Generates phylogenetic trees and distance matrices from a list of taxon names, or from a higher taxon expanded down to a chosen lower rank. Trees are obtained as induced subtrees of the Open Tree of Life synthetic tree using the rotl package (Michonneau, Brown and Winter, 2016, <doi:10.1111/2041-210X.12593>). Expansion of a higher taxon to its descendants uses taxize (Chamberlain and Szocs, 2013, <doi:10.12688/f1000research.2-191.v2>).

An interactive document on the topic of one-way and two-way analysis of variance using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://kartikeyab.shinyapps.io/ANOVAShiny/>.

Stepwise Uncertainty Reduction criterion and algorithm for sequentially learning a Gaussian Process Classifier as described in Menz et al. (2025).

Color palettes inspired by characters and nations from the animated series Avatar: The Last Airbender for use in R graphics.

Offers a graphical user interface for the calculation of the mean measure of divergence, with facilities for trait selection and graphical representations <doi:10.1002/ajpa.23336>.

Retrieve air quality data via the AirNow <https://www.airnow.gov/> API.

This package implements adaptive generalized Bayesian quantile regression with quantile-specific learning rates, HAC-based calibration, Gibbs posterior simulation, posterior summaries, predictive evaluation, and visualization tools. The package builds on the generalized Bayesian composite quantile regression framework of Hardy and Korobilis (2026) <doi:10.2139/ssrn.6618603> by allowing learning rates to vary across quantile levels. The implementation is designed for empirical work with small and moderate time-series samples where posterior calibration and tail-specific inference are important.

Display air quality model output and monitoring data using scatterplots, grids, and legends.

An offline verifier for verifiable credentials and delegation chains issued by the Attestix Python core. Verifies Ed25519 (RFC 8032) signatures over W3C Verifiable Credentials, decodes Ed25519 did:key identifiers, and verifies UCAN delegation chains (EdDSA JWT's) including capability attenuation, with no Python runtime required. Reproduces the Attestix JCS-style JSON canonical form (a practical subset of RFC 8785 that additionally applies NFC Unicode normalization) byte-for-byte so that signatures produced by the reference implementation verify here. Useful for compliance, research and biostatistics users who work in R and need to check AI-agent compliance credentials. See <https://attestix.io> for the project and <https://attestix.io/spec/bundle/v1> for the bundle wire format.

This package provides a toolbox for programming Clinical Data Standards Interchange Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA). Analysis derivations are implemented in accordance with the "Analysis Data Model Implementation Guide" (CDISC Analysis Data Model Team, 2021, <https://www.cdisc.org/standards/foundational/adam>). The package is an extension package of the admiral package for pediatric clinical trials.

Utilities for simulating and prototyping relational event models, including helpers to generate dynamic event sequences and covariate processes for sender and receiver sets. The endogenous-effect and case-control estimation machinery follows Juozaitiene and Wit (2024) <doi:10.1093/jrsssa/qnae132>.

Adaptive wavelet lifting transforms for signal denoising using optimal local neighbourhood regression, from Nunes et al. (2006) <doi:10.1007/s11222-006-6560-y>.

Reads Agricultural Production Systems sIMulator ('APSIM') Classic 7.x .out files, pairs simulated series with sparse observed measurements, and computes the goodness-of-fit statistics used in crop-model calibration and validation, including the decomposition of root mean squared error into systematic and unsystematic components. Produces publication grade figures with ggplot2': one-to-one scatter plots, residual and Taylor diagrams, probability of exceedance curves, multi-variable timelines with a secondary axis, and distribution plots, assembled into multi-panel figures at journal column widths in colour, greyscale or line art. Treatment structure encoded in simulation names is parsed into factor columns, and the sensitivity of an output to those factors is quantified by variance decomposition, one-at-a-time analysis or range screening. A built-in shiny interface watches the output directory and refreshes incrementally as APSIM regenerates its files.

This package implements a two-stage, design-informed framework for selecting the truncation time in time-to-event trials analyzed with the average hazard estimand, motivated by pediatric oncology settings with small samples, slow accrual, and limited follow-up. Stage 1 validates a clinically proposed truncation time against the planned design using simulation-based diagnostics for risk-set support, follow-up coverage, estimability, and estimator stability, classifying it as Pass, Borderline, or Fail. Stage 2 performs constrained optimization over a grid of candidate truncation times within a clinical-distance window, maximizing a utility subject to feasibility constraints, with an independent evaluation run to assess the selected time. Supports proportional-hazards, early-, and delayed-effect patterns, uniform accrual with administrative censoring, and calibrated exponential random censoring.

The goal of the package aldvmm is to fit adjusted limited dependent variable mixture models of health state utilities. Adjusted limited dependent variable mixture models are finite mixtures of normal distributions with an accumulation of density mass at the limits, and a gap between 100% quality of life and the next smaller utility value. The package aldvmm uses the likelihood and expected value functions proposed by Hernandez Alava and Wailoo (2015) <doi:10.1177/1536867X1501500307> using normal component distributions and a multinomial logit model of probabilities of component membership.

This is an implementation of the Generalized Discrimination Score (also known as Two Alternatives Forced Choice Score, 2AFC) for various representations of forecasts and verifying observations. The Generalized Discrimination Score is a generic forecast verification framework which can be applied to any of the following verification contexts: dichotomous, polychotomous (ordinal and nominal), continuous, probabilistic, and ensemble. A comprehensive description of the Generalized Discrimination Score, including all equations used in this package, is provided by Mason and Weigel (2009).

In total it has 7 functions, three for calculating machine calibration, which determine application rate (L/ha), nozzle flow (L/min) and amount of product (L or kg) to be added. to the tank with each sprayer filling. Two functions for graphs of the flow distribution of the nozzles (L/min) in the application bar and, of the temporal variability of the meteorological conditions (air temperature, relative humidity of the air and wind speed). Two functions to determine the spray deposit (uL/cm2), through the methodology called spectrophotometry, with the aid of bright blue (Palladini, L.A., Raetano, C.G., Velini, E.D. (2005), <doi:10.1590/S0103-90162005000500005>) or metallic markers (Chaim, A., Castro, V.L.S.S., Correles, F.M., Galvão, J.A.H., Cabral, O.M.R., Nicolella, G. (1999), <doi:10.1590/S0100-204X1999000500003>). The package supports the analysis and representation of information, using a single free software that meets the most diverse areas of activity in application technology.

An efficient Rcpp implementation of the Adaptive Rejection Metropolis Sampling (ARMS) algorithm proposed by Gilks, W. R., Best, N. G. and Tan, K. K. C. (1995) <doi:10.2307/2986138>. This allows for sampling from a univariate target probability distribution specified by its (potentially unnormalised) log density.

This package provides tools for the analysis of interval-valued data, including construction, visualization, and statistical modeling. The package provides the intData class for representing interval-valued data, along with functions to aggregate microdata and to estimate parameters of latent distributions. Barycenter and covariance matrix estimation is implemented based on the Mallows distance (Oliveira et al. (2025) <doi:10.48550/arXiv.2407.05105>). Robust estimation of the symbolic covariance matrix is implemented via the Interval Minimum Covariance Determinant (IMCD) estimator, enabling outlier detection based on the robust squared Interval-Mahalanobis distance, as proposed by Loureiro et al. (2026b) <doi:10.48550/arXiv.2604.26769>. Explainable outlier detection is supported through Shapley value based decomposition of the squared robust Interval-Mahalanobis distance, allowing assessment of variable contributions to outlyingness (Loureiro et al. (2026a) <doi:10.48550/arXiv.2606.26307>). Shapley interaction indices are also implemented, along with visualization tools to support interpretation of the results.

Estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent response and covariates are mismatched and observed intermittently within subjects. Kernel weighted estimating equations are used for generalized linear models with either time-invariant or time-dependent coefficients. Cao, H., Li, J., and Fine, J. P. (2016) <doi:10.1214/16-EJS1141>. Cao, H., Zeng, D., and Fine, J. P. (2015) <doi:10.1111/rssb.12086>.

The maximum likelihood estimator (MLE) is a technology: under regularity conditions, any MLE is asymptotically normal with variance given by the inverse Fisher information. This package exploits that structure by defining an algebra over MLEs. Compose independent estimators into joint MLEs via block-diagonal covariance ('joint'), optimally combine repeated estimates via inverse-variance weighting ('combine'), propagate transformations via the delta method ('rmap'), and bridge to distribution algebra via conversion to normal or multivariate normal objects ('as_dist'). Supports asymptotic ('mle', mle_numerical') and bootstrap ('mle_boot') estimators with a unified interface for inference: confidence intervals, standard errors, AIC, Fisher information, and predictive intervals. For background on maximum likelihood estimation, see Casella and Berger (2002, ISBN:978-0534243128). For the delta method and variance estimation, see Lehmann and Casella (1998, ISBN:978-0387985022).

This package implements the alternating k-means biclustering algorithm in Fraiman and Li (2020) <arXiv:2009.04550>.

Client package for the AWS Key Management Service <https://aws.amazon.com/kms/>, a cloud service for managing encryption keys.

This package provides functions to simplify and standardise antimicrobial resistance (AMR) data analysis and to work with microbial and antimicrobial properties by using evidence-based methods, as described in <doi:10.18637/jss.v104.i03>.