Functions to generate K-fold cross validation (CV) folds
    and CV test error estimates that take into account
    how a survey dataset's sampling design was constructed
    (SRS, clustering, stratification, and/or unequal sampling weights).
    You can input linear and logistic regression models, along with data and a 
    type of survey design in order to get an output that can help you determine
    which model best fits the data using K-fold cross validation.
    Our paper on "K-Fold Cross-Validation for Complex Sample Surveys"
    by Wieczorek, Guerin, and McMahon (2022)
    <doi:10.1002/sta4.454>
    explains why differing how we take folds based on survey design is useful.
Data obtained from surveys contains information not only about the
    survey responses, but also the survey metadata, e.g. the original survey
    questions and the answer options. The 'surveydata' package makes it easy to
    keep track of this metadata, and to easily extract columns with
    specific questions.
This tool is designed to analyze up to 5 Fraud Detection Questions integrated into a survey, focusing on potential fraudulent participants to clean the survey dataset from potential fraud. Fraud Detection Questions and further information available at <https://surveydefense.org>.
Visualize and tabulate single-choice, multiple-choice, matrix-style questions from survey data. 
    Includes ability to group cross-tabulations, frequency distributions, and plots by categorical variables and 
    to integrate survey weights. Ideal for quickly uncovering descriptive patterns in survey data.
A tool for computing network representations of attitudes,
  extracted from tabular data such as sociological surveys. Development of
  surveygraph software and training materials was initially funded by the
  European Union under the ERC Proof-of-concept programme (ERC,
  Attitude-Maps-4-All, project number: 101069264). Views and opinions expressed
  are however those of the author(s) only and do not necessarily reflect those
  of the European Union or the European Research Council Executive Agency.
  Neither the European Union nor the granting authority can be held responsible
  for them.
Tools for sample survey planning, including sample size calculation, estimation of expected precision for the estimates of totals, and calculation of optimal sample size allocation.
Provides a pipeline to perform small area estimation and prevalence mapping of binary indicators using health and demographic survey data, described in Fuglstad et al. (2022) <doi:10.48550/arXiv.2110.09576> and Wakefield et al. (2020) <doi:10.1111/insr.12400>.
Calculate point estimates and their standard errors in complex household surveys using bootstrap replicates. Bootstrapping considers survey design with a rotating panel. A comprehensive description of the methodology can be found under <https://statistikat.github.io/surveysd/articles/methodology.html>.
Short and understandable commands that generate tabulated, 
    formatted, and rounded survey estimates. Mostly a wrapper for the 
    'survey' package (Lumley (2004) <doi:10.18637/jss.v009.i08> 
    <https://CRAN.R-project.org/package=survey>) that identifies 
    low-precision estimates using the National Center for Health 
    Statistics (NCHS) presentation standards (Parker et al. (2017) 
    <https://www.cdc.gov/nchs/data/series/sr_02/sr02_175.pdf>,
    Parker et al. (2023) <doi:10.15620/cdc:124368>).
Decision support tool for prioritizing sites for ecological
    surveys based on their potential to improve plans for conserving
    biodiversity (e.g. plans for establishing protected areas). Given a set of
    sites that could potentially be acquired for conservation management,
    it can be used to generate and evaluate plans for surveying additional
    sites. Specifically, plans for ecological surveys can be
    generated using various conventional approaches (e.g. maximizing expected
    species richness, geographic coverage, diversity of sampled environmental
    algorithms. After generating such survey plans, they can be evaluated using
    conditions) and maximizing value of information. Please note that several
    functions depend on the 'Gurobi' optimization software (available from
    <https://www.gurobi.com>). Additionally, the 'JAGS' software (available from
    <https://mcmc-jags.sourceforge.io/>) is required to fit hierarchical
    generalized linear models. For further details, see Hanson et al. (2022) <doi:10.1111/1365-2664.14309>.
Contains a suite of functions for survival analysis in health economics.
    These can be used to run survival models under a frequentist (based on maximum likelihood)
    or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian
    Monte Carlo). To run the Bayesian models, the user needs to install additional modules 
    (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed using 
    'remotes::install_github' from their GitHub repositories: 
    (<https://github.com/giabaio/survHEhmc> and <https://github.com/giabaio/survHEinla/> 
    respectively). 'survHEinla' is based on the package INLA, which is available for download at
    <https://inla.r-inla-download.org/R/stable/>. The user can specify a set of parametric models 
    using a common notation and select the preferred mode of inference. The results can also be 
    post-processed to produce probabilistic sensitivity analysis and can be used to export the 
    output to an Excel file (e.g. for a Markov model, as often done by modellers and 
    practitioners). <doi:10.18637/jss.v095.i14>.
High dimensional time to events data analysis with variable selection technique.
             Currently support LASSO, clustering and Bonferroni's correction.
Performs inference for a class of measures to compare competing risk prediction models with censored survival data. The class includes the integrated discrimination improvement index (IDI) and category-less net reclassification index (NRI).
Newly developed methods for the estimation of several probabilities
      in an illness-death model. The package can be used to obtain nonparametric and 
      semiparametric estimates for: transition probabilities, occupation probabilities, 
      cumulative incidence function and the sojourn time distributions. 
      Additionally, it is possible to fit proportional hazards regression models
      in each transition of the Illness-Death Model. Several auxiliary 
      functions are also provided which can be used for marginal 
      estimation of the survival functions.
High dimensional survival data analysis with Markov Chain Monte Carlo(MCMC). 
              Currently supports frailty data analysis. Allows for Weibull and 
              Exponential distribution. Includes function for interval censored data.
Contains the core survival analysis routines, including
	     definition of Surv objects, 
	     Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
	     and parametric accelerated failure time models.
Implementation of methodology designed to perform: (i) variable 
    selection, (ii) effect estimation, and (iii) uncertainty quantification, 
    for high-dimensional survival data. Our method uses a spike-and-slab prior 
    with Laplace slab and Dirac spike and approximates the corresponding 
    posterior using variational inference, a popular method in machine learning 
    for scalable conditional inference. Although approximate, the variational 
    posterior provides excellent point estimates and good control of the false 
    discovery rate. For more information see Komodromos et al. (2021) 
    <arXiv:2112.10270>.
A high-level interface to perform survival analysis, 
    including Kaplan-Meier analysis and log-rank tests and Cox regression.
    Aims at providing a clear and elegant syntax, support for use in a pipeline, structured output and plotting.
    Builds upon the 'survminer' package for Kaplan-Meier plots and provides
    a customizable implementation for forest plots.
    Kaplan & Meier (1958) <doi:10.1080/01621459.1958.10501452>
    Cox (1972) <JSTOR:2985181>
    Peto & Peto (1972) <JSTOR:2344317>.
An outcome-guided algorithm is developed to identify clusters of samples with similar characteristics and survival rate. The algorithm first builds a random forest and then defines distances between samples based on the fitted random forest. Given the distances, we can apply hierarchical clustering algorithms to define clusters. Details about this method is described in <https://github.com/luyouepiusf/SurvivalClusteringTree>.
Implementations of classical and machine learning models for survival analysis, including deep neural networks via 'keras' and 'tensorflow'. Each model includes a separated fit and predict interface with consistent prediction types for predicting risk or survival probabilities. Models are either implemented from 'Python' via 'reticulate' <https://CRAN.R-project.org/package=reticulate>, from code in GitHub packages, or novel implementations using 'Rcpp' <https://CRAN.R-project.org/package=Rcpp>. Neural networks are implemented from the 'Python' package 'pycox' <https://github.com/havakv/pycox>.
Estimate the regression coefficients and the baseline hazard 
      of proportional hazard Cox models with left, right or interval censored survival data 
      using maximum penalised likelihood. A 'non-parametric' smooth estimate of the baseline hazard
      function is provided.
Fitting Cox proportional hazard model under dependent right censoring using copula and maximum penalised likelihood methods.
Several functions and S3 methods to predict survival by using neural networks. We implemented Partial Logistic Artificial Neural Networks (PLANN) as proposed by Biganzoli et al. (1998)  <https://pubmed.ncbi.nlm.nih.gov/9618776>.
Provides estimates for the bivariate and trivariate distribution 
             functions and bivariate and trivariate survival functions for 
             censored gap times. Two approaches, using existing methodologies, 
             are considered: (i) the Lin's estimator, which is based on the 
             extension the Kaplan-Meier estimator of the distribution function 
             for the first event time and the Inverse Probability of Censoring 
             Weights for the second time (Lin DY, Sun W, Ying Z (1999) 
             <doi:10.1093/biomet/86.1.59> and (ii) another estimator
             based on Kaplan-Meier weights (Una-Alvarez J, Meira-Machado L (2008)
             <https://w3.math.uminho.pt/~lmachado/Biometria_conference.pdf>). 
             The proposed methods are the landmark estimators based on 
             subsampling approach, and the estimator based on weighted cumulative
             hazard estimator. The package also provides nonparametric estimator
             conditional to a given continuous covariate. All these methods have
             been submitted to be published.
Compute time-dependent ROC curve from censored survival
        data using Kaplan-Meier (KM) or Nearest Neighbor Estimation
        (NNE) method of Heagerty, Lumley & Pepe (Biometrics, Vol 56 No
        2, 2000, PP 337-344).
Several functions and S3 methods to construct a super learner in the presence of censored times-to-event and to evaluate its prognostic capacities.
Performs support vectors analysis for data sets with survival
    outcome. Three approaches are available in the package: The regression approach
    takes censoring into account when formulating the inequality constraints of
    the support vector problem. In the ranking approach, the inequality constraints
    set the objective to maximize the concordance index for comparable pairs
    of observations. The hybrid approach combines the regression and ranking
    constraints in the same model.
Performs survival analysis for one-way layout. The package includes the generalized test for survival ANOVA (Tsui and Weerahandi (1989) <doi:10.2307/2289949> and (Weerahandi, 2004; ISBN:978-0471470175)). It also performs pairwise comparisons and graphical approaches. Moreover, it assesses the weibullness of data in each group via test. The package computes mean and confidence interval under Weibull distribution.
Vignettes for the 'survival' package. Split from the 'survival' 
    package since the vignettes were getting large. Also, since 'survival' is a 
    recommended package it cannot make use of other packages outside of 
    base+recommended (e.g. 'rmarkdown').
Several datasets which detail the results and events of each season of Survivor. This includes 
  details on the cast, voting history, immunity and reward challenges, jury votes and viewers. This data is 
  useful for practicing data wrangling, graph analytics and analysing how each season of Survivor played out. 
  Includes 'ggplot2' scales and colour palettes for visualisation.
Provides kernel weighting methods for estimation of proportional 
    hazards models with intermittently observed longitudinal covariates. 
    Cao H., Churpek M. M., Zeng D., and Fine J. P. (2015) 
    <doi:10.1080/01621459.2014.957289>.
An implementation of popular evaluation metrics that are commonly used in survival prediction 
  including Concordance Index, Brier Score, Integrated Brier Score, 
  Integrated Square Error, Integrated Absolute Error and Mean Absolute Error.
  For a detailed information, see (Ishwaran H, Kogalur UB, Blackstone EH and Lauer MS (2008) <doi:10.1214/08-AOAS169>) and
  (Moradian H, Larocque D and Bellavance F (2017) <doi:10.1007/s10985-016-9372-1>) for different evaluation metrics.
In clinical trials, endpoints are sometimes evaluated with uncertainty. Adjudication is commonly adopted to ensure the study integrity. We propose to use multiple imputation (MI) introduced by Robin (1987) <doi:10.1002/9780470316696> to incorporate these uncertainties if reasonable event probabilities were provided. The method has been applied to Cox Proportional Hazard (PH) model, Kaplan-Meier (KM) estimation and Log-rank test in this package. Moreover, weighted estimations discussed in Cook (2004) <doi:10.1016/S0197-2456(00)00053-2> were also implemented with weights calculated from event probabilities. In conclusion, this package can handle time-to-event analysis if events presented with uncertainty by different methods.
Contains the function 'ggsurvplot()' for drawing easily beautiful
    and 'ready-to-publish' survival curves with the 'number at risk' table
    and 'censoring count plot'. Other functions are also available to plot 
    adjusted curves for `Cox` model and to visually examine 'Cox' model assumptions.
A collection of functions to help in the analysis of
    right-censored survival data. These extend the methods available in
    package:survival.
Sample size and effect size calculations for survival endpoints based on mixture survival-by-response model. The methods implemented can be found in  Bofill, Shen & Gómez (2021) <arXiv:2008.12887>.
Tools for flexible estimation of conditional survival 
              functions using off-the-shelf machine learning tools. Implements both 
              global and local survival stacking. See Wolock CJ, Gilbert PB,
              Simon N, and Carone M (2024) <doi:10.1080/10618600.2024.2304070>.
Generate objects that simulate survival times. Random values for the distributions are generated using the method described by Bender (2003) <https://epub.ub.uni-muenchen.de/id/eprint/1716> and Leemis (1987) in Operations Research, 35(6), 892–894.
Simple and quick method of exporting the most often used
    survival analysis results to an Excel sheet.
Perform survival simulation with parametric survival model generated from 'survreg' function in 'survival' package.
    In each simulation coefficients are resampled from variance-covariance matrix of parameter estimates to 
    capture uncertainty in model parameters.
    Prediction intervals of Kaplan-Meier estimates and hazard ratio of treatment effect can be further calculated using simulated survival data.
Fits hazard and excess hazard models with multidimensional penalized splines allowing for 
        time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for 
        multidimensional penalized hazard and excess hazard models. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics. 
	See Fauvernier et al. (2019a) <doi:10.21105/joss.01434> for an overview of the package and Fauvernier et al. (2019b) <doi:10.1111/rssc.12368> for the method.
Presmoothed estimators of survival, density, cumulative and non-cumulative hazard functions with right-censored survival data. For details, see Lopez-de-Ullibarri and Jacome (2013) <doi:10.18637/jss.v054.i11>.
The function SurvRegCens() of this package allows estimation of a Weibull Regression for a right-censored endpoint, one interval-censored covariate, and an arbitrary number of non-censored covariates. Additional functions allow to switch between different parametrizations of Weibull regression used by different R functions, inference for the mean difference of two arbitrarily censored Normal samples, and estimation of canonical parameters from censored samples for several distributional assumptions. Hubeaux, S. and Rufibach, K. (2014) <arXiv:1402.0432>.
Performs two-sample comparisons using the restricted mean survival time (RMST) as a summary measure of the survival time distribution. Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed. It performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures.
Estimates the restricted mean survival time (RMST) with the time window [0, tau], where tau is adaptively selected from the procedure, proposed by Horiguchi et al. (2018) <doi:10.1002/sim.7661>. It also estimates the RMST with the time window [tau1, tau2], where tau1 is adaptively selected from the procedure, proposed by Horiguchi et al. (2023) <doi:10.1002/sim.9662>.
Performs the permutation test using difference in the restricted mean survival time (RMST) between groups as a summary measure of the survival time distribution. When the sample size is less than 50 per group, it has been shown that there is non-negligible inflation of the type I error rate in the commonly used asymptotic test for the RMST comparison. Generally, permutation tests can be useful in such a situation. However, when we apply the permutation test for the RMST comparison, particularly in small sample situations, there are some cases where the survival function in either group cannot be defined due to censoring in the permutation process. Horiguchi and Uno (2020) <doi:10.1002/sim.8565> have examined six workable solutions to handle this numerical issue. It performs permutation tests with implementation of the six methods outlined in the paper when the numerical issue arises during the permutation process. The result of the asymptotic test is also provided for a reference.
Incorporate various statistics and layout customization options to enhance the efficiency and adaptability of the Kaplan-Meier plots.
Performs a dual-parameter sensitivity analysis of treatment effect to unmeasured confounding in observational studies with either survival or competing risks outcomes. Huang, R., Xu, R. and Dulai, P.S.(2020) <doi:10.1002/sim.8672>.
Simulation of simple and complex survival data including recurrent and multiple events and competing risks. See Moriña D, Navarro A. (2014) <doi:10.18637/jss.v059.i02> and Moriña D, Navarro A. (2017) <doi:10.1080/03610918.2016.1175621>.
Conduct asymptotic and empirical power and sample size calculations for Single-Nucleotide Polymorphism (SNP) association studies with right censored time to event outcomes.
Survival analysis with sparse longitudinal covariates under right censoring scheme. Different hazards models are involved. Please cite the manuscripts corresponding to this package:  Sun, Z. et al. (2022) <doi:10.1007/s10985-022-09548-6>, Sun, Z. and Cao, H. (2023)  <arXiv:2310.15877> and Sun, D. et al. (2023) <arXiv:2308.15549>.
Nonparametric estimation of Spearman's rank correlation with bivariate survival (right-censored) data as described in Eden, S.K., Li, C., Shepherd B.E. (2021), Nonparametric Estimation of Spearman's Rank Correlation with Bivariate Survival Data, Biometrics (under revision). The package also provides functions that visualize bivariate survival data and bivariate probability mass function.
Parametric survival regression models under the maximum likelihood approach via 'Stan'. Implemented regression models include accelerated failure time models, proportional hazards models, proportional odds models, accelerated hazard models, Yang and Prentice models, and extended hazard models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue (Birnbaum-Saunders) distributions. References: Lawless (2002) <ISBN:9780471372158>; Bennett (1982) <doi:10.1002/sim.4780020223>; Chen and Wang(2000) <doi:10.1080/01621459.2000.10474236>; Demarqui and Mayrink (2021) <doi:10.1214/20-BJPS471>.
Package performs Cox regression and survival distribution function estimation when the survival times are subject to double truncation. In case that the survival and truncation times are quasi-independent, the estimation procedure for each method involves inverse probability weighting, where the weights correspond to the inverse of the selection probabilities and are estimated using the survival times and truncation times only. A test for checking this independence assumption is also included in this package. The functions available in this package for Cox regression, survival distribution function estimation, and testing independence under double truncation are based on the following methods, respectively: Rennert and Xie (2018) <doi:10.1111/biom.12809>, Shen (2010) <doi:10.1007/s10463-008-0192-2>, Martin and Betensky (2005) <doi:10.1198/016214504000001538>. When the survival times are dependent on at least one of the truncation times, an EM algorithm is employed to obtain point estimates for the regression coefficients. The standard errors are calculated using the bootstrap method. See Rennert and Xie (2022) <doi:10.1111/biom.13451>. Both the independent and dependent cases assume no censoring is present in the data. Please contact Lior Rennert <liorr@clemson.edu> for questions regarding function coxDT and Yidan Shi <yidan.shi@pennmedicine.upenn.edu> for questions regarding function coxDTdep.
Implements methods for variable selection in linear
    regression based on the "Sum of Single Effects" (SuSiE) model, as
    described in Wang et al (2020) <DOI:10.1101/501114> and Zou et al
    (2021) <DOI:10.1101/2021.11.03.467167>. These methods provide
    simple summaries, called "Credible Sets", for accurately
    quantifying uncertainty in which variables should be selected.
    The methods are motivated by genetic fine-mapping applications,
    and are particularly well-suited to settings where variables are
    highly correlated and detectable effects are sparse. The fitting
    algorithm, a Bayesian analogue of stepwise selection methods
    called "Iterative Bayesian Stepwise Selection" (IBSS), is simple
    and fast, allowing the SuSiE model be fit to large data sets
    (thousands of samples and hundreds of thousands of variables).
Provides a complete suite of tools for interacting
    with the Survey Solutions 'GraphQL' API
    <https://demo.mysurvey.solutions/graphql/>. This package encompasses
    all currently available queries and mutations, including the latest
    features for map uploads. It is built on the modern 'httr2' package,
    offering a streamlined and efficient interface without relying on
    external 'GraphQL' client packages. In addition to core API
    functionalities, the package includes a range of helper functions
    designed to facilitate the use of available query filters.
Computes synchrony as windowed cross-correlation based on two-dimensional time series in a text file you can upload. 'SUSY' works as described in Tschacher & Meier (2020) <doi:10.1080/10503307.2019.1612114>.
Implements the s-values proposed by Ed. Leamer.
    It provides a context-minimal approach for sensitivity analysis using extreme
    bounds to assess the sturdiness of regression coefficients.
Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. 
             Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural 
             impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>),  patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>),
             independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), 
             smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).
R bindings to SVD and eigensolvers (PROPACK, nuTRLan).
Quickly construct standard dialog boxes for your GUI, including 
  message boxes, input boxes, list, file or directory selection, ... In case R
  cannot display GUI dialog boxes, a simpler command line version of these
  interactive elements is also provided as fallback solution.
Reimplementation of the 'svDialogs' dialog boxes in Tcl/Tk.
Implements the discrete nonlinear filter (DNF) of Kitagawa (1987) <doi:10.1080/01621459.1987.10478534> to a wide class of stochastic volatility (SV) models with return and volatility jumps following the work of Bégin and Boudreault (2021) <doi:10.1080/10618600.2020.1840995>. Offers several built-in SV models and a flexible framework for users to create customized models by specifying drift and diffusion functions along with a jump arrival distribution for the return and volatility dynamics. Allows for the estimation of factor models with stochastic volatility (e.g., heteroskedastic volatility CAPM) by incorporating expected return predictors. `Includes functions to compute likelihood evaluations, filtering and prediction distribution estimates, maximum likelihood parameter estimates, to simulate data from built-in and custom SV models with jumps, and to forecast future returns and volatility values using Monte Carlo simulation from a given SV model.
Obtain parameters of Svensson's Method, including percentage agreement, 
    systematic change and individual change. Also, the contingency table can be generated. 
    Svensson's Method is a rank-invariant nonparametric method for the analysis of ordered scales 
    which measures the level of change both from systematic and individual aspects. For the details, 
    please refer to Svensson E. Analysis of systematic and random differences between paired ordinal 
    categorical data [dissertation]. Stockholm: Almqvist & Wiksell International; 1993.
A graphics device for R that produces 'Scalable Vector
    Graphics'. 'svglite' is a fork of the older 'RSvgDevice' package.
This 'htmlwidget' provides pan and zoom interactivity to R
    graphics, including 'base', 'lattice', and 'ggplot2'. The interactivity is
    provided through the 'svg-pan-zoom.js' library. Various options to the widget
    can tailor the pan and zoom experience to nearly any user desire.
The purpose of this package is to manipulate SVG files that are templates of charts the user wants to produce. 
    In vector graphics one copes with x-/y-coordinates of elements (e.g. lines, rectangles, text). Their scale is often dependent on the program that is used to produce the graphics. 
    In applied statistics one usually has numeric values on a fixed scale (e.g. percentage values between 0 and 100) to show in a chart. 
    Basically, 'svgtools' transforms the statistical values into coordinates and widths/heights of the vector graphics.
    This is done by stackedBar() for bar charts, by linesSymbols() for charts with lines and/or symbols (dot markers) and scatterSymbols() for scatterplots.
The 'SciViews' 'svGUI' package eases the management of Graphical
  User Interfaces (GUI) in R. It is independent from any particular GUI widgets
  ('Tk', 'Gtk2', native, ...). It centralizes info about GUI elements currently
  used, and it dispatches GUI calls to the particular toolkits in use in
  function of the context (is R run at the terminal, within a 'Tk' application,
  a HTML page?).
Creates 3D animated, interactive visualizations that can be viewed in a web browser.
A simple HTTP server allows to connect GUI clients to R.
Function for the GUI API to interact with external IDE/code editors.
Provides functionality to fit and simulate from stationary vine 
  copula models for time series, see Nagler et al. (2022) 
  <doi:10.1016/j.jeconom.2021.11.015>.
R-side code to implement an R editor and IDE in Komodo IDE
  with the SciViews-K extension.
In practice, it is difficult to determine the number of decomposition modes, K, for Variational Mode Decomposition (VMD). To overcome this issue, this study offers Spearman Variational Mode Decomposition (SVMD), a method that uses the Spearman correlation coefficient to calculate the ideal mode number. Unlike the Pearson correlation coefficient, which only returns a perfect value when X and Y are linearly connected, the Spearman correlation can be calculated without knowing the probability distributions of X and Y. The Spearman correlation coefficient, also called Spearman's rank correlation coefficient, is a subset of a wider correlation coefficient. As VMD decomposes a signal, the Spearman correlation coefficient between the reconstructed and original sequences rises as the mode number K increases. Once the signal has been fully decomposed, subsequent increases in K cause the correlation to gradually level off. When the correlation reaches a specific level, VMD is said to have adequately decomposed the signal. Numerous experiments revealed that a threshold of 0.997 produces the best denoising effect, so the threshold is set at 0.997. This package has been developed using concept of Yang et al. (2021)<doi:10.1016/j.aej.2021.01.055>.
Miscellaneous functions for 'SciViews' or general use: manage a
  temporary environment attached to the search path for temporary variables you
  do not want to save() or load(), test if 'Aqua', 'Mac', 'Win', ... Show
  progress bar, etc.
Implements the SVM-Maj algorithm to train data with support vector machine
  <doi:10.1007/s11634-008-0020-9>.
  This algorithm uses two efficient updates, one for linear kernel and one 
  for the nonlinear kernel.
Computes the entire regularization path for the two-class svm classifier
		with essentially the same cost as a single SVM fit.
Determines networks of significant synchronization between the discrete states of nodes; see Tumminello et al <doi:10.1371/journal.pone.0017994>.
Two versions of sample variance plots, Sv-plot1 and Sv-plot2, will be provided illustrating 
             the squared deviations from sample variance. Besides indicating the contribution of squared 
             deviations for the sample variability, these plots are capable of detecting characteristics of the
             distribution such as symmetry, skewness and outliers. A remarkable graphical method based on 
             Sv-plot2 can determine the decision on testing hypotheses over one or two population means. 
             In sum, Sv-plots will be appealing visualization tools. Complete description of this methodology  
             can be found in the article, Wijesuriya (2020) <doi:10.1080/03610918.2020.1851716>.
Provides tools for creating and working with survey replicate weights,
  extending functionality of the 'survey' package from Lumley (2004) <doi:10.18637/jss.v009.i08>.
  Implements bootstrap methods for complex surveys, including the generalized survey bootstrap
  as described by Beaumont and Patak (2012) <doi:10.1111/j.1751-5823.2011.00166.x>.
  Methods are provided for applying nonresponse adjustments to
  both full-sample and replicate weights as described by 
  Rust and Rao (1996) <doi:10.1177/096228029600500305>.
  Implements methods for sample-based calibration described by Opsomer and Erciulescu (2021) 
  <https://www150.statcan.gc.ca/n1/pub/12-001-x/2021002/article/00006-eng.htm>.
  Diagnostic functions are included to compare weights and weighted estimates
  from different sets of replicate weights.
Computes the entire solution paths for Support Vector Regression(SVR) with respect to the regularization parameter, lambda and epsilon in epsilon-intensive loss function, efficiently. We call each path algorithm svrpath and epspath. See Wang, G. et al (2008) <doi:10.1109/TNN.2008.2002077> for details regarding the method.
Various tools for semantic vector spaces, such as
    correspondence analysis (simple, multiple and discriminant), latent
    semantic analysis, probabilistic latent semantic analysis, non-negative
    matrix factorization, latent class analysis, EM clustering, logratio
	analysis and log-multiplicative (association) analysis. Furthermore,
    there are specialized distance measures, plotting functions and some helper
    functions.
A socket server allows to connect clients to R.
Functions to enumerate and reference figures, tables and equations
  in R Markdown documents that do not support these features (thus not
  'bookdown' or 'quarto'. Supporting functions for using 'Sweave' and 'Knitr'
  with 'LyX'.
Set of tools aimed at wrapping some of the functionalities of the
  packages tools, utils and codetools into a nicer format so that an IDE can use
  them.
A complete unit test system and functions to implement its GUI part.
High level management of widgets, windows and other graphical resources.
Estimates the population average controlled difference for a given outcome between levels of a binary treatment, exposure, or other group membership variable of interest for clustered, stratified survey samples where sample selection depends on the comparison group. Provides three methods for estimation, namely outcome modeling and two factorizations of inverse probability weighting. Under stronger assumptions, these methods estimate the causal population average treatment effect. Salerno et al., (2024) <doi:10.48550/arXiv.2406.19597>.
Diagnostics for fixed effects linear regression models fitted with survey data. Extensions of standard diagnostics to complex survey data are included: standardized residuals, leverages, Cook's D, dfbetas, dffits, condition indexes, and variance inflation factors as found in Li and Valliant (Surv. Meth., 2009, 35(1), pp. 15-24; Jnl. of Off. Stat., 2011, 27(1), pp. 99-119; Jnl. of Off. Stat., 2015, 31(1), pp. 61-75); Liao and Valliant (Surv. Meth., 2012, 38(1), pp. 53-62; Surv. Meth., 2012, 38(2), pp. 189-202).  Variance inflation factors are also computed for some general linear models (binomial, gaussian, poisson, quasibinomial, and quasipoisson) as described in Liao (U. Maryland thesis, 2010).
Linear mixed models for complex survey data, by pairwise composite likelihood, as described in Lumley & Huang (2023) <arXiv:2311.13048>. Supports nested and crossed random effects, and correlated random effects as in genetic models.  Allows for multistage sampling and for other designs where pairwise sampling probabilities are specified or can be calculated.
Builds, evaluates and validates a nomogram with survey data
    and right-censored outcomes. As described in Capanu (2015)
    <doi:10.18637/jss.v064.c01>, the package contains functions to create
    the nomogram, validate it using bootstrap, as well as produce the
    calibration plots.
Provides inference based on the survey package for the wide range of parametric models in the 'VGAM' package.
Quickly and flexibly calculates weights for survey data, in order to correct
    for survey non-response or other sampling issues. Uses rake weighting, a common technique 
    also know as rim weighting or iterative proportional fitting.  This technique allows for weighting 
    on multiple variables, even when the interlocked distribution of the two
    variables is not known. Interacts with Thomas Lumley's 'survey' package, as described in 
    Lumley, Thomas (2011, ISBN:978-1-118-21093-2). Adds additional functionality, more adaptable syntax, and error-checking
    to the base weighting functionality in 'survey.'
This algorithm conducts variable selection in the classification setting. It repeatedly subsamples variables and runs linear discriminant analysis (LDA) on the subsampled variables. Variables are scored based on the AUC and the t-statistics. Variables then enter a competition and the semi-finalist variables will be evaluated in a final round of LDA classification. The algorithm then outputs a list of variable selected. Qiao, Sun and Fan (2017) <http://people.math.binghamton.edu/qiao/swa.html>.
An algorithm that trains a meta-learning procedure that combines 
    screening and wrapper methods to find a set of extremely low-dimensional attribute 
    combinations. This package works on top of the 'caret' package and proceeds in a 
    forward-step manner. More specifically, it builds and tests learners starting 
    from very few attributes until it includes a maximal number of attributes by 
    increasing the number of attributes at each step. Hence, for each fixed number
    of attributes, the algorithm tests various (randomly selected) learners and 
    picks those with the best performance in terms of training error. Throughout,
    the algorithm uses the information coming from the best learners at the previous
    step to build and test learners in the following step. In the end, it outputs
    a set of strong low-dimensional learners.
A collection of 'HTML', 'JavaScript', and 'CSS' assets that
  dynamically generate beautiful documentation from a 'Swagger' compliant API:
  <https://swagger.io/specification/>.
Collection of functions to connect the structure of the data with the information on the samples. Three types of associations are covered: 1. linear model of principal components. 2. hierarchical clustering analysis. 3. distribution of features-sample annotation associations. Additionally, the inter-relation between sample annotations can be analyzed. Simple methods are provided for the correction of batch effects and removal of principal components.
Obtaining accurate and stable estimates of regression coefficients
    can be challenging when the suggested statistical model has issues related to
    multicollinearity, convergence, or overfitting. One solution is to use
    principal component analysis (PCA) results in the regression, as discussed in
    Chan and Park (2005) <doi:10.1080/01446190500039812>. The swaprinc() package
    streamlines comparisons between a raw regression model with the full set of
    raw independent variables and a principal component regression model where
    principal components are estimated on a subset of the independent variables,
    then swapped into the regression model in place of those variables. The
    swaprinc() function compares one raw regression model to one principal
    component regression model, while the compswap() function compares one raw
    regression model to many principal component regression models. Package
    functions include parameters to center, scale, and undo centering and scaling,
    as described by Harvey and Hansen (2022)
    <https://cran.r-project.org/package=LearnPCA/vignettes/Vig_03_Step_By_Step_PCA.pdf>.
    Additionally, the package supports using Gifi methods to extract principal
    components from categorical variables, as outlined by Rossiter (2021)
    <https://www.css.cornell.edu/faculty/dgr2/_static/files/R_html/NonlinearPCA.html#2_Package>.
Function library for processing collective movement data (e.g. fish
    schools, ungulate herds, baboon troops) collected from GPS trackers or 
    computer vision tracking software.
Provides a pipeline for the comparative analysis of 
  collective movement data (e.g. fish schools, bird flocks, baboon troops) by 
  processing 2-dimensional positional data (x,y,t) from GPS trackers or computer
  vision tracking systems, discretizing events of collective motion, calculating
  a set of established metrics that characterize each event, and placing the
  events in a multi-dimensional swarm space constructed from these metrics.
  The swarm space concept, the metrics and data sets included are described in:
  Papadopoulou Marina, Furtbauer Ines, O'Bryan Lisa R., Garnier Simon, 
  Georgopoulou Dimitra G., Bracken Anna M., Christensen Charlotte and King
  Andrew J. (2023) <doi:10.1098/rstb.2022.0068>.
There are numerous places to create and download color
    palettes. These are usually shared in 'Adobe' swatch file formats of
    some kind. There is also often the need to use standard palettes
    developed within an organization to ensure that aesthetics are carried over
    into all projects and output. Now there is a way to read these swatch
    files in R and avoid transcribing or converting color values by hand or
    or with other programs. This package provides functions to read and
    inspect 'Adobe Color' ('ACO'), 'Adobe Swatch Exchange' ('ASE'), 'GIMP Palette' ('GPL'),
    'OpenOffice' palette ('SOC') files and 'KDE Palette' ('colors') files. Detailed
    descriptions of 'Adobe Color' and 'Swatch Exchange' file formats as well as other
    swatch file formats can be found at
    <http://www.selapa.net/swatches/colors/fileformats.php>.
A set of tools for examining the design and analysis aspects of stepped wedge cluster randomized trials (SW CRT) based on a repeated cross-sectional sampling scheme (Hussey MA and Hughes JP (2007) Contemporary Clinical Trials 28:182-191. <doi:10.1016/j.cct.2006.05.007>).
Implements the Sliding Window Discrete Fourier Transform (SWDFT). Also provides 
             statistical methods based on the SWDFT, and graphical tools to display the outputs.
To meet the needs of statistical power calculation for stepped wedge cluster randomized trials, we developed this software. Different parameters can be specified by users for different scenarios, including: cross-sectional and cohort designs, binary and continuous outcomes, marginal (GEE) and conditional models (mixed effects model), three link functions (identity, log, logit links), with and without time effects under exchangeable, nested exchangeable and block exchangeable correlation structures. Unequal numbers of clusters per sequence are also allowed. The methods included in this package: Zhou et al. (2020) <doi:10.1093/biostatistics/kxy031>, Li et al. (2018) <doi:10.1111/biom.12918>. Supplementary documents can be found at: <https://ysph.yale.edu/cmips/research/software/swdpwr/>. The Shiny app for swdpwr can be accessed at: <https://jiachenchen322.shinyapps.io/swdpwr_shinyapp/>. The package also includes functions that perform calculations for the intra-cluster correlation coefficients based on the random effects variances as input variables for continuous and binary outcomes, respectively.
Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), <doi:10.1126/science.aal4230>, Relative Norm Distance (Garg et al., 2018), <doi:10.1073/pnas.1720347115>, Mean Average Cosine Similarity (Mazini et al., 2019) <arXiv:1904.04047>, SemAxis (An et al., 2018) <arXiv:1806.05521>, Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) <doi:10.18653/v1/P19-1162>, and Embedding Coherence Test (Dev & Phillips, 2019) <arXiv:1901.07656>.
Tidies up the forecasting modeling and prediction work flow, 
    extends the 'broom' package 
    with 'sw_tidy', 'sw_glance', 'sw_augment', and 'sw_tidy_decomp' functions 
    for various forecasting models,
    and enables converting 'forecast' objects to 
    "tidy" data frames with 'sw_sweep'.
Selective sweep is a biological phenomenon in which genetic variation between neighboring beneficial mutant alleles is swept away due to the effect of genetic hitchhiking. Detection of selective sweep is not well acquainted as well as it is a laborious job. This package is a user friendly approach for detecting selective sweep in genomic regions. It uses a Random Forest based machine learning approach to predict selective sweep from VCF files as an input. Input of this function, train data and new data, can be computed using the project <https://github.com/AbhikSarkar1999/SweepDiscovery> in 'GitHub'. This package has been developed by using the concept of  Pavlidis and Alachiotis (2017) <doi:10.1186/s40709-017-0064-0>.
Structural handling of identity numbers used in the Swedish
    administration such as personal identity numbers ('personnummer') and
    organizational identity numbers ('organisationsnummer').
The Swiss Ephemeris (version 2.10.03) is a high precision ephemeris based upon the
    DE431 ephemerides from NASA's JPL. It covers the time range 13201 BCE to
    17191 CE. This package uses the semi-analytic theory by Steve Moshier.
    For faster and more accurate calculations, the compressed Swiss Ephemeris
    data is available in the 'swephRdata' package. To access this data package,
    run 'install.packages("swephRdata", repos = "https://rstub.r-universe.dev",
    type = "source")'. The size of the 'swephRdata' package is approximately
    115 MB. The user can also use the original JPL DE431 data.
Process and summarize aerial survey 'DAS' data (AirDAS) 
    <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-185.PDF>
    collected using an aerial survey program from the 
    Southwest Fisheries Science Center (SWFSC) 
    <https://www.fisheries.noaa.gov/west-coast/science-data/california-current-marine-mammal-assessment-program>.
    PDF files detailing the relevant AirDAS data formats are included in this package.
Process and summarize DAS data files. 
    These files are typically, but do not have to be 
    DAS <https://swfsc-publications.fisheries.noaa.gov/publications/TM/SWFSC/NOAA-TM-NMFS-SWFSC-305.PDF> data
    produced by the Southwest Fisheries Science Center (SWFSC) program 'WinCruz'.
    This package standardizes and streamlines basic DAS data processing,
    and includes a PDF with the DAS data format requirements expected by the package.
Collection of conversion, analytical, geodesic, mapping, and
    plotting functions. Used to support packages and code written by
    researchers at the Southwest Fisheries Science Center of the National
    Oceanic and Atmospheric Administration.
Simulation extrapolation and inverse probability weighted generalized estimating equations method for longitudinal data with missing observations and measurement error in covariates. References: Yi, G. Y. (2008) <doi:10.1093/biostatistics/kxm054>; Cook, J. R. and Stefanski, L. A. (1994) <doi:10.1080/01621459.1994.10476871>; Little, R. J. A. and Rubin, D. B. (2002, ISBN:978-0-471-18386-0).
An efficient sensitivity analysis for stochastic models based on 
    Monte Carlo samples. Provides weights on simulated scenarios from a 
    stochastic model, such that stressed random variables fulfil given 
    probabilistic constraints (e.g. specified values for risk measures), 
    under the new scenario weights. Scenario weights are selected by 
    constrained minimisation of the relative entropy to the baseline model. 
    The 'SWIM' package is based on Pesenti S.M., Millossovich P., Tsanakas A. (2019)
    "Reverse Sensitivity Testing: What does it take to break the model" 
    <openaccess.city.ac.uk/id/eprint/18896/> and Pesenti S.M. (2021) 
    "Reverse Sensitivity Analysis for Risk Modelling" <https://www.ssrn.com/abstract=3878879>.
The goal of the 'SwimmeR' package is to provide means of acquiring, and then analyzing, data from swimming (and diving) competitions.  To that end 'SwimmeR' allows results to be read in from .html sources, like 'Hy-Tek' real time results pages, '.pdf' files, 'ISL' results, 'Omega' results, and (on a development basis) '.hy3' files.  Once read in, 'SwimmeR' can convert swimming times (performances) between the computationally useful format of seconds reported to the '100ths' place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community.  'SwimmeR' can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', 'SCM', 'SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data.  This is a developmental package, not yet mature.
Used for creating swimmers plots with functions to customize the bars, add points, add lines, add text, and add arrows.
Create carousels using the 'JavaScript' library 'Swiper' and
    the package 'htmlwidgets'. The carousels can be displayed in the
    'RStudio' viewer pane, in 'Shiny' applications and in 'R markdown'
    documents. The package also provides a 'RStudio' addin allowing to 
    choose image files and to display them in the viewer pane.
Use the R console as an interactive learning
    environment. Users receive immediate feedback as they are guided through
    self-paced lessons in data science and R programming.
A set of tools for writing and sharing interactive courses
    to be used with swirl.
Ozone, NOx (= Sum of nitrogen monoxide and nitrogen dioxide), nitrogen monoxide, ambient temperature, dew point, wind speed and wind direction at 3 sites around lake of Lucerne in Central Switzerland in 30 min time resolution for year 2004.
Retrieves the most important data on parliamentary activities of the Swiss Federal Assembly via 
    an open, machine-readable interface (see <https://ws.parlament.ch/odata.svc/>).
An unsorted collection of visualization widgets rendered in 'Tcl/Tk'<https://www.tcl.tk/> to generate agile dashboards for your iterative simulations. Widgets include progress bars, counters, eavesdroppers, injectors, switches, and sliders for dynamic manipulation and visualization of simulation parameters.
Provides a switch-case construct for 'R', as it is known from other programming languages. It allows to test multiple, similar conditions in an efficient, easy-to-read manner, so nested if-else constructs can be avoided. The switch-case construct is designed as an 'R' function that allows to return values depending on which condition is met and lets the programmer flexibly decide whether or not to leave the switch-case construct after a case block has been executed.
Provides an abstraction for managing, installing,
    and switching between sets of installed R packages. This allows users to
    maintain multiple package libraries simultaneously, e.g. to maintain
    strict, package-version-specific reproducibility of many analyses, or
    work within a development/production release paradigm. Introduces a
    generalized package installation process which supports multiple repository
    and non-repository sources and tracks package provenance.
Estimate the parameters of multivariate endogenous switching and sample selection models using methods described in Newey (2009) <doi:10.1111/j.1368-423X.2008.00263.x>, E. Kossova, B. Potanin (2018) <https://ideas.repec.org/a/ris/apltrx/0346.html>, E. Kossova, L. Kupriianova, B. Potanin (2020) <https://ideas.repec.org/a/ris/apltrx/0391.html> and E. Kossova, B. Potanin (2022) <https://ideas.repec.org/a/ris/apltrx/0455.html>.
Tools for retrieving, organizing, and analyzing environmental
    data from the System Wide Monitoring Program of the National Estuarine
    Research Reserve System <https://cdmo.baruch.sc.edu/>. These tools
    address common challenges associated with continuous time series data
    for environmental decision making.
Tools for performing routine analysis and plotting tasks with environmental
    data from the System Wide Monitoring Program of the National Estuarine
    Research Reserve System <https://cdmo.baruch.sc.edu/>. This package builds
    on the functionality of the 'SWMPr' package <https://cran.r-project.org/package=SWMPr>,
    which is used to retrieve and organize the data. The combined set of tools
    address common challenges associated with continuous time series data
    for environmental decision making, and are intended for use in annual reporting activities.
    References:
    Beck, Marcus W. (2016) <ISSN 2073-4859><https://journal.r-project.org/archive/2016-1/beck.pdf>
    Rudis, Bob (2014) <https://rud.is/b/2014/11/16/moving-the-earth-well-alaska-hawaii-with-r/>.
    United States Environmental Protection Agency (2015) <https://cfpub.epa.gov/si/si_public_record_Report.cfm?Lab=OWOW&dirEntryId=327030>.
Functions to speed up work flow for hydrological analysis. 
    Focused on Australian climate data (SILO climate data), hydrological models (eWater Source) and in particular South Australia (<https://water.data.sa.gov.au> hydrological data).
An English language syllable counter, plus readability score
    measure-er. For readability, we support 'Flesch' Reading Ease and
    'Flesch-Kincaid' Grade Level ('Kincaid' 'et al'. 1975)
    <https://stars.library.ucf.edu/cgi/viewcontent.cgi?article=1055&context=istlibrary>,
    Automated Readability Index ('Senter' and Smith 1967)
    <https://apps.dtic.mil/sti/citations/AD0667273>,
    Simple Measure of Gobbledygook (McLaughlin 1969),
    and 'Coleman-Liau' (Coleman and 'Liau' 1975) <doi:10.1037/h0076540>. The
    package has been carefully optimized and should be very efficient, both in
    terms of run time performance and memory consumption. The main methods are
    'vectorized' by document, and scores for multiple documents are computed in
    parallel via 'OpenMP'.
Implements tidy syllabification of transcription. 
  Based on @kylebgorman's 'python' implementation <https://github.com/kylebgorman/syllabify>.
Collection (syllogi in greek) of real and fictitious data sets for teaching purposes.
	     The datasets were manually entered by the author from the respective references as listed in the individual dataset documentation.
	     The fictions datasets are the creation of the author, that he has found useful for teaching statistics.
Provides the hyphenation algorithm used for 'TeX'/'LaTeX' and similar software, as proposed by Liang (1983, <https://tug.org/docs/liang/>). Mainly contains the
                    function hyphen() to be used for hyphenation/syllable counting of text objects. It was originally developed for and part of the 'koRpus' package, but later
                    released as a separate package so it's lighter to have this particular functionality available for other packages. Support for various languages needs be added
                    on-the-fly or by plugin packages (<https://undocumeantit.github.io/repos/>); this package does not include any language specific data. Due to some restrictions
                    on CRAN, the full package sources are only available from the project homepage. To ask for help, report bugs, request features, or discuss the development of
                    the package, please subscribe to the koRpus-dev mailing list (<http://korpusml.reaktanz.de>).
Adds support for the English language to the 'sylly'
        package. Due to some restrictions on CRAN, the full package
        sources are only available from the project homepage. To ask
        for help, report bugs, suggest feature improvements, or discuss
        the global development of the package, please consider
        subscribing to the koRpus-dev mailing list
        (<http://korpusml.reaktanz.de>).
Functions for fitting the Autoregressive and Moving Average Symmetric Model for univariate time series introduced by Maior and Cysneiros (2018), <doi:10.1007/s00362-016-0753-z>. Fitting method: conditional maximum likelihood estimation. For details see: Wei (2006), Time Series Analysis: Univariate and Multivariate Methods, Section 7.2.
Powerful user interface for adding symbols, smileys, arrows, building mathematical equations using 'LaTeX' or 'r2symbols'. Built for use in development of 'Markdown' and 'Shiny' Outputs.
Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation.
 (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>,
 Breiman, L. (1996), <doi:10.1007/BF00058655>,
 Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>,
 Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>,
 Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>,
 Calinski, T., Harabasz, J. (1974) <doi:10.1080/03610927408827101>,
 Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>,
 Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>,
 Walesiak, M., Dudek, A. (2008) <doi:10.1007/978-3-540-78246-9_11>,
 Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).
Creates a numeric guide for writing the formula for the determinant of a square matrix (a detguide) as a function of the elements of the matrix and writes out that formula, the symbolic representation.
Introduces the 'symbolicQspray' objects. Such an object
    represents a multivariate polynomial whose coefficients are fractions of
    multivariate polynomials with rational coefficients. The package
    allows arithmetic on such polynomials. It is based on the 'qspray' and 
    'ratioOfQsprays' packages. Some functions for 'qspray' polynomials have
    their counterpart for 'symbolicQspray' polynomials. A 'symbolicQspray' 
    polynomial should not be seen as a polynomial on the field of fractions 
    of rational polynomials, but should rather be seen as a polynomial with 
    rational coefficients depending on some parameters, symbolically 
    represented, with a dependence given by fractions of rational polynomials.
A matrix-like class to represent a symmetric matrix partitioned
    into file-backed blocks.
Provides an R interface to 'SymEngine' <https://github.com/symengine/>,
    a standalone 'C++' library for fast symbolic manipulation. The package has functionalities
    for symbolic computation like calculating exact mathematical expressions, solving
    systems of linear equations and code generation.
Implementations of a large number of tests for symmetry and their 
    bootstrap variants, which can be used for testing the symmetry of random
    samples around a known or unknown mean. Functions are also there for testing
    the symmetry of model residuals around zero. Currently, the supported models
    are linear models and generalized autoregressive conditional
    heteroskedasticity (GARCH) models (fitted with the 'fGarch' package). All
    tests are implemented using the 'Rcpp' package which ensures great
    performance of the code.
Symbolic central and non-central moments of the multivariate normal distribution. Computes a standard representation, LateX code, and values at specified mean and covariance matrices.
Implements the Symphony single-cell reference building and query mapping algorithms and additional functions described in Kang et al <https://www.nature.com/articles/s41467-021-25957-x>.
Easily analyze and visualize the performance of symptom
    checkers. This package can be used to gain comprehensive insights into
    the performance of single symptom checkers or the performance of
    multiple symptom checkers. It can be used to easily compare these
    symptom checkers across several metrics to gain an understanding of
    their strengths and weaknesses. The metrics are developed in 
    Kopka et al. (2023) <doi:10.1177/20552076231194929>.
A collection of functions for symbolic computation using the
			 'caracas' package for structural equation models and other
			 statistical analyses. Among its features is the ability
			 to calculate the model-implied covariance (and correlation)
			 matrix and the sampling covariance matrix of variable functions
			 using the delta method.
Contains methods for simulation and for evaluating the pdf, cdf, and quantile functions for symmetric stable, symmetric classical tempered stable, and symmetric power tempered stable distributions.
Generates synonyms from a given word drawing from a synonym list
    from the 'moby' project <http://moby-thesaurus.org/>.
Methods for computing spatial, temporal, and spatiotemporal
    statistics as described in Gouhier and Guichard (2014) 
    <doi:10.1111/2041-210X.12188>. These methods include 
    empirical univariate, bivariate and multivariate
    variograms; fitting variogram models; phase locking and synchrony analysis;
    generating autocorrelated and cross-correlated matrices.
The synchrosqueezed wavelet transform is implemented. The package is a translation of MATLAB Synchrosqueezing Toolbox, version 1.1 originally developed by Eugene Brevdo (2012). The C code for curve_ext was authored by Jianfeng Lu, and translated to Fortran by Dongik Jang. Synchrosqueezing is based on the papers: [1] Daubechies, I., Lu, J. and Wu, H. T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30. 243-261. [2] Thakur, G., Brevdo, E., Fukar, N. S. and Wu, H-T. (2013) The Synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications. Signal Processing, 93, 1079-1094.
The function 'syncSubsample' subsamples temporal data of different entities so that the result only contains synchronal events. The function 'mci' calculates the Movement Coordination Index (MCI, see reference on help page for function 'mci') of a data set created with the function 'syncSubsample'.
Generate the same random numbers in R and Python.
Analysis of metacommunities based on functional traits and
    phylogeny of the community components. The functions that are offered here
    implement for the R environment methods that have been available in the
    SYNCSA application written in C++ (by Valerio Pillar, available at 
    <http://ecoqua.ecologia.ufrgs.br/SYNCSA.html>).
Regression inference for multiple populations by integrating 
    summary-level data using stacked imputations. Gu, T., Taylor, J.M.G. and 
    Mukherjee, B. (2021) A synthetic data integration framework to leverage 
    external summary-level information from heterogeneous populations 
    <arXiv:2106.06835>.
Framework to perform synthetic likelihood inference
    for models where the likelihood function is unavailable or
    intractable.
This tool fits a non-parametric Bayesian model called a "hierarchically coupled mixture model with local dependence (HCMM-LD)" to the original microdata in order to generate synthetic microdata for privacy protection. The non-parametric feature of the adopted model is useful for capturing the joint distribution of the original input data in a highly flexible manner, leading to the generation of synthetic data whose distributional features are similar to that of the input data. The package allows the original input data to have missing values and impute them with the posterior predictive distribution, so no missing values exist in the synthetic data output. The method builds on the work of Murray and Reiter (2016) <doi:10.1080/01621459.2016.1174132>.
Explore synesthesia
  consistency test data, calculate consistency scores, 
  and classify participant data as valid or invalid.
A set of functions for generating 'SPSS' syntax files from the R environment.
Draw syntenic relationships between genome assemblies.
    There are 3 functions which take a tab delimited file containing alignment data for 
    syntenic blocks between genomes to produce either a linear alignment plot, 
    an evolution highway style plot, or a painted ideogram representing syntenic relationships.
    There is also a function to convert alignment data in the DESCHRAMBLER/inferCAR format
    to the required data structure.
Implements the synthetic control group method for comparative case studies as described in Abadie and Gardeazabal (2003) and Abadie, Diamond, and Hainmueller (2010, 2011, 2014). The synthetic control method allows for effect estimation in settings where a single unit (a state, country, firm, etc.) is exposed to an event or intervention. It provides a data-driven procedure to construct synthetic control units based on a weighted combination of comparison units that approximates the characteristics of the unit that is exposed to the intervention. A combination of comparison units often provides a better comparison for the unit exposed to the intervention than any comparison unit alone.
Provides access to curated American Community Survey (ACS) base tables via a wrapper
  to library(acs). Builds synthetic micro-datasets at any user-specified geographic level with
  ten default attributes; and, conducts spatial microsimulation modeling (SMSM) via simulated
  annealing.  SMSM is conducted in parallel by default. Lastly, we provide functionality for
  data-extensibility of micro-datasets <doi:10.18637/jss.v104.i07>.
Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) <doi:10.1016/j.advwatres.2019.103430> and Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962> associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>.
A critical first step in systematic literature reviews
  and mining of academic texts is to identify relevant texts from a range
  of sources, particularly databases such as 'Web of Science' or 'Scopus'.
  These databases often export in different formats or with different metadata
  tags. 'synthesisr' expands on the tools outlined by Westgate (2019)
  <doi:10.1002/jrsm.1374> to import bibliographic data from a range of formats
  (such as 'bibtex', 'ris', or 'ciw') in a standard way, and allows merging
  and deduplication of the resulting dataset.
Data is synthesized using a combination of inverse transform
    sampling from the emperical quantile functions for each variable, and 
    then copying the rank order structure from the original dataset. The
    package also includes a number of functions to measure the utility of
    synthesized datasets.
Creation of an individual claims simulator which generates various
    features of non-life insurance claims. An initial set of test parameters,
    designed to mirror the experience of an Auto Liability portfolio, were set
    up and applied by default to generate a realistic test data set of
    individual claims (see vignette). The simulated data set then allows
    practitioners to back-test the validity of various reserving models and to
    prove and/or disprove certain actuarial assumptions made in claims
    modelling. The distributional assumptions used to generate this data set can
    be easily modified by users to match their experiences. Reference: Avanzi B,
    Taylor G, Wang M, Wong B (2020) "SynthETIC: an individual insurance claim
    simulator with feature control" <arXiv:2008.05693>.
A tool for producing synthetic versions of microdata containing confidential information so that they are safe to be released to users for exploratory analysis. The key objective of generating synthetic data is to replace sensitive original values with synthetic ones causing minimal distortion of the statistical information contained in the data set. Variables, which can be categorical or continuous, are synthesised one-by-one using sequential modelling. Replacements are generated by drawing from conditional distributions fitted to the original data using parametric or classification and regression trees models. Data are synthesised via the function syn() which can be largely automated, if default settings are used, or with methods defined by the user. Optional parameters can be used to influence the disclosure risk and the analytical quality of the synthesised data. For a description of the implemented method see Nowok, Raab and Dibben (2016) <doi:10.18637/jss.v074.i11>.
A set of functions to support experimentation in the utility of partially synthetic data sets.  All functions compare an observed data set to one or a set of partially synthetic data sets derived from the observed data to (1) check that data sets have identical attributes, (2) calculate overall and specific variable perturbation rates, (3) check for potential logical inconsistencies, and (4) calculate confidence intervals and standard errors of desired variables in multiple imputed data sets. Confidence interval and standard error formulas have options for either synthetic data sets or multiple imputed data sets. For more information on the formulas and methods used, see Reiter & Raghunathan (2007) <doi:10.1198/016214507000000932>.
Measures memory and CPU usage of R code by regularly taking 
    snapshots of calls to the system command 'ps'. The package provides an entry 
    point (albeit coarse) to profile usage of system resources by R code run 
    in parallel.
Drop-in replacements for the base system2() function with fine control
    and consistent behavior across platforms. Supports clean interruption, timeout, 
    background tasks, and streaming STDIN / STDOUT / STDERR over binary or text 
    connections. Arguments on Windows automatically get encoded and quoted to work 
    on different locales.
Quasi-Monte-Carlo algorithm for systematic generation of shock scenarios from an arbitrary multivariate elliptical distribution. The algorithm selects a systematic mesh of arbitrary fineness that approximately evenly covers an isoprobability ellipsoid in d dimensions (Flood, Mark D. & Korenko, George G. (2013) <doi:10.1080/14697688.2014.926018>).
  This package is the 'R' analogy to the 'Matlab' code published by Flood & Korenko in above-mentioned paper.
Loading system fonts and Google Fonts
    <https://fonts.google.com/> into R, in order to
    support other packages such as 'R2SWF' and 'showtext'.
Provides functions for constructing mathematical models of dynamical systems from measured input-output data.
Send 'syslog' protocol messages to a remote 'syslog' server
    specified by host name and TCP network port.
In the past decade, genome-scale metabolic reconstructions have widely been 
    used to comprehend the systems biology of metabolic pathways within an organism. 
    Different GSMs are constructed using various techniques that 
    require distinct steps, but the input data, information conversion and software tools
    are neither concisely defined nor mathematically or programmatically formulated
    in a context-specific manner.The tool that quantitatively and qualitatively specifies
    each reconstruction steps and can generate a template list of reconstruction steps dynamically
    selected from a reconstruction step reservoir, constructed based on all available published papers.
Econometric estimation of simultaneous
 systems of linear and nonlinear equations using Ordinary Least
 Squares (OLS), Weighted Least Squares (WLS), Seemingly Unrelated
 Regressions (SUR), Two-Stage Least Squares (2SLS), Weighted
 Two-Stage Least Squares (W2SLS), and Three-Stage Least Squares (3SLS)
 as suggested, e.g., by Zellner (1962) <doi:10.2307/2281644>,
 Zellner and Theil (1962) <doi:10.2307/1911287>, and
 Schmidt (1990) <doi:10.1016/0304-4076(90)90127-F>.
Provides system native access to the font catalogue. As font
    handling varies between systems it is difficult to correctly locate
    installed fonts across different operating systems. The 'systemfonts'
    package provides bindings to the native libraries on Windows, macOS
    and Linux for finding font files that can then be used further by e.g.
    graphic devices. The main use is intended to be from compiled code but
    'systemfonts' also provides access from R.
The past decade has demonstrated an increased need to better understand risks leading to systemic crises. This framework offers scholars, practitioners and policymakers a useful toolbox to explore such risks in financial systems. Specifically, this framework provides popular econometric and network measures to monitor systemic risk and to measure the consequences of regulatory decisions. These systemic risk measures are based on the frameworks of Adrian and Brunnermeier (2016) <doi:10.1257/aer.20120555> and Billio, Getmansky, Lo and Pelizzon (2012) <doi:10.1016/j.jfineco.2011.12.010>.
Analysis of risk through liability matrices. Contains a Gibbs sampler for network reconstruction,  where only row and column sums of the liabilities matrix as well as some other fixed entries are observed, following the methodology of Gandy&Veraart (2016) <doi:10.1287/mnsc.2016.2546>. It also incorporates models that use a power law distribution on the degree distribution.
Deals with Young tableaux (field of combinatorics). For standard
    Young tabeaux, performs enumeration, counting, random generation, the
    Robinson-Schensted correspondence, and conversion to and from paths on
    the Young lattice. Also performs enumeration and counting of 
    semistandard Young tableaux, enumeration of skew semistandard Young 
    tableaux, enumeration of Gelfand-Tsetlin patterns, and computation of 
    Kostka numbers.
Extracts sentiment and sentiment-derived plot arcs
    from text using a variety of sentiment dictionaries conveniently
    packaged for consumption by R users.  Implemented dictionaries include
    "syuzhet" (default) developed in the Nebraska Literary Lab
    "afinn" developed by Finn Årup Nielsen, "bing" developed by Minqing Hu
    and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D.
    Applicable references are available in README.md and in the documentation
    for the "get_sentiment" function.  The package also provides a hack for
    implementing Stanford's coreNLP sentiment parser. The package provides
    several methods for plot arc normalization.
A system for personalized exercise plan recommendations for T2D (Type 2 Diabetes) patients  based on the primary outcome of HbA1c (Glycated Hemoglobin). You provide the individual's information, and 'T2DFitTailor' details the exercise plan and predicts the intervention's effectiveness.
Contains functions for applying the T^2-test for equivalence.
    The T^2-test for equivalence is a multivariate two-sample equivalence test. 
    Distance measure of the test is the Mahalanobis distance.
    For multivariate normally distributed data the T^2-test for equivalence 
    is exact and UMPI.
    The function T2EQ() implements the T^2-test for equivalence 
    according to Wellek (2010) <DOI:10.1201/ebk1439808184>.
    The function T2EQ.dissolution.profiles.hoffelder() implements a variant 
    of the T^2-test for equivalence according to Hoffelder (2016) 
    <http://www.ecv.de/suse_item.php?suseId=Z|pi|8430> for the 
    equivalence comparison of highly variable dissolution profiles.
Covers k-table control analysis using multivariate control charts for qualitative variables using fundamentals of multiple correspondence analysis and multiple factor analysis. The graphs can be shown in a flat or interactive way, in the same way all the outputs can be shown in an interactive shiny panel.
Cluster analysis is one of the most fundamental problems in data science. We provide a variety of algorithms from clustering to the learning on the space of partitions. See Hennig, Meila, and Rocci (2016, ISBN:9781466551886) for general exposition to cluster analysis.
Transport theory has seen much success in many fields of statistics and machine learning. We provide a variety of algorithms to compute Wasserstein distance, barycenter, and others. See Peyré and Cuturi (2019) <doi:10.1561/2200000073> for the general exposition to the study of computational optimal transport.
Contains functions for creating various types of summary tables, e.g. comparing characteristics across levels of a categorical variable and summarizing fitted generalized linear models, generalized estimating equations, and Cox proportional hazards models. Functions are available to handle data from simple random samples as well as complex surveys.
Calculates the robust Taba linear, Taba rank (monotonic), TabWil, and TabWil rank 
    correlations. Test statistics as well as one sided or two sided p-values are provided 
    for all correlations. Multiple correlations and p-values can be calculated 
    simultaneously across multiple variables. In addition, users will have the option to use 
    the partial, semipartial, and generalized partial correlations; where the partial and 
    semipartial correlations use linear, logistic, or Poisson regression to modify the specified
    variable.
Functions for tabulating and summarising categorical variables. 
    Most functions are designed to work with dataframes, and use the 'tidyverse' 
    idiom of taking the dataframe as the first argument so they work within 
    pipelines. Equivalent functions that operate directly on vectors are also 
    provided where it makes sense. This package aims to make exploratory data 
    analysis involving categorical variables quicker, simpler and more robust.
Sometimes you need to split your data and work on the two chunks independently before bringing them back together. 'Taber' allows you to do that with its two functions.
Simple tabulation should be dead simple. 
    This package is an opinionated approach to easy tabulations while also 
    providing exact numbers and allowing for re-usability. 
    This is achieved by providing tabulations as data.frames with columns for 
    values, optional variable names, frequency counts including and excluding 
    NAs and percentages for counts including and excluding NAs. Also values are
    automatically sorted by in decreasing order of frequency counts to allow for 
    fast skimming of the most important information.
For writing tables with custom formats in a Excel file ready to be distributed.
A specialization of 'dplyr' data manipulation verbs that parse and build expressions
    which are ultimately evaluated by 'data.table', letting it handle all optimizations. A set of
    additional verbs is also provided to facilitate some common operations on a subset of the data.
Translate double and integer valued data into
    character values formatted for tabulation in manuscripts
    or other types of academic reports.
Create HTML tables of descriptive statistics, as one would expect
    to see as the first table (i.e. "Table 1") in a medical/epidemiological journal
    article.
Table 1 is the classical way to describe the patients in a
    clinical study. The amount of splits in the data in such a table is
    limited. Table1Heatmap draws a heatmap of all crosstables that can be
    generated with the data. Users can choose between showing the actual
    crosstables or direction of effect of associations, and highlight
    associations by number of patients or p-values.
    v1.2 - fixed "missing "no visible global function definition for .."
A toolbox for comparing two data frames. This package is defunct. I recommend you use the "versus" package instead.
Create publication quality plots and tables for Item Response Theory and Classical Test theory based item analysis, exploratory and confirmatory factor analysis.
Creates some tables of clinical study. 'Table 1' is created by table1() to describe baseline characteristics, which is essential in every clinical study. Created by table2(), the function of 'Table 2' is to explore influence factors. And 'Table 3' created by table3() is able to make stratified analysis.
Higher Criticism (HC) test between two frequency tables. Test is based on an adaptation of the Tukey-Donoho-Jin HC statistic to testing frequency tables described in Kipnis (2019) <arXiv:1911.01208>.
A tool to create and style HTML tables with CSS. These can
    be exported and used in any application that accepts HTML (e.g. 'shiny',
    'rmarkdown', 'PowerPoint'). It also provides functions to create CSS files
    (which also work with shiny).
Provides two classes extending 'data.table' class. Simple
    'tableList' class wraps 'data.table' and any additional structures together.
    More complex 'tableMatrix' class combines 'data.table' and
    'matrix'. See <http://github.com/InferenceTechnologies/tableMatrix> for more
    information and examples.
Provides a user friendly interface to 
  generation of booktab style tables using 'xtable'.
Creates 'Table 1', i.e., description of baseline patient
    characteristics, which is essential in every medical research.
    Supports both continuous and categorical variables, as well as
    p-values and standardized mean differences. Weighted data are
    supported via the 'survey' package.
'R' interface to the 'Tabler' HTML template. See more here <https://tabler.io>. 
    'tablerDash' is a light 'Bootstrap 4' dashboard template. There are different 
     layouts available such as a one page dashboard or a multi page template,
     where the navigation menu is contained in the navigation bar. A fancy example
     is available at <https://dgranjon.shinyapps.io/shinyMons/>.
Computes and displays complex tables of summary statistics.
  Output may be in LaTeX, HTML, plain text, or an R
  matrix for further processing.
Allows to work with 'Table Schema' (<https://specs.frictionlessdata.io/table-schema/>). 'Table Schema' is well suited for use cases around handling and validating tabular data in text formats such as 'csv', but its utility extends well beyond this core usage, towards a range of applications where data benefits from a portable schema format. The 'tableschema.r' package can load and validate any table schema descriptor, allow the creation and modification of descriptors, expose methods for reading and streaming data that conforms to a 'Table Schema' via the 'Tabular Data Resource' abstraction.
Presentation-quality tables are displayed as plots on an R 
  graphics device.  Although there are other packages that format tables 
  for display, this package is unique in combining two features: (a) It is 
  aware of the logical structure of the table being presented, and makes 
  use of that for automatic layout and styling of the table.  This avoids 
  the need for most manual adjustments to achieve an attractive result. 
  (b) It displays tables using 'ggplot2' graphics.  Therefore a table can 
  be presented anywhere a graph could be, with no more effort.  External 
  software such as LaTeX or HTML or their viewers is not required.  The 
  package provides a full set of tools to control the style and appearance 
  of tables, including titles, footnotes and reference marks, horizontal 
  and vertical rules, and spacing of rows and columns.  Methods are included 
  to display matrices; data frames; tables created by R's ftable(), table(), 
  and xtabs() functions; and tables created by the 'tables' and 'xtable' 
  packages.  Methods can be added to display other table-like objects.  A 
  vignette is included that illustrates usage and options available in the 
  package.
Creates a table of descriptive statistics
 for factor and numeric columns in a data frame. Displays
 these by groups, if any. Highly customizable, with support
 for 'html' and 'pdf' provided by 'kableExtra'. Respects
 original column order, column labels, and factor level order.
 See ?tablet.data.frame and vignettes.
A wrapper to a set of algorithms designed to recognise positional cues present in hierarchical for-human Tables (which would normally be interpreted visually by the human brain) to decompose, then reconstruct the data into machine-readable LongForm Dataframes.
Collection of functions that allow to export data frames to excel workbook.
Implements the 'TabNet' model by Sercan O. Arik et al. (2019)
    <doi:10.48550/arXiv.1908.07442> with 'Coherent Hierarchical Multi-label
    Classification Networks' by Giunchiglia et al.  <doi:10.48550/arXiv.2010.10151> and
    provides a consistent interface for fitting and creating predictions.
    It's also fully compatible with the 'tidymodels' ecosystem.
Collect  your data on digital marketing campaigns from 'Taboola' using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Provides a music notation syntax and a collection of music
    programming functions for generating, manipulating, organizing, and analyzing
    musical information in R. Music syntax can be entered directly in character
    strings, for example to quickly transcribe short pieces of music. The
    package contains functions for directly performing various mathematical,
    logical and organizational operations and musical transformations on special
    object classes that facilitate working with music data and notation. The
    same music data can be organized in tidy data frames for a familiar and
    powerful approach to the analysis of large amounts of structured music data.
    Functions are available for mapping seamlessly between these formats and
    their representations of musical information. The package also provides an
    API to 'LilyPond' (<https://lilypond.org/>) for transcribing musical
    representations in R into tablature ("tabs") and sheet music. 'LilyPond' is
    open source music engraving software for generating high quality sheet music
    based on markup syntax. The package generates 'LilyPond' files from R code
    and can pass them to the 'LilyPond' command line interface to be rendered
    into sheet music PDF files or inserted into R markdown documents. The
    package offers nominal MIDI file output support in conjunction with
    rendering sheet music. The package can read MIDI files and attempts to
    structure the MIDI data to integrate as best as possible with the data
    structures and functionality found throughout the package.
Helps the user to build and register schema descriptions of 
    disorganised (messy) tables. Disorganised tables are tables that are 
    not in a topologically coherent form, where packages such as 'tidyr' could 
    be used for reshaping. The schema description documents the arrangement of 
    input tables and is used to reshape them into a standardised (tidy) output 
    format.
An easy way to examine archaeological count data. This
    package provides several tests and measures of diversity:
    heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.),
    richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and
    similarity (Brainerd-Robinson, etc.). It allows to easily visualize
    count data and statistical thresholds: rank vs abundance plots,
    heatmaps, Ford (1962) and Bertin (1977) diagrams, etc.
Bindings for the 'Tabula' <https://tabula.technology/> 'Java'
    library, which can extract tables from PDF files. This tool can reduce time
    and effort in data extraction processes in fields like investigative
    journalism. It allows for automatic and manual table extraction, the latter
    facilitated through a 'Shiny' interface, enabling manual areas selection\
    with a computer mouse for data retrieval.
Facilities to work with vector and raster data in efficient 
 repeatable and systematic work flow. Missing functionality in existing packages 
 is included here to allow extraction from raster data with 'simple features' and 
 'Spatial' types and to make extraction consistent and straightforward. Extract cell 
 numbers from raster data and  return the cells as a data frame 
 rather than as lists of matrices or vectors. The functions here allow spatial data 
 to be used without special handling for the format currently in use.
The 'tabularmap' is one of the visualization methods for 
    efficiently displaying data consisting of multiple elements by tiling them.
    When dealing with geospatial, it corrects for differences in visibility 
    between areas.
The maximum likelihood classifier (MLC) is one of the most common classifiers used for 
  remote sensing imagery. 
  This package uses 'RcppArmadillo' to provide a fast implementation of the MLC to train 
  and predict over tabular data (data.frame).
  The algorithms were based on Mather (1985) <doi:10.1080/01431168508948456> method.
Generates pretty console output for tables allowing for full customization
  of cell colors, font type, borders and many others attributes. It also supports 'multibyte'
  characters and nested tables.
Efficient tabulation with Stata-like output.
	For each unique value of the variable, it shows the number of 
	observations with that value, proportion of observations with that
	value, and cumulative proportion, in descending order of frequency.
	Accepts data.table, tibble, or data.frame as input. 
	Efficient with big data: if you give it a data.table, 
	tab() uses data.table syntax.
Convert semi-structured log files (such as 'Apache' access.log files)
    into a tabular format (data.frame) using a standard template system.
Tabu search algorithm for binary configurations. A basic version of the algorithm as described by Fouskakis and Draper (2007) <doi:10.1111/j.1751-5823.2002.tb00174.x>.
Make it easy to deal with multiple cross-tables in data exploration, by
  creating them, manipulating them, and adding color helpers to highlight
  important informations (differences from totals, comparisons between lines or
  columns, contributions to variance, confidence intervals, odds ratios, etc.). 
  All functions are pipe-friendly and render data frames which can be easily 
  manipulated. In the same time, time-taking operations are done with 'data.table'
  to go faster with big dataframes. Tables can be exported with formats and colors 
  to 'Excel', plot and html.
To facilitate the analysis of positron emission tomography (PET) 
    time activity curve (TAC) data, and to encourage open science and 
    replicability, this package supports data loading and analysis of multiple 
    TAC file formats. Functions are available to analyze loaded TAC data for 
    individual participants or in batches. Major functionality includes weighted
    TAC merging by region of interest (ROI), calculating models including 
    standardized uptake value ratio (SUVR) and distribution volume ratio (DVR, 
    Logan et al. 1996 <doi:10.1097/00004647-199609000-00008>), basic plotting 
    functions and calculation of cut-off values (Aizenstein et al. 2008
    <doi:10.1001/archneur.65.11.1509>). Please see the walkthrough vignette for
    a detailed overview of 'tacmagic' functions.
Extensions to 'lattice', providing new high-level
    functions, methods for existing functions, panel functions, and a theme.
Suite of tools to support the practice of tada science. It includes an
    engaging package roulette that is designed to facilitate learning about 
    new packages.
Functions to organize data, methods, and results used in scientific
  analyses. A TAF analysis consists of four scripts (data.R, model.R, output.R,
  report.R) that are run sequentially. Each script starts by reading files from
  a previous step and ends with writing out files for the next step. Convenience
  functions are provided to version control the required data and software, run
  analyses, clean residues from previous runs, manage files, manipulate tables,
  and produce figures. With a focus on stability and reproducible analyses, TAF
  is designed to have no package dependencies. TAF forms a base layer for the
  'icesTAF' package and other scientific applications.
Implement the transformed additive Gaussian (TAG) process and the  transformed approximately additive Gaussian (TAAG) process proposed in Lin and Joseph (2020) <DOI:10.1080/00401706.2019.1665592>. These functions can be used to model deterministic computer experiments, obtain predictions at new inputs, and quantify the uncertainty of the predictions. This research is supported by a U.S. National Science Foundation grant DMS-1712642 and a U.S. Army Research Office grant W911NF-17-1-0007.
Provides functions for attaching tags to R objects,
    searching for objects based on tags, and removing tags from objects.
    It also includes a function for removing all tags from an object, as well
    as a function for deleting all objects with a specific tag from the R
    environment. The package is useful for organizing and managing large
    collections of objects in R.
High-resolution movement-sensor tags typically include accelerometers 
    to measure body posture and sudden movements or changes in speed, 
    magnetometers to measure direction of travel, and pressure sensors
    to measure dive depth in aquatic or marine animals. The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; 
    calibration (from raw data to calibrated data in scientific units); 
    visualization (for example, multi-panel time-series plots); 
    data processing (such as event detection, calculation of derived metrics like jerk and 
    dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis).
The function TailClassifier() suggests one of the following types of tail for your discrete data: 1) Power decaying tail; 2) Sub-exponential decaying tail; and 3) Near-exponential decaying tail. The function also provides an estimate of the parameter for the classified-distribution as a reference.
Provides functions implementing minimal distance estimation methods for parametric tail dependence models, as proposed in  
	 Einmahl, J.H.J., Kiriliouk, A., Krajina, A., and Segers, J. (2016) <doi:10.1111/rssb.12114> and 
	 Einmahl, J.H.J., Kiriliouk, A., and Segers, J. (2018) <doi:10.1007/s10687-017-0303-7>.
Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.
Implements the tail-rank statistic for selecting biomarkers
  from a microarray data set, an efficient nonparametric test focused
  on the distributional tails. See
  <https://gitlab.com/krcoombes/coombeslab/-/blob/master/doc/papers/tolstoy-new.pdf>.
When plotting treated-minus-control differences, after-minus-before changes, or difference-in-differences, the ttrans() function symmetrically transforms the positive and negative tails to aid plotting.  The package includes an observational study with three control groups and an unaffected outcome; see Rosenbaum (2020) <doi:10.1111/biom.13558>.
A tool to help create shiny apps for selecting and annotating 
    elements of images. Users must supply images, questions, and answer
    choices. The user interface is a dynamic shiny app, that displays the images
    and questions and answer choices. The data generated can be saved to a
    file that can be used for subsequent analysis. The original purpose was to
    annotate still images from tennis video for face recognition and emotion 
    detection purposes.
It includes functions for applying methodologies utilized for single-process kinetic analysis of solid-state processes were recently summarized and described in the Recommendation of ICTAC Kinetic Committee. These methods work with the basic kinetic equation. The Methodologies included refers to  Avrami, Friedman, Kissinger, Ozawa, OFM, Mo, Starink, isoconversional methodology (Vyazovkin) according to ICATAC Kinetics Committee recommendations as reported in Vyazovkin S, Chrissafis K, Di Lorenzo ML, et al. ICTAC Kinetics Committee recommendations for collecting experimental thermal analysis data for kinetic computations. Thermochim Acta. 2014;590:1-23. <doi:10.1016/J.TCA.2014.05.036> .
Includes marginal maximum likelihood estimation and joint maximum
    likelihood estimation for unidimensional and multidimensional 
    item response models. The package functionality covers the 
    Rasch model, 2PL model, 3PL model, generalized partial credit model, 
    multi-faceted Rasch model, nominal item response model, 
    structured latent class model, mixture distribution IRT models, 
    and located latent class models. Latent regression models and 
    plausible value imputation are also supported. For details see
    Adams, Wilson and Wang, 1997 <doi:10.1177/0146621697211001>,
    Adams, Wilson and Wu, 1997 <doi:10.3102/10769986022001047>,
    Formann, 1982 <doi:10.1002/bimj.4710240209>,
    Formann, 1992 <doi:10.1080/01621459.1992.10475229>.
Agglomerative hierarchical clustering with a bespoke distance 
  measure based on medication similarities in the Anatomical Therapeutic 
  Chemical Classification System, medication timing and 
  medication amount or dosage. Tools for summarizing, illustrating and 
  manipulating the cluster objects are also available.
The Tanaka method enhances the representation of topography on
    a map using shaded contour lines. In this simplified implementation of
    the method, north-west white contours represent illuminated topography
    and south-east black contours represent shaded topography. See Tanaka
    (1950) <doi:10.2307/211219>.
A two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, <doi:10.1093/bioinformatics/btw449>), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.
Feature selection algorithm that extracts features in highly
    correlated spaces. The extracted features are meant to be fed into
    simple explainable models such as linear or logistic regressions. The
    package is useful in the field of explainable modelling as a
    way to understand variable behavior.
Provides an extensible formula system to quickly and easily create
    production quality tables. The processing steps are a formula parser,
    statistical content generation from data as defined by formula, followed by
    rendering into a table. Each step of the processing is separate and user
    definable thus creating a set of composable building blocks for
    highly customizable table generation. A user is not limited by any of the 
    choices of the package creator other than the formula grammar. For example,
    one could chose to add a different S3 rendering function and output a format
    not provided in the default package, or possibly one would rather have Gini
    coefficients for their statistical content in a resulting table. Routines to
    achieve New England Journal of Medicine style, Lancet style and
    Hmisc::summaryM() statistics are provided. The package contains rendering
    for HTML5, Rmarkdown and an indexing format for use in tracing and tracking
    are provided.
Builds tables with customizable rows. Users can specify the type
             of data to use for each row, as well as how to handle missing
             data and the types of comparison tests to run on the table
             columns.
Implementation of functions for fitting taper curves (a semiparametric 
  linear mixed effects taper model) to diameter measurements along stems. Further 
  functions are provided to estimate the uncertainty around the predicted curves, 
  to calculate timber volume (also by sections) and marginal (e.g., upper) diameters. 
  For cases where tree heights are not measured, methods for estimating
  additional variance in volume predictions resulting from uncertainties in
  tree height models (tariffs) are provided.  The example data include the taper 
  curve parameters for Norway spruce used in the 3rd German NFI fitted to 380 trees 
  and a subset of section-wise diameter measurements of these trees. The functions 
  implemented here are detailed in Kublin, E., Breidenbach, J., Kaendler, G. (2013)
  <doi:10.1007/s10342-013-0715-0>.
Providing new german-wide 'TapeR' Models and functions for their 
  evaluation. Included are the most common tree species in Germany (Norway 
  spruce, Scots pine, European larch, Douglas fir, Silver fir as well as 
  European beech, Common/Sessile oak and Red oak). Many other species are mapped
  to them so that 36 tree species / groups can be processed. Single trees are 
  defined by species code, one or multiple diameters in arbitrary measuring 
  height and tree height. The functions then provide information on diameters 
  along the stem, bark thickness, height of diameters, volume of the total or 
  parts of the trunk and total and component above-ground biomass. It is also 
  possible to calculate assortments from the taper curves. For diameter and 
  volume estimation, uncertainty information is given.
Wrapper for using 'tapkee' command line utility,
 it allows to run it from inside R and catch the results for further analysis and plotting.
 'Tapkee' is a program for fast dimension reduction, see 'package?tapkee' and <http://tapkee.lisitsyn.me/>
 for installation and other details.
Functions to produce, fit and predict from bipartite networks with abundance, trait and phylogenetic information. Its methods are described in detail in Benadi, G., Dormann, C.F., Fruend, J., Stephan, R. & Vazquez, D.P. (2021) Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny. The American Naturalist, in press.
Manager of tick-by-tick transaction data that performs 'cleaning', 'aggregation' and 'import' in an efficient and fast way. The package engine, written in C++, exploits the 'zlib' and 'gzstream' libraries to handle gzipped data without need to uncompress them. 'Cleaning' and 'aggregation' are performed according to Brownlees and Gallo (2006) <DOI:10.1016/j.csda.2006.09.030>. Currently, TAQMNGR processes raw data from WRDS (Wharton Research Data Service, <https://wrds-web.wharton.upenn.edu/wrds/>).
Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <DOI:10.1081/STA-200054435>.
Function-oriented Make-like declarative pipelines for
  Statistics and data science are supported in the 'targets' R package.
  As an extension to 'targets', the 'tarchetypes' package provides
  convenient user-side functions to make 'targets' easier to use.
  By establishing reusable archetypes for common kinds of
  targets and pipelines, these functions help express complicated
  reproducible pipelines concisely and compactly.
  The methods in this package were influenced by the 'drake' R package
  by Will Landau (2018) <doi:10.21105/joss.00550>.
Measure text's sentiment with dictionaries and simple rules covering
    negations and modifiers. User-supplied dictionaries are supported, including
    Unicode emojis and multi-word tokens, so this package can also be used to
    study constructs beyond sentiment.
Various methods for targeted and semiparametric inference including
	     augmented inverse probability weighted (AIPW) estimators for missing data and
	     causal inference (Bang and Robins (2005) <doi:10.1111/j.1541-0420.2005.00377.x>),
         variable importance and conditional average treatment effects (CATE)
         (van der Laan (2006) <doi:10.2202/1557-4679.1008>),
	     estimators for risk differences and relative risks (Richardson et al. (2017)
	     <doi:10.1080/01621459.2016.1192546>), assumption lean inference for generalized
         linear model parameters (Vansteelandt et al. (2022) <doi:10.1111/rssb.12504>).
Pipeline tools coordinate the pieces of computationally
  demanding analysis projects.
  The 'targets' package is a 'Make'-like pipeline tool for statistics and
  data science in R. The package skips costly runtime for tasks
  that are already up to date,
  orchestrates the necessary computation with implicit parallel computing,
  and abstracts files as R objects. If all the current output matches
  the current upstream code and data, then the whole pipeline is up
  to date, and the results are more trustworthy than otherwise.
  The methodology in this package
  borrows from GNU 'Make' (2015, ISBN:978-9881443519)
  and 'drake' (2018, <doi:10.21105/joss.00550>).
Implement the Tariff algorithm for coding cause-of-death from verbal autopsies. The Tariff method was originally proposed in James et al (2011) <DOI:10.1186/1478-7954-9-31> and later refined as Tariff 2.0 in Serina, et al. (2015) <DOI:10.1186/s12916-015-0527-9>. Note that this package was not developed by authors affiliated with the Institute for Health Metrics and Evaluation and thus unintentional discrepancies may exist between the this implementation and the implementation available from IHME.
Interactive shiny application for working with textmining and text analytics. Various visualizations  are provided.
Provides functions for analyzing citizens' bicycle usage pattern and predicting rental amount on specific conditions.
    Functions on this package interacts with data on 'tashudata' package, a 'drat' repository.
    'tashudata' package contains rental/return history on public bicycle system('Tashu'), weather for 3 years and bicycle station information. 
    To install this data package, see the instructions at <https://github.com/zeee1/Tashu_Rpackage>.
    top10_stations(), top10_paths() function visualizes image showing the most used top 10 stations and paths.
    daily_bike_rental() and monthly_bike_rental() shows daily, monthly amount of bicycle rental.
    create_train_dataset(), create_test_dataset() is data processing function for prediction.
    Bicycle rental history from 2013 to 2014 is used to create training dataset and that on 2015 is for test dataset.
    Users can make random-forest prediction model by using create_train_model() 
    and predict amount of bicycle rental in 2015 by using predict_bike_rental().
Schedule R scripts/processes with the Windows task scheduler. This
    allows R users to automate R processes on specific time points from R itself.
You only need to type 'why pie charts are bad' on Google to find
    thousands of articles full of (valid) reasons why other types of charts 
    should be preferred over this one.
    Therefore, because of the little use due to the reasons already mentioned,
    making pie charts (and related) in R is not straightforward, so other 
    functions are needed to simplify things.
    In this R package there are useful functions to make 'tasty' pie charts 
    immediately by exploiting the many cool templates provided.
The ta-test is a modified two-sample or two-group t-test of Gosset (1908). In small samples with less than 15 replicates,the ta-test significantly reduces type I error rate but has almost the same power with the t-test and hence can greatly enhance reliability or reproducibility of discoveries in biology and medicine. The ta-test can test single null hypothesis or multiple null hypotheses without needing to correct p-values.
Functions to combine data.frames in ways that require additional effort in 
  base R, and to add metadata (id, title, ...) that can be used for printing and 
  xlsx export. The 'Tatoo_report' class is provided as a 
  convenient helper to write several such tables to a workbook, one table per 
  worksheet. Tatoo is built on top of 'openxlsx', but intimate knowledge of 
  that package is not required to use tatoo.
A clinically meaningful measures of treatment effects for right-censored data 
  are provided, based on the concept of Kendall's tau, along with the corresponding inference procedures.
  Two plots of tau processes, with the option to account for the cure fraction or not, are available.
  The plots of tau processes serve as useful graphical tools for monitoring the relative performances over time.
'Tautulli' (<http://tautulli.com>) is a monitoring application for 'Plex' Media Servers 
  (<https://www.plex.tv>) which collects a lot of data about media items and server 
  usage such as play counts. This package interacts with the 'Tautulli' API of any 
  specified server to get said data into R. The 'Tautulli' API documentation is available
  at <https://github.com/Tautulli/Tautulli/blob/master/API.md>.
Provides classes for storing and manipulating taxonomic data. 
 Most of the classes can be treated like base R vectors (e.g. can be used 
 in tables as columns and can be named). Vectorized classes can store taxon names
 and authorities, taxon IDs from databases, taxon ranks, and other types of
 information. More complex classes are provided to store taxonomic trees and
 user-defined data associated with them.
Creates a local database of many commonly used taxonomic authorities
             and provides functions that can quickly query this data.
Creates a local Lightning Memory-Mapped Database ('LMDB') 
             of many commonly used taxonomic authorities
             and provides functions that can quickly query this data.
             Supported taxonomic authorities include 
             the Integrated Taxonomic Information System ('ITIS'),
             National Center for Biotechnology Information ('NCBI'),
             Global Biodiversity Information Facility ('GBIF'), 
             Catalogue of Life ('COL'), and Open Tree Taxonomy ('OTT'). 
             Name and identifier resolution using 'LMDB' can
             be hundreds of times faster than either relational databases or
             internet-based queries. Precise data provenance information for
             data derived from naming providers is also included.
A novel feature-wise normalization method based on a zero-inflated negative binomial model. This method assumes that the effects of sequencing depth vary for each taxon on their mean and also incorporates a rational link of zero probability and taxon dispersion as a function of sequencing depth. Ziyue Wang, Dillon Lloyd, Shanshan Zhao, Alison Motsinger-Reif (2023) <doi:10.1101/2023.10.31.563648>.
Computation and visualization of Taxicab Correspondence Analysis, Choulakian (2006) <doi:10.1007/s11336-004-1231-4>.  Classical correspondence analysis (CA) is a statistical method to analyse 2-dimensional tables of positive numbers and is typically applied to contingency tables (Benzecri, J.-P. (1973). L'Analyse des Donnees. Volume II. L'Analyse des Correspondances. Paris, France: Dunod).  Classical CA is based on the Euclidean distance.  Taxicab CA is like classical CA but is based on the Taxicab or Manhattan distance.  For some tables, Taxicab CA gives more informative results than classical CA.
Interacts with a suite of web 'APIs' for taxonomic tasks,
    such as getting database specific taxonomic identifiers, verifying
    species names, getting taxonomic hierarchies, fetching downstream and
    upstream taxonomic names, getting taxonomic synonyms, converting
    scientific to common names and vice versa, and more.
Handling taxonomic lists through objects of class 'taxlist'.
    This package provides functions to import species lists from 'Turboveg'
    (<https://www.synbiosys.alterra.nl/turboveg/>) and the possibility to create
    backups from resulting R-objects.
    Also quick displays are implemented as summary-methods.
A collection of functions used to format taxonomic names in Markdown
    documents. Those functions work with data structured according to
    Alvarez and Luebert (2018) <doi:10.3897/bdj.6.e23635>.
Functions for assigning taxonomy to NCBI accession numbers and taxon IDs based on NCBI's accession2taxid and taxdump files. This package allows the user to download NCBI data dumps and create a local database for fast and local taxonomic assignment.
A comprehensive resource for data on Taylor Swift songs. Data
    is included for all officially released studio albums, extended plays
    (EPs), and individual singles are included. Data comes from 'Genius'
    (lyrics) and 'Spotify' (song characteristics). Additional functions
    are included for easily creating data visualizations with color
    palettes inspired by Taylor Swift's album covers.
The Taylor Russell model is a widely used method for assessing test validity in personnel selection tasks. The three functions in this package extend this model in a number of notable ways. TR() estimates test validity for a single selection test via the original Taylor Russell model. It extends this model by allowing users greater flexibility in argument choice. For example, users can specify any three of the four parameters (base rate, selection ratio, criterion validity, and positive predictive value) of the Taylor Russell model and estimate the remaining parameter (see the help file for examples).  The TaylorRussell() function generalizes the original Taylor Russell model to allow for multiple selection tests (predictors).  To our knowledge, this is the first generalization of the Taylor Russell model to allow for three or more selection tests (it is also the first to correctly handle models with two selection tests). TRDemo() is a 'shiny' program for illustrating the underlying logic of the Taylor Russell model. Taylor, HC and Russell, JT (1939) "The relationship of validity coefficients to the practical effectiveness of tests in selection: Discussion and tables" <doi:10.1037/h0057079>.
For when your colors absolutely should not be excluded from the narrative.
Estimation of the survivor average causal effect under outcomes truncated by death, which requires the existence of a substitution variable. It can be applied to both experimental and observational data.
Functions are provided for prior specification in divergence time
    estimation using fossils as well as other kinds of data. It
    provides tools for interacting with the input and output of Bayesian
    platforms in evolutionary biology such as 'BEAST2', 'MrBayes', 'RevBayes',
    or 'MCMCTree'.
    It Implements a simple measure similarity between probability
    density functions for comparing prior and
    posterior Bayesian densities, as well as code for calculating the
    combination of distributions using conflation of Hill (2008). Functions for estimating the
    origination time in collections of distributions using the x-intercept (e.g., Draper and Smith, 1998) and
    stratigraphic intervals (Marshall 2010) are also available.
    Hill, T. 2008. "Conflations of probability distributions". Transactions of the American Mathematical Society, 363:3351-3372. <doi:10.48550/arXiv.0808.1808>,
    Draper, N. R. and Smith, H. 1998. "Applied Regression Analysis". 1--706. Wiley Interscience, New York. <DOI:10.1002/9781118625590>,
    Marshall, C. R. 2010. "Using confidence intervals to quantify the uncertainty in the end-points of stratigraphic ranges". Quantitative Methods in Paleobiology, 291--316. <DOI:10.1017/S1089332600001911>.
Our method introduces mathematically well-defined measures for tightness of branches in a hierarchical tree. Statistical significance of the findings is determined, for all branches of the tree, by performing permutation tests, optionally with generalized Pareto p-value estimation.
Extends the test-based Bayes factor (TBF) methodology to multinomial regression models and discrete time-to-event models with competing risks. The TBF methodology has been well developed and implemented for the generalised linear model [Held et al. (2015) <doi:10.1214/14-STS510>] and for the Cox model [Held et al. (2016) <doi:10.1002/sim.7089>].
Facilitate the movement between data frames to 'xts'. Particularly
    useful when moving from 'tidyverse' to the widely used 'xts' package, which is
    the input format of choice to various other packages. It also allows the user 
    to use a 'spread_by' argument for a character column 'xts' conversion.
Boosting the likelihood of conditional and shift transformation models as introduced in \doi{10.1007/s11222-019-09870-4}.
Creates simulated clinical trial data with realistic correlation structures and assumed efficacy levels by using a tilted bootstrap resampling approach. Samples are drawn from observed data with some samples appearing more frequently than others. May also be used for simulating from a joint Bayesian distribution along with clinical trials based on the Bayesian distribution.
Tools to help developers and producers manipulate R objects and outputs. It includes tools for displaying results and objects, and for formatting them in the correct format.
Fast calculation of the Subtree Prune and Regraft (SPR),
  Tree Bisection and Reconnection (TBR) and Replug distances between 
  unrooted trees, using the algorithms of Whidden and 
  Matsen (2017) <arxiv:1511.07529>.
Provides rolling statistical functions based
    on date and time windows instead of n-lagged observations.
Tensor Composition Analysis (TCA) allows the deconvolution of two-dimensional data (features by observations) coming from a mixture of heterogeneous sources into a three-dimensional matrix of signals (features by observations by sources). The TCA framework further allows to test the features in the data for different statistical relations with an outcome of interest while modeling source-specific effects; particularly, it allows to look for statistical relations between source-specific signals and an outcome. For example, TCA can deconvolve bulk tissue-level DNA methylation data (methylation sites by individuals) into a three-dimensional tensor of cell-type-specific methylation levels for each individual (i.e. methylation sites by individuals by cell types) and it allows to detect cell-type-specific statistical relations (associations) with phenotypes. For more details see Rahmani et al. (2019) <DOI:10.1038/s41467-019-11052-9>.
The classical two-sample t-test works well for the normally distributed data or 
    data with large sample size. The tcfu() and tt() tests implemented in this package provide 
    better type-I-error control with more accurate power when testing the equality of two-sample 
    means for skewed populations having unequal variances. These tests are especially useful 
    when the sample sizes are moderate. The tcfu() uses the Cornish-Fisher expansion to achieve 
    a better approximation to the true percentiles. The tt() provides transformations of the Welch's 
    t-statistic so that the sampling distribution become more symmetric. For more technical details, 
    please refer to Zhang (2019) <http://hdl.handle.net/2097/40235>.
The Cancer Genome Atlas (TCGA) is a program aimed at improving our understanding of Cancer Biology. Several TCGA Datasets are available online. 'TCGAretriever' helps accessing and downloading TCGA data hosted on 'cBioPortal' via its Web Interface (see <https://www.cbioportal.org/> for more information).
Differential analysis of tumor tissue immune cell type
    abundance based on RNA-seq gene-level expression from The Cancer
    Genome Atlas (TCGA; <https://pancanatlas.xenahubs.net>) database.
Implementation of Time-course Gene Set Analysis (TcGSA), a method for 
	analyzing longitudinal gene-expression data at the gene set level. Method is
	detailed in: Hejblum, Skinner & Thiebaut (2015) <doi: 10.1371/journal.pcbi.1004310>.
Methods for generating modelled parametric Tropical Cyclone (TC) spatial hazard fields and time series output at point locations from TC tracks.  R's compatibility to simply use fast 'cpp' code via the 'Rcpp' package and the wide range spatial analysis tools via the 'terra' package makes it an attractive open source environment to study 'TCs'.  This package estimates TC vortex wind and pressure fields using parametric equations originally coded up in 'python' by 'TCRM' <https://github.com/GeoscienceAustralia/tcrm> and then coded up in 'Cuda' 'cpp' by 'TCwindgen' <https://github.com/CyprienBosserelle/TCwindgen>.
Implementation of target-controlled infusion algorithms for compartmental pharmacokinetic and pharmacokinetic-pharmacodynamic models. Jacobs (1990) <doi:10.1109/10.43622>; Marsh et al. (1991) <doi:10.1093/bja/67.1.41>; Shafer and Gregg (1993) <doi:10.1007/BF01070999>; Schnider et al. (1998) <doi:10.1097/00000542-199805000-00006>; Abuhelwa, Foster, and Upton (2015) <doi:10.1016/j.vascn.2015.03.004>; Eleveld et al. (2018) <doi:10.1016/j.bja.2018.01.018>.
A wrapper for The Cancer Imaging Archive's REST API. The Cancer
    Imaging Archive (TCIA) hosts de-identified medical images of cancer
    available for public download, as well as rich metadata for each image
    series. TCIA provides a REST API for programmatic access to the data.
    This package provides simple functions to access each API endpoint.
    For more information, see <https://github.com/pamelarussell/TCIApathfinder>
    and TCIA's website.
Provide the core functionality to transform longitudinal data to
    complex-time (kime) data using analytic and numerical techniques, visualize the original 
    time-series and reconstructed kime-surfaces, perform model based (e.g., tensor-linear regression)
    and model-free classification and clustering methods in the book Dinov, ID and Velev, MV. (2021)
    "Data Science: Time Complexity, Inferential Uncertainty, and Spacekime Analytics", De Gruyter STEM Series,
    ISBN 978-3-11-069780-3. <https://www.degruyter.com/view/title/576646>.
    The package includes 18 core functions which can be separated into three groups.
    1) draw longitudinal data, such as Functional magnetic resonance imaging(fMRI) time-series, and forecast or transform the time-series data.
    2) simulate real-valued time-series data, e.g., fMRI time-courses, detect the activated areas,
    report the corresponding p-values, and visualize the p-values in the 3D brain space.
    3) Laplace transform and kimesurface reconstructions of the fMRI data.
An implementation of hypothesis testing in an extended Rasch modeling framework,
    including sample size planning procedures and power computations. Provides 4 statistical tests, 
    i.e., gradient test (GR), likelihood ratio test (LR), Rao score or Lagrange multiplier test (RS), 
    and Wald test, for testing a number of hypotheses referring to the Rasch model (RM), linear 
    logistic test model (LLTM), rating scale model (RSM), and partial credit model (PCM). Three 
    types of functions for power and sample size computations are provided. Firstly, functions to 
    compute the sample size given a user-specified (predetermined) deviation from the hypothesis 
    to be tested, the level alpha, and the power of the test. Secondly, functions to evaluate the 
    power of the tests given a user-specified (predetermined) deviation from the hypothesis to be 
    tested, the level alpha of the test, and the sample size. Thirdly, functions to evaluate the 
    so-called post hoc power of the tests. This is the power of the tests given the observed 
    deviation of the data from the hypothesis to be tested and a user-specified level alpha of the
    test. Power and sample size computations are based on a Monte Carlo simulation approach. It is
    computationally very efficient. The variance of the random error in computing power and sample
    size arising from the simulation approach is analytically derived by using the delta method. 
    Draxler, C., & Alexandrowicz, R. W. (2015), <doi:10.1007/s11336-015-9472-y>.
A series of additional Tcl commands and Tk widgets with style
  and various functions (under Windows: DDE exchange, access to the
  registry and icon manipulation) to supplement the tcltk package
Provides functions for robust trimmed clustering. The methods are 
    described in Garcia-Escudero (2008) <doi:10.1214/07-AOS515>, 
    Fritz et al. (2012) <doi:10.18637/jss.v047.i12>, 
    Garcia-Escudero et al. (2011)  <doi:10.1007/s11222-010-9194-z> and others.
The 1311 time series from the tourism forecasting competition conducted in 2010 and described in Athanasopoulos et al. (2011) <DOI:10.1016/j.ijforecast.2010.04.009>.
A set of tools for processing and modeling high-throughput and
    high-content chemical screening data. The package was developed for the
    the chemical screening data generated by the US EPA ToxCast program, but
    can be used for diverse chemical screening efforts.
Performs the basic concentration response curve fitting used 
    in the 'tcpl' package. It is a substitute for the original tcplFit() function 
    (and sub-functions) and allows a wider variety of concentration-response models. 
    All of the models included in the 'BMDExpress' package are now part of this package,
    and the output includes a calculation of the bmd (Benchmark Dose or concentration) 
    value.
Enables the computation of the 'two cut-points with maximum odds ratio (OR)
    value method' for data analysis, particularly suited for binary classification
    tasks. Users can identify optimal cut-points in a continuous variable by
    maximizing the odds ratio while maintaining an equal risk level, useful for
    tasks such as medical diagnostics, risk assessment, or predictive modeling.
The 'twelvedata' REST service offers access to current and historical
 data on stocks, standard as well as digital 'crypto' currencies, and other financial
 assets covering a wide variety of course and time spans. See <https://twelvedata.com/>
 for details, to create an account, and to request an API key for free-but-capped access
 to the data.
Tools for the statistical analysis of persistent homology and for
    density clustering. For that, this package provides an R interface for the
    efficient algorithms of the C++ libraries 'GUDHI' <https://project.inria.fr/gudhi/software/>, 'Dionysus' <https://www.mrzv.org/software/dionysus/>, and 'PHAT' <https://bitbucket.org/phat-code/phat/>. This package also implements the methods in Fasy et al. (2014) <doi:10.1214/14-AOS1252> and Chazal et al. (2014) <doi:10.1145/2582112.2582128>  for analyzing the statistical significance of persistent homology features.
Topological data analysis studies structure and shape of the data using topological features. We provide a variety of algorithms to learn with persistent homology of the data based on functional summaries for clustering, hypothesis testing, visualization, and others. We refer to Wasserman (2018) <doi:10.1146/annurev-statistics-031017-100045> for a statistical perspective on the topic.
Topological data analysis is a powerful tool for finding non-linear global structure
    in whole datasets. The main tool of topological data analysis is persistent homology, which computes
    a topological shape descriptor of a dataset called a persistence diagram. 'TDApplied' provides 
    useful and efficient methods for analyzing groups of persistence diagrams with machine learning and statistical inference,
    and these functions can also interface with other data science packages to form flexible and integrated
    topological data analysis pipelines.
A comprehensive toolset for any
    useR conducting topological data analysis, specifically via the
    calculation of persistent homology in a Vietoris-Rips complex.
    The tools this package currently provides can be conveniently split
    into three main sections: (1) calculating persistent homology; (2)
    conducting statistical inference on persistent homology calculations;
    (3) visualizing persistent homology and statistical inference.
    The published form of TDAstats can be found in Wadhwa et al. (2018)
    <doi:10.21105/joss.00860>.   
    For a general background on computing persistent homology for
    topological data analysis, see Otter et al. (2017)
    <doi:10.1140/epjds/s13688-017-0109-5>.
    To learn more about how the permutation test is used for
    nonparametric statistical inference in topological data analysis,
    read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>.
    To learn more about how TDAstats calculates persistent homology,
    you can visit the GitHub repository for Ripser, the software that
    works behind the scenes at <https://github.com/Ripser/ripser>.
    This package has been published as Wadhwa et al. (2018)
    <doi:10.21105/joss.00860>.
Provides a set of tools for managing time-series data,
    with a particular emphasis on defining various frequency types such
    as daily and weekly. It also includes functionality for converting
    data between different frequencies.
Uniform random samples from simple manifolds, sometimes with noise,
    are commonly used to test topological data analytic (TDA) tools.
    This package includes samplers powered by two techniques: analytic
    volume-preserving parameterizations, as employed by Arvo (1995)
    <doi:10.1145/218380.218500>, and rejection sampling, as employed by
    Diaconis, Holmes, and Shahshahani (2013) <doi:10.1214/12-IMSCOLL1006>.
Tools for computing various vector summaries of persistence diagrams 
    studied in Topological Data Analysis. For improved computational efficiency, 
    all code for the vector summaries is written in 'C++' using the 'Rcpp' package.
The companion package that provides all the datasets used in the book
 "Data Integration, Manipulation and Visualization of Phylogenetic Trees" by Guangchuang Yu (2022, ISBN:9781032233574).
An implementation of a boosted Tweedie compound Poisson model proposed by Yang, Y., Qian, W. and Zou, H. (2018) <doi:10.1080/07350015.2016.1200981>. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing high-order interactions among predictors. This package is based on the 'gbm' package originally developed by Greg Ridgeway.
Estimate the transition diagnostic classification model (TDCM) 
    described in Madison & Bradshaw (2018) <doi:10.1007/s11336-018-9638-5>, a 
    longitudinal extension of the log-linear cognitive diagnosis model (LCDM) in 
    Henson, Templin & Willse (2009) <doi:10.1007/s11336-008-9089-5>. As the LCDM 
    subsumes many other diagnostic classification models (DCMs), many other DCMs 
    can be estimated longitudinally via the TDCM. The 'TDCM' package includes 
    functions to estimate the single-group and multigroup TDCM, summarize 
    results of interest including item parameters, growth proportions, 
    transition probabilities, transitional reliability, attribute correlations, 
    model fit, and growth plots.
A collection of functions for automatically creating 'Stan'
      code for transition diagnostic classification models (TDCMs) as they are
      defined by Madison and Bradshaw (2018) <DOI:10.1007/s11336-018-9638-5>.
      This package supports automating the creation of 'Stan' code for TDCMs,
      fungible TDCMs (i.e., TDCMs with item parameters constrained to be equal
      across all items), and multi-threaded TDCMs.
The Time-Delay Correlation algorithm (TDCor) reconstructs the topology of a gene regulatory network (GRN) from time-series transcriptomic data.  The algorithm is described in details in Lavenus et al., Plant Cell, 2015.  It was initially developed to infer the topology of the GRN controlling lateral root formation in Arabidopsis thaliana.  The time-series transcriptomic dataset which was used in this study is included in the package to illustrate how to use it.
The t-Digest construction algorithm, by 
    Dunning et al., (2019) <doi:10.48550/arXiv.1902.04023>, uses a variant of 1-dimensional 
    k-means clustering to produce a very compact data structure that allows 
    accurate estimation of quantiles. This t-Digest data structure can be used 
    to estimate quantiles, compute other rank statistics or even to estimate 
    related measures like trimmed means. The advantage of the t-Digest over 
    previous digests for this purpose is that the t-Digest handles data with 
    full floating point resolution. The accuracy of quantile estimates produced 
    by t-Digests can be orders of magnitude more accurate than those produced 
    by previous digest algorithms. Methods are provided to create and update 
    t-Digests and retrieve quantiles from the accumulated distributions.
The main purpose of this package is to propose a rigorous framework to fairly compare trip distribution laws and models as described in Lenormand et al. (2016) <doi:10.1016/j.jtrangeo.2015.12.008>.
Set of functions designed to help in the
    analysis of TDP sensors. Features includes dates and time conversion, weather
    data interpolation, daily maximum of tension analysis and calculations required
    to convert sap flow density data to sap flow rates at the tree and plot scale (For more information see : Granier (1985) <DOI:10.1051/forest:19850204> & Granier (1987) <DOI:10.1093/treephys/3.4.309>).
Implementation of target diagrams using 'lattice' and 'ggplot2' graphics. Target diagrams provide a graphical overview of the respective contributions of the unbiased RMSE and MBE to the total RMSE (Jolliff, J. et al., 2009. "Summary Diagrams for Coupled Hydrodynamic-Ecosystem Model Skill Assessment." Journal of Marine Systems 76: 64–82.)
The tdROC package facilitates the estimation of time-dependent ROC 
    (Receiver Operating Characteristic) curves and the Area Under the time-dependent 
    ROC Curve (AUC) in the context of survival data, accommodating scenarios with 
    right censored data and the option to account for competing risks. In addition 
    to the ROC/AUC estimation, the package also estimates time-dependent Brier score and 
    survival difference. Confidence intervals of various estimated quantities can be 
    obtained from bootstrap. The package also offers plotting functions for visualizing 
    time-dependent ROC curves.
Functions that can be used to calculate time-dependent state and parameter sensitivities for both continuous- and discrete-time deterministic models. See Ng et al. (in press) <doi:10.1086/726143> for more information about time-dependent sensitivity analysis.
Functions for performing time domain signal coding as used in Chesmore (2001) <doi:10.1016/S0003-682X(01)00009-3>, and related tasks. This package creates the standard S-matrix and A-matrix (with variable lag), has tools to convert coding matrices into distributed matrices, provides published codebooks and allows for extraction of code sequences.
STARMA (Space-Time Autoregressive Moving Average) models are commonly utilized in modeling and forecasting spatiotemporal time series data. However, the intricate nonlinear dynamics observed in many space-time rainfall patterns often exceed the capabilities of conventional STARMA models. This R package enables the fitting of Time Delay Spatio-Temporal Neural Networks, which are adept at handling such complex nonlinear dynamics efficiently. For detailed methodology, please refer to Saha et al. (2020) <doi:10.1007/s00704-020-03374-2>.
Functions and examples are provided for Transmission/disequilibrium tests
             for extended marker haplotypes, as in
             Clayton, D. and Jones, H. (1999) "Transmission/disequilibrium tests
             for extended marker haplotypes". Amer. J. Hum. Genet., 65:1161-1169,
             <doi:10.1086/302566>.
Provides functions to estimate the insertion and deletion rates of transposable element (TE) families. The estimation of insertion rate consists of an improved estimate of the age distribution that takes into account random mutations, and an adjustment by the deletion rate. A hypothesis test for a uniform insertion rate is also implemented. This package implements the methods proposed in Dai et al (2018).
Different approaches for selecting the threshold in generalized Pareto distributions. Most of them are based on minimizing the AMSE-criterion or at least by reducing the bias of the assumed GPD-model. Others are heuristically motivated by searching for stable sample paths, i.e. a nearly constant region of the tail index estimator with respect to k, which is the number of data in the tail. The third class is motivated by graphical inspection. In addition, a sequential testing procedure for GPD-GoF-tests is also implemented here.
Statistics students often have problems understanding 
    the relation between a random variable's true scale and its z-values. 
    To allow instructors to better better visualize histograms for these 
    students, the package provides histograms with two horizontal 
    axis containing z-values and the true scale of the variable. 
    The function TeachHistDens() provides a density histogram with two axis.  
    TeachHistCounts() and TeachHistRelFreq() are variations for count and 
    relative frequency histograms, respectively. TeachConfInterv() and 
    TeachHypTest() help instructors to visualize confidence levels 
    and the results of hypothesis tests.
Demonstration functions that can be used in a classroom to demonstrate statistical concepts, or on your own to better understand the concepts or the programming.
Allows the user to draw probabilistic samples and make inferences from a finite population based on several sampling designs.
Can fit neural networks with up to two hidden layer and two different error functions. Also able to handle a weight decay. But just able to compute one output neuron and very slow.
A 'shiny' based interactive exploration framework for
    analyzing clinical trials data. 'teal' currently provides a dynamic
    filtering facility and different data viewers. 'teal' 'shiny'
    applications are built using standard 'shiny' modules.
Introduction of 'qenv' S4 class, that facilitates code
    execution and reproducibility in 'teal' applications.
Provides a 'teal_data' class as a unified data model for
    'teal' applications focusing on reproducibility and relational data.
Utilizing the 'logger' framework to record events within a
    package, specific to 'teal' family of packages.  Supports logging
    namespaces, hierarchical logging, various log destinations,
    vectorization, and more.
Provides user-friendly tools for creating and customizing
    clinical trial reports. By leveraging the 'teal' framework, this
    package provides 'teal' modules to easily create an interactive panel
    that allows for seamless adjustments to data presentation, thereby
    streamlining the creation of detailed and accurate reports.
Prebuilt 'shiny' modules containing tools for viewing data,
    visualizing data, understanding missing and outlier values within your
    data and performing simple data analysis.  This extends 'teal'
    framework that supports reproducible research and analysis.
Prebuilt 'shiny' modules containing tools for the generation
    of 'rmarkdown' reports, supporting reproducible research and analysis.
Data filtering module for 'teal' applications.  Allows for
    interactive filtering of data stored in 'data.frame' and
    'MultiAssayExperiment' objects. Also displays filtered and unfiltered
    observation counts.
A standardized user interface for column selection, that
    facilitates dataset merging in 'teal' framework.
Collection of 'shiny' widgets to support 'teal' applications.
    Enables the manipulation of application layout and plot or table
    settings.
Implements models of leaf temperature using energy balance. It uses units to ensure that parameters are properly specified and transformed before calculations. It allows separate lower and upper surface conductances to heat and water vapour, so sensible and latent heat loss are calculated for each surface separately  as in Foster and Smith (1986) <doi:10.1111/j.1365-3040.1986.tb02108.x>. It's straightforward to model leaf temperature over environmental gradients such as light, air temperature, humidity, and wind. It can also model leaf temperature over trait gradients such as leaf size or stomatal conductance. Other references are Monteith and Unsworth (2013, ISBN:9780123869104), Nobel (2009, ISBN:9780123741431), and Okajima et al. (2012) <doi:10.1007/s11284-011-0905-5>.
An implementation of the TEAM algorithm to identify local differences 
    between two (e.g. case and control) independent, univariate distributions, as 
    described in J Pura, C Chan, and J Xie (2019) <arXiv:1906.07757>. The algorithm 
    is based on embedding a multiple-testing procedure on a hierarchical structure 
    to identify high-resolution differences between two distributions. The 
    hierarchical structure is designed to identify strong, short-range differences 
    at lower layers and weaker, but long-range differences at increasing layers. 
    TEAM yields consistent layer-specific and overall false discovery rate control.
Provides color palettes corresponding to professional and amateur, 
    sports teams. These can be useful in creating data graphics that are themed 
    for particular teams.
Package of wrapper functions using R6 class to send requests to 
  Microsoft 'Teams' <https://products.office.com/en-us/microsoft-teams/group-chat-software> through webhooks.
  When you need to share information or data from R to 'Teams', rather than copying/pasting, you
  can use this package to send well-formatted output from multiple R objects.
Models the direction of the maximum horizontal stress using
    relative plate motion parameters. Statistical algorithms to evaluate
    the modeling results compared with the observed data. Provides plots
    to visualize the results. Methods described in Stephan et al. (2023)
    <doi:10.1038/s41598-023-42433-2> and Wdowinski (1998)
    <doi:10.1016/S0079-1946(98)00091-3>.
The typicality and eccentricity data analysis (TEDA) framework was
    put forward by Angelov (2013) <DOI:10.14313/JAMRIS_2-2014/16>. It has been further developed into multiple
    different techniques since, and provides a non-parametric way of determining how
    similar an observation, from a process that is not purely random, is to other
    observations generated by the process. This package provides code to use the
    batch and recursive TEDA methods that have been published.
Implements methods to fit Virtual Twins models (Foster et al. 
  (2011) <doi:10.1002/sim.4322>) for identifying subgroups with differential
  effects in the context of clinical trials while controlling the probability
  of falsely detecting a differential effect when the conditional average
  treatment effect is uniform across the study population using parameter
  selection methods proposed in Wolf et al. (2022) 
  <doi:10.1177/17407745221095855>.
Fits mixtures of multivariate t-distributions (with eigen-decomposed covariance structure) via the expectation conditional-maximization algorithm under a clustering or classification paradigm.
Functions for interacting directly with the Taiwan Economic Journal API to offer data in R. For more information go to <https://api.tej.com.tw>.
Implementation of the remote effects spatial process (RESP) model for teleconnection.  The RESP model is a geostatistical model that allows a spatially-referenced variable (like average precipitation) to be influenced by covariates defined on a remote domain (like sea surface temperatures).  The RESP model is introduced in Hewitt et al. (2018) <doi:10.1002/env.2523>.  Sample code for working with the RESP model is available at <https://jmhewitt.github.io/research/resp_example>. This material is based upon work supported by the National Science Foundation under grant number AGS 1419558. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
R wrapper around the Telegram Bot API (http://core.telegram.org/bots/api) to access Telegram's messaging facilities with ease (e.g. you send messages, images, files from R to your smartphone).
Provides a pure interface for the 'Telegram Bot API'
    <http://core.telegram.org/bots/api>. In addition to the pure API
    implementation, it features a number of tools to make the development of
    'Telegram' bots with R easy and straightforward, providing an easy-to-use
    interface that takes some work off the programmer.
Analyze telemetry datasets generalized to allow
    any technology. The filtering steps check for false positives caused by 
    reflected transmissions from surfaces and false pings from other noise 
    generating equipment. The filters are based on JSATS filtering algorithms
    found in package 'filteRjsats' <https://CRAN.R-project.org/package=filteRjsats>
    but have been generalized to allow the user to define many of the filtering 
    variables. Additionally, this package contains scripts used to help identify 
    an optimal maximum blanking period as defined in Capello et al (2015) 
    <doi:10.1371/journal.pone.0134002>. The functions were written according to
    their manuscript description, but have not been reviewed by the authors for 
    accuracy. It is included here as is, without warranty.
Fits Bayesian finite mixtures with an unknown number of components using the telescoping sampler and different component distributions. For more details see Frühwirth-Schnatter et al. (2021) <doi:10.1214/21-BA1294>.
Using The Free Evocation of Words Technique method with some functions, this package will make a
    social representation and other analysis. The Free Evocation of Words Technique consists of collecting a number of words evoked by a subject facing exposure to an inducer term. The purpose of this technique is to understand the relationships created between words evoked by the individual and the inducer term. This technique is included in the theory of social representations, therefore, on the information transmitted by an individual, seeks to create a profile that define a social group.
Streamline the processing of 'Telraam' data, sourced from open data
    mobility sensors. These tools range from data retrieval (without the need for
    API knowledge) to data visualization, including data preprocessing.
Method to estimate the effect of the trend in predictor variables on the observed trend
 of the response variable using mixed models with temporal autocorrelation. See Fernández-Martínez et
 al. (2017 and 2019) <doi:10.1038/s41598-017-08755-8> <doi:10.1038/s41558-018-0367-7>.
Temporal disaggregation methods are used to disaggregate and
    interpolate a low frequency time series to a higher frequency series, where
    either the sum, the mean, the first or the last value of the resulting
    high frequency series is consistent with the low frequency series. Temporal
    disaggregation can be performed with or without one or more high frequency
    indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso,
    Fernandez, Litterman, Denton and Denton-Cholette, summarized in Sax and
    Steiner (2013) <doi:10.32614/RJ-2013-028>. Supports most R time series
    classes.
Fits temperature response models to rate measurements taken at different temperatures. Etienne Low-Decarie,Tobias G. Boatman, Noah Bennett,Will Passfield,Antonio Gavalas-Olea,Philipp Siegel, Richard J. Geider (2017) <doi:10.1002/ece3.3576> .
Implements the template ICA (independent components analysis) model
    proposed in Mejia et al. (2020) <doi:10.1080/01621459.2019.1679638> and the 
    spatial template ICA model proposed in proposed in Mejia et al. (2022)
    <doi:10.1080/10618600.2022.2104289>. Both models estimate subject-level 
    brain as deviations from known population-level networks, which are 
    estimated using standard ICA algorithms. Both models employ an 
    expectation-maximization algorithm for estimation of the latent brain 
    networks and unknown model parameters. Includes direct support for 'CIFTI',
    'GIFTI', and 'NIFTI' neuroimaging file formats.
Provides tools to work with template code and text in R. It aims to
    provide a simple substitution mechanism for R-expressions inside these
    templates. Templates can be written in other languages like 'SQL', can
    simply be represented by characters in R, or can themselves be R-expressions
    or functions.
Helper functions for MASCOTNUM algorithm template, for design of numerical experiments practice:
    algorithm template parser to support MASCOTNUM specification <https://www.gdr-mascotnum.fr/template.html>, 
    'ask & tell' decoupling injection (inspired by <https://search.r-project.org/CRAN/refmans/sensitivity/html/decoupling.html>) 
    to use "crimped" algorithms (like uniroot(), optim(), ...) from outside R,
    basic template examples: Brent algorithm for 1 dim root finding and L-BFGS-B from base optim().
Performs maximum likelihood based estimation and inference on time to event data, possibly subject to non-informative right censoring. FitParaSurv() provides maximum likelihood estimates of model parameters and distributional characteristics, including the mean, median, variance, and restricted mean. CompParaSurv() compares the mean, median, and restricted mean survival experiences of two treatment groups. Candidate distributions include the exponential, gamma, generalized gamma, log-normal, and Weibull.
Analysis and visualization of data from temporal sensory methods, including for temporal check-all-that-apply (TCATA) and temporal dominance of sensations (TDS). Methods are mainly from manuscripts by Castura, J.C., Antúnez, L., Giménez, A., and Ares, G. (2016) <doi:10.1016/j.foodqual.2015.06.017>, Castura, Baker, and Ross (2016) <doi:10.1016/j.foodqual.2016.06.011>, and Pineau et al. (2009) <doi:10.1016/j.foodqual.2009.04.005>.
A collection of methods to estimate parameters of different tempered stable 
  distributions (TSD). Currently, there are seven different tempered stable 
  distributions to choose from: Tempered stable subordinator distribution, 
  classical TSD, generalized classical TSD, normal TSD, modified TSD, rapid 
  decreasing TSD, and Kim-Rachev TSD.
  The package also provides functions to compute density and probability 
  functions and tools to run Monte Carlo simulations.
  This package has already been used for the estimation of tempered stable 
  distributions (Massing (2023) <arXiv:2303.07060>).
  The following references form the theoretical background for various functions
  in this package. References for each function are explicitly listed in its 
  documentation:
  Bianchi et al. (2010) <doi:10.1007/978-88-470-1481-7_4>
  Bianchi et al. (2011) <doi:10.1137/S0040585X97984632>
  Carrasco (2017) <doi:10.1017/S0266466616000025>
  Feuerverger (1981) <doi:10.1111/j.2517-6161.1981.tb01143.x>
  Hansen et al. (1996) <doi:10.1080/07350015.1996.10524656>
  Hansen (1982) <doi:10.2307/1912775>
  Hofert (2011) <doi:10.1145/2043635.2043638>
  Kawai & Masuda (2011) <doi:10.1016/j.cam.2010.12.014>
  Kim et al. (2008) <doi:10.1016/j.jbankfin.2007.11.004>
  Kim et al. (2009) <doi:10.1007/978-3-7908-2050-8_5>
  Kim et al. (2010) <doi:10.1016/j.jbankfin.2010.01.015>
  Kuechler & Tappe (2013) <doi:10.1016/j.spa.2013.06.012>
  Rachev et al. (2011) <doi:10.1002/9781118268070>.
TEMPoral TEnsor Decomposition (TEMPTED), is a dimension reduction method for multivariate longitudinal data with varying temporal sampling. It formats the data into a temporal tensor and decomposes it into a summation of low-dimensional components, each consisting of a subject loading vector, a feature loading vector, and a continuous temporal loading function. These loadings provide a low-dimensional representation of subjects or samples and can be used to identify features associated with clusters of subjects or samples. TEMPTED provides the flexibility of allowing subjects to have different temporal sampling, so time points do not need to be binned, and missing time points do not need to be imputed.
Compute the coordinates to produce a tendril plot. 
    In the tendril plot, each tendril (branch) represents a type of events, 
    and the direction of the tendril is dictated by on which treatment arm the 
    event is occurring. If an event is occurring on the first of the two 
    specified treatment arms, the tendril bends in a clockwise direction. 
    If an event is occurring on the second of the treatment arms, the
    tendril bends in an anti-clockwise direction. 
    Ref: Karpefors, M and Weatherall, J., "The Tendril Plot - a novel visual summary 
    of the incidence, significance and temporal aspects of adverse events in 
    clinical trials" - JAMIA 2018; 25(8): 1069-1073 <doi:10.1093/jamia/ocy016>.
Implementation of ZENIT-POLAR substitution cipher method of encryption using by default the TENIS-POLAR cipher. This last cipher of encryption became famous through the collection of Brazilian books "Os Karas" by the author Pedro Bandeira. For more details, see "A Cryptographic Dictionary" (GC&CS, 1944).
Implements methods and functions to calibrate 
  time-specific niche models (multi-temporal calibration), letting users 
  execute a strict calibration and selection process of niche models based 
  on ellipsoids, as well as functions to project the potential distribution in 
  the present and in global change  scenarios.The 'tenm' package has functions
  to recover information that may be lost or overlooked while applying a data 
  curation protocol. This curation involves preserving occurrences that may 
  appear spatially redundant (occurring in the same pixel) but originate from 
  different time periods. A novel aspect of this package is that it might 
  reconstruct the fundamental niche more accurately than mono-calibrated 
  approaches. The theoretical background of the package can be found in 
  Peterson et al. (2011)<doi:10.5860/CHOICE.49-6266>.
The tensor product of two arrays is notionally an outer
        product of the arrays collapsed in specific extents by summing
        along the appropriate diagonals.
Provides convenience functions for advanced
        linear algebra with tensors and computation with data sets of
        tensors on a higher level abstraction. It includes Einstein and
        Riemann summing conventions, dragging, co- and contravariate
        indices, parallel computations on sequences of tensors.
Bayesian Tensor Factorization for decomposition of tensor data sets using the trilinear CANDECOMP/PARAFAC (CP) factorization, with automatic component selection. The complete data analysis pipeline is provided, including functions and recommendations for data normalization and model definition, as well as missing value prediction and model visualization. The method performs factorization for three-way tensor datasets and the inference is implemented with Gibbs sampling.
Contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: 'tPCA', 'tFOBI', 'tJADE', k-tJADE', 'tgFOBI', 'tgJADE', 'tSOBI', 'tNSS.SD', 'tNSS.JD', 'tNSS.TD.JD', 'tPP' and 'tTUCKER'.
Performs model-based tensor clustering methods including Tensor Gaussian Mixture Model (TGMM), Tensor Envelope Mixture Model (TEMM) by Deng and Zhang (2021) <DOI: 10.1111/biom.13486>, Doubly-Enhanced EM (DEEM) algorithm by Mai, Zhang, Pan and Deng (2021) <DOI: 10.1080/01621459.2021.1904959>.
Efficient algorithms for tensor noise reduction and completion. This package includes a suite of parametric and nonparametric tools for estimating tensor signals from noisy, possibly incomplete observations. The methods allow a broad range of data types, including continuous, binary, and ordinal-valued tensor entries. The algorithms employ the alternating optimization. The detailed algorithm description can be found in the following three references.
Here we provide tools for the computation and factorization of high-dimensional
	     tensor products that are formed by smaller matrices. The methods are based on
	     properties of Kronecker products (Searle 1982, p. 265, ISBN-10: 0470009616).
	     We evaluated this methodology by benchmark testing and illustrated its use in
	     Gaussian Linear Models ('Lopez-Cruz et al., 2024') <doi:10.1093/g3journal/jkae001>.
Interface to 'TensorFlow' <https://www.tensorflow.org/>,
  an open source software library for numerical computation using data
  flow graphs. Nodes in the graph represent mathematical operations,
  while the graph edges represent the multidimensional data arrays
  (tensors) communicated between them. The flexible architecture allows
  you to deploy computation to one or more 'CPUs' or 'GPUs' in a desktop,
  server, or mobile device with a single 'API'. 'TensorFlow' was originally
  developed by researchers and engineers working on the Google Brain Team
  within Google's Machine Intelligence research organization for the
  purposes of conducting machine learning and deep neural networks research,
  but the system is general enough to be applicable in a wide variety
  of other domains as well.
To handle higher-order tensor data. See Kolda and Bader (2009) <doi:10.1137/07070111X> for details on tensor. While existing packages on tensor data extend the base 'array' class to some data classes, this package serves as an alternative resort to handle tensor only as 'array' class.
    Some functionalities related to missingness are also supported.
A set of functions to estimate rank and factor loadings of time series tensor factor models. A tensor is a multidimensional array. To analyze high-dimensional tensor time series, factor model is a major dimension reduction tool. 'TensorPreAve' provides functions to estimate the rank of core tensors and factor loading spaces of tensor time series. More specifically, a pre-averaging method that accumulates information from tensor fibres is used to estimate the factor loading spaces. The estimated directions corresponding to the strongest factors are then used for projecting the data for a potentially improved re-estimation of the factor loading spaces themselves. A new rank estimation method is also implemented to utilizes correlation information from the projected data. 
    See Chen and Lam (2023) <arXiv:2208.04012> for more details.
Implement the alternating algorithm for supervised tensor decomposition with interactive side information. Details can be found in the publication Hu, Jiaxin, Chanwoo Lee, and Miaoyan Wang. "Generalized Tensor Decomposition with features on multiple modes." Journal of Computational and Graphical Statistics, Vol. 31, No. 1, 204-218, 2022 <doi:10.1080/10618600.2021.1978471>.
Implements the multiway sparse clustering approach of M. Wang and Y. Zeng, "Multiway clustering via tensor block models". Advances in Neural Information Processing System 32 (NeurIPS), 715-725, 2019.
An implementation of fitting generalized linear models on
    second-order tensor type data. The functions within this package mainly focus on
    parameter estimation, including parameter coefficients and standard deviation.
Factor and autoregressive models for matrix and tensor valued time series. We provide functions for estimation, simulation and prediction. The models are discussed in 
    Li et al (2021) <doi:10.48550/arXiv.2110.00928>, Chen et al (2020) <DOI:10.1080/01621459.2021.1912757>, 
    Chen et al (2020) <DOI:10.1016/j.jeconom.2020.07.015>, and Xiao et al (2020) <doi:10.48550/arXiv.2006.02611>.
A collection of functions for Kronecker structured covariance
    estimation and testing under the array normal model. For estimation,
    maximum likelihood and Bayesian equivariant estimation procedures are
    implemented. For testing, a likelihood ratio testing procedure is
    available. This package also contains additional functions for manipulating
    and decomposing tensor data sets. This work was partially supported by NSF
    grant DMS-1505136. Details of the methods are described in
    Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and
    Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.
The TEQR package contains software to calculate the operating characteristics for the TEQR and the ACT designs.The TEQR (toxicity equivalence range) design is a toxicity based cumulative cohort design with added safety rules. The ACT (Activity constrained for toxicity) design  is also a cumulative cohort design with additional safety rules. The unique feature of this design is that dose is escalated based on lack of activity rather than on lack of toxicity and is de-escalated only if an unacceptable level of toxicity is experienced.
An integrated set of extensions to the 'ergm' package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). 'tergm' is a part of the 'statnet' suite of packages for network analysis. See Krivitsky and Handcock (2014) <doi:10.1111/rssb.12014> and Carnegie, Krivitsky, Hunter, and Goodreau (2015) <doi:10.1080/10618600.2014.903087>.
Provides functions for the computationally efficient simulation of 
    dynamic networks estimated with the statistical framework of temporal 
    exponential random graph models, implemented in the 'tergm' package.
Creates, manipulates, queries and repairs vectors of
    parameter terms.  Parameter terms are the labels used to reference
    values in vectors, matrices and arrays. They represent the names in
    coefficient tables and the column names in 'mcmc' and 'mcmc.list'
    objects.
Implements simulated tests for the hypothesis that terminal digits are uniformly
    distributed (chi-squared goodness-of-fit) and the hypothesis that terminal digits
    are independent from preceding digits (several tests of independence for r x c 
    contingency tables). Also, for a number of distributions, implements Monte Carlo 
    simulations for type I errors and power for the test of independence.
Table, Listings, and Graphs (TLG) library for common outputs
    used in clinical trials.
Generalized estimating equations (GEE) are a popular choice
    for analyzing longitudinal binary outcomes. This package provides an
    interface for fitting GEE, currently for logistic regression, within
    the 'tern' <https://cran.r-project.org/package=tern> framework (Zhu,
    Sabanés Bové et al., 2023) and tabulate results easily using 'rtables'
    <https://cran.r-project.org/package=rtables> (Becker, Waddell et al.,
    2023).  It builds on 'geepack' <doi:10.18637/jss.v015.i02> (Højsgaard,
    Halekoh and Yan, 2006) for the actual GEE model fitting.
Mixed models for repeated measures (MMRM) are a popular
    choice for analyzing longitudinal continuous outcomes in randomized
    clinical trials and beyond; see for example Cnaan, Laird and Slasor
    (1997)
    <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E>.
    This package provides an interface for fitting MMRM within the 'tern'
    <https://cran.r-project.org/package=tern> framework by Zhu et al.
    (2023) and tabulate results easily using 'rtables'
    <https://cran.r-project.org/package=rtables> by Becker et al. (2023).
    It builds on 'mmrm' <https://cran.r-project.org/package=mmrm> by
    Sabanés Bové et al. (2023) for the actual MMRM computations.
Plots ternary diagrams (simplex plots / Gibbs triangles) and
  Holdridge life zone plots <doi:10.1126/science.105.2727.367> using the
  standard graphics functions.
  Allows custom annotation, interpolating, contouring and scaling of plotting
  region.
  Includes a 'Shiny' user interface for point-and-click ternary plotting.
  An alternative to 'ggtern', which uses the 'ggplot2' family of plotting 
  functions.
A suite of functions for visualising ternary probabilistic forecasts, as discussed in the paper by Jupp (2012) <doi:10.1098/rsta.2011.0350>.
Methods for spatial data analysis with vector (points, lines, polygons) and raster (grid) data. Methods for vector data include geometric operations such as intersect and buffer. Raster methods include local, focal, global, zonal and geometric operations. The predict and interpolate methods facilitate the use of regression type (interpolation, machine learning) models for spatial prediction, including with satellite remote sensing data. Processing of very large files is supported. See the manual and tutorials on <https://rspatial.org/> to get started. 'terra' replaces the 'raster' package ('terra' can do more, and it is faster and easier to use).
Provides triangulations of regular height fields, based on the methods described in "Fast Polygonal Approximation of Terrains and Height Fields" Michael Garland and Paul S. Heckbert (1995) <https://www.mgarland.org/files/papers/scape.pdf> using code from the 'hmm' library written by Michael Fogleman <https://www.github.com/fogleman/hmm>.
Functions for the retrieval, manipulation, and visualization
    of 'geospatial' data, with an aim towards producing '3D' landscape
    visualizations in the 'Unity' '3D' rendering engine. Functions are
    also provided for retrieving elevation data and base map tiles from
    the 'USGS' National Map <https://apps.nationalmap.gov/services/>.
Calculate the failure probability of civil engineering problems with Level I up to Level III Methods. Have fun and enjoy. References: Spaethe (1991, ISBN:3-211-82348-4) "Die Sicherheit tragender Baukonstruktionen", AU,BECK (2001) "Estimation of small failure probabilities in high dimensions by subset simulation." <doi:10.1016/S0266-8920(01)00019-4>, Breitung (1989) "Asymptotic approximations for probability integrals." <doi:10.1016/0266-8920(89)90024-6>.
Simulation of reconstructed phylogenetic trees under tree-wide time-heterogeneous birth-death processes and estimation of diversification parameters under the same model. Speciation and extinction rates can be any function of time and mass-extinction events at specific times can be provided. Trees can be simulated either conditioned on the number of species, the time of the process, or both. Additionally, the likelihood equations are implemented for convenience and can be used for Maximum Likelihood (ML) estimation and Bayesian inference.
Delaunay and Voronoï tessellations, with emphasis on the
    two-dimensional and the three-dimensional cases (the package provides
    functions to plot the tessellations for these cases). Delaunay
    tessellations are computed in C with the help of the 'Qhull' library
    <http://www.qhull.org/>.
Easy install and load key packages from the 'tesselle' suite
    in a single step. The 'tesselle' suite is a collection of packages for
    research and teaching in archaeology. These packages focus on
    quantitative analysis methods developed for archaeology. The
    'tesselle' packages are designed to work seamlessly together and to
    complement general-purpose and other specialized statistical packages.
    These packages can be used to explore and analyze common data types in
    archaeology: count data, compositional data and chronological data.
    Learn more about 'tesselle' at <https://www.tesselle.org>.
Bindings to 'Tesseract': 
     a powerful optical character recognition (OCR) engine that supports over 100 languages.
     The engine is highly configurable in order to tune the detection algorithms and
     obtain the best possible results.
Package test2norm contains functions to generate formulas for 
    normative standards applied to cognitive tests. It takes raw test scores 
    (e.g., number of correct responses) and converts them to scaled scores and 
    demographically adjusted scores, using methods described in Heaton et al. 
    (2003) <doi:10.1016/B978-012703570-3/50010-9> & Heaton et al. (2009, 
    ISBN:9780199702800). The scaled scores are calculated as quantiles of the 
    raw test scores, scaled to have the mean of 10 and standard deviation of 3, 
    such that higher values always correspond to better performance on the test. 
    The demographically adjusted scores are calculated from the residuals of a 
    model that regresses scaled scores on demographic predictors (e.g., age). 
    The norming procedure makes use of the mfp2() function from the 'mfp2' 
    package to explore nonlinear associations between cognition and demographic 
    variables.
This application provides exploratory and confirmatory factor analysis, classical test theory, unidimensional and multidimensional item response theory, and continuous item response model analysis, through the 'shiny' interactive interface. In addition,  it offers rich functionalities for visualizing and downloading results. Users can download figures, tables, and analysis reports via the interactive interface.
Finding the best values for user-specified arguments of a prediction algorithm can be difficult, particularly if there is an interaction between argument levels. This package automates the testing of any user-defined prediction algorithm over an arbitrary number of arguments. It includes functions for testing the algorithm over the given arguments with respect to an arbitrary number of user-defined diagnostics, visualising the results of these tests, and finding the optimal argument combinations with respect to each diagnostic.
A common way of validating a biological assay for is through a
    procedure, where m levels of an analyte are measured with n replicates at each
    level, and if all m estimates of the coefficient of variation (CV) are less
    than some prespecified level, then the assay is declared validated for precision
    within the range of the m analyte levels. Two limitations of this procedure are:
    there is no clear statistical statement of precision upon passing, and it is
    unclear how to modify the procedure for assays with constant standard deviation.
    We provide tools to convert such a procedure into a set of m hypothesis tests.
    This reframing motivates the m:n:q procedure, which upon completion delivers
    a 100q% upper confidence limit on the CV. Additionally, for a post-validation
    assay output of y, the method gives an ``effective standard deviation interval''
    of log(y) plus or minus r, which is a 68% confidence interval on log(mu), where
    mu is the expected value of the assay output for that sample. Further, the m:n:q
    procedure can be straightforwardly applied to constant standard deviation assays.
    We illustrate these tools by applying them to a growth inhibition assay. This is
    an implementation of the methods described in Fay, Sachs, and Miura (2018) 
    <doi:10.1002/sim.7528>.
Provides a method for comparing the results of two binary diagnostic tests using paired data. 
    Users can rapidly perform descriptive and inferential statistics in a single function call. Options permit users to select which parameters they are interested in comparing and methods for correction for multiple comparisons.
    Confidence intervals are calculated using the methods with the best coverage. Hypothesis tests use the methods with the best asymptotic performance. A summary of the methods is available in Roldán-Nofuentes (2020) <doi:10.1186/s12874-020-00988-y>.
    This package is targeted at clinical researchers who want to rapidly and effectively compare results from binary diagnostic tests.
Different multiple testing procedures for correlation tests are implemented. These procedures were shown to theoretically control asymptotically the Family Wise Error Rate (Roux (2018) <https://tel.archives-ouvertes.fr/tel-01971574v1>) or the False Discovery Rate (Cai & Liu (2016) <doi:10.1080/01621459.2014.999157>). The package gather four test statistics used in correlation testing, four FWER procedures with either single step or stepdown versions, and four FDR procedures.
Computes the test statistics for examining the significance of autocorrelation in univariate time series, cross-correlation in bivariate time series, Pearson correlations in multivariate series and test statistics for i.i.d. property of univariate series given in Dalla, Giraitis and Phillips (2020), <https://cowles.yale.edu/sites/default/files/files/pub/d21/d2194-r.pdf>.
Test your data! An extension of the 'testthat' unit testing
    framework with a family of functions and reporting tools for checking
    and validating data frames.
Functions for imputing missing item responses for dichotomous and
    polytomous test and assessment data. This package enables missing imputation
    methods that are suitable for test and assessment data, including: 
    listwise (LW) deletion (see De Ayala et al. 2001 <doi:10.1111/j.1745-3984.2001.tb01124.x>), 
    treating as incorrect (IN, see Lord, 1974 <doi: 10.1111/j.1745-3984.1974.tb00996.x>; 
    Mislevy & Wu, 1996 <doi: 10.1002/j.2333-8504.1996.tb01708.x>; 
    Pohl et al., 2014 <doi: 10.1177/0013164413504926>), person mean imputation (PM), 
    item mean imputation (IM), two-way (TW) and response function (RF) imputation,
    (see Sijtsma & van der Ark, 2003 <doi: 10.1207/s15327906mbr3804_4>),
    logistic regression (LR) imputation, predictive mean matching (PMM), and expectation–maximization (EM) 
    imputation (see Finch, 2008 <doi: 10.1111/j.1745-3984.2008.00062.x>).
Uses the optimal test design approach by Birnbaum (1968, ISBN:9781593119348) and
    van der Linden (2018) <doi:10.1201/9781315117430> to construct fixed, adaptive, and parallel tests.
    Supports the following mixed-integer programming (MIP) solver packages: 'Rsymphony',
    'highs', 'gurobi', 'lpSolve', and 'Rglpk'. The 'gurobi' package is not available from CRAN; see <https://www.gurobi.com/downloads/>.
Offers a solution for the unavailability of raw data in most anthropological studies by facilitating the calculations of several sexual dimorphism related analyses using the published summary statistics of metric data (mean, standard deviation and sex specific sample size) as illustrated by the works of Relethford, J. H., & Hodges, D. C. (1985) <doi:10.1002/ajpa.1330660105>, Greene, D. L. (1989) <doi:10.1002/ajpa.1330790113> and Konigsberg, L. W. (1991) <doi:10.1002/ajpa.1330840110>.
Provides data sets for teaching statistics and data science courses. 
    It includes a sample of data from John Edmund Kerrich's famous 
    coinflip experiment. These are data that I used for teaching SOC 4015 / SOC 
    5050 at Saint Louis University (SLU). The package also contains an R Markdown
    template with the required formatting for assignments in my courses 
    SOC 4015, SOC 4650, SOC 5050, and SOC 5650 at SLU.
Tests the hypothesis  that variances are homogeneous or not using bootstrap. The procedure uses a variance-based statistic, and is derived from a normal-theory test.
             The test equivalently expressed the hypothesis as a function of the log contrasts of the population variances. A box-type acceptance region is constructed to test the hypothesis. See Cahoy (2010) \doi{10.1016/j.csda.2010.04.012}.
Allows users to test characteristics of common R objects.
Add tests in-line in examples. Provides standalone functions for
    facilitating easier test writing in Rd files. However, a more familiar
    interface is provided using 'roxygen2' tags. Tools are also provided for
    facilitating package configuration and use with 'testthat'.
Provides test statistics, p-value, and confidence intervals based on 9 hypothesis tests for dependence.
Test functions are often used to test computer code.
  They are used in optimization to test algorithms and in
  metamodeling to evaluate model predictions. This package provides
  test functions that can be used for any purpose.
Develop, evaluate, and score multiple choice examinations, 
 psychological scales, questionnaires, and similar types of data involving
 sequences of choices among one or more sets of answers.
 This version of the package should be considered as brand new.  Almost all
 of the functions have been changed, including their argument list.
 See the file NEWS.Rd in the Inst folder for more information.
 Using the package does not require any formal statistical knowledge 
 beyond what would be provided by a first course in statistics in a 
 social science department.  There the user would encounter the concept 
 of probability and how it is used to model data and make decisions, 
 and would become familiar with basic mathematical and statistical notation.
 Most of the output is in graphical form.
Push a sample population for unit testing on data mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model.
Test the nullity of covariances, in a set of variables, using a simple univariate procedure. See Marques, Diago, Norouzirad, Bispo (2023) <doi:10.1002/mma.9130>.
Provides a bootstrap test which decides whether two dose response curves can be assumed as equal concerning their maximum absolute deviation. A plenty of choices for the model types are available, which can be found in the 'DoseFinding' package, which is used for the fitting of the models. See <doi:10.1080/01621459.2017.1281813> for details.
Provides two convenience functions assert() and test_pkg() to
    facilitate testing R packages.
GUI for entering test items and obtaining raw
   and transformed scores. The results are shown on the
   console and can be saved to a tabular text file for further
   statistical analysis. The user can define his own tests and
   scoring procedures through a GUI.
Provides functionality of a statistical testing implementation whether a dataset comes from a symmetric distribution when the center of symmetry is unknown, including Wilcoxon test and sign test procedure. In addition, sample size determination for both tests is provided. The Wilcoxon test procedure is described in Vexler et al. (2023) <https://www.sciencedirect.com/science/article/abs/pii/S0167947323000579>, and the sign test is outlined in Gastwirth (1971) <https://www.jstor.org/stable/2284233>.
Software testing is important, but, in part because it is
    frustrating and boring, many of us avoid it. 'testthat' is a testing
    framework for R that is easy to learn and use, and integrates with
    your existing 'workflow'.
Runs tests using the 'testthat' package but allows for multiple
    attempts for a single test. This is useful for noisy or flaky tests
    that generally pass but can fail due to occasional random errors,
    such as numeric instability or using random data.
Utility functions and 'RStudio' addins for writing,
    running and organizing automated tests. Integrates tightly with the
    packages 'testthat', 'devtools' and 'usethis'.  Hotkeys can be
    assigned to the 'RStudio' addins for running tests in a single file or
    to switch between a source file and the associated test file. In
    addition, testthis provides function to manage and run tests in
    subdirectories of the test/testthat directory.
Tests one hypothesis with several test statistics, correcting for multiple testing.  The central function in the package is testtwice().  In a sensitivity analysis, the method has the largest design sensitivity of its component tests.  The package implements the method and examples in Rosenbaum, P. R. (2012) <doi:10.1093/biomet/ass032> Testing one hypothesis twice in observational studies. Biometrika, 99(4), 763-774.
The satisfaction Analysis using the tetraclasse model from Sylvie Llosa. Llosa (1997) <http://www.jstor.org/stable/40592578>.
Each sequence is predicted by expanding the distance matrix. The compact set of hyper-parameters is tuned through random search.
Uses the Distorted Wave Born Approximation (DWBA) to compute the
  acoustic backward scattering, the geometry of the object is formed by a
  volumetric mesh, composed of tetrahedrons. This computation is done efficiently
  through an analytical 3D integration that allows for a solution which is
  expressed in terms of elementary functions for each tetrahedron.
  It is important to note that this method is only valid for objects whose
  acoustic properties, such as density and sound speed, do not vary significantly
  compared to the surrounding medium. (See Lavia, Cascallares and Gonzalez, J. D. (2023). 
  TetraScatt model: Born approximation for the estimation of acoustic dispersion of 
  fluid-like objects of arbitrary geometries. arXiv preprint <arXiv:2312.16721>).
A game inspired by 'Tetris'. Opens a plot window device and starts a game of 'tetRys' in it. Steer the tetrominos with the arrow keys, press Pause to pause and Esc to end the game.
When using the R package  'exams'  to write mathematics questions in 'Sweave' files, the output of a lot of R functions need to be adjusted for display in mathematical formulas. Specifically, the functions were accumulated when writing questions for the  topics of the mathematics courses College Algebra, Precalculus, Calculus, Differential Equations, Introduction to Probability, and Linear Algebra.  The output of the developed functions can be used in 'Sweave' files.
Checks LaTeX documents and .bib files for typing errors, such as spelling errors, incorrect quotation marks. Also provides useful functions for parsing and linting bibliography files.
Randomizing exams with 'LaTeX'.
    If you can compile your main document with 'LaTeX', the program should be able to compile the randomized
    versions without much extra effort when creating the document.
Statistical extreme value modelling of threshold excesses, maxima
    and multivariate extremes. Univariate models for threshold excesses and maxima
    are the Generalised Pareto, and Generalised Extreme Value model respectively.
    These models may be fitted by using maximum (optionally penalised-)likelihood,
    or Bayesian estimation, and both classes of models may be fitted with covariates
    in any/all model parameters. Model diagnostics support the fitting process.
    Graphical output for visualising fitted models and return level estimates is
    provided. For serially dependent sequences, the intervals declustering algorithm
    of Ferro and Segers (2003) <doi:10.1111/1467-9868.00401> is provided, with
    diagnostic support to aid selection of threshold and declustering horizon.
    Multivariate modelling is performed via the conditional approach of Heffernan
    and Tawn (2004) <doi:10.1111/j.1467-9868.2004.02050.x>, with graphical tools for
    threshold selection and to diagnose estimation convergence.
Articles in the 'R Journal' were first authored in 'LaTeX', which performs 
    admirably for 'PDF' files but is less than ideal for modern online interfaces.
    The 'texor' package does all the transitional chores and conversions necessary
    to move to the online versions.
An extension of ExPosition for two table analyses, specifically, discriminant analyses.
Compile snippets of 'LaTeX' directly into images
    from the R console to view in the 'RStudio' viewer pane, Shiny apps
    and 'RMarkdown' documents.
Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft <doi:10.18637/jss.v055.i08>.)
Link R with Transformers from Hugging Face to transform text variables to word embeddings; where the word embeddings are used to statistically test the mean difference between set of texts, compute semantic similarity scores between texts, predict numerical variables, and visual statistically significant words according to various dimensions etc. For more information see  <https://www.r-text.org>.
Find similarities between texts using the Smith-Waterman algorithm. The algorithm performs local sequence alignment and determines similar regions between two strings.
    The Smith-Waterman algorithm is explained in the paper: "Identification of common molecular subsequences" by T.F.Smith and M.S.Waterman (1981), available at <doi:10.1016/0022-2836(81)90087-5>. 
    This package implements the same logic for sequences of words and letters instead of molecular sequences.
This is a collection of functions optimized for working with
             with various kinds of text matrices. Focusing on 
             the text matrix as the primary object - represented 
             either as a base R dense matrix or a 'Matrix' package sparse 
             matrix - allows for a consistent and intuitive interface 
             that stays close to the underlying mathematical foundation 
             of computational text analysis. In particular, the package
             includes functions for working with word embeddings, 
             text networks, and document-term matrices. Methods developed in 
             Stoltz and Taylor (2019) <doi:10.1007/s42001-019-00048-6>, 
             Taylor and Stoltz (2020) <doi:10.1007/s42001-020-00075-8>, 
             Taylor and Stoltz (2020) <doi:10.15195/v7.a23>, and
             Stoltz and Taylor (2021) <doi:10.1016/j.poetic.2021.101567>.
The United Nations’ Sustainable Development Goals (SDGs) have become an important guideline for organisations to monitor and plan their contributions to social, economic, and environmental transformations. The 'text2sdg' package is an open-source analysis package that identifies SDGs in text using scientifically developed query systems, opening up the opportunity to monitor any type of text-based data, such as scientific output or corporate publications. For more information regarding the methodology see Meier, Mata & Wulff (2022) <arXiv:2110.05856>.
This is a companion package for the 'text2sdg' package. It contains the trained ensemble models needed by the 'detect_sdg' function from the 'text2sdg' package. See Wulff, Meier and Mata (2023) <arXiv:2301.11353> and Meier, Wulff and Mata (2021) <arXiv:2110.05856> for reference.
Converts text into speech using various text-to-speech (TTS) engines and provides an unified interface for accessing their functionality.
  With this package, users can easily generate audio files of spoken words, phrases, or sentences from plain text data. The package supports multiple TTS engines, 
  including Google's 'Cloud Text-to-Speech API', 'Amazon Polly', Microsoft's 'Cognitive Services Text to Speech REST API',  and a free TTS engine called 'Coqui TTS'.
Fast and memory-friendly tools for text vectorization, topic
    modeling (LDA, LSA), word embeddings (GloVe), similarities. This package
    provides a source-agnostic streaming API, which allows researchers to perform
    analysis of collections of documents which are larger than available RAM. All
    core functions are parallelized to benefit from multicore machines.
Generate 'LaTeX' tables directly from R. It builds 'LaTeX' tables in blocks in the spirit of 'ggplot2' using the '+' and '/' operators for concatenation in the vertical and horizontal dimensions, respectively. It exports tables in the 'LaTeX' tabular environment using '.tex' code. It can compile '.tex' code to 'PDF' automatically.
Compute a non-overlapping layout of text boxes to label multiple overlain curves. For each curve, iteratively search for an adjacent x,y position for the text box that does not overlap with the other curves. If this process fails, then offsets are computed to add to the y values for each curve, that results in sufficient space to add all of the text labels.
Tools to clean and process text.  Tools are geared at checking for substrings that
          are not optimal for analysis and replacing or removing them (normalizing) with more
          analysis friendly substrings (see Sproat, Black, Chen, Kumar, Ostendorf, & Richards
          (2001) <doi:10.1006/csla.2001.0169>) or extracting them into new variables. For
          example, emoticons are often used in text but not always easily handled by analysis
          algorithms.  The replace_emoticon() function replaces emoticons with word
          equivalents.
Provides a framework to download, parse, and store text
    datasets on the disk and load them when needed. Includes various
    sentiment lexicons and labeled text data sets for classification and
    analysis.
Implements the approach described in Fong and Grimmer (2016) <https://aclweb.org/anthology/P/P16/P16-1151.pdf> for 
	automatically discovering latent treatments from a corpus and estimating the average marginal component effect (AMCE) of 
	each treatment.  The data is divided into a training and test set.  The supervised Indian Buffet Process (sibp) is used 
	to discover latent treatments in the training set.  The fitted model is then applied to the test set to infer the values 
	of the latent treatments in the test set.  Finally, Y is regressed on the latent treatments in the test set to estimate 
	the causal effect of each treatment.
Implement text and sentiment analysis with 'texter'. 
             Generate sentiment scores on text data and also visualize sentiments.
             'texter' allows you to quickly generate insights on your data.
             It includes support for lexicons such as 'NRC' and 'Bing'.
Provides functionalities based on the paper "Time Varying Dictionary and the Predictive Power of FED Minutes" (Lima, 2018) <doi:10.2139/ssrn.3312483>. It selects the most predictive terms, that we call time-varying dictionary using supervised machine learning techniques as lasso and elastic net.
The software application Praat can be used to annotate
    waveform data (e.g., to mark intervals of interest or to label events).
    (See <http://www.fon.hum.uva.nl/praat/> for more information about Praat.)
    These annotations are stored in a Praat TextGrid object, which consists of
    a number of interval tiers and point tiers. An interval tier consists of
    sequential (i.e., not overlapping) labeled intervals. A point tier consists
    of labeled events that have no duration. The 'textgRid' package provides
    S4 classes, generics, and methods for accessing information that is stored
    in Praat TextGrid objects.
Contains real images of the same textile material 
    with/without local defects, which were used in 
    Bui and Apley (2018) <doi:10.1080/00401706.2017.1302362>.
Multinomial (inverse) regression inference for text documents and associated attributes.  For details see: Taddy (2013 JASA) Multinomial Inverse Regression for Text Analysis <arXiv:1012.2098> and Taddy (2015, AoAS), Distributed Multinomial Regression, <arXiv:1311.6139>. A minimalist partial least squares routine is also included.  Note that the topic modeling capability of earlier 'textir' is now a separate package, 'maptpx'.
An aid for text mining in R, with a syntax that
    should be familiar to experienced R users. Provides a wrapper for several 
    topic models that take similarly-formatted input and give similarly-formatted
    output. Has additional functionality for analyzing and diagnostics for
    topic models.
Graphic interface for text analysis, implement a few methods such as biplots, correspondence analysis, co-occurrence, clustering, topic models, correlations and sentiments.
Statistical exploration of textual corpora using several methods
    from French 'Textometrie' (new name of 'Lexicometrie') and French 'Data Analysis' schools.
    It includes methods for exploring irregularity of distribution of lexicon features across
    text sets or parts of texts (Specificity analysis); multi-dimensional exploration (Factorial analysis), etc. 
    Those methods are used in the TXM software.
Visualise complex relations in texts. This is done by providing functionalities for displaying 
    text co-occurrence networks, text correlation networks, dependency relationships as well as text clustering and semantic text 'embeddings'. 
    Feel free to join the effort of providing interesting text visualisations.
The 'textrank' algorithm is an extension of the 'Pagerank' algorithm for text. The algorithm allows to summarize text by calculating how sentences are related to one another. This is done by looking at overlapping terminology used in sentences in order to set up links between sentences. The resulting sentence network is next plugged into the 'Pagerank' algorithm which identifies the most important sentences in your text and ranks them. 
    In a similar way 'textrank' can also be used to extract keywords. A word network is constructed by looking if words are following one another. On top of that network the 'Pagerank' algorithm is applied to extract relevant words after which relevant words which are following one another are combined to get keywords.  
    More information can be found in the paper from Mihalcea, Rada & Tarau, Paul (2004) <https://www.aclweb.org/anthology/W04-3252/>.
A wrapper for the 'TexTra' API <https://mt-auto-minhon-mlt.ucri.jgn-x.jp/>, 
    a web service for translating texts between different languages. 
    'TexTra' API account is required to use the service.
Converting text to numerical features requires specifically
    created procedures, which are implemented as steps according to the
    'recipes' package. These steps allows for tokenization, filtering,
    counting (tf and tfidf) and feature hashing.
Function for sparse regression on raw text, regressing a labeling
    vector onto a feature space consisting of all possible phrases.
Tools for measuring similarity among documents and detecting
    passages which have been reused. Implements shingled n-gram, skip n-gram,
    and other tokenizers; similarity/dissimilarity functions; pairwise
    comparisons; minhash and locality sensitive hashing algorithms; and a
    version of the Smith-Waterman local alignment algorithm suitable for
    natural language.
Tools that can be used to reshape and restructure text data.
Provides access to the text shaping functionality in the
    'HarfBuzz' library and the bidirectional algorithm in the 'Fribidi'
    library.  'textshaping' is a low-level utility package mainly for
    graphic devices that expands upon the font tool-set provided by the
    'systemfonts' package.
Tools that stem and lemmatize text.  Stemming is a process that removes
         endings such as affixes.  Lemmatization is the process of grouping inflected
         forms together as a single base form.
It offers functions for splitting, parsing, tokenizing and creating a vocabulary for big text data files. Moreover, it includes functions for building a document-term matrix and extracting information from those (term-associations, most frequent terms). It also embodies functions for calculating token statistics (collocations, look-up tables, string dissimilarities) and functions to work with sparse matrices. Lastly, it includes functions for Word Vector Representations (i.e. 'GloVe', 'fasttext') and incorporates functions for the calculation of (pairwise) text document dissimilarities. The source code is based on 'C++11' and exported in R through the 'Rcpp', 'RcppArmadillo' and 'BH' packages.
A framework for text cleansing and analysis. Conveniently prepare and process large amounts of text for analysis. 
  Includes various metrics for word counts/frequencies that scale efficiently. Quickly 
  analyze large amounts of text data using a text.table (a data.table created with one word (or unit of text analysis) per row, similar to the tidytext format). 
  Offers flexibility to efficiently work with text data stored in vectors as well as text data formatted as a text.table.
Utilities for handling character vectors
  that store human-readable text (either plain or with
  markup, such as HTML or LaTeX). The package provides,
  in particular, functions that help with the
  preparation of plain-text reports, e.g. for expanding
  and aligning strings that form the lines of such
  reports. The package also provides generic functions for
  transforming R objects to HTML and to plain text.
Defines S3 vector data types for vectors of functional data 
   (grid-based, spline-based or functional principal components-based) with all 
   arithmetic and summary methods, derivation, integration and smoothing, 
   plotting, data import and export, and data wrangling, such as re-evaluating, 
   subsetting, sub-assigning, zooming into sub-domains, or extracting functional 
   features like minima/maxima and their locations. 
   The implementation allows including such vectors in data frames for joint 
   analysis of functional and scalar variables.
R implementation of 'TFactS' to predict which are the 
    transcription factors (TFs), regulated in a biological condition based on
    lists of differentially expressed genes (DEGs) obtained from transcriptome
    experiments. This package is based on the 'TFactS' concept by 
    Essaghir et al. (2010) <doi:10.1093/nar/gkq149> and expands
    it. It allows users to perform 'TFactS'-like enrichment approach. The
    package can import and use the original catalogue file from the 'TFactS'
    as well as users' defined catalogues of interest that are not supported 
    by 'TFactS' (e.g., Arabidopsis).
'TensorFlow SIG Addons' <https://www.tensorflow.org/addons> is a repository 
             of community contributions that conform to well-established API patterns, 
             but implement new functionality not available in core 'TensorFlow'. 
             'TensorFlow' natively supports a large number of operators, layers, metrics, 
             losses, optimizers, and more. However, in a fast moving field like Machine Learning, 
             there are many interesting new developments that cannot be integrated into 
             core 'TensorFlow' (because their broad applicability is not yet clear, or 
             it is mostly used by a smaller subset of the community).
Building customized transfer function and ARIMA models with multiple operators and parameter restrictions. Functions for model identification, model estimation (exact or conditional maximum likelihood), model diagnostic checking, automatic outlier detection, calendar effects, forecasting and seasonal adjustment. See Bell and Hillmer (1983) <doi:10.1080/01621459.1983.10478005>, Box, Jenkins, Reinsel and Ljung <ISBN:978-1-118-67502-1>, Box, Pierce and Newbold (1987) <doi:10.1080/01621459.1987.10478430>, Box and Tiao (1975) <doi:10.1080/01621459.1975.10480264>, Chen and Liu (1993) <doi:10.1080/01621459.1993.10594321>.
Translate R control flow expressions into 'Tensorflow' graphs.
In Cox's proportional hazard model, covariates are modeled as linear function and may not be flexible. This package implements additive trend filtering Cox proportional hazards model as proposed in Jiacheng Wu & Daniela Witten (2019) "Flexible and Interpretable Models for Survival Data", Journal of Computational and Graphical Statistics, <DOI:10.1080/10618600.2019.1592758>. The fitted functions are piecewise polynomial with adaptively chosen knots.
Interface to 'TensorFlow' Datasets, a high-level library for
    building complex input pipelines from simple, re-usable pieces.
    See <https://www.tensorflow.org/guide> for additional
    details.
Tools to deploy 'TensorFlow' <https://www.tensorflow.org/> models across 
  multiple services. Currently, it provides a local server for testing 'cloudml' 
  compatible services.
Statistical interpretation of forensic glass transfer (Simulation of the probability distribution of recovered glass fragments).
Interface to 'TensorFlow' Estimators 
    <https://www.tensorflow.org/guide/estimator>, a high-level 
    API that provides implementations of many different model types 
    including linear models and deep neural networks.
Provides a convenient way to log scalars, images, audio, and histograms in the 'tfevent' record file format. 
  Logged data can be visualized on the fly using 'TensorBoard', a web based tool that focuses on visualizing the training 
  progress of machine learning models.
'TensorFlow' Hub is a library for the publication, discovery, and
    consumption of reusable parts of machine learning models. A module is a 
    self-contained piece of a 'TensorFlow' graph, along with its weights and 
    assets, that can be reused across different tasks in a process known as
    transfer learning. Transfer learning train a model with a smaller dataset,
    improve generalization, and speed up training.
Interface to 'TensorFlow IO', Datasets and filesystem extensions maintained by `TensorFlow SIG-IO` <https://github.com/tensorflow/community/blob/master/sigs/io/CHARTER.md>.
We provide the cumulative distribution function (CDF),
    quantile, and statistical power calculator for a collection of 
    thresholding Fisher's p-value combination methods, including Fisher's 
    p-value combination method, truncated product method and, in particular, soft-thresholding
    Fisher's p-value combination method which is proven to be optimal in some
    context of signal detection. The p-value calculator for the omnibus version
    of these tests are also included. For reference, please see Hong Zhang and Zheyang Wu. "TFisher Tests: Optimal and Adaptive Thresholding for Combining p-Values", submitted.
In putative Transcription Factor Binding Sites (TFBSs) 
             identification from sequence/alignments,
             we are interested in the significance of certain match score.
             TFMPvalue provides the accurate calculation of P-value with 
             score threshold for Position Weight Matrices, 
             or the score with given P-value. 
             It is an interface to code originally made available by 
             Helene Touzet and Jean-Stephane Varre, 2007, 
             Algorithms Mol Biol:2, 15. <doi:10.1186/1748-7188-2-15>.
Provides a framework for the creation and use of Neural ordinary
    differential equations with the 'tensorflow' and 'keras' packages. 
    The idea of Neural ordinary differential equations comes from 
    Chen et al. (2018) <doi:10.48550/arXiv.1806.07366>, and 
    presents a novel way of learning and solving differential systems.
Utilities for simple manipulation and quick  	
	plotting of time series data. These utilities use the 'tframe' package
	which provides a programming kernel for time series. Extensions to
	'tframe' provided in 'tframePlus' can also be used. See the Guide
	vignette for examples.
Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow'
    that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU').
    'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers,
    variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.
A kernel of functions for programming 
	time series methods in a way that is relatively independently of the 
	representation of time. Also provides plotting, time windowing, 
	and some
	other utility functions which are specifically intended for time series.
	See the Guide distributed as a vignette, or ?tframe.Intro for more
	details. (User utilities are in package tfplot.)
Extensions and additional 'tframe' utilities.
Provide functions to estimate the coefficients in high-dimensional linear regressions via a tuning-free and robust approach. The method was published in Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "A Tuning-free Robust and Efficient Approach to High-dimensional Regression", Journal of the American Statistical Association, 115:532, 1700-1714(JASA’s discussion paper), <doi:10.1080/01621459.2020.1840989>. See also Wang, L., Peng, B., Bradic, J., Li, R. and Wu, Y. (2020), "Rejoinder to “A tuning-free robust and efficient approach to high-dimensional regression". Journal of the American Statistical Association, 115, 1726-1729, <doi:10.1080/01621459.2020.1843865>; Peng, B. and Wang, L. (2015), "An Iterative Coordinate Descent Algorithm for High-Dimensional Nonconvex Penalized Quantile Regression", Journal of Computational and Graphical Statistics, 24:3, 676-694, <doi:10.1080/10618600.2014.913516>; Clémençon, S., Colin, I., and Bellet, A. (2016), "Scaling-up empirical risk minimization: optimization of incomplete u-statistics", The Journal of Machine Learning Research, 17(1):2682–2717; Fan, J. and Li, R. (2001), "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties", Journal of the American Statistical Association, 96:456, 1348-1360, <doi:10.1198/016214501753382273>.
Creates a framework to store and apply display metadata to Analysis
    Results Datasets (ARDs). The use of 'tfrmt' allows users to define table 
    format and styling without the data, and later apply the format to the data.
Provides an interactive interface to the 'tfrmt' package. Users 
    can import, modify, and export tables and templates with little to no code.
Create and manage unique directories for each 'TensorFlow' 
  training run. Provides a unique, time stamped directory for each run
  along with functions to retrieve the directory of the latest run or 
  latest several runs.
Extension of 'funHDDC' Schmutz et al. (2018) 
    <doi:10.1007/s00180-020-00958-4> for cases including
    outliers by fitting t-distributions for robust groups. 'TFunHDDC' can cluster
    univariate or multivariate data produced by the 'fda' package for data using
    a b-splines or Fourier basis.
Theme and colour palettes for The Globe and Mail's graphics. Includes colour and fill scale functions, colour palette helpers and a Globe-styled 'ggplot2' theme object.
Deconvolving thermoluminescence glow curves according to various 
        kinetic models (first-order, second-order, general-order, and mixed-order) using 
        a modified Levenberg-Marquardt algorithm (More, 1978) <DOI:10.1007/BFb0067700>. 
        It provides the possibility of setting constraints or fixing any of parameters. 
        It offers an interactive way to initialize parameters by clicking with a mouse on 
        a plot at positions where peak maxima should be located. The optimal estimate is 
        obtained by "trial-and-error". It also provides routines for simulating first-order, 
        second-order, and general-order glow peaks.
Density, distribution function, quantile function and random generation for the Truncated Generalised Gamma Distribution (also in log10(x) and ln(x) space).
Efficient implementation of K-Means++ algorithm. For more
    information see (1) "kmeans++ the advantages of the k-means++
    algorithm" by David Arthur and Sergei Vassilvitskii (2007),
    Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete
    algorithms, Society for Industrial and Applied Mathematics,
    Philadelphia, PA, USA, pp. 1027-1035, and (2) "The Effectiveness of
    Lloyd-Type Methods for the k-Means Problem" by Rafail Ostrovsky, Yuval
    Rabani, Leonard J. Schulman and Chaitanya Swamy
    <doi:10.1145/2395116.2395117>.
Bayesian nonstationary, semiparametric nonlinear regression 
 and design by treed Gaussian processes (GPs) with jumps to the limiting 
 linear model (LLM).  Special cases also implemented include Bayesian 
 linear models, CART, treed linear models, stationary separable and 
 isotropic GPs, and GP single-index models.  Provides 1-d and 2-d plotting functions 
 (with projection and slice capabilities) and tree drawing, designed for 
 visualization of tgp-class output.  Sensitivity analysis and 
 multi-resolution models are supported. Sequential experimental 
 design and adaptive sampling functions are also provided, including ALM, 
 ALC, and expected improvement.  The latter supports derivative-free
 optimization of noisy black-box functions.  For details and tutorials, 
 see Gramacy (2007) <doi:10.18637/jss.v019.i09> and Gramacy & Taddy (2010) 
 <doi:10.18637/jss.v033.i06>.
Functions to compute and plot tracheidograms, as in De Soto et al. (2011) <doi:10.1139/x11-045>.
Rapid advancements in high-throughput gene sequencing
    technologies have resulted in genome-scale time-series datasets. 
    Uncovering the underlying temporal sequence of gene regulatory events 
    in the form of time-varying gene regulatory networks demands 
    accurate and computationally efficient algorithms. Such an
    algorithm is 'TGS'. It is proposed in Saptarshi Pyne, Alok Ranjan 
    Kumar, and Ashish Anand. Rapid reconstruction of time-varying 
    gene regulatory networks. IEEE/ACM Transactions on Computational 
    Biology and Bioinformatics, 17(1):278{291, Jan-Feb 2020. The TGS 
    algorithm is shown to consume only 29 minutes for a microarray 
    dataset with 4028 genes. This package provides an implementation 
    of the TGS algorithm and its variants.
Functions for implementing the targeted gold standard (GS) testing. You provide the true disease or treatment failure status and the risk score, tell 'TGST' the availability of GS tests and which method to use, and it returns the optimal tripartite rules. Please refer to Liu et al. (2013) <doi:10.1080/01621459.2013.810149> for more details.
A collection of high performance utilities to compute
    distance, correlation, auto correlation, clustering and other tasks.
    Contains graph clustering algorithm described in "MetaCell: analysis
    of single-cell RNA-seq data using K-nn graph partitions" (Yael Baran,
    Akhiad Bercovich, Arnau Sebe-Pedros, Yaniv Lubling, Amir Giladi, Elad
    Chomsky, Zohar Meir, Michael Hoichman, Aviezer Lifshitz & Amos Tanay,
    2019 <doi:10.1186/s13059-019-1812-2>).
Turing Geovisualization Engine R package for geospatial visualization and analysis.
Contains data sets used in other packages Torsten Hothorn
  maintains.
Provide R Markdown templates and LaTeX preamble
    which are necessary for creating PDF from R Markdown documents in Thai language.
Implements the truncated harmonic mean estimator (THAMES) 
    of the reciprocal marginal likelihood using posterior samples and
    unnormalized log posterior values via reciprocal importance sampling.
    Metodiev, Perrot-Dockès, Ouadah, Irons, & Raftery (2023) <arXiv:2305.08952>.
Find out who maintains the packages you use in
             your current session or in your package library and
             maybe say 'thank you'.
A tool for starring GitHub repositories.
Consolidates and calculates different sets of time-series features from multiple
    'R' and 'Python' packages including 'Rcatch22' Henderson, T. (2021) <doi:10.5281/zenodo.5546815>,
    'feasts' O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>,
    'tsfeatures' Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020)
    <https://CRAN.R-project.org/package=tsfeatures>, 'tsfresh' Christ, M., Braun, N., Neuffer, J.,
    and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, 'TSFEL' Barandas, M., et al. (2020)
    <doi:10.1016/j.softx.2020.100456>, and 'Kats' Facebook Infrastructure Data Science (2021)
    <https://facebookresearch.github.io/Kats/>.
Provides a suite of functions for analysing, interpreting, and visualising
    time-series features calculated from different feature sets from the 'theft' package. 
    Implements statistical learning methodologies described in Henderson, T., 
    Bryant, A., and Fulcher, B. (2023) <arXiv:2303.17809>.
Provides a simple interface to search available data provided by
    Theia (<https://theia.cnes.fr>), download it, and manage it. Data can be downloaded
    based on a search result or from a cart file downloaded from Theia website.
Theme 'ggplot2', 'lattice', and 'base' graphics based on a
    few choices, including foreground color, background color, accent
    color, and font family. Fonts that aren't available on the system, but
    are available via download on 'Google Fonts', can be automatically
    downloaded, cached, and registered for use with the 'showtext' and
    'ragg' packages.
A dataset with an uneven number of cases in each class is
    said to be unbalanced. Many models produce a subpar performance on
    unbalanced datasets. A dataset can be balanced by increasing the
    number of minority cases using SMOTE 2011 <arXiv:1106.1813>,
    BorderlineSMOTE 2005 <doi:10.1007/11538059_91> and ADASYN 2008
    <https://ieeexplore.ieee.org/document/4633969>. Or by decreasing the
    number of majority cases using NearMiss 2003
    <https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf> or Tomek
    link removal 1976 <https://ieeexplore.ieee.org/document/4309452>.
Utilizing the 'OpenAI' API as the back end (<https://platform.openai.com/docs/api-reference>), 'TheOpenAIR' offers 'R' wrapper functions for the 'ChatGPT' endpoint and several high-level functions that enable the integration of 'ChatGPT' capabilities in diverse data-related tasks, such as data cleansing and automated analytics script generation.
We present a range of simulations to aid researchers in determining appropriate sample sizes when performing critical
    thermal limits studies (e.g. CTmin/CTmin experiments). A number of wrapper functions are provided for plotting and summarising outputs
    from these simulations. This package is presented in van Steenderen, C.J.M., Sutton, G.F., Owen, C.A., Martin, G.D., and Coetzee, J.A. Sample size assessments for thermal physiology studies: An R
    package and R Shiny application. 2023. Physiological Entomology. <doi:10.1111/phen.12416>. The GUI version of this package is available on the R Shiny online server at: 
    <https://clarkevansteenderen.shinyapps.io/ThermalSampleR_Shiny/> , or it is accessible via GitHub at <https://github.com/clarkevansteenderen/ThermalSampleR_Shiny/>. We would like
    to thank Grant Duffy (University of Otago, Dundedin, New Zealand) for granting us permission to use the source code for the Test of Total Equivalency function.
A collection of functions and routines for inputting thermal
    image video files, plotting and converting binary raw data into estimates of
    temperature.  First published 2015-03-26.  Written primarily for research purposes
    in biological applications of thermal images.  v1 included the base calculations 
    for converting thermal image binary values to temperatures. v2 included additional
    equations for providing heat transfer calculations and an import function for thermal
    image files (v2.2.3 fixed error importing thermal image to windows OS). v3. Added numerous
    functions for converting thermal image, videos, rewriting and exporting.  
    v3.1. Added new functions to convert files. v3.2.  Fixed the various functions related to finding frame times.
    v4.0. fixed an error in atmospheric attenuation constants, affecting raw2temp and temp2raw functions.
    Recommend update for use with long distance calculations. v.4.1.3 changed to frameLocates to reflect change to as.character() to format().
Calculates several thermal comfort indexes using temperature, wind speed and relative humidity values, calculating indexes such as Humidex, windchill, Discomfort Index and others.
Given the circuit topology, simulating the Voltage vs. Temperature
    curve with a set of Resistors (R1, R2, R3 and R4) and Thermistors values
    (TH1 and TH2) for a given thermistor network shown in Seth DeLand (2024) 
    "Optimal Component Selection Using the Mixed-Integer Genetic Algorithm"  
    <https://ww2.mathworks.cn/matlabcentral/fileexchange/35810-optimal-component-selection-using-the-mixed-integer-genetic-algorithm>
    and the application in Mak and Wu (2019) <doi:10.1080/00401706.2019.1593246>.
Temperature measurement data, equations and methods for thermocouples,
    wire RTD, thermistors, IC thermometers, bimetallic strips and the ITS-90.
Estimation of the SF-ACE, a Causal Inference estimand proposed in the paper "The Subtype-Free Average Causal Effect For Heterogeneous Disease Etiology" (soon on arXiv).
A user-friendly R data package that is intended to make Turkish higher education statistics more accessible.
A step-up test for genetic rare variants in a gene or in a pathway. The method determines an optimal grouping of rare variants analytically. The method has been described in Hoffmann TJ, Marini NJ, and Witte JS (2010) <doi:10.1371/journal.pone.0013584>.
Methods and tools for generating forecasts at different temporal
    frequencies using a hierarchical time series approach.
Some tools for cleaning up messy 'Excel' files to be suitable
    for R. People who have been working with 'Excel' for years built more
    or less complicated sheets with names, characters, formats that are
    not homogeneous. To be able to use them in R nowadays, we built a set
    of functions that will avoid the majority of importation problems and
    keep all the data at best.
Determine the path of the executing script. Compatible
        with a few popular GUIs: 'Rgui', 'RStudio', 'VSCode',
        'Jupyter', 'Emacs', and 'Rscript' (shell). Compatible with
        several functions and packages: 'source()', 'sys.source()',
        'debugSource()' in 'RStudio', 'compiler::loadcmp()',
        'utils::Sweave()', 'box::use()', 'knitr::knit()',
        'plumber::plumb()', 'shiny::runApp()', 'package:targets', and
        'testthat::source_file()'.
Functions for admin needs of employees of
      Thomas Jefferson University and Thomas Jefferson
      University Hospital, Philadelphia, PA.
Key-value store, implemented as a wrapper around 'LMDB';
    the "lightning memory-mapped database" <https://www.symas.com/lmdb>.
    'LMDB' is a transactional key value store that uses a memory map
    for efficient access.  This package wraps the entire 'LMDB'
    interface (except duplicated keys), and provides objects for
    transactions and cursors.
Creates some 'WebGL' shaders. They can be used as the background of a 'Shiny' app. They also can be visualized in the 'RStudio' viewer pane or included in 'Rmd' documents, but this is pretty useless, besides contemplating them.
A tool that allows users to estimate tree height in the long-term forest experiments in Sweden. It utilizes the multilevel nonlinear mixed-effect height models developed for the forest experiments and consists of four functions for the main species, other conifer species, and other broadleaves. Each function within the system returns a data frame that includes the input data and the estimated heights for any missing values. Ogana et al. (2023) <doi:10.1016/j.foreco.2023.120843>\n Arias-Rodil et al. (2015) <doi:10.1371/JOURNAL.PONE.0143521>.
Provides a crawler for programmatically navigating THREDDS Data Server (<https://www.unidata.ucar.edu/software/tds/>) 
  catalogs, and access dataset metadata and resources.
Implements the Maximum Likelihood estimator for baseline, placebo, and treatment groups (three-group) experiments with non-compliance proposed by Gerber, Green, Kaplan, and Kern (2010).
Create interactive 3D scatter plots, network plots, and
    globes using the 'three.js' visualization library (<https://threejs.org>).
Access open data from <https://www.threesixtygiving.org>, a 
    database of charitable grant giving in the UK operated by '360Giving'.
    The package provides functions to search and retrieve data on charitable 
    grant giving, and process that data into tidy formats. It relies on the 
    '360Giving' data standard, described at 
    <https://standard.threesixtygiving.org/>.
Component analysis for three-way data arrays by means of Candecomp/Parafac, Tucker3, Tucker2 and Tucker1 models.
Does nothing useful, but perhaps does that nothing in an entertaining or informative fashion.
A connector to the 'What3Words' (http://what3words.com/) service, which represents each 3m by 3m square on earth
             with a unique trio of English-language words.
Fit a threshold regression model based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Fit a threshold regression model for Interval Censored Data based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.
Defines the classes used to identify
  outliers (threshing) and compute the number of significant principal
  components and number of clusters (reaping) in a joint application
  of PCA and hierarchical clustering. See Wang et al., 2018,
  <doi:10.1186/s12859-017-1998-9>.
Functions that provide point and interval estimations of optimum thresholds for continuous diagnostic tests. The methodology used is based on minimizing an overall cost function in the two- and three-state settings. We also provide functions for sample size determination and estimation of diagnostic accuracy measures. We also include graphical tools. The statistical methodology used here can be found in Perez-Jaume et al (2017) <doi:10.18637/jss.v082.i04> and in Skaltsa et al (2010, 2012) <doi:10.1002/bimj.200900294>, <doi:10.1002/sim.4369>.
We focus on the diagnostic ability assessment of medical tests when the outcome of interest is the status (alive or dead) of the subjects at a certain time-point t. This binary status is determined by right-censored times to event and it is unknown for those subjects censored before t. Here we provide three methods (unknown status exclusion, imputation of censored times and using time-dependent ROC curves) to evaluate the diagnostic ability of binary and continuous tests in this context. Two references for the methods used here are Skaltsa et al. (2010) <doi:10.1002/bimj.200900294> and Heagerty et al. (2000) <doi:10.1111/j.0006-341x.2000.00337.x>.
Provides functions for the selection of thresholds for use in 
    extreme value models, based mainly on the methodology in 
    Northrop, Attalides and Jonathan (2017) <doi:10.1111/rssc.12159>.
    It also performs predictive inferences about future extreme values, 
    based either on a single threshold or on a weighted average of inferences 
    from multiple thresholds, using the 'revdbayes' package 
    <https://cran.r-project.org/package=revdbayes>.   
    At the moment only the case where the data can be treated as 
    independent identically distributed observations is considered.
Pure R implementation of Apache Thrift.
    This library doesn't require any code generation.
    To learn more about Thrift go to <https://thrift.apache.org>.
Allow to compute and visualise convective parameters commonly
  used in the operational prediction of severe convective storms. Core algorithm
  is based on a highly optimized 'C++' code linked into 'R' via 'Rcpp'. Highly
  efficient engine allows to derive thermodynamic and kinematic parameters from
  large numerical datasets such as reanalyses or operational Numerical Weather 
  Prediction models in a reasonable amount of time. Package has been developed
  since 2017 by research meteorologists specializing in severe thunderstorms.
  The most relevant methods used in the package based on the following publications
  Stipanuk (1973) <https://apps.dtic.mil/sti/pdfs/AD0769739.pdf>,
  McCann et al. (1994) <doi:10.1175/1520-0434(1994)009%3C0532:WNIFFM%3E2.0.CO;2>,
  Bunkers et al. (2000) <doi:10.1175/1520-0434(2000)015%3C0061:PSMUAN%3E2.0.CO;2>,
  Corfidi et al. (2003) <doi:10.1175/1520-0434(2003)018%3C0997:CPAMPF%3E2.0.CO;2>,
  Showalter (1953) <doi:10.1175/1520-0477-34.6.250>,
  Coffer et al. (2019) <doi:10.1175/WAF-D-19-0115.1>,
  Gropp and Davenport (2019) <doi:10.1175/WAF-D-17-0150.1>,
  Czernecki et al. (2019) <doi:10.1016/j.atmosres.2019.05.010>,
  Taszarek et al. (2020) <doi:10.1175/JCLI-D-20-0346.1>,
  Sherburn and Parker (2014) <doi:10.1175/WAF-D-13-00041.1>,
  Romanic et al. (2022) <doi:10.1016/j.wace.2022.100474>.
Fit Thurstonian forced-choice models (CFA (simple and factor) and IRT) in R. This package allows for the analysis of item response modeling (IRT) as well as confirmatory factor analysis (CFA) in the Thurstonian framework. Currently, estimation can be performed by 'Mplus' and 'lavaan'. References:
  Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>;
  Jansen, M. T., & Schulze, R. (in review). The Thurstonian linked block design: Improving Thurstonian modeling for paired comparison and ranking data.;
  Maydeu-Olivares & Böckenholt (2005) <doi:10.1037/1082-989X.10.3.285>.
Fit Thurstonian Item Response Theory (IRT) models in R. This 
  package supports fitting Thurstonian IRT models and its extensions using 
  'Stan', 'lavaan', or 'Mplus' for the model estimation. Functionality for 
  extracting results, making predictions, and simulating data is provided as 
  well. References: 
  Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>;
  Bürkner et al. (2019) <doi:10.1177/0013164419832063>.
Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the traditional
    data frame.
Built on top of the 'tibble' package, 'tibbletime' is an extension
  that allows for the creation of time aware tibbles. Some immediate
  advantages of this include: the ability to perform time-based subsetting
  on tibbles, quickly summarising and aggregating results by time periods,
  and creating columns that can be used as 'dplyr' time-based groups.
A tool to rectangle a nested list, that is to convert it into
    a tibble. This is done automatically or according to a given
    specification.  A common use case is for nested lists coming from
    parsing JSON files or the JSON response of REST APIs. It is supported
    by the 'vctrs' package and therefore offers a wide support of vector
    types.
Plots and analyzes time-intensity curve data, such as data from 
  (contrast-enhanced) ultrasound. Values such as peak intensity, time to 
  peak and area under the curve are calculated.
Implements tic-tac-toe game to play on console, either with human or AI players.
  Various levels of AI players are trained through the Q-learning algorithm.
Code execution timing functions 'tic' and 'toc' that
    can be nested. One can record all timings while a complex script is
    running, and examine the values later. It is also possible to instrument
    the timing calls with custom callbacks. In addition, this package provides
    class 'Stack', implemented as a vector, and class 'StackList', which is a
    stack implemented as a
    list, both of which support operations 'push', 'pop', 'first_element',
    'last_element' and 'clear'.
Tidal analysis of evenly spaced observed time series (time step 1 to 60 min) with or
    without shorter gaps using the harmonic representation of inequalities.
    The analysis should preferably cover an observation period of at least 19 years.
    For shorter periods low frequency constituents are not taken into account, in accordance with the Rayleigh-Criterion.
    The main objective of this package is to synthesize or predict a tidal time series.
Implements harmonic analysis of tidal and sea-level data.
  Over 400 harmonic tidal constituents can be estimated, all with 
  daily nodal corrections. Time-varying mean sea-levels can also
  be used.
Calculate Characteristics of Quasi-Periodic Time Series, e.g. Estuarine Water Levels.
Tide analysis and prediction of predominantly semi-diurnal tides
    with two high waters and two low waters during one lunar day (~24.842 hours,
    ~1.035 days). The analysis should preferably cover an observation period of at
    least 19 years. For shorter periods, for example, the nodal cycle can not be
    taken into account, which particularly affects the height calculation. The main
    objective of this package is to produce tide tables.
Provides 'Apache Spark' style window aggregation for R dataframes and remote 'dbplyr' tables via 'mutate' in 'dplyr' flavour.
The goal of this package will be to provide a simple interface for automatic machine learning that fits the 'tidymodels' framework. The intention is to work for regression and classification problems with a simple verb framework.
Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models
    ('JAGS', 'Stan', 'rstanarm', 'brms', 'MCMCglmm', 'coda', ...) in a tidy data format. Functions are provided
    to help extract tidy data frames of draws from Bayesian models and that generate point
    summaries and intervals in a tidy format. In addition, 'ggplot2' 'geoms' and 'stats' are provided for
    common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus
    densities), and fit curves with multiple, arbitrary uncertainty bands.
Tools to download data series from 'Banco de España' ('BdE')
    on 'tibble' format. 'Banco de España' is the national central bank
    and, within the framework of the Single Supervisory Mechanism ('SSM'),
    the supervisor of the Spanish banking system along with the European
    Central Bank. This package is in no way sponsored endorsed or
    administered by 'Banco de España'.
Multiple ways to bin numeric columns with a tidy output. Wraps a variety of existing binning methods into one function, and includes a new method for binning by equal value, which is useful for sales data. Provides a function to automatically summarize the properties of the binned columns.
Compute arbitrary non-parametric bootstrap statistics on data in
    tidy data frames.
Create additional rows and columns on broom::tidy() output to allow for easier control on categorical parameter estimates.
Provides users a quick exploratory dive into common
    visualizations without writing a single line of code given the users
    data follows the Analysis Data Model (ADaM) standards put forth by the
    Clinical Data Interchange Standards Consortium (CDISC)
    <https://www.cdisc.org>. Prominent modules/ features of the
    application are the Table Generator, Population Explorer, and the
    Individual Explorer. The Table Generator allows users to drag and drop
    variables and desired statistics (frequencies, means, ANOVA, t-test,
    and other summary statistics) into bins that automagically create
    stunning tables with validated information. The Population Explorer
    offers various plots to visualize general trends in the population
    from various vantage points. Plot modules currently include scatter
    plot, spaghetti plot, box plot, histogram, means plot, and bar plot.
    Each plot type allows the user to plot uploaded variables against one
    another, and dissect the population by filtering out certain subjects.
    Last, the Individual Explorer establishes a cohesive patient
    narrative, allowing the user to interact with patient metrics (params)
    by visit or plotting important patient events on a timeline. All
    modules allow for concise filtering & downloading bulk outputs into
    html or pdf formats to save for later.
An integrated R interface to several United States Census Bureau 
    APIs (<https://www.census.gov/data/developers/data-sets.html>) and the US Census Bureau's 
    geographic boundary files. Allows R users to return Census and ACS data as 
    tidyverse-ready data frames, and optionally returns a list-column with feature geometry for mapping 
    and spatial analysis.
Changepoint detection algorithms for R are widespread but have
    different interfaces and reporting conventions. 
    This makes the comparative analysis of results difficult. 
    We solve this problem by providing a tidy, unified interface for several 
    different changepoint detection algorithms. 
    We also provide consistent numerical and graphical reporting leveraging 
    the 'broom' and 'ggplot2' packages.
There is a wide range of R packages created for data visualization, but still, there was no simple and easily accessible way to create clean and transparent charts - up to now. The 'tidycharts' package enables the user to generate charts compliant with International Business Communication Standards ('IBCS').
    It means unified bar widths, colors, chart sizes, etc. Creating homogeneous reports has never been that easy! Additionally, users can apply semantic notation to indicate different data scenarios (plan, budget, forecast). What's more, it is possible to customize the charts by creating a personal color pallet with the possibility of switching to default options after the experiments.
    We wanted the package to be helpful in writing reports, so we also made joining charts in a one, clear image possible.
    All charts are generated in SVG format and can be shown in the 'RStudio' viewer pane or exported to HTML output of 'knitr'/'markdown'.
A common interface to specifying clustering models, in the
    same style as 'parsnip'. Creates unified interface across different
    functions and computational engines.
Provides an intuitive interface for working with the
    competing risk endpoints. The package wraps the 'cmprsk' package, and
    exports functions for univariate cumulative incidence estimates and
    competing risk regression. Methods follow those introduced in Fine and
    Gray (1999) <doi:10.1002/sim.7501>.
Analyze lines of R code using tidy principles. This allows you to 
    input lines of R code and output a data frame with one row per function 
    included. Additionally, it facilitates code classification via included lexicons.
Provides convenience functions for common data
    modification and analysis tasks in communication research. This
    includes functions for univariate and bivariate data analysis, index
    generation and reliability computation, and intercoder reliability
    tests. All functions follow the style and syntax of the tidyverse, and
    are construed to perform their computations on multiple variables at
    once. Functions for univariate and bivariate data analysis comprise
    summary statistics for continuous and categorical variables, as well
    as several tests of bivariate association including effect sizes.
    Functions for data modification comprise index generation and
    automated reliability analysis of index variables. Functions for
    intercoder reliability comprise tests of several intercoder
    reliability estimates, including simple and mean pairwise percent
    agreement, Krippendorff's Alpha (Krippendorff 2004, ISBN:
    9780761915454), and various Kappa coefficients (Brennan & Prediger
    1981 <doi: 10.1177/001316448104100307>; Cohen 1960 <doi:
    10.1177/001316446002000104>; Fleiss 1971 <doi: 10.1037/h0031619>).
Loads the 5 packages in the Tidy Consultant Universe. This collection of packages is useful for anyone doing data science,
    data analysis, or quantitative consulting. The functions in these packages range from data cleaning, data validation, data binning, statistical modeling, and file exporting.
A comprehensive and user-friendly interface for accessing, manipulating, 
    and analyzing country-level data from around the world. It allows users to retrieve detailed 
    information on countries, including names, regions, continents, populations, currencies, calling codes, 
    and more, all in a tidy data format. The package is designed to work seamlessly within 
    the 'tidyverse' ecosystem, making it easy to filter, arrange, and visualize country-level data in R.
Core parts of the C API of R are wrapped in a C++ namespace via a set
 of inline functions giving a tidier representation of the underlying data structures
 and functionality using a header-only implementation without additional dependencies.
The Common Workflow Language <https://www.commonwl.org/> is an
    open standard for describing data analysis workflows. This package takes
    the raw Common Workflow Language workflows encoded in JSON or 'YAML'
    and turns the workflow elements into tidy data frames or lists.
    A graph representation for the workflow can be constructed and visualized
    with the parsed workflow inputs, outputs, and steps. Users can embed the
    visualizations in their 'Shiny' applications, and export them
    as HTML files or static images.
Provides model specifications, tuning parameters for models in 
    'dann' package. Models based on Hastie (1996) 
    <https://web.stanford.edu/~hastie/Papers/dann_IEEE.pdf>.
Visualize your 'Tidyverse' data analysis pipelines via the 
    'Tidy Data Tutor'(<https://tidydatatutor.com/>) web application.
Delta Method implementation to estimate standard errors with known 
    asymptotic properties within the 'tidyverse' workflow. The Delta Method is a
    statistical tool that approximates an estimator’s behaviour using a Taylor 
    Expansion. For a comprehensive explanation, please refer to Chapter 3 of van
    der Vaart (1998, ISBN: 9780511802256).
Cleans spectrophotometry data obtained from the Denovix instrument. The package also provides an option to normalize the data in order to compare the quality of the samples obtained.
To make it easy to generate random numbers based upon the underlying stats 
    distribution functions. All data is returned in a tidy and structured
    format making working with the data simple and straight forward. Given that the
    data is returned in a tidy 'tibble' it lends itself to working with the rest of the
    'tidyverse'.
Utils for basic statistical experiments, that can be used for teaching 
    introductory statistics. Each experiment generates a tibble.
    Dice rolls and coin flips are simulated using sample().
    The properties of the dice can be changed, like the number of sides.
    A coin flip is simulated using a two sided dice.
    Experiments can be combined with the pipe-operator.
Dimensionality reduction (DR) is widely used in many domain for analyzing and visualizing high-dimensional data. 'tidydr' provides uniform output and is compatible with multiple methods, including 'prcomp', 'mds', 'Rtsne'. etc.
Streamline the process of accessing fundamental financial data from the United States Securities and Exchange Commission's ('SEC') Electronic Data Gathering, Analysis, and Retrieval system ('EDGAR') 'API' <https://www.sec.gov/edgar/sec-api-documentation>, transforming it into a tidy, analysis-ready format.
Takes objects of class edsurvey.data.frame and converts them to a data.frame within the calling environment of 'dplyr' and 'ggplot2' functions. Additionally, for plotting with 'ggplot2', users can map aesthetics to subject scales and all plausible values will be used. This package supports student level data; to work with school or teacher level data, see '?EdSurvey::getData'.
Unicodes are not friendly to work with, and not all Unicodes are
    Emoji per se, making obtaining Emoji statistics a difficult task. This
    tool can help your experience of working with Emoji as smooth as possible,
    as it has the 'tidyverse' style.
The 'ESTIMATE' package infers tumor purity from expression data as a 
  function of immune and stromal infiltrate, but requires writing of intermediate 
  files, is un-pipeable, and performs poorly when presented with modern datasets 
  with current gene symbols. 'tidyestimate' a fast, tidy, modern reimagination of
  'ESTIMATE' (2013) <doi:10.1038/ncomms3612>.
Tidying functions built on 'data.table'
    to provide quick and efficient data manipulation with
    minimal overhead.
Helper functions for empirical research in financial
    economics, addressing a variety of topics covered in Scheuch, Voigt,
    and Weiss (2023) <doi:10.1201/b23237>.  The package is designed to
    provide shortcuts for issues extensively discussed in the book,
    facilitating easier application of its concepts. For more information
    and resources related to the book, visit
    <https://www.tidy-finance.org/r/index.html>.
An extension to the 'R' tidy data environment for automated machine learning. The package allows fitting and cross validation of linear regression and classification algorithms on grouped data.
Provides the function 'tidyformula()', which translates formulas containing 'tidyselect'-style selection helpers. It expands these helpers by evaluating 'dplyr::select()' with the relevant selection helper and a supplied data frame. The package contains methods for traversing abstract syntax trees from Wickham, Hadley (2019) <doi:10.1201/9781351201315>.
A toolkit of tidy data manipulation verbs with 'data.table' as the backend.
  Combining the merits of syntax elegance from 'dplyr' and computing performance from 'data.table', 
  'tidyfst' intends to provide users with state-of-the-art data manipulation tools with least pain.
  This package is an extension of 'data.table'. While enjoying a tidy syntax, 
  it also wraps combinations of efficient functions to facilitate frequently-used data operations.
Tidy syntax for 'data.table', using modification by reference whenever possible.
 This toolkit is designed for big data analysis in high-performance desktop or laptop computers.
 The syntax of the package is similar or identical to 'tidyverse'.
 It is user friendly, memory efficient and time saving. For more information,
 check its ancestor package 'tidyfst'.
Provides functions that compute predictions from Generalised
    Additive Models (GAMs) fitted with 'mgcv' and return them as a tibble.
    These can be plotted with a generic plot()-method that uses 'ggplot2' or
    plotted as any other data frame. The main function is predict_gam().
A toolset that allows you to easily import and tidy data sheets retrieved
    from Gapminder data web tools. It will therefore contribute to reduce the time
    used in data cleaning of Gapminder indicator data sheets as they are very messy.
Interactively gate points on a scatter plot. Interactively drawn gates are 
  recorded and can be applied programmatically to reproduce results exactly. Programmatic gating is 
  based on the package gatepoints by Wajid Jawaid (who is also an author of this package).
Handle genomic data within data frames just as you would with 'GRanges'.
    This packages provides method to deal with genomic intervals the "tidy-way" which makes
    it simpler to integrate in the the general data munging process. The API is inspired by the
    popular 'bedtools' and the genome_join() method from the 'fuzzyjoin' package.
An intuitive interface for getting data from geocoding services.
In order to easily integrate geoRSS data into analysis,
    'tidygeoRSS' parses 'geo' feeds and returns 
    tidy simple features data frames.
A graph, while not "tidy" in itself, can be thought of as two
    tidy data frames describing node and edge data respectively.
    'tidygraph' provides an approach to manipulate these two virtual data
    frames using the API defined in the 'dplyr' package, as well as
    provides tidy interfaces to a lot of common graph algorithms.
This is a tidy implementation for heatmap.  At the
    moment it is based on the (great) package 'ComplexHeatmap'.  The goal
    of this package is to interface a tidy data frame with this powerful
    tool.  Some of the advantages are: Row and/or columns colour
    annotations are easy to integrate just specifying one parameter
    (column names).  Custom grouping of rows is easy to specify providing
    a grouped tbl. For example: df %>% group_by(...).  Labels size
    adjusted by row and column total number.  Default use of Brewer and
    Viridis palettes.
The goal of 'tidyheatmaps' is to simplify the generation of publication-ready heatmaps from tidy data. By offering an interface to the powerful 'pheatmap' package, it allows for the effortless creation of intricate heatmaps with minimal code.
Estimates heterogeneous treatment effects using tidy semantics 
    on experimental or observational data.  Methods are based on the doubly-robust
    learner of Kennedy (n.d.) <arXiv:2004.14497>. You provide a simple
    recipe for what machine learning algorithms to use in estimating the nuisance
    functions and 'tidyhte' will take care of cross-validation, estimation, model
    selection, diagnostics and construction of relevant quantities of interest about
    the variability of treatment effects.
Provides functions to access historical and real-time national 'hydrometric'
    data from Water Survey of Canada data sources (<https://dd.weather.gc.ca/hydrometric/csv/> and
    <https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/www/>) and then applies tidy data principles.
Construct and analyse indexes in a pipeline tidy workflow.
    'tidyindex' contains modules for transforming variables, aggregating 
    variables across time, reducing data dimension through weighting, and 
    fitting distributions. A manuscript describing the methodology can be 
    found at <https://github.com/huizezhang-sherry/paper-tidyindex>.
Turn complex 'JSON' data into tidy data frames.
Implements an algorithm for Latent Dirichlet
    Allocation (LDA), Blei et at. (2003) <https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf>,
    using style conventions from the 'tidyverse',
    Wickham et al. (2019)<doi:10.21105/joss.01686>,
    and 'tidymodels', Kuhn et al.<https://tidymodels.github.io/model-implementation-principles/>.
    Fitting is done via collapsed Gibbs sampling.
    Also implements several novel features for LDA such as guided models and
    transfer learning based on ongoing and, as yet, unpublished research.
How can we measure how the usage or frequency of some
    feature, such as words, differs across some group or set, such as
    documents? One option is to use the log odds ratio, but the log odds
    ratio alone does not account for sampling variability; we haven't
    counted every feature the same number of times so how do we know which
    differences are meaningful? Enter the weighted log odds, which
    'tidylo' provides an implementation for, using tidy data principles.
    In particular, here we use the method outlined in Monroe, Colaresi,
    and Quinn (2008) <doi:10.1093/pan/mpn018> to weight the log odds ratio
    by a prior. By default, the prior is estimated from the data itself,
    an empirical Bayes approach, but an uninformative prior is also
    available.
Provides feedback about 'dplyr' and 'tidyr' operations.
An interface to the 'mclust' package to easily
    carry out latent profile analysis ("LPA"). Provides functionality to
    estimate commonly-specified models. Follows a tidy approach, in that
    output is in the form of a data frame that can subsequently be
    computed on. Also has functions to interface to the commercial 'MPlus'
    software via the 'MplusAutomation' package.
Framework to run Monte Carlo simulations over a parameter grid. 
             Allows to parallelize the simulations. 
             Generates plots and 'LaTeX' tables
             summarizing the results from the simulation.
The tidy modeling "verse" is a collection of packages for
    modeling and statistical analysis that share the underlying design
    philosophy, grammar, and data structures of the tidyverse.
Transforms long data into a matrix form to allow for ease of input into modelling packages for regression, principal components, imputation or machine learning. It does this by pivoting on user defined columns, generating a key-value table for variable names to ensure one-to-one mappings are preserved. It is particularly useful when the indicator names in the columns are long descriptive strings, for example "Energy imports, net (% of energy use)". High level analysis wrapper functions for correlation and principal components analysis are provided.
Provides the means to convert 'multiqc_data.json' files,
    produced by the wonderful 'MultiQC' tool, into tidy data frames for downstream
    analysis in R. This analysis might involve cohort analysis, quality control visualisation,
    change-point detection, statistical process control, clustering, or any other
    type of quality analysis.
Provides functions for visualising generalised
    additive models and getting predicted values using tidy tools from the 'tidyverse' packages.
Tidy tools for 'NetCDF' data sources. Explore the contents of a 
 'NetCDF' source (file or URL) presented as variables organized by grid with a 
 database-like interface. The hyper_filter() interactive function translates the 
 filter value or index expressions to array-slicing form. No data is read until 
 explicitly requested, as a data frame or list of arrays via hyper_tibble() or 
 hyper_array().
Provides a set of functions with a common framework for age-depth model management, 
  stratigraphic visualization, and common statistical transformations. The focus of the
  package is stratigraphic visualization, for which 'ggplot2' components are provided
  to reproduce the scales, geometries, facets, and theme elements commonly used in
  publication-quality stratigraphic diagrams. Helpers are also provided to reproduce
  the exploratory statistical summaries that are frequently included on
  stratigraphic diagrams. See Dunnington et al. (2021) <doi:10.18637/jss.v101.i07>.
The goal of 'tidyplate' is to help researchers convert different types of
    microplates into tidy dataframes which can be used in data analysis. It accepts xlsx and
    csv files formatted in a specific way as input. It supports all types of 
    standard microplate formats such as 6-well, 12-well, 24-well, 48-well, 96-well, 384-well,
    and, 1536-well plates.
Provides functions such as str_crush(), add_missing_column(), 
  coalesce_data() and drop_na_all() that complement 'tidyverse' functionality
  or functions that provide alternative behaviors such as if_else2()
  and str_detect2().
Parse XML documents from the Open Access subset of Europe PubMed Central <https://europepmc.org>
    including section paragraphs, tables, captions and references.
Bayesian analysis used here to answer the question: "when
    looking at resampling results, are the differences between models
    'real'?" To answer this, a model can be created were the performance
    statistic is the resampling statistics (e.g. accuracy or RMSE). These
    values are explained by the model types. In doing this, we can get
    parameter estimates for each model's affect on performance and make
    statistical (and practical) comparisons between models. The methods
    included here are similar to Benavoli et al (2017)
    <https://jmlr.org/papers/v18/16-305.html>.
It parses a fitted 'R' model object, and returns a formula in
    'Tidy Eval' code that calculates the predictions.  It works with
    several databases back-ends because it leverages 'dplyr' and 'dbplyr'
    for the final 'SQL' translation of the algorithm. It currently
    supports lm(), glm(), randomForest(), ranger(), earth(),
    xgb.Booster.complete(), cubist(), and ctree() models.
Bringing business and financial analysis to the 'tidyverse'. The 'tidyquant' 
    package provides a convenient wrapper to various 'xts', 'zoo', 'quantmod', 'TTR' 
    and 'PerformanceAnalytics' package 
    functions and returns the objects in the tidy 'tibble' format. The main 
    advantage is being able to use quantitative functions with the 'tidyverse'
    functions including 'purrr', 'dplyr', 'tidyr', 'ggplot2', 'lubridate', etc. See 
    the 'tidyquant' website for more information, documentation and examples.
Use 'SQL' 'SELECT' statements to query 'R' data
    frames.
Tools to help to create tidy data, where each column is a
    variable, each row is an observation, and each cell contains a single
    value.  'tidyr' contains tools for changing the shape (pivoting) and
    hierarchy (nesting and 'unnesting') of a dataset, turning deeply
    nested lists into rectangular data frames ('rectangling'), and
    extracting values out of string columns. It also includes tools for
    working with missing values (both implicit and explicit).
Compute age-adjusted rates by direct and indirect methods and other epidemiological indicators in a tidy way, wrapping functions from the 'epitools' package.
Helper functions for processing 'REDCap' data in R. 'REDCap' is a 
    web-enabled application for building and managing surveys and databases 
    developed at Vanderbilt University.
Provides 'tidyverse' methods for wrangling
    and analyzing 'Earth Engine' <https://earthengine.google.com/> data. These methods help the user with filtering, 
    joining and summarising 'Earth Engine' image collections.
With the objective of including data from RSS feeds into your analysis, 
    'tidyRSS' parses RSS, Atom and JSON feeds and returns a tidy data frame.
Provides a framework to work with decision rules. Rules can be extracted from supported models, augmented with (custom) metrics using validation data, manipulated using standard dataframe operations, reordered and pruned based on a metric, predict on unseen (test) data. Utilities include; Creating a rulelist manually, Exporting a rulelist as a SQL case statement and so on. The package offers two classes; rulelist and ruleset based on dataframe.
Fit species distribution models (SDMs) using the 'tidymodels' framework, 
  which provides a standardised interface to define models and process their 
  outputs. 'tidysdm' expands 'tidymodels' by providing methods for spatial objects,
  models and metrics specific to SDMs,
  as well as a number of specialised functions to process occurrences
  for contemporary and palaeo datasets. The full 
  functionalities of the package are described
  in Leonardi et al. (2023) <doi:10.1101/2023.07.24.550358>.
A backend for the selecting functions of the 'tidyverse'.  It
    makes it easy to implement select-like functions in your own packages
    in a way that is consistent with other 'tidyverse' interfaces for
    selection.
A tidy workflow for generating, estimating, reporting,
    and plotting structural equation models using 'lavaan', 'OpenMx', or
    'Mplus'. Throughout this workflow, elements of syntax, results, and graphs
    are represented as 'tidy' data, making them easy to customize.
    Includes functionality to estimate latent class analyses.
It creates an invisible layer that allow to see the 'Seurat' object 
    as tibble and interact seamlessly with the tidyverse.
Tidy standardized mean differences ('SMDs'). 'tidysmd' uses
    the 'smd' package to calculate standardized mean differences for
    variables in a data frame, returning the results in a tidy format.
A tidy approach to analysis of biological sequences. All processing and data-storage functions are heavily optimized to allow the fastest and most efficient data storage.
Save the output of statistical tests in an organized file that can 
  be shared with others or used to report statistics in scientific papers.
Functions to generate stop-word lists in 110 languages, in a way consistent across all the languages supported. The generated lists are based on the morphological tagset from the Universal Dependencies.
Calculation of string distance following the tidy
    data principles. Built on top of the 'stringdist' package.
A synthetic control offers a way of evaluating the effect of an intervention in comparative case studies. The package makes a number of improvements when implementing the method in R. These improvements allow users to inspect, visualize, and tune the synthetic control more easily. A key benefit of a tidy implementation is that the entire preparation process for building the synthetic control can be accomplished in a single pipe.
A tidy interface to 'data.table',
  giving users the speed of 'data.table' while using tidyverse-like syntax.
Extension of the 'tidyverse' for 'SpatRaster' and
    'SpatVector' objects of the 'terra' package. It includes also new
    'geom_' functions that provide a convenient way of visualizing 'terra'
    objects with 'ggplot2'.
Using tidy data principles can make many text mining tasks
    easier, more effective, and consistent with tools already in wide use.
    Much of the infrastructure needed for text mining with tidy data
    frames already exists in packages like 'dplyr', 'broom', 'tidyr', and
    'ggplot2'. In this package, we provide functions and supporting data
    sets to allow conversion of text to and from tidy formats, and to
    switch seamlessly between tidy tools and existing text mining
    packages.
A selection of various tools to extend a data analysis workflow based on the 'tidyverse' packages.
  This includes high-level data frame editing methods (in the style of 'mutate'/'mutate_at'), some methods in the style of
  'purrr' and 'forcats', 'lookup' methods for dict-like lists, a generic method for lumping a data frame by a given count,
  various low-level methods for special treatment of 'NA' values, 'python'-style tuple-assignment and 'truthy'/'falsy' checks,
  saving to PDF and PNG from a pipe and various small utilities.
Generate tables, listings, and graphs (TLG) using 'tidyverse.'
  Tables can be created functionally, using a standard TLG process, or by
  specifying table and column metadata to create generic analysis summaries.
  The 'envsetup' package can also be leveraged to create environments for table
  creation.
Read General Transit Feed Specification (GTFS) zipfiles into a list of R dataframes. Perform validation of the data structure against the specification. Analyze the headways and frequencies at routes and stops. Create maps and perform spatial analysis on the routes and stops. Please see the GTFS documentation here for more detail: <https://gtfs.org/>.
Functions for extracting tidy data from Bayesian treatment effect models, in particular BART, but extensions are possible. Functionality includes extracting tidy posterior summaries as in 'tidybayes' <https://github.com/mjskay/tidybayes>, estimating (average) treatment effects, common support calculations, and plotting useful summaries of these.
Phylogenetic tree generally contains multiple components including node, edge, branch and associated data. 'tidytree' provides an approach to convert tree object to tidy data frame as well as provides tidy interfaces to manipulate tree data.
'TidyTuesday' is a project by the 'Data Science Learning
    Community' in which they post a weekly dataset in a public data
    repository (<https://github.com/rfordatascience/tidytuesday>) for
    people to analyze and visualize. This package provides the tools to
    easily download this data and the description of the source.
Provides a consistent API to pull United States Department of
    Agriculture census and survey data from the National Agricultural
    Statistics Service (NASS) QuickStats service.
The 'tidyverse' is a set of packages that work in harmony
    because they share common data representations and 'API' design. This
    package is designed to make it easy to install and load multiple
    'tidyverse' packages in a single step. Learn more about the
    'tidyverse' at <https://www.tidyverse.org>.
Perform a Visual Predictive Check (VPC), while accounting for 
    stratification, censoring, and prediction correction. Using piping from 
    'magrittr', the intuitive syntax gives users a flexible and powerful method 
    to generate VPCs using both traditional binning and a new binless approach 
    Jamsen et al. (2018) <doi:10.1002/psp4.12319> with Additive Quantile 
    Regression (AQR) and Locally Estimated Scatterplot Smoothing (LOESS) 
    prediction correction.
Query 'Wikidata' API <https://www.wikidata.org/wiki/Wikidata:Main_Page> with ease, get tidy data frames in response, and cache data in a local database.
Imports non-tabular from Excel files into R.  Exposes cell content,
    position and formatting in a tidy structure for further manipulation.
    Tokenizes Excel formulas.  Supports '.xlsx' and '.xlsm' via the embedded
    'RapidXML' C++ library <https://rapidxml.sourceforge.net>.  Does not support
    '.xlsb' or '.xls'.
Functions to read, write and display bitmap images stored in the TIFF format. It can read and write both files and in-memory raw vectors, including native image representation.
Pre-process for discrete time series data set which is not continuous at the column
    of 'date'. Refilling records of missing 'date' and other columns to the hollow data set so that
    final data set is able to be dealt with time series analysis.
The R implementation of TIGER. 
    TIGER integrates random forest algorithm into an innovative ensemble learning architecture. Benefiting from this advanced architecture, TIGER is resilient to outliers, free from model tuning and less likely to be affected by specific hyperparameters.
    TIGER supports targeted and untargeted metabolomics data and is competent to perform both intra- and inter-batch technical variation removal. TIGER can also be used for cross-kit adjustment to ensure data obtained from different analytical assays can be effectively combined and compared.
    Reference: Han S. et al. (2022) <doi:10.1093/bib/bbab535>.
Handling and manipulation polygons, coordinates, and other geographical objects. The tools include: polygon areas, barycentric and trilinear coordinates (Hormann and Floater, 2006, <doi:10.1145/1183287.1183295>), convex hull for polygons (Graham and Yao, 1983, <doi:10.1016/0196-6774(83)90013-5>), polygon triangulation (Toussaint, 1991, <doi:10.1007/BF01905693>), great circle and geodesic distances, Hausdorff distance, and reduced major axis.
A collection of data sets and functions that are useful in the
    teaching of statistics at an elementary level to students who may have
    little or no previous experience with the command line.  The functions for
    elementary inferential procedures follow a uniform interface for user
    input.  Some of the functions are instructional applets that can only be
    run on the R Studio integrated development environment with package
    'manipulate' installed.  Other instructional applets are Shiny apps
    that may be run locally. In teaching the package is used alongside of
    package 'mosaic', 'mosaicData' and 'abd', which are therefore listed as
    dependencies.
Infers the V genotype of an individual from immunoglobulin (Ig)
    repertoire sequencing data (AIRR-Seq, Rep-Seq). Includes detection of 
    any novel alleles. This information is then used to correct existing V 
    allele calls from among the sample sequences.
    Citations:
    Gadala-Maria, et al (2015) <doi:10.1073/pnas.1417683112>,
    Gadala-Maria, et al (2019) <doi:10.3389/fimmu.2019.00129>.
The functions needed to perform tight clustering Algorithm.
Tightens an observational block design into a smaller design with either smaller or fewer blocks while controlling for covariates. The method uses fine balance, optimal subset matching (Rosenbaum, 2012 <doi:10.1198/jcgs.2011.09219>) and two-criteria matching (Zhang et al 2023 <doi:10.1080/01621459.2021.1981337>).  The main function is tighten().  The suggested 'rrelaxiv' package for solving minimum cost flow problems: (i) derives from Bertsekas and Tseng (1988) <doi:10.1007/BF02288322>, (ii) is not available on CRAN due to its academic license, (iii) may be downloaded from GitHub at <https://github.com/josherrickson/rrelaxiv/>, (iv) is not essential to use the package.
Write modelling results into a database for
    'tigreBrowser', a web-based tool for browsing figures and summary
    data of independent model fits, such as Gaussian process models
    fitted for each gene or other genomic element. The browser is
    available at <https://github.com/PROBIC/tigreBrowser>.
Download TIGER/Line shapefiles from the United States Census Bureau 
    (<https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html>) 
    and load into R as 'sf' objects.
Collect  marketing data from TikTok Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Provides a graphics output device for R that records plots
        in a LaTeX-friendly format. The device transforms plotting
        commands issued by R functions into LaTeX code blocks. When
        included in a LaTeX document, these blocks are interpreted with
        the help of 'TikZ'---a graphics package for TeX and friends
        written by Till Tantau. Using the 'tikzDevice', the text of R
        plots can contain LaTeX commands such as mathematical formula.
        The device also allows arbitrary LaTeX code to be inserted into
        the output stream.
The modern database 'TileDB' introduces a powerful on-disk
  format for storing and accessing any complex data based on multi-dimensional
  arrays. It supports dense and sparse arrays, dataframes and key-values stores,
  cloud storage ('S3', 'GCS', 'Azure'), chunked arrays, multiple compression,
  encryption and checksum filters, uses a fully multi-threaded implementation,
  supports parallel I/O, data versioning ('time travel'), metadata and groups.
  It is implemented as an embeddable cross-platform C++ library with APIs from
  several languages, and integrations. This package provides the R support.
R spatial objects for Tilegrams.
  Tilegrams are tiled maps where the region size is proportional to
  the certain characteristics of the dataset.
Implements an algorithm for generating maps, known as tile maps,
    in which each region is represented by a single tile of the same shape and
    size. The algorithm was first proposed in "Generating Tile Maps" by Graham
    McNeill and Scott Hale (2017) <doi:10.1111/cgf.13200>. Functions allow
    users to generate, plot, and compare square or hexagon tile maps.
Creates geographic map tiles from geospatial map files or 
    non-geographic map tiles from simple image files. This package provides a 
    tile generator function for creating map tile sets for use with packages 
    such as 'leaflet'. In addition to generating map tiles based on a common 
    raster layer source, it also handles the non-geographic edge case, producing 
    map tiles from arbitrary images. These map tiles, which have a 
    non-geographic, simple coordinate reference system (CRS), can also be used 
    with 'leaflet' when applying the simple CRS option. Map tiles can be created 
    from an input file with any of the following extensions: tif, grd and nc for 
    spatial maps and png, jpg and bmp for basic images. This package requires 
    'Python' and the 'gdal' library for 'Python'. 'Windows' users are 
    recommended to install 'OSGeo4W' (<https://trac.osgeo.org/osgeo4w/>) as an 
    easy way to obtain the required 'gdal' support for 'Python'.
Implements an algorithm for variable selection in high-dimensional linear regression using the "tilted correlation", a new way of measuring the contribution of each variable to the response which takes into account high correlations among the variables in a data-driven way.
Functions for estimation of wood volumes, number of logs, diameters along the stem and heights at which certain diameters occur, based on taper functions and other parameters. References: McTague, J. P., & Weiskittel, A. (2021). <doi:10.1139/cjfr-2020-0326>.
Provides data frames for forest or tree data structures. You can 
    create forest data structures from data frames and process them based on 
    their hierarchies.
Generate weekly timetables as a ggplot2 layer. Add
    informative timeslots with elements such as title, key-value pairs, or
    colour to reveal trends.
Efficient routines for manipulation of date-time objects while
   accounting for time-zones and daylight saving times. The package includes
   utilities for updating of date-time components (year, month, day etc.),
   modification of time-zones, rounding of date-times, period addition and
   subtraction etc. Parts of the 'CCTZ' source code, released under the Apache
   2.0 License, are included in this package. See
   <https://github.com/google/cctz> for more details.
The 'timeDate' class fulfils the conventions of the ISO 8601 
	standard as well as of the ANSI C and POSIX standards. Beyond
	these standards it provides the "Financial Center" concept
	which allows to handle data records collected in different time 
	zones and mix them up to have always the proper time stamps with 
	respect to your personal financial center, or alternatively to the GMT
	reference time. It can thus also handle time stamps from historical 
	data records from the same time zone, even if the financial 
	centers changed day light saving times at different calendar
	dates.
We provide a toolbox to estimate the time delay between the brightness time series of gravitationally lensed quasar images via Bayesian and profile likelihood approaches. The model is based on a state-space representation for  irregularly observed time series data generated from a latent continuous-time Ornstein-Uhlenbeck process. Our Bayesian method adopts scientifically motivated hyper-prior distributions and a Metropolis-Hastings within Gibbs sampler, producing posterior samples of the model parameters that include the time delay. A profile likelihood of the time delay is a simple approximation to the marginal posterior distribution of the time delay. Both Bayesian and profile likelihood approaches complement each other, producing almost identical results; the Bayesian way is more principled but the profile likelihood is easier to implement. A new functionality is added in version 1.0.9 for estimating the time delay between doubly-lensed light curves observed in two bands. See also Tak et al. (2017) <doi:10.1214/17-AOAS1027>, Tak et al. (2018) <doi:10.1080/10618600.2017.1415911>, Hu and Tak (2020) <arXiv:2005.08049>.
Infer constant and stochastic, time-dependent parameters to consider intrinsic stochasticity of a dynamic model and/or to analyze model structure modifications that could reduce model deficits.
  The concept is based on inferring time-dependent parameters as stochastic processes in the form of Ornstein-Uhlenbeck processes jointly with inferring constant model parameters and parameters of the Ornstein-Uhlenbeck processes.
  The package also contains functions to sample from and calculate densities of Ornstein-Uhlenbeck processes.
  References:
  Tomassini, L., Reichert, P., Kuensch, H.-R. Buser, C., Knutti, R. and Borsuk, M.E. (2009), A smoothing algorithm for estimating stochastic, continuous-time model parameters and its application to a simple climate model, Journal of the Royal Statistical Society: Series C (Applied Statistics) 58, 679-704, <doi:10.1111/j.1467-9876.2009.00678.x>
  Reichert, P., and Mieleitner, J. (2009), Analyzing input and structural uncertainty of nonlinear dynamic models with stochastic, time-dependent parameters. Water Resources Research, 45, W10402, <doi:10.1029/2009WR007814>
  Reichert, P., Ammann, L. and Fenicia, F. (2021), Potential and challenges of investigating intrinsic uncertainty of hydrological models with time-dependent, stochastic parameters. Water Resources Research 57(8), e2020WR028311, <doi:10.1029/2020WR028311>
  Reichert, P. (2022), timedeppar: An R package for inferring stochastic, time-dependent model parameters, in preparation.
Data frames with time information are subset and flagged with period information. Data frames with times are dealt as timeDF objects and periods are represented as periodDF objects.
Computation of t-year survival probabilities and t-year
    risks with right censored survival data. The Kaplan-Meier estimator
    is used to provide estimates for data without competing risks and
    the Aalen-Johansen estimator is used when there are competing risks.
    Confidence intervals and p-values are obtained using either usual
    Wald-type inference or empirical likelihood inference, as described
    in Thomas and Grunkemeier (1975) <doi:10.1080/01621459.1975.10480315>
    and Blanche (2020) <doi:10.1007/s10985-018-09458-6>. Functions for
    both one-sample and two-sample inference are provided. Unlike Wald-type
    inference, empirical likelihood inference always leads to consistent
    conclusions, in terms of statistical significance, when comparing
    two risks (or survival probabilities) via either a ratio or a difference.
Produce an HTML page containing horizontal strips that symbolize events in a person's lsife. Since this is entirely a visualization, the image <https://barryzee.github.io/henry-timeline/henry.html> will show the basic use to show a timeline of events. The image <https://barryzee.github.io/vermeer/cssOverlay.html> shows how to correlate two timelines of events. A brief description is available at <https://barryzee.github.io/timeLineGraphics_manuscript/golden_age.html>.
An easy tool for plotting annotated timelines, grouped timelines, and exploratory graphics (boxplot/histogram/density plot/scatter plot/line plot). Filter, summarize date data by duration and convert to calendar units.
Approaches for incorporating time into network analysis. Methods include: construction of time-ordered networks (temporal graphs); shortest-time and shortest-path-length analyses; resource spread calculations; data resampling and rarefaction for null model construction; reduction to time-aggregated networks with variable window sizes; application of common descriptive statistics to these networks; vector clock latencies; and plotting functionalities. The package supports <doi:10.1371/journal.pone.0020298>.
Simple definition of time intervals for the current, previous, and next week, month, quarter and year.
A set of fast tidy functions for wrangling, completing and
    summarising date and date-time data. It combines 'tidyverse' syntax
    with the efficiency of 'data.table' and speed of 'collapse'.
Provides a 'timeR' class that makes timing codes easier. One can create 'timeR' objects and use them to record all timings, and extract recordings as data frame for later use.
Programs for Martinussen and Scheike (2006), `Dynamic Regression
    Models for Survival Data', Springer Verlag.  Plus more recent developments.
    Additive survival model, semiparametric proportional odds model, fast
    cumulative residuals, excess risk models and more. Flexible competing risks
    regression including GOF-tests. Two-stage frailty modelling. PLS for the
    additive risk model. Lasso in the 'ahaz' package.
Estimation of time-dependent ROC curve and area under time dependent ROC curve (AUC) in the presence of censored data, with or without competing risks. Confidence intervals of AUCs and tests for comparing AUCs of two rival markers measured on the same subjects can be computed, using the iid-representation of the AUC estimator. Plot functions for time-dependent ROC curves and AUC curves are provided. Time-dependent Positive Predictive Values (PPV) and Negative Predictive Values (NPV) can also be computed. See Blanche et al. (2013) <doi:10.1002/sim.5958> and references therein for the details of the methods implemented in the package.
'S4' classes and various tools for financial time series:
  Basic functions such as scaling and sorting, subsetting,
  mathematical operations and statistical functions.
Provides a diverse collection of time series datasets
    spanning various fields such as economics, finance, energy, healthcare, and more.
    Designed to support time series analysis in R by offering datasets from
    multiple disciplines, making it a valuable resource for researchers and analysts.
Archive and manage times series data from official statistics. The 'timeseriesdb' package was designed to manage a large catalog of time series from official statistics which are typically published on a monthly, quarterly or yearly basis. Thus timeseriesdb is optimized to handle updates caused by data revision as well as elaborate, multi-lingual meta information.
Easy visualization, wrangling, and feature engineering of time series data for 
    forecasting and machine learning prediction. Consolidates and extends time series functionality 
    from packages including 'dplyr', 'stats', 'xts', 'forecast', 'slider', 'padr', 'recipes', and 'rsample'.
Objects to manipulate sequential and seasonal time series. Sequential time series based on time instants and time durations are handled. Both can be regularly or unevenly spaced (overlapping durations are allowed). Only POSIX* format are used for dates and times. The following classes are provided : 'POSIXcti', 'POSIXctp', 'TimeIntervalDataFrame', 'TimeInstantDataFrame', 'SubtimeDataFrame' ; methods to switch from a class to another and to modify the time support of series (hourly time series to daily time series for instance) are also defined. Tools provided can be used for instance to handle environmental monitoring data (not always produced on a regular time base).
Provides a hypothesis test and variable selection algorithm for use in time-varying, concurrent regression models. The hypothesis test function is also accompanied by a plotting function which will show the estimated beta(s) and confidence band(s) from the hypothesis test. The hypothesis test function helps the user identify significant covariates within the scope of a time-varying concurrent model. The plots will show the amount of area that falls outside the confidence band(s) which is used for the test statistic within the hypothesis test.
Computes how the correlation between 2 time-series changes over time.
    To do so, the package follows the method from Choi & Shin (2021) <doi:10.1007/s42952-020-00073-6>.
    It performs a non-parametric kernel smoothing (using a common bandwidth) of all underlying components required for the computation of a correlation coefficient (i.e., x, y, x^2, y^2, xy).
    An automatic selection procedure for the bandwidth parameter is implemented.
    Alternative kernels can be used (Epanechnikov, box and normal).
    Both Pearson and Spearman correlation coefficients can be estimated and change in correlation over time can be tested.
Create rich and fully interactive timeline visualizations.
    Timelines can be included in Shiny apps or R markdown documents.
    'timevis' includes an extensive API to manipulate a timeline after creation,
    and supports getting data out of the visualization into R. Based on the
    'vis.js' Timeline JavaScript library.
Unleash the power of time-series data visualization with ease using our package. Designed with simplicity 
 in mind, it offers three key features through the 'shiny' package output. The first tab shows time- series 
 charts with forecasts, allowing users to visualize trends and changes effortlessly. The second one displays 
 Averages per country presented in tables with accompanying sparklines, providing a quick and attractive 
 overview of the data. The last tab presents A customizable world map colored based on user-defined 
 variables for any chosen number of countries, offering an advanced visual approach to understanding 
 geographical data distributions. This package operates with just a few simple arguments, enabling users 
 to conduct sophisticated analyses without the need for complex programming skills. Transform your 
 time-series data analysis experience with our user-friendly tool.
Estimates time varying regression effects under Cox type models in
    survival data using classification and regression tree. The codes in this package were 
    originally written in S-Plus for the paper "Survival Analysis with Time-Varying Regression
    Effects Using a Tree-Based Approach," by Xu, R. and Adak, S. (2002) <doi:10.1111/j.0006-341X.2002.00305.x>, Biometrics, 58: 305-315.
    Development of this package was supported by NIH grants AG053983 and AG057707,
    and by the UCSD Altman Translational Research Institute, NIH grant UL1TR001442.
    The content is solely the responsibility of the authors and does not necessarily
    represent the official views of the NIH.
    The example data are from the Honolulu Heart Program/Honolulu Asia Aging Study (HHP/HAAS).
A problem solving environment (PSE) for fitting separable
    nonlinear models to measurements arising in physics and chemistry
    experiments, as described by Mullen & van Stokkum (2007)
    <doi:10.18637/jss.v018.i03> for its use in fitting time resolved
    spectroscopy data, and as described by Laptenok et al. (2007)
    <doi:10.18637/jss.v018.i08> for its use in fitting Fluorescence
    Lifetime Imaging Microscopy (FLIM) data, in the study of Förster
    Resonance Energy Transfer (FRET).  `TIMP` also serves as the
    computation backend for the `GloTarAn` software, a graphical user
    interface for the package, as described in Snellenburg et al. (2012)
    <doi:10.18637/jss.v049.i03>.
Functions for statistical analysis, prediction and control of time
 series based mainly on Akaike and Nakagawa (1988) <ISBN 978-90-277-2786-2>.
A universal non-uniform random number generator
  for quite arbitrary distributions with piecewise twice
  differentiable densities.
Parsing '(R)Markdown' files with numerous regular expressions can
    be fraught with peril, but it does not have to be this way. Converting
    '(R)Markdown' files to 'XML' using the 'commonmark' package allows
    in-memory editing via of 'markdown' elements via 'XPath' through the
    extensible 'R6' class called 'yarn'. These modified 'XML' representations
    can be written to '(R)Markdown' documents via an 'xslt' stylesheet which
    implements an extended version of 'GitHub'-flavoured 'markdown' so that you
    can tinker to your hearts content.
Instead of nesting function calls, annotate and transform 
    functions using "#." comments.
A 'tufte'-alike style for 'rmarkdown'.
 A modern take on the 'Tufte' design for pdf and html vignettes,
 building on the 'tufte' package with additional contributions
 from the 'knitr' and 'ggtufte' package, and also acknowledging
 the key influence of 'envisioned css'.
Generate a palette of tints, shades or both from a single colour.
Palettes generated from Tintin covers. There is one palette per
  cover, with a total of 24 palettes of 5 colours each. Includes functions to
  interpolate colors in order to create more colors based on the provided
  palettes.The data is based on Cyr, et al. (2004) <doi:10.1503/cmaj.1041405>
  and Wikipedia <https://en.wikipedia.org/wiki/The_Adventures_of_Tintin>.
The Gene Expression Omnibus (<https://www.ncbi.nlm.nih.gov/geo/>) and The Cancer Genome Atlas (<https://portal.gdc.cancer.gov/>) are widely used medical public databases. Our platform integrates routine analysis and visualization tools for expression data to provide concise and intuitive data analysis and presentation.
Adds some functions to help in your coding etiquette.
    'tinycodet' primarily focuses on 4 aspects.
    1) Safer decimal (in)equality testing,
    standard-evaluated alternatives to with() and aes(),
    and other functions for safer coding.
    2) A new package import system,
    that attempts to combine the benefits of using a package without attaching it,
    with the benefits of attaching a package.
    3) Extending the string manipulation capabilities of the 'stringi' R package.
    4) Reducing repetitive code.
    Besides linking to 'Rcpp', 'tinycodet' has only one other dependency, namely 'stringi'.
Assign, extract, or remove variable labels from R vectors.
  Lightweight and dependency-free.
Lightweight extension of the base R graphics system, with support
      for automatic legends, facets, and various other enhancements.
Creates useful files and folders for data
  analysis  projects and provides functions to manage data,
  scripts and output files. Also provides a project
  template for 'Rstudio'.
Provides functions to get personal 'Google Scholar'
    profile data from web API and show it in table or figure format.
Snapshots for unit tests using the 'tinytest' framework for R. Includes expectations to test base R and 'ggplot2' plots as well as console output from print().
An R wrapper for the 'Spotify' Web API 
  <https://developer.spotify.com/web-api/>.
Create highly customized tables with this simple and dependency-free package. Data frames can be converted to 'HTML', 'LaTeX', 'Markdown', 'Word', 'PNG', 'PDF', or 'Typst' tables. The user interface is minimalist and easy to learn. The syntax is concise. 'HTML' tables can be customized using the flexible 'Bootstrap' framework, and 'LaTeX' code with the 'tabularray' package.
Provides a lightweight (zero-dependency) and easy to use 
    unit testing framework. Main features: install tests with 
    the package. Test results are treated as data that can be stored and 
    manipulated. Test files are R scripts interspersed with test commands, that
    can be programmed over. Fully automated build-install-test sequence for 
    packages. Skip tests when not run locally (e.g. on CRAN). Flexible and 
    configurable output printing. Compare computed output with output stored 
    with the package. Run tests in parallel. Extensible by other packages.
    Report side effects.
Unit testing is a solid component of automated CI/CD pipelines. 
	'tinytest' - a lightweight, zero-dependency  alternative to 'testthat' was developed.
	To be able to integrate 'tinytests' results into common CI/CD systems the test results from 
	tinytest need to be caputred and converted to JUnit XML format. 
	'tinytest2JUnit' enables this conversion while staying also lightweight 
	and only have 'tinytest' as its dependency.
Helper functions to install and maintain the 'LaTeX' distribution
  named 'TinyTeX' (<https://yihui.org/tinytex/>), a lightweight, cross-platform,
  portable, and easy-to-maintain version of 'TeX Live'. This package also
  contains helper functions to compile 'LaTeX' documents, and install missing
  'LaTeX' packages automatically.
Themes for 'ggplot2' are a convenient way to style plots. The 'hrbrthemes' package
 contains a particularly nice one, but brings along a significant tail of dependencies. So this
 (currently experimental) package brings along just the 'theme_ipsum_rc' theme using the 'Roboto'
 'Condensed' font.  Should the font not be installed on your system, see the help in the package
 'hrbrthemes' on how to install 'Roboto Condensed'.
Download geographic shapes from the United States Census Bureau 
    TIGER/Line Shapefiles <https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.html>.
    Functions support downloading and reading in geographic boundary data.
    All downloads can be set up with a cache to avoid multiple downloads.
    Data is available back to 2000 for most geographies.
Cluster data without specifying the number of clusters using the Table Invitation Prior (TIP) introduced in the paper "Clustering Gene Expression Using the Table Invitation Prior" by Charles W. Harrison, Qing He, and Hsin-Hsiung Huang (2022) <doi:10.3390/genes13112036>. TIP is a Bayesian prior that uses pairwise distance and similarity information to cluster vectors, matrices, or tensors.
Accurately estimates phase shifts by accounting for period
  changes and for the point in the circadian cycle at which the stimulus occurs.
  See Tackenberg et al. (2018) <doi:10.1177/0748730418768116>.
Assists performing tip-dating of phylogenetic trees with BEAST 
    BEAST is a popular software for phylogenetic analysis.
    The package assists the implementation of various phylogenetic tip-
	dating tests using BEAST. It contains two main functions.
	The first one allows preparing date randomization analyses, 
	which assess the temporal signal of a data set. 
	The second function allows performing leave-one-out analyses,
	which test for the consistency between independent calibration sequences
	and allow pinpointing those leading to potential bias.
	The included tutorial provides detailed step-by-step instructions.
	An expanded description of the package can be found in article:
	Rieux, A. and Khatchikian, C.E. (2017), 
	TIPDATINGBEAST: an R package to assist the implementation of phylogenetic
	tip-dating tests using BEAST. Molecular Ecology Resources, 17: 608-613.
	<onlinelibrary.wiley.com/doi/full/10.1111/1755-0998.12603>.
Provides access to the complete Pali Canon, or
    Tipitaka, the canonical scripture for Theravadin Buddhists 
    worldwide. Based on the Chattha Sangayana Tipitaka version
    4 (Vipassana Research Institute, 1990).
Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider.
'Htmlwidget' of 'Tippyjs' to add tooltips to 'Shiny' apps and 'R markdown' documents.
The strength of evidence provided by epidemiological and
    observational studies is inherently limited by the potential for
    unmeasured confounding.  We focus on three key quantities: the
    observed bound of the confidence interval closest to the null, the
    relationship between an unmeasured confounder and the outcome, for
    example a plausible residual effect size for an unmeasured continuous
    or binary confounder, and the relationship between an unmeasured
    confounder and the exposure, for example a realistic mean difference
    or prevalence difference for this hypothetical confounder between
    exposure groups. Building on the methods put forth by Cornfield et al.
    (1959), Bross (1966), Schlesselman (1978), Rosenbaum & Rubin (1983),
    Lin et al. (1998), Lash et al. (2009), Rosenbaum (1986), Cinelli &
    Hazlett (2020), VanderWeele & Ding (2017), and Ding & VanderWeele
    (2016), we can use these quantities to assess how an unmeasured
    confounder may tip our result to insignificance.
Generates stochastic time series and genealogies associated with a population dynamics model. Times series are simulated using the Gillespie exact and approximate algorithms and a new algorithm we introduce that uses both approaches to optimize the time execution of the simulations. Genealogies are simulated from a trajectory using a backwards-in-time based approach. Methods are described in Danesh G et al (2022) <doi:10.1111/2041-210X.14038>.
It allows for mapping proportions and indicators defined on the unit interval. It implements Beta-based small area methods comprising the classical Beta regression models, the Flexible Beta model and Zero and/or One Inflated extensions (Janicki 2020 <doi:10.1080/03610926.2019.1570266>). Such methods, developed within a Bayesian framework through Stan <https://mc-stan.org/>, come equipped with a set of diagnostics and complementary tools, visualizing and exporting functions. A Shiny application with a user-friendly interface can be launched to further simplify the process. For further details, refer to De Nicolò and Gardini (2024 <doi:10.18637/jss.v108.i01>).
Functions and S3 classes for time indexes and time indexed
        series, which are compatible with FAME frequencies.
Uses indicator species scores across binary partitions of
  a sample set to detect congruence in taxon-specific changes of abundance
  and occurrence frequency along an environmental gradient as evidence of
  an ecological community threshold.  Relevant references include Baker and King 
  (2010) <doi:10.1111/j.2041-210X.2009.00007.x>, King and Baker (2010) 
  <doi:10.1899/09-144.1>, and Baker and King (2013) <doi:10.1899/12-142.1>.
This data set provides information on the fate of passengers on
    the fatal maiden voyage of the ocean liner "Titanic", summarized according
    to economic status (class), sex, age and survival. Whereas the base R
    Titanic data found by calling data("Titanic") is an array resulting from
    cross-tabulating 2201 observations, these data sets are the individual
    non-aggregated observations and formatted in a machine learning context
    with a training sample, a testing sample, and two additional data sets
    that can be used for deeper machine learning analysis. These data sets
    are also the data sets downloaded from the Kaggle competition and thus
    lowers the barrier to entry for users new to R or machine learing.
In some phase I trials, the design goal is to find the dose associated with a certain target toxicity rate or the dose with a certain weighted sum of rates of various toxicity grades. 'TITEgBOIN' provides the set up and calculations needed to run a dose-finding trial using bayesian optimal interval (BOIN) (Yuan et al. (2016) <doi:10.1158/1078-0432.CCR-16-0592>), generalized bayesian optimal interval (gBOIN) (Mu et al. (2019) <doi:10.1111/rssc.12263>), time-to-event bayesian optimal interval (TITEBOIN) (Lin et al. (2020) <doi:10.1093/biostatistics/kxz007>) and time-to-event generalized bayesian optimal interval (TITEgBOIN) (Takeda et al. (2022) <doi:10.1002/pst.2182>) designs. 'TITEgBOIN' can conduct tasks: run simulations and get operating characteristics; determine the dose for the next cohort; select maximum tolerated dose (MTD). These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose limiting toxicity (DLT) rates or target normalized equivalent toxicity score (ETS) rates to account for discrete toxicity score, and incorporate safety and/or stopping rules.
Functions to design phase 1 trials using an isotonic regression based design incorporating time-to-event information. Simulation and design functions are available, which incorporate information about followup and DLTs, and apply isotonic regression to devise estimates of DLT probability.
A collection of functions to plot acid/base titration 
    curves (pH vs. volume of titrant), complexation titration curves 
    (pMetal vs. volume of EDTA), redox titration curves (potential 
    vs.volume of titrant), and precipitation titration curves (either 
    pAnalyte or pTitrant vs. volume of titrant). Options include the 
    titration of mixtures, the ability to overlay two or more 
    titration curves, and the ability to show equivalence points.
Facilitate the management of data from knowledge
   resources that are frequently used alone or together
   in research environments.
   In 'TKCat', knowledge resources are manipulated as modeled database (MDB)
   objects. These objects provide access to the data tables along with a general
   description of the resource and a detail data model documenting the
   tables, their fields and their relationships.
   These MDBs are then gathered in catalogs that can be easily
   explored an shared.
   Finally, 'TKCat' provides tools to easily subset, filter and combine MDBs and
   create new catalogs suited for specific needs.
A 'Tcl/Tk' Graphical User Interface (GUI) to display images than can be zoomed and panned using the mouse and keyboard shortcuts. 'tkImgR' read and write different image formats (PPM/PGM, PNG and GIF) using the standard 'Tcl/Tk' distribution (>=8.6), but other formats (JPEG, TIFF, CR2) can be handled using the 'tkImg' package for 'Tcl/Tk'.
Simple mechanism for placing R graphics in a Tk widget.
Implements inverse and augmented inverse probability weighted 
    estimators for common treatment effect parameters at an interim analysis 
    with time-lagged outcome that may not be available for all enrolled subjects.  
    Produces estimators, standard errors, and information that can be used to 
    compute stopping boundaries using software that assumes that the 
    estimators/test statistics have independent increments. 
    Tsiatis, A. A. and Davidian, M., (2022) <arXiv:2204.10739> .
Implements a semiparametric estimator for the odds ratio model with
    censored, time-lagged, ordered categorical outcome in a randomized clinical
    trial that incorporates baseline and time-dependent information. 
    Tsiatis, A. A. and Davidian, M. (2021) <arXiv:2106.15559>.
Computes the solution path of the Terminating-LARS (T-LARS) algorithm. The T-LARS algorithm 
    is a major building block of the T-Rex selector (see R package 'TRexSelector').
    The package is based on the papers Machkour, Muma, and Palomar (2022) <arXiv:2110.06048>, Efron, Hastie, Johnstone, 
    and Tibshirani (2004) <doi:10.1214/009053604000000067>, and Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>.
An optimal alternating optimization algorithm for estimation of precision matrices of sparse tensor graphical models, and an efficient inference procedure for support recovery of the precision matrices.
Provides a comprehensive suite of statistical tools for analyzing, simulating, and computing properties of the Topp-Leone Cauchy Rayleigh (TLCAR) distribution, a versatile distribution amalgamating features of the Topp-Leone, Cauchy, and Rayleigh distributions, ideal for modeling intricate, heterogeneous data across scientific domains. See Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2023) <doi:10.1007/s44199-023-00066-4> and Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2024) <doi:10.1007/s44199-023-00069-1> for further insights.
Gives a number of functions to aid common data 
    analysis processes and reporting statistical results in an 'RMarkdown' file. 
    Data analysis functions combine multiple base R functions used to describe 
    simple bivariate relationships into a single, easy to use function. 
    Reporting functions will return character strings to report p-values, 
    confidence intervals, and hypothesis test and regression results. Strings 
    will be LaTeX-formatted as necessary and will knit pretty in an 'RMarkdown' 
    document. The package also provides wrappers function in the 'tableone' 
    package to make the results knit-able.
This comprehensive toolkit for T-distribution regression, known as the analysis of "TLIC" (T-distribution Linear regression Integrated Corrector), adopts ordinary least squares method and assumes that errors follow a T-distribution. This approach gives it an advantage when dealing with small samples or non-normal error distributions, and can provide more robust parameter estimation and hypothesis testing results.The philosophy of the package is described in Guo G. (2020) <doi:10.1080/02664763.2022.2053949>.
Computation of effects under linear, logistic and Poisson regression models with transformed variables. Logarithm and power transformations are allowed. Effects can be displayed both numerically and graphically in both the original and the transformed space of the variables.
Calculates empirical TL-moments (trimmed L-moments) of arbitrary 
    order and trimming, and converts them to distribution parameters.
Implementation of the classic Genz algorithm and a novel tile-low-rank algorithm for computing	relatively high-dimensional multivariate normal (MVN) and Student-t (MVT) probabilities. 
	References used for this package:
	Foley, James, Andries van Dam, Steven Feiner, and John Hughes. 
	"Computer Graphics: Principle and Practice". Addison-Wesley 
	Publishing Company. Reading, Massachusetts (1987, ISBN:0-201-84840-6 1);
	Genz, A., "Numerical computation of multivariate
	normal probabilities," Journal of Computational and
	Graphical Statistics, 1, 141-149 (1992) <doi:10.1080/10618600.1992.10477010>;
    Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "Exploiting Low
    Rank Covariance Structures for Computing High-Dimensional Normal and Student-
    t Probabilities," Statistics and Computing, 31.1, 1-16 (2021) 
    <doi:10.1007/s11222-020-09978-y>;
    Cao, J., Genton, M. G., Keyes, D. E., & Turkiyyah, G. M. "tlrmvnmvt: 
    Computing High-Dimensional Multivariate Normal and Student-t Probabilities 
    with Low-Rank Methods in R," Journal of Statistical Software, 101.4, 
    1-25 (2022) <doi:10.18637/jss.v101.i04>.
Functions for point and interval estimation in
	     error-in-variables models via total least squares or
	     generalized total least squares method. See Golub and Van
	     Loan (1980) <doi:10.1137/0717073>, Gleser (1981)
	     <https://www.jstor.org/stable/2240867>, Ivan Markovsky
	     and Huffel (2007) <doi:10.1016/j.sigpro.2007.04.004> for
	     more information.
A framework for text mining applications within R.
Provides a 'tm' Source to create corpora from
  a corpus prepared in the format used by the 'Alceste' application (i.e.
  a single text file with inline meta-data). It is able to import both
  text contents and meta-data (starred) variables.
A plug-in for the text mining framework tm to support text mining 
             in a distributed way. The package provides a convenient interface for
             handling distributed corpus objects based on distributed list objects.
Provides a 'tm' Source to create corpora from
  articles exported from the 'Europresse' content provider as
  HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author and pages).
Provides a 'tm' Source to create corpora from
  articles exported from the Dow Jones 'Factiva' content provider as
  XML or HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author, subject,
  geographical coverage, company, industry, and various
  provider-specific fields).
Enhances 'koRpus' text object classes and methods to also support large
          corpora. Hierarchical ordering of corpus texts into arbitrary categories will
          be preserved. Provided classes and methods also improve the ability of using
          the 'koRpus' package together with the 'tm' package. To ask for help, report
          bugs, suggest feature improvements, or discuss the global development of the
          package, please subscribe to the koRpus-dev mailing list
          (<https://korpusml.reaktanz.de>).
Provides a 'tm' Source to create corpora from
  articles exported from the 'LexisNexis' content provider as
  HTML files. It is able to read both text content and meta-data
  information (including source, date, title, author and pages).
  Note that the file format is highly unstable: there is no warranty
  that this package will work for your corpus, and you may have
  to adjust the code to adapt it to your particular format.
A plug-in for the tm text mining framework providing mail handling
  functionality.
Useful functions to connect to 'TM1' <https://www.ibm.com/uk-en/products/planning-and-analytics> instance from R via REST API. With the functions in the package, data can be imported from 'TM1' via mdx view or native view, data can be sent to 'TM1', processes and chores can be executed, and cube and dimension metadata information can be taken.
Thematic maps are geographical maps in which spatial data distributions are visualized. This package offers a flexible, layer-based, and easy to use approach to create thematic maps, such as choropleths and bubble maps.
Set of tools for reading and processing spatial data. The aim is to supply the workflow to create thematic maps. This package also facilitates 'tmap', the package for visualizing thematic maps.
With this tool, a user should be able to quickly implement
    complex random effect models through simple C++ templates. The package combines
    'CppAD' (C++ automatic differentiation), 'Eigen' (templated matrix-vector
    library) and 'CHOLMOD' (sparse matrix routines available from R) to obtain
    an efficient implementation of the applied Laplace approximation with exact
    derivatives. Key features are: Automatic sparseness detection, parallelism
    through 'BLAS' and parallel user templates.
Enables all 'rstan' functionality for a 'TMB' model object, in particular MCMC sampling and chain visualization. Sampling can be performed with or without Laplace approximation for the random effects. This is demonstrated in Monnahan & Kristensen (2018) <DOI:10.1371/journal.pone.0197954>.
A Text mining toolkit for Chinese, which includes facilities for 
    Chinese string processing, Chinese NLP supporting, encoding detecting and 
    converting. Moreover, it provides some functions to support 'tm' package 
    in Chinese.
Provides an R-interface to the TMDb API (see TMDb API on <https://developers.themoviedb.org/3/getting-started/introduction>). The Movie Database (TMDb) is a popular user editable database for movies and TV shows (see <https://www.themoviedb.org>).
Miscellaneous utility functions for data manipulation,
    data tidying, and working with gene expression data.
Suite of tropical geometric tools for use in machine learning applications. These methods may be summarized in the following references: Yoshida, et al. (2022) <doi:10.2140/astat.2023.14.37>, Barnhill et al. (2023) <doi:10.48550/arXiv.2303.02539>, Barnhill and Yoshida (2023) <doi:10.3390/math11153433>, Aliatimis et al. (2023) <doi:10.1007/s11538-024-01327-8>, Yoshida et al. (2022) <doi:10.1109/TCBB.2024.3420815>, and Yoshida et al. (2019) <doi:10.1007/s11538-018-0493-4>.
Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006.  This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC).  The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes.  Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values.  The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments.
Methods and feature set definitions for feature or gene set
  enrichment analysis in transcriptional and metabolic profiling data.
  Package includes tests for enrichment based on ranked lists of features,
  functions for visualisation and multivariate functional analysis. See Zyla et al (2019)
  <doi:10.1093/bioinformatics/btz447>.
Define general templates with tags that can be replaced by content depending on arguments and objects to modify the final output of the document.
Trauma Mortality prediction for ICD-9, ICD-10, and AIS lexicons in
    long or wide format based on Dr. Alan Cook's tmpm mortality model.
Sensitivity analysis using the trimmed means estimator.
Provides conditional maximum likelihood (CML) item parameter estimation of both sequential and cumulative deterministic multistage designs (Zwitser & Maris, 2015, <doi:10.1007/s11336-013-9369-6>) and probabilistic sequential and cumulative multistage designs (Steinfeld & Robitzsch, 2021, <doi:10.31234/osf.io/ew27f>). Supports CML item parameter estimation of conventional linear designs and additional functions for the likelihood ratio test (Andersen, 1973, <doi:10.1007/BF02291180>) as well as functions for simulating various types of multistage designs.
Efficient sampling of truncated multivariate (scale) mixtures of normals under linear inequality constraints is nontrivial due to the analytically intractable normalizing constant. Meanwhile, traditional methods may subject to numerical issues, especially when the dimension is high and dependence is strong.    Algorithms proposed by Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> are adopted for overcoming difficulties in simulating truncated distributions. Efficient rejection sampling for simulating truncated univariate normal distribution is included in the package, which shows superiority in terms of acceptance rate and numerical stability compared to existing methods and R packages. An efficient function for sampling from truncated multivariate normal distribution subject to convex polytope restriction regions based on Gibbs sampler for conditional truncated univariate distribution is provided. By extending the sampling method, a function for sampling truncated multivariate Student's t distribution is also developed.     Moreover, the proposed method and computation remain valid for high dimensional and strong dependence scenarios. Empirical results in Li and Ghosh (2015) <doi: 10.1080/15598608.2014.996690> illustrated the superior performance in terms of various criteria (e.g. mixing and integrated auto-correlation time).
Importance sampling from the truncated multivariate normal using the GHK (Geweke-Hajivassiliou-Keane) simulator.
  Unlike Gibbs sampling which can get stuck in one truncation sub-region depending on initial values, this package allows 
  truncation based on disjoint regions that are created by truncation of absolute values. The GHK algorithm uses simple Cholesky
  transformation followed by recursive simulation of univariate truncated normals hence there are also no convergence issues. 
  Importance sample is returned along with sampling weights, based on which, one can calculate integrals over truncated regions
  for multivariate normals.
Random number generation for the truncated multivariate normal and Student t distribution. 
  Computes probabilities, quantiles and densities, 
  including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.
Simulation of random vectors from truncated multivariate normal 
  and t distributions based on the algorithms proposed by Yifang Li and Sujit K. Ghosh (2015) <doi:10.1080/15598608.2014.996690>.
Node centrality measures for temporal networks. Available measures are temporal degree centrality, temporal closeness centrality and temporal betweenness centrality defined by Kim and Anderson (2012) <doi:10.1103/PhysRevE.85.026107>. Applying the REN algorithm by Hanke and Foraita (2017) <doi:10.1186/s12859-017-1677-x> when calculating the centrality measures keeps the computational running time linear in the number of graph snapshots. Further, all methods can run in parallel up to the number of nodes in the network.
Binary ties limit the richness of network analyses as relations are unique. The two-mode structure contains a number of features lost when projection it to a one-mode network. Longitudinal datasets allow for an understanding of the causal relationship among ties, which is not the case in cross-sectional datasets as ties are dependent upon each other.
Performing the hypothesis tests for the two
    sample problem based on order statistics and power comparisons. 
    Calculate the test statistic, density, distribution function, 
    quantile function, random number generation and others.
Provides access to the Taxonomic Name Resolution Service <https://github.com/ojalaquellueva/tnrsapi> through R.  The user supplies plant taxonomic names and the package returns resolved taxonomic names along with information on decisions.  Optionally, the package can also be used to parse taxonomic names.
An assortment of functions and templates customized to meet 
    the needs of data analysts at the non-profit organization TNTP. 
    Includes functions for branded colors and plots, credentials management,
    repository set-up, and other common analytic tasks.
Create interactive tables, calendars and charts with 'TOAST UI' <https://ui.toast.com/> libraries to
  integrate in 'shiny' applications or 'rmarkdown' 'HTML' documents.
Construction of the Total Operating Characteristic (TOC) Curve and the Receiver (aka Relative) Operating Characteristic (ROC) Curve for spatial and non-spatial data. The TOC method is a modification of the ROC method which measures the ability of an index variable to diagnose either presence or absence of a characteristic. The diagnosis depends on whether the value of an index variable is above a threshold. Each threshold generates a two-by-two contingency table, which contains four entries: hits (H), misses (M), false alarms (FA), and correct rejections (CR). While ROC shows for each threshold only two ratios, H/(H + M) and FA/(FA + CR), TOC reveals the size of every entry in the contingency table for each threshold (Pontius Jr., R.G., Si, K. 2014. <doi:10.1080/13658816.2013.862623>).
This is a simple addin to 'RStudio' that finds all 'TODO', 'FIX ME', 'CHANGED' etc. comments in your project and shows them as a markers list.
Use the <https://toggl.com> time tracker api through R.
Approximations of global p-values when testing hypothesis in presence of non-identifiable nuisance parameters. The method relies on the Euler characteristic heuristic and the expected Euler characteristic is efficiently computed by  in Algeri and van Dyk (2018) <arXiv:1803.03858>.
Interfaces with the 'Hugging Face' tokenizers library to provide implementations
  of today's most used tokenizers such as the 'Byte-Pair Encoding' algorithm 
  <https://huggingface.co/docs/tokenizers/index>. It's extremely fast for both 
  training new vocabularies and tokenizing texts.
Create browsers for reading full texts from a token list format.
    Information obtained from text analyses (e.g., topic modeling, word scaling)
    can be used to annotate the texts.
Convert natural language text into tokens. Includes tokenizers for
    shingled n-grams, skip n-grams, words, word stems, sentences, paragraphs,
    characters, shingled characters, lines, Penn Treebank, regular
    expressions, as well as functions for counting characters, words, and sentences,
    and a function for splitting longer texts into separate documents, each with
    the same number of words.  The tokenizers have a consistent interface, and
    the package is built on the 'stringi' and 'Rcpp' packages for  fast
    yet correct tokenization in 'UTF-8'.
Unsupervised text tokenizer focused on computational efficiency. Wraps the 'YouTokenToMe' library <https://github.com/VKCOM/YouTokenToMe> which is an implementation of fast Byte Pair Encoding (BPE) <https://aclanthology.org/P16-1162/>.
Statistical tolerance limits provide the limits between which we can expect to find a specified proportion of a sampled population with a given level of confidence.  This package provides functions for estimating tolerance limits (intervals) for various univariate distributions (binomial, Cauchy, discrete Pareto, exponential, two-parameter exponential, extreme value, hypergeometric, Laplace, logistic, negative binomial, negative hypergeometric, normal, Pareto, Poisson-Lindley, Poisson, uniform, and Zipf-Mandelbrot), Bayesian normal tolerance limits, multivariate normal tolerance regions, nonparametric tolerance intervals, tolerance bands for regression settings (linear regression, nonlinear regression, nonparametric regression, and multivariate regression), and analysis of variance tolerance intervals.  Visualizations are also available for most of these settings.
Email Finder R Client Library.
        Search emails are based on the website You give one domain name and it returns all the email addresses found on the internet.
        Email Finder generates or retrieves the most likely email address from a domain name, a first name and a last name.
        Email verify checks the deliverability of a given email address, verifies if it has been found in our database, and returns their sources.
Transcriptome visualization from sample trait statistics to gene expression analysis. Six categories include "Samples Statistics", "Traits Analysis", "Differential Expression Analysis", "Advanced Analysis", "GO and KEGG Enrichment", "Tables Operations", with complete sample data.
Several functions to allow comparisons of data across different geographies, in particular for Canadian census data from different censuses.
Includes functions for mapping named lists to function arguments, random strings,
  pasting and combining rows together across columns, etc.
Implements two tests for same-source of toolmarks. The chumbley_non_random() test follows the paper "An Improved Version of a Tool Mark Comparison Algorithm" by Hadler and Morris (2017) <doi:10.1111/1556-4029.13640>. This is an extension of the Chumbley score as previously described in "Validation of Tool Mark Comparisons Obtained Using a Quantitative, Comparative, Statistical Algorithm" by Chumbley et al (2010) <doi:10.1111/j.1556-4029.2010.01424.x>. fixed_width_no_modeling() is based on correlation measures in a diamond shaped area of the toolmark as described in Hadler (2017).
Uplift modeling aims at predicting the causal effect of an action such as a marketing campaign on a particular individual. In order to simplify the task for practitioners in uplift modeling, we propose a combination of tools that can be separated into the following ingredients: i) quantization, ii) visualization, iii) variable selection, iv) parameters estimation and, v) model validation. For more details, see <https://dms.umontreal.ca/~murua/research/UpliftRegression.pdf>.
Provides functions for multivariate analysis with compositional data. Includes a function for doing compositional canonical correlation analysis.  This analysis requires two data matrices of compositions, which can be adequately transformed and used as entries in a specialized program for canonical correlation analysis, that is able to deal with singular covariance matrices. The methodology is described in Graffelman et al. (2017) <doi:10.1101/144584>. Functions for log-ratio principal component analysis with condition number computations and log-ratio discriminant analysis have been added to the package.
Tools to calculate stability indices with parametric,
 non-parametric and probabilistic approaches. The basic data format requirement for 'toolStability' is a data frame with 3 columns including numeric trait values, genotype,and environmental labels. Output format of each function is the dataframe with chosen stability index for each genotype. 
 Function "table_stability" offers the summary table of all stability indices in this package. 
 This R package toolStability is part of the main publication: 
 Wang, Casadebaig and Chen (2023) <doi:10.1007/s00122-023-04264-7>. 
 Analysis pipeline for main publication can be found on github: <https://github.com/Illustratien/Wang_2023_TAAG/tree/V1.0.0>. 
 Sample dataset in this package is derived from another publication:  
 Casadebaig P, Zheng B, Chapman S et al. (2016) <doi:10.1371/journal.pone.0146385>. 
 For detailed documentation of dataset, please see on Zenodo <doi:10.5281/zenodo.4729636>. 
 Indices used in this package are from: 
 Döring TF, Reckling M (2018) <doi:10.1016/j.eja.2018.06.007>. 
 Eberhart SA, Russell WA (1966) <doi:10.2135/cropsci1966.0011183X000600010011x>. 
 Eskridge KM (1990) <doi:10.2135/cropsci1990.0011183X003000020025x>. 
 Finlay KW, Wilkinson GN (1963) <doi:10.1071/AR9630742>. 
 Hanson WD (1970) Genotypic stability. <doi:10.1007/BF00285245>. 
 Lin CS, Binns MR (1988) <https://cdnsciencepub.com/doi/abs/10.4141/cjps88-018>. 
 Nassar R, Hühn M (1987). 
 Pinthus MJ (1973) <doi:10.1007/BF00021563>. 
 Römer T (1917). 
 Shukla GK (1972). 
 Wricke G (1962).
An R wrapper for using 'TooManyCells', a command line program for clustering, visualizing, and quantifying cell clade relationships. See <https://gregoryschwartz.github.io/too-many-cells/> for more details.
Language specific cardinal to ordinal number conversion.
Two- and three-dimensional morphometric maps of enamel and 
	dentine thickness and multivariate analysis. 
	Volume calculation of dental materials. 
	Principal component analysis of thickness maps with 
	associated morphometric map variations.
Several datasets which describe the chef contestants in Top Chef, 
    the challenges that they compete in, and the results of those challenges. 
    This data is useful for practicing data wrangling, graphing, and analyzing 
    how each season of Top Chef played out.
The 'TopDom' method identifies topological domains in genomes from Hi-C sequence data (Shin et al., 2016 <doi:10.1093/nar/gkv1505>).  The authors published an implementation of their method as an R script (two different versions; also available in this package).  This package originates from those original 'TopDom' R scripts and provides help pages adopted from the original 'TopDom' PDF documentation.  It also provides a small number of bug fixes to the original code.
Data collected on movement behavior is often in the form of time-
    stamped latitude/longitude coordinates sampled from the underlying movement
    behavior. These data can be compressed into a set of segments via the Top-
    Down Time Ratio Segmentation method described in Meratnia and de By (2004)
    <doi:10.1007/978-3-540-24741-8_44> which, with some loss of information, 
    can both reduce the size of the data as well as provide corrective smoothing
    mechanisms to help reduce the impact of measurement error.  This is an
    improvement on the well-known Douglas-Peucker algorithm for segmentation
    that operates not on the basis of perpendicular distances. Top-Down Time
    Ratio segmentation allows for disparate sampling time intervals by
    calculating the distance between locations and segments with respect to
    time. Provided a trajectory with timestamps, tdtr() returns a set of straight-
    line segments that can represent the full trajectory. McCool, Lugtig,
    and Schouten (2022) <doi:10.1007/s11116-022-10328-2> describe this method as
    implemented here in more detail.
Calculates topic-specific diagnostics (e.g. mean token length, exclusivity) for 
    Latent Dirichlet Allocation and Correlated Topic Models fit using the 'topicmodels' package.
    For more details, see Chapter 12 in Airoldi et al. (2014, ISBN:9781466504080), 
    pp 262-272 Mimno et al. (2011, ISBN:9781937284114), and Bischof et al. (2014) <arXiv:1206.4631v1>.
Leveraging (large) language models for automatic topic labeling. The main function converts a list of top terms into a label for each topic. Hence, it is complementary to any topic modeling package that produces a list of top terms for each topic. While human judgement is indispensable for topic validation (i.e., inspecting top terms and most representative documents), automatic topic labeling can be a valuable tool for researchers in various scenarios.
Provides an interface to the C code for Latent Dirichlet
	     Allocation (LDA) models and Correlated Topics Models
	     (CTM) by David M. Blei and co-authors and the C++ code
	     for fitting LDA models using Gibbs sampling by Xuan-Hieu
	     Phan and co-authors.
Find topics in texts which are semantically embedded using techniques like word2vec or Glove. 
    This topic modelling technique models each word with a categorical distribution whose natural parameter is the inner product between a word embedding and an embedding of its assigned topic.
    The techniques are explained in detail in the paper 'Topic Modeling in Embedding Spaces' by Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei (2019), available at <arXiv:1907.04907>.
Provides implementation of the "Topic SCORE" algorithm that is
	     proposed by Tracy Ke and Minzhe Wang. The singular value decomposition
	     step is optimized through the usage of svds() function in 'RSpectra'
	     package, on a 'dgRMatrix' sparse matrix. Also provides a column-wise
	     error measure in the word-topic matrix A, and an algorithm for
	     recovering the topic-document matrix W given A and D based on
	     quadratic programming.
		The details about the techniques are explained in the paper "A new SVD approach to optimal topic estimation" by Tracy Ke and Minzhe Wang (2017) <arXiv:1704.07016>.
For multiple ranked input lists (full or partial) representing the same set of N objects, the package 'TopKLists' <doi:10.1515/sagmb-2014-0093> offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results. Note that RGtk2 and gWidgets2RGtk2 have been archived on CRAN. See <https://github.com/pievos101/TopKLists> for installation instructions.
A mathematical optimization procedure in combination with statistical bootstrap for the estimation of the latent signals (sometimes called scores) informing the global consensus ranking (often named aggregation ranking). To solve mid/large-scale problems, users should install the 'gurobi' optimiser (available from <https://www.gurobi.com/>).
Set of hydrological functions including an R
        implementation of the hydrological model TOPMODEL, which is
        based on the 1995 FORTRAN version by Keith Beven. From version
        0.7.0, the package is put into maintenance mode.
A toolkit for calculating topographic distances and identifying and plotting topographic paths. Topographic distances can be calculated along shortest topographic paths (Wang (2009) <doi:10.1111/j.1365-294X.2009.04338.x>), weighted topographic paths (Zhan et al. (1993) <doi:10.1007/3-540-57207-4_29>), and topographic least cost paths (Wang and Summers (2010) <doi:10.1111/j.1365-294X.2009.04465.x>). Functions can map topographic paths on colored or hill shade maps and plot topographic cross sections (elevation profiles) for the paths.
Using Gaussian graphical models we propose a novel approach to
             perform pathway analysis using gene expression. Given the
             structure of a graph (a pathway) we introduce two statistical
             tests to compare the mean and the concentration matrices between
             two groups. Specifically, these tests can be performed on the
             graph and on its connected components (cliques). The package is
             based on the method described in Massa M.S., Chiogna M., Romualdi
             C. (2010) <doi:10.1186/1752-0509-4-121>.
Flexible and ergonomic topological sorting implementation for
    R. Supports a variety of input data encoding (lists of edges or
    adjacency matrices, graphs edge direction), stable sort variants as
    well as cycle detection with detailed diagnosis.
A collection of functions for visualizing,exploring and annotating genetic association results.Association results from multiple traits can be viewed simultaneously along with gene annotation, over the entire genome (Manhattan plot) or in the more detailed regional view.
Gene and exon information from Ensembl genome builds GRCh38.p13 (104) and GRCh37 (v40) to use with the 'topr' package.
Evaluation of alternatives based on multiple criteria using TOPSIS method.
Assists in the TOPSIS analysis process, designed to return at the end of the answer of the TOPSIS multicriteria analysis,  a ranking table with the best option as the analysis proposes. TOPSIS is basically a technique developed by Hwang and Yoon in 1981, starting from the point that the best alternative should be closest to the positive ideal solution and farthest from the negative one, based on several criteria to result in the best benefit. (LIU, H. et al., 2019) <doi:10.1016/j.agwat.2019.105787>.
The goal of tor (to-R) is to help you to import multiple
    files from a single directory at once, and to do so as quickly,
    flexibly, and simply as possible.
Provides functionality to define and train neural networks similar to
    'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R
    using the 'libtorch' library. Also supports low-level tensor operations and
    'GPU' acceleration.
Provides access to datasets, models and processing
    facilities for deep learning in audio.
Provides datasets in a format that can be easily consumed by torch 'dataloaders'.
  Handles data downloading from multiple sources, caching and pre-processing so
  users can focus only on their model implementations.
Optimizers for 'torch' deep learning library. These
    functions include recent results published in the literature and are
    not part of the optimizers offered in 'torch'. Prospective users
    should test these optimizers with their data, since performance
    depends on the specific problem being solved.  The packages includes
    the following optimizers: (a) 'adabelief' by Zhuang et al (2020),
    <arXiv:2010.07468>; (b) 'adabound' by Luo et al.(2019),
    <arXiv:1902.09843>; (c) 'adahessian' by Yao et al.(2021)
    <arXiv:2006.00719>; (d) 'adamw' by Loshchilov & Hutter (2019),
    <arXiv:1711.05101>; (e) 'madgrad' by Defazio and Jelassi (2021),
    <arXiv:2101.11075>; (f) 'nadam' by Dozat (2019),
    <https://openreview.net/pdf/OM0jvwB8jIp57ZJjtNEZ.pdf>; (g) 'qhadam' by
    Ma and Yarats(2019), <arXiv:1810.06801>; (h) 'radam' by Liu et al.
    (2019), <arXiv:1908.03265>; (i) 'swats' by Shekar and Sochee (2018),
    <arXiv:1712.07628>; (j) 'yogi' by Zaheer et al.(2019),
    <https://papers.nips.cc/paper/8186-adaptive-methods-for-nonconvex-optimization>.
Provides access to datasets, models and preprocessing
    facilities for deep learning with images. Integrates seamlessly
    with the 'torch' package and it's 'API' borrows heavily from
    'PyTorch' vision package.
Implements additional operators for computer vision models, including
    operators necessary for image segmentation and object detection deep learning
    models.
Third order response surface designs (M. Hemavathi, Shashi Shekhar, Eldho Varghese, Seema Jaggi, Bikas Sinha & Nripes Kumar Mandal (2022) <DOI:10.1080/03610926.2021.1944213>."Theoretical developments in response surface designs: an informative review and further thoughts") are classified into two types viz., designs which are suitable for sequential experimentation and designs for non-sequential experimentation (M. Hemavathi, Eldho Varghese, Shashi Shekhar & Seema Jaggi (2022)<DOI:10.1080/02664763.2020.1864817>." Sequential asymmetric third order rotatable designs (SATORDs)"). The sequential experimentation approach involves conducting the trials step by step whereas, in the non-sequential experimentation approach, the entire runs are executed in one go.This package contains functions named STORDs() and NSTORDs() for generating sequential/non-sequential TORDs given in Das, M. N., and V. L. Narasimham (1962). <DOI:10.1214/aoms/1177704374>. "Construction of rotatable designs through balanced incomplete block designs" along with the randomized layout. It also contains another function named Pred3.var() for generating the variance of predicted response as well as the moment matrix based on a third order response surface model.
Draws tornado plots for model sensitivity to univariate changes.  Implements methods for many modeling methods including linear models, generalized linear models, survival regression models, and arbitrary machine learning models in the caret package.  Also draws variable importance plots.
A framework for statistical analysis in content analysis. In addition to a pipeline for preprocessing text corpora and linking to the latent Dirichlet allocation from the 'lda' package, plots are offered for the descriptive analysis of text corpora and topic models. In addition, an implementation of Chang's intruder words and intruder topics is provided. Sample data for the vignette is included in the toscaData package, which is available on gitHub: <https://github.com/Docma-TU/toscaData>.
Operators and functions provided by base R sometimes lack some
    features found in other programming languages, such as the ability to
    concatenate strings using + or to repeat strings using *. This package aims
    at providing such functionality without breaking existing code, i.e., only
    statements, that would throw errors in pure base R are patched.
Base R sometimes requires verbose statements for simple,
    often recurring tasks, such as printing text without trailing
    space, ending with newline. This package aims at providing
    shorthands for such tasks.
A general framework of two directional simultaneous inference
    is provided for high-dimensional as well as the fixed dimensional models with manifest
    variable or latent variable structure, such as high-dimensional mean models, high-
    dimensional sparse regression models, and high-dimensional latent factors models.
    It is making the simultaneous inference on a set of parameters from two directions,
    one is testing whether the estimated zero parameters indeed are zero and the other is
    testing whether there exists zero in the parameter set of non-zero. More details can be 
    referred to Wei Liu, et al. (2022) <doi:10.48550/arXiv.2012.11100>.
The goal of 'tosr' is to create the Tree of Science from 
             Web of Science (WoS) and Scopus data. It can read files 
             from both sources at the same time. More information 
             can be found in Valencia-Hernández (2020) 
             <https://revistas.unal.edu.co/index.php/ingeinv/article/view/77718>.
Two one-sided tests (TOST) procedure to test equivalence for t-tests, correlations, differences between proportions, and meta-analyses, including power analysis for t-tests and correlations. Allows you to specify equivalence bounds in raw scale units or in terms of effect sizes. See: Lakens (2017) <doi:10.1177/1948550617697177>.
Download summary files from Census Bureau <https://www2.census.gov/> 
    and extract data, in particular high resolution data at 
    block, block group, and tract level, from decennial census and 
    American Community Survey 1-year and 5-year estimates.
Measures the degree of balance for a given phylogenetic tree by 
  calculating the Total Cophenetic Index.
  Reference: A. Mir, F. Rossello, L. A. Rotger (2013).
  A new balance index for phylogenetic trees.
  Math. Biosci. 241, 125-136 <doi:10.1016/j.mbs.2012.10.005>.
Estimates the parameters of a Transformed Ornstein-Uhlenbeck (TOU) stochastic model for adsorption data and also the parameters of the related pseudo-n-order (PNO) model, such as the maximum adsorption capacity (qe), the adsorption rate constant (kn) and the order of the model (n).
R implementation of the software tools developed in the H-CUP
    (Healthcare Cost and Utilization Project) <https://www.hcup-us.ahrq.gov>
    and AHRQ (Agency for Healthcare Research and Quality)
    <https://www.ahrq.gov>.  It currently contains functions for mapping ICD-9
    codes to the AHRQ comorbidity measures and translating ICD-9
    (resp. ICD-10) codes to ICD-10 (resp. ICD-9) codes based on GEM (General
    Equivalence Mappings) from CMS (Centers for Medicare and Medicaid
    Services).
Several datasets which describe the challenges and results of
    competitions in Tournament of Champions.  This data is useful for
    practicing data wrangling, graphing, and analyzing how each season of
    Tournament of Champions played out.
Implements geodesic interpolation and basis
    generation functions that allow you to create new tour
    methods from R.
Non-imputational method for handling missing values in 
   a prediction context, meaning that not only are there missing
   values in the training dataset, but also some values may be missing
   in future cases to be predicted. Based on the notion of regression
   averaging (Matloff (2017, ISBN: 9781498710916)).
Computation of stopping boundaries for a single-arm trial using a
    Bayesian criterion; i.e., for each m<=n (n= total patient number of the 
    trial) the smallest number of observed toxicities is calculated
    leading to the termination of the trial/accrual according to the specified 
    criteria. The probabilities of stopping the trial/accrual at and up until 
    (resp.) the m-th patient (m<=n) is also calculated. This design is more 
    conservative than the frequentist approach (using Clopper Pearson CIs)
    which might be preferred as it concerns safety.See also Aamot et.al.(2010) 
    "Continuous monitoring of toxicity in clinical trials - simulating the risk 
    of stopping prematurely" <doi:10.5414/cpp48476>.
Data analysis package for estimating potential biological effects from chemical concentrations in environmental samples. Included are a set of functions to analyze, visualize, and organize measured concentration data as it relates to user-selected chemical-biological interaction benchmark data such as water quality criteria. The intent of these analyses is to develop a better understanding of the potential biological relevance of environmental chemistry data. Results can be used to prioritize which chemicals at which sites may be of greatest concern. These methods are meant to be used as a screening technique to predict potential for biological influence from chemicals that ultimately need to be validated with direct biological assays. A description of the analysis can be found in Blackwell (2017) <doi:10.1021/acs.est.7b01613>.
Enables users to build 'ToxPi' prioritization models and provides 
  functionality within the grid framework for plotting ToxPi graphs.
  'toxpiR' allows for more customization than the 'ToxPi GUI' 
  (<https://toxpi.org>) and integration into existing workflows for greater 
  ease-of-use, reproducibility, and transparency.
  toxpiR package behaves nearly identically to the GUI; the package 
  documentation includes notes about all differences.
  The vignettes download example files from 
  <https://github.com/ToxPi/ToxPi-example-files>.
A 'shiny' app that generates plots and summary tables from repeat-dose
        toxicology study results to facilitate holistic evaluation of the drug safety of 
        active pharmaceutical ingredients (API) prior to initiation of clinical trials.
Estimation of transition probabilities for the illness-death model. Both the Aalen-Johansen estimator for a Markov model and a novel non-Markovian estimator by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>, see also Balboa and de Una-Alvarez (2018) <doi:10.18637/jss.v083.i10>, are included.
Contains logic for single sample gene set testing of cancer transcriptomic data with adjustment for normal tissue-specificity.
  Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.
Contains summary data on gene expression in normal human tissues from the Human Protein Atlas for use with the 
  Tissue-Adjusted Pathway Analysis of cancer (TPAC) method.
  Frost, H. Robert (2023) "Tissue-adjusted pathway analysis of cancer (TPAC)" <doi:10.1101/2022.03.17.484779>.
Tools for estimating and inferring two-way partial area under receiver operating characteristic curves (two-way pAUC), partial area under receiver operating characteristic curves (pAUC), and partial area under ordinal dominance curves (pODC). Methods includes Mann-Whitney statistic and Jackknife, etc.
Constraint-based causal discovery using the PC algorithm while
    accounting for a partial node ordering, for example a partial temporal ordering
    when the data were collected in different waves of a cohort study.
    Andrews RM, Foraita R, Didelez V, Witte J (2021) <arXiv:2108.13395>  
    provide a guide how to use tpc to analyse cohort data.
A threshold partial correlation approach to selecting 
             important variables in linear models of L. and others (2017) at
             <doi:10.5705/ss.202015.0473>, and in partial linear models
             of L. and others (2018) at <doi:10.1016/j.jmva.2018.06.005>. 
             This package also extends the PC-simple algorithm of
             B. and others (2010) at <doi:10.1093/biomet/asq008> to 
             partial linear models.
Tools to calculate trait probability density functions (TPD) at
    any scale (e.g. populations, species, communities). TPD functions are used to compute
    several indices of functional diversity, as well as its partition across scales.
    These indices constitute a unified framework that incorporates the underlying
    probabilistic nature of trait distributions into uni- or multidimensional
    functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for
    further information.
We described a novel Topology-based pathway enrichment analysis, which integrated the global position of the nodes and the topological property of the pathways in  Kyoto Encyclopedia of Genes and Genomes Database.
             We also provide some functions to obtain the latest information about pathways to finish pathway enrichment analysis using this method.
A bioinformatics tool for the estimation of the tumor purity from sequencing data. 
    It uses the set of putative clonal somatic single nucleotide variants within copy number neutral
    segments to call tumor cellularity.
Calculates the number of true positives and false positives from a dataset formatted for Jackknife alternative free-response receiver operating characteristic which is used for statistical analysis which is explained in the book 'Chakraborty' 'DP' (2017), "Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples", Taylor-Francis <https://www.crcpress.com/9781482214840>.
Uses thresholded partial least squares algorithm to create a regression or classification model. For more information, see Lee, Bradlow, and Kable <doi:10.1016/j.crmeth.2022.100227>.
A traceability focused tool created to simplify the data manipulation necessary to create clinical summaries.
Contains functions for calculating the Federal Highway Administration (FHWA) Transportation Performance Management (TPM) performance measures. Currently, the package provides methods for the System Reliability and Freight (PM3) performance measures calculated from travel time data provided by The National Performance Management Research Data Set (NPMRDS), including Level of Travel Time Reliability (LOTTR), Truck Travel Time Reliability (TTTR), and Peak Hour Excessive Delay (PHED) metric scores for calculating statewide reliability performance measures. Implements <https://www.fhwa.dot.gov/tpm/guidance/pm3_hpms.pdf>.
Provides a simple approach for constructing dynamic materials
 modeling suggested by Prasad and Gegel (1984) <doi:10.1007/BF02664902>. It
 can easily generate various processing-maps based on this model as well. The
 calculation result in this package contains full materials constants, information
 about power dissipation efficiency factor, and rheological properties, can
 be exported completely also, through which further analysis and customized
 plots will be applicable as well.
Estimation of transition probabilities for the
	illness-death model and or the three-state progressive model.
Provide data generation and estimation tools for the truncated positive normal (tpn)
             model discussed in Gomez, Olmos, Varela and Bolfarine (2018) 
             <doi:10.1007/s11766-018-3354-x>, the slash tpn distribution discussed in Gomez, 
             Gallardo and Santoro (2021) <doi:10.3390/sym13112164>, the bimodal tpn distribution 
             discussed in Gomez et al. (2022) <doi:10.3390/sym14040665> and the flexible tpn model.
Regression models for temporal process responses with
        time-varying coefficient.
Density, distribution function, the quantile function, 
    random generation function, and maximum likelihood estimation.
Enables the acquisition of Korean financial market data, 
            designed to integrate seamlessly with the 'tidyquant' package.
Automates translating the instructions of 'iatgen' generated qsf
             (Qualtrics survey files) to other languages using either officially
             supported or user-supplied translations (for tutorial see Santos
             et al., 2023 <doi:10.17504/protocols.io.kxygx34jdg8j/v1>).
Plant ecologists often need to collect "traits" data
    about plant species which are often scattered among various
    databases: TR8 contains a set of tools which take care of
    automatically retrieving some of those functional traits data
    for plant species from publicly available databases (Biolflor,
    The Ecological Flora of the British Isles, LEDA traitbase, Ellenberg
    values for Italian Flora, Mycorrhizal intensity databases, Catminat, BROT,
    PLANTS, Jepson Flora Project).
    The TR8 name, inspired by "car plates" jokes, was chosen since
    it both reminds of the main object of the package and is
    extremely short to type.
Efficient method for fitting nonparametric matrix trace regression model. The detailed description can be found in  C. Lee, L. Li, H. Zhang, and M. Wang (2021). Nonparametric Trace Regression via Sign Series Representation. <arXiv:2105.01783>. The method employs the aggregation of structured sign series for trace regression (ASSIST) algorithm.
Write output (plots and tables) ensuring traceability back to code. Includes a graphics saver with simple automation of stamping with source, destination and creation time. A list of plots can be saved at once. A user-friendly selection of output dimensions for presentations, on-screen inspections, and more available.
Functions to access the database of 217 data-frames with aggregate 
    study-level characteristics (that may act as effect modifiers) extracted 
    from published systematic reviews with network meta-analysis. The database shall 
    only be used for developing and appraising the methodology to assess 
    the transitivity assumption quantitatively.
Better looking call stacks after an error.
'BEAST2' (<https://www.beast2.org>) is a widely used
  Bayesian phylogenetic tool, that uses DNA/RNA/protein data
  and many model priors to create a posterior of jointly estimated 
  phylogenies and parameters.
  'Tracer' (<https://github.com/beast-dev/tracer/>) is a GUI tool 
  to parse and analyze the files generated by 'BEAST2'.
  This package provides a way to parse and analyze 'BEAST2' input
  files without active user input, but using
  R function calls instead.
Contains functions to standardize tracheid profiles
    using the traditional method (Vaganov) and a new method to standardize
    tracheidograms based on the relative position of tracheids within tree rings.
Functions for preparing and analyzing animal tracking data, 
    with the intention of identifying areas which are potentially important at 
    the population level and therefore of conservation interest. Areas identified 
    using this package may be checked against global or regionally-defined criteria,
    such as those set by the Key Biodiversity Area program. The method
    published herein is described in full in Beal et al. 2021 <doi:10.1111/2041-210X.13713>.
Obtain population density and body size structure, using video material or image sequences as input. Functions assist in the creation of image sequences from videos, background detection and subtraction, particle identification and tracking. An artificial neural network can be trained for noise filtering. The goal is to supply accurate estimates of population size, structure and/or individual behavior, for use in  evolutionary and ecological studies.
Data frame class for storing collective movement data (e.g. fish
    schools, ungulate herds, baboon troops) collected from GPS trackers or 
    computer vision tracking software.
Collaborative writing and editing of R Markdown (or Sweave) documents. The local .Rmd (or .Rnw) is uploaded as a plain-text file to Google Drive. By taking advantage of the easily readable Markdown (or LaTeX) syntax and the well-known online interface offered by Google Docs, collaborators can easily contribute to the writing and editing process. After integrating all authors’ contributions, the final document can be downloaded and rendered locally.
Provides infrastructure for handling running, cycling and swimming data from GPS-enabled tracking devices within R. The package provides methods to extract, clean and organise workout and competition data into session-based and unit-aware data objects of class 'trackeRdata' (S3 class). The information can then be visualised, summarised, and analysed through flexible and extensible methods. Frick and Kosmidis (2017) <doi: 10.18637/jss.v082.i07>, which is updated and maintained as one of the vignettes, provides detailed descriptions of the package and its methods, and real-data demonstrations of the package functionality.
Provides an integrated user interface and workflow for
             the analysis of running, cycling and swimming data from GPS-enabled
             tracking devices through the 'trackeR' <https://CRAN.R-project.org/package=trackeR> R package.
Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data.  Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals.
Functions for working with magnetic resonance images. Reading and
    writing of popular file formats (DICOM, Analyze, NIfTI-1, NIfTI-2, MGH);
    interactive and non-interactive visualisation; flexible image manipulation;
    metadata and sparse image handling.
A collection of tools for trade practitioners, including the ability to calibrate different consumer demand systems and simulate the effects of tariffs and quotas under different competitive regimes. These tools are derived from Anderson et al. (2001) <doi:10.1016/S0047-2727(00)00085-2> and Froeb et al. (2003) <doi:10.1016/S0304-4076(02)00166-5>.
Datasets from Yotov, et al. (2016, ISBN:978-92-870-4367-2) "An
    Advanced Guide to Trade Policy Analysis" and functions to report regression
    summaries with clustered robust standard errors.
Tree Ring Analysis of Disturbance Events in R (TRADER) package provides functions for disturbance reconstruction from tree-ring data, e.g. boundary line, absolute increase, growth averaging methods.
Access 'Open Trade Statistics' API from R to download
    international trade data.
Contains performance analysis metrics of track records including entropy-based
            correlation and dynamic beta based on the Kalman filter. The normalized sample entropy method
            has been implemented which produces accurate entropy estimation even on smaller datasets while for
            the dynamic beta calculation the Kalman filter methodology has been utilized.
            On a separate stream, trades from the five major assets classes and also
            functionality to use pricing curves, rating tables, CSAs and add-on tables. The
            implementation follows an object oriented logic whereby each trade inherits from
            more abstract classes while also the curves/tables are objects. Furthermore, odds calculators
            and P&L back-testing functionality has been implemented for the most widely used betting/trading
            strategies including martingale, DAlembert, Labouchere and Fibonacci. Back-testing has also been included for the EuroMillions and EuroJackpot lotteries.
			Furthermore, some basic functionality about climate risk has been included.
Estimate and return either the traffic speed or the car entries in the city of Thessaloniki using historical traffic data. It's used in transport pilot of the 'BigDataEurope' project. There are functions for processing these data, training a neural network, select the most appropriate model and predict the traffic speed or the car entries for a selected time date.
Estimation, selection and comparison of several families of
 transformations. The families of transformations included in the package are 
 the following: Bickel-Doksum (Bickel and Doksum 1981 <doi:10.2307/2287831>), 
 Box-Cox, Dual (Yang 2006 <doi:10.1016/j.econlet.2006.01.011>), 
 Glog (Durbin et al. 2002 <doi:10.1093/bioinformatics/18.suppl_1.S105>), 
 gpower (Kelmansky et al. 2013 <doi:10.1515/sagmb-2012-0030>), 
 Log, Log-shift opt (Feng et al. 2016 <doi:10.1002/sta4.104>),
 Manly, modulus (John and Draper 1980 <doi:10.2307/2986305>), 
 Neglog (Whittaker et al. 2005 <doi:10.1111/j.1467-9876.2005.00520.x>), 
 Reciprocal and Yeo-Johnson. The package simplifies to 
 compare linear models with untransformed and transformed dependent variable 
 as well as linear models where the dependent variable is transformed with 
 different transformations. Furthermore, the package employs maximum likelihood 
 approaches, moments optimization and divergence minimization to estimate the optimal 
 transformation parameter.
Methods to unify the different ways of creating predictive models and their different predictive formats for classification and regression. It includes 
       methods such as K-Nearest Neighbors Schliep, K. P. (2004) <doi:10.5282/ubm/epub.1769>, Decision Trees Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (2017) <doi:10.1201/9781315139470>, 
       ADA Boosting Esteban Alfaro, Matias Gamez, Noelia García (2013) <doi:10.18637/jss.v054.i02>, Extreme Gradient Boosting Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>, 
       Random Forest Breiman (2001) <doi:10.1023/A:1010933404324>, Neural Networks Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>,
       Support Vector Machines Bennett, K. P. & Campbell, C. (2000) <doi:10.1145/380995.380999>, Bayesian Methods Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (1995) <doi:10.1201/9780429258411>, 
       Linear Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, Quadratic Discriminant Analysis Venables, W. N., & Ripley, B. D. (2002) <ISBN:0-387-95457-0>, 
       Logistic Regression Dobson, A. J., & Barnett, A. G. (2018) <doi:10.1201/9781315182780> and Penalized Logistic Regression Friedman, J. H., Hastie, T., & Tibshirani, R. (2010) <doi:10.18637/jss.v033.i01>.
The goal of 'trainR' is to provide a simple interface to the 
    National Rail Enquiries (NRE) systems. There are few data feeds 
    available, the simplest of them is Darwin, which provides real-time 
    arrival and departure predictions, platform numbers, delay estimates, 
    schedule changes and cancellations. Other data feeds provide historical 
    data, Historic Service Performance (HSP), and much more. 'trainR' 
    simplifies the data retrieval, so that the users can focus on their 
    analyses. For more details visit 
    <https://www.nationalrail.co.uk/46391.aspx>.
Split a dataframe, tibble, or data.table into training and test sets. Return either a list, an index, or directly assign training and test sets into memory.
A collection of commonly used tools for animal movement and other tracking 
 data. Variously distance, angle, bearing, distance-to, bearing-to and speed are 
 provided for geographic data that can be used directly or within 'tidyverse' 
 syntax. Distances and bearings are calculated using modern geodesic methods as 
 provided by Charles F. F. Karney (2013) <doi:10.1007/s00190-012-0578-z> 
 via the 'geodist' and 'geosphere' packages.
Species trait data from many different sources, including
    sequence data from 'NCBI' (<https://www.ncbi.nlm.nih.gov/>),
    plant trait data from 'BETYdb', data from 'EOL' 'Traitbank',
    'Birdlife' International, and more.
Calculates trait moments from trait and community data using the 
  methods developed in Maitner et al (2021) <doi:10.22541/au.162196147.76797968/v1>.
Implements a three-step procedure in the spirit of Leffondre et al. (2004) to identify clusters of individual longitudinal trajectories. The procedure involves (1) computing a number of "measures of change" capturing various features of the trajectories; (2) using a Principal Component Analysis based dimension reduction algorithm to select a subset of measures and (3) using the k-means clustering algorithm to identify clusters of trajectories.
Classes and methods for trajectory data, with support for nesting individual Track objects in track sets (Tracks) and track sets for different entities in collections of Tracks. Methods include selection, generalization, aggregation, intersection, simulation, and plotting.
Implements marginal structural models combined with a latent class growth analysis framework for assessing the causal effect of treatment trajectories. Based on the approach described in "Marginal Structural Models with Latent Class Growth Analysis of Treatment Trajectories" Diop, A., Sirois, C., Guertin, J.R., Schnitzer, M.E., Candas, B., Cossette, B., Poirier, P., Brophy, J., Mésidor, M., Blais, C. and Hamel, D., (2023) <doi:10.1177/09622802231202384>.
A toolbox to assist with statistical analysis of animal trajectories.
    It provides simple access to algorithms for calculating and assessing a variety of 
    characteristics such as speed and acceleration, as well as multiple measures of 
    straightness or tortuosity. Some support is provided for 3-dimensional trajectories. 
    McLean & Skowron Volponi (2018) <doi:10.1111/eth.12739>.
Formula-based user-interfaces to specific transformation models
  implemented in package 'mlt'. Available models include Cox models, some parametric 
  survival models (Weibull, etc.), models for ordered categorical variables,
  normal and non-normal (Box-Cox type) linear models, and continuous outcome logistic regression
  (Lohse et al., 2017, <DOI:10.12688/f1000research.12934.1>). The underlying theory
  is described in Hothorn et al. (2018) <DOI:10.1111/sjos.12291>. An extension to
  transformation models for clustered data is provided (Barbanti and Hothorn, 2022,
  <DOI:10.1093/biostatistics/kxac048>). Multivariate conditional transformation models 
  (Klein et al, 2022, <DOI:10.1111/sjos.12501>) and shift-scale transformation models (Siegfried et al, 2023,
  <DOI:10.1080/00031305.2023.2203177>) can be fitted as well.
Extends invariant causal prediction (Peters et al., 2016, 
    <doi:10.1111/rssb.12167>) to generalized linear and transformation models 
    (Hothorn et al., 2018, <doi:10.1111/sjos.12291>).
    The methodology is described in Kook et al. (2023,
    <doi:10.48550/arXiv.2309.12833>).
Set of sequence analysis tools for manipulating, describing and rendering categorical sequences, and more generally mining sequence data in the field of social sciences. Although this sequence analysis package is primarily intended for state or event sequences that describe time use or life courses such as family formation histories or professional careers, its features also apply to many other kinds of categorical sequence data. It accepts many different sequence representations as input and provides tools for converting sequences from one format to another. It offers several functions for describing and rendering sequences, for computing distances between sequences with different metrics (among which optimal matching), original dissimilarity-based analysis tools, and functions for extracting the most frequent event subsequences and identifying the most discriminating ones among them. A user's guide can be found on the TraMineR web page.
Collection of ancillary functions and utilities to be used in conjunction with the 'TraMineR' package for sequence data exploration. Includes, among others, specific functions such as state survival plots, position-wise group-typical states, dynamic sequence indicators, and dissimilarities between event sequences. Also includes contributions by non-members of the TraMineR team such as methods for polyadic data and for the comparison of groups of sequences.
Likelihood-based estimation of mixed-effects transformation models
    using the Template Model Builder ('TMB', Kristensen et al., 2016)
    <doi:10.18637/jss.v070.i05>. The technical details of transformation models
    are given in Hothorn et al. (2018) <doi:10.1111/sjos.12291>. Likelihood
    contributions of exact, randomly censored (left, right, interval) and
    truncated observations are supported. The random effects are assumed to be
    normally distributed on the scale of the transformation function, the
    marginal likelihood is evaluated using the Laplace approximation, and the
    gradients are calculated with automatic differentiation (Tamasi & Hothorn,
    2021) <doi:10.32614/RJ-2021-075>. Penalized smooth shift terms can be
    defined using 'mgcv'.
Partially penalized versions of specific transformation models
    implemented in package 'mlt'. Available models include a fully parametric version
    of the Cox model, other parametric survival models (Weibull, etc.), models for
    binary and ordered categorical variables, normal and transformed-normal (Box-Cox type)
    linear models, and continuous outcome logistic regression. Hyperparameter tuning
    is facilitated through model-based optimization functionalities from package 'mlrMBO'.
    The accompanying vignette describes the methodology used in 'tramnet' in detail.
    Transformation models and model-based optimization are described in
    Hothorn et al. (2019) <doi:10.1111/sjos.12291> and
    Bischl et al. (2016) <arxiv:1703.03373>, respectively.
Implements a trampoline algorithm for R that let's users write recursive functions
    that get around R's stack call limitations, enabling theoretically infinite recursion. The
    algorithm is based around generator function as implemented in the 'coro' package, and is
    based almost completely on the 'trampoline' module from Python <https://gitlab.com/ferreum/trampoline>.
Matching terminal restriction fragment length
        polymorphism ('TRFLP') profiles between unknown samples and a
        database of known samples.  'TRAMPR' facilitates analysis of
        many unknown profiles at once, and provides tools for working
        directly with electrophoresis output through to generating
        summaries suitable for community analyses with R's rich set of
        statistical functions.  'TRAMPR' also resolves the issues of
        multiple 'TRFLP' profiles within a species, and shared 'TRFLP'
        profiles across species.
Greedy optimal subset selection for transformation models 
    (Hothorn et al., 2018, <doi:10.1111/sjos.12291> ) based on the abess
    algorithm (Zhu et al., 2020, <doi:10.1073/pnas.2014241117> ). Applicable to
    models from packages 'tram' and 'cotram'.
Implements sentiment analysis using huggingface <https://huggingface.co> transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa <https://huggingface.co/cross-encoder/nli-distilroberta-base> and default image/video pipeline is Open AI's CLIP  <https://huggingface.co/openai/clip-vit-base-patch32>. All other zero-shot classification model pipelines can be implemented using their model name from <https://huggingface.co/models?pipeline_tag=zero-shot-classification>.
Performs various statistical transformations; Box-Cox and Log (Box and Cox, 1964) <doi:10.1111/j.2517-6161.1964.tb00553.x>, Glog (Durbin et al., 2002) <doi:10.1093/bioinformatics/18.suppl_1.S105>, Neglog (Whittaker et al., 2005) <doi:10.1111/j.1467-9876.2005.00520.x>, Reciprocal (Tukey, 1957), Log Shift (Feng et al., 2016) <doi:10.1002/sta4.104>, Bickel-Docksum (Bickel and Doksum, 1981) <doi:10.1080/01621459.1981.10477649>, Yeo-Johnson (Yeo and Johnson, 2000)  <doi:10.1093/biomet/87.4.954>, Square Root (Medina et al., 2019), Manly (Manly, 1976) <doi:10.2307/2988129>, Modulus (John and Draper, 1980)  <doi:10.2307/2986305>, Dual (Yang, 2006) <doi:10.1016/j.econlet.2006.01.011>, Gpower (Kelmansky et al., 2013) <doi:10.1515/sagmb-2012-0030>. It also performs graphical approaches, assesses the success of the transformation via tests and plots.
Targets parameters that solve Ordinary Differential
    Equations (ODEs) driven by a vector of cumulative hazard functions.
    The package provides a method for estimating these parameters using
    an estimator defined by a corresponding Stochastic Differential Equation
    (SDE) system driven by cumulative hazard estimates. By providing cumulative
    hazard estimates as input, the package gives estimates of the parameter as
    output, along with pointwise (co)variances derived from an asymptotic
    expression. Examples of parameters that can be targeted in this way include
    the survival function, the restricted mean survival function, cumulative
    incidence functions, among others; see Ryalen, Stensrud, and Røysland (2018)
    <doi:10.1093/biomet/asy035>, and further applications in
    Stensrud, Røysland, and Ryalen (2019) <doi:10.1111/biom.13102>
    and Ryalen et al. (2021) <doi:10.1093/biostatistics/kxab009>.
Transformer is a Deep Neural Network Architecture based i.a. on the Attention mechanism (Vaswani et al. (2017) <doi:10.48550/arXiv.1706.03762>).
Implementation of the transformation of the Mean Opinion Scores (MOS) to be used before applying the rank based statistical techniques. The method and its necessity is described in: Babak Naderi, Sebastian Möller (2020) <arXiv:2004.11490>.
In order to smoothly animate the transformation of polygons and 
    paths, many aspects needs to be taken into account, such as differing number 
    of control points, changing center of rotation, etc. The 'transformr' 
    package provides an extensive framework for manipulating the shapes of 
    polygons and paths and can be seen as the spatial brother to the 'tweenr' 
    package.
A geomorphology-based hydrological modelling for transferring
    streamflow measurements from gauged to ungauged catchments. Inverse
    modelling enables to estimate net rainfall from streamflow measurements 
    following Boudhraâ et al. (2018) <doi:10.1080/02626667.2018.1425801>. 
    Resulting net rainfall is then estimated on the ungauged catchments 
    by spatial interpolation in order to finally simulate streamflow 
    following de Lavenne et al. (2016) <doi:10.1002/2016WR018716>.
Translation of logit models coefficients into percentages, following Deauvieau (2010) <doi:10.1177/0759106309352586>.
Allows translating with formatted string literals, grouped entries, and configurable system of plurals. Have a separate file for each locale and use inheritance to handle dialect differences.
Provides a robust and user-friendly solution for transliterating Ukrainian strings into Latin symbols.
To make the semiparametric transformation models easier to apply in real studies, 
    we introduce this R package, in which the MLE in transformation models via 
    an EM algorithm proposed by Zeng D, Lin DY(2007) <doi:10.1111/j.1369-7412.2007.00606.x> 
    and adaptive lasso method in transformation models proposed by Liu XX, Zeng 
    D(2013) <doi:10.1093/biomet/ast029> are implemented.  
    C++ functions are used to compute complex loops. The coefficient vector and 
    cumulative baseline hazard function can be estimated, along with the 
    corresponding standard errors and P values.
Treatment and visualization of membrane (selective) transport 
    data. Transport profiles involving up to three species are produced as
    publication-ready plots and several membrane performance parameters 
    (e.g. separation factors as defined in Koros et al. (1996) 
    <doi:10.1351/pac199668071479> and non-linear regression parameters
    for the equations described in Rodriguez de San Miguel et al. (2014)
    <doi:10.1016/j.jhazmat.2014.03.052>) can be obtained. Many widely used 
    experimental setups (e.g. membrane physical aging) can be easily studied
    through the package's graphical representations.
A unified estimation procedure for the analysis of right censored data using linear transformation models. An introduction can be found in Jie Zhou et al. (2022) <doi:10.18637/jss.v101.i09>.
Implementation of two transportation problem algorithms. 
             1. North West Corner Method 
             2. Minimum Cost Method or Least cost method.
             For more technical details about the algorithms please refer below URLs.
             <http://www.universalteacherpublications.com/univ/ebooks/or/Ch5/nw.htm>.
             <http://personal.maths.surrey.ac.uk/st/J.F/chapter7.pdf>.
Inference of transmission tree from a dated phylogeny. 
    Includes methods to simulate and analyse outbreaks.
    The methodology is described in
    Didelot et al. (2014) <doi:10.1093/molbev/msu121>,
    Didelot et al. (2017) <doi:10.1093/molbev/msw275>.
A set of vectorised functions to calculate medical equations used in transplantation, 
    focused mainly on transplantation of abdominal organs. These functions include donor and recipient
    risk indices as used by NHS Blood & Transplant, OPTN/UNOS and Eurotransplant, tools for 
    quantifying HLA mismatches, functions for calculating estimated glomerular filtration rate (eGFR), 
    a function to calculate the APRI (AST to platelet ratio) score used in initial screening of suitability to receive a 
    transplant from a hepatitis C seropositive donor and some biochemical unit converter functions. 
    All functions are designed to work with either US or international units.
    References for the equations are provided in the vignettes and function documentation.
To visualize the gene structure with multiple isoforms better, I developed this package to draw different transcript structures easily.
Solve optimal transport problems. Compute Wasserstein distances (a.k.a. Kantorovitch, Fortet--Mourier, Mallows, Earth Mover's, or minimal L_p distances), return the corresponding transference plans, and display them graphically. Objects that can be compared include grey-scale images, (weighted) point patterns, and mass vectors.
Improves the predictive performance of ridge and lasso regression exploiting one or more sources of prior information on the importance and direction of effects (Rauschenberger and others 2023, <doi:10.1093/bioinformatics/btad680>). For running the vignette, install 'fwelnet' from 'GitHub' <https://github.com/kjytay/fwelnet>.
Tensor Gaussian graphical models (GGMs) have important applications in numerous areas, which can interpret conditional independence structures within tensor data. Yet, the available tensor data in one single study is often limited due to high acquisition costs. 
             Although relevant studies can provide additional data, it remains an open question how to pool such heterogeneous data. This package implements a transfer learning framework for tensor GGMs, which takes full advantage of informative auxiliary domains even when non-informative auxiliary domains are present, benefiting from the carefully designed data-adaptive weights. 
             Reference: 
             Ren, M., Zhen Y., and Wang J. (2022). "Transfer learning for tensor graphical models" <arXiv:2211.09391>.
A latent, quasi-independent truncation time is assumed to be linked with the observed dependent truncation time, the event time, and an unknown transformation parameter via a structural transformation model. The transformation parameter is chosen to minimize the conditional Kendall's tau (Martin and Betensky, 2005) <doi:10.1198/016214504000001538> or the regression coefficient estimates (Jones and Crowley, 1992) <doi:10.2307/2336782>. The marginal distribution for the truncation time and the event time are completely left unspecified. The methodology is applied to survival curve estimation and regression analysis.
Univariate time series operations that follow an opinionated design. 
    The main principle of 'transx' is to keep the number of observations the same. 
    Operations that reduce this number have to fill the observations gap.
The trapezoid package provides 'dtrapezoid', 'ptrapezoid', 'qtrapezoid',
    and 'rtrapezoid' functions for the trapezoidal distribution.
Pacote para a analise de experimentos com um ou dois fatores com
             testemunhas adicionais conduzidos no delineamento inteiramente 
             casualizado ou em blocos casualizados. 
             "Package for the analysis of one or two-way experiments with 
             additional controls conducted in a completely randomized design 
             or in a randomized block design".
Simple trustworthy utility functions to use TauDEM 
    (Terrain Analysis Using Digital Elevation Models <https://hydrology.usu.edu/taudem/taudem5/>) command-line interface.
    This package provides a guide to installation of TauDEM and its dependencies GDAL (Geopatial Data Abstraction Library) 
    and MPI (Message Passing Interface) for different operating systems.
    Moreover, it checks that TauDEM and its dependencies are correctly installed and included to the PATH, 
    and it provides wrapper commands for calling TauDEM methods from R.
'Travel Time' API <https://docs.traveltime.com/api/overview/introduction> helps users find locations by journey time rather than using ‘as the crow flies’ distance.
  Time-based searching gives users more opportunities for personalisation and delivers a more relevant search.
Contains R functions for simulating and estimating integer-valued trawl processes as 
    described in the article Veraart (2019),"Modeling, simulation and inference for
    multivariate time series of counts using trawl processes", Journal of Multivariate Analysis,
    169, pages 110-129,
    <doi:10.1016/j.jmva.2018.08.012> and for 
    simulating random vectors from the bivariate negative binomial and the bi- and trivariate 
    logarithmic series distributions.
Transmission Ratio Distortion (TRD) is a genetic phenomenon where the two alleles from either parent are not transmitted to the offspring at the expected 1:1 ratio under Mendelian inheritance, leading to spurious signals in genetic association studies. Functions in this package are developed to account for this phenomenon using loglinear model and Transmission Disequilibrium Test (TDT). Some population information can also be calculated.
Truncation of univariate probability distributions. The probability distribution can come from other packages so long as the function names follow the standard d, p, q, r naming format. Also other univariate probability distributions are included.
Download daily interest rates from the US Treasury XML feed.
    Leveraging
    <https://home.treasury.gov/treasury-daily-interest-rate-xml-feed>,
    this package serves as a wrapper, facilitating the retrieval of daily
    treasury rates across various categories, including par yield curves,
    treasury bills, long-term rates, and real yield curves.
Computes treatment patterns within a given cohort using the Observational
    Medical Outcomes Partnership (OMOP) common data model (CDM). As described
    in Markus, Verhamme, Kors, and Rijnbeek (2022) <doi:10.1016/j.cmpb.2022.107081>.
A suite of descriptive and inferential methods designed to evaluate one or more biomarkers for their ability to guide patient treatment recommendations.  Package includes functions to assess the calibration of risk models; and plot, evaluate, and compare markers. Please see the reference Janes H, Brown MD, Huang Y, et al. (2014) <doi:10.1515/ijb-2012-0052> for further details.
A modular package for simulating phylogenetic trees and species traits jointly. Trees can be simulated using modular birth-death parameters (e.g. changing starting parameters or algorithm rules). Traits can be simulated in any way designed by the user. The growth of the tree and the traits can influence each other through modifiers objects providing rules for affecting each other. Finally, events can be created to modify both the tree and the traits under specific conditions.
An R re-implementation of the 'treeinterpreter' package on PyPI
        <https://pypi.org/project/treeinterpreter/>. Each prediction can be
        decomposed as 'prediction = bias + feature_1_contribution + ... +
        feature_n_contribution'. This decomposition is then used to calculate
        the Mean Decrease Impurity (MDI) and Mean Decrease Impurity using
        out-of-bag samples (MDI-oob) feature importance measures based on the
        work of Li et al. (2019) <arXiv:1906.10845>.
Provides customizable 3D tree models (as 'OBJ' files) for use in data visualization. Includes both planar and solid tree models, various crown types (columnar, oval, palm, pyramidal, rounded, spreading, vase, weeping), and options to change the diameter, height, and color of the tree's crown and trunk.
Interface to the API for 'TreeBASE' <http://treebase.org>
    from 'R.' 'TreeBASE' is a repository of user-submitted phylogenetic
    trees (of species, population, or genes) and the data used to create
    them.
User-friendly analysis of hierarchical multinomial processing tree (MPT) 
    models that are often used in cognitive psychology. Implements the latent-trait 
    MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT 
    approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model 
    heterogeneity of participants. MPT models are conveniently specified by an
    .eqn-file as used by other MPT software and data are provided by a .csv-file 
    or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored 
    Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides 
    tests of heterogeneity and MPT-tailored summaries and plotting functions.
    A detailed documentation is available in Heck, Arnold, & Arnold (2018) 
    <DOI:10.3758/s13428-017-0869-7> and a tutorial on MPT modeling can be found 
    in Schmidt, Erdfelder, & Heck (2022) <DOI:10.31234/osf.io/gh8md>.
Create a measure of inter-point dissimilarity useful 
 for clustering mixed data, and, optionally, perform the clustering.
Performs sparse discriminant analysis on a combination of
    node and leaf predictors when the predictor variables are
    structured according to a tree, as described in Fukuyama et
    al. (2017) <doi:10.1371/journal.pcbi.1005706>.
Functions for estimating times of common ancestry and molecular clock rates of evolution using a variety of evolutionary models, parametric and nonparametric bootstrap confidence intervals, methods for detecting outlier lineages, root-to-tip regression, and a statistical test for selecting molecular clock models. The methods are described in Volz, E.M. and S.D.W. Frost (2017) <doi:10.1093/ve/vex025>.
The model estimates air pollution removal by dry deposition on trees. It also estimates or uses hourly values for aerodynamic resistance, boundary layer resistance, canopy resistance, stomatal resistance, cuticular resistance, mesophyll resistance, soil resistance, friction velocity and deposition velocity. It also allows plotting graphical results for a specific time period. The pollutants are nitrogen dioxide, ozone, sulphur dioxide, carbon monoxide and particulate matter. Baldocchi D (1994) <doi:10.1093/treephys/14.7-8-9.1069>. Farquhar GD, von Caemmerer S, Berry JA (1980) Planta 149: 78-90. Hirabayashi S, Kroll CN, Nowak DJ (2015) i-Tree Eco Dry Deposition Model. Nowak DJ, Crane DE, Stevens JC (2006) <doi:10.1016/j.ufug.2006.01.007>. US EPA (1999) PCRAMMET User's Guide. EPA-454/B-96-001. Weiss A, Norman JM (1985) Agricultural and Forest Meteorology 34: 205—213.
Visualizing cuts for either axis-align or non axis-align tree methods (e.g. decision tree, random tessellation process).
Perform test to detect differences in structure between families of
             trees. The method is based on cophenetic distances and aggregated
             Student's tests.
Testing for trajectory presence and heterogeneity on
 multivariate data. Two statistical methods (Tenha & Song 2022) 
 <doi:10.1371/journal.pcbi.1009829> are implemented. The tree dimension
 test quantifies the statistical evidence for trajectory presence. The
 subset specificity measure summarizes pattern heterogeneity using the
 minimum subtree cover. There is no user tunable parameters for either
 method. Examples are included to illustrate how to use the methods on 
 single-cell data for studying gene and pathway expression dynamics and
 pathway expression specificity.
Implements measures of tree similarity, including 
  information-based generalized Robinson-Foulds distances
  (Phylogenetic Information Distance, Clustering Information Distance,
  Matching Split Information Distance; Smith 2020)
  <doi:10.1093/bioinformatics/btaa614>; 
  Jaccard-Robinson-Foulds distances (Bocker et al. 2013)
  <doi:10.1007/978-3-642-40453-5_13>, 
  including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>;
  the Matching Split Distance (Bogdanowicz & Giaro 2012)
  <doi:10.1109/TCBB.2011.48>;
  Maximum Agreement Subtree distances;
  the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the
  Nearest Neighbour Interchange (NNI) distance, approximated per Li et al. 
  (1996) <doi:10.1007/3-540-61332-3_168>.
  Includes tools for visualizing mappings of tree space (Smith 2022)
  <doi:10.1093/sysbio/syab100>,
  for identifying islands of trees (Silva and Wilkinson 2021)
  <doi:10.1093/sysbio/syab015>,
  for calculating the median of sets of trees,
  and for computing the information content of trees and splits.
Perform two types of analysis: 1) checking the
    goodness-of-fit of tree models to your single-cell gene expression
    data; and 2) deciding which tree best fits your data.
Creates interpretable decision tree visualizations 
    with the data represented as a heatmap at the tree's leaf nodes.
    'treeheatr' utilizes the customizable 'ggparty' package for 
    drawing decision trees.
A treemap is a space-filling visualization of hierarchical
    structures. This package offers great flexibility to draw treemaps.
Provides 'ggplot2' geoms for drawing treemaps.
Implementation of unconditional Bernoulli Scan Statistic developed
             by Kulldorff et al. (2003) <doi:10.1111/1541-0420.00039> 
             for hierarchical tree structures. Tree-based Scan Statistics are an
             exploratory method to identify event clusters across the space of a 
             hierarchical tree.
Provides functionality for creation and comparison of polynomials that uniquely
  describe trees as introduced in Liu (2019, <arXiv:1904.03332>). The core method
  converts rooted unlabeled phylo objects from 'ape' to the tree defining polynomials 
  described with coefficient matrices. Additionally, a conversion for rooted binary trees 
  with binary trait labels is also provided. Once the polynomials of trees are calculated 
  there are functions to calculate distances, distance matrices and plot different distance 
  trees from a target tree. Manipulation and conversion to the tree defining polynomials is 
  implemented in C++ with 'Rcpp' and 'RcppArmadillo'. Furthermore, parallel programming with 
  'RcppThread' is used to improve performance converting to polynomials and calculating distances.
The German national forest inventory uses angle count sampling, 
    a sampling method first published as `Bitterlich, W.: Die Winkelzählmessung.
    Allgemeine Forst- und Holzwirtschaftliche Zeitung, 58. Jahrg., Folge 11/12 
    vom Juni 1947` and extended by Grosenbaugh
    (<https://academic.oup.com/jof/article-abstract/50/1/32/4684174>)
    as probability proportional to size sampling.
    When plots are located near stand boundaries, their sizes and hence
    their probabilities need to be corrected.
Record all tree-ring Shapefile of tree disk with GIS soft ('Qgis'<https://www.qgis.org/en/site/>) and interpolating model from high resolution tree disk image.
Reconstruct phylogenetic trees from discrete data.
  Inapplicable character states are handled using the algorithm of Brazeau,
  Guillerme and Smith (2019) <doi:10.1093/sysbio/syy083> with the "Morphy"
  library, under equal or implied step weights.
  Contains a "shiny" user interface for interactive tree search and exploration
  of results, including character visualization, rogue taxon detection,
  tree space mapping, and cluster consensus trees (Smith 2022a, b)
  <doi:10.1093/sysbio/syab099>, <doi:10.1093/sysbio/syab100>.
  Profile Parsimony (Faith and Trueman, 2001) <doi:10.1080/10635150118627>, 
  Successive Approximations (Farris, 1969) <doi:10.2307/2412182>
  and custom optimality criteria are implemented.
An efficient implementation of the 'TreeSHAP' algorithm
    introduced by Lundberg et al., (2020) <doi:10.1038/s42256-019-0138-9>.
    It is capable of calculating SHAP (SHapley Additive exPlanations) values 
    for tree-based models in polynomial time.  Currently supported models include 
    'gbm', 'randomForest', 'ranger', 'xgboost', 'lightgbm'.
Simulation methods for phylogenetic trees where (i) all tips are sampled at one time point or (ii) tips are sampled sequentially through time. (i) For sampling at one time point, simulations are performed under a constant rate birth-death process, conditioned on having a fixed number of final tips (sim.bd.taxa()), or a fixed age (sim.bd.age()), or a fixed age and number of tips (sim.bd.taxa.age()). When conditioning on the number of final tips, the method allows for shifts in rates and mass extinction events during the birth-death process (sim.rateshift.taxa()). The function sim.bd.age() (and sim.rateshift.taxa() without extinction) allow the speciation rate to change in a density-dependent way. The LTT plots of the simulations can be displayed using LTT.plot(), LTT.plot.gen() and LTT.average.root(). TreeSim further samples trees with n final tips from a set of trees generated by the common sampling algorithm stopping when a fixed number m>>n of tips is first reached (sim.gsa.taxa()). This latter method is appropriate for m-tip trees generated under a big class of models (details in the sim.gsa.taxa() man page). For incomplete phylogeny, the missing speciation events can be added through simulations (corsim()). (ii) sim.rateshifts.taxa() is generalized to sim.bdsky.stt() for serially sampled trees, where the trees are conditioned on either the number of sampled tips or the age. Furthermore, for a multitype-branching process with sequential sampling, trees on a fixed number of tips can be simulated using sim.bdtypes.stt.taxa(). This function further allows to simulate under epidemiological models with an exposed class. The function sim.genespeciestree() simulates coalescent gene trees within birth-death species trees, and sim.genetree() simulates coalescent gene trees.
Provides a flexible simulation tool for phylogenetic trees under a general model for speciation and extinction. Trees with a user-specified number of extant tips, or a user-specified stem age are simulated. It is possible to assume any probability distribution for the waiting time until speciation and extinction. Furthermore, the waiting times to speciation / extinction may be scaled in different parts of the tree, meaning we can simulate trees with clade-dependent diversification processes. At a speciation event, one species splits into two. We allow for two different modes at these splits: (i) symmetric, where for every speciation event new waiting times until speciation and extinction are drawn for both daughter lineages; and (ii) asymmetric, where a speciation event results in one species with new waiting times, and another that carries the extinction time and age of its ancestor. The symmetric mode can be seen as an vicariant or allopatric process where divided populations suffer equal evolutionary forces while the asymmetric mode could be seen as a peripatric speciation where a mother lineage continues to exist.  Reference: O. Hagen and T. Stadler (2017). TreeSimGM: Simulating phylogenetic trees under general Bellman Harris models with lineage-specific shifts of speciation and extinction in R. Methods in Ecology and Evolution. <doi:10.1111/2041-210X.12917>.
Provides bindings to 'Tree-sitter', an incremental parsing
    system for programming tools. 'Tree-sitter' builds concrete syntax
    trees for source files of any language, and can efficiently update
    those syntax trees as the source file is edited. It also includes a
    robust error recovery system that provides useful parse results even
    in the presence of syntax errors.
Provides bindings to an 'R' grammar for 'Tree-sitter', to be
    used alongside the 'treesitter' package. 'Tree-sitter' builds concrete
    syntax trees for source files of any language, and can efficiently
    update those syntax trees as the source file is edited.
Provide a range of functions with multiple criteria for cutting phylogenetic trees at any evolutionary depth. It enables users to cut trees in any orientation, such as rootwardly (from root to tips) and tipwardly (from tips to its root), or allows users to define a specific time interval of interest. It can also be used to create multiple tree pieces of equal temporal width. Moreover, it allows the assessment of novel temporal rates for various phylogenetic indexes, which can be quickly displayed graphically.
Tools for the exploration of distributions of phylogenetic trees.
    This package includes a 'shiny' interface which can be started from R using
    treespaceServer(). 
    For further details see Jombart et al. (2017) <DOI:10.1111/1755-0998.12676>.
Combine a list of taxa with a phylogeny to generate a starting tree for use in
    total evidence dating analyses.
Collection of phylogenetic tree statistics,
             collected throughout the literature. All functions have been 
             written to maximize computation speed. The package includes 
             umbrella functions to calculate all statistics, all balance 
             associated statistics, or all branching time related statistics. 
             Furthermore, the 'treestats' package supports summary statistic 
             calculations on Ltables, provides speed-improved coding of 
             branching times, Ltable conversion and includes algorithms to
             create intermediately balanced trees. Full description can be
             found in Janzen (2024) <doi:10.1016/j.ympev.2024.108168>.
Algorithms for detecting population structure from the history of coalescent events recorded in phylogenetic trees. This method classifies each tip and internal node of a tree into disjoint sets characterized by similar coalescent patterns. The methods are described in Volz, E., Wiuf, C., Grad, Y., Frost, S., Dennis, A., & Didelot, X. (2020) <doi:10.1093/sysbio/syaa009>.
Efficient implementations of functions for the creation, 
  modification and analysis of phylogenetic trees.
  Applications include:
  generation of trees with specified shapes;
  tree rearrangement;
  analysis of tree shape;
  rooting of trees and extraction of subtrees;
  calculation and depiction of split support;
  plotting the position of rogue taxa (Klopfstein & Spasojevic 2019)
  <doi:10.1371/journal.pone.0212942>;
  calculation of ancestor-descendant relationships,
  of 'stemwardness' (Asher & Smith, 2022) <doi:10.1093/sysbio/syab072>,
  and of tree balance (Mir et al. 2013, Lemant et al. 2022)
  <doi:10.1016/j.mbs.2012.10.005>, <doi:10.1093/sysbio/syac027>;
  artificial extinction (Asher & Smith, 2022) <doi:10.1093/sysbio/syab072>;
  import and export of trees from Newick, Nexus (Maddison et al. 1997)
  <doi:10.1093/sysbio/46.4.590>,
  and TNT <https://www.lillo.org.ar/phylogeny/tnt/> formats;
  and analysis of splits and cladistic information.
Provides a dataset of predefined color palettes based on the Star Trek science fiction series, associated color palette functions, 
    and additional functions for generating customized palettes that are on theme. The package also offers functions for applying 
    the palettes to plots made using the 'ggplot2' package.
Provides a collection of true type and open type Star Trek-themed fonts.
Trelliscope is a scalable, flexible, interactive approach to visualizing data (Hafen, 2013 <doi:10.1109/LDAV.2013.6675164>). This package provides methods that make it easy to create a Trelliscope display specification for TrelliscopeJS. High-level functions are provided for creating displays from within 'tidyverse' or 'ggplot2' workflows. Low-level functions are also provided for creating new interfaces.
An R client for the Trello API. Supports free-tier features such as
    access to private boards, creating and updating cards and other resources,
    and downloading data in a structured way.
A slightly-opinionated R interface for the 'Tremendous' API (<https://www.tremendous.com/>). 
    In addition to supporting GET and POST requests, 'tremendousr' has, dare I say, 
    tremendously intuitive functions for sending digital rewards and incentives directly from R.
Tools for translating environmental change into organismal response. Microclimate models to vertically scale weather station data to organismal heights. The biophysical modeling tools include both general models for heat flows and specific models to predict body temperatures for a variety of ectothermic taxa. Additional functions model and temporally partition air and soil temperatures and solar radiation. Utility functions estimate the organismal and environmental parameters needed for biophysical ecology. 'TrenchR' focuses on relatively simple and modular functions so users can create transparent and flexible biophysical models. Many functions are derived from Gates (1980) <doi:10.1007/978-1-4612-6024-0> and Campbell and Norman (1988) <isbn:9780387949376>.
The analysis of environmental data often requires
	     the detection of trends and change-points. 
	     This package includes tests for trend detection
	     (Cox-Stuart Trend Test, Mann-Kendall Trend Test, 
	     (correlated) Hirsch-Slack Test,
             partial Mann-Kendall Trend Test, multivariate (multisite)
	     Mann-Kendall Trend Test, (Seasonal) Sen's slope, 
	     partial Pearson and Spearman correlation trend test),
             change-point detection (Lanzante's test procedures, 
	     Pettitt's test, Buishand Range Test,
	     Buishand U Test, Standard Normal Homogeinity Test),
	     detection of non-randomness (Wallis-Moore Phase Frequency Test,
	     Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test)
	     and the two sample Robust Rank-Order Distributional Test.
Innovative Trend Analysis is a graphical method to examine the trends in time series data. Sequential Mann-Kendall test uses the intersection of prograde and retrograde series to indicate the possible change point in time series data. Distribution free cumulative sum charts indicate location and significance of the change point in time series.
  Zekai, S. (2011). <doi:10.1061/(ASCE)HE.1943-5584.0000556>.
  Grayson, R. B. et al. (1996). Hydrological Recipes: Estimation Techniques in Australian Hydrology. Cooperative Research Centre for Catchment Hydrology, Australia, p. 125. 
  Sneyers, S. (1990). On the statistical analysis of series of observations. Technical note no 5 143, WMO No 725 415. Secretariat of the World Meteorological Organization, Geneva, 192 pp.
Provides a coherent interface for evaluating models fit with the
  trending package.  This package is part of the RECON
  (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.
Provides a coherent interface to multiple modelling tools for
  fitting trends along with a standardised approach for generating confidence
  and prediction intervals.
Estimation of causal odds ratio and power calculation given trends in exposure prevalence    and outcome frequencies of stratified data.
Fitting models for, and simulation of, trend locally stationary 
    wavelet (TLSW) time series models, which take account of time-varying 
    trend and dependence structure in a univariate time series. The TLSW model, 
    and its estimation, is described in McGonigle, Killick and Nunes (2022a) 
    <doi:10.1111/jtsa.12643>, (2022b) <doi:10.1214/22-EJS2044>. New users will 
    likely want to start with the TLSW function.
Performs the detection of linear trend changes for univariate time series 
    by implementing the bottom-up unbalanced wavelet transformation proposed by 
    H. Maeng and P. Fryzlewicz (2023). The estimated number and locations of the 
    change-points are returned with the piecewise-linear estimator for signal.
Analysis of annual average ocean water level time series, providing
    improved estimates of trend (mean sea level) and associated real-time velocities and
    accelerations. Improved trend estimates are based on singular spectrum analysis
    methods. Various gap-filling options are included to accommodate incomplete time
    series records. The package also includes a range of diagnostic tools to inspect
    the components comprising the original time series which enables expert
    interpretation and selection of likely trend components. A wide range of
    screen and plot to file options are available in the package.
Matrix factorization for multivariate time series with both low rank and temporal structures. The procedure is the one proposed by Alquier, P. and Marie, N. Matrix factorization for multivariate time series analysis. Electronic journal of statistics, 13(2), 4346-4366 (2019).
Non-parametric trend comparison of two independent samples with sequential subsamples. For more details, please refer to Wang, Stapleton, and Chen (2018) <doi:10.1080/00949655.2018.1482492>.
Access Google Trends information. This package provides a tidy wrapper to the 'gtrendsR' package.
    Use four spaces when indenting paragraphs within the Description.
Provides three estimators for tensor response regression (TRR) and tensor predictor regression (TPR) models with tensor envelope structure. The three types of estimation approaches are generic and can be applied to any envelope estimation problems. The full Grassmannian (FG) optimization is often associated with likelihood-based estimation but requires heavy computation and good initialization; the one-directional optimization approaches (1D and ECD algorithms) are faster, stable and does not require carefully chosen initial values; the SIMPLS-type is motivated by the partial least squares regression and is computationally the least expensive. For details of TRR, see Li L, Zhang X (2017) <doi:10.1080/01621459.2016.1193022>. For details of TPR, see Zhang X, Li L (2017) <doi:10.1080/00401706.2016.1272495>. For details of 1D algorithm, see Cook RD, Zhang X (2016) <doi:10.1080/10618600.2015.1029577>. For details of ECD algorithm, see Cook RD, Zhang X (2018) <doi:10.5705/ss.202016.0037>. For more details of the package, see Zeng J, Wang W, Zhang X (2021) <doi:10.18637/jss.v099.i12>.
Performs fast variable selection in high-dimensional settings while controlling the 
    false discovery rate (FDR) at a user-defined target level. The package is based on the paper 
    Machkour, Muma, and Palomar (2022) <arXiv:2110.06048>.
Tri-hierarchical incomplete block design is defined as an arrangement of v  treatments each replicated r  times in a three system of blocks if, each block of the first system contains m_1 blocks of second system and each block of the second system contains m_2 blocks of the third system. Ignoring the first and second system of blocks, it leaves an incomplete block design with b_3 blocks of size k_3i units; ignoring first and third system of blocks, it leaves an incomplete block design with b_2 blocks each of size k_2i units and ignoring the second and third system of blocks, it leaves an incomplete block design with b_1 blocks each of size k_1 units. For dealing with experimental circumstances where there are three nested sources of variation, a tri-hierarchical incomplete block design can be adopted. Tri - hierarchical incomplete block designs can find application potential in obtaining mating-environmental designs for breeding trials. To know more about nested block designs one can refer Preece (1967) <doi:10.1093/biomet/54.3-4.479>. This package includes series1(), series2(), series3() and series4() functions. This package generates tri-hierarchical designs with six component designs under certain parameter restrictions.
Assists in analyzing the lying behavior of cows from raw data 
    recorded with a triaxial accelerometer attached to the hind leg of a cow. Allows 
    the determination of common measures for lying behavior including total lying duration, the 
    number of lying bouts, and the mean duration of lying bouts. Further capabilities are the 
    description of lying laterality and the calculation of proxies for the level of physical 
    activity of the cow. Reference: Simmler M., Brouwers S. P. (2023) <https://gitlab.com/AgroSimi/triact_manuscript>.
Simulate genotypes for case-parent triads, case-control, and quantitative trait samples with realistic linkage diequilibrium structure and allele frequency distribution. For studies of epistasis one can simulate models that involve specific SNPs at specific sets of loci, which we will refer to as "pathways". TriadSim generates genotype data by resampling triad genotypes from existing data. The details of the method is described in the manuscript under preparation "Simulating Autosomal Genotypes with Realistic Linkage Disequilibrium and a Spiked in Genetic Effect" Shi, M., Umbach, D.M., Wise A.S., Weinberg, C.R.
Implements target trial emulation methods to apply randomized
    clinical trial design and analysis in an observational setting. Using
    marginal structural models, it can estimate intention-to-treat and
    per-protocol effects in emulated trials using electronic health
    records. A description and application of the method can be found in
    Danaei et al (2013) <doi:10.1177/0962280211403603>.
A collection of clinical trial designs and methods, implemented in 
    'rstan' and R, including: the Continual Reassessment Method by O'Quigley et 
    al. (1990) <doi:10.2307/2531628>; EffTox by Thall & Cook (2004) 
    <doi:10.1111/j.0006-341X.2004.00218.x>; the two-parameter logistic method of
    Neuenschwander, Branson & Sponer (2008) <doi:10.1002/sim.3230>; and the 
    Augmented Binary method by Wason & Seaman (2013) <doi:10.1002/sim.5867>; and
    more. We provide functions to aid model-fitting and analysis. 
    The 'rstan' implementations may also serve as a cookbook to anyone looking 
    to extend or embellish these models. We hope that this package encourages 
    the use of Bayesian methods in clinical trials. There is a preponderance of 
    early phase trial designs because this is where Bayesian methods are used 
    most. If there is a method you would like implemented, please get in touch.
Functions and Examples in Sample Size Calculation in
        Clinical Research.
Provides the "r, q, p, and d" distribution functions for the triangle distribution.  Also includes maximum likelihood estimation of parameters.
Measuring angles between points in a landscape is much easier
    than measuring distances. When the location of three points is known the
    position of the observer can be determined based solely on the angles between
    these points as seen by the observer. This task (known as triangulation)
    however requires onerous calculations - these calculations are automated by this
    package.
A collection of high-performance functions for the triangular
    distribution that consists of the probability density function, cumulative
    distribution function, quantile function, random variate generator, moment
    generating function, characteristic function, and expected shortfall
    function. References: Samuel Kotz, Johan Ren Van Dorp (2004)
    <doi:10.1142/5720> and Acerbi, Carlo and Tasche, Dirk. (2002)
    <doi:10.1111/1468-0300.00091>.
Compositional data consisting of three-parts can be color
  mapped with a ternary color scale. Such a scale is provided by
  the tricolore packages with options for discrete and continuous
  colors, mean-centering and scaling. See 
  Jonas Schöley (2021) "The centered ternary balance scheme. A technique
  to visualize surfaces of unbalanced three-part compositions"
  <doi:10.4054/DemRes.2021.44.19>,
  Jonas Schöley, Frans Willekens (2017) "Visualizing compositional data
  on the Lexis surface" <doi:10.4054/DemRes.2017.36.21>, and
  Ilya Kashnitsky, Jonas Schöley (2018) "Regional population structures
  at a glance" <doi:10.1016/S0140-6736(18)31194-2>.
Fits 2D and 3D geometric transformations via 'Stan' probabilistic programming engine ( 
    Stan Development Team (2021) <https://mc-stan.org>). Returns posterior distribution for individual
    parameters of the fitted distribution. Allows for computation of LOO and WAIC information criteria 
    (Vehtari A, Gelman A, Gabry J (2017) <doi:10.1007/s11222-016-9696-4>) as well as Bayesian R-squared
    (Gelman A, Goodrich B, Gabry J, and Vehtari A (2018) <doi:10.1080/00031305.2018.1549100>).
'Radix trees', or 'tries', are key-value data structures optimised for efficient lookups, similar in purpose
             to hash tables. 'triebeard' provides an implementation of 'radix trees' for use in R programming and in
             developing packages with 'Rcpp'.
The trigger strategy is a general framework for a multistage statistical design with multiple hypotheses, allowing an adaptive selection of interim analyses. The selection of interim stages can be associated with some prespecified endpoints which serve as the trigger. This selection allows us to refine the critical boundaries in hypotheses testing procedures, and potentially increase the statistical power. This package includes several trial designs using the trigger strategy. 
    See Gou, J. (2023), "Trigger strategy in repeated tests on multiple hypotheses", Statistics in Biopharmaceutical Research, 15(1), 133-140, and Gou, J. (2022), "Sample size optimization and initial allocation of the significance levels in group sequential trials with multiple endpoints", Biometrical Journal, 64(2), 301-311.
Processing and analysis of pathomics, omics and other medical datasets.
    'tRigon' serves as a toolbox for descriptive and statistical analysis, correlations, 
    plotting and many other methods for exploratory analysis of high-dimensional datasets.
A complete data set of historic GB trig points in British National Grid (OSGB36) coordinate reference system. Trig points (aka triangulation stations) are fixed survey points used to improve the accuracy of map making in Great Britain during the 20th Century. Trig points are typically located on hilltops so still serve as a useful navigational aid for walkers and hikers today.
Propensity score matching for non-binary treatments.
Trimmed k-means clustering. The method is described in Cuesta-Albertos et al. (1997) <doi:10.1214/aos/1031833664>.
Information on all of the TriMet stops in the Portland Metro Area. It includes information such as the longitude, latitude, cross street, and direction of the stop. TriMet has catalogued these stops, 6880 in total.
A lightweight toolkit to reduce the size of a list object. The
    object is minimized by recursively removing elements from the object 
    one-by-one. The process is constrained by a reference function call
    specified by the user, where the target object is given as an argument. 
    The procedure will not allow elements to be removed from the object, that 
    will cause results from the function call to diverge from the function 
    call with the original object.
Provides various commonly-used response time trimming
    methods, including the recursive / moving-criterion methods reported by
    Van Selst and Jolicoeur (1994). By passing trimming functions raw data files,
    the package will return trimmed data ready for inferential testing.
Several statistical test functions as well as a function for exploratory data analysis to investigate classifiers allocating individuals to one of three disjoint and ordered classes. In a single classifier assessment the discriminatory power is compared to classification by chance. In a comparison of two classifiers the null hypothesis corresponds to equal discriminatory power of the two classifiers.
Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) <doi:10.1080/10618600.2012.681250>.
Access and manipulate spatial tracking data, with straightforward 
 coercion from and to other formats. Filter for speed and create time spent 
 maps from tracking data. There are coercion methods to convert between 'trip'
 and 'ltraj' from 'adehabitatLT', and between 'trip' and 'psp' and 'ppp' from 
 'spatstat'. Trip objects can be created from raw or grouped data frames, and 
 from types in the 'sp', sf', 'amt', 'trackeR', 'mousetrap', and other packages, 
 Sumner, MD (2011) <https://figshare.utas.edu.au/articles/thesis/The_tag_location_problem/23209538>.
A constrained two-dimensional Delaunay triangulation package
  providing both triangulation and generation of voronoi mosaics of 
  irregular spaced data.
Data handling and estimation functions for animal movement
    estimation from archival or satellite tags. Helper functions are included
    for making image summaries binned by time interval from Markov Chain Monte Carlo
    simulations.
Social Relation Model (SRM) analyses for single or multiple
    round-robin groups are performed. These analyses are either based on one
    manifest variable, one latent construct measured by two manifest variables,
    two manifest variables and their bivariate relations, or two latent
    constructs each measured by two manifest variables. Within-group t-tests
    for variance components and covariances are provided for single groups.
    For multiple groups two types of significance tests are provided:
    between-groups t-tests (as in SOREMO) and enhanced standard errors based on
    Lashley and Bond (1997) <DOI:10.1037/1082-989X.2.3.278>. Handling for missing values is provided.
Attain excellent covariate balance by matching two treated units
    and one control unit or vice versa within strata. Using such triples, as
    opposed to also allowing pairs of treated and control units, 
    allows easier interpretation of the two possible 
    weights of observations and better insensitivity to unmeasured bias in the test
    statistic. Using triples instead of matching in a fixed 1:2 or 2:1 ratio
    allows for the match to be feasible in more situations.
    The 'rrelaxiv' package, which provides an alternative solver for the underlying 
    network flow problems, carries an academic license and is not available on CRAN, but
    may be downloaded from 'GitHub' at <https://github.com/josherrickson/rrelaxiv/>.
    The 'Gurobi' commercial optimization software is required to use the two functions
    [infsentrip()] and [triplesIP()]. These functions are not essential
    to the main purpose of this package. A free academic license can be obtained at 
    <https://www.gurobi.com/features/academic-named-user-license/>. 
    The 'gurobi' R package can then be installed following 
    the instructions at <https://www.gurobi.com/documentation/9.1/refman/ins_the_r_package.html>.
Tools for exploring effects of correlated features in predictive 
    models. The predict_triplot() function delivers instance-level explanations 
    that calculate the importance of the groups of explanatory variables. The 
    model_triplot() function delivers data-level explanations. The generic plot 
    function visualises in a concise way importance of hierarchical groups of 
    predictors. All of the the tools are model agnostic, therefore works for any
    predictive machine learning models. Find more details in Biecek (2018) 
    <arXiv:1806.08915>.
Uses read counts for biallelic single nucleotide polymorphisms (SNPs)
    to compare the likelihoods for the observed read counts given that a sample is 
    either diploid or triploid. It allows parameters to be specified to account for 
    sequencing error rates and allelic bias. For details of the algorithm, please see
    Delomas (2019) <doi:10.1111/1755-0998.13073>.
Overall predictive performance is measured by a mean score
    (or loss), which decomposes into miscalibration, discrimination, and
    uncertainty components. The main focus is visualization of these distinct
    and complementary aspects in joint displays.
    See Dimitriadis, Gneiting, Jordan, Vogel (2024) <doi:10.1016/j.ijforecast.2023.09.007>.
Computing diversity measures on tripartite graphs. This package first implements a parametrized family of such diversity measures which apply on probability distributions. Sometimes called "True Diversity", this family contains famous measures such as the richness, the Shannon entropy, the Herfindahl-Hirschman index, and the Berger-Parker index. Second, the package allows to apply these measures on probability distributions resulting from random walks between the levels of tripartite graphs. By defining an initial distribution at a given level of the graph and a path to follow between the three levels, the probability of the walker's position within the final level is then computed, thus providing a particular instance of diversity to measure.
Functions to estimate temporally regularized matrix factorizations (TRMF) for forecasting and imputing values in short but high-dimensional time series. Uses regularized alternating least squares to compute the factorization, allows for several types of constraints on matrix factors and natively handles weighted and missing data.
Evaluate inline or chunks of R code in template files and replace with their output modifying the resulting template.
These functions generate data frames on troop deployments and military basing using U.S. Department of Defense data on overseas military deployments. This package provides functions for pulling country-year troop deployment and basing data. Subsequent versions will hopefully include cross-national data on deploying countries.
It includes functions like tropical addition, tropical multiplication for vectors and matrices. In tropical algebra, the tropical sum of two numbers is their minimum and the tropical product of two numbers is their ordinary sum. For more information see also I. Simon (1988) Recognizable sets with multiplicities in the tropical semi ring: Volume 324 Lecture Notes I Computer Science, pages 107-120 <doi: 10.1007/BFb0017135>.
A compilation of fish stock assessment methods for the
    analysis of length-frequency data in the context of data-poor
    fisheries. Includes methods and examples included in the FAO
    Manual by P. Sparre and S.C. Venema (1998), "Introduction to tropical fish
    stock assessment" (<https://www.fao.org/documents/card/en/c/9bb12a06-2f05-5dcb-a6ca-2d6dd3080f65/>),
    as well as other more recent methods.
Class definitions and constructors for pseudo-vectors containing
    all permutations, combinations and subsets of objects taken from a vector.
    Simplifies working with structures commonly encountered in combinatorics.
The main function of the package aims to update 'lmer()'/'glmer()' models depending on their warnings, so trying to avoid convergence and singularity problems.
Provides methods for generating .dat files for use with the 'AMPL' 
    software using spatial data, particularly rasters. It includes support for 
    various spatial data formats and different problem types. By automating the 
    process of generating 'AMPL' datasets, this package can help streamline 
    optimization workflows and make it easier to solve complex optimization 
    problems. The methods implemented in this package are described in detail
    in a publication by Fourer et al. (<doi:10.1287/mnsc.36.5.519>).
Gives the required 2^n treatment combinations in a 2^n symmetric factorial experiment in their respective standard order.
Recursive partytioning of transformation models with
  corresponding random forest for conditional transformation models 
  as described in 'Transformation Forests' (Hothorn and Zeileis, 2021, <doi:10.1080/10618600.2021.1872581>) 
  and 'Top-Down Transformation Choice' (Hothorn, 2018, <DOI:10.1177/1471082X17748081>).
A convenient R interface to the 'National Health Service (NHS) 
    Technology Reference Update Distribution (TRUD) API'. Retrieve available
    releases for items that you are subscribed to and download these with ease.
    For more information on the API, see <https://isd.digital.nhs.uk/trud/users/guest/filters/0/api>.
Implements Bayesian methods, described in
    Hugh-Jones (2019) <doi:10.1007/s40881-019-00069-x>, for estimating the
    proportion of liars in coin flip-style experiments, where subjects
    report a random outcome and are paid for reporting a "good" outcome.
Most estimators implemented by the video game industry cannot obtain reliable initial estimates nor guarantee comparability between distant estimates. TrueSkill Through Time solves all these problems by modeling the entire history of activities using a single Bayesian network allowing the information to propagate correctly throughout the system. This algorithm requires only a few iterations to converge, allowing millions of observations to be analyzed using any low-end computer.
    The core ideas implemented in this project were developed by Dangauthier P, Herbrich R, Minka T, Graepel T (2007). "Trueskill through time: Revisiting the history of chess." <https://dl.acm.org/doi/10.5555/2981562.2981605>.
Implements the TRUH test statistic for two sample testing under heterogeneity. TRUH incorporates the underlying heterogeneity and imbalance in the samples, and provides a conservative test for the composite null hypothesis that the two samples arise from the 
   same mixture distribution but may differ with respect to the mixing weights. See Trambak Banerjee, Bhaswar B. Bhattacharya, Gourab Mukherjee Ann. Appl. Stat. 14(4): 1777-1805 (December 2020). <DOI:10.1214/20-AOAS1362> for more details.
Visualizes the relationship between allele frequency and effect size in genetic association studies. The input is a data frame containing association results. The output is a plot with the effect size of risk variants in the Y axis, and the allele frequency spectrum in the X axis. Corte et al (2023) <doi:10.1101/2023.04.21.23288923>.
Doubly robust estimation for the mean of an arbitrarily transformed survival time under covariate-induced dependent left truncation and noninformative right censoring. The functions truncAIPW(), truncAIPW_cen1(), and truncAIPW_cen2() compute the doubly robust estimators under the scenario without censoring and the two censoring scenarios, respectively. The package also contains three simulated data sets 'simu', 'simu_c1', and 'simu_c2', which are used to illustrate the usage of the functions in this package.
    Reference: Wang, Y., Ying, A., Xu, R. (2022) "Doubly robust estimation under covariate-induced dependent left truncation" <arXiv:2208.06836>.
A collection of functions to deal with the truncated univariate and multivariate normal and Student distributions, described in Botev (2017) <doi:10.1111/rssb.12162> and Botev and L'Ecuyer (2015) <doi:10.1109/WSC.2015.7408180>.
A collection of tools to evaluate probability density
        functions, cumulative distribution functions, quantile functions
        and random numbers for truncated random variables.  These functions are
        provided to also compute the expected value and variance. Nadarajah
        and Kotz (2006) developed most of the functions. QQ plots can be produced.
        All the probability functions in the stats, stats4 and evd
        packages are automatically available for truncation..
Handles truncated members from the exponential family of
	probability distributions. Contains functions such as rtruncnorm() and
	dtruncpois(), which are truncated versions of rnorm() and dpois() from the
	stats package that also offer richer output containing, for example, the
	distribution parameters. It also provides functions to retrieve the original
	distribution parameters from a truncated sample by maximum-likelihood
	estimation.
Density, probability, quantile and random number
        generation functions for the truncated normal distribution.
Finds the posterior modes for the mean and standard deviation for a
  truncated normal distribution with one or two known truncation points.
  The method used extends Bayesian methods for parameter estimation for a singly
  truncated normal distribution under the Jeffreys prior (see Zhou X,
  Giacometti R, Fabozzi FJ, Tucker AH (2014). "Bayesian estimation of truncated
  data with applications to operational risk measurement".
  <doi:10.1080/14697688.2012.752103>). This package additionally allows for a
  doubly truncated normal distribution.
Estimation of models for truncated Gaussian variables by maximum likelihood.
Semi-parametric estimation of truncated linear regression models
Does local optimization using two derivatives and trust regions.
    Guaranteed to converge to local minimum of objective function.
Trusted Timestamps (tts) are created by incorporating a hash of a file or dataset into a transaction on the decentralized blockchain (Stellar network). 
    The package makes use of a free service provided by <https://stellarapi.io>.
Trust region algorithm for nonlinear optimization. Efficient when
    the Hessian of the objective function is sparse (i.e., relatively few nonzero
    cross-partial derivatives). See Braun, M. (2014) <doi:10.18637/jss.v060.i04>.
Advanced tryCatch() and try() functions for better error handling
             (logging, stack trace with source code references and support for post-mortem analysis via dump files).
Differentiate client errors (4xx)
  from server errors (5xx) for the 'plumber' and 
  'RestRserve' HTTP API frameworks. The package
  also includes a built-in logging mechanism
  to standard output (STDOUT) or standard error
  (STDERR) depending on the log level.
Contains R functions and datasets detailed in the book
        "Time Series Analysis with Applications in R (second edition)" by Jonathan Cryer and Kung-Sik Chan.
Tsallis distribution also known as the q-exponential family distribution. Provide distribution d, p, q, r functions, fitting and testing functions. Project initiated by Paul Higbie and based on Cosma Shalizi's code.
The best ANN structure for time series data analysis is a demanding need in the present era.
    This package will find the best-fitted ANN model based on forecasting accuracy.
    The optimum size of the hidden layers was also determined after determining the number of lags to be included.
    This package has been developed using the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978>  "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.
Time series toolkit with identical behavior for all
  time series classes: 'ts','xts', 'data.frame', 'data.table', 'tibble', 'zoo',
  'timeSeries', 'tsibble', 'tis' or 'irts'. Also converts reliably between these classes.
Different estimators are provided to solve the blind source separation problem for multivariate time series with stochastic volatility and supervised dimension reduction problem for multivariate time series. Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace. The package is fully described in Nordhausen, Matilainen, Miettinen, Virta and Taskinen (2021) <doi:10.18637/jss.v098.i15>.
Two stage curvature identification with machine learning for causal 
    inference in settings when instrumental variable regression is not suitable
    because of potentially invalid instrumental variables. Based on Guo and 
    Buehlmann (2022) "Two Stage Curvature Identification with Machine Learning: 
    Causal Inference with Possibly Invalid Instrumental Variables"
    <arXiv:2203.12808>. The vignette is available in Carl, Emmenegger, Bühlmann and Guo (2023) 
    "TSCI: two stage curvature identification for causal inference with 
    invalid instruments" <arXiv:2304.00513>.
A set of measures of dissimilarity between time series to perform time series clustering. Metrics based on raw data, on generating models and on the forecast behavior are implemented. Some additional utilities related to time series clustering are also provided, such as clustering algorithms and cluster evaluation metrics.
Functions for the analysis of time series using copula models.  
    The package is based on methodology described in the following references.
    McNeil, A.J. (2021) <doi:10.3390/risks9010014>,
    Bladt, M., & McNeil, A.J. (2021) <doi:10.1016/j.ecosta.2021.07.004>,
    Bladt, M., & McNeil, A.J. (2022) <doi:10.1515/demo-2022-0105>.
Likelihood-based methods for model fitting and assessment, prediction and intervention analysis of count time series following generalized linear models are provided. Models with the identity and with the logarithmic link function are allowed. The conditional distribution can be Poisson or Negative Binomial.
A set of functions to implement Time Series Cointegrated System (TSCS)
    spatial interpolation and relevant data visualization.
Forecasting competitions are of increasing importance as a mean to learn best practices and gain knowledge. Data leakage is one of the most common issues that can often be found in competitions. Data leaks can happen when the training data contains information about the test data. For example: randomly chosen blocks of time series are concatenated to form a new time series, scale-shifts, repeating patterns in time series,  white noise is added in the original time series to form a new time series, etc.  'tsdataleaks' package can be used to detect data leakages in a collection of  time series.
A terribly-simple data base for numeric
  time series, written purely in R, so no external
  database-software is needed. Series are stored in
  plain-text files (the most-portable and enduring file
  type) in CSV format. Timestamps are encoded using R's
  native numeric representation for 'Date'/'POSIXct',
  which makes them fast to parse, but keeps them
  accessible with other software. The package provides
  tools for saving and updating series in this
  standardised format, for retrieving and joining data,
  for summarising files and directories, and for
  coercing series from and to other data types (such as
  'zoo' series).
ARIMA-model-based decomposition of quarterly and 
 monthly time series data.
 The methodology is developed and described, among others, in 
 Burman (1980) <DOI:10.2307/2982132> and 
 Hillmer and Tiao (1982) <DOI:10.2307/2287770>.
RNNs are preferred for sequential data like time series, speech, text, etc. but when dealing with long range dependencies, vanishing gradient problems account for their poor performance. LSTM and GRU are effective solutions which are nothing but RNN networks with the abilities of learning both short-term and long-term dependencies. Their structural makeup enables them to remember information for a long period without any difficulty. LSTM consists of one cell state and three gates, namely, forget gate, input gate and output gate whereas GRU comprises only two gates, namely, reset gate and update gate. This package consists of three different functions for the application of RNN, LSTM and GRU to any time series data for its forecasting. For method details see Jaiswal, R. et al. (2022). <doi:10.1007/s00521-021-06621-3>.
Calculate optimal Zhong's two-/three-stage Phase II designs (see Zhong (2012) <doi:10.1016/j.cct.2012.07.006>). Generate Target Toxicity decision table for Phase I dose-finding (two-/three-stage). This package also allows users to run dose-finding simulations based on customized decision table.
We propose an optimality criterion to determine the required training set, r-score, which is derived directly from Pearson's correlation between the genomic estimated breeding values and phenotypic values of the test set <doi:10.1007/s00122-019-03387-0>. This package provides two main functions to determine a good training set and its size.
Disaggregates low frequency time series data to higher frequency series. Implements the following methods for temporal disaggregation: Boot, Feibes and Lisman (1967) <DOI:10.2307/2985238>, Chow and Lin (1971) <DOI:10.2307/1928739>, Fernandez (1981) <DOI:10.2307/1924371> and Litterman (1983) <DOI:10.2307/1391858>.
A set of commonly used distance measures and some additional functions which, although initially not designed for this purpose, can be used to measure the dissimilarity between time series. These measures can be used to perform clustering, classification or other data mining tasks which require the definition of a distance measure between time series. U. Mori, A. Mendiburu and J.A. Lozano (2016), <doi:10.32614/RJ-2016-058>.
Location-Scale based distributions parameterized in terms of mean, standard deviation, skew and shape parameters and estimation using automatic differentiation. Distributions include the Normal, Student and GED as well as their skewed variants ('Fernandez and Steel'), the 'Johnson SU', and the Generalized Hyperbolic. Also included is the semi-parametric piece wise distribution ('spd') with Pareto tails and kernel interior.
Implements a method for identifying subgroups with superior response relative to the overall sample.
Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).
Calculates total survey error (TSE) for one or more surveys, using common scale-dependent and/or scale-independent metrics.  On TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>.
The library allows to perform a multivariate time series
    classification based on the use of Discrete Wavelet Transform for
    feature extraction, a step wise discriminant to select the most
    relevant features and finally, the use of a linear or quadratic
    discriminant for classification. Note that all these steps can be done
    separately which allows to implement new steps.  Velasco, I., Sipols,
    A., de Blas, C. S., Pastor, L., & Bayona, S. (2023)
    <doi:10.1186/S12938-023-01079-X>.  Percival, D. B., & Walden, A. T.
    (2000,ISBN:0521640687).  Maharaj, E. A., & Alonso, A. M. (2014)
    <doi:10.1016/j.csda.2013.09.006>.
Calculates total survey error (TSE) for one or more surveys, using both scale-dependent and scale-independent metrics.  Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTIND and its documentation), properly input column names (see functions documentation), and run your functions.  For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.
A framework for dynamically combining forecasting models for time series forecasting predictive tasks. It leverages machine learning models from other packages to automatically combine expert advice using metalearning and other state-of-the-art forecasting combination approaches. The predictive methods receive a data matrix as input, representing an embedded time series, and return a predictive ensemble model. The ensemble use generic functions 'predict()' and 'forecast()' to forecast future values of the time series. Moreover, an ensemble can be updated using methods, such as 'update_weights()' or 'update_base_models()'. A complete description of the methods can be found in: Cerqueira, V., Torgo, L., Pinto, F., and Soares, C. "Arbitrated Ensemble for Time Series Forecasting." to appear at: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017; and Cerqueira, V., Torgo, L., and Soares, C.: "Arbitrated Ensemble for Solar Radiation Forecasting." International Work-Conference on Artificial Neural Networks. Springer, 2017 <doi:10.1007/978-3-319-59153-7_62>.
Which uses Twitter APIs for the necessary data in sentiment analysis, acts as a middleware with the approved Twitter Application.
    A special access key is given to users who subscribe to the application with their Twitter account. With this special access key, the user defined keyword for sentiment analysis can be searched in twitter recent searches and results can be obtained( more information <https://github.com/hakkisabah/tsentiment> ).
    In addition, a service named tsentiment-services has been developed to provide all these operations ( for more information <https://github.com/hakkisabah/tsentiment-services> ).
    After the successful results obtained and in line with the permissions given by the user, the results of the analysis of the word cloud and bar graph saved in the user folder directory can be seen. In each analysis performed, the previous analysis visual result is deleted and this is the basic information you need to know as a practice rule.
    'tsentiment' package provides a free service that acts as a middleware for easy data extraction from Twitter, and in return, the user rate limit is reduced by 30 requests from the total limit and the remaining requests are used. These 30 requests are reserved for use in application analytics. For information about endpoints, you can refer to the limit information in the "GET search/tweets" row in the Endpoints column in the list at <https://developer.twitter.com/en/docs/twitter-api/v1/rate-limits>.
Computes various entropies of given time series. This is the initial version that includes ApEn() and SampEn() functions for calculating approximate entropy and sample entropy. Approximate entropy was proposed by S.M. Pincus in "Approximate entropy as a measure of system complexity", Proceedings of the National Academy of Sciences of the United States of America, 88, 2297-2301 (March 1991). Sample entropy was proposed by J. S. Richman and J. R. Moorman in "Physiological time-series analysis using approximate entropy and sample entropy", American Journal of Physiology, Heart and Circulatory Physiology, 278, 2039-2049 (June 2000). This package also contains FastApEn() and FastSampEn() functions for calculating fast approximate entropy and fast sample entropy. These are newly designed very fast algorithms, resulting from the modification of the original algorithms. The calculated values of these entropies are not the same as the original ones, but the entropy trend of the analyzed time series determines equally reliably. Their main advantage is their speed, which is up to a thousand times higher. A scientific article describing their properties has been submitted to The Journal of Supercomputing and in present time it is waiting for the acceptance.
Time series analysis and computational finance.
Routines for the analysis of nonlinear time series. This
        work is largely inspired by the TISEAN project, by Rainer
        Hegger, Holger Kantz and Thomas Schreiber:
        <http://www.mpipks-dresden.mpg.de/~tisean/>.
Implements an Entropy measure of dependence based on the Bhattacharya-Hellinger-Matusita distance. Can be used as a (nonlinear) autocorrelation/crosscorrelation function for continuous and categorical time series. The package includes tests for serial and cross dependence and nonlinearity based on it. Some routines have a parallel version that can be used in a multicore/cluster environment. The package makes use of S4 classes.
Multiscale multifractal analysis (MMA) (Gierałtowski et al.,
    2012)<DOI:10.1103/PhysRevE.85.021915> is a time series analysis method,
    designed to describe scaling properties of fluctuations within the signal
    analyzed. The main result of this procedure is the so called Hurst surface
    h(q,s) , which is a dependence of the local Hurst exponent h (fluctuation
    scaling exponent) on the multifractal parameter q and the scale of observation s
    (data window width).
Routines for nonlinear time series analysis based on Threshold Autoregressive Moving Average models. 
 It provides functions and methods for: TARMA model fitting and forecasting, tests for threshold effects, unit-root tests based on TARMA models.
Tools to perform some descriptive data analysis for assets. Manage the portfolio and capital of assets. It also downloads and organizes data from the Tehran Stock Exchange (TSE).
Calculates total survey error (TSE) for a survey under multiple, different weighting schemes, using both scale-dependent and scale-independent metrics.  Package works directly from the data set, with no hand calculations required: just upload a properly structured data set (see TESTWGT and its documentation), properly input column names (see functions documentation), and run your functions.  For more on TSE, see: Weisberg, Herbert (2005, ISBN:0-226-89128-3); Biemer, Paul (2010) <doi:10.1093/poq/nfq058>; Biemer, Paul et.al. (2017, ISBN:9781119041672); etc.
Forecasting of long memory time series in presence of structural break by using TSF algorithm by Papailias and Dias (2015) <doi:10.1016/j.ijforecast.2015.01.006>.
Methods for extracting various features from time series data. The features provided are those from Hyndman, Wang and Laptev (2013) <doi:10.1109/ICDMW.2015.104>, Kang, Hyndman and Smith-Miles (2017) <doi:10.1016/j.ijforecast.2016.09.004> and from Fulcher, Little and Jones (2013) <doi:10.1098/rsif.2013.0048>. Features include spectral entropy, autocorrelations, measures of the strength of seasonality and trend, and so on. Users can also define their own feature functions.
A general regression neural network (GRNN) is a variant of a
    Radial Basis Function Network characterized by a fast single-pass learning.
    'tsfgrnn' allows you to forecast time series using a GRNN model Francisco 
    Martinez et al. (2019) <doi:10.1007/978-3-030-20521-8_17> and Francisco
    Martinez et al. (2022) <doi:10.1016/j.neucom.2021.12.028>. When the forecasting
    horizon is higher than 1, two multi-step ahead forecasting strategies can be used.
    The model built is autoregressive, that is, it is only based on the 
    observations of the time series. You can consult and plot how the
    prediction was done. It is also possible to assess the forecasting accuracy
    of the model using rolling origin evaluation.
Allows forecasting time series using nearest neighbors regression
    Francisco Martinez, Maria P. Frias, Maria D. Perez-Godoy and Antonio J.
    Rivera (2019) <doi:10.1007/s10462-017-9593-z>. When the forecasting horizon
    is higher than 1, two multi-step ahead forecasting strategies can be used.
    The model built is autoregressive, that is, it is only based on the 
    observations of the time series. The nearest neighbors used in a prediction
    can be consulted and plotted.
Nonlinear growth models are extremely useful in gaining insight into the underlying mechanism. These models are generally 'mechanistic,' with parameters that have biological meaning. This package allows you to fit and forecast time series data using nonlinear growth models.
Multiple flavors of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a large choice of conditional distributions. Methods for specification, estimation, prediction, filtering, simulation, statistical testing and more. Represents a partial re-write and re-think of 'rugarch', making use of automatic differentiation for estimation.
The 'tsgc' package provides comprehensive tools for the analysis and forecasting of epidemic trajectories.
    It is designed to model the progression of an epidemic over time while accounting for the various uncertainties
    inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach,
    using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in
    epidemic data. The reinitialization feature enhances the model’s ability to adapt to the emergence of new waves.
    The forecasts generated by the package are of value to public health officials and researchers who need to
    understand and predict the course of an epidemic to inform decision-making. Beyond its application in public
    health, the package is also a useful resource for researchers and practitioners in fields where the trajectories
    of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for
    data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy.
    The core methodologies implemented in 'tsgc' are based on well-established statistical techniques as described in
    Harvey and Kattuman (2020) <doi:10.1162/99608f92.828f40de>, Harvey and Kattuman (2021)
    <doi:10.1098/rsif.2021.0179>, and Ashby, Harvey, Kattuman, and Thamotheram (2024)
    <https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.
Obtaining relevant set of trait specific genes from gene expression data is important for clinical diagnosis of disease and discovery of disease mechanisms in plants and animals. This process involves identification of relevant genes and removal of redundant genes as much as possible from a whole gene set. This package returns the trait specific gene set from the high dimensional RNA-seq count data by applying combination of two conventional machine learning algorithms, support vector machine (SVM) and genetic algorithm (GA). GA is used to control and optimize the subset of genes sent to the SVM for classification and evaluation. Genetic algorithm uses repeated learning steps and cross validation over number of possible solution and selects the best. The algorithm selects the set of genes based on a fitness function that is obtained via support vector machines. Using SVM as the classifier performance and the genetic algorithm for feature selection, a set of trait specific gene set is obtained.
To provide a high dimensional grouped variable selection approach for detection of whole-genome SNP effects and SNP-SNP interactions, as described in Fang et al. (2017, under review).
Two-stage procedure compares hazard rate functions,
    which may or may not cross each other.
Provides a 'tbl_ts' class (the 'tsibble') for
    temporal data in an data- and model-oriented format. The 'tsibble'
    provides tools to easily manipulate and analyse temporal data, such as
    filling in time gaps and aggregating over calendar periods.
Provides diverse datasets in the 'tsibble' data structure. These datasets are useful for learning and demonstrating how tidy temporal data can tidied, visualised, and forecasted.
A shared tsibble data easily communicates between
    htmlwidgets on both client and server sides, powered by 'crosstalk'. A
    shiny module is provided to visually explore periodic/aperiodic
    temporal patterns.
Time series methods for intermittent demand forecasting. Includes Croston's method and its variants (Moving Average, SBA), and the TSB method. Users can obtain optimal parameters on a variety of loss functions, or use fixed ones (Kourenztes (2014) <doi:10.1016/j.ijpe.2014.06.007>). Intermittent time series classification methods and iMAPA that uses multiple temporal aggregation levels are also provided (Petropoulos & Kourenztes (2015) <doi:10.1057/jors.2014.62>).
An implementation of the time-series Susceptible-Infected-Recovered (TSIR) model using a number of different fitting options for infectious disease time series data. The manuscript based on this package can be found here <doi:10.1371/journal.pone.0185528>. The method implemented here is described by Finkenstadt and Grenfell (2000) <doi:10.1111/1467-9876.00187>.
The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Min-Max transformation has been used for data preparation. Here, we have used one LSTM layer as a simple LSTM model and a Dense layer is used as the output layer. Then, compile the model using the loss function, optimizer and metrics. This package is based on Keras and TensorFlow modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
The LSTM (Long Short-Term Memory) model is a Recurrent Neural Network (RNN) based architecture that is widely used for time series forecasting. Customizable configurations for the model are allowed, improving the capabilities and usability of this model compared to other packages. This package is based on 'keras' and 'tensorflow' modules and the algorithm of Paul and Garai (2021) <doi:10.1007/s00500-021-06087-4>.
It is a versatile tool for predicting time series data using Long Short-Term Memory (LSTM) models. It is specifically designed to handle time series with an exogenous variable, allowing users to denote whether data was available for a particular period or not. The package encompasses various functionalities, including hyperparameter tuning, custom loss function support, model evaluation, and one-step-ahead forecasting. With an emphasis on ease of use and flexibility, it empowers users to explore, evaluate, and deploy LSTM models for accurate time series predictions and forecasting in diverse applications. More details can be found in Garai and Paul (2023) <doi:10.1016/j.iswa.2023.200202>.
Generic methods for use in a time series probabilistic framework, allowing for a common calling convention across packages. Additional methods for time series prediction ensembles and probabilistic plotting of predictions is included. A more detailed description is available at <https://www.nopredict.com/packages/tsmethods> which shows the currently implemented methods in the 'tsmodels' framework.
Return the first four moments of the SMN distributions (Normal, Student-t, Pearson VII, Slash or Contaminated Normal).
Tools for specifying time series regression models.
A toolkit implementing the Matrix Profile concept
    that was created by CS-UCR
    <http://www.cs.ucr.edu/~eamonn/MatrixProfile.html>.
Return the first four moments, estimation of parameters and sample of the TSMSN distributions (Skew Normal, Skew t, Skew Slash or Skew Contaminated Normal).
Temporal SNA tools for continuous- and discrete-time longitudinal networks having vertex, edge, and attribute dynamics stored in the 'networkDynamic' format. This work was supported by grant R01HD68395 from the National Institute of Health.
A "pure R" implementation of the t-SNE algorithm.
Fit, compare, and visualize Bayesian graphical vector autoregressive (GVAR) network models using 'Stan'. These models are commonly used in psychology to represent temporal and contemporaneous relationships between multiple variables in intensive longitudinal data. Fitted models can be compared with a test based on matrix norm differences of posterior point estimates to quantify the differences between two estimated networks. See also Siepe, Kloft & Heck (2024) <doi:10.31234/osf.io/uwfjc>.
Detection of outliers in time series following the 
    Chen and Liu (1993) <DOI:10.2307/2290724> procedure. 
    Innovational outliers, additive outliers, level shifts, 
    temporary changes and seasonal level shifts are considered.
Basic infrastructure and some algorithms for the traveling
    salesperson problem (also traveling salesman problem; TSP).
    The package provides some simple algorithms and
    an interface to the Concorde TSP solver and its implementation of the
    Chained-Lin-Kernighan heuristic. The code for Concorde
    itself is not included in the package and has to be obtained separately.
    Hahsler and Hornik (2007) <doi:10.18637/jss.v023.i02>.
Prediction intervals for ARIMA and structural time series
    models using importance sampling approach with uninformative priors for model
    parameters, leading to more accurate coverage probabilities in frequentist
    sense. Instead of sampling the future observations and hidden states of the
    state space representation of the model, only model parameters are sampled,
    and the method is based solving the equations corresponding to the conditional
    coverage probability of the prediction intervals. This makes method relatively
    fast compared to for example MCMC methods, and standard errors of prediction
    limits can also be computed straightforwardly.
Instance feature calculation and evolutionary instance generation
    for the traveling salesman problem. Also contains code to "morph" two TSP
    instances into each other. And the possibility to conveniently run a couple
    of solvers on TSP instances.
Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Prediction is one of the most important activities while working with time series. There are many alternative ways to model the time series. Finding the right one is challenging to model them. Most data-driven models (either statistical or machine learning) demand tuning. Setting them right is mandatory for good predictions. It is even more complex since time series prediction also demands choosing a data pre-processing that complies with the chosen model. Many time series frameworks have features to build and tune models. The package differs as it provides a framework that seamlessly integrates tuning data pre-processing activities with the building of models. The package provides functions for defining and conducting time series prediction, including data pre(post)processing, decomposition, tuning, modeling, prediction, and accuracy assessment. More information is available at Izau et al. <doi:10.5753/sbbd.2022.224330>.
Time Series Qn is a package with applications of the Qn estimator of Rousseeuw and Croux (1993) <doi:10.1080/01621459.1993.10476408> to univariate and multivariate Time Series in time and frequency domains. More specifically, the robust estimation of autocorrelation or autocovariance matrix functions from Ma and Genton (2000, 2001) <doi:10.1111/1467-9892.00203>, <doi:10.1006/jmva.2000.1942> and Cotta (2017) <doi:10.13140/RG.2.2.14092.10883> are provided. The robust pseudo-periodogram of Molinares et. al. (2009) <doi:10.1016/j.jspi.2008.12.014> is also given. This packages also provides the M-estimator of the long-memory parameter d based on the robustification of the GPH estimator proposed by Reisen et al. (2017) <doi:10.1016/j.jspi.2017.02.008>.
Methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining.
    Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson),
    and forecasting accuracy measures are implemented.
Additive hazards models with two stage residual inclusion method are fitted under either survival data or competing risks data. The estimator incorporates an instrumental variable and therefore can recover causal estimand in the presence of unmeasured confounding under some assumptions. A.Ying, R. Xu and J. Murphy. (2019) <doi:10.1002/sim.8071>.
Methods for handling the missing values outliers are introduced in
    this package. The recognized missing values and outliers are replaced 
    using a model-based approach. The model may consist of both autoregressive
    components and external regressors. The methods work robust and efficient,
    and they are fully tunable. The primary motivation for writing the package
    was preprocessing of the energy systems data, e.g. power plant production
    time series, but the package could be used with any time series data. For 
    details, see Narajewski et al. (2021) <doi:10.1016/j.softx.2021.100809>.
Time Series Segmented Residual Trends is a method for the automated detection of land degradation from remotely sensed vegetation and climate datasets. TSS-RESTREND incorporates aspects of two existing degradation detection methods: RESTREND which is used to control for climate variability, and BFAST which is used to look for structural changes in the ecosystem. The full details of the testing and justification of the TSS-RESTREND method (version 0.1.02) are published in Burrell et al., (2017). <doi:10.1016/j.rse.2017.05.018>. The changes to the method introduced in version 0.2.03 focus on the inclusion  of temperature as an additional climate variable. This allows for land  degradation assessment in temperature limited drylands. A paper that details this work is currently under review. There are also a number of bug fixes and speed improvements. Version 0.3.0 introduces additional attribution for eCO2,  climate change and climate variability the details of which are in press in Burrell et al., (2020).  The version under active development and additional example scripts showing  how the package can be applied can be found at <https://github.com/ArdenB/TSSRESTREND>.
Execution of various time series models and choosing the best one
    either by a specific error metric or by picking the best one by majority vote.
    The models are based on the "forecast" package, written by Prof. Rob Hyndman.
Flexible simulation of time series using time
    series components, including seasonal, calendar and outlier effects.
    Algorithm described in Ollech, D. (2021) <doi:10.1515/jtse-2020-0028>.
It performs the smoothing approach provided by penalized least squares for univariate and bivariate time series, as proposed by Guerrero (2007) and Gerrero et al. (2017). 
          This allows to estimate the time series trend by controlling the amount of resulting (joint) smoothness.
          ---
          Guerrero, V.M (2007)  <DOI:10.1016/j.spl.2007.03.006>.
          Guerrero, V.M; Islas-Camargo, A. and Ramirez-Ramirez, L.L. (2017) <DOI:10.1080/03610926.2015.1133826>.
Functions for statistical analysis, modeling and simulation of time
 series with state space model, based on the methodology in Kitagawa
 (2020, ISBN: 978-0-367-18733-0).
Provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.
Implementation and forecasting univariate time series data using the Support Vector Machine model. Support Vector Machine is one of the prominent machine learning approach for non-linear time series forecasting. For method details see Kim, K. (2003) <doi:10.1016/S0925-2312(03)00372-2>.
Goodness of Fit and Forecast Evaluation Tests for timeseries models. Includes, among others, the Generalized Method of Moments (GMM) Orthogonality Test of Hansen (1982), the Nyblom (1989) parameter constancy test, the sign-bias test of Engle and Ng (1993), and a range of tests for value at risk and expected shortfall evaluation.
Plot official statistics' time series conveniently: automatic
    legends, highlight windows, stacked bar chars with positive and
    negative contributions, sum-as-line option, two y-axes with automatic
    horizontal grids that fit both axes and other popular chart types.
    'tstools' comes with a plethora of defaults to let you plot without
    setting an abundance of parameters first, but gives you the
    flexibility to tweak the defaults. In addition to charts, 'tstools'
    provides a super fast, 'data.table' backed time series I/O that allows
    the user to export / import long format, wide format and transposed
    wide format data to various file types.
Interactive laboratory of Time Series based in Box-Jenkins methodology.
Includes: (i) tests and visualisations that can help the modeller explore time series components and perform decomposition; (ii) modelling shortcuts, such as functions to construct lagmatrices and seasonal dummy variables of various forms; (iii) an implementation of the Theta method; (iv) tools to facilitate the design of the forecasting process, such as ABC-XYZ analyses; and (v) "quality of life" functions, such as treating time series for trailing and leading values.
Fitting tree-structured varying coefficient models (Berger et al. (2019), <doi:10.1007/s11222-018-9804-8>). Simultaneous detection of covariates with varying coefficients and effect modifiers that induce varying coefficients if they are present.
Utilities for rapidly loading specified rows and/or columns of data from large tab-separated value (tsv) files (large: e.g. 1 GB file of 10000 x 10000 matrix). 'tsvio' is an R wrapper to 'C' code that creates an index file for the rows of the tsv file, and uses that index file to collect rows and/or columns from the tsv file without reading the whole file into memory.
An 'RStudio' add-in to visualize time series. Time series are searched in the global environment as data.frame objects with a column of type date and a column of type numeric. Interactive charts are produced using 'plotly' package.
Tools for timescale decomposition of the classic variance ratio of community ecology. Tools are as described in Zhao et al (in prep), extending commonly used methods introduced by Peterson et al (1975) <doi: 10.2307/1936306>.
Accompanies the texts Time Series for Data Science with R by Woodward, Sadler and Robertson & Applied Time Series Analysis with R, 2nd edition by Woodward, Gray, and Elliott.  It is helpful for data analysis and for time series instruction.
Characterisation of the extremal dependence structure of time series, avoiding pre-processing and filtering as done typically with peaks-over-threshold methods. It uses the conditional approach of Heffernan and Tawn (2004) <DOI:10.1111/j.1467-9868.2004.02050.x> which is very flexible in terms of extremal and asymptotic dependence structures, and Bayesian methods improve efficiency and allow for deriving measures of uncertainty. For example, the extremal index, related to the size of clusters in time, can be estimated and samples from its posterior distribution obtained.
Wrapper of datatable widget, allowing display of data.tree objects.
            All arguments of the data.tree become columns and each node is a row.
            Adds column with buttons allowing folding and unfolding the levels.
This interface was created to develop a standard procedure 
 to analyse temporal trend in the framework of the OSPAR convention.
 The analysis process run through 4 successive steps : 1) manipulate your data, 2)
 select the parameters you want to analyse, 3) build your regulated 
 time series, 4) perform diagnosis and analysis and 5) read the results. 
 Statistical analysis call other package function such as Kendall tests
 or cusum() function.
Computes a point pattern in R^2 or on a graph that is representative of a collection of many data patterns. The result is an approximate barycenter (also known as Fréchet mean or prototype) based on a transport-transform metric. Possible choices include Optimal SubPattern Assignment (OSPA) and Spike Time metrics. Details can be found in Müller, Schuhmacher and Mateu (2020) <doi:10.1007/s11222-020-09932-y>.
U.S. Department of the Treasury, Alcohol and Tobacco Tax and
    Trade Bureau (TTB) collects data and reports on monthly beer
    industry production and operations. This data package includes
    a collection of 10 years (2006 - 2015) worth of data on materials
    used at U.S. breweries in pounds reported by the Brewer's Report
    of Operations and the Quarterly Brewer's Report of Operations
    forms, ready for data analysis. This package also includes historical
    tax rates on distilled spirits, wine, beer, champagne, and tobacco
    products as individual data sets.
The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Paper: Albrecht, Marco, et al. (2017)<DOI:10.1186/s12859-016-1440-8>.
Some accelerated three-term conjugate gradient algorithms implemented purely in R with the same user interface as optim(). The search directions and acceleration scheme are described in Andrei, N. (2013) <doi:10.1016/j.amc.2012.11.097>, Andrei, N. (2013) <doi:10.1016/j.cam.2012.10.002>, and Andrei, N (2015) <doi:10.1007/s11075-014-9845-9>. Line search is done by a hybrid algorithm incorporating the ideas in Oliveia and Takahashi (2020) <doi:10.1145/3423597> and More and Thuente (1994) <doi:10.1145/192115.192132>.
The 'tinytest' package offers a light-weight zero-dependency unit-testing
 framework to which this package adds support of the 'diffobj' package for 'diff'-style
 comparison of R objects.
C source code and R wrappers for the tth/ttm TeX-to-HTML/MathML translators.
Likelihood ratio and maximum likelihood statistics are provided 
     that can be used as alternatives to p-values Colquhoun (2017) 
     <doi:10.1098/rsos.171085>. Arguments can be either p-values or t-statistics. 
     together with degrees of freedom. For the function 'tTOlr', the argument
     'twoSided' has the default 'twoSided = TRUE'.
A collection of over 50 technical indicators for creating technical trading rules. The package also provides fast implementations of common rolling-window functions, and several volatility calculations.
Time-Temperature Superposition analysis is often applied to frequency modulated data obtained by Dynamic
		Mechanic Analysis (DMA) and Rheometry in the analytical chemistry and physics
		areas. These techniques provide estimates of material mechanical properties
		(such as moduli) at different temperatures in a wider range of time. This
		package provides the Time-Temperature superposition Master Curve at a referred
		temperature by the three methods: the two wider used methods, Arrhenius based
		methods and WLF, and the newer methodology based on derivatives procedure.
		The Master Curve is smoothed by B-splines basis. The package output is composed
		of plots of experimental data, horizontal and vertical shifts, TTS data, and TTS
		data fitted using B-splines with bootstrap confidence intervals.
A screening process utilizing training and testing samples to filter out uninformative DNA methylation sites. Surrogate variables (SVs) of DNA methylation are included in the filtering process to explain unknown factor effects.
It provides generic methods that are used by more than one package, avoiding conflicts. This package will be imported by 'tidySingleCellExperiment' and 'tidyseurat'.
Create structured, formatted HTML tables of in a flexible and
    convenient way.
Implementation of Time to Target plot based on the work 
             of Ribeiro and Rosseti (2015) <DOI:10.1007/s11590-014-0760-8>, 
             that describe a numerical method that gives the probability of 
             an algorithm A finds a solution at least as good as a given 
             target value in smaller computation time than algorithm B.
Get comments posted on YouTube videos, information on how many 
    times a video has been liked, search for videos with particular content, and 
    much more. You can also scrape captions from a few videos. To learn more about
    the YouTube API, see <https://developers.google.com/youtube/v3/>.
Get statistics and reports from YouTube. To learn more about
    the YouTube Analytics and Reporting API, see <https://developers.google.com/youtube/reporting/>.
Performs Three-Mode Principal Components Analysis,
    which carries out Tucker Models.
Helper functions for 'TUFLOW FV' models. Current functionality includes reading in and plotting
    output POINTS files and generating initial conditions based on point observations.
Provides R Markdown output formats to use Tufte styles for
    PDF and HTML output.
Custom template and output formats for use with rmarkdown. Produce
    Edward Tufte-style handouts in html formats with full support for rmarkdown
    features
Perform the conventional Tukey test from formula, 
             lm, aov, aovlist and lmer objects.
Functions for density, cumulative density, quantile and simulation
         of Tukey g-and-h (1977) distributions. The quantile-based
         transformation (Hoaglin 1985 <doi:10.1002/9781118150702.ch11>) and its
         reverse transformation, as well as the letter-value based estimates
         (Hoaglin 1985), are also provided.
Tukey regions are polytopes in the Euclidean space, viz. 
    upper-level sets of the Tukey depth function on given data. The bordering 
    hyperplanes of a Tukey region are computed as well as its vertices, facets, 
    centroid, and volume. In addition, the Tukey median set, which is the 
    non-empty Tukey region having highest depth level, and its barycenter 
    (= Tukey median) are calculated. Tukey regions are visualized in dimension 
    two and three. For details see Liu, Mosler, and Mozharovskyi 
    (2019, <doi:10.1080/10618600.2018.1546595>). See file LICENSE.note for 
    additional license information.
Provides wrapper functions to the multiple marginal model function mmm() of package 'multcomp' to implement the trend test of Tukey, Ciminera and Heyse (1985) <DOI:10.2307/2530666> for general parametric models.
Integrates several popular high-dimensional methods based on Linear Discriminant Analysis (LDA) and provides a comprehensive and user-friendly toolbox for linear, semi-parametric and tensor-variate classification as mentioned in Yuqing Pan, Qing Mai and Xin Zhang (2019) <arXiv:1904.03469>. Functions are included for covariate adjustment, model fitting, cross validation and prediction.
A tool to obtain tumor growth rates from clinical trial patient data.  Output includes individual and summary data for tumor growth rate estimates as well as optional plots of the observed and predicted tumor quantity over time.
The ability to tune models is important. 'tune' contains
    functions and classes to be used in conjunction with other
    'tidymodels' packages for finding reasonable values of
    hyper-parameters in models, pre-processing methods, and
    post-processing steps.
Generic methods for parameter tuning of classification algorithms using multiple scoring functions (Muessel et al. (2012), <doi:10.18637/jss.v046.i05>).
Analyze music and speech, extract features like MFCCs, handle wave files and their representation in various ways, read mp3, read midi, perform steps of a transcription, ...
        Also contains functions ported from the 'rastamat' 'Matlab' package.
Tuning random forest with one line. The package is mainly based on the packages 'ranger' and 'mlrMBO'.
Functions to find all matches or non-matches, orphans, and
    duplicate or other replicated elements.
Algorithms for accelerating the convergence of slow,
        monotone sequences from smooth, contraction mapping such as the
        EM and MM algorithms. It can be used to accelerate any smooth,
        linearly convergent acceleration scheme.  A tutorial style
        introduction to this package is available in a vignette on the
        CRAN download page or, when the package is loaded in an R
        session, with vignette("turboEM").
Includes the results of general, local, and presidential elections held in Turkey between 1995 and 2023, broken down by provinces and overall national results. It facilitates easy processing of this data and the creation of visual representations based on these election results.
Package designed for working with vectors and lists of vectors,
    mainly for turning them into other indexed data structures.
An implementation of turtle graphics
    <http://en.wikipedia.org/wiki/Turtle_graphics>.
    Turtle graphics comes from Papert's language Logo and has
    been used to teach concepts of computer programming.
Helps the R users to get data from 'Tushare Pro'<https://tushare.pro>.
    'Tushare Pro' is a platform as well as a community with a lot of staffs working in financial area.
    We support financial data such as stock price, financial report statements and digital coins data.
Helper functions for creating, editing, and testing tutorials
    created with the 'learnr' package. Provides a simple method for allowing
    students to download their answers to tutorial questions. For examples
    of its use, see the 'r4ds.tutorials' and 'primer.tutorials' packages.
The main objective of cooperative games is to allocate a good among
  the agents involved. This package includes the most well-known   allocation 
  rules, i.e., the Shapley value, the Banzhaf value, the egalitarian rule, and 
  the equal surplus division value. In addition, it considers the point of view 
  of a priori unions (situations in which agents can form coalitions). For this
  purpose, the package includes the Owen value, the Banzhaf-Owen value, and the
  corresponding extensions of the egalitarian rules. All these values can be 
  calculated exactly or estimated by sampling.
The model, developed at the Vienna University of Technology, is a lumped conceptual rainfall-runoff model, following the structure of the HBV model. 
             The model can also be run in a semi-distributed fashion and with dual representation of soil layer.
             The model runs on a daily or shorter time step and consists of a snow routine, a soil moisture routine and a flow routing routine. 
             See Parajka, J., R. Merz, G. Bloeschl (2007) <DOI:10.1002/hyp.6253> Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments, Hydrological Processes, 21, 435-446.
Create a time-varying dataset using features, exposure, and
    look back specifications.
Fits time-varying effect models (TVEM). These are a kind of application of varying-coefficient models in the context of longitudinal data, allowing the strength of linear, logistic, or Poisson regression relationships to change over time.  These models are described further in Tan, Shiyko, Li, Li & Dierker (2012) <doi:10.1037/a0025814>.  We thank Kaylee Litson, Patricia Berglund, Yajnaseni Chakraborti, and Hanjoo Kim for their valuable help with testing the package and the documentation. The development of this package was part of a research project supported by National Institutes of Health grants P50 DA039838 from the National Institute of Drug Abuse and 1R01 CA229542-01 from the National Cancer Institute and the NIH Office of Behavioral and Social Science Research. Content is solely the responsibility of the authors and does not necessarily represent the official views of the funding institutions mentioned above. This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
Simulation, estimation and inference for univariate and multivariate TV(s)-GARCH(p,q,r)-X models, where s indicates the number and shape of the transition functions, p is the ARCH order, q is the GARCH order, r is the asymmetry order, and 'X' indicates that covariates can be included; see Campos-Martins and Sucarrat (2024) <doi:10.18637/jss.v108.i09>. In the multivariate case, variances are estimated equation by equation and dynamic conditional correlations are allowed. The TV long-term component of the variance as in the multiplicative TV-GARCH model of Amado and Terasvirta (2013) <doi:10.1016/j.jeconom.2013.03.006> introduces non-stationarity whereas the GARCH-X short-term component describes conditional heteroscedasticity. Maximisation by parts leads to consistent and asymptotically normal estimates.
Probability mass (d), distribution (p), quantile (q), and random 
    number generating (r and rt) functions for the time-varying right-truncated 
    geometric (tvgeom) distribution. Also provided are functions to calculate the first 
    and second central moments of the distribution. The tvgeom distribution
    is similar to the geometric distribution, but the probability 
    of success is allowed to vary at each time step, and there are a limited 
    number of trials. This distribution is essentially a Markov chain, and it
    is useful for modeling Markov chain systems with a set 
    number of time steps.
Functions for managing cashflows and interest rate curves.
Functions for compounding and discounting calculations included here serve as a complete reference for various scenarios of time value of money.
    Raymond M. Brooks (“Financial Management,” 2018, ISBN: 9780134730417).
    Sheridan Titman, Arthur J. Keown, John D. Martin (“Financial Management: Principles and Applications,” 2017, ISBN: 9780134417219).
    Jonathan Berk, Peter DeMarzo, David Stangeland, Andras Marosi (“Fundamentals of Corporate Finance,” 2019, ISBN: 9780134735313).
    S. A. Hummelbrunner, Kelly Halliday, Ali R. Hassanlou (“Contemporary Business Mathematics with Canadian Applications,” 2020, ISBN: 9780135285015).
Provides functions for estimating mediation effects that vary over 
   time as described in Cai X, Coffman DL, Piper ME, Li R. 
   Estimation and inference for the mediation effect in a time-varying mediation model. 
   BMC Med Res Methodol. 2022;22(1):1-12.
This is a statistical tool interactive that provides multivariate statistical tests that are more powerful than traditional Hotelling T2 test and LRT (likelihood ratio test) for the vector of normal mean populations with and without contamination and non-normal populations (Henrique J. P. Alves & Daniel F. Ferreira (2019) <DOI: 10.1080/03610918.2019.1693596>).
Provides tools for denoising noisy signal and images via
    Total Variation Regularization. Reducing the total variation of
    the given signal is known to remove spurious detail while preserving
    essential structural details. For the seminal work on the topic,
    see Rudin et al (1992) <doi:10.1016/0167-2789(92)90242-F>.
Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques.
Contains various 'ggplot2' themes and color palettes based on TV shows 
    such as 'Game of Thrones', 'Brooklyn Nine-Nine', 'Avatar: The Last Airbender',
    'Spongebob Squarepants', and more.
Provides functions for propensity score
        estimating and weighting, nonresponse weighting, and diagnosis
        of the weights.
Provides functions for propensity score
        estimation and weighting for continuous exposures as described in Zhu, Y., 
        Coffman, D. L., & Ghosh, D. (2015). A boosting algorithm for
        estimating generalized propensity scores with continuous treatments.
        Journal of Causal Inference, 3(1), 25-40. <doi:10.1515/jci-2014-0022>.
Provides functions for estimating natural direct and indirect effects for mediation analysis. It uses weighting where the weights are functions of estimates of the probability of exposure or treatment assignment (Hong, G (2010). <https://cepa.stanford.edu/sites/default/files/workshops/GH_JSM%20Proceedings%202010.pdf> Huber, M. (2014). <doi:10.1002/jae.2341>). Estimation of probabilities can use generalized boosting or logistic regression. Additional functions provide diagnostics of the model fit and weights. The vignette provides details and examples.
Provides functions for gradient boosted weighting to correct linkage 
    failures or generate comparison groups.
Implements Time-Weighted Dynamic Time Warping (TWDTW), 
    a measure for quantifying time series similarity. The TWDTW algorithm, 
    described in Maus et al. (2016) <doi:10.1109/JSTARS.2016.2517118> and 
    Maus et al. (2019) <doi:10.18637/jss.v088.i05>, is applicable to multi-dimensional 
    time series of various resolutions. It is particularly suitable for comparing 
    time series with seasonality for environmental and ecological data analysis, 
    covering domains such as remote sensing imagery, climate data, hydrology, 
    and animal movement. The 'twdtw' package offers a user-friendly 'R' interface, 
    efficient 'Fortran' routines for TWDTW calculations, flexible time weighting 
    definitions, as well as utilities for time series preprocessing and visualization.
Maximum likelihood computations for Tweedie families, including the series expansion (Dunn and Smyth, 2005; <doi:10.1007/s11222-005-4070-y>) and the Fourier inversion  (Dunn and Smyth, 2008; <doi:10.1007/s11222-007-9039-6>), and related methods.
In order to create smooth animation between states of data,
    tweening is necessary. This package provides a range of functions for
    creating tweened data that can be used as basis for animation. Furthermore 
    it adds a number of vectorized interpolaters for common R data 
    types such as numeric, date and colour.
An interface to 'twitter-text', a 'JavaScript' library which
   is responsible for determining the length/validity of a tweet and 
   identifying/linking any URLs or special tags (e.g. mentions or
   hashtags) which may be present.
Generates a game of 2048 that can be played in the
    console.  Supports grids of arbitrary sizes, undoing the last move,
    and resuming a game that was exited during the current session.
An R wrapper around the API of TheyWorkForYou, a parliamentary 
    monitoring site that scrapes and repackages Hansard (the UK's parliamentary 
    record) and augments it with information from the Register of Members' 
    Interests, election results, and voting records to provide a unified 
    source of information about UK legislators and their activities. See 
    <http://www.theyworkforyou.com> for details.
The Twilio web service provides an API for computer programs
    to interact with telephony. The included functions wrap the SMS and MMS 
    portions of Twilio's API, allowing users to send and receive text messages
    from R. See <https://www.twilio.com/docs/> for more information.
An efficient algorithm for data twinning. This work is supported by U.S. National Science 
	Foundation grants DMREF-1921873 and CMMI-1921646.
Tailored for topic modeling with tweets and fit for visualization tasks in R.
    Collect, pre-process and analyze the contents of tweets using 
    LDA and structural topic models (STM). Comes with visualizing capabilities like tweet and hashtag maps 
    and built-in support for 'LDAvis'.
Provides an interface to the Twitter web API.
Collect  your data on digital marketing campaigns from Twitter Ads using the 'Windsor.ai' API <https://windsor.ai/api-fields/>.
Provides an integration to the 'metatrader 5'. 
    The functionalities carry out automated trading using
    sentiment indexes computed from 'twitter' and/or 'stockwits'. 
    The sentiment indexes are based on the ph.d. dissertation 
    "Essays on Economic Forecasting Models" (Godeiro,2018) <https://repositorio.ufpb.br/jspui/handle/123456789/15198>
    The integration between the 'R' and the 'metatrader 5' allows sending buy/sell orders to the brokerage.
Include the Twitter status widgets in HTML pages created
  using R markdown. The package uses the Twitter javascript APIs to
  embed in your document Twitter cards associated to specific statuses.
  The main targets are regular HTML pages or dashboards.
Implementation of a Bayesian two-way latent structure model for integrative genomic clustering.  The model clusters samples in relation to distinct data sources, with each subject-dataset receiving a latent cluster label, though cluster labels have across-dataset meaning because of the model formulation.  A common scaling across data sources is unneeded, and inference is obtained by a Gibbs Sampler.  The model can fit multivariate Gaussian distributed clusters or a heavier-tailed modification of a Gaussian density.  Uniquely among integrative clustering models, the formulation makes no nestedness assumptions of samples across data sources -- the user can still fit the model if a study subject only has information from one data source. The package provides a variety of post-processing functions for model examination including ones for quantifying observed alignment of clusterings across genomic data sources.  Run time is optimized so that analyses of datasets on the order of thousands of features on fewer than 5 datasets and hundreds of subjects can converge in 1 or 2 days on a single CPU.  See "Swanson DM, Lien T, Bergholtz H, Sorlie T, Frigessi A, Investigating Coordinated Architectures Across Clusters in Integrative Studies: a Bayesian Two-Way Latent Structure Model, 2018, <doi:10.1101/387076>, Cold Spring Harbor Laboratory" at <https://www.biorxiv.org/content/early/2018/08/07/387076.full.pdf> for model details.
The TWN-list (Taxa Waterbeheer Nederland) is the Dutch standard for naming 
    taxons in Dutch Watermanagement. This package makes it easier to use the 
    TWN-list for ecological analyses. It  consists of two parts. First it makes the 
    TWN-list itself available in R. Second, it has a few functions that make it 
    easy to perform some basic and often recurring tasks for checking and consulting
    taxonomic data from the TWN-list.
A system to simulate clinical trials with time to event endpoints. Event simulation is based on Cox models allowing for covariates in addition to the treatment or group factor. Specific drop-out rates (separate from administrative censoring) can be controlled in the simulation. Other features include stratified randomization, non-proportional hazards, different accrual patterns, and event projection (timing to reach the target event) based on interim data.
Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and
  confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) <doi:10.1177/1536867X1501500102>.
Density, distribution function, quantile function, 
    and random generation function, maximum likelihood estimation (MLE), 
    penalized maximum likelihood estimation (PMLE), the quartiles method estimation (QM),
    and median rank estimation (MEDRANK) for the two-parameter exponential distribution.
    MLE and PMLE are based on Mengjie Zheng (2013)<https://scse.d.umn.edu/sites/scse.d.umn.edu/files/mengjie-thesis_masters-1.pdf>.
    QM is based on Entisar Elgmati and Nadia Gregni (2016)<doi:10.5539/ijsp.v5n5p12>.
    MEDRANK is based on Matthew Reid (2022)<doi:10.5281/ZENODO.3938000>.
Estimation of gene-treatment interactions in randomized clinical trials exploiting gene-treatment independence. Methods used in the package refer to J. Y. Dai, M. LeBlanc, and C. Kooperberg (2009) Biometrics <doi:10.1111/j.1541-0420.2008.01046.x>.
Fast randomization based two sample tests. 
  Testing the hypothesis that two samples come from the same distribution using randomization to create p-values. Included tests are: Kolmogorov-Smirnov, Kuiper, Cramer-von Mises, Anderson-Darling, Wasserstein, and DTS. The default test (two_sample) is based on the DTS test statistic, as it is the most powerful, and thus most useful to most users. 
  The DTS test statistic builds on the Wasserstein distance by using a weighting scheme like that of Anderson-Darling. See the companion paper at <arXiv:2007.01360> or <https://codowd.com/public/DTS.pdf> for details of that test statistic, and non-standard uses of the package (parallel for big N, weighted observations, one sample tests, etc). We also include the permutation scheme to make test building simple for others.
For high-dimensional data whose main feature is a large number, p, of variables but a small sample size, the null hypothesis that the marginal distributions of p variables are the same for two groups is tested. We propose a test statistic motivated by the simple idea of comparing, for each of the p variables, the empirical characteristic functions computed from the two samples. If one rejects this global null hypothesis of no differences in distributions between the two groups, a set of permutation p-values is reported to identify which variables are not equally distributed in both groups.
Implements the TWO-Component Single Cell Model-Based Association Method (TWO-SIGMA) for gene-level differential expression (DE) analysis and DE-based gene set testing of single-cell RNA-sequencing datasets. See Van Buren et al. (2020) <doi:10.1002/gepi.22361> and Van Buren et al. (2021) <doi:10.1101/2021.01.24.427979>.
An inverse probability of censoring weighted (IPCW) targeted maximum likelihood estimator (TMLE) for evaluating a marginal point treatment effect from data where some variables were collected on only a subset of participants using a two-stage design (or marginal mean outcome for a single arm study). A TMLE for conditional parameters defined by a marginal structural model (MSM) is also available.
Conditional logistic regression with longitudinal follow up and
    individual-level random coefficients: A stable and efficient
    two-step estimation method.
Carries out analyses of two-way tables with one observation per cell, together with graphical displays
    for an additive fit and
    a diagnostic plot for removable 'non-additivity' via a power transformation of the response.
    It implements Tukey's Exploratory Data Analysis (1973) <ISBN: 978-0201076165> methods, including a
    1-degree-of-freedom test for row*column 'non-additivity', linear in the row and column effects.
Estimates the weights and measure of robustness to treatment effect heterogeneity attached to two-way fixed effects regressions.
    Clément de Chaisemartin, Xavier D'Haultfœuille (2020) <DOI: 10.1257/aer.20181169>.
Performs two-way tests in independent groups designs. These are two-way ANOVA, two-way ANOVA under heteroscedasticity: parametric bootstrap based generalized test and generalized pivotal quantity based generalized test (Ananda et al., 2022) <doi:10.1080/03610926.2022.2059682>, two-way ANOVA for medians, trimmed means, M-estimators (Wilcox, 2011; ISBN:978-0-12-386983-8). The package performs descriptive statistics and graphical approaches. Moreover, it assesses variance homogeneity and normality of data in each group via tests and plots. All 'twowaytests' functions are designed for two-way layout.
A collection of functions for data analysis with two-by-two contingency tables. The package provides tools to compute measures of effect (odds ratio, risk ratio, and risk difference), calculate impact numbers and attributable fractions, and perform hypothesis testing. Statistical analysis methods are oriented towards epidemiological investigation of relationships between exposures and outcomes.
A model for the growth of self-limiting populations using three, four, or five parameter 
    functions, which have wide applications in a variety of fields.
    The dependent variable in a dynamical modeling could be the population size at time x, 
    where x is the independent variable. In the analysis of quantitative polymerase chain reaction 
    (qPCR), the dependent variable would be the fluorescence intensity and the independent variable 
    the cycle number. This package then would calculate the TWW cycle threshold.
Efficient estimation of the population-level causal effects of
    stochastic interventions on a continuous-valued exposure. Both one-step and
    targeted minimum loss estimators are implemented for the counterfactual mean
    value of an outcome of interest under an additive modified treatment policy,
    a stochastic intervention that may depend on the natural value of the
    exposure. To accommodate settings with outcome-dependent two-phase
    sampling, procedures incorporating inverse probability of censoring
    weighting are provided to facilitate the construction of inefficient and
    efficient one-step and targeted minimum loss estimators.  The causal
    parameter and its estimation were first described by Díaz and van der Laan
    (2013) <doi:10.1111/j.1541-0420.2011.01685.x>, while the multiply robust
    estimation procedure and its application to data from two-phase sampling
    designs is detailed in NS Hejazi, MJ van der Laan, HE Janes, PB Gilbert,
    and DC Benkeser (2020) <doi:10.1111/biom.13375>. The software package
    implementation is described in NS Hejazi and DC Benkeser (2020)
    <doi:10.21105/joss.02447>. Estimation of nuisance parameters may be
    enhanced through the Super Learner ensemble model in 'sl3', available for
    download from GitHub using 'remotes::install_github("tlverse/sl3")'.
Provides functions to produce rudimentary ascii graphics
        directly in the terminal window. Provides a basic plotting
        function (and equivalents of curve, density, acf and barplot)
        as well as boxplot and image functions.
This queue is a data structure that lets
  parallel processes send and receive messages,
  and it can help coordinate the work
  of complicated parallel tasks.
  Processes can push new messages to the queue,
  pop old messages, and obtain a
  log of all the messages ever pushed. File locking
  preserves the integrity of the data even when
  multiple processes access the queue simultaneously.
A type system for R. It supports setting variable types in a script or the body of a function, so variables can't be assigned illegal values. Moreover it supports setting argument and return types for functions.
Type hints are special comments within a function body indicating the intended nature of the function's arguments in terms of data types, dimensions and permitted values. The actual parameters with which the function is called are evaluated against these type hint comments at run-time.
Provides a simple type annotation for R that is usable in scripts,
    in the R console and in packages. It is intended as a convention to allow other
    packages to use the type information to provide error checking,
    automatic documentation or optimizations.
The 'R' language includes a set of defined types, but the language
    itself is "absurdly dynamic" (Turcotte & Vitek (2019)
    <doi:10.1145/3340670.3342426>), and lacks any way to specify which types are
    expected by any expression. The 'typetracer' package enables code to be
    traced to extract detailed information on the properties of parameters
    passed to 'R' functions. 'typetracer' can trace individual functions or
    entire packages.
Provides an up-to-date copy of the Internet Assigned Numbers
    Authority (IANA) Time Zone Database. It is updated periodically to
    reflect changes made by political bodies to time zone boundaries, UTC
    offsets, and daylight saving time rules. Additionally, this package
    provides a C++ interface for working with the 'date' library. 'date'
    provides comprehensive support for working with dates and date-times,
    which this package exposes to make it easier for other R packages to
    utilize. Headers are provided for calendar specific calculations,
    along with a limited interface for time zone manipulations.
Download and compile any version of the
  IANA Time Zone Database (also known as Olson database)
  and make it current in your R session.
  Beware: on Windows 'Cygwin' is required!
Contains functions for calculating under-five child mortality estimates using the Trussell version of the Brass method (United Nations (1990) <https://www.un.org/en/development/desa/population/publications/pdf/mortality/stepguide_childmort.pdf> and United Nations (1983) <https://www.un.org/en/development/desa/population/publications/pdf/mortality/stepguide_childmort.pdf>) as well as applying the cohort-derived methods by Rajaratnam and colleagues (Rajaratnam JK, Tran LN, Lopez AD, Murray CJL (2010) "Measuring Under-Five Mortality: Validation of New Low-Cost Methods" <doi:10.1371/journal.pmed.1000253>).
Despite there being a section in RFC 7231
    <https://tools.ietf.org/html/rfc7231#section-5.5.3> defining a suggested
    structure for 'User-Agent' headers this data is notoriously difficult
    to parse consistently. Tools are provided that will take in user agent
    strings and return structured R objects. This is a 'V8'-backed package
    based on the 'ua-parser' project <https://github.com/ua-parser>.
The Unmanned Aerial Vehicle Mission Planner provides an easy to use work flow for planning autonomous obstacle avoiding surveys of ready to fly unmanned aerial vehicles to retrieve aerial or spot related data. It creates either intermediate flight control files for the DJI-Litchi supported series or ready to upload control files for the pixhawk-based flight controller. Additionally it contains some useful tools for digitizing and data manipulation.
The framework proposed in Jenul et al., (2022) <doi:10.1007/s10994-022-06221-9>, together with an interactive Shiny dashboard. 'UBayFS' is an ensemble feature selection technique embedded in a Bayesian statistical framework. The method combines data and user knowledge, where the first is extracted via data-driven ensemble feature selection. The user can control the feature selection by assigning prior weights to features and penalizing specific feature combinations. 'UBayFS' can be used for common feature selection as well as block feature selection.
Two Phase I designs are implemented in the package: the classical 3+3 and the Continual Reassessment Method (<doi:10.2307/2531628>). Simulations tools are also available to estimate the operating characteristics of the methods with several user-dependent options.
Complete work flow for the analysis of pharmacokinetic pharmacodynamic (PKPD), physiologically-based pharmacokinetic (PBPK) and systems pharmacology models including: creation of ordinary differential equation-based models, pooled parameter estimation, individual/population based simulations, rule-based simulations for clinical trial design and modeling assays, deployment with a customizable 'Shiny' app, and non-compartmental analysis. System-specific analysis templates can be generated and each element includes integrated reporting with 'PowerPoint' and 'Word'.
Provides a set of functions that can be used to obtain better predictive performance on cost-sensitive and cost/benefits tasks (for both regression and classification). This includes re-sampling approaches that modify the original data set biasing it towards the user preferences.
Fit Bayesian hierarchical models of animal abundance and occurrence
    via the 'rstan' package, the R interface to the 'Stan' C++ library.
    Supported models include single-season occupancy, dynamic occupancy, and
    N-mixture abundance models. Covariates on model parameters are specified
    using a formula-based interface similar to package 'unmarked', while also
    allowing for estimation of random slope and intercept terms. References:
    Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>;
    Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Basic statistical analyses. The package has been developed
    to be used in statistics courses at Bocconi University (Milan, Italy).
    Currently, the package includes some exploratory and inferential analyses
    usually presented in introductory statistics courses.
Calculates the Urban Centrality Index (UCI) as in Pereira et al., 
             (2013) <doi:10.1111/gean.12002>. The UCI measures the 
             extent to which the spatial organization of a city or region varies 
             from extreme polycentric to extreme monocentric in a continuous 
             scale from 0 to 1. Values closer to 0 indicate more polycentric patterns
             and values closer to 1 indicate a more monocentric urban form.
Returns a data frame with the names of the input data points and hex colors (or CIELab coordinates). Data can be mapped to colors for use in data visualization. It optimally maps data points into a polygon that represents the CIELab colour space. Since Euclidean distance approximates relative perceptual differences in CIELab color space, the result is a color encoding that aims to capture much of the structure of the original data.
Find and import datasets from the
    University of California Irvine Machine Learning (UCI ML) Repository into R.
    Supports working with data from UCI ML repository inside of R scripts, notebooks, 
    and 'Quarto'/'RMarkdown' documents. Access the UCI ML repository directly at
    <https://archive.ics.uci.edu/>.
Clustering and classification inference for high dimension low sample size (HDLSS)
    data with U-statistics. The package contains implementations of nonparametric statistical
    tests for sample homogeneity, group separation, clustering, and classification of 
    multivariate data. The methods have high statistical power and are tailored for data
    in which the dimension L is much larger than sample size n. See Gabriela B. Cybis,
    Marcio Valk and Sílvia RC Lopes (2018) <doi:10.1080/00949655.2017.1374387>, Marcio 
    Valk and Gabriela B. Cybis (2020) <doi:10.1080/10618600.2020.1796398>, Debora Z. Bello, Marcio 
    Valk and Gabriela B. Cybis (2021) <arXiv:2106.09115>.
An algorithm for general-purpose unconstrained non-linear optimization.
             The algorithm is of quasi-Newton type with BFGS updating of the inverse
             Hessian and soft line search with a trust region type monitoring of the
             input to the line search algorithm. The interface of 'ucminf' is
             designed for easy interchange with 'optim'.
Comprehensive analysis and forecasting 
             of univariate time series using automatic 
             time series models of many kinds.
             Harvey AC (1989) <doi:10.1017/CBO9781107049994>.
             Pedregal DJ and Young PC (2002) <doi:10.1002/9780470996430>.
             Durbin J and Koopman SJ (2012) <doi:10.1093/acprof:oso/9780199641178.001.0001>.
             Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) <doi:10.1007/978-3-540-71918-2>.
             Gómez V, Maravall A (2000) <doi:10.1002/9781118032978>.
             Pedregal DJ, Trapero JR and Holgado E (2024) <doi:10.1016/j.ijforecast.2023.09.004>.
Changes the column names of the inputted dataset to the correct
    names from the Uniform Crime Report codebook for the "Offenses Known and
    Clearance by Arrest" datasets from 1998-2014.
Provides functions and a Shiny application for downloading,
    analyzing and visualizing datasets from UCSC Xena
    (<http://xena.ucsc.edu/>), which is a collection of UCSC-hosted public
    databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.
Download and explore datasets from UCSC Xena data hubs, which
    are a collection of UCSC-hosted public databases such as TCGA, ICGC,
    TARGET, GTEx, CCLE, and others.  Databases are normalized so they can
    be combined, linked, filtered, explored and downloaded.
The udder quarter infection data set contains infection times of individual cow udder quarters with Corynebacterium bovis (Laevens et al. 1997 <DOI:10.3168/jds.S0022-0302(97)76295-7>). Obviously, the four udder quarters are clustered within a cow, and udder quarters are sampled only approximately monthly, generating interval-censored data. The data set contains both covariates that change within a cow (e.g., front and rear udder quarters) and covariates that change between cows (e.g., parity [the number of previous calvings]). The correlation between udder infection times within a cow also is of interest, because this is a measure of the infectivity of the agent causing the disease. Various models have been applied to address the problem of interdependence for right-censored event times. These models, as applied to this data set, can be found back in the publications found in the reference list.
This natural language processing toolkit provides language-agnostic
    'tokenization', 'parts of speech tagging', 'lemmatization' and 'dependency
    parsing' of raw text. Next to text parsing, the package also allows you to train
    annotation models based on data of 'treebanks' in 'CoNLL-U' format as provided
    at <https://universaldependencies.org/format.html>. The techniques are explained
    in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0
    with UDPipe', available at <doi:10.18653/v1/K17-3009>. 
    The toolkit also contains functionalities for commonly used data manipulations on texts 
    which are enriched with the output of the parser. Namely functionalities and algorithms 
    for collocations, token co-occurrence, document term matrix handling, 
    term frequency inverse document frequency calculations,
    information retrieval metrics (Okapi BM25), handling of multi-word expressions,
    keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) 
    sentiment scoring and semantic similarity analysis.
Provides simple bindings to Unidata's udunits library.
Uniform Error Index is the weighted average of different error measures. Uniform Error Index utilizes output from different error function and gives more robust and stable error values. This package has been developed to compute Uniform Error Index from ten different loss function like Error Square, Square of Square Error, Quasi Likelihood Error, LogR-Square, Absolute Error, Absolute Square Error etc. The weights are determined using Principal Component Analysis (PCA) algorithm of Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.
Enables the user to calculate Value at Risk (VaR) and Expected 
    Shortfall (ES) by means of various parametric and semiparametric 
    GARCH-type models. For the latter the estimation of the nonparametric scale
    function is carried out by means of a data-driven smoothing approach. Model
    quality, in terms of forecasting VaR and ES, can be assessed by means of 
    various backtesting methods such as the traffic light test for VaR and a 
    newly developed traffic light test for ES. The approaches implemented in 
    this package are described in e.g. Feng Y., Beran J., Letmathe S. and 
    Ghosh S. (2020) <https://ideas.repec.org/p/pdn/ciepap/137.html> as well as 
    Letmathe S., Feng Y. and Uhde A. (2021) 
    <https://ideas.repec.org/p/pdn/ciepap/141.html>.
This is a new version of the 'userfriendlyscience' package,
    which has grown a bit unwieldy. Therefore, distinct functionalities
    are being 'consciously uncoupled' into different packages. This package
    contains the general-purpose tools and utilities (see the
    'behaviorchange' package, the 'rosetta' package, and the
    soon-to-be-released 'scd' package for other functionality), and
    is the most direct 'successor' of the original 'userfriendlyscience' package.
    For example, this package contains a number of basic functions to create
    higher level plots, such as diamond plots, to easily plot sampling
    distributions, to generate confidence intervals, to plan study sample sizes
    for confidence intervals, and to do some basic operations such as
    (dis)attenuate effect size estimates.
An R API providing easy access to a relational database with macroeconomic, 
             financial and development related time series data for Uganda. 
             Overall more than 5000 series at varying frequency (daily, monthly, 
             quarterly, annual in fiscal or calendar years) can be accessed through 
             the API. The data is provided by the Bank of Uganda, 
             the Ugandan Ministry of Finance, Planning and Economic Development,
             the IMF and the World Bank. The database is being updated once a month.
Maximum likelihood estimation of univariate Gaussian Mixture Autoregressive (GMAR),
    Student's t Mixture Autoregressive (StMAR), and Gaussian and Student's t Mixture Autoregressive (G-StMAR) models, 
    quantile residual tests, graphical diagnostics, forecast and simulate from GMAR, StMAR and G-StMAR processes. 
    Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) <doi:10.1111/jtsa.12108>, 
    Mika Meitz, Daniel Preve, Pentti Saikkonen (2023) <doi:10.1080/03610926.2021.1916531>,
    Savi Virolainen (2022) <doi:10.1515/snde-2020-0060>.
Unit-Gompertz density, cumulative distribution, quantile functions and random deviate
   	generation of the unit-Gompertz distribution. In addition, there are a function for fitting the 
   	Generalized Additive Models for Location, Scale and Shape.
Run a Gibbs sampler for hurdle models to analyze data showing an excess of zeros, which is common in zero-inflated count and semi-continuous models. The package includes the hurdle model under Gaussian, Gamma, inverse Gaussian, Weibull, Exponential, Beta, Poisson, negative binomial, logarithmic, Bell, generalized Poisson, and binomial distributional assumptions. The models described in Ganjali et al. (2024).
Construct a Hidden Markov Model with states learnt by unsupervised classification.
Implements functions to derive uncertainty intervals for (i) regression (linear and probit) parameters when outcome is missing not at random (non-ignorable missingness) introduced in Genbaeck, M., Stanghellini, E., de Luna, X. (2015) <doi:10.1007/s00362-014-0610-x> and Genbaeck, M., Ng, N., Stanghellini, E., de Luna, X. (2018) <doi:10.1007/s10433-017-0448-x>; and (ii) double robust and outcome regression estimators of average causal effects (on the treated) with possibly unobserved confounding introduced in Genbaeck, M., de Luna, X. (2018) <doi:10.1111/biom.13001>.
A set of custom 'R' 'Markdown' templates for documents and
   presentations with the University of Illinois at Urbana-Champaign (UIUC)
   color scheme and identity standards.
Functions for converting between UK and US spellings of English words.
Process UK Biobank COVID-19 test result data for susceptibility, severity and mortality analyses, perform potential non-genetic COVID-19 risk factor and co-morbidity association tests. Wang et al. (2021) <doi:10.5281/zenodo.5174381>.
Full listing of UK baby names occurring more than three times per year between 1974 and 2020, and rankings of baby name popularity by decade from 1904 to 1994.
A suite of utilities for working with the UK Biobank 
    <https://www.ukbiobank.ac.uk/> Nuclear Magnetic Resonance spectroscopy (NMR) 
    metabolomics data <https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=220>. 
    Includes functions for extracting biomarkers from decoded UK Biobank field 
    data, removing unwanted technical variation from biomarker concentrations,
    computing an extended set of lipid, fatty acid, and cholesterol fractions, 
    and for re-deriving composite biomarkers and ratios after adjusting data 
    for unwanted biological variation. For further details on methods see
    Ritchie SC et al. Sci Data (2023) <doi:10.1038/s41597-023-01949-y>.
A set of tools to create a UK Biobank <http://www.ukbiobank.ac.uk/> dataset from a UKB fileset (.tab, .r, .html),
    visualize primary demographic data for a sample subset, query ICD diagnoses,
    retrieve genetic metadata, read and write standard file formats for genetic analyses.
Functions to implement the methods of the Flood Estimation Handbook (FEH), associated updates and the revitalised flood hydrograph model (ReFH). Currently the package uses NRFA peak flow dataset version 13. Aside from FEH functionality, further hydrological functions are available. Most of the methods implemented in this package are described in one or more of the following: "Flood Estimation Handbook", Centre for Ecology & Hydrology (1999, ISBN:0 948540 94 X). "Flood Estimation Handbook Supplementary Report No. 1", Kjeldsen (2007, ISBN:0 903741 15 7). "Regional Frequency Analysis - an approach based on L-moments", Hosking & Wallis (1997, ISBN: 978 0 521 01940 8). "Proposal of the extreme rank plot for extreme value analysis: with an emphasis on flood frequency studies", Hammond (2019, <doi:10.2166/nh.2019.157>). "Making better use of local data in flood frequency estimation", Environment Agency (2017, ISBN: 978 1 84911 387 8). "Sampling uncertainty of UK design flood estimation" , Hammond (2021, <doi:10.2166/nh.2021.059>). "Improving the FEH statistical procedures for flood frequency estimation", Environment Agency (2008, ISBN: 978 1 84432 920 5). "Low flow estimation in the United Kingdom", Institute of Hydrology (1992, ISBN 0 948540 45 1). Wallingford HydroSolutions, (2016, <http://software.hydrosolutions.co.uk/winfap4/Urban-Adjustment-Procedure-Technical-Note.pdf>). Data from the UK National River Flow Archive (<https://nrfa.ceh.ac.uk/>, terms and conditions: <https://nrfa.ceh.ac.uk/costs-terms-and-conditions>).
Allows users to access live UK energy market information via various APIs.
A time series of the national grid demand (high-voltage electric power transmission network) in the UK since 2011.
Access data from Land Registry Open Data
    <http://landregistry.data.gov.uk/> through 'SPARQL' queries. 'uklr'
    supports the house price index, transaction and price paid data.
Downloads data from the 'UK Police' public data API, 
    the full docs of which are available at <https://data.police.uk/docs/>. 
    Includes data on police forces and police force areas, crime reports, 
    and the use of stop-and-search powers.
Extracts coordinates of an event location from text based on dictionaries of landmarks, roads, and areas. Only returns the location of an event of interest and ignores other location references; for example, if determining the location of a road traffic crash from the text "crash near [location 1] heading towards [location 2]", only the coordinates of "location 1" would be returned. Moreover, accounts for differences in spelling between how a user references a location and how a location is captured in location dictionaries.
Universally unique identifiers ('UUIDs') can be sub-optimal for many
  uses-cases because they are not the most character efficient way of encoding
  128 bits of randomness; v1/v2 versions are impractical in many environments,
  as they require access to a unique, stable MAC address; v3/v5 versions require
  a unique seed and produce randomly distributed IDs, which can cause fragmentation
  in many data structures; v4 provides no other information than randomness which
  can cause fragmentation in many data structures. Providing an alternative, 'ULIDs'
  (<https://github.com/ulid/spec>) have 128-bit compatibility with 'UUID', 1.21e+24
  unique 'ULIDs' per millisecond, support standard (text) sorting, canonically encoded
  as a 26 character string, as opposed to the 36 character 'UUID', use 'base32'
  encoding for better efficiency and readability (5 bits per character), are case
  insensitive, have no special characters (i.e. are 'URL' safe) and have a monotonic
  sort order (correctly detects and handles the same millisecond).
A tool to define rare biosphere. 'ulrb' solves the problem of the 
    definition of rarity by replacing arbitrary thresholds with an unsupervised machine 
    learning algorithm (partitioning around medoids, or k-medoids). This algorithm 
    works for any type of microbiome data, provided there is a species abundance table.
    For validation of this method to different species abundance tables see Pascoal et al, 2024 (in peer-review).
    This method also works for non-microbiome data.
A generic reference Bayesian analysis of unidimensional mixture distributions obtained by a location-scale parameterisation of the model is implemented. The including functions simulate and summarize posterior samples for location-scale mixture models using a weakly informative prior. There is no need to define priors for scale-location parameters except two hyperparameters in which are associated with a Dirichlet prior for weights and a simplex.
Plots traced ultrasound tongue imaging data according to a polar coordinate system. 
    There is currently support for plotting means and standard deviations of each category's trace;
    Smoothing Splines Analysis of Variance (SSANOVA) could be implemented as well.  The origin of the 
    polar coordinates may be defined manually or automatically determined based on different 
    algorithms.
    Currently 'ultrapolaRplot' supports ultrasound tongue imaging trace data from 'UltraTrace' 
    (<https://github.com/SwatPhonLab/UltraTrace>). 'UltraTrace' is capable of importing data from 
    Articulate Instruments AAA.
    'read_textgrid.R' is required for opening TextGrids to determine category and alignment
    information of ultrasound traces.
Uniform manifold approximation and projection is a technique
    for dimension reduction. The algorithm was described by McInnes and
    Healy (2018) in <arXiv:1802.03426>. This package provides an interface
    for two implementations. One is written from scratch, including components
    for nearest-neighbor search and for embedding. The second implementation
    is a wrapper for 'python' package 'umap-learn' (requires separate
    installation, see vignette for more details).
By gaining the property of emergence through self-organization, the enhancement of SOMs(self organizing maps) is called Emergent SOM (ESOM). The result of the projection by ESOM is a grid of neurons which can be visualised as a three dimensional landscape in form of the Umatrix. Further details can be found in the referenced publications (see url). This package offers tools for calculating and visualising the ESOM as well as Umatrix, Pmatrix and UStarMatrix. All the functionality is also available through graphical user interfaces implemented in 'shiny'. Based on the recognized data structures, the method can be used to generate new data.
A convenient wrapper for the UM-Bridge protocol. UM-Bridge is a protocol designed for coupling uncertainty quantification (or statistical / optimization) software to numerical models. A model is represented as a mathematical function with optional support for derivatives via Jacobian actions etc.
Tools for analyzing sequencing data containing unique
    molecular identifiers generated by 'UMIErrorCorrect'
    (<https://github.com/stahlberggroup/umierrorcorrect>).
Calculates one-sample unbiased central moment estimates and 
    two-sample pooled estimates up to 6th order, including estimates of 
    powers and products of central moments. Provides the machinery for 
    obtaining unbiased central moment estimators beyond 6th order by generating
    expressions for expectations of raw sample moments and their powers and 
    products. 
    Gerlovina and Hubbard (2019) <doi:10.1080/25742558.2019.1701917>.
Does uniformly most powerful (UMP) and uniformly most
    powerful unbiased (UMPU) tests.  At present only distribution implemented
    is binomial distribution.  Also does fuzzy tests and confidence intervals
    (following Geyer and Meeden, 2005, <doi:10.1214/088342305000000340>)
    for the binomial
    distribution (one-tailed procedures based on UMP test and two-tailed
    procedures based on UMPU test).
The Ultimate Microrray Prediction, Reality and Inference
  Engine (UMPIRE) is a package to facilitate the simulation of realistic
  microarray data sets with links to associated outcomes. See Zhang and
  Coombes (2012) <doi:10.1186/1471-2105-13-S13-S1>. Version 2.0 adds the
  ability to simulate realistic mixed-typed clinical data.
Quickly create, run, and report structural equation models, and twin models.
    See '?umx' for help, and umx_open_CRAN_page("umx") for NEWS.
    Timothy C. Bates, Michael C. Neale, Hermine H. Maes, (2019). umx: A library for Structural Equation and Twin Modelling in R.
    Twin Research and Human Genetics, 22, 27-41. <doi:10.1017/thg.2019.2>.
Una herramienta rápida y consistente para la disposición de microdatos
    y la visualización de las cifras y estadísticas oficiales de la Universidad
    Nacional de Colombia <https://unal.edu.co>. Contiene una biblioteca de
    funciones gráficas, tanto estáticas como interactivas, que ofrece numerosos
    tipos de gráficos con una sintaxis altamente configurable y simple. Entre estos
    encontramos la visualización de tablas HTML, series, gráficos de barras y
    circulares, mapas, etc. Todo lo anterior apoyado en bibliotecas de JavaScript.
    English: A fast and consistent tool for the arrangement of microdata and the
    visualization of official figures and statistics from the National University
    of Colombia <https://unal.edu.co>. It includes a library of graphical functions,
    both static and interactive, offering numerous types of charts with a highly
    configurable and simple syntax. Among these, we find the visualization of HTML
    tables, series, bar and pie charts, maps, etc. It provides the capability to
    transition from the interactive to the dynamic world and from one library to
    another without changing function or syntax.
Top-down and bottom-up algorithms
        for nonparametric function estimation in Gaussian noise using
        Unbalanced Haar wavelets.
If a procedure consists of several stages and there are several models that can be selected for each stage,
	uncertainty of the procedure can be decomposed by stages or models.
	This package includes the ANOVA-based method, the cumulative uncertainty-based method, and the balanced decomposition method.
	Yongdai Kim et al. (2019) <doi:10.1016/j.hydroa.2019.100024> is a related paper which is accessible via the URL below.
Implements the Gaussian method of first and second order, the Kragten numerical method and the Monte Carlo simulation method for uncertainty estimation and analysis.
Provides functions for estimating uncertainty in the number of fatalities in the Uppsala Conflict Data Program (UCDP) data. The package implements a parametric reported-value Gumbel mixture distribution that accounts for the uncertainty in the number of fatalities in the UCDP data. The model is based on information from a survey on UCDP coders and how they view the uncertainty of the number of fatalities from UCDP events. The package provides functions for making random draws of fatalities from the mixture distribution, as well as to estimate percentiles, quantiles, means, and other statistics of the distribution. Full details on the survey and estimation procedure can be found in Vesco et al (2024).
Dataset contains select attributes for each match result since 1949-1950 season for UNC men's basketball team.
Implements Minimum Torsion for portfolio diversification as
    described in Meucci, Attilio (2013) <doi:10.2139/ssrn.2276632>.
Model data with a suspected clustering structure (either in 
    co-variate space, regression space or both) using a Bayesian product model 
    with a logistic regression likelihood. Observations are represented 
    graphically and clusters are formed through various edge removals or 
    additions. Cluster quality is assessed through the log Bayesian evidence of 
    the overall model, which is estimated using either a Sequential Monte Carlo 
    sampler or a suitable transformation of the Bayesian Information Criterion 
    as a fast approximation of the former. The internal Iterated Batch 
    Importance Sampling scheme (Chopin (2002 <doi:10.1093/biomet/89.3.539>)) is 
    made available as a free standing function.
Calculate several understandability metrics of BPMN models. BPMN stands for business process modelling notation and is a language for expressing business processes into business process diagrams. Examples of these understandability metrics are: average connector degree, maximum connector degree, sequentiality, cyclicity, diameter, depth, token split, control flow complexity, connector mismatch, connector heterogeneity, separability, structuredness and cross connectivity. See R documentation and paper on metric implementation included in this package for more information concerning the metrics.
Use syntax inspired by the package 'glue' to extract matched substrings in a more intuitive and compact way than by using standard regular expressions.
Versatile method for ungrouping histograms (binned count data) 
 assuming that counts are Poisson distributed and that the underlying sequence 
 on a fine grid to be estimated is smooth. The method is based on the composite 
 link model and estimation is achieved by maximizing a penalized likelihood. 
 Smooth detailed sequences of counts and rates are so estimated from the binned 
 counts. Ungrouping binned data can be desirable for many reasons: Bins can be 
 too coarse to allow for accurate analysis; comparisons can be hindered when 
 different grouping approaches are used in different histograms; and the last 
 interval is often wide and open-ended and, thus, covers a lot of information 
 in the tail area. Age-at-death distributions grouped in age classes and 
 abridged life tables are examples of binned data. Because of modest assumptions, 
 the approach is suitable for many demographic and epidemiological applications. 
 For a detailed description of the method and applications see 
 Rizzi et al. (2015) <doi:10.1093/aje/kwv020>.
A 'ggplot2' theme and color palettes following
    the United Nations High Commissioner for Refugees (UNHCR) Data Visualization Guidelines recommendations.
Verb-like functions to work with messy data, often derived from 
             spreadsheets or parsed PDF tables. Includes functions for unwrapping 
             values broken up across rows, relocating embedded grouping values, 
             and to annotate meaningful formatting in spreadsheet files.
Implement a shrinkage estimation for the univariate normal mean based on a preliminary test (pretest) estimator. This package also provides the confidence interval based on pivoting the cumulative density function. The methodologies are published in Taketomi et al.(2024) <doi:10.1007/s42081-023-00221-2> and Taketomi et al.(2024-)(under review).
A classification (decision) tree is constructed from survival data with high-dimensional covariates.
 The method is a robust version of the logrank tree, where the variance is stabilized.
 The main function "uni.tree" returns a classification tree for a given survival dataset.
 The inner nodes (splitting criterion) are selected by minimizing the P-value of the two-sample the score tests.
 The decision of declaring terminal nodes (stopping criterion) is the P-value threshold given by an argument (specified by user).
 This tree construction algorithm is proposed by Emura et al. (2021, in review).
Nonparametric estimation of a unimodal or U-shape covariate effect under additive hazards model.
UNIfied Cross-Omics deconvolution (Unico) deconvolves
    standard 2-dimensional bulk matrices of samples by features into a
    3-dimensional tensors representing samples by features by cell types.
    Unico stands out as the first principled model-based deconvolution
    method that is theoretically justified for any heterogeneous genomic
    data. For more details see Chen and Rahmani et al. (2024)
    <doi:10.1101/2024.01.27.577588>.
Data from Unicode 15.1.0 and related utilities.
Most universities use specific color combinations to express their unique brand identity. The 'unicol' package provides the colors and color palettes of various universities for easy plotting and printing in R. We collect and provide a diverse range of color palettes for creating scientific visualizations.
Testing whether two discrete variables have a functional
  relationship under null distributions where the two variables are
  statistically independent with fixed marginal counts.
  The fast enumeration algorithm was based on (Nguyen et al. 2020) <doi:10.24963/ijcai.2020/372>.
Uniform sampling of Directed Acyclic Graphs (DAG) using exact
 enumeration by relating each DAG to a sequence of outpoints (nodes with no
 incoming edges) and then to a composition of integers as suggested by
 Kuipers, J. and Moffa, G. (2015) <doi:10.1007/s11222-013-9428-y>.
Probability functions, family for glm() and Stan code for working with the unifed distribution (Quijano Xacur, 2019; <doi:10.1186/s40488-019-0102-6>).
In many phase I trials, the design goal is to find the dose associated with a certain target toxicity rate. In some trials, the goal can be to find the dose with a certain weighted sum of rates of various toxicity grades. For others, the goal is to find the dose with a certain mean value of a continuous response. This package provides the setup and calculations needed to run a dose-finding trial with non-binary endpoints and performs simulations to assess design’s operating characteristics under various scenarios. Three dose finding designs are included in this package: unified phase I design (Ivanova et al. (2009) <doi:10.1111/j.1541-0420.2008.01045.x>), Quasi-CRM/Robust-Quasi-CRM (Yuan et al. (2007) <doi:10.1111/j.1541-0420.2006.00666.x>, Pan et al. (2014) <doi:10.1371/journal.pone.0098147>) and generalized BOIN design (Mu et al. (2018) <doi:10.1111/rssc.12263>). The toxicity endpoints can be handled with these functions including equivalent toxicity score (ETS), total toxicity burden (TTB), general continuous toxicity endpoints, with incorporating ordinal grade toxicity information into dose-finding procedure. These functions allow customization of design characteristics to vary sample size, cohort sizes, target dose-limiting toxicity (DLT) rates, discrete or continuous toxicity score, and incorporate safety and/or stopping rules.
Functions for the creation and manipulation of scenes and objects
    within the 'Unity' '3D' video game engine (<https://unity.com/>). Specific
    focuses include the creation and import of terrain data and 'GameObjects' as
    well as scene management.
Uniform sampling on various geometric shapes, such as
    spheres, ellipsoids, simplices.
A unified R graphics backend. Render R graphics fast and easy to many common file formats.
    Provides a thread safe 'C' interface for asynchronous rendering of R graphics.
Perform L1 or L2 isotonic and unimodal regression on 1D weighted or unweighted input vector and isotonic regression on 2D weighted or unweighted input vector. It also performs L infinity isotonic and unimodal regression on 1D unweighted input vector. Reference: Quentin F. Stout (2008) <doi:10.1016/j.csda.2008.08.005>. Spouge, J., Wan, H. & Wilbur, W.(2003) <doi:10.1023/A:1023901806339>. Q.F. Stout (2013) <doi:10.1007/s00453-012-9628-4>.
Define and use graphical elements of corporate design manuals in R. The 'unikn' package provides color functions (by defining dedicated colors and color palettes, and commands for finding, changing, viewing, and using them) and styled text elements (e.g., for marking, underlining, or plotting colored titles). The pre-defined range of colors and text decoration functions is based on the corporate design of the University of Konstanz <https://www.uni-konstanz.de/>, but can be adapted and extended for other purposes or institutions.
Implementation of "light" stemmers for French, German, Italian, Spanish, Portuguese, Finnish, Swedish. 
  They are based on the same work as the "light" stemmers found in 'SolR' <https://lucene.apache.org/solr/> or 'ElasticSearch' <https://www.elastic.co/fr/products/elasticsearch>. 
  A "light" stemmer consists in removing inflections only for noun and adjectives. 
  Indexing verbs for these languages is not of primary importance compared to nouns and adjectives. 
  The stemming procedure for French is described in (Savoy, 1999) <doi:10.1002/(SICI)1097-4571(1999)50:10%3C944::AID-ASI9%3E3.3.CO;2-H>.
Connect to Uniprot <https://www.uniprot.org/> to retrieve information about proteins using their accession number such information could be name or taxonomy information, For detailed information kindly read the publication <https://www.sciencedirect.com/science/article/pii/S1874391919303859>.
For each string in a set of strings, determine a unique tag that is a substring of fixed size k unique to that string, if it has one. If no such unique substring exists, the least frequent substring is used. If multiple unique substrings exist, the lexicographically smallest substring is used. This lexicographically smallest substring of size k is called the "UniqTag" of that string.
Univariate spline regression. It is possible to add the shape constraint of unimodality and predefined or
	self-defined penalties on the B-spline coefficients.
Provides the ability to read 'Unisens' data into R. 'Unisens' is a universal data format for multi sensor data.
Any package (subsequently called 'target package') is enabled to provide its users an easily accessible, user-friendly and human readable text file where key=value pairs (used by functions defined in the target package) can be saved. This settings file lives in a location defined by the user of the target package, and its user-defined values remain unchanged even when the author of the target package is introducing or deleting keys, or when the target package is updated or re-installed.
The uc.check() function checks whether the roots of a given polynomial lie outside the Unit circle. You can also easily draw an unit circle.
United is a software tool which can be downloaded at the following
    website <http://www.schroepl.net/pbm/software/united/>. In general, it is
    a virtual manager game for football teams. This package contains helpful
    functions for determining an optimal formation for a virtual match in
    United. E.g. knowing that the opponent has a strong defensive it is
    advisable to beat him in the midfield. Furthermore, this package contains
    functions for computing the optimal usage of hardness in a game.
Simplifies regression tests by comparing objects produced by test
    code with earlier versions of those same objects.  If objects are unchanged
    the tests pass, otherwise execution stops with error details.  If in
    interactive mode, tests can be reviewed through the provided interactive
    environment.
A collection of parametric quantile regression models for bounded data. At present, the package provides 13 parametric quantile regression models. It can specify regression structure for any quantile and shape parameters. It also provides several S3 methods to extract information from fitted model, such as residual analysis, prediction, plotting, and model comparison. For more computation efficient the [dpqr]'s, likelihood, score and hessian functions are written in C++. For further details see Mazucheli et. al (2022) <doi:10.1016/j.cmpb.2022.106816>.
Support for measurement units in R vectors, matrices
    and arrays: automatic propagation, conversion, derivation
    and simplification of units; raising errors in case of unit
    incompatibility. Compatible with the POSIXct, Date and difftime 
    classes. Uses the UNIDATA udunits library and unit database for 
    unit compatibility checking and conversion.
    Documentation about 'units' is provided in the paper by Pebesma, Mailund &
    Hiebert (2016, <doi:10.32614/RJ-2016-061>), included in this package as a
    vignette; see 'citation("units")' for details.
A test to understand the stability of the underlying 
    stochastic data. Helps the user’s understand whether the random variable under 
    consideration is stationary or non-stationary without any manual interpretation 
    of the results. It further ensures to check all the prerequisites and assumptions which 
    are underlying the unit root test statistics and if the underlying data is found to be non-stationary in all
    the 4 lags the function diagnoses the input data and returns with an optimised solution on the same.
Concise TAP <http://testanything.org/> compliant unit testing package. Authored tests can be run using CMD check with minimal implementation overhead.
Assess essential unidimensionality using external validity
    information  using the procedure proposed by Ferrando & Lorenzo-Seva
    (2019) <doi:10.1177/0013164418824755>. Provides two indices for assessing
    differential  and incremental validity, both based on a second-order 
    modelling schema for the general factor.
User-friendly maximum likelihood estimation (Fisher (1921) 
    <doi:10.1098/rsta.1922.0009>) of univariate densities.
Algorithms for checking the accuracy of a clustering result with known classes, computing cluster validity indices, and generating plots for comparing them.
    The package is compatible with K-means, fuzzy C means, EM clustering, and hierarchical clustering (single, average, and complete linkage).
    The details of the indices in this package can be found in:
    C. Alok. (2010) <https://hdl.handle.net/10603/93443>,
    J. C. Bezdek, M. Moshtaghi, T. Runkler, C. Leckie (2016) <doi:10.1109/TFUZZ.2016.2540063>,
    T. Calinski, J. Harabasz (1974) <doi:10.1080/03610927408827101>,
    C. H. Chou, M. C. Su, E. Lai (2004) <doi:10.1007/s10044-004-0218-1>,
    D. L. Davies, D. W. Bouldin (1979) <doi:10.1109/TPAMI.1979.4766909>,
    J. C. Dunn (1973) <doi:10.1080/01969727308546046>,
    F. Haouas, Z. Ben Dhiaf, A. Hammouda, B. Solaiman (2017) <doi:10.1109/FUZZ-IEEE.2017.8015651>,
    M. Kim, R. S. Ramakrishna (2005) <doi:10.1016/j.patrec.2005.04.007>,
    S. H. Kwon (1998) <doi:10.1049/EL:19981523>,
    S. H. Kwon, J. Kim, S. H. Son (2021) <doi:10.1049/ell2.12249>,
    G. W. Miligan (1980) <doi:10.1007/BF02293907>,
    M. K. Pakhira, S. Bandyopadhyay, U. Maulik (2004) <doi:10.1016/j.patcog.2003.06.005>,
    M. Popescu, J. C. Bezdek, T. C. Havens, J. M. Keller (2013) <doi:10.1109/TSMCB.2012.2205679>,
    S. Saitta, B. Raphael, I. Smith (2007) <doi:10.1007/978-3-540-73499-4_14>,
    A. Starczewski (2017) <doi:10.1007/s10044-015-0525-8>,
    Y. Tang, F. Sun, Z. Sun (2005) <doi:10.1109/ACC.2005.1470111>,
    N. Wiroonsri (2024) <doi:10.1016/j.patcog.2023.109910>,
    N. Wiroonsri, O. Preedasawakul (2023) <arXiv:2308.14785>,
    C. H. Wu, C. S. Ouyang, L. W. Chen, L. W. Lu (2015) <doi:10.1109/TFUZZ.2014.2322495> and
    X. Xie, G. Beni (1991) <doi:10.1109/34.85677>.
Provides S3 generic methods and some default implementations
    for Bayesian analyses that generate Markov Chain Monte Carlo (MCMC)
    samples.  The purpose of 'universals' is to reduce package
    dependencies and conflicts.  The 'nlist' package implements many of
    the methods for its 'nlist' class.
Well known outlier detection techniques in the univariate case. Methods to deal with skewed distribution are included too. The Hidiroglou-Berthelot (1986) method to search for outliers in ratios of historical data is implemented as well. When available, survey weights can be used in outliers detection.
Pseudo-random number generation of 17 univariate distributions proposed by Demirtas. (2005) <DOI:10.22237/jmasm/1114907220>.
Bindings to system utilities found in most Unix systems such as
    POSIX functions which are not part of the Standard C Library.
Separate a data frame in two based on key columns. The function
 unjoin() provides an inside-out version of a nested data frame. This is used
 to identify duplication and normalize it (in the database sense) by linking
 two tables with the redundancy removed. This is a basic requirement for
 detecting topology within spatial structures that has motivated the need for
 this package as a building block for workflows within more applied projects.
Fits hierarchical models of animal abundance and occurrence to data collected using survey methods such as point counts, site occupancy sampling, distance sampling, removal sampling, and double observer sampling. Parameters governing the state and observation processes can be modeled as functions of covariates. References: Kellner et al. (2023) <doi:10.1111/2041-210X.14123>, Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.
Tools for fitting and assessing Bayesian multilevel regression 
  models that account for unmeasured confounders.
Fast flattening of hierarchical data structures (e.g. JSON, XML)
             into data.frames with a flexible spec language.
Assess the significance of identified clusters and estimates the true number of clusters by comparing the explained variation due to the clustering from the original data to that produced by clustering a unimodal reference distribution which preserves the covariance structure in the data. The reference distribution is generated using kernel density estimation and a Gaussian copula framework. A dimension reduction strategy and sparse covariance estimation optimize this method for the high-dimensional, low-sample size setting. This method is described in Helgeson, Vock, and Bair (2021) <doi:10.1111/biom.13376>.
Tools for converting data from complex or irregular layouts to a
    columnar structure.  For example, tables with multilevel column or row
    headers, or spreadsheets.  Header and data cells are selected by their
    contents and position, as well as formatting and comments where available,
    and are associated with one other by their proximity in given directions.
    Functions for data frames and HTML tables are provided.
Provides half-normal plots, reference plots, and Pareto plots
    of effects from an unreplicated experiment, along with various 
    pseudo-standard-error measures, simulated reference distributions, 
    and other tools. Many of these methods are described in 
    Daniel C. (1959) <doi:10.1080/00401706.1959.10489866> and/or
    Lenth R.V. (1989) <doi:10.1080/00401706.1989.10488595>, but some new
    approaches are added and integrated in one package.
Wraps the 'unrtf' utility <https://www.gnu.org/software/unrtf/> to 
    extract text from RTF files. Supports document conversion to HTML, LaTeX or
    plain text. Output in HTML is recommended because 'unrtf' has limited 
    support for converting between character encodings.
Automatically converts language-specific verbal information, e.g., "1st half of the 19th century," to its standardized numerical counterparts, e.g., "1801-01-01/1850-12-31." It follows the recommendations of the 'MIDAS' ('Marburger Informations-, Dokumentations- und Administrations-System'), see <doi:10.11588/artdok.00003770>.
Performs a test for second-order stationarity of time series based
    on unsystematic sub-samples.
Hubbell's Unified Neutral Theory of Biodiversity.
Calculates a Mahalanobis distance for every row of a set of
    outcome variables (Mahalanobis, 1936
    <doi:10.1007/s13171-019-00164-5>). The conditional Mahalanobis
    distance is calculated using a conditional covariance matrix (i.e., a
    covariance matrix of the outcome variables after controlling for a set
    of predictors). Plotting the output of the cond_maha() function can
    help identify which elements of a profile are unusual after
    controlling for the predictors.
Historical voting data of the United Nations General Assembly. This
    includes votes for each country in each roll call, as well as descriptions and
    topic classifications for each vote.
We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance not pay attention to the accuracy of prediction, but focus on model selection uncertainty and providing more information of the model selection results. And to estimate the model measures, we propose an simplify and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance focuses on model selection uncertainty and providing more information of the model selection results. To estimate the model uncertainty variance, we propose an simplified and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. Please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403.<DOI:10.1111/biom.13024> for more information.
Displays percentage changes by height and absolute changes by area for up to three nested or non-nested levels. The plots visualise changes in indices and markets, showing how the changes for sectors or for individual components contribute to the overall change. Data can be classified by up to three levels of grouping variables in a layered, hierarchical plot. Each level can be ordered in several ways including by baseline, by percentage change, and by absolute change. The vignettes give examples.
Provides an extension to the Partial Credit Model and Generalized Partial Credit Models which allows for an additional person parameter that characterizes the uncertainty of the person. The method was originally proposed by Tutz and Schauberger (2020) <doi:10.1177/0146621620920932>.
When a package is loaded, the source repository is checked for
    new versions and a message is shown in the console indicating whether the
    package is out of date.
When updating major or minor R versions all packages should
    be re-installed. The utilities in this package assist in getting a
    user up-and-running again by installing all previously installed R
    packages. The package uses 'renv' to install; immediately replenishing
    your 'renv' package cache.
Implements empirical Bayes approaches to genotype
       polyploids from next generation sequencing data while
       accounting for allele bias, overdispersion, and sequencing
       error. The main functions are flexdog() and multidog(), 
       which allow the specification
       of many different genotype distributions. Also provided are functions to
       simulate genotypes, rgeno(), and read-counts, rflexdog(), as well as
       functions to calculate oracle genotyping error rates, oracle_mis(), and
       correlation with the true genotypes, oracle_cor(). These latter two
       functions are useful for read depth calculations. Run
       browseVignettes(package = "updog") in R for example usage. See
       Gerard et al. (2018) <doi:10.1534/genetics.118.301468> and
       Gerard and Ferrao (2020) <doi:10.1093/bioinformatics/btz852> for details 
       on the implemented methods.
Provides an algorithm to detect and characterize disturbances (start, end dates, intensity) that can occur at different hierarchical levels by studying the dynamics of longitudinal observations at the unit level and group level based on Nadaraya-Watson's smoothing curves, but also a shiny app which allows to visualize the observations and the detected disturbances. Finally the package provides a dataframe mimicking a pig farming system subsected to disturbances simulated according to Le et al.(2022) <doi:10.1016/j.animal.2022.100496>.
Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms described in "Gregor Zens, Sylvia Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers – Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association <doi:10.1080/01621459.2023.2259030>".
Up-and-Down is the most popular design approach for dose-finding, but has been severely under-served by the statistical computing community. This is the first package to address Up-and-Down's needs. For a recent methodological tutorial on Up-and-Down, see Oron et al. (2022) <doi:10.1097/ALN.0000000000004282>.
'UpSet.js' is a re-implementation of 'UpSetR' to create interactive set visualizations for more than three sets.
  This is a 'htmlwidget' wrapper around the 'JavaScript' library 'UpSet.js'.
Creates visualizations of intersecting sets using a novel matrix
    design, along with visualizations of several common set, element and attribute
    related tasks (Conway 2017) <doi:10.1093/bioinformatics/btx364>.
Using matrix layout to visualize the unique, common, or individual contribution of each predictor (or matrix of predictors) towards explained variation on canonical analysis. These contributions were derived from variance partitioning analysis (VPA) and hierarchical partitioning (HP), applying the algorithm of Lai J., Zou Y., Zhang J., Peres-Neto P. (2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution, 13: 782-788 <doi:10.1111/2041-210X.13800>.
Core functions necessary for using The Globe and Mail's R data journalism template, 'startr', along with utilities for day-to-day data journalism tasks, such as reading and writing files, producing graphics and cleaning up datasets.
Variance approximations for the 
    Horvitz-Thompson total estimator in Unequal Probability Sampling
    using only first-order inclusion probabilities. 
    See Matei and Tillé (2005) and Haziza, Mecatti and Rao (2008) for details.
'Elasticsearch' is an open-source, distributed, document-based datastore
    (<https://www.elastic.co/products/elasticsearch>).
    It provides an 'HTTP' 'API' for querying the database and extracting datasets, but that
    'API' was not designed for common data science workflows like pulling large batches of
    records and normalizing those documents into a data frame that can be used as a training
    dataset for statistical models. 'uptasticsearch' provides an interface for 'Elasticsearch'
    that is explicitly designed to make these data science workflows easy and fun.
Provide a set of wrappers to call all the endpoints of UptimeRobot API
  which includes various kind of ping, keep-alive and speed tests.
  See <https://uptimerobot.com/> for more information.
Provides researchers with a simple set of diagnostic tools for monitoring the progress and reliability of raters conducting content coding tasks. Goehring (2024) <https://bengoehring.github.io/improving-content-analysis-tools-for-working-with-undergraduate-research-assistants.pdf> argues that supervisors---especially supervisors of small teams---should utilize computational tools to monitor reliability in real time. As such, this package provides easy-to-use functions for calculating inter-rater reliability statistics and measuring the reliability of one coder compared to the rest of the team.
Calculate unified measures
  that quantify the effect of a covariate on a binary dependent variable
  (e.g., for meta-analyses).
  This can be particularly important
  if the estimation results are obtained
  with different models/estimators
  (e.g., linear probability model, logit, probit, ...)
  and/or with different transformations of the explanatory variable of interest
  (e.g., linear, quadratic, interval-coded, ...).
  The calculated unified measures are:
  (a) semi-elasticities of linear, quadratic, or interval-coded covariates and
  (b) effects of linear, quadratic, interval-coded, or categorical covariates
  when a linear or quadratic covariate changes between distinct intervals,
  the reference category of a categorical variable
  or the reference interval of an interval-coded variable needs to be changed,
  or some categories of a categorical covariate
  or some intervals of an interval-coded covariate need to be grouped together.
  Approximate standard errors of the unified measures are also calculated.
  All methods that are implemented in this package 
  are described in the 'vignette'  
  "Extracting and Unifying Semi-Elasticities and Effect Sizes
  from Studies with Binary Dependent Variables"
  that is included in this package.
Unit root and cointegration tests encountered in applied 
 econometric analysis are implemented.
Provide the URL checking tools available in R 4.1+ as a
    package for earlier versions of R. Also uses concurrent requests so
    can be much faster than the serial versions.
Allows using two URL shortening services, which also provide
    expanding and analytic functions. Specifically developed for 'Bit.ly' (which requires OAuth 2.0) and 'is.gd' (no API key).
A toolkit for all URL-handling needs, including encoding and decoding,
    parsing, parameter extraction and modification. All functions are
    designed to be both fast and entirely vectorised. It is intended to be
    useful for people dealing with web-related datasets, such as server-side
    logs, although may be useful for other situations involving large sets of
    URLs.
Seasonal unit roots and seasonal stability tests.
    P-values based on response surface regressions are available for both tests.
    P-values based on bootstrap are available for seasonal unit root tests.
Conduct unit root tests based on 'EViews' (<https://eviews.com>) routines and report them in tables. 'EViews' (Econometric Views) is a commercial software for econometrics.
S3 classes and methods for manipulation with georeferenced raster data: reading/writing, processing, multi-panel visualization.
A simple interface to the Geographic Header information
  from the "2010 US Census Summary File 2".  The entire Summary File 2
  is described at
  <https://catalog.data.gov/dataset/census-2000-summary-file-2-sf2>,
  but note that this package only provides access to parts of the
  geographic header ('geoheader') of the file.  In particular, only
  the first 101 columns of the geoheader are included and, more
  importantly, only rows with summary levels (SUMLEVs) 010 through 050
  (nation down through county level) are included.  In addition to
  access to (part of) the geoheader, the package also provides a
  decode function that takes a column name and value and, for certain
  columns, returns "the meaning" of that column (i.e., a "SUMLEV"
  value of 40 means "State"); without a value, the decode function
  attempts to describe the column itself.
Updated versions of the 1970's "US State Facts and Figures"
    objects from the 'datasets' package included with R. The new data is
    compiled from a number of sources, primarily from United States Census
    Bureau or the relevant federal agency.
Contains a WGS84 datum map of the USA, which includes all
    Commonwealth and State boundaries & also includes Puerto Rico and the U.S.
    Virgin Islands. This map is a reprojection of the NAD83 datum map from the
    USGS National Map. This package contains a subset of the data included in
    the 'USA.state.boundaries.data' package, which is available in a 'drat'
    repository. To install that data package, please follow the instructions at
    <https://gitlab.com/iembry/usa.state.boundaries.data>.
Intended to be used by the United States Copyright Office Product Management Division Business Analysts. Include algorithms for the United States Copyright Office Product Management Division SR Audit Data dataset. The algorithm takes in the SR Audit Data excel file and reformat the spreadsheet such that the values and variables fit the format of the online database. Support functions in this package include clean_str(), which cleans instances of variable AUDIT_LOG; clean_data_to_excel(), which cleans and output the reorganized SR Audit Data dataset in excel format; clean_data_to_dataframe(), which cleans and stores the reorganized SR Audit Data data set to a data frame; format_from_excel(), which reads in the outputted excel file from the clean_data_to_excel() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys. format_from_dataframe(), which reads in the outputted data frame from the clean_data_to_dataframe() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys; support_function(), which takes in the dictionary outputted either from the format_from_dataframe() or format_from_excel() function and returns the data as a formatted data frame according to the original U.S. Copyright Office SR Audit Data online database. The main function of this package is clean_format_all(), which takes in an excel file and returns the formatted data into a new excel and text file according to the format from the U.S. Copyright Office SR Audit Data online database.
Interface to easily access data via the United States Department of Agriculture (USDA)'s Livestock Mandatory Reporting ('LMR')
  Data API at <https://mpr.datamart.ams.usda.gov/>. The downloaded data can be saved for later off-line use. 
  Also provide relevant information and metadata for each of the input variables needed for sending the data inquiry.
An alternative for downloading various United States Department of
    Agriculture (USDA) data from <https://quickstats.nass.usda.gov/> through R. 
    You must sign up for an API token from the mentioned website in order for 
    this package to work.
Demographic data on the United States at the county and state levels spanning multiple years.
This is a framework that aims to provide methods and tools for assessing the impact of different sources of uncertainties (e.g.positional uncertainty) on performance of species distribution models (SDMs).)
Functions to re-arrange, extract, and work with distances.
Consistent with 'knitr' syntax highlighting, 'usedthese' adds
    a summary table of package & function usage to a Quarto document and
    enables aggregation of usage across a website.
A set of little functions that have been found useful to do little
    odds and ends such as plotting the results of K-means clustering, substituting
    special text characters, viewing parts of a data.frame, constructing formulas
    from text and building design and response matrices.
A set of general functions that I have used in various
    projects and other R packages. Miscellaneous operations on data
    frames, matrices and vectors, ROC and PR statistics.
Code snippets to fit models using the tidymodels framework
    can be easily created for a given data set.
Automate package and project setup tasks that are otherwise
    performed manually. This includes setting up unit testing, test
    coverage, continuous integration, Git, 'GitHub', licenses, 'Rcpp',
    'RStudio' projects, and more.
Compiled and cleaned the county-level estimates of fertilizer, 
    nitrogen and phosphorus, from 1945 to 2012 in United States of America (USA).  The commercial fertilizer data were originally 
    generated by USGS based on the sales data of commercial fertilizer. The manure data were estimated
    based on county-level population data of livestock, poultry, and other animals.
    See the user manual for detailed data sources and cleaning methods.  
    'usfertilizer' utilized the tidyverse to clean the original data and provide 
    user-friendly dataframe. Please note that USGS does not endorse this package. Also data from 1986 is not available for now.
Provides an overview of the demand for natural gas in the US by state and country level. Data source: US Energy Information Administration <https://www.eia.gov/>.
Provides a set of regular time-series datasets, describing the US electricity grid. That includes the total demand and supply, and as well as the demand by energy source (coal, solar, wind, etc.). Source: US Energy Information Administration (Dec 2019) <https://www.eia.gov/>.
Analyzes longitudinal data of HIV decline in patients on antiretroviral therapy using the canonical biphasic exponential decay model (pioneered, for example, by work in Perelson et al. (1997) <doi:10.1038/387188a0>; and Wu and Ding (1999) <doi:10.1111/j.0006-341X.1999.00410.x>). Model fitting and parameter estimation are performed, with additional options to calculate the time to viral suppression. Plotting and summary tools are also provided for fast assessment of model results.
Calculates federal and state income taxes in the United States. It acts as a wrapper 
    to the NBER's TAXSIM 35 (<http://taxsim.nber.org/taxsim35/>) tax simulator. TAXSIM 35 conducts 
    the calculations, while 'usincometaxes' prepares the data for TAXSIM 35, sends the data to 
    TAXSIM 35's server or communicates with the Web Assembly file, retrieves the data, and places it 
    into a data frame. All without the user worrying about this process.
A collection of data sets to accompany the
    textbook "Using R for Introductory Statistics," second
    edition.
Implements mixtures of unrestricted skew-t factor analyzer models via the EM algorithm.
The Universal Scalability Law (Gunther 2007)
    <doi:10.1007/978-3-540-31010-5> is a model to predict hardware and
    software scalability. It uses system capacity as a function of load to
    forecast the scalability for the system.
Obtain United States map data frames of varying region types (e.g. county,
    state). The map data frames include Alaska and Hawaii conveniently placed to the
    bottom left, as they appear in most maps of the US. Convenience functions for plotting
    choropleths, visualizing spatial data, and working with FIPS codes are also provided.
Provides a container for data used by the 'usmap' package.
    The data used by 'usmap' has been extracted into this package so that the
    file size of the 'usmap' package can be reduced greatly. The data in this
    package will be updated roughly once per year as new map data files are
    provided by the US Census Bureau.
Implements various independence tests for discrete, continuous, and infinite-dimensional data. The tests are based on a U-statistic permutation test, the USP of Berrett, Kontoyiannis and Samworth (2020) <arXiv:2001.05513>, and shown to be minimax rate optimal in a wide range of settings. As the permutation principle is used, all tests have exact, non-asymptotic Type I error control at the nominal level.
Centers of population (centroid) data for census areas in the
    United States.
The "ussher" data set is drawn from original chronological textual historic events. Commonly known as James Ussher's Annals of the World, the source text was originally written in Latin in 1650, and published in English translation in 1658.The data are classified by index, year, epoch (or one of the 7 ancient "Ages of the World"), Biblical source book if referenced (rarely), as well as alternate dating mechanisms, such as "Anno Mundi" (age of the world) or "Julian Period" (dates based upon the Julian calendar). Additional file "usshfull" includes variables that may be of further interest to historians, such as Southern Kingdom and Northern Kingdom discrepant dates, and the original amalgamated dating mechanic used by Ussher in the original text. The raw data can also be called using "usshraw", as described in: Ussher, J. (1658) <https://archive.org/stream/AnnalsOfTheWorld/Annals_djvu.txt>.
A set of functions leading to multivariate response L1 regression. 
    This includes functions on computing Euclidean inner products and norms, 
    weighted least squares estimates on multivariate responses, function to compute 
    fitted values and residuals. This package is a companion to the book "U-Statistics,
    M-estimation and Resampling", by Arup Bose and Snigdhansu Chatterjee, to appear 
    in 2017 as part of the "Texts and Readings in Mathematics" (TRIM) series of 
    Hindustan Book Agency and Springer-Verlag.
Make requests from the US Treasury Fiscal Data API endpoints.
Forms a query to submit for US Treasury yield curve data, posting
    this query to the US Treasury web site's data feed service.  By default the
    download includes data yield data for 12 products from January 1, 1990,
    some of which are NA during this span.  The caller can pass parameters to
    limit the query to a certain year or year and month, but the full download
    is not especially large.  The download data from the service is in XML
    format.  The package's main function transforms that XML data into a numeric 
    data frame with treasury product items (constant maturity yields for 12 kinds 
    of bills, notes, and bonds) as columns and dates as row names. The function 
    returns a list which includes an item for this data frame as well as query-related
    values for reference and the update date from the service.
Three functions are provided: first function changes time from local to UTC, other changes from UTC to local and third returns difference between local and UTC. %h+% operator is also provided it adds hours to a time.
Process and print 'UTF-8' encoded international
    text (Unicode). Input, validate, normalize, encode, format, and
    display.
Functions for building customized ready-to-export tables for publication.
Tools for formatting and summarizing data for outcomes research.
A set of functions to aid in the production of visuals in ggplot2.
Construct and plot objective hierarchies and associated value and utility functions. 
  Evaluate the values and utilities and visualize the results as colored objective hierarchies or tables. 
  Visualize uncertainty by plotting median and quantile intervals within the nodes of objective hierarchies.
  Get numerical results of the evaluations in standard R data types for further processing.
Contains functions for simulating phase 12 trial designs described by Chapple and Thall (2019) including simulation and the EFF-TOX trial and simulation and implementation of the U12 trial. Functions for implementing the EFF-TOX trial are found in the package 'Phase123'.
Predicts a smooth and continuous (individual) utility function from utility points, and computes measures of intensity for risk and higher order risk measures (or any other measure computed with user-written function) based on this utility function and its derivatives according to the method introduced in Schneider (2017) <http://hdl.handle.net/21.11130/00-1735-0000-002E-E306-0>.
The most used functions on IPEA (Instituto de Pesquisa Economica Aplicada). 
             Most of functions deal with brazilian names. 
             It can guess the women single's name, extract prepositions or extract the first name.
Multi-label learning strategies and others procedures to support multi-
  label classification in R. The package provides a set of multi-label procedures such as
  sampling methods, transformation strategies, threshold functions, pre-processing 
  techniques and evaluation metrics. A complete overview of the matter can be seen in
  Zhang, M. and Zhou, Z. (2014) <doi:10.1109/TKDE.2013.39> and Gibaja, E. and 
  Ventura, S. (2015) A Tutorial on Multi-label Learning.
Tools for generating and handling of UUIDs (Universally Unique Identifiers).
A tool for checking how much information is disclosed when
    reporting summary statistics.
A method for estimating log-normalizing constants (or free
        energies) and expectations from multiple distributions (such as
        multiple generalized ensembles).
An implementation of the Uniform Manifold Approximation and
    Projection dimensionality reduction by McInnes et al. (2018)
    <doi:10.48550/arXiv.1802.03426>. It also provides means to transform new data and
    to carry out supervised dimensionality reduction. An implementation of
    the related LargeVis method of Tang et al. (2016) <doi:10.48550/arXiv.1602.00370>
    is also provided. This is a complete re-implementation in R (and C++,
    via the 'Rcpp' package): no Python installation is required. See the
    uwot website (<https://github.com/jlmelville/uwot>) for more
    documentation and examples.
Provides convenience functions for user experience research
    with an emphasis on quantitative user experience testing and reporting.
    The functions are designed to translate statistical approaches to 
    applied user experience research.
An R interface to V8 <https://v8.dev>: Google's open source JavaScript
    and WebAssembly engine. This package can be compiled either with V8 version 6 
    and up or NodeJS when built as a shared library.
Various semiparametric and nonparametric statistical tools for
    immune correlates analysis of vaccine clinical trial data. This includes
    calculation of summary statistics and estimation of risk, vaccine efficacy,
    controlled effects (controlled risk and controlled vaccine efficacy), and
    mediation effects (natural direct effect, natural indirect effect,
    proportion mediated). See Gilbert P, Fong Y, Kenny A, and Carone, M (2022)
    <doi:10.1093/biostatistics/kxac024> and Fay MP and Follmann DA (2023)
    <doi:10.48550/arXiv.2208.06465>.
A method to visualize pharmacometric analyses which are impacted by
    covariate effects. Variability-aligned covariate harmonized-effects
    and time-transformation equivalent ('vachette') facilitates intuitive
    overlays of data and model predictions, allowing for comprehensive
    comparison without dilution effects. 'vachette' improves upon previous
    methods Lommerse et al. (2021) <doi:10.1002/psp4.12679>, enabling its
    application to all pharmacometric models and enhancing Visual Predictive
    Checks (VPC) by integrating data into cohesive plots that can highlight
    model misspecification.
An implementation of three procedures developed by 
    John Tukey: FUNOP (FUll NOrmal Plot), FUNOR-FUNOM 
    (FUll NOrmal Rejection-FUll NOrmal Modification), and vacuum cleaner. 
    Combined, they provide a way to identify, treat, and analyze outliers 
    in two-way (i.e., contingency) tables, as described in his 
    landmark paper "The Future of Data Analysis", Tukey, John W. (1962) 
    <https://www.jstor.org/stable/2237638>.
A lexicon and rule-based sentiment analysis tool that is specifically 
    attuned to sentiments expressed in social media, and works well on texts from other 
    domains. Hutto & Gilbert (2014) <https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8109/8122>.
Non-Domestic VAERS vaccine data for 01/01/2016 - 06/14/2016. If
    you want to explore the full VAERS data for 1990 - Present (data, symptoms,
    and vaccines), then check out the 'vaersND' package from the URL below. The
    URL and BugReports below correspond to the 'vaersND' package, of which
    'vaersNDvax' is a small subset (2016 only). 'vaersND' is not hosted on CRAN
    due to the large size of the data set. To install the Suggested 'vaers' and
    'vaersND' packages, use the following R code:
    'devtools::install_git("https://gitlab.com/iembry/vaers.git",
    build_vignettes = TRUE)' and
    'devtools::install_git("https://gitlab.com/iembry/vaersND.git",
    build_vignettes = TRUE)'. "VAERS is a national vaccine safety
    surveillance program co-sponsored by the US Centers for Disease Control and
    Prevention (CDC) and the US Food and Drug Administration (FDA). VAERS is a
    post-marketing safety surveillance program, collecting information about
    adverse events (possible side effects) that occur after the administration
    of vaccines licensed for use in the United States." For more information
    about the data, visit <https://vaers.hhs.gov/index>. For information about
    vaccination/immunization hazards, visit
    <http://www.questionuniverse.com/rethink.html/#vaccine>.
US VAERS vaccine data for 01/01/2018 - 06/14/2018. If you want to
    explore the full VAERS data for 1990 - Present (data, symptoms, and
    vaccines), then check out the 'vaers' package from the URL below. The URL
    and BugReports below correspond to the 'vaers' package, of which 'vaersvax'
    is a small subset (2018 only). 'vaers' is not hosted on CRAN due to the
    large size of the data set. To install the Suggested 'vaers' and 'vaersND'
    packages, use the following R code:
    'devtools::install_git("<https://gitlab.com/iembry/vaers.git>",
    build_vignettes = TRUE)' and
    'devtools::install_git("<https://gitlab.com/iembry/vaersND.git>",
    build_vignettes = TRUE)'. "The Vaccine Adverse Event Reporting System (VAERS)
    is a national early warning system to detect possible safety problems in
    U.S.-licensed vaccines. VAERS is co-managed by the Centers for Disease Control
    and Prevention (CDC) and the U.S. Food and Drug Administration (FDA)." For
    more information about the data, visit <https://vaers.hhs.gov/>. For
    information about
    vaccination/immunization hazards, visit
    <http://www.questionuniverse.com/rethink.html#vaccine>.
Provides access to the 'Vagalume' API <https://api.vagalume.com.br>. 
    The data extracted is basically lyrics of songs and information about
    artists/bands.
Fits generalized additive models (GAMs) using a variational approximations (VA) framework. In brief, the VA framework provides a fully or at least closed to fully tractable lower bound approximation to the marginal likelihood of a GAM when it is parameterized as a mixed model (using penalized splines, say). In doing so, the VA framework aims offers both the stability and natural inference tools available in the mixed model approach to GAMs, while achieving computation times comparable to that of using the penalized likelihood approach to GAMs. See Hui et al. (2018) <doi:10.1080/01621459.2018.1518235>.
A set of basic tools to transform functions into functions with
    input validation checks, in a manner suitable for both programmatic and
    interactive use.
A binding for the 'valection' program which offers various ways to 
  sample the outputs of competing algorithms or parameterizations, and fairly 
  assess their performance against each other. The 'valection' C library is 
  required to use this package and can be downloaded from:
  <http://labs.oicr.on.ca/boutros-lab/software/valection>. Cooper CI, et al; Valection: Design Optimization for Validation and Verification Studies; Biorxiv 2018; <doi:10.1101/254839>.
Uses 'ChatGPT' <https://openai.com/> to create poems about R packages.
   Currently contains the roses() function to make "roses are red, ..." style 
   poems and the prompt() function to only assemble the prompt without submitting 
   it to 'ChatGPT'.
Alternative splicing produces a variety of different protein products from a given gene. 'VALERIE' enables visualisation of alternative splicing events from high-throughput single-cell RNA-sequencing experiments. 'VALERIE' computes percent spliced-in (PSI) values for user-specified genomic coordinates corresponding to alternative splicing events. PSI is the proportion of sequencing reads supporting the included exon/intron as defined by Shiozawa (2018) <doi:10.1038/s41467-018-06063-x>. PSI are inferred from sequencing reads data based on specialised infrastructures for representing and computing annotated genomic ranges by Lawrence (2013) <doi:10.1371/journal.pcbi.1003118>. Computed PSI for each single cell are subsequently presented in the form of a heatmap implemented using the 'pheatmap' package by Kolde (2010) <https://CRAN.R-project.org/package=pheatmap>. Board overview of the mean PSI difference and associated p-values across different user-defined groups of single cells are presented in the form of a line graph using the 'ggplot2' package by Wickham (2007) <https://CRAN.R-project.org/package=ggplot2>.
The Bank of Canada updated their Valet API
             <https://www.bankofcanada.ca/valet/docs>, and no R client
             currently exists. This provides access to all of Valet's endpoints
             and serves responses in wide format easy for researchers to
             handle but also provides tools to access API responses as a list.
An interface to the 'Valhalla' routing engine’s
    application programming interfaces (APIs) for turn-by-turn routing,
    isochrones, and origin-destination analyses. Also includes several
    user-friendly functions for plotting outputs, and strives to follow
    "tidy" design principles. Please note that this package requires
    access to a running instance of 'Valhalla', which is open source and
    can be downloaded from <https://github.com/valhalla/valhalla>.
Methods and tools for analysing and validating the outputs
    and modelled functions of artificial neural networks (ANNs) in terms
    of predictive, replicative and structural validity. Also provides a
    method for fitting feed-forward ANNs with a single hidden layer.
Functions for validating the structure and properties of data frames. Answers essential questions about a data set after initial import or modification. What are the unique or missing values? What columns form a primary key? What are the properties of the numeric or categorical columns? What kind of overlap or mapping exists between 2 columns?
Declare data validation rules and data quality indicators;
        confront data with them and analyze or visualize the results.
        The package supports rules that are per-field, in-record,
        cross-record or cross-dataset. Rules can be automatically
        analyzed for rule type and connectivity. Supports checks implied
        by an SDMX DSD file as well. See also Van der Loo
        and De Jonge (2018) <doi:10.1002/9781118897126>, Chapter 6
        and the JSS paper (2021) <doi:10.18637/jss.v097.i10>.
By creating crowd-sourcing tasks that can be easily posted and results retrieved using Amazon's Mechanical Turk (MTurk) API, researchers can use this solution to validate the quality of topics obtained from unsupervised or semi-supervised learning methods, and the relevance of topic labels assigned. This helps ensure that the topic modeling results are accurate and useful for research purposes. See Ying and others (2022) <doi:10.1101/2023.05.02.538599>. For more information, please visit <https://github.com/Triads-Developer/Topic_Model_Validation>.
Generate suggestions for validation rules 
  from a reference data set, which can be used as a starting point for domain specific 
   rules to be checked with package 'validate'.
Rule sets with validation rules may contain redundancies or contradictions. 
  Functions for finding redundancies and problematic rules are provided, 
  given a set a rules formulated with 'validate'.
Given a partition resulting from any clustering algorithm, the implemented tests allow valid post-clustering inference by testing if a given variable significantly separates two of the estimated clusters. 
             Methods are detailed in: Hivert B, Agniel D, Thiebaut R & Hejblum BP (2022). 
             "Post-clustering difference testing: valid inference and practical considerations", <arXiv:2210.13172>.
Deploy, execute, and analyze the results of models hosted on the
  'ValidMind' platform <https://validmind.com>. This package interfaces with the
  'Python' client library in order to allow advanced diagnostics and insight
  into trained models all from an 'R' environment.
Functions for metrics and plots for model evaluation. Based on vectors of observed and predicted values. Method: Kristin Piikki, Johanna Wetterlind, Mats Soderstrom and Bo Stenberg (2021). <doi:10.1111/SUM.12694>.
The algorithm implemented in this package was
    designed to quickly estimates the distribution of the 
    log-rank especially for heavy unbalanced groups. VALORATE 
    estimates the null distribution and the p-value of the 
    log-rank test based on a recent formulation. For a given 
    number of alterations that define the size of survival 
    groups, the estimation involves a weighted sum of 
    distributions that are conditional on a co-occurrence term 
    where mutations and events are both present. The estimation 
    of conditional distributions is quite fast allowing the 
    analysis of large datasets in few minutes 
    <http://bioinformatica.mty.itesm.mx/valorate>.
Historical results for the state of Virginia lottery draw games. Data were downloaded from https://www.valottery.com/.
Read and manipulate genome intervals and signals. Provides
    functionality similar to command-line tool suites within R, enabling
    interactive analysis and visualization of genome-scale data.  Riemondy
    et al. (2017) <doi:10.12688/f1000research.11997.1>.
EQ-5D is a standard instrument (<https://euroqol.org/eq-5d-instruments/>) that measures the quality of life 
    often used in clinical and economic evaluations of health care technologies. Both adult versions of EQ-5D (EQ-5D-3L and EQ-5D-5L)
    contain a descriptive system and visual analog scale. The descriptive system measures the patient's health in 5 dimensions: 
    the 5L versions has 5 levels and 3L version has 3 levels. The descriptive system scores are usually converted to index values 
    using country specific values sets (that incorporates the country preferences). This package allows the calculation of both descriptive system
    scores to the index value scores. The value sets for EQ-5D-3L are from the references mentioned in the website <https://euroqol.org/eq-5d-instruments/eq-5d-3l-about/valuation/>
    The value sets for EQ-5D-3L for a total of 31 countries are used for the valuation (see the user guide for a complete list of references).
    The value sets for EQ-5D-5L are obtained from references mentioned in the <https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/valuation-standard-value-sets/> and other sources.
    The value sets for EQ-5D-5L for a total of 17 countries are used for the valuation (see the user guide for a complete list of references).
    The package can also be used to map 5L scores to 3L index values for 10 countries: Denmark, France, Germany, Japan, Netherlands, Spain, Thailand, UK, USA, and Zimbabwe. 
    The value set and method for mapping are obtained from Van Hout et al (2012) <doi: 10.1016/j.jval.2012.02.008>.
You can easily visualize your 'sf' polygons or data.frame with h3 address.
    While 'leaflet' package is too raw for data analysis, 
    this package can save data analysts' efforts & time with pre-set visualize options.
The number of countries with multiple Health Related Quality of Life (HRQL) value sets is growing, and this trend is expected to continue. 
    Each instrument and value set characterizes and values health differently. Identical health states can yield different utility values when valued 
    using different value sets. The 'valueSetCompare' package facilitates comparisons of HRQoL value sets, enabling both theoretical and empirical comparisons.
    For empirical comparisons, it employs a novel simulation-based method by Jiang et al. (2022) <doi:10.1186/s12955-022-02031-8>, allowing users to investigate
    the responsiveness of HRQoL instruments across the entire health spectrum using cross-sectional data with external health anchors.
Contains logic for cell-specific gene set scoring of single cell RNA sequencing data.
Implementation of a Monte Carlo simulation engine for valuing synthetic portfolios of 
    variable annuities, which reflect realistic features of common annuity contracts in practice. 
    It aims to facilitate the development and dissemination of research related to the efficient 
    valuation of a portfolio of large variable annuities. The main valuation methodology was 
    proposed by Gan (2017) <doi:10.1515/demo-2017-0021>.
Vampirize the response biases from a dataset! Performs 
    factor analysis controlling the effects of social desirability
    and acquiescence using the method described in Ferrando, 
    Lorenzo-Seva & Chico (2009) <doi:10.1080/10705510902751374>.
Wrapper around the 'City of Vancouver' Open Data API <https://opendata.vancouver.ca/api/v2/console> to simplify and standardize access to 'City of Vancouver' open data. 
  Functionality to list the data catalogue and access data and geographic records.
Collection of functions to evaluate presence-absence models. It 
    comprises functions to adjust discrimination statistics for the 
    representativeness effect through case-weighting, along with functions for 
    visualizing the outcomes. Originally outlined in: 
    Jiménez-Valverde (2022) The uniform AUC: dealing with the 
    representativeness effect in presence-absence models. Methods Ecol. Evol, 
    13, 1224-1236.
Palettes generated from Vincent van Gogh's paintings.
Simplifies functions assess normality for bivariate and multivariate statistical techniques. Includes functions designed to replicate plots and tables that would result from similar calls in 'SPSS', including hst(), box(), qq(), tab(), cormat(), and residplot(). Also includes simplified formulae, such as mode(), scatter(), p.corr(), ow.anova(), and rm.anova().
Provides low-level access to 'GDAL' functionality.  
  'GDAL' is the 'Geospatial Data Abstraction Library' a translator for raster and vector geospatial data formats 
  that presents a single raster abstract data model and single vector abstract data model to the calling application 
  for all supported formats <https://gdal.org/>. This package is focussed on providing exactly and only what GDAL does, to enable
  developing further tools.
A collection of the functions for estimation, hypothesis testing, prediction for stationary vector autoregressive models.
The spectral characteristics of a bivariate series (Marginal Spectra, Coherency- and Phase-Spectrum) determine whether there is a strong presence of short-, medium-, or long-term fluctuations (components of certain frequencies in the spectral representation of the series) in each one of them.  These are induced by strong peaks of the marginal spectra of each series at the corresponding frequencies. The spectral characteristics also determine how strongly these short-, medium-, or long-term fluctuations of the two series are correlated between the two series. Information on this is provided by the Coherency spectrum at the corresponding frequencies. Finally, certain fluctuations of the two series may be lagged to each other. Information on this is provided by the Phase spectrum at the corresponding frequencies. The idea in this package is to define a VAR (Vector autoregression) model with desired spectral characteristics by specifying a number of polynomials, required to define the VAR. See Ioannidis(2007) <doi:10.1016/j.jspi.2005.12.013>. These are specified via their roots, instead of via their coefficients. This is an idea borrowed from the Time Series Library of R. Dahlhaus, where it is used for defining ARMA models for univariate time series. This way, one may e.g. specify a VAR inducing a strong presence of long-term fluctuations in series 1 and in series 2, which are weakly correlated, but lagged by a number of time units to each other, while short-term fluctuations in series 1 and in series 2, are strongly present only in one of the two series, while they are strongly correlated to each other between the two series. Simulation from such models allows studying the behavior of data-analysis tools, such as estimation of the spectra, under different circumstances, as e.g. peaks in the spectra, generating bias, induced by leakage.
Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at <https://arxiv.org/abs/1604.07451>.
Tool for easy and efficient discretization of continuous and categorical data. 
    The package calculates the most optimal binning of a given explanatory variable with respect to a 
    user-specified target variable. The purpose is to assign a unique Weight-of-Evidence value 
    to each of the calculated binpoints in order to recode the original variable. 
    The package allows users to impose certain restrictions on the functional form on the resulting 
    binning while maximizing the overall information value in the original data. 
    The package is well suited for logistic scoring models where input variables may be subject to 
    restrictions such as linearity by e.g. regulatory authorities. An excellent source describing in 
    detail the development of scorecards, and the role of Weight-of-Evidence coding in credit scoring 
    is (Siddiqi 2006, ISBN: 978–0-471–75451–0). The package utilizes the discrete nature of decision trees and 
    Isotonic Regression to accommodate the trade-off between flexible functional forms and maximum 
    information value.
Fast algorithms for fitting Bayesian variable selection
    models and computing Bayes factors, in which the outcome (or
    response variable) is modeled using a linear regression or a
    logistic regression. The algorithms are based on the variational
    approximations described in "Scalable variational inference for
    Bayesian variable selection in regression, and its accuracy in
    genetic association studies" (P. Carbonetto & M. Stephens, 2012,
    <DOI:10.1214/12-BA703>). This software has been applied to large
    data sets with over a million variables and thousands of samples.
Performs clustering of quantitative variables,
    assuming that clusters lie in low-dimensional subspaces. Segmentation of
    variables, number of clusters and their dimensions are selected based on
    BIC. Candidate models are identified based on many runs of K-means
    algorithm with different random initializations of cluster centers.
Implementations of Thresholded Block Segmentation Scheme (TBSS) and Low-rank plus Sparse Two Step Procedure (LSTSP) algorithms for detecting multiple changes in structural VAR models. The package aims to address the problem of change point detection in piece-wise stationary VAR models, under different settings regarding the structure of their transition matrices (autoregressive dynamics); specifically, the following cases are included: (i) (weakly) sparse, (ii) structured sparse, and (iii) low rank plus sparse. It includes multiple algorithms and related extensions from Safikhani and Shojaie (2020) <doi:10.1080/01621459.2020.1770097> and Bai, Safikhani and Michailidis (2020) <doi:10.1109/TSP.2020.2993145>.
Generation of domain variables, linearization of several non-linear population statistics (the ratio of two totals, weighted income percentile, relative median income ratio, at-risk-of-poverty rate, at-risk-of-poverty threshold, Gini coefficient, gender pay gap, the aggregate replacement ratio, the relative median income ratio, median income below at-risk-of-poverty gap, income quintile share ratio, relative median at-risk-of-poverty gap), computation of regression residuals in case of weight calibration, variance estimation of sample surveys by the ultimate cluster method (Hansen, Hurwitz and Madow, Sample Survey Methods And Theory, vol. I: Methods and Applications; vol. II: Theory. 1953, New York: John Wiley and Sons), variance estimation for longitudinal, cross-sectional measures and measures of change for single and multistage stage cluster sampling designs (Berger, Y. G., 2015, <doi:10.1111/rssa.12116>). Several other precision measures are derived - standard error, the coefficient of variation, the margin of error, confidence interval, design effect.
Generating functions for both optimal and ordinary difference sequences, and the difference-based estimation functions.
Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions, including all commonly known distributions.  Also computed are the corresponding probability density function and cumulative distribution function. See Chan, Nadarajah and Afuecheta (2015) <doi:10.1080/03610918.2014.944658> for more details.
Error variance estimation in ultrahigh dimensional datasets with four different methods, viz. Refitted cross validation, k-fold refitted cross validation, Bootstrap-refitted cross validation, Ensemble method.
Variables are the fundamental parts of each programming language but handling them efficiently might be frustrating for programmers. This package contains some functions to help user (especially data explorers) to make more sense of their variables and take the most out of variables and hardware resources. These functions are written and crafted since 2014 with years of experience in statistical data analysis on high-dimensional data, and for each of them there was a need. Functions in this package are supposed to be efficient and easy to use, hence they will be frequently updated to make them more convenient.
Performs analysis of various genetic parameters like genotypic and phenotypic coefficient of variance, heritability, genetic advance, genetic advance as a percentage of mean. The package also has functions for genotypic and phenotypic covariance, correlation and path analysis. Dataset has been added to facilitate example. For more information refer Singh, R.K. and Chaudhary, B.D. (1977, ISBN:81766330709788176633079).
Abstract descriptions of (yet) unobserved variables.
Implements variable screening techniques for ultra-high
    dimensional regression settings.  Techniques for independent (iid) data,
    varying-coefficient models, and longitudinal data are implemented. The package
     currently contains three screen functions: screenIID(), screenLD() and screenVCM(),
     and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(),
     simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of
    Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) <DOI:10.1198/jasa.2011.tm10563>,
    Runze LI, Wei ZHONG, & Liping ZHU (2012) <DOI:10.1080/01621459.2012.695654>,
    Jingyuan LIU, Runze LI, & Rongling WU (2014) <DOI:10.1080/01621459.2013.850086>
    Hengjian CUI, Runze LI, & Wei ZHONG (2015) <DOI:10.1080/01621459.2014.920256>, and
    Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) <DOI:10.1214/16-AOAS912>.
Uses a Bayesian model to
    estimate the variability in a repeated
    measure outcome and use that as an outcome or a predictor
    in a second stage model.
Provides functions for the variance gamma
        distribution. Density, distribution and quantile functions.
        Functions for random number generation and fitting of the
        variance gamma to data. Also, functions for computing moments
        of the variance gamma distribution of any order about any
        location. In addition, there are functions for checking the
        validity of parameters and to interchange different sets of
        parameterizations for the variance gamma distribution.
This is a 'sparklyr' extension integrating 'VariantSpark' and R. 'VariantSpark' is 
  a framework based on 'scala' and 'spark' to analyze genome datasets, 
  see <https://bioinformatics.csiro.au/>. It was tested on datasets with 3000 samples 
  each one containing 80 million features in either unsupervised clustering approaches 
  and supervised applications, like classification and regression. The genome datasets
  are usually writing in VCF, a specific text file format used 
  in bioinformatics for storing gene sequence variations. So, 'VariantSpark' is a great 
  tool for genome research, because it is able to read VCF files, run analyses and return 
  the output in a 'spark' data frame.
Enables computationally efficient parameters-estimation by variational Bayesian methods for various diagnostic classification models (DCMs). DCMs are a class of discrete latent variable models for classifying respondents into latent classes that typically represent distinct combinations of skills they possess. Recently, to meet the growing need of large-scale diagnostic measurement in the field of educational, psychological, and psychiatric measurements, variational Bayesian inference has been developed as a computationally efficient alternative to the Markov chain Monte Carlo methods, e.g., Yamaguchi and Okada (2020a) <doi:10.1007/s11336-020-09739-w>, Yamaguchi and Okada (2020b) <doi:10.3102/1076998620911934>, Yamaguchi (2020) <doi:10.1007/s41237-020-00104-w>, Oka and Okada (2023) <doi:10.1007/s11336-022-09884-4>, and Yamaguchi and Martinez (2023) <doi:10.1111/bmsp.12308>. To facilitate their applications, 'variationalDCM' is developed to provide a collection of recently-proposed variational Bayesian estimation methods for various DCMs.
Computes the random forest variable importance (VIMP) for the conditional inference random forest (cforest) of the 'party' package. Includes a function (varImp) that computes the VIMP for arbitrary measures from the 'measures' package. For calculating the VIMP regarding the measures accuracy and AUC two extra functions exist (varImpACC and varImpAUC).
Applying Monte Carlo permutation to generate pointwise variogram envelope and checking for spatial dependence at different scales using permutation test. Empirical Brown's method and Fisher's method are used to compute overall p-value for hypothesis test.
Estimates the predicted 10-year cardiovascular (CVD) risk score (in probability) for women military service 
    members and veterans by inputting patient profiles. The proposed women CVD risk score improves the accuracy of the 
    existing American College of Cardiology/American Heart Association CVD risk assessment
    tool in predicting long‐term CVD risk for VA women, particularly in young and racial/ethnic 
    minority women. See the reference: Jeon‐Slaughter, H., Chen, X., Tsai, S., Ramanan, B., & Ebrahimi, R. 
    (2021) <doi:10.1161/JAHA.120.019217>.
Multi-caller variant analysis pipeline for targeted analysis sequencing (TAS) data. Features a modular, automated workflow that can start with raw reads and produces a user-friendly PDF summary and a spreadsheet containing consensus variant information.
The goal of the package is to equip the 'jmcm' package (current version 0.2.1) 
    with estimations of the covariance of estimated parameters. Two methods are provided. 
    The first method is to use the inverse of estimated Fisher's information matrix, see 
    M. Pourahmadi (2000) <doi:10.1093/biomet/87.2.425>, M. Maadooliat, M. Pourahmadi and 
    J. Z. Huang (2013) <doi:10.1007/s11222-011-9284-6>, and W. Zhang, C. Leng, C. Tang (2015) 
    <doi:10.1111/rssb.12065>. The second method is bootstrap based, see Liu, R.Y. (1988) 
    <doi:10.1214/aos/1176351062> for reference.
A continuous version of the receiver operating characteristics (ROC) curve to visualize and assess the classification and continuity performances of biomarkers, diagnostic tests, or risk prediction models.
A computational toolbox of heuristics approaches for performing variable ranking and feature selection based on mutual information well adapted for multivariate system epidemiology datasets. The core function is a general implementation of the minimum redundancy maximum relevance model. R. Battiti (1994) <doi:10.1109/72.298224>. Continuous variables are discretized using a large choice of rule. Variables ranking can be learned with a sequential forward/backward search algorithm. The two main problems that can be addressed by this package is the selection of the most representative variable within a group of variables of interest (i.e. dimension reduction) and variable ranking with respect to a set of features of interest.
In order to make it easy to use variance reduction algorithms for any simulation, this framework can help you.
    We propose user friendly and easy to extend framework. Antithetic Variates, Inner Control Variates,
    Outer Control Variates and Importance Sampling algorithms are available in the framework. User can
    write its own simulation function and use the Variance Reduction techniques in this package to
    obtain more efficient simulations. An implementation of Asian Option simulation is already 
    available within the package. See Kemal Dinçer Dingeç & Wolfgang Hörmann (2012) <doi:10.1016/j.ejor.2012.03.046>.
Methods for fitting semi-parametric mean and variance models, with normal or censored data. Extended to allow a regression in the location, scale and shape parameters, and further for multiple regression in each.
Estimation, lag selection, diagnostic testing, forecasting, causality analysis, forecast error variance decomposition and impulse response functions of VAR models and estimation of SVAR and SVEC models.
Feature selection using Sequential Forward Floating feature Selection and Jeffries-Matusita distance. It returns a suboptimal set of features to use for image classification. Reference: Dalponte, M., Oerka, H.O., Gobakken, T., Gianelle, D. & Naesset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51, 2632-2645, <DOI:10.1109/TGRS.2012.2216272>.
Utilizes multiple variable selection methods to estimate Average Treatment Effect.
Full model selection (detection of the relevant features and estimation of the number of clusters) for model-based clustering (see reference here <doi:10.1007/s11222-016-9670-1>). Data to analyze can be continuous, categorical, integer or mixed. Moreover, missing values can occur and do not necessitate any pre-processing. Shiny application permits an easy interpretation of the results.
Variable selection from random forests using both
        backwards variable elimination (for the selection of small sets
        of non-redundant variables) and selection based on the
        importance spectrum (somewhat similar to scree plots; for the
        selection of large, potentially highly-correlated variables).
        Main applications in high-dimensional data (e.g., microarray
        data, and other genomics and proteomics applications).
Vector autoregressive (VAR) model is a fundamental and effective approach
    for multivariate time series analysis. Shrinkage estimation methods can be
    applied to high-dimensional VAR models with dimensionality greater than
    the number of observations, contrary to the standard ordinary least squares
    method. This package is an integrative package delivering nonparametric,
    parametric, and semiparametric methods in a unified and consistent manner,
    such as the multivariate ridge regression in Golub, Heath, and Wahba (1979)
    <doi:10.2307/1268518>, a James-Stein type nonparametric shrinkage method in
    Opgen-Rhein and Strimmer (2007) <doi:10.1186/1471-2105-8-S2-S3>, and
    Bayesian estimation methods using noninformative and informative priors
    in Lee, Choi, and S.-H. Kim (2016) <doi:10.1016/j.csda.2016.03.007> and
    Ni and Sun (2005) <doi:10.1198/073500104000000622>.
Performs 20 omnibus tests for testing the composite hypothesis of variance homogeneity.
An implementation of the Likelihood ratio Test (LRT) for testing that,
    in a (non)linear mixed effects model, the variances of a subset of the random
    effects are equal to zero. There is no restriction on the subset of variances
    that can be tested: for example, it is possible to test that all the variances
    are equal to zero. Note that the implemented test is asymptotic.
    This package should be used on model fits from packages 'nlme', 'lmer', and 'saemix'.
    Charlotte Baey and Estelle Kuhn (2019) <doi:10.18637/jss.v107.i06>.
Implements a maximum likelihood estimation (MLE) method for
    estimation and prediction of Gaussian process-based spatially varying
    coefficient (SVC) models (Dambon et al. (2021a)
    <doi:10.1016/j.spasta.2020.100470>).  Covariance tapering (Furrer et
    al. (2006) <doi:10.1198/106186006X132178>) can be applied such that
    the method scales to large data. Further, it implements a joint
    variable selection of the fixed and random effects (Dambon et al.
    (2021b) <doi:10.1080/13658816.2022.2097684>). The package and its 
    capabilities are described in (Dambon et al. (2021c) <arXiv:2106.02364>).
Provide a collection of miscellaneous R functions
    related to the Vasicek distribution with the intent to make
    the lives of risk modelers easier.
Vasicek density, cumulative distribution, quantile functions and random deviate
   	generation of Vasicek distribution. In addition, there are two functions for fitting the 
   	Generalized Additive Models for Location Scale and Shape introduced by Rigby and 
   	Stasinopoulos (2005, <doi:10.1111/j.1467-9876.2005.00510.x>). Some functions 
   	are written in C++ using 'Rcpp', developed by Eddelbuettel and Francois (2011, <doi:10.18637/jss.v040.i08>).
An R client for the 'vatcheckapi.com' VAT number validation API. The API requires registration of an API key. Basic features are free, some require a paid subscription. You can find the full API documentation at <https://vatcheckapi.com/docs> .
Provides an interface to a 'HashiCorp' vault server over
  its http API (typically these are self-hosted; see
  <https://www.vaultproject.io>).  This allows for secure storage and
  retrieval of secrets over a network, such as tokens, passwords and
  certificates.  Authentication with vault is supported through
  several backends including user name/password and authentication via
  'GitHub'.
Estimate vaccine efficacy (VE) using immunogenicity data.
    The inclusion of immunogenicity data in regression models can increase precision in VE. 
    The methods are described in the publication "Elucidating vaccine efficacy using a correlate of protection, demographics, and logistic regression" by Julie Dudasova, Zdenek Valenta, and Jeffrey R. Sachs (2024).
Computes the Gaussian variational approximation of the Bayesian 
    empirical likelihood posterior. This is an implementation of the function 
    found in Yu, W., & Bondell, H. D. (2023) <doi:10.1080/01621459.2023.2169701>.
The shared random effects joint model is one of the most widely used approaches to study the associations between longitudinal biomarkers and a survival outcome and make dynamic risk predictions using the longitudinally measured biomarkers. 
    One major limitation of joint models is that they could be computationally expensive for complex models where the number of the shared random effects is large.  
    This package can be used to fit complex multivariate joint models using our newly developed algorithm Jieqi Tu and Jiehuan Sun (2023) <doi:10.1002/sim.9619>, which is based on Gaussian variational approximate inference and is computationally efficient.
Fit and simulate latent position and cluster models for network data, using a fast Variational Bayes approximation developed in Salter-Townshend and Murphy (2013) <doi:10.1016/j.csda.2012.08.004>.
Contains functions for a variational Bayesian method for sparse PCA proposed by Ning (2020) <arXiv:2102.00305>. There are two algorithms: the PX-CAVI algorithm (if assuming the loadings matrix is jointly row-sparse) and the batch PX-CAVI algorithm (if without this assumption). The outputs of the main function, VBsparsePCA(), include the mean and covariance of the loadings matrix, the score functions, the variable selection results, and the estimated variance of the random noise.
Vector binary tree provides a new data structure, to
 make your data visiting and management more efficient. If the
 data has structured column names, it can read these names and
 factorize them through specific split pattern, then build the mappings
 within double list, vector binary tree, array and tensor mutually, through
 which the batched data processing is achievable easily. The methods of
 array and tensor are also applicable. Detailed methods are described in
 Chen Zhang et al. (2020) <doi:10.35566/isdsa2019c8>.
Time series decomposition for univariate time series using the
    "Verallgemeinerte Berliner Verfahren" (Generalized Berlin Method) as
    described in 'Kontinuierliche Messgrößen und Stichprobenstrategien in
    Raum und Zeit mit Anwendungen in den Natur-, Umwelt-, Wirtschafts-
    und Finanzwissenschaften', by
    Hebbel and Steuer, Springer Berlin Heidelberg, 2022
    <doi:10.1007/978-3-662-65638-9>, or
    'Decomposition of Time Series using the Generalised Berlin 
    Method (VBV)' by Hebbel and Steuer, in Jan Beran, Yuanhua Feng, Hartmut
    Hebbel (Eds.): Empirical Economic and Financial Research - Theory,
    Methods and Practice, Festschrift in Honour of Prof. Siegfried Heiler.
    Series: Advanced Studies in Theoretical and Applied Econometrics.
    Springer 2014, p. 9-40.
Provides new classes for (rotated) BB1, BB6, BB7, BB8, and 
  Tawn copulas, extends the existing Gumbel and Clayton families with 
  rotations, and allows to set up a vine copula model using the 'copula' API.
  Corresponding objects from the 'VineCopula' API can easily be converted.
ANOVA and REML estimation of linear mixed models is implemented, once following
 Searle et al. (1991, ANOVA for unbalanced data), once making use of the 'lme4' package.
 The primary objective of this package is to perform a variance component analysis (VCA)
 according to CLSI EP05-A3 guideline "Evaluation of Precision of Quantitative Measurement
 Procedures" (2014). There are plotting methods for visualization of an experimental design,
 plotting random effects and residuals. For ANOVA type estimation two methods for computing
 ANOVA mean squares are implemented (SWEEP and quadratic forms). The covariance matrix of 
 variance components can be derived, which is used in estimating confidence intervals. Linear
 hypotheses of fixed effects and LS means can be computed. LS means can be computed at specific
 values of covariables and with custom weighting schemes for factor variables. See ?VCA for a
 more comprehensive description of the features.
Implements the Vine Copula Change Point (VCCP) methodology for the estimation of the number and location of multiple change points in the vine copula structure of multivariate time series. The method uses vine copulas, various state-of-the-art segmentation methods to identify multiple change points, and a likelihood ratio test or the stationary bootstrap for inference. The vine copulas allow for various forms of dependence between time series including tail, symmetric and asymmetric dependence. The functions have been extensively tested on simulated multivariate time series data and fMRI data. For details on the VCCP methodology, please see Xiong & Cribben (2021).
Visualization techniques, data sets, summary and inference
        procedures aimed particularly at categorical data. Special
        emphasis is given to highly extensible grid graphics. The
        package was package was originally inspired by the book 
	"Visualizing Categorical Data" by Michael Friendly and is 
	now the main support package for a new book, 
	"Discrete Data Analysis with R" by Michael Friendly and 
	David Meyer (2015).
Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data
    and the 'gnm' package for Generalized Nonlinear Models.
	In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and
	adds a 3D version in 'mosaic3d'.  Additionally, methods are provided for comparing and visualizing lists of
	'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by
  Michael Friendly and David Meyer.
The 'vcfpp.h' (<https://github.com/Zilong-Li/vcfpp>) provides an easy-to-use 'C++' 'API' of 'htslib', offering full functionality for manipulating Variant Call Format (VCF) files. The 'vcfppR' package serves as the R bindings of the 'vcfpp.h' library, enabling rapid processing of both compressed and uncompressed VCF files. Explore a range of powerful features for efficient VCF data manipulation.
Facilitates easy manipulation of variant call format (VCF) data.
    Functions are provided to rapidly read from and write to VCF files. Once
    VCF data is read into R a parser function extracts matrices of data. This
    information can then be used for quality control or other purposes. Additional
    functions provide visualization of genomic data. Once processing is complete
    data may be written to a VCF file (*.vcf.gz). It also may be converted into
    other popular R objects (e.g., genlight, DNAbin). VcfR provides a link between
    VCF data and familiar R software.
Implements functions for varying coefficient meta-analysis methods. 
  These methods do not assume effect size homogeneity. Subgroup effect size 
  comparisons, general linear effect size contrasts, and linear models of 
  effect sizes based on varying coefficient methods can be used to describe 
  effect size heterogeneity. Varying coefficient meta-analysis methods do not 
  require the unrealistic assumptions of the traditional fixed-effect and 
  random-effects meta-analysis methods.  
  For details see:  Statistical Methods for Psychologists, Volume 5, <https://dgbonett.sites.ucsc.edu/>.
Methods for faster extraction (about 5x faster in a few test cases) of variance-covariance matrices and standard errors from models. Methods in the 'stats' package tend to rely on the summary method, which may waste time computing other summary statistics which are summarily ignored.
Estimating the disparity between two groups based on the extended model of the Peters-Belson (PB) method. Our model is the first work on the longitudinal data, and also can set a varying variable to find the complicated association between other variables and the varying variable. Our work is an extension of the Peters-Belson method which was originally published in Peters (1941)<doi:10.1080/00220671.1941.10881036> and Belson (1956)<doi:10.2307/2985420>.
Method to perform penalized variance component analysis.
Record test suite 'HTTP' requests and replays them during
    future runs. A port of the Ruby gem of the same name
    (<https://github.com/vcr/vcr/>). Works by hooking into the 'webmockr'
    R package for matching 'HTTP' requests by various rules ('HTTP' method,
    'URL', query parameters, headers, body, etc.), and then caching
    real 'HTTP' responses on disk in 'cassettes'. Subsequent 'HTTP' requests
    matching any previous requests in the same 'cassette' use a cached
    'HTTP' response.
Recursive partitioning for varying coefficient generalized linear models and ordinal linear mixed models. Special features are coefficient-wise partitioning, non-varying coefficients and partitioning of time-varying variables in longitudinal regression. A description of a part of this package was published by Burgin and Ritschard (2017) <doi:10.18637/jss.v080.i06>.
Defines new notions of prototype and size that are used to
    provide tools for consistent and well-founded type-coercion and
    size-recycling, and are in turn connected to ideas of type- and
    size-stability useful for analysing function interfaces.
A way of visualizing collections of time series and, optionally
    their future values, forecasts for their future values and prediction
    intervals for the forecasts. A web-based GUI can be used to display the
    information in a collection of time series.
Comparison of variance - covariance patterns using relative principal component analysis (relative eigenanalysis), as described in Le Maitre and Mitteroecker (2019) <doi:10.1111/2041-210X.13253>. Also provides functions to compute group covariance matrices, distance matrices, and perform proportionality tests. A worked sample on the body shape of cichlid fishes is included, based on the dataset from Kerschbaumer et al. (2013) <doi:10.5061/dryad.fc02f>.
Analyze Peptide Array Data and characterize peptide
    sequence space. Allows for high level visualization of global signal, Quality control based
    on replicate correlation and/or relative Kd, calculation of peptide Length/Charge/Kd parameters,
    Hits selection based on RFU Signal, and amino acid composition/basic motif recognition with RFU
    signal weighting. Basic signal trends can be used to generate peptides that follow the observed
    compositional trends.
The qda() function from package 'MASS' is extended to calculate a weighted linear (LDA) and quadratic discriminant analysis (QDA) by changing the group variances and group means based on cell-wise uncertainties.
    The uncertainties can be derived e.g. through relative errors for each individual measurement (cell), not only row-wise or column-wise uncertainties.
    The method can be applied compositional data (e.g. portions of substances, concentrations) and non-compositional data.
Facilities for constructing variance dispersion graphs, fraction-
    of-design-space plots and similar graphics for exploring the properties of
    experimental designs. The design region is explored via random sampling, which
    allows for more flexibility than traditional variance dispersion graphs. A
    formula interface is leveraged to provide access to complex model formulae.
    Graphics can be constructed simultaneously for multiple experimental designs
    and/or multiple model formulae. Instead of using pointwise optimization to
    find the minimum and maximum scaled prediction variance curves, which can be
    inaccurate and time consuming, this package uses quantile regression as an
    alternative.
Uses a modification of the published FORTRAN code in "A Computer Program for Generating Variance Dispersion Graphs" by G. Vining, Journal of Quality Technology, Vol. 25 No. 1 January 1993, to produce variance dispersion graphs. Also produces fraction of design space plots, and contains data frames for several minimal run response surface designs.
An extension to the 'testthat' package that makes it easy
    to add graphical unit tests. It provides a Shiny application to
    manage the test cases.
To ease the visualization of outputs from Diversity Motif Analyser ('DiMA';
    <https://github.com/BVU-BILSAB/DiMA>). 'vDiveR' allows visualization of the diversity 
    motifs (index and its variants – major, minor and unique) for elucidation of 
    the underlying inherent dynamics. Please refer <https://vdiver-manual.readthedocs.io/en/latest/>
    for more information.
Contains variable, diversity, and joining sequences and accompanying functions that enable both the extraction of and comparison between immune V-D-J genomic segments from a variety of species. Sources include IMGT from MP Lefranc (2009) <doi:10.1093/nar/gkn838> and Vgenerepertoire from publication DN Olivieri (2014) <doi:10.1007/s00251-014-0784-3>.
Although model selection is ubiquitous in scientific discovery, the stability and uncertainty of the selected model is often hard to evaluate. How to characterize the random behavior of the model selection procedure is the key to understand and quantify the model selection uncertainty. This R package offers several graphical tools to visualize the distribution of the selected model. For example, Gplot(), Hplot(), VDSM_scatterplot() and VDSM_heatmap(). To the best of our knowledge, this is the first attempt to visualize such a distribution. About what distribution of selected model is and how it work please see Qin,Y.and Wang,L. (2021) "Visualization of Model Selection Uncertainty" <https://homepages.uc.edu/~qinyn/VDSM/VDSM.html>.
Provides statistical methods for the design and analysis of a calibration study, which aims for calibrating measurements using two different methods. The package includes sample size calculation, sample selection,  regression analysis with error-in measurements and change-point regression. The method is described in Tian, Durazo-Arvizu, Myers, et al. (2014) <DOI:10.1002/sim.6235>.
Applies affine and similarity transformations on vector spatial data (sp objects). Transformations can be defined from control points or directly from parameters. If redundant control points are provided Least Squares is applied allowing to obtain residuals and RMSE.
Automates set operations (i.e., comparisons of overlap) between multiple vectors.
    It also contains a function for automating reporting in 'RMarkdown', by generating markdown output for easy analysis, as well as an 'RMarkdown' template for use with 'RStudio'.
Under a different representation of the multivariate normal (MVN) probability, we can use the Vecchia approximation to sample the integrand at a linear complexity with respect to n. Additionally, both the SOV algorithm from Genz (92) and the exponential-tilting method from Botev (2017) can be adapted to linear complexity. The reference for the method implemented in this package is Jian Cao and Matthias Katzfuss (2024) "Linear-Cost Vecchia Approximation of Multivariate Normal Probabilities" <doi:10.48550/arXiv.2311.09426>. Two major references for the development of our method are Alan Genz (1992) "Numerical Computation of Multivariate Normal Probabilities" <doi:10.1080/10618600.1992.10477010> and Z. I. Botev (2017) "The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting" <doi:10.48550/arXiv.1603.04166>.
The 'base'  tools  union() intersect(), etc.,  follow the algebraic definition
 that each element of a set must be unique. 
 Since it's often helpful to compare all elements of two vectors,
 this toolset treats every element as unique for counting purposes.
 For ease of use, all functions in vecsets have an argument
 'multiple' which, when set to FALSE,
 reverts them to the base::sets (alias for all the items) tools functionality.
A tool for fast, efficient bitwise operations along the
    elements within a vector. Provides such functionality for AND, OR and
    XOR, as well as infix operators for all of the binary bitwise
    operations.
Facilitate the analysis of inter-limb and intra-limb coordination in human movement. 
  It provides functions for calculating the phase angle between two segments, enabling researchers and practitioners to quantify the coordination patterns within and between limbs during various motor tasks.
  Needham, R., Naemi, R., & Chockalingam, N. (2014) <doi:10.1016/j.jbiomech.2013.12.032>.
  Needham, R., Naemi, R., & Chockalingam, N. (2015) <doi:10.1016/j.jbiomech.2015.07.023>.
  Tepavac, D., & Field-Fote, E. C. (2001) <doi:10.1123/jab.17.3.259>.
  Park, J.H., Lee, H., Cho, Js. et al. (2021) <doi:10.1038/s41598-020-80237-w>.
Allows registered 'VectorSurv' <https://vectorsurv.org/> users access to data through the 'VectorSurv API' <https://api.vectorsurv.org/>. Additionally provides functions for analysis and visualization.
New wavelet methodology (vector wavelet coherence) (Oygur, T., Unal, G, 2020 <doi:10.1007/s40435-020-00706-y>) 
  to handle dynamic co-movements of multivariate time series via extending multiple and quadruple wavelet coherence methodologies. 
  This package can be used to perform multiple wavelet coherence, quadruple wavelet coherence, and n-dimensional vector wavelet coherence analyses.
Provides platform for Vedic calendar system having several
    functionalities to facilitate conversion between Gregorian and Vedic
    calendar systems, and helpful in examining its impact in the
    time series analysis domain.
The 'Vega-Lite' 'JavaScript' framework provides a higher-level grammar
    for visual analysis, akin to 'ggplot' or 'Tableau', that generates complete 'Vega'
    specifications. Functions exist which enable building a valid 'spec' from scratch
    or importing a previously created 'spec' file. Functions also exist to export 'spec'
    files and to generate code which will enable plots to be embedded in properly
    configured web pages. The default behavior is to generate an 'htmlwidget'.
Ordination methods, diversity analysis and other
  functions for community and vegetation ecologists.
Static and dynamic 3D plots to be used with ordination
    results and in diversity analysis, especially with the vegan package.
'Vega' and 'Vega-Lite' parse text in 'JSON' notation to render 
  chart-specifications into 'HTML'. This package is used to facilitate the 
  rendering. It also provides a means to interact with signals, events,
  and datasets in a 'Vega' chart using 'JavaScript' or 'Shiny'.
A set of functions to: (1) perform fuzzy clustering of vegetation data (De Caceres et al, 2010) <doi:10.1111/j.1654-1103.2010.01211.x>; (2) to assess ecological community similarity on the basis of structure and composition (De Caceres et al, 2013) <doi:10.1111/2041-210X.12116>.
Handling of vegetation data from different sources (
	Turboveg 2.0 <https://www.synbiosys.alterra.nl/turboveg/>; 
	the German national repository <https://www.vegetweb.de> and others. 
    Taxonomic harmonization (given appropriate taxonomic lists,
    e.g. the German taxonomic standard list "GermanSL", <https://germansl.infinitenature.org>).
Collection of common methods to determine growing season length in
  a simple manner. Start and end dates of the vegetation periods are calculated
  solely based on daily mean temperatures and the day of the year.
Import and handling data from vegetation-plot databases, especially
    data stored in 'Turboveg 2' (<https://www.synbiosys.alterra.nl/turboveg/>).
    Also import/export routines for exchange of data with 'Juice'
    (<https://www.sci.muni.cz/botany/juice/>) are implemented.
Elaboration of vehicular emissions inventories,
    consisting in four stages, pre-processing activity data, preparing 
    emissions factors, estimating the emissions and post-processing of emissions 
    in maps and databases. More details in Ibarra-Espinosa et al (2018) <doi:10.5194/gmd-11-2209-2018>.
    Before using VEIN you need to know the vehicular composition of your study area, in other words,
    the combination of of type of vehicles, size and fuel of the fleet. Then, it is recommended to
    start with the project to download a template to create a structure of directories and scripts.
Predicate helper functions for testing atomic vectors in R. All functions take
  a single argument 'x' and check whether it's of the target type of base-R
  atomic vector (i.e. no class extensions nor attributes other than 'names'),
  returning TRUE or FALSE. Some additionally check for value (e.g. absence of
  missing values, infinities, blank characters, or 'names' attribute; or having
  length 1).
Functions for downloading, reshaping, culling, cleaning, and analyzing fossil data from the Paleobiology Database <https://paleobiodb.org>.
A set of functions for generating HTML to
    embed hosted video in your R Markdown documents or Shiny applications.
A close to zero dependency package to draw and display Venn 
    diagrams up to 7 sets, and any Boolean union of set intersections.
A set of functions to generate high-resolution Venn and Euler plots. Includes handling for several special cases, including two-case scaling, and extensive customization of plot shape and structure.
Calculates and displays Venn and Euler Diagrams.
Calculate and plot Venn diagrams in 2D and 3D.
Turn R analysis outputs into full sentences, by writing vectors into in-sentence lists, pluralising words conditionally, spelling out numbers if they are at the start of sentences, writing out dates in full following US or UK style, and managing capitalisations in tidy data.
Describe in words the genealogical relationship between two
    members of a given pedigree, using the algorithm in Vigeland (2022)
    <doi:10.1186/s12859-022-04759-y>. 'verbalisr' is part of the
    'pedsuite' collection of packages for pedigree analysis. For a
    demonstration of 'verbalisr', see the online app 'QuickPed' at
    <https://magnusdv.shinyapps.io/quickped>.
An implementation of the 'Verhoeff' algorithm for calculating 
    check digits (Verhoeff, J. (1969) <doi:10.1002/zamm.19710510323>). 
    Functions are provided to calculate a check digit given an input number, 
    calculate and append a check digit to an input number, and validate that a 
    check digit is correct given an input number.
Utilities for verifying discrete, continuous and probabilistic forecasts, and forecasts expressed as parametric distributions are included.
The biomarker data set by Vermeulen et al. (2009)
    <doi:10.1016/S1470-2045(09)70154-8> is provided. The data
    source, however, is by Ruijter et al. (2013)
    <doi:10.1016/j.ymeth.2012.08.011>. The original data set may
    be downloaded from
    <https://medischebiologie.nl/wp-content/uploads/2019/02/qpcrdatamethods.zip>.
    This data set is for a real-time quantitative polymerase chain reaction
    (PCR) experiment that comprises the raw fluorescence data of 24,576
    amplification curves. This data set comprises 59 genes of interest and 5
    reference genes. Each gene was assessed on 366 neuroblastoma complementary
    DNA (cDNA) samples and on 18 standard dilution series samples (10-fold
    5-point dilution series x 3 replicates + no template controls (NTC) x 3
    replicates).
R data pipelines commonly require reading and writing data to versioned 
    directories. Each directory might correspond to one step of a multi-step process,
    where that version corresponds to particular settings for that step and a chain of
    previous steps that each have their own versions. This package creates a configuration
    object that makes it easy to read and write versioned data, based on YAML
    configuration files loaded and saved to each versioned folder.
Installs specified versions of R packages hosted on CRAN and
    provides functions to list available versions and the versions of currently
    installed packages. These tools can be used to help make R projects and
    packages more reproducible. 'versions' fits in the narrow gap between
    the 'devtools' install_version() function and the 'checkpoint' package.
    devtools::install_version() installs a stated package version from source files
    stored on the CRAN archives. However CRAN does not store binary versions of
    packages so Windows users need to have RTools installed and Windows and OSX
    users get longer installation times. 'checkpoint' uses the Revolution Analytics
    MRAN server to install packages (from source or binary) as they were available
    on a given date. It also provides a helpful interface to detect the packages
    in use in a directory and install all of those packages for a given date.
    'checkpoint' doesn't provide install.packages-like functionality however, and
    that's what 'versions' aims to do, by querying MRAN. As MRAN only goes back to
    2014-09-17, 'versions' can't install packages archived before this date.
A lightweight package for sorting version codes in various forms. No strong dependencies guaranteed.
A toolset for interactively exploring the differences between two data frames.
Creates Vertex Similarity matrix of an undirected graph based
  on the method stated by E. A. Leicht, Petter Holme, AND M. E. J. Newman in
  their paper <DOI:10.1103/PhysRevE.73.026120>.
Provides functions to run statistical analyses on
    surface-based neuroimaging data, computing measures including cortical
    thickness and surface area of the whole-brain and of the hippocampi.
    It can make use of 'FreeSurfer', 'fMRIprep' and 'HCP' preprocessed
    datasets and 'HippUnfold' hippocampal segmentation outputs for a given
    sample by restructuring the data values into a single file. The single
    file can then be used by the package for analyses independently from
    its base dataset and without need for its access.
Multi-precision library that allows to store and operate with arbitrarily big integers without
    loss of precision. It includes a large list of tools to work with them, like:
      - Arithmetic and logic operators
      - Modular-arithmetic operators
      - Computer Number Theory utilities
      - Probabilistic primality tests
      - Factorization algorithms
      - Random generators of diferent types of integers.
Simple functions for calculating the image gradient, image hessian, volume ratio filter, and Frangi vesselness filter of 3-dimensional volumes.
The goal of 'vetiver' is to provide fluent tooling to
    version, share, deploy, and monitor a trained model. Functions handle
    both recording and checking the model's input data prototype, and
    predicting from a remote API endpoint. The 'vetiver' package is
    extensible, with generics that can support many kinds of models.
Declarative template-based framework for verifying that objects
  meet structural requirements, and auto-composing error messages when they do
  not.
R Codes and Datasets for Duchateau, L. and Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with applications in Veterinary Research. International Livestock Research Institute.
Implements methods for inference on potential waning of vaccine
    efficacy and for estimation of vaccine efficacy at a user-specified time
    after vaccination based on data from a randomized, double-blind,
    placebo-controlled vaccine trial in which participants may be unblinded
    and placebo subjects may be crossed over to the study vaccine.  The
    methods also allow adjustment for possible confounding via inverse
    probability weighting through specification of models for the trial
    entry process, unblinding mechanisms, and the probability an unblinded
    placebo participant accepts study vaccine:  Tsiatis, A. A. and Davidian,
    M. (2021) <arXiv:2102.13103> .
Implements methods for inference on potential waning of vaccine efficacy and for estimation of vaccine efficacy at a user-specified time after vaccination based on data from a randomized, double-blind, placebo-controlled vaccine trial in which participants may be unblinded and placebo subjects may be crossed over to the study vaccine.  The methods also for variant stratification and allow adjustment for possible confounding via inverse probability weighting through specification of models for the trial entry process, unblinding mechanisms, and the probability an unblinded placebo participant accepts study vaccine.
Computation the value of one of two uniformly 
    distributed marginals if the copula probability value is known
    and the value of the second marginal is also known.
    Computation and plotting corresponding cumulative
    distribution function or survival function.
    The numerical definition of a common area limited by lines
    of the cumulative distribution function and survival function.
    Approximate quantification of the probability of this area.
    In addition to 'amh', the copula dimension may be larger than 2.
A set of visual input controls for Shiny apps to facilitate filtering across multiple outputs.
Variance function estimation for models proposed by W. Sadler in his variance function program 
  ('VFP', <http://www.aacb.asn.au/resources/useful-tools/variance-function-program-v14>). Here, the idea is
  to fit multiple variance functions to a data set and consequently assess which function reflects
  the relationship 'Var ~ Mean' best. For 'in-vitro diagnostic' ('IVD') assays modeling this relationship
  is of great importance when individual test-results are used for defining follow-up treatment of patients.
Realization of published methods to analyze visual field (VF) progression. Introduction to the plotting methods (designed by author TE) for VF output visualization. A sample dataset for two eyes, each with 10 follow-ups is included. The VF analysis methods could be found in -- Musch et al. (1999) <doi:10.1016/S0161-6420(99)90147-1>, Nouri-Mahdavi et at. (2012) <doi:10.1167/iovs.11-9021>, Schell et at. (2014) <doi:10.1016/j.ophtha.2014.02.021>, Aptel et al. (2015) <doi:10.1111/aos.12788>.
Empirical models for runoff, erosion, and phosphorus loss 
    across a vegetated filter strip, given slope, soils, climate, and 
    vegetation (Gall et al., 2018) <doi:10.1007/s00477-017-1505-x>. 
    It also includes functions for deriving climate parameters from 
    measured daily weather data, and for simulating rainfall. Models 
    implemented include MUSLE (Williams, 1975) and APLE (Vadas et al., 
    2009 <doi:10.2134/jeq2008.0337>).
An implementation of about 6 major classes of
    statistical regression models. The central algorithm is
    Fisher scoring and iterative reweighted least squares.
    At the heart of this package are the vector generalized linear
    and additive model (VGLM/VGAM) classes. VGLMs can be loosely
    thought of as multivariate GLMs. VGAMs are data-driven
    VGLMs that use smoothing. The book "Vector Generalized
    Linear and Additive Models: With an Implementation in R"
    (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of
    the statistical framework and the package. Currently only
    fixed-effects models are implemented. Many (100+) models and
    distributions are estimated by maximum likelihood estimation
    (MLE) or penalized MLE. The other classes are RR-VGLMs
    (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained
    RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs,
    RCIMs (row-column interaction models)---these classes perform
    constrained and unconstrained quadratic ordination (CQO/UQO)
    models in ecology, as well as constrained additive ordination
    (CAO). Hauck-Donner effect detection is implemented.
    Note that these functions are subject to change;
    see the NEWS and ChangeLog files for latest changes.
Mainly data sets to accompany the VGAM package and
	the book "Vector Generalized Linear and
	Additive Models: With an Implementation in R" (Yee, 2015)
	<DOI:10.1007/978-1-4939-2818-7>.
	These are used to illustrate vector generalized
	linear and additive models (VGLMs/VGAMs), and associated models
	(Reduced-Rank VGLMs, Quadratic RR-VGLMs, Row-Column
	Interaction Models, and constrained and unconstrained ordination
	models in ecology). This package now contains some
	old VGAM family functions which have been replaced by newer
	ones (often because they are now special cases).
Extending the functionalities of the 'VGAM' package with
         additional functions and datasets. At present, 'VGAMextra'
         comprises new family functions (ffs) to estimate several time
         series models by maximum likelihood using Fisher scoring, 
         unlike popular packages in CRAN relying on optim(), including
         ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non-
         seasonal), the Order-(p) VAR model, error correction models
         for cointegrated time series, and ARMA-structures with Student-t 
         errors. For independent data, new ffs to estimate the inverse-
         Weibull, the inverse-gamma, the generalized beta of the second
         kind and the general multivariate normal distributions are
         available. In addition, 'VGAMextra' incorporates new VGLM-links
         for the mean-function, and the quantile-function (as an alternative
         to ordinary quantile modelling) of several 1-parameter distributions,
         that are compatible with the class of VGLM/VGAM family functions.
         Currently, only fixed-effects models are implemented. All functions
         are subject to change; see the NEWS for further details on the
         latest changes.
Estimates hierarchical models using mean-field variational Bayes. 
    At present, it can estimate logistic, linear, and negative binomial models. 
    It can accommodate models with an arbitrary number of random effects and 
    requires no integration to estimate. It also provides the ability to improve 
    the quality of the approximation using marginal augmentation. 
    Goplerud (2022) <doi:10.1214/21-BA1266> and Goplerud (2024) <doi:10.1017/S0003055423000035> 
    provide details on the variational algorithms.
Analyze the co-adaptation of codon usage between a virus and its host, calculate various codon usage bias measurements as: effective number of codons (ENc) Novembre (2002) <doi:10.1093/oxfordjournals.molbev.a004201>, codon adaptation index (CAI) Sharp  and  Li (1987) <doi:10.1093/nar/15.3.1281>, relative  codon deoptimization index (RCDI) Puigbò et al (2010) <doi:10.1186/1756-0500-3-87>, similarity index (SiD) Zhou et al (2013) <doi:10.1371/journal.pone.0077239>,  synonymous codon usage orderliness (SCUO) Wan et al (2004) <doi:10.1186/1471-2148-4-19> and, relative synonymous    codon usage (RSCU) Sharp et al (1986) <doi:10.1093/nar/14.13.5125>. Also, it provides a statistical dinucleotide over- and underrepresentation with three different models. Implement several methods for visualization of codon usage as ENc.GC3plot() and PR2.plot().
The "Vertical and Horizontal Inheritance Consistence Analysis" method is described in the following publication: "VHICA: a new method to discriminate between vertical and horizontal transposon transfer: application to the mariner family within Drosophila" by G. Wallau. et al. (2016) <DOI:10.1093/molbev/msv341>. The purpose of the method is to detect horizontal transfers of transposable elements, by contrasting the divergence of transposable element sequences with that of regular genes.
The base class 'VirtualArray' is defined, which acts as a wrapper around lists allowing users to fold arbitrary sequential data into n-dimensional, R-style virtual arrays. The derived 'XArray' class is defined to be used for homogeneous lists that contain a single class of objects. The 'RasterArray' and 'SfArray' classes enable the use of stacked spatial data instead of lists.
Provides direct access to linked names for the same entity across the world's major name authority files, including national and regional variations in language, character set, and spelling. For more information go to <https://viaf.org/>.
The Variable Infiltration Capacity (VIC) model is a macroscale
    hydrologic model that solves full water and energy balances, originally
    developed by Xu Liang at the University of Washington (UW).
    The version of VIC source code used is of 5.0.1 on <https://github.com/UW-Hydro/VIC/>,
    see Hamman et al. (2018).
    Development and maintenance of the current official version
    of the VIC model at present is led by the UW Hydro (Computational Hydrology
    group) in the Department of Civil and Environmental Engineering at UW. VIC is
    a research model and in its various forms it has been applied to most of the
    major river basins around the world, as well as globally 
    <http://vic.readthedocs.io/en/master/Documentation/References/>. 
    References: "Liang, X., D. P. Lettenmaier, E. F. Wood, and
    S. J. Burges (1994), A simple hydrologically based model of land surface water
    and energy fluxes for general circulation models, J. Geophys. Res., 99(D7),
    14415-14428, <doi:10.1029/94JD00483>"; 
    "Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y. (2018), The
    Variable Infiltration Capacity model version 5 (VIC-5): infrastructure improvements
    for new applications and reproducibility, Geosci. Model Dev., 11, 3481-3496, 
    <doi:10.5194/gmd-11-3481-2018>".
Compute group-specific intraclass correlation coefficients, 
    Bayesian testing of homogenous within-group variance, and spike-and-slab
    model selection to determine which groups share a common within-group 
    variance in a one-way random effects model <10.31234/osf.io/hpq7w>.
A shiny app for accurate estimation of vaccine induced immunogenicity 
    with bivariate linear modeling. Method is detailed in: Lhomme, Hejblum, Lacabaratz, 
    Wiedemann, Lelievre, Levy, Thiebaut & Richert (2020). Journal of Immunological Methods, 
    477:112711. <doi:10.1016/j.jim.2019.112711>.
Easily interfaces R to spatial datasets available through 
  the Victorian Government's WFS (Web Feature Service): <https://opendata.maps.vic.gov.au/geoserver/ows?request=GetCapabilities&service=wfs>, 
  which allows users to read in 'sf' data from these sources. VicmapR uses the lazy querying approach and code developed by Teucher et al. (2021) for the 'bcdata' R package <doi:10.21105/joss.02927>.
Compared with the similar graph embedding method such as Laplacian Eigenmaps, 'Vicus' can exploit more local structures of graph data. For the details of the methods, see the reference section of 'GitHub' README.md <https://github.com/rikenbit/Vicus>.
Video interactivity within 'shiny' applications using 'video.js'. Enables
    the status of the video to be sent from the UI to the server, and allows events
    such as playing and pausing the video to be triggered from the server.
Converts Vietnam's provinces' names and ID
    across different formats. Handles diacritics and different spellings.
Conversion of characters from unsupported Vietnamese character encodings to Unicode characters. These Vietnamese encodings (TCVN3, VISCII, VPS) are not natively supported in R and lead to printing of wrong characters and garbled text (mojibake). This package fixes that problem and provides readable output with the correct Unicode characters (with or without diacritics).
Debugging pipe chains often consists of viewing the output after 
  each step. This package adds RStudio addins and two functions that allow 
  outputing each or select steps in a convenient way.
Provides a graphical user interface to integrate, visualize and explore results 
            from linkage and quantitative trait loci analysis, together with genomic information for autopolyploid 
            species. The app is meant for interactive use and allows users to optionally upload different sources 
            of information, including  gene annotation and alignment files, enabling the exploitation and search for 
            candidate genes in a genome browser. In its current version, 'VIEWpoly' supports inputs from 'MAPpoly', 
            'polymapR', 'diaQTL', 'QTLpoly', 'polyqtlR', 'GWASpoly', and 'HIDECAN' packages.
A collection of functions to make R a more effective viewscape analysis 
    tool for calculating viewscape metrics based on computing the viewable area for 
    given a point/multiple viewpoints and a digital elevation model.The method of calculating 
    viewscape metrics implemented in this package are based on the work of 
    Tabrizian et al. (2020) <doi:10.1016/j.landurbplan.2019.103704>. The algorithm of computing 
    viewshed is based on the work of 
    Franklin & Ray. (1994) <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=555780f6f5d7e537eb1edb28862c86d1519af2be>.
Conducts linear regression using variational Bayesian inference, particularly optimized for genome-wide association mapping and whole-genome prediction which use a number of DNA markers as the explanatory variables. Provides seven regression models which select the important variables (i.e., the variables related to response variables) among the given explanatory variables in different ways (i.e., model structures).
Inference methods for state-space models, relying on the Kalman Filter or on Viking (Variational Bayesian VarIance tracKING). See J. de Vilmarest (2022) <https://theses.hal.science/tel-03716104/>.
This is a package for creating and running Agent Based Models (ABM). It provides a set of base classes with core functionality to allow bootstrapped models. For more intensive modeling, the supplied classes can be extended to fit researcher needs.
New tools for the visualization of missing and/or imputed values
    are introduced, which can be used for exploring the data and the structure of
    the missing and/or imputed values. Depending on this structure of the missing
    values, the corresponding methods may help to identify the mechanism generating
    the missing values and allows to explore the data including missing values.
    In addition, the quality of imputation can be visually explored using various
    univariate, bivariate, multiple and multivariate plot methods. A graphical user
    interface available in the separate package VIMGUI allows an easy handling of
    the implemented plot methods.
To computed the variability independent of mean (VIM) or variation
    independent of mean (VIM). The methodology can be found at
    Peter M Rothwell et al. (2010) <doi:10.1016/S1474-4422(10)70067-3>.
Calculate point estimates of and valid confidence intervals for
    nonparametric, algorithm-agnostic variable importance measures in high and low dimensions,
    using flexible estimators of the underlying regression functions. For more information
    about the methods, please see Williamson et al. (Biometrics, 2020),  Williamson et al. (JASA, 2021), and Williamson and Feng (ICML, 2020).
An implementation of methods related to sparse clustering and variable importance 
    in clustering. The package currently allows to perform sparse k-means clustering with a group 
    penalty, so that it automatically selects groups of numerical features. It also allows to 
    perform sparse clustering and variable selection on mixed data (categorical and numerical 
    features), by preprocessing each categorical feature as a group of numerical features.
    Several methods for visualizing and exploring the results are also provided. 
    M. Chavent, J. Lacaille, A. Mourer and M. Olteanu (2020)<https://www.esann.org/sites/default/files/proceedings/2020/ES2020-103.pdf>.
The variable importance is calculated using knock off variables. Then output can be provided in numerical and graphical form. Meredith L Wallace (2023) <doi:10.1186/s12874-023-01965-x>.
Provides a programmatic interface in R for the US Department of 
    Transportation (DOT) National Highway Transportation Safety Administration 
    (NHTSA) vehicle identification number (VIN) API, located at 
    <https://vpic.nhtsa.dot.gov/api/>. 
    The API can decode up to 50 vehicle identification numbers in one call, and 
    provides manufacturer information about the vehicles, including make, model, 
    model year, and gross vehicle weight rating (GVWR).
Provides tools for the statistical analysis of regular vine copula 
    models, see Aas et al. (2009) <doi:10.1016/j.insmatheco.2007.02.001> and 
    Dissman et al. (2013) <doi:10.1016/j.csda.2012.08.010>.
    The package includes tools for parameter estimation, model selection,
    simulation, goodness-of-fit tests, and visualization. Tools for estimation,
    selection and exploratory data analysis of bivariate copula models are also
    provided.
Implements D-vine quantile regression models with
  parametric or nonparametric pair-copulas. See 
  Kraus and Czado (2017) <doi:10.1016/j.csda.2016.12.009> and
  Schallhorn et al. (2017) <arXiv:1705.08310>.
Implementation of the vine graphical model for building
    high-dimensional probability distributions as a factorization of
    bivariate copulas and marginal density functions. This package
    provides S4 classes for vines (C-vines and D-vines) and methods
    for inference, goodness-of-fit tests, density/distribution
    function evaluation, and simulation.
Produces violin plots with optional nonparametric (Mann-Whitney test) and parametric (Tukey's honest significant difference) mean comparison and linear regression. This package aims to be a simple and quick visualization tool for comparing means and assessing trends of categorical factors.
A violin plot is a combination of a box plot and a kernel density plot. This package allows extensive customisation of violin plots.
A general framework for constructing variable importance plots from 
  various types of machine learning models in R. Aside from some standard model-
  specific variable importance measures, this package also provides model-
  agnostic approaches that can be applied to any supervised learning algorithm.
  These include 1) an efficient permutation-based variable importance measure, 
  2) variable importance based on Shapley values (Strumbelj and Kononenko, 
  2014) <doi:10.1007/s10115-013-0679-x>, and 3) the variance-based 
  approach described in Greenwell et al. (2018) <arXiv:1805.04755>. A 
  variance-based method for quantifying the relative strength of interaction 
  effects is also included (see the previous reference for details).
Generate a violin point plot, a combination of a violin/histogram
    plot and a scatter plot by offsetting points within a category based on their
    density using quasirandom noise.
Provides methods for assessing the applicability domain of models that predict viral load and CD4 (Cluster of Differentiation 4) lymphocyte counts. These methods help determine the extent of extrapolation when making predictions.
Provides a comprehensive framework for building, evaluating, and visualizing regression models for analyzing viral load and CD4 (Cluster of Differentiation 4) lymphocytes data. It leverages the principles of the tidymodels ecosystem of Max Kuhn and Hadley Wickham (2020) <https://www.tidymodels.org> to offer a user-friendly experience in model development. This package includes functions for data preprocessing, feature engineering, model training, tuning, and evaluation, along with visualization tools to enhance the interpretation of model results. It is specifically designed for researchers in biostatistics, computational biology, and HIV research who aim to perform reproducible and rigorous analyses to gain insights into disease dynamics. The main focus is on improving the understanding of the relationships between viral load, CD4 lymphocytes, and other relevant covariates to contribute to HIV research and the visibility of vulnerable seropositive populations.
A dedicated viral-explainer model tool designed to empower researchers in the field of HIV research, particularly in viral load and CD4 (Cluster of Differentiation 4) lymphocytes regression modeling. Drawing inspiration from the 'tidymodels' framework for rigorous model building of Max Kuhn and Hadley Wickham (2020) <https://www.tidymodels.org>, and the 'DALEXtra' tool for explainability by Przemyslaw Biecek (2020) <arXiv:2009.13248>. It aims to facilitate interpretable and reproducible research in biostatistics and computational biology for the benefit of understanding HIV dynamics.
Computation of volatility impulse response function for multivariate time series model using algorithm by Jin, Lin and Tamvakis (2012) <doi.org/10.1016/j.eneco.2012.03.003>.
Color maps designed to improve graph readability for readers with 
    common forms of color blindness and/or color vision deficiency. The color 
    maps are also perceptually-uniform, both in regular form and also when 
    converted to black-and-white for printing. This package also contains 
    'ggplot2' bindings for discrete and continuous color and fill scales. A lean
    version of the package called 'viridisLite' that does not include the 
    'ggplot2' bindings can be found at 
    <https://cran.r-project.org/package=viridisLite>.
Color maps designed to improve graph readability for readers with 
    common forms of color blindness and/or color vision deficiency. The color 
    maps are also perceptually-uniform, both in regular form and also when 
    converted to black-and-white for printing. This is the 'lite' version of the 
    'viridis' package that also contains 'ggplot2' bindings for discrete and 
    continuous color and fill scales and can be found at 
    <https://cran.r-project.org/package=viridis>.
Tools to generate virtual environmental drivers with a given temporal autocorrelation, and to simulate pollen curves at annual resolution over millennial time-scales based on these drivers and virtual taxa with different life traits and niche features. It also provides the means to simulate quasi-realistic pollen-data conditions by applying simulated accumulation rates and given depth intervals between consecutive samples.
Constructs a virtual population from fertility and mortality rates for any country,
	calendar year and birth cohort in the Human Mortality Database <https://www.mortality.org> and the Human Fertility Database <https://www.humanfertility.org>.  Fertility histories are simulated for every individual and their offspring, producing a multi-generation virtual population.
Provides a framework for generating virtual species distributions,
    a procedure increasingly used in ecology to improve species distribution
    models. This package integrates the existing methodological approaches 
    with the objective of generating virtual species distributions with 
    increased ecological realism.
Provides users with a simple and convenient
             mechanism to manage and query a 'Virtuoso' database using the 'DBI' (Data-Base Interface)
             compatible 'ODBC' (Open Database Connectivity) interface.
             'Virtuoso' is a high-performance "universal server," which can act
             as both a relational database, supporting standard Structured Query
             Language ('SQL') queries, while also supporting data following the
             Resource Description Framework ('RDF') model for Linked Data.
             'RDF' data can be queried using 'SPARQL' ('SPARQL' Protocol and 'RDF' Query Language)
             queries, a graph-based query that supports semantic reasoning.
             This allows users to leverage the performance of local or remote 'Virtuoso' servers using
             popular 'R' packages such as 'DBI' and 'dplyr', while also providing a 
             high-performance solution for working with large 'RDF' 'triplestores' from 'R.'
             The package also provides helper routines to install, launch, and manage
             a 'Virtuoso' server locally on 'Mac', 'Windows' and 'Linux' platforms using
             the standard interactive installers from the 'R' command-line.  By 
             automatically handling these setup steps, the package can make using 'Virtuoso'
             considerably faster and easier for a most users to deploy in a local
             environment. Managing the bulk import of triples
             from common serializations with a single intuitive command is another key
             feature of this package.  Bulk import performance can be tens to
             hundreds of times faster than the comparable imports using existing 'R' tools,
             including 'rdflib' and 'redland' packages.
Use VirusTotal, a Google service that analyzes files and URLs 
    for viruses, worms, trojans etc., provides category of the content hosted by a 
    domain from a variety of prominent services, provides passive DNS information,
    among other things. See <http://www.virustotal.com> for more information.
Provides easy-to-use tools for data analysis and visualization for hyperspectral remote sensing (also known as imaging spectroscopy), with 
    a particular focus on vegetation hyperspectral data analysis. It consists of a set of functions, ranging from the organization of hyperspectral data 
    in the proper data structure for spectral feature selection, calculation of vegetation index, multivariate analysis, as well as to the visualization 
    of spectra and results of analysis in the 'ggplot2' style.
Provides a set of wrapper functions for 'Visa Chart Components'.
  'Visa Chart Components' <https://github.com/visa/visa-chart-components> is an accessibility focused,
  framework agnostic set of data experience design systems components for the web.
Valid Improved Sparsity A-Learning (VISA) provides a new method for selecting important variables involved in optimal treatment regime from a multiply robust perspective. The VISA estimator achieves its success by borrowing the strengths of both model averaging (ARM, Yuhong Yang, 2001) <doi:10.1198/016214501753168262> and variable selection (PAL, Chengchun Shi, Ailin Fan, Rui Song and Wenbin Lu, 2018) <doi:10.1214/17-AOS1570>. The package is an implementation of Zishu Zhan and Jingxiao Zhang. (2022+).
Provides methods to calculate diagnostics for multicollinearity among predictors in a linear or
  generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009),
  "Where’s Waldo: Visualizing Collinearity Diagnostics", <doi:10.1198/tast.2009.0012>.
  These include better tabular presentation of collinearity diagnostics that highlight the important numbers,
  a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient,
  and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent.
A set of functions providing several visualization tools for exploring the behavior of the components in a network meta-analysis of multi-component (complex) interventions: 
   - components descriptive analysis
   - heat plot of the two-by-two component combinations 
   - leaving one component combination out scatter plot 
   - violin plot for specific component combinations' effects
   - density plot for components' effects  
   - waterfall plot for the interventions' effects that differ by a certain component combination 
   - network graph of components
   - rank heat plot of components for multiple outcomes.
   The implemented tools are described by Seitidis et al. (2023) <doi:10.1002/jrsm.1617>.
Functionality for creating phase portraits of functions in the
    complex number plane. Works with R base graphics, whose full 
    functionality is available. Parallel processing is used for optimum 
    performance.
Create preliminary exploratory data visualisations of an entire 
    dataset to identify problems or unexpected features using 'ggplot2'.
Designed to help the user to determine the sensitivity of an proposed causal effect to unconsidered common causes. Users can create visualizations of sensitivity, effect sizes, and determine which pattern of effects would support a causal claim for between group differences. Number needed to treat formula from Kraemer H.C. & Kupfer D.J. (2006) <doi:10.1016/j.biopsych.2005.09.014>.
A graphical R package designed to visualize behavioral observations over time. Based on raw time data extracted from video recorded sessions of experimental observations, ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps for all participants in a single graph. Individuals and/or group behavior can easily be assessed. Supplementary features allow users to further inspect their data by adding summary statistics (mean, standard deviation, quantile or statistical test) and/or time constraints to assess the accuracy of the realized actions.
Phase I clinical trials are the first step in drug development to test
    a new drug or drug combination on humans. Typical designs of Phase I 
    trials use toxicity as the primary endpoint and aim to find the maximum 
    tolerable dosage. However, these designs are poorly applicable for the 
    development of cancer therapeutic vaccines because the expected safety 
    concerns for these vaccines are not as much as cytotoxic agents. The 
    primary objectives of a cancer therapeutic vaccine phase I trial thus 
    often include determining whether the vaccine shows biologic activity 
    and the minimum dose necessary to achieve a full immune or even clinical 
    response. This package implements a Bayesian Phase I cancer vaccine
    trial design that allows simultaneous evaluation of safety and
    immunogenicity outcomes. See Wang et al. (2019) <DOI:10.1002/sim.8021>
    for further details.
Performs modeling and forecasting of park visitor counts
	using social media data and (partial) on-site visitor counts.
	Specifically, the model is built based on an automatic decomposition
	of the trend and seasonal components of the social media-based park visitor counts,
	from which short-term forecasts of the visitor counts and percent changes
	in the visitor counts can be made. A reference for the underlying model that 'VisitorCounts' uses can be found at 
    Russell Goebel, Austin Schmaltz, Beth Ann Brackett, Spencer A. Wood, Kimihiro Noguchi (2023) <doi:10.1002/for.2965> .
Provides a suite of analytical functionalities to process and analyze
    visual meteor observations from the Visual Meteor Database
    of the International Meteor Organization <https://www.imo.net/>.
Provides an R interface to the 'vis.js' JavaScript charting
    library. It allows an interactive visualization of networks.
Provides methods to transform omop_result objects into
    formatted tables and figures, facilitating the visualization of study
    results working with the Observational Medical Outcomes Partnership
    (OMOP) Common Data Model.
Provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', 'glm', 'coxph', 'rlm', 'gam', 'locfit', 'lmer', 'randomForest', etc.)
Visualization of the most powerful statistical hypothesis test.
    The function vistat() visualizes the statistical hypothesis testing between the 
    dependent variable (response) varsample and the independent variable (feature) varfactor. 
    The statistical hypothesis test (including the eventual corresponding post-hoc analysis) with
    the highest statistical power fulfilling the assumptions of the corresponding test is chosen based on a decision tree.
    A graph displaying the raw data accordingly to the chosen test is generated, the test statistics including eventual 
    post-hoc-analysis are returned. The automated workflow is especially suited for browser based interfaces to 
    server-based deployments of R. Implemented tests: lm(), t.test(), wilcox.test(), aov(), kruskal.test(), fisher.test(), chisqu.test().
    Implemented tests to check the normal distribution of standardized residuals: shapiro.test() and ad.test().
    Implemented post-hoc tests: TukeyHSD() for aov() and pairwise.wilcox.test() for kruskal.test().
    For the comparison of averages, the following algorithm is implemented: 
    If the p-values of the standardized residuals of both shapiro.test() or ad.test() are smaller 
    than 1-conf.level, kruskal.test() resp. wilcox.test() are performed, otherwise the oneway.test()
    and aov() resp. t.test() are performed and displayed. Exception: 
    If the sample size is bigger than 100, t.test() is always performed and wilcox.test() is never executed 
    (Lumley et al. (2002) <doi:10.1146/annurev.publhealth.23.100901.140546>).
    For the test of independence of count data, Cochran's rule (Cochran (1954) <doi:10.2307/3001666>) is implemented: 
    If more than 20 percent of all cells have a count smaller than 5, fisher.test() is performed and displayed,
    otherwise chisqu.test(). In both cases case an additional mosaic plot is generated.
A library for creating time based charts, like Gantt or timelines. Possible outputs 
  include 'ggplot2' diagrams, 'plotly.js' graphs, 'Highcharts.js' widgets and data.frames. Results can be
  used in the 'RStudio' viewer pane, in 'RMarkdown' documents or in Shiny apps. In the 
  interactive outputs created by vistime() and hc_vistime(), you can interact with the 
  plot using mouse hover or zoom.
Traces information spread through interactions between features, utilising information theory measures and a higher-order generalisation of the concept of widest paths in graphs.
  In particular, 'vistla' can be used to better understand the results of high-throughput biomedical experiments, by organising the effects of the investigated intervention in a tree-like hierarchy from direct to indirect ones, following the plausible information relay circuits.
  Due to its higher-order nature, 'vistla' can handle multi-modality and assign multiple roles to a single feature.
Provides a visualization for characterizing subgroups defined by a decision tree structure. The visualization simplifies the ability to interpret individual pathways to subgroups; each sub-plot describes the distribution of observations within individual terminal nodes and percentile ranges for the associated inner nodes.
Visualize and compute percentiles/probabilities of normal, t, f, chi square 
    and binomial distributions.
Estimates and plots as a heat map the correlation coefficients obtained via the wavelet local multiple correlation 'WLMC' (Fernández-Macho 2018) and the 'dominant' variable/s, i.e., the variable/s that maximizes the multiple correlation through time and scale (Polanco-Martínez et al. 2020, Polanco-Martínez 2022). We improve the graphical outputs of WLMC proposing a didactic and useful way to visualize the 'dominant' variable(s) for a set of time series. The WLMC was designed for financial time series, but other kinds of data (e.g., climatic, ecological, etc.) can be used. The functions contained in 'VisualDom' are highly flexible since these contains several parameters to personalize the time series under analysis and the heat maps. In addition, we have also included two data sets (named 'rdata_climate' and 'rdata_Lorenz') to exemplify the use of the functions contained in 'VisualDom'. Methods derived from Fernández-Macho (2018) <doi:10.1016/j.physa.2017.11.050>, Polanco-Martínez et al. (2020) <doi:10.1038/s41598-020-77767-8> and Polanco-Martínez (2023, in press).
A collection of tools for analyzing the field of vision. It
    provides a framework for development and use of innovative methods for
    visualization, statistical analysis, and clinical interpretation of
    visual-field loss and its change over time. It is intended to be a tool for
    collaborative research. The package is described in Marin-Franch and Swanson
    (2013) <doi:10.1167/13.4.10> and is part of the Open Perimetry Initiative
    (OPI) [Turpin, Artes, and McKendrick (2012) <doi:10.1167/12.11.22>].
Graphs the pdf or pmf and highlights what area or probability is
    present in user defined locations. Visualize is able to provide lower tail,
    bounded, upper tail, and two tail calculations. Supports strict and equal
    to inequalities.  Also provided on the graph is the mean and variance of
    the distribution.
Visualize the trends and historical downloads from packages in the 'CRAN' repository. Data is obtained by using the 'API' to query the database from the 'RStudio' 'CRAN' mirror.
To visualize the probabilities of early
      termination, fail and success of Simon's two-stage
      design.  To evaluate and visualize the operating
      characteristics of Simon's two-stage design.
Visualizes vowel variation in f0, F1, F2, F3 and duration.
A Shiny application and functions for visual exploration of hierarchical clustering with numeric datasets. Allows users to iterative set hyperparameters, select features and evaluate results through various plots and computation of evaluation criteria.
Implements the novel testing approach by Janitza et al.(2015)
    <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4>
    for the permutation variable importance measure in a random forest and the
    PIMP-algorithm by Altmann et al.(2010) <doi:10.1093/bioinformatics/btq134>.
    Janitza et al.(2015) <http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25587-4>
    do not use the "standard" permutation variable
    importance but the cross-validated permutation variable
    importance for the novel test approach. The cross-validated
    permutation variable importance is not based on the out-of-bag
    observations but uses a similar strategy which is inspired by
    the cross-validation procedure. The novel test approach can be
    applied for classification trees as well as for regression
    trees. However, the use of the novel testing approach has not
    been tested for regression trees so far, so this routine is
    meant for the expert user only and its current state is rather
    experimental.
Provides templates and functions to simplify the production and maintenance of curriculum vitae.
Analysing vital statistics based on tools
    consistent with the tidyverse. Tools are provided for data visualization,
    lifetable calculations, computing net migration numbers, Lee-Carter
    modelling; functional data modelling and forecasting.
Provides fitting routines for four versions of the
        Vitality family of mortality models.
Provides a versatile range of functions, including exploratory data analysis, time-series analysis, organizational network analysis, and data validation, whilst at the same time implements a set of best practices in analyzing and visualizing data specific to 'Microsoft Viva Insights'.
Analysis of minor alleles in Illumina sequencing data of viral
    genomes. Functions in 'vivaldi' primarily operate on vcf files.
A suite of plots for displaying variable importance and two-way variable interaction jointly. Can also display partial dependence plots laid out in a pairs plot or 'zenplots' style.
Provides an easy to calculate local variable importance measure based on Ceteris Paribus profile 
  and global variable importance measure based on Partial Dependence Profiles.
Interactive visualization for Bayesian prior and posterior distributions. 
    This package facilitates an animated transition between prior and posterior distributions. 
    Additionally, it splits the distribution into bars based on the provided 'breaks,' displaying 
    the probability for each region. If no 'breaks' are provided, it defaults to zero.
Provides an interface to the VK API <https://vk.com/dev/methods>.
      VK <https://vk.com/> is the largest European online social networking
      service, based in Russia.
Using frequency matrices, very low frequency variants (VLFs) are assessed for amino acid and nucleotide sequences.  The VLFs are then compared to see if they occur in only one member of a species, singleton VLFs, or if they occur in multiple members of a species, shared VLFs.  The amino acid and nucleotide VLFs are then compared to see if they are concordant with one another.  Amino acid VLFs are also assessed to determine if they lead to a change in amino acid residue type, and potential changes to protein structures. Based on Stoeckle and Kerr (2012) <doi:10.1371/journal.pone.0043992>.
Functions, Classes & Methods for estimation, prediction, and
  simulation (bootstrap) of Variable Length Markov Chain ('VLMC') Models.
Models categorical time series through a Markov Chain when a) covariates are predictors for transitioning into the next state/symbol and b) when the dependence in the past states has variable length. The probability of transitioning to the next state in the Markov Chain is defined by a multinomial regression whose parameters depend on the past states of the chain and, moreover, the number of states in the past needed to predict the next state also depends on the observed states themselves. See Zambom, Kim, and Garcia (2022) <doi:10.1111/jtsa.12615>.
A framework to infer causality on a pair of time series of real numbers based on variable-lag Granger causality and transfer entropy. Typically, Granger causality and transfer entropy have an assumption of a fixed and constant time delay between the cause and effect. However, for a non-stationary time series, this assumption is not true. For example, considering two time series of velocity of person A and person B where B follows A. At some time, B stops tying his shoes, then running to catch up A. The fixed-lag assumption is not true in this case. We propose a framework that allows variable-lags between cause and effect in Granger causality and transfer entropy to allow them to deal with variable-lag non-stationary time series. Please see Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf (2021) <doi:10.1145/3441452> when referring to this package in publications.
'RcppArmadillo' implementation for the Matlab code of the 'Variational Mode Decomposition' and 'Two-Dimensional Variational Mode Decomposition'. For more information, see (i) 'Variational Mode Decomposition' by K. Dragomiretskiy and D. Zosso in IEEE Transactions on Signal Processing, vol. 62, no. 3, pp. 531-544, Feb.1, 2014, <doi:10.1109/TSP.2013.2288675>; (ii) 'Two-Dimensional Variational Mode Decomposition' by Dragomiretskiy, K., Zosso, D. (2015), In: Tai, XC., Bae, E., Chan, T.F., Lysaker, M. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2015. Lecture Notes in Computer Science, vol 8932. Springer, <doi:10.1007/978-3-319-14612-6_15>.
Application of Variational Mode Decomposition based different Machine Learning models for univariate time series forecasting. For method details see (i) K. Dragomiretskiy and D. Zosso (2014) <doi:10.1109/TSP.2013.2288675>; (ii)  Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.
Forecasting univariate time series with Variational Mode Decomposition (VMD) based time delay neural network models.For method details see Konstantin, D.and Dominique, Z. (2014). <doi:10.1109/TSP.2013.2288675>.
A variety of tools to allow the quantification of videos of the 
    lymphatic vasculature taken under an operating microscope. Lymphatic vessels
    that have been injected with a variety of blue dyes can be tracked throughout
    the video to determine their width over time. Code is optimised
    for efficient processing of multiple large video files. Functions to calculate
    physiologically relevant parameters and generate graphs from these values 
    are also included.
Provides fast sampling from von Mises-Fisher distribution using the method proposed by Andrew T.A Wood (1994) <doi:10.1080/03610919408813161>.
Manage, provision and use Virtual Machines pre-configured for R.
             Develop, test and build package in a clean environment.
             'Vagrant' tool and a provider (such as 'Virtualbox') have to be installed.
An implementation of the variable neighborhood trust region 
    algorithm Bierlaire et al. (2009) "A Heuristic for Nonlinear Global 
    Optimization" <doi:10.1287/ijoc.1090.0343>.
Create adjacency matrices of vocalisation graphs from
  dataframes containing sequences of speech and silence intervals,
  transforming these matrices into Markov diagrams, and generating
  datasets for classification of these diagrams by 'flattening' them
  and adding global properties (functionals) etc.  Vocalisation
  diagrams date back to early work in psychiatry (Jaffe and Feldstein,
  1970) and social psychology (Dabbs and Ruback, 1987) but have only
  recently been employed as a data representation method for machine
  learning tasks including meeting segmentation (Luz, 2012)
  <doi:10.1145/2328967.2328970> and classification (Luz,
  2013) <doi:10.1145/2522848.2533788>.
Methods to calculate the expected value of information from a decision-analytic model.  This includes the expected value of perfect information (EVPI), partial perfect information (EVPPI) and sample information (EVSI), and the expected net benefit of sampling (ENBS).  A range of alternative computational methods are provided under the same user interface.  See Heath et al. (2024) <doi:10.1201/9781003156109>, Jackson et al. (2022) <doi:10.1146/annurev-statistics-040120-010730>.
Tools for voice analysis, speaker recognition and mood inference. 
    Gathers 'R' and 'Python' tools to solve problems concerning voice and audio 
    in general.
Simplifies and largely automates practical voice analytics for social science research. This package offers an accessible and easy-to-use interface, including an interactive Shiny app, that simplifies the processing, extraction, analysis, and reporting of voice recording data in the behavioral and social sciences. The package includes batch processing capabilities to read and analyze multiple voice files in parallel, automates the extraction of key vocal features for further analysis, and automatically generates APA formatted reports for typical between-group comparisons in experimental social science research. A more extensive methodological introduction that inspired the development of the 'voiceR' package is provided in Hildebrand et al. 2020 <doi:10.1016/j.jbusres.2020.09.020>.
'R' implementation of the 'vol2bird' software for generating vertical profiles
    of birds and other biological signals in weather radar data. See Dokter et al.
    (2011) <doi:10.1098/rsif.2010.0116> for a paper describing the methodology.
Trading Strategies for high Option Volatility environment are represented here through their Graphs. The graphic indicators, strategies, calculations, functions and all the discussions are for academic, research, and educational purposes only and should not be construed as investment advice and come with absolutely no Liability.
    Guy Cohen (“The Bible of Options Strategies (2nd ed.)”, 2015, ISBN: 9780133964028).
    Zura Kakushadze, Juan A. Serur (“151 Trading Strategies”, 2018, ISBN: 9783030027919).
    John C. Hull (“Options, Futures, and Other Derivatives (11th ed.)”, 2022, ISBN: 9780136939979).
Generates interactive plots for analysing and visualising 
    three-class high dimensional data. It is particularly suited to visualising 
    differences in continuous attributes such as gene/protein/biomarker 
    expression levels between three groups. Differential gene/biomarker 
    expression analysis between two classes is typically shown as a volcano 
    plot. However, with three groups this type of visualisation is particularly 
    difficult to interpret. This package generates 3D volcano plots and 3-way 
    polar plots for easier interpretation of three-class data.
Interactive adverse event (AE) volcano plot for monitoring clinical trial safety. This tool allows users to view the overall distribution of AEs in a clinical trial using standard (e.g. MedDRA preferred term) or custom (e.g. Gender) categories using a volcano plot similar to proposal by Zink et al. (2013) <doi:10.1177/1740774513485311>. This tool provides a stand-along shiny application and flexible shiny modules allowing this tool to be used as a part of more robust safety monitoring framework like the Shiny app from the 'safetyGraphics' R package.
Provides an R interface for 'volesti' C++ package. 'volesti' computes estimations of volume
             of polytopes given by (i) a set of points, (ii) linear inequalities or (iii) Minkowski sum of segments
             (a.k.a. zonotopes). There are three algorithms for volume estimation as well as algorithms
             for sampling, rounding and rotating polytopes. Moreover, 'volesti' provides algorithms for
             estimating copulas useful in computational finance. Methods implemented in 'volesti' are described
             in A. Chalkis and V. Fisikopoulos (2022) <doi:10.32614/RJ-2021-077> and references therein.
Craft polished tables and plots in Markdown reports. 
             Simply choose whether to treat your data as counts or metrics, 
             and the package will automatically generate well-designed default tables and plots for you.
             Boiled down to the basics, with labeling features and simple interactive reports.
             All functions are 'tidyverse' compatible.
Volleyball match statistics of the German volleyball first division league (seasons 2013/2014 to 
    2018/2019). The data has been collected from the official volleyball first division homepage 
    (<www.volleyball-bundesliga.de>) and contains information on teams, staff, sets, matches, and player-in-match 
    statistics (extracted automatically from the official match reports).
Facilitates modeling species' ecological niches and 
  geographic distributions based on occurrences and environments that 
  have a vertical as well as horizontal component, and projecting models 
  into three-dimensional geographic space. Working in three dimensions is 
  useful in an aquatic context when the organisms one wishes to model can 
  be found across a wide range of depths in the water column. The package
  also contains functions to automatically generate marine training
  model training regions using machine learning, and interpolate and smooth
  patchily sampled environmental rasters using thin plate splines.
  Davis Rabosky AR, Cox CL, Rabosky DL, Title PO, Holmes IA, Feldman A, McGuire JA (2016) <doi:10.1038/ncomms11484>.
  Nychka D, Furrer R, Paige J, Sain S (2021) <doi:10.5065/D6W957CT>.
  Pateiro-Lopez B, Rodriguez-Casal A (2022) <https://CRAN.R-project.org/package=alphahull>.
The d3.js framework with the plugins d3-voronoi-map, d3-voronoi-treemap and d3-weighted-voronoi
        are used to generate Voronoi treemaps in R and in a shiny application.
        The computation of the Voronoi treemaps are based on Nocaj and Brandes (2012)
        <doi:10.1111/j.1467-8659.2012.03078.x>.
Export dataframes and automatically start importing into 
    'Vorteks'. 'Vorteks Visualization Environment (VVE)' and 'Vorteks Data Manager (VDM)' will start an import. 'Vorteks Processing Environment (VPE)' will start a new project and
    add a file reader with the dataframe file already set.
    Warning: WINDOWS ONLY. Requires installation of 'Vorteks' software.
Contains selected data from two publications,
    Campbell 'et' 'al'. (2016) <DOI:10.1080/14486563.2015.1028486>
    and 'Pacioni' 'et' 'al'. (2017) <DOI:10.1071/PC17002>.
    The data is provided both as raw outputs from the population viability
    analysis software 'Vortex' and packaged as R objects.
    The R package 'vortexR' uses the raw data provided here to illustrate its
    functionality of parsing raw 'Vortex' output into R objects.
Collects tweets and metadata for threaded conversations and
    generates networks.
A 'Shiny' application for the interactive visualisation and
    analysis of networks that also provides a web interface for collecting
    social media data using 'vosonSML'.
A suite of easy to use functions for collecting social media
    data and generating networks for analysis. Supports Twitter, YouTube,
    Reddit and web site data sources.
Generating realizations of a fractal Brownian function on uniform 1D & 2D grid with classic and generic versions of the Voss algorithm (random sequential additions).
Counting election votes and determining election results by different methods, including
    the single transferable vote or ranked choice, approval, score, plurality, condorcet and two-round runoff methods (Raftery et al., 2021 <doi:10.32614/RJ-2021-086>).
'ProPublica' <https://projects.propublica.org/represent/> makes United States
    Congress member votes available and has developed their own unique cartogram to visually
    represent this data. Tools are provided to retrieve voting data, prepare voting data 
    for plotting with 'ggplot2', create vote cartograms and theme them.
Provide functions to generate random simulated evaluations on candidates by voters for evaluation-based elections. Functions are based on several models for continuous or discrete evaluations.
An R interface to the Project 'VoteSmart'<https://justfacts.votesmart.org/> API.
Various methods to count ballots in voting systems are provided.
	Functions to check validity of ballots are also provided to ensure flexibility.
Calculates voter transitions comparing two elections, using the function solve.QP() in package 'quadprog'.
A wrapper around a CSS library called 'vov.css', intended for use 
  in 'shiny' applications. Simply wrap a UI element in one of the animation 
  functions to see it move.
Procedures for the manipulation, normalization, and plotting of phonetic and sociophonetic vowel formant data.  vowels is the backend for the NORM website.
Functions for the mass-univariate voxelwise analysis of medical imaging data that follows the NIfTI <http://nifti.nimh.nih.gov> format.
Tools for 3D point cloud voxelisation, projection, geometrical and morphological description of trees (DBH, height, volume, crown diameter), analyses of temporal changes between different measurement times, distance based clustering and visualisation of 3D voxel clouds and 2D projection. Most analyses and algorithms provided in the package are based on the concept of space exploration and are described in Lecigne et al. (2018, <doi:10.1093/aob/mcx095>).
Visual predictive checks are a commonly used diagnostic plot in pharmacometrics, showing how certain statistics (percentiles) for observed data compare to those same statistics for data simulated from a model. The package can generate VPCs for continuous, categorical, censored, and (repeated) time-to-event data.
Variable Penalty Dynamic Time Warping (VPdtw) for aligning chromatographic signals. With an appropriate penalty this method performs good alignment of chromatographic data without deforming the peaks (Clifford, D., Stone, G., Montoliu, I., Rezzi S., Martin F., Guy P., Bruce S., and Kochhar S.(2009) <doi:10.1021/ac802041e>; Clifford, D. and Stone, G. (2012) <doi:10.18637/jss.v047.i08>).
An oceanographic data processing package for analyzing and visualizing Video 
    Plankton Recorder data.  This package was developed at 'Bedford Institute of Oceanography'.
    Functions are designed to process automated image classification output and create organized and 
    easily portable data products.
Implements a set of routines to perform structured matrix factorization with minimum volume constraints. The NMF procedure decomposes a matrix X into a product C * D. Given conditions such that the matrix C is non-negative and has sufficiently spread columns, then volume minimization of a matrix D delivers a correct and unique, up to a scale and permutation, solution (C, D). This package provides both an implementation of volume-regularized NMF and "anchor-free" NMF, whereby the standard NMF problem is reformulated in the covariance domain. This algorithm was applied in Vladimir B. Seplyarskiy Ruslan A. Soldatov, et al. "Population sequencing data reveal a compendium of mutational processes in the human germ line". Science, 12 Aug 2021. <doi:10.1126/science.aba7408>. This package interacts with data available through the 'simulatedNMF' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/vrnmf>. The size of the 'simulatedNMF' package is approximately 8 MB.
The goal of 'vroom' is to read and write data (like 'csv',
    'tsv' and 'fwf') quickly. When reading it uses a quick initial
    indexing step, then reads the values lazily , so only the data you
    actually use needs to be read.  The writer formats the data in
    parallel and writes to disk asynchronously from formatting.
A  collection of statistical tests for martingale difference hypothesis, including automatic portmanteau test (Escansiano and Lobato, 2009) <doi:10.1016/j.jeconom.2009.03.001> and automatic variance ratio test (Kim, 2009) <doi:10.1016/j.frl.2009.04.003>.
Performs variable selection/feature reduction under a clustering or 
  classification framework. In particular, it can be used in an automated fashion 
  using mixture model-based methods ('teigen' and 'mclust' are currently supported).
  Can account for mixtures of non-Gaussian distributions via Manly transform (via 'ManlyMix').
  See Andrews and McNicholas (2014) <doi:10.1007/s00357-013-9139-2> and Neal and McNicholas (2023)
  <doi:10.48550/arXiv.2305.16464>.
Provides a shim command for survival analysis graphic generation.
Methods for calculating the variance scale exponent to
    identify memory patterns in time series data. Includes tests for white
    noise, short memory, and long memory. See Fu, H. et al. (2018)
    <doi:10.1016/j.physa.2018.06.092>.
An implementation of Vasicek and Song goodness-of-fit tests. Several functions are provided to estimate differential Shannon entropy, i.e., estimate Shannon entropy of real random variables with density, and test the goodness-of-fit of some family of distributions, including uniform, Gaussian, log-normal, exponential, gamma, Weibull, Pareto, Fisher, Laplace and beta distributions; see Lequesne and Regnault (2020) <doi:10.18637/jss.v096.c01>.
Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data and penalized estimating equations for generalized linear models with multiple imputation.
  Reference:
  Li, Y., Yang, H., Yu, H., Huang, H., Shen, Y*. (2023) "Penalized estimating equations for generalized linear models with multiple imputation", <doi:10.1214/22-AOAS1721>.
  Li, Y., Yang, H., Yu, H., Huang, H., Shen, Y*. (2023) "Penalized weighted least-squares estimate for variable selection on correlated multiply imputed data", <doi:10.1093/jrsssc/qlad028>.
A wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient, stable, and high confidential variables from omics-based data. Using a bagging strategy in combination of a parametric method or inflection point search method for cut-off threshold determination. This package can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates. Luo H, Zhao Q, et al (2020) <doi:10.1126/scitranslmed.aax7533> for more details.
Provides fast spectral estimation of latent factors in random
    dot product graphs using the vsp estimator. Under mild assumptions,
    the vsp estimator is consistent for (degree-corrected) stochastic
    blockmodels, (degree-corrected) mixed-membership stochastic
    blockmodels, and degree-corrected overlapping stochastic blockmodels.
A nonparametric method to estimate Toeplitz covariance matrices from a sample of n independently and identically distributed p-dimensional vectors with mean zero. The data is preprocessed with the discrete cosine matrix and a variance stabilization transformation to obtain an approximate Gaussian regression setting for the log-spectral density function. Estimates of the spectral density function and the inverse of the covariance matrix are provided as well. Functions for simulating data and a protein data example are included. For details see (Klockmann, Krivobokova; 2023), <arXiv:2303.10018>.
Implementation of 'Azure DevOps' <https://azure.microsoft.com/> API calls. 
    It enables the extraction of information about repositories, build and release definitions and individual releases. 
    It also helps create repositories and work items within a project without logging into 'Azure DevOps'. 
    There is the ability to use any API service with a shell for any non-predefined call.
Three steps variable selection procedure based on random forests.
    Initially developed to handle high dimensional data (for which number of
    variables largely exceeds number of observations), the package is very
    versatile and can treat most dimensions of data, for regression and
    supervised classification problems. First step is dedicated to eliminate
    irrelevant variables from the dataset. Second step aims to select all
    variables related to the response for interpretation purpose. Third step
    refines the selection by eliminating redundancy in the set of variables
    selected by the second step, for prediction purpose.
    Genuer, R. Poggi, J.-M. and Tuleau-Malot, C. (2015)
    <https://journal.r-project.org/archive/2015-2/genuer-poggi-tuleaumalot.pdf>.
Bayesian variable selection using shrinkage priors to identify significant variables in high-dimensional datasets. The package includes methods for determining the number of significant variables through innovative clustering techniques of posterior distributions, specifically utilizing the 2-Means and Sequential 2-Means (S2M) approaches. The package aims to simplify the variable selection process with minimal tuning required in statistical analysis.
Automatically generates HTML variable documentation including variable names, labels, classes, value labels (if applicable), value ranges, and summary statistics. See the vignette "vtable" for a package overview.
A 'data.frame' processor/conditioner that prepares real-world data for predictive modeling in a statistically sound manner.
    'vtreat' prepares variables so that data has fewer exceptional cases, making
    it easier to safely use models in production. Common problems 'vtreat' defends
    against: 'Inf', 'NA', too many categorical levels, rare categorical levels, and new
    categorical levels (levels seen during application, but not during training). Reference: 
    "'vtreat': a data.frame Processor for Predictive Modeling", Zumel, Mount, 2016, <DOI:10.5281/zenodo.1173313>.
A tool for calculating and drawing "variable trees". Variable trees display information about nested subsets of a data frame.
An interactive document on  the topic of variance analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://predanalyticssessions1.shinyapps.io/chisquareVarianceTest/>.
Estimates the type of variables in non-quality controlled data. The prediction is based on a random forest model, trained on over 5000 medical variables with accuracy of 99%. The accuracy can hardy depend on type and coding style of data.
Contains functions for visualization univariate data: ccdplot and qddplot.
Make it easy to use 'vue' in R with helper
              dependency functions and examples.
Contains functions for analysis and summary of tidal datasets. Also provides access to tidal data collected by the National Oceanic and Atmospheric Administration's Center for Operational Oceanographic Products and Services and the Permanent Service for Mean Sea Level. For detailed description and application examples, see Hill, T.D. and S.C. Anisfeld (2021) <doi:10.6084/m9.figshare.14161202.v1> and Hill, T.D. and S.C. Anisfeld (2015) <doi:10.1016/j.ecss.2015.06.004>.
Calculates the volume under the ROC surface and its (co)variance for ordered multi-class ROC analysis as well as certain bivariate ordinal measures of association.
Offers a comprehensive set of assertion tests to help users validate the integrity of their data. 
    These tests can be used to check for specific conditions or properties within a dataset and 
    help ensure that data is accurate and reliable. 
    The package is designed to make it easy to add quality control checks to data analysis 
    workflows and to aid in identifying and correcting any errors or inconsistencies in data.
Allow R users to interact with the 'Canvas' Learning Management System (LMS) API (see
    <https://canvas.instructure.com/doc/api/all_resources.html> for details).
    It provides a set of functions to access and manipulate course data, assignments, grades, users, 
    and other resources available through the 'Canvas' API.
Provides a set of functions for data transformations.
    Transformations are performed on character and numeric data. As the scope of the package is
    within Student Analytics, there are functions focused around the academic year.
Provides a user-friendly R 'shiny' app for performing various statistical tests on datasets. 
  It allows users to upload data in numerous formats and perform statistical analyses. 
  The app dynamically adapts its options based on the selected columns and supports both single and multiple column comparisons.
  The app's user interface is designed to streamline the process of selecting datasets, columns, and test options, 
  making it easy for users to explore and interpret their data. The underlying functions for statistical tests are well-organized and can be used independently within other R scripts.
Provides numerous functions to fill data.
    These can be applied either to missing or skewed data.
    The functions are designed within the scope of Student Analytics.
Offers a wide range of functions for reading and writing data in various file formats, 
    including CSV, RDS, Excel and ZIP files. 
    Additionally, it provides functions for retrieving metadata associated with files, 
    such as file size and creation date, making it easy to manage and organize large data sets. 
    This package is designed to simplify data import and export tasks, 
    and provide users with a comprehensive set of tools to work with different types of data files.
Provides a set of functions for manipulating data frames in accordance with specific business rules. 
    In addition, it includes wrapper functions for commonly used functions from the popular 'tidyverse' package, 
    making it easy to integrate these functions into data analysis workflows. 
    The package is designed to streamline data preprocessing and help users quickly and efficiently 
    perform data transformations that are specific to their business needs.
Helper and Wrapper functions for making shiny dashboards more easily. Functions are made modular and lower level functions are exported as well, so many use-cases are supported.
Provides an R interface for interacting with the 'Tableau' Server.
    It allows users to perform various operations such as publishing workbooks, refreshing data extracts, 
    and managing users using the 'Tableau' REST API (see <https://help.tableau.com/current/api/rest_api/en-us/REST/rest_api_ref.htm> for details). Additionally, 
    it includes functions to perform manipulations on local 'Tableau' workbooks.
Provides an R interface for interacting with the 'Semestry TermTime' services.
    It allows users to retrieve scheduling data from the API. see
    <https://github.com/vusaverse/vvtermtime/blob/main/openapi_7.7.0.pdf> for details.
Provides R functions to draw lines and curves with the width
             of the curve allowed to vary along the length of the curve.
Gaze data from the Visual World Paradigm requires significant
    preprocessing prior to plotting and analyzing the data. This package 
    provides functions for preparing visual world eye-tracking data for 
    statistical analysis and plotting. It can prepare data for linear 
    analyses (e.g., ANOVA, Gaussian-family LMER, Gaussian-family GAMM) as
    well as logistic analyses (e.g., binomial-family LMER and binomial-family GAMM).
    Additionally, it contains various plotting functions for creating grand average and
    conditional average plots. See the vignette for samples of the functionality.
    Currently, the functions in this package are designed for handling data
    collected with SR Research Eyelink eye trackers using Sample Reports created
    in SR Research Data Viewer.  While we would like to add functionality 
    for data collected with other systems in the future, the current package is 
    considered to be feature-complete; further updates will mainly entail maintenance
	and the addition of minor functionality.
Set of functions that improves the graphical presentations of the functions: wave.correlation and spin.correlation (waveslim package, Whitcher 2012) and the wave.multiple.correlation and wave.multiple.cross.correlation (wavemulcor package, Fernandez-Macho 2012b). The plot outputs (heatmaps) can be displayed in the screen or can be saved as PNG or JPG images or as PDF or EPS formats. The W2CWM2C package also helps to handle the (input data) multivariate time series easily as a list of N elements (times series) and provides a multivariate data set (dataexample) to exemplify its use. A description of the package was published in a scientific paper: Polanco-Martinez and Fernandez-Macho (2014), <doi:10.1109/MCSE.2014.96>.
R interface to a W3C Markup Validation service.
  See <https://validator.w3.org/> for more information.
Provides a set of utility function to prevent the spread of
    utilities script in W4M (Workflow4Metabolomics) scripts, and centralize
    them in a single package.
    Some are meant to be replaced by real packages in a near future, like
    the parse_args() function: it is here only to prepare the ground for
    more global changes in W4M scripts and tools.
Contains inferential and graphical routines for multi-group analysis of while-alive loss (or event) rate for possibly recurrent nonfatal event in the presence of death.
Color palettes taken from the landscapes and cities of Washington 
    state. Colors were extracted from a set of photographs, and then combined to 
    form a set of continuous and discrete palettes.  Continuous palettes were 
    designed to be perceptually uniform, while discrete palettes were chosen to
    maximize contrast at several different levels of overall brightness and 
    saturation. Each palette has been evaluated to ensure colors are 
    distinguishable by colorblind people.
A multivariate weather generator for daily climate variables based 
    on weather-states (Flecher et al. (2010) <doi:10.1029/2009WR008098>). 
    It uses a Markov chain for modeling the succession of weather states. 
    Conditionally to the weather states, the multivariate variables are modeled using the family 
    of Complete Skew-Normal distributions. Parameters are estimated on measured series. Must 
    include the variable 'Rain' and can accept as many other variables as desired.
A user-friendly factor-like interface for converting strings of
    text into numeric vectors and rectangular data structures.
Square pie charts (a.k.a. waffle charts) can be used
    to communicate parts of a whole for categorical quantities. To emulate the
    percentage view of a pie chart, a 10x10 grid should be used with each square
    representing 1% of the total. Modern uses of waffle charts do not
    necessarily adhere to this rule and can be created with a grid of any
    rectangular shape. Best practices suggest keeping the number of categories
    small, just as should be done when creating pie charts. Tools are provided
    to create waffle charts as well as stitch them together, and to use glyphs
    for making isotype pictograms.
Full screen and partial loading screens for 'Shiny' with spinners, progress bars, and notifications.
Generates random data sets including: data.frames, lists,
        and vectors.
Read 'Quake' assets including bitmap images and textures in 'wal' file format. This package also provides support for extracting these assets from 'WAD' and 'PAK' file archives. It can also read models in 'MDL' and 'MD2' formats.
Compare complex R objects and reveal the key differences.
    Designed particularly for use in testing packages where being able to
    quickly isolate key differences makes understanding test failures much
    easier.
Process GPS and accelerometry data to generate walk bouts. A walk bout is a period of activity with accelerometer movement matching the patterns of walking with corresponding GPS measurements that confirm travel. The inputs of the 'walkboutr' package are individual-level accelerometry and GPS data. The outputs of the model are walk bouts with corresponding times, duration, and summary statistics on the sample population, which collapse all personally identifying information. These bouts can be used to measure walking both as an outcome of a change to the built environment or as a predictor of health outcomes such as a cardioprotective behavior. Kang B, Moudon AV, Hurvitz PM, Saelens BE (2017) <doi:10.1016/j.trd.2017.09.026>.
Efficient Bayesian generalized linear models with time-varying coefficients 
    as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial 
    observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using 
    Hamiltonian Monte Carlo provided by Stan, using a state space representation 
    of the model in order to marginalise over the coefficients for efficient sampling. 
    For non-Gaussian models, the package uses the importance sampling type estimators based on 
    approximate marginal MCMC as in Vihola, Helske, Franks (2020, <doi:10.1111/sjos.12492>).
Easily collect walk scores, bike scores, and transit scores (where
    available) from the 'Walk Score' API <https://www.walkscore.com/professional/api.php>, 
    a proprietary API that assigns locations a walkability score between 0 and 100.
A collection of functions to perform the Application
        Programming Interface (API) calls associated with the Walk
        Score website (www.walkscore.com) within the R environment.
        These functions can be used to query the Walk Score and Transit
        Score database for a wide variety of information using R
        scripts. This package includes the simple Walk Score and
        Transit Score API calls, which return the scores associated
        with an input location, as well as calls which return some data
        used to calculate the scores. These functions are especially
        useful for mass data collection and gathering Walk Score and
        Transit Score values for large lists of locations.
The 'shiny' application Wallace is a modular platform for 
    reproducible modeling of species niches and distributions. Wallace
    guides users through a complete analysis, from the acquisition of species
    occurrence and environmental data to visualizing model predictions on an 
    interactive map, thus bundling complex workflows into a single, 
    streamlined interface. An extensive vignette, which guides users through 
    most package functionality can be found on the package's GitHub Pages 
    website: <https://wallaceecomod.github.io/wallace/articles/tutorial-v2.html>.
Datasets from the WallOmics project. Contains phenomics, metabolomics, proteomics and transcriptomics data collected from two organs of five ecotypes of the model plant Arabidopsis thaliana exposed to two temperature growth conditions. Exploratory and integrative analyses of these data are presented in Durufle et al (2020) <doi:10.1093/bib/bbaa166> and Durufle et al (2020) <doi:10.3390/cells9102249>.
A prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events.
Provides API access to the Walmart Open API <https://developer.walmartlabs.com/>, 
    that contains data about stores, Value of the day and products which 
    includes names, sale prices, shipping rates and taxonomies.
A toolbox of common robust statistical tests, including robust
  descriptives, robust t-tests, and robust ANOVA. It is also available as a
  module for 'jamovi' (see <https://www.jamovi.org> for more information).
  Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on
  the scripts and work of Rand Wilcox. These analyses are described in depth in
  the book 'Introduction to Robust Estimation & Hypothesis Testing'.
Implements Weighted-Average Least Squares model averaging
    for negative binomial regression models of Huynh (2024) <doi:10.48550/arXiv.2404.11324>,
    generalized linear models of De Luca, Magnus, Peracchi (2018) 
    <doi:10.1016/j.jeconom.2017.12.007> and linear regression models of 
    Magnus, Powell, Pruefer (2010) <doi:10.1016/j.jeconom.2009.07.004>, see also 
    Magnus, De Luca (2016) <doi:10.1111/joes.12094>. Weighted-Average Least Squares
    for the linear regression model is based on the original 'MATLAB' code by 
    Magnus and De Luca <https://www.janmagnus.nl/items/WALS.pdf>, see also 
    Kumar, Magnus (2013) <doi:10.1007/s13571-013-0060-9> and 
    De Luca, Magnus (2011) <doi:10.1177/1536867X1201100402>.
The outcome of various rehabilitation strategies for water distribution systems can be modeled with the Water Management Simulator (WaMaSim). Pipe breaks and the corresponding damage and rehabilitation costs are simulated. It is mainly intended to be used as educational tool for the Water Infrastructure Experimental and Computer Laboratory at ETH Zurich, Switzerland.
'MIME' types are shorthand descriptors for
      file contents and can be determined from "magic"
      bytes in file headers, file contents or intuited
      from file extensions. Tools are provided to
      perform curated "magic" tests as well as mapping
      'MIME' types from a database of over 1,500
      extension mappings.
Provides the functions to perform a Welch's one-way Anova with fixed effects
  based on summary statistics (sample size, means, standard deviation) and the Games-Howell post hoc test
  for multiple comparisons and provides the effect size estimator adjusted omega squared.
  In addition sample size estimation can be computed based on Levy's method, and a Monte Carlo
  simulation is included to bootstrap residual normality and homoscedasticity
  Welch, B. L. (1951) <doi:10.1093/biomet/38.3-4.330>
  Kirk, R. E. (1996) <doi:10.1177/0013164496056005002>
  Carroll, R. M., & Nordholm, L. A. (1975) <doi:10.1177/001316447503500304>
  Albers, C., & Lakens, D. (2018) <doi:10.1016/j.jesp.2017.09.004>
  Games, P. A., & Howell, J. F. (1976) <doi:10.2307/1164979>
  Levy, K. J. (1978a) <doi:10.1080/00949657808810246>
  Show-Li, J., & Gwowen, S. (2014) <doi:10.1111/bmsp.12006>.
It generates the roster of turn for an outlet which is flowing 
        (water) 24X7 or 168 hours towards the area under command or agricutural 
        area (to be irrigated). The area under command is differentially owned 
        by different individual farmers. The Outlet runs for free of cost to 
        irrigate the area under command 24X7.
        So, flow time of the outlet has to be divided based on an area owned by 
        an individual farmer and the location of his land or farm. This roster 
        is known as 'warabandi' and its generation in agriculture practices is a 
        very tedious task. Calculations of time in microseconds are more 
        error-prone, especially whenever it is performed by hands. That division
        of flow time for an individual farmer can be calculated by 'warabandi'. 
        However, it generates a full publishable report for an outlet and all the 
        farmers who have farms subjected to be irrigated. 
        It reduces error risk and makes a more reproducible roster. For more 
        details about warabandi system you can found elsewhere in 
        Bandaragoda DJ(1995) <https://publications.iwmi.org/pdf/H_17571i.pdf>.
Functions aiming to facilitate the analysis of the structure of animal acoustic signals in 'R'. 'warbleR' makes use of the basic sound analysis tools from the packages 'tuneR' and 'seewave', and offers new tools for acoustic structure analysis. The main features of the package are the use of loops to apply tasks through acoustic signals referenced in a selection (annotation) table and the production of spectrograms in image files that allow to organize data and verify acoustic analyzes. The package offers functions to explore, organize and manipulate multiple sound files, explore and download 'Xeno-Canto' recordings, create spectrograms of complete recordings or individual signals, run different measures of acoustic signal structure, evaluate the performance of measurement methods, catalog signals, characterize different structural levels in acoustic signals, run statistical analysis of duet coordination and consolidate databases and annotation tables, among others.
This estimates precise weaning ages
	for a given skeletal population
	by analyzing the stable nitrogen isotope ratios of them.
	Bone collagen turnover rates estimated anew and
	the approximate Bayesian computation (ABC)
	were adopted in this package.
Tooling to group dates by a variety of periods including:
    yearly, monthly, by second, by week of the month, and more.  The
    groups are defined in such a way that they also represent the distance
    between dates in terms of the period. This extracts valuable
    information that can be used in further calculations that rely on a
    specific temporal spacing between observations.
Mixed effects modeling with warping for functional data using B-
    spline. Warping coefficients are considered as random effects, and warping
    functions are general functions, parameters representing the projection onto B-
    spline basis of a part of the warping functions. Warped data are modelled by a
    linear mixed effect functional model, the noise is Gaussian and independent from
    the warping functions.
Urban water and sanitation survey dataset collected by Water and
    Sanitation for the Urban Poor (WSUP) with technical support from 
    Valid International. These citywide surveys have been collecting data 
    allowing water and sanitation service levels across the entire city to be 
    characterised, while also allowing more detailed data to be collected in 
    areas of the city of particular interest. These surveys are intended to 
    generate useful information for others working in the water and sanitation
    sector. Current release version includes datasets collected from a survey 
    conducted in Dhaka, Bangladesh in March 2017. This survey in Dhaka is one of 
    a series of surveys to be conducted by WSUP in various
    cities in which they operate including Accra, Ghana; Nakuru, Kenya; 
    Antananarivo, Madagascar; Maputo, Mozambique; and, Lusaka, Zambia. This 
    package will be updated once the surveys in other cities are completed and 
    datasets have been made available.
Time series outlier detection with non parametric test. This is a new outlier detection methodology (washer): efficient for time saving elaboration and implementation procedures, adaptable for general assumptions and for needing very short time series, reliable and effective as involving robust non parametric test. You can find two approaches: single time series (a vector) and grouped time series (a data frame). For other informations: Andrea Venturini (2011) Statistica - Universita di Bologna, Vol.71, pp.329-344. For an informal explanation look at R-bloggers on web.
Gets data from the Washington State Legislature.
Create plots and tables in a consistent style with WaSHI
    (Washington Soil Health Initiative) branding. Use 'washi' to easily
    style your 'ggplot2' plots and 'flextable' tables.
The wavelet-based variance transformation method is used for system modelling and prediction. It refines predictor spectral representation using Wavelet Theory, which leads to improved model specifications and prediction accuracy. Details of methodologies used in the package can be found in Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962>, Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>, and Jiang, Z., Sharma, A., & Johnson, F. (2021) <doi:10.1016/J.JHYDROL.2021.126816>.
Provides a set of functions to implement decision-making systems
    based on the W.A.S.P.A.S. method (Weighted Aggregated Sum Product Assessment),
    Chakraborty and Zavadskas (2012) <doi:10.5755/j01.eee.122.6.1810>.
    So this package offers functions that analyze and validate the
    raw data, which must be entered in a determined format;
    extract specific vectors and matrices from this raw database;
    normalize the input data; calculate rankings by intermediate methods;
    apply the lambda parameter for the main method; and a function that does
    everything at once. The package has an example database called choppers,
    with which the user can see how the input data should be organized so that
    everything works as recommended by the decision methods based on multiple
    criteria that this package solves. Basically, the data are composed of a set
    of alternatives, which will be ranked, a set of choice criteria, a matrix
    of values for each Alternative-Criterion relationship, a vector of weights
    associated with the criteria, since certain criteria are considered more
    important than others, as well as a vector that defines each criterion as
    cost or benefit, this determines the calculation formula, as there are those
    criteria that we want the highest possible value (e.g. durability)
    and others that we want the lowest possible value (e.g. price).
Functions to compute Wasserstein barycenters
    of subset posteriors using the swapping algorithm developed by Puccetti, 
    Rüschendorf and Vanduffel (2020) <doi:10.1016/j.jmaa.2017.02.003>. The 
    Wasserstein barycenter is a geometric approach for combining subset 
    posteriors. It allows for parallel and distributed computation of the 
    posterior in case of complex models and/or big datasets, thereby increasing
    computational speed tremendously.
Imports U.S. Geological Survey (USGS) daily hydrologic data from USGS web services (see <https://waterservices.usgs.gov/> for more information), plots the data, addresses some common data problems, and calculates and plots anomalies.
Provides support for creating waterfall charts in R
    using both traditional base and lattice graphics.
A not uncommon task for quants is to create 'waterfall charts'. There seems to be no simple way to do this in 'ggplot2' currently. This package contains a single function (waterfall) that simply draws a waterfall chart in a 'ggplot2' object. Some flexibility is provided, though often the object created will need to be modified through a theme.
The main purpose of waterquality is to quickly and easily convert
    satellite-based reflectance imagery into one or many well-known water quality
    algorithms designed for the detection of harmful algal blooms or the following
    pigment proxies: chlorophyll-a, blue-green algae (phycocyanin), and turbidity.
    Johansen et al. (2019) <doi:10.21079/11681/35053>.
Provides Water Year Hydrologic Classification Indices based on measured 
    unimpaired runoff (in million acre-feet). Data is provided by California Department of Water Resources
    and subject to revision.
Wrap-around Time Series (WATS) plots for interrupted time series
    designs with seasonal patterns.
    Longitudinal trajectories are shown in both Cartesian and polar coordinates.
    In many scenarios, a WATS plot more clearly shows the existence and effect size of
    of an intervention.
    This package accompanies
    "Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs"
    by Rodgers, Beasley, & Schuelke (2014)
    <doi:10.1080/00273171.2014.946589>;
    see 'citation("Wats")' for details.
Tools for fitting and simulating mixtures of Watson distributions. 
             The random sampling scheme of the package offers two sampling 
             algorithms that are based of the results of Sablica, Hornik and Leydold (2022)
             <https://research.wu.ac.at/en/publications/random-sampling-from-the-watson-distribution>.
             What is more, the package offers a smart tool to combine these two methods,
             and based on the selected parameters, it approximates the relative sampling
             speed for both methods and picks the faster one. In addition, the package
             offers a fitting function for the mixtures of Watson distribution, that
             uses the expectation-maximization (EM) algorithm. Special features are
             the possibility to use multiple variants of the E-step and M-step,
             sparse matrices for the data representation and state of the art methods
             for numerical evaluation of needed special functions using the results of
             Sablica and Hornik (2022) <https://www.ams.org/journals/mcom/2022-91-334/S0025-5718-2021-03690-X/>.
Efficiently read and write Waveform (WAV) audio files <https://en.wikipedia.org/wiki/WAV>. 
  Support for unsigned 8 bit Pulse-code modulation (PCM), signed 12, 16, 24 and 32 bit 
  PCM and other encodings.
Computes Bayesian wavelet shrinkage credible intervals for
	nonparametric regression.
	The method uses cumulants to derive Bayesian credible intervals for
	wavelet regression estimates.
	The first four cumulants of the posterior distribution of the
	estimates are expressed in terms of the observed data and integer
	powers of the mother wavelet functions.
	These powers are closely approximated by linear combinations of
	wavelet scaling functions at an appropriate finer scale.
	Hence, a suitable modification of the discrete wavelet transform allows
	the posterior cumulants to be found efficiently for any data set.
	Johnson transformations then yield the credible intervals themselves.
	Barber, S., Nason, G.P. and Silverman, B.W. (2002)
	<doi:10.1111/1467-9868.00332>.
Makes available code necessary to reproduce figures and
        tables in papers on the WaveD method for wavelet deconvolution
        of noisy signals as presented in The WaveD Transform in R,
        Journal of Statistical Software Volume 21, No. 3, 2007.
The wavelet and ANN technique have been combined to reduce the effect of data noise. This wavelet-ANN conjunction model is able to forecast time series data with better accuracy than the traditional time series model. This package fits hybrid Wavelet ANN model for time series forecasting using algorithm by Anjoy and Paul (2017) <DOI: 10.1007/s00521-017-3289-9>.
Noise in the time-series data significantly affects the accuracy of the ARIMA model. Wavelet transformation decomposes the time series data into subcomponents to reduce the noise and help to improve the model performance. The wavelet-ARIMA model can achieve higher prediction accuracy than the traditional ARIMA model. This package provides Wavelet-ARIMA model for time series forecasting based on the algorithm by Aminghafari and Poggi (2012) and Paul and Anjoy (2018) <doi:10.1142/S0219691307002002> <doi:10.1007/s00704-017-2271-x>.
Wavelet analysis and reconstruction of time series, cross-wavelets and phase-difference (with filtering options), significance with simulation algorithms.
ETS stands for Error, Trend, and Seasonality, and it is a popular time series forecasting method. Wavelet decomposition can be used for denoising, compression, and feature extraction of signals. By removing the high-frequency components, wavelet decomposition can remove noise from the data while preserving important features. A hybrid Wavelet ETS  (Error Trend-Seasonality) model has been developed for time series forecasting using algorithm of Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.
Fits the combination of Wavelet-GARCH model for time series forecasting using algorithm by Paul (2015) <doi:10.3233/MAS-150328>.
Wavelet decomposition method is very useful for modelling noisy time series data. Wavelet decomposition using 'haar' algorithm has been implemented to developed hybrid Wavelet GBM (Gradient Boosting Method) model for time series forecasting using algorithm by Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.
The employment of the Wavelet decomposition technique proves to be highly advantageous in the modelling of noisy time series data. Wavelet decomposition technique using the "haar" algorithm has been incorporated to formulate a hybrid Wavelet KNN (K-Nearest Neighbour) model for time series forecasting, as proposed by Anjoy and Paul (2017) <DOI:10.1007/s00521-017-3289-9>.
A wavelet-based LSTM model is a type of neural network architecture that uses wavelet technique to pre-process the input data before passing it through a Long Short-Term Memory (LSTM) network. The wavelet-based LSTM model is a powerful approach that combines the benefits of wavelet analysis and LSTM networks to improve the accuracy of predictions in various applications. This package has been developed using the algorithm of Anjoy and Paul (2017) and Paul and Garai (2021) <DOI:10.1007/s00521-017-3289-9> <doi:10.1007/s00500-021-06087-4>.
Wavelet decomposes a series into multiple sub series called detailed and smooth components which helps to capture volatility at multi resolution level by various models. Two hybrid Machine Learning (ML) models (Artificial Neural Network and Support Vector Regression have been used) have been developed in combination with stochastic models, feature selection, and optimization algorithms for prediction of the data. The algorithms have been developed following Paul and Garai (2021)  <doi:10.1007/s00500-021-06087-4>.
Four filters have been chosen namely 'haar', 'c6', 'la8', and 'bl14' (Kindly refer to 'wavelets' in 'CRAN' repository for more supported filters). Levels of decomposition are 2, 3, 4, etc. up to maximum decomposition level which is ceiling value of logarithm of length of the series base 2. For each combination two models are run separately. Results are stored in 'input'. First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as 'MIN' and other values are denoted as 'NA'. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as 'MAX' and other values are denoted as 'NA'. 'output' contains the similar number of rows (which is 8) and columns (which is number filter-level combinations) as of 'input'. Values in 'output' are corresponding 'NA', 'MIN' or 'MAX'. Finally, the column containing minimum number of 'NA' values is denoted as the best ('FL'). In special case, if two columns having equal 'NA', it has been checked among these two columns which one is having least 'NA' in first five rows and has been inferred as the best. 'FL_metrics_values' are the corresponding metrics values. 'WARIGAANbest' is the data frame (dimension: 1*8) containing different metrics of the best filter-level combination.  More details can be found in Garai and others (2023) <doi:10.13140/RG.2.2.11977.42087>.
The Wavelet Decomposition followed by Random Forest Regression (RF) models have been applied for time series forecasting. The maximum overlap discrete wavelet transform (MODWT) algorithm was chosen as it works for any length of the series. The series is first divided into training and testing sets. In each of the wavelet decomposed series, the  supervised machine learning approach namely random forest was employed to train the model. This package also provides accuracy metrics in the form of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Ding et al. (2021) <DOI: 10.1007/s11356-020-12298-3>.
Contains functions for computing and plotting
        discrete wavelet transforms (DWT) and maximal overlap discrete
        wavelet transforms (MODWT), as well as their inverses.
        Additionally, it contains functionality for computing and
        plotting wavelet transform filters that are used in the above
        decompositions as well as multiresolution analyses.
The main aim of this package is to combine the advantage of wavelet and support vector machine models for time series forecasting. This package also gives the accuracy measurements in terms of RMSE and MAPE. This package fits the hybrid Wavelet SVR model for time series forecasting The main aim of this package is to combine the advantage of wavelet and Support Vector Regression (SVR) models for time series forecasting. This package also gives the accuracy measurements in terms of Root Mean Square Error (RMSE) and Mean Absolute Prediction Error (MAPE). This package is based on the algorithm of Raimundo and Okamoto (2018) <DOI: 10.1109/INFOCT.2018.8356851>.
Wavelet routines that calculate single sets of wavelet
    multiple regressions and correlations, and cross-regressions and
    cross-correlations from a multivariate time series.  Dynamic versions
    of the routines allow the wavelet local multiple (cross-)regressions
    and (cross-)correlations to evolve over time.
Functions for calculating the
    fetch (length of open water distance along given directions)
    and estimating wave energy from wind and wave monitoring data.
For multivariate datasets, this function enables the estimation of missing data using the Weighted AVERage of all possible Regressions using the data available.
Originally designed application in the context of
    resource-limited plant research and breeding programs, 'waves'
    provides an open-source solution to spectral data processing and model
    development by bringing useful packages together into a streamlined
    pipeline.  This package is wrapper for functions related to the
    analysis of point visible and near-infrared reflectance measurements.
    It includes visualization, filtering, aggregation, preprocessing,
    cross-validation set formation, model training, and prediction
    functions to enable open-source association of spectral and reference
    data. This package is documented in a peer-reviewed manuscript in the
    Plant Phenome Journal <doi:10.1002/ppj2.20012>.  Specialized
    cross-validation schemes are described in detail in Jarquín et al.
    (2017) <doi:10.3835/plantgenome2016.12.0130>. Example data is from
    Ikeogu et al. (2017) <doi:10.1371/journal.pone.0188918>.
Spatial data are generally auto-correlated, meaning that if two 
  units selected are close to each other, then it is likely that they share the
  same properties. For this reason, when sampling in the population it is often
  needed that the sample is well spread over space. A new method to draw a sample
  from a population with spatial coordinates is proposed. This method is called
  wave (Weakly Associated Vectors) sampling. It uses the less correlated vector
  to a spatial weights matrix to update the inclusion probabilities vector
  into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) <doi:10.1007/s13253-020-00407-1>.
Basic wavelet routines for time series (1D), image (2D) and array 
  (3D) analysis.  The code provided here is based on wavelet methodology 
  developed in Percival and Walden (2000); Gencay, Selcuk and Whitcher (2001); 
  the dual-tree complex wavelet transform (DTCWT) from Kingsbury (1999, 2001) as
  implemented by Selesnick; and Hilbert wavelet pairs (Selesnick 2001, 2002).  
  All figures in chapters 4-7 of GSW (2001) are reproducible using this package 
  and R code available at the book website(s) below.
Performs 1, 2 and 3D real and complex-valued wavelet transforms,
	nondecimated transforms, wavelet packet transforms, nondecimated
	wavelet packet transforms, multiple wavelet transforms,
	complex-valued wavelet transforms, wavelet shrinkage for
	various kinds of data, locally stationary wavelet time series,
	nonstationary multiscale transfer function modeling, density
	estimation.
Provides scalogram based wavelet tools for time series analysis: wavelet power spectrum, scalogram, windowed scalogram, windowed scalogram difference (see Bolos et al. (2017) <doi:10.1016/j.amc.2017.05.046>), scale index and windowed scale index (Benitez et al. (2010) <doi:10.1016/j.camwa.2010.05.010>).
Computation of approximate potentials for both gradient and non
    gradient fields. It is known from physics that only gradient fields, also
    known as conservative, have a well defined potential function. Here we
    present an algorithm, based on the classical Helmholtz decomposition, to
    obtain an approximate potential function for non gradient fields.
    More information in
    Rodríguez-Sánchez (2020) <doi:10.1371/journal.pcbi.1007788>.
Converts pathways from 'WikiPathways' GPML format into
  'igraph' objects. Includes tools to find all cycles in the
  resulting graphs and determine which ones involve negative feedback
  (inhibition).
Assessing predictive models of spatial data can be
    challenging, both because these models are typically built for
    extrapolating outside the original region represented by training data
    and due to potential spatially structured errors, with "hot spots" of
    higher than expected error clustered geographically due to spatial
    structure in the underlying data. Methods are provided for assessing
    models fit to spatial data, including approaches for measuring the
    spatial structure of model errors, assessing model predictions at
    multiple spatial scales, and evaluating where predictions can be made
    safely. Methods are particularly useful for models fit using the
    'tidymodels' framework. Methods include Moran's I ('Moran' (1950)
    <doi:10.2307/2332142>), Geary's C ('Geary' (1954)
    <doi:10.2307/2986645>), Getis-Ord's G ('Ord' and 'Getis' (1995)
    <doi:10.1111/j.1538-4632.1995.tb00912.x>), agreement coefficients from
    'Ji' and Gallo (2006) (<doi: 10.14358/PERS.72.7.823>), agreement
    metrics from 'Willmott' (1981) (<doi: 10.1080/02723646.1981.10642213>)
    and 'Willmott' 'et' 'al'. (2012) (<doi: 10.1002/joc.2419>), an
    implementation of the area of applicability methodology from 'Meyer'
    and 'Pebesma' (2021) (<doi:10.1111/2041-210X.13650>), and an
    implementation of multi-scale assessment as described in 'Riemann'
    'et' 'al'. (2010) (<doi:10.1016/j.rse.2010.05.010>).
The BACON algorithms are methods for multivariate outlier
    nomination (detection) and robust linear regression by Billor, Hadi,
    and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2>. The extension
    to weighted problems is due to Beguin and Hulliger (2008)
    <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X200800110616>; see
    also <doi:10.21105/joss.03238>.
Provides efficient implementation of the Wild Binary Segmentation and Binary
    Segmentation algorithms for estimation of the number and locations of
    multiple change-points in the piecewise constant function plus Gaussian
    noise model.
Implements the diagnostic "theta" developed in Poetscher and Preinerstorfer (2020) "How Reliable are Bootstrap-based Heteroskedasticity Robust Tests?" <arXiv:2005.04089>. This diagnostic can be used to detect and weed out bootstrap-based procedures that provably have size equal to one for a given testing problem. The implementation covers a large variety of bootstrap-based procedures, cf. the above mentioned article for details. A function for computing bootstrap p-values is provided.
Search and download data from the World Bank Data API.
Implements detection for the number and locations of
    the change-points in a time series using the Wild Binary Segmentation and
    the Locally Stationary Wavelet model of Korkas and Fryzlewicz (2017) <doi:10.5705/ss.202015.0262>.
Download and plot education specific demographic data from the Wittgenstein Centre for Demography and Human Capital Data Explorer <http://dataexplorer.wittgensteincentre.org/>.
A flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.
Analyze given data frame with multiple endpoints and return Kaplan-Meier survival probabilities together with the specified confidence interval. See Nabipoor M, Westerhout CM, Rathwell S, and Bakal JA (2023) <doi:10.1186/s12874-023-01857-0>.
K-means clustering, hierarchical clustering, and PCA with observational 
    weights and/or variable weights. It also includes the corresponding functions 
    for data nuggets which serve as representative samples of large datasets.
    Cherasia et al., (2022) <doi:10.1007/978-3-031-22687-8_20>. 
    Amaratunga et al., (2009) <doi:10.1002/9780470317129>.
Retrieval the leaf area index (LAI) and soil moisture (SM) from  microwave backscattering data using water cloud model (WCM) model . The WCM algorithm attributed to Pervot et al.(1993) <doi:10.1016/0034-4257(93)90053-Z>. The authors are grateful to SAC, ISRO, Ahmedabad for providing financial support to Dr. Prashant K Srivastava to conduct this research work.
Implements inferential and graphic procedures for the semiparametric proportional 
  means regression of weighted composite endpoint of recurrent event and death (Mao and Lin, 
  2016, <doi:10.1093/biostatistics/kxv050>).
Allows users to create weighted confusion matrices and accuracy
    metrics that help with the model selection process for classification
    problems, where distance from the correct category is important. The
    package includes several weighting schemes which can be parameterized, as
    well as custom configuration options. Furthermore, users can decide
    whether they wish to positively or negatively affect the accuracy score
    as a result of applying weights to the confusion matrix. Functions are
    included to calculate accuracy metrics for imbalanced data. Finally,
    'wconf' integrates well with the 'caret' package, but it can also work
    standalone when provided data in matrix form.
    References:
    Kuhn, M. (2008) "Building Perspective Models in R Using the caret Package"
    <doi:10.18637/jss.v028.i05>
    Monahov, A. (2021) "Model Evaluation with Weighted Threshold Optimization
    (and the mewto R package)" <doi:10.2139/ssrn.3805911>
    Monahov, A. (2024) "Improved Accuracy Metrics for Classification with
    Imbalanced Data and Where Distance from the Truth Matters, with the wconf R
    Package" <doi:10.2139/ssrn.4802336>
    Starovoitov, V., Golub, Y. (2020). New Function for Estimating Imbalanced
    Data Classification Results. Pattern Recognition and Image Analysis, 295–302
    Van de Velden, M., Iodice D'Enza, A., Markos, A., Cavicchia, C. (2023)
    "A general framework for implementing distances for categorical variables"
    <doi:10.48550/arXiv.2301.02190>.
Calculates Pearson, Spearman, polychoric, and polyserial correlation coefficients, in weighted or unweighted form. The package implements tetrachoric correlation as a special case of the polychoric and biserial correlation as a specific case of the polyserial.
A new inverse probability of selection weighted Cox model to deal with outcome-dependent sampling in survival analysis.
Search and download data from over 40 databases hosted by the World Bank, including the World Development Indicators ('WDI'), International Debt Statistics, Doing Business, Human Capital Index, and Sub-national Poverty indicators.
A modern, flexible interface for accessing the World Bank’s World Development Indicators 
  (WDI) API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation>. 
  Similar to the existing 'WDI' package, 'wdi2' allows users to download, process, and analyze 
  indicator data for multiple countries and years. However, 'wdi2' differs by relying on 'httr2' 
  for multi-page request and error handling, providing support for downloading multiple indicators 
  with a single function call, using progress bars to keep users informed about the data processing,
  and returning the processed data in a tidy data format in line with Wickham (2014) <doi:10.18637/jss.v059.i10>.
Provides efficient implementations of weighted dependence measures
  and related asymptotic tests for independence. Implemented measures are
  the Pearson correlation, Spearman's rho, Kendall's tau, Blomqvist's beta, and
  Hoeffding's D; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0> and
  Hollander et al. (2015, ISBN:9780470387375).
There are a number of binary files associated with the
    'Webdriver'/'Selenium' project. This package provides functions to download
    these binaries and to manage processes involving them.
Assortativity coefficients, centrality measures, 
    and clustering coefficients for weighted and directed networks.
    Rewiring unweighted networks with given assortativity coefficients.
    Generating general preferential attachment networks.
Functions for finding and pulling data from the
  'Wisconsin Department of Natural Resources ArcGIS REST APIs'
  <https://dnrmaps.wi.gov/arcgis/rest/services> and 
  <https://dnrmaps.wi.gov/arcgis2/rest/services>.
Fetch and clean data from the World Database on Protected
    Areas (WDPA) and the World Database on Other Effective Area-Based
    Conservation Measures (WDOECM). Data is obtained from Protected Planet
    <https://www.protectedplanet.net/en>. To augment data cleaning procedures,
    users can install the 'prepr' R package (available at
    <https://github.com/dickoa/prepr>). For more information on this package,
    see Hanson (2022) <doi:10.21105/joss.04594>.
Functions are collected to analyse weather data for agriculture 
    purposes including to read weather records in multiple formats, 
    calculate extreme climate index.
Numerous time series admit autoregressive moving average (ARMA)
  representations, in which the errors are uncorrelated but not necessarily
  independent.
  These models are called weak ARMA by opposition to the standard ARMA models, 
  also called strong ARMA models, in which the error terms are supposed to be 
  independent and identically distributed (iid).
  This package allows the study of nonlinear time series models through weak 
  ARMA representations.
  It determines identification, estimation and validation for ARMA models and 
  for AR and MA models in particular. 
  Functions can also be used in the strong case.
  This package also works on white noises by omitting arguments 'p', 'q', 'ar'
  and 'ma'.
  See Francq, C. and Zakoïan, J. (1998) <doi:10.1016/S0378-3758(97)00139-0> and 
  Boubacar Maïnassara, Y. and Saussereau, B. (2018)
  <doi:10.1080/01621459.2017.1380030> for more details.
Package to read Empatica E4 data, perform several transformations, perform signal
    processing and analyses, including batch analyses.
Weather indices represent the overall weekly effect of a weather variable on crop yield throughout the cropping season. This package contains functions that can convert the weekly weather data into yearly weighted Weather indices with weights being the correlation coefficient between weekly weather data over the years and crop yield over the years. This can be done for an individual weather variable and for two weather variables at a time as the interaction effect. This method was first devised by Jain, RC, Agrawal R, and Jha, MP (1980), "Effect of climatic variables on rice yield and its forecast",MAUSAM, 31(4), 591–596, <doi:10.54302/mausam.v31i4.3477>. Later, the method have been used by various researchers and the latest can found in Gupta, AK, Sarkar, KA, Dhakre, DS, & Bhattacharya, D (2022), "Weather Based Potato Yield Modelling using Statistical and Machine Learning Technique",Environment and Ecology, 40(3B), 1444–1449,<https://www.environmentandecology.com/volume-40-2022>.
Functions to convert between weather metrics, including conversions
    for metrics of temperature, air moisture, wind speed, and precipitation.
    This package also includes functions to calculate the heat index from
    air temperature and air moisture.
Provides automated downloading, parsing and formatting of
    weather data for Australia through API endpoints provided by the
    Department of Primary Industries and Regional Development ('DPIRD') of
    Western Australia and by the Science and Technology Division of the
    Queensland Government's Department of Environment and Science ('DES').
    As well as the Bureau of Meteorology ('BOM') of the Australian
    government precis and coastal forecasts, agriculture bulletin data,
    and downloading and importing radar and satellite imagery files.
    'DPIRD' weather data are accessed through public 'APIs' provided by
    'DPIRD', <https://www.agric.wa.gov.au/weather-api-20>, providing
    access to weather station data from the 'DPIRD' weather station
    network.  Australia-wide weather data are based on data from the
    Australian Bureau of Meteorology ('BOM') data and accessed through
    'SILO' (Scientific Information for Land Owners) Jeffrey et al. (2001)
    <doi:10.1016/S1364-8152(01)00008-1>.  'DPIRD' data are made available
    under a Creative Commons Attribution 3.0 Licence (CC BY 3.0 AU)
    license <https://creativecommons.org/licenses/by/3.0/au/deed.en>.
    SILO data are released under a Creative Commons Attribution 4.0
    International licence (CC BY 4.0)
    <https://creativecommons.org/licenses/by/4.0/>. 'BOM' data are (c)
    Australian Government Bureau of Meteorology and released under a
    Creative Commons (CC) Attribution 3.0 licence or Public Access Licence
    ('PAL') as appropriate, see
    <http://www.bom.gov.au/other/copyright.shtml> for further details.
A comprehensive suite of functions for processing,
  analyzing, and visualizing textual data from tweets is offered.  
  Users can clean tweets, analyze their sentiments, visualize data, 
  and examine the correlation between sentiments and environmental 
  data such as weather conditions. Main features include text processing,  
  sentiment analysis, data visualization, correlation analysis, and 
  synthetic data generation. Text processing involves cleaning and preparing  
  tweets by removing textual noise and irrelevant words. Sentiment analysis 
  extracts and accurately analyzes sentiments from tweet texts using advanced 
  algorithms. Data visualization creates various charts like word clouds
  and sentiment polarity graphs for visual representation of data. Correlation 
  analysis examines and calculates the correlation between tweet sentiments 
  and environmental variables such as weather conditions. Additionally, 
  random tweets can be generated for testing and evaluating the performance 
  of analyses, empowering users to effectively analyze and interpret 'Twitter' 
  data for research and commercial purposes.
Provides Apache and IIS log analytics for transaction performance, client populations and workload definitions.
Chemical information from around the web. This package interacts 
    with a suite of web services for chemical information. Sources include: Alan
    Wood's Compendium of Pesticide Common Names, Chemical Identifier Resolver,
    ChEBI, Chemical Translation Service, ChemSpider, ETOX, Flavornet,
    NIST Chemistry WebBook, OPSIN, PubChem, SRS, Wikidata.
Organizational framework for web development in R including functions to  
    serve static and dynamic content via HTTP methods, includes the html5 package to
    create HTML pages, and offers other utility functions for common tasks related 
    to web development.
A client for the 'WebDriver' 'API'. It allows driving a
    (probably headless) web browser, and can be used to test web
    applications, including 'Shiny' apps. In theory it works with any
    'WebDriver' implementation, but it was only tested with 'PhantomJS'.
Functions for easily creating interactive web pages using
    'R Markdown' that students can use in self-guided learning.
Create a web app that makes it easier to test web clients
    without using the internet. It includes a web app framework with path
    matching, parameters and templates. Can parse various 'HTTP' request
    bodies. Can send 'JSON' data or files from the disk. Includes a web
    app that implements the 'httpbin.org' web service.
The web version WebGestalt <https://www.webgestalt.org> supports 12 organisms, 354 gene identifiers and 321,251 function categories. Users can upload the data and functional categories with their own gene identifiers. In addition to the Over-Representation Analysis, WebGestalt also supports Gene Set Enrichment Analysis and Network Topology Analysis. The user-friendly output report allows interactive and efficient exploration of enrichment results. The WebGestaltR package not only supports all above functions but also can be integrated into other pipeline or simultaneously analyze multiple gene lists.
Displays geospatial data on an interactive 3D globe in the web browser.
Creates interactive web maps using the 'JavaScript' 'Leaflet'
    library with base layers of 'The National Map' ('TNM'). 'TNM' services
    provide access to base geospatial information that describes the landscape
    of the United States and its territories. This package is dependent on, and
    intended to be used with, the 'leaflet' package.
Stubbing and setting expectations on 'HTTP' requests.
    Includes tools for stubbing 'HTTP' requests, including expected
    request conditions and response conditions. Match on
    'HTTP' method, query parameters, request body, headers and
    more. Can be used for unit tests or outside of a testing 
    context.
Lossless webp images are 26% smaller in size compared to PNG. Lossy
    webp images are 25-34% smaller in size compared to JPEG. This package reads
    and writes webp images into a 3 (rgb) or 4 (rgba) channel bitmap array using
    conventions from the 'jpeg' and 'png' packages.
This is a collection of tools for conducting both basic and advanced statistical power analysis including correlation, proportion, t-test, one-way ANOVA, two-way ANOVA, linear regression, logistic regression, Poisson regression, mediation analysis, longitudinal data analysis, structural equation modeling and multilevel modeling. It also serves as the engine for conducting power analysis online at <https://webpower.psychstat.org>.
Several analysis-related functions for the book entitled
    "Web-based Analysis without R in Your Computer"(written in Korean, ISBN 978-89-5566-185-9)
    by Keon-Woong Moon. The main function plot.htest() shows the distribution of statistic for
    the object of class 'htest'.
R is used by a vast array of people for a vast array of purposes
    - including web analytics. This package contains functions for consuming and
    munging various common forms of request log, including the Common and Combined
    Web Log formats and various Amazon access logs.
A collection of tools to fit and work with trophic Species Distribution Models. Trophic Species Distribution Models combine knowledge of trophic interactions with Bayesian structural equation models that model each species as a function of its prey (or predators) and environmental conditions. It exploits the topological ordering of the known trophic interaction network to predict species distribution in space and/or time, where the prey (or predator) distribution is unavailable. The method implemented by the package is described in Poggiato, Andréoletti, Pollock and Thuiller (2022) <doi:10.22541/au.166853394.45823739/v1>.
Functions that allow for accessing domains and a number of search engines.
Takes screenshots of web pages, including Shiny applications and R
    Markdown documents.
Takes screenshots of web pages, including Shiny applications
    and R Markdown documents. 'webshot2' uses headless Chrome or Chromium
    as the browser back-end.
Provides a 'WebSocket' client interface for R.
    'WebSocket' is a protocol for low-overhead real-time communication:
    <https://en.wikipedia.org/wiki/WebSocket>.
Data structures and methods to work with web tracking data. The functions cover data preprocessing steps, enriching web tracking data with external information and methods for the analysis of digital behavior as used in several academic papers (e.g., Clemm von Hohenberg et al., 2023 <doi:10.17605/OSF.IO/M3U9P>; Stier et al., 2022 <doi:10.1017/S0003055421001222>).
A Stata-style `webuse()` function for importing named datasets from Stata's online collection.
Parses http request data in application/json, multipart/form-data, 
    or application/x-www-form-urlencoded format. Includes example of hosting
    and parsing html form data in R using either 'httpuv' or 'Rhttpd'.
Provides functions to create factor variables with contrasts based on weighted effect coding, and their interactions. In weighted effect coding the estimates from a first order regression model show the deviations per group from the sample mean. This is especially useful when a researcher has no directional hypotheses and uses a sample from a population in which the number of observation per group is different.
Makes research involving EMDAT and related datasets easier. These Datasets are manually filled and have several formatting and compatibility issues. Weed aims to resolve these with its functions.
Calculates the WEGE (Weighted Endemism including Global 
    Endangerment index) index for a particular area. Additionally it also 
    calculates rasters of KBA's (Key Biodiversity Area) criteria (A1a, A1b, A1e, 
    and B1), Weighted endemism (WE), the EDGE (Evolutionarily Distinct and
    Globally Endangered) score, Evolutionary Distinctiveness (ED) and Extinction
    risk (ER). Farooq, H., Azevedo, J., Belluardo F., Nanvonamuquitxo, C.,
    Bennett, D., Moat, J., Soares, A., Faurby, S. & Antonelli, A. (2020)
    <doi:10.1101/2020.01.17.910299>.
A utility for working with women's basketball data. A scraping and aggregating interface for the WNBA Stats API <https://stats.wnba.com/> and ESPN's <https://www.espn.com> women's college basketball and WNBA statistics. It provides users with the capability to access the game play-by-plays, box scores, standings and results to analyze the data for themselves.
Provides a single function to fit data of an input data frame into one of the selected Weibull functions (w2, w3 and it's truncated versions), calculating the scale, location and shape parameters accordingly. The resulting plots and files are saved into the 'folder' parameter provided by the user. References: a) John C. Nash, Ravi Varadhan (2011). "Unifying Optimization Algorithms to Aid Software System Users: optimx for R" <doi:10.18637/jss.v043.i09>.
Conducts a goodness-of-fit test for the Weibull distribution (referred to as the weibullness test) and furnishes parameter estimations for both the two-parameter and three-parameter Weibull distributions. Notably, the threshold parameter is derived through correlation from the Weibull plot. Additionally, this package conducts goodness-of-fit assessments for the exponential, Gumbel, and inverse Weibull distributions, accompanied by parameter estimations.
  For more details, see Park (2017) <doi:10.23055/ijietap.2017.24.4.2848>, 
  Park (2018) <doi:10.1155/2018/6056975>, and Park (2023) <doi:10.3390/math11143156>. 
  This work was supported by the National Research Foundation of Korea (NRF) grants funded by 
  the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528).
Life data analysis in the graphical tradition of Waloddi Weibull. Methods derived from Robert B. Abernethy (2008, ISBN 0-965306-3-2), Wayne Nelson (1982, ISBN: 9780471094586), William Q. Meeker and  Lois A. Escobar (1998, ISBN: 1-471-14328-6), John I. McCool, (2012, ISBN: 9781118217986).
Graphical data analysis of accelerated life tests. Methods derived from  Wayne Nelson (1990, ISBN: 9780471522775), William Q. Meeker and  Lois A. Escobar (1998, ISBN: 1-471-14328-6).
An interactive introduction to Life Data Analysis that depends on 'WeibullR' 
  by David Silkworth and Jurgen Symynck (2022) <https://CRAN.R-project.org/package=WeibullR>, 
  a R package for Weibull Analysis, and 'learnr' by Garrick Aden-Buie et al. (2023) 
  <https://CRAN.R-project.org/package=learnr>, a framework for building interactive learning 
  modules in R.
Build interactive Weibull Probability 
  Plots with 'WeibullR' by David Silkworth and Jurgen Symynck (2022) 
  <https://CRAN.R-project.org/package=WeibullR>, an R package for 
  Weibull analysis, and 'plotly' by Carson Sievert (2020) <https://plotly-r.com>, 
  an interactive web-based graphing library.
A 'Shiny' web application for life data analysis that depends on 'WeibullR' by David Silkworth and Jurgen Symynck (2022) 
  <https://CRAN.R-project.org/package=WeibullR>, an R package for Weibull analysis.
Weighted descriptive statistics is the discipline of quantitatively describing the main features of real-valued fuzzy data which usually given from a fuzzy population. One can summarize this special kind of fuzzy data numerically or graphically using this package. To interpret some of the properties of one or several sets of real-valued fuzzy data, numerically summarize is possible by some weighted statistics which are designed in this package such as mean, variance, covariance and correlation coefficent. Also, graphically interpretation can be given by weighted histogram and weighted scatter plot using this package to describe properties of real-valued fuzzy data set.
Estimates high-dimensional multivariate normal copula regression models with the weighted composite likelihood estimating equations in Nikoloulopoulos (2022) <arXiv:2203.04619>. It provides autoregressive moving average correlation structures and binary, ordinal, Poisson, and negative binomial regressions.
Clusters state sequences and weighted data. It provides an optimized weighted PAM algorithm as well as functions for aggregating replicated cases, computing cluster quality measures for a range of clustering solutions and plotting (fuzzy) clusters of state sequences. Parametric bootstraps methods to validate typology of sequences are also provided.
The weighted ensemble method is a valuable approach for combining forecasts. This algorithm employs several optimization techniques to generate optimized weights. This package has been developed using algorithm of Armstrong (1989) <doi:10.1016/0024-6301(90)90317-W>.
A conditional independence test that can be applied both to
    univariate and multivariate random variables. The test is based on a
    weighted form of the sample covariance of the residuals after a
    nonlinear regression on the conditioning variables. Details are
    described in Scheidegger, Hoerrmann and Buehlmann (2021) "The Weighted
    Generalised Covariance Measure" <arXiv:2111.04361>. The test is a
    generalisation of the Generalised Covariance Measure (GCM) implemented
    in the R package 'GeneralisedCovarianceMeasure' by Jonas Peters and
    Rajen D. Shah based on Shah and Peters (2020) "The Hardness of
    Conditional Independence Testing and the Generalised Covariance
    Measure" <arXiv:1804.07203>.
An implementation of the Weighted Portmanteau Tests described
      in "New Weighted Portmanteau Statistics for Time Series Goodness-of-Fit Testing"
      published by the Journal of the American Statistical Association, Volume 107, 
      Issue 498, pages 777-787, 2012.
Performs a sensitivity analysis using weighted rank tests in observational studies with I blocks of size J; see Rosenbaum (2024) <doi:10.1080/01621459.2023.2221402>.  The package can perform adaptive inference in block designs; see Rosenbaum (2012) <doi:10.1093/biomet/ass032>.  The main functions are wgtRank(), wgtRankCI() and wgtRanktt().
Fast computation of
 Receiver Operating Characteristic (ROC) curves
 and Area Under the Curve (AUC)
 for weighted binary classification problems
 (weights are example-specific cost values).
The weighted scores method and composite likelihood information criteria as an intermediate step for variable/correlation selection for longitudinal ordinal and count data in Nikoloulopoulos, Joe and Chaganty (2011)  <doi:10.1093/biostatistics/kxr005>, Nikoloulopoulos (2016) <doi:10.1002/sim.6871> and Nikoloulopoulos (2017) <arXiv:1510.07376>.
Treemaps are a visually appealing graphical representation of
    numerical data using a space-filling approach. A plane or 'map' is
    subdivided into smaller areas called cells.  The cells in the map are
    scaled according to an underlying metric which allows to grasp the
    hierarchical organization and relative importance of many objects at
    once. This package contains two different implementations of treemaps,
    Voronoi treemaps and Sunburst treemaps.  The Voronoi treemap function
    subdivides the plot area in polygonal cells according to the highest
    hierarchical level, then continues to subdivide those parental cells
    on the next lower hierarchical level, and so on. The Sunburst treemap
    is a computationally less demanding treemap that does not require
    iterative refinement, but simply generates circle sectors that are
    sized according to predefined weights.  The Voronoi tesselation is
    based on functions from Paul Murrell (2012)
    <https://www.stat.auckland.ac.nz/~paul/Reports/VoronoiTreemap/voronoiTreeMap.html>.
Generates balancing weights for causal effect estimation in observational studies with
             binary, multi-category, or continuous point or longitudinal treatments by easing and
             extending the functionality of several R packages and providing in-house estimation methods.
             Available methods include those that rely on parametric modeling, optimization, and machine learning. Also
             allows for assessment of weights and checking of covariate balance by interfacing directly
             with the 'cobalt' package. Methods for estimating weighted regression models that take into account 
             uncertainty in the estimation of the weights via M-estimation or bootstrapping are available. See the vignette "Installing Supporting Packages" for instructions on how
             to install any package 'WeightIt' uses, including those that may not be on CRAN.
Estimation of observation-specific weights for incomplete longitudinal data and bootstrap procedure for weighted quantile regressions. See Jacqmin-Gadda, Rouanet, Mba, Philipps, Dartigues (2020) for details <doi:10.1177/0962280220909986>.
Estimates the Vevea and Hedges (1995)
    weight-function model. By specifying arguments, users can
    also estimate the modified model described in Vevea and Woods (2005), 
    which may be more practical with small datasets. Users 
    can also specify moderators to estimate a linear model. 
    The package functionality allows users to easily extract the 
    results of these analyses as R objects for other uses. In addition, 
    the package includes a function to launch both models as 
    a Shiny application. Although the Shiny application is also available online, 
    this function allows users to launch it locally if they choose.
Provides a variety of functions for producing simple weighted statistics, such as weighted Pearson's correlations, partial correlations, Chi-Squared statistics, histograms, and t-tests.  Also now includes some software for quickly recoding survey data and plotting estimates from interaction terms in regressions (and multiply imputed regressions) both with and without weights. NOTE: Weighted partial correlation calculations pulled to address a bug.
Functions for subject/instance weighted support vector machines (SVM). 
    It uses a modified version of 'libsvm' and is compatible with package 'e1071'. It also allows user defined kernel matrix.
All functions and data sets required for the examples in the book
    Hyndman (2024) "That's Weird: Anomaly Detection Using R" <https://OTexts.com/weird/>.  
    All packages needed to run the examples are also loaded.
Provides computational support for flow over weirs, such as sharp-crested, broad-crested, and embankments. Initially, the package supports broad- and sharp-crested weirs.
Implementation of Johansen's general formulation of Welch-James's statistic with Approximate Degrees of Freedom, which makes it suitable for testing 
    any linear hypothesis concerning cell means in univariate and multivariate mixed model designs when the data pose non-normality and non-homogeneous variance. Some 
    improvements, namely trimmed means and Winsorized variances, and bootstrapping for calculating an empirical critical value, have been added to the classical formulation. 
    The code departs from a previous SAS implementation by L.M. Lix and H.J. Keselman, available at <http://supp.apa.org/psycarticles/supplemental/met_13_2_110/SAS_Program.pdf> and
    published in Keselman, H.J., Wilcox, R.R., and Lix, L.M. (2003) <DOI:10.1111/1469-8986.00060>.
Estimates the standard and weighted Elo (WElo, Angelini et al., 2022 <doi:10.1016/j.ejor.2021.04.011>) rates. The current version provides Elo and WElo rates for tennis, according to different systems of weights (games or sets) and scale factors (constant, proportional to the number of matches, with more weight on Grand Slam matches or matches played on a specific surface). Moreover, the package gives the possibility of estimating the (bootstrap) standard errors for the rates. Finally, the package includes betting functions that automatically select the matches on which place a bet.
Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels. Random effects are estimated using the PIRLS algorithm from 'lme4pureR' (Walker and Bates (2013) <https://github.com/lme4/lme4pureR>).
Palettes generated mostly from 'Wes Anderson' movies.
The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test (McKeigue (2019), <doi:10.1177/0962280218776989>). The package can be used with any test dataset on which you have observed case-control status and have computed prior and posterior probabilities of case status using a model learned on a training dataset. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.
A suite of routines for Weyl algebras.  Notation follows
   Coutinho (1995, ISBN 0-521-55119-6, "A Primer of Algebraic
   D-Modules").  Uses 'disordR' discipline
   (Hankin 2022 <doi:10.48550/arXiv.2210.03856>).  To cite
   the package in publications, use Hankin
   2022 <doi:10.48550/arXiv.2212.09230>.
Provides a computationally efficient way of fitting
	     weighted linear fixed effects estimators for causal
	     inference with various weighting schemes. Weighted linear
	     fixed effects estimators can be used to estimate the
	     average treatment effects under different identification
	     strategies. This includes stratified randomized
	     experiments, matching and stratification for
	     observational studies, first differencing, and
	     difference-in-differences. The package implements methods
	     described in Imai and Kim (2017) "When should We Use
	     Linear Fixed Effects Regression Models for Causal
	     Inference with Longitudinal Data?", available at
	     <https://imai.fas.harvard.edu/research/FEmatch.html>.
Implementation of Weighted Fast Greedy algorithm for community detection in networks with mixed types of attributes.
Provides a large English words list and tools to find words by patterns. In particular, anagram finder and scrabble word finder.
Provides a convenient data set, a set of helper functions, and a benchmark function for
    economically (profit) driven wind farm layout optimization. This enables researchers in the field
    of the NP-hard (non-deterministic polynomial-time hard) problem of wind farm layout optimization
    to focus on their optimization methodology contribution and also provides a realistic benchmark
    setting for comparability among contributions. See Croonenbroeck, Carsten & Hennecke, David (2020)
    <doi:10.1016/j.energy.2020.119244>.
A computationally efficient whole genome approach to detecting and estimating significant QTL in linkage maps using the flexible linear mixed modelling functionality of ASReml-R.
Functions necessary to perform Weighted Correlation Network Analysis on high-dimensional data as originally described in Horvath and Zhang (2005) <doi:10.2202/1544-6115.1128> and Langfelder and Horvath (2008) <doi:10.1186/1471-2105-9-559>. Includes functions for rudimentary data cleaning, construction of correlation networks, module identification, summarization, and relating of variables and modules to sample traits. Also includes a number of utility functions for data manipulation and visualization.
Provides function, wget_set(), to change the method (default to 'wget -c') using in download.file().
  Using 'wget -c' allowing continued downloading, which is especially useful for slow internet connection and for downloading large files.
  User can run wget_unset() to restore previous setting.
Functions for the analysis of whole-genome sequencing studies to simultaneously detect the existence, and estimate the locations of association signals at genome-wide scale. The functions allow genome-wide association scan, candidate region scan and single window test.
Functions for determining the effect of data weights on the variance of survey data: users will load a data set which has a weights column, and the package will calculate the design effect (DEFF), weighting loss, root design effect (DEFT), effective sample size (ESS), and/or weighted margin of error.
An enhanced implementation of Whittaker-Henderson smoothing for the gradation 
    of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks.
    'WH' is based on the methods described in Biessy (2023) <doi:10.48550/arXiv.2306.06932>.
    Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, 
    automatically selects the smoothing parameter(s) and extrapolates beyond the range of data.
Inferences about counterfactuals are essential for prediction,
      answering what if questions, and estimating causal effects.
      However, when the counterfactuals posed are too far from the data at
      hand, conclusions drawn from well-specified statistical analyses
      become based largely on speculation hidden in convenient modeling
      assumptions that few would be willing to defend. Unfortunately,
      standard statistical approaches assume the veracity of the model
      rather than revealing the degree of model-dependence, which makes this
      problem hard to detect. WhatIf offers easy-to-apply methods to
      evaluate counterfactuals that do not require sensitivity testing over
      specified classes of models. If an analysis fails the tests offered
      here, then we know that substantive inferences will be sensitive to at
      least some modeling choices that are not based on empirical evidence,
      no matter what method of inference one chooses to use. WhatIf
      implements the methods for evaluating counterfactuals discussed in
      Gary King and Langche Zeng, 2006, "The Dangers of Extreme
      Counterfactuals," Political Analysis 14 (2) <DOI:10.1093/pan/mpj004>; 
      and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The 
      Pitfalls of Counterfactual Inference," International Studies 
      Quarterly 51 (March) <DOI:10.1111/j.1468-2478.2007.00445.x>.
Imports 'WhatsApp' chat logs and parses them into
    a usable dataframe object. The parser works on chats exported 
    from Android or iOS phones and on Linux, macOS and Windows. The parser has  multiple options for extracting smileys and emojis
    from the messages, extracting URLs and domains from the messages, extracting names and types of sent 
    media files from the messages, extracting timestamps from messages, extracting and anonymizing author 
    names from messages. Can be used to create anonymized versions of data.
Use the 'what3words' API 
    <https://developer.what3words.com/public-api> to return three words which 
    uniquely identify every 3m x 3m square on Earth. It is also possible to 
    return coordinates from any valid three words location. Supports multiple 
    languages.
Builds complex plots, heatmaps in particular, using natural semantics. Bigger plots can be assembled using directives such as 'LeftOf', 'RightOf', 'TopOf', and 'Beneath' and more. Other features include clustering, dendrograms and integration with 'ggplot2' generated grid objects. This package is particularly designed for bioinformaticians to assemble complex plots for publication.
In Multidimensional Systems the When dimension allows us to
    express when the analysed facts have occurred. The purpose of this
    package is to provide support for implementing this dimension in the
    form of date and time tables for Relational On-Line Analytical Processing 
    star database systems.
Robust and reliable functions to return informative outputs to 
        console with the run or source location of a command. This can be from
        the  'RScript'/R terminal commands or 'RStudio' console, source editor, 
        'Rmarkdown' document and a Shiny application.
Retrieve geographical information for airports using their IATA or ICAO codes.
Set of tools for manipulating gas exchange data from cardiopulmonary exercise testing.
An R frontend for the 'WhiteboxTools' library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. 'WhiteboxTools' can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. 'WhiteboxTools' also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) <doi:10.1016/j.cageo.2016.07.003>.
Provides a set of functions to make tracking the hidden movements 
  of the 'Jack' player easier. By tracking every possible path Jack might have 
  traveled from the point of the initial murder including special movement such 
  as through alleyways and via carriages, the police can more accurately narrow 
  the field of their search. Additionally, by tracking all possible hideouts from 
  round to round, rounds 3 and 4 should have a vastly reduced field of search.
A collection of functions related to novel methods for estimating R(t), 
  created by the lab of Professor Laura White. Currently implemented methods include 
  two-step Bayesian back-calculation and now-casting for line-list data with missing reporting delays, 
  adapted in 'STAN' from Li (2021) <doi:10.1371/journal.pcbi.1009210>, 
  and calculation of time-varying reproduction number assuming a flux between various adjacent states, adapted into 'STAN' from 
  Zhou (2021) <doi:10.1371/journal.pcbi.1010434>.
Implements the whitening methods (ZCA, PCA, Cholesky,
 ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018)
 "Optimal whitening and decorrelation", <doi:10.1080/00031305.2016.1277159>,
 as well as the whitening approach to canonical correlation analysis allowing
 negative canonical correlations described in Jendoubi and Strimmer (2019)
 "A whitening approach to probabilistic canonical correlation analysis for omics
 data integration", <doi:10.1186/s12859-018-2572-9>. The package also offers
 functions to simulate random orthogonal matrices, compute (correlation) loadings
 and explained variation. It also contains four example data sets (extended UCI 
 wine data, TCGA LUSC data, nutrimouse data, extended pitprops data).
Formal implementation of White test of heteroskedasticity and a bootstrapped version of it, developed under the methodology of Jeong, J., Lee, K. (1999) <https://yonsei.pure.elsevier.com/en/publications/bootstrapped-whites-test-for-heteroskedasticity-in-regression-mod>.
Shinohara (2014) <doi:10.1016/j.nicl.2014.08.008>
    introduced 'WhiteStripe', an intensity-based normalization of T1 
    and T2 images, where normal 
    appearing white matter performs well, but requires segmentation.
    This method performs white matter mean and standard deviation
    estimates on data that has been rigidly-registered to the 'MNI'
    template and uses histogram-based methods.
Provides methods for retrieving United States Geological Survey (USGS) water data using sequential and parallel processing (Bengtsson, 2022 <doi:10.32614/RJ-2021-048>). In addition to parallel methods, data wrangling and additional statistical attributes are provided.
This is a small, lightweight package that lets users investigate the distribution of genotypes in genotype-by-sequencing (GBS) data where they expect (by and large) Hardy-Weinberg equilibrium, in order to assess rates of genotyping errors and the dependence of those rates on read depth.  It implements a Markov chain Monte Carlo (MCMC) sampler using 'Rcpp' to compute a Bayesian estimate of what we call the heterozygote miscall rate for restriction-associated digest (RAD) sequencing data and other types of reduced representation GBS data. It also provides functions to generate plots of expected and observed genotype frequencies. Some background on these topics can be found in a recent paper "Recent advances in conservation and population genomics data analysis" by Hendricks et al. (2018) <doi:10.1111/eva.12659>, and another paper describing the MCMC approach is in preparation with Gordon Luikart and Thierry Gosselin.
Look up the username and full name of the current user,
    the current user's email address and 'GitHub' username,
    using various sources of system and configuration information.
The Model Disability Survey (MDS) <https://www.who.int/activities/collection-of-data-on-disability> is a World Health Organization (WHO) general population survey
    instrument to assess the distribution of disability within a country or 
    region, grounded in the International Classification of Functioning, 
    Disability and Health <https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health>. This package provides fit-for-purpose functions 
    for calculating and presenting the results from this survey, as used by 
    the WHO. The package primarily provides functions for implementing
    Rasch Analysis (see Andrich (2011) <doi:10.1586/erp.11.59>) to
    calculate a metric scale for disability.
Interactive tools for generating random samples. Users select an
    .xlsx, .csv, or delimited .txt file with population data and are walked through selecting the
    sample type (Simple Random Sample or Stratified), the number of backups
    desired, and a "stratify_on" value (if desired). The sample size is determined
    using a normal approximation to the hypergeometric distribution based on
    Nicholson (1956) <doi:10.1214/aoms/1177728270>. An .xlsx file is created
    with the sample and key metadata for reference. It is menu-driven and lets
    users pick an output directory. See vignettes for a detailed walk-through.
Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data.
Provides two functions 'frameableWidget()', and 'frameWidget()'.
  The 'frameableWidget()' is used to add extra code to a 'htmlwidget' which
  allows is to be rendered correctly inside a responsive 'iframe'.
  The 'frameWidget()' is a 'htmlwidget' which displays content of another 'htmlwidget'
  inside a responsive 'iframe'.
  These functions allow for easier embedding of 'htmlwidgets' in content management systems
  such as 'wordpress', 'blogger' etc.
  They also allow for separation of widget content from main HTML content where
  CSS of the main HTML could interfere with the widget.
Encapsulates the pattern of untidying data into a wide
    matrix, performing some processing, then turning it back into a tidy
    form. This is useful for several operations such as co-occurrence
    counts, correlations, or clustering that are mathematically convenient
    on wide matrices.
Retrieve and import data from different databases of the Federal Statistical Office of Germany (DESTATIS) using their SOAP XML web service <https://www-genesis.destatis.de/>.
The german Wikibook "GNU R" introduces R to new users. This package is a collection of functions and datas used in the german WikiBook "GNU R".
An API client for the 'Wikidata Query Service'
    <https://query.wikidata.org/>.
Read from, interogate, and write to Wikidata <https://www.wikidata.org> -
    the multilingual, interdisciplinary, semantic knowledgebase. Includes functions to:
    read from wikidata (single items, properties, or properties); query wikidata (retrieving
    all items that match a set of criterial via Wikidata SPARQL query service); write to
    Wikidata (adding new items or statements via QuickStatements); and handle and manipulate
    Wikidata objects (as lists and tibbles). Uses the Wikidata and Quickstatements APIs.
Query Wikidata and get facts from current and historic Wikipedia main pages.
Scrape lake metadata tables from Wikipedia <https://www.wikipedia.org/>.
A wrapper for the MediaWiki API, aimed particularly at the
    Wikimedia 'production' wikis, such as Wikipedia. It can be used to retrieve
    page text, information about users or the history of pages, and elements of
    the category tree.
Queries online 'WikiPathway' graphics and allows mapping user data (e.g., expression values) on the graph. The package designs a grammar of graphic syntax that using pipe operator to add graphic layer.
'Taxonomic' information from 'Wikipedia', 'Wikicommons',
    'Wikispecies', and 'Wikidata'. Functions included for getting
    taxonomic information from each of the sources just listed, as
    well performing taxonomic search.
A set of wrappers intended to check, read and download information from the Wikimedia sources. It is specifically created to work with names of celebrities, in which case their information and statistics can be downloaded. Additionally, it also builds links and snippets to use in combination with the function gallery() in netCoin package.
Access 'Wikipedia' through the several 'MediaWiki' APIs
    (<https://www.mediawiki.org/wiki/API>), as well as through the
    'XTools' API (<https://www.mediawiki.org/wiki/XTools/API>). Ensure
    your API calls are correct, and receive results in tidy tibbles.
An implementation of the 1-Sample Wilcoxon Sign rank test for medians. 
              It includes 2 functions, W_stat(), which computes the 
              exact probabilities of the Wilcoxon Sign Rank Test Statistic, W.
              The second function, Wilcox.m.test() allows the user to conduct the
              1-Sample Wilcoxon Sign Rank hypothesis test for medians, this also 
              allows the user to conduct the hypothesis test for the normal approximation, 
              based on the techniques of Bickel and Doksum (1973, ISBN:013850363X).
Dynamic interaction refers to spatial-temporal associations in the movements of two (or more) animals. This package provides tools for calculating a suite of indices used for quantifying dynamic interaction with wildlife telemetry data. For more information on each of the methods employed see the references within. The package (as of version >= 0.3) also has new tools for automating contact analysis in large tracking datasets. The package (as of version 1.0) uses the 'move2' class of objects for working with tracking dataset.
Conducts single coefficient tests and multiple-contrast hypothesis tests of meta-regression models using cluster wild bootstrapping, based on methods examined in Joshi, Pustejovsky, and Beretvas (2022) <DOI:10.1002/jrsm.1554>.
Provides insight into how the best hand for a poker game changes based on the game dealt, players who stay in until the showdown and wildcards added to the base game.  At this time the package does not support player tactics, so draw poker variants are not included.
Tool-set of modules for creating web-based applications that use plot based strategies to visualize and analyze multi-omics data.
    This package utilizes the 'shiny' and 'plotly' frameworks to provide a user friendly dashboard for interactive plotting.
Obtain the native stack trace and fuse it with R's
    stack trace for easier debugging of R packages with native code.
Post-construction fatality monitoring studies at wind facilities are based on data from searches for bird and bat carcasses in plots beneath turbines. Bird and bat carcasses can fall outside of the search plot. Bird and bat carcasses from wind turbines often fall outside of the searched area. To compensate, area correction (AC) estimations are calculated to estimate the percentage of fatalities that fall within the searched area versus those that fall outside of it. This package provides two likelihood based methods and one physics based method (Hull and Muir (2010) <doi:10.1080/14486563.2010.9725253>, Huso and Dalthorp (2014) <doi:10.1002/jwmg.663>) to estimate the carcass fall distribution. There are also functions for calculating the proportion of area searched within one unit annuli, log logistic distribution functions, and truncated distribution functions.
Provides a tool to fit and compare the wind turbine power curves 
    with successful curve fitting techniques. Facilitates to examine and compare 
    the performance of a user-defined power curve fitting techniques. 
    Also, provide features to generate power curve discrete points from a graphical 
    power curves. Data on the power curves of the wind turbine from major 
    manufacturers are provided.
Analysing convergent evolution using the Wheatsheaf index, described in Arbuckle et al. (2014) <doi: 10.1111/2041-210X.12195>, and some other unrelated but perhaps useful functions.
Evaluation of prediction performance of smaller regions of
    spectra for Chemometrics. Segmentation of spectra, evolving dimensions
    regions and sliding windows as selection methods. Election of the best
    model among those computed based on error metrics. Chen et al.(2017)
    <doi:10.1007/s00216-017-0218-9>.
Collect multichannel marketing data from sources such as Google analytics, Facebook Ads, and many others using the 'Windsor.ai' API <https://www.windsor.ai/api-fields/>.
Computes inequality measures of a given variable taking into account weights. Suitable for ratio, interval and ordered scale. Includes Gini, Theil, Leti index, Palma ratio, 20:20 ratio, Allison and Foster index, Jenkins index, Cowell and Flechaire index, Abul Naga and Yalcin index, Apouey index, Blair and Lacy index. Bootstrap provides distribution of inequality measures enabling significance tests.
Obtain information on peak flow data from the National River Flow Archive (NRFA) in the United Kingdom, either from the Peak Flow Dataset files <https://nrfa.ceh.ac.uk/peak-flow-dataset> once these have been downloaded to the user's computer or using the NRFA's API. These files are in a format suitable for direct use in the 'WINFAP' software, hence the name of the package.
Generate continuous maps of genetic diversity using moving windows with options for rarefaction, interpolation, and masking as described in Bishop et al. (2023) <doi:10.1111/2041-210X.14090>.
Using a time-varying random parameters model developed in
    Koutchade et al., (2024) <https://hal.science/hal-04318163>, this
    package allows allocating variable input costs among crops produced by
    farmers based on panel data including information on input expenditure
    aggregated at the farm level and acreage shares. It also considers in
    fairly way the weighting data and can allow integrating time-varying
    and time-constant control variables.
Calculate the win ratio for prioritized outcomes and the 95% confidence interval based on Bebu and Lachin (2016) <doi:10.1093/biostatistics/kxv032>. Three type of outcomes can be analyzed: survival "failure-time" events, repeated survival "failure-time" events and continuous or ordinal "non-failure time" events that are captured at specific time-points in the study.
Calculate the win statistics (win ratio, net benefit and win odds) for prioritized multiple endpoints, plot the win statistics and win proportions over study time if at least one time-to-event endpoint is analyzed, and simulate datasets with dependent endpoints. The package can handle any type of outcomes (continuous, ordinal, binary, time-to-event) and allow users to perform stratified analysis, inverse probability of censoring weighting (IPCW) and inverse probability of treatment weighting (IPTW) analysis.
Performs an analysis of time-to-event clinical trial data using various "win time" methods, 
  including 'ewt', 'ewtr', 'rmt', 'max', 'wtr', 'rwtr', and 'pwt'. These methods are used to calculate and compare 
  treatment effects on ordered composite endpoints. The package handles event times, event indicators, and treatment 
  arm indicators and supports calculations on observed and resampled data. Detailed explanations of each method and 
  usage examples are provided in "Use of win time for ordered composite endpoints in clinical trials," by Troendle et al. 
  (2024)<https://pubmed.ncbi.nlm.nih.gov/38417455/>. For more information, see the package documentation or the vignette titled "Introduction to wintime."
Provides simple, fast functions for maximum likelihood and Bayesian estimates of wildlife population parameters, suitable for use with simulated data or bootstraps. Early versions were indeed quick and dirty, but optional error-checking routines and meaningful error messages have been added. Includes single and multi-season occupancy, closed capture population estimation, survival, species richness and distance measures.
In the course of a genome-wide association study, the situation often arises that some phenotypes are known with greater precision than others.
    It could be that some individuals are known to harbor more micro-environmental variance than others.
    In the case of inbred strains of model organisms, it could be the case that more organisms were observed from some strains than others, so the strains with more organisms have better-estimated means.
    Package 'wISAM' handles this situation by allowing for weighting of each observation according to residual variance.
    Specifically, the 'weight' parameter to the function conduct_scan() takes the precision of each observation (one over the variance).
Provides functions for computing moments and coefficients related to the Beta-Wishart and Inverse Beta-Wishart distributions. 
    It includes functions for calculating the expectation of matrix-valued functions of the Beta-Wishart distribution, 
    coefficient matrices C_k and H_k, expectation of matrix-valued functions of the inverse Beta-Wishart distribution, 
    and coefficient matrices \tilde{C}_k and \tilde{H}_k. For more details, refer Hillier and Kan (2024) <https://www-2.rotman.utoronto.ca/~kan/papers/wishmom.pdf>, 
    "On the Expectations of Equivariant Matrix-valued Functions of Wishart and Inverse Wishart Matrices".
Adds ... to a function's argument list so that it can
    tolerate non-matching arguments.
Lets you temporarily execute an expression or a local block with a different here() root in the 'here' package. This is useful for sourcing code in other projects which expect the root directory of here() to be the project directory of those projects. This may be the case with git submodules for example.
A set of functions to run code 'with' safely and temporarily
    modified global state. Many of these functions were originally a part
    of the 'devtools' package, this provides a simple package with limited
    dependencies to provide access to these functions.
Provides a minimal R and C++ API for parsing
  well-known binary and well-known text representation of
  geometries to and from R-native formats.
  Well-known binary is compact
  and fast to parse; well-known text is human-readable
  and is useful for writing tests. These formats are
  useful in R only if the information they contain can be
  accessed in R, for which high-performance functions
  are provided here.
Utility functions to convert between the 'Spatial' classes
  specified by the package 'sp', and the well-known binary '(WKB)'
  representation for geometry specified by the 'Open Geospatial Consortium'.
  Supports 'Spatial' objects of class 'SpatialPoints',
  'SpatialPointsDataFrame', 'SpatialLines', 'SpatialLinesDataFrame',
  'SpatialPolygons', and 'SpatialPolygonsDataFrame'. Supports 'WKB' geometry
  types 'Point', 'LineString', 'Polygon', 'MultiPoint', 'MultiLineString', and
  'MultiPolygon'. Includes extensions to enable creation of maps with
  'TIBCO Spotfire'.
Converts weekly data to monthly data.
     Users can use three types of week formats: ISO week, epidemiology week (epi week) and calendar date.
Provides extra utilities for well-known formats in the
  'wk' package that are outside the scope of that package. Utilities
  to parse coordinates from data frames, plot well-known geometry
  vectors, extract meta information from well-known geometry vectors,
  and calculate bounding boxes are provided.
It proposes a novel variable selection approach in classification problem that takes into account the correlations that may exist between the predictors of the design matrix in a high-dimensional logistic model. Our approach consists in rewriting the initial high-dimensional logistic model to remove the correlation between the predictors and in applying the generalized Lasso criterion.
Use various regression models for the analysis of win loss endpoints 
             adjusting for non-binary and multivariate covariates.
Calculate magnetic field at a given location and time according to 
  the World Magnetic Model (WMM). Both the main field and secular variation 
  components are returned. This functionality is useful for physicists and 
  geophysicists who need orthogonal components from WMM. Currently, this package 
  supports annualized time inputs between 2000 and 2025. If desired, users can
  specify which WMM version to use, e.g., the original WMM2015 release or the 
  recent out-of-cycle WMM2015 release. Methods used to implement WMM, including 
  the Gauss coefficients for each release, are described in the following 
  publications: Chulliat et al (2020) <doi:10.25923/ytk1-yx35>,
  Chulliat et al (2019) <doi:10.25921/xhr3-0t19>, 
  Chulliat et al (2015) <doi:10.7289/V5TB14V7>, 
  Maus et al (2010) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/WMM2010_Report.pdf>, 
  McLean et al (2004) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/TRWMM_2005.pdf>,
  and Macmillian et al (2000) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/wmm2000.pdf>.
Performs Wilcoxon-Mann-Whitney test in the presence of missing data with controlled Type I error regardless of the values of missing data.
Power calculator for the two-sample Wilcoxon-Mann-Whitney rank-sum test for a continuous outcome (Mollan, Trumble, Reifeis et. al., Mar. 2020) <doi:10.1080/10543406.2020.1730866> <arXiv:1901.04597>, (Mann and Whitney 1947) <doi:10.1214/aoms/1177730491>, (Shieh, Jan, and Randles 2006) <doi:10.1080/10485250500473099>.
Calculates the minimal sample size for the Wilcoxon-Mann-Whitney test
    that is needed for a given power and two sided type I error rate. The method works for metric data with and without
    ties, count data, ordered categorical data, and even dichotomous data.
    But data is needed for the reference group to generate synthetic data for the treatment group based on a relevant effect.
    For details, see Brunner, E., Bathke A. C. and Konietschke, F: Rank- and Pseudo-Rank Procedures in Factorial Designs - Using R and SAS, Springer Verlag, to appear.
This is a set of minimization tools (maximum likelihood estimation and least square fitting) to solve examples in the Johan Gabrielsson and Dan Weiner's book "Pharmacokinetic and Pharmacodynamic Data Analysis - Concepts and Applications" 5th ed. (ISBN:9198299107). Examples include linear and nonlinear compartmental model, turn-over model, single or multiple dosing bolus/infusion/oral models, allometry, toxicokinetics, reversible metabolism, in-vitro/in-vivo extrapolation, enterohepatic circulation, metabolite modeling, Emax model, inhibitory model, tolerance model, oscillating response model, enantiomer interaction model, effect compartment model, drug-drug interaction model, receptor occupancy model, and rebound phenomena model.
New tools for the imputation of missing values in high-dimensional
    data are introduced using the non-parametric nearest neighbor methods. It
    includes weighted nearest neighbor imputation methods that use specific
    distances for selected variables. It includes an automatic procedure of cross
    validation and does not require prespecified values of the tuning parameters.
    It can be used to impute missing values in high-dimensional data when the sample
    size is smaller than the number of predictors. For more information see Faisal
    and Tutz (2017) <doi:10.1515/sagmb-2015-0098>.
Estimates Poole and Rosenthal's (1985 <doi:10.2307/2111172>, 1991 <doi:10.2307/2111445>)
	W-NOMINATE scores from roll call votes supplied though a 'rollcall'
	object from the 'pscl' package.
Implements the Whale Optimization Algorithm(WOA) for k-medoids clustering, providing tools for effective and efficient cluster analysis in various data sets. The methodology is based on "The Whale Optimization Algorithm" by Mirjalili and Lewis (2016) <doi:10.1016/j.advengsoft.2016.01.008>.
Descriptive statistics for large data tend to be low resolution on the tails. 
    Whisker Odds generate a table of descriptive statistics for large data. This is the same as
    letter-values, but with an alternative naming of depths which allow for depths beyond 26. For 
    a reference to letter-values see 'Heike Hofmann' and 'Hadley Wickham' and 'Karen Kafadar' (2017) <doi:10.1080/10618600.2017.1305277>.
Shows the relationship between an independent and dependent variable through Weight of Evidence and Information Value.
Implements an automated binning of numeric variables and factors with
 respect to a dichotomous target variable.
 Two approaches are provided: An implementation of fine and coarse classing that
 merges granular classes and levels step by step. And a tree-like approach that
 iteratively segments the initial bins via binary splits. Both procedures merge,
 respectively split, bins based on similar weight of evidence (WOE) values and
 stop via an information value (IV) based criteria.
 The package can be used with single variables or an entire data frame. It provides
 flexible tools for exploring different binning solutions and for deploying them to
 (new) data.
Implements a spatiotemporal boundary detection model with a dissimilarity
    metric for areal data with inference in a Bayesian setting using Markov chain
    Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget),
    probit or Tobit link and spatial correlation is introduced at each time point
    through a conditional autoregressive (CAR) prior. Temporal correlation is introduced
    through a hierarchical structure and can be specified as exponential or first-order
    autoregressive. Full details of the package can be found in the accompanying vignette.
    Furthermore, the details of the package can be found in "Diagnosing Glaucoma 
    Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method", 
    by Berchuck et al (2018), <arXiv:1805.11636>. The paper is in press at the Journal of 
    the American Statistical Association.
A hierarchy of classes and methods for manipulating matrices formed implicitly from the sums of the inverses of other matrices, a situation commonly encountered in spatial statistics and related fields. Enables easy use of the Woodbury matrix identity and the matrix determinant lemma to allow computation (e.g., solving linear systems) without having to form the actual matrix. More information on the underlying linear algebra can be found in Harville, D. A. (1997) <doi:10.1007/b98818>.
Tools for generating simulated sawn timber
    strength grading data with a main focus on statistical simulation based on
    covariance matrices. Simulation data
    for Norway spruce sawn timber from Austria and reference values of means and
    standard deviations of grade determining properties from literature
    for a number of European countries are provided, as well.
Monetary valuation of wood in German forests
             (stumpage values), including estimations of harvest quantities, 
             wood revenues, and harvest costs. The functions are sensitive to
             tree species, mean diameter of the harvested trees, stand quality,
             and logging method. The functions include estimations for the
             consequences of disturbances on revenues and costs. The underlying
             assortment tables are taken from Offer and Staupendahl (2018) with
             corresponding functions for salable and skidded volume derived in
             Fuchs et al. (2023). Wood revenue and harvest cost
             functions were taken from v. Bodelschwingh (2018). The consequences
             of disturbances refer to Dieter (2001), Moellmann and Moehring
             (2017), and Fuchs et al. (2022a, 2022b). For the full references 
             see documentation of the functions, package README, and Fuchs et
             al. (2023). Apart from Dieter (2001) and Moellmann and
             Moehring (2017), all functions and factors are based on data from
             HessenForst, the forest administration of the Federal State of
             Hesse in Germany.
Students learning both econometrics and R may find the introduction 
    to both challenging. The wooldridge data package aims to lighten the task by efficiently 
    loading any data set found in the text with a single command. Data sets have been 
    compressed to a fraction of their original size. Documentation files contain page numbers, 
    the original source, time of publication, and notes from the author suggesting avenues for 
    further analysis and research. If one needs an introduction to R model syntax, a 
    vignette contains solutions to examples from chapters of the text. 
    Data sets are from the 7th edition (Wooldridge 2020, ISBN-13 978-1-337-55886-0), 
    and are backwards compatible with all previous versions of the text.
Create reproducible and transparent research projects in 'R'.
    This package is based on the Workflow for Open
    Reproducible Code in Science (WORCS), a step-by-step procedure based on best
    practices for
    Open Science. It includes an 'RStudio' project template, several
    convenience functions, and all dependencies required to make your project
    reproducible and transparent. WORCS is explained in the tutorial paper
    by Van Lissa, Brandmaier, Brinkman, Lamprecht, Struiksma, & Vreede (2021).
    <doi:10.3233/DS-210031>.
For a given Sentence-Aligned Parallel Corpus, it aligns words for each sentence pair. It considers one-to-many and symmetrization alignments. Moreover, it evaluates the quality of word alignment based on this package and some other software. It also builds an automatic dictionary of two languages based on given parallel corpus.
Learn vector representations of words by continuous bag of words and skip-gram implementations of the 'word2vec' algorithm. 
    The techniques are detailed in the paper "Distributed Representations of Words and Phrases and their Compositionality" by Mikolov et al. (2013), available at <arXiv:1310.4546>.
Functionality to create pretty word clouds, visualize differences and similarity between documents, and avoid over-plotting in scatter plots with text.
A fast visualization tool for creating wordcloud
    by using 'wordcloud2.js'. 'wordcloud2.js' is a JavaScript library to create 
    wordle presentation on 2D canvas or HTML <https://timdream.org/wordcloud2.js/>.
The 'Wordle' game. Players have six attempts to guess a 
    five-letter word. After each guess, the player is informed which 
    letters in their guess are either: anywhere in the word; in the right 
    position in the word. This can be used to inform the next guess. Can be 
    played interactively in the console, or programmatically. Based on Josh 
    Wardle's game <https://www.powerlanguage.co.uk/wordle/>.
Application to estimate statistical values using properties provided by a group of individuals to describe
  concepts using 'shiny'. It estimates the underlying distribution to generate new descriptive words
  Canessa et al. (2023) <doi:10.3758/s13428-022-01811-w>, applies a new clustering model, and uses simulations to estimate 
  the probability that two persons describe the same words based on their descriptions
  Canessa et al. (2022) <doi:10.3758/s13428-022-02030-z>.
Extract features and classify documents with noisy labels given by document-meta data or keyword matching Watanabe & Zhou (2020) <doi:10.1177/0894439320907027>.
An interface to WordNet using the Jawbone Java API to WordNet.
  WordNet (<https://wordnet.princeton.edu/>) is a large lexical database of
  English.  Nouns, verbs, adjectives and adverbs are grouped into sets of
  cognitive synonyms (synsets), each expressing a distinct concept.  Synsets
  are interlinked by means of conceptual-semantic and lexical relations.
  Please note that WordNet(R) is a registered tradename.  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>).
  The WordNet database files need to be made available separately,
  either via package 'wordnetDicts' from <https://datacube.wu.ac.at>,
  installing system packages where available, or direct download from
  <https://wordnetcode.princeton.edu/3.0/WNdb-3.0.tar.gz>.
Methods for estimating profit, profit-maximizing price, demand and consumer surplus of Word-of-Mouth-campaigns on mean-field networks.
Apply 'Wordpiece' (<arXiv:1609.08144>) tokenization to input text, 
 given an appropriate vocabulary. The 'BERT' (<arXiv:1810.04805>) tokenization 
 conventions are used by default.
Provides data to be used by the wordpiece algorithm in order to 
    tokenize text into somewhat meaningful chunks. Included vocabularies were 
    retrieved from 
    <https://huggingface.co/bert-base-cased/resolve/main/vocab.txt> and 
    <https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt> and parsed
    into an R-friendly format.
Collects several classical word pools used most often
    to provide lists of words in psychological studies of learning and memory. It
    provides a simple function, 'pickList' for selecting random samples of words
    within given ranges.
A framework for developing n-gram models for text prediction.
    It provides data cleaning, data sampling, extracting tokens from text,
    model generation, model evaluation and word prediction. For information on how n-gram models 
    work we referred to: "Speech and Language Processing"
    <https://web.stanford.edu/~jurafsky/slp3/3.pdf>. For optimizing R code and
    using R6 classes we referred to "Advanced R" 
    <https://adv-r.hadley.nz/r6.html>. For writing R extensions we referred to 
    "R Packages", <https://r-pkgs.org/index.html>.
The word puzzle game requires you to find out the letters in a word within a limited number of guesses. In each round, if your guess hit any letters in the word, they reveal themselves. If all letters are revealed before your guesses run out, you win this game; otherwise you fail. You may run multiple games to guess different words.
Serves for rendering MS Word documents with R inline code and inserting tables and plots.
List of english scrabble words as listed in the OTCWL2014
    <https://www.scrabbleplayers.org/w/Official_Tournament_and_Club_Word_List_2014_Edition>.
    Words are collated from the 'Word Game Dictionary' <https://www.wordgamedictionary.com/word-lists/>.
Provides access to various word embedding methods (GloVe, 
    fasttext and word2vec) to extract word vectors using a unified framework to
    increase reproducibility and correctness.
An interactive laboratory for research on distributional semantic models ('DSM',
             see <https://en.wikipedia.org/wiki/Distributional_semantics> for more information).
Provides a workflow for your analysis projects by combining
  literate programming ('knitr' and 'rmarkdown') and version control
  ('Git', via 'git2r') to generate a website containing time-stamped,
  versioned, and documented results.
Managing both a 'parsnip' model and a preprocessor, such as a
    model formula or recipe from 'recipes', can often be challenging. The
    goal of 'workflows' is to streamline this process by bundling the
    model alongside the preprocessor, all within the same object.
A workflow is a combination of a model and preprocessors
    (e.g, a formula, recipe, etc.) (Kuhn and Silge (2021)
    <https://www.tmwr.org/>). In order to try different combinations of
    these, an object can be created that contains many workflows. There
    are functions to create workflows en masse as well as training them
    and visualizing the results.
Functions for the import, transformation, and analysis of data 
    from muscle physiology experiments. The work loop technique is used to 
    evaluate the mechanical work and power output of muscle. Josephson (1985) 
    <doi:10.1242/jeb.114.1.493> modernized the technique for
    application in comparative biomechanics. Although our initial motivation 
    was to provide functions to analyze work loop experiment data, as we 
    developed the package we incorporated the ability to analyze data from 
    experiments that are often complementary to work loops. There are currently 
    three supported experiment types: work loops, simple twitches, and tetanus 
    trials. Data can be imported directly from .ddf files or via an object 
    constructor function. Through either method, data can then be cleaned or 
    transformed via methods typically used in studies of muscle physiology. 
    Data can then be analyzed to determine the timing and magnitude of force 
    development and relaxation (for isometric trials) or the magnitude of work, 
    net power, and instantaneous power among other things (for work loops). 
    Although we do not provide plotting functions, all resultant objects are 
    designed to be friendly to visualization via either base-R plotting or 
    'tidyverse' functions.
 This package has been peer-reviewed by rOpenSci (v. 1.1.0).
Download and search data from the 'World Bank Indicators
    API', which provides access to nearly 16,000 time series indicators.
    See
    <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation>
    for further details about the API.
World Flora Online is an online flora of all known plants, available from <https://www.worldfloraonline.org/>. Methods are provided of matching a list of plant names (scientific names, taxonomic names, botanical names) against a static copy of the World Flora Online Taxonomic Backbone data that can be downloaded from the World Flora Online website. The World Flora Online Taxonomic Backbone is an updated version of The Plant List (<http://www.theplantlist.org/>), a working list of plant names that has become static since 2013.
Allow users to obtain clean and tidy
    football (soccer) game, team and player data. Data is collected from a
    number of popular sites, including 'FBref',
    transfer and valuations data from
    'Transfermarkt'<https://www.transfermarkt.com/> and shooting location
    and other match stats data from 'Understat'<https://understat.com/>
    and 'fotmob'<https://www.fotmob.com/>. It gives users the
    ability to access data more efficiently, rather than having to export
    data tables to files before being able to complete their analysis.
Easily plot heat maps of the world, based on continuous or categorical data. Country labels can also be added to the map.
Functions to import data from more than 30,000 surface
    meteorological sites around the world managed by the National Oceanic and Atmospheric Administration (NOAA) Integrated Surface
    Database (ISD).
A toolkit to detect clusters from distance matrices. 
    The distance matrices are assumed to be calculated between the cells of 
    multiple animals ('Caenorhabditis elegans') from input time-series matrices. 
    Some functions for generating distance matrices, performing clustering, 
    evaluating the clustering, and visualizing the results of clustering and 
    evaluation are available. We're also providing the download function to 
    retrieve the calculated distance matrices from 
    'figshare' <https://figshare.com>.
Client for World Register of Marine Species
    (<https://www.marinespecies.org/>). Includes functions for each
    of the API methods, including searching for names by name, date and
    common names, searching using external identifiers, fetching
    synonyms, as well as fetching taxonomic children and
    taxonomic classification.
Generate wordsearch and crossword puzzles using
    custom lists of words (and clues).  Make them easy or hard,
    and print them to solve offline with paper and pencil!
R clients to the 'Web of Science' and 'InCites' 
  <https://clarivate.com/products/data-integration/> APIs, which 
  allow you to programmatically download publication and citation data
  indexed in the 'Web of Science' and 'InCites' databases.
It shows the connections between selected clusters from the latest time point and the clusters from all the previous time points. The transition matrices between time point t and t+1 are obtained from Waddington-OT analysis <https://github.com/ScialdoneLab/WOTPLY>.
Introduce weights into Ordered Weighted Averages and extend bivariate means based on n-ary tree construction. Please refer to the following:
	G. Beliakov, H. Bustince, and T. Calvo (2016, ISBN: 978-3-319-24753-3),
	G. Beliakov(2018) <doi:10.1002/int.21913>,
	G. Beliakov, J.J. Dujmovic (2016) <doi:10.1016/j.ins.2015.10.040>,
	J.J. Dujmovic and G. Beliakov (2017) <doi:10.1002/int.21828>.
Opinionated functions that enable easier and faster
    analysis of Viva Insights data. There are three main types of functions in 'wpa':
    (i) Standard functions create a 'ggplot' visual or a summary table based on a specific
    Viva Insights metric; (2) Report Generation functions generate HTML reports on
    a specific analysis area, e.g. Collaboration; (3) Other miscellaneous functions cover
    more specific applications (e.g. Subject Line text mining) of Viva Insights data.
    This package adheres to 'tidyverse' principles and works well with the pipe syntax.
    'wpa' is built with the beginner-to-intermediate R users in mind, and is optimised for
    simplicity.
Supplies permutation-test alternatives to traditional hypothesis-test
  procedures such as two-sample tests for means, medians, and standard deviations;
  correlation tests; tests for homogeneity and independence; and more. Suitable for
  general audiences, including individual and group users, introductory statistics
  courses, and more advanced statistics courses that desire an introduction to
  permutation tests.
Weighted Piecewise Kernel Density Estimation for large data.
Data from the United Nation's World Population Prospects 2008
Data from the United Nation's World Population Prospects
        2010
Data from the United Nation's World Population Prospects 2012
Provides data from the United Nation's World Population Prospects 2015.
Provides data from the United Nation's World Population Prospects 2017.
Provides data from the United Nation's World Population Prospects 2019.
Explore data in the 'wpp2019' (or 2017, 2015, ...) package using a 'shiny' interface.
Performs Wasserstein projections from the predictive distributions of any model into the space of predictive distributions of linear models. We utilize L1 penalties to also reduce the complexity of the model space. This package employs the methods as described in Dunipace, Eric and Lorenzo Trippa (2020) <arXiv:2012.09999>.
Functions to assist in the processing and
    exploration of data from environmental monitoring programs.
    The package name stands for "water quality" and reflects the
    original focus on time series data for physical and chemical
    properties of water, as well as the biota. Intended for
    programs that sample approximately monthly, quarterly or
    annually at discrete stations, a feature of many legacy data
    sets. Most of the functions should be useful for analysis of
    similar-frequency time series regardless of the subject
    matter.
Fits weighted quantile sum regression models, calculates weighted quantile sum index and estimated component weights.
Implements a permutation test method for the weighted quantile sum (WQS) regression, building off the 'gWQS' package (Renzetti et al. (2021) <https://CRAN.R-project.org/package=gWQS>). Weighted quantile sum regression is a statistical technique to evaluate the effect of complex exposure mixtures on an outcome (Carrico et al. (2015) <doi:10.1007/s13253-014-0180-3>). The model features a statistical power and Type I error (i.e., false positive) rate trade-off, as there is a machine learning step to determine the weights that optimize the linear model fit. This package provides an alternative method based on a permutation test that should reliably allow for both high power and low false positive rate when utilizing WQS regression (Day et al. (2022) <doi:10.1289/EHP10570>).
Assess Water Quality Trends for Long-Term Monitoring Data in Estuaries using
  Generalized Additive Models following Wood (2017) <doi:10.1201/9781315370279> and Error
  Propagation with Mixed-Effects Meta-Analysis following Sera et al. (2019) <doi:10.1002/sim.8362>.
  Methods are available for model fitting, assessment of fit, annual and seasonal trend tests, and
  visualization of results.
Implements various win ratio methodologies for composite endpoints of death and 
  non-fatal events, including the (stratified) proportional win-fractions (PW) regression models 
  (Mao and Wang, 2020 <doi:10.1111/biom.13382>), (stratified) two-sample tests with possibly 
  recurrent nonfatal event, and sample size calculation for standard win ratio test (Mao et al., 
  2021 <doi:10.1111/biom.13501>).
Supports systematic scrutiny, modification, and integration of
    data. The function status() counts rows that have missing values in 
    grouping columns (returned by na() ), have non-unique combinations of 
    grouping columns (returned by dup() ), and that are not locally sorted
    (returned by unsorted() ). Functions enumerate() and itemize() give 
    sorted unique combinations of columns, with or without occurrence counts,
    respectively. Function ignore() drops columns in x that are present in y,
    and informative() drops columns in x that are entirely NA; constant() returns
    values that are constant, given a key.  Data that have defined unique 
    combinations of grouping values behave more predictably during merge operations.
The main functionalities of 'wrappedtools' are:
    adding backticks to variable names; rounding to desired precision 
    with special case for p-values; 
    selecting columns based on pattern and storing their position, name, 
    and backticked name; computing and formatting of descriptive statistics 
    (e.g. mean±SD), comparing groups and creating publication-ready tables with 
    descriptive statistics and p-values; creating specialized plots for 
    correlation matrices. Functions were mainly written for my own daily work or 
    teaching, but may be of use to others as well.
Helper functions to easily add functionality to functions.  The package can 
  assign functions to have an lazy evaluation allowing you to save and update the 
  arguments before and after each function call.  You can set a temporary working 
  directory within functions and wrap console messages around other functions.
Tools for writing and debugging R code. Provides: 
    '%.>%' dot-pipe (an 'S3' configurable pipe), unpack/to (R style multiple assignment/return),
    'build_frame()'/'draw_frame()' ('data.frame' example tools),
    'qc()' (quoting concatenate), 
    ':=' (named map builder), 'let()' (converts non-standard evaluation interfaces to parametric standard
    evaluation interfaces, inspired by 'gtools::strmacro()' and 'base::bquote()'), and more.
A wrapper around Michel Scheffers's 'libassp' (<https://libassp.sourceforge.net/>).
    The 'libassp' (Advanced Speech Signal Processor) library aims at providing
    functionality for handling speech signal files in most common audio formats
    and for performing analyses common in phonetic science/speech science. This
    includes the calculation of formants, fundamental frequency, root mean
    square, auto correlation, a variety of spectral analyses, zero crossing
    rate, filtering etc. This wrapper provides R with a large subset of
    'libassp's signal processing functions and provides them to the user in a
    (hopefully) user-friendly manner.
A collection of color palettes that were extracted from various books on my sons(Wren) bookshelf. Also included are a number of functions and wrappers to utilize them, as well as to subset the palettes to desired number/specific colors.
Calculates non-parametric estimates of the sample size, power and confidence intervals for the win-ratio. For more detail on the theory behind the methodologies implemented see Yu, R. X. and Ganju, J. (2022) <doi:10.1002/sim.9297>.
Additional options for making graphics in the context of analyzing high-throughput data are available here.
  This includes automatic segmenting of the current device (eg window) to accommodate multiple new plots, 
  automatic checking for optimal location of legends in plots, small histograms to insert as legends, 
  histograms re-transforming axis labels to linear when plotting log2-transformed data,
  a violin-plot <doi:10.1080/00031305.1998.10480559> function for a wide variety of input-formats, 
  principal components analysis (PCA) <doi:10.1080/14786440109462720> with bag-plots <doi:10.1080/00031305.1999.10474494> to highlight and compare the center areas for groups of samples, 
  generic MA-plots (differential- versus average-value plots) <doi:10.1093/nar/30.4.e15>, 
  staggered count plots and generation of mouse-over interactive html pages.
Implementation of the methodologies described in 1) Alexander Petersen, Xi Liu and Afshin A. Divani (2021) <doi:10.1214/20-aos1971>, including global F tests, partial F tests, intrinsic Wasserstein-infinity bands and Wasserstein density bands, and 2) Chao Zhang, Piotr Kokoszka and Alexander Petersen (2022) <doi:10.1111/jtsa.12590>, including estimation, prediction, and inference of the Wasserstein autoregressive models.
A powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The 'WrightMap' package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. 'TAM', 'mirt', 'eRm' or 'IRToys' in 'R', or 'Stata', 'Mplus', etc.),  'WrightMap' features special integration with 'ConQuest' to facilitate reading and plotting its output directly.The 'wrightMap' function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The 'CQmodel' function reads output files created using 'ConQuest' software and creates a set of data frames for easy data manipulation, bundled in a 'CQmodel' object. The 'wrightMap' function can take a 'CQmodel' object as input or it can be used to create Wright Maps directly from data frames of person and item parameters.
Function for writing a SNNS pattern file from a data.frame
        or matrix.
Zero-dependency data frame to xlsx exporter based on 'libxlsxwriter'. 
    Fast and no Java or Excel required.
Cross-platform Perl based R function to create Excel 2003 (XLS) and Excel 2007 (XLSX)
             files from one or more data frames. Each data frame will be
             written to a separate named worksheet in the Excel spreadsheet.
             The worksheet name will be the name of the data frame it contains
             or can be specified by the user.
The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. 
  Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc.  
  Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM)  
  for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). 
  A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. 
  Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. 
  Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples.
  Many times large experimental datasets need some additional filtering, adequate functions are provided. 
  Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too.
  Batch reading (or writing) of sets of files and combining data to arrays is supported, too.
A collection of robust statistical methods based on Wilcox' WRS functions. It implements robust t-tests (independent and dependent samples), robust ANOVA (including between-within subject designs), quantile ANOVA, robust correlation, robust mediation, and nonparametric ANCOVA models based on robust location measures.
Water resources system simulator is a tool for simulation and analysis of large-scale water resources systems. 'WRSS' proposes functions and methods for construction, simulation and analysis of primary storage and hydropower water resources features (e.g. reservoirs, aquifers, and etc.) based on Standard Operating Policy (SOP).
A collection of implementations of classical and
    novel algorithms for weighted sampling without replacement.
An adaptation for estuaries (tidal waters) of weighted regression
    on time, discharge, and season to evaluate trends in water quality time series. 
    Please see Beck and Hagy (2015) <doi:10.1007/s10666-015-9452-8> for 
    details.
Top-Down mass spectrometry aims to identify entire proteins as well as their (post-translational) modifications or ions bound (eg Chen et al (2018) <doi:10.1021/acs.analchem.7b04747>). 
    The pattern of internal fragments (Haverland et al (2017) <doi:10.1007/s13361-017-1635-x>) may reveal important information about the original structure of the proteins studied 
    (Skinner et al (2018) <doi:10.1038/nchembio.2515> and Li et al (2018) <doi:10.1038/nchem.2908>). 
    However, the number of possible internal fragments gets huge with longer proteins and subsequent identification of internal fragments remains challenging, 
    in particular since the the accuracy of measurements with current mass spectrometers represents a limiting factor.   
    This package attempts to deal with the complexity of internal fragments and allows identification of terminal and internal fragments from deconvoluted mass-spectrometry data.
Predicts individual race/ethnicity using surname, first name,
    middle name, geolocation, and other attributes, such as gender and
    age. The method utilizes Bayes' Rule (with optional measurement error
    correction) to compute the posterior probability of each racial
    category for any given individual. The package implements methods
    described in Imai and Khanna (2016) "Improving Ecological Inference by
    Predicting Individual Ethnicity from Voter Registration Records"
    Political Analysis <DOI:10.1093/pan/mpw001> and Imai, Olivella, and
    Rosenman (2022) "Addressing census data problems in race imputation
    via fully Bayesian Improved Surname Geocoding and name supplements"
    <DOI:10.1126/sciadv.adc9824>.  The package also incorporates the data
    described in Rosenman, Olivella, and Imai (2023) "Race and ethnicity
    data for first, middle, and surnames"
    <DOI:10.1038/s41597-023-02202-2>.
Non- and semiparametric regression for generalized additive, partial linear, and varying coefficient models as well as their combinations via smoothed backfitting. Based on Roca-Pardinas J and Sperlich S (2010) <doi:10.1007/s11222-009-9130-2>; Mammen E, Linton O and Nielsen J (1999) <doi:10.1214/aos/1017939138>; Lee YK, Mammen E, Park BU (2012) <doi:10.1214/12-AOS1026>.
Easily override the default visual choices in 'ggplot2' to make 
    your time series plots look more like the Wall Street Journal. Specific 
    theme design choices include omitting x-axis grid lines and displaying 
    sparse light grey y-axis grid lines. Additionally, this allows to label 
    the y-axis scales with your units only displayed on the top-most number, 
    while also removing the bottom most number (unless specifically 
    overridden). The goal is visual simplicity, because who has time to waste 
    looking at a cluttered graph?
Entropy weighted k-means (ewkm) by Liping Jing, Michael
        K. Ng and Joshua Zhexue Huang (2007)
        <doi:10.1109/TKDE.2007.1048> is a weighted subspace clustering
        algorithm that is well suited to very high dimensional data.
        Weights are calculated as the importance of a variable with
        regard to cluster membership.  The two-level variable
        weighting clustering algorithm tw-k-means (twkm) by Xiaojun
        Chen, Xiaofei Xu, Joshua Zhexue Huang and Yunming Ye (2013)
        <doi:10.1109/TKDE.2011.262> introduces two types of weights,
        the weights on individual variables and the weights on
        variable groups, and they are calculated during the clustering
        process.  The feature group weighted k-means (fgkm) by Xiaojun
        Chen, Yunminng Ye, Xiaofei Xu and Joshua Zhexue Huang (2012)
        <doi:10.1016/j.patcog.2011.06.004> extends this concept by
        grouping features and weighting the group in addition to
        weighting individual features.
A weighted selection probability to locate rare variants associated with multiple phenotypes.
A parallel implementation of Weighted Subspace Random Forest.  The
    Weighted Subspace Random Forest algorithm was proposed in the
    International Journal of Data Warehousing and Mining by Baoxun Xu,
    Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye
    (2012) <DOI:10.4018/jdwm.2012040103>.  The algorithm can classify
    very high-dimensional data with random forests built using small
    subspaces.  A novel variable weighting method is used for variable
    subspace selection in place of the traditional random variable
    sampling.This new approach is particularly useful in building
    models from high-dimensional data.
Tools for a wavelet-based approach to analyzing spatial synchrony, principally in ecological data. Some tools will be useful for studying community synchrony. See, for instance, Sheppard et al (2016) <doi: 10.1038/NCLIMATE2991>, Sheppard et al (2017) <doi: 10.1051/epjnbp/2017000>, Sheppard et al (2019) <doi: 10.1371/journal.pcbi.1006744>.
Perform the calculation of W-test, diagnostic checking, calculate
    minor allele frequency (MAF) and odds ratio.
Computes the Weighted Topological Overlap with positive and negative signs (wTO) networks given a data frame containing the mRNA count/ expression/ abundance per sample, and a vector containing the interested nodes of interaction (a subset of the elements of the full data frame). It also computes the cut-off threshold or p-value based on the individuals bootstrap or the values reshuffle per individual. It also allows the construction of a consensus network, based on multiple wTO networks. The package includes a visualization tool for the networks.  More about the methodology can be found at <doi:10.1186/s12859-018-2351-7>.
Evaluation of alternatives based on multiple criteria using weighted technique for Order preference by similarity to an ideal solution method. Reference: Hwang CL. (1981, ISBN:978-3-540-10558-9).
Allows form managers to download entries from their respondents
    using Wufoo JSON API (<https://www.wufoo.com>). Additionally, the Wufoo reports - when public - can be
    also acquired programmatically. Note that building new forms within this package
    is not supported.
Provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh, A., Walden, A.T., & Percival, D.B. (2000) <doi:10.2307/2669537> and Guerrier, S. & Molinari, R. (2016) <arXiv:1607.05858>.
Select data analysis plots, under a standardized calling interface implemented on top of 'ggplot2' and 'plotly'.  
   Plots of interest include: 'ROC', gain curve, scatter plot with marginal distributions, 
   conditioned scatter plot with marginal densities,
   box and stem with matching theoretical distribution, and density with matching theoretical distribution.
Book is "Linear Mixed Models: A Practical Guide Using
        Statistical Software" published in 2006 by Chapman Hall / CRC
        Press.
Provides a collection of white noise hypothesis tests for functional time series and related visualizations. 
  These include tests based on the norms of autocovariance operators that are built under both strong and weak 
  white noise assumptions. Additionally, tests based on the spectral density operator and on principal component
  dimensional reduction are included, which are built under strong white noise assumptions. 
  Also, this package provides goodness-of-fit tests for functional autoregressive of order 1 models.
  These methods are described in Kokoszka et al. (2017) <doi:10.1016/j.jmva.2017.08.004>, Characiejus and Rice (2019) 
  <doi:10.1016/j.ecosta.2019.01.003>, Gabrys and Kokoszka (2007) <doi:10.1198/016214507000001111>, 
  and Kim et al. (2023) <doi: 10.1214/23-SS143>
  respectively.
A weather generator to simulate precipitation and temperature for regions with seasonality. Users input training data containing precipitation, temperature, and seasonality (up to 26 seasons). Including weather season as a training variable allows users to explore the effects of potential changes in season duration as well as average start- and end-time dates due to phenomena like climate change. Data for training should be a single time series but can originate from station data, basin averages, grid cells, etc.
    Bearup, L., Gangopadhyay, S., & Mikkelson, K. (2021). "Hydroclimate Analysis Lower Santa Cruz River Basin Study (Technical Memorandum No ENV-2020-056)." Bureau of Reclamation.
    Gangopadhyay, S., Bearup, L. A., Verdin, A., Pruitt, T., Halper, E., & Shamir, E. (2019, December 1). "A collaborative stochastic weather generator for climate impacts assessment in the Lower Santa Cruz River Basin, Arizona." Fall Meeting 2019, American Geophysical Union. <https://ui.adsabs.harvard.edu/abs/2019AGUFMGC41G1267G>.
Meta testing is the ability to test a function without having to 
    provide its parameter values.
    Those values will be generated, based on semantic naming of parameters, as 
    introduced by package 'wyz.code.offensiveProgramming'.
    Value generation logic can be completed with your own data types 
    and generation schemes. This to meet your most specific requirements and to 
    answer to a wide variety of usages, from general use case to very specific
    ones.
    While using meta testing, it becomes easier to generate stress test 
    campaigns, non-regression test campaigns and robustness test campaigns, as 
    generated tests can be saved and reused from session to session. 
    Main benefits of using 'wyz.code.metaTesting' is ability to discover valid 
    and invalid function parameter combinations, ability to infer valid 
    parameter values, and to provide smart summaries that allows you to focus
    on dysfunctional cases.
Allows to turn standard R code into offensive programming code. 
    Provides code instrumentation to ease this change and tools to assist and 
    accelerate code production and tuning while using offensive programming code 
    technics. 
    Should improve code robustness and quality. Function calls can be easily 
    verified on-demand or in batch mode to assess parameter types and length 
    conformities. 
    Should improve coders productivity as offensive programming reduces the code
    size due to reduced number of controls all along the call chain. 
    Should speed up processing as many checks will be reduced to one single check.
Allows to generate on-demand or by batch, any R documentation file,
    whatever is kind, data, function, class or package. It populates
    documentation sections, either automatically or by considering
    your input. Input code could be standard R code or offensive programming code. 
    Documentation content completeness depends on the type of code you use. With
    offensive programming code, expect generated documentation to be fully 
    completed, from a format and content point of view. With some standard R 
    code, you will have to activate post processing to fill-in any section that 
    requires complements. Produced manual page validity is automatically tested 
    against R documentation compliance rules. Documentation language 
    proficiency, wording style, and phrasal adjustments remains your job.
Allows to generate automatically 'testthat' code files from offensive 
    programming test cases. Generated test files are complete and ready to run.
    Using 'wyz.code.testthat' you will earn a lot of time, reduce the number of
    errors in test case production, be able to test immediately generated files 
    without any need to view or modify them, and enter a zero time latency between 
    code implementation and industrial testing. As with 'testthat', you may
    complete provided test cases according to your needs to push testing further, 
    but this need is nearly void when using 'wyz.code.offensiveProgramming'.
Provides a tool for extracting information (entities and relations between them) in text datasets. It also emphasizes the results exploration with graphical displays. It is a rule-based system and works with hand-made dictionaries and local grammars defined by users. 'x.ent' uses parsing with Perl functions and JavaScript to define user preferences through a browser and R to display and support analysis of the results extracted. Local grammars are defined and compiled with the tool Unitex, a tool developed by University Paris Est that supports multiple languages. See ?xconfig for an introduction.
The 'X13-ARIMA-SEATS' <https://www.census.gov/data/software/x13as.html> methodology and software is a widely used software and developed by the US Census Bureau. It can be accessed from 'R' with this package and 'X13-ARIMA-SEATS' binaries are provided by the 'R' package 'x13binary'.
The US Census Bureau provides a seasonal adjustment program now
 called 'X-13ARIMA-SEATS' building on both earlier programs called X-11 and
 X-12 as well as the SEATS program by the Bank of Spain. The US Census Bureau
 offers both source and binary versions -- which this package integrates for
 use by other R packages.
The x3p file format  is specified in ISO standard 5436:2000 to 
    describe 3d surface measurements. 'x3ptools' allows reading, writing and 
    basic modifications to the 3D surface measurements.
A few functions which provide a quick way of subsetting
    genomic admixture data and generating customizable stacked barplots.
Create HTML5 slides with R Markdown and the JavaScript library
    'remark.js' (<https://remarkjs.com>).
Extras and extensions for 'xaringan' slides. Navigate your
    slides with tile view. Make your slides editable, live! Announce slide
    changes with subtle tones. Animate slide transitions with
    'animate.css'. Add tabbed panels to slides with 'panelset'. Use the
    'Tachyons CSS' utility toolkit for rapid slide development. Scribble
    on your slides. Add a copy button to your code chunks with
    'clipboard'. Add a logo or top or bottom banner to every slide.
    Broadcast slides to stay in sync with remote viewers. Include yourself
    in your slides with 'webcam'.  Plus a whole lot more!
Create beautifully color-coordinated and customized themes
    for your 'xaringan' slides, without writing any CSS. Complete your
    slide theme with 'ggplot2' themes that match the font and colors used
    in your slides.  Customized styles can be created directly in your
    slides' 'R Markdown' source file or in a separate external script.
Functions to extract business financial information from
  an Extensible Business Reporting Language ('XBRL') instance file and the
  associated collection of files that defines its 'Discoverable' Taxonomy
  Set ('DTS').
Functions for Estimating a (c)DCC-GARCH Model in large dimensions
    based on a publication by Engle et,al (2017) <doi:10.1080/07350015.2017.1345683> and Nakagawa et,al (2018) <doi:10.3390/ijfs6020052>.
    This estimation method is consist of composite likelihood method by Pakel et al. (2014) <http://paneldataconference2015.ceu.hu/Program/Cavit-Pakel.pdf>
    and (Non-)linear shrinkage estimation of covariance matrices by Ledoit and Wolf (2004,2015,2016). (<doi:10.1016/S0047-259X(03)00096-4>,
    <doi:10.1214/12-AOS989>, <doi:10.1016/j.jmva.2015.04.006>).
Hamiltonian Monte Carlo for both continuous and discontinuous
  posterior distributions with customisable trajectory length 
  termination criterion. See Nishimura et al. (2020) <doi:10.1093/biomet/asz083> for
  the original Discontinuous Hamiltonian Monte Carlo, 
  Hoffman et al. (2014) <doi:10.48550/arXiv.1111.4246> and Betancourt (2016) 
  <doi:10.48550/arXiv.1601.00225> for the definition of possible Hamiltonian
  Monte Carlo termination criteria.
Miscellaneous functions used for x-engineering (feature engineering) or 
  for supporting in other packages maintained by 'Shichen Xie'.
Implementation of a scalable, highly configurable, and 
        e(x)tended architecture for (e)volutionary and (g)enetic (a)lgorithms.
        Multiple representations (binary, real-coded, permutation, and 
        derivation-tree), a rich collection of genetic operators, 
        as well as an extended processing pipeline are provided 
        for genetic algorithms (Goldberg, D. E. (1989, ISBN:0-201-15767-5)),
        differential evolution (Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005)
        <doi:10.1007/3-540-31306-0>), simulated annealing (Aarts, E., and Korst, J.
        (1989, ISBN:0-471-92146-7)), grammar-based genetic programming 
        (Geyer-Schulz (1997, ISBN:978-3-7908-0830-X)), and grammatical evolution 
        (Ryan, C., O'Neill, M., and Collins, J. J. (2018) <doi:10.1007/978-3-319-78717-6>).
        All algorithms reuse basic adaptive mechanisms for performance optimization.
        Sequential or parallel execution (on multi-core machines, 
        local clusters, and high performance computing environments) 
        is available for all algorithms. See 
        <https://github.com/ageyerschulz/xega/tree/main/examples/executionModel>.
Translates a BNF (Backus-Naur Form) specification of a 
             context-free language into an R grammar object
             which consists of the start symbol, the symbol table, 
             the production table, and a short production table.
             The short production table is non-recursive. 
             The grammar object contains the file name from 
             which it was generated (without a path).
             In addition, it provides functions to determine the type 
             of a symbol (isTerminal() and isNonterminal()) and functions
             to access the production table (rules() and derives()).
             For the BNF specification, see Backus, John et al. (1962)
             "Revised Report on the Algorithmic Language ALGOL 60".
             (ALGOL60 standards page <http://www.algol60.org/2standards.htm>,
              html-edition <https://www.masswerk.at/algol60/report.htm>)
             The grammar compiler is based on the APL2 implementation in 
             Geyer-Schulz, Andreas (1997, ISBN:978-3-7908-0830-X).
Derivation tree operations are needed for implementing 
            grammar-based genetic programming and grammatical evolution:  
            Generating of a random derivation trees of a context-free grammar 
            of bounded depth, decoding a derivation tree, 
            choosing a random node in a derivation tree, 
            extracting a tree whose root is a specified node, and 
            inserting a subtree into a derivation tree at a specified node.
            These operations are necessary for the initializiation and 
            for decoders of a random population of programs, 
            as well as for implementing crossover and mutation operators.
            Depth-bounds are guaranteed by switching to a grammar 
            without recursive production rules. 
            For executing the examples, the package 'BNF' is needed.
            The basic tree operations of generating, extracting, and 
            inserting of derivation trees as well as the conditions 
            for guaranteeing complete derivation trees have been 
            presented in Geyer-Schulz (1997, ISBN:978-3-7908-0830-X).
            The use of random integer vectors for the generation 
            of derivation trees has been introduced in 
            Ryan, C., Collins, J. J., and  O'Neill, M. (1998)
            <doi:10.1007/BFb0055930>.
Representation-dependent gene level operations  
        for genetic and evolutionary algorithms with real-coded genes
        are collected in this package. The common feature of the gene
        operations is that all of them are useful for derivation-free
        optimization algorithms. At the moment the package  
        implements initialization, mutation, crossover, and replication 
        operations for differential evolution as described in
        Price, Kenneth V., Storn, Rainer M. and Lampinen, Jouni A. (2005)
        <doi:10.1007/3-540-31306-0>.
Representation-dependent gene level operations of a 
        genetic algorithm with binary coded genes:
        Initialization of random binary genes, several gene maps for 
        binary genes, several mutation operators, several crossover
        operators with 1 and 2 kids, replication 
        pipelines for 1 and 2 kids, and, last but not least, function 
        factories for configuration. 
        See Goldberg, D. E. (1989, ISBN:0-201-15767-5).
        For crossover operators, see 
        Syswerda, G. (1989, ISBN:1-55860-066-3),
        Spears, W. and De Jong, K. (1991, ISBN:1-55860-208-9).
        For mutation operators, see 
        Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7).
Grammatical evolution  (see O'Neil, M. and 
        Ryan, C.  (2003,ISBN:1-4020-7444-1)) uses decoders to 
        convert linear (binary or integer genes) into programs.  
        In addition, automatic determination of codon precision 
        with a limited rule choice bias is provided.
        For a recent survey of grammatical evolution, 
        see Ryan, C., O'Neill, M., and Collins, J. J. (2018)
        <doi:10.1007/978-3-319-78717-6>.
An implementation of
        the representation-dependent gene level operations of grammar-based
        genetic programming with genes which are derivation trees
        of a context-free grammar: Initialization of a gene with a 
        complete random derivation tree, decoding of a derivation tree.
        Crossover is implemented by exchanging subtrees. Depth-bounds
        for the minimal and the maximal depth of the roots of the subtrees
        exchanged by crossover can be set. 
        Mutation is implemented by replacing a subtree by a random subtree. 
        The depth of the random subtree and the insertion node are 
        configurable. For details, 
        see Geyer-Schulz (1997, ISBN:978-3-7908-0830-X).
An implementation of 
        representation-dependent gene level operations for
        genetic algorithms with genes which represent permutations: 
        initialization of genes, mutation and crossover.
        The crossover operation provided is position-based crossover 
        (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8). 
        For mutation, several variants are included: Order-based mutation
        (Syswerda, G., Chap. 21 in Davis, L. (1991, ISBN:0-442-00173-8), 
        randomized Lin-Kernighan heuristics
        (Croes, G. A. (1958) <doi:10.1287/opre.6.6.791> and
         Lin, S. and Kernighan. B. W. (1973) 
        <doi:10.1287/opre.21.2.498>), 
        and randomized greedy operators.
        A random mix operator for mutation selects a mutation variant 
        randomly.
This collection of gene representation-independent functions 
   implements the population layer of extended evolutionary and genetic 
   algorithms and its support. The population layer consists of functions 
   for initializing, logging, observing, evaluating a population of genes, 
   as well as of computing the next population. For parallel evaluation of a 
   population of genes 4 execution models - named Sequential, MultiCore, 
   FutureApply, and Cluster - are provided. They are implemented by 
   configuring the lapply() function. The execution model FutureApply can be 
   externally configured as recommended by Bengtsson (2021) 
   <doi:10.32614/RJ-2021-048>. Configurable acceptance rules and cooling 
   schedules (see Kirkpatrick, S., Gelatt, C. D. J, and Vecchi, M. P. (1983) 
   <doi:10.1126/science.220.4598.671>, and Aarts, E., and Korst, J. 
   (1989, ISBN:0-471-92146-7) offer simulated annealing or greedy randomized 
   approximate search procedure elements. Adaptive crossover and mutation 
   rates depending on population statistics generalize the approach of 
   Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7).
Datasets and definitions of generic functions used in dependencies of the 'xergm' package.
Read and write XES Files to create event log objects used by the 'bupaR' framework. XES (Extensible Event Stream) is the `IEEE` standard for storing and sharing event data (see <http://standards.ieee.org/findstds/standard/1849-2016.html> for more info).
Miscellaneous functions commonly used in other packages maintained by 'Yihui Xie'.
This tool enables in-database scoring of 'XGBoost' models built in R, by translating trained model objects into SQL query. 
  'XGBoost' <https://xgboost.readthedocs.io/en/latest/index.html> provides parallel tree boosting (also known as gradient boosting machine, or GBM) algorithms
  in a highly efficient, flexible and portable way. GBM algorithm is introduced by Friedman (2001) <doi:10.1214/aos/1013203451>, 
  and more details on 'XGBoost' can be found in Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
Extreme Gradient Boosting, which is an efficient implementation
    of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
    This package is its R interface. The package includes efficient linear
    model solver and tree learning algorithms. The package can automatically
    do parallel computation on a single machine which could be more than 10
    times faster than existing gradient boosting packages. It supports
    various objective functions, including regression, classification and ranking.
    The package is made to be extensible, so that users are also allowed to define
    their own objectives easily.
Compute surrogate explanation groves for predictive machine learning models and analyze complexity vs. explanatory power of an explanation according to Szepannek, G. and von Holt, B. (2023) <doi:10.1007/s41237-023-00205-2>.
Supports a structured approach
    for exploring PKPD data <https://opensource.nibr.com/xgx/>. It also
    contains helper functions for enabling the modeler to follow best R
    practices (by appending the program name, figure name location, and
    draft status to each plot). In addition, it enables the modeler to
    follow best graphical practices (by providing a theme that
    reduces chart ink, and by providing time-scale, log-scale, and
    reverse-log-transform-scale functions for more readable axes).
    Finally, it provides some data checking and summarizing functions for
    rapidly exploring pharmacokinetics and pharmacodynamics (PKPD) datasets.
Fits relative survival regression models with or without proportional excess hazards and with the additional possibility to correct for background mortality by one or more parameter(s). These models are relevant when the observed mortality in the studied group is not comparable to that of the general population or in population-based studies where the available life tables used for net survival estimation are insufficiently stratified. In the latter case, the proposed model by Touraine et al. (2020) <doi:10.1177/0962280218823234> can be used. The user can also fit a model that relaxes the proportional expected hazards assumption considered in the Touraine et al. excess hazard model. This extension was proposed by Mba et al. (2020) <doi:10.1186/s12874-020-01139-z> to allow non-proportional effects of the additional variable on the general population mortality. In non-population-based studies, researchers can identify non-comparability source of bias in terms of expected mortality of selected individuals. An excess hazard model correcting this selection bias is presented in Goungounga et al. (2019) <doi:10.1186/s12874-019-0747-3>. This class of model with a random effect at the cluster level on excess hazard is presented in Goungounga et al. (2023) <doi:10.1002/bimj.202100210>.
Computes robust association measures that do not 
    presuppose linearity. The xi correlation (xicor) is based
    on cross correlation between ranked increments.
    The reference for the methods implemented here is 
    Chatterjee, Sourav (2020) <arXiv:1909.10140>
    This package includes the Galton peas example.
Provides a simple XML tree parser/generator. It includes
        functions to read XML files into R objects, get information out of
        and into nodes, and write R objects back to XML code. It's not as
        powerful as the 'XML' package and doesn't aim to be, but for simple
        XML handling it could be useful. It was originally developed for
        the R GUI and IDE 'RKWard' <https://rkward.kde.org>, to make plugin
        development easier.
Plotting ggplot2 graphs using the XKCD style.
The XKCD color survey asked participants to name colours. Randall Munroe published the top thousand(roughly) names and their sRGB hex values. This package lets you use them.
Download data from individual XKCD comics, written by Randall Munroe <https://xkcd.com/>.
An R interface to the 'OpenPyXL' 'Python' library to create 
  native 'Excel' charts and work with Microsoft 'Excel' files.
Provides comprehensive functionality to read, write and format Excel data.
Parse entire folders of non-rectangular 'xlsx' files into a single
  rectangular and tidy 'data.frame' based on a custom template file defining the 
  column names of the output.
The expression of X-chromosome undergoes three possible biological processes: X-chromosome
             inactivation (XCI), escape of the X-chromosome inactivation (XCI-E),and skewed X-chromosome                    
             inactivation (XCI-S). To analyze the X-linked genetic association for phenotype such as                         
             continuous, binary, and time-to-event outcomes with the actual process unknown, we propose a unified            
             approach of maximizing the likelihood or partial likelihood over all of the potential biological             
             processes. The methods are described in Wei Xu, Meiling Hao (2017) <doi:10.1002/gepi.22097>. And 
             also see Dongxiao Han, Meiling Hao, Lianqiang Qu, Wei Xu (2019) <doi:10.1177/0962280219859037>.
Provides a tool for non linear mapping (non linear regression) using a mixture of regression model and an inverse regression strategy. The methods include the GLLiM model (see Deleforge et al (2015) <DOI:10.1007/s11222-014-9461-5>) based on Gaussian mixtures and a robust version of GLLiM, named SLLiM (see Perthame et al (2016) <DOI:10.1016/j.jmva.2017.09.009>) based on a mixture of Generalized Student distributions. The methods also include BLLiM (see Devijver et al (2017) <arXiv:1701.07899>) which is an extension of GLLiM with a sparse block diagonal structure for large covariance matrices (particularly interesting for transcriptomic data).
Given the date column as an ascending entry, future errors are included in the sum of squares of error that should be minimized based on the number of steps and weights you determine. Thus, it is prevented that the variables affect each other's coefficients unrealistically.
Provide R functions to read/write/format Excel 2007 and Excel 97/2000/XP/2003 file formats.
Reading and writing sheets of a single 'Excel' file into and from a list of data frames. Eases I/O of tabular data in bioinformatics while keeping them in a human readable format.
The xlsxjars package collects all the external jars
  required for the xlxs package. This release corresponds to POI
  3.10.1.
A toolbox for meta-analysis. This package includes: 1,a robust multivariate meta-analysis of continuous or binary outcomes; 2, a bivariate Egger's test for detecting small study effects; 3, Galaxy Plot: A New Visualization Tool of Bivariate Meta-Analysis Studies; 4, a bivariate T&F method accounting for publication bias in bivariate meta-analysis, based on symmetry of the galaxy plot. Hong C. et al(2020) <doi:10.1093/aje/kwz286>, Chongliang L. et al(2020) <doi:10.1101/2020.07.27.20161562>.
Many approaches for both reading and
        creating XML (and HTML) documents (including DTDs), both local
        and accessible via HTTP or FTP.  Also offers access to an
        'XPath' "interpreter".
Work with XML files using a simple, consistent interface.
    Built on top of the 'libxml2' C library.
Helpers for transforming
    XML content into number of tables while preserving parent to child
    relationships.
Import an XML document with nested object structures and convert
    it into a relational data model. The result is a set of R dataframes 
    with foreign key relationships. The data model and the data can be exported as
    SQL code of different SQL flavors.
Converts XML documents to R dataframes and dataframes to XML documents.
  A wide variety of options allows for different XML formats and flexible control
  of the conversion process. Results can be exported to CSV and Excel, if desired. 
  Also converts XML data to R lists.
Convert the output of 'utils::getParseData()' to an 'XML'
    tree, that one can search via 'XPath', and easier to manipulate in
    general.
'XML' package for creating and reading and manipulating 'XML', with an object model based on 'Reference Classes'.
The 'XML-RPC' is a remote procedure call protocol 
    based on 'XML'. The 'xmlrpc2' package is inspired by the 'XMLRPC'
	package but uses the 'curl' and 'xml2' packages instead 'RCurl' and 'XML'.
Edit 'XMP' metadata <https://en.wikipedia.org/wiki/Extensible_Metadata_Platform> 
    in a variety of media file formats as well as 
    edit bookmarks (aka outline aka table of contents) and documentation info entries in 'pdf' files.
    Can detect and use a variety of command-line tools to perform these operations such as
    'exiftool' <https://exiftool.org/>, 'ghostscript' <https://www.ghostscript.com/>, 
    and/or 'pdftk' <https://gitlab.com/pdftk-java/pdftk>.
XMRs combine X-Bar control charts and Moving Range control charts. These functions also will recalculate the reference lines when significant change has occurred.
Fit a two-step kernel ridge regression model for
        predicting edges in networks, and carry out cross-validation
        using shortcuts for swift and accurate performance assessment
        (Stock et al, 2018 <doi:10.1093/bib/bby095> ).
Tests whether a set of counts fit a given expected ratio. For
    example, a genetic cross might be expected to produce four types in the
    relative frequencies of 9:3:3:1. To see whether a set of observed counts
    fits this expectation, one can examine all possible outcomes with xmulti() or a
    random sample of them with xmonte() and find the probability of an observation
    deviating from the expectation by at least as much as the observed. As a
    measure of deviation from the expected, one can use the log-likelihood
    ratio, the multinomial probability, or the classic chi-square statistic. A
    histogram of the test statistic can also be plotted and compared with the
    asymptotic curve.
Analysis of crossover interference in experimental crosses,
    particularly regarding the gamma model. See, for example,
    Broman and Weber (2000) <doi:10.1086/302923>.
Cross platform solution to open files, directories or 'URLs'
    with their associated programs.
Helps systematize and ease the process of 
    building unit tests with the 'testthat' package by providing 
    tools for generating expectations.
Allows to provide live interpretations and explanations of statistical 
    functions in R. These interpretations and explanations are shown when the explained function 
    is called by the user. They can interact with the values of the explained function's actual 
    results to offer relevant, meaningful insights. The 'xplain' interpretations and explanations 
    are based on an easy-to-use XML format that allows to include R code to interact with the 
    returns of the explained function.
Tools for interactive data exploration built using 'shiny'. Includes apps for descriptive 
    statistics, visualizing probability distributions, inferential statistics, linear regression, 
    logistic regression and RFM analysis.
Provides a set of functions devoted to multivariate exploratory statistics on textual data. Classical methods such as correspondence analysis and agglomerative hierarchical clustering are available. Chronologically constrained agglomerative hierarchical clustering enriched with labelled-by-words trees is offered. Given a division of the corpus into parts, their characteristic words and documents are identified. Further, accessing to 'FactoMineR' functions is very easy. Two of them are relevant in textual domain. MFA() addresses multiple lexical table allowing applications such as dealing with multilingual corpora as well as simultaneously analyzing both open-ended and closed questions in surveys. See <http://xplortext.unileon.es> for examples.
Tools to build CDISC compliant data sets and check for CDISC
    compliance.
Diagnostics for non-linear mixed-effects (population) 
    models from 'NONMEM' <https://www.iconplc.com/solutions/technologies/nonmem/>. 
    'xpose' facilitates data import, creation of numerical run summary 
    and provide 'ggplot2'-based graphics for data exploration and model 
    diagnostics.
Extension to 'xpose' to support 'nlmixr2'. Provides functions to import 'nlmixr2' fit data into an 'xpose' data object, allowing the use of 'xpose' for 'nlmixr2' model diagnostics.
A model building aid for nonlinear mixed-effects 
    (population) model analysis using NONMEM, facilitating data set 
    checkout, exploration and visualization, model diagnostics, candidate 
    covariate identification and model comparison. The methods are described 
    in Keizer et al. (2013) <doi:10.1038/psp.2013.24>, and Jonsson et al. (1999) 
    <doi:10.1016/s0169-2607(98)00067-4>.
Support for interfaces from R to other languages,
    built around a class for evaluators and a combination of functions, classes and
    methods for communication. Will be used through a specific language interface
    package. Described in the book "Extending R".
Tools to analyze datasets previous to any statistical modeling.
    Has various functions designed to find inconsistencies and understanding the distribution of the data.
An implementation of the RuleFit algorithm as described in Friedman & Popescu 
  (2008) <doi:10.1214/07-AOAS148>. eXtreme Gradient Boosting ('XGBoost') is used 
  to build rules, and 'glmnet' is used to fit a sparse linear model on the raw and rule features. The result
  is a model that learns similarly to a tree ensemble, while often offering improved interpretability
  and achieving improved scoring runtime in live applications. Several algorithms for
  reducing rule complexity are provided, most notably hyperrectangle de-overlapping. All algorithms scale to 
  several million rows and support sparse representations to handle tens of thousands of dimensions.
Contains functions to identify tree-ring borders based on X-ray micro-density profiles and a Graphical User Interface (GUI) to visualize density profiles and correct tree-ring borders. Campelo F, Mayer K, Grabner M. (2019) <doi:10.1016/j.dendro.2018.11.002>.
A Julia interface structured according to the general
	     form described in package 'XR' and in the book "Extending R".
Fits hierarchical regularized regression models
    to incorporate potentially informative external data, Weaver and Lewinger (2019) <doi:10.21105/joss.01761>. 
    Utilizes coordinate descent to efficiently fit regularized regression models both with and without
    external information with the most common penalties used in practice (i.e. ridge, lasso, elastic net). 
    Support for standard R matrices, sparse matrices and big.matrix objects.
A 'Python' interface structured according to the general
    form described in package 'XR' and in the book "Extending R".
This is a set of statistical quality control functions,  that allows plotting control charts and its iterations, process capability for variable and attribute control, highlighting the xrs_gr() function, like a first iteration for variable chart, meanwhile the we_rules() function detects non random patterns in sample.
An extension for the 'xml2' package to transform XML documents
    by applying an 'xslt' style-sheet.
The circadian period of a time series data is predicted and the statistical significance of the periodicity are calculated using the chi-square periodogram.
Tools to download and merge data files on sub-national conflict, violence and protests from <http://www.x-sub.org>.
There are two new network metrics, RWC (random walk centrality) and CBET (counting betweenness). Also available are the normalized versions of those metrics. These measures of centrality and betweenness are particularly useful for the analysis of very dense weighted networks which include loops. Traditional measures do not work as well for those network characteristics. The main reference is DePaolis at al (2022) <doi:10.1007/s41109-022-00519-2>.
Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.
Based on 'STATA' xtsum command, it is used to compute summary statistics for a panel data set. It generates overall, between-group, and within-group statistics for specified variables in a panel data set, as presented in S. Porter (2023) <https://stephenporter.org/files/xtsum_handout.pdf>, StataCorp (2023) <https://www.stata.com/manuals/xtxtsum.pdf>.
Extends standard penalized regression (Lasso, Ridge, and Elastic-net) to allow feature-specific shrinkage based on external information with the goal of achieving a better prediction accuracy and variable selection. Examples of external information include the grouping of predictors, prior knowledge of biological importance, external p-values, function annotations, etc. The choice of multiple tuning parameters is done using an Empirical Bayes approach. A majorization-minimization algorithm is employed for implementation.
This is a collection of some useful functions when dealing with text data. 
    Currently it only contains a very efficient function of decoding HTML entities
    in character vectors by 'Rcpp' routine.
Calculates a number of valuation adjustments including CVA, DVA,
    FBA, FCA, MVA and KVA. A two-way margin agreement has been implemented. For
    the KVA calculation three regulatory frameworks are supported: CEM, (simplified) SA-CCR, OEM
	and IMM. The probability of default is implied through the credit spreads curve.
    The package supports an exposure calculation based on SA-CCR which includes several trade types
    and a simulated path which is currently available only for IRSwaps. The latest regulatory capital charge methodologies
    have been implementing including BA-CVA & SA-CVA.
Extrema-weighted feature extraction for varying length functional data. Functional data analysis method that performs dimensionality reduction based on predefined features and allows for quantile weighting. Method implemented as presented in van den Boom et al. (2018) <doi:10.1093/bioinformatics/bty120>.
Institutional performance assessment remains a key challenge to a multitude of stakeholders. Existing indicators such as h-type indicators, g-type indicators, and many others do not reflect expertise of institutions that defines their research portfolio. The package offers functionality to compute and visualise two novel indices: the x-index and the xd-index. The x-index evaluates an institution's scholarly expertise within a specific discipline or field, while the xd-index provides a broader assessment of overall scholarly expertise considering an institution's publication pattern and strengths across coarse thematic areas. These indices offer a nuanced understanding of institutional research capabilities, aiding stakeholders in research management and resource allocation decisions. Lathabai, H.H., Nandy, A., and Singh, V.K. (2021) <doi:10.1007/s11192-021-04188-3>. Nandy, A., Lathabai, H.H., and Singh, V.K. (2023) <doi:10.5281/zenodo.8305585>.
Extremely fast hashing of R objects using 'xxHash'.  R objects are hashed via
    the standard serialization mechanism in R.  Raw byte vectors and strings
    can be handled directly for compatibility with hashes created on 
    other systems.  This implementation is a wrapper around the 'xxHash' 'C'
    library which is available from <https://github.com/Cyan4973/xxHash>.
A suite of psychometric analysis tools for research and operation, including:
    (1) computation of probability, information, and likelihood for the 3PL, GPCM, and GRM;
    (2) parameter estimation using joint or marginal likelihood estimation method;
    (3) simulation of computerized adaptive testing using built-in or customized algorithms;
    (4) assembly and simulation of multistage testing. 
    The full documentation and tutorials are at <https://github.com/xluo11/xxIRT>.
An alternative canonical correlation/redundancy analysis function, with associated print, plot, and summary methods.  A method for generating helio plots is also included.
Procedures to perform consensus clustering starting from a dissimilarity matrix or a data matrix. It's allowed to select if the subsampling has to be by samples or features. In case of computational heavy load, the procedures can run in parallel.
Another implementation of general regression neural network in R
    based on Specht (1991) <DOI:10.1109/72.97934>. It is applicable to the 
    functional approximation or the classification.
Obtain historical and near real time data related to stocks, index 
    and currencies from the Yahoo Finance API. This package is community maintained 
    and is not officially supported by 'Yahoo'. The accuracy of data is only as 
    correct as provided on <https://finance.yahoo.com/>.
Performs nearest neighbor-based imputation using one or more alternative 
    approaches to processing multivariate data. These include methods based on canonical 
    correlation: analysis, canonical correspondence analysis, and a multivariate adaptation 
    of the random forest classification and regression techniques of Leo Breiman and Adele 
    Cutler. Additional methods are also offered. The package includes functions for 
    comparing the results from running alternative techniques, detecting imputation targets 
    that are notably distant from reference observations, detecting and correcting 
    for bias, bootstrapping and building ensemble imputations, and mapping results.
This collection of data exploration tools was developed at
    Yale University for the graphical exploration of complex
    multivariate data; barcode and gpairs now have their own
    packages.  The big.read.table() function provided here may be
    useful for large files when only a subset is needed (but please
    see the note in the help page for this function).
Implements the 'libyaml' 'YAML' 1.1 parser and emitter
  (<https://pyyaml.org/wiki/LibYAML>) for R.
A YAML-based
 mechanism for working with table metadata. Supports
 compact syntax for creating, modifying, viewing, exporting,
 importing, displaying, and plotting metadata coded as column 
 attributes. The 'yamlet' dialect is valid 'YAML' with
 defaults and conventions chosen to improve readability. 
 See ?yamlet, ?decorate, ?modify, ?io_csv, and ?ggplot.decorated.
Setting layout through 'YAML' headers in 'R-Markdown' documents,
    enabling their automatic generation.
    Functions and methods may summarize 'R' objects in automatic reports, for
    instance check-lists and further reports applied to the packages 'taxlist'
    and 'vegtable'.
Tidy tools for quantifying how well model fits to a data set
    such as confusion matrices, class probability curve summaries, and
    regression metrics (e.g., RMSE).
Contains a mixture of functions and data sets referred to in the introductory e-book "YaRrr!: The Pirate's Guide to R". The latest version of the e-book is available for free at <https://www.thepiratesguidetor.com>.
A collection of string functions designed for writing 
    compact and expressive R code. 'yasp' (Yet Another String Package) is simple,
    fast, dependency-free, and written in pure R. The package provides: a
    coherent set of abbreviations for paste() from package 'base' with a
    variety of defaults, such as p() for "paste" and pcc() for "paste and
    collapse with commas"; wrap(), bracket(), and others for wrapping a
    string in flanking characters; unwrap() for removing pairs of characters
    (at any position in a string); and sentence() for cleaning whitespace
    around punctuation and capitalization appropriate for prose sentences.
Provides functions to manage taxonomy when lineages are
    described with strings and ranks separated with special patterns like
    "|*__" or ";*__".
Test of linearity originally proposed by Yatchew (1997) <doi:10.1016/S0165-1765(97)00218-8> and improved by de Chaisemartin & D'Haultfoeuille (2024) <doi:10.2139/ssrn.4284811> to be robust under heteroskedasticity.
Determines the sum of squares of the (2^n)-1 factorial effects in a 2^n factorial experiment using Yates' algorithm.
Nonparametric estimation of discount functions and yield curves from 
    transaction data of coupon paying bonds. 
    Koo, B., La Vecchia, D., & Linton, O. B. (2021) <doi:10.1016/j.jeconom.2020.04.014> 
    describe an application of this package using the Center for Research in 
    Security Prices (CRSP) Bond Data and document its implementation.
Asks Yes-No questions with variable or custom responses.
Facilitates download of financial data from Yahoo Finance <https://finance.yahoo.com/>, 
 a vast repository of stock price data across multiple financial exchanges. The package offers a local caching system
 and support for parallel computation.
The purpose of this package is to provide methods to interpret multiple
  linear regression and canonical correlation results including beta weights,structure coefficients, 
  validity coefficients, product measures, relative weights, all-possible-subsets regression,
  dominance analysis, commonality analysis, and adjusted effect sizes.
Deploy, maintain, and invoke models via the Yhat
    REST API.
Modelling the yield curve with some parametric models.
        The models implemented are: 
		Nelson, C.R., and A.F. Siegel (1987) <doi: 10.1086/296409>, 
		Diebold, F.X. and Li, C. (2006) <doi: 10.1016/j.jeconom.2005.03.005> 
		and Svensson, L.E. (1994) <doi: 10.3386/w4871>. 
		The package also includes the data of the term structure of interest rate of Federal Reserve Bank and European Central Bank.
Compute the standard expected years of life lost (YLL),
    as developed by the Global Burden of Disease Study
    (Murray, C.J., Lopez, A.D. and World Health Organization, 1996).
    The YLL is based on comparing the age of death to an external standard life
    expectancy curve. It also computes the average YLL, which highlights premature
    causes of death and brings attention to preventable deaths
    (Aragon et al., 2008).
Convert 'YMD' format number or string to Date efficiently, using Rust's
    standard library. It also provides helper functions to handle Date, e.g., quick
    finding the beginning or end of the given period, adding months to Date, etc.
The Author's personal R Package that contains miscellaneous functions. 
    The current version of package contains miscellaneous functions for brain data 
    to compute Asymmetry Index (AI) and bilateral (L+R) measures and reshape the data.
Write 'YAML' front matter for R Markdown and related
    documents. Work with 'YAML' objects more naturally and write the
    resulting 'YAML' to your clipboard or to 'YAML' files related to your
    project.
Provides a collection of lightweight helper functions (imps) both
  for interactive use and for inclusion within other packages. These include
  functions for minimal input assertions, visualising colour palettes,
  quoting user input, searching rows of a data frame and capturing string
  tokens.
Provides helper functions to perform Bayesian model averaging
    using Markov chain Monte Carlo samples from separate models. Calculates
    weights and obtains draws from the model-averaged posterior for quantities
    of interest specified by the user. Weight calculations can be done using
    marginal likelihoods or log-predictive likelihoods as in Ando, T., & Tsay,
    R. (2010) <doi:10.1016/j.ijforecast.2009.08.001>.
Analyzing performances of cricketers and cricket teams
             based on 'yaml' match data from Cricsheet <https://cricsheet.org/>.
Dataset from the young elite swimmers study.
Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with baseline hazard/odds modeled with Bernstein polynomials. Details about the model can be found in Demarqui et al. (2019) <arXiv:1910.04475>. Model fitting can be carried out via both maximum likelihood and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.
For any spending function specified by the user, this 
    package provides corresponding boundaries for interim testing using
    the adaptively weighted log-rank test developed by Yang and Prentice
    (2010 <doi:10.1111/j.1541-0420.2009.01243.x>). 
    The package uses a re-sampling method to obtain stopping boundaries 
    at the interim looks.The output consists of stopping boundaries 
    and observed values of the test statistics at the interim looks, 
    along with nominal p-values defined as the probability of the test 
    exceeding the specific observed test statistic value or critical 
    value, regardless of the test behavior at other looks. 
    The asymptotic validity of the stopping boundaries is established
    in Yang (2018 <doi:10.1002/sim.7958>).
Inference procedures accommodate a flexible range of hazard ratio patterns with a two-sample semi-parametric model. This model contains the proportional hazards model and the proportional odds model as sub-models, and accommodates non-proportional hazards situations to the extreme of having crossing hazards and crossing survivor functions. Overall, this package has four major functions: 1) the parameter estimation, namely short-term and long-term hazard ratio parameters; 2) 95 percent and 90 percent point-wise confidence intervals and simultaneous confidence bands for the hazard ratio function; 3) p-value of the adaptive weighted log-rank test; 4) p-values of two lack-of-fit tests for the model. See the included "read_me_first.pdf" for brief instructions. In this version (1.1), there is no need to sort the data before applying this package.
Provides covariate-adjusted comparison of two groups of right 
    censored data, where the binary group variable has separate short-term 
    and long-term effects on the hazard function, while effects of covariates 
    such as age, blood pressure, etc. are proportional on the hazard. The model 
    was studied in Yang and Prentice (2015) <doi:10.1002/sim.6453> and it extends 
    the two sample version of the short-term and long-term hazard ratio model
    proposed in Yang and Prentice (2005) <doi:10.1093/biomet/92.1.1>. The model 
    extends the usual Cox proportional hazards model to allow more flexible 
    hazard ratio patterns, such as gradual onset of effect, diminishing effect, 
    and crossing hazard or survival functions. This package provides 
    the following: 1) point estimates and confidence intervals for model 
    parameters; 2) point estimate and confidence interval of the average hazard 
    ratio; and 3) plots of estimated hazard ratio function with point-wise and 
    simultaneous confidence bands.
Semiparametric modeling of lifetime data with crossing survival curves via Yang and Prentice model with piecewise exponential baseline distribution. Details about the model can be found in Demarqui and Mayrink (2019) <arXiv:1910.02406>. Model fitting carried out via likelihood-based and Bayesian approaches. The package also provides point and interval estimation for the crossing survival times.
An implementation of equilibrium-based yield per recruit
    methods.  Yield per recruit methods can used to estimate the optimal
    yield for a fish population as described by Walters and Martell (2004)
    <isbn:0-691-11544-3>.  The yield can be based on the number of fish
    caught (or harvested) or biomass caught for all fish or just large
    (trophy) individuals.
An extension for 'NetSurfP-2.0' (Klausen et al. (2019) <doi:10.1002/prot.25674>)
    which is specifically designed to analyze the results of bottom-up-proteomics that is
    primarily analyzed with 'MaxQuant' (Cox, J., Mann, M. (2008) <doi:10.1038/nbt.1511>).
    This tool is designed to process a large number of yeast peptides that produced as a
    results of whole yeast cell-proteome digestion and provide a coherent picture of
    secondary structure of proteins.
Miscellaneous functions for data analysis, portfolio management, graphics, data manipulation, statistical investigation, including descriptive statistics, creating leading and lagging variables, portfolio return analysis, time series difference and percentage change calculation, stacking data for higher efficient analysis.
Simplify working with the 'YouTube Analytics' API <https://developers.google.com/youtube/analytics>. Collect 
    data for your channel including geography, traffic sources, time period, etc.
Simulation and Inference for SDEs and Other Stochastic Processes.
Provides a graphical user interface for the 'yuima' package.
Miscellaneous functions commonly used by 'YuLab-SMU'.
Provides a number of functions to facilitate
  extracting information in 'YAML' fragments from one or 
  multiple files, optionally structuring the information
  in a 'data.tree'. 'YAML' (recursive acronym for "YAML ain't
  Markup Language") is a convention for specifying structured
  data in a format that is both machine- and human-readable.
  'YAML' therefore lends itself well for embedding (meta)data
  in plain text files, such as Markdown files. This principle
  is implemented in 'yum' with minimal dependencies (i.e. only
  the 'yaml' packages, and the 'data.tree' package can be
  used to enable additional functionality).
A fast 'JSON' parser, generator and validator which converts 'JSON', 
    'NDJSON' (Newline Delimited 'JSON') and 'GeoJSON' (Geographic 'JSON') data 
    to/from R objects.  The standard R data types are 
    supported (e.g. logical, numeric, integer) with configurable handling of NULL 
    and NA values. Data frames, atomic vectors and lists are all supported as data 
    containers translated to/from 'JSON'.  'GeoJSON' data is read in as
    'simple features' objects.
    This implementation wraps the 'yyjson' 'C' library which 
    is available from <https://github.com/ibireme/yyjson>.
Provides simple statistics from instruments and observations at sites in the NEON network, and acts as a simple interface for v0 of the National Ecological Observatory Network (NEON) API.
  Statistics are generated for meteorologic and soil-based observations, and are presented for daily, annual, and one-time observations at all available NEON sites.
  Users can also retrieve any dataset publicly hosted by NEON. Metadata for NEON sites and data products can be returned, as well as information on data product availability by site and date. 
  For more information on NEON, please visit <https://www.neonscience.org>. For detailed data product information, please see the NEON data product catalog at <https://data.neonscience.org/data-product-catalog>.
A suite of statistics for identifying areas of the genome under selective pressure. See Jacobs, Sluckin and Kivisild (2016) <doi:10.1534/genetics.115.185900>.
Principled methods for the imputation of zeros, left-censored and missing data in
    compositional data sets (Palarea-Albaladejo and Martin-Fernandez (2015) <doi:10.1016/j.chemolab.2015.02.019>).
Contains the US Census Bureau's 2020 ZCTA to County Relationship 
    File, as well as convenience functions to translate between States, Counties
    and ZIP Code Tabulation Areas (ZCTAs).
An implementation of z-curves - a method for estimating expected discovery 
    and replicability rates on the bases of test-statistics of published studies. The package 
    provides functions for fitting the new density and EM version 
    (Bartoš & Schimmack, 2020, <doi:10.31234/osf.io/urgtn>), censored observations,  
    as well as the original density z-curve (Brunner & Schimmack, 2020, <doi:10.15626/MP.2018.874>). 
    Furthermore, the package provides summarizing and plotting functions for the fitted z-curve objects. 
    See the aforementioned articles for more information about the z-curves, expected discovery 
    and replicability rates, validation studies, and limitations.
Facilitates making a connection to the 
  'Zendesk' API and executing various queries. You can use it to
  get ticket, ticket metrics, and user data. The 'Zendesk' documentation is 
  available at <https://developer.zendesk.com/rest_api
  /docs/support/introduction>. This package is not supported by 
  'Zendesk' (owner of the software).
Provides a %<-% operator to perform multiple,
    unpacking, and destructuring assignment in R. The 
    operator unpacks the right-hand side of an assignment
    into multiple values and assigns these values to 
    variables on the left-hand side of the assignment.
R package accompanying the book Working with dynamic models for agriculture and environment, by Daniel Wallach (INRA), David Makowski (INRA), James W. Jones (U.of Florida), Francois Brun (ACTA). 3rd edition 2018-09-27.
Implements the estimation of local (and global) association measures: Lewontin's D, Ducher's Z, pointwise mutual information, normalized pointwise mutual information and chi-squared residuals. The significance of local (and global) association is accessed using p-values estimated by permutations.
Use behavioural variables to compute period, rhythmicity and other circadian parameters.
  Methods include computation of chi square periodograms (Sokolove and Bushell (1978) <DOI:10.1016/0022-5193(78)90022-X>),
  Lomb-Scargle periodograms (Lomb (1976) <DOI:10.1007/BF00648343>, Scargle (1982) <DOI:10.1086/160554>, Ruf (1999) <DOI:10.1076/brhm.30.2.178.1422>),
  and autocorrelation-based periodograms.
Provides an Interface to 'Zenodo' (<https://zenodo.org>) REST API, 
  including management of depositions, attribution of DOIs by 'Zenodo' and 
  upload and download of files.
This package provides an R wrapper for the Zendesk API
Access, download and locally cache files deposited on 'Zenodo' <https://zenodo.org>.
Graphical tools for visualizing high-dimensional data along a path
 of alternating one- and two-dimensional plots. Note that this
 includes interactive graphics plots based on 'loon' in turn based on 'tcltk'
 (included as part of the standard R distribution).  It also requires 'graph' from Bioconductor.
 For more detail on use and algorithms, see <doi:10.18637/jss.v095.i04>.
Fetch statistics about views, downloads and data volume from 'Zenodo' deposits. The package collects a 'Zenodo' (<https://zenodo.org>) deposit file information, respecting the website scrapping policies.
Uses bootstrap to test zero order correlation being equal to a 
    partial or semi-partial correlation (one or two tailed). Confidence 
    intervals for the parameter (zero order minus partial) can also be
    determined. Implements the bias-corrected and accelerated bootstrap
    method as described in "An Introduction to the Bootstrap" Efron (1983) 
    <0-412-04231-2>.
A system for creating the bilateral trade flow between a country
    pair equal to zero. You provide the data, tell get_zerotradeflow() which 
    variables are of interest and it expands the base by creating the bilateral
    zero trade flow. The bases on the flow of trade between countries only report
    positive trade (greater than zero), however, for some analyzes of gravitacional
    models, data on zero flow is also necessary. Some examples for Gravity Model:
    Figueiredo and Loures (2016) <doi:10.5935/0034-7140.20160015> and
    Yotov, Piermartini, Monteiro and Larch <https://vi.unctad.org/tpa/web/docs/vol2/book.pdf>.
Functions to compute compositional turnover using zeta-diversity,
    the number of species shared by multiple assemblages. The package includes
    functions to compute zeta-diversity for a specific number of
    assemblages and to compute zeta-diversity for a range of numbers of
    assemblages. It also includes functions to explain how zeta-diversity
    varies with distance and with differences in environmental variables
    between assemblages, using generalised linear models, linear models
    with negative constraints, generalised additive models,shape
    constrained additive models, and I-splines.
The advent of genomic technologies has enabled the generation of two-dimensional or even multi-dimensional high-throughput data, e.g., monitoring multiple changes in gene expression in genome-wide siRNA screens across many different cell types (E Robert McDonald 3rd (2017) <doi: 10.1016/j.cell.2017.07.005> and Tsherniak A (2017) <doi: 10.1016/j.cell.2017.06.010>) or single cell transcriptomics under different experimental conditions. We found that simple computational methods based on a single statistical criterion is no longer adequate for analyzing such multi-dimensional data. We herein introduce 'ZetaSuite', a statistical package initially designed to score hits from two-dimensional RNAi screens.We also illustrate a unique utility of 'ZetaSuite' in analyzing single cell transcriptomics to differentiate rare cells from damaged ones (Vento-Tormo R (2018) <doi: 10.1038/s41586-018-0698-6>). In 'ZetaSuite', we  have the following steps: QC of input datasets, normalization using Z-transformation, Zeta score calculation and hits selection based on defined Screen Strength.
Performs Zoom-Focus Algorithm (ZFA) to optimize testing regions for rare variant association tests in exome sequencing data.
Improve the usage of model fitting functions within a piped
    work flow.
A two-part zero-inflated Beta regression model with random 
    effects (ZIBR) for testing the association between microbial abundance 
    and clinical covariates for longitudinal microbiome data. Eric Z. Chen 
    and Hongzhe Li (2016) <doi:10.1093/bioinformatics/btw308>.
Provides MCMC algorithms for the analysis of
        zero-inflated count models. The case of stochastic search
        variable selection (SVS) is also considered.  All MCMC samplers
        are coded in C++ for improved efficiency. A data set
        considering the demand for health care is provided.
Zillow, an online real estate company, provides real estate
    and mortgage data for the United States through a REST API. The
    ZillowR package provides an R function for each API service, making it
    easy to make API calls and process the response into convenient,
    R-friendly data structures.  See
    <https://www.zillow.com/howto/api/APIOverview.htm> for the Zillow API
    Documentation. NOTE: Zillow deprecated their API on 2021-09-30, and
    this package is now deprecated as a result.
Analyze count time series with excess zeros. 
    Two types of statistical models are supported: Markov regression by Yang et al.
    (2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al. 
    (2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and 
    parameter-driven models respectively in the time series literature. The functions used for 
    Markov regression or observation-driven models can also be used to fit ordinary regression models 
    with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) 
    assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, 
    quantile, and generate random numbers from ZIP and ZINB distributions.
Generates Realizations of First-Order Integer Valued Autoregressive Processes with Zero-Inflated Innovations (ZINAR(1)) and Estimates its Parameters as described in Garay et al. (2021) <doi:10.1007/978-3-030-82110-4_2>.
Simulation, exploratory data analysis and Bayesian analysis of the p-order Integer-valued Autoregressive (INAR(p)) and Zero-inflated p-order Integer-valued Autoregressive (ZINAR(p)) processes, as described in Garay et al. (2020) <doi:10.1080/00949655.2020.1754819>.
Cross-Platform 'zip' Compression Library. A replacement for
    the 'zip' function, that does not require any additional external
    tools on any platform.
Some data treated by the Japanese R user require
    unique operations and processing. These are caused by address, Kanji,
    and traditional year representations. 'zipangu' transforms specific
    to Japan into something more general one.
Implementation of zero-inflated Poisson models under Bayesian framework using data augmentation as discussed in Chapter 5 of Zhang (2020) <https://hdl.handle.net/10012/16378>. This package is constructed in accommodating four different scenarios: the general scenario, the scenario with measurement error in responses, the external validation scenario, and the internal validation scenario.
Make working with ZIP codes in R painless with an integrated dataset of U.S. ZIP codes and functions for working with them. 
             Search ZIP codes by multiple geographies, including state, county, city & across time zones. Also included are functions for relating
             ZIP codes to Census data, geocoding & distance calculations.
Estimation methods for zero-inflated Poisson factor analysis (ZIPFA) on sparse data. 
    It provides estimates of coefficients in a new type of zero-inflated regression. 
    It provides a cross-validation method to determine the potential rank of the data in the ZIPFA 
    and conducts zero-inflated Poisson factor analysis based on the determined rank.
Implementation of four extensions of the Zipf distribution: the Marshall-Olkin 
  Extended Zipf (MOEZipf) Pérez-Casany, M., & Casellas, A. (2013) <arXiv:1304.4540>, the Zipf-Poisson Extreme (Zipf-PE), the 
  Zipf-Poisson Stopped Sum (Zipf-PSS) and the Zipf-Polylog distributions. 
  In log-log scale, the two first extensions allow for top-concavity 
  and top-convexity while the third one only allows for top-concavity. 
  All the extensions maintain the linearity associated with the Zipf model in the tail.
Statistical models and utilities for the analysis of word frequency distributions.
	The utilities include functions for loading, manipulating and visualizing word frequency
	data and vocabulary growth curves.  The package also implements several statistical 
	models for the distribution of word frequencies in a population.  (The name of this package 
	derives from the most famous word frequency distribution, Zipf's law.)
We provide a flexible Zero-inflated Poisson-Gamma Model (ZIPG) by connecting both the mean abundance and the variability to different covariates, and build valid statistical inference procedures for both parameter estimation and hypothesis testing. These functions can be used to analyze microbiome count data with zero-inflation and overdispersion. The model is discussed in Jiang et al (2023) <doi:10.1080/01621459.2022.2151447>.
Provides a set of functions for working with American postal codes,
   which are known as ZIP Codes. These include accessing ZIP Code to ZIP Code 
   Tabulation Area (ZCTA) crosswalks, retrieving demographic data for ZCTAs, and 
   tabulating demographic data for three-digit ZCTAs.
Implements Python-style zip for R. Is a more flexible version of cbind.
Permutations tests to identify factor correlated to zero-inflated proportions response. Provide a performance indicator based on Spearman correlation to quantify the part of correlation explained by the selected set of factors. See details for the method at the following preprint e.g.: <https://hal.archives-ouvertes.fr/hal-02936779v3>.
This function produces empirical best linier unbiased predictions (EBLUPs) for Zero-Inflated data and its Relative Standard Error. Small Area Estimation with Zero-Inflated Model (SAE-ZIP) is a model developed for Zero-Inflated data that can lead us to overdispersion situation. To handle this kind of situation, this model is created. The model in this package is based on Small Area Estimation with Zero-Inflated Poisson model proposed by Dian Christien Arisona (2018)<https://repository.ipb.ac.id/handle/123456789/92308>. For the data sample itself, we use combination method between Roberto Benavent and Domingo Morales (2015)<doi:10.1016/j.csda.2015.07.013> and Sabine Krieg, Harm Jan Boonstra and Marc Smeets (2016)<doi:10.1515/jos-2016-0051>.
Assesses evidence for Zipf's Law of Abbreviation in animal vocalisation using IDs, note class and note duration. The package also provides a web plot function for visualisation.
The 'zlib' package for R aims to offer an R-based equivalent of 'Python's' built-in 'zlib' module for data compression and decompression. This package provides a suite of functions for working with 'zlib' compression, including utilities for compressing and decompressing data streams, manipulating compressed files, and working with 'gzip', 'zlib', and 'deflate' formats.
Transformation of laboratory measurements into z or z(log)-value
    based on given or empirical reference limits as proposed in
    Hoffmann et al. 2017 <doi:10.1515/labmed-2016-0087>.
A collection of utility functions that facilitate looking up
    vector values from a lookup table, annotate values in at table for 
    clearer viewing, and support a safer approach to vector sampling, 
    sequence generation, and aggregation.
Calculate and plot scattering matrix coefficients for plane waves at interface.
Fits beta regression and zero-or-one inflated beta regression and obtains Bayesian Inference of the model via the Markov Chain Monte Carlo approach implemented in JAGS.
Fits Dirichlet regression and zero-and-one inflated Dirichlet regression with Bayesian methods implemented in Stan. These models are sometimes referred to as trinomial mixture models; covariates and overdispersion can optionally be included.
'Zoltar' <https://www.zoltardata.com/> is a website that provides a repository of model forecast results
    in a standardized format and a central location. It supports storing, retrieving, comparing, and analyzing time
    series forecasts for prediction challenges of interest to the modeling community. This package provides functions
    for working with the 'Zoltar' API, including connecting and authenticating, getting meta information (projects,
    models, and forecasts, and truth), and uploading, downloading, and deleting forecast and truth data.
Functions, documentation and example data to help divide
    geographic space into discrete polygons (zones).
    The functions are motivated by research into the merits of different zoning systems
    <doi:10.1068/a090169>. A flexible 'ClockBoard' zoning system is
    provided, which breaks-up space by concentric rings
    and radial lines emanating from a central point.
    By default, the diameter of the rings grow according the triangular number sequence
    <doi:10.1080/26375451.2019.1598687> with the first 4 'doughnuts'
    (or 'annuli') measuring 1, 3, 6, and 10 km wide.
    These annuli are subdivided into equal segments (12 by default), creating the
    visual impression of a dartboard. Zones are labelled according to
    distance to the centre and angular distance from North, creating a simple
    geographic zoning and labelling system useful for visualising geographic
    phenomena with a clearly demarcated central location such as cities.
Computes a zonohedron from real vector generators.  The package also computes zonogons (2D zonotopes) and zonosegs (1D zonotopes).  An elementary S3 class for matroids is included, which supports matroids with rank 3, 2, and 1.  Optimization methods are taken from Heckbert (1985) <https://www.cs.cmu.edu/~ph/zono.ps.gz>.
An S3 class with methods for totally ordered indexed
             observations. It is particularly aimed at irregular time series
             of numeric vectors/matrices and factors. zoo's key design goals
             are independence of a particular index/date/time class and
             consistency with ts and base R by providing methods to extend
             standard generics.
This tool provides functions to load, segment and classify zooplankton images. 
  The image processing algorithms and the machine learning classifiers in this package are (will be, since these have not been added yet) direct ports of 
  an early 'python' implementation that can be found at <https://github.com/arickGrootveld/ZooID>. The model weights and datasets (also not added yet) 
  that are a part of this package can also be found at Arick Grootveld, Eva R. Kozak, Carmen Franco-Gordo (2023) <doi:10.5281/zenodo.7979996>.
A free (open source) solution for analyzing digital
  images of plankton. In combination with ImageJ, a free image analysis
  system, it processes digital images, measures individuals, trains for
  automatic classification of taxa, and finally, measures plankton samples
  (abundances, total and partial size spectra or biomasses, etc.).
Includes functions and reference data to 
    generate and manipulate log-ratios (also known as log size index (LSI)
    values) from measurements obtained on zooarchaeological material. Log
    ratios are used to compare the relative (rather than the absolute)
    dimensions of animals from archaeological contexts 
    (Meadow 1999, ISBN: 9783896463883).
    zoolog is also able to seamlessly integrate data and references 
    with heterogeneous nomenclature, which is internally managed by a 
    zoolog thesaurus. 
    A preliminary version of the zoolog methods was first used by 
    Trentacoste, Nieto-Espinet, and Valenzuela-Lamas (2018) 
    <doi:10.1371/journal.pone.0208109>.
You can call zm(), when displaying any active plot to enter an
    interactive session to zoom/navigate any plot. The development
    version, as well as binary releases can be found at
    <https://github.com/cbarbu/R-package-zoom>.
Empowers users to fuzzily-merge data frames with millions or tens of millions of rows in minutes with low memory usage.  The package uses the locality sensitive hashing algorithms developed by Datar, Immorlica, Indyk and Mirrokni (2004) <doi:10.1145/997817.997857>, and Broder (1998) <doi:10.1109/SEQUEN.1997.666900> to avoid having to compare every pair of records in each dataset, resulting in fuzzy-merges that finish in linear time.
Provides utilities for processing and analyzing the files that are exported from a recorded 'Zoom' Meeting. This includes analyzing data captured through video cameras and microphones, the text-based chat, and meta-data. You can analyze aspects of the conversation among meeting participants and their emotional expressions throughout the meeting.
Facilitates making a connection to the 'Zoom' API and executing
  various queries. You can use it to get data on 'Zoom' webinars and 'Zoom'
  meetings. The 'Zoom' documentation is available at 
  <https://developers.zoom.us/docs/api/>. This package is 
  not supported by 'Zoom' (owner of the software).
Combines a forecast of a time series, using the function forecast(), with the dynamic plots from dygraphs.
A tool for calculating z-scores and centiles for weight-for-age, 
    length/height-for-age, weight-for-length/height, BMI-for-age, 
    head circumference-for-age, age circumference-for-age, 
    subscapular skinfold-for-age, triceps skinfold-for-age based on the 
    WHO Child Growth Standards.
Generates well-known integer sequences. 'gmp' package is adopted for computing with arbitrarily large numbers. Every function has hyperlink to its corresponding item in OEIS (The On-Line Encyclopedia of Integer Sequences) in the function help page. For interested readers, see Sloane and Plouffe (1995, ISBN:978-0125586306).
Makes zebra-striped tables (tables with alternating row colors)
    in LaTeX and HTML formats easily from a data.frame, matrix, lm, aov, anova,
    glm, coxph, nls, fitdistr, mytable and cbind.mytable objects.
Functions for obtaining the density, random variates
             and maximum likelihood estimates of the Zero-truncated Poisson lognormal
             distribution and their mixture distribution.
Read '.xls' and '.sbj' files which are written by the
   Microsoft Windows program 'z-Tree'. The latter is a software for
   developing and carrying out economic experiments
   (see <http://www.ztree.uzh.ch/> for more information).
How fast can you type R functions on your keyboard? Find out by running a 'zty.pe' game: export R functions as instructions to type to destroy opponents vessels.
Stein control variates can be used to improve Monte Carlo estimates of expectations when the derivatives of the log target are available. This package implements a variety of such methods, including zero-variance control variates (ZV-CV, Mira et al. (2013) <doi:10.1007/s11222-012-9344-6>), regularised ZV-CV (South et al., 2018 <arXiv:1811.05073>), control functionals (CF, Oates et al. (2017) <doi:10.1111/rssb.12185>) and semi-exact control functionals (SECF, South et al., 2020 <arXiv:2002.00033>). ZV-CV is a parametric approach that is exact for (low order) polynomial integrands with Gaussian targets. CF is a non-parametric alternative that offers better than the standard Monte Carlo convergence rates. SECF has both a parametric and a non-parametric component and it offers the advantages of both for an additional computational cost. Functions for applying ZV-CV and CF to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied in this package. The basic requirements for using the package are a set of samples, derivatives and function evaluations.
An efficient implementation of the slope method described by Sen (1968) <doi:10.1080/01621459.1968.10480934> plus implementation of prewhitening approaches to determining trends in climate data described by Zhang, Vincent, Hogg, and Niitsoo (2000) <doi:10.1080/07055900.2000.9649654> and Yue, Pilon, Phinney, and Cavadias (2002) <doi:10.1002/hyp.1095>.
A minor collection of HTTP wrappers for the 'Zamzar File Conversion'
    API. The wrappers makes it easy to utilize the API and thus convert
    between more than 100 different file formats (ranging from audio files,
    images, movie formats, etc., etc.) through an R session.  
    For specifics regarding the API, please see <https://developers.zamzar.com/>.
Functions and datasets for bootstrapping from the
book "Bootstrap Methods and Their Application" by A. C. Davison and
D. V. Hinkley (1997, CUP), originally written by Angelo Canty for S.
Various functions for classification, including k-nearest
neighbour, Learning Vector Quantization and Self-Organizing Maps.
Methods for Cluster analysis.  Much extended the original from
Peter Rousseeuw, Anja Struyf and Mia Hubert,
based on Kaufman and Rousseeuw (1990) "Finding Groups in Data".
Reading and writing data stored by some versions of
'Epi Info', 'Minitab', 'S', 'SAS', 'SPSS', 'Stata', 'Systat', 'Weka',
and for reading and writing some 'dBase' files.
Functions for kernel smoothing (and density estimation)
corresponding to the book:
Wand, M.P. and Jones, M.C. (1995) "Kernel Smoothing".
A powerful and elegant high-level data visualization
system inspired by Trellis graphics, with an emphasis on
multivariate data. Lattice is sufficient for typical graphics needs,
and is also flexible enough to handle most nonstandard requirements.
See ?Lattice for an introduction.
Functions and datasets to support Venables and Ripley,
"Modern Applied Statistics with S" (4th edition, 2002).
A rich hierarchy of sparse and dense matrix classes,
including general, symmetric, triangular, and diagonal matrices
with numeric, logical, or pattern entries.  Efficient methods for
operating on such matrices, often wrapping the 'BLAS', 'LAPACK',
and 'SuiteSparse' libraries.
Generalized additive (mixed) models, some of their extensions and
other generalized ridge regression with multiple smoothing
parameter estimation by (Restricted) Marginal Likelihood,
Generalized Cross Validation and similar, or using iterated
nested Laplace approximation for fully Bayesian inference. See
Wood (2017) <doi:10.1201/9781315370279> for an overview.
Includes a gam() function, a wide variety of smoothers, 'JAGS'
support and distributions beyond the exponential family.
Software for feed-forward neural networks with a single
hidden layer, and for multinomial log-linear models.
Recursive partitioning for classification,
regression and survival trees.  An implementation of most of the
functionality of the 1984 book by Breiman, Friedman, Olshen and Stone.
Contains the core survival analysis routines, including
definition of Surv objects,
Kaplan-Meier and Aalen-Johansen (multi-state) curves, Cox models,
and parametric accelerated failure time models.
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Tharso Augustus Rossiter Araújo Monteiro <tharso.augustus@ufpe.br>
Department of Mathematics and Statistics, University of
Agriculture Faisalabad, Faisalabad-Pakistan.
Package 'rjags' requires JAGS executabe file (compatiable
version) should be already installed.
The optmatch package, which is useful for running many of the
provided functions, may be downloaded from Github at
<https://github.com/markmfredrickson/optmatch> if not available
on CRAN. The rrelaxiv package, which provides an alternative
solver for the underlying network flow problems, carries an
academic license and is not available on CRAN, but may be
downloaded from Github at
<https://github.com/josherrickson/rrelaxiv/>.
Maintainers are not available to give advice on using a package
they did not author.
This version is a generalisation for (v=wnl) treatments. In the
version 1.0 we used rectangular right angular (m) association
schemes with (v=2nl) and m=4,5 and 7 associated classes. The
Association schemes used in this R-package are : Rectangular
association scheme; Generalized rectangular right angular
association scheme (4); Generalized rectangular right angular
association scheme (5); Generalized rectangular right angular
association scheme (7).
1. Asian Development Bank (ADB), Islamabad, Pakistan. 2. Benazir
Income Support Programme (BISP), Islamabad, Pakistan. 3.
Department of Mathematics and Statistics, University of
Agriculture Faisalabad, Faisalabad-Pakistan.
One minimum cost flow problem may have several or many solutions
that are equivalent in providing the same minimum total or mean
cost. Minor differences between computers or implementations
may have the minor consequence of altering which equivalent
solution is produced. The optmatch package, which is useful for
running many of the provided functions, may be downloaded from
Github at <https://github.com/markmfredrickson/optmatch> if not
available on CRAN. The rrelaxiv package, which provides an
alternative solver for the underlying network flow problems,
carries an academic license and is not available on CRAN, but
may be downloaded from Github at
<https://github.com/josherrickson/rrelaxiv/>.
This publication was prepared for NCES under Contract No.
ED-IES-12-D-0002/0004 with the American Institutes for
Research. Mention of trade names, commercial products, or
organizations does not imply endorsement by the U.S.
Government.
Supported by internal research project ING01-1516 from
'Universidad de Playa Ancha'
School of Mathematical and Statistical Sciences, Clemson
University, Clemson, South Carolina, USA.
Ver. 1.0: The author obtained financial resources as part of
financing the doctoral scholarship from the Polish National
Science Centre (DEC-2018/28/T/HS4/00095). Ver. 2.0: The
research was supported by the program ''Excellence Initiative -
Research University (IDUB)'' through the project
BOB-IDUB-622-89/2021 - Nowe Idee POB III IDUB, 01/IDUB/2019/94
at the University of Warsaw, Poland.
1. Asian Development Bank (ADB), Islamabad, Pakistan. 2. Benazir
Income Support Programme (BISP), Islamabad, Pakistan. 3.
Department of Mathematics and Statistics, University of
Agriculture Faisalabad, Pakistan.
This publication was prepared for NCES under Contract No.
ED-IES-12-D-0002 with the American Institutes for Research.
Mention of trade names, commercial products, or organizations
does not imply endorsement by the U.S. Government.
This update fixes bugs noted because of global variables not
being recognised as global variables in some environments.
This update fixes bugs noted because of global variables not
being recognised as global variables in some environments and
some errors noted on specific choices in mixed effects
regression.
Research funded by the Polish National Science Centre grant under
the contract number DEC-2015/19/N/HS4/00205.
Few parts are still preliminary and might be changed in the near
future. And more functions will be add as easier tools to
higher efficiency in analyzing.
While reporting issues on github start subject line with
[cran-package]
The authors are grateful to Mikhail Kanevski, Federico Amato and
Luciano Telesca for many fruitful discussions about the use and
the application of Fisher-Shannon method.
This software comes with NO warranty! It is NOT intended to be
used in clinical applications! For evaluation only!
The package is also based on the discussion
https://stats.stackexchange.com/questions/168964/building-a-regression-tree-with-r-from-scratch/168967#168967
research product of the RISCOPE project (ANR, project
No.16CE04-0011) <https://perso.math.univ-toulouse.fr/riscope/>.
Department of Genetics and Plant Breeding, University of
Agricultural Sciecnes, Dharwad.
Package 'R2jags' requires JAGS executable file installed
The authors are grateful to Mikhail Kanevski, Michael
Leuenberger, Carmen D. Vega Orozco and Fabian Guignard for many
fruitful discussions about the use of intrinsic dimension in
data mining.
Because IPEDS requirements may change from year to year, having
the most recent version of this package is highly recommended.
Old versions can be found as GitHub branches. The package can
also be used to convert any correctly-prepared data into a
key-value pair format for any survey (IPEDS or non-IPEDS).
Please, cite this package as: Marina Saez (2021). KarsTS: An
Interface For Microclimate Time Series Analysis. R package
version 2.4.1.
Department of Statistics, Bahauddin Zakariya University, Multan,
Pakistan.
Department of Statistics, Bahauddin Zakariya University, Multan,
Pakistan
The originale code was in matlab, see details below.
The developer version of 'keras' should be used, rather than the
CRAN version. The latter will cause tests to fail on an initial
run, but work on subsequent tries. To avoid this, use
devtools::install_github("rstudio/keras"). The user also must
have an installation of 'Python 3'.
a previous version of the library (main version number: 3) was
developed as part of the European Union's Horizon 2020 research
and innovation programme under grant agreement No. 727040
(Virt-EU). The package uses functions from eclat
<https://borgelt.net/eclat.html>, for association rule mining,
Infomap <https://www.mapequation.org>, for the Infomap
community detection method (please refer to their web site for
up-to-date code), and Howard Hinnant's date and time library
<https://github.com/HowardHinnant/date>. The code from these
libraries has been included in our source package.
For convenience, the package includes the necessary header files
for the Eigen distribution. (This is all that is needed to use
that functionality.) You can use an alternative installation of
Eigen on your system or the one we provide. The license for the
Eigen code is very permissive and allows us to distribute it
with this package. See <http://eigen.tuxfamily.org/index.php?
title=Main_Page> and also the License section on that page.
The APT samplers within this package were adapted from MCMC
samplers provided in the NIMBLE package
(https://github.com/nimble-dev/nimble) and developed by the
cited members of the NIMBLE Development Team. The adaptations
for APT included in this package were the work of the package
developer. Many thanks to the NIMBLE Development Team for their
support during the development of this package.
The current version (0.4.2) fixes a numerical issue with
symmetric implementation of the paco_links function.
Research funded by the grant of the National Science Centre,
Poland, under the contract number DEC-2018/31/B/HS4/02021.
Package is in active development; please report errors and
suggestions to rchlumsk@uwaterloo.ca.
NOTICE regarding the transfer of data from Retrosheet: The
information used here was obtained free of charge from and is
copyrighted by Retrosheet. Interested parties may contact
Retrosheet at "www.retrosheet.org".
See http://www.json.org/JSON_checker/test.zip for tests. We now
use code from the libjson project
(http://libjson.sourceforge.net).
Free to use for non-Norwegian institutions, otherwise see
LICENSE.
The development of this package was supported by Carlos
Jaramillo, Center for Tropical Paleoecology and Archaeology at
Smithsonian Tropical Research Institute.
This publication was prepared for NCES under Contract No.
ED-IES-12-D-0009/0005 with Sanametrix and Westat. Mention of
trade names, commercial products, or organizations does not
imply endorsement by the U.S. Government.
The GPU computing version can be found soon, on following url
https://sites.google.com/site/mohamedlaibwebpage/Software.
This package is developed as part of ongoing Ph.D. (Agricultural
Statistics) thesis research work of first author at ICAR-Indian
Agricultural Statistics Research Institute, New Delhi, India.
This package is developed as part of ongoing Ph.D (Agricultural
Statistics) thesis research work of first author at ICAR-Indian
Agricultural Statistics Research Institute, New Delhi, India.
The author acknowledges financial support from Sao Paulo Research
Foundation (FAPESP) grant 2016/18599-4
This is experimental software distributed free of charge and
comes with no warranty!  Exercise professional caution.
Package TSE works directly from the data set – no hand
calculations required.  Just upload a properly structured data
set (see TESTNUMB and its documentation), properly input column
names (see examples in the functions documentation), and run
your functions.
'TSEind' is a companion package to 'TSE'. Both calculate TSE for
your surveys, but use 'TSEind' if your surveys and the "gold
standard" survey have independent samples, and use 'TSE' if
your surveys and the "gold standard" survey have paired
samples.
'TSEwgt' is a companion package to 'TSE'. Each package calculates
TSE, but the former for multiple, different surveys, and the
latter for a single survey under multiple, different weighting
schemes.
This publication was prepared for NCES under Contract No.
ED-IES-12-D-0002 with American Institutes for Research. Mention
of trade names, commercial products, or organizations does not
imply endorsement by the U.S. government.
2022-11-15 06:15:38 UTC; Kosuke Kashiwabara
2021-12-04 12:57:38 UTC; Javaria Ahamd Khan
2020-09-22 06:18:07 UTC; Benjamin Schlegel
2023-01-18 00:21:38 UTC; Sidharth Macherla
2024-09-08 09:19:25 UTC; Pavlos Pantatosakis
2023-05-17 21:39:10 UTC; Andreas Kuehnapfel
2021-11-28 13:38:57 UTC; Javaria Ahamd Khan
2022-09-20 14:28:28 UTC; Javaria Ahamd Khan
2023-03-26 20:33:56 UTC; Sebastian Weirich
2024-04-04 21:14:33 UTC; Sebastian Weirich
2020-07-14 07:43:10 UTC; Bernardo Sousa Pinto
2024-09-16 09:45:12 UTC; Lennart Oelschläger
2024-06-17 04:42:11 UTC; Carlos Perez Carmona
2018-10-24 16:05:44 UTC; Ashutosh Srivastava
2024-05-17 08:35:02 UTC; Friedemann von Lampe
2015-08-23 02:08:23 UTC; Fernando de Villar
2023-06-11 18:14:04 UTC; Jonas Christoffer
2024-09-06 17:23:04 UTC; Eleanor Pullenayegum
2024-03-20 23:16:49 UTC; Atanu Bhattacharjee
2020-01-08 09:11:00 UTC; Nadezda Spiridovska
2021-04-16 08:59:10 UTC; Ilias Moutsopoulos
2024-09-17 13:25:34 UTC; Lennart Oelschläger
2021-07-12 03:13:21 UTC; Guillaume Boucher
2024-05-22 14:17:13 UTC; Rasmus Puggaard-Rode
2018-04-29 13:45:20 UTC; Alberto Vico Moreno
2023-06-20 20:30:41 UTC; Gonzalo Vazquez-Bare
2017-01-03 15:34:03 UTC; Guybrush Threepwood
2020-05-07 11:07:40 UTC; Kanhaiiya Agrawal
2020-06-08 13:17:19 UTC; Kanhaiiya Agrawal
2019-08-02 17:03:10 UTC; Bernardo Sousa Pinto
2017-08-15 05:32:43 UTC; Aurnov Chattopadhyay
2024-03-27 21:47:34 UTC; Atanu Bhattacharjee
2022-01-08 14:42:05 UTC; Sandeep Patakamuri
2016-03-19 23:54:53 UTC; Sophie Baillargeon
OfficeToPDF -
https://github.com/cognidox/OfficeToPDF, pandoc (>= 1.14) -
https://pandoc.org
Image Magick (for reading non PPM format), dcraw
(for reading RAW images).
ADMIXTOOLS suite of command-line utilities for
population genetics. See
<https://reich.hms.harvard.edu/software> for the most recent
installation instructions and further information.
Alfresco Content Repository (Community or
Enterprise)
Python (>= 3.6.0), (Python) Altair (>= 4.2.0),
vega_datasets (>= 0.9.0). To use image functions for MacOS: X11
None, but greater than 12 GB of RAM highly
recommended.
ImageMagick (http://imagemagick.org) or
GraphicsMagick (http://www.graphicsmagick.org) or LyX
(http://www.lyx.org) for saveGIF(); (PDF)LaTeX for saveLatex();
SWF Tools (http://swftools.org) for saveSWF(); FFmpeg
(http://ffmpeg.org) or avconv (https://libav.org/avconv.html)
for saveVideo()
The exact (anti)clustering algorithms require that
either the GNU linear programming kit (GLPK library) is
installed (<http://www.gnu.org/software/glpk/>) or the SYMPHONY
open source MILP solver
(<https://github.com/coin-or/SYMPHONY>). Rendering the vignette
requires pandoc (<https://pandoc.org/>).
Cargo (Rust's package manager), rustc, OpenSSL
libarchive: libarchive-dev (deb), libarchive-devel
(rpm), libarchive (homebrew), libarchive_dev (csw)
Cargo (Rust's package manager), rustc >= 1.70
C++17; for AWS S3 support on Linux, libcurl and
openssl (optional); cmake >= 3.16 (build-time only, and only
for full source build)
Quarto command line tools
(https://github.com/quarto-dev/quarto-cli).
FFmpeg (>= 3.2); with at least libx264 and lame
(mp3) drivers. MacOS Homebrew: ffmpeg. Debian/Ubuntu:
libavfilter-dev. Fedora/RHEL: either ffmpeg-devel from
https://rpmfusion.org (preferred), or libavfilter-free-devel
which is a more limited version available on Fedora/EPEL9.
AWS Java SDK (see README.md for more details)
JAGS (>= 4.3.0) (see
http://mcmc-jags.sourceforge.net)
GDAL (>= 2.2.3): gdal-bin (deb), libgdal-dev (deb)
or gdal-devel (rmp), GEOS (>= 3.4.0), PROJ (>= 4.9.3):
libproj-dev (deb), sqlite3, ImageMagick++: imagemagick (deb),
libmagic-dev (deb), libmagick++-dev (deb) or
ImageMagick-c++-devel (rpm) ImageMagick
(http://imagemagick.org) or GraphicsMagick
(http://www.graphicsmagick.org) or LyX (http://www.lyx.org) for
saveGIF(); (PDF)LaTeX for saveLatex(); SWF Tools
(http://swftools.org) for saveSWF(); FFmpeg (http://ffmpeg.org)
or avconv (https://libav.org/avconv.html) for saveVideo()
Rtools (>= 3.3) for Windows, Xcode (>= 9.0) for Mac
OS X
Preferred genomic operations engine: 'BEDTools',
'BEDOPS' and 'Tabix (>= 1.3)'.
JAGS >=4.3.0 <https://mcmc-jags.sourceforge.io>,
Java JDK >=1.4 <https://www.java.com/en/download/manual.jsp>
grpc/protobuf headers and compilers. For example
libprotobuf-dev libgrpc++-dev protobuf-compiler-grpc (Debian)
or protobuf-devel grpc-devel grpc-plugins (Fedora/RHEL)
Julia (>= 1.7), Bigsimr.jl, Distributions.jl
C++11. Also a few functions from package 'bigsnpr'
wrap existing software such as 'PLINK'
<www.cog-genomics.org/plink2>. Functions are provided to
download these software. Note that these external software
might not work for some operating systems (e.g. 'PLINK' might
not work on Solaris).
A system with a recent-enough C++11 compiler (such
as g++-4.8 or later).
'monobit' for reading/writing additional bitmap
font formats.
Hugo (<https://gohugo.io>) and Pandoc
(<https://pandoc.org>)
LaTeX(texi2dvi) must be present in the system to
create PDF reports
OpenBUGS (>= 3.2.2), hence Windows or x86 Linux
pandoc (>= 1.12.3, needed for vignettes)
'Microsoft Visual Code', 'Rtools' on 'Windows',
'gdb' or 'lldb' depending on which OS
Rtools (suitable for the installed R) for Windows,
Xcode for Mac
FFTW (>=3.3.1); possible package: fftw-devel (rpm),
libfftw3-dev (deb) or fftw (brew).
Python and numpyro are needed for Bayesian
inference computations; python (>= 3.8) with header files and
shared library; numpyro (= v0.12.1;
https://https://num.pyro.ai/en/latest/index.html); arviz (=
v0.15.1; https://https://python.arviz.org/en/stable/)
Cargo (Rust's package manager), rustc (>= 1.66.1)
Google Chrome or other Chromium-based browser.
chromium: chromium (rpm) or chromium-browser (deb)
gsl (with development libraries libgsl-dev)
Cargo (Rust's package manager), rustc (>= 1.67.1),
and GNU Make
pandoc (to create a clinical data review report)
pandoc (for export clin DT to a file - inclusion of
list of interactive plots/objects with knitr)
xclip (https://github.com/astrand/xclip) or xsel
(http://www.vergenet.net/~conrad/software/xsel/) for accessing
the X11 clipboard, or wl-clipboard
(https://github.com/bugaevc/wl-clipboard) for systems using
Wayland.
ZeroMQ (libzmq) >= 4.3.0 (optional; otherwise
bundled)
libarmadillo: apt-get install -y libarmadillo-dev
(deb), libblas: apt-get install -y libblas-dev (deb),
liblapack: apt-get install -y liblapack-dev (deb),
libarpack++2: apt-get install -y libarpack++2-dev (deb),
gfortran: apt-get install -y gfortran (deb), libgmp3: apt-get
install -y libgmp3-dev (deb), libfftw3: apt-get install -y
libfftw3-dev (deb), libtiff5: apt-get install -y libtiff5-dev
(deb)
MOSEK (http://www.mosek.com) and MOSEK License for
use of Rmosek
libcurl: libcurl-devel (rpm) or
libcurl4-openssl-dev (deb).
ImageMagick++: ImageMagick-c++-devel (rpm) or
libmagick++-dev (deb)
awscli: apt install -y awscli (deb), aws-configure:
aws configure (deb)
pdfcrop (part of TexLive) is required to rebuild
the vignettes.
Java (>= 7.0); Stanford CoreNLP
<http://nlp.stanford.edu/ software/corenlp.shtml> (>= 3.5.2)
GNU make, pandoc (>=1.12.3, needed for vignettes)
pandoc (>= 2.0; https://pandoc.org), git, whoami
none, one or more of JAGS (>= 3.0.0), WinBUGS (>=
1.4), OpenBUGS (>= 3.2.2), Stan
A version of Fortran supporting the LOG_GAMMA
Intrinsic and the ieee_arithmetic module.
OpenSSL(>= 1.0.1), NVIDIA CUDA GPU with compute
capability 3.0 or above and NVIDIA CUDA Toolkit 9.0 or above
fontconfig or zlib (only needed for platforms other
than modern OSX and Windows)
Julia (>= 1.6), DifferentialEquations.jl,
ModelingToolkit.jl
Python (>= 2.7.0), TensorFlow
(https://www.tensorflow.org/)
libpq >= 9.0: libpq-dev (deb) or postgresql-devel
(rpm)
LibreOffice (<https://www.libreoffice.org/>)
required to extract data from .doc files or perform .pptx
conversion.
'unrar' (Linux/macOS) or '7-Zip' (Windows) to work
with '.rar' files, GDAL (>= 3.0.2), GEOS (>= 3.8.0), PROJ (>=
6.2.1)
Requires the OpenMP library for parallel computing.
If the OpenMP library is not available, the code is executed
sequentially and a warning is printed.
The package required the jq command line library.
Please check https://stedolan.github.io/jq/download/ for
download instructions.
Python (>= 2.7 or >= 3.5) and elex package
The function render() requires Pandoc
(http://pandoc.org). To use PGP/GnuPG encryption requires gpg.
PROJ (>= 6, https://proj.org/download.html), GDAL
(>= 3, https://gdal.org/download.html), NetCDF(>= 4,
https://www.unidata.ucar.edu/software/netcdf/).
JAGS (>= 4.0.0). For installation instructions, see
http://mcmc-jags.sourceforge.net/
ProtoBuf libraries and compiler version 3.3.0 or
later; On Debian/Ubuntu these can be installed as
libprotoc-dev, libprotobuf-dev and protobuf-compiler, while on
Fedora/CentOS protobuf-devel and protobuf-compiler are needed.
apt-get-pip: apt-get install -y python3-pip (deb),
python3-pip: python3 -m pip install -U pip (deb), numpy: pip3
install -U numpy (deb), cv2: pip3 install -U opencv-python
(deb), matplotlib: pip3 install -U matplotlib (deb), skimage:
pip3 install -U scikit-image (deb), libarmadillo: apt-get
install -y libarmadillo-dev (deb), libblas: apt-get install -y
libblas-dev (deb), liblapack: apt-get install -y liblapack-dev
(deb), libarpack++2: apt-get install -y libarpack++2-dev (deb),
gfortran: apt-get install -y gfortran (deb)
Generally, fastText builds on modern Mac OS and
Linux distributions. Since it uses some C++11 features, it
requires a compiler with good C++11 support. These include a
(g++-4.7.2 or newer) or a (clang-3.3 or newer).
fftw3 (libfftw3-dev (deb), or fftw-devel (rpm))
Cargo (Rust's package manager), rustc >= 1.67.1, xz
Display resolution >= 1280x800; RAM >= 4GB
fluidsynth: fluidsynth-devel (rpm) or
libfluidsynth-dev (deb). On Linux you also need a soundfont
provided by 'fluid-soundfont-gm' (Fedora) or 'sf3-soundfont-gm'
(Debian/Ubuntu)
libarmadillo: apt-get install -y libarmadillo-dev
(deb), libblas: apt-get install -y libblas-dev (deb),
liblapack: apt-get install -y liblapack-dev (deb),
libarpack++2: apt-get install -y libarpack++2-dev (deb),
gfortran: apt-get install -y gfortran (deb)
GNU Scientific Library (GSL). Note: users should
have GSL installed.
(license-free) MATLAB Runtime (MCR) V 9.12, Java
(>=8)
Python (>= 2.4), difflib, fuzzywuzzy ( >=0.15.0 ),
python-Levenshtein ( >=0.12.0 ). Detailed installation
instructions for each operating system can be found in the
README file.
Debian or Ubuntu system with access to Goto Blas,
Intel MKL, Atlas development build as well as a Nvidia GPU with
CUDA support
GDAL (>= 3.1.0, built against GEOS), PROJ, libxml2
libarmadillo: apt-get install -y libarmadillo-dev
(deb)
MongoDB (>= 3.4.0), Python (>= 2.7). Installation
instructions and links can be found in the README file.
'unrar' (Linux/macOS) or '7-Zip' (Windows) to work
with '.rar' files, C++11, GDAL (>= 2.0.1), GEOS (>= 3.8.0),
PROJ (>= 6.2.1)
libgit2 (>= 1.0): libgit2-devel (rpm) or
libgit2-dev (deb)
Chromium-based browser (Google Chrome, Brave, ...)
By default, git2r uses a system installation of the
libgit2 headers and library. However, if a system installation
is not available, builds and uses a bundled version of the
libgit2 source. zlib headers and library. OpenSSL headers and
library (non-macOS). LibSSH2 (optional on non-Windows) to
enable the SSH transport.
GPGME: libgpgme-dev (deb), gpgme-devel (rpm) gpgme
(brew). On Linux 'haveged' is recommended for generating
entropy when using the GPG key generator.
C++11 (supporting at least std=c++0x), OpenCL
shared library (provided by an SDK such as AMD/NVIDIA) and
OpenCL headers including the C++ header file (provided by
Khronos if not by SDK)
Python (>= 2.7.0) with header files and shared
library; TensorFlow (v1.14; https://www.tensorflow.org/);
TensorFlow Probability (v0.7.0;
https://www.tensorflow.org/probability/)
Python (>= 3.6) with header files and shared
library; H2O4GPU (https://github.com/h2oai/h2o4gpu)
V8 engine version 6+ is needed for modern JS and
WASM support. On Debian / Ubuntu install either libv8-dev or
libnode-dev, on Fedora use v8-devel.
GNU make, zlib: zlib1g-dev (deb), zlib-devel (rpm)
Cargo (Rust's package manager), rustc >= 1.56.0
Apache Hadoop >= 2.7.2
(https://hadoop.apache.org/releases.html#Download); Obsolete:
Hadoop core >= 0.19.1 and <= 1.0.3 or CDH3
(https://www.cloudera.com); standard unix tools (e.g., chmod)
LaTeX packages: adjustbox, array, calc, caption,
colortbl, fontspec, graphicx, hhline, hyperref, multirow,
siunitx, tabularx, threeparttable, ulem, wrapfig
Julia (>= 1.0) and Interpretable AI System Image
(>= 1.0.0)
gdal-bin: apt install -y gdal-bin (deb),
libgdal-dev: apt install -y libgdal-dev (deb), libudunits2-dev:
apt install -y libudunits2-dev (deb), libgeos-dev: apt install
-y libgeos-dev (deb), libproj-dev: apt install -y libproj-dev
(deb), unzip: apt install unzip (deb)
ImageMagick (http://imagemagick.org) or
GraphicsMagick (http://www.graphicsmagick.org)
A C library with the fesetround/fegetround
functions.
libxml2 (optional), glpk (>= 4.57, optional)
libtiff, libbz2, libdeflate, libjpeg, liblzma,
libwebp, libzstd, zlib
C++17 or C++11 and OpenCV 3 or newer: libopencv-dev
(Debian, Ubuntu) or opencv-devel (Fedora)
Impala driver to support a 'DBI'-compatible R
interface
libcurl4-openssl-dev pandoc libarchive-dev
libv8-dev (deb)
Complete functionality necessitates Amazon Corretto
(win), and default-jre, pandoc, libxml2-dev,
libpoppler-cpp-dev, libmagick++-dev, optipng, libtesseract-dev,
libleptonica-dev, tesseract-ocr-eng (deb)
pandoc (to export an interactive summary table to a
file)
jupyter, jupyter_kernel_test (Python package for
testing)
libjq: jq-devel (rpm) or libjq-dev (deb)
Optional: libsecret on Linux (libsecret-1-dev on
Debian/Ubuntu, libsecret-devel on Fedora/CentOS)
MOSEK (http://www.mosek.com) and MOSEK license.
Package vignettes based on R Markdown v2 or
reStructuredText require Pandoc (http://pandoc.org). The
function rst2pdf() requires rst2pdf
(https://github.com/rst2pdf/rst2pdf).
procps (linux), ps (macos), wmic (windows)
ImageJ (>=1.48), ij.jar (see
http://imagej.nih.gov/ij/), Java (>=1.6.0)
GNU make (optional), libxml2 (optional), glpk (>=
4.57, optional)
JAGS (>= 3.0.0) (see
http://mcmc-jags.sourceforge.net)
At least trial version of LocalSolver to be
downloaded from http://www.localsolver.com/download.html
C++11, A system with zoneinfo data (e.g.
/usr/share/zoneinfo). On Windows the zoneinfo included with R
is used.
Python 3.x with following modules: PyGMO, NumPy,
and cloudpickle
ADMB version 11 <http://admb-project.org/> for
use.admb=TRUE; see readme.txt
On a Unix-alike, one of the C functions
mach_absolute_time (macOS), clock_gettime or gethrtime. If none
of these is found, the obsolescent POSIX function gettimeofday
will be tried.
memory (>= 4GB) Ubuntu: build-essential,
libxml2-dev, libssl-dev, libcurl4-openssl-dev Windows: Rtools
(>= 3.5)
TensorFlow (https://www.tensorflow.org), Keras
(https://keras.io), TensorFlow Probability
(https://www.tensorflow.org/probability)
A C++17 compiler. Version 8 or later of GCC will be
fine.
gdal (optional), geos (optional), proj (optional),
udunits (optional), gsl (optional), gmp (optional), glu
(optional), jags (optional), mpfr (optional), openmpi
(optional)
JAGS (>= 4.0.0) (see
https://mcmc-jags.sourceforge.io)
libcurl: libcurl-devel (rpm) or
libcurl4-openssl-dev (deb)
A valid account MUST be registered with Microsoft's
Cognitive Services website
<https://www.microsoft.com/cognitive-services/> in order to
obtain a (free) API key. Without an API key, this package will
not work properly.
'libnng' >= 1.6 and 'libmbedtls' >= 2.5, or 'cmake'
and 'xz' to compile NNG and/or Mbed TLS included in package
sources
pandoc, jupyter, jupyter nbconvert ("nbconvert"
python package), pywin32 (on windows)
neo4j (>=3 AND <=5) <https://neo4j.com/>
neo4j - http://www.neo4j.com, cypher-shell -
https://github.com/neo4j/cypher-shell/releases
Python (>= 3.0.0), Neptune (https://neptune.ai/)
cmake (>= 3.2.0) which is used only on Linux or
macOS systems when no system build of nlopt (>= 2.7.0) can be
found.
NetLogo (>= 5.3.1), Java (>= 11), pandoc, OpenSSL,
udunits-2, PROJ.4, GEOS, GDAL, libxml2
python3-dev: apt-get install -y python3-dev (deb),
python3-pip: apt-get install -y python3-pip (deb), numpy: pip3
install numpy (deb), scipy: pip3 install scipy (deb), nmslib:
pip3 install --no-binary :all: nmslib (deb)
JAGS (http://mcmc-jags.sourceforge.net/) for
analysis of Bayesian hierarchical models
A chromium based browser, required by "webshot2"
<https://rstudio.github.io/webshot2/>
GNU make, An ODBC3 driver manager and drivers.
GNU make, pandoc (>= 1.12.3), pandoc-citeproc,
C++17
OpenCV 3 or newer: libopencv-dev (Debian, Ubuntu)
or opencv-devel (Fedora). The QR code detector requires at
least libopencv 4.5.2.
libarmadillo: apt-get install -y libarmadillo-dev
(deb), libblas: apt-get install -y libblas-dev (deb),
liblapack: apt-get install -y liblapack-dev (deb),
libarpack++2: apt-get install -y libarpack++2-dev (deb),
gfortran: apt-get install -y gfortran (deb), libjpeg-dev:
apt-get install -y libjpeg-dev (deb), libpng-dev: apt-get
install -y libpng-dev (deb), libfftw3-dev: apt-get install -y
libfftw3-dev (deb), libtiff5-dev: apt-get install -y
libtiff5-dev (deb)
To use the Localhost of OSRM, you need to build
OSRM
<https://github.com/Project-OSRM/osrm-backend/wiki/Building-OSRM>
locally
pandoc (https://johnmacfarlane.net/pandoc) for
exporting markdown files to other formats.
Rendering the document template requires pandoc (>=
2.0; https://pandoc.org) and for PDFs a TeX distribution, such
as TinyTeX (>= 0.12; https://yihui.org/tinytex/)
For multipoint linkage analysis, 'MERLIN'
(http://csg.sph.umich.edu/abecasis/merlin/index.html).
MERLIN (https://csg.sph.umich.edu/abecasis/merlin/)
for multipoint linkage analysis.
The 'Japanese.Rnw' vignette requires uplatex and
dvipdfmx.
OpenMPI (>= 1.5.4) on Linux, Mac, and FreeBSD.
MS-MPI (Microsoft MPI v7.1 (SDK) and Microsoft HPC Pack 2012 R2
MS-MPI Redistributable Package) on Windows.
'OpenMPI' (>= 1.5.4) on Solaris, Linux, Mac, and
FreeBSD. 'MS-MPI' (Microsoft HPC Pack 2012 R2 MS-MPI
Redistributable Package) on Windows.
Linux, Mac OSX, and Windows, or 'ZeroMQ' library >=
4.0.4. Solaris 10 needs 'ZeroMQ' library 4.0.7 and 'OpenCSW'.
Poppler C++ API: libpoppler-cpp-dev (deb) or
poppler-cpp-devel (rpm), and poppler-data (rpm/deb) package.
MERLIN (https://csg.sph.umich.edu/abecasis/merlin/)
for calculations involving multiple linked markers.
OpenSSL library and headers (openssl-dev or
similar)
aria2: apt-get install -y aria2 (deb), gdal-bin:
apt-get install -y gdal-bin (deb), libgdal-dev: apt-get install
-y libgdal-dev (deb)
libssh >= 0.6.0 (the original, not libssh2)
For Windows users, Rtools (see
https://cran.r-project.org/bin/windows/Rtools/).
pandoc (>= 1.12.3) -
http://johnmacfarlane.net/pandoc
JAGS (>= 4.0.0) (see
https://mcmc-jags.sourceforge.io/)
A POSIX system. Currently Linux and OS X are known
to work. GNU make.
proj 4.4.6 or higher (https://proj.org/)
pandoc (http://pandoc.org) for building Vignettes
and output reports as HTML
'python3' needs to built for the same architecture
R is built for (32 or 64 bit).
Python (>= 3.7), pip (for installing Python
packages), pytrends, pandas, requests
'QGIS' latest or long-term release currently
supported according to the 'QGIS' release schedule
(<https://www.qgis.org/en/site/getinvolved/development/roadmap.html>).
Older 'QGIS' releases are not officially supported, but may
work since 'QGIS' 3.16.
Chromium-based browser (e.g., Chrome, Chromium, or
Brave)
Quarto command line tool
(<https://github.com/quarto-dev/quarto-cli>).
OpenBugs for functions bugs() and openbugs() or
WinBUGS 1.4 for function bugs()
package manual and Vignettes require pandoc (>=
1.14) http://pandoc.org)
python >= 2.6, by default rasciidoc uses a system
installation of asciidoc. If a system installation of asciidoc
is not available, it downloads the sources from
(<http://gitlab.com/asciidoc>). GNU source-highlight is
recommended.
fftw3 (libfftw3-dev (deb), or fftw-devel (rpm)),
pkg-config
Needs a running BaseX server instance. The testuser
with credentials ('Test'/'testBasex') should have admin rights.
cmake, zlib, GNU make, Solaris: g++ is a
requirement
GNU make, pandoc (>= 1.12.3), pngquant, C++17
A valid Bloomberg installation. The API headers and
dynamic library are downloaded from
<https://github.com/Rblp/blp> during the build step. See
<https://bloomberg.github.io/blpapi-docs/cpp/3.24.6> as well as
<https://www.bloomberg.com/professional/support/api-library/>
for API documentation. Currently supported platforms are
Windows and Linux (both for x86-64 processors only) as well as
macOS (for arm64 only).  Other os-cpu pairings are not
supported by Bloomberg and can therefore not be supported here.
MOSEK (http://www.mosek.com) and MOSEK License for
use of Rmosek,
GMP (GNU MP bignum library from
<https://gmplib.org/>)
A 64-bit POSIX OS such as Linux or OS X with IANA
time zone data in /usr/share/zoneinfo as well as a
recent-enough C++11 compiler (such as g++-4.9 or later which is
preferred, g++-4.8 works too). On Windows the zoneinfo included
with R is used; and time parsing support is enabled via a
backport of std::get_time from the LLVM libc++ library.
GNU make, pcre2 (>= 10.00), GLib (>= 2.0.0). On
Windows, no prior installations are necessary, as pre-built
(i.e. cross-compiled) binaries of required libraries are
downloaded from a GitHub repository
(<https://github.com/PolMine/libcl>) during installation. On
macOS, static libraries of Glib are downloaded
(<https://github.com/PolMine/libglib>) if Glib is not present.
A POSIX system. Currently Linux and OS X are known
to work.
MeCab 0.996 (or mecab-ko 0.9.2) or higher, GNU make
GNU make, Intel TBB, Windows: cmd.exe and
cscript.exe, Solaris: g++ is required
An available hiredis library (eg via package
libhiredis-dev on Debian/Ubuntu, hiredis-devel on
Fedora/RedHat, or directly from source from
<https://github.com/redis/hiredis>) can be used but version
1.0.2 is also included and built on demand if needed. The
optional 'rredis' package can be installed from the 'ghrr'
'drat' repo for additional illustrations and tests.
Python (>= 3.7), retriever (>= 3.0.0) (version must
be listed to patch to allow parsing)
xclip (https://github.com/astrand/xclip) or xsel
(http://www.vergenet.net/~conrad/software/xsel/) for accessing
the X11 clipboard
GNU GSL for the GSL random-number generators
HDF5 (>= 1.8.13), little-endian platform
Subversion for install_svn, git for install_git
Presentations written in (R)Markdown require Pandoc
(http://pandoc.org).
'unrar' (Linux/macOS) or '7-Zip' (Windows) to work
with '.rar' files.
Rust 'cargo'; the crate 'libR-sys' must compile
without error
tk.browse() requires Tcl-tk >= 8.6 (shipped with R
>= 3.4.0 on Windows, may have to be manually updated on other
systems)
Python (>= 3.7), rgf_python, scikit-learn (>=
0.18.0), scipy, numpy. Detailed installation instructions for
each operating system can be found in the README file.
OpenGL and GLU Library (Required for display in R.
See "Installing OpenGL support" in README.md. Not needed if
only browser displays using rglwidget() are wanted.), zlib
(optional), libpng (>=1.2.9, optional), FreeType (optional)
GLPK library package (e.g., libglpk-dev on
Debian/Ubuntu)
Maxima (https://sourceforge.net/projects/maxima/,
tested with versions 5.42.0 - 5.46.0), needs to be on PATH
iRODS C++ HTTP API 0.2.0
(<https://github.com/irods/irods_client_http_api>)
Java JDK 1.2 or higher (for JRI/REngine JDK 1.4 or
higher), GNU make
GNU make, cmake (>= 3.2.0), gcc, gfortran
libmariadb-client-lgpl-dev or libmariadb-dev or
libmysqlclient-dev, with libssl-dev (deb),
mariadb-connector-c-devel or mariadb-devel (rpm),
mariadb-connector-c or mysql-connector-c (brew)
notepad.exe, mark.exe (>= 8.0) (or mark32.exe and
mark64.exe) and rel_32.exe (see README.txt)
pandoc (>=2.11.3 with 'commonmark_x' processing and
'sourcepos' extension, needed for 'rmarkdown' formats).
pandoc (>=1.14, needed for the vignette)
gmp (>= 4.2.3), mpfr (>= 3.0.0), pdfcrop (part of
TexLive) is required to rebuild the vignettes.
libmariadb-client-dev | libmariadb-client-lgpl-dev
| libmysqlclient-dev (deb), mariadb-devel (rpm), mariadb |
mysql-connector-c (brew), mysql56_dev (csw)
The tools to run 'R CMD SHLIB' on 'Fortran' code.
The used 'Fortran' compiler must support pointer initialization
which is a feature of the 2008 standard.
roger-base (for interface functions only)
GNU Make, GCC Compiler Suite (C and Fortran)
Oracle Instant Client or Oracle Database Client
Poppler Glib interface headers and libraries
(<http://poppler.freedesktop.org/>) [Debian/Ubuntu:
libpoppler-glib-dev, Fedora: poppler-glib-devel]
C++11, GNU make, Windows: cmd.exe and cscript.exe
ProtoBuf libraries and compiler version 3.3.0 or
later; On Debian/Ubuntu these can be installed as
libprotoc-dev, libprotobuf-dev and protobuf-compiler, while on
Fedora/CentOS protobuf-devel and protobuf-compiler are needed.
A C++17 compiler is required as well.
A user API key (which one can request from the
website at <http://www.pushbullet.com>), and one or more
devices to push messages to which may be any one of an (Android
or iOS) phone, a (Chrome or Firefox, or Opera or Safari)
browser or the (Windows or Mac) desktop application provided
the corresponding Pushbullet 'app' has been installed on any
one of these.
QuantLib library (>= 1.25) from
https://quantlib.org, Boost library from https://www.boost.org
GNU make, pandoc (>= 1.12.3), pandoc-citeproc
GNU make, CMake (>= 3.10), pandoc, libarchive,
libregex, libexpat, liblzma, libzstd, liblz4, libz2, libz,
libbcrypt
"conda (python>=3.6 pytorch torchvision cpuonly
numpy ( >= 1.14.0) matplotlib pandas -c pytorch);
python-minimal, pandoc pandoc-citeproc, qpdf; Python (>=3.6),
pytorch (>=1.4), torchvision, cpuonly, numpy ( >= 1.14.0);
pandoc (>= 2.0), qpdf ( >= 7.0) on Solaris"
ZeroMQ >= 3.0.0: libzmq3-dev (deb) or zeromq-devel
(rpm)
Rust tool chain w/ cargo, libclang/llvm-config
GLPK library, including -dev or -devel part
C++17, zlib headers and libraries, GNU make,
optionally also bzip2 and POSIX-compliant regex functions.
(PDF)LaTeX (https://www.latex-project.org/) with
'pdfpages' package for studentGrowthPlot option in visualizeSGP
to bind together student growth plots into school catalogs
yarn 1.22.17 or higher, Node 12 or higher
Pandoc (http://pandoc.org) needs to installed and
available in the search path.
'SLiM' is a forward simulation software for
population genetics and evolutionary biology. See
<https://messerlab.org/slim/> for installation instructions and
further information. The 'Python' coalescent framework
'msprime' and the 'tskit' module can by installed by following
the instructions at <https://tskit.dev/>.
For playing sounds, a command line system tool for
playing wav-files is required.
Python (>= 3.6.0), ssw-py (>= 1.0.0). Detailed
installation instructions can be found in the README file.
C++, arrow package with gzip codec, StarPU
For all functions resp. apps: pandoc, LaTeX. For
the edapy() function resp. Statsomat/EDAPY app: Python (>=3).
LilyPond v2.22.1-2+ (needed for rendering sheet
music or writing MIDI files)
Java (>= 7.0): openjdk-11-jdk (deb),
java-11-openjdk.x86_64 (rpm), openjdk@11 (brew)
Tcl/Tk (>= 8.5), Tktable (>= 2.9, optional)
Tesseract >= 3.03 (libtesseract-dev /
tesseract-devel) and Leptonica (libleptonica-dev /
leptonica-devel). On Debian you need to install the English
training data separately (tesseract-ocr-eng)
pandoc (>= 1.12.3) suggested for using wordreg
function; LaTeX packages tikz, booktabs, dcolumn, rotating,
thumbpdf, longtable, paralist for the vignette
TensorFlow >= 1.13.0 (https://www.tensorflow.org/)
and TensorFlow IO >= 0.4.0 (https://github.com/tensorflow/io)
PhantomJS (https://phantomjs.org), pandoc (>=
1.12.3) - https://pandoc.org.
A C++17 compiler is required; on macOS compilation
version 11.0 or later is required. Optionally cmake (only when
TileDB source build selected), curl (only when TileDB source
build selected)), and git (only when TileDB source build
selected); on x86_64 and M1 platforms pre-built TileDB Embedded
libraries are available at GitHub and are used if no TileDB
installation is detected, and no other option to build or
download was specified by the user.
Python (>= 2.7), python-gdal library (For Windows,
gdal installed via OSGeo4W <https://trac.osgeo.org/osgeo4w/>
recommended) clipboard
A system with zoneinfo data (e.g.
/usr/share/zoneinfo) as well as a recent-enough C++11 compiler
(such as g++-4.8 or later). On Windows the zoneinfo included
with R is used.
Tcl/Tk (>= 8.6). To read and write other formats
than PPM/PGM, PNG and GIF it is required the 'tkImg' for
'Tcl/Tk' (https://sourceforge.net/projects/tkimg/), or the
debian 'libtk-img' package (Ubuntu) or the RPM 'tkimg' package
(Fedora or openSUSE).
LibTorch (https://pytorch.org/); Only x86_64
platforms are currently supported except for ARM system running
macOS.
under Linux, fftwtools needs libfftw3-dev
udunits (>= 2) from
https://www.unidata.ucar.edu/software/udunits/
PARI/GP >= 2.3.0 [strongly recommended for
logkda()]
V8 engine version 6+ is needed for ES6 and WASM
support. On Linux you can build against libv8-dev (Debian) or
v8-devel (Fedora). We also provide static libv8 binaries for
most platforms, see the README for details.
libcurl: libcurl-devel (rpm) or
libcurl4-openssl-dev (deb), GNU make.
Miniconda3 (v23.10.0); BrainStat (reticulate
installation, v0.4.2); Numpy (v1.26.4)
virtuoso-opensource (Linux). For Mac & Windows,
this package can automate Virtuoso installation.
fftw3, GNU make, sox, Ghostscript, libsndfile, GDAL
PhantomJS for taking screenshots, ImageMagick or
GraphicsMagick and OptiPNG for manipulating images.
Java (>= 5.0); WordNet database files (direct
download:
<https://wordnetcode.princeton.edu/3.0/WNdb-3.0.tar.gz>; Debian
and Fedora package: wordnet)
Perl (>= 5.0), Unitex (>= 3.0
http://www-igm.univ-mlv.fr/~unitex/)
Fortran sources included in `tools/x13as_html` are
compiled via `configure`.
libxml2: libxml2-dev (deb), libxml2-devel (rpm)
'ghostscript' or 'pdftk' for editing pdf bookmarks
and/or documentation info entries. 'exiftool' for editing pdf
documentation info entries and/or xmp metadata.
libxslt: libxslt1-dev (deb), libxslt-devel (rpm)
Cargo (>= 1.56) (Rust's package manager), rustc
Accurate, Adaptable, and Accessible Error Metrics for Predictive
Models
Reliability and Scoring Routines for the Approach-Avoidance Task
Apps Based Activities for Communicating and Understanding
Statistics
Tools for Approximate Bayesian Computation (ABC)
Data Only: Tools for Approximate Bayesian Computation (ABC)
Array Based CpG Region Analysis Pipeline
Implementation of Artificial Bee Colony (ABC) Optimization
Approximate Bayesian Computational Model for Estimating P2
Approximate Bayesian Computation via Random Forests
Asymptotically Bias-Corrected Regularized Linear Discriminant
Analysis
Databases Used Routinely by the Brazilian Jurimetrics
Association
Useful Tools for Jurimetrical Analysis Used by the Brazilian
Jurimetrics Association
Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models
Modelling Multivariate Data with Additive Bayesian Networks
Measure a Subject's Abnormality with Respect to a Reference
Population
The Abnormal Blood Profile Score to Detect Blood Doping
Variable Selection in Nonparametric Models using B-Splines
R Interface to the A/B Street Transport System Simulation
Software
An R-Shiny Application for Creating Visual Abstracts
Survival Analysis using Time Dependent Covariate for Animal
Breeding
High-Dimensional Principal Fitted Components and Abundant
Regression
Inferring Directional Conservative Causal Core Gene Networks
Abrupt Change-Point or Aberration Detection in Point Series
Colour Plots with Palettes from Academic Institutions
Access the Twitter Academic Research Product Track V2 API
Endpoint
A Canonical Correlation Analysis with Inferential Guaranties
Functions for Processing Accelerometer Data
Missing Value Imputation for Accelerometer Data
The Acute COPD Exacerbation Prediction Tool (ACCEPT)
Creation and Evaluation of Acceptance Sampling Plans
Acceptance-Rejection Method for Generating Pseudo-Random
Observations
Command Line Tools to Produce Accessible Documents using 'R
Markdown'
Improving the Accessibility of 'rmarkdown' Documents
Accrual Plots and Predictions for Clinical Trials
Natural Abundance Correction of Mass Spectrometer Data
Query the Attentional Control Data Collection
Tools for Autoregressive Conditional Duration Models
Analysis of Correlated High-Dimensional Expression (ACE) Data
Optimal Bayesian Experimental Design using the ACE Algorithm
Analisis Computacional de Eventos de Protesta
ACE and AVAS for Selecting Multiple Regression Transformations
ACE Kin Pair Data Simulations and Model Fitting
Estimating Dynamic Heritability and Twin Model Comparison
Robust Estimation of the ACF from the M-Periodogram
Automated Retrieval of ACLED Conflict Event Data
Affymetrix SNP Probe-Summarization using Non-Negative Matrix
Factorization
Modelling Dependence with Multivariate Archimax (or any
User-Defined Continuous) Copulas
A Tool for Automating the Statistical Disclosure Control of
Research Outputs
A Cost-Minimal Regular Spanning Subgraph with TreeClust
Download, Manipulate, and Present American Community Survey and
Decennial Data from the US Census
Algorithmic Complexity for Short Strings
Data Only: Algorithmic Complexity of Short Strings (Computed via
Coding Theorem Method)
ACSS, Corresponding ACSS, and GLP Algorithm
A Companion Package for the Book "A Course in Statistics with R"
Asymptotic Classification Theory for Cognitive Diagnosis
Extract Circadian Rhythms Metrics from Actigraphy Data
Activity Fragmentation Metrics Extracted from Minute Level
Activity Data
Generate Activity Counts from Raw Accelerometer Data
A 'Shiny' App to Analyze Accelerometer-Measured Daily Physical
Behavior Data
Utilities for Parsing and Plotting Activities
Finding Cancer Driver Proteins with Enriched Mutations in
Post-Translational Modification Sites
Gaussian Process Based Design and Analysis for the Active
Subspace Method
Integrative Pathway Enrichment Analysis of Multivariate Omics
Data
Activity Index Calculation using Raw 'Accelerometry' Data
Advanced Processing and Chart Generation from activPAL Events
Files
Read in Activity Data and Plot Actograms
Actuarial Functions and Heavy Tailed Distributions
Handling Hierarchically Structured Risk Factors using Random
Effects Models
Computation of Actuarial Measures Using Bell G Family
Create Actuarial Experience Studies: Prepare Data, Summarize
Results, and Create Reports
A Package for Displaying Visual Scenes as They May Appear to an
Animal with Lower Acuity
Optimal Out-of-Sample Forecast Evaluation and Testing under
Stationarity
Acute Chronic Workload Ratio Calculation
The R Package Ada for Stochastic Boosting
Applies Multiclass AdaBoost.M1, SAMME and Bagging
Estimator of the Adherer Average Causal Effect
Discrete and Global Optimization Routines
Adaptive Huber Estimation and Regression
Archetypoid Algorithms and Anomaly Detection
Adaptive Nature-Inspired Algorithms for Hybrid Genetic
Optimization
Adaptive Approaches for Signal Detection in Pharmacovigilance
Bayesian Adaptive Designs for Diagnostic Trials
Adaptive Bioequivalence Design for In-Vitro Permeation Tests
Implementation of a Generic Adaptive Monte Carlo Markov Chain
Sampler
Adaptive P-Value Thresholding for Multiple Hypothesis Testing
with Side Information
Automated Deconvolution Augmentation of Profiles for Tissue
Specific Cells
Adaptive Sampling for Positive Unlabeled and Label Noise
Learning
Adaptive Smoothing Spline (AdaSS) Estimator for the
Function-on-Function Linear Regression
'Arrow' Database Connectivity ('ADBC') Driver Manager
'Arrow' Database Connectivity ('ADBC') 'PostgreSQL' Driver
'Arrow' Database Connectivity ('ADBC') 'SQLite' Driver
'DBI' Compliant Database Access Using 'ADBC'
Binomial and Multinomial Additive Hazard Models
Fit Additive Hazards Models for Survival Analysis
Discover and Install Useful RStudio Addins
'RStudio' Addins for Show Outline of a R Markdown/'LaTeX'
Project
Additive Data Envelopment Analysis Models
Additivity Tests in the Two Way Anova with Single Sub-Class
Numbers
Adds Labeled Center Line and Scale Lines/Regions to Trellis
Plots
Analysis of Accelerated Destructive Degradation Test Data
Analysis of Ecological Data: Exploratory and Euclidean Methods
in Environmental Sciences
Exploratory Analysis of Genetic and Genomic Data
An S4 Lattice-Based Package for the Representation of
Multivariate Data
Analysis of Habitat Selection by Animals
Group Lasso and Elastic Net Solver for Generalized Linear Models
Exploratory Analyses for the Phylogenetic Comparative Method
A 'shiny' Application for the (Audio-)Visualization of Adverse
Event Profiles
Adequacy of Probabilistic Models and General Purpose
Optimization
Estimation in Optimal Adaptive Two-Stage Designs
Import from and Export to Amiga Disk Files
Get Adform Ads Data via the 'Windsor.ai' API
Assess Medication Adherence from Pharmaceutical Claims Data
Adjacency-Constrained Clustering of a Block-Diagonal Similarity
Matrix
Computing Sensitivity at a Fix Value of Specificity and Vice
Versa as Well as Bootstrap Metrics for ROC Curves
Parameter and Adjusted Probability Estimation for Right-Censored
Data
Remove Automated and Repeated Downloads from 'RStudio' 'CRAN'
Download Logs
Confounder-Adjusted Survival Curves and Cumulative Incidence
Functions
Accident and Development Period Adjusted Linear Pools for
Actuarial Stochastic Reserving
Utility Functions and Development Tools for the Admiral Package
Family
Oncology Extension Package for ADaM in 'R' Asset Library
Pediatrics Extension Package for ADaM in 'R' Asset Library
Vaccine Extension Package for ADaM in 'R' Asset Library
Adaptive Mixture of Student-t Distributions
Package Admix for Admixture (aka Contamination) Models
An Interface for Running 'ADMIXTOOLS' Analyses
Algorithms using Alternating Direction Method of Multipliers
Alternating Direction Method of Multipliers to Solve Dense
Dubmatrix Problem
Penalized Precision Matrix Estimation via ADMM
Estimate and Manipulate Age-Depth Models
Ancient Demographic Modelling Using Radiocarbon
No-U-Turn MCMC Sampling for 'ADMB' Models
Adoption Probability, Triers and Users Rate of a New Product
Fast Clustering Using Adaptive Density Peak Detection
Use Least Squares Polynomial Regression and Statistical Testing
to Improve Savitzky-Golay
Design and Analysis of Locally or Globally Efficient Adaptive
Designs
Upper Clopper-Pearson Confidence Limits for Burn-in Studies
under Additional Available Information
Solving 1D Advection Bi-Flux Diffusion Equation
Interactive R Tutorials to Accompany Field (2016), "An Adventure
in Statistics"
Automatic Direct Variable Selection via Interrupted Coefficient
Estimation
Wavelet Analysis of Genomic Data from Admixed Populations
Access Data from the Atlas do Estado Brasileiro
Automated and Early Detection of Disease Outbreaks
Change Point Analysis in ARIMA Forecasting
Automated and Early Detection of Seasonal Epidemic Onset
Statistical Modelling for Asymmetric Exponential Power
Distribution
A Computational Tool for Aerobiological Data
Estimate Aerosol Particle Collection Through Sample Lines
Model-Based Estimation of Confounder-Adjusted Attributable
Fractions
Government Analysis Function Recommended Accessible Colour
Palette
Diagnosis Performance Using Attributable Fraction
Accelerated Functional Failure Time Model with
Error-Contaminated Survival Times
Raster Georeferencing, Grid Affine Transforms, Cell Abstraction
The Attributable Fraction (AF) Described as a Function of
Disease Heritability, Prevalence and Intervention Specific
Factors
Functions for Atomic Force Microscope Force-Distance Curves
Analysis
Tools for Modelling of Animal Flight Performance
Toolkit for Regression Analysis of Kazakhstan Banking Sector
Data
Africa Macroeconomic Monitor Database API
Accelerated Failure Time Model with Generalized Estimating
Equations
Accelerated Failure Time for High Dimensional Data with MCMC
R-Squared Measure under Accelerated Failure Time (AFT) Models
Semiparametric Accelerated Failure Time Model
Model Diagnostics for Accelerated Failure Time Models
Toolbox for Downloading and Extracting Copernicus AgERA5 Data
Calculate 'ActiGraph' Counts from Accelerometer Data
Collection of Functions for Working with Age Intervals
Agnostic Fay-Herriot Model for Small Area Statistics
Aggregate Numeric, Date and Categorical Variables
Utilities for Aggregating Probabilistic Forecasts
Relationship Matrices for Diploid and Autopolyploid Species
Adaptive Gauss Hermite Quadrature for Bayesian Inference
Aggregation Operators and Preordered Sets
Agreement Interval of Two Measurement Methods
Statistical Procedures for Agricultural Research
Visualization of Design of Experiments from the 'agricolae'
Package
Tutorial Analysis of Some Agricultural Experiments
Utilities for Data Analysis in Agriculture
Evapotranspiration and Energy Fluxes Spatial Analysis
Calculate Concentration and Dispersion in Ordered Rating Scales
Experimental Statistics and Graphics for Agricultural Sciences
Regression Analysis Linear and Nonlinear for Agriculture
Stability Analysis for Agricultural Research
Data Analysis of Pesticide Application Technology
Crosswalk Municipality and District Statistics in Germany
Adaptive and Automatic Gradient Boosting Computations
An R Implementation of the 'Align-GVGD' Method
Regularization for Semiparametric Additive Hazards Regression
Additive Heredity Model: Method for the Mixture-of-Mixtures
Experiments
Functions and Data for the Book 'Applied Hierarchical Modeling
in Ecology' Vols 1 and 2
An Implementation of the Artificial Hydrocarbon Networks
Fast Searching for Multiple Keywords in Multiple Texts
Analytic Hierarchy Process for Survey Data
Consistency in the Analytic Hierarchy Process
Hybrid Method for Multiple Criteria Decision-Making (MCDM)
Flexible Parametric Accelerated Hazards Models
Calculates Continuous Time Likelihood Ratio Weights Assuming
Multiplicative Intensity Models and Additive Hazard Models
Testing for Compositional Pathologies in Datasets
Model Selection and Multimodel Inference Based on (Q)AIC(c)
Model Selection of PERMANOVA Models Using AICc
Additive Information & Details of Evidence Synthesis
Read AIFF Files and Convert to WAVE Format
Experimental Evaluation of Algorithm-Assisted Human
Decision-Making
Irish Weather Observing Stations Hourly Records for 2017
AI Assistant to Write and Understand R Code
Download Mexico City Pollution, Wind, and Temperature Data
Exposure Model to Air Pollutants Based on Mobility and Daily
Activities
Suite of GR Hydrological Models for Precipitation-Runoff
Modelling
Hydro-Meteorological Catchments Datasets for the 'airGR'
Packages
Ensemble-Based Data Assimilation with GR Hydrological Models
'airGR' Integrated Water Resource Management
Teaching Hydrological Modelling with the GR Rainfall-Runoff
Models ('Shiny' Interface Included)
Retrieve 'AirNow' Air Quality Observations and Forecasts
Convenience Tools for Working with Airport Data
Air Quality Measurements in Spain from 2011 to 2018
AIRR Data Representation Reference Library
Evaluation of Algorithm Collections Using Item Response Theory
Additive Isotonic Proportional Hazards Model
Interpolation of Irregularly and Regularly Spaced Data
Immunoglobulin Clonal Lineage and Diversity Analysis
Download, Merge, and Process Redistricting Data
Adaptive Lasso for the Cox Regression with Interval Censored and
Possibly Left Truncated Data
PARAFAC Analysis of Fluorescence Excitation-Emission Matrices
Estimating Length-Based Indicators for Fish Stock
Quantile Regression Using Asymmetric Laplace Distribution
Adjusted Limited Dependent Variable Mixture Models
Interpretable Machine Learning and Statistical Inference with
Accumulated Local Effects (ALE)
Accumulated Local Effects (ALE) Plots and Partial Dependence
(PD) Plots
Dynamic Model of Ammonia Emission from Field-Applied Manure
Estimating Absolute Protein Quantities from Label-Free LC-MS/MS
Proteomics Data
Connectivity to 'Alfresco' Content Management Repositories
Downloading Time Series from ALFRED Database for Various
Vintages
Tools to Standardize Phytoplankton Taxonomic Data and Perform
Functional Group Classifications
Haplotype Two Snips Out of a Paired Group of Patients
Implement an Address Search Auto Completion Menu on 'Shiny' Text
Inputs Using the 'Algolia Places' 'Javascript' Library
Allows you to Easily Interact with the Algorithmia Platform
Converts Human gene symbols to entrez IDs
Estimate Invasive and Alien Species (IAS) Introduction Rates
A Modified DTW Algorithm for Stratigraphic Time Series Alignment
Pre-Processing XY Data from Experimental Methods
Acknowledge all Contributors to a Project
Allele Imputation and Haplotype Reconstruction from Pedigree
Databases
Identifying Unique Multilocus Genotypes where Genotyping Error
and Missing Data may be Present
Allele Retention, Inbreeding, and Demography
Predict and Visualize Population-Level Changes in Allele
Frequencies in Response to Climate Change
Calculating Multiple Performance Metrics of a Prediction Model
Acute Lymphoblastic Leukemia Maintenance Therapy Analysis
Interface for 'All of Us' Researcher Workbench
Removing Allometric Effects of Body Size in Morphological
Analysis
Datasets from the Survival TV Series Alone
Model Classifier for Binary Classification
Fit GLM's with High-Dimensional k-Way Fixed Effects
Algorithms for High-Throughput Sequencing of Antigen-Specific T
Cells
Confidence Intervals for Coefficient Alpha and Standardized
Alpha
Generalization of the Convex Hull of a Sample of Points in the
Plane
Set Alpha Based on Sample Size Using Bayes Factors
Obtain Alpha-Outlier Regions for Well-Known Probability
Distributions
Partition/Decomposition of Breeding Values by Paths of
Information
Implementation of the 3D Alpha-Shape for the Reconstruction of
3D Sets from a Point Cloud
Lightweight Interface to the Alpha Vantage API
Adaptive Lasso Quantile Regression with Fixed Effects
Data to Accompany Applied Linear Regression 4th Edition
Multivariate Curve Resolution Alternating Least Squares
(MCR-ALS)
Accelerated line search algorithm for simultaneous orthogonal
transformation of several positive definite symmetric matrices
to nearly diagonal form
Package Documentation Websites with 'Quarto', 'Docsify',
'Docute', or 'MkDocs'
Detection of Altered Protein Quantitative Relationships
Provides an Interface to the NREL Alternate Fuels Locator
Optimal Experimental Designs for Accelerated Life Testing
Alternative Estimators to Adjusted R-Squared
Accessing and Analyzing Large-Scale Environmental Data
Alternating Manifold Proximal Gradient Method for Sparse PCA
An Interface to the 'AutoNavi Maps' API Geocoding Services
Get Amazon Ads Data via the 'Windsor.ai' API
Get Amazon S3 Data via the 'Windsor.ai' API
Get Amazon Sp Data via the 'Windsor.ai' API
Simulation and Estimation of Ambit Processes
Generate LaTeX Code for Auto-Multiple-Choice (AMC)
Anomaly Detection with Normal Probability Functions
Additive and Multiplicative Effects Models for Networks and
Relational Data
American Politics-Inspired Color Palette Generator
Checks for Various Computing Environments
Compute the Adjusted Market Inefficiency Measure
Additive Main Effects and Multiplicative Interaction Model
Stability Parameters
Automated Moderated Nonlinear Factor Analysis Using 'M-plus'
Tractable Parametric Odds-Based Regression Models
Statistical Test for the Multivariate Point Null Hypotheses
An Algorithm for Automatic Peak Detection in Noisy Periodic and
Quasi-Periodic Signals
Shiny Apps to Support Capacity Building on Harvest Control Rules
Statistical Analysis of Amplicon Data of the Same Sample to
Identify Artefacts
Clinical Scores Calculator for Healthcare
Analysis of Codon Data under Stationarity using a Bayesian
Framework
Targeted Differential and Global Enrichment Analysis of
Taxonomic Rank by Shared Asvs
Simple and Canonical Correspondence Analysis
Analogue and Weighted Averaging Methods for Palaeoecology
Data Analysis and Automated R Notebook Generation
Assigning Ancestry Based on Population References
Construct Natural-Language Lists with Internationalization
Asynchronous Disk-Based Representation of Massive Data
Make Spectroscopic Data Processing Easier
Simulate a Bus Route Creel Survey of Anglers
Imports Log Files from Angstrom Engineering Thermal Evaporator
Tools for 'ROMS' the Regional Ocean Modeling System
Inferring Dominance Hierarchies and Estimating Uncertainty
Extended Kalman Filters for Animal Movement
Networks Characterising the Physical Configurations of Animal
Habitats
A Web-Based Graphics Device for Animated Visualisations
A Gallery of Animations in Statistics and Utilities to Create
Animations
Visualizing Changes in Performance Measures and Demographic
Affiliations using Animation
Animated Interactive Grammar of Graphics
A Collection of ML Tools for Conservation Research
Animation of Multiple Trajectories with Uncertainty
Statistical Network Analysis of Animal Social Networks
Animate Shiny and R Markdown Content when it Comes into View
Compute High Dimensional Orthant Probabilities
Artificial Neural Networks for Anomaly Detection
Annotated Matrix: Matrices with Persistent Row and Column
Annotations
Annotate the Gene Symbols for Probes in Expression Array
Extract Subsequences from GenBank Annotations
Image Annotation and Polygon Outlining using Free Drawing
A Non-Parametric Statistical Test to Compare Clustering
Structures
Analysis of Moderation with the Monte Carlo Confidence Interval
Method
Analyses of Proportions using Anscombe Transform
Interactive Document for Working with Analysis of Variance
Create Datasets with Identical Summary Statistics
Functions and Data for the Book "Applied Nonparametric
Statistical Methods", 5th Edition
Import, Manipulate and Explore the Results of an 'Antares'
Simulation
Computation of the WHO Child Growth Standards
Computation of the WHO 2007 References for School-Age Children
and Adolescents (5 to 19 Years)
An R Package for the Mean Measure of Divergence (MMD)
Statistical Methods for Anthropometric Data
Antibody Titer Analysis of Vaccinated Patients
Extract Text from Microsoft Word Documents
Sleep Quality and Anxiety in Confinement
Query 'nycflights13'-Like Air Travel Data for Given Years and
Airports
Install and Load Any Package from CRAN, Bioconductor or Github
Anything to 'POSIXct' or 'Date' Converter
Bootstrapping in Different One-Way and Two-Way ANOVA
Data from the 'Access to Opportunities Project (AOP)'
A-Optimal Block Designs for Comparing Test Treatments with
Controls
Inference in the Balanced One-Way ANOVA Model with Random Factor
Classic, Nonparametric and Bayesian One-Way Analysis of Variance
Panel
Format Outputs of Statistical Tests According to APA Guidelines
Visualization of Clonal Expansion for Single Cell Immune
Profiles
Create American Psychological Association (APA) Style Tables
Create R Markdown Text for Results in the Style of the American
Psychological Association (APA)
Resolving Plant Taxon Names Using the Australian Plant Census
Analysis of Unreplicated Orthogonal Experiments using All
Possible Comparisons
A New Age-Period-Cohort Model for Describing and Investigating
Inter-Cohort Differences and Life Course Dynamics
Routines for Descriptive and Model-Based APC Analysis
An Implementation of the Additive Polynomial Design Matrix
Advanced Pathway Enrichment Analysis Representation
Phylogenetic Methods for Multiple Gene Data
Create Interactive Chart with the JavaScript 'ApexCharts'
Library
Multiple Testing Approach using Average Power Function (APF) and
Bayes FDR Robust Estimation
Analysis with Profile Hidden Markov Models
Statistical Inference and Prediction of Annotations in
Phylogenetic Trees
Functions and Data Sets for the Book "A Progressive Introduction
to Linear Models"
Auto-Adaptive Parentage Inference Software Tolerant to Missing
Parents
Interface to 'episensr' for Sensitivity Analysis of
Epidemiological Results
Datasets from Hosmer, Lemeshow and Sturdivant, "Applied Logistic
Regression" (3rd Ed., 2013)
Decorate a 'ggplot' with Associated Information
Another Plot Package: 'Bagplots', 'Iconplots', 'Summaryplots',
Slider Functions and Others
Automated Population Pharmacokinetic Dataset Assembly
Prioritize and Delete Erroneous Taxa in a Large Phylogenetic
Tree
Tools for Choice Model Estimation and Application
Interface to 'AppEEARS' NASA Web Services
Adjusted Prediction Model Performance Estimation
Get Apple Search Ads Data via the 'Windsor.ai' API
A Compilation of Applicability Domain Methods
Functions and Data Sets for 'Applied Predictive Modeling'
Amyloid Propensity Prediction Neural Network
Code and Obtain Customized Planned Comparisons with 'appRiori'
Bayesian Prediction of Complex Computer Codes
Approximately Optimal Fine Balance Matching with Multiple Groups
Approximate and Exact Optimal Transport Methods
Get Data from 'Appsflyer' via the 'Windsor.ai' API
Datasets from Draper and Smith "Applied Regression Analysis"
(3rd Ed., 1998)
Amdahl's Profiler, Directed Optimization Made Easy
Inspect, Read, Edit and Run 'APSIM' "Next Generation" and
'APSIM' Classic
Average Positive Predictive Values (AP) for Binary Outcomes and
Censored Event Times
Visualize Population Pyramids Aggregated by Age
Display Air Quality Model Output and Monitoring Data
Integrated Development Toolbox for Aquatic Chemical Model
Generation
Access to Aquo domaintables from R (Dutch)
Another Look at the Acceptance-Rejection Method
Simulate Auto Regressive Data from Precision Matricies
Rigorous Data Reduction and Error Propagation of Ar40 / Ar39
Data
Fitting Univariate Censored Linear Regression Model with
Autoregressive Errors
Geocoding with the 'ArcGIS' REST API Service
A Robust Interface to ArcGIS 'Geocoding Services'
Search for POIs using ArcGIS 'Places Service'
Post-Processing of Markov Chain Monte Carlo Simulations for
Chronological Modelling
Example Datasets from Archaeological Research
Refitting and Spatial Analysis in Archaeology
Visualisation, Exploration, and Web Communication of
Archaeological Spatial Data
Finds the Archetypal Analysis of a Data Frame
An Archetype for Data Mining and Data Science Projects
Plant Root System Architecture Analysis Using DART and RSML
Files
Multi-Format Archive and Compression Support
Retrieve Archived Web Pages from the 'Internet Archive'
Tools for Storing, Restoring and Searching for R Objects
Tools for Archiving, Managing and Sharing R Objects via GitHub
Autoregressive Cokriging Models for Multifidelity Codes
Process ArcGIS Protocol Buffer FeatureCollections
Processing and Physical Activity Summaries of Minute Level
Activity Data
Annual Regional Database of the European Commission (ARDECO)
ARDL, ECM and Bounds-Test for Cointegration
Linear and Nonlinear Autoregressive Distributed Lag Models:
General-to-Specific Approach
Calculate Area of Triangles and Polygons
Harmonise and Integrate Heterogeneous Areal Data
Plot Stacked Areas and Confidence Bands as Filled Polygons
Plots, Summary Statistics and Tools for Arena Simulation Users
Arena for the Exploration and Comparison of any ML Models
Fractional ARIMA (and Other Long Memory) Time Series Modeling
Accurate Estimation of Influenza Epidemics using Google Search
Data
Automatic Regression for Governing Equations (ARGOS)
Command Line Optional and Positional Argument Parser
Random Variate Generator for the Argus Distribution
Adaptable Regularized Hotelling's T^2 Test for High-Dimensional
Data
Efficient Computations of Standard Clustering Comparison
Measures
Hybrid ARIMA-GARCH and Two Specially Designed ML-Based Models
Likelihood Based Inference for ARIMA Modeling
Time Series Forecasting using ARIMA-ANN Hybrid Model
Archive and Unarchive Databases Using Flat Files
Exploring Social Network Structures Through Friendship-Driven
Community Detection with Association Rules Mining
Data Analysis Using Regression and Multilevel/Hierarchical
Models
Adaptive Rejection Metropolis Sampling (ARMS) via 'Rcpp'
Analysis of Large Affymetrix Microarray Data Sets
A Probe-Level Data File Format Used by 'aroma.affymetrix'
[deprecated]
Copy-Number Analysis of Large Microarray Data Sets
Core Methods and Classes Used by 'aroma.*' Packages Part of the
Aroma Framework
Geometric Morphometric Methods and Virtual Anthropology Tools
Retrieving and Analyzing Air Quality and Weather Data from ARPA
Lombardia
Censored Mixed-Effects Models with Different Correlation
Structures
Tools for Simulating Direct Behavioral Observation Recording
Procedures Based on Alternating Renewal Processes
Fast Generators and Iterators for Permutations, Combinations,
Integer Partitions and Compositions
Apply a Function to a Margin of an Array
An Arsenal of 'R' Functions for Large-Scale Statistical
Summaries
Aligned Rank Transform for Nonparametric Factorial Analysis
To Insert Title, Divider, and Block of Comments
Mining Association Rules and Frequent Itemsets
Classification Based on Association Rules
Mining NB-Frequent Itemsets and NB-Precise Rules
Visualizing Association Rules and Frequent Itemsets
Management and Processing of Autonomous Recording Unit (ARU)
Data
Data Sets for "Applied Survival Analysis Using R""
A Collection of Statistical Tools for Biologists
Export R Objects to Several Markup Languages
Create 'Ascii' Screen Casts from R Scripts
Reads Fixed-Width ASCII Data Files (.txt or .dat) that Have
Accompanying Setup Files (.sps or .sas)
Simulate the Spread of Ascochyta Blight in Chickpea
Simulations for Adaptive Seamless Designs
Interface to the Australian Statistical Geography Standard
Methods for Adaptive Shrinkage, using Empirical Bayes
Password Entry Utilities for R, Git, and SSH
Interface to the Algorithm Selection Benchmark Library
Optimal Convex M-Estimation for Linear Regression via Antitonic
Score Matching
Linkage Map Construction using the MSTmap Algorithm
Functions for the Book "Applied Statistical Modeling for
Ecologists"
Predicting and Classifying Patient Phenotypes with Multi-Omics
Data
Animal Social Network Inference and Permutations for Ecologists
Functions for Estimating Centrographic Statistics
A General Framework for Multivariate Analysis with Optimal
Scaling
Analysis of Symmetry of Parasitic Infections
Spline Regression with Adaptive Knot Selection
Augments 'ASReml-R' in Fitting Mixed Models and Packages
Generally in Exploring Prediction Differences
Applied Singular Spectrum Analysis (ASSA)
Verbose Assertions for Tabular Data (Data.frames and
Data.tables)
Simple Assertions for Beautiful and Customisable Error Messages
Assertive Programming for R Analysis Pipelines
Assessment Tools for Regression Models with Discrete and
Semicontinuous Outcomes
Backtesting Simple Asset Allocation Strategies
Estimating Asset Correlations from Default Data
Optimal Pricing of Assets with Fixed Expiry Date
Population Assignment using Genetic, Non-Genetic or Integrated
Data in a Machine Learning Framework
Infer Geographic Origin from Isotopic Data
A Suite of R Functions Implementing Spline Smoothing Techniques
Adaptive Subgroup Selection in Group Sequential Trials
Measuring Association with Recursive Binning
Implements New and Existing Association Indices for Constructing
Animal Social Networks
Calculate the Assortativity Coefficient of Weighted and Binary
Networks
Translates an R Function to a C++ Function
A Computational Tool for Astrochronology
Astronomy: Time and Position Functions, Misc. Utilities
Applied Statistical Time Series Analysis
Adaptive SURE Thresholding Using Side Information
Stochastic Volatility Models with or without Leverage
Data on Asylum and Resettlement for the UK
Asymmetric Linkage Disequilibrium (ALD) for Polymorphic Genetic
Data
Asymmetric Second Order Rotatable Designs (AsymmetricSORDs)
Multidimensional Scaling of Asymmetric Proximities
Asymmetry Measures for Probability Density Functions
A Simple R Package for Classical Parametric Statistical Tests
and Confidence Intervals in Large Samples
Estimate Asymptomatic Cases via Capture/Recapture Methods
Regression Analysis of Sparse Asynchronous Longitudinal Data
Create Tables for Reporting Clinical Trials
Automatic Time Series Analysis and Forecasting using the Ata
Method
Bounding Treatment Effects by Limited Information Pooling
Estimating ATE with Misclassified Outcomes and Mismeasured
Covariates
Average Treatment Effects with Measurement Error and Variable
Selection for Confounders
Download Air Quality and Meteorological Information of Chile
Tools to Handle and Publish Metadata as 'Atom' XML Format
Alert Time Quality - Evaluating Timely Epidemic Metrics
Wrapper for the 'AT' Protocol Behind 'Bluesky'
Correcting for Attenuation Due to Measurement Error
Threshold Independent Performance Measures for Probabilistic
Classifiers
Standard Conform Pure Tone Audiometry (PTA) Plots
Model Audit - Verification, Validation, and Error Analysis
Automatic Dynamic Regression using Extreme Gradient Boosting
Analysis of Augmented Randomised Complete Block Designs
Analysis of Data with Mixed Measurement Error and
Misclassification in Covariates
eBird Data Extraction and Processing in R
Area Under Minimum of False Positives and Negatives
Creates a Neighbourhood Using Locality Sensitive Hashing for
Gaussian Projections
TERN AusPlots Australian Ecosystem Monitoring Data
Provides Datasets About Australian Politicians
Approximate Unconditional and Permutation Tests
Automatic Variable Reduction Using Principal Component Analysis
Learning to Rank Bagging Workflows with Metalearning
Automated Covariate Selection Using HDPS Algorithm
An Interface to the 'AutoDesk' 'API' Platform
Automated Stacked Ensemble Classifier for Severe Class Imbalance
Automatic Construction of Forced-Choice Tests
Auto-GO: Reproducible, Robust and High Quality Ontology
Enrichment Visualizations
Semi-Automatic Grading of R and Rmd Scripts
Automated Generation of Data-Informed GLM Models in Task-Based
fMRI Data Analysis
Multiple Heat Maps for Projected Coordinates
Automagically Document and Install Packages Necessary to Run R
Code
Algorithms for Automatically Fitting MFA Models
Auto-Generate Changelog using Conventional Commits
Automated Transcriptome Classifier Pipeline: Comprehensive
Transcriptome Analysis
Automatic Generation of Interactive Visualizations for
Statistical Results
Development of Visualization Tools for Protein Sequence
Creating Echarts Visualizations as Easy as Possible
Automatic Linear and Logistic Regression and Survival Analysis
Minimum Distance Estimation in Autoregressive Model
An Interpretable Machine Learning-Based Automatic Clinical Score
Generator
Fully Automatic Generation of Scorecards
Retrieve Disease-Related Genes from Public Sources
Automatic Transformation of an 'R' Function into a 'shiny' App
Builds Stepwise GLMs via Train and Test Approach
An R Port of the 'ImageJ' Plugin 'Auto Threshold'
A Novel Automatic Shifted Log Transformation
Automatic Model Selection and Prediction for Univariate Time
Series
Auto Visual Inference with Computer Vision Models
Check if the Title of a Package is Available, Appropriate and
Interesting
Calculation of Average Population Attributable Fractions and
Confidence Intervals
Access to the List of Birds Species of Peru
A Variable Selection using Genetic Algorithms
A Computational Biologist’s Toolkit To Get Data From 'avidaDB'
Calculate the Inertial Properties of a Flying Bird
Adaptation of Virtual Twins Method from Jared Foster
A Global Dataset of Anthropogenic Extinct Birds and their
Traits: Phylogeny Builder
Convert Dates to Arbitrary Week Definitions
Get Data from 'Awin' via the 'Windsor.ai' API
Amazon 'Kinesis' Consumer Application for Stream Processing
Client for the Amazon Alexa Web Information Services API
Communicating with AWS EC2 and ECS using AWS REST APIs
'AWS Key Management Service' Client Package
Data Processing of SMN Hi-Res Weather Forecast from 'AWS'
Class and Methods Definitions for Packages 'aws', 'adimpro',
'fmri', 'dwi'
Analyzing Zero-Inflated and Zero-Altered Data
Logging in 'R' and Post to 'Azure Log Analytics' Workspace
Include Azure Application Insights in Shiny Apps
Authentication Services for Azure Active Directory
Interface to 'Container Instances', 'Docker Registry' and
'Kubernetes' in 'Azure'
Interface to the 'Azure Cosmos DB' 'NoSQL' Database Service
Interface to 'Kusto'/'Azure Data Explorer'
Interface to the 'Azure Machine Learning' 'SDK'
Interface to the Table Storage Service in 'Azure'
Interface to Azure Computer Vision Services
Interface to Azure Virtual Machine Instance Metadata
Gene Orthologs for Model Organisms in a Tidy Data Format
Use 'nlmixr2' to Interact with Open Source and Commercial
Software
Talking to 'Docker' and 'Singularity' Containers
Bayesian Analysis of Computer Code Output (BACCO)
Bayesian Augmented Control for Clinical Trials
Bayesian Classification and Information Sharing (BaCIS) Tool for
the Design of Multi-Group Phase II Clinical Trials
Reimplementations of Functions Introduced Since R-3.0.0
Learning Causal Cyclic Graphs from Unknown Shift Interventions
Exploring Portfolio-Based Conjectures About Financial
Instruments
Bayesian Estimation and Forecasting of Age-Specific Rates
Robust Explainable Outlier Detection Based on OutlierTree
Bagging Bandwidth Selection in Kernel Density and Regression
Estimation
A Binary Regression Adaptive Goodness-of-Fit Test (BAGofT)
Filter Covariance and Correlation Matrices with
Bootstrapped-Averaged Hierarchical Ansatz
Bayes Factors for Informative Hypotheses
Analyze and Compare Nucleotide Recoding RNA Sequencing Datasets
Balance Check for Multiple Covariates in Matched Observational
Studies
Balanced and Spatially Balanced Sampling
Bayesian Hierarchical Modeling for Label-Free Proteomics
Statistical Inference and Sure Independence Screening via Ball
Statistics
Expression RNA-Seq Data Analysis Based on Linear Mixed Model
Functions and Datasets for "Bayesian Methods: A Social and
Behavioral Sciences Approach"
High Dimensional Bayesian Mediation Analysis
Bayesian Meta-Analysis of Diagnostic Test Data
Bayesian Additive Models for Location, Scale, and Shape (and
Beyond)
Species Distribution Models as a Function of Biotic, Abiotic and
Movement Factors (BAM)
Analysis and Visualization of Macroevolutionary Dynamics on
Phylogenetic Trees
Bayesian Age-Period-Cohort Modeling and Prediction
Bayesian Analysis of the Network Autocorrelation Model
Functions for Simple a/B Split Test and Multi-Armed Bandit
Analysis
Compute Upper Prediction Bounds on the FDP in Competition-Based
Setups
Automatize Diagnosis Standardized Assignation Using the Banff
Classification
Provides Ready to Use Shapefiles for Geographical Map of
Bangladesh
Make Banner Comments with a Consistent Format
Batch Effect Removal and Addon Normalization (in Phenotype
Prediction using Gene Data)
Label Creation for Tracking and Collecting Data from Biological
Samples
Species Identification using DNA Barcodes
Complete Works of William Shakespeare in Tidy Format
Calculate Bark Beetle Phenology Using Different Models
Causal Inference using Bayesian Additive Regression Trees
Bayesian Additive Regression Trees for Confounder Selection
Bayesian Variable Selection and Model Averaging using Bayesian
Adaptive Sampling
Bayesian Variable Selection with Shrinking and Diffusing Priors
Convert Regression Between Base Function and 'rms' Package
Fast and URL-Safe Base64 Encoder and Decoder
Utilities for Streamlined Data Import, Imputation and Modelling
Infrastructure for Computing with Basis Functions
Baseline Models for Classification and Regression
Basic Sequence Processing Tool for Biological Data
Estimation of Seed Germination Base Temperature in Thermal
Modelling
Plot Simple Features with 'base' Sensibilities
Trace Plots, Density Plots and Chain Comparisons for MCMC
Samples
Construct Rich Tables for Output to 'HTML'/'Excel'
Retrieval and Processing of the Spanish National Forest
Inventory
Classification of RNA Sequences using Complex Network Theory
Classification of RNA Sequences using Complex Network and
Information Theory
Bayesian Model to Archaeological Faunal Skeletal Profiles
Analysis and Visualization of Basketball Data
Analytical Calculation of Basket Trial Operating Characteristics
Simulation-Based Calculation of Basket Trial Operating
Characteristics
Age-Specific Bayesian Survival Trajectory Analysis from
Incomplete Census or Capture-Recapture/Recovery Data
Managing Packages Removal and Installation
Batching Routines in Parallel and Passing Command-Line Arguments
to R
Statistical Experiments on Batch Computing Clusters
Downloads and Organizes Financial Data for Multiple Tickers
Consistent Batch Means Estimation of Monte Carlo Standard Errors
Semi-Supervised Bayesian Mixture Models Incorporating Batch
Correction
Convert Categorical Representations of Logicals to Actual
Logicals
Real-Time PCR Data Sets by Batsch et al. (2008)
Bayesian Adaptive Trial Simulator Software (BATSS) for
Generalised Linear Models
An R Package for Data Reduction by Battery Reduction
Bayesian Analysis of Clumped Isotope Datasets
Bayesian Estimation and Optimisation of Expected False Discovery
Rate
Bayesian Quantile Elastic Net for Genetic Study
User Friendly Bayesian Data Analysis for Psychology
Bayesian Estimation of the Additive Main Effects and
Multiplicative Interaction Model
Bayesian Inference in the Analysis of Variance via Markov Chain
Monte Carlo in Gaussian Mixture Models
Bayesian Estimation of Bivariate Volatility Model
Bayesian Estimation of Mixtures of Multivariate Bernoulli
Distributions
Miscellaneous Functions for Bioinformatics and Bayesian
Statistics
An Implementation of Rubin's (1981) Bayesian Bootstrap
Bayesian Estimation using Bernstein Polynomial Fits Rate Matrix
Power Calculations and Bayesian Analysis of Count Distributions
and FECRT Data using MCMC
Bayesian Analysis of Censored Regression Models Under Scale
Mixture of Skew Normal Distributions
Simulation and Analysis of Adaptive Bayesian Clinical Trials
Two Arm Bayesian Clinical Trial Design with and Without
Historical Control Data
Bayesian Cure Rate Modeling for Time-to-Event Data
Functions and Datasets for the book "Bayesian Data Analysis"
Methods and Tools for Bayesian Dynamic Conditional Correlation
GARCH(1,1) Model
Bayesian Single-Arm Design with Survival Endpoints
Bayesian Dynamic Factor Analysis (DFA) with 'Stan'
Bayesian Decreasingly Informative Priors for Early Termination
Phase II Trials
Statistical Analysis for Task-Based Fmri Data
Implementation of the Bayesian Discount Prior Approach for
Clinical Trials
Bayesian Smoothing of Remote Sensing Image Classification
Computation of Bayes Factors for Common Designs
Bayesian Dynamic Borrowing with Flexible Baseline Hazard
Function
Implementation of Bayesian Neural Networks
Spatial Bayesian Methods for Task Functional MRI Studies
Fit Multivariate Response Generalized Additive Models using
Hamiltonian Monte Carlo
Bayesian Estimation of the GARCH(1,1) Model with Student-t
Innovations
Bayesian Causal Mediation Analysis using 'Stan'
Bayesian Modeling via Frequentist Goodness-of-Fit
Fast Bayesian Gaussian Process Regression Fitting
Estimate Fish Growth Using MCMC Analysis
Bayesian Grouped Weighted Quantile Sum Regression
Bayesian Estimation of the ETAS Model for Earthquake Occurrences
Bayesian Solutions for the Factor Zoo: We Just Ran Two
Quadrillion Models
Bayesian Gamma Regression: Joint Mean and Shape Modeling
Predict Brain Asymmetry Based on Handedness and Dichotic
Listening
Simulate, Evaluate, and Analyze Dose Finding Trials with
Bayesian MCPMod
Simulate and Analyse Bayesian Platform Trial with Time Trend
Sample Size and Power for Comparing Inequality Constrained
Hypotheses
Plot Positive and Negative Predictive Values for Medical Tests
General-Purpose MCMC and SMC Samplers and Tools for Bayesian
Statistics
Bayesian t Regression for Modeling Mean and Scale Parameters
MCMC Estimation of Bayesian Vectorautoregressions
Bayesian Methods for Image Segmentation using a Potts Model
Efficient Sampling for Gaussian Linear Regression with Arbitrary
Priors
Adjust Longitudinal Regression Models Using Bayesian Methodology
Bayesian Preference Learning with the Mallows Rank Model
Bayesian Post-Estimation on the Mean Scale
Bayesian Random-Effects Meta-Analysis and Meta-Regression
Bayesian Mixture Survival Models using Additive
Mixture-of-Weibull Hazards, with Lasso Shrinkage and
Stratification
A Multistate Life Table (MSLT) Methodology Based on Bayesian
Approach
Non-Parametric Bayesian Analyses of Animal Movement
A Bayesian No-Effect- Concentration (NEC) Algorithm
Bayesian Analysis of Non-Stationary Gaussian Process Models
Bayesian Group Sequential Design for Ordinal Data
Variable Selection in a Hierarchical Bayesian Model for a Hazard
Function
Bayesian Inference for Presence-Only Data
Generate Postestimation Quantities for Bayesian MCMC Estimation
Bayesian Power Prior Design for Survival Data
Fast Projection Direction for Multivariate Changepoint Detection
Bayesian Regression Models with Global-Local Shrinkage Priors
Bayesian Analysis of Replication Studies
Fits the Bayesian Piecewise Linear Log-Hazard Model
Bayesian Robust Generalized Mixed Models for Longitudinal Data
Bayes Factors for Hierarchical Linear Models with Continuous
Predictors
Datasets and Supplemental Functions from Bayes Rules! Book
Bayesian Variable Selection Using Simplified Shotgun Stochastic
Search with Screening (S5)
Bayes Linear Estimators for Finite Population
Different Models of Posterior Distributions of Adjusted Odds
Ratio
Bayesian Seemingly Unrelated Regression Models in
High-Dimensional Settings
Bayesian Survival Regression with Flexible Error and Random
Effects Distributions
Bayesian Survival Analysis for Right Censored Data
Bayesian Survival Models for High-Dimensional Data
Effect Size Targeted Bayesian Two-Sample t-Tests via Markov
Chain Monte Carlo in Gaussian Mixture Models
Understand and Describe Bayesian Models and Posterior
Distributions
Bayesian Analysis of Item-Level Twin Data
Bayes Factors, Model Choice and Variable Selection in Linear
Models
Visually Learning the Graphical Structure of Bayesian Networks
and Performing MCMC with 'Stan'
Bayesian Change-Point Detection for Process Monitoring with
Fault Detection
R Utilities Accompanying the Software Package BayesX
Distribution of the 'BayesX' C++ Sources
Bayesian Zero-Inflated Bernoulli Regression Model
Global Bayesian Foraminifera Core Top Calibration
Bayesian Shrinkage Estimators for Precision Matrices in Gaussian
Graphical Models
Chronological Bayesian Models Integrating Optically Stimulated
Luminescence and Radiocarbon Age Dating
Bayesian Multidimensional Scaling and Choice of Dimension
Computation of Bayes Factors for Common Biomedical Designs
Bayesian Stability Analysis of Genotype by Environment
Interaction (GEI)
On Bayesian Analysis of Threshold Autoregressive Models
Solving and Optimizing Large-Scale Nonlinear Systems
Identification of Bull and Bear States of the Market
Bayesian Estimation of Incoherent Neutron Scattering Backgrounds
Benthic Biotic Indices Calculation from Composition Data
Client for the Deutsche Bundesbank and European Central Bank
APIs
Miscellaneous Helper Functions for B. Bischl
Bayesian Model for Genotyping using RNA-Seq
Tools for General Maximum Likelihood Estimation
Create Simple Predictive Models on Bayesian Belief Networks
Bessel and Beta Regressions via Expectation-Maximization
Algorithm for Continuous Bounded Data
Gene Regulatory Network Inference with Bc3net
Block Coordinate Ascent with One-Step Generalized Rosen
Algorithm
Bias Corrected Bootstrap Confidence Intervals
Bias-Corrected Bayesian Classification with Selected Features
Calculation of Option Prices Based on a Universal Solution
Bayesian Consensus Clustering for Multiple Longitudinal Features
Bayesian Structure and Causal Learning of Gaussian Directed
Graphs
Search and Retrieve Data from the BC Data Catalogue
Business Cycle Dating and Plotting Tools
The Bayesian Causal Effect Estimation Algorithm
Causal Inference using Bayesian Causal Forests
Semiparametric Bivariate Correlated Frailty Models Fit
Bivariate Correlated Frailty Models with Varied Variances
Bayesian Constrained Generalised Linear Models
Bias-Corrected Estimates for Generalized Linear Models for
Dependent Data
Clinical Trial Calculation Based on BCHM Design
Radiocarbon Dating, Age-Depth Modelling, Relative Sea Level Rate
Estimation, and Non-Parametric Phase Modelling
A Dirichlet Process Mixture Model for Clustering Longitudinal
Gene Expression Data
Map Layers and Spatial Utilities for British Columbia
Mixed Effect Model with the Box-Cox Transformation
Behavioral Change Point Analysis of Animal Movement
Bayesian Continual Reassessment Method for Phase I
Dose-Escalation Trials
Bias-Corrected Methods for Estimating the ROC Surface of
Continuous Diagnostic Tests
A Bayesian Semiparametric Factor Analysis Model for Subtype
Identification (Clustering)
Bayesian Context Trees for Discrete Time Series
Cross-Validation for the SVD (Bi-Cross-Validation)
Darwinizer: Darwin Core (DwC) Field Names Standardization
Efficient Determination of Sample Size in Balanced Design of
Experiments
Bayesian Structure Learning in Graphical Models using
Birth-Death MCMC
Bayesian Distributed Lag Interaction Models
Transparent and Reproducible Artificial Data Generation
Bayesian Adaptive Designs for Phase II Trials with Binary
Endpoint
Inference and Design for Predictive Values in Diagnostic Tests
Bayesian Detection of Potential Risk Using Inference on Blinded
Safety Data
Remove Weekends and Holidays from ggplot2 Axes
Routines for Block Diagonal Symmetric Matrices
Block Structure Detection Using Singular Vectors
Bayesian Inference for Discrete Weibull Regression
Biometric Exploratory Analysis Creation House
Analysis of Multiplex Cytometric Bead Assays
Bootstrap Evaluation of Association Matrices
Visualization via Beanplots (Like Boxplot/Stripchart/Violin
Plot)
Bayesian Analysis of Heterogeneous Treatment Effect
Bayesian Estimation of Change-Points in the Slope of
Multivariate Time-Series
Bayesian Evaluation, Analysis, and Simulation Software Tools for
Trials
Bayesian Model Averaging of Covariate Adjusted Negative-Binomial
Dose-Response
Quantifies Effects of Geo/Eco Distance on Genetic
Differentiation
Extract Genotypes from a PLINK .bed File
Genomic Region Processing using Tools Such as 'BEDTools',
'BEDOPS' and 'Tabix'
Binary Endpoint Estimation with Covariate Adjustment
General Unified Threshold Model of Survival for Bees using
Bayesian Inference
Easily Play Notification Sounds on any Platform
The Bee Swarm Plot, an Alternative to Stripchart
Tools for Behavior Change Researchers and Professionals
Canonical Data Structure for Behavioural Data
Multivariate Conditional Volatility Modelling and Forecasting
Download Historical Data from the Belgrade Stock Exchange
Boltzmann Entropy of a Landscape Gradient
Likelihood Method for Evaluating Bioequivalence
Count Regression Models Based on the Bell Distribution
28 Benchmark Densities from Berlinet/Devroye (1994)
Benchmark and Frontier Analysis Using DEA and SFA
High Precise Measurement of R Expressions Execution Time
Bayesian Estimation of Nonlinear Data (BEND)
Benford Analysis for Data Validation and Forensic Analytics
Statistical Tests for Evaluating Conformity to Benford's Law
Bayesian Estimation of Naloxone Kit Number Under-Reporting
Bent-Cable Regression for Independent Data or Autoregressive
Time Series
Bayesian Estimation for Quantile Regression Mixed Models
Bayesian Exponential Random Graph Models
Bayesian Inference and Model Selection for Stochastic Epidemics
Function Collection Related to Plotting and Hydrology
Best Subset Selection in Linear, Logistic and CoxPH Models
Computations and Approximations for Bessel Functions
Best Subset GLM and Regression Utilities
Generating Bootstrap Estimation Distributions of HR Data
Bayesian Estimation of Intervention Effects
A Comprehensive R Package for Best Subset Selection
Burden Estimate of Common Communicable Diseases in Settlements
of Displaced Populations
Mini Games from Adventures of Beta and Bit
A Family of Beta Mixture Models for Clustering Beta-Valued DNA
Methylation Data
Confidence Intervals for Standardized Regression Coefficients
Functions for Working with Two- And Four-Parameter Beta
Probability Distributions and Psychometric Analysis of
Classifications
Partitioning Beta Diversity into Turnover and Nestedness
Components
Calculate Power and Sample Size with Beta Regression
Taxonomic Uncertainty on Multivariate Analyses of Ecological
Data
Robust Confidence Intervals for Standardized Regression
Coefficients
Simulation, Estimation and Forecasting of Beta-Skew-t-EGARCH
Models
Better Statistics for OLS and Binomial Logistic Regression
Bayesian Evaluation of Variant Involvement in Mendelian Disease
Visualize and Parse the Output of 'BeXY'
Compare the Goodness of Fit of Benford's and Blondeau Da Silva's
Digit Distributions to a Given Dataset
Bayesian Spectral Inference for Time Series
Flexible Bayes Factor Testing of Scientific Expectations
Get Data from the Swiss Federal Statistical Office
Plot Maps from Switzerland by Swiss Federal Statistical Office
Bayesian Framework for Computational Modeling
A Suite of Packages for Analysis of Big Genomic Data
Bimodal GEV Distribution with Location Parameter
Binary Generalized Extreme Value Additive Models
Bell-G and Complementary Bell-G Family of Distributions
Bayesian Genomic Linear Models Applied to GE Genome Selection
Bayesian Estimation of Generalized Graded Unfolding Model
Parameters
Example BGM Files for the Atlantis Ecosystem Model
An R Package for Extended Behavior Genetics Analysis
Gaussian Mixture Modeling Algorithms and the Belief-Based
Mixture Modeling
Bayesian Analysis of Networks of Binary and/or Ordinal Variables
Markov Beta and Gamma Processes for Modeling Hazard Rates
Bayesian Group Sparse Multi-Task Regression
Estimate the Burden of Healthcare-Associated Infections
Useful Functions for Box, Hunter and Hunter II
Bayesian Hierarchical Models for Basket Trials
Bayesian Hierarchical Multi-Subject Multiscale Analysis of
Functional MRI (fMRI) Data
Bayesian Hierarchical Poisson Models for Multiple Grouped
Outcomes with Clustering
Structural Bayesian Vector Autoregression Models
Blinding Assessment Indexes for Randomized, Controlled, Clinical
Trials
A GUI to Correct Measurement Bias in DNA Methylation Analyses
R Wrapper for Java Implementation of BiBit
Create Different Types of Bibliometric Networks
Document-Level Matching Between Bibliographic Datasets
Bayesian Inference for the Birnbaum-Saunders Distribution
Binary Classification Using Extensions of Discriminant Analysis
Integrative Inference of De Novo Cis-Regulatory Modules
Bayesian Inference for Directed Acyclic Graphs
Efficient Estimation of Bid-Ask Spreads from Open, High, Low,
and Close Prices
Calculates the Bidimensional Regression Between Two 2D
Configurations
Tools for Accessing the Botanical Information and Ecology
Network Database
Tools for Survey Statistics in Educational Assessment
'BLAS' and 'LAPACK' Routines for Native R Matrices and
'big.matrix' Objects
Utilities for 'big.matrix' Objects from Package 'bigmemory'
Perform Boolean Operations on Large Numbers
Read, Write, Manipulate, Explore Chess PGN Files and R API to
UCI Chess Engines
Flexibly Format Dates and Times to a Given Locale
Distances for Machine Learning and Statistics in the Context of
Big Data
Case-Control Analysis of Multi-Allelic Loci
Scalable Bayesian Disease Mapping Models for High-Dimensional
Data
Fit, Simulate, and Diagnose Hierarchical Exponential-Family
Models for Big Networks
Biochemically Intuitive Generalized Loewe Model
Extending Lasso Model Fitting to Big Data
Physical and Physiological Ecosystem Properties from Eddy
Covariance Data
Bounded Memory Linear and Generalized Linear Models
Making Optimal Matching Size-Scalable Using Optimal Calipers
Manage Massive Matrices with Shared Memory and Memory-Mapped
Files
A Shared Resource Interface for Bigmemory Project Packages
Arbitrary-Precision Integer and Floating-Point Mathematics
Big Quadratic Inverse Covariance Estimation
Generalized Linear Models (GLM) for Large Data Sets
An Interface to Google's 'BigQuery' 'API'
An Interface to Google's 'BigQuery Storage' API
Fast Generation of High-Dimensional Random Vectors
Statistical Tools for Filebacked Big Matrices
Big Survival Analysis Using Stochastic Gradient Descent
Table, Apply, and Split Functionality for Matrix and
'big.matrix' Objects
Nonparametric Analysis of Bivariate Gap Time with Competing
Risks
Sparse Estimation of Large Time Series Models
Dimension Reduction Methods for Multivariate Time Series
Co-Clustering Adjusted Rand Index and Bikm1 Procedure for
Contingency and Binary Data-Sets
Contains Data of Billboard Hot 100 Songs
Create Interactive Chart with the JavaScript 'Billboard' Library
Threshold Cut Point of Probability for a Binary Classifier Model
Creates, Merges, and Reads Binary Dosage Files
Variable Selection for Binary Data Using the EM Algorithm
Mean and Scale-Factor Modeling of Under- And Over-Dispersed
Binary Data
Fit and Predict a Gaussian Process Model with (Time-Series)
Binary Response
Flexible Marginalized Models for Binary Correlated Outcomes
Estimate the Correlation Between Two Irregular Time Series
Multi-Class Discriminant Analysis using Binary Predictors
Methods for Analyzing Binned Income Data
Estimates Parameters of Functions Driving Binomial Random
Variables
Get Bing Ads Data via the 'Windsor.ai' API
Evaluation and Experimental Design for Binomial Group Testing
Identification and Estimation using Group Testing
Calculation for Single Arm Group Sequential Test with Binary
Endpoint
Processes Binary Data Obtained from Fragment Analysis (Such as
AFLPs, ISSRs, and RFLPs)
Data Generation with Binary and Continuous Non-Normal Components
Simultaneous Generation of Multivariate Binary and Normal
Variates
Binomial Confidence Intervals for Several Parameterizations
Confidence Intervals for a Binomial Proportion
Binomial Random Forest Feature Selection
Calculates the Statistical Significance of a Trend in a Set of
Measurements
Concurrent Generation of Binary, Ordinal and Continuous Data
Depth-Dependent Binocular Visual Fields Simulation
Cut Numeric Values into Evenly Distributed Groups
Piecewise Smooth Regression by Bootstrapped Binary Segmentation
Efficient Implementation of Binary Segmentation
Exact Binary Sequential Designs and Analysis
Bayesian Inference for Neyman-Scott Point Processes
Data Preprocessing, Binning for Classification and Regression
Bayesian BIN (Bias, Information, Noise) Model of Forecasting
Predict Environmental Conditions from Biological Observations
Analyse Audio Recordings and Automatically Extract Animal
Vocalizations
Access Data Dependencies Installed Through 'Biobricks.ai'
Interactive Circular Visualization of Genomic Data using
'htmlwidgets' and 'BioCircos.js'
Access the Bioconductor Project Package Repository
Create and Manipulate BioCompute Objects
Package for Community Ecology and Suitability Analysis
An R Shiny Application for Biological Dosimetry
Multilevel Modeling of Dendroclimatical Fluctuations
Process Biogas Data and Predict Biogas Production
Mathematical Modelling of (Dynamic) Microbial Inactivation
Create Hyperlinks to Biological Databases and Resources
Biologically Explainable Machine Learning Framework
Find Biomarkers in Two-Class Discrimination Problems
Estimating Aboveground Biomass and Its Uncertainty in Tropical
Forests
Functions to Assist Design and Analysis of Agronomic Experiments
Ensemble Platform for Species Distribution Modeling
Biomarker Prognostic Enrichment Tool for Time-to-Event Trial
Extracting Insights from Biological Images
Simulation of deterministic and stochastic biochemical reaction
networks using Petri Nets
An R Package for Biomarkers Analysis in Precision Medicine
Biological Analysis and Visualization of Weather Radar Data
From Biological Sequences to Multidimensional Scaling
Biodiversity Index Calculation and Bootstrap Confidence Interval
Estimation
Distributional Data Analysis Techniques for Biosensor Data
A Toolbox for Manipulating Biological Sequences
Tools for Electromyogram Signals (EMG) Analysis
Bioacoustic Basic Operations with Decibels and the Passive Sonar
Equation
Biomarker Selection in Penalized Regression Models
Utility Functions, Datasets and Extended Examples for Survival
Analysis
Practical Functions for Biostatistics Beginners
Nested Data Summary, Adverse Events and REDCap
Calculation of Freshwater Biotic Indices
Tools to Use and Explore the 'BioTIME' Database
Tools for Biometry and Applied Statistics in Agricultural
Science
Create Area-Proportional Venn Diagrams from Biological Lists
Visualising Bipartite Networks and Calculating Some (Ecological)
Indices
Partition Bipartite Network into Non-Overlapping Biclusters by
Optimizing Bipartite Modularity
Bayesian Individual Patient Data Meta-Analysis using 'JAGS'
Construct Reactive Calibrated Axes Biplots
Bootstrap on Classical Biplots and Clustering Disjoint Biplot
Ranking Nodes in Bipartite and Weighted Networks
Bayesian Instrumental Regression for Disparity Estimation
Methods to Analyse Ring Re-Encounter Data
Migration Traffic Rate Calculation Package for 'Birdscan MR1'
Radars
The R Package for "The Basics of Item Response Theory Using R"
Tools and Palettes for Bivariate Thematic Mapping
Download Data from the Bank for International Settlements (BIS)
Tools to find bad commits with git bisect
Toolkit to Identify Candidate Synthetic Lethality
Statistical Tools for Bivariate Spatial Dependence Analysis
Approximate Bayesian Inference via Sparse Grid Quadrature
Evaluation (BISQuE) for Hierarchical Models
Decomposition of Bulk Expression with Single-Cell Sequencing
Cumulative History Analysis for Bistable Perception Time Series
Classes and Methods for Fast Memory-Efficient Boolean Selections
A S3 Class for Vectors of 64bit Integers
Binarization and Trinarization of One-Dimensional Data
Bivariate Alternating Recurrent Event Data Analysis
Tolerance Interval and EIV Regression - Method Comparison
Studies
Bivariate Residual Plots with Simulation Polygons
Generation of Bivariate Uniform Data and Its Relation to
Bivariate Binary Data
Conduct Univariate and Bivariate Wavelet Analyses
Compute the Biweight Mean Vector and Covariance & Correlation
Matrice
Business Days Calculations and Utilities
Bivariate Zero-Inflated Count Models Using Copulas
Parallel Chain Tools for Bayesian Kernel Machine Regression
Black Box Optimization and Exploration of Parameter Space
Read and Write FWF Files in the 'Blaise' Format
Blaker's Binomial and Poisson Confidence Limits
Plots (Slightly Extended) Bland-Altman Plots
Bayesian Inference with Laplace Approximations and P-Splines
Native R Implementation of an Efficient BLAST-Like Algorithm
Black-Litterman and Copula Opinion Pooling Frameworks
Analyze biotic homogenization of landscapes
Joint Analysis of Experiments with Mixtures and Random Effects
Record Linkage for Empirically Motivated Priors
Bayesian Functional Linear Regression with Sparse Step Functions
Data Files and Functions Accompanying the Book "Bayesian Data
Analysis in Ecology using R, BUGS and Stan"
A Simple S3 Class for Representing Vectors of Binary Data
('BLOBS')
Co-Clustering Package for Binary, Categorical, Contingency and
Continuous Data-Sets
Estimation of Large Block Covariance Matrices
Spatial and Environmental Blocking for K-Fold and LOO
Cross-Validation
Block Forests: Random Forests for Blocks of Clinical and Omics
Covariate Data
Select an Optimal Block-Length to Bootstrap Dependent Data
(Block Bootstrap)
blockmatrix: Tools to solve algebraic systems with partitioned
matrices
Integrating Multi-Source Block-Wise Missing Data in Model
Selection
Generalized and Classical Blockmodeling of Valued Networks
GUI for the Generalised Blockmodeling of Valued Networks
Latent and Stochastic Block Model Estimation by a 'V-EM'
Algorithm
Randomization for Block Random Clinical Trials
Nested and Crossed Block Designs for Factorial and Unstructured
Treatment Sets
Block, Assign, and Diagnose Potential Interference in Randomized
Experiments
Estimate and Visualize Voting Blocs' Partisan Contributions
Create Blogs and Websites with R Markdown
Impute and Analyze Data with BLOQ Observations
Tools for Developing Binary Logistic Regression Models
Dose Escalation Design in Phase I Oncology Trial Using Bayesian
Logistic Regression Modeling
Stochastic Rainfall Generator Bartlett-Lewis Rectangular Pulse
Model
Interactive Document for Working with Binary Logistic Regression
Analysis
Data Viewer for Bureau of Labor Statistics Data
An API Wrapper for the United States Bureau of Labor Statistics
Make Requests from the Bureau of Labor Statistics API
Blyth-Still-Casella Exact Binomial Confidence Intervals
Bayesian Model Averaging for Basket Trials
Multivariate Extremes: Bayesian Estimation of the Spectral
Measure
Bayesian Multivariate Analysis of Summary Statistics
CONCOR for Structural- And Regular-Equivalence Blockmodeling
Mediation Analysis with Missing Data Using Bootstrap
Mediation Analysis with Missing Data and Non-Normal Data
Bayesian Multidimensional Generalized Graded Unfolding Model
Miscellaneous Functions for Panel Data, Quantiles, and Printing
Results
Bayesian Estimation for Finite Mixture of Distributions
Easy and Accessible Bayesian Measurement Models Using 'brms'
An Implementation of the Bayesian Markov (Renewal) Mixed Models
Bayesian Multilevel Single Case Models using 'Stan'
Bayesian Modeling of Spatio-Temporal Data with R
Bayesian Approach for MTAR Models with Missing Data
Learning Discrete Bayesian Network Classifiers from Data
Access Data from the Brazilian Development Bank (BNDES)
Bounding Omitted Variable Bias Using Auxiliary Data
Bayesian Network Structure Learning, Parameter Learning and
Inference
Bayesian Network Meta-Analysis using 'JAGS'
An Implementation of Sensitivity Analysis in Bayesian Networks
Bagged k-Nearest Neighbors Survival Prediction
Ferguson-Klass Type Algorithm for Posterior Normalized Random
Measures
Simulate Genotypes from the BN-PSD Admixture Model
A Bayesian Nonparametric Algorithm for Time Series Clustering
Pathway Enrichment Analysis Based on Bayesian Network
Bayesian Non- And Semi-Parametric Model Fitting
Bayesian Network Structure Learning from Data with Missing
Values
Bayesian Networks Interactive Visualization and Explainable
Artificial Intelligence
Bayesian Output Analysis Program (BOA) for MCMC
Board Games and Tools for Building Board Games
Profiling of Peripheral Blood Mononuclear Cells using CyTOF
Boosting Diversity in Regression Ensembles
Call the Bus Open Data Service ('BODS') API Through R
Percent Body Fat Values Using Anthropometric Prediction
Equations
Reference Values for CT-Assessed Body Composition
Real-Time PCR Data Sets by Boggy et al. (2010)
Interface to the Boilerpipe Java Library
Bayesian Optimal INterval (BOIN) Design for Single-Agent and
Drug- Combination Phase I Clinical Trials
Conduct Simulation Study of Bayesian Optimal Interval Design
with BOIN-ET Family
Model Consistent Lasso Estimation Through the Bootstrap
Functions for Elementary Bayesian Inference
Methods for Fixed-Income Valuation, Risk and Return
Fixed Coupon Bond Valuation Allowing for Odd Coupon Periods and
Various Day Count Conventions
An Improved Multiple Testing Procedure for Controlling False
Discovery Rates
Some Distributions from the 'Boost' Library and More
Authoring Books and Technical Documents with R Markdown
Optimal Estimation of Partially Observed Boolean Dynamical
Systems
Construction, Simulation and Analysis of Boolean Networks
Debugging Tools to Inspect the Intermediate Steps of a Call
A Boosting Approach to Data Envelopment Analysis
Boosting for Multivariate Longitudinal Responses
Boosted Multivariate Trees for Longitudinal Data
Bootstrap Functions (Originally by Angelo Canty for S)
A Bootstrap-Based Heterogeneity Test for Meta-Analysis
Bootstrapping Estimates of Clustering Stability
Combine Parameter Estimates via Parametric Bootstrap
Bootstrapping the ARDL Tests for Cointegration
Bootstrap Confidence Intervals on Effect Sizes
Simulation and Comparison of Dissolution Profiles
Bootstrap Inference for Multiple Imputation
Bootstrapped Confidence Intervals for (Negative) Likelihood
Ratio Tests
Bootstrap-MRMR Technique for Informative Gene Selection
Bootstrap Methods for Various Network Estimation Routines
Bootstrap Hyperparameter Selection for PLS Models and Extensions
Bootstrap Prediction Intervals and Bias-Corrected Forecasting
A Bootstrap Test for the Probability of Ruin in the Classical
Risk Process
Functions for the Book "An Introduction to the Bootstrap"
Bootstrap Algorithms for Finite Population Inference
Bootstrap cis-QTL Method that Corrects for the Winner's Curse
Bootstrap Methods for Complete Survey Data
Fast, Exact Bootstrap Principal Component Analysis for High
Dimensional Data
Nonparametric Bootstrap Test with Pooled Resampling Card Game
Test Stationarity using Bootstrap Wavelet Packet Tests
Transforming Behavioral Observation Records into Data Matrices
Bayesian Ordination and Regression AnaLysis
Estimate Causal Effects with Borrowing Between Data Sources
Wrapper Algorithm for All Relevant Feature Selection
A Bunch of Structure and Sequence Analysis
Best Orthogonalized Subset Selection (BOSS)
Bound on the Error of the First-Order Edgeworth Expansion
Analytic Solutions for (Ground-Water) Boussinesq Equation
Outlier Detection and Influence Diagnostics for Meta-Analysis
Functions to Create Publication-Quality Plots
Write Reusable, Composable and Modular R Code
Provides 'box' Compatibility for 'languageserver'
Box-Cox-Type Transformations for Linear and Logistic Models with
Random Effects
Clustering Method Based on Boxplot Statistics
Double Box Plot for Two-Axes Correlation
Blood Pressure Device Accuracy Evaluation: Statistical
Considerations
Nonparametric Bounds for the Average Causal Effect Due to Balke
and Pearl and Extensions
Biplot of Multivariate Data Based on Principal Components
Analysis
Beta Product Confidence Procedure for Right Censored Data
Bayesian Model Selection Approach for Parsimonious Gaussian
Mixture Models
Bayesian Purity Model to Estimate Tumor Purity
Support for BPMN (Business Process Management Notation) Models
Visualize Process Execution Data on 'BPMN' Diagrams
Bayesian Projected Normal Regression Models for Circular Data
Computations Around Bayesian Predictive Power
Causal Effects in Principal Strata Defined by Antidrug
Antibodies
Bayesian Quantile Regression for Ordinal Models
A Method to Estimate the Accuracy and Biogeographical Status of
Georeferenced Biological Data
Bias Reduction Through Analysis of Competing Events (BRACE)
BRACoD: Bayesian Regression Analysis of Compositional Data
Calculate the Revealed Aggregator of Probability Predictions
Visualize Combined Action Response Surfaces and Report BRAID
Analyses
Fitting Dose Response with the BRAID Combined Action Model
Inference the Partial Correlations Based on Time Series Data
Region-Level Connectivity Network Construction via Kernel
Canonical Correlation Analysis
Helper Functions to 'misc3d' and 'rgl' Packages for Brain
Imaging
Efficient Best Subset Selection for GLMs via Branch and Bound
Algorithms
Simulation and Estimation for Branching Processes
Bootstrapped Robustness Assessment for Qualitative Comparative
Analysis
Bayesian Screening and Variable Selection
Accesses Brazilian Public Security Data from SINESP Since 2015
Download and Processing of Automatic Weather Stations (AWS) Data
of INMET-Brazil
Variable Selection and Ranking in Copula Survival Models
Affected by General Censoring Scheme
Fetch Zonal Statistics of Weather Indicators for Brazilian
Municipalities
Analyze Big Files Without Loading Them in Memory
Estimates Degrees of Relatedness (Up to the Second Degree) for
Extreme Low-Coverage Data
Species Richness Estimation and Modeling
Model Agnostic Explainers for Individual Predictions
Methods for Fast Multiple Change-Point/Break-Point Detection and
Estimation
An R Package for Multiple Break-Point Detection via the
Cross-Entropy Method
Core Functions to Read and Fit 13c Time Series from Breath Tests
Stan-Based Fit to Gastric Emptying Curves
Brain Cell Type Specific Gene Expression Analysis
Templating Framework for Report Generation
Bias Reduction in Binomial-Response Generalized Linear Models
Bias Reduction in Generalized Linear Models
Interface with the Brickset API for Getting Data About LEGO Sets
An Implementation of the Bridge Distribution with Logit-Link as
in Wang and Louis (2003)
Bridge Hand Generator with Criteria Selector
Genome-Wide RNA Degradation Analysis Using BRIC-Seq Data
Bridge Sampling for Marginal Likelihoods and Bayes Factors
A Tree and Forest Tool for Classification and Regression
Package for Brik, Fabrik and Fdebrik Algorithms to Initialise
Kmeans
Computation of BRINDA Adjusted Micronutrient Biomarkers for
Inflammation
Fast Inference for Large Spatial Datasets using BRISC
Bias Reduction with Missing Binary Response
Bayesian Marginal Effects for 'brms' Models
Bayesian Regularization for Feed-Forward Neural Networks
Broken Adaptive Ridge Regression with Cyclops
Broken Stick Model for Irregular Longitudinal Data
Browse Over Longitudinal Data Graphically and Analytically in R
Convert Statistical Objects into Tidy Tibbles
A Compression Format Optimized for the Web
Bayesian Analysis of Quantile Regression Models
Broadly Useful Convenient and Efficient R Functions
Interface to the 'OpenBUGS' MCMC Software
High-Level Modeling Functions with 'torch'
Retrieve Historical Data of Companies Listed on the 'BRVM' Stock
Exchange
A 'Bootstrap 4' Version of 'shinydashboard'
Safety Assessment in Agricultural Field Trials
Bayesian State-Space Models for Animal Movement
Bayesian Spectral Analysis Models using Gaussian Process Priors
Bayesian Survival Model with Lasso Shrinkage Using Generalized
Weibull Regression
Nonparametric Smoothing of the Hazard Function
Modeling and Computing Biogenic Silica ('bSi') from Inland and
Pelagic Sediments
Bayesian Super Imposition by Translation and Rotation Growth
Curve Analysis
Custom 'Bootstrap' 'Sass' Themes for 'shiny' and 'rmarkdown'
Bayes Screening and Model Discrimination
Build Decision Trees with Optimal Multivariate Splits
Bayesian Proposal to Fit Spatial Econometric Models
Bayesian Spatial Blind Source Separation
Bayesian Sparse Estimation of a Covariance Matrix
Bayesian Nonparametric Spectral Density Estimation Using
B-Spline Priors
Adds Functionality to the R Markdown + Shiny Bootstrap Framework
Bayesian Semiparametric Models for Meta-Analysis
Asymptotic Covariance Matrices of Some BSS Mixing and Unmixing
Matrix Estimates
Bayesian Inference of Non-Linear and Non-Gaussian State Space
Models
Blind Source Separation for Multivariate Spatial Data using
Eigen Analysis
Whitening Data as Preparation for Blind Source Separation
Create HTML Content with Bootstrap 5 Classes and Layouts
Submitter and Monitor of the 'LSF Cluster'
Bayesian Estimation of Structural Vector Autoregressive Models
Bayesian SVARs with Sign, Zero, and Narrative Restrictions
Fitting a Log-Binomial Model using the Bekhit-Schöpe-Wagenpfeil
(BSW) Algorithm
Beyond the Border - Kernel Density Estimation for Urban
Geography
Bradley-Terry Model with Exponential Time Decayed Log-Likelihood
and Adaptive Lasso
Temporal Exponential Random Graph Models by Bootstrapped
Pseudolikelihood
Modelling Heterogeneity in Paired Comparison Data
Estimate the Number of Factors in Large Nonstationary Datasets
Implementing BTYD Models with the Log Sum Exp Patch
Probabilistic Models for Assessing and Predicting your Customer
Base
Produces 'bubbleHeatmap' Plots for Visualising Metabolomics Data
Beautiful Bubbles for 'shiny' and 'rmarkdown' Backgrounds
Bias and Uncertainty Corrected Sample Size
A Deep Learning for Statistical Classification and Regression
Analysis with Random Effects
Stepwise Elimination and Term Reordering for Mixed-Effects
Regression
Organize & Run Build Scripts Comfortably
System for Building and Debugging C/C++ Dynamic Libraries
Buckley-James Regression for Survival Data with High-Dimensional
Covariates
Interactive Shiny App for Bulk Sequencing Data
Quality Control and Outlier Identification in Bulk for
Multicenter Trials
The Ultimate Tool for Reading Data in Bulk
Automatic Groove Identification via Bayesian Changepoint
Detection
Bullish Trading Strategies Through Graphs
Visualising Multiple Pairwise Variable Correlations and Other
Scores
Tools for Modeling Bumblebee Colony Growth and Decline
Quantify Disease Transmission Within and Between Population
Groups
Analyze Bunching in a Kink or Notch Setting
Serialize Model Objects with a Consistent Interface
'Burgle': Stealing the Necessary Parts of Model Objects
R Wrapper for Bursa Municipality Open Data Portal
Markov Model for Bursty Behavior in Streams
Calculates Business Duration Between Two Dates
Simple Modelling Tools for Business Plans
Bayesian Analysis of a Vector Autoregressive Model with
Stochastic Volatility and Time-Varying Parameters
Bayesian Inference of Vector Autoregressive and Error Correction
Models
Bayesian Vector Heterogeneous Autoregressive Modeling
The Stark-Parker algorithm for bounded-variable least squares
Bayesian Variable Selection in High Dimensional Settings using
Nonlocal Priors
Backward Procedure for Change-Point Detection
Describe Image Patterns in Natural Structures
Bandwidth Selectors for Local Linear Quantile Regression
Baumgartner Weiss Schindler Test of Equal Distributions
Tools for Case 1 Best-Worst Scaling (MaxDiff) Designs
Statistics About Bytes Contained in a File as a Circle Plot
Bivariate Zero-Inflated Negative Binomial Model Estimator
Extended Inference for Lasso and Elastic-Net Regularized Cox and
Generalized Linear Models
Dataset of Regional COVID-19 Deaths per 100,000 Pop (NUTS)
Methods for Detecting Safety Signals in Clinical Trials Using
Body-Systems (System Organ Classes)
Compare Two Classifications or Clustering Solutions of Varying
Structure
Inferring Large-Scale Gene Networks with C3NET
C5.0 Decision Trees and Rule-Based Models
Simple, Multiple and Joint Correspondence Analysis
Project Specific Workspace Organization Templates
Bootstrap Confidence Regions for Simple and Multiple
Correspondence Analysis
Conjunctive Analysis of Case Configurations
Cross-Species Analysis of Cell Identities, Markers and
Regulations
Classification Accuracy and Consistency under Item Response
Theory
Conditional Density Estimation Network Construction and
Evaluation
A Package to Perform Covariate Augmented Dickey-Fuller Unit Root
Tests
CAESAR: a Cross-Technology and Cross-Resolution Framework for
Spatial Omics Annotation
Simulation of Plasma Caffeine Concentrations by Using Population
Pharmacokinetic Model
Conditional Akaike Information Criterion for 'lme4' and 'nlme'
Graphical Aid in Correspondence Analysis Interpretation and
Significance Testings
R Graphics Device using Cairo Graphics Library for Creating
High-Quality Bitmap (PNG, JPEG, TIFF), Vector (PDF, SVG,
PostScript) and Display (X11 and Win32) Output
Comparison of Algorithms with Iterative Sample Size Estimation
Calculations for All Common Subsequences
Comparative Analysis with Annotation-Based Genomic Components
Calculate Pesticide Risk Metric (PRM) Values from Multiple
Pesticides...Calc Them All
High Dimensional Numerical and Symbolic Calculus
Simple Wrapper for Computationally Expensive Functions
Create, Read, Write, and Work with 'iCalendar' Files, Calendars
and Scheduling Data
Ready to Print Monthly and Yearly Calendars Made with 'ggplot2'
'calendR' Fork with Additional Features (Backwards Compatible)
Multiple Imputation Using MICE and Random Forest
Calibration Plots for the Transition Probabilities from
Multistate Models
Automated Parameter Estimation for Complex Models
Calibration of Scatterplot and Biplot Axes
Calibration for Computer Experiments with Binary Responses
Weighting and Estimation for Panel Data with Non-Response
Bayesian Calibration of Complex Computer Codes
Mapping ML Scores to Calibrated Predictions
Assesses the Quality of Estimates Made by Complex Sample Designs
Computes Statistics from Discrimination Experimental Data
Easily Compile and Call Inline C Functions
Recording Synchronisation, Call Detection and Assignment, Audio
Analysis
Improved Allele-Specific Copy Number of SNP Microarrays for
Downstream Segmentation
Canonical Associative Learning Models and their Representations
Finite Mixture Models and Meta-Analysis Tools - Based on C.A.MAN
'CamelUp' Board Game as a Teaching Aid for Introductory
Statistics
Datasets from "Microeconometrics: Methods and Applications" by
Cameron and Trivedi
Cell-Typing using Variance Adjusted Mahalanobis Distances with
Multi-Labeling
Connect to Campaign Manager via the 'Windsor.ai' API
Generic PK/PD Simulation Platform CAMPSIS
Read and Manipulate Camera Trap Data Packages
Camera Trap Data Management and Preparation of Occupancy and
Spatial Capture-Recapture Analyses
Maps of the Political and Administrative Divisions of Canada
Effortlessly Create Stunning Canadian Maps
Categorical Analysis of Neo- And Paleo-Endemism
Access, Retrieve, and Work with Canadian Census Data and
Geography
Publication Quality Phylogenetic Tree Plots
Classification of Cancer Using Administrative Data
Streamline Access to Cancer Screening Data
Visualizing Generalized Canonical Discriminant and Canonical
Correlation Analysis
Batch Correction of Single Cell Transcriptome Data
Accessing Intra-Tumor Heterogeneity and Tracking Longitudinal
and Spatial Clonal Evolutionary History by Next-Generation
Sequencing
Accessing Statistics Canada Data Table and Vectors
Directly Extracts Complete CANSIM Data Tables
Visualization Package for CanvasXpress in R
Combined Analysis of Pleiotropy and Epistasis for Diversity
Outbred Mice
Comparative Analyses of Phylogenetics and Evolution in R
Data on Scholarships in CAPES International Mobility Programs
Capital Budgeting Analysis, Annuity Loan Calculations and
Amortization Schedules
Compute and Visualize CAPL-2 Scores and Interpretations
Correspondence Analysis with Geometric Frequency Interpretation
A Virtual Patient Simulator in the Context of Calcium and
Phosphate Homeostasis