Sequential Monte Carlo (SMC) inference for fully Bayesian Gaussian process (GP) regression and classification models by particle learning (PL) following Gramacy & Polson (2011) <doi:10.48550/arXiv.0909.5262>. The sequential nature of inference and the active learning (AL) hooks provided facilitate thrifty sequential design (by entropy) and optimization (by improvement) for classification and regression models, respectively. This package essentially provides a generic PL interface, and functions (arguments to the interface) which implement the GP models and AL heuristics. Functions for a special, linked, regression/classification GP model and an integrated expected conditional improvement (IECI) statistic provide for optimization in the presence of unknown constraints. Separable and isotropic Gaussian, and single-index correlation functions are supported. See the examples section of ?plgp and demo(package="plgp") for an index of demos.

This package provides a collection of functions that primarily produce graphics to aid in a Propensity Score Analysis (PSA). Functions include: cat.psa and box.psa to test balance within strata of categorical and quantitative covariates, circ.psa for a representation of the estimated effect size by stratum, loess.psa that provides a graphic and loess based effect size estimate, and various balance functions that provide measures of the balance achieved via a PSA in a categorical covariate.

This package provides tools to process legacy format summary redistricting data files produced by the United States Census Bureau pursuant to P.L. 94-171. These files are generally available earlier but are difficult to work with as-is.

Parallelized version of the "segment" function from Bioconductor package "DNAcopy", utilizing multi-core computation on host CPU.

This package provides functions and datasets to accompany J. Albert and J. Hu, "Probability and Bayesian Modeling", CRC Press, (2019, ISBN: 1138492566).

Provide easy methods to translate pieces of text. Functions send requests to translation services online.

This package provides a nonparametric, multicore-capable plausible naive Bayes classifier based on Pareto density estimation (PDE). It addresses low-evidence cases through a plausibility correction. To enhance the interpretability of the flexible naive Bayes classifier by revealing its posterior structure and feature-wise, class-specific evidence, posterior probabilities can be visualized as class-wise line plots for one-dimensional data or color-coded Voronoi diagrams for pairwise feature projections, and class-conditional PDE likelihoods as overlaid, mirrored density profiles resembling violin plots. Methodological details are provided by Stier, Q., Hoffmann, J. and Thrun, M. C. (2026) "Classifying with the Fine Structure of Distributions: Leveraging Distributional Information for Robust and Plausible Naive Bayes" <DOI:10.3390/make8010013>. For multicore computations, the implementation applies the general memory-sharing approach described by Thrun, M. C. and Märte, J. (2026) "memshare: Memory Sharing for Multicore Computation in R with an Application to Feature Selection by Mutual Information using PDE" <DOI:10.32614/RJ-2025-043>.

This package contains functions for data preparation, prediction of transition probabilities, estimating semi-parametric regression models and for implementing nonparametric estimators for other quantities. See Meira-Machado and Roca-Pardiñas (2011) <doi:10.18637/jss.v038.i03>.

You can use this program for 3 sets of categorical data for propensity score matching. Assume that the data has 3 different categorical variables. You can use it to perform propensity matching of baseline indicator groupings. The matching will make the differences in the baseline data smaller. This method was described by Alvaro Fuentes (2022) <doi:10.1080/00273171.2021.1925521>.

This package provides tools for Bayesian estimation of meta-analysis models that account for publications bias or p-hacking. For publication bias, this package implements a variant of the p-value based selection model of Hedges (1992) <doi:10.1214/ss/1177011364> with discrete selection probabilities. It also implements the mixture of truncated normals model for p-hacking described in Moss and De Bin (2019) <arXiv:1911.12445>.

Power analysis for AB testing. The calculations are based on the Welch's unequal variances t-test, which is generally preferred over the Student's t-test when sample sizes and variances of the two groups are unequal, which is frequently the case in AB testing. In such situations, the Student's t-test will give biased results due to using the pooled standard deviation, unlike the Welch's t-test.

This package provides a set of raw datasets used to create SDTM domains in pharmaversesdtm package.

An implementation of the Elston-Stewart algorithm for calculating pedigree likelihoods given genetic marker data (Elston and Stewart (1971) <doi:10.1159/000152448>). The standard algorithm is extended to allow inbred founders. pedprobr is part of the pedsuite', a collection of packages for pedigree analysis in R. In particular, pedprobr depends on pedtools for pedigree manipulations and pedmut for mutation modelling. For more information, see Pedigree Analysis in R (Vigeland, 2021, ISBN:9780128244302).

Several person-fit statistics (PFSs; Meijer and Sijtsma, 2001, <doi:10.1177/01466210122031957>) are offered. These statistics allow assessing whether individual response patterns to tests or questionnaires are (im)plausible given the other respondents in the sample or given a specified item response theory model. Some PFSs apply to dichotomous data, such as the likelihood-based PFSs (lz, lz*) and the group-based PFSs (personal biserial correlation, caution index, (normed) number of Guttman errors, agreement/disagreement/dependability statistics, U3, ZU3, NCI, Ht). PFSs suitable to polytomous data include extensions of lz, U3, and (normed) number of Guttman errors.

Power estimation and sample size calculation for 10X Visium Spatial Transcriptomics data to detect differential expressed genes between two conditions based on bootstrap resampling. See Shui et al. (2025) <doi:10.1371/journal.pcbi.1013293> for method details.

Generative simulation of experimental and behavioural data sets from a portable JavaScript Object Notation (JSON) design specification shared with the Python package of the same name. Supports user-specified fixed effect sizes, crossed by-subject and by-item random intercepts and slopes, predictors measured with error, realistic response families (Gaussian, lognormal, shifted lognormal, ex-Gaussian, Bernoulli, Poisson, ordinal and Beta), and simulation-based power and precision-based design analysis, including the Type S and Type M errors of Gelman and Carlin (2014) <doi:10.1177/1745691614551642> and a region of practical equivalence. A shared cross-language random-number generator means that, given the same specification and seed, the R and Python implementations produce identical data: exactly for the Gaussian family and for any family with rounding set, and to within the last unit in the last place for families applying a transcendental function to the linear predictor, whose rounding the IEEE-754 standard does not fix.

This package provides several data sets and functions to accompany the book "Population Genetics with R: An Introduction for Life Scientists" (2021, ISBN:9780198829546).

Store and retrieve data from options() using syntax derived from the here package. potions makes it straightforward to update and retrieve options, either in the workspace or during package development, without overwriting global options.

Visualizes the coverage depth of a complete plastid genome as well as the equality of its inverted repeat regions in relation to the circular, quadripartite genome structure and the location of individual genes. For more information, please see Gruenstaeudl and Jenke (2020) <doi:10.1186/s12859-020-3475-0>.

This package provides fast, dependency-minimal tools to strip all cell formulas from standard Excel (.xlsx) files while preserving evaluated calculated values intact. It also supports exporting multi-sheet workbooks into standalone single-sheet files. By design, the package extracts only cell values and their formatting (styles) to ensure maximum speed and safety, discarding complex embedded objects such as images or charts.

Use phenotype risk scores based on linked clinical and genetic data to study Mendelian disease and rare genetic variants. See Bastarache et al. 2018 <doi:10.1126/science.aal4043>.

Implementation of PsychroLib <https://github.com/psychrometrics/psychrolib> library which contains functions to enable the calculation properties of moist and dry air in both metric (SI) and imperial (IP) systems of units. References: Meyer, D. and Thevenard, D (2019) <doi:10.21105/joss.01137>.

This package provides functions for simulating from and fitting the latent hidden Markov models for response process data (Tang, 2024) <doi:10.1007/s11336-023-09938-1>. It also includes functions for simulating from and fitting ordinary hidden Markov models.

Permutation Distribution Clustering is a clustering method for time series. Dissimilarity of time series is formalized as the divergence between their permutation distributions. The permutation distribution was proposed as measure of the complexity of a time series.