Analysis of DNA mixtures involving relatives by computation of likelihood ratios that account for dropout and drop-in, mutations, silent alleles and population substructure. This is useful in kinship cases, like non-invasive prenatal paternity testing, where deductions about individuals relationships rely on DNA mixtures, and in criminal cases where the contributors to a mixed DNA stain may be related. Relationships are represented by pedigrees and can include kinship between more than two individuals. The main function is relMix() and its graphical user interface relMixGUI(). The implementation and method is described in Dorum et al. (2017) <doi:10.1007/s00414-016-1526-x>, Hernandis et al. (2019) <doi:10.1016/j.fsigss.2019.09.085> and Kaur et al. (2016) <doi:10.1007/s00414-015-1276-1>.

This package provides functions from the book "Reinsurance: Actuarial and Statistical Aspects" (2017) by Hansjoerg Albrecher, Jan Beirlant and Jef Teugels <https://www.wiley.com/en-us/Reinsurance%3A+Actuarial+and+Statistical+Aspects-p-9780470772683>.

This package provides tools for basic and advance cancer statistics and graphics. Groups individual data, merges registry data and population data, calculates age-specific rate, age-standardized rate, cumulative risk, estimated annual percentage rate with standards error. Creates graphics across variable and time, such as age-specific trends, bar chart and period-cohort trends.

Defines classes and methods to process text-based cytogenetics using the CytoGPS web site, then import the results into R for further analysis and graphing.

This package creates reports from Trello, a collaborative, project organization and list-making application. <https://trello.com/> Reports are created by comparing individual Trello board cards from two different points in time and documenting any changes made to the cards.

Embeds sources and headers from Tina's Random Number Generator ('TRNG') C++ library. Exposes some functionality for easier access, testing and benchmarking into R. Provides examples of how to use parallel RNG with RcppParallel'. The methods and techniques behind TRNG are illustrated in the package vignettes and examples. Full documentation is available in Bauke (2021) <https://github.com/rabauke/trng4/blob/v4.23.1/doc/trng.pdf>.

Solve some conic related problems (intersection of conics with lines and conics, arc length of an ellipse, polar lines, etc.).

This package provides a platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.

Implementation of the following methods for event history analysis. Risk regression models for survival endpoints also in the presence of competing risks are fitted using binomial regression based on a time sequence of binary event status variables. A formula interface for the Fine-Gray regression model and an interface for the combination of cause-specific Cox regression models. A toolbox for assessing and comparing performance of risk predictions (risk markers and risk prediction models). Prediction performance is measured by the Brier score and the area under the ROC curve for binary possibly time-dependent outcome. Inverse probability of censoring weighting and pseudo values are used to deal with right censored data. Lists of risk markers and lists of risk models are assessed simultaneously. Cross-validation repeatedly splits the data, trains the risk prediction models on one part of each split and then summarizes and compares the performance across splits.

Infer log-linear Poisson Graphical Model with an auxiliary data set. Hot-deck multiple imputation method is used to improve the reliability of the inference with an auxiliary dataset. Standard log-linear Poisson graphical model can also be used for the inference and the Stability Approach for Regularization Selection (StARS) is implemented to drive the selection of the regularization parameter. The method is fully described in <doi:10.1093/bioinformatics/btx819>.

Calculates relevance and significance values for simple models and for many types of regression models. These are introduced in Stahel, Werner A. (2021) "Measuring Significance and Relevance instead of p-values." <https://stat.ethz.ch/~stahel/relevance/stahel-relevance2103.pdf>. These notions are also applied to replication studies, as described in the manuscript Stahel, Werner A. (2022) "'Replicability': Terminology, Measuring Success, and Strategy" available in the documentation.

This package provides functionality to prepare data and analyze plausibility of both forecasted and reported epidemiological signals. The functions implement a set of plausibility algorithms that are agnostic to geographic and time resolutions and are calculated independently then presented as a combined score.

Imports log and data files from "Eosense" ecosystem gas flux chambers into dataframes that can directly be used with "fluxible" by Gaudard et al (2025) <doi:10.1111/2041-210X.70161>.

Statistical tools based on the probabilistic properties of the record occurrence in a sequence of independent and identically distributed continuous random variables. In particular, tools to prepare a time series as well as distribution-free trend and change-point tests and graphical tools to study the record occurrence. Details about the implemented tools can be found in Castillo-Mateo et al. (2023a) <doi:10.18637/jss.v106.i05> and Castillo-Mateo et al. (2023b) <doi:10.1016/j.atmosres.2023.106934>.

This package contains functions for random generation of R x C and 2 x 2 x K contingency tables. In addition to the generation of contingency tables over predetermined intraclass-correlated clusters, it is possible to generate contingency tables without intraclass correlations under product multinomial, multinomial, and Poisson sampling plans. It also consists of a function for generation of random data from a given discrete probability distribution function. See Demirhan (2016) <https://journal.r-project.org/archive/2016-1/demirhan.pdf> for more information.

Doubly ranked tests are nonparametric tests for grouped functional and multivariate data. The testing procedure first ranks a matrix (or three dimensional array) of data by column (if a matrix) or by cell (across the third dimension if an array). By default, it calculates a sufficient statistic for the subject's order within the sample using the observed ranks, taken over the columns or cells. Depending on the number of groups, G, the summarized ranks are then analyzed using either a Wilcoxon Rank Sum test (G = 2) or a Kruskal-Wallis (G greater than 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.

Easily Download Analysis-Ready Crash Data from the U.S. National Highway Traffic Safety Administration.

An extension for roxygen2 to embed Shinylive applications in the package documentation.

Fetches NCBI data (RefSeq <https://www.ncbi.nlm.nih.gov/refseq/> database) and provides an environment to extract information at the level of gene, mRNA or protein accessions.

This package provides tools for manipulating, exploring, and visualising multiple-response data, including scored or ranked responses. Conversions to and from factors, lists, strings, matrices; reordering, lumping, flattening; set operations; tables; frequency and co-occurrence plots.

Includes sysdata.rda file for packages of the RobASt - family of packages; is currently used by package RobExtremes only.

This function conducts variation partitioning and hierarchical partitioning to calculate the unique, shared (referred as to "common") and individual contributions of each predictor (or matrix) towards explained variation (R-square and adjusted R-square) on canonical analysis (RDA,CCA and db-RDA), applying the algorithm of Lai J.,Zou Y., Zhang J.,Peres-Neto P.(2022) Generalizing hierarchical and variation partitioning in multiple regression and canonical analyses using the rdacca.hp R package.Methods in Ecology and Evolution,13: 782-788 <DOI:10.1111/2041-210X.13800>.

Enhances the R Optimization Infrastructure ('ROI') package with the alabama solver for solving nonlinear optimization problems.