Assess the proportion of treatment effect explained by a longitudinal surrogate marker as described in Agniel D and Parast L (2021) <doi:10.1111/biom.13310>; and estimate the treatment effect on a longitudinal surrogate marker as described in Wang et al. (2025) <doi:10.1093/biomtc/ujaf104>. A tutorial for this package can be found at <https://www.laylaparast.com/longsurr>.

BEAST2 (<https://www.beast2.org>) is a widely used Bayesian phylogenetic tool, that uses DNA/RNA/protein data and many model priors to create a posterior of jointly estimated phylogenies and parameters. BEAST2 is commonly accompanied by BEAUti 2 (<https://www.beast2.org>), which, among others, allows one to install BEAST2 package. This package allows to work with BEAST2 packages from R'.

This package provides an algorithm for creating mandalas. From the perspective of classic mathematical curves and rigid movements on the plane, the package allows you to select curves and produce mandalas from the curve. The algorithm was developed based on the book by Alcoforado et. al. entitled "Art, Geometry and Mandalas with R" (2022) in press by the USP Open Books Portal.

This package provides a framework based on S3 dispatch for constructing models of mosquito-borne pathogen transmission which are constructed from submodels of various components (i.e. immature and adult mosquitoes, human populations). A consistent mathematical expression for the distribution of bites on hosts means that different models (stochastic, deterministic, etc.) can be coherently incorporated and updated over a discrete time step.

Fits the neighboring models of a fitted structural equation model and assesses the model uncertainty of the fitted model based on BIC posterior probabilities (BPP), using the method presented in Wu, Cheung, and Leung (2020) <doi:10.1080/00273171.2019.1574546>. See Pesigan, Cheung, Wu, Chang, and Leung (2026) <doi:10.3758/s13428-025-02921-x> for an introduction to the package.

Cross-Entropy optimisation of unconstrained deterministic and noisy functions illustrated in Rubinstein and Kroese (2004, ISBN: 978-1-4419-1940-3) through a highly flexible and customisable function which allows user to define custom variable domains, sampling distributions, updating and smoothing rules, and stopping criteria. Several built-in methods and settings make the package very easy-to-use under standard optimisation problems.

Data used in compiling the Handbook of UK Urban Tree Allometric Equations and Size Characteristics (Fennel 2024). The data include measurements of height, crown radius and diameter at breast height (DBH) for UK urban trees. For more details see Fennell (2024) Handbook of UK Urban Tree Allometric Equations and Size Characteristics (Version 1.4). <doi:10.13140/RG.2.2.28745.04961>.

Automatic generation of maximally distinct qualitative color palettes, optionally tailored to color deficiency. A set of colors or a subspace of a color space is used as input and a final palette of specified size is generated by picking colors that maximize the minimum pairwise difference among the chosen colors. Adaptations to color vision deficiency, background colors, and white points are supported.

Apache Drill is a low-latency distributed query engine designed to enable data exploration and analysis on both relational and non-relational data stores, scaling to petabytes of data. Methods are provided that enable working with Apache Drill instances via the REST API, DBI methods and using dplyr'/'dbplyr idioms. Helper functions are included to facilitate using official Drill Docker images/containers.

Enables drag-and-drop behaviour in Shiny apps, by exposing the functionality of the SortableJS <https://sortablejs.github.io/Sortable/> JavaScript library as an htmlwidget'. You can use this in Shiny apps and widgets, learnr tutorials as well as R Markdown. In addition, provides a custom learnr question type - question_rank() - that allows ranking questions with drag-and-drop.

An implementation of local and global statistical complexity measures (aka Information Theory Quantifiers, ITQ) for time series analysis based on ordinal statistics (Bandt and Pompe (2002) <DOI:10.1103/PhysRevLett.88.174102>). Several distance measures that operate on ordinal pattern distributions, auxiliary functions for ordinal pattern analysis, and generating functions for stochastic and deterministic-chaotic processes for ITQ testing are provided.

This package provides a set of statistical tools for spatio-temporal data exploration. Includes simple plotting functions, covariance calculations and computations similar to principal component analysis for spatio-temporal data. Can use both dataframes and stars objects for all plots and computations. For more details refer Spatio-Temporal Statistics with R (Christopher K. Wikle, Andrew Zammit-Mangion, Noel Cressie, 2019, ISBN:9781138711136).

This package implements the Vector Matching algorithm to match multiple treatment groups based on previously estimated generalized propensity scores. The package includes tools for visualizing initial confounder imbalances, estimating treatment assignment probabilities using various methods, defining the common support region, performing matching across multiple groups, and evaluating matching quality. For more details, see Lopez and Gutman (2017) <doi:10.1214/17-STS612>.

This package provides a low-level spell checker and morphological analyzer based on the famous hunspell library. The package can analyze or check individual words as well as parse text, LaTeX, HTML or XML documents. For a more user-friendly interface use the spelling package which builds on this package to automate checking of files, documentation and vignettes in all common formats.

This is a package for variable elimination (Gaussian elimination, Fourier-Motzkin elimination), Moore-Penrose pseudoinverse, reduction to reduced row echelon form, value substitution, projecting a vector on the convex polytope described by a system of (in)equations, simplify systems by removing spurious columns and rows and collapse implied equalities, test if a matrix is totally unimodular, compute variable ranges implied by linear (in)equalities.

This package lets you interface to Nocedal et al. L-BFGS-B.3.0 limited memory BFGS minimizer with bounds on parameters. This registers a R compatible C interface to L-BFGS-B.3.0 that uses the same function types and optimization as the optim() function. This package also adds more stopping criteria as well as allowing the adjustment of more tolerances.

FHIR R4 bundles in JSON format are derived from https://synthea.mitre.org/downloads. Transformation inspired by a kaggle notebook published by Dr Alexander Scarlat, https://www.kaggle.com/code/drscarlat/fhir-starter-parse-healthcare-bundles-into-tables. This is a very limited illustration of some basic parsing and reorganization processes. Additional tooling will be required to move beyond the Synthea data illustrations.

ClustIRR analyzes repertoires of B- and T-cell receptors. It starts by identifying communities of immune receptors with similar specificities, based on the sequences of their complementarity-determining regions (CDRs). Next, it employs a Bayesian probabilistic models to quantify differential community occupancy (DCO) between repertoires, allowing the identification of expanding or contracting communities in response to e.g. infection or cancer treatment.

The funOmics package ggregates or summarizes omics data into higher level functional representations such as GO terms gene sets or KEGG metabolic pathways. The aggregated data matrix represents functional activity scores that facilitate the analysis of functional molecular sets while allowing to reduce dimensionality and provide easier and faster biological interpretations. Coordinated functional activity scores can be as informative as single molecules!

This package provides a collection of microRNAs/targets from external resources, including validated microRNA-target databases (miRecords, miRTarBase and TarBase), predicted microRNA-target databases (DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA and TargetScan) and microRNA-disease/drug databases (miR2Disease, Pharmaco-miR VerSe and PhenomiR).

Use BirdNET', a state-of-the-art deep learning classifier, to automatically identify (bird) sounds. Analyze bioacoustic datasets without any computer science background using a pre-trained model or a custom trained classifier. Predict bird species occurrence based on location and week of the year. Kahl, S., Wood, C. M., Eibl, M., & Klinck, H. (2021) <doi:10.1016/j.ecoinf.2021.101236>.

Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random effects is described in Zhao et al. (2024) <doi:10.1093/jrsssc/qlad102>.

Covariance is of universal prevalence across various disciplines within statistics. We provide a rich collection of geometric and inferential tools for convenient analysis of covariance structures, topics including distance measures, mean covariance estimator, covariance hypothesis test for one-sample and two-sample cases, and covariance estimation. For an introduction to covariance in multivariate statistical analysis, see Schervish (1987) <doi:10.1214/ss/1177013111>.

Generate multivariate color palettes to represent two-dimensional or three-dimensional data in graphics (in contrast to standard color palettes that represent just one variable). You tell colors3d how to map color space onto your data, and it gives you a color for each data point. You can then use these colors to make plots in base R', ggplot2', or other graphics frameworks.