Allows users to identify similar cases for qualitative case studies using statistical matching methods.

This package contains functions to detect and visualise periods of climate sensitivity (climate windows) for a given biological response. Please see van de Pol et al. (2016) <doi:10.1111/2041-210X.12590> and Bailey and van de Pol (2016) <doi:10.1371/journal.pone.0167980> for details.

Generates tree plots with precise branch lengths, gene annotations, and cellular prevalence. The package handles complex tree structures (angles, lengths, etc.) and can be further refined as needed by the user.

Fits predictive and symmetric co-correspondence analysis (CoCA) models to relate one data matrix to another data matrix. More specifically, CoCA maximises the weighted covariance between the weighted averaged species scores of one community and the weighted averaged species scores of another community. CoCA attempts to find patterns that are common to both communities.

C5.0 decision trees and rule-based models for pattern recognition that extend the work of Quinlan (1993, ISBN:1-55860-238-0).

The Large Language Model (LLM) represents a groundbreaking advancement in data science and programming, and also allows us to extend the world of R. A seamless interface for integrating the OpenAI Web APIs into R is provided in this package. This package leverages LLM-based AI techniques, enabling efficient knowledge discovery and data analysis. The previous functions such as seamless translation and image generation have been moved to other packages deepRstudio and stableDiffusion4R'.

Generate cohorts and subsets using an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) Database. Cohorts are defined using CIRCE (<https://github.com/ohdsi/circe-be>) or SQL compatible with SqlRender (<https://github.com/OHDSI/SqlRender>).

This package provides various tools of for clustering multivariate angular data on the torus. The package provides angular adaptations of usual clustering methods such as the k-means clustering, pairwise angular distances, which can be used as an input for distance-based clustering algorithms, and implements clustering based on the conformal prediction framework. Options for the conformal scores include scores based on a kernel density estimate, multivariate von Mises mixtures, and naive k-means clusters. Moreover, the package provides some basic data handling tools for angular data.

This package provides functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) <http://ftp.cs.ucla.edu/pub/stat_ser/r329-uai.pdf>, an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf>, and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>. All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) <http://ftp.cs.ucla.edu/pub/stat_ser/r309.pdf>.

This package provides a modeling tool allowing gene selection, reverse engineering, and prediction in cascade networks. Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014) <doi:10.1093/bioinformatics/btt705>.

This package provides a collection of functions for exploratory chemometrics of 2D spectroscopic data sets such as COSY (correlated spectroscopy) and HSQC (heteronuclear single quantum coherence) 2D NMR (nuclear magnetic resonance) spectra. ChemoSpec2D deploys methods aimed primarily at classification of samples and the identification of spectral features which are important in distinguishing samples from each other. Each 2D spectrum (a matrix) is treated as the unit of observation, and thus the physical sample in the spectrometer corresponds to the sample from a statistical perspective. In addition to chemometric tools, a few tools are provided for plotting 2D spectra, but these are not intended to replace the functionality typically available on the spectrometer. ChemoSpec2D takes many of its cues from ChemoSpec and tries to create consistent graphical output and to be very user friendly.

This package provides functions to make lifetables and to calculate hazard function estimate using Poisson regression model with splines. Includes function to draw simple flowchart of cohort study. Function boxesLx() makes boxes of transition rates between states. It utilizes Epi package Lexis data.

This package provides a curated list of copepod-fish ecological interaction records. It contains the taxonomy of the copepod and the fish and the publication from which the information was obtained. This database contains only marine and brackish water fish species. It excludes fish species that inhabit only freshwater.

Apply and visualize conditional formatting to data frames in R. It renders a data frame with cells formatted according to criteria defined by rules, using a tidy evaluation syntax. The table is printed either opening a web browser or within the RStudio viewer if available. The conditional formatting rules allow to highlight cells matching a condition or add a gradient background to a given column. This package supports both HTML and LaTeX outputs in knitr reports, and exporting to an xlsx file.

Providing a cluster allocation for n samples, either with an $n \times p$ data matrix or an $n \times n$ distance matrix, a bootstrap procedure is performed. The proportion of bootstrap replicates where a pair of samples cluster in the same cluster indicates who tightly the samples in a particular cluster clusters together.

We implement a new correlation estimator, CorSym, designed for exchangeable variables, where the ordering of the two values in the pair is arbitrary. This kind of data arises frequently in the study of assortative pairing (for example, income in a couple). The standard Pearson estimator is sensitive to such ordering and can be highly biased when the order is biased (when the first value tends to have lower or higher values than the second value). CorSym gives the same estimate deterministically for all orders within each pair, and estimates the desired correlation without bias (variables must be exchangeable). The package also includes utilities to simulate biased orders and test for order bias. Described in Kennedy and Ochoa (2026) <doi:10.64898/2026.08.22.746446>.

Management and analysis of camera trap wildlife data through an integrated workflow. Provides functions for image/video organization and metadata extraction, species/individual identification. Creates detection histories for occupancy and spatial capture-recapture analyses, with support for multi-season studies. Includes tools for fitting community occupancy models in JAGS and NIMBLE, and an interactive dashboard for survey data visualization and analysis. Features visualization of species distributions and activity patterns, plus export capabilities for GIS and reports. Emphasizes automation and reproducibility while maintaining flexibility for different study designs.

This package performs regression analysis for longitudinal count data, allowing for serial dependence among observations from a given individual and two dimensional random effects on the linear predictor. Estimation is via maximization of the exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed. Details can be found in the accompanying scientific papers: Goncalves & Cabral (2021, Journal of Statistical Software, <doi:10.18637/jss.v099.i03>) and Goncalves et al. (2007, Computational Statistics & Data Analysis, <doi:10.1016/j.csda.2007.03.002>).

Estimate sample sizes needed to capture target levels of genetic diversity from a population (multivariate allele frequencies) for applications like germplasm conservation and breeding efforts. Compares bootstrap samples to a full population using linear regression, employing the R-squared value to represent the proportion of diversity captured. Iteratively increases sample size until a user-defined target R-squared is met. Offers a parallelized R implementation of a previously developed python method. All ploidy levels are supported. For more details, see Sandercock et al. (2024) <doi:10.1073/pnas.2403505121>.

Implementation of models to analyse compositional microbiome time series taking into account the interaction between groups of bacteria. The models implemented are described in Creus-Martà et al (2018, ISBN:978-84-09-07541-6), Creus-Martà et al (2021) <doi:10.1155/2021/9951817> and Creus-Martà et al (2022) <doi:10.1155/2022/4907527>.

Provide the CrossClustering algorithm (Tellaroli et al. (2016) <doi:10.1371/journal.pone.0152333>), which is a partial clustering algorithm that combines the Ward's minimum variance and Complete Linkage algorithms, providing automatic estimation of a suitable number of clusters and identification of outlier elements.

Check your R code for some of the most common layout flaws. Many tried to teach us how to write code less dreadful, be it implicitly as B. W. Kernighan and D. M. Ritchie (1988) <ISBN:0-13-110362-8> in The C Programming Language did, be it explicitly as R.C. Martin (2008) <ISBN:0-13-235088-2> in Clean Code: A Handbook of Agile Software Craftsmanship did. So we should check our code for files too long or wide, functions with too many lines, too wide lines, too many arguments or too many levels of nesting. Note: This is not a static code analyzer like pylint or the like. Checkout <https://cran.r-project.org/package=lintr> instead.

This package provides functions and Data to support Context Driven Exploratory Projection Pursuit.

This package provides a GUI with which users can construct and interact with Canonical Correspondence Analysis and Canonical Non-Symmetrical Correspondence Analysis and provides inferential results by using Bootstrap Methods.