It allows to learn the structure of univariate time series, learning parameters and forecasting. Implements a model of Dynamic Bayesian Networks with temporal windows, with collections of linear regressors for Gaussian nodes, based on the introductory texts of Korb and Nicholson (2010) <doi:10.1201/b10391> and Nagarajan, Scutari and Lèbre (2013) <doi:10.1007/978-1-4614-6446-4>.

Design, conduct and analyze DCEs from a virtual interface in shiny. Reference: Perez-Troncoso, D. (2022) <https://github.com/danielpereztr/DCEtool>.

Estimates Two-way Fixed Effects difference-in-differences/event-study models using the imputation-based approach proposed by Borusyak, Jaravel, and Spiess (2021).

Distribution (S4-)classes for elliptically contoured distributions (based on package distr').

Analysis, visualisation and simulation of digital polymerase chain reaction (dPCR) (Burdukiewicz et al. (2016) <doi:10.1016/j.bdq.2016.06.004>). Supports data formats of commercial systems (Bio-Rad QX100 and QX200; Fluidigm BioMark) and other systems.

The gap statistic approach is extended to estimate the number of clusters for categorical response format data. This approach and accompanying software is designed to be used with the output of any clustering algorithm and with distances specifically designed for categorical (i.e. multiple choice) or ordinal survey response data.

Tool collection for common and not so common data science use cases. This includes custom made algorithms for data management as well as value calculations that are hard to find elsewhere because of their specificity but would be a waste to get lost nonetheless. Currently available functionality: find sub-graphs in an edge list data.frame, find mode or modes in a vector of values, extract (a) specific regular expression group(s), generate ISO time stamps that play well with file names, or generate URL parameter lists by expanding value combinations.

Estimate population kin counts and its distribution by type, age and sex. The package implements one-sex and two-sex framework for studying living-death availability, with time varying rates or not, and multi-stage model.

Interface for Rcpp users to dlib <http://dlib.net> which is a C++ toolkit containing machine learning algorithms and computer vision tools. It is used in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. This package allows R users to use dlib through Rcpp'.

Make inference in a mixture of discrete Laplace distributions using the EM algorithm. This can e.g. be used for modelling the distribution of Y chromosomal haplotypes as described in [1, 2] (refer to the URL section).

Several statistical methods for analyzing survival data under various forms of dependent censoring are implemented in the package. In addition to accounting for dependent censoring, it offers tools to adjust for unmeasured confounding factors. The implemented approaches allow users to estimate the dependency between survival time and dependent censoring time, based solely on observed survival data. For more details on the methods, refer to Deresa and Van Keilegom (2021) <doi:10.1093/biomet/asaa095>, Czado and Van Keilegom (2023) <doi:10.1093/biomet/asac067>, Crommen et al. (2024) <doi:10.1007/s11749-023-00903-9>, Deresa and Van Keilegom (2024) <doi:10.1080/01621459.2022.2161387>, Willems et al. (2025) <doi:10.48550/arXiv.2403.11860>, Ding and Van Keilegom (2025) and D'Haen et al. (2025) <doi:10.1007/s10985-025-09647-0>.

This package provides a common interface for applying dimensionality reduction methods, such as Principal Component Analysis ('PCA'), Independent Component Analysis ('ICA'), diffusion maps, Locally-Linear Embedding ('LLE'), t-distributed Stochastic Neighbor Embedding ('t-SNE'), and Uniform Manifold Approximation and Projection ('UMAP'). Has built-in support for sparse matrices.

Discretization-based random sampling algorithm that is useful for a complex model in high dimension is implemented. The normalizing constant of a target distribution is not needed. Posterior summaries are compared with those by OpenBUGS'. The method is described: Wang and Lee (2014) <doi:10.1016/j.csda.2013.06.011> and exercised in Lee (2009) <http://hdl.handle.net/1993/21352>.

This package performs cluster analysis using an ensemble clustering framework, Chiu & Talhouk (2018) <doi:10.1186/s12859-017-1996-y>. Results from a diverse set of algorithms are pooled together using methods such as majority voting, K-Modes, LinkCluE, and CSPA. There are options to compare cluster assignments across algorithms using internal and external indices, visualizations such as heatmaps, and significance testing for the existence of clusters.

This package provides access to Dataverse APIs <https://dataverse.org/> (versions 4-5), enabling data search, retrieval, and deposit. For Dataverse versions <= 3.0, use the archived dvn package <https://cran.r-project.org/package=dvn>.

This package provides fast methods to work with Merton's distance to default model introduced in Merton (1974) <doi:10.1111/j.1540-6261.1974.tb03058.x>. The methods includes simulation and estimation of the parameters.

This package provides a metapackage that brings together a curated collection of R packages containing domain-specific datasets. It includes time series data, educational metrics, crime records, medical datasets, and oncology research data. Designed to provide researchers, analysts, educators, and data scientists with centralized access to structured and well-documented datasets, this metapackage facilitates reproducible research, data exploration, and teaching applications across a wide range of domains. Included packages: - timeSeriesDataSets': Time series data from economics, finance, energy, and healthcare. - educationR': Datasets related to education, learning outcomes, and school metrics. - crimedatasets': Datasets on global and local crime and criminal behavior. - MedDataSets': Datasets related to medicine, public health, treatments, and clinical trials. - OncoDataSets': Datasets focused on cancer research, survival, genetics, and biomarkers.

This package provides an easy to use implementation of life expectancy decomposition formulas for age bands, derived from Ponnapalli, K. (2005). A comparison of different methods for decomposition of changes in expectation of life at birth and differentials in life expectancy at birth. Demographic Research, 12, pp.141â 172. <doi:10.4054/demres.2005.12.7> In addition, there is a decomposition function for disease cause breakdown and a couple helpful plot functions.

Detection of runs of homozygosity and of heterozygosity in diploid genomes using two methods: sliding windows (Purcell et al (2007) <doi:10.1086/519795>) and consecutive runs (Marras et al (2015) <doi:10.1111/age.12259>).

This package provides a key-value dictionary data structure based on R6 class which is designed to be similar usages with other languages dictionary (e.g. Python') with reference semantics and extendabilities by R6.

Computes small-sample degrees of freedom adjustment for heteroskedasticity robust standard errors, and for clustered standard errors in linear regression. See Imbens and Kolesár (2016) <doi:10.1162/REST_a_00552> for a discussion of these adjustments.

This package provides a statistically and computationally efficient debiasing method for conducting valid inference on the high-dimensional linear regression function with missing outcomes. The reference paper is Zhang, Giessing, and Chen (2023) <arXiv:2309.06429>.

Algorithms implementing populations of agents that interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here, a swarm system called Databionic swarm (DBS) is introduced which was published in Thrun, M.C., Ultsch A.: "Swarm Intelligence for Self-Organized Clustering" (2020), Artificial Intelligence, <DOI:10.1016/j.artint.2020.103237>. DBS is able to adapt itself to structures of high-dimensional data such as natural clusters characterized by distance and/or density based structures in the data space. The first module is the parameter-free projection method called Pswarm (Pswarm()), which exploits the concepts of self-organization and emergence, game theory, swarm intelligence and symmetry considerations. The second module is the parameter-free high-dimensional data visualization technique, which generates projected points on the topographic map with hypsometric tints defined by the generalized U-matrix (GeneratePswarmVisualization()). The third module is the clustering method itself with non-critical parameters (DBSclustering()). Clustering can be verified by the visualization and vice versa. The term DBS refers to the method as a whole. It enables even a non-professional in the field of data mining to apply its algorithms for visualization and/or clustering to data sets with completely different structures drawn from diverse research fields. The comparison to common projection methods can be found in the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.

Assists in finding the most suitable thread count for the various data.table routines that support parallel processing.