The ggarrow package is a ggplot2 extension that plots a variety of different arrow segments with many options to customize. The arrowheadr package makes it easy to create custom arrowheads and fins within the parameters that ggarrow functions expect. It has preset arrowheads and a collection of functions to create and transform data for customizing arrows.

Storing very large data objects on a local drive, while still making it possible to manipulate the data in an efficient manner.

Anytime-valid sequential estimation of the p-value of a test calibrated by Monte-Carlo simulation, as described in Stoepker & Castro (2024) <doi:10.48550/arXiv.2409.18908>.

The method of anticlustering partitions a pool of elements into groups (i.e., anticlusters) with the goal of maximizing between-group similarity or within-group heterogeneity. The anticlustering approach thereby reverses the logic of cluster analysis that strives for high within-group homogeneity and clear separation between groups. Computationally, anticlustering is accomplished by maximizing instead of minimizing a clustering objective function, such as the intra-cluster variance (used in k-means clustering) or the sum of pairwise distances within clusters. The main function anticlustering() gives access to optimal and heuristic anticlustering methods described in Papenberg and Klau (2021; <doi:10.1037/met0000301>), Brusco et al. (2020; <doi:10.1111/bmsp.12186>), Papenberg (2024; <doi:10.1111/bmsp.12315>), Papenberg, Wang, et al. (2025; <doi:10.1016/j.crmeth.2025.101137>), Papenberg, Breuer, et al. (2025; <doi:10.1017/psy.2025.10052>), and Yang et al. (2022; <doi:10.1016/j.ejor.2022.02.003>). The optimal algorithms require that an integer linear programming solver is installed. This package will install lpSolve (<https://cran.r-project.org/package=lpSolve>) as a default solver, but it is also possible to use the package Rglpk (<https://cran.r-project.org/package=Rglpk>), which requires the GNU linear programming kit (<https://www.gnu.org/software/glpk/glpk.html>), the package Rsymphony (<https://cran.r-project.org/package=Rsymphony>), which requires the SYMPHONY ILP solver (<https://github.com/coin-or/SYMPHONY>), or the commercial solver Gurobi, which provides its own R package that is not available via CRAN (<https://www.gurobi.com/downloads/>). Rglpk', Rsymphony', gurobi and their system dependencies have to be manually installed by the user because they are only suggested dependencies. Full access to the bicriterion anticlustering method proposed by Brusco et al. (2020) is given via the function bicriterion_anticlustering(), while kplus_anticlustering() implements the full functionality of the k-plus anticlustering approach proposed by Papenberg (2024). Some other functions are available to solve classical clustering problems. The function balanced_clustering() applies a cluster analysis under size constraints, i.e., creates equal-sized clusters. The function matching() can be used for (unrestricted, bipartite, or K-partite) matching. The function wce() can be used optimally solve the (weighted) cluster editing problem, also known as correlation clustering, clique partitioning problem or transitivity clustering.

Browse through a continuously updated list of existing RStudio addins and install/uninstall their corresponding packages.

This package implements a parsimonious evolutionary model to analyze and predict gene-functional annotations in phylogenetic trees as described in Vega Yon et al. (2021) <doi:10.1371/journal.pcbi.1007948>. Focusing on computational efficiency, aphylo makes it possible to estimate pooled phylogenetic models, including thousands (hundreds) of annotations (trees) in the same run. The package also provides the tools for visualization of annotated phylogenies, calculation of posterior probabilities (prediction) and goodness-of-fit assessment featured in Vega Yon et al. (2021).

Runs projections of groups of matrix projection models (MPMs), allowing density dependence mechanisms to work across MPMs. This package was developed to run both adaptive dynamics simulations such as pairwise and multiple invasibility analyses, and community projections in which species are represented by MPMs. All forms of MPMs are allowed, including integral projection models (IPMs).

Nonparametric data-driven approach to discovering heterogeneous subgroups in a selection-on-observables framework. aggTrees allows researchers to assess whether there exists relevant heterogeneity in treatment effects by generating a sequence of optimal groupings, one for each level of granularity. For each grouping, we obtain point estimation and inference about the group average treatment effects. Please reference the use as Di Francesco (2022) <doi:10.2139/ssrn.4304256>.

Linear and nonlinear regression analysis common in agricultural science articles (Archontoulis & Miguez (2015). <doi:10.2134/agronj2012.0506>). The package includes polynomial, exponential, gaussian, logistic, logarithmic, segmented, non-parametric models, among others. The functions return the model coefficients and their respective p values, coefficient of determination, root mean square error, AIC, BIC, as well as graphs with the equations automatically.

Finds, prioritizes and deletes erroneous taxa in a phylogenetic tree. This package calculates scores for taxa in a tree. Higher score means the taxon is more erroneous. If the score is zero for a taxon, the taxon is not erroneous. This package also can remove all erroneous taxa automatically by iterating score calculation and pruning taxa with the highest score.

We propose an age-dependent topic modelling (ATM) model, providing a low-rank representation of longitudinal records of hundreds of distinct diseases in large electronic health record data sets. The model assigns to each individual topic weights for several disease topics; each disease topic reflects a set of diseases that tend to co-occur as a function of age, quantified by age-dependent topic loadings for each disease. The model assumes that for each disease diagnosis, a topic is sampled based on the individualâ s topic weights (which sum to 1 across topics, for a given individual), and a disease is sampled based on the individualâ s age and the age-dependent topic loadings (which sum to 1 across diseases, for a given topic at a given age). The model generalises the Latent Dirichlet Allocation (LDA) model by allowing topic loadings for each topic to vary with age. References: Jiang (2023) <doi:10.1038/s41588-023-01522-8>.

Modern software often poorly support older file formats. This package intends to handle many file formats that were native to the antiquated Commodore Amiga machine. This package focuses on file types from the older Amiga operating systems (<= 3.0). It will read and write specific file formats and coerces them into more contemporary data.

Trigger animation effects on scroll on any HTML element of shiny and rmarkdown', such as any text or plot, thanks to the AOS Animate On Scroll jQuery library.

Bindings to libarchive <http://www.libarchive.org> the Multi-format archive and compression library. Offers R connections and direct extraction for many archive formats including tar', ZIP', 7-zip', RAR', CAB and compression formats including gzip', bzip2', compress', lzma and xz'.

Fast generators and iterators for permutations, combinations, integer partitions and compositions. The arrangements are in lexicographical order and generated iteratively in a memory efficient manner. It has been demonstrated that arrangements outperforms most existing packages of similar kind. Benchmarks could be found at <https://randy3k.github.io/arrangements/articles/benchmark.html>.

Exclusion-based parentage assignment is essential for studies in biodiversity conservation and breeding programs - Kang Huang, Rui Mi, Derek W Dunn, Tongcheng Wang, Baoguo Li, (2018), <doi:10.1534/genetics.118.301592>. The tool compares multilocus genotype data of potential parents and offspring, identifying likely parentage relationships while accounting for genotyping errors, missing data, and duplicate genotypes. acoRn includes two algorithms: one generates synthetic genotype data based on user-defined parameters, while the other analyzes existing genotype data to identify parentage patterns. The package is versatile, applicable to diverse organisms, and offers clear visual outputs, making it a valuable resource for researchers.

The main application concerns to a new robust optimization package with two major contributions. The first contribution refers to the assessment of the adequacy of probabilistic models through a combination of several statistics, which measure the relative quality of statistical models for a given data set. The second one provides a general purpose optimization method based on meta-heuristics functions for maximizing or minimizing an arbitrary objective function.

This package implements an innovative approach to community detection in social networks using Association Rules Learning. The package provides tools for processing graph and rules objects, generating association rules, and detecting communities based on node interactions. Designed to facilitate advanced research in Social Network Analysis, this package leverages association rules learning for enhanced community detection. This approach is described in El-Moussaoui et al. (2021) <doi:10.1007/978-3-030-66840-2_3>.

Evaluates acute lymphoblastic leukemia maintenance therapy practice at patient and cohort level.

Two unordered pairs of data of two different snips positions is haplotyped by resolving a small number ob closed equations.

An R console utility that lets you ask R related questions to the OpenAI large language model. It can answer how-to questions by providing code, and what-is questions by explaining what given code does. You must provision your own key for the OpenAI API <https://platform.openai.com/docs/api-reference>.

Collect your data on digital marketing campaigns from Adform Ads using the Windsor.ai API <https://windsor.ai/api-fields/>.

This package provides a method for quantifying resilience after a stress event. A set of functions calculate the area of resilience that is created by the departure of baseline y (i.e., robustness) and the time taken x to return to baseline (i.e., rapidity) after a stress event using the Cartesian coordinates of the data. This package has the capability to calculate areas of resilience, growth, and cases in which resilience is not achieved (e.g., diminished performance without return to baseline).

This package provides tools to construct (or add to) cell-type signature matrices using flow sorted or single cell samples and deconvolve bulk gene expression data. Useful for assessing the quality of single cell RNAseq experiments, estimating the accuracy of signature matrices, and determining cell-type spillover. Please cite: Danziger SA et al. (2019) ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells <doi:10.1371/journal.pone.0224693>.