP' requests
    matching any previous requests in the same 'cassette' use a cached
    'HTTP' response.
Recursive partitioning for varying coefficient generalized linear models and ordinal linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal regression.
Defines new notions of prototype and size that are
    used to provide tools for consistent and well-founded type-coercion
    and size-recycling, and are in turn connected to ideas of type- and
    size-stability useful for analysing function interfaces.
Comparison of variance - covariance patterns using relative principal component analysis (relative eigenanalysis). Also provides functions to compute group covariance matrices, distance matrices, and perform proportionality tests. A working sample on the body shape of cichlid fishes is included.
Analyze Peptide Array Data and characterize peptide
    sequence space. Allows for high level visualization of global signal, Quality control based
    on replicate correlation and/or relative Kd, calculation of peptide Length/Charge/Kd parameters,
    Hits selection based on RFU Signal, and amino acid composition/basic motif recognition with RFU
    signal weighting. Basic signal trends can be used to generate peptides that follow the observed
    compositional trends.
The qda() function from package 'MASS' is extended to calculate a weighted linear (LDA) and quadratic discriminant analysis (QDA) by changing the group variances and group means based on cell-wise uncertainties.
    The uncertainties can be derived e.g. through relative errors for each individual measurement (cell), not only row-wise or column-wise uncertainties.
    The method can be applied compositional data (e.g. portions of substances, concentrations) and non-compositional data.
Facilities for constructing variance dispersion graphs, fraction-
    of-design-space plots and similar graphics for exploring the properties of
    experimental designs. The design region is explored via random sampling, which
    allows for more flexibility than traditional variance dispersion graphs. A
    formula interface is leveraged to provide access to complex model formulae.
    Graphics can be constructed simultaneously for multiple experimental designs
    and/or multiple model formulae. Instead of using pointwise optimization to
    find the minimum and maximum scaled prediction variance curves, which can be
    inaccurate and time consuming, this package uses quantile regression as an
    alternative.
Uses a modification of the published FORTRAN code in "A Computer Program for Generating Variance Dispersion Graphs" by G. Vining, Journal of Quality Technology, Vol. 25 No. 1 January 1993, to produce variance dispersion graphs. Also produces fraction of design space plots, and contains data frames for several minimal run response surface designs.
Functions for creating variance dispersion graphs, fraction of design space plots, and contour plots of scaled prediction variances for second-order response surface designs in spherical and cuboidal regions. Also, some standard response surface designs can be generated.
An extension to the 'testthat' package that makes it easy
    to add graphical unit tests. It provides a Shiny application to
    manage the test cases.
Contains variable, diversity, and joining sequences and accompanying functions that enable both the extraction of and comparison between immune V-D-J genomic segments from a variety of species. Sources include IMGT from MP Lefranc (2009) <doi:10.1093/nar/gkn838> and Vgenerepertoire from publication DN Olivieri (2014) <doi:10.1007/s00251-014-0784-3>.
Provides statistical methods for the design and analysis of a calibration study, which aims for calibrating measurements using two different methods. The package includes sample size calculation, sample selection,  regression analysis with error-in measurements and change-point regression. The method is described in Tian, Durazo-Arvizu, Myers, et al. (2014) <DOI:10.1002/sim.6235>.
A package for applying affine and similarity transformations on vector spatial data (sp objects). Transformations can be defined from control points or directly from parameters. If redundant control points are provided Least Squares is applied allowing to obtain residuals and RMSE.
Automates set operations (i.e., comparisons of overlap) between multiple vectors.
    It also contains a function for automating reporting in 'RMarkdown', by generating markdown output for easy analysis, as well as an 'RMarkdown' template for use with 'RStudio'.
The base 'sets' tools follow the algebraic definition
 that each element of a set must be unique. 
 Since it's often helpful to compare all elements of two vectors,
 this toolset treats every element as unique for counting purposes.
 For ease of use, all functions in vecsets have an argument
 'multiple' which, when set to FALSE,
 reverts them to the base::set tools functionality.
A 2D statistical analysis is performed, both numerical and graphical, of a set of vectors. Since a vector has two components (module and azimuth) vector analysis is performed in three stages: modules are analyzed by means of linear statistics, azimuths are analyzed by circular statistics, and the joint analysis of modules and azimuths is done using density maps that allow detecting another distribution properties (i.e. anisotropy) and outliers. Tests and circular statistic parameters have associated a full range of graphing: histograms, maps of distributions, point maps, vector maps, density maps, distribution modules and azimuths.
This package performs a 3D statistical analysis, both numerical and graphical, of a set of vectors. Since a vector has three components (a module and two angles) vectorial analysis is performed in two stages: modules are analyzed by means of linear statistics and orientations are analyzed by spherical statistics. Tests and spherical statistic parameters are accompanied by a graphs as: density maps, distribution modules and angles. The tests, spherical statistic parameters and graphs allow us detecting another distribution properties (i.e. anisotropy) and outliers.
Block matrices and object arrays, as general purpose S4 data structures, with automatic formatting. Matrix arrays (that is, arrays of matrices), with binary operators for succinct/convenient matrix array multiplication. Set/sequence related functions, including index/interval finding functions. Also, SQL-like select functions, analytic geometry functions, head/tail generalizations and prototype pooling/convolution functions.
The 'Vega-Lite' 'JavaScript' framework provides a higher-level grammar
    for visual analysis, akin to 'ggplot' or 'Tableau', that generates complete 'Vega'
    specifications. Functions exist which enable building a valid 'spec' from scratch
    or importing a previously created 'spec' file. Functions also exist to export 'spec'
    files and to generate code which will enable plots to be embedded in properly
    configured web pages. The default behavior is to generate an 'htmlwidget'.
Ordination methods, diversity analysis and other
  functions for community and vegetation ecologists.
Static and dynamic 3D plots to be used with ordination
    results and in diversity analysis, especially with the vegan package.
'Vega' and 'Vega-Lite' parse text in 'JSON' notation to render 
  chart-specifications into 'HTML'. This package is used to facilitate the 
  rendering. It also provides a means to interact with signals, events,
  and datasets in a 'Vega' chart using 'JavaScript' or 'Shiny'.
A set of functions to: (1) perform fuzzy clustering of vegetation data [De Caceres et al. (2010) <doi:10.1111/j.1654-1103.2010.01211.x>]; (2) to assess ecological community ressemblance on the basis of structure and composition [De Caceres et al. (2013): <doi:10.1111/2041-210X.12116>]; and (3) to perform community trajectory analysis [De Caceres et al. (2019): <doi:10.1002/ecm.1350>].
Handling of vegetation data from different sources (
	Turboveg <http://www.synbiosys.alterra.nl/turboveg/>; 
	the German national repository <http://www.vegetweb.de> and others. 
    Taxonomic harmonization (given appropriate taxonomic lists,
    e.g. the German taxonomic standard list "GermanSL", <http://germansl.infinitenature.org>).
This package computes diversity for community data sets
        using the methods outlined by Jost (2006, 2007). While there
        are differing opinions on the ideal way to calculate diversity
        (e.g. Magurran 2004), this method offers the advantage of
        providing diversity numbers equivalents, independent alpha and
        beta diversities, and the ability to incorporate 'order' (q) as
        a continuous measure of the importance of rare species in the
        metrics. The functions provided in this package largely
        correspond with the equations offered by Jost in the cited
        papers. The package computes alpha diversities, beta
        diversities, gamma diversities, and similarity indices.
        Confidence intervals for diversity measures are calculated
        using a bootstrap method described by Chao et al. (2008).  For
        datasets with many samples (sites, plots), sim.table creates
        tables of all pairwise comparisons possible, and for grouped
        samples sim.groups calculates pairwise combinations of within-
        and between-group comparisons.
Collection of common methods to determine growing season length in
  a simple manner. Start and end dates of the vegetation periods are calculated
  solely based on daily mean temperatures and the day of the year.
Import and handling data from vegetation-plot databases, especially
    data stored in 'Turboveg' (<https://www.synbiosys.alterra.nl/turboveg>).
    Also import/export routines for exchange of data with 'Juice'
    (<http://www.sci.muni.cz/botany/juice>) are implemented.
Elaboration of vehicular emissions inventories,
    consisting in four stages, pre-processing activity data, preparing 
    emissions factors, estimating the emissions and post-processing of emissions 
    in maps and databases. More details in Ibarra-Espinosa et al (2018) <doi:10.5194/gmd-11-2209-2018>.
    Before using VEIN you need to know the vehicular composition of your study area, in other words,
    the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to
    start with the project to download a template to create a structure of directories and scripts.
Functions for downloading, reshaping, culling, cleaning, and analyzing fossil data from the Paleobiology Database <https://paleobiodb.org>.
A set of functions for generating HTML to
    embed hosted video in your R Markdown documents or Shiny applications.
Draws and displays Venn diagrams up to 7 sets, and any Boolean union of set intersections.
A set of functions to generate high-resolution Venn and Euler plots. Includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.
Calculates and displays Venn and Euler Diagrams
Provides variable selection and estimation routines for models
    with main effects stratified on multiple binary factors. The 'vennLasso' package
    is an implementation of the method introduced in Huling, et al. (2017) <doi:10.1111/biom.12769>.
Calculate and plot Venn diagrams in 2D and 3D.
Utilities for verifying discrete, continuous and probabilistic forecasts, and forecasts expressed as parametric distributions are included.
Installs specified versions of R packages hosted on CRAN and
    provides functions to list available versions and the versions of currently
    installed packages. These tools can be used to help make R projects and
    packages more reproducible. 'versions' fits in the narrow gap between
    the 'devtools' install_version() function and the 'checkpoint' package.
    devtools::install_version() installs a stated package version from source files
    stored on the CRAN archives. However CRAN does not store binary versions of
    packages so Windows users need to have RTools installed and Windows and OSX
    users get longer installation times. 'checkpoint' uses the Revolution Analytics
    MRAN server to install packages (from source or binary) as they were available
    on a given date. It also provides a helpful interface to detect the packages
    in use in a directory and install all of those packages for a given date.
    'checkpoint' doesn't provide install.packages-like functionality however, and
    that's what 'versions' aims to do, by querying MRAN. As MRAN only goes back to
    2014-09-17, 'versions' can't install packages archived before this date.
When given a description of a polyhedral set by a system of linear inequalities Ax <= b, produce the list of the vertices of the set.
Creates Vertex Similarity matrix of an undirected graph based
  on the method stated by E. A. Leicht, Petter Holme, AND M. E. J. Newman in
  their paper <DOI:10.1103/PhysRevE.73.026120>.
Permits to apply the 'Vertex Sort' algorithm (Jothi et al. (2009) <10.1038/msb.2009.52>) to a graph in order to elucidate its hierarchical structure. It also allows graphic visualization of the sorted graph by exporting the results to a cytoscape friendly format. Moreover, it offers five different algorithms of graph randomization: 1) Randomize a graph with preserving node degrees, 2) with preserving similar node degrees, 3) without preserving node degrees, 4) with preserving node in-degrees and 5) with preserving node out-degrees.
Multi-precision library that allows to store and operate with arbitrarily big integers without
    loss of precision. It includes a large list of tools to work with them, like:
      - Arithmetic and logic operators
      - Modular-arithmetic operators
      - Computer Number Theory utilities
      - Probabilistic primality tests
      - Factorization algorithms
      - Random generators of diferent types of integers.
Simple functions for calculating the image gradient, image hessian, volume ratio filter, and Frangi vesselness filter of 3-dimensional volumes.
Integrated data management library that offers a variety of tools concerning the loading and manipulation of environmental data available from different Venezuelan governmental sources. Facilities are provided to plot temporal and spatial data as well as understand the health of a collection of meteorological data.
Declarative template-based framework for verifying that objects
  meet structural requirements, and auto-composing error messages when they do
  not.
R Codes and Datasets for Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Computation the value of one of two uniformly 
    distributed marginals if the copula probability value is known
    and the value of the second marginal is also known.
    Computation and plotting corresponding cumulative
    distribution function or survival function.
    The numerical definition of a common area limited by lines
    of the cumulative distribution function and survival function.
    Approximate quantification of the probability of this area.
    In addition to 'amh', the copula dimension may be larger than 2.
A set of visual input controls for Shiny apps to facilitate filtering across multiple outputs.
Variance function estimation for models proposed by W. Sadler in his variance function program 
  ('VFP', <http://www.aacb.asn.au/resources/useful-tools/variance-function-program-v14>). Here, the idea is
  to fit multiple variance functions to a data set and consequently assess which function reflects
  the relationship 'Var ~ Mean' best. For 'in-vitro diagnostic' ('IVD') assays modeling this relationship
  is of great importance when individual test-results are used for defining follow-up treatment of patients.
Realization of published methods to analyze visual field (VF) progression. Introduction to the plotting methods (designed by author TE) for VF output visualization. A sample dataset for two eyes, each with 10 follow-ups is included. The VF analysis methods could be found in -- Musch et al. (1999) <doi:10.1016/S0161-6420(99)90147-1>, Nouri-Mahdavi et at. (2012) <doi:10.1167/iovs.11-9021>, Schell et at. (2014) <doi:10.1016/j.ophtha.2014.02.021>, Aptel et al. (2015) <doi:10.1111/aos.12788>.
Empirical models for runoff, erosion, and phosphorus loss 
    across a vegetated filter strip, given slope, soils, climate, and 
    vegetation (Gall et al., 2018) <doi:10.1007/s00477-017-1505-x>. 
    It also includes functions for deriving climate parameters from 
    measured daily weather data, and for simulating rainfall. Models 
    implemented include MUSLE (Williams, 1975) and APLE (Vadas et al., 
    2009 <doi:10.2134/jeq2008.0337>).
An implementation of about 6 major classes of
    statistical regression models. The central algorithm is
    Fisher scoring and iterative reweighted least squares.
    At the heart of this package are the vector generalized linear
    and additive model (VGLM/VGAM) classes. VGLMs can be loosely
    thought of as multivariate GLMs. VGAMs are data-driven
    VGLMs that use smoothing. The book "Vector Generalized
    Linear and Additive Models: With an Implementation in R"
    (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of
    the statistical framework and the package. Currently only
    fixed-effects models are implemented. Many (150+) models and
    distributions are estimated by maximum likelihood estimation
    (MLE) or penalized MLE. The other classes are RR-VGLMs
    (reduced-rank VGLMs), quadratic RR-VGLMs, reduced-rank VGAMs,
    RCIMs (row-column interaction models)---these classes perform
    constrained and unconstrained quadratic ordination (CQO/UQO)
    models in ecology, as well as constrained additive ordination
    (CAO). Hauck-Donner effect detection is implemented.
    Note that these functions are subject to change;
    see the NEWS and ChangeLog files for latest changes.
Mainly data sets to accompany the VGAM package and
	the book "Vector Generalized Linear and
	Additive Models: With an Implementation in R" (Yee, 2015)
	<DOI:10.1007/978-1-4939-2818-7>.
	These are used to illustrate vector generalized
	linear and additive models (VGLMs/VGAMs), and associated models
	(Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column
	Interaction Models, and constrained and unconstrained ordination
	models in ecology). This package now contains some
	old VGAM family functions which have been replaced by newer
	ones (often because they are now special cases).
Extending the functionalities of the 'VGAM' package with
         additional functions and datasets. At present, 'VGAMextra'
         comprises new family functions (ffs) to estimate several time
         series models by maximum likelihood using Fisher scoring, 
         unlike popular packages in CRAN relying on optim(), including
         ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non-
         seasonal), the Order-(p) VAR model, error correction models
         for cointegrated time series, and ARMA-structures with Student-t 
         errors. For independent data, new ffs to estimate the inverse-
         Weibull, the inverse-gamma, the generalized beta of the second
         kind and the general multivariate normal distributions are
         available. In addition, 'VGAMextra' incorporates new VGLM-links
         for the mean-function, and the quantile-function (as an alternative
         to ordinary quantile modelling) of several 1-parameter distributions,
         that are compatible with the class of VGLM/VGAM family functions.
         Currently, only fixed-effects models are implemented. All functions
         are subject to change; see the NEWS for further details on the
         latest changes.
Analyze the co-adaptation of codon usage between a virus and its host, calculate various codon usage bias measurements as: effective number of codons (ENc) Novembre (2002) <doi:10.1093/oxfordjournals.molbev.a004201>, codon adaptation index (CAI) Sharp  and  Li (1987) <doi:10.1093/nar/15.3.1281>, relative  codon deoptimization index (RCDI) Puigbò et al (2010) <doi:10.1186/1756-0500-3-87>, similarity index (SiD) Zhou et al (2013) <doi:10.1371/journal.pone.0077239>,  synonymous codon usage orderliness (SCUO) Wan et al (2004) <doi:10.1186/1471-2148-4-19> and, relative synonymous    codon usage (RSCU) Sharp et al (1986) <doi:10.1093/nar/14.13.5125>. Also, it provides a statistical dinucleotide over- and underrepresentation with three different models. Implement several methods for visualization of codon usage as ENc.GC3plot() and PR2.plot().
This package provides an implementation of Linear discriminant analysis and quadratic discriminant analysis that works fine in very high dimension (when there are many more variables than observations).
The "Vertical and Horizontal Inheritance Consistence Analysis" method is described in the following publication: "VHICA: a new method to discriminate between vertical and horizontal transposon transfer: application to the mariner family within Drosophila" by G. Wallau. et al. (2016) <DOI:10.1093/molbev/msv341>. The purpose of the method is to detect horizontal transfers of transposable elements, by contrasting the divergence of transposable element sequences with that of regular genes.
Provides direct access to linked names for the same entity across the world's major name authority files, including national and regional variations in language, character set, and spelling. For more information go to <https://viaf.org/>.
A shiny app for accurate estimation of vaccine induced immunogenicity 
    with bivariate linear modeling. Method is detailed in: Lhomme E, Hejblum BP, Lacabaratz C, 
    Wiedemann A, Lelièvre J-D, Levy Y, Thiebaut R & Richert L (2019). Submitted.
Converts Vietnam's provinces' names and ID
    across different formats. Handles diacritics and different spellings.
A set of tools to compute viewshed in 3D from Terrestrial Laser Scanner data and prepare the data prior to visibility estimation.
This package implements a fast regression algorithm for
        building linear model for large data as defined in the paper
        "VIF-Regression: A Fast Regression Algorithm for Large Data
        (2011), Journal of the American Statistical Association, Vol.
        106, No. 493: 232-247" by Dongyu Lin, Dean P. Foster, and Lyle
        H. Ungar.
Contains a function to support the following paper:
    Xiaoping Shi, Xiang-Sheng Wang, Dongwei Wei, Yuehua Wu (2016), <DOI:10.1007/s00180-015-0587-5>,
    A sequential multiple change-point detection procedure via VIF regression,
    Computational Statistics, 31(2): 671-691.
Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).
New tools for the visualization of missing and/or imputed values
    are introduced, which can be used for exploring the data and the structure of
    the missing and/or imputed values. Depending on this structure of the missing
    values, the corresponding methods may help to identify the mechanism generating
    the missing values and allows to explore the data including missing values.
    In addition, the quality of imputation can be visually explored using various
    univariate, bivariate, multiple and multivariate plot methods. A graphical user
    interface available in the separate package VIMGUI allows an easy handling of
    the implemented plot methods.
Calculate point estimates of and valid confidence intervals for
    nonparametric, algorithm-agnostic variable importance measures in high and low dimensions,
    using flexible estimators of the underlying regression functions. For more information
    about the methods, please see Williamson et al. (Biometrics, 2020),  Williamson et al. (arXiv, 2020+) <arXiv:2004.03683>, and Williamson and Feng (ICML, 2020) <arXiv:>.
Provides a programmatic interface in R for the US Department of 
    Transportation (DOT) National Highway Transportation Safety Administration 
    (NHTSA) vehicle identification number (VIN) API, located at 
    <https://vpic.nhtsa.dot.gov/api/>. 
    The API can decode up to 50 vehicle identification numbers in one call, and 
    provides manufacturer information about the vehicles, including make, model, 
    model year, and gross vehicle weight rating (GVWR).
Provides tools for the statistical analysis of vine copula models.
    The package includes tools for parameter estimation, model selection,
    simulation, goodness-of-fit tests, and visualization. Tools for estimation,
    selection and exploratory data analysis of bivariate copula models are also
    provided.
Implements D-vine quantile regression models with
  parametric or nonparametric pair-copulas. See 
  Kraus and Czado (2017) <doi:10.1016/j.csda.2016.12.009> and
  Schallhorn et al. (2017) <arXiv:1705.08310>.
Implementation of the vine graphical model for building
    high-dimensional probability distributions as a factorization of
    bivariate copulas and marginal density functions. This package
    provides S4 classes for vines (C-vines and D-vines) and methods
    for inference, goodness-of-fit tests, density/distribution
    function evaluation, and simulation.
A lattice violin-plot is overlayed with the arithmetic
        mean and standard deviation.
Produces violin plots with optional mean comparison with Tukey's honest significant difference and linear regression. This package aims to be a simple and quick visualization tool for comparing means and assessing trends of categorical factors.
A violin plot is a combination of a box plot and a kernel density plot. This package allows extensive customisation of violin plots.
viopoints draws one dimensional scatter plots with jitter
        using kernel density estimates in a similar way to violin
        plots.
A general framework for constructing variable importance plots from 
  various types of machine learning models in R. Aside from some standard model-
  specific variable importance measures, this package also provides model-
  agnostic approaches that can be applied to any supervised learning algorithm.
  These include 1) an efficient permutation-based variable importance measure, 
  2) variable importance based on Shapley values (Strumbelj and Kononenko, 
  2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based 
  approach described in Greenwell et al. (2018) <arXiv:1805.04755>. A 
  variance-based method for quantifying the relative strength of interaction 
  effects is also included (see the previous reference for details).
Generate a violin point plot, a combination of a violin/histogram
    plot and a scatter plot by offsetting points within a category based on their
    density using quasirandom noise.
Computation of volatility impulse response function for multivariate time series model using algorithm by Jin, Lin and Tamvakis (2012) <doi.org/10.1016/j.eneco.2012.03.003>.
Implementation of the 'viridis' - the default -, 'magma', 'plasma', 
    'inferno', and 'cividis' color maps for 'R'. 'viridis', 'magma', 'plasma', 
    and 'inferno' are ported from 'matplotlib' <http://matplotlib.org/>, a 
    popular plotting library for 'python'. 'cividis', was developed by Jamie R. 
    Nuñez and Sean M. Colby. These color maps are designed in such a way that 
    they will analytically be perfectly perceptually-uniform, both in regular 
    form and also when converted to black-and-white. They are also designed to 
    be perceived by readers with the most common form of color blindness (all 
    color maps in this package) and color vision deficiency ('cividis' only).
Implementation of the 'viridis' - the default -, 'magma', 'plasma', 
    'inferno', and 'cividis' color maps for 'R'. 'viridis', 'magma', 'plasma', 
    and 'inferno' are ported from 'matplotlib' <http://matplotlib.org/>, a 
    popular plotting library for 'python'. 'cividis', was developed by Jamie R. 
    Nuñez and Sean M. Colby. These color maps are designed in such a way that 
    they will analytically be perfectly perceptually-uniform, both in regular 
    form and also when converted to black-and-white. They are also designed to 
    be perceived by readers with the most common form of color blindness (all 
    color maps in this package) and color vision deficiency ('cividis' only). 
    This is the 'lite' version of the more complete 'viridis' package that can 
    be found at <https://cran.r-project.org/package=viridis>.
Experiments in which highly complex virome sequencing data is generated are difficult to visualize and unpack for a person without programming experience. 
    After processing of the raw sequencing data by a next generation sequencing (NGS) processing pipeline the usual output consists of contiguous sequences (contigs) in fasta format and an annotation file linking the 
    contigs to a reference sequence. The virome analysis browser app imports an annotation file and a corresponding fasta file containing the annotated contigs. 
    It facilitates browsing of annotations in multiple files and allows users to select and export specific annotated sequences from the associated fasta files. Various
    annotation quality thresholds can be set to filter contigs from the annotation files. Further inspection of selected contigs can be done in the form of automatic open
    reading frame (ORF) detection. Separate contigs and/or separate ORFs can be downloaded in nucleotide or amino acid format for further analysis.
The ability to generate virtual niches enables virtual ecology 
    experiments that have been advocated for evaluating species-environment 
    relationship models. Using multivariate normal distributions and matrix 
    algebra this package provides a unique computational framework to generate 
    integrated fundamental and realised niches, which is important given 
    differences in possible complexity of realised niche shapes that result 
    from biotic interactions Etherington and Omondiagbe (2019) <doi:10.21105/joss.01661>.
Tools to generate virtual environmental drivers with a given temporal autocorrelation, and to simulate pollen curves at annual resolution over millennial time-scales based on these drivers and virtual taxa with different life traits and niche features. It also provides the means to simulate quasi-realistic pollen-data conditions by applying simulated accumulation rates and given depth intervals between consecutive samples.
Provides a framework for generating virtual species distributions,
    a procedure increasingly used in ecology to improve species distribution
    models. This package integrates the existing methodological approaches with the
    objective of generating virtual species distributions with increased ecological
    realism.
Provides users with a simple and convenient
             mechanism to manage and query a 'Virtuoso' database using the 'DBI' (Data-Base Interface)
             compatible 'ODBC' (Open Database Connectivity) interface.
             'Virtuoso' is a high-performance "universal server," which can act
             as both a relational database, supporting standard Structured Query
             Language ('SQL') queries, while also supporting data following the
             Resource Description Framework ('RDF') model for Linked Data.
             'RDF' data can be queried using 'SPARQL' ('SPARQL' Protocol and 'RDF' Query Language)
             queries, a graph-based query that supports semantic reasoning.
             This allows users to leverage the performance of local or remote 'Virtuoso' servers using
             popular 'R' packages such as 'DBI' and 'dplyr', while also providing a 
             high-performance solution for working with large 'RDF' 'triplestores' from 'R.'
             The package also provides helper routines to install, launch, and manage
             a 'Virtuoso' server locally on 'Mac', 'Windows' and 'Linux' platforms using
             the standard interactive installers from the 'R' command-line.  By 
             automatically handling these setup steps, the package can make using 'Virtuoso'
             considerably faster and easier for a most users to deploy in a local
             environment. Managing the bulk import of triples
             from common serializations with a single intuitive command is another key
             feature of this package.  Bulk import performance can be tens to
             hundreds of times faster than the comparable imports using existing 'R' tools,
             including 'rdflib' and 'redland' packages.
Use VirusTotal, a Google service that analyzes files and URLs 
    for viruses, worms, trojans etc., provides category of the content hosted by a 
    domain from a variety of prominent services, provides passive DNS information,
    among other things. See <http://www.virustotal.com> for more information.
Functionality for creating phase portraits of functions in the
    complex number plane. Works with R base graphics, whose full 
    functionality is available. Parallel processing is used for optimum 
    performance.
Create preliminary exploratory data visualisations of an entire 
    dataset to identify problems or unexpected features using 'ggplot2'.
A graphical R package designed to visualize behavioral observations over time. Based on raw time data extracted from video recorded sessions of experimental observations, ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps for all participants in a single graph. Individuals and/or group behavior can easily be assessed. Supplementary features allow users to further inspect their data by adding summary statistics (mean, standard deviation, quantile or statistical test) and/or time constraints to assess the accuracy of the realized actions.
A Bayesian Phase I cancer vaccine
    trial design is implemented in this package. The design allows simultaneous evaluation
    of safety and immunogenicity outcomes in the context of vaccine studies.
    See Wang (2019) <DOI:10.1002/sim.8021> for the details of the Phase I cancer vaccine trial
    design.
Provides an R interface to the 'vis.js' JavaScript charting
    library. It allows an interactive visualization of networks.
Provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', 'glm', 'coxph', 'rlm', 'gam', 'locfit', 'lmer', 'randomForest', etc.)
A library for creating time based charts, like Gantt or timelines. Possible outputs 
  include 'ggplot' diagrams, 'Plotly' graphs, 'Highchart' widgets and 'data.frames'. Results can be
  used in the 'RStudio' viewer pane, in 'RMarkdown' documents or in 'Shiny' apps. In the 
  interactive outputs created by 'Plotly.js' and 'Highcharts.js', you can interact with the 
  plot using mouse hover or zoom.
Provides a visualization for characterizing subgroups defined by a decision tree structure. The visualization simplifies the ability to interpret individual pathways to subgroups; each sub-plot describes the distribution of observations within individual terminal nodes and percentile ranges for the associated inner nodes.
Visualize and compute percentiles/probabilities of normal, t, f, chi square 
    and binomial distributions.
A collection of tools for analyzing the field of vision. It provides a framework for development and use of innovative methods for visualization, statistical analysis, and clinical interpretation of visual-field loss and its change over time. It is intended to be a tool for collaborative research.
Graphs the pdf or pmf and highlights what area or probability is
    present in user defined locations. Visualize is able to provide lower tail,
    bounded, upper tail, and two tail calculations. Supports strict and equal
    to inequalities.  Also provided on the graph is the mean and variance of
    the distribution.
Visualize the trends and historical downloads from packages in the 'CRAN' repository. Data is obtained by using the 'API' to query the database from the 'RStudio' 'CRAN' mirror.
Visual 2D point and contour plots for binary classification modeling under algorithms such as glm(), randomForest(), gbm(), nnet() and svm(), presented over two dimensions generated by FAMD and MCA methods. Package 'FactoMineR' for multivariate reduction functions and package 'MBA' for interpolation functions are used. The package can be used to visualize the discriminant power of input variables and algorithmic modeling, explore outliers, compare algorithm behaviour, etc. It has been created initially for teaching purposes, but it has also many practical uses.
Visualizes vowel variation in f0, F1, F2, F3 and duration.
Implements the novel testing approach by Janitza et al.(2015)
    <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4>
    for the permutation variable importance measure in a random forest and the
    PIMP-algorithm by Altmann et al.(2010) <doi:10.1093/bioinformatics/btq134>.
    Janitza et al.(2015) <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4>
    do not use the "standard" permutation variable
    importance but the cross-validated permutation variable
    importance for the novel test approach. The cross-validated
    permutation variable importance is not based on the out-of-bag
    observations but uses a similar strategy which is inspired by
    the cross-validation procedure. The novel test approach can be
    applied for classification trees as well as for regression
    trees. However, the use of the novel testing approach has not
    been tested for regression trees so far, so this routine is
    meant for the expert user only and its current state is rather
    experimental.
Provides templates and functions to simplify the production and maintenance of curriculum vitae.
Provides fitting routines for four versions of the
        Vitality family of mortality models.
Provides an easy to calculate local variable importance measure based on Ceteris Paribus profile 
  and global variable importance measure based on Partial Dependence Profiles.
Interactive visualization for Bayesian prior and posterior distributions. 
             When both distributions are provided the animation shows a transition from 
             prior to posterior. Finally, the animation splits the distribution using the provided 
             'breaks' into bars that show the probability for each region. 
             If no 'breaks' are providers it will use zero by default.
Provides an interface to the VK API <https://vk.com/dev/methods>.
      VK <https://vk.com/> is the largest European online social networking
      service, based in Russia.
Contains functions to set up risk-adjusted quality control charts in health care. For the variable life adjusted display (VLAD) proposed by Lovegrove et al. (1997) <doi:10.1016/S0140-6736(97)06507-0> signaling rules derived in Wittenberg et al. (2018) <doi:10.1002/sim.7647> are implemented. Additionally, for the risk-adjusted cumulative sum chart based on log-likelihood ratio statistic introduced by Steiner et al. (2000) <doi:10.1093/biostatistics/1.4.441> average run length and control limits can be computed with fast and accurate Markov chain approximations developed in Knoth et al. (2019) <doi:10.1002/sim.8104>.
Assists in producing a plot that more effectively expresses changes over time for two different types (long format and wide format) using a consistent calling scheme for longitudinal data. It provides the ability to projection supplementary information (supplementary objects and variables) that can often occur in longitudinal data to graphs, as well as provides a new interactive implementation to perform the additional interpretation, so it is also useful for longitudinal data visuals analysis (see <http://lib.pusan.ac.kr/resource/e-article/?app=eds&mod=detail&record_id=edsker.000004649097&db_id=edsker> for more information).
Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences.  The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs.  The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another.  Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures.
Functions, Classes & Methods for estimation, prediction, and
  simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.
A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2019) <arXiv:1912.10829> when referring to this package in publications.