Radioactive doses estimation using individual chromosomal aberrations information. See Higueras M, Puig P, Ainsbury E, Rothkamm K. (2015) <doi:10.1088/0952-4746/35/3/557>.

Determine the number of dimensions to retain in exploratory factor analysis. The main function, nest(), returns the solution and the plot(nest()) returns a plot.

The Resource Description Framework, or RDF is a widely used data representation model that forms the cornerstone of the Semantic Web. RDF represents data as a graph rather than the familiar data table or rectangle of relational databases. The rdflib package provides a friendly and concise user interface for performing common tasks on RDF data, such as reading, writing and converting between the various serializations of RDF data, including rdfxml', turtle', nquads', ntriples', and json-ld'; creating new RDF graphs, and performing graph queries using SPARQL'. This package wraps the low level redland R package which provides direct bindings to the redland C library. Additionally, the package supports the newer and more developer friendly JSON-LD format through the jsonld package. The package interface takes inspiration from the Python rdflib library.

Loads Axon Binary Files (both ABF and ABF2') created by Axon Instruments/Molecular Devices software such as pClamp'.

This package implements safe policy learning under regression discontinuity designs with multiple cutoffs, based on Zhang et al. (2022) <doi:10.48550/arXiv.2208.13323>. The learned cutoffs are guaranteed to perform no worse than the existing cutoffs in terms of overall outcomes. The rdlearn package also includes features for visualizing the learned cutoffs relative to the baseline and conducting sensitivity analyses.

Tskit enables efficient storage, manipulation, and analysis of ancestral recombination graphs (ARGs) using succinct tree sequence encoding. The tree sequence encoding of an ARG is described in Wong et al. (2024) <doi:10.1093/genetics/iyae100>, while `tskit` project is described in Jeffrey et al. (2026) <doi:10.48550/arXiv.2602.09649>. See also <https://tskit.dev> for project news, documentation, and tutorials. Tskit provides Python', C', and Rust application programming interfaces (APIs). The Python API can be called from R via the reticulate package to load and analyse tree sequences as described at <https://tskit.dev/tutorials/tskitr.html>. RcppTskit provides R access to the tskit C API for cases where the reticulate option is not optimal; for example, high-performance or low-level work with tree sequences. Currently, RcppTskit provides a limited set of R functions because the Python API and reticulate already covers most needs.

Robust (outlier-resistant) estimators of finite population characteristics like of means, totals, ratios, regression, etc. Available methods are M- and GM-estimators of regression, weight reduction, trimming, and winsorization. The package extends the survey <https://CRAN.R-project.org/package=survey> package.

Download, prepare and analyze data from large-scale assessments and surveys with complex sampling and assessment design (see Rutkowski', 2010 <doi:10.3102/0013189X10363170>). Such studies are, for example, international assessments like TIMSS', PIRLS and PISA'. A graphical interface is available for the non-technical user.The package includes functions to covert the original data from SPSS into R data sets keeping the user-defined missing values, merge data from different respondents and/or countries, generate variable dictionaries, modify data, produce descriptive statistics (percentages, means, percentiles, benchmarks) and multivariate statistics (correlations, linear regression, binary logistic regression). The number of supported studies and analysis types will increase in future. For a general presentation of the package, see Mirazchiyski', 2021a (<doi:10.1186/s40536-021-00114-4>). For detailed technical aspects of the package, see Mirazchiyski', 2021b (<doi:10.3390/psych3020018>).

Turns nested lists into data.frames in an orderly manner.

Parameter estimation, computation of probability, information, and (log-)likelihood, and visualization of item/test characteristic curves and item/test information functions for three uni-dimensional item response theory models: the 3-parameter-logistic model, generalized partial credit model, and graded response model. The full documentation and tutorials are at <https://github.com/xluo11/Rirt>.

Calculate rarefaction-based alpha- and beta-diversity. Offer parametric extrapolation to estimate the total expected species in a single community and the total expected shared species between two communities. Visualize the curve-fitting for these estimators.

This package provides a collection of functions to simulate dice rolls and the like. In particular, experiments and exercises can be performed looking at combinations and permutations of values in dice rolls and coin flips, together with the corresponding frequencies of occurrences. When applying each function, the user has to input the number of times (rolls, flips) to toss the dice. Needless to say, the more the tosses, the more the frequencies approximate the actual probabilities. Moreover, the package provides functions to generate non-transitive sets of dice (like Efron's) and to check whether a given set of dice is non-transitive with given probability.

This package provides functions used in the R: Einführung durch angewandte Statistik (second edition).

This package provides functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) <doi:10.1080/10618600.2023.2231051> for the covariance-based method and Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537> for the operator-based rational approximation. These can be generated by the citation function in R.

This package provides and extends the Fuzzy Coco algorithm by wrapping the FuzzyCoCo C++ Library, cf <https://github.com/Lonza-RND-Data-Science/fuzzycoco>. Fuzzy Coco constructs systems that predict the outcome of a human decision-making process while providing an understandable explanation of a possible reasoning leading to it. The constructed fuzzy systems are composed of rules and linguistic variables. This package provides a S3 classic interface (fit_xy()/fit()/predict()/evaluate()) and a tidymodels'/'parsnip interface, a custom engine with custom iteration stop criterion and progress bar support as well as a systematic implementation that do not rely on genetic programming but rather explore all possible combinations.

This package provides functions to conduct hypothesis tests and derive confidence intervals for quantiles, linear combinations of quantiles, ratios of dependent linear combinations and differences and ratios of all of the above for comparisons between independent samples. Additionally, quantile-based measures of inequality are also considered.

This package performs aggregation of ordered lists based on the ranks using several different algorithms: Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a brute force algorithm (for small problems).

STK++ <http://www.stkpp.org> is a collection of C++ classes for statistics, clustering, linear algebra, arrays (with an Eigen'-like API), regression, dimension reduction, etc. The integration of the library to R is using Rcpp'. The rtkore package includes the header files from the STK++ core library. All files contain only template classes and/or inline functions. STK++ is licensed under the GNU LGPL version 2 or later. rtkore (the stkpp integration into R') is licensed under the GNU GPL version 2 or later. See file LICENSE.note for details.

Yandex Clickhouse (<https://clickhouse.com/>) is a high-performance relational column-store database to enable big data exploration and analytics scaling to petabytes of data. Methods are provided that enable working with Yandex Clickhouse databases via DBI methods and using dplyr'/'dbplyr idioms.

This package provides a collection of methods for the robust analysis of univariate and multivariate functional data, possibly in high-dimensional cases, and hence with attention to computational efficiency and simplicity of use. See the R Journal publication of Ieva et al. (2019) <doi:10.32614/RJ-2019-032> for an in-depth presentation of the roahd package. See Aleman-Gomez et al. (2021) <arXiv:2103.08874> for details about the concept of depthgram.

The goal of readsdr is to bridge the design capabilities from specialised System Dynamics software with the powerful numerical tools offered by R libraries. The package accomplishes this goal by parsing XMILE files ('Vensim and Stella') models into R objects to construct networks (graph theory); ODE functions for Stan'; and inputs to simulate via deSolve as described in Duggan (2016) <doi:10.1007/978-3-319-34043-2>.

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. The rdpower package provides tools to perform power, sample size and MDE calculations in RD designs: rdpower() calculates the power of an RD design, rdsampsi() calculates the required sample size to achieve a desired power and rdmde() calculates minimum detectable effects. See Cattaneo, Titiunik and Vazquez-Bare (2019) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2019_Stata.pdf> for further methodological details.

This package provides a complete interface to LibBi', a library for Bayesian inference (see <https://libbi.org> and Murray, 2015 <doi:10.18637/jss.v067.i10> for more information). This includes functions for manipulating LibBi models, for reading and writing LibBi input/output files, for converting LibBi output to provide traces for use with the coda package, and for running LibBi to conduct inference.

This package provides a useful statistical tool for the construction and analysis of Honeycomb Selection Designs. More information about this type of designs: Fasoula V. (2013) <doi:10.1002/9781118497869.ch6> Fasoula V.A., and Tokatlidis I.S. (2012) <doi:10.1007/s13593-011-0034-0> Fasoulas A.C., and Fasoula V.A. (1995) <doi:10.1002/9780470650059.ch3> Tokatlidis I. (2016) <doi:10.1017/S0014479715000150> Tokatlidis I., and Vlachostergios D. (2016) <doi:10.3390/d8040029>.