Conversion between attitude representations: DCM, Euler angles, Quaternions, and Euler vectors.
    Plus conversion between 2 Euler angle set types (xyx, yzy, zxz, xzx, yxy, zyz, xyz, yzx, zxy, xzy, yxz, zyx).
    Fully vectorized code, with warnings/errors for Euler angles (singularity, out of range, invalid angle order), 
    DCM (orthogonality, not proper, exceeded tolerance to unity determinant) and Euler vectors(not unity).
    Also quaternion and other useful functions.
    Based on SpinCalc by John Fuller and SpinConv by Paolo de Leva.
Implementation of various spirometry equations
    in R, currently the GLI-2012 (Global Lung Initiative;
    Quanjer et al. 2012 <doi:10.1183/09031936.00080312>),
    the race-neutral GLI global 2022 (Global Lung Initiative;
    Bowerman et al. 2023 <doi:10.1164/rccm.202205-0963OC>), the
    NHANES3 (National Health and Nutrition Examination Survey;
    Hankinson et al. 1999 <doi:10.1164/ajrccm.159.1.9712108>)
    and the JRS 2014 (Japanese Respiratory Society; Kubota
    et al. 2014 <doi:10.1016/j.resinv.2014.03.003>) equations.
    Also the GLI-2017 diffusing capacity equations 
    <doi:10.1183/13993003.00010-2017> are implemented.
    Contains user-friendly functions to calculate predicted 
    and LLN (Lower Limit of Normal) values for different 
    spirometric parameters such as FEV1 (Forced Expiratory 
    Volume in 1 second), FVC (Forced Vital Capacity), etc, 
    and to convert absolute spirometry measurements 
    to percent (%) predicted and z-scores.
Enables binary package installations on Linux distributions.
    Provides access to 'RStudio' public repositories at
    <https://packagemanager.posit.co>, and transparent management of
    system requirements without administrative privileges. Currently supported
    distributions are 'CentOS' / 'RHEL', and several 'RHEL' derivatives
    ('Rocky Linux', 'AlmaLinux', 'Oracle Linux', and 'Amazon Linux'),
    'openSUSE' / 'SLES', 'Debian', and 'Ubuntu' LTS.
Provides an interface to the Spotify API <https://developer.spotify.com/documentation/web-api/>.
An implementation of functionalities to transform directed graphs that are bound to a set of
  known forbidden paths. There are several transformations, following the rules provided by Villeneuve 
  and Desaulniers (2005) <doi: 10.1016/j.ejor.2004.01.032>, and Hsu et al. (2009) <doi: 10.1007/978-3-642-03095-6_60>. 
  The resulting graph is generated in a data-frame format. See rsppfp website for more information, 
  documentation an examples.
The SPRITE algorithm creates possible distributions of discrete responses
    based on reported sample parameters, such as mean, standard deviation and range 
    (Heathers et al., 2018, <doi:10.7287/peerj.preprints.26968v1>). This package implements it, 
    drawing heavily on the code for Nick Brown's 'rSPRITE' Shiny app <https://shiny.ieis.tue.nl/sprite/>. 
    In addition, it supports the modeling of distributions based on multi-item (Likert-type) 
    scales and the use of restrictions on the frequency of particular responses.
Calculate generalized R-squared, partial R-squared, and partial correlation coefficients for generalized linear (mixed) models (including quasi models with well defined variance functions).
Allows the user to generate and execute select, insert, update and delete 'SQL' queries the underlying database without having to explicitly write 'SQL' code.
Embeds the SQLite database engine in R and provides an
    interface compliant with the DBI package. The source for the SQLite
    engine (version 3.51.2) and for various extensions is included.
    System libraries will never be consulted because this package relies
    on static linking for the plugins it includes; this also ensures a
    consistent experience across all installations.
A lightweight wrapper around the 'RSQLite' package for streamlined loading of
    data from tabular files (i,e. text delimited files like Comma Separated Values and
    Tab Separated Values, Microsoft Excel, and Arrow Inter-process Communication files)
    in 'SQLite' databases. Includes helper functions for inspecting the structure of
    the input files, and some functions to simplify activities on the 'SQLite' tables.
Parser for 'SQL' statements. Currently, it supports parsing of only 'SELECT' statements.
An implementation of calculating the R-squared measure as a total mediation effect size measure and its confidence interval for moderate- or high-dimensional mediator models. It gives an option to filter out non-mediators using variable selection methods. The original R package is directly related to the paper Yang et al (2021) "Estimation of mediation effect for high-dimensional omics mediators with application to the Framingham Heart Study" <doi:10.1101/774877>. The new version contains a choice of using cross-fitting, which is computationally faster. The details of the cross-fitting method are available in the paper Xu et al (2023) "Speeding up interval estimation for R2-based mediation effect of high-dimensional mediators via cross-fitting" <doi:10.1101/2023.02.06.527391>.
Provides R-squared values and standardized regression coefficients for linear models applied to multiply imputed datasets as obtained by 'mice'. Confidence intervals, zero-order correlations, and alternative adjusted R-squared estimates are also available. The methods are described in Van Ginkel and Karch (2024) <doi:10.1111/bmsp.12344> and in Van Ginkel (2020) <doi:10.1007/s11336-020-09696-4>.
We provide an implementation for Sum of Ranking Differences (SRD), 
    a novel statistical test introduced by Héberger (2010) 
    <doi:10.1016/j.trac.2009.09.009>. The test allows the comparison of 
    different solutions through a reference by first performing a rank 
    transformation on the input, then calculating and comparing the distances 
    between the solutions and the reference - the latter is measured in the 
    L1 norm. The reference can be an external benchmark (e.g. an established 
    gold standard) or can be aggregated from the data. The calculated distances, 
    called SRD scores, are validated in two ways, see Héberger and Kollár-Hunek 
    (2011) <doi:10.1002/cem.1320>. A randomization test (also called permutation 
    test) compares the SRD scores of the solutions to the SRD scores of randomly 
    generated rankings. The second validation option is cross-validation that 
    checks whether the rankings generated from the solutions come from the same 
    distribution or not. For a detailed analysis about the cross-validation 
    process see Sziklai, Baranyi and Héberger (2021) 
    <doi:10.48550/arXiv.2105.11939>. The package offers a wide array of 
    features related to SRD including the computation of the SRD scores, 
    validation options, input preprocessing and plotting tools.
Methods and tools for Singular Spectrum Analysis including decomposition,
             forecasting and gap-filling for univariate and multivariate time series.
             General description of the methods with many examples can be found in the book
             Golyandina (2018, <doi:10.1007/978-3-662-57380-8>).
             See 'citation("Rssa")' for details.
Ranked set sampling (RSS) is introduced as an advanced method for data collection which is substantial for the statistical and methodological analysis in scientific studies by McIntyre (1952) (reprinted in 2005) <doi:10.1198/000313005X54180>. This package introduces the first package  that implements the RSS and its modified versions for sampling. With 'RSSampling', the researchers can sample with basic RSS and the modified versions, namely, Median RSS, Extreme RSS, Percentile RSS, Balanced groups RSS, Double RSS, L-RSS, Truncation-based RSS, Robust extreme RSS. The 'RSSampling' also allows imperfect ranking using an auxiliary variable (concomitant) which is widely used in the real life applications. Applicants can also use this package for parametric and nonparametric inference such as mean, median and variance estimation, regression analysis and some distribution-free tests where the the samples are obtained via basic RSS.
Provides useful tools which supplement the use of 'Simulx' software and 'R' connectors ('Monolix Suite'). 'Simulx' is an easy, efficient and flexible application for clinical trial simulations. You need 'Simulx' software to be installed in order to use 'RsSimulx' package. Among others tasks, 'RsSimulx' provides the same functions as package 'mlxR' does with a compatibility with 'Simulx' software.
A collection of implementations of semi-supervised classifiers
    and methods to evaluate their performance. The package includes implementations
    of, among others, Implicitly Constrained Learning, Moment Constrained Learning,
    the Transductive SVM, Manifold regularization, Maximum Contrastive Pessimistic
    Likelihood estimation, S4VM and WellSVM.
Reservoir Systems Standard Operation Policy. A system for simulation of supply reservoirs. It proposes functionalities for plotting and evaluation of supply reservoirs systems.
Testing the equality of two means using Ranked Set Sampling
             and Median Ranked Set Sampling are provided under normal distribution. Data generation functions are also given RSS and MRSS. 
             Also, data generation functions are given under imperfect ranking data for Ranked Set Sampling and Median Ranked Set Sampling.
             Ozdemir Y.A., Ebegil M., & Gokpinar F. (2019), <doi:10.1007/s40995-018-0558-0>
             Ozdemir Y.A., Ebegil M., & Gokpinar F. (2017), <doi:10.1080/03610918.2016.1263736>.
Defines colour palettes and themes for Royal Statistical 
  Society (RSS) publications, including Significance magazine. Palettes and 
  themes are supported in both base R and 'ggplot2' graphics, and are intended 
  to be used by authors submitting to RSS publications.
Provides functions to access, search and download spacetime earth
    observation data via SpatioTemporal Asset Catalog (STAC). This package 
    supports the version 1.0.0 (and older) of the STAC specification
    (<https://github.com/radiantearth/stac-spec>). 
    For further details see Simoes et al. (2021) <doi:10.1109/IGARSS47720.2021.9553518>.
An extremely simple stack data type, implemented with 'R6'
    classes. The size of the stack increases as needed, and the amortized
    time complexity is O(1). The stack may contain arbitrary objects.
Provides fast, persistent (side-effect-free) stack, queue and
    deque (double-ended-queue) data structures. While deques include a superset
    of functionality provided by queues, in these implementations queues are
    more efficient in some specialized situations. See the documentation for
    rstack, rdeque, and rpqueue for details.
User-facing R functions are provided to parse, compile, test,
    estimate, and analyze Stan models by accessing the header-only Stan library
    provided by the 'StanHeaders' package. The Stan project develops a probabilistic
    programming language that implements full Bayesian statistical inference
    via Markov Chain Monte Carlo, rough Bayesian inference via 'variational'
    approximation, and (optionally penalized) maximum likelihood estimation via
    optimization. In all three cases, automatic differentiation is used to quickly
    and accurately evaluate gradients without burdening the user with the need to
    derive the partial derivatives.
Estimates previously compiled regression models using the 'rstan'
    package, which provides the R interface to the Stan C++ library for Bayesian
    estimation. Users specify models via the customary R syntax with a formula and
    data.frame plus some additional arguments for priors.
Regression methods to quantify the relation 
    between two measurement methods are provided by this package. The
    focus is on a Bayesian Deming regressions family. With a Bayesian
    method the Deming regression can be run in a traditional fashion or
    can be run in a robust way just decreasing the degree of freedom
    d.f. of the sampling distribution. With d.f. = 1 an extremely robust
    Cauchy distribution can be sampled. Moreover, models for dealing
    with heteroscedastic data are also provided. For reference see
    G. Pioda (2024) <https://piodag.github.io/bd1/>.
Perform sigmoidal Emax model fit using 'Stan' in a formula notation, without writing 'Stan' model code.
Provides various tools for developers of R packages interfacing
    with 'Stan' <https://mc-stan.org>, including functions to set up the required
    package structure, S3 generics and default methods to unify function naming
    across 'Stan'-based R packages, and vignettes with recommendations for
    developers.
'Stan' implementation of the Theory of Visual Attention (TVA; Bundesen, 1990; <doi:10.1037/0033-295X.97.4.523>) and numerous convenience functions for generating, compiling, fitting, and analyzing TVA models.
A simple R -> Stata interface allowing the user to
    execute Stata commands (both inline and from a .do file)
    from R.
Provides a simple and intuitive pipe-friendly framework, coherent with the 'tidyverse' design philosophy, 
    for performing basic statistical tests, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses. 
    The output of each test is automatically transformed into a tidy data frame to facilitate visualization. 
    Additional functions are available for reshaping, reordering, manipulating and visualizing correlation matrix.  
    Functions are also included to facilitate the analysis of factorial experiments, including purely 'within-Ss' designs 
    (repeated measures), purely 'between-Ss' designs, and mixed 'within-and-between-Ss' designs. 
    It's also possible to compute several effect size metrics, including "eta squared" for ANOVA, "Cohen's d" for t-test and 
    'Cramer V' for the association between categorical variables. 
    The package contains helper functions for identifying univariate and multivariate outliers, assessing normality and homogeneity of variances.
R interface for china national data <http://data.stats.gov.cn/>, 
    some convenient functions for accessing the national data are provided.
'STG' is a method for feature selection in neural network. The procedure is based on probabilistic relaxation of the l0 norm of features, or the count of the number of selected features. The framework simultaneously learns either a nonlinear regression or classification function while selecting a small subset of features. Read more: Yamada et al. (2020) <https://proceedings.mlr.press/v119/yamada20a.html>.
Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate  Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" <doi:10.1198/jcgs.2009.07177>. The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" <doi:10.1007/s10107-012-0584-1>.
Toolbox for remote sensing image processing and analysis such as
    calculating spectral indexes, principal component transformation, unsupervised
    and supervised classification or fractional cover analyses.
R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, this re-implements Stata's 'stpm2' function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the 'mgcv' package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects <doi:10.1002/sim.7451>, and copulas. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.
Takes Poisson or Binomial discrete spatial data and runs a Gibbs sampler for a variety of Spatiotemporal Conditional Autoregressive (CAR) models. Includes measures to prevent estimate over-smoothing through a restriction of model informativeness for select models. Also provides tools to load output and get median estimates. Implements methods from Besag, York, and Mollié (1991) "Bayesian image restoration, with two applications in spatial statistics" <doi:10.1007/BF00116466>, Gelfand and Vounatsou (2003) "Proper multivariate conditional autoregressive models for spatial data analysis" <doi:10.1093/biostatistics/4.1.11>, Quick et al. (2017) "Multivariate spatiotemporal modeling of age-specific stroke mortality" <doi:10.1214/17-AOAS1068>, and Quick et al. (2021) "Evaluating the informativeness of the Besag-York-Mollié CAR model" <doi:10.1016/j.sste.2021.100420>.
Functions to access data from the 'Strava v3 API' <https://developers.strava.com/>.
Unified object oriented interface for multiple independent streams of random numbers from different sources.
As of 'RStudio' v1.3, the preferences in the Global Options
    dialog (and a number of other preferences that aren’t) are now saved
    in simple, plain-text JSON files. This package provides an interface
    for working with these 'RStudio' JSON preference files to easily make
    modifications without using the point-and-click option menus. This is
    particularly helpful when working on teams to ensure a unified
    experience across machines and utilizing settings for best practices.
Access the RStudio API (if available) and provide informative error
    messages when it's not.
Create and install 'RStudio' themes derived from Visual
    Studio Code, Positron and 'TextMate' themes. Provides functions to
    convert between 'TextMate' and Visual Studio Code or Positron themes,
    as well as ports of several Visual Studio Code themes.
Create densities, probabilities, random numbers, quantiles, and maximum likelihood estimation for several 
    distributions, mainly the symmetric and asymmetric power exponential (AEP), a.k.a. the Subbottin family of 
    distributions, also known as the generalized error distribution.
    Estimation is made using the design of Bottazzi (2004) <https://ideas.repec.org/p/ssa/lemwps/2004-14.html>, where 
    the likelihood is maximized by several optimization procedures using the 'GNU Scientific Library (GSL)', translated to 'C++' 
    code, which makes it both fast and accurate. The package also provides methods for the gamma, Laplace, and Asymmetric
    Laplace distributions.
A collection of efficient and effective tools and
	algorithms for subgroup discovery and analytics. The package
	integrates an R interface to the org.vikamine.kernel library
	of the VIKAMINE system <http://www.vikamine.org> implementing
	subgroup discovery, pattern mining and analytics in Java.
Produces tables with the level of replication (number of replicates) and the
    experimental uncoded values of the quantitative factors to be used for rotatable Central
    Composite Design (CCD) experimentation and a 2-D contour plot of the corresponding
    variance of the predicted response according to
    Mead et al. (2012) <doi:10.1017/CBO9781139020879> design_ccd(), and analyzes
    CCD data with response surface methodology ccd_analysis(). A rotatable CCD
    provides values of the variance of the predicted response that are concentrically
    distributed around the average treatment combination used in the experimentation, which
    with uniform precision (implied by the use of several replicates at the average
    treatment combination) improves greatly the search and finding of an optimum response.
    These properties of a rotatable CCD represent undeniable advantages over the classical
    factorial design, as discussed by Panneton et al. (1999) <doi:10.13031/2013.13267> and
    Mead et al. (2012) <doi:10.1017/CBO9781139020879.018> among others.
Provides functions to estimate the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate marker.
Random generation of survival data from a wide range of regression models, including accelerated failure time (AFT), proportional hazards (PH), proportional odds (PO), accelerated hazard (AH), Yang and Prentice (YP), and extended hazard (EH) models. The package 'rsurv' also stands out by its ability to generate survival data from an unlimited number of baseline distributions provided that an implementation of the quantile function of the chosen baseline distribution is available in R. Another nice feature of the package 'rsurv' lies in the fact that linear predictors are specified via a formula-based approach, facilitating the inclusion of categorical variables and interaction terms. The functions implemented in the package 'rsurv' can also be employed to simulate survival data with more complex structures, such as survival data with different types of censoring mechanisms, survival data with cure fraction, survival data with random effects (frailties), multivariate survival data, and competing risks survival data. Details about the R package 'rsurv' can be found in Demarqui (2024) <doi:10.48550/arXiv.2406.01750>.
A range of functions for the design and
    analysis of disease surveillance activities. These functions were
    originally developed for animal health surveillance activities but can be
    equally applied to aquatic animal, wildlife, plant and human health
    surveillance activities. Utilities are included for sample size calculation
    and analysis of representative surveys for disease freedom, risk-based
    studies for disease freedom and for prevalence estimation.
    This package is based on Cameron A., Conraths F., Frohlich A., Schauer B.,
    Schulz K., Sergeant E., Sonnenburg J., Staubach C. (2015). R package of 
    functions for risk-based surveillance. Deliverable 6.24, WP 6 - Decision 
    making tools for implementing risk-based surveillance, Grant Number 
    no. 310806, RISKSUR (<https://www.fp7-risksur.eu/sites/default/files/documents/Deliverables/RISKSUR_%28310806%29_D6.24.pdf>). 
    Many of the 'RSurveillance' functions are incorporated into the 'epitools'
    website: Sergeant, ESG, 2019. Epitools epidemiological calculators. 
    Ausvet Pty Ltd. Available at: <http://epitools.ausvet.com.au>.
'SurveyCTO' is a platform for mobile data collection in offline settings.
  The 'rsurveycto' R package uses the 'SurveyCTO' REST API
  <https://docs.surveycto.com/05-exporting-and-publishing-data/05-api-access/01.api-access.html>
  to read datasets and forms from a 'SurveyCTO' server into R as 'data.table's
  and to download file attachments. The package also has limited support to
  write datasets to a server.
Provides an interface to the 'SurvStat' web service from the
  Robert Koch Institute (<https://tools.rki.de/SurvStat/SurvStatWebService.svc>) 
  allowing downloads of disease time series stratified
  by pathogen type and subtype, age, and geography from notifiable disease
  reports in Germany.
Low-rank matrix decompositions are fundamental tools and widely used for data
  analysis, dimension reduction, and data compression. Classically, highly accurate 
  deterministic matrix algorithms are used for this task. However, the emergence of 
  large-scale data has severely challenged our computational ability to analyze big data. 
  The concept of randomness has been demonstrated as an effective strategy to quickly produce
  approximate answers to familiar problems such as the singular value decomposition (SVD). 
  The rsvd package provides several randomized matrix algorithms such as the randomized 
  singular value decomposition (rsvd), randomized principal component analysis (rpca), 
  randomized robust principal component analysis (rrpca), randomized interpolative 
  decomposition (rid), and the randomized CUR decomposition (rcur). In addition several plot 
  functions are provided.
Computing singular value decomposition with robustness is a challenging task. 
    This package provides an implementation of computing robust SVD using density power 
    divergence (<doi:10.48550/arXiv.2109.10680>). It combines the idea of robustness and efficiency in estimation
    based on a tuning parameter. It also provides utility functions to simulate various
    scenarios to compare performances of different algorithms.
Renders vector-based svg images into high-quality custom-size
    bitmap arrays using 'librsvg2'. The resulting bitmap can be written to
    e.g. png, jpeg or webp format. In addition, the package can convert
    images directly to various formats such as pdf or postscript.
Interface to 'SWI'-'Prolog', <https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package 'rolog' instead. The purpose of this package is to provide the 'Prolog' runtime on systems that do not have a software installation of 'SWI'-'Prolog'.
An R interface to the SYMPHONY solver for mixed-integer linear programs.
'SyncroSim' is a generalized framework for managing scenario-based 
    datasets (<https://syncrosim.com/>). 'rsyncrosim' provides an interface to 
    'SyncroSim'. Simulation models can be added to 'SyncroSim' in order to 
    transform these datasets, taking advantage of general features such as 
    defining scenarios of model inputs, running Monte Carlo simulations, and 
    summarizing model outputs. 'rsyncrosim' requires 'SyncroSim' 2.3.5 or higher 
    (API documentation: <https://docs.syncrosim.com/>).
Various functions for querying and reshaping dependency trees, 
    as for instance created with the 'spacyr' or 'udpipe' packages.
    This enables the automatic extraction of useful semantic relations from texts,
    such as quotes (who said what) and clauses (who did what). Method proposed in 
    Van Atteveldt et al. (2017) <doi:10.1017/pan.2016.12>.
Access Synthesize Bio models from their API <https://app.synthesize.bio/> using
  this wrapper that provides a convenient interface to the Synthesize Bio API, allowing users
  to generate realistic gene expression data based on specified biological conditions. This package
  enables researchers to easily access AI-generated transcriptomic data for various
  modalities including bulk RNA-seq, single-cell RNA-seq, microarray data, and more.
Functions to write messages to the 'syslog' system logger API,
  available on all 'POSIX'-compatible operating systems. Features include
  tagging messages with a priority level and application type, as well as
  masking (hiding) messages below a given priority level.
Provides a programmatic interface to the 'Request Tracker' (RT)
  HTTP API <https://rt-wiki.bestpractical.com/wiki/REST>. 'RT' is a popular
  ticket tracking system.
Performs one-sample t-test based on robustified statistics using median/MAD (TA) and Hodges-Lehmann/Shamos (TB). For more details, see Park and Wang (2018)<arXiv:1807.02215>. This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169).
Play the classic game of tic-tac-toe (naughts and crosses).
Contains functions for random generation of R x C and 2 x 2 x K contingency tables. In addition to the generation of contingency tables over predetermined intraclass-correlated clusters, it is possible to generate contingency tables without intraclass correlations under product multinomial, multinomial, and Poisson sampling plans. It also consists of a function for generation of random data from a given discrete probability distribution function. See Demirhan (2016) <https://journal.r-project.org/archive/2016-1/demirhan.pdf> for more information.
Reporting tables often have structure that goes beyond simple
    rectangular data. The 'rtables' package provides a framework for
    declaring complex multi-level tabulations and then applying them to
    data. This framework models both tabulation and the resulting tables
    as hierarchical, tree-like objects which support sibling sub-tables,
    arbitrary splitting or grouping of data in row and column dimensions,
    cells containing multiple values, and the concept of contextual
    summary computations. A convenient pipe-able interface is provided for
    declaring table layouts and the corresponding computations, and then
    applying them to data.
Designed to create and display complex tables with R, the
    'rtables' R package allows cells in an 'rtables' object to contain any
    high-dimensional data structure, which can then be displayed with
    cell-specific formatting instructions. Additionally, the
    'rtables.officer' package supports export formats related to the
    Microsoft Office software suite, including Microsoft Word ('docx') and
    Microsoft PowerPoint ('pptx').
Provides R bindings for 'Tabulator JS' <https://tabulator.info/>.
  Makes it a breeze to create highly customizable interactive tables in 'rmarkdown' documents
  and 'shiny' applications. It includes filtering, grouping, editing, input validation,
  history recording, column formatters, packaged themes and more.
Applies a given global-fit method to random partial tanglegrams of a fixed size
             to identify the associations, terminals, and nodes that maximize phylogenetic
             (in)congruence. It also includes functions to compute more easily the confidence
             intervals of classification metrics and plot results, reducing computational time.
             See Llaberia-Robledillo et al., (2023) <doi:10.1093/sysbio/syad016>.
Storing huge data in RData format causes problems because
        of the necessity to load the whole file to the memory in order
        to access and manipulate objects inside such file; rtape is a
        simple solution to this problem. The package contains several
        wrappers of R built-in serialize/unserialize mechanism allowing
        user to quickly append objects to a tape-like file and later
        iterate over them requiring only one copy of each stored object
        to reside in memory a time.
Discretize AR(1) process following Tauchen (1986) <http://www.sciencedirect.com/science/article/pii/0165176586901680>. A discrete Markov chain that approximates in the sense of weak convergence a continuous-valued univariate Autoregressive process of first order is generated. It is a popular method used in economics and in finance.
We provide functions to perform taxometric analyses. This package contains 46 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method.
The Randomized Trait Community Clustering method (Triado-Margarit et al., 2019,
    <doi:10.1038/s41396-019-0454-4>) is a statistical approach which allows to determine whether
    if an observed trait clustering pattern is related to an increasing environmental constrain.
    The method 1) determines whether exists or not a trait clustering on the sampled communities
    and 2) assess if the observed clustering signal is related or not to an increasing environmental
    constrain along an environmental gradient. Also, when the effect of the environmental gradient
    is not linear, allows to determine consistent thresholds on the community assembly based on trait-values.
The 'TRUST4' or 'MiXCR' is used to identify the clonotypes.
    The goal of 'rTCRBCRr' is to process the results from these clonotyping tools,
    and analyze the clonotype repertoire metrics based on chain names and IGH isotypes.
    The manuscript is still under preparation for publication for now.
    The references describing the methods in this package will be added later.
Upload R data.frame to Arm Treasure Data, see <https://www.treasuredata.com/>. 
  You can execute database or table handling for resources on Arm Treasure Data.
Robust tail dependence estimation for bivariate models. This package is based on two papers by the authors:'Robust and bias-corrected estimation of the coefficient of tail dependence' and 'Robust and bias-corrected estimation of probabilities of extreme failure sets'. This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).
Provides response time distributions (density/PDF,
       distribution function/CDF, quantile function, and random
       generation): (a) Ratcliff diffusion model (Ratcliff &
       McKoon, 2008, <doi:10.1162/neco.2008.12-06-420>) based on C
       code by Andreas and Jochen Voss and (b) linear ballistic
       accumulator (LBA; Brown & Heathcote, 2008,
       <doi:10.1016/j.cogpsych.2007.12.002>) with different
       distributions underlying the drift rate.
Exploit controlled vocabularies organized on tematres servers.
Machine learning and visualization package with an 'S7' backend 
    featuring comprehensive type checking and validation, paired with an efficient functional
    user-facing API. train(), cluster(), and decomp() provide one-call access to supervised and 
    unsupervised learning. All configuration steps are performed using setup functions and 
    validated. A single call to train() handles preprocessing, hyperparameter tuning, and testing 
    with nested resampling. Supports 'data.frame', 'data.table', and 'tibble' inputs, parallel 
    execution, and interactive visualizations. The package first appeared in E.D. Gennatas (2017)
    <https://repository.upenn.edu/entities/publication/d81892ea-3087-4b71-a6f5-739c58626d64>.
A collection of R Markdown templates for nicely structured, reproducible 
    data analyses in R. The templates have embedded examples on how to write 
    citations, footnotes, equations and use colored message/info boxes, how to 
    cross-reference different parts/sections in the report, provide a nice 
    table of contents (toc) with a References section and proper R session 
    information as well as examples using DT tables and ggplot2 graphs. 
    The bookdown Lite template theme supports code folding.
A set of tools for creation, manipulation, and modeling
    of tensors with arbitrary number of modes. A tensor in the context of data
    analysis is a multidimensional array. rTensor does this by providing a S4
    class 'Tensor' that wraps around the base 'array' class. rTensor
    provides common tensor operations as methods, including matrix unfolding,
    summing/averaging across modes, calculating the Frobenius norm, and taking
    the inner product between two tensors. Familiar array operations are
    overloaded, such as index subsetting via '[' and element-wise operations.
    rTensor also implements various tensor decomposition, including CP, GLRAM,
    MPCA, PVD, and Tucker. For tensors with 3 modes, rTensor also implements
    transpose, t-product, and t-SVD, as defined in Kilmer et al. (2013). Some
    auxiliary functions include the Khatri-Rao product, Kronecker product, and
    the Hadamard product for a list of matrices.
Models and displays tephra transport through custom (windy, turbulent, heterogeneous) atmosphere over custom topography. Includes a Lagrangian (particle-tracking) tephra transport model and a function to save snapshots of model as png files.
A small language extension for succinct conditional assignment using `?` and `:`, emulating the conditional ternary operator syntax using in C, Java, JavaScript and other languages.
Use trend filtering, a type of regularized nonparametric
    regression, to estimate the instantaneous reproduction number, also
    called Rt. This value roughly says how many new infections will result
    from each new infection today.  Values larger than 1 indicate that an
    epidemic is growing while those less than 1 indicate decline. For more 
    details about this methodology, see Liu, Cai, Gustafson, and McDonald (2024)
    <doi:10.1371/journal.pcbi.1012324>.
A machine learning package for automatic text classification 
	that makes it simple for novice users to get started with machine 
	learning, while allowing experienced users to easily experiment 
	with different settings and algorithm combinations. The package 
	includes eight algorithms for ensemble classification (svm, slda, 
	boosting, bagging, random forests, glmnet, decision trees, neural 
	networks), comprehensive analytics, and thorough documentation.
A set of R functions to output Rich Text Format (RTF) files with high resolution tables and graphics that may be edited with a standard word processor such as Microsoft Word.
Tensor Factor Models (TFM) are appealing dimension reduction tools for high-order tensor time series, and have wide applications in economics, finance and medical imaging. We propose an one-step projection estimator by minimizing the least-square loss function, and further propose a robust estimator with an iterative weighted projection technique by utilizing the Huber loss function. The methods are discussed in Barigozzi et al. (2022) <arXiv:2206.09800>, and Barigozzi et al. (2023) <arXiv:2303.18163>.
Methods for comparing different regression algorithms for 
    describing the temporal dynamics of secondary tree growth (xylem and 
    phloem). Users can compare the accuracy of the most common fitting methods 
    usually used to analyse xylem and phloem data, i.e., Gompertz function, 
    Double Gompertz function, General Additive Models (GAMs); and an algorithm 
    newly introduced to the field, i.e., Bayesian Regularised Neural Networks 
    (brnn). The core function of the package is XPSgrowth(), while the results 
    can be interpreted using implemented generic S3 methods, such as plot() and 
    summary().
Allow function for using 'TGStat Stat API' and
    'TGStat Search API', for more details see <https://api.tgstat.ru/docs/ru/start/intro.html>.
    'TGStat' provide telegram channel analytics data.
The goal of 'Rthingsboard' is to provide interaction with the API of 'ThingsBoard' (<https://thingsboard.io/>), an open-source IoT platform for device management, data collection, processing and visualization.
A suite of custom R Markdown formats and templates for
    authoring journal articles and conference submissions.
A tool for processing Articulate
    Assistant Advanced™ (AAA) ultrasound tongue imaging data and Carstens AG500/1 electro-magnetic articulographic data.
'TiddlyWiki' is a unique non-linear notebook for capturing, organising and sharing complex information. 'rtiddlywiki' is a R interface of 'TiddlyWiki' <https://tiddlywiki.com> to create new tiddler from 'R Markdown' file, and then put into a local 'TiddlyWiki' server if it is available.
Calculates tide heights based on tide station harmonics.  It
    includes the harmonics data for 637 US stations.  The harmonics data
    was converted from
    <https://github.com/poissonconsulting/rtide/blob/main/data-raw/harmonics-dwf-20151227-free.tar.bz2>,
    NOAA web site data processed by David Flater for 'XTide'.  The code to
    calculate tide heights from the harmonics is based on 'XTide'.
Extract text or metadata from over a thousand file types, using Apache Tika <https://tika.apache.org/>. Get either plain text or structured XHTML content.
Loading data from 'tiktok Marketing API' <https://business-api.tiktok.com/portal> 
    by business centers, advertisers, budgets and reports.
A thin wrapper around the tiktoken-rs crate, allowing to encode text into Byte-Pair-Encoding (BPE) tokens and decode tokens back to text. This is useful to understand how Large Language Models (LLMs) perceive text.
R tools to measure and compare inequality, welfare and poverty using the EU statistics on income and living conditions surveys.
Color palettes from famous artists and paintings.
Rarefy data, calculate diversity and plot the results.
A toolkit for Commodities 'analytics', risk management and
    trading professionals. Includes functions for API calls to
    <https://commodities.morningstar.com/#/>, <https://developer.genscape.com/>,
    and <https://www.bankofcanada.ca/valet/docs>.
Provide seamless support for right-to-left (RTL) languages, such as Persian and Arabic,
  in R Markdown documents and 'LaTeX' output. It includes functions and hooks that enable easy
  integration of RTL language content, allowing users to create documents that adhere to RTL writing
  conventions. For in-depth insights into dynamic documents and the 'knitr' package, consider referring
  to Xie, Y (2014) <ISBN: 978-1-482-20353-0>.
Convenience functions to make some common tasks with right-to-left string
    printing easier, more convenient and with no need to remember long Unicode
    characters. Specifically helpful for right-to-left languages such as Arabic,
    Persian and Hebrew.
A set of tools to process and calculate 
             metrics on point clouds derived from terrestrial LiDAR 
             (Light Detection and Ranging; TLS). Its creation is based on key 
             aspects of the TLS application in forestry and ecology. 
             Currently, the main routines are based on filtering, neighboring 
             features of points, voxelization, canopy structure, 
             and the creation of artificial stands. It is written using 
             data.table and C++ language and in most of the functions it is 
             possible to use parallel processing to speed-up the routines.
Terrestrial laser scanning (TLS) data processing and post-hurricane damage severity classification at the individual tree level using deep Learning. Further details were published in Klauberg et al. (2023) <doi:10.3390/rs15041165>.
Native 'R' interface to 'TMB' (Template Model Builder) so models can be written entirely in 'R' rather than 'C++'. Automatic differentiation, to any order, is available for a rich subset of 'R' features, including linear algebra for dense and sparse matrices, complex arithmetic, Fast Fourier Transform, probability distributions and special functions. 'RTMB' provides easy access to model fitting and validation following the principles of Kristensen, K., Nielsen, A., Berg, C. W., Skaug, H., & Bell, B. M. (2016) <DOI:10.18637/jss.v070.i05> and Thygesen, U.H., Albertsen, C.M., Berg, C.W. et al. (2017) <DOI:10.1007/s10651-017-0372-4>.
Extends the functionality of the 'RTMB' <https://kaskr.r-universe.dev/RTMB> package by providing a collection of non-standard probability distributions compatible with automatic differentiation (AD). While 'RTMB' enables flexible and efficient modelling, including random effects, its built-in support is limited to standard distributions. The package adds additional AD-compatible distributions, broadening the range of models that can be implemented and estimated using 'RTMB'. Automatic differentiation and Laplace approximation are described in Kristensen et al. (2016) <doi:10.18637/jss.v070.i05>.
The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv)
    is a two-stage estimation method that reformulates structured table-based
    systems - such as allocation problems, transaction matrices, and
    input-output tables - as structured least-squares problems. Based on the
    Convex Least Squares Programming (CLSP) framework, TMPinv solves systems
    with row and column constraints, block structure, and optionally reduced
    dimensionality by (1) constructing a canonical constraint form and applying
    a pseudoinverse-based projection, followed by (2) a convex-programming
    refinement stage to improve fit, coherence, and regularization (e.g., via
    Lasso, Ridge, or Elastic Net).
Provides an interactive wrapper for the 'tmpinv()' function from
    the 'rtmpinv' package with options extending its functionality to pre-
    and post-estimation processing and streamlined incorporation of prior cell
    information. The Tabular Matrix Problems via Pseudoinverse Estimation
    (TMPinv) is a two-stage estimation method that reformulates structured
    table-based systems - such as allocation problems, transaction matrices,
    and input-output tables - as structured least-squares problems. Based
    on the Convex Least Squares Programming (CLSP) framework, TMPinv solves
    systems with row and column constraints, block structure, and optionally
    reduced dimensionality by (1) constructing a canonical constraint form
    and applying a pseudoinverse-based projection, followed by (2) a
    convex-programming refinement stage to improve fit, coherence, and
    regularization (e.g., via Lasso, Ridge, or Elastic Net).
Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models 
      by Klauer and	Kellen (2018) <doi:10.1016/j.jmp.2017.12.003> and Klauer, Hartmann, and Meyer-Grant (submitted). 
      The RT-MPT class not only incorporate	frequencies like traditional multinomial processing tree (MPT) models, 
      but also latencies. This enables it	to estimate process completion times and encoding plus motor execution times 
      next to the process probabilities	of traditional MPTs. 'rtmpt' is a hierarchical Bayesian framework and posterior 
      samples are sampled using a Metropolis-within-Gibbs sampler (for exponential RT-MPTs) or Hamiltonian-within-Gibbs 
      sampler (for diffusion RT-MPTs).
Quickly imports, processes, analyzes, and visualizes mass-spectrometric 
	data.  Includes functions for easily extracting specific data and measurements
	from large (multi-gigabyte) raw Bruker data files, as well as a set of S3 object
	classes for manipulating and measuring mass spectrometric peaks and plotting
	peaks and spectra using the 'ggplot2' package.
Read raw and processed data from acoustic ejection mass
	spectrometry (AEMS) files produced by the Sciex EchoMS instrument.  Includes
	functions to create interactive reader objects, extract raw intensity 
	measurements, mass spectra, and fully-processed mass-transition intensity
	areas.  Methods for data processing and analysis are described in Rimmer et
	al. (2025) <doi:10.1021/acs.analchem.5c03730>. Supports both multiple
	reaction monitoring (MRM) and full-scan (neutral loss and precursor ion)
	data formats.
Truncated Newton function minimization with bounds constraints
	based on the 'Matlab'/'Octave' codes of Stephen Nash.
Allows you to interact with the API of the "Todoist"
    platform.  'Todoist' <https://www.todoist.com/> provides an online task
    manager service for teams.
An implementation of calls designed to collect and organize
    Mastodon data via its Application Program Interfaces (API), which can
    be found at the following URL: <https://docs.joinmastodon.org/>.
Data with irregular spatial support, such as runoff related data or data from administrative units, can with 'rtop' be interpolated to locations without observations with the top-kriging method. A description of the package is given by Skøien et al (2014) <doi:10.1016/j.cageo.2014.02.009>.
Helps to fit thermal performance curves (TPCs). 'rTPC' contains 26 model formulations previously used to fit TPCs and has helper functions to set sensible start parameters, upper and lower parameter limits and estimate parameters useful in downstream analyses, such as cardinal temperatures, maximum rate and optimum temperature. See Padfield et al. (2021) <doi:10.1111/2041-210X.13585>.
Tools for qPCR data analysis using Delta Ct and Delta Delta Ct methods, including t-test, Wilcoxon-test, ANOVA models, and publication-ready visualizations. The package supports multiple target, and multiple reference genes, and uses a calculation framework adopted from Ganger et al. (2017) <doi:10.1186/s12859-017-1949-5> and Taylor et al. (2019) <doi:10.1016/j.tibtech.2018.12.002>, covering both the Livak and Pfaffl methods.
A toolkit for the analysis of paths from spatial tracking experiments and calculation of goal-finding strategies. 
    This package is centered on an approach using machine learning for path classification.
Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) <doi:10.1515/snde-2012-0044> and Dimpfl and Peter (2014) <doi:10.1016/j.intfin.2014.03.004> for theory and applications to financial time series. Additional references can be found in the theory part of the vignette.
Provides datasets related to the Star Trek fictional universe and functions for working with the data.
    The package also provides access to real world datasets based on the televised series and other related licensed media productions.
    It interfaces with the Star Trek API (STAPI) (<http://stapi.co/>), 
    Memory Alpha (<https://memory-alpha.fandom.com/wiki/Portal:Main>), and Memory Beta (<https://memory-beta.fandom.com/wiki/Main_Page>) 
    to retrieve data, metadata and other information relating to Star Trek.
    It also contains several local datasets covering a variety of topics. 
    The package also provides functions for working with data from other Star Trek-related 
    R data packages containing larger datasets not stored in 'rtrek'.
The traditional linear regression trend, Modified Mann-Kendall (MK)
    non-parameter trend and bootstrap trend are included in this package. Linear 
    regression trend is rewritten by '.lm.fit'. MK trend is rewritten by 'Rcpp'.
    Finally, those functions are about 10 times faster than previous version 
    in R.
    Reference:
    Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for 
    autocorrelated data. Journal of hydrology, 204(1-4), 182-196. 
    <doi:10.1016/S0022-1694(97)00125-X>.
Analyze download logs from the CRAN RStudio mirror 
            (<http://cran.rstudio.com/>). 
            This CRAN mirror is the default one used in RStudio.
            The available data is the result of parsed and anonymised raw log data from
            that CRAN mirror.
This is a port of Jonathan Shewchuk's Triangle library to
    R. From his description: "Triangle generates exact Delaunay
    triangulations, constrained Delaunay triangulations, conforming
    Delaunay triangulations, Voronoi diagrams, and high-quality
    triangular meshes. The latter can be generated with no small or
    large angles, and are thus suitable for finite element analysis."
The TRIM model is widely used for estimating growth and decline of
    animal populations based on (possibly sparsely available) count data. The
    current package is a reimplementation of the original TRIM software developed
    at Statistics Netherlands by Jeroen Pannekoek. See
    <https://www.cbs.nl/en-gb/society/nature-and-environment/indices-and-trends%2d%2dtrim%2d%2d>
    for more information about TRIM.
A carbon-water coupled model (TRIPLEX-CW-Flux) is based on two well-established models, TRIPLEX-Flux model and Penman–Monteith model, integrates soil water and water vapor pressure deficits into the stomata conductance submodule to estimate net ecosystem production and evapotranspiration in forest ecosystems.<https://github.com/ShulanSun/rTRIPLEX_CW_Flux>.
Embeds sources and headers from Tina's Random
    Number Generator ('TRNG') C++ library. Exposes some functionality for
    easier access, testing and benchmarking into R. Provides examples of
    how to use parallel RNG with 'RcppParallel'. The methods and
    techniques behind 'TRNG' are illustrated in the package vignettes and
    examples. Full documentation is available in Bauke (2021)
    <https://github.com/rabauke/trng4/blob/v4.23.1/doc/trng.pdf>.
Process phylogenetic trees with tropical support vector machine and principal component analysis defined with tropical geometry. Details about tropical support vector machine are available in : Tang, X., Wang, H. & Yoshida, R. (2020) <arXiv:2003.00677>. Details about tropical principle component analysis are available in : Page, R., Yoshida, R. & Zhang L. (2020) <doi:10.1093/bioinformatics/btaa564> and Yoshida, R., Zhang, L. & Zhang, X. (2019) <doi:10.1007/s11538-018-0493-4>.
Designed to support the application of plant trait data providing easy applicable functions
    for the basic steps of data preprocessing, e.g. data import, data exploration, selection of columns
    and rows, excluding trait data according to different attributes, geocoding, long- to wide-table
    transformation, and data export. 'rtry' was initially developed as part of the TRY R project to
    preprocess trait data received via the TRY database.
This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).
Supports modelling real-time case data to facilitate the real-time
    surveillance of infectious diseases and other point phenomena. The package provides automated computational grid generation over
    an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, 
    and predictions and visualisation. Both Bayesian and maximum likelihood methods are provided. Log-Gaussian Cox Processes are described by 
    Diggle et al. (2013) <doi:10.1214/13-STS441> and we provide both the low-rank approximation for Gaussian processes 
    described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and Riutort-Mayol et al (2023) <doi:10.1007/s11222-022-10167-2> and the
    nearest neighbour Gaussian process described by Datta et al (2016) <doi:10.1080/01621459.2015.1044091>.
Frequentist sequential meta-analysis based on 
    'Trial Sequential Analysis' (TSA) in programmed in Java by the Copenhagen 
    Trial Unit (CTU). The primary function is the calculation of group 
    sequential designs for meta-analysis to be used for planning and analysis of
    both prospective and retrospective sequential meta-analyses to preserve 
    type-I-error control under sequential testing. 'RTSA' includes tools for 
    sample size and trial size calculation for meta-analysis and core 
    meta-analyses methods such as fixed-effect and random-effects models and
    forest plots. TSA is described in Wetterslev et. al (2008) 
    <doi:10.1016/j.jclinepi.2007.03.013>. The methods for deriving the
    group sequential designs are based on Jennison and Turnbull (1999,
    ISBN:9780849303166).
Adaptation of the 'Matlab' 'tsEVA' toolbox developed by Lorenzo Mentaschi
    available here:
    <https://github.com/menta78/tsEva>. It contains an implementation of the
    Transformed-Stationary (TS) methodology for non-stationary extreme 
    value Analysis (EVA) as described in Mentaschi et al. (2016) <doi:10.5194/hess-20-3527-2016>.  
    In synthesis this approach consists in:
    (i) transforming a non-stationary time series into a
    stationary one to which the stationary extreme value theory can be applied; and
    (ii) reverse-transforming the result into a non-stationary extreme
    value distribution.
    'RtsEva' offers several options for trend estimation (mean, extremes, seasonal)
    and contains multiple plotting functions displaying different aspects
    of the non-stationarity of extremes.
An R wrapper around the fast T-distributed Stochastic
    Neighbor Embedding implementation by Van der Maaten  (see <https://github.com/lvdmaaten/bhtsne/> for more information on the original implementation).
TSON, short for Typed JSON, is a binary-encoded serialization of
    JSON like document that support JavaScript typed data (https://github.com/tercen/TSON).
Contains the program 'ttf2pt1', for use with the
    'extrafont' package. This product includes software developed by the 'TTF2PT1'
    Project and its contributors.
An implementation of calls designed to collect 'Tumblr' data via its Application Program Interfaces (API), which can be found at the following URL: <https://www.tumblr.com/docs/en/api/v2>.
Implements the t-walk algorithm, a general-purpose, self-adjusting
    Markov Chain Monte Carlo (MCMC) sampler for continuous distributions as
    described by Christen & Fox (2010) <doi:10.1214/10-BA603>. The t-walk requires
    no tuning and is robust for a wide range of target distributions, including
    high-dimensional and multimodal problems. This implementation includes an
    option for running multiple chains in parallel to accelerate sampling and
    facilitate convergence diagnostics.
Real Twig is a method to correct branch overestimation in quantitative structure models. Overestimated cylinders are correctly tapered using measured twig diameters of corresponding tree species. Supported quantitative structure modeling software includes 'TreeQSM', 'SimpleForest', 'Treegraph', and 'aRchi'. Also included is a novel database of twig diameters and tools for fractal analysis of point clouds.
A trimmed down copy of the "kent-core source tree"
	turned into a 'C' library for manipulation of '.2bit' files.
	See <https://genome.ucsc.edu/FAQ/FAQformat.html#format7>
	for a quick overview of the '2bit' format. The "kent-core source tree"
	can be found here: <https://github.com/ucscGenomeBrowser/kent-core/>.
	Only the '.c' and '.h' files from the source tree that are related
	to manipulation of '.2bit' files were kept. Note that the package
	is primarily useful to developers of other R packages who wish
	to use the '2bit' 'C' library in their own 'C'/'C++' code.
A strong type system for R which supports
    symbol declaration and assignment with type checking
    and condition checking.
An R interface to the 'typeform' <https://www.typeform.com/>
    application program interface.  Also provides functions for
    downloading your results.
R bindings for functions from the UCR Suite by Rakthanmanon et al. (2012) <DOI:10.1145/2339530.2339576>, which enables ultrafast subsequence
      search for a best match under Dynamic Time Warping and Euclidean Distance.
ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.
Automatically flags common spatial errors in biological collection data using metadata and specialists' information. RuHere implements a workflow to manage occurrence data through six steps: dataset merging, metadata flagging, validation against expert-derived distribution maps, visualization of flagged records, and sampling bias exploration. It specifically integrates specialist-curated range information to identify geographic errors and introductions that often escape standard automated validation procedures. For details on the methodology, see: Trindade & Caron (2026) <doi:10.64898/2026.02.02.703373>.
Accessible and flexible implementation of three ecoacoustic indices that are less commonly available in existing R frameworks: Background Noise, Soundscape Power and Soundscape Saturation. The functions were design to accommodate a variety of sampling designs. Users can tailor calculations by specifying spectrogram time bin size, amplitude thresholds and normality tests. By simplifying computation and standardizing reproducible methods, the package aims to support ecoacoustics studies. For more details about the indices read Towsey (2017) <https://eprints.qut.edu.au/110634/> and Burivalova (2017) <doi:10.1111/cobi.12968>.
The real-time quantitative polymerase chain reaction
    (qPCR) technical data sets by Ruijter et al. (2013)
    <doi:10.1016/j.ymeth.2012.08.011>: (i) the four-point 10-fold dilution
    series; (ii) 380 replicates; and (iii) the competimer data set. These
    three data sets can be used to benchmark qPCR methods. Original data set
    is available at
    <https://medischebiologie.nl/wp-content/uploads/2019/02/qpcrdatamethods.zip>.
    This package fixes incorrect annotations in the original data sets.
Tools for creating data validation pipelines and
    tidy reports. This package offers a framework for exploring and
    validating data frame like objects using 'dplyr' grammar of data
    manipulation.
Bindings for additional models for use with the 'parsnip'
    package.  Models include prediction rule ensembles (Friedman and
    Popescu, 2008) <doi:10.1214/07-AOAS148>, C5.0 rules (Quinlan, 1992
    ISBN: 1558602380), and Cubist (Kuhn and Johnson, 2013)
    <doi:10.1007/978-1-4614-6849-3>.
Streamlines data preprocessing, analysis, and visualization for association rule mining. Designed to work with the 'arules' package, features include discretizing data frames, generating rule set intersections, and visualizing rules with heatmaps and Euler diagrams. 'RulesTools' also includes a dataset on Brook trout detection from Nolan et al. (2022) <doi:10.1007/s13412-022-00800-x>.
This holds r markdown and quarto templates for academic papers and 
    slide decks. It also has templates to create research projects which 
    contain academic papers as vignettes.
Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components to a variety of GARCH and MEM (Engle (2002) <doi:10.1002/jae.683>, Engle and Gallo (2006) <doi:10.1016/j.jeconom.2005.01.018>, and Amendola et al. (2024) <doi:10.1016/j.seps.2023.101764>) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. 'rumidas' also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.
Exploration of pharmacometrics data involves both general tools (transformation and plotting) and specific techniques (non-compartmental analysis). This kind of exploration is generally accomplished by utilizing different packages. The purpose of 'ruminate' is to create a 'shiny' interface to make these tools more broadly available while creating reproducible results.
Plots multiple run charts, finds successive signals of 
    improvement, and revises medians when each signal occurs. Finds runs
    above, below, or on both sides of the median, and returns a plot and
    a data.table summarising original medians and any revisions, for all
    groups within the supplied data.
Doubly ranked tests are nonparametric tests for grouped functional and multivariate data.
    The testing procedure first ranks a matrix (or three dimensional array) of data by column (if a matrix) or by cell (across the third dimension if an array).
    By default, it calculates a sufficient statistic for the subject's order within the sample using the observed ranks, taken over the columns or cells.
    Depending on the number of groups, G, the summarized ranks are then analyzed using either a Wilcoxon Rank Sum test (G = 2) or a Kruskal-Wallis (G greater than 2).
Convert a string of text characters to Elder Futhark Runes <https://en.wikipedia.org/wiki/Elder_Futhark>.
Captures errors encountered when running 'run_examples()', and processes and archives them. The function 'run_examples()' within the 'devtools' package allows batch execution of all of the examples within a given package. This is much more convenient than testing each example manually. However, a major inconvenience is that if an error is encountered, the program stops and does not complete testing the remaining examples. Also, there is not a systematic record of the results, namely which package functions had no examples, which had examples that failed, and which had examples that succeeded. The current package provides the missing functionality.
Implements two methods of estimating runs scored in a softball 
    scenario: (1) theoretical expectation using discrete Markov chains and (2) empirical
    distribution using multinomial random simulation.  Scores are based on player-specific input 
    probabilities (out, single, double, triple, walk, and homerun).  Optional inputs include probability
    of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether
    a player is "fast" (e.g. the player could stretch home).  These probabilities may be 
    calculated from common player statistics that are publicly available on team's webpages. 
    Scores are evaluated based on a nine-player lineup and may be used to compare lineups, 
    evaluate base scenarios, and compare the offensive potential of individual players.  
    Manuscript forthcoming.  See Bukiet & Harold (1997) <doi:10.1287/opre.45.1.14> for 
    implementation of discrete Markov chains.
R functions implementing a standard Unit Testing
        framework, with additional code inspection and report
        generation tools.
User-friendly interface utilities for MCMC models via
    Just Another Gibbs Sampler (JAGS), facilitating the use of parallel
    (or distributed) processors for multiple chains, automated control
    of convergence and sample length diagnostics, and evaluation of the
    performance of a model using drop-k validation or against simulated
    data. Template model specifications can be generated using a standard
    lme4-style formula interface to assist users less familiar with the
    BUGS syntax.  A JAGS extension module provides additional distributions
    including the Pareto family of distributions, the DuMouchel prior and
    the half-Cauchy prior.
The function runMCMC_btadjust() returns a mcmc.list object which is the output of a 
    Markov Chain Monte Carlo obtained - from either 'JAGS', 'nimble' or 'greta' -
	after adjusting burn-in and thinning parameters to meet pre-specified criteria
	in terms of convergence & effective sample size. Used with 'nimble', runMCMC_btadjust()
	allows extra calculations (e.g. information criteria for model comparison and goodness-of-fit
	p-values for model diagnosis).
Lightweight library for rolling windows operations. Package enables
  full control over the window length, window lag and a time indices. With a runner 
  one can apply any R function on a rolling windows. The package eases work with 
  equally and unequally spaced time series.
Package 'runonce' helps automating the saving of long-running code
    to help running the same code multiple times. If you run some long-running 
    code once, it saves the result in a file on disk. Then, if the result 
    already exists, i.e. if the code has already been run and its output has 
    already been saved, it just reads the result from the stored file instead 
    of running the code again.
Provides methods for fast computation of running sample 
    statistics for time series. These include: (1) mean, (2) 
    standard deviation, and (3) variance over a fixed-length window 
    of time-series, (4) correlation, (5) covariance, and (6) 
    Euclidean distance (L2 norm) between short-time pattern and 
    time-series. Implemented methods utilize Convolution Theorem to 
    compute convolutions via Fast Fourier Transform (FFT).
Interface to the 'UNU.RAN' library for Universal Non-Uniform RANdom variate generators. 
	     Thus it allows to build non-uniform random number generators from quite arbitrary
	     distributions. In particular, it provides an algorithm for fast numerical inversion
	     for distribution with given density function.
	     In addition, the package contains densities, distribution functions and quantiles
	     from a couple of distributions.
A collection of efficient implementations of popular offline change-point detection algorithms, featuring a consistent, object-oriented interface for practical use.
Package to tackle large-scale problems asynchronously across
    a distributed network. Employing a database centric model, rush
    enables workers to communicate tasks and their results over a shared
    'Redis' database. Key features include low task overhead, efficient
    caching, and robust error handling. The package powers the
    asynchronous optimization algorithms in the 'bbotk' and 'mlr3tuning'
    packages.
By placing on a circle 10 points numbered from 1 to 10, and connecting them by a straight line to the point corresponding to its multiplication by 2. (1 must be connected to 1 * 2 = 2, point 2 must be set to 2 * 2 = 4, point 3 to 3 * 2 = 6 and so on). You will obtain an amazing geometric figure that complicates and beautifies itself by varying the number of points and the multiplication table you use.
Uses the generalized ratio-of-uniforms (RU) method to simulate
    from univariate and (low-dimensional) multivariate continuous distributions.
    The user specifies the log-density, up to an additive constant. The RU
    algorithm is applied after relocation of mode of the density to zero, and
    the user can choose a tuning parameter r. For details see Wakefield, Gelfand
    and Smith (1991) <DOI:10.1007/BF01889987>, Efficient generation of random
    variates via the ratio-of-uniforms method, Statistics and Computing (1991)
    1, 129-133.  A Box-Cox variable transformation can be used to make the input
    density suitable for the RU method and to improve efficiency.  In the
    multivariate case rotation of axes can also be used to improve efficiency.
    From version 1.2.0 the 'Rcpp' package 
    <https://cran.r-project.org/package=Rcpp> can be used to improve efficiency.
A RUT (Rol Unico Tributario) is an unique and personal identification number implemented in Chile to identify citizens and taxpayers. Rutifier allows to validate if a RUT exist or not and change between the different formats a RUT can have.
Real-time quantitative polymerase chain reaction (qPCR) data
    by Rutledge et al. (2004) <doi:10.1093/nar/gnh177> in tidy format. The
    data comprises a six-point, ten-fold dilution series, repeated in five
    independent runs, for two different amplicons. In each run, each standard
    concentration is replicated four times. For the original raw data file see
    the Supplementary Data section:
    <https://academic.oup.com/nar/article/32/22/e178/2375678#supplementary-data>.
Implements the 'RUV' (Remove Unwanted Variation) algorithms.  These algorithms attempt to adjust for systematic errors of unknown origin in high-dimensional data.  The algorithms were originally developed for use with genomic data, especially microarray data, but may be useful with other types of high-dimensional data as well.  These algorithms were proposed in Gagnon-Bartsch and Speed (2012) <doi:10.1093/nar/gkz433>, Gagnon-Bartsch, Jacob and Speed (2013), and Molania, et. al. (2019) <doi:10.1093/nar/gkz433>.  The algorithms require the user to specify a set of negative control variables, as described in the references.  The algorithms included in this package are 'RUV-2', 'RUV-4', 'RUV-inv', 'RUV-rinv', 'RUV-I', and RUV-III', along with various supporting algorithms.
Contains miscellaneous functions useful in biostatistics, mostly univariate and multivariate testing procedures with a special emphasis on permutation tests. Many functions intend to simplify user's life by shortening existing procedures or by implementing plotting functions that can be used with as many methods from different packages as possible.
A collection of functions for computing "r-values" from various
    kinds of user input such as MCMC output or a list of effect size estimates
    and associated standard errors. Given a large collection of measurement units,
    the r-value, r, of a particular unit is a reported percentile that may be
    interpreted as the smallest percentile at which the unit should be placed in the
    top r-fraction of units.
Operations on triangular meshes based on 'VCGLIB'. This package
    integrates nicely with the R-package 'rgl' to render the meshes processed by
    'Rvcg'. The Visualization and Computer Graphics Library (VCG for short) is
    an open source portable C++ templated library for manipulation, processing
    and displaying with OpenGL of triangle and tetrahedral meshes. The library,
    composed by more than 100k lines of code, is released under the GPL license,
    and it is the base of most of the software tools of the Visual Computing Lab of
    the Italian National Research Council Institute ISTI <https://vcg.isti.cnr.it/>,
    like 'metro' and 'MeshLab'. The 'VCGLIB' source is pulled from trunk
    <https://github.com/cnr-isti-vclab/vcglib> and patched to work with options
    determined by the configure script as well as to work with the header files
    included by 'RcppEigen'.
Check latest release version of R and R package (both in 'CRAN', 'Bioconductor' or 'Github').
A framework with tools to compare two random variables via stochastic dominance. See the README.md at <https://github.com/EtorArza/RVCompare> for a quick start guide. It can compute the Cp and Cd of two probability distributions and the Cumulative Difference Plot as explained in E. Arza (2022) <doi:10.1080/10618600.2022.2084405>. Uses bootstrap or DKW-bounds to compute the confidence bands of the cumulative distributions. These two methods are described in B. Efron. (1979) <doi:10.1214/aos/1176344552> and P. Massart (1990) <doi:10.1214/aop/1176990746>.
Random vectors, called rvecs. An rvec holds
    multiple draws, but tries to behave like a standard
    R vector, including working well in data frames.
    Rvecs are useful for analysing
    output from a simulation or a Bayesian analysis.
Simple and fast tool for transforming phytosociological 
    vegetation data into digital form for the following analysis.
    Danihelka, Chrtek, and Kaplan (2012, ISSN:00327786).
    Hennekens, and Schaminée (2001) <doi:10.2307/3237010>.
    Tichý (2002) <doi:10.1111/j.1654-1103.2002.tb02069.x>.
    Wickham, François, Henry, Müller (2022) <https://CRAN.R-project.org/package=dplyr>.
Build regular expressions using grammar and functionality inspired 
    by <https://github.com/VerbalExpressions>. Usage of the %>% is encouraged to
    build expressions in a chain-like fashion.
Query the main 'R' 'SVN' repository to find the
    versions 'r-release' and 'r-oldrel' refer to, and also all previous
    'R' versions and their release dates.
Retrieve, map and summarize data from the 'VertNet.org' 
    archives (<https://vertnet.org/>).  Functions allow searching by many 
    parameters, including 'taxonomic' names, places, and dates. In addition, 
    there is an interface for conducting spatially delimited searches, and 
    another for requesting large 'datasets' via email.
Wrappers around the 'xml2' and 'httr' packages to make it
    easy to download, then manipulate, HTML and XML.
Vector Graphics devices for 'Microsoft PowerPoint' and
    'Microsoft Excel'. Functions extending package 'officer' are provided
    to embed 'DrawingML' graphics into 'Microsoft PowerPoint'
    presentations and 'Microsoft Excel' workbooks.
To detecting rare variants for binary traits using general pedigrees, the pedigree disequilibrium tests are proposed by collapsing rare haplotypes/variants with/without weights. To run the test, MERLIN is needed in Linux for haplotyping.
'ViennaCL' is a free open-source linear algebra library 
  for computations on many-core architectures (GPUs, MIC) and 
  multi-core CPUs. The library is written in C++ and supports 'CUDA', 
  'OpenCL', and 'OpenMP' (including switches at runtime). 
  I have placed these libraries in this package as a more efficient 
  distribution system for CRAN. The idea is that you can write a package 
  that depends on the 'ViennaCL' library and yet you do not need to 
  distribute a copy of this code with your package.
Provides 'Java' graphical user interfaces 
    for viewing, manipulating and plotting graphs. 
    Graphs may be directed or undirected.
The detection of troubling approximate collinearity in a multiple linear regression model is a classical problem in Econometrics. This package is focused on determining whether or not the degree of approximate multicollinearity in a multiple linear regression model is of concern, meaning that it affects the statistical analysis (i.e. individual significance tests) of the model. This objective is achieved by using the variance inflation factor redefined and the scatterplot between the variance inflation factor and the coefficient of variation. For more details see Salmerón R., García C.B. and García J. (2018) <doi:10.1080/00949655.2018.1463376>, Salmerón, R., Rodríguez, A. and García C. (2020) <doi:10.1007/s00180-019-00922-x>, Salmerón, R., García, C.B, Rodríguez, A. and García, C. (2022) <doi:10.32614/RJ-2023-010>, Salmerón, R., García, C.B. and García, J. (2025) <doi:10.1007/s10614-024-10575-8> and Salmerón, R., García, C.B, García J. (2023, working paper) <doi:10.48550/arXiv.2005.02245>. You can also view the package vignette using 'browseVignettes("rvif")', the package website (<https://www.ugr.es/local/romansg/rvif/index.html>) using 'browseURL(system.file("docs/index.html", package = "rvif"))' or version control on GitHub (<https://github.com/rnoremlas/rvif_package>).
Provides an interface to 'vinecopulib', a C++ library for vine 
 copula modeling. The 'rvinecopulib' package implements the core features of the
 popular 'VineCopula' package, in particular inference algorithms for both vine 
 copula and bivariate copula models. Advantages over 'VineCopula' are a sleeker 
 and more modern API, improved performances, especially in high dimensions, 
 nonparametric and multi-parameter families, and the ability to model discrete 
 variables. The 'rvinecopulib' package includes 'vinecopulib' as header-only 
 C++ library (currently version 0.7.2). Thus users do not need to install 
 'vinecopulib' itself in order to use 'rvinecopulib'. Since their initial 
 releases, 'vinecopulib' is licensed under the MIT License, and 'rvinecopulib' 
 is licensed under the GNU GPL version 3.
The Regional Vulnerability Index (RVI), a statistical measure of brain structural abnormality, quantifies an individual's similarity to the expected pattern (effect size) of deficits in schizophrenia (Kochunov P, Fan F, Ryan MC, et al. (2020) <doi:10.1002/hbm.25045>).
Open any data frame with 'visidata', a terminal-based spreadsheet application <https://www.visidata.org>.
Load data from vk.com api about your communiti users and views,
    ads performance, post on user wall and etc.	For more information 
    see API Documentation <https://vk.com/dev/first_guide>.
Generates pseudo-random vectors that follow an arbitrary von Mises-Fisher distribution on a sphere. This method is fast and efficient when generating a large number of pseudo-random vectors. Functions to generate random variates and compute density for the distribution of an inner product between von Mises-Fisher random vector and its mean direction are also provided. Details are in Kang and Oh (2024) <doi:10.1007/s11222-024-10419-3>.
Gather boxscore, play-by-play, and auxiliary data from Major League Volleyball (MLV) <https://provolleyball.com>, League One Volleyball Pro (LOVB) <https://www.lovb.com/pro-league>, and Athletes Unlimited Pro Volleyball (AU) <https://auprosports.com/volleyball/> to create a repository of basic and advanced statistics for teams and players.
Perform a Relative Weights Analysis (RWA) (a.k.a. Key Drivers Analysis) as per the method described 
    in Tonidandel & LeBreton (2015) <DOI:10.1007/s10869-014-9351-z>, with its original roots in Johnson (2000) <DOI:10.1207/S15327906MBR3501_1>. In essence, RWA decomposes
    the total variance predicted in a regression model into weights that accurately reflect the proportional 
    contribution of the predictor variables, which addresses the issue of multi-collinearity. In typical scenarios,
    RWA returns similar results to Shapley regression, but with a significant advantage on computational performance.
Provides API to Melbourne pedestrian and weather data
    <https://data.melbourne.vic.gov.au> in tidy data form.
A port of Ruby Warrior.
    Teaches R programming in a fun and interactive way.
A set of R functions which provide an
        environment for the Time-Frequency analysis of 1-D signals (and
        especially for the wavelet and Gabor transforms of noisy
        signals). It was originally written for Splus by Rene Carmona,
        Bruno Torresani, and Wen L. Hwang, first at the University of
        California at Irvine and then at Princeton University.  Credit
        should also be given to Andrea Wang whose functions on the
        dyadic wavelet transform are included. Rwave is based on the
        book: "Practical Time-Frequency Analysis: Gabor and Wavelet
        Transforms with an Implementation in S", by Rene Carmona, Wen
        L. Hwang and Bruno Torresani (1998, eBook ISBN:978008053942), Academic Press.
Perform wavelet analysis (orthogonal,translation invariant, tensorial, 1-2-3d transforms, thresholding, block thresholding, linear,...) with applications to data compression or denoising/regression. The core of the code is a port of 'MATLAB' Wavelab toolbox written by D. Donoho, A. Maleki and M. Shahram (<https://statweb.stanford.edu/~wavelab/>).
Provides a wrapper for running the bundled 'Open-WBO' Maximum Satisfiability
    (MaxSAT) solver (<https://github.com/sat-group/open-wbo>).
    Users can pass command-line arguments to the solver and capture its output
    as a character string or file.
Code to facilitate simulation and inference when connectivity is defined by underlying random walks. Methods for spatially-correlated pairwise distance data are especially considered. This provides core code to conduct analyses similar to that in Hanks and Hooten (2013) <doi:10.1080/01621459.2012.724647>.
A tool to read and manipulate data generated from 'RiverWare'(TM) 
    <https://www.riverware.org/> simulations. 'RiverWare' and 'RiverSMART' 
    generate data in "rdf", "csv", and "nc" format. This package provides an 
    interface to read, aggregate, and summarize data from one or more 
    simulations in a 'dplyr' pipeline.
Weave and tangle drivers for Sweave extending the
  standard drivers. RweaveExtraLatex and RtangleExtra provide options
  to completely ignore code chunks on weaving, tangling, or both.
  Chunks ignored on weaving are not parsed, yet are written out
  verbatim on tangling. Chunks ignored on tangling may be evaluated as
  usual on weaving, but are completely left out of the tangled
  scripts. The driver RtangleExtra also provides options to control
  the separation between code chunks in the tangled script, and to
  specify the extension of the file name (or remove it entirely) when
  splitting is selected.
An R interface to Weka (Version 3.9.3).
   Weka is a collection of machine learning algorithms for data mining
   tasks written in Java, containing tools for data pre-processing,
   classification, regression, clustering, association rules, and
   visualization.  Package 'RWeka' contains the interface code, the
   Weka jar is in a separate package 'RWekajars'.  For more information
   on Weka see <https://www.cs.waikato.ac.nz/ml/weka/>.
External jars required for package 'RWeka'.
Communications simulation package supporting forward error correction.
Random walk functions to extract new variables based on clients transactional behaviour. For more details, see Eddin et al. (2021) <arXiv:2112.07508v3> and Oliveira et al. (2021) <arXiv:2102.05373v2>.
A straightforward, easy-to-use and robust parsing package which aims to
    digest history files from the popular messenger service 'WhatsApp' in all locales
    and from all devices.
Provides Wiener process distribution functions,
  namely the Wiener first passage time density, CDF, quantile and random
  functions. Additionally supplies a modelling function (wdm) and further
  methods for the resulting object.
Provides an interface with the 'Wildbook' mark-recapture ecological database framework. It 
    helps users to pull data from the 'Wildbook' framework and format data for further analysis
    with mark-recapture applications like 'Program MARK' (which can be accessed via the 'RMark' package in 'R').
    Further information on the 'Wildbook' framework is available at: <http://www.wildbook.org/doku.php>.
A plug in for using 'WinEdt' as an editor for R.
An expansion of R's 'stats' random wishart matrix generation. 
  This package allows the user to generate singular, Uhlig and Harald (1994) 
  <doi:10.1214/aos/1176325375>, and pseudo wishart, Diaz-Garcia, et al.(1997) 
  <doi:10.1006/jmva.1997.1689>, matrices. In addition the user can generate 
  wishart matrices with fractional degrees of freedom, Adhikari (2008) 
  <doi:10.1061/(ASCE)0733-9399(2008)134:12(1029)>, commonly used in volatility 
  modeling. Users can also use this package to create random covariance matrices.
Implementation of the Integrated Simple Weighted Sum Product Method (WISP), a multiple criteria sorting method create by Dragisa Stanujkic (2021) <doi:10.1109/TEM.2021.3075783>.
Estimates life tables, specifically (crude) death rates and (raw and graduated) death probabilities, using rolling windows in one (e.g., age), two (e.g., age and time) or three (e.g., age, time and income) dimensions. The package can also be utilised for summarising statistics and smoothing continuous variables through rolling windows in other domains, such as estimating averages of self-positioning ideology in political science.
   Acknowledgements:
   The authors wish to thank Ministerio de Ciencia, Innovación y Universidades (grant PID2021-128228NB-I00) and Generalitat Valenciana (grants HIECPU/2023/2, Conselleria de Hacienda, Economía y Administración Pública, and CIGE/2023/7, Conselleria de Educación, Cultura, Universidades y Empleo) for supporting this research.
Contains convenience functions for working with spatial data across
    multiple UTM zones, raster-vector operations common in the analysis of 
    conflict data, and converting degrees, minutes, and seconds latitude and
    longitude coordinates to decimal degrees.
Creation, estimation, and prediction of random weight neural networks (RWNN), Schmidt et al. (1992) <doi:10.1109/ICPR.1992.201708>, including popular variants like extreme learning machines, Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, sparse RWNN, Zhang et al. (2019) <doi:10.1016/j.neunet.2019.01.007>, and deep RWNN, Henríquez et al. (2018) <doi:10.1109/IJCNN.2018.8489703>. It further allows for the creation of ensemble RWNNs like bagging RWNN, Sui et al. (2021) <doi:10.1109/ECCE47101.2021.9595113>, boosting RWNN, stacking RWNN, and ensemble deep RWNN, Shi et al. (2021) <doi:10.1016/j.patcog.2021.107978>.
An implementation of the WOFOST ("World Food Studies") crop growth model. WOFOST is a dynamic simulation model that uses daily weather data, and crop, soil and management parameters to simulate crop growth and development. See De Wit et al. (2019) <doi:10.1016/j.agsy.2018.06.018> for a recent review of the history and use of the model.
Reproducibility is essential to the progress of research, 
  yet achieving it remains elusive even in computational fields. 
  Continuous Integration (CI) platforms offer a powerful way to launch automated workflows 
  to check and document code, but often require considerable time, effort, 
  and technical expertise to setup. We therefore developed the rworkflows suite 
  to make robust CI workflows easy and freely accessible to all R package developers. 
  rworkflows consists of 1) a CRAN/Bioconductor-compatible R package template, 
  2) an R package to quickly implement a standardised workflow, and 
  3) a centrally maintained GitHub Action.
Enables mapping of country level and gridded user datasets.
High resolution vector country boundaries derived from
        Natural Earth data, can be plotted in rworldmap.
Search by keywords in R packages, task views, CRAN, the web and display the results in the console or in txt, html or pdf files. Download the package documentation (html index, README, NEWS, pdf manual, vignettes, source code, binaries) with a single instruction. Visualize the package dependencies and CRAN checks. Compare the package versions, unload and install the packages and their dependencies in a safe order. Explore CRAN archives. Use the above functions for task view maintenance. Access web search engines from the console thanks to 80+ bookmarks. All functions accept standard and non-standard evaluation.
It contains Chinese character frequency data based on 
    news data from 2017 to 2019. Source of these news include Sina, China daily and Tencent.
Implements various tests, visualizations, and metrics
    for diagnosing convergence of MCMC chains in phylogenetics.  It implements
    and automates many of the functions of the AWTY package in the R
    environment, as well as a host of other functions.  Warren, Geneva, and Lanfear (2017), <doi:10.1093/molbev/msw279>.
Tools for getting historical weather information and forecasts 
    from wunderground.com. Historical weather and forecast data includes, but 
    is not limited to, temperature, humidity, windchill, wind speed, dew point, 
    heat index. Additionally, the weather underground weather API also includes 
    information on sunrise/sunset, tidal conditions, satellite/webcam imagery, 
    weather alerts, hurricane alerts and historical high/low temperatures.
Visualize your favorite XKCD comic strip directly from
        R. XKCD <https://xkcd.com> web comic content is provided under the Creative
        Commons Attribution-NonCommercial 2.5 License.
Methods to compute chemical similarity between two or more reactions and molecules. Allows masking of chemical substructures for weighted similarity computations. Uses packages 'rCDK' and 'fingerprint' for cheminformatics functionality. Methods for reaction similarity and sub-structure masking are as described in: Giri et al. (2015) <doi:10.1093/bioinformatics/btv416>.
Facilities for running simulations from ordinary
    differential equation ('ODE') models, such as pharmacometrics and other
    compartmental models.  A compilation manager translates the ODE model
    into C, compiles it, and dynamically loads the object code into R for
    improved computational efficiency.  An event table object facilitates
    the specification of complex dosing regimens (optional) and sampling
    schedules.  NB: The use of this package requires both C and
    Fortran compilers, for details on their use with R please see
    Section 6.3, Appendix A, and Appendix D in the "R Administration and
    Installation" manual. Also the code is mostly released under GPL.  The
    'VODE' and 'LSODA' are in the public domain.  The information is available
    in the inst/COPYRIGHTS.
Provides the log-likelihoods with gradients from 'stan'
    (Carpenter et al (2015), <doi:10.48550/arXiv.1509.07164>) needed
    for generalized log-likelihood estimation in 'nlmixr2'
    (Fidler et al (2019) <doi:10.1002/psp4.12445>). This is
    split of to reduce computational burden of recompiling 'rxode2'
    (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) which runs
    the 'nlmixr2' models during estimation.
Analysis of combined total and allele specific reads from the reciprocal cross study with RNA-seq data.
Provides access to the 'xylib' C library for to import xy 
  data from powder diffraction, spectroscopy and other experimental methods.
Interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).
Load data from 'Yandex Direct' API V5 
    <https://yandex.ru/dev/direct/doc/dg/concepts/about-docpage> into R.
	Provide function for load lists of campaings, ads, keywords and other 
	objects from 'Yandex Direct' account. Also you can load statistic from
	API 'Reports Service' <https://yandex.ru/dev/direct/doc/reports/reports-docpage>.
	And allows keyword bids management.
'Yandex Translate' (https://translate.yandex.com/) is a statistical machine translation system.
	The system translates separate words, complete texts, and webpages.
	This package can be used to detect language from text and to translate it to supported target language.
	For more info: https://tech.yandex.com/translate/doc/dg/concepts/About-docpage/ .
Allows work with 'Management API' for load counters, segments, filters,
	user permissions and goals list from Yandex Metrica, 'Reporting API' allows you to get 
	information about the statistics of site visits and other data without
	using the web interface, 'Logs API' allows to receive non-aggregated data and 
	'Compatible with Google Analytics Core Reporting API v3' allows 
	receive information about site traffic and other data using field names 
	from Google Analytics Core API.	For more information see official 
	documents <https://yandex.ru/dev/metrika/doc/api2/concept/about-docpage>.
A programmatic interface to web-services of YouTheria. YouTheria is
    an online database of mammalian trait data <http://www.utheria.org/>.
Provide function for get data from 'YouTube Data API' 
    <https://developers.google.com/youtube/v3/docs/>, 'YouTube Analytics API' 
    <https://developers.google.com/youtube/analytics/reference/> and 
    'YouTube Reporting API' <https://developers.google.com/youtube/reporting/v1/reports>.
This tool proposes a new ranking algorithm that utilizes a "Y*WAASB" biplot generated by the 'metan'. The aim of the current package is to effectively distinguish the top-ranked genotypes in MET (Multi-Environmental Trials). For a detailed explanation of the process of obtaining "WAASB", "WAASBY" indices, and a "Y*WAASB" biplot, refer to the manual included in this package as well as the study by Olivoto & Lúcio (2020) <doi:10.1111/2041-210X.13384>. In this context, "WAASB" refers to the "Weighted Average of Absolute Scores" provided by Olivoto et al. (2019) <doi:10.2134/agronj2019.03.0220>, which quantifies the stability of genotypes across different environments using linear mixed-effect models. To run the package, you need to extract the "WAASB" and "WAASBY" coefficients using the 'metan' and apply them. This tool utilizes PCA (Principal Component Analysis) and differentiates the entries which may be genotypes, hybrids, varieties, etc using "WAASB", "WAASBY", and a combination of the specified trait and WAASB index.
Provides functionality to read settings, statuses and readings of
  weather stations from the 'ZENTRA Cloud' API 
  <https://zentracloud.com/api/v1/guide#APIGuidelines>.
Implements the Zig-Zag algorithm (Bierkens, Fearnhead, Roberts, 2016) <arXiv:1607.03188> applied and Bouncy Particle Sampler <arXiv:1510.02451> for a Gaussian target and Student distribution.
Interface to the 'ZeroMQ' lightweight messaging kernel (see <https://zeromq.org/> for more information).
Functions to identify Homozygous-by-Descent (HBD) segments associated with runs of homozygosity (ROH) and to
    estimate individual autozygosity (or inbreeding coefficient). HBD segments and autozygosity are assigned to multiple HBD classes
    with a model-based approach relying on a mixture of exponential distributions. The rate of the exponential distribution is distinct
    for each HBD class and defines the expected length of the HBD segments. These HBD classes are therefore related to the age of the
    segments (longer segments and smaller rates for recent autozygosity / recent common ancestor). The functions allow to estimate the
    parameters of the model (rates of the exponential distributions, mixing proportions), to estimate global and local autozygosity
    probabilities and to identify HBD segments with the Viterbi decoding. The method is fully described in Druet and Gautier (2017)
    <doi:10.1111/mec.14324> and Druet and Gautier (2022) <doi:10.1016/j.tpb.2022.03.001>.
Provides R bindings for Google's s2 library for geometric calculations on
    the sphere. High-performance constructors and exporters provide high compatibility
    with existing spatial packages, transformers construct new geometries from existing
    geometries, predicates provide a means to select geometries based on spatial
    relationships, and accessors extract information about geometries.
A set of functions used in teaching STATS 201/208 Data Analysis at
    the University of Auckland. The functions are designed to make parts of R more
    accessible to a large undergraduate population who are mostly not statistics
    majors.
An advanced version of package 's2dverification'. Intended for 
    seasonal to decadal (s2d) climate forecast verification, but also applicable
    to other types of forecasts or general climate analysis. This package is 
    specifically designed for comparing experimental and observational datasets. 
    It provides functionality for data retrieval, post-processing, skill score 
    computation against observations, and visualization. Compared to 
    's2dverification', 's2dv' is more compatible with the package 'startR', able 
    to use multiple cores for computation and handle multi-dimensional arrays 
    with a higher flexibility. The Climate Data Operators (CDO) version used in 
    development is 1.9.8. Implements methods described in Wilks (2011) 
    <doi:10.1016/B978-0-12-385022-5.00008-7>, DelSole and Tippett 
    (2016) <doi:10.1175/MWR-D-15-0218.1>, Kharin et al. (2012) 
    <doi:10.1029/2012GL052647>, Doblas-Reyes et al. (2003) 
    <doi:10.1007/s00382-003-0350-4>.
Implements the generalized semi-supervised elastic-net. This method extends the supervised elastic-net problem, and thus it is a practical solution to the problem of feature selection in semi-supervised contexts. Its mathematical formulation is presented from a general perspective, covering a wide range of models.  We focus on linear and logistic responses, but the implementation could be easily extended to other losses in generalized linear models. We develop a flexible and fast implementation, written in 'C++' using 'RcppArmadillo' and integrated into R via 'Rcpp' modules. See Culp, M. 2013 <doi:10.1080/10618600.2012.657139> for references on the Joint Trained Elastic-Net.
Download files hosted on 'AWS S3' (Amazon Web Services Simple Storage Service; <https://aws.amazon.com/s3/>) to a local directory based on their URI. Avoid downloading files that are already present locally.  Allow for customization of where to store downloaded files.
A S3 resource is provided by Amazon Web Services S3 or a S3-compatible object store (such as Minio). 
    The resource can be a tidy file to be downloaded from the object store, or a data lake (such as Delta Lake) 
    Parquet file to be read by Apache Spark.
Access 'Amazon Web Service Simple Storage Service' ('S3') <https://aws.amazon.com/s3/>
    as if it were a file system. Interface based on the R package 'fs'.
Performs variable selection using the structured screen-and-select (S3VS) framework in linear models, generalized linear models with binary data, and survival models such as the Cox model and accelerated failure time (AFT) model.
Implements a set of distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species). While these methods can also be used on well-sampled taxa, they are united by the fact that they can be utilized with relatively few data points. More details on the currently implemented methodologies can be found in Drake and Richards (2018) <doi:10.1002/ecs2.2373>, Drake (2015) <doi:10.1098/rsif.2015.0086>, and Drake (2014) <doi:10.1890/ES13-00202.1>.
A new object oriented programming system designed to be a
    successor to S3 and S4. It includes formal class, generic, and method
    specification, and a limited form of multiple dispatch. It has been
    designed and implemented collaboratively by the R Consortium
    Object-Oriented Programming Working Group, which includes
    representatives from R-Core, 'Bioconductor', 'Posit'/'tidyverse', and
    the wider R community.
Provides a generic framework for working with YAML (YAML
    Ain't Markup Language) configuration files. Uses 'ajv' (Another JSON
    Schema Validator) via 'V8' to validate configurations against JSON
    Schema definitions. Configuration objects inherit from 'S7' classes
    and base lists, supporting downstream extension through custom classes
    and methods.
Perform joint segmentation on two signal dimensions derived from 
    total read depth (intensity) and allele specific read depth (intensity) for 
    whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data.
Parses R source files into Abstract Syntax Tree (AST) symbol indices, traces function
    callers across projects, discovers project dependency graphs, generates
    project briefings, and provides package introspection tools. Designed
    for AI coding agents that need structured code understanding.
Calculates a degree of spatial association between regionalizations 
    or categorical maps using the information-theoretical V-measure 
    (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>). It also
    offers an R implementation of the MapCurve method 
    (Hargrove et al. (2006) <doi:10.1007/s10109-006-0025-x>).
A modular and extendable approach to extract (micro)saccades from gaze samples via an ensemble of methods.
  Although there is an agreement about a general definition of a saccade, the more specific details are harder to agree upon.
  Therefore, there are numerous algorithms that extract saccades based on various heuristics, which differ in the assumptions about velocity,
  acceleration, etc. The package uses three methods (Engbert and Kliegl (2003) <doi:10.1016/S0042-6989(03)00084-1>,
  Otero-Millan et al. (2014)<doi:10.1167/14.2.18>, and Nyström and Holmqvist (2010) <doi:10.3758/BRM.42.1.188>)
  to label individual samples and then applies a majority vote approach to identify saccades. The package includes three
  methods but can be extended via custom functions. It also uses a modular approach to compute velocity and
  acceleration from noisy samples. Finally, you can obtain methods votes per gaze sample instead of saccades.
Computes the Exposure-At-Default based on  the standardized approach
    of CRR2 (SA-CCR). The simplified version of SA-CCR has been included, as well as the OEM methodology.
	Multiple trade types of all the five major asset classes are being supported including the Other Exposure and, given the inheritance-
    based structure of the application, the addition of further trade types
    is straightforward. The application returns a list of trees per Counterparty and CSA after
    automatically separating the trades based on the Counterparty, the CSAs, the hedging sets, the
    netting sets and the risk factors. The basis and volatility transactions are
    also identified and treated in specific hedging sets whereby the corresponding 
    penalty factors are applied. All the examples appearing on the
    regulatory papers (both for the margined and the unmargined workflow) have been
    implemented including the latest CRR2 developments.
Implementation of the BLEU-Score in 'C++' to evaluate the
    quality of generated text. The BLEU-Score, introduced by Papineni et al. (2002)
    <doi:10.3115/1073083.1073135>, is a metric for evaluating the quality of
    generated text. It is based on the n-gram overlap between the generated
    text and reference texts. Additionally, the package provides some smoothing
    methods as described in Chen and Cherry (2014) <doi:10.3115/v1/W14-3346>.
Implementation of the wavelet-based spatial verification method of Buschow and Friederichs "SAD: Verifying the Scale, Anisotropy and Direction of precipitation forecasts" (2020, submitted to QJRMS). Forecasts and Observations are transformed by a decimated or redundant dual-tree complex wavelet transform to analyze the spatial scale, degree of anisotropy and preferred direction in each field. These structural attributes are compared by a series of scores. An experimental algorithm for the correction of these errors is included as well.
We analyzed the nucleotide composition of genes with a special emphasis on stability of DNA sequences. Besides, in a variety of different organisms unequal use of synonymous codons, or codon usage bias, occurs which also show variation among genes in the same genome. Seemingly, codon usage bias is affected by both selective constraints and mutation bias which allows and enables us to examine and detect changes in these two evolutionary forces between genomes or along one genome. Therefore, we determined the codon adaptation index (CAI), effective number of codons (ENC) and codon usage analysis with calculation of the relative synonymous codon usage (RSCU), and subsequently predicted the translation efficiency and accuracy through GC-rich codon usages. Furthermore, we estimated the relative stability of the DNA sequence following calculation of the average free energy (Delta G) and Dimer base-stacking energy level.
Computes the probability of a set of species abundances of a single or multiple samples of individuals with one or more guilds under a mainland-island model. One must specify the mainland (metacommunity) model and the island (local) community model. It assumes that species fluctuate independently. The package also contains functions to simulate under this model. See Haegeman, B. & R.S. Etienne (2017). A general sampling formula for community structure data. Methods in Ecology & Evolution 8: 1506-1519 <doi:10.1111/2041-210X.12807>.
Provides the density, distribution, quantile and generation functions of some obscure probability 
    distributions, including the doubly non-central t, F, Beta, and Eta distributions; 
    the lambda-prime and K-prime; the upsilon distribution; the (weighted) sum of 
    non-central chi-squares to a power; the (weighted) sum of log non-central chi-squares;
    the product of non-central chi-squares to powers; the product of doubly non-central
    F variables; the product of independent normals.
Maximum likelihood tools to fit and compare models of species
    abundance distributions and of species rank-abundance distributions.
Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) <doi:10.1093/biomet/asr063>.
Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP). The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is based on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.
Time series area-level models for small area estimation. 
      The package supplements the functionality of the sae package. Specifically, it includes
      EBLUP fitting of the Rao-Yu model in the original form without a spatial component. 
      The package also offers a modified ("dynamic") version of the Rao-Yu model, replacing
      the assumption of stationarity. Both univariate and multivariate applications are
      supported. Of particular note is the allowance for covariance of the area-level sample 
      estimates over time, as encountered in rotating panel designs such as the U.S. National 
      Crime Victimization Survey or present in a time-series of 5-year estimates from the 
      American Community Survey. Key references to the methods include
      J.N.K. Rao and I. Molina (2015, ISBN:9781118735787),
      J.N.K. Rao and M. Yu (1994) <doi:10.2307/3315407>, and
      R.E. Fay and R.A. Herriot (1979) <doi:10.1080/01621459.1979.10482505>.
Enables small area estimation (SAE) of health and demographic indicators in low- and middle-income countries (LMICs). It powers an R 'shiny' application for generating subnational estimates and prevalence maps of 150+ binary indicators from Demographic and Health Surveys (DHS). It builds on the SAE analysis workflow from the 'surveyPrev' package. For documentation, visit <https://sae4health.stat.uw.edu/>. Methodological details can be found at Wu et al. (2025) <doi:10.48550/arXiv.2505.01467>.
Select best combination of auxiliary variables with certain criterion.
Estimates the parameter of small area in binary data without
             auxiliary variable using Empirical Bayes technique, mainly from Rao 
             and Molina (2015,ISBN:9781118735787) with book entitled "Small Area Estimation Second Edition". 
             This package provides another option of direct estimation using weight.
             This package also features alpha and beta parameter estimation on calculating process of small area.
             Those methods are Newton-Raphson and Moment which based on Wilcox (1979) <doi:10.1177/001316447903900302> 
             and Kleinman (1973) <doi:10.1080/01621459.1973.10481332>.
Provides functionality to fit a zero-inflated estimator for small area estimation.
    This estimator is a combines a linear mixed effects regression model and a logistic
    mixed effects regression model via a two-stage modeling approach. The estimator's mean
    squared error is estimated via a parametric bootstrap method. Chandra and others
    (2012, <doi:10.1080/03610918.2011.598991>) introduce and describe this estimator and mean
    squared error estimator. White and others (2024+, <doi:10.48550/arXiv.2402.03263>) describe the 
    applicability of this estimator to estimation of forest attributes and further assess the
    estimator's properties.
Provides small area estimation for count data type and gives option whether to use covariates in the estimation or not. By implementing Empirical Bayes (EB) Poisson-Gamma model, each function returns EB estimators and mean squared error (MSE) estimators for each area. The EB estimators without covariates are obtained using the model proposed by Clayton & Kaldor (1987) <doi:10.2307/2532003>, the EB estimators with covariates are obtained using the model proposed by Wakefield (2006) <doi:10.1093/biostatistics/kxl008> and the MSE estimators are obtained using Jackknife method by Jiang et. al. (2002) <doi:10.1214/aos/1043351257>.
Provides several functions for area level of small area estimation using hierarchical Bayesian (HB) methods with several univariate distributions for variables of interest. The dataset that is used in every function is generated accordingly in the Example. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involve the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.
Implementation of small area estimation using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error. The 'rjags' package is employed to obtain parameter estimates. For the references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).
Implementation of Small Area Estimation (SAE) using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error under Beta Distribution. The 'rjags' package is employed to obtain parameter estimates. For the references, see J.N.K & Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Sharon (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).
We designed this package to provide several functions for area level of small area estimation using hierarchical Bayesian (HB) method. This package provides model using panel data for variable interest.This package also provides a dataset produced by a data generation. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean. For the reference, see Rao and Molina (2015).
Several functions are provided for small area estimation at
    the area level using the hierarchical bayesian (HB) method with panel
    data under beta distribution for variable interest. This package also
    provides a dataset produced by data generation. The 'rjags' package is
    employed to obtain parameter estimates. Model-based estimators involve
    the HB estimators, which include the mean and the variation of the
    mean. For the reference, see Rao and Molina (2015, ISBN:
    978-1-118-73578-7).
Provides several functions and datasets for area level of Small Area Estimation under Spatial Model using Hierarchical Bayesian (HB) Method. Model-based estimators include the HB estimators based on a Spatial Fay-Herriot model with univariate normal distribution for variable of interest.The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.
Estimates area and subarea level proportions using the Small Area Estimation (SAE) Twofold Subarea Model with a hierarchical Bayesian (HB) approach under Beta distribution. A number of simulated datasets generated for illustration purposes are also included. The 'rstan' package is employed to estimate parameters via the Hamiltonian Monte Carlo and No U-Turn Sampler algorithm. The  model-based estimators include the HB mean, the variation of the mean, and quantiles. For references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Torabi and Rao (2014) <doi:10.1016/j.jmva.2014.02.001>, Leyla Mohadjer et al.(2007) <http://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000559.pdf>, Erciulescu et al.(2019) <doi:10.1111/rssa.12390>, and Yudasena (2024).
We designed this package to provides several functions for area and subarea level of small area estimation under Twofold Subarea Level Model using hierarchical Bayesian (HB) method with Univariate Normal distribution for variables of interest. Some dataset simulated by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates using Gibbs Sampling algorithm. Model-based estimators involves the HB estimators which include the mean, the variation of mean, and the quantile. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Torabi and Rao (2014) <doi:10.1016/j.jmva.2014.02.001>, Leyla Mohadjer et al.(2007) <http://www.asasrms.org/Proceedings/y2007/Files/JSM2007-000559.pdf>, and Erciulescu et al.(2019) <doi:10.1111/rssa.12390>.
Small area estimation unit level models (Battese-Harter-Fuller model) with a Bayesian Hierarchical approach. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Battese et al. (1988) <doi:10.1080/01621459.1988.10478561>.
Provides function for area level of small area estimation using hierarchical Bayesian (HB) method with Zero-Inflated Binomial distribution for variables of interest. Some dataset produced by a data generation are also provided. The 'rjags' package is employed to obtain parameter estimates. Model-based estimators involves the HB estimators which include the mean and the variation of mean.
Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) <http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf>.
A set of functions and datasets implementation of small area estimation when auxiliary variable is measured with error. These functions provide a empirical best linear unbiased prediction (EBLUP) estimator and mean squared error (MSE) estimator of the EBLUP. These models were developed by Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>.
The 'saemix' package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. It (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for 'saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.
Compute various common mean squared predictive error (MSPE) estimators, as well as several existing variance component predictors as a byproduct, for FH model (Fay and Herriot, 1979) and NER model (Battese et al., 1988) in small area estimation.
Implementation of small area estimation (Fay-Herriot model) with EBLUP (Empirical Best Linear Unbiased Prediction) Approach for non-sampled area estimation by adding cluster information and assuming that there are similarities among particular areas. See also Rao & Molina (2015, ISBN:978-1-118-73578-7) and Anisa et al. (2013) <doi:10.9790/5728-10121519>.
Provides function for small area estimation at area level
    using averaging pseudo area level model for variables of interest.
    A dataset produced by data generation is also provided. This package
    estimates small areas at the village level and then aggregates them
    to the sub-district, region, and provincial levels.
Methods to fit robust alternatives to commonly used models used in
    Small Area Estimation. The methods here used are based on best linear
    unbiased predictions and linear mixed models. At this time available models
    include area level models incorporating spatial and temporal correlation in
    the random effects.
Functions to calculate EBLUPs (Empirical Best Linear Unbiased Predictor) estimators and their MSEs (Mean Squared Errors). Estimators are based on an area-level linear mixed model introduced by Rao and Yu (1994) <doi:10.2307/3315407>. The REML (Residual Maximum Likelihood) method is used for fitting the model.
Tools for the simulation of data in the context of small area
    estimation. Combine all steps of your simulation - from data generation
    over drawing samples to model fitting - in one object. This enables easy
    modification and combination of different scenarios. You can store your
    results in a folder or start the simulation in parallel.
The aim of this package is to offer new methodology for unit-level 
    small area models under transformations and limited population auxiliary 
    information. In addition to this new methodology, the widely used nested 
    error regression model without transformations (see "An Error-Components 
    Model for Prediction of County Crop Areas Using Survey and Satellite Data" 
    by Battese, Harter and Fuller (1988) <doi:10.1080/01621459.1988.10478561>) 
    and its well-known uncertainty estimate (see "The estimation of the mean 
    squared error of small-area estimators" by Prasad and Rao (1990) 
    <doi:10.1080/01621459.1995.10476570>) are provided. In this package, the 
    log transformation and the data-driven log-shift transformation are 
    provided. If a transformation is selected, an appropriate method is chosen 
    depending on the respective input of the population data: Individual 
    population data (see "Empirical best prediction under a nested error model 
    with log transformation" by Molina and Martín (2018) 
    <doi:10.1214/17-aos1608>) but also aggregated population data (see 
    "Estimating regional income indicators under transformations and access to 
    limited population auxiliary information" by Würz, Schmid and Tzavidis 
    <unpublished>) can be entered. Especially under limited data access, new 
    methodologies are provided in saeTrafo. Several options are available to 
    assess the used model and to judge, present and export its results. For a 
    detailed description of the package and the methods used see the 
    corresponding vignette.
Allows users to produce diagnostic procedures and graphic tools for the evaluation of Small Area estimators.
Provides functionality for image processing and shape analysis in 
    the context of reconstructed medical images generated by deep learning-based
    methods or standard image processing algorithms and produced from different
    medical imaging types, such as X-ray, Computational Tomography (CT),
    Magnetic Resonance Imaging (MRI), and pathology imaging. Specifically,
    offers tools to segment regions of interest and to extract quantitative
    shape descriptors for applications in signal processing,
    statistical analysis and modeling, and machine learning.
The aim of the package is to provide some basic functions
        for doing statistics with one dimensional Fuzzy Data (in the
        form of polygonal fuzzy numbers). In particular, the package
        contains functions for the basic operations on the class of
        fuzzy numbers (sum, scalar product, mean, median, Hukuhara difference) 
        as well as for calculating (Bertoluzza) distance and sample variance. 
        Moreover a function to simulate fuzzy random variables and bootstrap tests 
        for the equality of means is included. Version 2.1 fixes some bugs
        of previous versions.
Provides tools to help tag and validate data according to user-specified rules. The 'safeframe' class adds variable level attributes to 'data.frame' columns. Once tagged, these variables can be seamlessly used in downstream analyses, making data pipelines clearer, more robust, and more reliable.
The goal of 'safejoin' is to guarantee that when performing joins extra rows are not added to your data. 'safejoin' provides a wrapper around 'dplyr::left_join' that will raise an error when extra rows are unexpectedly added to your data. This can be useful when working with data where you expect there to be a many to one relationship but you are not certain the relationship holds.
The goal of 'SAFEPG' is to predict climate-related extreme losses by fitting a frequency-severity model. It improves predictive performance by introducing a sign-aligned regularization term, which ensures consistent signs for the coefficients across the frequency and severity components. This enhancement not only increases model accuracy but also enhances its interpretability, making it more suitable for practical applications in risk assessment.
A consistent interface to encrypt and decrypt strings, R objects and files using symmetric and asymmetric key encryption.
Functions to design and apply tests that are anytime valid. The
  functions can be used to design hypothesis tests in the prospective/randomised
  control trial setting or in the observational/retrospective setting. The
  resulting tests remain valid under both optional stopping and optional
  continuation. The current version includes safe t-tests and safe tests of
  two proportions. For details on the theory of safe tests, see
  Grunwald, de Heide and Koolen (2019) "Safe Testing" <arXiv:1906.07801>, 
  for details on safe logrank tests see ter Schure, Perez-Ortiz, Ly and Grunwald
  (2020) "The Safe Logrank Test: Error Control under Continuous Monitoring with 
  Unlimited Horizon" <arXiv:2011.06931v3> and Turner, Ly and Grunwald (2021)
  "Safe Tests and Always-Valid Confidence Intervals for contingency tables and 
  beyond" <arXiv:2106.02693> for details on safe contingency table tests.
A file format for storing tensors that is secure (doesn't allow for
    code execution), fast and simple to implement. 'safetensors' also enables cross
    language and cross frameworks compatibility making it an ideal format for
    storing machine learning model weights.
Example clinical trial data sets formatted for easy use in R.
Fork of 'vote_2.3-2', Raftery et al. (2021)
  <DOI:10.32614/RJ-2021-086>, with additional support 
  for stochastic experimentation.
Datasets and functions for the book "Statistiques pour l’économie et la gestion", "Théorie et applications en entreprise", F. Bertrand, Ch. Derquenne, G. Dufrénot, F. Jawadi and M. Maumy, C. Borsenberger editor, (2021, ISBN:9782807319448, De Boeck Supérieur, Louvain-la-Neuve). 
    The first chapter of the book is dedicated to an introduction to statistics and their world. 
    The second chapter deals with univariate exploratory statistics and graphics. 
    The third chapter deals with bivariate and multivariate exploratory statistics and graphics. 
    The fourth chapter is dedicated to data exploration with Principal Component Analysis. 
    The fifth chapter is dedicated to data exploration with Correspondance Analysis.
    The sixth chapter is dedicated to data exploration with Multiple Correspondance Analysis. 
    The seventh chapter is dedicated to data exploration with automatic clustering. 
    The eighth chapter is dedicated to an introduction to probability theory and classical probability distributions.
    The ninth chapter is dedicated to an estimation theory, one-sample and two-sample tests.
    The tenth chapter is dedicated to an Gaussian linear model.
    The eleventh chapter is dedicated to an introduction to time series.
    The twelfth chapter is dedicated to an introduction to probit and logit models.
    Various example datasets are shipped with the package as well as some new functions.
Implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2023) <doi:10.48550/arXiv.2308.04325>, and allows for the statistical modeling of asymmetric between-location effects, as well as within-location effects using spatial autoregressive graphical models. The package allows for the generation of spatial weight matrices to capture asymmetric effects for strip-type intercropping designs, although it can handle any type of spatial data commonly found in other sciences.
Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) <doi:10.1201/9780429446177>. It also provides some test data generation and plotting functionality to assist with this process.
Highest posterior model is widely accepted as a good model among available models. In terms of variable selection highest posterior model is often the true model. Our stochastic search process SAHPM based on simulated annealing maximization method tries to find the highest posterior model by maximizing the model space with respect to the posterior probabilities of the models. This package currently contains the SAHPM method only for linear models. The codes for GLM will be added in future.
Computes the Akaike information criterion for the generalized linear models (logistic regression, Poisson regression, and Gaussian graphical models) estimated by the lasso.
A new diagram for the verification of vector variables (wind, current, etc) generated by multiple models against a set of observations is presented in this package. It has been designed as a generalization of the Taylor diagram to two dimensional quantities. It is based on the analysis of the two-dimensional structure of the mean squared error matrix between model and observations. The matrix is divided into the part corresponding to the relative rotation and the bias of the empirical orthogonal functions of the data. The full set of diagnostics produced by the analysis of the errors between model and observational vector datasets comprises the errors in the means, the analysis of the total variance of both datasets, the rotation matrix corresponding to the principal components in observation and model, the angle of rotation of model-derived empirical orthogonal functions respect to the ones from observations, the standard deviation of model and observations, the root mean squared error between both datasets and the squared two-dimensional correlation coefficient. See the output of function UVError() in this package.
Holds functions developed by the University of Ottawa's SAiVE
    (Spatio-temporal Analysis of isotope Variations in the Environment)
    research group with the intention of facilitating the re-use of code,
    foster good code writing practices, and to allow others to benefit
    from the work done by the SAiVE group. Contributions are welcome via
    the 'GitHub' repository <https://github.com/UO-SAiVE/SAiVE> by group members as well as non-members.
Surveys to collect employment data so as to obtain data estimates on the number of employed people, the number of unemployed, and other employment indicators.
Extends the functionality of R serialization by augmenting the
    built-in reference hook system. This enhanced implementation allows optimal,
    one-pass integrated serialization that combines R serialization with
    third-party serialization methods. Facilitates the serialization of even
    complex R objects, which contain non-system reference objects, such as those
    accessed via external pointers, for use in parallel and distributed
    computing.
Handles both vector and matrices, using a flexible S4 class for automatic differentiation.
  The method used is forward automatic differentiation. Many functions and methods have been defined, 
  so that in most cases, functions written without automatic differentiation in mind can be 
  used without change.
Data related to the Salem Witch Trials
    Datasets and tutorials documenting the witch accusations and trials 
    centered around Salem, Massachusetts in 1692. Originally assembled by
    Richard B. Latner of Tulane University for his website 
    <https://www2.tulane.edu/~salem/index.html>. The data sets include information
    on 152 accused witches, members of the Salem Village Committee, 
    signatories of petitions related to the events, and tax data for 
    Salem Village.
A coordinate descent algorithm for computing the solution paths of
    the sparse and coupled sparse asymmetric least squares, including the
    (adaptive) elastic net and Lasso penalized SALES and COSALES regressions.
Functions connecting to the 'Salesforce' Platform APIs (REST, SOAP, 
    Bulk 1.0, Bulk 2.0, Metadata, Reports and Dashboards) 
    <https://trailhead.salesforce.com/content/learn/modules/api_basics/api_basics_overview>. 
    "API" is an acronym for "application programming interface". Most all calls 
    from these APIs are supported as they use CSV, XML or JSON data that can be 
    parsed into R data structures. For more details please see the 'Salesforce' 
    API documentation and this package's website 
    <https://stevenmmortimer.github.io/salesforcer/> for more information, 
    documentation, and examples.
Simulation tools to evaluate the long-term effects of salmon management strategies, including a combination of habitat, harvest, and
  habitat actions. The stochastic age-structured operating model accommodates complex life histories, including freshwater survival across 
  early life stages, juvenile survival and fishery exploitation in the marine life stage, partial maturity by age class, and fitness impacts of 
  hatchery programs on natural spawning populations. 'salmonMSE' also provides an age-structured conditioning model to develop operating models 
  fitted to data.
The SALSO algorithm is an efficient randomized greedy search method to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. The algorithm is implemented for many loss functions, including the Binder loss and a generalization of the variation of information loss, both of which allow for unequal weights on the two types of clustering mistakes. Efficient implementations are also provided for Monte Carlo estimation of the posterior expected loss of a given clustering estimate. See Dahl, Johnson, Müller (2022) <doi:10.1080/10618600.2022.2069779>.
The SALTSampler package facilitates Monte Carlo Markov Chain (MCMC)
    sampling of random variables on a simplex. A Self-Adjusting Logit Transform
    (SALT) proposal is used so that sampling is still efficient even in difficult
    cases, such as those in high dimensions or with parameters that differ by orders
    of magnitude. Special care is also taken to maintain accuracy even when some
    coordinates approach 0 or 1 numerically. Diagnostic and graphic functions are
    included in the package, enabling easy assessment of the convergence and mixing
    of the chain within the constrained space.
Take real or simulated data and salt it with errors commonly 
    found in the wild, such as pseudo-OCR errors, Unicode problems, numeric 
    fields with nonsensical punctuation, bad dates, etc.
Computationally efficient tools for high dimensional predictive
        modeling (regression and classification). SAM is short for sparse 
        additive modeling, and adopts the computationally efficient basis 
        spline technique. We solve  the optimization problems by various 
        computational algorithms including the block coordinate descent 
        algorithm, fast iterative soft-thresholding algorithm, and newton method. 
        The computation is further accelerated by warm-start and active-set tricks.
An R API providing access to a relational database with macroeconomic time series data for South Africa,
 obtained from the South African Reserve Bank (SARB) and Statistics South Africa (STATSSA), and updated on a weekly basis
 via the EconData <https://www.econdata.co.za/> platform and automated scraping of the SARB and STATSSA websites.
 The database is maintained at the Department of Economics at Stellenbosch University.
Health research using data from electronic health records (EHR) has gained
    popularity, but misclassification of EHR-derived disease status and lack of
    representativeness of the study sample can result in substantial bias in
    effect estimates and can impact power and type I error for association
    tests. Here, the assumed target of inference is the relationship between
    binary disease status and predictors modeled using a logistic regression
    model. 'SAMBA' implements several methods for obtaining bias-corrected
    point estimates along with valid standard errors as proposed in Beesley and
    Mukherjee (2020) <doi:10.1101/2019.12.26.19015859>, currently under review.
A collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) <doi:10.1155/2017/7847531>. The methods may be used for further purposes where weighting and generation of new observations is needed.
Implements functions for working with absorbing Markov chains. The
    implementation is based on the framework described in "Toward a unified
    framework for connectivity that disentangles movement and mortality in space
    and time" by Fletcher et al. (2019) <doi:10.1111/ele.13333>, which applies
    them to spatial ecology. This framework incorporates both resistance and 
    absorption with spatial absorbing Markov chains (SAMC) to provide several
    short-term and long-term predictions for metrics related to connectivity in 
    landscapes. Despite the ecological context of the framework, this package
    can be used in any application of absorbing Markov chains.
Design a Bayesian seamless multi-arm biomarker-enriched phase II/III 
             design with the survival endpoint with allowing sample size re-estimation.
             James M S Wason, Jean E Abraham, Richard D Baird, Ioannis Gournaris, Anne-Laure Vallier, James D Brenton, Helena M Earl, Adrian P Mander (2015) <doi:10.1038/bjc.2015.278>.
             Guosheng Yin, Nan Chen, J. Jack Lee (2018) <doi:10.1007/s12561-017-9199-7>.
             Ying Yuan, Beibei Guo, Mark Munsell, Karen Lu, Amir Jazaeri (2016) <doi:10.1002/sim.6971>.
Renders plots to a temporary image using the ragg graphics device and returns knitr::include_graphics() output. Optionally saves the image to a specified path. This helps ensure consistent appearance across interactive sessions, saved files, and knitted documents. For more details see Pedersen and Shemanarev (2025) <doi: 10.32614/CRAN.package.ragg>.
A novel semi-supervised machine learning algorithm to predict phenotype event times using Electronic Health Record (EHR) data.
In a clinical trial with repeated measures designs, outcomes are often taken from subjects at fixed time-points.  The focus of the trial may be to compare the mean outcome in two or more groups at some pre-specified time after enrollment. In the presence of missing data auxiliary assumptions are necessary to perform such comparisons.  One commonly employed assumption is the missing at random assumption (MAR).   The 'samon' package allows the user to perform a (parameterized) sensitivity analysis of this assumption.  In particular it can be used to examine the sensitivity of tests in the difference in outcomes to violations of the MAR assumption.  The sensitivity analysis can be performed under two scenarios, a) where the data exhibit a monotone missing data pattern (see the samon() function), and, b) where in addition to a monotone missing data pattern the data exhibit intermittent missing values (see the samonIM() function).
Evaluating the biasing impact of geographic features such as airports, cities, roads, rivers in datasets of coordinates based biological collection datasets, by Bayesian estimation of the parameters of a Poisson process. Enables also spatial visualization of sampling bias and includes a set of convenience functions for publication level plotting. Also available as 'shiny' app. The reference for the methodology is: Zizka et al. (2020) <doi:10.1111/ecog.05102>.
Easily analyze and visualize differences between samples (e.g., benchmark comparisons, nonresponse comparisons in surveys) on three levels. The comparisons can be univariate, bivariate or multivariate. On univariate level the variables of interest of a survey and a comparison survey (i.e. benchmark) are compared, by calculating one of several difference measures (e.g., relative difference in mean), and an average difference between the surveys. On bivariate level a function can calculate significant differences in correlations for the surveys. And on multivariate levels a function can calculate significant differences in model coefficients between the surveys of comparison. All of those differences can be easily plotted and outputted as a table. For more  detailed information on the methods and example use see Rohr, B., Silber, H., & Felderer, B. (2024). Comparing the Accuracy of Univariate, Bivariate, and Multivariate Estimates across Probability and Nonprobability Surveys with Population Benchmarks. Sociological Methodology <doi:10.1177/00811750241280963>.
Provides a collection of sample datasets 
    on various fields such as automotive performance and safety data to historical demographics and socioeconomic indicators, as well as recreational data.
    It serves as a resource for researchers and analysts seeking to perform analyses and derive insights from classic data sets in R.
Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs.
Two-step
   and maximum likelihood estimation
   of Heckman-type sample selection models:
   standard sample selection models (Tobit-2),
   endogenous switching regression models (Tobit-5),
   sample selection models with binary dependent outcome variable,
   interval regression with sample selection (only ML estimation),
   and endogenous treatment effects models.
   These methods are described in the three vignettes
   that are included in this package 
   and in econometric textbooks such as
   Greene (2011, Econometric Analysis, 7th edition, Pearson).
Computes sample size for Student's t-test and for the Wilcoxon-Mann-Whitney test for categorical data. The t-test function allows paired and unpaired (balanced / unbalanced) designs as well as homogeneous and heterogeneous variances. The Wilcoxon function allows for ties.
There are four categories of Phase III clinical trials according to different research goals, including (1) Testing for equality, (2) Superiority trial, (3) Non-inferiority trial, and (4) Equivalence trial. This package aims to help researchers to calculate sample size when comparing means or proportions in Phase III clinical trials with different research goals.
Computes the required sample size for estimation of totals, means
    and proportions under complex sampling designs.
It helps in determination of sample size for estimating population mean or proportion under simple random sampling with or without replacement and stratified random sampling without replacement.
             When prior information on the population coefficient of variation (CV) is unavailable, then a preliminary sample is drawn to estimate the CV which is used to compute the final sample size. If the final
             size exceeds the preliminary sample size, then additional units are drawn; otherwise, the preliminary sample size is considered as final sample size. For stratified random sampling without replacement design,
             it also calculates the sample size in each stratum under different allocation methods for estimation of population mean and proportion based upon the availability of prior information on sizes of the strata,
             standard deviations of the strata and costs of drawing a sampling unit in the strata.For details on sampling methodology, see, Cochran (1977) "Sampling Techniques" <https://archive.org/details/samplingtechniqu0000coch_t4x6>.
Calculates the power and sample size for Cochran-Mantel-Haenszel tests. 
    There are also several helper functions for working with probability,
    odds, relative risk, and odds ratio values.
Calculates the sample size needed for evaluating a diagnostic test based on sensitivity, specificity, prevalence, and desired precision. Based on Buderer (1996) <doi:10.1111/j.1553-2712.1996.tb03538.x>.
Calculates sample size for various scenarios, such as sample size
    to estimate population proportion with stated absolute or relative
    precision, testing a single proportion with a reference value, to estimate
    the population mean with stated absolute or relative precision, testing
    single mean with a reference value and sample size for comparing two
    unpaired or independent means, comparing two paired means, the sample size
    For case control studies, estimating the odds ratio with stated precision,
    testing the odds ratio with a reference value, estimating relative risk with
    stated precision, testing relative risk with a reference value, testing
    a correlation coefficient with a specified value, etc. 
    <https://www.academia.edu/39511442/Adequacy_of_Sample_Size_in_Health_Studies#:~:text=Determining%20the%20sample%20size%20for,may%20yield%20statistically%20inconclusive%20results.>.
To determine sample size or power for case-control studies to be analyzed using logistic regression.
Sample size requirements calculation
        using three different Bayesian criteria in the
        context of designing an experiment to estimate a normal mean or
        the difference between two normal means.  Functions for
        calculation of required sample sizes for the Average Length
        Criterion, the Average Coverage Criterion and the Worst Outcome
        Criterion in the context of normal means are provided.
        Functions for both the fully Bayesian and the mixed
        Bayesian/likelihood approaches are provided.
        For reference see Joseph L. and Bélisle P. (1997) <https://www.jstor.org/stable/2988525>.
Sample size requirements calculation 
        using three different Bayesian criteria in the
        context of designing an experiment to estimate the difference
        between two binomial proportions. Functions for calculation of
        required sample sizes for the Average Length Criterion, the
        Average Coverage Criterion and the Worst Outcome Criterion in
        the context of binomial observations are provided. In all
        cases, estimation of the difference between two binomial
        proportions is considered. Functions for both the fully
        Bayesian and the mixed Bayesian/likelihood approaches are
        provided.
        For reference see Joseph L., du Berger R. and Bélisle P. (1997)
        <doi:10.1002/(sici)1097-0258(19970415)16:7%3C769::aid-sim495%3E3.0.co;2-v>.
Provides methods to calculate sample size for single-arm survival studies using the arcsine transformation, incorporating uniform accrual and exponential survival assumptions. Includes functionality for detailed numerical integration and simulation. This method is based on Nagashima et al. (2021) <doi:10.1002/pst.2090>.
Affords researchers the ability to draw stratified samples from the U.S. Department of Veteran's Affairs/Department of Defense Identity Repository (VADIR) database according to a variety of population characteristics. The VADIR database contains information for all veterans who were separated from the military after 1980. The central utility of the present package is to integrate data cleaning and formatting for the VADIR database with the stratification methods described by Mahto (2019) <https://CRAN.R-project.org/package=splitstackshape>. Data from VADIR are not provided as part of this package.
An interactive 'shiny' application to assist in determining
    sample sizes for common survey designs such as 'simple random
    sampling', 'stratified sampling', and 'cluster sampling'. It includes
    formulas, helper calculators, and illustrative examples.
Simplifies the process of generating samples from a variety of probability distributions, allowing users to quickly create data frames for demonstrations, troubleshooting, or teaching purposes. Data is available in multiple sizes—small, medium, and large. For more information, refer to the package documentation.
Functions to draw random samples using different sampling schemes are available. Functions are also provided to obtain (generalized) calibration weights, different estimators, as well some variance estimators.
Select sampling methods for probability samples using large data sets.  This includes spatially balanced sampling in multi-dimensional spaces with any prescribed inclusion probabilities. All implementations are written in C with efficient data structures such as k-d trees that easily scale to several million rows on a modern desktop computer.
Sampling procedures from the book 'Stichproben - Methoden und praktische Umsetzung mit R' by Goeran Kauermann and Helmut Kuechenhoff (2010).
Package provides the possibility to sampling complete datasets 
  from a normal distribution to simulate cluster randomized trails for different study designs.
A robust solution employing the SRS (Simple Random Sampling),
    systematic and PPS (Probability Proportional to Size) sampling
    methods, ensuring a methodical and representative selection of data.
    Seamlessly allocate predetermined allocations to smaller levels.
Functions to take samples of data, sample size estimation and getting useful estimators such as total, mean, proportion about its population using simple random, stratified, systematic and cluster sampling.
Tools for the optimization of stratified sampling design. It determines a stratification of a sampling frame that minimizes sample cost while satisfying precision constraints in a multivariate and multidomain context. The approach relies on a genetic algorithm; each candidate partition of the frame is an individual whose fitness is evaluated via the Bethel-Chromy allocation to meet target precisions. Functions support analysis of optimization results, labeling of the frame with new strata, and drawing a sample according to the optimal allocation. Algorithmic components adapt code from the 'genalg' package. See M. Ballin and G. Barcaroli (2020) "R package SamplingStrata: new developments and extension to Spatial Sampling" <doi:10.48550/arXiv.2004.09366>.
Functions to calculate some point estimators and estimate their variance under unequal probability sampling without replacement. Single and two-stage sampling designs are considered. Some approximations for the second-order inclusion probabilities (joint inclusion probabilities) are available (sample and population based). A variety of Jackknife variance estimators are implemented. Almost every function is written in C (compiled) code for faster results. The functions incorporate some performance improvements for faster results with large datasets.
Understand human performance from the perspective of sampling, both looking at how people generate samples and how people use the samples they have generated. A longer overview and other resources can be found at <https://sampling.warwick.ac.uk>.
Contains human behaviour datasets collected by the SAMPLING project (<https://sampling.warwick.ac.uk>).
Implementation of the SAM prior and generation of its
     operating characteristics for dynamically borrowing information
     from historical data. For details, please refer to Yang et al. (2023)
     <doi:10.1111/biom.13927>.
Estimation of the required sample size to validate a risk model for binary outcomes, based on the sample size equations proposed by Pavlou et al. (2021) <doi:10.1177/09622802211007522>. For precision-based sample size calculations, the user is required to enter the anticipated values of the C-statistic and outcome prevalence, which can be obtained from a previous study. The user also needs to specify the required precision (standard error) for the C-statistic, the calibration slope and the calibration in the large. The calculations are valid under the assumption of marginal normality for the distribution of the linear predictor.
Significance Analysis of Microarrays for differential expression analysis, RNAseq data and related problems.
Markov chain Monte Carlo samplers for posterior simulations of conjugate Bayesian nonparametric
    mixture models. Functionality is provided for Gibbs sampling as in Algorithm 3 of Neal (2000)
    <DOI:10.1080/10618600.2000.10474879>, restricted Gibbs merge-split sampling as described in Jain & Neal
    (2004) <DOI:10.1198/1061860043001>, and sequentially-allocated merge-split sampling <DOI:10.1080/00949655.2021.1998502>, as well as
    summary and utility functions.
Simulation tools for closed-loop simulation are provided for the 'MSEtool' operating model to inform data-rich fisheries. 
  'SAMtool' provides a conditioning model, assessment models of varying complexity with standardized reporting, 
  model-based management procedures, and diagnostic tools for evaluating assessments inside closed-loop simulation.
A sensitivity analysis approach for unmeasured confounding in observational data with multiple treatments and a binary outcome. This approach derives the general bias formula and provides adjusted causal effect estimates in response to various assumptions about the degree of unmeasured confounding. Nested multiple imputation is embedded within the Bayesian framework to integrate   uncertainty about the sensitivity parameters and sampling variability.  Bayesian Additive Regression Model (BART) is used for outcome modeling. The causal estimands are the conditional average treatment effects (CATE) based on the risk difference.  For more details, see paper: Hu L et al. (2020) A flexible sensitivity analysis approach for unmeasured confounding with multiple treatments and a binary outcome with application to SEER-Medicare lung cancer data <arXiv:2012.06093>.
Augmenting a matched data set by generating multiple stochastic, matched samples from the data using a
  multi-dimensional histogram constructed from dropping the input matched data into a multi-dimensional grid built on
  the full data set. The resulting stochastic, matched sets will likely provide a collectively higher coverage of the full
  data set compared to the single matched set. Each stochastic match is without duplication, thus allowing downstream
  validation techniques such as cross-validation to be applied to each set without concern for overfitting.
An efficient tool for fitting nested mixture models based on a shared set of 
    atoms via Markov Chain Monte Carlo and variational inference algorithms. 
    Specifically, the package implements the common atoms model (Denti et al., 2023), 
    its finite version (similar to D'Angelo et al., 2023), and a hybrid finite-infinite 
    model (D'Angelo and Denti, 2024). All models implement univariate nested mixtures 
    with Gaussian kernels equipped with a normal-inverse gamma prior distribution 
    on the parameters. Additional functions are provided to help analyze the 
    results of the fitting procedure.   
    References:       
    Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>,      
    D’Angelo, Canale, Yu, Guindani (2023) <doi:10.1111/biom.13626>,      
    D’Angelo, Denti (2024) <doi:10.1214/24-BA1458>.
Data sets and code blocks for the book 'Statistical Analysis of 
  Network Data with R, 2nd Edition'.
A flexible framework for definition and application of time/depth-
    based rules for sets of parameters for single grains that can be used to 
    create artificial sediment profiles. Such profiles can be used for virtual 
    sample preparation and synthetic, for instance, luminescence measurements.
Object-oriented software for model-robust covariance matrix estimators. Starting out from the basic 
             robust Eicker-Huber-White sandwich covariance methods include: heteroscedasticity-consistent (HC)
	     covariances for cross-section data; heteroscedasticity- and autocorrelation-consistent (HAC)
	     covariances for time series data (such as Andrews' kernel HAC, Newey-West, and WEAVE estimators);
	     clustered covariances (one-way and multi-way); panel and panel-corrected covariances;
	     outer-product-of-gradients covariances; and (clustered) bootstrap covariances. All methods are
	     applicable to (generalized) linear model objects fitted by lm() and glm() but can also be adapted
	     to other classes through S3 methods. Details can be found in Zeileis et al. (2020) <doi:10.18637/jss.v095.i01>,
	     Zeileis (2004) <doi:10.18637/jss.v011.i10> and Zeileis (2006) <doi:10.18637/jss.v016.i09>.
Downloads and tidies the San Francisco Public Utilities Commission Beach Water Quality Monitoring Program data. Data sets can be downloaded per beach, or the raw data can be downloaded. See <https://sfwater.org/cfapps/lims/beachmain1.cfm>.
Created for population health analytics and monitoring.
    The functions in this package work best when working with patient level Master Patient Index-like datasets .
    Built to be used by  NHS bodies and other health service providers.
Routines for solving large systems of linear equations and
  eigenproblems in R. Direct and iterative solvers from the Eigen C++ library
  are made available. Solvers include Cholesky, LU, QR, and Krylov subspace
  methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are
  supported.
Recent gcc and clang compiler versions provide functionality to
 test for memory violations and other undefined behaviour; this is often 
 referred to as "Address Sanitizer" (or 'ASAN') and "Undefined Behaviour 
 Sanitizer" ('UBSAN'). The Writing R Extension manual describes this in some
 detail in Section 4.3 title "Checking Memory Access".

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

    As an alternative, pre-built Docker containers such as the Rocker container
 'r-devel-san' or the multi-purpose container 'r-debug' can be used.

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

    The code samples are based on the examples from the Address Sanitizer
 Wiki at <https://github.com/google/sanitizers/wiki>.
During the preparation of data set(s) one usually performs
    some sanity checks. The idea is that irrespective of where the
    checks are performed, they are centralized by this package in order
    to list all at once with examples if a check failed.
Facilitate phonetic transliteration between different languages. With support for both Hindi and English, this package provides a way to convert text between Hindi and English dataset. Whether you're working with multilingual data or need to convert dataset for analysis or presentation purposes, it offers a simple and efficient solution and harness the power of phonetic transliteration in your projects with this versatile package.
Plots that illustrate the flow of information or material.
By binding R functions and the 'Highcharts' <http://www.highcharts.com/> charting library, 'sankeywheel' package provides a simple way to draw dependency wheels and sankey diagrams.
There are several functions to implement the method for analysis in a randomized clinical trial with strata with following key features. A stratified Mann-Whitney estimator addresses the comparison between two randomized groups for a strictly ordinal response variable. The multivariate vector of such stratified Mann-Whitney estimators for multivariate response variables can be considered for one or more response variables such as in repeated measurements and these can have missing completely at random (MCAR) data. Non-parametric covariance adjustment is also considered with the minimal assumption of randomization. The p-value for hypothesis test and confidence interval are provided.
Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), and hybrid finite-infinite models. 
             All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure.  
             References:
             Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>,   
             D’Angelo, Denti (2024) <doi:10.1214/24-BA1458>.
Machine learning is widely used in information-systems design. Yet, training algorithms on imbalanced datasets may severely affect performance on unseen data. For example, in some cases in healthcare, financial, or internet-security contexts, certain sub-classes are difficult to learn because they are underrepresented in training data. This 'R' package offers a flexible and efficient solution based on a new synthetic average neighborhood sampling algorithm ('SANSA'), which, in contrast to other solutions, introduces a novel “placement” parameter that can be tuned to adapt to each datasets unique manifestation of the imbalance. More information about the algorithm's parameters can be found at Nasir et al. (2022) <https://murtaza.cc/SANSA/>.
A graphical and automated pipeline for the analysis 
		of short time-series in R ('santaR'). This approach is designed to accommodate asynchronous 
		time sampling (i.e. different time points for different individuals), 
		inter-individual variability, noisy measurements and large numbers of variables. 
		Based on a smoothing splines functional model, 'santaR' is able to detect variables
		highlighting significantly different temporal trajectories between study groups.
		Designed initially for metabolic phenotyping, 'santaR' is also suited for other Systems Biology 
		disciplines. Command line and graphical analysis (via a 'shiny' application) enable fast and
		parallel automated analysis and reporting, intuitive visualisation and comprehensive plotting
		options for non-specialist users.
A tool for cutting data into intervals. Allows singleton intervals.
  Always includes the whole range of data by default. Flexible labelling. 
  Convenience functions for cutting by quantiles etc. Handles dates, times, units
  and other vectors.
An efficient tool for fitting the nested common and shared atoms models using variational Bayes approximate inference for fast computation. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite model. 
            All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure.  
            References:   
            Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>,    
            D’Angelo, Canale, Yu, Guindani (2023) <doi:10.1111/biom.13626>.
Inspired by the art and color research of Sanzo Wada (1883-1967), 
    his "Dictionary Of Color Combinations" (2011, ISBN:978-4861522475), and the 
    interactive site by Dain M. Blodorn Kim <https://github.com/dblodorn/sanzo-wada>, 
    this package brings Wada's color combinations to R for easy use in data 
    visualizations. This package honors 60 of Wada's color combinations: 
    20 duos, 20 trios, and 20 quads.
The Hypothesis tests for the means of independent or paired groups.
    This package investigates the normality assumption automatically.
    Then, it tests the hypothesis tests for two independent or paired group means
    by using parametric or non-parametric tests. It uses the Shapiro-Wilk test to
    test the normality assumption. For independent two groups, If data comes from
    the normal distribution, the package uses the Z or t-test according to whether
    variances are known. For paired groups, it uses paired t-test under normal data
    sets. If data does not come from the normal distribution, the package uses the
    Wilcoxon test for independent and paired cases.
Implementation of SAPEVO-M, a Group Ordinal Method for Multiple Criteria Decision-Making (MCDM). SAPEVO-M is an acronym for Simple Aggregation of Preferences Expressed by Ordinal Vectors Group Decision Making. This method provides alternatives ranking given decision makers' preferences: criteria preferences and alternatives preferences for each criterion.This method is described in Gomes et al. (2020) <doi: 10.1590/0101-7438.2020.040.00226524 >.
Access, modify, aggregate and plot data from the 'Sapfluxnet' project,
  the first global database of sap flow measurements.
In ecology, spatial data is often represented using polygons. These
  polygons can represent a variety of spatial entities, such as ecological
  patches, animal home ranges, or gaps in the forest canopy.  Researchers often
  need to determine if two spatial processes, represented by these polygons, are
  independent of each other. For instance, they might want to test if the home
  range of a particular animal species is influenced by the presence of a
  certain type of vegetation.  To address this, Godoy et al. (2022)
  (<doi:10.1016/j.spasta.2022.100695>) developed conditional Monte Carlo
  tests. These tests are designed to assess spatial independence while taking
  into account the shape and size of the polygons.
Functions for statistical analysis of point processes.
A collection of tools to access prepared air quality monitoring
    data files from web servers with ease and speed. Air quality data are 
    sourced from open and publicly accessible repositories and can be found in 
    these locations: 
    <https://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8> 
    and <https://discomap.eea.europa.eu/map/fme/AirQualityExport.htm>. The web 
    server space has been provided by Ricardo Energy & Environment.
'Smart Adaptive Recommendations' (SAR) is the name of a fast, scalable, adaptive algorithm for personalized recommendations based on user transactions and item descriptions. It produces easily explainable/interpretable recommendations and handles "cold item" and "semi-cold user" scenarios. This package provides two implementations of 'SAR': a standalone implementation, and an interface to a web service in Microsoft's 'Azure' cloud: <https://github.com/Microsoft/Product-Recommendations/blob/master/doc/sar.md>. The former allows fast and easy experimentation, and the latter provides robust scalability and extra features for production use.
A Graphical user interface to calculate the rainfall-runoff relation using the Natural Resources Conservation Service - Curve Number method (NRCS-CN method) but include modifications by Hawkins et al., (2002) about the Initial Abstraction. This GUI follows the programming logic of a previously published software (Hernandez-Guzman et al., 2011)<doi:10.1016/j.envsoft.2011.07.006>. It is a raster-based GIS tool that outputs runoff estimates from Land use/land cover and hydrologic soil group maps.
    This package has already been published in Journal of Hydroinformatics (Hernandez-Guzman et al., 2021)<doi:10.2166/hydro.2020.087> but it is under constant development at the Institute about Natural Resources Research (INIRENA) from the Universidad Michoacana de San Nicolas de Hidalgo and represents a collaborative effort between the Hydro-Geomatic Lab (INIRENA) with the Environmental Management Lab (CIAD, A.C.).
Functions, classes and methods for time series modelling with ARIMA
    and related models. The aim of the package is to provide consistent
    interface for the user. For example, a single function autocorrelations()
    computes various kinds of theoretical and sample autocorrelations. This is
    work in progress, see the documentation and vignettes for the current
    functionality.  Function sarima() fits extended multiplicative seasonal
    ARIMA models with trends, exogenous variables and arbitrary roots on the
    unit circle, which can be fixed or estimated (for the algebraic basis for
    this see <doi:10.48550/arXiv.2208.05055>, a paper on the methodology is being prepared).
Offers a systematic way for conditional reporting of figures and tables for many
    (and bivariate combinations of) variables, typically from survey data.
    Contains interactive 'ggiraph'-based 
    (<https://CRAN.R-project.org/package=ggiraph>) plotting functions and
    data frame-based summary tables (bivariate significance tests, 
    frequencies/proportions, unique open ended responses, etc) with
    many arguments for customization, and extensions possible. Uses a global 
    options() system for neatly reducing redundant code.
    Also contains tools for immediate saving of objects and returning a hashed link to the object,
    useful for creating download links to high resolution images upon rendering in 'Quarto'. 
    Suitable for highly customized reports, primarily intended for survey
    research.
Scaffold an entire web-based report using template chunks, based on a small chapter overview and a dataset. 
    Highly adaptable with prefixes, suffixes, translations, etc. Also contains tools for password-protecting,
    e.g. for each organization's report on a website. Developed for the common case of a survey across multiple organizations/sites
    where each organization wants to obtain results for their organization compared with everyone else.
    See 'saros' (<https://CRAN.R-project.org/package=saros>) for tools used for authors in the drafted reports.
Provides a set of functions to interpret changes in
 compositional data based on a network representation of all pairwise ratio
 comparisons: computation of all pairwise ratio, construction of a
 p-value matrix of all pairwise tests of these ratios between
 conditions, conversion of this matrix to a network.
Provides a set of basic functions for creating Moodle XML
 output files suited for importing questions in Moodle (a learning
 management system, see <https://moodle.org/> for more information).
Analysis and plotting tools for snow profile data produced from manual snowpack 
  observations and physical snowpack models. The functions in this package support snowpack 
  and avalanche research by reading various formats of data (including CAAML, SMET,
  generic csv, and outputs from the snow cover model SNOWPACK), manipulate the data, and 
  produce graphics such as stratigraphy and time series profiles. Package developed by 
  the Simon Fraser University Avalanche Research Program <http://www.avalancheresearch.ca>. 
  Graphics apply visualization concepts from Horton, Nowak, and Haegeli (2020, 
  <doi:10.5194/nhess-20-1557-2020>).
The development of post-processing functionality for simulated snow profiles 
    by the snow and avalanche community is often done in 'python'. This package
    aims to make some of these tools accessible to 'R' users. Currently integrated 
    modules contain functions to calculate dry snow layer instabilities in support 
    of avalache hazard assessments following the publications of Richter, 
    Schweizer, Rotach, and Van Herwijnen (2019) <doi:10.5194/tc-13-3353-2019>, and 
    Mayer, Van Herwijnen, Techel, and Schweizer (2022) <doi:10.5194/tc-2022-34>.
Implements the basic elements of the multi-model
    inference paradigm for up to twenty species-area relationship models (SAR), using simple
    R list-objects and functions, as in Triantis et al. 2012 <DOI:10.1111/j.1365-2699.2011.02652.x>.
    The package is scalable and users can easily create their own model and data objects. Additional
    SAR related functions are provided.
A toolkit for Partially Observed Markov Decision Processes (POMDP). Provides
    bindings to C++ libraries implementing the algorithm SARSOP (Successive Approximations
    of the Reachable Space under Optimal Policies) and described in Kurniawati et al (2008),
    <doi:10.15607/RSS.2008.IV.009>.  This package also provides a high-level interface
    for generating, solving and simulating POMDP problems and their solutions.
Documentation and prototypes for the earliest (circa 2010) open-source effort to reverse engineer the sas7bdat file format. The package includes a prototype reader for sas7bdat files. However, newer packages may contain more robust readers for sas7bdat files.
Using any importation code designed for 'SAS' users to read ASCII files into 'sas7bdat' files, this package parses through the INPUT block of a '.sas' syntax file to design the parameters needed for a 'read.fwf()' function call.  This allows the user to specify the location of the ASCII (often a '.dat') file and the location of the 'SAS' syntax file, and then load the data frame directly into R in just one step.
Converts the dates to different 'SAS' date formats. In 'SAS' dates are a special case of 
             numeric values. Each day is assigned a specific numeric value, starting from January 1, 1960.
             This date is assigned the date value 0, and the next date has a date value of 1 and so on.
             The previous days to this date are represented by -1 , -2 and so on. With this approach, 
             'SAS' can represent any date in the future or any date in the past. There are many date 
             formats used in 'SAS' to represent date-time. Here, we try to develop functions which will
             convert the date to different 'SAS' date formats.
Provides 'SAS'-style IF/ELSE chains, independent IF rules,
    and DELETE logic for 'data.table', enabling clinical programmers to
    express Study Data Tabulation Model (SDTM) and Analysis Data Model
    (ADaM)-style derivations in familiar SAS-like syntax.
    Methods are informed by clinical data standards described in CDISC
    SDTM and ADaM implementation guides. See
    <https://www.cdisc.org/standards/foundational/sdtm> and
    <https://www.cdisc.org/standards/foundational/adam>.
This is a core implementation of 'SAS' procedures for linear models - GLM, REG, ANOVA, TTEST, FREQ, and UNIVARIATE. Some R packages provide type II and type III SS. However, the results of nested and complex designs are often different from those of 'SAS.' Different results does not necessarily mean incorrectness. However, many wants the same results to SAS. This package aims to achieve that.
             Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).
Settings and functions to extend the 'knitr' 'SAS' engine.
Data sets and sample lmer analyses corresponding
  to the examples in Littell, Milliken, Stroup and Wolfinger
  (1996), "SAS System for Mixed Models", SAS Institute.
Use R and 'SAS' within reproducible multilingual 'quarto'
    documents. Run 'SAS' code blocks interactively, send data back and
    forth between 'SAS' and R, and render 'SAS' output within 'quarto'
    documents. 'SAS' connections are established through a combination of
    'SASPy' and 'reticulate'.
Provides a 'SAS' interface, through
    'SASPy'(<https://sassoftware.github.io/saspy/>) and
    'reticulate'(<https://rstudio.github.io/reticulate/>).  This package
    helps you create 'SAS' sessions, execute 'SAS' code in remote 'SAS'
    servers, retrieve execution results and log, and exchange datasets
    between 'SAS' and 'R'.  It also helps you to install 'SASPy' and
    create a configuration file for the connection. Please review the
    'SASPy' license file as instructed so that you comply with its
    separate and independent license.
An 'SCSS' compiler, powered by the 'LibSass' library. With this,
    R developers can use variables, inheritance, and functions to generate
    dynamic style sheets. The package uses the 'Sass CSS' extension language,
    which is stable, powerful, and CSS compatible.
A meta-package that aims to make 'R' easier for everyone, 
    especially programmers who have a background in 'SAS®' software.
    This set of packages brings many useful concepts to 'R', including
    data libraries, data dictionaries, formats 
    and format catalogs, a data step, and a traceable log.  The system
    also includes a package that replicates several commonly-used 'SAS®' procedures,
    like 'PROC FREQ', 'PROC MEANS', and 'PROC REG'.
Tools for analyzing tail dependence in any sample or in particular theoretical models.  The package uses only theoretical and non parametric methods, without inference.  The primary goals of the package are to provide: (a)symmetric multivariate extreme value models in any dimension;  theoretical and empirical indices to order tail dependence;  theoretical and empirical graphical methods to visualize tail dependence.
Bundles functions used to analyze the harmfulness of trial errors in criminal trials.
    Functions in the Scientific Analysis of Trial Errors ('sate') package help users estimate the 
    probability that a jury will find a defendant guilty given jurors' preferences for a guilty 
    verdict and the uncertainty of that estimate. Users can also compare actual and hypothetical 
    trial conditions to conduct harmful error analysis. The conceptual framework is discussed
    by Barry Edwards, A Scientific Framework for Analyzing the Harmfulness of Trial Errors,
    UCLA Criminal Justice Law Review (2024) <doi:10.5070/CJ88164341> and Barry Edwards, 
    If The Jury Only Knew: The Effect Of Omitted Mitigation Evidence On The Probability Of A Death Sentence,
    Virginia Journal of Social Policy & the Law (2025)
    <https://vasocialpolicy.org/wp-content/uploads/2025/05/Edwards-If-The-Jury-Only-Knew.pdf>.
    The relationship between individual jurors' 
    verdict preferences and the probability that a jury returns a guilty verdict has been studied 
    by Davis (1973) <doi:10.1037/h0033951>; MacCoun & Kerr (1988) <doi:10.1037/0022-3514.54.1.21>, 
    and Devine et el. (2001) <doi:10.1037/1076-8971.7.3.622>, among others.
Herein, we provide a broad variety of functions which are useful
    for handling, manipulating, and visualizing satellite-based remote sensing 
    data. These operations range from mere data import and layer handling (eg 
    subsetting), over Raster* typical data wrangling (eg crop, extend), to more 
    sophisticated (pre-)processing tasks typically applied to satellite imagery 
    (eg atmospheric and topographic correction). This functionality is 
    complemented by a full access to the satellite layers' metadata at any 
    stage and the documentation of performed actions in a separate log file. 
    Currently available sensors include Landsat 4-5 (TM), 7 (ETM+), and 8 
    (OLI/TIRS Combined), and additional compatibility is ensured for the Landsat 
    Global Land Survey data set.
With 'satin' functions, visualisation, data extraction and further analysis like producing climatologies from several images, and anomalies of satellite derived ocean data can be easily done.  Reading functions can import a user defined geographical extent of data stored in netCDF files.  Currently supported ocean data sources include NASA's Oceancolor web page <https://oceancolor.gsfc.nasa.gov/>, sensors VIIRS-SNPP; MODIS-Terra; MODIS-Aqua; and SeaWiFS.  Available variables from this source includes chlorophyll concentration, sea surface temperature (SST), and several others.  Data sources specific for SST that can be imported too includes Pathfinder AVHRR <https://www.ncei.noaa.gov/products/avhrr-pathfinder-sst> and GHRSST <https://www.ghrsst.org/>.  In addition, ocean productivity data produced by Oregon State University can also be handled previous conversion from HDF4 to HDF5 format.  Many other ocean variables can be processed by importing netCDF data files from two European Union's Copernicus Marine Service databases <https://marine.copernicus.eu/>, namely Global Ocean Physical Reanalysis and Global Ocean Biogeochemistry Hindcast.
Given raster files directly downloaded from various websites,
    it generates a raster structure where it merges them if they are tiles
    of the same scene and classifies them according to their spectral and
    spatial resolution for easy access by name.
A metric expressing the quality of a UMAP layout. This is a
    package that contains the Saturn_coefficient() function that reads an
    input matrix, its dimensionality reduction produced by UMAP, and
    evaluates the quality of this dimensionality reduction by producing a
    real value in the [0; 1] interval. We call this real value Saturn
    coefficient. A higher value means better dimensionality reduction; a
    lower value means worse dimensionality reduction.
    Reference: Davide Chicco et al. (February 2026), "The advantages of
    our proposed Saturn coefficient over continuity and trustworthiness
    for UMAP dimensionality reduction evaluation", PeerJ Computer
    Science 12:e3424 (pp. 1-30), <doi:10.7717/peerj-cs.3424>.
Machine learning provides algorithms that can learn from data and make inferences or predictions. Stochastic automata is a class of input/output devices which can model components. This work provides implementation an inference algorithm for stochastic automata which is similar to the Viterbi algorithm. Moreover, we specify a learning algorithm using the expectation-maximization technique and provide a more efficient implementation of the Baum-Welch algorithm for stochastic automata. This work is based on Inference and learning in stochastic automata was by Karl-Heinz Zimmermann(2017) <doi:10.12732/ijpam.v115i3.15>.
An implementation of a regularized regression prediction and 
    empirical Bayes method to recover the true gene expression profile in 
    noisy and sparse single-cell RNA-seq data. See Huang M, et al (2018) 
    <doi:10.1038/s41592-018-0033-z> for more details.
Provides a toolbox that allows the user to implement accessibility related concepts.
Implements the Savvy Parity Regression 'savvyPR' methodology
    for multivariate linear regression analysis. The package solves an
    optimization problem that balances the contribution of each predictor
    variable to ensure estimation stability in the presence of
    multicollinearity. It supports two distinct parameterization methods,
    a Budget-based approach that allocates a fixed loss contribution to
    each predictor, and a Target-based approach (t-tuning) that utilizes
    a relative elasticity weight for the response variable. The package
    provides comprehensive tools for model estimation, risk distribution
    analysis, and parameter tuning via cross-validation (PR1, PR2, and
    PR3 model types) to optimize predictive accuracy. Methods are based
    on Asimit, Chen, Ichim and Millossovich (2026)
    <https://openaccess.city.ac.uk/id/eprint/37017/>.
The SAVVY (Survival Analysis for AdVerse Events with VarYing Follow-Up Times)
    project is a consortium of academic and pharmaceutical
    industry partners that aims to improve the analyses of adverse event (AE)
    data in clinical trials through the use of survival techniques appropriately
    dealing with varying follow-up times and competing events, see
    Stegherr, Schmoor, Beyersmann, et al. (2021) <doi:10.1186/s13063-021-05354-x>.
    Although statistical methodologies have advanced,
    in AE analyses often the incidence proportion, the incidence density or a
    non-parametric Kaplan-Meier estimator are used, which either ignore censoring or
    competing events. This package contains functions to easily conduct the
    proposed improved AE analyses.
Implements a suite of shrinkage estimators for multivariate linear 
    regression to improve estimation stability and predictive accuracy. 
    Provides methods including the Stein estimator, Diagonal Shrinkage, 
    the general Shrinkage estimator (solving a Sylvester equation), and 
    Slab Regression (Simple and Generalized). These methods address Stein's 
    paradox by introducing structured bias to reduce variance without requiring 
    cross-validation, except for 'ShrinkageRR' where the intensity 
    is chosen by minimizing an explicit Mean Squared Error (MSE) criterion. 
    Methods are based on Asimit, V., Cidota, M. A., Chen, Z., and Asimit, J. (2025) 
    <https://openaccess.city.ac.uk/id/eprint/35005/>.
The SAWNUTI algorithm performs sequence comparison for finite sequences of discrete events with non-uniform time intervals. Further description of the algorithm can be found in the paper: A. Murph, A. Flynt, B. R. King (2021). Comparing finite sequences of discrete events with non-uniform time intervals, Sequential Analysis, 40(3), 291-313. <doi:10.1080/07474946.2021.1940491>.
Tests coefficients with sandwich estimator of variance and with small samples. Regression types supported are gee, linear regression, and conditional logistic regression.
Spectral and Average Autocorrelation Zero Distance Density
    ('sazed') is a method for estimating the season length of a 
    seasonal time series. 'sazed' is aimed at practitioners, as it employs only 
    domain-agnostic preprocessing and does not depend on parameter tuning or 
    empirical constants. The computation of 'sazed' relies on the efficient 
    autocorrelation computation methods suggested by Thibauld Nion (2012, URL: 
    <https://etudes.tibonihoo.net/literate_musing/autocorrelations.html>) and by 
    Bob Carpenter (2012, URL: 
    <https://lingpipe-blog.com/2012/06/08/autocorrelation-fft-kiss-eigen/>).
Get the most appropriate autoregressive integrated moving average, generalized auto-regressive conditional heteroscedasticity and Markov switching GARCH model. For method details see Haas M, Mittnik S, Paolella MS (2004). <doi:10.1093/jjfinec/nbh020>, Bollerslev T (1986). <doi:10.1016/0304-4076(86)90063-1>.
Fitting and plotting parametric or non-parametric size-biased 
    non-negative distributions, with optional covariates if parametric. 
    Rowcliffe, M. et al. (2016) <doi:10.1002/rse2.17>.
Offers Bayesian semiparametric density estimation 
             and tail-index estimation for heavy tailed data, by
             using a parametric, tail-respecting transformation
             of the data to the unit interval and then modeling
             the transformed data with a purely nonparametric
             logistic Gaussian process density prior. Based on 
             Tokdar et al. (2022) <doi:10.1080/01621459.2022.2104727>.
Uses parametric and nonparametric methods to quantify the proportion of the estimated selection bias (SB) explained by each observed confounder when estimating propensity score weighted treatment effects. Parast, L and Griffin, BA (2020). "Quantifying the Bias due to Observed Individual Confounders in Causal Treatment Effect Estimates". Statistics in Medicine, 39(18): 2447- 2476 <doi: 10.1002/sim.8549>.
An MCMC algorithm for simultaneous feature selection and classification, 
    and visualization of the selected features and feature interactions. 
    An implementation of SBFC by Krakovna, Du and Liu (2015), <arXiv:1506.02371>.
Estimation and inference for parameters 
        in a Gaussian copula model,
        treating the univariate marginal distributions as nuisance
        parameters as described in Hoff (2007) <doi:10.1214/07-AOAS107>. 
        This package also provides a
        semiparametric imputation procedure for missing multivariate
        data.
Computes a simple blinding index for randomized controlled trials introduced in Petroff, Bacak, Dagres, Dilk, Wachter: A simple blinding index for randomized controlled trials. Contemp Clin Trials Commun. 2024 Nov 26;42:101393. <doi:10.1016/j.conctc.2024.101393>. PMID: 39686958.
Computes the sBIC for various singular model collections including:
    binomial mixtures, factor analysis models, Gaussian mixtures,
    latent forests, latent class analyses, and reduced rank regressions.
This is the implementation of the novel structural Bayesian information criterion by Zhou, 2020 (under review).
   In this method, the prior structure is modeled and incorporated into the Bayesian information criterion framework. 
   Additionally, we also provide the implementation of a two-step algorithm to generate the candidate model pool.
Parameter inference methods for models defined implicitly using a random simulator. Inference is carried out using simulation-based estimates of the log-likelihood of the data. The inference methods implemented in this package are explained in Park, J. (2025) <doi:10.48550/arxiv.2311.09446>. These methods are built on a simulation metamodel which assumes that the estimates of the log-likelihood are approximately normally distributed with the mean function that is locally quadratic around its maximum. Parameter estimation and uncertainty quantification can be carried out using the ht() function (for hypothesis testing) and the ci() function (for constructing a confidence interval for one-dimensional parameters).
Implements sparse Bayesian learning method for QTL mapping and genome-wide association studies.
A collection of tools and functions to adjust a variety of stochastic blockmodels (SBM). 
  Supports at the moment Simple, Bipartite, 'Multipartite' and Multiplex SBM (undirected or directed with Bernoulli, Poisson or Gaussian emission laws on the edges, and possibly covariate for Simple and Bipartite SBM). See Léger (2016) <doi:10.48550/arXiv.1602.07587>, 'Barbillon et al.' (2020) <doi:10.1111/rssa.12193> and 'Bar-Hen et al.' (2020) <doi:10.48550/arXiv.1807.10138>.
Median-of-means is a generic yet powerful framework for scalable and robust estimation. A framework for Bayesian analysis is called M-posterior, which estimates a median of subset posterior measures. For general exposition to the topic, see the paper by Minsker (2015) <doi:10.3150/14-BEJ645>.
An ADMM implementation of SDP-1, a semidefinite programming relaxation of the maximum likelihood estimator for fitting a block model. SDP-1 has a tendency to produce equal-sized blocks and is ideal for producing a form of network histogram approximating a nonparametric graphon model. Alternatively, it can be used for community detection. (This is experimental code, proceed with caution.)
Implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings.
Generate Stochastic Branching Networks ('SBNs'). Used to model the branching structure of rivers.
Creates classifier for binary outcomes using Adaptive Boosting 
    (AdaBoost) algorithm on decision stumps with a fast C++ implementation. 
    For a description of AdaBoost, see Freund and Schapire (1997) 
    <doi:10.1006/jcss.1997.1504>. This type of classifier is nonlinear, but
    easy to interpret and visualize. Feature vectors may be a combination of
    continuous (numeric) and categorical (string, factor) elements. Methods 
    for classifier assessment, predictions, and cross-validation also included.
An efficient implementation of Scalable Bayesian Rule Lists Algorithm, a competitor algorithm for decision tree algorithms; see Hongyu Yang, Cynthia Rudin, Margo Seltzer (2017) <https://proceedings.mlr.press/v70/yang17h.html>. It builds from pre-mined association rules and have a logical structure identical to a decision list or one-sided decision tree. Fully optimized over rule lists, this algorithm strikes practical balance between accuracy, interpretability, and computational speed.
Calculates a Satorra-Bentler scaled chi-squared difference test between nested models that were estimated using maximum likelihood (ML) with robust standard errors, which cannot be calculated the traditional way. For details see Satorra & Bentler (2001) <doi:10.1007/bf02296192> and Satorra & Bentler (2010) <doi:10.1007/s11336-009-9135-y>. This package may be particularly helpful when used in conjunction with 'Mplus' software, specifically when implementing the complex survey option. In such cases, the model estimator in 'Mplus' defaults to ML with robust standard errors.
Tools for interacting with U.S. Geological Survey ScienceBase
    <https://www.sciencebase.gov> interfaces. ScienceBase is a data cataloging and
    collaborative data management platform. Functions included for querying
    ScienceBase, and creating and fetching datasets.
Implements the Stable Balancing Weights by Zubizarreta (2015) <DOI:10.1080/01621459.2015.1023805>. These are the weights of minimum variance that approximately balance the empirical distribution of the observed covariates. For an overview, see Chattopadhyay, Hase and Zubizarreta (2020) <DOI:10.1002/sim.8659>. To solve the optimization problem in 'sbw', the default solver is 'quadprog', which is readily available through CRAN. The solver 'osqp' is also posted on CRAN. To enhance the performance of 'sbw', users are encouraged to install other solvers such as 'gurobi' and 'Rmosek', which require special installation. For the installation of gurobi and pogs, please follow the instructions at <https://docs.gurobi.com/projects/optimizer/en/current/reference/r.html> and <http://foges.github.io/pogs/stp/r>.
A wrapper for Blizzard's Starcraft II (a 2010 real-time strategy game) Application Programming Interface (API). All documented API calls are implemented in an easy-to-use and consistent manner.
Transfers/imputes statistics among Spanish spatial polygons (census sections or postal code areas) from different moments in time (2001-2023) without need of spatial files, just linking statistics to the ID codes of the spatial units. 
    The data available in the census sections of a partition/division (cartography) into force in a moment of time is transferred to the census sections of another partition/division employing the geometric approach (also known as areal weighting or polygon overlay). 
    References: 
    Goerlich (2022) <doi:10.12842/WPIVIE_0322>.
    Pavía and Cantarino (2017a, b) <doi:10.1111/gean.12112>, <doi:10.1016/j.apgeog.2017.06.021>.
    Pérez and Pavía (2024a, b) <doi:10.4995/CARMA2024.2024.17796>, <doi:10.38191/iirr-jorr.24.057>.
    Acknowledgements:
    The authors wish to thank Consellería de Educación, Cultura, Universidades y Empleo, Generalitat Valenciana (grant CIACIO/2023/031), Consellería de Educación, Universidades y Empleo, Generalitat Valenciana (grant AICO/2021/257), Ministerio de Economía e Innovación (grant PID2021-128228NB-I00) and Fundación Mapfre for supporting this research.
Simple Component Analysis (SCA) often provides much more
   interpretable components than Principal Components (PCA) while still
   representing much of the variability in the data.
Calculates graph theoretic scagnostics. Scagnostics describe various measures of interest for pairs of variables, based on their appearance on a scatterplot.  They are useful tool for discovering interesting or unusual scatterplots from a scatterplot matrix, without having to look at every individual plot.
Estimates unit-level and population-level parameters from a hierarchical model in marketing applications. The package includes:
  Hierarchical Linear Models with a mixture of normals prior and covariates,
  Hierarchical Multinomial Logits with a mixture of normals prior and covariates,
  Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates. For more details, see Bumbaca, F. (Rico), Misra, S., & Rossi, P. E. (2020) <doi:10.1177/0022243720952410> "Scalable Target Marketing: Distributed Markov Chain Monte Carlo for Bayesian Hierarchical Models". Journal of Marketing Research, 57(6), 999-1018.
Scale alignment is a new procedure for rescaling dimensions of 
    between-items multidimensional Rasch family models so that dimensions scores
    can be compared directly (Feuerstahler & Wilson, 2019; under review) 
	<doi:10.1111/jedm.12209>. This package includes functions for implementing 
    delta-dimensional alignment (DDA) and logistic regression alignment (LRA) 
    for dichotomous or polytomous data. This function also includes a wrapper 
	for models fit using the 'TAM' package.
Provides functions to format and summarise already computed outputs from commonly used statistical and psychometric functions into compact, single-row tables and simple graphs, with utilities to export results to CSV, Word, and Excel formats. The package does not implement new statistical methods or estimation procedures; instead, it organises and presents results obtained from existing functions such as psych::describe(), psych::alpha(), stats::t.test(), and gtsummary::tbl_summary() to streamline reporting workflows in clinical and psychological research.
Perform a 'probabilistic' linkage of two data files using a scaling procedure using the methods described in Goldstein, H., Harron, K. and Cortina-Borja, M. (2017) <doi:10.1002/sim.7287>.
Graphical scales map data to aesthetics, and provide methods
    for automatically determining breaks and labels for axes and legends.
A scalable Gibbs sampling implementation for high dimensional Bayesian regression with the continuous spike-and-slab prior. Niloy Biswas, Lester Mackey and Xiao-Li Meng, "Scalable Spike-and-Slab" (2022) <arXiv:2204.01668>.
Algorithms for fitting scaled sparse linear regression and estimating precision matrices.
Generalized additive models under shape
        constraints on the component functions of the linear predictor.       
        Models can include multiple shape-constrained (univariate
        and bivariate) and unconstrained terms. Routines of the 
        package 'mgcv' are used to set up the model matrix, print, 
        and plot the results. Multiple smoothing parameter 
        estimation by the Generalized Cross Validation or similar.
        See Pya and Wood (2015) <doi:10.1007/s11222-013-9448-7> 
        for an overview. A broad selection of shape-constrained 
        smoothers, linear functionals of smooths with shape constraints,
        and Gaussian models with AR1 residuals.
A collection of procedures for analysing, visualising, 
  and managing single-case data. Multi-phase and multi-baseline designs are 
  supported. Analysing methods include regression models 
  (multilevel, multivariate, bayesian), between case standardised mean difference, 
  overlap indices ('PND', 'PEM', 'PAND', 'NAP', 'PET', 'tau-u', 'IRD', 'baseline corrected tau', 
  'CDC'), and randomization tests. Data preparation functions support outlier 
  detection, handling missing values, scaling, and custom transformations. 
  An export function helps to generate html, word, and latex tables in a 
  publication friendly style. A shiny app allows to use scan in a graphical 
  user interface.
  More details can be found in the online book 'Analyzing single-case data with 
  R and scan', Juergen Wilbert (2026)
  <https://jazznbass.github.io/scan-Book/>.
An entirely data-driven cell type annotation tools, which requires training data to learn the classifier, but not biological knowledge to make subjective decisions. It consists of three steps: preprocessing training and test data, model fitting on training data, and cell classification on test data. See Xiangling Ji,Danielle Tsao, Kailun Bai, Min Tsao, Li Xing, Xuekui Zhang.(2022)<doi:10.1101/2022.02.19.481159> for more details.
Detection of anomalous space-time clusters using the scan 
  statistics methodology. Focuses on prospective surveillance of data streams, 
  scanning for clusters with ongoing anomalies. Hypothesis testing is made 
  possible by Monte Carlo simulation. Allévius (2018) <doi:10.21105/joss.00515>.
Import, plot, and diagnose results from statistical
  catch-at-age models, used in fisheries stock assessment.
Generates cell-level cytokine activity estimates using relevant information from gene sets constructed with the 'CytoSig' and the 'Reactome' databases and scored using the modified 'Variance-adjusted Mahalanobis (VAM)' framework for single-cell RNA-sequencing (scRNA-seq) data. 'CytoSig' database is described in: Jiang at al., (2021) <doi:10.1038/s41592-021-01274-5>. 'Reactome' database is described in: Gillespie et al., (2021) <doi:10.1093/nar/gkab1028>. The 'VAM' method is outlined in: Frost (2020) <doi:10.1093/nar/gkaa582>.
Series of algorithms to translate  users' mental models of seascapes, 
  landscapes and, more generally, of geographic features into computer representations 
  (classifications). Spaces and geographic objects are classified with user-defined 
  rules taking into account spatial data as well as spatial relationships among 
  different classes and objects.
It is a single cell active pathway analysis tool based on the graph neural network (F. Scarselli (2009) <doi:10.1109/TNN.2008.2005605>; Thomas N. Kipf (2017) <arXiv:1609.02907v4>) to construct the gene-cell association network, infer pathway activity scores from different single cell modalities data, integrate multiple modality data on the same cells into one pathway activity score matrix, identify cell phenotype activated gene modules and parse association networks of gene modules under multiple cell phenotype. In addition, abundant visualization programs are provided to display the results.
Computes the maximum likelihood estimator of the generalised additive and index regression with shape constraints. Each additive component function is assumed to obey one of the nine possible shape restrictions: linear, increasing, decreasing, convex, convex increasing, convex decreasing, concave, concave increasing, or concave decreasing. For details, see Chen and Samworth (2016) <doi:10.1111/rssb.12137>.
A port of the Scarabee toolkit originally written as a 
  Matlab-based application. scaRabee provides a framework for simulation and optimization 
  of pharmacokinetic-pharmacodynamic models at the individual and population level.
  It is built on top of the neldermead package, which provides the direct search 
  algorithm proposed by Nelder and Mead for model optimization.
Allows you to make clean, good-looking scatter plots with the option to 
    easily add marginal density or box plots on the axes. It is also available as a module for 'jamovi'
    (see <https://www.jamovi.org> for more information). 'Scatr' is based on the 
    'cowplot' package by Claus O. Wilke and the 'ggplot2' package by Hadley Wickham.
Provides a powerful and flexible tool for visualizing proportional data across spatially resolved contexts. By combining the concepts of scatter plots and stacked bar charts, 'scatterbar' allows users to create scattered bar chart plots, which effectively display the proportions of different categories at each (x, y) location. This visualization is particularly useful for applications where understanding the distribution of categories across spatial coordinates is essential. This package features automatic determination of optimal scaling factors based on data, customizable scaling and padding options for both x and y axes, flexibility to specify custom colors for each category, options to customize the legend title, and integration with 'ggplot2' for robust and high-quality visualizations. For more details, see Velazquez et al. (2024) <doi:10.1101/2024.08.14.606810>.
Creates 'D3' 'JavaScript' scatterplots from 'R' with interactive
    features : panning, zooming, tooltips, etc.
The user has the option to utilize the two-dimensional density estimation techniques called smoothed density published by Eilers and Goeman (2004) <doi:10.1093/bioinformatics/btg454>, and pareto density which was evaluated for univariate data by Thrun, Gehlert and Ultsch, 2020 <doi:10.1371/journal.pone.0238835>. Moreover, it provides visualizations of the density estimation in the form of two-dimensional scatter plots in which the points are color-coded based on increasing density. Colors are defined by the one-dimensional clustering technique called 1D distribution cluster algorithm (DDCAL) published by Lux and Rinderle-Ma (2023) <doi:10.1007/s00357-022-09428-6>.
C-based conversion of large scatterplot data to rasters plus other 
             operations such as data blurring or data alpha blending. Speeds up 
             plotting of data with millions of points.
Creates scatterpie plots, especially useful for plotting pies on a map.
Plots a three dimensional (3D) point cloud.
Create a scatter plot matrix, using `htmlwidgets` package and `d3.js`.
Identifies a bicluster, a submatrix of the data such that the features and observations within the submatrix differ from those not contained in submatrix, using a two-step method. In the first step, observations in the bicluster are  identified to maximize the sum of weighted between cluster feature differences. The method is described in Helgeson et al. (2020) <doi:10.1111/biom.13136>. 'SCBiclust' can be used to identify biclusters which differ based on feature means, feature variances, or more general differences.
Cellular population mapping (CPM) a deconvolution algorithm in which single-cell genomics is required in only one or a few samples, where in other samples of the same tissue, only bulk genomics is measured and the underlying fine resolution cellular heterogeneity is inferred.
Statistical methods for estimating and inferring the mean of functional data. The methods include simultaneous confidence bands, local polynomial fitting,  bandwidth selection by plug-in and cross-validation, goodness-of-fit tests for parametric models, equality tests for two-sample problems, and plotting functions.
Identifying spatially variable genes is critical in linking molecular cell functions 
	with tissue phenotypes. This package utilizes a granularity-based dimension-agnostic tool, 
	single-cell big-small patch (scBSP), implementing sparse matrix operation and KD tree 
	methods for distance calculation, for the identification of spatially variable genes on 
	large-scale data. The detailed description of this method is available at Wang, J. 
	and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>).
Provides tools to import and export from several existing pieces of ion-channel analysis software such as 'TAC', 'QUB', 'SCAN', and 'Clampfit', implements procedures such as dwell-time correction and defining bursts with a critical time, and provides tools for analysis of bursts, such as tools for sorting and plotting.
An automatic cluster-based annotation pipeline based on evidence-based score by matching the marker genes with known cell markers in tissue-specific cell taxonomy reference database for single-cell RNA-seq data. See Shao X, et al (2020) <doi:10.1016/j.isci.2020.100882> for more details.
Performing cell type annotation based on cell markers from a unified database. The approach utilizes correlation-based approach combined with association analysis using Fisher-exact and phyper statistical tests (Upton, Graham JG. (1992) <DOI:10.2307/2982890>).
An efficient implementation of SCCI using 'Rcpp'. SCCI is short for the Stochastic Complexity-based Conditional Independence criterium (Marx and Vreeken, 2019). SCCI is an asymptotically unbiased and L2 consistent estimator of (conditional) mutual information for discrete data.
Provides wrappers for 'scclust', a C library for computationally efficient
    size-constrained clustering with near-optimal performance.
    See <https://github.com/fsavje/scclust> for more information.
Core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with differential expression (DE) matrices and count matrices, a collection of functions for manipulating and plotting data via 'ggplot2', and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP <doi:10.21105/joss.00861>, collapsing vertices of each cluster in the graph, and propagating graph labels.
The SC-SR Algorithm is used to calculate fully non-parametric and self-consistent estimators of the cause-specific failure probabilities in the presence of interval-censoring and possible making of the failure cause in a competing risks environment. In the version 2.0 the function creating the probability matrix from double-censored data is added.
Various self-controlled case series models used to investigate 
        associations between time-varying exposures such as vaccines or
        other drugs or non drug exposures and an adverse event can be
        fitted. Detailed information on the self-controlled case series
        method and its extensions with more examples can be found in 
        Farrington, P., Whitaker, H., and Ghebremichael Weldeselassie, Y.
        (2018, ISBN: 978-1-4987-8159-6. Self-controlled Case Series studies:
        A modelling Guide with R. Boca Raton: Chapman & Hall/CRC Press) 
        and <https://sccs-studies.info/index.html>.
Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'.  'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions.  For citation please use: Marsh SE (2021) "Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing" <doi:10.5281/zenodo.5706430> RRID:SCR_024675.
Contains functions for statistical data analysis based on spatially-clustered techniques.
    The package allows estimating the spatially-clustered spatial regression models presented in Cerqueti, Maranzano \& Mattera (2024), "Spatially-clustered spatial autoregressive models
    with application to agricultural market concentration in Europe", arXiv preprint 2407.15874 <doi:10.48550/arXiv.2407.15874>.
    Specifically, the current release allows the estimation of the spatially-clustered linear regression model (SCLM), the spatially-clustered spatial autoregressive model (SCSAR),
    the spatially-clustered spatial Durbin model (SCSEM), and the spatially-clustered linear regression model with spatially-lagged exogenous covariates (SCSLX).
    From release 0.0.2, the library contains functions to estimate spatial clustering based on Adiajacent Matrix K-Means (AMKM) as described in Zhou, Liu \& Zhu (2019), "Weighted adjacent matrix for K-means clustering", Multimedia Tools and Applications, 78 (23) <doi:10.1007/s11042-019-08009-x>.
A collection of functions that enable easy access and updating of a database of data over time.
             More specifically, the package facilitates type-2 history for data-warehouses and provides a number
             of Quality of life improvements for working on SQL databases with R.
             For reference see Ralph Kimball and Margy Ross (2013, ISBN 9781118530801).
Implementations for two different Bayesian models of differential co-expression. scdeco.cop() fits the bivariate Gaussian copula model from Zichen Ma, Shannon W. Davis, Yen-Yi Ho (2023) <doi:10.1111/biom.13701>, while scdeco.pg() fits the bivariate Poisson-Gamma model from Zhen Yang, Yen-Yi Ho (2022) <doi:10.1111/biom.13457>.
Streamlined workflow from deconvolution of bulk RNA-seq data to downstream differential expression and gene-set enrichment analysis. Provide various visualization functions.
Implements methods for obtaining kernel density estimates
    subject to a variety of shape constraints (unimodality, bimodality, 
    symmetry, tail monotonicity, bounds, and constraints on the number of 
    inflection points). Enforcing constraints can eliminate unwanted waves or 
    kinks in the estimate, which improves its subjective appearance and can 
    also improve statistical performance. The main function scdensity() is 
    very similar to the density() function in 'stats', allowing 
    shape-restricted estimates to be obtained with little effort. The 
    methods implemented in this package are described in Wolters and Braun 
    (2017) <doi:10.1080/03610918.2017.1288247>, Wolters (2012) 
    <doi:10.18637/jss.v047.i06>, and Hall and Huang (2002) 
    <https://www3.stat.sinica.edu.tw/statistica/j12n4/j12n41/j12n41.htm>.
    See the scdensity() help for for full citations.
Provides a fast and accurate pipeline for single-cell analyses. 
    The 'scDHA' software package can perform clustering, dimension reduction and visualization, classification, and time-trajectory inference on single-cell data (Tran et.al. (2021) <DOI:10.1038/s41467-021-21312-2>).
Provides a set of tools for estimating hierarchical linear
    models and effect sizes based on data from single-case designs. 
    Functions are provided for calculating standardized mean difference effect sizes that 
    are directly comparable to standardized mean differences estimated from between-subjects randomized experiments,
    as described in Hedges, Pustejovsky, and Shadish (2012) <DOI:10.1002/jrsm.1052>; 
    Hedges, Pustejovsky, and Shadish (2013) <DOI:10.1002/jrsm.1086>; 
    Pustejovsky, Hedges, and Shadish (2014) <DOI:10.3102/1076998614547577>; 
    and Chen, Pustejovsky, Klingbeil, and Van Norman (2023) <DOI:10.1016/j.jsp.2023.02.002>. 
    Includes an interactive web interface.
Analysis tools to investigate changes in intercellular
    communication from scRNA-seq data. Using a Seurat object as input,
    the package infers which cell-cell interactions are present in the dataset
    and how these interactions change between two conditions of interest
    (e.g. young vs old). It relies on an internal database of ligand-receptor
    interactions (available for human, mouse and rat) that have been gathered
    from several published studies. Detection and differential analyses
    rely on permutation tests. The package also contains several tools
    to perform over-representation analysis and visualize the results. See
    Lagger, C. et al. (2023) <doi:10.1038/s43587-023-00514-x> for a full 
    description of the methodology.
Detection of item-wise Differential Item Functioning (DIF) 
             in fitted 'mirt', 'multipleGroup' or 'bfactor' models  
             using score-based structural change tests. Under the hood 
             the sctest() function from the 'strucchange' package is used.
In some situations where researchers would like to demonstrate
    causal effects, it is hard to obtain a sample size that would allow for a
    well-powered randomized controlled trial. Single case designs are experimental
    designs that can be used to demonstrate causal effects with only one participant
    or with only a few participants. The 'scdtb' package provides a suite of tools
    for analyzing data from studies that use single case designs. The nap() function
    can be used to compute the nonoverlap of all pairs as outlined by the What
    Works Clearinghouse (2022) <https://ies.ed.gov/ncee/wwc/Handbooks>. The
    package also offers the mixed_model_analysis() and cross_lagged() functions
    which implement mixed effects models and cross lagged analyses as described
    in Maric & van der Werff (2020) <doi:10.4324/9780429273872-9>. The
    randomization_test() function implements randomization tests based on methods
    presented in Onghena (2020) <doi:10.4324/9780429273872-8>. The scdtb() 'shiny'
    application can be used to upload single case design data and access various
    'scdtb' tools for plotting and analysis.
Implementation of Stepwise Clustered Ensemble (SCE) and Stepwise Cluster Analysis (SCA) for multivariate data analysis. The package provides comprehensive tools for feature selection, model training, prediction, and evaluation in hydrological and environmental modeling applications. Key functionalities include recursive feature elimination (RFE), Wilks feature importance analysis, model validation through out-of-bag (OOB) validation, and ensemble prediction capabilities. The package supports both single and multivariate response variables, making it suitable for complex environmental modeling scenarios. For more details see Li et al. (2021) <doi:10.5194/hess-25-4947-2021>.
PAM (Partitioning Around Medoids) algorithm application to samples of single cell sequencing techniques
	     with a high number of cells (as many as the computer memory allows). The package uses a binary format to
	     store matrices (either full, sparse or symmetric) in files written in the disk that can contain any data type
	     (not just double) which allows its manipulation when memory is sufficient to load them as int or float, but not
	     as double. The PAM implementation is done in parallel, using several/all the cores of the machine, if it has them.
	     This package shares a great part of its code with packages 'jmatrix' and 'parallelpam' but their functionality is
	     included here so there is no need to install them.
Sometimes it is useful to serve up alternative 'shiny' UIs 
    depending on information passed in the request object, such as the value of 
    a cookie or a query parameter. This packages facilitates such switches.
A specialized selection algorithm designed to align simulated fire perimeters with specific fire size distribution scenarios. The foundation of this approach lies in generating a vast collection of plausible simulated fires across a wide range of conditions, assuming a random pattern of ignition. The algorithm then assembles individual fire perimeters based on their specific probabilities of occurrence, e.g., determined by (i) the likelihood of ignition and (ii) the probability of particular fire-weather scenarios, including wind speed and direction. Implements the method presented in Rodrigues (2025a) <doi:10.5194/egusphere-egu25-8974>. Demo data and code examples can be found in Rodrigues (2025b) <doi:10.5281/zenodo.15282605>.
Analyse single cell RNA sequencing data using entropy to
    calculate heterogeneity and homogeneity of genes amongst the cell
    population. From the work of Michael J. Casey, Ruben J. Sanchez-Garcia 
    and Ben D. MacArthur.
A pipeline for estimating the stellar
        age, mass, and radius given observational 
        effective temperature, [Fe/H], and astroseismic
	parameters. The results are obtained adopting a maximum likelihood
	technique over a grid of pre-computed stellar models, as
	described in Valle et al. (2014) <doi:10.1051/0004-6361/201322210>.
SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models.
Analyze public-use micro data from the Survey of Consumer Finances.
  Provides tools to download prepared data files, construct replicate-weighted
  multiply imputed survey designs, compute descriptive statistics and model
  estimates, and produce plots and tables. Methods follow design-based inference
  for complex surveys and pooling across multiple imputations. See the package
  website and the code book for background.
Self-Consistent Field(SCF) calculation method is one of the most important steps in the calculation methods of quantum chemistry. Ehrenreich, H., & Cohen, M. H. (1959). <doi:10.1103/PhysRev.115.786> However, the most prevailing software in this area, 'Gaussian''s SCF convergence process is hard to monitor, especially while the job is still running, causing researchers difficulty in knowing whether the oscillation has started or not, wasting time and energy on useless configurations or abandoning the jobs that can actually work. M.J. Frisch, G.W. Trucks, H.B. Schlegel et al. (2016). <https://gaussian.com> 'SCFMonitor' enables 'Gaussian' quantum chemistry calculation software users to easily read the 'Gaussian' .log files and monitor the SCF convergence and geometry optimization process with little effort and clear, beautiful, and clean outputs. It can generate graphs using 'tidyverse' to let users check SCF convergence and geometry optimization processes in real-time. The software supports processing .log files remotely using with rbase::url(). This software is a suitcase for saving time and energy for the researchers, supporting multiple versions of 'Gaussian'.
A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. 'scGate' automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. Briefly, 'scGate' takes as input: i) a gene expression matrix stored in a 'Seurat' object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 'scGate' evaluates the strength of signature marker expression in each cell using the rank-based method 'UCell', and then performs k-nearest neighbor (kNN) smoothing by calculating the mean 'UCell' score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest. See the related publication Andreatta et al. (2022) <doi:10.1093/bioinformatics/btac141>.
An extension of the Fisher Scoring Algorithm to combine PLS regression with GLM 
    estimation in the multivariate context. Covariates can also be grouped in themes.
Traditional methods for analyzing single cell RNA-seq datasets focus solely on gene expression, but this package introduces a novel approach that goes beyond this limitation. Using Gene Ontology terms as features, the package allows for the functional profile of cell populations, and comparison within and between datasets from the same or different species. Our approach enables the discovery of previously unrecognized functional similarities and differences between cell types and has demonstrated success in identifying cell types' functional correspondence even between evolutionarily distant species.
Fast and regularized version of GWR for large dataset, detailed in Murakami, Tsutsumida, Yoshida, Nakaya, and Lu (2019) <arXiv:1905.00266>.
Provides methods to detect structural changes in time series or random fields (spatial data). Focus is on the detection of abrupt changes or trends in independent data, but the package also provides a function to de-correlate data with dependence. The functions are based on the test suggested in Schmidt (2024) <DOI:10.3150/23-BEJ1686> and the work in Görz and Fried (2025) <DOI:10.48550/arXiv.2512.11599>.
Offers classes and functions to contact web servers while enforcing scheduling rules required by the sites. The URL class makes it easy to construct a URL by providing parameters as a vector. The Request class allows to describes SOAP (Simple Object Access Protocol) or standard requests: URL, method (POST or GET), header, body. The Scheduler class controls the request frequency for each server address by mean of rules (Rule class). The RequestResult class permits to get the request status to handle error cases and the content.
Validate data.frames against schemas to ensure that data matches expectations. Define schemas using 'tidyselect' and predicate functions for type consistency, nullability, and more. Schema failure messages can be tailored for non-technical users and are ideal for user-facing applications such as in 'shiny' or 'plumber'.
A fast and adaptable tool to convert photos and images into usable colour schemes for data visualisation. Contains functionality to extract colour palettes from images, as well for the conversion of images between colour spaces.
Functions for creating and manipulating 12-tone (i.e., dodecaphonic) musical matrices using Arnold Schoenberg's (1923) serialism technique. This package can generate random 12-tone matrices and can generate matrices using a pre-determined sequence of notes.
Provides functions to extract citation data from Google
    Scholar.  Convenience functions are also provided for comparing
    multiple scholars and predicting future h-index values.
Tools for detecting, normalizing, classifying, and extracting
    scholarly identifier strings. The package provides lightweight,
    dependency-free helpers for common identifier systems such as DOIs,
    ORCID iDs, ISBNs, ISSNs, arXiv identifiers, and PubMed identifiers.
    Functions are designed to be vectorized, predictable, and suitable as
    low-level building blocks for other R packages and data workflows.
Enables querying of scholarly identifier services to
    verify identifier existence, convert identifiers across systems,
    retrieve bibliographic metadata, and discover linked identifiers.
    Supports common identifier types including DOI, PMID, PMCID,
    arXiv, and ORCID.
Compiles and displays the available data sets regarding the Italian school system, with a focus on the infrastructural aspects.
    Input datasets are downloaded from the web, with the aim of updating everything to real time.  
    The functions are divided in four main modules, namely 
    'Get', to scrape raw data from the web
    'Util', various utilities needed to process raw data
    'Group', to aggregate data at the municipality or province level
    'Map', to visualize the output datasets.
Contains functions and datasets for math taught in school. A main focus is set to prime-calculation.
Offers a helping hand to psychologists and other behavioral scientists who routinely deal with experimental data from factorial experiments. It includes several functions to format output from other R functions according to the style guidelines of the APA (American Psychological Association). This formatted output can be copied directly into manuscripts to facilitate data reporting. These features are backed up by a toolkit of several small helper functions, e.g., offering out-of-the-box outlier removal. The package lends its name to Georg "Schorsch" Schuessler, ingenious technician at the Department of Psychology III, University of Wuerzburg. For details on the implemented methods, see Roland Pfister and Markus Janczyk (2016) <doi: 10.20982/tqmp.12.2.p147>.
The complete scripts from the American version of the Office
    television show in tibble format. Use this package to analyze and have
    fun with text from the best series of all time.
This is a shape preserving spline <doi:10.1137/0720057>
    which is guaranteed to be monotonic and concave or convex if the
    data is monotonic and concave or convex. It does not use any 
    optimisation and is therefore quick and smoothly converges to a 
    fixed point in economic dynamics problems including value function 
    iteration. It also automatically gives the first two derivatives 
    of the spline and options for determining behaviour when evaluated 
    outside the interpolation domain.
Use R to interface with the 'Charles Schwab Trade API' <https://developer.schwab.com/>.
    Functions include authentication, trading, price requests, account information, and option 
    chains. A user will need a Schwab brokerage account and Schwab Individual Developer app. See README 
    for authentication process and examples.
Functions for generating Standardized Climate Indices (SCI).
  Functions for generating Standardized Climate Indices (SCI). 
  SCI is a transformation of (smoothed) climate (or environmental) time series 
  that removes seasonality and forces the data to take values of the standard 
  normal distribution. SCI was originally developed for precipitation. 
  In this case it is known as the Standardized Precipitation Index (SPI).
Remove batch effects by projecting query batches into the reference batch space.
Colour choice in information visualisation is important in order to
    avoid being mislead by inherent bias in the used colour palette. The 'scico'
    package provides access to the perceptually uniform and colour-blindness 
    friendly palettes developed by Fabio Crameri and released under the 
    "Scientific Colour-Maps" moniker. The package contains 24 different palettes 
    and includes both diverging and sequential types.
Set of tools to import, summarize, wrangle, and visualize data. 
    These functions were originally written based on the needs of the 
    various synthesis working groups that were supported by the 
    National Center for Ecological Analysis and Synthesis (NCEAS). 
    These tools are meant to be useful inside and 
    outside of the context for which they were designed.
Data used in Taback, N. (2022). Design and Analysis of Experiments and Observational Studies using R. Chapman & Hall/CRC.
Offers 'markdown' output formats designed with various styles, allowing users to generate HTML reports tailored for scientific or machine learning showcase. The output has a contemporary appearance with vibrant visuals, providing numerous styles for effective highlighting. Created using the 'tufte' <https://rstudio.github.io/tufte/> package code as a starting point.
It involves bibliometric indicators calculation from bibliometric data.It also deals pattern analysis using the text part of bibliometric data.The bibliometric data are obtained from mainly Web of Science and Scopus.
Users may specify what fundamental qualities of a new study have 
             or have not changed in an attempt to reproduce or replicate an 
             original study. A comparison of the differences is visualized. 
             Visualization approach follows 'Patil', 'Peng', and 
             'Leek' (2016) <doi:10.1101/066803>.
Tools for quantitative research in scientometrics and bibliometrics.
    This package provides routines for importing bibliographic data from
    Clarivate Web of Science (<https://www.webofscience.com/wos/>) and
    performing bibliometric analysis.
Omics data (e.g. transcriptomics, proteomics,
    metagenomics...)  offer a detailed and multi-dimensional perspective
    on the molecular components and interactions within complex biological
    (eco)systems.  Analyzing these data requires adapted procedures, which
    are implemented as steps according to the 'recipes' package.
An automatic cell type detection and assignment algorithm for single cell RNA-Seq and Cytof/FACS data. 'SCINA' is capable of assigning cell type identities to a pool of cells profiled by scRNA-Seq or Cytof/FACS data with prior knowledge of markers, such as genes and protein symbols that are highly or lowly expressed in each category. See Zhang Z, et al (2019) <doi:10.3390/genes10070531> for more details.
We develop a novel matrix factorization tool named 'scINSIGHT' to jointly analyze multiple single-cell gene expression samples from biologically heterogeneous sources, such as different disease phases, treatment groups, or developmental stages. Given multiple gene expression samples from different biological conditions, 'scINSIGHT' simultaneously identifies common and condition-specific gene modules and quantify their expression levels in each sample in a lower-dimensional space. With the factorized results, the inferred expression levels and memberships of common gene modules can be used to cluster cells and detect cell identities, and the condition-specific gene modules can help compare functional differences in transcriptomes from distinct conditions. Please also see Qian K, Fu SW, Li HW, Li WV (2022) <doi:10.1186/s13059-022-02649-3>.
Provides an R interface to SCIP (Solving Constraint Integer Programs), a framework for mixed-integer
	     programming (MIP), mixed-integer nonlinear programming (MINLP), and constraint integer programming
	     (2025, <doi:10.48550/arXiv.2511.18580>). Supports linear, quadratic, SOS, indicator, and knapsack
	     constraints with continuous, binary, and integer variables. Includes a one-shot solver interface
	     and a model-building API for incremental problem construction.
A collection of functions that creates graphs with error
             bars for data collected from one-way or higher factorial
             designs.
Provides a template for new research projects structured as an R
    package-based research compendium. Everything - data, R scripts, custom
    functions and manuscript or reports - is contained within the same package to
    facilitate collaboration and promote reproducible research, following the FAIR 
    principles.
Create and format tables and APA statistics for
    scientific publication. This includes making a 'Table 1'
    to summarize demographics across groups, correlation tables
    with significance indicated by stars, and extracting formatted
    statistical summarizes from simple tests for in-text notation.
    The package also includes functions for Winsorizing data based
    on a Z-statistic cutoff.
A set of 'Rmarkdown' themes for creating scientific and professional documents. Simple interface with features to ease navigation across the page and sub-pages.
Provides an imputation pipeline for single-cell RNA sequencing data. 
  The 'scISR' method uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and estimates the dropout values using a subspace regression model (Tran et.al. (2022) <DOI:10.1038/s41598-022-06500-4>).
Fast maximum-likelihood phylogeny inference from noisy single-cell data using the 'ScisTree' algorithm by Yufeng Wu (2019) <doi:10.1093/bioinformatics/btz676>. 'scistreer' provides an 'R' interface and improves speed via 'Rcpp' and 'RcppParallel', making the method applicable to massive single-cell datasets (>10,000 cells).
You can use the functions provided by the package to make various statistical tables, 
             such as baseline data tables. Creates 'Table 1', i.e., a description of the 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.
             This method was described by Mary L McHugh (2013) <doi:10.11613/bm.2013.018>.
Single-cell Interpretable Tensor Decomposition (scITD) employs the 
    Tucker tensor decomposition to extract multicell-type gene expression patterns 
    that vary across donors/individuals. This tool is geared for use with single-cell
    RNA-sequencing datasets consisting of many source donors. The method has a wide
    range of potential applications, including the study of inter-individual variation
    at the population-level, patient sub-grouping/stratification, and the analysis
    of sample-level batch effects. Each "multicellular process" that is extracted 
    consists of (A) a multi cell type gene loadings matrix and (B) a
    corresponding donor scores vector indicating the level at which the corresponding
    loadings matrix is expressed in each donor. Additional methods are implemented
    to aid in selecting an appropriate number of factors and to evaluate stability
    of the decomposition. Additional tools are provided for downstream analysis,
    including integration of gene set enrichment analysis and ligand-receptor analysis. 
    Tucker, L.R. (1966) <doi:10.1007/BF02289464>. Unkel, S., Hannachi, A., Trendafilov, N. T., & Jolliffe, I. T. (2011) <doi:10.1007/s13253-011-0055-9>. Zhou, G., & Cichocki, A. (2012) <doi:10.2478/v10175-012-0051-4>.
Functions to install SciViews additions to R, and more
        tools.
Uses statistical network modeling to understand the co-expression relationships among genes and to construct sparse gene co-expression networks from single-cell gene expression data.
Toolbox containing a variety of spectral clustering tools functions. Among the tools available are the hierarchical spectral clustering algorithm, the Shi and Malik clustering algorithm, the Perona and Freeman algorithm, the non-normalized clustering, the Von Luxburg algorithm, the Partition Around Medoids clustering algorithm, a multi-level clustering algorithm, recursive clustering and the fast method for all clustering algorithm. As well as other tools needed to run these algorithms or useful for unsupervised spectral clustering. This toolbox aims to gather the main tools for unsupervised spectral classification. See <http://mawenzi.univ-littoral.fr/> for more information and documentation.
Perform meta-analysis of single-case experiments, including calculating various effect size measures (SMD, PND, PEM and NAP) and probability combining (additive and multiplicative method), as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.
The single cell mapper (scMappR) R package contains a suite of bioinformatic tools that provide experimentally relevant cell-type specific information to a list of differentially expressed genes (DEG). The function "scMappR_and_pathway_analysis" reranks DEGs to generate cell-type specificity scores called cell-weighted fold-changes. Users input a list of DEGs, normalized counts, and a signature matrix into this function. scMappR then re-weights bulk DEGs by cell-type specific expression from the signature matrix, cell-type proportions from RNA-seq deconvolution and the ratio of cell-type proportions between the two conditions to account for changes in cell-type proportion. With cwFold-changes calculated, scMappR uses two approaches to utilize cwFold-changes to complete cell-type specific pathway analysis. The "process_dgTMatrix_lists" function in the scMappR package contains an automated scRNA-seq processing pipeline where users input scRNA-seq count data, which is made compatible for scMappR and other R packages that analyze scRNA-seq data. We further used this to store hundreds up regularly updating signature matrices. The functions "tissue_by_celltype_enrichment", "tissue_scMappR_internal", and "tissue_scMappR_custom" combine these consistently processed scRNAseq count data with gene-set enrichment tools to allow for cell-type marker enrichment of a generic gene list (e.g. GWAS hits). Reference: Sokolowski,D.J., Faykoo-Martinez,M., Erdman,L., Hou,H., Chan,C., Zhu,H., Holmes,M.M., Goldenberg,A. and Wilson,M.D. (2021) Single-cell mapper (scMappR): using scRNA-seq to infer cell-type specificities of differentially expressed genes. NAR Genomics and Bioinformatics. 3(1). Iqab011. <doi:10.1093/nargab/lqab011>.
Provides a framework for modeling cellular metabolic states and continuous 
    metabolic trajectories from single-cell RNA-seq data using pathway-level 
    scoring. Enables lineage-restricted metabolic analysis, metabolic 
    'pseudotime' inference, module-level trend analysis, and visualization of 
    metabolic state transitions.
A general-purpose implementation of synthetic control methods 
    that accounts for potential spillover effects between units. Based on 
    the methodology of Cao and Dowd (2019) <doi:10.48550/arXiv.1902.07343>
    "Estimation and Inference for Synthetic Control Methods with Spillover Effects".
Unequal granularity of cell type annotation makes it difficult to compare scRNA-seq datasets at scale. Leveraging the ontology system for defining cell type hierarchy, 'scOntoMatch' aims to align cell type annotations to make them comparable across studies. The alignment involves two core steps: first is to trim the cell type tree within each dataset so each cell type does not have descendants, and then map cell type labels cross-studies by direct matching and mapping descendants to ancestors. Various functions for plotting cell type trees and manipulating ontology terms are also provided. In the Single Cell Expression Atlas hosted at EBI, a compendium of datasets with curated ontology labels are great inputs to this package.
Provides a computational framework for identification of B cell clones from
             Adaptive Immune Receptor Repertoire sequencing (AIRR-Seq) data. Three main
             functions are included (identicalClones, hierarchicalClones, and spectralClones)
             that perform clustering among sequences of BCRs/IGs (B cell receptors/immunoglobulins)
             which share the same V gene, J gene and junction length.
             Nouri N and Kleinstein SH (2018) <doi: 10.1093/bioinformatics/bty235>.
             Nouri N and Kleinstein SH (2019) <doi: 10.1101/788620>.
             Gupta NT, et al. (2017) <doi: 10.4049/jimmunol.1601850>.
Handling of behavioural data from the Ethoscope platform 
    (Geissmann, Garcia Rodriguez, Beckwith, French, Jamasb and Gilestro (2017) <DOI:10.1371/journal.pbio.2003026>).
    Ethoscopes (<https://giorgiogilestro.notion.site/Ethoscope-User-Manual-a9739373ae9f4840aa45b277f2f0e3a7>) are an open source/open hardware framework made of 
    interconnected raspberry pis (<https://www.raspberrypi.org>) designed to quantify the behaviour of multiple 
    small animals in a distributed and real-time fashion. The default tracking algorithm records primary variables
    such as xy coordinates, dimensions and speed.
    This package is part of the rethomics framework <https://rethomics.github.io/>.
Assigns a score projection from 0 to 1 between a given in 'vivo' stage and each single cluster from an in 'vitro' dataset. The score is assigned based on the the fraction of specific markers of the in 'vivo' stage that are conserved in the in 'vitro' clusters <https://github.com/ScialdoneLab>.
A non convex optimization package that optimizes any function under the criterion, combination of variables are on the surface of a unit sphere, as described in the paper : Das et al. (2019) <arXiv:1909.04024> .
Provides routines for scoring behavioral questionnaires. Includes scoring procedures for the 'International Physical Activity Questionnaire (IPAQ)' <http://www.ipaq.ki.se>. Compares physical functional performance to the age- and gender-specific normal ranges.
The `scorecard` package makes the development of credit risk scorecard 
  easier and efficient by providing functions for some common tasks, 
  such as data partition, variable selection, woe binning, scorecard scaling,
  performance evaluation and report generation. These functions can also used
  in the development of machine learning models.
    The references including: 
  1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: 
  Development and Implementation Using SAS. 
  2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. 
  Developing and Implementing Intelligent Credit Scoring.
Provides infrastructure functionalities such as missing value treatment, information value calculation, GINI calculation etc. which are used for developing a traditional credit scorecard as well as a machine learning based model. The functionalities defined are standard steps for any credit underwriting scorecard development, extensively used in financial domain.
S-Core Graph Decomposition algorithm for graphs. This is a method
 for decomposition of a weighted graph, as proposed by Eidsaa and
 Almaas (2013) <doi:10.1103/PhysRevE.88.062819>. The high speed and the low
 memory usage make it suitable for large graphs.
Perform association test within linear mixed model framework using score test integrated with Empirical Bayes for genome-wide association study. Firstly, score test was conducted for each marker under linear mixed model framework, taking into account the genetic relatedness and population structure. And then all the potentially associated markers were selected with a less stringent criterion. Finally, all the selected markers were placed into a multi-locus model to identify the true quantitative trait nucleotide.
Hyvärinen's score matching (Hyvärinen, 2005) <https://jmlr.org/papers/v6/hyvarinen05a.html> is a useful estimation technique when the normalising constant for a probability distribution is difficult to compute. This package implements score matching estimators using automatic differentiation in the 'CppAD' library <https://github.com/coin-or/CppAD> and is designed for quickly implementing score matching estimators for new models. Also available is general robustification (Windham, 1995) <https://www.jstor.org/stable/2346159>. Already in the package are estimators for directional distributions (Mardia, Kent and Laha, 2016) <doi:10.48550/arXiv.1604.08470> and the flexible Polynomially-Tilted Pairwise Interaction model for compositional data. The latter estimators perform well when there are zeros in the compositions (Scealy and Wood, 2023) <doi:10.1080/01621459.2021.2016422>, even many zeros (Scealy, Hingee, Kent, and Wood, 2024) <doi:10.1007/s11222-024-10412-w>. A partial interface to CppAD's ADFun objects is also available.
Provides peak functions, which enable us to detect peaks in time series. The methods implemented in this package are based on Girish Keshav Palshikar (2009) <https://www.researchgate.net/publication/228853276_Simple_Algorithms_for_Peak_Detection_in_Time-Series>.
Provides computational tools for estimating inverse regions and 
  constructing the corresponding simultaneous outer and inner confidence regions. 
  Acceptable input includes both one-dimensional and two-dimensional data for linear, 
  logistic, functional, and spatial generalized least squares regression models. 
  Functions are also available for constructing simultaneous confidence bands (SCBs) 
  for these models. The definition of simultaneous confidence regions (SCRs) follows 
  Sommerfeld et al. (2018) <doi:10.1080/01621459.2017.1341838>. Methods for estimating 
  inverse regions, SCRs, and the nonparametric bootstrap are based on 
  Ren et al. (2024) <doi:10.1093/jrsssc/qlae027>. Methods for constructing SCBs 
  are described in Crainiceanu et al. (2024) <doi:10.1201/9781003278726> 
  and Telschow et al. (2022) <doi:10.1016/j.jspi.2021.05.008>.
Evaluating probabilistic forecasts via proper scoring rules.  scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families.  Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score.  For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.
Implements multiple consistent scoring functions
    (Gneiting T (2011) <doi:10.1198/jasa.2011.r10138>) for assessing point
    forecasts and point predictions. Detailed documentation of scoring
    functions' properties is included for facilitating interpretation of
    results.
Dictionary-like reference for computing scoring rules in a wide
    range of situations. Covers both parametric forecast distributions (such as
    mixtures of Gaussians) and distributions generated via simulation. Further 
    details can be found in the package vignettes <doi:10.18637/jss.v090.i12>, 
    <doi:10.18637/jss.v110.i08>.
Facilitate the evaluation of forecasts in a convenient
    framework based on data.table. It allows user to to check their forecasts
    and diagnose issues, to visualise forecasts and missing data, to transform
    data before scoring, to handle missing forecasts, to aggregate scores, and
    to visualise the results of the evaluation. The package mostly focuses on
    the evaluation of probabilistic forecasts and allows evaluating several
    different forecast types and input formats. Find more information about the
    package in the Vignettes as well as in the accompanying paper,
    <doi:10.48550/arXiv.2205.07090>.
A consistent, semi-supervised, non-parametric survival curve estimator optimized for efficient use of Electronic Health Record (EHR) data with a limited number of current status labels. See van der Laan and Robins (1997) <doi:10.2307/2670119>.
Constructs cell-type–specific gene regulatory networks from single-cell RNA-sequencing data. The method implements the SCORPION algorithm, which first aggregates individual cells into super-cells and then applies PANDA (Passing Attributes between Networks for Data Assimilation) to infer transcription factor–target regulatory relationships. It also provides statistical methods for differential edge analysis.
Perform the balanced (Scott and Knott, 1974) and unbalanced <doi:10.1590/1984-70332017v17n1a1> Scott & Knott algorithm.
The Scott-Knott Effect Size Difference (ESD) test is a mean comparison approach that leverages a hierarchical clustering to partition the set of treatment means (e.g., means of variable importance scores, means of model performance) into statistically distinct groups with non-negligible difference [Tantithamthavorn et al., (2018) <doi:10.1109/TSE.2018.2794977>].
Implements the Scout method for regression, described in "Covariance-regularized regression and classification for high-dimensional problems", by Witten and Tibshirani (2008),  Journal of the Royal Statistical Society, Series B 71(3): 615-636.
Creates a contextual menu that can be triggered with keyboard shortcuts or programmatically.
    This can replace traditional sidebars or navigation bars, thereby enhancing the 
    user experience with lighter user interfaces.
Using principal component analysis as a base model, 'SCOUTer' 
    offers a new approach to simulate outliers in a simple and precise way. 
    The user can generate new observations defining them by a pair of well-known 
    statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2) 
    statistics. Just by introducing the target values of the SPE and T^2, 'SCOUTer' 
    returns a new set of observations with the desired target properties. 
    Authors: Alba González, Abel Folch-Fortuny, Francisco Arteaga and 
    Alberto Ferrer (2020).
Implements estimators for structured covariance matrices in the
    presence of pairwise and spatial covariates.
    Metodiev, Perrot-Dockès, Ouadah, Fosdick, Robin, Latouche & Raftery (2025)
    <doi:10.48550/arXiv.2411.04520>.
Discovers synergistic gene pairs in single-cell RNA-seq and spatial transcriptomics data. Unlike conventional pairwise co-expression analyses that rely on a single correlation metric, scPairs integrates 14 complementary metrics across five orthogonal evidence layers to compute a composite synergy score with optional permutation-based significance testing. The five evidence layers span cell-level co-expression (Pearson, Spearman, biweight midcorrelation, mutual information, ratio consistency), neighbourhood-aware smoothing (KNN-smoothed correlation, neighbourhood co-expression, cluster pseudo-bulk, cross-cell-type, neighbourhood synergy), prior biological knowledge (GO/KEGG co-annotation Jaccard, pathway bridge score), trans-cellular interaction, and spatial co-variation (Lee's L, co-location quotient). This multi-scale design enables researchers to move beyond simple co-expression towards a comprehensive characterisation of cooperative gene regulation at transcriptomic and spatial resolution. For more information, see the package documentation at <https://github.com/zhaoqing-wang/scPairs>.
Implements different inventory models, the bullwhip effect and other supply chain performance variables. Marchena Marlene (2010) <arXiv:1009.3977>.
R version of 'scperturb' tool for single-cell perturbation 
    analysis. Contains wrappers for performing E-statistics for Seurat objects. 
    More details on the method can be found in Peidli et al. (2023) 
    <doi:10.1101/2022.08.20.504663> and in Székely and Rizzo (2004).
Implementation of prediction and inference procedures for Synthetic Control methods using least square, lasso, ridge, or simplex-type constraints. Uncertainty is quantified with prediction intervals as developed in Cattaneo, Feng, and Titiunik (2021) <doi:10.1080/01621459.2021.1979561> for a single treated unit and in Cattaneo, Feng, Palomba, and Titiunik (2025) <doi:10.1162/rest_a_01588> for multiple treated units and staggered adoption. More details about the software implementation can be found in Cattaneo, Feng, Palomba, and Titiunik (2025) <doi:10.18637/jss.v113.i01>.
Compute ploidy of single cells (or nuclei)
    based on single-cell (or single-nucleus)
    ATAC-seq (Assay for Transposase-Accessible Chromatin using sequencing)
    data <https://github.com/fumi-github/scPloidy>.
Add-on for the 'scan' package that creates plots 
  from single-case data frames ('scdf'). It includes functions for styling 
  single-case plots, adding phase-based lines to indicate various statistical 
  parameters, and predefined themes for presentations and publications. More 
  information and in depth examples can be found in the online book 
  "Analyzing Single-Case Data with R and 'scan" 
  Jürgen Wilbert (2026) <https://jazznbass.github.io/scan-Book/>.
Provide a workflow to jointly embed chromatin accessibility peaks and expressed genes into a shared low-dimensional space using paired single-cell ATAC-seq (scATAC-seq) and single-cell RNA-seq (scRNA-seq) data. It integrates regulatory relationships among peak-peak interactions (via 'Cicero'), peak-gene interactions (via Lasso, random forest, and XGBoost), and gene-gene interactions (via principal component regression). With the input of paired scATAC-seq and scRNA-seq data matrices, it assigns a low-dimensional feature vector to each gene and peak. Additionally, it supports the reconstruction of gene-gene network with low-dimensional projections (via epsilon-NN) and then the comparison of the networks of two conditions through manifold alignment implemented in 'scTenifoldNet'. See <doi:10.1093/bioinformatics/btaf483> for more details.
Useful to visualize the Poissoneity (an independent Poisson statistical framework, 
  where each RNA measurement for each cell comes from its own independent Poisson distribution) of 
  Unique Molecular Identifier (UMI) based single cell RNA sequencing (scRNA-seq) data, and explore 
  cell clustering based on model departure as a novel data representation.
Simplicially constrained regression models for proportions in both sides. The constraint is always that the betas are non-negative and sum to 1. References: Iverson S.J.., Field C., Bowen W.D. and Blanchard W. (2004) "Quantitative Fatty Acid Signature Analysis: A New Method of Estimating Predator Diets". Ecological Monographs, 74(2): 211-235. <doi:10.1890/02-4105>.
A system that provides a streamlined way of generating
    publication ready plots for known Single-Cell transcriptomics data in
    a “publication ready” format. This is, the goal is to automatically
    generate plots with the highest quality possible, that can be used
    right away or with minimal modifications for a research article.
Functions to implement the stability controlled 
    quasi-experiment (SCQE) approach to study the effects of 
    newly adopted treatments that were not assigned at random. This package 
    contains tools to help users avoid making statistical assumptions that rely 
    on infeasible assumptions. Methods developed in Hazlett (2019) 
    <doi:10.1002/sim.8717>.
Given bincount data from single-cell copy number profiling (segmented or unsegmented), estimates ploidy, and uses the ploidy estimate to scale the data to absolute copy numbers. Uses the modular quantogram proposed by Kendall (1986) <doi:10.1002/0471667196.ess2129.pub2>, modified by weighting segments according to confidence, and quantifying confidence in the estimate using a theoretical quantogram. Includes optional fused-lasso segmentation with the algorithm in Johnson (2013) <doi:10.1080/10618600.2012.681238>, using the implementation from glmgen by Arnold, Sadhanala, and Tibshirani.
We provide a suite of tools for estimating the sample complexity of a chosen model through theoretical bounds and simulation. The package incorporates methods for estimating the Vapnik-Chervonenkis dimension (VCD) of a chosen algorithm, which can be used to estimate its sample complexity. Alternatively, we provide simulation methods to estimate sample complexity directly. For more details, see Carter, P & Choi, D (2024). "Learning from Noise: Applying Sample Complexity for Political Science Research" <doi:10.31219/osf.io/evrcj>.
Tools for scraping information from webpages and other XML contents, using XPath or CSS selectors.
The 'scrapeR' package utilizes functions that fetch and extract text content from specified web pages. It handles HTTP errors and parses HTML efficiently. The package can handle hundreds of websites at a time using the scrapeR_in_batches() command.
A group of functions to scrape data from different websites, for 
    academic purposes.
Routines for a collection of screen-and-clean type
        variable selection procedures, including UPS and GS.
Optimal hard thresholding of singular values. The procedure adaptively estimates the best singular value threshold under unknown noise characteristics. The threshold chosen by 'ScreeNOT' is optimal (asymptotically, in the sense of minimum Frobenius error) under the the so-called "Spiked model" of a low-rank matrix observed in additive noise. In contrast to previous works, the noise is not assumed to be i.i.d. or white; it can have an essentially arbitrary and unknown correlation structure, across either rows, columns or both. 'ScreeNOT' is proposed to practitioners as a mathematically solid alternative to Cattell's ever-popular but vague Scree Plot heuristic from 1966. 
    If you use this package, please cite our paper: David L. Donoho, Matan Gavish and Elad Romanov (2023). "ScreeNOT: Exact MSE-optimal singular value thresholding in correlated noise." Annals of Statistics, 2023 (To appear). <arXiv:2009.12297>.
Take screenshots from R command and locate an image position.
Minimal R client for the 'Screenshotbase' API to render website screenshots
    and query account status. Provides functions to set the API key, call the
    status endpoint, and take a screenshot as a raw image response.
Implementation of the scregclust algorithm
  described in Larsson, Held, et al. (2024) <doi:10.1038/s41467-024-53954-3>
  which reconstructs regulatory programs of target genes in scRNA-seq data.
  Target genes are clustered into modules and each module is associated with a linear
  model describing the regulatory program.
A base dependency solution with basic argument parsing for use with
    'Rscript'.
Tools for the analysis of high-dimensional data developed/implemented
  at the group "Statistical Complexity Reduction In Molecular Epidemiology" (SCRIME).
  Main focus is on SNP data. But most of the functions can also be applied to other
  types of categorical data.
Identifies the name of the current script
    in a variety of contexts, e.g. interactively or 
    when sourced.  Attempts to support RStudio environment.
    Based on <https://stackoverflow.com/a/32016824/2292993>
    and <https://stackoverflow.com/a/35842176/2292993>.
Run complex native scripts with a single command, similar to system commands.
Provides functions to retrieve the location of R scripts loaded through the source() function or run from the command line using the Rscript command. This functionality is analogous to the Bash shell's ${BASH_SOURCE[0]}. Users can first set the project root's path relative to the script path and then all subsequent paths relative to the root. This system ensures that all paths lead to the same location regardless of where any script is executed/loaded from without resorting to the use of setwd() at the top of the scripts.
Displays the content of a R script into the 'Cytoscape' network-visualization app <https://cytoscape.org/>.
A small set of functions wrapping up the call stack and command
  line inspection needed to determine a running script's filename from
  within the script itself.
Full text, in data frames containing one row per verse, of the 
    Standard Works of The Church of Jesus Christ of Latter-day Saints (LDS). 
    These are the Old Testament, (KJV), the New Testament (KJV), the Book of 
    Mormon, the Doctrine and Covenants, and the Pearl of Great Price.
A coalescent simulator that allows the rapid simulation of
    biological sequences under neutral models of evolution, see
    Staab et al. (2015) <doi:10.1093/bioinformatics/btu861>.
    Different to other coalescent based simulations,
    it has an optional approximation parameter that
    allows for high accuracy while maintaining a linear run time cost for long
    sequences. It is optimized for simulating massive data sets as produced by Next-
    Generation Sequencing technologies for up to several thousand sequences.
A pipeline that can process single or multiple Single Cell RNAseq 
    samples primarily specializes in Clustering and Dimensionality Reduction. 
    Meanwhile we use common cell type marker genes for T cells, B cells, Myeloid cells, 
    Epithelial cells, and stromal cells (Fiboblast, Endothelial cells, Pericyte,
    Smooth muscle cells) to visualize the Seurat clusters, to facilitate labeling 
    them by biological names. Once users named each cluster, they can evaluate the 
    quality of them again and find the de novo marker genes also.
'Last.fm'<https://www.last.fm> is a music platform focussed on building a 
    detailed profile of a users listening habits. It does this by 'scrobbling' (recording) 
    every track you listen to on other platforms ('spotify', 'youtube', 'soundcloud' etc)
    and transferring them to your 'Last.fm' database. This allows 'Last.fm' to act as a 
    complete record of your entire listening history. 'scrobbler' provides helper functions
    to download and analyse your listening history in R.
Allows the user to animate 'shiny' elements when scrolling to view them.
    The animations are activated using the 'scrollrevealjs' library. See <https://scrollrevealjs.org/> for more information.
Identifying cell types based on expression profiles is a pillar of single cell analysis. 'scROSHI' identifies cell types based on expression profiles of single cell analysis by utilizing previously obtained cell type specific gene sets. It takes into account the hierarchical nature of cell type relationship and does not require training or annotated data. A detailed description of the method can be found at: Prummer, Bertolini, Bosshard, Barkmann, Yates, Boeva, The Tumor Profiler Consortium, Stekhoven, and Singer (2022) <doi:10.1101/2022.04.05.487176>.
Design single-case phase, alternation and multiple-baseline experiments, and conduct randomization tests on data gathered by means of such designs, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>.
Test published summary statistics for consistency
    (Brown and Heathers, 2017, <doi:10.1177/1948550616673876>;
    Allard, 2018, <https://aurelienallard.netlify.app/post/anaytic-grimmer-possibility-standard-deviations/>;
    Heathers and Brown, 2019, <https://osf.io/5vb3u/>).
    The package also provides infrastructure for implementing new
    error detection techniques.
Functions for working with the scrypt key derivation functions
    originally described by Colin Percival
    <https://www.tarsnap.com/scrypt/scrypt.pdf> and in Percival and Josefsson
    (2016) <doi:10.17487/RFC7914>. Scrypt is a password-based key derivation
    function created by Colin Percival. The algorithm was specifically designed
    to make it costly to perform large-scale custom hardware attacks by
    requiring large amounts of memory.
A simple, light, and robust interface between R and
    the 'Scryfall' card data API <https://scryfall.com/docs/api>.
Solves convex cone programs via operator splitting. Can solve:
    linear programs ('LPs'), second-order cone programs ('SOCPs'), semidefinite programs
    ('SDPs'), exponential cone programs ('ECPs'), and power cone programs ('PCPs'), or
    problems with any combination of those cones. 'SCS' uses 'AMD' (a set of routines for permuting sparse matrices prior to factorization) and 'LDL' (a sparse 'LDL' factorization and solve package) from 'SuiteSparse' (<https://people.engr.tamu.edu/davis/suitesparse.html>).
Single cell resolution data has been valuable in learning about tissue microenvironments and interactions between cells or spots. This package allows for the simulation of this level of data, be it single cell or ‘spots’, in both a univariate (single metric or cell type) and bivariate (2 or more metrics or cell types) ways. As more technologies come to marker, more methods will be developed to derive spatial metrics from the data which will require a way to benchmark methods against each other. Additionally, as the field currently stands, there is not a gold standard method to be compared against. We set out to develop an R package that will allow users to simulate point patterns that can be biologically informed from different tissue domains, holes, and varying degrees of clustering/colocalization. The data can be exported as spatial files and a summary file (like 'HALO'). <https://github.com/FridleyLab/scSpatialSIM/>.
Provides functions for evaluating the stability of low-dimensional embeddings and cluster assignments in single‑cell RNA sequencing (scRNA‑seq) datasets. Starting from a principal component analysis (PCA) object, users can generate multiple replicates of t‑Distributed Stochastic Neighbor Embedding (t‑SNE) or Uniform Manifold Approximation and Projection (UMAP) embeddings. Embedding stability is quantified by computing pairwise Kendall’s Tau correlations across replicates and summarizing the distribution of correlation coefficients. In addition to dimensionality reduction, 'scStability' assesses clustering consistency using either Louvain or Leiden algorithms and calculating the Normalized Mutual Information (NMI) between all pairs of cluster assignments. For background on UMAP and t-SNE algorithms, see McInnes et al. (2020, <doi:10.21105/joss.00861>) and van der Maaten & Hinton (2008, <https://github.com/lvdmaaten/bhtsne>), respectively.
A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input.  First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene’s outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene’s function in the analyzed cells.
A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.
Extends the functionality of the package 'Synth' as detailed in Abadie, Diamond, and Hainmueller (2011) <doi:10.18637/jss.v042.i13>. Includes generating and plotting placebos, post/pre-MSPE (Mean Squared Prediction Error) significance tests and plots, and calculating average treatment effects for multiple treated units.
A normalization method for single-cell UMI count data using a 
  variance stabilizing transformation. The transformation is based on a 
  negative binomial regression model with regularized parameters. As part of the
  same regression framework, this package also provides functions for
  batch correction, and data correction. See Hafemeister and Satija (2019)
  <doi:10.1186/s13059-019-1874-1>, and Choudhary and Satija (2022) <doi:10.1186/s13059-021-02584-9>
  for more details.
Code for describing and manipulating scuba diving profiles 
	(depth-time curves) and decompression models, 
        for calculating the predictions of decompression models,
	for calculating maximum no-decompression time and decompression tables,
	and for performing mixed gas calculations.
Imbalanced training datasets impede many popular classifiers. To balance training data, a combination of oversampling minority classes and undersampling majority classes is useful. This package implements the SCUT (SMOTE and Cluster-based Undersampling Technique) algorithm as described in Agrawal et. al. (2015) <doi:10.5220/0005595502260234>. Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package.
Make graphical representations of single case data and transform graphical displays back to raw data, as discussed in Bulte and Onghena (2013) <doi:10.22237/jmasm/1383280020>. The package also includes tools for visually analyzing single-case data, by displaying central location, variability and trend.
Provides Stable Diffusion image generation in R using the 'ggmlR'
    tensor library. Supports text-to-image and image-to-image generation with
    multiple model versions (SD 1.x, SD 2.x, 'SDXL', Flux). Implements the
    full inference pipeline including CLIP text encoding, 'UNet' noise removal,
    and 'VAE' encoding/decoding. Unified sd_generate() entry point with
    automatic strategy selection (direct, tiled sampling, high-resolution fix)
    based on output resolution and available 'VRAM'. High-resolution generation
    (2K, 4K+) via tiled 'VAE' decoding, tiled diffusion sampling
    ('MultiDiffusion'), and classic two-pass refinement (text-to-image, then
    upscale with image-to-image). Multi-GPU parallel generation via
    sd_generate_multi_gpu(). Multi-GPU model parallelism via 'device_layout'
    in sd_ctx(): distribute diffusion, text encoders, and 'VAE' across
    separate 'Vulkan' devices. Built-in profiling (sd_profile_start(),
    sd_profile_summary()) for per-stage timing of text encoding, sampling,
    and 'VAE' decode. Supports CPU and 'Vulkan' GPU. No 'Python' or external
    API dependencies required. Cross-platform: Linux, macOS, Windows.
Provides an efficient framework for 
   high-dimensional linear and diagonal discriminant analysis with 
   variable selection.  The classifier is trained using James-Stein-type 
   shrinkage estimators and predictor variables are ranked using 
   correlation-adjusted t-scores (CAT scores).  Variable selection error 
   is controlled using false non-discovery rates or higher criticism.
Functions and Datasets from Lohr, S. (1999), Sampling:
        Design and Analysis, Duxbury.
We develop a new class of distribution free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data driven threshold along the ranking to control the FDR. For more information, see the website below and the accompanying paper: Du et al. (2023), "False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation", <doi:10.1080/01621459.2021.1945459>. Some optional functionality uses the archived R packages ‘huge’ and ‘pfa’, which are not available from CRAN’s main repositories. Users who need this optional functionality can obtain them from the CRAN Archive as follows: ‘huge’ at <https://cran.r-project.org/src/contrib/Archive/huge/>; ‘pfa’ at <https://cran.r-project.org/src/contrib/Archive/pfa/>.
Provides digital tools for performing analyses within Social Dynamics and complexity in the Ancient Mediterranean (SDAM), which is a research group based at the Department of History and Classical Studies at Aarhus University.
Data sets and functions to support the books "Statistics: Data 
        analysis and modelling" by Speekenbrink, M. (2021) 
        <https://mspeekenbrink.github.io/sdam-book/> and "An R companion to 
        Statistics: data analysis and modelling" by Speekenbrink, M. (2021) 
        <https://mspeekenbrink.github.io/sdam-r-companion/>. All datasets 
        analysed in these books are provided in this package. In addition, the 
        package provides functions to compute sample statistics (variance, 
        standard deviation, mode), create raincloud and enhanced Q-Q plots, and 
        expand Anova results into omnibus tests and tests of individual 
        contrasts.
A fast, consistent tool for plotting and facilitating the analysis of stratigraphic
    and sedimentological data. Taking advantage of the flexible plotting tools available in R, 
    'SDAR' uses stratigraphic and sedimentological data to produce detailed graphic logs for 
    outcrop sections and borehole logs. These logs can include multiple features (e.g., bed thickness,
    lithology, samples, sedimentary structures, colors, fossil content, bioturbation index, 
    gamma ray logs) (Johnson, 1992, <ISSN 0037-0738>).
Includes all the datasets of 'Sampling: Design and Analysis'
    (3rd edition by Sharon Lohr) in R format and additional functions for
    analyzing and graphing probability samples.
Stock-and-flow models are a computational method from the field of 
    system dynamics. They represent how systems change over time and are 
    mathematically equivalent to ordinary differential equations. 'sdbuildR'
    (system dynamics builder) provides an intuitive interface for constructing 
    stock-and-flow models without requiring extensive domain knowledge. Models 
    can quickly be simulated and revised, supporting iterative development. 
    'sdbuildR' simulates models in 'R' and 'Julia', where 'Julia' offers unit
    support and large-scale ensemble simulations. Additionally, 'sdbuildR' can 
    import models created in 'Insight Maker' (<https://insightmaker.com/>).
Provides functionality to generate, (interactively) modify (by adding, removing and renaming nodes) and convert nested hierarchies between different formats.
  These tree like structures can be used to define for example complex hierarchical tables used for statistical disclosure control.
Tools for researchers to explicitly show that their results
    comply to rules for statistical disclosure control imposed by research
    data centers. These tools help in checking descriptive statistics and
    models and in calculating extreme values that are not individual data.
    Also included is a simple function to create log files. The methods
    used here are described in the "Guidelines for the checking of output
    based on microdata research" by Bond, Brandt, and de Wolf (2015)
    <https://cros.ec.europa.eu/system/files/2024-02/Output-checking-guidelines.pdf>.
Data from statistical agencies and other institutions are mostly
    confidential. This package, introduced in Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v067.i04>, can be used for the generation of anonymized
    (micro)data, i.e. for the creation of public- and scientific-use files.
    The theoretical basis for the methods implemented can be found in Templ (2017) <doi:10.1007/978-3-319-50272-4>.
    Various risk estimation and anonymization methods are included. Note that the package
    includes a graphical user interface published in Meindl and Templ (2019) <doi:10.3390/a12090191> that allows to use various methods of this
    package.
Tools for calculating disclosure risk measures for microdata,
    including record-level and file-level measures. The record-level disclosure
    risk is estimated primarily using exhaustive tabulation. The file-level
    disclosure risk is estimated by fitting loglinear models on the observed 
    sample counts in cells formed by key variables and their interactions. 
    Funded by the National Center for Education Statistics. See Skinner and
    Shlomo (2008) <doi:10.1198/016214507000001328> for a description of the
    file-level risk measures and the loglinear model approach.
Privacy protected raster maps 
  can be created from spatial point data. Protection
  methods include smoothing of dichotomous variables by de Jonge and de Wolf (2016) 
  <doi:10.1007/978-3-319-45381-1_9>, continuous variables by de Wolf and 
  de Jonge (2018) <doi:10.1007/978-3-319-99771-1_23>, suppressing 
  revealing values and a generalization of the quad tree method by 
  Suñé, Rovira, Ibáñez and Farré (2017) <doi:10.2901/EUROSTAT.C2017.001>.
Methods for statistical disclosure control in
    tabular data such as primary and secondary cell suppression as described for example
    in Hundepol et al. (2012) <doi:10.1002/9781118348239> are covered in this package.
Description: Provides functions for simulation and inference for stochastic differential equations (SDEs). It accompanies the book "Simulation and Inference for Stochastic Differential Equations: With R Examples" (Iacus, 2008, Springer; ISBN: 978-0-387-75838-1).
Set of functions for Stochastic Data Envelopment Analysis. Chance constrained versions of radial, directional and additive DEA models are implemented, as long as super-efficiency models. See: Cooper, W.W.; Deng, H.; Huang, Z.; Li, S.X. (2002). <doi:10.1057/palgrave.jors.2601433>, Bolós, V.J.; Benítez, R.; Coll-Serrano, V. (2024) <doi:10.1016/j.orp.2024.100307>.
Implementation of statistical methods for the estimation of
    toroidal diffusions. Several diffusive models are provided, most of them
    belonging to the Langevin family of diffusions on the torus. Specifically,
    the wrapped normal and von Mises processes are included, which can be seen
    as toroidal analogues of the Ornstein-Uhlenbeck diffusion. A collection of
    methods for approximate maximum likelihood estimation, organized in four
    blocks, is given: (i) based on the exact transition probability density,
    obtained as the numerical solution to the Fokker-Plank equation; (ii) based
    on wrapped pseudo-likelihoods; (iii) based on specific analytic
    approximations by wrapped processes; (iv) based on maximum likelihood of
    the stationary densities. The package allows the replicability of the
    results in García-Portugués et al. (2019) <doi:10.1007/s11222-017-9790-2>.
Nonparametric method for testing the equality of the spectral densities of two time series of possibly different lengths. The time series are preprocessed with the discrete cosine transform and the variance stabilising transform to obtain an approximate Gaussian regression setting for the log-spectral density function. The test statistic is based on the squared L2 norm of the difference between the estimated log-spectral densities. The test returns the result, the statistic value, and the p-value. It also provides the estimated empirical quantile and null distribution under the hypothesis of equal spectral densities. An example using EEG data is included. For details see Nadin, Krivobokova, Enikeeva (2026), <doi:10.48550/arXiv.2602.10774>.
Identify 17 Sustainable Development Goals and associated 169 targets in text.
Implements scalable Markov chain Monte Carlo (Sca-MCMC) algorithms for Bayesian inference in dynamic generalized linear models (DGLMs). The package supports Pareto-type and Gamma-type DGLMs, which are suitable for modeling heavy-tailed phenomena such as wealth allocation and financial returns. It provides simulation tools for synthetic DGLM data, adaptive mutation-rate strategies (ScaI, ScaII, ScaIII), geometric temperature ladders for parallel tempering, and posterior predictive evaluation metrics (e.g., R2, RMSE). The methodology is based on the scalable MCMC framework described in Guo et al. (2025).
Functions to filter GPS/Argos locations, as well as assessing the sample size for the analysis of animal distributions. The filters remove temporal and spatial duplicates, fixes located at a given height from estimated high tide line, and locations with high error as described in Shimada et al. (2012) <doi:10.3354/meps09747> and Shimada et al. (2016) <doi:10.1007/s00227-015-2771-0>. Sample size for the analysis of animal distributions can be assessed by the conventional area-based approach or the alternative probability-based approach as described in Shimada et al. (2021) <doi:10.1111/2041-210X.13506>.
Implementation of the modified skew discrete Laplace (SDL) regression model.
    The package provides a set of functions for a complete analysis of integer-valued data, 
    where the dependent variable is assumed to follow a modified SDL distribution. This regression
    model is useful for the analysis of integer-valued data and experimental studies in which
    paired discrete observations are collected.
An extensible framework for developing species distribution
    models using individual and community-based approaches, generate ensembles of
    models, evaluate the models, and predict species potential distributions in
    space and time. For more information, please check the following paper:
    Naimi, B., Araujo, M.B. (2016) <doi:10.1111/ecog.01881>.
Screen for and analyze non-linear sparse direct effects in the presence of unobserved confounding using the spectral deconfounding techniques (Ćevid, Bühlmann, and Meinshausen (2020)<jmlr.org/papers/v21/19-545.html>, Guo, Ćevid, and Bühlmann (2022) <doi:10.1214/21-AOS2152>). These methods have been shown to be a good estimate for the true direct effect if we observe many covariates, e.g., high-dimensional settings, and we have fairly dense confounding. Even if the assumptions are violated, it seems like there is not much to lose, and the deconfounded models will, in general, estimate a function closer to the true one than classical least squares optimization. 'SDModels' provides functions SDAM() for Spectrally Deconfounded Additive Models (Scheidegger, Guo, and Bühlmann (2025) <doi:10.1145/3711116>) and SDForest() for Spectrally Deconfounded Random Forests (Ulmer, Scheidegger, and Bühlmann (2025) <doi:10.1080/10618600.2025.2569602>).
Terrestrial and marine predictors for species distribution modelling
    from multiple sources, including WorldClim <https://www.worldclim.org/>,,
    ENVIREM <https://envirem.github.io/>, Bio-ORACLE <https://bio-oracle.org/>
    and MARSPEC <http://www.marspec.org/>.
Implements spatial and spatiotemporal GLMMs (Generalized Linear
    Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial
    Differential Equation) Gaussian Markov random field approximation to
    Gaussian random fields. One common application is for spatially explicit
    species distribution models (SDMs).
    See Anderson et al. (2025) <doi:10.18637/jss.v115.i02>.
User-friendly framework that enables the training and the
    evaluation of species distribution models (SDMs). The package implements
    functions for data driven variable selection and model tuning and includes
    numerous utilities to display the results. All the functions used to select
    variables or to tune model hyperparameters have an interactive real-time
    chart displayed in the 'RStudio' viewer pane during their execution.
A software package help user to create virtual species for species distribution modelling. It includes
    several methods to help user to create virtual species distribution map.
    Those maps can be used for Species Distribution Modelling (SDM) study. SDM use
    environmental data for sites of occurrence of a species to predict all the sites
    where the environmental conditions are suitable for the species to persist, and
    may be expected to occur.
Spatial model calculation for static and dynamic panel data models, weights matrix creation and  Bayesian model comparison.
  Bayesian model comparison methods were described by 'LeSage' (2014) <doi:10.1016/j.spasta.2014.02.002>.
  The 'Lee'-'Yu' transformation approach is described in 'Yu', 'De Jong' and 'Lee' (2008) <doi:10.1016/j.jeconom.2008.08.002>, 'Lee' and 'Yu' (2010) <doi:10.1016/j.jeconom.2009.08.001> and 'Lee' and 'Yu' (2010) <doi:10.1017/S0266466609100099>.
Utility functions for scale-dependent and alternative hyperpriors. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend
  on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. Hyperpriors for all effects can be elicitated within the package. Including complex tensor product interaction terms and variable selection priors. The basic model is explained in in Klein and Kneib (2016) <doi:10.1214/15-BA983>.
Based on the compound Poisson risk process that is perturbed by
    a Brownian motion, saddlepoint approximations to some measures of risk are
    provided. Various approximation methods for the probability of ruin are
    also included. Furthermore, exact values of both the risk measures as well
    as the probability of ruin are available if the individual claims follow
    a hypo-exponential distribution (i. e., if it can be represented as a sum
    of independent exponentially distributed random variables with different
    rate parameters). For more details see Gatto and Baumgartner (2014)
    <doi:10.1007/s11009-012-9316-5>.
We visualize the standard deviation of a data set as the size of a prism whose volume equals the total volume of several prisms made from the Empirical Cumulative Distribution Function.
The sdrt() function is designed for estimating subspaces for Sufficient Dimension Reduction (SDR) in time series, with a specific focus on the Time Series Central Mean subspace (TS-CMS). The package employs the Fourier transformation method proposed by Samadi and De Alwis (2023) <doi:10.48550/arXiv.2312.02110> and the Nadaraya-Watson kernel smoother method proposed by Park et al. (2009) <doi:10.1198/jcgs.2009.08076> for estimating the TS-CMS. The package provides tools for estimating distances between subspaces and includes functions for selecting model parameters using the Fourier transformation method.
Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, 
             while serving as a basis for other packages maintained by Wenbo Lv.
Companion package that supports the 'surveydown' survey platform (<https://surveydown.org>). The default method for working with a 'surveydown' survey is to edit the plain text 'survey.qmd' and 'app.R' files. With 'sdstudio', you can create, preview and manage surveys with a 'shiny' application as a graphical user interface.
Functions for self-determination motivation theory (SDT) to compute
    measures of motivation internalization, motivation simplex structure, and of
    the original and adjusted self-determination or relative autonomy index. SDT
    was introduced by Deci and Ryan (1985) <doi:10.1007/978-1-4899-2271-7>. See
    package?SDT for an overview.
An Electronic Data Capture system (EDC) and Data Standard agnostic 
    solution that enables the pharmaceutical programming community to develop 
    Clinical Data Interchange Standards Consortium (CDISC)
    Study Data Tabulation Model (SDTM) datasets in R. The reusable algorithms 
    concept in 'sdtm.oak' provides a framework for modular programming and can 
    potentially automate the conversion of raw clinical data to SDTM through 
    standardized SDTM specifications. SDTM is one of the required standards for data 
    submission to the Food and Drug Administration (FDA) in the United States and 
    Pharmaceuticals and Medical Devices Agency (PMDA) in Japan. SDTM standards 
    are implemented following the SDTM Implementation Guide as defined by 
    CDISC <https://www.cdisc.org/standards/foundational/sdtmig>.
Clinical Data Interchange Standards Consortium (CDISC) Standard Data
    Tabulation Model (SDTM) controlled terminology, 2025-03-25.
    Source: <https://evs.nci.nih.gov/ftp1/CDISC/SDTM/>.
A series of checks to identify common issues in Study Data Tabulation Model (SDTM) datasets. These checks are intended to be generalizable, actionable, and meaningful for analysis.
Provides a set of tools to assist statistical programmers 
    in validating Study Data Tabulation Model (SDTM) domain data sets. 
    Statistical programmers are required to validate that a SDTM data set domain 
    has been programmed correctly, per the SDTM Implementation Guide (SDTMIG) by 
    'CDISC' (<https://www.cdisc.org/standards/foundational/sdtmig>), 
    study specification, and study protocol using a process called double 
    programming. Double programming involves two different programmers 
    independently converting the raw electronic data cut (EDC) data into a SDTM 
    domain data table and comparing their results to ensure accurate 
    standardization of the data. One of these attempts is termed 'production' 
    and the other 'validation'. Generally, production runs are the official 
    programs for submittals and these are written in 'SAS'. Validation runs can 
    be programmed in another language, in this case 'R'.
Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties. More details about the methodology SDWD is seen on Wang and Zou (2016) (<doi:10.1080/10618600.2015.1049700>).
Implements the SE-test for equivalence
    according to Hoffelder et al. (2015) <DOI:10.1080/10543406.2014.920344>.
    The SE-test for equivalence is a multivariate two-sample equivalence test. 
    Distance measure of the test is the sum of standardized differences
    between the expected values or in other words: the sum of effect sizes (SE)
    of all components of the two multivariate samples. 
    The test is an asymptotically valid test for normally distributed data 
    (see Hoffelder et al.,2015).
    The function SE.EQ() implements the SE-test for equivalence 
    according to Hoffelder et al. (2015).
    The function SE.EQ.dissolution.profiles() implements a variant 
    of the SE-test for equivalence for similarity analyses of dissolution 
    profiles as mentioned in Suarez-Sharp et al.(2020) 
    <DOI:10.1208/s12248-020-00458-9>). The equivalence margin used in 
    SE.EQ.dissolution.profiles() is analogically defined as for the T2EQ
    approach according to Hoffelder (2019) <DOI:10.1002/bimj.201700257>) 
    by means of a systematic shift in location
    of 10 [\% of label claim] of both dissolution profile populations. 
    SE.EQ.dissolution.profiles() checks whether the weighted mean of the 
    differences of the expected values of both dissolution profile populations 
    is statistically significantly smaller than 10 [\% of label claim]. The 
    weights are built up by the inverse variances.
A few major genes and a series of polygene are responsive for each quantitative trait. Major genes are individually identified while polygene is collectively detected. This is mixed major genes plus polygene inheritance analysis or segregation analysis (SEA). In the SEA, phenotypes from a single or multiple bi-parental segregation populations along with their parents are used to fit all the possible models and the best model of the trait for population phenotypic distributions is viewed as the model of the trait. There are fourteen types of population combinations available. Zhang Yuan-Ming, Gai Jun-Yi, Yang Yong-Hua (2003, <doi:10.1017/S0016672303006141>).
Calculates parameters of the seawater carbonate system and assists the design of ocean acidification perturbation experiments.
The explosion of biobank data offers immediate opportunities for 
    gene-environment (GxE) interaction studies of complex diseases because of the 
    large sample sizes and rich collection in genetic and non-genetic information. 
    However, the extremely large sample size also introduces new computational 
    challenges in GxE assessment, especially for set-based GxE variance component (VC) 
    tests, a widely used strategy to boost overall GxE signals and to evaluate the 
    joint GxE effect of multiple variants from a biologically meaningful unit 
    (e.g., gene). 
    We present 'SEAGLE', a Scalable Exact AlGorithm for Large-scale Set-based 
    GxE tests, to permit GxE VC test scalable to biobank data. 'SEAGLE' employs modern 
    matrix computations to achieve the same “exact” results as the original GxE VC 
    tests, and does not impose additional assumptions nor relies on approximations. 
    'SEAGLE' can easily accommodate sample sizes in the order of 10^5, is implementable 
    on standard laptops, and does not require specialized equipment. 
    The accompanying manuscript for this package can be found at Chi, Ipsen, Hsiao, 
    Lin, Wang, Lee, Lu, and Tzeng. (2021+) <arXiv:2105.03228>.
Transformation of sea currents to connectivity data. Two files of
 horizontal and vertical currents flows are transformed into connectivity data
 in the form of 'sfnetwork', shapefile, edge list and adjacency matrix. An
 application example is shown at Nagkoulis et al. (2025)
 <doi:10.1016/j.dib.2024.111268>.
An R 'Shiny' application dedicated to the intra-site spatial analysis of piece-plotted archaeological remains, making the two and three-dimensional spatial exploration of archaeological data as user-friendly as possible.  Documentation about 'SEAHORS' is provided by the vignette included in this package and by the companion scientific paper: Royer, Discamps, Plutniak, Thomas (2023, PCI Archaeology, <doi:10.5281/zenodo.7674698>).
Standard error adjusted adaptive lasso (SEA-lasso) is a version of the adaptive lasso, which incorporates OLS standard error to the L1 penalty weight. This method is intended for variable selection under linear regression settings (n > p). This new weight assignment strategy is especially useful when the collinearity of the design matrix is a concern.
Provides comprehensive analytics, reporting, and testing capabilities 
    for systematic review search strategies. The package focuses on validating search 
    performance, generating standardized 'PRISMA'-compliant reports, and ensuring 
    reproducibility in evidence synthesis. Features include precision-recall analysis, 
    cross-database performance comparison, benchmark validation against gold standards, 
    sensitivity analysis, temporal coverage assessment, automated report generation,
    and statistical comparison of search strategies. Supports multiple export formats 
    including 'CSV', 'Excel', 'RIS', 'BibTeX', and 'EndNote'. Includes tools for duplicate 
    detection, search strategy optimization, cross-validation frameworks, meta-analysis 
    of benchmark results, power analysis for study design, and reproducibility package 
    creation. Optionally connects to 'PubMed' for direct database searching and real-time 
    strategy comparison using the 'E-utilities' 'API'. Enhanced with bootstrap comparison 
    methods, 'McNemar' test for strategy evaluation, and comprehensive visualization tools 
    for performance assessment. Methods based on Manning et al. (2008) 
    for information retrieval metrics, Moher et al. (2009)
    for 'PRISMA' guidelines, and Sampson et al. (2006) for 
    systematic review search methodology.
Provides a search interface to look up terms
    on 'Google', 'Bing', 'DuckDuckGo', 'Startpage', 'Ecosia', 'rseek',
    'Twitter', 'StackOverflow', 'RStudio Community', 'GitHub', and 'BitBucket'.
    Upon searching, a browser window will open with the aforementioned search
    results.
The QuadTree data structure is useful for fast,
	     neighborhood-restricted lookups. We use it to implement fast k-Nearest
        Neighbor and Rectangular range lookups in 2 dimenions. The
        primary target is high performance interactive graphics.
A seamless design that combines phase I dose escalation based on toxicity with phase II dose expansion and dose comparison based on efficacy.
Capable of deriving seasonal statistics, such as "normals", and
  analysis of seasonal data, such as departures. This package also has
  graphics capabilities for representing seasonal data, including boxplots for
  seasonal parameters, and bars for summed normals. There are many specific
  functions related to climatology, including precipitation normals,
  temperature normals, cumulative precipitation departures and precipitation
  interarrivals. However, this package is designed to represent any
  time-varying parameter with a discernible seasonal signal, such as found
  in hydrology and ecology.
Spatiotemporal individual-level model of seasonal infectious disease transmission within the Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) framework are applied to model seasonal infectious disease transmission. This package employs a likelihood based Monte Carlo Expectation Conditional Maximization (MCECM) algorithm for estimating model parameters. In addition to model fitting and parameter estimation, the package offers functions for calculating AIC using real pandemic data and conducting simulation studies customized to user-specified model configurations.
Routines for the seasonal analysis of health data, including
    regression models, time-stratified case-crossover, plotting functions
    and residual checks, see Barnett and Dobson (2010) ISBN
    978-3-642-10748-1. Thanks to Yuming Guo for checking the
    case-crossover code.
Easy-to-use interface to X-13-ARIMA-SEATS, the seasonal adjustment
    software by the US Census Bureau. It offers full access to almost all
    options and outputs of X-13, including X-11 and SEATS, automatic ARIMA model
    search, outlier detection and support for user defined holiday variables,
    such as Chinese New Year or Indian Diwali. A graphical user interface can be
    used through the 'seasonalview' package. Uses the X-13-binaries from the
    'x13binary' package.
The price action at any given time is determined by investor 
  sentiment and market conditions. Although there is no established principle, 
  over a long period of time, things often move with a certain periodicity.
  This is sometimes referred to as anomaly. 
  The seasonPlot() function in this package calculates and visualizes the 
  average value of price movements over a year for any given period. 
  In addition, the monthly increase or decrease in price movement is 
  represented with a colored background. 
  This seasonPlot() function can use the same symbols as the 'quantmod' package 
  (e.g. ^IXIC, ^DJI, SPY, BTC-USD, and ETH-USD etc).
A graphical user interface to the 'seasonal' package and
  'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software.
The computation of a seasonal index is a fundamental step in time-series forecasting when the data exhibits seasonality. Specifically, a seasonal index quantifies — for each season (e.g. month, quarter, week) — the relative magnitude of the seasonal effect compared to the overall average level of the series. This package has been developed to compute seasonal index for time series data and it also seasonalise and desesaonalise the time series data.
Read CODAR's SeaSonde High-Frequency Radar spectra files, compute radial metrics, and generate plots for spectra and antenna pattern data. Implementation is based in technical manuals, publications and patents, please refer to the following documents for more information: Barrick and Lipa (1999) <https://codar.com/images/about/patents/05990834.PDF>; CODAR Ocean Sensors (2002) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Docs/Informative/FirstOrder_Settings.pdf>; Lipa et al. (2006) <doi:10.1109/joe.2006.886104>; Paolo et al. (2007) <doi:10.1109/oceans.2007.4449265>; CODAR Ocean Sensors (2009a) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Docs/GuidesToFileFormats/File_AntennaPattern.pdf>; CODAR Ocean Sensors (2009b) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Docs/GuidesToFileFormats/File_CrossSpectraReduced.pdf>; CODAR Ocean Sensors (2016a) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Manuals_Documentation_Release_8/File_Formats/File_Cross_Spectra_V6.pdf>; CODAR Ocean Sensors (2016b) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Manuals_Documentation_Release_8/File_Formats/FIle_Reduced_Spectra.pdf>; CODAR Ocean Sensors (2016c) <http://support.codar.com/Technicians_Information_Page_for_SeaSondes/Manuals_Documentation_Release_8/Application_Guides/Guide_SpectraPlotterMap.pdf>; Bushnell and Worthington (2022) <doi:10.25923/4c5x-g538>.
An overall test for seasonality of a given time
    series in addition to a set of individual seasonality tests as
    described by Ollech and Webel (forthcoming): An overall seasonality
    test. Bundesbank Discussion Paper.
Provides tools for processing and evaluating seasonal weather forecasts, 
    with an emphasis on tercile forecasts. We follow the World Meteorological Organization's 
    "Guidance on Verification of Operational Seasonal Climate Forecasts", 
    S.J.Mason (2018, ISBN: 978-92-63-11220-0, URL: <https://library.wmo.int/idurl/4/56227>). 
    The development was supported by the European Union’s Horizon 2020 research and innovation 
    programme under grant agreement no. 869730 (CONFER).
    A comprehensive online tutorial is available at <https://seasonalforecastingengine.github.io/SeaValDoc/>.
Monte Carlo sampling algorithms for semiparametric
    Bayesian regression analysis. These models feature a nonparametric
    (unknown) transformation of the data paired with widely-used
    regression models including linear regression, spline regression,
    quantile regression, and Gaussian processes. The transformation
    enables broader applicability of these key models, including for
    real-valued, positive, and compactly-supported data with challenging
    distributional features. The samplers prioritize computational
    scalability and, for most cases, Monte Carlo (not MCMC) sampling for
    greater efficiency. Details of the methods and algorithms are provided
    in Kowal and Wu (2024) <doi:10.1080/01621459.2024.2395586>.
These functions were developed within SECFISH project (Strengthening regional cooperation in the area of fisheries  data collection-Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level). They are aimed at identifying  correlations between costs and transversal variables by metier using individual vessel data and for disaggregating variable costs from  fleet segment to metier  level.
Estimating parameters of site clusters on 2D & 3D square lattice with various lattice sizes, relative fractions of open sites (occupation probability), iso- & anisotropy, von Neumann & Moore (1,d)-neighborhoods, described by Moskalev P.V. et al. (2011) <arXiv:1105.2334v1>.
Functions to estimate the density and size of a spatially 
  distributed animal population sampled with an array of passive detectors, 
  such as traps, or by searching polygons or transects. Models incorporating 
  distance-dependent detection are fitted by maximizing the likelihood. 
  Tools are included for data manipulation and model selection.
Tools for designing spatially explicit capture-recapture studies of animal populations. This is primarily a simulation manager for package 'secr'. Extensions in version 2.5.0 include costing and evaluation of detector spacing.
Allow sharing sensitive information, for example passwords,
    'API' keys, etc., in R packages, using public key cryptography.
Fast and memory-efficient streaming hash functions,
    binary/text encoding and serialization. Hashes strings and raw vectors
    directly.  Stream hashes files which can be larger than memory, as
    well as in-memory objects through R's serialization mechanism.
    Implements the SHA-256, SHA-3 and 'Keccak' cryptographic hash
    functions, SHAKE256 extendable-output function (XOF), 'SipHash'
    pseudo-random function, base64 and base58 encoding, 'CBOR' and 'JSON'
    serialization.
Facilitates secret management by storing credentials in a dedicated file, 
          keeping them out of your code base. 
          The secrets are stored without encryption.  
          This package is compatible with secrets stored by the 'SecretsProvider' 'Python' package <https://pypi.org/project/SecretsProvider/>.
Encrypt text using a simple shifting substitution cipher with setcode(), providing two numeric keys used to define the encryption algorithm. The resulting text can be decoded using decode() function and the two numeric keys specified during encryption.
Functions are provided for internal use by the spatial 
  capture-recapture package 'secr' (from version 5.4.0). The idea is to speed up 
  the installation of 'secr', and possibly reduce its size. Initially the functions 
  are those for area and transect search that use numerical integration code from 
  'RcppNumerical' and 'RcppEigen'. The functions are not intended to be user-friendly 
  and require considerable preprocessing of data.
Tools for spatially explicit capture-recapture analysis of animal populations in linear habitats, extending package 'secr'.
Simultaneously infers state-dependent diversification across
 two or more states of a single or multiple traits while accounting for the
 role of a possible concealed trait. See Herrera-Alsina et al. (2019)
 <doi:10.1093/sysbio/syy057>.
Determining potential output and the output gap - two inherently unobservable variables - is a major challenge for macroeconomists. 'sectorgap' features a flexible modeling and estimation framework for a multivariate Bayesian state space model identifying economic output fluctuations consistent with subsectors of the economy. The proposed model is able to capture various correlations between output and a set of aggregate as well as subsector indicators. Estimation of the latent states and parameters is achieved using a simple Gibbs sampling procedure and various plotting options facilitate the assessment of the results. For details on the methodology and an illustrative example, see Streicher (2024) <https://www.research-collection.ethz.ch/handle/20.500.11850/653682>.
Seamless and standardized interaction with data
    exported from the clinical data management system (CDMS)
    'secuTrial'<https://www.secutrial.com>.
    The primary data export the package works with is a standard non-rectangular export.
Proxy forward modelling for sediment archived climate proxies such 
  as Mg/Ca, d18O or Alkenones. The user provides a hypothesised "true" past climate, 
  such as output from a climate model, and details of the sedimentation rate and 
  sampling scheme of a sediment core. Sedproxy returns simulated proxy records. 
  Implements the methods described in Dolman and Laepple (2018) 
  <doi:10.5194/cp-14-1851-2018>.
Provides plotting utilities supporting packages in the 'easystats'
    ecosystem (<https://github.com/easystats/easystats>) and some extra themes,
    geoms, and scales for 'ggplot2'. Color scales are based on
    <https://materialui.co/>.
    References: Lüdecke et al. (2021) <doi:10.21105/joss.03393>.
Provides a wrapper to access data from the SeeClickFix
  web API for R. SeeClickFix is a central platform employed by many cities
  that allows citizens to request their city's services. This package
  creates several functions to work with all the built-in calls to the
  SeeClickFix API. Allows users to download service request data from
  numerous locations in easy-to-use dataframe format manipulable in
  standard R functions.
Output colors used in literal vectors, palettes and plot objects (ggplot).
Functions for dimension reduction through the seeded canonical correlation analysis are provided. A classical canonical correlation analysis (CCA) is one of useful statistical methods in multivariate data analysis, but it is limited in use due to the matrix inversion for large p small n data. To overcome this, a seeded CCA has been proposed in Im, Gang and Yoo (2015) \doi{10.1002/cem.2691}. The seeded CCA is a two-step procedure. The sets of variables are initially reduced by successively projecting cov(X,Y) or cov(Y,X) onto cov(X) and cov(Y), respectively, without loss of information on canonical correlation analysis, following Cook, Li and Chiaromonte (2007) \doi{10.1093/biomet/asm038} and Lee and Yoo (2014) \doi{10.1111/anzs.12057}. Then, the canonical correlation is finalized with the initially-reduced two sets of variables.
Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) <doi:10.1177/08944393231178605>.
    Implements Seeded LDA (Lu et al., 2010) <doi:10.1109/ICDMW.2011.125> and Sequential LDA (Du et al., 2012) <doi:10.1007/s10115-011-0425-1> with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing.
Imbibition causes seeds to expand, which results in the seed coat or testa being broken. Seed germination begins with imbibition. Imbibition aids in the transport of water into the developing ovules. Imbibition is required during the first stages of root water absorption.
A mechanism for easily generating and organizing a collection of 
  seeds from a single seed, which may be subsequently used to ensure 
  reproducibility in processes/pipelines that utilize multiple random 
  components (e.g., trial simulation).
Analysis of seed germination data
    using the physiological time modelling approach. Includes functions
    to fit hydrotime and thermal-time models with the traditional approaches
    of Bradford (1990) <doi:10.1104/pp.94.2.840>
    and Garcia-Huidobro (1982) <doi:10.1093/jxb/33.2.288>. 
    Allows to fit models to grouped datasets,
    i.e. datasets containing multiple species, seedlots or experiments.
Seed vigor is defined as the sum total of those properties of the seed which determine the level of activity and performance of the seed or seed lot during germination and seedling emergence.  Testing for vigor becomes more important for carryover seeds, especially if seeds were stored under unknown conditions or under unfavorable storage conditions. Seed vigor testing is also used as indicator of the storage potential of a seed lot and in ranking various seed lots with different qualities. The vigour index is calculated using the equation given by (Ling et al. 2014) <doi:10.1038/srep05859>.
Provides a simple interface to recursively list files from a
    directory, filter them using a regular expression, read their
    contents, and extract lines that match a user-defined pattern. The
    package returns a dataframe containing the matched lines, their
    line numbers, file paths, and the corresponding matched substrings.
    Designed for quick code base exploration, log inspection, or any use
    case involving pattern-based file and line filtering.
A novel meta-learning framework for forecast model selection using time series features. Many applications require a large number of time series to be forecast. Providing better forecasts for these time series is important in decision and policy making. We propose a classification framework which selects forecast models based on features calculated from the time series. We call this framework FFORMS (Feature-based FORecast Model Selection). FFORMS builds a mapping that relates the features of time series to the best forecast model using a random forest. 'seer' package is the implementation of the FFORMS algorithm. For more details see our paper at <https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp06-2018.pdf>.
Creates SEER (Surveillance, Epidemiology and End Results) and 
    A-bomb data binaries from ASCII sources and provides tools for estimating
    SEER second cancer risks. Methods are described in <doi:10.1038/leu.2015.258>.
Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound).  This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses. See Sueur et al. (2008) <doi:10.1080/09524622.2008.9753600> and Sueur (2018) <doi:10.1007/978-3-319-77647-7>.
Provides two methods for segmentation and joint segmentation/clustering of
    bivariate time-series. Originally intended for ecological segmentation
    (home-range and behavioural modes) but easily applied on other series,
    the package also provides tools for analysing outputs from R packages 'moveHMM' and 'marcher'.
    The segmentation method is a bivariate extension of  Lavielle's method available in 'adehabitatLT' 
    (Lavielle, 1999 <doi:10.1016/S0304-4149(99)00023-X> and 2005 <doi:10.1016/j.sigpro.2005.01.012>).
    This method rely on dynamic programming for efficient segmentation.
    The segmentation/clustering method alternates steps of dynamic programming with an Expectation-Maximization algorithm.
    This is an extension of Picard et al (2007) <doi:10.1111/j.1541-0420.2006.00729.x> method 
    (formerly available in 'cghseg' package) to the bivariate case.
    The method is fully described in Patin et al (2018) <doi:10.1101/444794>.
Performs correlation matrix segmentation and applies a test
    procedure to detect highly correlated regions in gene expression.
Proposes an application for sequence prediction generalizing the similarity within the network of previous sequences.
A set of segregation-based indices and randomization methods to
            make robust environmental inequality assessments, as described in
            Schaeffer and Tivadar (2019) "Measuring Environmental Inequalities:
            Insights from the Residential Segregation Literature"
            <doi:10.1016/j.ecolecon.2019.05.009>.
Contains functions that help to determine event
    boundaries in event segmentation experiments by bootstrapping a critical
    segmentation magnitude under the null hypothesis that all key presses were
    randomly distributed across the experiment. Segmentation magnitude is
    defined as the sum of Gaussians centered at the times of the segmentation
    key presses performed by the participants. Within a participant, the maximum
    of the overlaid Gaussians is used to prevent an excessive influence of a
    single participant on the overall outcome (e.g. if a participant is pressing
    the key multiple times in succession). Further functions are included, such
    as plotting the results.
Fitting regression models where, in addition to possible linear terms, one or more covariates have segmented (i.e., broken-line or piece-wise linear) or stepmented (i.e. piece-wise constant) effects. Multiple breakpoints for the same variable are allowed. 
  The estimation method is discussed in Muggeo (2003, <doi:10.1002/sim.1545>) and 
  illustrated in Muggeo (2008, <https://www.r-project.org/doc/Rnews/Rnews_2008-1.pdf>). An approach for hypothesis testing is presented 
  in Muggeo (2016, <doi:10.1080/00949655.2016.1149855>), and interval estimation for the breakpoint is discussed in Muggeo (2017, <doi:10.1111/anzs.12200>). 
  Segmented mixed models, i.e. random effects in the change point, are discussed in Muggeo (2014, <doi:10.1177/1471082X13504721>).
  Estimation of piecewise-constant relationships and changepoints (mean-shift models) is 
  discussed in Fasola et al. (2018, <doi:10.1007/s00180-017-0740-4>).
A dynamic programming solution to segmentation based on
        maximization of arbitrary similarity measures within segments.
	The general idea, theory and this implementation are described in
	Machne, Murray & Stadler (2017) <doi:10.1038/s41598-017-12401-8>.
	In addition to the core algorithm, the package provides time-series
	processing and clustering functions as described in the publication.
	These are generally applicable where a `k-means` clustering yields
	meaningful results, and have been specifically developed for
	clustering of the Discrete Fourier Transform of periodic gene
	expression data (`circadian' or `yeast metabolic oscillations').
	This clustering approach is outlined in the supplemental material of
	Machne & Murray (2012) <doi:10.1371/journal.pone.0037906>), and here
	is used as a basis of segment similarity measures. Notably, the
	time-series processing and clustering functions can also be used as
	stand-alone tools, independent of segmentation, e.g., for 
        transcriptome data already mapped to genes.
Given a likelihood provided by the user, this package applies it
    to a given matrix dataset in order to find change points in the data that
    maximize the sum of the likelihoods of all the segments. This package provides
    a handful of algorithms with different time complexities and assumption compromises
    so the user is able to choose the best one for the problem at hand. The implementation
    of the segmentation algorithms in this package are based on the paper by Bruno M. de Castro,
	Florencia Leonardi (2018) <arXiv:1501.01756>. The Berlin
	weather sample dataset was provided by Deutscher Wetterdienst <https://dwd.de/>.
	You can find all the references in the Acknowledgments section of this package's
	repository via the URL below.
A system that computes metrics to assess the segmentation 
    accuracy of geospatial data. These metrics calculate the discrepancy 
    between segmented and reference objects, and indicate the segmentation 
    accuracy. For more details on choosing evaluation metrics, we 
    suggest seeing Costa et al. (2018) <doi:10.1016/j.rse.2017.11.024> and 
    Jozdani et al. (2020) <doi:10.1016/j.isprsjprs.2020.01.002>.
Implements a segmentation algorithm for multiple change-point detection in high-dimensional GARCH processes. It simultaneously segments GARCH processes by identifying 'common' change-points, each of which can be shared by a subset or all of the component time series as a change-point in their within-series and/or cross-sectional correlation structure.
Tools for modeling non-continuous linear responses of ecological communities to environmental data. The package is straightforward through three steps: (1) data ordering (function OrdData()), (2) split-moving-window analysis (function SMW()) and (3) piecewise redundancy analysis (function pwRDA()). Relevant references include Cornelius and Reynolds (1991) <doi:10.2307/1941559> and Legendre and Legendre (2012, ISBN: 9780444538697).
Computes segregation indices, including the Index of Dissimilarity,
    as well as the information-theoretic indices developed by
    Theil (1971) <isbn:978-0471858454>, namely
    the Mutual Information Index (M) and Theil's Information Index (H).
    The M, further described by Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x>
    and Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008>,
    is a measure of segregation that is highly decomposable. The package provides
    tools to decompose the index by units and groups (local segregation),
    and by within and between terms. The package also provides a method to decompose
    differences in segregation as described by Elbers (2021) <doi:10.1177/0049124121986204>.
    The package includes standard error estimation by bootstrapping, which also corrects for
    small sample bias. The package also contains functions for visualizing segregation patterns.
An implementation of the full-likelihood Bayes factor (FLB)
    for evaluating segregation evidence in clinical medical genetics. The
    method was introduced by Thompson et al. (2003) <doi:10.1086/378100>.
    This implementation supports custom penetrance values and liability
    classes, and allows visualisations and robustness analysis as
    presented in Ratajska et al. (2023) <doi:10.1002/mgg3.2107>. See also the 
    online app 'shinyseg', <https://chrcarrizosa.shinyapps.io/shinyseg>, which 
    offers interactive segregation analysis with many additional features 
    (Carrizosa et al. (2024) <doi:10.1093/bioinformatics/btae201>).
Provides tests for segregation distortion in F1 
    polyploid populations under different assumptions of meiosis. 
    These tests can account for double reduction, partial preferential pairing,
    and genotype uncertainty through the use of genotype likelihoods. 
    Parallelization support is provided. Details of these methods are 
    described in Gerard et al. (2025a) <doi:10.1007/s00122-025-04816-z>
    and Gerard et al. (2025b) <doi:10.1101/2025.06.23.661114>.
    Part of this material is based upon work supported by the
    National Science Foundation under Grant No. 2132247.  The
    opinions, findings, and conclusions or recommendations expressed
    are those of the author and do not necessarily reflect the views
    of the National Science Foundation.
Implementation of the original Sequence Globally Unique Identifier (SEGUID) algorithm [Babnigg and Giometti (2006) <doi:10.1002/pmic.200600032>] and SEGUID v2 (<https://www.seguid.org>), which extends SEGUID v1 with support for linear, circular, single- and double-stranded biological sequences, e.g. DNA, RNA, and proteins.
A very nice interface to Princeton's 'WordNet' without 'rJava' dependency. 'WordNet' data is not included. Princeton University makes 'WordNet' available to research and commercial users free of charge provided the terms of their license (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed, and proper reference is made to the project using an appropriate citation (<https://wordnet.princeton.edu/citing-wordnet>).
Convert a time series of observations to a time series of standardised indices that can be used to monitor variables on a common and probabilistically interpretable scale. The indices can be aggregated and rescaled to different time scales, visualised using plot capabilities, and calculated using a range of distributions. This includes flexible non-parametric and non-stationary methods.
Implements the Seinhorst model to analyze the relationship between initial 
          nematode densities and plant growth response using nonlinear least squares 
          estimation. The package provides tools for model fitting, prediction, and 
          visualization, facilitating the study of plant-nematode interactions. Model 
          parameters can be estimated or set to predefined values based on Seinhorst (1986)
          <doi:10.1007/978-1-4613-2251-1_11>.
Extended Susceptible-Exposed-Infected-Recovery Model for 
    handling high false negative rate and symptom based administration of 
    diagnostic tests. <doi:10.1101/2020.09.24.20200238>.
An implementation of self-exciting point process model for information cascades, which occurs when many people engage in the same acts after observing the actions of others (e.g. post resharings on Facebook or Twitter). It provides functions to estimate the infectiousness of an information cascade and predict its popularity given the observed history. See <http://snap.stanford.edu/seismic/> for more information and datasets.
Fast versions of seismic analysis functions that 'roll' over a
    vector of values. See the 'RcppRoll' package for alternative
    versions of basic statistical functions such as rolling mean,
    median, etc.
Set of functions that access information about deputies and votings
    in Polish diet from webpage <http://www.sejm.gov.pl>. The package was developed
    as a result of an internship in MI2 Group - <http://mi2.mini.pw.edu.pl>, Faculty
    of Mathematics and Information Science, Warsaw University of Technology.
Sejong(http://www.sejong.or.kr/) corpus and
        Hannanum(http://semanticweb.kaist.ac.kr/home/index.php/HanNanum)
        dictionaries for KoNLP
Implements a method for fitting a bounded probability
        distribution to quantiles (for example stated by an expert),
        see Bornkamp and Ickstadt (2009) for details.  For this
        purpose B-splines are used, and the density is obtained by
        penalized least squares based on a Brier entropy penalty.  The
        package provides methods for fitting the distribution as well
        as methods for evaluating the underlying density and cdf. In
        addition methods for plotting the distribution, drawing random
        numbers and calculating quantiles of the obtained distribution
        are provided.
Calculates (unconditional) post-selection confidence intervals
    and p-values for the coefficients of (generalized) linear models.
The objective of these functions is to derive a species assemblage
    that satisfies a functional trait profile. Restoring resilient ecosystems
    requires a flexible framework for selecting assemblages that are based on the
    functional traits of species. However, current trait-based models have been
    limited to algorithms that can only select species by optimising specific trait
    values, and could not elegantly accommodate the common desire among restoration
    ecologists to produce functionally diverse assemblages. We have solved this
    problem by applying a non-linear optimisation algorithm that optimises Rao Q,
    a closed-form functional trait diversity index that incorporates species
    abundances, subject to other linear constraints. This framework generalises
    previous models that only optimised the entropy of the community, and can
    optimise both functional diversity and entropy simultaneously. This package
    can also be used to generate experimental assemblages to test the effects
    of community-level traits on community dynamics and ecosystem function. The
    method is based on theory discussed in Laughlin (2014, Ecology Letters)
    <doi:10.1111/ele.12288>.
Provides indices such as Manly's alpha, foraging ratio, and Ivlev's selectivity to allow for analysis of dietary selectivity and preference. Can accommodate multiple experimental designs such as constant prey number of prey depletion. Please contact the package maintainer with any publications making use of this package in an effort to maintain a repository of dietary selections studies.
An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.
Adds variable-selection functions for Beta regression models
    (both mean and phi submodels) so they can be used within the 'SelectBoost' algorithm.
    Includes stepwise AIC, BIC, and corrected AIC on betareg() fits, 'gamlss'-based 
    LASSO/Elastic-Net, a pure 'glmnet' iterative re-weighted least squares-based selector 
    with an optional standardization speedup, and 'C++' helpers for iterative 
    re-weighted least squares working steps and precision updates. Also provides
    a fastboost_interval() variant for interval responses, comparison helpers,
    and a flexible simulator simulation_DATA.beta() for interval-valued data. 
    For more details see Bertrand and Maumy (2023) <doi:10.7490/f1000research.1119552.1>.
Extends the 'SelectBoost' approach to Generalized Additive Models for
    Location, Scale and Shape (GAMLSS). Implements bootstrap stability-selection
    across parameter-specific formulas (mu, sigma, nu, tau) via gamlss::stepGAIC().
    Includes optional standardization of predictors and helper functions for
    corrected AIC calculation. More details can be found in Bertrand and Maumy (2024) 
    <https://hal.science/hal-05352041> that highlights correlation-aware resampling to improve
    variable selection for GAMLSS and quantile regression when predictors are
    numerous and highly correlated.
Provides tools for the simultaneous improvement of multiple traits in plant breeding. Building upon the classical selection index (Smith 1937 <doi:10.1111/j.1469-1809.1936.tb02143.x>) and modern quantitative genetics (Kang 2020 <doi:10.1007/978-3-319-91223-3>), this package calculates classical phenotypic, genomic, marker-assisted, restricted/constrained, and eigen selection indices. It also incorporates multi-stage selection evaluation and stochastic simulations to estimate genetic advance based on economic weights, heritability, and genetic correlations.
Computes bounds and sensitivity parameters as part of sensitivity 
    analysis for selection bias. Different bounds are provided: the SV (Smith 
    and VanderWeele), sharp bounds,  AF (assumption-free) bound, GAF (generalized 
    AF), and CAF (counterfactual AF) bounds. The calculation of the sensitivity 
    parameters for the SV, sharp, and GAF bounds assume an additional dependence 
    structure in form of a generalized M-structure. The bounds can be
    calculated for any structure as long as the necessary assumptions hold. See 
    Smith and VanderWeele (2019) <doi:10.1097/EDE.0000000000001032>,
    Zetterstrom, Sjölander, and Waernabum (2025) <doi:10.1177/09622802251374168>,
    Zetterstrom and Waernbaum (2022) <doi:10.1515/em-2022-0108>, and
    Zetterstrom (2024) <doi:10.1515/em-2023-0033>.
Multi-stage selection is practiced in numerous fields of life
    and social sciences and particularly in breeding. A special characteristic of
    multi-stage selection is that candidates are evaluated in successive stages
    with increasing intensity and effort, and only a fraction of the superior
    candidates is selected and promoted to the next stage. For the optimum design
    of such selection programs, the selection gain plays a crucial role. It can be
    calculated by integration of a truncated multivariate normal (MVN) distribution.
    While mathematical formulas for calculating the selection gain and the variance
    among selected candidates were developed long time ago, solutions for numerical
    calculation were not available. This package can also be used for optimizing
    multi-stage selection programs for a given total budget and different costs of
    evaluating the candidates in each stage.
New tools for post-selection inference, for use with forward
    stepwise regression, least angle regression, the lasso, and the many means
    problem. The lasso function implements Gaussian, logistic and Cox survival
    models.
Publication bias, the fact that studies identified for inclusion in a meta analysis do not represent all studies on the topic of interest, is commonly recognized as a threat to the validity of the results of a meta analysis. One way to explicitly model publication bias is via selection models or weighted probability distributions. In this package we provide implementations of several parametric and nonparametric weight functions. The novelty in Rufibach (2011) is the proposal of a non-increasing variant of the nonparametric weight function of Dear & Begg (1992). The new approach potentially offers more insight in the selection process than other methods, but is more flexible than parametric approaches. To maximize the log-likelihood function proposed by Dear & Begg (1992) under a monotonicity constraint we use a differential evolution algorithm proposed by Ardia et al (2010a, b) and implemented in Mullen et al (2009). In addition, we offer a method to compute a confidence interval for the overall effect size theta, adjusted for selection bias as well as a function that computes the simulation-based p-value to assess the null hypothesis of no selection as described in Rufibach (2011, Section 6).
Translates a CSS selector into an equivalent XPath
  expression. This allows us to use CSS selectors when working with
  the XML package as it can only evaluate XPath expressions. Also
  provided are convenience functions useful for using CSS selectors on
  XML nodes. This package is a port of the Python package 'cssselect'
  (<https://cssselect.readthedocs.io/>).
Fit and selects point pattern models based on minimum contrast, AIC and and goodness of fit.
Detection of outliers and influential errors using a latent variable model.
A user-friendly wrapper for web automation, using either
    'chromote' or 'selenium'. Provides a simple and consistent API to make
    web scraping and testing scripts easy to write and understand.
    Elements are lazy, and automatically wait for the website to be valid,
    resulting in reliable and reproducible code, with no visible impact on
    the experience of the programmer.
An implementation of 'W3C WebDriver 2.0'
  (<https://w3c.github.io/webdriver/>), allowing interaction
  with a 'Selenium Server' (<https://www.selenium.dev/documentation/grid/>)
  instance from 'R'. Allows a web browser to be automated from 'R'.
Provides the SELF criteria to learn causal structure. Please cite "Ruichu Cai, Jie Qiao, Zhenjie Zhang, Zhifeng Hao. SELF: Structural Equational Embedded Likelihood Framework for Causal Discovery. AAAI. 2018."
Execute the self-controlled case series (SCCS) design using observational 
  data in the OMOP Common Data Model. Extracts all necessary data from the database and 
	transforms it to the format required for SCCS. Age and season can be modeled
	using splines assuming constant hazard within calendar months. Event-dependent 
	censoring of the observation period can be corrected for. Many exposures can be
	included at once (MSCCS), with regularization on all coefficients except for the
	exposure of interest. Includes diagnostics for all major assumptions of the SCCS.
A probability tree allows to compute probabilities of
	     complex events, such as genotype probabilities in intermediate generations of inbreeding
	     through recurrent self-fertilization (selfing). This package implements functionality to compute
	     probability trees for two- and three-marker genotypes in the F2 to F7 selfing
	     generations. The conditional probabilities are derived automatically
	     and in symbolic form. The package also provides functionality to
	     extract and evaluate the relevant probabilities.
Selection index is one of the efficient and acurrate method for selection of animals. This package is useful for construction of selection indices.
    It uses mixed and random model least squares analysis to estimate the heritability of
    traits and genetic correlation between traits. The package uses the sire model as it is considered as
    random effect. The genetic and phenotypic (co)variances along with the relative economic values are 
    used to construct the selection index for any number of traits. It also estimates the accuracy of the
    index and the genetic gain expected for different traits. Fisher (1936) <doi:10.1111/j.1469-1809.1936.tb02137.x>.
Functions for fitting general linear structural
    equation models (with observed and latent variables) using the RAM approach, 
    and for fitting structural equations in observed-variable models by two-stage least squares.
Style sheets and JavaScript assets for 'shiny.semantic' package.
It offers functions for creating dashboard with Fomantic UI.
Cleans and formats language transcripts guided by a series of transformation options (e.g., lemmatize words, omit stopwords, split strings across rows). 'SemanticDistance' computes two distinct metrics of cosine semantic distance (experiential and embedding). These values reflect pairwise cosine distance between different elements or chunks of a language sample. 'SemanticDistance' can process monologues (e.g., stories, ordered text), dialogues (e.g., conversation transcripts), word pairs arrayed in columns, and unordered word lists. Users specify options for how they wish to chunk distance calculations. These options include: rolling ngram-to-word distance (window of n-words to each new word), ngram-to-ngram distance (2-word chunk to the next 2-word chunk), pairwise distance between words arrayed in columns, matrix comparisons (i.e., all possible pairwise distances between words in an unordered list), turn-by-turn distance (talker to talker in a dialogue transcript). 'SemanticDistance' includes visualization options for analyzing distances as time series data and simple semantic network dynamics (e.g., clustering, undirected graph network).
Implements named semaphores from the 'boost' 'C++' library
    <https://www.boost.org/> for interprocess communication. Multiple 'R' 
    sessions on the same host can block (with optional timeout) on a semaphore
    until it becomes positive, then atomically decrement it and unblock. Any
    session can increment the semaphore.
We present a rank-based Mercer kernel to compute a pair-wise similarity metric corresponding to informative representation of data. We tailor the development of a kernel to encode our prior knowledge about the data distribution over a probability space. The philosophical concept behind our construction is that objects whose feature values fall on the extreme of that feature’s probability mass distribution are more similar to each other, than objects whose feature values lie closer to the mean. Semblance emphasizes features whose values lie far away from the mean of their probability distribution. The kernel relies on properties empirically determined from the data and does not assume an underlying distribution. The use of feature ranks on a probability space ensures that Semblance is computational efficacious, robust to outliers, and statistically stable, thus making it widely applicable algorithm for pattern analysis. The output from the kernel is a square, symmetric matrix that gives proximity values between pairs of observations.
A collection of helper functions for forming
  bootstrapping confidence intervals and examining bootstrap
  estimates in structural equation modelling,
  introduced in Yang and Cheung (2026) <doi: 10.3758/s13428-025-02911-z>
  The function currently
  support models fitted by the 'lavaan' package by
  Rosseel (2012) <doi:10.18637/jss.v048.i02>.
Training and validation of a custom (or data-driven) Structural
    Equation Models using Deep Neural Networks or Machine Learning algorithms, which
	extend the fitting procedures of the 'SEMgraph' R package <doi:10.32614/CRAN.package.SEMgraph>.
Interactive 'shiny' application for working with Structural Equation Modelling technique. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/semwebappk/> .
Fits a structural equation multidimensional scaling (SEMDS) model for asymmetric and three-way input dissimilarities. It assumes that the dissimilarities are measured with errors. The latent dissimilarities are estimated as factor scores within an SEM framework while the objects are represented in a low-dimensional space as in MDS.
Automatically calculate direct, indirect, and total effects for 
    piecewise structural equation models, comprising lists of fitted models 
    representing structured equations (Lefcheck, 2016 <doi:10/f8s8rb>). 
    Confidence intervals are provided via bootstrapping.
Provides standardized effect decomposition (direct, indirect, and total effects) for three major structural equation modeling frameworks: 
	'lavaan', 'piecewiseSEM', and 'plspm'. Automatically handles zero-effect variables, generates publication-ready 'ggplot2' visualizations, and returns 
	both wide-format and long-format effect tables. Supports effect filtering, multi-model object inputs, and customizable visualization parameters.
	For a general overview of the methods used in this package, see Rosseel (2012) <doi:10.18637/jss.v048.i02> and Lefcheck (2016) <doi:10.1111/2041-210X.12512>.
Sensitivity analysis in structural equation modeling using
    influence measures and diagnostic plots. Support leave-one-out casewise
    sensitivity analysis presented by Pek and MacCallum (2011)
    <doi:10.1080/00273171.2011.561068> and approximate casewise influence
    using scores and casewise likelihood. An introduction to
    the package can be found in Cheung and Lai (2026)
    <doi:10.1080/00273171.2026.2634293>.
A framework for extracting semantic motifs around entities in textual data. It implements an entity-centered semantic grammar that distinguishes six classes of motifs: actions of an entity, treatments of an entity, agents acting upon an entity, patients acted upon by an entity, characterizations of an entity, and possessions of an entity. Motifs are identified by applying a set of extraction rules to a parsed text object that includes part-of-speech tags and dependency annotations - such as those generated by 'spacyr'. For further reference, see: Stuhler (2022) <doi: 10.1177/00491241221099551>.
Estimate networks and causal relationships in complex systems through
  Structural Equation Modeling. This package also includes functions for importing,
  weight, manipulate, and fit biological network models within the
  Structural Equation Modeling framework as outlined in the Supplementary Material of
  Grassi M, Palluzzi F, Tarantino B (2022) <doi:10.1093/bioinformatics/btac567>.
An assortment of helper functions for doing structural equation
  modeling, mainly by 'lavaan' for now. Most of them are time-saving functions
  for common tasks in doing structural equation modeling and reading the
  output. This package is not for functions that implement advanced statistical
  procedures. It is a light-weight package for simple functions that do simple
  tasks conveniently, with as few dependencies as possible.
Contains methods to generate and evaluate semi-artificial data sets. 
 Based on a given data set different methods learn data properties using machine learning algorithms and
 generate new data with the same properties.
 The package currently includes the following data generators:
  i) a RBF network based generator using rbfDDA() from package 'RSNNS',
  ii) a Random Forest based generator for both classification and regression problems
  iii) a density forest based generator for unsupervised data
 Data evaluation support tools include:
  a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance
  b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM
  c) evaluation based on classification performance with various learning models, e.g., random forests.
Use inverse probability weighting methods to estimate treatment effect under marginal structure model (MSM) for the transition hazard of semi competing risk data, i.e. illness death model. We implement two specific such models, the usual Markov illness death structural model and the general Markov illness death structural model. We also provide the predicted three risks functions from the marginal structure models. Zhang, Y. and Xu, R. (2022) <arXiv:2204.10426>.
Hierarchical multistate models are considered to perform the analysis of independent/clustered semi-competing risks data. The package allows to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions and cluster-specific random effects distribution; a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation approach for several parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.
Implements Multivariate ANalysis Of VAriance (MANOVA) parameters' inference and test with regularization for semicontinuous high-dimensional data. The method can be applied also in presence of low-dimensional data. The p-value can be obtained through asymptotic distribution or using a permutation procedure. The package gives also the possibility to simulate this type of data. Method is described in Elena Sabbioni, Claudio Agostinelli and Alessio Farcomeni (2025) A regularized MANOVA test for semicontinuous high-dimensional data. Biometrical Journal, 67:e70054. DOI <doi:10.1002/bimj.70054>, arXiv DOI <doi:10.48550/arXiv.2401.04036>.
Provides routines to check identifiability of linear structural 
    equation models and factor analysis models. The routines are based on the 
    graphical representation of structural equation models.
Semi-distance and mean-variance (MV) index are proposed to measure the dependence between a categorical random variable and a continuous variable.
    Test of independence and feature screening for classification problems can be implemented via the two dependence measures.
    For the details of the methods, see Zhong et al. (2023) <doi:10.1080/01621459.2023.2284988>;
    Cui and Zhong (2019) <doi:10.1016/j.csda.2019.05.004>;
    Cui, Li and Zhong (2015) <doi:10.1080/01621459.2014.920256>.
Functions for fitting multi-state semi-Markov models to longitudinal data. A parametric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Exponentiated Weibull distributions is considered. Right-censoring can be taken into account and both constant and time-varying covariates can be included using a Cox proportional model. Reference: A. Krol and P. Saint-Pierre (2015) 	<doi:10.18637/jss.v066.i06>.
A powerful, easy to use syntax for specifying and estimating complex 
    Structural Equation Models. Models can be estimated using Partial 
    Least Squares Path Modeling or Covariance-Based Structural Equation 
    Modeling or covariance based Confirmatory Factor Analysis (Ray, Danks, and Valdez 2021 <doi:10.2139/ssrn.3900621>).
Supplemental functions for estimating and analysing structural equation models including Cross Validated Prediction and Testing (CVPAT, Liengaard et al., 2021 <doi:10.1111/deci.12445>).
Copula based Cox proportional hazards models for survival data subject to dependent 
    censoring. This approach does not assume that the parameter defining the copula is known. The 
    dependency parameter is estimated with other finite model parameters by maximizing a Pseudo 
    likelihood function. The cumulative hazard function is estimated via estimating equations 
    derived based on martingale ideas. Available copula functions include Frank, Gumbel and Normal
    copulas. Only Weibull and lognormal models are allowed for the censoring model, even though any
    parametric model that satisfies certain identifiability conditions could be used. Implemented 
    methods are described in the article "Copula based Cox proportional hazards models for dependent
    censoring" by Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>.
Fits a semiparametric spatiotemporal model for data with mixed frequencies,
    specifically where the response variable is observed at a lower frequency than some covariates.
    The estimation uses an iterative backfitting algorithm that combines a non-parametric smoothing spline
    for high-frequency data, parametric estimation for low-frequency and spatial neighborhood effects,
    and an autoregressive error structure. Methodology based on Malabanan, Lansangan, and Barrios (2022)
    <https://scienggj.org/2022/SciEnggJ%202022-vol15-no02-p90-107-Malabanan%20et%20al.pdf>.
Forms likelihood-based confidence intervals
    (LBCIs) for parameters in structural equation modeling,
    introduced in Cheung and Pesigan (2023)
    <doi:10.1080/10705511.2023.2183860>. Currently
    implements the algorithm illustrated by Pek and Wu
    (2018) <doi:10.1037/met0000163>, and supports the robust
    LBCI proposed by Falk (2018)
    <doi:10.1080/10705511.2017.1367254>.
Computes likelihood ratio test (LRT) p-values
  for free parameters in a structural equation model.
  Currently supports models fitted by the 'lavaan' package by
  Rosseel (2012) <doi:10.18637/jss.v048.i02>.
Monte Carlo confidence intervals for free and defined parameters
    in models fitted in the structural equation modeling package 'lavaan'
    can be generated using the 'semmcci' package.
    'semmcci' has three main functions, namely, MC(), MCMI(), and MCStd().
    The output of 'lavaan' is passed as the first argument
    to the MC() function or the MCMI() function to generate Monte Carlo confidence intervals.
    Monte Carlo confidence intervals for the standardized estimates
    can also be generated by passing the output of the MC() function or the MCMI() function
    to the MCStd() function.
    A description of the package and code examples
    are presented in Pesigan and Cheung (2024) <doi:10.3758/s13428-023-02114-4>.
Provides Markov Chain Monte Carlo (MCMC) routine for the 
             structural equation modelling described in 
             Maity et. al. (2020) <doi:10.1093/bioinformatics/btaa286>. This MCMC sampler is 
             useful when one attempts to perform an integrative survival analysis for multiple 
             platforms of the Omics data where the response is time to event and the 
             predictors are different omics expressions for different platforms.
Provides methods for constructing and maintaining a database of presentations in R. The presentations are either ones that the user gives or gave or presentations at a particular event or event series. The package also provides a plot method for the interactive mapping of the presentations using 'leaflet' by grouping them according to country, city, year and other presentation attributes. The markers on the map come with popups providing presentation details (title, institution, event, links to materials and events, and so on).
Implements several functions for the analysis of semantic networks including different network estimation algorithms, partial node bootstrapping (Kenett, Anaki, & Faust, 2014 <doi:10.3389/fnhum.2014.00407>), random walk simulation (Kenett & Austerweil, 2016 <http://alab.psych.wisc.edu/papers/files/Kenett16CreativityRW.pdf>), and a function to compute global network measures. Significance tests and plotting features are also implemented.
Implements several functions that automates the cleaning and spell-checking of text data. Also converges, finalizes, removes plurals and continuous strings, and puts text data in binary format for semantic network analysis. Uses the 'SemNetDictionaries' package to make the cleaning process more accurate, efficient, and reproducible.
Implements dictionaries that can be used in the 'SemNetCleaner' package. Also includes several functions aimed at facilitating the text cleaning analysis in the 'SemNetCleaner' package. This package is designed to integrate and update word lists and dictionaries based on each user's individual needs by allowing users to store and save their own dictionaries. Dictionaries can be added to the 'SemNetDictionaries' package by submitting user-defined dictionaries to <https://github.com/AlexChristensen/SemNetDictionaries>.
Latent repeated measures ANOVA (L-RM-ANOVA) is a structural 
    equation modeling based alternative to traditional repeated measures ANOVA.
    L-RM-ANOVA extends the latent growth components approach by 
    Mayer et al. (2012) <doi:10.1080/10705511.2012.713242> and introduces
    latent variables to repeated measures analysis.
Path diagrams and visual analysis of various SEM packages' output.
Provides a-priori, post-hoc, and compromise power-analyses
    for structural equation models (SEM).
Most function focus on specific ways to customize a graph.
             They use a 'qgraph' output  as the first argument, and return a
             modified 'qgraph' object. This allows the functions to be chained
             by a pipe operator.
Perform sensitivity analysis in structural equation modeling using 
    meta-heuristic optimization methods (e.g., ant colony optimization and others). 
    The references for the proposed methods are: 
    (1) Leite, W., & Shen, Z., Marcoulides, K., Fish, C., & Harring, J. (2022). 
    <doi:10.1080/10705511.2021.1881786>
    (2) Harring, J. R., McNeish, D. M., & Hancock, G. R. (2017)
    <doi:10.1080/10705511.2018.1506925>;
    (3) Fisk, C., Harring, J., Shen, Z., Leite, W., Suen, K., & Marcoulides, K. 
    (2022). <doi:10.1177/00131644211073121>;
    (4) Socha, K., & Dorigo, M. (2008) <doi:10.1016/j.ejor.2006.06.046>.
    We also thank Dr. Krzysztof Socha for sharing his research on ant colony optimization
    algorithm with continuous domains and associated R code,
    which provided the base for the development of this package.
Performs sensitivity analysis for Structural Equation Modeling (SEM). It determines which sample points need to be removed for the sign of a specific path in the SEM model to change, thus assessing the robustness of the model. Methodological manuscript in preparation.
Semiparametric Estimation of Stochastic Frontier Models following a two step procedure: in the first step semiparametric or nonparametric regression techniques are used to relax parametric restrictions of the functional form representing technology and in the second step variance parameters are obtained by pseudolikelihood estimators or by method of moments.
Supports eigenvalue block-averaging p-values (Foldnes, Grønneberg, 2018) <doi:10.1080/10705511.2017.1373021>,
    penalized eigenvalue block-averaging p-values (Foldnes, Moss, Grønneberg, 2024) <doi:10.1080/10705511.2024.2372028>, penalized
    regression p-values (Foldnes, Moss, Grønneberg, 2024) <doi:10.1080/10705511.2024.2372028>, as well as traditional p-values such as Satorra-Bentler. All p-values can
    be calculated using unbiased or biased gamma estimates (Du, Bentler, 2022) <doi:10.1080/10705511.2022.2063870> 
    and two choices of chi square statistics.
Provides miscellaneous tools for structural equation modeling, 
             many of which extend the 'lavaan' package. For example, latent
             interactions can be estimated using product indicators (Lin et al.,
             2010, <doi:10.1080/10705511.2010.488999>) and simple effects probed;
             analytical power analyses can be conducted (Jak et al., 2021,
             <doi:10.3758/s13428-020-01479-0>); and scale reliability
             can be estimated based on estimated factor-model parameters.
SEM Trees and SEM Forests -- an extension of model-based decision
    trees and forests to Structural Equation Models (SEM). SEM trees hierarchically
    split empirical data into homogeneous groups each sharing similar data patterns
    with respect to a SEM by recursively selecting optimal predictors of these
    differences. SEM forests are an extension of SEM trees. They are ensembles of
    SEM trees each built on a random sample of the original data. By aggregating
    over a forest, we obtain measures of variable importance that are more robust
    than measures from single trees. A description of the method was published by
    Brandmaier, von Oertzen, McArdle, & Lindenberger (2013) <doi:10.1037/a0030001> 
    and Arnold, Voelkle, & Brandmaier (2020) <doi:10.3389/fpsyg.2020.564403>.
Tools and functions for parsing, rendering and operating on
    semantic version strings. Semantic versioning is a simple set of rules
    and requirements that dictate how version numbers are assigned and
    incremented as outlined at <https://semver.org>.
Send email using 'Sendgrid' <https://sendgrid.com/> 
            mail API(v3) <https://docs.sendgrid.com/api-reference/how-to-use-the-sendgrid-v3-api/authentication>.
A system enables cross study Analysis by extracting and filtering study data for 
    control animals from  'CDISC' 'SEND' Study Repository. 
    These data types are supported: Body Weights, Laboratory test results and Microscopic findings. 
    These database types are supported: 'SQLite' and 'Oracle'.
Package contains a simple SMTP client with minimal dependencies which 
        provides a portable solution for sending email, including file attachments and inline html reports, 
        from within R. SMTP Authentication and SSL/STARTTLS is implemented using curl.
Stacked ensemble for regression tasks based on 'mlr3' framework with a pipeline for preprocessing numeric and factor features and hyper-parameter tuning using grid or random search.
Implements a suite of sensitivity analysis tools 
  that extends the traditional omitted variable bias framework and makes it easier 
  to understand the impact of omitted variables in regression models, as discussed in Cinelli, C. and Hazlett, C. (2020), "Making Sense of Sensitivity: Extending Omitted Variable Bias." Journal of the Royal Statistical Society, Series B (Statistical Methodology) <doi:10.1111/rssb.12348>.
Provides statistical diagnostics to evaluate whether proxy indicators reliably represent an unobservable target construct. The main function 'senser()' assesses proxies across multiple dimensions including monotonicity, information content, stability, distributional alignment, and potential bias risk. It prints a concise, interpretable summary suitable for analytical and policy-oriented assessment, without claiming causal inference.
Provides tools to conduct interpretable sensitivity analyses for weighted estimators,
    introduced in Huang (2024) <doi:10.1093/jrsssa/qnae012> and 
    Hartman and Huang (2024) <doi:10.1017/pan.2023.12>. 
    The package allows researchers to generate the set of recommended sensitivity summaries
    to evaluate the sensitivity in their underlying weighting estimators to omitted moderators or confounders. 
    The tools can be flexibly applied in causal inference settings (i.e., in external and internal validity contexts)
    or survey contexts.
Sensitivity analysis for trials with irregular and informative 
    assessment times, based on a new influence function-based, augmented 
    inverse intensity-weighted estimator.
Provides an interface to the 'Sensibo Sky' API which allows to remotely control non-smart air conditioning units.
  See <https://sensibo.com> for more informations.
An implementation of sensitivity analysis for phylogenetic comparative
 methods. The package is an umbrella of statistical and graphical methods that 
 estimate and report different types of uncertainty in PCM:
 (i) Species Sampling uncertainty (sample size; influential species and clades).
 (ii) Phylogenetic uncertainty (different topologies and/or branch lengths).
 (iii) Data uncertainty (intraspecific variation and measurement error).
A collection of functions for sensitivity analysis of model outputs (factor screening, global sensitivity analysis and robustness analysis), for variable importance measures of data, as well as for interpretability of machine learning models. Most of the functions have to be applied on scalar output, but several functions support multi-dimensional outputs.
Performs exact or approximate adaptive or nonadaptive Cochran-Mantel-Haenszel-Birch tests and sensitivity analyses for one or two 2x2xk tables in observational studies.
Implements the calibrated sensitivity analysis approach for matched observational studies. Our sensitivity analysis framework views matched sets as drawn from a super-population. The unmeasured confounder is modeled as a random variable. We combine matching and model-based covariate-adjustment methods to estimate the treatment effect. The hypothesized unmeasured confounder enters the picture as a missing covariate. We adopt a state-of-art Expectation Maximization (EM) algorithm to handle this missing covariate problem in generalized linear models (GLMs). As our method also estimates the effect of each observed covariate on the outcome and treatment assignment, we are able to calibrate the unmeasured confounder to observed covariates.
      Zhang, B., Small, D. S. (2018). <arXiv:1812.00215>.
Sensitivity analysis for case-control studies in which some cases may meet a more narrow definition of being a case compared to other cases which only meet a broad definition.  The sensitivity analyses are described in Small, Cheng, Halloran and Rosenbaum (2013, "Case Definition and Sensitivity Analysis", Journal of the American Statistical Association, 1457-1468).  The functions sens.analysis.mh and sens.analysis.aberrant.rank provide sensitivity analyses based on the Mantel-Haenszel test statistic and aberrant rank test statistic as described in Rosenbaum (1991, "Sensitivity Analysis for Matched Case Control Studies", Biometrics); see also Section 1 of Small et al.  The function adaptive.case.test provides adaptive inferences as described in Section 5 of Small et al.  The function adaptive.noether.brown provides a sensitivity analysis for a matched cohort study based on an adaptive test.  The other functions in the package are internal functions.
Sensitivity to unmeasured biases in an observational study that is a full match.  Function senfm() performs tests and function senfmCI() creates confidence intervals.  The method uses Huber's M-statistics, including least squares, and is described in Rosenbaum (2007, Biometrics) <DOI:10.1111/j.1541-0420.2006.00717.x>.
Implements exact, normally approximated, and sampling-based sensitivity 
    analysis for observational studies with contingency tables. Includes exact 
    (kernel-based), normal approximation, and sequential importance sampling (SIS) 
    methods using 'Rcpp' for computational efficiency. The methods build upon the 
    framework introduced in Rosenbaum (2002) <doi:10.1007/978-1-4757-3692-2> and 
    the generalized design sensitivity framework developed by Chiu (2025) 
    <doi:10.48550/arXiv.2507.17207>.
Sensitivity analysis for multiple outcomes in observational studies.  For instance, all linear combinations of several outcomes may be explored using Scheffe projections in the comparison() function; see Rosenbaum (2016, Annals of Applied Statistics) <doi:10.1214/16-AOAS942>.  Alternatively, attention may focus on a few principal components in the principal() function.  The package includes parallel methods for individual outcomes, including tests in the senm() function and confidence intervals in the senmCI() function.
The package performs a sensitivity analysis in an observational study using an M-statistic, for instance, the mean.  The main function in the package is senmv(), but amplify() and truncatedP() are also useful.  The method is developed in Rosenbaum Biometrics, 2007, 63, 456-464, <doi:10.1111/j.1541-0420.2006.00717.x>.
Sensitivity analysis for tests, confidence intervals and estimates in matched observational studies with one or more controls using weighted or unweighted Huber-Maritz M-tests (including the permutational t-test).   The method is from Rosenbaum (2014) Weighted M-statistics with superior design sensitivity in matched observational studies with multiple controls JASA, 109(507), 1145-1158 <doi:10.1080/01621459.2013.879261>.
Provides Sensory and Consumer Data 
    mapping and analysis <doi:10.14569/IJACSA.2017.081266>. The mapping visualization is made available
    from several features : options in dimension reduction methods and prediction models ranging from 
    linear to non linear regressions. A smoothed version of the map performed using locally weighted regression algorithm 
    is available. A selection process of map stability is provided. A 'shiny' application is included. 
    It presents an easy GUI for the implemented functions as well as a comparative tool of fit models
    using several criteria. Basic analysis such as characterization of products,
    panelists and sessions likewise consumer segmentation are also made available.
We implement functions to estimate and perform sensitivity analysis to unobserved confounding of direct and indirect effects introduced in Lindmark, de Luna and Eriksson (2018) <doi:10.1002/sim.7620> and Lindmark (2022) <doi:10.1007/s10260-021-00611-4>. The estimation and sensitivity analysis are parametric, based on probit and/or linear regression models. Sensitivity analysis is implemented for unobserved confounding of the exposure-mediator, mediator-outcome and exposure-outcome relationships.
It allows to rapidly compute, bootstrap and plot up to fourth-order Sobol'-based sensitivity indices using several state-of-the-art first and total-order estimators. Sobol' indices can be computed either for models that yield a scalar as a model output or for systems of differential equations. The package also provides a suit of benchmark tests functions and several options to obtain publication-ready figures of the model output uncertainty and sensitivity-related analysis. An overview of the package can be found in Puy et al. (2022) <doi:10.18637/jss.v102.i05>.
Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008).
Interface to the 'Sensor Tower' API <https://app.sensortower.com/api/docs/app_analysis> 
    for mobile app analytics and market intelligence. Provides functions to retrieve 
    app metadata, publisher information, download and revenue estimates, active user 
    metrics, category rankings, and market trends. The package includes data processing 
    utilities to clean and aggregate metrics across platforms, automatic app name 
    resolution, and tools for generating professional analytics dashboards. Supports 
    both iOS and Android app ecosystems with unified data structures for cross-platform 
    analysis.
Contains the function CUUimpute() which performs model-based clustering and imputation simultaneously.
Perform biomarker evaluation and comparison in terms of specificity at a controlled sensitivity level, or sensitivity at a controlled specificity level. Point estimation and exact bootstrap of Huang, Parakati, Patil, and Sanda (2023) <doi:10.5705/ss.202021.0020> for the one- and two-biomarker problems are implemented.
Provides methods for sensory discrimination methods;
  duotrio, tetrad, triangle, 2-AFC, 3-AFC, A-not A, same-different,
  2-AC and degree-of-difference.
  This enables the calculation of d-primes, standard errors of
  d-primes, sample size and power computations, and
  comparisons of different d-primes. Methods for profile likelihood
  confidence intervals and plotting are included. Most methods are 
  described in Brockhoff, P.B. and Christensen, R.H.B. (2010) 
  <doi:10.1016/j.foodqual.2009.04.003>.
Collection of datasets from Sen & Srivastava: "Regression
        Analysis, Theory, Methods and Applications", Springer.  Sources
        for individual data files are more fully documented in the
        book.
Sensitivity analysis in unmatched observational studies, with or without strata.  The main functions are sen2sample() and senstrat().  See Rosenbaum, P. R. and Krieger, A. M. (1990), JASA, 85, 493-498, <doi:10.1080/01621459.1990.10476226> and Gastwirth, Krieger and Rosenbaum (2000), JRSS-B, 62, 545–555 <doi:10.1111/1467-9868.00249> .
Unsupervised text tokenizer allowing to perform byte pair encoding and unigram modelling. 
    Wraps the 'sentencepiece' library <https://github.com/google/sentencepiece> which provides a language independent tokenizer to split text in words and smaller subword units. 
    The techniques are explained in the paper "SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing" by Taku Kudo and John Richardson (2018) <doi:10.18653/v1/D18-2012>.
    Provides as well straightforward access to pretrained byte pair encoding models and subword embeddings trained on Wikipedia using 'word2vec', 
    as described in "BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages" by Benjamin Heinzerling and Michael Strube (2018) <http://www.lrec-conf.org/proceedings/lrec2018/pdf/1049.pdf>.
Sentiment Analysis via deep learning and gradient boosting models with a lot of the underlying hassle taken care of to make the process as simple as possible. 
  In addition to out-performing traditional, lexicon-based sentiment analysis (see <https://benwiseman.github.io/sentiment.ai/#Benchmarks>),
  it also allows the user to create embedding vectors for text which can be used in other analyses.
  GPU acceleration is supported on Windows and Linux.
Performs a sentiment analysis of textual contents in R. This implementation
    utilizes various existing dictionaries, such as Harvard IV, or finance-specific 
    dictionaries. Furthermore, it can also create customized dictionaries. The latter 
    uses LASSO regularization as a statistical approach to select relevant terms based on 
    an exogenous response variable.
Calculate text polarity sentiment at the sentence level and
         optionally aggregate by rows or grouping variable(s).
Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) <doi:10.18637/jss.v099.i02>.
A framework that joins topic modeling and sentiment analysis of
  textual data. The package implements a fast Gibbs sampling estimation of
  Latent Dirichlet Allocation (Griffiths and Steyvers (2004)
  <doi:10.1073/pnas.0307752101>) and Joint Sentiment/Topic Model (Lin, He,
  Everson and Ruger (2012) <doi:10.1109/TKDE.2011.48>). It offers a variety of
  helpers and visualizations to analyze the result of topic modeling. The
  framework also allows enriching topic models with dates and externally
  computed sentiment measures. A flexible aggregation scheme enables the
  creation of time series of sentiment or topical proportions from the enriched
  topic models. Moreover, a novel method jointly aggregates topic proportions
  and sentiment measures to derive time series of topical sentiment.
Unofficial client for 'Sentry' <https://sentry.io>,
  a self-hosted or cloud-based error-monitoring service. It will inform about
  errors in real-time, and includes integration with the 'Plumber' package.
To calculate the standard error of measurement (SEM) to assess the observer variability (inter- and intra-observer variation).
    The methods used in this package are referenced from Zoran B. Popović (2017) <doi:10.21037/cdt.2017.03.12>.
Implements the Segment Profile Extraction via Pattern
  Analysis method for row-mean-centered multivariate data. Core
  capabilities include SVD-based row-isometric biplot
  construction, bias-corrected and accelerated, and percentile
  bootstrap confidence intervals for domain coordinates and
  per-person direction cosines, Procrustes alignment of bootstrap
  replicates across planes, parallel analysis for dimensionality
  selection, and segment profile reconstruction in planes defined
  by pairs of singular dimensions. A synthetic Woodcock-Johnson IV
  look-alike dataset is provided for examples and testing. The
  method is described in Kim and Grochowalski (2019)
  <doi:10.1007/s00357-018-9277-7>.
Regression context for the Partial Least Squares framework for 
    Extreme values. Estimations of the Shrinkage for Extreme Partial Least-Squares 
    (SEPaLS) estimators, an adaptation of the original Partial Least Squares 
    (PLS) method tailored to the extreme-value framework.
    The SEPaLS project is a joint work by Stephane Girard, Hadrien Lorenzo and 
    Julyan Arbel.
    R code to replicate the results of the paper is available at 
    <https://github.com/hlorenzo/SEPaLS_simus>.
    Extremes within PLS was already studied by one of the authors, see M 
    Bousebeta, G Enjolras, S Girard (2023) <doi:10.1016/j.jmva.2022.105101>.
Maximum likelihood estimation of the parameters
    of matrix and 3rd-order tensor normal distributions with unstructured
    factor variance covariance matrices, two procedures, and for unbiased
    modified likelihood ratio testing of simple and double separability
    for variance-covariance structures, two procedures. References:
    Dutilleul P. (1999) <doi:10.1080/00949659908811970>, 
    Manceur AM, Dutilleul P. (2013)
    <doi:10.1016/j.cam.2012.09.017>, and Manceur AM, Dutilleul 
    P. (2013) <doi:10.1016/j.spl.2012.10.020>.
Visual representations of model fit or predictive success in the form of "separation plots."  See Greenhill, Brian, Michael D. Ward, and Audrey Sacks. "The separation plot: A new visual method for evaluating the fit of binary models." American Journal of Political Science 55.4 (2011): 991-1002.
Stratigraphic ranges of fossil marine animal genera from Sepkoski's
    (2002) published compendium. No changes have been made to any taxonomic 
    names. However, first and last appearance intervals have been updated to be
    consistent with stages of the International Geological Timescale. 
    Functionality for generating a plot of Sepkoski's evolutionary fauna is also
    included. For specific details on the compendium see:
    Sepkoski, J. J. (2002). A compendium of fossil marine animal genera.
    Bulletins of American Paleontology, 363, pp. 1–560 (ISBN 0-87710-450-6).
    Access: <https://www.biodiversitylibrary.org/item/40634#page/5/mode/1up>.
Provides statistical tools for testing first-order separability in spatio-temporal point processes, that is, assessing whether the spatio-temporal intensity function can be expressed as the product of spatial and temporal components. The package implements several hypothesis tests, including exact and asymptotic methods for Poisson and non-Poisson processes. Methods include global envelope tests, chi-squared type statistics, and a novel 
 Hilbert-Schmidt independence criterion (HSIC) test, all with both block and pure permutation procedures. Simulation studies and real world examples, including the 2001 UK foot and mouth disease outbreak data, illustrate the utility of the proposed methods. The package contains all simulation studies and applications presented in Ghorbani et al. (2021) <doi:10.1016/j.csda.2021.107245> and Ghorbani et al. (2025) <doi:10.1007/s11749-025-00972-y>.
This software is useful for loading '.fasta' or '.gbk' files, and for retrieving sequences from 'GenBank' dataset <https://www.ncbi.nlm.nih.gov/genbank/>. This package allows  to detect differences or asymmetries based on nucleotide composition by using local linear kernel smoothers. Also, it is possible to draw inference about critical points (i. e. maximum or minimum points) related with the derivative curves. Additionally, bootstrap methods have been used  for estimating confidence intervals and speed computational techniques (binning techniques) have been implemented in 'seq2R'.
Computes the optimal alignment of two character sequences.
    Visualizes the result of the alignment in a matrix plot.
    Needleman, Saul B.; Wunsch, Christian D. (1970) "A general method applicable to the search for similarities in the amino acid sequence of two proteins" <doi:10.1016/0022-2836(70)90057-4>.
Potential randomization schemes are prospectively evaluated when
    units are assigned to treatment arms upon entry into the experiment. The schemes
    are evaluated for balance on covariates and on predictability (i.e., how well
    could a site worker guess the treatment of the next unit enrolled).
A modification of the preventive vaccine efficacy trial design of Gilbert, Grove et al. (2011, Statistical Communications in Infectious Diseases) is implemented, with application generally to individual-randomized clinical trials with multiple active treatment groups and a shared control group, and a study endpoint that is a time-to-event endpoint subject to right-censoring. The design accounts for the issues that the efficacy of the treatment/vaccine groups may take time to accrue while the multiple treatment administrations/vaccinations are given; there is interest in assessing the durability of treatment efficacy over time; and group sequential monitoring of each treatment group for potential harm, non-efficacy/efficacy futility, and high efficacy is warranted. The design divides the trial into two stages of time periods, where each treatment is first evaluated for efficacy in the first stage of follow-up, and, if and only if it shows significant treatment efficacy in stage one, it is evaluated for longer-term durability of efficacy in stage two. The package produces plots and tables describing operating characteristics of a specified design including an unconditional power for intention-to-treat and per-protocol/as-treated analyses; trial duration; probabilities of the different possible trial monitoring outcomes (e.g., stopping early for non-efficacy); unconditional power for comparing treatment efficacies; and distributions of numbers of endpoint events occurring after the treatments/vaccinations are given, useful as input parameters for the design of studies of the association of biomarkers with a clinical outcome (surrogate endpoint problem). The code can be used for a single active treatment versus control design and for a single-stage design.
Provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.
Generates/modifies RNA-seq data for use in simulations. We provide
    a suite of functions that will add a known amount of signal to a real 
    RNA-seq dataset. The advantage of using this approach over simulating under
    a theoretical distribution is that common/annoying aspects of the data
    are more preserved, giving a more realistic evaluation of your method. 
    The main functions are select_counts(), thin_diff(), thin_lib(), 
    thin_gene(), thin_2group(), thin_all(), and effective_cor(). See
    Gerard (2020) <doi:10.1186/s12859-020-3450-9> for details on the
    implemented methods.
It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) <doi:10.1177/0049124115591013>), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) <doi:10.1177/0081175015570976>), smoothing sequences for index plots (see Piccarreta (2012) <doi:10.1177/0049124112452394>), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) <doi:10.1111/j.1467-985X.2009.00606.x>), symmetrical (or canonical) Partial Least Squares (see Bry (1996)).
Designed for estimating variants of hidden (latent) Markov models
    (HMMs), mixture HMMs, and non-homogeneous HMMs (NHMMs) for social sequence
    data and other categorical time series. Special cases include
    feedback-augmented NHMMs, Markov models without latent layer, mixture
    Markov models, and latent class models. The package supports models for one
    or multiple subjects with one or multiple parallel sequences (channels).
    External covariates can be added to explain cluster membership in mixture 
    models as well as initial, transition and emission probabilities in NHMMs.
    The package provides functions for evaluating and comparing models, as well
    as functions for visualizing of multichannel sequence data and HMMs. For
    NHMMs, methods for computing average causal effects and marginal state and
    emission probabilities are available. Models are estimated using maximum
    likelihood via the EM algorithm or direct numerical maximization with
    analytical gradients. Documentation is available via several vignettes,
    and Helske and Helske (2019, <doi:10.18637/jss.v088.i03>). For methodology
    behind the NHMMs, see Helske (2025, <doi:10.48550/arXiv.2503.16014>).
Contains an implementation of invariant causal prediction for sequential data. The main function in the package is 'seqICP', which performs linear sequential invariant causal prediction and has guaranteed type I error control. For non-linear dependencies the package also contains a non-linear method 'seqICPnl', which allows to input any regression procedure and performs tests based on a permutation approach that is only approximately correct. In order to test whether an individual set S is invariant the package contains the subroutines 'seqICP.s' and 'seqICPnl.s' corresponding to the respective main methods.
Multiple imputation of missing data in a dataset using MICT or 
    MICT-timing methods. The core idea of the algorithms is to fill gaps of 
    missing data, which is the typical form of missing data in a longitudinal 
    setting, recursively from their edges. Prediction is based on either a 
    multinomial or random forest regression model. Covariates and 
    time-dependent covariates can be included in the model.
Exploratory data analysis and data visualization
  for biological sequence (DNA and protein) data. Seqinr includes
  utilities for sequence data management under the ACNUC system
  described in Gouy, M. et al. (1984) Nucleic Acids Res.
  12:121-127 <doi:10.1093/nar/12.1Part1.121>.
Kataegis is a localized hypermutation occurring when a region is enriched in somatic SNVs. Kataegis can result from multiple cytosine deaminations catalyzed by the AID/APOBEC family of proteins. This package contains functions to detect kataegis from SNVs in BED format. This package reports two scores per kataegic event, a hypermutation score and an APOBEC mediated kataegic score. Yousif, F. et al.; The Origins and Consequences of Localized and Global Somatic Hypermutation; Biorxiv 2018 <doi:10.1101/287839>.
A network module-based generalized linear model for differential expression analysis with the count-based sequence data from RNA-Seq.
Tools for reading and writing biological sequences in multiple
    formats, including 'FASTA', 'PHYLIP', 'CLUSTAL', 'STOCKHOLM', 'MEGA' and
    'GenBank'. Supports interleaved and sequential layouts where applicable,
    converts between formats, and manipulates sequence sets (e.g., filtering by
    patterns and computing consensus sequences from alignments). Also includes
    functions to download nucleotide records from NCBI by accession.
Integrate sequencing data (Variant call format, e.g. VCF or BCF) or meta-analysis results in R. This package can help you (1) read VCF/BCF/BGEN files by chromosomal ranges (e.g. 1:100-200); (2) read 'RareMETAL' summary statistics files; (3) read tables from a 'tabix'-indexed files; (4) annotate VCF/BCF files; (5) create customized workflow based on Makefile.
S4 class object for creating and managing group sequential designs. It calculates the efficacy and futility boundaries at each look. It allows modifying the design and tracking the design update history.
Based on the structure of the SPSS version of the Swiss
    Household Panel (SHP) data, provides a function seqFromWaves()
    that seeks the data of variables specified by the user in each 
    of the wave files and collects them as sequences. The function also 
    matches the sequences with variables from other files such as the 
    master files of persons (MP) and households (MH), and social origins (SO). 
    It can also match with activity calendar data (CA).
Implementation of sequential trial emulation for the analysis of observational databases.
    The 'SEQTaRget' software accommodates time-varying treatments and confounders, as well as binary
    and failure time outcomes. 'SEQTaRget' allows to compare both static and dynamic strategies,
    can be used to estimate observational analogs of intention-to-treat
    and per-protocol effects, and can adjust for potential selection bias
    induced by losses-to-follow-up. (Paper to come).
Sequential triangular test for the arithmetic mean in one- and two-
    samples, proportions in one- and two-samples, and the Pearson's correlation
    coefficient.
A collection of Radix Tree and Trie algorithms for finding similar sequences and calculating sequence distances (Levenshtein and other distance metrics). This work was inspired by a trie implementation in Python: "Fast and Easy Levenshtein distance using a Trie." Hanov (2011) <https://stevehanov.ca/blog/index.php?id=114>.
A rudimentary sequencer to define, manipulate and mix sound samples.
    The underlying motivation is to sonify data, as demonstrated in the blog <https://globxblog.github.io/>, the presentation by Renard and Le Bescond (2022, <https://hal.science/hal-03710340v1>) or the poster by Renard et al. (2023, <https://hal.inrae.fr/hal-04388845v1>).
Contains fast functions to calculate the exact Bayes posterior
    for the Sparse Normal Sequence Model, implementing the algorithms
    described in Van Erven and Szabo (2021,
    <doi:10.1214/20-BA1227>). For general hierarchical
    priors, sample sizes up to 10,000 are feasible within half an hour
    on a standard laptop. For beta-binomial spike-and-slab priors, a
    faster algorithm is provided, which can handle sample sizes of
    100,000 in half an hour. In the implementation, special care has
    been taken to assure numerical stability of the methods even for
    such large sample sizes.
Functions to calculate exact critical values, statistical power, expected time to signal, and required sample sizes for performing exact sequential analysis. All these	calculations can be done for either Poisson or binomial data, for continuous or group sequential analyses, and for different types of rejection boundaries. In case of group sequential analyses, the group sizes do not have to be specified in advance and the alpha spending can be arbitrarily settled. For regression versions of the methods, Monte Carlo and asymptotic methods are used.
In population management, data come at more or less regular intervals over time in sampling batches (bouts) and decisions should be made with the minimum number of samples and as quickly as possible. This package provides tools to implement, produce charts with stop lines, summarize results and assess sequential analyses that test hypotheses about population sizes. Two approaches are included: the sequential test of Bayesian posterior probabilities (Rincon, D.F. et al. 2025 <doi:10.1111/2041-210X.70053>), and the sequential probability ratio test (Wald, A. 1945 <http://www.jstor.org/stable/2235829>).
Functions to be used in conjunction with the 'Sequential' package that allows for planning of observational database studies that will be analyzed with exact sequential analysis. This package supports Poisson- and binomial-based data. The primary function, seq_wrapper(...), accepts parameters for simulation of a simple exposure pattern and for the 'Sequential' package setup and analysis functions. The exposure matrix is used to simulate the true and false positive and negative populations (Green (1983) <doi:10.1093/oxfordjournals.aje.a113521>, Brenner (1993) <doi:10.1093/oxfordjournals.aje.a116805>). Functions are then run from the 'Sequential' package on these populations, which allows for the exploration of outcome misclassification in data.
Multi-generational pedigree inference from incomplete data on
    hundreds of SNPs, including parentage assignment and sibship clustering.
    See Huisman (2017) (<DOI:10.1111/1755-0998.12665>) for more information.
Apache Drill is a low-latency distributed query engine designed to enable 
    data exploration and analysis on both relational and non-relational data stores, 
    scaling to petabytes of data. Methods are provided that enable working with Apache 
    Drill instances via the REST API, DBI methods
    and using 'dplyr'/'dbplyr' idioms. Helper functions are included to facilitate
    using official Drill Docker images/containers.
Enables reading and writing binary and ASCII data to 
             RS232/RS422/RS485 or any other virtual serial interface of the
             computer.
Infrastructure for ordering objects with an implementation of several
    seriation/sequencing/ordination techniques to reorder matrices, dissimilarity
    matrices, and dendrograms. Also provides (optimally) reordered heatmaps,
    color images and clustering visualizations like dissimilarity plots, and
    visual assessment of cluster tendency plots (VAT and iVAT). Hahsler et al (2008) <doi:10.18637/jss.v025.i03>.
Calculate and compare lower confidence bounds for binomial series system reliability. The R 'shiny' application, launched by the function launch_app(), weaves together a workflow of customized simulations and delta coverage calculations to output recommended lower confidence bound methods.
Translates antibody levels measured in cross-sectional
    population samples into estimates of the frequency with which
    seroconversions (infections) occur in the sampled populations.
    Replaces the previous `seroincidence` package.
Estimating the force of infection from time varying, age varying, or constant
    serocatalytic models from population based seroprevalence studies using a Bayesian framework,
    including data simulation functions enabling the generation of serological surveys based on this
    models. This tool also provides a flexible prior specification syntax for the force of infection
    and the seroreversion rate, as well as methods to assess model convergence and comparison
    criteria along with useful visualisation functions.
Speeds up the process of loading raw data from MBA (Multiplex Bead Assay) examinations, performs quality control checks, and automatically normalises the data, preparing it for more advanced, downstream tasks. The main objective of the package is to create a simple environment for a user, who does not necessarily have experience with R language. The package is developed within the project 'PvSTATEM', which is an international project aiming for malaria elimination.
A Bayesian framework for inferring influenza infection status from
    serial antibody measurements. Jointly estimates season-specific infection
    probabilities, antibody boosting and waning after infection, and baseline
    hemagglutination inhibition (HAI) titer distributions via Markov chain
    Monte Carlo (MCMC). Supports multi-season analysis and subgroup
    comparisons via a group_by interface. See Tsang et al. (2022)
    <doi:10.1038/s41467-022-29310-8> for methodological details.
An easy-to-use and efficient tool to estimate infectious diseases parameters using serological data. Implemented models include SIR models (basic_sir_model(), static_sir_model(), mseir_model(), sir_subpops_model()), parametric models (polynomial_model(), fp_model()), nonparametric models (lp_model()), semiparametric models (penalized_splines_model()), hierarchical models (hierarchical_bayesian_model()).
  The package is based on the book "Modeling Infectious Disease Parameters Based on Serological and Social Contact Data: A Modern Statistical Perspective" (Hens, Niel & Shkedy, Ziv & Aerts, Marc & Faes, Christel & Damme, Pierre & Beutels, Philippe., 2013) <doi:10.1007/978-1-4614-4072-7>.
Data wrangling and cleaning, quality control checks and 
  implementation of machine learning classification algorithm.
Implements a regularization method for cumulative link models using the 
    Smooth-Effect-on-Response Penalty (SERP). This method allows flexible 
    modeling of ordinal data by enabling a smooth transition from a general 
    cumulative link model to a simplified version of the same model. As the 
    tuning parameter increases from zero to infinity, the subject-specific 
    effects for each variable converge to a single global effect. 
    The approach addresses common issues in cumulative link models, such as 
    parameter unidentifiability and numerical instability, by maximizing a 
    penalized log-likelihood instead of the standard non-penalized version. 
    Fitting is performed using a modified Newton's method. Additionally, the 
    package includes various model performance metrics and descriptive tools.
    For details on the implemented penalty method, see 
    Ugba (2021) <doi:10.21105/joss.03705> and 
    Ugba et al. (2021) <doi:10.3390/stats4030037>.
The primary goal of 'Serpstat' API <https://api-docs.serpstat.com/docs/serpstat-public-api/jenasqbwtxdlr-introduction-to-serpstat-api> 
    is to reduce manual SEO (search engine optimization) and PPC (pay-per-click) 
    tasks. You can automate your keywords research or competitors analysis 
    with this API wrapper.
Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive
    mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS),
    using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate
    observations of SERRS are highly collinear and lend themselves to a reduced-rank
    representation. The GAMM separates the SERRS signal into three components: a
    sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline;
    and additive white noise. The parameters of each component of the model are estimated
    iteratively using SMC. The posterior distributions of the parameters given the observed
    spectra are represented as a population of weighted particles.
Start an HTTP server in R to serve static files, or dynamic
    documents that can be converted to HTML files (e.g., R Markdown) under a
    given directory.
Tools to compute and analyze the set of statistically-equivalent (Gaussian, linear) path models which generate the input precision or (partial) correlation matrix. 
             This procedure is useful for understanding how statistical network models such as the Gaussian Graphical Model (GGM) perform as causal discovery tools. 
             The statistical-equivalence set of a given GGM expresses the uncertainty we have about the sign, size and direction of directed relationships based on the weights matrix of the GGM alone. 
             The derivation of the equivalence set and its use for understanding GGMs as causal discovery tools is described by Ryan, O., Bringmann, L.F., & Schuurman, N.K. (2022) <doi: 10.31234/osf.io/ryg69>.
Randomization of presence/absence species distribution raster
    data with or without including spatial structure for calculating
    standardized effect sizes and testing null hypothesis. The
    randomization algorithms are based on classical algorithms for
    matrices (Gotelli 2000, <doi:10.2307/177478>) implemented for raster
    data.
Provides tools to check variables contained in the user environment, and
    inspect the currently loaded package namespaces. The intended use is to
    allow user scripts to throw errors or warnings if unwanted variables exist
    or if unwanted packages are loaded.
Query and print information about the current R session.  It
    is similar to 'utils::sessionInfo()', but includes more information
    about packages, and where they were installed from.
More easy to get intersection, union or complementary set and 
    combinations.
Setaria viridis (green foxtail) is a common weed.  This
    package contains measurements from individual branches of a wild
    Setaria viridis plant collected near the author's home.  The data is
    intended for use in data analysis practice.
The implementation of a forecasting-specific tree-based model that is in particular suitable for global time series forecasting, as proposed in Godahewa et al. (2022) <arXiv:2211.08661v1>. The model uses the concept of Self Exciting Threshold Autoregressive (SETAR) models to define the node splits and thus, the model is named SETAR-Tree. The SETAR-Tree uses some time-series-specific splitting and stopping procedures. It trains global pooled regression models in the leaves allowing the models to learn cross-series information. The depth of the tree is controlled by conducting a statistical linearity test as well as measuring the error reduction percentage at each node split. Thus, the SETAR-Tree requires minimal external hyperparameter tuning and provides competitive results under its default configuration. A forest is developed by extending the SETAR-Tree. The SETAR-Forest combines the forecasts provided by a collection of diverse SETAR-Trees during the forecasting process.
Functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the 	examples in the books by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", Cambridge University Press.
Provides utilities to help set and record the setting of
	the seed and the uniform and normal generators used when a random
	experiment is run. The utilities can be used in other functions 
	that do random experiments to simplify recording and/or setting all the 
	necessary information for reproducibility. 
	See the vignette and reference manual for examples.
Data structures and basic operations for ordinary sets,
             generalizations such as fuzzy sets, multisets, and
             fuzzy multisets, customizable sets, and intervals.
It provides cumulative distribution function (CDF),
    quantile, p-value, statistical power calculator and random number generator
    for a collection of group-testing procedures, including the Higher Criticism
    tests, the one-sided Kolmogorov-Smirnov tests, the one-sided Berk-Jones tests,
    the one-sided phi-divergence tests, etc. The input are a group of p-values.
    The null hypothesis is that they are i.i.d. Uniform(0,1). In the context of
    signal detection, the null hypothesis means no signals. In the context of the
    goodness-of-fit testing, which contrasts a group of i.i.d. random variables to
    a given continuous distribution, the input p-values can be obtained by the CDF
    transformation. The null hypothesis means that these random variables follow the
    given distribution. For reference, see [1]Hong Zhang, Jiashun Jin and Zheyang Wu. 
    "Distributions and power of optimal signal-detection statistics in finite case",
    IEEE Transactions on Signal Processing (2020) 68, 1021-1033; [2] Hong Zhang and Zheyang Wu.
    "The general goodness-of-fit tests for correlated data", Computational Statistics & 
    Data Analysis (2022) 167, 107379.
Provides option settings management that goes
    beyond R's default 'options' function. With this package, users can define
    their own option settings manager holding option names, default values and 
    (if so desired) ranges or sets of allowed option values that will be 
    automatically checked. Settings can then be retrieved, altered and reset 
    to defaults with ease. For R programmers and package developers it offers 
    cloning and merging functionality which allows for conveniently defining 
    global and local options, possibly in a multilevel options hierarchy. See 
    the package vignette for some examples concerning functions, S4 classes, 
    and reference classes. There are convenience functions to reset par() 
    and options() to their 'factory defaults'.
Provides a 'Rstudio' addin to download, merge and upload 'Rstudio'
    settings and keymaps, essentially 'syncing them' at will. It uses 'Google Drive'
    as a cloud storage to keep the settings and keymaps files.
Create sets of variables based on a mutual
    information approach. In this context, a set is a collection of
    distinct elements (e.g., variables) that can also be treated as a
    single entity.  Mutual information, a concept from probability theory,
    quantifies the dependence between two variables by expressing how much
    information about one variable can be gained from observing the other.
    Furthermore, you can analyze, and visualize
    these sets in order to better understand the relationships among
    variables.
Automatically sets the value of options("width") when the
   terminal emulator is resized. The functions of this package only work if R is compiled for Unix systems and it is running interactively in a terminal emulator.
A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>, Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>, Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>, and Hao, Hao, et al (2020) <doi:10.1101/2020.10.12.335331> for more details.
A simple, one-command package which runs an interactive dashboard capable of common visualizations for single cell RNA-seq. 'SeuratExplorer' requires a processed 'Seurat' object, which is saved as 'rds' or 'qs2' file.
Defines S4 classes for single-cell genomic data and associated
  information, such as dimensionality reduction embeddings, nearest-neighbor
  graphs, and spatially-resolved coordinates. Provides data access methods and
  R-native hooks to ensure the Seurat object is familiar to other R users. See
  Satija R, Farrell J, Gennert D, et al (2015) <doi:10.1038/nbt.3192>,
  Macosko E, Basu A, Satija R, et al (2015) <doi:10.1016/j.cell.2015.05.002>,
  and Stuart T, Butler A, et al (2019) <doi:10.1016/j.cell.2019.05.031>,
  Hao Y, Hao S, et al (2021) <doi:10.1016/j.cell.2021.04.048> and
  Hao Y, et al (2023) <doi:10.1101/2022.02.24.481684> for
  more details.
R client and utilities for 'Seven Bridges Platform' API, from 'Cancer Genomics Cloud' 
    to other 'Seven Bridges' supported platforms. API documentation is hosted publicly 
    at <https://docs.sevenbridges.com/docs/the-api>.
Customise 'Shiny' disconnected screens as well as sanitize error messages to make them clearer and friendlier to the user.
Provides a simple interface to developing complex data pipelines which can be executed in a single call.
    'sewage' makes it easy to test, debug, and share data pipelines through it's interface and visualizations.
Support for simple feature access, a standardized way to
    encode and analyze spatial vector data. Binds to 'GDAL' 
	<doi:10.5281/zenodo.5884351> for reading and writing data, to 'GEOS'
    <doi:10.5281/zenodo.11396894> for geometrical operations,
    and to 'PROJ' <doi:10.5281/zenodo.5884394> for projection
    conversions and datum transformations. Uses by default the 's2'
    package for geometry operations on geodetic (long/lat degree)
	coordinates.
Provides a user-friendly framework for estimating a wide variety of cross-sectional and panel stochastic frontier models. Suitable for a broad range of applications, the implementation offers extensive flexibility in specification and estimation techniques.
Stochastic frontier analysis with advanced methods.
 In particular, it applies the approach proposed by Latruffe et al. (2017) 
 <DOI:10.1093/ajae/aaw077> to estimate a stochastic frontier with technical 
 inefficiency effects when one input is endogenous.
Maximum likelihood estimation for stochastic frontier
    analysis (SFA) of production (profit) and cost functions. The package
    includes the basic stochastic frontier for cross-sectional or pooled
    data with several distributions for the one-sided error term (i.e.,
    Rayleigh, gamma, Weibull, lognormal, uniform, generalized exponential
    and truncated skewed Laplace), the latent class stochastic frontier
    model (LCM) as described in Dakpo et al. (2021)
    <doi:10.1111/1477-9552.12422>, for cross-sectional and pooled data,
    and the sample selection model as described in Greene (2010)
    <doi:10.1007/s11123-009-0159-1>, and applied in Dakpo et al. (2021)
    <doi:10.1111/agec.12683>.  Several possibilities in terms of
    optimization algorithms are proposed.
Support for reading/writing simple feature ('sf') spatial objects from/to 'Parquet' files. 'Parquet' files are an open-source, column-oriented data storage format from Apache (<https://parquet.apache.org/>), now popular across programming languages. This implementation converts simple feature list geometries into well-known binary format for use by 'arrow', and coordinate reference system information is maintained in a standard metadata format.
Provides a function sfc() to compute the substance flow
    with the input files --- "data" and "model". If sample.size is
    set more than 1, uncertainty analysis will be executed while
    the distributions and parameters are supplied in the file "data".
Compute centrographic statistics (central points, standard 
    distance, standard deviation ellipse, standard deviation box) for 
    observations taken at point locations in 2D or 3D. The 'sfcentral' library 
    was inspired in 'aspace' package but conceived to be used in a spatial
    'tidyverse' context.
Bayesian clustering of spatial regions with similar functional shapes using
    spanning trees and latent Gaussian models. The method enforces spatial contiguity
    within clusters and supports a wide range of latent Gaussian models, including
    non-Gaussian likelihoods, via the R-INLA framework. The algorithm is based on Zhong,
    R., Chacón-Montalván, E. A., and Moraga, P. (2024) <doi:10.48550/arXiv.2407.12633>,
    extending the approach of Zhang, B., Sang, H., Luo, Z. T., and Huang, H. (2023)
    <doi:10.1214/22-AOAS1643>. The package includes tools for model fitting, convergence
    diagnostics, visualization, and summarization of clustering results.
Routines to write, simulate, and validate stock-flow consistent (SFC) models. The accounting structure of SFC models are described in Godley and Lavoie (2007, ISBN:978-1-137-08599-3). The algorithms implemented to solve the models (Gauss-Seidel and Broyden) are described in Kinsella and O'Shea (2010) <doi:10.2139/ssrn.1729205> and Peressini and Sullivan (1988, ISBN:0-387-96614-5).
Implementation of all possible forms of 2x2 and 3x3 space-filling curves, 
    i.e., the generalized forms of the Hilbert curve <https://en.wikipedia.org/wiki/Hilbert_curve>, 
    the Peano curve <https://en.wikipedia.org/wiki/Peano_curve> and the Peano curve in the 
    meander type (Figure 5 in <https://eudml.org/doc/141086>). It can generates nxn curves expanded from
    any specific level-1 units. It also implements the H-curve and the three-dimensional Hilbert curve.
A collection of pre-optimized space-filling designs, for up to ten 
    parameters, is contained here. Functions are provided to access designs 
    described by Husslage et al (2011) <doi:10.1007/s11081-010-9129-8> and
    Wang and Fang (2005) <doi:10.1142/9789812701190_0040>. The design types 
    included are Audze-Eglais, MaxiMin, and uniform.
Build a constrained high quality Delaunay triangulation from simple 
   features objects, applying constraints based on input line segments, and 
   triangle properties including maximum area, minimum internal angle. The 
   triangulation code in 'RTriangle' uses the method of Cheng, Dey and Shewchuk 
   (2012, ISBN:9781584887300). For a low-dependency alternative with low-quality 
   path-based constrained  triangulation see <https://CRAN.R-project.org/package=decido> and for high-quality configurable
    triangulation see <https://github.com/hypertidy/anglr>. Also consider comparison
    with the 'GEOS' lib which since version 3.10.0 includes a low quality 
    polygon triangulation method that starts with ear clipping and refines to Delaunay.
An interface to 'spdep' to integrate with 'sf' objects and the 'tidyverse'.
Construct various types of space-filling designs, including Latin hypercube designs, clustering-based designs, maximin designs, maximum projection designs, and uniform designs (Joseph 2016 <doi:10.1080/08982112.2015.1100447>). It also offers the option to optimize designs based on user-defined criteria. This work is supported by U.S. National Science Foundation grant DMS-2310637.
Pleiotropy-informed significance analysis of genome-wide association studies with surrogate functional false discovery rates (sfFDR). The sfFDR framework  adapts the fFDR to leverage informative data from multiple sets of GWAS summary statistics to increase power in study while accommodating for linkage disequilibrium. sfFDR provides estimates of key FDR quantities in a significance analysis such as the functional local FDR and $q$-value, and uses these estimates to derive a functional $p$-value for type I error rate control and a functional local Bayes' factor for post-GWAS analyses (e.g., fine mapping and colocalization).
Gives design points from a sequential full factorial-based
 Latin hypercube design, as described in Duan, Ankenman, Sanchez,
 and Sanchez (2015, Technometrics,
 <doi:10.1080/00401706.2015.1108233>).
Converts between R and Simple Feature 'sf' objects, without depending
  on the Simple Feature library. Conversion functions are available at both the R level, 
  and through 'Rcpp'.
A group of functions that support the 'sf' package, focused primarily on repairing polygons that break when re-projected.
Implements the structural forest methodology for the heterogeneous
    newsvendor model. The package provides tools to prepare data, fit honest
    newsvendor trees and forests, and obtain point and distributional predictions
    for demand decisions under uncertainty.
Identify and understand clusters of points (typically representing 
    the locations of places or events) stored in simple-features (SF) objects.
    This is useful for analysing, for example, hot-spots of crime events. The 
    package emphasises producing results from point SF data in a single step 
    using reasonable default values for all other arguments, to aid rapid data 
    analysis by users who are starting out. Functions available include kernel
    density estimation (for details, see Yip (2020) 
    <doi:10.22224/gistbok/2020.1.12>), analysis of spatial association (Getis
    and Ord (1992) <doi:10.1111/j.1538-4632.1992.tb00261.x>) and hot-spot
    classification (Chainey (2020) ISBN:158948584X).
The straightforward filtering index (SFINX) identifies true positive
    protein interactions in a fast, user-friendly, and highly accurate way.
    It is not only useful for the filtering of affinity purification -
    mass spectrometry (AP-MS) data, but also for similar types of data
    resulting from other co-complex interactomics technologies, such as TAP-MS,
    Virotrap and BioID. SFINX can also be used via the website interface at
    <http://sfinx.ugent.be>.
Helpers for addressing the issue of disconnected spatial units. 
    It allows for convenient adding and removal of neighbourhood connectivity between areal units prior to modelling, with the visual aid of maps.
    Post-modelling, it reduces the human workload for extracting, tidying and mapping predictions from areal models.
Generates Skew Factor Models data and applies Sparse Online Principal Component (SOPC), Incremental Principal Component (IPC), Projected Principal Component (PPC), Perturbation Principal Component (PPC), Stochastic Approximation Principal Component (SAPC), Sparse Principal Component (SPC) and other PC methods to estimate model parameters. It includes capabilities for calculating mean squared error, relative error, and sparsity of the loading matrix.The philosophy of the package is described in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.
Provides a tidy approach to spatial network
    analysis, in the form of classes and functions that enable a seamless
    interaction between the network analysis package 'tidygraph' and the
    spatial analysis package 'sf'.
We have designed this package to address experimental scenarios involving multiple covariates. It focuses on construction of Optimal Covariate Designs (OCDs), checking space filling property of the developed design. The primary objective of the package is to generate OCDs using four methods viz., M array method, Juxtapose method, Orthogonal Integer Array and Hadamard method. The package also evaluates space filling properties of both the base design and OCDs using the MaxPro criterion, providing a meaningful basis for comparison. In addition, it includes tool to visualize the spread offered by the design points in the form of scatterplot, which help users to assess distribution and coverage of design points.
Implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2024) <doi:10.48550/arXiv.2308.04325>, and allows for the statistical modeling of multi-group rank data in combination with object variables. The package also allows for the simulation of synthetic multi-group rank data.
Useful utilities ['goodies'] from Seminar fuer Statistik ETH Zurich,
   some of which were ported from S-plus in the 1990s.
 For graphics, have pretty (Log-scale) axes eaxis(), an enhanced Tukey-Anscombe
   plot, combining histogram and boxplot, 2d-residual plots, a 'tachoPlot()',
   pretty arrows, etc.
 For robustness, have a robust F test and robust range().
 For system support, notably on Linux, provides 'Sys.*()' functions with
   more access to system and CPU information.
 Finally, miscellaneous utilities such as simple efficient prime numbers,
   integer codes, Duplicated(), toLatex.numeric() and is.whole().
A series of tools for analyzing Systems Factorial Technology data.  This includes functions for plotting and statistically testing capacity coefficient functions and survivor interaction contrast functions.  Houpt, Blaha, McIntire, Havig, and Townsend (2013) <doi:10.3758/s13428-013-0377-3> provide a basic introduction to Systems Factorial Technology along with examples using the sft R package.
Classes and methods for spatial objects that have a registered time 
    column, in particular for irregular spatiotemporal data. The time column can 
    be of any type, but needs to be ordinal. Regularly laid out spatiotemporal 
    data (vector or raster data cubes) are handled by package 'stars'.
Contains space filling based tools for
    machine learning and data mining. Some functions offer
    several computational techniques and deal with the out of
    memory for large big data by using the ff package.
Modern classes for tracking and movement data, building
    on 'sf' spatial infrastructure, and early theoretical work from
    Turchin (1998, ISBN: 9780878938476), and Calenge et al. (2009)
    <doi:10.1016/j.ecoinf.2008.10.002>. Tracking data are series of
    locations with at least 2-dimensional spatial coordinates (x,y), a
    time index (t), and individual identification (id) of the object
    being monitored; movement data are made of trajectories, i.e. the
    line representation of the path, composed by steps (the
    straight-line segments connecting successive locations). 'sftrack'
    is designed to handle movement of both living organisms and
    inanimate objects.
Simple 'SendGrid' Email API client for creating and sending emails. 
    For more information, visit the official 'SendGrid' Email API documentation: <https://sendgrid.com/en-us/solutions/email-api>.
Facilitates extraction of geospatial data from the 'Office for National Statistics Open Geography' and 'nomis' Application Programming Interfaces (APIs). Simplifies process of querying 'nomis' datasets <https://www.nomisweb.co.uk/> and extracting desired datasets in dataframe format. Extracts area shapefiles at chosen resolution from 'Office for National Statistics Open Geography' <https://geoportal.statistics.gov.uk/>.
Main properties and regression procedures using a generalization of the Dirichlet distribution called Simplicial Generalized Beta distribution. It is a new distribution on the simplex (i.e. on the space of compositions or positive vectors with sum of components equal to 1). The Dirichlet distribution can be constructed from a random vector of independent Gamma variables divided by their sum. The SGB follows the same construction with generalized Gamma instead of Gamma variables. The Dirichlet exponents are supplemented by an overall shape parameter and a vector of scales. The scale vector is itself a composition and can be modeled with auxiliary variables through a log-ratio transformation. Graf, M. (2017, ISBN: 978-84-947240-0-8). See also the vignette enclosed in the package.
Implements an extension of the Generalized Berk-Jones (GBJ) statistic for
    survival data, sGBJ. It computes the sGBJ statistic and its p-value for testing 
    the association between a gene set and a time-to-event outcome with possible 
    adjustment on additional covariates. Detailed method is available at Villain L, Ferte T, 
    Thiebaut R and Hejblum BP (2021) <doi:10.1101/2021.09.07.459329>.
Sparse-group boosting to be used in conjunction with the 'mboost' for modeling grouped data.
    Applicable to all sparse-group lasso type problems where within-group and between-group sparsity is desired.
    Interprets and visualizes individual variables and groups.
A fast and flexible set of tools for large scale estimation. It
    features many stochastic gradient methods, built-in models, visualization
    tools, automated hyperparameter tuning, model checking, interval estimation,
    and convergence diagnostics.
Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, <doi:10.48550/arXiv.2412.20509>). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, <http://jmlr.org/papers/v23/20-1104.html>).
Estimation and inference methods for large-scale mean and quantile regression models via stochastic (sub-)gradient descent (S-subGD) algorithms. 
    The inference procedure handles cross-sectional data sequentially: 
    (i) updating the parameter estimate with each incoming "new observation", 
    (ii) aggregating it as a Polyak-Ruppert average, and 
    (iii) computing an asymptotically pivotal statistic for inference through random scaling. 
    The methodology used in the 'SGDinference' package is described in detail in the following papers: 
    (i) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2022) <doi:10.1609/aaai.v36i7.20701> "Fast and robust online inference with stochastic gradient descent via random scaling".
    (ii) Lee, S., Liao, Y., Seo, M.H. and Shin, Y. (2023) <arXiv:2209.14502> "Fast Inference for Quantile Regression with Tens of Millions of Observations".
Stagewise techniques implemented with Generalized Estimating Equations to handle individual, group, bi-level, and interaction selection. Stagewise approaches start with an empty model and slowly build the model over several iterations, which yields a 'path' of candidate models from which model selection can be performed. This 'slow brewing' approach gives stagewise techniques a unique flexibility that allows simple incorporation of Generalized Estimating Equations; see Vaughan, G., Aseltine, R., Chen, K., Yan, J., (2017) <doi:10.1111/biom.12669> for details.
An Object-oriented Framework for Geostatistical Modeling in S+ 
  containing functions for variogram estimation, variogram fitting and kriging
  as well as some plot functions. Written entirely in S, therefore works only
  for small data sets in acceptable computing time.
Fit a regularized generalized linear model via penalized
        maximum likelihood.  The model is fit for a path of values of
        the penalty parameter. Fits linear, logistic and Cox models.
RCON(V, E) models are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. 'sglasso' package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm.
Set of tools to fit a linear multiple or semi-parametric regression
    models with the possibility of non-informative random right or left censoring. 
    Under this setup, the localization parameter of the response variable distribution is modeled by using linear multiple regression
    or semi-parametric functions, whose non-parametric components may be approximated
    by natural cubic spline or P-splines. The supported distribution for the model error is a generalized log-gamma distribution which includes
    the generalized extreme value and standard normal distributions as important special cases. Inference is based on likelihood, penalized likelihood and bootstrap methods.   
    Lastly, some numerical and graphical devices for diagnostic of the fitted models are offered.
We provide functions for computing the decision boundaries for pre-licensure vaccine trials using the Generalized Likelihood Ratio tests proposed by Shih, Lai, Heyse and Chen (2010, <doi:10.1002/sim.4036>).
It computes the solutions to a generic stochastic growth model for a given set of user supplied parameters. It includes
    the solutions to the model, plots of the solution, 	
    a summary of the features of the model, a function that covers different types of consumption preferences,
    and a function that computes the moments of a Markov process.	
	Merton, Robert C (1971) <doi:10.1016/0022-0531(71)90038-X>,
	Tauchen, George (1986) <doi:10.1016/0165-1765(86)90168-0>,
	Wickham, Hadley (2009, ISBN:978-0-387-98140-6 ).
An implementation of sparse Gaussian Markov random field mixtures 
  presented by Ide et al. (2016) <doi:10.1109/ICDM.2016.0119>.
  It provides a novel anomaly detection method for multivariate noisy sensor data.
  It can automatically handle multiple operational modes.
  And it can also compute variable-wise anomaly scores.
Methods focused in performing the OSGB36/ETRS89 transformation 
  (Great Britain and the Isle of Man only) by using the Ordnance Survey's 
  OSTN15/OSGM15 transformation model. Calculation of distances and areas from 
  sets of points defined in any of the supported Coordinated Systems is also
  available.
Seven different methods for multiple testing problems. The SGoF-type methods (see for example, Carvajal Rodríguez et al., 2009 <doi:10.1186/1471-2105-10-209>;  de Uña Álvarez, 2012 <doi:10.1515/1544-6115.1812>; Castro Conde et al., 2015 <doi:10.1177/0962280215597580>) and the BH and BY false discovery rate controlling procedures.
Smoothing signals and computing their derivatives is a common
 requirement in signal processing workflows. Savitzky-Golay filters are a
 established method able to do both (Savitzky and Golay, 1964 <doi:10.1021/ac60214a047>).
 This package implements one dimensional Savitzky-Golay filters that can be applied to
 vectors and matrices (either row-wise or column-wise).
 Vectorization and memory allocations have been profiled to reduce computational
 fingerprint. Short filter lengths are implemented in the direct space, while
 longer filters are implemented in frequency space, using a Fast Fourier
 Transform (FFT).
An analytic framework for the calculation of norm- and criterion-referenced academic growth estimates using large scale, longitudinal education assessment data as developed in Betebenner (2009) <doi:10.1111/j.1745-3992.2009.00161.x>.
Data sets utilized by the 'SGP' package as exemplars for users to conduct their own student growth percentiles (SGP) analyses.
A 'ggplot2' theme and colour palettes to create accessible
    data visualisations in the Scottish Government.
Regularized version of partial least square approaches
 providing sparse, group, and sparse group versions of partial
 least square regression models (Liquet, B., Lafaye de Micheaux, P.,
 Hejblum B., Thiebaut, R. (2016) <doi:10.1093/bioinformatics/btv535>).
 Version of PLS Discriminant analysis is also provided.
Fits the regularization path of regression models
    (linear and logistic) with additively combined penalty terms. All
    possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), 
    Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and 
    Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), 
    Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE).
    For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) <doi:10.1002/bimj.202200334>.
Computation of second-generation p-values as described in Blume et al. (2018) <doi:10.1371/journal.pone.0188299> and Blume et al. (2019) <doi:10.1080/00031305.2018.1537893>. There are additional functions which provide power and type I error calculations, create graphs (particularly suited for large-scale inference usage), and a function to estimate false discovery rates based on second-generation p-value inference.
Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.
Interactive visualizations of graphs created with the 'igraph' package using a 'htmlwidgets' wrapper for the 'sigma.js' network visualization v2.4.0 <https://www.sigmajs.org/>, enabling to display several thousands of nodes. While several 'R' packages have been developed to interface 'sigma.js', all were developed for v1.x.x and none have migrated to v2.4.0 nor are they planning to. This package builds upon the 'sigmaNet' package, and users familiar with it will recognize the similar design approach. Two extensions have been added to the classic 'sigma.js' visualizations by overriding the underlying 'JavaScript' code, enabling to draw a frame around node labels, and to display labels on multiple lines by parsing line breaks. Other additional functionalities that did not require overriding 'sigma.js' code include toggling node visibility when clicked using a node attribute and highlighting specific edges. 'sigma.js' is currently preparing a stable release v3.0.0, and this package plans to update to it when it is available.
Implementation of Sparse-group SLOPE (SGS) (Feser and Evangelou (2023) <doi:10.48550/arXiv.2305.09467>) models. Linear and logistic regression models are supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported. In addition, a general Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) implementation is provided. Group SLOPE (gSLOPE) (Brzyski et al. (2019) <doi:10.1080/01621459.2017.1411269>) and group-based OSCAR models (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) are also implemented. All models are available with strong screening rules (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.15357>) for computational speed-up.
Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS; LIDAR). Primary functions provide means 
    to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information 
    about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management
    practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data
    distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used, 
    enabling data-driven stratifications and sampling approaches.
A set of function that implements for seasonal multivariate time series analysis based on Seasonal Generalized Space
            Time Autoregressive with Seemingly Unrelated Regression (S-GSTAR-SUR) Model by Setiawan(2016)<https://www.researchgate.net/publication/316517889_S-GSTAR-SUR_model_for_seasonal_spatio_temporal_data_forecasting>.
Density, distribution function, quantile function and random generation for the skewed generalized t distribution. This package also provides a function that can fit data to the skewed generalized t distribution using maximum likelihood estimation.
Functions for easily manipulating colours, creating colour scales and calculating colour distances.
Shadow Document Object Model is a web standard that offers component style and markup encapsulation. 
  It is a critically important piece of the Web Components story as it ensures that a component will work in any environment even if other CSS or JavaScript is at play on the page.
  Custom HTML tags can't be directly identified with selenium tools, because Selenium doesn't provide any way to deal with shadow elements. 
  Using this plugin you can handle any custom HTML tags.
Implement shadowtextGrob() for 'grid' and geom_shadowtext() layer for 'ggplot2'.
             These functions create/draw text grob with background shadow.
A statistical method for reducing the number of covariates in
    an analysis by evaluating Variable Importance Measures (VIMPs) derived
    from the Random Forest algorithm. It performs statistical tests on the
    VIMPs and outputs whether the covariate is significant along with the
    p-values.
A comprehensive framework for quantifying the fundamental thermodynamic parameters of adsorption reactions—changes in the standard Gibbs free energy (delta G), enthalpy (delta H), and entropy (delta S)—is essential for understanding the spontaneity, heat effects, and molecular ordering associated with sorption processes. By analysing temperature-dependent equilibrium data, thermodynamic interpretation expands adsorption studies beyond conventional isotherm fitting, offering deeper insight into underlying mechanisms and surface–solute interactions. Such an approach typically involves evaluating equilibrium coefficients across multiple temperatures and non-temperature treatments, deriving thermodynamic parameters using established thermodynamic relationships, and determining delta G as a temperature-specific indicator of adsorption favourability. This analytical pathway is widely applicable across environmental science, soil science, chemistry, materials science, and engineering, where reliable assessment of sorption behaviour is critical for examining contaminant retention, nutrient dynamics, and the behaviour of natural and engineered surfaces. By focusing specifically on thermodynamic inference, this framework complements existing adsorption isotherm-fitting packages such as “AdIsMF” <https://CRAN.R-project.org/package=AdIsMF> <doi:10.32614/CRAN.package.AdIsMF>, and strengthens the scientific basis for interpreting adsorption energetics in both research and applied contexts. Details can be found in Roy et al. (2025) <doi:10.1007/s11270-025-07963-7>.
The functions allow for the numerical evaluation of some commonly used entropy measures, such as Shannon entropy, Rényi entropy, Havrda and Charvat entropy, and Arimoto entropy, at selected parametric values from several well-known and widely used probability distributions. Moreover, the functions also compute the relative loss of these entropies using the truncated distributions. Related works include: Awad, A. M., & Alawneh, A. J. (1987). Application of entropy to a life-time model. IMA Journal of Mathematical Control and Information, 4(2), 143-148. <doi:10.1093/imamci/4.2.143>.
The implementation of SHAPBoost, a boosting-based feature selection technique that ranks features iteratively based on Shapley values.
Estimating the Shapley values using the algorithm in 
    the paper Liuqing Yang, Yongdao Zhou, Haoda Fu, Min-Qian Liu and 
    Wei Zheng (2024) <doi:10.1080/01621459.2023.2257364> "Fast Approximation 
    of the Shapley Values Based on Order-of-Addition Experimental Designs".
    You provide the data and define the value function, it retures the 
    estimated Shapley values based on sampling methods or experimental designs.
Functions for plotting graphical shapes
  such as ellipses, circles, cylinders, arrows, ...
In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrap confidence interval for the change-point is incorporated in the main routine.
Functions to read and write ESRI shapefiles.
M-estimators of location and shape following the power family 
    (Frahm, Nordhausen, Oja (2020) <doi:10.1016/j.jmva.2019.104569>) are 
    provided in the case of complete data and also when observations have 
    missing values together with functions aiding their visualization.
This is an evolving and growing collection of tools for the quantification, assessment, and comparison of shape and pattern. This collection provides tools for: (1) the spatial decomposition of planar shapes using 'ShrinkShape' to incrementally shrink shapes to extinction while computing area, perimeter, and number of parts at each iteration of shrinking; the spectra of results are returned in graphic and tabular formats (Remmel 2015) <doi:10.1111/cag.12222>, (2) simulating landscape patterns, (3) provision of tools for estimating composition and configuration parameters from a categorical (binary) landscape map (grid) and then simulates a selected number of statistically similar landscapes. Class-focused pattern metrics are computed for each simulated map to produce empirical distributions against which statistical comparisons can be made. The code permits the analysis of single maps or pairs of maps (Remmel and Fortin 2013) <doi:10.1007/s10980-013-9905-x>, (4) counting the number of each first-order pattern element and converting that information into both frequency and empirical probability vectors (Remmel 2020) <doi:10.3390/e22040420>, and (5) computing the porosity of raster patches <doi:10.3390/su10103413>. NOTE: This is a consolidation of existing packages ('PatternClass', 'ShapePattern') to begin warehousing all shape and pattern code in a common package. Additional utility tools for handling data are provided and this package will be added to as more tools are created, cleaned-up, and documented.  Note that all future developments will appear in this package and that 'PatternClass' will eventually be archived.
Studies otolith shape variation among fish populations.
  Otoliths are calcified structures found in the inner ear of teleost fish and their shape has
  been known to vary among several fish populations and stocks, making them very useful in taxonomy,
  species identification and to study geographic variations. The package extends previously described
  software used for otolith shape analysis by allowing the user to automatically extract closed
  contour outlines from a large number of images, perform smoothing to eliminate pixel noise described in Haines and Crampton (2000) <doi:10.1111/1475-4983.00148>,
  choose from conducting either a Fourier or wavelet see Gençay et al (2001) <doi:10.1016/S0378-4371(00)00463-5> transform to the outlines and visualize
  the mean shape. The output of the package are independent Fourier or wavelet coefficients
  which can be directly imported into a wide range of statistical packages in R. The package
  might prove useful in studies of any two dimensional objects.
Routines for the statistical analysis of landmark 
  shapes, including Procrustes analysis, graphical displays, principal
  components analysis, permutation and bootstrap tests, thin-plate 
  spline transformation grids and comparing covariance matrices. 
     See Dryden, I.L. and Mardia, K.V. (2016). Statistical shape analysis, 
     with Applications in R (2nd Edition), John Wiley and Sons.
Landsat satellites collect important data about global forest conditions. Documentation about Landsat's role in forest disturbance estimation is available at the site <https://landsat.gsfc.nasa.gov/>. By constrained quadratic B-splines, this package delivers an optimal shape-restricted trajectory to a time series of Landsat imagery for the purpose of modeling annual forest disturbance dynamics to behave in an ecologically sensible manner assuming one of seven possible "shapes", namely, flat, decreasing, one-jump (decreasing, jump up, decreasing), inverted vee (increasing then decreasing), vee (decreasing then increasing), linear increasing, and double-jump (decreasing, jump up, decreasing, jump up, decreasing). The main routine selects the best shape according to the minimum Bayes information criterion (BIC) or the cone information criterion (CIC), which is defined as the log of the estimated predictive squared error. The package also provides parameters summarizing the temporal pattern including year(s) of inflection, magnitude of change, pre- and post-inflection rates of growth or recovery. In addition, it contains routines for converting a flat map of disturbance agents to time-series disturbance maps and a graphical routine displaying the fitted trajectory of Landsat imagery.
Aid in visual data investigations
 using SHAP (SHapley Additive exPlanation) visualization plots for 'XGBoost' and 'LightGBM'. 
 It provides summary plot, dependence plot, interaction plot, and force plot and relies on
 the SHAP implementation provided by 'XGBoost' and 'LightGBM'.
Based on Shapley values to explain multivariate outlyingness and to detect and impute cellwise outliers.
    Includes implementations of methods described in Mayrhofer and Filzmoser (2023) <doi:10.1016/j.ecosta.2023.04.003>.
Shapley Value Regression for calculating the relative importance of independent variables in linear regression with avoiding the collinearity.
Provides SHAP explanations of machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the Interpretable Machine Learning, there are more and more new ideas for explaining black-box models. One of the best known method for local explanations is SHapley Additive exPlanations (SHAP) introduced by Lundberg, S., et al., (2016) <arXiv:1705.07874> The SHAP method is used to calculate influences of variables on the particular observation. This method is based on Shapley values, a technique used in game theory. The R package 'shapper' is a port of the Python library 'shap'.
Complex machine learning models are often hard to interpret. However, in
  many situations it is crucial to understand and explain why a model made a specific
  prediction. Shapley values is the only method for such prediction explanation framework
  with a solid theoretical foundation. Previously known methods for estimating the Shapley
  values do, however, assume feature independence. This package implements methods which accounts for any feature
  dependence, and thereby produces more accurate estimates of the true Shapley values.
  An accompanying 'Python' wrapper ('shaprpy') is available through PyPI.
Visualizations for SHAP (SHapley Additive exPlanations), such
    as waterfall plots, force plots, various types of importance plots,
    dependence plots, and interaction plots.  These plots act on a
    'shapviz' object created from a matrix of SHAP values and a
    corresponding feature dataset. Wrappers for the R packages 'xgboost',
    'lightgbm', 'fastshap', 'shapr', 'h2o', 'treeshap', 'DALEX', and
    'kernelshap' are added for convenience.  By separating visualization
    and computation, it is possible to display factor variables in graphs,
    even if the SHAP values are calculated by a model that requires
    numerical features. The plots are inspired by those provided by the
    'shap' package in Python, but there is no dependency on it.
Analyse species-habitat associations in R. Therefore, information about the location 
  of the species (as a point pattern) is needed together with environmental conditions 
  (as a categorical raster). To test for significance habitat associations, one of 
  the two components is randomized. Methods are mainly based on Plotkin et al. (2000) 
  <doi:10.1006/jtbi.2000.2158> and Harms et al. (2001) <doi:10.1111/j.1365-2745.2001.00615.x>.
Provides a parallel execution runtime for R that emphasizes
    deterministic memory behavior and efficient handling of large shared inputs.
    'shard' enables zero-copy parallel reads via shared/memory-mapped segments,
    encourages explicit output buffers to avoid large result aggregation, and
    supervises worker processes to mitigate memory drift via controlled recycling.
    Diagnostics report peak memory usage, end-of-run memory return, and hidden
    copy/materialization events to support reproducible performance benchmarking.
Provides functions to retrieve, process, analyze, and
    quality-control marine physical, chemical, and biological data. The
    main focus is on Swedish monitoring data available through the 'SHARK'
    database <https://shark.smhi.se/en/>, with additional API support for 'Nordic
    Microalgae' <https://nordicmicroalgae.org/>, 'Dyntaxa'
    <https://artfakta.se/>, World Register of Marine Species ('WoRMS') <https://www.marinespecies.org>,
    'AlgaeBase' <https://www.algaebase.org>, OBIS 'xylookup' web service 
    <https://iobis.github.io/xylookup/> and Intergovernmental Oceanographic Commission (IOC) - 
    UNESCO databases on harmful algae  <https://www.marinespecies.org/hab/> and toxins
    <https://toxins.hais.ioc-unesco.org/>.
Run Leslie Matrix models using Monte Carlo simulations for any specified shark species. 
    This package was developed during the publication of Smart, JJ, White, WT, Baje, L, et al. (2020)
    "Can multi-species shark longline fisheries be managed sustainably using size limits? 
    Theoretically, yes. Realistically, no".J Appl Ecol. 2020; 57; 1847–1860. 
    <doi:10.1111/1365-2664.13659>.
In stability selection (N Meinshausen, P Bühlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>) and consensus clustering (S Monti et al (2003) <doi:10.1023/A:1023949509487>), resampling techniques are used to enhance the reliability of the results. In this package (B Bodinier et al (2025) <doi:10.18637/jss.v112.i05>), hyper-parameters are calibrated by maximising model stability, which is measured under the null hypothesis that all selection (or co-membership) probabilities are identical (B Bodinier et al (2023a) <doi:10.1093/jrsssc/qlad058> and B Bodinier et al (2023b) <doi:10.1093/bioinformatics/btad635>). Functions are readily implemented for the use of LASSO regression, sparse PCA, sparse (group) PLS or graphical LASSO in stability selection, and hierarchical clustering, partitioning around medoids, K means or Gaussian mixture models in consensus clustering.
Functions and data sets inspired by data sharpening -
             data perturbation to achieve improved performance in
             nonparametric estimation, as described in Choi, E., Hall, P.
             and Rousson, V. (2000).  
             Capabilities for enhanced local linear regression function
             and derivative estimation are included, as well as an
             asymptotically correct iterated data sharpening estimator
             for any degree of local polynomial regression estimation.
             A cross-validation-based bandwidth selector is included which,
             in concert with the iterated sharpener, will often provide
             superior performance, according to a median integrated squared
             error criterion.  Sample data sets are provided to illustrate
             function usage.
A collection of tools for analyzing significance of assets,
    funds, and trading strategies, based on the Sharpe ratio and overfit 
    of the same. Provides density, distribution, quantile and random generation 
    of the Sharpe ratio distribution based on normal returns, as well
    as the optimal Sharpe ratio over multiple assets. Computes confidence intervals
    on the Sharpe and provides a test of equality of Sharpe ratios based on 
    the Delta method. The statistical foundations of the Sharpe can be found in
    the author's Short Sharpe Course  <doi:10.2139/ssrn.3036276>.
An efficient moment-free estimator of the Sharpe ratio, or signal-to-noise ratio, for heavy-tailed data (see <arXiv:1505.01333>).
Functions and data sets for data sharpening.
    Nonparametric regressions are computed subject to smoothness
    and other kinds of penalties.
An algorithm for identifying high-resolution driver elements for datasets from a high-definition reporter assay library. Xinchen Wang, Liang He, Sarah Goggin, Alham Saadat, Li Wang, Melina Claussnitzer, Manolis Kellis (2017) <doi:10.1101/193136>.
A collection of data processing, visualization, and export functions to support soil survey operations. Many of the functions build on the `SoilProfileCollection` S4 class provided by the aqp package, extending baseline visualization to more elaborate depictions in the context of spatial and taxonomic data. While this package is primarily developed by and for the USDA-NRCS, in support of the National Cooperative Soil Survey, the authors strive for generalization sufficient to support any soil survey operation. Many of the included functions are used by the SoilWeb suite of websites and movile applications. These functions are provided here, with additional documentation, to enable others to replicate high quality versions of these figures for their own purposes.
Hierarchical models for the analysis of species-area 
  relationships (SARs) by combining several data sets and covariates; 
  with a global data set combining individual SAR studies; 
  as described in Solymos and Lele 
  (2012) <doi:10.1111/j.1466-8238.2011.00655.x>.
The Statistical Learning Theory (SLT) provides the theoretical background to ensure that a supervised algorithm generalizes the mapping f:X -> Y given f is selected from its search space bias F. This formal result depends on the Shattering coefficient function N(F,2n) to upper bound the empirical risk minimization principle, from which one can estimate the necessary training sample size to ensure the probabilistic learning convergence and, most importantly, the characterization of the capacity of F, including its under and overfitting abilities while addressing specific target problems. In this context, we propose a new approach to estimate the maximal number of hyperplanes required to shatter a given sample, i.e., to separate every pair of points from one another, based on the recent contributions by Har-Peled and Jones in the dataset partitioning scenario, and use such foundation to analytically compute the Shattering coefficient function for both binary and multi-class problems. As main contributions, one can use our approach to study the complexity of the search space bias F, estimate training sample sizes, and parametrize the number of hyperplanes a learning algorithm needs to address some supervised task, what is specially appealing to deep neural networks. Reference: de Mello, R.F. (2019) "On the Shattering Coefficient of Supervised Learning Algorithms" <arXiv:1911.05461>; de Mello, R.F., Ponti, M.A. (2018, ISBN: 978-3319949888) "Machine Learning: A Practical Approach on the Statistical Learning Theory".
Provides a computational framework for analyzing mutations in
    immunoglobulin (Ig) sequences. Includes methods for Bayesian estimation of
    antigen-driven selection pressure, mutational load quantification, building of
    somatic hypermutation (SHM) models, and model-dependent distance calculations.
    Also includes empirically derived models of SHM for both mice and humans.
    Citations: 
    Gupta and Vander Heiden, et al (2015) <doi:10.1093/bioinformatics/btv359>, 
    Yaari, et al (2012) <doi:10.1093/nar/gks457>, 
    Yaari, et al (2013) <doi:10.3389/fimmu.2013.00358>, 
    Cui, et al (2016) <doi:10.4049/jimmunol.1502263>.
Uses C++ via the 'Rcpp' package to parse modern Excel files ('.xlsx').
    Memory usage is kept minimal by decompressing only parts of the file at a time,
    while employing multiple threads to achieve significant runtime reduction.
    Uses <https://github.com/richgel999/miniz> and <https://github.com/lemire/fast_double_parser>.
Implements various methods for eliciting a probability
    distribution for a single parameter from an expert or a group of
    experts. The expert provides a small number of probability judgements,
    corresponding to points on his or her cumulative distribution
    function. A range of parametric distributions can then be fitted and
    displayed, with feedback provided in the form of fitted probabilities
    and percentiles. For multiple experts, a weighted linear pool can be
    calculated. Also includes functions for eliciting beliefs about
    population distributions; eliciting multivariate distributions using a
    Gaussian copula; eliciting a Dirichlet distribution; eliciting
    distributions for variance parameters in a random effects
    meta-analysis model; survival extrapolation. R Shiny apps for most of the methods are
    included.
Takes as input a stable oxygen isotope (d18O) profile measured in growth direction (D)
	through a shell + uncertainties in both variables (d18O_err & D_err). It then models the seasonality
	in the d18O record by fitting a combination of a growth and temperature sine wave to year-length chunks of
	the data (see Judd et al., (2018) <doi:10.1016/j.palaeo.2017.09.034>). This modeling is carried out along a sliding window through the data and yields estimates of
	the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modeling
	routine and the data itself are propagated and pooled to obtain a confidence envelope around the age of
	each data point in the shell. The end result is a shell chronology consisting of estimated ages of shell
	formation relative to the annual cycle with their uncertainties. All formulae in the package serve this
	purpose, but the user can customize the model (e.g. number of days in a year and the mineralogy of the
	shell carbonate) through input parameters.
Contains all the formulae of the growth and trace element uptake model described in the equally-named
    Geoscientific Model Development paper (de Winter, 2017, <doi:10.5194/gmd-2017-137>). The model takes as input a file with X- and Y-coordinates of digitized
    growth increments recognized on a longitudinal cross section through the bivalve shell, as well as a BMP file of an elemental map of the
    cross section surface with chemically distinct phases separated by phase analysis. It proceeds by a step-by-step process described in
    the paper, by which digitized growth increments are used to calculate changes in shell height, shell thickness, shell volume, shell mass
    and shell growth rate through the bivalve's life time. Then, results of this growth modelling are combined with the trace element mapping
    results to trace the incorporation of trace elements into the bivalve shell. Results of various modelling parameters can be exported in
    the form of XLSX files.
Secure handling of API keys can be difficult. This package provides
    secure convenience functions for 
    entering / handling API keys and opening connections via inversion of control
    on those keys. Works seamlessly between production and developer environments.
Powerful graphical displays and statistical tools for structured problem solving and diagnosis. 
    The functions of the 'sherlock' package are especially useful for applying the process of elimination as a problem diagnosis technique.
    The 'sherlock' package was designed to seamlessly work with the 'tidyverse' set of packages and provides a collection of graphical displays 
    built on top of the 'ggplot' and 'plotly' packages, such as different kinds of small multiple plots as well as helper functions such as 
    adding reference lines, normalizing observations, reading in data or saving analysis results in an Excel file.
    References:
    David Hartshorne (2019, ISBN: 978-1-5272-5139-7).
    Stefan H. Steiner, R. Jock MacKay (2005, ISBN: 0873896467).
Compute the frequency distribution of a search term in a series of texts. For example, Arthur Conan Doyle wrote a total of 60 Sherlock Holmes stories, comprised of 54 short stories and 4 longer novels. I wanted to test my own subjective impression that, in many of the stories, Sherlock Holmes' popularity was used as bait to induce the reader to read a story that is essentially not primarily a Sherlock Holmes story. I used the term "Holmes" as a search pattern, since Watson would frequently address him by name, or use his name to describe something that he was doing. My hypothesis is that the frequency distribution of the search pattern "Holmes" is a good proxy for the degree to which a story is or is not truly a Sherlock Holmes story. The results are presented in a manuscript that is available as a vignette and online at <https://barryzee.github.io/Concordance/index.html>.
A template dashboard system with AI agent integrated. 
    Comes with default themes that can be customized. 
    Developers can upload modified templates on 'Github', and users can 
    easily download templates with 'RStudio' project wizard.
    The key features of the default template include light and dark theme 
    switcher, resizing graphs, synchronizing inputs across sessions, 
    new notification system, fancy progress bars, and card-like flip 
    panels with back sides, as well as various of 'HTML' tool widgets.
Implements S3 classes for storing dates and date-times based on the Jalali calendar. 
    The main design goal of 'shide' is consistency with base R's 'Date' and 'POSIXct'.
    It provide features such as: date-time parsing, formatting and arithmetic.
An exact method for computing the Poisson-Binomial Distribution (PBD). The package provides a function for generating a random sample from the PBD, as well as two distinct approaches for computing the density, distribution, and quantile functions of the PBD. The first method uses direct-convolution, or a dynamic-programming approach which is numerically stable but can be slow for a large input due to its quadratic complexity. The second method is much faster on large inputs thanks to its use of Fast Fourier Transform (FFT) based convolutions. Notably in this case the package uses an exponential shift to practically guarantee the relative accuracy of the computation of an arbitrarily small tail of the PBD -- something that FFT-based methods often struggle with. This ShiftConvolvePoiBin method is described in Peres, Lee and Keich (2020) <arXiv:2004.07429> where it is also shown to be competitive with the fastest implementations for exactly computing the entire Poisson-Binomial distribution.
Fast enrichment analysis for locally correlated statistics
        via circular permutations.
        The analysis can be performed at multiple significance thresholds
        for both primary and auxiliary data sets with
        efficient correction for multiple testing.
Provides confidence intervals in least-squares regressions when the
  variable of interest has a shift-share structure, and in instrumental
  variables regressions when the instrument has a shift-share structure. The
  confidence intervals implement the AKM and AKM0 methods developed in Adão,
  Kolesár, and Morales (2019) <doi:10.1093/qje/qjz025>.
Prototype your shiny apps quickly with these
    Lorem-Ipsum-like Helpers.
Makes it incredibly easy to build interactive web
    applications with R. Automatic "reactive" binding between inputs and
    outputs and extensive prebuilt widgets make it possible to build
    beautiful, responsive, and powerful applications with minimal effort.
Easily use 'Blueprint', the popular 'React' library from Palantir, in your 'Shiny' app.
  'Blueprint' provides a rich set of UI components for creating visually appealing applications
  and is optimized for building complex, data-dense web interfaces.
  This package provides most components from the underlying library,
  as well as special wrappers for some components
  to make it easy to use them in 'R' without writing 'JavaScript' code.
Enables the complete removal of various 'Shiny' components, such
    as inputs, outputs and modules. It also aids in the removal of observers that have been 
    created in dynamically created modules.
Offers a comprehensive solution for managing 'empty states' in 'Shiny' applications.
    It provides tools to create both default and customizable components for scenarios where
    data is absent or doesn't match user-defined filters. The package prioritizes user experience,
    ensuring clarity and consistency even when data is not available to display.
Launch an application by a simple click without opening R or RStudio. The package has 3 functions of which only one is essential in its use, `shiny.exe()`. It generates a script in the open shiny project then create a shortcut in the same folder that allows you to launch the app by clicking.If you set `host = 'public'`, the application will be launched on the public server to which you are connected. Thus, all other devices connected to the same server will be able to access the application through the link of your `IPv4` extended by the port. You can stop the application by leaving the terminal opened by the shortcut.
A rich set of UI components for building Shiny applications,
  including inputs, containers, overlays, menus, and various utilities.
  All components from Fluent UI (the underlying JavaScript library)
  are available and have usage examples in R.
It provides easy internationalization of Shiny
    applications. It can be used as standalone translation package
    to translate reports, interactive visualizations or
    graphical elements as well.
Chat with large language models on your machine without internet with complete privacy via 'ollama', powered by R 'shiny' interface. For more information on 'ollama', visit <https://ollama.com>.
A toolbox for defining React component wrappers which can be used seamlessly in Shiny apps.
It is a simple router for your Shiny apps.
    The router allows you to create dynamic web applications with real-time
    User Interface and easily share url to pages within your Shiny apps.
Creating a great user interface for your Shiny apps can be a
    hassle, especially if you want to work purely in R and don't want to
    use, for instance HTML templates. This package adds support for a
    powerful UI library Fomantic UI - <https://fomantic-ui.com/> (before
    Semantic). It also supports universal UI input binding that works with
    various DOM elements.
Allows 'TailwindCSS' to be used in Shiny apps with just-in-time 
  compiling, custom css with '@apply' directive, and custom tailwind 
  configurations.
Enables instrumentation of 'Shiny' apps for tracking
    user session events such as input changes, browser type, and session
    duration.  These events can be sent to any of the available storage
    backends and analyzed using the included 'Shiny' app to gain insights
    about app usage and adoption.
Automates the creation of Dockerfiles for deploying 'Shiny' applications.
    By integrating with 'renv' for dependency management and leveraging Docker-based
    solutions, it simplifies the process of containerizing 'Shiny' apps,
    ensuring reproducibility and consistency across different environments.
    Additionally, it facilitates the setup of CI/CD pipelines for building Docker images
    on both GitLab and GitHub.
Ace editor bindings to enable a rich text editing environment
    within Shiny.
Make R data available in Web-based virtual reality experiences
  for immersive, cross-platform data visualizations. Includes the 'gg-aframe'
  JavaScript package for a Grammar of Graphics declarative HTML syntax to create
  3-dimensional data visualizations with 'Mozilla A-Frame' <https://aframe.io>.
Easily create pretty popup messages (modals) in 'Shiny'. A modal can
    contain text, images, OK/Cancel buttons, an input to get a response from the
    user, and many more customizable options.
An extension of 'animate.css' that allows user to easily add animations to any UI element in 'shiny' app using the elements id.
Add in-app user authentication to 'shiny', 
    allowing you to secure publicly hosted apps and 
    build dynamic user interfaces from user information.
An 'htmlwidget' of the human body that allows you
    to hide/show and assign colors to 79 different body parts. The 'human'
    widget is an 'htmlwidget', so it works in Quarto documents,
    R Markdown documents, or any other HTML medium.
    It also functions as an input/output widget in a 'shiny' app.
A graphical user interface (GUI) for fitting Bayesian regression
    models using the package 'brms' which in turn relies on 'Stan'
    (<https://mc-stan.org/>). The 'shinybrms' GUI is a 'shiny' app.
Sometimes it's useful to know some information about your user in a 'Shiny' app. The available
    information is: browser name (such as 'Chrome' or 'Safari') and version, device type 
    (mobile or desktop), operating system (such as 'Windows' or 'Mac' or 'Android') and version,
    and browser dimensions.
Adds easy access to additional Twitter Bootstrap components to Shiny.
Add indicators (spinner, progress bar, gif) in your 'shiny'
  applications to show the user that the server is busy. And other tools to let
  your users know something is happening (send notifications, reports, ...).
Provides a combined slider and numeric input for usage in a 'Shiny' app. The slider and the numeric input are linked together: each one is updated when the other one changes. Many styling properties are customizable (e.g. colors and size).
Makes the 'React' library 'Chakra UI' usable in 'Shiny' apps. 'Chakra UI' components include alert dialogs, drawers (sliding panels), menus, modals, popovers, sliders, and more.
Provides a scrolling chat interface with multiline input,
    suitable for creating chatbot apps based on Large Language Models
    (LLMs). Designed to work particularly well with the 'ellmer' R package
    for calling LLMs.
Provides an easy-to-use module for adding a chat to a Shiny app. Allows
    users to send messages and view messages from other users. 
    Messages can be stored in a database or a .rds file.
A 'shiny' application estimating the operating characteristics of the Student's t-test by Student (1908) <doi:10.1093/biomet/6.1.1>, Welch's t-test by Welch (1947) <doi:10.1093/biomet/34.1-2.28>, and Wilcoxon test by Wilcoxon (1945) <doi:10.2307/3001968> in one-sample or two-sample cases, in settings defined by the user (conditional distribution, sample size per group, location parameter per group, nuisance parameter per group), using Monte Carlo simulations Malvin H. Kalos, Paula A. Whitlock (2008) <doi:10.1002/9783527626212>.
You can easily add advanced cohort-building component
    to your analytical dashboard or simple 'Shiny' app.
    Then you can instantly start building cohorts using multiple
    filters of different types, filtering datasets, and filtering steps.
    Filters can be complex and data-specific, and together
    with multiple filtering steps you can use complex filtering rules.
    The cohort-building sidebar panel allows you to easily
    work with filters, add and remove filtering steps.
    It helps you with handling missing values during filtering,
    and provides instant filtering feedback with filter feedback plots.
    The GUI panel is not only compatible with native shiny
    bookmarking, but also provides reproducible R code.
Takes one or more fitted Cox proportional hazards models and writes
    a 'shiny' application to a directory specified by the user. The 'shiny' 
    application displays predicted survival curves based on user input, and 
    contains none of the original data used to create the Cox model or models. 
    The goal is towards visualization and presentation of predicted survival
    curves.
A widget for 'shiny' apps to handle schedule expression input, using the 'cron-expression-input' JavaScript component. Note that this does not edit the 'crontab' file, it is just an input element for the schedules. See <https://github.com/DatalabFabriek/shinycroneditor/blob/main/inst/examples/shiny-app.R> for an example implementation.
When a 'Shiny' output (such as a plot, table, map, etc.) is recalculating, it remains 
    visible but gets greyed out. Using 'shinycssloaders', you can add a loading animation ("spinner")
    to outputs instead. By wrapping a 'Shiny' output in 'withSpinner()', a spinner will automatically
    appear while the output is recalculating. You can also manually show and hide the spinner, or add
    a full-page spinner to cover the entire page.
    See the demo online at <https://daattali.com/shiny/shinycssloaders-demo/>.
A custom css/html or gif/image file for the loading screen in R 'shiny'. It also can use the marquee to have custom text loading screen.
Create Interactive Graph (Network) Visualizations. 
  'shinyCyJS' can be used in 'Shiny' apps or viewed from 'Rstudio' Viewer.
  'shinyCyJS' includes API to build Graph model like node or edge with customized attributes for R. 
  'shinyCyJS' is built with 'cytoscape.js' and 'htmlwidgets' R package.
Create dashboards with 'Shiny'. This package provides a theme
    on top of 'Shiny', making it easy to create attractive dashboards.
Extend 'shinydashboard' with 'AdminLTE2' components. 
             'AdminLTE2' is a free 'Bootstrap 3' dashboard template available
             at <https://adminlte.io>. Customize boxes, add timelines and a lot more.
Provides three types of datetime pickers for usage in a
    'Shiny' UI. A datetime picker is an input field for selecting both a
    date and a time.
Provides a simple authentification mechanism for single 'shiny' applications.
    Authentification and password change functionality are performed calling user provided functions that typically access some database backend.
    Source code of main applications is protected until authentication is successful.
A 'Shiny' app can disconnect for a variety of reasons: an unrecoverable error occurred in
    the app, the server went down, the user lost internet connection, or any other reason
    that might cause the 'Shiny' app to lose connection to its server. With 'shinydisconnect', you can
    call disonnectMessage() anywhere in a Shiny app's UI to add a nice message when this happens. 
    Works locally (running Shiny apps within 'RStudio') and on Shiny servers (such as shinyapps.io,
    'RStudio Connect', 'Shiny Server Open Source', 'Shiny Server Pro'). See demo online at 
    <https://daattali.com/shiny/shinydisconnect-demo/>.
Add functionality to create drag and drop div elements in shiny.
Shiny module for easily sharing files between users. Admin can add, remove, edit and download file.
    User can only download file. It's also possible to manage files using R functions directly.
A dynamic timer control (DTC) is a 'shiny' widget that enables time-based processes in applications. It allows users to execute these processes manually in individual steps or at customizable speeds. The timer can be paused, resumed, or restarted. This control is particularly well-suited for simulations, animations, countdowns, or interactive visualizations.
'Ace' and 'Monaco' editor bindings to enable a rich text widget
    within 'shiny' application and provide more features, e.g. text comparison,
    spell checking and an extra 'SAS' code highlight mode.
Add fancy CSS effects to your 'shinydashboards' or 'shiny' apps.
             100% compatible with 'shinydashboardPlus' and 'bs4Dash'.
Enables deploying configuration file-based 'shiny' apps with minimal programming for interactive exploration and analysis showcase of molecular expression data. For exploration, supports visualization of correlations between rows of an expression matrix and a table of observations, such as clinical measures, and comparison of changes in expression over time. For showcase, enables visualizing the results of differential expression from package such as 'limma', co-expression modules from 'WGCNA' and lower dimensional projections.
Provides tools for analyzing and understanding the file contents of 
    large 'shiny' application directories. The package extracts key information about 
    render functions, reactive functions, and their inputs from app files, 
    organizing them into structured data frames for easy reference. This streamlines 
    the onboarding process for new contributors and helps identify areas for 
    optimization in complex 'shiny' codebases with multiple files and sourcing chains.
Easily display user feedback in Shiny apps.
Provides functionality for client-side navigation of
    the server side file system in shiny apps. In case the app is running
    locally this gives the user direct access to the file system without the
    need to "download" files to a temporary location. Both file and folder
    selection as well as file saving is available.
Allows to connect 'selectizeInputs' widgets as filters to a 'reactable' table. 
  As known from spreadsheet applications, column filters are interdependent, so each 
  filter only shows the values that are really available at the moment based on 
  the current selection in other filters. Filter values currently not available 
  (and also those being available) can be shown via popovers or tooltips.
Provides an interface to 'shiny' inputs used for filtering
    vectors, data.frames, and other objects. 'S7'-based implementation
    allows for seamless extensibility.
In 'Shiny' apps, it is sometimes useful to see a plot or a
    table in full screen. Using 'Shinyfullscreen', you can easily designate
    the 'HTML' elements that can be displayed on fullscreen and use buttons to 
    trigger the fullscreen view.
Provides useful UI components and input widgets for 'Shiny'
    applications.  The offered components allow to apply non-standard
    operations and view to your 'Shiny' application, but also help to
    overcome common performance issues.
Insert Glide JavaScript component into Shiny applications for
    carousel or assistant-like user interfaces.
Collection of 'shiny' application styling that are the based
    on the GOV.UK Design System.  See
    <https://design-system.service.gov.uk/components/> for details.
Access functionality of the 'heatmaply' package through 'Shiny UI'.
Creates a lightweight way to add markdown helpfiles to 'shiny' apps,
    using modal dialog boxes, with no need to observe each help button separately.
Modifies the progress() function from 'httr' package to let it 
      send output to progressBar() function from 'shinyWidgets' package. 
      It is just a tweak at the original functions from 'httr' package to 
      make it smooth for 'shiny' developers.
A tool to plot data with a large sample size using 'shiny' and 'plotly'.
    Relatively small samples are obtained from the original data using a specific algorithm.
    The samples are updated according to a user-defined x range.
    Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost (2022) <https://github.com/predict-idlab/plotly-resampler>.
Generate an invoice containing a header with invoice number and businesses details. The invoice table contains
  any of: salary, one-liner costs, grouped costs. Under the table signature and bank account details appear. 
  Pages are numbered when more than one. Source .json and .Rmd files are editable in the app. A .csv file with raw data can be downloaded.
  This package includes functions for getting exchange rates between currencies based on 
  'quantmod' (Ryan and Ulrich, 2023 <https://CRAN.R-project.org/package=quantmod>).
Performing Item Response Theory analysis such as parameter estimation, ability estimation, item and model fit analyse, local independence assumption, dimensionality assumption, characteristic and information curves under various models with a user friendly 'shiny' interface.
Package including functions and interactive shiny application
    for the psychometric analysis of educational tests, psychological
    assessments, health-related and other types of multi-item
    measurements, or ratings from multiple raters.
An extension to shiny that brings interactions and animation effects from
    'jQuery UI' library.
Perform common useful JavaScript operations in Shiny apps that will
    greatly improve your apps without having to know any JavaScript. Examples
    include: hiding an element, disabling an input, resetting an input back to
    its original value, delaying code execution by a few seconds, and many more
    useful functions for both the end user and the developer. 'shinyjs' can also
    be used to easily call your own custom JavaScript functions from R.
Provides an interactive Kanban board widget for 'shiny' applications. 
    It allows users to manage tasks using a drag-and-drop interface and offers customizable styling options. 
    'shinykanban' is ideal for project management, task tracking, and agile workflows within 'shiny' apps.
An interactive Shiny application to perform fast parameter inference on
    dynamical systems (described by ordinary differential equations) using gradient matching.
    Please see the project page for more details.
A collection of highly configurable, touch-enabled knob input controls for 'shiny'. These components can be styled to fit in perfectly in any app, and allow users to set precise values through many input modalities. Users can touch-and-drag, click-and-drag, scroll their mouse wheel, double click, or use keyboard input.
Web front end for your 'R' functions producing plots or tables.
    If you have a function or set of related functions, you can make them
    available over the internet through a web browser. This is the same
    motivation as the 'shiny' package, but note that the development of
    'shinylight' is not in any way linked to that of 'shiny' (beyond the use of
    the 'httpuv' package). You might prefer 'shinylight' to 'shiny' if you want
    a lighter weight deployment with easier horizontal scaling, or if you want
    to develop your front end yourself in JavaScript and HTML just using
    a lightweight remote procedure call interface to your R code on the
    server.
Exporting 'shiny' applications with 'shinylive' allows you to
    run them entirely in a web browser, without the need for a separate R
    server. The traditional way of deploying 'shiny' applications involves
    in a separate server and client: the server runs R and 'shiny', and
    clients connect via the web browser. When an application is deployed
    with 'shinylive', R and 'shiny' run in the web browser (via 'webR'):
    the browser is effectively both the client and server for the
    application. This allows for your 'shiny' application exported by
    'shinylive' to be hosted by a static web server.
Assesses the number of concurrent users 'shiny' applications
    are capable of supporting, and for directing application changes in
    order to support a higher number of users. Provides facilities for
    recording 'shiny' application sessions, playing recorded sessions
    against a target server at load, and analyzing the resulting metrics.
Track and record the use of applications and the user's interactions with 'Shiny' inputs.
  Allows to trace the inputs with which the user interacts, the outputs generated, 
  as well as the errors displayed in the interface.
Easily integrate and control 'Lottie' animations within 'shiny' applications', without the need for idiosyncratic expression or use of 'JavaScript'. This includes utilities for generating animation instances, controlling playback, manipulating animation properties, and more. For more information on 'Lottie', see: <https://airbnb.io/lottie/#/>. Additionally, see the official 'Lottie' GitHub repository at <https://github.com/airbnb/lottie>.
Provides functions that wrap HTML Bootstrap
    components code to enable the design and layout of informative landing home
    pages for Shiny applications. This can lead to a better user experience for
    the users and writing less HTML for the developer.
Simple and secure authentification mechanism for single 'Shiny' applications.
    Credentials are stored in an encrypted 'SQLite' database. Source code of main application
    is protected until authentication is successful.
Allows shiny developers to incorporate UI elements based on Google's Material design. See <https://material.io/guidelines/> for more information.
A 'Shiny' app allowing to compare and merge two files, with syntax highlighting for several coding languages.
Provides tools for capturing logic in a Shiny app and
    exposing it as code that can be run outside of Shiny (e.g., from an R
    console). It also provides tools for bundling both the code and
    results to the end user.
A unifying framework for managing and deploying 'shiny' applications 
    that consist of modules, where an "app" is a tab-based workflow that guides 
    a user step-by-step through an analysis. The 'shinymgr' app builder 
    "stitches" 'shiny' modules together so that outputs from one module serve as 
    inputs to the next, creating an analysis pipeline that is easy to implement 
    and maintain. Users of 'shinymgr' apps can save analyses as an RDS file that
    fully reproduces the analytic steps and can be ingested into an R Markdown 
    report for rapid reporting. In short, developers use the 'shinymgr' 
    framework to write modules and seamlessly combine them into 'shiny' apps, and 
    users of these apps can execute reproducible analyses that can be 
    incorporated into reports for rapid dissemination.
An R shiny user interface for the 'nlmixr2' (Fidler et al (2019) <doi:10.1002/psp4.12445>) package,
  designed to simplify the modeling process for users. Additionally, this package includes supplementary functions
  to further enhances the usage of 'nlmixr2'.
Implementation of a shiny app to easily compare supervised machine learning model performances. 
    You provide the data and configure each model parameter directly on the shiny app.  
    Different supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression and classification tasks.
    Implementation of available machine learning models on R has been done by Lantz (2013, ISBN:9781782162148).
Develop outstanding 'shiny' apps for 'iOS' and 'Android' as well as beautiful 'shiny' gadgets.
    'shinyMobile' is built on top of the latest 'Framework7' template <https://framework7.io>.
    Discover 14 new input widgets (sliders, vertical sliders, stepper, 
    grouped action buttons, toggles, picker, smart select, ...), 2 themes (light and dark), 
    12 new widgets (expandable cards, badges, chips, timelines, gauges, progress bars, ...) 
    combined with the power of server-side notifications such as alerts, modals, toasts,
    action sheets, sheets (and more) as well as 3 layouts (single, tabs and split).
Launch a 'shiny' application for 'tidymodels' results. For
    classification or regression models, the app can be used to determine
    if there is lack of fit or poorly predicted points.
A 'Shiny' app including the 'Monaco' editor. The 'Monaco' editor is the code editor which powers 'VS Code'. It is particularly well developed for 'JavaScript'. In addition to the 'Monaco' editor features, the app provides prettifiers and minifiers for multiple languages, 'SCSS' and 'TypeScript' compilers, code checking for 'C' and 'C++' (requires 'cppcheck').
Dual interfaces, graphical and programmatic, designed for
    intuitive applications of Multilevel Regression and Poststratification (MRP).
    Users can apply the method to a variety of datasets, from electronic health records
    to sample survey data, through an end-to-end Bayesian data analysis workflow.
    The package provides robust tools for data cleaning, exploratory analysis,
    flexible model building, and insightful result visualization. For more details, see
    Si et al. (2020) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2020002/article/00003-eng.pdf?st=iF1_Fbrh>
    and Si (2025) <doi:10.1214/24-STS932>.
A set of user interface components to create outstanding 'shiny' apps <https://shiny.posit.co/>,  with
    the power of 'React' 'JavaScript' <https://react.dev/>. Seamlessly support dark and light themes, customize CSS with
    'tailwind' <https://tailwindcss.com/>.
The computer program is an efficient igneous norm algorithm and rock classification system written in R but run as shiny app.
An enterprise-targeted scalable and customizable 'shiny' module providing an easy way to incorporate free-form note taking or discussion boards into applications.
    The package includes a 'shiny' module that can be included in any 'shiny' application to create a panel containing searchable, editable text broken down by section headers.
    Can be used with a local 'SQLite' database, or a compatible remote database of choice.
Provides a simple, configurable, provider-agnostic 'OAuth 2.0' and
    'OpenID Connect' (OIDC) authentication framework for 'shiny' applications
    using 'S7' classes. Defines providers, clients, and tokens, as well
    as various supporting functions and a 'shiny' module. Features include
    cross-site request forgery (CSRF) protection, state encryption,
    'Proof Key for Code Exchange' (PKCE) handling, validation of OIDC identity
    tokens (nonces, signatures, claims), automatic user info retrieval, asynchronous
    flows, and hooks for audit logging.
Troubleshooting reactive data in 'shiny' can be difficult. These functions will convert reactive data frames into functions and load all assigned objects into your local environment. If you create a dummy input object, as the function will suggest, you will be able to test your server and ui functions interactively.
Add shiny inputs with one or more inline buttons that grow and shrink with inputs. 
 Also add tool tips to input buttons and styling and messages for input validation.
Handle POST requests on a custom path (e.g., /ingress) inside the same 'shiny' HTTP server using user interface functions and HTTP responses. Expose latest payload as a reactive and provide helpers for query parameters.
Shiny Module to create, visualize, customize and export Excel-like pivot table.
Creates 'shiny' application ('app.R') for making predictions based on lm(), glm(), or coxph() models.
Provides functions to parse and analyze logs generated by 'ShinyProxy' containers.
    It extracts metadata from log file names, reads log contents, 
    and computes summary statistics (such as the total number of lines and lines containing 
    error messages), facilitating efficient monitoring and debugging of 'ShinyProxy' deployments.
Input widget that allows to construct complex filtering queries in 'Shiny'.
  It's a wrapper for 'JavaScript' library 'jQuery-QueryBuilder', check <https://querybuilder.js.org/>.
This 'RStudio' addin makes the creation of 'Shiny' and 'ShinyDashboard' apps more efficient.  Besides the necessary folder structure, entire apps can be created using a drag and drop interface and customized with respect to a specific use case. The addin allows the export of the required user interface and server code at any time. By allowing the creation of modules, the addin can be used throughout the entire app development process.
Simple and flexible quizzes in 'shiny'. Easily create quizzes from various pre-built question and choice types or create your own using 'htmltools' and 'shiny' packages as building blocks. Integrates with larger 'shiny' applications. Ideal for non-web-developers such as educators, data scientists, and anyone who wants to assess responses interactively in a small form factor.
It builds dynamic R shiny based dashboards to analyze any CSV files. It provides simple dashboard design to subset the data, perform exploratory data analysis and preliminary machine learning (supervised and unsupervised). It also provides filters based on columns of interest.
An input controller for R Shiny: a matrix with radio buttons, 
    where only one option per row can be selected.
A simple interface to integrate star ratings into your 'shiny' apps. 
            It can be used for customer feedback systems, user reviews, or any application that requires user ratings. 
            'shinyRatings' offers a straightforward and customisable solution that enhances user engagement and facilitates valuable feedback collection.
This gadget allows you to use the 'recipes' package belonging to 'tidymodels' to carry 
    out the data preprocessing tasks in an interactive way. Build your 'recipe' by dragging the variables, 
    visually analyze your data to decide which steps to use, add those steps and preprocess your data.
Provides functionality to extract reactive expressions from a 'shiny' application and 
    convert them into stand-alone R scripts. This enables users to reproduce tables and visualisations 
    outside the interactive UI, facilitating integration into static reports or automated workflows 
    without requiring access to the original application source code.
Shiny wrappers for the RGL package. This package exposes RGL's
    ability to export WebGL visualization in a shiny-friendly format.
A 'shiny' interface for a simpler use of the 'sbm' R package.
    It also contains useful functions to easily explore the 'sbm' package
    results.  With this package you should be able to use the stochastic
    block model without any knowledge in R, get automatic reports and nice
    visuals, as well as learning the basic functions of 'sbm'.
Create a skeleton 'shiny' application with create_template() that is
    reproducible, can be saved and meets academic standards for attribution.
    Forked from 'wallace'. Code is split into modules that are loaded and linked
    together automatically and each call one function. Guidance pages explain
    modules to users and flexible logging informs them of any errors. Options
    enable asynchronous operations, viewing of source code, interactive maps and
    data tables. Use to create complex analytical applications, following best
    practices in open science and software development. Includes functions for
    automating repetitive development tasks and an example application at
    run_shinyscholar() that requires
    install.packages("shinyscholar", dependencies = TRUE). A guide to developing
    applications can be found on the package website.
Capture screenshots in 'Shiny' applications. Screenshots can either be
    of the entire viewable page, or a specific section of the page. The captured
    image is automatically downloaded as a PNG image, or it can also be saved on
    the server. Powered by the 'html2canvas' JavaScript library.
Add a searchbar widget to your 'Shiny' application. The widget
  quickly integrates with any existing element containing text to highlight
  matches. Highlighting is done with the 'JavaScript' library 'mark.js'. The
  widget includes buttons to cycle through multiple instances of the match and
  automatically scroll to the matches in an overflow element (or window). The
  widget also displays the total number of matches and which match is currently
  being cycled through. The widget is structured as a 'Bootstrap 3' input
  group.
Provides a select control widget for 'Shiny'. It is easily
    customizable, and one can easily use HTML in the items and KaTeX to
    type mathematics.
Create in-app purchasing and subscriptions through 'Servicebot' payment using the 'Stripe' framework.
Provides interactive plotting for mathematical models of infectious disease spread. Users can choose from a variety of common built-in ordinary differential equation (ODE) models (such as the SIR, SIRS, and SIS models), or create their own. This latter flexibility allows 'shinySIR' to be applied to simple ODEs from any discipline. The package is a useful teaching tool as students can visualize how changing different parameters can impact model dynamics, with minimal knowledge of coding in R. The built-in models are inspired by those featured in Keeling and Rohani (2008) <doi:10.2307/j.ctvcm4gk0> and Bjornstad (2018) <doi:10.1007/978-3-319-97487-3>.
A graphical user interface for interactive Markov chain Monte
    Carlo (MCMC) diagnostics and plots and tables helpful for analyzing a
    posterior sample. The interface is powered by the 'Shiny' web
    application framework from 'RStudio' and works with the output of MCMC 
    programs written in any programming language (and has extended 
    functionality for 'Stan' models fit using the 'rstan' and 'rstanarm' 
    packages).
Enhance the bookmarkable state feature of 'shiny' with additional
    customization such as storage location and storage repositories leveraging
    the 'pins' package.
Store persistent and synchronized data from 'shiny' inputs within the browser. Refresh 'shiny' applications and preserve user-inputs over multiple sessions. A database-like storage format is implemented using 'Dexie.js' <https://dexie.org>, a minimal wrapper for 'IndexedDB'. Transfer browser link parameters to 'shiny' input or output values. Store app visitor views, likes and followers.
Sequences sampled at different time points can be used to infer molecular phylogenies on natural time scales, but if the sequences records inaccurate sampling times, that are not the actual sampling times, then it will affect the molecular phylogenetic analysis. This shiny application helps exploring temporal characteristics of the evolutionary trees through linear regression analysis and with the ability to identify and remove incorrect labels. The method was extended to support exploring other phylogenetic signals under strict and relaxed models.
Please see the 'shinytest' to 'shinytest2' migration guide at <https://rstudio.github.io/shinytest2/articles/z-migration.html>.
Automated unit testing of Shiny applications through a
    headless 'Chromium' browser.
It's my experience that working with 'shiny' is intuitive once you're
    into it, but can be quite daunting at first. Several common mistakes are fairly
    predictable, and therefore we can control for these. The functions in this
    package help match up the assets listed in the UI and the SERVER files, and
    Visualize the ad hoc structure of the 'shiny' App.
Create mocked bindings to 'Shiny' update functions within test function calls
    to automatically update input values. The mocked bindings simulate the communication
    between the server and UI components of a 'Shiny' module in testServer().
Themes for use with Shiny. Includes several Bootstrap themes
    from <https://bootswatch.com/>, which are packaged for use with Shiny
    applications.
Provides a time input widget for Shiny. This widget allows intuitive time input in the
    '[hh]:[mm]:[ss]' or '[hh]:[mm]' (24H) format by using a separate numeric input for each time
    component. The interface with R uses date-time objects. See the project page for more
    information and examples.
Provides a customizable timer widget for 'shiny' applications. Key 
    features include countdown and count-up mode, multiple display formats (including 
    simple seconds, minutes-seconds, hours-minutes-seconds, and minutes-seconds-centiseconds), 
    ability to pause, resume, and reset the timer. 'shinytimer' widget can be 
    particularly useful for creating interactive and time-sensitive applications, 
    tracking session times, setting time limits for tasks or quizzes, and more.
Enables the ability to change or flash the title of the browser window during a 'shiny' session.
This is a wrapper of the 'React' library 'React-Toastify'. It allows to show some notifications (toasts) in 'Shiny' applications. There are options for the style, the position, the transition effect, and more.
Browser notifications in 'Shiny' apps, using
    'toastr': <https://github.com/CodeSeven/toastr#readme>.
Exposes bindings to jsTree -- a JavaScript library
    that supports interactive trees -- to enable a rich, editable trees in
    Shiny.
Improves the user experience of Shiny apps by helping to
    provide feedback when required inputs are missing, or input values
    are not valid.
Provides a comprehensive 'Shiny' application for analyzing 
    Whole Genome Duplication ('WGD') events.
    This package provides a user-friendly 'Shiny' web application for 
    non-experienced researchers to prepare input data and execute command lines 
    for several well-known 'WGD' analysis tools, including 'wgd', 'ksrates', 
    'i-ADHoRe', 'OrthoFinder', and 'Whale'. This package also provides the source code 
    for experienced researchers to adjust and install the package to their own server. 
    Key Features
    1) Input Data Preparation 
    This package allows users to conveniently upload and format their data, 
	making it compatible with various 'WGD' analysis tools.
    2) Command Line Generation
    This package automatically generates the necessary command lines for 
	selected 'WGD' analysis tools, reducing manual errors and saving time.
    3) Visualization
    This package offers interactive visualizations to explore and interpret 'WGD' results, 
	facilitating in-depth 'WGD' analysis.
    4) Comparative Genomics
    Users can study and compare 'WGD' events across different species, 
	aiding in evolutionary and comparative genomics studies.
    5) User-Friendly Interface 
	This 'Shiny' web application provides an intuitive and accessible interface, 
	making 'WGD' analysis accessible to researchers and 'bioinformaticians' of all levels.
Collection of custom input controls and user interface components for 'Shiny' applications. 
  Give your applications a unique and colorful style !
Design, build, and deploy R packages demo presentations by an interactive wizard. Set up unique title, logo and themes. Add personalized tabs exposing applicability. And deploy as a part of a package or an independent app.
Provides an XY pad input for the 'Shiny' framework. An XY pad is like a bivariate slider. It allows to pick up a pair of numbers.
Implements estimation methods for shrinkage covariance matrices
    using user-specified covariance targets. The covariance target is a structured
    matrix towards which the unbiased sample covariance is shrunk, optionally
    incorporating prior knowledge. Shrinkage intensity is computed analytically.
    The method is described and applied to microarray gene expression data in
    Jelizarow et al. (2010) <doi:10.1093/bioinformatics/btq323>.
Implements statistical methods for detecting evolutionary shifts
    in both the optimal trait value (mean) and evolutionary diffusion variance.
    The method uses an L1-penalized optimization framework to identify branches 
    where shifts occur, and the shift magnitudes. It also supports the 
    inclusion of measurement error. For more details, see Zhang, Ho, and Kenney (2023) <doi:10.48550/arXiv.2312.17480>.
Collect  your data on digital marketing campaigns from 'Shopify' Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Provides several datasets useful for processing and analysis of text in Turkish from an online shopping platform.
Provides tools for shoreline dating coastal Stone Age sites. The 
  implemented method was developed in Roalkvam (2023) 
  <doi:10.1016/j.quascirev.2022.107880> for the Norwegian Skagerrak coast. 
  Although it can be extended to other areas, this also forms the core area for 
  application of the package. Shoreline dating is based on the present-day 
  elevation of a site, a reconstruction of past relative sea-level change, and
  empirically derived estimates of the likely elevation of the sites above the 
  contemporaneous sea-level when they were in use. The geographical and temporal 
  coverage of the method thus follows from the availability of local geological 
  reconstructions of shoreline displacement and the degree to which the 
  settlements to be dated have been located on or close to the shoreline when 
  they were in use. Methods for numerical treatment and visualisation of the 
  dates are provided, along with basic tools for visualising and evaluating the 
  location of sites.
Integrates clipboard copied data in R Studio, loads and installs libraries within a R script and returns all valid arguments of a selected function.
Performs automatic creation of short forms of scales with an 
    ant colony optimization algorithm and a Tabu search. As implemented in the 
    package, the ant colony algorithm randomly selects items to build a model of 
    a specified length, then updates the probability of item selection according 
    to the fit of the best model within each set of searches. The algorithm 
    continues until the same items are selected by multiple ants a given number 
    of times in a row. On the other hand, the Tabu search changes one parameter at
    a time to be either free, constrained, or fixed while keeping track of the
    changes made and putting changes that result in worse fit in a "tabu" list
    so that the algorithm does not revisit them for some number of searches. 
    See Leite, Huang, & Marcoulides (2008) <doi:10.1080/00273170802285743> for
    an applied example of the ant colony algorithm, and Marcoulides & Falk (2018)
    <doi:10.1080/10705511.2017.1409074> for an applied example of the Tabu search.
Implement different Item Response Theory (IRT) based procedures for the development 
        of static short test forms (STFs) from a test. Two main procedures are considered (Epifania, Anselmi & Robusto, 2022 <doi:10.1007/978-3-031-27781-8_7>).
        The procedures differ in how the most informative items are selected for the inclusion in the STF, either by considering their item information functions without any reference to any specific latent trait level (benchmark procedure) or by considering their information 
        with respect to specific latent trait levels, denoted as theta targets (theta target procedure). 
        Three methods are implemented for the definition of the theta targets: (i) as the midpoints of equal intervals on the latent trait, 
        (ii) as the centroids of the clusters obtained by clustering the latent trait, 
        and (iii) as user-defined values. Importantly, the number of theta targets defines the number of items included in the STF.
        For further details on the procedure, please refer to Epifania, Anselmi & Robusto (2022) <doi:10.1007/978-3-031-27781-8_7>.
Identifies what optimal subset of a desired number of items should be retained in a short version of a psychometric instrument to assess the “broadest” proportion of the construct-level content of the set of items included in the original version of the said psychometric instrument. Expects a symmetric adjacency matrix as input (undirected weighted network model). Supports brute force and simulated annealing combinatorial search algorithms.
Create short sprint acceleration-velocity (AVP) and force-velocity (FVP) profiles
     and predict kinematic and kinetic variables using the timing-gate split times, laser or 
     radar gun data, tether devices data, as well as the data provided by the GPS and LPS 
     monitoring systems. The modeling method utilized in this package is based on the works of 
    Furusawa K, Hill AV, Parkinson JL (1927) <doi: 10.1098/rspb.1927.0035>, 
    Greene PR. (1986) <doi: 10.1016/0025-5564(86)90063-5>, 
    Chelly SM, Denis C. (2001) <doi: 10.1097/00005768-200102000-00024>,
    Clark KP, Rieger RH, Bruno RF, Stearne DJ. (2017) <doi: 10.1519/JSC.0000000000002081>, 
    Samozino P. (2018) <doi: 10.1007/978-3-319-05633-3_11>, 
    Samozino P. and Peyrot N., et al (2022) <doi: 10.1111/sms.14097>, 
    Clavel, P., et al (2023) <doi: 10.1016/j.jbiomech.2023.111602>, 
    Jovanovic M. (2023) <doi: 10.1080/10255842.2023.2170713>, and
    Jovanovic M., et al (2024) <doi: 10.3390/s24092894>.
Generate and translate standard Universally Unique Identifiers (UUIDs) into shorter - or just different - formats and back. Also implements base58 encoders and decoders.
Analyzes shooting data with respect to group shape,
        precision, and accuracy. This includes graphical methods,
        descriptive statistics, and inference tests using standard,
        but also non-parametric and robust statistical methods.
        Implements distributions for radial error in bivariate normal
        variables. Works with files exported by 'OnTarget PC/TDS',
        'Silver Mountain' e-target, 'ShotMarker' e-target, 'SIUS'
        e-target, or 'Taran', as well as with custom data files in
        text format. Supports inference from range statistics such as
        extreme spread. Includes a set of web-based graphical user
        interfaces.
Sometimes it is handy to be able to view an image file on an
    'R' graphics device. This package just does that. Currently it supports
    'PNG' files.
Making it easy to use various types of fonts ('TrueType',
    'OpenType', Type 1, web fonts, etc.) in R graphs, and supporting most output
    formats of R graphics including PNG, PDF and SVG. Text glyphs will be converted
    into polygons or raster images, hence after the plot has been created, it no
    longer relies on the font files. No external software such as 'Ghostscript' is
    needed to use this package.
Providing font files that can be used by the 'showtext' package.
Functions for converting and processing network data from a
        'SpatialLinesDataFrame' -Class object to an 'igraph'-Class object.
Setwise Hierarchical Rate of Erroneous Discovery (SHRED) methods for setwise variable selection with false discovery rate (FDR) control. Setwise variable selection means that sets of variables may be selected when the true variable cannot be identified. This allows us to maintain FDR control but increase power. Details of the SHRED methods are in Organ, Kenney & Gu (2026) <doi:10.48550/arXiv.2603.02160>.
The predictive value of a statistical model can often be improved
    by applying shrinkage methods. This can be achieved, e.g., by regularized
    regression or empirical Bayes approaches. Various types of shrinkage factors can
    also be estimated after a maximum likelihood. While global shrinkage modifies
    all regression coefficients by the same factor, parameterwise shrinkage factors
    differ between regression coefficients. With variables which are either highly
    correlated or associated with regard to contents, such as several columns of a
    design matrix describing a nonlinear effect, parameterwise shrinkage factors are
    not interpretable and a compromise between global and parameterwise shrinkage,
    termed 'joint shrinkage', is a useful extension. A computational shortcut to
    resampling-based shrinkage factor estimation based on DFBETA residuals can be
    applied. Global, parameterwise and joint shrinkage for models fitted by lm(),
    glm(), coxph(), or mfp() is available.
Bayesian regression tree ensembles for survival analysis and causal
 inference. Implements BART, DART, Bayesian Causal 
  Forests (BCF), and Horseshoe Forests models.
  Supports right-censored survival outcomes 
  via accelerated failure time (AFT) formulations. Designed for 
  high-dimensional prediction and heterogeneous treatment effect 
  estimation in causal inference.
Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.
Efficient Markov chain Monte Carlo (MCMC) algorithms for fully 
    Bayesian estimation of dynamic survival models with shrinkage priors. 
    Details on the algorithms used are provided in Wagner (2011) <doi:10.1007/s11222-009-9164-5>, 
    Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and
    Cadonna et al. (2020) <doi:10.3390/econometrics8020020>.
Approximate Bayesian regularization using Gaussian approximations. The input is a vector of estimates
             and a Gaussian error covariance matrix of the key parameters. Bayesian shrinkage is then applied
             to obtain parsimonious solutions. The method is described on 
             Karimova, van Erp, Leenders, and Mulder (2024) <DOI:10.31234/osf.io/2g8qm>. Gibbs samplers are used
             for model fitting. The shrinkage priors that are supported are Gaussian (ridge) priors, Laplace
             (lasso) priors (Park and Casella, 2008 <DOI:10.1198/016214508000000337>), and horseshoe priors
             (Carvalho, et al., 2010; <DOI:10.1093/biomet/asq017>). These priors include an option
             for grouped regularization of different subsets of parameters (Meier et al., 2008; 
             <DOI:10.1111/j.1467-9868.2007.00627.x>). F priors are used for the penalty
             parameters lambda^2 (Mulder and Pericchi, 2018 <DOI:10.1214/17-BA1092>). This correspond to
             half-Cauchy priors on lambda (Carvalho, Polson, Scott, 2010 <DOI:10.1093/biomet/asq017>).
Efficient variational inference methods for fully Bayesian univariate 
  and multivariate Gaussian and t-process regression models. Hierarchical shrinkage priors, 
  including the triple gamma prior, are used for effective variable selection and 
  covariance shrinkage in high-dimensional settings. The package leverages normalizing 
  flows to approximate complex posterior distributions. For details on implementation, 
  see Knaus (2025) <doi:10.48550/arXiv.2501.13173>.
Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors, both dynamic and static. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and  
  Cadonna et al. (2020) <doi:10.3390/econometrics8020020> and Knaus and Frühwirth-Schnatter (2023)  <doi:10.48550/arXiv.2312.10487>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>. For the multivariate extension, see the 'shrinkTVPVAR' package.
Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter 
  vector autoregressive models with stochastic volatility (TVP-VAR-SV) under shrinkage priors and dynamic shrinkage processes. 
  Details on the TVP-VAR-SV model and the shrinkage priors can be found in Cadonna et al. (2020) <doi:10.3390/econometrics8020020>, 
  details on the software can be found in Knaus et al. (2021) <doi:10.18637/jss.v100.i13>, while details on the dynamic shrinkage process
  can be found in Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>.
Programmatic access to Flipside Crypto data via the Compass RPC API: <https://api-docs.flipsidecrypto.xyz/>. As simple as auto_paginate_query() but with core functions as needed for troubleshooting. Note, 0.1.1 support deprecated 2023-05-31.
We provide a collection of statistical hypothesis testing procedures ranging from classical to modern methods for non-trivial settings such as high-dimensional scenario. For the general treatment of statistical hypothesis testing, see the book by Lehmann and Romano (2005) <doi:10.1007/0-387-27605-X>.
Implementation of the shuffle estimator, a non-parametric estimator for signal and noise variance under mild noise correlations.
Scripts and exercises that use card shuffling to teach confidence interval comparisons for different estimators.
Shows the scatter plot along with the fitted regression lines. It 
  depicts min, max, the three quartiles, mean, and sd for each variable.
  It also depicts sd-line, sd-box, r, r-square, prediction boundaries, and regression outliers.
Package including additional modules for interactive
    'ShinyItemAnalysis' application for the psychometric analysis of
    educational tests, psychological assessments, health-related and other
    types of multi-item measurements, or ratings from multiple raters.
A comprehensive suite of functions designed for constructing
    and managing 'ShinyItemAnalysis' modules, supplemented with detailed
    guides, ready-to-use templates, linters, and tests. This package
    allows developers to seamlessly create and integrate one or more
    modules into their existing packages or to start a new module project
    from scratch.
Fits bi-variate ellipses to stable isotope data using Bayesian
    inference with the aim being to describe and compare their isotopic
    niche.
Provides models to identify bimodally expressed genes from
 RNAseq data based on the Bimodality Index. SIBERG models the  RNAseq data in
 the finite mixture modeling framework and incorporates mechanisms  for
 dealing with RNAseq normalization. Three types of mixture models are
 implemented,  namely, the mixture of log normal, negative binomial, or 
 generalized Poisson distribution. See Tong et al. (2013)
 <doi:10.1093/bioinformatics/bts713>.
Aims to quantify time intensity data by using sigmoidal and
    double sigmoidal curves. It fits straight lines, sigmoidal, 
    and double sigmoidal curves on to time vs intensity data. 
    Then all the fits are used to make decision on which model 
    best describes the data. This method was first developed 
    in the context of single-cell viral growth analysis (for
    details, see Caglar et al. (2018) <doi:10.7717/peerj.4251>),
    and the package name stands for "SIngle CEll Growth Analysis in R".
Provides a lightweight runtime type system for 'R' that enables
    developers to declare and enforce variable types during execution.
    Inspired by 'TypeScript', the package introduces intuitive syntax for
    annotating variables and validating data structures, helping catch
    type-related errors early and making 'R' code more robust and easier
    to maintain.
Fetch data on targeted public investments from Plataforma +Brasil (SICONV) <http://plataformamaisbrasil.gov.br/>, 
    the responsible system for requests, execution, and monitoring of federal discretionary transfers in Brazil.
Single-index mixture cure models allow estimating the probability of cure and the latency depending on a vector (or functional) covariate, avoiding the curse of dimensionality. The vector of parameters that defines the model can be estimated by maximum likelihood. A nonparametric estimator for the conditional density of the susceptible population is provided. For more details, see Piñeiro-Lamas (2024) (<https://ruc.udc.es/dspace/handle/2183/37035>). Funding: This work, integrated into the framework of PERTE for Vanguard Health, has been co-financed by the Spanish Ministry of Science, Innovation and Universities with funds from the European Union NextGenerationEU, from the Recovery, Transformation and Resilience Plan (PRTR-C17.I1) and from the Autonomous Community of Galicia within the framework of the Biotechnology Plan Applied to Health.
The code computes the structural intervention distance (SID) between a true directed acyclic graph (DAG) and an estimated DAG. Definition and details about the implementation can be found in J. Peters and P. Bühlmann: "Structural intervention distance (SID) for evaluating causal graphs", Neural Computation 27, pages 771-799, 2015  <doi:10.1162/NECO_a_00708>.
Provides function to apply "Subgroup Identification based on Differential Effect Search" (SIDES) method proposed by Lipkovich et al. (2011) <doi:10.1002/sim.4289>.
Evolutionary reconstruction based on substitutions and insertion-deletion (indels) analyses in a distance-based framework as described in Muñoz-Pajares (2013) <doi:10.1111/2041-210X.12118>.
Allows the user to connect with IBGE's (Instituto Brasileiro de 
    Geografia e Estatistica, see <https://www.ibge.gov.br/> for more information)
    SIDRA API in a flexible way. SIDRA is the acronym to "Sistema IBGE de 
    Recuperacao Automatica" and is the system where IBGE turns available 
    aggregate data from their researches.
We provide functions for estimation and inference of locally-stationary time series using the sieve methods and bootstrapping procedure. In addition, it also contains functions to generate Daubechies and Coiflet wavelet by Cascade algorithm and to process data visualization.
Allows to retrieve time series of all indicators available in the Bank of Mexico's Economic Information System (<http://www.banxico.org.mx/SieInternet/>).
Methods for regression with high-dimensional predictors and  univariate or maltivariate response variables. It considers the decomposition of the coefficient matrix that leads to the best approximation to the signal part in the response given any rank, and estimates the decomposition by solving a penalized generalized eigenvalue problem followed by a least squares procedure. Ruiyan Luo and Xin Qi (2017) <doi:10.1016/j.jmva.2016.09.005>.
Analysis Results Standard (ARS), a foundational standard by CDISC
    (Clinical Data Interchange Standards Consortium), provides a logical
    data model for metadata describing all components to calculate Analysis Results. 
    <https://www.cdisc.org/standards/foundational/analysis-results-standard>
    Using 'siera' package, ARS metadata is ingested (JSON or Excel format),
    producing programmes to generate Analysis Results Datasets (ARDs).
Performs multivariate nonparametric regression/classification by the method of sieves (using orthogonal basis). The method is suitable for moderate high-dimensional features (dimension < 100). The l1-penalized sieve estimator, a nonparametric generalization of Lasso, is adaptive to the feature dimension with provable theoretical guarantees. We also include a nonparametric stochastic gradient descent estimator, Sieve-SGD, for online or large scale batch problems. Details of the methods can be found in: <arXiv:2206.02994> <arXiv:2104.00846><arXiv:2310.12140>.
Functions for making particle-size analysis. Sieve tests are widely used to obtain particle-size distribution of powders or granular materials.
Cluster-independent method based on topology structure of gene co-expression network for identifying feature gene sets, extracting cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Without prior cell clustering, SifiNet circumvents potential inaccuracies in clustering that may influence subsequent analyses. This method is introduced in Qi Gao, Zhicheng Ji, Liuyang Wang, Kouros Owzar, Qi-Jing Li, Cliburn Chan, Jichun Xie "SifiNet: a robust and accurate method to identify feature gene sets and annotate cells" (2024) <doi:10.1093/nar/gkae307>.
Print function signatures and find overly complicated code.
Provides fundamental function support for 'SigBridgeR' and its single-cell phenotypic screening algorithm, including optional functions.
SigClust is a statistical method for testing the
        significance of clustering results. SigClust can be applied to
        assess the statistical significance of splitting a data set
        into two clusters. For more than two clusters, SigClust can be
        used iteratively.
Uses logistic regression to model the probability of detection as a function of covariates. 
             This model is then used with observational survey data to estimate population size, while
             accounting for uncertain detection.  See Steinhorst and Samuel (1989).
Provides pseudo-likelihood methods for empirically analyzing common signaling games in international relations as described in Crisman-Cox and Gibilisco (2019) <doi:10.1017/psrm.2019.58>.
Interface to 'sigma.js' graph visualization library including animations, plugins and shiny proxies.
Genomic alterations including single nucleotide substitution,
    copy number alteration, etc. are the major force for cancer
    initialization and development. Due to the specificity of molecular
    lesions caused by genomic alterations, we can generate characteristic
    alteration spectra, called 'signature' (Wang, Shixiang, et al. (2021)
    <DOI:10.1371/journal.pgen.1009557> & Alexandrov, Ludmil B., et al.
    (2020) <DOI:10.1038/s41586-020-1943-3> & Steele Christopher D., et al.
    (2022) <DOI:10.1038/s41586-022-04738-6>).  This package helps users to
    extract, analyze and visualize signatures from genomic alteration
    records, thus providing new insight into cancer study.
Several different sigmoid functions are implemented, including a wrapper function, SoftMax preprocessing and inverse functions.
Provides a classification framework to use expression patterns of pathways as features to identify similarity between biological samples. It provides a new measure for quantifying similarity between expression patterns of pathways.
A framework for the analysis and exploration of single-cell chromatin data.
    The 'Signac' package contains functions for quantifying single-cell chromatin data,
    computing per-cell quality control metrics, dimension reduction
    and normalization, visualization, and DNA sequence motif analysis.
    Reference: Stuart et al. (2021) <doi:10.1038/s41592-021-01282-5>.
An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.
A set of signal processing functions originally written for 'Matlab' and 'Octave'.
  Includes filter generation utilities, filtering functions,
  resampling routines, and visualization of filter models. It also
  includes interpolation functions.
Predicts the presence of signal peptides in eukaryotic protein
    using hidden semi-Markov models. The implemented algorithm can be accessed from
    both the command line and GUI.
Provides a comprehensive toolkit for extracting latent signals from
    panel data through multivariate time series analysis. Implements spectral
    decomposition methods including wavelet multiresolution analysis via maximal
    overlap discrete wavelet transform, Percival and Walden (2000)
    <doi:10.1017/CBO9780511841040>, empirical mode decomposition for
    non-stationary signals, Huang et al. (1998) <doi:10.1098/rspa.1998.0193>,
    and Bayesian trend extraction via the Grant-Chan embedded Hodrick-Prescott
    filter, Grant and Chan (2017) <doi:10.1016/j.jedc.2016.12.007>. Features
    Bayesian variable selection through regularized Horseshoe priors, Piironen
    and Vehtari (2017) <doi:10.1214/17-EJS1337SI>, for identifying structurally
    relevant predictors from high-dimensional candidate sets. Includes dynamic
    factor model estimation, principal component analysis with bootstrap
    significance testing, and automated technical interpretation of signal
    morphology and variance topology.
Extract the signed backbones of intrinsically dense weighted networks based on the significance filter and vigor filter as described in the following paper. Please cite it if you find this software useful in your work.	
	Furkan Gursoy and Bertan Badur. "Extracting the signed backbone of intrinsically dense weighted networks." Journal of Complex Networks. <arXiv:2012.05216>.
Add significance marks to any R Boxplot, including a given significance niveau.
Provides significance controlled variable selection algorithms with different directions (forward, backward, stepwise) based on diverse criteria (AIC, BIC, adjusted r-square, PRESS, or p-value). The algorithm selects a final model with only significant variables defined as those with significant p-values after multiple testing correction such as Bonferroni, False Discovery Rate, etc. See Zambom and Kim (2018) <doi:10.1002/sta4.210>.
Methods for the analysis of signed networks. This includes
    several measures for structural balance as introduced by Cartwright
    and Harary (1956) <doi:10.1037/h0046049>, blockmodeling algorithms
    from Doreian (2008) <doi:10.1016/j.socnet.2008.03.005>, various
    centrality indices, and projections of signed two-mode networks
    introduced by Schoch (2020) <doi:10.1080/0022250X.2019.1711376>.
Provides convenience functions to replace hyphen-minuses (ASCII 45)
    with proper minus signs (Unicode character 2212). The true minus matches
    the plus symbol in width, line thickness, and height above the baseline.
    It was designed for mathematics, looks better in presentation,
    and is understood properly by screen readers.
Interfaces with the 'SigOpt' API. More info at <https://sigopt.com>.
Pathway Analysis is statistically linking observations on 
     the molecular level to biological processes or pathways on 
     the systems(i.e., organism, organ, tissue, cell) level.  
     Traditionally, pathway analysis methods regard pathways 
     as collections of single genes and treat all genes in a pathway
     as equally informative. However, this can lead to identifying
     spurious pathways as statistically significant since components
     are often shared amongst pathways. SIGORA seeks to avoid this
     pitfall by focusing on genes or gene pairs that are (as a combination)
     specific to a single pathway.
     In relying on such pathway gene-pair signatures (Pathway-GPS),
     SIGORA inherently uses the status of other genes in the
     experimental context to identify the most relevant pathways.
     The current version allows for pathway analysis of human
     and mouse datasets. In addition, it contains pre-computed
     Pathway-GPS data for pathways in the KEGG and  Reactome
     pathway repositories and mechanisms for extracting GPS
     for user-supplied repositories.
Succinctly and correctly format statistical summaries of
    various models and tests (F-test, Chi-Sq-test, Fisher-test, T-test, and rank-significance). 
    This package also includes empirical tests, such as Monte Carlo and bootstrap distribution estimates.
Streamlines geographic data transformation, storage and
    publication, simplifying data preparation and enhancing
    interoperability across formats and platforms.
The goal of SIHR is to provide inference procedures in the high-dimensional generalized linear regression setting for:
    (1) linear functionals <doi:10.48550/arXiv.1904.12891> <doi:10.48550/arXiv.2012.07133>,
    (2) conditional average treatment effects,
    (3) quadratic functionals <doi:10.48550/arXiv.1909.01503>,
    (4) inner product,
    (5) distance.
Calculate superior identification index and its extensions.
    Measure the performance of journals based on how well they could 
    identify the top papers by any index (e.g. citation indices) according to Huang & Yang. 
    (2022) <doi:10.1007/s11192-022-04372-z>. These methods could be extended to 
    evaluate other entities such as institutes, countries, etc.
Method to estimate the spatial influence scales of landscape variables on a response variable. The method is based on Chandler and Hepinstall-Cymerman (2016) Estimating the spatial scales of landscape effects on abundance, Landscape ecology, 31: 1383-1394, <doi:10.1007/s10980-016-0380-z>.
In various domains, many datasets exhibit both high variable dependency and group structures, which necessitates their simultaneous estimation. This package provides functions for two subgroup identification methods based on penalized functions, both of which utilize factor model structures to adapt to data with cross-sectional dependency. The first method is the Subgroup Identification with Latent Factor Structure Method (SILFSM) we proposed. By employing Center-Augmented Regularization and factor structures, the SILFSM effectively eliminates data dependencies while identifying subgroups within datasets. For this model, we offer optimization functions based on two different methods: Coordinate Descent and our newly developed Difference of Convex-Alternating Direction Method of Multipliers (DC-ADMM) algorithms; the latter can be applied to cases where the distance function in Center-Augmented Regularization takes L1 and L2 forms. The other method is the Factor-Adjusted Pairwise Fusion Penalty (FA-PFP) model, which incorporates factor augmentation into the Pairwise Fusion Penalty (PFP) developed by Ma, S. and Huang, J. (2017) <doi:10.1080/01621459.2016.1148039>. Additionally, we provide a function for the Standard CAR (S-CAR) method, which does not consider the dependency and is for comparative analysis with other approaches. Furthermore, functions based on the Bayesian Information Criterion (BIC) of the SILFSM and the FA-PFP method are also included in 'SILFS' for selecting tuning parameters. For more details of Subgroup Identification with Latent Factor Structure Method, please refer to He et al. (2024) <doi:10.48550/arXiv.2407.00882>.
Provides a general framework to perform statistical inference of each gene pair 
        and global inference of whole-scale gene pairs in gene networks using the well known 
        Gaussian graphical model (GGM) in a time-efficient manner. We focus on the high-dimensional 
        settings where p (the number of genes) is allowed to be far larger than n (the number of subjects). 
        Four main approaches are supported in this package: (1) the bivariate nodewise scaled Lasso 
        (Ren et al (2015) <doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled Lasso 
        (Jankova and van de Geer (2017) <doi:10.1007/s11749-016-0503-5>) (3) the de-sparsified 
        graphical Lasso (Jankova and van de Geer (2015) <doi:10.1214/15-EJS1031>) (4) the GGM 
        estimation with false discovery rate control (FDR) using scaled Lasso or Lasso 
        (Liu (2013) <doi:10.1214/13-AOS1169>). Windows users should install 'Rtools' before the 
        installation of this package.
Quantifies clustering quality by measuring both cohesion within clusters and separation between clusters. Implements advanced silhouette width computations for diverse clustering structures, including: simplified silhouette (Van der Laan et al., 2003) <doi:10.1080/0094965031000136012>, Probability of Alternative Cluster normalization methods (Raymaekers & Rousseeuw, 2022) <doi:10.1080/10618600.2022.2050249>, fuzzy clustering and silhouette diagnostics using membership probabilities (Campello & Hruschka, 2006; Menardi, 2011; Bhat & Kiruthika, 2024) <doi:10.1016/j.fss.2006.07.006>, <doi:10.1007/s11222-010-9169-0>, <doi:10.1080/23737484.2024.2408534>, and multi-way clustering extensions such as block and tensor clustering (Schepers et al., 2008; Bhat & Kiruthika, 2025) <doi:10.1007/s00357-008-9005-9>, <doi:10.21203/rs.3.rs-6973596/v1>. Provides tools for computation and visualization (Rousseeuw, 1987) <doi:10.1016/0377-0427(87)90125-7> to support robust and reproducible cluster diagnostics across standard, soft, and multi-way clustering settings.
Generate common data forms for complex data suitable for conversions and
 transmission by decomposition as paths or primitives. Paths are sequentially-linked records, 
 primitives are basic atomic elements and both can model many forms and be grouped into hierarchical 
 structures.  The universal models 'SC0' (structural) and 'SC' (labelled, relational) are composed of 
 edges and can represent any hierarchical form. Specialist models 'PATH', 'ARC' and 'TRI' provide the 
 most common intermediate forms used for converting from one form to another. The methods are
 inspired by the simplicial complex <https://en.wikipedia.org/wiki/Simplicial_complex> and 
 provide intermediate forms that relate spatial data structures to this mathematical construct.
Implements a simple, novel clustering algorithm based on
  optimizing the silhouette width. See <doi:10.1101/2023.11.07.566055>
  for details.
Simultaneous inference procedures for high-dimensional linear models as described by Zhang, X., and Cheng, G. (2017) <doi:10.1080/01621459.2016.1166114>.
Utilizes the Reliability-Adjusted Product Indicator (RAPI) method to 
    estimate effects among latent variables, thus allowing for more precise definition and analysis of 
    mediation and moderation models. Our simulation studies reveal that while 'silp' may exhibit 
    instability with smaller sample sizes and lower reliability scores (e.g., N = 100, 'omega' = 0.7), 
    implementing nearest positive definite matrix correction and bootstrap confidence interval 
    estimation can significantly ameliorate this volatility. When these adjustments are applied, 
    'silp' achieves estimations akin in quality to those derived from LMS. In conclusion, the 'silp' 
    package is a valuable tool for researchers seeking to explore complex relational structures between 
    variables without resorting to commercial software.
    Cheung et al.(2021)<doi:10.1007/s10869-020-09717-0>
    Hsiao et al.(2018)<doi:10.1177/0013164416679877>.
Perform common dendrometry operations such as inventory preparing, and inventory data analysis.
Allows user to conduct a simulation based quantitative
             bias analysis using covariate structures generated with individual-level
             data to characterize the bias arising from unmeasured confounding. Users
             can specify their desired data generating mechanisms to simulate data
             and quantitatively summarize findings in an end-to-end application
             using this package.
It provides users with a wide range of tools to simulate, estimate, analyze, and visualize the dynamics of stochastic differential systems in both forms Ito and Stratonovich. Statistical analysis with parallel Monte Carlo and moment equations methods of SDEs <doi:10.18637/jss.v096.i02>. Enabled many searchers in different domains to use these equations to modeling practical problems in financial and actuarial modeling and other areas of application, e.g., modeling and simulate of first passage time problem in shallow water using the attractive center (Boukhetala K, 1996) ISBN:1-56252-342-2.
The definition of fuzzy random variable and the methods of simulation from fuzzy random variables are two challenging statistical problems in three recent decades. This package is organized based on a special definition of fuzzy random variable and simulate fuzzy random variable by Piecewise Linear Fuzzy Numbers (PLFNs); see Coroianua et al. (2013) <doi:10.1016/j.fss.2013.02.005> for details about PLFNs. Some important statistical functions are considered for obtaining the membership function of main statistics, such as mean, variance, summation, standard deviation and coefficient of variance. Some of applied advantages of 'Sim.PLFN' package are:  (1) Easily generating / simulation a random sample of PLFN, (2) drawing the membership functions of the simulated PLFNs or the membership function of the statistical result, and (3) Considering the simulated PLFNs for arithmetic operation or importing into some statistical computation. Finally, it must be mentioned that 'Sim.PLFN' package works on the basis of 'FuzzyNumbers' package.
Provides tools for simulating spatially dependent predictors (continuous or binary),
    which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous
    predictors are generated using traditional multivariate normal distributions or Gauss Markov random
    fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288>
    and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using
    a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors 
	exhibiting spatial clustering can also be easily specified by the user.
Monitoring reporting rates of subject-level clinical events (e.g.
  adverse events, protocol deviations) reported by clinical trial sites is an
  important aspect of risk-based quality monitoring strategy. Sites that are 
  under-reporting or over-reporting events can be detected using bootstrap
  simulations during which patients are redistributed between sites. Site-specific
  distributions of event reporting rates  are generated that are used to assign
  probabilities to the observed reporting rates.
  (Koneswarakantha 2024 <doi:10.1007/s43441-024-00631-8>).
Functions for obtaining p-values (for hypothesis tests), confidence intervals, and multivariate confidence sets. In particular, the method is compatible with differentially private dataset, as long as the privacy mechanism is known. For more details, see Awan and Wang (2024), "Simulation-based, Finite-sample Inference for Privatized Data", <doi:10.48550/arXiv.2303.05328>.
Provides some code to run simulations of state-space models, and then
             use these in the Approximate Bayesian Computation Sequential Monte Carlo (ABC-SMC) 
             algorithm of Toni et al. (2009) <doi:10.1098/rsif.2008.0172> and a bootstrap particle
             filter based particle Markov chain Monte Carlo (PMCMC) algorithm 
             (Andrieu et al., 2010 <doi:10.1111/j.1467-9868.2009.00736.x>). 
             Also provides functions to plot and summarise the outputs.
Provides a suite of helper functions to support Bayesian Kernel 
    Machine Regression (BKMR) analyses in environmental health research. It 
    enables the simulation of realistic multivariate exposure data using 
    Multivariate Skewed Gamma distributions, estimation of distributional 
    parameters by subgroup, and application of adaptive, data-driven thresholds 
    for feature selection via Posterior Inclusion Probabilities (PIPs). It is 
    especially suited for handling skewed exposure data and enhancing the 
    interpretability of BKMR results through principled variable selection. The
    methodology is shown in Hasan et. al. (2025) <doi:10.1101/2025.04.14.25325822>.
Provides estimation of simultaneous bootstrap and asymptotic confidence intervals for diversity indices, namely the Shannon and the Simpson index. Several pre--specified multiple comparison types are available to choose. Further user--defined contrast matrices are applicable. In addition, simboot estimates adjusted as well as unadjusted p--values for two of the three proposed bootstrap methods. Further simboot allows for comparing biological diversities of two or more groups while simultaneously testing a user-defined selection of Hill numbers of orders q, which are considered as appropriate and useful indices for measuring diversity.
Computerized Adaptive Testing simulations with dichotomous and polytomous items. Selects items with Maximum Fisher Information method or randomly, with or without constraints (content balancing and item exposure control). Evaluates the simulation results in terms of precision, item exposure, and test length. Inspired on Magis & Barrada (2017) <doi:10.18637/jss.v076.c01>.
A flexible tool for simulating complex longitudinal data using
    structural equations, with emphasis on problems in causal inference.
    Specify interventions and simulate from intervened data generating
    distributions. Define and evaluate treatment-specific means, the average
    treatment effects and coefficients from working marginal structural models.
    User interface designed to facilitate the conduct of transparent and
    reproducible simulation studies, and allows concise expression of complex
    functional dependencies for a large number of time-varying nodes. See the
    package vignette for more information, documentation and examples.
Provides efficient R and 'C++' routines to simulate cognitive diagnostic
    model data for Deterministic Input, Noisy "And" Gate ('DINA') and
    reduced Reparameterized Unified Model ('rRUM') from 
    Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>,
    Culpepper (2015) <doi:10.3102/1076998615595403>, and
    de la Torre (2009) <doi:10.3102/1076998607309474>.
Simultaneous tests and confidence intervals are provided for one-way experimental designs with one or many normally distributed, primary response variables (endpoints). Differences (Hasler and Hothorn, 2011 <doi:10.2202/1557-4679.1258>) or ratios (Hasler and Hothorn, 2012 <doi:10.1080/19466315.2011.633868>) of means can be considered. Various contrasts can be chosen, unbalanced sample sizes are allowed as well as heterogeneous variances (Hasler and Hothorn, 2008 <doi:10.1002/bimj.200710466>) or covariance matrices (Hasler, 2014 <doi:10.1515/ijb-2012-0015>).
Provides a framework to generating random variates from
             arbitrary multivariate copulae, while concentrating on
             (bivariate) extreme value copulae.  Particularly useful if
             the multivariate copulae are not available in closed
             form.  Detailed discussion of the methodologies used can
             be found in Tajvidi and Turlach (2018)
             <doi:10.1111/anzs.12209>.
Simulates correlated multinomial responses conditional on a marginal model specification.
Simulate and plot general experimental crosses. The focus is on simulating genotypes with an aim towards flexibility rather than speed. Meiosis is simulated following the Stahl model, in which chiasma locations are the superposition of two processes: a proportion p coming from a process exhibiting no interference, and the remainder coming from a process following the chi-square model.
Algorithms for the implementation and evaluation of Monte Carlo tests, as well as for their use in multiple testing procedures.
Simulate complex data from a given directed acyclic graph and information about each individual node.
	Root nodes are simply sampled from the specified distribution. Child Nodes are simulated according to
	one of many implemented regressions, such as logistic regression, linear
	regression, poisson regression or any other function. Also includes a comprehensive framework for discrete-time
	simulation, discrete-event simulation, and networks-based simulation which can generate even more complex longitudinal and dependent data.
	For more details, see Robin Denz, Nina Timmesfeld (2025) <doi:10.48550/arXiv.2506.01498>.
Generate simulated datasets from an initial underlying
    distribution and apply transformations to obtain realistic data. Implements
    the 'NORTA' (Normal-to-anything) approach from Cario and Nelson (1997)
    and other data generating mechanisms. Simple network visualization tools 
    are provided to facilitate communicating the simulation setup.
Simulation methods for the Fisher Bingham distribution on the unit sphere, the matrix Bingham distribution on a Grassmann manifold, the matrix Fisher distribution on SO(3), and the bivariate von Mises sine model on the torus.
 The methods use an acceptance/rejection simulation algorithm for the Bingham distribution and are described fully by Kent, Ganeiber and Mardia (2018) <doi:10.1080/10618600.2017.1390468>.
 These methods supersede earlier MCMC simulation methods and are more general than earlier simulation methods.
 The methods can be slower in specific situations where there are existing non-MCMC simulation methods (see Section 8 of Kent, Ganeiber and Mardia (2018) <doi:10.1080/10618600.2017.1390468> for further details).
Provides tools to safely and efficiently organize and execute 
    Monte Carlo simulation experiments in R.
    The package controls the structure and back-end of Monte Carlo simulation experiments
    by utilizing a generate-analyse-summarise workflow. The workflow safeguards against 
    common simulation coding issues, such as automatically re-simulating non-convergent results, 
    prevents inadvertently overwriting simulation files, catches error and warning messages
    during execution, implicitly supports parallel processing with high-quality random number 
    generation, and provides tools for managing high-performance computing (HPC) array jobs
    submitted to schedulers such as SLURM. For a pedagogical introduction to the package see
    Sigal and Chalmers (2016) <doi:10.1080/10691898.2016.1246953>. For a more in-depth overview of 
    the package and its design philosophy see Chalmers and Adkins (2020) <doi:10.20982/tqmp.16.4.p248>.
Implementation of a model-based bootstrap approach for testing whether two formulations are similar. The package provides a function for fitting a pharmacokinetic model to time-concentration data and comparing the results for all five candidate models regarding the Residual Sum of Squares (RSS). The candidate set contains a First order, Hixson-Crowell, Higuchi, Weibull and a logistic model. The assessment of similarity implemented in this package is performed regarding the maximum deviation of the profiles. See Moellenhoff et al. (2018) <doi:10.1002/sim.7689> for details.
Assessment of the distributions of baseline continuous and categorical 
    variables in randomised trials. This method is based on the Carlisle-Stouffer 
    method with Monte Carlo simulations. It calculates p-values for each trial 
    baseline variable, as well as combined p-values for each trial - these p-values 
    measure how compatible are distributions of trials baseline variables with 
    random sampling. This package also allows for graphically plotting the 
    cumulative frequencies of computed p-values.
    Please note that code was partly adapted from Carlisle JB, Loadsman JA. 
    (2017) <doi:10.1111/anae.13650>.
Mixed DNA profiles can be sampled according to models for probabilistic genotyping. Peak height variability is modelled using a log normal distribution or a gamma distribution. Sample contributors may be related according to a pedigree.
An object oriented framework to simulate
  ecological (and other) dynamic systems. It can be used for
  differential equations, individual-based (or agent-based) and other
  models as well. It supports structuring of simulation scenarios (to avoid copy
  and paste) and aims to improve readability and re-usability of code.
Contains various functions to be used for simulation education,
    including simple Monte Carlo simulation functions, queueing simulation
    functions, variate generation functions capable of producing independent
    streams and antithetic variates, functions for illustrating random variate
    generation for various discrete and continuous distributions, and functions
    to compute time-persistent statistics.  Also contains functions for 
    visualizing: event-driven details of a single-server queue model; a Lehmer
    random number generator; variate generation via acceptance-rejection; and
    of generating a non-homogeneous Poisson process via thinning.  Also
    contains two queueing data sets (one fabricated, one real-world) to
    facilitate input modeling.  More details on the use of these functions can
    be found in Lawson and Leemis (2015) <doi:10.1109/WSC.2017.8248124>, in
    Kudlay, Lawson, and Leemis (2020) <doi:10.1109/WSC48552.2020.9384010>, and
    in Lawson and Leemis (2021) <doi:10.1109/WSC52266.2021.9715299>.
An open-source R package for structuring, maintaining, running, and debugging statistical simulations on both local and cluster-based computing environments.See full documentation at <https://avi-kenny.github.io/SimEngine/>.
Data simulator including genotype, phenotype, pedigree, 
    selection and reproduction in R. It simulates most of reproduction process
    of animals or plants and provides data for GS (Genomic Selection),
    GWAS (Genome-Wide Association Study), and Breeding.
    For ADI model, please see Kao C and Zeng Z (2002) <doi:10.1093/genetics/160.3.1243>.
    For build.cov, please see B. D. Ripley (1987) <ISBN:9780470009604>.
Estimation of function and index vector in single index model ('sim')
    with (and w/o) shape constraints including different smoothness conditions.
    See, e.g., Kuchibhotla and Patra (2020) <doi:10.3150/19-BEJ1183>.
Supports the calculation of meteorological characteristics in 
      evapotranspiration research and reference crop evapotranspiration, and offers 
      three models to simulate crop evapotranspiration and soil water balance in 
      the field, including single crop coefficient and dual crop coefficient, 
      as well as the Shuttleworth-Wallace model. These calculations main refer to 
      Allen et al.(1998, ISBN:92-5-104219-5), Teh (2006, ISBN:1-58-112-998-X), and 
      Liu et al.(2006) <doi:10.1016/j.agwat.2006.01.018>.
Monte Carlo simulations of a game-theoretic model for the 
	legal exemption system of the European cartel law are implemented
	in order to estimate the (mean) deterrent effect of this system.
	The input and output parameters of the simulated cartel 
	opportunities can be visualized by three-dimensional projections.
	A description of the model is given in Moritz et al. (2018)
	<doi:10.1515/bejeap-2017-0235>.
Simulate event history data from a framework where treatment decisions 
    and disease progression are represented as counting process. The user can specify 
    number of events and parameters of intensities thereby creating a flexible 
    simulation framework.
Implementation of the SIMEX-Algorithm by Cook & Stefanski (1994) <doi:10.1080/01621459.1994.10476871> and
    MCSIMEX by Küchenhoff, Mwalili & Lesaffre (2006) <doi:10.1111/j.1541-0420.2005.00396.x>.
Generates binary test data based on Item Response Theory using the two-parameter logistic model (Lord, 1980 <doi:10.4324/9780203056615>). Useful functions for test equating are included, e.g. functions for generating internal and external common items between test forms and a function to create a linkage plans between those forms. Ancillary functions for generating true item and person parameters as well as for calculating the probability of a person correctly answering an item are also included.
Implementation of the boosting procedure with the simulation and extrapolation approach to address variable selection and estimation for high-dimensional data subject to measurement error in predictors. It can be used to address generalized linear models (GLM) in Chen (2023) <doi: 10.1007/s11222-023-10209-3> and the accelerated failure time (AFT) model in Chen and Qiu (2023) <doi: 10.1111/biom.13898>. Some relevant references include Chen and Yi (2021) <doi:10.1111/biom.13331> and Hastie, Tibshirani, and Friedman (2008, ISBN:978-0387848570).
The focus is on simulating and modeling families with founders drawn from a structured population (for example, with different ancestries or other potentially non-family relatedness), in contrast to traditional pedigree analysis that treats all founders as equally unrelated.  Main function simulates a random pedigree for many generations, avoiding close relatives, pairing closest individuals according to a 1D geography and their randomly-drawn sex, and with variable children sizes to result in a target population size per generation.  Auxiliary functions calculate kinship matrices, admixture matrices, and draw random genotypes across arbitrary pedigree structures starting from the corresponding founder values.  The code is built around the plink FAM table format for pedigrees.  Described in Yao and Ochoa (2022) <doi:10.1101/2022.03.25.485885>.
Conducting Bayesian Optimal Interval (BOIN) design for phase I 
    dose-finding trials. 'simFastBOIN' provides functions for pre-computing 
    decision tables, conducting trial simulations, and evaluating operating 
    characteristics. The package uses vectorized operations and the 
    Iso::pava() function for isotonic regression to achieve efficient 
    performance while maintaining full compatibility with BOIN methodology. 
    Version 1.3.2 adds p_saf and p_tox parameters for customizable safety and 
    toxicity thresholds. Version 1.3.1 fixes Date field. Version 1.2.1 adds 
    comprehensive 'roxygen2' documentation and enhanced print formatting with 
    flexible table output options. Version 1.2.0 integrated C-based PAVA for 
    isotonic regression. Version 1.1.0 introduced conservative MTD selection 
    (boundMTD) and flexible early stopping rules (n_earlystop_rule). Methods 
    are described in Liu and Yuan (2015) <doi:10.1111/rssc.12089>.
Through simfinapi, you can intuitively access the 'SimFin'
    Web-API (<https://www.simfin.com/>) to make 'SimFin' data easily
    available in R. To obtain an 'SimFin' API key (and thus to use this
    package), you need to register at <https://app.simfin.com/login>.
Simulates data from model objects (e.g., from lm(), glm()),
    and plots this along with the original data to compare how well the
    simulated data matches the original data to determine model fit.
A general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort.  The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results.  It follows a clear object-oriented design and supports parallel computing to increase computational performance.
Simulates regression models,
    including both simple regression and generalized linear mixed
    models with up to three level of nesting. Power simulations that are
    flexible allowing the specification of missing data, unbalanced designs,
    and different random error distributions are built into the package.
Routine that allows the user to run several goodness-of-fit tests.  
    It also combines the tests and returns a properly adjusted family-wise p value.
    Details can be found in <arXiv:2007.04727>.
Calculates performance criteria measures and associated Monte Carlo standard errors for simulation results. Includes functions to help run simulation studies, following a general simulation workflow that closely aligns with the approach described by Morris, White, and Crowther (2019) <DOI:10.1002/sim.8086>. Also includes functions for calculating bootstrap confidence intervals (including normal, basic, studentized, percentile, bias-corrected, and bias-corrected-and-accelerated) with tidy output, as well as for extrapolating confidence interval coverage rates and hypothesis test rejection rates following techniques suggested by Boos and Zhang (2000) <DOI:10.1080/01621459.2000.10474226>.
Provides tools to simulate and analyze survival data with interval-, 
  left-, right-, and uncensored observations under common parametric distributions, 
  including "Weibull", "Exponential", "Log-Normal", "Log-Logistic", "Gamma", 
  "Gompertz", "Normal", "Logistic", and "EMV". The package supports both direct 
  maximum likelihood estimation and imputation-based methods, making it suitable 
  for methodological research, simulation benchmarking, and teaching. A web-based 
  companion app is also available for demonstration purposes.
Contains tests for association between a set of genetic variants and multiple correlated outcomes that are interval censored. Interval-censored data arises when the exact time of the onset of an outcome of interest is unknown but known to fall between two time points.
Based on the illness-death model a large number of clinical
    trials with oncology endpoints progression-free survival (PFS) and
    overall survival (OS) can be simulated, see Meller, Beyersmann and
    Rufibach (2019) <doi:10.1002/sim.8295>.  The simulation set-up allows
    for random and event-driven censoring, an arbitrary number of
    treatment arms, staggered study entry and drop-out.  Exponentially,
    Weibull and piecewise exponentially distributed survival times can be
    generated. The correlation between PFS and OS can be calculated.
Functions to run and assist four
  different similarity measures. The similarity
  measures included are: longest common
  subsequence (LCSS), Frechet distance, edit distance
  and dynamic time warping (DTW). Each of these
  similarity measures can be calculated from two
  n-dimensional trajectories, both in matrix form.
Allows a Simile model saved as a compiled binary to be
  loaded, parameterized, executed and interrogated. This version works 
  with Simile v6 on.
A fast implementation of the weighted information similarity aggregation (WISE) 
    test for detecting serial dependence, particularly suited for high-dimensional and 
    non-Euclidean time series. Includes functions for constructing similarity matrices 
    and conducting hypothesis testing. Users can use different similarity 
    measures and define their own weighting schemes. For more details see Q Zhu, M Liu, 
    Y Han, D Zhou (2025) <doi:10.48550/arXiv.2509.05678>.
Provides an efficient and very flexible framework to
    conduct data-driven epidemiological modeling in realistic large
    scale disease spread simulations. The framework integrates
    infection dynamics in subpopulations as continuous-time Markov
    chains using the Gillespie stochastic simulation algorithm and
    incorporates available data such as births, deaths and movements
    as scheduled events at predefined time-points. Using C code for
    the numerical solvers and 'OpenMP' (if available) to divide work
    over multiple processors ensures high performance when simulating
    a sample outcome. One of our design goals was to make the package
    extendable and enable usage of the numerical solvers from other R
    extension packages in order to facilitate complex epidemiological
    research. The package contains template models and can be extended
    with user-defined models. For more details see the paper by
    Widgren, Bauer, Eriksson and Engblom (2019)
    <doi:10.18637/jss.v091.i12>. The package also provides
    functionality to fit models to time series data using the
    Approximate Bayesian Computation Sequential Monte Carlo
    ('ABC-SMC') algorithm of Toni and others (2009)
    <doi:10.1098/rsif.2008.0172> or the Particle Markov Chain Monte
    Carlo ('PMCMC') algorithm of 'Andrieu' and others (2010)
    <doi:10.1111/j.1467-9868.2009.00736.x>.
Provides tools for the stochastic simulation of effectiveness scores to mitigate
  data-related limitations of Information Retrieval evaluation research, as described in Urbano and
  Nagler (2018) <doi:10.1145/3209978.3210043>. These tools include: fitting, selection and plotting
  distributions to model system effectiveness, transformation towards a prespecified expected value,
  proxy to fitting of copula models based on these distributions, and simulation of new evaluation
  data from these distributions and copula models.
Provides tools for generating and analyzing simulation studies. Users may easily specify all terms of a simulation study, often in a single line of code. Common univariate and bivariate methods, such as t tests, proportions tests, and chi squared tests, are integrated. Multivariate studies involving linear or logistic regression may also be specified with symbolic inputs. The simulation studies generate data for n observations in each of B experiments. Analyses of each experiment are integrated, and empirical results across the experiments are also provided.
Uses simulation to create prediction intervals for
    post-policy outcomes in interrupted time series (ITS) designs,
    following Miratrix (2020) <arXiv:2002.05746>. This package provides
    methods for fitting ITS models with lagged outcomes and variables to
    account for temporal dependencies.  It then conducts inference via
    simulation, simulating a set of plausible counterfactual post-policy
    series to compare to the observed post-policy series. This package
    also provides methods to visualize such data, and also to incorporate
    seasonality models and smoothing and aggregation/summarization.  This
    work partially funded by Arnold Ventures in collaboration with
    MDRC.
Simulate multivariate correlated data given nonparametric marginals and their joint structure characterized by a Pearson or Spearman correlation matrix. The simulator engages the problem from a purely computational perspective. It assumes no statistical models such as copulas or parametric distributions, and can approximate the target correlations regardless of theoretical feasibility. The algorithm integrates and advances the Iman-Conover (1982) approach <doi:10.1080/03610918208812265> and the Ruscio-Kaczetow iteration (2008) <doi:10.1080/00273170802285693>. Package functions are carefully implemented in C++ for squeezing computing speed, suitable for large input in a manycore environment. Precision of the approximation and computing speed both substantially outperform various CRAN packages to date. Benchmarks are detailed in function examples. A simple heuristic algorithm is additionally designed to optimize the joint distribution in the post-simulation stage. The heuristic demonstrated good potential of achieving the same level of precision of approximation without the enhanced Iman-Conover-Ruscio-Kaczetow. The package contains a copy of Permuted Congruential Generator.
Simulate a virtual population of subjects that has demographic distributions (height, weight, and BMI) and correlations (height and weight), by sex and age, which mimic those reported in real-world anthropometric growth charts (CDC, WHO, or Fenton).
A toolbox for constructing potential landscapes for dynamical
    systems using Monte Carlo simulation.  The method is based on the
    potential landscape definition by Wang et al. (2008)
    <doi:10.1073/pnas.0800579105> (also see Zhou & Li, 2016
    <doi:10.1063/1.4943096> for further mathematical discussions) and can
    be used for a large variety of models.
We provide functions for estimation and inference of nonlinear and non-stationary time series regression using the sieve methods and bootstrapping procedure.
A process-oriented and trajectory-based Discrete-Event Simulation
    (DES) package for R. It is designed as a generic yet powerful framework. The
    architecture encloses a robust and fast simulation core written in 'C++' with
    automatic monitoring capabilities. It provides a rich and flexible R API that
    revolves around the concept of trajectory, a common path in the simulation
    model for entities of the same type. Documentation about 'simmer' is provided
    by several vignettes included in this package, via the paper by Ucar, Smeets
    & Azcorra (2019, <doi:10.18637/jss.v090.i02>), and the paper by Ucar,
    Hernández, Serrano & Azcorra (2018, <doi:10.1109/MCOM.2018.1700960>);
    see 'citation("simmer")' for details.
Provides wrappers for common activity patterns in 'simmer' trajectories.
A set of plotting methods for 'simmer' trajectories and simulations.
Allows users to quickly apply individual or multiple metrics to evaluate Monte Carlo simulation studies.
A major challenge in estimating treatment decision rules from a randomized clinical trial dataset with covariates measured at baseline lies in detecting relatively small treatment effect modification-related variability (i.e., the treatment-by-covariates interaction effects on treatment outcomes) against a relatively large non-treatment-related variability (i.e., the main effects of covariates on treatment outcomes). The class of Single-Index Models with Multiple-Links is a novel single-index model specifically designed to estimate a single-index (a linear combination) of the covariates associated with the treatment effect modification-related variability, while allowing a nonlinear association with the treatment outcomes via flexible link functions. The models provide a flexible regression approach to developing treatment decision rules based on patients' data measured at baseline. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1016/j.jspi.2019.05.008> and Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (that allows an unspecified X main effect) for detail of the method. The main function of this package is simml().
Fits Stable Isotope Mixing Models (SIMMs) and is meant as a longer term replacement to the previous widely-used package SIAR. SIMMs are used to infer dietary proportions of organisms consuming various food sources from observations on the stable isotope values taken from the organisms' tissue samples. However SIMMs can also be used in other scenarios, such as in sediment mixing or the composition of fatty acids. The main functions are simmr_load() and simmr_mcmc(). The two vignettes contain a quick start and a full listing of all the features. The methods used are detailed in the papers Parnell et al 2010 <doi:10.1371/journal.pone.0009672>, and Parnell et al 2013 <doi:10.1002/env.2221>.
Algorithms to create prognostic biomarkers using biological genesets or networks.
Simulation of event histories with possibly non-linear baseline hazard rate functions, non-linear (time-varying) covariate effect functions, and dependencies on the past of the history. Random generation of event histories is performed using inversion sampling on the cumulative all-cause hazard rate functions.
A toolkit for simulation studies concerning time-to-event endpoints
    with non-proportional hazards. 'SimNPH' encompasses functions for simulating
    time-to-event data in various scenarios, simulating different trial designs
    like fixed-followup, event-driven, and group sequential designs. The package
    provides functions to calculate the true values of common summary statistics
    for the implemented scenarios and offers common analysis methods for
    time-to-event data. Helper functions for running simulations with the
    'SimDesign' package and for aggregating and presenting the results are also
    included. Results of the conducted simulation study are available in the 
    paper: "A Comparison of Statistical Methods for Time-To-Event Analyses in 
    Randomized Controlled Trials Under Non-Proportional Hazards", 
    Klinglmüller et al. (2025) <doi:10.1002/sim.70019>.
Implements statistical inference for systems of ordinary differential equations,
  that uses the integral-matching criterion and takes advantage of the separability of parameters,
  in order to obtain initial parameter estimates for nonlinear least squares optimization. 
  Dattner & Yaari (2018) <arXiv:1807.04202>. 
  Dattner et al. (2017) <doi:10.1098/rsif.2016.0525>.
  Dattner & Klaassen (2015) <doi:10.1214/15-EJS1053>.
Develop spatial interaction models (SIMs).  SIMs predict the
    amount of interaction, for example number of trips per day, between
    geographic entities representing trip origins and destinations.
    Contains functions for creating origin-destination datasets
    from geographic input datasets and calculating movement between
    origin-destination pairs with constrained, production-constrained,
    and attraction-constrained models (Wilson 1979) <doi:10.1068/a030001>.
Simulates and plots quantities of interest (relative
    hazards, first differences, and hazard ratios) for linear coefficients,
    multiplicative interactions, polynomials, penalised splines, and
    non-proportional hazards, as well as stratified survival curves from Cox
    Proportional Hazard models. It also simulates and plots marginal effects
    for multiplicative interactions. Methods described in Gandrud (2015)
    <doi:10.18637/jss.v065.i03>.
A tool for simulating rhythmic data: transcriptome data using
  Gaussian or negative binomial distributions, and behavioral activity data
  using Bernoulli or Poisson distributions. See Singer et al. (2019)
  <doi:10.7717/peerj.6985>.
Interface to interact with the modelling framework 'SIMPLACE' and to
    parse the results of simulations.
A simple function that anonymises a list of variables
    in a consistent way: anonymised factors are not recycled and
    the same original levels receive the same anonymised factor
    even if located in different datasets.
Provides SPSS- and SAS-like output for least squares multiple regression,
    logistic regression, and count variable regressions. Detailed output is also provided for
    OLS moderated regression, interaction plots, and Johnson-Neyman
    regions of significance. The output includes standardized
    coefficients, partial and semi-partial correlations, collinearity diagnostics,
    plots of residuals, and detailed information about simple slopes for interactions. 
    The output for some functions includes Bayes Factors and, if requested,  
    regression coefficients from Bayesian Markov Chain Monte Carlo analyses.
    There are numerous options for model plots.
    The REGIONS_OF_SIGNIFICANCE function also provides
    Johnson-Neyman regions of significance and plots of interactions for both lm
    and lme models. There is also a function for partial and semipartial
    correlations and a function for conducting Cohen's
    set correlation analyses.
Provides intuitive functions for caching R objects, encouraging
    reproducible, restartable, and distributed R analysis. The user selects a
    location to store caches, and then provides  nothing more than a cache name
    and instructions (R code) for how to produce the R object. Also
    provides some advanced options like environment assignments, recreating or
    reloading caches, and cluster compute bindings (using the 'batchtools'
    package) making it flexible enough for use in large-scale data analysis
    projects.
A curated set of colors that are called using
    a standardized syntax: saturation + hue + lightness. For example, 
    "brightblue4" and "mutedred2". Functions exists to return individual colors 
    by name or to build palettes across or within hues. Most functions allow you 
    to visualize the palettes in addition to returning the desired hex codes.
Using the adjustment method from Benjamini & Hochberg (1995) <doi:10.1111/j.2517-6161.1995.tb02031.x>, this package determines which variables are significant under repeated testing with a given dataframe of p values and an user defined "q" threshold.  It then returns the original dataframe along with a significance column where an asterisk denotes a significant p value after FDR calculation, and NA denotes all other p values. This package uses the Benjamini & Hochberg method specifically as described in Lee, S., & Lee, D. K. (2018) <doi:10.4097/kja.d.18.00242>.
Simple classic graph algorithms for simple graph classes.
    Graphs may possess vertex and edge attributes. 'simplegraph' has
    no dependencies and it is written entirely in R, so it is easy to
    install.
This is a graph database in 'SQLite'.  It is inspired by Denis Papathanasiou's Python simple-graph project on 'GitHub'.
A very bare-bones interface to use the Metropolis-Hastings Monte 
    Carlo Markov Chain algorithm. It is suitable for teaching and testing 
    purposes.
Trains neural networks (multilayer perceptrons with one hidden layer) for bi- or multi-class classification.
The number of studies involving correlated traits and the availability of tools to handle this type of data has increased considerably in the last decade. With such a demand, we need tools for testing hypotheses related to single and multi-trait (correlated) phenotypes based on many genetic settings. Thus, we implemented various options for simulation of pleiotropy and Linkage Disequilibrium under additive, dominance and epistatic models. The simulation currently takes a marker data set as an input and then uses it for simulating multiple traits as described in Fernandes and Lipka (2020) <doi:10.1186/s12859-020-03804-y>.
Simple result caching in R based on R.cache. The global environment is not 
    considered when caching results simplifying moving files between multiple instances 
    of R. Relies on more base functions than R.cache (e.g. cached results are saved using 
    saveRDS() and readRDS()).
Runs R-code present in a pandoc markdown file and 
  includes the resulting output in the resulting markdown file. This
  file can then be converted into any of the output formats 
  supported by pandoc. The package can also be used as an engine
  for writing package vignettes.
When working across multiple machines and, similarly for
    reproducible research, it can be time consuming to ensure that you have
    all of the needed packages installed and loaded and that the correct working
    directory is set. 'simpleSetup' provides simple functions for making these
    tasks more straightforward.
By calling the 'SimpleTex' <https://simpletex.cn/> open API implements 
    text and mathematical formula recognition on the image, and the output formula 
    can be used directly with 'Markdown' and 'LaTeX'.
Create Upset plots using a combination of 'ggplot2' and 'patchwork'.
Identifies constant, additive, multiplicative, and user-defined 
    simplivariate components in numeric data matrices using a genetic algorithm. 
    Supports flexible pattern definitions and provides visualization for 
    general biclustering applications across diverse domains. The method builds 
    on simplivariate models as introduced in Hageman et al. (2008) 
    <doi:10.1371/journal.pone.0003259> and is related to biclustering 
    frameworks as reviewed by Madeira and Oliveira (2004) <doi:10.1109/TCBB.2004.2>.
Provides an implementation of simplicial complexes for 
    Topological Data Analysis (TDA). The package includes functions to 
    compute faces, boundary operators, Betti numbers, Euler characteristic, 
    and to construct simplicial complexes. It also implements persistent 
    homology, from building filtrations to computing persistence diagrams, 
    with the aim of helping readers understand the core concepts of 
    computational topology.
    Methods are based on standard references in persistent homology such as 
    Zomorodian and Carlsson (2005) <doi:10.1007/s00454-004-1146-y> and 
    Chazal and Michel (2021) <doi:10.3389/frai.2021.667963>.
Provides methods to integrate functions over m-dimensional simplices
 in n-dimensional Euclidean space.  There are exact methods for polynomials and
 adaptive methods for integrating an arbitrary function.
Network sparsification with a variety of novel and known network sparsification 
    techniques. All network sparsification techniques reduce the number of edges, not the number 
    of nodes. Network sparsification is sometimes referred to as network dimensionality reduction. 
    This package is based on the work of Spielman, D., Srivastava, N. (2009)<arXiv:0803.0929>.
    Koutis I., Levin, A., Peng, R. (2013)<arXiv:1209.5821>. Toivonen, H., Mahler, S., Zhou, F. 
    (2010)<doi:10.1007>. Foti, N., Hughes, J., Rockmore, D. (2011)<doi:10.1371>.
Reliability and agreement analyses often have limited software support. Therefore, this package was created to make agreement and reliability analyses easier for the average researcher. The functions within this package include simple tests of agreement, agreement analysis for nested and replicate data, and provide robust analyses of reliability. In addition, this package contains a set of functions to help when planning studies looking to assess measurement agreement.
An extension of the 'AlphaSimR' package
  (<https://cran.r-project.org/package=AlphaSimR>) for stochastic simulations of
  honeybee populations and breeding programmes. 'SIMplyBee' enables simulation of
  individual bees that form a colony, which includes a queen, fathers (drones
  the queen mated with), virgin queens, workers, and drones. Multiple colony can
  be merged into a population of colonies, such as an apiary or a whole country
  of colonies. Functions enable operations on castes, colony, or colonies, to
  ease 'R' scripting of whole populations. All 'AlphaSimR' functionality with
  respect to genomes and genetic and phenotype values is available and further
  extended for honeybees, including haplo-diploidy, complementary sex determiner
  locus, colony events (swarming, supersedure, etc.), and colony phenotype values.
Tools and methods to simulate populations for surveys based
    on auxiliary data. The tools include model-based methods, calibration and
    combinatorial optimization algorithms, see Templ, Kowarik and Meindl (2017) <doi:10.18637/jss.v079.i10>) and
    Templ (2017) <doi:10.1007/978-3-319-50272-4>. The package was developed with support of
    the International Household Survey Network, DFID Trust Fund TF011722 and funds
    from the World bank.
Uses simulations from generalized linear mixed-effects models to incorporate random effects across multiple sources and levels of variation, and a dispersion parameter to account for overdispersion and capture unexplained variability. Covers design scenarios for both short-term and long-term trials evaluating the impact of single or combined vector control interventions. Methods build on Kipingu et al. (2025) <doi:10.1186/s12936-025-05454-y> and Johnson et al. (2015) <doi:10.1111/2041-210X.12306>.
Provides functionality for analytically calculating parameters (via the 
    'InteractionPoweR' package) useful for simulation of moderated multiple 
    regression, based on the correlations among the predictors and outcome and 
    the reliability of predictors.
Easy to use interfaces to a number of imputation methods
        that fit in the not-a-pipe operator of the 'magrittr' package.
This is an all-encompassing suite to facilitate the simulation of
    so-called quantities of interest by way of a multivariate normal distribution
    of the regression model's coefficients and variance-covariance matrix.
Calculate power for generalised linear mixed models, using
    simulation. Designed to work with models fit using the 'lme4' package.
    Described in Green and MacLeod, 2016 <doi:10.1111/2041-210X.12504>.
Simulate populations  with desired 
  properties and extract respondent driven samples. To better understand the 
  usage of the package and the algorithm used, please refer to Perera, A., and 
  Ramanayake, A. (2019) 
  <https://www.aimr.tirdiconference.com/assets/images/portfolio/Conference-Proceeding-AIMR-19.pdf>.
Simulation of recurrent event data for non-constant baseline
    hazard in the total time model with risk-free intervals and possibly a competing event.
    Possibility to cut the data to an interim data set. Data can be plotted.
    Details about the method can be found in Jahn-Eimermacher, A. et al. (2015) <doi:10.1186/s12874-015-0005-2>.
Similarity regression,
    evaluating the probability of association between sets of ontological terms
    and binary response vector. A no-association model is compared with one in which
    the log odds of a true response is linked to the semantic similarity
    between terms and a latent characteristic ontological profile - 'Phenotype Similarity Regression for Identifying the Genetic Determinants of Rare Diseases', Greene et al 2016 <doi:10.1016/j.ajhg.2016.01.008>.
Researchers have been using simulated data from a multivariate linear model to compare and evaluate different methods, ideas and models. Additionally, teachers and educators have been using a simulation tool to demonstrate and teach various statistical and machine learning concepts.
    This package helps users to simulate linear model data with a wide range of properties by tuning few parameters such as relevant latent components. In addition, a shiny app as an 'RStudio' gadget gives users a simple interface for using the simulation function. See more on: Sæbø, S., Almøy, T., Helland, I.S. (2015) <doi:10.1016/j.chemolab.2015.05.012> and Rimal, R., Almøy, T., Sæbø, S. (2018) <doi:10.1016/j.chemolab.2018.02.009>.
Simulation methods to study the effect of management policies on
    efforts to restore populations back to their original genetic composition.
    Allows for single-scenario simulation and for optimization of specific chosen
    scenarios. Further information can be found in Hernandez, Janzen and Lavretsky 
    (2023) <doi:10.1111/1755-0998.13892>.
Provides functions to generate and analyze spatially-explicit individual-based multistate movements in rivers,
  heterogeneous and homogeneous spaces. This is done by incorporating landscape bias on local behaviour, based on
  resistance rasters. Although originally conceived and designed to simulate trajectories of species constrained to
  linear habitats/dendritic ecological networks (e.g. river networks), the simulation algorithm is built to be
  highly flexible and can be applied to any (aquatic, semi-aquatic or terrestrial) organism, independently on the
  landscape in which it moves. Thus, the user will be able to use the package to simulate movements either in
  homogeneous landscapes, heterogeneous landscapes (e.g. semi-aquatic animal moving mainly along rivers but also using
  the matrix), or even in highly contrasted landscapes (e.g. fish in a river network). The algorithm and its input
  parameters are the same for all cases, so that results are comparable. Simulated trajectories can then be used as
  mechanistic null models (Potts & Lewis 2014, <DOI:10.1098/rspb.2014.0231>) to test a variety of 'Movement Ecology'
  hypotheses (Nathan et al. 2008, <DOI:10.1073/pnas.0800375105>), including landscape effects (e.g. resources, 
  infrastructures) on animal movement and species site fidelity, or for predictive purposes (e.g. road mortality risk,
  dispersal/connectivity). The package should be relevant to explore a broad spectrum of ecological phenomena, such as
  those at the interface of animal behaviour, management, landscape and movement ecology, disease and invasive species
  spread, and population dynamics.
Generates data from R or 'JAGS' code for use in simulation
    studies.  The data are returned as an 'nlist::nlists' object and/or
    saved to file as individual '.rds' files.  Parallelization is
    implemented using the 'future' package.  Progress is reported using
    the 'progressr' package.
Tools for setting up ("design"), conducting, and evaluating
  large-scale simulation studies with graphics and tables, including
  parallel computations.
Provides an easy framework for Monte Carlo simulation in structural equation modeling, which can be used for various purposes, such as such as model fit evaluation, power analysis, or missing data handling and planning.
RNA sequencing analysis methods are often derived by relying on hypothetical parametric models for read counts that are not likely to be precisely satisfied in practice. Methods are often tested by analyzing data that have been simulated according to the assumed model. This testing strategy can result in an overly optimistic view of the performance of an RNA-seq analysis method. We develop a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user. Users control the proportion of genes simulated to be differentially expressed (DE) and can provide a vector of weights to control the distribution of effect sizes. The algorithm requires a matrix of RNA-seq read counts with large sample sizes in at least two treatment groups. Many datasets are available that fit this standard.
An implementation of a single-index regression for optimizing individualized dose rules from an observational study. To model interaction effects between baseline covariates and a treatment variable defined on a continuum, we employ two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear combination of the covariates (a single-index). An unspecified main effect for the covariates is allowed, which can also be modeled through a parametric model. A unique contribution of this work is in the parsimonious single-index parametrization specifically defined for the interaction effect term. We refer to Park, Petkova, Tarpey, and Ogden (2020) <doi:10.1111/biom.13320> (for the case of a discrete treatment) and Park, Petkova, Tarpey, and Ogden (2021) "A single-index model with a surface-link for optimizing individualized dose rules" <arXiv:2006.00267v2> for detail of the method. The model can take a member of the exponential family as a response variable and can also take an ordinal categorical response. The main function of this package is simsl().
Stop signal task  data of go and stop trials is generated per participant. The simulation process is based on the generally non-independent horse race model and fixed stop signal delay or tracking method. Each of go and stop process is assumed having exponentially modified Gaussian(ExG) or Shifted Wald (SW) distributions. The output data can be converted to 'BEESTS' software input data enabling researchers to test and evaluate various brain stopping processes manifested by ExG or SW distributional parameters of interest. Methods are described in: Soltanifar M (2020) <https://hdl.handle.net/1807/101208>, Matzke D, Love J, Wiecki TV, Brown SD, Logan GD and Wagenmakers E-J (2013) <doi:10.3389/fpsyg.2013.00918>, Logan GD, Van Zandt T, Verbruggen F, Wagenmakers EJ. (2014) <doi:10.1037/a0035230>.
Creates simulated data from structural equation models with standardized loading. Data generation methods are described in Schneider (2013) <doi:10.1177/0734282913478046>.
Provides a streamlined and user-friendly framework
    for simulating data in state space models,
    particularly when the number of subjects/units (n) exceeds one,
    a scenario commonly encountered in social and behavioral sciences.
    This package was designed to generate data for the simulations
    performed in Pesigan, Russell, and Chow (2025) <doi:10.1037/met0000779>.
Simulates data sets in order to explore modeling
    techniques or better understand data generating processes. The user
    specifies a set of relationships between covariates, and generates
    data based on these specifications. The final data sets can represent
    data from randomized control trials, repeated measure (longitudinal)
    designs, and cluster randomized trials. Missingness can be generated
    using various mechanisms (MCAR, MAR, NMAR).
Simulate survival times from standard parametric survival
    distributions (exponential, Weibull, Gompertz), 2-component mixture
    distributions, or a user-defined hazard, log hazard, cumulative hazard,
    or log cumulative hazard function. Baseline covariates can be included
    under a proportional hazards assumption.
    Time dependent effects (i.e. non-proportional hazards) can be included by
    interacting covariates with linear time or a user-defined function of time.
    Clustered event times are also accommodated.
    The 2-component mixture distributions can allow for a variety of flexible
    baseline hazard functions reflecting those seen in practice.
    If the user wishes to provide a user-defined
    hazard or log hazard function then this is possible, and the resulting
    cumulative hazard function does not need to have a closed-form solution.
    For details see the supporting paper <doi:10.18637/jss.v097.i03>.
    Note that this package is modelled on the 'survsim' package available in
    the 'Stata' software (see Crowther and Lambert (2012)
    <https://www.stata-journal.com/sjpdf.html?articlenum=st0275> or
    Crowther and Lambert (2013) <doi:10.1002/sim.5823>).
Simulate age-structured populations that vary in space and time and 
    explore the efficacy of a range of built-in or user-defined sampling 
    protocols to reproduce the population parameters of the known population. 
    (See Regular et al. (2020) <doi:10.1371/journal.pone.0232822> for more
    details).
Streamlines the creation of descriptive frequency tables ('Table 1'), 
    diagnostic test accuracy evaluations (sensitivity, specificity, predictive values), 
    and multi-outcome regression summaries. Features automatic tables, 
    prevalence and odds ratio calculations, and seamless integration with 'flextable' 
    for exporting results to 'Microsoft Word' and 'PowerPoint'.
Transforms or simulates data with a target empirical covariance matrix supplied
             by the user. The method to obtain the data with the target empirical covariance
             matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019)
             <arXiv:1812.05678>.
Handles datetimes as integers for the usage inside
        Discrete-Event Simulations (DES). The conversion is made using
        the internally generic function as.numeric() of the base
        package. DES is described in Simulation Modeling and Analysis
        by Averill Law and David Kelton (1999) <doi:10.2307/2288169>.
Flexibly simulates a dataset with time-varying covariates with user-specified exchangeable correlation structures across and within clusters. Covariates can be normal or binary and can be static within a cluster or time-varying. Time-varying normal variables can optionally have linear trajectories within each cluster. See ?make_one_dataset for the main wrapper function. See Montez-Rath et al. <arXiv:1709.10074> for methodological details.
Tool for statistical simulations that have two components. 
    One component generates the data and the other one
    analyzes the data. The main aims of the package are the reduction
    of the administrative source code (mainly loops and management code for the
    results) and a simple applicability of the package that allows the user to
    quickly learn how to work with it. Parallel computing is
    also supported. Finally, convenient functions are provided to summarize the
    simulation results.
Sample size estimation for bio-equivalence trials is supported through a simulation-based approach 
    that extends the Two One-Sided Tests (TOST) procedure. The methodology provides flexibility in 
    hypothesis testing, accommodates multiple treatment comparisons, and accounts for correlated endpoints. 
    Users can model complex trial scenarios, including parallel and crossover designs, intra-subject variability, 
    and different equivalence margins. Monte Carlo simulations enable accurate estimation of power and type I error 
    rates, ensuring well-calibrated study designs. The statistical framework builds on established methods for 
    equivalence testing and multiple hypothesis testing in bio-equivalence studies, as described in Schuirmann (1987) 
    <doi:10.1007/BF01068419>, Mielke et al. (2018) <doi:10.1080/19466315.2017.1371071>, Shieh (2022) 
    <doi:10.1371/journal.pone.0269128>, and Sozu et al. (2015) <doi:10.1007/978-3-319-22005-5>. 
    Comprehensive documentation and vignettes guide users through implementation and interpretation of results.
Simulate complex traits given a SNP genotype matrix and model parameters (the desired heritability, number of causal loci, and either the true ancestral allele frequencies used to generate the genotypes or the mean kinship for a real dataset).  Emphasis on avoiding common biases due to the use of estimated allele frequencies.  The code selects random loci to be causal, constructs coefficients for these loci and random independent non-genetic effects, and can optionally generate random group effects.  Traits can follow three models: random coefficients, fixed effect sizes, and infinitesimal (multivariate normal).  GWAS method benchmarking functions are also provided.  Described in Yao and Ochoa (2022) <doi:10.1101/2022.03.25.485885>.
Provides some basic routines for simulating a
    clinical trial. The primary intent is to provide some tools to
    generate trial simulations for trials with time to event outcomes.
    Piecewise exponential failure rates and piecewise constant
    enrollment rates are the underlying mechanism used to simulate
    a broad range of scenarios such as those presented in
    Lin et al. (2020) <doi:10.1080/19466315.2019.1697738>.
    However, the basic generation of data is done using pipes to allow
    maximum flexibility for users to meet different needs.
A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.
Implements two iterative techniques called T3Clus and 3Fkmeans, aimed at simultaneously clustering objects and a factorial dimensionality reduction of variables and occasions on three-mode datasets developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>. Also, we provide a convex combination of these two simultaneous procedures called CT3Clus and based on a hyperparameter alpha (alpha in [0,1], with 3FKMeans for alpha=0 and T3Clus for alpha= 1) also developed by Vichi et al. (2007) <doi:10.1007/s00357-007-0006-x>. Furthermore, we implemented the traditional tandem procedures of T3Clus (TWCFTA) and 3FKMeans (TWFCTA) for sequential clustering-factorial decomposition (TWCFTA), and vice-versa (TWFCTA) proposed by P. Arabie and L. Hubert (1996) <doi:10.1007/978-3-642-79999-0_1>.
Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample. Duong (2017) <doi:10.18637/jss.v021.i07>, Christian P. Robert and George Casella (2010 ISBN:978-1-4419-1575-7) <doi:10.1007/978-1-4419-1576-4>.
A set of tools developed at Simularia for Simularia, to help
    preprocessing and post-processing of meteorological and air quality data.
A framework for performing simulations such as those common in
    methodological statistics papers.  The design principles of this package
    are described in greater depth in Bien, J. (2016) "The simulator: An Engine
    to Streamline Simulations," which is available at <arXiv:1607.00021>.
This is an R implementation of a constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models (SIMULE). The SIMULE algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogenous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(simuleDemo) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Ritambhara Singh, Yanjun Qi (2017) <DOI:10.1007/s10994-017-5635-7>.
Tools to simulate realistic raw case data for an epidemic in
    the form of line lists and contacts using a branching process.
    Simulated outbreaks are parameterised with epidemiological parameters
    and can have age-structured populations, age-stratified
    hospitalisation and death risk and time-varying case fatality risk.
Simulate genotypes in SNP (single nucleotide polymorphisms) Matrix as random numbers from an uniform distribution, for diploid organisms (coded by 0, 1, 2), Sikorska et al., (2013) <doi:10.1186/1471-2105-14-166>, or half-sib/full-sib SNP matrix from real or simulated parents SNP data, assuming mendelian segregation. Simulate phenotypic traits for real or simulated SNP data, controlled by a specific number of quantitative trait loci  and their effects, sampled from a Normal or an Uniform distributions, assuming a pure additive model. This is useful for testing association and genomic prediction models or for educational purposes.
Simulation tools for planning Vitamin D studies. Individual vitamin D status 
             profiles are simulated, modelling population heterogeneity in trial arms. 
             Exposures to infectious agents are generated, with infection depending on vitamin D status.
The sinaplot is a data visualization chart suitable for plotting
    any single variable in a multiclass data set. It is an enhanced jitter strip
    chart, where the width of the jitter is controlled by the density
    distribution of the data within each class.
Implementation of the Conditional Least Square (CLS) estimates and
    its covariance matrix for the first-order spatial integer-valued
    autoregressive model (SINAR(1,1)) proposed by Ghodsi (2012)
    <doi:10.1080/03610926.2011.560739>.
This implements the Brunton et al (2016; PNAS <doi:10.1073/pnas.1517384113>) sparse
    identification algorithm for finding ordinary differential equations for a measured 
    system from raw data (SINDy). The package includes a set of additional tools for 
    working with raw data, with an emphasis on cognitive science applications (Dale 
    and Bhat, 2018 <doi:10.1016/j.cogsys.2018.06.020>). See 
    <https://github.com/racdale/sindyr> for examples and updates.
Manage package documentation and namespaces from the command line. 
             Programmatically attach namespaces in R and Rmd script, populates 
             'Roxygen2' skeletons with information scraped from within functions and 
             populate the Imports field of the DESCRIPTION file.
When comparing single cases to control populations and no parameters are known researchers and clinicians must estimate these with a control sample. This is often done when testing a case's abnormality on some variable or testing abnormality of the discrepancy between two variables. Appropriate frequentist and Bayesian methods for doing this are here implemented, including tests allowing for the inclusion of covariates. These have been developed first and foremost by John Crawford and Paul Garthwaite, e.g. in Crawford and Howell (1998) <doi:10.1076/clin.12.4.482.7241>, Crawford and Garthwaite (2005) <doi:10.1037/0894-4105.19.3.318>, Crawford and Garthwaite (2007) <doi:10.1080/02643290701290146> and Crawford, Garthwaite and Ryan (2011) <doi:10.1016/j.cortex.2011.02.017>. The package is also equipped with power calculators for each method.
Provides functions to calculate and visualise the Regional
    Consistency Probability (RCP) for single-arm multi-regional clinical
    trials (MRCTs) using the Effect Retention Approach (ERA). Six endpoint
    types are supported: continuous, binary, count (negative binomial),
    time-to-event via hazard ratio, milestone survival, and restricted mean
    survival time (RMST). For each endpoint, both a closed-form (or
    semi-analytical) solution and a Monte Carlo simulation approach are
    implemented. Two consistency evaluation methods are available: Method 1
    (effect retention in Region 1 relative to the overall population) and
    Method 2 (simultaneous positive effect across all regions). Plotting
    functions generate faceted visualisations of RCP as a function of the
    regional allocation proportion, overlaying formula and simulation results
    for direct comparison. The methodology follows the Japanese MHLW
    guidelines for MRCTs.
    Abbreviations used: RCP (Regional Consistency Probability), MRCT
    (Multi-Regional Clinical Trial), RMST (Restricted Mean Survival Time),
    MHLW (Ministry of Health, Labour and Welfare).
Provides R functions for calculating basic effect size indices for 
  single-case designs, including several non-overlap measures and parametric 
  effect size measures, and for estimating the gradual effects model developed 
  by Swan and Pustejovsky (2018) <DOI:10.1080/00273171.2018.1466681>. 
  Standard errors and confidence intervals (based on the assumption that the outcome 
  measurements are mutually independent) are provided for the subset of effect sizes 
  indices with known sampling distributions.
Creates complex heatmaps for single cell RNA-seq data that simultaneously 
    display gene expression levels (as color intensity) and expression percentages 
    (as circle sizes). Supports gene grouping, cell type annotations, and time point 
    comparisons. Built on top of 'ComplexHeatmap' and integrates with 'Seurat' objects.
    For more details see Gu (2022) <doi:10.1002/imt2.43> and Hao (2024) <doi:10.1038/s41587-023-01767-y>.
One key exploratory analysis step in single-cell genomics data analysis
    is the prediction of features with different activity levels. For example, we want 
    to predict differentially expressed genes (DEGs) in single-cell RNA-seq data, 
    spatial DEGs in spatial transcriptomics data, or differentially accessible 
    regions (DARs) in single-cell ATAC-seq data. 'singleCellHaystack' predicts differentially
    active features in single cell omics datasets without relying on the clustering
    of cells into arbitrary clusters. 'singleCellHaystack' uses Kullback-Leibler 
    divergence to find features (e.g., genes, genomic regions, etc) that are active
    in subsets of cells that are non-randomly positioned inside an input space (such as 
    1D trajectories, 2D tissue sections, multi-dimensional embeddings, etc). For 
    the theoretical background of 'singleCellHaystack' we refer to our original paper
    Vandenbon and Diez (Nature Communications, 2020) <doi:10.1038/s41467-020-17900-3>
    and our update Vandenbon and Diez (Scientific Reports, 2023) <doi:10.1038/s41598-023-38965-2>.
A suite of statistical methods for analysis of single-cell omics data including linear model-based methods for differential abundance analysis for individual level single-cell RNA-seq data. For more details see Zhang, et al. (Submitted to Bioinformatics)<https://github.com/Lujun995/DiSC_Replication_Code>.
Implementation of single-source capture-recapture methods for population size estimation using zero-truncated, zero-one truncated and zero-truncated one-inflated Poisson, Geometric and Negative Binomial regression as well as Zelterman's and Chao's regression. Package includes point and interval estimators for the population size with variances estimated using analytical or bootstrap method. Details can be found in: van der Heijden et all. (2003) <doi:10.1191/1471082X03st057oa>, Böhning and van der Heijden (2019) <doi:10.1214/18-AOAS1232>, Böhning et al. (2020) Capture-Recapture Methods for the Social and Medical Sciences or Böhning and Friedl (2021) <doi:10.1007/s10260-021-00556-8>.
Implementation of SING algorithm to extract joint and individual non-Gaussian components from two datasets. SING uses an objective function that maximizes the skewness and kurtosis of latent components with a penalty to enhance the similarity between subject scores. Unlike other existing methods, SING does not use PCA for dimension reduction, but rather uses non-Gaussianity, which can improve feature extraction. Benjamin B.Risk, Irina Gaynanova (2021) <doi:10.1214/21-AOAS1466>.
Density, distribution function, quantile function 
	and random generation for the sum of independent non-identical
	binomial distribution with parameters \code{size} and \code{prob}.
Implements an approach aimed at assessing the accuracy and
    effectiveness of raw scores obtained in scales that contain locally dependent
    items. The program uses as input the calibration (structural) item estimates
    obtained from fitting extended unidimensional factor-analytic solutions in
    which the existing local dependencies are included. Measures of reliability
    (Omega) and information are proposed at three levels: (a) total score, (b)
    bivariate-doublet, and (c) item-by-item deletion, and  are compared to those
    that would be obtained if all the items had been locally independent. All the
    implemented procedures can be obtained from: (a) linear factor-analytic
    solutions in which the item scores are treated as approximately continuous,
    and (b) non-linear solutions in which the item scores are treated as
    ordered-categorical. A detailed guide can be obtained at the following url.
A single, phenome-wide permutation of large-scale biobank data.
    When a large number of phenotypes are analyzed in parallel, a single 
    permutation across all phenotypes followed by genetic association analyses 
    of the permuted data enables estimation of false discovery rates (FDRs) 
    across the phenome. These FDR estimates provide a significance criterion 
    for interpreting genetic associations in a biobank context. For the basic
    permutation of unrelated samples, this package takes a sample-by-variable 
    file with ID, genotypic covariates, phenotypic covariates, and phenotypes 
    as input. For data with related samples, it also takes a file with 
    sample pair-wise identity-by-descent information. 
    The function outputs a permuted sample-by-variable file ready 
    for genome-wide association analysis. See Annis et al. (2021) <doi:10.21203/rs.3.rs-873449/v1> 
    for details.
Provides access to packages developed for downloading, reading and analyzing
  microdata from household surveys in Integrated System of Household Surveys - SIPD
  conducted by Brazilian Institute of Geography and Statistics - IBGE.
	More information can be obtained from the official website <https://www.ibge.gov.br/>.
Processor for selected ion flow tube mass spectrometer
            (SIFT-MS) output file from breath analysis. It allows the filtering of the SIFT output file (i.e., variation
            over time of the target analyte concentration) and the following
            analysis for the determination of: maximum, average, and standard deviation
            value of target concentration measured at each exhalation, and the
            respiratory rate over the measurement. Additionally, it is possible to
            align the SIFT-MS data with other on-line techniques such as 
            cardio pulmonary exercise test (CPET) for a comprehensive 
            characterization of breath samples.
A simulator for reticulate evolution under a birth-death-hybridization process. Here the birth-death process is extended to consider reticulate Evolution by allowing hybridization events to occur. The general purpose simulator allows the modeling of three different reticulate patterns: lineage generative hybridization, lineage neutral hybridization, and lineage degenerative hybridization. Users can also specify hybridization events to be dependent on a trait value or genetic distance. We also extend some phylogenetic tree utility and plotting functions for networks. We allow two different stopping conditions: simulated to a fixed time or number of taxa. When simulating to a fixed number of taxa, the user can simulate under the Generalized Sampling Approach that properly simulates phylogenies when assuming a uniform prior on the root age.
A platform for computing competition indices and experimenting
    with spatially explicit individual-based vegetation models.
Calculating daily global solar radiation at horizontal surface using several well-known models (i.e. Angstrom-Prescott, Supit-Van Kappel, Hargreaves, Bristow and Campbell, and Mahmood-Hubbard), and model calibration based on ground-truth data, and (3) model auto-calibration. The FAO Penmann-Monteith equation to calculate evapotranspiration is also included.
Provides two main functionalities.
    1 - Given a system of simultaneous equation,
    it decomposes  the matrix of coefficients weighting the endogenous variables 
    into three submatrices: one includes the subset of coefficients that have a causal nature
    in the model, two include the subset of coefficients that have a interdependent nature
    in the model, either at systematic level or induced by the correlation between error terms.
    2 - Given a decomposed model,
    it tests for the significance of the interdependent relationships acting in the system, 
    via Maximum likelihood and Wald test, which can be built starting from the function output.
    For theoretical reference see Faliva (1992) <doi:10.1007/BF02589085> and 
    Faliva and Zoia (1994) <doi:10.1007/BF02589041>.
Performs hybrid multi-stage factor analytic procedure for controlling acquiescence in restricted solutions (Ferrando & Lorenzo-Seva, 2000 <https://www.uv.es/revispsi/articulos3.00/ferran7.pdf>).
We build an Susceptible-Infectious-Recovered (SIR) model where the rate of infection is the sum of the household rate and the community rate. We estimate the posterior distribution of the parameters using the Metropolis algorithm. Further details may be found in: F Scott Dahlgren, Ivo M Foppa, Melissa S Stockwell, Celibell Y Vargas, Philip LaRussa, Carrie Reed (2021) "Household transmission of influenza A and B within a prospective cohort during the 2013-2014 and 2014-2015 seasons" <doi:10.1002/sim.9181>.
Supplementary functions for item response models aiming
    to complement existing R packages. The functionality includes among others
    multidimensional compensatory and noncompensatory IRT models
    (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), 
    MCMC for hierarchical IRT models and testlet models
    (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), 
    NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), 
    Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>;
    Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>),
    faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011,
    <doi:10.1111/j.1745-3984.2011.00143.x>),
    ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), 
    DETECT statistic (Stout, Habing, Douglas & Kim, 1996, 
    <doi:10.1177/014662169602000403>), local structural equation modeling 
    (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016,
    <doi:10.1080/00273171.2016.1142856>).
Implements a thresholded version of the Sliced Inverse Regression method (Li, K. C. (1991) <doi:10.2307/2290563>), which allows to do variable selection.
Variable selection techniques are essential tools for model
    selection and estimation in high-dimensional statistical models. Through this
    publicly available package, we provide a unified environment to carry out
    variable selection using iterative sure independence screening (SIS) (Fan and Lv (2008)<doi:10.1111/j.1467-9868.2008.00674.x>) and all
    of its variants in generalized linear models (Fan and Song (2009)<doi:10.1214/10-AOS798>) and the Cox proportional hazards
    model (Fan, Feng and Wu (2010)<doi:10.1214/10-IMSCOLL606>).
Implements the SISAL algorithm by Tikka and Hollmén. It is
        a sequential backward selection algorithm which uses a linear
        model in a cross-validation setting. Starting from the full
        model, one variable at a time is removed based on the
        regression coefficients. From this set of models, a
        parsimonious (sparse) model is found by choosing the model with
        the smallest number of variables among those models where the
        validation error is smaller than a threshold. Also implements
        extensions which explore larger parts of the search space
        and/or use ridge regression instead of ordinary least squares.
Interval fusion and selection procedures for regression with 
             functional inputs. Methods include a semiparametric approach based
             on Sliced Inverse Regression (SIR), as described in 
             <doi:10.1007/s11222-018-9806-6> (standard ridge and sparse SIR are 
             also included in the package) and a random forest based approach, 
             as described in <doi:10.1002/sam.11705>.
Implementation of the SSR-Algorithm. The Sign-Simplicity-Regression model is a nonparametric statistical model which is based on residual signs and simplicity assumptions on the regression function. Goal is to calculate the most parsimonious regression function satisfying the statistical adequacy requirements. Theory and functions are specified in Metzner (2020, ISBN: 979-8-68239-420-3, "Trendbasierte Prognostik") and Metzner (2021, ISBN: 979-8-59347-027-0, "Adäquates Maschinelles Lernen").
The Brazilian system for diploma registration and validation on technical and superior
    courses are managing by 'Sistec' platform, see <https://sistec.mec.gov.br/>. This package provides 
    tools for Brazilian institutions to update the student's registration and make data analysis 
    about their situation, retention and drop out.
This data package contains four datasets of quantitative PCR (qPCR)
  amplification curves that were used as supplementary data in the research
  article by Sisti et al. (2010), <doi:10.1186/1471-2105-11-186>.
  The primary dataset comprises a ten-fold dilution series spanning
  copy numbers from 3.14 × 10^7 to 3.14 × 10^2, with twelve replicates per
  concentration. These samples are based on a pGEM-T Promega plasmid containing
  a 104 bp fragment of the mitochondrial gene NADH dehydrogenase 1 (MT-ND1),
  amplified using the ND1/ND2 primer pair. The remaining three datasets contain
  qPCR results in the presence of specific PCR inhibitors: tannic acid,
  immunoglobulin G (IgG), and quercetin, respectively, to assess their effects
  on the amplification process. These datasets are useful for researchers 
  interested in PCR kinetics. The original raw data file is available as
  Additional File 1:
  <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-11-186/MediaObjects/12859_2009_3643_MOESM1_ESM.XLS>.
Functions common to members of the SISTM team.
Selects invalid instruments amongst a candidate of potentially bad instruments. The algorithm selects potentially invalid instruments and provides an estimate of the causal effect between exposure and outcome.
Computes the sit coefficient between two vectors x and y, 
  possibly all paired coefficients for a matrix. The reference for the methods implemented here is 
  Zhang, Yilin, Canyi Chen, and Liping Zhu. 2022. "Sliced Independence Test." Statistica Sinica. <doi:10.5705/ss.202021.0203>. 
  This package incorporates the Galton peas example.
Functions for fitting and plotting SITAR (Super
    Imposition by Translation And Rotation) growth curve models. SITAR is
    a shape-invariant model with a regression B-spline mean curve and
    subject-specific random effects on both the measurement and age
    scales.  The model was first described by Lindstrom (1995)
    <doi:10.1002/sim.4780141807> and developed as the SITAR method by Cole
    et al (2010) <doi:10.1093/ije/dyq115>.
Carries out a two-level sample selection where the possibility of an initially selected site not wanting to participate is anticipated, and the site is optimally replaced. The procedure aims to reduce bias (and/or loss of external validity) with respect to the target population. In selecting units and sub-units, 'sitepickR' uses the cube method developed by 'Deville & Tillé', (2004) <http://www.math.helsinki.fi/msm/banocoss/Deville_Tille_2004.pdf> and described in Tillé (2011) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2011002/article/11609-eng.pdf?st=5-sx8Q8n>. The cube method is a probability sampling method that is designed to satisfy criteria for balance between the sample and the population. Recent research has shown that this method performs well in simulations for studies of educational programs (see Fay & Olsen (2021, under review). To implement the cube method, 'sitepickR' uses the sampling R package <https://cran.r-project.org/package=sampling>. To implement statistical matching, 'sitepickR' uses the 'MatchIt' R package <https://cran.r-project.org/package=MatchIt>.
The goal of 'siteymlgen' is to make it easy to organise
  the building of your 'R Markdown' website.
  The init() function placed within the first code chunk of the index.Rmd
  file of an 'R' project directory will initiate the generation of an automatically
  written _site.yml file. 'siteymlgen' recommends a specific naming
  convention for your 'R Markdown' files. This naming will ensure that
  your navbar layout is ordered according to a hierarchy.
Implements a three-dimensional stochastic model of cancer growth and mutation similar to the one described in Waclaw et al. (2015) <doi:10.1038/nature14971>. Allows for interactive 3D visualizations of the simulated tumor. Provides a comprehensive summary of the spatial distribution of mutants within the tumor. Contains functions which create synthetic sequencing datasets from the generated tumor.
Provided within are two high quality and fast PPRNGs that may be
    used in an 'OpenMP' parallel environment. In addition, there is a generator
    for one dimensional low-discrepancy sequence. The objective of this library
    to consolidate the distribution of the 'sitmo' (C++98 & C++11), 'threefry' and
    'vandercorput' (C++11-only) engines on CRAN by enabling others to link to the
    header files inside of 'sitmo' instead of including a copy of each engine
    within their individual package. Lastly, the package contains example
    implementations using the 'sitmo' package and three accompanying vignette that
    provide additional information.
Format a number (or a list of numbers) to a string (or a
        list of strings) with SI prefix. Use SI prefixes as constants
        like (4 * milli)^2
Framework to build an individual tree simulator.
Provides extensions for package 'sitree' for allometric variables, growth, mortality, recruitment, management, tree removal and external modifiers functions.
A meta-package that loads the complete sitrep ecosystem for
    applied epidemiology analysis. This package provides report templates
    and automatically loads companion packages, including 'epitabulate' (for
    epidemiological tables), 'epidict' (for data dictionaries), 'epikit' (for
    epidemiological utilities), and 'apyramid' (for age-sex pyramids).
    Simply load 'sitrep' to access all functions from the ecosystem.
An end-to-end toolkit for land use and land cover classification
    using big Earth observation data. Builds satellite image data cubes from cloud collections.
    Supports visualization methods for images and time series and 
    smoothing filters for dealing with noisy time series.
    Enables merging of multi-source imagery (SAR, optical, DEM).
    Includes functions for quality assessment of training samples using self-organized maps and  
    to reduce training samples imbalance. Provides machine learning algorithms including support vector machines, 
    random forests, extreme gradient boosting, multi-layer perceptrons,
    temporal convolution neural networks, and temporal attention encoders.
    Performs efficient classification of big Earth observation data cubes and includes 
    functions for post-classification smoothing based on Bayesian inference. 
    Enables best practices for estimating area and assessing accuracy of land change. 
    Includes object-based spatio-temporal segmentation for space-time OBIA.
    Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.
An iterative feature selection method that internally utilizes
    various Machine Learning methods that have embedded feature reduction in
    order to shrink down the feature space into a small and yet robust set.
Functions and utilities to perform
    Statistical Analyses in the Six Sigma way.
    Through the DMAIC cycle (Define, Measure, Analyze, Improve, Control),
    you can manage several Quality Management studies:
    Gage R&R, Capability Analysis, Control Charts, Loss Function Analysis,
    etc. Data frames used in the books "Six Sigma with R" [ISBN 978-1-4614-3652-2]
    and "Quality Control with R" [ISBN 978-3-319-24046-6], 
    are also included in the package.
An opinionated interface to Amazon Web Services <https://aws.amazon.com>, 
    with functions for interacting with 'IAM' (Identity and Access Management),
    'S3' (Simple Storage Service), 'RDS' (Relational Data Service), Redshift, and Billing.
    Lower level functions ('aws_' prefix) are for do it yourself workflows, while
    higher level functions ('six_' prefix) automate common tasks.
Estimate morphometric and gonadal size at sexual maturity for organisms, usually fish and invertebrates. It includes methods for classification based on relative growth (using principal components analysis, hierarchical clustering, discriminant analysis), logistic regression (Frequentist or Bayes), parameters estimation and some basic plots.
Calculates and plots the SiZer map for scatterplot data.  A 
  SiZer map is a way of examining when the p-th derivative of a 
  scatterplot-smoother is significantly negative, possibly zero or 
  significantly positive across a range of smoothing bandwidths.
Provides a database-independent JDBC interface.
Collection of functions dealing with labelled data, like reading and 
    writing data between R and other statistical software packages like 'SPSS',
    'SAS' or 'Stata', and working with labelled data. This includes easy ways 
    to get, set or change value and variable label attributes, to convert 
    labelled vectors into factors or numeric (and vice versa), or to deal with 
    multiple declared missing values.
Collection of miscellaneous utility functions, supporting data
    transformation tasks like recoding, dichotomizing or grouping variables,
    setting and replacing missing values. The data transformation functions
    also support labelled data, and all integrate seamlessly into a
    'tidyverse'-workflow.
Collection of plotting and table output functions for data
    visualization. Results of various statistical analyses (that are commonly used
    in social sciences) can be visualized using this package, including simple and
    cross tabulated frequencies, histograms, box plots, (generalized) linear models,
    mixed effects models, principal component analysis and correlation matrices,
    cluster analyses, scatter plots, stacked scales, effects plots of regression
    models (including interaction terms) and much more. This package supports
    labelled data.
A scalable and fast method for estimating joint Species Distribution Models (jSDMs) for big community data, including eDNA data. The package estimates a full (i.e. non-latent) jSDM with different response distributions (including the traditional multivariate probit model). The package allows to perform variation partitioning (VP) / ANOVA on the fitted models to separate the contribution of environmental, spatial, and biotic associations. In addition, the total R-squared can be further partitioned per species and site to reveal the internal metacommunity structure, see Leibold et al., <doi:10.1111/oik.08618>. The internal structure can then be regressed against environmental and spatial distinctiveness, richness, and traits to analyze metacommunity assembly processes.  The package includes support for accounting for spatial autocorrelation and the option to fit responses using deep neural networks instead of a standard linear predictor. As described in Pichler & Hartig (2021) <doi:10.1111/2041-210X.13687>, scalability is achieved by using a Monte Carlo approximation of the joint likelihood implemented via 'PyTorch' and 'reticulate', which can be run on CPUs or GPUs.
Collection of convenient functions for common statistical computations,
             which are not directly provided by R's base or stats packages.
             This package aims at providing, first, shortcuts for statistical measures,
             which otherwise could only be calculated with additional effort
             (like Cramer's V, Phi, or effect size statistics like Eta or Omega squared),
             or for which currently no functions available. Second, another focus
             lies on weighted variants of common statistical measures and tests
             like weighted standard error, mean, t-test, correlation, and more.
A small set of helper functions to convert 'sjPlot'
    HTML-tables to R data.frame objects / knitr::kable-tables.
In forensics, it is common and effective practice to analyse glass fragments from the scene and suspects 
  to gain evidence of placing a suspect at the crime scene. 
  This kind of analysis involves comparing the physical and chemical attributes of glass fragments that exist on both the 
  person and at the crime scene, and assessing the significance in a likeness that they share. 
  The package implements the Scott-Knott Modification 2 algorithm (SKM2) (Christopher M. Triggs and 
  James M. Curran and John S. Buckleton and Kevan A.J. Walsh (1997) <doi:10.1016/S0379-0738(96)02037-3> 
  "The grouping problem in forensic glass analysis: a divisive approach", Forensic Science International, 85(1), 1--14)
  for small sample glass fragment analysis using the refractive index (ri) of a set of glass samples. 
  It also includes an experimental multivariate analog to the Scott-Knott algorithm for similar analysis on glass samples
  with multiple chemical concentration variables and multiple samples of the same item; testing against the Hotellings T^2 
  distribution (J.M. Curran and C.M. Triggs and J.R. Almirall and J.S. Buckleton and K.A.J. Walsh (1997) 
  <doi:10.1016/S1355-0306(97)72197-X> "The interpretation of elemental composition measurements from forensic 
  glass evidence", Science & Justice, 37(4), 241--244).
Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.
Utilities for single nucleotide polymorphism (SNP) based kinship analysis
    testing and evaluation. The 'skater' package contains functions for importing, parsing, 
    and analyzing pedigree data, performing relationship degree inference, benchmarking 
    relationship degree classification, and summarizing identity by descent (IBD) segment data.
    Package functions and methods are described in Turner et al. (2021) "skater: An R package 
    for SNP-based Kinship Analysis, Testing, and Evaluation" <doi:10.1101/2021.07.21.453083>.
Allows search and visualisation of a collection of uniformly processed skeletal transcriptomic datasets. Includes methods to identify datasets where genes of interest are differentially expressed and find datasets with a similar gene expression pattern to a query dataset Soul J, Hardingham TE, Boot-Handford RP, Schwartz JM (2019) <doi:10.1093/bioinformatics/bty947>.
A tool for bootstrapping new packages with useful defaults,
    including a test suite outline that passes checks and helpers for running
    tests, checking test coverage, building vignettes, and more. Package
    skeletons it creates are set up for pushing your package to
    'GitHub' and using other hosted services for building and test automation.
Functions for the Skellam distribution, including: density
    (pmf), cdf, quantiles and regression.
Creates static / animated / interactive visualisations embeddable
    in R Markdown documents. It implements an R-to-JavaScript transpiler and
    enables users to write JavaScript applications using the syntax of R.
An implementation of image processing effects that convert a photo into a line drawing image. 
    For details, please refer to Tsuda, H. (2020). sketcher: An R package for converting a photo into a sketch style image. 
    <doi:10.31234/osf.io/svmw5>.
Construct sketches of data via random subspace embeddings. 
  For more details, see the following papers.
  Lee, S. and Ng, S. (2022). "Least Squares Estimation Using Sketched Data with Heteroskedastic Errors," Proceedings of the 39th International Conference on Machine Learning (ICML22), 162:12498-12520.
  Lee, S. and Ng, S. (2020). "An Econometric Perspective on Algorithmic Subsampling," Annual Review of Economics, 12(1): 45–80.
Provides functions to create and manage research compendiums for data analysis. Research compendiums are a standard and intuitive folder structure for organizing the digital materials of a research project, which can significantly improve reproducibility. The package offers several compendium structure options that fit different research project as well as the ability of duplicating the folder structure of existing projects or implementing custom structures. It also simplifies the use of version control.
Functions are provided for the density function,
	     distribution function, quantiles and random number
	     generation for the skew hyperbolic
	     t-distribution. There are also functions that fit
	     the distribution to data. There are functions for the
	     mean, variance, skewness, kurtosis and mode of a given
	     distribution and to calculate moments of any order about
	     any centre. To assess goodness of fit, there are
	     functions to generate a Q-Q plot, a P-P plot and a tail plot.
It fits scale mixture of skew-normal linear mixed models using either an expectation–maximization (EM) type algorithm or its accelerated version (Damped Anderson Acceleration with Epsilon Monotonicity, DAAREM), including some possibilities for modeling the within-subject dependence <doi:10.18637/jss.v115.i07>.
Provide data generation and estimation tools for the multivariate scale mixtures of normal 
             presented in Lange and Sinsheimer (1993) <doi:10.2307/1390698>, the multivariate scale 
             mixtures of skew-normal presented in Zeller, Lachos and Vilca (2011) 
             <doi:10.1080/02664760903406504>, the multivariate skew scale mixtures of normal 
             presented in Louredo, Zeller and Ferreira (2021) <doi:10.1007/s13571-021-00257-y>
             and the multivariate scale mixtures of skew-normal-Cauchy presented in Kahrari et al. (2020)
	     <doi:10.1080/03610918.2020.1804582>.
Estimate necessary sample sizes for comparing the location
    of data from two groups or categories when the distribution of the 
    data is skewed. The package 
    offers a non-parametric method for a Wilcoxon Mann-Whitney test of 
    location shift as well as methods for several generalized linear 
    models, for instance, Gamma regression.
Density, distribution function, quantile function and
        random generation for the skewed t distribution of Fernandez
        and Steel.
Provide estimation and data generation tools for the skew-unit family
		discussed based on Mukhopadhyay and Brani (1995) <doi:10.2307/2348710>. 
		The family contains extensions for popular distributions such as the ArcSin 
		discussed in Arnold and Groeneveld  (1980) <doi:10.1080/01621459.1980.10477449>, 
		triangular, U-quadratic and Johnson-SB proposed in Cortina-Borja (2006) <doi:10.1111/j.1467-985X.2006.00446_12.x> 
		distributions, among others.
Sequential Kalman filter for scalable online changepoint detection by temporally correlated data. It enables fast single and multiple change points with missing values. See the reference: Hanmo Li, Yuedong Wang, Mengyang Gu (2023), <arXiv:2310.18611>.
SKIFTI files contain brain imaging data in coordinates across Tract Based Spatial Statistics (TBSS) skeleton, which 
    represent the brain white matter intensity values. 
    'skiftiTools' provides a unified environment for reading, writing, 
    visualizing and manipulating SKIFTI-format data. It supports the "subsetting", 
    "concatenating", and using data as data.frame for R statistical functions. The SKIFTI data is structured for convenient access to the data and metadata,
    and includes support for visualizations. For more information see Merisaari et al. (2024) <doi:10.57736/87d2-0608>.
A generalization of the statistic used in Friedman's ANOVA method and in Durbin's rank test. This nonparametric statistical test is useful for the data obtained from block designs with missing observations occurring randomly. A resulting p-value is based on the chi-squared distribution and Monte Carlo method.
Functions that simplify calls to the 'Skilljar' API. See 
    <https://api.skilljar.com/docs/> for documentation on the 'Skilljar' API.
    This package is not supported by 'Skilljar'.
A simple to use summary function that can be used with pipes
    and displays nicely in the console. The default summary statistics may
    be modified by the user as can the default formatting.  Support for
    data frames and vectors is included, and users can implement their own
    skim methods for specific object types as described in a vignette.
    Default summaries include support for inline spark graphs.
    Instructions for managing these on specific operating systems are
    given in the "Using skimr" vignette and the README.
Implements a Bayesian hierarchical model designed to identify skips in mobile menstrual cycle self-tracking on mobile apps. Future developments will allow for the inclusion of covariates affecting cycle mean and regularity, as well as extra information regarding tracking non-adherence. Main methods to be outlined in a forthcoming paper, with alternative models from Li et al. (2022) <doi:10.1093/jamia/ocab182>.
Provides tools for applying Sklar's Omega (Hughes, 2022) <doi:10.1007/s11222-022-10105-2>
    methodology to nominal scores, ordinal scores, percentages, counts, amounts (i.e.,
    non-negative real numbers), and balances (i.e., any real number). The framework can
    accommodate any number of units, any number of coders, and missingness; and
    can be used to measure agreement with a gold standard, intra-coder agreement,
    and/or inter-coder agreement. Frequentist inference is supported for all levels
    of measurement. Bayesian inference is supported for continuous scores only.
Algorithms to compute spherical k-means partitions.
  Features several methods, including a genetic and a fixed-point
  algorithm and an interface to the CLUTO vcluster program.
It's a Super K-Nearest Neighbor(SKNN) classification method with using kernel density to describe weight of the distance between a training observation and the testing sample. Comparison of performance between SKNN and KNN shows that SKNN is significantly superior to KNN.
Generates and evaluates D, I, A, Alias, E, T, and G optimal designs. Supports generation and evaluation of blocked and split/split-split/.../N-split plot designs. Includes parametric and Monte Carlo power evaluation functions, and supports calculating power for censored responses. Provides a framework to evaluate power using functions provided in other packages or written by the user. Includes a Shiny graphical user interface that displays the underlying code used to create and evaluate the design to improve ease-of-use and make analyses more reproducible. For details, see Morgan-Wall et al. (2021) <doi:10.18637/jss.v099.i01>.
A tool to calculate sky illuminance values (in lux) for both sun and moon. The
 model is a translation of the Fortran code by Janiczek and DeYoung (1987)
 <https://archive.org/details/DTIC_ADA182110>.
Calculate geolocations by light using template matching. 
 The routine uses a calibration free optimization of a sky illuminance model to 
 determine locations robustly using a template matching approach,
 as described by Ekstrom (2004) <https://nipr.repo.nii.ac.jp/records/2496>,
 and behaviourly informed constraints (step-selection).
'Slack' <https://slack.com/> provides a service for teams to
    collaborate by sharing messages, images, links, files and more.
    Functions are provided that make it possible to interact with the
    'Slack' platform 'API'. When you need to share information or data
    from R, rather than resort to copy/ paste in e-mails or other services
    like 'Skype' <https://www.skype.com/en/>, you can use this package to
    send well-formatted output from multiple R objects and expressions to
    all teammates at the same time with little effort. You can also send
    images from the current graphics device, R objects, and upload files.
Data structures and algorithms for sparse arrays and matrices,
  based on index arrays and simple triplet representations, respectively.
Slanted matrices and ordered clustering for better visualization of similarity data.
Alternative to using withCallingHandlers() in the simple case of catch 
  and rethrow. The `%!%` operator evaluates the expression on its left hand side, 
  and if an error occurs, the right hand side is used to construct a new error
  that embeds the original error.
Allows users to list data structures using path-based navigation. 
    Provides intuitive methods for storing, accessing,
    and manipulating nested data through simple path strings.
    Key features include strict mode validation, path existence checking, recursive
    operations, and automatic parent-level creation. Designed for use cases requiring
    organized storage of complex nested data while maintaining simple access patterns.
    Particularly useful for configuration management, nested settings, and any
    application where data naturally forms a tree-like structure.
Implements the smooth LASSO estimator for the function-on-function linear regression model described in Centofanti et al. (2022) <doi:10.1016/j.csda.2022.107556>.
Provides comprehensive tools for the implementation of Structural Latent Class Models (SLCM), including
    Latent Transition Analysis (LTA; Linda M. Collins and Stephanie T. Lanza, 2009) <doi:10.1002/9780470567333>,
    Latent Class Profile Analysis (LCPA; Hwan Chung et al., 2010) <doi:10.1111/j.1467-985x.2010.00674.x>, and
    Joint Latent Class Analysis (JLCA; Saebom Jeon et al., 2017) <doi:10.1080/10705511.2017.1340844>, and 
    any other extended models involving multiple latent class variables.
Efficient R package for latent class analysis of recurrent events, based on the semiparametric multiplicative intensity model by Zhao et al. (2022) <doi:10.1111/rssb.12499>. SLCARE returns estimates for non-functional model parameters along with the associated variance estimates and p-values. Visualization tools are provided to depict the estimated functional model parameters and related functional quantities of interest. SLCARE also delivers a model checking plot to help assess the adequacy of the fitted model.
Perform a Bayesian estimation of the exploratory 
    Sparse Latent Class Model for Binary Data 
    described by Chen, Y., Culpepper, S. A., and Liang, F. (2020) 
    <doi:10.1007/s11336-019-09693-2>.
The skew logistic distribution is a quantile-defined generalisation
 of the logistic distribution (van Staden and King 2015).  Provides random 
 numbers, quantiles, probabilities, densities and density quantiles for the distribution.
 It provides Quantile-Quantile plots and method of L-Moments estimation 
 (including asymptotic standard errors) for the distribution.
Calculates maximum likelihood estimate, exact and asymptotic confidence intervals, and exact and asymptotic goodness of fit p-values for concentration of infectious units from serial limiting dilution assays. This package uses the likelihood equation, exact goodness of fit p-values, and exact confidence intervals described in Meyers et al. (1994) <http://jcm.asm.org/content/32/3/732.full.pdf>. This software is also implemented as a web application through the Shiny R package <https://iupm.shinyapps.io/sldassay/>.
In the recent past, measurement of coverage has been mainly through
    two-stage cluster sampled surveys either as part of a nutrition assessment
    or through a specific coverage survey known as Centric Systematic Area
    Sampling (CSAS). However, such methods are resource intensive and often only
    used for final programme evaluation meaning results arrive too late for
    programme adaptation. SLEAC, which stands for Simplified Lot Quality
    Assurance Sampling Evaluation of Access and Coverage, is a low resource
    method designed specifically to address this limitation and is used
    regularly for monitoring, planning and importantly, timely improvement to
    programme quality, both for agency and Ministry of Health (MoH) led
    programmes. SLEAC is designed to complement the Semi-quantitative Evaluation
    of Access and Coverage (SQUEAC) method. This package provides functions for
    use in conducting a SLEAC assessment.
Compute Time series Resistant Smooth 4253H, twice smoothing method.
Sleep cycles are largely detected according to the originally proposed criteria by Feinberg & Floyd (1979) <doi:10.1111/j.1469-8986.1979.tb02991.x> as described in Blume & Cajochen (2021) <doi:10.1016/j.mex.2021.101318>.
Use behavioural variables to score activity and infer sleep from bouts of immobility. 
    It is primarily designed to score sleep in fruit flies from Drosophila Activity Monitor (TriKinetics) and Ethoscope data.
    It implements sleep scoring using the "five-minute rule" (Hendricks et al. (2000) <DOI:10.1016/S0896-6273(00)80877-6>),
    activity classification for Ethoscopes (Geissmann et al. (2017) <DOI:10.1371/journal.pbio.2003026>) 
    and a new algorithm to detect when animals are dead.
A tool to interactively explore the
  embeddings created by dimension reduction methods such as 
  Principal Components Analysis (PCA), Multidimensional Scaling (MDS), 
  T-distributed Stochastic Neighbour Embedding (t-SNE),
  Uniform Manifold Approximation and Projection (UMAP) or any other.
Efficient regression analysis under general two-phase sampling, where Phase I includes error-prone data and Phase II contains validated data on a subset.
The implementation of the algorithm for estimation of mutual information and channel capacity from experimental data by classification procedures (logistic regression). Technically, it allows to estimate information-theoretic measures between finite-state input and multivariate, continuous output. Method described in Jetka et al. (2019) <doi:10.1371/journal.pcbi.1007132>.
A framework for simulating spatially explicit genomic data which
    leverages real cartographic information for programmatic and visual encoding
    of spatiotemporal population dynamics on real geographic landscapes. Population
    genetic models are then automatically executed by the 'SLiM' software by Haller
    et al. (2019) <doi:10.1093/molbev/msy228> behind the scenes, using a custom
    built-in simulation 'SLiM' script. Additionally, fully abstract spatial models
    not tied to a specific geographic location are supported, and users can also
    simulate data from standard, non-spatial, random-mating models. These can be
    simulated either with the 'SLiM' built-in back-end script, or using an efficient
    coalescent population genetics simulator 'msprime' by Baumdicker et al. (2022)
    <doi:10.1093/genetics/iyab229> with a custom-built 'Python' script bundled with the
    R package. Simulated genomic data is saved in a tree-sequence format and can be
    loaded, manipulated, and summarised using tree-sequence functionality via an R
    interface to the 'Python' module 'tskit' by Kelleher et al. (2019)
    <doi:10.1038/s41588-019-0483-y>. Complete model configuration, simulation and
    analysis pipelines can be therefore constructed without a need to leave the R
    environment, eliminating friction between disparate tools for population genetic
    simulations and data analysis.
Data sets from Ramsey, F.L. and Schafer, D.W. (2002), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (2nd
    ed)", Duxbury.
Data sets from Ramsey, F.L. and Schafer, D.W. (2013), "The
    Statistical Sleuth: A Course in Methods of Data Analysis (3rd
    ed)", Cengage Learning.
Set of tools to find coherent patterns in gene expression (microarray) data using a Bayesian Sparse Latent Factor Model (SLFM) <DOI:10.1007/978-3-319-12454-4_15>. Considerable effort has been put to build a fast and memory efficient package, which makes this proposal an interesting and computationally convenient alternative to study patterns of gene expressions exhibited in matrices. The package contains the implementation of two versions of the model based on different mixture priors for the loadings: one relies on a degenerate component at zero and the other uses a small variance normal distribution for the spike part of the mixture.
Implementation for sparse logistic functional principal component analysis (SLFPCA). SLFPCA is specifically developed for functional binary data, and the estimated eigenfunction can be strictly zero on some sub-intervals, which is helpful for interpretation. The crucial function of this package is SLFPCA().
Implements the Bayesian model selection method with suspected latent 
    grouping factor methodology of Metzger and Franck (2020), 
    <doi:10.1080/00401706.2020.1739561>. SLGF detects latent 
    heteroscedasticity or group-based regression effects based on the levels of a 
    user-specified categorical predictor.
Provides tools for conditional and spatially dependent 
    density estimation using Spatial Logistic Gaussian Processes (SLGPs). 
    The approach represents probability densities through finite-rank 
    Gaussian process priors transformed via a spatial logistic density 
    transformation, enabling flexible non-parametric modeling of 
    heterogeneous data. Functionality includes density prediction, 
    quantile and moment estimation, sampling methods, and preprocessing 
    routines for basis functions. Applications arise in spatial statistics, 
    machine learning, and uncertainty quantification. 
    The methodology builds on the framework of Leonard (1978) 
    <doi:10.1111/j.2517-6161.1978.tb01655.x>, Lenk (1988) <doi:10.1080/01621459.1988.10478625>, 
    Tokdar (2007) <doi:10.1198/106186007X210206>, Tokdar (2010) <doi:10.1214/10-BA605>, 
    and is further aligned with recent developments 
    in Bayesian non-parametric modelling: see Gautier (2023) <https://boristheses.unibe.ch/4377/>, 
    and Gautier (2025) <doi:10.48550/arXiv.2110.02876>).
Generate the optimal Latin Hypercube Designs (LHDs) for computer experiments with quantitative factors and the optimal Sliced Latin Hypercube Designs (SLHDs) for computer experiments with both quantitative and qualitative factors. Details of the algorithm can be found in Ba, S., Brenneman, W. A. and Myers, W. R. (2015), "Optimal Sliced Latin Hypercube Designs," Technometrics. Important function in this package is "maximinSLHD".
This comprehensive toolkit for skewed regression is designated as "SLIC" (The LIC for Distributed Skewed Regression Analysis). It is predicated on the assumption that the error term follows a skewed distribution, such as the Skew-Normal, Skew-t, or Skew-Laplace. The methodology and theoretical foundation of the package are described in Guo G.(2020) <doi:10.1080/02664763.2022.2053949>.
A facility to generate sliced (orthogonal) Latin hypercube designs with four and five slices. For details about sliced and orthogonal Latin hypercube designs, see Yang, J. F., Lin, C. D., Qian, P. Z., and Lin, D. K. (2013). "Construction of sliced orthogonal Latin hypercube designs". Statistica Sinica, 1117-1130, <doi:10.5705/ss.2012.037>.
A framework for visualizing and exploring results of a Management
    Strategy Evaluation (MSE). The publication quality figures and tables
    can be developed directly from the R console, or interactively explored
    with the 'Slick' App. For more details, see the 'Slick' website 
    <https://slick.bluematterscience.com>.
This statistical method uses the nearest neighbor algorithm to estimate absolute distances between single cells 
	based on a chosen constellation of surface proteins, with these distances being a measure of the similarity between the 
	two cells being compared. Based on Sen, N., Mukherjee, G., and Arvin, A.M. (2015) <DOI:10.1016/j.ymeth.2015.07.008>.
This takes spatial single-cell-type RNA-seq data (specifically designed for Slide-seq v2) that calls copy number alterations (CNAs) using pseudo-spatial binning, clusters cellular units (e.g. beads) based on CNA profile, and visualizes spatial CNA patterns. Documentation about 'SlideCNA' is included in the the pre-print by Zhang et al. (2022, <doi:10.1101/2022.11.25.517982>). The package 'enrichR' (>= 3.0), conditionally used to annotate SlideCNA-determined clusters with gene ontology terms, can be installed at <https://github.com/wjawaid/enrichR> or with install_github("wjawaid/enrichR").
Fast k-nearest neighbors (K-NN) and principal component
    analysis (PCA) imputation algorithms for missing values in
    high-dimensional numeric matrices, i.e., epigenetic data. For
    extremely high-dimensional data with ordered features, a sliding
    window approach for K-NN or PCA imputation is provided.  Additional
    features include group-wise imputation (e.g., by chromosome),
    hyperparameter tuning with repeated cross-validation, multi-core
    parallelization, and optional subset imputation. The K-NN algorithm is
    described in: Hastie, T., Tibshirani, R., Sherlock, G., Eisen, M.,
    Brown, P. and Botstein, D.  (1999) "Imputing Missing Data for Gene
    Expression Arrays". The PCA imputation is an optimized version of the
    imputePCA() function from the 'missMDA' package described in: Josse,
    J. and Husson, F.  (2016) <doi:10.18637/jss.v070.i01> "missMDA: A
    Package for Handling Missing Values in Multivariate Data Analysis".
Provides type-stable rolling window functions over any R data
    type. Cumulative and expanding windows are also supported. For more
    advanced usage, an index can be used as a secondary vector that
    defines how sliding windows are to be created.
Create a side-by-side view of raster(image)s with an interactive 
    slider to switch between regions of the images. This can be especially useful
    for image comparison of the same region at different time stamps.
A collection of functions to perform Detrended Fluctuation Analysis (DFA exponent), GUEDES et al. (2019) <doi:10.1016/j.physa.2019.04.132> , Detrended cross-correlation coefficient (RHODCCA), GUEDES & ZEBENDE (2019) <doi:10.1016/j.physa.2019.121286>, DMCA cross-correlation coefficient and Detrended multiple cross-correlation coefficient (DMC), GUEDES & SILVA-FILHO & ZEBENDE (2018) <doi:10.1016/j.physa.2021.125990>, both with sliding windows approach.
Fits singular linear models to longitudinal data. Singular linear
    models are useful when the number, or timing, of longitudinal observations
    may be informative about the observations themselves. They are described
    in Farewell (2010) <doi:10.1093/biomet/asp068>, and are extensions of the
    linear increments model <doi:10.1111/j.1467-9876.2007.00590.x> to general
    longitudinal data.
Annotates single-cell and spatial-transcriptomic (ST) data using context-matching marker datasets. It creates a unified marker list (`Markers_list`) from multiple sources: built-in curated databases ('Cellmarker2', 'PanglaoDB', 'ScType', 'scIBD', 'TCellSI', 'PCTIT', 'PCTAM'), Seurat objects with cell labels, or user-provided Excel tables. SlimR first uses adaptive machine learning for parameter optimization, and then offers two automated annotation approaches: 'cluster-based' and 'per-cell'. Cluster-based annotation assigns one label per cluster, expression-based probability calculation, and AUC validation. Per-cell annotation assigns labels to individual cells using three scoring methods with adaptive thresholds and ratio-based confidence filtering, plus optional UMAP spatial smoothing, making it ideal for heterogeneous clusters and rare cell types. The package also supports semi-automated workflows with heatmaps, feature plots, and combined visualizations for manual annotation. For more information, see the package documentation at <https://github.com/zhaoqing-wang/SlimR>.
Provides functions for performing common tasks when working with
  slippy map tile service APIs e.g. Google maps, Open Street Map, Mapbox, Stamen,
  among others. Functionality includes converting from latitude and longitude to
  tile numbers, determining tile bounding boxes, and compositing tiles to a
  georeferenced raster image.
Provides statistical procedures for linear regression in the general context where the errors are assumed to be correlated. Different ways to estimate the asymptotic covariance matrix of the least squares estimators are available. Starting from this estimation of the covariance matrix, the confidence intervals and the usual tests on the parameters are modified. The functions of this package are very similar to those of 'lm': it contains methods such as summary(), plot(), confint() and predict(). The 'slm' package is described in the paper by E. Caron, J. Dedecker and B. Michel (2019), "Linear regression with stationary errors: the R package slm", arXiv preprint <arXiv:1906.06583>.
Stepwise models for the optimal linear combination of continuous variables in binary classification problems under Youden Index optimisation. Information on the models implemented can be found at Aznar-Gimeno et al. (2021) <doi:10.3390/math9192497>.
A collection of helper functions designed to help
    you to better understand object oriented programming in R,
    particularly using 'S3'.
Efficient implementations for Sorted L-One Penalized Estimation
    (SLOPE): generalized linear models regularized with the sorted L1-norm
    (Bogdan et al. 2015). Supported models include
    ordinary least-squares regression, binomial regression, multinomial
    regression, and Poisson regression. Both dense and sparse  predictor
    matrices are supported. In addition, the package features predictor
    screening rules that enable fast and efficient solutions to high-dimensional
    problems.
Calculates the slope (longitudinal gradient or steepness)
  of linear geographic features such as roads (for more details, see Ariza-López et al. (2019) 
  <doi:10.1038/s41597-019-0147-x>) and rivers (for more details, see Cohen et al. (2018) 
  <doi:10.1016/j.jhydrol.2018.06.066>). It can use local Digital Elevation Model (DEM) 
  data or download DEM data via the 'ceramic' package. The package also provides functions 
  to add elevation data to linestrings and visualize elevation profiles.
Provides tools for predicting ICU length of stay and assessing ICU efficiency. 
  It is based on the methodologies proposed by Peres et al. (2022, 2023), which utilize data-driven approaches for modeling and validation, offering insights into ICU performance and patient outcomes. References: Peres et al. (2022)<https://pubmed.ncbi.nlm.nih.gov/35988701/>, Peres et al. (2023)<https://pubmed.ncbi.nlm.nih.gov/37922007/>. 
  More information: <https://github.com/igor-peres/ICU-Length-of-Stay-Prediction>.
Catch advice for data-limited vertebrate and invertebrate 
    fisheries managed by harvest slot limits using the SlotLim harvest 
    control rule. The package accompanies the manuscript "SlotLim: catch advice 
    for data-limited vertebrate and invertebrate fisheries managed by harvest 
    slot limits" (Pritchard et al., in prep). Minimum data requirements: at 
    least two consecutive years of catch data, length–frequency distributions, 
    and biomass or abundance indices (all from fishery-dependent sources); 
    species-specific growth rate parameters (either von Bertalanffy, Gompertz, 
    or Schnute); and either the natural mortality rate ('M') or the maximum 
    observed age ('tmax'), from which M is estimated. The following functions 
    have optional plotting capabilities that require 'ggplot2' installed: 
    prop_target(), TBA(), SAM(), catch_advice(), catch_adjust(), and 
    slotlim_once().
An implementation of a phylogenetic comparative method. It can fit univariate among-species Ornstein-Uhlenbeck models of phenotypic trait evolution, where the trait evolves towards a primary optimum. The optimum can be modelled as a single parameter, as multiple discrete regimes on the phylogenetic tree, and/or with continuous covariates. See also Hansen (1997) <doi:10.2307/2411186>, Butler & King (2004) <doi:10.1086/426002>, Hansen et al. (2008) <doi:10.1111/j.1558-5646.2008.00412.x>.
A mostly pure-R implementation of the RAKE algorithm (Rose, S., Engel, D., Cramer, N. and Cowley, W. (2010) <doi:10.1002/9780470689646.ch1>), which can be used to extract keywords from documents without any training data.
Presidential Election data of "Sri Lanka"" is stored in Pdf
    files, through Pdf scraping they are converted into data-frames and
    stored in this R package.
A facility to generate balanced semi-Latin rectangles with any cell size (preferably up to ten) with given number of treatments, see Uto, N.P. and Bailey, R.A. (2020). "Balanced Semi-Latin rectangles: properties, existence and constructions for block size two". Journal of Statistical Theory and Practice, 14(3), 1-11, <doi:10.1007/s42519-020-00118-3>. It also provides facility to generate partially balanced semi-Latin rectangles for cell size 2, 3 and 4 for any number of treatments.
Ordinary and modified statistics for symmetrical linear
    regression models with small samples. The supported ordinary
    statistics include Wald, score, likelihood ratio and gradient. The
    modified statistics include score, likelihood ratio and gradient.
    Diagnostic tools associated with the fitted model are implemented. For
    more details see Medeiros and Ferrari (2017) <DOI:10.1111/stan.12107>.
With given inputs that include number of points, discrete design space, a measure of skewness, models and parameter value, this package calculates the objective value, optimal designs and plot the equivalence theory under A- and D-optimal criteria under the second-order Least squares estimator. This package is based on the paper "Properties of optimal regression designs under the second-order least squares estimator" by Chi-Kuang Yeh and Julie Zhou (2021) <doi:10.1007/s00362-018-01076-6>.
Conduct latent trajectory class analysis with longitudinal data. Our method supports longitudinal continuous, binary and count data. For more methodological details, please refer to Hart, K.R., Fei, T. and Hanfelt, J.J. (2020), Scalable and robust latent trajectory class analysis using artificial likelihood. Biometrics <doi:10.1111/biom.13366>.
User-friendly functions which parse output of
 command line programs used to query Slurm.
 Morris A. Jette and Tim Wickberg (2023)
 <doi:10.1007/978-3-031-43943-8_1> describe Slurm in detail.
'Slurm', Simple Linux Utility for Resource Management
          <https://slurm.schedmd.com/>, is a popular 'Linux' based software used to 
          schedule jobs in 'HPC' (High Performance Computing) clusters. This R package
          provides a specialized lightweight wrapper of 'Slurm' with a syntax similar to
          that found in the 'parallel' R package. The package also includes a method for
          creating socket cluster objects spanning multiple nodes that can be used with
          the 'parallel' package.
This is software linked to the book
  'Applied Smoothing Techniques for Data Analysis -
  The Kernel Approach with S-Plus Illustrations' Oxford University Press.
Implementation of the Stochastic Multi-Criteria Acceptability Analysis (SMAA) family of Multiple Criteria Decision Analysis (MCDA) methods. Tervonen, T. and Figueira,  J. R. (2008) <doi:10.1002/mcda.407>.
Implements the following approaches for multidimensional scaling (MDS) based on stress minimization using majorization (smacof): ratio/interval/ordinal/spline MDS on symmetric dissimilarity matrices, MDS with external constraints on the configuration, individual differences scaling (idioscal, indscal), MDS with spherical restrictions, and ratio/interval/ordinal/spline unfolding (circular restrictions, row-conditional). Various tools and extensions like jackknife MDS, bootstrap MDS, permutation tests, MDS biplots, gravity models, unidimensional scaling, drift vectors (asymmetric MDS), classical scaling, and Procrustes are implemented as well.
Statistical methods for analyzing case-control point data.  Methods include the ratio of kernel densities, the difference in K Functions, the spatial scan statistic, and q nearest neighbors of cases.
SMAHP (pronounced as SOO-MAP) is a novel multi-omics framework for causal mediation analysis of high-dimensional proteogenomic data with survival outcomes. The full methodological details can be found in our recent preprint by Ahn S et al. (2025) <doi:10.48550/arXiv.2503.08606>.
Inference techniques for Fay Herriot Model.
A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data>.
Data practitioners regularly use the 'R' and 'Python' programming languages to 
    prepare data for analyses. Thus, they encode important data preprocessing decisions in 
    'R' and 'Python' code. The 'smallsets' package subsequently decodes these decisions into 
    a Smallset Timeline, a static, compact visualisation of data preprocessing decisions 
    (Lucchesi et al. (2022) <doi:10.1145/3531146.3533175>). The visualisation consists of 
    small data snapshots of different preprocessing steps. The 'smallsets' package builds this 
    visualisation from a user's dataset and preprocessing code located in an 'R', 'R Markdown', 
    'Python', or 'Jupyter Notebook' file. Users simply add structured comments with snapshot  
    instructions to the preprocessing code. One optional feature in 'smallsets' requires 
    installation of the 'Gurobi' optimisation software and 'gurobi' 'R' package, available 
    from <https://www.gurobi.com>. More information regarding the optional feature and 
    'gurobi' installation can be found in the 'smallsets' vignette.
Collection of utility functions supporting statistical modeling, 
   regression analysis, and network analysis workflows used in data 
   science research. Includes tools for model selection, matrix 
   operations, graph analysis, and related statistical computations.
Animal movement models including Moving-Resting Process
    with Embedded Brownian Motion
    (Yan et al., 2014, <doi:10.1007/s10144-013-0428-8>;
    Pozdnyakov et al., 2017, <doi:10.1007/s11009-017-9547-6>),
    Brownian Motion with Measurement Error
    (Pozdnyakov et al., 2014, <doi:10.1890/13-0532.1>),
    Moving-Resting-Handling Process with Embedded Brownian Motion
    (Pozdnyakov et al., 2020, <doi:10.1007/s11009-020-09774-1>),
    Moving-Resting Process with Measurement Error
    (Hu et al., 2021, <doi:10.1111/2041-210X.13694>),
    Moving-Moving Process with two Embedded Brownian Motions.
Permits determination of a set of 
    optimal dynamic treatment regimes and sample size for a SMART design 
    in the Bayesian setting with binary outcomes. Please see Artman (2020) <arXiv:2008.02341>.
SMART trial design, as described by He, J., McClish, D., Sabo, R. (2021)  <doi:10.1080/19466315.2021.1883472>, includes multiple stages of randomization, where participants are randomized to an initial treatment in the first stage and then subsequently re-randomized between treatments in the following stage.
Exploratory analysis on any input data describing the structure and the relationships present in the data. The package automatically select the variable and does related descriptive statistics. Analyzing information value, weight of evidence, custom tables, summary statistics, graphical techniques will be performed for both numeric and categorical predictors.
A collection of recycled and modified R functions to aid in file manipulation, data exploration, wrangling, optimization, and object manipulation. Other functions aid in convenient data visualization, loop progression, software packaging, and installation.
Tools for accessing and processing datasets prepared by the Foundation SmarterPoland.pl. Among all: access to API of Google Maps, Central Statistical Office of Poland, MojePanstwo, Eurostat, WHO and other sources.
Preview spatial data as 'leaflet' maps with minimal
  effort. smartmap is optimized for interactive use and distinguishes itself 
  from similar packages because it does not need real spatial ('sp' or 'sf')
  objects an input; instead, it tries to automatically coerce everything that 
  looks like spatial data to sf objects or leaflet maps. It - for example -  
  supports direct mapping of: a vector containing a single coordinate pair,
  a two column matrix, a data.frame with longitude and latitude columns, or
  the path or URL to a (possibly compressed) 'shapefile'.
Methods for analysis of energy consumption data (electricity, gas, 
    water) at different data measurement intervals. The package provides feature extraction 
    methods and algorithms to prepare data for data mining and machine learning 
    applications. Deatiled descriptions of the methods and their application can be found 
    in Hopf (2019, ISBN:978-3-86309-669-4) "Predictive Analytics for Energy Efficiency and 
    Energy Retailing" <doi:10.20378/irbo-54833> and Hopf et al. (2016) <doi:10.1007/s12525-018-0290-9> 
    "Enhancing energy efficiency in the residential sector with smart meter data analytics".
Sample size calculation to detect dynamic treatment regime (DTR) effects based on change in clinical attachment level (CAL) outcomes from a non-surgical chronic periodontitis treatments study. The experiment is performed under a Sequential Multiple Assignment Randomized Trial (SMART) design. The clustered tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and non-randomly missing. The implemented algorithm is available in Xu et al. (2019+) <arXiv:1902.09386>.
Interact with the 'Smartsheet' platform through the 'Smartsheet' 
  API 2.0. <https://smartsheet.redoc.ly/>. API is an acronym for
  application programming interface; the 'Smartsheet' API allows users to
  interact with 'Smartsheet' sheets directly within R.
A set of tools for determining the necessary sample size
    in order to identify the optimal dynamic treatment regime
    in a sequential, multiple assignment, randomized trial (SMART). 
    Utilizes multiple comparisons with the best methodology 
    to adjust for multiple comparisons.
    Designed for an arbitrary SMART design. Please see Artman (2018) <doi:10.1093/biostatistics/kxy064> for more details.
Fast computation of multivariate analyses of small (10s to 100s markers) to big (1000s to 100000s) genotype data. Runs Principal Component Analysis allowing for centering, z-score standardization and scaling for genetic drift, projection of ancient samples to modern genetic space and multivariate tests for differences in group location (Permutation-Based Multivariate Analysis of Variance) and dispersion (Permutation-Based Multivariate Analysis of Dispersion).
Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional data sets. The algorithm is built on the 'irwsva.build' function of the 'sva' package and proposes a revision on it that achieves an order of magnitude faster running time while trading no accuracy loss in return.
Cluster user-supplied somatic read counts with corresponding allele-specific copy number and tumor purity to infer feasible underlying intra-tumor heterogeneity in terms of  number of subclones, multiplicity, and allocation (Little et al. (2019) <doi:10.1186/s13073-019-0643-9>).
Fast, wavelet-based Empirical Bayes shrinkage methods for
    signal denoising, including smoothing Poisson-distributed data and
    Gaussian-distributed data with possibly heteroskedastic error. The
    algorithms implement the methods described Z. Xing, P. Carbonetto & 
    M. Stephens (2021) <https://jmlr.org/papers/v22/19-042.html>.
Methods for fitting bivariate lines in
    allometry using the major axis (MA) or standardised major axis (SMA), and
    for making inferences about such lines. The available methods of inference
    include confidence intervals and one-sample tests for slope and elevation,
    testing for a common slope or elevation amongst several allometric lines,
    constructing a confidence interval for a common slope or elevation, and
    testing for no shift along a common axis, amongst several samples. 
    See Warton et al. 2012 <doi:10.1111/j.2041-210X.2011.00153.x> for methods description.
All data in the book "Statistical Methods in Biology" by Welham et al. (2015) <doi:10.1201/b17336> with a corresponding documentation and illustrative analysis of the data.
A set of functions to build a scoring model from beginning to end, leading the user
	to follow an efficient and organized development process, reducing significantly the time
	spent on data exploration, variable selection, feature engineering, binning and model selection 
	among other recurrent tasks. 
	The package also incorporates monotonic and customized binning, scaling capabilities that 
	transforms logistic coefficients into points for a better business understanding and 
	calculates and visualizes classic performance metrics of a classification model.
Implements multiple imputation of missing covariates by
    Substantive Model Compatible Fully Conditional Specification.
    This is a modification of the popular FCS/chained equations
    multiple imputation approach, and allows imputation of missing
    covariate values from models which are compatible with the user
    specified substantive model.
An R-package for Estimating Semiparametric PH and AFT Mixture Cure Models.
Computes standardized mean differences and confidence intervals for 
    multiple data types based on Yang, D., & Dalton, J. E. (2012) 
    <https://support.sas.com/resources/papers/proceedings12/335-2012.pdf>.
Contains data files to accompany Smithson & Merkle (2013), Generalized Linear Models for Categorical and Continuous Limited Dependent Variables.
An easy to use implementation of routine structural missing data diagnostics with functions to visualize the proportions of missing observations, investigate missing data patterns and conduct various empirical missing data diagnostic tests. Reference: Weberpals J, Raman SR, Shaw PA, Lee H, Hammill BG, Toh S, Connolly JG, Dandreo KJ, Tian F, Liu W, Li J, Hernández-Muñoz JJ, Glynn RJ, Desai RJ. smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies. JAMIA Open. 2024 Jan 31;7(1):ooae008. <doi:10.1093/jamiaopen/ooae008>.
A computing tool is developed to automated identify somatic mutation-driven immune cells. The operation modes including: i) inferring the relative abundance matrix of tumor-infiltrating immune cells and integrating it with a particular gene mutation status, ii) detecting differential immune cells with respect to the gene mutation status and converting the abundance matrix of significant differential immune cell into two binary matrices (one for up-regulated and one for down-regulated), iii) identifying somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and iv) visualization of immune cell abundance of samples in different mutation status..
Allows users to easily build custom 'docker images' <https://docs.docker.com/> from
    'Amazon Web Service Sagemaker' <https://aws.amazon.com/sagemaker/> using
    'Amazon Web Service CodeBuild' <https://aws.amazon.com/codebuild/>.
The Sparse Marginal Epistasis Test is a computationally efficient 
    genetics method which detects statistical epistasis in complex traits;
    see Stamp et al. (2025, <doi:10.1101/2025.01.11.632557>) for details.
Implements statistical methods for analyzing the counts of areal data, with a focus on the detection of spatial clusters and clustering.  The package has a heavy emphasis on spatial scan methods, which were first introduced by Kulldorff and Nagarwalla (1995) <doi:10.1002/sim.4780140809> and Kulldorff (1997) <doi:10.1080/03610929708831995>.
Simple class to hold contents of a SMET file as specified in
    Bavay (2021) <https://code.wsl.ch/snow-models/meteoio/-/blob/master/doc/SMET_specifications.pdf>.
    There numerical meteorological measurements are all based on MKS (SI) units
    and timestamp is standardized to UTC time.
Provides the filtering algorithms for the state space models on the Stiefel manifold as well as the corresponding sampling algorithms for uniform, vector Langevin-Bingham and matrix Langevin-Bingham distributions on the Stiefel manifold.
Discovery of spatial patterns with Hidden Markov Random Field. This package is designed for spatial transcriptomic data and single molecule fluorescent in situ hybridization (FISH) data such as sequential fluorescence in situ hybridization (seqFISH) and multiplexed error-robust fluorescence in situ hybridization (MERFISH). The methods implemented in this package are described in Zhu et al. (2018) <doi:10.1038/nbt.4260>.
Code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second and third editions of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press.
Functions that provide statistical methods for interval-censored (grouped) data. The package supports the estimation of linear and linear mixed regression models with interval-censored dependent variables. Parameter estimates are obtained by a stochastic expectation maximization algorithm. Furthermore, the package enables the direct (without covariates) estimation of statistical indicators from interval-censored data via an iterative kernel density algorithm. Survey and Organisation for Economic Co-operation and Development (OECD) weights can be included into the direct estimation (see, Walter, P. (2019) <doi:10.17169/refubium-1621>).
Statistical models for specific coronavirus disease 2019 use cases at German local health authorities. All models of Statistical modelling for infectious disease management 'smidm' are part of the decision support toolkit in the 'EsteR' project. More information is published in Sonja Jäckle, Rieke Alpers, Lisa Kühne, Jakob Schumacher, Benjamin Geisler, Max Westphal "'EsteR' – A Digital Toolkit for COVID-19 Decision Support in Local Health Authorities" (2022) <doi:10.3233/SHTI220799> and Sonja Jäckle, Elias Röger, Volker Dicken, Benjamin Geisler, Jakob Schumacher, Max Westphal "A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions" (2021) <doi:10.3390/ijerph18179166>.
Provides functions to estimate, predict and interpolate areal
        data. For estimation and prediction we assume areal data is an average
        of an underlying continuous spatial process as in Moraga et
        al. (2017) <doi:10.1016/j.spasta.2017.04.006>, Johnson et al. (2020)
        <doi:10.1186/s12942-020-00200-w>, and Wilson and Wakefield (2020)
        <doi:10.1093/biostatistics/kxy041>. The interpolation methodology is
        (mostly) based on Goodchild and Lam (1980, ISSN:01652273).
Implements a general algorithm for estimating Sparse Multiple Index
    (SMI) models for nonparametric forecasting and prediction. Estimation of SMI
    models requires the Gurobi mixed integer programming (MIP) solver via the 
    gurobi R package. To use this functionality, the Gurobi Optimizer must be 
    installed, and a valid license obtained and activated from 
    <https://www.gurobi.com>. The gurobi R package must then be installed and 
    configured following the instructions at 
    <https://support.gurobi.com/hc/en-us/articles/14462206790033-How-do-I-install-Gurobi-for-R>. 
    The package also includes functions for fitting nonparametric additive 
    models with backward elimination, group-wise additive index models, and 
    projection pursuit regression models as benchmark comparison methods. In 
    addition, it provides tools for generating prediction intervals to quantify 
    uncertainty in point forecasts produced by the SMI model and benchmark 
    models, using the classical block bootstrap and a new method called 
    conformal bootstrap, which integrates block bootstrap with split conformal 
    prediction.
This tiny package contains one function smirnov() which
        calculates two scaled taxonomic coefficients, Txy (coefficient
        of similarity) and Txx (coefficient of originality). These two
        characteristics may be used for the analysis of similarities
        between any number of taxonomic groups, and also for assessing
        uniqueness of giving taxon. It is possible to use smirnov()
        output as a distance measure: convert it to distance by
        "as.dist(1 - smirnov(x))".
Constructs a yield curve by the Smith-Wilson method from a table of
              libor and swap rates. Now updated to take bond coupons and prices 
              in the same table.
Statistical Methods for Inferring Transmissions of Infectious Diseases from deep sequencing data (SMITID).
  It allow sequence-space-time host and viral population data storage, indexation and querying.
Feature screening is a powerful tool in processing ultrahigh dimensional data. It attempts to screen out most irrelevant features in preparation for a more elaborate analysis. Xu and Chen (2014)<doi:10.1080/01621459.2013.879531> proposed an effective screening method SMLE, which naturally incorporates the joint effects among features in the screening process. This package provides an efficient implementation of SMLE-screening for high-dimensional linear, logistic, and Poisson models. The package also provides a function for conducting accurate post-screening feature selection based on an iterative hard-thresholding procedure and a user-specified selection criterion. Zang, Xu, and Burkett (2025)<doi:10.18637/jss.v115.i08>.
Fitting the full likelihood proportional hazards model and
    extracting the residuals.
A pair of functions that allow for the generation and tracking of coordinate data clouds without a time dimension,
    primarily for use in super-resolution plant micro-tubule image segmentation.
Local Correlation Integral (LOCI) method for outlier identification is implemented here. The LOCI method developed here is invented in Breunig, et al. (2000), see <doi:10.1145/342009.335388>.
Performs parametric and non-parametric estimation and simulation for multi-state discrete-time semi-Markov processes. 
	For the parametric estimation, several discrete distributions are considered for the sojourn times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial. The non-parametric estimation concerns the sojourn time distributions, where no assumptions are done on the shape of distributions. 
	Moreover, the estimation can be done on the basis of one or several sample paths, with or without censoring at the beginning or/and at the end of the sample paths. The implemented methods are described in Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and 
	Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>. 
	Estimation and simulation of discrete-time k-th order Markov chains are also considered.
Provides a pipeline for estimating the average treatment effect via semi-supervised learning. Outcome regression is fit with cross-fitting using various machine learning method or user customized function. Doubly robust ATE estimation leverages both labeled and unlabeled data under a semi-supervised missing-data framework. For more details see Hou et al. (2021) <doi:10.48550/arxiv.2110.12336>. A detailed vignette is included.
Performs parametric and non-parametric estimation and simulation 
    for multi-state discrete-time semi-Markov processes. For the parametric 
    estimation, several discrete distributions are considered for the sojourn 
    times: Uniform, Geometric, Poisson, Discrete Weibull and Negative Binomial.
    The non-parametric estimation concerns the sojourn time distributions, 
    where no assumptions are done on the shape of distributions. Moreover, the 
    estimation can be done on the basis of one or several sample paths, with or
    without censoring at the beginning or/and at the end of the sample paths. 
    Reliability indicators such as reliability, maintainability, availability, 
    BMP-failure rate, RG-failure rate, mean time to failure and mean time to 
    repair are available as well. The implemented methods are described in 
    Barbu, V.S., Limnios, N. (2008) <doi:10.1007/978-0-387-73173-5>, 
    Barbu, V.S., Limnios, N. (2008) <doi:10.1080/10485250701261913> and 
    Trevezas, S., Limnios, N. (2011) <doi:10.1080/10485252.2011.555543>.
    Estimation and simulation of discrete-time k-th order Markov chains are 
    also considered.
In Switzerland, the landscape of municipalities is changing rapidly
  mainly due to mergers. The Swiss Municipal Data Merger Tool 
  automatically detects these mutations and maps municipalities over time, i.e. municipalities of an old state
  to municipalities of a new state. This functionality is helpful when working 
  with datasets that are based on different spatial references. The package's idea and use 
  case is discussed in the following article: <doi:10.1111/spsr.12487>.
Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions.
Bayesian analysis of censored linear mixed-effects models that replace Gaussian assumptions with a flexible class of distributions, such as the scale mixture of normal family distributions, considering a damped exponential correlation structure which was employed to account for within-subject autocorrelation among irregularly observed measures. For more details, see Kelin Zhong, Fernanda L. Schumacher, Luis M. Castro, Victor H. Lachos (2025) <doi:10.1002/sim.10295>.
Starting from a given object representing a fitted model
  (within a certain set of model classes) whose (non-)linear predictor
  includes some ordered factor(s) among the explanatory variables, a new
  model is constructed and fitted where each named factor is replaced by a
  single numeric score, suitably chosen so that the new variable produces a
  fit comparable with the standard methodology based on a set of polynomial
  contrasts. Two variants of the present approach have been developed, one in
  each of the next references: Azzalini (2023) <doi:10.1002/sta4.624>, (2024)
  <doi:10.48550/arXiv.2406.15933>.
Bio-Layer Interferometry (BLI) is a technology to determine the binding kinetics between biomolecules. BLI signals are small and noisy when small molecules are investigated as ligands (analytes). We develop this package to process and analyze the BLI data acquired on Octet Red96 from Fortebio more accurately. 
    Sun Q., Li X., et al (2020) <doi:10.1038/s41467-019-14238-3>. 
    In this new version, we organize the BLI experiment data and analysis methods into a S4 class with self-explaining structure.
Provides generators for a high number of both single- and multi-
    objective test functions which are frequently used for the benchmarking of
    (numerical) optimization algorithms. Moreover, it offers a set of convenient
    functions to generate, plot and work with objective functions.
Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting.
             The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>),
             Exponential Smoothing (Hyndman et al., 2008, <doi:10.1007/978-3-540-71918-2>),
             SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>),
             Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi:10.13140/RG.2.2.24986.29123>),
             Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi:10.1080/00207543.2017.1380326>)
             and several simulation functions. It also allows dealing with intermittent demand based on the
             iETS framework (Svetunkov & Boylan, 2019, <doi:10.13140/RG.2.2.35897.06242>).
Implements several methods to estimate effects of generalized 
    time-varying treatment strategies on the mean of an outcome at one or more 
    selected follow-up times of interest. Specifically, the package implements 
    the time-smoothed inverse probability weighted estimators described in  
    McGrath et al. (2025) <doi:10.48550/arXiv.2509.13971>. Outcomes may be repeatedly, 
    non-monotonically, informatively, and sparsely measured in the data source. 
    The package also supports settings where outcomes are truncated by death, 
    i.e. some individuals die during follow-up which renders the outcome of 
    interest undefined at the follow-up time of interest.
We provide full functionality to smooth L1 penalized regression operators and to compute regression estimates thereof. For this, the objective function of a user-specified regression operator is first smoothed using Nesterov smoothing (see Y. Nesterov (2005) <doi:10.1007/s10107-004-0552-5>), resulting in a modified objective function with explicit gradients everywhere. The smoothed objective function and its gradient are minimized via BFGS, and the obtained minimizer is returned. Using Nesterov smoothing, the smoothed objective function can be made arbitrarily close to the original (unsmoothed) one. In particular, the Nesterov approach has the advantage that it comes with explicit accuracy bounds, both on the L1/L2 difference of the unsmoothed to the smoothed objective functions as well as on their respective minimizers (see G. Hahn, S.M. Lutz, N. Laha, C. Lange (2020) <doi:10.1101/2020.09.17.301788>). A progressive smoothing approach is provided which iteratively smoothes the objective function, resulting in more stable regression estimates. A function to perform cross validation for selection of the regularization parameter is provided.
Empirical likelihood methods for asymptotically efficient
    estimation of models based on conditional or unconditional moment
    restrictions; see Kitamura, Tripathi & Ahn (2004)
    <doi:10.1111/j.1468-0262.2004.00550.x> and Owen (2013)
    <doi:10.1002/cjs.11183>.
    Kernel-based non-parametric methods for density/regression estimation and
    numerical routines for empirical likelihood maximisation are implemented in
    'Rcpp' for speed.
Estimation of two-state (survival) models and irreversible illness-
    death models with possibly interval-censored, left-truncated and right-censored
    data. Proportional intensities regression models can be specified to allow for
    covariates effects separately for each transition. We use either a parametric
    approach with Weibull baseline intensities or a semi-parametric approach with
    M-splines approximation of baseline intensities in order to obtain smooth
    estimates of the hazard functions. Parameter estimates are obtained by maximum
    likelihood in the parametric approach and by penalized maximum likelihood in the
    semi-parametric approach.
Provides flexible hazard ratio curves allowing non-linear
  relationships between continuous predictors and survival.
  To better understand the effects that each continuous covariate
  has on the outcome, results are expressed in terms of hazard
  ratio curves, taking a specific covariate value as reference.
  Confidence bands for these curves are also derived.
Implementation of the SIC epsilon-telescope method, either
    using single or distributional (multiparameter) regression. Includes classical regression
    with normally distributed errors and robust regression, where the errors are from
    the Laplace distribution. The "smooth generalized normal distribution" is used,
    where the estimation of an additional shape parameter allows the user to move
    smoothly between both types of regression. See O'Neill and Burke (2022)
    "Robust Distributional Regression with Automatic Variable Selection" for more details.
    <doi:10.48550/arXiv.2212.07317>. This package also contains the data analyses from O'Neill and
    Burke (2023). "Variable selection using a smooth information criterion for distributional
    regression models". <doi:10.1007/s11222-023-10204-8>.
Perform two-dimensional smoothing for spatial fields using FFT and the convolution theorem (see Gilleland 2013, <doi:10.5065/D61834G2>).
Some M-estimators for 1-dimensional location (Bisquare,
             ML for the Cauchy distribution, and the estimators from 
             application of the smoothing principle introduced in Hampel, 
             Hennig and Ronchetti (2011) to the above, the Huber
             M-estimator, and the median, main function is smoothm), 
             and Pitman estimator.
Tools for smoothing and tidying spatial features
    (i.e. lines and polygons) to make them more aesthetically pleasing.
    Smooth curves, fill holes, and remove small fragments from lines and
    polygons.
Computes smooth estimations for the Cumulative/Dynamic and Incident/Dynamic ROC curves, in presence of right censorship, based on the bivariate kernel density estimation of the joint distribution function of the Marker and Time-to-event variables.
Contains, as a main contribution, a function to fit
             a regression model with possibly right, left or interval
             censored observations and with the error distribution
             expressed as a mixture of G-splines. Core part
             of the computation is done in compiled 'C++' written
             using the 'Scythe' Statistical Library Version 0.3.
Given independent and identically distributed observations X(1), ..., X(n) from a Generalized Pareto distribution with shape parameter gamma in [-1,0], offers several estimates to compute estimates of gamma. The estimates are based on the principle of replacing the order statistics by quantiles of a distribution function based on a log--concave density function. This procedure is justified by the fact that the GPD density is log--concave for gamma in [-1,0].
A flexible moving average algorithm for modeling drug exposure in pharmacoepidemiology studies as presented in the article: Ouchi, D.,  Giner-Soriano, M., Gómez-Lumbreras, A., Vedia Urgell, C.,Torres, F., & Morros, R. (2022). "Automatic Estimation of the Most Likely Drug Combination in Electronic Health Records Using the Smooth Algorithm : Development and Validation Study." JMIR medical informatics, 10(11), e37976. <doi:10.2196/37976>.
The nonparametric trend and its derivatives in equidistant time 
    series (TS) with short-memory stationary errors can be estimated. The 
    estimation is conducted via local polynomial regression using an 
    automatically selected bandwidth obtained by a built-in iterative plug-in 
    algorithm or a bandwidth fixed by the user. A Nadaraya-Watson kernel 
    smoother is also built-in as a comparison. With version 1.1.0, a linearity 
    test for the trend function, forecasting methods and backtesting 
    approaches are implemented as well.
    The smoothing methods of the package are described in Feng, Y., Gries, T., 
    and Fritz, M. (2020) <doi:10.1080/10485252.2020.1759598>.
Provides functions that automate accessing, downloading and exploring Soil Moisture
    and Ocean Salinity (SMOS) Level 4 (L4) data developed by Barcelona Expert Center (BEC). 
    Particularly, it includes functions to search for, acquire, extract, and plot BEC-SMOS L4 soil 
    moisture data downscaled to ~1 km spatial resolution. Note that SMOS is one of Earth Explorer 
    Opportunity missions by the European Space Agency (ESA). More information about SMOS  
    products can be found at <https://earth.esa.int/eogateway/missions/smos/data>.
A collection of various oversampling techniques developed from SMOTE is provided. SMOTE is a oversampling technique which synthesizes a new minority instance between a pair of one minority instance and one of its K nearest neighbor.  Other techniques adopt this concept with other criteria in order to generate balanced dataset for class imbalance problem.
Provides the SMOTE with Boosting (SMOTEWB) algorithm. See
      F. Sağlam, M. A. Cengiz (2022) <doi:10.1016/j.eswa.2022.117023>. It is a
      SMOTE-based resampling technique which creates synthetic data on the links 
      between nearest neighbors. SMOTEWB uses boosting weights to determine where
      to generate new samples and automatically decides the number of neighbors 
      for each sample. It is robust to noise and outperforms most of the 
      alternatives according to Matthew Correlation Coefficient metric.
      Alternative resampling methods are also available in the package.
Provides movies to help students to understand statistical 
  concepts.  The 'rpanel' package  <https://cran.r-project.org/package=rpanel> 
  is used to create interactive plots that move to illustrate key statistical 
  ideas and methods.  There are movies to: visualise probability distributions
  (including user-supplied ones); illustrate sampling distributions of the
  sample mean (central limit theorem), the median, the sample maximum 
  (extremal types theorem) and (the Fisher transformation of the) 
  product moment correlation coefficient; examine the influence of an 
  individual observation in simple linear regression; illustrate key concepts 
  in statistical hypothesis testing. Also provided are dpqr functions for the 
  distribution of the Fisher transformation of the correlation coefficient 
  under sampling from a bivariate normal distribution.
Provides functions for creating and annotating a composite plot in 'ggplot2'. Offers background themes and shortcut plotting functions that produce figures that are appropriate for the format of scientific journals. Some methods are described in Min and Zhou (2021) <doi:10.3389/fgene.2021.802894>.
Contains the datasets and a few functions for use with 
        the practicals outlined in Appendix A of the book
        Statistical Models (Davison, 2003, Cambridge University Press), 
        which can be found at <doi:10.1017/CBO9780511815850>.
Computes the studentized midrange distribution (pdf, cdf and quantile) and generates random numbers.
Produce small area population estimates by fitting census data to
    survey data.
A collection of simple parameter estimation and tests for the comparison of multivariate means and variation, to accompany Chapters 4 and 5 of the book Multivariate Statistical Methods. A Primer (5th edition), by Manly BFJ, Navarro Alberto JA & Gerow K (2024) <doi:10.1201/9781003453482>.
Provides estimations of the Receiver Operating Characteristic (ROC) curve and the Area Under the Curve (AUC) based on the two-stages mixed-subjects ROC curve estimator (Diaz-Coto et al. (2020) <doi:10.1515/ijb-2019-0097> and Diaz-Coto et al. (2020) <doi:10.1080/00949655.2020.1736071>).
Datasets used in "Statistical Methods for the Social Sciences"
    (SMSS) by Alan Agresti and Barbara Finlay.
Implements L0-constrained Multi-Task Learning and domain generalization algorithms. The algorithms are coded in Julia allowing for fast implementations of the coordinate descent and local combinatorial search algorithms. For more details, see a preprint of the paper: Loewinger et al., (2022) <arXiv:2212.08697>.
Implementation of the SMuRF algorithm of Devriendt et al. (2021) <doi:10.1016/j.insmatheco.2020.11.010> to fit generalized linear models (GLMs) with multiple types of predictors via regularized maximum likelihood.
Testing the mediation effect of multiple SNPs on an outcome through a mediator.
Implementation of the structural model for variances in order to
        detect differentially expressed genes from gene expression data.
Mosaic diagram, scatterplot matrix, Andrews curves, parallel coordinate diagram, radar diagram, 
   and Chernoff plots as a Shiny app, which allow the order of variables to be changed interactively. 
   The apps are intended as teaching examples.
Simple implementation of Semantic Versioning 2.0.0 ('SemVer') on the
    'vctrs' package.  This package provides a simple way to create,
    compare, and manipulate semantic versions in R. It is designed to be
    lightweight and easy to use.
Build and manipulate probability distributions of the skew-normal 
  family and some related ones, notably the skew-t and the SUN families. 
  For the skew-normal and the skew-t distributions, statistical methods are 
  provided for data fitting and model diagnostics, in the univariate and the 
  multivariate case.
A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.
'RStudio' addin which provides a GUI to visualize and analyse networks. 
    After finishing a session, the code to produce the plot is inserted in the current script.
    Alternatively, the function SNAhelperGadget() can be used directly from the console.
    Additional addins include the Netreader() for reading network files, Netbuilder() to create
    small networks via point and click, and the Componentlayouter() to layout networks with many components manually.
Implements snake in R as a programming example, see <https://en.wikipedia.org/wiki/Snake_(video_game_genre)>.
A consistent, flexible and easy to use tool to parse and convert strings into cases like snake or camel among others.
Visualize long timelines, extended sequences and temporally
    chained survey responses and experience sampling data using intuitive
    serpentine (snake) plots. Supports distribution bars,
    tick-mark plots, inter-item correlation arcs, faceted multi-construct
    panels, and daily time-of-day positioning for ecological momentary
    assessment data.
Plays the game of Snakes and Ladders and has tools for analyses. The tools included allow you to find the average moves to win, frequency of each square, importance of the snakes and the ladders, the most common square and the plotting of the game played.
A simple wrapper to easily design vanilla deep neural networks using 'Tensorflow'/'Keras' backend for regression, classification and multi-label tasks, with some tweaks and tricks (skip shortcuts, embedding, feature selection and anomaly detection).
Collect  your data on digital marketing campaigns from Snapchat Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Geostatistical modeling and kriging with
    gridded data using spatially separable covariance functions (Kronecker
    covariances). Kronecker products in these models provide shortcuts for
    solving large matrix problems in likelihood and conditional mean,
    making 'snapKrig' computationally efficient with large grids. The package
    supplies its own S3 grid object class, and a host of methods including
    plot, print, Ops, square bracket replace/assign, and more. Our computational
    methods are described in Koch, Lele, Lewis (2020) <doi:10.7939/r3-g6qb-bq70>.
Functions for reading and writing Gadget N-body snapshots. The Gadget code is popular in astronomy for running N-body / hydrodynamical cosmological and merger simulations. To find out more about Gadget see the main distribution page at www.mpa-garching.mpg.de/gadget/
Because your linear models deserve better than console output.
  A sleek color palette and kable styling to make your regression results look sharper than they are.
  Includes support for Partial Least Squares (PLS) regression via both the SVD and NIPALS algorithms,
  along with a unified interface for model fitting and fabulous LaTeX and console output formatting.
  See the package website at 
  <https://finitesample.space/snazzier>.
Download data (tables and datasets) from the Swiss
     National Bank (SNB; <https://www.snb.ch/en>), the Swiss
     central bank.  The package is lightweight and comes with few
     dependencies; suggested packages are used only if data is to
     be transformed into particular data structures, for instance
     into 'zoo' objects.  Downloaded data can optionally be
     cached, to avoid repeated downloads of the same files.
Computes Strongest Neighbor Coherence (SNC), a structural diagnostic that replaces Cronbach's alpha using top-k correlation structure.
  For methodology, see Wells (2025) <https://github.com/TheotherDrWells/snc>.
Efficient estimation of multivariate skew-normal distribution in closed form.
Fitting of non-parametric production frontiers for use in efficiency analysis.
    Methods are provided for both a smooth analogue of Data Envelopment Analysis (DEA) and a 
    non-parametric analogue of Stochastic Frontier Analysis (SFA). Frontiers are constructed for 
    multiple inputs and a single output using constrained kernel smoothing as in 
    Racine et al. (2009), which allow for the imposition of monotonicity and concavity constraints 
    on the estimated frontier.
Similarity Network Fusion takes multiple views of a network and fuses them together to construct an overall status matrix. The input to our algorithm can be feature vectors, pairwise distances, or pairwise similarities. The learned status matrix can then be used for retrieval, clustering, and classification.
Create correlation networks using St. Nicolas House Analysis ('SNHA'). 
    The package can be used for visualizing multivariate data similar to Principal
    Component Analysis or Multidimensional Scaling using a ranking approach.
    In contrast to 'MDS' and 'PCA', 'SNHA' uses a network approach to explore
    interacting variables. 
    For details see 'Hermanussen et. al. 2021', <doi:10.3390/ijerph18041741>.
Implements the Simple Non-Iterative Clustering algorithm for 
    superpixel segmentation of multi-band images, as introduced by Achanta 
    and Susstrunk (2017) <doi:10.1109/CVPR.2017.520>. Supports both standard 
    image arrays and geospatial raster objects, with a design that can be 
    extended to other spatial data frameworks. The algorithm groups adjacent 
    pixels into compact, coherent regions based on spectral similarity and 
    spatial proximity. A high-performance implementation supports images 
    with arbitrary spectral bands.
Convert laboratory data to the Portuguese Information System 
    for Water Resources 'SNIRH' file format. 'SNIRH' is Portugal's national 
    water resources information system <https://snirh.apambiente.pt/>. 
    The package validates station data, converts parameters and units, 
    and generates compliant output files for data submission.
Calculating home ranges and movements of animals in complex stream environments
    is often challenging, and standard home range estimators do not apply. This
    package provides a series of tools for assessing movements in a stream
    network, such as calculating the total length of stream used, distances
    between points, and movement patterns over time. See Vignette for additional
    details. This package was originally released on 'GitHub' under the name 'SNM'.
    SNMA was developed for analyses in McKnight et al. (2025) <doi:10.3354/esr01442>
    which contains additional examples and information.
Implement K-nearest neighbor classifier, weighted nearest neighbor classifier, bagged nearest neighbor classifier, optimal weighted nearest neighbor classifier and stabilized nearest neighbor classifier, and perform model selection via 5 fold cross-validation for them. This package also provides functions for computing the classification error and classification instability of a classification procedure.
Programmatic interface to the 'SNOTEL' snow data
  (<https://www.nrcs.usda.gov/programs-initiatives/sswsf-snow-survey-and-water-supply-forecasting-program>). Provides easy downloads of snow 
  data into your R work space or a local directory. Additional post-processing 
  routines to extract snow season indexes are provided.
Support for simple parallel computing in R.
An R interface to the C 'libstemmer' library that implements
  Porter's word stemming algorithm for collapsing words to a common
  root to aid comparison of vocabulary. Currently supported languages are
  Arabic, Basque, Catalan, Danish, Dutch, English, Finnish, French, German, Greek,
  Hindi, Hungarian, Indonesian, Irish, Italian, Lithuanian, Nepali,
  Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Tamil
  and Turkish.
Functions for analysis of network objects, which are imported or simulated by the package. The non-parametric methods of analysis center on snowball and bootstrap sampling for estimating functions of network degree distribution. For other parameters of interest, see, e.g., 'bootnet' package.
Provides historical datasets related to John Snow's 1854 cholera outbreak study in London. Includes data on cholera cases, water pump locations, and the street layout, enabling analysis and visualisation of the outbreak.
Usability wrapper around snow for easier development of
        parallel R programs. This package offers e.g. extended error
        checks, and additional functions. All functions work in
        sequential mode, too, if no cluster is present or wished.
        Package is also designed as connector to the cluster management
        tool sfCluster, but can also used without it.
Authentication helpers for 'Snowflake'.
    It provides compatibility with authentication approaches supported
    by the 'Snowflake Connector for Python' <https://pypi.org/project/snowflake-connector-python>
    and the 'Snowflake CLI' <https://pypi.org/project/snowflake-cli>.
The function generates and plots random snowflakes. Each snowflake is defined by a given diameter, width of the crystal, color, and random seed. Snowflakes are plotted in such way that they always remain round, no matter what the aspect ratio of the plot is. Snowflakes can be created using transparent colors, which creates a more interesting, somewhat realistic, image. Images of the snowflakes can be separately saved as svg files and used in websites as static or animated images.
Extension of the snow package supporting fault tolerant and reproducible applications, as well as supporting easy-to-use parallel programming - only one function is needed. Dynamic cluster size is also available.
Run 'SQL' queries across 'Snowflake', 'Amazon Redshift', 'PostgreSQL', 'SQLite', and 'DuckDB' from R with a single function. Optionally stream and cache large query results to a local 'DuckDB' database for efficient work with larger-than-memory datasets.
Population genetics package for designing diagnostic panels. 
    Candidate markers, marker combinations, and different panel sizes are 
    assessed for how well they can predict the source population of known 
    samples. Requires a genotype file of candidate markers in STRUCTURE format. 
    Methods for population cross-validation are described in Jombart (2008) 
    <doi:10.1093/bioinformatics/btn129>.
To automated functional annotation of genetic variants and linked proxies. Linked SNPs in moderate to high linkage disequilibrium (e.g. r2>0.50) with the corresponding index SNPs will be selected for further analysis.
Is designed to interactively and reproducibly visualize and filter SNP
	(single-nucleotide polymorphism) datasets. This R-based implementation of SNP
	and genotype filters facilitates 
	an interactive and iterative SNP filtering pipeline, which can be documented
	reproducibly via 'rmarkdown'. 'SNPfiltR' contains functions for visualizing 
	various quality and missing data metrics for a SNP dataset, and then filtering
	the dataset based on user specified cutoffs.
	All functions take 'vcfR' objects as input, which can easily be
	generated by reading standard vcf (variant call format) files into R using 
	the R package 'vcfR' authored by Knaus and Grünwald (2017) <doi:10.1111/1755-0998.12549>. 
	Each 'SNPfiltR' function can return a newly filtered 'vcfR' object, which can then be
	written to a local directory in standard vcf format using the 'vcfR' package,
	for downstream population genetic and phylogenetic analyses.
Linkage disequilibrium visualizations of up to several hundreds of single nucleotide polymorphisms (SNPs), annotated with chromosomic positions and gene names. Two types of plots are available for small numbers of SNPs (<40) and for large numbers (tested up to 500). Both can be extended by combining other ggplots, e.g. association studies results, and functions enable to directly visualize the effect of SNP selection methods, as minor allele frequency filtering and TagSNP selection, with a second correlation heatmap. The SNPs correlations are computed on Genotype Data objects from the 'GWASTools' package using the 'SNPRelate' package, and the plots are customizable 'ggplot2' and 'gtable' objects and are annotated using the 'biomaRt' package. Usage is detailed in the vignette with example data and results from up to 500 SNPs of 1,200 scans are in Charlon T. (2019) <doi:10.13097/archive-ouverte/unige:161795>.
A set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example to export to a PLINK set.
Tools for performing variable selection in three-way data using N-PLS 
    in combination with L1 penalization, Selectivity Ratio and VIP scores. 
    The N-PLS model (Rasmus Bro, 1996 <DOI:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) 
    is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries 
    to maximize the covariance between X and Y data arrays. The package also adds
    variable selection through L1 penalization, Selectivity Ratio and VIP scores.
The goal of 'snpsettest' is to provide simple tools that perform
             set-based association tests (e.g., gene-based association tests)
             using GWAS (genome-wide association study) summary statistics. A
             set-based association test in this package is based on the
             statistical model described in VEGAS (versatile gene-based
             association study), which combines the effects of a set of SNPs
             accounting for linkage disequilibrium between markers. This package
             uses a different approach from the original VEGAS implementation to
             compute set-level p values more efficiently, as described in
             <https://github.com/HimesGroup/snpsettest/wiki/Statistical-test-in-snpsettest>.
A spectral framework to map quantitative trait loci (QTLs) affecting joint differential networks of gene co-Expression. Test the equivalence among multiple biological networks via spectral statistics. See reference Hu, J., Weber, J. N., Fuess, L. E., Steinel, N. C., Bolnick, D. I., & Wang, M. (2025) <doi:10.1371/journal.pcbi.1012953>.
Models with skew‑normally distributed and thus asymmetric error terms, implementing the methods developed in Badunenko and Henderson (2023) "Production analysis with asymmetric noise" <doi:10.1007/s11123-023-00680-5>. The package provides tools to estimate regression models with skew‑normal error terms, allowing both the variance and skewness parameters to be heteroskedastic. It also includes a stochastic frontier framework that accommodates both i.i.d. and heteroskedastic inefficiency terms.
Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided. Note: Formerly available versions of the MfUSampler can be obtained from the archive <https://cran.r-project.org/src/contrib/Archive/MfUSampler/>.
The methods discussed in this package are new non-parametric methods
    based on sequential normal scores 'SNS' (Conover et al (2017)
    <doi:10.1080/07474946.2017.1360091>), designed for sequences of observations,
    usually time series data, which may occur singly or in batches,
    and may be univariate or multivariate. These methods are designed
    to detect changes in the process, which may occur as changes in location
    (mean or median), changes in scale (standard deviation, or variance), or
    other changes of interest in the distribution of the observations,
    over the time observed. They usually apply to large data sets,
    so computations need to be simple enough to be done in a reasonable
    time on a computer, and easily updated as each new observation
    (or batch of observations) becomes available. Some examples and more detail
    in 'SNS' is presented in the work by Conover et al (2019) <arXiv:1901.04443>.
Implementations self-normalization (SN) based algorithms for 
    change-points estimation in time series data. This comprises nested 
    local-window algorithms for detecting changes in both univariate and 
    multivariate time series developed in Zhao, Jiang and Shao (2022) 
    <doi:10.1111/rssb.12552>.
Contains functions to perform various models and
    methods for test equating (Kolen and Brennan, 2014 
    <doi:10.1007/978-1-4939-0317-7> ; Gonzalez and Wiberg, 2017 
    <doi:10.1007/978-3-319-51824-4> ; von Davier et. al, 2004 
    <doi:10.1007/b97446>). It currently implements the traditional mean, linear 
    and equipercentile equating methods. Both IRT observed-score and true-score 
    equating are also supported, as well as the mean-mean, mean-sigma, Haebara 
    and Stocking-Lord IRT linking methods. It also supports newest methods such 
    that local equating, kernel equating (using Gaussian, logistic, 
    Epanechnikov, uniform and adaptive kernels) with presmoothing, and IRT 
    parameter linking methods based on asymmetric item characteristic functions. 
    Functions to obtain both standard error of equating (SEE) and standard error 
    of equating differences between two equating functions (SEED) are also 
    implemented for the kernel method of equating.
Download and read datasets from the Swiss National
     Science Foundation (SNF, FNS, SNSF; <https://snf.ch>).  The
     package is lightweight and without dependencies.  Downloaded
     data can optionally be cached, to avoid repeated downloads of
     the same files.  There are also utilities for comparing
     different versions of datasets, i.e. to report added, removed
     and changed entries.
Consolidated data simulation, sample size calculation and
    analysis functions for several snSMART (small sample sequential,
    multiple assignment, randomized trial) designs under one library.  See
    Wei, B., Braun, T.M., Tamura, R.N. and Kidwell, K.M. "A Bayesian
    analysis of small n sequential multiple assignment randomized trials
    (snSMARTs)." (2018) Statistics in medicine, 37(26), pp.3723-3732
    <doi:10.1002/sim.7900>.
Identifies single nucleotide variants in next-generation sequencing data by estimating their local false discovery rates. For more details, see Karimnezhad, A. and Perkins, T. J. (2024) <doi:10.1038/s41598-024-51958-z>.
Allows objects to be stored on disc and automatically
       		recalled into memory, as required, by delayed assignment.
Creates stratum orthogonal arrays (also known as strong orthogonal arrays). These are arrays with more levels per column than the typical orthogonal array, and whose low order projections behave like orthogonal arrays, when collapsing levels to coarser strata. Details are described in Groemping (2022) "A unifying implementation of stratum (aka strong) orthogonal arrays" <http://www1.bht-berlin.de/FB_II/reports/Report-2022-002.pdf>.
Tools to design experiments, compute Sobol sensitivity indices,
    and summarise stochastic responses inspired by the strategy described by
    Zhu and Sudret (2021) <doi:10.1016/j.ress.2021.107815>. Includes helpers
    to optimise toy models implemented in C++, visualise indices with
    uncertainty quantification, and derive reliability-oriented sensitivity
    measures based on failure probabilities.
    It is further detailed in Logosha, Maumy and Bertrand (2022) 
    <doi:10.1063/5.0246026> and (2023) <doi:10.1063/5.0246024> or in Bertrand, 
    Logosha and Maumy (2024) <https://hal.science/hal-05371803>, 
    <https://hal.science/hal-05371795> and <https://hal.science/hal-05371798>.
Algorithm to estimate the Sobol indices using a non-parametric fit of the regression curve. The bandwidth is estimated using bootstrap to reduce the finite-sample bias. The package is based on the paper Solís, M. (2018) <arXiv:1803.03333>.
Functions for evaluating tournament predictions, simulating results from individual soccer matches and tournaments. See <http://sandsynligvis.dk/2018/08/03/world-cup-prediction-winners/> for more information.
A set of functions to quantify and visualise 
    social autocorrelation.
Tools developed to facilitate the establishment of the rank and social hierarchy 
             for gregarious animals by the Si method developed by 
             Kondo & Hurnik (1990)<doi:10.1016/0168-1591(90)90125-W>. It is
             also possible to determine the number of agonistic interactions between two 
             individuals, sociometric and dyadics matrix from dataset obtained through 
             electronic bins. In addition, it is possible plotting the results using a bar plot, box plot, and sociogram.
Provides methods for sampling contact matrices from diary
    data for use in infectious disease modelling, as discussed in Mossong
    et al. (2008) <doi:10.1371/journal.pmed.0050074>.
The notion of power index has been widely used in literature to evaluate the influence of individual players (e.g., voters, political parties, nations, stockholders, etc.) involved in a collective decision situation like an electoral system, a parliament, a council, a management board, etc., where players may form coalitions. Traditionally this ranking is determined through numerical evaluation. More often than not however only ordinal data between coalitions is known. The package 'socialranking' offers a set of solutions to rank players based on a transitive ranking between coalitions, including through CP-Majority, ordinal Banzhaf or lexicographic excellence solution summarized by Tahar Allouche, Bruno Escoffier, Stefano Moretti and Meltem Öztürk (2020, <doi:10.24963/ijcai.2020/3>).
Social risks are increasingly becoming a critical component of health
    care research. One of the most common ways to identify social needs is by using
    ICD-10-CM "Z-codes." This package identifies social risks using varying taxonomies
    of ICD-10-CM Z-codes from administrative health care data. The conceptual
    taxonomies come from:
    Centers for Medicare and Medicaid Services (2021) <https://www.cms.gov/files/document/zcodes-infographic.pdf>,
    Reidhead (2018) <https://web.mhanet.com/>,
    A Arons, S DeSilvey, C Fichtenberg, L Gottlieb (2018) <https://sirenetwork.ucsf.edu/tools-resources/resources/compendium-medical-terminology-codes-social-risk-factors>.
Provides tools to simulate and analyse datasets of social interactions 
    between individuals using hierarchical Bayesian models implemented in Stan. 
    Model fitting is performed via the 'rstan' package. Users can generate 
    realistic interaction data where individual phenotypes influence and respond 
    to those of their partners, with control over sampling design parameters such 
    as the number of individuals, partners, and repeated dyads. The simulation 
    framework allows flexible control over variation and correlation in mean trait 
    values, social responsiveness, and social impact, making it suitable for 
    research on interacting phenotypes and on direct and indirect genetic effects 
    ('DGEs' and 'IGEs'). The package also includes functions to fit and compare 
    alternative models of social effects, including impact–responsiveness, 
    variance–partitioning, and trait-based models, and to summarise model 
    performance in terms of bias and dispersion. For a more detailed description 
    of the available models and impact–responsiveness, see the accompanying 
    article Wijnhorst et al. (2026) <doi:10.1093/jeb/voag013>.
Accesses raw data via API and calculates social
    determinants of health measures for user-specified locations in the
    US, returning them in tidyverse- and sf-compatible data frames.
Provides an interface to search, read, query, and retrieve metadata for 
    datasets hosted on 'Socrata' open data portals. Supports all 'Socrata' data types, 
    including spatial data returned as 'sf' objects.
Supporting materials for a course and book on data visualization. It contains utility functions for graphs and several sample data sets. See Healy (2019) <ISBN 978-0691181622>.
Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.
Bindings to 'libsodium' <https://doc.libsodium.org/>: a modern, 
    easy-to-use software library for encryption, decryption, signatures, password
    hashing and more. Sodium uses curve25519, a state-of-the-art Diffie-Hellman 
    function by Daniel Bernstein, which has become very popular after it was 
    discovered that the NSA had backdoored Dual EC DRBG.
Provides an interface to the 'NoSQL' database 'CouchDB'
    (<http://couchdb.apache.org>). Methods are provided for managing
    databases within 'CouchDB', including creating/deleting/updating/transferring,
    and managing documents within databases. One can connect with a local
    'CouchDB' instance, or a remote 'CouchDB' databases such as 'Cloudant'. 
    Documents can be inserted directly from vectors, lists, data.frames, 
    and 'JSON'. Targeted at 'CouchDB' v2 or greater.
Este paquete tiene la finalidad de ayudar a aprender de una forma interactiva, teniendo ejemplos y la posibilidad de resolver nuevos al mismo tiempo. Apuntes de clase interactivos.
Software that leverages the capabilities of Circos by manipulating data, preparing configuration files, and running the Perl-native Circos directly from the R environment with minimal user intervention. Circos is a novel software that addresses the challenges in visualizing genetic data by creating circular ideograms composed of tracks of heatmaps, scatter plots, line plots, histograms, links between common markers, glyphs, text, and etc. Please see <http://www.circos.ca>.
Implements the SoftBart model of described by Linero and Yang (2018) <doi:10.1111/rssb.12293>, with the optional use of a sparsity-inducing prior to allow for variable selection. For usability, the package maintains the same style as the 'BayesTree' package.
Detect libraries used in a project and automatically create software bibliographies in 'PDF', 'Word', 'Rmarkdown', and 'BibTeX' formats.
An extension of sensitivity, specificity, positive and negative
    predictive value to continuous predicted and reference memberships in
    [0, 1].
It contains soft clustering algorithms, in particular approaches derived from rough set theory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k-means. It also contains classic k-means and a corresponding illustrative demo.
Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an 'EM' flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components).
It leverages the network-like architecture of scientific models together with software quality metrics to identify chains of function calls that are more prone to generating and propagating errors. It operates on tbl_graph objects representing call dependencies between functions (callers and callees) and computes risk scores for individual functions and for paths (sequences of function calls) based on cyclomatic complexity, in-degree and betweenness centrality. The package supports variance-based uncertainty and sensitivity analyses after Puy et al. (2022) <doi:10.18637/jss.v102.i05> to assess how risk scores change under alternative risk definitions.
'SOHPIE' (pronounced as SOFIE) is a novel pseudo-value regression approach for differential co-abundance network analysis of microbiome data, which can include additional clinical covariate in the model. The full methodological details can be found in Ahn S and Datta S (2023) <arXiv:2303.13702v1>.
Sparsity Oriented Importance Learning (SOIL) provides a new variable importance measure for high dimensional linear regression and logistic regression from a sparse penalization perspective, by taking into account the variable selection uncertainty via the use of a sensible model weighting. The package is an implementation of Ye, C., Yang, Y., and Yang, Y. (2017+).
Facilitates basic and equation-based analyses of some important soil properties related to soil chemical environment and nutrient availability to plants. Freundlich H (1907). <doi:10.1515/zpch-1907-5723>. Datta SP, Bhadoria PBS (1999). <doi:10.1002%2F%28SICI%291522-2624%28199903%29162%3A2%3C183%3A%3AAID-JPLN183%3E3.0.CO%3B2-A>."Boron adsorption and desorption in some acid soils of West Bengal, India". Langmuir I (1918). <doi:10.1021/ja02242a004> "The adsorption of gases on plane surfaces of glass, mica, and platinum". Khasawneh FE (1971). <doi:10.2136/sssaj1971.03615995003500030029x> "Solution ion activity and plant growth".
Includes four functions: RFactor_calc(), RFactor_est(), KFactor() and SoilLoss(). The rainfall erosivity factors can be calculated or estimated, and soil erodibility will be estimated by the equation extracted from the monograph. Soil loss will be estimated by the product of five factors (rainfall erosivity, soil erodibility, length and steepness slope, cover-management factor and support practice factor. In the future, additional functions can be included. This efforts to advance research in soil and water conservation, with fast and accurate results.
A collection of functions for reading soil data from U.S. Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) and National Cooperative Soil Survey (NCSS) databases.
Function for the computation of fractal dimension based on mass of soil particle size distribution by Tyler & Wheatcraft (1992) <doi:10.2136/sssaj1992.03615995005600020005x>. It also provides functions for calculation of mean weight and geometric mean diameter of particle size distribution by Perfect et al. (1992) <doi:10.2136/sssaj1992.03615995005600050012x>.
Implements a physics-informed one-dimensional convolutional
    neural network (CNN1D-PINN) for estimating the complete soil water
    retention curve (SWRC) as a continuous function of matric potential,
    from soil texture, organic carbon, bulk density, and depth.
    The network architecture ensures strict monotonic decrease of
    volumetric water content with increasing suction by construction,
    through cumulative integration of non-negative slope outputs
    (monotone integral architecture). Four physics-based residual
    constraints adapted from Norouzi et al. (2025) <doi:10.1029/2024WR038149>
    are embedded in the loss function: (S1) linearity at the dry end
    (pF in [5, 7.6]); (S2) non-negativity at pF = 6.2;
    (S3) non-positivity at pF = 7.6; and (S4) a near-zero derivative
    in the saturated plateau region (pF in [-2, -0.3]). Includes tools
    for data preparation, model training, dense prediction, performance
    metrics, texture classification, and publication-quality visualisation.
Analyzing soil food webs or any food web measured at equilibrium. The package
  calculates carbon and nitrogen fluxes and stability properties using methods
  described by Hunt et al. (1987) <doi:10.1007/BF00260580>, de Ruiter et al. 
  (1995) <doi:10.1126/science.269.5228.1257>, Holtkamp et al. (2011)
  <doi:10.1016/j.soilbio.2010.10.004>, and Buchkowski and Lindo (2021) 
  <doi:10.1111/1365-2435.13706>. The package can also manipulate the structure 
  of the food web as well as simulate food webs away from equilibrium and run 
  decomposition experiments.
Generally, soil functionality is characterized by its capability to sustain microbial activity, nutritional element supply, structural stability and aid for crop production. Since soil functions can be linked to 80% of ecosystem services, conservation of degraded land should strive to restore not only the capacity of soil to sustain flora but also ecosystem provisions. The primary ecosystem services of soil are carbon sequestration, food or biomass production, provision of microbial habitat, nutrient recycling. However, the actual magnitude of soil functions provided by agricultural land uses has never been quantified. Nutrient supply capacity (NSC) is a measure of nutrient dynamics in restored land uses. Carbon accumulation proficiency (CAP) is a measure of ecosystem carbon sequestration. Biological activity index (BAI) is the average of responses of all enzyme activities in treated land over control/reference land. The CAP parameter investigates how land uses may affect carbon flows, retention, and sequestration. The CAP provides a signal for C cycles, flows, and the systems' relative operational supremacy.
Provides functions for (1) soil water retention (SWC) and unsaturated hydraulic conductivity (Ku) (van Genuchten-Mualem (vGM or vG) [1, 2], Peters-Durner-Iden (PDI) [3, 4, 5], Brooks and Corey (bc) [8]), (2) fitting of parameter for SWC  and/or Ku using Shuffled Complex Evolution (SCE) optimisation and (3) calculation of soil hydraulic properties (Ku and soil water contents) based on the simplified evaporation method (SEM) [6, 7].
    Main references:
    [1] van Genuchten (1980) <doi:10.2136/sssaj1980.03615995004400050002x>, 
    [2] Mualem (1976) <doi:10.1029/WR012i003p00513>, 
    [3] Peters (2013) <doi:10.1002/wrcr.20548>, 
    [4] Iden and Durner (2013) <doi:10.1002/2014WR015937>, 
    [5] Peters (2014) <doi:10.1002/2014WR015937>,
    [6] Wind G. P. (1966),
    [7] Peters and Durner (2008) <doi:10.1016/j.jhydrol.2008.04.016> and
    [8] Brooks and Corey (1964).
Provides functions for efficiently estimating properties of 
         the Van Genuchten-Mualem model for soil hydraulic parameters from 
         possibly sparse soil water retention and hydraulic conductivity data 
         by multi-response parameter estimation methods 
         (Stewart, W.E., Caracotsios, M. Soerensen, J.P. (1992) 
         "Parameter estimation from multi-response data" 
         <doi:10.1002/aic.690380502>). Parameter estimation is simplified 
         by exploiting the fact that residual and saturated water contents 
         and saturated conductivity are conditionally linear parameters 
         (Bates, D. M.  and Watts, D. G. (1988) 
         "Nonlinear Regression Analysis and Its Applications" 
         <doi:10.1002/9780470316757>). 
         Estimated parameters are optionally constrained by the evaporation
         characteristic length (Lehmann, P., Bickel, S., Wei, Z. and Or, D. (2020)
         "Physical Constraints for Improved Soil Hydraulic Parameter 
         Estimation by Pedotransfer Functions" <doi:10.1029/2019WR025963>) 
         to ensure that the estimated parameters are physically valid. 
         Common S3 methods and further utility functions allow to process, 
         explore and visualise estimation results.
Calculate numerical agricultural soil management indicators from on a management timeline of an arable field. Currently, indicators for carbon (C) input into the soil system, soil tillage intensity rating (STIR), number of soil cover and living plant cover days, N fertilization and livestock intensity, and plant diversity are implemented.
    The functions can also be used independently of the management timeline to calculate some indicators. The package contains tables with reference information for the functions, as well as a '*.xlsx' template to collect the management data.
Basic and model-based soil physical analyses.
Functions for modeling Soil Organic Matter decomposition
        in terrestrial ecosystems with linear and nonlinear systems of differential equations. 
        The package implements
        models according to the compartmental system representation described in 
        Sierra and others (2012) <doi:10.5194/gmd-5-1045-2012> and Sierra and others (2014)
        <doi:10.5194/gmd-7-1919-2014>.
The developed function generates soil salinity indices using satellite data, utilizing multiple spectral bands such as Blue, Green, Red, Near-Infrared (NIR),
             and Shortwave Infrared (SWIR1, SWIR2). It computes 24 different salinity indices crucial for monitoring and analyzing salt-affected soils efficiently.
             For more details see, Rani, et al. (2022). <DOI: 10.1007/s12517-022-09682-3>. One of the key features of the developed function is its flexibility.
             Users can provide any combination of the required spectral bands, and the function will automatically calculate only the relevant indices based on the available data.
             This dynamic capability ensures that users can maximize the utility of their data without the need for all spectral bands, making the package versatile and user-friendly.
             Outputs are provided as GeoTIFF file format, facilitating easy integration with GIS workflows.
Taxonomic dictionaries, formative element lists, and functions related to the maintenance, development and application of U.S. Soil Taxonomy. 
   Data and functionality are based on official U.S. Department of Agriculture sources including the latest edition of the Keys to Soil Taxonomy. Descriptions and metadata are obtained from the National Soil Information System or Soil Survey Geographic databases. Other sources are referenced in the data documentation. 
   Provides tools for understanding and interacting with concepts in the U.S. Soil Taxonomic System. Most of the current utilities are for working with taxonomic concepts at the "higher" taxonomic levels: Order, Suborder, Great Group, and Subgroup.
A compilation of functions designed to assist users on the correlation analysis of crop yield and soil test values. Functions to estimate crop response patterns to soil nutrient availability and critical soil test values using various approaches such as: 1) the modified arcsine-log calibration curve (Correndo et al. (2017) <doi:10.1071/CP16444>); 2) the graphical Cate-Nelson quadrants analysis (Cate & Nelson (1965)), 3) the statistical Cate-Nelson quadrants analysis (Cate & Nelson (1971) <doi:10.2136/sssaj1971.03615995003500040048x>), 4) the linear-plateau regression (Anderson & Nelson (1975) <doi:10.2307/2529422>), 5) the quadratic-plateau regression (Bullock & Bullock (1994) <doi:10.2134/agronj1994.00021962008600010033x>), and 6) the Mitscherlich-type exponential regression (Melsted & Peck (1977) <doi:10.2134/asaspecpub29.c1>). The package development stemmed from ongoing work with the Fertilizer Recommendation Support Tool (FRST) and Feed the Future Innovation Lab for Collaborative Research on Sustainable Intensification (SIIL) projects.
Testing of soil for the contents of organic carbon, and available macro- and micro-nutrients is a crucial part of soil fertility assessment. This package computes some routinely tested soil properties viz. organic carbon (C), total nitrogen (N), available N, mineral N, available phosphorus (P), available potassium (K), available iron (Fe), available zinc (Zn), available manganese (Mn), available copper (Cu), and available nickel (Ni) in soil based on laboratory analysis data obtained by most commonly followed protocols. Besides, it can also draw standard curves based on absorption/emission vs. concentration data, and give out unknown concentrations from absorption/emission readings.
"The Soil Texture Wizard" is a set of R functions designed to produce texture triangles (also called texture plots, texture diagrams, texture ternary plots), classify and transform soil textures data. These functions virtually allows to plot any soil texture triangle (classification) into any triangle geometry (isosceles, right-angled triangles, etc.). This set of function is expected to be useful to people using soil textures data from different soil texture classification or different particle size systems. Many (> 15) texture triangles from all around the world are predefined in the package. A simple text based graphical user interface is provided: soiltexture_gui().
Provides tools to record, validate, and analyse soil tillage
    depth and erosion across years and field treatments. Includes functions
    for year-wise tillage operation summaries, erosion depth tracking,
    compaction detection, soil loss estimation, and visualisation of
    temporal changes in tillage and erosion profiles. Methods follow
    Lal (2001) <doi:10.1201/9780203739280> and
    Renard et al. (1997) "Predicting Soil Erosion by Water: A Guide to
    Conservation Planning with the Revised Universal Soil Loss Equation
    (RUSLE)"
    <https://ntrl.ntis.gov/NTRL/dashboard/searchResults/titleDetail/PB97153704.xhtml>.
Supervised latent-variable regression for high-dimensional predictors
    such as soil reflectance spectra. The model uses an encoder-decoder neural
    network with a stochastic Gaussian latent representation regularized by a
    Kullback-Leibler term, and a supervised prediction head trained jointly with
    the reconstruction objective. The implementation interfaces R with a 'Python'
    deep-learning backend and provides utilities for training, tuning, and
    prediction.
It implements parametric formulas of soil
    water retention or conductivity curve. At the moment, only Van Genuchten
    (for soil water retention curve) and Mualem (for hydraulic conductivity)
    were implemented. 
    See reference (<http://en.wikipedia.org/wiki/Water_retention_curve>).
Provides a simple way for utilizing Sojourn methods for
    accelerometer processing, as detailed in Lyden K, Keadle S,
    Staudenmayer J, & Freedson P (2014) <doi:10.1249/MSS.0b013e3182a42a2d>,
    Ellingson LD, Schwabacher IJ, Kim Y, Welk GJ, & Cook DB (2016)
    <doi:10.1249/MSS.0000000000000915>, and Hibbing PR, Ellingson LD,
    Dixon PM, & Welk GJ (2018) <doi:10.1249/MSS.0000000000001486>.
Stores objects (e.g. neural networks) that are needed for
    using Sojourn accelerometer methods. For more information, see
    Lyden K, Keadle S, Staudenmayer J, & Freedson P (2014)
    <doi:10.1249/MSS.0b013e3182a42a2d>, Ellingson LD, Schwabacher IJ,
    Kim Y, Welk GJ, & Cook DB (2016) <doi:10.1249/MSS.0000000000000915>,
    and Hibbing PR, Ellingson LD, Dixon PM, & Welk GJ (2018)
    <doi:10.1249/MSS.0000000000001486>.
Interactively play a game of sokoban ,which has nine game levels.Sokoban is a type of transport puzzle, in which the player pushes boxes or crates around in a warehouse, trying to get them to storage locations.
Calculation methods of solar radiation and performance of photovoltaic systems from daily and intradaily irradiation data sources.
Calculation of solar zenith and azimuth angles.
Provide utilities to work with solar time, 
  i.e. where noon is exactly when sun culminates.
  Provides functions for computing sun position and times of sunrise and sunset.
Isolation forest is anomaly detection method introduced by the paper Isolation based Anomaly Detection (Liu, Ting and Zhou <doi:10.1145/2133360.2133363>).
For surface energy models and estimation of solar positions and components with varying topography, time and locations. The functions calculate solar top-of-atmosphere, open, diffuse and direct components, atmospheric transmittance and diffuse factors, day length, sunrise and sunset, solar azimuth, zenith, altitude, incidence, and hour angles, earth declination angle, equation of time, and solar constant. Details about the methods and equations are explained in Seyednasrollah, Bijan, Mukesh Kumar, and Timothy E. Link. 'On the role of vegetation density on net snow cover radiation at the forest floor.' Journal of Geophysical Research: Atmospheres 118.15 (2013): 8359-8374, <doi:10.1002/jgrd.50575>.
Provides a set of functions for querying and parsing data
    from 'Solr' (<https://solr.apache.org/>) 'endpoints' (local and
    remote), including search, 'faceting', 'highlighting', 'stats', and
    'more like this'. In addition, some functionality is included for
    creating, deleting, and updating documents in a 'Solr' 'database'.
R language bindings for SolveBio's API.
    SolveBio is a biomedical knowledge hub that enables life science
    organizations to collect and harmonize the complex, disparate
    "multi-omic" data essential for today's R&D and BI needs.
Universal and robust algorithm for solving the total alkalinity-pH equation presented in G. Munhoven (2013) <doi:10.5194/gmd-6-1367-2013> and G. Munhoven (2021) <doi:10.5194/gmd-2020-447>. The total alkalinity-pH equation relates total alkalinity and pH for a given set of acid-base concentrations in a given water sample, among which carbonic acid. This package is particularly useful in marine chemistry involving dissolved inorganic carbon. Original package in Fortran can be found at <doi:10.5281/zenodo.4328965>.
Self-Organizing Map (with application in gene clustering).
A topological version of k-NN: An abstract model is build
             as 2-dimensional self-organising map. Samples of unknown
             class are predicted by mapping them on the SOM and analysing
             class membership of neurons in the neighbourhood.
An R implementation of the Self-Organising Migrating Algorithm, a general-purpose, stochastic optimisation algorithm. The approach is similar to that of genetic algorithms, although it is based on the idea of a series of ``migrations'' by a fixed set of individuals, rather than the development of successive generations. It can be applied to any cost-minimisation problem with a bounded parameter space, and is robust to local minima.
Load and export 'SomaScan' data via the
    'SomaLogic Operating Co., Inc.' structured text file
    called an ADAT ('*.adat'). For file format see
    <https://github.com/SomaLogic/SomaLogic-Data/blob/main/README.md>.
    The package also exports auxiliary functions for
    manipulating, wrangling, and extracting relevant
    information from an ADAT object once in memory.
Database of genes which frequently sustain somatic mutations, but are unlikely to drive cancer.
The stochastic (also called on-line) version of the Self-Organising
             Map (SOM) algorithm is provided. Different versions of the
             algorithm are implemented, for numeric and relational data and for
             contingency tables as described, respectively, in Kohonen (2001)
             <isbn:3-540-67921-9>, Olteanu & Villa-Vialaneix (2005)
             <doi:10.1016/j.neucom.2013.11.047> and Cottrell et al (2004)
             <doi:10.1016/j.neunet.2004.07.010>. The package also contains many
             plotting features (to help the user interpret the results), can 
             handle (and impute) missing values and is delivered with a 
             graphical user interface based on 'shiny'.
It's a collection of functions for Multiplicity Correction and Multiple Testing.
Analysis of multivariate environmental high frequency data by Self-Organizing Map and k-means clustering algorithms. By means of the graphical user interface it provides a comfortable way to elaborate by self-organizing map algorithm rather big datasets (txt files up to 100 MB ) obtained by environmental high-frequency monitoring by sensors/instruments. The functions present in the package are based on 'kohonen' and 'openair' packages implemented by functions embedding Vesanto et al. (2001) <http://www.cis.hut.fi/projects/somtoolbox/package/papers/techrep.pdf>  heuristic rules for map initialization parameters, k-means clustering algorithm and map features visualization. Cluster profiles visualization as well as graphs dedicated to the visualization of time-dependent variables Licen et al. (2020) <doi:10.4209/aaqr.2019.08.0414> are provided.
Implements self-organising maps combined with hierarchical cluster analysis (SOM-HCA) for clustering and visualization of high-dimensional data.
    The package includes functions to estimate the optimal map size based on various quality measures
    and to generate a model using the selected dimensions.
    It also performs hierarchical clustering on the map nodes to group similar units.
    Documentation about the SOM-HCA method is provided in Pastorelli et al. (2024)
    <doi:10.1002/xrs.3388>.
Processes data from Molecular Dynamics simulations using 
    Self Organising Maps. Features include the ability to read different 
    input formats. Trajectories can be analysed to identify groups of important
    frames. Output visualisation can be generated for maps and pathways.
    Methodological details can be found in Motta S et al (2022) 
    <doi:10.1021/acs.jctc.1c01163>.
    I/O functions for xtc format files were implemented using the 'xdrfile'
    library available under open source license. The relevant information can 
    be found in inst/COPYRIGHT.
Structural multivariate-univariate linear mixed model solver for estimation of multiple random effects with unknown variance-covariance structures (e.g., heterogeneous and unstructured) and known covariance among levels of random effects (e.g., pedigree and genomic relationship matrices) (Covarrubias-Pazaran, 2016 <doi:10.1371/journal.pone.0156744>; Maier et al., 2015 <doi:10.1016/j.ajhg.2014.12.006>; Jensen et al., 1997). REML estimates can be obtained using the Direct-Inversion Newton-Raphson and Direct-Inversion Average Information algorithms for the problems r x r (r being the number of records) or using the Henderson-based average information algorithm for the problem c x c (c being the number of coefficients to estimate). Spatial models can also be fitted using the two-dimensional spline functionality available.
Integrates the 13C nuclear magnetic resonance spectra using different integration ranges. Output depends on the method chosen. For the Molecular Mixing Model, a measurement of the fitting quality is given by its R-factor. For more details see: <doi:10.5281/zenodo.10137768>.
Formulas for calculating sound velocity, water pressure, depth, 
    density, absorption and sonar equations.
Simulates the cultural evolution of quantitative traits of bird song. 'SongEvo' is an individual- (agent-) based model. 'SongEvo' is spatially-explicit and can be parameterized with, and tested against, measured song data. Functions are available for model implementation, sensitivity analyses, parameter optimization, model validation, and hypothesis testing.
Estimate the abundance of cell clones from the
	     distribution of lengths of DNA fragments (as created by
	     sonication, whence `sonicLength').  The algorithm in
	     "Estimating abundances of retroviral insertion sites from
	     DNA fragment length data" by Berry CC, Gillet NA, Melamed
	     A, Gormley N, Bangham CR, Bushman FD. Bioinformatics;
	     2012 Mar 15;28(6):755-62 is implemented.  The
	     experimental setting and estimation details are described
	     in detail there. Briefly, integration of new DNA in a
	     host genome (due to retroviral infection or gene therapy)
	     can be tracked using DNA sequencing, potentially allowing
	     characterization of the abundance of individual cell
	     clones bearing distinct integration sites. The locations
	     of integration sites can be determined by fragmenting the
	     host DNA (via sonication or fragmentase), breaking the
	     newly integrated DNA at a known sequence, amplifying the
	     fragments containing both host and integrated DNA,
	     sequencing those amplicons, then mapping the host
	     sequences to positions on the reference genome. The
	     relative number of fragments containing a given position
	     in the host genome estimates the relative abundance of
	     cells hosting the corresponding integration site, but
	     that number is not available and the count of amplicons
	     per fragment varies widely.  However, the expected number
	     of distinct fragment lengths is a function of the
	     abundance of cells hosting an integration site at a given
	     position and a certain nuisance parameter. The algorithm
	     implicitly estimates that function to estimate the
	     relative abundance.
Provides tools for manipulating sound files for bioacoustic
  analysis, and preparing analyses these for publication. The package validates
  that values are physically possible wherever feasible.
Sonification (or audification) is the process of representing data by sounds in the audible range. This package provides the R function sonify() that transforms univariate data, sampled at regular or irregular intervals, into a continuous sound with time-varying frequency. The ups and downs in frequency represent the ups and downs in the data. Sonify provides a substitute for R's plot function to simplify data analysis for the visually impaired.
Computes scores of outlyingness for data sets consisting of nominal variables and includes various evaluation metrics for assessing performance of outlier identification algorithms producing scores of outlyingness. The scores of nominal outlyingness are computed based on the framework of Costa and Papatsouma (2025) <doi:10.48550/arXiv.2408.07463>.
Obtains lists of files of remote sensing collections for Southern Ocean surface 
 properties. Commonly used data sources of sea surface temperature, sea ice concentration, and 
 altimetry products such as sea surface height and sea surface currents are cached in object storage
 on the Pawsey Supercomputing Research Centre facility. Patterns of working to retrieve data from these object storage
 catalogues are described. The catalogues include complete collections of datasets Reynolds et al. (2008) 
 "NOAA Optimum Interpolation Sea Surface Temperature (OISST) Analysis, Version 2.1" <doi:10.7289/V5SQ8XB5>, 
 Spreen et al. (2008) "Artist Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) 
 sea ice concentration" <doi:10.1029/2005JC003384>. In future releases helpers will be added to identify 
 particular data collections and target specific dates for earth observation data for reading, as well as 
 helpers to retrieve data set citation and provenance details.  This work was supported by resources provided 
 by the Pawsey Supercomputing Research Centre with funding from the Australian Government and the Government of 
 Western Australia.  This software was developed by the Integrated Digital East Antarctica program of the Australian
 Antarctic Division.
Generalised additive P-spline regression models estimation using the separation of overlapping precision matrices (SOP) method. Estimation is based on the equivalence between P-splines and linear mixed models, and variance/smoothing parameters are estimated based on restricted maximum likelihood (REML). The package enables users to estimate P-spline models with overlapping penalties. Based on the work described in Rodriguez-Alvarez et al. (2015) <doi:10.1007/s11222-014-9464-2>; Rodriguez-Alvarez et al. (2019) <doi:10.1007/s11222-018-9818-2>, and Eilers and Marx (1996) <doi:10.1214/ss/1038425655>.
The sparse online principal component can not only process the online data set, but also obtain a sparse solution of the online data set. The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.
Provides functions to non-parametrically estimate the off-pulse interval of a source
    function originating from a pulsar. The technique is based on a sequential application of P-values
    obtained from goodness-of-fit tests for the uniform distribution, such as the Kolmogorov-Smirnov,
    Cramer-von Mises, Anderson-Darling and Rayleigh goodness-of-fit tests.
Computes sequential A-, MV-, D- and E-optimal or near-optimal block and row-column designs for two-colour cDNA microarray experiments using the linear fixed effects and mixed effects models where the interest is in a comparison of all possible elementary treatment contrasts. The package also provides an optional method of using the graphical user interface (GUI) R package 'tcltk' to ensure that it is user friendly.
Used to construct the URLs and parameters of 'Socrata Open Data API' <https://dev.socrata.com> calls, using the API's 'SoQL' parameter format. Has method-chained and sensical syntax. Plays well with pipes.
Estimation for longitudinal data following outcome dependent sampling using the sequential offsetted regression technique.  Includes support for binary, count, and continuous data.  The first regression is a logistic regression, which uses a known ratio (the probability of being sampled given that the subject/observation was referred divided by the probability of being sampled given that the subject/observation was no referred) as an offset to estimate the probability of being referred given outcome and covariates.  The second regression uses this estimated probability to calculate the mean population response given covariates.
Can be used to model the fate of soil organic carbon and soil organic nitrogen and to calculate N mineralisation rates. Provides a framework that numerically solves differential equations of soil organic carbon models based on first-order kinetics and extends these models to include the nitrogen component. The name 'sorcering' is an acronym for 'Soil ORganic Carbon & CN Ratio drIven Nitrogen modellinG framework'.
A Bayesian semiparametric Dirichlet process mixtures to estimate correlated receiver operating characteristic (ROC) surfaces and the associated volume under the surface (VUS) with stochastic order constraints. The reference paper is:Zhen Chen, Beom Seuk Hwang, (2018) "A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints". Biometrics, 75, 539-550. <doi:10.1111/biom.12997>.
Provides tools to efficiently analyze and visualize laboratory data from aqueous static adsorption experiments. The package provides functions to plot Langmuir, Freundlich, and Temkin isotherms and functions to determine the statistical conformity of data points to the Langmuir, Freundlich, and Temkin adsorption models through statistical characterization of the isothermic least squares regressions lines. Scientific Reference: Dada, A.O, Olalekan, A., Olatunya, A. (2012) <doi:10.9790/5736-0313845>.
Enables drag-and-drop behaviour in Shiny apps, by exposing
    the functionality of the 'SortableJS'
    <https://sortablejs.github.io/Sortable/> JavaScript library as an
    'htmlwidget'.  You can use this in Shiny apps and widgets, 'learnr'
    tutorials as well as R Markdown. In addition, provides a custom
    'learnr' question type - 'question_rank()' - that allows ranking
    questions with drag-and-drop.
Misc support functions for rOpenGov and open data downloads.
Search contributed R packages, sort by package.
Access statistical information on welfare and health in Finland 
    from the Sotkanet open data portal <https://sotkanet.fi/sotkanet/fi/index>.
The President of the United States is constitutionally obligated to provide
  a report known as the 'State of the Union'. The report summarizes the current challenges
  facing the country and the president's upcoming legislative agenda. While historically
  the State of the Union was often a written document, in recent decades it has always
  taken the form of an oral address to a joint session of the United States Congress.
  This package provides the raw text from every such address with the intention of
  being used for meaningful examples of text analysis in R. The corpus is well suited
  to the task as it is historically important, includes material intended to be read
  and material intended to be spoken, and it falls in the public domain. As the corpus
  spans over two centuries it is also a good test of how well various methods hold up
  to the idiosyncrasies of historical texts. Associated data about each address, such
  as the year, president, party, and format, are also included.
Basic functions for dealing with wav files and sound samples.
Provides an all-in-one solution for automatic classification of 
    sound events using convolutional neural networks (CNN). The main purpose 
    is to provide a sound classification workflow, from annotating sound events
    in recordings to training and automating model usage in real-life
    situations. Using the package requires a pre-compiled collection of 
    recordings with sound events of interest and it can be employed for: 
    1) Annotation: create a database of annotated recordings, 
    2) Training: prepare train data from annotated recordings and fit CNN models, 
    3) Classification: automate the use of the fitted model for classifying 
    new recordings. By using automatic feature selection and a user-friendly GUI
    for managing data and training/deploying models, this package is intended 
    to be used by a broad audience as it does not require specific expertise in 
    statistics, programming or sound analysis. Please refer to the vignette for
    further information.
    Gibb, R., et al. (2019) <doi:10.1111/2041-210X.13101>
    Mac Aodha, O., et al. (2018) <doi:10.1371/journal.pcbi.1005995>
    Stowell, D., et al. (2019) <doi:10.1111/2041-210X.13103>
    LeCun, Y., et al. (2012) <doi:10.1007/978-3-642-35289-8_3>.
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>.
Calculates vote-specific and traditional Shapley-Owen power indices (vs-SOVs and SOVs) for spatial voting games in one to four dimensions. Evaluates voter influence through an a posteriori analysis of relative preferences. Supports weighted voting and various voting thresholds. Compatible with ideal point estimates from NOMINATE, Optimal Classification, and 'MCMCpack'. The method builds on Bibina and Dougherty (2025) <doi:10.2139/ssrn.6324519>.
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.
Implements the Stratigraphic Plug Alignment (SPA) procedure for
    integrating sparsely sampled plug-based measurements (e.g., total organic
    carbon, porosity, mineralogy) with high-resolution X-ray fluorescence
    (XRF) geochemical data. SPA uses linear interpolation via the base
    approx() function with constrained extrapolation (rule = 1) to preserve
    stratigraphic order and avoid estimation beyond observed depths. The
    method aligns all datasets to a common depth grid, enabling high-resolution
    multivariate analysis and stratigraphic interpretation of core-based
    datasets such as those from the Utica and Point Pleasant formations.
    See R Core Team (2025)
    <https://stat.ethz.ch/R-manual/R-devel/library/stats/html/stats-package.html>
    and Omodolor (2025)
    <http://rave.ohiolink.edu/etdc/view?acc_num=case175262671767524>
    for methodological background and geological context.
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' <https://github.com/jinworks/CellChat>, 'CellPhoneDB' <https://www.cellphonedb.org/>, 'Cellinker' <https://www.rna-society.org/cellinker/>, '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>.
A set of tools dedicated to modeling food web transfer based on an
  initial ground raster. It provides a directed acyclic graph structure for a 
  set of rasters representing the flow of elements (e.g., food, energy, 
  contaminants). It also includes tools for working with dispersal algorithms, 
  enabling the combination of flux data with population movement.
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.
Spatial components offer tools for dimension reduction and 
  spatially variable gene detection for high dimensional spatial transcriptomics 
  data. Construction of a projection onto low-dimensional feature space of 
  spatially dependent metagenes offers pre-processing to clustering, testing for 
  spatial variability and denoising of spatial expression patterns. For more 
  details, see Koehler et al. (2026) <doi:10.1093/bioinformatics/btag052>.
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>).
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.  Methods
    described in Kotov et al. (2026) <doi:10.1177/23998083251415040>.
    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.
Implements algorithms for terrestrial, mobile, and airborne lidar processing, tree detection, 
    segmentation, and attribute estimation (Donager et al., 2021) 
    <doi:10.3390/rs13122297>, and a hierarchical patch delineation algorithm 
    'PatchMorph' (Girvetz & Greco, 2007) <doi:10.1007/s10980-007-9104-8>. Tree 
    detection uses rasterized point cloud metrics (relative neighborhood density and 
    verticality) combined with RANSAC cylinder fitting to locate tree boles and estimate 
    diameter at breast height. Tree segmentation applies graph-theory approaches inspired 
    by Tao et al. (2015) <doi:10.1016/j.isprsjprs.2015.08.007> with cylinder fitting 
    methods from de Conto et al. (2017) <doi:10.1016/j.compag.2017.07.019>. PatchMorph 
    delineates habitat patches across spatial scales using organism-specific thresholds. 
    Built on 'lidR' (Roussel et al., 2020) <doi:10.1016/j.rse.2020.112061>.
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'.
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 a client for running SPARQL queries directly from R.
    SPARQL (short for SPARQL Protocol and RDF Query Language) is a query
    language used to retrieve and manipulate data stored in RDF (Resource
    Description Framework) format.
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.
Implements spectral clustering algorithms for community detection
    in sparse networks under the stochastic block model ('SBM') and
    degree-corrected stochastic block model ('DCSBM'), following the methods of
    Lei and Rinaldo (2015) <doi:10.1214/14-AOS1274>. Provides a regularized
    normalized Laplacian embedding, spherical k-median clustering for 'DCSBM',
    standard k-means for 'SBM', simulation utilities for both models, and a
    misclustering rate evaluation metric. Also includes the 'NCAA' college
    football network of Girvan and Newman (2002) <doi:10.1073/pnas.122653799>
    as a benchmark dataset, and the Bethe-Hessian community number estimator
    of Hwang (2023) <doi:10.1080/01621459.2023.2223793>.
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
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>.
Provides an R interface to the 'sparseLM' C library for
    large-scale nonlinear least squares problems with arbitrarily sparse
    Jacobians. The underlying solver implements a sparse variant of the
    Levenberg-Marquardt algorithm for minimizing sum-of-squares objective
    functions, supports user-supplied analytic Jacobians or finite-difference
    approximation, and is designed to exploit sparsity for improved memory use
    and performance. This package exposes the solver in R and uses sparse
    matrix classes and the 'CHOLMOD' sparse Cholesky factorization routines
    through the 'Matrix' package interface. Methods from the C library are
    described in Lourakis (2010) <doi:10.1007/978-3-642-15552-9_4>.
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.
Provides an interface to the SuiteSparse UMFPACK LU factorisation routines for sparse matrices stored in compressed column format. Implements the algorithm described in Davis (2004) <doi:10.1145/992200.992206>.
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>.
Computes sparse vector autoregressive coefficients and sparse
  precision matrices for time series chain graphical models. Methods are
  described in Abegaz and Wit (2013)
  <doi:10.1093/biostatistics/kxt005>.
A wrapper for sparse VAR (Vector Autoregression) and
             VECM (Vector Error Correction Model) time series models estimation
             using penalties like ENET (Elastic Net), SCAD (Smoothly Clipped
             Absolute Deviation) and MCP (Minimax Concave Penalty).
             Based on the work of Basu and Michailidis (2015)
             <doi:10.1214/15-AOS1315>.
Implements variational Bayesian algorithms to perform scalable variable selection for sparse, high-dimensional linear and logistic regression models. Features include a novel prioritized updating scheme, which uses a preliminary estimator of the variational means during initialization to generate an updating order prioritizing large, more relevant, coefficients. Sparsity is induced via spike-and-slab priors with either Laplace or Gaussian slabs. By default, the heavier-tailed Laplace density is used. Formal derivations of the algorithms and asymptotic consistency results may be found in Kolyan Ray and Botond Szabo (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 (2026) <doi:10.1016/j.jmva.2025.105536>. 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.
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.
Implements atom-based regression models (ABRM) for analyzing spatially 
    misaligned data. Provides functions for simulating misaligned spatial data, 
    preparing NIMBLE model inputs, running MCMC diagnostics, and providing results. All main functions return
    S3 objects with print(), summary(), and plot() methods for intuitive result exploration.
    Methods originally described in Mugglin et al. (2000) <doi:10.1080/01621459.2000.10474279>,
    further investigated in Trevisani & Gelfand (2013), and applied in Nethery et al. (2023) 
    <doi:10.1101/2023.01.10.23284410>.
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.
The aim of the spatial downscaling is to increase the spatial resolution of the gridded geospatial input data. This package contains two deep learning based spatial downscaling methods, super-resolution deep residual network (SRDRN) (Wang et al., 2021 <doi:10.1029/2020WR029308>) and UNet (Ronneberger et al., 2015 <doi:10.1007/978-3-319-24574-4_28>), along with a statistical baseline method bias correction and spatial disaggregation (Wood et al., 2004 <doi:10.1023/B:CLIM.0000013685.99609.9e>). The SRDRN and UNet methods are implemented to optionally account for cyclical temporal patterns in case of spatio-temporal data. For more details of the methods, see Sipilä et al. (2025) <doi:10.48550/arXiv.2512.13753>.
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().
Fast computation of Conley (1999) <doi:10.1016/S0304-4076(98)00084-0>
    spatial heteroskedasticity and autocorrelation consistent (HAC) standard
    errors for linear regression models with geo-coded data, with a fast C++
    implementation by Christensen, Hartman, and Samii (2021)
    <doi:10.1017/S0020818321000187>. Performance-critical distance calculations,
    kernel weighting, and variance component accumulation are implemented in C++
    via 'Rcpp' and 'RcppArmadillo'. Includes tools for estimating the spatial
    correlation range from covariograms and correlograms following the bandwidth
    selection method proposed in Lehner (2026) <doi:10.48550/arXiv.2603.03997>,
    and diagnostic visualizations for bandwidth selection.
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>.
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 Random Forest models fitted to spatially structured data. Spatial predictors are constructed from a distance matrix among training samples using Moran's Eigenvector Maps (MEMs; Dray, Legendre, and Peres-Neto 2006 <DOI:10.1016/j.ecolmodel.2006.02.015>) or the RFsp approach (Hengl et al. <DOI:10.7717/peerj.5518>). These predictors are used alongside user-supplied explanatory variables in Random Forest models. The package provides functions for model fitting, multicollinearity reduction, interaction identification, hyperparameter tuning, evaluation via spatial cross-validation, and result visualization using partial dependence and interaction plots. Model fitting relies on the '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>.
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, 2025) <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>.
Provides functions for coarse-to-fine spatial modeling (CFSM), enabling fast spatial prediction, regression, and uncertainty quantification. For further details, see Murakami et al. (2025) <doi:10.48550/arXiv.2510.00968>.
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>.
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, perform principal component analysis (PCA), and assemble 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>.
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>, together with geographical pattern causality proposed in Zhang & Wang (2025) <doi:10.1080/13658816.2025.2581207>.
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.
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.
Implements a parameter expanded variational Bayes algorithm for linear regression models with high-dimensional variable selection. The methodology utilizes spike-and-slab priors to perform simultaneous estimation and selection. Details can be found in Olejua et al. (2024) <doi:10.21203/rs.3.rs-7208847/v1>.
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).
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.
Provides tools for descriptive data analysis,
    variable inspection, data management, and tabulation workflows in 'R'.
    Summarizes variable metadata, labels, classes, missing values, and
    representative values, with support for readable frequency tables,
    cross-tabulations, association measures for contingency tables
    (Cramer's V, Phi, Goodman-Kruskal Gamma, Kendall's Tau-b, Somers' D,
    and others), categorical and continuous summary tables, including
    APA-style reporting outputs. Includes helpers for interactive
    codebooks, variable label extraction, clipboard export, and row-wise
    descriptive summaries. Designed to make descriptive analysis faster,
    clearer, and easier to work with in practice.
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.
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.
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.
Fit additive mixed meta-analysis (AMMA) models, extending
  the 'mixmeta' package <https://cran.r-project.org/package=mixmeta> 
  to allow for spline-based meta-regression. Functions combine features of 
  'mgcv' <https://cran.r-project.org/package=mgcv> for building spline 
  components and 'mixmeta' for estimating general mixed-effects meta-analysis 
  models.
Creates 'ggplot2'-based visualizations of smooth effects from GAM (Generalized Additive Models)
    fitted with 'mgcv' and spline effects from GLM (Generalized Linear Models). Supports survey-weighted
    models ('svyglm', 'svycoxph') from the 'survey' package, 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.
Create mixed models with repeated measures using natural
    cubic splines applied to an observed continuous time variable, as
    described by Donohue et al. (2023) <doi:10.1002/pst.2285>. Iterate
    through multiple covariance structure types until one converges.
    Categorize observed time according to scheduled visits. Perform
    subgroup analyses.
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>.
Provides tools for splitting large Excel worksheets into multiple smaller sheets based on a specified number of rows per chunk. The package reads Excel files, partitions the data efficiently using the 'data.table' package, and writes the resulting subsets into a single workbook with multiple sheets using the 'openxlsx' package. This is useful for handling large datasets, preparing data for reporting, and exporting manageable Excel files for downstream analysis.
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 comprehensive toolkit for mining, analyzing, and visualizing
    scientific literature in sport science domains. Provides functions for
    retrieving abstracts from 'Scopus', preprocessing text data, performing
    advanced topic modeling using Latent Dirichlet Allocation ('LDA'),
    Structural Topic Models ('STM'), and Correlated Topic Models ('CTM'),
    and creating publication-ready visualizations including keyword
    co-occurrence networks and topic trends. For methodological details see
    Blei et al. (2003) <doi:10.1162/jmlr.2003.3.4-5.993> for 'LDA',
    Roberts et al. (2014) <doi:10.1111/ajps.12103> for 'STM', and
    Blei and Lafferty (2007) <doi:10.1214/07-AOAS114> for 'CTM'.
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. For 
   further details see the associated publication in
   Chalmers (2025) <doi:10.3758/s13428-025-02787-z>.
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.
Provides a methodology to analyze how species occurrences change over time, particularly in relation to spatial and thermal factors. It facilitates the development of explanatory hypotheses about the impact of environmental shifts on species by analyzing historical presence data that includes temporal and geographic information. Approach described in Lobo et al., 2023 <doi:10.1002/ece3.10674>.
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/>.
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:10.1214/25-BA1582> 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.
Implements a test for distinguishing between true long memory
    and spurious long memory. Reference: Qu, Z. (2011). "A Test Against
    Spurious Long Memory." Journal of Business & Economic Statistics,
    29(3), 423–438. <doi:10.1198/jbes.2010.09153>.
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.
An R interface to the 'Python' 'sqlfluff' 'SQL' linter and
    formatter via the 'reticulate' package. Enables linting, fixing, and
    parsing of 'SQL' queries with support for multiple dialects. Includes
    special handling for 'glue' 'SQL' syntax with curly-brace placeholders.
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.
Fits linear regression models on datasets residing in SQL
    databases without pulling data into R memory. Computes sufficient
    statistics inside the database engine via a single
    aggregation query and solves the normal equations in R.
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.
Lightweight helpers for connecting to Microsoft 'SQL Server' using 'DBI', 'odbc', and 'pool'. Provides simple wrappers for building connection arguments, establishing connections, and safely disconnecting.
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 mappings such as the EM algorithm. 
    It can be used to accelerate any smooth, linearly convergent scheme. 
    A tutorial-style introduction is available in vignette("SQUAREM"). 
    See also: Varadhan & Roland (2008) <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 robust variable selection and regression using the Fast and Scalable 
             Cellwise-Robust Ensemble (FSCRE) algorithm. The approach establishes a robust foundation 
             using the Detect Deviating Cells (DDC) algorithm and robust correlation estimates. It then 
             employs a competitive ensemble architecture where a robust Least Angle Regression (LARS) 
             engine proposes candidate variables and cross-validation arbitrates their assignment. A final 
             robust MM-estimator is applied to the selected predictors.
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).
Select the most suitable shape to describe the relationship between the exposure and the outcome among increasing, decreasing, convex, and concave shapes (Yin et al. (2021) <DOI:10.1007/s13571-020-00246-7>); estimate the direct and indirect effects with prior knowledge on the relationship between the mediator and the outcome with binary exposure (Yin et al. (2024) <DOI:10.1007/s13571-024-00336-w>); estimate the direct and indirect effects using linear regression-based approach (VanderWeele (2015, ISBN:9780199325870)).
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.
Stress Response score (SRscore) is a stress responsiveness 
    measure for transcriptome datasets and is based on the vote-counting 
    method. The SRscore is determined to evaluate and score genes on the 
    basis of the consistency of the direction of their regulation 
    (Up-regulation, Down-regulation, or No change) under stress conditions 
    across multiple analyzed research projects. This package is based on 
    the HN-score (score based on the ratio of gene expression between
    hypoxic and normoxic conditions) proposed by 
    Tamura and Bono (2022) <doi:10.3390/life12071079>, and can calculate both 
    the original method and an extended calculation method described in 
    Fukuda et al. (2025) <doi:10.1093/plphys/kiaf105>.
Read 'SubRip' <https://sourceforge.net/projects/subrip/>
    subtitle files as data frames for easy text analysis or manipulation.
    Easily shift numeric timings and export subtitles back into valid
    'SubRip' timestamp format to sync subtitles and audio.
Srt file is a common subtitle format for videos, it contains subtitle and when the subtitle showed.
    This package is for align time of srt file, and also change color, style and position of subtitle in videos,
	the srt file will be read as a vector into R, and can be write into srt file after modified using this package.
Use piping, verbs like 'group_by' and 'summarize', and other
    'dplyr' inspired syntactic style when calculating summary statistics on survey
    data using functions from the 'survey' package.
Develops a framework for fisheries stock assessment simulation
    testing with Stock Synthesis (SS) as described in Anderson et al.
    (2014) <doi:10.1371/journal.pone.0092725>.
Stationary subspace analysis (SSA) is a blind source separation (BSS) variant where stationary components are separated from non-stationary components. Several SSA methods for multivariate time series are provided here (Flumian et al. (2024) <doi:10.1016/j.cam.2023.115379>; 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.
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.
Implements the Shimazaki-Shinomoto method for optimizing the
    bin width of a histogram. This method minimizes the mean integrated
    squared error (MISE) and features a 'C++' backend for high performance
    and shift-averaging to remove edge-position bias. Ideally suits for
    time-dependent rate estimation and identifying intrinsic data
    structures.  Supports both 1D and 2D data distributions. For more
    details see Shimazaki and Shinomoto (2007) "A Method for Selecting the
    Bin Size of a Time Histogram" <doi:10.1162/neco.2007.19.6.1503>.
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>.
Implements a semi-supervised learning framework for finite mixture
    models under a mixed-missingness mechanism. The approach models both
    missing completely at random (MCAR) and entropy-based missing at random
    (MAR) processes using a logistic–entropy formulation. Estimation is carried
    out via an Expectation–-Conditional Maximisation (ECM) algorithm with robust
    initialisation routines for stable convergence. The methodology relates to
    the statistical perspective and informative missingness behaviour discussed
    in Ahfock and McLachlan (2020) <doi:10.1007/s11222-020-09971-5> and
    Ahfock and McLachlan (2023) <doi:10.1016/j.ecosta.2022.03.007>. The package
    provides functions for data simulation, model estimation, prediction, and
    theoretical Bayes error evaluation for analysing partially labelled data
    under a mixed-missingness mechanism.
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.
Wraps the 'rstac' package with a pipe-friendly, tidy API. All results return 'tibbles' instead of nested lists. Ships with a catalog registry of known 'STAC' endpoints including Planetary Computer, Earth Search, and 'USGS', while supporting any 'STAC' API URL.
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>.
Fits linear difference-in-differences models in scenarios where
    intervention roll-outs are staggered over time. The package implements a 
    version of an approach proposed by Sun and Abraham (2021) 
    <doi:10.1016/j.jeconom.2020.09.006> to estimate cohort- and 
    time-since-treatment specific difference-in-differences parameters, and it 
    provides convenience functions both for specifying the model and for 
    flexibly aggregating coefficients to answer a variety of research questions.
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.
Provides representative standard load profiles (SLPs) for
  electricity published by the German Association of Energy and Water
  Industries (BDEW Bundesverband der Energie- und Wasserwirtschaft e.V.)
  in a tidy format. Covers the 1999 profiles — households (H0), commerce
  (G0–G6), and agriculture (L0–L2) — and the updated 2025 profiles (H25,
  G25, L25, P25, S25), which additionally represent households with
  photovoltaic systems and battery storage. Also provides an interface for
  generating a standard load profile over a user-defined date range.
  The 1999 data and methodology are described in VDEW (1999),
  "Repräsentative VDEW-Lastprofile",
  <https://www.bdew.de/media/documents/1999_Repraesentative-VDEW-Lastprofile.pdf>.
  The generation algorithm is described in 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 2025 profiles are described in BDEW (2025), "Standardlastprofile
  Strom", <https://www.bdew.de/energie/standardlastprofile-strom/>.
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.
Reconstructs forest stand basal area and stem density at
    user-specified reference years using tree-level inventory data. The
    method estimates missing tree ages, back-calculates diameters using
    species-specific growth rates, and incorporates decay-class-based
    decomposition to infer mortality timing for dead trees. Results are
    returned in a tidy long format suitable for analysis and
    visualization.
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.
A 'future' backend that enables seamless execution of parallel R
    workloads on 'Amazon Web Services' ('AWS', <https://aws.amazon.com>),
    including 'EC2' and 'Fargate'. 'staRburst' handles environment
    synchronization, data transfer, quota management, and worker orchestration
    automatically, allowing users to scale from local execution to 100+ cloud
    workers with a single line of code change.
'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.
Facilitates probabilistic record linkage between infectious disease
    surveillance datasets (notifiable disease registers, outbreak line-lists),
    vaccination registries, and hospitalization records using methods based on
    Fellegi and Sunter (1969) <doi:10.1080/01621459.1969.10501049> and
    Sayers et al. (2016) <doi:10.1093/ije/dyv322>. The package provides
    core functions for data preparation, linkage, and analysis: clean_the_nest() 
    standardizes variable names and formats across heterogeneous datasets; 
    murmuration() performs machine learning-based record linkage using blocking 
    variables and similarity metrics; molting() deidentifies datasets for secure 
    sharing; homing() re-identifies previously deidentified datasets; plumage() 
    identifies and categorizes comorbidities; and preening() creates analysis-ready 
    variables including age categories and temporal groupings. Designed for 
    epidemiological research linking acute and post-acute disease outcomes to 
    vaccination status and healthcare utilization. Supports multiple linkage 
    scenarios including case-to-vaccination, case-to-hospitalization, and
    event-based vaccination status determination (e.g., outbreak attendees, flight
    passengers, exposure site visitors).
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.
Datasets for the textbook Stat2: Modeling with Regression and ANOVA (second edition). 
    The package also includes data for the first edition, Stat2: Building Models for a World of Data
    and a few functions for plotting diagnostics.
A comprehensive statistical toolbox for National Statistics
    Institutes (INS) in Africa. Provides functions for survey data import
    ('KoboToolbox', 'ODK', 'CSPro', 'Excel', 'Stata', 'SPSS'), data
    processing and validation, weighted statistical analysis (descriptive
    statistics, cross-tabulations, regression, Human Development Index (HDI),
    Multidimensional Poverty Index (MPI) following Alkire and Foster (2011)
    <doi:10.1093/oep/gpr051>, inequalities), visualization (age pyramids,
    thematic maps, official charts) and dissemination ('SDMX' export, 'DDI'
    metadata, anonymization, Word/PDF reports). Designed to work in
    resource-constrained environments, offline and in French.
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>.
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.
Provides tools for the integration and exploration of data tables measured on the same set of observational units. The package includes methods to assess similarities among tables, extract common patterns across variable blocks, and create visual summaries that highlight shared structures in multiblock data.
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.
Provides utility functions for validation and quality control of
    clinical trial datasets and outputs across 'SDTM', 'ADaM' and 'TFL' workflows.
    The package supports dataset loading, metadata inspection, frequency and
    summary calculations, table-ready aggregations, and compare-style dataset
    review similar to 'SAS' 'PROC COMPARE'. Functions are designed to support
    reproducible execution, transparent review, and independent verification of
    statistical programming results. Dataset comparisons may leverage 'arsenal'
    <https://cran.r-project.org/package=arsenal>.
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.
The statistical tools in this package do one of four things:
    1) Enhance basic statistical functions with more 
    flexible inputs, smarter defaults, and richer, clearer, and ready-to-use 
    output (e.g., t.test2())
    2) Produce publication-ready commonly needed figures with one line of code (e.g., plot_cdf())
    3) Implement novel analytical tools developed by the authors (e.g., twolines())
    4) Deliver niche functions of high value to the authors that are not easily
    available elsewhere (e.g., clear(), convert_to_sql(), resize_images()).
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>.
Implements the ST-DBSCAN (spatio-temporal density-based spatial
    clustering of applications with noise) clustering algorithm for detecting
    spatially and temporally dense regions in point data, with a fast C++ backend
    via 'Rcpp'. Birant and Kut (2007) <doi:10.1016/j.datak.2006.01.013>.
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.
Provides functions to compute standardized differences for numeric, binary,
    and categorical variables on Apache Spark DataFrames using 'sparklyr'.
    The implementation mirrors the methods used in the 'stddiff' package but
    operates on distributed data. See Zhicheng Du, Yuantao Hao (2022) 
    <doi:10.32614/CRAN.package.stddiff> for reference.
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.