This package provides a statistical method for reducing the number of covariates in an analysis by evaluating Variable Importance Measures (VIMPs) derived from the Random Forest algorithm. It performs statistical tests on the VIMPs and outputs whether the covariate is significant along with the p-values.

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 original globally optimal Dynamic Programming (DP) solver of Reddy and Khan (2020) as the default and adds two faster derivative-free alternatives for interactive and large-scale use: a multi-start COBYLA solver and a two-phase global solver that couples DIRECT-L with COBYLA refinement. 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>.

Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) <doi:10.1080/10618600.1999.10474847>. This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories.

This package provides functions and data sets for data sharpening. Nonparametric regressions are computed subject to smoothness and other kinds of penalties.

Set of functions that access information about deputies and votings in Polish diet from webpage <http://www.sejm.gov.pl>. The package was developed as a result of an internship in MI2 Group - <http://mi2.mini.pw.edu.pl>, Faculty of Mathematics and Information Science, Warsaw University of Technology.

Selection index is one of the efficient and acurrate method for selection of animals. This package is useful for construction of selection indices. It uses mixed and random model least squares analysis to estimate the heritability of traits and genetic correlation between traits. The package uses the sire model as it is considered as random effect. The genetic and phenotypic (co)variances along with the relative economic values are used to construct the selection index for any number of traits. It also estimates the accuracy of the index and the genetic gain expected for different traits. Fisher (1936) <doi:10.1111/j.1469-1809.1936.tb02137.x>.

This package provides functions for hormesis screening by classifying the shapes of dose-response curves based on semiparametric tests. The shapes are indications of different potential toxicology effect. It also offers a scalable visualization scheme to present testing conclusions for large-scale dataset with a large number of dose-response curves. For more information, see Jin et al. (2026) <https://github.com/YinglJin-0203/shorm/blob/main/Manuscripts/BotanicalHormesisTestingFinalDraft.docx>.

This package provides a simulator for reticulate evolution under a birth-death-hybridization process. Here the birth-death process is extended to consider reticulate Evolution by allowing hybridization events to occur. The general purpose simulator allows the modeling of three different reticulate patterns: lineage generative hybridization, lineage neutral hybridization, and lineage degenerative hybridization. Users can also specify hybridization events to be dependent on a trait value or genetic distance. We also extend some phylogenetic tree utility and plotting functions for networks. We allow two different stopping conditions: simulated to a fixed time or number of taxa. When simulating to a fixed number of taxa, the user can simulate under the Generalized Sampling Approach that properly simulates phylogenies when assuming a uniform prior on the root age.

Analysis of field trial experiments by modelling spatial trends using two-dimensional Penalised spline (P-spline) models.

Testing the mediation effect of multiple SNPs on an outcome through a mediator.

Access statistical information on welfare and health in Finland from the Sotkanet open data portal <https://sotkanet.fi/sotkanet/fi/index>.

This package provides functions to calculate and visualise the Regional Consistency Probability (RCP) for single-arm multi-regional clinical trials (MRCTs) using the Effect Retention Approach (ERA). Six endpoint types are supported: continuous, binary, count (negative binomial), time-to-event via hazard ratio, milestone survival, and restricted mean survival time (RMST). For each endpoint, both a closed-form (or semi-analytical) solution and a Monte Carlo simulation approach are implemented. Two consistency evaluation methods are available: Method 1 (effect retention in Region 1 relative to the overall population) and Method 2 (simultaneous positive effect across all regions). Plotting functions generate faceted visualisations of RCP as a function of the regional allocation proportion, overlaying formula and simulation results for direct comparison. The methodology follows the Japanese MHLW guidelines for MRCTs. Abbreviations used: RCP (Regional Consistency Probability), MRCT (Multi-Regional Clinical Trial), RMST (Restricted Mean Survival Time), MHLW (Ministry of Health, Labour and Welfare).

Seeded Sequential LDA can classify sentences of texts into pre-define topics with a small number of seed words (Watanabe & Baturo, 2023) <doi:10.1177/08944393231178605>. Implements Seeded LDA (Lu et al., 2010) <doi:10.1109/ICDMW.2011.125> and Sequential LDA (Du et al., 2012) <doi:10.1007/s10115-011-0425-1> with the distributed LDA algorithm (Newman, et al., 2009) for parallel computing.

Estimates split-half reliabilities for scoring algorithms of cognitive tasks and questionnaires. The splithalfr supports researcher-provided scoring algorithms, with six vignettes illustrating how on included datasets. The package provides four splitting methods (first-second, odd-even, permutated, Monte Carlo), the option to stratify splits by task design, a number of reliability coefficients, the option to sub-sample data, and bootstrapped confidence intervals.

This package provides a set of tools for writing and sharing interactive courses to be used with swirl.

This package provides a set of methods to implement Generalized Method of Moments and Maximal Likelihood methods for Random Utility Models. These methods are meant to provide inference on rank comparison data. These methods accept full, partial, and pairwise rankings, and provides methods to break down full or partial rankings into their pairwise components. Please see Generalized Method-of-Moments for Rank Aggregation from NIPS 2013 for a description of some of our methods.

This package provides functions for converting and processing network data from a SpatialLinesDataFrame -Class object to an igraph'-Class object.

Facilitate the evaluation of forecasts in a convenient framework based on data.table. It allows user to to check their forecasts and diagnose issues, to visualise forecasts and missing data, to transform data before scoring, to handle missing forecasts, to aggregate scores, and to visualise the results of the evaluation. The package mostly focuses on the evaluation of probabilistic forecasts and allows evaluating several different forecast types and input formats. Find more information about the package in the Vignettes as well as in the accompanying paper, <doi:10.48550/arXiv.2205.07090>.

Interface to sigma.js graph visualization library including animations, plugins and shiny proxies.

Routines for creating, manipulating, and performing Bayesian inference about Gaussian processes in one and two dimensions using the Fourier basis approximation: simulation and plotting of processes, calculation of coefficient variances, calculation of process density, coefficient proposals (for use in MCMC). It uses R environments to store GP objects as references/pointers.

Detects spatial and temporal groups in GPS relocations (Robitaille et al. (2019) <doi:10.1111/2041-210X.13215>). It can be used to convert GPS relocations to gambit-of-the-group format to build proximity-based social networks, and perform data-stream randomization methods suitable for GPS data. Also provides measures of intragroup social dynamics including distance and direction to leaders, centroids and nearest neighbours.

This package provides a lightweight tool that provides a reproducible workflow for selecting and executing appropriate statistical analysis in one-way or two-way experimental designs. The package automatically checks for data normality, conducts parametric (ANOVA) or non-parametric (Kruskal-Wallis) tests, performs post-hoc comparisons with Compact Letter Displays (CLD), and generates publication-ready boxplots, faceted plots, and heatmaps. It is designed for researchers seeking fast, automated statistical summaries and visualization. Based on established statistical methods including Shapiro and Wilk (1965) <doi:10.2307/2333709>, Kruskal and Wallis (1952) <doi:10.1080/01621459.1952.10483441>, Tukey (1949) <doi:10.2307/3001913>, Fisher (1925) <ISBN:0050021702>, and Wickham (2016) <ISBN:978-3-319-24277-4>.

It implements parametric formulas of soil water retention or conductivity curve. At the moment, only Van Genuchten (for soil water retention curve) and Mualem (for hydraulic conductivity) were implemented. See reference (<http://en.wikipedia.org/wiki/Water_retention_curve>).

Structurally guided sampling (SGS) approaches for airborne laser scanning (ALS; LIDAR). Primary functions provide means to generate data-driven stratifications & methods for allocating samples. Intermediate functions for calculating and extracting important information about input covariates and samples are also included. Processing outcomes are intended to help forest and environmental management practitioners better optimize field sample placement as well as assess and augment existing sample networks in the context of data distributions and conditions. ALS data is the primary intended use case, however any rasterized remote sensing data can be used, enabling data-driven stratifications and sampling approaches.