This package provides an interactive RStudio gadget for working with an AI assistant during package and script development. The gadget can use selected editor text, the active source file, package metadata, and uploaded files as context for code explanation, code generation, documentation, and review workflows. It offers model presets, assistant behavior settings, responsive code-focused output, and explicit copy, insert, and replace actions for the active source editor. API interactions via the httr package are performed asynchronously using promises and future to avoid blocking the R console. The backend is configured via the OPENAI_API_KEY environment variable.

An implementation of the one-step privacy-protecting method for estimating the overall and site-specific hazard ratios using inverse probability weighted Cox models in distributed data network studies, as proposed by Shu, Yoshida, Fireman, and Toh (2019) <doi: 10.1177/0962280219869742>. This method only requires sharing of summary-level riskset tables instead of individual-level data. Both the conventional inverse probability weights and the stabilized weights are implemented.

This package provides an interface to the GenderAPI.io Phone Number Validation & Formatter API (<https://www.genderapi.io>) for validating international phone numbers, detecting number type (mobile, landline, Voice over Internet Protocol (VoIP)), retrieving region and country metadata, and formatting numbers to E.164 or national format. Designed to simplify integration into R workflows for data validation, Customer Relationship Management (CRM) data cleaning, and analytics tasks. Full documentation is available at <https://www.genderapi.io/docs-phone-validation-formatter-api>.

This package provides tools for meta-analysis of proportions and prevalence from studies reporting event counts and sample sizes. Provides transformed and untransformed inverse-variance models, random-effects estimation, heterogeneity statistics, prediction intervals, subgroup analysis, meta-regression, leave-one-out sensitivity analysis, influence diagnostics, forest plots, funnel plots, and an optional binomial generalized linear mixed model interface. The package is designed for epidemiological, veterinary, medical, and One Health applications, including antimicrobial resistance prevalence studies.

The Piece-wise exponential (Additive Mixed) Model (PAMM; Bender and others (2018) <doi: 10.1177/1471082X17748083>) is a powerful model class for the analysis of survival (or time-to-event) data, based on Generalized Additive (Mixed) Models (GA(M)Ms). It offers intuitive specification and robust estimation of complex survival models with stratified baseline hazards, random effects, time-varying effects, time-dependent covariates and cumulative effects (Bender and others (2019)), as well as support for left-truncated data as well as competing risks, recurrent events and multi-state settings. pammtools provides tidy workflow for survival analysis with PAMMs, including data simulation, transformation and other functions for data preprocessing and model post-processing as well as visualization.

Calculate (stratified) percentiles on a data.frame Stratification will split the data.frame into subgroups and calculate percentiles for each independently.

This package provides methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.

In short, this package is a locator for cool, refreshing beverages. It will find and return the nearest location where you can get a cold one.

Imputes missing species trait data for comparative analyses by combining three sources of information: phylogenetic similarity (closely related species share similar traits), cross-trait correlations (observed traits inform missing ones), and optional environmental covariates (climate, habitat, geography). Handles continuous measurements, counts, binary variables, ordered categories, unordered categories, bounded proportions, zero-inflated counts, and compositional multi-proportion data in a single call. The method blends a phylogenetic baseline with a graph neural network correction; a per-trait gate calibrated on held-out data ensures the network only contributes when it improves on the baseline. Provides conformal prediction intervals for continuous, count, and ordinal traits and an experimental analysis-aware multiple-imputation workflow for one missing continuous covariate in Gaussian linear, binomial-logit, and Gaussian random-intercept models, with Rubin pooling limited to fixed effects. Stochastic graph-network and posterior-tree completions are prediction diagnostics rather than validated inferential imputations. Tested up to 10,000 species. Bundled datasets include 300-species and 9,993-species bird-trait subsets with matching example phylogenetic trees. Rubin (1987, ISBN:978-0-471-08705-2); Vovk et al. (2005, ISBN:978-0-387-25061-8); Nakagawa and de Villemereuil (2019) <doi:10.1093/sysbio/syy089>.

Kernel density estimation on the polysphere, (hyper)sphere, and circle. Includes functions for density estimation, regression estimation, ridge estimation, bandwidth selection, kernels, samplers, and homogeneity tests. Companion package to Garcà a-Portugués and Meilán-Vila (2025) <doi:10.1080/01621459.2025.2521898> and Garcà a-Portugués and Meilán-Vila (2023) <doi:10.1007/978-3-031-32729-2_4>.

Collection of tutorials for working with Positron and for learning how to apply generative AI when coding with R. Covers scripts, Quarto documents, Git', GitHub', and Quarto websites. Makes extensive use of the tools in the tutorial.helpers package.

This package provides functions to make board game graphics with the ggplot2', grid', rayrender', rayvertex', and rgl packages. Specializes in game diagrams, animations, and "Print & Play" layouts for the piecepack <https://www.ludism.org/ppwiki> but can make graphics for other board game systems. Includes configurations for several public domain game systems such as checkers, (double-18) dominoes, go, piecepack', playing cards, etc.

Conduct dsep tests (piecewise SEM) of a directed, or mixed, acyclic graph without latent variables (but possibly with implicitly marginalized or conditioned latent variables that create dependent errors) based on linear, generalized linear, or additive modelswith or without a nesting structure for the data. Also included are functions to do desp tests step-by-step,exploratory path analysis, and Monte Carlo X2 probabilities. This package accompanies Shipley, B, (2026).Cause and Correlation in Biology: A User's Guide to Path Analysis, StructuralEquations and Causal Inference (3rd edition). Cambridge University Press.

Fits Bayesian nonparametric models based on Pólya tree processes, including adaptive Pólya trees, Markov adaptive Pólya trees, optional Pólya trees, and their conditional-density counterparts. Methods are described in Ma (2017) <doi:10.1214/16-BA1021>, Ma (2017) <doi:10.1214/17-EJS1254>, and Wong and Ma (2010) <doi:10.1214/09-AOS755>.

Generalized Least Squares (GLS) estimation of Seemingly Unrelated Regression (SUR) systems on unbalanced panel in the one/two-way cases also taking into account the possibility of cross equation restrictions. Methodological details can be found in Biørn (2004) <doi:10.1016/j.jeconom.2003.10.023> and Platoni, Sckokai, Moro (2012) <doi:10.1080/07474938.2011.607098>.

Run simulations to assess the impact of various designs features and the underlying biological behaviour on the outcome of a Patient Derived Xenograft (PDX) population study. This project can either be deployed to a server as a shiny app or installed locally as a package and run the app using the command populationPDXdesignApp()'.

This package provides functions for creating color palettes, visualizing palettes, modifying colors, and assigning colors for plotting.

Latent class analysis and latent class regression models for polytomous outcome variables. Also known as latent structure analysis.

This package provides a package for selecting the most relevant features (genes) in the high-dimensional binary classification problems. The discriminative features are identified using analyzing the overlap between the expression values across both classes. The package includes functions for measuring the proportional overlapping score for each gene avoiding the outliers effect. The used measure for the overlap is the one defined in the "Proportional Overlapping Score (POS)" technique for feature selection. A gene mask which represents a gene's classification power can also be produced for each gene (feature). The set size of the selected genes might be set by the user. The minimum set of genes that correctly classify the maximum number of the given tissue samples (observations) can be also produced.

Density, distribution function, quantile function, and random generation function based on Kittipong Klinjan,Tipat Sottiwan and Sirinapa Aryuyuen (2024)<DOI:10.28919/cmbn/8833>.

Collection of pivotal algorithms for: relabelling the MCMC chains in order to undo the label switching problem in Bayesian mixture models; fitting sparse finite mixtures; initializing the centers of the classical k-means algorithm in order to obtain a better clustering solution. For further details see Egidi, Pappadà , Pauli and Torelli (2018b)<ISBN:9788891910233>.

Power and sample size calculation for bulk tissue and single-cell eQTL analysis based on ANOVA, simple linear regression, or linear mixed effects model. It can also calculate power/sample size for testing the association of a SNP to a continuous type phenotype. Please see the reference: Dong X, Li X, Chang T-W, Scherzer CR, Weiss ST, Qiu W. (2021) <doi:10.1093/bioinformatics/btab385>.

This package implements Progressive Regularized Vine Copula (Prog-Vine) frameworks for high-dimensional dependent competing risks with masked failure causes under Progressive Type-II Censoring. Fits Weibull marginals, estimates pair-copula trees using Expectation-Maximization (EM) algorithms, computes Louis observed information confidence intervals, and implements Data Augmentation Gibbs Samplers for Bayesian credible intervals.

Create, transform, and summarize custom random variables with distribution functions (analogues of p*()', d*()', q*()', and r*() functions from base R). Two types of distributions are supported: "discrete" (random variable has finite number of output values) and "continuous" (infinite number of values in the form of continuous random variable). Functions for distribution transformations and summaries are available. Implemented approaches often emphasize approximate and numerical solutions: all distributions assume finite support and finite values of density function; some methods implemented with simulation techniques.