This package provides an infrastructure for handling multiple R Markdown reports, including automated curation and time-stamping of outputs, parameterisation and provision of helper functions to manage dependencies.

This package provides an accessible and efficient implementation of a randomized feature and bootstrap-enhanced Gaussian naive Bayes classifier. The method combines stratified bootstrap resampling with random feature subsampling and aggregates predictions via posterior averaging. Support is provided for mixed-type predictors and parallel computation. Methods are described in Srisuradetchai (2025) <doi:10.3389/fdata.2025.1706417> "Posterior averaging with Gaussian naive Bayes and the R package RandomGaussianNB for big-data classification".

Predicts morphological parameters of rorquals (e.g. body mass, flipper length, maximum engulfment capacity) from body length using allometric equations from Kahane-Rapport and Goldbogen (2018) <doi:10.1002/jmor.20846>.

Suite of tools for using D3', a library for producing dynamic, interactive data visualizations. Supports translating objects into D3 friendly data structures, rendering D3 scripts, publishing D3 visualizations, incorporating D3 in R Markdown, creating interactive D3 applications with Shiny, and distributing D3 based htmlwidgets in R packages.

Nonparametric maximum likelihood estimation methods for random coefficient binary response models and some related functionality for sequential processing of hyperplane arrangements. See J. Gu and R. Koenker (2020) <DOI:10.1080/01621459.2020.1802284>.

This package performs genome-wide association studies (GWAS) on individuals that are both related and have repeated measurements. For each Single Nucleotide Polymorphism (SNP), it computes score statistic based p-values for a linear mixed model including random polygenic effects and a random effect for repeated measurements. The computed p-values can be visualized in a Manhattan plot. For more details see Ronnegard et al. (2016) <doi:10.1111/2041-210X.12535> and for more examples see <https://github.com/larsronn/RepeatABEL_Tutorials>.

Play the classic game of tic-tac-toe (naughts and crosses).

This package provides functions used in the R: Einführung durch angewandte Statistik (second edition).

Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.

This package creates interactive analytic graphs with R'. It joins the data analysis power of R and the visualization libraries of JavaScript in one package. The package provides interactive networks, timelines, barplots, image galleries and evolving networks. Graphs are represented as D3.js graphs embedded in a web page ready for its interactive analysis and exploration.

An implementation of robust boosting algorithms for regression in R. This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) <doi:10.1016/j.csda.2020.107065>. It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) <doi:10.1214/aos/1013203451>) and Robloss (Lutz et al. (2008) <doi:10.1016/j.csda.2007.11.006>).

An R interface to estimate structured additive regression (STAR) models with BayesX'.

This package provides a set of tools to reconstruct ordered ontogenic trajectories from single cell RNAseq data.

Perform sigmoidal Emax model fit using Stan in a formula notation, without writing Stan model code.

The rearrangement operator (Hardy, Littlewood, and Polya 1952) for univariate, bivariate, and trivariate point estimates of monotonic functions. The package additionally provides a function that creates simultaneous confidence intervals for univariate functions and applies the rearrangement operator to these confidence intervals.

This package provides functions to query (filter or transform), pivot (convert from array-of-objects to object-of-arrays, for easy import as R data frame), search, patch (edit), and validate (against JSON Schema') JSON and NDJSON strings, files, or URLs. Query and pivot support JSONpointer', JSONpath or JMESpath expressions. The implementation uses the jsoncons <https://danielaparker.github.io/jsoncons/> header-only library; the library is easily linked to other packages for direct access to C++ functionality not implemented here.

This package performs RNA emulation and active learning proposed by Heo and Sung (2025) <doi:10.1080/00401706.2024.2376173> for multi-fidelity computer experiments. The RNA emulator is particularly useful when the simulations with different fidelity level are nonlinearly correlated. The hyperparameters in the model are estimated by maximum likelihood estimation.

This package provides functions to compile and load Rust code from R, similar to how Rcpp or cpp11 allow easy interfacing with C++ code. Also provides helper functions to create R packages that use Rust code. Under the hood, the Rust crate extendr is used to do all the heavy lifting.

Perform a Relative Weights Analysis (RWA) (a.k.a. Key Drivers Analysis) as per the method described in Tonidandel & LeBreton (2015) <DOI:10.1007/s10869-014-9351-z>, with its original roots in Johnson (2000) <DOI:10.1207/S15327906MBR3501_1>. In essence, RWA decomposes the total variance predicted in a regression model into weights that accurately reflect the proportional contribution of the predictor variables, which addresses the issue of multi-collinearity. In typical scenarios, RWA returns similar results to Shapley regression, but with a significant advantage on computational performance.

The handling of an API key (misnomer for password) for protected data can be difficult. This package provides secure convenience functions for entering / handling API keys and pulling data directly into memory. By default it will load from REDCap instances, but other sources are injectable via inversion of control.

This package provides an R interface to the Data Retriever <https://retriever.readthedocs.io/en/latest/> via the Data Retriever's command line interface. The Data Retriever automates the tasks of finding, downloading, and cleaning public datasets, and then stores them in a local database.

This package provides implementations of a classifier based on the "Classification Based on Associations" (CBA). It can be used for building classification models from association rules. Rules are pruned in the order of precedence given by the sort criteria and a default rule is added. The final classifier labels provided instances. CBA was originally proposed by Liu, B. Hsu, W. and Ma, Y. Integrating Classification and Association Rule Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86 (1998, ISBN:1-57735-070-7).

This package provides tools are provided for estimating, testing, and simulating abundance in a two-event (Petersen) mark-recapture experiment. Functions are given to calculate the Petersen, Chapman, and Bailey estimators and associated variances. However, the principal utility is a set of functions to simulate random draws from these estimators, and use these to conduct hypothesis tests and power calculations. Additionally, a set of functions are provided for generating confidence intervals via bootstrapping. Functions are also provided to test abundance estimator consistency under complete or partial stratification, and to calculate stratified or partially stratified estimators. Functions are also provided to calculate recommended sample sizes. Referenced methods can be found in Arnason et al. (1996) <ISSN:0706-6457>, Bailey (1951) <DOI:10.2307/2332575>, Bailey (1952) <DOI:10.2307/1913>, Chapman (1951) NAID:20001644490, Cohen (1988) ISBN:0-12-179060-6, Darroch (1961) <DOI:10.2307/2332748>, and Robson and Regier (1964) <ISSN:1548-8659>.

This package provides wrappers around base::grep() where the first argument is standardized to take the data object. This makes it less of a pain to use regular expressions with magrittr or other pipe operators.