Basic functions for dealing with wav files and sound samples.
Provides an all-in-one solution for automatic classification of 
    sound events using convolutional neural networks (CNN). The main purpose 
    is to provide a sound classification workflow, from annotating sound events
    in recordings to training and automating model usage in real-life
    situations. Using the package requires a pre-compiled collection of 
    recordings with sound events of interest and it can be employed for: 
    1) Annotation: create a database of annotated recordings, 
    2) Training: prepare train data from annotated recordings and fit CNN models, 
    3) Classification: automate the use of the fitted model for classifying 
    new recordings. By using automatic feature selection and a user-friendly GUI
    for managing data and training/deploying models, this package is intended 
    to be used by a broad audience as it does not require specific expertise in 
    statistics, programming or sound analysis. Please refer to the vignette for
    further information.
    Gibb, R., et al. (2019) <doi:10.1111/2041-210X.13101>
    Mac Aodha, O., et al. (2018) <doi:10.1371/journal.pcbi.1005995>
    Stowell, D., et al. (2019) <doi:10.1111/2041-210X.13103>
    LeCun, Y., et al. (2012) <doi:10.1007/978-3-642-35289-8_3>.
Functions to calculate indices for soundscape ecology and other ecology research that uses audio recordings.
The SoundexBR package provides an algorithm for decoding names
    into phonetic codes, as pronounced in Portuguese. The goal is for
    homophones to be encoded to the same representation so that they can be
    matched despite minor differences in spelling. The algorithm mainly encodes
    consonants; a vowel will not be encoded unless it is the first letter. The
    soundex code resultant consists of a four digits long string composed by
    one letter followed by three numerical digits: the letter is the first
    letter of the name, and the digits encode the remaining consonants.
Performs parametric synthesis of sounds with harmonic and noise 
    components such as animal vocalizations or human voice. Also offers tools 
    for audio manipulation and acoustic analysis, including pitch tracking, 
    spectral analysis, audio segmentation, pitch and formant shifting, etc. 
    Includes four interactive web apps for synthesizing and annotating audio, 
    manually correcting pitch contours, and measuring formant frequencies. 
    Reference: Anikin (2019) <doi:10.3758/s13428-018-1095-7>.
Implement a promising, and yet little explored protocol for bioacoustical analysis, the eigensound method by MacLeod, Krieger and Jones (2013) <doi:10.4404/hystrix-24.1-6299>. Eigensound is a multidisciplinary method focused on the direct comparison between stereotyped sounds from different species. 'SoundShape', in turn, provide the tools required for anyone to go from sound waves to Principal Components Analysis, using tools extracted from traditional bioacoustics (i.e. 'tuneR' and 'seewave' packages), geometric morphometrics (i.e. 'geomorph' package) and multivariate analysis (e.g. 'stats' package). For more information, please see Rocha and Romano (2021) and check 'SoundShape' repository on GitHub for news and updates <https://github.com/p-rocha/SoundShape>.
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>.
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.
Provides a function that behaves nearly as base::source() but implements a caching mechanism on disk, project based.
    It allows to quasi source() R scripts that gather data but can fail or consume to much time to respond even if nothing new is expected.
    It comes with tools to check and execute on demand or when cache is invalid the script.
Ratings, votes, swear words and sentiments are analysed for
    the show 'SouthPark' through a 'Shiny' application after web scraping 
    from 'IMDB' and the website <https://southpark.fandom.com/wiki/South_Park_Archives>.
A set of tools for state-dependent 
  empirical analysis through both VAR- and local projection-based 
  state-dependent forecasts, impulse response functions, 
  historical decompositions, and forecast error variance decompositions.
Efficient procedures for fitting and cross-validating the structurally-regularized time-dependent Cox models.
Genomic and multi-environmental soybean data. Soybean Nested
    Association Mapping (SoyNAM) project dataset funded by the United Soybean Board
    (USB). BLUP function formats data for genome-wide prediction and association analysis.
Data sets used by 'Krause et al. (2022)'  <doi:10.1101/2022.04.11.487885>. It comprises phenotypic records obtained from the USDA Northern Region Uniform Soybean Tests from 1989 to 2019 for maturity groups II and III. In addition, soil and weather variables are provided for the 591 observed environments (combination of locations and years).
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. From this
  version, 'rgdal', 'maptools', and 'rgeos' are no longer used at all,
  see <https://r-spatial.org/r/2023/05/15/evolution4.html> for details.
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.
Fits single-species (univariate) and multi-species (multivariate) non-spatial and spatial abundance models in a Bayesian framework using Markov Chain Monte Carlo (MCMC). Spatial models are fit using Nearest Neighbor Gaussian Processes (NNGPs). Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Fits single-species and multi-species spatial and non-spatial versions of generalized linear mixed models (Gaussian, Poisson, Negative Binomial), N-mixture models (Royle 2004 <doi:10.1111/j.0006-341X.2004.00142.x>) and hierarchical distance sampling models (Royle, Dawson, Bates (2004) <doi:10.1890/03-3127>). Multi-species spatial models are fit using a spatial factor modeling approach with NNGPs for computational efficiency.
Provides tools for analyzing spatial cell-cell interactions based on ligand-receptor pairs, including functions for local, regional, and global analysis using spatial transcriptomics data. Integrates with databases like 'CellChat' <http://www.cellchat.org/>, 'CellPhoneDB' <https://www.cellphonedb.org/>, 'Cellinker' <https://www.rna-society.org/cellinker/index.html>, 'ICELLNET' <https://github.com/soumelis-lab/ICELLNET>, and 'ConnectomeDB' <https://humanconnectome.org/software/connectomedb/> to identify ligand-receptor pairs, visualize interactions through heatmaps, chord diagrams, and infer interactions on different spatial scales.
Generates random and quasi-random space-filling sequences. Supports the following sequences: 'Halton', 'Sobol', 'Owen'-scrambled 'Sobol',  'Owen'-scrambled 'Sobol' with errors distributed as blue noise, progressive jittered, progressive multi-jittered ('PMJ'), 'PMJ' with blue noise, 'PMJ02', and 'PMJ02' with blue noise. Includes a 'C++' 'API'. Methods derived from "Constructing Sobol sequences with better two-dimensional projections" (2012) <doi:10.1137/070709359> S. Joe and F. Y. Kuo, "Progressive Multi-Jittered Sample Sequences" (2018) <https://graphics.pixar.com/library/ProgressiveMultiJitteredSampling/paper.pdf> Christensen, P., Kensler, A. and Kilpatrick, C., and "A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space" (2019) E. Heitz, B. Laurent, O. Victor, C. David and I. Jean-Claude, <doi:10.1145/3306307.3328191>.
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, plus the forward direction of the 'CIECAM02' color appearance model.
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.
Simultaneous/joint diagonalization of local autocovariance matrices to estimate spatio-temporally uncorrelated random fields.
A spatial covariate-augmented overdispersed Poisson factor model is proposed to perform efficient latent representation learning method for high-dimensional large-scale spatial count data with additional covariates.
An R wrapper to the 'Python' 'spaCy' 'NLP' library,
    from <https://spacy.io>.
Provides easy to use functions to create all-sky grid plots of widely used astronomical coordinate systems (equatorial, ecliptic, galactic) and scatter plots of data on any of these systems including on-the-fly system conversion. It supports any type of spherical projection to the plane defined by the 'mapproj' package.
A set of RStudio addins that are designed to be used in
             combination with user-defined RStudio keyboard shortcuts. These
             addins either:
             1) insert text at a cursor position (e.g. insert
             operators %>%, <<-, %$%, etc.),
             2) replace symbols in selected pieces of text (e.g., convert
             backslashes to forward slashes which results in stings like
             "c:\data\" converted into "c:/data/") or
             3) enclose text with special symbols (e.g., converts "bold" into
             "**bold**") which is convenient for editing R Markdown files.
Estimation of various biodiversity indices and related (dis)similarity measures based on individual-based (abundance) data or sampling-unit-based (incidence) data taken from one or multiple communities/assemblages.
Metapackage for implementing a variety of event-based models, with
    a focus on spatially explicit models. These include raster-based,
    event-based, and agent-based models. The core simulation components
    (provided by 'SpaDES.core') are built upon a discrete event simulation (DES;
    see Matloff (2011) ch 7.8.3 <https://nostarch.com/artofr.htm>)
    framework that facilitates modularity, and easily enables the user to
    include additional functionality by running user-built simulation modules
    (see also 'SpaDES.tools'). Included are numerous tools to visualize rasters
    and other maps (via 'quickPlot'), and caching methods for reproducible
    simulations (via 'reproducible'). Tools for running simulation experiments are
    provided by 'SpaDES.experiment'. Additional functionality is provided by
    the 'SpaDES.addins' and 'SpaDES.shiny' packages.
Provides the core framework for a discrete event system to 
    implement a complete data-to-decisions, reproducible workflow.
    The core components facilitate the development of modular pieces, 
    and 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 within-project dependencies. 
    The suggested package 'NLMR' can be installed from the repository 
    (<https://PredictiveEcology.r-universe.dev>).
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.  The suggested package 'NLMR' can
    be installed from the following repository:
    (<https://PredictiveEcology.r-universe.dev>).
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.
Simple utilities to design and generate density functions on bounded regions in space and space-time, and simulate independent, identically distributed data therefrom. See Davies & Lawson (2019) <doi:10.1080/00949655.2019.1575066> for example.
Set of functions for sparse matrix algebra.
    Differences with other sparse matrix packages are:
    (1) we only support (essentially) one sparse matrix format,
    (2) based on transparent and simple structure(s),
    (3) tailored for MCMC calculations within G(M)RF.
    (4) and it is fast and scalable (with the extension package spam64).
    Documentation about 'spam' is provided by vignettes included in this package, see also Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>; see 'citation("spam")' for details.
Provides the Fortran code of the R package 'spam'
    with 64-bit integers. Loading this package together with the R package
    spam enables the sparse matrix class spam to handle huge sparse matrices
    with more than 2^31-1 non-zero elements.
    Documentation is provided in Gerber, Moesinger and Furrer (2017) <doi:10.1016/j.cageo.2016.11.015>.
Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted  likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.
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.
Gain seamless access to origin-destination (OD) data from the
    Spanish Ministry of Transport, hosted at
    <https://www.transportes.gob.es/ministerio/proyectos-singulares/estudios-de-movilidad-con-big-data/opendata-movilidad>.
    This package simplifies the management of these large datasets by
    providing tools to download zone boundaries, handle associated
    origin-destination data, and process it efficiently with the 'duckdb'
    database interface.  Local caching minimizes repeated downloads,
    streamlining workflows for researchers and analysts. Extensive
    documentation is available at
    <https://ropenspain.github.io/spanishoddata/index.html>, offering
    guides on creating static and dynamic mobility flow visualizations and
    transforming large datasets into analysis-ready formats.
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. The package includes methods for spectral fitting: Wilson
    (2021) <DOI:10.1002/mrm.28385>, Wilson (2025) <DOI:10.1002/mrm.30462> and
    spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.
Provides a comprehensive suite of portfolio spanning tests for asset 
             pricing, such as Huberman and Kandel (1987) <doi:10.1111/j.1540-6261.1987.tb03917.x>, 
             Gibbons et al. (1989) <doi:10.2307/1913625>, Kempf and Memmel (2006) <doi:10.1007/BF03396737>, 
             Pesaran and Yamagata (2024) <doi:10.1093/jjfinec/nbad002>, and Gungor and 
             Luger (2016) <doi:10.1080/07350015.2015.1019510>.
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.
A flexible framework combining 
  variable screening and random projection techniques for fitting ensembles of 
  predictive generalized linear models to high-dimensional data.
  Designed for extensibility, the package implements
  key techniques as S3 classes with user-friendly constructors,
  enabling easy integration and development of new procedures for 
  high-dimensional applications. For more details see 
  Parzer et al (2024a) <doi:10.48550/arXiv.2312.00130> and
  Parzer et al (2024b) <doi:10.48550/arXiv.2410.00971>.
Read in 'SAS' Data ('.sas7bdat' Files) into 'Apache Spark' from R. 'Apache Spark' is an open source cluster computing framework available at <http://spark.apache.org>. This R package uses the 'spark-sas7bdat' 'Spark' package (<https://spark-packages.org/package/saurfang/spark-sas7bdat>) to import and process 'SAS' data in parallel using 'Spark'. Hereby allowing to execute 'dplyr' statements in parallel on top of 'SAS' data.
Load Avro Files into 'Apache Spark' using 'sparklyr'. This
    allows to read files from 'Apache Avro' <https://avro.apache.org/>.
A 'sparklyr' extension package providing an integration with Google 'BigQuery'.
  It supports direct import/export where records are directly streamed from/to 'BigQuery'.
  In addition, data may be imported/exported via intermediate data extracts on Google 'Cloud Storage'.
'Hail' is an open-source, general-purpose, 'python' based data analysis
    tool with additional data types and methods for working with genomic data, see
    <https://hail.is/>. 'Hail' is built to scale and has first-class support for 
    multi-dimensional structured data, like the genomic data in a genome-wide 
    association study (GWAS). 'Hail' is exposed as a 'python' library, using primitives
    for distributed queries and linear algebra implemented in 'scala', 'spark', and 
    increasingly 'C++'. The 'sparkhail' is an R extension using 'sparklyr' package. 
    The idea is to help R users to use 'hail' functionalities with the well-know
    'tidyverse' syntax, see <https://www.tidyverse.org/>.
Include interactive sparkline charts
    <http://omnipotent.net/jquery.sparkline> in 
    all R contexts with the convenience of 'htmlwidgets'.
R interface to Apache Spark, a fast and general
    engine for big data processing, see <https://spark.apache.org/>. This
    package supports connecting to local and remote Apache Spark clusters,
    provides a 'dplyr' compatible back-end, and provides an interface to
    Spark's built-in machine learning algorithms.
This sparklyr extension makes 'Flint' time series
    library functionalities (<https://github.com/twosigma/flint>) easily
    accessible through R.
A 'sparklyr' extension adding the capability to work easily with nested data.
Generate syntax for use with the sparklines package for
        LaTeX.
A 'sparklyr' extension that enables reading and writing 'TensorFlow'
  TFRecord files via 'Apache Spark'.
Load WARC (Web ARChive) files into Apache Spark using 'sparklyr'. This
    allows to read files from the Common Crawl project <http://commoncrawl.org/>.
A 'sparklyr' <https://spark.posit.co/> extension that provides an R
  interface for 'XGBoost' <https://github.com/dmlc/xgboost> on 'Apache Spark'. 
  'XGBoost' is an optimized distributed gradient boosting library.
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>.
The SPARRA risk score (Scottish Patients At Risk of admission and Re-Admission) estimates yearly risk of emergency hospital admission using electronic health records on a monthly basis for most of the Scottish population. This package implements a suite of functions used to analyse the behaviour and performance of the score, focusing particularly on differential performance over demographically-defined groups. It includes useful utility functions to plot receiver-operator-characteristic, precision-recall and calibration curves, draw stock human figures, estimate counterfactual quantities without the need to re-compute risk scores, to simulate a semi-realistic dataset. Our manuscript can be found at: <doi:10.1371/journal.pdig.0000675>.
Calculate the statistical power to detect clusters using kernel-based 
        spatial relative risk functions that are estimated using the 'sparr' package.
        Details about the 'sparr' package methods can be found in the tutorial: Davies
        et al. (2018) <doi:10.1002/sim.7577>. Details about kernel density estimation 
        can be found in J. F. Bithell (1990) <doi:10.1002/sim.4780090616>. More 
        information about relative risk functions using kernel density estimation can 
        be found in J. F. Bithell (1991) <doi:10.1002/sim.4780101112>.
The 'HJ-Biplot' is a multivariate method that represents high-dimensional data in a low-dimensional subspace, capturing most of the information’s variability in just a few dimensions. This package implements three new regularized versions of the HJ-Biplot: Ridge, LASSO, and Elastic Net. These versions introduce restrictions that shrink or zero-out variable weights to improve interpretability based on regularization theory. All methods provide graphical representations using 'ggplot2'.
'C++' classes for sparse matrix methods including implementation of sparse LDL decomposition of symmetric matrices and solvers described by Timothy A. Davis (2016) 
  <https://fossies.org/linux/SuiteSparse/LDL/Doc/ldl_userguide.pdf>. Provides a set of C++ classes for basic sparse
  matrix specification and linear algebra, and a class to implement sparse LDL decomposition and solvers. See <https://github.com/samuel-watson/SparseChol>
  for details.
A sparse covariance estimator based on different thresholding operators.
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.
Implementation of various estimation methods for dynamic factor models (DFMs) including principal components analysis (PCA) Stock and Watson (2002) <doi:10.1198/016214502388618960>, 2Stage Giannone et al. (2008) <doi:10.1016/j.jmoneco.2008.05.010>, expectation-maximisation (EM) Banbura and Modugno (2014) <doi:10.1002/jae.2306>, and the novel EM-sparse approach for sparse DFMs Mosley et al. (2023) <arXiv:2303.11892>. Options to use classic multivariate Kalman filter and smoother (KFS) equations from Shumway and Stoffer (1982) <doi:10.1111/j.1467-9892.1982.tb00349.x> or fast univariate KFS equations from Koopman and Durbin (2000) <doi:10.1111/1467-9892.00186>, and options for independent and identically distributed (IID) white noise or auto-regressive (AR(1)) idiosyncratic errors. Algorithms coded in 'C++' and linked to R via 'RcppArmadillo'.
A collection of sparse and regularized discriminant analysis
    methods intended for small-sample, high-dimensional data sets. The package
    features the High-Dimensional Regularized Discriminant Analysis classifier
    from Ramey et al. (2017) <arXiv:1602.01182>. Other classifiers include
    those from Dudoit et al. (2002) <doi:10.1198/016214502753479248>, Pang et
    al. (2009) <doi:10.1111/j.1541-0420.2009.01200.x>, and Tong et al. (2012)
    <doi:10.1093/bioinformatics/btr690>.
Computation of sparse eigenvectors of a matrix (aka sparse PCA)
    with running time 2-3 orders of magnitude lower than existing methods and
    better final performance in terms of recovery of sparsity pattern and 
    estimation of numerical values. Can handle covariance matrices as well as 
    data matrices with real or complex-valued entries. Different levels of 
    sparsity can be specified for each individual ordered eigenvector and the 
    method is robust in parameter selection. See vignette for a detailed 
    documentation and comparison, with several illustrative examples. 
    The package is based on the paper:
    K. Benidis, Y. Sun, P. Babu, and D. P. Palomar (2016). "Orthogonal Sparse PCA 
    and Covariance Estimation via Procrustes Reformulation," IEEE Transactions on 
    Signal Processing <doi:10.1109/TSP.2016.2605073>.
Estimation of functional linear mixed models for irregularly or
    sparsely sampled data based on functional principal component analysis.
Provides a general framework for performing sparse functional
    clustering as originally described in Floriello and Vitelli (2017)
    <doi:10.1016/j.jmva.2016.10.008>, with the possibility of jointly handling
    data misalignment (see Vitelli, 2019, <doi:10.48550/arXiv.1912.00687>).
Implements sparse generalized factor models (sparseGFM) for dimension reduction
    and variable selection in high-dimensional data with automatic adaptation to weak factor
    scenarios. The package supports multiple data types (continuous, count, binary) through
    generalized linear model frameworks and handles missing values automatically. It provides
    12 different penalty functions including Least Absolute Shrinkage and Selection Operator (Lasso),
    adaptive Lasso, Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP), group Lasso,
    and their adaptive versions for inducing row-wise sparsity in factor loadings. Key features
    include cross-validation for regularization parameter selection using Sparsity Information
    Criterion (SIC), automatic determination of the number of factors via multiple information
    criteria, and specialized algorithms for row-sparse loading structures. The methodology
    employs alternating minimization with Singular Value Decomposition (SVD)-based identifiability
    constraints and is particularly effective for high-dimensional applications in genomics, economics,
    and social sciences where interpretable sparse dimension reduction is crucial.
    For penalty functions, see Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>,
    Fan and Li (2001) <doi:10.1198/016214501753382273>, and Zhang (2010) <doi:10.1214/09-AOS729>.
Efficient implementation of sparse group lasso with optional
    bound constraints on the coefficients; see
    <doi:10.18637/jss.v110.i06>.  It supports the use of a sparse design
    matrix as well as returning coefficient estimates in a sparse matrix.
    Furthermore, it correctly calculates the degrees of freedom to allow
    for information criteria rather than cross-validation with very large
    data. Finally, the interface to compiled code avoids unnecessary
    copies and allows for the use of long integers.
SparseGrid is a package to create sparse grids for numerical integration, based on code from www.sparse-grids.de
Estimates Hessian of a scalar-valued function, and returns it
    in a sparse Matrix format. The sparsity pattern must be known in advance. The
    algorithm is especially efficient for hierarchical models with a large number of
    heterogeneous units.  See Braun, M. (2017) <doi:10.18637/jss.v082.i10>.
Provides an implementation of the Sparse ICA method in Wang et al. (2024) <doi:10.1080/01621459.2024.2370593> for estimating sparse independent source components of cortical surface functional MRI data, by addressing a non-smooth, non-convex optimization problem through the relax-and-split framework. This method effectively balances statistical independence and sparsity while maintaining computational efficiency.
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.
Estimates sparse regression models (i.e., with few non-zero coefficients) in high-dimensional multi-task learning and transfer learning settings, as proposed by Rauschenberger et al. (2025) <https://orbilu.uni.lu/handle/10993/63425>.
Models the nonnegative entries of a rectangular adjacency matrix using a sparse latent position model, as illustrated in Rastelli, R. (2018) "The Sparse Latent Position Model for nonnegative weighted networks" <arXiv:1808.09262>.
Provides an S4 class for representing and
    interacting with sparse plus rank matrices. At the moment the
    implementation is quite spare, but the plan is eventually subclass
    Matrix objects.
Use RcppEigen to fit least trimmed squares
    regression models with an L1 penalty in order to obtain
    sparse models.
Some basic linear algebra functionality for sparse matrices is
  provided:  including Cholesky decomposition and backsolving as well as
  standard R subsetting and Kronecker products.
The 'sparseMatEst' package provides functions for estimating sparse 
  covariance and precision matrices with error control. A false positive
  rate is fixed corresponding to the probability of falsely including a matrix 
  entry in the support of the estimator.  It uses the binary search
  method outlined in Kashlak and Kong (2019) <arXiv:1705.02679> and in 
  Kashlak (2019) <arXiv:1903.10988>.
Implements the algorithm described in
    Barron, M., and Li, J. (Not yet published). This algorithm clusters
    samples from multiple ordered populations, links the clusters across the
    conditions and identifies marker genes for these changes. The package was
    designed for scRNA-Seq data but is also applicable to many other data
    types, just replace cells with samples and genes with variables. The
    package also contains functions for estimating the parameters for SparseMDC
    as outlined in the paper. We recommend that users further select their
    marker genes using the magnitude of the cluster centers.
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).
An implementation of ranked sparsity methods, including
    penalized regression methods such as the sparsity-ranked lasso, its
    non-convex alternatives, and elastic net, as well as the sparsity-ranked
    Bayesian Information Criterion. As described in Peterson and
    Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked
    sparsity is a philosophy with methods primarily useful for
    variable selection in the presence of prior informational
    asymmetry, which occurs in the context of trying to perform variable
    selection in the presence of interactions and/or polynomials. Ultimately,
    this package attempts to facilitate dealing with cumbersome interactions
    and polynomials while not avoiding them entirely. Typically, models
    selected under ranked sparsity principles will also be more transparent,
    having fewer falsely selected interactions and polynomials than other
    methods.
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.
Provides elastic net penalized maximum likelihood estimator for structural equation models (SEM). The package implements `lasso` and `elastic net` (l1/l2) penalized SEM and estimates the model parameters with an efficient block coordinate ascent algorithm that maximizes the penalized likelihood of the SEM.  Hyperparameters are inferred from cross-validation (CV).  A Stability Selection (STS) function is also available to provide accurate causal effect selection. The software achieves high accuracy performance through a `Network Generative Pre-trained Transformer` (Network GPT) Framework with two steps: 1) pre-trains the model to generate a complete (fully connected) graph; and 2) uses the complete graph as the initial state to fit the `elastic net` penalized SEM.
Implements the SparseStep model for solving regression
    problems with a sparsity constraint on the parameters. The SparseStep
    regression model was proposed in Van den Burg, Groenen, and Alfons (2017)
    <arXiv:1701.06967>. In the model, a regularization term is added to the
    regression problem which approximates the counting norm of the parameters.
    By iteratively improving the approximation a sparse solution to the
    regression problem can be obtained.  In this package both the standard
    SparseStep algorithm is implemented as well as a path algorithm which uses
    golden section search to determine solutions with different values for the
    regularization parameter.
Functions for fitting, forecasting, and early detection of outbreaks in
    sparse surveillance count time series. Supports negative binomial (NB),
    self-exciting NB, generalise autoregressive moving average (GARMA) NB , zero-inflated NB (ZINB), self-exciting ZINB, generalise autoregressive moving average ZINB, and hurdle formulations. Climatic and environmental covariates
    can be included in the regression component and/or the zero-modified components.
    Includes outbreak-detection algorithms for NB, ZINB, and hurdle models, with
    utilities for prediction and diagnostics.
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.
Offers a fast algorithm for fitting solution paths of sparse SVM models with lasso or elastic-net regularization. Reference: Congrui Yi and Jian Huang (2017) <doi:10.1080/10618600.2016.1256816>.
Implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. Features include a novel prioritized updating scheme, which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large, more relevant, coefficients. Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs. By default, the heavier-tailed Laplace density is used. Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo (JASA 2020) and Kolyan Ray, Botond Szabo, and Gabriel Clara (NeurIPS 2020).
Provides sparse vectors powered by ALTREP (Alternative
    Representations for R Objects) that behave like regular vectors, and
    can thus be used in data frames. Also provides tools to convert
    between sparse matrices and data frames with sparse columns and
    functions to interact with sparse vectors.
The sparse vector field consensus 
		(SparseVFC) algorithm (Ma et al., 2013 <doi:10.1016/j.patcog.2013.05.017>) for robust vector 
		field learning. Largely translated from the Matlab functions in <https://github.com/jiayi-ma/VFC>.
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 <doi:10.18637/jss.v111.i02>.
Statistical pattern recognition and dating using archaeological artefacts assemblages.
    Package of statistical tools for archaeology.
    hclustcompro()/perioclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021, ISBN:978-3-030-60103-4).
    mapclust(): Bellanger Lise, Coulon Arthur, Husi Philippe (2021) <doi:10.1016/j.jas.2021.105431>.
    seriograph(): Desachy Bruno (2004) <doi:10.3406/pica.2004.2396>.
    cerardat(): Bellanger Lise, Husi Philippe (2012) <doi:10.1016/j.jas.2011.06.031>.
This is an implementation of the algorithm described in Section 3 of Hosszejni and Frühwirth-Schnatter (2022) <doi:10.48550/arXiv.2211.00671>. The algorithm is used to verify that the counting rule CR(r,1) holds for the sparsity pattern of the transpose of a factor loading matrix. As detailed in Section 2 of the same paper, if CR(r,1) holds, then the idiosyncratic variances are generically identified. If CR(r,1) does not hold, then we do not know whether the idiosyncratic variances are identified or not.
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) <doi:10.2307/2332748>.
    When s<t, SPAS implements the methods described in
    Plante, Rivest, and Tremblay (1988) <doi:10.2307/2533994>.
    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 of the same name.
Sample size estimation and blinded sample size reestimation in Adaptive Study Design.
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.
Modeling spatial dependencies in dependent variables, extending traditional spatial regression approaches. It allows for the joint modeling of both the mean and the variance of the dependent variable, incorporating semiparametric effects in both models. Based on generalized additive models (GAM), the package enables the inclusion of non-parametric terms while maintaining the classical theoretical framework of spatial regression. Additionally, it implements the Generalized Spatial Autoregression (GSAR) model, which extends classical methods like logistic Spatial Autoregresive Models (SAR), probit Spatial Autoregresive Models (SAR), and Poisson Spatial Autoregresive Models (SAR), offering greater flexibility in modeling spatial dependencies and significantly improving computational efficiency and the statistical properties of the estimators. Related work includes: a) J.D. Toloza-Delgado, Melo O.O., Cruz N.A. (2024). "Joint spatial modeling of mean and non-homogeneous variance combining semiparametric SAR and GAMLSS models for hedonic prices". <doi:10.1016/j.spasta.2024.100864>. b) Cruz, N. A., Toloza-Delgado, J. D., Melo, O. O. (2024). "Generalized spatial autoregressive model". <doi:10.48550/arXiv.2412.00945>.
The heterogeneity of spatial data presenting a finite number of categories can be measured via computation of spatial entropy. Functions are available for the computation of the main entropy and spatial entropy measures in the literature. They include the traditional version of Shannon's entropy (Shannon, 1948 <doi:10.1002/j.1538-7305.1948.tb01338.x>), Batty's spatial entropy (Batty, 1974 <doi:10.1111/j.1538-4632.1974.tb01014.x>), O'Neill's entropy (O'Neill et al., 1998 <doi:10.1007/BF00162741>), Li and Reynolds' contagion index (Li and Reynolds, 1993 <doi:10.1007/BF00125347>), Karlstrom and Ceccato's entropy (Karlstrom and Ceccato, 2002 <https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-61351>), Leibovici's entropy (Leibovici, 2009 <doi:10.1007/978-3-642-03832-7_24>), Parresol and Edwards' entropy (Parresol and Edwards, 2014 <doi:10.3390/e16041842>) and Altieri's entropy (Altieri et al., 2018, <doi:10.1007/s10651-017-0383-1>). Full references for all measures can be found under the topic 'SpatEntropy'. The package is able to work with lattice and point data. The updated version works with the updated 'spatstat' package (>= 3.0-2).
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>.
Performance of functional kriging, cokriging, optimal sampling and simulation for spatial prediction of functional data. The framework of spatial prediction, optimal sampling and simulation are extended from scalar to functional data. 'SpatFD' is based on the Karhunen-Loève expansion that allows to represent the observed functions in terms of its empirical functional principal components. Based on this approach, the functional auto-covariances and cross-covariances required for  spatial functional predictions and optimal sampling, are completely determined by the sum of the spatial auto-covariances and cross-covariances of the respective score components. The package provides new classes of data and functions for modeling spatial dependence structure among curves. The spatial prediction of curves at unsampled locations can be carried out using two types of predictors, and both of them report, the respective variances of the prediction error. In addition, there is a function for the determination of spatial locations sampling configuration that ensures minimum variance of spatial functional prediction. There are also two functions for plotting predicted curves at each location and mapping the surface at each time point, respectively. References Bohorquez, M., Giraldo, R., and Mateu, J. (2016) <doi:10.1007/s10260-015-0340-9>, Bohorquez, M., Giraldo, R., and Mateu, J. (2016) <doi:10.1007/s00477-016-1266-y>, Bohorquez M., Giraldo R. and Mateu J. (2021) <doi:10.1002/9781119387916>.
Provides a collection of functions for preparing data and fitting Bayesian count spatial regression models, with a specific focus on the Gamma-Count (GC) model. The GC model is well-suited for modeling dispersed count data, including under-dispersed or over-dispersed counts, or counts with equivalent dispersion, using Integrated Nested Laplace Approximations (INLA). The package includes functions for generating data from the GC model, as well as spatially correlated versions of the model. See Nadifar, Baghishani, Fallah (2023) <doi:10.1007/s13253-023-00550-5>.
The implementation to perform the geometric spatial point analysis developed in Hernández & Solís (2022) <doi:10.1007/s00180-022-01244-1>. It estimates the geometric goodness-of-fit index for a set of variables against a response one based on the 'sf' package. The package has methods to print and plot the results.
Graphs (or networks) and graph component
        calculations for spatial locations in 1D, 2D, 3D etc.
The developed function is designed for the generation of spatial grids based on user-specified longitude and latitude coordinates. The function first validates the input longitude and latitude values,
              ensuring they fall within the appropriate geographic ranges. It then creates a polygon from the coordinates and determines the appropriate Universal Transverse Mercator zone based on the provided 
              hemisphere and longitude values. Subsequently, transforming the input Shapefile to the Universal Transverse Mercator projection when necessary. Finally, a spatial grid is generated with the specified interval and saved as a Shapefile. 
              For method details see, Brus,D.J.(2022).<DOI:10.1201/9781003258940>. The function takes into account crucial parameters such as the hemisphere (north or south), desired grid interval, and the output Shapefile path.
              The developed function is an efficient tool, simplifying the process of empty spatial grid generation for applications such as, geo-statistical analysis, digital soil mapping product generation, etc. Whether for environmental studies, 
              urban planning, or any other geo-spatial analysis, this package caters to the diverse needs of users working with spatial data, enhancing the accessibility and ease of spatial data processing and visualization.
Shortest paths between points in grids. Optional barriers and custom transition functions. Applications regarding planet Earth, as well as 
  generally spheres and planes. Optimized for computational performance, customizability, and user friendliness. Graph-theoretical implementation tailored 
  to gridded data. Currently focused on Dijkstra's (1959) <doi:10.1007/BF01386390> algorithm. Future updates broaden the scope to other least cost path
  algorithms and to centrality measures.
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.
Blind source separation for multivariate spatial data based on simultaneous/joint diagonalization of (robust) 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>.
Seamlessly create interactive online catalogues for geospatial data. Items can be mapped as points or areas and retrieved using either a map or a dynamic table with search form and optional column filters.
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.
Deconvolution of spatial transcriptomics data based on neural networks and single-cell RNA-seq data. SpatialDDLS implements a workflow to create neural network models able to make accurate estimates of cell composition of spots from spatial transcriptomics data using deep learning and the meaningful information provided by single-cell RNA-seq data. See Torroja and Sanchez-Cabo (2019) <doi:10.3389/fgene.2019.00978> and Mañanes et al. (2024) <doi:10.1093/bioinformatics/btae072> to get an overview of the method and see some examples of its performance.
Utilities to support spatial data manipulation, query, sampling
    and modelling in ecological applications. Functions include models for species 
	population density, spatial smoothing, multivariate separability, point process 
	model for creating pseudo- absences and sub-sampling, Quadrant-based sampling and 
	analysis, auto-logistic modeling, sampling models, cluster optimization, statistical 
	exploratory tools and raster-based metrics.
Methods and data for cluster detection and disease mapping.
Tools for the statistical modelling of spatial extremes using max-stable processes, copula or Bayesian hierarchical models. More precisely, this package allows (conditional) simulations from various parametric max-stable models, analysis of the extremal spatial dependence, the fitting of such processes using composite likelihoods or least square (simple max-stable processes only), model checking and selection and prediction. Other approaches (although not completely in agreement with the extreme value theory) are available such as the use of (spatial) copula and Bayesian hierarchical models assuming the so-called conditional assumptions. The latter approaches is handled through an (efficient) Gibbs sampler. Some key references: Davison et al. (2012) <doi:10.1214/11-STS376>, Padoan et al. (2010) <doi:10.1198/jasa.2009.tm08577>, Dombry et al. (2013) <doi:10.1093/biomet/ass067>.
Visualization and analysis of spatially resolved transcriptomics data. The 'spatialGE'
	R package provides methods for visualizing and analyzing spatially resolved transcriptomics data, 
	such as 10X Visium, CosMx, or csv/tsv gene expression matrices. It includes tools for spatial 
	interpolation, autocorrelation analysis, tissue domain detection, gene set enrichment, and 
	differential expression analysis using spatial mixed models.
Fit latent variable models with the GEV distribution as the data likelihood and the GEV parameters following latent Gaussian processes. The models in this package are built using the template model builder 'TMB' in R, which has the fast ability to integrate out the latent variables using Laplace approximation. This package allows the users to choose in the fit function which GEV parameter(s) is considered as a spatially varying random effect following a Gaussian process, so the users can fit spatial GEV models with different complexities to their dataset without having to write the models in 'TMB' by themselves. This package also offers methods to sample from both fixed and random effects posteriors as well as the posterior predictive distributions at different spatial locations. Methods for fitting this class of models are described in Chen, Ramezan, and Lysy (2024) <doi:10.48550/arXiv.2110.07051>.
Provision of the S4 SpatialGraph class built on top of objects provided by 'igraph' and 'sp' packages, and associated utilities. See the documentation of the SpatialGraph-class within this package for further description. An example of how from a few points one can arrive to a SpatialGraph is provided in the function sl2sg().
Performs simulations of binary spatial raster data using
    the Ising model (Ising (1925) <doi:10.1007/BF02980577>; 
    Onsager (1944) <doi:10.1103/PhysRev.65.117>). It allows to set a few
    parameters that represent internal and external pressures, and the number 
    of simulations (Stepinski and Nowosad (2023) <doi:10.1098/rsos.231005>).
Calculate Kernel Density Estimation (KDE) for spatial data. 
  The algorithm is inspired by the tool 'Heatmap' from 'QGIS'. The method is described by:
  Hart, T., Zandbergen, P. (2014) <doi:10.1108/PIJPSM-04-2013-0039>, 
  Nelson, T. A., Boots, B. (2008) <doi:10.1111/j.0906-7590.2008.05548.x>,
  Chainey, S., Tompson, L., Uhlig, S.(2008) <doi:10.1057/palgrave.sj.8350066>.
Contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances between pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020), <doi:10.1137/19M1261195>). All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm. The package has four main helper functions: compareOneToOne() (to compare two spatial maps), compareOneToMany() (to compare a reference map with a list of other maps), compareAll() (to compute a matrix of distances between a list of maps), and focusArea() (to compute the KWD distance within a focus area). In non-convex maps, the helper functions first build the convex-hull of the input bins and pad the weights with zeros.
Implements a spatial extension of the random forest algorithm 
             (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>).
             Allows for a geographically weighted random forest regression 
             including a function to find the optical bandwidth. (Georganos 
             and Kalogirou (2022) <https://www.mdpi.com/2220-9964/11/9/471>).
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.
  Functions contained in the package are intended to accompany Vallejos, R., Osorio, F.,
  Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables:
  With Applications in R. Springer, Cham <doi:10.1007/978-3-030-56681-4>.
A spatial population can be generated based on spatially varying regression model under the assumption that observations are collected from a uniform two-dimensional grid consist of (m * m) lattice points with unit distance between any two neighbouring points. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).<DOI:10.1080/10485252.2018.1499907>.  This spatially generated data can be used to test different issues related to the statistical analysis of spatial data.
 This generated spatial data can be utilized in geographically weighted regression analysis for studying the spatially varying relationships among the variables.
Computes spatial position models: the potential model as defined
    by Stewart (1941) <doi:10.1126/science.93.2404.89> and catchment areas as
    defined by Reilly (1931) or Huff (1964) <doi:10.2307/1249154>.
A collection of methods for the Bayesian estimation of Spatial Probit, Spatial Ordered Probit and Spatial Tobit Models. Original implementations from the works of 'LeSage and Pace' (2009, ISBN: 1420064258) were ported and adjusted for R, as described in 'Wilhelm and de Matos' (2013) <doi:10.32614/RJ-2013-013>.
Spatial versions of Regression Discontinuity Designs (RDDs) are becoming increasingly popular as tools for causal inference. However, conducting state-of-the-art analyses often involves tedious and time-consuming steps. This package offers comprehensive functionalities for executing all required spatial and econometric tasks in a streamlined manner. Moreover, it equips researchers with tools for performing essential placebo and balancing checks comprehensively. The fact that researchers do not have to rely on 'APIs' of external 'GIS' software ensures replicability and raises the standard for spatial RDDs.
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'. 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. A recent review is provided by 'Bivand', 'Millo' and 'Piras' (2021) <doi:10.3390/math9111276>. 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'. From version 1.3-6, add Anselin-Kelejian (1997) test to `stsls` for residual spatial autocorrelation <doi:10.1177/016001769702000109>.
Conducts hierarchical partitioning to calculate individual contributions of spatial and  predictors (groups) towards total R2  for spatial simultaneous autoregressive model.
A collection of functions for estimating spatial regimes, aggregations of neighboring spatial units that are homogeneous in functional terms. The term spatial regime, therefore, should not be understood as a synonym for cluster. More precisely, the term cluster does not presuppose any functional relationship between the variables considered, while the term regime is linked to a regressive relationship underlying the spatial process.
Automatic generation and selection of spatial predictors for spatial regression with Random Forest. Spatial predictors are surrogates of variables driving the spatial structure of a response variable. The package offers two methods to generate spatial predictors from a distance matrix among training cases: 1) Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>): computed as the eigenvectors of a weighted matrix of distances; 2) RFsp (Hengl et al. <DOI:10.7717/peerj.5518>): columns of the distance matrix used as spatial predictors. Spatial predictors help minimize the spatial autocorrelation of the model residuals and facilitate an honest assessment of the importance scores of the non-spatial predictors. Additionally, functions to reduce multicollinearity, identify relevant variable interactions, tune random forest hyperparameters, assess model transferability via spatial cross-validation, and explore model results via partial dependence curves and interaction surfaces are included in the package. The modelling functions are built around the highly efficient 'ranger' package (Wright and Ziegler 2017 <DOI:10.18637/jss.v077.i01>).
Provides methods for spatial risk calculations, focusing on 
    efficient determination of the sum of observations within a circle of a 
    given radius. These methods are particularly relevant for applications such 
    as insurance, where recent European Commission regulations require the 
    calculation of the maximum insured value of fire risk policies for all 
    buildings that are partly or fully located within a 200 m radius. The 
    underlying problem is described by Church (1974) <doi:10.1007/BF01942293>.
Provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).
Functions and classes for spatial resampling to use with the
    'rsample' package, such as spatial cross-validation (Brenning, 2012)
    <doi:10.1109/IGARSS.2012.6352393>. The scope of 'rsample' and
    'spatialsample' is to provide the basic building blocks for creating
    and analyzing resamples of a spatial data set, but neither package
    includes functions for modeling or computing statistics. The resampled
    spatial data sets created by 'spatialsample' do not contain much
    overhead in memory.
Visualization and analysis  of Vectra Immunoflourescent
    data. Options for calculating both the univariate and bivariate Ripley's K
    are included. Calculations are performed using a permutation-based 
    approach presented by Wilson et al.  <doi:10.1101/2021.04.27.21256104>.
Tools for spatial data analysis.  Emphasis on kriging.  Provides functions for prediction and simulation.  Intended to be relatively straightforward, fast, and flexible.
Perform variable selection for the spatial Poisson regression model under the adaptive elastic net penalty. Spatial count data with covariates is the input. We use a spatial Poisson regression model to link the spatial counts and covariates. For maximization of the likelihood under adaptive elastic net penalty, we implemented the penalized quasi-likelihood (PQL) and the approximate penalized loglikelihood (APL) methods. The proposed methods can automatically select important covariates, while adjusting for possible spatial correlations among the responses. More details are available in Xie et al. (2018, <arXiv:1809.06418>). The package also contains the Lyme disease dataset, which consists of the disease case data from 2006 to 2011, and demographic data and land cover data in Virginia. The Lyme disease case data were collected by the Virginia Department of Health. The demographic data (e.g., population density, median income, and average age) are from the 2010 census. Land cover data were obtained from the Multi-Resolution Land Cover Consortium for 2006.
Spatial forecast verification refers to verifying weather forecasts when the verification set (forecast and observations) is on a spatial field, usually a high-resolution gridded spatial field.  Most of the functions here require the forecast and observed fields to be gridded and on the same grid. For a thorough review of most of the methods in this package, please see Gilleland et al. (2009) <doi: 10.1175/2009WAF2222269.1> and for a tutorial on some of the main functions available here, see Gilleland (2022) <doi: 10.5065/4px3-5a05>.
Tools to compute and assess significance of early-warnings signals (EWS) of ecosystem degradation. EWS are spatial metrics derived from raster data -- 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.
Provide regularized maximum covariance analysis incorporating smoothness,
  sparseness and orthogonality of couple patterns by using the alternating direction method
  of multipliers algorithm. The method can be applied to either regularly or irregularly
  spaced data,  including 1D, 2D, and 3D (Wang and Huang, 2018 <doi:10.1002/env.2481>).
A novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. 'SpaTopic' is scalable to large-scale image datasets without extracting neighborhood information for every single cell. For more details on the methodology, see <https://xiyupeng.github.io/SpaTopic/>.
Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions
  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.
Inference on panel data using spatiotemporal partially-observed Markov process (SpatPOMP) models. The 'spatPomp' package extends 'pomp' to include algorithms taking advantage of the spatial structure in order to assist with handling high dimensional processes. See Asfaw et al. (2024) <doi:10.48550/arXiv.2101.01157> for further description of the package.
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. (2019) <doi:10.1111/2041-210X.13215>).  It can be
    used to convert GPS relocations to gambit-of-the-group format to build
    proximity-based social networks In addition, the randomizations
    function provides data-stream randomization methods suitable for GPS
    data.
Contains all the datasets for the 'spatstat' family of packages.
Functionality for exploratory data analysis and nonparametric analysis of
	     spatial data, mainly spatial point patterns,
	     in the 'spatstat' family of packages.
	     (Excludes analysis of spatial data on a linear network,
	     which is covered by the separate package 'spatstat.linnet'.)
	     Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Defines spatial data types and supports geometrical operations
	     on them. Data types include point patterns, windows (domains),
	     pixel images, line segment patterns, tessellations and hyperframes.
	     Capabilities include creation and manipulation of data
	     (using command line or graphical interaction),
	     plotting, geometrical operations (rotation, shift, rescale,
	     affine transformation), convex hull, discretisation and
	     pixellation, Dirichlet tessellation, Delaunay triangulation,
	     pairwise distances, nearest-neighbour distances,
	     distance transform, morphological operations
	     (erosion, dilation, closing, opening), quadrat counting,
	     geometrical measurement, geometrical covariance,
	     colour maps, calculus on spatial domains,
	     Gaussian blur, level sets of images, transects of images,
	     intersections between objects, minimum distance matching.
	     (Excludes spatial data on a network, which are supported by
	     the package 'spatstat.linnet'.)
Extension to the 'spatstat' package, containing
	     interactive graphics capabilities.
Extension to the 'spatstat' family of packages, for analysing
	     large datasets of spatial points on a network. The geometrically-
	     corrected K function is computed using a memory-efficient
	     tree-based algorithm described by Rakshit, Baddeley and Nair (2019).
Defines types of spatial data on a linear network
	     and provides functionality for geometrical operations,
	     data analysis and modelling of data on a linear network,
	     in the 'spatstat' family of packages.
	     Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks.
	     Data types defined on a network include point patterns, pixel images, functions, and tessellations.
	     Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
	Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood.
	Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
	Random point patterns on a network can be generated using a variety of models.
Extension to the 'spatstat' package, enabling the user
	     to fit point process models to point pattern data
	     by local composite likelihood ('geographically weighted
	     regression').
Functionality for parametric statistical modelling and inference for spatial data,
	     mainly spatial point patterns, in the 'spatstat' family of packages.
	     (Excludes analysis of spatial data on a linear network,
	     which is covered by the separate package 'spatstat.linnet'.)
	     Supports parametric modelling, formal statistical inference, and model validation.
	     Parametric models include Poisson point processes, Cox point processes, Neyman-Scott cluster processes, Gibbs point processes and determinantal point processes. Models can be fitted to data using maximum likelihood, maximum pseudolikelihood, maximum composite likelihood and the method of minimum contrast. Fitted models can be simulated and predicted. Formal inference includes hypothesis tests (quadrat counting tests, Cressie-Read tests, Clark-Evans test, Berman test, Diggle-Cressie-Loosmore-Ford test, scan test, studentised permutation test, segregation test, ANOVA tests of fitted models, adjusted composite likelihood ratio test, envelope tests, Dao-Genton test, balanced independent two-stage test), confidence intervals for parameters, and prediction intervals for point counts. Model validation techniques include leverage, influence, partial residuals, added variable plots, diagnostic plots, pseudoscore residual plots, model compensators and Q-Q plots.
Functionality for random generation of spatial data in the 'spatstat' family of packages.
	     Generates random spatial patterns of points according to many simple rules (complete spatial randomness,
	     Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns
	     (thinning, random shift, jittering),  simulated realisations of random point processes including
	     simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes
	     (using direct, Brix-Kendall, or hybrid algorithms),
	     log-Gaussian Cox processes, product shot noise cluster processes
	     and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm,
	     alternating Gibbs sampler, or coupling-from-the-past perfect simulation).
	     Also generates random spatial patterns of line segments,
	     random tessellations, and random images (random noise, random mosaics).
	     Excludes random generation on a linear network,
	     which is covered by the separate package 'spatstat.linnet'.
Defines sparse three-dimensional arrays
	     and supports standard operations on them.
	     The package also includes utility functions for
	     matrix calculations that are common in
	     statistics, such as quadratic forms.
Estimation of one-dimensional probability distributions
	     including kernel density estimation, weighted empirical cumulative
	     distribution functions, Kaplan-Meier and reduced-sample estimators
	     for right-censored data, heat kernels, kernel properties,
	     quantiles and integration.
Contains utility functions for the 'spatstat' family of packages
             which may also be useful for other purposes.
Bayesian inference for parametric proportional hazards spatial
    survival models; flexible spatial survival models. See Benjamin M. Taylor, Barry S. Rowlingson (2017) <doi:10.18637/jss.v077.i04>.
Provides a simple progress bar to use for basic and advanced users that suits all those who prefer procedural programming. It is especially useful for integration into markdown files thanks to the progress bar's customisable appearance.
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.
Encapsulates a number of spatially balanced sampling algorithms,
    namely, Balanced Acceptance Sampling (equal, unequal, seed point, panels), Halton frames 
    (for discretizing a continuous resource), Halton Iterative Partitioning (equal probability) 
    and Simple Random Sampling.
    Robertson, B. L., Brown, J. A., McDonald, T. and Jaksons, P. (2013) <doi:10.1111/biom.12059>.
    Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2017) <doi:10.1016/j.spl.2017.05.004>.
    Robertson, B. L., McDonald, T., Price, C. J. and Brown, J. A. (2018) <doi:10.1007/s10651-018-0406-6>.
    Robertson, B. L., van Dam-Bates, P. and Gansell, O. (2021a) <doi:10.1007/s10651-020-00481-1>.
    Robertson, B. L., Davies, P., Gansell, O., van Dam-Bates, P., McDonald, T. (2025) <doi:10.1111/anzs.12435>.
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 and Banerjee <doi:10.1016/j.envsoft.2019.104608>.
Provides several Bayesian survival models for spatial/non-spatial survival data: proportional hazards (PH), accelerated failure time (AFT), proportional odds (PO), and accelerated hazards (AH), a super model that includes PH, AFT, PO and AH as special cases, Bayesian nonparametric nonproportional hazards (LDDPM), generalized accelerated failure time (GAFT), and spatially smoothed Polya tree density estimation. The spatial dependence is modeled via frailties under PH, AFT, PO, AH and GAFT, and via copulas under LDDPM and PH. Model choice is carried out via the logarithm of the pseudo marginal likelihood (LPML), the deviance information criterion (DIC), and the Watanabe-Akaike information criterion (WAIC). See Zhou, Hanson and Zhang (2020) <doi:10.18637/jss.v092.i09>.
Implements a spatial Bayesian non-parametric factor analysis model 
    with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). 
    Spatial correlation is introduced in the columns of the factor loadings 
    matrix using a Bayesian non-parametric prior, the probit stick-breaking 
    process. Areal spatial data is modeled using a conditional autoregressive 
    (CAR) prior and point-referenced spatial data is treated using a Gaussian 
    process. The response variable can be modeled as Gaussian, probit, Tobit, or
    Binomial (using Polya-Gamma augmentation). Temporal correlation is 
    introduced for the latent factors through a hierarchical structure and can 
    be specified as exponential or first-order autoregressive. Full details of 
    the package can be found in the accompanying vignette. Furthermore, the 
    details of the package can be found in "Bayesian Non-Parametric Factor 
    Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019), 
    <doi:10.1214/20-BA1253> in Bayesian Analysis.
Provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2024) <doi:10.48550/arXiv.2410.09504>. This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios.
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, Pantalone F, Benedetti R, and Piersimoni F (2022) <doi:10.18637/jss.v103.c02>, Benedetti R and Piersimoni F (2017) <doi:10.1002/bimj.201600194>, and Benedetti R and Piersimoni F (2017) <arXiv:1710.09116>. The implementation has been done in C++ through the use of 'Rcpp' and 'RcppArmadillo'.
Evaluation of control charts by means of
        the zero-state, steady-state ARL (Average Run Length) and RL quantiles.
        Setting up control charts for given in-control ARL. The control charts
        under consideration are one- and two-sided EWMA, CUSUM, and
        Shiryaev-Roberts schemes for monitoring the mean or variance of normally
        distributed independent data. ARL calculation of the same set of schemes under drift (in the mean) are added.
        Eventually, all ARL measures for the multivariate EWMA (MEWMA) are provided.
Provides statistical process control tools for stochastic
    textured surfaces. The current version supports the following tools:
    (1) generic modeling of stochastic textured surfaces.
    (2) local defect monitoring and diagnostics in stochastic 
        textured surfaces, which was proposed by Bui and Apley (2018a) 
        <doi:10.1080/00401706.2017.1302362>.
    (3) global change monitoring in the nature of stochastic 
        textured surfaces, which was proposed by Bui and Apley (2018b) 
        <doi:10.1080/00224065.2018.1507559>.
    (4) computation of dissimilarity matrix of stochastic textured 
        surface images, which was proposed by Bui and Apley (2019b)
        <doi:10.1016/j.csda.2019.01.019>.
Calibration of thresholds of control charts such as
    CUSUM charts based on past data, taking estimation error into account.
A new reduced-rank LDA method which works for high dimensional multi-class data.
Implements the SPCAvRP algorithm, developed and analysed in "Sparse principal component analysis via random projections" Gataric, M., Wang, T. and Samworth, R. J. (2018) <arXiv:1712.05630>. The algorithm is based on the aggregation of eigenvector information from carefully-selected random projections of the sample covariance matrix.
Programs to find the sample size or power of studies using the 
    Sequential Parallel Comparison Design (SPCD) and programs to analyze 
    such studies. This is a clinical trial design where 
    patients initially on placebo who did not respond are re-randomized between 
    placebo and active drug in a second phase and the results of the two phases are 
    pooled. The method of analyzing binary data with this design is described in 
    Fava,Evins, Dorer and Schoenfeld(2003) <doi:10.1159/000069738>, and the 
    method of analyzing continuous data is described in Chen, Yang, 
    Hung and Wang (2011) <doi:10.1016/j.cct.2011.04.006>.
Fast computation of the required sample size or the
    achieved power, for GWAS studies with different types of covariate
    effects and different types of covariate-gene dependency structure.
    For the detailed description of the methodology, see Zhang (2022)
    "Power and Sample Size Computation for Genetic Association Studies
     of Binary Traits: Accounting for Covariate Effects" <arXiv:2203.15641>.
Computes the effective range of a smoothing matrix, 
    which is a measure of the distance to which smoothing occurs. 
    This is motivated by the application of spatial splines for adjusting 
    for unmeasured spatial confounding in regression models, but the
    calculation of effective range can be applied to smoothing 
    matrices in other contexts. For algorithmic details, see Rainey and Keller
    (2024) "spconfShiny: an R Shiny application..." <doi:10.1371/journal.pone.0311440>
    and Keller and Szpiro (2020)
    "Selecting a Scale for Spatial Confounding Adjustment" <doi:10.1111/rssa.12556>.
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 published in Spatial Statistics
    by Berchuck et al (2019): "A spatially varying change points model for monitoring glaucoma 
    progression using visual field data", <doi:10.1016/j.spasta.2019.02.001>.
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 and GeoPackage, but from version 2.3.4, no longer ESRI Shapefile - use GeoPackage instead. 
    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.
    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.
Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. This is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods. For an overview of stated choice experimental design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. & Daly. A. <doi:10.4337/9781781003152>. The package website can be accessed at <https://spdesign.edsandorf.me>. We acknowledge funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163).
Provides functionality for simulating data generation processes across various spatial regression models, conceptually aligned with the 'dgp' module of the 'Python' library 'spreg' <https://pysal.org/spreg/api.html#dgp>.
Logging functions in 'RcppSpdlog' provide access to the logging
 functionality from the 'spdlog' 'C++' library. This package offers shorter convenience
 wrappers for the 'R' functions which match the 'C++' functions, namely via, say,
 'spdl::debug()' at the debug level. The actual formatting is done by the
 'fmt::format()' function from the 'fmtlib' library (that is also 'std::format()'
 in 'C++20' or later).
Spatial downscaling of climate data (Global Circulation Models/Regional Climate Models) using quantile-quantile bias correction technique.
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) and Berkson and Gage 
    (1952), and extended in Schmidt and Witte (1989).
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>.
A graph community detection algorithm that aims to be performant
    on large graphs and robust, returning consistent results across runs.
    SpeakEasy 2 (SE2), the underlying algorithm, is described in Chris Gaiteri,
    David R. Connell & Faraz A. Sultan et al. (2023)
    <doi:10.1186/s13059-023-03062-0>. The core algorithm is written in 'C',
    providing speed and keeping the memory requirements low. This implementation
    can take advantage of multiple computing cores without increasing memory
    usage. SE2 can detect community structure across scales, making it a good
    choice for biological data, which often has hierarchical structure. Graphs
    can be passed to the algorithm as adjacency matrices using base 'R'
    matrices, the 'Matrix' library, 'igraph' graphs, or any data that can be
    coerced into a matrix.
It allows running 'Praat' scripts from R and it provides some
    wrappers for basic plotting. It also adds support for literate markdown
    tangling. The package is designed to bring reproducible phonetic research
    into R.
Makes Nuclear Magnetic Resonance spectroscopy (NMR spectroscopy) data analysis as easy as possible by only requiring a small set of functions to perform an entire analysis. 'speaq' offers the possibility of raw spectra alignment and quantitation but also an analysis based on features whereby the spectra are converted to peaks which are then grouped and turned into features. These features can be processed with any number of statistical tools either included in 'speaq' or available elsewhere on CRAN. More details can be found in Vu et al. (2011) <doi:10.1186/1471-2105-12-405> and Beirnaert et al. (2018) <doi:10.1371/journal.pcbi.1006018>.
Functions for conducting jackknife Euclidean / empirical likelihood inference for Spearman's rho (de Carvalho and Marques (2012) <doi: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().
Making specification curve analysis easy, fast, and pretty. It 
  improves upon existing offerings with additional features and 'tidyverse' 
  integration. Users can easily visualize and evaluate how their models behave 
  under different specifications with a high degree of customization. For a 
  description and applications of specification curve analysis see Simonsohn, 
  Simmons, and Nelson (2020) <doi:10.1038/s41562-020-0912-z>.
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.
Implementation of various methods in estimation of species richness or diversity in Wang (2011)<doi:10.18637/jss.v040.i09>.
A simple method to display and characterise the multidimensional ecological niche of a species. The method also estimates the optimums and amplitudes along each niche dimension. Give also an estimation of the degree of niche overlapping between species. See Kleparski and Beaugrand (2022) <doi:10.1002/ece3.8830> for further details.
Surface Protein abundance Estimation using CKmeans-based clustered thresholding ('SPECK') is an unsupervised learning-based method that performs receptor abundance estimation for single cell RNA-sequencing data based on reduced rank reconstruction (RRR) and a clustered thresholding mechanism. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for the RRR is further detailed in: Erichson et al., (2019) <doi:10.18637/jss.v089.i11> and Halko et al., (2009) <doi:10.48550/arXiv.0909.4061>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.
A set of functions for obtaining positional parameters and magnitude difference between components of binary and multiple stellar systems from series of speckle images.
Provides utilities for conducting specification curve analyses (Simonsohn, Simmons & Nelson (2020, <doi: 10.1038/s41562-020-0912-z>) 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 (2021) <doi:10.1016/j.jeconom.2020.07.021>. 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.
A tool for survival analysis using a discrete time approach with ensemble binary classification. 'spect' provides a
	simple interface consistent with commonly used R data analysis packages, such as 'caret', a variety of parameter options
	to help facilitate search automation, a high degree of transparency to the end-user - all intermediate data sets and
	parameters are made available for further analysis and useful, out-of-the-box visualizations of model performance. Methods
	for transforming survival data into discrete-time are adapted from the 'autosurv' package by Suresh et al., (2022)
	<doi:10.1186/s12874-022-01679-6>.
Stores and eases the manipulation of spectra and associated data,
    with dedicated classes for spatial and soil-related data.
Provides interface to the 'Spectator Earth' API <https://api.spectator.earth/>, 
    mainly for obtaining the acquisition plans and satellite overpasses for Sentinel-1, 
    Sentinel-2, Landsat-8 and Landsat-9 satellites. Current position and trajectory can 
    also be obtained for a much larger set of satellites. It is also possible to search 
    the archive for available images over the area of interest for a given (past) period, 
    get the URL links to download the whole image tiles, or alternatively to download the 
    image for just the area of interest based on selected spectral bands.
Provides a consistent interface to use various methods to calculate
  the periodogram and estimate the period of a rhythmic time-course. Methods
  include Lomb-Scargle, fast Fourier transform, and three versions of the
  chi-square periodogram. See Tackenberg and Hughey (2021)
  <doi:10.1371/journal.pcbi.1008567>.
Provides functions to combine, normalize and visualize spectral data, and for assembling customizable image grids suitable for publication-quality scientific figures.
On discrete data spectral analysis is performed by Fourier and Hilbert
    transforms as well as with model based analysis called Lomb-Scargle method.
    Fragmented and irregularly spaced data can be processed in almost all methods. Both,
    FFT as well as LOMB methods take multivariate data and return standardized PSD. 
    For didactic reasons an analytical approach for deconvolution of noise spectra and 
    sampling function is provided.
    A user friendly interface helps to interpret the results.
Apply the spectral residual algorithm to data, such as a
    time series, to detect anomalies. Anomaly scores can be used to determine
    outliers based upon a threshold or fed into more sophisticated prediction
    models. Methods are based upon "Time-Series Anomaly Detection Service at
    Microsoft", Ren, H., Xu, B., Wang, Y., et al., (2019)
    <doi:10.48550/arXiv.1906.03821>.
Performs cluster analysis of mixed-type data using Spectral Clustering, see F. Mbuga and, C. Tortora (2022) <doi:10.3390/stats5010001>.
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.
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.
Tools for obtaining, processing, and visualizing spectral reflectance data for the user-defined land or water surface classes for visual exploring in which wavelength the classes differ. Input should be a shapefile with polygons of surface classes (it might be different habitat types, crops, vegetation, etc.). The Sentinel-2 L2A satellite mission optical bands pixel data are obtained through the Google Earth Engine service (<https://earthengine.google.com/>) and used as a source of spectral data.
Analyse light spectra for visual and non-visual (often called
 melanopic) needs, wrapped up in a Shiny App. 'Spectran' allows for the import
 of spectra in various CSV forms but also provides a wide range of example
 spectra and even the creation of own spectral power distributions. The goal of
 the app is to provide easy access and a visual overview of the spectral
 calculations underlying common parameters used in the field. It is thus ideal
 for educational purposes or the creation of presentation ready graphs in
 lighting research and application. 'Spectran' uses equations and action spectra
 described in CIE S026 (2018) <doi:10.25039/S026.2018>, DIN/TS 5031-100 (2021)
 <doi:10.31030/3287213>, and ISO/CIE 23539 (2023)
 <doi:10.25039/IS0.CIE.23539.2023>.
Predict regional community composition at a fine spatial 
    resolution using only sparse biological and environmental data. The package 
    is based on the DynamicFOAM algorithm described 
    in Mokany et al. (2011) <doi:10.1111/j.1461-0248.2011.01675.x>.
Spectra viewer, organizer, data preparation and property blocks
        from within R or stand-alone. Binary (application) part is 
        installed separately using spnInstallApp() from spectrino package.
Input/Output, processing and visualization of spectra taken with different spectrometers, including SVC (Spectra Vista), ASD and PSR (Spectral Evolution). Implements an S3 class spectra that other packages can build on. Provides methods to access, plot, manipulate, splice sensor overlap, vector normalize and smooth spectra.
A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.
Do multi-gene descent probabilities
   (Thompson, 1983, <doi:10.1098/rspb.1983.0072>)
   and special cases thereof
   (Thompson, 1986, <doi:10.1002/zoo.1430050210>)
   including inbreeding and kinship coefficients.  But does much more:
   probabilities of any set of genes descending from any other
   set of genes.
Implements the Smoothness-Penalized Deconvolution method for estimating a probability density under measurement error of Kent and Ruppert (2023) <doi:10.1080/01621459.2023.2259028>. The estimator is formed by computing a histogram of the error-contaminated data, and then finding an estimate that minimizes a reconstruction error plus a smoothness-inducing penalty term. The primary function, sped(), takes the data and error distribution, and returns the estimator as a function.
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.
Inferring causation from spatial cross-sectional data through empirical dynamic modeling (EDM), with methodological extensions including geographical convergent cross mapping from Gao et al. (2023) <doi:10.1038/s41467-023-41619-6>, as well as the spatial causality test following the approach of Herrera et al. (2016) <doi:10.1111/pirs.12144>.
Converts the floor speeches of Uruguayan legislators, extracted from the 
    parliamentary minutes, to tidy data.frame where each observation is the intervention of a single legislator.
Scrap speech text and speaker informations of speeches of House of 
          Representatives of Brazil, and transform in a cleaned tibble.
Fitting linear models and generalized linear models to large data sets by updating algorithms, according to the method described in Enea (2009, ISBN: 9788861294257).
A suite of functions that allow a full, fast, and efficient Bayesian treatment of the Bradley--Terry model. 
             Prior assumptions about the model parameters can be encoded through a multivariate normal prior distribution. 
             Inference is performed using a latent variable representation of the model.
Label, recode, rename, and convert datasets and ASCII files more efficiently. 'speedycode' automates the code necessary for labeling variables with the 'labelled' package, recoding and renaming variables with 'dplyr' syntax, and converting ASCII files with the 'readroper' package. Most functions require only the name of the dataset and the code will be automatically written. Some convenience functions useful for converting ASCII files are also included.
Import classification results from the 'RDP Classifier' (Ribosomal Database Project),' 'USEARCH sintax,' 'vsearch sintax' and the 'QIIME2' (Quantitative Insights into Microbial Ecology) classifiers into 'phyloseq' tax_table objects.
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>.
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).
Automatically replaces "misspelled" words in a character vector
    based on their string distance from a list of words sorted by their frequency
    in a corpus. The default word list provided in the package comes from 
    the Corpus of Contemporary American English. Uses the Jaro-Winkler distance
    metric for string similarity as implemented in van der Loo (2014) 
    <doi:10.32614/RJ-2014-011>. The word frequency data is derived from 
    Davies (2008-) "The Corpus of Contemporary American English (COCA)" 
    <https://www.english-corpora.org/coca/>.
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.
Performs non-parametric tests of parametric specifications.
    Five tests are available. 
    Specific bandwidth and kernel methods can be chosen along with many other options. 
    Allows parallel computing to quickly compute p-values based on the bootstrap. 
    Methods implemented in the package are H.J. Bierens (1982) <doi:10.1016/0304-4076(82)90105-1>,
    J.C. Escanciano (2006) <doi:10.1017/S0266466606060506>,
    P.L. Gozalo (1997) <doi:10.1016/S0304-4076(97)86571-2>,
    P. Lavergne and V. Patilea (2008) <doi:10.1016/j.jeconom.2007.08.014>,
    P. Lavergne and V. Patilea (2012) <doi:10.1198/jbes.2011.07152>,
    J.H. Stock and M.W. Watson (2006) <doi:10.1111/j.1538-4616.2007.00014.x>,
    C.F.J. Wu (1986) <doi:10.1214/aos/1176350142>,
    J. Yin, Z. Geng, R. Li, H. Wang (2010) <https://www.jstor.org/stable/24309002>
    and J.X. Zheng (1996) <doi:10.1016/0304-4076(95)01760-7>.
We provide functionality to implement penalized PCA with an option to smooth the objective function using Nesterov smoothing. Two functions are available to compute a user-specified number of eigenvectors. The function unsmoothed_penalized_EV() computes a penalized PCA without smoothing and has three parameters (the input matrix, the Lasso penalty, and the number of desired eigenvectors). The function smoothed_penalized_EV() computes a smoothed penalized PCA using the same parameters and additionally requires the specification of a smoothing parameter. Both functions return a matrix having the desired eigenvectors as columns.
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.
Estimation, scoring, and plotting functions for the semi-parametric factor model proposed by Liu & Wang (2022) <doi:10.1007/s11336-021-09832-8> and Liu & Wang (2023) <arXiv:2303.10079>. Both the conditional densities of observed responses given the latent factors and the joint density of latent factors are estimated non-parametrically. Functional parameters are approximated by smoothing splines, whose coefficients are estimated by penalized maximum likelihood using an expectation-maximization (EM) algorithm. E- and M-steps can be parallelized on multi-thread computing platforms that support 'OpenMP'. Both continuous and unordered categorical response variables are supported.
Implements a group-bridge penalized function-on-scalar regression
    model proposed by Wang et al. (2023) <doi:10.1111/biom.13684>, to simultaneously
    estimate functional coefficient and recover the local sparsity.
Tools to decompose (transformed) spatial connectivity matrices and perform supervised or unsupervised semiparametric spatial filtering in a regression framework. The package supports unsupervised spatial filtering in standard linear as well as some generalized linear regression models.
An implementation of feature selection, weighting and ranking via simultaneous perturbation
    stochastic approximation (SPSA). The SPSA-FSR algorithm searches for a locally optimal set of
    features that yield the best predictive performance using some error measures such as mean 
    squared error (for regression problems) and accuracy rate (for classification problems).
Estimation and Prediction Functions Using Bayesian Hierarchical Spatial Finlay-Wilkinson Model for Analysis of Multi-Environment Field Trials.
A collection of functions to deal with spatial and spatiotemporal autoregressive conditional heteroscedasticity (spatial ARCH and GARCH models) by Otto, Schmid, Garthoff (2018, Spatial Statistics) <doi:10.1016/j.spasta.2018.07.005>: simulation of spatial ARCH-type processes (spARCH, log/exponential-spARCH, complex-spARCH); quasi-maximum-likelihood estimation of the parameters of spARCH models and spatial autoregressive models with spARCH disturbances, diagnostic checks, visualizations.
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 multidimensional dataset of students' performance assessment in high school physics. The SPHERE dataset was collected from 497 students in four public high schools specifically measuring their conceptual understanding, scientific ability, and attitude toward physics [see Santoso et al. (2024) <doi:10.17632/88d7m2fv7p.1>]. The data collection was conducted using some research based assessments established by the physics education research community. They include the Force Concept Inventory, the Force and Motion Conceptual Evaluation, the Rotational and Rolling Motion Conceptual Survey, the Fluid Mechanics Concept Inventory, the Mechanical Waves Conceptual Survey, the Thermal Concept Evaluation, the Survey of Thermodynamic Processes and First and Second Laws, the Scientific Abilities Assessment Rubrics, and the Colorado Learning Attitudes about Science Survey. Students' attributes related to gender, age, socioeconomic status, domicile, literacy, physics identity, and test results administered using teachers' developed items are also reported in this dataset.
A simple package facilitating ML based analysis for physics education research (PER) purposes. The implemented machine learning technique is random forest optimized by item response theory (IRT) for feature selection and genetic algorithm (GA) for hyperparameter tuning. The data analyzed here has been made available in the CRAN repository through the 'spheredata' package. The SPHERE stands for Students' Performance in Physics Education Research (PER). The students are the eleventh graders learning physics at the high school curriculum. We follow the stream of multidimensional students' assessment as probed by some research based assessments in PER. The goal is to predict the students' performance at the end of the learning process. Three learning domains are measured including conceptual understanding, scientific ability, and scientific attitude. Furthermore, demographic backgrounds and potential variables predicting students' performance on physics are also demonstrated.
A simple tool for numerical optimization on the unit sphere. This is achieved by combining the spherical coordinating system with L-BFGS-B optimization. This algorithm is implemented in Kolkiewicz, A., Rice, G., & Xie, Y. (2020) <doi:10.1016/j.jspi.2020.07.001>.
Fitting dimension reduction methods to data lying on two-dimensional sphere.
    This package provides principal geodesic analysis, principal circle, principal curves 
    proposed by Hauberg, and spherical principal curves. Moreover, it offers the method of locally   
    defined principal geodesics which is underway. The detailed procedures 
    are described in Lee, J., Kim, J.-H. and Oh, H.-S. (2021) <doi:10.1109/TPAMI.2020.3025327>. 
    Also see Kim, J.-H., Lee, J. and Oh, H.-S. (2020) <arXiv:2003.02578>.
Various functions for creating spherical coordinate system plots via extensions to rgl.
Fitting a smooth path to a given set of noisy spherical data observed at known time points. It implements a piecewise geodesic curve fitting method on the unit sphere based on a velocity-based penalization scheme. The proposed approach is implemented using the Riemannian block coordinate descent algorithm. To understand the method and algorithm, one can refer to Bak, K. Y., Shin, J. K., & Koo, J. Y. (2023) <doi:10.1080/02664763.2022.2054962> for the case of order 1. Additionally, this package includes various functions necessary for handling spherical data.
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.
Functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables.
Implementation of uniformity tests on the circle and
    (hyper)sphere. The main function of the package is unif_test(), which
    conveniently collects more than 35 tests for assessing uniformity on
    S^{p-1} = {x in R^p : ||x|| = 1}, p >= 2. The test statistics are 
    implemented in the unif_stat() function, which allows computing several
    statistics for different samples within a single call, thus facilitating
    Monte Carlo experiments. Furthermore, the unif_stat_MC() function allows
    parallelizing them in a simple way. The asymptotic null distributions of
    the statistics are available through the function unif_stat_distr(). The
    core of 'sphunif' is coded in C++ by relying on the 'Rcpp' package.
    The package also provides several novel datasets and gives the replicability
    for the data applications/simulations in García-Portugués et al. (2021)
    <doi:10.1007/978-3-030-69944-4_12>, García-Portugués et al. (2023)
    <doi:10.3150/21-BEJ1454>, Fernández-de-Marcos and
    García-Portugués (2024) <doi:10.1016/j.spl.2024.110218>, and
    García-Portugués et al. (2025) <doi:10.1080/01621459.2025.2566414>.
A set of functions allowing to implement the 'SpiceFP' approach
  which is iterative. It involves transformation of functional predictors into
  several candidate explanatory matrices (based on contingency tables), to which
  relative edge matrices with contiguity constraints are associated. Generalized
  Fused Lasso regression are performed in order to identify the best candidate
  matrix, the best class intervals and related coefficients at each iteration.
  The approach is stopped when the maximal number of iterations is reached or
  when retained coefficients are zeros. Supplementary functions allow to get
  coefficients of any candidate matrix or mean of coefficients of many candidates.
  The methods in this package are describing in Girault Gnanguenon Guesse, 
  Patrice Loisel, Bénedicte Fontez, Thierry Simonneau, Nadine Hilgert (2021)
  "An exploratory penalized regression to identify combined effects of functional
  variables -Application to agri-environmental issues" 
  <https://hal.archives-ouvertes.fr/hal-03298977>.
Improves the interpretation of the Standardized Precipitation
    Index under changing climate conditions. The package uses the
    nonstationary approach proposed in Blain et al. (2022)
    <doi:10.1002/joc.7550> to detect trends in rainfall quantities and to
    quantify the effect of such trends on the probability of a drought event
    occurring.
Extracts and summarizes metadata from data frames, including variable names, labels, types, and missing values. Computes compact descriptive statistics, frequency tables, and cross-tabulations to assist with efficient data exploration. Includes an interactive and exportable codebook generator for documenting variable metadata. Facilitates the identification of missing data patterns and structural issues in datasets. Designed to streamline initial data management and exploratory analysis workflows within 'R'.
Analysis of species limits and DNA barcoding data. Included are functions for generating important summary statistics from DNA barcode data, assessing specimen identification efficacy, testing and optimizing divergence threshold limits, assessment of diagnostic nucleotides, and calculation of the probability of reciprocal monophyly. Additionally, a sliding window function offers opportunities to analyse information across a gene, often used for marker design in degraded DNA studies. Further information on the package has been published in Brown et al (2012) <doi:10.1111/j.1755-0998.2011.03108.x>.
The 'Robots Exclusion Protocol' <https://www.robotstxt.org/orig.html> documents
    a set of standards for allowing or excluding robot/spider crawling of different areas of
    site content. Tools are provided which wrap The 'rep-cpp' <https://github.com/seomoz/rep-cpp>
    C++ library for processing these 'robots.txt' files.
Calculate the Standardized Precipitation Index (SPI) for monitoring drought, using Artificial Intelligence techniques (SPIGA) and traditional numerical technique Maximum Likelihood (SPIML).  For more information see: http://drought.unl.edu/monitoringtools/downloadablespiprogram.aspx.
Applies re-sampled kernel density method to detect vote fraud. It estimates the proportion of coarse vote-shares in the observed data relative to the null hypothesis of no fraud.
Spike and slab for prediction and variable selection in linear regression models. Uses a generalized elastic net for variable selection.
Bayesian variable selection, model choice, and regularized
    estimation for (spatial) generalized additive mixed regression models
    via stochastic search variable selection with spike-and-slab priors.
A user-friendly tool for estimating both total and directional connectedness 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>.
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>.
Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. (2022) <doi:10.1007/s10260-022-00655-0>) for the semiparametric setting introduced in Drost et al. (2009) <doi:10.1111/j.1467-9868.2008.00687.x>, different procedures to bootstrap INAR data (Jentsch, C. and Weiß, C.H. (2017) <doi:10.3150/18-BEJ1057>) and flexible simulation of INAR data.
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. (2020) <doi:10.1002/sim.8434>). Furthermore, the proposed method can conduct 
   structural identification by distinguishing nonlinear interactions from main effects only 
   case within Bayesian framework. Spike-and-slab priors are incorporated on both individual 
   and group level to shrink coefficients corresponding to irrelevant main and interaction 
   effects to zero exactly. The Markov chain Monte Carlo algorithms of the proposed and 
   alternative methods are  efficiently implemented in C++.
Functions for spatial methods based on generalized estimating equations (GEE) and
  wavelet-revised methods (WRM), functions for scaling by wavelet multiresolution regression (WMRR),
  conducting multi-model inference, and stepwise model selection. Further, contains functions 
  for spatially corrected model accuracy measures.
Projection pursuit is used to find interesting low-dimensional
    projections of high-dimensional data by optimizing an index over all
    possible projections. The 'spinebil' package contains methods to evaluate
    the performance of projection pursuit index functions using tour methods. 
    A paper describing the methods can be found at <doi:10.1007/s00180-020-00954-8>.
Data visualization tours animates linear projection 
  of multivariate data as its basis (ie. orientation) changes. The 'spinifex' 
  packages generates paths for manual tours by manipulating the contribution of 
  a single variable at a time Cook & Buja (1997) 
  <doi:10.1080/10618600.1997.10474754>. Other types of tours, such as grand 
  (random walk) and guided (optimizing some objective function) are available 
  in the 'tourr' package Wickham et al. <doi:10.18637/jss.v040.i02>. 
  'spinifex' builds on 'tourr' and can render tours with 'gganimate' and 
  'plotly' graphics, and allows for exporting as an .html widget and as an .gif, 
  respectively. This work is fully discussed in Spyrison & Cook (2020) 
  <doi:10.32614/RJ-2020-027>.
Proposes a 'torch' implementation of Graph Net architecture allowing different options for message passing and feature embedding.
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.
It visualizes data along an Archimedean spiral <https://en.wikipedia.org/wiki/Archimedean_spiral>, 
    makes so-called spiral graph or spiral chart. 
    It has two major advantages for visualization: 1. It is able to visualize data with very long axis with high 
    resolution. 2. It is efficient for time series data to reveal periodic patterns.
Contains an R Markdown template for a clinical trial 
    protocol adhering to the SPIRIT statement. The SPIRIT (Standard Protocol 
    Items for Interventional Trials) statement outlines recommendations for a 
    minimum set of elements to be addressed in a  clinical trial protocol. 
    Also contains functions to create a xml document from the template and 
    upload it to clinicaltrials.gov<https://www.clinicaltrials.gov/> for 
    trial registration.
Import, process, summarize and visualize raw data from 
    metabolic carts. See Robergs, Dwyer, and Astorino (2010) <doi:10.2165/11319670-000000000-00000> for more details on data processing.
The Splancs package was written as an enhancement to S-Plus for display and analysis of spatial point pattern data; it has been ported to R and is in "maintenance mode".
This program calculates bioclimatic indices and fluxes (radiation, 
    evapotranspiration, soil moisture) for use in studies of ecosystem function, 
    species distribution, and vegetation dynamics under changing climate 
    scenarios. Predictions are based on a minimum of required inputs: latitude, 
    precipitation, air temperature, and cloudiness. 
    Davis et al. (2017) <doi:10.5194/gmd-10-689-2017>.
The current version of this package estimates spatial autoregressive models for binary dependent variables using GMM estimators <doi:10.18637/jss.v107.i08>. It supports one-step (Pinkse and Slade, 1998) <doi:10.1016/S0304-4076(97)00097-3> and two-step GMM estimator along with the linearized GMM estimator proposed by Klier and McMillen (2008) <doi:10.1198/073500107000000188>. It also allows for either Probit or Logit model and compute the average marginal effects. All these models are presented in Sarrias and Piras (2023) <doi:10.1016/j.jocm.2023.100432>.
An extension to the individual claim simulator called 'SynthETIC'
    (on CRAN), to simulate the evolution of case estimates of incurred losses
    through the lifetime of an insurance claim. The transactional simulation
    output now comprises key dates, and both claim payments and revisions of
    estimated incurred losses. An initial set of test parameters, designed to
    mirror the experience of a real insurance portfolio, were set up and applied
    by default to generate a realistic test data set of incurred histories (see
    vignette). However, the distributional assumptions used to generate this
    data set can be easily modified by users to match their experiences.
    Reference: Avanzi B, Taylor G, Wang M (2021) "SPLICE: A Synthetic Paid Loss
    and Incurred Cost Experience Simulator" <arXiv:2109.04058>.
Implements a two-stage estimation approach for Cox 
    regression using five-parameter M-spline functions to model the baseline hazard. It allows
    for flexible hazard shapes and model selection based on log-likelihood criteria as described in
    Teranishi et al.(2025).
    In addition, the package provides functions for constructing and evaluating B-spline copulas 
    based on five M-spline or I-spline basis functions, allowing users to flexibly model and 
    compute bivariate dependence structures. Both the copula function and its density can be evaluated.
    Furthermore, the package supports computation of dependence measures such as Kendall's tau and 
    Spearman's rho, derived analytically from the copula parameters.
Creates 'ggplot2'-based visualizations of smooth effects from GAM (Generalized Additive Models)
    fitted with 'mgcv' and spline effects from GLM (Generalized Linear Models). Supports interaction terms
    and provides hazard ratio plots with histograms for survival analysis.
    Wood (2017, ISBN:9781498728331) provides comprehensive methodology for generalized additive models.
Constructs basis functions of B-splines, M-splines,
    I-splines, convex splines (C-splines), periodic splines,
    natural cubic splines, generalized Bernstein polynomials,
    their derivatives, and integrals (except C-splines)
    by closed-form recursive formulas.
    It also contains a C++ head-only library integrated with Rcpp.
    See Wang and Yan (2021) <doi:10.6339/21-JDS1020> for details.
Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) <doi:10.1080/10618600.1999.10474847>. This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories.
Splines are efficiently represented through their Taylor expansion at the knots. The representation accounts for the support sets and is thus suitable for sparse functional data. Two cases of boundary conditions are considered: zero-boundary or periodic-boundary for all derivatives except the last. The periodical splines are represented graphically using polar coordinates. The B-splines and orthogonal bases of splines that reside on small total support are implemented. The orthogonal bases are referred to as 'splinets' and are utilized for functional data analysis. Random spline generator is implemented as well as  all fundamental algebraic and calculus operations on splines.  The optimal, in the least square sense, functional fit by 'splinets' to data consisting of sampled values of functions as well as splines build over another set of knots is obtained and used for functional data analysis. The S4-version of the object oriented R is used.  <doi:10.48550/arXiv.2102.00733>, <doi:10.1016/j.cam.2022.114444>, <doi:10.48550/arXiv.2302.07552>.
Procedure to optimally split a dataset for training and testing. 
    'SPlit' is based on the method of support points, which is independent of modeling methods.
    Please see Joseph and Vakayil (2021) <doi:10.1080/00401706.2021.1921037> for details.
    This work is supported by U.S. National Science Foundation grant DMREF-1921873.
Some R functions, such as optim(), require a function
  its gradient passed as separate arguments. When these are
  expensive to calculate it may be much faster to calculate
  the function (fn) and gradient (gr) together since they often share
  many calculations (chain rule). This package allows the user
  to pass in a single function that returns both the function
  and gradient, then splits (hence 'splitfngr') them
  so the results can be accessed
  separately. The functions provided allow this to be done with
  any number of functions/values, not just for functions and gradients.
Functions to compute split generalized linear models. The approach fits 
             generalized linear models that split the covariates into groups. The 
             optimal split of the variables into groups and the regularized estimation 
             of the coefficients are performed by minimizing an objective function 
             that encourages sparsity within each group and diversity among them.
             Example applications can be found in Christidis et al. (2021)
             <doi:10.48550/arXiv.2102.08591>.
Estimate the internal consistency of your tasks with a permutation based split-half reliability approach.
    Unofficial release name: "I eat stickers all the time, dude!".
Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires. The 'splithalfr' supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, the option to sub-sample data, and bootstrapped confidence intervals.
Split Knockoff is a data adaptive variable selection framework for controlling the
             (directional) false discovery rate (FDR) in structural sparsity, where variable 
             selection on linear transformation of parameters is of concern. This proposed scheme
             relaxes the linear subspace constraint to its neighborhood, often known as variable
             splitting in optimization.
             Simulation experiments can be reproduced following the Vignette. 
             'Split Knockoffs' is first defined in Cao et al. (2021) <doi:10.48550/arXiv.2103.16159>.
Functions for computing split regularized estimators defined in Christidis, Lakshmanan, 
             Smucler and Zamar (2019) <doi:10.48550/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>.
Performs analysis of split-split plot experiments in both completely randomized and randomized complete block designs. With the results, you can obtain ANOVA, mean tests, and regression analysis (Este pacote faz a analise de experimentos em parcela subsubdivididas no delineamento inteiramente casualizado e delineamento em blocos casualizados. Com resultados e possível obter a ANOVA, testes de medias e análise de regressao) <https://www.expstat.com/pacotes-do-r>.
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.
Implements 'SplitWise', a hybrid regression approach that transforms
    numeric variables into either single-split (0/1) dummy variables or retains
    them as continuous predictors. The transformation is followed by stepwise
    selection to identify the most relevant variables. The default 'iterative'
    mode adaptively explores partial synergies among variables to enhance model
    performance, while an alternative 'univariate' mode applies simpler
    transformations independently to each predictor. For details, see Kurbucz et al. (2025) <doi:10.48550/arXiv.2505.15423>.
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
  indexed rectangular data. The index values can be
  calendar (timeSeries class) or numeric (signalSeries class).
  Methods are included for aggregation, alignment, merging, and summaries.
  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://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/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://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/metadata/74030>.
An updated and extended version of 'spm' package, by introducing some further novel functions for modern statistical methods (i.e., generalised linear models, glmnet, generalised least squares), thin plate splines, support vector machine, kriging methods (i.e., simple kriging, universal kriging, block kriging, kriging with an external drift), and novel hybrid methods (228 hybrids plus numerous variants) of modern statistical methods or machine learning methods with mathematical and/or univariate geostatistical methods for spatial predictive modelling. For each method, two functions are provided, with one function for assessing the predictive errors and accuracy of the method based on cross-validation, and the other for generating spatial predictions. It also contains a couple of functions for data preparation and predictive accuracy assessment.
Build custom Europe SpatialPolygonsDataFrame, if you don't know what is
    a SpatialPolygonsDataFrame see SpatialPolygons() in 'sp', by example for mapLayout() in 'antaresViz'. 
    Antares is a powerful software developed by RTE to simulate and study electric power systems 
    (more information about 'Antares' here: <https://antares-simulator.org/>).
A set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) <doi:10.32614/RJ-2013-022> and Sartore et al. (2016) <doi:10.1016/j.cageo.2016.06.001>.
Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.
A collection of functions for estimating spatial and spatio-temporal regression models. Moran eigenvectors are used as spatial basis functions to efficiently approximate spatially dependent Gaussian processes (i.e., random effects eigenvector spatial filtering; see Murakami and Griffith 2015 <doi: 10.1007/s10109-015-0213-7>). The implemented models include linear regression with residual spatial dependence, spatially/spatio-temporally varying coefficient models (Murakami et al., 2017, 2024; <doi:10.1016/j.spasta.2016.12.001>,<doi:10.48550/arXiv.2410.07229>), spatially filtered unconditional quantile regression (Murakami and Seya, 2019 <doi:10.1002/env.2556>), Gaussian and non-Gaussian spatial mixed models through compositionally-warping (Murakami et al. 2021, <doi:10.1016/j.spasta.2021.100520>).
Identify statistically significant flow clusters using the local spatial network autocorrelation statistic G_ij* 
    proposed by 'Berglund' and 'Karlström' (1999) <doi:10.1007/s101090050013>. 
    The metric, an extended statistic of 'Getis/Ord' G ('Getis' and 'Ord' 1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>, 
    detects a group of flows having similar traits in terms of directionality. 
    You provide OD data and the associated polygon to get results 
    with several parameters, some of which are defined by spdep package.
Perform spatial analysis on network.
    Implement several methods for spatial analysis on network: Network Kernel Density estimation, 
    building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation 
    for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation
    References: Okabe et al (2019) <doi:10.1080/13658810802475491>;
    Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).
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 (2022) <doi:10.18637/jss.v103.i05>, 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'), 'iNaturalist',
    'eBird', Integrated Digitized 'Biocollections' ('iDigBio'), 'VertNet', 
    Ocean 'Biogeographic' Information System ('OBIS'), and 
    Atlas of Living Australia ('ALA'). Includes functionality for retrieving
    species occurrence data, and combining those data.
Fits single-species, multi-species, and integrated non-spatial and spatial occupancy models using Markov Chain Monte Carlo (MCMC). Models are fit using Polya-Gamma data augmentation detailed in Polson, Scott, and Windle (2013) <doi:10.1080/01621459.2013.829001>. Spatial models are fit using either Gaussian processes or Nearest Neighbor Gaussian Processes (NNGP) for large spatial datasets. Details on NNGP models are given in Datta, Banerjee, Finley, and Gelfand (2016) <doi:10.1080/01621459.2015.1044091> and Finley, Datta, and Banerjee (2022) <doi:10.18637/jss.v103.i05>. Provides functionality for data integration of multiple single-species occupancy data sets using a joint likelihood framework. Details on data integration are given in Miller, Pacifici, Sanderlin, and Reich (2019) <doi:10.1111/2041-210X.13110>. Details on single-species and multi-species models are found in MacKenzie, Nichols, Lachman, Droege, Royle, and Langtimm (2002) <doi:10.1890/0012-9658(2002)083[2248:ESORWD]2.0.CO;2> and Dorazio and Royle <doi:10.1198/016214505000000015>, respectively.
It can be useful to temporarily hide some text or other HTML elements 
    in 'Shiny' applications. Building on 'Spoiler-Alert.js', it is possible to select the
    elements to hide at startup, to partially reveal them by hovering them, and to 
    completely show them when clicking on them.
Implements an ensemble machine learning approach to predict the sporulation potential of metagenome-assembled genomes (MAGs) from uncultivated Firmicutes based on the presence/absence of sporulation-associated genes.
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.
Proposes application of spectral analysis and jack-knife resampling for multivariate sequence forecasting. The application allows for a fast random search in a compact space of hyper-parameters composed by Sequence Length and Jack-Knife Leave-N-Out.
The 'spork' syntax describes
 label formatting concisely, supporting
 mixed nesting of subscripts and superscripts
 to arbitrary depth. It intends to be easy
 to read and write in plain text, and easy
 to convert to equivalent presentations
 in 'plotmath', 'latex', and 'html'.  Greek symbols
 and a multiplication symbol are explicitly
 supported. See ?as_spork and ?as_previews.
Calculates ratings for two-player or 
  multi-player challenges. Methods included in package such as are able to 
  estimate ratings (players strengths) and their evolution in time, also able to 
  predict output of challenge. Algorithms are based on Bayesian Approximation 
  Method, and they don't involve any matrix inversions nor likelihood estimation. 
  Parameters are updated sequentially, and computation doesn't require any 
  additional RAM to make estimation feasible. Additionally, base of the package 
  is written in C++ what makes sport computation even faster. Methods used in the 
  package refer to Mark E. Glickman (1999) 
  <https://www.glicko.net/research/glicko.pdf>; 
  Mark E. Glickman (2001) <doi:10.1080/02664760120059219>; 
  Ruby C. Weng, Chih-Jen Lin (2011) <https://www.jmlr.org/papers/volume12/weng11a/weng11a.pdf>; 
  W. Penny, Stephen J. Roberts (1999) <doi:10.1109/IJCNN.1999.832603>.
A time series causal inference model for Randomized Controlled Trial (RCT) under spillover effect. 'SPORTSCausal' (Spillover Time Series Causal Inference) separates treatment effect and spillover effect from given responses of experiment group and control group by predicting the response without treatment. It reports both effects by fitting the Bayesian Structural Time Series (BSTS) model based on 'CausalImpact', as described in Brodersen et al. (2015) <doi:10.1214/14-AOAS788>.
Use of Knock Out and Round Robin Techniques in preparing tournament fixtures as discussed in the Book Health and Physical Education by 'Dr. V K Sharma'(2018,ISBN:978-93-5272-134-4).
Create scaled 'ggplot' representations of playing surfaces.
    Playing surfaces are drawn pursuant to rule-book specifications.
    This package should be used as a baseline plot for displaying any type of
    tracking data.
Providing convenience functions to connect R with the 'Spotify' application programming interface ('API'). At first it aims to help setting up 
  the OAuth2.0 Authentication flow. The default output of the get_*() functions is tidy, but optionally the functions could return the raw response from the 
  'API' as well. The search_*() and get_*() functions can be combined. See the vignette for more information and examples and the official Spotify for 
  Developers website <https://developer.spotify.com/documentation/web-api/> for information about the Web 'API'.
An R wrapper for pulling data from the 'Spotify' Web API 
  <https://developer.spotify.com/documentation/web-api/> in bulk, or post items on a
  'Spotify' user's playlist.
An algorithm to cluster satellite hot spot data spatially and temporally.
Compute relative or absolute population trends across space and time using predictions from models fitted to ecological population abundance data, as described in 
             Knape (2025) <doi:10.1016/j.ecolind.2025.113435>. The package supports models fitted by 'mgcv' or 'brms', and draws from posterior predictive distributions.
Summary ellipses superimposed on a scatter plot contain all bi-variate summary
            statistics for regression analysis. Furthermore, the outer ellipse flags potential
            outliers. Multiple groups can be compared in terms of centers and spreads as illustrated
            in the examples.
Provides a general purpose simulation-based power analysis 
   API for routine and customized simulation experimental designs. 
   The package focuses exclusively on Monte Carlo simulation experiment variants of 
   (expected) prospective power analyses, 
   criterion analyses, compromise analyses, sensitivity analyses, and 
   a priori/post-hoc analyses. The default simulation experiment functions defined within the package 
   provide stochastic variants of the power analysis subroutines in 
   G*Power 3.1 (Faul, Erdfelder, Buchner, and Lang, 2009) <doi:10.3758/brm.41.4.1149>,
   along with various other parametric and non-parametric power analysis 
   applications (e.g., mediation analyses) and support for Bayesian power analysis 
   by way of Bayes factors or posterior probability evaluations. 
   Additional functions for building 
   empirical power curves, reanalyzing simulation information, and 
   for increasing the precision of the resulting power 
   estimates are also included, each of which utilize similar API structures.
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>.
Predicts the occurrence times (in day-of-year) of spring phenological events. Three methods, including the accumulated degree days (ADD) method, the accumulated days transferred to a standardized temperature (ADTS) method, and the accumulated developmental progress (ADP) method, were used. See Shi et al. (2017a) <doi:10.1016/j.agrformet.2017.04.001> and Shi et al. (2017b) <doi:10.1093/aesa/sax063> for details.
The developed package can be used to generate a spatial population for different
             levels of relationships among the dependent and auxiliary variables along with spatially
             varying model parameters. A spatial layout is designed as a [0,k-1]x[0,k-1] square region on which observations
             are collected at (k x k) lattice points with a unit distance between any two neighbouring points along the horizontal
             and vertical axes. For method details see Chao, Liu., Chuanhua, Wei. and Yunan, Su. (2018).<doi:10.1080/10485252.2018.1499907>.
             The generated spatial population can be utilized in Geographically Weighted Regression model based analysis for studying the 
             spatially varying relationships among the variables. Furthermore, various statistical analysis can be performed on this spatially 
             generated data.
Testing for Spatial Dependence of Qualitative Data in Cross Section. The list of functions includes join-count tests, Q test, spatial scan test, similarity test and spatial runs test. The methodology of these models can be found in <doi:10.1007/s10109-009-0100-1> and <doi:10.1080/13658816.2011.586327>.
Sparse arrays interpreted as multivariate polynomials.
    Uses 'disordR' discipline (Hankin, 2022,
    <doi:10.48550/ARXIV.2210.03856>).  To cite the package in
    publications please use Hankin (2022) <doi:10.48550/ARXIV.2210.10848>.
The Statistical Package for REliability Data Analysis (SPREDA) implements recently-developed statistical methods for the analysis of reliability data. Modern technological developments, such as sensors and smart chips, allow us to dynamically track product/system usage as well as other environmental variables, such as temperature and humidity. We refer to these variables as dynamic covariates. The package contains functions for the analysis of time-to-event data with dynamic covariates and degradation data with dynamic covariates. The package also contains functions that can be used for analyzing time-to-event data with right censoring, and with left truncation and right censoring. Financial support from NSF and DuPont are acknowledged.
Provides a function for the estimation of parameters 
		in a binary regression with the skew-probit link function. 
		Naive MLE, Jeffrey type of prior and Cauchy prior type of penalization are implemented,
		as described in DongHyuk Lee and Samiran Sinha (2019+) <doi:10.1080/00949655.2019.1590579>.
Processing and analysis of field collected or simulated sprinkler 
    system catch data (depths) to characterize irrigation uniformity and efficiency using
    standard and other measures. Standard measures include the Christiansen coefficient
    of uniformity (CU) as found in Christiansen, J.E.(1942, ISBN:0138779295,
    "Irrigation by Sprinkling"); and distribution uniformity (DU), potential
    efficiency of the low quarter (PELQ), and application efficiency of the low quarter (AELQ)
    that are implementations of measures of the same notation in Keller, J. and Merriam, 
    J.L. (1978) "Farm Irrigation System Evaluation: A Guide for Management"
    <https://pdf.usaid.gov/pdf_docs/PNAAG745.pdf>. spreval::DU.lh is similar to spreval::DU
    but is the distribution uniformity of the low half instead of low quarter as in DU.
    spreval::PELQT is a version of spreval::PELQ adapted for traveling systems instead
    of lateral move or solid-set sprinkler systems. The function spreval::eff is
    analogous to the method used to compute application efficiency for furrow irrigation
    presented in Walker, W. and Skogerboe, G.V. (1987,ISBN:0138779295, "Surface
    Irrigation: Theory and Practice"),that uses piecewise integration of infiltrated
    depth compared against soil-moisture deficit (SMD), when the argument "target"
    is set equal to SMD.  The other functions contained in the package provide 
    graphical representation of sprinkler system uniformity, and other standard
    univariate parametric and non-parametric statistical measures as applied to
    sprinkler system catch depths. A sample data set of field test data spreval::catchcan
    (catch depths) is provided and is used in examples and vignettes. Agricultural systems
    emphasized, but this package can be used for landscape irrigation evaluation, and a
    landscape (turf) vignette is included as an example application.
Functions for calculating species richness for rarefaction and 
  extrapolation, primarily non-parametric species richness such as 
  jackknife, Chao1, and ACE. Also available are functions for plotting 
  species richness and extrapolation curves, and computing standard diversity 
  and entropy indices.
Recently, regularized variable selection has emerged as a powerful tool to identify and dissect gene-environment interactions. Nevertheless, in longitudinal studies with high dimensional genetic factors, regularization methods for G×E interactions have not been systematically developed. In this package, we provide the implementation of sparse group variable selection, based on both the quadratic inference function (QIF) and generalized estimating equation (GEE), to accommodate the bi-level selection for longitudinal G×E studies with high dimensional genomic features. Alternative methods conducting only the group or individual level selection have also been included. The core modules of the package have been developed in C++.
Computes the extended spring indices (SI-x) and false spring 
    exposure indices (FSEI). The SI-x indices are standard indices used for 
    analysis in spring phenology studies. In addition, the FSEI is also from 
    research on the climatology of false springs and adjusted to include an 
    early and late false spring exposure index. The indices  include the 
    first leaf index, first bloom index, and false spring exposure indices, 
    along with all calculations for all functions needed to calculate 
    each index. The main function returns all indices, but each function can 
    also be run separately. 
    Allstadt et al. (2015) <doi: 10.1088/1748-9326/10/10/104008> 
    Ault et al. (2015) <doi: 10.1016/j.cageo.2015.06.015> 
    Peterson and Abatzoglou (2014) <doi: 10.1002/2014GL059266>
    Schwarz et al. (2006) <doi: 10.1111/j.1365-2486.2005.01097.x> 
    Schwarz et al. (2013) <doi: 10.1002/joc.3625>.
An R data package containing setlists from all Bruce Springsteen concerts over 1973-2021. 
    Also includes all his song details such as lyrics and albums. Data extracted from: 
    <http://brucebase.wikidot.com/>.
An implementation of a computationally efficient method to fit large-scale interaction models based on the reluctant interaction selection principle. The method and its properties are described in greater depth in Yu, G., Bien, J., and Tibshirani, R.J. (2019) "Reluctant interaction modeling", which is available at <arXiv:1907.08414>.
Provides functions to perform the Sequential Probability Ratio Test (SPRT) 
  for hypothesis testing in Binomial, Poisson and Normal distributions. 
  The package allows users to specify Type I and Type II error probabilities, 
  decision thresholds, and compare null and alternative hypotheses sequentially 
  as data accumulate. It includes visualization tools for plotting the likelihood 
  ratio path and decision boundaries, making it easier to interpret results. 
  The methods are based on Wald (1945) <doi:10.1214/aoms/1177731118>, 
  who introduced the SPRT as one of the earliest and most powerful sequential 
  analysis techniques. This package is useful in quality control, clinical trials, 
  and other applications requiring early decision-making.The term 'SPRT' is an abbreviation and used intentionally.
It is a toolbox for Sequential Probability Ratio Tests (SPRT),  Wald (1945) <doi:10.2134/agronj1947.00021962003900070011x>.
    SPRTs are applied to the data during the sampling process, ideally after each observation.
    At any stage, the test will return a decision to either continue sampling or terminate and accept one of the specified hypotheses.
    The seq_ttest() function performs one-sample, two-sample, and paired t-tests for testing one- and two-sided hypotheses (Schnuerch & Erdfelder (2019) <doi:10.1037/met0000234>).
    The seq_anova() function allows to perform a sequential one-way fixed effects ANOVA (Steinhilber et al. (2023) <doi:10.31234/osf.io/m64ne>).
    Learn more about the package by using vignettes "browseVignettes(package = "sprtt")" or go to the website <https://meikesteinhilber.github.io/sprtt/>.
Sequential Poisson sampling is a variation of Poisson sampling for
    drawing probability-proportional-to-size samples with a given number of
    units, and is commonly used for price-index surveys. This package gives
    functions to draw stratified sequential Poisson samples according to the
    method by Ohlsson (1998, ISSN:0282-423X), as well as other order sample
    designs by Rosén (1997, <doi:10.1016/S0378-3758(96)00186-3>), and generate
    approximate bootstrap replicate weights according to the generalized
    bootstrap method by Beaumont and Patak
    (2012, <doi:10.1111/j.1751-5823.2011.00166.x>).
The systemPipeShiny (SPS) framework comes with many UI and server components. However, installing the whole framework is heavy and takes some time. If you would like to use UI and server components from SPS in your own Shiny apps, do not hesitate to try this package.
Fits spatial scale (SS) forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso models.  All area-level covariates are considered at all available scales to enter a model, but the SS algorithms are constrained to select each area-level covariate at a single spatial scale.
Provides basic functionality for labeling 
        iso- & anisotropic percolation clusters on 2D & 3D square lattices 
        with various lattice sizes, occupation probabilities, von Neumann & 
        Moore (1,d)-neighborhoods, and random variables weighting 
        the percolation lattice sites.
An implementation of the feature Selection procedure by Partitioning the entire Solution Paths
            (namely SPSP) to identify the relevant features rather than using a single tuning parameter. 
            By utilizing the entire solution paths, this procedure can obtain better selection accuracy than 
            the commonly used approach of selecting only one tuning parameter based on existing criteria, 
            cross-validation (CV), generalized CV, AIC, BIC, and extended BIC (Liu, Y., & Wang, P. (2018) 
            <doi:10.1214/18-EJS1434>). It is more stable and accurate (low false positive and 
            false negative rates) than other variable selection approaches. In addition, it can be flexibly 
            coupled with the solution paths of Lasso, adaptive Lasso, ridge regression, and other penalized 
            estimators.
Fits Bayesian hierarchical spatial and spatial-temporal process
    models for point-referenced Gaussian, Poisson, binomial, and binary data
    using stacking of predictive densities. It involves sampling from
    analytically available posterior distributions conditional upon candidate
    values of the spatial process parameters and, subsequently assimilate
    inference from these individual posterior distributions using Bayesian
    predictive stacking. Our algorithm is highly parallelizable and hence, much
    faster than traditional Markov chain Monte Carlo algorithms while delivering
    competitive predictive performance. See Zhang, Tang, and Banerjee (2025)
    <doi:10.1080/01621459.2025.2566449>, and, Pan, Zhang, Bradley, and Banerjee
    (2025) <doi:10.48550/arXiv.2406.04655> for details.
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 
    Minguez, R., Lopez, F.A., and Mur, J. (2022) <doi:10.18637/jss.v104.i11>
    Mur, J., Lopez, F.A., and Herrera, M. (2010) <doi:10.1080/17421772.2010.516443> 
    Lopez, F.A., Mur, J., and Angulo, A. (2014) <doi:10.1007/s00168-014-0624-2>.
A set of reliable routines to ease semiparametric survival regression modeling based on Bernstein polynomials. 'spsurv' includes proportional hazards, proportional odds and accelerated failure time frameworks for right-censored data. RV Panaro (2020) <arXiv:2003.10548>.
A design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more. For additional details, see Dumelle et al. (2023) <doi:10.18637/jss.v105.i03>.
The systemPipeShiny (SPS) framework comes with many useful utility functions. However, installing the whole framework is heavy and takes some time. If you like only a few useful utility functions from SPS, install this package is enough.
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). Bakar et al., (2015).
Spatio-temporal data have become increasingly popular in many research fields. Such data often have complex structures that are difficult to describe and estimate. This package provides reliable tools for modeling complicated spatio-temporal data. It also includes tools of online process monitoring to detect possible change-points in a spatio-temporal process over time. More specifically, the package implements the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) <doi:10.1002/sim.7622>, the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019) <doi:10.1002/sim.8315>, the three-step method for the joint estimation of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022) <doi:10.1007/s10463-021-00787-2>, the spatio-temporal disease surveillance method discussed in Qiu and Yang (2021) <doi:10.1002/sim.9150> that can accommodate the covariate effect, the spatial-LASSO-based process monitoring method proposed by Qiu and Yang (2023) <doi:10.1080/00224065.2022.2081104>, and the online spatio-temporal disease surveillance method described in Yang and Qiu (2020) <doi:10.1080/24725854.2019.1696496>.
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>.
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 datasets from Vigen (2015) 
    <https://web.archive.org/web/20230607181247/https%3A/tylervigen.com/spurious-correlations> rescued from the Internet
    Wayback Machine. These should be preserved for statistics introductory courses as these make it very clear that
    correlation is not causation.
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.
Data on the Spy vs. Spy comic strip of Mad magazine, created and
   written by Antonio Prohias.
The overall performance of soil ecosystem services and productivity greatly relies on soil health, making it a crucial indicator. The evaluation of soil physical, chemical, and biological parameters is necessary to determine the overall soil quality index. In our package, three commonly used methods, including linear scoring, regression-based, and principal component-based soil quality indexing, are employed to calculate the soil quality index. This package has been developed using concept of Bastida et al. (2008) and Doran and Parkin (1994) <doi:10.1016/j.geoderma.2008.08.007> <doi:10.2136/sssaspecpub35.c1>.
Runs 'SQL' statements on in-memory data frames within a temporary in-memory 'duckdb' data base.
Includes built-in methods for generating long 'SQL' CASE statements,
             and other 'SQL' statements that may otherwise be arduous to construct
             by hand.The generated statement can easily be concatenated to string
             literals to form queries to 'SQL'-like databases, such as when using 
             the 'RODBC' package. The current methods include casewhen() for 
             building CASE statements, inlist() for building IN statements, and
             updatetable() for building UPDATE statements.
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.
A convenient interface for formatting 'SQL' queries directly
    within 'R'. It acts as a wrapper around the 'sql_format' Rust crate.
    The package allows you to format 'SQL' code with customizable options,
    including indentation, case formatting, and more, ensuring your 'SQL'
    queries are clean, readable, and consistent.
Execute files of 'SQL' and manage database connections. 'SQL' statements and queries may be interpolated with string literals. Execution of individual statements and queries may be controlled with keywords. Multiple connections may be defined with 'YAML' and accessed by name.
Includes functions for interacting with common meta data fields, 
  writing insert statements, calling functions, and more for 'T-SQL' and 'Postgresql'.
sqliter helps users, mainly data munging practioneers, to organize
    their sql calls in a clean structure. It simplifies the process of
    extracting and transforming data into useful formats.
A tool for working with 'SQLite' databases. 'SQLite' has some idiosyncrasies and limitations that impose some hurdles to the R developer who is using this database as a repository. For instance, 'SQLite' doesn't have a date type and 'sqliteutils' has some functions to deal with that.
The nature of working with structured query language ('SQL') scripts 
             efficiently often requires the creation of temporary tables and there 
             are few clean and simple 'R' 'SQL' execution approaches that allow 
             you to complete this kind of work with the 'R' environment. This 
             package seeks to give 'SQL' implementations in 'R' a little love 
             by deploying functions that allow you to deploy complex 'SQL' 
             scripts within a typical 'R' workflow.
Wrapper for the non-validating 'SQL' parser 'Python' module 'sqlparse' <https://github.com/andialbrecht/sqlparse>. It allows parsing, splitting, and formatting 'SQL' statements.
Allows to build complex 'SQL' (Structured Query Language) queries dynamically. Classes and/or factory functions are used to produce a syntax tree from which the final character string is generated. Strings and identifiers are automatically quoted using the right quotes, using either ANSI (American National Standards Institute) quoting or the quoting style of an existing database connector. Style can be configured to set uppercase/lowercase for keywords, remove unnecessary spaces, or omit optional keywords.
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', 'Snowflake', 
  'Azure Synapse Analytics Dedicated', 'Apache Spark', 'SQLite', and 'InterSystems IRIS'.
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.
Provides a helper function, to bulk read 'SQL' code from separate files and load it into an 'R' list, where the list elements contain the individual statements and queries as strings. This works by annotating the 'SQL' code with a name comment, which also will be the name of the list element.
Provides an extension for 'SQL' queries as separate file 
    within 'targets' pipelines. The shorthand creates two targets,
    the query file and the query result.
'SqueezeMeta' is a versatile pipeline for the automated analysis of metagenomics/metatranscriptomics data (<https://github.com/jtamames/SqueezeMeta>). This package provides functions loading 'SqueezeMeta' results into R, filtering them based on different criteria, and visualizing the results using basic plots. The 'SqueezeMeta' project (and any subsets of it generated by the different filtering functions) is parsed into a single object, whose different components (e.g. tables with the taxonomic or functional composition across samples, contig/gene abundance profiles) can be easily analyzed using other R packages such as 'vegan' or 'DESeq2'. The methods in this package are further described in Puente-Sánchez et al., (2020) <doi:10.1186/s12859-020-03703-2>.
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].
Provides simple and powerful interfaces that facilitate interaction
    with 'ODBC' data sources. Each data source gets its own unique and dedicated
    interface, wrapped around 'RODBC'. Communication settings are remembered
    between queries, and are managed silently in the background. The interfaces
    support multi-statement 'SQL' scripts, which can be parameterised via
    metaprogramming structures and embedded 'R' expressions.
A subgroup identification method for precision medicine based on 
    quantitative objectives. This method can handle continuous, binary and 
    survival endpoint for both prognostic and predictive case. For the 
    predictive case, the method aims at identifying a subgroup for which 
    treatment is better than control by at least a pre-specified or 
    auto-selected constant. For the prognostic case, the method aims at 
    identifying a subgroup that is at least better than a 
    pre-specified/auto-selected constant. The derived signature is a linear 
    combination of predictors, and the selected subgroup are subjects with 
    the signature > 0. The false discover rate when no true subgroup exists
    is controlled at a user-specified level.
Algorithms for accelerating the convergence of slow,
        monotone sequences from smooth, contraction mapping such as the
        EM algorithm. It can be used to accelerate any smooth, linearly
        convergent acceleration scheme.  A tutorial style introduction
        to this package is available in a vignette on the CRAN download
        page or, when the package is loaded in an R session, with
        vignette("SQUAREM"). Refer to the J Stat Software article: <doi:10.18637/jss.v092.i07>.
Functions for color-based visualization of multivariate data, i.e. colorgrams or heatmaps.  Lower-level functions  map numeric values to colors, display a matrix as an array of colors, and draw color keys.  Higher-level plotting functions generate a bivariate histogram, a dendrogram aligned with a color-coded matrix, a triangular distance matrix, and more.
An implementation of statistical tools for the analysis 
    of rotation-valued time series and functional data. It relies on 
    pre-existing quaternion data structure provided by the 'Eigen' 'C++' 
    library.
Data processing and visualizations for rodent vocalizations exported from
    'DeepSqueak'. These functions are compatible with the 'SqueakR' Shiny Dashboard,
    which can be used to visualize experimental results and analyses.
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>].
It is often useful to produce short, quasi-unique
    identifiers (SQUIDs) without the benefit of a central authority to
    prevent duplication. Although Universally Unique Identifiers (UUIDs)
    provide for this, these are also unwieldy; for example, the most used UUID,
    version 4, is 36 characters long. SQUIDs are short (8 characters) at the
    expense of having more collisions, which can be mitigated by combining them
    with human-produced suffixes, yielding relatively brief, half
    human-readable, almost-unique identifiers (see for example the identifiers
    used for Decentralized Construct Taxonomies; Peters & Crutzen,
    2024 <doi:10.15626/MP.2022.3638>). SQUIDs are the number of 
    centiseconds elapsed since the beginning of 1970 converted to a base 30
    system. This package contains functions to produce SQUIDs as well as
    convert them back into dates and times.
Finds causal connections in precision data, finds lags and embeddings in 
  time series, guides training of neural networks and other smooth models, evaluates 
  their performance, gives a mathematically grounded answer to the over-training 
  problem.  Smooth regression is based on the Gamma test, which measures smoothness
  in a multivariate relationship.  Causal relations are smooth, noise is not.  
  'sr' includes the Gamma test and search techniques that use it. 
  References: Evans & Jones (2002) <doi:10.1098/rspa.2002.1010>, 
  AJ Jones (2004) <doi:10.1007/s10287-003-0006-1>.
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.
Manage a collection/library of R source packages. Discover, document, load, test 
  source packages. Enable to use those packages as if they were actually installed. Quickly reload
  only what is needed on source code change. Run tests and checks in parallel.
Connecting to databases requires boilerplate code to specify
    connection parameters and to set up sessions properly with the DBMS.
    This package provides a simple tool to fill two purposes: abstracting
    connection details, including secret credentials, out of your source
    code and managing configuration for frequently-used database connections
    in a persistent and flexible way, while minimizing requirements on the
    runtime environment.
Implementation of the SRCS method for a color-based visualization of the
    results of multiple pairwise tests on a large number of problem configurations, proposed in: 
    I.G. del Amo, D.A. Pelta. SRCS: a technique for comparing multiple algorithms under several
    factors in dynamic optimization problems. In: E. Alba, A. Nakib, P. Siarry
    (Eds.), Metaheuristics for Dynamic Optimization. Series: Studies in
    Computational Intelligence 433, Springer, Berlin/Heidelberg, 2012.
Sparse redundancy analysis
	for high dimensional (biomedical) data. Directional multivariate analysis 
	to express the maximum variance in the predicted data set by a linear 
	combination of variables of the predictive data set. Implemented in a 
	partial least squares framework, for more details see Csala et al. (2017) <doi:10.1093/bioinformatics/btx374>.
Provides you with easy, programmatic access to SRDP data.
Estimate average treatment effects (ATEs) in stratified randomized experiments. `sreg` supports a wide range of stratification designs, including matched pairs, n-tuple designs, and larger strata with many units — possibly of unequal size across strata. 'sreg' is designed to accommodate scenarios with multiple treatments and cluster-level treatment assignments, and accommodates optimal linear covariate adjustment based on baseline observable characteristics. 'sreg' computes estimators and standard errors based on Bugni, Canay, Shaikh (2018) <doi:10.1080/01621459.2017.1375934>; Bugni, Canay, Shaikh, Tabord-Meehan (2024+) <doi:10.48550/arXiv.2204.08356>; Jiang, Linton, Tang, Zhang (2023+) <doi:10.48550/arXiv.2201.13004>; Bai, Jiang, Romano, Shaikh, and Zhang (2024) <doi:10.1016/j.jeconom.2024.105740>; Bai (2022) <doi:10.1257/aer.20201856>; Bai, Romano, and Shaikh (2022) <doi:10.1080/01621459.2021.1883437>; Liu (2024+) <doi:10.48550/arXiv.2301.09016>; and Cytrynbaum (2024) <doi:10.3982/QE2475>.
It is a framework to fit semiparametric regression estimators for the total parameter of a finite population when the interest variable is asymmetric distributed. The main references for this package are Sarndal C.E., Swensson B., and Wretman J. (2003,ISBN: 978-0-387-40620-6, "Model Assisted Survey Sampling." Springer-Verlag) 
  Cardozo C.A, Paula G.A. and Vanegas L.H. (2022) "Generalized log-gamma additive partial linear mdoels with P-spline smoothing", Statistical Papers. 
  Cardozo C.A and Alonso-Malaver C.E. (2022). "Semi-parametric model assisted estimation in finite populations." In preparation.
Functions to perform split robust least angle regression. The approach first uses the
             least angle regression algorithm to split the variables into the models of an ensemble
             and robust estimates of the correlation between predictors. An elastic net estimator is 
             then applied to the selected predictors in each model using the imputed data from the
             detect deviating cell (DDC) method.
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>).
Provides most of the data files used in the textbook "Scientific Research and Methodology" by Dunn (2025, ISBN: 9781032496726).
The most important function of the R package is the genetic effects analysis of small RNA in hybrid plants via two methods, and at the same time, it provides various forms of graph related to data characteristics and expression analysis. In terms of two classification methods, one is the calculation of the additive (a) and dominant (d), the other is the evaluation of expression level dominance by comparing the total expression of the small RNA in progeny with the expression level in the parent species.
Performs the change-point detection in regression coefficients of linear model 
    by partitioning the regression coefficients into two classes of smoothness. The change-point and
    the regression coefficients are jointly estimated.
Generate data objects from XML versions of the Swiss
  Register of Plant Protection Products. An online version of the
  register can be accessed at <https://www.psm.admin.ch/de/produkte>. There is no
  guarantee of correspondence of the data read in using this package with that
  online version, or with the original registration documents.  Also, the
  Federal Food Safety and Veterinary Office, coordinating the authorisation of
  plant protection products in Switzerland, does not answer requests regarding
  this package.
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.
An independent, reproducible, and flexible Spatially Resolved Transcriptomics (SRT) simulation framework that can be used to facilitate the development of SRT analytical methods for a wide variety of SRT-specific analyses. It utilizes spatial localization information to simulate SRT expression count data in a reproducible and scalable fashion. Two major simulation schemes are implemented in 'SRTsim': reference-based and reference-free.
Srt file is a common subtitle format for videos, it contains subtitle and when the subtitle showed.
    This package is for align time of srt file, and also change color, style and position of subtitle in videos,
	the srt file will be read as a vector into R, and can be write into srt file after modified using this package.
Use piping, verbs like 'group_by' and 'summarize', and other
    'dplyr' inspired syntactic style when calculating summary statistics on survey
    data using functions from the 'survey' package.
Develops a framework for fisheries stock assessment simulation
    testing with Stock Synthesis (SS) as described in Anderson et al.
    (2014) <doi:10.1371/journal.pone.0092725>.
Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2021); Hara et al. (2010) <doi:10.1007/978-3-642-17537-4_52>) along with functions to simulate time series with time-varying variance and autocovariance (Patilea and Raissi(2014) <doi:10.1080/01621459.2014.884504>).
Singular spectrum analysis (SSA) decomposes a time series into interpretable components like trends, oscillations, and noise without strict distributional and structural assumptions. For method details see Golyandina N, Zhigljavsky A (2013). <doi:10.1007/978-3-642-34913-3>.
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.
Computes the trimmed-k mean by removing the k smallest and k largest values from a numeric vector. Created for STAT 5400 at the University of Iowa.
Includes general data manipulation functions, algorithms for statistical disclosure control (Langsrud, 2024) <doi:10.1007/978-3-031-69651-0_6> and functions for hierarchical computations by sparse model matrices (Langsrud, 2023) <doi:10.32614/RJ-2023-088>.
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.
It helps in determination of sample size for estimation of population mean and proportion based upon the availability of prior information on coefficient of variation (CV) of the population under Simple Random Sampling (SRS) with or without replacement sampling design. If there is no prior information on the population CV, then a small preliminary sample of size is selected to estimate the population CV which is then used for determination of final sample size. If the final sample size is more than the preliminary sample size, then the preliminary sample is augmented by drawing additional units from the remaining population units so that the size of the augmented sample is equal to the final sample size. On the other hand, if the preliminary sample size is larger than the final sample size, then the preliminary sample is considered as the final sample.
Web application using 'shiny' for the SSD (Species
    Sensitivity Distribution) module of the MOSAIC (MOdeling and
    StAtistical tools for ecotoxICology) platform. It estimates the
    Hazardous Concentration for x% of the species (HCx) from toxicity
    values that can be censored and provides various plotting options for
    a better understanding of the results. See our companion paper
    Kon Kam King et al. (2014) <doi:10.48550/arXiv.1311.5772>.
Reference data sets of species sensitivities to compare the 
    results of fitting species sensitivity distributions 
    using software such as 'ssdtools' and 'Burrlioz'. 
    It consists of 17 primary data sets from
    four different Australian and Canadian organizations as well as five
    datasets from anonymous sources. It also includes a data set of the
    results of fitting various distributions using different software.
Functions to visually and statistically analyze single system data.
A method that inherits the standard gene set variation analysis (GSVA) method and also provides the option to use summary statistics from any analysis (disease vs healthy, lesional side vs nonlesional side, etc..) input to define the direction of gene sets used for directional gene set score calculation for a given disease. Note to use this package, GSVA(>= 1.52.1) is needed to pre-installed. 
    Hanzelmann, S., Castelo, R., and Guinney, J. (2013) <doi:10.1186/1471-2105-14-7>.
Allows to map species richness and endemism based on stacked
    species distribution models (SSDM). Individuals SDMs can be created using a
    single or multiple algorithms (ensemble SDMs). For each species, an SDM can
    yield a habitat suitability map, a binary map, a between-algorithm variance
    map, and can assess variable importance, algorithm accuracy, and between-
    algorithm correlation. Methods to stack individual SDMs include summing
    individual probabilities and thresholding then summing. Thresholding can be
    based on a specific evaluation metric or by drawing repeatedly from a Bernoulli
    distribution. The SSDM package also provides a user-friendly interface.
Performs structured OLS (sOLS) and structured SIR (sSIR).
Species sensitivity distributions are cumulative probability
    distributions which are fitted to toxicity concentrations for
    different species as described by Posthuma et al.(2001)
    <isbn:9781566705783>. The ssdtools package uses Maximum Likelihood to
    fit distributions such as the gamma, log-logistic, log-normal and
    log-normal log-normal mixture. Multiple distributions can be averaged
    using Akaike Information Criteria.  Confidence intervals on hazard
    concentrations and proportions are produced by bootstrapping.
Calculates a modified Simplified Surface Energy Balance Index (SSEBI) and the Evaporative Fraction (EF) using geospatial raster data such as albedo and surface-air temperature difference (TS–TA). The SSEBI is computed from albedo and TS–TA to estimate surface moisture and evaporative dynamics, providing a robust assessment of surface dryness while accounting for atmospheric variations. Based on Roerink, Su, and Menenti (2000) <doi:10.1016/S1464-1909(99)00128-8>.
Functionality to parse server-sent events with a high-level
    interface that can be extended for custom applications.
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.
Fits group-regularized generalized linear models (GLMs) using the spike-and-slab group lasso (SSGL) prior of Bai et al. (2022) <doi:10.1080/01621459.2020.1765784> and extended to GLMs by Bai (2023) <doi:10.48550/arXiv.2007.07021>. This package supports fitting the SSGL model for the following GLMs with group sparsity: Gaussian linear regression, binary logistic regression, and Poisson regression.
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.
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, HLA alleles, or HLA haplotypes, and calculates frequencies 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. Users may also specify their own datasets, but file conventions must follow the prebundled Solberg dataset, or the mock haplotype dataset.
Estimation of an S-shaped function and its corresponding inflection point via a least squares approach. A sequential mixed primal-dual based algorithm is implemented for the fast computation. Details can be found in Feng et al. (2022) <doi:10.1111/rssb.12481>.
Spatial stratified heterogeneity (SSH) denotes the coexistence of within-strata homogeneity and between-strata heterogeneity. Information consistency-based methods provide a rigorous approach to quantify SSH and evaluate its role in spatial processes, grounded in principles of geographical stratification and information theory (Bai, H. et al. (2023) <doi:10.1080/24694452.2023.2223700>; Wang, J. et al. (2024) <doi:10.1080/24694452.2023.2289982>).
Convenient tools for exchanging files securely from within R. By
    encrypting the content safe passage of files (shipment) can be provided by
    common but insecure carriers such as ftp and email. Based on asymmetric
    cryptography no management of shared secrets is needed to make a secure
    shipment as long as authentic public keys are available. Public keys used
    for secure shipments may also be obtained from external providers as part of
    the overall process. Transportation of files will require that relevant
    services such as ftp and email servers are available.
Evaluating the consistency assumption of Network Meta-Analysis both globally and locally in the Bayesian framework. Inconsistencies are located by applying Bayesian variable selection to the inconsistency factors. The implementation of the method is described by Seitidis et al. (2023) <doi:10.1002/sim.9891>.
Extends the classical SSIM method proposed by 'Wang', 'Bovik', 'Sheikh', and 'Simoncelli'(2004) <doi:10.1109/TIP.2003.819861>. 
  for irregular lattice-based maps and raster images.
  The geographical SSIM method incorporates well-developed 'geographically weighted summary statistics'('Brunsdon', 'Fotheringham' and 'Charlton' 2002) <doi:10.1016/S0198-9715(01)00009-6> 
  with an adaptive bandwidth kernel function for irregular lattice-based maps.
Parse Standard Schedules Information file (types 2 and 3) into a Data Frame.
    Can also expand schedules into flights.
Functions that calculate appropriate sample sizes for one-sample t-tests, two-sample t-tests, and F-tests for microarray experiments based on desired power while controlling for false discovery rates. For all tests, the standard deviations (variances) among genes can be assumed fixed or random.  This is also true for effect sizes among genes in one-sample and two sample experiments. Functions also output a chart of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes.
We propose a procedure for sample size calculation while
    controlling false discovery rate for RNA-seq experimental design. Our
    procedure depends on the Voom method proposed for RNA-seq data analysis
    by Law et al. (2014) <DOI:10.1186/gb-2014-15-2-r29> and the sample size 
    calculation method proposed for microarray experiments by Liu and Hwang 
    (2007) <DOI:10.1093/bioinformatics/btl664>. We develop a set of functions
    that calculates appropriate sample sizes for two-sample t-test for RNA-seq
    experiments with fixed or varied set of parameters. The outputs also contain a
    plot of power versus sample size, a table of power at different sample sizes,
    and a table of critical test values at different sample sizes. 
    To install this package, please use 
    'source("http://bioconductor.org/biocLite.R"); biocLite("ssizeRNA")'. 
    For R version 3.5 or greater, please use  
    'if(!requireNamespace("BiocManager", quietly = TRUE)){install.packages("BiocManager")}; BiocManager::install("ssizeRNA")'.
Efficient coordinate ascent algorithm for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO of Rockova and George (2018) <doi:10.1080/01621459.2016.1260469>.
Creates an S4 class "SSM" and defines functions for fitting smooth
    supersaturated models, a polynomial model with spline-like behaviour.
    Functions are defined for the computation of Sobol indices for sensitivity
    analysis and plotting the main effects using FANOVA methods. It also
    implements the estimation of the SSM metamodel error using a GP model with
    a variety of defined correlation functions.
Performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.
In order to facilitate the adjustment of the sample selection models
  existing in the literature, we created the 'ssmodels' package. Our package
  allows the adjustment of the classic Heckman model (Heckman (1976),
  Heckman (1979) <doi:10.2307/1912352>), and the estimation of the parameters of
  this model via the maximum likelihood method and two-step method, in addition
  to the adjustment of the Heckman-t models introduced in the literature by
  Marchenko and Genton (2012) <doi:10.1080/01621459.2012.656011> and the
  Heckman-Skew model introduced in the literature by Ogundimu and Hutton (2016)
  <doi:10.1111/sjos.12171>. We also implemented functions to adjust the
  generalized version of the Heckman model, introduced by Bastos, Barreto-Souza,
  and Genton (2021) <doi:10.5705/ss.202021.0068>, that allows the inclusion of
  covariables to the dispersion and correlation parameters, and a function to
  adjust the Heckman-BS model introduced by Bastos and Barreto-Souza (2020)
  <doi:10.1080/02664763.2020.1780570> that uses the Birnbaum-Saunders
  distribution as a joint distribution of the selection and primary regression
  variables. This package extends and complements existing R packages such as 
  'sampleSelection' (Toomet and Henningsen, 2008) and 'ssmrob' (Zhelonkin et al., 2016), providing additional robust and flexible sample selection models.
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.
Estimation of robust estimators for multi-group and spatial data including the casewise robust Spatially Smoothed Minimum Regularized Determinant (ssMRCD) estimator and its usage for local outlier detection as described in Puchhammer and Filzmoser (2023) <doi:10.1080/10618600.2023.2277875> as well as for sparse robust PCA for multi-source data described in Puchhammer, Wilms and Filzmoser (2024) <doi:10.48550/arXiv.2407.16299>. Moreover, a cellwise robust multi-group Gaussian mixture model (MG-GMM) is implemented as described in Puchhammer, Wilms and Filzmoser (2024) <doi:10.48550/arXiv.2504.02547>. Included are also complementary visualization and parameter tuning tools.
Package provides a set of tools for robust estimation and inference for models with sample selectivity and endogenous treatment model. For details, see Zhelonkin and Ronchetti (2021) <doi:10.18637/jss.v099.i04>.
It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) <doi:10.1007/s00180-016-0691-1>.
A systematic bioinformatics tool to perform single-sample mutation-based pathway analysis by integrating somatic mutation data with the Protein-Protein Interaction (PPI) network. In this method, we use local and global weighted strategies to evaluate the effects of network genes from mutations according to the network topology and then calculate the mutation-based pathway enrichment score (ssMutPES) to reflect the accumulated effect of mutations of each pathway. Subsequently, the ssMutPES profiles are used for unsupervised spectral clustering to identify cancer subtypes.
Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., (2010) <DOI:10.1198/jasa.2009.ap08248>.) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood.  Mapping and other graphical functions are included.
Fits Bayesian spatio-temporal models and makes predictions on stream networks using the approach by Santos-Fernandez, Edgar, et al. (2022)."Bayesian spatio-temporal models for stream networks". <arXiv:2103.03538>. In these models, spatial dependence is captured using stream distance and flow connectivity, while temporal autocorrelation is modelled using vector autoregression methods.
Import, create and assemble data needed to fit spatial-statistical stream-network models using the 'SSN2' package for 'R'. Streams, observations, and prediction locations are represented as simple features and specific tools provided to define topological relationships between features; calculate the hydrologic distances (with flow-direction preserved) and the spatial additive function used to weight converging stream segments; and export the topological, spatial, and attribute information to an `SSN` (spatial stream network) object, which can be efficiently stored, accessed and analysed in 'R'. A detailed description of methods used to calculate and format the spatial data can be found in Peterson, E.E. and Ver Hoef, J.M., (2014) <doi:10.18637/jss.v056.i02>.
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.
The Simulation-based Sampling Protocol (SSP) is an R package designed to estimate sampling effort in studies of ecological communities. It is based on the concept of pseudo-multivariate standard error (MultSE) (Anderson & Santana-Garcon, 2015, <doi:10.1111/ele.12385>) and the simulation of ecological data. The theoretical background is described in Guerra-Castro et al. (2020, <doi:10.1111/ecog.05284>).
Pauly et al. (2008) <http://legacy.seaaroundus.s3.amazonaws.com/doc/Researcher+Publications/dpauly/PDF/2008/Books%26Chapters/FisheriesInLargeMarineEcosystems.pdf> created (and coined the name) 'Stock Status Plots' for a UNEP compendium on Large Marine Ecosystems(LMEs, Sherman and Hempel (2009)<https://marineinfo.org/imis?module=ref&refid=142061&printversion=1&dropIMIStitle=1>). Stock status plots are bivariate graphs summarizing the status (e.g., developing, fully exploited, overexploited, etc.), through time, of the multispecies fisheries of a fished area or ecosystem. This package contains three functions to generate stock status plots viz., SSplots_pauly() (as per the criteria proposed by Pauly et al.,2008), SSplots_kleisner() (as per the criteria proposed by Kleisner and Pauly (2011) <http://www.ecomarres.com/downloads/regional.pdf> and Kleisner et al. (2013) <doi:10.1111/j.1467-2979.2012.00469.x>)and SSplots_EPI() (as per the criteria proposed by Jayasankar et al.,2021 <https://eprints.cmfri.org.in/11364/>).
Implement a GAM-based (Generalized Additive Models) spatial surplus 
    production model (spatial SPM), aimed at modeling northern shrimp population 
    in Atlantic Canada but potentially to any stock in any location. The package 
    is opinionated in its implementation of SPMs as it internally makes the choice 
    to use penalized spatial gams with time lags. However, it also aims to provide 
    options for the user to customize their model. The methods are described in 
    Pedersen et al. (2022, <https://www.dfo-mpo.gc.ca/csas-sccs/Publications/ResDocs-DocRech/2022/2022_062-eng.html>).
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>,
    Handcock, Gile and Mar (2015) <doi:10.1111/biom.12255>,
    Kim and Handcock (2021) <doi:10.1093/jssam/smz055>, and
    McLaughlin, et. al. (2023) <doi:10.1214/23-AOAS1807>.
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.
'Takea Semantic Structure Analysis' (TSSA) and 'Sakai Sequential 
    Relation Analysis' (SSRA) for polytomous items. Package includes functions for
    generating a sequential relation table and a treegram to visualize the
    sequential relations between pairs of items.
Provides the necessary sample size for a longitudinal study with binary outcome in order to attain a pre-specified power while strictly maintaining the Type I error rate. 
 Kapur K, Bhaumik R, Tang XC, Hur K, Reda DJ, Bhaumik D (2014) <doi:10.1002/sim.6203>.
Calculates the power and sample size based on the difference in Restricted Mean Survival Time.
Implement the algorithm provided in scan for estimating the 
  transmission route on railway network using passenger volume. It is a 
  generalization of the scan statistic approach for railway network to identify
  the hot railway route for transmitting infectious diseases.
This is a collection of functions to calculate stop-signal reaction time (SSRT). Includes functions for both "integration" and "mean" methods; both fixed and adaptive stop-signal delays are supported (see appropriate functions). Calculation is based on Verbruggen et al. (2019) <doi:10.7554/eLife.46323.001> and Verbruggen et al. (2013) <doi:10.1177/0956797612457390>.
Tools to import survey files in the '.sss' (triple-s) format. 
    The package provides the function 'read.sss()' that reads the '.asc' 
    (or '.csv') and '.sss' files of a triple-s survey data file. 
    See also <https://triple-s.org/>.
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>.
Implements the Self-Similarity Test for Normality (SSTN), a new 
    statistical test designed to assess whether a given sample originates from 
    a normal distribution. The procedure is based on iteratively estimating the 
    characteristic function of the sum of standardized i.i.d. random variables and 
    comparing it to the characteristic function of the standard normal distribution. 
    A Monte Carlo procedure is used to determine the empirical distribution of the 
    test statistic under the null hypothesis. Details of the methodology are 
    described in Anarat and Schwender (2025), "A normality test based on self-similarity" 
    (Submitted).
Penalized and non-penalized maximum likelihood estimation of smooth
  transition vector autoregressive models with various types of transition weight
  functions, conditional distributions, and identification methods. Constrained
  estimation with various types of constraints is available. Residual based
  model diagnostics, forecasting, simulations, counterfactual analysis, and
  computation of impulse response functions, generalized impulse response functions,
  generalized forecast error variance decompositions, as well as historical
  decompositions. See
  Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>,
  Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>,
  Markku Lanne, Savi Virolainen (2025) <doi:10.1016/j.jedc.2025.105162>,
  Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.
Functions for sample size estimation and simulation in clinical
  trials. Includes methods for selecting the best group using the
  Indifference-zone approach, as well as designs for non-inferiority,
  equivalence, and negative binomial models. For the sample size calculation
  for non-inferiority of vaccines, the approach is based on Fleming, Powers,
  and Huang (2021) <doi:10.1177/1740774520988244>. The Indifference-zone
  approach is based on Sobel and Huyett (1957) <doi:10.1002/j.1538-7305.1957.tb02411.x> and
  Bechhofer, Santner, and Goldsman (1995, ISBN:978-0-471-57427-9).
Functions for performing stochastic search variable selection (SSVS) 
    for binary and continuous outcomes and visualizing the results. 
    SSVS is a Bayesian variable selection method used to estimate the probability 
    that individual predictors should be included in a regression model. 
    Using MCMC estimation, the method samples thousands of regression models 
    in order to characterize the model uncertainty regarding both the predictor 
    set and the regression parameters. For details see Bainter, McCauley, Wager, 
    and Losin (2020) Improving practices for selecting a subset of important 
    predictors in psychology: An application to predicting pain, Advances in 
    Methods and Practices in Psychological Science 3(1), 66-80 
    <DOI:10.1177/2515245919885617>.
Provides an R interface for 'SSW' (Striped Smith-Waterman)
    via its 'Python' binding 'ssw-py'. 'SSW' is a fast 'C' and 'C++'
    implementation of the Smith-Waterman algorithm for pairwise sequence
    alignment using Single-Instruction-Multiple-Data (SIMD) instructions.
    'SSW' enhances the standard algorithm by efficiently returning alignment
    information and suboptimal alignment scores.
    The core 'SSW' library offers performance improvements for various
    bioinformatics tasks, including protein database searches,
    short-read alignments, primary and split-read mapping,
    structural variant detection, and read-overlap graph generation.
    These features make 'SSW' particularly useful for genomic applications.
    Zhao et al. (2013) <doi:10.1371/journal.pone.0082138> developed the
    original 'C' and 'C++' implementation.
Set of tools to fit a semi-parametric regression model suitable for analysis of data sets in which the response variable is continuous, strictly positive, asymmetric and possibly, censored. Under this setup, both the median and the skewness of the response variable distribution are explicitly modeled by using semi-parametric functions, whose non-parametric components may be approximated by natural cubic splines or P-splines. Supported distributions for the model error include log-normal, log-Student-t, log-power-exponential, log-hyperbolic, log-contaminated-normal, log-slash, Birnbaum-Saunders and Birnbaum-Saunders-t distributions.
Implements the "shrinkage t" statistic 
   introduced in Opgen-Rhein and Strimmer (2007) <DOI:10.2202/1544-6115.1252> and 
   a shrinkage estimate of the "correlation-adjusted t-score" (CAT score) described 
   in Zuber and Strimmer (2009) <DOI:10.1093/bioinformatics/btp460>.  
   It also offers a convenient interface  to a number of other regularized 
   t-statistics commonly employed in high-dimensional case-control studies.
Efficiently estimate shape parameters of periodic time series 
    imagery with which  a statistical seasonal trend analysis (STA) is subsequently performed. 
    STA output can be exported in conventional raster formats. 
    Methods to visualize STA output are also implemented as well as the calculation 
    of additional basic statistics. STA is based on (R. Eastman, F. Sangermano, 
    B. Ghimire, H. Zhu, H. Chen, N. Neeti, Y. Cai, E. Machado and S. Crema, 2009) <doi:10.1080/01431160902755338>.
A stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) <doi:10.1038/s41598-020-79317-8>, Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.
Provides functionalities for performing stability analysis of genotype by environment interaction (GEI) to identify superior and stable genotypes across diverse environments. It implements Eberhart and Russell’s ANOVA method (1966)(<doi:10.2135/cropsci1966.0011183X000600010011x>), Finlay and Wilkinson’s Joint Linear Regression method (1963) (<doi:10.1071/AR9630742>), Wricke’s Ecovalence (1962, 1964), Shukla’s stability variance parameter (1972) (<doi:10.1038/hdy.1972.87>), Kang’s simultaneous selection for high yield and stability (1991) (<doi:10.2134/agronj1991.00021962008300010037x>), Additive Main Effects and Multiplicative Interaction (AMMI) method and Genotype plus Genotypes by Environment (GGE) Interaction methods.
Provides tools for Genotype by Environment Interaction (GEI) analysis, using statistical models and visualizations to assess genotype performance across environments. It helps researchers explore interaction effects, stability, and adaptability in multi-environment trials, identifying the best-performing genotypes in different conditions. Which Win Where!
Contains an implementation of 'StabilizedRegression', a regression framework for heterogeneous data introduced in Pfister et al. (2021) <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.
Functions for stabilometric signal quantification.      
    The input is a data frame containing the x, y coordinates of the center-of-pressure displacement.     
    Jose Magalhaes de Oliveira (2017) <doi:10.3758/s13428-016-0706-4> "Statokinesigram normalization method";       
    T E Prieto, J B Myklebust, R G Hoffmann, E G Lovett, B M Myklebust (1996) <doi:10.1109/10.532130> "Measures of postural steadiness: Differences between healthy young and elderly adults";      
    L F Oliveira et al (1996) <doi:10.1088/0967-3334/17/4/008> "Calculation of area of stabilometric signals using principal component analisys".
Density, distribution, quantile and hazard functions of a
    stable variate; generalized regression models for the parameters
    of a stable distribution. See the README for how to make equivalent calls
    to those of 'stabledist' (i.e., Nolan's 0-parameterization and 
    1-parameterization as detailed in Nolan (2020)). 
    See github for Lambert and Lindsey 1999 JRSS-C journal article, 
    which details the parameterization of the Buck (1995) stable.   
    See the Details section of the `?dstable` help file for context and 
    references.
Density, Probability and Quantile functions, and random number
  generation for (skew) stable distributions, using the parametrizations of
  Nolan.
Estimate the four parameters of stable laws using maximum
             likelihood method, generalised method of moments with
             finite and continuum number of points, iterative
             Koutrouvelis regression and Kogon-McCulloch method.  The
             asymptotic properties of the estimators (covariance
             matrix, confidence intervals) are also provided.
Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC) samplers to sample from a posterior distribution. This package determines whether the MCMC sample is large enough   to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected  and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and  Knudson (2018) <arXiv:1812:09384>.
Graphical and computational methods that can be used to assess the 
  stability of results from supervised statistical learning.
Provides tools to calculate the alpha parameter of the Weibull distribution, given beta
 and the age-specific fertility of a species, so that the population remains stable and stationary.
 Methods are inspired by "Survival profiles from linear models versus Weibull models: Estimating stable
 and stationary population structures for Pleistocene large mammals" (Martín-González et al. 2019)
 <doi:10.1016/j.jasrep.2019.03.031>.
An exploratory and heuristic approach for specification search in
    Structural Equation Modeling. The basic idea is to subsample the original data
    and then search for optimal models on each subset. Optimality is defined through
    two objectives: model fit and parsimony. As these objectives are conflicting,
    we apply a multi-objective optimization methods, specifically NSGA-II, to obtain
    optimal models for the whole range of model complexities. From these optimal
    models, we consider only the relevant model specifications (structures), i.e.,
    those that are both stable (occur frequently) and parsimonious and use those to
    infer a causal model.
An implementation of many measures for the
    assessment of the stability of feature selection. Both simple measures
    and measures which take into account the similarities between features
    are available, see Bommert (2020) <doi:10.17877/DE290R-21906>.
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.
Standardized accuracy (staccuracy) is a framework for expressing accuracy scores such that 50% represents a reference level of performance and 100% is a perfect prediction. The 'staccuracy' package provides tools for creating staccuracy functions as well as some recommended staccuracy measures. It also provides functions for some classic performance metrics such as mean absolute error (MAE), root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUCROC), as well as their winsorized versions when applicable.
A minimalist implementation of model stacking by
    Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> for boosted tree models.
    A classic, two-layer stacking model is implemented, where the first layer
    generates features using gradient boosting trees, and the second layer
    employs a logistic regression model that uses these features as inputs.
    Utilities for training the base models and parameters tuning are provided,
    allowing users to experiment with different ensemble configurations easily.
    It aims to provide a simple and efficient way to combine multiple
    gradient boosting models to improve predictive model performance
    and robustness.
Provides methods for inference using stacked multiple 
    imputations augmented with weights. The vignette provides example R code for 
    implementation in general multiple imputation settings. For additional details 
    about the estimation algorithm, we refer the reader to Beesley, Lauren J and 
    Taylor, Jeremy M G (2020) “A stacked approach for chained equations multiple 
    imputation incorporating the substantive model” <doi:10.1111/biom.13372>, 
    and Beesley, Lauren J and Taylor, Jeremy M G (2021) “Accounting for not-at-random 
    missingness through imputation stacking” <arXiv:2101.07954>.
Building predictive models with stacking which is a type of ensemble learning. Learners can be specified from those implemented in 'caret'. For more information of the package, see Nukui and Onogi (2023) <doi:10.1101/2023.06.06.543970>.
Model stacking is an ensemble technique that involves
    training a model to combine the outputs of many diverse statistical
    models, and has been shown to improve predictive performance in a
    variety of settings. 'stacks' implements a grammar for
    'tidymodels'-aligned model stacking.
S4 class wrappers for the 'ODBC' and Pool DBI connection, also provides some 
    utilities to paste small datasets to clipboard, rename columns. It is used by the package 'stacomiR' for
    connections to the database. Development versions of 'stacomiR' are available in R-forge.
Creates and fits staged event tree probability models,
             which are probabilistic graphical models capable of representing
             asymmetric conditional independence statements
             for categorical variables.
             Includes functions to create, plot and fit staged
             event trees from data, as well as many efficient structure
             learning algorithms.
             References:
             Carli F, Leonelli M, Riccomagno E, Varando G (2022).
             <doi: 10.18637/jss.v102.i06>.
             Collazo R. A., Görgen C. and Smith J. Q.
             (2018, ISBN:9781498729604).
             Görgen C., Bigatti A., Riccomagno E. and Smith J. Q. (2018)
             <arXiv:1705.09457>.
             Thwaites P. A., Smith, J. Q. (2017) <arXiv:1510.00186>.
             Barclay L. M., Hutton J. L. and Smith J. Q. (2013)
             <doi:10.1016/j.ijar.2013.05.006>.
             Smith J. Q. and Anderson P. E. (2008)
             <doi:10.1016/j.artint.2007.05.004>.
Provides facilities to implement and run population models of
    stage-structured species...
Efficiently estimates treatment effects in settings with randomized staggered rollouts, using tools 
    proposed by Roth and Sant'Anna (2023) <doi:10.48550/arXiv.2102.01291>.
Proposes an original instrument for measuring stakeholder influence on the development of an infrastructure project that is carried through by a municipality, drawing on stakeholder classifications (Mitchell, Agle, & Wood, 1997) and input-output modelling (Hester & Adams, 2013). Mitchell R., Agle B.R., & Wood D.J. <doi:10.2307/259247> Hester, P.T., & Adams, K.M. (2013) <doi:10.1016/j.procs.2013.09.282>.
Allows users to calculate pairwise Nei's Genetic Distances (Nei 1972), pairwise Fixation
 Indexes (Fst) (Weir & Cockerham 1984) and also Genomic Relationship matrixes following Yang et al. (2010) in mixed and single
 ploidy populations. Bootstrapping across loci is implemented during Fst calculation to generate confidence intervals and p-values
 around pairwise Fst values. StAMPP utilises SNP genotype data of any ploidy level (with the ability to handle missing data) and is coded to  
 utilise multithreading where available to allow efficient analysis of large datasets. StAMPP is able to handle genotype data from genlight objects 
 allowing integration with other packages such adegenet.
 Please refer to LW Pembleton, NOI Cogan & JW Forster, 2013, Molecular Ecology Resources, 13(5), 946-952. <doi:10.1111/1755-0998.12129> for the appropriate citation and user manual. Thank you in advance.
Perform spatial temporal analysis of moving polygons; a
    longstanding analysis problem in Geographic Information Systems. Facilitates
    directional analysis, distance analysis, and some other simple functionality for
    examining spatial-temporal patterns of moving polygons.
Fits semiparametric linear and multilevel models with non-parametric additive Bayesian additive regression tree (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) components and Stan (Stan Development Team (2021) <https://mc-stan.org/>) sampled parametric ones. Multilevel models can be expressed using 'lme4' syntax (Bates, Maechler, Bolker, and Walker (2015) <doi:10.18637/jss.v067.i01>).
Provides functions for the analysis of
        occupational and environmental data with non-detects. Maximum
        likelihood (ML) methods for censored log-normal data and
        non-parametric methods based on the product limit estimate (PLE)
        for left censored data are used to calculate all of the
        statistics recommended by the American Industrial Hygiene
        Association (AIHA) for the complete data case. Functions for
        the analysis of complete samples using exact methods are also
        provided for the lognormal model. Revised from 2007-11-05
        'survfit~1'.
Tools which allow regression variables to be placed on similar
    scales, offering computational benefits as well as easing interpretation of
    regression output.
Data on standard load profiles from the German Association of 
  Energy and Water Industries (BDEW Bundesverband der Energie- und 
  Wasserwirtschaft e.V.) in a tidy format. The data and methodology are 
  described in VDEW (1999), "Repräsentative VDEW-Lastprofile", 
  <https://www.bdew.de/media/documents/1999_Repraesentative-VDEW-Lastprofile.pdf>.
  The package also offers an interface for generating a standard load profile 
  over a user-defined period. For the algorithm, see VDEW (2000), 
  "Anwendung der Repräsentativen VDEW-Lastprofile step-by-step",
  <https://www.bdew.de/media/documents/2000131_Anwendung-repraesentativen_Lastprofile-Step-by-step.pdf>.
The <http://standartox.uni-landau.de> database offers cleaned,
    harmonized and aggregated ecotoxicological test data, which can
    be used for assessing effects and risks of chemical concentrations
    found in the environment.
Easily create alerts, notifications, modals, info tips and loading 
    screens in 'Shiny'. Includes several options to customize alerts and 
    notifications by including text, icons, images and buttons. When wrapped 
    around a 'Shiny' output, loading screen is automatically displayed while 
    the output is being recalculated.
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.
Infrastructure and functions that can be used for 
    integrating 'Stan' (Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>) code into 
    stand alone R packages which in turn use the  'CmdStan' engine  which is often accessed through 
    'CmdStanR'. Details given in Stan Development Team (2025) <https://mc-stan.org/cmdstanr/>. 
    Using 'CmdStanR' and pre-written 'Stan' code can make package installation easy. Using 'staninside'
    offers a way to cache user-compiled 'Stan' models in user-specified directories reducing the need
    to recompile the same model multiple times.
Implementation of popular mortality models using the 'rstan' 
    package, which provides the R interface to the 'Stan' C++ library for 
    Bayesian estimation. The package supports well-known models proposed in the 
    actuarial and demographic literature including the Lee-Carter (1992) 
    <doi:10.1080/01621459.1992.10475265> and the Cairns-Blake-Dowd (2006) 
    <doi:10.1111/j.1539-6975.2006.00195.x> models. By a simple call, the user 
    inputs deaths and exposures and the package outputs the MCMC simulations for
    each parameter, the log likelihoods and predictions. Moreover, the package 
    includes tools for model selection and Bayesian model averaging by leave 
    future-out validation.
An interface to the 'Python' package 'stanza' <https://stanfordnlp.github.io/stanza/index.html>.
    'stanza' is a 'Python' 'NLP' library for many human languages.
    It contains support for running various accurate natural language processing tools on 60+ languages.
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 functions 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.
'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.'
A robust and powerful empirical Bayesian approach is developed for replicability analysis of two large-scale experimental studies. The method controls the false discovery rate by using the joint local false discovery rate based on the replicability null as the test statistic. An EM algorithm combined with a shape constraint nonparametric method is used to estimate unknown parameters and functions. [Li, Y. et al., (2024), <doi:10.1371/journal.pgen.1011423>].
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 2021 <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>).
Provides modular functions and applications for quickly generating 
    plots and tables. Each modular function opens a graphical user interface 
    providing the user with options to create and customise plots and tables.
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 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 of this package is to define transformations that allow
    obtaining stars from flat tables easily. In addition, it includes
    basic data cleaning, dimension enrichment, incremental data refresh
    and query operations, adapted to this context.
Miscellaneous functions for working with 'stars' objects, mainly single-band rasters. Currently includes functions for: (1) focal filtering, (2) detrending of Digital Elevation Models, (3) calculating flow length, (4) calculating the Convergence Index, (5) calculating topographic aspect and topographic slope.
Makes it possible to serve map tiles for web maps (e.g. leaflet) based on a function or a stars object without having to render them in advance. This enables parallelization of the rendering, separating the data source and visualization location and to provide web services.
The package is used for calibrating the design parameters for single-to-double arm transition design proposed by Shi and Yin (2017). The calibration is performed via numerical enumeration to find the optimal design that satisfies the constraints on the type I and II error rates.
Get started with new projects by dropping a skeleton of a new
    project into a new or existing directory, initialise git repositories,
    and create reproducible environments with the 'renv' package. The
    package allows for dynamically named files, folders, file content, as
    well as the functionality to drop individual template files into
    existing projects.
Automatically fetch, transform and arrange subsets of
    multidimensional data sets (collections of files) stored in local and/or
    remote file systems or servers, using multicore capabilities where possible.
    This 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.
An interactive document on  the topic of basic statistical analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://jarvisatharva.shinyapps.io/StatisticsPrimer/>.
Datasets for the textbook Stat2: Modeling with Regression and ANOVA (second edition). 
    The package also includes data for the first edition, Stat2: Building Models for a World of Data
    and a few functions for plotting diagnostics.
Settings and functions to extend the 'knitr' 'Stata' engine.
A set of tools inspired by 'Stata' to explore data.frames ('summarize',
    'tabulate', 'xtile', 'pctile', 'binscatter', elapsed quarters/month, lead/lag).
Basic statistical methods with some modifications for the course Statistical Methods at Federal University of Bahia (Brazil). All methods in this packages are explained in the text book of Montgomery and Runger (2010) <ISBN: 978-1-119-74635-5>.
An easy connection with R to Statistics Canada's Web Data Service. Open economic data (formerly known as CANSIM tables, now identified by Product IDs (PID)) are accessible as a data frame, directly in the user's R environment.
    Warin, Le Duc (2019) <doi:10.6084/m9.figshare.10544735>.
A "spellchecker" for statistics. It checks whether your
    p-values match their accompanying test statistic and degrees of
    freedom. statcheck searches for null-hypothesis significance test
    (NHST) in APA style (e.g., t(28) = 2.2, p < .05). It recalculates the
    p-value using the reported test statistic and degrees of freedom. If
    the reported and computed p-values don't match, statcheck will flag
    the result as an error. If the reported p-value is statistically
    significant and the recomputed one is not, or vice versa, the result
    will be flagged as a decision error.  You can use statcheck directly
    on a string of text, but you can also scan a PDF or HTML file, or even
    a folder of PDF and/or HTML files.  Statcheck needs an external
    program to convert PDF to text: Xpdf. Instructions on where and how to
    download this program, how to install statcheck, and more details on
    what statcheck can and cannot do can be found in the online manual:
    <https://rpubs.com/michelenuijten/statcheckmanual>.  You can find a
    point-and-click web interface to scan PDF or HTML or DOCX articles on
    <http://statcheck.io>.
The goal of statcodelists is to promote the reuse and exchange of statistical 
    information and related metadata with making the internationally standardized SDMX code 
    lists available for the R user. SDMX has been published as an ISO International Standard 
    (ISO 17369). The metadata definitions, including the codelists are updated regularly 
    according to the standard. The authoritative version of the code lists made available in this
    package is <https://sdmx.org/?page_id=3215/>.
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.
Provides fitting functions and other tools for decision confidence 
    and metacognition researchers, including meta-d'/d', often considered to be 
    the gold standard to measure metacognitive efficiency, and information-theoretic measures of metacognition. 
    Also allows to fit and compare several static models of decision making and confidence.
Import data from the 'STATcube' REST API or from the open data
    portal of Statistics Austria. This package includes a client for API
    requests as well as parsing utilities for data which originates from
    'STATcube'. Documentation about 'STATcubeR' is provided by several vignettes 
    included in the package as well as on the public 'pkgdown' page at 
    <https://statistikat.github.io/STATcubeR/>.
Statistical analysis methods for environmental data are implemented. There is a particular focus on robust methods, and on methods for compositional data. In addition, larger data sets from geochemistry are provided. The statistical methods are described in Reimann, Filzmoser, Garrett, Dutter (2008, ISBN:978-0-470-98581-6).
Support for reading and writing files in StatDataML---an XML-based data exchange format.
A lightweight tool that provides a reproducible workflow for selecting and executing appropriate statistical analysis in one-way or two-way experimental designs. The package automatically checks for data normality, conducts parametric (ANOVA) or non-parametric (Kruskal-Wallis) tests, performs post-hoc comparisons with Compact Letter Displays (CLD), and generates publication-ready boxplots, faceted plots, and heatmaps. It is designed for researchers seeking fast, automated statistical summaries and visualization. Based on established statistical methods including Shapiro and Wilk (1965) <doi:10.2307/2333709>, Kruskal and Wallis (1952) <doi:10.1080/01621459.1952.10483441>, Tukey (1949) <doi:10.2307/3001913>, Fisher (1925) <ISBN:0050021702>, and Wickham (2016) <ISBN:978-3-319-24277-4>.
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.
Provides a unique dataset of historical forest cover across all states in the United States, spanning from 1907 to 2017, along with 1630 as a reference year. This dataset is important for understanding environmental changes and land use trends over time. It includes functionality for easy access of the 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.
Contains several tools for nonlinear regression analyses and general data analysis in biology and agriculture. Contains also datasets for practicing and teaching purposes. Supports the blog: Onofri (2024) "Fixing the bridge between biologists and statisticians" <https://www.statforbiology.com> and the book: Onofri (2024) "Experimental Methods in Agriculture" <https://www.statforbiology.com/_statbookeng/>. The blog is a collection of short articles aimed at improving the efficiency of communication between biologists and statisticians, as pointed out in Kozak (2016) <doi:10.1590/0103-9016-2015-0399>, spreading a better awareness of the potential usefulness, beauty and limitations of biostatistic.
Fast single trait Genome Wide Association Studies (GWAS) following 
    the method described in Kang et al. (2010), <doi:10.1038/ng.548>.        
    One of a series of statistical genetic packages for streamlining the 
    analysis of typical plant breeding experiments developed by Biometris.
Analysis of multi environment data of plant breeding experiments
    following the analyses described in Malosetti, Ribaut, 
    and van Eeuwijk (2013), <doi:10.3389/fphys.2013.00044>.
    One of a series of statistical genetic packages for streamlining the analysis of 
    typical plant breeding experiments developed by Biometris.
    Some functions have been created to be used in conjunction with the R 
    package 'asreml' for the 'ASReml' software, which can be obtained upon 
    purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).
Phenotypic analysis of data coming from high throughput 
    phenotyping (HTP) platforms, including different types of outlier detection,
    spatial analysis, and parameter estimation. The package is being developed
    within the EPPN2020 project (<https://cordis.europa.eu/project/id/731013>).
    Some functions have been created to be used in conjunction with the R 
    package 'asreml' for the 'ASReml' software, which can be obtained upon 
    purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).
For biparental, three and four-way crosses Identity by Descent 
    (IBD) probabilities can be calculated using Hidden Markov Models and 
    inheritance vectors following Lander and Green
    (<https://www.jstor.org/stable/29713>) and Huang
    (<doi:10.1073/pnas.1100465108>). One of a series of statistical genetic 
    packages for streamlining the analysis of typical plant breeding experiments
    developed by Biometris.
For Multi Parent Populations (MPP) Identity By Descend (IBD) 
    probabilities are computed using Hidden Markov Models. These probabilities 
    are then used in a mixed model approach for QTL Mapping as described in 
    Li et al. (<doi:10.1007/s00122-021-03919-7>).
Fast multi-trait and multi-trail Genome Wide Association Studies 
    (GWAS) following the method described in Zhou and Stephens. (2014), 
    <doi:10.1038/nmeth.2848>. One of a series of statistical genetic packages 
    for streamlining the analysis of typical plant breeding experiments 
    developed by Biometris.
Phenotypic analysis of field trials using mixed models with and 
    without spatial components. One of a series of statistical genetic packages 
    for streamlining the analysis of typical plant breeding experiments developed
    by Biometris.    
    Some functions have been created to be used in conjunction with the R 
    package 'asreml' for the 'ASReml' software, which can be obtained upon 
    purchase from 'VSN' international (<https://vsni.co.uk/software/asreml-r/>).
Contains statistical methods to analyze graphs, such as graph parameter estimation, 
    model selection based on the Graph Information Criterion, statistical tests to discriminate two or more populations of graphs, 
    correlation between graphs, and clustering of graphs.
    References: Takahashi et al. (2012) <doi:10.1371/journal.pone.0049949>, Fujita et al. (2017) <doi:10.3389/fnins.2017.00066>,
    Fujita et al. (2017) <doi:10.1016/j.csda.2016.11.016>, Fujita et al. (2019) <doi:10.1093/comnet/cnz028>.
Provides a convenient interface to the 'staticrypt' by Robin Moisson <https://github.com/robinmoisson/staticrypt>---'Node.js' package for 
    adding a password protection layer to static HTML pages. This package can be 
    integrated into the post-render process of 'quarto' documents to secure them 
    with a password.
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>.
Detrending multivariate time-series to approximate stationarity when dealing with intensive longitudinal data, prior to Vector Autoregressive (VAR) or multilevel-VAR estimation. Classical VAR assumes weak stationarity (constant first two moments), and deterministic trends inflate spurious autocorrelation, biasing Granger-causality and impulse-response analyses. All functions operate on raw panel data and write detrended columns back to the data set, but differ in the level at which the trend is estimated. See, for instance, Wang & Maxwell (2015) <doi:10.1037/met0000030>; Burger et al. (2022) <doi:10.4324/9781003111238-13>; Epskamp et al. (2018) <doi:10.1177/2167702617744325>.
A collection of miscellaneous statistical functions for 
    probability distributions: 'dbern()', 'pbern()', 'qbern()', 'rbern()' for 
    the Bernoulli distribution, and 'distr2name()', 'name2distr()' for 
    distribution names; 
    probability density estimation: 'densityfun()'; 
    most frequent value estimation: 'mfv()', 'mfv1()'; 
    other statistical measures of location: 'cv()' (coefficient of variation),
    'midhinge()', 'midrange()', 'trimean()'; 
    construction of histograms: 'histo()', 'find_breaks()'; 
    calculation of the Hellinger distance: 'hellinger()'; 
    use of classical kernels: 'kernelfun()', 'kernel_properties()'; 
    univariate piecewise-constant regression: 'picor()'.
A collection of statistical and geometrical tools
	including the aligned rank transform (ART; Higgins et al.
	1990 <doi:10.4148/2475-7772.1443>; Peterson 2002
	<doi:10.22237/jmasm/1020255240>; Wobbrock et al. 2011
	<doi:10.1145/1978942.1978963>), 2-D histograms and
	histograms with overlapping bins, a function for making all
	possible formulae within a set of constraints, amongst others.
Transform complex statistical output into straightforward, understandable, and context-aware natural language descriptions using Large Language Models (LLMs), making complex analyses more accessible to individuals with varying statistical expertise. It relies on the 'ellmer' package to interface with LLM providers including OpenAI <https://openai.com/>, Google AI Studio <https://aistudio.google.com/>, and Anthropic <https://www.anthropic.com/> (API keys are required and managed via 'ellmer').
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.
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, secure convergence and exact distributional calculations for unit deviances.
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 cross-sectional network modeling 
    with the 'statnet' software suite <https://github.com/statnet>.
Get programmatic access to data from the Czech public
    budgeting and accounting database, Státní pokladna
    <https://monitor.statnipokladna.gov.cz/>.
Statistical performance measures used in the econometric literature to evaluate conditional covariance/correlation matrix estimates (MSE, MAE, Euclidean distance, Frobenius distance, Stein distance, asymmetric loss function, eigenvalue loss function and the loss function defined in Eq. (4.6) of Engle et al. (2016) <doi:10.2139/ssrn.2814555>). Additionally, compute Eq. (3.1) and (4.2) of Li et al. (2016) <doi:10.1080/07350015.2015.1092975> to compare the factor loading matrix. The statistical performance measures implemented have been previously used in, for instance, Laurent et al. (2012) <doi:10.1002/jae.1248>,  Amendola et al. (2015) <doi:10.1002/for.2322> and  Becker et al. (2015) <doi:10.1016/j.ijforecast.2013.11.007>.
A small collection of data on graduate statistics programs from the United States.
Implements confidence interval and sample size methods that 
    are especially useful in psychological research. The methods can be 
    applied in 1-group, 2-group, paired-samples, and multiple-group designs
    and to a variety of parameters including means, medians, proportions, 
    slopes, standardized mean differences, standardized linear contrasts 
    of means, plus several measures of correlation and association. 
    Confidence interval and sample size functions are given for single 
    parameters as well as differences, ratios, and linear contrasts of
    parameters. The sample size functions can be used to approximate the 
    sample size needed to estimate a parameter or function of parameters 
    with desired confidence interval precision or to perform a variety of
    hypothesis tests (directional two-sided, equivalence, superiority, 
    noninferiority) with desired power. For details see: Statistical Methods
    for Psychologists, Volumes 1 – 4, <https://dgbonett.sites.ucsc.edu/>.
Generates a random quotation from a database of quotes on topics
    in statistics, data visualization and science. Other functions allow searching
    the quotes database by key term tags, or authors or creating a word cloud.
    The output is designed to be suitable for use at the console, in Rmarkdown
    and LaTeX.
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.
Implements the methodological developments found in Hermes (2025) <doi:10.48550/arXiv.2503.02786>, and allows for the statistical modeling of data consisting of multiple users that provide an ordinal rating for one or multiple items.
Work with and download road traffic casualty data from Great
    Britain. Enables access to the UK's official road safety statistics,
    'STATS19'. Enables users to specify a download directory for the data,
    which can be set permanently by adding `STATS19_DOWNLOAD_DIRECTORY=/path/to/a/dir`
    to your `.Renviron` file, which can be opened with `usethis::edit_r_environ()`.
    The data is provided as a series of `.csv` files.
    This package downloads, reads-in and formats the data, making it suitable
    for analysis. See the stats19 vignette for details. Data available from
    1979 to 2024. See the official data series at
    <https://www.data.gov.uk/dataset/cb7ae6f0-4be6-4935-9277-47e5ce24a11f/road-accidents-safety-data>.
  The package is described in a paper in the Journal of Open Source Software
  (Lovelace et al. 2019) <doi:10.21105/joss.01181>.
  See 
  Gilardi et al. (2022) <doi:10.1111/rssa.12823>,
  Vidal-Tortosa et al. (2021) <doi:10.1016/j.jth.2021.101291>,
  Tait et al. (2023) <doi:10.1016/j.aap.2022.106895>, and
  León et al. (2025) <doi:10.18637/jss.v114.i09>
  for examples of how the data can be used for
  methodological and empirical research.
Univariate and multivariate normal data simulation. They also supply a brief summary of the analysis for each experiment/design:
    - Independent samples.
    - One-way and two-way Anova.
    - Paired samples (T-Test & Regression).
    - Repeated measures (Anova & Multiple Regression).
    - Clinical Assay.
Pull data from the 'STAT Search Analytics' API <https://help.getstat.com/knowledgebase/api-services/>. It was developed by the Search Discovery team to help analyze keyword ranking data.
Utilities for producing dataframes with rich details for the
    most common types of statistical approaches and tests: parametric,
    nonparametric, robust, and Bayesian t-test, one-way ANOVA, correlation
    analyses, contingency table analyses, and meta-analyses. The functions
    are pipe-friendly and provide a consistent syntax to work with tidy
    data. These dataframes additionally contain expressions with
    statistical details, and can be used in graphing packages. This
    package also forms the statistical processing backend for
    'ggstatsplot'. References: Patil (2021) <doi:10.21105/joss.03236>.
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.
Includes an interactive application designed to support
    educators in wide-ranging disciplines, with a particular focus on
    those teaching introductory statistical methods (descriptive and/or
    inferential) for data analysis. Users are able to randomly generate
    data, make new versions of existing data through common adjustments
    (e.g., add random normal noise and perform transformations), and check
    the suitability of the resulting data for statistical analyses.
Computes confidence intervals for variance using the Chi-Square distribution, without requiring raw data. Wikipedia (2025) <https://en.wikipedia.org/wiki/Chi-squared_distribution>. 'All-in-One Chi Distribution CI' provides functions to calculate confidence intervals for the population variance based on a chi-squared distribution, utilizing a sample variance and sample size. It offers only a simple all-in-one method for quick calculations to find the CI for Chi Distribution.
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.
A set of consistent, opinionated functions to quickly check
    function arguments, coerce them to the desired configuration, or
    deliver informative error messages when that is not possible.
Provides functions to model and forecast crop yields using a spatial temporal conditional copula approach. 
    The package incorporates extreme weather covariates and Bayesian Structural Time Series models to analyze crop 
    yield dependencies across multiple regions. Includes tools for fitting, simulating, and visualizing results. 
    This method build upon established R packages, including 'Hofert' 'et' 'al'. (2025) <doi:10.32614/CRAN.package.copula>, 
    'Scott' (2024) <doi:10.32614/CRAN.package.bsts>, and 'Stephenson' 'et' 'al'. (2024) <doi:10.32614/CRAN.package.evd>.
Enables the creation of Chain Event Graphs over spatial areas, with an optional 'Shiny' user interface. Allows users to fully customise both the structure and underlying model of the Chain Event Graph, offering a high degree of flexibility for tailored analyses. For more details on Chain Event Graphs, see Freeman, G., & Smith, J. Q. (2011) <doi:10.1016/j.jmva.2011.03.008>, Collazo R. A., Görgen C. and Smith J. Q. (2018, ISBN:9781498729604) and Barclay, L. M., Hutton, J. L., & Smith, J. Q. (2014) <doi:10.1214/13-BA843>.
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.
Algorithms of nonparametric sequential test and online 
  change-point detection for streams of univariate (sub-)Gaussian, binary, 
  and bounded random variables, introduced in following publications -
  Shin et al. (2024) <doi:10.48550/arXiv.2203.03532>,
  Shin et al. (2021) <doi:10.48550/arXiv.2010.08082>.
Provides crop yield and meteorological data for Ontario, Canada. 
  Includes functions for fitting and predicting data using spatio-temporal models, as well as
  tools for visualizing the results. The package builds upon existing R packages, including
  'copula' (Hofert et al., 2025) <doi:10.32614/CRAN.package.copula>, and
  'bsts' (Scott, 2024) <doi:10.32614/CRAN.package.bsts>.
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.
Analysis of spatial relationships between cell types in spatial transcriptomics data. Spatial proximity is a critical factor in cell-cell communication. The package calculates nearest neighbor distances between specified cell types and provides visualization tools to explore spatial patterns. Applications include studying cell-cell interactions, immune microenvironment characterization, and spatial organization of tissues.
Functions for computing a standardized moderation effect
             in moderated regression and forming its confidence interval
             by nonparametric bootstrapping as proposed in
             Cheung, Cheung, Lau, Hui, and Vong (2022)
             <doi:10.1037/hea0001188>. Also includes simple-to-use
             functions for computing conditional effects (unstandardized
             or standardized) and plotting moderation effects.
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>.
Contains more modern tools for causal inference using regression 
      standardization. Four general classes of models are implemented; generalized 
      linear models, conditional generalized estimating equation models, 
      Cox proportional hazards models, and shared frailty gamma-Weibull models. 
      Methodological details are described in Sjölander, A. (2016) <doi:10.1007/s10654-016-0157-3>. 
      Also includes functionality for doubly robust estimation for generalized linear models 
      in some special cases, and the ability to implement custom models.
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.
The steepness package 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.
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>.
Fit Hawkes and log-Gaussian Cox process models with extensions. Introduced in Hawkes (1971) <doi:10.2307/2334319> a Hawkes process is a self-exciting temporal point process where the occurrence of an event immediately increases the chance of another. We extend this to consider self-inhibiting process and a non-homogeneous background rate. A log-Gaussian Cox process is a Poisson point process where the log-intensity is given by a Gaussian random field. We extend this  to a joint likelihood formulation fitting a marked log-Gaussian Cox model. In addition, the package offers functionality to fit self-exciting spatiotemporal point processes. Models are fitted via maximum likelihood using 'TMB' (Template Model Builder). Where included 1) random fields are assumed to be Gaussian and are integrated over using the Laplace approximation and 2) a stochastic partial differential equation model, introduced by Lindgren, Rue, and Lindström. (2011) <doi:10.1111/j.1467-9868.2011.00777.x>, is defined for the field(s).
Manages and display stellar tracks and isochrones from 
	     Pisa low-mass database. Includes tools for isochrones
	     construction and tracks interpolation.
Use stem analysis data to reconstructing tree growth and carbon accumulation. Users can independently or in combination perform a number of standard tasks for any tree species.
    (i) Age class determination.
    (ii) The cumulative growth, mean annual increment, and current annual increment of diameter at breast height (DBH) with bark, tree height, and stem volume with bark are estimated. 
    (iii) Tree biomass and carbon storage estimation from volume and allometric models are calculated. 
    (iv) Height-diameter relationship is fitted with nonlinear models, if diameter at breast height (DBH) or tree height are available, which can be used to retrieve tree height and diameter at breast height (DBH).
    <https://github.com/forestscientist/StemAnalysis>.
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 spatio-temporal models with censored or missing data using the SAEM algorithm (Delyon et al., 1999). This algorithm is a stochastic approximation of the widely used EM algorithm and is particularly valuable for models in which the E-step lacks a closed-form expression. It also provides a function to compute the observed information matrix using the method developed by Louis (1982). To assess the performance of the fitted model, case-deletion diagnostics are provided.
Provides R bindings for the Stencila Schema <https://schema.stenci.la>. This package is primarily aimed at R developers wanting to programmatically generate, or modify, executable documents.
A comprehensive logging framework for R applications that provides hierarchical logging levels, database integration, and contextual logging capabilities. The package supports 'SQLite' storage for persistent logs, provides colour-coded console output for better readability, includes parallel processing support, and implements structured error reporting with 'JSON' formatting.
An user-friendly framework to preprocess raw item
             scores of questionnaires into factors or scores and standardize
             them. Standardization can be made either by their normalization
             in representative sample, or by import of premade scoring table.
Starting from a Regression Model, it provides a stepwise 
              procedure to select the linear predictor.
Collection of model estimation, and model plotting functions
    related to the STEPCAM family of community assembly models. STEPCAM is
    a STEPwise Community Assembly Model that infers the relative
    contribution of Dispersal Assembly, Habitat Filtering and Limiting
    Similarity from a dataset consisting of the combination of trait and
    abundance data. See also <doi:10.1890/14-0454.1> for more information.
Interfaces the 'stepcount' Python 
    module <https://github.com/OxWearables/stepcount> to estimate
    step counts and other activities from 'accelerometry' data.
Provides a step-down procedure for controlling the False
  Discovery Proportion (FDP) in a competition-based setup, implementing 
  Dong et al. (2020) <arXiv:2011.11939>. Such setups include target-decoy 
  competition (TDC) in computational mass spectrometry and the knockoff 
  construction in linear regression.
An introduction to a couple of novel predictive variable selection methods for generalised boosted regression modeling (gbm). They are based on various variable influence methods (i.e., relative variable influence (RVI) and knowledge informed RVI (i.e., KIRVI, and KIRVI2)) that adopted similar ideas as AVI, KIAVI and KIAVI2 in the 'steprf' package, and also based on predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <DOI: 10.13140/RG.2.2.27686.22085>.
It is a hybrid spatial model that combines the variable selection capabilities of stepwise regression methods with the predictive power of the Geographically 
             Weighted Regression(GWR) model.The developed hybrid model follows a two-step approach where the stepwise variable selection method is applied first to identify 
             the subset of predictors that have the most significant impact on the response variable, and then a GWR model is fitted using those selected variables for spatial 
             prediction at test or unknown locations. For method details,see Leung, Y., Mei, C. L. and Zhang, W. X. (2000).<DOI:10.1068/a3162>.This hybrid spatial model aims to 
             improve the accuracy and interpretability of GWR predictions by selecting a subset of relevant variables through a stepwise selection process.This approach is particularly 
             useful for modeling spatially varying relationships and improving the accuracy of spatial predictions.
A Package for selecting variables for the joint modeling of mean and dispersion (including models for mixture experiments) based on hypothesis testing and the quality of model's fit. In each iteration of the selection process, a criterion for checking the goodness of fit is used as a filter for choosing the terms that will be evaluated by a hypothesis test. Pinto & Pereira (2021) <arXiv:2109.07978>.
Provides functions to calculate step- and cadence-based metrics from 
    timestamped accelerometer and wearable device data. Supports CSV and AGD files from 
    'ActiGraph' devices, CSV files from 'Fitbit' devices, and step counts derived   
    with R package 'GGIR' <https://github.com/wadpac/GGIR>, with automatic handling 
    of epoch lengths from 1 to 60 seconds. Metrics include total steps, cadence 
    peaks, minutes and steps in predefined cadence bands, and time and steps in 
    moderate-to-vigorous physical activity (MVPA). Methods and thresholds are 
    informed by the literature, e.g., 
    Tudor-Locke and Rowe (2012) <doi:10.2165/11599170-000000000-00000>, 
    Barreira et al. (2012) <doi:10.1249/MSS.0b013e318254f2a3>, 
    and Tudor-Locke et al. (2018) <doi:10.1136/bjsports-2017-097628>. 
    The package record is also available on Zenodo (2023) <doi:10.5281/zenodo.7858094>.
This is an interface for the 'Python' package
  'StepMix'. It is a 'Python' package following the scikit-learn API for
  model-based clustering and generalized mixture modeling (latent class/profile
  analysis) of continuous and categorical data. 'StepMix' handles missing values
  through Full Information Maximum Likelihood (FIML) and provides multiple stepwise
  Expectation-Maximization (EM) estimation methods based on pseudolikelihood
  theory. Additional features include support for covariates and distal outcomes,
  various simulation utilities, and non-parametric bootstrapping, which allows
  inference in semi-supervised and unsupervised settings. Software paper available
  at <doi:10.18637/jss.v113.i08>.
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.
Tools for power and sample size 
    calculation as well as design diagnostics for 
    longitudinal mixed model settings, with a focus on stepped wedge designs.
    All calculations are oracle estimates i.e. assume random effect variances 
    to be known (or guessed) in advance.  
    The method is introduced in Hussey and Hughes (2007) <doi:10.1016/j.cct.2006.05.007>,
    extensions are discussed in Li et al. (2020) <doi:10.1177/0962280220932962>.
Provide various functions and tools to help fit models for
    estimating treatment effects in stepped wedge cluster randomized trials.
    Implements methods described in Kenny, Voldal, Xia, and Heagerty (2022) 
    "Analysis of stepped wedge cluster randomized trials in the presence of a 
    time-varying treatment effect", <doi:10.1002/sim.9511>.
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, simulataneous multiscale changepoint estimator, (K. Frick, A. Munk and H. Sieling, 2014) <doi:10.1111/rssb.12047> and HSMUCE, heterogeneous SMUCE, (F. Pein, H. Sieling and A. Munk, 2017) <doi:10.1111/rssb.12202>. In addition, confidence intervals for the change-point locations and bands for the unknown signal can be obtained.
Stepwise regression is a statistical technique used for model selection. This package streamlines stepwise regression analysis by supporting multiple regression types(linear, Cox, logistic, Poisson, Gamma, and negative binomial), incorporating popular selection strategies(forward, backward, bidirectional, and subset), and offering essential metrics. It enables users to apply multiple selection strategies and metrics in a single function call, visualize variable selection processes, and export results in various formats. StepReg offers a data-splitting option to address potential issues with invalid statistical inference and a randomized forward selection option to avoid overfitting. We validated StepReg's accuracy using public datasets within the SAS software environment. For an interactive web interface, users can install the companion 'StepRegShiny' package.
A web-based 'shiny' interface for the 'StepReg' package enables stepwise regression analysis across linear, generalized linear (including logistic, Poisson, Gamma, and negative binomial), and Cox models. It supports forward, backward, bidirectional, and best-subset selection under a range of criteria. The package also supports stepwise regression to multivariate settings, allowing multiple dependent variables to be modeled simultaneously. Users can explore and combine multiple selection strategies and criteria to optimize model selection. For enhanced robustness, the package offers optional randomized forward selection to reduce overfitting, and a data-splitting workflow for more reliable post-selection inference. Additional features include logging and visualization of the selection process, as well as the ability to export results in common formats.
An introduction to several novel predictive variable selection methods for random forest. They are based on various variable importance methods (i.e., averaged variable importance (AVI), and knowledge informed AVI (i.e., KIAVI, and KIAVI2)) and predictive accuracy in stepwise algorithms. For details of the variable selection methods, please see: Li, J., Siwabessy, J., Huang, Z. and Nichol, S. (2019) <doi:10.3390/geosciences9040180>. Li, J., Alvarez, B., Siwabessy, J., Tran, M., Huang, Z., Przeslawski, R., Radke, L., Howard, F., Nichol, S. (2017). <DOI: 10.13140/RG.2.2.27686.22085>.
Software to simulate population change across space and time. Visintin et al. (2020) <doi:10.1111/2041-210X.13354>.
Provides function to estimate multiple change points
    using marginal likelihood method. See the Manual file in data folder for
    a detailed description of all functions, and a walk through tutorial.
    For more information of the method, please see Du, Kao and Kou (2016) 
    <doi:10.1080/01621459.2015.1006365>.
Functions to perform stepwise split regularized regression. The approach first 
             uses a stepwise algorithm to split the variables into the models with a goodness
             of fit criterion, and then regularization is applied to each model. The weights 
             of the models in the ensemble are determined based on a criterion selected by 
             the user.
Collection of stepwise procedures to conduct multiple hypotheses testing. The details of the stepwise algorithm can be found in Romano and Wolf (2007) <DOI:10.1214/009053606000001622> and Hsu, Kuan, and Yen (2014) <DOI:10.1093/jjfinec/nbu014>.
Functions for the collection of 3D points and curves using a stereo camera setup.
This is a collection of various kinds of data with broad uses for teaching. 
    My students, and academics like me who teach the same topics I teach, should find 
    this useful if their teaching workflow is also built around the R programming 
    language. The applications are multiple but mostly cluster on topics of statistical
    methodology, international relations, and political economy.
Work with containers over the Docker API.  Rather than
    using system calls to interact with a docker client, using the
    API directly means that we can receive richer information from
    docker.  The interface in the package is automatically generated
    using the 'OpenAPI' (a.k.a., 'swagger') specification, and all
    return values are checked in order to make them type stable.
These are miscellaneous functions that I find useful for my research and teaching.
    The contents include themes for plots, functions for simulating
    quantities of interest from regression models, functions for simulating various 
    forms of fake data for instructional/research purposes, and many more. All told, the functions
    provided here are broadly useful for data organization, data presentation, data recoding, 
    and data simulation.
These are my collection of 'R Markdown' templates, mostly for compilation to PDF.
    These are useful for all things academic and professional, if you are using 'R Markdown'
    for things like your CV or your articles and manuscripts.
This is a compilation of my preferred themes and
    related theme elements for 'ggplot2'. I believe these themes
    and theme elements are aesthetically pleasing, both for pedagogical 
    instruction and for the presentation of applied statistical research to a wide
    audience. These themes imply routine use of easily obtained/free fonts, simple
    forms of which are included in this package.
A general spatiotemporal satellite image imputation method based on sparse functional data analytic techniques. The imputation method applies and extends the Functional Principal Analysis by Conditional Estimation (PACE). The underlying idea for the proposed procedure is to impute a missing pixel by borrowing information from temporally and spatially contiguous pixels based on the best linear unbiased prediction.
A collection of statistical hypothesis tests of functional time series. While it will include more tests when the related literature are enriched, this package contains the following key tests: functional stationarity test, functional trend stationarity test, functional unit root test, to name a few.
A framework for specifying spatially, temporally and spatially-and-temporally varying coefficient models using Generalized Additive Models with smooths. The smooths are parameterised with location, time and predictor variables. The framework supports the investigation of the presence and nature of any space-time dependencies in the data by evaluating multiple model forms (specifications) using a Generalized Cross-Validation score. The workflow sequence is to: i) Prepare the data by lengthening it to have a single location and time variables for each observation. ii) Evaluate all possible spatial and/or temporal models in which each predictor is specified in different ways. iii) Evaluate each model and pick the best one. iv) Create the final model. v) Calculate the varying coefficient estimates to quantify how the relationships between the target and predictor variables vary over space, time or space-time. vi) Create maps, time series plots etc. For more details see: Comber et al (2023) <doi:10.4230/LIPIcs.GIScience.2023.22>, Comber et al (2024) <doi:10.1080/13658816.2023.2270285>  and Comber et al (2004) <doi:10.3390/ijgi13120459>.
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.
Provides data frames that hold certain columns and attributes 
    persistently for data processing in 'dplyr'.
A collection of functions for processing raw data from Stream Temperature, Intermittency, and Conductivity (STIC) loggers. 'STICr' (pronounced "sticker") includes functions for tidying, calibrating, classifying, and doing quality checks on data from STIC sensors. Some package functionality is described in Wheeler/Zipper et al. (2023) <doi:10.31223/X5636K>.
Manipulating input and output files of the 'STICS' crop
    model. Files are either 'JavaSTICS' XML files or text files used by
    the model 'fortran' executable. Most basic functionalities are reading
    or writing parameter names and values in both XML or text input files,
    and getting data from output files.  Advanced functionalities include
    XML files generation from XML templates and/or spreadsheets, or text
    files generation from XML files by using 'xslt' transformation.
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.
The goal of 'stim' is to provide a function for estimating the Stability Informed Model. The Stability Informed Model integrates stability information (how much a variable correlates with itself in the future) into cross-sectional estimates. Wysocki and Rhemtulla (2022) <https://psyarxiv.com/vg5as>.
Regression trunk model estimation proposed by Dusseldorp and Meulman (2004) <doi:10.1007/bf02295641> and Dusseldorp, Conversano, Van Os (2010) <doi:10.1198/jcgs.2010.06089>, integrating a regression tree and a multiple regression model.
A consistently well behaved method of interpolation based on piecewise rational functions using Stineman's algorithm.
Univariate time series forecasting with STL decomposition based auto regressive integrated moving average (ARIMA) hybrid  model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.
Univariate time series forecasting with STL decomposition based Extreme Learning Machine hybrid  model. For method details see Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.
Statistical analysis of spatio-temporal point processes on linear networks. This packages provides tools to visualise and analyse spatio-temporal point patterns on linear networks using first, second, and higher-order summary statistics.
Decompose a time series into seasonal, trend, and remainder
    components using an implementation of Seasonal Decomposition of Time
    Series by Loess (STL) that provides several enhancements over the STL
    method in the stats package.  These enhancements include handling missing
    values, providing higher order (quadratic) loess smoothing with automated
    parameter choices, frequency component smoothing beyond the seasonal and
    trend components, and some basic plot methods for diagnostics.
Implementation of hybrid STL decomposition based time delay neural network model for univariate time series forecasting. For method details see Jha G K, Sinha, K (2014). <doi:10.1007/s00521-012-1264-z>, Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.
The Structural Topic Model (STM) allows researchers 
  to estimate topic models with document-level covariates. 
  The package also includes tools for model selection, visualization,
  and estimation of topic-covariate regressions. Methods developed in
  Roberts et. al. (2014) <doi:10.1111/ajps.12103> and 
  Roberts et. al. (2016) <doi:10.1080/01621459.2016.1141684>. Vignette
  is Roberts et. al. (2019) <doi:10.18637/jss.v091.i02>.
Rapidly build accurate genetic prediction models for genome-wide association or whole-genome sequencing study data by smooth-threshold multivariate genetic prediction (STMGP) method. Variable selection is performed using marginal association test p-values with an optimal p-value cutoff selected by Cp-type criterion. Quantitative and binary traits are modeled respectively via linear and logistic regression models. A function that works through PLINK software (Purcell et al. 2007 <DOI:10.1086/519795>, Chang et al. 2015 <DOI:10.1186/s13742-015-0047-8>) <https://www.cog-genomics.org/plink2> is provided. Covariates can be included in regression model.
Provides an application that acts as a GUI for the 'stm' text analysis package.
This app enables interactive validation, interpretation and visualization of structural topic models from the 'stm' package by Roberts and others (2014) <doi:10.1111/ajps.12103>. It also includes helper functions for model diagnostics and extracting data from effect estimates.
Implementation of the family of generalised age-period-cohort
    stochastic mortality models. This family of models encompasses many models
    proposed in the actuarial and demographic literature including the 
    Lee-Carter (1992) <doi:10.2307/2290201> and
    the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. 
    It includes functions for fitting mortality models, analysing their 
    goodness-of-fit and performing mortality projections and simulations.
Plots a QQ-Norm Plot with several Gaussian simulations.
Allow to identify motifs in spatial-time series. A motif is a previously unknown subsequence of a (spatial) time series with relevant number of occurrences. For this purpose, the Combined Series Approach (CSA) is used.
Strength training prescription using percent-based approach requires
    numerous computations and assumptions. 'STMr' package allow users to estimate 
    individual reps-max relationships, implement various progression tables, and
    create numerous set and rep schemes. The 'STMr' package is originally created as
    a tool to help writing Jovanović M. (2020) Strength Training Manual
    <ISBN:979-8604459898>.
Fit a spatial-temporal occupancy models using
    a probit formulation instead of a traditional logit
    model.
Stochastic blockmodeling of one-mode and linked networks as presented in Škulj and Žiberna (2022) <doi:10.1016/j.socnet.2022.02.001>. The optimization is done via CEM (Classification Expectation Maximization) algorithm that can be initialized by random partitions or the results of k-means algorithm. The development of this package is financially supported by the Slovenian Research Agency (<https://www.arrs.si/>) within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).
Performs simulation and inference of diffusion processes on circle. Stochastic correlation models based on circular diffusion models are provided. For details see Majumdar, S. and Laha, A.K. (2024) "Diffusion on the circle and a stochastic correlation model"  <doi:10.48550/arXiv.2412.06343>.
Collision Risk Models for avian fauna (seabird and migratory birds) at
    offshore wind farms. The base deterministic model is derived from 
    Band (2012) <https://tethys.pnnl.gov/publications/using-collision-risk-model-assess-bird-collision-risks-offshore-wind-farms>. 
    This was further expanded on by Masden (2015) <doi:10.7489/1659-1> and code 
    used here is heavily derived from this work with input from Dr A. Cook at the 
    British Trust for Ornithology. These collision risk models are useful for 
    marine ornithologists who are working in the offshore wind industry, particularly 
    in UK waters. However, many of the species included in the stochastic collision 
    risk models can also be found in the North Atlantic in the 
    United States and Canada, and could be applied there.
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++.
Flexible stochastic tree ensemble software. 
    Robust implementations of Bayesian Additive Regression Trees (BART) 
    Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285> 
    for supervised learning and Bayesian Causal Forests (BCF) 
    Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195> 
    for causal inference. Enables model serialization and parallel sampling 
    and provides a low-level interface for custom stochastic forest samplers.
Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Hosszejni and Kastner (2021) <doi:10.18637/jss.v100.i12> and Kastner (2016) <doi:10.18637/jss.v069.i05> and the package examples.
Parameter estimation for stochastic volatility models using maximum likelihood. The latent log-volatility is 
    integrated out of the likelihood using the Laplace approximation. The models are fitted via 'TMB' (Template Model
    Builder) (Kristensen, Nielsen, Berg, Skaug, and Bell (2016) <doi:10.18637/jss.v070.i05>).
Methods of Fundamental Analysis for Valuation of Equity included here serve as a quick reference for undergraduate courses on Stock Valuation and Chartered Financial Analyst Levels 1 and 2 Readings on Equity Valuation.
    Jerald E. Pinto (“Equity Asset Valuation (4th Edition)”, 2020, ISBN: 9781119628194).
    Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level I Volumes 1-6. (Vol. 4, pp. 445-491)", 2019, ISBN: 9781119593577).
    Chartered Financial Analyst Institute ("Chartered Financial Analyst Program Curriculum 2020 Level II Volumes 1-6. (Vol. 4, pp. 197-447)", 2019, ISBN: 9781119593614).
The 'StockDistFit' package provides functions for fitting probability distributions to stock price data. The package uses maximum likelihood estimation to find the best-fitting distribution for a given stock. It also offers a function to fit several distributions to one or more assets and compare the distribution with the Akaike Information Criterion (AIC) and then pick the best distribution. References are as follows: Siew et al. (2008) <https://www.jstage.jst.go.jp/article/jappstat/37/1/37_1_1/_pdf/-char/ja> and Benth et al. (2008) <https://books.google.co.ke/books?hl=en&lr=&id=MHNpDQAAQBAJ&oi=fnd&pg=PR7&dq=Stochastic+modeling+of+commodity+prices+using+the+Variance+Gamma+(VG)+model.+&ots=YNIL2QmEYg&sig=XZtGU0lp4oqXHVyPZ-O8x5i7N3w&redir_esc=y#v=onepage&q&f=false>.
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 finding good starting values via a K-means clustering of the genetic data. Details can be found in: Foster, S. D.; Feutry, P.; Grewe, P. M.; Berry, O.; Hui, F. K. C. & Davies (2020) <doi:10.1111/1755-0998.12920>.
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.
Stochastic dominance tests help ranking different distributions. The package implements the consistent test for stochastic dominance by Barrett and Donald (2003) <doi:10.1111/1468-0262.00390>. Specifically, it implements Barrett and Donald's Kolmogorov-Smirnov type tests for first- and second-order stochastic dominance based on bootstrapping 2 and 1.
Given a list of substance compositions, a list of substances 
  involved in a process, and a list of constraints in addition to mass 
  conservation of elementary constituents, the package contains functions 
  to build the substance composition matrix, to analyze the uniqueness of 
  process stoichiometry, and to calculate stoichiometric coefficients if 
  process stoichiometry is unique. 
  (See Reichert, P. and Schuwirth, N., A generic framework for deriving 
  process stoichiometry in enviromental models, Environmental Modelling 
  and Software 25, 1241-1251, 2010 for more details.)
Provides functionality for working with tensors, alternating
           forms, wedge products, Stokes's theorem, and related concepts
	   from the exterior calculus.  Uses 'disordR' discipline
	   (Hankin, 2022, <doi:10.48550/arXiv.2210.03856>).  The
	   canonical reference would be M. Spivak
	   (1965, ISBN:0-8053-9021-9) "Calculus on Manifolds".  To cite
	   the package in publications please use Hankin (2022)
           <doi:10.48550/arXiv.2210.17008>.
Implements variable selection procedures for low to moderate size generalized linear regressions models. It includes the STOPES functions for linear regression (Capanu M, Giurcanu M, Begg C, Gonen M, Optimized variable selection via repeated data splitting, Statistics in Medicine, 2020, 19(6):2167-2184) as well as subsampling based optimization methods for generalized linear regression models (Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gonen, Subsampling based variable selection for generalized linear models).
Toolbox for different kinds of spatio-temporal analyses to be performed on observed point patterns, following the growing stream of literature on point process theory. This R package implements functions to perform different kinds of analyses on point processes, proposed in the papers (Siino, Adelfio, and Mateu 2018<doi:10.1007/s00477-018-1579-0>; Siino et al. 2018<doi:10.1002/env.2463>; Adelfio et al. 2020<doi:10.1007/s00477-019-01748-1>; D’Angelo, Adelfio, and Mateu 2021<doi:10.1016/j.spasta.2021.100534>; D’Angelo, Adelfio, and Mateu 2022<doi:10.1007/s00362-022-01338-4>; D’Angelo, Adelfio, and Mateu 2023<doi:10.1016/j.csda.2022.107679>). The main topics include modeling, statistical inference, and simulation issues on spatio-temporal point processes on Euclidean space and linear networks. Version 1.0.0 has been updated for accompanying the journal publication D Angelo and Adelfio 2025 <doi:10.18637/jss.v113.i10>.
Provides functions for creating, displaying, and evaluating stopping rules for safety monitoring in clinical studies.
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.
Set of functions to quantify and map the
    behaviour of winds generated by tropical storms and cyclones in space
    and time. It includes functions to compute and analyze fields such as the maximum sustained wind field, power dissipation index and duration of exposure to winds above a given threshold. It also includes functions to map the trajectories as well as characteristics of the storms.
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.
Fast and efficient sampling from general univariate probability
    density functions. Implements a rejection sampling approach designed to take
    advantage of modern CPU caches and minimise evaluation of the target
    density for most samples. Many standard densities are internally implemented
    in 'C' for high performance, with general user defined densities also
    supported. A paper describing the methodology will be released soon.
Functions for retrieving general and specific data from the Norwegian Parliament,
  through the Norwegian Parliament API at <https://data.stortinget.no>.
Download, explore, and analyze Literary Theme Ontology themes
    and thematically annotated story data. To learn more about the project
    visit <https://github.com/theme-ontology/theming> and
    <https://www.themeontology.org>.
An interface to explore trends in Twitter data using the 
    'Storywrangler' Application Programming Interface (API), which can be found
    here: <https://github.com/janeadams/storywrangler>.
A toolkit for Reliability Availability and Maintainability (RAM) modeling of industrial process systems.
Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.
Tools for transport planning with an emphasis on spatial
    transport data and non-motorized modes.
    The package was originally developed to support the 'Propensity to Cycle Tool', a publicly available strategic cycle network planning tool
    (Lovelace et al. 2017) <doi:10.5198/jtlu.2016.862>, but has since been
    extended to support public transport routing and accessibility analysis
    (Moreno-Monroy et al. 2017) <doi:10.1016/j.jtrangeo.2017.08.012> and
    routing with locally hosted routing engines such as 'OSRM'
    (Lowans et al. 2023) <doi:10.1016/j.enconman.2023.117337>.
    The main functions are for creating and manipulating geographic "desire
    lines" from origin-destination (OD) data (building on the 'od'
    package); calculating routes on the transport network locally and via
    interfaces to routing services such as <https://cyclestreets.net/>
    (Desjardins et al. 2021) <doi:10.1007/s11116-021-10197-1>;
    and calculating route segment attributes such as bearing. The package
    implements the 'travel flow aggregration' method described in Morgan
    and Lovelace (2020) <doi:10.1177/2399808320942779> and the
    'OD jittering' method described in Lovelace et al. (2022)
    <doi:10.32866/001c.33873>.
    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>, and in a paper outlining the landscape of
    open source software for geographic methods in transport planning
    (Lovelace, 2021) <doi:10.1007/s10109-020-00342-2>.
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>.
Estimation of model parameters for marked Hawkes process.
    Accounts for missing data in the estimation of the parameters.
    Technical details found in (Tucker et al., 2019 <DOI:10.1016/j.spasta.2018.12.004>).
Generates artificial point patterns marked by their
  spatial and temporal signatures. The resulting point cloud
  may exhibit inherent interactions between both signatures.
  The simulation integrates microsimulation 
  (Holm, E., (2017)<doi:10.1002/9781118786352.wbieg0320>) 
  and agent-based models (Bonabeau, E., (2002)<doi:10.1073/pnas.082080899>),
  beginning with the configuration of movement characteristics for 
  the specified agents (referred to as 'walkers') and their interactions 
  within the simulation environment. These interactions 
  (Quaglietta, L. and Porto, M., (2019)<doi:10.1186/s40462-019-0154-8>) 
  result in specific spatiotemporal patterns that can be visualized, 
  analyzed, and used for various analytical purposes. 
  Given the growing scarcity of detailed spatiotemporal data 
  across many domains, this package provides an alternative data source 
  for applications in social and life sciences.
Methods for decomposing seasonal data: STR (a Seasonal-Trend 
  time series 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 also be used for forecasting.
Offers a suite of functions for converting to and from
    (atomic) vectors, matrices, data.frames, and (3D+) arrays as well
    as lists of these objects. It is an alternative to the base R
    as.<str>.<method>() functions (e.g., as.data.frame.array()) that
    provides more useful and/or flexible restructuring of R objects.
    To do so, it only works with common structuring of R objects (e.g.,
    data.frames with only atomic vector columns).
Regression-based ranking of pathogen strains with respect to their contributions to natural epidemics, using demographic and genetic data sampled in the curse of the epidemics. This package also includes the GMCPIC test.
Provides a framework for performing discrete (share-level) simulations of
  investment strategies. Simulated portfolios optimize exposure to an input signal subject
  to constraints such as position size and factor exposure. For background see L. Chincarini
  and D. Kim (2010, ISBN:978-0-07-145939-6) "Quantitative Equity Portfolio Management".
Message translation is often managed with 'po' files and the 'gettext' programme, 
    but sometimes another solution is needed. In contrast to 'po' files, a more flexible approach 
    is used as in the Fluent <https://projectfluent.org/> project with R Markdown snippets. 
    The key-value approach allows easier handling of the translated messages.
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.
Build a project framework for users with 
    access to only the most basic of automation tools.
Non-proportional hazard (NPH) is commonly observed in immuno-oncology studies, where the survival curves of the treatment and control groups show delayed separation. To properly account for NPH, several statistical methods have been developed. One such method is Max-Combo test, which is a straightforward and flexible hypothesis testing method that can simultaneously test for constant, early, middle, and late treatment effects. However, the majority of the Max-Combo test performed in clinical studies are unstratified, ignoring the important prognostic stratification factors. To fill this gap, we have developed an R package for stratified Max-Combo testing that accounts for stratified baseline factors. Our package explores various methods for calculating combined test statistics, estimating joint distributions, and determining the p-values.
Functions in this package provide solution to classical problem in
  survey methodology - an optimum sample allocation in stratified sampling. In
  this context, the optimum allocation is in the classical Tschuprow-Neyman's
  sense and it satisfies additional lower or upper bounds restrictions imposed
  on sample sizes in strata. There are few different algorithms available to
  use, and one them is based on popular sample allocation method that applies
  Neyman allocation to recursively reduced set of strata.
  This package also provides the function that computes a solution to the
  minimum cost allocation problem, which is a minor modification of the
  classical optimum sample allocation. This problem lies in the determination
  of a vector of strata sample sizes that minimizes total cost of the survey,
  under assumed fixed level of the stratified estimator's variance. As in the
  case of the classical optimum allocation, the problem of minimum cost
  allocation can be complemented by imposing upper-bounds constraints on sample
  sizes in strata.
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.
Offers a comprehensive approach for analysing stratified 2x2 contingency tables. It facilitates the calculation of odds ratios, 95% confidence intervals, and conducts chi-squared, Cochran-Mantel-Haenszel, Mantel-Haenszel, and Breslow-Day-Tarone tests. The package is particularly useful in fields like epidemiology and social sciences where stratified analysis is essential. The package also provides interpretative insights into the results, aiding in the understanding of statistical outcomes.
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.
Quantify stratigraphic disorder using the metrics defined by Burgess (2016) <doi:10.2110/jsr.2016.10>. Contains a range of utility tools to construct and manipulate stratigraphic columns.
Identifies individuals in a social network who should be the intervention
    subjects for a network intervention in which you have a group of targets, a
    group of avoiders, and a group that is neither.
Users can build and test customized quantitative trading strategies. Some quantitative trading strategies are already implemented, e.g. various moving-average filters with trend following approaches.
    The implemented class called "Strategy" allows users to access several methods to analyze performance figures, plots and backtest the strategies.
    Furthermore, custom strategies can be added, a generic template is available. The custom strategies require a certain input and output so they can be called from the Strategy-constructor.
Variants of strategy estimation (Dal Bo & Frechette, 2011, <doi:10.1257/aer.101.1.411>), including the model with parameters for the choice probabilities of the strategies (Breitmoser, 2015, <doi:10.1257/aer.20130675>), and the model with individual level covariates for the selection of strategies by individuals (Dvorak & Fehrler, 2018, <doi:10.2139/ssrn.2986445>).
A dynamic model of 
    the big-picture, whole ecosystem effects of hydrodynamics, 
	temperature, nutrients, and fishing on continental shelf marine 
	food webs. The package is described in: Heath, M.R., 
	Speirs, D.C., Thurlbeck, I. and Wilson, R.J. (2020) 
	<doi:10.1111/2041-210X.13510> StrathE2E2: An R package 
	for modelling the dynamics of marine food webs and 
	fisheries. 8pp.
Univariate stratification of survey populations with a generalization of the 
  Lavallee-Hidiroglou method of stratum construction. The generalized method takes into account 
  a discrepancy between the stratification variable and the survey variable. The determination 
  of the optimal boundaries also incorporate, if desired, an anticipated non-response, a take-all 
  stratum for large units, a take-none stratum for small units, and a certainty stratum to ensure 
  that some specific units are in the sample. The well known cumulative root frequency rule of 
  Dalenius and Hodges and the geometric rule of Gunning and Horgan are also implemented.
Performs Stratified Covariate Balancing with Markov blanket feature selection and use of synthetic cases. See Alemi et al. (2016) <DOI:10.1111/1475-6773.12628>.
A toolkit for stratified medicine, subgroup identification, and precision medicine.
    Current tools include (1) filtering models (reduce covariate space), (2) patient-level estimate
    models (counterfactual patient-level quantities, such as the conditional average treatment effect), 
    (3) subgroup identification models (find subsets of patients with similar treatment effects), 
    and (4) treatment effect estimation and inference (for the overall population and discovered 
    subgroups). These tools can be customized and are directly used in PRISM 
    (patient response identifiers for stratified medicine; Jemielita and Mehrotra 2019
    <arXiv:1912.03337>. This package is in beta and will be continually updated.
Random Forest-like tree ensemble that works with groups of predictor variables. When building a tree, a number of variables is taken randomly from each group separately, thus ensuring that it considers variables from each group for the splits. Useful when rows contain information about different things (e.g. user information and product information) and it's not sensible to make a prediction with information from only one group of variables, or when there are far more variables from one group than the other and it's desired to have groups appear evenly on trees.
    Trees are grown using the C5.0 algorithm rather than the usual CART algorithm. Supports parallelization (multithreaded), missing values in predictors, and categorical variables (without doing One-Hot encoding in the processing). Can also be used to create a regular (non-stratified) Random Forest-like model, but made up of C5.0 trees and with some additional control options.
    As it's built with C5.0 trees, it works only for classification (not for regression).
Integrating a stratified structure in the population in a sampling design can considerably reduce the variance of the Horvitz-Thompson estimator. We propose in this package different methods to handle the selection of a balanced sample in stratified population. For more details see Raphaël Jauslin, Esther Eustache and Yves Tillé (2021) <doi:10.1007/s42081-021-00134-y>. The package propose also a method based on optimal transport and balanced sampling, see Raphaël Jauslin and Yves Tillé <doi:10.1016/j.jspi.2022.12.003>.
Provides functions for stratified sampling and assigning custom labels to data, ensuring randomness within groups. The package supports various sampling methods such as stratified, cluster, and systematic sampling. It allows users to apply transformations and customize the sampling process. This package can be useful for statistical analysis and data preparation tasks.
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 
    Sebastien Wouters, Anne-Christine Da Silva, Frederic Boulvain and 
    Xavier Devleeschouwer, 2021. The R Journal 13:2, 153-178.
    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>.
The fossil record is a joint expression of ecological, taphonomic, 
    evolutionary, and stratigraphic processes (Holland and Patzkowsky, 2012, ISBN:978-0226649382).
    This package allowing to simulate biological processes in the time domain
    (e.g., trait evolution, fossil abundance, phylogenetic trees), and examine how their expression
    in the rock record (stratigraphic domain) is influenced based on 
    age-depth models, ecological niche models, and taphonomic effects.
    Functions simulating common processes used in modeling trait evolution, biostratigraphy or 
    event type data such as first/last occurrences are provided and can be used 
    standalone or as part of a pipeline. The package comes with example 
    data sets and tutorials in several vignettes, which can be used as a 
    template to set up one's own simulation.
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.
The main function is icweib(), which fits a stratified Weibull
    proportional hazards model for left censored, right censored, interval censored,
    and non-censored survival data. We parameterize the Weibull regression model
    so that it allows a stratum-specific baseline hazard function, but where the
    effects of other covariates are assumed to be constant across strata. 
    Please refer to Xiangdong Gu, David Shapiro, Michael D. Hughes and
    Raji Balasubramanian (2014) <doi:10.32614/RJ-2014-003> for more details.
API for fast data extraction for .hic files that provides programmatic access to the matrices. It doesn't store the pointer data for all the matrices, only the one queried, and currently we are only supporting matrices (not vectors).
This is a modification of 'HDoutliers' package. The 'HDoutliers' algorithm is a powerful 
    unsupervised algorithm for detecting anomalies in high-dimensional data, with a 
    strong theoretical foundation. However, it suffers from some limitations that 
    significantly hinder its performance level, under certain circumstances. This package 
    implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) 
    <arXiv:1908.04000>  for detecting anomalies in high-dimensional data
    that addresses these limitations of 'HDoutliers' algorithm. We define an anomaly as an observation that deviates markedly from the majority
    with a large distance gap. An approach based on extreme value theory is used 
    for the anomalous threshold calculation.
Performs receptor abundance estimation for single cell RNA-sequencing data using a supervised feature selection mechanism and a thresholded gene set scoring procedure. Seurat's normalization method is described in: Hao et al., (2021) <doi:10.1016/j.cell.2021.04.048>, Stuart et al., (2019) <doi:10.1016/j.cell.2019.05.031>, Butler et al., (2018) <doi:10.1038/nbt.4096> and Satija et al., (2015) <doi:10.1038/nbt.3192>. Method for reduced rank reconstruction and rank-k selection is detailed in: Javaid et al., (2022) <doi:10.1101/2022.10.08.511197>. Gene set scoring procedure is described in: Frost et al., (2020) <doi:10.1093/nar/gkaa582>. Clustering method is outlined in: Song et al., (2020) <doi:10.1093/bioinformatics/btaa613> and Wang et al., (2011) <doi:10.32614/RJ-2011-015>.
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, NSF CMMI
    1728612, 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>.
Tools for using the 'StreamCat' and 'LakeCat' API and 
             interacting with the 'StreamCat' and 'LakeCat' database. 
             Convenience functions in the package wrap the API for 'StreamCat' 
             on <https://api.epa.gov/StreamCat/streams/metrics>.
Provides indices and tools for directed acyclic graphs (DAGs), particularly DAG representations of intermittent streams.  A detailed introduction to the package can be found in the publication: "Non-perennial stream networks as directed acyclic graphs: The R-package streamDAG" (Aho et al., 2023) <doi:10.1016/j.envsoft.2023.105775>, and in the introductory package vignette.
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.
Characterize daily stream discharge and water quality data
    and subsample water quality data. Provide dates, discharge, and water
    quality measurements and 'streamsampler' can find gaps, get summary
    statistics, and subsample according to common stream sampling protocols.
    Stream sampling protocols are described in Lee et al. (2016) <doi:10.1016/j.jhydrol.2016.08.059> 
    and Lee et al. (2019) <doi:10.3133/sir20195084>.
Given a 'coro' asynchronous generator instance that produces text,
    write that text into a document selection in 'RStudio' and 'Positron'. This
    is particularly helpful for streaming large language model responses into
    the user's editor.
A collection of functions to search and download street view imagery 
             ('Mapilary' <https://www.mapillary.com/developer/api-documentation>) and 
             to extract, quantify, and visualize visual features. Moreover, there are 
             functions provided to generate Qualtrics survey in TXT format using 
             the collection of street views for various research purposes.
It contains functions to estimate multivariate Student's t dynamic and static regression models for given degrees of freedom and lag length. Users can also specify the trends and dummies of any kind in matrix form.
    Poudyal, N., and Spanos, A. (2022) <doi:10.3390/econometrics10020017>.
    Spanos, A. (1994) <http://www.jstor.org/stable/3532870>.
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>.
Traditional model evaluation metrics fail to capture model 
    performance under less than ideal conditions. This package employs 
    techniques to evaluate models "under-stress". This includes testing 
    models' extrapolation ability, or testing accuracy on specific 
    sub-samples of the overall model space. Details describing stress-testing 
    methods in this package are provided in 
    Haycock (2023) <doi:10.26076/2am5-9f67>. The other primary contribution of
    this package is provided to R users access to the 'Python' library 'PyCaret'
    <https://pycaret.org/> for quick and easy access to auto-tuned 
    machine learning models.
Forms queries to submit to the Cleveland Federal Reserve Bank web
    site's financial stress index data site.  Provides query functions for both
    the composite stress index and the components data. By default the download
    includes daily time series data starting September 25, 1991.  The functions
    return a class of either type easing or cfsi which contain a list of items
    related to the query and its graphical presentation.  The list includes the
    time series data as an xts object.  The package provides four lattice time
    series plots to render the time series data in a manner similar to the
    bank's own presentation.
Reliability of (normal) stress-strength models and for building two-sided or one-sided confidence intervals according to different approximate procedures.
There are some things that I wish were easier with the
    'stringr' or 'stringi' packages. The foremost of these is the
    extraction of numbers from strings. 'stringr' and 'stringi' make you
    figure out the regular expression for yourself; 'strex' takes care of
    this for you. There are many other handy functionalities in 'strex'.
    Contributions to this package are encouraged; it is intended as a
    miscellany of string manipulation functions that cannot be found in
    'stringi' or 'stringr'.
The strided iterator adapts multidimensional buffers to work with 
  the C++ standard library and range-based for-loops. Given a pointer or iterator
  into a multidimensional data buffer, one can generate an iterator range using
  make_strided to construct strided versions of the standard library's begin and
  end. For constructing range-based for-loops, a strided_range class is provided.
  These help authors to avoid integer-based indexing, which in some cases can impede
  algorithm performance and introduce indexing errors. This library exists
  primarily to expose the header file to other R projects.
Takes a list of character strings and forms an adjacency matrix for
    the times the specified characters appear together in the strings provided. For
    use in social network analysis and data wrangling. Simple package, comprised of
    three functions.
Extract glyph information from font data, and translate the
    outline curves to flattened paths or tessellated polygons. The converted
    data is returned as a 'data.frame' in easy-to-plot format.
Implements an approximate string matching version of R's native
    'match' function. Also offers fuzzy text search based on various string
     distance measures. Can calculate various string distances based on edits
    (Damerau-Levenshtein, Hamming, Levenshtein, optimal sting alignment), qgrams (q-
    gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An
    implementation of soundex is provided as well. Distances can be computed between
    character vectors while taking proper care of encoding or between integer
    vectors representing generic sequences. This package is built for speed and
    runs in parallel by using 'openMP'. An API for C or C++ is exposed as well.
    Reference: MPJ van der Loo (2014) <doi:10.32614/RJ-2014-011>.
Provides an extendable, performant and multithreaded 'alt-string' implementation backed by 'C++' vectors and strings.
Pass named and unnamed character vectors into specified positions
    in strings. This represents an attempt to replicate some of python's string
    formatting.
A collection of character string/text/natural language
    processing tools for pattern searching (e.g., with 'Java'-like regular
    expressions or the 'Unicode' collation algorithm), random string generation,
    case mapping, string transliteration, concatenation, sorting, padding,
    wrapping, Unicode normalisation, date-time formatting and parsing,
    and many more. They are fast, consistent, convenient, and -
    thanks to 'ICU' (International Components for Unicode) -
    portable across all locales and platforms. Documentation about 'stringi' is
    provided via its website at <https://stringi.gagolewski.com/> and
    the paper by Gagolewski (2022, <doi:10.18637/jss.v103.i02>).
Performs complex string operations compactly and efficiently. Supports string interpolation jointly with over 50 string operations. Also enhances regular string functions (like grep() and co). See an introduction at <https://lrberge.github.io/stringmagic/>.
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.
Provides drop-in replacements for functions from the
    'stringr' package, with the same user interface. These functions have
    no external dependencies and can be copied directly into your package
    code using the 'staticimports' package.
English is the native language for only 5% of the World population.
    Also, only 17% of us can understand this text. Moreover, the Latin alphabet
    is the main one for merely 36% of the total.
    The early computer era, now a very long time ago, was dominated by the US.
    Due to the proliferation of the internet, smartphones, social media, and
    other technologies and communication platforms, this is no longer the case.
    This package replaces base R string functions (such as grep(),
    tolower(), sprintf(), and strptime()) with ones that fully
    support the Unicode standards related to natural language and
    date-time processing. It also fixes some long-standing inconsistencies,
    and introduces some new, useful features.
    Thanks to 'ICU' (International Components for Unicode) and 'stringi',
    they are fast, reliable, and portable across different platforms.
The strip function deletes components of R model outputs that are useless for specific purposes, such as predict[ing], print[ing], summary[izing], etc.
For making Trellis-type conditioning plots without strip labels.
    This is useful for displaying the structure of results from factorial designs
    and other studies when many conditioning variables would clutter the display
    with layers of redundant strip labels. Settings of the variables are encoded by
    layout and spacing in the trellis array and decoded by a separate legend. The
    functionality is implemented by a single S3 generic strucplot() function that
    is a wrapper for the Lattice package's xyplot() function. This allows access to
    all Lattice graphics capabilities in the usual way.
Extracts plain text from RTF (Rich Text Format) file.
A collection of tools for clinical trial data management and 
    analysis in research and teaching.
    The package is mainly collected for personal use, but any use beyond that is encouraged.
    This package has migrated functions from 'agdamsbo/daDoctoR', and new functions has been added.
    Version follows months and year. See NEWS/Changelog for release notes.
    This package includes sampled data from the TALOS trial (Kraglund et al (2018) <doi:10.1161/STROKEAHA.117.020067>).
    The win_prob() function is based on work by Zou et al (2022) <doi:10.1161/STROKEAHA.121.037744>.
    The age_calc() function is based on work by Becker (2020) <doi:10.18637/jss.v093.i02>.
R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press.
A comprehensive set of string manipulation functions based on those found in 'Python'
    without relying on 'reticulate'. It provides functions that intend to (1) make it easier for
    users familiar with 'Python' to work with strings, (2) reduce the complexity often associated
    with string operations, (3) and enable users to write more readable and maintainable code
    that manipulates strings.
Testing, monitoring and dating structural changes in (linear)
             regression models. strucchange features tests/methods from
	     the generalized fluctuation test framework as well as from
	     the F test (Chow test) framework. This includes methods to
	     fit, plot and test fluctuation processes (e.g., CUSUM, MOSUM,
	     recursive/moving estimates) and F statistics, respectively.
             It is possible to monitor incoming data online using
             fluctuation processes.
             Finally, the breakpoints in regression models with structural
             changes can be estimated together with confidence intervals.
             Emphasis is always given to methods for visualizing the data.
A fast implementation with additional experimental features for
             testing, monitoring and dating structural changes in (linear)
             regression models. 'strucchangeRcpp' features tests/methods from
	     the generalized fluctuation test framework as well as from
	     the F test (Chow test) framework. This includes methods to
             fit, plot and test fluctuation processes (e.g. cumulative/moving
             sum, recursive/moving estimates) and F statistics, respectively.
             These methods are described in Zeileis et al. (2002)
             <doi:10.18637/jss.v007.i02>.
             Finally, the breakpoints in regression models with structural
             changes can be estimated together with confidence intervals,
             and their magnitude as well as the model fit can be evaluated
             using a variety of statistical measures.
Enforcement of field types in lists. A drop-in tool to allow for 
    dynamic input data that might be questionably parsed or cast to be coerced
    into the specific desired format in a reasonably performant manner.
Tree-structured modelling of categorical predictors (Tutz and Berger (2018), <doi:10.1007/s11634-017-0298-6>) or measurement
    units (Berger and Tutz (2018), <doi:10.1080/10618600.2017.1371030>).
Performs multiple testing corrections that take specific structure
    of hypotheses into account, as described in Sankaran & Holmes (2014)
    <doi:10.18637/jss.v059.i13>.
Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals. 
             It is built to perform very well in the presence of significant level shifts. It is designed to play 
             well with any breakpoint algorithm and any smoothing algorithm. Currently defaults to 'lowess' for smoothing
             and 'strucchange' for breakpoint identification. The package is useful in areas such as trend analysis, time series 
             decomposition, breakpoint identification and anomaly detection.
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.
The Structural Topic and Sentiment-Discourse (STS) model allows researchers to estimate topic models with document-level metadata that determines both topic prevalence and sentiment-discourse. The sentiment-discourse is modeled as a document-level latent variable for each topic that modulates the word frequency within a topic. These latent topic sentiment-discourse variables are controlled by the document-level metadata. The STS model can be useful for regression analysis with text data in addition to topic modeling’s traditional use of descriptive analysis. The method was developed in Chen and Mankad (2024) <doi:10.1287/mnsc.2022.00261>.
These are tools that allow users to do time series diagnostics, primarily
    tests of unit root, by way of simulation. While there is nothing necessarily
    wrong with the received wisdom of critical values generated decades ago, 
    simulation provides its own perks. Not only is simulation broadly informative
    as to what these various test statistics do and what are their plausible 
    values, simulation provides more flexibility for assessing unit root by way
    of different thresholds or different hypothesized distributions.
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>.
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>.
Non-parametric test, originally proposed by Stute (1997) <https://www.jstor.org/stable/2242560>, that the expectation of a dependent variable Y given an independent variable D is linear in D.
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.
A set of statistical tools for spatio-temporal data exploration.     
    Includes simple plotting functions, covariance calculations and computations 
    similar to principal component analysis for spatio-temporal data. Can use 
    both dataframes and stars objects for all plots and computations. For more 
    details refer 'Spatio-Temporal Statistics with R' (Christopher K. Wikle, 
    Andrew Zammit-Mangion, Noel Cressie, 2019, ISBN:9781138711136).
Pretty-prints R code without changing the user's formatting
    intent.
Estimates the authors or speakers of texts. Methods developed in Huang, Perry, and Spirling (2020) <doi:10.1017/pan.2019.49>. The model is built on a Bayesian framework in which the distinctiveness of each speaker is defined by how different, on average, the speaker's terms are to everyone else in the corpus of texts. An optional cross-validation method is implemented to select the subset of terms that generate the most accurate speaker predictions. Once a set of terms is selected, the model can be estimated. Speaker distinctiveness and term influence can be recovered from parameters in the model using package functions. Once fitted, the model can be used to predict authorship of new texts.
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.
Implements the S-type estimators, novel robust estimators for general linear regression models, addressing challenges such as outlier contamination and leverage points. This package introduces robust regression techniques to provide a robust alternative to classical methods and includes diagnostic tools for assessing model fit and performance. The methodology is based on the study, "Comparison of the Robust Methods in the General Linear Regression Model" by Sazak and Mutlu (2023). This package is designed for statisticians and applied researchers seeking advanced tools for robust regression analysis.
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.
Implementation of Sequential BATTing (bootstrapping and aggregating of thresholds from trees) for developing threshold-based multivariate (prognostic/predictive) biomarker signatures. Variable selection is automatically built-in. Final signatures are returned with interaction plots for predictive signatures. Cross-validation performance evaluation and testing dataset results are also output. Detail algorithms are described in Huang et al (2017) <doi:10.1002/sim.7236>.
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.
Provides access to granular sub-national income data from the
    MCC-PIK Database Of Sub-national Economic Output (DOSE). The package
    downloads and processes the data from its open repository on 'Zenodo'
    (<https://zenodo.org/records/13773040>). Functions are provided to
    fetch data at multiple geographic levels, match coordinates to
    administrative regions, and access associated geometries.
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. (2018) <doi:10.1111/biom.12884> does an overall test and separate tests in subgroups, correcting for multiple testing using the joint distribution.
The subplex algorithm for unconstrained optimization, developed by Tom Rowan.
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.
Balancing computational and statistical efficiency, subsampling techniques offer
    a practical solution for handling large-scale data analysis. Subsampling methods
    enhance statistical modeling for massive datasets by efficiently drawing
    representative subsamples from full dataset based on tailored sampling probabilities. These
    probabilities are optimized for specific goals, such as minimizing the variance
    of coefficient estimates or reducing prediction error.
Functions for computing test subscores using different
    methods in both classical test theory (CTT) and item response theory (IRT). This
    package enables three types of subscoring methods within the framework of CTT
    and IRT, including (1) Wainer's augmentation method (Wainer et. al., 2001) 
    <doi:10.4324/9781410604729>, (2) Haberman's subscoring methods (Haberman, 2008) 
    <doi:10.3102/1076998607302636>, and (3) Yen's objective performance index (OPI; Yen, 1987) 
    <https://www.ets.org/research/policy_research_reports/publications/paper/1987/hrap>. 
    It also includes functions 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. In addition, the package includes 
    a function to assess the local independence assumption of IRT with
    Yen's Q3 statistic (Yen, 1984 <doi:10.1177/014662168400800201>; Yen, 1993 
    <doi:10.1111/j.1745-3984.1993.tb00423.x>).
Identifying outcome relevant subgroups has now become as simple as possible!
 The formerly lengthy and tedious search for the needle in a haystack will be replaced by a single, comprehensive and coherent presentation.
 The central result of a subgroup screening is a diagram in which each single dot stands for a subgroup. The diagram may show thousands of them. The position of the dot in the diagram is determined by the
 sample size of the subgroup and the statistical measure of the treatment effect in that subgroup. The sample size is shown on the horizontal axis while the treatment effect is displayed on the vertical axis. Furthermore, the diagram shows the line of no effect and the overall study results. For small subgroups, which are found on the left side of the plot, larger random deviations from the mean study effect are expected, while for larger subgroups only small deviations from the study mean can be expected to be chance findings.
 So for a study with no conspicuous subgroup effects, the dots in the figure are expected to form a kind of funnel. Any deviations from this funnel shape hint to conspicuous subgroups.
A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria. Theoretical support for the heuristic search methods and exploratory data analysis criteria is in Cadima, Cerdeira, Minhoto (2003, <doi:10.1016/j.csda.2003.11.001>). Theoretical support for the leap and bounds algorithm and the criteria for the general multivariate linear model is in Duarte Silva (2001, <doi:10.1006/jmva.2000.1920>). There is a package vignette "subselect", which includes additional references.
The Subsemble algorithm is a general subset ensemble prediction method, which can be used for small, moderate, or large datasets. Subsemble partitions the full dataset into subsets of observations, fits a specified underlying algorithm on each subset, and uses a unique form of k-fold cross-validation to output a prediction function that combines the subset-specific fits. An oracle result provides a theoretical performance guarantee for Subsemble. The paper, "Subsemble: An ensemble method for combining subset-specific algorithm fits" is authored by Stephanie Sapp, Mark J. van der Laan & John Canny (2014) <doi:10.1080/02664763.2013.864263>.
An interface to access data from Substack publications via API. 
    Users can fetch the latest, top, search for specific posts, or retrieve a single post by its slug. 
    This functionality is useful for developers and researchers looking to analyze Substack content or 
    integrate it into their applications. For more information, visit the API documentation at <https://substackapi.dev/introduction>.
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>.
Contains methods for the simulation of positive tempered stable distributions and related subordinators. Including classical tempered stable, rapidly deceasing tempered stable, truncated stable, truncated tempered stable, generalized Dickman, truncated gamma, generalized gamma, and p-gamma. For details, see Dassios et al (2019) <doi:10.1017/jpr.2019.6>, Dassios et al (2020) <doi:10.1145/3368088>, Grabchak (2021) <doi:10.1016/j.spl.2020.109015>.
A systematic biology tool was developed to prioritize cancer subtype-specific drugs by integrating genetic perturbation, drug action, biological pathway, and cancer subtype. 
    The capabilities of this tool include inferring patient-specific subpathway activity profiles in the context of gene expression profiles with subtype labels, calculating differentially 
    expressed subpathways based on cultured human cells treated with drugs in the 'cMap' (connectivity map) database, prioritizing cancer subtype specific drugs according to drug-disease 
    reverse association score based on subpathway, and visualization of results (Castelo (2013) <doi:10.1186/1471-2105-14-7>; Han et al (2019) <doi:10.1093/bioinformatics/btz894>; Lamb and Justin (2006) <doi:10.1126/science.1132939>). Please cite using <doi:10.1093/bioinformatics/btab011>.
Substitution matrices are important parameters in protein alignment algorithms. These matrices represent the likelihood that an amino acid will be substituted for another during mutation. This tool allows users to apply predefined and custom matrices and then explore the resulting alignments with interactive visualizations.  'SubVis' requires the availability of a web browser.
Quality control charts for survival outcomes.
    Allows users to construct the Continuous Time Generalized
    Rapid Response CUSUM (CGR-CUSUM) <doi:10.1093/biostatistics/kxac041>, 
    the Biswas & Kalbfleisch (2008)  <doi:10.1002/sim.3216> CUSUM, 
    the Bernoulli CUSUM and the risk-adjusted funnel plot for survival data 
    <doi:10.1002/sim.1970>. 
    These procedures can be used to monitor survival processes for a change 
    in the failure rate.
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.
Sudoku designs (Bailey et al., 2008<doi:10.1080/00029890.2008.11920542>) can be used as experimental designs which tackle one extra source of variation than conventional Latin square designs. Although Sudoku designs are similar to Latin square designs, only addition is the region concept. Some very important functions related to row-column designs as well as block designs along with basic functions are included in this package.
Generates region-specific Suess and Laws corrections for 
    stable carbon isotope data from marine organisms collected between 1850 and 2023. Version
    0.1.6 of 'SuessR' contains four built-in regions: the Bering Sea ('Bering Sea'), the
    Aleutian archipelago ('Aleutian 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.
The sufficient forecasting (SF) method is implemented by this package for a single time series forecasting using many predictors and a possibly nonlinear forecasting function. Assuming that the predictors are driven by some latent factors, the SF first conducts factor analysis and then performs sufficient dimension reduction on the estimated factors to derive predictive indices for forecasting. The package implements several dimension reduction approaches, including principal components (PC), sliced inverse regression (SIR), and directional regression (DR). Methods for dimension reduction are as described in: Fan, J., Xue, L. and Yao, J. (2017) <doi:10.1016/j.jeconom.2017.08.009>, Luo, W., Xue, L., Yao, J. and Yu, X. (2022) <doi:10.1093/biomet/asab037> and Yu, X., Yao, J. and Xue, L. (2022) <doi:10.1080/07350015.2020.1813589>.
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' extensions to construct glyph-maps for visualizing seasonality in spatiotemporal data. See the Journal of Statistical Software reference: Zhang, H. S., Cook, D., Laa, U., Langrené, N., & Menéndez, P. (2024) <doi:10.18637/jss.v110.i07>. The manuscript for this package is currently under preparation and can be found on GitHub at <https://github.com/maliny12/paper-sugarglider>.
By adding dependencies to the "Suggests" field of a package's
    DESCRIPTION file, and then declaring that they are needed within any
    dependent functionality, it is often possible to significantly reduce the
    number of "hard" dependencies required by a package. This package provides
    a minimal way to declare when a suggested package is needed.
Provides 'ggplot2' graphics for analysing time
    series data. It aims to fit into the 'tidyverse' and grammar of
    graphics framework for handling temporal data.
Visualizes sulcal morphometry data derived from 'BrainVisa' <https://brainvisa.info/> including width, depth, surface area, and length. The package enables mapping of statistical group results or subject-level values onto cortical surface maps, with options to focus on all sulci or only selected regions of interest. Users can display all four measures simultaneously or restrict plots to chosen measures, creating composite, publication-quality brain visualizations in R to support the analysis and interpretation of sulcal morphology.
Estimation of mean squared prediction error of a small area predictor is provided. In particular, the recent method of Simple, Unified, Monte-Carlo Assisted approach for the mean squared prediction error estimation of small area predictor is provided. We also provide other existing methods of mean squared prediction error estimation such as jackknife method for the mixed logistic model.
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.
    See for details:
    Svishcheva et al (2019) Gene-based association tests using GWAS summary
    statistics. Bioinformatics.
    Belonogova et al (2022) SumSTAAR: A flexible framework for gene-based
    association studies using GWAS summary statistics. PLOS Comp Biol.
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.
Produces tables with descriptive statistics for continuous, categorical and dichotomous variables. It is largely based on the package 'gtsummary'; Sjoberg DD et al. (2021) <doi:10.32614/RJ-2021-053>.
Provides functions for tabulating and summarizing 
    categorical, multiple response, ordinal, and continuous 
    variables in R data frames. Makes it easy to create clear, 
    structured summary tables, so you spend less time wrangling 
    data and more time interpreting it.
Data frame summaries, cross-tabulations,
  weight-enabled frequency tables and common descriptive
  (univariate) statistics in concise tables available in a
  variety of formats (plain ASCII, Markdown and HTML). A good 
  point-of-entry for exploring data, both for experienced
  and new R users.
Provides methods for spatial and spatio-temporal smoothing of demographic and health indicators using survey data, with particular focus on estimating and projecting under-five mortality rates, described in Mercer et al. (2015) <doi:10.1214/15-AOAS872>, Li et al. (2019) <doi:10.1371/journal.pone.0210645>, Wu et al. (DHS Spatial Analysis Reports No. 21, 2021), and Li et al. (2023) <doi:10.48550/arXiv.2007.05117>.
Provides tools to simulate multi-omics datasets with predefined signal structures. The generated data can be used for testing, validating, and benchmarking integrative analysis methods such as factor models and clustering approaches. This version includes enhanced signal customization, visualization tools (scatter, histogram, 3D), MOFA-based analysis pipelines, PowerPoint export, and statistical profiling of datasets. Designed for both method development and teaching, SUMO supports real and synthetic data pipelines with interpretable outputs. Tini, Giulia, et al (2019) <doi:10.1093/bib/bbx167>.
Application of theoretical results which ensure that the summation
  of an infinite discrete series is within an arbitrary margin of error of its
  true value. The C code under the hood is shared through header files to allow
  users to sum their own low level functions as well. Based on the paper by
  Braden (1992) <doi: 10.2307/2324995>.
It allows to quickly perform permutation-based closed testing by sum-based global tests, and construct lower confidence bounds for the TDP, simultaneously over all subsets of hypotheses. As a main feature, it produces simultaneous lower confidence bounds for the proportion of active voxels in different clusters for fMRI cluster analysis. Details may be found in Vesely, Finos, and Goeman (2020) <arXiv:2102.11759>.
Combine topic modeling and sentiment analysis to identify individual students' gaps, and highlight their strengths and weaknesses across predefined competency domains and professional activities.
Explore continuous, date and categorical variables. 'sumvar' aims to bring the ease and simplicity of the "sum" and "tab" functions from 'stata'.
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>.
Compute the position of the sun, and local solar time using Meeus'
    formulae. Compute day and/or night length using different
    twilight definitions or arbitrary sun elevation angles. This package is 
    part of the 'r4photobiology' suite, Aphalo, P. J. (2015)
    <doi:10.19232/uv4pb.2015.1.14>. Algorithms from Meeus (1998, ISBN:0943396611).
Survival analysis for unbalanced clusters using Archimedean copulas (Prenen et al. (2016) <DOI:10.1111/rssb.12174>).
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>.
Provides a set of convenient functions for calculating sun-related information, including the sun's position (elevation and azimuth), and the times of sunrise, sunset, solar noon, and twilight for any given geographical location on Earth. These calculations are based on equations provided by the National Oceanic & Atmospheric Administration (NOAA) <https://gml.noaa.gov/grad/solcalc/calcdetails.html> as described in "Astronomical Algorithms" by Jean Meeus (1991, ISBN: 978-0-943396-35-4).
Methodology for supervised grouping aka "clustering" of
   potentially many predictor variables, such as genes etc, implementing
   algorithms 'PELORA' and 'WILMA'.
An implementation of interpreted string literals. Based on the
  'glue' package by Hester & Bryan (2024) <doi:10.32614/CRAN.package.glue> but
  with a focus on efficiency and simplicity at a cost of flexibility.
Computes standard error and confidence interval of various descriptive statistics under 
    various designs and sampling schemes. The main function, superb(), return a plot. It can 
    also be used to obtain a dataframe with the statistics and their precision intervals 
    so that other plotting environments (e.g., Excel)
    can be used. See Cousineau and colleagues (2021) <doi:10.1177/25152459211035109> 
    or Cousineau (2017) <doi:10.5709/acp-0214-z> for a review as well as Cousineau (2005)
    <doi:10.20982/tqmp.01.1.p042>, Morey (2008) <doi:10.20982/tqmp.04.2.p061>, Baguley (2012)
    <doi:10.3758/s13428-011-0123-7>, Cousineau & Laurencelle (2016) <doi:10.1037/met0000055>,
    Cousineau & O'Brien (2014) <doi:10.3758/s13428-013-0441-z>, Calderini & Harding 
    <doi:10.20982/tqmp.15.1.p001> for specific references.
    The documentation is available at <https://dcousin3.github.io/superb/> .
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.
Aggregates large single-cell data into metacell dataset by merging together gene expression of very similar cells. 'SuperCell' uses 'velocyto.R' <doi:10.1038/s41586-018-0414-6> <https://github.com/velocyto-team/velocyto.R> for RNA velocity. We also recommend installing 'scater' Bioconductor package <doi:10.18129/B9.bioc.scater> <https://bioconductor.org/packages/release/bioc/html/scater.html>.
Creates superpixels based on input spatial data. 
  This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters).
  It is based on the SLIC algorithm (Achanta et al. (2012) <doi:10.1109/TPAMI.2012.120>), and readapts it to work with arbitrary dissimilarity measures.
A supervised compression method that incorporates the response for reducing big data to a carefully selected subset. Please see Joseph and Mak (2021) <doi:10.1002/sam.11508>. This research is supported by a U.S. National Science Foundation (NSF) grant CMMI-1921646.
The 'superdiag' package provides a comprehensive test suite for testing
  Markov Chain nonconvergence. It integrates five standard empirical MCMC convergence
  diagnostics (Gelman-Rubin, Geweke, Heidelberger-Welch, Raftery-Lewis, and Hellinger
  distance) and plotting functions for trace plots and density histograms. The functions
  of the package can be used to present all diagnostic statistics and graphs at once
  for conveniently checking MCMC nonconvergence.
Identification of sets of objects with shared features is a common operation in all disciplines. Analysis of intersections among multiple sets is fundamental for in-depth understanding of their complex relationships. This package implements a theoretical framework for efficient computation of statistical distributions of multi-set intersections based upon combinatorial theory, and provides multiple scalable techniques for visualizing the intersection statistics. The statistical algorithm behind this package was published in Wang et al. (2015) <doi:10.1038/srep16923>.
Likelihood evaluations for stationary Gaussian time series are typically obtained via the Durbin-Levinson algorithm, which scales as O(n^2) in the number of time series observations.  This package provides a "superfast" O(n log^2 n) algorithm written in C++, crossing over with Durbin-Levinson around n = 300.  Efficient implementations of the score and Hessian functions are also provided, leading to superfast versions of inference algorithms such as Newton-Raphson and Hamiltonian Monte Carlo.  The C++ code provides a Toeplitz matrix class packaged as a header-only library, to simplify low-level usage in other packages and outside of R.
A system for generating extendable and customizable heatmaps for exploring complex datasets, including big data and data with multiple data types.
Implements the super learner prediction method and contains a
    library of prediction algorithms to be used in the super learner.
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.
Image Segmentation using Superpixels, Affinity Propagation and Kmeans Clustering. The R code is based primarily on the article "Image Segmentation using SLIC Superpixels and Affinity Propagation Clustering, Bao Zhou, International Journal of Science and Research (IJSR), 2013" <https://www.ijsr.net/archive/v4i4/SUB152869.pdf>.
Functions to perform simulations of ANOVA designs of up to three factors. Calculates the observed power and average observed effect size for all main effects and interactions in the ANOVA, and all simple comparisons between conditions. Includes functions for analytic power calculations and additional helper functions that compute effect sizes for ANOVA designs, observed error rates in the simulations, and functions to plot power curves. Please see Lakens, D., & Caldwell, A. R. (2021). "Simulation-Based Power Analysis for Factorial Analysis of Variance Designs". <doi:10.1177/2515245920951503>.
Tools for analysing the agreement of two or more rankings of the same items. Examples are importance rankings of predictor variables and risk predictions of subjects. Benchmarks for agreement are computed based on random permutation and bootstrap. See Ekstrøm CT, Gerds TA, Jensen, AK (2018). "Sequential rank agreement methods for comparison of ranked lists." _Biostatistics_, *20*(4), 582-598 <doi:10.1093/biostatistics/kxy017> for more information.
Estimate and understand individual-level variation in
    transmission. Implements density and cumulative compound Poisson
    discrete distribution functions (Kremer et al. (2021) 
    <doi:10.1038/s41598-021-93578-x>), as well as functions to calculate
    infectious disease outbreak statistics given epidemiological
    parameters on individual-level transmission; including the probability
    of an outbreak becoming an epidemic/extinct (Kucharski et al. (2020) 
    <doi:10.1016/S1473-3099(20)30144-4>), or the cluster size
    statistics, e.g. what proportion of cases cause X\% of transmission
    (Lloyd-Smith et al. (2005) <doi:10.1038/nature04153>).
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>).
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.
The functions sp() and sp_seq() compute the support points in Mak and Joseph (2018) <DOI:10.1214/17-AOS1629>. Support points can be used as a representative sample of a desired distribution, or a representative reduction of a big dataset (e.g., an "optimal" thinning of Markov-chain Monte Carlo sample chains). This work was supported by USARO grant W911NF-14-1-0024 and NSF DMS grant 1712642.
Provides basic functions that support an implementation of object case (Case 1) best-worst scaling: a function for converting a two-level orthogonal main-effect design/balanced incomplete block design into questions; two functions for creating a data set suitable for analysis; a function for calculating count-based scores; a function for calculating shares of preference; and a function for generating artificial responses to questions. See Louviere et al. (2015) <doi:10.1017/CBO9781107337855> for details on best-worst scaling, and Aizaki and Fogarty (2023) <doi:10.1016/j.jocm.2022.100394> for the package.
Provides three basic functions that support an implementation of Case 2 (profile case) best-worst scaling. The first is to convert an orthogonal main-effect design into questions, the second is to create a dataset suitable for analysis, and the third is to calculate count-based scores. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.
Provides basic functions that support an implementation of multi-profile case (Case 3) best-worst scaling (BWS). Case 3 BWS is a question-based survey method to elicit people's preferences for attribute levels. Case 3 BWS constructs various combinations of attribute levels (profiles) and then asks respondents to select the best and worst profiles in each choice set. A main function creates a dataset for the analysis from the choice sets and the responses to the questions. For details on Case 3 BWS, refer to Louviere et al. (2015) <doi:10.1017/CBO9781107337855>.
Provides basic functions that support an implementation of (discrete) choice experiments (CEs). CEs is a question-based survey method measuring people's preferences for goods/services and their characteristics. Refer to Louviere et al. (2000) <doi:10.1017/CBO9780511753831> for details on CEs, and Aizaki (2012) <doi:10.18637/jss.v050.c02> for the package.
Suite of helper functions for data wrangling and visualization.
    The only theme for these functions is that they tend towards simple, short, and narrowly-scoped.
    These functions are built for tasks that often recur but are not large enough in scope to warrant an ecosystem of interdependent functions.
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.
This data-driven phylogenetic comparative method fits stabilizing selection models to continuous trait data, building on the 'ouch' methodology of Butler and King (2004) <doi:10.1086/426002>. The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak. For more information see Ingram and Mahler (2013) <doi:10.1111/2041-210X.12034>.
A collection of functions for statistical and multivariate analysis of surface-related data, with a focus on antimicrobial activity and omniphobicity. Designed to support materials scientists and researchers in exploring structure–function relationships in surface-engineered materials through reproducible and interpretable workflows. For more details, see Li et al. (2021) <doi:10.1002/advs.202100368>, and Kwon et al. (2020) <doi:10.3390/polym12081826>.
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.
Methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) <doi:10.1016/j.geomorph.2023.108838>). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface  (i.e., without de-trending) using increments of order 2. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices.
Implements Surprisal analysis for gene expression data such as RNA-seq or microarray experiments. Surprisal analysis is an information-theoretic method that decomposes gene expression data into a baseline state and constraint-associated deviations, capturing coordinated gene expression patterns under different biological conditions. References: Kravchenko-Balasha N. et al. (2014) <doi:10.1371/journal.pone.0108549>. Zadran S. et al. (2014) <doi:10.1073/pnas.1414714111>. Su Y. et al. (2019) <doi:10.1371/journal.pcbi.1007034>. Bogaert K. A. et al. (2018) <doi:10.1371/journal.pone.0195142>.
Implements the "Residual (Sur)Realism" algorithm described by
    Stefanski (2007) <doi:10.1198/000313007X190079> to generate datasets 
    that reveal hidden images or messages in their residual plots. It offers
    both predefined datasets and tools to embed custom text or images
    into residual structures. Allowing users to create intriguing visual
    demonstrations for teaching model diagnostics.
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 
  (Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt 
  University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland 
  Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine.
Provides functions to estimate the proportion of treatment effect explained by the surrogate marker using a Bayesian Model Averaging approach. Duan and Parast (2023) <doi:10.1002/sim.9986>.
Estimates the proportion of treatment effect on a censored primary outcome that is explained by the treatment effect on a censored surrogate outcome/event. All methods are described in detail in Parast, et al (2020) "Assessing the Value of a Censored Surrogate Outcome" <doi:10.1007/s10985-019-09473-1> and Wang et al (2025) "Model-free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival" <doi:10.1002/sim.70268>.  A tutorial for this package can be found at <https://www.laylaparast.com/surrogateoutcome>.
Provides functions to nonparametrically assess assumptions necessary to prevent the surrogate paradox through hypothesis tests of stochastic dominance, monotonicity of regression functions, and non-negative residual treatment effects. More details are available in Hsiao et al 2025 (under review). A tutorial for this package can be found at <https://laylaparast.com/home/SurrogateParadoxTest.html>.
Uses a novel rank-based nonparametric approach to evaluate a surrogate marker
  in a small sample size setting. Details are described in Parast et al (2024)
  <doi:10.1093/biomtc/ujad035> and Hughes A et al (2025)
  <doi:10.1002/sim.70241>. A tutorial for this package can be found at
  <https://www.laylaparast.com/surrogaterank> and a Shiny App 
  implementing the package can be found at 
  <https://parastlab.shinyapps.io/SurrogateRankApp/>.
Performs estimation and inference on a partially missing target outcome (e.g. gene expression in an inaccessible tissue) while borrowing information from a correlated surrogate outcome (e.g. gene expression in an accessible tissue). Rather than regarding the surrogate outcome as a proxy for the target outcome, this package jointly models the target and surrogate outcomes within a bivariate regression framework. Unobserved values of either outcome are treated as missing data. In contrast to imputation-based inference, no assumptions are required regarding the relationship between the target and surrogate outcomes. Estimation in the presence of bilateral outcome missingness is performed via an expectation conditional maximization either algorithm. In the case of unilateral target missingness, estimation is performed using an accelerated least squares procedure. A flexible association test is provided for evaluating hypotheses about the target regression parameters. For additional details, see: McCaw ZR, Gaynor SM, Sun R, Lin X: "Leveraging a surrogate outcome to improve inference on a partially missing target outcome" <doi:10.1111/biom.13629>.
Provides functions to implement group sequential procedures that allow for early stopping to declare efficacy using a surrogate marker and the possibility of futility stopping.   More details are available in: Parast, L. and Bartroff, J (2024) <doi:10.1093/biomtc/ujae108>. A tutorial for this package can be found at <https://laylaparast.com/home/SurrogateSeq.html>.
Provides functions to test for a treatment effect in terms of the difference in survival between a treatment group and a control group using surrogate marker information obtained at some early time point in a time-to-event outcome setting. Nonparametric kernel estimation is used to estimate the test statistic and perturbation resampling is used for variance estimation. More details will be available in the future in: Parast L, Cai T, Tian L (2019) ``Using a Surrogate Marker for Early Testing of a Treatment Effect" Biometrics, 75(4):1253-1263. <doi:10.1111/biom.13067>.
Provides functions for the evaluation of
    surrogate endpoints when both the surrogate and the true endpoint are failure
    time variables. The approaches implemented are:
    (1) the two-step approach (Burzykowski et al, 2001) <DOI:10.1111/1467-9876.00244> with a copula model (Clayton, Plackett, Hougaard) at
    the first step and either a linear regression of log-hazard ratios at the second
    step (either adjusted or not for measurement error);
    (2) mixed proportional hazard models estimated via mixed Poisson GLM
    (Rotolo et al, 2017 <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.
Fit Cox non-proportional hazards models with time-varying coefficients. 
    Both unpenalized procedures (Newton and proximal Newton) and penalized procedures 
    (P-splines and smoothing splines) are included using B-spline basis functions for 
    estimating time-varying coefficients. For penalized procedures, cross validations, 
    mAIC, TIC or GIC are implemented to select tuning parameters. Utilities for 
    carrying out post-estimation visualization, summarization, point-wise confidence 
    interval and hypothesis testing are also provided.
    For more information, see Wu et al. (2022) <doi: 10.1007/s10985-021-09544-2> and 
    Luo et al. (2023) <doi:10.1177/09622802231181471>.
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.
Performs two-sample comparisons based on average hazard with survival weight (AHSW) or general censoring-free incidence rate (CFIR) proposed by Uno and Horiguchi (2023) <doi:10.1002/sim.9651>.
Provides a variety of functions to estimate time-dependent true/false positive rates and AUC curves from a set of censored survival data.
Tests for equality of two survival functions based on integrated weighted differences of two Kaplan-Meier curves.
Three new methods to perform outlier detection in a survival context. In total there are six methods provided, the first three methods are traditional residual-based outlier detection methods, the second three are the concordance-based. Package developed during the work on the two following publications: Pinto J., Carvalho A. and Vinga S. (2015) <doi:10.5220/0005225300750082>; Pinto J.D., Carvalho A.M., Vinga S. (2015) <doi:10.1007/978-3-319-27926-8_22>.
Provides helper functions to compute linear predictors, time-dependent ROC curves,
    and Harrell's concordance index for Cox proportional hazards models as described in Therneau (2024) <https://CRAN.R-project.org/package=survival>,
    Therneau and Grambsch (2000, ISBN:0-387-98784-3), Hung and Chiang (2010) <doi:10.1002/cjs.10046>, Uno et al. (2007) <doi:10.1198/016214507000000149>,
    Blanche, Dartigues, and Jacqmin-Gadda (2013) <doi:10.1002/sim.5958>, Blanche, Latouche, and Viallon (2013) <doi:10.1007/978-1-4614-8981-8_11>,
    Harrell et al. (1982) <doi:10.1001/jama.1982.03320430047030>, Peto and Peto (1972) <doi:10.2307/2344317>, Schemper (1992) <doi:10.2307/2349009>,
    and Uno et al. (2011) <doi:10.1002/sim.4154>.
Performs inference for C of risk prediction models with censored survival data, using the method proposed by Uno et al. (2011) <doi:10.1002/sim.4154>. Inference for the difference in C between two competing prediction models is also implemented.
Performs repeated nested cross-validation for Cox Proportionate Hazards, Cox Lasso, Survival Random Forest, and their ensemble. Returns internally validated concordance index, time-dependent area under the curve, Brier score, calibration slope, and statistical testing of non-linear ensemble outperforming the baseline Cox model. In this, it helps researchers to quantify the gain of using a more complex survival model, or justify its redundancy. Equally, it shows the performance value of the non-linear and interaction terms, and may highlight the need of further feature transformation. Further details can be found in Shamsutdinova, Stamate, Roberts, & Stahl (2022) "Combining Cox Model and Tree-Based Algorithms to Boost Performance and Preserve Interpretability for Health Outcomes" <doi:10.1007/978-3-031-08337-2_15>, where the method is described as Ensemble 1.
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 (2013) <doi:10.1002/sim.5874>. 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.
Provides deep learning models for right-censored survival data using the 'torch' backend. 
    Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox, 
    and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation, 
    hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score, 
    and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>.
Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) <doi:10.2196/34589>; Stan Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).
Computing the one-sided/two-sided integrated/maximally selected EL statistics for simultaneous testing, the one-sided/two-sided EL tests for pointwise testing, and an initial test that precedes one-sided testing to exclude the possibility of crossings or alternative orderings among the survival functions.
Provides predictive accuracy tools to evaluate time-to-event survival models. This includes calculating the concordance probability estimate that incorporates the follow-up time for a particular study developed by Devlin, Gonen, Heller (2020)<doi:10.1007/s10985-020-09503-3>. It also evaluates the concordance probability estimate for nested Cox proportional hazards models using a projection-based approach by Heller and Devlin (under review).
Survival analysis models are commonly used in medicine and other areas. Many of them
    are too complex to be interpreted by human. Exploration and explanation is needed, but
    standard methods do not give a broad enough picture. 'survex' provides easy-to-apply
    methods for explaining survival models, both complex black-boxes and simpler statistical models.
    They include methods specific to survival analysis such as SurvSHAP(t) introduced in Krzyzinski et al., (2023)
    <doi:10.1016/j.knosys.2022.110234>, SurvLIME described in Kovalev et al., (2020) <doi:10.1016/j.knosys.2020.106164> as well as
    extensions of existing ones described in Biecek et al., (2021) <doi:10.1201/9780429027192>.
It computes Relative survival, AER and SMR based on French death rates.
Survival analysis using a flexible Bayesian model for individual-level right-censored data, optionally combined with aggregate data on counts of survivors in different periods of time. An M-spline is used to describe the hazard function, with a prior on the coefficients that controls over-fitting. Proportional hazards or flexible non-proportional hazards models can be used to relate survival to predictors. Additive hazards (relative survival) models, waning treatment effects, and mixture cure models are also supported. Priors can be customised and calibrated to substantive beliefs. Posterior distributions are estimated using 'Stan', and outputs are arranged in a tidy format. See Jackson (2023) <doi:10.1186/s12874-023-02094-1>.
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. Small-area estimation.
Implements different kinds of bootstraps
    to estimate sampling variation from survey data with 
    complex designs. Includes the rescaled bootstrap
    described in Rust and Rao (1996) <doi:10.1177/096228029600500305> and
    Rao and Wu (1988) <doi:10.1080/01621459.1988.10478591>.
Performs canonical correlation for survey data, including 
  multiple tests of significance for secondary canonical correlations. 
  A key feature of this package is that it incorporates survey data structure 
  directly in a novel test of significance via a sequence of simple linear 
  regression models on the canonical variates. See reference - Cruz-Cano, 
  Cohen, and Mead-Morse (2024) "Canonical Correlation Analysis of Survey data: the SurveyCC R package" 
  The R Journal under review.
Functions to generate K-fold cross validation (CV) folds
    and CV test error estimates that take into account
    how a survey dataset's sampling design was constructed
    (SRS, clustering, stratification, and/or unequal sampling weights).
    You can input linear and logistic regression models, along with data and a 
    type of survey design in order to get an output that can help you determine
    which model best fits the data using K-fold cross validation.
    Our paper on "K-Fold Cross-Validation for Complex Sample Surveys"
    by Wieczorek, Guerin, and McMahon (2022)
    <doi:10.1002/sta4.454>
    explains why differing how we take folds based on survey design is useful.
Data obtained from surveys contains information not only about the
    survey responses, but also the survey metadata, e.g. the original survey
    questions and the answer options. The 'surveydata' package makes it easy to
    keep track of this metadata, and to easily extract columns with
    specific questions.
This tool is designed to analyze up to 5 Fraud Detection Questions integrated into a survey, focusing on potential fraudulent participants to clean the survey dataset from potential fraud. Fraud Detection Questions and further information available at <https://surveydefense.org>.
Generate programmable surveys using markdown and R code chunks. Surveys
    are composed of two files: a survey.qmd 'Quarto' file defining the
    survey content (pages, questions, etc), and an app.R file defining a
    'shiny' app with global settings (libraries, database configuration,
    etc.) and server configuration options (e.g., conditional skipping /
    display, etc.). Survey data collected from respondents is stored in a
    'PostgreSQL' database. Features include controls for conditional skip
    logic (skip to a page based on an answer to a question), conditional
    display logic (display a question based on an answer to a question), a
    customizable progress bar, and a wide variety of question types,
    including multiple choice (single choice and multiple choices),
    select, text, numeric, multiple choice buttons, text area, and dates.
    Because the surveys render into a 'shiny' app, designers can also
    leverage the reactive capabilities of 'shiny' to create dynamic and
    interactive surveys.
Visualize and tabulate single-choice, multiple-choice, matrix-style questions from survey data. 
    Includes ability to group cross-tabulations, frequency distributions, and plots by categorical variables and 
    to integrate survey weights. Ideal for quickly uncovering descriptive patterns in survey data.
A tool for computing network representations of attitudes,
  extracted from tabular data such as sociological surveys. Development of
  surveygraph software and training materials was initially funded by the
  European Union under the ERC Proof-of-concept programme (ERC,
  Attitude-Maps-4-All, project number: 101069264). Views and opinions expressed
  are however those of the author(s) only and do not necessarily reflect those
  of the European Union or the European Research Council Executive Agency.
  Neither the European Union nor the granting authority can be held responsible
  for them.
The goal of 'surveynnet' is to extend the functionality of 'nnet', 
  which already supports survey weights, by enabling it to handle clustered
  and stratified data. It achieves this by incorporating design effects 
  through the use of effective sample sizes as outlined by Chen
  and Rust (2017), <doi:10.1093/jssam/smw036>, and performed by  
  'deffCR' in the package 'PracTools' (Valliant, Dever, and Kreuter (2018), 
  <doi:10.1007/978-3-319-93632-1>).
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.
Provides a pipeline to perform small area estimation and prevalence mapping of binary indicators using health and demographic survey data, described in Fuglstad et al. (2022) <doi:10.48550/arXiv.2110.09576> and Wakefield et al. (2020) <doi:10.1111/insr.12400>.
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>.
Sample surveys use scientific methods to draw inferences about population parameters by observing a representative part of the population, called sample. The SRSWOR (Simple Random Sampling Without Replacement) is one of the most widely used probability sampling designs, wherein every unit has an equal chance of being selected and units are not repeated.This function draws multiple SRSWOR samples from a finite population and estimates the population parameter i.e. total of HT, Ratio, and Regression estimators. Repeated simulations (e.g., 500 times) are used to assess and compare estimators using metrics such as percent relative bias (%RB), percent relative root means square error (%RRMSE).For details on sampling methodology,
             see, Cochran (1977) "Sampling Techniques" <https://archive.org/details/samplingtechniqu0000coch_t4x6>.
Short and understandable commands that generate tabulated, 
    formatted, and rounded survey estimates. Mostly a wrapper for the 
    'survey' package (Lumley (2004) <doi:10.18637/jss.v009.i08> 
    <https://CRAN.R-project.org/package=survey>) that identifies 
    low-precision estimates using the National Center for Health 
    Statistics (NCHS) presentation standards (Parker et al. (2017) 
    <https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf>,
    Parker et al. (2023) <doi:10.15620/cdc:124368>).
Decision support tool for prioritizing sites for ecological
    surveys based on their potential to improve plans for conserving
    biodiversity (e.g. plans for establishing protected areas). Given a set of
    sites that could potentially be acquired for conservation management,
    it can be used to generate and evaluate plans for surveying additional
    sites. Specifically, plans for ecological surveys can be
    generated using various conventional approaches (e.g. maximizing expected
    species richness, geographic coverage, diversity of sampled environmental
    algorithms. After generating such survey plans, they can be evaluated using
    conditions) and maximizing value of information. Please note that several
    functions depend on the 'Gurobi' optimization software (available from
    <https://www.gurobi.com>). Additionally, the 'JAGS' software (available from
    <https://mcmc-jags.sourceforge.io/>) is required to fit hierarchical
    generalized linear models. For further details, see Hanson et al. (2023) <doi:10.1111/1365-2664.14309>.
The estimation method proposed by Chen and Yi (2021) 
 <doi:10.1111/biom.13331> is extended to the analysis of survival data, 
 accommodating  commonly used survival models while accounting for measurement
 error and network structures among covariates.
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). To run the Bayesian models, the user needs to install additional modules 
    (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed from 
    <https://giabaio.r-universe.dev/> using 
    'install.packages("survHEhmc", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org"))'
    and 
    'install.packages("survHEinla", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org"))'
    respectively. 'survHEinla' is based on the package INLA, which is available for download at
    <https://inla.r-inla-download.org/R/stable/>. The user can specify a set of parametric models 
    using a common notation and select the preferred mode of inference. The results can also be 
    post-processed to produce probabilistic sensitivity analysis and can be used to export the 
    output to an Excel file (e.g. for a Markov model, as often done by modellers and 
    practitioners). <doi:10.18637/jss.v095.i14>.
High dimensional time to events data analysis with variable selection technique.
             Currently support LASSO, clustering and Bonferroni's correction.
Performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI).
Newly developed methods for the estimation of several probabilities
      in an illness-death model. The package can be used to obtain nonparametric and 
      semiparametric estimates for: transition probabilities, occupation probabilities, 
      cumulative incidence function and the sojourn time distributions. 
      Additionally, it is possible to fit proportional hazards regression models
      in each transition of the Illness-Death Model. Several auxiliary 
      functions are also provided which can be used for marginal 
      estimation of the survival functions.
High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). 
              Currently supports frailty data analysis. Allows for Weibull and 
              Exponential distribution. Includes function for interval censored data.
Generates multiple imputed datasets from a substantive model compatible fully conditional specification model for time-to-event data. Our method assumes that the censoring process also depends on the covariates with missing values. Details will be available in an upcoming publication.
Contains the core survival analysis routines, including
	     definition of Surv objects, 
	     Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
	     and parametric accelerated failure time models.
Implementation of methodology designed to perform: (i) variable 
    selection, (ii) effect estimation, and (iii) uncertainty quantification, 
    for high-dimensional survival data. Our method uses a spike-and-slab prior 
    with Laplace slab and Dirac spike and approximates the corresponding 
    posterior using variational inference, a popular method in machine learning 
    for scalable conditional inference. Although approximate, the variational 
    posterior provides excellent point estimates and good control of the false 
    discovery rate. For more information see Komodromos et al. (2021) 
    <arXiv:2112.10270>.
An outcome-guided algorithm is developed to identify clusters of samples with similar characteristics and survival rate. The algorithm first builds a random forest and then defines distances between samples based on the fitted random forest. Given the distances, we can apply hierarchical clustering algorithms to define clusters. Details about this method is described in <https://github.com/luyouepiusf/SurvivalClusteringTree>.
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 or survival probabilities. 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>. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/pycox>.
Estimate the regression coefficients and the baseline hazard 
      of proportional hazard Cox models with left, right or interval censored survival data 
      using maximum penalised likelihood. A 'non-parametric' smooth estimate of the baseline hazard
      function is provided.
Fitting Cox proportional hazard model under dependent right censoring using copula and maximum penalised likelihood methods.
Several functions and S3 methods to predict survival by using neural networks. We implemented Partial Logistic Artificial Neural Networks (PLANN) as proposed by Biganzoli et al. (1998)  <https://pubmed.ncbi.nlm.nih.gov/9618776>.
Provides estimates for the bivariate and trivariate distribution 
             functions and bivariate and trivariate survival functions for 
             censored gap times. Two approaches, using existing methodologies, 
             are considered: (i) the Lin's estimator, which is based on the 
             extension the Kaplan-Meier estimator of the distribution function 
             for the first event time and the Inverse Probability of Censoring 
             Weights for the second time (Lin DY, Sun W, Ying Z (1999) 
             <doi:10.1093/biomet/86.1.59> and (ii) another estimator
             based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008)
             <https://w3.math.uminho.pt/~lmachado/Biometria_conference.pdf>). 
             The proposed methods are the landmark estimators based on 
             subsampling approach, and the estimator based on weighted cumulative
             hazard estimator. The package also provides nonparametric estimator
             conditional to a given continuous covariate. All these methods have
             been submitted to be published.
Compute time-dependent ROC curve from censored survival
        data using Kaplan-Meier (KM) or Nearest Neighbor Estimation
        (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No
        2, 2000, PP 337-344).
Several functions and S3 methods to construct a super learner in the presence of censored times-to-event and to evaluate its prognostic capacities.
Provides influence function-based methods to evaluate a longitudinal surrogate marker in a censored time-to-event outcome setting, with plug-in and targeted maximum likelihood estimation options. Details are described in: Agniel D and Parast L (2025). "Robust Evaluation of Longitudinal Surrogate Markers with Censored Data." Journal of the Royal Statistical Society: Series B <doi:10.1093/jrsssb/qkae119>. A tutorial for this package can be found at <https://www.laylaparast.com/survivalsurrogate> and a Shiny App 
  implementing the package can be found at 
  <https://parastlab.shinyapps.io/survivalsurrogateApp/>.
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.
Performs survival analysis for one-way layout. The package includes the generalized test for survival ANOVA (Tsui and Weerahandi (1989) <doi:10.2307/2289949> and (Weerahandi, 2004; ISBN:978-0471470175)). It also performs pairwise comparisons and graphical approaches. Moreover, it assesses the weibullness of data in each group via test. The package computes mean and confidence interval under Weibull distribution.
Vignettes for the 'survival' package. Split from the 'survival' 
    package since the vignettes were getting large. Also, since 'survival' is a 
    recommended package it cannot make use of other packages outside of 
    base+recommended (e.g. 'rmarkdown').
Datasets detailing the results, castaways, and events of each 
  season of Survivor for the US, Australia, South Africa, New Zealand, and the 
  UK. This includes details on the cast, voting history, immunity and reward 
  challenges, jury votes, boot order, advantage details, and episode ratings. 
  Use this for analysis of trends and statistics of the game.
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>.
Provide model averaging-based approaches that can be used to predict personalized survival probabilities. The key underlying idea is to approximate the conditional survival function using a weighted average of multiple candidate models. Two scenarios of candidate models are allowed: (Scenario 1) partial linear Cox model and (Scenario 2) time-varying coefficient Cox model. A reference of the underlying methods is Li and Wang (2023) <doi:10.1016/j.csda.2023.107759>.
An implementation of popular evaluation metrics that are commonly used in survival prediction including Concordance Index, Brier Score, Integrated Brier Score, 
  Integrated Square Error, Integrated Absolute Error and Mean Absolute Error.
  For a detailed information, see (Ishwaran H, Kogalur UB, Blackstone EH and Lauer MS (2008) <doi:10.1214/08-AOAS169>) , 
  (Moradian H, Larocque D and Bellavance F (2017) <doi:10.1007/s10985-016-9372-1>), (Hanpu Zhou, Hong Wang, Sizheng Wang and Yi Zou (2023) <doi:10.32614/rj-2023-009>) for different evaluation metrics.
In clinical trials, endpoints are sometimes evaluated with uncertainty. Adjudication is commonly adopted to ensure the study integrity. We propose to use multiple imputation (MI) introduced by Robin (1987) <doi:10.1002/9780470316696> to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) <doi:10.1016/S0197-2456(00)00053-2> were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods.
Contains the function 'ggsurvplot()' for drawing easily beautiful
    and 'ready-to-publish' survival curves with the 'number at risk' table
    and 'censoring count plot'. Other functions are also available to plot 
    adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions.
A collection of functions to help in the analysis of
    right-censored survival data. These extend the methods available in
    package:survival.
Sample size and effect size calculations for survival endpoints based on mixture survival-by-response model. The methods implemented can be found in  Bofill, Shen & Gómez (2021) <arXiv:2008.12887>.
Statistical tools for analyzing time-to-event data using
             machine learning. Implements survival stacking for conditional 
             survival estimation, standardized survival function estimation for 
             current status data, and methods for algorithm-agnostic variable
             importance. See Wolock CJ, Gilbert PB, Simon N, 
             and Carone M (2024) <doi:10.1080/10618600.2024.2304070>.
Network meta-analysis for survival outcome data often involves several studies only involve dichotomized outcomes (e.g., the numbers of event and sample sizes of individual arms).  To combine these different outcome data, Woods et al. (2010) <doi:10.1186/1471-2288-10-54> proposed a Bayesian approach using complicated hierarchical models. Besides, frequentist approaches have been alternative standard methods for the statistical analyses of network meta-analysis, and the methodology has been well established. We proposed an easy-to-implement method for the network meta-analysis based on the frequentist framework in Noma and Maruo (2025) <doi:10.1101/2025.01.23.25321051>. This package involves some convenient functions to implement the simple synthesis method.
Generate objects that simulate survival times. Random values for the distributions are generated using the method described by Bender (2003) <https://epub.ub.uni-muenchen.de/id/eprint/1716> and Leemis (1987) in Operations Research, 35(6), 892–894.
Simple and quick method of exporting the most often used
    survival analysis results to an Excel sheet.
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 (excess) hazard, relative mortality ratio or marginal intensity 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. Later versions (>2.0.0) include penalized models for relative mortality ratio, and marginal intensity in recurrent event setting.
	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, 
	3) wish to model the relative mortality ratio between a cohort and a reference population, 4) wish to describe the marginal intensity for recurrent event data. 
	See Fauvernier et al. (2019a) <doi:10.21105/joss.01434> for an overview of the package and Fauvernier et al. (2019b) <doi:10.1111/rssc.12368> for the method.
Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data. For details, see Lopez-de-Ullibarri and Jacome (2013) <doi:10.18637/jss.v054.i11>.
The function SurvRegCens() of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions. Hubeaux, S. and Rufibach, K. (2014) <doi:10.48550/arXiv.1402.0432>.
Implements Bayesian inference in accelerated failure time (AFT) models for 
    right-censored survival times assuming a log-logistic distribution. Details of the 
    variational Bayes algorithms, with and without shared frailty, are described in 
    Xian et al. (2024) <doi:10.1007/s11222-023-10365-6> and 
    Xian et al. (2024) <doi:10.48550/arXiv.2408.00177>, respectively.
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.
Estimates the restricted mean survival time (RMST) with the time window [0, tau], where tau is adaptively selected from the procedure, proposed by Horiguchi et al. (2018) <doi:10.1002/sim.7661>. It also estimates the RMST with the time window [tau1, tau2], where tau1 is adaptively selected from the procedure, proposed by Horiguchi et al. (2023) <doi:10.1002/sim.9662>.
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.
Incorporate various statistics and layout customization options to enhance the efficiency and adaptability of the Kaplan-Meier plots.
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.(2020) <doi:10.1002/sim.8672>.
Simulation of simple and complex survival data including recurrent and multiple events and competing risks. See Moriña D, Navarro A. (2014) <doi:10.18637/jss.v059.i02> and Moriña D, Navarro A. (2017) <doi:10.1080/03610918.2016.1175621>.
Conduct asymptotic and empirical power and sample size calculations for Single-Nucleotide Polymorphism (SNP) association studies with right censored time to event outcomes.
Survival analysis with sparse longitudinal covariates under right censoring scheme. Different hazards models are involved. Please cite the manuscripts corresponding to this package:  Sun, Z. et al. (2022) <doi:10.1007/s10985-022-09548-6>, Sun, Z. and Cao, H. (2023)  <arXiv:2310.15877> and Sun, D. et al. (2023) <arXiv:2308.15549>.
Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021), Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival Data, Biometrics (under revision). The package also provides functions that visualize bivariate survival data and bivariate probability mass function.
Parametric survival regression models under the maximum likelihood approach via 'Stan'. Implemented regression models include accelerated failure time models, proportional hazards models, proportional odds models, accelerated hazard models, Yang and Prentice models, and extended hazard models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue (Birnbaum-Saunders) distributions. References: Lawless (2002) <ISBN:9780471372158>; Bennett (1982) <doi:10.1002/sim.4780020223>; Chen and Wang(2000) <doi:10.1080/01621459.2000.10474236>; Demarqui and Mayrink (2021) <doi:10.1214/20-BJPS471>.
Package performs Cox regression and survival distribution function estimation when the survival times are subject to double truncation. In case that the survival and truncation times are quasi-independent, 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. 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 (2018) <doi:10.1111/biom.12809>, Shen (2010) <doi:10.1007/s10463-008-0192-2>, Martin and Betensky (2005) <doi:10.1198/016214504000001538>. When the survival times are dependent on at least one of the truncation times, an EM algorithm is employed to obtain point estimates for the regression coefficients. The standard errors are calculated using the bootstrap method. See Rennert and Xie (2022) <doi:10.1111/biom.13451>. Both the independent and dependent cases assume no censoring is present in the data. Please contact Lior Rennert <liorr@clemson.edu> for questions regarding function coxDT and Yidan Shi <yidan.shi@pennmedicine.upenn.edu> for questions regarding function coxDTdep.
Survey to collect data about the social and economic conditions of Indonesian society. This activity aims to include: As a data source for planning and evaluating national, sectoral development programs, and providing indicators for Sustainable Development Goals (TPB), National Medium Term Development Plan (RPJMN), and Nawacita, GDP/GRDP and annual Integrated Institutional Balance Sheet.
Implements methods for variable selection in linear
    regression based on the "Sum of Single Effects" (SuSiE) model, as
    described in Wang et al (2020) <DOI:10.1101/501114> and Zou et al
    (2021) <DOI:10.1101/2021.11.03.467167>. These methods provide
    simple summaries, called "Credible Sets", for accurately
    quantifying uncertainty in which variables should be selected.
    The methods are motivated by genetic fine-mapping applications,
    and are particularly well-suited to settings where variables are
    highly correlated and detectable effects are sparse. The fitting
    algorithm, a Bayesian analogue of stepwise selection methods
    called "Iterative Bayesian Stepwise Selection" (IBSS), is simple
    and fast, allowing the SuSiE model be fit to large data sets
    (thousands of samples and hundreds of thousands of variables).
Provides a complete suite of tools for interacting
    with the Survey Solutions 'GraphQL' API
    <https://demo.mysurvey.solutions/graphql/>. This package encompasses
    all currently available queries and mutations, including the latest
    features for map uploads. It is built on the modern 'httr2' package,
    offering a streamlined and efficient interface without relying on
    external 'GraphQL' client packages. In addition to core API
    functionalities, the package includes a range of helper functions
    designed to facilitate the use of available query filters.
Computes synchrony as windowed cross-correlation based on two-dimensional time series in a text file you can upload. 'SUSY' works as described in Tschacher & Meier (2020) <doi:10.1080/10503307.2019.1612114>.
Implements a method to combine multiple levels of multiple
  sequence alignment to uncover the structure of complex DNA rearrangements.
Implements the s-values proposed by Ed. Leamer.
    It provides a context-minimal approach for sensitivity analysis using extreme
    bounds to assess the sturdiness of regression coefficients.
Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. 
             Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural 
             impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>),  patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>),
             independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), 
             smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).
R bindings to SVD and eigensolvers (PROPACK, nuTRLan).
Quickly construct standard dialog boxes for your GUI, including 
  message boxes, input boxes, list, file or directory selection, ... In case R
  cannot display GUI dialog boxes, a simpler command line version of these
  interactive elements is also provided as fallback solution.
Reimplementation of the 'svDialogs' dialog boxes in Tcl/Tk.
Model age schedules of mortality, nqx, suitable for a life table. This package 
    implements the SVD-Comp mortality model indexed by either child or child/adult mortality. 
    Given input value(s) of either 5q0 or (5q0, 45q15), the qx() function generates 
    single-year 1qx or 5-year 5qx conditional age-specific probabilities of dying. 
    See Clark (2016) <doi:10.48550/arXiv.1612.01408> and Clark (2019) <doi:10.1007/s13524-019-00785-3>.
Implements the discrete nonlinear filter (DNF) of Kitagawa (1987) <doi:10.1080/01621459.1987.10478534> to a wide class of stochastic volatility (SV) models with return and volatility jumps following the work of Bégin and Boudreault (2021) <doi:10.1080/10618600.2020.1840995> to obtain likelihood evaluations and maximum likelihood parameter estimates. Offers several built-in SV models and a flexible framework for users to create customized models by specifying drift and diffusion functions along with an arrival distribution for the return and volatility jumps. Allows for the estimation of factor models with stochastic volatility (e.g., heteroskedastic volatility CAPM) by incorporating expected return predictors. Also includes functions to compute filtering and prediction distribution estimates, to simulate data from built-in and custom SV models with jumps, and to forecast future returns and volatility values using Monte Carlo simulation from a given SV model.
An interactive charting library built on 'Svelte' and 'D3' to easily produce SVG charts in R.
  Designed to simplify 'shiny' development by eliminating the need for renderUI(), insertUI(), removeUI(),
  and 'shiny' proxy functions, using 'Svelte''s reactive state system instead.
Implements Self-Validated Ensemble Models (SVEM; Lemkus et al. (2021) <doi:10.1016/j.chemolab.2021.104439>) using elastic net regression via 'glmnet' (Friedman et al. (2010) <doi:10.18637/jss.v033.i01>). SVEM averages predictions from multiple models fitted to fractionally weighted bootstraps of the data, tuned with anti-correlated validation weights. Supports Gaussian and binomial responses. Also implements the randomized permutation whole-model test for SVEM with Gaussian responses (Karl (2024) <doi:10.1016/j.chemolab.2024.105122>). Some parts of the package code were drafted with assistance from generative AI tools.
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.
Manage 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.
A simple HTTP server allows to connect GUI clients to R.
Function for the GUI API to interact with external IDE/code editors.
Provides functionality to fit and simulate from stationary vine 
  copula models for time series, see Nagler et al. (2022) 
  <doi:10.1016/j.jeconom.2021.11.015>.
R-side code to implement an R editor and IDE in Komodo IDE
  with the SciViews-K extension.
In practice, it is difficult to determine the number of decomposition modes, K, for Variational Mode Decomposition (VMD). To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021)<doi:10.1016/j.aej.2021.01.055>.
Functions required for the 'SciViews::R' dialect or for general
  use: manage a temporary environment attached to the search path, define
  synonyms for R functions using aka(), test if 'Aqua', 'Mac', 'Win' ... Show
  progress bar, etc.
Implements the SVM-Maj algorithm to train data with support vector machine
  <doi:10.1007/s11634-008-0020-9>.
  This algorithm uses two efficient updates, one for linear kernel and one 
  for the nonlinear kernel.
Computes the entire regularization path for the two-class svm classifier
		with essentially the same cost as a single SVM fit.
Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>.
Two versions of sample variance plots, Sv-plot1 and Sv-plot2, will be provided illustrating 
             the squared deviations from sample variance. Besides indicating the contribution of squared 
             deviations for the sample variability, these plots are capable of detecting characteristics of the
             distribution such as symmetry, skewness and outliers. A remarkable graphical method based on 
             Sv-plot2 can determine the decision on testing hypotheses over one or two population means. 
             In sum, Sv-plots will be appealing visualization tools. Complete description of this methodology  
             can be found in the article, Wijesuriya (2020) <doi:10.1080/03610918.2020.1851716>.
Provides tools for creating and working with survey replicate weights,
  extending functionality of the 'survey' package from Lumley (2004) <doi:10.18637/jss.v009.i08>.
  Implements bootstrap methods for complex surveys, including the generalized survey bootstrap
  as described by Beaumont and Patak (2012) <doi:10.1111/j.1751-5823.2011.00166.x>.
  Methods are provided for applying nonresponse adjustments to
  both full-sample and replicate weights as described by 
  Rust and Rao (1996) <doi:10.1177/096228029600500305>.
  Implements methods for sample-based calibration described by Opsomer and Erciulescu (2021) 
  <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021002/article/00006-eng.htm>.
  Diagnostic functions are included to compare weights and weighted estimates
  from different sets of replicate weights.
Computes the entire solution paths for Support Vector Regression(SVR) with respect to the regularization parameter, lambda and epsilon in epsilon-intensive loss function, efficiently. We call each path algorithm svrpath and epspath. See Wang, G. et al (2008) <doi:10.1109/TNN.2008.2002077> for details regarding the method.
Various tools for semantic vector spaces, such as
    correspondence analysis (simple, multiple and discriminant), latent
    semantic analysis, probabilistic latent semantic analysis, non-negative
    matrix factorization, latent class analysis, EM clustering, logratio
	analysis and log-multiplicative (association) analysis. Furthermore,
    there are specialized distance measures, plotting functions and some helper
    functions.
A socket server allows to connect clients to R.
Functions to enumerate and reference figures, tables and equations
  in R Markdown documents that do not support these features (thus not
  'bookdown' or 'quarto'. Supporting functions for using 'Sweave' and 'Knitr'
  with 'LyX'.
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.
High level management of widgets, windows and other graphical resources.
Estimates the population average controlled difference for a given outcome between levels of a binary treatment, exposure, or other group membership variable of interest for clustered, stratified survey samples where sample selection depends on the comparison group. Provides three methods for estimation, namely outcome modeling and two factorizations of inverse probability weighting. Under stronger assumptions, these methods estimate the causal population average treatment effect. Salerno et al., (2024) <doi:10.48550/arXiv.2406.19597>.
Mixed-effect proportional hazards models for multistage stratified, 
   cluster-sampled, unequally weighted survey samples. Provides variance 
   estimation by Taylor series linearisation or replicate weights.
Diagnostics for fixed effects linear and general 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 as found in Li and Valliant (Surv. Meth., 2009, 35(1), pp. 15-24; Jnl. of Off. Stat., 2011, 27(1), pp. 99-119; Jnl. of Off. Stat., 2015, 31(1), pp. 61-75); Liao and Valliant (Surv. Meth., 2012, 38(1), pp. 53-62; Surv. Meth., 2012, 38(2), pp. 189-202).  Variance inflation factors and condition indexes are also computed for some general linear models as described in Liao (U. Maryland thesis, 2010).
Linear mixed models for complex survey data, by pairwise composite likelihood, as described in Lumley & Huang (2023) <arXiv:2311.13048>. Supports nested and crossed random effects, and correlated random effects as in genetic models.  Allows for multistage sampling and for other designs where pairwise sampling probabilities are specified or can be calculated.
Builds, evaluates and validates a nomogram with survey data
    and right-censored outcomes. As described in Capanu (2015)
    <doi:10.18637/jss.v064.c01>, the package contains functions to create
    the nomogram, validate it using bootstrap, as well as produce the
    calibration plots.
Estimate the receiver operating characteristic (ROC) curve, area under the curve (AUC) and optimal cut-off points for individual classification taking into account complex sampling designs when working with complex survey data. Methods implemented in this package are described in: A. Iparragirre, I. Barrio, I. Arostegui (2024) <doi:10.1002/sta4.635>; A. Iparragirre, I. Barrio, J. Aramendi, I. Arostegui (2022) <doi:10.2436/20.8080.02.121>; A. Iparragirre, I. Barrio (2024) <doi:10.1007/978-3-031-65723-8_7>.
Provides diagnostic tests for assessing the informativeness of survey weights in regression models. Implements difference-in-coefficients tests (Hausman 1978 <doi:10.2307/1913827>; Pfeffermann 1993 <doi:10.2307/1403631>), weight-association tests (DuMouchel and Duncan 1983 <doi:10.2307/2288185>; Pfeffermann and Sverchkov 1999 <https://www.jstor.org/stable/25051118>; Pfeffermann and Sverchkov 2003 <ISBN:9780470845672>; Wu and Fuller 2005 <https://www.jstor.org/stable/27590461>), estimating equations tests (Pfeffermann and Sverchkov 2003 <ISBN:9780470845672>), and non-parametric permutation tests. Includes simulation utilities replicating Wang et al. (2023 <doi:10.1111/insr.12509>) and extensions.
Fit design-based linear and logistic elastic nets with complex survey data considering the sampling design when defining training and test sets using replicate weights. Methods implemented in this package are described in: A. Iparragirre, T. Lumley, I. Barrio, I. Arostegui (2024) <doi:10.1002/sta4.578>.
Provides inference based on the survey package for the wide range of parametric models in the 'VGAM' package.
Quickly and flexibly calculates weights for survey data, in order to correct
    for survey non-response or other sampling issues. Uses rake weighting, a common technique 
    also know as rim weighting or iterative proportional fitting.  This technique allows for weighting 
    on multiple variables, even when the interlocked distribution of the two
    variables is not known. Interacts with Thomas Lumley's 'survey' package, as described in 
    Lumley, Thomas (2011, ISBN:978-1-118-21093-2). Adds additional functionality, more adaptable syntax, and error-checking
    to the base weighting functionality in 'survey.'
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/>.
Provides a fast implementation of the SWAG algorithm for Generalized Linear Models which allows to perform a meta-learning procedure that combines 
    screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. The package then performs test on the network of selected models to identify the variables that are highly predictive by using entropy-based network measures.
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.
Obtaining accurate and stable estimates of regression coefficients
    can be challenging when the suggested statistical model has issues related to
    multicollinearity, convergence, or overfitting. One solution is to use
    principal component analysis (PCA) results in the regression, as discussed in
    Chan and Park (2005) <doi:10.1080/01446190500039812>. The swaprinc() package
    streamlines comparisons between a raw regression model with the full set of
    raw independent variables and a principal component regression model where
    principal components are estimated on a subset of the independent variables,
    then swapped into the regression model in place of those variables. The
    swaprinc() function compares one raw regression model to one principal
    component regression model, while the compswap() function compares one raw
    regression model to many principal component regression models. Package
    functions include parameters to center, scale, and undo centering and scaling,
    as described by Harvey and Hansen (2022)
    <https://cran.r-project.org/package=LearnPCA/vignettes/Vig_03_Step_By_Step_PCA.pdf>.
    Additionally, the package supports using Gifi methods to extract principal
    components from categorical variables, as outlined by Rossiter (2021)
    <https://www.css.cornell.edu/faculty/dgr2/_static/files/R_html/NonlinearPCA.html#2_Package>.
Function library for processing collective movement data (e.g. fish
    schools, ungulate herds, baboon troops) collected from GPS trackers or 
    computer vision tracking software.
Provides a pipeline for the comparative analysis of 
  collective movement data (e.g. fish schools, bird flocks, baboon troops) by 
  processing 2-dimensional positional data (x,y,t) from GPS trackers or computer
  vision tracking systems, discretizing events of collective motion, calculating
  a set of established metrics that characterize each event, and placing the
  events in a multi-dimensional swarm space constructed from these metrics.
  The swarm space concept, the metrics and data sets included are described in:
  Papadopoulou Marina, Furtbauer Ines, O'Bryan Lisa R., Garnier Simon, 
  Georgopoulou Dimitra G., Bracken Anna M., Christensen Charlotte and King
  Andrew J. (2023) <doi:10.1098/rstb.2022.0068>.
The Swash-Backwash Model for the Single Epidemic Wave was developed by Cliff and Haggett (2006) <doi:10.1007/s10109-006-0027-8> to model the velocity of spread of infectious diseases across space. This package enables the calculation of the Swash-Backwash Model for user-supplied panel data on regional infections. The package also provides additional functions for bootstrap confidence intervals, country comparison, visualization of results, and data management.
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 or cohort sampling scheme (Hussey MA and Hughes JP (2007) 
	Contemporary Clinical Trials 28:182-191).
Implements the Sliding Window Discrete Fourier Transform (SWDFT). Also provides 
             statistical methods based on the SWDFT, and graphical tools to display the outputs.
To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects (the default specification assumes no-time-effect) under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) <doi:10.1093/biostatistics/kxy031>, Li et al. (2018) <doi:10.1111/biom.12918>. Supplementary documents can be found at: <https://ysph.yale.edu/cmips/research/software/study-design-power-calculation/swdpwr/>. The Shiny app for swdpwr can be accessed at: <https://jiachenchen322.shinyapps.io/swdpwr_shinyapp/>. The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively.
Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), <doi:10.1126/science.aal4230>, Relative Norm Distance (Garg et al., 2018), <doi:10.1073/pnas.1720347115>, Mean Average Cosine Similarity (Mazini et al., 2019) <arXiv:1904.04047>, SemAxis (An et al., 2018) <arXiv:1806.05521>, Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) <doi:10.18653/v1/P19-1162>, and Embedding Coherence Test (Dev & Phillips, 2019) <arXiv:1901.07656>.
Tidies up the forecasting modeling and prediction work flow, 
    extends the 'broom' package 
    with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions 
    for various forecasting models,
    and enables converting 'forecast' objects to 
    "tidy" data frames with 'sw_sweep'.
Selective sweep is a biological phenomenon in which genetic variation between neighboring beneficial mutant alleles is swept away due to the effect of genetic hitchhiking. Detection of selective sweep is not well acquainted as well as it is a laborious job. This package is a user friendly approach for detecting selective sweep in genomic regions. It uses a Random Forest based machine learning approach to predict selective sweep from VCF files as an input. Input of this function, train data and new data, can be computed using the project <https://github.com/AbhikSarkar1999/SweepDiscovery> in 'GitHub'. This package has been developed by using the concept of  Pavlidis and Alachiotis (2017) <doi:10.1186/s40709-017-0064-0>.
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 2.10.03) 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.r-universe.dev",
    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 DAS data files. 
    These files are typically, but do not have to be 
    DAS <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF> data
    produced by the Southwest Fisheries Science Center (SWFSC) program 'WinCruz'.
    This package standardizes and streamlines basic DAS data processing,
    and includes a PDF with the DAS data format requirements expected by the 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/> and Pesenti S.M. (2021) 
    "Reverse Sensitivity Analysis for Risk Modelling" <https://www.ssrn.com/abstract=3878879>.
The goal of the 'SwimmeR' package is to provide means of acquiring, and then analyzing, data from swimming (and diving) competitions.  To that end 'SwimmeR' allows results to be read in from .html sources, like 'Hy-Tek' real time results pages, '.pdf' files, 'ISL' results, 'Omega' results, and (on a development basis) '.hy3' files.  Once read in, 'SwimmeR' can convert swimming times (performances) between the computationally useful format of seconds reported to the '100ths' place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community.  'SwimmeR' can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', 'SCM', 'SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data.  This is a developmental package, not yet mature.
Used for creating swimmers plots with functions to customize the bars, add points, add lines, add text, and add arrows.
Create carousels using the 'JavaScript' library 'Swiper' and
    the package 'htmlwidgets'. The carousels can be displayed in the
    'RStudio' viewer pane, in 'Shiny' applications and in 'R markdown'
    documents. The package also provides a 'RStudio' addin allowing to 
    choose image files and to display them in the viewer pane.
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.
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.
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.
Estimate the parameters of multivariate endogenous switching and sample selection models using methods described in Newey (2009) <doi:10.1111/j.1368-423X.2008.00263.x>, E. Kossova, B. Potanin (2018) <https://ideas.repec.org/a/ris/apltrx/0346.html>, E. Kossova, L. Kupriianova, B. Potanin (2020) <https://ideas.repec.org/a/ris/apltrx/0391.html> and E. Kossova, B. Potanin (2022) <https://ideas.repec.org/a/ris/apltrx/0455.html>.
Tools for retrieving, organizing, and analyzing environmental
    data from the System Wide Monitoring Program of the National Estuarine
    Research Reserve System <https://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 <https://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>.
An index is created using a mathematical model that transforms multi-dimensional variables into a single value. These variables are often correlated, and while PCA-based indices can address the issue of multicollinearity, they typically do not account for survey weights, which can lead to inaccurate rankings of survey units such as households, districts, or states. To resolve this, the current package facilitates the development of a principal component analysis-based composite index by incorporating survey weights for each sample observation. This ensures the generation of a survey-weighted principal component-based normalized composite index. Additionally, the package provides a normalized principal component-based composite index and ranks the sample observations based on the values of the composite indices.
            For method details see, Skinner, C. J., Holmes, D. J. and Smith, T. M. F. (1986) <DOI:10.1080/01621459.1986.10478336>, Singh, D., Basak, P., Kumar, R. and Ahmad, T. (2023) <DOI:10.3389/fams.2023.1274530>.
Functions to speed up work flow for hydrological analysis. 
    Focused on Australian climate data (SILO climate data), hydrological models (eWater Source) and in particular South Australia (<https://water.data.sa.gov.au> hydrological data).
An English language syllable counter, plus readability score
    measure-er. For readability, we support 'Flesch' Reading Ease and
    'Flesch-Kincaid' Grade Level ('Kincaid' 'et al'. 1975)
    <https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1055&context=istlibrary>,
    Automated Readability Index ('Senter' and Smith 1967)
    <https://apps.dtic.mil/sti/citations/AD0667273>,
    Simple Measure of Gobbledygook (McLaughlin 1969),
    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>.
Collection (syllogi in greek) of real and fictitious data sets for teaching purposes.
	     The datasets were manually entered by the author from the respective references as listed in the individual dataset documentation.
	     The fictions datasets are the creation of the author, that he has found useful for teaching statistics.
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.
Powerful user interface for adding symbols, smileys, arrows, building mathematical equations using 'LaTeX' or 'r2symbols'. Built for use in development of 'Markdown' and 'Shiny' Outputs.
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>,
 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>,
 Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).
Creates a numeric guide for writing the formula for the determinant of a square matrix (a detguide) as a function of the elements of the matrix and writes out that formula, the symbolic representation.
Introduces the 'symbolicQspray' objects. Such an object
    represents a multivariate polynomial whose coefficients are fractions of
    multivariate polynomials with rational coefficients. The package
    allows arithmetic on such polynomials. It is based on the 'qspray' and 
    'ratioOfQsprays' packages. Some functions for 'qspray' polynomials have
    their counterpart for 'symbolicQspray' polynomials. A 'symbolicQspray' 
    polynomial should not be seen as a polynomial on the field of fractions 
    of rational polynomials, but should rather be seen as a polynomial with 
    rational coefficients depending on some parameters, symbolically 
    represented, with a dependence given by fractions of rational polynomials.
A matrix-like class to represent a symmetric matrix partitioned
    into file-backed blocks.
Provides an R interface to 'SymEngine' <https://github.com/symengine/>,
    a standalone 'C++' library for fast symbolic manipulation. The package has functionalities
    for symbolic computation like calculating exact mathematical expressions, solving
    systems of linear equations and code generation.
Implementations of a large number of tests for symmetry and their 
    bootstrap variants, which can be used for testing the symmetry of random
    samples around a known or unknown mean. Functions are also there for testing
    the symmetry of model residuals around zero. Currently, the supported models
    are linear models and generalized autoregressive conditional
    heteroskedasticity (GARCH) models (fitted with the 'fGarch' package). All
    tests are implemented using the 'Rcpp' package which ensures great
    performance of the code.
Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.
Implements the Symphony single-cell reference building and query mapping algorithms and additional functions described in Kang et al <https://www.nature.com/articles/s41467-021-25957-x>.
Easily analyze and visualize the performance of symptom
    checkers. This package can be used to gain comprehensive insights into
    the performance of single symptom checkers or the performance of
    multiple symptom checkers. It can be used to easily compare these
    symptom checkers across several metrics to gain an understanding of
    their strengths and weaknesses. The metrics are developed in 
    Kopka et al. (2023) <doi:10.1177/20552076231194929>.
A collection of functions for symbolic computation using the
			 'caracas' package for structural equation models and other
			 statistical analyses. Among its features is the ability
			 to calculate the model-implied covariance (and correlation)
			 matrix and the sampling covariance matrix of variable functions
			 using the delta method.
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.
The synchrosqueezed wavelet transform is implemented. 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'.
Generate the same random numbers in R and Python.
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>).
Regression inference for multiple populations by integrating 
    summary-level data using stacked imputations. Gu, T., Taylor, J.M.G. and 
    Mukherjee, B. (2021) A synthetic data integration framework to leverage 
    external summary-level information from heterogeneous populations 
    <arXiv:2106.06835>.
A framework for evaluating drug combination effects in preclinical in vivo studies.
    'SynergyLMM' provides functions to analyze longitudinal tumor growth experiments using mixed-effects models, 
    perform time-resolved analyses of synergy and antagonism, evaluate model diagnostics and performance, 
    and assess both post-hoc and a priori statistical power.
    The calculation of drug combination synergy follows the statistical framework provided by Demidenko and Miller (2019, <doi:10.1371/journal.pone.0224137>).
    The implementation and analysis of linear mixed-effect models is based on the methods described by Pinheiro and Bates (2000, <doi:10.1007/b98882>),
    and Gałecki and Burzykowski (2013, <doi:10.1007/978-1-4614-3900-4>).
This tool fits a non-parametric Bayesian model called a "hierarchically coupled mixture model with local dependence (HCMM-LD)" to the original microdata in order to generate synthetic microdata for privacy protection. The non-parametric feature of the adopted model is useful for capturing the joint distribution of the original input data in a highly flexible manner, leading to the generation of synthetic data whose distributional features are similar to that of the input data. The package allows the original input data to have missing values and impute them with the posterior predictive distribution, so no missing values exist in the synthetic data output. The method builds on the work of Murray and Reiter (2016) <doi:10.1080/01621459.2016.1174132>.
Explore synesthesia
  consistency test data, calculate consistency scores, 
  and classify participant data as valid or invalid.
A set of functions for generating 'SPSS' syntax files from the R environment.
Draw syntenic relationships between genome assemblies.
    There are 3 functions which take a tab delimited file containing alignment data for 
    syntenic blocks between genomes to produce either a linear alignment plot, 
    an evolution highway style plot, or a painted ideogram representing syntenic relationships.
    There is also a function to convert alignment data in the DESCHRAMBLER/inferCAR format
    to the required data structure.
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.
Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) <doi:10.1016/j.advwatres.2019.103430> and Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962> associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>.
A critical first step in systematic literature reviews
  and mining of academic texts is to identify relevant texts from a range
  of sources, particularly databases such as 'Web of Science' or 'Scopus'.
  These databases often export in different formats or with different metadata
  tags. 'synthesisr' expands on the tools outlined by Westgate (2019)
  <doi:10.1002/jrsm.1374> to import bibliographic data from a range of formats
  (such as 'bibtex', 'ris', or 'ciw') in a standard way, and allows merging
  and deduplication of the resulting dataset.
Synthesize numeric, categorical, mixed and time series data. Data 
    circumstances including mixed (or zero-inflated) distributions and missing
    data patterns are reproduced in the synthetic data. A single parameter allows
    balancing between high-quality synthetic data that represents correlations of
    the original data and lower quality but more privacy safe synthetic data
    without correlations. Tuning can be done per variable or for the whole
    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" <doi:10.48550/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>. Functions to assess identity and attribute disclosure for the original and for the synthetic data are included in the package, and their use is illustrated in a vignette on disclosure (Practical Privacy Metrics for Synthetic Data).
Implements the revised Synthetic Matching Algorithm of Kreitmeir, Lane, and Raschky (2025) <doi:10.2139/ssrn.3751162>, building on 
  the original approach of Acemoglu, Johnson, Kermani, Kwak, and Mitton (2016) <doi:10.1016/j.jfineco.2015.10.001>, to estimate the cumulative 
  treatment effect of an event on treated firms’ stock returns.
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>.
Measures memory and CPU usage of R code by regularly taking 
    snapshots of calls to the system command 'ps'. The package provides an entry 
    point (albeit coarse) to profile usage of system resources by R code run 
    in parallel.
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.
There is variation across AgNPs due to differences in characterization techniques and testing metrics employed in studies. To address this problem, we have developed a systematic evaluation framework called 'sysAgNPs'. Within this framework, Distribution Entropy (DE) is utilized to measure the uncertainty of  feature categories of AgNPs, Proclivity Entropy (PE) assesses the preference of these categories, and Combination Entropy (CE) quantifies the uncertainty of feature combinations of AgNPs. Additionally, a Markov chain model is employed to examine the relationships among the sub-features of AgNPs and to determine a Transition Score (TS) scoring standard that is based on steady-state probabilities. The 'sysAgNPs' framework provides metrics for evaluating AgNPs, which helps to unravel their complexity and facilitates effective comparisons among different AgNPs, thereby advancing the scientific research and application of these AgNPs.
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.
In the past decade, genome-scale metabolic reconstructions have widely been 
    used to comprehend the systems biology of metabolic pathways within an organism. 
    Different GSMs are constructed using various techniques that 
    require distinct steps, but the input data, information conversion and software tools
    are neither concisely defined nor mathematically or programmatically formulated
    in a context-specific manner.The tool that quantitatively and qualitatively specifies
    each reconstruction steps and can generate a template list of reconstruction steps dynamically
    selected from a reconstruction step reservoir, constructed based on all available published papers.
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>.
Analysis of risk through liability matrices. Contains a Gibbs sampler for network reconstruction,  where only row and column sums of the liabilities matrix as well as some other fixed entries are observed, following the methodology of Gandy&Veraart (2016) <doi:10.1287/mnsc.2016.2546>. It also incorporates models that use a power law distribution on the degree distribution.
Deals with Young tableaux (field of combinatorics). For standard
    Young tabeaux, performs enumeration, counting, random generation, the
    Robinson-Schensted correspondence, and conversion to and from paths on
    the Young lattice. Also performs enumeration and counting of 
    semistandard Young tableaux, enumeration of skew semistandard Young 
    tableaux, enumeration of Gelfand-Tsetlin patterns, and computation of 
    Kostka numbers.
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.
A system for personalized exercise plan recommendations for T2D (Type 2 Diabetes) patients  based on the primary outcome of HbA1c (Glycated Hemoglobin). You provide the individual's information, and 'T2DFitTailor' details the exercise plan and predicts the intervention's effectiveness.
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.
Covers k-table control analysis using multivariate control charts for qualitative variables using fundamentals of multiple correspondence analysis and multiple factor analysis. The graphs can be shown in a flat or interactive way, in the same way all the outputs can be shown in an interactive shiny panel.
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.
Converting structured data from tables into XML format using
    predefined templates ensures consistency and flexibility, making it
    ideal for data exchange, reporting, and automated workflows.
Calculates the robust Taba linear, Taba rank (monotonic), TabWil, and TabWil rank 
    correlations. Test statistics as well as one sided or two sided p-values are provided 
    for all correlations. Multiple correlations and p-values can be calculated 
    simultaneously across multiple variables. In addition, users will have the option to use 
    the partial, semipartial, and generalized partial correlations; where the partial and 
    semipartial correlations use linear, logistic, or Poisson regression to modify the specified
    variable.
Functions for tabulating and summarising categorical variables. 
    Most functions are designed to work with dataframes, and use the 'tidyverse' 
    idiom of taking the dataframe as the first argument so they work within 
    pipelines. Equivalent functions that operate directly on vectors are also 
    provided where it makes sense. This package aims to make exploratory data 
    analysis involving categorical variables quicker, simpler and more robust.
Sometimes you need to split your data and work on the two chunks independently before bringing them back together. 'Taber' allows you to do that with its two functions.
Simple tabulation should be dead simple. 
    This package is an opinionated approach to easy tabulations while also 
    providing exact numbers and allowing for re-usability. 
    This is achieved by providing tabulations as data.frames with columns for 
    values, optional variable names, frequency counts including and excluding 
    NAs and percentages for counts including and excluding NAs. Also values are
    automatically sorted by in decreasing order of frequency counts to allow for 
    fast skimming of the most important information.
For writing tables with custom formats in a Excel file ready to be distributed.
A specialization of 'dplyr' data manipulation verbs that parse and build expressions
    which are ultimately evaluated by 'data.table', letting it handle all optimizations. A set of
    additional verbs is also provided to facilitate some common operations on a subset of the data.
Translate double and integer valued data into
    character values formatted for tabulation in manuscripts
    or other types of academic reports.
Create HTML tables of descriptive statistics, as one would expect
    to see as the first table (i.e. "Table 1") in a medical/epidemiological journal
    article.
Table 1 is the classical way to describe the patients in a
    clinical study. The amount of splits in the data in such a table is
    limited. Table1Heatmap draws a heatmap of all crosstables that can be
    generated with the data. Users can choose between showing the actual
    crosstables or direction of effect of associations, and highlight
    associations by number of patients or p-values.
    v1.2 - fixed "missing "no visible global function definition for .."
A toolbox for comparing two data frames. This package is defunct. I recommend you use the "versus" package instead.
Offers a TableContainer() function to create tables enriched with row, column, and table annotations. This package is similar to 'SummarizedExperiment' in Bioconductor <doi:10.18129/B9.bioc.SummarizedExperiment>, but designed to work independently of Bioconductor, it ensures annotations are automatically updated when the table is subset. Additionally, it includes format_tbl() methods for enhanced table formatting and display.
Create publication quality plots and tables for Item Response Theory and Classical Test theory based item analysis, exploratory and confirmatory factor analysis.
Creates some tables of clinical study. 'Table 1' is created by table1() to describe baseline characteristics, which is essential in every clinical study. Created by table2(), the function of 'Table 2' is to explore influence factors. And 'Table 3' created by table3() is able to make stratified analysis.
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.
Provides functions to build interactive dashboards combining the 'Tabler UI Kit' with 'Shiny', making it
    easy to create professional-looking web applications. 'Tabler' is fully responsive and compatible with all modern
    browsers. Offers customizable layouts and components built with 'HTML5' and 'CSS3'. The underlying 'Tabler'
    (<https://github.com/tabler/tabler>) and 'Tabler Icons' (<https://github.com/tabler/tabler-icons>)
    were pre-built from source to eliminate the need for 'Node.js' and 'NPM' on package installation.
'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.
Computes and displays complex tables of summary statistics.
  Output may be in LaTeX, HTML, plain text, or an R
  matrix for further processing.
Presentation-quality tables are displayed as plots on an R 
  graphics device.  Although there are other packages that format tables 
  for display, this package is unique in combining two features: (a) It is 
  aware of the logical structure of the table being presented, and makes 
  use of that for automatic layout and styling of the table.  This avoids 
  the need for most manual adjustments to achieve an attractive result. 
  (b) It displays tables using 'ggplot2' graphics.  Therefore a table can 
  be presented anywhere a graph could be, with no more effort.  External 
  software such as LaTeX or HTML or their viewers is not required.  The 
  package provides a full set of tools to control the style and appearance 
  of tables, including titles, footnotes and reference marks, horizontal 
  and vertical rules, and spacing of rows and columns.  Methods are included 
  to display matrices; data frames; tables created by R's ftable(), table(), 
  and xtabs() functions; and tables created by the 'tables' and 'xtable' 
  packages.  Methods can be added to display other table-like objects.  A 
  vignette is included that illustrates usage and options available in the 
  package.
Create "good enough" tables with a single formula. 'tablespan' tables 
  can be exported to 'Excel', 'HTML', 'LaTeX', and 'RTF' by leveraging
  the packages 'openxlsx' and 'gt'. See <https://jhorzek.github.io/tablespan/> for
  an introduction.
Creates a table of descriptive statistics
 for factor and numeric columns in a data frame. Displays
 these by groups, if any. Highly customizable, with support
 for 'html' and 'pdf' provided by 'kableExtra'. Respects
 original column order, column labels, and factor level order.
 See ?tablet.data.frame and vignettes.
A wrapper to a set of algorithms designed to recognise positional cues present in hierarchical for-human Tables (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into machine-readable LongForm Dataframes.
Collection of functions that allow to export data frames to excel workbook.
Implements the 'TabNet' model by Sercan O. Arik et al. (2019)
    <doi:10.48550/arXiv.1908.07442> with 'Coherent Hierarchical Multi-label
    Classification Networks' by Giunchiglia et al.  <doi:10.48550/arXiv.2010.10151> and
    provides a consistent interface for fitting and creating predictions.
    It's also fully compatible with the 'tidymodels' ecosystem.
Collect  your data on digital marketing campaigns from 'Taboola' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Provides a music notation syntax and a collection of music
    programming functions for generating, manipulating, organizing, and analyzing
    musical information in R. Music syntax can be entered directly in character
    strings, for example to quickly transcribe short pieces of music. The
    package contains functions for directly performing various mathematical,
    logical and organizational operations and musical transformations on special
    object classes that facilitate working with music data and notation. The
    same music data can be organized in tidy data frames for a familiar and
    powerful approach to the analysis of large amounts of structured music data.
    Functions are available for mapping seamlessly between these formats and
    their representations of musical information. The package also provides an
    API to 'LilyPond' (<https://lilypond.org/>) for transcribing musical
    representations in R into tablature ("tabs") and sheet music. 'LilyPond' is
    open source music engraving software for generating high quality sheet music
    based on markup syntax. The package generates 'LilyPond' files from R code
    and can pass them to the 'LilyPond' command line interface to be rendered
    into sheet music PDF files or inserted into R markdown documents. The
    package offers nominal MIDI file output support in conjunction with
    rendering sheet music. The package can read MIDI files and attempts to
    structure the MIDI data to integrate as best as possible with the data
    structures and functionality found throughout the package.
A standardized workflow to reconstruct spatial configurations of altitude-bounded biogeographic systems over time. For example, 'tabs' can model how island archipelagos expand or contract with changing sea levels or how alpine biomes shift in response to tree line movements. It provides functionality to account for various geophysical processes such as crustal deformation and other tectonic changes, allowing for a more accurate representation of biogeographic system dynamics. For more information see De Groeve et al. (2025) <doi:10.3897/arphapreprints.e151900>.
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.
Simplify reporting many tables by creating tibbles of tables. With
  'tabtibble', a tibble of tables is created with captions and automatic
  printing using 'knit_print()'.
An easy way to examine archaeological count data. This
    package provides several tests and measures of diversity:
    heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.),
    richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and
    similarity (Brainerd-Robinson, etc.). It allows to easily visualize
    count data and statistical thresholds: rank vs abundance plots,
    heatmaps, Ford (1962) and Bertin (1977) diagrams, etc.
Bindings for the 'Tabula' <https://tabula.technology/> 'Java'
    library, which can extract tables from PDF files. This tool can reduce time
    and effort in data extraction processes in fields like investigative
    journalism. It allows for automatic and manual table extraction, the latter
    facilitated through a 'Shiny' interface, enabling manual areas selection\
    with a computer mouse for data retrieval.
Facilities to work with vector and raster data in efficient 
 repeatable and systematic work flow. Missing functionality in existing packages 
 is included here to allow extraction from raster data with 'simple features' and 
 'Spatial' types and to make extraction consistent and straightforward. Extract cell 
 numbers from raster data and  return the cells as a data frame 
 rather than as lists of matrices or vectors. The functions here allow spatial data 
 to be used without special handling for the format currently in use.
The 'tabularmap' is one of the visualization methods for 
    efficiently displaying data consisting of multiple elements by tiling them.
    When dealing with geospatial, it corrects for differences in visibility 
    between areas.
The maximum likelihood classifier (MLC) is one of the most common classifiers used for 
  remote sensing imagery. 
  This package uses 'RcppArmadillo' to provide a fast implementation of the MLC to train 
  and predict over tabular data (data.frame).
  The algorithms were based on Mather (1985) <doi:10.1080/01431168508948456> method.
Efficient tabulation with Stata-like output.
	For each unique value of the variable, it shows the number of 
	observations with that value, proportion of observations with that
	value, and cumulative proportion, in descending order of frequency.
	Accepts data.table, tibble, or data.frame as input. 
	Efficient with big data: if you give it a data.table, 
	tab() uses data.table syntax.
Convert semi-structured log files (such as 'Apache' access.log files)
    into a tabular format (data.frame) using a standard template system.
Tabu search algorithm for binary configurations. A basic version of the algorithm as described by Fouskakis and Draper (2007) <doi:10.1111/j.1751-5823.2002.tb00174.x>.
Make it easy to deal with multiple cross-tables in data exploration, by
  creating them, manipulating them, and adding color helpers to highlight
  important informations (differences from totals, comparisons between lines or
  columns, contributions to variance, confidence intervals, odds ratios, etc.). 
  All functions are pipe-friendly and render data frames which can be easily 
  manipulated. In the same time, time-taking operations are done with 'data.table'
  to go faster with big dataframes. Tables can be exported with formats and colors 
  to 'Excel', plot and html.
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.
This analytical framework is based on an analysis of the shape of the trait abundance distributions to better understand community assembly processes, and predict community dynamics under environmental changes. This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve).
Suite of tools to support the practice of tada science. It includes an
    engaging package roulette that is designed to facilitate learning about 
    new packages.
General framework to organize data, methods, and results used in
  reproducible scientific analyses. A TAF analysis consists of four scripts
  (data.R, model.R, output.R, report.R) that are run sequentially. Each script
  starts by reading files from a previous step and ends with writing out files
  for the next step. Convenience functions are provided to version control the
  required data and software, run analyses, clean residues from previous runs,
  manage files, manipulate tables, and produce figures. With a focus on
  stability and reproducible analyses, the TAF package comes with no
  dependencies. TAF forms a base layer for the 'icesTAF' package and other
  scientific applications.
Implement the transformed additive Gaussian (TAG) process and the  transformed approximately additive Gaussian (TAAG) process proposed in Lin and Joseph (2020) <DOI:10.1080/00401706.2019.1665592>. These functions can be used to model deterministic computer experiments, obtain predictions at new inputs, and quantify the uncertainty of the predictions. This research is supported by a U.S. National Science Foundation grant DMS-1712642 and a U.S. Army Research Office grant W911NF-17-1-0007.
Provides functions for attaching tags to R objects,
    searching for objects based on tags, and removing tags from objects.
    It also includes a function for removing all tags from an object, as well
    as a function for deleting all objects with a specific tag from the R
    environment. The package is useful for organizing and managing large
    collections of objects in R.
High-resolution movement-sensor tags typically include accelerometers 
    to measure body posture and sudden movements or changes in speed, 
    magnetometers to measure direction of travel, and pressure sensors
    to measure dive depth in aquatic or marine animals. The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; 
    calibration (from raw data to calibrated data in scientific units); 
    visualization (for example, multi-panel time-series plots); 
    data processing (such as event detection, calculation of derived metrics like jerk and 
    dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis).
Functions and example files to calculate the tRNA adaptation index,
  a measure of the level of co-adaptation between the set of tRNA genes and the 
  codon usage bias of protein-coding genes in a given genome. The methodology is
  described in dos Reis, Wernisch and Savva (2003) <doi:10.1093/nar/gkg897>, and 
  dos Reis, Savva and Wernisch (2004) <doi:10.1093/nar/gkh834>.
The function TailClassifier() suggests one of the following types of tail for your discrete data: 1) Power decaying tail; 2) Sub-exponential decaying tail; and 3) Near-exponential decaying tail. The function also provides an estimate of the parameter for the classified-distribution as a reference.
Provides functions implementing minimal distance estimation methods for parametric tail dependence models, as proposed in  
	 Einmahl, J.H.J., Kiriliouk, A., Krajina, A., and Segers, J. (2016) <doi:10.1111/rssb.12114> and 
	 Einmahl, J.H.J., Kiriliouk, A., and Segers, J. (2018) <doi:10.1007/s10687-017-0303-7>.
The goal of 'TailID' is to detect sensitive points in the tail of a dataset using techniques from Extreme Value Theory (EVT). It utilizes the Generalized Pareto Distribution (GPD) for assessing tail behavior and detecting inconsistent points with the Identical Distribution hypothesis of the tail. For more details see Manau (2025)<doi:10.4230/LIPIcs.ECRTS.2025.20>.
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.
Postprocessors refine predictions outputted from machine
    learning models to improve predictive performance or better satisfy
    distributional limitations. This package introduces 'tailor' objects,
    which compose iterative adjustments to model predictions. A number of
    pre-written adjustments are provided with the package, such as
    calibration. See Lichtenstein, Fischhoff, and Phillips (1977) 
    <doi:10.1007/978-94-010-1276-8_19>. Other methods and utilities to compose 
    new adjustments are also included. Tailors are tightly integrated with the 
    'tidymodels' framework.
Estimators for two functionals used to detect Gamma, Pareto or Lognormal distributions, as well as distributions exhibiting similar tail behavior, as introduced by Iwashita and Klar (2023) <doi:10.1111/stan.12316> and 
            Klar (2024) <doi:10.1080/00031305.2024.2413081>. 
            One of these functionals, g, originally proposed by Asmussen and Lehtomaa (2017) <doi:10.3390/risks5010010>, distinguishes between log-convex and log-concave tail behavior. 
            Furthermore the characterization of the lognormal distribution is based on the work of Mosimann (1970) <doi:10.2307/2284599>.
            The package also includes methods for visualizing these estimators and their associated confidence intervals across various threshold values.
Implements the tail-rank statistic for selecting biomarkers
  from a microarray data set, an efficient nonparametric test focused
  on the distributional tails. See
  <https://gitlab.com/krcoombes/coombeslab/-/blob/master/doc/papers/tolstoy-new.pdf>.
When plotting treated-minus-control differences, after-minus-before changes, or difference-in-differences, the ttrans() function symmetrically transforms the positive and negative tails to aid plotting.  The package includes an observational study with three control groups and an unaffected outcome; see Rosenbaum (2022) <doi:10.1080/00031305.2022.2063944>.
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.
Visualisation, analysis and quality control of conversational data.
             Rapid and visual insights into the nature, timing and quality of
             time-aligned annotations in conversational corpora.
             For more details, see 
             Dingemanse et al., (2022) <doi:10.18653/v1/2022.acl-long.385>.
An R 'shiny' app designed for diverse text analysis tasks, offering a wide range of methodologies tailored to Natural Language Processing (NLP) needs. 
             It is a versatile, general-purpose tool for analyzing textual data. 
             'tall' features a comprehensive workflow, including data cleaning, preprocessing, statistical analysis, and visualization, all integrated for effective text analysis.
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>.
Agglomerative hierarchical clustering with a bespoke distance 
  measure based on medication similarities in the Anatomical Therapeutic 
  Chemical Classification System, medication timing and 
  medication amount or dosage. Tools for summarizing, illustrating and 
  manipulating the cluster objects are also available.
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>.
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.
Feature selection algorithm that extracts features in highly
    correlated spaces. The extracted features are meant to be fed into
    simple explainable models such as linear or logistic regressions. The
    package is useful in the field of explainable modelling as a
    way to understand variable behavior.
Provides an extensible formula system to quickly and easily create
    production quality tables. The processing steps are a formula parser,
    statistical content generation from data as defined by formula, followed by
    rendering into a table. Each step of the processing is separate and user
    definable thus creating a set of composable building blocks for
    highly customizable 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 in a resulting table. Routines to
    achieve New England Journal of Medicine style, Lancet style and
    Hmisc::summaryM() statistics are provided. The package contains rendering
    for HTML5, Rmarkdown and an indexing format for use in tracing and tracking
    are provided.
Builds tables with customizable rows. Users can specify the type
             of data to use for each row, as well as how to handle missing
             data and the types of comparison tests to run on the table
             columns.
Implementation of functions for fitting taper curves (a semiparametric 
  linear mixed effects taper model) to diameter measurements along stems. Further 
  functions are provided to estimate the uncertainty around the predicted curves, 
  to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. 
  For cases where tree heights are not measured, methods for estimating
  additional variance in volume predictions resulting from uncertainties in
  tree height models (tariffs) are provided.  The example data include the taper 
  curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees 
  and a subset of section-wise diameter measurements of these trees. The functions 
  implemented here are detailed in Kublin, E., Breidenbach, J., Kaendler, G. (2013)
  <doi:10.1007/s10342-013-0715-0>.
Providing new german-wide 'TapeR' Models and functions for their 
  evaluation. Included are the most common tree species in Germany (Norway 
  spruce, Scots pine, European larch, Douglas fir, Silver fir as well as 
  European beech, Common/Sessile oak and Red oak). Many other species are mapped
  to them so that 36 tree species / groups can be processed. Single trees are 
  defined by species code, one or multiple diameters in arbitrary measuring 
  height and tree height. The functions then provide information on diameters 
  along the stem, bark thickness, height of diameters, volume of the total or 
  parts of the trunk and total and component above-ground biomass. It is also 
  possible to calculate assortments from the taper curves. Uncertainty 
  information is provided for diameter, volume and component biomass estimation.
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.
Swift and seamless Single Sign-On (SSO) integration. Designed
    for effortless compatibility with popular Single Sign-On providers
    like Google and Microsoft, it streamlines authentication, enhancing
    both user experience and application security. Elevate your 'shiny'
    applications for a simplified, unified, and secure authentication process.
Functions to produce, fit and predict from bipartite networks with abundance, trait and phylogenetic information. Its methods are described in detail in Benadi, G., Dormann, C.F., Fruend, J., Stephan, R. & Vazquez, D.P. (2021) Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny. The American Naturalist, in press.
Manager of tick-by-tick transaction data that performs 'cleaning', 'aggregation' and 'import' in an efficient and fast way. The package engine, written in C++, exploits the 'zlib' and 'gzstream' libraries to handle gzipped data without need to uncompress them. 'Cleaning' and 'aggregation' are performed according to Brownlees and Gallo (2006) <DOI:10.1016/j.csda.2006.09.030>. Currently, TAQMNGR processes raw data from WRDS (Wharton Research Data Service, <https://wrds-web.wharton.upenn.edu/wrds/>).
Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <DOI:10.1081/STA-200054435>.
Function-oriented Make-like declarative pipelines for
  Statistics and data science are supported in the 'targets' R package.
  As an extension to 'targets', the 'tarchetypes' package provides
  convenient user-side functions to make 'targets' easier to use.
  By establishing reusable archetypes for common kinds of
  targets and pipelines, these functions help express complicated
  reproducible pipelines concisely and compactly.
  The methods in this package were influenced by the 'targets' R package.
  by Will Landau (2018) <doi:10.21105/joss.00550>.
Runs 'targets' pipeline in '/inst/tarchives' and stores the results 
    in the R user directory. This means that the user does not have to run the
    process repeatedly, and the developer has the flexibility to update the data
    as versions are updated.
Measure text's sentiment with dictionaries and simple rules covering
    negations and modifiers. User-supplied dictionaries are supported, including
    Unicode emojis and multi-word tokens, so this package can also be used to
    study constructs beyond sentiment.
Various methods for targeted and semiparametric inference including
	     augmented inverse probability weighted (AIPW) estimators for missing data and
	     causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>),
         variable importance and conditional average treatment effects (CATE)
         (van der Laan (2006) <doi:10.2202/1557-4679.1008>),
	     estimators for risk differences and relative risks (Richardson et al. (2017)
	     <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized
         linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).
Pipeline tools coordinate the pieces of computationally
  demanding analysis projects.
  The 'targets' package is a 'Make'-like pipeline tool for statistics and
  data science in R. The package skips costly runtime for tasks
  that are already up to date,
  orchestrates the necessary computation with implicit parallel computing,
  and abstracts files as R objects. If all the current output matches
  the current upstream code and data, then the whole pipeline is up
  to date, and the results are more trustworthy than otherwise.
  The methodology in this package
  borrows from GNU 'Make' (2015, ISBN:978-9881443519)
  and 'drake' (2018, <doi:10.21105/joss.00550>).
Implement the Tariff algorithm for coding cause-of-death from verbal autopsies. The Tariff method was originally proposed in James et al (2011) <DOI:10.1186/1478-7954-9-31> and later refined as Tariff 2.0 in Serina, et al. (2015) <DOI:10.1186/s12916-015-0527-9>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist between the this implementation and the implementation available from IHME.
Interactive shiny application for working with textmining and text analytics. Various visualizations  are provided.
Provides functions for analyzing citizens' bicycle usage pattern and predicting rental amount on specific conditions.
    Functions on this package interacts with data on 'tashudata' package, a 'drat' repository.
    'tashudata' package contains rental/return history on public bicycle system('Tashu'), weather for 3 years and bicycle station information. 
    To install this data package, see the instructions at <https://github.com/zeee1/Tashu_Rpackage>.
    top10_stations(), top10_paths() function visualizes image showing the most used top 10 stations and paths.
    daily_bike_rental() and monthly_bike_rental() shows daily, monthly amount of bicycle rental.
    create_train_dataset(), create_test_dataset() is data processing function for prediction.
    Bicycle rental history from 2013 to 2014 is used to create training dataset and that on 2015 is for test dataset.
    Users can make random-forest prediction model by using create_train_model() 
    and predict amount of bicycle rental in 2015 by using predict_bike_rental().
Schedule R scripts/processes with the Windows task scheduler. This
    allows R users to automate R processes on specific time points from R itself.
You only need to type 'why pie charts are bad' on Google to find
    thousands of articles full of (valid) reasons why other types of charts 
    should be preferred over this one.
    Therefore, because of the little use due to the reasons already mentioned,
    making pie charts (and related) in R is not straightforward, so other 
    functions are needed to simplify things.
    In this R package there are useful functions to make 'tasty' pie charts 
    immediately by exploiting the many cool templates provided.
A collection of recipe datasets scraped from <https://www.allrecipes.com/>, containing two complementary datasets: 'allrecipes' with 14,426 general recipes, and 'cuisines' with 2,218 recipes categorized by country of origin. Both datasets include comprehensive recipe information such as ingredients, nutritional facts (calories, fat, carbs, protein), cooking times (preparation and cooking), ratings, and review metadata. All data has been cleaned and standardized, ready for analysis.
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.
Functions to support economic modelling in R based on the methods of the 
    Dutch guideline for economic evaluations in healthcare <https://www.zorginstituutnederland.nl/publicaties/publicatie/2024/01/16/richtlijn-voor-het-uitvoeren-van-economische-evaluaties-in-de-gezondheidszorg>, 
    CBS data <https://www.cbs.nl/>, and OECD data <https://www.oecd.org/en.html>.
A clinically meaningful measures of treatment effects for right-censored data 
  are provided, based on the concept of Kendall's tau, along with the corresponding inference procedures.
  Two plots of tau processes, with the option to account for the cure fraction or not, are available.
  The plots of tau processes serve as useful graphical tools for monitoring the relative performances over time.
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) <DOI:10.48550/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) <DOI:10.1214/16-EJS1166>.
'Tautulli' (<http://tautulli.com>) is a monitoring application for 'Plex' Media Servers 
  (<https://www.plex.tv>) which collects a lot of data about media items and server 
  usage such as play counts. This package interacts with the 'Tautulli' API of any 
  specified server to get said data into R. The 'Tautulli' API documentation is available
  at <https://github.com/Tautulli/Tautulli/blob/master/API.md>.
Provides classes for storing and manipulating taxonomic data. 
 Most of the classes can be treated like base R vectors (e.g. can be used 
 in tables as columns and can be named). Vectorized classes can store taxon names
 and authorities, taxon IDs from databases, taxon ranks, and other types of
 information. More complex classes are provided to store taxonomic trees and
 user-defined data associated with them.
Creates a local database of many commonly used taxonomic authorities
             and provides functions that can quickly query this data.
Creates a local Lightning Memory-Mapped Database ('LMDB') 
             of many commonly used taxonomic authorities
             and provides functions that can quickly query this data.
             Supported taxonomic authorities include 
             the Integrated Taxonomic Information System ('ITIS'),
             National Center for Biotechnology Information ('NCBI'),
             Global Biodiversity Information Facility ('GBIF'), 
             Catalogue of Life ('COL'), and Open Tree Taxonomy ('OTT'). 
             Name and identifier resolution using 'LMDB' can
             be hundreds of times faster than either relational databases or
             internet-based queries. Precise data provenance information for
             data derived from naming providers is also included.
A novel feature-wise normalization method based on a zero-inflated negative binomial model. This method assumes that the effects of sequencing depth vary for each taxon on their mean and also incorporates a rational link of zero probability and taxon dispersion as a function of sequencing depth. Ziyue Wang, Dillon Lloyd, Shanshan Zhao, Alison Motsinger-Reif (2023) <doi:10.1101/2023.10.31.563648>.
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 application programming interfaces
    (API) 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. Some of the services
    supported include 'NCBI E-utilities' (<https://www.ncbi.nlm.nih.gov/books/NBK25501/>),
    'Encyclopedia of Life' (<https://eol.org/docs/what-is-eol/data-services>),
    'Global Biodiversity Information Facility' (<https://techdocs.gbif.org/en/openapi/>),
    and many more. Links to the API documentation for other supported services are
    available in the documentation for their respective functions in this package.
Download taxonomic databases, convert them into 'SQLite' format, 
    and query them locally for fast, reliable, and reproducible access to 
    taxonomic data.
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.
A collection of functions used to format taxonomic names in Markdown
    documents. Those functions work with data structured according to
    Alvarez and Luebert (2018) <doi:10.3897/bdj.6.e23635>.
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 download NCBI data dumps and create a local database for fast and local taxonomic assignment.
Taxonomic lists matching and merging, casting and melting 
  scientific names, managing taxonomic lists from  Global Biodiversity 
  Information Facility 'GBIF' <https://www.gbif.org/> or Integrated Taxonomic 
  Information System 'ITIS', <https://itis.gov/> harvesting names from 
  Wikipedia and fuzzy matching.
A comprehensive resource for data on Taylor Swift songs. Data
    is included for all officially released studio albums, extended plays
    (EPs), and individual singles are included. Data comes from 'Genius'
    (lyrics) and 'Spotify' (song characteristics). Additional functions
    are included for easily creating data visualizations with color
    palettes inspired by Taylor Swift's album covers.
The Taylor Russell model is a widely used method for assessing test validity in personnel selection tasks. The three functions in this package extend this model in a number of notable ways. TR() estimates test validity for a single selection test via the original Taylor Russell model. It extends this model by allowing users greater flexibility in argument choice. For example, users can specify any three of the four parameters (base rate, selection ratio, criterion validity, and positive predictive value) of the Taylor Russell model and estimate the remaining parameter (see the help file for examples).  The TaylorRussell() function generalizes the original Taylor Russell model to allow for multiple selection tests (predictors).  To our knowledge, this is the first generalization of the Taylor Russell model to allow for three or more selection tests (it is also the first to correctly handle models with two selection tests). TRDemo() is a 'shiny' program for illustrating the underlying logic of the Taylor Russell model. Taylor, HC and Russell, JT (1939) "The relationship of validity coefficients to the practical effectiveness of tests in selection: Discussion and tables" <doi:10.1037/h0057079>.
For when your colors absolutely should not be excluded from the narrative.
A collection of interactive 'shiny' applications for performing comprehensive analyses in the field of tree breeding and genetics. The package is designed to assist users in visualizing and interpreting experimental data through a user-friendly interface. Each application is launched via a simple function, and users can upload data in 'Excel' format for analysis. For more information, refer to Singh, R.K. and Chaudhary, B.D. (1977, ISBN:9788176633079).
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.
Functions are provided for prior specification in divergence time
    estimation using fossils as well as other kinds of data. It
    provides tools for interacting with the input and output of Bayesian
    platforms in evolutionary biology such as 'BEAST2', 'MrBayes', 'RevBayes',
    or 'MCMCTree'.
    It Implements a simple measure similarity between probability
    density functions for comparing prior and
    posterior Bayesian densities, as well as code for calculating the
    combination of distributions using conflation of Hill (2008). Functions for estimating the
    origination time in collections of distributions using the x-intercept (e.g., Draper and Smith, 1998) and
    stratigraphic intervals (Marshall 2010) are also available.
    Hill, T. 2008. "Conflations of probability distributions". Transactions of the American Mathematical Society, 363:3351-3372. <doi:10.48550/arXiv.0808.1808>,
    Draper, N. R. and Smith, H. 1998. "Applied Regression Analysis". 1--706. Wiley Interscience, New York. <DOI:10.1002/9781118625590>,
    Marshall, C. R. 2010. "Using confidence intervals to quantify the uncertainty in the end-points of stratigraphic ranges". Quantitative Methods in Paleobiology, 291--316. <DOI:10.1017/S1089332600001911>.
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>.
Creates simulated clinical trial data with realistic correlation structures and assumed efficacy levels by using a tilted bootstrap resampling approach. Samples are drawn from observed data with some samples appearing more frequently than others. May also be used for simulating from a joint Bayesian distribution along with clinical trials based on the Bayesian distribution.
Tools to help developers and producers manipulate R objects
    and outputs. It includes tools for displaying results and objects, and
    for formatting them in the correct format.
Fast calculation of the Subtree Prune and Regraft (SPR),
  Tree Bisection and Reconnection (TBR) and Replug distances between 
  unrooted trees, using the algorithms of Whidden and 
  Matsen (2017) <arxiv:1511.07529>.
Provides rolling statistical functions based
    on date and time windows instead of n-lagged observations.
Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of heterogeneous sources into a three-dimensional matrix of signals (features by observations by sources). The TCA framework further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects; particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a three-dimensional tensor of cell-type-specific methylation levels for each individual (i.e. methylation sites by individuals by cell types) and it allows to detect cell-type-specific statistical relations (associations) with phenotypes. For more details see Rahmani et al. (2019) <DOI:10.1038/s41467-019-11052-9>.
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 <https://www.cbioportal.org/> for more information).
Differential analysis of tumor tissue immune cell type
    abundance based on RNA-seq gene-level expression from The Cancer
    Genome Atlas (TCGA; <https://pancanatlas.xenahubs.net>) database.
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>.
Methods for generating modelled parametric Tropical Cyclone (TC) spatial hazard fields and time series output at point locations from TC tracks.  R's compatibility to simply use fast 'cpp' code via the 'Rcpp' package and the wide range spatial analysis tools via the 'terra' package makes it an attractive open source environment to study 'TCs'.  This package estimates TC vortex wind and pressure fields using parametric equations originally coded up in 'python' by 'TCRM' <https://github.com/GeoscienceAustralia/tcrm> and then coded up in 'Cuda' 'cpp' by 'TCwindgen' <https://github.com/CyprienBosserelle/TCwindgen>.
Implementation of target-controlled infusion algorithms for compartmental pharmacokinetic and pharmacokinetic-pharmacodynamic models. Jacobs (1990) <doi:10.1109/10.43622>; Marsh et al. (1991) <doi:10.1093/bja/67.1.41>; Shafer and Gregg (1993) <doi:10.1007/BF01070999>; Schnider et al. (1998) <doi:10.1097/00000542-199805000-00006>; Abuhelwa, Foster, and Upton (2015) <doi:10.1016/j.vascn.2015.03.004>; Eleveld et al. (2018) <doi:10.1016/j.bja.2018.01.018>.
A wrapper for The Cancer Imaging Archive's REST API. The Cancer
    Imaging Archive (TCIA) hosts de-identified medical images of cancer
    available for public download, as well as rich metadata for each image
    series. TCIA provides a REST API for programmatic access to the data.
    This package provides simple functions to access each API endpoint.
    For more information, see <https://github.com/pamelarussell/TCIApathfinder>
    and TCIA's website.
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 Functional magnetic resonance imaging(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 hypothesis testing in an extended Rasch modeling framework,
    including sample size planning procedures and power computations. Provides 4 statistical tests, 
    i.e., gradient test (GR), likelihood ratio test (LR), Rao score or Lagrange multiplier test (RS), 
    and Wald test, for testing a number of hypotheses referring to the Rasch model (RM), linear 
    logistic test model (LLTM), rating scale model (RSM), and partial credit model (PCM). Three 
    types of functions for power and sample size computations are provided. Firstly, functions to 
    compute the sample size given a user-specified (predetermined) deviation from the hypothesis 
    to be tested, the level alpha, and the power of the test. Secondly, functions to evaluate the 
    power of the tests given a user-specified (predetermined) deviation from the hypothesis to be 
    tested, the level alpha of the test, and the sample size. Thirdly, functions to evaluate the 
    so-called post hoc power of the tests. This is the power of the tests given the observed 
    deviation of the data from the hypothesis to be tested and a user-specified level alpha of the
    test. Power and sample size computations are based on a Monte Carlo simulation approach. It is
    computationally very efficient. The variance of the random error in computing power and sample
    size arising from the simulation approach is analytically derived by using the delta method. 
    Additionally, functions to compute the power of the tests as a function of an effect measure 
    interpreted as explained variance are provided.
    Draxler, C., & Alexandrowicz, R. W. (2015), <doi:10.1007/s11336-015-9472-y>.
A series of additional Tcl commands and Tk widgets 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>.
The ToxCast Data Analysis Pipeline ('tcpl') is an R package that manages, curve-fits, plots, and stores ToxCast data to populate its linked MySQL database, 'invitrodb'. The package was developed for the chemical screening data curated by the US EPA's Toxicity Forecaster (ToxCast) program, but 'tcpl' can be used to support diverse chemical screening efforts.
The tcplfit2 R package performs basic concentration-response curve fitting. The original tcplFit() function in the tcpl R package performed basic concentration-response curvefitting to 3 models. With tcplfit2, the core tcpl concentration-response functionality has been expanded to process diverse high-throughput screen (HTS) data generated at the US Environmental Protection Agency, including targeted ToxCast, high-throughput transcriptomics (HTTr) and high-throughput phenotypic profiling (HTPP). tcplfit2 can be used independently to support analysis for diverse chemical screening efforts.
Convert T Cell Receptor (TCR) gene names between the 10X Genomics, Adaptive Biotechnologies, and ImMunoGeneTics (IMGT) nomenclatures.
Implements methods for selecting the number of factors in Poisson
  factor models, with a primary focus on Thinning Cross-Validation (TCV). The
  TCV method is based on the 'data thinning' technique, which probabilistically
  partitions each count observation into training and test sets while preserving
  the underlying factor structure. The Poisson factor model is then fit on the
  training set, and model selection is performed by comparing predictive
  performance on the test set. This toolkit is designed for researchers working
  with high-dimensional count data in fields such as genomics, text mining, and
  social sciences. The data thinning methodology is detailed in Dharamshi et al.
  (2025) <doi:10.1080/01621459.2024.2353948> and Wang et al. (2025)
  <doi:10.1080/01621459.2025.2546577>.
Framework provides functions to parse 'Training Center XML (TCX)' files
    and extract key activity metrics such as total distance, total time, calories burned,
    maximum altitude, and power values (watts). This package is useful for analyzing workout
    and training data from devices that export 'TCX' format.
The 'twelvedata' REST service offers access to current and historical
 data on stocks, standard as well as digital 'crypto' currencies, and other financial
 assets covering a wide variety of course and time spans. See <https://twelvedata.com/>
 for details, to create an account, and to request an API key for free-but-capped access
 to the data.
Tools for Topological Data Analysis. The package focuses on statistical analysis of persistent homology and density clustering. For that, this package provides an R interface for the efficient algorithms of the C++ libraries 'GUDHI' <https://project.inria.fr/gudhi/software/>, 'Dionysus' <https://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements methods from Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2015) <doi:10.20382/jocg.v6i2a8>  for analyzing the statistical significance of persistent homology features.
Topological data analysis studies structure and shape of the data using topological features. We provide a variety of algorithms to learn with persistent homology of the data based on functional summaries for clustering, hypothesis testing, visualization, and others. We refer to Wasserman (2018) <doi:10.1146/annurev-statistics-031017-100045> for a statistical perspective on the topic.
Topological data analysis is a powerful tool for finding non-linear global structure
    in whole datasets. The main tool of topological data analysis is persistent homology, which computes
    a topological shape descriptor of a dataset called a persistence diagram. 'TDApplied' provides 
    useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference,
    and these functions can also interface with other data science packages to form flexible and integrated
    topological data analysis pipelines.
Topological data analytic methods in machine learning rely on
    vectorizations of the persistence diagrams that encode persistent
    homology, as surveyed by Ali &al (2000)
    <doi:10.48550/arXiv.2212.09703>.  Persistent homology can be computed
    using 'TDA' and 'ripserr' and vectorized using 'TDAvec'.  The
    Tidymodels package collection modularizes machine learning in R for
    straightforward extensibility; see Kuhn & Silge (2022,
    ISBN:978-1-4920-9644-3).  These 'recipe' steps and 'dials' tuners make
    efficient algorithms for computing and vectorizing persistence
    diagrams available for Tidymodels workflows.
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>.
Provides a set of tools for managing time-series data,
    with a particular emphasis on defining various frequency types such
    as daily and weekly. It also includes functionality for converting
    data between different frequencies.
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>.
Provides tools for computing various vector summaries of persistence diagrams studied in Topological Data Analysis. For improved computational efficiency, 
    all code for the vector summaries is written in 'C++' using the 'Rcpp' and 'RcppArmadillo' packages.
The companion package that provides all the datasets used in the book
 "Data Integration, Manipulation and Visualization of Phylogenetic Trees" by Guangchuang Yu (2022, ISBN:9781032233574).
An implementation of a boosted Tweedie compound Poisson model proposed by Yang, Y., Qian, W. and Zou, H. (2018) <doi:10.1080/07350015.2016.1200981>. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing high-order interactions among predictors. This package is based on the 'gbm' package originally developed by Greg Ridgeway.
Estimate the transition diagnostic classification model (TDCM) 
    described in Madison & Bradshaw (2018) <doi:10.1007/s11336-018-9638-5>, a 
    longitudinal extension of the log-linear cognitive diagnosis model (LCDM) in 
    Henson, Templin & Willse (2009) <doi:10.1007/s11336-008-9089-5>. As the LCDM 
    subsumes many other diagnostic classification models (DCMs), many other DCMs 
    can be estimated longitudinally via the TDCM. The 'TDCM' package includes 
    functions to estimate the single-group and multigroup TDCM, summarize 
    results of interest including item parameters, growth proportions, 
    transition probabilities, transitional reliability, attribute correlations, 
    model fit, and growth plots.
A collection of functions for automatically creating 'Stan'
      code for transition diagnostic classification models (TDCMs) as they are
      defined by Madison and Bradshaw (2018) <DOI:10.1007/s11336-018-9638-5>.
      This package supports automating the creation of 'Stan' code for TDCMs,
      fungible TDCMs (i.e., TDCMs with item parameters constrained to be equal
      across all items), and multi-threaded TDCMs.
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.
The total deviation index (TDI) is an unscaled statistical measure used to evaluate the deviation between paired quantitative measurements when assessing the extent of agreement between different raters. It describes a boundary such that a large specified proportion of the differences in paired measurements are within the boundary (Lin, 2000) <https://pubmed.ncbi.nlm.nih.gov/10641028/>.
    This R package implements some methodologies existing in the literature for TDI estimation and inference in the case of two raters.
The t-Digest construction algorithm, by 
    Dunning et al., (2019) <doi:10.48550/arXiv.1902.04023>, 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.
The main purpose of this package is to propose a rigorous framework to fairly compare trip distribution laws and models as described in Lenormand et al. (2016) <doi:10.1016/j.jtrangeo.2015.12.008>.
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>).
This contains functions that can be used to estimate the time-dependent 
              precision-recall curve (PRC) and the corresponding area under the PRC 
              for right-censored survival data. It also compute time-dependent 
              ROC curve and its corresponding area under the ROC curve (AUC). See
              Beyene, Chen and Kifle (2024) <doi:10.1002/bimj.202300135>.
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.)
The tdROC package facilitates the estimation of time-dependent ROC 
    (Receiver Operating Characteristic) curves and the Area Under the time-dependent 
    ROC Curve (AUC) in the context of survival data, accommodating scenarios with 
    right censored data and the option to account for competing risks. In addition 
    to the ROC/AUC estimation, the package also estimates time-dependent Brier score and 
    survival difference. Confidence intervals of various estimated quantities can be 
    obtained from bootstrap. The package also offers plotting functions for visualizing 
    time-dependent ROC curves.
Functions that can be used to calculate time-dependent state and parameter sensitivities for both continuous- and discrete-time deterministic models. See Ng et al. (2023) <doi:10.1086/726143> for more information about time-dependent sensitivity analysis.
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.
STARMA (Space-Time Autoregressive Moving Average) models are commonly utilized in modeling and forecasting spatiotemporal time series data. However, the intricate nonlinear dynamics observed in many space-time rainfall patterns often exceed the capabilities of conventional STARMA models. This R package enables the fitting of Time Delay Spatio-Temporal Neural Networks, which are adept at handling such complex nonlinear dynamics efficiently. For detailed methodology, please refer to Saha et al. (2020) <doi:10.1007/s00704-020-03374-2>.
Functions and examples are provided for Transmission/disequilibrium tests
             for extended marker haplotypes, as in
             Clayton, D. and Jones, H. (1999) "Transmission/disequilibrium tests
             for extended marker haplotypes". Amer. J. Hum. Genet., 65:1161-1169,
             <doi:10.1086/302566>.
Provides functions to estimate the insertion and deletion rates of transposable element (TE) families. The estimation of insertion rate consists of an improved estimate of the age distribution that takes into account random mutations, and an adjustment by the deletion rate. A hypothesis test for a uniform insertion rate is also implemented. This package implements the methods proposed in Dai et al (2018).
Different approaches for selecting the threshold in generalized Pareto distributions. Most of them are based on minimizing the AMSE-criterion or at least by reducing the bias of the assumed GPD-model. Others are heuristically motivated by searching for stable sample paths, i.e. a nearly constant region of the tail index estimator with respect to k, which is the number of data in the tail. The third class is motivated by graphical inspection. In addition, a sequential testing procedure for GPD-GoF-tests is also implemented here.
Statistics students often have problems understanding 
    the relation between a random variable's true scale and its z-values. 
    To allow instructors to better better visualize histograms for these 
    students, the package provides histograms with two horizontal 
    axis containing z-values and the true scale of the variable. 
    The function TeachHistDens() provides a density histogram with two axis.  
    TeachHistCounts() and TeachHistRelFreq() are variations for count and 
    relative frequency histograms, respectively. TeachConfInterv() and 
    TeachHypTest() help instructors to visualize confidence levels 
    and the results of hypothesis tests.
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.
A 'shiny' based interactive exploration framework for
    analyzing clinical trials data. 'teal' currently provides a dynamic
    filtering facility and different data viewers. 'teal' 'shiny'
    applications are built using standard 'shiny' modules.
Introduction of 'qenv' S4 class, that facilitates code
    execution and reproducibility in 'teal' applications.
Provides a 'teal_data' class as a unified data model for
    'teal' applications focusing on reproducibility and relational data.
Utilizing the 'logger' framework to record events within a
    package, specific to 'teal' family of packages.  Supports logging
    namespaces, hierarchical logging, various log destinations,
    vectorization, and more.
Provides user-friendly tools for creating and customizing
    clinical trial reports. By leveraging the 'teal' framework, this
    package provides 'teal' modules to easily create an interactive panel
    that allows for seamless adjustments to data presentation, thereby
    streamlining the creation of detailed and accurate reports.
Prebuilt 'shiny' modules containing tools for viewing data,
    visualizing data, understanding missing and outlier values within your
    data and performing simple data analysis.  This extends 'teal'
    framework that supports reproducible research and analysis.
Prebuilt 'shiny' modules containing tools for the generation
    of 'rmarkdown' reports, supporting reproducible research and analysis.
Data filtering module for 'teal' applications.  Allows for
    interactive filtering of data stored in 'data.frame' and
    'MultiAssayExperiment' objects. Also displays filtered and unfiltered
    observation counts.
A standardized user interface for column selection, that
    facilitates dataset merging in 'teal' framework.
Collection of 'shiny' widgets to support 'teal' applications.
    Enables the manipulation of application layout and plot or table
    settings.
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>.
Provides color palettes corresponding to professional and amateur, 
    sports teams. These can be useful in creating data graphics that are themed 
    for particular teams.
Models the direction of the maximum horizontal stress using
    relative plate motion parameters. Statistical algorithms to evaluate
    the modeling results compared with the observed data. Provides plots
    to visualize the results. Methods described in Stephan et al. (2023)
    <doi:10.1038/s41598-023-42433-2> and Wdowinski (1998)
    <doi:10.1016/S0079-1946(98)00091-3>.
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.
Inferring causation from time series data through empirical dynamic modeling (EDM), with methods such as convergent cross mapping from Sugihara et al. (2012) <doi:10.1126/science.1227079>, partial cross mapping as outlined in Leng et al. (2020) <doi:10.1038/s41467-020-16238-0>, and cross mapping cardinality as described in Tao et al. (2023) <doi:10.1016/j.fmre.2023.01.007>.
Implements methods to fit Virtual Twins models (Foster et al. 
  (2011) <doi:10.1002/sim.4322>) for identifying subgroups with differential
  effects in the context of clinical trials while controlling the probability
  of falsely detecting a differential effect when the conditional average
  treatment effect is uniform across the study population using parameter
  selection methods proposed in Wolf et al. (2022) 
  <doi:10.1177/17407745221095855>.
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>.
A simple wrapper around the Telegram Bot API (<https://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.
Analyze telemetry datasets generalized to allow
    any technology. The filtering steps check for false positives caused by 
    reflected transmissions from surfaces and false pings from other noise 
    generating equipment. The filters are based on JSATS filtering algorithms
    found in package 'filteRjsats' <https://CRAN.R-project.org/package=filteRjsats>
    but have been generalized to allow the user to define many of the filtering 
    variables. Additionally, this package contains scripts used to help identify 
    an optimal maximum blanking period as defined in Capello et al (2015) 
    <doi:10.1371/journal.pone.0134002>. The functions were written according to
    their manuscript description, but have not been reviewed by the authors for 
    accuracy. It is included here as is, without warranty.
Fits Bayesian finite mixtures with an unknown number of components using the telescoping sampler and different component distributions. For more details see Frühwirth-Schnatter et al. (2021) <doi:10.1214/21-BA1294>.
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.
Streamline the processing of 'Telraam' data, sourced from open data
    mobility sensors. These tools range from data retrieval (without the need for
    API knowledge) to data visualization, including data preprocessing.
Method to estimate the effect of the trend in predictor variables on the observed trend
 of the response variable using mixed models with temporal autocorrelation. See Fernández-Martínez et
 al. (2017 and 2019) <doi:10.1038/s41598-017-08755-8> <doi:10.1038/s41558-018-0367-7>.
Temporal disaggregation methods are used to disaggregate and
    interpolate a low frequency time series to a higher frequency series, where
    either the sum, the mean, the first or the last value of the resulting
    high frequency series is consistent with the low frequency series. Temporal
    disaggregation can be performed with or without one or more high frequency
    indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso,
    Fernandez, Litterman, Denton and Denton-Cholette, summarized in Sax and
    Steiner (2013) <doi:10.32614/RJ-2013-028>. Supports most R time series
    classes.
Implements a probabilistic ensemble time-series forecaster that combines an auto-encoder with a neural decision forest whose split variables are learned through a differentiable feature-mask layer. Functions are written with 'torch' tensors and provide CRPS (Continuous Ranked Probability Scores) training plus mixture-distribution post-processing.
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> .
Implements the template ICA (independent components analysis) model
    proposed in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638> and the 
    spatial template ICA model proposed in proposed in Mejia et al. (2022)
    <doi:10.1080/10618600.2022.2104289>. Both models estimate subject-level 
    brain as deviations from known population-level networks, which are 
    estimated using standard ICA algorithms. Both models employ an 
    expectation-maximization algorithm for estimation of the latent brain 
    networks and unknown model parameters. Includes direct support for 'CIFTI',
    'GIFTI', and 'NIFTI' neuroimaging file formats.
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.
Helper functions for MASCOTNUM / RT-UQ <https://uq.math.cnrs.fr/> algorithm template, for design of numerical experiments practice:
    algorithm template parser to support MASCOTNUM specification <https://github.com/MASCOTNUM/algorithms>, 
    'ask & tell' decoupling injection (inspired by <https://search.r-project.org/CRAN/refmans/sensitivity/html/decoupling.html>) 
    to use "crimped" algorithms (like uniroot(), optim(), ...) from outside R,
    basic template examples: Brent algorithm for 1 dim root finding and L-BFGS-B from base optim().
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). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) <doi:10.1016/j.foodqual.2015.06.017>, Castura, Baker, and Ross (2016) <doi:10.1016/j.foodqual.2016.06.011>, and Pineau et al. (2009) <doi:10.1016/j.foodqual.2009.04.005>.
A collection of methods to estimate parameters of different tempered stable 
  distributions (TSD). Currently, there are seven different tempered stable 
  distributions to choose from: Tempered stable subordinator distribution, 
  classical TSD, generalized classical TSD, normal TSD, modified TSD, rapid 
  decreasing TSD, and Kim-Rachev TSD.
  The package also provides functions to compute density and probability 
  functions and tools to run Monte Carlo simulations.
  This package has already been used for the estimation of tempered stable 
  distributions (Massing (2023) <arXiv:2303.07060>).
  The following references form the theoretical background for various functions
  in this package. References for each function are explicitly listed in its 
  documentation:
  Bianchi et al. (2010) <doi:10.1007/978-88-470-1481-7_4>
  Bianchi et al. (2011) <doi:10.1137/S0040585X97984632>
  Carrasco (2017) <doi:10.1017/S0266466616000025>
  Feuerverger (1981) <doi:10.1111/j.2517-6161.1981.tb01143.x>
  Hansen et al. (1996) <doi:10.1080/07350015.1996.10524656>
  Hansen (1982) <doi:10.2307/1912775>
  Hofert (2011) <doi:10.1145/2043635.2043638>
  Kawai & Masuda (2011) <doi:10.1016/j.cam.2010.12.014>
  Kim et al. (2008) <doi:10.1016/j.jbankfin.2007.11.004>
  Kim et al. (2009) <doi:10.1007/978-3-7908-2050-8_5>
  Kim et al. (2010) <doi:10.1016/j.jbankfin.2010.01.015>
  Kuechler & Tappe (2013) <doi:10.1016/j.spa.2013.06.012>
  Rachev et al. (2011) <doi:10.1002/9781118268070>.
TEMPoral TEnsor Decomposition (TEMPTED), is a dimension reduction method for multivariate longitudinal data with varying temporal sampling. It formats the data into a temporal tensor and decomposes it into a summation of low-dimensional components, each consisting of a subject loading vector, a feature loading vector, and a continuous temporal loading function. These loadings provide a low-dimensional representation of subjects or samples and can be used to identify features associated with clusters of subjects or samples. TEMPTED provides the flexibility of allowing subjects to have different temporal sampling, so time points do not need to be binned, and missing time points do not need to be imputed.
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).
Implements methods and functions to calibrate 
  time-specific niche models (multi-temporal calibration), letting users 
  execute a strict calibration and selection process of niche models based 
  on ellipsoids, as well as functions to project the potential distribution in 
  the present and in global change  scenarios.The 'tenm' package has functions
  to recover information that may be lost or overlooked while applying a data 
  curation protocol. This curation involves preserving occurrences that may 
  appear spatially redundant (occurring in the same pixel) but originate from 
  different time periods. A novel aspect of this package is that it might 
  reconstruct the fundamental niche more accurately than mono-calibrated 
  approaches. The theoretical background of the package can be found in 
  Peterson et al. (2011)<doi:10.5860/CHOICE.49-6266>.
The tensor product of two arrays is notionally an outer
        product of the arrays collapsed in specific extents by summing
        along the appropriate diagonals.
Provides convenience functions for advanced
        linear algebra with tensors and computation with data sets of
        tensors on a higher level abstraction. It includes Einstein and
        Riemann summing conventions, dragging, co- and contravariate
        indices, parallel computations on sequences of tensors.
Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.
Contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: 'tPCA', 'tFOBI', 'tJADE', k-tJADE', 'tgFOBI', 'tgJADE', 'tSOBI', 'tNSS.SD', 'tNSS.JD', 'tNSS.TD.JD', 'tPP' and 'tTUCKER'.
Performs model-based tensor clustering methods including Tensor Gaussian Mixture Model (TGMM), Tensor Envelope Mixture Model (TEMM) by Deng and Zhang (2021) <DOI: 10.1111/biom.13486>, Doubly-Enhanced EM (DEEM) algorithm by Mai, Zhang, Pan and Deng (2021) <DOI: 10.1080/01621459.2021.1904959>.
Here we provide tools for the computation and factorization of high-dimensional
	     tensor products that are formed by smaller matrices. The methods are based on
	     properties of Kronecker products (Searle 1982, p. 265, ISBN-10: 0470009616).
	     We evaluated this methodology by benchmark testing and illustrated its use in
	     Gaussian Linear Models ('Lopez-Cruz et al., 2024') <doi:10.1093/g3journal/jkae001>.
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.
To handle higher-order tensor data. See Kolda and Bader (2009) <doi:10.1137/07070111X> for details on tensor. While existing packages on tensor data extend the base 'array' class to some data classes, this package serves as an alternative resort to handle tensor only as 'array' class.
    Some functionalities related to missingness are also supported.
A set of functions to estimate rank and factor loadings of time series tensor factor models. A tensor is a multidimensional array. To analyze high-dimensional tensor time series, factor model is a major dimension reduction tool. 'TensorPreAve' provides functions to estimate the rank of core tensors and factor loading spaces of tensor time series. More specifically, a pre-averaging method that accumulates information from tensor fibres is used to estimate the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves. A new rank estimation method is also implemented to utilizes correlation information from the projected data. 
    See Chen and Lam (2023) <arXiv:2208.04012> for more details.
Implement the alternating algorithm for supervised tensor decomposition with interactive side information. Details can be found in the publication Hu, Jiaxin, Chanwoo Lee, and Miaoyan Wang. "Generalized Tensor Decomposition with features on multiple modes." Journal of Computational and Graphical Statistics, Vol. 31, No. 1, 204-218, 2022 <doi:10.1080/10618600.2021.1978471>.
Implements the multiway sparse clustering approach of M. Wang and Y. Zeng, "Multiway clustering via tensor block models". Advances in Neural Information Processing System 32 (NeurIPS), 715-725, 2019.
An implementation of fitting generalized linear models on
    second-order tensor type data. The functions within this package mainly focus on
    parameter estimation, including parameter coefficients and standard deviation.
Factor and autoregressive models for matrix and tensor valued time series. We provide functions for estimation, simulation and prediction. The models are discussed in 
    Li et al (2021) <doi:10.48550/arXiv.2110.00928>, Chen et al (2020) <DOI:10.1080/01621459.2021.1912757>, 
    Chen et al (2020) <DOI:10.1016/j.jeconom.2020.07.015>, and Xiao et al (2020) <doi:10.48550/arXiv.2006.02611>.
A collection of functions for Kronecker structured covariance
    estimation and testing under the array normal model. For estimation,
    maximum likelihood and Bayesian equivariant estimation procedures are
    implemented. For testing, a likelihood ratio testing procedure is
    available. This package also contains additional functions for manipulating
    and decomposing tensor data sets. This work was partially supported by NSF
    grant DMS-1505136. Details of the methods are described in
    Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and
    Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.
The general principle relies on calculating the cumulative signal of nascent
    RNA sequencing over the gene body of any given gene or transcription unit.
    'tepr' can identify transcription attenuation sites by comparing profile to
    a null model which assumes uniform read density over the entirety of the
    transcription unit. It can also identify increased or diminished
    transcription attenuation by comparing two conditions. Besides rigorous
    statistical testing and high sensitivity, a major feature of 'tepr' is its
    ability to provide the elongation pattern of each individual gene, including
    the position of the main attenuation point when such a phenomenon occurs.
    Using 'tepr', users can visualize and refine genome-wide aggregated analyses
    of elongation patterns to robustly identify effects specific to subsets of
    genes. These metrics are suitable for internal comparisons (between genes in
    each condition) and for studying elongation of the same gene in different
    conditions or comparing it to a perfect theoretical uniform elongation.
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.
An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis. See Krivitsky and Handcock (2014) <doi:10.1111/rssb.12014> and Carnegie, Krivitsky, Hunter, and Goodreau (2015) <doi:10.1080/10618600.2014.903087>.
Provides functions for the computationally efficient simulation of 
    dynamic networks estimated with the statistical framework of temporal 
    exponential random graph models, implemented in the 'tergm' package.
Provides a set of functions
    that allow users for styling their R code according to
    the 'tidyverse' style guide. The package uses a native
    Rust implementation to ensure the highest performance.
    Learn more about 'tergo' at <https://rtergo.pagacz.io>.
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.
Implements simulated tests for the hypothesis that terminal digits are uniformly
    distributed (chi-squared goodness-of-fit) and the hypothesis that terminal digits
    are independent from preceding digits (several tests of independence for r x c 
    contingency tables). Also, for a number of distributions, implements Monte Carlo 
    simulations for type I errors and power for the test of independence.
Defines a graphics device and functions for graphical output in
    terminal emulators that support graphical output. Currently terminals that
    support the Terminal Graphics Protocol
    (<https://sw.kovidgoyal.net/kitty/graphics-protocol/>) and terminal supporting Sixel 
    (<https://en.wikipedia.org/wiki/Sixel>) are supported.
Table, Listings, and Graphs (TLG) library for common outputs
    used in clinical trials.
Generalized estimating equations (GEE) are a popular choice
    for analyzing longitudinal binary outcomes. This package provides an
    interface for fitting GEE, currently for logistic regression, within
    the 'tern' <https://cran.r-project.org/package=tern> framework (Zhu,
    Sabanés Bové et al., 2023) and tabulate results easily using 'rtables'
    <https://cran.r-project.org/package=rtables> (Becker, Waddell et al.,
    2023).  It builds on 'geepack' <doi:10.18637/jss.v015.i02> (Højsgaard,
    Halekoh and Yan, 2006) for the actual GEE model fitting.
Mixed models for repeated measures (MMRM) are a popular
    choice for analyzing longitudinal continuous outcomes in randomized
    clinical trials and beyond; see for example Cnaan, Laird and Slasor
    (1997)
    <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>.
    This package provides an interface for fitting MMRM within the 'tern'
    <https://cran.r-project.org/package=tern> framework by Zhu et al.
    (2023) and tabulate results easily using 'rtables'
    <https://cran.r-project.org/package=rtables> by Becker et al. (2023).
    It builds on 'mmrm' <https://cran.r-project.org/package=mmrm> by
    Sabanés Bové et al. (2023) for the actual MMRM computations.
'RBMI' implements standard and reference based multiple
    imputation methods for continuous longitudinal endpoints (Gower-Page
    et al. (2022) <doi:10.21105/joss.04251>). This package provides an
    interface for 'RBMI' uses the 'tern'
    <https://cran.r-project.org/package=tern> framework by Zhu et al.
    (2023) and tabulate results easily using 'rtables'
    <https://cran.r-project.org/package=rtables> by Becker et al. (2023).
Plots ternary diagrams (simplex plots / Gibbs triangles) and
  Holdridge life zone plots <doi:10.1126/science.105.2727.367> using the
  standard graphics functions.
  Allows custom annotation, interpolating, contouring and scaling of plotting
  region.
  Includes a 'Shiny' user interface for point-and-click ternary plotting.
  An alternative to 'ggtern', which uses the 'ggplot2' family of plotting 
  functions.
A suite of functions for visualising ternary probabilistic forecasts, as discussed in the paper by Jupp (2012) <doi:10.1098/rsta.2011.0350>.
Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/> to get started. 'terra' replaces the 'raster' package ('terra' can do more, and it is faster and easier to use).
Provides triangulations of regular height fields, based on the methods described in "Fast Polygonal Approximation of Terrains and Height Fields" Michael Garland and Paul S. Heckbert (1995) <https://www.mgarland.org/files/papers/scape.pdf> using code from the 'hmm' library written by Michael Fogleman <https://www.github.com/fogleman/hmm>.
Functions for the retrieval, manipulation, and visualization
    of 'geospatial' data, with an aim towards producing '3D' landscape
    visualizations in the 'Unity' '3D' rendering engine. Functions are
    also provided for retrieving elevation data and base map tiles from
    the 'USGS' National Map <https://apps.nationalmap.gov/services/>.
Calculate the failure probability of civil engineering problems with Level I up to Level III Methods. Have fun and enjoy. References: Spaethe (1991, ISBN:3-211-82348-4) "Die Sicherheit tragender Baukonstruktionen", AU,BECK (2001) "Estimation of small failure probabilities in high dimensions by subset simulation." <doi:10.1016/S0266-8920(01)00019-4>, Breitung (1989) "Asymptotic approximations for probability integrals." <doi:10.1016/0266-8920(89)90024-6>.
Simulation of reconstructed phylogenetic trees under tree-wide time-heterogeneous birth-death processes and estimation of diversification parameters under the same model. Speciation and extinction rates can be any function of time and mass-extinction events at specific times can be provided. Trees can be simulated either conditioned on the number of species, the time of the process, or both. Additionally, the likelihood equations are implemented for convenience and can be used for Maximum Likelihood (ML) estimation and Bayesian inference.
Easy install and load key packages from the 'tesselle' suite
    in a single step. The 'tesselle' suite is a collection of packages for
    research and teaching in archaeology. These packages focus on
    quantitative analysis methods developed for archaeology. The
    'tesselle' packages are designed to work seamlessly together and to
    complement general-purpose and other specialized statistical packages.
    These packages can be used to explore and analyze common data types in
    archaeology: count data, compositional data and chronological data.
    Learn more about 'tesselle' at <https://www.tesselle.org>.
Bindings to 'Tesseract': 
     a powerful optical character recognition (OCR) engine that supports over 100 languages.
     The engine is highly configurable in order to tune the detection algorithms and
     obtain the best possible results.
Package test2norm contains functions to generate formulas for 
    normative standards applied to cognitive tests. It takes raw test scores 
    (e.g., number of correct responses) and converts them to scaled scores and 
    demographically adjusted scores, using methods described in Heaton et al. 
    (2003) <doi:10.1016/B978-012703570-3/50010-9> & Heaton et al. (2009, 
    ISBN:9780199702800). The scaled scores are calculated as quantiles of the 
    raw test scores, scaled to have the mean of 10 and standard deviation of 3, 
    such that higher values always correspond to better performance on the test. 
    The demographically adjusted scores are calculated from the residuals of a 
    model that regresses scaled scores on demographic predictors (e.g., age). 
    The norming procedure makes use of the mfp2() function from the 'mfp2' 
    package to explore nonlinear associations between cognition and demographic 
    variables.
This application provides exploratory and confirmatory factor analysis, classical test theory, unidimensional and multidimensional item response theory, and continuous item response model analysis, through the 'shiny' interactive interface. In addition,  it offers rich functionalities for visualizing and downloading results. Users can download figures, tables, and analysis reports via the interactive interface.
Finding the best values for user-specified arguments of a prediction algorithm can be difficult, particularly if there is an interaction between argument levels. This package automates the testing of any user-defined prediction algorithm over an arbitrary number of arguments. It includes functions for testing the algorithm over the given arguments with respect to an arbitrary number of user-defined diagnostics, visualising the results of these tests, and finding the optimal argument combinations with respect to each diagnostic.
A common way of validating a biological assay for is through a
    procedure, where m levels of an analyte are measured with n replicates at each
    level, and if all m estimates of the coefficient of variation (CV) are less
    than some prespecified level, then the assay is declared validated for precision
    within the range of the m analyte levels. Two limitations of this procedure are:
    there is no clear statistical statement of precision upon passing, and it is
    unclear how to modify the procedure for assays with constant standard deviation.
    We provide tools to convert such a procedure into a set of m hypothesis tests.
    This reframing motivates the m:n:q procedure, which upon completion delivers
    a 100q% upper confidence limit on the CV. Additionally, for a post-validation
    assay output of y, the method gives an ``effective standard deviation interval''
    of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where
    mu is the expected value of the assay output for that sample. Further, the m:n:q
    procedure can be straightforwardly applied to constant standard deviation assays.
    We illustrate these tools by applying them to a growth inhibition assay. This is
    an implementation of the methods described in Fay, Sachs, and Miura (2018) 
    <doi:10.1002/sim.7528>.
Provides a method for comparing the results of two binary diagnostic tests using paired data. 
    Users can rapidly perform descriptive and inferential statistics in a single function call. Options permit users to select which parameters they are interested in comparing and methods for correction for multiple comparisons.
    Confidence intervals are calculated using the methods with the best coverage. Hypothesis tests use the methods with the best asymptotic performance. A summary of the methods is available in Roldán-Nofuentes (2020) <doi:10.1186/s12874-020-00988-y>.
    This package is targeted at clinical researchers who want to rapidly and effectively compare results from binary diagnostic tests.
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 (2022), <https://www.cambridge.org/core/journals/econometric-theory/article/abs/robust-tests-for-white-noise-and-crosscorrelation/4D77C12C52433F4C6735E584C779403A>, <https://elischolar.library.yale.edu/cowles-discussion-paper-series/57/>.
Test your data! An extension of the 'testthat' unit testing
    framework with a family of functions and reporting tools for checking
    and validating data frames.
Functions for imputing missing item responses for dichotomous and
    polytomous test and assessment data. This package enables missing imputation
    methods that are suitable for test and assessment data, including: 
    listwise (LW) deletion (see De Ayala et al. 2001 <doi:10.1111/j.1745-3984.2001.tb01124.x>), 
    treating as incorrect (IN, see Lord, 1974 <doi: 10.1111/j.1745-3984.1974.tb00996.x>; 
    Mislevy & Wu, 1996 <doi: 10.1002/j.2333-8504.1996.tb01708.x>; 
    Pohl et al., 2014 <doi: 10.1177/0013164413504926>), person mean imputation (PM), 
    item mean imputation (IM), two-way (TW) and response function (RF) imputation,
    (see Sijtsma & van der Ark, 2003 <doi: 10.1207/s15327906mbr3804_4>),
    logistic regression (LR) imputation, predictive mean matching (PMM), and expectation–maximization (EM) 
    imputation (see Finch, 2008 <doi: 10.1111/j.1745-3984.2008.00062.x>).
Uses the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and
    van der Linden (2018) <doi:10.1201/9781315117430> to construct fixed, adaptive, and parallel tests.
    Supports the following mixed-integer programming (MIP) solver packages: 'Rsymphony',
    'highs', 'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see <https://www.gurobi.com/downloads/>.
Offers a solution for the unavailability of raw data in most anthropological studies by facilitating the calculations of several sexual dimorphism related analyses using the published summary statistics of metric data (mean, standard deviation and sex specific sample size) as illustrated by the works of Relethford, J. H., & Hodges, D. C. (1985) <doi:10.1002/ajpa.1330660105>, 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 used for statistics. The package 
    also contains an R Markdown template with the required formatting for 
    assignments in my former courses.
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}.
Allows users to test characteristics of common R objects.
Add tests in-line in examples. Provides standalone functions for
    facilitating easier test writing in Rd files. However, a more familiar
    interface is provided using 'roxygen2' tags. Tools are also provided for
    facilitating package configuration and use with 'testthat'.
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.
Develop, evaluate, and score multiple choice examinations, 
 psychological scales, questionnaires, and similar types of data involving
 sequences of choices among one or more sets of answers.
 This version of the package should be considered as brand new.  Almost all
 of the functions have been changed, including their argument list.
 See the file NEWS.Rd in the Inst folder for more information.
 Using the package does not require any formal statistical knowledge 
 beyond what would be provided by a first course in statistics in a 
 social science department.  There the user would encounter the concept 
 of probability and how it is used to model data and make decisions, 
 and would become familiar with basic mathematical and statistical notation.
 Most of the output is in graphical form.
An R interface to load testing data in the 'OMOP' Common Data Model ('CDM'). An input file, csv or xlsx, can be converted to a 'CDMConnector' object. This object can be used to execute and test studies that use the 'CDM' <https://www.ohdsi.org/data-standardization/>.
Test the nullity of covariances, in a set of variables, using a simple univariate procedure. See Marques, Diago, Norouzirad, Bispo (2023) <doi:10.1002/mma.9130>.
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.
Provides functionality of a statistical testing implementation whether a dataset comes from a symmetric distribution when the center of symmetry is unknown, including Wilcoxon test and sign test procedure. In addition, sample size determination for both tests is provided. The Wilcoxon test procedure is described in Vexler et al. (2023) <https://www.sciencedirect.com/science/article/abs/pii/S0167947323000579>, and the sign test is outlined in Gastwirth (1971) <https://www.jstor.org/stable/2284233>.
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'.
Automates documentation of test_that() calls within R test files. 
    The package scans test sources, extracts human-readable test titles (even when 
    composed with functions like paste() or glue::glue(), ... etc.), and generates 
    reproducible roxygen2-style listings that can be inserted both globally and 
    per-section. It ensures idempotent updates and supports customizable numbering 
    templates with hierarchical indices. Designed for developers, QA teams, and package
    maintainers seeking consistent, self-documenting test inventories.
Runs tests using the 'testthat' package but allows for multiple
    attempts for a single test. This is useful for noisy or flaky tests
    that generally pass but can fail due to occasional random errors,
    such as numeric instability or using random data.
Utility functions and 'RStudio' addins for writing,
    running and organizing automated tests. Integrates tightly with the
    packages 'testthat', 'devtools' and 'usethis'.  Hotkeys can be
    assigned to the 'RStudio' addins for running tests in a single file or
    to switch between a source file and the associated test file. In
    addition, testthis provides function to manage and run tests in
    subdirectories of the test/testthat directory.
Tests one hypothesis with several test statistics, correcting for multiple testing.  The central function in the package is testtwice().  In a sensitivity analysis, the method has the largest design sensitivity of its component tests.  The package implements the method and examples in Rosenbaum, P. R. (2012) <doi:10.1093/biomet/ass032> Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774.
The satisfaction Analysis using the tetraclasse model from Sylvie Llosa. Llosa (1997) <http://www.jstor.org/stable/40592578>.
Each sequence is predicted by expanding the distance matrix. The compact set of hyper-parameters is tuned through random search.
Uses the Distorted Wave Born Approximation (DWBA) to compute the
  acoustic backward scattering, the geometry of the object is formed by a
  volumetric mesh, composed of tetrahedrons. This computation is done efficiently
  through an analytical 3D integration that allows for a solution which is
  expressed in terms of elementary functions for each tetrahedron.
  It is important to note that this method is only valid for objects whose
  acoustic properties, such as density and sound speed, do not vary significantly
  compared to the surrounding medium. (See Lavia, Cascallares and Gonzalez, J. D. (2023). 
  TetraScatt model: Born approximation for the estimation of acoustic dispersion of 
  fluid-like objects of arbitrary geometries. arXiv preprint <arXiv:2312.16721>).
A game inspired by 'Tetris'. Opens a plot window device and starts a game of 'tetRys' in it. Steer the tetrominos with the arrow keys, press Pause to pause and Esc to end the game.
When using the R package  'exams'  to write mathematics questions in 'Sweave' files, the output of a lot of R functions need to be adjusted for display in mathematical formulas. Specifically, the functions were accumulated when writing questions for the  topics of the mathematics courses College Algebra, Precalculus, Calculus, Differential Equations, Introduction to Probability, and Linear Algebra.  The output of the developed functions can be used in 'Sweave' files.
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.
Articles in the 'R Journal' were first authored in 'LaTeX', which performs 
    admirably for 'PDF' files but is less than ideal for modern online interfaces.
    The 'texor' package does all the transitional chores and conversions necessary
    to move to the online versions.
An extension of ExPosition for two table analyses, specifically, discriminant analyses.
Compile snippets of 'LaTeX' directly into images
    from the R console to view in the 'RStudio' viewer pane, Shiny apps
    and 'RMarkdown' documents.
Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft <doi:10.18637/jss.v055.i08>.)
Link R with Transformers from Hugging Face to transform 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.
Allows users to analyze text and classify emotions
    such as happiness, sadness, anger, fear, and neutrality.
    It combines text preprocessing, TF-IDF (Term Frequency-Inverse Document Frequency) 
    feature extraction, and Random Forest classification to predict emotions
    and map them to corresponding emojis for enhanced sentiment visualization.
This is a collection of functions optimized for working with
             with various kinds of text matrices. Focusing on 
             the text matrix as the primary object - represented 
             either as a base R dense matrix or a 'Matrix' package sparse 
             matrix - allows for a consistent and intuitive interface 
             that stays close to the underlying mathematical foundation 
             of computational text analysis. In particular, the package
             includes functions for working with word embeddings, 
             text networks, and document-term matrices. Methods developed in 
             Stoltz and Taylor (2019) <doi:10.1007/s42001-019-00048-6>, 
             Taylor and Stoltz (2020) <doi:10.1007/s42001-020-00075-8>, 
             Taylor and Stoltz (2020) <doi:10.15195/v7.a23>, and
             Stoltz and Taylor (2021) <doi:10.1016/j.poetic.2021.101567>.
The United Nations' Sustainable Development Goals (SDGs) have become an important guideline for organisations to monitor and plan their contributions to social, economic, and environmental transformations. The 'text2sdg' package is an open-source analysis package that identifies SDGs in text using scientifically developed query systems, opening up the opportunity to monitor any type of text-based data, such as scientific output or corporate publications. For more information see Meier, Mata & Wulff (2025) <doi:10.32614/RJ-2024-005> and Wulff, Meier & Mata (2024) <doi:10.1007/s11625-024-01516-3>.
This is a companion package for the 'text2sdg' package. It contains the trained ensemble models needed by the 'detect_sdg' function from the 'text2sdg' package. See Wulff, Meier and Mata (2023) <arXiv:2301.11353> and Meier, Wulff and Mata (2021) <arXiv:2110.05856> for reference.
Converts text into speech using various text-to-speech (TTS) engines and provides an unified interface for accessing their functionality.
  With this package, users can easily generate audio files of spoken words, phrases, or sentences from plain text data. The package supports multiple TTS engines, 
  including Google's 'Cloud Text-to-Speech API', 'Amazon Polly', Microsoft's 'Cognitive Services Text to Speech REST API',  and a free TTS engine called 'Coqui TTS'.
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.
Generate 'LaTeX' tables directly from R. It builds 'LaTeX' tables in blocks in the spirit of 'ggplot2' using the '+' and '/' operators for concatenation in the vertical and horizontal dimensions, respectively. It exports tables in the 'LaTeX' tabular environment using '.tex' code. It can compile '.tex' code to 'PDF' automatically.
It analyzes text to create a count of top n-grams, including tokens (one-word), bigrams(two-word), and trigrams (three-word), while removing all stopwords. It also plots the n-grams and corresponding counts as a bar chart.
A lightweight and focused text annotation tool built with 'shiny'. Provides an 
    interactive graphical user interface for coding text documents,
    managing code hierarchies, creating memos, and analyzing coding patterns.
    Features include code co-occurrence analysis, visualization of coding patterns,
    comparison of multiple coding sets, and export capabilities. Supports
    collaborative qualitative research through standardized annotation formats
    and analysis tools.
Compute a non-overlapping layout of text boxes to label multiple overlain curves. For each curve, iteratively search for an adjacent x,y position for the text box that does not overlap with the other curves. If this process fails, then offsets are computed to add to the y values for each curve, that results in sufficient space to add all of the text labels.
Tools to clean and process text.  Tools are geared at checking for substrings that
          are not optimal for analysis and replacing or removing them (normalizing) with more
          analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards
          (2001) <doi:10.1006/csla.2001.0169>) or extracting them into new variables. For
          example, emoticons are often used in text but not always easily handled by analysis
          algorithms.  The replace_emoticon() function replaces emoticons with word
          equivalents.
Provides a framework to download, parse, and store text
    datasets on the disk and load them when needed. Includes various
    sentiment lexicons and labeled text data sets for classification and
    analysis.
Implements the approach described in Fong and Grimmer (2016) <https://aclweb.org/anthology/P/P16/P16-1151.pdf> for 
	automatically discovering latent treatments from a corpus and estimating the average marginal component effect (AMCE) of 
	each treatment.  The data is divided into a training and test set.  The supervised Indian Buffet Process (sibp) is used 
	to discover latent treatments in the training set.  The fitted model is then applied to the test set to infer the values 
	of the latent treatments in the test set.  Finally, Y is regressed on the latent treatments in the test set to estimate 
	the causal effect of each treatment.
Implement text and sentiment analysis with 'texter'. 
             Generate sentiment scores on text data and also visualize sentiments.
             'texter' allows you to quickly generate insights on your data.
             It includes support for lexicons such as 'NRC' and 'Bing'.
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.
Multinomial (inverse) regression inference for text documents and associated attributes.  For details see: Taddy (2013 JASA) Multinomial Inverse Regression for Text Analysis <arXiv:1012.2098> and Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>. A minimalist partial least squares routine is also included.  Note that the topic modeling capability of earlier 'textir' is now a separate package, 'maptpx'.
An aid for text mining in R, with a syntax that
    should be familiar to experienced R users. Provides a wrapper for several 
    topic models that take similarly-formatted input and give similarly-formatted
    output. Has additional functionality for analyzing and diagnostics for
    topic models.
Graphic interface for text analysis, implement a few methods such as biplots, correspondence analysis, co-occurrence, clustering, topic models, correlations and sentiments.
Statistical exploration of textual corpora using several methods
    from French 'Textometrie' (new name of 'Lexicometrie') and French 'Data Analysis' schools.
    It includes methods for exploring irregularity of distribution of lexicon features across
    text sets or parts of texts (Specificity analysis); multi-dimensional exploration (Factorial analysis), etc. 
    Those methods are used in the TXM software.
Visualise complex relations in texts. This is done by providing functionalities for displaying 
    text co-occurrence networks, text correlation networks, dependency relationships as well as text clustering and semantic text 'embeddings'. 
    Feel free to join the effort of providing interesting text visualisations.
A simple Natural Language Processing (NLP) toolkit focused on search-centric workflows with minimal dependencies. The package offers key features for web scraping, text processing, corpus search, and text embedding generation via the 'HuggingFace API' <https://huggingface.co/docs/api-inference/index>.
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 wrapper for the 'TexTra' API <https://mt-auto-minhon-mlt.ucri.jgn-x.jp/>, 
    a web service for translating texts between different languages. 
    'TexTra' API account is required to use the service.
Converting text to numerical features requires specifically
    created procedures, which are implemented as steps according to the
    'recipes' package. These steps allows for tokenization, filtering,
    counting (tf and tfidf) and feature hashing.
Function for sparse regression on raw text, regressing a labeling
    vector onto a feature space consisting of all possible phrases.
Tools for measuring similarity among documents and detecting
    passages which have been reused. Implements shingled n-gram, skip n-gram,
    and other tokenizers; similarity/dissimilarity functions; pairwise
    comparisons; minhash and locality sensitive hashing algorithms; and a
    version of the Smith-Waterman local alignment algorithm suitable for
    natural language.
Tools that can be used to reshape and restructure text data.
Provides access to the text shaping functionality in the
    'HarfBuzz' library and the bidirectional algorithm in the 'Fribidi'
    library. 'textshaping' is a low-level utility package mainly for
    graphic devices that expands upon the font tool-set provided by the
    'systemfonts' package.
Tools that stem and lemmatize text.  Stemming is a process that removes
         endings such as affixes.  Lemmatization is the process of grouping inflected
         forms together as a single base form.
It offers functions for splitting, parsing, tokenizing and creating a vocabulary for big text data files. Moreover, it includes functions for building a document-term matrix and extracting information from those (term-associations, most frequent terms). It also embodies functions for calculating token statistics (collocations, look-up tables, string dissimilarities) and functions to work with sparse matrices. Lastly, it includes functions for Word Vector Representations (i.e. 'GloVe', 'fasttext') and incorporates functions for the calculation of (pairwise) text document dissimilarities. The source code is based on 'C++11' and exported in R through the 'Rcpp', 'RcppArmadillo' and 'BH' packages.
A framework for text cleansing and analysis. Conveniently prepare and process large amounts of text for analysis. 
  Includes various metrics for word counts/frequencies that scale efficiently. Quickly 
  analyze large amounts of text data using a text.table (a data.table created with one word (or unit of text analysis) per row, similar to the tidytext format). 
  Offers flexibility to efficiently work with text data stored in vectors as well as text data formatted as a text.table.
Utilities for handling character vectors
  that store human-readable text (either plain or with
  markup, such as HTML or LaTeX). The package provides,
  in particular, functions that help with the
  preparation of plain-text reports, e.g. for expanding
  and aligning strings that form the lines of such
  reports. The package also provides generic functions for
  transforming R objects to HTML and to plain text.
Defines S3 vector data types for vectors of functional data 
   (grid-based, spline-based or functional principal components-based) with all 
   arithmetic and summary methods, derivation, integration and smoothing, 
   plotting, data import and export, and data wrangling, such as re-evaluating, 
   subsetting, sub-assigning, zooming into sub-domains, or extracting functional 
   features like minima/maxima and their locations. 
   The implementation allows including such vectors in data frames for joint 
   analysis of functional and scalar variables.
R implementation of 'TFactS' to predict which are the 
    transcription factors (TFs), regulated in a biological condition based on
    lists of differentially expressed genes (DEGs) obtained from transcriptome
    experiments. This package is based on the 'TFactS' concept by 
    Essaghir et al. (2010) <doi:10.1093/nar/gkq149> and expands
    it. It allows users to perform 'TFactS'-like enrichment approach. The
    package can import and use the original catalogue file from the 'TFactS'
    as well as users' defined catalogues of interest that are not supported 
    by 'TFactS' (e.g., Arabidopsis).
'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).
Build customized transfer function and ARIMA models with multiple operators 
    and parameter restrictions. Provides tools for model identification, estimation 
    using exact or conditional maximum likelihood, diagnostic checking, automatic outlier 
    detection, calendar effects, forecasting, and seasonal adjustment. The new version 
    also supports unobserved component ARIMA model specification and estimation 
    for structural time series analysis.
Translate R control flow expressions into 'Tensorflow' graphs.
In Cox's proportional hazard model, covariates are modeled as linear function and may not be flexible. This package implements additive trend filtering Cox proportional hazards model as proposed in Jiacheng Wu & Daniela Witten (2019) "Flexible and Interpretable Models for Survival Data", Journal of Computational and Graphical Statistics, <DOI:10.1080/10618600.2019.1592758>. The fitted functions are piecewise polynomial with adaptively chosen knots.
Interface to 'TensorFlow' Datasets, a high-level library for
    building complex input pipelines from simple, re-usable pieces.
    See <https://www.tensorflow.org/guide> for additional
    details.
Tools to deploy 'TensorFlow' <https://www.tensorflow.org/> models across 
  multiple services. Currently, it provides a local server for testing 'cloudml' 
  compatible services.
Statistical interpretation of forensic glass transfer (Simulation of the probability distribution of recovered glass fragments).
Interface to 'TensorFlow' Estimators 
    <https://www.tensorflow.org/guide/estimator>, a high-level 
    API that provides implementations of many different model types 
    including linear models and deep neural networks.
Provides a convenient way to log scalars, images, audio, and histograms in the 'tfevent' record file format. 
  Logged data can be visualized on the fly using 'TensorBoard', a web based tool that focuses on visualizing the training 
  progress of machine learning models.
'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.
The Truncated Factor Model is a statistical model designed to handle specific data structures in data analysis. This R package focuses on the Sparse Online Principal Component Estimation method, which is used to calculate data such as the loading matrix and specific variance matrix for truncated data, thereby better explaining the relationship between common factors and original variables. Additionally, the R package also provides other equations for comparison with the Sparse Online Principal Component Estimation method.The philosophy of the package is described in thesis. (2023) <doi:10.1007/s00180-022-01270-z>.
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. <doi:10.1186/1748-7188-2-15>.
Provides a framework for the creation and use of Neural ordinary
    differential equations with the 'tensorflow' and 'keras' packages. 
    The idea of Neural ordinary differential equations comes from 
    Chen et al. (2018) <doi:10.48550/arXiv.1806.07366>, and 
    presents a novel way of learning and solving differential systems.
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.)
Provide functions to estimate the coefficients in high-dimensional linear regressions via a tuning-free and robust approach. The method was published in Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "A Tuning-free Robust and Efficient Approach to High-dimensional Regression", Journal of the American Statistical Association, 115:532, 1700-1714(JASA’s discussion paper), <doi:10.1080/01621459.2020.1840989>. See also Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "Rejoinder to “A tuning-free robust and efficient approach to high-dimensional regression". Journal of the American Statistical Association, 115, 1726-1729, <doi:10.1080/01621459.2020.1843865>; Peng, B. and Wang, L. (2015), "An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression", Journal of Computational and Graphical Statistics, 24:3, 676-694, <doi:10.1080/10618600.2014.913516>; Clémençon, S., Colin, I., and Bellet, A. (2016), "Scaling-up empirical risk minimization: optimization of incomplete u-statistics", The Journal of Machine Learning Research, 17(1):2682–2717; Fan, J. and Li, R. (2001), "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties", Journal of the American Statistical Association, 96:456, 1348-1360, <doi:10.1198/016214501753382273>.
Creates a framework to store and apply display metadata to
    Analysis Results Datasets (ARDs). The use of 'tfrmt' allows users to
    define table format and styling without the data, and later apply the
    format to the data.
Provides an interactive interface to the 'tfrmt' package. Users 
    can import, modify, and export tables and templates with little to no code.
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.
Extension of 'funHDDC' Schmutz et al. (2018) 
    <doi:10.1007/s00180-020-00958-4> for cases including
    outliers by fitting t-distributions for robust groups. 'TFunHDDC' can cluster
    univariate or multivariate data produced by the 'fda' package for data using
    a b-splines or Fourier basis.
Theme and colour palettes for The Globe and Mail's graphics. Includes colour and fill scale functions, colour palette helpers and a Globe-styled 'ggplot2' theme object.
Deconvolving thermoluminescence glow curves according to various 
        kinetic models (first-order, second-order, general-order, and mixed-order) using 
        a modified Levenberg-Marquardt algorithm (More, 1978) <DOI:10.1007/BFb0067700>. 
        It provides the possibility of setting constraints or fixing any of parameters. 
        It offers an interactive way to initialize parameters by clicking with a mouse on 
        a plot at positions where peak maxima should be located. The optimal estimate is 
        obtained by "trial-and-error". It also provides routines for simulating first-order, 
        second-order, and general-order glow peaks.
Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).
Efficient implementation of K-Means++ algorithm. For more
    information see (1) "kmeans++ the advantages of the k-means++
    algorithm" by David Arthur and Sergei Vassilvitskii (2007),
    Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete
    algorithms, Society for Industrial and Applied Mathematics,
    Philadelphia, PA, USA, pp. 1027-1035, and (2) "The Effectiveness of
    Lloyd-Type Methods for the k-Means Problem" by Rafail Ostrovsky, Yuval
    Rabani, Leonard J. Schulman and Chaitanya Swamy
    <doi:10.1145/2395116.2395117>.
Generalization of the classification and regression tree (CART) model that partitions subjects into terminal nodes and tailors machine learning model to each terminal node.
Bayesian nonstationary, semiparametric nonlinear regression 
 and design by treed Gaussian processes (GPs) with jumps to the limiting 
 linear model (LLM).  Special cases also implemented include Bayesian 
 linear models, CART, treed linear models, stationary separable and 
 isotropic GPs, and GP single-index models.  Provides 1-d and 2-d plotting functions 
 (with projection and slice capabilities) and tree drawing, designed for 
 visualization of tgp-class output.  Sensitivity analysis and 
 multi-resolution models are supported. Sequential experimental 
 design and adaptive sampling functions are also provided, including ALM, 
 ALC, and expected improvement.  The latter supports derivative-free
 optimization of noisy black-box functions.  For details and tutorials, 
 see Gramacy (2007) <doi:10.18637/jss.v019.i09> and Gramacy & Taddy (2010) 
 <doi:10.18637/jss.v033.i06>.
Functions to compute and plot tracheidograms, as in De Soto et al. (2011) <doi:10.1139/x11-045>.
Rapid advancements in high-throughput gene sequencing
    technologies have resulted in genome-scale time-series datasets. 
    Uncovering the underlying temporal sequence of gene regulatory events 
    in the form of time-varying gene regulatory networks demands 
    accurate and computationally efficient algorithms. Such an
    algorithm is 'TGS'. It is proposed in Saptarshi Pyne, Alok Ranjan 
    Kumar, and Ashish Anand. Rapid reconstruction of time-varying 
    gene regulatory networks. IEEE/ACM Transactions on Computational 
    Biology and Bioinformatics, 17(1):278{291, Jan-Feb 2020. The TGS 
    algorithm is shown to consume only 29 minutes for a microarray 
    dataset with 4028 genes. This package provides an implementation 
    of the TGS algorithm and its variants.
Functions for implementing the targeted gold standard (GS) testing. You provide the true disease or treatment failure status and the risk score, tell 'TGST' the availability of GS tests and which method to use, and it returns the optimal tripartite rules. Please refer to Liu et al. (2013) <doi:10.1080/01621459.2013.810149> for more details.
A collection of high performance utilities to compute
    distance, correlation, auto correlation, clustering and other tasks.
    Contains graph clustering algorithm described in "MetaCell: analysis
    of single-cell RNA-seq data using K-nn graph partitions" (Yael Baran,
    Akhiad Bercovich, Arnau Sebe-Pedros, Yaniv Lubling, Amir Giladi, Elad
    Chomsky, Zohar Meir, Michael Hoichman, Aviezer Lifshitz & Amos Tanay,
    2019 <doi:10.1186/s13059-019-1812-2>).
Turing Geovisualization Engine R package for geospatial visualization and analysis.
Contains data sets used in other packages Torsten Hothorn
  maintains.
Implements the truncated harmonic mean estimator (THAMES) 
    of the reciprocal marginal likelihood using posterior samples and
    unnormalized log posterior values via reciprocal importance sampling.
    Metodiev, Perrot-Dockès, Ouadah, Irons, Latouche, & Raftery (2024). 
    Bayesian Analysis. <doi:10.1214/24-BA1422>.
Implements the truncated harmonic mean estimator (THAMES) 
    of the reciprocal marginal likelihood for uni- and multivariate mixture 
    models using posterior samples and unnormalized log posterior values via 
    reciprocal importance sampling.
    Metodiev, Irons, Perrot-Dockès, Latouche & Raftery (2025)
    <doi:10.48550/arXiv.2504.21812>.
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.
Consolidates and calculates different sets of time-series features from multiple
    'R' and 'Python' packages including 'Rcatch22' Henderson, T. (2021) <doi:10.5281/zenodo.5546815>,
    'feasts' O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>,
    'tsfeatures' Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020)
    <https://CRAN.R-project.org/package=tsfeatures>, 'tsfresh' Christ, M., Braun, N., Neuffer, J.,
    and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, 'TSFEL' Barandas, M., et al. (2020)
    <doi:10.1016/j.softx.2020.100456>, and 'Kats' Facebook Infrastructure Data Science (2021)
    <https://facebookresearch.github.io/Kats/>.
Provides a suite of functions for analysing, interpreting, and visualising
    time-series features calculated from different feature sets from the 'theft' package. 
    Implements statistical learning methodologies described in Henderson, T.,  
    Bryant, A., and Fulcher, B. (2023) <doi:10.48550/arXiv.2303.17809>.
Provides a simple interface to search available data provided by
    Theia (<https://theia.cnes.fr>), download it, and manage it. Data can be downloaded
    based on a search result or from a cart file downloaded from Theia website.
Theme 'ggplot2', 'lattice', and 'base' graphics based on a
    few choices, including foreground color, background color, accent
    color, and font family. Fonts that aren't available on the system, but
    are available via download on 'Google Fonts', can be automatically
    downloaded, cached, and registered for use with the 'showtext' and
    'ragg' packages.
A dataset with an uneven number of cases in each class is
    said to be unbalanced. Many models produce a subpar performance on
    unbalanced datasets. A dataset can be balanced by increasing the
    number of minority cases using SMOTE 2011 <doi:10.48550/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>.
Utilizing the 'OpenAI' API as the back end (<https://platform.openai.com/docs/api-reference>), 'TheOpenAIR' offers 'R' wrapper functions for the 'ChatGPT' endpoint and several high-level functions that enable the integration of 'ChatGPT' capabilities in diverse data-related tasks, such as data cleansing and automated analytics script generation.
An integrated suite of tools for creating, maintaining, and reusing
    FAIR (Findable, Accessible, Interoperable, Reusable) theories.
    Designed to support transparent and collaborative theory development,
    the package enables users to formalize theories,
    track changes with version control,
    assess pre-empirical coherence,
    and derive testable hypotheses.
    Aligning with open science principles and workflows,
    'theorytools' facilitates the systematic improvement of theoretical frameworks
    and enhances their discoverability and usability.
We present a range of simulations to aid researchers in determining appropriate sample sizes when performing critical
    thermal limits studies (e.g. CTmin/CTmin experiments). A number of wrapper functions are provided for plotting and summarising outputs
    from these simulations. This package is presented in van Steenderen, C.J.M., Sutton, G.F., Owen, C.A., Martin, G.D., and Coetzee, J.A. Sample size assessments for thermal physiology studies: An R
    package and R Shiny application. 2023. Physiological Entomology. <doi:10.1111/phen.12416>. The GUI version of this package is available on the R Shiny online server at: 
    <https://clarkevansteenderen.shinyapps.io/ThermalSampleR_Shiny/> , or it is accessible via GitHub at <https://github.com/clarkevansteenderen/ThermalSampleR_Shiny/>. We would like
    to thank Grant Duffy (University of Otago, Dundedin, New Zealand) for granting us permission to use the source code for the Test of Total Equivalency function.
A collection of functions and routines for inputting thermal
    image video files, plotting and converting binary raw data into estimates of
    temperature.  First published 2015-03-26.  Written primarily for research purposes
    in biological applications of thermal images.  v1 included the base calculations 
    for converting thermal image binary values to temperatures. v2 included additional
    equations for providing heat transfer calculations and an import function for thermal
    image files (v2.2.3 fixed error importing thermal image to windows OS). v3. Added numerous
    functions for converting thermal image, videos, rewriting and exporting.  
    v3.1. Added new functions to convert files. v3.2.  Fixed the various functions related to finding frame times.
    v4.0. fixed an error in atmospheric attenuation constants, affecting raw2temp and temp2raw functions.
    Recommend update for use with long distance calculations. v.4.1.3 changed to frameLocates to reflect change to as.character() to format().
Calculates several thermal comfort indexes using temperature, wind speed and relative humidity values, calculating indexes such as Humidex, windchill, Discomfort Index and others.
Temperature measurement data, equations and methods for thermocouples,
    wire RTD, thermistors, IC thermometers, bimetallic strips and the ITS-90.
Provides tools for decomposing differences in rate metrics between
    two groups into contributions from individual subgroups and visualizing them
    as a "Theseus Plot". Inspired by the story of the Ship of Theseus, the method
    replaces subgroup data from one group with that of another step by step,
    recalculating the overall metric at each stage to quantify subgroup contributions.
    A Theseus Plot combines the stepwise progression of a waterfall plot with
    the comparative bars of a bar chart, offering an intuitive way to understand
    subgroup-level effects.
Estimation of the SF-ACE, a Causal Inference estimand proposed in the paper "The Subtype-Free Average Causal Effect For Heterogeneous Disease Etiology" (soon on arXiv).
A user-friendly R data package that is intended to make Turkish higher education statistics more accessible.
Testing, Implementation, and Forecasting of the THETA-SVM hybrid model. The THETA-SVM hybrid model combines the distinct strengths of the THETA model and the Support Vector Machine (SVM) model for time series forecasting.For method details see Bhattacharyya et al. (2022) <doi:10.1007/s11071-021-07099-3>.
A step-up test for genetic rare variants in a gene or in a pathway. The method determines an optimal grouping of rare variants analytically. The method has been described in Hoffmann TJ, Marini NJ, and Witte JS (2010) <doi:10.1371/journal.pone.0013584>.
Methods and tools for generating forecasts at different temporal
    frequencies using a hierarchical time series approach.
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 path of the executing script. Compatible
        with several popular GUIs: 'Rgui', 'RStudio', 'Positron',
        'VSCode', 'Jupyter', 'Emacs', and 'Rscript' (shell). Compatible
        with several functions and packages: 'source()',
        'sys.source()', 'debugSource()' in 'RStudio',
        'compiler::loadcmp()', 'utils::Sweave()', 'box::use()',
        'knitr::knit()', 'plumber::plumb()', 'shiny::runApp()',
        'package:targets', and 'testthat::source_file()'.
Provides utility functions for data analysis and scientific computing. Includes functions for parallel processing, and other computational tasks to streamline workflows.
Functions for admin needs of employees of
      Thomas Jefferson University and Thomas Jefferson
      University Hospital, Philadelphia, PA.
Key-value store, implemented as a wrapper around 'LMDB';
    the "lightning memory-mapped database" <https://www.symas.com/mdb>.
    '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.
A tool that allows users to estimate tree height in the long-term forest experiments in Sweden. It utilizes the multilevel nonlinear mixed-effect height models developed for the forest experiments and consists of four functions for the main species, other conifer species, and other broadleaves. Each function within the system returns a data frame that includes the input data and the estimated heights for any missing values. Ogana et al. (2023) <doi:10.1016/j.foreco.2023.120843>\n Arias-Rodil et al. (2015) <doi:10.1371/JOURNAL.PONE.0143521>.
Provides a crawler for programmatically navigating THREDDS Data Server (<https://www.unidata.ucar.edu/software/tds/>) 
  catalogs, and access dataset metadata and resources.
A fast, interactive cross-platform, and easy to share 
    'WebGL'-based 3D brain viewer that visualizes 'FreeSurfer' and/or
    'AFNI/SUMA' surfaces. The viewer widget can be either standalone or 
    embedded into 'R-shiny' applications. The standalone version only require
    a web browser with 'WebGL2' support (for example, 'Chrome', 'Firefox', 
    'Safari'), and can be inserted into any websites. The 'R-shiny' 
    support allows the 3D viewer to be dynamically generated from reactive user 
    inputs. Please check the publication by Wang, Magnotti, Zhang,
    and Beauchamp (2023, <doi:10.1523/ENEURO.0328-23.2023>) for electrode 
    localization. This viewer has been fully adopted by 'RAVE' 
    <https://openwetware.org/wiki/RAVE>, an interactive toolbox to 
    analyze 'iEEG' data by Magnotti, Wang, and Beauchamp (2020, 
    <doi:10.1016/j.neuroimage.2020.117341>). Please check 
    'citation("threeBrain")' for details.
Implements the Maximum Likelihood estimator for baseline, placebo, and treatment groups (three-group) experiments with non-compliance proposed by Gerber, Green, Kaplan, and Kern (2010).
Create interactive 3D scatter plots, network plots, and
    globes using the 'three.js' visualization library (<https://threejs.org>).
Access open data from <https://www.threesixtygiving.org>, a 
    database of charitable grant giving in the UK operated by '360Giving'.
    The package provides functions to search and retrieve data on charitable 
    grant giving, and process that data into tidy formats. It relies on the 
    '360Giving' data standard, described at 
    <https://standard.threesixtygiving.org/>.
Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.
Does nothing useful, but perhaps does that nothing in an entertaining or informative fashion.
A connector to the 'What3Words' (http://what3words.com/) service, which represents each 3m by 3m square on earth
             with a unique trio of English-language words.
Fit a threshold regression model based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Fit a threshold regression model for Interval Censored Data based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Defines the classes used to identify
  outliers (threshing) and compute the number of significant principal
  components and number of clusters (reaping) in a joint application
  of PCA and hierarchical clustering. See Wang et al., 2018,
  <doi:10.1186/s12859-017-1998-9>.
Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. We also provide functions for sample size determination and estimation of diagnostic accuracy measures. We also include graphical tools. The statistical methodology used here can be found in Perez-Jaume et al (2017) <doi:10.18637/jss.v082.i04> and in Skaltsa et al (2010, 2012) <doi:10.1002/bimj.200900294>, <doi:10.1002/sim.4369>.
We focus on the diagnostic ability assessment of medical tests when the outcome of interest is the status (alive or dead) of the subjects at a certain time-point t. This binary status is determined by right-censored times to event and it is unknown for those subjects censored before t. Here we provide three methods (unknown status exclusion, imputation of censored times and using time-dependent ROC curves) to evaluate the diagnostic ability of binary and continuous tests in this context. Two references for the methods used here are Skaltsa et al. (2010) <doi:10.1002/bimj.200900294> and Heagerty et al. (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.
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>.
Allow to compute and visualise convective parameters commonly
  used in the operational prediction of severe convective storms. Core algorithm
  is based on a highly optimized 'C++' code linked into 'R' via 'Rcpp'. Highly
  efficient engine allows to derive thermodynamic and kinematic parameters from
  large numerical datasets such as reanalyses or operational Numerical Weather 
  Prediction models in a reasonable amount of time. Package has been developed
  since 2017 by research meteorologists specializing in severe thunderstorms.
  The most relevant methods used in the package based on the following publications
  Stipanuk (1973) <https://apps.dtic.mil/sti/pdfs/AD0769739.pdf>,
  McCann et al. (1994) <doi:10.1175/1520-0434(1994)009%3C0532:WNIFFM%3E2.0.CO;2>,
  Bunkers et al. (2000) <doi:10.1175/1520-0434(2000)015%3C0061:PSMUAN%3E2.0.CO;2>,
  Corfidi et al. (2003) <doi:10.1175/1520-0434(2003)018%3C0997:CPAMPF%3E2.0.CO;2>,
  Showalter (1953) <doi:10.1175/1520-0477-34.6.250>,
  Coffer et al. (2019) <doi:10.1175/WAF-D-19-0115.1>,
  Gropp and Davenport (2019) <doi:10.1175/WAF-D-17-0150.1>,
  Czernecki et al. (2019) <doi:10.1016/j.atmosres.2019.05.010>,
  Taszarek et al. (2020) <doi:10.1175/JCLI-D-20-0346.1>,
  Sherburn and Parker (2014) <doi:10.1175/WAF-D-13-00041.1>,
  Romanic et al. (2022) <doi:10.1016/j.wace.2022.100474>.
Fit Thurstonian forced-choice models (CFA (simple and factor) and IRT) in R. This package allows for the analysis of item response modeling (IRT) as well as confirmatory factor analysis (CFA) in the Thurstonian framework. Currently, estimation can be performed by 'Mplus' and 'lavaan'. References:
  Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>;
  Jansen, M. T., & Schulze, R. (in review). The Thurstonian linked block design: Improving Thurstonian modeling for paired comparison and ranking data.;
  Maydeu-Olivares & Böckenholt (2005) <doi:10.1037/1082-989X.10.3.285>.
Fit Thurstonian Item Response Theory (IRT) models in R. This 
  package supports fitting Thurstonian IRT models and its extensions using 
  'Stan', 'lavaan', or 'Mplus' for the model estimation. Functionality for 
  extracting results, making predictions, and simulating data is provided as 
  well. References: 
  Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>;
  Bürkner et al. (2019) <doi:10.1177/0013164419832063>.
Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the traditional
    data frame.
Built on top of the 'tibble' package, 'tibbletime' is an extension
  that allows for the creation of time aware tibbles. Some immediate
  advantages of this include: the ability to perform time-based subsetting
  on tibbles, quickly summarising and aggregating results by time periods,
  and creating columns that can be used as 'dplyr' time-based groups.
A tool to rectangle a nested list, that is to convert it into
    a tibble. This is done automatically or according to a given
    specification.  A common use case is for nested lists coming from
    parsing JSON files or the JSON response of REST APIs. It is supported
    by the 'vctrs' package and therefore offers a wide support of vector
    types.
Plots and analyzes time-intensity curve data, such as data from 
  (contrast-enhanced) ultrasound. Values such as peak intensity, time to 
  peak, area under the curve, wash in rate and wash out rate are calculated.
This is a small package to provide consistent tick marks for plotting 'ggplot2' figures. 
    It provides breaks and labels for 'ggplot2' without requiring 'ggplot2' to be installed.
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.
Code execution 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 'StackList', which is a
    stack implemented as a
    list, both of which support operations 'push', 'pop', 'first_element',
    'last_element' and 'clear'.
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.
Provides 'Apache Spark' style window aggregation for R dataframes and remote 'dbplyr' tables via 'mutate' in 'dplyr' flavour.
The goal of this package will be to provide a simple interface for automatic machine learning that fits the 'tidymodels' framework. The intention is to work for regression and classification problems with a simple verb framework.
Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models
    ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided
    to help extract tidy data frames of draws from Bayesian models and that generate point
    summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for
    common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus
    densities), and fit curves with multiple, arbitrary uncertainty bands.
Tools to download data series from 'Banco de España' ('BdE')
    on 'tibble' format. 'Banco de España' is the national central bank
    and, within the framework of the Single Supervisory Mechanism ('SSM'),
    the supervisor of the Spanish banking system along with the European
    Central Bank. This package is in no way sponsored endorsed or
    administered by 'Banco de España'.
Multiple ways to bin numeric columns with a tidy output. Wraps a variety of existing binning methods into one function, and includes a new method for binning by equal value, which is useful for sales data. Provides a function to automatically summarize the properties of the binned columns.
Compute arbitrary non-parametric bootstrap statistics on data in
    tidy data frames.
Create additional rows and columns on broom::tidy() output to allow for easier control on categorical parameter estimates.
Provides users a quick exploratory dive into common
    visualizations without writing a single line of code given the users
    data follows the Analysis Data Model (ADaM) standards put forth by the
    Clinical Data Interchange Standards Consortium (CDISC)
    <https://www.cdisc.org>. Prominent modules/ features of the
    application are the Table Generator, Population Explorer, and the
    Individual Explorer. The Table Generator allows users to drag and drop
    variables and desired statistics (frequencies, means, ANOVA, t-test,
    and other summary statistics) into bins that automagically create
    stunning tables with validated information. The Population Explorer
    offers various plots to visualize general trends in the population
    from various vantage points. Plot modules currently include scatter
    plot, spaghetti plot, box plot, histogram, means plot, and bar plot.
    Each plot type allows the user to plot uploaded variables against one
    another, and dissect the population by filtering out certain subjects.
    Last, the Individual Explorer establishes a cohesive patient
    narrative, allowing the user to interact with patient metrics (params)
    by visit or plotting important patient events on a timeline. All
    modules allow for concise filtering & downloading bulk outputs into
    html or pdf formats to save for later.
An integrated R interface to several United States Census Bureau 
    APIs (<https://www.census.gov/data/developers/data-sets.html>) and the US Census Bureau's 
    geographic boundary files. Allows R users to return Census and ACS data as 
    tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping 
    and spatial analysis.
Census and administrative data in South Korea are a basic source of
    quantitative and mixed-methods research for social and urban scientists.
    This package provides a 'sf' (Pebesma et al., 2024 <doi:10.32614/CRAN.package.sf>) 
    based standardized workflow based on direct open API access to the major census and
    administrative data sources and pre-generated files in South Korea.
Changepoint detection algorithms for R are widespread but have
    different interfaces and reporting conventions. 
    This makes the comparative analysis of results difficult. 
    We solve this problem by providing a tidy, unified interface for several 
    different changepoint detection algorithms. 
    We also provide consistent numerical and graphical reporting leveraging 
    the 'broom' and 'ggplot2' packages.
There is a wide range of R packages created for data visualization, but still, there was no simple and easily accessible way to create clean and transparent charts - up to now. The 'tidycharts' package enables the user to generate charts compliant with International Business Communication Standards ('IBCS').
    It means unified bar widths, colors, chart sizes, etc. Creating homogeneous reports has never been that easy! Additionally, users can apply semantic notation to indicate different data scenarios (plan, budget, forecast). What's more, it is possible to customize the charts by creating a personal color pallet with the possibility of switching to default options after the experiments.
    We wanted the package to be helpful in writing reports, so we also made joining charts in a one, clear image possible.
    All charts are generated in SVG format and can be shown in the 'RStudio' viewer pane or exported to HTML output of 'knitr'/'markdown'.
A common interface to specifying clustering models, in the
    same style as 'parsnip'. Creates unified interface across different
    functions and computational engines.
Provides an intuitive interface for working with the
    competing risk endpoints. The package wraps the 'cmprsk' package, and
    exports functions for univariate cumulative incidence estimates and
    competing risk regression. Methods follow those introduced in Fine and
    Gray (1999) <doi:10.1002/sim.7501>.
Analyze lines of R code using tidy principles. This allows you to 
    input lines of R code and output a data frame with one row per function 
    included. Additionally, it facilitates code classification via included lexicons.
Provides convenience functions for common data modification
    and analysis tasks in communication research. This includes functions
    for univariate and bivariate data analysis, index generation and
    reliability computation, and intercoder reliability tests. All
    functions follow the style and syntax of the tidyverse, and are
    construed to perform their computations on multiple variables at once.
    Functions for univariate and bivariate data analysis comprise summary
    statistics for continuous and categorical variables, as well as
    several tests of bivariate association including effect sizes.
    Functions for data modification comprise index generation and
    automated reliability analysis of index variables. Functions for
    intercoder reliability comprise tests of several intercoder
    reliability estimates, including simple and mean pairwise percent
    agreement, Krippendorff's Alpha (Krippendorff 2004, ISBN:
    9780761915454), and various Kappa coefficients (Brennan & Prediger
    1981 <doi: 10.1177/001316448104100307>; Cohen 1960 <doi:
    10.1177/001316446002000104>; Fleiss 1971 <doi: 10.1037/h0031619>).
Loads the 5 packages in the Tidy Consultant Universe. This collection of packages is useful for anyone doing data science,
    data analysis, or quantitative consulting. The functions in these packages range from data cleaning, data validation, data binning, statistical modeling, and file exporting.
A comprehensive and user-friendly interface for accessing, manipulating, 
    and analyzing country-level data from around the world. It allows users to retrieve detailed 
    information on countries, including names, regions, continents, populations, currencies, calling codes, 
    and more, all in a tidy data format. The package is designed to work seamlessly within 
    the 'tidyverse' ecosystem, making it easy to filter, arrange, and visualize country-level data in R.
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.
Provides model specifications, tuning parameters for models in 
    'dann' package. Models based on Hastie (1996) 
    <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.
Visualize your 'Tidyverse' data analysis pipelines via the 
    'Tidy Data Tutor'(<https://tidydatatutor.com/>) web application.
Delta Method implementation to estimate standard errors with known 
    asymptotic properties within the 'tidyverse' workflow. The Delta Method is a
    statistical tool that approximates an estimator’s behaviour using a Taylor 
    Expansion. For a comprehensive explanation, please refer to Chapter 3 of van
    der Vaart (1998, ISBN: 9780511802256).
Cleans spectrophotometry data obtained from the Denovix instrument. The package also provides an option to normalize the data in order to compare the quality of the samples obtained.
To make it easy to generate random numbers based upon the underlying stats 
    distribution functions. All data is returned in a tidy and structured
    format making working with the data simple and straight forward. Given that the
    data is returned in a tidy 'tibble' it lends itself to working with the rest of the
    'tidyverse'.
Provides extended data frames, with a special data frame column which contains two indexes, with potentially a nesting structure, and support for tibbles and methods for 'dplyr'.
Utils for basic statistical experiments, that can be used for teaching 
    introductory statistics. Each experiment generates a tibble.
    Dice rolls and coin flips are simulated using sample().
    The properties of the dice can be changed, like the number of sides.
    A coin flip is simulated using a two sided dice.
    Experiments can be combined with the pipe-operator.
Dimensionality reduction (DR) is widely used in many domain for analyzing and visualizing high-dimensional data. 'tidydr' provides uniform output and is compatible with multiple methods, including 'prcomp', 'mds', 'Rtsne'. etc.
Streamline the process of accessing fundamental financial data from the United States Securities and Exchange Commission's ('SEC') Electronic Data Gathering, Analysis, and Retrieval system ('EDGAR') 'API' <https://www.sec.gov/edgar/sec-api-documentation>, transforming it into a tidy, analysis-ready format.
Unicodes are not friendly to work with, and not all Unicodes are
    Emoji per se, making obtaining Emoji statistics a difficult task. This
    tool can help your experience of working with Emoji as smooth as possible,
    as it has the 'tidyverse' style.
The 'ESTIMATE' package infers tumor purity from expression data as a 
  function of immune and stromal infiltrate, but requires writing of intermediate 
  files, is un-pipeable, and performs poorly when presented with modern datasets 
  with current gene symbols. 'tidyestimate' a fast, tidy, modern reimagination of
  'ESTIMATE' (2013) <doi:10.1038/ncomms3612>.
Tidying functions built on 'data.table'
    to provide quick and efficient data manipulation with
    minimal overhead.
Helper functions for empirical research in financial
    economics, addressing a variety of topics covered in Scheuch, Voigt,
    and Weiss (2023) <doi:10.1201/b23237>.  The package is designed to
    provide shortcuts for issues extensively discussed in the book,
    facilitating easier application of its concepts. For more information
    and resources related to the book, visit
    <https://www.tidy-finance.org/r/index.html>.
An extension to the 'R' tidy data environment for automated machine learning. The package allows fitting and cross validation of linear regression and classification algorithms on grouped data.
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'.
Provides functions that compute predictions from Generalised
    Additive Models (GAMs) fitted with 'mgcv' and return them as a tibble.
    These can be plotted with a generic plot()-method that uses 'ggplot2' or
    plotted as any other data frame. The main function is predict_gam().
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.
Interactively gate points on a scatter plot. Interactively drawn gates are 
  recorded and can be applied programmatically to reproduce results exactly. Programmatic gating is 
  based on the package gatepoints by Wajid Jawaid (who is also an author of this package).
Handle genomic data within data frames just as you would with 'GRanges'.
    This packages provides method to deal with genomic intervals the "tidy-way" which makes
    it simpler to integrate in the the general data munging process. The API is inspired by the
    popular 'bedtools' and the genome_join() method from the 'fuzzyjoin' package.
An intuitive interface for getting data from geocoding services.
In order to easily integrate geoRSS data into analysis,
    'tidygeoRSS' parses 'geo' feeds and returns 
    tidy simple features data frames.
A graph, while not "tidy" in itself, can be thought of as two
    tidy data frames describing node and edge data respectively.
    'tidygraph' provides an approach to manipulate these two virtual data
    frames using the API defined in the 'dplyr' package, as well as
    provides tidy interfaces to a lot of common graph algorithms.
This is a tidy implementation for heatmap.  At the
    moment it is based on the (great) package 'ComplexHeatmap'.  The goal
    of this package is to interface a tidy data frame with this powerful
    tool.  Some of the advantages are: Row and/or columns colour
    annotations are easy to integrate just specifying one parameter
    (column names).  Custom grouping of rows is easy to specify providing
    a grouped tbl. For example: df %>% group_by(...).  Labels size
    adjusted by row and column total number.  Default use of Brewer and
    Viridis palettes.
The goal of 'tidyheatmaps' is to simplify the generation of publication-ready heatmaps from tidy data. By offering an interface to the powerful 'pheatmap' package, it allows for the effortless creation of intricate heatmaps with minimal code.
Estimates heterogeneous treatment effects using tidy semantics 
    on experimental or observational data.  Methods are based on the doubly-robust
    learner of Kennedy (2023) <doi:10.1214/23-EJS2157>. You provide a simple
    recipe for what machine learning algorithms to use in estimating the nuisance
    functions and 'tidyhte' will take care of cross-validation, estimation, model
    selection, diagnostics and construction of relevant quantities of interest about
    the variability of treatment effects.
Provides functions to access historical and real-time national 'hydrometric'
    data from Water Survey of Canada data sources and then applies tidy data principles.
Construct and analyse indexes in a pipeline tidy workflow.
    'tidyindex' contains modules for transforming variables, aggregating 
    variables across time, reducing data dimension through weighting, and 
    fitting distributions. A manuscript describing the methodology can be 
    found at <https://github.com/huizezhang-sherry/paper-tidyindex>.
Turn complex 'JSON' data into tidy data frames.
Loading the Korea Labor Institute's KLIPS (Korea Labor & Income Panel Study) panel data and returning data frames. Users must download 26 years of panel data from the Korea Labor Institute website and save it in a folder in an appropriate path. Afterwards, users can easily convert the data into a data frame using this package.
Implements an algorithm for Latent Dirichlet
    Allocation (LDA), Blei et at. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>,
    using style conventions from the 'tidyverse',
    Wickham et al. (2019)<doi:10.21105/joss.01686>,
    and 'tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>.
    Fitting is done via collapsed Gibbs sampling.
    Also implements several novel features for LDA such as guided models and
    transfer learning.
A tidy interface for integrating large language model (LLM) APIs such as 'Claude', 'Openai', 'Gemini','Mistral' and local models via 'Ollama' into R workflows. The package supports text and media-based interactions, interactive message history, batch request APIs, and a tidy, pipeline-oriented interface for streamlined integration into data workflows. Web services are available at <https://www.anthropic.com>, <https://openai.com>, <https://aistudio.google.com/>, <https://mistral.ai/> and <https://ollama.com>.
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.
Framework to run Monte Carlo simulations over a parameter grid. 
             Allows to parallelize the simulations. 
             Generates plots and 'LaTeX' tables
             summarizing the results from the simulation.
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.
Transforms long data into a matrix form to allow for ease of input into modelling packages for regression, principal components, imputation or machine learning. It does this by pivoting on user defined columns, generating a key-value table for variable names to ensure one-to-one mappings are preserved. It is particularly useful when the indicator names in the columns are long descriptive strings, for example "Energy imports, net (% of energy use)". High level analysis wrapper functions for correlation and principal components analysis are provided.
Provides the means to convert 'multiqc_data.json' files,
    produced by the wonderful 'MultiQC' tool, into tidy data frames for downstream
    analysis in R. This analysis might involve cohort analysis, quality control visualisation,
    change-point detection, statistical process control, clustering, or any other
    type of quality analysis.
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().
An implementation of tidy speaker vowel normalization.
    This includes generic functions for defining new normalization methods for
    points, formant tracks, and Discrete Cosine Transform coefficients, as well
    as convenience functions implementing established normalization methods.
    References for the implemented methods are:
    Johnson, Keith (2020) <doi:10.5334/labphon.196>
    Lobanov, Boris (1971) <doi:10.1121/1.1912396>
    Nearey, Terrance M. (1978) <https://sites.ualberta.ca/~tnearey/Nearey1978_compressed.pdf>
    Syrdal, Ann K., and Gopal, H. S. (1986) <doi:10.1121/1.393381>
    Watt, Dominic, and Fabricius, Anne (2002) <https://www.latl.leeds.ac.uk/article/evaluation-of-a-technique-for-improving-the-mapping-of-multiple-speakers-vowel-spaces-in-the-f1-f2-plane/>.
Provides a set of functions with a common framework for age-depth model management, 
  stratigraphic visualization, and common statistical transformations. The focus of the
  package is stratigraphic visualization, for which 'ggplot2' components are provided
  to reproduce the scales, geometries, facets, and theme elements commonly used in
  publication-quality stratigraphic diagrams. Helpers are also provided to reproduce
  the exploratory statistical summaries that are frequently included on
  stratigraphic diagrams. See Dunnington et al. (2021) <doi:10.18637/jss.v101.i07>.
The goal of 'tidyplate' is to help researchers convert
    different types of microplates into tibbles which can be used in data
    analysis. It accepts xlsx and csv files formatted in a specific way as
    input. It supports all types of standard microplate formats such as
    6-well, 12-well, 24-well, 48-well, 96-well, 384-well, and, 1536-well
    plates.
The goal of 'tidyplots' is to streamline the creation of publication-ready plots for scientific papers. It allows to gradually add, remove and adjust plot components using a consistent and intuitive syntax.
Provides functions such as str_crush(), add_missing_column(), 
  coalesce_data() and drop_na_all() that complement 'tidyverse' functionality
  or functions that provide alternative behaviors such as if_else2()
  and str_detect2().
Parse XML documents from the Open Access subset of Europe PubMed Central <https://europepmc.org>
    including section paragraphs, tables, captions and references.
We provide a tidy grammar of population genetics, facilitating 
    the manipulation and analysis of data on biallelic single nucleotide
    polymorphisms (SNPs). 'tidypopgen' scales to very large genetic datasets
    by storing genotypes on disk, and performing operations on them in 
    chunks, without ever loading all data in memory. The full
    functionalities of the package are described in Carter et al. (2025)
    <doi:10.1101/2025.06.06.658325>.
Bayesian analysis used here to answer the question: "when
    looking at resampling results, are the differences between models
    'real'?" To answer this, a model can be created were the performance
    statistic is the resampling statistics (e.g. accuracy or RMSE). These
    values are explained by the model types. In doing this, we can get
    parameter estimates for each model's affect on performance and make
    statistical (and practical) comparisons between models. The methods
    included here are similar to Benavoli et al (2017)
    <https://jmlr.org/papers/v18/16-305.html>.
It parses a fitted 'R' model object, and returns a formula in
    'Tidy Eval' code that calculates the predictions.  It works with
    several databases back-ends because it leverages 'dplyr' and 'dbplyr'
    for the final 'SQL' translation of the algorithm. It currently
    supports lm(), glm(), randomForest(), ranger(), earth(),
    xgb.Booster.complete(), cubist(), and ctree() models.
Easily construct prompts and associated logic for interacting with 
  large language models (LLMs). 
  'tidyprompt' introduces the concept of prompt wraps, which are building
  blocks that you can use to quickly turn a simple prompt into a complex
  one. Prompt wraps do not just modify the prompt text, but also add
  extraction and validation functions that will be applied to the response
  of the LLM. This ensures that the user gets the desired output.
  'tidyprompt' can add various features to prompts and their evaluation
  by LLMs, such as structured output, automatic feedback, retries, reasoning
  modes, autonomous R function calling, and R code generation and evaluation.
  It is designed to be compatible with any LLM provider that offers
  chat completion.
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.
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).
Compute age-adjusted rates by direct and indirect methods and other epidemiological indicators in a tidy way, wrapping functions from the 'epitools' package.
Helper functions for processing 'REDCap' data in R. 'REDCap' is a 
    web-enabled application for building and managing surveys and databases 
    developed at Vanderbilt University.
Provides 'tidyverse' methods for wrangling
    and analyzing 'Earth Engine' <https://earthengine.google.com/> data. These methods help the user with filtering, 
    joining and summarising 'Earth Engine' image collections.
Allows users to quickly load multiple patients' electrocardiographic 
    (ECG) data at once and conduct relevant time analysis of heart rate variability 
    (HRV) without manual edits from a physician or data cleaning specialist. 
    The package provides the unique ability to iteratively filter, plot, 
    and store time analysis results in a data frame while writing plots to a 
    predefined folder. This streamlines the workflow for HRV analysis across 
    multiple datasets. Methods are based on Rodríguez-Liñares et al. (2011) 
    <doi:10.1016/j.cmpb.2010.05.012>. Examples of applications using this 
    package include Kwon et al. (2022) <doi:10.1007/s10286-022-00865-2> and 
    Lawrence et al. (2023) <doi:10.1016/j.autneu.2022.103056>.
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.
Provides functions to scale, log-transform and fit linear models within a 'tidyverse'-style R code framework.
    Intended to smooth over inconsistencies in output of base R statistical functions, allowing ease of teaching, learning and daily use. Inspired by the tidy principles used in 'broom' Robinson (2017) <doi:10.21105/joss.00341>.
Provides a framework to work with decision rules. Rules can be extracted from supported models, augmented with (custom) metrics using validation data, manipulated using standard dataframe operations, reordered and pruned based on a metric, predict on unseen (test) data. Utilities include; Creating a rulelist manually, Exporting a rulelist as a SQL case statement and so on. The package offers two classes; rulelist and ruleset based on dataframe.
Fit species distribution models (SDMs) using the 'tidymodels' framework, 
  which provides a standardised interface to define models and process their 
  outputs. 'tidysdm' expands 'tidymodels' by providing methods for spatial objects,
  models and metrics specific to SDMs,
  as well as a number of specialised functions to process occurrences
  for contemporary and palaeo datasets. The full 
  functionalities of the package are described
  in Leonardi et al. (2023) <doi:10.1101/2023.07.24.550358>.
A backend for the selecting functions of the 'tidyverse'.  It
    makes it easy to implement select-like functions in your own packages
    in a way that is consistent with other 'tidyverse' interfaces for
    selection.
A tidy workflow for generating, estimating, reporting,
    and plotting structural equation models using 'lavaan', 'OpenMx', or
    'Mplus'. Throughout this workflow, elements of syntax, results, and graphs
    are represented as 'tidy' data, making them easy to customize.
    Includes functionality to estimate latent class analyses, and to plot
    'dagitty' and 'igraph' objects.
It creates an invisible layer that allow to see the 'Seurat' object 
    as tibble and interact seamlessly with the tidyverse.
Tidy standardized mean differences ('SMDs'). 'tidysmd' uses
    the 'smd' package to calculate standardized mean differences for
    variables in a data frame, returning the results in a tidy format.
Enables the analysis of spectroscopy data such as infrared ('IR'), Raman, and nuclear magnetic resonance ('NMR') using the tidy data framework from the 'tidyverse'. The 'tidyspec' package provides functions for data transformation, normalization, baseline correction, smoothing, derivatives, and both interactive and static visualization. It promotes structured, reproducible workflows for spectral data exploration and preprocessing. Implemented methods include Savitzky and Golay (1964) "Smoothing and Differentiation of Data by Simplified Least Squares Procedures" <doi:10.1021/ac60214a047>, Sternberg (1983) "Biomedical Image Processing" <https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1654163>, Zimmermann and Kohler (1996) "Baseline correction using the rolling ball algorithm" <doi:10.1016/0168-583X(95)00908-6>, Beattie and Esmonde-White (2021) "Exploration of Principal Component Analysis: Deriving Principal Component Analysis Visually Using Spectra" <doi:10.1177/0003702820987847>, Wickham et al. (2019) "Welcome to the tidyverse" <doi:10.21105/joss.01686>, and Kuhn, Wickham and Hvitfeldt (2024) "recipes: Preprocessing and Feature Engineering Steps for Modeling" <https://CRAN.R-project.org/package=recipes>.
A tidy approach to analysis of biological sequences. All processing and data-storage functions are heavily optimized to allow the fastest and most efficient data storage.
Functions to generate stop-word lists in 110 languages, in a way consistent across all the languages supported. The generated lists are based on the morphological tagset from the Universal Dependencies.
Calculation of string distance following the tidy
    data principles. Built on top of the 'stringdist' package.
Provides a tidy set of functions for summarising data, 
    including descriptive statistics, frequency tables with normality testing, 
    and group-wise significance testing. Designed for fast, readable, 
    and easy exploration of both numeric and categorical data.
Streamlines the analysis of clinical data by automatically selecting appropriate statistical descriptions and inference methods based on variable types. For method details see Motulsky H J (2016) <https://www.graphpad.com/guides/prism/10/statistics/index.htm> and d'Agostino R B (1971) <doi:10.1093/biomet/58.2.341>.
A synthetic control offers a way of evaluating the effect of an intervention in comparative case studies. The package makes a number of improvements when implementing the method in R. These improvements allow users to inspect, visualize, and tune the synthetic control more easily. A key benefit of a tidy implementation is that the entire preparation process for building the synthetic control can be accomplished in a single pipe.
A system built on 'tidymodels' for generating synthetic tabular
    data. We provide tools for ordering a sequential synthesis, feature and 
    target engineering, sampling, hyperparameter tuning, enforcing constraints, 
    and adding extra noise during a synthesis.
A tidy interface to 'data.table',
  giving users the speed of 'data.table' while using tidyverse-like syntax.
Extension of the 'tidyverse' for 'SpatRaster' and
    'SpatVector' objects of the 'terra' package. It includes also new
    'geom_' functions that provide a convenient way of visualizing 'terra'
    objects with 'ggplot2'.
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 version of the Titanic survival data tailored for people analytics demonstrations and practice. While another package, 'titanic', reproduces the Kaggle competition files with minimal preprocessing, 'tidytitanic' combines the train and test datasets into the single dataset, 'passengers', for exploration and summary across all passengers. It also extracts personal identifiers—such as first names, last names, and titles from the raw 'name' field, enabling demographic analysis. The 'passengers' data does not cover the crew, but this package also provides the more bare-bones, crew-containing datasets 'tidy_titanic' and 'flat_titanic' based on the 'Titanic' data set from 'datasets' for further exploration. This human-centered data package is designed to support exploratory data analysis, feature engineering, and pedagogical use cases.
Generate tables, listings, and graphs (TLG) using 'tidyverse.'
  Tables can be created functionally, using a standard TLG process, or by
  specifying table and column metadata to create generic analysis summaries.
  The 'envsetup' package can also be leveraged to create environments for table
  creation.
Read General Transit Feed Specification (GTFS) zipfiles into a list of R dataframes. Perform validation of the data structure against the specification. Analyze the headways and frequencies at routes and stops. Create maps and perform spatial analysis on the routes and stops. Please see the GTFS documentation here for more detail: <https://gtfs.org/>.
Functions for extracting tidy data from Bayesian treatment effect models, in particular BART, but extensions are possible. Functionality includes extracting tidy posterior summaries as in 'tidybayes' <https://github.com/mjskay/tidybayes>, estimating (average) treatment effects, common support calculations, and plotting useful summaries of these.
Phylogenetic tree generally contains multiple components including node, edge, branch and associated data. 'tidytree' provides an approach to convert tree object to tidy data frame as well as provides tidy interfaces to manipulate tree data.
'TidyTuesday' is a project by the 'Data Science Learning
    Community' in which they 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.
Utilities to retrieve and tidy U.S. macroeconomic data series
    from public government data providers. Functions streamline access to series
    from the Federal Reserve Bank of St. Louis Federal Reserve Economic Data (FRED),
    the Bureau of Labor Statistics flat files, and the Bureau of Economic
    Analysis National Income and Product Accounts tables, then return
    consistent, tidy data frames ready for modeling and graphics. The package includes
    helpers for date alignment, log-linear projections, and common macro diagnostics,
    along with convenience plot builders for quick publication-quality charts.
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://www.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.
Provides multiple water chemistry-based models and published empirical models in one standard format.
    As many models have been included as possible, however, users should be aware that models have varying degrees of accuracy and applicability.
    To learn more, read the references provided below for the models implemented.
    Functions can be chained together to model a complete treatment process and are designed to work in a 'tidyverse' workflow.
    Models are primarily based on these sources:
    Benjamin, M. M. (2002, ISBN:147862308X),
    Crittenden, J. C., Trussell, R., Hand, D., Howe, J. K., & Tchobanoglous, G., Borchardt, J. H. (2012, ISBN:9781118131473),
    USEPA. (2001) <https://www.epa.gov/sites/default/files/2017-03/documents/wtp_model_v._2.0_manual_508.pdf>.
Query 'Wikidata' API <https://www.wikidata.org/wiki/Wikidata:Main_Page> with ease, get tidy data frames in response, and cache data in a local database.
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 <https://rapidxml.sourceforge.net>.  Does not support
    '.xlsb' or '.xls'.
Functions to read, write and display bitmap images stored in the TIFF format. It can read and write both files and in-memory raw vectors, including native image representation.
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.
The R implementation of TIGER. 
    TIGER integrates random forest algorithm into an innovative ensemble learning architecture. Benefiting from this advanced architecture, TIGER is resilient to outliers, free from model tuning and less likely to be affected by specific hyperparameters.
    TIGER supports targeted and untargeted metabolomics data and is competent to perform both intra- and inter-batch technical variation removal. TIGER can also be used for cross-kit adjustment to ensure data obtained from different analytical assays can be effectively combined and compared.
    Reference: Han S. et al. (2022) <doi:10.1093/bib/bbab535>.
Handling and manipulation polygons, coordinates, and other geographical objects. The tools include: polygon areas, barycentric and trilinear coordinates (Hormann and Floater, 2006, <doi:10.1145/1183287.1183295>), convex hull for polygons (Graham and Yao, 1983, <doi:10.1016/0196-6774(83)90013-5>), polygon triangulation (Toussaint, 1991, <doi:10.1007/BF01905693>), great circle and geodesic distances, Hausdorff distance, and reduced major axis.
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.
Tightens an observational block design into a smaller design with either smaller or fewer blocks while controlling for covariates. The method uses fine balance, optimal subset matching (Rosenbaum, 2012 <doi:10.1198/jcgs.2011.09219>) and two-criteria matching (Zhang et al 2023 <doi:10.1080/01621459.2021.1981337>).  The main function is tighten().  The suggested 'rrelaxiv' package for solving minimum cost flow problems: (i) derives from Bertsekas and Tseng (1988) <doi:10.1007/BF02288322>, (ii) is not available on CRAN due to its academic license, (iii) may be downloaded from GitHub at <https://github.com/josherrickson/rrelaxiv/>, (iv) is not essential to use the package.
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/geographies/mapping-files/time-series/geo/tiger-line-file.html>)
    and load into R as 'sf' objects.
Tools to import, clean, validate, and analyze freshwater quality data in Brazil.
    Implements water quality indices including the Water Quality Index (WQI/IQA),
    the Trophic State Index (TSI/IET) after Carlson (1977) <doi:10.4319/lo.1977.22.2.0361>
    and Lamparelli (2004) <https://www.teses.usp.br/teses/disponiveis/41/41134/tde-20032006-075813/publico/TeseLamparelli2004.pdf>,
    and the National Sanitation Foundation Water Quality Index (NSF WQI)
    <doi:10.1007/s11157-023-09650-7>. The package also checks
    compliance with Brazilian standard CONAMA Resolution 357/2005
    <https://conama.mma.gov.br/?id=450&option=com_sisconama&task=arquivo.download>
    and generates reproducible reports for routine monitoring workflows.
Collect  marketing data from TikTok Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
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 modern database 'TileDB' introduces a powerful on-disk
  format for storing and accessing any complex data based on 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. This package provides the R support.
R spatial objects for Tilegrams.
  Tilegrams are tiled maps where the region size is proportional to
  the certain characteristics of the dataset.
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.
Functions for estimation of wood volumes, number of logs, diameters along the stem and heights at which certain diameters occur, based on taper functions and other parameters. References: McTague, J. P., & Weiskittel, A. (2021). <doi:10.1139/cjfr-2020-0326>.
Provides data frames for forest or tree data structures. You can 
    create forest data structures from data frames and process them based on 
    their hierarchies.
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>.
Fits time-dependent shared frailty Cox model (specifically the adapted Paik et al.'s Model) based on the paper "Centre-Effect on Survival After Bone Marrow Transplantation: Application of Time-Dependent Frailty Models", by C.M. Wintrebert, H. Putter, A.H. Zwinderman and J.C. van Houwelingen (2004) <doi:10.1002/bimj.200310051>.
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. (2021), Potential and challenges of investigating intrinsic uncertainty of hydrological models with time-dependent, stochastic parameters. Water Resources Research 57(8), e2020WR028311, <doi:10.1029/2020WR028311>
  Reichert, P. (2022), timedeppar: An R package for inferring stochastic, time-dependent model parameters, in preparation.
Data frames with time information are subset and flagged with period information. Data frames with times are dealt as timeDF objects and periods are represented as periodDF objects.
Computation of t-year survival probabilities and t-year
    risks with right censored survival data. The Kaplan-Meier estimator
    is used to provide estimates for data without competing risks and
    the Aalen-Johansen estimator is used when there are competing risks.
    Confidence intervals and p-values are obtained using either usual
    Wald-type inference or empirical likelihood inference, as described
    in Thomas and Grunkemeier (1975) <doi:10.1080/01621459.1975.10480315>
    and Blanche (2020) <doi:10.1007/s10985-018-09458-6>. Functions for
    both one-sample and two-sample inference are provided. Unlike Wald-type
    inference, empirical likelihood inference always leads to consistent
    conclusions, in terms of statistical significance, when comparing
    two risks (or survival probabilities) via either a ratio or a difference.
Produce an HTML page containing horizontal strips that symbolize events in a person's lsife. Since this is entirely a visualization, the image <https://barryzee.github.io/henry-timeline/henry.html> will show the basic use to show a timeline of events. The image <https://barryzee.github.io/vermeer/cssOverlay.html> shows how to correlate two timelines of events. A brief description is available at <https://barryzee.github.io/timeLineGraphics_manuscript/golden_age.html>.
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.
A set of fast tidy functions for wrangling, completing and
    summarising date and date-time data. It combines 'tidyverse' syntax
    with the efficiency of 'data.table' and speed of 'collapse'.
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.
'S4' classes and various tools for financial time series:
  Basic functions such as scaling and sorting, subsetting,
  mathematical operations and statistical functions.
Provides a diverse collection of time series datasets
    spanning various fields such as economics, finance, energy, healthcare, and more.
    Designed to support time series analysis in R by offering datasets from
    multiple disciplines, making it a valuable resource for researchers and analysts.
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 duration are handled. Both can be regularly or unevenly spaced (overlapping duration 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).
Provides a hypothesis test and variable selection algorithm for use in time-varying, concurrent regression models. The hypothesis test function is also accompanied by a plotting function which will show the estimated beta(s) and confidence band(s) from the hypothesis test. The hypothesis test function helps the user identify significant covariates within the scope of a time-varying concurrent model. The plots will show the amount of area that falls outside the confidence band(s) which is used for the test statistic within the hypothesis test.
Computes how the correlation between 2 time-series changes over time.
    To do so, the package follows the method from Choi & Shin (2021) <doi:10.1007/s42952-020-00073-6>.
    It performs a non-parametric kernel smoothing (using a common bandwidth) of all underlying components required for the computation of a correlation coefficient (i.e., x, y, x^2, y^2, xy).
    An automatic selection procedure for the bandwidth parameter is implemented.
    Alternative kernels can be used (Epanechnikov, box and normal).
    Both Pearson and Spearman correlation coefficients can be estimated and change in correlation over time can be tested.
Create rich and fully interactive timeline visualizations.
    Timelines can be included in Shiny apps or R markdown documents.
    '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 JavaScript library.
Unleash the power of time-series data visualization with ease using our package. Designed with simplicity 
 in mind, it offers three key features through the 'shiny' package output. The first tab shows time- series 
 charts with forecasts, allowing users to visualize trends and changes effortlessly. The second one displays 
 Averages per country presented in tables with accompanying sparklines, providing a quick and attractive 
 overview of the data. The last tab presents A customizable world map colored based on user-defined 
 variables for any chosen number of countries, offering an advanced visual approach to understanding 
 geographical data distributions. This package operates with just a few simple arguments, enabling users 
 to conduct sophisticated analyses without the need for complex programming skills. Transform your 
 time-series data analysis experience with our user-friendly tool.
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).
A problem solving environment (PSE) for fitting separable
    nonlinear models to measurements arising in physics and chemistry
    experiments, as described by Mullen & van Stokkum (2007)
    <doi:10.18637/jss.v018.i03> for its use in fitting time resolved
    spectroscopy data, and as described by Laptenok et al. (2007)
    <doi:10.18637/jss.v018.i08> for its use in fitting Fluorescence
    Lifetime Imaging Microscopy (FLIM) data, in the study of Förster
    Resonance Energy Transfer (FRET).  `TIMP` also serves as the
    computation backend for the `GloTarAn` software, a graphical user
    interface for the package, as described in Snellenburg et al. (2012)
    <doi:10.18637/jss.v049.i03>.
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.
Parsing '(R)Markdown' files with numerous regular expressions can
    be fraught with peril, but it does not have to be this way. Converting
    '(R)Markdown' files to 'XML' using the 'commonmark' package allows
    in-memory editing via of 'markdown' elements via 'XPath' through the
    extensible 'R6' class called 'yarn'. These modified 'XML' representations
    can be written to '(R)Markdown' documents via an 'xslt' stylesheet which
    implements an extended version of 'GitHub'-flavoured 'markdown' so that you
    can tinker to your hearts content.
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.
Palettes generated from Tintin covers. There is one palette per
  cover, with a total of 24 palettes of 5 colours each. Includes functions to
  interpolate colors in order to create more colors based on the provided
  palettes.The data is based on Cyr, et al. (2004) <doi:10.1503/cmaj.1041405>
  and Wikipedia <https://en.wikipedia.org/wiki/The_Adventures_of_Tintin>.
The Gene Expression Omnibus (<https://www.ncbi.nlm.nih.gov/geo/>) and The Cancer Genome Atlas (<https://portal.gdc.cancer.gov/>) are widely used medical public databases. Our platform integrates routine analysis and visualization tools for expression data to provide concise and intuitive data analysis and presentation.
Adds some functions to help in your coding etiquette.
    'tinycodet' primarily focuses on 4 aspects.
    1) Safer decimal (in)equality testing,
    standard-evaluated alternatives to with() and aes(),
    and other functions for safer coding.
    2) A new package import system,
    that attempts to combine the benefits of using a package without attaching it,
    with the benefits of attaching a package.
    3) Extending the string manipulation capabilities of the 'stringi' R package.
    4) Reducing repetitive code.
    Besides linking to 'Rcpp', 'tinycodet' has only one other dependency, namely 'stringi'.
Assign, extract, or remove variable labels from R vectors.
  Lightweight and dependency-free.
Provides utilities to create and use lenses to simplify data
    manipulation. Lenses are composable getter/setter pairs that provide a
    functional approach to manipulating deeply nested data structures, e.g.,
    elements within list columns in data frames. The implementation is based on
    the earlier 'lenses' R package <https://github.com/cfhammill/lenses>,
    which was inspired by the Haskell 'lens' package by Kmett (2012)
    <https://github.com/ekmett/lens>, one of the most widely referenced
    implementations of lenses. For additional background and history on the
    theory of lenses, see the 'lens' package wiki:
    <https://github.com/ekmett/lens/wiki/History-of-Lenses>.
Lightweight extension of the base R graphics system, with support
      for automatic legends, facets, themes, and various other enhancements.
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.
Snapshots for unit tests using the 'tinytest' framework for R. Includes expectations to test base R and 'ggplot2' plots as well as console output from print().
An R wrapper for the 'Spotify' Web API 
  <https://developer.spotify.com/web-api/>.
Create highly customized tables with this simple and dependency-free package. Data frames can be converted to 'HTML', 'LaTeX', 'Markdown', 'Word', 'PNG', 'PDF', or 'Typst' tables. The user interface is minimalist and easy to learn. The syntax is concise. 'HTML' tables can be customized using the flexible 'Bootstrap' framework, and 'LaTeX' code with the 'tabularray' package.
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.
Unit testing is a solid component of automated CI/CD pipelines. 
	'tinytest' - a lightweight, zero-dependency  alternative to 'testthat' was developed.
	To be able to integrate 'tinytests' results into common CI/CD systems the test results from 
	tinytest need to be caputred and converted to JUnit XML format. 
	'tinytest2JUnit' enables this conversion while staying also lightweight 
	and only have 'tinytest' as its dependency.
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.
Themes for 'ggplot2' are a convenient way to style plots. The 'hrbrthemes' package
 contains a particularly nice one, but brings along a significant tail of dependencies. So this
 (currently experimental) package brings along just the 'theme_ipsum_rc' theme using the 'Roboto'
 'Condensed' font.  Should the font not be installed on your system, see the help in the package
 'hrbrthemes' on how to install 'Roboto Condensed'. Note that 'hrbrthemes' is now archived at
 CRAN.
Download geographic shapes from the United States Census Bureau 
    TIGER/Line Shapefiles <https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html>.
    Functions support downloading and reading in geographic boundary data.
    All downloads can be set up with a cache to avoid multiple downloads.
    Data is available back to 2000 for most geographies.
Fits a wide variety of multivariate spatio-temporal models
    with simultaneous and lagged interactions among variables (including
    vector autoregressive spatio-temporal ('VAST') dynamics)
    for areal, continuous, or network spatial domains.  
    It includes time-variable, space-variable, and space-time-variable 
    interactions using dynamic structural equation models ('DSEM') 
    as expressive interface, and the 'mgcv' package to specify splines 
    via the formula interface.  See Thorson et al. (2025)
    <doi:10.1111/geb.70035> for more details.
Cluster data without specifying the number of clusters using the Table Invitation Prior (TIP) introduced in the paper "Clustering Gene Expression Using the Table Invitation Prior" by Charles W. Harrison, Qing He, and Hsin-Hsiung Huang (2022) <doi:10.3390/genes13112036>. TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors.
Accurately estimates phase shifts by accounting for period
  changes and for the point in the circadian cycle at which the stimulus occurs.
  See Tackenberg et al. (2018) <doi:10.1177/0748730418768116>.
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).
Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider.
'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, the
    relationship between an unmeasured confounder and the outcome, for
    example a plausible residual effect size for an unmeasured continuous
    or binary confounder, and the relationship between an unmeasured
    confounder and the exposure, for example a realistic mean difference
    or prevalence difference for this hypothetical confounder between
    exposure groups. Building on the methods put forth by Cornfield et al.
    (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983),
    Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli &
    Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele
    (2016), we can use these quantities to assess how an unmeasured
    confounder may tip our result to insignificance.
Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) <doi:10.1111/2041-210X.14038>.
It allows for mapping proportions and indicators defined on the unit interval. It implements Beta-based small area methods comprising the classical Beta regression models, the Flexible Beta model and Zero and/or One Inflated extensions (Janicki 2020 <doi:10.1080/03610926.2019.1570266>). Such methods, developed within a Bayesian framework through Stan <https://mc-stan.org/>, come equipped with a set of diagnostics and complementary tools, visualizing and exporting functions. A Shiny application with a user-friendly interface can be launched to further simplify the process. For further details, refer to De Nicolò and Gardini (2024 <doi:10.18637/jss.v108.i01>).
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 and King 
  (2010) <doi:10.1111/j.2041-210X.2009.00007.x>, King and Baker (2010) 
  <doi:10.1899/09-144.1>, and Baker and King (2013) <doi:10.1899/12-142.1>.
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.
In some phase I trials, the design goal is to find the dose associated with a certain target toxicity rate or the dose with a certain weighted sum of rates of various toxicity grades. 'TITEgBOIN' provides the set up and calculations needed to run a dose-finding trial using bayesian optimal interval (BOIN) (Yuan et al. (2016) <doi:10.1158/1078-0432.CCR-16-0592>), generalized bayesian optimal interval (gBOIN) (Mu et al. (2019) <doi:10.1111/rssc.12263>), time-to-event bayesian optimal interval (TITEBOIN) (Lin et al. (2020) <doi:10.1093/biostatistics/kxz007>) and time-to-event generalized bayesian optimal interval (TITEgBOIN) (Takeda et al. (2022) <doi:10.1002/pst.2182>) designs. 'TITEgBOIN' can conduct tasks: run simulations and get operating characteristics; determine the dose for the next cohort; select maximum tolerated dose (MTD). These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose limiting toxicity (DLT) rates or target normalized equivalent toxicity score (ETS) rates to account for discrete toxicity score, and incorporate safety and/or stopping rules.
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.
Specialized toolkit for processing biological and fisheries data from Peru's anchovy (Engraulis ringens) fishery. Provides functions to analyze fishing logbooks, calculate biological indicators (length-weight relationships, juvenile percentages), generate spatial fishing indicators, and visualize regulatory measures from Peru's Ministry of Production. Features automated data processing from multiple file formats, coordinate validation, spatial analysis of fishing zones, and tools for analyzing fishing closure announcements and regulatory compliance. Includes built-in datasets of Peruvian coastal coordinates and parallel lines for analyzing fishing activities within regulatory zones.
Facilitate the management of data from knowledge
   resources that are frequently used alone or together
   in research environments.
   In 'TKCat', knowledge resources are manipulated as modeled database (MDB)
   objects. These objects provide access to the data tables along with a general
   description of the resource and a detail data model documenting the
   tables, their fields and their relationships.
   These MDBs are then gathered in catalogs that can be easily
   explored an shared.
   Finally, 'TKCat' provides tools to easily subset, filter and combine MDBs and
   create new catalogs suited for specific needs.
A 'Tcl/Tk' Graphical User Interface (GUI) to display images than can be zoomed and panned using the mouse and keyboard shortcuts. 'tkImgR' read and write different image formats (PPM/PGM, PNG and GIF) using the standard 'Tcl/Tk' distribution (>=8.6), but other formats (JPEG, TIFF, CR2) can be handled using the 'tkImg' package for 'Tcl/Tk'.
Simple mechanism for placing R graphics in a Tk widget.
Implements inverse and augmented inverse probability weighted 
    estimators for common treatment effect parameters at an interim analysis 
    with time-lagged outcome that may not be available for all enrolled subjects.  
    Produces estimators, standard errors, and information that can be used to 
    compute stopping boundaries using software that assumes that the 
    estimators/test statistics have independent increments. 
    Tsiatis, A. A. and Davidian, M., (2022) <doi:10.1002/sim.9580> .
Implements a semiparametric estimator for the odds ratio model with
    censored, time-lagged, ordered categorical outcome in a randomized clinical
    trial that incorporates baseline and time-dependent information. 
    Tsiatis AA, Davidian M, Holloway ST (2023) <doi:10.1111/biom.13603>.
Computes the solution path of the Terminating-LARS (T-LARS) algorithm. The T-LARS algorithm 
    is a major building block of the T-Rex selector (see R package 'TRexSelector').
    The package is based on the papers Machkour, Muma, and Palomar (2022) <arXiv:2110.06048>, Efron, Hastie, Johnstone, 
    and Tibshirani (2004) <doi:10.1214/009053604000000067>, and Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>.
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.
Provides a comprehensive suite of statistical tools for analyzing, simulating, and computing properties of the Topp-Leone Cauchy Rayleigh (TLCAR) distribution, a versatile distribution amalgamating features of the Topp-Leone, Cauchy, and Rayleigh distributions, ideal for modeling intricate, heterogeneous data across scientific domains. See Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2023) <doi:10.1007/s44199-023-00066-4> and Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2024) <doi:10.1007/s44199-023-00069-1> for further insights.
Support functions and datasets to facilitate the analysis of linguistic
    data. The current focus is on the calculation of corpus-linguistic dispersion
    measures as described in Gries (2021) <doi:10.1007/978-3-030-46216-1_5> and 
    Soenning (2025) <doi:10.3366/cor.2025.0326>. The most commonly used parts-based 
    indices are implemented, including different formulas and modifications that are 
    found in the literature, with the additional option to obtain frequency-adjusted 
    scores. Dispersion scores can be computed based on individual count variables or 
    a term-document matrix.
Gives a number of functions to aid common data 
    analysis processes and reporting statistical results in an 'RMarkdown' file. 
    Data analysis functions combine multiple base R functions used to describe 
    simple bivariate relationships into a single, easy to use function. 
    Reporting functions will return character strings to report p-values, 
    confidence intervals, and hypothesis test and regression results. Strings 
    will be LaTeX-formatted as necessary and will knit pretty in an 'RMarkdown' 
    document. The package also provides wrappers function in the 'tableone' 
    package to make the results knit-able.
Documentation for commonly-used objects included in the base distribution of 
    R. Note that 'tldrDocs' does not export any functions itself, its purpose is
    to write .Rd files during its installation for 'tldr()' to find.
This comprehensive toolkit for T-distributed regression is designated as "TLIC" (The LIC for T Distribution Regression Analysis) analysis. It is predicated on the assumption that the error term adheres to a T-distribution. The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02664763.2022.2053949>.
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. The methods are described in Barrera-Gomez
    and Basagana (2015) <doi:10.1097/EDE.0000000000000247>.
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," Statistics and Computing, 31.1, 1-16 (2021) 
    <doi:10.1007/s11222-020-09978-y>;
    Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "tlrmvnmvt: 
    Computing High-Dimensional Multivariate Normal and Student-t Probabilities 
    with Low-Rank Methods in R," Journal of Statistical Software, 101.4, 
    1-25 (2022) <doi:10.18637/jss.v101.i04>.
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.
A framework for text mining applications within R.
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).
Enhances 'koRpus' text object classes and methods to also support large
          corpora. Hierarchical ordering of corpus texts into arbitrary categories will
          be preserved. Provided classes and methods also improve the ability of using
          the 'koRpus' package together with the 'tm' package. To ask for help, report
          bugs, suggest feature improvements, or discuss the global development of the
          package, please subscribe to the koRpus-dev mailing list
          (<https://korpusml.reaktanz.de>).
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.
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.
A robust computational framework for analyzing complex multimodal data. Extends existing state-dependent models to account for diverse data streams, addressing challenges such as varying temporal scales and learner characteristics to improve the robustness and interpretability of findings. For methodological details, see Shaffer, Wang, and Ruis (2025) "Transmodal Analysis" <doi:10.18608/jla.2025.8423>.
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.
Provides new layer functions to 'tmap' for creating various types of cartograms. A cartogram is a type of thematic map in which geographic areas are resized or distorted based on a quantitative variable, such as population. The goal is to make the area sizes proportional to the selected variable while preserving geographic positions as much as possible.
Provides new layer functions to 'tmap' for drawing glyphs. A glyph is a small chart (e.g., donut chart) shown at specific map locations to visualize multivariate or time-series data. The functions work with the syntax of 'tmap' and allow flexible control over size, layout, and appearance.
The 'tmap' package provides two plotting modes for static and interactive thematic maps. This package extends 'tmap' with two additional modes based on 'Mapbox GL JS' and 'MapLibre GL JS'. These modes feature interactive vector tiles, globe views, and other modern web-mapping capabilities, while maintaining a consistent 'tmap' interface across all plotting modes.
Provides functions for visualizing networks with 'tmap'. It supports 'sfnetworks' objects natively but is not limited to them. Useful for adding network layers such as edges and nodes to 'tmap' maps. More features may be added in future versions.
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.
Attaches a set of packages commonly used for spatial plotting with 'tmap'. It includes 'tmap' and its extensions ('tmap.glyphs', 'tmap.networks', 'tmap.cartogram', 'tmap.mapgl'), as well as supporting spatial data packages ('sf', 'stars', 'terra') and 'cols4all' for exploring color palettes. The collection is designed for thematic mapping workflows and does not include the full set of packages from the R-spatial ecosystem.
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. This is demonstrated in Monnahan & Kristensen (2018) <DOI:10.1371/journal.pone.0197954>.
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>).
Miscellaneous utility functions for data manipulation,
    data tidying, and working with gene expression data.
Suite of tropical geometric tools for use in machine learning applications. These methods may be summarized in the following references: Yoshida, et al. (2022) <doi:10.2140/astat.2023.14.37>, Barnhill et al. (2023) <doi:10.48550/arXiv.2303.02539>, Barnhill and Yoshida (2023) <doi:10.3390/math11153433>, Aliatimis et al. (2023) <doi:10.1007/s11538-024-01327-8>, Yoshida et al. (2022) <doi:10.1109/TCBB.2024.3420815>, and Yoshida et al. (2019) <doi:10.1007/s11538-018-0493-4>.
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.
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. See Zyla et al (2019)
  <doi:10.1093/bioinformatics/btz447>.
Define general templates with tags that can be replaced by content depending on arguments and objects to modify the final output of the document.
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.
Sensitivity analysis using the trimmed means estimator.
Provides conditional maximum likelihood (CML) item parameter estimation of both sequential and cumulative deterministic multistage designs (Zwitser & Maris, 2015, <doi:10.1007/s11336-013-9369-6>) and probabilistic sequential and cumulative multistage designs (Steinfeld & Robitzsch, 2024, <doi:10.1007/s41237-024-00228-3>). Supports CML item parameter estimation of conventional linear designs and additional functions for the likelihood ratio test (Andersen, 1973, <doi:10.1007/BF02291180>) as well as functions for simulating various types of multistage designs.
Methods for computing joint tests, controlling the Familywise Error Rate (FWER) and getting lower bounds on the number of false hypotheses in a set. The methods implemented here are described in Mogensen and Markussen (2021) <doi:10.48550/arXiv.2108.04731>.
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.
Simulation of random vectors from truncated multivariate normal 
  and t distributions based on the algorithms proposed by Yifang Li and Sujit K. Ghosh (2015) <doi:10.1080/15598608.2014.996690>.
Provides tools for performing Transition Network Analysis (TNA) to 
    study relational dynamics, including functions for building and plotting TNA 
    models, calculating centrality measures, and identifying dominant events and 
    patterns. TNA statistical techniques (e.g., bootstrapping and permutation 
    tests) ensure the reliability of observed insights and confirm that 
    identified dynamics are meaningful. See (Saqr et al., 2025) 
    <doi:10.1145/3706468.3706513> for more details on TNA.
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.
Simplifies access to Tunisian government open data from <https://data.gov.tn/fr/>.
      Queries datasets by theme, author, or keywords, retrieves metadata, and gets structured results ready for analysis; all through the official 'CKAN' API.
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.
Performing the hypothesis tests for the two
    sample problem based on order statistics and power comparisons. 
    Calculate the test statistic, density, distribution function, 
    quantile function, random number generation and others.
Provides access to the Taxonomic Name Resolution Service <https://github.com/ojalaquellueva/tnrsapi> through R.  The user supplies plant taxonomic names and the package returns resolved taxonomic names along with information on decisions.  Optionally, the package can also be used to parse taxonomic names.
Create interactive tables, calendars, charts and markdown WYSIWYG editor with 'TOAST UI' <https://ui.toast.com/> libraries to
  integrate in 'shiny' applications or 'rmarkdown' 'HTML' documents.
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 <https://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>.
Interfaces with the 'Hugging Face' tokenizers library to provide implementations
  of today's most used tokenizers such as the 'Byte-Pair Encoding' algorithm 
  <https://huggingface.co/docs/tokenizers/index>. It's extremely fast for both 
  training new vocabularies and tokenizing texts.
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, 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://aclanthology.org/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.
Email Finder R Client Library.
        Search emails are based on the website You give one domain name and it returns all the email addresses found on the internet.
        Email Finder generates or retrieves the most likely email address from a domain name, a first name and a last name.
        Email verify checks the deliverability of a given email address, verifies if it has been found in our database, and returns their sources.
Simple toolkit for working with TOML text. Based on tomledit which
    allows for modifying TOML while preserving order, comments,and whitespace.
A toolkit for working with 'TOML' files in R while preserving
    formatting, comments, and structure. 'tomledit' enables serialization of R
    objects such as lists, data.frames, numeric, logical, and date vectors.
Several functions to allow comparisons of data across different geographies, in particular for Canadian census data from different censuses.
Includes functions for mapping named lists to function arguments, random strings,
  pasting and combining rows together across columns, etc.
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 marketing campaign on a particular individual. 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) variable selection, iv) parameters estimation and, v) model validation. For more details, see <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>. Functions for log-ratio principal component analysis with condition number computations and log-ratio discriminant analysis have been added to the package.
Tools to calculate stability indices with parametric,
 non-parametric and probabilistic approaches. The basic data format requirement for 'toolStability' is a data frame with 3 columns including numeric trait values, genotype,and environmental labels. Output format of each function is the dataframe with chosen stability index for each genotype. 
 Function "table_stability" offers the summary table of all stability indices in this package. 
 This R package toolStability is part of the main publication: 
 Wang, Casadebaig and Chen (2023) <doi:10.1007/s00122-023-04264-7>. 
 Analysis pipeline for main publication can be found on github: <https://github.com/Illustratien/Wang_2023_TAAG>. 
 Sample dataset in this package is derived from another publication:  
 Casadebaig P, Zheng B, Chapman S et al. (2016) <doi:10.1371/journal.pone.0146385>. 
 For detailed documentation of dataset, please see on Zenodo <doi:10.5281/zenodo.4729636>. 
 Indices used in this package are from: 
 Döring TF, Reckling M (2018) <doi:10.1016/j.eja.2018.06.007>. 
 Eberhart SA, Russell WA (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>. 
 Eskridge KM (1990) <doi:10.2135/cropsci1990.0011183X003000020025x>. 
 Finlay KW, Wilkinson GN (1963) <doi:10.1071/AR9630742>. 
 Hanson WD (1970) Genotypic stability. <doi:10.1007/BF00285245>. 
 Lin CS, Binns MR (1988). 
 Nassar R, Hühn M (1987). 
 Pinthus MJ (1973) <doi:10.1007/BF00021563>. 
 Römer T (1917). 
 Shukla GK (1972). 
 Wricke G (1962).
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.
Token-Oriented Object Notation (TOON) is a compact, human-readable 
    serialization format designed for passing structured data to Large Language
    Models with significantly reduced token usage. It's intended for LLM input
    as a lossless, drop-in representation of JSON data.
Language specific cardinal to ordinal number conversion.
Two- and three-dimensional morphometric maps of enamel and 
	dentine thickness and multivariate analysis. 
	Volume calculation of dental materials. 
	Principal component analysis of thickness maps with 
	associated morphometric map variations.
Several datasets which describe the chef contestants in Top Chef, 
    the challenges that they compete in, and the results of those challenges. 
    This data is useful for practicing data wrangling, graphing, and analyzing 
    how each season of Top Chef played out.
The 'TopDom' method identifies topological domains in genomes from Hi-C sequence data (Shin et al., 2016 <doi:10.1093/nar/gkv1505>).  The authors published an implementation of their method as an R script (two different versions; also available in this package).  This package originates from those original 'TopDom' R scripts and provides help pages adopted from the original 'TopDom' PDF documentation.  It also provides a small number of bug fixes to the original code.
Data collected on movement behavior is often in the form of time-
    stamped latitude/longitude coordinates sampled from the underlying movement
    behavior. These data can be compressed into a set of segments via the Top-
    Down Time Ratio Segmentation method described in Meratnia and de By (2004)
    <doi:10.1007/978-3-540-24741-8_44> which, with some loss of information, 
    can both reduce the size of the data as well as provide corrective smoothing
    mechanisms to help reduce the impact of measurement error.  This is an
    improvement on the well-known Douglas-Peucker algorithm for segmentation
    that operates not on the basis of perpendicular distances. Top-Down Time
    Ratio segmentation allows for disparate sampling time intervals by
    calculating the distance between locations and segments with respect to
    time. Provided a trajectory with timestamps, tdtr() returns a set of straight-
    line segments that can represent the full trajectory. McCool, Lugtig,
    and Schouten (2022) <doi:10.1007/s11116-022-10328-2> describe this method as
    implemented here in more detail.
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>.
Leveraging (large) language models for automatic topic labeling. The main function converts a list of top terms into a label for each topic. Hence, it is complementary to any topic modeling package that produces a list of top terms for each topic. While human judgement is indispensable for topic validation (i.e., inspecting top terms and most representative documents), automatic topic labeling can be a valuable tool for researchers in various scenarios.
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.
Find topics in texts which are semantically embedded using techniques like word2vec or Glove. 
    This topic modelling technique models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic.
    The techniques are explained in detail in the paper 'Topic Modeling in Embedding Spaces' by Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei (2019), available at <arXiv:1907.04907>.
Implements differential language analysis with statistical tests and offers various language visualization techniques for n-grams and topics. It also supports the 'text' package. For more information, visit <https://r-topics.org/> and <https://www.r-text.org/>.
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' <doi:10.1515/sagmb-2014-0093> 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. Note that RGtk2 and gWidgets2RGtk2 have been archived on CRAN. See <https://github.com/pievos101/TopKLists> for installation instructions.
A mathematical optimization procedure in combination with statistical bootstrap for the estimation of the latent signals (sometimes called scores) informing the global consensus ranking (often named aggregation ranking). To solve mid/large-scale problems, users should install the 'gurobi' optimiser (available from <https://www.gurobi.com/>).
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). The package is
             based on the method described in Massa M.S., Chiogna M., Romualdi
             C. (2010) <doi:10.1186/1752-0509-4-121>.
Description: Implementation of topological data analysis methods based on 
    graph-theoretic approaches for discovering topological structures in data.
    The core algorithm constructs topological spaces from graphs following
    Nada et al. (2018) <doi:10.1002/mma.5096> "New types of topological 
    structures via graphs".
Deciphering hierarchy of agents exhibiting observable dominance events is a crucial problem in several disciplines, in particular in behavioural analysis of social animals, but also in social sciences and game theory. This package implements an inference approach based on graph theory, namely to extract the optimal acyclic subset of a weighted graph of dominance; this allows for hierarchy estimation through topological sorting. The package also contains infrastructure to investigate partially defined hierarchies and hierarchy dynamics.
Flexible and ergonomic topological sorting implementation for
    R. Supports a variety of input data encoding (lists of edges or
    adjacency matrices, graphs edge direction), stable sort variants as
    well as cycle detection with detailed diagnosis.
A collection of functions for visualizing,exploring and annotating genetic association results.Association results from multiple traits can be viewed simultaneously along with gene annotation, over the entire genome (Manhattan plot) or in the more detailed regional view.
Gene and exon information from Ensembl genome builds GRCh38.p13 (104) and GRCh37 (v40) to use with the 'topr' package.
Evaluation of alternatives based on multiple criteria using TOPSIS method.
Assists in the TOPSIS analysis process, designed to return at the end of the answer of the TOPSIS multicriteria analysis,  a ranking table with the best option as the analysis proposes. TOPSIS is basically a technique developed by Hwang and Yoon in 1981, starting from the point that the best alternative should be closest to the positive ideal solution and farthest from the negative one, based on several criteria to result in the best benefit. (LIU, H. et al., 2019) <doi:10.1016/j.agwat.2019.105787>.
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) <doi:10.48550/arXiv.1912.01703> but written entirely in R
    using the 'libtorch' library. Also supports low-level tensor operations and
    'GPU' acceleration.
Provides datasets in a format that can be easily consumed by torch 'dataloaders'.
  Handles data downloading from multiple sources, caching and pre-processing so
  users can focus only on their model implementations.
Torch code for computing multi-class Area Under The Minimum,
 <https://www.jmlr.org/papers/v24/21-0751.html>, Generalization.
 Useful for optimizing Area under the curve.
Optimizers for 'torch' deep learning library. These
    functions include recent results published in the literature and are
    not part of the optimizers offered in 'torch'. Prospective users
    should test these optimizers with their data, since performance
    depends on the specific problem being solved.  The packages includes
    the following optimizers: (a) 'adabelief' by Zhuang et al (2020),
    <arXiv:2010.07468>; (b) 'adabound' by Luo et al.(2019),
    <arXiv:1902.09843>; (c) 'adahessian' by Yao et al.(2021)
    <arXiv:2006.00719>; (d) 'adamw' by Loshchilov & Hutter (2019),
    <arXiv:1711.05101>; (e) 'madgrad' by Defazio and Jelassi (2021),
    <arXiv:2101.11075>; (f) 'nadam' by Dozat (2019),
    <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) 'qhadam' by
    Ma and Yarats(2019), <arXiv:1810.06801>; (h) 'radam' by Liu et al.
    (2019), <arXiv:1908.03265>; (i) 'swats' by Shekar and Sochee (2018),
    <arXiv:1712.07628>; (j) 'yogi' by Zaheer et al.(2019),
    <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.
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.
Implements additional operators for computer vision models, including
    operators necessary for image segmentation and object detection deep learning
    models.
Third order response surface designs (M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI:10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts") are classified into two types viz., designs which are suitable for sequential experimentation and designs for non-sequential experimentation (M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2022)<DOI:10.1080/02664763.2020.1864817>." Sequential asymmetric third order rotatable designs (SATORDs)"). The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go.This package contains functions named STORDs() and NSTORDs() for generating sequential/non-sequential TORDs given in Das, M. N., and V. L. Narasimham (1962). <DOI:10.1214/aoms/1177704374>. "Construction of rotatable designs through balanced incomplete block designs" along with the randomized layout. It also contains another function named Pred3.var() for generating the variance of predicted response as well as the moment matrix based on a third order response surface model.
Draws tornado plots for model sensitivity to univariate changes.  Implements methods for many modeling methods including linear models, generalized linear models, survival regression models, and arbitrary machine learning models in the caret package.  Also draws variable importance plots.
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. Sample data for the vignette is included in the toscaData package, which is available on gitHub: <https://github.com/Docma-TU/toscaData>.
Performs Thresholded Ordered Sparse Canonical Correlation Analysis (CCA). For more details see Senar, N. (2024) <doi:10.1093/bioadv/vbae021> and Senar, N. et al. (2025) <doi:10.48550/arXiv.2503.15140>.
Operators and functions provided by base R sometimes lack some
    features found in other programming languages, such as the ability to
    concatenate strings using + or to repeat strings using *. This package aims
    at providing such functionality without breaking existing code, i.e., only
    statements, that would throw errors in pure base R are patched.
Base R sometimes requires verbose statements for simple,
    often recurring tasks, such as printing text without trailing
    space, ending with newline. This package aims at providing
    shorthands for such tasks.
A general framework of two directional simultaneous inference
    is provided for high-dimensional as well as the fixed dimensional models with manifest
    variable or latent variable structure, such as high-dimensional mean models, high-
    dimensional sparse regression models, and high-dimensional latent factors models.
    It is making the simultaneous inference on a set of parameters from two directions,
    one is testing whether the estimated zero parameters indeed are zero and the other is
    testing whether there exists zero in the parameter set of non-zero. More details can be 
    referred to Wei Liu, et al. (2022) <doi:10.48550/arXiv.2012.11100>.
The goal of 'tosr' is to create the Tree of Science from
             Web of Science (WoS) and Scopus data. It can read files
             from both sources at the same time. More information
             can be found in Valencia-Hernández (2020)
             <https://revistas.unal.edu.co/index.php/ingeinv/article/view/77718>.
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.
Measures the degree of balance for a given phylogenetic tree by 
  calculating 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>.
Estimates the parameters of a Transformed Ornstein-Uhlenbeck (TOU) stochastic model for adsorption data and also the parameters of the related pseudo-n-order (PNO) model, such as the maximum adsorption capacity (qe), the adsorption rate constant (kn) and the order of the model (n).
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).
Several datasets which describe the challenges and results of
    competitions in Tournament of Champions.  This data is useful for
    practicing data wrangling, graphing, and analyzing how each season of
    Tournament of Champions played out.
Implements geodesic interpolation and basis
    generation functions that allow you to create new tour
    methods from R.
Find the optimal decision rules (AKA progression criteria)
    and sample size for clinical trials with three (stop/pause/go)
    outcomes. Both binary and continuous endpoints can be accommodated,
    as can cases where an adjustment is planned following a pause outcome. 
    For more details see Wilson et al. (2024) <doi:10.1186/s12874-024-02351-x>.
The best way to implement middle ware for 'shiny' Applications. 'tower'
    is designed to make implementing behavior on top of 'shiny' easy with a layering
    model for incoming HTTP requests and server sessions. 'tower' is a very minimal
    package with little overhead, it is mainly meant for other package developers
    to implement new behavior.
Non-imputational method for handling missing values in 
   a prediction context, meaning that not only are there missing
   values in the training dataset, but also some values may be missing
   in future cases to be predicted. Based on the notion of regression
   averaging (Matloff (2017, ISBN: 9781498710916)).
Computation of stopping boundaries for a single-arm trial using a
    Bayesian criterion; i.e., for each m<=n (n= total patient number of the 
    trial) the smallest number of observed toxicities is calculated
    leading to the termination of the trial/accrual according to the specified 
    criteria. The probabilities of stopping the trial/accrual at and up until 
    (resp.) the m-th patient (m<=n) is also calculated. This design is more 
    conservative than the frequentist approach (using Clopper Pearson CIs)
    which might be preferred as it concerns safety.See also Aamot et.al.(2010) 
    "Continuous monitoring of toxicity in clinical trials - simulating the risk 
    of stopping prematurely" <doi:10.5414/cpp48476>.
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 (2017) <doi:10.1021/acs.est.7b01613>.
Enables users to build 'ToxPi' prioritization models and provides 
  functionality within the grid framework for plotting ToxPi graphs.
  'toxpiR' allows for more customization than the 'ToxPi GUI' 
  (<https://toxpi.org>) and integration into existing workflows for greater 
  ease-of-use, reproducibility, and transparency.
  toxpiR package behaves nearly identically to the GUI; the package 
  documentation includes notes about all differences.
  The vignettes download example files from 
  <https://github.com/ToxPi/ToxPi-example-files>.
A 'shiny' app that generates plots and summary tables from repeat-dose
        toxicology study results to facilitate holistic evaluation of the drug safety of 
        active pharmaceutical ingredients (API) prior to initiation of clinical trials.
Contains logic for single sample gene set testing of cancer transcriptomic data with adjustment for normal tissue-specificity.
  Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.
Contains summary data on gene expression in normal human tissues from the Human Protein Atlas for use with the 
  Tissue-Adjusted Pathway Analysis of cancer (TPAC) method.
  Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.
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.
Constraint-based causal discovery using the PC algorithm while
    accounting for a partial node ordering, for example a partial temporal ordering
    when the data were collected in different waves of a cohort study.
    Andrews RM, Foraita R, Didelez V, Witte J (2021) <arXiv:2108.13395>  
    provide a guide how to use tpc to analyse cohort data.
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.
Provides functions to construct two-phase design layouts, compute treatment- and block-incidence matrices, derive C-matrices for residual, direct, and interaction effects, and calculate the efficiency factor for two-phase experimental designs with factorial treatment structure.
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.
Calculates the number of true positives and false positives from a dataset formatted for Jackknife alternative free-response receiver operating characteristic which is used for statistical analysis which is explained in the book 'Chakraborty' 'DP' (2017), "Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples", Taylor-Francis <https://www.crcpress.com/9781482214840>.
Uses thresholded partial least squares algorithm to create a regression or classification model. For more information, see Lee, Bradlow, and Kable <doi:10.1016/j.crmeth.2022.100227>.
A traceability focused tool created to simplify the data manipulation necessary to create clinical summaries.
Contains functions for calculating the Federal Highway Administration (FHWA) Transportation Performance Management (TPM) performance measures. Currently, the package provides methods for the System Reliability and Freight (PM3) performance measures calculated from travel time data provided by The National Performance Management Research Data Set (NPMRDS), including Level of Travel Time Reliability (LOTTR), Truck Travel Time Reliability (TTTR), and Peak Hour Excessive Delay (PHED) metric scores for calculating statewide reliability performance measures. Implements <https://www.fhwa.dot.gov/tpm/guidance/pm3_hpms.pdf>.
Provides a simple approach for constructing dynamic materials
 modeling suggested by Prasad and Gegel (1984) <doi:10.1007/BF02664902>. It
 can easily generate various processing-maps based on this model as well. The
 calculation result in this package contains full materials constants, information
 about power dissipation efficiency factor, and rheological properties, can
 be exported completely also, through which further analysis and customized
 plots will be applicable as well.
Estimation of transition probabilities for the
	illness-death model and or the three-state progressive model.
Provide data generation and estimation tools for the truncated positive normal (tpn)
             model discussed in Gomez, Olmos, Varela and Bolfarine (2018) 
             <doi:10.1007/s11766-018-3354-x>, the slash tpn distribution discussed in Gomez, 
             Gallardo and Santoro (2021) <doi:10.3390/sym13112164>, the bimodal tpn distribution 
             discussed in Gomez et al. (2022) <doi:10.3390/sym14040665>, the flexible tpn model
	     <doi:10.3390/math11214431> and the unit tpn distribution <doi:10.1016/j.chemolab.2025.105322>.
Regression models for temporal process responses with
        time-varying coefficient.
Density, distribution function, the quantile function, 
    random generation function, and maximum likelihood estimation.
The two-parameter Xgamma and Poisson Xgamma distributions are analyzed, covering standard distribution and regression functions, maximum likelihood estimation, quantile functions, probability density and mass functions, cumulative distribution functions, and random number generation. References include: "Sen, S., Chandra, N. and Maiti, S. S. (2018). On properties and applications of a two-parameter XGamma distribution. Journal of Statistical Theory and Applications, 17(4): 674--685. <doi:10.2991/jsta.2018.17.4.9>." "Wani, M. A., Ahmad, P. B., Para, B. A. and Elah, N. (2023). A new regression model for count data with applications to health care data. International Journal of Data Science and Analytics. <doi:10.1007/s41060-023-00453-1>."
Enables the acquisition of Korean financial market data, 
            designed to integrate seamlessly with the 'tidyquant' package.
Automates translating the instructions of 'iatgen' generated qsf
             (Qualtrics survey files) to other languages using either officially
             supported or user-supplied translations (for tutorial see Santos
             et al., 2023 <doi:10.17504/protocols.io.kxygx34jdg8j/v1>).
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 (The Ecological Flora
    of the British Isles, LEDA traitbase, Ellenberg
    values for Italian Flora, Mycorrhizal intensity databases, 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.
A pipeline for short tandem repeat instability analysis from fragment analysis data. Inputs of fsa files or peak tables, and a user supplied metadata data-frame. The package identifies ladders, calls peaks, identifies the modal peaks, calls repeats, then calculates repeat instability metrics (e.g. expansion index from Lee et al. (2010) <doi:10.1186/1752-0509-4-29>).
Efficient method for fitting nonparametric matrix trace regression model. The detailed description can be found in  C. Lee, L. Li, H. Zhang, and M. Wang (2021). Nonparametric Trace Regression via Sign Series Representation. <arXiv:2105.01783>. The method employs the aggregation of structured sign series for trace regression (ASSIST) algorithm.
Write output (plots and tables) ensuring traceability back to code. Includes a graphics saver with simple automation of stamping with source, destination and creation time. A list of plots can be saved at once. A user-friendly selection of output dimensions for presentations, on-screen inspections, and more available.
Functions to access the database of 217 data-frames with aggregate 
    study-level characteristics (that may act as effect modifiers) extracted 
    from published systematic reviews with network meta-analysis. The database shall 
    only be used for developing and appraising the methodology to assess 
    the transitivity assumption quantitatively.
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' (<https://github.com/beast-dev/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.
Functions for preparing and analyzing animal tracking data, 
    with the intention of identifying areas which are potentially important at 
    the population level and therefore of conservation interest. Areas identified 
    using this package may be checked against global or regionally-defined criteria,
    such as those set by the Key Biodiversity Area program. The method
    published herein is described in full in Beal et al. 2021 <doi:10.1111/2041-210X.13713>.
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.
Collaborative writing and editing of R Markdown (or Sweave) documents. The local .Rmd (or .Rnw) is uploaded as a plain-text file to Google Drive. By taking advantage of the easily readable Markdown (or LaTeX) syntax and the well-known online interface offered by Google Docs, collaborators can easily contribute to the writing and editing process. After integrating all authors’ contributions, the final document can be downloaded and rendered locally.
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.
Tracks parameter value, gradient, and Hessian at each iteration 
    of numerical optimizers. Useful for analyzing optimization progress, 
    diagnosing issues, and studying convergence behavior.
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.
The ts objects in R are managed using a very specific date format (in the form c(2022, 9) for September 2022 or c(2021, 2) for the second quarter of 2021, depending on the frequency, for example). We focus solely on monthly and quarterly series to manage the dates of ts objects. The general idea is to offer a set of functions to manage this date format without it being too restrictive or too imprecise depending on the rounding. This is a compromise between simplicity, precision and use of the basic 'stats' functions for creating and managing time series (ts(), window()).
    Les objets ts en R sont gérés par un format de date très particulier (sous la forme c(2022, 9) pour septembre 2022 ou c(2021, 2) pour le deuxième trimestre 2021 selon la fréquence par exemple). On se concentre uniquement sur les séries mensuelles et trimestrielles pour gérer les dates des objets ts. Lidée générale est de proposer un ensemble de fonctions pour gérer ce format de date sans que ce soit trop contraignant ou trop imprécis selon les arrondis. Cest un compromis entre simplicité, précision et utilisation des fonctions du package 'stats' de création et de gestion des séries temporelles (ts(), window()).
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>.
Datasets from Yotov, et al. (2016, ISBN:978-92-870-4367-2) "An
    Advanced Guide to Trade Policy Analysis" and functions to report regression
    summaries with clustered robust standard errors.
Tree Ring Analysis of Disturbance Events in R (TRADER) package provides functions for disturbance reconstruction from tree-ring data, e.g. boundary line, absolute increase, growth averaging methods.
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 a state/space algorithm. The normalized sample entropy method
            has been implemented which produces accurate entropy estimation even on smaller datasets.
            On a separate stream, trades from the five major assets classes and also
            functionality to use pricing curves, rating tables, Credit Support Annex 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. Furthermore, odds calculators
            and P&L back-testing functionality has been implemented for the most widely used betting/trading
            strategies including martingale, 'DAlembert', 'Labouchere' and Fibonacci. Back testing has also been included for the 'EuroMillions',
      			the 'EuroJackpot', the UK Lotto, the Set For Life and the UK 'ThunderBall' lotteries.
      			Furthermore, some basic functionality about climate risk has been included.
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 of the 'BigDataEurope' project. 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 for classification and regression. It includes  methods such as K-Nearest Neighbors Schliep, K. P. (2004) <doi:10.5282/ubm/epub.1769>, Decision Trees Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (2017) <doi:10.1201/9781315139470>,  ADA Boosting Esteban Alfaro, Matias Gamez, Noelia García (2013) <doi:10.18637/jss.v054.i02>, Extreme Gradient Boosting Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>,  Random Forest Breiman (2001) <doi:10.1023/A:1010933404324>, Neural Networks Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Support Vector Machines Bennett, K. P. & Campbell, C. (2000) <doi:10.1145/380995.380999>, Bayesian Methods Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995) <doi:10.1201/9780429258411>,  Linear Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Quadratic Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>,  Logistic Regression Dobson, A. J., & Barnett, A. G. (2018) <doi:10.1201/9781315182780> and Penalized Logistic Regression Friedman, J. H., Hastie, T., & Tibshirani, R. (2010) <doi:10.18637/jss.v033.i01>.
The goal of 'trainR' is to provide a simple interface to the 
    National Rail Enquiries (NRE) systems. There are few data feeds 
    available, the simplest of them is Darwin, which provides real-time 
    arrival and departure predictions, platform numbers, delay estimates, 
    schedule changes and cancellations. Other data feeds provide historical 
    data, Historic Service Performance (HSP), and much more. 'trainR' 
    simplifies the data retrieval, so that the users can focus on their 
    analyses. For more details visit 
    <https://www.nationalrail.co.uk/46391.aspx>.
Split a dataframe, tibble, or data.table into training and test sets. Return either a list, an index, or directly assign training and test sets into memory.
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.
Calculates trait moments from trait and community data using the 
  methods developed in Maitner et al (2021) <doi:10.22541/au.162196147.76797968/v1>.
Implements a three-step procedure in the spirit of Leffondre et al. (2004) to identify clusters of individual longitudinal trajectories. The procedure involves (1) computing a number of "measures of change" capturing various features of the trajectories; (2) using a Principal Component Analysis based dimension reduction algorithm to select a subset of measures and (3) using the k-medoids or k-means algorithm to identify clusters of trajectories.
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.
Estimation of group-based trajectory models, including finite mixture models for longitudinal data, 
    supporting censored normal, zero-inflated Poisson, logit, and beta distributions, 
    using expectation-maximization and quasi-Newton methods, with tools for model selection, 
    diagnostics, and visualization of latent trajectory groups,
    <doi:10.4159/9780674041318>, Nagin, D. (2005). Group-Based Modeling of Development. Cambridge, MA: Harvard University Press.
    and Noel (2022), <https://orbilu.uni.lu/>, thesis.
Implements marginal structural models combined with a latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" Diop, A., Sirois, C., Guertin, J.R., Schnitzer, M.E., Candas, B., Cossette, B., Poirier, P., Brophy, J., Mésidor, M., Blais, C. and Hamel, D., (2023) <doi:10.1177/09622802231202384>.
A toolbox to assist with statistical analysis of 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. Some support is provided for 3-dimensional trajectories. 
    McLean & Skowron Volponi (2018) <doi:10.1111/eth.12739>.
Formula-based user-interfaces to specific transformation models
  implemented in package 'mlt' (<DOI:10.32614/CRAN.package.mlt>, <DOI:10.32614/CRAN.package.mlt.docreg>). 
  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 (Barbanti and Hothorn, 2022,
  <DOI:10.1093/biostatistics/kxac048>). Multivariate conditional transformation models 
  (Klein et al, 2022, <DOI:10.1111/sjos.12501>) and shift-scale transformation models (Siegfried et al, 2023,
  <DOI:10.1080/00031305.2023.2203177>) can be fitted as well. The package contains an implementation of 
  a doubly robust score test, described in  Kook et al. (2024, <DOI:10.1080/01621459.2024.2395588>).
Extends invariant causal prediction (Peters et al., 2016, 
    <doi:10.1111/rssb.12167>) to generalized linear and transformation models 
    (Hothorn et al., 2018, <doi:10.1111/sjos.12291>).
    The methodology is described in Kook et al. (2023,
    <doi:10.1080/01621459.2024.2395588>).
Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or 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 functions for extracting the most frequent event 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. Includes, among others, specific functions such as state survival plots, position-wise group-typical states, dynamic sequence indicators, and dissimilarities between event sequences. Also includes contributions by non-members of the TraMineR team such as methods for polyadic data and for the comparison of groups of sequences.
Likelihood-based estimation of mixed-effects transformation models
    using the Template Model Builder ('TMB', Kristensen et al., 2016)
    <doi:10.18637/jss.v070.i05>. The technical details of transformation models
    are given in Hothorn et al. (2018) <doi:10.1111/sjos.12291>. Likelihood
    contributions of exact, randomly censored (left, right, interval) and
    truncated observations are supported. 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 (Tamasi & Hothorn,
    2021) <doi:10.32614/RJ-2021-075>. Penalized smooth shift terms can be
    defined using the 'mgcv' notation. Additive mixed-effects transformation
    models are described in Tamasi (2025) <doi:10.18637/jss.v114.i11>.
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) <doi:10.48550/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.
Greedy optimal subset selection for transformation models 
    (Hothorn et al., 2018, <doi:10.1111/sjos.12291> ) based on the abess
    algorithm (Zhu et al., 2020, <doi:10.1073/pnas.2014241117> ). Applicable to
    models from packages 'tram' and 'cotram'. Application to shift-scale
    transformation models are described in Siegfried et al. (2024,
    <doi:10.1080/00031305.2023.2203177>).
Performs transformation discrimination analysis and non-transformation discrimination analysis. It also includes functions for Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Mixture Discriminant Analysis. In the context of mixture discriminant analysis, it offers options for both common covariance matrix (common sigma) and individual covariance matrices (uncommon sigma) for the mixture components.
Implements sentiment analysis using huggingface <https://huggingface.co> transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa <https://huggingface.co/cross-encoder/nli-distilroberta-base> and default image/video pipeline is Open AI's CLIP  <https://huggingface.co/openai/clip-vit-base-patch32>. All other zero-shot classification model pipelines can be implemented using their model name from <https://huggingface.co/models?pipeline_tag=zero-shot-classification>.
Performs various statistical transformations; Box-Cox and Log (Box and Cox, 1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Glog (Durbin et al., 2002) <doi:10.1093/bioinformatics/18.suppl_1.S105>, Neglog (Whittaker et al., 2005) <doi:10.1111/j.1467-9876.2005.00520.x>, Reciprocal (Tukey, 1957), Log Shift (Feng et al., 2016) <doi:10.1002/sta4.104>, Bickel-Docksum (Bickel and Doksum, 1981) <doi:10.1080/01621459.1981.10477649>, Yeo-Johnson (Yeo and Johnson, 2000)  <doi:10.1093/biomet/87.4.954>, Square Root (Medina et al., 2019), Manly (Manly, 1976) <doi:10.2307/2988129>, Modulus (John and Draper, 1980)  <doi:10.2307/2986305>, Dual (Yang, 2006) <doi:10.1016/j.econlet.2006.01.011>, Gpower (Kelmansky et al., 2013) <doi:10.1515/sagmb-2012-0030>. It also performs graphical approaches, assesses the success of the transformation via tests and plots.
Targets parameters that solve Ordinary Differential
    Equations (ODEs) driven by a vector of cumulative hazard functions.
    The package provides a method for estimating these parameters using
    an estimator defined by a corresponding Stochastic Differential Equation
    (SDE) system driven by cumulative hazard estimates. By providing cumulative
    hazard estimates as input, the package gives estimates of the parameter as
    output, along with pointwise (co)variances derived from an asymptotic
    expression. Examples of parameters that can be targeted in this way include
    the survival function, the restricted mean survival function, cumulative
    incidence functions, among others; see Ryalen, Stensrud, and Røysland (2018)
    <doi:10.1093/biomet/asy035>, and further applications in
    Stensrud, Røysland, and Ryalen (2019) <doi:10.1111/biom.13102>
    and Ryalen et al. (2021) <doi:10.1093/biostatistics/kxab009>.
Transformer is a Deep Neural Network Architecture based i.a. on the Attention mechanism (Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>).
Time series forecasting faces challenges due to the non-stationarity, nonlinearity, and chaotic nature of the data. Traditional deep learning models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) process data sequentially but are inefficient for long sequences. To overcome the limitations of these models, we proposed a transformer-based deep learning architecture utilizing an attention mechanism for parallel processing, enhancing prediction accuracy and efficiency. This paper presents user-friendly code for the implementation of the proposed transformer-based deep learning architecture utilizing an attention mechanism for parallel processing. References:  Nayak et al. (2024) <doi:10.1007/s40808-023-01944-7> and Nayak et al. (2024) <doi:10.1016/j.simpa.2024.100716>.
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.
A geomorphology-based hydrological modelling for transferring
    streamflow measurements from gauged to ungauged catchments. Inverse
    modelling enables to estimate net rainfall from streamflow measurements 
    following Boudhraâ et al. (2018) <doi:10.1080/02626667.2018.1425801>. 
    Resulting net rainfall is then estimated on the ungauged catchments 
    by spatial interpolation in order to finally simulate streamflow 
    following de Lavenne et al. (2016) <doi:10.1002/2016WR018716>.
Transfer learning for generalized factor models with support for continuous, count (Poisson), and binary data types. The package provides functions for single and multiple source transfer learning, source detection to identify positive and negative transfer sources, factor decomposition using Maximum Likelihood Estimation (MLE), and information criteria ('IC1' and 'IC2') for rank selection. The methods are particularly useful for high-dimensional data analysis where auxiliary information from related source datasets can improve estimation efficiency in the target domain.
Analyse data from longitudinal studies to characterise changes in values of
   semi-quantitative outcome variables within individual subjects, using high performance C++
   code to enable rapid processing of large datasets. A flexible methodology is available for
   codifying these state transitions.
Translation of logit models coefficients into percentages, following Deauvieau (2010) <doi:10.1177/0759106309352586>.
Provides a robust and user-friendly solution for transliterating Ukrainian strings into Latin symbols.
An object model for source text and translations. Find and extract
    translatable strings. Provide translations and seamlessly retrieve them at
    runtime.
To make the semiparametric transformation models easier to apply in real studies, 
    we introduce this R package, in which the MLE in transformation models via 
    an EM algorithm proposed by Zeng D, Lin DY(2007) <doi:10.1111/j.1369-7412.2007.00606.x> 
    and adaptive lasso method in transformation models proposed by Liu XX, Zeng 
    D(2013) <doi:10.1093/biomet/ast029> are implemented.  
    C++ functions are used to compute complex loops. The coefficient vector and 
    cumulative baseline hazard function can be estimated, along with the 
    corresponding standard errors and P values.
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. An introduction can be found in Jie Zhou et al. (2022) <doi:10.18637/jss.v101.i09>.
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.
To visualize the gene structure with multiple isoforms better, I developed this package to draw different transcript structures easily.
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.
Doubly-robust, non-parametric estimators for the transported average treatment effect from 
  Rudolph, Williams, Stuart, and Diaz (2023) <doi:10.48550/arXiv.2304.00117> and the 
  intent-to-treatment average treatment effect from Rudolph and van der Laan (2017)
  <doi:10.1111/rssb.12213>. Estimators are fit using cross-fitting and nuisance parameters are 
  estimated using the Super Learner algorithm.
A tool for comprehensive transcriptomic data analysis, with a focus on transcript-level data preprocessing, expression profiling, differential expression analysis, and functional enrichment. It enables researchers to identify key biological processes, disease biomarkers, and gene regulatory mechanisms. 'TransProR' is aimed at researchers and bioinformaticians working with RNA-Seq data, providing an intuitive framework for in-depth analysis and visualization of transcriptomic datasets. The package includes comprehensive documentation and usage examples to guide users through the entire analysis pipeline. The differential expression analysis methods incorporated in the package include 'limma' (Ritchie et al., 2015, <doi:10.1093/nar/gkv007>; Smyth, 2005, <doi:10.1007/0-387-29362-0_23>), 'edgeR' (Robinson et al., 2010, <doi:10.1093/bioinformatics/btp616>), 'DESeq2' (Love et al., 2014, <doi:10.1186/s13059-014-0550-8>), and Wilcoxon tests (Li et al., 2022, <doi:10.1186/s13059-022-02648-4>), providing flexible and robust approaches to RNA-Seq data analysis. For more information, refer to the package vignettes and related publications.
Improves the predictive performance of ridge and lasso regression exploiting one or more sources of prior information on the importance and direction of effects (Rauschenberger and others 2023, <doi:10.1093/bioinformatics/btad680>). For running the vignette (optional), install 'fwelnet' and 'ecpc' from <https://github.com/kjytay/fwelnet> and <https://github.com/Mirrelijn/ecpc>, respectively.
Tensor Gaussian graphical models (GGMs) have important applications in numerous areas, which can interpret conditional independence structures within tensor data. Yet, the available tensor data in one single study is often limited due to high acquisition costs. 
             Although relevant studies can provide additional data, it remains an open question how to pool such heterogeneous data. This package implements a transfer learning framework for tensor GGMs, which takes full advantage of informative auxiliary domains even when non-informative auxiliary domains are present, benefiting from the carefully designed data-adaptive weights. 
             Reference: 
             Ren, M., Zhen Y., and Wang J. (2022). "Transfer learning for tensor graphical models" <arXiv:2211.09391>.
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.
Pacote para a analise de experimentos com um ou dois fatores com
             testemunhas adicionais conduzidos no delineamento inteiramente 
             casualizado ou em blocos casualizados. 
             "Package for the analysis of one or two-way experiments with 
             additional controls conducted in a completely randomized design 
             or in a randomized block design".
Simple trustworthy utility functions to use TauDEM 
    (Terrain Analysis Using Digital Elevation Models <https://hydrology.usu.edu/taudem/taudem5/>) command-line interface.
    This package provides a guide to installation of TauDEM and its dependencies GDAL (Geopatial Data Abstraction Library) 
    and MPI (Message Passing Interface) for different operating systems.
    Moreover, it checks that TauDEM and its dependencies are correctly installed and included to the PATH, 
    and it provides wrapper commands for calling TauDEM methods from R.
Hospitals, hospital systems, and even trauma systems that
    provide care to injured patients may not be aware of robust metrics
    that can help gauge the efficacy of their programs in saving the lives
    of injured patients.  'traumar' provides robust functions driven by
    the academic literature to automate the calculation of relevant
    metrics to individuals desiring to measure the performance of their
    trauma center or even a trauma system.  'traumar' also provides some
    helper functions for the data analysis journey. Users can refer to the
    following publications for descriptions of the methods used in
    'traumar'.  TRISS methodology, including probability of survival, and
    the W, M, and Z Scores - Flora (1978)
    <doi:10.1097/00005373-197810000-00003>, Boyd et al. (1987,
    PMID:3106646), Llullaku et al. (2009) <doi:10.1186/1749-7922-4-2>,
    Singh et al. (2011) <doi:10.4103/0974-2700.86626>, Baker et al. (1974,
    PMID:4814394), and Champion et al. (1989)
    <doi:10.1097/00005373-198905000-00017>. For the Relative Mortality
    Metric, see Napoli et al. (2017) <doi:10.1080/24725579.2017.1325948>,
    Schroeder et al. (2019) <doi:10.1080/10903127.2018.1489021>, and
    Kassar et al. (2016) <doi:10.1177/00031348221093563>. For more
    information about methods to calculate over- and under-triage in
    trauma hospital populations and samples, please see the following
    publications - Peng & Xiang (2016) <doi:10.1016/j.ajem.2016.08.061>,
    Beam et al. (2022) <doi:10.23937/2474-3674/1510136>, Roden-Foreman et
    al. (2017) <doi:10.1097/JTN.0000000000000283>.
'Travel Time' API <https://docs.traveltime.com/api/overview/introduction> helps users find locations by journey time rather than using ‘as the crow flies’ distance.
  Time-based searching gives users more opportunities for personalisation and delivers a more relevant search.
Contains R functions for simulating and estimating integer-valued trawl processes as 
    described in the article Veraart (2019),"Modeling, simulation and inference for
    multivariate time series of counts using trawl processes", Journal of Multivariate Analysis,
    169, pages 110-129,
    <doi:10.1016/j.jmva.2018.08.012> and for 
    simulating random vectors from the bivariate negative binomial and the bi- and trivariate 
    logarithmic series distributions.
A new measure of similarity between a pair of mass spectrometry (MS) experiments, called truncated rank correlation (TRC). To provide a robust metric of similarity in noisy high-dimensional data, TRC uses truncated top ranks (or top m-ranks) for calculating correlation. Truncated rank correlation as a robust measure of test-retest reliability in mass spectrometry data. For more details see Lim et al. (2019) <doi:10.1515/sagmb-2018-0056>.
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.
Truncation of univariate probability distributions. The probability distribution can come from other packages so long as the function names follow the standard d, p, q, r naming format. Also other univariate probability distributions are included.
Download daily interest rates from the US Treasury XML feed.
    Leveraging
    <https://home.treasury.gov/treasury-daily-interest-rate-xml-feed>,
    this package serves as a wrapper, facilitating the retrieval of daily
    treasury rates across various categories, including par yield curves,
    treasury bills, long-term rates, and real yield curves.
Computes treatment patterns within a given cohort using the Observational
    Medical Outcomes Partnership (OMOP) common data model (CDM). As described
    in Markus, Verhamme, Kors, and Rijnbeek (2022) <doi:10.1016/j.cmpb.2022.107081>.
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.
A modular package for simulating phylogenetic trees and species traits jointly. Trees can be simulated using modular birth-death parameters (e.g. changing starting parameters or algorithm rules). Traits can be simulated in any way designed by the user. The growth of the tree and the traits can influence each other through modifiers objects providing rules for affecting each other. Finally, events can be created to modify both the tree and the traits under specific conditions ( Guillerme, 2024 <DOI:10.1111/2041-210X.14306>).
There are some experimental scenarios where each experimental unit receives a sequence of treatments across multiple periods, and treatment effects persist beyond the period of application. It focuses on the construction and calculation of the parametric value of the residual effect designs balanced for carryover effects, also referred to as crossover designs, change-over designs, or repeated measurements designs (Aggarwal and Jha, 2010<doi:10.1080/15598608.2010.10412013>). The primary objective of the package is to generate a new class of Balanced Ternary Residual Effect Designs (BTREDs), balanced for carryover effects tailored explicitly for situations where the number of periods is less than or equal to the number of treatments. In addition, the package provides four new classes of Partially Balanced Ternary Residual Effect Designs (PBTREDs), constructed using incomplete block designs, initial sequences, and rectangular association scheme. In addition, one extra function is included to help study the parametric properties of a given residual effect design.
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) <doi:10.48550/arXiv.1906.10845>.
Provides customizable 3D tree models (as 'OBJ' files) for use in data visualization. Includes both planar and solid tree models, various crown types (columnar, oval, palm, pyramidal, rounded, spreading, vase, weeping), and options to change the diameter, height, and color of the tree's crown and trunk.
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> and a tutorial on MPT modeling can be found 
    in Schmidt, Erdfelder, & Heck (2023) <DOI:10.1037/met0000561>.
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.
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.
    For more details see Roman-Palacios et al. (2021) <doi:10.7717/peerj.12450>.
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. For more details see Volz and 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).
Perform test to detect differences in structure between families of
             trees. The method is based on cophenetic distances and aggregated
             Student's tests.
Testing for trajectory presence and heterogeneity on
 multivariate data. Two statistical methods (Tenha & Song 2022) 
 <doi:10.1371/journal.pcbi.1009829> are implemented. The tree dimension
 test quantifies the statistical evidence for trajectory presence. The
 subset specificity measure summarizes pattern heterogeneity using the
 minimum subtree cover. There is no user tunable parameters for either
 method. Examples are included to illustrate how to use the methods on 
 single-cell data for studying gene and pathway expression dynamics and
 pathway expression specificity.
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>;
  the Hierarchical Mutual Information (Perotti et al. 2015)
  <doi:10.1103/PhysRevE.92.062825>;
  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>.
  Includes tools for visualizing mappings of tree space (Smith 2022)
  <doi:10.1093/sysbio/syab100>,
  for identifying islands of trees (Silva and Wilkinson 2021)
  <doi:10.1093/sysbio/syab015>,
  for calculating the median of sets of trees,
  and for computing the information content of trees and splits.
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.
A treemap is a space-filling visualization of hierarchical
    structures. This package offers great flexibility to draw treemaps.
Provides 'ggplot2' geoms for drawing treemaps.
Implementation of unconditional Bernoulli Scan Statistic developed
             by Kulldorff et al. (2003) <doi:10.1111/1541-0420.00039> 
             for hierarchical tree structures. Tree-based Scan Statistics are an
             exploratory method to identify event clusters across the space of a 
             hierarchical tree.
Implements a likelihood ratio test and two pairwise standardized mean difference tests for testing equality of means against tree ordered alternatives in one-way ANOVA. The null hypothesis assumes all group means are equal, while the alternative assumes the control mean is less than or equal to each treatment mean with at least one strict inequality. Inputs are a list of numeric vectors (groups) and a significance level; outputs include the test statistic, critical value, and decision. Methods described in "Testing Against Tree Ordered Alternatives in One-way ANOVA" <doi:10.48550/arXiv.2507.17229>.
The German national forest inventory uses angle count sampling, 
    a sampling method first published as `Bitterlich, W.: Die Winkelzählmessung.
    Allgemeine Forst- und Holzwirtschaftliche Zeitung, 58. Jahrg., Folge 11/12 
    vom Juni 1947` and extended by Grosenbaugh
    (<https://academic.oup.com/jof/article-abstract/50/1/32/4684174>)
    as probability proportional to size sampling.
    When plots are located near stand boundaries, their sizes and hence
    their probabilities need to be corrected.
Record all tree-ring Shapefile of tree disk with GIS soft 'Qgis' and interpolating model from high resolution tree disk image.
Reconstruct phylogenetic trees from discrete data.
  Inapplicable character states are handled using the algorithm of Brazeau,
  Guillerme and Smith (2019) <doi:10.1093/sysbio/syy083> with the "Morphy"
  library, under equal or implied step weights.
  Contains a "shiny" user interface for interactive tree search and exploration
  of results, including character visualization, rogue taxon detection,
  tree space mapping, and cluster consensus trees (Smith 2022a, b)
  <doi:10.1093/sysbio/syab099>, <doi:10.1093/sysbio/syab100>.
  Profile Parsimony (Faith and Trueman, 2001) <doi:10.1080/10635150118627>, 
  Successive Approximations (Farris, 1969) <doi:10.2307/2412182>
  and custom optimality criteria are implemented.
An efficient implementation of the 'TreeSHAP' algorithm
    introduced by Lundberg et al., (2020) <doi:10.1038/s42256-019-0138-9>.
    It is capable of calculating SHAP (SHapley Additive exPlanations) values 
    for tree-based models in polynomial time.  Currently supported models include 
    'gbm', 'randomForest', 'ranger', 'xgboost', 'lightgbm'.
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>.
Provides bindings to 'Tree-sitter', an incremental parsing
    system for programming tools. 'Tree-sitter' builds concrete syntax
    trees for source files of any language, and can efficiently update
    those syntax trees as the source file is edited. It also includes a
    robust error recovery system that provides useful parse results even
    in the presence of syntax errors.
Provides bindings to an 'R' grammar for 'Tree-sitter', to be
    used alongside the 'treesitter' package. 'Tree-sitter' builds concrete
    syntax trees for source files of any language, and can efficiently
    update those syntax trees as the source file is edited.
Provide a range of functions with multiple criteria for cutting phylogenetic trees at any evolutionary depth. It enables users to cut trees in any orientation, such as rootwardly (from root to tips) and tipwardly (from tips to its root), or allows users to define a specific time interval of interest. It can also be used to create multiple tree pieces of equal temporal width. Moreover, it allows the assessment of novel temporal rates for various phylogenetic indexes, which can be quickly displayed graphically.
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.
Collection of phylogenetic tree statistics,
             collected throughout the literature. All functions have been 
             written to maximize computation speed. The package includes 
             umbrella functions to calculate all statistics, all balance 
             associated statistics, or all branching time related statistics. 
             Furthermore, the 'treestats' package supports summary statistic 
             calculations on Ltables, provides speed-improved coding of 
             branching times, Ltable conversion and includes algorithms to
             create intermediately balanced trees. Full description can be
             found in Janzen (2024) <doi:10.1016/j.ympev.2024.108168>.
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.
Efficient implementations of functions for the creation, 
  modification and analysis of phylogenetic trees.
  Applications include:
  generation of trees with specified shapes;
  tree rearrangement;
  analysis of tree shape;
  rooting of trees and extraction of subtrees;
  calculation and depiction of split support;
  plotting the position of rogue taxa (Klopfstein & Spasojevic 2019)
  <doi:10.1371/journal.pone.0212942>;
  calculation of ancestor-descendant relationships,
  of 'stemwardness' (Asher & Smith, 2022) <doi:10.1093/sysbio/syab072>,
  and of tree balance (Mir et al. 2013, Lemant et al. 2022)
  <doi:10.1016/j.mbs.2012.10.005>, <doi:10.1093/sysbio/syac027>;
  artificial extinction (Asher & Smith, 2022) <doi:10.1093/sysbio/syab072>;
  import and export of trees from Newick, Nexus (Maddison et al. 1997)
  <doi:10.1093/sysbio/46.4.590>,
  and TNT <https://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.
A slightly-opinionated R interface for the 'Tremendous' API (<https://www.tremendous.com/>). 
    In addition to supporting GET and POST requests, 'tremendousr' has, dare I say, 
    tremendously intuitive functions for sending digital rewards and incentives directly from R.
Tools for translating environmental change into organismal response. Microclimate models to vertically scale weather station data to organismal heights. The biophysical modeling tools include both general models for heat flows and specific models to predict body temperatures for a variety of ectothermic taxa. Additional functions model and temporally partition air and soil temperatures and solar radiation. Utility functions estimate the organismal and environmental parameters needed for biophysical ecology. 'TrenchR' focuses on relatively simple and modular functions so users can create transparent and flexible biophysical models. Many functions are derived from Gates (1980) <doi:10.1007/978-1-4612-6024-0> and Campbell and Norman (1988) <isbn:9780387949376>.
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.
Fitting models for, and simulation of, trend locally stationary 
    wavelet (TLSW) time series models, which take account of time-varying 
    trend and dependence structure in a univariate time series. The TLSW model, 
    and its estimation, is described in McGonigle, Killick and Nunes (2022a) 
    <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. New users will 
    likely want to start with the TLSW function.
Performs the detection of linear trend changes for univariate time series 
    by implementing the bottom-up unbalanced wavelet transformation proposed by 
    H. Maeng and P. Fryzlewicz (2023). The estimated number and locations of the 
    change-points are returned with the piecewise-linear estimator for signal.
Extract trends from monthly and quarterly economic time series.
    Provides two main functions: augment_trends() for pipe-friendly 'tibble' workflows
    and extract_trends() for direct time series analysis. Includes key econometric
    filters and modern parameter experimentation tools.
Provides a set of exploratory data analysis (EDA) tools for 
    visualizing trends, diagnosing data types for beginner-friendly workflows, 
    and automatically routing to suitable statistical tests or trend exploration 
    models. Includes unified plotting functions for trend lines, grouped boxplots, 
    and comparative scatterplots; automated statistical testing (e.g., t-test, 
    Wilcoxon, ANOVA, Kruskal-Wallis, Tukey, Dunn) with optional effect size 
    calculation; and model-based trend analysis using generalized additive 
    models (GAM) for count data, generalized linear models (GLM) for continuous 
    data, and zero-inflated models (ZIP/ZINB) for count data with potential 
    zero-inflation. 
    Also supports time-window continuity checks, cross-year 
    handling in compare_monthly_cases(), and ARIMA-ready preparation with 
    stationarity diagnostics, ensuring consistent parameter styles for 
    reproducible research and user-friendly workflows.Methods are 
    based on R Core Team (2024) <https://www.R-project.org/>, 
    Wood, S.N.(2017, ISBN:978-1498728331),
    Hyndman RJ, Khandakar Y (2008) <doi:10.18637/jss.v027.i03>, 
    Simon Jackman (2024) <https://github.com/atahk/pscl/>,    
    Achim Zeileis, Christian Kleiber, Simon Jackman (2008) <doi:10.18637/jss.v027.i08>.
Matrix factorization for multivariate time series with both low rank and temporal structures. 
    The procedure is the one proposed by Alquier, P. and Marie, N. 
    "Matrix factorization for multivariate time series analysis." 
    Electronic Journal of Statistics, 13(2), 4346-4366 (2019).
Non-parametric trend comparison of two independent samples with sequential subsamples. For more details, please refer to Wang, Stapleton, and Chen (2018) <doi:10.1080/00949655.2018.1482492>.
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>. For more details of the package, see Zeng J, Wang W, Zhang X (2021) <doi:10.18637/jss.v099.i12>.
Performs fast variable selection in high-dimensional settings while controlling the 
    false discovery rate (FDR) at a user-defined target level. The package is based on the paper 
    Machkour, Muma, and Palomar (2022) <arXiv:2110.06048>.
Tri-hierarchical incomplete block design is defined as an arrangement of v  treatments each replicated r  times in a three system of blocks if, each block of the first system contains m_1 blocks of second system and each block of the second system contains m_2 blocks of the third system. Ignoring the first and second system of blocks, it leaves an incomplete block design with b_3 blocks of size k_3i units; ignoring first and third system of blocks, it leaves an incomplete block design with b_2 blocks each of size k_2i units and ignoring the second and third system of blocks, it leaves an incomplete block design with b_1 blocks each of size k_1 units. For dealing with experimental circumstances where there are three nested sources of variation, a tri-hierarchical incomplete block design can be adopted. Tri - hierarchical incomplete block designs can find application potential in obtaining mating-environmental designs for breeding trials. To know more about nested block designs one can refer Preece (1967) <doi:10.1093/biomet/54.3-4.479>. This package includes series1(), series2(), series3() and series4() functions. This package generates tri-hierarchical designs with six component designs under certain parameter restrictions.
Assists in analyzing the lying behavior of cows from raw data 
    recorded with a triaxial accelerometer attached to the hind leg of a cow. Allows 
    the determination of common measures for lying behavior including total lying duration, the 
    number of lying bouts, and the mean duration of lying bouts. Further capabilities are the 
    description of lying laterality and the calculation of proxies for the level of physical 
    activity of the cow. Reference: Simmler M., Brouwers S. P. (2024) <doi:10.7717/peerj.17036>.
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.
Implements target trial emulation methods to apply randomized
    clinical trial design and analysis in an observational setting. Using
    marginal structural models, it can estimate intention-to-treat and
    per-protocol effects in emulated trials using electronic health
    records. A description and application of the method can be found in
    Danaei et al (2013) <doi:10.1177/0962280211403603>.
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.
Simulate phase II and/or phase III clinical trials. It supports various types of endpoints and adaptive strategies. Tools for carrying out graphical testing procedure and combination test under group sequential design are also provided.
Functions and Examples in Sample Size Calculation in
        Clinical Research.
Provides the "r, q, p, and d" distribution functions for the triangle distribution.  Also includes maximum likelihood estimation of parameters.
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> and Acerbi, Carlo and Tasche, Dirk. (2002)
    <doi:10.1111/1468-0300.00091>.
Compositional data consisting of three-parts can be color
  mapped with a ternary color scale. Such a scale is provided by
  the tricolore packages with options for discrete and continuous
  colors, mean-centering and scaling. See 
  Jonas Schöley (2021) "The centered ternary balance scheme. A technique
  to visualize surfaces of unbalanced three-part compositions"
  <doi:10.4054/DemRes.2021.44.19>,
  Jonas Schöley, Frans Willekens (2017) "Visualizing compositional data
  on the Lexis surface" <doi:10.4054/DemRes.2017.36.21>, and
  Ilya Kashnitsky, Jonas Schöley (2018) "Regional population structures
  at a glance" <doi:10.1016/S0140-6736(18)31194-2>.
Fits 2D and 3D geometric transformations via 'Stan' probabilistic programming engine ( 
    Stan Development Team (2021) <https://mc-stan.org>). Returns posterior distribution for individual
    parameters of the fitted distribution. Allows for computation of LOO and WAIC information criteria 
    (Vehtari A, Gelman A, Gabry J (2017) <doi:10.1007/s11222-016-9696-4>) as well as Bayesian R-squared
    (Gelman A, Goodrich B, Gabry J, and Vehtari A (2018) <doi:10.1080/00031305.2018.1549100>).
'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'.
The trigger strategy is a general framework for a multistage statistical design with multiple hypotheses, allowing an adaptive selection of interim analyses. The selection of interim stages can be associated with some prespecified endpoints which serve as the trigger. This selection allows us to refine the critical boundaries in hypotheses testing procedures, and potentially increase the statistical power. This package includes several trial designs using the trigger strategy. 
    See Gou, J. (2023), "Trigger strategy in repeated tests on multiple hypotheses", Statistics in Biopharmaceutical Research, 15(1), 133-140, and Gou, J. (2022), "Sample size optimization and initial allocation of the significance levels in group sequential trials with multiple endpoints", Biometrical Journal, 64(2), 301-311.
Processing and analysis of pathomics, omics and other medical datasets.
    'tRigon' serves as a toolbox for descriptive and statistical analysis, correlations, 
    plotting and many other methods for exploratory analysis of high-dimensional datasets.
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.
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.
  See also "ROC Analysis for Classification and Prediction in Practice" by Nakas, Bantis
  and Gatsonis (2023), ISBN 9781482233704.
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>.
Access and manipulate spatial tracking data, with straightforward 
 coercion from and to other formats. Filter for speed and create time spent 
 maps from tracking 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, 
 Sumner, MD (2011) <https://figshare.utas.edu.au/articles/thesis/The_tag_location_problem/23209538>.
A constrained two-dimensional Delaunay triangulation package
  providing both triangulation and generation of voronoi mosaics of 
  irregular spaced data.
  Please note that most of the functions are now also covered in package
  interp, which is a re-implementation from scratch under a free license
  based on a different triangulation algorithm.
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.
Implements triple-difference (DDD) estimators for both average
              treatment effects and event-study parameters. Methods include regression
              adjustment, inverse-probability weighting, and doubly-robust estimators,
              all of which rely on a conditional DDD parallel-trends assumption and
              allow covariate adjustment across multiple pre- and post-treatment
              periods. The methodology is detailed in Ortiz-Villavicencio and
              Sant'Anna (2025) <doi:10.48550/arXiv.2505.09942>.
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.
Attain excellent covariate balance by matching two treated units
    and one control unit or vice versa within strata. Using such triples, as
    opposed to also allowing pairs of treated and control units, 
    allows easier interpretation of the two possible 
    weights of observations and better insensitivity to unmeasured bias in the test
    statistic. Using triples instead of matching in a fixed 1:2 or 2:1 ratio
    allows for the match to be feasible in more situations.
    The 'rrelaxiv' package, which provides an alternative solver for the underlying 
    network flow problems, carries an academic license and is not available on CRAN, but
    may be downloaded from 'GitHub' at <https://github.com/josherrickson/rrelaxiv/>.
    The 'Gurobi' commercial optimization software is required to use the two functions
    [infsentrip()] and [triplesIP()]. These functions are not essential
    to the main purpose of this package. A free academic license can be obtained at 
    <https://www.gurobi.com/features/academic-named-user-license/>. 
    The 'gurobi' R package can then be installed following 
    the instructions at <https://www.gurobi.com/documentation/9.1/refman/ins_the_r_package.html>.
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>.
Overall predictive performance is measured by a mean score
    (or loss), which decomposes into miscalibration, discrimination, and
    uncertainty components. The main focus is visualization of these distinct
    and complementary aspects in joint displays.
    See Dimitriadis, Gneiting, Jordan, Vogel (2024) <doi:10.1016/j.ijforecast.2023.09.007>.
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.
Evaluate inline or chunks of R code in template files and replace with their output modifying the resulting template.
These functions generate data frames on troop deployments and military basing using U.S. Department of Defense data on overseas military deployments. This package provides functions for pulling country-year troop deployment and basing data. Subsequent versions will hopefully include cross-national data on deploying countries.
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" (<https://openknowledge.fao.org/server/api/core/bitstreams/bc7c37b6-30df-49c0-b5b4-8367a872c97e/content>),
    as well as other more recent methods.
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.
The main function of the package aims to update 'lmer()'/'glmer()' models depending on their warnings, so trying to avoid convergence and singularity problems.
Provides methods for generating .dat files for use with the 'AMPL' 
    software using spatial data, particularly rasters. It includes support for 
    various spatial data formats and different problem types. By automating the 
    process of generating 'AMPL' datasets, this package can help streamline 
    optimization workflows and make it easier to solve complex optimization 
    problems. The methods implemented in this package are described in detail
    in a publication by Fourer et al. (<doi:10.1287/mnsc.36.5.519>).
Bayesian trophic position models using 'stan' by leveraging 'brms' for 
             stable isotope data. Trophic position models are derived by using 
             equations from 
             Post (2002) <doi:10.1890/0012-9658(2002)083[0703:USITET]2.0.CO;2>,
             Vander Zanden and Vadeboncoeur (2002) <doi:10.1890/0012-9658(2002)083[2152:FAIOBA]2.0.CO;2>, 
             and 
             Heuvel et al. (2024) <doi:10.1139/cjfas-2024-0028>.
Gives the required 2^n treatment combinations in a 2^n symmetric factorial experiment in their respective standard order.
Recursive partytioning of transformation models with
  corresponding random forest for conditional transformation models 
  as described in 'Transformation Forests' (Hothorn and Zeileis, 2021, <doi:10.1080/10618600.2021.1872581>) 
  and 'Top-Down Transformation Choice' (Hothorn, 2018, <DOI:10.1177/1471082X17748081>).
Implements rank preserving structural failure time model (RPSFTM), iterative parameter estimation (IPE), inverse probability of censoring weights (IPCW), marginal structural model (MSM), simple two-stage estimation (TSEsimp), and improved two-stage estimation with g-estimation (TSEgest) methods for treatment switching in randomized clinical trials.
Provides a convenient R interface to the 'National Health Service 
    NHS Technology Reference Update Distribution (TRUD) API', allowing users 
    to list available releases for their subscribed items, retrieve metadata, 
    and download release files. For more information on the API, see 
    <https://isd.digital.nhs.uk/trud/users/guest/filters/0/api>.
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.
Most estimators implemented by the video game industry cannot obtain reliable initial estimates nor guarantee comparability between distant estimates. TrueSkill Through Time solves all these problems by modeling the entire history of activities using a single Bayesian network allowing the information to propagate correctly throughout the system. This algorithm requires only a few iterations to converge, allowing millions of observations to be analyzed using any low-end computer.
    Landfried G, Mocskos E (2025). "TrueSkill Through Time: Reliable Initial Skill Estimates and Historical Comparability with Julia, Python, and R." <doi:10.18637/jss.v112.i06>.
    The core ideas implemented in this project were developed by Dangauthier P, Herbrich R, Minka T, Graepel T (2007). "Trueskill through time: Revisiting the history of chess.".
This groundbreaking technical indicator directly integrates volatility into price averaging by weighting median range-bound prices using the True Range.  Unlike conventional metrics such as TWAP (Time-Weighted Average Price), which focuses solely on time, or VWAP (Volume-Weighted Average Price), which emphasizes volume, 'TrueWAP' captures fluctuating market behavior by reflecting true price movement within high/low performance boundaries.
Implements the TRUH test statistic for two sample testing under heterogeneity. TRUH incorporates the underlying heterogeneity and imbalance in the samples, and provides a conservative test for the composite null hypothesis that the two samples arise from the 
   same mixture distribution but may differ with respect to the mixing weights. See Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee Ann. Appl. Stat. 14(4): 1777-1805 (December 2020). <DOI:10.1214/20-AOAS1362> for more details.
Visualizes the relationship between allele frequency and effect size in genetic association studies. The input is a data frame containing association results. The output is a plot with the effect size of risk variants in the Y axis, and the allele frequency spectrum in the X axis. Corte et al (2023) <doi:10.1101/2023.04.21.23288923>.
Doubly robust estimation for the mean of an arbitrarily transformed survival time under covariate-induced dependent left truncation and noninformative right censoring. The functions truncAIPW(), truncAIPW_cen1(), and truncAIPW_cen2() compute the doubly robust estimators under the scenario without censoring and the two censoring scenarios, respectively. The package also contains three simulated data sets 'simu', 'simu_c1', and 'simu_c2', which are used to illustrate the usage of the functions in this package.
    Reference: Wang, Y., Ying, A., Xu, R. (2022) "Doubly robust estimation under covariate-induced dependent left truncation" <arXiv:2208.06836>.
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..
Handles truncated members from the exponential family of
	probability distributions. Contains functions such as rtruncnorm() and
	dtruncpois(), which are truncated versions of rnorm() and dpois() from the
	stats package that also offer richer output containing, for example, the
	distribution parameters. It also provides functions to retrieve the original
	distribution parameters from a truncated sample by maximum-likelihood
	estimation.
Density, probability, quantile and random number
        generation functions for the truncated normal distribution.
Finds the posterior modes for the mean and standard deviation for a
  truncated normal distribution with one or two known truncation points.
  The method used extends Bayesian methods for parameter estimation for a singly
  truncated normal distribution under the Jeffreys prior (see Zhou X,
  Giacometti R, Fabozzi FJ, Tucker AH (2014). "Bayesian estimation of truncated
  data with applications to operational risk measurement".
  <doi:10.1080/14697688.2012.752103>). This package additionally allows for a
  doubly truncated normal distribution.
Estimation of models for truncated Gaussian variables by maximum likelihood.
Estimators for semi-parametric linear regression models with truncated response variables (fixed truncation point). The estimators implemented are the Symmetrically Trimmed Least Squares (STLS) estimator introduced by Powell (1986) <doi:10.2307/1914308>, the Quadratic Mode (QME) estimator introduced by Lee (1993) <doi:10.1016/0304-4076(93)90056-B>, and the Left Truncated (LT) estimator introduced by Karlsson (2006) <doi:10.1007/s00184-005-0023-x>.
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>.
Makes data wrangling with ID-related aspects more comfortable.
    Provides functions that make it easy to inspect various subject-generated ID codes (SGIC) for plausibility.
    Also helps with inspecting other common identifiers, ensuring that your data stays clean and reliable.
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).
Differentiate client errors (4xx)
  from server errors (5xx) for the 'plumber' and 
  'RestRserve' HTTP API frameworks. The package
  also includes a built-in logging mechanism
  to standard output (STDOUT) or standard error
  (STDERR) depending on the log level.
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.
The best ANN structure for time series data analysis is a demanding need in the present era.
    This package will find the best-fitted ANN model based on forecasting accuracy.
    The optimum size of the hidden layers was also determined after determining the number of lags to be included.
    This package has been developed using the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
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.
A suite of auxiliary functions that enhance time series estimation and forecasting, including a robust anomaly detection routine based on Chen and Liu (1993) <doi:10.2307/2290724> (imported and wrapped from the 'tsoutliers' package), utilities for managing calendar and time conversions, performance metrics to assess both point forecasts and distributional predictions, advanced simulation by allowing the generation of time series components—such as trend, seasonal, ARMA, irregular, and anomalies—in a modular fashion based on the innovations form of the state space model and a number of transformation methods including Box-Cox, Logit, 'Softplus-Logit' and Sigmoid.
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 and supervised dimension reduction problem for multivariate time series. Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace. The package is fully described in Nordhausen, Matilainen, Miettinen, Virta and Taskinen (2021) <doi:10.18637/jss.v098.i15>.
Two stage curvature identification with machine learning for causal 
    inference in settings when instrumental variable regression is not suitable
    because of potentially invalid instrumental variables. Based on Guo and 
    Buehlmann (2022) "Two Stage Curvature Identification with Machine Learning: 
    Causal Inference with Possibly Invalid Instrumental Variables"
    <doi:10.48550/arXiv.2203.12808>. The vignette is available in Carl, Emmenegger, Bühlmann and Guo (2025) 
    "TSCI: Two Stage Curvature Identification for Causal Inference with Invalid Instruments in R" <doi:10.18637/jss.v114.i07>.
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.
Functions for the analysis of time series using copula models.  
    The package is based on methodology described in the following references.
    McNeil, A.J. (2021) <doi:10.3390/risks9010014>,
    Bladt, M., & McNeil, A.J. (2021) <doi:10.1016/j.ecosta.2021.07.004>,
    Bladt, M., & McNeil, A.J. (2022) <doi:10.1515/demo-2022-0105>.
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.
Forecasting competitions are of increasing importance as a mean to learn best practices and gain knowledge. Data leakage is one of the most common issues that can often be found in competitions. Data leaks can happen when the training data contains information about the test data. For example: randomly chosen blocks of time series are concatenated to form a new time series, scale-shifts, repeating patterns in time series,  white noise is added in the original time series to form a new time series, etc.  'tsdataleaks' package can be used to detect data leakages in a collection of  time series.
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).
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>.
RNNs are preferred for sequential data like time series, speech, text, etc. but when dealing with long range dependencies, vanishing gradient problems account for their poor performance. LSTM and GRU are effective solutions which are nothing but RNN networks with the abilities of learning both short-term and long-term dependencies. Their structural makeup enables them to remember information for a long period without any difficulty. LSTM consists of one cell state and three gates, namely, forget gate, input gate and output gate whereas GRU comprises only two gates, namely, reset gate and update gate. This package consists of three different functions for the application of RNN, LSTM and GRU to any time series data for its forecasting. For method details see Jaiswal, R. et al. (2022). <doi:10.1007/s00521-021-06621-3>.
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.
We propose an optimality criterion to determine the required training set, r-score, which is derived directly from Pearson's correlation between the genomic estimated breeding values and phenotypic values of the test set <doi:10.1007/s00122-019-03387-0>. This package provides two main functions to determine a good training set and its size.
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>.
First - Generates (potentially high-dimensional) high-frequency and low-frequency series for simulation studies in temporal disaggregation; Second - a toolkit utilizing temporal disaggregation and benchmarking techniques with a low-dimensional matrix of indicator series previously proposed in Dagum and Cholette (2006, ISBN:978-0-387-35439-2) ; and Third - novel techniques proposed by Mosley, Gibberd and Eckley (2021) <arXiv:2108.05783> for disaggregating low-frequency series in the presence of high-dimensional indicator matrices.
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>.
Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior.
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>.
The library allows to perform a multivariate time series
    classification based on the use of Discrete Wavelet Transform for
    feature extraction, a step wise discriminant to select the most
    relevant features and finally, the use of a linear or quadratic
    discriminant for classification. Note that all these steps can be done
    separately which allows to implement new steps.  Velasco, I., Sipols,
    A., de Blas, C. S., Pastor, L., & Bayona, S. (2023)
    <doi:10.1186/S12938-023-01079-X>.  Percival, D. B., & Walden, A. T.
    (2000,ISBN:0521640687).  Maharaj, E. A., & Alonso, A. M. (2014)
    <doi:10.1016/j.csda.2013.09.006>.
Autoregressive distributed lag (A[R]DL) models (and their reparameterized equivalent, the Generalized Error-Correction Model [GECM]) (see De Boef and Keele 2008 <doi:10.1111/j.1540-5907.2007.00307.x>) are the workhorse models in uncovering dynamic inferences. ADL models are simple to estimate; this is what makes them attractive. Once these models are estimated, what is less clear is how to uncover a rich set of dynamic inferences from these models. We provide tools for recovering those inferences in three forms: causal inferences from ADL models, traditional time series quantities of interest (short- and long-run effects), and dynamic conditional relationships.
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>.
Which uses Twitter APIs for the necessary data in sentiment analysis, acts as a middleware with the approved Twitter Application.
    A special access key is given to users who subscribe to the application with their Twitter account. With this special access key, the user defined keyword for sentiment analysis can be searched in twitter recent searches and results can be obtained( more information <https://github.com/hakkisabah/tsentiment> ).
    In addition, a service named tsentiment-services has been developed to provide all these operations ( for more information <https://github.com/hakkisabah/tsentiment-services> ).
    After the successful results obtained and in line with the permissions given by the user, the results of the analysis of the word cloud and bar graph saved in the user folder directory can be seen. In each analysis performed, the previous analysis visual result is deleted and this is the basic information you need to know as a practice rule.
    'tsentiment' package provides a free service that acts as a middleware for easy data extraction from Twitter, and in return, the user rate limit is reduced by 30 requests from the total limit and the remaining requests are used. These 30 requests are reserved for use in application analytics. For information about endpoints, you can refer to the limit information in the "GET search/tweets" row in the Endpoints column in the list at <https://developer.twitter.com/en/docs/twitter-api/v1/rate-limits>.
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 and cross 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).
Routines for nonlinear time series analysis based on Threshold Autoregressive Moving Average (TARMA) models. It provides functions and methods for: TARMA model fitting and forecasting, including robust estimators, see Goracci et al. JBES (2025) <doi:10.1080/07350015.2024.2412011>; tests for threshold effects, see Giannerini et al. JoE (2024) <doi:10.1016/j.jeconom.2023.01.004>, Goracci et al. Statistica Sinica (2023) <doi:10.5705/ss.202021.0120>, Angelini et al. (2024) <doi:10.48550/arXiv.2308.00444>;  unit-root tests based on TARMA models, see Chan et al. Statistica Sinica (2024) <doi:10.5705/ss.202022.0125>.
Tools to perform some descriptive data analysis for assets. Manage the portfolio and capital of assets. It also downloads and organizes data from the Tehran Stock Exchange (TSE).
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>.
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 Francisco
    Martinez et al. (2022) <doi:10.1016/j.neucom.2021.12.028>. 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 forecasting time series using nearest neighbors regression
    Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J.
    Rivera (2019) <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.
Nonlinear growth models are extremely useful in gaining insight into the underlying mechanism. These models are generally 'mechanistic,' with parameters that have biological meaning. This package allows you to fit and forecast time series data using nonlinear growth models.
A collection of functions for generating frequency tables and cross-tabulations of categorical variables. The resulting tables can be exported to various formats (Excel, PDF, HTML, etc.) with extensive formatting and layout customization options.
Multiple flavors of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a large choice of conditional distributions. Methods for specification, estimation, prediction, filtering, simulation, statistical testing and more. Represents a partial re-write and re-think of 'rugarch', making use of automatic differentiation for estimation.
The 'tsgc' package provides comprehensive tools for the analysis and forecasting of epidemic trajectories.
    It is designed to model the progression of an epidemic over time while accounting for the various uncertainties
    inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach,
    using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in
    epidemic data. The reinitialization feature enhances the model’s ability to adapt to the emergence of new waves.
    The forecasts generated by the package are of value to public health officials and researchers who need to
    understand and predict the course of an epidemic to inform decision-making. Beyond its application in public
    health, the package is also a useful resource for researchers and practitioners in fields where the trajectories
    of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for
    data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy.
    The core methodologies implemented in 'tsgc' are based on well-established statistical techniques as described in
    Harvey and Kattuman (2020) <doi:10.1162/99608f92.828f40de>, Harvey and Kattuman (2021)
    <doi:10.1098/rsif.2021.0179>, and Ashby, Harvey, Kattuman, and Thamotheram (2024)
    <https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.
Obtaining relevant set of trait specific genes from gene expression data is important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package returns the trait specific gene set from the high dimensional RNA-seq count data by applying combination of two conventional machine learning algorithms, support vector machine (SVM) and genetic algorithm (GA). GA is used to control and optimize the subset of genes sent to the SVM for classification and evaluation. Genetic algorithm uses repeated learning steps and cross validation over number of possible solution and selects the best. The algorithm selects the set of genes based on a fitness function that is obtained via support vector machines. Using SVM as the classifier performance and the genetic algorithm for feature selection, a set of trait specific gene set is obtained.
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).
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.
Time series methods for intermittent demand forecasting. Includes Croston's method and its variants (Moving Average, SBA), and the TSB method. Users can obtain optimal parameters on a variety of loss functions, or use fixed ones (Kourenztes (2014) <doi:10.1016/j.ijpe.2014.06.007>). Intermittent time series classification methods and iMAPA that uses multiple temporal aggregation levels are also provided (Petropoulos & Kourenztes (2015) <doi:10.1057/jors.2014.62>).
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 <doi:10.1371/journal.pone.0185528>. The method implemented here is described by Finkenstadt and Grenfell (2000) <doi:10.1111/1467-9876.00187>.
Unobserved components time series model using the linear innovations state space representation (single source of error) with choice of error distributions and option for dynamic variance. Methods for estimation using automatic differentiation, automatic model selection and ensembling, prediction, filtering, simulation and backtesting. Based on the model described in Hyndman et al (2012) <doi:10.1198/jasa.2011.tm09771>.
Implementation of the tree-guided feature selection and logic aggregation approach introduced in Chen et al. (2024) <doi:10.1080/01621459.2024.2326621>. The method enables the selection and aggregation of large-scale rare binary features with a known hierarchical structure using a convex, linearly-constrained regularized regression framework. The package facilitates the application of this method to both linear regression and binary classification problems by solving the optimization problem via the smoothing proximal gradient descent algorithm (Chen et al. (2012) <doi:10.1214/11-AOAS514>).
The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on Keras and TensorFlow modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Customizable configurations for the model are allowed, improving the capabilities and usability of this model compared to other packages. This package is based on 'keras' and 'tensorflow' modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
It is a versatile tool for predicting time series data using Long Short-Term Memory (LSTM) models. It is specifically designed to handle time series with an exogenous variable, allowing users to denote whether data was available for a particular period or not. The package encompasses various functionalities, including hyperparameter tuning, custom loss function support, model evaluation, and one-step-ahead forecasting. With an emphasis on ease of use and flexibility, it empowers users to explore, evaluate, and deploy LSTM models for accurate time series predictions and forecasting in diverse applications. More details can be found in Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.
Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) <doi:10.1016/bs.host.2019.01.001>.
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.
Generic methods for use in a time series probabilistic framework, allowing for a common calling convention across packages. Additional methods for time series prediction ensembles and probabilistic plotting of predictions is included. A more detailed description is available at <https://www.nopredict.com/packages/tsmethods> which shows the currently implemented methods in the 'tsmodels' framework.
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).
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.
Fit, compare, and visualize Bayesian graphical vector autoregressive (GVAR) network models using 'Stan'. These models are commonly used in psychology to represent temporal and contemporaneous relationships between multiple variables in intensive longitudinal data. Fitted models can be compared with a test based on matrix norm differences of posterior point estimates to quantify the differences between two estimated networks. See also Siepe, Kloft & Heck (2024) <doi:10.31234/osf.io/uwfjc>.
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>.
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.
Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated 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 prediction is a critical task in data analysis, requiring not only the selection of appropriate models, but also suitable data preprocessing and tuning strategies. 
  TSPredIT (Time Series Prediction with Integrated Tuning) is a framework that provides a seamless integration of data preprocessing, decomposition, model training, hyperparameter optimization, and evaluation. 
  Unlike other frameworks, TSPredIT emphasizes the co-optimization of both preprocessing and modeling steps, improving predictive performance. 
  It supports a variety of statistical and machine learning models, filtering techniques, outlier detection, data augmentation, and ensemble strategies. 
  More information is available in Salles et al. <doi:10.1007/978-3-662-68014-8_2>.
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. For 
    details, see Narajewski et al. (2021) <doi:10.1016/j.softx.2021.100809>.
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>.
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.
Flexible simulation of time series using time series
    components, including seasonal, calendar and outlier effects. Main
    algorithm described in Ollech, D. (2021) <doi:10.1515/jtse-2020-0028>.
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>.
Functions for statistical analysis, modeling and simulation of time
 series with state space model, based on the methodology in Kitagawa
 (2020, ISBN: 978-0-367-18733-0).
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.
Implementation and forecasting univariate time series data using the Support Vector Machine model. Support Vector Machine is one of the prominent machine learning approach for non-linear time series forecasting. For method details see Kim, K. (2003) <doi:10.1016/S0925-2312(03)00372-2>.
Goodness of Fit and Forecast Evaluation Tests for timeseries models. Includes, among others, the Generalized Method of Moments (GMM) Orthogonality Test of Hansen (1982), the Nyblom (1989) parameter constancy test, the sign-bias test of Engle and Ng (1993), and a range of tests for value at risk and expected shortfall evaluation.
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.
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. (2019), <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.
Utilities for rapidly loading specified rows and/or columns of data from large tab-separated value (tsv) files (large: e.g. 1 GB file of 10000 x 10000 matrix). 'tsvio' is an R wrapper to 'C' code that creates an index file for the rows of the tsv file, and uses that index file to collect rows and/or columns from the tsv file without reading the whole file into memory.
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 texts Time Series for Data Science with R by Woodward, Sadler and Robertson & 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 (2020) <doi:10.1007/s11222-020-09932-y>.
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>.
Some accelerated three-term conjugate gradient algorithms implemented purely in R with the same user interface as optim(). The search directions and acceleration scheme are described in Andrei, N. (2013) <doi:10.1016/j.amc.2012.11.097>, Andrei, N. (2013) <doi:10.1016/j.cam.2012.10.002>, and Andrei, N (2015) <doi:10.1007/s11075-014-9845-9>. Line search is done by a hybrid algorithm incorporating the ideas in Oliveia and Takahashi (2020) <doi:10.1145/3423597> and More and Thuente (1994) <doi:10.1145/192115.192132>.
The 'tinytest' package offers a light-weight zero-dependency unit-testing
 framework to which this package adds support via the 'diffobj' package for 'diff'-style
 textual comparison of R objects, as well as via 'tinysnapshot' package for visual
 differences in plots.
Analyzing treatment effects in clinical trials with time-to-event outcomes is complicated by intercurrent events. This package implements methods for estimating and inferring the cumulative incidence functions for time-to-event (TTE) outcomes with intercurrent events (ICEs) under the five strategies outlined in the ICH E9 (R1) addendum, see Deng (2025)<doi:10.1002/sim.70091>. This package can be used for analyzing data from both randomized controlled trials and observational studies. In general, we have a primary outcome event and possibly an intercurrent event. Two data structures are allowed: competing risks, where only the time to the first event is recorded, and semicompeting risks, where the times to both the primary outcome event and intercurrent event (or censoring) are recorded. For estimation methods, users can choose nonparametric estimation (which does not use covariates) and semiparametrically efficient estimation.
C source code and R wrappers for the tth/ttm TeX-to-HTML/MathML translators.
Likelihood ratio and maximum likelihood statistics are provided 
     that can be used as alternatives to p-values Colquhoun (2017) 
     <doi:10.1098/rsos.171085>. Arguments can be either p-values or t-statistics. 
     together with degrees of freedom. For the function 'tTOlr', the argument
     'twoSided' has the default 'twoSided = TRUE'.
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.
An implementation of the Thornley transport resistance plant growth model. The package can be used to simulate plant growth as forced by climate system variables. The package provides methods for formatting forcing variables, simulating growth dynamics and calibrating model parameters. For more information see Higgins et al. (2025) TTR.PGM: An R package for modelling the distributions and dynamics of plants using the Thornley transport resistance plant growth model. Methods in Ecology and Evolution. in press.
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.
It provides generic methods that are used by more than one package, avoiding conflicts. This package will be imported by 'tidySingleCellExperiment' and 'tidyseurat'.
Create structured, formatted HTML tables of in a flexible and
    convenient way.
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) <doi:10.48550/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.
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.
Helper functions for 'TUFLOW FV' models. Current functionality includes reading in and plotting
    output POINTS files and generating initial conditions based on point observations.
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
Simple utilities to generate a Dockerfile from a directory or
    project, build the corresponding Docker image, push the image to
    DockerHub, and publicly share the project via Binder.
Cooperative game theory models decision-making situations in
    which a group of agents, called players, may achieve certain benefits
    by cooperating to reach an optimal outcome. It has great potential in
    different fields, since it offers a scenario to analyze and solve
    problems in which cooperation is essential to achieve a common goal.
    The 'TUGLab' (Transferable Utility Games Laboratory) R package
    contains a set of scripts that could serve as a helpful complement to
    the books and other materials used in courses on cooperative game
    theory, and also as a practical tool for researchers working in this
    field. The 'TUGLab' project was born in 2006 trying to highlight the
    geometrical aspects of the theory of cooperative games for 3 and 4
    players. 'TUGlabWeb' is an online platform on which the basic
    functions of 'TUGLab' are implemented, and it is being used all over
    the world as a resource in degree, master's and doctoral programs.
    This package is an extension of the first versions and enables users
    to work with games in general (computational restrictions aside). The
    user can check properties of games, compute well-known games and
    calculate several set-valued and single-valued solutions such as the
    core, the Shapley value, the nucleolus or the core-center. The package
    also illustrates how the Shapley value flexibly adapts to various
    cooperative game settings, including weighted players and coalitions,
    a priori unions, and restricted communication structures. In keeping
    with the original philosophy of the first versions, special emphasis
    is placed on the graphical representation of the solution concepts for
    3 and 4 players.
Provides tools to perform multiple comparison analyses, based on the well-known Tukey's "Honestly Significant Difference" (HSD) test. In models involving interactions, 'TukeyC' stands out from other R packages by implementing intuitive and easy-to-use functions. In addition to accommodating traditional R methods such as lm() and aov(), it has also been extended to objects of the lmer() class, that is, mixed models with fixed effects. For more details see Tukey (1949) <doi:10.2307/3001913>.
Functions for density, cumulative density, quantile and
       simulation of Tukey g-and-h (1977) distributions. The
       quantile-based transformation (Hoaglin 1985
       <doi:10.1002/9781118150702.ch11>) and its reverse
       transformation, as well as the letter-value based estimates
       (Hoaglin 1985), are also provided.
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.
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 (Muessel et al. (2012), <doi:10.18637/jss.v046.i05>).
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").
Includes the results of general, local, and presidential elections held in Turkey between 1995 and 2023, broken down by provinces and overall national results. It facilitates easy processing of this data and the creation of visual representations based on these election results.
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.
Helper functions for creating, editing, and testing tutorials
    created with the 'learnr' package. Provides a simple method for allowing
    students to download their answers to tutorial questions. For examples
    of its use, see the 'r4ds.tutorials' package.
The main objective of cooperative games is to allocate a good among
  the agents involved. This package includes the most well-known   allocation 
  rules, i.e., the Shapley value, the Banzhaf value, the egalitarian rule, and 
  the equal surplus division value. In addition, it considers the point of view 
  of a priori unions (situations in which agents can form coalitions). For this
  purpose, the package includes the Owen value, the Banzhaf-Owen value, and the
  corresponding extensions of the egalitarian rules. All these values can be 
  calculated exactly or estimated by sampling.
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.
Create a time-varying dataset using features, exposure, and
    look back specifications.
Fit of a double additive cure survival model with time-varying covariates. The additive terms in the long- and short-term survival submodels, modelling the cure probability and the event timing for susceptible units, are estimated using Laplace P-splines. For more details, see Lambert and Kreyenfeld (2025) <doi:10.1093/jrsssa/qnaf035>.
Total variation denoising can be used to approximate a given
    sequence of noisy observations by a piecewise constant sequence, with 
    adaptively-chosen break points. An efficient linear-time algorithm for 
    total variation denoising is provided here, based on Johnson (2013)
    <doi:10.1080/10618600.2012.681238>.
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, Yajnaseni Chakraborti, and Hanjoo Kim for their valuable help with testing the package and the 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.
Simulation, estimation and inference for univariate and multivariate TV(s)-GARCH(p,q,r)-X models, where s indicates the number and shape of the transition functions, p is the ARCH order, q is the GARCH order, r is the asymmetry order, and 'X' indicates that covariates can be included; see Campos-Martins and Sucarrat (2024) <doi:10.18637/jss.v108.i09>. In the multivariate case, variances are estimated equation by equation and dynamic conditional correlations are allowed. The TV long-term component of the variance as in the multiplicative TV-GARCH model of Amado and Terasvirta (2013) <doi:10.1016/j.jeconom.2013.03.006> introduces non-stationarity whereas the GARCH-X short-term component describes conditional heteroscedasticity. Maximisation by parts leads to consistent and asymptotically normal estimates.
Estimates the time-varying (tv) parameters of the GARCH(1,1) model, enabling the modeling of non-stationary volatilities by allowing the model parameters to change gradually over time. The estimation and prediction processes are facilitated through the application of the Kalman filter and state-space equations. This package supports the estimation of tv parameters for various deterministic functions, which can be identified through exploratory analysis of different time periods or segments of return data. The methodology is grounded in the framework presented by Ferreira et al. (2017) <doi:10.1080/00949655.2017.1334778>.
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.
Functions for compounding and discounting calculations included here serve as a complete reference for various scenarios of time value of money.
    Raymond M. Brooks (“Financial Management,” 2018, ISBN: 9780134730417).
    Sheridan Titman, Arthur J. Keown, John D. Martin (“Financial Management: Principles and Applications,” 2017, ISBN: 9780134417219).
    Jonathan Berk, Peter DeMarzo, David Stangeland, Andras Marosi (“Fundamentals of Corporate Finance,” 2019, ISBN: 9780134735313).
    S. A. Hummelbrunner, Kelly Halliday, Ali R. Hassanlou (“Contemporary Business Mathematics with Canadian Applications,” 2020, ISBN: 9780135285015).
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 the estimation of a time-dependent covariance matrix of returns with the intended use for portfolio optimization. The package offers methods for determining the optimal number of factors to be used in the covariance estimation, a hypothesis test of time-varying covariance, and user-friendly functions for portfolio optimization and rolling window evaluation. The local PCA method, method for determining the number of factors, and associated hypothesis test are based on Su and Wang (2017) <doi:10.1016/j.jeconom.2016.12.004>. The approach to time-varying portfolio optimization follows Fan et al. (2024) <doi:10.1016/j.jeconom.2022.08.007>. The regularisation applied to the residual covariance matrix adopts the technique introduced by Chen et al. (2019) <doi:10.1016/j.jeconom.2019.04.025>.
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.
Longitudinal data offers insights into population changes over
 time but often requires a flexible structure, especially with varying
 follow-up intervals. Panel data is one way to store such records, though
 it adds complexity to analysis. The 'tvtools' package for R simplifies
 exploring and analyzing panel data.
Provides functions for propensity score
        estimating and weighting, nonresponse weighting, and diagnosis
        of the weights.
Provides functions for propensity score
        estimation and weighting for continuous exposures as described in Zhu, Y., 
        Coffman, D. L., & Ghosh, D. (2015). A boosting algorithm for
        estimating generalized propensity scores with continuous treatments.
        Journal of Causal Inference, 3(1), 25-40. <doi:10.1515/jci-2014-0022>.
Provides functions for estimating natural direct and indirect effects for mediation analysis. It uses weighting where the weights are functions of estimates of the probability of exposure or treatment assignment (Hong, G (2010). <https://cepa.stanford.edu/sites/default/files/workshops/GH_JSM%20Proceedings%202010.pdf> Huber, M. (2014). <doi:10.1002/jae.2341>). Estimation of probabilities can use generalized boosting or logistic regression. Additional functions provide diagnostics of the model fit and weights. The vignette provides details and examples.
High-performance parsing of 'Tableau' workbook files into
    tidy data frames and dependency graphs for other visualization tools
    like R 'Shiny' or 'Power BI' replication.
An open-access tool/framework that constitutes the core functions
  to analyze terrestrial water cycle data across various spatio-temporal scales.
Implements Time-Weighted Dynamic Time Warping (TWDTW), 
    a measure for quantifying time series similarity. The TWDTW algorithm, 
    described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and 
    Maus et al. (2019) <doi:10.18637/jss.v088.i05>, is applicable to multi-dimensional 
    time series of various resolutions. It is particularly suitable for comparing 
    time series with seasonality for environmental and ecological data analysis, 
    covering domains such as remote sensing imagery, climate data, hydrology, 
    and animal movement. The 'twdtw' package offers a user-friendly 'R' interface, 
    efficient 'Fortran' routines for TWDTW calculations, flexible time weighting 
    definitions, as well as utilities for time series preprocessing and visualization.
Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; <doi:10.1007/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.
An interface to 'twitter-text', a 'JavaScript' library which
   is responsible for determining the length/validity of a tweet and 
   identifying/linking any URLs or special tags (e.g. mentions or
   hashtags) which may be present.
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.
Implements a decomposition of the two-way fixed effects instrumental variable estimator into all possible Wald difference-in-differences estimators. 
  Provides functions to summarize the contribution of different cohort comparisons to the overall two-way fixed effects instrumental variable estimate, with or without controls. 
  The method is described in Miyaji (2024) <doi:10.48550/arXiv.2405.16467>.
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.
Provides tools for building decision and cost-effectiveness analysis models. It enables users to write these models concisely, simulate outcomes—including probabilistic analyses—efficiently using optimized vectorized processes and parallel computing, and produce results. The package employs a Grammar of Modeling approach, inspired by the Grammar of Graphics, to streamline model construction. For an interactive graphical user interface, see 'DecisionTwig' at <https://www.dashlab.ca/projects/decision_twig/>. Comprehensive tutorials and vignettes are available at <https://hjalal.github.io/twig/>.
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.
A global-local approximation framework for large-scale Gaussian process modeling. 
                Please see Vakayil and Joseph (2024) <doi:10.1080/00401706.2023.2296451> for details.
                This work is supported by U.S. NSF grants CMMI-1921646 and DMREF-1921873.
An efficient algorithm for data twinning. This work is supported by U.S. National Science 
	Foundation grants DMREF-1921873 and CMMI-1921646.
Collect  your data on digital marketing campaigns from Twitter Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
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.
A system to simulate clinical trials with time to event endpoints. Event simulation is based on Cox models allowing for covariates in addition to the treatment or group factor. Specific drop-out rates (separate from administrative censoring) can be controlled in the simulation. Other features include stratified randomization, non-proportional hazards, different accrual patterns, and event projection (timing to reach the target event) based on interim data.
Provides a calculator for the two-dimensional clinical Disease Activity 
    index for Psoriatic Arthritis (TwoDcDAPSA), a principal component-derived measure 
    that complements the conventional clinical DAPSA score. The TwoDcDAPSA captures 
    residual variation in patient-reported outcomes (pain and patient global assessment) 
    and joint counts (swollen and tender) after adjusting for standardized cDAPSA using 
    natural spline coefficients derived from published models. Residuals are 
    standardized and combined with fixed principal component loadings to yield a 
    continuous PROs-Joint Contrast (PJC) score and quartile groupings. The package 
    applies pre-specified coefficients and loadings to new datasets but does not 
    estimate spline models or principal components itself.
Implements two-mode clustering (biclustering) using 
    genetic algorithms. The method was first introduced in 
    Hageman et al. (2008) <doi:10.1007/s11306-008-0105-7>. The 
    package provides tools for fitting, visualization, and 
    validation of two-mode cluster structures in data matrices.
Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and
  confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) <doi:10.1177/1536867X1501500102>.
Density, distribution function, quantile function, 
    and random generation function, maximum likelihood estimation (MLE), 
    penalized maximum likelihood estimation (PMLE), the quartiles method estimation (QM),
    and median rank estimation (MEDRANK) for the two-parameter exponential distribution.
    MLE and PMLE are based on Mengjie Zheng (2013)<https://scse.d.umn.edu/sites/scse.d.umn.edu/files/mengjie-thesis_masters-1.pdf>.
    QM is based on Entisar Elgmati and Nadia Gregni (2016)<doi:10.5539/ijsp.v5n5p12>.
    MEDRANK is based on Matthew Reid (2022)<doi:10.5281/ZENODO.3938000>.
Tools for constructing and analyzing two-phase experimental designs under correlated error structures. Version 1.1.1 includes improved efficiency factor classification with tolerance control, updated plot visualizations, and improved clarity of the results. The conceptual framework and the term two-phase were introduced by McIntyre (1955) <doi:10.2307/3001770>).
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 the TWO-Component Single Cell Model-Based Association Method (TWO-SIGMA) for gene-level differential expression (DE) analysis and DE-based gene set testing of single-cell RNA-sequencing datasets. See Van Buren et al. (2020) <doi:10.1002/gepi.22361> and Van Buren et al. (2021) <doi:10.1101/2021.01.24.427979>.
An inverse probability of censoring weighted (IPCW) targeted maximum likelihood estimator (TMLE) for evaluating a marginal point treatment effect from data where some variables were collected on only a subset of participants using a two-stage design (or marginal mean outcome for a single arm study). A TMLE for conditional parameters defined by a marginal structural model (MSM) is also available.
Conditional logistic regression with longitudinal follow up and
    individual-level random coefficients: A stable and efficient
    two-step estimation method.
Analyse time to event data with two time scales by estimating a smooth hazard that varies over two time scales and also, if covariates are available, to estimate a proportional hazards model with such a two-dimensional baseline hazard. 
  Functions are provided to prepare the raw data for estimation, to estimate and to plot the two-dimensional smooth hazard. 
  Extension to a competing risks model are implemented. For details about the method please refer to Carollo et al. (2024) <doi:10.1002/sim.10297>.
Implements combined p-value functions for two trials along with compatible combined point and interval estimates as described in Pawel, Roos, and Held (2025) <doi:10.48550/arXiv.2503.10246>.
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.
Estimates the weights and measure of robustness to treatment effect heterogeneity attached to two-way fixed effects regressions.
    Clément de Chaisemartin, Xavier D'Haultfœuille (2020) <DOI: 10.1257/aer.20181169>.
Performs two-way tests in independent groups designs. These are two-way ANOVA, two-way ANOVA under heteroscedasticity: parametric bootstrap based generalized test and generalized pivotal quantity based generalized test, two-way ANOVA for medians, trimmed means, M-estimators. 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  (Dag et al., 2024, <doi:10.1016/j.softx.2024.101862>).
A collection of functions for data analysis with two-by-two contingency tables. The package provides tools to compute measures of effect (odds ratio, risk ratio, and risk difference), calculate impact numbers and attributable fractions, and perform hypothesis testing. Statistical analysis methods are oriented towards epidemiological investigation of relationships between exposures and outcomes.
A model for the growth of self-limiting populations using three, four, or five parameter 
    functions, which have wide applications in a variety of fields.
    The dependent variable in a dynamical modeling could be the population size at time x, 
    where x is the independent variable. In the analysis of quantitative polymerase chain reaction 
    (qPCR), the dependent variable would be the fluorescence intensity and the independent variable 
    the cycle number. This package then would calculate the TWW cycle threshold.
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 the counterfactual mean
    value of an outcome of interest under an additive modified treatment policy,
    a stochastic intervention that may depend on the natural value of the
    exposure. To accommodate settings with outcome-dependent two-phase
    sampling, procedures incorporating inverse probability of censoring
    weighting are provided to facilitate the construction of inefficient and
    efficient one-step and targeted minimum loss estimators.  The causal
    parameter and its estimation were first described by Díaz and van der Laan
    (2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust
    estimation procedure and its application to data from two-phase sampling
    designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert,
    and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package
    implementation is described in NS Hejazi and DC Benkeser (2020)
    <doi:10.21105/joss.02447>. 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.
A type system for R. It supports setting variable types in a script or the body of a function, so variables can't be assigned illegal values. Moreover it supports setting argument and return types for functions.
Type hints are special comments within a function body indicating the intended nature of the function's arguments in terms of data types, dimensions and permitted values. The actual parameters with which the function is called are evaluated against these type hint comments at run-time.
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.
The 'R' language includes a set of defined types, but the language
    itself is "absurdly dynamic" (Turcotte & Vitek (2019)
    <doi:10.1145/3340670.3342426>), and lacks any way to specify which types are
    expected by any expression. The 'typetracer' package enables code to be
    traced to extract detailed information on the properties of parameters
    passed to 'R' functions. 'typetracer' can trace individual functions or
    entire packages.
Compile 'Typst' files using the 'typst-cli' (<https://typst.app>)
    command line tool. Automatically falls back to rendering via embedded 'Typst'
    from 'Quarto' (<https://quarto.org>) if 'Typst' is not installed. Includes
    utilities to check for 'typst-cli' availability and run 'Typst' commands.
Provides an up-to-date copy of the Internet Assigned Numbers
    Authority (IANA) Time Zone Database. It is updated periodically to
    reflect changes made by political bodies to time zone boundaries, UTC
    offsets, and daylight saving time rules. Additionally, this package
    provides a C++ interface for working with the 'date' library. 'date'
    provides comprehensive support for working with dates and date-times,
    which this package exposes to make it easier for other R packages to
    utilize. Headers are provided for calendar specific calculations,
    along with a limited interface for time zone manipulations.
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!
Contains functions for calculating under-five child mortality estimates using the Trussell version of the Brass method (United Nations (1990) <https://www.un.org/en/development/desa/population/publications/pdf/mortality/stepguide_childmort.pdf> and United Nations (1983) <https://www.un.org/en/development/desa/population/publications/pdf/mortality/stepguide_childmort.pdf>) as well as applying the cohort-derived methods by Rajaratnam and colleagues (Rajaratnam JK, Tran LN, Lopez AD, Murray CJL (2010) "Measuring Under-Five Mortality: Validation of New Low-Cost Methods" <doi:10.1371/journal.pmed.1000253>).
An educational toolkit for learning statistical concepts through 
    interactive exploration. Provides functions for basic statistics (mean, 
    variance, etc.) and probability distributions with step-by-step explanations 
    and interactive learning modes. Each function can be used for simple 
    calculations, detailed learning with explanations, or interactive practice 
    with feedback.
A comprehensive educational package combining clustering algorithms with 
    detailed step-by-step explanations. Provides implementations of both traditional 
    (hierarchical, k-means) and modern (Density-Based Spatial Clustering of Applications with Noise (DBSCAN), 
    Gaussian Mixture Models (GMM), genetic k-means) clustering methods 
    as described in Ezugwu et. al., (2022) <doi:10.1016/j.engappai.2022.104743>. 
    Includes educational datasets highlighting different clustering challenges, based on 
    'scikit-learn' examples (Pedregosa et al., 2011) 
    <https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.html>. Features detailed 
    algorithm explanations, visualizations, and weighted distance calculations for 
    enhanced learning.
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 ready to fly unmanned aerial vehicles to retrieve aerial or spot related data. It creates either intermediate flight control files for the DJI-Litchi supported series or ready to upload control files for the pixhawk-based flight controller. Additionally it contains some useful tools for digitizing and data manipulation.
Analyzes the impact of external conditions on air quality using counterfactual approaches, featuring methods for data preparation, modeling, and visualization.
The framework proposed in Jenul et al., (2022) <doi:10.1007/s10994-022-06221-9>, together with an interactive Shiny dashboard. 'UBayFS' is an ensemble feature selection technique embedded in a Bayesian statistical framework. The method combines data and user knowledge, where the first is extracted via data-driven ensemble feature selection. The user can control the feature selection by assigning prior weights to features and penalizing specific feature combinations. 'UBayFS' can be used for common feature selection as well as block feature selection.
Two Phase I designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method (<doi:10.2307/2531628>). 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'.
Fit Bayesian hierarchical models of animal abundance and occurrence
    via the 'rstan' package, the R interface to the 'Stan' C++ library.
    Supported models include single-season occupancy, dynamic occupancy, and
    N-mixture abundance models. Covariates on model parameters are specified
    using a formula-based interface similar to package 'unmarked', while also
    allowing for estimation of random slope and intercept terms. References:
    Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>;
    Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Basic statistical analyses. The package has been developed
    to be used in statistics courses at Bocconi University (Milan, Italy).
    Currently, the package includes some exploratory and inferential analyses
    usually presented in introductory statistics courses.
Calculates the Urban Centrality Index (UCI) as in Pereira et al., 
             (2013) <doi:10.1111/gean.12002>. The UCI measures the 
             extent to which the spatial organization of a city or region varies 
             from extreme polycentric to extreme monocentric in a continuous 
             scale from 0 to 1. Values closer to 0 indicate more polycentric patterns
             and values closer to 1 indicate a more monocentric urban form.
Returns a data frame with the names of the input data points and hex colors (or CIELab coordinates). Data can be mapped to colors for use in data visualization. It optimally maps data points into a polygon that represents the CIELab colour space. Since Euclidean distance approximates relative perceptual differences in CIELab color space, the result is a color encoding that aims to capture much of the structure of the original data.
Find and import datasets from the
    University of California Irvine Machine Learning (UCI ML) Repository into R.
    Supports working with data from UCI ML repository inside of R scripts, notebooks, 
    and 'Quarto'/'RMarkdown' documents. Access the UCI ML repository directly at
    <https://archive.ics.uci.edu/>.
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>, Marcio 
    Valk and Gabriela B. Cybis (2020) <doi:10.1080/10618600.2020.1796398>, Debora Z. Bello, Marcio 
    Valk and Gabriela B. Cybis (2021) <arXiv:2106.09115>.
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'.
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 functions and a Shiny application 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.
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.
Uniform Error Index is the weighted average of different error measures. Uniform Error Index utilizes output from different error function and gives more robust and stable error values. This package has been developed to compute Uniform Error Index from ten different loss function like Error Square, Square of Square Error, Quasi Likelihood Error, LogR-Square, Absolute Error, Absolute Square Error etc. The weights are determined using Principal Component Analysis (PCA) algorithm of Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.
Enables the user to calculate Value at Risk (VaR) and Expected 
    Shortfall (ES) by means of various parametric and semiparametric 
    GARCH-type models. For the latter the estimation of the nonparametric scale
    function is carried out by means of a data-driven smoothing approach. Model
    quality, in terms of forecasting VaR and ES, can be assessed by means of 
    various backtesting methods such as the traffic light test for VaR and a 
    newly developed traffic light test for ES. The approaches implemented in 
    this package are described in e.g. Feng Y., Beran J., Letmathe S. and 
    Ghosh S. (2020) <https://ideas.repec.org/p/pdn/ciepap/137.html> as well as 
    Letmathe S., Feng Y. and Uhde A. (2021) 
    <https://ideas.repec.org/p/pdn/ciepap/141.html>.
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.
Density, distribution function, quantile function, and random generating function of the Unit-Garima distribution
    based on Ayuyuen, S., & Bodhisuwan, W. (2024)<doi:10.18187/pjsor.v20i1.4307>.
An R API providing easy access to a relational database with macroeconomic, 
             financial and development related time series data for Uganda. 
             Overall more than 5000 series at varying frequency (daily, monthly, 
             quarterly, annual in fiscal or calendar years) can be accessed through 
             the API. The data is provided by the Bank of Uganda, 
             the Ugandan Ministry of Finance, Planning and Economic Development,
             the IMF and the World Bank. The database is being updated once a month.
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 (2023) <doi:10.1080/03610926.2021.1916531>,
    Savi Virolainen (2022) <doi:10.1515/snde-2020-0060>.
Unit-Gompertz density, cumulative distribution, quantile functions and random deviate
   	generation of the unit-Gompertz distribution. In addition, there are a function for fitting the 
   	Generalized Additive Models for Location, Scale and Shape.
Run a Gibbs sampler for hurdle models to analyze data showing an excess of zeros, which is common in zero-inflated count and semi-continuous models. The package includes the hurdle model under Gaussian, Gamma, inverse Gaussian, Weibull, Exponential, Beta, Poisson, negative binomial, logarithmic, Bell, generalized Poisson, and binomial distributional assumptions. The models described in Ganjali et al. (2024).
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>.
Retrieve data from the UNESCO Institute for Statistics (UIS) API
    <https://api.uis.unesco.org/api/public/documentation/>. UIS provides public 
    access to more than 4,000 indicators focusing on education, science and
    technology, culture, and communication.
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.
Functions for converting between UK and US spellings of English words.
Full listing of UK baby names occurring more than three times per year between 1974 and 2020, and rankings of baby name popularity by decade from 1904 to 1994.
A suite of utilities for working with the UK Biobank 
    <https://www.ukbiobank.ac.uk/> Nuclear Magnetic Resonance spectroscopy (NMR) 
    metabolomics data <https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=220>. 
    Includes functions for extracting biomarkers from decoded UK Biobank field 
    data, removing unwanted technical variation from biomarker concentrations,
    computing an extended set of lipid, fatty acid, and cholesterol fractions, 
    and for re-deriving composite biomarkers and ratios after adjusting data 
    for unwanted biological variation. For further details on methods see
    Ritchie SC et al. Sci Data (2023) <doi:10.1038/s41597-023-01949-y>.
Functions to implement the methods of the Flood Estimation Handbook (FEH), associated updates and the revitalised flood hydrograph model (ReFH). Currently the package uses NRFA peak flow dataset version 14. 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). "Making better use of local data in flood frequency estimation", Environment Agency (2017, ISBN: 978 1 84911 387 8). "Sampling uncertainty of UK design flood estimation" , Hammond (2021, <doi:10.2166/nh.2021.059>). "The FEH 2025 statistical method update", UK Centre for Ecology and Hydrology (2025). "Low flow estimation in the United Kingdom", Institute of Hydrology (1992, ISBN 0 948540 45 1). Data from the UK National River Flow Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions: <https://nrfa.ceh.ac.uk/help/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.
Programmatic interface to access data from the UK Health Security Agency (UKHSA) Data Dashboard API. The package was originally based on the 'ukcovid19' package by Pouria Hadjibagheri and has been substantially rewritten and extended. For more information on the API, see <https://ukhsa-dashboard.data.gov.uk/access-our-data>.
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.
Extracts coordinates of an event location from text based on dictionaries of landmarks, roads, and areas. Only returns the location of an event of interest and ignores other location references; for example, if determining the location of a road traffic crash from the text "crash near [location 1] heading towards [location 2]", only the coordinates of "location 1" would be returned. Moreover, accounts for differences in spelling between how a user references a location and how a location is captured in location dictionaries.
Universally unique identifiers ('UUIDs') can be sub-optimal for many
  uses-cases because they are not 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. Providing an alternative, 'ULIDs'
  (<https://github.com/ulid/spec>) have 128-bit compatibility with 'UUID', 1.21e+24
  unique 'ULIDs' per millisecond, support standard (text) sorting, canonically encoded
  as a 26 character string, as opposed to the 36 character 'UUID', use 'base32'
  encoding for better efficiency and readability (5 bits per character), are case
  insensitive, have no special characters (i.e. are 'URL' safe) and have a monotonic
  sort order (correctly detects and handles the same millisecond).
A tool to define the rare biosphere. 'ulrb' solves the problem of the 
    definition of rarity by replacing arbitrary thresholds with an unsupervised machine 
    learning algorithm (partitioning around medoids, or k-medoids). This algorithm 
    works for any type of microbiome data, provided there is an abundance table.
    This method also works for non-microbiome data.
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.
Plots traced ultrasound tongue imaging data according to a polar coordinate system. 
    There is currently support for plotting means and standard deviations of each category's trace;
    Smoothing Splines Analysis of Variance (SSANOVA) could be implemented as well.  The origin of the 
    polar coordinates may be defined manually or automatically determined based on different 
    algorithms.
    Currently 'ultrapolaRplot' supports ultrasound tongue imaging trace data from 'UltraTrace' 
    (<https://github.com/SwatPhonLab/UltraTrace>). 'UltraTrace' is capable of importing data from 
    Articulate Instruments AAA.
    'read_textgrid.R' is required for opening TextGrids to determine category and alignment
    information of ultrasound traces.
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'. Based on the recognized data structures, the method can be used to generate new data.
A convenient wrapper for the UM-Bridge protocol. UM-Bridge is a protocol designed for coupling uncertainty quantification (or statistical / optimization) software to numerical models. A model is represented as a mathematical function with optional support for derivatives via Jacobian actions etc.
Tools for analyzing sequencing data containing unique
    molecular identifiers generated by 'UMIErrorCorrect'
    (<https://github.com/stahlberggroup/umierrorcorrect>).
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.
Quickly create, run, and report structural equation models, and twin models.
    See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS.
    Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R.
    Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.
Una herramienta rápida y consistente para la disposición de
    microdatos y la visualización de las cifras y estadísticas oficiales
    de la Universidad Nacional de Colombia <https://unal.edu.co>. Contiene
    una biblioteca de funciones gráficas, tanto estáticas como
    interactivas, que ofrece numerosos tipos de gráficos con una sintaxis
    altamente configurable y simple. Entre estos encontramos la
    visualización de tablas HTML, series, gráficos de barras y circulares,
    mapas, etc. Todo lo anterior apoyado en bibliotecas de JavaScript.
    English: A fast and consistent tool for the arrangement of microdata
    and the visualization of official figures and statistics from the
    National University of Colombia <https://unal.edu.co>. It includes a
    library of graphical functions, both static and interactive, offering
    numerous types of charts with a highly configurable and simple syntax.
    Among these, we find the visualization of HTML tables, series, bar and
    pie charts, maps, etc. It provides the capability to transition from
    the interactive to the dynamic world and from one library to another
    without changing function or syntax.
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.
Implements the Gaussian method of first and second order, the Kragten numerical method and the Monte Carlo simulation method for uncertainty estimation and analysis.
Provides functions for estimating uncertainty in the number of fatalities in the Uppsala Conflict Data Program (UCDP) data. The package implements a parametric reported-value Gumbel mixture distribution that accounts for the uncertainty in the number of fatalities in the UCDP data. The model is based on information from a survey on UCDP coders and how they view the uncertainty of the number of fatalities from UCDP events. The package provides functions for making random draws of fatalities from the mixture distribution, as well as to estimate percentiles, quantiles, means, and other statistics of the distribution. Full details on the survey and estimation procedure can be found in Vesco et al (2024).
Dataset contains select attributes for each match result since 1949-1950 season for UNC men's basketball team.
Implements Minimum Torsion for portfolio diversification as
    described in Meucci, Attilio (2013) <doi:10.2139/ssrn.2276632>.
Model data with a suspected clustering structure (either in 
    co-variate space, regression space or both) using a Bayesian product model 
    with a logistic regression likelihood. Observations are represented 
    graphically and clusters are formed through various edge removals or 
    additions. Cluster quality is assessed through the log Bayesian evidence of 
    the overall model, which is estimated using either a Sequential Monte Carlo 
    sampler or a suitable transformation of the Bayesian Information Criterion 
    as a fast approximation of the former. The internal Iterated Batch 
    Importance Sampling scheme (Chopin (2002 <doi:10.1093/biomet/89.3.539>)) is 
    made available as a free standing function.
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.
A framework for estimating difference-in-differences with
 unpoolable data, based on Karim, Webb, Austin, and Strumpf (2024)
 <doi:10.48550/arXiv.2403.15910>. Supports common or staggered adoption, multiple
 groups, and the inclusion of covariates. Also computes p-values for the
 aggregate average treatment effect on the treated via the
 randomization inference procedure described in MacKinnon and Webb (2020)
 <doi:10.1016/j.jeconom.2020.04.024>.
Detects values imported from spreadsheets that were auto-converted 
    to Excel date serials and reconstructs the originally intended day.month
    decimals (for example, '30.3' that Excel displayed as '30/03/2025').
    The functions work in a vectorized manner, preserve non-serial values, and 
    support both the 1900 and 1904 date systems.
A variational mapping approach that reveals and expands future temporal dynamics from folded high-dimensional geometric distance spaces, unfold turns a set of time series into a 4D block of pairwise distances between reframed windows, learns a variational mapper that maps those distances to the next reframed window, and produces horizon-wise predictive functions for each input series. In short: it unfolds the future path of each series from a folded geometric distance representation.
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>.
Create United Nations High Commissioner for Refugees (UNHCR)
    branded documents, presentations, and reports using R Markdown templates.
    This package provides customized formats that align with UNHCR's official
    brand guidelines for creating professional PDF reports, Word documents,
    PowerPoint presentations, and HTML outputs.
A 'ggplot2' theme and color palettes following
    the United Nations High Commissioner for Refugees (UNHCR) Data Visualization Guidelines recommendations.
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.
Implement a shrinkage estimation for the univariate normal mean based on a preliminary test (pretest) estimator. This package also provides the confidence interval based on pivoting the cumulative density function. The methodologies are published in Taketomi et al.(2024) <doi:10.1007/s42081-023-00221-2> and Taketomi et al.(2024-)(under review).
A classification (decision) tree is constructed from survival data with high-dimensional covariates.
 The method is a robust version of the logrank tree, where the variance is stabilized.
 The main function "uni.tree" returns a classification tree for a given survival dataset.
 The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests.
 The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user).
 This tree construction algorithm is proposed by Emura et al. (2021, in review).
Nonparametric estimation of a unimodal or U-shape covariate effect under additive hazards model.
UNIfied Cross-Omics deconvolution (Unico) deconvolves
    standard 2-dimensional bulk matrices of samples by features into a
    3-dimensional tensors representing samples by features by cell types.
    Unico stands out as the first principled model-based deconvolution
    method that is theoretically justified for any heterogeneous genomic
    data. For more details see Chen and Rahmani et al. (2024)
    <doi:10.1101/2024.01.27.577588>.
Data from Unicode 16.0.0 and related utilities.
Most universities use specific color combinations to express their unique brand identity. The 'unicol' package provides the colors and color palettes of various universities for easy plotting and printing in R. We collect and provide a diverse range of color palettes for creating scientific visualizations.
Testing whether two discrete variables have a functional
  relationship under null distributions where the two variables are
  statistically independent with fixed marginal counts.
  The fast enumeration algorithm was based on (Nguyen et al. 2020) <doi:10.24963/ijcai.2020/372>.
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.
Provides a unified R6-based interface for various machine learning models with automatic interface detection, consistent cross-validation, model interpretations via numerical derivatives, and visualization. Supports both regression and classification tasks with any model function that follows R's standard modeling conventions (formula or matrix interface).
Functions for the creation and manipulation of scenes and objects
    within the 'Unity' '3D' video game engine (<https://unity.com/>). Specific
    focuses include the creation and import of terrain data and 'GameObjects' as
    well as scene management.
Uniform sampling on various geometric shapes, such as
    spheres, ellipsoids, simplices.
In diagnostic contexts, individuals are often assessed using multiple
 tests that measure the same latent variable (e.g., intelligence). These test scores
 are typically not exactly identical. Simple averaging neglects the correlation
 between tests and the reduced variance of their combination. The 'unifyR' package
 provides functions to compute statistically accurate unified scores, reliabilities
 and validities of multiple tests. The underlying algorithms build on and extend the
 method proposed by Evans (1996, <DOI:10.3758/BF03204767>) and have been validated
 through simulations.
A unified R graphics backend. Render R graphics fast and easy to many common file formats.
    Provides a thread safe 'C' interface for asynchronous rendering of R graphics.
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 finding, 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 decoration 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 purposes or institutions.
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 <doi:10.1016/j.jprot.2019.103613>.
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.
A collection of parametric quantile regression models for bounded data. At present, the package provides 13 parametric quantile regression models. It can specify regression structure for any quantile and shape parameters. It also provides several S3 methods to extract information from fitted model, such as residual analysis, prediction, plotting, and model comparison. For more computation efficient the [dpqr]'s, likelihood, score and hessian functions are written in C++. For further details see Mazucheli et. al (2022) <doi:10.1016/j.cmpb.2022.106816>.
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.
A test to understand the stability of the underlying 
    stochastic data. Helps the user’s understand whether the random variable under 
    consideration is stationary or non-stationary without any manual interpretation 
    of the results. It further ensures to check all the prerequisites and assumptions which 
    are underlying the unit root test statistics and if the underlying data is found to be non-stationary in all
    the 4 lags the function diagnoses the input data and returns with an optimised solution on the same.
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.
Algorithms for checking the accuracy of a clustering result with known classes, computing cluster validity indices, and generating plots for comparing them.
    The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage).
    The details of the indices in this package can be found in:
    J. C. Bezdek, M. Moshtaghi, T. Runkler, C. Leckie (2016) <doi:10.1109/TFUZZ.2016.2540063>,
    T. Calinski, J. Harabasz (1974) <doi:10.1080/03610927408827101>,
    C. H. Chou, M. C. Su, E. Lai (2004) <doi:10.1007/s10044-004-0218-1>,
    D. L. Davies, D. W. Bouldin (1979) <doi:10.1109/TPAMI.1979.4766909>,
    J. C. Dunn (1973) <doi:10.1080/01969727308546046>,
    F. Haouas, Z. Ben Dhiaf, A. Hammouda, B. Solaiman (2017) <doi:10.1109/FUZZ-IEEE.2017.8015651>,
    M. Kim, R. S. Ramakrishna (2005) <doi:10.1016/j.patrec.2005.04.007>,
    S. H. Kwon (1998) <doi:10.1049/EL:19981523>,
    S. H. Kwon, J. Kim, S. H. Son (2021) <doi:10.1049/ell2.12249>,
    G. W. Miligan (1980) <doi:10.1007/BF02293907>,
    M. K. Pakhira, S. Bandyopadhyay, U. Maulik (2004) <doi:10.1016/j.patcog.2003.06.005>,
    M. Popescu, J. C. Bezdek, T. C. Havens, J. M. Keller (2013) <doi:10.1109/TSMCB.2012.2205679>,
    S. Saitta, B. Raphael, I. Smith (2007) <doi:10.1007/978-3-540-73499-4_14>,
    A. Starczewski (2017) <doi:10.1007/s10044-015-0525-8>,
    Y. Tang, F. Sun, Z. Sun (2005) <doi:10.1109/ACC.2005.1470111>,
    N. Wiroonsri (2024) <doi:10.1016/j.patcog.2023.109910>,
    N. Wiroonsri, O. Preedasawakul (2023) <doi:10.48550/arXiv.2308.14785>,
    C. H. Wu, C. S. Ouyang, L. W. Chen, L. W. Lu (2015) <doi:10.1109/TFUZZ.2014.2322495>, 
    X. Xie, G. Beni (1991) <doi:10.1109/34.85677> and
    Rousseeuw (1987) and Kaufman and Rousseeuw(2009) <doi:10.1016/0377-0427(87)90125-7> and 
    <doi:10.1002/9780470316801>
    C. Alok. (2010).
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. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Tools for fitting and assessing Bayesian multilevel regression 
  models that account for unmeasured confounders.
Fast flattening of hierarchical data structures (e.g. JSON, XML)
             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 described in Helgeson, Vock, and Bair (2021) <doi:10.1111/biom.13376>.
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.
Methods for managing under- and over-enrollment in Simon's Two-Stage Design are offered by providing adaptive threshold adjustments and sample size recalibration. It also includes post-inference analysis tools to support clinical trial design and evaluation. The package is designed to enhance flexibility and accuracy in trial design, ensuring better outcomes in oncology and other clinical studies. Yunhe Liu, Haitao Pan (2024). Submitted.
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 <https://www.gnu.org/software/unrtf/> 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.
Automatically converts language-specific verbal information, e.g., "1st half of the 19th century," to its standardized numerical counterparts, e.g., "1801-01-01/1850-12-31." It follows the recommendations of the 'MIDAS' ('Marburger Informations-, Dokumentations- und Administrations-System'), see <doi:10.11588/artdok.00003770>.
Reconstructs all possible raw data that could have led to reported
    summary statistics. Provides a wrapper for the 'Rust' implementation of the
    'CLOSURE' algorithm.
Performs a test for second-order stationarity of time series based
    on unsystematic sub-samples.
Hubbell's Unified Neutral Theory of Biodiversity.
Calculates a Mahalanobis distance for every row of a set of
    outcome variables (Mahalanobis, 1936
    <doi:10.1007/s13171-019-00164-5>). The conditional Mahalanobis
    distance is calculated using a conditional covariance matrix (i.e., a
    covariance matrix of the outcome variables after controlling for a set
    of predictors). Plotting the output of the cond_maha() function can
    help identify which elements of a profile are unusual after
    controlling for the predictors.
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.
This program realizes a universal estimation approach that accommodates
    multi-category variables and effect scales, making up for the deficiencies
    of the existing approaches when dealing with non-binary exposures and
    complex models. The estimation via bootstrapping can simultaneously provide results of
    causal mediation on risk difference (RD), odds ratio (OR) and risk ratio (RR) scales with tests of the effects' difference.
    The estimation is also applicable to many other settings, e.g., moderated mediation, inconsistent covariates,
    panel data, etc. The high flexibility and compatibility
    make it possible to apply for any type of model, greatly meeting the needs of
    current empirical researches.
We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance not pay attention to the accuracy of prediction, but focus on model selection uncertainty and providing more information of the model selection results. And to estimate the model measures, we propose an simplify and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance focuses on model selection uncertainty and providing more information of the model selection results. To estimate the model uncertainty variance, we propose an simplified and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. Please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403.<DOI:10.1111/biom.13024> for more information.
Displays percentage changes by height and absolute changes by area for up to three nested or non-nested levels. The plots visualise changes in indices and markets, showing how the changes for sectors or for individual components contribute to the overall change. Data can be classified by up to three levels of grouping variables in a layered, hierarchical plot. Each level can be ordered in several ways including by baseline, by percentage change, and by absolute change. The vignettes give examples.
Provides an extension to the Partial Credit Model and Generalized Partial Credit Models which allows for an additional person parameter that characterizes the uncertainty of the person. The method was originally proposed by Tutz and Schauberger (2020) <doi:10.1177/0146621620920932>.
When a package is loaded, the source repository is checked for
    new versions and a message is shown in the console indicating whether the
    package is out of date.
When updating major or minor R versions all packages should
    be re-installed. The utilities in this package assist in getting a
    user up-and-running again by installing all previously installed R
    packages. The package uses 'renv' to install; immediately replenishing
    your 'renv' package cache.
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.
Provides an algorithm to detect and characterize disturbances (start, end dates, intensity) that can occur at different hierarchical levels by studying the dynamics of longitudinal observations at the unit level and group level based on Nadaraya-Watson's smoothing curves, but also a shiny app which allows to visualize the observations and the detected disturbances. Finally the package provides a dataframe mimicking a pig farming system subsected to disturbances simulated according to Le et al.(2022) <doi:10.1016/j.animal.2022.100496>.
Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms described in "Gregor Zens, Sylvia Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers – Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association <doi:10.1080/01621459.2023.2259030>".
Up-and-Down (UD) is the most popular design approach for dose-finding, but it has been severely under-served by the statistical and computing communities. This is the first package that comprehensively addresses UD's needs. Recent applied UD tutorial: Oron et al., 2022 <doi:10.1097/ALN.0000000000004282>. Recent methodological overview: Oron and Flournoy, 2024 <doi:10.51387/24-NEJSDS74>.
Using matrix layout to visualize the unique, common, or individual contribution of each predictor (or matrix of predictors) towards explained variation on different models. These contributions were derived from variation partitioning (VP) and hierarchical partitioning (HP), applying the algorithm of "Lai et al. (2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution, 13: 782-788 <doi:10.1111/2041-210X.13800>".
'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>.
Core functions necessary for using The Globe and Mail's R data journalism template, 'startr', along with utilities for day-to-day data journalism tasks, such as reading and writing files, producing graphics and cleaning up datasets.
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.
Provides researchers with a simple set of diagnostic tools for monitoring the progress and reliability of raters conducting content coding tasks. Goehring (2024) <https://bengoehring.github.io/improving-content-analysis-tools-for-working-with-undergraduate-research-assistants.pdf> argues that supervisors---especially supervisors of small teams---should utilize computational tools to monitor reliability in real time. As such, this package provides easy-to-use functions for calculating inter-rater reliability statistics and measuring the reliability of one coder compared to the rest of the team.
Calculate unified measures
  that quantify the effect of a covariate on a binary dependent variable
  (e.g., for meta-analyses).
  This can be particularly important
  if the estimation results are obtained
  with different models/estimators
  (e.g., linear probability model, logit, probit, ...)
  and/or with different transformations of the explanatory variable of interest
  (e.g., linear, quadratic, interval-coded, ...).
  The calculated unified measures are:
  (a) semi-elasticities of linear, quadratic, or interval-coded covariates and
  (b) effects of linear, quadratic, interval-coded, or categorical covariates
  when a linear or quadratic covariate changes between distinct intervals,
  the reference category of a categorical variable
  or the reference interval of an interval-coded variable needs to be changed,
  or some categories of a categorical covariate
  or some intervals of an interval-coded covariate need to be grouped together.
  Approximate standard errors of the unified measures are also calculated.
  All methods that are implemented in this package 
  are described in the 'vignette'  
  "Extracting and Unifying Semi-Elasticities and Effect Sizes
  from Studies with Binary Dependent Variables"
  that is included in this package.
Unit root and cointegration tests encountered in applied 
 econometric analysis are implemented.
Provide the URL checking tools available in R 4.1+ as a
    package for earlier versions of R. Also uses concurrent requests so
    can be much faster than the serial versions.
Offers tools for parsing and analyzing URL datasets,
    extracting key components and identifying common patterns. It aids in 
    examining website architecture and identifying SEO issues, helping users
    optimize web presence and content strategy.
A fast and simple 'URL' parser package for 'R'. This package provides
    functions to parse 'URLs' into their components, such as scheme, user, password, host, port,
    path, query, and fragment.
Allows using two URL shortening services, which also provide
    expanding and analytic functions. Specifically developed for 'Bit.ly' (which requires OAuth 2.0) 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.
Conduct unit root tests based on 'EViews' (<https://eviews.com>) routines and report them in tables. 'EViews' (Econometric Views) is a commercial software for econometrics.
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.
Contains a WGS84 datum map of the USA, which includes all
    Commonwealth and State boundaries & also includes Puerto Rico and the U.S.
    Virgin Islands. This map is a reprojection of the NAD83 datum map from the
    USGS National Map. This package contains a subset of the data included in
    the 'USA.state.boundaries.data' package, which is available in a 'drat'
    repository. To install that data package, please follow the instructions at
    <https://gitlab.com/iembry/usa.state.boundaries.data>.
Intended to be used by the United States Copyright Office Product Management Division Business Analysts. Include algorithms for the United States Copyright Office Product Management Division SR Audit Data dataset. The algorithm takes in the SR Audit Data excel file and reformat the spreadsheet such that the values and variables fit the format of the online database. Support functions in this package include clean_str(), which cleans instances of variable AUDIT_LOG; clean_data_to_excel(), which cleans and output the reorganized SR Audit Data dataset in excel format; clean_data_to_dataframe(), which cleans and stores the reorganized SR Audit Data data set to a data frame; format_from_excel(), which reads in the outputted excel file from the clean_data_to_excel() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys. format_from_dataframe(), which reads in the outputted data frame from the clean_data_to_dataframe() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys; support_function(), which takes in the dictionary outputted either from the format_from_dataframe() or format_from_excel() function and returns the data as a formatted data frame according to the original U.S. Copyright Office SR Audit Data online database. The main function of this package is clean_format_all(), which takes in an excel file and returns the formatted data into a new excel and text file according to the format from the U.S. Copyright Office SR Audit Data online database.
Fetch United States Congressional Records from their API <https://api.govinfo.gov/docs/> such as congressional 
    speeches, speaker names, and metadata about congressional sessions, and detailed granule records. 
    Optional parameters allow users to specify congressional sessions, 
    and the maximum number of speeches to retrieve. Data is parsed, cleaned, and 
    returned in a structured dataframe for analysis.
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.
Provides a diverse collection of U.S. datasets 
    encompassing various fields such as crime, economics, education, finance, energy, healthcare, and more.
    It serves as a valuable resource for researchers and analysts seeking to perform in-depth analyses and derive insights from U.S.-specific data.
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).)
Fetch data from the <https://www.justice.gov/developer/api-documentation/api_v1> API such as press releases, blog entries, and speeches. Optional parameters allow users to specify the number of results starting from the earliest or latest entries, and whether these results contain keywords. Data is cleaned for analysis and returned in a dataframe.
Provides functions for uniform sampling of the environmental space, designed to assist species distribution modellers in gathering ecologically relevant pseudo-absence data. The method ensures balanced representation of environmental conditions and helps reduce sampling bias in model calibration. Based on the framework described by Da Re et al. (2023) <doi:10.1111/2041-210X.14209>.
Functions to re-arrange, extract, and work with distances.
Consistent with 'knitr' syntax highlighting, 'usedthese' adds
    a summary table of package & function usage to a Quarto document and
    enables aggregation of usage across a website.
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 other R packages. Miscellaneous operations on data
    frames, matrices and vectors, ROC and PR statistics.
Code snippets to fit models using the tidymodels framework
    can be easily created for a given data set.
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 an overview of the demand for natural gas in the US by state and country level. Data source: US Energy Information Administration <https://www.eia.gov/>.
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.
Calculates federal and state income taxes in the United States. It acts as a wrapper 
    to the NBER's TAXSIM 35 (<http://taxsim.nber.org/taxsim35/>) tax simulator. TAXSIM 35 conducts 
    the calculations, while 'usincometaxes' prepares the data for TAXSIM 35, sends the data to 
    TAXSIM 35's server or communicates with the Web Assembly file, retrieves the data, and places it 
    into a data frame. All without the user worrying about this process.
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, visualizing spatial data, and working with FIPS codes are also provided.
Provides a container for data used by the 'usmap' package.
    The data used by 'usmap' has been extracted into this package so that the
    file size of the 'usmap' package can be reduced greatly. The data in this
    package will be updated roughly once per year as new map data files are
    provided by the US Census Bureau.
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.
Centers of population (centroid) data for census areas in the
    United States.
The "ussher" data set is drawn from original chronological textual historic events. Commonly known as James Ussher's Annals of the World, the source text was originally written in Latin in 1650, and published in English translation in 1658.The data are classified by index, year, epoch (or one of the 7 ancient "Ages of the World"), Biblical source book if referenced (rarely), as well as alternate dating mechanisms, such as "Anno Mundi" (age of the world) or "Julian Period" (dates based upon the Julian calendar). Additional file "usshfull" includes variables that may be of further interest to historians, such as Southern Kingdom and Northern Kingdom discrepant dates, and the original amalgamated dating mechanic used by Ussher in the original text. The raw data can also be called using "usshraw", as described in: Ussher, J. (1658) <https://archive.org/stream/AnnalsOfTheWorld/Annals_djvu.txt>.