Providing the container for the DockerParallel package.

Compiling regression results into a publishable format, conducting post-hoc hypothesis testing, and plotting moderating effects (the effect of X on Y becomes stronger/weaker as Z increases).

This package implements ROC (Receiver Operating Characteristic)â Optimizing Binary Classifiers, supporting both linear and kernel models. Both model types provide a variety of surrogate loss functions. In addition, linear models offer multiple regularization penalties, whereas kernel models support a range of kernel functions. Scalability for large datasets is achieved through approximation-based options, which accelerate training and make fitting feasible on large data. Utilities are provided for model training, prediction, and cross-validation. The implementation builds on the ROC-Optimizing Support Vector Machines. For more information, see Hernà ndez-Orallo, José, et al. (2004) <doi:10.1145/1046456.1046489>, presented in the ROC Analysis in AI Workshop (ROCAI-2004).

Takes matched and unmatched data and calculates Rosenbaum bounds for the treatment effect. Calculates bounds for binary outcome data, Hodges-Lehmann point estimates, Wilcoxon signed-rank test for matched data and matched IV estimators, Wilcoxon sum rank test, and for data with multiple matched controls. The sensitivity analysis methods in this package are documented in Rosenbaum (2002) Observational Studies, <doi:10.1007/978-1-4757-3692-2>, Springer-Verlag.

This package performs random projection using Johnson-Lindenstrauss (JL) Lemma (see William B.Johnson and Joram Lindenstrauss (1984) <doi:10.1090/conm/026/737400>). Random Projection is a dimension reduction technique, where the data in the high dimensional space is projected into the low dimensional space using JL transform. The original high dimensional data matrix is multiplied with the low dimensional projection matrix which results in reduced matrix. The projection matrix can be generated using the projection function that is independent to the original data. Then finally apply the classification task on the projected data.

Perform robust estimation and inference in platform trials and other master protocol trials. Yuhan Qian, Yifan Yi, Jun Shao, Yanyao Yi, Gregory Levin, Nicole Mayer-Hamblett, Patrick J. Heagerty, Ting Ye (2025) <doi:10.48550/arXiv.2411.12944>.

Interface to use and access Wilensky's NetLogo (Wilensky 1999) from R using either headless (no GUI) or interactive GUI mode. Provides functions to load models, execute commands, and get values from reporters. Mostly analogous to the NetLogo Mathematica Link <https://github.com/NetLogo/Mathematica-Link>.

Helper functions to accompany the Blair, Coppock, and Humphreys (2022) "Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign" <https://book.declaredesign.org>. rdss includes datasets, helper functions, and plotting components to enable use and replication of the book.

Translation of the MATLAB program Carb (Nathan and Mauz 2008 <DOI:10.1016/j.radmeas.2007.12.012>; Mauz and Hoffmann 2014) for dose rate modelling for carbonate-rich samples in the context of trapped charged dating (e.g., luminescence dating) applications.

R interface for calling OpenCV routines that works by translating R objects to OpenCV classes and back. Low-level wrappers for several OpenCV routines are provided as Rcpp modules. In addition, high level interfaces are provided for a limited selection of common operations.

As an advanced approach to computerized adaptive testing (CAT), shadow testing (van der Linden(2005) <doi:10.1007/0-387-29054-0>) dynamically assembles entire shadow tests as a part of selecting items throughout the testing process. Selecting items from shadow tests guarantees the compliance of all content constraints defined by the blueprint. RSCAT is an R package for the shadow-test approach to CAT. The objective of RSCAT is twofold: 1) Enhancing the effectiveness of shadow-test CAT simulation; 2) Contributing to the academic and scientific community for CAT research. RSCAT is currently designed for dichotomous items based on the three-parameter logistic (3PL) model.

An interface to the software package MARK that constructs input files for MARK and extracts the output. MARK was developed by Gary White and is freely available at <http://www.phidot.org/software/mark/downloads/> but is not open source.

Creation, manipulation, simulation of linear Gaussian Bayesian networks from text files and more...

High resolution vector country boundaries derived from Natural Earth data, can be plotted in rworldmap.

Streamlined statistical reporting in Rmarkdown environments. Facilitates the automated reporting of descriptive statistics, multiple univariate models, multivariable models and tables combining these outputs. Plotting functions include customisable survival curves, forest plots from logistic and ordinal regression and bivariate comparison plots.

This package implements causal inference methods for incorporating external control data into randomized controlled trials (RCTs) with longitudinal outcomes. Provides an analysis module supporting weighting-based methods such as inverse probability weighting (IPW) and augmented inverse probability weighting (AIPW), difference-in-differences (DID), and synthetic control approaches for borrowing external control information, as well as a simulation module for generating trial and external control data, evaluating estimator performance via Monte Carlo studies, and conducting power analyses for sample size determination. Methods are based on Zhou et al. (2025) <doi:10.1093/jrsssa/qnae075> and Zhou et al. (2024) <doi:10.1080/10543406.2024.2330209>.

This package contains functions to generate random numbers from the beta distribution and random vectors from the Dirichlet distribution.

The Ryan-Holm step-down Bonferroni or Sidak procedure is to control the family-wise (experiment-wise) type I error rate in the multiple comparisons. This procedure provides the adjusting p-values and adjusting CIs. The methods used in this package are referenced from John Ludbrook (2000) <doi:10.1046/j.1440-1681.2000.03223.x>.

Scans manuscripts, bibliographies, and reference lists for citations to retracted publications so that authors can avoid citing retracted work. retraction reads bibliographic formats (BibTeX, BibLaTeX, CSL-JSON, RIS, EndNote XML) and document formats (R Markdown, Quarto, LaTeX', Markdown, HTML, JATS XML, Word, and PDF), extracts and normalizes identifiers, and checks them against retraction data. The default data source is the Retraction Watch database served through the XeraRetractionTracker API; Crossref', OpenAlex', Europe PMC', PubMed', DataCite', and a preprint source ('arXiv and bioRxiv withdrawals) are available as additional sources ('OpenAlex retraction data is itself derived from Retraction Watch). Within each source, matching proceeds from exact Digital Object Identifier (DOI) and PubMed identifier lookups to fuzzy title matching, and results are returned as a tidy table with a match-quality score and an optional self-contained HTML report.

Some response-adaptive randomization methods commonly found in literature are included in this package. These methods include the randomized play-the-winner rule for binary endpoint (Wei and Durham (1978) <doi:10.2307/2286290>), the doubly adaptive biased coin design with minimal variance strategy for binary endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>, Rosenberger and Lachin (2015) <doi:10.1002/9781118742112>) and maximal power strategy targeting Neyman allocation for binary endpoint (Tymofyeyev, Rosenberger, and Hu (2007) <doi:10.1198/016214506000000906>) and RSIHR allocation with each letter representing the first character of the names of the individuals who first proposed this rule (Youngsook and Hu (2010) <doi:10.1198/sbr.2009.0056>, Bello and Sabo (2016) <doi:10.1080/00949655.2015.1114116>), A-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), Aa-optimal Allocation for continuous endpoint (Sverdlov and Rosenberger (2013) <doi:10.1080/15598608.2013.783726>), generalized RSIHR allocation for continuous endpoint (Atkinson and Biswas (2013) <doi:10.1201/b16101>), Bayesian response-adaptive randomization with a control group using the Thall \& Wathen method for binary and continuous endpoints (Thall and Wathen (2007) <doi:10.1016/j.ejca.2007.01.006>) and the forward-looking Gittins index rule for binary and continuous endpoints (Villar, Wason, and Bowden (2015) <doi:10.1111/biom.12337>, Williamson and Villar (2019) <doi:10.1111/biom.13119>).

This package infers V-D-J (Variable-Diversity-Joining) haplotypes and gene deletions from AIRR-seq (Adaptive Immune Receptor Repertoire sequencing) data for Ig (Immunoglobulin) and TR (T cell Receptor) chains, based on J (Joining), D (Diversity), or V (Variable) genes as anchor, by adapting a Bayesian framework. It also calculates a Bayes factor, a number that indicates the certainty level of the inference, for each haplotyped gene. Citation: Gidoni, et al (2019) <doi:10.1038/s41467-019-08489-3>. Peres and Gidoni, et al (2019) <doi:10.1093/bioinformatics/btz481>.

Designing repetitive group acceptance sampling inspection plans for time-truncated life tests. The package uses a distribution-free formulation in which the user supplies the failure probability. The functions compute operating characteristic probabilities and average sample numbers subject to a consumer's risk constraint. The package also provides graphical and comparative tools for comparing repetitive group, group and single sampling inspection plans. Sherman (1965) <doi:10.2307/1266124>; Aslam and Jun (2013) <doi:10.1007/s00170-013-4747-x>. Saha et al. (2025) <doi:10.1007/s41872-025-00305-w>; Tripathi et al. (2020) <doi:10.1080/02664763.2020.1759031>; Tripathi and Aslam (2024) <doi:10.1285/i20705948v17n3p636>; Tripathi et al. (2022) <doi:10.1007/s40745-020-00267-z>; Saha et al. (2021) <doi:10.1080/21681015.2021.1893843>; Tripathi et al. (2023) <doi:10.1007/s41872-023-00221-x>.

Calculates and visualises speech rhythm and timing metrics. The rhythm.metrics package provides a standardised workflow to compute common metrics including Delta C, Delta V, VarcoC, VarcoV, the percentage of vocalic intervals (%V), and both raw and normalised Pairwise Variability Indices (rPVI, nPVI). It includes functions for calculating and visualising these measures to facilitate cross-linguistic and developmental rhythm research. Delta C, Delta V, and %V measures are based on Ramus et al. (1999) <doi:10.1016/S0010-0277(99)00058-X>; VarcoC and VarcoV measures are based on Dellwo (2006, ISBN: 9783631554777); and rPVI-C and nPVI-V are based on Grabe & Low (2002) <doi:10.1515/9783110197105.2.515>.

Analyze multi-level one-way experimental designs where there are unequal sample sizes and population variance homogeneity can not be assumed. To conduct the Gabriel test <doi:10.2307/2286265>, create two vectors: one for your observations and one for the factor level of each observation. The function, rgabriel, conduct the test and save the output as a vector to input into the gabriel.plot function, which produces a confidence interval plot for Multiple Comparison.