Multimodal distributions can be modelled as a mixture of components. The model is derived using the Pareto Density Estimation (PDE) for an estimation of the pdf. PDE has been designed in particular to identify groups/classes in a dataset. Precise limits for the classes can be calculated using the theorem of Bayes. Verification of the model is possible by QQ plot, Chi-squared test and Kolmogorov-Smirnov test. The package is based on the publication of Ultsch, A., Thrun, M.C., Hansen-Goos, O., Lotsch, J. (2015) <DOI:10.3390/ijms161025897>.

Choice models are a widely used technique across numerous scientific disciplines. The Apollo package is a very flexible tool for the estimation and application of choice models in R. Users are able to write their own model functions or use a mix of already available ones. Random heterogeneity, both continuous and discrete and at the level of individuals and choices, can be incorporated for all models. There is support for both standalone models and hybrid model structures. Both classical and Bayesian estimation is available, and multiple discrete continuous models are covered in addition to discrete choice. Multi-threading processing is supported for estimation and a large number of pre and post-estimation routines, including for computing posterior (individual-level) distributions are available. For examples, a manual, and a support forum, visit <https://www.ApolloChoiceModelling.com>. For more information on choice models see Train, K. (2009) <isbn:978-0-521-74738-7> and Hess, S. & Daly, A.J. (2014) <isbn:978-1-781-00314-5> for an overview of the field.

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>.

This package provides nine computational algorithms for dimensionality reduction via Principal Component Analysis (PCA), built using an object-oriented (S3) architecture. The package includes classical and modern methods: Singular Value Decomposition (SVD) based on Eckart and Young (1936) <doi:10.1007/BF02288367>, Power Iteration based on Hotelling (1933) <doi:10.1037/h0071325>, QR Algorithm based on Francis (1961) <doi:10.1093/comjnl/4.3.265>, Jacobi Algorithm based on Jacobi (1846) <doi:10.1515/crll.1846.30.51>, Arnoldi Iteration based on Arnoldi (1951) <doi:10.1090/qam/42792>, NIPALS based on Wold (1975) <doi:10.1017/S0021900200047604>, Alternating Least Squares (ALS) based on Kolda and Bader (2009) <doi:10.1137/07070111X>, Probabilistic PCA (PPCA) with EM Algorithm based on Tipping and Bishop (1999) <doi:10.1111/1467-9868.00196>, and Generalized Hebbian Algorithm (GHA) based on Sanger (1989) <doi:10.1016/0893-6080(89)90044-0>.

This package provides three stability-validated pipelines for computing an Aggregated Latent Space Index (ALSI): a binary MCA pipeline (alsi_workflow()), an ordinal pipeline using homals alternating least squares optimal scaling (alsi_workflow_ordinal()), and a continuous ipsatized SVD pipeline (calsi_workflow()). All three pipelines share a common bootstrap dual-criterion stability framework (principal angles and Tucker congruence phi) for determining the number of dimensions to retain before index construction. The package is designed to complement Segmented Profile Analysis (SEPA) and is intended for psychometric scale construction and dimensional reduction in survey and clinical research.

Parsing R code is key to build tools such as linters and stylers. This package provides a binding to the Rust crate ast-grep so that one can parse and explore R code.

Simulation and pricing routines for rare-event options using Adaptive Multilevel Splitting and standard Monte Carlo under Black-Scholes and Heston models. Core routines are implemented in C++ via Rcpp and RcppArmadillo with lightweight R wrappers.

This package provides functions to compute summary scores (besides proprietary ones) reported in the tabulated data resource that is released by the Adolescent Brain Cognitive Development (ABCD) study.

This package provides WHO 2007 References for School-age Children and Adolescents (5 to 19 years) (z-scores) with confidence intervals and standard errors around the prevalence estimates, taking into account complex sample designs. More information on the methods is available online: <https://www.who.int/tools/growth-reference-data-for-5to19-years>.

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.

Constructs sparse adjacency matrices from spatial coordinates, feature measurements, class labels, and temporal indices. Supports nearest-neighbor graphs, heat-kernel weights, graph Laplacians, diffusion operators, and bilateral smoothers for graph-based data analysis, following spectral graph methods in von Luxburg (2007) <doi:10.1007/s11222-007-9033-z>, diffusion maps in Coifman and Lafon (2006) <doi:10.1016/j.acha.2006.04.006>, and bilateral filtering in Tomasi and Manduchi (1998) <doi:10.1109/ICCV.1998.710815>.

This package provides a collection of measures for measuring ecological diversity. Ecological diversity comes in two flavors: alpha diversity measures the diversity within a single site or sample, and beta diversity measures the diversity across two sites or samples. This package overlaps considerably with other R packages such as vegan', gUniFrac', betapart', and fossil'. We also include a wide range of functions that are implemented in software outside the R ecosystem, such as scipy', Mothur', and scikit-bio'. The implementations here are designed to be basic and clear to the reader.

This package provides functions to access data from public RESTful APIs including World Bank API and REST Countries API', retrieving real-time or historical information related to Algeria. The package enables users to query economic indicators and international demographic and geopolitical statistics in a reproducible way. It is designed for researchers, analysts, and developers who require reliable and programmatic access to Algerian data through established APIs. For more information on the APIs, see: World Bank API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392> and REST Countries API <https://restcountries.com/>.

This package provides a set of Study Data Tabulation Model (SDTM) datasets from the Clinical Data Interchange Standards Consortium (CDISC) pilot project used for testing and developing Analysis Data Model (ADaM) derivations inside the admiral package.

This package implements a framework for creating boxplots where the whisker lengths are determined by formal multiple testing procedures, making them adaptive to sample size and data characteristics. The function bh_boxplot() generates boxplots that control the False Discovery Rate (FDR) via the Benjamini-Hochberg procedure, and the function holm_boxplot() generates boxplots that control the Family-Wise Error Rate (FWER) via the Holm procedure. The methods are based on the framework in Gang, Lin, and Tong (2025) <doi:10.48550/arXiv.2510.20259>.

This package implements the Analytic Hierarchy Process (AHP) method using Gaussian normalization (AHPGaussian) to derive the relative weights of the criteria and alternatives. It also includes functions for visualizing the results and generating graphical outputs. Method as described in: dos Santos, Marcos (2021) <doi:10.13033/ijahp.v13i1.833>.

Programming oncology specific Clinical Data Interchange Standards 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.

An R Shiny application for visual and statistical exploration and web communication of archaeological spatial data, either remains or sites. It offers interactive 3D and 2D visualisations (cross sections and maps of remains, timeline of the work made in a site) which can be exported in SVG and HTML formats. It performs simple spatial statistics (convex hull, regression surfaces, 2D kernel density estimation) and allows exporting data to other online applications for more complex methods. archeoViz can be used offline locally or deployed on a server, either with interactive input of data or with a static data set. Example is provided at <https://analytics.huma-num.fr/archeoviz/en>.

This package provides a number of functions to access the National Energy Research Laboratory Alternate Fuel Locator API <https://developer.nrel.gov/docs/transportation/alt-fuel-stations-v1/>. The Alternate Fuel Locator shows the location of alternate fuel stations in the United States and Canada. This package also includes the data from the US Department of Energy Alternate Fuel database as a data set.

Implementation of the technique of Lleonart et al. (2000) <doi:10.1006/jtbi.2000.2043> to scale body measurements that exhibit an allometric growth. This procedure is a theoretical generalization of the technique used by Thorpe (1975) <doi:10.1111/j.1095-8312.1975.tb00732.x> and Thorpe (1976) <doi:10.1111/j.1469-185X.1976.tb01063.x>.

Aho-Corasick is an optimal algorithm for finding many keywords in a text. It can locate all matches in a text in O(N+M) time; i.e., the time needed scales linearly with the number of keywords (N) and the size of the text (M). Compare this to the naive approach which takes O(N*M) time to loop through each pattern and scan for it in the text. This implementation builds the trie (the generic name of the data structure) and runs the search in a single function call. If you want to search multiple texts with the same trie, the function will take a list or vector of texts and return a list of matches to each text. By default, all 128 ASCII characters are allowed in both the keywords and the text. A more efficient trie is possible if the alphabet size can be reduced. For example, DNA sequences use at most 19 distinct characters and usually only 4; protein sequences use at most 26 distinct characters and usually only 20. UTF-8 (Unicode) matching is not currently supported.

An iterative implementation of a recursive binary partitioning algorithm to measure pairwise dependence with a modular design that allows user specification of the splitting logic and stop criteria. Helper functions provide suggested versions of both and support visualization and the computation of summary statistics on final binnings. For a thorough discussion and demonstration of the algorithm, see Salahub and Oldford (2025) <doi:10.1002/sam.70042>.

The anomalize package enables a "tidy" workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it's quite simple to decompose time series, detect anomalies, and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). Time series decomposition is used to remove trend and seasonal components via the time_decompose() function and methods include seasonal decomposition of time series by Loess ("stl") and seasonal decomposition by piecewise medians ("twitter"). The anomalize() function implements two methods for anomaly detection of residuals including using an inner quartile range ("iqr") and generalized extreme studentized deviation ("gesd"). These methods are based on those used in the forecast package and the Twitter AnomalyDetection package. Refer to the associated functions for specific references for these methods.

This package provides an S3 class to represent graph adjacency lists using vctrs'. Allows for creation, subsetting, combining, and pretty printing of these lists. Adjacency lists can be easily converted to zero-indexed lists, which allows for easy passing of objects to low-level languages for processing.