Detrending multivariate time-series to approximate stationarity when dealing with intensive longitudinal data, prior to Vector Autoregressive (VAR) or multilevel-VAR estimation. Classical VAR assumes weak stationarity (constant first two moments), and deterministic trends inflate spurious autocorrelation, biasing Granger-causality and impulse-response analyses. All functions operate on raw panel data and write detrended columns back to the data set, but differ in the level at which the trend is estimated. See, for instance, Wang & Maxwell (2015) <doi:10.1037/met0000030>; Burger et al. (2022) <doi:10.4324/9781003111238-13>; Epskamp et al. (2018) <doi:10.1177/2167702617744325>.

This package provides functionalities for performing stability analysis of genotype by environment interaction (GEI) to identify superior and stable genotypes across diverse environments. It implements Eberhart and Russellâ s ANOVA method (1966)(<doi:10.2135/cropsci1966.0011183X000600010011x>), Finlay and Wilkinsonâ s Joint Linear Regression method (1963) (<doi:10.1071/AR9630742>), Wrickeâ s Ecovalence (1962, 1964), Shuklaâ s stability variance parameter (1972) (<doi:10.1038/hdy.1972.87>), Kangâ s simultaneous selection for high yield and stability (1991) (<doi:10.2134/agronj1991.00021962008300010037x>), Additive Main Effects and Multiplicative Interaction (AMMI) method and Genotype plus Genotypes by Environment (GGE) Interaction methods.

The cartogram heatmaps generated by the included methods are an alternative to choropleth maps for the United States and are based on work by the Washington Post graphics department in their report on "The states most threatened by trade" (<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>). "State bins" preserve as much of the geographic placement of the states as possible but have the look and feel of a traditional heatmap. Functions are provided that allow for use of a binned, discrete scale, a continuous scale or manually specified colors depending on what is needed for the underlying data.

Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) (Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285>) for supervised learning and Bayesian Causal Forests (BCF) (Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195>) for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers. Includes the grow-from-root algorithm for accelerated forest sampling (He and Hahn (2021) <doi:10.1080/01621459.2021.1942012>), a log-linear leaf model for forest-based heteroskedasticity (Murray (2020) <doi:10.1080/01621459.2020.1813587>), and the cloglog BART model of Alam and Linero (2025) <doi:10.48550/arXiv.2502.00606> for ordinal outcomes.

This package implements confidence interval and sample size methods that are especially useful in psychological research but are also useful in educational, social science, business, and biological research. This package includes more than 100 confidence interval functions and more than 80 sample size functions for 1-group, 2-group, paired-samples, and multiple-group designs and for a variety of parameters including means, medians, proportions, slopes, standardized mean differences, standardized linear contrasts of means, and several measures of correlation and association. The sample size functions can be used to approximate the sample size needed to estimate a parameter or function of parameters with desired confidence interval precision or to perform a variety of hypothesis tests (directional two-sided, equivalence, superiority, noninferiority) with desired power. For details about these methods see: Statistical Methods for Psychologists, Volumes 1 â 4, <https://dgbonett.sites.ucsc.edu/>.

This package provides a modular simulation engine for a wide range of stochastic processes. Provides exact and approximate simulation methods for Poisson processes (homogeneous and inhomogeneous), Brownian motion (standard, drifted, and bridge), discrete- and continuous-time Markov chains, birth-death processes, the Yule pure-birth process, infinitesimal generator matrix utilities, Markovian queuing systems (M/M/1, M/M/c, M/M/c/K) with exact steady-state statistics, Levy processes (gamma, normal inverse Gaussian, variance-gamma, alpha-stable), Merton jump-diffusion models, Hawkes self-exciting processes, geometric Brownian motion, and Ornstein-Uhlenbeck mean-reverting diffusions. Includes variance reduction techniques (antithetic variates, control variates, importance sampling, stratified sampling), parallel simulation via the future framework, rare-event simulation (cross-entropy and multilevel splitting), path visualisation, and summary statistics. Methods are based on Glasserman (2003) <doi:10.1007/978-0-387-21617-1>, Asmussen & Glynn (2007) <doi:10.1007/978-0-387-69033-9>, Norris (1997) <doi:10.1017/CBO9780511810633>, and Kleinrock (1975, ISBN:0471491101).

Determines Optimum Strata Boundaries (OSB) and Optimum Sample Sizes (OSS) for univariate stratified sampling designs under Neyman allocation. The stratification variable is described by a best-fitting parametric distribution, selected automatically by AIC from a set of continuous families (normal, log-normal, gamma, Weibull, exponential, Cauchy, uniform, Pareto, triangular and right-triangular), and the optimum boundaries are obtained by minimising the Neyman objective. Version 2.0 keeps the Dynamic Programming (DP) solver of Reddy and Khan (2020), which is globally optimal for the fitted distribution, as the default. It adds two fast derivative-free solvers, a multi-start COBYLA solver and a two-phase global solver that couples DIRECT-L with BOBYQA refinement, and an exact solver that runs dynamic programming on the sorted data and returns the partition with the smallest empirical Neyman cost. It also provides cost-constrained allocation with unequal per-stratum costs, a design-efficiency comparison (compare_designs), two- and three-dimensional and interactive visualisations, solution-quality diagnostics (a Cauchy-Schwarz optimality gap and KKT first-order residuals for the derivative-free solvers) and a self-contained shiny application, while remaining backward compatible with the strata.data() and strata.distr() interface of version 1.x. The methodology follows Khan et al. (2008) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2008002/article/10761-eng.pdf>, Reddy and Khan (2018) <doi:10.1111/anzs.12244> and Reddy and Khan (2020) <doi:10.1111/anzs.12301>.

This package provides string parsing functionalities for generating plotnames, filenames and paths.

This package provides utilities to create or suppress start-up messages.

Allows the creation and manipulation of C++ std::vector's in R.

Support for reading and writing files in StatDataML---an XML-based data exchange format.

This package provides an extendable, performant and multithreaded alt-string implementation backed by C++ vectors and strings.

This package provides density, probability and quantile functions, and random number generation for (skew) stable distributions, using the parametrizations of Nolan.

R Codes and Datasets for Stroup, W. W. (2012). Generalized Linear Mixed Models Modern Concepts, Methods and Applications, CRC Press.

This package provides a graphical user interface for cross-sectional network modeling with the statnet software suite <https://github.com/statnet>.

The <http://standartox.uni-landau.de> database offers cleaned, harmonized and aggregated ecotoxicological test data, which can be used for assessing effects and risks of chemical concentrations found in the environment.

This package provides data about the Star Wars movie franchise in a set of relational tables or as a complete DuckDB database. All data was collected from the open source Star Wars API.

Estimation of model parameters for marked Hawkes process. Accounts for missing data in the estimation of the parameters. Technical details found in (Tucker et al., 2019 <DOI:10.1016/j.spasta.2018.12.004>).

This package provides a streamlined tool provides a graphical user interface for quality control based signal drift correction (QC-RFSC), integration of data from multi-batch MS-based experiments, and the comprehensive statistical analysis in metabolomics and proteomics.

Basic statistical methods with some modifications for the course Statistical Methods at Federal University of Bahia (Brazil). All methods in this packages are explained in the text book of Montgomery and Runger (2010) <ISBN: 978-1-119-74635-5>.

Miscellaneous functions for working with stars objects, mainly single-band rasters. Currently includes functions for: (1) focal filtering, (2) detrending of Digital Elevation Models, (3) calculating flow length, (4) calculating the Convergence Index, (5) calculating topographic aspect and topographic slope.

For Multi Parent Populations (MPP) Identity By Descend (IBD) probabilities are computed using Hidden Markov Models. These probabilities are then used in a mixed model approach for QTL Mapping as described in Li et al. (<doi:10.1007/s00122-021-03919-7>).

This package provides a stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) <doi:10.1038/s41598-020-79317-8>, Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.

Message translation is often managed with po files and the gettext programme, but sometimes another solution is needed. In contrast to po files, a more flexible approach is used as in the Fluent <https://projectfluent.org/> project with R Markdown snippets. The key-value approach allows easier handling of the translated messages.